esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Algorithms for HPC Systems in the Exascale Era
### Parallel Solvers for Exascale Computing
#### 7.2.3 Parallel Iterative and Direct Linear Solver
And let \(\mathcal{T}_{\mathbb{I}\times I}(L)\) be the block cluster quad-tree on the index set 1 whose admissibility condition \(AC(\tau\times\sigma)\) is defined as_  
\[AC(\tau\times\sigma)=\left\{ \begin{array}{ll}1&\text{if }A_{|\tau\times\sigma} \text{ can be approximated by an $\mathbf{R}$k-matrix in a specified norm}\|\cdot\|\\ 0&\text{otherwise}\end{array} \right.\]  
_Then, the matrix \(A\) is called the \(\mathcal{H}\)-matrix of blockwise rank \(k\) defined on block cluster quad-tree \(\mathcal{T}_{\mathbb{I}\times I}(L)\)._  
_Let us remember that, given a matrix \(A\), the matrix \(\bar{A}\) is said to be an approximation of \(A\) in a specified norm \(\|\cdot\|\) if there exists \(\epsilon\in\Re\) such that \(\big{\|}A-\bar{A}\big{\|}<\epsilon\)._  
Different approaches can be used to compute an \(\mathbf{R}\)\(k\)-matrix approximation \(\tilde{M}=\tilde{M}_{1}\tilde{M}_{2}\) of an arbitrary matrix \(M\)[188]; as an example, we cite the one based on the _\"truncated singular value decomposition,\"_ which represents the best approximation of an arbitrary matrix in the spectral and Frobenius norm (see [189] for details). As an example of the use of the \(\mathcal{H}\)-matrices to obtain algorithms of reduced complexity, we propose the algorithm for the calculation of the matrix-vector product (see Algorithm 14): the complexity for that matrix-vector multiplication algorithm is \(O(kn\log(n))\). Other algorithms of interest from basic matrix algebra that use \(\mathcal{H}\)-matrices are described in Hackbusch et al. [189]. ```
1:procedureHmatrix-MVM(\(A\), \(y\), \(x\), \(\mathcal{T}_{\mathbb{I}\times I}(L)\), \(\tau\times\sigma\))
2:Input:\(A\), \(y\), \(x\), \(\mathcal{T}_{\mathbb{I}\times I}(L)\), \(\tau\times\sigma\)
3:Output:\(y\)
4:if\(S(\tau\times\sigma)\
eq\emptyset\)then\(\triangleright\)\(\tau\times\sigma\) is not a leaf of \(\mathcal{T}_{\mathbb{I}\times I}(L)\)
5:for each \(\tau^{\prime}\times\sigma^{\prime}\in S(\tau\times\sigma)\)do
6: HMatrix-MVM(\(A\), \(y\), \(x\), \(\mathcal{T}_{\mathbb{I}\times I}(L)\), \(\tau^{\prime}\times\sigma^{\prime}\))
7:endfor
8:else\(\triangleright\)\(\tau\times\sigma\) is a leaf of \(\mathcal{T}_{\mathbb{I}\times I}(L)\)
9:\(y_{|_{\tau}}\gets y_{|_{\tau}}+A_{|\tau\times\sigma}x_{|\sigma}\)\(\triangleright\)\(A_{|\tau\times\sigma}\) could be an unstructured or an \(\mathbf{R}\)\(k\)-matrix
10:endif
11:endprocedure
```  
**Algorithm 14** Matrix-vector multiplication \(y=y+Ax\) of the \(\mathcal{H}\)-matrix \(A\in\Re^{I\times I}\) (defined on block cluster quad-tree \(\mathcal{T}_{\mathbb{I}\times I}(L)\)) with vector \(x\in\Re^{I}\). The index sets \(\tau\times\sigma\) to be used in the first call to the recursive HMatrix-MVM procedure are such that \(\tau=\sigma=I\).
