esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### External Constraints: Depletion of Abiotic and Biotic Natural Resources

Table 5: Water depletion and water yield in the watershed in the study area. and local patterns of overland flow requires detailed data that are not available or appropriate for our approach. Even so, this concept provides a useful initial assessment of how much area is required to compensate for water consumption in a specific watershed. As can be seen, the impacts on abiotic resources of scaling the process to 1 year of operation would be 0.159 Mt oil/y for scenario C1, 0.124 Mt oil/y for scenario C2, and 0.099 Mt oil/y for scenario C3 as fossil depletion or 1148 million Nm\({}^{3}\) of NG for C1 and 800 Mm\({}^{3}\), on average, for C2 and C3 as the requirement of PES of the CHP plant (Table 4 (b)). If the PES requirements for the CHP plant, according to the data shown in Table 4 (b), are compared with the 1P hydrocarbon reserves [58], the impact of scenario C1 on the total reserves for Mexico would only be 0.0019%. Also, a contribution of only 0.0003% for the NG reserves in m\({}^{3}\) billion was estimated (Table 6). From a spatial point of view, the Mexican hydrocarbon reserves (stock-flow) are in an area of 200,965,000 ha [59], which includes offshore and onshore land and represents only 0.39% of the national territory. For 1P reserves, a value of 0.0032 ha/toe is estimated. From this reference value and considering the estimation of fossil fuel depletion, the CHP plant would require 516.2 ha for C1, 405.1 ha for C2, and 323.4 ha for C3 to cover its annual demand of PES for one year of operation at the local scale (Table 6). From a local scale point of view, for this energetic system, those values represent a low pressure on the environment. However, on a national scale, Mexico only has hydrocarbon 1P reserves for 8.5 more years, (Table 6), which is a short time as fossil fuels provide fuel for most of the country. Thus, this period of reserves could be reduced due to an increase in PES consumption to generate more EC. Particularly, for this abiotic resource, Mexico has external biophysical constraints, which are related to the overall size of the energy flow requirement of the country [68]. For instance, the country imports 8 billion ft\({}^{3}\)/d of NG, which represents 85% of its consumption. Of that amount, the Mexican oil and gas industry (PEMEX) demands 2 billion ft\({}^{3}\)/d, and the remaining 6 billion is used by the electricity sector, industry, and personal residences [69].
## 3 Results and Discussion  
### Definition of the Debonding Damage Map  
As the accuracy of the proposed machine-learning-based identification model is considerably affected by the input data, appropriate relevant key features of natural frequencies should be chosen to reflect debonding damage. The natural frequency change rate is affected by the damage configuration according to the structural characteristics of the blade, and there are many different configurations. Therefore, at least 1000 sets of debonding damage configurations are required to build a debonding damage map for accurate and reliable identification. This study accordingly developed the 1105 damage configurations defined in Table 2 according to damage variables such as single or multiple damages, damage start location, and damage length. Three damage configuration types were evaluated: Configuration Type 1 (Figure 5a) means single damage at one joint, Configuration Type 2 (Figure 5b) shows two damages at one joint, and Configuration Type 3 (Figure 5c) is single damage at each of two joints. The damage start locations were defined as 10 m, 22 m, and 34 m from the root, as shown in Figure 6. Finally, the damage length was increased from 2 m to 8 m in 2 m intervals, starting from the damage start location. In the configuration of the 10 m damage start location, the damage length extended from the root up to 18 m from the root, as shown in Figure 7. The longest length was selected to include the location at 30-35% of the total blade length from the root, where structural integrity is most lacking, and thus the maximum stress is experienced under extreme loads, as identified in a previous study [24]. The various blade damage configurations were then modeled using the MPC technique, and modal analyses were performed to obtain the resulting natural frequencies for the 1 st to the 6 th, as shown in Table 3.  
Figure 4: ANN architecture for identification of debonding damage. Figure 5: Debonding damage: (**a**) Configuration type 1; (**b**) Configuration type 2; (**c**) Configuration type 3 (shown schematically). Figure 6: Debonding damage start location.
