esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To assess the influence of an intermediate coarse layer on the slope stability during heavy rainfall, knowledge about water movement and how slope failure occurs is important. To clarify the characteristics of water infiltration in a multi-layered slope and assess its influence on the slope failure modes, eight groups of physical slope models were investigated. It was found that the unsaturated hydraulic conductivity in the coarse layer (\(5.54\times 10^{-6}\) cm/s) was much lower than that of the fine layer (\(1.08\times 10^{-4}\) cm/s), which resulted in the capillary barrier working at a lower water content. Intermediate coarse layers embedded between finer ones may initially confine the infiltration within the overlying finer layers, delaying the infiltration and eventually inducing a lateral flow diversion in the inclined slope. Two different failure modes occurred in the model experiments: surface sliding occurred at the toe in the single-layer slope group and piping occurred at the toe in the multi-layered slope as the rainfall water accumulated, was diverted along the interface, and then broke through in the downslope direction of the intermediate coarse layer. The lateral flow diversion caused by the capillary barrier and the tilt angle may be the major factors influencing the difference of the failure modes. The result also revealed that the coarser layers may have negative effects on the slope stability.  
unsaturated soil; capillary barrier; multi-layer slope; slope failure Article
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 4 Simulation Setup
### Model Description
In order to meet the IPv6 minimum MTU requirement and facilitate two-way communication with conventional IPv6-capable networks, IETF developed RFC 4944 [36] for transmission of IPv6 packets over IEEE 802.15.4 networks. The standard recommends an adaptation layer below the network layer for fragmentation and reassembly of frames. The realisation that IEEE 802.15.4 networks (LoWPANs) share certain PHY/MAC characteristics with PLC renders them attractive to be adapted for low-power, low-rate communication over power lines. At PHY, OFDM with Differential Binary Phase Shift Keying (DBPSK) is employed. As this work is experimental in nature, first, we emulate the HEMS in NS-3 and investigate the network performance for the given values of impulsive and background noise. The Signal to Noise Ratio (SNR) obtained is then fed into MATLAB to evaluate the Bit Error Rate (BER) performance as a function of SNR.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT
### NB-IoT Key Technologies
#### 2.2.2 Deployment Scenario  
The NB-IoT deployment is classified into in-band, standalone, and guard-band scenarios according to its relationship with the legacy cellular network as shown in Figure 3. The in-band scenario allocates 200 kHz of the LTE carrier to the NB-IoT and reuses the frequency that is already secured by the network operator. Hence, the deployment cost can be reduced because additional spectrum is not necessary. However, the resource map should be adjusted to guarantee the orthogonality with existing network and the available bandwidth for the LTE network can decrease. Next, the standalone scenario does not use LTE band resources, but assigns a dedicated spectrum to the NB-IoT. In addition, it can coexist with the GSM network by re-farming the network frequency. Finally, the guard-band scenario operates in the LTE guard band. Hence, it does not to require additional spectrum, but the number of available channels is limited. For instance, in the case of LTE with 20 MHz channel bandwidth, as shown in the Figure 3, only two 1 MHz guard bands are available, whereas the 10 MHz channel bandwidth has two 500 kHz guard bands [12; 13].
## 2 Methodology
By analyzing the EEG data from participants engaged in gameplay, the study provides valuable insights into the role of video games in promoting emotional well-being and cognitive engagement. The findings can be generalized to demonstrate the broader impact of video games on human concentration, and the research opens up opportunities for further exploration of video game applications in various areas, such as education, health, and cognitive treatments. The methodology consisted of two phases: the first phase involved explaining different video games, providing a video tutorial, and making necessary adjustments to use the VR headset. Brainwave data were recorded using electroencephalography while participants played video games during this phase. The second phase focused on analyzing the brainwave data to determine the concentration level exhibited during different categories of VR video games. The first phase can be seen in Figure 1, where different video games are explained, along with a video tutorial for making all the adjustments needed to use the virtual reality headset. After all the necessary adjustments are made, the person starts playing the video games while their brainwave data are simultaneously recorded using an EEG (MUSE 2), as shown in Figure 2. The MUSE 2 is a headband designed for measuring EEG signals. These brain activity readings are transmitted wirelessly to a tablet through Bluetooth, enabling the data to be analyzed online or offline. The MUSE 2 headband has proven effective in capturing EEG data for activities like neurofeedback and brain-computer interfacing (BCI) sessions [23; 31]. Participants try on the Meta Quest 2 and remove it to fit the EEG to their heads. Once the EEG signal is transmitted to the tablet, users put on the Meta Quest 2 again to begin the video game testing, as shown in Figure 3.  
