Sentiment analysis plays a crucial role in tracking opinions on social media and gaining insights into user sentiment. However, traditional methods mainly focus on the overall sentiment of the text and struggle to identify nuanced emotions. This study presents a fine-grained sentiment analysis model called BERT-RCNN-SE, designed to address the immediacy, irregularity, and complexity of sentiment expression on social media platforms, such as Twitter. The model utilized BERT for semantic representation and the RCNN architecture to extract detailed local aspects of phrases. Furthermore, the SENet module adaptsively modifies the weights of feature channels, improving the model's ability to recognize nuanced emotions in social media comments. The results indicate that the BERT-RCNN-SE model has significantly outperformed the TextCNN model on a publicly accessible Twitter dataset, with a Macro F1 score improvement of 38.02%, compared to the baseline BERT model which improved by 1.15%. This confirms the model's effectiveness and superiority in fine-grained sentiment analysis of social media comments.

Multiple spectral infrared (IR) observations onboard geostationary satellites are effective and widely used in estimating precipitation with high spatiotemporal resolutions. Currently, FengYun-4A (FY-4A) and FengYun-4B (FY-4B), representing the most advanced Chinese geostationary meteorological satellites, are equipped with Advanced Geosynchronous Radiation Imager (AGRI) to continuously observe the climate and weather over vast eastern Asia region. However, geographic factors, such as viewing zenith angle (VZA) and solar zenith angle (SZA), could result in systematic errors in estimating and merging precipitation. Therefore, motivated by analyzing the influence patterns of these physical factors on precipitation estimation, the precipitation estimation using the chromatographic analysis method by merging enhanced multispectral IR observations (PECAM) is proposed to generate the merged precipitation data covering observed fields of both FY-4A and FY-4B. The main conclusions are summarized as follows: 1) latitude, view zenith angle (VZA), and elevation exert a negative influence (up to 20 K) on averaged TBBs of single IR band (as$T_{10.8}$), causing precipitation overestimation; 2)${\Delta T}_{7.1A-13.5A}$and${\Delta T}_{6.95B-13.3B},\Delta T_{3.75H-13.5A}$and${\Delta T}_{3.75H-13.3B}$, and${\Delta T}_{3.75H-3.75L}$are mainly influenced by ecliptic obliquity angle (EOA), SZA, and shadow effect, causing seasonal patterns, diurnal fluctuations, and shadow effects, respectively; 3) compared to PERSIANN-CCS, FY4A-Official, and FY4B-Official, at hourly scale, PECAM-FY4A&B consistently outperforms them across CC, RMSE, and CSI metrics, with minimum improvements of approximately 0.046 in CC, 0.30 mm/h in RMSE, and 0.033 in CSI; and 4) meanwhile at daily scales, merged data from FY-4A&B shows overall improvements in CC, RMSE, and CSI, with at least 0.041, 0.80 mm/day, and 0.022, respectively. Foreseeably, the signal processing and merging strategy in PECAM has significant potential to serve as references for the estimation and integration of precipitation data from the FY-4 series, as well as the GOES, Meteosat, and Himawari series.

With the rapid expansion of Industry 4.0 technology, the proliferation of large-scale devices faces increasingly severe cyber threats, underscoring the critical importance of cryptographic technology for secure communication and authentication. However, cryptographic systems, as the bedrock of security, have faced a barrage of attacks in recent years, including potential threats from quantum computing and memory disclosure vulnerabilities. In this article, we focus on enhancing the security of two standard quantum-safe cryptographic algorithms, Dilithium and eXtended Merkle signature scheme (XMSS), by leveraging hardware transactional memory (HTM) to create a secure operational environment. Unlike traditional cryptography such as Rivest–Shamir–Adleman (RSA) and elliptic curve cryptography (ECC), Dilithium, and XMSS involve more and larger sensitive variables, rendering conventional solutions inadequate. By conducting a comprehensive sensitivity analysis of variables within the abovementioned algorithms, we confine sensitive operations to transactional execution regions and employ transaction-splitting technology for efficiency. Our prototype, utilizing Intel transactional synchronization extension (TSX), demonstrates robust protection against memory disclosure attacks with acceptable performance overheads. Notably, our security-enhanced Dilithium and XMSS software implementations, recommended by NIST, achieve an average throughput factor of 0.75 compared to the (unprotected) reference implementations.