# Application of Dynamic Time Warping (DTW) in Comparing MRT Signals of Steel Ropes
## 3 Results  
The input data for the analysis of time-series synchronization using the DTW algorithm consisted of text files exported by the MD121 View software (developed by Zawada NDT)  
Figure 12: Cross-sectional views of selected steel wire ropes: (**a**) Seale 6x19-NF rope by Drumet, consisting of 115 wires; (**b**) round-stranded rope 22.0 17x7+Ao Z/s-n-I-N-g-1570-WT-75/TT-2, consisting of 119 wires; (**c**) NRHD-24 rope by ArcelorMittal, consisting of 360 wires [16]; (**d**) STRATOPLAST MF 40.0 8x26 WS+EPIWRC 1960 B/N(Zn/Al) z/Z (s/S) rope by CASAR, polymer impregnated, consisting of 331 wires [10].  
from binary files generated during periodic MRT inspections of steel wire ropes. These files are archived by certified inspectors performing MRT diagnostics.  
Extended analysis of the MRT data was carried out using the commercial software DEWESoftX 2024.3 for signal filtering and spectral analysis [69] and the open-source R 4.4.2 environment with the DTW package [60] developed by [PERSON] at the Institute of Biophysics (IBF-CNR), Milano. This software is distributed under the terms of the GNU General Public License Version 2, June 1991.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 3 EEDOR Void Avoidance with Hop Count Discovery Mechanism
### Description of the Proposed EEDOR-VA Protocol
#### 3.3.1 Hop Count Discovery Phase
In each step, every neighbor node that receives a reply updates its hop count from the surface sink(s) if its hop count is greater than 1+ hop count in the received HCREP. Figure 3b shows the routes from sink s1 to node n1.
## 2 Data and Methods
### Source Analysis Methods  
Factor analysis is used in the analysis of pollution sources. Factor analysis is a method of variable dimensionality reduction that assumes the existence of multiple unobservable hidden factors behind the original variables. It starts with the study of the correlation matrix of the original variables and reduces the number of original variables into a few composite factors by means of dimensionality reduction, which usually reveals the internal links and important information hidden between the original variables, especially the causal (pollution source) links. The KMO (Kaiser-Meyer-Olkin) test and Bartlett's sphericity test are used to determine the correlation between indicators before factor analysis to determine whether the original variables are suitable for principal component analysis, and the specific evaluation methods are established as follows:  
1. Min-max normalization: eliminate the effects of dimensions and orders of magnitude by subtracting the mean of all variables and dividing by the standard deviation of each variable;
2. Calculate the correlation coefficient matrix: use the standardized data matrix to calculate its corresponding correlation coefficient matrix, R, and calculate the eigenvalue and eigenvector of R;
3. Determine the number of principal components: determine the number of principal components m according to the principle that the eigenvalue is greater than 1 or the cumulative contribution rate of principal component variance is greater than 85%;  
\begin{table}
\begin{tabular}{c c c} \hline
**Grade** & \(P_{i}\) & **Pollution Assessment** \\ \hline I & \(\leq\)1 & Non-polluting \\ II & \((1,2]\) & Light pollution \\ III & \((2,3]\) & Light pollution \\ IV & \((3,5]\) & Moderate pollution \\ V & \(>\)5 & Heavy pollution \\ \hline \end{tabular}
\end{table}
Table 1: Environmental quality assessment grading criteria for the single factor pollution index method.  
\begin{table}
\begin{tabular}{c c c} \hline
**Grade** & \(P_{i}\) & **Pollution Assessment** \\ \hline I & \(\leq\)0.7 & Cleaning \\ II & \((0.7,1.0]\) & Cleaner \\ III & \((1.0,2.0]\) & Light pollution \\ IV & \((2.0,3.0]\) & Moderate pollution \\ V & \(>\)3.0 & Heavy pollution \\ \hline \end{tabular}
\end{table}
Table 2: Environmental quality assessment grading criteria for the Nemerow pollution index method.  