## 2 Methods  
### Elastic Full-Waveform Inversion (EFWI)  
EFWI can provide high-resolution multi-parameter models of underground media by fitting the full wave information of multi-component seismic data. The objective function of EFWI using multi-component seismic data can be written as follows [12],  
\[\sigma_{1}\big{(}\lambda,\mathbf{\mu},\mathbf{v}_{p},\mathbf{v}_{s}\big{)}=\sum_{i}^{ns}\sum_{ j}^{nr}\bigg{[}\Big{\|}\dot{\mathbf{u}}_{i,j}^{x}\big{(}\lambda,\mathbf{\mu},\mathbf{v}_{p}, \mathbf{v}_{s}\big{)}-\dot{\mathbf{d}}_{i,j}^{x}\Big{\|}^{2}+\Big{\|}\mathbf{u}_{i,j}^{z} \big{(}\lambda,\mathbf{\mu},\mathbf{v}_{p},\mathbf{v}_{s}\big{)}-\dot{\mathbf{d}}_{i,j}^{z} \Big{\|}^{2}\bigg{]}\, \tag{1}\]  
where \(\sigma_{1}\) denotes the objective function value; \(i\) and \(j\) denote the source and receiver index, respectively; \(ns\) and \(nr\) denote the total number of sources and receivers, respectively; \(\mathbf{u}_{i,j}^{x}\) and \(\mathbf{u}_{i,j}^{z}\) denote the x- and z-component of the synthetic data in the time domain, respectively; \(\dot{\mathbf{d}}_{i,j}^{x}\) and \(\dot{\mathbf{d}}_{i,j}^{z}\) denote the x- and z-component of the observed data in the time domain, respectively; \(\lambda\) and \(\mathbf{\mu}\) are same constants; and \(\mathbf{v}_{p}\) and \(\mathbf{v}_{s}\) denote P-wave velocity and S-wave velocity, respectively. In Equation (1), the density parameter is assumed to be constant. Equation (1) represents the objective function, which assesses waveform matching by computing the L2-norm of the residuals between observed and synthetic data. Amplitude errors in the observed data can lead to waveform differences not solely attributable to the velocity model. Consequently, the inversion process may converge to a local minimum, impeding reliable inversion outcomes. To address this issue, employing an objective function reliant on cross-correlation operations proves advantageous, given that [19]  
\[\sigma_{2}\big{(}\lambda,\mathbf{\mu},\mathbf{v}_{p},\mathbf{v}_{s}\big{)}=\sum_{i}^{ns} \sum_{j}^{nr}\Big{[}-\dot{\mathbf{u}}_{i,j}^{x}\big{(}\lambda,\mathbf{\mu},\mathbf{v}_{p}, \mathbf{v}_{s}\big{)}\cdot\dot{\mathbf{d}}_{i,j}^{x}-\dot{\mathbf{u}}_{i,j}^{z}\big{(} \lambda,\mathbf{\mu},\mathbf{v}_{p},\mathbf{v}_{s}\big{)}\cdot\dot{\mathbf{d}}_{i,j}^{z}\Big{]}, \tag{2}\]  
where \(\dot{\mathbf{u}}_{i,j}^{x}\) and \(\dot{\mathbf{u}}_{i,j}^{z}\) denote the normalized x- and z-component of the synthetic data, respectively, i.e., \(\dot{\mathbf{u}}_{i,j}^{x}=\mathbf{u}_{i,j}^{x}\Big{/}\Big{\|}\dot{\mathbf{u}}_{i,j}^{x} \Big{\|}\), \(\dot{\mathbf{u}}_{i,j}^{z}=\mathbf{u}_{i,j}^{z}\Big{/}\Big{\|}\dot{\mathbf{u}}_{i,j}^{z} \Big{\|}\), and \(\dot{\mathbf{d}}_{i,j}^{x}\) and \(\dot{\mathbf{d}}_{i,j}^{z}\) denote the normalized x- and z-component of the observed data, respectively, i.e., \(\dot{\mathbf{d}}_{i,j}^{x}=\dot{\mathbf{d}}_{i,j}^{x}\Big{/}\Big{\|}\dot{\mathbf{d}}_{i,j}^{ x}\Big{\|}\), \(\dot{\mathbf{d}}_{i,j}^{z}=\dot{\mathbf{d}}_{i,j}^{z}\Big{/}\Big{\|}\dot{\mathbf{d}}_{i,j}^{ z}\Big{\|}\).
