Over 150 countries have signed the Global Methane Pledge, aiming to reduce anthropogenic methane emissions by 30% by 2030. Reducing methane emissions from the energy sector is crucial to achieving this target. The current emission inventories for the energy sector have a spatial resolution of 1 km, suitable for regional-scale methane flux inversion but inadequate for identifying and monitoring point source emissions which is the most important type of anthropogenic methane emissions in the energy sector. To address this issue, we propose a multiattention mechanism, MAM-YOLOv9, for identifying emission facilities in the oil and gas industry, based on YOLOv9. We integrate SimAM and cascaded group attention (CGA) modules into the network, focusing on target objects under complex backgrounds while improving detection accuracy. In addition, we introduce the dynamic convolution module to replace the convolution in the YOLOv9 backbone network, improving computational efficiency and accurate object detection capability. Using submeter-level optical images provided by the high-resolution satellite images, we achieve large-scale monitoring of facility-level emission sources on a regional scale. Experiments demonstrate that our new method achieved SOTA performance, achieving the best results across various metrics compared with the baseline. We also conduct batch detection tasks in Shengli Oilfield, the second-largest oilfield in China, identifying over 38000 emission facilities. Based on the results, we further compile a facility-level methane emission inventory, which can better serve the global efforts for mitigating methane emissions from the oil and gas industry.

Generative Adversarial Networks (GANs) have become widely used in model training, as they can improve performance and/or protect sensitive information by generating data. However, this also raises potential risks, as malicious GANs may compromise or sabotage models by poisoning their training data. Therefore, it is important to verify the origin of a model’s training data for accountability purposes. In this work, we take the first step in the forensic analysis of models trained on GAN-generated data. Specifically, we first detect whether a model is trained on GAN-generated or real data. We then attribute these models, trained on GAN-generated data, to their respective source GANs. We conduct extensive experiments on three datasets, using four popular GAN architectures and four common model architectures. Empirical results show the remarkable performance of our detection and attribution methods. Furthermore, we conduct a more in-depth study and reveal that models trained on various data sources exhibit different decision boundaries and behaviours.

Deep learning models, pivotal in AI applications, are susceptible to model hijacking attacks. In model hijacking attacks, adversaries can misuse models for unintended tasks, shifting blame and maintenance costs onto the models’ deployers. Existing attack methods re-purpose target models by poisoning their training sets during training. However, leading models like GPT-4 and BERT with vast parameters are often pruned before deployment on resource-limited devices, which presents challenges for in-training attacks, including existing model hi- jacking attacks. In this paper, we propose PRJack, the first pruning-resistant hijacking attack. Specifically, the adversary re-purposes a model to perform a hijacking task different from the original task, which can still be activated even after model pruning. Our experiments across multiple datasets and pruning techniques highlight PRJack’s remarkable superiority on pruned models over existing model hijacking attacks.

Carbon observation satellites based on passive theory (e.g., OCO-2/3, GOSAT-1/2, and TanSat) have relatively high carbon dioxide column concentration (XCO2) accuracy when the observation conditions are met. Passive satellites have data bias and coverage deficiencies due to cloud cover, low albedo, low-light conditions, and aerosol scattering, resulting in carbon observation satellites based on passive theory that cannot meet the demand for high-precision, all-day, all-weather XCO2 monitoring. Active detection satellites are urgently needed to support global carbon sources, sinks, and carbon neutrality. China intends to launch a sensor satellite with active detection of XCO2 in the coming years. In this work, based on the satellite’s scaled-down airborne experiments, a spectral energy model was developed to optimize the conventional inversion algorithm and achieve a more accurate XCO2 inversion. The 1.572-$\mu \text{m}$integrated path differential absorption (IPDA) lidar column length is used indirectly to evaluate the accuracy of the spectral energy model for signal extraction. Also, the experimental results show that the accuracy of the signal extracted by the 1.572-$\mu \text{m}$IPDA lidar column length is 0.74 and 6.20 m at sea and on land based on the indirect evaluation of the length of the 1.572-$\mu \text{m}$IPDA lidar column length. The optimized XCO2 was evaluated (standard deviation as an evaluation metric) and its XCO2 standard deviation reduced by 31%, 63%, and 66% in the ocean, plains, and mountains, respectively. Our algorithm can obtain the XCO2 with a consistent trend by using XCO2 from the OCO-2 satellite as a reference. The calculated XCO2 is more accurate in areas dominated by anthropogenic factors (plains), due to the accuracy of the IPDA detection mechanism. This algorithm improves the accuracy and robustness of XCO2 inversion and has important reference significance for the IPDA lidar carried by China’s satellites to be launched in this year.

