esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Reference Track
#### 2.5.2 Comparison of Measurement Data  
Figures 10 and 11 show the complete B-scan of the middle channel (No. 4) for both real and simulated data. To better illustrate this, the areas where objects were buried in the B-scan of the real measurement are marked in red. In comparing real and simulated measurements, the first key observation is the presence of consistent reflections from the upper area's sleepers. Similar reflections further down indicate transitions between the ballast layer, subballast, or subgrade due to varying materials. Expectedly, these transitions show gradually changing reflection heights. However, the upper steeper contours are echoed in lower layers, with high-frequency waviness along the path axis due to electromagnetic wave traversal. In the simulated data, the absence of low-frequency components is noted due to constant subgrade layer thickness, unlike the real dataset, where gradual subgrade changes are observed over long track segments. Distinct variations are evident in the real data for buried objects beneath the track. Metal containers at 10 m and 50 m are distinctly visible, exhibiting hyperbolas of differing prominence, most probably attributed to variations in their depths and orientations. In simulations, these containers also exhibit clear hyperbolas. The water container at 19.7 m, while similar to the metal containers, shows unique multiple reflections for differentiation, a feature well represented in simulations. Distinguishing between the buried log and rock at 30 m and 40 m is challenging. While the relative permittivity of wood is about \(\varepsilon_{r}\approx\) 1.5 to 3.5, slightly lower than that of granite (\(\varepsilon_{r}\approx\) 5), the permittivity of the rock can be assumed to be similar to that of granite. The slight difference in permittivity between wood and granite-like rock did not produce clear reflections in either real or simulated data. However, a certain irregularity in the signal is noticeable at both positions (30 m and 40 m) directly below the sleepers. Regardless of whether  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance** & **Object** & **Dimensions (L \(\times\) W \(\times\) H)** \\ \hline
10 m & Metal canister & 30 cm \(\times\) 50 cm \(\times\) 30 cm \\
19.7 m & Plastic canister (water-filled) & 30 cm \(\times\) 40 cm \(\times\) 20 cm \\
30 m & Wooden log & 30 cm \(\times\) 40 cm \(\times\) 25 cm \\
39 m & Stone & 17 cm \(\times\) 28 cm \(\times\) 17 cm \\
50 m & Metal canister & 30 cm \(\times\) 40 cm \(\times\) 30 cm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Buried objects.  
these changes were due to the objects or whether the excavation work carried out at these points itself affected the measurement, these positions served as control points. They allowed for the verification of whether the subsequently implemented algorithms actually responded to the sought-after objects or were equally sensitive to general structural changes beneath the track.
## 4 Discussion
### GPP Changes  
Utilizing long-term GLASS GPP products, we analyzed GPP changes across two distinct periods within the boreal region. GLASS GPP was particularly well-suited for long-term trend analysis, as it provided consistent and reliable estimates of GPP across multiple years, which was crucial for understanding the impact of climate change on productivity. While MODIS is widely used and provides valuable insights, it often struggles with the complexities of high-latitude ecosystems [37]. Compared to the trend in P1, our findings suggested that GPP increases notably decelerated during P2. These observations aligned with previous studies [10; 59] that had reported a slowing down of GPP growth in boreal regions, which they attributed to increasing temperature and altered precipitation patterns affecting vegetation productivity. Additionally, gross primary production derived from two satellite-based models had also exhibited persistent and widespread decreases since the late 1990s [13]. Low GPPs are typical of environments with harsh conditions. These include high latitudes, where the growing season is short and temperatures are low, and dry areas where water availability limits plant productivity [42]. Common methods for estimating global GPP rely on meteorological and biotic factors such as temperature, leaf area index (LAI), and radiation [38]. A significant source of uncertainty in GPP estimation stems from the quality of meteorological input data [6]. In addition to climatic changes, our study further suggested that the deceleration in GPP was also linked to changes in vegetation phenology, as warmer temperatures had led to an extension of the growing season [42], which did not necessarily result in proportional increases in GPP. In some boreal regions, GPP trends continued to decrease due to prolonged growing seasons [43], highlighting the complex and context-dependent nature of GPP trends across different regions. In the context of our findings, the deceleration of GPP in P2 could have been partly explained by a saturation effect [42], where further temperature increases no longer yield linear productivity gains. This may have been due to nutrient limitations or changes in vegetation structure that reduce the carbon sink potential [60], and it also contributed to the discussion by suggesting that this trend may have been driven by multiple interacting factors, including climate variability, phenological shifts, and ecological feedbacks [36; 61]. Accurately representing inter-annual variability and long-term GPP trends continues to present substantial challenges, particularly in regions where climatic and ecological processes are shifting in complex and sometimes opposing directions.
