esa-sceva/satcom-chunk-collection huggingface dataset:
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 3 Delay Tolerant Network Protocols  
Routing protocols in DTNs can be separated into two broad categories: replication-based and forwarding-based. In a replication-based protocol, the main principle is that a message is copied multiple times. Replication of a message allows it to be distributed quickly throughout a network as well as decreasing latency and increasing the chance of a successful delivery. However, having too many packets in a network and using replication-based protocols can mean the network gets congested easily and slows down transmission of all messages. In a forwarding-based protocol, the message only exists as a single copy. When it is passed between nodes, once the receiver has got the message, the sender deletes it. This keeps network traffic down to a minimum but vastly increases latency and the chance of a successful delivery is significantly reduced.  
In the experiments four different protocols were used:  
* Epidemic [29]--A basic benchmark protocol requiring a vast amount of resources to produce reasonable results (Algorithm 1)
* Spray and Wait [30]--A more complex algorithm building upon Epidemic's success and reducing the need for resources (Algorithm 2)
* PRoPHET [31]--A complex algorithm relying on probabilities to route messages as directly as possible and minimising duplications (Algorithm 3)
* MaxProp [32]--An intricate, state-of-the-art protocol relying on probabilities and hop-counts to provide the highest delivery probability using the least resources [33] (Algorithm 4)
## Nomenclature \begin{tabular}{l l} \(A_{\phi}\) & Output of MMC leg averaging voltage control \\ \(C_{daa}\), \(C_{dcb}\) & Capacitance of the MMC DC-bus capacitors \\ \(f\) & Electrical grid fundamental frequency \\ \(f_{sw}\) & Individual SM switching frequency \\ \(i_{A}\), \(i_{B}\), \(i_{C}\) & Equivalent switching frequency \\ \(I_{A}\), \(I_{B}\), \(I_{C}\) & Phase \(A\), phase \(B\) and phase \(C\) instantaneous currents before compensation \\ \(I_{A1}\), \(I_{B1}\), \(I_{C}\) & Phase \(A\), phase \(B\) and phase \(C\) RMS currents before compensation \\ \(I_{A1}\), \(I_{B1}\), \(I_{C1}\) & Phase \(A\), phase \(B\) and phase \(C\) RMS currents after active power balance \\ \(I_{A2}\), \(I_{B2}\), \(I_{C2}\) & Phase \(A\), phase \(B\) and phase \(C\) RMS currents after active power balance and \\ \(I_{A2}\), \(I_{B2}\), \(I_{C2}\) & reactive power compensation \\ \(I_{x}\), \(i_{y}\), \(i_{z}\) & Phase \(x\), phase \(y\) and phase \(z\) instantaneous currents of the secondary windings of \\ \(i_{x}\), \(i_{y}\), \(i_{z}\) & the V/V power transformer before compensation. \\ \(I_{x}\), \(I_{y}\), \(I_{z}\) & Phase \(x\), phase \(y\) and phase \(z\) RMS currents of the secondary windings of the V/V power transformer before compensation. \\ \end{tabular}   
\(I_{x1},I_{y1},I_{z1}\)  
Phase \(x\), phase \(y\) and phase \(z\) instantaneous currents of the secondary windings of the V/V power transformer after active power balance. Phase \(x\), phase \(y\) and phase \(z\) instantaneous currents of the secondary windings of the V/V power transformer after active power balance and reactive power compensation.
## 3 Polyomino Tiling Theory Applied to Our Robotic Platform
### Area Decomposition Based on Furniture Layout  
As mentioned before, reconfigurability allows the robot to navigate around and under obstacles with different shapes. This results in accessing narrow spaces and inaccessible corners. However, this is possible while adhering to a tiling map as the navigation strategy; therefore, we can utilize the polyomino tiling theory. In order to apply these theorems perfectly for full area coverage, any space must satisfy the conditions of the theorem accurately, which is an unrealistic claim. That is why in this study, the entire area is manually decomposed into five sub-areas such that each sub-area will better satisfy the conditions of a theorem.  
