esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 1 Introduction  
Soil moisture (SM) is a key parameter that has an impact on the hydrological cycle and energy exchanges between the land and the atmosphere [1, 2]. The accurate acquisition of SM with high temporal and spatial resolution is particularly important for precision irrigation, crop growth monitoring, and production estimation [3, 4]. The synthetic aperture radar (SAR) has specific properties such as both a day/night data acquisition capability and high sensitivity to soil moisture, and it is widely used to estimate the surface SM in agricultural areas [5, 6, 7]. The soil roughness and the scattering of vegetation are two main disruptive factors in the research of soil moisture extraction using SAR data [8, 9, 10].  
The Water Cloud Model (WCM) is usually used in most studies to invert the soil moisture information over areas covered with vegetation by combining SAR and optical remote sensing data [11, 12, 13]. The Normalized Difference Vegetation Index (NDVI) derived from optical remote sensing is often applied to estimate the Vegetation Water Content (WWC) which is the parameter required by the WCM to eliminate the vegetation impact [14, 15, 16]. Eliminating the effect of soil roughness is another thorny problem in the process of SM inversion. Some surface scattering models have been proposed to parameterize the surface roughness parameters and simulate the backscattering coefficient of the soil surface, such as the Oh model [17] and the Dubois model [18, 19]. The problem with the methods mentioned above is the need for the in situ SM data to calibrate the parametrized model. Therefore, these algorithms have significant limitations in practical applications, especially in areas where the in situ SM data are difficult to obtain or cannot be measured in time. Fortunately, some theoretical models have been developed with higher simulation accuracy and flexibility, such as the PROGAIL model, the Advanced Integral Equation Model (AIEM) [20] and the MichiganMicrowave Canopy Scattering (MIMICS) model [21]. However, the research on SM inversion based entirely on theoretical models is not very common in relevant previous studies.  
In this study, an improved ratio model is proposed based on the simulation data set of the MIMICS to remove the vegetation layer effect. Based on the parameterization of the AIEM, the effect of the soil roughness on the inversion of soil moisture is eliminated. The Sentinel-1 SAR and Sentinel-2 optical data are collected to retrieve SM in winter wheat fields.
# Drone and Controller Detection and Localization: Trends and Challenges
## 2 UAV Architecture and Security Concerns
### Security Concerns  
Regarding UAV security, two main topics are discussed in the literature: the security and safety of UAVs and the potential misuse of UAVs against critical infrastructures and privacy-related issues.  
Threats to UAV security are well-researched concerning targeting its hardware, software, and communication module. In [6], threats to various components of the UAV system  
Figure 1: High-level architecture of a UAV.  
are discussed. GCS's physical, network, and cloud security have been highlighted as vulnerabilities that can be exploited. Moreover, threats exist against the UAV communication according to the communication medium technology or type (WiFi, cellular network, GPS, and other RF solutions). The possible common attacks are eavesdropping, jamming, replay, denial of service, hijacking, etc. Other threats include mission disruption and itinerary tracking [7; 8].  
Despite promising application benefits using UAVs, threats also exist by their prevalence in the public domain. UAV security threats and incidents are mainly caused by privacy violations of sensitive sites, airplane flight disruption, damage and explosion in targeted areas, and sensitive data leakage through eavesdropping [7; 8].
## 4 Results
### Comparison  
We demonstrated the effectiveness of our method through a comparative analysis with two existing techniques, as discussed in the studies by [PERSON] et al. [38] and [PERSON] et al. [37]. We computed the Chamfer Distance between the reconstructed models and the ground truth using Equation (10). Although our method yields Chamfer distances slightly higher than those reported in the studies by [PERSON] et al. [38] and [PERSON] et al. [37], the distances consistently clustered between 0 and 1 for most models. While our method may not often achieve the lowest Chamfer distances, it reliably produces high-quality reconstructions that closely match the ground truth (as illustrated in Figure 8).  