Cache side-channel attacks pose a significant threat to the data security of multi-tenant public clouds. However, currently proposed defenses either lack transparency (requiring user involvement) or incur a significant performance penalty. This paper is motivated by our insightful observation for the behavior of cache side-channel attackers who employ rdtsc/rdtscp instructions for timing purposes. We have discerned a behavior pattern that enables comprehensive identification of potential attackers. Building upon this observation, we introduce TF -timer, which operates on the core principle of inspecting cache side-channel attacks using the pre-identified behavior pattern while obscuring the return values of rdtsc/rdtscp instructions. Our proposed technique preserves the properties of rdtsc/rdtscp, only blurring the attacker's timing to minimize the impact on other applications. We have implemented the prototype of TF-timer at the hypervisor layer. It is completely transparent to users and requires no hardware modifications. Our evaluation results demonstrate that TF -timer efficiently and precisely miti-gates cache side-channel attacks that exploit rdtsc/rdtscp for timing, with performance penalties within 1 %.

A cloud-dependent 1-D variational (1DVAR) precipitation retrieval algorithm applied to FengYun-3D microwave soundings (CD1DVAR-FY3DMS) is developed in this study. Compared with the current 1DVAR precipitation retrieval framework, cloud scene identification (CSI) is first proposed to delineate the different weather conditions including clear sky, stratiform clouds, and convective clouds. Results of this study in a typical tropical cyclone event demonstrate that: 1) the precipitation retrievals considering CSI (correlation coefficient ~0.72 and root mean square error ~1.63 mm/h) outperform those without distinguishing cloud scenes (correlation coefficient ~0.66 and root mean square error ~1.82 mm/h); 2) identifying cloud scenes in a variational scheme could significantly improve the accuracy of retrieving heavy precipitation volumes, with the capturing abilities improved by ~100%; and 3) the FengYun-3 constellation has great potential to complement global microwave retrievals. In addition, these findings could provide valuable references and pathfinders for further improving the retrieval accuracy of microwave-based precipitation estimates, especially for strong convective zones.

Infrared (IR) observations from geostationary satellites have been widely used in estimating the precipitation with high spatiotemporal resolutions. Currently, it is acknowledged that single-channel IR observations are capable to extract the properties of cloud tops, while are insufficient for deeper vertical information. However, based on developments of multispectral IR imagers onboard geostationary satellites, distinguishing different precipitation regions with different vertical structures becomes possible, which has not been fully explored yet. Therefore, combining FengYun-4A/advanced geosynchronous radiation imager (FY-4A/AGRI) and global precipitation measurement/dual-frequency precipitation radar (GPM/DPR), we innovatively proposed frontal precipitation cloud index (FPCI) and saturated precipitation cloud index (SPCI) for IR signals by exploiting the characteristic responses of channel combinations to different precipitation systems. It is proven that multispectral IR observations are capable of responding to certain precipitation systems with different vertical structures. Meanwhile, IR imagers onboard geostationary satellites have significant advantages on tracking developing processes of precipitation systems with high spatiotemporal resolutions than microwave detectors onboard low-earth-orbit (LEO) satellites. In addition, this study is beneficial for impelling IR precipitation estimation to consider more radiation-based physical theory, especially for FY-4A official precipitation product.

Calculating the motion vectors based on the infrared observations can complement the gaps of the passive microwave and radar observations. So, it is an essential step in numerical weather models and other meteorological applications. FengYun-4A (FY4A) geosynchronous satellite was launched in 2016 by the Chinese government. Carrying on board the Advanced Geosynchronous Radiation Imager (AGRI), FY4A can provide instantaneous observations from visible to infrared wavelength. However, the ability of FY4A to capture precipitation motions still needs to be explored, and the optimal scale for generating the motion vectors remains unclear. In this study, a reasonable sliding window strategy is applied to calculate the correlation coefficients (CCs) between two image sequences to determine the motion vectors. Based on this, the China Merged Precipitation Analysis (CMPA) data are recognized as the true precipitation data and are compared with the 10.7-$\mu \text{m}$observations of AGRI aboard FY4A. The results in August 2018 show that based on the quantified directions of the motion vectors between the CMPA and FY4A-TBB (the Temperature of Black Body from FY4A), the CC and root mean square error (RMSE) are about 0.64 and 2.1, respectively, which are acceptable at the resolution of 0.5°. The result indicates that FY4A can capture precipitation motions. Meanwhile, as the resolution becomes lower, the probability distributions of the motion vectors in FY4A-TBB are more similar to those in CMPA, and they become stable at 0.5°. Therefore, 0.5° is considered the optimal resolution to generate the motion vectors for FY4A-TBB. The study results have great potentials for precipitation estimate retrieval and fusion for FY4A and also FY4B.

