esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The increment of the values can be justified with the same reasoning used in the Figure 7. If the data are analyzed at a single energy, the same order of values is obtained once again, with S5 demonstrating the lowest values and Pb-S1 demonstrating the greatest. It is important to observe, however, that there is a higher discrepancy between the values at 80 keV than at 20 keV. At 20 keV, the difference between the highest and lowest TVL values (Pb-S1 and Pb-S5) is equal to 0.004 cm, while the difference at 80 keV is 0.152 cm. The fact that the difference is much greater at 80 keV highlights the superior shielding ability of the Pb-S5 glass. At the same time though, if the glasses are being used against radiation energies of 20 keV, the discrepancy between the shielding ab  
Figure 7: The HVL for the Pb-SX glasses at four energies (20, 40, 60 and 80 keV). practically negligible. The advantage of the Pb-S5 glass over the other samples can only be observed as energy further increases.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 4 Multi-Carrier Transmission Schemes with Dimming Control
### Aco-Ofdm
#### 4.2.1 PD Receivers  
ACO-OFDM's received signal can be obtained in a similar manner to DCO-OFDM, taking into consideration its different frame structure. Therefore, the SNR of the \(k\)th subcarrier for ACO-OFDM can be expressed as [34]  
\[\Phi[k]^{\text{ACO}}=\frac{2\eta_{\text{ACO}}^{2}|H[k]|^{2}}{\sigma_{\text{ clip}}^{2}|H[k]|^{2}+\sigma_{\text{rx}}^{2}}, \tag{48}\]  
The attenuation factor and clipping noise variance are given, respectively, by [34; 46]  
\[\eta_{\text{ACO}}=\frac{1}{2}-Q(\kappa), \tag{49}\] \[\sigma_{\text{clip,ACO}}^{2}=\kappa^{2}Q(\kappa)-\kappa\phi( \kappa)+Q(\kappa)-2Q(\kappa)^{2}, \tag{50}\]
## 4 Met-Ocean Strategy for Assessing the Feasibility of Marine Renewable Energy near Ports
### Bathymetric, Port Contour/Typologies, Instrumental Data, and Forcing Used  
One of the fundamental and crucial tasks in developing the operational agitation system, which paves the way for a proper setup of the numerical strategy, is related to the concept of data normalization. Instrumental data from field campaigns or permanent measurement networks of Puertos del Estado, were initially provided in a free format (ASCII). Data regarding port section typologies include vital information about the various typologies that comprise each port's perimeters, structures, docks, and protective elements. Spectral forcing data, obtained from previous numerical executions under the coverage of the regional SAPO system of the Puertos del Estado, were provided as spectral wave files in the predefined format of the SWAN wave propagation model. Data about the general port geometry were aligned with the updated reality of each port facility. Bathymetric data standardized within a Geographic Information System (GIS) platform in native ArcGIS\({}^{\circledR}\) formats defining the final port geometry (port perimeter) were considered for each study port (Sagunto, Valencia, and Gandia), see Figure 2.  
Figure 2: Valencia, Sagunto, and Gandia ports’ numerical domain and outer forcing locations along with numerical domain and bathymetry definition. The numbers in each image indicate the identification of the control points chosen in the model **(a)** Port of Valencia; **(b)** Port of Sagunto; **(c)** Port of Gandia.
## 5 Implementation and Comparison
### Description of the 50 Benchmarks  
The first set (F1-F25) of 50 benchmark functions, as shown in Table A3, include various functions, such as \"Steptint\", \"Step\", \"Sphere\", \"SumSquares\", \"Quartic\", and \"Beale\", and primarily feature unimodal and separable characteristics. These functions typically involve simple algebraic formulas, which make them useful for evaluating basic aspects of optimization algorithms, such as convergence rate and precision. For example, the \"Steptint\" and \"Step\" functions, with their use of the floor operation, challenge an algorithm's ability to handle discontinuities within a defined range. The \"Sphere\" and \"SumSquares\" functions are quintessential for testing an algorithm's performance in handling different scales of variables due to their simple, sum-of-squares form. The inclusion of a random component in the \"Quartic\" function adds a stochastic element to the evaluation, providing insights into how algorithms cope with noise [35].  
