esa-sceva/satcom-chunk-collection huggingface dataset:
# Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios +
## 4 Cramer-Rao Bound for the TDOA Synchronous Architecture  
The CRB is a maximum likelihood estimator based on the Fisher Information Matrix (FIM) commonly used in the localization field to characterize the quality of a node distribution. The CRB is the common metric for characterizing the quality of a node distribution [61]. It provides the minimum localization error achieved by any algorithm in a defined location given a particular sensor distribution. In [61] a matrix form of the CRB was introduced enabling the characterization of the covariance matrix of the sensor distribution. In LPS, this uncertainties definition requires a heteroscedastic noise consideration [62] of the architecture at study since the positioning signal varies its path among the target and the different receivers of the architecture. Thus, the heteroscedasticity must be introduced into the covariance matrix of the FIM:  
Figure 2: Representation of the 3D urban scenario of simulations with the Target Location Environment (TLE) region in blue showing the coverage of the Local Positioning System (LPS). \[J_{mn}=\bigg{(}\frac{\partial h(TS)}{\partial TS_{m}}\bigg{)}^{T}\mathbf{R}^{-1}( \mathbf{TS})\bigg{(}\frac{\partial h(TS)}{\partial TS_{n}}\bigg{)}+\frac{1}{2}tr \bigg{(}\mathbf{R}^{-1}(\mathbf{TS})\bigg{(}\frac{\partial R(TS)}{\partial TS_{m}} \bigg{)}\mathbf{R}^{-1}(\mathbf{TS})\bigg{(}\frac{\partial R(TS)}{\partial TS_{n}} \bigg{)}\bigg{)} \tag{8}\]  
where \(J_{mn}\) represents the Fisher Information Matrix (FIM) element \((m,n)\), \(\mathbf{R}\) is the covariance matrix of the system in which we introduce the uncertainties characterization, \(\mathbf{h}\) is the vector with the path followed by the positioning signal and consequently with the time measurements of the TDOA architecture at study, TS is the target sensor and \(m,n\); the spatial coordinates of the target sensor in the reference system employed. The definition of the covariance matrix must include an actual characterization of the system uncertainties. In this paper, we consider the most extensive consideration of the literature: Noise in LOS [47] and NLOS environments [48] and clock errors [25]. In the TDOA architecture, the time measurements are proved to be correlated [63], thus a complete characterization of the covariance matrix must be accomplished.
## 2 Materials and Methods  
The design structure of the absorber is shown in Figure 1. It consists of a three-layer structure: VO2 on the top layer, a dielectric layer in the middle layer and a metal substrate on the bottom layer. The period of the structural unit is \(p=30\)\(\upmu\)m. The top layer is composed of VO2 with a thickness of 0.08 \(\upmu\)m. As is shown in Figure 1b, the pattern is composed of two perpendicularly intersecting ellipses and a circular hole in the center. The lengths of the semi-major axis and semi-minor axis of the ellipse and the radius of the inner circle are a = 14 \(\upmu\)m, b = 9 \(\upmu\)m and c = 6 \(\upmu\)m, respectively. The dielectric constant of VO2 can be expressed by the Drude model [28], which is:  
\[\varepsilon(\omega)=\varepsilon_{\infty}-\frac{\omega_{\text{p}}^{2}(\sigma)}{ (\omega^{2}+i\gamma\omega)} \tag{1}\]  
where \(\varepsilon_{\infty}\) and \(\gamma\) are 12 and \(5.75\times 10^{13}\) rad/s, respectively. The relationship between plasma frequency and conductivity is:  
\[\omega_{\text{p}}^{2}(\sigma)=\frac{\sigma}{\sigma_{0}}\omega_{\text{p}}^{2}( \sigma_{0}) \tag{2}\]with \(\sigma_{0}=3\times 10^{5}\) S/m and \(\omega_{\rm p}(\sigma_{0})=1.4\times 10^{15}\) rad/s. The middle layer is silicon dioxide (SiO\({}_{2}\)) with a thickness of 12 \(\upmu\)m and a permittivity (\(\varepsilon\)) of 3.8 [29]. The bottom layer is composed of gold with a thickness of 0.2 \(\upmu\)m and a conductivity of \(4.56\times 10^{7}\) S/m. As shown in Figure 1a, the incident wave propagates along the \(Z\)-axis from top to bottom, and the electric field polarization direction is along the \(X\)-axis. The spectral response of the structure is calculated by the finite element method. Periodic boundary conditions are applied in the X and Y directions, and a perfect matching layer is applied in the Z direction. The absorptance can be calculated by calculating the reflection coefficient (\(S_{11}\)) and the transmission coefficient (\(S_{21}\)). The expression of absorptance is:  
\[A(\omega)=1-R(\omega)-T(\omega)=1-\left|S_{11}(\omega)\right|^{2}-\left|S_{21 }(\omega)\right|^{2} \tag{3}\]  