Accurate and near-real-time rain information at fine scales is critical for forecasting local weather and floods. Traditional classical infrared (IR) cloud-top brightness temperature data alone do not contain sufficient precipitation-related information, and the introduction of visible (VIS) observations limits their applications to daytime. Methods for the efficient and comprehensive utilization of multichannel IR observations for accurately retrieving all-day near-real-time rain rates with consistent high quality warrant further exploration. In this study, we propose an all-day near-real-time quantitative precipitation estimation framework based on multispectral analysis (MSA) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard Chinese FY-4 series satellites; the proposed framework is called FY4QPE- MSA. Multiple IR bands are comprehensively and efficiently considered by adopting the principal component analysis technique to reduce the dimensionality to a few independent features while preserving most of the variations. The main conclusions include, but are not limited to, the following aspects: 1) the MSA from IR channels provides valuable information that facilitates the more accurate delineations of the precipitation occurrences; 2) FY4AQPE -MSA outperforms FY4AQPE- Offical [$\sim 20$% gain in the Pearson correlation coefficient (CC),$\sim 25$% gain in the root mean square error (RMSE), and$\sim 15$% gain in the critical success index (CSI)], FY4AQPE-Single ($\sim 10$% gain in CC,$\sim 15$% gain in RMSE, and$\sim 15$% gain in CSI), and Precipitation Estimation from Remotely Sensed Information using artificial neural network-cloud classification system (PERSIANN-CCS) ($\sim 30$% gain in CC,$\sim 20$% gain in RMSE, and$\sim 25$% gain in CSI); and 3) compared with the international IR-based baseline precipitation product, i.e., PERSIANN-CCS, FY4AQPE-MSA demonstrates no spatial gaps over the Tibetan Plateau. In addition, the results of this study suggest that the proposed general framework is promising and applicable for Chinese FY-4 series satellites in generating all-day near-real-time rain-rate information.

Gridded precipitation products with fine resolutions and qualities are of great importance for understanding the global water–carbon-energy cycles at various spatiotemporal scales. Though continuous developments in Satellite Remote Sensing fields have been providing great strengths for measuring the precipitation from space, merging precipitation products from different sources, especially the gauge observations, is still the optimal way for obtaining high-quality precipitation data. Currently, the mainstream merging methods mainly focus on merging the rain rates without the considerations of rain events. In this study, we propose a new assumption that both rain events and rain rates should be considered in the merging procedures rather than only the rain rates. To meet our assumption, a morphology-based adaptive spatio-temporal merging algorithm (MASTMA) for combining various precipitation products is proposed, in which the morphology theory is first introduced to comprehensively consider the influences from both rain events and rain rates. The multisource and multiscale precipitation products including the gauge-based data (CPC-U, 0.5°, daily), the satellite-based data [Global Satellite Mapping of Precipitation by Moving Vector with Kalman (GSMaP-MVK), 0.1°, hourly; integrated multisatellite retrievals for global precipitation measurement late run (IMERG-LR), 0.1°, half-hourly], and the reanalysis data (ERA5-land, 0.1°, hourly), have been comprehensively considered in MASTMA for generating the final estimates (MASTMA-F, 0.1°, hourly) over the southeastern regions of the Mainland China in the periods from 2016 to 2019. The main conclusions include but are not limited to: 1) considerations on rain events contribute significantly to the final merged results, especially when eliminating false extreme values over the regions where precipitation is greatly overestimated; 2) the MASTMA could optimally integrate the advantages from multisource precipitation products with different resolutions, particularly from the perspective of the spatial distributions; and 3) the final merged estimates using MASTMA outperform the contemporary state-of-the-art precipitation products especially in terms of modified Kling–Gupta Efficiency (mKGE) and critical success index (CSI). Additionally, the results of this study suggest that MASMTA is a new promising merging approach with great robustness and applicability, and has the foreseeable potentials for the operational run to generate the optimal global merged precipitation products.

