esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Evaluation
### Experience Setting  
The experiments were carried out on a 64-bit Ubuntu 18.04 LTS with a 3.20 GHz Intel(R) Core(TM) i5-10505 processor and 16 GB memory. MCLSM uses the OPTICS clustering algorithm to cluster feature values. During the clustering process, we set _min_samples_ to 5, and selected the range of _eps_ according to the reachability plot. In the clustering process, _eps_ represents the radius of the cluster and is used to determine whether one point can reach another. By adjusting the size of the _eps_ value, we can change the shape of the cluster. Smaller _eps_ may result in the formation of more and smaller clusters, while larger _eps_ may combine more points into one cluster. Therefore, it is very important to select the appropriate _eps_ in datasets with uneven densities. We determined the range of _eps_ using the reachability plot and adjusted the _eps_ in this range to achieve the best clustering effect. In a reachability plot, the boundaries of a cluster usually correspond to points with large variation in reachable distance, that is, high or low points. First, we set a threshold. Then, we found all the high and low points in the reachability plot and removed points larger than the threshold. Next, we determined the range of _eps_ using the maximum and minimum values of these points. Finally, we selected the appropriate step size to cluster within the defined range and obtained the most suitable _eps_ value for the current data according to the clustering effect. For example, Figure 7 shows the reachability plot generated for the ArrayList class, where the horizontal axis represents each feature value and the vertical axis stands for the reachability distance of the current feature value with reference to the previous one. For this class, we selected the range of _eps_ from 0.3 to 4.3, with _eps_ increasing by 0.5 for each cluster.  
The MCLSM uses the LTL formulas to guide Algorithm 2 to refine the FSA. When obtaining the LTL formula using Texada, we set the confidence thresholds in Texada [6] to 1.0 as we were only interested in properties that were never falsified. In the above process, we obtained a total of 94 LTL formulas, and these LTL formulas were used to refine FSAs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Library Class1** & **M** & **Generated Test Case** & **Recorded Method Calls** \\ \hline Hashtable & 8 & 79,403 & 89,811 \\ Signature & 5 & 79,096 & 205,386 \\ Socket & 21 & 80,035 & 130,876 \\ ZipOutputStream & 5 & 162,971 & 43,626 \\ SringTokenizer & 5 & 148,649 & 336,924 \\ StackAr & 7 & 549,648 & 132,826 \\ NFST & 5 & 158,998 & 95,149 \\ ToHTMLStream & 17 & 103,562 & 278,631 \\ SMTPProtocol & 15 & 57,281 & 136,271 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 7: Reachability plot generated by clustering algorithm.
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 6 Computational Results
### MILP Formulations Results
#### 6.2.1 Solving CG Sub-Problems
Note that for a given CG iteration, the computation efficiency of sub-problems i significantly varies following the number of jobs that the technician considered in a sub-problem can realize. At the end of the CG algorithm, it tends to solve quickly the sub-problems with the less jobs, proving that no column of negative reduced cost exist, and the sub-problems with remaining columns to add are the sub-problems with the maximal number of jobs with the competence constraint. With the different nature of MILP optimization searching for columns with negative reduced costs, the differences between the FGG formulation and the most compact MILP formulation are emphasized, with larger gaps in the quality of LP relaxations. In order to provide dual bounds of sub-problems, as in Section 5.7, it is better to use the FGG formulation. For using the primal heuristics and solving in truncated time in matheuristics, the most compact formulation (1)-(8) is more efficient. Note that the efficiency of the sub tour cuts is lessened (10). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **(1)–(8)** & **(1)–(8)** & **(12)–(19)** & **(12)–(19)** \\  & **No Cuts** & **+(10)** & **No Cuts** & **+(21)** \\ \hline