due to the thickness of metal is significantly larger than the skin depth to ensure no transmission, \(T(\omega)=0\). The absorptance here can be expressed as:  
\[(\omega)=1-R(\omega)=1-\left|S_{11}(\omega)\right|^{2} \tag{4}\]
# A Gamified AI-Driven System for Depression Monitoring and Management
## 4 Results: Simulation-Based Evaluation of Depression Detection Models
### Machine Learning Pipeline
#### 4.3.3 Results Summary  
Table 2 summarizes the classification performance across three machine learning models. The random forest model achieved the highest overall accuracy (0.88) and F1 score (0.878), suggesting it offered the most balanced performance in terms of precision and recall. The neural network model showed the highest ROC-AUC (0.931), indicating strong discriminative ability and potential for detecting subtle depressive patterns. While its F1 score was slightly lower than that of random forest, its sensitivity to class separation could prove valuable in future real-world scenarios.  
While the logistic regression model was less accurate overall, its simplicity and interpretability may be advantageous in clinical or resource-constrained settings. These findings highlight a trade-off between accuracy and interpretability that will be further evaluated in real-world studies.  
In addition to classification metrics, our feature importance analysis (Figure 4) showed that mood score and environmental features, such as temperature, were the most influential predictors. This confirms the value of multimodal data integration.  
Furthermore, the adaptive survey simulation over a 12-week virtual period demonstrated the system's capacity to adjust to evolving user states, reducing question fatigue while maintaining data relevance. This validates the theoretical design of the adaptive EMA logic embedded in the platform.  
Together, these results establish the proof-of-concept value of the proposed system. The combination of high model performance, real-time personalization, and adaptive feedback highlights the system's potential as both a research platform and a low-intensity self-regulation tool for depression support.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **ROC-AUC** \\ \hline Random Forest & 0.880 & 0.887 & 0.869 & 0.878 & 0.915 \\ Logistic Regression & 0.670 & 0.685 & 0.616 & 0.649 & 0.723 \\ Neural Network & 0.865 & 0.883 & 0.838 & 0.860 & 0.931 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance on simulated depression dataset.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 4 Simulation Results And Errors Analysis  
Considering the simulation in an indoor multipath Rician fading channel, in order to improve the tracking capability, we suggest configuring the Co-Band PRS as a continuous signal. This is achieved by increasing the symbol length of PRS in one time slot (\(L_{\text{PRS}}=12\)) and configuring \(T_{\text{P}\text{P}\text{S}}^{\text{PRS}}\) and \(T_{\text{P}\text{P}\text{S}}^{\text{PRS}}\) to be equal. This configuration increase the duty cycle of the Co-Band PRS signal in the time domain. The specific configuration is shown in Table 1.
# Alluvial Fan Scree Deposits: Formation Characteristics and Erosion Mitigation Strategies
## 5 Environmental Friendliness Assessment of SH Polymer Material
### Methodology  
The experimental protocol comprised three operational phases:  
1. Substrate preparation: A thin layer of soil was applied to the wooden cultivation pots, followed by the burial of tall fescue seeds in a grid pattern within the soil.
2. Vegetation pre-establishment: Two spraying methods were employed: one involved covering the soil surface with dry soil after burying the seeds, and then spraying SH polymer material, simulating a scenario where vegetation was already present on the soil surface before the application of SH polymer material. In this case, there was a layer of soil between the seeds and the SH polymer material.
3. Bare soil conditions: The second method included spraying SH polymer material after burying the seeds and then covering with dry soil, simulating a situation where vegetation was not initially present on the soil surface. In this case, the contact between seeds and the SH polymer material was more direct.  
SH polymer material was then sprayed, with application rates of 0.6, 0.9, 1.2, 1.5, and 2.0 kg/m\({}^{2}\). The detailed experimental process is illustrated in Figure 10 and Table 6. The experiment involved observing the growth of grass samples in the test area, evaluating the water retention and permeability of the SH polymer material to assess its environmental friendliness. A control group with no application of SH polymer material was also included.  
