esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Database of RCDBs
#### 2.1.2 Feature Selection  
According to numerous experimental and theoretical investigations, the shear strength in RCDBs is governed by different key factors such as concrete properties, geometrical dimensions, and longitudinal and web reinforcement characteristics. Hence, 16 important features were selected and defined as the input parameters, and thus categorized as follows: (1) concrete property: compressive strength (\(f_{e}^{c}\)); (2) geometric dimensions: width (\(b\)), beam span (\(l_{e}\)), height (\(h\)), shear span (\(a\)), beam depth (\(d\)), effective span-to-height proportion (\(l_{e}/h\)), and shear span-to-depth proportion (\(a/d\)); (3) longitudinal steel: steel percentage (\(\rho_{l}\)) and strength (\(f_{yl}\)); (4) horizontal web reinforcement: steel percentage (\(\rho_{h}\)), space (\(s_{h}\)), and strength (\(f_{yh}\)); (5) vertical web reinforcement: steel percentage (\(\rho_{v}\)), space (\(s_{v}\)), and strength (\(f_{yv}\)). The experimental shear strength (\(V_{u}\), \(Exp.\)) was set as the corresponding output parameter. Earlier investigations [83; 84] indicated that the size of the database ought to exceed ten times of number of input parameters. Therefore, this study meets the basic requirements for utilizing machine learning in regression problems. Table 1 provides a comprehensive overview of the selected features incorporated in the database, outlining their type, measurement units, and statistical information (min, max, mean, standard deviation (\(SD\)), etc.). Figure 3 illustrates the plots of the statistical information, offering significant insights. In which the horizontal axes represent the selected features, and the vertical axes refer to the number of specimens in the database. In general, the established database exhibits a broad spectrum and diversity, enhancing ML models to have superior generalization abilities.  
Figure 2: Types of RCDBs based on web reinforcement.
## 3 Results and Discussion
### Reduction Rate of NH\({}_{3}\)-N  
The concentration of NH\({}_{3}\)-N in wastewater can be unpredictable due to the decomposition of organic waste matter and nitrogen fixation processes [30]. Figure 3 shows the reduction rate of NH\({}_{3}\)-N using _C. asiatica_, _I. aquatica_, _S. molesta_, _P. stratiotes_, and _E. crassipes_ plotted against the days of the treatment. _C. asiatica_ reduced the most NH\({}_{3}\)-N concentration by the end of the experiment compared to the other four species, reaching 98% reduction. _C. asiatica_ is known as a medicinal plant for its high nutritional value and because of its ability to take in organic substances and nutrition from the environment, which explains why it serves as an excellent phytoremediation agent [31].  
This study shows _E. crassipes_ and _P. stratiotes_ serve more effectively as phytoremediation agents in catfish aquaculture wastewater than in tilapia fish aquaculture wastewater. The reduction rates of NH\({}_{3}\)-N by _E. crassipes_ and _P. stratiotes_ in catfish aquaculture wastewater reached until 74% and 78% respectively, compared to a reduction rate of only 69% and 65% respectively, in tilapia fish aquaculture wastewater [32].  
The total average removed concentrations of NH\({}_{3}\)-N in the wastewater sample after 14 days of treatment with _C. asiatica_, _I. aquatica_, _S. molesta_, _P. stratiotes_, and _E. crassipes_ were 4.07 mg/L, 2.87 mg/L, 2.65 mg/L, 3.3 mg/L, and 3.1 mg/L, respectively. Although the percentage of removal and the total average concentration showed some distinct differences, statistical independent t-tests showed the opposite, and the value was not significant (\(p<0.05\)). However, these species are believed to have high oxygen transfer abilities, providing suitable environments for bacterial nitrification while enhancing the removal of NH\({}_{3}\)-N [33]. In addition, all species reached a constant concentration of NH\({}_{3}\)-N after the 12 th day of treatment because they had reached their removal capacity. The concentration in the control tank was higher than those obtained in the tanks with plants at the end of the experiment.  
The increasing concentrations of NH\({}_{3}\)-N at day 10 by _S. molesta_ were caused by the death of the plants and their debris. Plant decomposition is a major cause of increased ammonia content in wastewater because the nitrogen compounds in the organic matter re-enter the water and are broken down by microorganisms, known as decomposers. The decomposition process produces ammonia, which undergoes nitrification. The decomposition of ammonia, however, yields hydrogen and nitrogen, and is an important process in nature [30]. The removal of NH\({}_{3}\)-N was also caused by the biological mechanisms in nutrient intake and ammonia absorption [34]. The other possible reason for the NH\({}_{3}\)-N removal was the high growth of denitrifying bacteria [35, 36].
## 4 Discussion
### Limitations and Outlook of This Study
This could affect the detection of subtle vegetation dynamics, especially in regions with frequent cloud cover or complex terrain. In future research, the authors plan to conduct analyses on smaller time scales, with a particular focus on the lag effects of climate factors on NDVI, while also improving data preprocessing techniques to better account for atmospheric interference and dataset inconsistencies. Additionally, this study will continue to monitor the development of longer time series remote sensing data and strive to expand the temporal coverage of our analyses when data availability permits. Our goal is to align future studies more closely with the 30-year climate cycle standard, thereby enhancing the robustness and applicability of our findings in the context of long-term climate-vegetation interactions. These improvements will help provide more detailed and accurate insights into the dynamic interactions between climate and vegetation.
