esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Existing ITS Technologies: Evaluation and Coexistence
## 2 Fundamentals and Related Work
### C-V2x MAC/PHY Layer  
#### 2.2.1 LTE-V2X  
LTE-V2X [4] is a synchronous network that can operate in the 5.9 GHz band with 10 MHz or 20 MHz channels for the vehicular environment. The LTE-V2X is based on single carrier frequency division multiple access (SC-FDMA), with a flexible resource allocation in the time-frequency domain. The resource is divided into subframes fixed to 1 ms in the time domain. In the frequency domain, the channel bandwidth is divided into subchannels with multiple resource blocks (RBs) of 180 kHz. Each RB is composed of 12 subcarriers separated by 15 kHz. Every subcarrier transmits 14 OFDMA symbols per subframe (9 symbols for data transmission, 4 symbols for demodulation reference signals (DMRS) and the last symbol to switch transmission/reception). For each transport block (TB), depending on the packet length, one or more subchannels are attributed for a fixed transmission time Interval (TTI) equal to 1 ms. The transmission of TB requires a control information SCI (sidelink control information) that occupies 2 RBs and helps the correct reception and demodulation of the TB. The resource allocation for the TB and their associated SCI are placed on adjacent or non-adjacent blocks, which must be transmitted in the same TTI.  
The communication mode between vehicles in coverage conditions stays on the base station (cellular-assisted) to manage radio resource allocation for direct communication via the PCS interface. Otherwise, LTE-V2X is based on pre-configured parameters for a sensing mechanism before transmission to attribute radio resources outside network coverage. However, one of the proposed approaches for sensing is based on the semi-persistent scheduling (SPS) mechanism. This mechanism uses various parameters defined at the PHY and MAC layers, referred to as channel sensing, resource allocation, reservation periods, etc. The vehicle needs to sense the transmission channel and identify potential resources. First, the available resources are selected in an interval [T1, T2]. These resources should be below a given threshold of measured power level, where (1 ms \(\leqq\) T1 \(\leqq\) ms) and (20 ms \(\leqq\) T2 \(\leqq\) 100 ms) present, respectively, the first and the last TTI of the selection window. Then, a small portion of those resources is usually considered the candidates' resources (the recommended value is around 20%). Next, the vehicle randomly selects candidate resources for subsequent transmission among all received candidate resources. This resource is used for a random re-selection counter in the range [5, 15], decremented at each communication. When this latter reaches zero, the vehicle can keep the same resource with a probability Pk (keeping probability) or repeat the resource selection process with probability (1-Pk).
# A Vulnerability Scanning Method for Web Services in Embedded Firmware
## 5 Evaluation
### Sensitive Path Reduction Analysis  
Because this paper filters the source point and sink point, the sensitive path is greatly reduced, as shown in Table 7. We count the number of danger functions filtered by WFFinder, the number of accurately identified external data input points, and the number of sensitive paths. We compare the results with SaTC. Using unfiltered hazard functions and external data input points containing many false positives as starting and ending points in SaTC generates a large number of sensitive paths, increasing the burden of stain analysis. On the contrary, due to the reduction in starting and ending points, WFinder obtains significantly fewer sensitive paths, which can greatly improve the efficiency of the taint analysis.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Device Series**} & \multirow{2}{*}{
\begin{tabular}{c} **Boundary** \\ **Binary** \\ \end{tabular} } & \multicolumn{4}{c}{**SaTC**} & \multicolumn{4}{c}{**WFinder**} \\ \cline{3-8}  & & **Alert** & **TP** & **Vulnerability** & **Alert** & **TP** & **Vulnerability** \\ \hline AX1803 & tdhttpd & 64 & 42 & 16 & 45 & 31 & 29 \\ AX1806 & tdhttpd & 61 & 45 & 19 & 51 & 29 & 25 \\ G1/G3 & httpd & 53 & 31 & 14 & 59 & 29 & 17 \\ DIR823G & goahead & 64 & 43 & 21 & 70 & 39 & 23 \\ DIR816 & goahead & 78 & 39 & 25 & 64 & 37 & 27 \\ X2000R & boa & 87 & 66 & 20 & 112 & 48 & 21 \\ A3002R & boa & 33 & 15 & 0 & 20 & 8 & 1 \\ R7000 & httpd & 96 & 78 & 32 & 121 & 64 & 41 \\ R7000P & httpd & 124 & 63 & 36 & 98 & 58 & 40 \\ CC8160 & httpd & 0 & 0 & 0 & 4 & 3 & 1 \\ \hline Total & 660 & 422 & 183 & 644 & 346 & 225 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with SaTC. We list the number of alerts (Alerts), true positives (TPs), and vulnerabilities.