4\_1\_20\_ex4 & 34.7 & 7.6 & 333.1 & 601.2 \\
4\_3\_20\_ex4 & 45.4 & 19.6 & 799.3 & 121.2 \\
4\_5\_20\_ex4 & 14.6 & 5.4 & 35.0 & 27.0 \\ \hline
5\_3\_20\_ex1 & 131.5 & 83.4 & \(>\)1200 & 955.5 \\
5\_3\_20\_ex2 & 21.3 & 24.9 & 224.8 & 86.2 \\
5\_3\_20\_ex3 & 34.7 & 14.4 & \(>\)1200 & 647.3 \\
5\_3\_20\_ex4 & 48.1 & 20.4 & 259.9 & 223.8 \\ \hline
5\_5\_20\_ex1 & 4.3 & 5.4 & 161.6 & 36.1 \\
5\_5\_20\_ex2 & 5.5 & 2.6 & 34.0 & 3.0 \\
5\_5\_20\_ex3 & 6.2 & 5.8 & 118.4 & 101.9 \\
5\_5\_20\_ex4 & 7.5 & 4.1 & 40.4 & 29.2 \\ \hline
8\_5\_20\_ex1 & 8.4 & 6.1 & 81.4 & 12.5 \\
8\_5\_20\_ex2 & 1.0 & 0.7 & 0.4 & 0.4 \\
8\_5\_20\_ex3 & 2.5 & 0.8 & 1.7 & 1.9 \\
8\_5\_20\_ex4 & 5.3 & 0.9 & 0.8 & 1.8 \\
8\_5\_20\_ex5 & 2.1 & 1.0 & 5.7 & 3.6 \\ \hline
15\_20\_40\_ex1 & 46.2 & 7.5 & 408.7 & 89.1 \\
15\_20\_40\_ex2 & 8.3 & 3.0 & 22.4 & 15.0 \\
15\_20\_40\_ex3 & 15.6 & 10.6 & 53.9 & 31.2 \\
15\_20\_40\_ex4 & 58.8 & 15.7 & 108.3 & 35.6 \\
15\_20\_40\_ex5 & 114.5 & 15.2 & 114.4 & 58.6 \\
15\_20\_40\_ex6 & 6.9 & 3.7 & 4.7 & 5.2 \\
15\_20\_40\_ex8 & 7.3 & 1.5 & 3.3 & 2.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of computation time (in seconds) of both compact MILP formulations of Section 3, with or without the 2-sub-tour cuts.
## 3 Materials and Methods
### Prototype Development
#### 3.3.1 Hardware  
The hardware block diagram is shown in Figure 5. The central processing unit is a single-board computer (Raspberry Pi (RPi) v3) [35]. It has a 1.2 GHz 64-bit ARMv8 quad-core microprocessor, 1 GB RAM, micro SD card slot supporting up to 32 GB, LCD interface (DSI), onboard BLE and Wi-Fi module, and other built-in peripheral hardware. Since image processing requires high memory and processing speed [36], the RPi is selected from other microcontrollers such as Advanced Virtual RISC (AVR) or Peripheral Interface Controller (PIC) that has lower memory and speed. Using the DSI and I2C port, a 7\" capacitive touch LCD [37] is interfaced with the RPi. The LCD has a 24-bit color depth and 800 \(\times\) 480 pixel screen resolution.  
An RPi HD camera module [38] is attached through a 4 mm hole near the center of the prototype microwave oven roof. The focal length is set to the height of the cavity of the oven which is 20 cm to get a sharper image. A FLIR thermal camera [28] is mounted near the center of the roof of the oven through a 4-mm hole. The thermal camera is interfaced with the RPi using Serial Peripheral Interface (SPI) protocol. To produce sounds, a speaker [39] is connected to the RPi board using a 3.5 mm stereo jack. One of the oven's door's switch is connected to an RPi interrupt pin, and the other door switch is connected to the AC circuit path so that the current can only flow when the door is closed. A daylight white LED bulb [40] is connected via relay contacts [41]. It was covered by stainless steel wire mesh which was connected with ground to shield the LED bulb from the microwave. For microwave generation, a Solid-State Relay (SSR) [42] is used to toggle power in the magnetron circuit. The RPi board and display are powered by means of a 110V AC to 5.1V DC adapter [43]. The RPi board produces 3.3 V and 5 V DC, with which the RPi HD camera, FLIR thermal camera, relay and speaker are powered.  
In this research, the two cameras are placed on the microwave oven's roof, where the cameras can sense the light and the heat through two small 4-mm holes. Thus, their bodies are not directly exposed to the microwaves, except for the lenses. The operating temperature of the thermal camera is \(-\)10 \({}^{\circ}\)C to \(+\)80 \({}^{\circ}\)C and it can measure temperature from \(-\)10 \({}^{\circ}\)C to \(+\)140 \({}^{\circ}\)C [28]. The RPi camera, used to capture the color image of the food for classification, can operate from \(-\)20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C [38].  
Figure 5: Block diagram of the hardware.