## 5 Discussion
### Assessment of Training Efficiency According to the Models  
The training time and accuracy of the models were tested for evaluating the efficiency. The identical hardware environment was used to train the machine learning models (i.e., AMD Ryzen 95900X 12-Core CPU, NVIDIA Geforce RTX 3060, and 64 GB RAM). The results are summarized in Table 6. There was a notable difference in training times between the ensemble learning-based machine learning models. The LightGBM model, compared to the RF model, has more parameters to be tuned [55,56] so that the longer computation time is required. The training accuracy for each Scheme for LightGBM and RF models is also shown to be unstable. It exhibited low accuracy in Site A and high accuracy in Site B. Although the computation speed of U-Net for burned area detection was not the fastest, U-Net demonstrated consistent accuracy regardless of Sites and Schemes. In order to detect burned areas, deep learning approaches like U-Net could produce reliable results.
## 4 Improvement of Thermal Performance
### Form-stable PCMs
#### 4.2.2 Inorganic Form-stable PCMs  
Form-stable PCMs can also be fabricated with inorganic mineral porous materials such as kaolin, diatomic, vermiculite, sepiolite, bentonite, attapulgite, fly ash, opal, etc., as well as with expanded materials such as expanded graphite and expanded perlite. The porous structure of these materials makes them suitable for their utilization as carries for the encapsulation of the PCM. The fabrication of inorganic porous form-stable PCMs can be performed with impregnation method, melting intercalation technology, and melting adsorption method [43].  
There is chemical compatibility between PCMs and the porous materials. The interactions developing between them are mainly surface tension, hydrogen bond, Van der Waals, etc. Due to these interactions, the form-stable composites show increased thermal stability and reliability. The absorbance rate, which is connected with latent heat capacity, is high in the case of expanded graphite, diatomic, and expanded perlite carriers. Thermal conductivity is vital for the energy storage and release efficiency. In mineral porous materials, thermal conductivity is relatively low but it can be easily enhanced with the addition of expanded graphite as an additive [43; 44].
# Forest Fire Detection Using New Routing Protocol
## 6 Simulation Setup  
The PCE-OLSR protocol is chosen to compare with the proposed LARRR protocol. To adopt these routing protocols in the fire conditions, the simulation environment is deemed to be a square forest (i.e., 1000 \(\times\) 1000 m\({}^{2}\)). In addition, 50 nodes are deployed in order to scan the forest. Table 1 depicts the simulation parameters for the proposed LARRR protocol.  
The nodes in this simulation have a coverage area with a radius of 250 m. In addition, each node moves in a circle form, where its diameter is up to 125 m. The initial energy of each node is equal to 2 kJ [36]. In normal conditions, the node's temperature is 20 \({}^{\circ}\)C. When the node scans a fire area, its temperature increases linearly with a step equal to 0.0055 \({}^{\circ}\)C. The experiment time of this simulation is 1500 s, and the forest fire will be initiated after 400 s of experiment time. The fire's location is selected in the center of the forest, as shown in Figure 10.  
The fire is assumed to be a circle, increasing with a speed of 0.5 m/s. In addition, the largest radius of the fire is 300 m. The packets inside the node are generated by using 2 Poisson variables. The first variable is used for generating the time with a mean equal to 15 s. Meanwhile, the second variable is used for generating the packets with a mean equal to 1 packet. The lifetime of each packet is 15 s. The simulation is conducted using MATLAB R2017B as the simulator software on a PC (Windows 10) with 6 GB RAM and a 1 TB HDD.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Experiment time & 1500 (s) \\ Average of logging data & 25 (s) \\ The ignition time of fire & 400 (s) \\ Nodes number & 50 (node) \\ Nodes energy & 2 (k) \\ Coverage area radius & 250 (m) \\ Average size of packet & 80 (bite) \\ Normal node temperature & 20 (degree) \\ Node speed & (7.5–12.5) m/s \\ Fire speed & 0.5 m/s \\ Fire radius & 300 (m) \\ Environment size & 1000 \(\times\) 1000 (m\({}^{2}\)) \\ Start point of fire & The forest center \\ Buffer size of data & 100 (packet) \\ Lifetime of data packet & 15 (s) \\ Interval arrival time & 15 (s) \\ Generation mean of data packet & 1 (packet) \\ Timeout of RREQM & 1.5 (s) \\ Buffer size of RREQM & 5000 (packet) \\ Buffer size of RREPM & 1000 (packet) \\ Temperature increment & 0.0055 degree/10 ms \\ Time unit & 10 ms \\ Simulator & MATLAB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 1 Introduction
### Outline  
The manuscript is organized as follows. First, the Probability of Test Success is introduced as the statistical power of a reliability demonstration test. The necessary hypotheses are formulated, mathematically defined, and illustrated. In Section 3, calculation methods for the Probability of Test Success are established. Here, four methods are introduced. The general method is capable of calculating the Probability of Test Success in all possible scenarios. It can easily be adapted thanks to the Bootstrap approach used here. The analytic method for SR tests enables a very fast and easily implementable calculation for failure-free tests, whereas the analytic and approximate calculation method for failure-based tests allows for an equally quick calculation of the Probability of Test Success of failure-based tests using the asymptotic properties of several statistics. Additionally, a calculation method which does not rely on the concept of the statistical power, and instead simulates the testing scheme, is explained. The advantages of these methods are worked out and compared to each other in Section 4. In order to illustrate the use and effect of the concept and the procedure for test planning of reliability demonstration tests, Section 5 uses a case study to demonstrate the holistic view one is able to obtain by employing the Probability of Test Success. This is additionally illustrated by the example of the monetary aspects of the case study, which concerns a high-voltage battery. Section 6 discusses the major work, contributions, challenges, and results of the presented approach and concludes the findings with recommendations for future works.
