esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 2 Wireless Sensor Deployment Optimization  
### Wireless Sensor Deployment  
#### 2.1.1 Problem Description  
Given a certain area of interest, \(V(|V|=N)\) is the set of monitored locations in the area. The goal of node deployment is to select a subset \(A\subset V(|A|=K<N)\), and the base station can efficiently estimate the value of any element in the set \(V\backslash A(|V\backslash A|=N-K)\) based on the observed values of the subset.  
Mutual information is a way to describe the correlation between two sets of events. This paper uses mutual information to describe the correlation between set A with deployed sensors and set \(V\backslash A\) without deployed sensors. Suppose set \(V=[V_{1},V_{2},\ldots,V_{N}]\) represents \(N\) positions, and \(X_{V}=[X_{1},X_{2},\ldots,X_{N}]\) describes the random variables of the observation results of these positions. For any subset \(A\subset V\), \(X_{A}\) is used to represent the set of random variables associated with the location subset \(A\). The objective function is to maximize the amount of information containing the unselected location set through the selected deployment set \(A\).  
\[\max_{A\subseteq V}F(A)=\max_{A\subseteq V}(H(X_{V\backslash A})-H(X_{V \backslash A}|X_{A})). \tag{1}\]  
In Equation (1), \(H(X_{V\backslash A})\) represents the entropy of the random variable \(X_{V/A}\), and \(H(X_{V\backslash A}|X_{A})\) represents the random variable \(X_{V/A}\) relative to the conditional entropy of \(X_{A}\).
## 4 Materials and Methods
### Data and Methodology
#### 4.2.4 Aerosol Optical Depth  
The aerosol optical depth (AOD) is the coefficient of irradiance attenuation by the atmosphere. AOD data used in the study were sourced from SODA [92] as 3-h averages. AOD 1240 nm, AOD 550 nm, AOD 500 nm, AOD 380 nm, and AOD 700 nm were calculated using the Angstrom law [93,94]. The Angstrom law methodology was also applied by [45,86] in their clear sky model studies. The Angstrom turbidity law [93,94] was applied as follows: the Angstrom exponent (\(\alpha\)) of the aerosol from the two known AOD wavelengths was found, and then the calculated \(\alpha\) from those two known wavelengths was used to calculate the unknown AO. Equations (13) to (18) from [93,94] were used for this:  
\[\frac{\tau_{\lambda}}{\tau_{\lambda 0}}=\left(\frac{\lambda}{\lambda_{0}}\right)^{-\alpha} \tag{13}\]  
\[\tau_{\lambda}=\tau_{\lambda 0}\bigg{(}\frac{\lambda}{\lambda_{0}}\bigg{)}^{-\alpha} \tag{14}\]  
\[\tau_{700 nm}=\tau_{\lambda 0}\bigg{(}\frac{700\ nm}{\lambda_{0}}\bigg{)}^{-\alpha} \tag{15}\]  
\[\tau_{500 nm}=\tau_{\lambda 0}\bigg{(}\frac{500\ nm}{\lambda_{0}}\bigg{)}^{-\alpha} \tag{16}\]\[\tau_{380 nm}=\tau_{\lambda 0}\left(\frac{380\ nm}{\lambda_{0}}\right)^{-\alpha} \tag{17}\]  
where:  
\(\tau_{\lambda}\) is the optical thickness at unknown wavelength (\(AOD_{380}\), \(AOD_{500}\) and \(AOD_{700}\));  
\(\tau_{\lambda 0}\) is the optical thickness at the known wavelength (\(AOD_{550}\) or \(AOD_{1240}\));  
\(\lambda\) is the wavelength of the unknown AOD ( \(AOD_{380}\), \(AOD_{500}\) and \(AOD_{700}\));  
\(\lambda_{0}\) is the wavelength of the known AOD (\(AOD_{550}\) or \(AOD_{1240}\));  
\(\alpha\) is the angstrom, or alpha exponent, of the aerosol. It is related to the particle size distribution.  