## 3 Proposed Simulation Model
### Charge Discharge Controller (CDC)  
The BESS battery's charging and discharging processes are controlled by the charge-discharge controller. To ensure that the required set charge or discharge power is factored in the BESS auxiliary power, the charge and discharge setting in kW is subtracted from the system auxiliary power. The charge setting (CS) defines that when the PV power is above the charge setting in kW, the (PCS) will start to charge the battery from the PV. The charge enable (\(\mathbf{C_{en}}\)) and disable are determined in (1), where the photovoltaic cell power is subtracted from the charge setting power. If the result is positive and the charge setting power is less than or equal to the photovoltaic cell power, the charge controller will deliver the necessary power resulting from the subtraction to charge the battery through the PCS once the charge enable (\(\mathbf{C_{en}}\)) is enabled. If the subtraction result is negative and the charge setting power is greater or equal to the photovoltaic cell power, the (\(\mathbf{C_{en}}\)) is disabled.  
\[\begin{split}\text{$\mathbf{C_{en}}$}=0&:\text{if}( \text{PV}_{\text{power}}-\text{CS})<0\text{ and }\text{CS}\geq\text{PV}_{\text{power}}\\ \text{$\mathbf{C_{en}}$}=1&:\text{if}(\text{PV}_{\text{ power}}-\text{CS})\geq 0\text{ and }\text{CS}\leq\text{PV}_{\text{power}}\end{split} \tag{1}\]  
Similarly, the discharge setting (DS) specifies that the PCS will begin to release energy from the battery to power the load when the photovoltaic cell's power falls below the discharge setting in kW. The discharge enable (\(\mathbf{D_{en}}\)) and disable is determined in (2), where the photovoltaic cell power is subtracted from the discharge setting power. If the result is negative and the discharge setting power is greater or equal to the photovoltaic cell power, the discharge controller will transmit the necessary power resulting from the subtraction to the load through the PCS once the (\(\mathbf{D_{en}}\)) is disabled. If the subtraction result is positive and discharge setting power less than or equal to the photovoltaic cell power the (\(\mathbf{D_{en}}\)) is disabled.  
\[\begin{split}\text{$\mathbf{D_{en}}$}=0&:\text{if} \big{(}\text{PV}_{\text{power}}-\text{DS}\big{)}>0\text{ and }\text{DS}\leq\text{PV}_{\text{power}}\\ \text{$\mathbf{D_{en}}$}=1&:\text{if}\big{(}\text{PV}_{ \text{power}}-\text{DS}\big{)}\leq 0\text{ and }\text{DS}\geq\text{PV}_{\text{power}}\end{split} \tag{2}\]
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 4 Engineering Background and Numerical Simulation Model
### Numerical Simulation Model
#### 4.2.1 Model Size and Boundary Conditions  
To establish a numerical simulation model, the western area of the foundation pit is chosen as the focus. The length of the retaining structure on the west side of the foundation pit measures 136 m. Consequently, the model dimensions are adjusted accordingly, with a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Statum** & **Natural Gravity** & **Cohesion** & **Friction Angle \(\varphi\)** & **Elastic Modulus** & & **Thickness** \\  & \(\gamma\) **(KN/m\({}^{3}\))** & \(c\) (kPa) & **(\({}^{\circ}\))** & **(MPa)** & **Poisson’s Ratio** & **(m)** \\ \hline Plain fill & 19.5 & 12 & 8 & 8 & 0.3 & 2 \\ Mucky soil & 18.2 & 12 & 6 & 4 & 0.35 & 5 \\ Silty clay & 19.5 & 30 & 18 & 16 & 0.25 & 5 \\ Coarse sand & 20.2 & 0 & 35 & 40 & 0.25 & 11 \\ Round & 20.5 & 0 & 40 & 60 & 0.25 & 7 \\ gravel & 21.9 & 40 & 40 & 82 & 0.25 & - \\ \hline \end{tabular}
\end{table}
Table 1: Stratum material parameters.  
Figure 5: Design scheme for the retaining structure on the west side of the foundation pit.  
length of 140 m, a longitudinal length of 120 m, and a height of 60 m. The arrangement of soil layers, as well as the positioning of piles and anchor cables, aligns with the depiction shown in Figure 5. This approach ensures consistency between the numerical simulation model and the specified soil layer distribution and support system layout.  
For this study, a numerical model is created utilizing the FLAC 3D finite difference software. The model consists of a total of 4,859,822 nodes, 3,689,843 elements, and is divided into seven groups. Figure 6 presents a visual representation of the comprehensive numerical simulation model.  
