esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Color  
The optical properties of the freeze-dried pumpkin samples are presented in Table 1. The biggest changes in color parameters were observed when PEF treatment was carried out at energy intakes of 0.5 kJ/kg (PEF\({}_{2}\)) and higher, and it was the most visible for the \(a^{*}\) and \(b^{*}\) chromaticity coordinates. For instance, the \(a^{*}\) values of samples subjected to PEF\({}_{4}\) treatment and then freeze-dried at 40 and 60 \({}^{\circ}\)C were equal to 22.86 and 19.43, respectively. For comparison, the untreated freeze-dried sample exhibited redness values of 18.92 and 17.95 when the process was conducted at 40 and 60 \({}^{\circ}\)C, respectively. Similar observations can be made for the yellowness parameter (\(b^{*}\)). The application of PEF\({}_{1}\) did not cause any relevant, from a statistical point of view, changes in the redness and yellowness of the samples when compared to the reference material. Most PEF-pretreated samples also  
Figure 2: Residual moisture content (\(RM\)) of untreated and PEF-treated freeze-dried pumpkin depending on the specific energy input \(W_{p}\). Different heating plate temperatures of 40 and 60 \({}^{\circ}\)C have been used. \(a^{*}\) Different letters indicate significant statistical differences (Tukey’s HSD, \(p<0.05\), \(n=3\)).  
exhibited lower \(L^{*}\) values. Only the materials treated with PEF\({}_{1}\) demonstrated a brighter color than the untreated pumpkins, regardless of the freeze-drying temperature.  
The \(BI\) shows in a complex way the browning pattern of the investigated samples. Based on this parameter, it can also be stated that PEF treatment with the lowest energy input, regardless of the freeze-drying temperature, resulted in a less brown color in comparison to the reference pumpkins. However, the application of higher energy resulted in a browner color--the values of \(BI\) significantly correlated with energy input for samples dried at 40 \({}^{\circ}\)C (r = 0.911) and 60 \({}^{\circ}\)C (r = 0.885). Two-way ANOVA for average values showed that energy input had a bigger impact then temperature (\(\eta^{2}\) = 0.825 and 0.377, respectively) on the variability in the \(BI\), although the effect was not significant (\(p\) = 0.175 and 0.387) on the assumed level of confidence. Nevertheless, the samples dried at a higher temperature in general exhibited higher values of \(BI\), which may be attributed to the surface release of sugars and caramelization reactions which can also take place at lower temperatures between 45 and 65 \({}^{\circ}\)C [33], and to [PERSON]'s reaction, which may occur at a temperature of 60 \({}^{\circ}\)C [34]. The total color difference in the PEF-treated samples, as calculated regarding the untreated freeze-dried material dried at the same temperature, ranged from 0.17 to 5.53. What is interesting is that, when comparing the effect of freeze-drying temperature, in general \(\Delta E\) was higher for materials dehydrated at 40 \({}^{\circ}\)C, which means that the effect of PEF was more pronounced in that case.
# Study of the Influence of Heat Flow on the Time to Ignition of Spruce and Beech Wood
## 4 Conclusions  
The primary objective of the experimental measurement was to observe the flammability of samples using radiant heat flux, independent of any other ignition source.  
The conclusions from the experiment can be summarized as follows:  
* With a radiant panel power of 5W, flaming combustion occurred for samples of spruce and beech wood at all distances (50, 70, and 100 mm), for all heat flux values, except for the last sample of beech wood placed at a distance of 100 mm, corresponding to a heat flux value of 64 kW\(\cdot\)m\({}^{-2}\).  
Figure 13: Visual observations of the effects different heat fluxes on residues of beech wood samples after burning at a radiant panel power of 10 kW. (**a**) Thermally stressed beech sample with a heat flux of 91 kW\(\cdot\)m\({}^{-2}\) (after 16.2 s); (**b**) wood exposed to a heat flux of 62 kW\(\cdot\)m\({}^{-2}\) and (**c**) burning with flames.  
* In the experimental results, the ignition time for beech wood was approximately twice that of spruce wood, likely due to the higher average wood density (\(700.72\pm 21.9\) kg\(\cdot\)m\({}^{-3}\) for beech wood compared to \(421.7\pm 30.46\) kg\(\cdot\)m\({}^{-3}\) for spruce wood).