# Analysis and Design of a Transient-State Resonant Converter Used as a Frequency Multiplier
## 3 Design Methodology and Experimental Testing
### Development of Experimental Tests
Figures 10 and 11 show the oscilloscope screen captures, where it is verified that the resonant converter is operating correctly, since, as seen in Figure 10, the inverter switching frequency is \(f_{sw}\approx 300\) kHz, while the frequency of the current in the resonant tank is oscillating at a frequency of \(f_{o}\approx 900\) kHz, which verifies the frequency multiplication according to the value of the multiplier \"\(n=3\)\". Furthermore, the plot of the resonant tank current (purple) satisfies the value proposed by the decay factor \(d_{f}=0.90\), since, as can be observed, the current amplitude does not decay to a large extent until the middle of the switching signal period, preserving a completely sinusoidal waveform. Figure 11 shows the output current, power, and voltage levels, which satisfy the operating parameters. Figure 11: Output voltage (blue), output current (green), output power (red). Figure 10: Class D inverter output voltage (blue) at \(f_{sw}\approx 300\) kHz, current in the resonant tank (purple) at \(f_{o}\approx 900\) kHz. Figure 9: Implementation of the resonant converter for experimental tests. To check the feasibility in the implementation and the performance of the transient operation, further tests were performed using values in the multiplier with \(n=3,5,7\) and for different values in the decay factor. These tests prove that the resonant converter can act as a high frequency multiplier without limitations in the operating power level, which is one of the drawbacks of frequency multipliers where only low power values are handled. As shown in Figure 12a-d, the current amplitude showed a slight decay up to the middle of the switching period. This was because the value in the decay factor used during the experimental tests was lower than \(d_{f}=0.9\) (current waveform without decay). Using low values in the decay factor such as \(d_{f}>0.9\), and with values in the frequency multiplier up to \(n=7\), allowed satisfying the established operating parameters and reaching the desired output power. In the experimental tests, the use of air core inductors was chosen to reduce losses in the resonant tank due to magnetic hysteresis, the efficiency of the tank being only affected by the value of the parasitic series resistance ESR\({}_{\text{L}}\) of the inductor L\({}_{\text{s}}\).
# Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
## 3 Approach
### Quality Index Based on Important Pixels for CNN-Based Localization
#### 3.2.3 CNN-Based Localization  
This section introduces the localization method based on the SPAD LiDAR and CNN. The localization method uses raw sensor data that can be acquired from the SPAD LiDAR and augmented data shown in Figure 6. The outputs of the localization are the position of a target. For example, in an application of AGVs, the targets are pallets in a warehouse.  
Before localization, the method requires a CNN model in an environment. A CNN model is determined from supervised training data (e.g., motion capture data, drive data made by a human operator, or existing localization data). After producing a CNN model, the localization method uses only range data, peak intensity data, and monocular data for localization.  
To explore the basic study of important pixels, we used a simple plain network architecture for our CNN that consists of three convolution layers, three pooling layers, and two fully connected layers. Our previous paper [15] presented the parameters of the network. The input data consist of three channels. The size of each data is 202 \(\times\) 96 pixels. Before the first convolutional layer, the CNN resizes the input data to 112 \(\times\) 112. The CNN has two output layers. One output layer is a classification output for the existence of a target. For example, in an AGV application, the target is a pallet, which the AGV must approach. The other output layer is a regression output for a 3D position and orientation of LiDAR.  