A novel framework was proposed to measure atmospheric concentration of$\delta ^{13}C$using a multiwavelength integrated path differential absorption (IPDA) LiDAR. The spectroscopy range of the multiwavelength IPDA LiDAR is recommended from 2264.5 to 2265.5 cm$^{-1}$. Using the proposed retrieving method, the relative error of$\delta ^{13}C$retrievals would be within 0.16‰ under reasonable settings. Moreover, the proposed method shows reliable performances in different circumstances. It would be of great significance for exploring the characteristic of$\delta ^{13}C$in the ecosystem and anthropogenic emissions in the future.

Ground-based differential absorption lidar (DIAL) can measure vertical CO2concentration profiles in the troposphere. Here, we propose a method of improving the accuracy and precision of CO2concentration profiles measurements. This method combines a conditional adjustment with Chebyshev fitting to reduce the error of the retrieved results in view of the received signal around the atmospheric boundary layer (ABL) with a high signal-to-noise ratio (SNR). Simulation experiments verified the effectiveness of this method. The accuracy of CO2concentration profiles can be improved larger than 83.4% when compared with that via traditional methods, and the standard deviation of the measured CO2concentration profiles calculated by our method was reduced by approximately 0.43–22.51 ppm when compared with the results calculated by traditional methods. Two real cases in different locations were also examined with the proposed technique. The results indicated the applicability of our method in measuring other trace gases by using DIAL.

With the rapid growth of GHG monitoring satellites, more and more studies focused on the issue of inversion/optimization of CO2 fluxes using satellite-derived XCO2 observations in recent years. A common and critical challenge in this framework is the separation of background and anomalies from XCO2 observations, which directly affect performance of the CO2 fluxes inversion. We proposed a novel method to accurately extract background XCO2 from satellite observations. A series of observing system simulation experiments were performed to test the performance of the method. We found that the bias and uncertainty of the background concentration are below 0.01 ppm and 0.05 ppm in the given cases, respectively. Based on this method, we selected five overpasses from 2014 to 2016 to demonstrate a regional-scale flux inversion near Riyadh. The comparison with the two previous methods shows that the posterior simulated XCO2 by the method proposed in this paper can match better with the observed XCO2 from OCO-2.

Continued drought can lead to vegetation mortality and reduced carbon sink capacity of terrestrial ecosystems. However, the complexity of the causes and processes of drought has led to a limited understanding of how vegetation performances under drought. Here we used solar-induced chlorophyll fluorescence (SIF) and enhanced vegetation index (EVI) data to explore the impact of U.S Midwest drought on vegetation in 2012. At the whole study area and flux tower scale, SIF is more sensitive to the decrease in precipitation than EVI. SIF also can more accurately monitor the growth of vegetation under drought. SIF is an effective index for monitoring environmental stress on vegetation.

CO2 is the most important warming gas in atmosphere, it's meaningful to measure the CO2 concentration with high precise by different sensors. In this manuscript, we introduced the campaign of QHD (Qinhuangdao) flights, which equipped with CO2- IPDA (integrated path of different absorption LIDAR), an in-situ CO2 sensor, thermometer, hygrometer and GPS (Global Positioning System). A fast and accurately retrieve method of CO2 by the oral data acquired by the airborne IPDA has been developed by our group. These flight contains three different landforms, including sea, city and mountain. It shows apparent difference on the distribution of CO2 among different landforms. Finally, we compared XCO2 data from OCO-2 and results of XCO2 calculated by IPDA system, it shows little difference.

