esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Attribute Collaborative Optimization for Multimodal Transportation Based on User Preferences
## 2 Literature Review and Research Gap
Meanwhile, this paper designs a GA-AFO hybrid algorithm that combines the advantages of GA and AFO [30]. It utilizes GA to generate an initial population and accelerates the convergence of AFO, with the aim of obtaining the optimal solution as soon as possible. The main innovations of this paper are as follows:  
1. In terms of modeling factors: This study fully considers the time window characteristics of various transportation modes. Differentiated time window models are established for the continuous time windows of highway transportation, the train schedule constraints of railway transportation, and the arrival and departure times of waterway transportation. 2. In terms of algorithm design: A fusion of GA and AFO is adopted. GA is utilized to generate the initial population, and then AFO is employed for iterative calculations. This approach avoids both the tendency of GA to fall into local optima and the slow iteration speed of AFO. 3. In terms of decision-making methods: A four-objective optimization model is constructed to minimize generalized transportation costs, transportation time, carbon emissions, and risks. By designing a fuzzy evaluation matrix, the demand preference information of multimodal transportation participants is quantified, thereby achieving a deep integration of transportation scheme formulation with user preferences.
## 2 Related Works  
A lot of research works have been done on efficient routing in a DTN environment. According to the main technology used in the routing, we classify the current research works as follows: routing based on history information, routing based on clustering, routing based on network coding and routing based on social networks.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 4 Multi-Application Scheduling Algorithm (MASA) Based on DRL
### Framework of MASA
#### 4.1.2 Heuristic Scheduler  
Because it involves the selection of PE type, the heterogeneous computing environment makes the assignment of PEs more difficult than the problem of assigning PEs in a homogeneous environment. Therefore, after obtaining the mapping probabilities corresponding to high-priority tasks, the task-mapping problem in the second scheduling phase is realized based on a heuristic algorithm. There are some implicit constraints in the resource allocation process, such as the sum of the computational requirements of multiple tasks deployed on a PE must not exceed the upper limit of this PE's computational resources, and the sum of the task data volume overlays must not exceed the physical bandwidth between the PEs.  
This paper aims to improve execution efficiency in a continuous dynamic environment through the inclusion of a heuristic algorithm that duplicates critical parent nodes, reducing application execution time. The mapping scheme of the selected node in this study is based on the DEFT algorithm [25]. Comparison of the computational results of the EFT algorithm and the Copy Parent Earliest Finish Time (CPEFT) algorithm helps select the smallest result as the most optimal value. Equations (11) and (12) detail how the CPEFT algorithm tries to replicate each parent node of the selected node in order to compare and arrive at the earliest possible completion time. Equation (13) explains how the DEFT algorithm selects the minimum result of the EFT and CPEFT algorithms, regardless of whether it involves duplicated parent nodes or not. This ensures that computations are performed with the minimum result.  
\[\mathrm{CPEST}(\mathrm{perd}(n_{i}),n_{i},p_{m})=\min_{n_{i}\in \mathrm{pred}(n_{i})}\big{(}\mathrm{AFT}(n_{j})+\mathrm{comm}\big{(}n_{j},n_{ i}\big{)}\big{)} \tag{11}\] \[\mathrm{CPEFT}(n_{i},p_{m})=\max_{n_{a},n_{b}\in\mathrm{pred}(n_{ i}),a\
eq b}\{\mathrm{CPEST}(n_{a},n_{i},p_{m}),\mathrm{CPEST}(n_{b},n_{i},p_{m})\}+ \mathrm{comp}(n_{i},p_{m})\] (12) \[\mathrm{DEFT}(n_{i})=\min\{\mathrm{CPEFT}(n_{i},p_{m}),\mathrm{ EFT}(n_{i},p_{m})\} \tag{13}\]