This decomposition depends on the position of each obstacle (furniture layout). In ideal circumstances, the robot must autonomously detect the presence, shape, and accessibility of the obstacles and tiny or unusual corners that require more attention. This experimental aspect is currently under investigation applying SLAM (Synchronous Localization And Mapping) and sensor fusion techniques. Consequently, depending upon the theorems in the robot's database, it will accordingly decompose the entire floor space into sub-areas for optimal area coverage. However, such decompositions do not always guarantee a perfect coverage of the sub-areas, resulting in until zones. That is why, optimizing the area decomposition based on furniture layout would serve as a future scope for this paper. Furthermore, the area decomposition will help to reduce the on-board computational cost working in each individual area with a restricted number of cells to be covered, which is the second reason supporting an area decomposition approach.  
In this paper, the positions of randomly-placed obstacles are considered to manually divide the entire space into five sub-areas, as depicted in Figure 5. Notice that, in Figure 5, the sub-areas are numbered, and the yellow sections represent the obstacles. The floor space chosen for this paper is an area of 11.54 m\({}^{2}\), such that the entire area can be covered exactly by 598 square cells. In correlation with the numbering on Figure 5, the respective areas of these distinct sub-areas are 2.058 m\({}^{2}\), 0.7056 m\({}^{2}\), 3.3124 m\({}^{2}\), 3.3124 m\({}^{2}\), and 2.156 m\({}^{2}\) without subtracting the area occupied by obstacles. After subtracting the obstacle-occupied area, the entire area to be cleaned is 10.5448 m\({}^{2}\) which is the sum of 1.8228 m\({}^{2}\), 0.7056 m\({}^{2}\), 3.0184 m\({}^{2}\), 2.9988 m\({}^{2}\), and 1.9992 m\({}^{2}\) respectively for each numbered sub-area.  
The general approach is to apply all four previously introduced theorems reasonably in each sub-area and propose consequent tiling sets that cover each sub-area. Finally, the resulting tiling sets will be analyzed in a Multi-Criteria Decision Making (MCDM) framework to test the effectiveness of these tiling theorems. Based on the MCDM results, the most efficient theorem for each sub-area is introduced that can result in a better balance between energy and area coverage.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 5 Experimental Evaluation
### Simulation Results  
In the following experiments, we will compare the proposed Channel estimator NDR-Net with the traditional LS algorithm, MMSE algorithm [26] and the Channel estimator using only \(CNN_{D}\) under different SNR, channel model and velocity. Among them, \(CNN_{D}\) denoising algorithm is the basic algorithm. The performance measure is the Channel estimation error MSE, defined as:  
\[MSE=\frac{1}{N}\sum_{n=1}^{N}\left\|\hat{H}-H\right\|_{2}^{2} \tag{14}\]
# Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction
## 3 Experiments and Analysis
### Metrics and Benchmark Methods  
The algorithm performance is evaluated using three metrics, namely average ranking, \(\gamma^{\text{\%}}\)-accuracy, and average error distance. The average ranking is the average location of the actual source in the list of nodes sorted in increasing order by the objective function value. We focus on the average value after many simulations. The \(\gamma\)%-accuracy represents the proportion of simulations for which the real source is ranked within the top \(\gamma\)% among all nodes. In particular, ties are broken randomly for nodes with the same ranking. As for the average error distance, it is defined as the average distance between the estimated source and the real source. Among those metrics, the average rankingreflects the average accuracy of source localization, the \(\gamma\%\)-accuracy focuses more on high-precision localization, and the average error distance mainly considers the approximate area of the source.  
The performance of GLAD-naive and GLAD-modified are compared with two well-known benchmark methods, namely Gaussian heuristic and time-reversal backward spreading:  
1. Gaussian heuristic (GAU). In [19], [PERSON] et al. first showed the possibility of estimating the location of the source from measurements collected by sparsely placed observers. They modeled the diffusion delays along edges with Gaussian distribution \(\mathcal{N}(\mu,\sigma)\), and built an MLE as the optimal solution for the source localization problem in arbitrary trees. Compared with our method, [PERSON] assumed that both \(\mu\) and \(\sigma\) were known parameters, whereas we allowed \(\sigma\) to be unknown and determined it via estimation process.
2. Time-reversal backward spreading (TRBS). The time-reversal backward spreading algorithm proposed by [PERSON] et al. [21] was an efficient method to infer the diffusion source based on a weighted network structure and partial timestamps. In their method, the variance of the differences between the true arrival times and the expected arrival from a node to all observers was calculated, as a measurement to evaluate the extent to which it is the diffusion source.  
Note that several topological-based methods, such as Rumor centrality and Jordan centrality, are not employed as benchmark methods since they cannot well exploit the timestamp information. (We have implemented these methods in experiments, and the numerical results suggest that they are far less accurate than the timestamp-based methods.)