Under the five application rates of SH polymer material and two application methods (covering soil before or after spraying), tall fescue grass successfully emerged and exhibited robust growth. The impact of different methods on tall fescue grass was minimal, indicating that the SH polymer material had good permeability and water retention compatible with the AFSDs. The experiment demonstrated that applying SH polymer material in this soil  
Figure 10: The overall growth state of tall fescue. (**a**) Experimental group and (**b**) control group (0 kg/m\({}^{2}\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Treatment Sequence** & **Application Rates (kg/m\({}^{2}\))** \\ \hline Soil coverage prior to spraying & 0.6, 0.9, 1.2, 1.5, 2.0 \\ \hline Spraying prior to soil coverage & 0.6, 0.9, 1.2, 1.5, 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental matrix.  
type does not negatively affect plant growth, highlighting its environmentally friendly nature and potential application in practical engineering.
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Fault Diagnosis and Prediction
#### 2.2.2 Method Based on Deep Learning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Application**} & \multicolumn{2}{c}{**Type of Equipment**} & \multirow{2}{*}{**Signal**} & \multirow{2}{*}{**Fault Type**} \\ \hline
[163] & \multirow{2}{*}{Fault detection} & Induction & Current & \\  & and isolation & motor & signal & \\
[168] & \multirow{2}{*}{Fault detection} & Brushless DC & Current and & \\  & and diagnosis & motor & vibration signal & \\
[171] & \multirow{2}{*}{Fault diagnosis} & Induction & Rotating sound signal & \\
[172] & \multirow{2}{*}{Fault diagnosis} & Induction & Vibration and current signal & Unbalance fault with different severity \\  & & motor & & \\  & & Permanent & Three-phase current signal & open and shot circuit fault \\
[183] & \multirow{2}{*}{Fault detection} & Magnet & and rotor & (instantaneous fault or gradual fault), \\  & & Synchronous & position & and winding resistance increase or \\  & & Motor & information & decrease (early faults) \\
[184] & \multirow{2}{*}{Fault diagnosis} & Three-phase & \multirow{2}{*}{Vibration} & \multirow{2}{*}{Vollage imbalance, rotor imbalance,} \\  & & asynchronous & signal & faulty bearing, broken rotor bars, and \\  & & motor & & \\
[186] & \multirow{2}{*}{Remaining} & Induction & signal & \\
[173] & \multirow{2}{*}{Fault prediction} & Induction & Current and voltage signal & Bearing fault \\  & & prediction & motor & \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Pump condition monitoring using recurrent neural network.
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 5 Simulation, Parameters and Metrics
To measure the RTT, we use a time stamp in the header option of the RREQ and RREP messages. (5) Gain: To better understand the gain that GA-AOMDV can achieve compared to other protocols, including GA-AODV, LEACH-GA, EPAR, DSR, EPAR-BFS, AODV, we calculate the gain as:  
\[\text{Gain}\backslash\text{Saving}=\pm\big{(}\big{(}\sum y_{1i}-\sum y_{2i} \big{)}/\sum y_{2i}\big{)}\times 100\ \ \ \ \ \ [\%] \tag{5}\]  
where  
\(y_{1i}\): is the performance value of the calculated metric for our protocol,  
\(y_{2i}\): is the performance value of the calculated metric for other protocols,  
\(\sum\) is the summation over the range from \(i=1\) to the number of x-axis points of the metric \(y\). The metrics we evaluate are packet delivery ratio, energy consumption, throughput, and RTT. If Equation (5) produces a positive value, this indicates the gain that our method can achieve; otherwise, if the output is negative, it indicates the saving which would happen in the case of energy consumption and RTT. (6) Average End-to-End delay: It denotes the average time required that a data packet delivers to the destination end. \[E2E=\sum_{i=1}^{n}(R_{i}-S_{i})/n\ \ \ \ \ \ \ [\text{Seconds}] \tag{6}\]  
where  
\(R_{i}\) represents the simulation time in which the data packet \(i\)th delivered. \(S_{i}\) denotes the simulation time when the packet \(i\)th sent. and \(n\) is the number of data packets delivered.