# Statistical Modeling of Wall Roughness and Its Influence on NLOS VLC Channels in Underground Mining
## 2 UM-VLC System Model
Other distributions serve as extensions to those utilized or are applied in particular instances that do not align with the context of this analysis. Some continuous distributions are extensions or particular instances of classical distributions, or they include specific cases that might be deemed irrelevant. For example, the chi-squared distribution exemplifies an extension of the gamma distribution. Furthermore, \(\xi_{qp}^{tilt}\) and \(\eta_{qp}^{tilt}\) pertain to the incidence and irradiance angles, respectively, additionally generated through random calculations utilizing normal, uniform, exponential, and gamma distributions. This describes the surface irregularity of the wall in different situations related to wall geometry, where each situation presents unique compositional arrangements. Alternative distributions are considered inadequate since they either incorporate or extend the four specified distributions which are applicable only in particular circumstances. In the given context, the normal distribution (NORM) function is expressed as follows:  
\[y(x)\ =\ \begin{cases}\frac{1}{\sigma\sqrt{2\pi}}\exp\left(-\frac{(x-\mu)^{2}} {2\sigma^{2}}\right)&\text{if }0\leq x\leq 1\\ 0&\text{otherwise},\end{cases}  \tag{7}\]  
where \(\mu\) is the average and \(\sigma\) the standard deviation. The uniform distribution (UNID) is depicted as follows:  
\[y(x)\ =\ \begin{cases}\frac{1}{b-a}&\text{if }\ a\leq x\leq b\\ 0&\text{otherwise},\end{cases}  \tag{8}\]  
where \(a\) and \(b\) are the limits of the distribution function. Whereas the gamma distribution (GAMD) is given as follows:  
\[y(x)\ =\ \frac{\beta^{\alpha}}{\Gamma(\alpha)}x^{\alpha-1}e^{-\beta x}, \tag{9}\]  
where \(\alpha\) is the form parameter and \(\beta\) is the scale parameter. Finally, the exponential distribution (EXPD) is given by:  
\[y(x)\ =\ \lambda e^{-\lambda x}. \tag{10}\]  
Within the specified distributions, the function \(y(x)\) serves as their representation, with \(\lambda\) acting as the scale parameter. Also, the simulation algorithm can be explained in detail by the following key flow diagram (Figure 2). The process involves iterating the grid in the XY plane and receiving a ray from the XZ plane grid. Upon completion of the iteration on the initial plane, the XZ plane engages the subsequent grid. This process repeats while calculating the average of the Power Distribution and the CIR. The simulation was conducted 21 times according to the Monte Carlo method [19; 20].
## 4 Stability Analysis of Slopes Under Freeze-Thaw Cycles
### The Influence of Freeze-Thaw Cycles on the Plastic Zones of Slopes  
The plastic zones of the slope profiles under varying freeze-thaw (F-T) cycles are presented in Figure 10. In the initial state (0 F-T cycles), the plastic zones exhibited a dispersed distribution with limited potential failure areas, indicating stable slope conditions. With the increasing of F-T cycles, the plastic zones progressively expanded and coalesced spatially. At F-T = 90, a fully developed shear band penetrated the slope profile, signifying complete slope failure.
## 3 Preliminary of Millimeter Radar  
The carrier wavelength of millimeter-wave radar is electromagnetic waves in the range of 1-10 mm, which has the characteristics of a short wavelength, a wide frequency band, and it is not easy for it to interfere. Millimeter-wave radar was used in the military in the early days, and in recent years, with the development of technology, it has been gradually used in automotive electronics, drones, and other fields. At present, the frequency bands of millimeter-wave radars are mainly divided into three categories: 24 GHz frequency band, 77 GHz frequency band, and 76-81 GHz frequency band. The radar used in this paper works at 76-81 GHz. The biggest feature of this frequency band is the wide bandwidth, so it has a high range resolution and is suitable for distinguishing different actions of the human body.  