## 4 Discussion
Also, complex physiographic nature of the study region may have an effect on the upward microwave radiation, which makes difficult for the satellite to resolve precipitation over areas with low precipitation amount, especially in the mountainous region [79; 80]. SBP datasets are indirect measurements that are based on satellite/sensor constellations, including PMW and IR sensors onboard LEO and geostationary satellites. These datasets may not accurately detect precipitation in high-elevation areas [81], especially in the winter season, when the ground surface is covered with snow and ice [13]. The errors of precipitation estimated by PMW are mainly based on scattering signal which cannot catch up warm/low-level precipitation frequently occursin low-elevation areas and algorithm to interpolate finer time-scales, such as using cloud motion vector by IR. Meanwhile, the used algorithm can not capture to interpolate finer time-scales, such as using cloud motion vector by IR. To reduce such bias, satellite-only estimates were calibrated using gauge-based GPCC and CPC datasets. The performances of the IMERG-C and GSMaP-Gauge datasets were also influenced by the quality and temporal range of the calibrated gauge-based GPCC and CPC datasets, respectively. Meanwhile, satellite-only datasets are only effectively adjusted for those areas where gauge data are available. Our results showed a substantial improvement in gauge-calibrated SBP datasets, which are more consistent than satellite-only datasets, due to the advantages of observed gauge adjustments. This result is similar to studies conducted in Central Asia [82], China [64; 83], East Africa [84] and Ethiopia [85]. However, deterioration of IMERG-C in low-elevation areas (Figures 3 and 7 and Table 5) as compared to IMERG-UC, as well as deterioration of GSMaP-Gauge in high-elevation areas (Figure 8c and Table 5) as compared to GSMaP-MVK, might be related to limitations of adjusted relevant rain-gauge density (GPCC and CPC). Such discrepancies indicate that the IMERG-C and GSMaP-Gauge retrieval algorithms need further improvements, particularly for mountainous areas, such as Nepal. Additionally, both PMW and IR satellites have complication in detecting shallow orographic precipitation [61; 86; 87]. We found that the local weather conditions and nature of the topography also influence the rainfall capturing capacity of SBP product. Figure 9: Scatter plot of differences in precipitation rates between gauge observed and SBP products, derived from monthly mean precipitation averaged over the monsoon season (mm/day). All the units of statistical metrics are in mm/day. Black, blue, and red colors indicate the performance statistics for precipitation rates less than 10 mm/day, between 10 and 20 mm/day, and above 20 mm/day, respectively. The continuous black and dotted black line represents the linear regression and 1:1 line, respectively.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 3 Methodology
### RGZ Classification Using Sentinel-1
The results also suggest that even minimal liquid water in shallow snowpacks (e.g., 5 cm) can lead to a sharp decrease in \(\sigma^{0}\) (Figure 3b). Much larger drops are found in the simulations of thicker snowpacks. Denser snowpacks with small grains are more sensitive to liquid water (Figure 3c,d). The spectral behavior of the wet snow can be dominated by the meltwater when the liquid water content is 1% by volume [58]. For the snowpacks thicker than 5 cm, C-band \(\sigma^{0}\) decreases by at least 4 dB in every case when liquid water reaches 1% by volume. Grain size is more than 2 mm in coarse-grained snowpacks and less than 2 mm in fine-grained snowpacks [59], which dominate the backscatter in frozen percolation and dry snow radar zones, respectively. As shown in Figure 4, the C-band \(\sigma^{0}\) in snowpacks with coarse grain (3-8 mm) and fine grain (0.5-2 mm) increase and decrease with snow depth. The \(\sigma^{0}\) of the coarse-grained snowpacks rises above \(-6.5\) dB quickly but keeps below \(-6.5\) dB in the fine-grained snowpacks. The simulations suggest that a frozen percolation radar zone experiencing heavy snowmelt in summer turns bright on SAR images in winter, whereas the dry snow radar zone with high accumulation would be dark. The results indicate a clear boundary (i.e., the DSL) between the two zones on SAR images. The differences in liquid snow content, snow grain size, and the relative elevations enable us to classify the RGZs based on Sentinel-1 SAR images. For the C-band Sentinel-1 images normalized to 30\({}^{\circ}\) incidence angle, the locations with \(\sigma^{0}\) 4 dB lower than the winter observations were defined as wet snow radar zone. The brightest parts on the images (\(\sigma^{0}\geq-6.5\) dB) were categorized as frozen percolation radar zone. The rest of the dark patches above and below 500 m were classified as dry snow and bare ice radar zone, respectively. The flowchart for Sentinel-1 SAR image processing and rules for RGZ classification are shown in Figure 5.  