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 4 Conclusions  
To solve the problems of large-power transmission and planar structure, it is proposed to design a Butler matrix beamforming network based on a square coaxial line. Square coaxial lines are easily fabricated and have a high microwave power threshold compared with circular ones. Shunt short-ended stubs on the branchline coupler are adopted in the strategy, which can increase the bandwidth of a conventional one by 10%. The whole Butler matrix with introduced short-ended stubs is simulated and optimized. The matrix with a 400 mm by 400 mm dimension was fabricated and measured. The measured results are consistent with those of the simulations. The measurement results of the developed large-power \(4\times 4\) Butler matrix 9 kW are presented.  
In follow-up work, further research will be conducted on a compact design and widening the microwave transmission bandwidth. Moreover, we will coat the inner conductors  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Refs** & **f\({}_{0}\)/GHz** & **d\({}_{\text{min}}\)/mm** & **20 dB-Bandwidth** & **Configuration** & **Power/kW** \\ \hline
[16] & 28 & 0.203 & 5.7\% & 1 \(\times\) 4 PCB & 0.01 \\
[12] & 9.6 & 10.16 & 6.25\% & 8 \(\times\) 8 WR90 & 6 \\
[17] & 5 & 2.75 & 4\% & 1 \(\times\) 8 square coaxial line & 8 \\ This Work & 4 & 2.35 & 10\% & 4 \(\times\) 4 square coaxial line & 9 \\ \hline \end{tabular}
\end{table}
Table 1: Performance with the prior Butler matrix.  
Figure 23: The experimental test setup of multipactor.  
with special materials to decrease the electron emission coefficient, and thus further enhance the microwave power threshold of the matrix. It could be used for large-power Butler matrices for SRS applications.  
Conceptualization, C.L. and W.C.; methodology, C.L. and J.L.; validation, W.C., H.B. and C.L.; writing, J.L.; supervision, C.L. and L.Y.; project administration, C.L., W.C. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant numbers U22A2015 and 62071316.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 4 Discussion
### Performance of OTCI-Driven Models in GPP Estimation  
The R\({}^{2}\) between GPP\({}_{\text{OTCI}}\) and GPP\({}_{\text{EC}}\) at seven sites showed that at least one of the three OTCI-driven models agreed well with the GPP. The US-WCr site performed best, whether the model used OTCI only or introduced either PAR or FAPAR. This result was also consistent with that in Section 3.2 using the OLCI FAPAR product. Interestingly, the inclusion ofradiation information (PAR and FAPAR) did  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Site ID** & **R\({}^{2}\)** & **RMSE** & **Bias** \\ \hline US-Bi1 & 0.39 & 4.61 & \(-\)2.6 \\ Us-Bi2 & 0.61 & 9.14 & \(-\)7.41 \\ US-Rls & **0.77** & **0.79** & **0.05** \\ US-Rws & 0.59 & 0.87 & \(-\)0.45 \\ US-WCr & 0.66 & 2.81 & \(-\)0.1 \\ US-KFS & 0.42 & 5.26 & \(-\)3.57 \\ US-Ton & 0.42 & 3.11 & 2.74 \\ \hline \hline \end{tabular} Note: Highest R\({}^{2}\), lowest RMSE, and lowest bias value are shown in bold. Significance levels: \(p<0.001\).  