The model's boundary conditions differ from other numerical simulation models and are as follows: a fixed constraint is applied to the bottom surface, while the top surface remains free. The front and rear surfaces have normal displacement constraints, and the left and right surfaces have both normal and tangential displacement constraints. This unique set of boundary conditions distinguishes it from other numerical simulation models used in similar studies. To meet the theoretical calculation assumptions, this study's model does not consider displacement at the foundation pit's corners. Therefore, it is necessary to constrain the tangential displacement of the model's left and right surfaces.
## 3 Hybrid FEM/SEA Model and Interstage Response Analysis
### The Interstage Models
An assessment process based on a comparison with an equivalent full deterministic analysis is also applied, either to limited portions of the interstage hybrid models to verify the accuracy of the coupling loss factors among specific SEA and FEM subsystems, or to the entire interstage models for an overall check of the hybrid approach's accuracy. For each examined system, the results are compared in the frequency range for which the two models (hybrid and full FEM) are expected to be accurate.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Printing
#### 3.4.2 Inkjet Printing  
Digital or direct printing technology is a way of producing low cost and high quantity conductive materials [259]. Inkjet printing is a way of writing patterns directly to substrates. Inkjet printing is a simple term characterized by dye drops as ink is poured into specific positions to form a printed design on a textile substrate with a specified resolution [260]. In essence, the procedure involves the ejection of a fixed quantity of ink in a liquid-filled chamber in response to applying an external voltage. This abrupt reduction produces a shock wave in the liquid, which induces a drop in the liquid to eject from the nozzle [261]. The expelled drop comes under gravity and air resistance gained in motion, and the surface tension helps slow it along the surface [262]. Then, the drop will dry by solvent evaporation.  
[PERSON] et al. developed a knit structure printed with silver inks by inkjet printing, which had a resistance of 0.09 \(\Omega\)/sq [263]. Low production costs, no masking, non-contact viability, lower material consumption, low temperature, and quick inkjet printing processing have gained interest in cost-effective manufacturing processes [264]. The design can be easily changed as no mask is used [261]. The major advantage of the inkjet printing process is that the conductive material can be deposited only in the required area on the substrate [265].
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.1 Alignment/Matching  
In the alignment task, we divided the solutions into two subtasks based on the processed data type. Their common goal was to align a new input image to a reference one with the highest achievable precision. An example of the matching process, performed on a set of misaligned input floor plans, is presented in Figure 15.  
**Grid Map Alignment** was a subtask represented by three articles found. One of the discovered use cases for such functionality was a service robot trying to match the current sensor readings and a newly created apartment grid map to a known map of the environment. [PERSON] et al. [130], in 2017, presented such a solution based on the graph-matching procedure. The whole system first segmented regions in the grid map (using morphological operations), extracted a graph from them, and represented it as a tree structure. The estimated transformation matrix was later checked for similarity, and a final alignment occurred. Similarly, in 2019, [PERSON] et al. [148] presented an area graph-based solution. In the method, they first segmented the grid map using the Area Graph. Then, for each area a set of features like area size or convex hull were calculated, relying on a novel feature called the \"passage distance\". Later processing calculated a weighted sum of the extracted features, found the best matches between areas, and used the overlap of well-matched ones to find the final transformation needed to align the maps. The topic of aligning two maps was also discussed in 2018 in the article by [PERSON] et al. [187]. They presented a solution for the layout map (blueprint, floor plan) alignment with the sensor map (grid map) and focused on the grid map deformation corrections. The proposed method started with decomposition-based segmentation and presented results as a Doubly-Connected Edge List (DCEL).  
The **Plan Alignment** task was found in the publication from 2016 by [PERSON] et al. [92]. Their main objective was to provide a solution for matching and retrieving similar floor plans, with a sample image being the search query. Like previous methods, this one also started with room segmentation (by boundary detection and morphological operations), adjacency recognition, and presentation in a topology graph form. A novel graph spectral embedding feature was proposed and used for floor plan representation, reducing the graph-matching computation time. The proposed method included information about the room decor in the retrieval process.
# Freeform Wide Field-of-View Spaceborne Imaging Telescope: From Design to Demonstrator
## 3 Telescope Proof-of-Concept Demonstrator
### Laboratory Demonstrator Setup
#### 3.2.1 Telescope Alignment Using Red Laser Source  
A fiber-pigtailed red laser source (Thorlabs S1 FC635, emission wavelength of 635 nm, 2.5 mW output power, single mode emission) is used for alignment purposes, supplemented with a collimating lens to achieve a collimated input beam (beam diameter 891 \(\upmu\)m). Using this laser source, we sought to accurately position the source and aperture in alignment with the primary mirror, such that the light passes through the aperture stop (Figure 8). The detector position is fixed below the fold mirror. The alignment and tilt of the laser source are set such that an angle of incidence of zero degrees results in a spot at the center of the image sensor.  
