esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of Wireless Power Harvesting Metrics
### Peak Passive Voltage  
A bridge rectifier, a single diode rectifier, or an ideal diode rectifier can be used to generate the passive peak voltage. Diodes are used to remove the input signal's negative voltages. A softening capacitor is connected to the rectifier circuits to decrease rectification ripples, resulting in a smooth DC output [69]. Defining the rectifier's sensitivity and efficiency is the relationship of Vpeak to VTH at the input. Allowing Vpeak to fall below VTH disables the multiplier, identifying the sensitivity. The rectifier output voltage is also related to Vpeak, as the number of stages in the rectifier ladder determines the output voltage. Passive voltage boosting can be achieved by connecting the reactive and resistive components, as shown in Equation (6) [70].  
\[\frac{V_{rec}}{V_{ant}}=\frac{R_{rec}+jX_{rec}}{(R_{ant}+R_{rec})+j(X_{ant}-X_{ rec})} \tag{6}\]  
where:  
\(V_{rec}\): the input voltage of the rectifier.  
\(V_{ant}\): the voltage source of the antenna.  
\(R_{ant}\): the resistor of the antenna.  
\(jX_{ant}\): the inductor of the antenna.
## 3 Research on an Improved CGAN Ground Grid Imaging Algorithm
### Objective Function and Training Process  
The loss function of the improved CGAN algorithm in this paper is mainly composed of the L1 loss and the adversarial loss. The weighted combination of the two losses constitutes the final total loss value. The L1 loss is defined as the average value of the absolute error between the generated sample and the real sample. Compared with the L2 loss, the L1 loss has better robustness and can generate smoother images, which is more suitable for ground grid corrosion imaging tasks [24]. Considering the objective functions of G and D, the objective function of the network structure proposed in this paper is expressed as follows:  
\[\begin{split}& L=\lambda_{1}\text{arg}\min_{G}\max_{D}\!\!L_{ CGAN}(D,G)+\lambda_{2}L_{I}\\ &\left\{\begin{array}{c}L_{CGAN}(D,G)=E_{x,y\sim p_{data}(x,y)} \left[\log D(x,y)\right]\\ +E_{x\sim p_{data}(x)}\left[\log(1-D(x,G(x)))\right]\\ L_{1}=E_{(x,y)\sim p_{data}(x,y)}\left[\parallel y-G(x)\parallel_{1}\right] \end{array}\right.\end{split} \tag{13}\]  
In Equation (13), \(x\) represents the conditional input image, \(y\) is the target real image and \(p_{data}\) represents the distribution of data.  
The entire training process is shown in Figure 8. During the training process, the training data and test data are first normalized, and then the structural parameters of the generative network and the discriminative network are set, including loss function parameters, activation function parameters, batch size parameters, number of iterations, etc., and initialized. The Adam optimizer is used for network training. The training process is mainly divided into two stages. First, the parameters of the discriminator D are fixed, and the EIT image data generated by G and the true conductivity distribution are combined with the conditional information, respectively, and input into the discriminator D to optimize the parameters of G and improve the image generation ability of the generative network; then, the parameters of the generator G are fixed, and the parameters of D are updated according to the output of G to enhance the ability of the discriminative network to identify true and false samples. The two are optimized alternately to continuously improve the overall performance.
## 4 The Proposed Model
### Dynamic Weight Average Weighting Scheme  
In multi-task learning, model performance is significantly influenced by the weights assigned to each task. If the weight of a task's loss is too high, the model will focus more on that task while neglecting others. Conversely, if the weight is too low, the model cannot effectively utilize the information from that task. Both situations can result in a decline in the model performance. However, manually adjusting the optimal weights for each task is often expensive and challenging [30]. Therefore, this paper introduces the Dynamic Weight Average (DWA) weighting scheme [14] on top of the existing multi-task learning model. During the training process, this scheme dynamically adapts the weights associated with each task, allowing the model to better balance different tasks and achieve optimal performance.  
The relative descending rate of the \(k\)-th task can be calculated as:  
\[\theta_{k}(c-1)=\frac{\mathcal{L}_{k}(c-1)}{\mathcal{L}_{k}(c-2)} \tag{14}\]  
where \(c\) is an iteration index. \(\mathcal{L}_{k}(c)\) represents each epoch's average loss calculated over several iterations. The smaller \(\theta_{k}(c-1)\) indicates that the value of the task's loss decreases more compared to the previous training round, which also means that the task gets better learning, so the attention to this task can be reduced appropriately in the \(c\)-th iteration. The weight assigned to the \(k\)-th task is calculated as:  
\[\lambda_{k}(c)=\frac{\textit{Mexp}(\theta_{k}(c-1)/T)}{\sum_{i}\textit{exp}( \theta_{i}(c-1)/T)} \tag{15}\]  
where \(T\) denotes a temperature parameter that controls the smoothness of the task weighting. Here the value of \(T\) is set to 2 following [PERSON] et al. [14]. To ensure consistency of loss value, the value of \(M\) is set to 3.  