In this investigation, we focus on determining which category of virtual reality video games people exhibit higher concentration levels. Figure 1: Preparation and adjustments to play video games in virtual reality.
## 3 Numerical Validation of the Damping Estimation Methodology
### Validation of the Application of the Prony Method  
As described in Section 2, the Prony method is a methodology that allows the decomposition of a signal in a set of exponential decaying sinusoids that are representative of each frequency of interest, to accurately estimate the damping coefficients. In this section, a numerical validation of this methodology will be performed through the simulation of a train passage over the Casalheira Bridge using a train-track-bridge interaction methodology developed by [PERSON] et al. [49]. The 3D FE model of this bridge and the Alfa Pendular model have been recently developed by the authors and presented in a previous publication [28]. The validation was carried out considering two different train speed scenarios and four different types of irregularities.  
Figure 5: Time history of the midspan displacement: bridge with (**a**) 7.00 m, (**b**) 11.00 m, (**c**) 11.50 m, and (**d**) 23.50 m span.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 5 Implementation
### Setup of the Randomness Processing Unit (RPU)  
The RPU is a specialized hardware device that generates true random numbers. It typically leverages physical phenomena such as quantum noise, thermal noise, or radioactive decay, which are inherently unpredictable. The RPU's design includes:  
* Noise Source: The physical process generating randomness.
* Entropy Extraction: Digital circuitry or algorithms to convert raw physical signals into uniformly distributed random numbers.
* Post-Processing: Additional steps to ensure the uniformity and independence of the generated numbers, such as whitening algorithms or hash functions.  
Before deployment, the RPU must be rigorously tested to ensure the quality and true randomness of the output. Statistical tests, such as the National Institute of Standards and Technology (NIST) suite, are used to assess randomness. Key metrics include:  
* Uniformity: Distribution of numbers should be uniform across the possible range.
* Independence: No correlation should exist between consecutive numbers.
* Entropy: High entropy indicates that the output is as unpredictable as possible.
# Bond Strength Properties of GFRP and CFRP according to Concrete Strength
## 1 Introduction
Similar to the process of verifying the bond strength of deformed bar and FRP, the examination of bond strength between reinforcing bar and concrete is required in the case of GFRP and CFRP before they are used in a reinforced concrete structure. The application of GFRP and CFRP is severely limited because the current structural design standards cannot be adopted for GFRP and CFRP owing to the difference in the mechanical behaviors such as tensile, compression, and adhesion performance from existing deformed rebars [14]. In this study, a bond strength experiment was conducted based on ASTM C 234, which was used in a previous study, to examine the bond strength of GFRP and CFRP. The water-cement ratio was set at 40%, 50%, and 60%; GFRP and CFRP rebars with a diameter of 13 mm were used to test the concrete strength.
## 5 Evaluation: Influence of the Main RPL Parameters on Network Convergence Performance  
In this section we study the influence of the two most crucial RPL parameters on the performance of the network convergence process, in the network scenarios presented in the previous section. The two considered RPL parameters are the redundancy constant, \(k\), and the minimum interval, \(I_{min}\). This section comprises two subsections, which focus respectively on the impact of each one of the aforementioned RPL parameters on network convergence performance.  
Figure 3: Percentage of networks converged in less than 10,000 s in three network size scenarios, for different node densities, as a function of the redundancy constant, \(k\): **(a)** Small network scenario, **(b)** medium network scenario, and **(c)** large network scenario, respectively.
# Semantic 3D Mapping from Deep Image Segmentation
## 4 Results
### Experiments of the Algorithm for 3D Segmentation  
The first experiment measures the precision with which an octomap represents a segmented object in the image. The proposed algorithm correctly detects the voxels of the octomap that belong to the detected object, and what we are going to show is how other approximations deteriorate this result. The first of these approximations corresponds to the original algorithm, in which a direct translation of pixels and voxels is made (suffering the problem of _borderline pixels_). The second approximation is when erosion is carried out to the segmentation in the image, trying to eliminate the border pixels, before translating to voxels.
## 5 User Preference Modeling
### User Preference Modeling by Deep Features and Social Tags  
In order to combine the advantages of deep features with social tags, user preference is jointly modeled with deep features and social tags. Set \(U_{\text{d}}\) represents the user preference model for deep features and \(U_{\text{t}}\) represents the user preference model for social tags. A user preference model can be formalized as  
\[U=\alpha U_{\text{t}}\ +\ (1\ -\ \alpha)U_{\text{d}} \tag{15}\]  
where \(\alpha\) is the parameter to balance the weight of user preference modeling for social tags and user preference modeling for deep features. Since it is hard to have an optimal \(\alpha\) mathematically, an experiment is implemented to decide the value of \(\alpha\). The detail of this experiment will be shown in Section 7.
