esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methods
Compressed sensing (CS) is used for reconstructing sparse signals from a small number of measurements, with the goal of data compression without a significant loss of information, especially when the signal of interest is sparse in a certain basis, such as the wavelet basis. In contrast, the LASSO algorithm is used in regression and feature selection within supervised learning to identify a robust predictive model and eliminate irrelevant features. Mathematical formulation for feature selection:  
In the following lines, we will briefly present the algorithms that have, as their starting point, the notion of sparsity and their implemented version for feature selection. Basis pursuit (BP) is an optimization technique used to find a sparse representation of a signal or a feature vector. In the context of feature selection, the goal is to identify a small subset of relevant features that significantly contribute to the performance of the classification model. Mathematically, BP can be formulated as follows:  
\[\min_{\beta}\lVert\beta\rVert_{1}\;s.t.\;\lVert y-X\beta\rVert_{2}\leq\epsilon,\]  
where \(\beta\) is the vector of coefficients corresponding to the features, X is the feature matrix, y is the label vector, and \(\epsilon\) is a constant that limits the approximation error. The classification problem formulation for BP and ADMM is to find a model that can correctly predict labels y based on features X. For feature selection, we formulate the problem to favour sparse solutions (\(\beta\) with many zero elements). The optimization formulation for BP and ADMM is:  
\[\min_{\beta}\lVert\beta\rVert_{1}\;s.t.\;y=X\beta.\]  
This is a simplified formulation, where we want to minimize the l1 norm of the coefficient vector, while ensuring that the model fits the data exactly. Orthogonal matching pursuit (OMP) is an efficient algorithm for feature selection in a classification problem and sparse signal reconstruction. Essentially, OMP is a greedy version of matching pursuit (MP) that adds an orthogonality constraint between selected features to improve accuracy and convergence. In feature selection, mathematically, the problem can be formulated as follows:  
\[y=X\beta+\epsilon,\]  
where \(y\) is the label vector (output vector), X is the feature matrix, \(\beta\) is the vector of coefficients corresponding to the features that we want to determine to be as sparse as possible, and \(\epsilon\) is the modeling error. In the case of the OMP algorithm, it tries to solve the problem in an iterative way by formulating the problem as:  
\[y=X\beta+\tau,\]  
where, at each step, the algorithm searches for the feature \(x_{j}\) that is most strongly correlated with the current residuals r, and adjusts the corresponding coefficients \(\beta\). In LASSO, the regression problem is formulated as:  
\[\min_{\beta}\biggl{(}\frac{1}{2n}\|y-X\beta\|_{2}^{2}+\lambda\|\beta\|_{1} \biggr{)}\]  
where X is the feature matrix, y represents the data labels, \(\lambda\) is a regularization factor that controls sparsity, and \(\beta\) is the vector of coefficients that LASSO tries to estimate.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 6 Guidance Navigation and Control (GNC)
### Guidance  
Guidance is usually executed based on data from the Global Navigation Satellite System (GNSS) and motion sensors such as accelerometers or gyroscopes. In the guidance system, the sailing path of USV is determined by path planning. Path planning refers to the system responsible for designing paths for autonomous missions [26]. The path planning process includes two main steps: the determination of a set of points on the map (waypoint), shown in Table 2, and the generation of a path based on the waypoints Figure 8. Zigzag trajectory has been selected as it is commonly used in bathymetry missions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Waypoint** & **Latitude** & **Longitude** \\ \hline
1 & 110.4094434 & \(-7.0806239\) \\
2 & 110.4093455 & \(-7.0806066\) \\
3 & 110.4093656 & \(-7.0805187\) \\
4 & 110.4094568 & \(-7.080536\) \\
5 & 110.4094823 & \(-7.0804482\) \\
6 & 110.4093884 & \(-7.0804282\) \\
7 & 110.4094112 & \(-7.0802885\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Waypoint data.  
Figure 6: The result prototype of USV.  
Figure 7: Interaction among GNC and vehicle.  
In addition, the cruise speed and cruise throttle parameter values are determined in the guidance system. When USV sailing follows the path, the cruise speed is the target speed in meters per second, while the cruise throttle is an estimate of how much throttle is needed in percent to attain cruise speed when sailing straight ahead. In this case, cruise speed value is given at 1.3 m/s.
## 4 Enabling Technologies for THz-FSO Communication  
Recent discoveries in material science, device engineering, and chip-to-fiber photonics integration have made terahertz (THz)-based free-space optical (FSO) communication systems more efficient [63; 67]. Enabling technologies are discussed in this section as major factors that are shaping developments in the study area, represented in Figure 6.  