Triple collocation (TC) is a novel method for quantifying the uncertainties of three data sets with mutually independent errors and has been widely used over different geographical fields. Researches in recent years report that TC shows potential in merging multiple data sets from different sources, while the TC-based merging method has not been used over precipitation. Using the TC formulation, this study merges precipitation from the Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5). The interim ECMWF Re-Analysis (ERA-Interim) is also involved to act as the substitute of ERA5 in some specific experiments for quality comparison between them. Merged data sets are produced at 0.25°×0.25°and daily resolutions from March 2000 to December 2013 over Mainland China, using ground observations from more than 2000 rain gauges as the validation benchmark. First, the effectiveness of the TC-based method for precipitation merging is assessed. Then, two weighting methods using root-mean-square error (RMSE) in logarithmic scale (log-RMSE) and modified scale (mod-RMSE) are compared because previous studies show that mod-RMSE is more suitable for characterizing errors within estimated data. Meanwhile, two merging strategies are designed, that is, merging rainfall and snowfall separately (RS) and merging precipitation directly (P). The results show that 1) all the merged products are superior to any input product which proves that the TC method is effective in precipitation merging; 2) TC-based merging generally has a better performance than dynamic Bayesian model averaging (DBMA)-based merging; 3) mod-RMSE shows worse performance in weight estimation than log-RMSE because mod-RMSE will deteriorate the impact of the underestimated inputs; and 4) RS-based merging is superior to P-based merging, and the superiority is particularly notable in winter. The RS strategy will be very helpful in improving the accuracy of precipitation estimates in cold climate such as over mountainous and high-altitude regions. Finally, the limitations of the TC method and potential solutions are discussed. This study demonstrates the great potential of the TC-based merging method in precipitation and provides insights into its application and development.

We introduce a spatially explicit statistical downscaling (SESD) method that fuses multiscale geospatial data with the soil moisture (SM) product from NASA's SM Active and Passive (SMAP) satellite. The multiscale data included the 9-km resolution SMAP SM image, 1-km resolution normalized difference vegetation index (NDVI), 1-km digital elevation model (DEM), 1-km resolution MODIS land surface temperature (LST), 500-m resolution gross primary productivity (GPP), 30-m resolution topographical water index (TWI), and West Texas Mesonet (WTM) station data. We used the random forest (RF) machine learning method to make a downscaled SM prediction at the 1-km resolution. Then, a regression kriging was applied to model the unpredicted variability at local scales to produce downscaled SM using the WTM station data. Due to the low number of ground truth samples, the validation was based on Monte -Carlo cross validation (CV) to calculate the unbiased root-mean-square deviation (ubRMSD), root-mean-square deviation (RMSD), and bias of the test set randomly separated from the training set from the WTM station data. Model validation showed that the downscaled SM data at the 1-km resolution can significantly improve the accuracy of the SM product as well as enhancing its spatial resolution. This article has its novelty in using the spatially explicit model to reconcile the scale difference from satellite data and ground observations.

With the emergence of virtualization technologies, various services have been migrated to the cloud. Beyond the tenants' own security controls implemented in the virtual machine (VM), the binary integrity verification mechanism in the virtual machine manager (VMM) provides stronger protections against malware. Unfortunately, none of existing integrity verification mechanisms in the cloud provides complete transparency and fine-grained efficiency. Some schemes selectively check the integrity of sensitive binaries, but they require modifications to the VMs (e.g., integrating monitoring libraries) to trigger verification. Others, although need no modification to the VMs, have to enforce checking on all the binaries, because they cannot distinguish binary images for the sensitive processes from the binaries for insensitive ones, leading to significant performance overheads. In this paper, we present TF-BIV, a transparent and fine-grained binary integrity verification scheme, which does not require any modification or software/driver installation in the VM. TF-BIV identifies the sensitive processes at the creation, and checks the integrity of the binaries (including the guest OS kernel and the dependant binaries) related to these processes. The provided transparency and efficiency are achieved by leveraging existing hardware virtualization supports (i.e., Intel extended page table) and debugging features (i.e., monitor trap flag). We have implemented the TF-BIV prototype based on QEMU-KVM. To demonstrate the usability of TF-BIV, we adopted it for cloud-based cryptographic services, to achieve the strict invoking controls. In addition to the password-based authentication, TF-BIV further achieves process-level authorization to the invokers. Intensive evaluation shows that TF-BIV implements the designed binary integrity verification with only about 3.6% performance overhead.