## 4 Results and Discussion
### Recommendations for Reliability Improvements  
For the manufacturers and service providers, this analysis indicates that developing a targeted maintenance approach for the critical subsystem and failure modes like the A/C evaporator and bluer can be effective. There is also a need to carry out regular maintenance (e.g., routine inspections, cleaning, and lubrication) with qualified professionals at authorized service centers. The qualified professional addresses minor issues before they escalate into major failures. The installation of the system should be performed following industry standards using high-quality components to avoid system inefficiencies and premature failures. Proper training should be provided to operators and maintenance personnel on HVAC system operation, maintenance procedures, and troubleshooting techniques.  
This analysis helps in optimizing the resource allocation, downtime, and repair costs. The establishment of effective preventive maintenance strategies (e.g., timely replacement of components, lubricating, oiling, etc.) based on the results can reduce the probability of unexpected maintenance, which is optimal as its cost is very high compared with other maintenance. The analysis also shows that there is a need to investigate the high-quality and reliable components as they lower the life cycle cost of the system. With the effective incorporation of these design and operational practices, the overall reliability of the HVAC system can be improved to improve customer satisfaction.  
This study provides several findings and have practical implications for the maintenance strategies of the analyzed HVAC system, particularly when used in a passenger car. The components that show the wear-out failure pattern need a proactive maintenance approach, such as scheduled maintenance for age-degradation. For example, the wear and tear in the compression and fan is due to continuous use. Periodic inspections and part replacement can minimize the unexpected failures. On other hand, a condition-based monitoring system can be proposed for the components with constant failure rates in which random failures were observed. This approach uses the sensors and electronic components that continuously monitor real-time data such as temperature, vibration, humidity, etc. to predict the potential failures before they occur.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 6 Conclusions  
In summary, data routing between flying UAVs in sky is a fairly complex task, and many routing protocols have been designed that use various path finding strategies and path recovery mechanisms to provide continuous routing to achieve data delivery. However, routing protocols are also facing some challenges while considering the characteristic of interruption easily in FANET which lead to performance reduction of the routing strategy during performing tasks. Therefore, the exiting routing strategies need to be improved so that the routing operation can be optimized synchronously with the dynamic changes of the network. The routing methods introduced in this article are all aimed at FANET, which fully represent the development of routing protocols in this field in recent years. At the same time, this paper proposes five new directions in the field of FANET research and provide clear research goals for interested new researchers.  
[PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] give suggestions and make modifications. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 61772175, Grant 61771185, Grant 61701172, Grant 61801170, Grant 61801171, and in part by the Postdoctoral Science Foundation of China under Grant 2018M632772.  
The authors wish to thank the editor and anonymous referees for their helpful comments in improving the quality of this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
FANETs Fly Ad Hoc Networks MANETs Mobile Ad Hoc Networks VANETs Vehicular Ad Hoc Networks UAVs Unmanned Air Vehicles
## 2 Materials and Methods
### Choice of the Electrode Array Configurations  
The choices of the electrode arrays for the acquisition sequences were dictated by the need to limit the number of current dipoles used, trying in the same way to keep the symmetricity and the regularity of the pseudosection. In consideration of this, among all the possible electrode configurations, three configurations were chosen. The first configuration, here called \(\alpha\), provides arrays with external current electrodes and internal potential electrodes in which the two dipoles have the same midpoint. This corresponds to the Wenner array if the electrodes are aligned. The second configuration, indicated by \(\beta\), is made by dipole-dipole arrays in which the current dipole and the potential dipole have the same length. Finally, the \(\gamma\) combination includes the measurements performed with the Full Range Gradient (FRG) array [24]. This latter is obtained by modifying the multiple gradient array [25], including in the sequence ofpotential dipoles that are external to the current dipole, also taking into account the Linear Grid array proposed by [PERSON] et al. [26]. The FRG array produces a pseudosection with extended lateral coverage, a much smaller geometric factor, and less noise contamination than the dipole-dipole array.  
For each areal electrode arrangement shown in Figure 1 the configurations \(\alpha\), \(\beta\), and \(\gamma\) were calculated. To limit the data noise, the measurements with a geometric factor \(G\) > 3000 m and, among the dipole-dipole measurements, those with a dipole length greater than 30 m were excluded. In this way, inverse models with low misfit and at the same time an adequate depth of investigation should be obtained. Table 1 summarizes the proposed electrode sequences, specifying the total number of measurements, the number of current dipoles, and the average number of potential measurements per current dipole.  