Figure 9 provides an example of reconstruction. Each image in this figure shows the overlap between the eyeglass frames model reconstructed using a specific method and the real model. The visual comparison demonstrates that our method yields a model that resembles the real one. This close alignment suggests that our approach is more accurate and reliable in capturing the intricacies of the original model.  
Figure 10 showcases our reconstruction outcomes across diverse eyeglass frames. The first row presents the successful reconstruction of an eyeglass frames model using different methods. Our method produces smoother edges, underscoring its capability to generate more refined and polished reconstructions. This smoothness is pivotal for achieving a high level of realism and visual appeal in the final model. In the second row, a subtle concavity at the corner of the ground truth model is highlighted, a feature that poses a significant challenge for accurate reconstruction. The methods by [PERSON] et al. [38]  
Figure 7: Input model and its corresponding synthetic sketch. (**a**) Input model; (**b**) synthetic sketch.  
and [PERSON] et al. (2020) result in coarse reconstructions that do not accurately capture this detail. In contrast, our method delivers a more detailed reconstruction, likely due to the incorporation of symmetry optimization during the process, which enhances the preservation and replication of the model's subtle features.  
Further examples in the third to fifth rows illustrate the model's ability to handle thin sections highlighted within the red box. While the reconstruction by [PERSON] et al. (2020) fails to connect these sections, and [PERSON] et al. (2020) only partially succeeds, our results are more congruent with the ground truth model. This fact suggests the effectiveness of our approach in maintaining delicate features.  
Figure 8: Comparison of Chamfer distances. (**Top**): histogram of Chamfer distances between the reconstructed models and the ground truth. (**Bottom**): average Chamfer distances between the reconstructed models and the ground truth (37; 38).  
Figure 9: The overlap between the reconstructed model and the ground truth, with higher overlap indicating better results (where gray represents the reconstructed model and purple represents the ground truth). (**a**) Meshsdf (38) (**b**) Sketch2 Mesh (37) (**c**) Ours.
# An In-Depth Review of the Weibull Model with a Focus on Various Parameterizations
## 5 Monte Carlo Simulation Studies
### MC Simulation Study I: Behavior of the ML Estimators
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{\(\beta_{1}=0.2\); \(\beta_{2}=1.0\); \(\zeta_{1}=0.2\); \(\zeta_{2}=1.0\)} \\ \cline{2-9}  & \multicolumn{6}{c}{Bias} & \multicolumn{6}{c}{MSE} \\ \hline \(n\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) \\ \hline
30 & \(-0.0019\) & \(0.0099\) & \(0.0192\) & \(0.1226\) & \(0.1699\) & \(0.4900\) & \(0.0889\) & \(0.3378\) \\
50 & \(0.0082\) & \(-0.0045\) & \(0.0149\) & \(0.0620\) & \(0.0877\) & \(0.2437\) & \(0.0454\) & \(0.1648\) \\
100 & \(0.0075\) & \(-0.0051\) & \(0.0050\) & \(0.0333\) & \(0.0659\) & \(0.1702\) & \(0.0158\) & \(0.0642\) \\
500 & \(0.0003\) & \(0.0001\) & \(0.0031\) & \(0.0022\) & \(0.0067\) & \(0.0177\) & \(0.0025\) & \(0.0110\) \\ \hline  & \multicolumn{6}{c}{Bias} & \multicolumn{6}{c}{MSE} \\ \hline \(n\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) \\ \hline
30 & \(0.0074\) & \(-0.0023\) & \(0.0188\) & \(0.1126\) & \(0.1963\) & \(0.5392\) & \(0.0832\) & \(0.3169\) \\
50 & \(-0.0019\) & \(0.0055\) & \(0.0123\) & \(0.0638\) & \(0.1380\) & \(0.3698\) & \(0.0434\) & \(0.1694\) \\
