esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Freezing State (\(-5\)\({}^{\circ}\)C) Reinforced Soil Interface Characteristics
The formula is as follows:  
\[f_{gs}=\frac{\tau_{\text{Pmax}}}{\sigma_{n}} \tag{3}\]  
Figure 13: The influence of water content on the peak shear stress of the reinforced soil interface under the freezing state (\(-5\)\({}^{\circ}\)C). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Strength Fitting**} & \multicolumn{1}{c}{**Goodness of Fit**} & \multicolumn{1}{c}{**Cohesion**} & \multicolumn{1}{c}{**Internal Friction Angle**} \\  & \multicolumn{1}{c}{**Curve**} & \multicolumn{1}{c}{\(R^{2}\)} & \multicolumn{1}{c}{\(c\)/kPa**} & \multicolumn{1}{c}{\(\varphi^{\circ}\)} \\ \hline  & 2\% & y = 0.41x + 9.97 & 0.99 & 9.97 & 22.29 \\
0\({}^{\circ}\)C & 4.5\% & y = 0.37x + 17.57 & 0.99 & 17.57 & 20.30 \\  & 7\% & y = 0.48x + 8.38 & 0.96 & 8.38 & 25.64 \\ \hline  & 2\% & y = 0.35x + 17.88 & 0.97 & 17.88 & 19.29 \\ \({}^{\circ}\)C & 4.5\% & y = 0.27x + 29.25 & 0.91 & 29.25 & 15.11 \\
0C & 7\% & y = 0.29x + 27.87 & 0.90 & 27.87 & 16.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Shear strength index of the reinforced soil interface. where \(f_{gs}\) is the apparent friction coefficient and \(\tau_{\text{pmax}}\) is the peak shear stress. The variation in the apparent friction coefficient is shown in Figure 14. The apparent friction coefficient decreases with increasing normal stress. When the normal stress increases from 40 kPa to 60 kPa, the apparent friction coefficient decreases rapidly. When the normal stress increases from 60 kPa to 80 kPa, the apparent friction coefficient decreases slowly, which is consistent with the conclusion of References [35,36]. This is because the lower normal stress on the soil particle constraint effect is poor. The soil particles have enough adjustment space, and the dilatancy is remarkable. With the increase in normal stress, the ability of normal stress to restrain the interface between reinforcement and soil increases, which limits the displacement and reversal of soil particles [37]. The dilatancy characteristics of the reinforced soil composite will be analyzed in detail in Section 3.4.
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 6 Experimental Results  
In this section, experimental results are presented to show the effectiveness of the proposed approach. Comparison results with other classification methods are also presented. For convenience, our approach is abbreviated as RBF-ISCC (RBF with Iterative Self-Constructing Clustering). We used a computer with Intel(R) Core(TM) i7 [EMAIL_ADDRESS] GHz and 16 GB of RAM to conduct the experiments.  
A five-fold cross validation was adopted to measure the performance of a classifier in the following experiments. Each dataset was randomly divided into five disjoint subsets, in such a way that the data of each category were evenly involved in five folds. Then, a classifier performed five runs for each dataset. In each run, four of the five subsets were used for training in the training phase and the remaining subset was used for testing in the testing phase. The results of the five runs were then averaged to indicate the performance of the classifier on the dataset. Note that the data for training were different from the data for testing in each run. The pre-defined constants, such as \(\alpha\) of our method, were determined in the training phase and were not changed in the testing phase.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Pitch Shifting Performance  
The classification results for Pitch Shifting augmentation are shown in Table 5. The use of Pitch Shifting for data augmentation leds to improved recognition performance compared to not using any data augmentation techniques. However, the recognition performance did not vary significantly with the range of Pitch Shifting in terms of pitch changes. The highest accuracy can reach 0.903 within the [\(-\)8, 8] pitch range, and the macro F1 score can reach 0.901. When combining spectrogram masking with Pitch Shifting, there is a noticeable improvement in recognition performance compared to using Pitch Shifting alone, with the highest accuracy reaching 0.923 within the [\(-\)5, 5] pitch range.
