esa-sceva/satcom-chunk-collection huggingface dataset:
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 3 Proposed Work
### False Report Attacks Information  
In this section, we explain our method that can calculate the current attacks ratio or false traffic ratio (FTR) without causing extra messages or energy costs at the sensor nodes.  
The communication is based on a query-driven model. The BS can send a query to determine an event in a a target area. The corresponding CH in the area along with event sensing nodes collaborate to respond. For one such communication, the BS has information for the number reports from CH in the area of interest.  
A validated event report arriving at the BS will cause increments in the legitimate report counter by one. Here, additional messages or energy consumption is not needed. A false report can be dropped either at the BS or en-route. When a false report is dropped at the BS, the false report counter is incremented by one. In case it is dropped at one of the nodes on the path, the BS will know it, as it will not be received within the time window. Therefore, using these two counter information available on the BS, FTR for \(m\) number of events in the WSN can thus be calculated as follows:  
\[FTR=\frac{\sum\limits_{i=1}^{m}(1\times F_{i})}{\sum\limits_{i=1}^{m}\left\{1 \times F_{i}+1\times L_{i}\right\}} \tag{9}\]  
where \(F_{i}\in[0,1]\) and \(L_{i}=F_{i}^{\prime}\) indicate the fabricated and legitimate reports, respectively, defined as follows:  
\[F_{i}=\left\{\begin{array}{ll}1\text{ if the }i_{\text{in}}\text{report is false.}\\ 0\text{ if the }i_{\text{in}}\text{report is valid.}\end{array}\right. \tag{10}\]  
In Equation (9), \(i\) represents the current number of events from 1 to \(m\) (i.e., total number of events in WSN). Fabricated reports are false reports injected from a compromised node in control of an adversary. Legitimate reports are valid reports generated by non-compromised nodes in WSN. The counters are at the BS, which has sufficient power. Therefore, we can model current attack information without costing extra messages or energy.
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
## 3 The Proposed Optical Steganography System Setup and Simulation Results  
The SPC-OCDMA system was simulated using Opti-System software (v 7.0). Opti-System is a software simulation kit from Optiwave(tm) (Nepean, OTT, Canada) that analyzes the performance of optical systems and networks. A schematic of the experimental setup is presented in Figure 5. The stealth signal is first encoded and then transmitted into the public channel. To simulate the system noise in a real optical network, a noise source is introduced to generate noise and couple it to the public and stealth signals. This enables convenient investigation of the impact of system noise on the stealth signal.  
Figure 4: Structure of FSO system.  
Figure 3: Structure of CFBG providing: (**a**) positive dispersion; and (**b**) negative dispersion.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 1 Introduction
### Objectives and Contributions  
The aim of this research is to develop an intelligent method for synthesizing TCs for C++ libraries based on a Q-learning agent, which achieves the reduction of the original test suite size without compromising the predefined characteristics of testing effectiveness. The method operates in the absence of formal specifications for valid API usage. In this research, branch code coverage, in particular, is used as the primary metric for evaluating testing effectiveness. However, the method is not limited to a specific metric and can be adapted to alternative criteria, such as line coverage or fault detection capabilities.  
To achieve this aim, the following objectives have been defined:  
1. To formalize the model of a Q-learning agent for the task of test case synthesis, considering the specific structure of states, actions, and reward function, and to implement a training algorithm based on classical Q-learning.
2. To develop a test case synthesis algorithm that rebuilds the original TCs at the API level using the memory of a trained agent, aiming to reduce TC length while preserving target branch coverage.
3. To design an intelligent method for test cases synthesis for C++ libraries that integrates the Q-learning agent with the test case synthesis algorithm.
4. To conduct an experimental evaluation of the method's effectiveness by applying it to two open-source C++ libraries, including an analysis of the agent's learning dynamics, the characteristics of the synthesized test cases, comparison with uniform random distribution-based TC synthesis as baseline, and assessment of the method's scalability.  
This work contributes an intelligent test case synthesis method for C++ libraries, capable of minimizing test suite size while preserving branch coverage and operating without specifications, validated through experiments on open-source C++ codebases. The remainder of the paper is organized as follows. Section 2 presents the formalization of the Q-learning agent model and reinforcement learning components implementation. In Section 2.10. the test case synthesis algorithm is presented. In Section 3, we provide an experimental evaluation, comparing the proposed approach with uniform random distribution-based TC synthesis as baseline across two open-source libraries. Section 4 discusses the results, including stability, effectiveness, limitations, and threats to validity of the method, and outlines directions for future work. Section 5 concludes the paper.