100 & \(0.0006\) & \(-0.0019\) & \(0.0035\) & \(0.0338\) & \(0.0360\) & \(0.1027\) & \(0.0158\) & \(0.0626\) \\
500 & \(0.0015\) & \(-0.0020\) & \(0.0009\) & \(0.0076\) & \(0.0066\) & \(0.0175\) & \(0.0026\) & \(0.0107\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bias and MSE for the WEI5 model parameters, with the indicated sample size and true values of the parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{\(\beta_{1}=0.2\); \(\beta_{2}=0.5\); \(\zeta_{1}=0.2\); \(\zeta_{2}=1.0\)} \\ \cline{2-9}  & \multicolumn{6}{c}{Bias} & \multicolumn{6}{c}{MSE} \\ \hline \(n\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) \\ \hline
30 & \(-0.0153\) & \(0.0185\) & \(0.0533\) & \(0.0503\) & \(0.0843\) & \(0.2499\) & \(0.1846\) & \(0.5915\) \\
50 & \(0.0021\) & \(-0.0036\) & \(0.0368\) & \(0.0318\) & \(0.0430\) & \(0.1333\) & \(0.0937\) & \(0.3019\) \\
100 & \(0.0013\) & \(-0.0010\) & \(0.0144\) & \(0.0223\) & \(0.0206\) & \(0.0619\) & \(0.0470\) & \(0.1478\) \\
500 & \(-0.0004\) & \(0.0001\) & \(0.0037\) & \(0.0029\) & \(0.0041\) & \(0.0134\) & \(0.0075\) & \(0.0238\) \\ \hline  & \multicolumn{6}{c}{Bias} & \multicolumn{6}{c}{MSE} \\ \hline \(n\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) \\ \hline
30 & \(0.0074\) & \(-0.0023\) & \(0.0188\) & \(0.1126\) & \(0.1963\) & \(0.5392\) & \(0.0832\) & \(0.3169\) \\
50 & \(-0.0019\) & \(0.0055\) & \(0.0123\) & \(0.0638\) & \(0.1380\) & \(0.3698\) & \(0.0434\) & \(0.1694\) \\
100 & \(0.0006\)
## 3 Study Area and Research Method
### Research Method
#### 3.2.2 Data Analysis
As shown in the equation below, FEM is adopted to examine the effects of various factors on technological innovation outputs. \[y_{i,t}=b_{0}+b_{i}x_{i,t}+a_{i}+e_{i,t}\]  
The dependent variable, \(y_{i,t}\), represents the level of technological innovation output of city i at year t. \(x_{i,t}\) represents all the independent variables explained in the last subsection. \(a_{i}\) is a city-dependent variable that is used to capture unobserved heterogeneity, and \(e_{i,t}\) is the error term.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 4 Discussion
### SEM Application on Gunshot Residue Identification  
Recent technological advancements have significantly enhanced the effectiveness of scanning electron microscopy (SEM) in gunshot residue (GSR) analysis, particularly through innovations in energy-dispersive spectroscopy (EDS) detectors. The development of new types of EDS detectors has greatly improved the sensitivity and accuracy of particle identification [50]. This improvement is complemented by the introduction of sophisticated algorithms, such as the binary tree-based classification algorithm proposed by [PERSON] et al. which optimizes the initial classification step and minimizes errors during the automatic run of GSR analysis [50; 51]. These algorithms are designed to reduce the incidence of false positives and false negatives, thus enhancing the reliability of the results [52]. Moreover, advancements in SEM/EDS technology have led to a substantial reduction in both the overall analysis time and the time required for reviewing particles of interest, thereby streamlining the entire GSR analysis process [53]. This time efficiency is further bolstered by automation, which not only speeds up the process but also ensures consistency and accuracy in particle detection and classification [54]. Collectively, these technological improvements have made SEM a more effective and reliable tool for forensic scientists, enabling quicker and more accurate GSR analyses while preserving the integrity of the evidence.