## 2 Materials and Methods
### Monitoring of Water Radioactivity and Physical-Chemical Parameters  
The Romanian drinking water standards expressing the maximum allowable values for chemicals and radioactivity are based on public health considerations [30; 31]. The details of the experimental measurements performed on the 65 investigated water samples are summarized in Table 1. This table also includes the maximum allowable limits of the measured physical-chemical parameters. Microbiological issues were not the subject of the present work. Water quality monitoring and a physical-chemical parameter evaluation of the 65 investigated samples were performed in the laboratories of the Directorate of Public Health of Sibiu, Romania, which are accredited by the national accreditation body (RENAR), using Romanian standards (SR), which adopted the European or International standards for analytical methods, such as European Norms (EN) or standards issued by the International Organization for Standards (ISO). The validation of the analytical methods, quality assurance, and control of the methods for drinking water, are carried out in accordance with the analytical performance characteristics laid down by the Law No. 458/2002 [32]. The gross alpha activity of water samples was measured using the alpha spectrometer Tennelec TC 256, USA, calibrated with Am-241, based on the described standard method [33]. The gross beta activity was measured using the beta spectrometer Robotron 20050, Germany, calibrated with Sr-90/Y-90, based on the described standard method [34]. The determined minimum detectable activities for gross alpha and beta measurements, were of 0.007 Bq and 0.06 Bq, respectively, calculated based on the ISO 11929-4: 2022 method [35]. The measurement of radon concentration in the water samples was conducted using the Alpha Guard DF 2000 radon monitor with the additional equipment AquaKit, based on the method SR EN ISO 13164-3: 2020 [36]. In accordance with the specific analytical method applied for each physical-chemical parameter as described in Table 1, the following equipment was used: Specord 200 Plus spectrophotometer/Analytik Jena; GBC Savant AAS Atomic Absorption Spectrometer; HQ40D Dual Channel Meter  
Figure 1: Location of sample collection points and water sources. with conductivity cell CDC401 + PHC series pH electrode; and Hach 2100Q Portable Turb-bidimeter. The traceability of measurements is achieved and maintained through periodic calibrations, in accordance with SR EN ISO/IEC 17025:2018 [37].
# A Subjective and Behavioral Assessment of Affordances in Virtual Architectural Walkthroughs
## 4 Results
### From the Time-Log and Observations  
Our understanding of behavior begins with the collective means for run-time and activities of all participants (shown in Table 2). The collective run-time for PW was 284 min 25 s (17,065 s). It was 337 min 38 s (20,258 s) for IW. Differences were observed in the run-time, durational and non-durational activities between the two scenarios, PW and IW.  
The ANOVA results below indicate no statistically significant results for all behaviors, barring click events, which demonstrated notable differences. This was expected, as subjects had more manipulation opportunities in IW compared to PW. Figure 5 illustrates the min-max plots for participant behavior.  
* Run-Time : \(F(1,\,62)=3.71\), \(p=0.06\), \(\eta^{2}p=0.056\)
* Stride State : \(F(1,\,62)=1.70\), \(p=0.19\), \(\eta^{2}p=0.027\)
* Still State : \(F(1,\,62)=0.61\), \(p=0.44\), \(\eta^{2}p=0.01\)
* Turn Event : \(F(1,\,62)=2.21\), \(p=0.14\), \(\eta^{2}p=0.034\)
* Bend Event : \(F(1,\,62)=2.0\), \(p=0.16\), \(\eta^{2}p=0.031\)
* Click Event : \(F(1,\,62)=4.77\), \(p=0.033\), \(\eta^{2}p=0.071\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Condition** & **Mean (\(\mu\))** & **Std.Dev** \\ \hline \multirow{2}{*}{Spatial Presence (SP)} & PW & 3.18 & 0.61 \\  & IW & 3.65 & 0.50 \\ \hline \multirow{2}{*}{Engagement (EN)} & PW & 3.56 & 0.57 \\  & IW & 3.89 & 0.50 \\ \hline \multirow{2}{*}{Naturalness (NV)} & PW & 3.66 & 0.73 \\  & IW & 3.84 & 0.64 \\ \hline \multirow{2}{*}{Negative Effects (NE)} & PW & 2.13 & 0.91 \\  & IW & 1.94 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collective means for ITC-SOPI items under both scenarios. Means were calculated for every question under each scenario, for all participants.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Condition** & **Mean (\(\mu\))** & **Std.Dev** \\ \hline \multirow{2}{*}{Run-Time (s)} & PW & 480.5 s & 221.2 \\  & IW & 593.47 & 247.2 \\ \hline \multirow{2}{*}{Stride (s)} & PW & 36.21 s & 23.1 \\  & IW & 44.92 s & 29.8 \\ \hline \multirow{2}{*}{Still (s)} & PW & 139.48 s & 26.9 \\  & IW & 33.58 s & 32.8 \\ \hline \multirow{2}{*}{Turn (no.)} & PW & 17.72 & 5.2 \\  & IW & 15.75 & 5.4 \\ \hline \multirow{2}{*}{Bend (no.)} & PW & 0.66 & 1.04 \\  & IW & 1.16 & 1.7 \\ \hline \multirow{2}{*}{Click (no.)} & PW & 23.28 & 12.0 \\  & IW & 30.47 & 14.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Collective means for observed behavior types of each participant in both scenarios.  