* With a radiant panel power of 10 W, flaming combustion was observed for samples of spruce and beech wood only at two distances (100 and 150 mm). At a distance of 200 mm (with a corresponding heat flux of 47 kW\(\cdot\)m\({}^{-2}\)), the samples did not undergo flaming combustion; however, the influence of heat flux was evidenced by non-flaming combustion.
* The statistical analysis, employing ANOVA followed by the Kruskal-Wallis test, examined variables including wood type, radiant panel output, distance, and heat flux in relation to ignition time. The analysis revealed a significant difference between ignition time and distance (\(p\)-value = 0.0000, H = 37.51583) as well as between ignition time and heat flux (\(p\)-value = 0.0000, H = 37.69726).
* Time-to-ignition values significantly depend on both the radiant panel output and the distance of the sample from the heat source.
* As the radiant panel power increased, the differences in the time to among the tested samples also increased.  
Conceptualization, J.H. and I.T.; methodology, J.H. and I.T.; software, I.M.; validation, J.H., I.T. and I.M.; formal analysis, A.H. and R.K.; investigation, J.H. and I.T.; resources, R.K.; data curation, I.M.; writing--original draft preparation, I.T.; writing--review and editing, A.H., I.T. and I.M.; supervision, A.H.; project administration, R.K.; funding acquisition, R.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 4 Proposed Algorithm
### Distributed Q-Learning in the Devices  
According to Equation (8), the BBU pool recommends more D2D modes for the devices. While the D2D mode has the advantage of increasing system capacity, it may not ensure an achievable data rate because of interference. To solve this problem, we use Q-learning agents in each device to maximize the received SINR.  
The state of device \(u\) at time \(t\) is defined as  
\[S_{u}^{t}=\left\{I_{u}^{t}\right\}, \tag{14}\]  
where \(I_{u}^{t}\) is the binary level of the received SINR of device \(u\) at time \(t\) based on current communication mode. If the mode of device is cellular mode, state of device \(u\) will be calculated based on received SINR of the cellular link. The action of device \(u\) at time \(t\) is defined as  
\[A_{u}^{t}=\left\{m_{u}^{t},\;p_{u}^{t}\right\}, \tag{15}\]  
where \(m_{u}^{t}\) is the transmission mode of device \(u\) at time \(t\), and \(p_{u}^{t}\) is the transmission power of device \(u\) at time \(t\). The reward of device \(u\) at time \(t\) is defined as  
\[R_{u}^{t}=\left\{\begin{array}{cc}\gamma_{u}&,I_{u}^{t}=1\\ -\gamma_{u}*\left(p_{max}-p_{u}^{t}\right),I_{u}^{t}=0,\;\gamma_{u}\geq 0\\ \gamma_{u}*\left(p_{max}-p_{u}^{t}\right),I_{u}^{t}=0,\;\gamma_{u}<0\end{array}\right., \tag{16}\]  
where \(\gamma_{u}\) is the SINR of device \(u\). In distributed Q-learning, the agent chooses between the action from centralized Q-learning and its own action to find the action that provides a higher expected SINR. Distributed Q-learning is summarized in Algorithm 2.
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
## 3 Results and Discussions
### Compaction Tests  
The compaction tests were performed in accordance with ASTM Standard test methods for laboratory compaction characteristics of soil using standard effort (600 kN-m/m\({}^{3}\)) [35]. The maximum dry density (MDD) for each mix type and its corresponding optimal moisture content (OMC) is presented in Figure 7. The effects of sediments contents on compaction characteristics of the sand-sediment mixes have been quantified accordingly (Table 3) and given in Figure 8.  
It can be observed from Figure 8 that the maximum dry density increases up to sediment contents (s) of 50% with a subsequent decrease in dry density with further increase in sediment contents. This is since initially the fine sediments fill the voids between the sand particles and increase the compatibility of the matrix. However, with fine sediments exceeding an optimum value (50%), the lighter particles of sediments start replacing the relatively heavier sand grains and hence the dry density decreases afterwards. Likewise, the optimum water content decreases till 25% sediment contents and starts  
Figure 5: Regressions of fine contents and void ratios.  