\end{table}
Table 4: Coefficient of determination (R\({}^{2}\)), root mean square error (RMSE, g C m\({}^{-2}\) d\({}^{-1}\)), and bias (g C m\({}^{-2}\) d\({}^{-1}\)) between GPP\({}_{\text{MODIS-FAPAR}}\) and GPP\({}_{\text{EC}}\) for seven sites.  
much to enhance model performance at the savannas and the two shrubland sites (Figure 4c,d,g). The probable reason is that the impact of soil background on the low vegetation coverage at these sites leads to failure of the OTCI in tracking GPP. By contrast, the addition of PAR and FAPAR appears to have little contribution to the GPP estimation at the US-Bi2, US-WCr, and US-KFS sites (Figure 4b,e,f), as OTCI can directly capture the pronounced seasonal variation of GPP at these sites [17; 74].
## 2 Security Requirement and S-Box Design Methods  
### S-Box Properties  
An S-box plays a crucial role in resisting cryptanalysis. A cryptographically strong S-box is classified based on its exhibited cryptographic properties. Moreover, an S-box must exhibit high nonlinearity, low differential uniformity, a high algebraic degree and complex algebraic expression to resist cryptanalysis, such as linear [3], differential [2], algebraic [7] and interpolation [6] attacks. Therefore, a lot of research is still in place to investigate and strengthen the cryptographic properties of each S-box proposed.  
There is also much research conducted in designing and constructing an S-box. Typically, the measurement of _cryptographically strong_ S-box properties depends on assumptions. To avoid more confusion in determining a cryptographically strong S-box, [PERSON] takes responsibility by facilitating and simplifying the desired properties in designing _good_ S-boxes [14]. In particular, the desired properties suggested by [PERSON] [14] and later followed by [PERSON] et al. [15], are bench-marked against AES's S-box. This is because AES's S-box is already conjectured as having the optimal cryptographic properties [9].  
Therefore, based on the suggestions made in [14; 15], we set the desired values for each of the S-box properties to be considered cryptographically strong, as summarized in Table 1. The following subsections explain each property in detail.  
Let \(\mathbb{F}\) be a finite field with \(\mathbf{2}\) elements, while \(\mathbb{F}_{2^{n}}\) is a finite field with \(\mathbf{2^{n}}\) elements. An \(n\times n\) S-box is a Boolean map:  
\[F:\mathbb{F}_{2^{n}}\rightarrow\mathbb{F}_{2^{n}}=(f_{1}(x_{1},\ldots,x_{n}), \ldots,f_{n}(x_{1},\ldots,x_{n}))\]  
where \(f_{1}(x_{1},\ldots,x_{n})\) to \(f_{n}(x_{1},\ldots,x_{n}))\) are called as component functions of \(F\).
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
###### Abstract  
This research article outlines our aim to perform topology optimization (TO) by reducing the mass of the connecting rod of an internal combustion engine based on static structural and dynamic analyses. The basic components of an internal combustion engine like the connecting rods, pistons, crankshaft, and cylinder liners were designed using Autodesk Inventor Professional 2025. Using topology optimization, we aimed to achieve lesser maximum von Mises stress during static structural analysis and maintain a factor of safety (FOS) above 2.5 during rigid body dynamics. A force of 64,500 N was applied at the small end of the connecting rod while the big end was fixed. Topology optimization was carried out using ANSYS Discovery software at various percentages on a trial-and-error basis to determine better topology with lesser maximum von Mises stress. Target reduction was set to 4%, and as a result, 5.66% mass reduction from the original design and 6.25% reduced maximum von Mises stress was achieved. Later, transient analysis was carried out to evaluate the irregular motion loads and moments acting on the connecting rod at 1000 rpm. The results showed that the FOS remained above 2.5. Finally, the optimized connecting rod was simulated and verified for longevity using Goodman fatigue life analysis.  
