esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Detection and Response via Optimized Alerts: Usability Study
## 3 Results
### Mann-Whitney U Test  
A Mann-Whitney U test of independent samples had participants' level of acceptance of alert output as its dependent variable, which is the ranked, summed scores across all scenarios (_S_tot_). The independent variable is Maximum Visual (_Vis_max_). The test determines whether the group who prefer VAO across all scenarios have a significantly different acceptance score than those who prefer TAO in some or all scenarios. Score totals are noted in Figure 3, while Table 6 provides a statistical summary.  
The Mann-Whitney U test indicates that there is a significant difference (\(U=863.5\), \(p=0.023\)) in the level of acceptance of alert output between the respondents who selected visual output across all scenarios (\(n=59\)) as compared to the respondents who provided mixed responses (\(n=22\)). As such, the null hypothesis, that there is no statistically significant difference in the level of acceptance of alert output between those who preferred VAO in all scenarios and those preferring TAO in some or all scenarios, is rejected.  
The effect size is calculated by dividing the Standardized Test Statistic, \(Z\), by the square root of the number of pairs: \(\frac{Z}{\sqrt{n}}=\frac{2.279}{\sqrt{81}}=0.253\). The effect size, according to [PERSON]'s classification of effect, is moderate, given 0.1 (small effect), 0.3 (moderate effect), and 0.5 and above (large effect).  
\begin{table}
\begin{tabular}{c l l l l} \hline \multicolumn{5}{l}{**Descriptive Statistics**} \\ \hline  & Maximum Visual & Mean & Std. Deviation & N \\ \hline \multirow{3}{*}{S1\_tot} & No & 107.7273 & 11.65856 & 22 \\  & Yes & 110.2034 & 15.15754 & 59 \\  & Total & 109.5309 & 14.26454 & 81 \\ \hline \multirow{3}{*}{S2\_tot} & No & 104.7727 & 14.91223 & 22 \\  & Yes & 109.9661 & 15.87556 & 59 \\  & Total & 108.5556 & 15.70032 & 81 \\ \hline \multirow{3}{*}{S3\_tot} & No & 88.6364 & 29.03618 & 22 \\  & Yes & 104.6102 & 21.62136 & 59 \\ \cline{1-1}  & Total & 100.2716 & 24.7255 & 81 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Frequency** & **Percent** & **Valid Percent** & **Cumulative Percent** \\ \hline \multirow{3}{*}{Valid} & No & 22 & 23.2 & 27.2 & 27.2 \\  & Yes & 59 & 62.1 & 72.8 & 100.0 \\ \cline{1-1}  & Total & 81 & 85.3 & 100.0 & \\ \hline Missing & 999.00 & 14 & 14.7 & & \\ \hline \multicolumn{2}{c}{Total} & 95 & 100.0 & & \\ \hline \end{tabular}
\end{table}
Table 5: Maximum Visual IVs (between-subjects factors).  
The Friedman test carried out to compare the score ranks for the three scenarios found there to be no significant difference between scenarios: \(x^{2}(2)=5.496,\ p<0.064\). The result indicates that scenario mean ranks did not differ significantly from scenario to scenario when not also factoring for responses based on output preference (Maximum Visual).  
Effect size was not applicable as no measurable significance was found.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 2 Materials and Methods
### Study Design  
In analyzing the scholarly literature on communication technologies for vehicles, this study responds to the increasing academic interest in the architecture, functionality, and evolution of in-vehicle communication protocols, including CAN, LIN, FlexRay, MOST, and Automotive Ethernet. These contributions span comparative evaluations, safety and security assessments, and integration into intelligent transportation systems. Building upon prior bibliometric approaches, our research aims to map both the volume and direction of scholarly work in these domains, especially in underexplored areas.  
To build our dataset, we used a combination of broad and targeted keyword searches (e.g., \"Controller Area Network\", \"CAN Bus\", \"Automotive Ethernet\", etc.) that ensured coverage of both foundational technologies and recent innovations. The final dataset captures 2919 peer-reviewed publications and represents a broad, interdisciplinary spectrum of research.  
For bibliometric mapping, we employed VOSviewer (version 1.6.20), which supports advanced network visualizations of bibliographic data. The following methodological configurations were applied:  
* Clustering layout: LinLog modularity-based layout to enhance the visibility of thematic clusters.
* Minimum threshold for inclusion: At least 10 occurrences for keywords and 10 citations for co-cited references to focus on the most significant patterns while reducing noise from sparsely cited items.