The alpha exponent, or Angstrom, of the aerosol (\(\alpha\)) is calculated as in Equation (18):  
\[\alpha=-\log\left[\frac{\left(\frac{\tau_{\lambda 1}}{\tau_{\lambda 2}}\right)}{ \left(\frac{\lambda_{1}}{\lambda_{2}}\right)}\right] \tag{18}\]  
where \(\frac{\tau_{\lambda 1}}{\tau_{\lambda 2}}\) is the ratio of known optical thickness measurements \(\frac{AOD_{580}}{AOD_{1240}}\) and \(\frac{\lambda_{1}}{\lambda_{2}}\) is the ratio between the known wavelengths \(\frac{550 nm}{1240 nm}\).
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 2 Methods
### Methods Used to Summarized Exposure Levels  
In order to make possible to compare the levels of exposure observed in different studies, all the values reported as power density were converted to E-field strength (V/m) by:  
\[E\left(\mathrm{V/m}\right)=\sqrt{Z_{0}*Power\ density\left(\mathrm{W/m^{2}} \right)} \tag{1}\]where \(Z_{0}\), equal to 377 \(\Omega\), is the impedance of vacuum.
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 1 Introduction  
Psoriasis is a chronic immune-mediated inflammatory skin disorder that affects approximately 2-3% of the global population [1]. It is characterized by red, scaly plaques that can appear on various parts of the body, causing significant physical and psychological burden to affected individuals [2]. The pathogenesis of psoriasis involves complex interactions among genetic, immunological, and environmental factors, leading to dysregulated immune responses and excessive proliferation of keratinocytes.  
Over the years, significant progress has been made in understanding the underlying mechanisms of psoriasis, leading to the development of various treatment options. Current management strategies for psoriasis aim to alleviate symptoms, improve quality of life, and prevent disease progression [3, 4]. These approaches encompass topical therapies, phototherapy, systemic medications, and biologic agents targeting specific immune pathways. Despite the advancements in psoriasis management, several challenges and unmet needs persist. Adverse effects, treatment resistance, long-term safety concerns, and high costs associated with some therapies limit their widespread use and efficacy. Additionally, the variability in treatment response among individuals highlights the need for personalized and tailored approaches in psoriasis management [5, 6].  
Emerging trends and future directions in psoriasis treatment hold promise for improved outcomes. These include the development of novel biologic agents targeting novel pathways, the exploration of combination therapies to enhance efficacy and minimize side effects [7, 8], the utilization of biomarkers for treatment selection and monitoring [9], and the advancement of gene- and cell-based therapies [10].  
This review aims to provide an overview of the current treatment landscape for psoriasis, highlighting the challenges faced by existing therapies and discussing the potential future trends that may shape the field. By addressing these aspects, we hope to contribute to the ongoing efforts to optimize psoriasis management and improve the lives of individuals affected by this chronic condition.
## 3 Experimental Design and Implementation
### Experimental Parameter Setting and Optimization
#### 3.4.2 Temporal Parameter Settings  
1. Time window division: Each time period is set to 2 h, matching the actual morning peak duration, ensuring sufficient data volume while avoiding confusion from cross-period traffic patterns.
2. Sampling interval threshold: When analyzing sampling characteristics, sampling time intervals are divided into two categories: less than 10 s and greater than or equal to 10 s. This threshold selection is based on data distribution characteristics and can effectively distinguish normal sampling from anomalous intervals.
# Effects of Different Aboveground Structural Parts of Grass Strips on the Sediment-Trapping Process
## 2 Materials and methods
### Measurement and Data Analysis  
#### 2.2.1 Sediment Process Calculations  
The sediment transport rate \(\mathit{Sed}(t)\) (g min\({}^{-1}\)) was defined as the mass of sediment transported per unit of time. \(\mathit{Sed}(t)\) at time \(t\) was calculated using the equation [18]  
\[\mathit{Sed}(t)=\mathit{SC}(t)\mathit{Q}(t) \tag{2}\]  
where \(\mathit{SC}(t)\) and \(\mathit{Q}(t)\) are the sediment concentration (g L\({}^{-1}\)) and flow rate (L min\({}^{-1}\)), respectively, at time \(t\) (min). The ISTE of a grass strip \(\mathit{le}(t)\) is usually used to indicate the real-time sediment-trapping performance of the grass strip over time [10; 11].  