Figure 3: C-band \(\sigma^{0}\) changes with liquid water content simulated by MEMLS3&a. Incidence angle was set as 30\({}^{\circ}\), and snow depth was set as 1, 10, 50, and 200 cm in (**a–d**) respectively. Simulations with snow density ranging from 350 kg/m\({}^{3}\) to 540 kg/m\({}^{3}\) and snow grain size ranging from 0.5 mm to 3 mm were conducted. Figure 4: C-band \(\sigma^{0}\) changes along with snow depth simulated by MEMLS3&a. Incidence angle was set as 30\({}^{\circ}\), coarse-grained (3–8 mm) snowpacks and fine-grained (0.5–2 mm) snowpacks of different snow densities (350–540 kg/m\({}^{3}\)) were considered. Figure 5: Flowchart of the RGZ mapping with Sentinel-1 synthetic aperture radar (SAR) images.
## 3 Performance Evaluations
### Results of Performance Evaluations  
The Single-model KAM Prediction System, which directly inputs the data into the KAM prediction model without prior gait pattern classification, achieves an average prediction error of \(8.82\ \mathrm{N}\cdot\mathrm{m}\) and a MAPE of \(12.95\%\). In comparison, the Multi-model Gait-based KAM Prediction System, which first classifies input data by gait pattern before predicting KAM, achieves a significantly lower average prediction error of \(6.84\ \mathrm{N}\cdot\mathrm{m}\) and a MAPE of \(12.95\%\). The Multi-model Gait-based KAM Prediction System, which is trained to predict the KAM prediction system, is trained to predict the KAM prediction system.  
Figure 8: Predictive accuracy visualization for the Multi-model and Single-model KAM Prediction Systems. Data points in the Multi-model system cluster more closely along the \(y=x\) line, highlighting its enhanced accuracy and robustness compared with the Single-model system, particularly in managing diverse gait patterns.  
Figure 7: comparison of the actual vs. predicted KAM values for the Multi-model and Single-model Gait-based KAM Prediction Systems. The close alignment of actual and predicted values in the Multi-model system demonstrates a higher prediction accuracy, especially across varying gait patterns.  
8.47%. These results represent the best performance achieved by each system and can be seen in Table 6.  
These results indicate that the Multi-model system offers more than a 25% improvement in prediction accuracy compared with the Single-model system. By classifying gait patterns prior to KAM prediction, the Multi-model system can better capture the pattern-specific characteristics, leading to more precise predictions. Specifically, the 4.48% reduction in MAPE reflects the Multi-model system's advantage in minimizing percentage-based prediction errors, which is critical for accurately assessing KAM levels across different gait patterns.  
The improvement in average prediction error and MAPE demonstrates that the Multi-model approach effectively addresses the limitations of a one-size-fits-all model by providing tailored predictions based on distinct gait patterns. This capability is particularly important in applications involving diverse movement patterns, as the system can adapt to specific variations in gait, such as the biomechanical differences between toe-in, toe-out, and trunk lean patterns. Such adaptability enhances the model's utility in real-world applications, where capturing subtle variations in KAM across varied gait types is essential for accurate and actionable predictions.  
In summary, the Multi-model Gait-based KAM Prediction System demonstrates a robust improvement over the Single-model system by leveraging gait pattern classification to enhance predictive accuracy. This distinction in performance metrics underscores the value of integrating pattern-specific processing steps for models aimed at predicting biomechanical measures like KAM.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 1 Introduction
### Our Motivation and Contribution  
The main motivation for this paper is to obtain a satisfactory performance over the entire range of SNR levels by improving the channel estimation and tracking performance (without changing the packet structure), and to comply with the IEEE 802.11p standard. In [31], we have proposed an adaptive channel estimation scheme that selectively uses two channel estimation schemes to improve the error performance for the entire SNR range of interest, and have investigated that the channel estimation performance can be improved by selectively using the better estimation scheme between schemes STA and MMSE. In this paper, we introduce an enhanced scheme of the adaptive channel estimation. The main contributions of this paper are given as follows.  
