esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology (SLF-RPL)
### Selecting Nodes for Routing  
In this phase of the trust-based routing model, the evaluated trust values of the nodes are used to establish a trusted network. The nodes with trust values greater than the set threshold value are selected for routing, and a trusted route is created between the root and child nodes. This ensures that only nodes with a certain level of trustworthiness are used for communication, thus reducing the risk of data being compromised or intercepted by malicious nodes. The trusted route serves as a secured channel for the nodes to exchange data between the trusted networks.
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
## 1 Introduction  
When an earthquake occurs, seismic waves do not propagate uniformly through the ground, leading to variations in ground motion even within nearby locations. As a result, the frequency components of seismic waves reaching different points of a structure's foundation may undergo an averaging effect, especially in the high frequency range above 10 Hz [1]. To accurately assess the seismic performance of structures while considering ground motion incoherency, the incoherency Soil-Structure Interaction (SSI) analysis was developed [1, 2]. The core element of an incoherency SSI analysis is plane-wave coherency function, which models coherency based on separation distance and frequency by considering the wave passage of seismic waves. Generally, these coherency functions are derivedfrom empirical data obtained through dense array measurements. The U.S. Nuclear Regulatory Commission (NRC) approved the plane-wave coherency function developed by [PERSON] [1], based on dense array data recorded at the Pinyon Flat site, which is a location that is classified as hard rock [3,4].  
The spatial variability of ground motion is primarily influenced by heterogeneity, with ground motion coherency varying based on site-specific conditions [1,3,5, 6, 7, 8]. Generally, ground motion coherency is higher in homogeneous and solid geological structures, while it is lower in heterogeneous and weak geological structures. In terms of coherency, soil types (e.g., sand and clay) exhibit lower coherency than rock types (e.g., granite and shale). Existing studies on coherency functions often rely on empirical models that require the installation of dense arrays, resulting in spatial and economic constraints. Moreover, these empirical models may be site-specific and thus cannot be universally applied to all sites with different stratigraphy [9].  
Considering the difficulties in developing an empirical coherency function from a site-specific dense array, efforts have been made to utilize numerical simulations for coherency model development. However, these studies have primarily applied idealized conditions. In this study, we use the Pinyon Flat, located in California, as the baseline site. It was selected because it is one of the few sites where a dense array was deployed on a rock profile, from which the empirical coherency function was derived. Additionally, the site has a measured _Vs_ profile available. A comprehensive parametric study evaluating the effect of various parameters influencing the calculated coherency function was performed. Systematically analyzing the sensitivity of variables influencing the target output derived from numerical simulations is essential for assessing the reliability of physics-based models and quantitatively evaluating uncertainty [10,11]. We identify which parameters have primary or secondary influences on the calculated coherency and compare the numerically derived function with the site-specific empirical model. Based on the results, we provide guidelines on how to use the numerical model to develop site-specific coherency models.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 3 A Multi-Application Scheduling Formal Definition  
Resource management for terminal heterogeneous SoCs is a domain-specific resource management problem, and the following are some of the definitions that will be used later.  
Definition 1: An RF application consists of multiple specific function modules, such as Fast Fourier Transforms (FFTs), encoders and decoders, and other computationally intensive software components. In this paper, an application (WiFi-TX/RX, Range Detection, etc.) is defined as a job, and a function module is defined as a task. The terms task and node are used interchangeably throughout this paper.  