Due to its high resolution, low signal processing complexity, and low cost, FMCW is the most used modulation method for millimeter-wave radar. FMCW radar system mainly includes transceiver antenna, RF front-end, modulation signal, a signal processing module, etc. Millimeter-wave radar can detect the distance, azimuth, and relative speed of the target through the correlation processing of the received signal and the transmitted signal. The principle of ranging and speed measurement of FMCW radar is introduced below.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 3 Proposed FH Sequence Recovery Method
### Input Data Format  
The frequency-hopping signal is non-stationary in nature. As the frequency-hopping signal may occupy a wide frequency range, the length of the sampled signal will be very long, which makes learning with the raw time domain signal as the input difficult. In this paper, we transform the frequency-hopping signal from the time domain to the time-frequency domain via STFT. STFT is used to preprocess the received signal, and the processed time-frequency diagram is used as the input of the neural network. The mathematical expression of the STFT is  
\[\left\{\begin{array}{l}STFT_{Y}(t,f)=\int_{-\infty}^{+\infty}Y(\tau)\sigma^{ *}(\tau-t)\exp(-j2\pi f\tau)d\tau\\ Y(t)=X(t)+J(t)\end{array}\right. \tag{2}\]  
where \(\sigma^{*}(t)\) represents the conjugate form of the window function, and \(Y(t)\) represents the received signal, which is composed of the frequency-hopping signal \(X(t)\) and the jamming \(J(t)\) in the channel.  
The receiver can take the frequency-hopping period as a known condition. It divides the received signal according to the frequency-hopping period and inputs the time-frequency diagram of each hopping signal into the network consecutively, and the output of the network at the current moment is the frequency-hopping sequence value of the hopping signal.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Public 5G Services
#### 4.5.3 Emergency Services  
In an emergency situation, responsiveness to the emergency can be improved by providing the data of a patient in an ambulance and getting remote medical care. In detail, when an accident occurs in a remote place and emergency actions are required, medical treatment can be provided through first-aid robots. The limitations of current technologies for these types of services include reliability and the requirement of low latency. The delivery of medical data obtained from emergency situations and the information on remote medical treatment and surgery should be delivered with the highest level of reliability and the lowest latency. In addition, high-quality videos regarding the emergency area and the medical data on the patients need to be sent with high speed and wider network coverage. Hence, 5G will be suitable for such types of emergency services, with its ultra-reliable wireless transmission and latency within 1 ms.
## 3 Results and Discussion  
### Characterization of DES with FTIR  
The FTIR spectra of the synthesized DES, based on choline chloride and ethylene glycol with the named compositions of CE0, CE1, CE2, and CE3, are shown in Figure 1.  
In all cases, after mixing the starting materials (HBAs and HBDs), homogenous and transparent phases were obtained, indicating the formation of the eutectic phases. The formation of the DES was also confirmed with the IR spectrum of the DES without water (CE0), as previously reported in [31]. The O-H vibration of the DES appears at 3300 cm\({}^{-1}\), increasing in the presence of water. Additionally, in all choline chloride-ethylene glycol-based DES spectra, there is a peak at 1086 cm\({}^{-1}\), which is indicative of the C-N vibration [31]. As seen here, the differences between the spectrum of the original DES and the spectra of the compositions with water, CE1, CE2, and CE3, are only related to the amount of water. This suggests that the DES retains its functionality for the potential extraction of fatty acids, even when water is added. The rise in the amplitude of some peaks is linked to the presence of water. This increase can be observed for the peak at 3300 cm\({}^{-1}\), corresponding to the stretching of the bond O-H, and it is produced by an increase in the amount of water from CE1 to CE3. Conversely, there is also an increase in the peak at 1650 cm\({}^{-1}\) when comparing the spectra of CE1, CE2, and CE3, which corresponds to the bending of the O-H bond due to the presence of water [32].  
Figure 2 shows the spectra of the DES based on choline chloride and fructose. Specifically, the spectra of the compositions CF2 and CF3 are presented. As in the case of choline chloride-ethylene glycol-based DES, CF2 and CF3 exhibit increasing peaks that correspond to the stretching and bending of the O-H bond in water, with no other significant differences between them.