Unlike traditional RF and optical communication, where direct modulation of the source is commonly used, many THz sources--such as quantum cascade lasers (QCLs) and photomixers--cannot be efficiently modulated using a pumping current due to slow carrier dynamics and thermal constraints [32]. In such cases, external THz modulators become essential for encoding information onto THz carrier waves. Depending on their operating principle, THz modulators can be classified into electronic, optical, and hybrid modulators [34]. Based on field-effect transistors (FETs) and Schottky diodes, electronic modulators enable high-speed amplitude and phase modulation, but suffer from limited efficiency at higher THz frequencies. Optical THz modulators, such as those using plasmonic metasurfaces and electro-optic materials (e.g., lithium niobate, graphene, and black phosphorus), provide ultra-fast modulation speeds and broadband tunability, making them more suitable for future THz communication networks [45]. Hybrid approaches, such as optically controlled modulators, integrate photonic and electronic components to achieve higher efficiency and faster response times. For efficient THz-FSO (free-space optical) communication, modulators based on graphene, metamaterials, and phase-change materials are being developed to achieve dynamic tunability and higher modulation depths [57]. Metasurface-based THz modulators offer enhanced beam steering, polarization control, and spatial multiplexing, making them ideal for use in adaptive THz communication architectures. Integrating machine learning-based adaptive modulation schemes is anotherpromising approach to optimizing the transmission efficiency of THz-FSO systems under varying atmospheric conditions. Given the critical role of THz modulators in enabling high-speed data transmission, future research should focus on low-loss, energy-efficient, and scalable modulator technologies that can be seamlessly integrated into 6G-and-beyond networks [68].
# Demand Control Strategies of a PCM Enhanced Ventilation System for Residential Buildings
###### Abstract  
A ventilated window system enhanced by phase change material (PCM) has been developed, and its energy-saving potential examined in previous works. In this paper, the ventilation control strategies are further developed, to improve the energy-saving potential of the PCM energy storage. The influence of ventilation airflow rate on the energy-saving potential of the PCM storage is firstly studied based on an EnergyPlus model of a sustainable low energy house located in New York. It shows that in summer, the optimized ventilation airflow rate is 300 m\({}^{3}\)/h. The energy-saving of utilizing a ventilated window with PCM energy storage is 10.1% compared to using a stand-alone ventilated window, and 12.0% compared to using a standard window. In winter, the optimized ventilation airflow rate is 102 m\({}^{3}\)/h. The energy-saving of utilizing a ventilated window with PCM energy storage is 26.6% compared to using a stand-alone ventilated window, and 32.8% compared to using a standard window. Based on the optimized ventilation airflow rate, a demand control ventilation strategy, which personalizes the air supply and heat pump setting based on the demand of each room, is proposed and its energy-saving potential examined. The results show that the energy savings of using demand control compared to a constant ventilation airflow rate in the house is 14.7% in summer and 30.4% in winter.  
phase change material; energy storage; ventilation control strategy; building energy conservation
# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 2 Related Work  
Although our method focuses on generating 3D digital stereoscopic holograms from a single black-and-white image using depth maps, previous studies have investigated the use of depth information for various 3D reconstruction and visualization tasks. [PERSON] et al. [16] introduced a method for generating digital holograms from photographs of real 3D objects, employing a time-of-flight 3D image sensor to acquire both color and depth images. Subsequently, a point cloud was extracted from the depth maps to generate the hologram. [PERSON] et al. [12] used digital holograms for creating color-reflection holograms of artwork and historic sites. They captured multiple perspective views via photography, videography, and 3D rendering and recorded interference patterns on holographic materials using specialized hardware. [PERSON] et al. [17] proposed a method for generating holograms from a reduced number of high-resolution perspective images, which is closely related to our study. They used a pixel rearrangement technique to improve the angular resolution of a hogel image without increasing the hogel size or degrading the spatial resolution. This technique involves capturing the perspective images of a 3D scene by recentering the camera and transforming them into parallax-related images for holographic printing. However, the existing methods often require multiple input images, specialized hardware, or color photographs. Our study differs in that it focuses on the generation of 3D digital stereoscopic holograms from single black-and-white images, a scenario not typically addressed in prior studies. Recent breakthroughs in CNN-based monocular depth estimation [18; 19; 20] have enabled accurate prediction of both relative and metric depth through innovative neural network architectures. Modern approaches leverage multiscale training datasets and scale-adaptive networks [21; 22; 23; 24] to generate precise depth maps that perform well across diverse scenes. However, these methods present certain limitations for holographic applications: they are not sufficiently optimized for integration with consumer-grade holographic stereogram printers, and they typically require high-quality RGB inputs while demonstrating limited performance on monochromatic images. To address these limitations, we used deep learning to create and refine depth maps, offering a unique and accessible approach for holographic reconstruction in cases where only limited visual data are available, such as historical photographs. This approach allows the generation of 3D holographic content from legacy images, for which traditional methods prove inadequate. Using the proposed method, a holographic portrait can be created within a viewing angle of 85-100\({}^{\circ}\).
