esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology  
### Time Domain Electromagnetics (TDEM)  
TDEM uses transient electromagnetic field diffusion under time-domain control [41; 42]. A time-varying magnetic field is created using a loop of wire. The methodology is based on a ground current injection through a transmitter (T\({}_{\rm X}\)) loop that is alternatively turned on and off. As a consequence of the current injection, a magnetic field perpendicular to the plane of the transmitter loop will be produced. When the current injection is turned off, the decay of the primary field induces electromotive forces within the surface, generating eddy currents that penetrate into the ground, creating a secondary magnetic field whose amplitude decreases with time (transient). The voltage against time for the decay secondary magnetic field associated with the eddy currents produced by the primary transmitter is measured during the turn-off period by the receiver loop (R\({}_{\rm x}\)). The decay is measured at several times, obtaining of a curve which amplitude and shape reflects the distribution of resistivity with depth, where early times provide information at shallow depths while later times provide the information about deeper depths. Detailed description of the physical background of the TDEM method can be found, for example, in [PERSON] and Stewart [43].
# Towards a Fast, Open-Path Laser Hygrometer for Airborne Eddy Covariance Measurements
## 2 Materials and Methods  
Figure 1 shows the schematic setup of the dTDLAS hygrometer. The light emitted by the fiber-coupled distributed feedback (DFB) diode laser is wavelength modulated by a laser current driver to sweep the H\({}_{2}\)O absorption line at 1369 nm. The laser beam is collimated inside the optics and passes through the multipass cell onto the InGaAs detector [26]. A transimpedance amplifier is used to amplify the photodiode signal and to prepare it for its digitization before the data are stored and evaluated with a PC.
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 4 Results
### RQ_1
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Approach** & \multicolumn{2}{c}{**EuroSAT**} & \multicolumn{2}{c}{**XAI4 SAR**} & \multicolumn{2}{c}{**UC Merced**} \\ \hline  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline PNs & 0.548 & 0.615 & 0.673 & 0.782 & 0.717 & 0.864 \\ SS & 0.554 & 0.621 & 0.704 & 0.803 & 0.754 & 0.869 \\ FT & 0.554 & 0.623 & 0.702 & 0.806 & 0.753 & 0.869 \\ BD-CSPN & 0.558 & 0.615 & **0.717** & 0.807 & **0.774** & 0.867 \\ LS & 0.551 & 0.603 & 0.694 & 0.777 & **0.774** & 0.869 \\ PT-MAP & **0.560** & **0.627** & 0.647 & 0.739 & 0.670 & 0.816 \\ TIM & 0.558 & **0.627** & 0.714 & **0.810** & 0.759 & **0.874** \\ TFT & 0.548 & 0.615 & 0.673 & 0.783 & 0.717 & 0.864 \\ \hline \end{tabular}
\end{table} 
Table 8: Average accuracy, 200 epochs: EuroSAT, XAI4 SAR, and UC Merced. As for 200 epochs and considering each dataset, the best performance for each shot configuration of a task is in **bold**. In **bold** and blue, the best result for all datasets is presented. Moreover, the cells with green background mean that these are the best results considering all the number of epochs. The cells with a yellow background and values in red mean these were the worst results considering all the number of epochs. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Approach** & \multicolumn{2}{c}{**WHU-RS19**} & \multicolumn{2}{c}{**AID**} & \multicolumn{2}{c}{**RESISC-45**} \\ \hline  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline PNs & 0.783 & 0.908 & 0.660 & 0.827 & 0.467 & 0.660 \\ SS & 0.831 & 0.916 & 0.703 & 0.837 & 0.503 & 0.676 \\ FT & 0.835 & 0.919 & 0.704 & 0.838 & 0.503 & **0.679** \\ BD-CSPN & 0.868 & 0.919 & 0.750 & 0.847 & **0.526** & 0.674 \\ LS & 0.856 & 0.912 & 0.746 & 0.842 & 0.519 & 0.663 \\ PT-MAP & 0.739 & 0.806 & 0.366 & 0.534 & 0.273 & 0.390 \\ TIM & 0.839 & 0.919 & 0.709 & 0.840 & 0.507 & **0.679** \\ TFT & 0.783 & 0.908 & 0.660 & 0.827 & 0.467 & 0.660 \\ \hline \end{tabular}
\end{table} 
Table 9: Average accuracy, 200 epochs: WHU-RS19, AID, and RESISC-45. As for 200 epochs and considering each dataset, the best performance for each shot configuration of a task is in **bold**. In **bold** and blue, the best result for all datasets is presented. Moreover, the cells with green background mean that these are the best results considering all the number of epochs. The cells with a yellow background and values in red mean these were the worst results considering all the number of epochs.