For \(c=1,2\), we initialize the value of \(\theta_{k}(c)\) to 1. Finally, the DWA technique is employed to ascertain each task's weight in the multi-loss function.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 2 Relevant Work
### Deep Learning
[21] addressed the challenge of limited labeled data in remotely sensed image scene classification by proposing a semi-supervised learning approach. The authors developed a co-training algorithm that utilizes both labeled and unlabeled data to increase the classification accuracy of the model. [PERSON] et al. [22] proposed an end-to-end ET-GSNet solution for remote-sensing image scene classification. It combines the strengths of Vision Transformer (ViT) and ResNet18 through knowledge distillation. The proposed method outperforms state-of-the-art algorithms in classification performance on remote-sensing databases. In many ways, it also demonstrates excellent generality for a wide range of occupations.
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 4 Simulation Setup  
This section illustrates the experimental setup and the network topology. Goodput, throughput, and convergence time are the parameters used to measure the performance of the proposed algorithm.
###### Abstract
As a result, it has strong potential to detect vegetation responses to water deficit and heat stress. However, coarse resolution and short time scales hinder the application of SIF data. Researchers have used machine learning methods based on the OCO-2 Global SIF product (GOSIF) to obtain data at fine resolution (0.05\({}^{\circ}\), 8 days) and longer time periods (2000-present) globally [36] to facilitate the study of SIF response to drought. However, it is still insufficient for drought and drought hazard analysis, which typically requires at least 30 years of data [37]. Therefore, this study proposes the use of the SIF data of a short time series (2000-2020), which is more sensitive to high temperatures and drought, as the dependent variable, and the meteorological drought index (SPEI), vegetation drought index (VHI), and soil drought index (SMCI) as independent variables. The relative weight method [38] was used to determine the weights of the independent variables to construct a comprehensive drought index for the long time series (1982-2020). Therefore, NEC, which is sensitive to climate change, was used as the study area in this study. First, a comprehensive drought indicator CDI reflecting drought and its effects was built based on the SPAC theory by integrating data from multi-source information, such as meteorology, remote sensing, and reanalysis, and its applicability in the NEC region was confirmed. Second, the spatial and temporal distribution characteristics of drought were analyzed based on CDI. Finally, we constructed static and dynamic drought hazard assessment models to quantitatively assess drought hazard during the crop-growing season (May to September). The findings of this study are intended to provide a scientific basis for drought risk management, disaster prevention, and mitigation strategies in NEC.
## 2 Materials and Methods
### 2.3. Wrist Angle Prediction with a Multi-Camera System and Open-Source Software (3 DCV)  
This method for wrist angle prediction used the DLC3D dataset with the addition of machine learning to determine if prediction accuracy could be improved over the DLC/Anipose approach outlined in Section 2.2.5. The XYZ marker locations established by DLC were used as the model features(attributes), and the FE and RUD angles were used for the model labels. The DLC3D dataset was divided into 4 shuffles that randomly selected 12 participants for training and 4 participants for testing without replacement. The 12-participant training set was then divided into an 80%/20% training/validation split for model training. The XYZ marker locations were used as the model features/attributes, and the FE and RUD angles were used for the model labels.  
We used a number of ML algorithms able to accommodate varying and increasing levels of complexity, including linear, quadratic, and cubic regressions, as well as XGBRegressor, ridge regression with radial basis function kernel approximation, Support Vector Regressor (SVR), and deep neural networks. The performance of each algorithm was optimized through hyperparameter tuning using a GridSearchCV approach to search automatically through the hyperparameters listed in a parameter grid. An Adam optimizer [46], Relu activation function, and mean absolute error loss function provided the best results for all networks.  
After the model was trained for each set of hyperparameters, the related training, validation, and testing error were recorded to determine the best model for this problem.  
Figure 3: Architecture for DeepLabCut data labeling, model training, and 3D kinematic analysis.  
Angles generated by these angle prediction models were compared to the angles generated from the OMC system during ROM trials. Training and validation mean absolute error (MAE) values were determined from the training dataset containing data from 12 of the participants. Testing MAE values were determined from the testing dataset containing data from the remaining 4 participants. MAE values were generated for each of the 4 shuffles.  
A global feature importance metric was calculated for the highest performing model. Global feature importance is used to measure the importance of a feature for the entire model rather than a local importance which measures the contribution to a specific observation [47]. SHapley Additive exPlanation (SHAP) values were selected because they are a commonly used explainability technique for tabular datasets [48]. To assess which body marker locations had the greatest influence on the outcome, the top features of importance were calculated for FE and RUD separately. This was determined by taking the average positional value of the 10 top features of importance, across the 4 shuffles.