**Definition 2**.: _Task scheduling includes task prioritization and task mapping, where task mapping describes the process of assigning PEs to tasks._
## 2 Problem Formulation
### Interdependency between Sub-Problems  
Figure 2 shows the relationships between the sub-problems based on the influences from the outputs of each sub-problem. The interdependencies between the sub-problems, illustrated in Figure 2, are as follows. Sub-problem 1 to sub-problem 2: for each STW, a set of selected clusters of customers in the network are considered to plan the delivery missions. Sub-problem 2 to sub-problem 3: for each customer cluster, a set of feasible collision-free UAV routings and accompanying schedules are created. Sub-problem 1 to sub-problem 3: each sub-mission is created inside a given STW where the range of wind speeds and direction is known. The creation of sub-missions is influenced by the nature of weather condition in each STW. Sub-problem 3 to sub-problem 4: the sequence of sub-missions from each FTW provides a full mission. The sub-missions created in sub-problem three are searched to find the sequence which provides the maximum customer satisfaction. As we consider the whole problem with the constraints and the system requirements, a general approach has to be designed to deliver feasible output addressing the whole problem with an overall quality solution. If each sub-problem is focused on individually, better different alternatives could be found but when all the sub-problems are considered together because of the interdependency between the sub-problems a general approach is proposed. An interdependency analysis substantiates that sub-problem 3 is the most difficult problem to focus on because all of the rest of the sub problems are  
Figure 2: Interdependency between the decomposed sub-problems. linked with it. Moreover, sub-problem 3 has the most generic aspects to focus on. Hence this paper focuses more on sub-problem 3. The mathematical formulation of the model considered employs the following symbols explained in Table 1.
# Digestate Management and Processing Practices: A Review
## 5 Digestate Processing
### Treatments of the Liquid Fraction
#### 5.3.2 Struvite Precipitation  
Struvite precipitation has been studied for the recovery of solubilized phosphate and ammonia N which is performed by the addition of magnesium salts that form a complex insoluble compound called magnesium ammonium phosphate or struvite (MgNH4 PO4.6H2O) [91]. Struvite is a white crystalline slightly alkaline substance composed of Mg, NH4+, and P in a molar ratio of 1:1:1. Struvite is an excellent fertilizer in which the nutrients have low water solubility and give a slow release in the soil. Since the availability of the free ions NH4+, Mg2+, and PO43- is strongly dependent on the H+ concentration, pH is used as a parameter to control struvite formation. The minimum solubility of struvite is at pH 10, but crystallization can also occur at pH values below 8. The main limiting factors for struvite precipitation are the cost of magnesium salts and of alkalis which are added for pH control [163] which make the cost of this process too high to be economically attractive [164]. However, since the pH is an essential parameter that promotes struvite precipitation, many of the studies are focused on increasing pH by adding alkalis NaOH, Mg(OH)2, MgO, MgCl2, and Ca(OH)2[165, 166] and through stripping the CO2 from the media being treated [167, 168]. Additionally, the use of low-cost sources of magnesium can be the solution to the high cost of the process. For this purpose, various substrates have been investigated, such as salt obtained by evaporation of seawater [169, 170], desalination waste such as brine [169, 171], and natural brucite [172, 173], etc. The results of the aforementioned studies emphasized that the application of the low-cost magnesium sources in struvite precipitation could achieve high ammonia removal efficiency, similar to the use of pure magnesium reagents [174].  
Struvite is known to be appropriate for use as fertilizer on turf, tree seedlings, ornamentals, vegetables, and flower boards [175] and would be also effective in grasslands and forests, where fertilizers are applied once every several years. However, up to now the agronomic value of struvite has not been fully examined [176].
## 2 Background
### Operational Costs (OPEX)  
After a chiller is purchased, its ownership costs persist throughout its lifespan due to operational and maintenance expenses. OPEX, an imperative component of the TCO, encompasses ongoing expenses required to operate and maintain the asset or system. Accurate calculations of these costs are essential for economic sustainability and cost optimization [100]. Various methods have been applied for cost analysis, including traditional methods such as historical data analysis [27] and basic cost estimation models [101], as well as more advanced approaches, such as predictive maintenance [102], energy-efficient technologies [87], and AI-driven analytics [49]. These advanced methods focus on minimizing operating costs by forecasting maintenance needs, identifying energy-saving opportunities, and enhancing overall efficiency, easing the financial burden on asset owners. OPEX primarily consists of energy and maintenance costs, as other expenses such as water, availabilityrisk, environmental, labor, and repair costs are considered minor in comparison [4; 6; 9; 30]. Moreover, maintenance costs can include software costs, as AI-based control algorithms often require ongoing updates and specialized maintenance [99]. Therefore, the upcoming sections provide a detailed exploration of OPEX components (energy and maintenance costs) and how they are calculated.