## 2 Related Work
The authors of [14] propose recognizing four emotion classes in VA, using feature extraction methods in the time and frequency domain and the mRMR method to select significant features. The authors implement statistical methods (mean, standard deviation, mean of absolute values of the first and second difference of the raw and processed signal), discrete wavelet transform, and the wavelet energy and wavelet entropy estimation. They use 15 EEG channels and the SVM classifier, obtaining accuracy percentages of 52.1% HAHV, 49.1% HALV, 49.6% LAHV, and 48.3% LALV, with an overall accuracy of 49.7%. However, the authors do not conclude which features provide better results. On the other hand, some authors use public access datasets such as DEAP for emotion classification. For example, the authors of [15], for emotion recognition, apply features such as power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), and rational asymmetry (RASM). They use the mean accuracy rate (mAR) metric, obtaining average results in VA space of 44.07% and 59.09% using SVM and ANN classifiers for the DEAP dataset. The authors of this study use frequency domain methods for feature analysis and individual participants. [PERSON] et al. propose the recognition of three (positive, negative, neutral) and four (VA space) affective states using PSD, DE, and Fourier transform features, obtaining accuracy results of 62.54% in DEAP using the ANN classifier [16]. Several works mentioned in the literature aim to achieve emotion recognition using different feature extraction methods in the time, frequency, time-frequency, and location (spatial) domains; however, their goal is not to determine which features or combination of them provide better results. Very few studies focus on determining which feature extraction methods from the different domains give better results in classifying emotional states. Therefore, there is still no agreement on the EEG features that provide better results to improve classification accuracy. One of the key contributions of this work lies in studying the recognition of emotional states defined in the four quadrants of the two-dimensional (VA) model, exploring a broader set of feature extraction methods in time, frequency, time-frequency, and location (spatial) domains to obtain input vectors for the SVM, k-NN, and ANN classifiers. The data to be analyzed come from the public access DEAP dataset that correlates emotional states with EEG signals using audio and image stimuli that acquire data based on BCI. Sixteen input vectors were created in order for the classifiers to evaluate which feature extraction method provides more information and produces a better performance.
## 4 Indoor Occupancy Detection
### CNN Model and Training  
To evaluate the performance of the proposed preprocessing method, we configure a three-channel CNN as shown in Figure 4. The CNN used in the experiment consists of an input layer, hidden layers, and an output layer. Since the proposed method expands the \(S\times h\) CSI amplitude data into three channels, we set the dimension of the CNN input layer to \(3\times S\times h\). After taking \(X_{R,c}(t:t+h),X_{G,c}(t:t+h)\), and \(X_{B,c}(t:t+h)\) at the input layer, convolution and polling operations are performed at the first hidden layer. For the convolution operation, we use a \(3\times 3\) kernel and the ReLU activation function. We set the padding size to one and the stride to one. For the polling operation, we use a \(2\times 2\) kernel with a stride of 2 and adopt the max pooling function. We set the dimension of the second hidden layer to \(32\times(S/2)\times(h/2)\) and perform the convolution and polling operations with the same hyper-parameters as those used for the first layer. We configure the dimension of the third hidden layer to \(64\times(S/4)\times(h/4)\). At this layer, we set the same hyper-parameters as those used for the first layer for the convolution and polling operation. The dimension of the fourth hidden layer is configured as \(128\times(S/8)\times(h/8)\). We perform a convolution operation with a \(3\times 3\) kernel with a padding size of one and a stride of one. After we perform the batch normalization and the ReLU activation function, we apply the adaptive average polling operation to produce a \(C\times 1\times 1\) tensor. Then, at the output layer, we apply the softmax function to determine the probability of the input CSI data belonging to each class.  
Figure 4: CNN architecture used for indoor occupancy detection.  
To train the CNN, we use cross entropy between the predicted class label and the true class label as the loss function, which is  
\[\Omega=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\log(p_{i,c}), \tag{9}\]  
where \(N\) is the number of data points, \(C\) is the number of class, and \(y_{i,c}=1\) if the \(i\)-th data point corresponds to class \(c\); otherwise, it is zero. \(p_{i,c}\) is the probability estimated by CNN that the \(i\)-th data point belongs to \(c\). We use the Adam optimizer. For model training, we set the number of epochs to 100, the batch size to 64, and the learning rate to 0.001.  
The hyper-parameters for model training are configured as follows. We set the epoch to 100 and the batch size to 64. The learning rate is configured to 0.001, and 80% of the total CSI data is used for training, while 20% of the data is used for testing. We use a computer equipped with an Intel i5-12400F CPU and Nvidia GeForce RTX 3060. The size of the random access memory is 32 GB, and its operating system is Window 10. When we run the CNN model, we use Python version 3.9.18 and PyTorch version 2.3.0.