* In our previous study [31], the proposed algorithm lacked a clear rationale. Therefore, in this study, we provide a theoretical analysis of the proposed algorithm. that can verify its validity.
* By using the standard IEEE 802.11p channel model of [32; 33], we evaluate the bit error rate (BER) and packet error rate (PER) of the proposed scheme. By adopting the standard IEEE 802.11p channel model, it can provide fair and objective performance assessment of the proposed scheme.
* We show that the proposed scheme can selectively use the STA and MMSE or STA and TRFI channel estimation schemes. This proves that the proposed scheme can be expanded for any combinations of a channel estimation scheme.
## 3 Reliability Models of Electric Distribution System Components
### Electrical Power Devices  
The elements of an electric distribution system, such as lines, transformers, power switches, busbar of switchgears, protection and control elements are modeled as objects, which can be functional or in failure state. The time between these states is represented as a random variable described by an adequate type of probability distribution. In reliability analyses, the following probability distribution types are most often used [57; 58]:  
* Exponential EXP (\(\lambda\)), \(\lambda>0\), \(\lambda\)--the rate parameter;
* Weibull WEI (\(\lambda\), \(\beta\)), \(\lambda>0\), \(\beta>0\), \(\lambda\)--the scale parameter, \(\beta\)--the shape parameter;
* Gamma GAM (\(b\), \(p\)), \(b>0\), \(p>0\), \(b\)--the rate (scale) parameter, \(p\)--the shape parameter;
* Normal NOR (\(\mu\), \(\sigma\)), \(\mu\geq 0\), \(\sigma>0\), \(\mu\)--the expected value, \(\sigma\)--the standard deviation;
* Pareto PAR (\(b\), \(b>0\), \(\delta>0\), \(b\)--the scale parameter, \(\delta\)--the shape parameter;
* Gumbel GUM (\(b_{0}\), \(t_{0}\)), \(b_{0}>0\), \(t_{0}\geq 0\), \(b_{0}\)--the scale parameter, \(t_{0}\)--the location parameter;
* Log-normal LNOR (\(\mu_{0}\), \(\sigma_{0}\)), \(\mu_{0}\geq 0\), \(\sigma_{0}>0\), \(\mu_{0}\)--the expected value of natural logarithm, \(\sigma_{0}\)--the standard deviation of natural logarithm.  
Additionally, one can take into consideration the third state that is a preventive repair state with the average annual maintenance duration. A two-state reliability model can be assumed for the MV networks belonging to Polish distribution system operators. Table 1 presents the reliability models of different types of electrical power devices. All the probability distribution parameters have been estimated based on the observations of Polish power distribution systems [59; 60].  
It is also necessary to determine the adequate reliability model of an equivalent point supplying an analyzed distribution system. The reliability characteristics of that point can be found by an assessment on the power transmission system level or statistical research.  
Some reliability analyses take into account the separate characteristics of protection devices (fuses, relays, releases, etc.) and automation equipment (automatic reclosing, stand-by switching on and others) [61].
# Statistical Methods in Bidding Decision Support for Construction Companies
## 2 Materials and Methods
### Evaluation of the Proposed Models  
To assess the quality and relevance of the performance of the proposed classification models [66], the following were proposed:  
* A relevance matrix that indicates the number and often the proportion of correctly and incorrectly classified cases;
* Diagnostic test parameters: sensitivity (7), specificity (8), positive (9), and negative (10) predictive value, test reliability (11) based on the contingency matrix:  
Sensitivity indicates the ratio of true positives to the sum of true positives and false negatives. In the problem under analysis, it describes the ability to detect the winning cases.  
\[sensitivity=\frac{TP}{TP+FN} \tag{7}\]Specificity means the ratio of true negatives to the sum of true negatives and false positives. In the problem examined, it describes the ability to detect the losing cases.  
\[Specificity=\frac{TN}{TN+FP} \tag{8}\]  
PPV (positive predictive value) denotes the probability that the case identified by the classifier as winning is indeed a winning case.  
\[PPV=\frac{TP}{TP+FP} \tag{9}\]  
NPV (negative predictive value) stands for the probability that the case identified by the classifier as loss is indeed a losing case.  
\[NPV=\frac{TN}{FN+TN} \tag{10}\]  
Effectiveness of the decision rule ACC (accuracy) implies the extent to which the results of the study reflect reality.  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
where:  
_TP_--true positive results,  
_FP_--false positive results,  
_TN_--false negative results,  
_FN_--true negative results.