Figure 8: Experimental proof-of-concept demonstrator. After reflection on M1, light passes through the aperture stop and is reflected by the secondary (M2) and fold mirror (M3) to the detector that is located beneath the telescope.
## 2 Materials and Methods  
This work aimed to develop algorithms that could determine whether pulmonary radiography images belong to people infected with COVID-19. To this end, we made use of artificial neural networks (ANNs). ANNs are computing algorithms that are somewhat inspired by biological neural networks. Generally speaking, ANNs constitute sequences of mathematical operations that are applied to given inputs, yielding corresponding outputs. These mathematical operations are usually deterministic, meaning that in a given neural network, the same input will always yield the same output. These mathematical operations are defined in terms of numerical parameters, usually referred to as weights. The weights characterize how the preliminary results obtained at each step in the network are propagated forward during the sequence of operations.  
There are multiple types of neural network, mainly differing in terms of the sequences and types of mathematical operations performed on their inputs. Among them, convolutional neural networks (CNNs) constitute a specific type that is well suited for image processing and computer vision. CNNs implement sequences of convolutional operations on their input images, represented by tensors, to extract the most relevant features that characterize the images. CNNs may have thousands of weights. Unlike spiking neural networks, CNNs do not take into consideration the signal propagation--i.e., signals are assumed to propagate instantaneously across the network.  
In order to use CNNs for image classification, one should determine what weights are optimal for the classification problem at hand. Determining the optimal weights of a CNN is analogous to computing the optimal coefficients of a polynomial function for best fitting. This process is known as training. In order to train a CNN such that it can detect whether pulmonary radiography exhibits signs of COVID-19, several radiographies from COVID-19-infected and uninfected people should be collected. These images will be used with their corresponding labels (infected or uninfected) to adjust the weights of a CNN (the training process). As a result, the network can properly classify new images as COVID-19-infected or uninfected. More details are provided in the following.
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 1 Introduction
The particular properties and morphology of biochar make it possible to use it as a suitable sorbent. In addition, this carbon-rich product requires less expensive technology, and its production  
Figure 1: Evolution of papers published in the last decade: pink, total; orange, soil; green, biofuel; and blue, adsorbent (Source: Scopus® database). demands lower energy requirements compared to more commonly employed adsorbents such as activated carbon [10]. There are diverse production methods, mainly considering thermochemical technologies to transform biomass into biochar. These can be classified into several categories (Figure 2): (i) slow or fast pyrolysis in the complete absence of oxygen, (ii) gasification, which occurs with reduced oxygen atmosphere, (iii) hydrothermal carbonization, applying heating to biomass in the water at subcritical with high levels of oxygen, (iv) dry and wet torrefraction under inert nitrogen and (v) microwave-assisted pyrolysis [11]. The physical and chemical transformations that occur in the fabrication process are very complex and rely on both the nature of the biomass and the conditions of the reactor. These conditions and the characteristics of the raw material (composition, particle size distribution and pore size, among others) largely determine these mentioned properties of the biochar [12]. Furthermore, chemical or physical post-treatments can be applied to the biochar with the aim to enhance surface characteristics or increase its catalytic activity (Figure 2). Therefore, based on the increasing interest in its use as an adsorbent, the aim of this review will focus on analyzing the most recent studies on the effect of main production variables in the biochar, discussing their influence in the application to wastewater remediation and evaluating the different strategies employed in order to enhance the uptake capacity of this low-cost adsorbents. An insight into the adsorption mechanism for organic and inorganic contaminants is provided. Furthermore, the recent advances in the regeneration of these carbonaceous adsorbents and the limitations in their use will be also displayed and discussed. Finally, an economic analysis is performed, and future prospects are analyzed.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Smoothing and Optimization  
#### 3.2.1 Smoothing of Non-Coplanar Trajectories  
First, based on the data presented in Table 1, the vertex 2 of PV panel 1 and PV panel 2 are designated as the start and end points of Bezier curve 1, corresponding to control points \(P_{1,0}\) and \(P_{1,3}\), respectively. Similarly, the vertex 3 of PV panel 1 and PV panel 2 serve as the start and end points of Bezier curve 2, represented by control points \(P_{2,0}\) and \(P_{2,3}\), respectively. Then, the non-coplanar trajectory design is executed using the Bezier curve formula with two scaling parameters. This ensures that the trajectory of Stage B is tangent at the node positions connecting the two PV panels (control points \(P_{0,0}\) and \(P_{1,3}\)). In other words, the smoothing of the non-coplanar trajectory is achieved as described by Equation (10). The effects of the smoothing process of Stage B during one working cycle are illustrated in Figure 7.