Quantification of the distribution of the CO2dry-air mixing ratio (XCO2) is crucial for understanding the carbon cycle. However, clouds and aerosols in the line of light create spectral interference with CO2signals. This interference can result in a low yield of XCO2retrievals, thus limiting the application of these valuable satellite data. In this study, we developed an innovative methodology to obtain XCO2maps of high spatial and temporal resolution using satellite data. The method first interpolates the spatial properties using an empirical Bayesian kriging (EBK) algorithm. Then, the temporal properties are modulated based on a CO2curve database that was constructed using temporal contours and transfer learning techniques. We applied this method to obtain spatiotemporal XCO2maps over mainland China using the Orbiting Carbon Observatory 2 (OCO-2) data product OCO-2_L2_Lite_FP 9r for the period from January 1 to December 31, 2019. The correlation coefficient ($R^{2}$) was 0.8056, and the average absolute prediction error [root-mean-square error (RMSE)] was 0.9951. In the research area of mainland China, the vacancy validation strategy was adopted and yielded$R^{2}$and RMSE of 0.8230 and 0.9746, respectively. We used the 2018–2019 ground-based data from four Total Carbon Column Observing Network (TCCON) sites in Europe and 2016 Hefei sites in mainland China to evaluate the performance of this new mapping method, respectively. Also, we obtained$R^{2}$of 0.8690 and the RMSE of 0.9056 in Europe and$R^{2}$of 0.8473 and the RMSE of 0.7026 in mainland China, proving the robustness and high precision of our method. This mapping technique is capable of filling the spatiotemporal gaps of satellite measurements with the high accuracy and resolution needed for its scientific application; thus, it has the potential to augment the scientific returns of satellite missions (e.g., USA OCO-2 Japan GOSAT and Chinese TanSat).

Recent years have witnessed the enormous growth of Android phones in the consumer market. On the other hand, as the most popular mobile platform, Android also attracts lots of attackers' attention. As a result, more and more Android malicious apps appear in the wild, which poses a serious threat to user's security and privacy. To such massive volume of Android malware, automated UI testing techniques have become the mainstream solutions because of the detection efficiency and accuracy. However, all existing UI testing techniques treat the Activity as the basic unit of UI interactions and cannot carry out a fine-grained analysis for Fragments. Due to the lack of Fragment-level analysis, the path coverage is usually quite limited. To fill this gap, in this paper, we propose FragDroid, a novel automated UI testing framework supporting both Activity and Fragment analysis. To achieve the Fragment-level testing, we design the Activity & Fragment Transition Model (AFTM) to simulate the internal interactions of an app, and ATFM could be utilized to generate test cases automatically through UI interactions. With the assist of AFTM, FragDroid achieves accessing most Activities and Fragments contained in the app along with the capability of detecting arbitrary API calls. We implemented a prototype of FragDroid and evaluated it on 15 popular apps. The results show FragDroid successfully covered 66% Fragments and the corresponding API calls of testing apps. Also, the traditional approaches have to miss at least 9.6% of API calls invoked in Fragments.

To facilitate understanding of the relationship between the most significant greenhouse gas carbon dioxide and human activities, we have developed a differential absorption lidar (DIAL) detection system at 1.57 μm. The goal of this lidar system is to detect the temporal and spatial distribution of atmospheric carbon dioxide gas from 0.3 km to 3 km in the atmosphere. Beginning in 2009, the system was initially completed in 2013. Since then, we have been constantly experimenting and repeated instrumentation improvements. From July 2015 to the present, we carried out vertical and horizontal measurement experiments in the urban area of Wuhan, Hubei Province and the suburb of Huainan, Anhui Province, China. This article presents a fast and optimized inversion algorithm to improve the speed and accuracy. Experimental results show that the DIAL system and inversion algorithm are stable and reliable.

To evaluate the performance of the Orbiting Carbon Observatory 2 (OCO-2) Lite File Product (Lite_FP) which has the highest amount of data and the highest utilization efficiency among the three products of OCO-2, we compared global atmospheric CO2observations for 20 months (September 2014 to April 2016) with GGG2014 data from the Total Carbon Column Observing Network (TCCON). We considered the latitude distribution of the TCCON sites and performed a site-by-site comparison at different latitude zones. The result demonstrated that the seasonal fluctuation of XCO2from Lite_FP is consistent with TCCON, and the biases of XCO2measurements ranged from -3 ppm to 4 ppm, with a 1% precision. Bias distribution differed in terms of latitude zones and observing modes. In addition, we analyzed the distribution characteristic of the bias of XCO2observations under land target mode in detail combined with surface and atmospheric properties.

The vertical profile of atmospheric CO2is of great scientific significance in identifying carbon sinks and sources, and estimating CO2emissions or uptakes. Differential absorption Light Detection And Ranging (DIAL), has been widely accepted as the most promising technique to sense atmospheric CO2. The classical method to retrieve measurements, generated from range-resolved detection, is derived from differentiating the measured column content, but its performance in dealing with aerosol backscatter signals is poor. To address this issue, this paper proposes a derivative method, which is based on Chebyshev fitting to the measured differential absorption optical depth. We created a performance evaluation model to assess the performance of the proposed method. Simulations revealed that the error of a single CO2profile in data retrieval can be reduced to less than 4 ppm in 6000 m. The precision of long-term mean CO2profile is expected to be less than 1 ppm. We believe that this novel method can be used in other applications also, e.g., trace gas measurements collected using DIAL, especially when the signal-to-noise-ratio of received signal is small.