# Experimental Study on Out-of-Plane Seismic Performance of New Type Masonry System
## 3 Shaking Table Tests
### Test Specimens  
In this study, three single-story, full-height, narrow specimens were designed and fabricated supposing the single-story storage building, as shown in Figure 6: (a) typical concrete block wall specimen with joint mortar (Specimen CB); (b) seismic block wall with H-shaped key block without joint mortar (Specimen HS); (c) seismic block wall with peanuts-shaped key block without joint mortar (Specimen PS). Their dimensions were approximately 2000 \(\times\) 800 \(\times\) 190 mm (10 \(\times\) 2 \(\times\) 1 layers) in height, length, and thickness, respectively. In this test, due to the restrictions of the experimental equipment, only the central part (2 layers) of the wide wall was considered, which has larger out-of-plane deformation because it is less affected by the transverse wall.  
The typical concrete block wall can resist only friction force to the horizontal load after losing the adhesive force between concrete block and joint mortar. On the other hand, the proposed concrete block wall systems are expected to have higher deformability, due to the interlocking mechanism between the main and the key blocks, as shown in Figures 4 and 5.
## 1 Introduction
Simultaneously, research endeavors focused on incorporating additional parameters, such as Receiver Autonomous Integrity Monitoring (RAIM) [30; 31; 32] and a Weighted Geometric Dilution of Precision (WGDOP) system [33; 34], among other considerations [35; 36; 37; 38], are also on the rise. The recursive method is difficult to apply in large-scale satellite constellations, and it requires too much computing power to move from high resolution to low resolution. The traditional algorithm assumes that the satellite positions are uniformly distributed, which lacks a theoretical basis. It is also incapable of handling the influence of obstructions in a complex working environment. The optimal distributions of satellites derived from theoretical analyses are symmetric with respect to the observing point. However, satellites at a negative angle cannot be observed under the GNSS observing scenarios. Therefore, there is a need to investigate the theory and obtain the preferred satellite distribution combinations under GNSS operation. A suitable optimization method is also examined to minimize the influence of obstructions according to the theory. The biggest problem with intelligent algorithms at present is that algorithms with fast computational speed require training and specify the number of satellites to be finally selected during training, while algorithms that do not require pre-training have poor budget speeds and are not well adapted to scenarios where large-scale LEO satellite constellations are used. Therefore, in the field of large-scale LEO satellite constellation positioning in the foreseeable future, it is necessary to derive configurations with the lowest GDOP through theoretical research and, at the same time, based on the results of the theoretical research, to propose an effective and efficient positioning satellite selection algorithm that can mitigate the influence of obstructions. The remainder of this study is organized as follows. Section 2 briefly introduces the criterion of the GDOP, traditional recursive algorithms for mathematically intractable GDOP problems, and the concept of classical genetic algorithm. In Section 3, configurations with the lowest GDOP are mathematically derived. A satellite selection algorithm incorporating a genetic algorithm is presented based on the derived formulas to overcome the effect of obstruction. In Section 4, real-world Starlink satellite data are used to analyze the performance of the proposed algorithm. Lastly, in Section 5, the content of this study is summarized, and the conclusions are presented.
## 3 Simulation Analysis
### Simulation Analysis: Effect of Flow Field on Wavefront
These findings indicate that when the number of holes per row is increased from 180 to 360, the low-frequency components of the wavefront aberration increase in strength, while the high-frequency components are reduced, which is helpful for facilitating other methods of correction to improve the quality of the beam. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Project** & **PV** & **RMS** \\ \hline Plan C & 0.214 & 0.049 \\ Plan D & 0.152 & 0.032 \\ Plan E & 0.144 & 0.031 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Peak-to-valley (PV) and root mean square (RMS) of wavefront aberration under different schemes. Figure 7: Contour maps of the fluid density in the beam expanding system for plans D and E: (**a**) contour map for plan D; (**b**) contour map for plan E.  
Through the above analysis, we found that when the number of air inlet holes in a single row is 360, the beam passing through the flow field inside the beam expanding system has the best quality. Therefore, when designing our experiment, we used plan E as the experimental scheme to verify the influence of the intake system on the flow field in the beam expanding system and on the light passing through the system.
## 2 Radiative Processes
### Absorption and Emission Spectrum  
The term \"spectrum\" usually denotes a curve thatrepresents the variations of an atomic or molecular property as a function of a parameter. Spectroscopy is the set of methods allowing the determination of these spectra and their analyses.  
When an atom is heated or even undergone an electric discharge, one of its electrons in its peripheral layer (more rarely the internal layers) goes from the fundamental level to a higher energy level called the excited state level. This phenomenon is called absorption. When the electron goes down, it re-emits energy in a light form (photon): this is the emission.  
