esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 3 Linear Programming Problems  
We strongly believe that it is important to understand linear programming very well, if a researcher intends to use optimization as his research tool in WSNs or in any design problem. Concepts in linear programming like linear duality, sensitivity analysis, complementary slackness and shadow prices are needed to understand NLPs and other advanced optimization techniques. Therefore, to have a solid understanding of NLP, a researcher who would like to lay their own foundation in using the tool is strongly advised not to bypass LP.  
Linear Programs (LPs) were the first type of optimization problems to be considered, and as mentioned earlier, they date back to the days of World War II. It is for that class of problems, [PERSON] proposed the _simplex_ method to find the optimum solution efficiently. Basically any LP can be represented in the form:  
\[z=\underset{\mathbf{x}}{min}\;\mathbf{c}^{\mathsf{T}}\mathbf{x}\] (1a) s.t. \[\mathbf{Ax}\leq\mathbf{b}, \tag{1b}\] \[\mathbf{x}_{LB}\leq\mathbf{x}\leq\mathbf{x}_{UB} \tag{1c}\]  
where \(\mathbf{x}\in\mathbb{R}^{n}\) is the vector of \(n\) decision variables, \(\mathbf{c}\in\mathbb{R}^{n}\) is the vector of costs per unit of the corresponding decision variables, \(\mathbf{A}\in\mathbb{R}^{m\times n}\) is the coefficient matrix of all the constraints,represents the vector of values a constraint must not exceed, \(\mathbf{x}_{LB}\) and \(\mathbf{x}_{UB}\) are vectors that hold the lower and upper bounds on each decision variable in \(\mathbf{x}\).  
Generally speaking, the constraint set given by Equation (1b) either:  
1. geometrically forms a polyhedron \(P\) of a set of infinitely many feasible solutions in the space of the decision variables i.e., \(P\subset\mathbb{R}^{n}\), or
2. no solution at all for which we say the LP is _infeasible_.  
An optimal solution for a feasible bounded LP is known to occur at a vertex point of the polyhedron representing the feasible region. We can have infinitely many optimal solutions when, for example, two vertices yield optimal solutions, then all points falling on the edge connecting them would return the same value of the objective function, hence all are optimal. The reader would probably guess that a key idea for devising an algorithm that solves general LPs, is to move around the vertices of the polyhedron of feasible solutions. In fact, that's a correct guess, as the classic simplex algorithm relies in its core on this idea.
# Effects of Differential Jump Training on Balance Performance in Female Volleyball Players
## 3 Results
The random intercept for participants yielded 65.95% of this model's overall variance (\(p<0.05\)). The final model with the best fit consisted of 18 parameters (\(X^{2}=29.29\), \(\Delta df=13\), \(p<0.01\), compared with the initial model). It was expressed by the following equation, where \(Y_{is}\) (WB performance), \(X_{is}\) (measurement), \(Z_{is}\) (leg), and \(\varepsilon_{is}\) (error) vary as functions of individual observations (\(i\); level 1 variable) and participants (\(s\); level 2 variable). \(b_{0}\) and \(u_{0s}\) represent a fixed and a random intercept, respectively; \(b_{1}\), \(b_{2}\), and \(b_{3}\) define the gradients. \[Y_{is}=(b_{0}+u_{0s})+b_{1}X_{is}+b_{2}Z_{is}+b_{3}X_{is}Z_{is}+\varepsilon_{is}\]  
Besides the effects of measurements (Table 1), no effect was found for leg (\(F(1,26.10)=0.33\), \(p=0.57\)), but an effect was found for the interaction of measurement by leg (\(F(2,20.19)=6.98\), \(p<0.01\)). The random intercept for participants yielded 0.41% of this model's overall variance (\(p<0.05\)). Additional fixed and random effects did not produce significantly improved models (best alternative model including player position as a random effect, compared with the final model: \(X^{2}=1.14\), \(\Delta df=1\), \(p=0.28\)). Model-based effects between the control and intervention phases are displayed in Figure 2.  