topology optimization; connecting rod; finite element method; multibody dynamics; factor of safety; fatigue life analysis Article
## 5 Application Case
### Requirement Description  
According to the needs of a petrochemical enterprise, the platform is designed to: (1) Collect and collate the actual working conditions, test and design data from multiple enterprises and research institutes, and establish a safety assessment system for key materials; (2) complement the tests (including relevant parameters and conditions); and, (3) integrate the original data and supplement the test data to lay the foundation for the safety assessment and post-assessment of key materials. The specific business flowchart of the petrochemical platform is shown in Figure 7.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Limitations of Traditional Detection Methods
#### 1.2.4 Dynamic Analysis Challenges  
_Single-path execution:_ dynamic analysis methods typically explore only a single execution path during testing, missing vulnerabilities in alternate code paths [14].  
_Performance overhead:_ runtime protection mechanisms introduce significant performance penalties that limit their practicality in production environments [15].  
_Browser engine differences:_ [PERSON] et al. demonstrate that dynamic analysis tools may miss XSS vulnerabilities that only manifest in specific browser engines or versions [16].
# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 4 Result and Discussion
### Comparative Study
#### 4.3.2 Results  
We chose four times augmented datasets to train with the YOLOv7 model based on the training details that were studied above. All the augmented data are trained by the same deep learning model, and the performance of the model is compared in Table 5. The performance of the model training on the datasets augmented by strategy in this paper is better than the datasets augmented by using the traditional method. Compared to the traditional method, the results show that three data augmentation methods by incorporating domain knowledge can improve the precision rate by 10.45%, 8.99%, and 9.92%, respectively, and improve the recall rate by 9.80%, 1.96%, and 3.22%, improve the F1_score rate by 10.22%, 5.31%, and 6.40%, improve the mAP_0.5 rate by 3.71%, 1.79%, and 3.32%. For crack detection, in addition to the gain compensation strategy, the precision of the crack decreased when using the other augmented strategies. The morphology of the crack in the GPR image shows a typical hyperbolic shape, and the data augmentation strategies change the shape of the hyperbola to a certain extent, which leads to a drop in precision.
# Wind Load Distribution in Float Photovoltaic System
## 4 Modeling Description
### Evaluation of Wind Load Distribution  
As a result of the flow analysis, in order to evaluate the load transmitted to the foundation by the wind load, the pressure shown in Table 3 was summarized as horizontal and vertical components in consideration of the area and installation angle of the PV module and shown in Table 4.  
The pressure in Table 3 is summarized into horizontal and vertical partitioning forces considering the area and installation angle of the solar module in Table 4. Forward means the direction of wind incident on the forward direction of the PV module, the \"\(-\)\" sign for the horizontal direction (\(x\)-axis) is opposite to the direction of the wind, and the vertical direction (\(y\)-axis) is opposite to the direction of gravity. Also, the backward means the direction of wind incident on the back of the PV module, and the \"\(-\)\" sign for the vertical direction is opposite to the direction of gravity. The loads in each direction shown in Tables 5 and 6 are graphically represented according to the array. Table 4 shows a significant difference in the loads on the PV modules depending on the direction of wind incidence. It can be seen that the largest loads occur in the first and second rows of the PV module array when the wind enters from the forward direction, and then the load decreases rapidly. In addition, when the wind is incident from the backward direction, the first row is evaluated to have the largest load, and then the load decreases rapidly. The vertical load distribution was evaluated similarly to the horizontal load distribution, and the wind loads on each column are shown in Table 6. It can be seen that when the wind is blowing from the forward direction, relatively large loads are generated in the first and second rows of the array, and the load decreases rapidly from the third row.