Cache timing side channels allow a remote attacker to disclose the cryptographic keys, by repeatedly invoking the encryption/decryption functions and measuring the execution time. Warm and Delay are two algorithm-independent and implementation-transparent countermeasures against remote cache-based timing side channels for block ciphers. They destroy the relationship between the execution time and the cache misses/hits which are determined by the secret key, but bring remarkable performance overhead. In this paper, we investigate the performance of cryptographic functions protected by Warm and Delay, and attempt to find the best strategy to integrate these two countermeasures with the optimal performance while effectively eliminate remote cache timing side channels for block ciphers implementations with lookup tables. To the best of our knowledge, this work is the first to systematically analyze the performance of integrating Warm and Delay against cache side channels.We derive the optimal scheme to integrate Warm and Delay, and apply it to AES. It is proven that the integration scheme achieves the optimal performance with the least extra operations on commodity systems. Finally, we implement it on Linux with Intel CPUs. Experimental results confirm that, ( a) the execution time does not leak information on cache access, ( b) the scheme outperforms other integration strategies of Warm and Delay, and ( c) the implementation works without any privileged operations on the computer.

The accurate knowledge of precipitation information over the Qinghai-Tibet Plateau, where the rain gauge networks are limited, is vital for various applications. While satellite-based precipitation estimates provide high spatial resolution (0.25°), large uncertainties and systematic anomalies still exist over this critical area. To derive more accurate monthly precipitation estimates, a spatial data-mining algorithm was used to remove the obvious anomalies compared with their neighbors from the original Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) 3B43 V7 data at an annual scale, as the TMPA data are more accurate than other satellite-based precipitation estimates. To supplement the international exchange stations, additional ground observations were used to calibrate and improve the TMPA data with anomalies removed at an annual scale. Finally, a disaggregation strategy was adopted to derive monthly precipitation estimates based on the calibrated TMPA data. We concluded that: 1) the obvious anomalies compared with their neighbors could be removed from the original TMPA data sets and 2) the calibrated results were of a higher quality than the original TMPA data in each month from 2000 to 2013. The improved TMPA 3B43 V7 data sets over the Qinghai-Tibet plateau, named NITMPA3B43_QTP, are available at http://agri.zju.edu.cn/NITMPA3B43_QTP/.

Cryptosystems are essential for computer and communication security, e.g., RSA or ECDSA in PGP Email clients and AES in full disk encryption. In practice, the cryptographic keys are loaded and stored in RAM as plain-text, and therefore vulnerable to cold-boot attacks exploiting the remanence effect of RAM chips to directly read memory data. To tackle this problem, we propose Copker, a cryptographic engine that implements asymmetric cryptosystems entirely within the CPU, without storing any plain-text sensitive data in RAM. Copker supports the popular asymmetric cryptosystems (i.e., RSA and ECDSA), and deterministic random bit generators (DRBGs) used in ECDSA signing. In its active mode, Copker stores kilobytes of sensitive data, including the private key, the DRBG seed and intermediate states, only in on-chip CPU caches (and registers). Decryption/signing operations are performed without storing any sensitive information in RAM. In the suspend mode, Copker stores symmetrically-encrypted private keys and DRBG seeds in memory, while employs existing solutions to keep the key-encryption key securely in CPU registers. Hence, Copker releases the system resources in the suspend mode. We implement Copker with the support of multiple private keys. With security analyses and intensive experiments, we demonstrate that Copker provides cryptographic services that are secure against cold-boot attacks and introduce reasonable overhead.