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 3 The Proposed Attention-Based Full-Scale Fusion Network
### Full-Scale Fusion with Attention (FSFA)  
The semantic information contained in features at different scales of the network varies, and their impact on semantic segmentation tasks also varies. We believe that fully fusing features from various scales is beneficial for improving semantic segmentation tasks. Meanwhile, when performing feature fusion, it is necessary to consider the differences in importance of features at different scales. Taking inspiration from the UNet 3+ [51] and attention mechanism [46], we design an attention-based full-scale feature fusion module. For the convenience of description, we number all modules (ARSU or ARSU-4F block) of the model according to the positive direction of information flow in the model. The first encoder module is numbered as 1, and the last decoder module is numbered as \(n_{E}+n_{D}\), where \(n_{E}\) and \(n_{D}\) are the number of modules included in the encoder and decoder, respectively.  
Figure 4: Illustration of the proposed AFSF network. Multiple connecting lines of the same color in the figure indicate that the same set of feature maps are simultaneously sent to multiple processing nodes in the network for feature fusion. For clarity, we mark the modules connected to the mask generator in purple.  
Figure 3: Illustration of Squeeze-and-Excitation block.  
The input of module \(n\) (\(1<n<=n_{E}+n_{D}\)) is the fusion of the outputs from modules 1 to \(n-1\), i.e., \(\{\mathrm{O}_{1},\mathrm{O}_{2},\ldots\mathrm{O}_{n-1}\}\). Taking the module numbered \(n\) as an example to explain our FSFA mechanism, firstly, we unify the size of all feature maps in \(\{\mathrm{O}_{1},\mathrm{O}_{2},\ldots\mathrm{O}_{n-1}\}\). Specifically, for feature maps with a size larger than \(\mathrm{O}_{n-1}\), we use adaptive max pooling to downsample the feature maps to the same size as \(\mathrm{O}_{n-1}\). For feature maps with a size smaller than \(\mathrm{O}_{n-1}\), we use bilinear interpolation to upsample the feature maps to the same size as \(\mathrm{O}_{n-1}\). Then, we concatenate all uniformly sized features and apply the SE operation [46] to them. Finally, we use a convolutional layer with a kernel size of 1, a batch-norm layer, and an ReLU layer to reduce the number of channels and control the size of the model parameters.
## 4 Results and Discussion
The RMSE, MAE, and MAX error of the LSTM model under DST at 30 \({}^{\circ}\)C were 1.5388%, 1.5786%, and 5.6996%, respectively. The RMSE, MAE, and MAX error of RNN model under DST at 30 \({}^{\circ}\)C were 0.2129%, 1.0532%, and 5.6613%, respectively. Compared with LSTM, the RMSE, MAE, and MAX error of GA-GRU were  
Figure 10: SOC estimation results and errors under US06 at 45 \({}^{\circ}\)C. (**a**) Comparison of SOC estimation results under US06 at 45 \({}^{\circ}\)C; (**b**) comparison of SOC estimation errors under US06 at 45 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Condition** & **RMSE (\%)** & **MAE (\%)** & **MAX Error (\%)** \\ \hline \multirow{6}{*}{RNN} & DST 0 \({}^{\circ}\)C & 2.0163 & 3.4739 & 10.3584 \\  & FUDS 10 \({}^{\circ}\)C & 0.3999 & 2.0255 & 11.6486 \\  & BIJST 20 \({}^{\circ}\)C & 1.3924 & 1.5474 & 7.9190 \\  & DST 30 \({}^{\circ}\)C & 0.2129 & 1.0532 & 5.6613 \\  & US06 45 \({}^{\circ}\)C & 1.1196 & 1.3768 & 4.6015 \\  & DST 0 \({}^{\circ}\)C & 0.6813 & 2.4416 & 7.0484 \\  & FUDS 10 \({}^{\circ}\)C & 1.5193 & 1.7937 & 7.3662 \\ LSTM & BIJST 20 \({}^{\circ}\)C & 1.5755 & 1.6566 & 4.4944 \\  & DST 30 \({}^{\circ}\)C & 1.5388 & 1.5786 & 5.6996 \\  & US06 45 \({}^{\circ}\)C & 0.6822 & 1.6672 & 4.5193 \\  & DST 0 \({}^{\circ}\)C & 0.1052 & 0.1965 & 0.9892 \\  & FUDS 10 \({}^{\circ}\)C & 0.0851 & 0.1750 & 0.8451 \\ GA-GRU & BIJST 20 \({}^{\circ}\)C & 0.2295 & 0.2754 & 2.0662 \\  & DST 30 \({}^{\circ}\)C & 0.2017 & 0.2383 & 1.3366 \\  & US06 45 \({}^{\circ}\)C & 0.0725 & 0.1960 & 0.9120 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of SOC estimation numerical results under different driving conditions. smaller, being reduced by 1.3371%, 1.3403%, and 4.363%, respectively. Compared with RNN, the RMSE, MAE, and MAX error of GA-GRU were reduced by 0.0112%, 0.8149%, and 4.3247%, respectively. The SOC estimation results indicate that GA-GRU had good accuracy and robustness.