###### Abstract  
Land cover change is the leading cause of global biodiversity decline. New satellite platforms allow for monitoring of habitats in increasingly fine detail, but most applications have been limited to forested ecosystems. I demonstrate the potential for detailed mapping and accurate dating of land cover change events in a highly biodverse, Critically Endangered, shrubland ecosystem--the Renosterveld of South Africa. Using supervised classification of Sentinel 2 data, and subsequent manual verification with very high resolution imagery, I locate all conversion of Renosterveld to non-natural land cover between 2016 and 2020. Land cover change events are further assigned dates using high temporal frequency data from Planet labs. A total area of 478.6 hectares of Renosterveld loss was observed over this period, accounting for 0.72% of the remaining natural vegetation in the region. In total, 50% of change events were dated to within two weeks of their actual occurrence, and 87% to within two months. The Renosterveld loss identified here is almost entirely attributable to conversion of natural vegetation to cropland through ploughing. Change often preceded the planting and harvesting seasons of rainfed annual grains. These results show the potential for new satellite platforms to accurately map land cover change in non-forest ecosystems, and detect change within days of its occurrence. There is potential to use this and similar datasets to automate the process of change detection and monitor change continuously.  
land cover monitoring; change detection; threatened ecosystems; Sentinel 2; planet labs; Cape Floristic region 202133834 [[https://dx.doi.org/10.3390/rs13050834](https://dx.doi.org/10.3390/rs13050834)]([https://dx.doi.org/10.3390/rs13050834](https://dx.doi.org/10.3390/rs13050834))
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
###### Abstract  
Teleportation is an important protocol in quantum communication. Realizing teleportation between arbitrary nodes in multi-hop quantum networks is of great value. Most of the existing multi-hop quantum networks are based on Bell states or Greeberger-Horne-Zeilinger (GHZ) states. Bell state is more susceptible to noise than GHZ states after purification, but generating a GHZ state consumes more basic states. In this paper, a new quantum multi-hop network scheme is proposed to improve the interference immunity of the network and avoid large consumption at the same time. Teleportation is realized in a network based on entanglement swapping, fusion, and purification. To ensure the robustness of the system, we also design the purification algorithm. The simulation results show the successful establishment of entanglement with high fidelity. Cirq is used to verify the network on the Noisy Intermediate-Scale Quantum (NISQ) platform. The robustness of the fusion scheme is better than the Bell states scheme, especially with the increasing number of nodes. This paper provides a solution to balance the performance and consumption in a multi-hop quantum network.  
multi-hop quantum networks; teleportation; fusion; entanglement purification
# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 3 Methodology
#### 3.1.1 Gaussian Supervector Representation  
First, we define the point cloud by the GSV representation. Let \(X=\{p_{s}\in\ R^{3},s=1,\ \ldots,\ S\}\) be the set of 3D points (the point cloud), where \(S\) indicates the point number in the set. A D-dimensional Gaussian mixture for a single 3D point (or vector) \(p_{s}\) is represented by the following form.  