## 3 Proposed System
### Classifier  
To develop a feature extraction method and evaluate the performance of our method, a conventional convolutional recurrent neural network (CRNN) with a mean-teacher model is adopted [40; 41; 42]. Figure 4 presents the details of the network structure of the CRNN classifier. The convolutional layers and the corresponding max-pooling layers are designed so that the \(n_{feature}\) axis of the output of the CNN layers is equal to unity. The RNN layers and the fully connected layer make the frame-wise probability of the classes, which is denoted by \"Time Stamps\" in Figure 4. To train the classifier using the weakly-labeled data, then it also generates the \"Clip Class\" output, which denotes the existing classes in a clip. The element of the clip class output \(\mathbf{C}\) is calculated by weighted average as  
\[\left[\mathbf{C}\right]_{c}=\frac{\sum_{n=0}^{N_{out}-1}\left[\mathbf{P} \right]_{n,c}\left[\mathbf{P}_{softmax}\right]_{n,c}}{\sum_{n=0}^{N_{out}-1} \left[\mathbf{P}_{softmax}\right]_{n,c}}, \tag{21}\]  
where \(N_{out}\) denotes the frame number of the classifier output of a sound clip.  
The mean-teacher model is adopted to train the classifier using both the labeled and unlabeled data, similar to state-of-the-art weakly-supervised SED systems [40; 41]. The network parameters of the student model, \(\boldsymbol{\theta}\), are optimized to minimize the cost function as  
\[J_{total}(\boldsymbol{\theta})=J_{class}(\boldsymbol{\theta})+\beta J_{consist} (\boldsymbol{\theta}), \tag{22}\]  
where \(J_{class}(\boldsymbol{\theta})\) and \(J_{consist}(\boldsymbol{\theta})\) denote the classification cost and the consistency cost, respectively, as presented in Figure 5. \(\beta\) is the weight for the consistency cost. The parameters of the teacher model, \(\boldsymbol{\theta}_{teacher}\), are fixed during the training procedure and updated at the end of the training batch as  
\[\boldsymbol{\theta}_{teacher}\leftarrow\alpha\boldsymbol{\theta}_{teacher}+(1- \alpha)\boldsymbol{\theta} \tag{23}\]  
where \(\alpha\) denotes the exponential weighting factor of the moving average.
# Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion
## 4 Methodology
### Recursion-Guided Payload Generation  
Most current automated payload generation methods rely on symbolic execution and constraint solving [37; 38]. However, these techniques are more mature in the context of C language, while symbolic execution technology for Java is still in a developmental and exploratory stage. There is an urgent need for reliable and efficient test case generation techniques and tools specifically for Java programs. At present, most Java symbolic execution tools [39] can only support basic and simple data types, such as symbolic representation of int and char. However, they are still unable to fully symbolize complex data types like string and object. This limitation restricts the use of symbolic execution for constraint solving on specified vulnerability paths.  
To address our targeted vulnerability types, i.e., spatial ACV, we propose a semi-automatic, procedural, and standardized payload generation method to support rapid vulnerability verification and exploitation. Our method process mainly includes:  
* Analyzing the recursive method's parameter processing logic. First, identify and extract all statements involved in processing the parameters within the recursive method according to the automatically filtered call chain. This includes all operations from the start of the recursive method until the parameters are passed to the next level of recursive calls.
* Constructing the initial actual parameter (argument). Based on the extracted parameter processing logic, generate an initial actual parameter (the initial value of the parameter) that should satisfy the conditions for at least one recursive call.
* Automatically iterative processing of actual parameters. Apply the extracted parameter processing logic to the initial actual parameter, repeating multiple times (e.g., 10,000 iterations). Each iteration simulates the changes in the arguments during a recursive call.
* Generating the final payload. After numerous iterations, the final value of the obtained actual parameter is used as the payload. This payload represents the final state of the argument after multiple recursive analysis and can be used for further verification or exploitation.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Supporting Functions
#### 3.1.2 Hash to Range Function H2 RF\({}_{i}\) (H\({}_{v}\), Z, n)  
Given a hash function \(H_{v}\) with output bit length \(v\), bit string \(Z\), integer \(n\), and integer index \(i\). The output is an integer \(h_{i}\). Algorithm 3 gives the pseudocode. The SM9 standard requires SM3 [32] to be used as the hash function.  