Furthermore, the second set (F26-F50) also includes benchmarks in Tables A4 and A5 and has complexity with functions like \"Booth\", \"Rastrigin\", \"Schwefel\", and \"Michalewicz\" models, which introduce multimodal characteristics and often non-separable dimensions. These functions are designed to test more advanced capabilities of optimization algorithms, such as escaping local minima and exploring multiple basins of attraction effectively. The \"Rastrigin\" function, for instance, is notorious for its large number of local minima, making it an excellent testbed for an algorithm's global search capabilities. The \"Michalewicz\" function, varying with dimensions, specifically challenges an algorithm's ability to solve problems that become progressively difficult as dimensions increase. Functions like \"Schwefel\" and \"Six Hump Camel Back\" further emphasize handling irregularities and asymmetries in the search space [36].
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 5 Conclusions  
In this paper, a novel multiple junction selection based routing protocol (DMJSR) is proposed. We first presented the major problems faced by some of the already well-known protocols in VANETs. Then we explained our new proposed protocol and explained how it overcomes the limitations faced by existing protocols. DMJSR selects two junctions at a time based on traffic density and shortest curve metric distance to the destination. DMJSR employs a novel multiple junction approach, an improved greedy forwarding strategy based on one-hop neighbor information to relay packets in between junctions, and a recovery strategy, which can incorporate urban surroundings challenges in a better way. The simulation results indicated that our proposed protocol convincingly outperforms TFOR, E-GyTAR, and GSR in terms of packet delivery ratio end-to-end delay because of its ability to incorporate in better ways the main challenges faced in the city environments as compared to the existing routing protocols.  
This work is fully funded by the Research and Innovation Management Center University of Malaysia Sarawak (RIMC-UNIMAS) under the grant number F08/SpSG/1403/16/4.  
**Author Contributions:** [PERSON] is the main author of this manuscript. All the authors have contributed to this manuscript. All authors have read and approved the final manuscript. [PERSON] conceived the novel idea and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed materials/analysis tools; [PERSON] wrote the entire paper. [PERSON], [PERSON] and [PERSON] checked, reviewed and revised the paper. [PERSON] accomplished final proofreading. [PERSON] supervised the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Study Limitations  
In this study, we grouped the individuals who performed similar work but not random independent participants as samples; therefore, this grouping could introduce a potential bias. The study did not consider the participants with RSP and HSP ailments; perhaps this may affect the mechanical parameters of muscles. Furthermore, only men were included as a part of the study, and in future comparisons, it may well be worth including women as part of a wider group. In addition, a relatively small sample size of the study could contribute to the type II statistical error.  
Lastly, while angle measurement is widespread, this is not the only or best way to evaluate postures. As a result, it would be desirable to widen the research to consider classification-based criteria, such as reference points collected from upper body X-rays.
## 2 Models and Methods
### Mathematical Model
#### 2.2.4 Solution Methods  
The temperature distribution inside the stacked product packages was simulated by means of the Fluent module of the commercial software package ANSYS Workbench, which uses the finite-volume method for numerical solving the governing equations of fluid dynamics, similarly to previous research [16]. The objective was to investigate the freezing completion effect within a product residence time of 24 h. The time step was 1800 s with 20 iterations per step. The k-\(\epsilon\) turbulence model was adopted. The type of the solver was Pressure-Based [16]. Discrete formats for momentum, energy, turbulent kinetic energy and turbulent dissipation rate were First Order Upwind [16]. The scheme of pressure-velocity coupling was SIMPLE [16].