## 3 Results
### p16+ Is an Independent Prognostic Biomarker in Immunotherapy-Treated R/M OPSCC Patients  
In Figure 2a, analysis of our R/M HNSCC study cohort based on p16 status revealed that p16+ patients had superior OS compared to p16- patients (log-rank, \(p=0.02\)). Subgroup analyses showed that the survival advantage conferred to p16+ status was limited to the oropharyngeal SCC (log-rank, \(p=0.004\)) patients and was not observed in the non-oropharyngeal SCC (log-rank, \(p=0.8\)) patients (Figure 2b). Median OS was 15.1 months and 4.5 months for p16+ and p16- oropharyngeal SCC patients, respectively. Patient characteristics were balanced between the p16+ and p16- R/M oropharyngeal cohorts with the exception of gender; p16+ patients tend to be males (Table 2). Anti-PD-1 immunotherapy choice showed no impact on OS when our R/M cohort was analyzed based on anatomical site (Figure 2c). Cox regression, adjusting for co-variates, revealed p16 status as an independent prognostic biomarker in the immunotherapy-treated R/M oropharyngeal SCC patients; p16- patients had a 7.67-fold (95% CI: 1.23 to 47.8; \(p=0.029\)) increase in risk of death compared to p16+ patients (Figure 2d).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Clinicopathologic Characteristics** & **p16+ (\(u=16\))** & **p16- (\(u=5\))** & \(p\) Value** \\ \hline Age & 65 & 58 & 0.23 \\ Gender & 94\% (\(u=15\)) & 40\% (\(u=2\)) & 0.03 \\ Male & 6\% (\(u=1\)) & 60\% (\(u=3\)) & \\ Female & 75\% (\(u=12\)) & 80\% (\(u=4\)) & 1 \\ No & 25\% (\(u=4\)) & 20\% (\(u=1\)) & \\ Anatomical Site & & & \\ Tonsil/Oropharynx & 69\% (\(u=11\)) & 60\% (\(u=3\)) & 1 \\ Base of Tongue & 31\% (\(u=5\)) & 40\% (\(u=2\)) & \\ Definitive treatment & & & \\ Non-surgical & 88\% (\(u=14\)) & 40\% (\(u=2\)) & 0.06 \\ Surgical & 12\% (\(u=2\)) & 60\% (\(u=3\)) & \\ Time to recurrence & & & \\ (months after definitive treatment) & 17 (1–57) & 7 (1–26) & 0.21 \\ Recurrence & & & \\ Local/Regional & 12\% (\(u=2\)) & 60\% (\(u=3\)) & \\ Distant & 88\% (\(u=14\)) & 40\% (\(u=2\)) & 0.23 \\ Immunotherapy & & & \\ Pembrolizumab & 25\% (\(u=4\)) & 60\% (\(u=3\)) & \\ Nivolumab & 69\% (\(u=11\)) & 40\% (\(u=2\)) & 0.29 \\ Both & 6\% (\(u=1\)) & & \\ \hline \end{tabular}
\end{table}
Table 2: Clinicopathologic characteristics of R/M OPSCC patients treated with anti-PD-1 immunotherapy.  
Figure 2: p16+ is an independent prognostic biomarker in immunotherapy-treated R/M OPSCC patients. (**a**) KM plot stratified based on p16 status for the entire cohort. (**b**) KM plot stratified based on p16 status for the oropharynx and non- oropharynx SCC cohorts. (**c**) KM plot stratified based on anti-PD-1 immunotherapy choice for the oropharynx and non- oropharynx SCC cohorts. (**d**) Multivariate model for the oropharynx SCC cohort presented as a forest plot. * \(p\) < 0.05.