# Failure Identification Method and LMI-Based Control System for Small Spacecraft
## 3 Attitude Controller
### State-Feedhack Controller  
The proposed control method consists of a state-feedback H\({}_{\infty}\) controller that is designed to solve an LMI problem. As already defined in the previous paragraph, a linearizeddynamic system (i.e., an LTI system, as in Equation (3)) is considered. The control policy is defined as  
\[u=-Kx\]  
such that it has  
\[\|G(s)\|_{\infty}<\gamma\]  
As previously mentioned, applying the bounded real lemma to the closed-loop system is equivalent to solving the following LMI problem  
\[\begin{bmatrix}AQ+QA^{T}+B_{u}Y+Y^{T}B_{u}^{T}&QC_{z}^{T}+Y^{T}D_{u}^{T}&B_{w} \\ C_{z}Q+D_{u}Y&-\gamma^{2}I&D_{w}\\ B_{w}^{T}&D_{w}^{T}&-1\end{bmatrix}<0 \tag{4}\]  
\[Q>0\]  
where the matrix is identified as \(M\), and \(Q\in\mathbb{R}^{6,6}\), \(Y\in\mathbb{R}^{3,6}\) and \(\gamma>0\) are the problem variables. The solution of these LMIs is processed using a mathematical solver. In particular, [PERSON] [32] integrated Matlab with the Yalmip [33] package, which is used for the evaluation of the H\({}_{\infty}\) suboptimal state-feedback controller; the author defined the gain matrix \(K\).  
The matrix \(A\) is subjected to uncertainties that are related to the value of reaction wheels' angular momentum. Considering \(N\) values of \(h_{rw,i}\) within the working range, it is possible to build different \(A_{j}\) matrices for each \(h_{rw,i}\) combination of values. For each \(A_{j}\) matrix, an \(M_{j}\) matrix is built from Equation (4). All of the \(M_{j}\) matrices are included in the LMI problem to achieve a unique state-feedback controller \(K\)  
\[\begin{array}{l}M_{1}<0\\ M_{2}<0\\ \vdots\\ M_{m}<0\\ Q>0\end{array}\quad\rightarrow\quad K=Y_{sol}Q_{sol}^{T} \tag{5}\]  
The controller gain \(K\) is defined considering a large combination of the reaction wheels' angular momentum. In this way, the controller guarantees system robustness to the uncertainties regarding the reaction wheels' angular momentum.
## 1 Introduction
used different thicknesses of CNT-PDMS composites on pure PDMS to discover better-performing transducers and found a decrease in the thermal conductivity of composite transducers due to a reduction in the thickness of the CNT film [13]. In another study, the characteristics of LGFUS were evaluated using a perforated photoacoustic lens, and a peak positive pressure of 46.5 MPa was attained [14]. These studies provide significant knowledge and an understanding of the CNT-PDMS composite structure and its relative impacts on shock wave characteristics. However, no study has been conducted on the characterization of shock waves using composite transducers with diameters that are more practical to use (up to 8 cm or more) in order to understand their relative effect on the properties of shock waves. CNT-PDMS composite transducers are a promising modality in the field of optoacoustic transducers due to their exceptional heat conduction and expansion properties. They can generate high optoacoustic pressure at the focal point. Shock waves, one of the characteristic features of acoustics, are used for many therapeutic purposes, including histotripsy and shock wave lithotripsy for kidney and liver stones [15; 16]. Therefore, optoacoustic transducers are another effective way of generating shock waves with similar or improved properties for use in different applications with improved performance. This paper investigates the effect of changing diameter in a CNT-PDMS composite transducer on the peak amplitude and properties of shock waves. The areas of interest are the maximum positive and negative pressure generation of shock waves at the focal point using larger diameters, compared to previous studies. The purpose of using larger diameters is to fabricate transducers with more suitable sizes and peak pressures at the focal point. Smaller diameters are beneficial for cell culture studies, but to further investigate the effect of shock waves, as well as acoustic attenuation and impedance, in transcranial studies, miniature composite transducers cannot be effectively used. Therefore, this paper aims to construct a CNT-PDMS composite transducer with maximum pressure generation using more practical diameters.