## 2 Materials and Methods
### Box-Behnken Plan and Statistical Analyses  
In this study, a three-level, three-factor Box-Behnken plan was implemented using Design-Expert v13 software from Stat-Ease in Minneapolis, MI, USA. The independent variables consisted of extraction time (\(X_{1}\)), ultrasound amplitude (\(X_{2}\)), and solvent concentration (\(X_{3}\)), while the dependent variables included total phenolic content (TPC), antioxidant activity measured using DPPH, FRAP, and ABTS assays, and pH. The experiment comprised a total of 15 randomly conducted combinations, including 3 center points utilised for estimating the pure error [20]. The levels of the three factors evaluated in the experiment are shown in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Run** & **Times** & **Ultrasound Amplitude** & **Solvent Concentration** \\ \hline
1. & 5 & 12 & 60 \\ \hline
2. & 5 & 24 & 30 \\ \hline
3. & 5 & 24 & 90 \\ \hline
4. & 5 & 36 & 60 \\ \hline
5. & 10 & 12 & 30 \\ \hline
6. & 10 & 12 & 90 \\ \hline
7. & 10 & 24 & 60 \\ \hline
8. & 10 & 24 & 60 \\ \hline
9. & 10 & 24 & 60 \\ \hline
10. & 10 & 36 & 30 \\ \hline
11. & 10 & 36 & 90 \\ \hline
12. & 15 & 12 & 60 \\ \hline
13. & 15 & 24 & 30 \\ \hline
14. & 15 & 24 & 90 \\ \hline
15. & 15 & 36 & 60 \\ \hline \end{tabular}
\end{table}
Table 1: The levels of the Box–Behnken plan.  
A second-order polynomial model was employed to demonstrate the impact of the independent variables on the response variable using the following equation [20].  
\[Y=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\beta_{3}X_{3}+\beta_{11}X_{1}^{2}+ \beta_{22}X_{2}^{2}+\beta_{33}X_{3}^{2}+\beta_{12}X_{1}X_{2}+\beta_{13}X_{1}X_{3 }+\beta_{23}X_{2}X_{3}\]  
In the provided equation, Y represents the response variables, including TPC, ABTS value, DPPH value, FRAP value, and pH. The independent variables are denoted as \(X_{1}\), \(X_{2}\), and \(X_{3}\). The equation includes the constant term \(\beta_{0}\), as well as the linear (\(\beta_{1,2,3}\)), quadratic (\(\beta_{11,22,33}\)), and interactive (\(\beta_{12,13,23}\)) coefficients [20].  
The statistical analysis was performed in ANOVA. The significance of the regression coefficient was assessed using an F-test, considering \(p\)-values below 0.05 as statistically significant. The best conditions were determined using Derringer's desirability function, aiming to maximise the response for each independent variable. The validity of the developed model was evaluated by comparing the predicted values with the actual experimental results [20].
# Chemical Analysis of Gunpowder and Gunshot Residues
## 1 Introduction  
Security is at the forefront of public policy, being one of the primary concerns of law enforcement agencies across the globe. A widely recognised threat to safety is firearms [1; 2], as their use or misuse is responsible for many deaths yearly. For example, in 2019, there were 250,000 estimated deaths related to firearms worldwide, of which approximately 177,000 resulted from physical violence, 20,000 from unintentional firearm cases, and 53,000 from self-harm. In the USA alone, in 2019, there were approximately 37,000 deaths associated with firearms, of which 13,000 resulted from physical violence, about 650 from unintentional firearm cases, and 23,000 from self-harm. By comparison, in the European Union (EU), where the purchase of firearms is heavily regulated [3], in the same year, there were almost 6500 deaths associated with firearms, of which around 1000 resulted from physical violence by firearms, 380 from unintentional firearm cases, and 5000 from self-harm by firearms [4]. The sheer number of occurrences worldwide emphasises the need for a reliable framework for the forensic analysis of evidence derived from firearms use.  
Ballistics is the discipline dedicated to study of the movement and behaviour of projectiles through the air. This study may focus on different parts of the projectile trajectory,allowing the division of this discipline into internal ballistics, external ballistics and terminal ballistics [5; 6; 7; 8]. The forensic analysis of firearms relies primarily on forensic ballistics, which comprehends the visual examination of engraved or printed marks on firearm-related elements, such as those on projectiles and casings, and subsequent comparison against samples obtained from a suspect firearm or references in databases [5; 6; 7; 8; 9]. Whenever this physical analysis is not possible, the chemical analysis of gunshot residue (GSR) may assist in the investigation since it provides additional information that can provide the basis for estimating the firing distance and time since discharge, or even the identification of the corresponding model and manufacturer of the ammunition [5; 10; 11], which can ultimately lead to the identification of the suspect [11; 12; 13; 14; 15; 16; 17; 18; 19].  