In classical physics, there can be an energy exchange between an electromagnetic wave of frequency n and a system having certain mechanical energy due to a periodic movement of frequency n\({}_{0}\) (see Figure 1) of the general form, Q = Q\({}_{0}\) cos 2 pnt.
## 4 Leak Detection Technologies
### Hardware-Based Methods
#### 4.1.2 Acoustic Methods
The limitations of the leak noise correlation technique in pinpointing leaks in plastic pipelines are due to the viscoelastic properties of the plastic pipes, which absorb sound energy and weaken the sound waves as they travel along the pipe [47]. Although the cross-correlation method was accurate within a 30 to 60 cm distance between the sensors, it was deemed too expensive, time-consuming, and had a limited range [48]. This method is enhanced by the use of noise loggers, which are compact units consisting of an acoustic sensor coupled with a programmable data logger and a communication module [46]. The loggers are arranged in a group of six or more at adjacent pipes, spaced between 200 to 500 m apart in metallic networks and as low as 80 m in plastic pipes. They are designed to automatically activate during nighttime hours to monitor the acoustic vibrations generated by leakages in the water system and provide actionable information about the location of the leaks. Using permanent acoustic noise loggers with wireless data transmission has significantly improved leak detection efficiency in water distribution networks. The data collected from these loggers can be analyzed using the limitations of leak noise loggers, including their reliance on acoustic detection susceptibility to external noise interference. Future research should focus on enhancing their capabilities through advanced sensor technologies and noise-filtering algorithms. Figure 7: Schematic representation of leak detection through the cross-correlation method.
# The Effects of Adhesive and Bonding Length on the Strain Transfer of Optical Fiber Sensors
## 4 Experimental Test Results and Discussions  
Uniaxial tensile tests were conducted to investigate the strain response measured by the surface bonded optical fiber sensor. A bare single mode optical fiber (Model No: SMF130V, Prime Optical Fiber Corporation, Taiwan) is bonded on the front surface of the central area of the specimen as the sensing fiber of the Mach-Zehnder interferometer. The test specimen is made of aluminum with dimensions of length 100 mm, width 12.5 mm and thickness 5 mm. A strain gauge is bonded on the back surface of the specimen to measure the strain \(\varepsilon_{0}\) of the host material induced by the tensile test. The material properties of the optical fiber, protective coating and host structure are shown in Table 1. The outer radii of the optical fiber and coating are \(r_{f}=62.5\ \mu\ m\) and \(r_{p}=125\ \mu m\), respectively; the thickness of the host structure \(h=5\ mm\). The index of refraction and Pockel's constants [20] are \(n_{0}=1.45,\ p_{11}=0.12,\ p_{12}=0.27\), respectively. The wavelength of the light emitted from the laser diode to the optic fiber is \(\lambda=1549.2\ nm\). The experimental setup is shown in Figure 3.  
The length of the surface bonded optical fiber is varied from \(2L_{f}=50\) mm to 120 mm, with increments of 10 mm in the experimental test to investigate the effect of the bonding length on the optical fiber strain. Three different adhesives, namely epoxy (Model No. 6670, Giuloo Chemicals Co. Ltd., Taiwan), acrylate (Model No. IPS66-GAL, Industrial Polych  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Host Material** & **Coating** & **Optical Fiber** \\ \hline [PERSON]’s modulus (GPa) & 72 & 0.0067 & 72 \\ Poisson’s ratio & 0.3 & 0.49 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties.  
Figure 3: Experimental setup of the tensile test measured by Mach–Zehnder interferometry.  
USA) and M-coat A (Vishay Precision Group, Inc., NC, USA), were used to investigate the influence of the adhesive on the optical fiber strain. The material properties of these three different adhesives are listed in Table 2. Figure 4 shows the results of the strain in the test specimen measured by the strain gauge and the light intensity of the Mach-Zehnder interferometer for epoxy adhesive with bonding length of 60 mm. In the case of acrylate and M-coat, the experimental results are shown in Figures 5 and 6 respectively.  
_i.e._, more strain is transferred to the optical fiber. The coefficient of strain transfer increases with the increase of the adhesive modulus. The influence of the adhesive on the strain transfer rate between the optical fiber and host material is investigated experimentally in this section. The experimental results for the general strain transfer trend related to the bonding length and adhesive agree well with the theoretical predictions.
## 4 Current and Advanced Methods in BEMS
### Automation
#### 4.2.1 Advanced Metering Infrastructure (AMI)  
Smart meters, communications networks, and data management systems are all part of the AMI, which allows utilities and customers to communicate in real time. The system can automatically and remotely assess electricity usage, connect and deactivate service, detect tampering and theft, identify faults and outages, and monitor voltage, among other features that were previously unavailable or required manual intervention [96; 97].