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 4 Review Methodology
### Search Strategy  
A systematic search for primary studies was conducted on 4 February 2025, using the following digital libraries: Springer Link, IEEE Xplore (Institute of Electrical and Electronics Engineers), Scopus, Multidisciplinary Digital Publishing Institute (MDPI),and PubMed. The search process was carried out by one researcher and validated by two additional researchers to ensure consistency and reliability. The search terms were defined based on the main components of the research, technology, task, and domain, and included the following combination of keywords and their synonyms: (\"generative adversarial network\" OR \"GAN\") AND (\"segmentation\" OR \"image segmentation\" OR \"semantic segmentation\") AND (\"histology\" OR \"histological\" OR \"histopathology\" OR \"histopathological\").
# Deep Reinforcement Learning for RIS-Aided Multiuser MISO System with Hardware Impairments
###### Abstract
* Due to the expectation operator along with the hardware impairment, it is challenging to derive the closed-form data rate expression. Furthermore, the objective function in terms of the max-min format is discontinuous and non-differentiable. As a result, the existing algorithms based on mathematical derivations are not applicable. Instead, we resort to the powerful deep deterministic policy gradient (DDPG) algorithm to solve this challenging optimization problem. * Note that the convergence speed is quite fast as it can converge within 600-900 iterations and the overall computational complexity are mainly from the calculation of rewards, which are only simple mathematical calculations. In addition, the calculated parameters can be used in subsequent steps and only need to be recalculated when the statistical CSI changes. Once the neural network is trained, it can be directly applied in real-time applications with only simple mathematical calculations. The neural networks only need to be retrained once the statistical CSI changes. Hence, the computational complexity is not high.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification  
Areas with abundant wind energy are generally far from load centers. Therefore, large-scale wind farms are established and wind power is transmitted to the power system over long distances. The typical DFIG-based wind farm is shown in Figure 1. The outlet voltage of the DFIG is generally 690 V. The wind power is transmitted through a wind turbine unit transformer, collector system, substation, and transmission lines. The voltage level of the collector system is usually 35 kV and the voltage levels of the transmission line are usually 110 kV or 220 kV. Notably, the 35 kV system is not grounded, and the neutral point of the high-voltage system is directly grounded.  
Figure 2 shows a typical DFIG with a rotor crowbar protection circuit. It mainly consists of a wind turbine, gearbox, asynchronous generator, RSC, grid side converter (GSC), rotor crowbar protection circuit, and control system. The DFIG stator is directly connected to the grid, whereas the rotor is connected to the grid by a back-to-back converter. The converter system enables variable speed operation of the wind turbine by decoupling the power system electrical frequency and the rotor mechanical frequency. A more detailed description of the DFIG system together with its control was previously reported [32].
## 3 Results and Discussion
### Multi-Criteria Decision Making with TOPSIS
#### 3.6.3 Formulation of the Decision Matrix (X) and Normalized Decision Matrix (R)  
The values obtained following a series of experiments and rigorous testing are enumerated in Table 7.  
Accordingly, the decision matrix, \(X\), is created and given in Equation (10).  
\[X=\begin{bmatrix}2.97&3.05&0.0400&176\\ 0.95&1.43&0.0025&176\\ 3.93&0.16&0.0020&12,000\\ \end{bmatrix} \tag{10}\]  
The norm of each column is calculated and given in Equations (11)-(14) below:  
* Compressive strength (positive criteria, \(\dagger\)): \[\sqrt{\left(2.97\right)^{2}+\left(0.95\right)^{2}+\left(3.93\right)^{2}}=5.02\] (11)
* Surface roughness (negative criteria, \(\downarrow\)): \[\sqrt{\left(3.05\right)^{2}+\left(1.43\right)^{2}+\left(0.16\right)^{2}}=3.37\] (12)
* Dimensional accuracy (negative criteria, \(\downarrow\)): \[\sqrt{\left(0.04\right)^{2}+\left(0.0025\right)^{2}+\left(0.002\right)^{2}}=0.04\] (13)
* Cost (negative criteria, \(\downarrow\)):  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Alternatives** & **Compressive Strength** & **Surface Roughness** & **Dimensional Accuracy** & **Cost** \\  & **(kN)** & **(Ra, \(\mu\)m)** & **(mm)** & **(b )** \\ \hline PLA-FDM & 2.97 & 3.05 & 0.0400 & 176 \\ ABS-FDM & 0.95 & 1.43 & 0.0025 & 176 \\ ABS-Injection & 3.93 & 0.16 & 0.0020 & Variable \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of criteria.  
\[\sqrt{\left(176\right)^{2}+\left(176\right)^{2}+\left(12,000\right)^{2}}=12000.26 \tag{14}\]  
Normalization of the decision matrix (\(R\)), dividing by the square root of the sum of the squares of each column, is given in Equations (15) and (16).  