Calculate the factor score: calculate the score of the top m principal components of each sample and plot the spatial distribution of the factor score.
# Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors
## 3 Method
### Nonconformity Measure with Image- and Instance-Level Representations  
The nonconformity measure is of great importance to efficient change-point detection, since it measures the strangeness between the current test datum and the obtained data, which is the key to increasing \(S_{n}\) after the change point. Existing works about domain adaptive object detectors mainly use the hidden feature map output by the base convolutional layers as the image-level representation of the original input image, and all of the final output vectors with high confidence of objects as the corresponding instance-level representation [7]. Building on those representations, the domain adaptation algorithms are called to decrease the gap in the representations between domains. In this spirit, we propose using the two representations to calculate the nonconformity measure, since the representations between two domains are different before the domain adaptation process.  
Our notations follow [7], which both concerns the image-level representation and instance-level representation. Define \(\phi_{u,v}(z_{i})\) as the activation at position \((u,v)\) of the feature map of the \(i\)th image \(z_{i}\) output by the backbone, and let \(p_{i,j}\) be the output vector of the \(j\)th object of \(i\)th image detected by the object detector. Following [7], we construct the image-level representation \(\tilde{\phi}(z_{i})\) and the instance-level representation \(\tilde{p}(z_{i})\) as follows:  
\[\tilde{\phi}(z_{i})=\frac{1}{N_{\phi}}\sum_{u,v}\phi_{u,v}(z_{i}),\]  
\[\tilde{p}(z_{i})=\frac{1}{N_{p_{i}}}\sum_{j}p_{i,j},\]  
where \(N_{\phi}\) is the total number of pixels of the feature map, \(N_{p_{i}}\) is the total number of detected objects and \(\tilde{p}(z_{i})\) is set to zero vector if \(N_{p_{i}}=0\). Finally, our whole representation vector \(v(z_{i})\) of each image is the concatenation of \(\tilde{\phi}(z_{i})\) and \(\tilde{p}(z_{i})\). After this vectorization of each image, we calculate the nonconformity score of \(z_{i}\) as  
\[\alpha_{i}=A(z_{i})=\min_{j}\sqrt{(v(z_{i})-v(z_{j}^{val}))^{2}} \tag{3}\]  
where \(z_{j}^{val}\) represents the \(j\)th sample in the source validation set and \(j\) ranges over \(\{1,\ldots,N^{v}\}\) with \(N^{v}\) being the validation size. This nonconformity measure is the 1-nearest neighbor nonconformity measure mentioned in [17], which measures the mathematical distance between \(v(z_{i})\) and the point set \(\{v(z_{1}^{val}),\ldots,v(z_{N^{val}}^{val})\}\) with the fact that larger \(\alpha_{i}\) is related to stranger \(z_{i}\) compared with the source validation data.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BER & Bit-error rate \\ CQSM & Complex quadrature spatial modulation \\ CSI & Channel state information \\ GSM & Generalized spatial modulation \\ IM & Index modulation \\ IQSM & Index quadrature spatial modulation \\ MA-SM & Multiple active antennas spatial modulation \\ MIMO & Multiple input multiple output \\ \end{tabular}  
Figure 5: BER of MA-SM using the optimal rotation angles, the angles derived in [20], and the all-zero angle set, for \(N_{T}=5\), \(N_{P}=3\), \(n_{R}=\)3, 4, and 6, using QPSK (**left sub-figure**) and 16-QAM (**right-sub-figure**).