Figure 6: Relationship between Atterberg limits and sediment contents.  
increasing afterwards. The reason behind the increase in optimum water content is that larger quantities of water are required to hydrate the increased amount of fine sediment. A similar trend was observed when dune sand was treated with incinerator ash [23] and cement by-pass dust [36].  
Figure 8: Effects of sediment contents on the compaction characteristics.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & **DS** & **DS-RS5** & **DS-RS10** & **DS-RS20** & **DS-RS50** & **DS-RS75** & **DS-RS90** & **DS-RS95** & **RS** \\ \hline MDD (kN/m3) & 16.54 & 16.64 & 17.78 & 17.38 & 17.78 & 16.88 & 15.35 & 15.1 & 14.99 \\ \hline OMC (\%) & 16.99 & 11 & 12 & 12.5 & 16.02 & 16.43 & 22.55 & 23.15 & 22.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum dry density (MDD) and optimum moisture content (OMC) for each mixture.  
Figure 7: Moisture density relationships of the dune sand-sediment mixes.
## 4 Typical Examples of Systems
### Propagation in a Dispersive Optical Fiber  
The optical pulses inside a medium, such as the single-mode fibers, are generally expressed by the nonlinear Schrodinger equation [13; 34; 35]. For the case of pulses with a width larger than 1 ps, this Schrodinger equation is reduced to:  
\[j\frac{\partial A}{\partial z}+\frac{j}{2}\alpha A-\frac{1}{2}\beta_{2}\frac{ \partial^{2}A}{\partial T^{2}}+\gamma|A|^{2}A \tag{29}\]  
Here:  
_A_: Slowly varying complex amplitude of the pulse envelope,  
_a_: Absorption coefficient,  
_\(\gamma\)_: Nonlinearity coefficient,  
_\(\beta_{2}\)_: 2 nd order dispersion coefficient,  
_z_: Observation distance,  
_T_: The time measured in a reference frame running with the pulse at the group velocity, \(v_{g}\) (\(T=t\)-\(z/v_{g}\)).  
Putting aside \(\gamma\) means \(\gamma=0\), and normalizing the complex amplitude and time scale leads to:  
\[j\frac{\partial g}{\partial z}=\frac{1}{2}\beta_{2}\frac{\partial^{2}g}{ \partial T^{2}} \tag{30}\]  
here, \(g(z,\tau)\) represents the normalized amplitude, where \(P_{0}\) is the peak power of the incident pulse:  
\[A(z,\tau)=\sqrt{P_{0}}\exp(-\alpha z/2)g(z,\tau) \tag{31}\]  
and \(\tau=T/T_{0}\) represents the normalized time, where \(T_{0}\) is the pulse width.  
The FT of the solution of Equation (30) is:  
\[G(\omega,z)=G(\omega)\exp\left(\frac{i}{2}\beta_{2}z\omega^{2}\right) \tag{32}\]  
Relations (32) and (3) are very similar. If we replace \(\beta_{2}\) by \(-\lambda/2\pi\), the two equations become identical. This inspires us to use optical elements, including diffractive elements, to reduce (or even cancel out) the effect of chromatic dispersion introduced by the propagation of the wave along the fiber. To do so, additional optical elements, such as Mach-Zender modulators and chirped fiber networks, are inserted in the telecommunication chain. It is preferable to have these additional elements, combined with the dispersive medium itself, namely the single-mode fiber, to form a compound system. This is straightforwardly doable with our approach.
## 5 Biological Activity
### Anti-Stress and Anti-Anxiety/Psychotropic Activity  
Withanolide glycosides show anti-stress properties and having positive effects on boosting memory [69]. They help treat stress-induced conditions such as gastric ulceration, cognitive deficit, irregular glucose homeostasis, sexual dysfunction, changes in plasma corticosterone levels, and immunosuppression [7].  
_Withania somifera_ root and seed extracts show anti-stress activity. An in vivo study was performed in mice to determine the anti-stress activity of the plant. An alcoholic extract of roots and seeds was prepared in normal saline and a single dose (100 mg/kg) was administered intraperitoneally to 20-25 g mice to test their swimming performance in water at 28\({}^{\prime}\)-30\({}^{\prime}\) C. The experiment showed a promising result; the swimming rate was almost double that of the control [70].  