## System Description
### Equivalent Signal-To-Noise Ratio from Source to the Destination  
In theoretical analyses, most of the existing work considers the forward process as completely ideal in the DF strategy [10; 11; 24], in which the relay correctly decodes and forwards the received messages. However, A few researchers consider this a non-ideal scenario. Due to possible errors at the relay, the S-R-D channel is clearly non-linear and non-Gaussian. However, one can think of the BER in S-R-D channel as the error probability at the receiver of an equivalent one-hop AWGN link. The BER of the two-hop S-R-D channel is given by [26]:  
\[P_{SRD}=(1-P_{SR})\times P_{RD}+(1-P_{RD})\times P_{SR} \tag{14}\]  
where \(P_{SR}\) and \(P_{RD}\) are the BER at source to relay (S-R) hop and relay to destination (R-D) hops, respectively. The equivalent SNR from the measured BER is applied to describe this non-ideal DF transmission scenario. Then the equivalent SNR is obtained.
## 2 Background  
### Automated Machine Learning (AutoML)  
AutoML is a state-of-the-art deep learning technique that automates the designing of neural network architecture for deep learning. Designing architecture is the most important stage in deep learning, since several types of performance are determined by the design. The majority of the current architecture is developed manually, which is both a time-consuming and error-prone process [1]. However, as problems for deep learning tasks have scaled up to more challenging and complex tasks, a manual design has become more difficult [28].  
The goal of autoML is to find the best performing learning algorithm without human intervention [29]. There are several approaches to autoML: hyper-parameter optimization (HPO), metalearning, and NAS.  
HPO aims to find an optimal hyperparameter in a specific neural network. A hyperparameter is a parameter that is necessary for a deep learning process [30]. The optimization of a hyperparameter is very difficult because each deep learning process has different hyperparameters [2,3]. HPO finds these hyperparameters automatically using various methods. Bayesian optimization (BO) is the most famous method for this.  
Metalearning can improve the performance of neural networks by using metadata. The challenge for the meta-learning methods is that it must learn from the prior experience in a systematic, data-driven way [2]. However, the final performance of the meta-learning methods is generally lower than the HPO or NAS.  
NAS techniques provide an auto-design process for the topology of deep neural network architecture. Recent deep neural networks have been more complex due to their several layers and nodes with links. However, most of the neural network architectures are designed by human experts [2]. Instead, NAS can generate the topology of a neural network architecture automatically. While NAS has similarities with HPO, the difference is that NAS does not focus on parameter tuning.
## 5 Studies on the Effects of _M. oleifera_ Leaf Extracts on Male Reproductive Function
### Food Additives  
In many studies, men who consume healthy food such as fish, fruits, vegetables, legumes, whole grains, and omega-3- and omega-6-fatty acids have increased semen quality as compared to men consuming caffeine, red meat, processed meat, pizza, sugary drinks, and sweets, etc. in their diet [121]. The latter-mentioned foods typically contain monosodium glutamate, a widely used food additive found in many ingredients and processed foods that can be obtained in every market and grocery store [122].  
Monosodium glutamate (MSG) has been associated with different toxicities and linked to obesity, metabolic disorders, and detrimental effects on the reproductive organs [122]. Monosodium glutamate toxicity can be linked to its ability to act on the glutamate receptors and release neurotransmitters playing a crucial role in normal pathological and physiological processes [123]. Due to the ability of MSG to influence cells of the reproductive system, MSG can cause sperm alterations, histological alterations, hormonal imbalance, and oxidative damage, which eventually leads to abnormalities in reproductive function [123].  