## 4 Numerical Analysis
### Critical Dynamic Buckling Load Affected by the Load Parameter \(k_{n}\)  
As is known, the dynamic buckling of the composite structure is affected by two axial loads. The buckling load is changed if the tensional load is applied in one direction and if the compressional load is performed in the other direction. Moreover, the buckling load is variable when two compressional axial forces are loaded. To analyze the regularity and mechanism of the buckling loads affected by two axial forces, the load parameters of the honeycomb reinforced laminated composite structure are defined as \(k_{n}\), where \(N_{y}=N_{0}\) and \(N_{x}=k_{n}N_{y}=k_{n}N_{0}\). The data are provided in Table 1. The buckling loads affected by \(k_{n}\) are plotted in Figure 2, where \(k_{a}=1\), and the length and width of the composite laminated plate are equal and denoted as \(a\). \(t_{1}=t_{3}\), \(t_{2}=4t_{1}\), \(d_{t}=1.5t_{1}\) and the length of the composite laminate ranges from 0.1 to 1 m. As shown in this figure, by increasing the load parameter, the buckling load is decreased significantly. Figure 3a shows that it is affected by the dimensions of the composite honeycomb reinforced laminate at the defined load parameters, and the buckling loads are decreased by increasing the laminate's geometry dimensions. To visualize the buckling load of the composite laminate quantitatively, the tensile and compression loads are numerically analyzed and plotted in Figure 3b. The reference load parameter is defined as \(k_{n}=1\), and the reference buckling load is \(N_{0}\) (\(k_{n}=1\)). The buckling load of the honeycomb reinforced laminate is increased by raising the tensile loads, whereas it is decreased by increasing the compression loads. It can be seen in the figure that the buckling load can be dramatically increased by the tensile forces, whereas it is decreased slowly by the compression forces.
## 5 Handling Adversarial Attacks  
Handling adversarial attacks means taking a more holistic view of the malignant tactics applied to mislead machine learning models and a proactive stance in implementing the corresponding defenses. Adversaries might craft inputs that look innocuous but actually are designed to subvert detection systems by exploiting model vulnerabilities. Theymay also conduct data poisoning during training or perform evasive actions at inference to reduce detection accuracy. Approaches to mitigate these threats include adversarial training, gradient masking, and rigorous data integrity checks. These methods are particularly relevant when considering high-risk systems, as defined in [6, 52], where strict regulatory requirements demand robust mechanisms to ensure model reliability and trustworthiness. In addition to gradient-level adversaries, 6G platforms must contend with cross-layer exploits such as coordinated eavesdropping + API-fuzzing campaigns documented in the recent literature. Our defense stack, therefore, binds adversarial training pipelines to the physical layer detectors and couples federated learning updates with blockchain-timed attestations at the connection layer to deliver provable end-to-end robustness. A concise illustration of common adversarial threats and potential mitigation strategies is shown below. Each strategy should be adapted to the specific context of the ML-driven intrusion detection system, ensuring compliance with legal and technical standards. Ensuring consistent monitoring of incoming data, performing robust outlier analysis, and dynamically retraining ML systems with clean data help maintain overall system resilience. Conceptual case study: As an illustrative example, suppose a hostile tenant injects forged flow rules into the slice controller so that malicious VoIP traffic imitates benign eMBB patterns. In a sandbox replay using Fast Gradient Sign Method (FGSM) perturbations, we observed qualitatively that a vanilla Isolation Forest detector misclassified many of these spoofed flows. Prior work proposed in [40] indicates that retraining with such adversarial samples can markedly improve recall; a full quantitative study is left for future work, but the scenario underscores why adversarial robustness must be treated as a first-class design goal. The threat-mitigation relationships introduced above are summarized in Figure 4.  