\[\mathit{le}(t)=\frac{\mathit{Sed}_{in}(t)-\mathit{Sed}_{out}(t)}{\mathit{Sed}_{ in}(t)}=1-\frac{\mathit{Sed}_{out}(t)}{\mathit{Sed}_{in}(t)} \tag{3}\]  
In Equation (3), \(\mathit{Sed}_{in}(t)\) and \(\mathit{Sed}_{out}(t)\) are the inflow and outflow sediment transport rates, respectively. The cumulative amount of sediment trapping \(\mathit{R}(t)\) can be calculated as  
\[\mathit{R}(t)=\sum_{i=1}^{n}(\mathit{Sed}_{in}(t_{i})-\mathit{Sed}_{out}(t_{i}) )\Delta t_{i}\ (i=1,\ 2,\ 3\ldots,\ n), \tag{4}\]  
where \(i\) and \(n\) indicate the \(i\)-th sample and the first \(n\) samples, respectively, and \(\Delta t_{i}\) is the sampling interval for the \(i\)-th sample.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**Particle Size Distribution**} & & \multicolumn{3}{c}{**Soil Type**} & **Soil Texture** \\ \hline Particle size (\(\mu\)m) & \(>\)1000 & 1000–500 & 500–250 & 250–100 & 100–50 & 50–20 & 20–2 & 2–1 & \(<\)1 & \\ \hline Percentage (\%) & 0 & 0.55 & 1.14 & 5.27 & 29.15 & 46.04 & 15.32 & 0.87 & 1.66 & \
\begin{tabular}{c} Lossial \\ soil \\ \end{tabular} &
\begin{tabular}{c} Sandy loam \\ soil \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties of the sediment used in the experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Flow Rate (Q)** & **Sediment Concentration (SC)** & **Slope** & **Treatment (Code)** & **Duration \(t\) (min)** & **Repeated** \\ \hline \multirow{3}{*}{30 L min\({}^{-1}\) m\({}^{-1}\)} & \multirow{3}{*}{120 g L\({}^{-1}\)} & \multirow{3}{*}{5\({}^{\circ}\)} & Completed grass strips (CG) & 150 & 3 \\  & & & Remove green grass (RG) & 120 & 3 \\ \cline{5-6}  & & & Remove green and witnessed grass (RGW) & 60 & 3 \\ \hline \end{tabular}
\end{table}
Table 2: Details of the sediment-trapping experiments using different structural components of the aboveground parts of the grass strips.
# Improving Performance of a Resonant String-Based Pulsation Attenuator in Hydraulic Systems
## 3 Adaptive Particle Swarm Optimization (PSO) Algorithm
### Adaptive PSO Algorithm  
The main objective to develop the APSO algorithm is to adaptively adjust the particle number while searching the globally best solution. The conventional PSO algorithm and most PSO variants usually work with a fixed number of particles. When the particles are distributed in a crowded way in a local region, each particle would have a similar distance to the globally best position. This means that some particles represent the same evolution process and are not necessary at this stage. In addition,for some complicated optimization problems, researchers generally prefer strong global search ability to allocate particles to a broader search space. However, this strategy lowers the computational efficiency for most unrotated problems [19].  