The vertical profile of atmospheric carbon dioxide is one of the most significant parameters for carbon cycle study. Ground-based differential absorption lidar (DIAL) is widely accepted as the most compromising technique to obtain vertical profile of atmospheric CO2. A container-integrated prototype was demonstrated in this paper. Both the configuration and preliminary results were presented here. This equipment will be tested along with the other 6 equipments of atmospheric soundings in Huainan this year, aiming to deepen our understanding about the interactions within neutral atmosphere layers.

This work evaluates the performance of OCO (Orbiting Carbon Observatory) -2 on global observations since its launch in Sep 2014. It is 10%~30% coverage that could be detected to obtain the concentration of atmospheric carbon dioxide in space dimension. However, about 65% data of OCO-2 has not be utilized to retrieve because of special atmospheric environment, such as thick aerosol depth and low pressure. In the accuracy aspect, compared with the TCCON, OCO-2 has good consistence of XCO2 at most sites. The average bias of monthly value is about 0.87 ppm and the standard deviation is 1.8 over TCCON sites. The mean monthly value of CO2can represent the actual value at a certain range.

A transportable two-wavelength (532 and 355 nm) depolarization Mie Lidar has been described. The 532 nm has a polarization channel. It has the ability to simultaneously measure vertical profiles of tropospheric aerosol extinction coefficients, attenuated depolarization ratio, attenuated color ratio and Ångström coefficient. Comparison with CALIPSO indicates that the measured data by the system is reliable.

Atmospheric aerosols have significant effects on raditive forcing and climate systems [1, 2]. Precise measurements of aerosol optical properties are required to be made on a global scale to understand the quantitative aerosol radiation effects. Therefore, we conducted a comprehensive aerosol experiment, which is the first of its kind, in urban Wuhan, central China in 2011. The means of the scattering coefficient, absorption coefficient, and SSA were 405.66 Mm-1, 131.64 Mm-1and 0.75, respectively. Atmospheric boundary layer heights (APLHs) played an important role in annual and diurnal variations of aerosol optical properties. Both scattering and absorption coefficients were large in winter and low in summer. And both were high at 7:00 LT because of the abundant motor vehicle exhaust emissions during the morning rush hours. These results can further provide a scientific basis for local environmental policies for the government.

Differential absorption lidar (DIAL) is widely accepted as the most promising remote sensing means to map the global CO2concentrations. Nevertheless, diurnal variations and vertical distributions of atmospheric CO2cannot be obtained by satellite-borne and airborne measurements. Ground-based DIAL systems are developed to fill this gap, as well as serve as validations for satellite-borne measurements. Atmospheric factors play significant roles in obtaining accurate range-resolved measurements of XCO2. However, the influence of atmospheric factors on the performance of a ground-based DIAL system aiming at CO2measurements has not been dedicatedly discussed yet. The pressure, temperature, and water vapor of the atmosphere have been taken into consideration for performance evaluation after preselection of absorption lines around 1.6 μm in this paper. In addition, errors caused by variations of aerosols have also been analyzed by using theoretical simulations and real measurements. We found that biases caused by temperature and pressure uncertainties were 0.11-0.45 ppm/K and 0.39 ppm/hPa, respectively, if the central wavelength was utilized as the online wavelength. In addition, the water vapor effect could be neglected by cautious selection of online and offline wavelength. Finally, if the online and offline wavelengths were transmitted alternatively, the temporal and range resolutions have to be determined very carefully to balance the signal-to-noise ratio of acquired data and tolerable errors derived from variations of aerosols. A variable range resolution is recommended for CO2measurements at different altitudes to fulfill the target precision.

The vertical profile of atmospheric carbon dioxide is of great significance for carbon cycle and budget study. However, that parameter can be only obtained by flask samples from profiling aircraft till now. Ground-based differential absorption lidar is widely accepted as a promising remote sensing means to obtain the vertical CO2concentration profile. Unfortunately, classic DIAL retrieving method cannot provide results of adequate accuracy and precision. Here, we propose an improved retrieving method to solve this problem. Experiments showed that the accuracy and precision of results are superior to 0.2 ppm (parts per million) and 4E-5 ppm respectively by means of the proposed method. That could be an ideal result for further carbon cycle and climate change research.