The current contribution reviews the composition of primer and gunpowder used in modern ammunitions and the chemical methodologies currently used for detecting, characterising, and identifying their compounds and elements. The present contribution will explore how the information that these methodologies provide can help in firearm identification and how these data drive the development of novel applications within forensic ballistics, particularly when associated with complex data analysis and statistical modelling. However, sample collection procedures will not be explored. For a detailed review on that topic, please refer to [PERSON] et al. [14] or [PERSON] et al. [15].
###### Abstract  
This paper reports the results of a systematic investigation into the magnetic properties of Bi\({}_{1-y}\)Ca\({}_{y}\)Fe\({}_{1-x}\)Mn\({}_{x}\)O\({}_{3}\) (\(0.1\leq y\leq 0.175\), \(0.35\leq x\leq 0.45\)) solid solutions. The substitution of Bi\({}^{3+}\) with Ca\({}^{2+}\) and the concurrent introduction of Mn\({}^{3+}\)/Mn\({}^{4+}\) ions result in the stabilization of various structural phases, with each exhibiting distinct magnetic characteristics. The investigation indicates that the samples containing the polar rhombohedral phase display metamagnetic transitions at low temperatures, characterized by pronounced jumps in magnetization. Single-phase samples with a nonpolar orthorhombic structure exhibit weak ferromagnetic behavior without metamagnetic features. The observed metamagnetic behavior, accompanied by anomalies in temperature-dependent magnetization and significant remnant magnetization at low temperatures, particularly in samples near the polar-anti(non)polar phase boundary, highlighted the presence of both antiferromagnetic and glassy magnetic components.  
BiFeO\({}_{3}\) magnetic properties; multiferroics; perovskites +
Footnote †: journal: Applied sciences
## 4 Results and Discussion
### Electrode Radius Effect  
The geometry of the electrodes and the aspect ratio between them will be determining factors to analyze the effect of the high-voltage electrode diameter. The radius of the corona electrode must be smaller compared to the collector electrode, achieving a greater intensity of the electric field around the emitting electrode and a discharge at lower inception voltages. For a positive corona discharge for air at standard conditions, from the Peek's law [37] the critical electric field \(\mathrm{E_{e}}\) can be specified on the effective surface of the emitting electrode:  
\[E_{e}=E_{o}(1+\frac{2.62\,\times\,10^{-2}}{\sqrt{Re}}) \tag{9}\]  
where \(E_{o}\) = 3.31 \(\times\) 10\({}^{6}\) V/m is the breakdown electric strength of air and the corona electrode radius Re is measured in meters. Figure 7 shows the detailed C-V curves for wires of different material (tungsten and stainless steel), with radii of 12.5, 25 and 50 microns and a separation between electrodes of 2 mm. It is important to emphasize that the selection of the material does not have a remarkable effect on the generation of the corona discharge, as several authors have analyzed [5; 38], but it is interesting to consider it to study the useful life of the device. As the radius of curvature of the corona electrode is increased, the C-V curve shifts towards higher voltages. In some industrial applications where the operating voltage is limited, it is necessary to consider lower voltages for devices based on corona discharge technology and use small electrode radii.  
Different heat-transfer coefficients h is obtained for thinner electrodes, see Figure 8. A bigger diameter seems to be more efficient in power consumption, but it requires higher voltages. A thicker corona electrode requires a higher voltage to start discharge, because of a lower concentration of electric field, see Figure 6, because of that, for the same power consumption, thinner electrodes will present a lower voltage, and acceleration of particles will be weaker. This means a higher air flow and an improvement in heat transfer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\xi\)(W/K) & **Gap 1.5 mm** & **Gap 2 mm** & **Gap 2.5 mm** \\ \hline diameter 25 um & 0.01 & 0.014 & 0.016 \\ diameter 50 um & 0.02 & 0.025 & 0.034 \\ diameter 100 um & 0.027 & 0.03 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variation of the power device with temperature parameter for different gaps and electrode diameters.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Climate Characteristics of Rainfall
#### 5.3.5 Other Studies  
A few other studies statistically analyzed rain types, warm rain systems, and rainfall morphology and some other aspects over the entire tropics. The conclusions of these individual works were expected to be verified by more studies and new versions of PR products.  