To improve the computational efficiency of the optimization process for RSHPA, APSO would reduce the particle close to the global best. First, a distance factor \(\delta_{i}\) is given as:  
\[\delta_{i}=\frac{f(X_{i})-f_{\min}}{f_{\max}-f_{\min}},i=1,2,\ldots,N \tag{20}\]  
where \(f(\cdot)\) denotes the objective function value (OFV), \(N\) denotes the total number of particles, \(f_{\max}\) denotes the OFV regarding the global best, and \(f_{\min}\) denotes the smallest OFV during all the calculation results of particles. The larger the distance factor, the closer the distance to the global best. In APSO, the particle removal is only conducted at the convergence stage, which satisfies:  
\[\sum_{k=i}^{i+W}\left|f_{\max}^{k}-f_{\max}^{k-1}\right|=0 \tag{21}\]  
where \(k\) denotes the iteration order and \(W\) denotes the consecutive times for the condition that the best OFV keeps unchanged. One should note that the number of particles has a lower bound \(N_{\min}\). Based on Equations (18)-(21), the flow chart of APSO is summarized as Figure 4 where the green parts denote the iteration steps in the conventional PSO algorithm, and the yellow parts denote the improved content in the APSO algorithm. The initialization step includes assigning initial position and velocity, personally best particle and globally best particle. During searching, each particle has position limitation, which represents the geometry limitation for the size variables of RSHPA in the optimization design. The velocity also has limitations and it affects the search speed of the APSO algorithm. Although a small limitation range can improve the optimization accuracy of each geometry parameters, the fabrication precision of RSHPA cannot reach too high in a practical structure. Thus, the velocity is set to a value close to the fabrication tolerance in this paper. Note that the unit of velocity in the optimization problem is equal to the unit of the size variables.
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 4 Conclusions  
In this paper, a neural network-assisted visual-inertial personnel localization method is proposed. Our goal is to enhance the accuracy of conventional visual-inertial under a moving base by means of LSTM networks. We developed an inertial measurement error model based on inertial measurement under a moving base, generated ground truth trajectories with visual-inertial SLAM, and regressed 6-dimensional IMU data from IMU data fragments of the shaky base using a trained neural network. Then, the localization accuracy was improved by combining with visual. The proposed algorithm was experimentally proven to be effective in improving the positioning accuracy of people under a moving base.  
The algorithm proposed in this paper mainly addresses the problem of low accuracy of person localization under a moving base, and the effect is not obvious for general scenes. Due to the small dataset and testers, the localization accuracy will be degraded for those who are not trained by the LSTM network. In addition, the scenes in underground spaces may be complex and multiple actions of testers may occur. Therefore, the follow-up work should increase the diversity of the data (adding testers and their possible actions) so that the proposed algorithm can be adapted to more complex environments.  
All named authors initially contributed a significant part to the paper. Conceptualization, Z.X. and Z.S.; methodology, Z.X.; software, Z.X.; validation, Z.X. and D.D.; formal analysis, Z.X.; investigation, Z.X. and D.D.; resources, Z.X. and D.D.; data curation, Z.X. and D.D.; writing--original draft preparation, Z.X.; writing--review and editing, Z.S.; visualization, Z.X.; supervision, Z.S.; project administration, Z.S.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (Grant No. 2020 YFC1511702, 2022 YFF0607400), National Natural Science Foundation of China (61971048), Beijing Science and Technology Project (Z221100005222024), Beijing Scholars Program, Key Laboratory of Modern Measurement & Control Technology, Ministry of Education.  
Not applicable.  
Figure 14: Building an experimental walking diagram.  
Figure 15: Building positioning track map.
###### Abstract  
This paper presents a method for extracting novel spectral features based on a sinusoidal model. The method is focused on characterizing the spectral shapes of audio signals using spectral peaks in frequency sub-bands. The extracted features are evaluated for predicting the levels of emotional dimensions, namely arousal and valence. Principal component regression, partial least squares regression, and deep convolutional neural network (CNN) models are used as prediction models for the levels of the emotional dimensions. The experimental results indicate that the proposed features include additional spectral information that common baseline features may not include. Since the quality of audio signals, especially timbre, plays a major role in affecting the perception of emotional valence in music, the inclusion of the presented features will contribute to decreasing the prediction error rate.  
musical emotion recognition; spectral feature extraction; sinusoidal model; principal component regression; deep learning; machine learning Article
# Dynamic Dark Channel Prior Dehazing with Polarization
## 5 Conclusions  
This paper proposes a polarization-based dark channel prior dehazing method to improve the efficiency of detecting and characterizing foggy targets. The main contributions of this paper are that the proposed method can effectively detect the target hidden in the background with similar reflectivity by using a combination of intensity and polarization. Moreover, a polarization imaging system with a split-amplitude structure is designed and built to enable real-time acquisition of transient scene targets. In the next step, we will attempt to carry out secondary development of the software architecture currently owned by the system, aiming to achieve real-time acquisition of the target scene while processing the target in real time. In addition, we intend to fuse the dehazed intensity image and the polarization image to shorten the time for target recognition and enhance the detection efficiency.  