\[R=\begin{bmatrix}2.97/5.02&3.05/3.37&0.0400/0.04&176/12,000.26\\ 0.95/5.02&1.43/3.37&0.0025/0.04&176/12,000.26\\ 3.93/5.02&0.16/3.37&0.0020/0.04&12,000/12,000.26\end{bmatrix} \tag{15}\]  
\[R=\begin{bmatrix}0.5920&0.9044&0.9968&0.0147\\ 0.1894&0.4240&0.0623&0.0147\\ 0.7834&0.0474&0.0498&0.9998\end{bmatrix} \tag{16}\]
## 3 Analysis and Results
### Distrito Tec Area
Figure 18: Number of main employment centres reachable within 15 min travelling by foot. Figure 20 presents the number of supermarkets that can be reached by foot within a travel time of a maximum of 15 min. There are five supermarkets within the Distrito Tec polygon; in the surrounding areas to the west and northeast, there is a high density. In contrast, to the east and southwest, there are no destinations available. To the south, the density is low with all supermarkets concentrating on Eugenio Garza Sada Avenue. The location of supermarkets has negative effects on the mobility of the Distrito Tec Area, as the population living in the eastern neighbourhoods has to travel to or through Distrito Tec to access a supermarket. From the 11 AGEBs that conform the Distrito Tec Area, 2 cannot access a single supermarket, 6 can access 1 destination, and 3 can access 2 destinations. On that account, most of the AGEBs meet the parameters of the 15 minutes city using foot as the travel mode. Figure 21 shows the number of supermarkets that can be reached by AGEB within a travel time of 15 min using a bicycle. Immediately, one can appreciate that the number of destinations becomes very high for pretty much the entire area, which is related to the fact that cycling speed is three-times higher than walking. By bike, all of the AGEBs within Distrito Tec show high accessibility levels, only one AGEB being in the 31-50 destinations class, one AGEB in the 51-69 class, five AGEBs in the 70-93 class, and three AGEBs in the 94-195 class. Such results demonstrate that the Distrito Tec Area comfortably meets the parameters to be considered a 15 minutes city in terms of access to commercial activities (supermarkets). Additionally, the evidence collected is key to understanding the massive potential that cycling has in the city in terms of accessibility. By using this transport mode, the local population can drastically increase their access to commercial activities without relying on motorised vehicles. Once again, this should promote local authorities to create adequate conditions to make cycling safe and comfortable for the local population. All the previous variables serve as a preliminary approach to understanding to what extent Distrito Tec is ready to become a 15 minutes city in terms of its road network and its destinations' availability. Nevertheless, as mentioned repeatedly, further variables and information have to be taken into consideration to provide a much more accurate analysis of the accessibility and the population's needs in the local area. The results presented highlight the importance of thinking locally and not just at a metropolitan or regional scale when infrastructure and urban equipment are planned. They also demonstrate that micromobility, especially via bicycles, has a massive potential to increase local accessibility without, necessarily, having to increase the number of amenities in a given area. Figure 20: Number of supermarkets reachable within 15 min of travel by foot.
## 3 Results
### The Results Obtained Through Quantitative Analysis
Only 1.8% of respondents strongly disagree with this statement, confirming that consumers place significant importance on specifications such as processing power, battery life, camera quality, and software capabilities over branding alone. The survey also investigates consumer behavior regarding online shopping habits. When asked whether they would test a smartphone in a physical store before purchasing it online, responses were mixed. The results indicate that 26.8% of respondents strongly agree with this approach, while 25.3% remain neutral, and 14.9% express disagreement. This suggests that a considerable proportion of consumers engage in \"showrooming\", a phenomenon where individuals explore products in physical stores before finalizing purchases online, often to secure better prices or deals. Furthermore, the significance of online reviews in consumer decision-making is evident in the responses. An overwhelming 73.2% of respondents strongly agree that they will read online reviews before purchasing a smartphone, whereas only 1.1% disagree. This highlights the crucial role of customer feedback and expert reviews in shaping purchasing decisions, reinforcing the importance of digital reputation management for smartphone brands and retailers. Regarding price perceptions, 32.7% of respondents strongly agree that they can find a better price for a smartphone online, whereas only 3.3% strongly disagree. This finding suggests that many consumers associate online shopping with cost savings, likely due to promotional discounts, e-commerce-exclusive deals, and greater price transparency compared to traditional stores. Demographic information about respondents provides further context for these findings. The survey sample consists of 65.6% female participants and 34.4% male participants, indicating that women were more heavily represented in the study. Additionally, the total monthly income distribution of respondents reveals that 21.2% fall within the 3500-4999 RON income range, while a smaller percentage (3.1%) report earning 20,000 RON or more per month.