\[v(p_{s})=\sum_{k=1}^{K}w_{k}v_{k}(p_{s};\mu_{k},\Sigma_{k}), \tag{1}\]  
where every \(v_{k},\ k=1,\ 2,\ \ldots,\ K\) is the D-dimensional Gaussian density with the mean vector \(\mu_{k}\), covariance matrix \(\Sigma_{k}\), and weights \(w_{k}>0\). Therefore, for every \(k\),  
\[v_{k}(p_{s};\mu_{k},\Sigma_{k})=\frac{1}{(2\pi)^{\frac{D}{2}}\big{(}\sqrt{det \Sigma_{k}}\big{)}}e^{-\frac{1}{2}(p_{s}-\mu_{k})^{T}\Sigma_{k}-1}(p_{s}-\mu_{ k}). \tag{2}\]  
In the case of a 3D point cloud (\(D=3\)), Equation (2) can be re-written as:  
\[v_{k}(p_{s};\mu_{k},\Sigma_{k})=\frac{1}{(2\pi)^{\frac{3}{2}}\big{(}\sqrt{det \Sigma_{k}}\big{)}}e^{-\frac{1}{2}(p_{s}-\mu_{k})^{T}\Sigma_{k}-1}(p_{s}-\mu_{ k}) \tag{3}\]  
[PERSON] law is used to estimate the posterior probability distribution for every \(k\) value [38]. The soft assignment of point \(p_{s}\) to the \(k\)-th Gaussian density is defined by:  
\[\gamma_{k}(p_{s})=\frac{w_{k}v_{k}(p_{s};\mu_{k},\Sigma_{k})}{\sum_{j=1}^{K}w_ {j}v_{j}\big{(}p_{s};\mu_{j},\Sigma_{j}\big{)}}. \tag{4}\]  
For every \(k=1,2,\ \ldots,K\), we define:  
\[n_{k}=\sum_{s=1}^{S}\gamma_{k}(p_{s}). \tag{5}\]  
Gaussian Supervectors in [39] are used to handle the issue of natural view categorization. Using Equations (1)-(5), first determine the vector:  
\[Z_{k}=\frac{\sum_{s=1}^{S}\gamma_{k}(p_{s})(p_{s}-\mu_{k})}{n_{k}}. \tag{6}\]  
Then, for every \(k\), calculate:  
\[Z_{k}^{\prime}=\left(\frac{\Sigma_{k}}{n_{k}}\right)^{-\frac{1}{2}}Z_{k}. \tag{7}\]  
Finally, concatenate the vectors \(Z_{k}^{\prime}\) to obtain the vector \(Z_{KGSV}^{\prime}\). The resulting \(Z_{KGSV}\) is normalized by the specimen size S [20]:  
\[Z_{KGSVN}^{\prime}=\frac{1}{S}\ Z_{KGSV}^{\prime}. \tag{8}\]  
Descriptions (particularly those concentrating on the maximum and minimum function) may improve the precision (see reference [38]). Hence, we apply the max and min functions on the \(Z_{KGSVN}^{\prime}\) vector to create Gaussian Supervector representation \(Z_{GSV}\) with the size at six by \(K\). We have \(K=m^{3}=8^{3}\) due to the optimal choice of \(m=8\), as explained in the Section 3.
## 4 Experimental Results
### Robustness to Distortions and Adversarial Attacks  
Real-world conditions such as compression and noise significantly affect performance. Table 2 compares accuracy under various distortions with XCeption showing higher robustness. Adversarial attacks via FGSM (\(\epsilon=0.01\)) lead to accuracy drops across all models (Table 3), underscoring the need for adversarial resilience strategies.
## 2 Study Area and Data Sets
### Data Set Description
#### 2.2.2 GPM IMERG Products  
The GPM IMERG series products used in this study enjoy high spatial (0.1\({}^{\circ}\) latitude/longitude) and temporal (daily) resolutions [6]. Meanwhile, the monthly IMERG data are obtained by accumulating the daily data. The IMERG product line covers the full 60\({}^{\circ}\) S-60\({}^{\circ}\) N globe and includes the ER, LR and FR products. The ER and LR products are near-real-time products with lag times of 4 h and 12 h, respectively. The algorithm of the post-real-time product FR integrates the satellite data and the ground-based rain gauge monthly observations, while the satellite data comprise the passive microwave and infrared data from GPM and other satellite constellations. The IMERG V05 products used in this study were released in November 2017 [6] and the Level-3 products were downloaded from the Precipitation Measurement Mission (PMM) website ([[https://pmm.nasa.gov/GPM](https://pmm.nasa.gov/GPM)]([https://pmm.nasa.gov/GPM](https://pmm.nasa.gov/GPM)); accessed on 5 September 2018). It is worth noting that, to make a quantitative assessment of the GPM data, the pixels that contain at least one ground station are first extracted, and then the in situ match-ups are acquired by averaging the ground station data within each extracted pixel.