## 2 Review of the Analytical Solutions for the Vertical Response of FREIs  
### Vertical Stiffness and Effective Compressive Modulus  
For a strip-shaped FREI with base side \(2a\), total height \(H\), thickness of the fiber reinforcement \(t_{f}\) and of the elastomeric layer \(t_{e}\), the vertical stiffness under a compressive load \(F_{v}\) (Figure 1a) and zero horizontal displacement \(\delta_{H}\) is given as [41]:  
\[K_{v}^{\delta_{H}=0}=\frac{E_{c}^{\delta_{H}=0}A_{c}}{t_{r}} \tag{1}\]  
where \(E_{c}^{\delta_{H}=0}\) is the effective compressive modulus under pure compression, \(A_{c}=2a\) is the contact area between the bearing and the supports, and \(t_{r}=n\cdot t_{e}\) is the total rubber height (\(n\) is the total number of elastomeric layers). The effective compressive modulus under pure compression is defined as [3]:  
\[E_{c}^{\delta_{H}=0}=\frac{\sigma_{v}t_{r}}{\delta_{v}} \tag{2}\]  
where \(\delta_{v}\) is the vertical displacement at the top of the bearing, and \(\sigma_{v}\) is the vertical pressure. Different analytical formulations were proposed for \(E_{c}^{\delta_{H}=0}\) of strip-shaped bearings with flexible reinforcements. Two main approaches are herein recalled, namely the _Pressure Solution_ (PS) [28] and the _Pressure Approach_ (PA) [34], considering the elastomer as incompressible and compressible, respectively. With the PS, the modulus can be obtained as:  
\[E_{c}^{\delta_{H}=0}\Big{|}_{PS}=G\delta_{1}^{2}\frac{12}{\left(\alpha a\right) ^{2}}\left[1-\frac{\tanh(\alpha a)}{\alpha a}\right] \tag{3}\]  
where \(G_{e}\) is the shear modulus of the elastomer, and \(\alpha=\sqrt{12G_{e}\left(1-v_{f}^{2}\right)/E/t_{f}t_{e}}\). The primary shape factor of a strip-shaped FREI can be obtained from \(S_{1}=a/t_{e}\). With the PA, the effective compressive modulus under pure compression is obtained from:  
\[E_{c}^{\delta_{H}=0}\Big{|}_{PA}=2G_{e}+\frac{2G_{e}\lambda}{\lambda+2G_{e}}+ \frac{\lambda^{2}}{\lambda+2G_{e}}\left(\frac{\alpha_{0}^{2}}{\beta_{0}^{2}} \right)\left[1-\frac{\tanh(\beta_{0}a)}{\beta_{0}a}\right] \tag{4}\]  
where \(\lambda=K-2G_{e}/3\) is the second Lame constant, \(\alpha_{0}=\frac{6}{t_{e}}\sqrt{\frac{G_{e}}{3K+4G_{e}}}\) (\(K\) is the bulk modulus of the rubber), and \(\beta_{0}=\sqrt{\alpha_{0}^{2}+\alpha^{2}}\).
## 1 Introduction
Notably, our method adopts a linear architectural design that achieves significantly efficient computational complexity and parameter scale while delivering notable accuracy improvements, making it suitable for practical weather forecasting. The code implementation is available at a GitHub repository: [[https://github.com/Fubukipara/PCCforWeatherTS/](https://github.com/Fubukipara/PCCforWeatherTS/)]([https://github.com/Fubukipara/PCCforWeatherTS/](https://github.com/Fubukipara/PCCforWeatherTS/)) (accessed on 26 March 2025). The main contributions of our work are threefold:  
1. We develop a plug-and-play deep learning module named PCC that incorporates variable relationships and maintains state reasonability for weather multivariate time series forecasting, enabling seamless integration with various forecasting models without requiring architectural modifications or additional preprocessing. 2. We design a computationally efficient architecture that significantly improves backbone model performance on weather time series forecasting tasks with minimal additional computational overhead, allowing the enhanced model to achieve superior performance with a significantly reduced parameter count. We conduct comprehensive experiments, ablation studies, and visualizations to demonstrate and analyze the superiority of our method.
# Attitudes towards the Potential Use of Aversive Geofencing Devices to Manage Wild Elephant Movement
## 2 Materials and Methods
### Data Analysis  
We analysed responses from 611 respondents based on three social groups (experts, farmers and others) and whether or not they have experienced HEC (HEC or no HEC). Respondents were categorised as a \"farmer\" or \"expert\" if they had indicated their involvement in farming (annual crops or perennial crops or livestock) or work related to Asian elephants in their responses to the survey (see Table S1). Those who did not belong to either of the groups were categorised as \"other\". Respondents were categorised as \"HEC\" if they had selected a severity level of HEC they experienced and/or mentioned at least one HEC related problem they have experienced (see Table S2), while the remaining respondents were categorised as \"no-HEC\".  