For this multi-output structure, the CNN uses the multi-task loss function presented by [PERSON] [16] and [PERSON] [17]. The loss function for the CNN is defined as  
\[Loss=L_{cls}(p,u)+\lambda[u\geq 1]L_{reg}(t^{u},v), \tag{5}\]  
where \(L_{cls}(p,u)\) denotes the classification loss for a true class \(u\), and \(p\) is an estimated class. If there is a target in input data, then the true class becomes one. In addition, \(L_{reg}(t^{u},v)\) is the regression loss for a predicted position of the LiDAR, \(v\) is the ground truth position and orientation, and \(\lambda\) controls the balance between the classification loss and regression loss. In this paper, \(\lambda\) is empirically decided. The method minimizes the multi-task loss function.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 2 System Model  
In this section, a detailed description of the gNB, channel, and UE model considered for the application of BM procedures in both gNB and UE-side directions during IA is presented, where the simulated system uses both the 5G toolbox and the Phased Array System Toolbox. The first one provides 3 GPP-standard-compliant functions and reference examples useful for modelling and simulation of a 5G NR end-to-end communication system, given the DL's physical channel and signal creation for transmission and reception of downlink control information (DCI) messages to the DL synchronization, demodulation, and further decoding of a live gNB signal at the UE. On the other hand, the Phased Array System Toolbox provides the necessary algorithms for enabling BF techniques with electronically steerable antennas, allowing the simulation of multipath fading environments to evaluate the performance of the employed BF antenna arrays.  
An overview of the main processing stages required for the dual-end beam-sweeping procedure at both the gNB and the UE ends, along with the main BM steps for effective beam pair establishment and maintenance, are highlighted in red in Figure 1.  
Figure 1: BSAE algorithm concept.
# Ontology-Based Regression Testing: A Systematic Literature Review
## 3 Research Method
### Inclusion and Exclusion Criteria  
The electronic database search process resulted in the selection of 53 studies. The selection of the studies underwent the review of titles, abstracts, and keywords. Next, we performed a full-length review of studies. As a result, 24 studies were finally selected for this SLR. Studies that had matched search strings with the titles, abstracts, and keywords were included in the first step of the selection process. The authors leveraged the guidelines and recommendations made by researchers [27; 29].  
We included peer-reviewed articles on the topic \"ontology-based regression testing\" and further explore the studies' inclusion and exclusion criteria as follows.  
The inclusion criteria of research studies are defined based on the titles, abstracts, and keywords as follows:  
* Publications that discuss ontology-based regression testing of web services were included.
* Publications that discuss the ontology-based test case prioritization techniques were included.
* Publications that discuss issues/challenges regarding ontology-based regression testing were included.  
Next, the authors defined the exclusion criteria of research articles as given in the following:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Repository Name** & **Search String** \\ \hline IEEE Xplore & ((((“All Metadata”: Ontology) AND ”All Metadata”: Regression Testing) OR ”All Metadata”: Test Case Prioritization) AND ”All Metadata”: Test Case Selection) AND ”All Metadata”: Test Case Generation) \\ \hline ACM digital Library & (+ Ontology + Regression + Testing + Test + Case + Prioritization + Test + Case + Selection + Test + Case + Generation) \\ \hline ScienceDirect & “Ontology, regression testing, test case prioritization, Test Case Selection, Test Case Generation.” \\ \hline SpringerLink & \begin{tabular}{c} ‘Ontology AND ”Regression Testing” AND (Test OR Case OR OR \\ Prioritization, OR Test OR Case OR Selection, OR Test OR Case OR Generation)” \\ \end{tabular} \\ \hline Web of Science & \
\begin{tabular}{c} (Ontology) AND TOPIC: (Regression Testing) OR TOPIC: (test case prioritization) OR TOPIC: (Test Case Selection) OR TOPIC: \\ (Test Case Generation) \\ \end{tabular} \\ \hline Wiley \& Sons &
\begin{tabular}{c} “Ontology” anywhere and “Regression Testing” anywhere and “Test Case Prioritization” anywhere and “Test Case Selection” anywhere and “Test Case Generation”. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Search strings used for paper’s retrieval.  
Duplicate papers on the research topic were excluded.
* Publications that did not discuss ontology-based regression testing of web services were excluded.
* Publications that did not provide a technical discussion on the research topic of this SLR were included.
* Publications written in a language other than English were excluded.