[PERSON] and [PERSON] (2015) calculated the fractions of stratiform and convective rainfall. There was an average of 40% for stratiform rainfall out of the total rainfall amounts, and the maximum stratiform rainfall fraction of about 60% occurred over the eastern-central Pacific while the minimum of about 25% occurred over the maritime continent. The stratiform rainfall fraction was generally larger over oceans than over land, and was generally larger with higher total rain amounts.  
[PERSON] and [PERSON] (2015) analyzed the characteristics of the warm rain. They found that the warm rain contributed 20% of the total rainfall amount over tropical oceans and 7.5% over tropical land areas. Large amounts of warm rainfall occurred over oceans near windward coasts during winters. Most of the warm rain systems were of small size (<100 km\({}^{2}\)) and had weak radar echoes (with maximum reflectivity of about 23 dBZ).  
[PERSON] et al. (2015) analyzed the rainfall morphology. They found that the rainfall from oceanic systems was dominated by small and large horizontal extents while the rainfall from continental systems was dominated by a midsized horizontal extent. Continental rainfall systems were more vertically developed compared with oceanic rainfall systems.  
[PERSON] et al. (2015) defined a concept of regional extreme rainfall and analyzed its geographical distribution. The regional extreme rainfall tended to occur late at night and in the morning over oceans while tended to occur in the afternoon over land. The intense and extensive extreme rainfall was mainly over oceans near coastal areas, the intense but less extensive extreme rainfall was mainly over land, and the extensive but less intense extreme rainfall was almost over oceans.  
[PERSON] et al. (2015) investigated the high-resolution spatial variability of precipitation climatology and verified the relationship of rainfall features to orography and geography. They found that storm-scale precipitation was remarkable for the rainfall enhancement over small islands while large-scale precipitation resulted in more rainfall over the adjacent ocean.
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 3 The Construction Process of the Synthesis Health Index for Metro Vehicle Wheelsets
### Performance Evaluation of the Comprehensive Health Index for Subway Vehicle Wheelsets  
The constructed SHI for subway vehicle wheelsets is typically evaluated comprehensively across several aspects: monotonicity, trend, robustness, and identifiability [3]. Among these aspects, the formulas for monotonicity, trend, and robustness align with those used in feature selection; thus, they will not be elaborated upon further. The following section will focus on explaining identifiability.  
During the RtF process, subway vehicle wheelsets are classified into several distinct health states. An effective SHI should be capable of identifying these varying health states. This capability is defined as identifiability, which measures the correlation between the SHI and the health states of subway vehicle wheelsets. Identifiability is quantified by the correlation coefficient between the SHI sequence and the health state sequence [3]:  
\[I_{s,h}=\frac{\sum_{i=1}^{m}(s_{i}-\overline{s})\times\big{(}h_{i}-\overline{ h}\big{)}}{\sqrt{\sum_{i=1}^{m}(s_{i}-\overline{s})^{2}\times\big{(}h_{i}- \overline{h}\big{)}^{2}}} \tag{15}\]In this equation, \(s_{i}\) represents the label value of the SHI, \(h_{i}\) denotes the SHI value, while \(\overline{s}\) and \(\overline{h}\) are the means of \(s_{i}\) and \(h_{i}\), respectively.
## 2 Data and Methods
### Feature Importance Analysis  
A crucial aspect of evaluating a model is analyzing the importance of its features. This analysis enables us to identify elements that require optimization. To assess feature importance, we employ the permutation feature importance (PFI) approach [59]. In this study, we evaluate the impact of 25 input variables on model fitting by sequentially shuffling the variables in each of the 25 input channels and independently estimating the loss increase due to each shuffling. In this study, we examine the relative importance of 25 variables, calculated as the absolute importance divided by the sum of the absolute importance of all variables.  
The absolute feature importance is calculated using the following steps:  
(1) Denote the loss on the neural network test set without shuffling as loss1. Then, choose one variable and randomly disrupt the data in the neural network's input field of the test set corresponding to the channel of that variable. Finally, feed it into the neural network to calculate the loss after disruption, which is denoted as loss2. To determine the increase in loss, calculate the difference between loss2 and loss1, denoted as loss3. This step is referred to as 1 permutation.  
(2) Complete 500 permutations for this variable and calculate the mean value of the 500 loss3s. This mean value is referred to as the feature importance of this variable.