## 6 Image Compression
### The Implementation on a Parallel System  
Compression and decompression parallel implementations are described, requiring O\((\alpha\log M)\) time with O\((n/\alpha)\) processors for any integer square value \(\alpha\in\Omega(\log n)\) on a PRAM EREW [8]. We partition an \(m\times m^{\prime}\) image \(I\) in \(x\times y\) rectangular areas, where \(x\) and \(y\) are \(\Theta(\alpha^{1/2})\). In parallel for each area, one processor applies the sequential rectangular block matching algorithm so that in O\((\alpha\log M)\) time each area is parsed into rectangles, some of which are monochromatic. In the theoretical context of unbounded parallelism, \(\alpha\) can be arbitrarily small and before encoding we wish to compute larger monochromatic rectangles. The monochromatic rectangles are computed by merging adjacent monochromatic areas without considering those monochromatic matches properly contained in some area. We denote with \(A_{i,j}\) for \(1\leq i\leq\lceil m/x\rceil\) and \(1\leq j\leq\lceil m^{\prime}/y\rceil\) the areas into which the image is partitioned. In parallel for \(1\leq i\leq\lceil m/x\rceil\), if \(i\) is odd, a processor merges areas \(A_{2i-1,j}\) and \(A_{2i,j}\) provided they are monochromatic and have the same color. The same is done horizontally for \(A_{i,2j-1}\) and \(A_{i,2j}\). At the \(k\)-th step, if areas \(A_{(i-1)2^{k-1}+1,j}\), \(A_{(i-1)2^{k-1}+2,j}\), \(\cdots\)\(A_{i2^{k-1},j}\), with \(i\) odd, were merged, then they will merge with areas \(A_{2^{k-1}+1,j}\), \(A_{i2^{k-1}+2,j}\), \(\cdots\)\(A_{(i+1)2^{k-1},j}\), if they are monochromatic with the same color.  
Figure 4: Computing the largest rectangle match in \((i,j)\) and \((k,h)\).  
The same is done horizontally for \(A_{i,(j-1)2^{k-1}+1}\), \(A_{i,(j-1)2^{k-1}+2}\), \(\cdots\)\(A_{i,j2^{k-1}}\), with \(j\) odd, and \(A_{i,j2^{k-1}+1}\), \(A_{i,j2^{k-1}+2}\), \(\cdots\)\(A_{i,(j+1)2^{k-1}}\). After \(\mathrm{O}(\log M)\) steps, the procedure is completed and each step takes \(\mathrm{O}(\alpha)\) time and \(\mathrm{O}(n/\alpha)\) processors since there is one processor for each area. Therefore, the image parsing phase is realized in \(\mathrm{O}(\alpha\log M)\) time with \(\mathrm{O}(n/\alpha)\) processors on an exclusive read, exclusive write shared memory machine. In order to implement parallel decompression, we need to indicate the end of the encoding of a specific area. So, we change the encoding scheme by associating the flag field 1110 to the raw match so that 1111 could indicate the end of the sequence of pointers corresponding to a given area. Then, the flag field 1110 is followed by sixteen bits uncompressed, flag fields 0 and 10 by the width and the length of the rectangle while flag field 110 is also followed by the position of the matching copy. The values following the flag fields are represented by a fixed length code. Since some areas could be entirely covered by a monochromatic rectangle 1111 is followed by the index associated with the next area by the raster scan. The interested reader can see how the coding and decoding phase are designed in [8].