* Counting method:
* Full counting was used for co-authorship networks to give equal weight to all contributors.
* Fractional counting was applied to co-citation analysis to normalize influence across highly connected items.  
Visual outputs--such as co-authorship, keyword co-occurrence, and journal co-citation networks--were created using VOSviewer (version 1.6.20) and Microsoft Excel (version Excel 2024). These tools allowed for complementary visual and statistical analysis of relationships among scholars, institutions, and thematic areas.  
Additionally, only documents classified as peer-reviewed journal articles, conference proceedings, review articles, or book chapters were included. Editorials, non-peer-reviewed reports, abstracts, and trade magazine articles were excluded to ensure analytical rigor.
## 4 Discussion  
### Position Difference of Vibration Intensity  
Figure 17 shows the rope vibrations above the cage and balance hammer. The law of vibration is still strongest in the short side direction (Z) and weakest in the long side direction (X), which indicates that the accuracy of the model is comprehensive and universal. The overall vibration value of the wire rope on the lowering side (above the hammer) is stronger than that on the lifting side (above the cage).  
Figure 16: Friction force produced by rope wrapping the pulleys. (**a**) Rope 300 m above cage; (**b**) Rope 350 m above cage; (**c**) Rope 400 m above cage.  
Figure 15: Contact force produced by the rope wrapping the pulleys. (**a**) Rope 300 m above cage; (**b**) rope 350 m above cage; (**c**) rope 400 m above cage.  
This is due to the short hanging length of the wire rope on the lower side. Short-span rope strings have relatively high rigidity and are more likely to produce strong vibration acceleration amplitude.  
With the increase of the distance from the cage, the vibration in all directions is significantly strengthened. In particular, the nonstationary vibration in the \(Z\) direction increases both in amplitude and quantity, and the amplitude in Y direction opposite to the acceleration direction increases. Similarly, the amplitude of the X-direction vibration increases with distance. This phenomenon can be explained by the fact that the higher the rope is, the higher the weight it bears. Therefore, as shown in Figure 18, the loading state of the rope goes from loosening to tightening, that is, from elasticity to rigidity.  
Figure 17: Rope vibration in three directions at different positions above the cage and the hammer. (**a1**) \(Z\)-axis—above the cage, (**a2**) \(Z\)-axis—above the hammer; (**b1**) Y axis—above of the cage, (**b2**) Y axis—above the hammer; (**c1**) X-axis—above the cage, (**c2**) X-axis—above the hammer.  
Greater stiffness will inevitably lead to greater vibration. This theory can also explain that the vibration amplitude in the Y direction increases at the lifting side and decreases at the lowering side. In addition, the nonstationary rope vibrations with increasing amplitude or quantity as distance increases in Figure 17(a1,b2) (purple circles in the figure) also belong to the \"loose-tight\" mechanism.
###### Abstract  
The investigation of mass movements is of major interest in mountain regions as these events represent a significant hazard for people and cause severe damage to crucial infrastructure. The torrential rainfall event that mainly occurred on the 14 July 2021 in western Central Europe not only led to severe flooding catastrophes (e.g., Meuse, Ahr and Erft rivers) but also triggered hundreds of mass movements in the low mountain range. Here, we investigate a hillslope debris flow that occurred in Biersdorf in the Eifael area (Rheinsh Massif, Rheinland-Pfalz) using a comprehensive geomorphological-geophysical approach in order to better understand the triggering mechanisms and process dynamics. We combined field studies by means of Electrical Resistivity Tomography (ERT), Direct Push Hydraulic Profiling (HPT) and sediment coring with UAV-generated photogrammetry, as well as debris flow runout modelling. Our results show that for the Biersdorf hillslope debris flow, the geomorphological and geotectonic position played a crucial role. The hillslope debris flow was triggered at a normal fault separating well-draining limestones of the Lower Muschelkalk, from dense weathered clay and sandstones of the Upper Buntsandstein. The combination of a large surface runoff and strong interflow at the sliding surface caused a transformation from an initial translational slide into the high-energy and widespread hillslope debris flow. We further created and validated a stand-alone model of the debris flow on a local scale achieving promising results. The model yields a 97% match to the observed runout area as well as to deposition spreads and heights. Thus, our study provides a pathway for analyzing hillslope debris flows triggered by torrential rainfall events in low mountain ranges. General knowledge on hillslope debris flows, risk assessment and hazard prevention were improved, and results can be transferred to other regions to improve risk assessment and hazard prevention.  