# Optimization of Transmission X-ray Target for Intense Pulsed Electron Beam Accelerators
## 2 Materials and Methods  
### IPEB Energy Spectrum Analysis  
As the MC method used in the particle transportation simulation essentially gives results on each primary electron, to provide a proper estimation of the output X-ray fluence and dose by IPEB, it is necessary to obtain the IPEB spectrum for reasonable MC calculation input. The IPEBs generated on the BIPPAB-450 pulsed particle accelerator [26] were taken for electron spectrum analysis. This facility generates pulsed high voltage with amplitude up to 250 kV with a magneto generator based on a pulsed transformer, and then the pulsed voltage is modulated by a Blumlein transforming line, further multiplied to over 450 kV by a pulsed autotransformer, applied on a graphite cathode of the electron beam diode and triggers micro-explosives via field emission at micro-tips on the surface with a plasma sheath being formed. The electrons in the plasma are accelerated by the electric field formed by the pulsed high voltage, and in this way, a high current of kA order can be obtained. Under certain conditions, the plasma emitter can be regarded as a source with an unlimited electron supply, and the current emission is only confined by the space-charge effect in the accelerating gap. As the emission happens on the cathode surface in an area with a diameter of 3.5 cm, compared with an anode-cathode gap of several mm, the emission current can be simplified into a plane-parallel diode model well described by [PERSON]'s law (also widely mentioned as the three halves law) [27]:  
\[J = \frac{4\epsilon_{0}\sqrt{2}e}{9\sqrt{m_{e}}}\cdot\frac{U^{3/2}}{d}. \tag{1}\]  
in which \(J\) is the current density, \(\epsilon_{0}\) is the vacuum permittivity, \(e\) and \(m_{e}\) are the electron charge and mass, \(U\) is the accelerating voltage on the cathode, and \(d\) is the width of the anode-cathode gap.  
Assuming that the initial kinetic energy of the electrons in the plasma is much lower than after acceleration, i.e., can be taken as zero, and no prominent electron scattering in the anode-cathode gap (corresponding to high vacuum), by applying [PERSON]'s law the correspondence between the accelerating voltage and the IPEB energy spectrum can be established. From the acquired working voltage of the IPEB diode (Figure 1), the IPEB spectrum can be deduced (Figure 2). Most of the electrons are distributed around 300 keV and over 370 keV. In the MC simulation, this energy spectrum is normalized to a probability density distribution for the sampling of the source particle. For the total number of electrons in one pulse, the beam current can be approximated as a triangular pulse with a typical peak value of 2.5 kA and full pulse length of 320 ns, and thus the number of electrons in one pulse is 2.5 \(\times\) 10\({}^{15}\).  
Figure 1: Typical oscilloscope curves of the accelerating gap voltage and beam current of the IPEB diode.
## 4 Results and Discussion
### Estimation of Water Demand between 2020 and 2029
#### 4.6.2 Tourist Water Demand
Recently, many projects have been initiated in Phuket Island to further its development, such as enhancing its status as a world-class tourism destination; making it a Smart city and MICE city; and building infrastructure (Phuket Airport expansion, light rail transit, and underpass), international schools, medical hub, international exhibition and conference \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Number of Tourists (People)**} & \multicolumn{3}{c}{**Tourist Water Demand (m3)**} \\ \cline{2-7}  & **Tourist** & **Excursionist** & **Total** & **Tourist** & **Excursionist** & **Total** \\ \hline
2020 & 3,764,171 & 239,119 & 4,003,290 & 4,517,005 & 7174 & 4,524,179 \\
2021 & 6,216,685 & 342,725 & 6,559,410 & 7,460,022 & 10,282 & 7,470,303 \\
2022 & 8,979,656 & 495,047 & 9,474,703 & 10,775,587 & 14,851 & 10,790,438 \\
2023 & 11,742,627 & 647,370 & 12,389,996 & 14,091,152 & 19,421 & 14,110,573 \\
2024 & 17,493,473 & 922,615 & 18,416,087 & 20,992,167 & 27,678 & 21,019,845 \\
2025 & 18,045,498 & 949,648 & 18,995,145 & 21,654,597 & 28,489 & 21,683,087 \\
2026 & 18,728,566 & 978,676 & 19,707,243 & 22,474,279 & 29,360 & 22,503,640 \\
2027 & 19,366,852 & 1,006,502 & 20,373,354 & 23,240,229 & 30,195 & 23,270,418 \\
2028 & 20,009,937 & 1,036,790 & 21,046,726 & 24,011,924 & 31,104 & 24,043,028 \\
2029 & 20,604,525 & 1,066,583 & 21,671,107 & 24,725,430 & 31,997 & 24,757,427 \\ \hline Average & 14,495,199 & 768,507 & 15,263,706 & 17,394,239 & 23,055 & 17,417,294 \\ \hline \hline \end{tabular} Note: Tourist water demand was calculated based on an average length of stay (day) by four days (the average between 2015 and 2019) from the TAT Intelligence Center, Tourism Authority of Thailand [3].  
