Abstract In order to align the virtual and real content precisely through augmented reality devices, especially in optical see-through head-mounted displays (OST-HMD), it is necessary to calibrate the device before using it. However, most existing methods estimated the parameters via 3D-2D correspondences based on the 2D alignment, which is cumbersome, time-consuming, theoretically complex, and results in insufficient robustness. To alleviate this issue, in this paper, we propose an efficient and simple calibration method based on the principle of directly calculating the projection transformation between virtual space and the real world via 3D-3D alignment. The proposed method merely needs to record the motion trajectory of the cube-marker in the real and virtual world, and then calculate the transformation matrix between the virtual space and the real world by aligning the two trajectories in the observed view. There are two advantages associated with the proposed method. First, the operation is simple. Theoretically, the user only needs to perform four alignment operations for calibration without changing the rotation variation. Second, the trajectory can be easily distributed throughout the entire observation view, resulting in more robust calibration results. To validate the effectiveness of the proposed method, we conducted extensive experiments on our self-built optical see-through head-mounted display (OST-HMD) device. The experimental results show that the proposed method can achieve better calibration results than other calibration methods.

Recently, in IEEE Transactions on Dependable and Secure Computing (TDSC), the VERSA scheme proposed by Hahnet al. uses a double aggregation method for verifying the correctness of results returned from the server. The authors proposed that the correctness of the model aggregation can be verified with lower verification overhead by utilizing only a lightweight pseudorandom generator. To support verifiability of results returned from the server, a method of sharing a pair of vectors$(a,b)$by all clients is proposed, which is one of the most important work in VERSA. Unfortunately, in this paper, we show that the method is incorrect, which leads clients to consistently conclude that the aggregated results are incorrect. Furthermore, the model training process in federated learning is forced to abort. Finally, we demonstrate our view through theory analysis and instantiation verification.

Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold$d$, the storage cost will grow exponentially or the precision of search results will greatly decrease as$d$increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this article, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over encrypted data, in which our scheme achieves fuzzy keyword search for any similarity threshold$d$with a constant storage size and accurate search results. Furthermore, we design a two-factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade-off in efficiency, security, and functionality. Recent work proposes a file-injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well-chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the T F × I D F $TF\times IDF$ rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID-19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient.

Due to a variety of advantages of data outsourcing, some Location Based Services (LBS) providers are motivated to outsource the geographic data and query service to commercial cloud. However, for protecting data confidentiality, the valuable data should be encrypted before outsourcing, which obstructs the utilization like geographic information query. To address this problem, some previous works regarding to secure search on encrypted database could be applied in outsourced LBS scenario directly, but none of them is tailor-made for linear region search (LRS). The LRS is a kind of LBS that widely used in navigation system, it finds the nearby points of interest (POI) for a query segment. In this paper, for the first time, we explore and solve the challenging problem of privacy-preserving linear region search. Specifically, we choose the quadtree structure to build index for POI database, then the results of LRS can be efficiently obtained by finding out the rectangular regions that query segment passes through. In order to preserve the privacy of both LBS providers and users, according to computational geometry and Asymmetric Scalar-product Preserving Encryption (ASPE) approach, we design a novel algorithm for accurately determining whether a segment intersects with a rectangle on ciphertext. Moreover, this algorithm also provides a new idea to solve other computational problems in encrypted 2-dimensional geometry space. Based on different privacy requirements of two threat models, we propose two privacy-preserving LRS schemes and corresponding dynamic update operations. Security analysis and experiments on real-world dataset show that our schemes are secure and efficient.

GF 5 is a hyperspectral imaging satellite which is configured with six forms of payloads. EMI is the first high-resolution imaging spectrometer used for the detection of atmospheric trace gases, the indicator of environmental pollution. The high spatial-temporal pollutants distribution information collected by EMI will assist government's decision-making and evaluation of air quality guarding in the future.