torrential rainfall event of 14 July 2021; Eifel flood; mass movement; hillslope debris flow; multi-methodological approach; runout modelling; MABEIS-project +
Footnote †: journal: Applicand  
Article Multi-Methodological Investigation of the Biersdorf Hillslope Debris Flow (Rheinland-Pfalz, Germany)
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 6 Some Final Considerations about Benchmarking, Decoherence Effects, and Fidelity
### QBER and a Brief Comparison with Other Similar QKD Protocols
For the current procedure, the QBER is on the range of the BB84 protocols, despite, it exhibits outstanding properties in terms of the reduced Eve success probability, his possible configuration, and the existence of quantum correlations during the quantum key generation.
## 3 Context-Aware Positioning Model
### Context Sensing
#### 3.1.2 Interacting with the Physical Environment  
This task deals with the use of a device's communication and navigation peripherals to interact with the environment. It is usually requested during the context-aware positioning stage, as a means to access positioning strategies or collaborate with neighboring devices.  
Communication peripherals include all types of peripherals used to send or receive information, such as Global System for Mobile Communications (GSM) and Wi-Fi antennas, RFID card readers, or GPS transceivers. Navigation peripherals include the magnetometer or digital compass, accelerometer, and  
\begin{table}
\begin{tabular}{c c} \hline
**Access Points** & List of access points available in a device’s context. \\ \hline
**Neighbors** & List of neighboring nodes available in a device’s context. \\ \hline
**Positioning Strategies** & List of positioning strategies available in a device’s context. \\ \hline \end{tabular}
\end{table}
Table 2: Scenario feature vector.  
gyroscope. These peripherals are used to determine the general direction and speed of a device, allowing for a degree of relative positioning (dead reckoning). The information gathered during this task is assembled as a file and sent to the management of contextual information stage for its processing.
## 4 Experimental Results and Analysis
### Analysis of Different Tampering Test Results
#### 4.2.2 Copy-Move Attack  
A copy-move attack involves copying part of an original image and moving it into another image. Figure 8 displays the specific process and Table 3 gives the PSNR and SSIM values of the different methods. As shown in Figure 8c, a part of the sky is copied from image Car1 and moved into the top of the watermarked Goldhill image. Based on Figure 8e-h, the tamper detection performance is essentially the same as that of the text addition attack and indicates that texture information and neighborhood adjustment play a significant role in image authentication and tamper location discovery. Although edge information is also an important part of an image, it reduces the detection accuracy and increases the redundancy. Compared with the other three recovery results in Figure 8i-k, the final image of the proposed method visually appears more similar to the watermarked image. Moreover, the values listed in Table 3 objectively demonstrate its quality. The tampered area consists of a large scale of smooth background at a distance, which is suitable for exploiting the advantages of the median filter. In this case, the image after filtering operation exhibits a better quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Indexes** & **[PERSON] et al. [8]** & **[PERSON] et al. [9]** & **[PERSON] et al. [7]** & **Proposed** \\ \hline PSNR (dB) & 35.01 & 40.85 & 45.52 & 45.68 \\ SSIM & 0.8441 & 0.8559 & 0.9925 & 0.9930 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of recovery performance for text addition attack on Lena image.  
Figure 7: Tamper detection and recovery results of text addition attack on Lena image: (**a**) original image; (**b**) watermarked image; (**c**) tamper reference; (**d**) tampered image; (**e**) tamper detection result of [8]; (**f**) tamper detection result of [9]; (**g**) tamper detection result of [7]; (**h**) tamper detection result of the proposed method; (**i**) recovery result of [8]; (**j**) recovery result of [9]; (**k**) recovery result of [7]; (**l**) recovery result of the proposed method.
## 2 Processes of Semiconductor Manufacturing and Measurement Methods
### Processes of Semiconductor Manufacturing
#### 2.1.2 Etching, Ion Implantation, and Deposition  
In the etching stage, the material is selectively removed from the exposed areas of the wafer to create the circuit patterns. Plasma etching, which utilizes gases such as CF4 and SF6, is a common technique. Following etching, the ion implantation process introduces  
Figure 1: Processes of semiconductor manufacturing.  
dopants into the silicon wafer to modify its electrical properties, essential for creating p-type or n-type regions that define the semiconductor's function [25; 26].  