Additionally, MSG leads to sperm membrane dysfunction, sperm DNA damage and sperm motility impairment [124]. These can be attributed to the effects of MSG in the induction of oxidative stress within the testes, marked by increased MDA and reduced GSH [125]. A study on male mice orally administered with monosodium glutamate food additive for 30 days showed a reduction in sperm motility of the rats, which was improved with the administration of _M. oleifera_ leaf extracts at 300 and 600 mg/kg doses [96]. Although this is the only study on the effects of _M. oleifera_ on monosodium glutamate-induced reduction of sperm quality, _M. oleifera_ can serve as a potential treatment for male infertility that might occur as a result of poor diet.
# An Efficient Routing Protocol with Overload Control for Group Mobility in Delay-Tolerant Networking
###### Abstract
Then, we propose an efficient DTN routing protocol with overload control for group mobility, which is an extension of our preliminary work in [22]. The group mobility model and the basic idea of using group delivery predictability in addition to individual delivery predictability are similar to our preliminary work in [22], but the work in this paper significantly extends the previous work in [22]. Our proposed work's main novelty and major contributions, which include a major extension from our preliminary work, are summarized as follows:  
* The proposed group mobility model was elaborated. * A proactive overload control protocol with overload control table was proposed in order to reduce the message forwarding of a message delivered to a node in the same group with a destination node. * A message forwarding condition and overload control protocol were efficiently combined for an efficient routing protocol with overload control for group mobility. * The performance of the proposed routing protocol with overload control was compared with comparing protocols, and the effectiveness of the proposed protocol was validated from the aspects of delivery ratio and overhead ratio. The remaining part of this paper is organized as follows. In Section 2, we propose a group mobility model and group delivery predictability model. In Section 3, we describe an algorithm of the proposed protocol in detail using examples. In Section 4, we analyze the performance of the proposed protocol extensively by using simulation. Finally, we conclude this work in Section 5.
## 2 Materials and Methods
### Automatic Classification  
In order to demonstrate the validity of the feature selection procedure, we conducted some experiments to check the classification power of the selected features to predict prosodic quality of the 605 selected recordings. We used the Weka machine learning toolkit [33]. To compare their performance, we employed three distinct classifiers that have shown success in similar classification tasks in our previous work: the C4.5 decision tree (DT), the multilayer perceptron (MLP), and the support vector machine (SVM). Given the size of the dataset, we used the default hyperparameters provided by the Weka software Version 3.8.6. The DT employed hyperparameters with a confidence threshold for pruning set at 0.25 and a minimum number of instances per leaf set at 2. For the SVM, a normalized polynomial kernel with an exponent of 2 was utilized. The complexity constant (C) was configured to 1, the tolerance parameter was established at 0.001, and the epsilon for round-off error was defined as \(1.0\times 10^{-12}\). Regarding the MLP architecture, it featured a single hidden layer with a number of neurons determined by the sum of input features and output classes divided by 2. The learning rate was set to 0.3, the momentum rate to 0.2, and the training proceeded for 500 epochs. To construct the training and testing sets, we implemented the leave-one-speaker-out cross-validation technique. The idea is to leave out one entire speaker's data as the test set in each iteration while training the model on the remaining data. This process is repeated for each speaker in the dataset. These classifiers used the label defined by the expert for the assessment of speech quality. In addition to verifying the effectiveness of the selected features in classifying the quality of the recordings, the use of automatic classifiers is justified by the need to integrate an automatic feedback system for players and therapists into the video game. This enables autonomous gaming and the potential correction of interventions after receiving such feedback.  
In this study, we also used three established feature selection methods in order to compare the classifier performance with our proposed method. The number of features selected is set to the same number obtained with the proposed method. Firstly, we employed information gain with respect to the class for ranking the features and selected the features with higher scores. Additionally, we utilized forward feature selection (FFS) with correlation-based feature subset selection [34] to identify the most relevant features. This method assesses the worth of a subset of features that exhibits high correlation with the class while maintaining low intercorrelation among the selected features. Furthermore, we employed forward feature selection (FFS) to select the most relevant features using the accuracy of a DT classifier as the evaluation metric.