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
###### Abstract  
Soil stabilization is a critical step in numerous engineering projects, preventing soil erosion, increasing soil strength, and reducing the risk of subsidence. Due to its inexpensive cost and potential environmental benefits, waste materials, such as waste marble powder (WMP), have been used as additives for soil stabilization in recent years. This study investigates waste marble powder's effects on unconfined compressive strength (UCS) and clayey soil's ultrasonic pulse velocity (UPV) at different water contents and curing times, and artificial neural networks (ANNs) are also used to predict the UCS and UPV values based on three input variables (percentage of waste marble dust, curing time, and moisture content). Geo-engineering experiments (Attereberg limits, compaction characteristics, specific gravity, UCS, and UPV) and analytical methods (ANNs) are used. The study results indicate that the soil is high-plasticity clay (CH) using the Unified Soil Classification System (USCS), and adding waste marble powder (WMP) can significantly improve the UCS and UPV of clay soils, especially at optimal water content, curing times of 28 days, and 60% WMP. It is found that the ANN models accurately predict the UCS and UPV values with high correlation coefficients approaching 1. In addition, this study shows that the optimum water content and curing time for stabilized clay soils depend on the grade and amount of waste marble powder utilized. Overall, the study demonstrates the potential of waste marble dust as a soil stabilization additive and the usefulness of ANNs in predicting UCS and UPV values. This study's results are relevant to engineers and researchers working on soil stabilization projects, such as foundations and backfills. They can contribute to the development of sustainable and cost-effective soil stabilization solutions.  
c 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
class: Soil, soil stabilization; waste marble powder (WMP); unconfined compressive strength (UCS); ultrasonic pulse velocity (UPV); artificial neural networks (ANNs)
# High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF
## 4 Range Ambiguities  
For traditional SAR systems with a uniform PRF, the position of the ambiguous area is fixed during the aperture time, as shown in Figure 13a. We make an assumption that \(A^{\prime}\) is the ambiguous component of point \(A\), and it is a strong pointlike target. As a result, after azimuth focusing is applied, the ambiguous energy of point \(A\) is focused at the same point, making the ambiguity of point \(A\) much worse than that of other points. In contrast, this situation will be different for SAR systems with changing pulse intervals because the ambiguous components of a strong pointlike target are located at different ranges [17]. As illustrated in Figure 13b, the ambiguous position of point \(A\) shifts from the red point to the blue point and eventually reaches the yellow point. In general, we assume that the red point \(A^{\prime}\) is a strong pointlike target, and both the blue and yellow points are normal targets. For point \(A\), its ambiguous energy exists only in one of the echo pulses instead of in almost all the echo pulses during the aperture time. Moreover, it has to be emphasized that along with the changing PI, the ambiguous energy of the strong pointlike target \(A^{\prime}\) would spread out to other points, resulting roughly in a uniform distribution of range ambiguity-to-signal ratio (RASR) along the range direction.  
A design example is presented in the following based on the parameters summarized in Table 2. Ten strong pointlike targets are distributed in the ambiguous area. In particular, the uniform PRF is chosen as 3714 Hz, and the varying PRFs are chosen in the interval [3535 Hz, 3714 Hz]. The performance prediction of the RASR is shown in Figure 14, where the red line and the blue line represent the RASR of the uniform PRF system and that of the CVPRF system, respectively. Due to ten strong pointlike targets distributed in the ambiguous area, the RASR of the uniform PRF system (red line) is above \(-20\) dB, which is regarded as a standard value. By dispersing the ambiguous component to different targets, instead of affecting the same point target, the RASR of all targets will keep under \(-20\) dB,  
Figure 12: Distribution of targets in the simulation scene (**a**) and the echo data of the uniform PRF (**b**) and CVPRF (**c**); Echo data of CVPRF with the step size of PRF up to 1 Hz (**d**) and 2 Hz (**e**).  
with the help of CVPRF technique. Furthermore, the RASR of the ten targets with strong point-like ambiguous component, is reduced by 4\(\sim\)6 dB.
## 2 Challenges in the Market Structure
### Data Requirements
#### 3.6.3 Technical Data for PV Parks and Wind Turbines  
If available, TSOs and DSOs have to give technical specifications for every type of installed wind turbine after choosing the appropriate POIs. Otherwise, TSOs and DSOs will substitute generic standard data. To be more precise, the technical specification data required are presented in Figure 7:For each turbine: the turbine type, longitude, latitude, altitude, rotor diameter, tower height, rotor height, A-factor, form factor c, annual average wind speed, vertical average shear component, extreme wind speed (10 min average), survival wind speed (3 sec average), automatic stop limit (10 min average), rated power, rotor speed, rated wind speed (30 sec average), cut in wind speed (3 sec average), cut out wind speed (10 min average), restart wind speed (10 min average), power curves.