Deposition processes then involve adding layers of material, such as silicon dioxide or metal films, onto the wafer. Chemical Vapor Deposition (CVD) is frequently employed, wherein reactive gases form thin films on the wafer's surface. These layers are critical for insulation and creating conductive pathways within the integrated circuits.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 3 Delay-Sensitive NOMA-HARQ
### Reliability and Throughput Analysis of Two-User D-NOMA-HARQ
This follows directly from the fact that the stationary distribution of the system can be characterized by the eigenvector of matrix \(\Pi^{\mathsf{T}}\), corresponds to eigenvalue \(1\) and the PER is simply the stationary probability of user \(i\) being in the error state._  
**Remark 2**: _With D-NOMA-HARQ the throughput of user \(i\), denoted by \(\eta_{i}(n,k_{i})\), is accordingly given by_  
\[\eta_{i}(n,k_{i})=\frac{k_{i}(1-\xi_{i})}{n}. \tag{6}\]  
_This is because with D-NOMA-HARQ, user \(i\) sends a new packet of length \(n\) with \(k_{i}\) message bits in each time slot and received correctly at the receiver with the error rate \(\xi_{i}\)._
# Estimation of Saprolite Thickness Needed to Remove _E. coli_ from Wastewater
## 2 Materials and Methods
### In Situ Saturated Hydraulic Conductivity  
Due to variations in soil horizon boundaries, in situ saturated hydraulic conductivity (Ksat) was measured at predetermined depth intervals associated with different horizons around the observation pit at each site by the constant-head well permeameter technique [18]. Due to the presence of rocks, tree roots, and other constraints preventing the boring of an auger hole, Ksat was not measured at all depths/horizons. Moreover, the locations and number of Ksat measurements (10 at four sites; 5 at four sites; 2, 4, 7, and 8 at each of the other four sites) were varied among the sites because of the differences in the landscape conditions and soil profile variations. For most cases, the area around the observation pit was divided into 10- by 10-m square areas, and one measurement for each of the selected depth interval/horizon was conducted within each of these areas. The Compact Constant Head Permeameter [19] was used to measure the steady-state flow rate of water (\(Q\)) under about 15 cm of water head (\(H\)) in a 6 cm diameter (\(r\) = 3 cm) auger hole dug to the desired depth. The Glover equation [20] was used to calculate Ksat.
## Materials and Methods
### Adsorption Studies of Cu\({}^{2+}\)  
Three experimental replicates were used to assess the sorption kinetics of Cu\({}^{2+}\) on biochar samples produced at different temperatures (BC300, BC450, and BC600), using a series of 50 mL centrifuge tubes (batch mode) prepared with a suspension of 1 g L\({}^{-1}\) biochar and 125 mg L\({}^{-1}\) Cu\({}^{2+}\) solution. The resulting suspension was stirred during the complete duration of the experiment at room temperature (23 \(\pm\) 2 \({}^{\circ}\)C), and 10 mL was sampled (filtered at 0.45 \(\upmu\)m) after 0.5, 1, 2, 4, 6, 12, and 24 h (0-1440 min) of suspension preparation. We obtained the Cu\({}^{2+}\) adsorption isotherms for the different biochar samples, from initial concentrations of Cu between 0 to 100 mg L\({}^{-1}\) and a biochar concentration of 1 g L\({}^{-1}\). The suspension of the mixture was stirred for 24 h at room temperature (22-25 \({}^{\circ}\)C) until sorption equilibrium was reached, and then filtered at 0.45 \(\upmu\)m. These conditions were determined based on the results obtained in the kinetic experiments. The Cu\({}^{2+}\) content was analyzed by flame atomic absorption spectroscopy (FAAS) at 324.7 nm using an AA-7000 spectrophotometer (Shimadzu, Japan) after filtering (0.45 \(\upmu\)m) and diluting the samples in 5% HNO\({}_{3}\).
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Experimental Results
#### 5.3.1 Evaluation of Privacy Protection Performance of Personalized PSD Spatial Index  
Based on the findings presented in Reference [35], the grid structure demonstrates superior performance compared to other spatial index data structures in terms of differential privacy protection. Consequently, we solely consider one spatial index data structure for comparison. Specifically, our experiments are conducted on four spatial indexes: k-d tree, UG unified grid, AG adaptive grid and PAG personalized adaptive grid. Each of these algorithms is utilized to identify different spatial indexes. Furthermore, the spatial indexes with added noise are repeatedly counted within a specific range, allowing us to calculate the disparity between the actual data count and the noise-infused count for the same query. A smaller variance between the actual count and the noise count signifies a stronger data protection effect.  