Concerning the electrode array configurations, the \(\gamma\) configuration is the one that allows us to acquire a greater average number of potential measures for each current dipole. This is certainly an advantage if multi-channel resistivity meters are used, capable of carrying out several potential measurements at the same time. Ultimately, among the nine possible combinations of electrode areal arrangement/electrode array configuration, the combination between \(B\) and \(\gamma\) is the choice that presents the best compromise since its number of total measurements is quite high and, at the same time, it shows the largest average number of potential measures for the current dipole (69). In this way, this combination should be the fastest while ensuring a good resolution.  
Figure 1: Three possible areal arrangements with 96 electrodes on a square area with a side of 120 m in conditions of limited accessibility.
Performance--Based Route Selection for Mountainous Highways: A Numerical Approach to Addressing Safety, Hydrological, and Geological Aspects  
[PERSON]  
1 Traffic and Highway Engineering, Public Works Department, Faculty of Engineering, Cairo University,  
Giza 12311, Egypt 1  
[PERSON]  
2 Irrigation and Hydraulics Department, Faculty of Engineering, Cairo University,  
Giza 12311, Egypt 2  
[PERSON]  
3 Geology Department, Faculty of Sciences, Cairo University,  
Giza 12311, Egypt; [EMAIL_ADDRESS]  
[PERSON]  
design alternatives with a focus on safety. When designing rural highways in mountainous regions, careful consideration of various factors, including safety, efficiency, and sustainability, should be taken. Recently, there has been a growing interest in performance-based design approaches, which enhance highway design based on various performance measures such as safety, mobility, and environmental impact [5]. NCHRP Report No. 839 suggests that traditional geometric design practices, relying on minimum standards, may not yield optimal solutions. Instead, the report proposes a process involving setting performance targets, establishing design criteria, generating and evaluating alternative designs, and selecting the preferred design. This flexible, data-driven approach to highway design decisions aims to balance competing objectives, resulting in safer and more efficient roadways.  
This method assists transportation officials in effectively managing their investments, addressing system-level requirements, and achieving performance objectives with limited resources. It utilizes quantitative analyses to facilitate systematic decision-making for enhanced performance [5].  
The paper will address the following key aspects:  
1. Geometric Design Evaluation: The evaluation will focus on ensuring that all horizontal and vertical alignment elements adhere to design standards and align with driving behavior. This involves assessing elements such as horizontal curve length, radius, consecutive horizontal reverse curves, vertical curve designs incorporating stopping sight distances, allowable maximum and minimum grades, etc.
2. Hydrological Aspects: Attention will be given to hydrological aspects to ensure the long-term stability of the highway. This involves considerations related to water flow, drainage, and other water-related factors that could impact the road's structural integrity over time.
3. Geological Aspects: Geological aspects will be examined to identify the rock type and properties of the study area. This information will be significant in determining the most suitable road path and addressing geological hazards that may affect the road's construction and long-term stability.
## 4 The ANN Design
### The ANN with Multi-Class (Three-Class) Classification  
We will conduct research when adding the third class--Warning, which will allow us to take into account the situation in which, as noted above, the collision state (significant events) is less than 0.01% of the total number of states, since most of the manipulator positions are not in a self-collision state. The small number of significant events prevents the ANN algorithms from correctly predicting the collision state at other random values. To eliminate this problem, we discard some of the states that do not affect learning, which will increase the percentage of significant events in the sample.  
To determine the training dataset, we will compile 2000 random positions for each manipulator. For the combination \"each manipulator with each\", we obtain 4,000,000 random positions. In the sample, we leave those cases when one or both manipulators fall along the X axis into a previously agreed corridor located in the center of the robot, or pass through it (Figures 5b,c and 6b,c). Figures 5a and 6a show the situation when data are excluded.  
Based on these rules, let us compose Dataset 3 for training. For this purpose, we will select 73,984 values from 4,000,000 data according to our rules, 9062 of which will be collisions. The percentage of significant events to the rest of the values in the new samples was approximately 12%.  
Let us consider Network 2 and use Dataset 3 to train it. Let us denote this network as Network 4.  
The results of calculation on a random sample are shown in Figure 11. The confusion matrix is shown in Figure 12.  
The results of the training are summarized in Table 10.  
Accuracy of collision detection in Network 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Sample** & **Test Sample** & **Random Sample** \\ \hline Precision & 98.59\% & 98.23\% & 95.95\% \\ Recall & 98.78\% & 98.08\% & 97.55\% \\ Error & 2.61\% & 3.67\% & 6.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy of collision detection in Network 4.  
Figure 11: Results of Network 4 training on a random sample (**a**) cross-entropy loss; (**b**) true positive rate.  
The obtained result is much higher than the previous solutions. However, the obtained result is not satisfactory and, therefore, we will return to the consideration of the binary classification problem and tuning of the network parameters.