Research conducted at University of Texas Health Science Center showed that _Withania somifera_ extract slows down the brain by reducing neuron excitability and inhibiting nerve transmission, closely related to the primary inhibitor Gama-Aminobutyric acid (a neurotransmitter) [71].  
Glycowithanolides of _Withania somifera_, sitoindoside IX and sitoindoside X at a concentration of 50-200 mg/kg, and glycosides (sitoindosides VII and VIII) at a concentration of 50-200 mg/kg orally, showed significant anti-stress activity in albino mice and rats and resulted in increased memory retention [8].  
Double-blind, placebo-controlled studies were performed to determine the anti-stress activity of _W. somifera_ in human trials. Sixty adults were randomly assigned to take 240 mg of a standardized ashwagandha extract (Shoden) once daily. The results were measured using the Depression, Anxiety, Stress Scale-21 (DASS-21), hormonal changes in cortisol, dehydroepiandrosterone-sulphate (DHEA-S), the Hamilton Anxiety Rating Scale (HAM-A), and testosterone levels. It was found that ashwagandha extract was associated with greater reductions than placebo for mean mHAM-A score, morning cortisol C-reactive protein, pulse rate, and blood pressure, and increased significantly for mean serum DHEAS and hemoglobin [72; 73; 74].  
Studies suggest that the stress-relieving property of ashwagandha is due to modulation of the HPA axis. The HPA axis is an important hormonal response system which controls stress. This axis ensures that the body quickly responds to nerve-racking moments and returns to a normal state.  
Figure 8: _W. somifera_ possesses various biological activities.
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 2 Materials and Methods
### Modeling and Pattern Selection
#### 2.3.2 Hyperparameters Selection
In our experiments, time intervals of 12 h, 8 h, 6 h, 4 h, 3 h, 2 h, 1 h, and 30 min were used. We used 144 time steps, 96 time steps, 72 time steps, 48 time steps, 36 time steps, 24 time steps, 12 time steps, and 6 time steps. To minimize the combination, we first tested the various time intervals, and once the optimal model for a time interval was found, we tested the model with the various time steps for that interval.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Coating
#### 3.3.2 Rod Coating  
The rod coating process is easy and inexpensive to penetrate the conductive material on the textile substrate in a continuous and controlled manner (see Figure 5). This technique can affect the uniformity and condition of penetrated thin conductive thin coat by key parameters such as the rod's size, solvents, and conductive solution's viscosity. The concentration of the coating is influenced by the groove size in the wire-wound rod and depends on the rod's diameter. The solution flows through the grooves in the wire-wound rod, resulting in a thin film deposition at room temperature [242]. [PERSON] et al. reported a simple dry-Mayer-rod-coating method that fabricates the conductive sheath/core-structured graphite/silk fibers and indicates their application as wearable strain sensors [244]. A clip is used to align the bundle of silk fiber at one end of the flat plate. Then, graphite was put on the fixed end of the silk fiber and graphite flakes, which were then moved by a Mayer rod pressed onto the fibers [242].
## 2 System Model  
### System Model of OFDM Signal Monitoring  
Figure 1 presents a model of a LEO satellite broadband OFDM signal monitoring system. At the OFDM communication link's transmission end, the process begins with channel convolutional encoding of the input information sequence. The encoded data are then rearranged via row-column interleaving to improve error resistance, followed by modulation using PSK or QAM to meet various communication environment needs [32]. Pilot signals are inserted for receiver-end channel estimation, and after serial-to-parallel conversion and IFFT, the signal's anti-interference capability is enhanced. A cyclic prefix and windowing functions are applied to reduce temporal interference and smooth signal edges. Finally, after PAPR suppression, the signal is transmitted with the synchronization header incorporated [33].  
The blind monitoring system for OFDM signals consists of several key modules, including OFDM signal detection, parameter estimation, signal identification, and blind demodulation. The detection module performs real-time signal collection and OFDM signal existence checks, along with preprocessing. The parameter estimation module estimates critical parameters such as subcarrier spacing, sampling rate, number of subcarriers, and cyclic prefix length using joint time-frequency analysis on preprocessed data. The signal identification module extracts multidimensional features from the OFDM signal post-cyclic prefix removal and identifies the modulation method for each subcarrier.  