Figure 5: The collective minima and maxima for user behaviors in PW and IW: (**a**) Run-time. (**b**) Still state. (**c**) Stride state. (**d**) Click event. (**e**) Turn event. (**f**) Bend event.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 3 Proposed UAVs-BS Network Coverage Model
### Proposed UAVs-BS Mobility Model  
After the CA initialization, the next step is the UAVs-BS mobility and selection of a selected CV for serving the cellular network. The UAVs-BS are working on the selected CA and enhancing the network performance. The selection of the UAVs-BS and its mobility in cellular networks is based on the link quality of the UAVs-BS and the CU density in the network. The proposed mobility model uses link quality and cellular network density because these parameters are more realistic in selecting the best UAVs-BS to serve the network. Sometimes, the cellular network is less congested, and the UAVs-BS mobility is based on coverage area decisions. The proposed mobility model improves the UAVs-BS mobility and only serves those areas which are more congested. The second parameters are link quality for better data communication with the ground BS and CUs.  
Most of the previous studies use the energy level or life of each UAV-BS because battery life is one of the significant parameters for decision making. However, the unpredictable cellular network condition is one of the causes of energy depletion. So, it is a better strategy to select the UAVs-BS by considering its link quality and user density at the ground. The proposed UAVs-BS mobility model decides the movement by using a decentralized decision-making method. This decision is also based on neighbour UAVs-BS within the CA radius range. Every UAV-BS is required to broadcast messages within a specific time for decision making. This model proposes maintaining the connectivity among neighbour UAVs, cellular networks, and the BS. The objective of the mobility model is to cover the CA, which is selected by using the proposed coverage area decision model. The UAVs-BS update their status by using control messages broadcasting where the message includes the current location of the UAVs-BS, the CA information, link quality, traffic density information, trailer, and addresses. The proposed mobility model has two main phases, including the information update phase and link quality plus a traffic density evaluation phase. The detail of these phases is discussed in the next sub-sections.
## 4 Discussion  
### Responses of Different Vegetation Types to Short-, Medium-, and Long-Term Droughs  
Based on land-use distribution (forests in western/northwestern margins, croplands in eastern/central-western/southern regions, and grasslands widely distributed across the basin), combined with NDVI-SPEI correlation analysis, vegetation responses to drought exhibit significant spatial heterogeneity and type-dependent characteristics.  
Figure 8: Spatial distribution of resistance and resilience indices of vegetation response to drought. Note: (**a**) spatial distribution of resistance indices of vegetation response to drought; (**a**’) spatial distribution of resilience indices of vegetation response to drought. Vegetation’s resistance and resilience were higher as the two indices increased.  
Forests in western/northwestern regions demonstrate deep-root buffering and lagged responses to drought. At the 1-month scale, weak negative correlations were observed, shifting to positive correlations after 6 months and 1 year and at 1-year lag. Deep roots (>3 m) utilizing groundwater minimize short-term drought impacts (1-month weak negative correlation), while the transition to non-significant positive correlations at 6-month and 1-year scales suggests long-term drought reduces productivity without showing significant one-year-lag effects. This response pattern reflects anisohydric stomatal regulation delaying water stress, where cumulative drought may deplete carbon reserves without manifesting significant 1-year-lag effects [12].  