We analysed the responses for the Likert-type questions using a logistic-regression model (a generalised linear model with a binomial distribution and a logit link function), by collapsing the responses to a binary variable (\(-\)2, \(-\)1 and 0 as a \"negative/neutral\" response and +1, +2 as a \"positive\" response). Such transformation of scale avoids issues with low frequency of responses in some response categories and simplifies the interpretation of data [78; 79]. We used the 'glm' function and the forestplot package [80] in the R statistical software [81] for this analysis. We used the Potential for Conflict Index\({}_{2}\) (PCI\({}_{2}\)) [82] to examine the mean responses given on the five-point scale (\(-\)2 to +2) and the level of consensus within the six groups: expert-HEC, expert-no HEC, farmer-HEC, farmer-no HEC, other-HEC, and other-no HEC. PCI\({}_{2}\) values range between 0 and 1 and depict the dispersion within the sample, with 0 indicating highest consensus within a group of respondents and 1 indicating the lowest consensus within a group (i.e., all responses are divided between the extreme response categories equally). We used the programs provided by [PERSON] et al. (2018) to calculate and graph \(\text{PCI}_{2}\) and mean values. The centre and the size of the bubble in the graph depict the mean score on the scale of the y axis and the \(\text{PCI}_{2}\) value, respectively. High potential for conflict within a group is depicted by larger bubbles and low potential for conflict within a group is depicted by smaller bubbles. Each analysed question (item) was reduced to shorter phrases for the convenience of display and are italicised when mentioned in the Results section (below). Table S3 contains the full-length questions. Responses to the open-ended questions were categorised according to themes, and due to the ambiguity in interpretation of the responses the approximate number of respondents commenting under each category are given within parenthesis.  
Figure 1: How aversive geofencing devices (satellite-linked electric shock collars) could be used to manage elephant movement.
## 1 Introduction  
Non-alcoholic fatty liver disease (NAFLD) is defined by the accumulation of fat in the liver after the exclusion of secondary causes of fat accumulation, among which alcohol consumption, steatosis medication, and genetic disorders play important roles. NAFLD is characterized by inflammation and progressive fibrosis, being a serious health issue not only in adults but also during childhood [1]. The progression of liver fibrosis, with a direct impact on the degree of portal hypertension (PHT) severity, represents a crucial element in the appearance of complications in cases of cirrhotic patients, influencing the mortality among these patients [2]. The quantification of this process has traditionally been performed by calculating the porto-suprahepatic pressure gradient with the threshold set at 10 mmHg, above which clinically significant portal hypertension (CSPH) can be defined [3, 4]. Due to its highly invasive character, the measurement of the porto-suprahepatic pressure gradient has become a marginal instrument that is mainly useful in the research procedures and characterized by low feasibility and reproducibility in usual practice [5].  
The Baveno VI consensus has implemented transient elastography as a reproducible assessment tool for the risk of occurrence of CSPH, excluding the need for endoscopic screening in order to identify the presence of high-risk varices (HRVs) [6]. Adopting the Baveno VI principles, against the background of the development of more and more reliable and reproducible methods for the non-invasive evaluation of liver fibrosis, creates perspectives for research in order to identify other feasible methods for the non-invasive quantification of PH and of the risk of variceal bleeding in patients with compensated advanced liver disease (cALD). Taking into account that 2D-SWE elastography has attracted considerable interest among clinicians thus far, we can consider it to be a potentially useful and promising resource in assessing the severity of PH [7; 8; 9; 10; 11; 12; 13].  
In this regard, the extension of the elastographic evaluation on the splenic parenchyma has also recently been discussed, with splenic fibrosis being potentially correlated with the degree of PH and the risk of bleeding [14; 15; 16; 17]. Moreover, spleen volume is referred to as an important diagnostic feature in NAFLD [18]. The clinical basis of this research approach is the fact that, up to this point, the parameters relevant to spleen dynamics--such as the bipolar diameter of the spleen, the caliber of the splenic vein, the number of platelets, or combinations of the above--have been useful benchmarks in assessing the degree of PH, as well as in estimating the risk of variceal bleeding, thereby creating a possible link between spleen elastography and non-invasive evaluation of CSPH [17; 19; 20].  
Given this state of knowledge, the aim of this study was to investigate the performance of shear-wave elastography of the spleen in the follow-up of patients with advanced chronic liver disease, and to prove its correlation with the presence of high-risk varices (HRVs).