Figure 1: Model of OFDM signal generation and blind detection system.  
This paper focuses on contributions to OFDM signal parameter estimation and identification, proposing a resampling filter-based wideband flexible monitoring method for the detection module, a dynamic compensation algorithm for satellite frequency offset based on ephemeris and coordinate system transformation for the estimation module, and an intrinsic feature-based OFDM signal identification method for the identification module.  
These contributions are detailed in Sections 3, 4, and 6 of the paper, respectively, providing a comprehensive approach to enhancing the monitoring and analysis of LEO satellite broadband OFDM signals.
# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 4 Simulation Results and Analysis
### Delivery Success Rate  
The probability to find the optimal cache placement node within a limited number of nodes is called the success rate of delivery. The higher the value is, the better the timeliness of the strategy being used. The delivery success rate of ESCPS is shown in Figure 8, when the node distribution range (1-10) and the node change energy consumption \(P_{c}\) (0.1-1) change.  
From Figure 8, we can see that the success rate of delivery is affected by the distribution range of nodes and the energy consumed by the change of nodes in ESCPS strategy. When \(P_{c}\) is a fixed constant, the success rate of delivery will increase as the distance between nodes increase. The reason is that _area_ is the main factor in the selection of cache nodes in ESCPS. That is to say, the larger the node's distribution range is, the farther the node's distance is. If the probability of the node which can be observed to satisfy the energy saving condition is higher, the observation times will be less and the success rate of delivery will be higher. If the node distribution range is a fixed constant, as \(P_{c}\) is continuously increased, the success rate of delivery will decrease slowly. The reason is that \(P_{c}\) is the extra energy consumed for each observation. The extra energy consumed will increase and the effect of the energy saving will downgrade when \(P_{c}\) is increased. In addition, the energy saving is less if the probability of being observed to satisfy the conditions is smaller and the number of observations used to find the best placement node is bigger. That means the success rate of delivery is lower. After analysis, we can see that there is no significant fluctuation in ESCPS under the condition of the change of parameters, and the success rate of delivery can always maintain better.
# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 3 Methodology  
### nmODE  
For a single-layer neural network, it is sufficient to multiply its input by the weight and add the bias term to obtain the neuron's output. It is typically expressed in the following functional form:  
\[y=f[Wx+b]. \tag{1}\]  
In general, such a single-layer neural network can only address linearly separable problems, and it is unable to capture nonlinear relationships, making it unsuitable for handling complex data patterns or tasks. In general, the computational capacity of neural networks tends to increase with the improvement of their nonlinearity. The nonlinearity of Equation (1) can be enhanced by constructing an implicit mapping equation, which takes the following form:  
\[y=f[y+Wx+b]. \tag{2}\]  
[PERSON] (2018) has already demonstrated that using sin\({}^{2}(\cdot)\) as the activation function in a single-layer neural network effectively enhances the network's expressive power, and addressing nonlinear problems such as the XOR problem. When employing sin\({}^{2}(\cdot)\) as the activation function in Equation (2), the following expression is obtained:  
\[y=\sin^{2}[y+Wx+b]. \tag{3}\]  
[PERSON] (2018) has already demonstrated that Equation (3) has one and only one global attractor. Let \(\dot{y}=0\), Equation (3) can be transformed into an ODE, referred to as the \"nmODE\", which we incorporate as a block in the neural network. Its expression is as follows:  
\[\dot{y}=-y+\sin^{2}[y+Wx+b]. \tag{4}\]  
Clearly, the nmODE exhibits a unique global attractor, a property intimately associated with memory within dynamic systems. Throughout the training process, the nmODE effectively decouples the initial values and external inputs, where \(x\) represents the external input, and the initial value \(y(0)\) is set to 0. This separation of initial values and external inputs endows the neural network with two distinct types of neurons: learning neurons and memory neurons. The primary function of learning neurons is to acquire learnable parameters, while memory neurons obtain features through ODEs. Any neuron representing the network's state at any time \(t\), referred to as \(y(t)\), is recognized as a memory neuron. Figure 1 provides an intuitive illustration of the nmODE.