Croplands in eastern/central-western/southern regions show irrigation-dependent rapid responses. Strong positive NDVI-SPEI correlations at the 1-month scale weaken at the 6-month scale, indicating the sensitivity of crops (e.g., maize) to seasonal precipitation [34]. Irrigation projects like \"Diverting Chaoer River to West Liao\" enhance 1-year-lag positive correlations in eastern croplands, while non-irrigated central-western areas show reduced drought resistance following grassland-to-cropland conversion (1995-2010) [35]. Shallow roots (<1 m) dependent on soil moisture make these systems vulnerable, with a documented 30% maize yield reduction when SPEI < \(-1.5\)[36].  
Grasslands across the basin display type-specific responses. In mesic grasslands, positive 1-month correlations strengthen at the 6-month scale, reflecting water accumulation effects [37]. In xeric grasslands (Horqin Sandy Land), there are dominant negative correlations, as drought-tolerant species (e.g., Artemisia) gain a competitive advantage during dry periods [6]. Specific management should be followed, such as the following: (1) in western forests, limiting groundwater extraction (maintain depth < 6 m) and planting deep-rooted drought-resistant species (e.g., Ulmus pumila) [38]; (2) in eastern croplands, promoting water-saving irrigation (60% drip coverage) and rotating low-water crops (e.g., replace maize with castor); and (3) in grasslands, implementing ecological water replenishment in sandy areas and restoring floodplain wetlands [39].
## 5 Conclusions  
In this paper, the NCCL technology is applied to a GPU-based FDTD parallel algorithm. This method can directly transfer data between GPUs without the help of CPU forwarding, which greatly improves the communication efficiency between the GPUs. The numerical results show that the proposed method is accurate and efficient and can provide a new idea and technique for high-performance computing based on GPUs, thereby demonstrating the practical implications of our research. The following main conclusions are drawn from this research:  
* Compared with an FDTD algorithm on a CPU platform, an FDTD algorithm based on GPUs can significantly improve the computational efficiency.
* Compared with traditional CPU forwarding for GPUs communication, GPUs direct communication based on the NCCL technology can improve the computational efficiency.
* Due to hardware resource limitations, this paper only performed calculations on four GPUs. As the number of GPUs increases, the efficiency improvement of this new communication mode becomes more significant.  
This paper is the first to apply the NCCL technology to an FDTD algorithm across GPU nodes, reducing the communication latency and improving the overall simulation efficiency. The implementation of this technology can greatly enhance the application scope of FDTD algorithms, providing a technical foundation for the efficient parallel implementation of FDTD algorithms and other electromagnetic algorithms. In addition, this method has not been widely applied in computational electromagnetics, so parallelizing different electromagnetic algorithms will be our next focus of work.  
Conceptualization, X.H. (Xirbo He) and B.W.; methodology, S.M.; validation, X.H. (Xudong Han); formal analysis, X.H. (Xirbo He) and B.W.; investigation, S.M.; data curation, S.M. and X.H. (Xirbo He); writing--original draft preparation, S.M. and X.H. (Xirbo He); writing--review and editing, X.H. (Xirbo He), visualization, X.H. (Xirbo He); supervision, X.H. (Xirbo He) and B.W.; funding acquisition, X.H. (Xirbo He) and B.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant Nos. 62201411, 62371378, and 62471352); Fundamental Research Funds for the Central Universities (XJSJ24035); and the National Key Laboratory of Electromagnetic Environment (JCKY2024210C61424030201).  
Not applicable.  
The data are only available on request due to restrictions on privacy.  
The authors declare no conflicts of interest.
## 1 Introduction
### Recycling of BBRM  
The reuse of remelted residues from comminuted bitumen-based roofing materials (BBRM) in the production of new products or materials is notably underrepresented in scientific literature. While some studies and industrial initiatives have explored recycling BBRM for applications such as road construction, there is limited research on utilizing remelted residues to create entirely new products.  