The final model for WB performance over the phases consisted of three parameters and was expressed by the following equation, where \(Y_{i}\) (WB performance), \(X_{i}\) (phase), and \(\varepsilon_{i}\) (error) vary as functions of individual observations (\(i\)). \(b_{0}\) represents a fixed intercept and \(b_{1}\) represents the gradient. \[Y_{i}=b_{0}+b_{1}X_{i}+\varepsilon_{i}\]  
The inclusion of random intercept, phase, leg, player position, and interactions as fixed and/or random effects did not deliver improved models (best models for each additional effect in comparison with the final model: \(0\leq X^{2}\leq 8.06\), \(1\leq\Delta df\leq 20\), \(0.09\leq p\leq 1\)). In these alternative models, no effects were found for leg (\(F(1,46)=2.87\), \(p=0.10\)), leg by phase (\(F(2,46)=1.53\), \(p=0.23\)), position (\(F(1,46)=2.24\), \(p=0.14\)), or position by phase (\(F(2,46)=1.48\), \(p=0.24\)). Figure 2: Discrepancies between development during the control and intervention phases, displayed as the model estimate (\(\beta\)) \(\pm\) standard error and 95% confidence interval (i.e., grey bars). Note: WB, wobble board performance; Asymmetry, inter-limb asymmetry; AP, anterior–posterior; ML, mediolateral; \(\
u_{\rm CoP}\), mean velocity of the center of pressure; \({}^{*}\)\(p<0.05\); \({}^{***}\)\(p<0.001\). For all other criterion variables, the final models included significant random intercepts for participants.
## 1 Introduction
This paper studies the performance, including the impact of the frequency-selective wireless channel and the non-linear rectifier, of pre-equalizing strategies maximizing the SNR (signal to noise ratio) at the receiver, the PAPR of the received signal, and the channel capacity in a multisine signal based SWIPT system. The pre-equalizer maximizing the SNR boosts the received power of each tone; while the pre-equalizer maximizing PAPR ensures the rectifier operates at the highest PCE at the same input power level. It is shown that simply maximizing the SNR of each tone or PAPR is not the best strategy for SWIPT. A balanced pre-equalizer, trading-off between the SNR and PAPR of the received signal, is proposed to improve the normalized PCE as much as possible. In addition, we also compare our proposed transmission strategy to the low-complexity transmission strategy optimizing solely WPT as proposed in [23], and the water-filling strategy optimizing solely channel capacity. Besides, the transmission strategies are also evaluated considering the conventional QAM modulation. The simulation and measurement results show that the normalized PCE can be improved up to a factor of 3 when using the optimal pre-equalizers with a channel capacity loss under 5% in the multisine PSK SWIPT system. On the other hand, the pre-equalizer proposed in [23] leads to the highest normalized PCE and the lowest capacity for the SWIPT system using QAM. Last but not least, it is shown that by applying the multisine PSK, the maximum normalized PCE is improved up to 60% compared to the one using QAM since the PAPR is improved. The rest of the paper is organized as follows. Section 2 introduces the system model. In Section 3, we demonstrate the multisine PSK and QAM modulations, and the corresponding information rate. Section 4 addresses the considered non-linear energy harvesting model as well as the PCE dependency on the PAPR and power level of the received signals. Section 5 presents the derivation of the transmission strategies for multisine PSK and QAM modulations. Section 6 compares the normalized PCE and information rate of the multisine PSK and QAM modulations, applying the proposed transmission strategies. We conclude this paper in Section 7. Notations: In this paper, we define the time domain signal using lower-case letters with time variation \(t\) as \(a\left(t\right)\). Vectors are represented by lowercase boldface vectors. \(\left\|\cdot\right\|_{1}\) and \(\left\|\cdot\right\|\) denotes the Taxicab norm and the Euclidean norm, respectively. The sign \(\circ\) represents the element-wise Hadamard operation [24].
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Data Retrieval Mechanism
#### 3.6.1 Sensing Module  
The Sensing Module (SM) supports the data collection process. The data can be collected from the sensors available on the MASU unit as well as retrieved from other units. For the MASU framework a sensor is any hardware or software component that act as source of information. Consequently, the SM module calls a number of services for accessing any type of sensor available on the unit.  
This module can obtain raw data, e.g., from the unit's physical sensors, but also high level information from other types of sensors. In the former case, a Collector role will be activated, while in the latter the role played by the unit will be a Processor. The SM also enables the use of data shared by other units as data source and establishes (for every sensor in the unit) the way how it will obtain the corresponding data. This way, the activation of particular Producer or Consumers roles can take place. Accordingly, the SM defines two basic data collection methods: direct and indirect. In the direct method the unit is responsible for capturing data without relying on any other source. In the second case, the sensor receives and processes data that has been previously collected by other units. In the former case the unit will play a producer role, whereas in the latter it will be a Consumer.  
Figure 5: Structure of the Collaborative Sensing Module (CoSM).  