\end{table} 
Table 22: The number of tourists and water demand between 2020 and 2029 under new normal conditions. Figure 14: Simple linear regression analysis between tourism growth and urban expansion in Phuket Island between 2020 and 2029: (**a**) normal condition; (**b**) new normal condition (COVID-19 pandemic). centers, and shopping malls [71]. This continuous development will lead to the expected LULC change and tourism growth over a short period.
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
## 1 Introduction
[38] demonstrated that TCN convincingly outperformed RNNs on various sequence modeling tasks. In this paper, we present a vibration-based method to estimate axle weights using DOVS and TCN. We first utilize a DOVS-based vibration sensing system and a data preprocessing process to acquire pavement vibration signal datasets. Then, we propose a deep learning-based method to reconstruct and estimate axle loads. To validate our method, we performed a series of experiments that tested the performance of estimating load with various load conditions and cement concrete pavement. The main innovations of this paper are as follows:  
* A pavement vibration acquisition method based on distributed optical vibration sensors (DOVS) is developed;
* A deep learning-based load reconstruction method tailored for pavement vibration data collected by DOVS is proposed. The remainder of this paper is organized as follows: First, a methodology is developed in Section 2, including data acquisition, schematics of algorithms, training strategies, and performance metrics. Second, conducted field tests are described in Section 3. The DOVS-based vibration sensing systems were employed in two different cases and other sensors were utilized to measure the ground truth of axle weights. In Section 4, the results of different traffic loads and pavement structures are compared and discussed.
## 3 Intelligent Jamming
### Minimal/Maximal Output on DL LTE Technology in 800 MHz Band  
Radio measurements were undertaken for the downlink connection, and the range for received signal level was found to be between \(-50\) and \(-80\) dBm neighbouring the cell (approximately \(200\) m), which corresponded to a good signal level, and \(-80\) to \(-100\) dBm at the edge of the cell coverage area [14].  
The minimal and maximal output power levels of the downlink of a wireless communication system can indeed vary depending on the technology and the specific frequency band or bandwidth being used:  
* Minimal output power: LTE devices can have minimal output powers in the range of \(-10\) to \(-13\) dBm, especially for low-power modes.
* Maximal output power: the maximum output power for LTE devices can range from \(-23\) to \(-33\) dBm or more, depending on the frequency band and the specific LTE category.
## 4 Results and Discussion
### Results
#### 4.3.2 The Weakest Nodes  
The weak nodes of the ADN have been represented in Table 2 through the probability of every bus not meeting the total electricity load in a period of 72 h. i.e., for a scenario of 72 h, if, in every hour of this scenario, a certain bus does not supply the entire energy demand, and its probability of default is 100%. The first column indicates the buses, where bus 1 has not been considered because it does not have a load at any time. The following four columns indicate the different scenarios of 72 h considered, and the column _Total_ shows the probability of every bus for ENS considering a 288 h period, i.e., assuming the four scenarios as a set of consecutive hours. The remaining columns possess the same meaning, but considering the DSR version model, and the last column _DERs installed_ indicates whether the bus possesses or is next to a DER.  
Therefore, following the results presented in the total columns, the buses with a DG or BESS installed have a lower probability of default when the weather conditions are not proper to generate enough power to meet demand with the capacity installed, and the support of the utility grid is not guaranteed. Specifically, the buses with WT installed are the ones that have the lowest probability of default followed by the nodes with PV panels and the nodes with a storage system. Moreover, as the objective function (1) and (24) minimize the energy flow through the lines when there is non-compliance, the nodes which are furthermost of the buses with a DER system, are the points with the highest probability to present load curtailment, as is the case of the upper section of the DN test system {23, 24, 25} and the lower section {19, 20, 21, 22}.  
The first two scenarios present the highest probability of ENS with or without DSR. The results are expected because they are the scenarios with more consecutive hours with low power generation (see Figure 3), in contrast with scenarios 3 and 4 which have a better power generation available. However, even though the ENS trend is the same, the probability decreases significantly in some buses when the DSR is included in the model. The clearest example is the case of scenario 4, where the ENS probability is zero for every bus when the DSR is considered.  
Figure 5: Scenario 4: SOC of batteries vs. load, and wind/irradiance resources available.