Figure 4: The diagram pairs four representative adversarial tactics—gradient-based evasion, data poisoning, cross-layer exploits, and model drift/obsolescence—with the corresponding defense mechanisms integrated into WIDA: offline adversarial training, differential validation with integrity hashing, blockchain-timed attestations plus zero-trust APIs, and an edge-resident SHAP drift monitor with self-adaptation hooks.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Energy Efficiency  
Energy efficiency refers to the energy saved during computational operations related to dynamic window creation, byte comparisons, and the verification of each data string. The low energy efficiency of the RAM scheme is linked to its high complexity in computation, its dynamic breakpoint adjustments, and the probabilistic nature of the byte comparisons. In CWCA, by controlling the chunk size within specified thresholds, the algorithm minimizes unnecessary computational operations and redundant data handling. Efficient string processing using delimiters and early termination conditions reduces energy consumption. The WCA-BF + ASE scheme has relatively low energy efficacy, as the Bloom filter's probabilistic approach involves multiple hash operations and bit manipulations for each data string, increasing the energy needed for verification and duplicate detection. This constant processing and data handling contribute to the overall higher energy usage. In the DSW technique, the energy consumption is higher due to the continuous computational operations required to manage two sliding windows. It involves constant byte comparisons and frequent hash calculations for each window position to determine the breakpoints. This demands significant processing power and memory usage, leading to increased energy consumption during these operations. The DICA achieves higher energy efficiency, as it has a lower computational cost and does not utilize excessive energy; it achieves this by identifying the dynamic chunk sizes based on delimiters and achieving better chunk sizes as well. Figure 7 shows that for a data size of 800 KB, the RAM, WCA-BF + ASE, DSW, CWCA, and DICA schemes maintain energy efficiency of 420, 530, 550, 635, and 860 operations per unit time, respectively.
# A Snapshot of the Microbiome of a Portuguese Abandoned Gold Mining Area
## 2 Materials and Methods  
### Sampling Site Description, Sample Collection and Processing  
Located at circa 11 km south of Gois (Coimbra, Portugal), the Escadia Grande mine is the most important mining site in the 252 km\({}^{2}\) homonymous exploration area (Au-Ag metallogenic belt). In general, the area's steep slope is covered by dense vegetation, mainly from _Eucalyptus globulus_ and _Pinus pinaster_. The exploitation of mineralogical resources has been dated back to the Roman period, although recently, the mine is known to have operated from 1940 to 1952. During this period of active exploitation, it is known that prospection occurred underground on seven levels and that circa 42,878 tons of material were handled [49]. Ore deposits are composed of tabular quartz veins consisting of quartz, arsenopyrite, pyrite, rare chalcopyrite, galena, sphalerite, gold and appetite [3; 50]. Au-Ag bearing quartz veins are 70 cm thick, up to 300 m long, with an average of 8 g/t of Au and 33 g/t of Ag [49; 50]. Mine dumps and tailings are mainly composed of quartz and other rock pieces with various sulfides (mainly arsenopyrite and galena). Moreover, mine wastes contain up to 8090 mg/kg of As and 70.1 mg/kg of Sb with arsenic, thus being the main human health concern trace element present in the Escadia Grande mining area [51].  
Three representative samples were selected for analysis: (P1) a control sample in an area located outside of the main mine zone, known to be deprived of relevant gold concentrations and without any history of past mining activities; (P2) a sample from the tailings located on the mining area hill, circa 25 m in front of the mine entrance (surrounded by dense fern vegetation); and (P3) a sample from the mine interior, collected at about 1 m next to the entrance (on one of the few lateral wall dry spots found, due to the mine being flooded) (Figure 1). Soil samples (circa 25 g) were collected to a depth of 5 to 10 cm after removal of the surface soil (\(\pm\)5 cm). Samples were properly conditioned to prevent contamination in individual sealed sterilized bags, cooled at 4 \({}^{\circ}\)C for transportation and maintained in sterile conditions (unopened and undisturbed) until further laboratory analysis. In the laboratory, samples were air-dried for 24 h and then sieved to remove the presence of plant material before environmental DNA extraction.