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 3 The Radial Gradient Seismic Metamaterial Structure
### The Influence of Material Parameters on Band Gap  
In order to further study the band gap characteristics of the triangular structure, the band gap of the structure under different material parameters is calculated. In general, the Young's modulus of soil varies in the range of 10-90 MPa, and the mass density of soil varies in the range of 1600-2200 kg/m\({}^{3}\). In the calculation, following the control variable method, the Young's modulus and material density change respectively, and other material and geometric parameters remain unchanged. The calculation results are shown in Figure 6a,b. The results show that as the Young's modulus of the soil increases from 10 MPa to 90 MPa, the first band gap increases rapidly and moves to a higher frequency, and the second band gap shows the same trend as the first band gap. Compared to the Young's modulus, the change of mass density has less influence on the band gap. As the mass density increases, the first band gap gradually narrows, the center frequency of the second band gap gradually decreases, and its width gradually narrows. Therefore, when optimizing the structure, we can consider adjusting the parameters of the soil to achieve a more flexible effect of adjusting the band gap.
# Structural Design and Optimization of Separated Air-Rib Tents Based on Response Surface Methodology
## 4 Optimized Design of the Wind Ropes Parameters Based on the Response Surface Methodology
### Optimization of Programmed Design  
The angle of the wind ropes, the number of the wind ropes on the side, the initial prestress of the wind ropes on the end face, and the initial prestress of the wind ropes on the side have been considered as the most important factors for response surface methodology. The maximum displacement of the tarpaulin is the target parameter. The test plan is defined by the central composite design method (CCD; four factors and three levels; Table 3). Each trial is performed twice. The corresponding results are presented in Table 4 and Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Factor Code**} & \multirow{2}{*}{**Parameter Name**} & \multicolumn{3}{c}{**Parameter Value**} \\ \cline{3-5}  & & \(-\)**1** & **0** & **+1** \\ \hline A & Angle of the wind ropes & 30\({}^{\circ}\) & 37.5\({}^{\circ}\) & 45\({}^{\circ}\) \\ B & Number of the wind ropes on the side & 2 & 6 & 10 \\ C & Initial prestress of the wind ropes on the end face & 400 Pa & 600 Pa & 800 Pa \\ D & Initial prestress of the wind ropes on the side & 0 Pa & 100 Pa & 200 Pa \\ \hline \hline \end{tabular}
\end{table}
Table 3: The table of the research parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & **A** & **B** & **C** & **D** & **Displacement of the Tarpaulin** \\
**Number** & **(\({}^{\circ}\))** & **(1)** & **(Pa)** & **(Pa)** & **(mm)** \\ \hline
1 & 30 & 2 & 600 & 100 & 409.2 \\
2 & 45 & 2 & 600 & 100 & 354.9 \\
3 & 30 & 10 & 600 & 100 & 420.0 \\
4 & 45 & 10
# Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging
## 2 Related Works and Theory Formulation
### Wavelet Analysis  
We use a wavelet transform as the decomposition basis to represent the original signal sparsely in this paper. The wavelet transform can perform the localized analysis on time (space) frequency by scale shifting and time (space) shifting of a mother wavelet. Thus, the information in signal's time domain (space domain) and signal's frequency domain can be analyzed simultaneously at multiple scales. Wavelet transform performs the property of time (space) subdivision at high frequency and frequency subdivision at low frequency [48]. In continuous time domain \(t\), the signal \(x(t)\) can be expressed by the wavelet transform as:  
\[WT(a,\tau)=\frac{1}{\sqrt{a}}\int_{-\infty}^{+\infty}x(t)W(\frac{t-\tau}{a})dt, \tag{5}\]  
where \(t\) denotes time, \(W_{a,\tau}(t)\) is a mother wavelet, \(a\) and \(\tau\) are the parameters of scale shifting and time shifting respectively [49]. Approximate coefficients and detailed coefficients in wavelet domain are generated by a low-pass wavelet filter and a high-pass wavelet filter, respectively.  
For continuous wavelets, scale \(a\), time \(t\), and time shifting \(\tau\) are all continuous. If the wavelet transform is used to process images or digital signals, Equation (5) should be discretized as discrete wavelet transform (DWT), and our work of optimizing wavelet decomposition basis in this paper is exactly based on DWT.