## 4 Deep Reinforcement Learning-Based Resource Allocation Policy
### Deep Actor-Critic Reinforcement Learning Algorithm
#### 4.2.1 The Critic with a DNN  
Figure 6 depicts the DNN at the critic, which is composed of an input layer, two hidden layers, and an output layer. The critic network is a feed-forward neural network that evaluates the action taken by the actor. Then, the evaluation of the critic is used by the actor to update its control policy. The input layer of the critic is an environment state, which contains \((2M+1)\) elements. Each hidden layer is a fully connected layer, which involves \(H_{C}\) neurons and uses a rectified linear unit (ReLU) activation function [45; 46] as follows:  
\[f_{ReLU}\left(z\right)=\max\left(0,z\right), \tag{18}\]  
where \(z=\sum\limits_{i=1}^{2M+1}\mathbf{\omega}_{i}s_{i}\left(t\right)\) is the estimated output of the layer before applying the activation function, in which \(s_{i}\left(t\right)\) indicates the \(i\)th element of the input state, \(s\left(t\right)\), and \(\mathbf{\omega}_{i}\) is the weight for the \(i\)th input.  
Figure 5: The structure of deep actor-critic reinforcement learning.  
The output layer of the DNN at the critic contains one neuron and uses the linear activation function to estimate the state-value function, \(V^{\pi}(s)\). In this paper, the value function parameter is optimized by adopting stochastic gradient descent with a back-propagation algorithm to minimize the loss function, defined as the mean squared error, which is computed by  
\[\mathcal{L}_{\mathbf{\omega}}=\delta^{2}\left(t\right), \tag{19}\]  
where \(\delta\left(t\right)\) is the TD error between the target value and the estimated value, which is given by  
\[\delta\left(t\right)=E[R\left(t\right)+\gamma V_{\mathbf{\omega}}\left(s\left(t+1 \right)\right)-V_{\mathbf{\omega}}\left(s\left(t\right)\right)], \tag{20}\]  
and it is utilized to evaluate selected action \(\mathbf{a}\left(t\right)\) of the actor. If the value of \(\delta\left(t\right)\) is positive, the tendency to choose action \(\mathbf{a}\left(t\right)\) in the future, when the system is in the same state, will be strengthened; otherwise, it will be weakened. The critic parameter can be updated in the direction of the gradient, as follows:  
\[\Delta\mathbf{\omega}=\alpha_{c}\delta\left(t\right)\
abla_{\mathbf{\omega}}V_{\mathbf{ \omega}}^{\pi}\left(s\left(t\right)\right), \tag{21}\]  
where \(\alpha_{c}\) is the learning rate of the critic.
## 3 Simulation Results
### Road Slope Influence  
The last analysis presented to validate and emphasize the potential of the power-weighted efficiency toolbox, is represented by the influence of the road slope on the map generation as well as on the powertrain mode selection. The map obtained on a flat road is compared against the uphill (\(\alpha=10\%\)) and downhill (\(\alpha=-10\%\)) scenarios; the correspondent power flow distribution among the three actuators is reported in Figure 15.  
Passing from downhill to uphill conditions, all maps tend to shift toward lower accelerations due to the presence of the road inclination. The MG1 mostly works as an electric generator, while MG2 provides extra power during extreme accelerations maneuvers. The presence of a road inclination (positive or negative) also contributes to modify the acceleration threshold for switching between Input Split and Compound Split modes, as shown in Figure 16, with the speed threshold kept fixed at 50 km/h.  
Figure 14: Power-weighted efficiency with (**left**) and without (**right**) the activation of the penalty factor \(P_{I}\) for the Compound Split mode and road slope \(\alpha=0\).  
Figure 15: Output shaft power maps of the ICE (**left**), MG1 (**center**) and MG2 (**right**) with a \(\alpha=-10\%\) (**top**), \(\alpha=0\%\) (**middle**) and \(\alpha=10\%\) (**bottom**) road slope to achieve the lower penalized power-weighted efficiency for the Input Split mode.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Preparation of Data for the System
#### 1.3.2 Description of Data and Disease Classes  
Overall, our data consist of 1318 objects, in the original database we consider 49 disease classes, in the publication we limit ourselves to 15, and we provide detailed results for five diseases. We consider 656 possible questions, which are appropriately levelled after further patient questions--a simple selection based on available disease symptoms. The number of objects in the classes we consider in detail is, \(|cl51|=71\), \(|cl112|=66\), \(|cl184|=65\), \(|cl188|=64\) and \(|cl135|=64\).  
The diagnostic system uses disease classes corresponding to different disease entities, which are classified and described below. Each class contains specific diagnostic questions to identify the disease in question based on the patient's responses.