The SM allows both, remote and local activation of the all the sensing services. This fact facilitates the activation of roles in the MASU units by the manager for collaborative sensing activities as well as the independent operation of the units for individual sensing tasks. The SM also allows flexible configuration of the sensing frequency and waiting times.  
The data collection methods specified by the SM allow a selective distribution of data from different sensors. That is, the MASU framework facilitates a flexible selection of the collection methods that will be used for each one of the sensors available on the units. For example, it can decide to use a direct method to capture data from the accelerometer of the unit but to use an indirect method to capture GPS data.  
Figure 6 illustrates a data sharing process conducted by three different units. These units have activated three kinds of sensors. Unit A shares data from two sensors. This data was collected directly by the unit, which means that these sensors were activated in direct mode. On the other hand, this unit also has a sensor activated in indirect mode, receiving data from one of the sensors of Unit C. In addition, Unit B has all its sensors activated in indirect mode. Then, this unit will receive all the data from Units A and C.
## 7 Review of Security and Privacy-Preserving Schemes in VANETs
### Trust-Based Schemes  
VANETs share many characteristics with Mobile _Ad Hoc_ Networks (MANETs). Trust-based routing schemes have been proposed for MANETs and the same idea of trust has also been applied to VANETs in [16, 21, 22]. However, apart from a lot of similarities between them, e.g., decentralized system, mobility, and openness, VANETs are different as they consist of a much larger number of nodes and their topology changes quickly as vehicles move fast [23]. Therefore, forming a network based on the trustworthiness of vehicles is quite challenging. In MANETs, the focus is on reliable packet delivery, whereas in VANETs the aim is to increase road safety and, therefore, the decision-making process has to be very fast due to the high speeds and limited time involved.  
In [24], authors propose trust establishment through infrastructure or in a self-organizing manner. The former means to use a central authority or security infrastructure and the latter involves developing a trust score dynamically. In infrastructure-based trust establishment, there has to be a centralized authority, but it is very difficult to do that quickly in VANETs due to the size and time restrictions. In self-organizing trust establishment, the trust is based on the direct (self-interaction), indirect (receiving interaction information from other nodes), and hybrid approaches (combination of the two) as shown in Figure 5.  
Figure 5: Classification of trust establishmentapproaches.  
As the vehicle's trust score has to be updated and linked with some identification (Pseudonym/Certificate, _etc_.), it again raises the problems of privacy. Moreover, in VANETs, the trust information might not be available in the beginning, or a default trust score has to be assigned. Also, once a group of vehicles form a network, it is then difficult to protect oneself from an insider. To account for this, data-centric trust schemes have been suggested in [25], where a malicious node can be identified based on the data/information being exchanged. In [26], a data-centric mechanism is proposed that enables users to aggregate the data and then determine if a rogue is sending false data. The rogue node can then be reported and its data is ignored.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 5 Discussion  
The results on the meningioma dataset and BraTS2021 dataset prove the model's effectiveness, which further improves segmentation accuracy while reducing computational and memory burdens. However, despite the good performance, the proposed method still has some limitations and deserves further study.  
This method performs dimensionality reduction using TSP, removes noise interference using EGA, and then extracts the edges using CFA because CFA gradually learns to form the shape of the tumor. This method is more applicable in tumor segmentation, but its generalization to other datasets, such as for blood vessel segmentation, should still be investigated. In addition, due to the lightweight nature of the model, it can be considered to continue its lightweight operation, and perhaps attempt to apply it to meningioma surgical video segmentation in subsequent studies.