BBRM recycling is particularly challenging due to the complexity of its composition, which often includes reinforcing materials like polyester or glass fibers. These components can degrade the quality of remelted bitumen, restricting its reuse in high-value applications. According to a report by the Asphalt Roofing Manufacturers Association, the recycling potential of asphalt roof membranes remains underexplored, particularly in North America [21].  
The report identifies several potential applications for recycled BBRM, including:  
_Cold Patch Material_. Utilizing recycled BBRM for pothole repairs.  
_New Roofing Products_. Incorporating recycled materials into the production of new bitumen-based roofing products.  
_Road Base Filler_. Employing recycled BBRM as a filler in road base layers.  
_Dust Suppression_. Using BBRM-derived substances to suppress dust on gravel or unpaved roads.  
These applications underscore the versatility of recycled BBRM and its potential to enhance sustainability in construction and maintenance sectors.  
Globally, the large-scale recycling of roofing materials remains limited, with small amounts of recycled material often incorporated into asphalt mixtures. However, innovative processes are paving the way for more efficient recycling practices, as follows:  
_BiELSo (\"Bitumen Endless Life Solutions\")_. Developed by Icopal, this process crushes bitumen-containing materials like roofing felt, melts the bitumen, and removes non-bituminous components. The recycled bitumen is mixed with virgin bitumen to produce new roofing materials of comparable quality to those made with primary bitumen. Each square meter produced reduces CO\({}_{2}\) emissions by approximately 100 g, equivalent to the emissions from driving one kilometer [22].  
_BitumenMix Granules_. Created by Tarpaper Recycling ApS, these granules consist of 45% stone material and 55% recycled bituminous material. This waste-free process is directly integrated into asphalt production, replacing up to 50% of virgin bitumen and reducing CO\({}_{2}\) emissions by approximately 60 kg per ton of asphalt [23],  
_Derbigum's Patented Process_. This method involves recycling bituminous waste, such as production scraps and demolition materials, into modified bitumen-based waterproofing membranes. The waste is ground, melted, and mixed with fresh bitumen, resulting in products containing 2-12% fibers [24].  
Our innovative solution aims to develop a novel material that performs multiple functions within the construction sector, leveraging recycled BBRM for enhanced sustainability and functionality.
## 5 Performance Evaluation Using Simulation  
In this section, the ability of the MEWMA control chart to detect abrupt changes in the network is assessed through simulation. To do so, average run lengths (ARL) of simulated networks in phase II are calculated after applying different shifts to the edge creation probabilities between nodes of the network. Therefore, 500 in-control consecutive networks with 600 nodes are generated to determine the UCL. The networks are modeled with HMTM, and their parameters are estimated through the MCMC algorithm. In this regard, the following steps are followed to generate 10,000 random networks in phase I and obtain the UCL:  
1. For \(t=1\) to 10000;
2. For \(1\leq i<j\leq 500\) and based on the probability of edge creation between nodes \(i\) and \(j\) generate \(y_{ij}\);
3. Use the MCMC algorithm to estimate vector \(L_{t}\) for the generated network;
4. For \(t=1\) to 10000, calculate \(Z_{t}\) based on relation (6);
5. For \(t=1\) to 10000, evaluate the \(T^{2}\) statistics based on relation (7);
6. For all \(T^{2}\) statistics, find a UCL that the type I error \(\alpha\) meets.  
For type I error \(\alpha=0.01\), the UCL is obtained. Therefore, the in-control ARL of the control chart is 100. For phase II, different probabilities of edges are used to evaluate the performance of the proposedcontrol chart in detecting network anomalies. Then, the following steps are followed to evaluate the out-of-control ARL of the control chart:  
1. For \(i=1\) to 10000; 1. Set \(RL_{i}=0\); 2. While \(T^{2}<\) UC; 1. Generate a random network based on different probabilities; 2. Estimate model parameters with the MCMC algorithm and obtain \(T^{2}\) statistic from relation (7); 3. Put \(RL_{i}=RL_{i}+1\); 2. Evaluate \(\text{ARL}=\sum\limits_{i=1}^{1000}RL_{i}/10000\).  
The results of the simulation for phase II are illustrated in Figure 1. It can be observed that the \(T^{2}\) control chart is capable of detecting changes in the HMTM parameters. The out-of-control ARL values are much less than the in-control ARL.