# Industrial Dust Explosions. A Brief Review
## 1 Brief Review of Dust Explosion Basics
### Materials That Can Give Dust Explosions  
\[\text{fuel}+\text{oxygen}\rightarrow\text{oxides}+\text{heat}\]  
Most often, the rapid energy that is released in a dust explosion is governed by the chemical reaction:  
Therefore, only materials that are not already stable oxides can give dust explosions. This excludes silicates, sulphates, nitrates, carbonates, and phosphates, and, therefore, dust clouds of Portland cement, sand, limestone, etc. cannot give dust explosions. The main four groups of materials that can give such explosions are:  
* natural organic materials (grain, wood, linen, sugar, etc.);
* synthetic organic materials (plastics, organic pigments, pesticides, pharmaceuticals, etc.);
* coal and peat; and,metals (aluminium, magnesium, titanium, zinc, iron, etc.).
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## Appendix A
### Statistical Analysis of Location Deviation  
To analyze the temporal evolution of location deviation and its variability under jamming conditions, we calculate the mean and standard deviation of the corrected deviations across all measurement trials at each discrete time point \(t\). Due to the varying lengths of the measurement trials, only trials with at least \(t\) samples are considered for the statistical analysis at that specific time point. Let \(S_{t}=\{i\ |\ T_{i}\geq t\}\) be the set of indices of trials containing a sample at time \(t\).  
The mean location deviation at time \(t\), denoted by \(\mu_{t}\), is computed as the average of the corrected deviation values across all relevant trials:  
\[\mu_{t}=\frac{\sum_{i\in S_{t}}\mathit{Dev}\prime_{i,j}}{|S_{t}|}. \tag{10}\]  
The temporal variability of the location deviation is quantified by the standard deviation \(\sigma_{t}\), calculated using Bessel's correction to provide an unbiased estimate of the population standard deviation:  
\[\sigma_{t}=\sqrt{\frac{\sum_{i\in S_{t}}\left(\mathit{Dev}\prime_{i,j}-\mu_{t }\right)^{2}}{|S_{t}|-1}}. \tag{11}\]
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 3 Materials and Methods
### Evaluation Metrics  
To assess the performance of each neural network classifier, we use accuracy (Acc), sensitivity (Se), specificity (Sp), area under curve (AUC), and time consumption (Tc). Acc, Se, and Sp are crucial clinical parameters that serve as a reference for us when we evaluate model performance. Acc is defined as the proportion of correctly classified samples to all samples. Se represents the classifier's ability to identify patients. Sp denotes the classifier's ability to identify healthy individuals. AUC is used to evaluate the model's performance with unbalanced data, which is one of our primary metrics for assessing model performance. Tc is used to evaluate how much computer resources each neural network consumes.  
\[\text{Acc}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{FN}+\text{TN}+\text{FP}} \tag{8}\] \[\text{Se}= \frac{\text{TP}}{\text{TP}+\text{FN}}\] (9) \[\text{Sp}= \frac{\text{TN}}{\text{TN}+\text{FP}} \tag{10}\]  
where, TP is a true positive signal, FN is a false negative signal, TN is a true negative signal, and FP is a false positive signal.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 5 Experiments
### Results of the Coarse Localization Stage
#### 5.1.2 Results of the Classification CNN  
Once the training is completed by using the augmented cloudy dataset, the resultant CNN was evaluated and the accuracy reached was 98.11% (i.e., percentage of times that the trained CNN retrieves correctly the room from which the input image was captured). Figure 2 shows the confusion matrix obtained. From this figure, the conclusion reached is that despite the fact that some errors appear, they are mostly due to confusions with images captured in transition areas between different rooms. For example, images from the corridor are retrieved as printer area, office and stairs area (which are adjacent to the corridor), but they are never predicted as toilet (which is totally disconnected from the corridor). Nonetheless, 21 images from the office room were retrieved as printer area despite the fact that those rooms are not adjacent. This may be due to the appearance similarity between them. Despite these few mistakes, the hit rate is significantly high. The high accuracy rate is noticed more clearly in the trajectory maps shown in Figure 3. First, Figure 3a shows the capture point of each test image with blue color, in case that the CNN correctly retrieves the room and with red color in case of wrong retrieval. Second, Figure 3b shows these capture points with different colors that indicate the room that the CNN has retrieved for each test image. Hence, from these results, the conclusion is that the CNN is properly trained to tackle the room retrieval task.  
Figure 2: Confusion matrix of the room retrieval classification. These results were obtained by using the trained CNN and the augmented dataset of images captured with cloudy illumination. The rows define the real room of the images and the columns the prediction addressed by the network.