Increasing marine activities taking place within the exclusive economic zone (EEZ) have made broadband maritime communications very attractive in recent years. In this paper, a coordinated satellite and terrestrial architecture (II-CST) is presented to implement real-time and broadband Internet access at sea. Further, since the mobility of shipborne base stations (S-BSs) may cause complex inter-cell interference (ICI), an S-BS sleeping control scheme is proposed to adapt the system resource allocation corresponding with the user load. When the proportion of blocked users is beyond threshold, the sleeping control process is triggered to reduce ICI and recover broadband access. Then we use the sailing position of accessed users as a constraint to adjust the downlink power allocation of S-BSs. Simulation results with real ship data of China's Yellow Sea show that under the II-CST, our adaptive S-BS sleeping control can contribute to achievable downlink data rate, mobility robustness, and power efficiency of the dynamic broadband maritime communication system.

Evapotranspiration (ET) is crucial to arid and semiarid environments because it substantially affects the energy and water cycles. Based on the estimation of continuous ET from barren or sparsely vegetated areas, this letter investigated how ET affects the regional land surface temperature (LST) in the Shiyanghe river catchment area, a typical inland river catchment in Northwest China. ET was estimated during 2008-2011 using a three-temperature model and MODIS data sets. LST was retrieved from the MODIS product (MOD11A2). Results taken from transects that began in the oasis area and ended in the desert reveal that ET decreased, with the daily average ET in the oasis being approximately 1.4 mm larger than that in the desert. By contrast, the LST in the oasis was 8 K lower than that in the desert. Statistical results suggested that ET and LST showed a negative relationship (R2=0.83$ ). Further analysis showed that the correlation was strongly dependent on the water availability in barren or sparsely vegetated areas. It is concluded that in water-limited barren or sparsely vegetated regions, the negative correlation between ET and LST may provide alternative information for water management, such as identifying groundwater recharge in arid regions.

This paper presents an algorithm for the retrieval of ozone profiles from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership satellite using a nonlinear optimal estimation method. The issue of channel selection is discussed. Based on a sensitivity analysis, we selected a spectral range of 990-1070 cm-1for the ozone profile retrievals. Compared with ERA-Interim ozone profiles and eigenvector regression method profiles, the ozone climatology profile is better able to construct the a priori state. The retrieved CrIS profiles are in good agreement with smoothed high-vertical-resolution ozonesonde profiles. An analysis of the information content of the CrIS retrievals demonstrates that the CrIS measurements can provide useful information for capturing the spatial and temporal variations in ozone and are insensitive to ozone below 400 hPa. An error analysis revealed that smoothing error represents the main error source for the retrieved CrIS ozone profiles.

The classification criteria are established to classify the water of Taihu Lake into four classes based on above-water remote sensing reflectance (Rrs), i.e., types A to D. Among the four water types, type A spectra represented the case of waters where algal blooms or aquatic plants appeared, while type B is referred to the water with high suspended matter concentration and low chlorophyll a concentration (Cchla). Both types A and B were not suitable for retrieving Cchlafrom image data. Hence, three-band, four-band, and two-band ratio algorithms were constructed to retrieve Cchlafrom water types C and D. The obtained results showed that the relation trends between Cchlaand Rrswere different between type C and type D waters. By using Medium Resolution Imaging Spectrometer images, acquired on November 11, 2007 and November 20, 2008, the Cchlaof Taihu Lake was mapped by band 9/band 7 models; it could be concluded that the Cchlamainly ranged from 0 to 20 mg · m-3, accounting for 83.70% of the whole lake area in 2007 image, while the area was 86.63% in 2008 image. The estimation accuracies varied from different Cchlaranges. The mean absolute percent errors obtained by band 9/band 7 models were 106.23%, 56.79%, 38.04%, 33.80%, and 58.74% for the ranges 0 mg · m-3chla-3, 5 mg · m-3chla-3, 10 Cchla-3, 20 mg · m-3chla-3chla, respectively. Correspondingly, the root-mean-square errors were 5.02, 4.45, 5.59, 8.72, and 32.55 mg · m-3, respectively.