# GNSS Multipath Detection Using Continuous Time-Series C/N0
## 2 Features of Measured C/N\({}_{0}\) in Dense Urban Areas
### Relationship Between Pseudo-Range Errors and C/N\({}_{0}\)
According to these results, the C/N\({}_{0}\) of satellites affected by strong NLOS signals can easily reach 40 dB-Hz and more while exhibiting decreases below 30 to 35 dB-Hz. Furthermore, the larger pseudo-range errors of these satellites continued for a long time, even though large fluctuations could be observed. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**SV** & **Flag** & \begin{tabular}{c} **Maximum** \\ **Error (m)** \\ **Errors (m)** \\ \end{tabular} & \begin{tabular}{c} **90 th** \\ **Percentage of** \\ **Errors (m)** \\ \end{tabular} & \begin{tabular}{c} **10 th Percentile** \\ **of Errors** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Percentage of** \\ **C/N\({}_{0}\) \(<\) 30** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Percentage of** \\ **C/N\({}_{0}\) \(>\) 30 and** \\ **(\%)** \\ \end{tabular} \\ \hline G01 & LOS & 1.5 & 1.1 & 0.6 & 0 & 0 & 100.0 \\ G03 & NLOS & 233.9 & 150.5 & 65.0 & 16.4 & 74.2 & 4.1 \\ G07 & LOS & 26.9 & 12.4 & 4.7 & 1.6 & 30.6 & 67.8 \\ G08 & NLOS & 103.6 & 61.9 & 35.4 & 7.6 & 60.4 & 28.5 \\ G11 & LOS & 5.2 & 4.1 & 1.1 & 1.0 & 35.4 & 63.6 \\ G22 & NLOS & 155.3 & 99.6 & 56.5 & 2.3 & 31.2 & 64.1 \\ G27 & NLOS & 556.4 & 111.4 & 77.4 & 19.9 & 38.3 & 6.7 \\ G28 & NLOS & 339.6 & 73.8 & 11.4 & 37.7 & 19.7 & 0 \\ G30 & NLOS & 320.8 & 90.5 & 35.5 & 4.5 & 39.3 & 3.3 \\ J01 & NLOS & 279.0 & 175.5 & 116.6 & 31.0 & 53.3 & 0 \\ J02 & LOS & 5.7 & 4.2 & 0.9 & 0 & 12.1 & 87.9 \\ J03 & LOS & 0.1 & 0.1 & 0.1 & 0 & 0 & 100 \\ J07 & LOS & 4.2 & 3.4 & 1.8 & 0 & 100 & 0 \\ E01 & LOS & 7.9 & 5.6 & 0.7 & 0 & 4.1 & 95.9 \\ E13 & NLOS & 198.1 & 121.1 & 39.0 & 68.3 & 30.1 & 0 \\ E21 & NLOS & 210.7 & 115.2 & 47.5 & 13.3 & 79.0 & 6.9 \\ E26 & Partial & 27.7 & 6.0 & 1.4 & 3.7 & 35.3 & 61.0 \\ E31 & NLOS & 305.4 & 30.2 & 3.8 & 21.9 & 43.8 & 27.6 \\ E33 & NLOS & 127.8 & 79.4 & 18.6 & 62.7 & 36.0 & 0 \\ C07 & LOS & 3.8 & 2.7 & 0.7 & 0 & 0 & 100.0 \\ C08 & LOS & 4.9 & 3.5 & 0.6 & 0 & 17.7 & 82.3 \\ C10 & LOS & 11.8 & 4.1 & 0.5 & 4.2 & 38.5 & 43.7 \\ C11 & Partial & 26.5 & 3.9 & 0.4 & 14.3 & 34.4 & 48.4 \\ C13 & LOS & 13.5 & 11.3 & 5.5 & 2.4 & 31.6 & 66.0 \\ C25 & LOS & 6.6 & 4.8 & 0.7 & 0 & 1.1 & 98.9 \\ R02 & NLOS & 631.9 & 154.5 & 75.0 & 12.6 & 45.7 & 36.2 \\ R15 & LOS & 8.2 & 6.4 & 3.2 & 0 & 0.6 & 99.4 \\ R16 & Partial & 113.0 & 55.2 & 1.8 & 2.7 & 9.6 & 62.7 \\ R17 & LOS & 9.8 & 7.3 & 4.3 & 0.2 & 3.7 & 96.1 \\ R18 & Partial & 123.8 & 13.2 & 0.2 & 10.4 & 30.5 & 59.1 \\ R19 & NLOS & 454.4 & 242.6 & 62.5 & 45.4 & 14.0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of pseudo-range errors and C/N\({}_{0}\).
# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 1 Introduction
In this method, multi-band EEG topology maps are introduced by combining the frequency domain characteristics, spatial information, and frequency band characteristics of multi-channel EEG signals, which can clearly represent the changes in EEG signals on the scalp under different emotional states. Inspired by the neuronal circuits in the human brain's nervous system, a lightweight emotion recognition network, ERENet, is proposed to learn the feature representation from the multi-band EEG topology. In ERENet, a multi-band discrete parallel processing module is designed firstly to extract the features of the EEG topology map of each frequency band in parallel, so that the spatial frequency features of each specific frequency band can be effectively learned. In addition, a multi-band information exchange and reorganization module is designed to extract the features of the feature map groups composed of different bands for learning the combined features of different bands. Finally, a weighted classification module is designed, in which the attention mechanism is integrated into the aggregation of feature values at the same location to explore the correlation of different combined features. In this paper, the effectiveness of the proposed framework is evaluated using three classification scenarios: intra-subject, inter-subject, and mixed-subject, and compared with the other methods. The experimental results show that the proposed method outperforms most existing methods in terms of three classification strategies. The main contributions are summarized as follows:  
1. Fusing the frequency domain features, spatial information, and frequency band characteristics of multi-channel EEG signals, a novel emotion recognition framework based on multi-band EEG topology maps is proposed. In this framework, the multi-band EEG topology maps are introduced, which not only reflect the relative positional relationship between EEG channels, but also reflect the differences in frequency bands in EEG frequency domain features. 2. A novel emotion recognition network, ERENet, is proposed. In ERENet, a multi-band discrete parallel processing module is designed for extracting spatial frequency domain features from the EEG topology map of each specific frequency band. In addition, a multi-band information exchange and recombination module is designed for the interaction of features in different frequency bands; thereby, more representational combined features can be extracted. Finally, a weighted classification module is designed to explore the correlation of different combined features. The remainder of this paper is structured as follows: Section 2 presents the dataset and the preprocessing of the data, and describes the proposed method. Section 3 presents the experiments and the results. The experimental results are further discussed in Section 4, and Section 5 concludes this paper.
## 3 Offshore Platform Overview and Finite Element Model
### Pile-Water Interface  
The hydrodynamic coupling between a vibrating pile and its ambient water medium introduces an inertial load component, quantified as the added mass effect (Equation (15)). This interaction alters the dynamic impedance of the pile-water system, necessitating its explicit consideration in offshore structural design [28].  
\[m_{\alpha}=C_{\alpha}A_{p}\rho W \tag{15}\]  
\(m_{\alpha}\) denotes the hydrodynamic added mass induced by the interaction between the pile and surrounding water, with units of kilograms (kg). It quantifies the inertial effects of water on the pile's dynamic response, thereby altering structural dynamic characteristics (e.g., natural frequency). The coefficient \(C_{\alpha}\), determined through the theoretical model in this study, represents the hydrodynamic added mass coefficient. This dimensionless parameter depends on the pile's geometry (e.g., cylindrical, square), dimensions, and flow conditions. \(A_{p}\) refers to the pile's cross-sectional area in square meters (m\({}^{2}\)), \(\rho\) is the density of ambient water in kilograms per cubic meter (kg/m\({}^{3}\)), and W represents the submerged length of the pile along its axial direction in meters (m).
## 2 Methodology
### Evaluation Metrics
#### 2.4.2 Testing Metrics  
When evaluating the model using the test set, three well-known evaluation metrics were used in this study: _precision_, _recall_, and _F1-score_. These evaluation metrics are calculated as follows:  
\[Precision=\frac{TP}{TP+FP}, \tag{1}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{2}\]  
\[F1\ score=2*\frac{Precision*Recall}{Precision+Recall} \tag{3}\]  
where _TP_ is the true positive, _FP_ is the false positive, _FN_ is the false negative, and _TN_ is the true negative.  
A visual confusion matrix was used to provide detailed classification results for each category, helping to analyze the performance of the model on different categories. Additionally, due to the imbalance of distress samples in the dataset, three metrics, macro-average precision, macro-average recall, and macro-average F1-score, have been introduced to overcome possible evaluation biases. The metrics are calculated by summing and averaging the values for each category. Macro-averaging assigns equal weight to each category, ensuring a balanced impact on the overall assessment and providing fairer and more comprehensive model assessment results.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods
### Methods
#### 2.3.4 Forest Stand Age Mapping and Accuracy Assessment  
Two indicators, namely the coefficient of determination (\(R^{2}\)) and root-mean-square error (\(RMSE\)) [40], were used to compare and evaluate the accuracy of forest stand age map ping and modeling using different stand growth models and machine learning methods.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}\bigl{(}y_{i}-y_{i}^{\prime}\bigr{)}^{2 }\Bigr{)}} \tag{1}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\bigl{(}y_{i}-y_{i}^{\prime}\bigr{)}^{2}}{\sum_{i=1} ^{n}\bigl{(}y_{i}-\overline{y}\bigr{)}^{2}} \tag{2}\]  
where \(n\) represents the number of samples, \(i\) represents the \(i\)-th sample point, \(y_{i}\) represents the reference forest stand age, \(y_{i}^{\prime}\) represents the mapped forest stand age and \(\overline{y}\) represents the mean value of the reference forest stand age.