* **For each section of PV panels in solar parks:** longitude, latitude, altitude, tracking or static panels, power conversion factor, panel's tilt angle.
* **For selected overhead lines:** longitude, latitude, altitude, tower type, tower total height and wire height. The overhead line route cross-section for the OHLs that will be covered by innovative forecasting must also be provided.
* The names of the two substations they connect, the voltage level, the number of circuits, and the parallel OHL index are all required for proposed critical lines (DSOs and TSOs).
# A Direct Search Algorithm for Global Optimization
## 3 A Direct Search Algorithm for Global Optimization
### The Basic Algorithm
#### 3.4.9. Implementation  
An implementation of the basic algorithm in pseudocode is given in Algorithm 1.  
```
1:functionBGDS(\(f,\mathbf{x}_{0}\))
2:parameters:\(\epsilon>0\), \(P\in\mathbb{N}\)
3:definitions:\(s(\boldsymbol{\xi}):=P\boldsymbol{\xi}-\lfloor P\boldsymbol{\xi}\rfloor, \phi(\boldsymbol{\xi}):=f(s(\boldsymbol{\xi}))\)
4:\(\mathbf{z}\leftarrow\operatorname{rand}(\dim(x_{0}))\)
5:\(\boldsymbol{\xi}_{0}\leftarrow(\lfloor P\mathbf{z}\rfloor+\mathbf{x}_{0})/P\)
6:\(\epsilon\leftarrow\epsilon/P\)
7:\(\boldsymbol{\xi}\leftarrow\operatorname{LDS}(\phi,\boldsymbol{\xi}_{0}, \epsilon)\)\(\triangleright\) Local direct search
8:return\(s(\boldsymbol{\xi})\)
9:endfunction
10:functionLDS(\(\phi,\boldsymbol{\xi}_{0},\epsilon\))
11:parameters:\(\rho,\sigma,c\in\mathbb{R}:1\leq\rho,0<\sigma<1,0<c,0<\Delta\)
12:definitions:\(\operatorname{vset}(\boldsymbol{\xi},\Delta):=\boldsymbol{\xi}\in\mathbb{R}^{n}, \Delta\in\mathbb{R}\), \(\operatorname{vmin}(\mathcal{V})\in\operatorname{arg}\min\{\phi(\boldsymbol{ \xi}_{i}):\boldsymbol{\xi}_{i}\in\mathcal{V}\}\)
13:\(\boldsymbol{\xi}\leftarrow\boldsymbol{\xi}_{0}\)
14:\(\mathcal{V}\leftarrow\operatorname{vset}(\boldsymbol{\xi},\Delta)\)
15:\(\boldsymbol{\xi}_{\min}\leftarrow\operatorname{vmin}(\mathcal{V})\)
16:while\(\epsilon<|\Delta|\)do
17:if\(\phi(\boldsymbol{\xi}_{\min})<\phi(\boldsymbol{\xi})-c\Delta^{2}\)then\(\triangleright\) Expansive translation
18:\(\boldsymbol{\xi}\leftarrow\boldsymbol{\xi}_{\min}\)
19:\(\Delta\leftarrow\rho\Delta\)
20:\(\mathcal{V}\leftarrow\operatorname{vset}(\boldsymbol{x},\Delta)\)
21:\(\boldsymbol{\xi}_{\min}\leftarrow\operatorname{vmin}(\mathcal{V})\)
22:endif
23:if\(\phi(\boldsymbol{\xi})-c\Delta^{2}\leq\phi(\boldsymbol{\xi}_{\min})\)then\(\triangleright\) Rotation
24:\(\Delta\leftarrow-\Delta\)
25:\(\mathcal{V}\leftarrow\operatorname{vset}(\boldsymbol{\xi},\Delta)\)
26:\(\boldsymbol{\xi}_{\min}\leftarrow\operatorname{vmin}(\mathcal{V})\)
27:endif
28:if\(\phi(\boldsymbol{\xi})-c\Delta^{2}\leq\phi(\boldsymbol{\xi}_{\min})\)then\(\triangleright\) Shrinkage
29:\(\Delta\leftarrow\sigma\Delta\)
30:\(\mathcal{V}\leftarrow\operatorname{vset}(\boldsymbol{\xi},\Delta)\)
31:\(\boldsymbol{\xi}_{\min}\leftarrow\operatorname{vmin}(\mathcal{V})\)
32:endif
33:endwhile
34:return\(\boldsymbol{\xi}_{\min}\)
35:endfunction
```  
**Algorithm 1** Basic algorithm.