###### Abstract
This paper aims to improve range resolution in indoor environments while enabling the construction of low-cost radar systems by increasing bandwidth through signal processing, in contrast to existing radars that require wide bandwidth. Among the methods to enhance FMCW radar's range resolution, the Neural Network Padding algorithm uses deep learning to extend the signal length by predicting and appending the signal at both the beginning and the end of the original signal, improving range resolution. A feedforward neural network was used, and Zoom-FFT (ZFFT) was applied to obtain accurate points at the neural network input. While this method improves range resolution, it has several issues. Firstly, the network must be retrained whenever the environment changes. Additionally, when targets are not equidistant, the central target signal can disappear due to the multiplication of prediction errors, leading to a degradation in range resolution. In this paper, we propose an Estimated Signal Padding (ESP) method to enhance range resolution by estimating and generating each target signal and padding it after the original signal to increase the sample count. The phase and amplitude of the target are derived from its location using FFT, and the original target signal is estimated. Since a signal similar to the original is padded, the ratio of the main lobe to the side lobe improves, thereby enhancing range resolution. Unlike deep learning methods, this approach achieves performance improvements without requiring training.
## 7 Vanadium Ion Transfer Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Membrane**} & \multicolumn{4}{c}{**Diffusivity /cm min\({}^{-1}\)**} \\ \cline{2-5}  & **V(II)** & **V(III)** & **V(IV)** & **V(V)** \\ \hline AMV & \(2.01\times 10^{-6}\) & \(1.11\times 10^{-6}\) & \(3.17\times 10^{-5}\) & \(5.67\times 10^{-5}\) \\ CMV & \(1.01\times 10^{-5}\) & \(1.34\times 10^{-5}\) & \(2.65\times 10^{-5}\) & \(2.14\times 10^{-4}\) \\ Nafion 117 & \(4.63\times 10^{-5}\) & \(4.24\times 10^{-5}\) & \(1.94\times 10^{-4}\) & \(1.41\times 10^{-4}\) \\ Daramic (0.15 mm) & \(6.21\times 10^{-4}\) & \(7.09\times 10^{-4}\) & \(1.46\times 10^{-3}\) & \(>\)\(1\times 10^{-2}\) \\ Composite membrane (0.23 mm) & \(1.26\times 10^{-4}\) & \(1.34\times 10^{-4}\) & \(3.64\times 10^{-4}\) & \(2.92\times 10^{-3}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Diffusivity (cm\(\cdot\cdot\)min\({}^{-1}\)) of vanadium ions across different membranes [65]. results, the chemical identity and the environment of the diffused vanadium ions was studied extensively and analysed. From the UV/Vis Absorption spectra, it was seen that the V\({}^{4+}\) ions were adsorbed inside the nafion channel network and could not be removed by mechanical agitation unlike the other vanadium ion species. By analysing the surface deposition by XPS, it was revealed that the V\({}^{5+}\) ions were the predominant species on both surfaces of the membrane. Additionally, the NMR spectroscopic analysis showed Nafion to be chemically stable and there were no changes in the structure on exposure to vanadium electrolytes. Overall, it was concluded that the vanadium ions were not directly bound to the sulfonic acid groups in Nafion but they were bound to the water molecules in the hydration shell [120]. Another important consequence of vanadium ion diffusion in VRB applications was further highlighted in a simulation study by [PERSON] _et al._[121], who demonstrated the effect of the resultant self- discharge reactions on stack and electrolyte reservoir temperatures. Using vanadium ion diffusion coefficient values for Nafion 115 [119], they found that not only does the vanadium ion diffusion across the membrane lead to capacity loss as a result of different diffusion across the membrane and self discharge reactions, but that for the membrane in question, the magnitude of the heat generated by the self-discharge processes, could lead to excessive heating of the electrolyte, especially when the pumps were turned off during stand-by periods. Temperatures above 55 \({}^{\circ}\)C were predicted within the stack over the first half-day on standby, levelling off to the environmental temperature levels after 2-3 days. The study highlighted the importance of improved membranes to reduce self-discharge rate and thermal effects on the VRB. The researchers concluded that while Nafion 115 is extensively used in laboratory studies of the VRB, it is unsuitable for commercial systems where diffusion coefficients one tenth of the Nafion 115 values are required for adequate performance and temperature control during long-term operation [121].