```
1\(hlen\leftarrow\left[8\times\left(5\times\left(\log_{2}n\right)\right)/32\right]\)
2\(Ha\gets KDF(H_{v},\ n||Z,hlen)\)
3 output \(h_{i}\leftarrow(Ha\ mod\ (n-1))+1\)
```  
**Algorithm 3**\(H2 RF_{i}(H_{v},\ Z,\ n)\)
###### Abstract
However, it is known to us that nonlinear properties are frequently encountered in a practical actuator, such as the hydraulic actuator and electromagnetic actuator. In addition, the actuating force can be affected by the real-time status of the suspension as well. Plenty of studies have been carried out to compensate the impact of non-ideal actuators. In [6; 10; 33], H-infinity control methods are used to handle time-varying gains, actuator faults, and saturation in active suspension systems. These approaches improve system stability and ride comfort under uncertainties and constraints. In [12], a unified model is proposed for actuators with dead-zone and hysteresis characteristics. The method addresses both nonlinearities without requiring prior knowledge of actuator parameters or output measurements. Compared with inverse-based approaches, it is simpler to implement and more robust to uncertainties, though it may require careful gain tuning. In [15; 32], actuator saturation is managed using auxiliary systems [15] via anti-windup compensation in adaptive robust control, and [32] with a neural network-based controller. These methods improve robustness but increase system complexity. In [21], an adaptive method is integrated with the SMC to compensate for the influence of unknown control gain, which improves system robustness but may increase computational complexity. It is also noted in [40; 41] that the prescribed performance function (PPF) has been widely adopted because it provides a shortcut to regulate both the transient and steady-state performance of the controlled system, and the system output and tracking error can be controlled to an arbitrarily small residual set. In this paper, an adaptive controller based on backstepping control and PPF is designed to improve ride comfort for heavy-duty vehicles. The main contributions of this study are as follows: (1) An APPC is designed to regulate the transient and steady-state performance of the active seat suspension system. (2) A new adaptive law is introduced for parameter uncertainty estimation and friction compensation. (3) A prototype of an active seat suspension is presented and both numerical simulations and experimental validation are conducted to demonstrate the effectiveness of the proposed controller. The remainder of this paper is organized as follows: Section 2 presents the dynamic model of the controlled driver-seat system and formulates the control problem. Section 3 introduces the necessary preliminaries and synthesizes the control algorithm. In Section 4, the proposed controller effectiveness is verified through numerical simulations and experimental validation. Finally, Section 5 offers conclusions and suggestions for future work.
## 1 Introduction
* Large Differences in Shape and Size: the polyp scale is large, and the shape characteristics are also different, making it easy to generate incorrect segmentation when dealing with small polyps. * Semantic Information Loss: through continuous sampling operations during the segmentation process, the semantic information contained in the polyp context is easily gradually lost. Inspired by the Pyramid Vision Transformer v2 (PVTv2) [11], this paper proposes a hierarchical path aggregation network with Pyramid Vision Transformer (HPANet) for colorectal polyp segmentation. This network effectively extracts multi-scale features of polyps and reduces noise interference, thereby improving segmentation accuracy. Three key technical innovations are introduced, which are outlined below:  
(1) A novel network, HPANet, for colorectal polyp segmentation was proposed. This network utilizes PVTv2 to extract multi-level polyp image features and then designs a backward multi-scale feature fusion module (BMFM). This module sequentially fuses adjacent feature maps from the back to the front to generate spatial attention maps, effectively addressing the scale variation issue of colorectal polyps. (2) A forward noise reduction module (FNRM) was developed to mitigate noise interference such as bubbles and illumination on the image by gradually integrating and learning the contextual texture features from front to back, thereby improving segmentation accuracy. (3) A boundary feature refinement module (BFRM) was designed to address the boundary blurring issue caused by repeated up-down sampling operations. BFRM combines channel and spatial attention to preserve more high-frequency boundary details during feature alignment. The remaining sections of the paper are arranged as follows: Section 2 introduces related works, including polyp segmentation networks and vision Transformer, Section 3 describes the structure of HPANet, Section 4 details the experimental settings and analysis of experimental results, and Section 5 concludes with a summary and discussion.