This paper focuses on assessing the effectiveness of applying orientation angle calibration to polarimetric synthetic aperture radar (PolSAR) data for soil moisture estimation. We employ Cloude-decomposition-based method to estimate the orientation angle because it can relate a scatter-distributed pixel to its major component of an equivalent "pure target," use the Jet Propulsion Laboratory/Airborne Synthetic Aperture Radar L-band fully polarimetric data to validate the proposed method, and observe results in good agreement after orientation angle compensation is employed. Specifically, root mean square errors of measured radar backscattering coefficients σhh0and σvv0and copolarization ratio versus advanced integral equation model predictions are reduced significantly from 1.95, 1.33, and 2.03 dB to 1.30, 1.15, and 1.43 dB, respectively. The compensated copolarized backscattering coefficients are also used as inputs to a novel inversion model to estimate the dielectric factor Rhhand volumetric soil moisture mv. The results show that the estimation errors are reduced significantly from 0.075 to 0.054 and 0.056 to 0.041 for Rhhand mv, respectively. This paper demonstrates the advantage of orientation angle calibration as a preprocessing for estimating bare soil moisture, particularly in agricultural areas, and the preponderance of fully PolSAR data on soil moisture estimation over dual and single polarizations.

On September 6, 2008, the satellite HJ-1B was launched into a sun-synchronous, near-polar orbit. In order to determine temporal changes of the absolute radiometric calibration of the HJ-1B satellite in flight, a program was carried out at DunHuang calibration field, Gansu province of China, from August 19 to 30, 2009. In this work, reflectance -based calibration method was employed to simulate the absolute calibration coefficients of the HJ-1B CCD sensors. Then the cotton field, cement court, water pool and test site was selected to validate the calibration coefficient. The validation results indicated that there had a good agreement between the imaged-based reflectance and the ground measurement of the type of cement court and test site. On the other hand, there had a disagreement of the type of cotton field and water because of the effect of mixed pixel.

The paper presents a new modification method of chlorophyll content index based on a new LAI index-sLAIDI to solve the problem of chlorophyll content index influenced by LAI. Based on the data stimulated by PROSPECT-SAIL modal and measured data, seven chlorophyll content indices are chosen and divided by sLAIDIkto reduce the influence of LAI. Based on the stimulated dataset, the normalized standard deviation of chlorophyll content indices are used to evaluate effect of modification to determine the parameter k. Measured dataset are used and to verify result of modification. The result shows that chlorophyll content index divided by sLAIDIkcan improve accuracy of estimation, and parameter k is different for different chlorophyll content indices.

A comparative research of snow cover is conducted, which is aimed to investigate the effect of spatial scale variation and the differences of different spatial resolution remote sensing images, by using moderate-resolution imaging spectroradiometer (MODIS) data and the satellite-B (HJ-1B) data of the small satellite constellation for environment and disaster monitoring and forecasting of China. Results show that: (1) the scaling-change within certain limits has little impact on snow cover mapping; (2) high spatial resolution image gives a better description of the detailed information of snow cover area; (3) the differences of snow cover between low spatial resolution and high resolution images are due to the different snow cover type.

Drought is a harmful and headachy natural disaster in the world, which has caused considerable loss to agricultural production and economy per year with high frequency. It is therefore very important and necessary for drought monitoring over large scales by remotely sensed techniques. In order to establish physically meaningful water stress index, the SWIR bands with strong water absorption features are used in this study, then the shortwave infrared water stress indices (SIWSI) are constructed using NIR and SWIR bands. A representative arid and semi-arid region over northwestern China, Ningxia region, where droughts occur frequently, is selected to assess the drought status using two indices of SIWSI6, 2and SIWSI7, 2derived from NIR channel 2 and SWIR channel 6 or 7 of Moderateresolution Imaging Spectroradiometer (MODIS) sensor, combining with ground truth data of soil moisture observed by the meteorological stations across the whole study area. The fitted regressions of negative exponential curves indicate that both indices have significantly correlated with the in-situ measurements (P6, 2performs slightly better than the SIWSI7, 2(r2=3D0.56 and 0.48).

A novel algorithm for the mobile station (MS) location estimation with none line of sight (NLOS) error in wireless network is proposed in this paper. In the proposed algorithm, the MS location estimation problem is formulated as a simple linear approximation problem in vector space with the data corrupted by the NLOS errors. The M-estimator is employed to eliminate the NLOS errors, and a recursive algorithm is developed to solve the M-estimation normal equations. Compared to the conventional algorithms, the proposed algorithm does not rely on the prior knowledge of the statistical model of the measurement noise. Another advantage is that the proposed algorithm can track the slow moment of MS due to the recursive nature, which is hardly to achieve in other algorithms. The algorithm also possesses low arithmetic complexity. Effectiveness of the proposed algorithm is verified by the numerical simulations.