## 4 Conclusions  
Feasibility of a low-profile UWB antenna array design with two highly selective wide rejection bands has been investigated. This was achieved by introducing metamaterial characteristics in the hexagonal patch antenna to extend its operational bandwidth. Dual notched bands were incorporated in the design to prevent the UWB system being affected by interference from WLAN (5.15-5.825 GHz) and X-band satellite downlink communication band (7.10-7.76 GHz). This was accomplished by loading the ground-plane of the antenna with electromagnetic bandgap slots. The proposed techniques had no effect on the dimensions of the antenna array. Unlike previous dual-notched band designs the proposed antenna is smaller and has a wider notched band characteristic to eliminate interfering signals.  
Conceptualization, A.A.A., M.A., B.S.V., P.S., and E.L.; methodology, A.A.A., M.A., and B.S.V.; software, A.A.A., M.A., and B.S.V.; validation, A.A.A., M.A., B.S.V., P.S., and E.L.; formal analysis, A.A.A., M.A., and B.S.V.; investigation, A.A.A., M.A., B.S.V., P.S., and E.L.; resources, A.A.A., M.A., and B.S.V.; data curation, A.A.A., M.A., B.S.V., and E.L.; writing--original draft preparation, A.A.A., M.A., and B.S.V.; writing--review and editing, A.A.A., M.A., B.S.V., P.S., and E.L.; visualization, A.A.A., M.A., B.S.V.; supervision, M.A., and E.L.; project administration, E.L.; funding acquisition, M.A., and E.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**References**} & **Antenna Size** & \multirow{2}{*}{**No. of EBGs**} & **Bandwidth of** & \multirow{2}{*}{**Controllable EBG Freq.**} & **Design** \\  & **(mm3)** & & & **each EBG (GHz)** & **Complexity** \\ \hline
[3] & 16 \(\times\) 25 \(\times\) 1.52 & Single & 0.71 & Yes & No \\
[19] & 44 \(\times\) 44.4 \(\times\) 0.1 & Dual & 0.21 and 0.21 & No & No \\
[20] & 48 \(\times\) 50 \(\times\) 1 & Single & 0.97 & Yes & No \\
[21] & 50 \(\times\) 50 \(\times\) 1.575 & Dual & 0.24 and 0.48 & No & No \\
[22] & 28 \(\times\) 18 \(\times\) 0.8 & Dual & 0.54 and 0.53 & No & Yes \\
[23] & 38 \(\times\) 42 \(\times\) 0.5 & Single & 0.76 & No & Yes \\
[24] & 8.5 \(\times\) 22 \(\times\) 0.8 & Single & 1.34 & No & Yes \\ Proposed Work & 20 \(\times\) 20 \(\times\) 0.87 & Dual & 1.14 and 1.41 & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with state-of-the-art UWB notched band antennas.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Dimensions (mm)** \\ \hline Radius of EBG slots & 0.2 \\ Gap between EBG slots & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geometrical parameters of the MTM inspired UWB antenna loaded with EBG slots. (Note, all other parameters are given in Tables 1 and 2).
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 5 System Description
### System Operation
#### 5.4.2 Transmission and Management of Energy Offers  
Energy offers are generated when a smart meter detects its household is generating more energy than consuming. In such case:  
1. The smart meter partitions the available surplus into the established power-time pieces (for instance 10 W for 10 min) and each piece is announced through an offer message (Section 5.3, Procedure 4).
2. The substation records all the offer messages so that they can later be paired with energy requests. An offer message is valid during an established period of time (this is why offer messages are timestamped).
3. If that time elapses, and the offer has not been paired, it expires.
4. If its energy is still available, the offering meter generates and transmits a new offer message for it.  
When an offer message is properly paired with a request (Section 5.3, Procedure 6):  
1. The smart meter of the offering household has to put the established power into the grid during the established time.
2. When the supply time established as a result of a pairing is about to elapse: * If the supplied power is still available, the meter generates and sends a new offer. * Otherwise, when the supply time elapses, the meter stops pouring that energy into the grid.  
The substation can establish a threshold so that when the number of available offers surpasses it, the substation generates and pairs energy requests by itself and then stores that energy or pours it into the global grid.