Given that our partition percentage is a randomly generated number during the actual internal division of the PAG grid, it becomes apparent that most workers allocate a significant portion of their privacy budget to the division of the first-level grid, with a comparatively smaller share allocated to the second-level grid nodes. Consequently, we set the privacy budget for k-d, UG and AG at 0.4, with a percentage of 0.5, and the privacy budget in PAG at 0.6. To ensure the accuracy and effectiveness of the experimental data, we conducted separate queries across various spatial indexes under different total privacy budgets. The average results from multiple seeds were calculated, followed by the computation of the average discrepancy between the query results of different spatial indexes under different total privacy budgets and the real data. Figure 4 illustrates the experimental test data for different total privacy budgets in a line graph, while Table 3 presents the average experimental values for different privacy budgets, with averageErr representing the average of the average difference between the actual query count and the noisy query count results.
## 3 Methods: Bayesian Modelling
### Features Analysis  
The collection of aerosol instrumentation presented in Table 1 permits determination of size-fractionated PM mass concentrations, including PM\({}_{1}\), PM\({}_{2.5}\), and PM number concentrations (PN): 0.01-1 \(\upmu\)m (PN\({}_{1-0.01}\)), 0.01-0.025 \(\upmu\)m (PN\({}_{0.025-0.01}\)), 0.025-1 \(\upmu\)m (PN\({}_{1-0.025}\)), and 0.025-0.3 \(\upmu\)m (PN\({}_{0.3-0.025}\)).  
The relationship between BC mass concentrations and size-fractionated PM mass and number concentrations is displayed in Figure 1. The use of all PM size fractions in the BC proxy may be redundant since some variables may have similar contributions and others may not have meaningful associations with BC. Therefore, it is important to analyse the features of the PM and PN concentrations. Three types of correlation analyses are performed including Pearson (r\({}_{p}\)), Spearman (r\({}_{s}\)), and mutual information (MI). The Pearson correlation coefficient is known to be effective for evaluating the linear relationship between two continuous variables [42], whereas the Spearman correlation coefficient is computed based on the ranked values for each variable rather than the raw data [43]. MI is also applied to ensure undetected non-linear correlations between these variables are captured [44; 45].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measured Variable** & **Instrument** & **Measurement Range** & **Maximum** \\ \hline Submicron particle number & CPC 3007-2 (TSI Inc.) & 0.01 \(\upmu\)m–1 \(\upmu\)m & \(4\times 10^{5}\) cm\({}^{-3}\) \\ concentration (cm\({}^{-3}\)) & P-Trak 8525 (TSI Inc.) & 0.02 \(\upmu\)m–1 \(\upmu\)m & \\ \hline Particle number size & AeroTrak 9306-V2 (TSI Inc.) & 0.3 \(\upmu\)m–25 \(\upmu\)m & 210 cm\({}^{-3}\) \\ distribution (cm\({}^{-3}\)) & AeroTrak 9306-V2 (TSI Inc.) & 0.3 \(\upmu\)m–25 \(\upmu\)m & 210 cm\({}^{-3}\) \\ \hline PM\({}_{x}\) (\(\upmu\)g/m\({}^{3}\)) & DustTrak DRX 8533 (TSI Inc.) & PM\({}_{1}\), PM\({}_{2.5}\), PM\({}_{10}\) & 150 mg/m\({}^{3}\) \\ \hline Black carbon, BC (\(\upmu\)g/m\({}^{3}\)) & microAeth AE51 (AethLabs) & Fine fraction & 1 mg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of aerosol instrumentation and measured variables involved in the mobile air pollution measurement campaign in Jordan.  
The relationship between these variables and BC mass concentrations are shown on each subplot of Figure 1. From the correlation analysis, it can be seen that the correlations between each PM variable with BC mass concentrations does not differ significantly. In this case, the variables of PM\({}_{2.5}\) and PN\({}_{0.3-0.025}\) (shown as red data points in Figure 1) are selected as the features for the inputs of the BC proxy based on physical characteristics of BC. The size distribution of BC is known to be in the range of 25-300 nm and that fraction contributes significantly to PM\({}_{2.5}\)[9]. The use of the PM\({}_{2.5}\) variable is advantageous because it is typically included in routine air quality monitoring around the world [46] and is increasingly used as part of low-cost air quality sensing networks [47].