## 4 Soil water and drought monitoring for early-warning applications
### Model Inputs
#### 4.1.1 Precipitation  
Precipitation is the single most important input of the model. Satellite-derived rainfall estimate (RFE) generated by the National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautic and Space Administration (NASA) are used to drive the water balance model. NOAA produces daily rainfall data at 10 km spatial resolution using a blend of Meteosat's cold cloud duration (CCD) and raingauge data for Africa ([PERSON] and [PERSON], 1997). NASA generates daily rainfall data at 25 km resolution from the Tropical Rainfall Mapping Mission (TRMM) satellite systems that covers the globe between 60 degrees latitude north and south of the equator.
## 3 Results and Discussion
### Pilot-Scale Trickling Filter Experiments
It can be concluded from Figure 4 that pH values increase (up to 7.01) during biological d-COD degradation. This is consistent with the observations of [PERSON] et al. [37] and [PERSON] et al. [62], the  
Figure 4: pH and temperature variations recorded in the trickling filter under batch operating mode with recirculation rates of 0.5 and 1.0 L/min for initial d-COD concentrations of: (**a**) 3500 mg/L, (**b**) 7500 mg/L, (**c**) 9000 mg/L and (**d**) 18,000 mg/L. latter stating that \"this happens due to the adaptation and type of microorganisms naturally occurring along time, which clearly prefer higher pH values\". According to [PERSON] et al. [63] a decrease in dissolved organic compounds is accompanied by an increase of dissolved inorganic carbon (during the oxidation of organic matter, CO\({}_{2}\) is released) that result in an increase of pH to more alkaline values. Concerning the temperature of the treated water, this increased up to 30 \({}^{\circ}\)C, mainly due to the recirculation applied via the recirculation pump. Although the d-COD removal levels reported using the trickling filter were significantly high, the final effluent showed d-COD values over the permissible limits for municipal and industrial effluents (125 mg/L) (Government Gazette (GR) 2011/354B [64]). Specifically, for the highest concentration of C\({}_{18000}\) and the volumetric flow rate of 0.5 L/min, the effluent's d-COD concentration was about 1500 mg/L. Therefore, a suitable post-treatment step is necessary to improve the quality of the final effluent.
## 4 Conclusions  
This work is motivated by the limitations of the Lambertian emission beam configuration-based conventional hybrid PLC and VLC techniques paradigm, which fail to cover the investigations of hybrid PLC and VLC based on distinct emission beam configurations. In this article, commercially available optical sources with distinct non-Lambertian beam patterns were employed to configure the novel hybrid PLC and VLC system and the relevant channel characteristics were explored for a future 6G IoT Network as well. For the typical side receiver position under a NSPW non-Lambertian configuration, the achievable transmission performance of the hybrid PLC and VLC channel is up to 165.7 Mbps, while the counterpart of the baseline Lambertian hybrid PLC and VLC configuration is just about 147.8 Mbps accordingly. In future work for the 6G IoT network, the exploration of hybrid PLC and VLC based on distinct optical beams could be extended to customized dynamic beam configurations, beam switching, beam steering, resource allocation, reconfigurable multiple-input multiple-output, and other design-enabling techniques.  
[PERSON], Determined the theme and structure of the article, provided professional knowledge in the field of visible light communications, wrote and modified the article, and replied to comments from the editors and reviewers; [PERSON]-L.I., Provided theoretical knowledge of mobile communication and wireless communication; [PERSON], Found the literature, participated in the discussion and writing of some of the content; [PERSON], Looked for the literature, participated in the discussion and writing of some of the content. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grants No. 62061043), the Tianshan Cedar Project of Xinjiang Uygur Autonomous Region (Grants No. 2020 XS27) and the High-level Talents Introduction Project in Autonomous Region (Grants No. 042419004).  
Not applicable.  
Data Availability Statement:Data are contained within the article.  
The authors declare no conflict of interest.  
Figure 6: Comparison of achievable rate versus azimuth rotation angle for hybrid PLC and VLC channel with typical rotating asymmetric beam configuration.