[PERSON] proposed the original idea. [PERSON] conducted the experiments and designed the instrument. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] collected and analyzed the data. [PERSON] wrote the manuscript. [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{4}{c}{**Polarization Information Record**} \\ \hline
**Region** & **Channel R** & **Channel G** & **Channel B** \\ \hline
1 & 0.169 & 0.264 & 0.443 \\
2 & 0.158 & 0.192 & 0.205 \\
3 & 0.205 & 0.117 & 0.106 \\ \hline \end{tabular}
\end{table}
Table 4: Polarization information in different regions.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{4}{c}{**Contrast**} \\ \hline
**Region** & **Channel R** & **Channel G** & **Channel B** \\ \hline
1 & 0.097 & 0.386 & 0.614 \\
2 & 0.123 & 0.243 & 0.318 \\ \hline \end{tabular}
\end{table}
Table 3: The contrast of polarized images of different color channels.  
**Funding:** This work was supported by the Fundamental Research Program of Shanxi Province (20210302124702), the National Natural Science Foundation of China (62005251, 62305237), and the Central Guidance on Local Science and Technology Development Fund of Shanxi Province (YDZJSX2022A031).  
**Data Availability Statement:** Data sharing is not applicable due to privacy restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 3 Experiment and Results
### Comparative Experiment
#### 3.5.1 Data Augmentation Methods  
In order to study the influence of data augmentation methods on the results, three data augmentation methods (random rotation, random scaling, and random movement) were applied to the input point cloud separately and then mixed for training. Figure 8 shows the impact of the data augmentation methods on the classification accuracy of ModelNet40, where A is a random rotation operation, B is a random scaling operation, C is a random movement operation, A + B refers to the mixed operation of random rotation and random scaling, A + C refers to the mixed operation of random rotation and random movement, B + C refers to the mixed operation of random scaling and random movement, and A + B + C refers to three mixed operations.  
As shown in Figure 8, the random rotation operation works best when only a single data augmentation operation is used. When the random rotation operation is combined with the other two operations, the accuracy rate is more than 80%, while the accuracy rate of the combined random scaling and random moving operations is about 70%. When the three data augmentation methods are combined, the classification accuracy is slightly improved, which shows that the performance of the network using multiple data augmentation methods is better than using a single data augmentation method for training. The classification accuracy using only random scaling or random moving point cloud is not high because the origin cloud and the transformed point cloud have a similar spatial distribution. When using the combination of rotation prediction and other transformations, it is easier for the neural network to distinguish positive and negative samples. Therefore, in order to learn generalizable features, it is very important to combine rotation prediction with other groups of data augmentation methods.
## 5 Conclusions  
In this paper, we propose a compact and highly isolated dual-polarized HA with continuous scanning using a pillbox feeding structure. The proposed pillbox structure can generate orthogonal surface waves with high isolation between low distortion in two polarizations with compact size. The hologram pattern consists of the proposed HA, which acts as an anisotropic impedance surface to control the surface impedance in each polarization independently by using Jerusalem cross-shaped unit cells. This feeding structure and hologram pattern suppress the unwanted cross-polarized fields, thereby overcoming the OSB phenomenon for continuous scanning. The proposed HA is fabricated by PCB etching and bonded with each layer and measures a realized gain of approximately 18.5 dBi in the center frequency. Moreover, it can steer the beam direction from +18\({}^{\circ}\) to -25\({}^{\circ}\) in the measured range of 21-27 GHz. In addition, the proposed HA has isolation between each polarization over 40 dB. Consequently, the proposed HA can be adapted to various communication systems requiring dual-polarization characteristics with continuous beam scanning through the broadside.  
Data curation and writing--original draft preparation, C.Y.P.; writing--review and editing, C.Y.P., I.-J.Y. and Y.J.Y.; fabrication and measurement, C.Y.P.; supervision, Y.J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1F1A1069725).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.