## 3 Optimizing In-Orbit SAR Operational Parameters for Altitude Variation  
Figure 10 shows the flowchart to optimize the SAR operational parameters when a SAR satellite is in orbit. At first, the target is defined by a user for image acquisition. In this process, the swath numbers corresponding to the target are determined to cover the incidence angle range at the predicted altitude. The nominal SAR operational parameters are extracted from the database, which includes values presented in Table 3. The extracted parameters are as follows: (1) antenna parameters such as a RF center frequency, an antenna Tx/Rx amplitude/phase excitation value, and an antenna beam pointing angle, (2) transceiver parameters such as Rx gain control and RF bandwidth, (3) SAR controller parameters such as a chirp rate, a Rx sampling frequency, a Block Adaptive Quantization (BAQ) compression ratio, and a Rx decimation ratio. As shown in Figure 10, those parameters are then used to recalculate the in-orbit SAR operational parameters for the predicted altitude. The following sections explain the processes for the target definition and the PRF optimization and finally, the optimization results.  
Figure 9: SAR performances at nominal altitude: (**a**) NESZ; (**b**) RAR; (**c**) AAR.
###### Abstract
The authors in [5] and [6] present the reliability and availability analysis of Ethernet communication architecture for a transmission substation in a power system. The authors used a reliability block diagram (RDB) approach to compare different network architectures (cascade, star, and ring). [PERSON] _et al._[7] present a method for reliability calculations of the electrical system (sub-sea cables, switchgear, transformers, and WTs) within offshore wind farms. Results of energy not supplied, using different WPF sizes and different architectures, were calculated and compared. In this paper, we propose different communication network architectures based on the electrical topology for small-scale WPFs. The reliability block diagram approach is used to calculate the availability of the proposed EPON model between optical line terminal (OLT) and WT-ONUs, with different protection schemes. The main objective of this work is to:  
* Propose communication network architectures for WPF monitoring based on EPON technology;
* Configure the network model of small-scale WPFs, based on electric power topologies;
* Analyze the optical power budget and path loss to ensure that the received signal power at the wind turbines side is sufficient to maintain acceptable performance;
* Make a reliability analysis of the communication network, consisting of OLT, feeder fiber (FF), passive optical splitter (POS), distributed fiber (DF), and ONU;
* Compare the connection availability between OLT and WT-ONUs with different protection schemes; and
* Compare the network costs for 20 different architectures with, and without, protection schemes. This paper is structured as follows. Section 2 provides related work. In Section 3, the proposed EPON-based wind power farm topologies are described. Section 4 provides the performance evaluation, results, and discussion. Finally, Section 5 presents the conclusion and the direction of future work.
# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 3 Proposed Method  
### Overall Architecture  
Infrared small target detection poses significant challenges due to the low signal-to-noise ratio, the presence of complex backgrounds, and the extremely small size of the targets, which often span only a few pixels. To address these difficulties, we propose a novel hybrid framework named PSHNet (Position-Scale Heatmap Network), which combines the dense localization capability of segmentation-style heatmaps with the geometric precision of structure-aware box refinement.  
The architecture of PSHNet is illustrated in Figure 1. It follows a U-Net-style encoder-decoder structure designed to preserve high-resolution spatial features throughout the network. The encoder is composed of successive convolutional layers that extract hierarchical features, while the decoder progressively restores spatial resolution by upsampling and fusing features from corresponding encoder layers. To enhance the model's sensitivity to small, subtle features--which are typically lost during deep-layer downsampling--we introduce an Enhanced Low-level Feature Module (ELFM) after the first encoding stage. ELFM strengthens shallow features via multi-scale dilated convolutions and channel-wise attention, improving the model's ability to capture fine-grained patterns necessary for small target detection.  
As shown in Figure 1, at the end of the decoder, PSHNet integrates a multi-branch localization head, consisting of three parallel output modules:  
* A heatmap head, which produces a dense spatial probability map indicating likely target center locations using a Gaussian encoding.
* An offset head, which refines the coarse grid-aligned center predictions by regressing sub-pixel shifts.
* A size head, which predicts the width and height \((w,h)\) of each target.  
These outputs are combined to form structured predictions representing the location and scale of each detected target. The heatmap ensures dense spatial coverage, while the offset and size predictions recover precise position-scale representations.  
Unlike traditional detection frameworks that treat CIoU-based box regression as the primary loss, PSHNet treats CIoU as an auxiliary geometric regularization term. After the predicted centers are refined using heatmap peaks and offsets, the CIoU loss is computed between the predicted and ground-truth bounding boxes. This encourages alignment in location and shape without compromising the dense spatial supervision provided by the heatmap structure.  
PSHNet thus unifies the spatial flexibility of segmentation-style prediction with the geometric rigor of box-level regularization. It is particularly suited for infrared small target scenarios, where precise localization must be achieved under conditions of minimal signal and high ambiguity. The complete network design supports accurate, interpretable, and efficient small object detection under weak visibility conditions.
# Glass Beads for Road Markings: Surface Damage and Retroreflection Decay Study
## 2 Glass Beads
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Glass Bead Type** & **Standard** & **Large** & **Premium** & **High Index 2** \\ \hline Sample code & _SF_ & _ML_ & _SP_ & _HI_ \\ Raw materials & Recycled & Virgin & Virgin & Virgin \\ Typical size range (\(\upmu\)m) & 100–850 & 600–2000 & 300–1000 & 212–1000 \\ Refractive index (RI) 1 & 1.5 & 1.5 & 1.6–1.7 & 1.9 2 \\ Density, calculated (g/cm\({}^{3}\)) & 2.5 & 2.5 & 2.9 & 4.3 2 \\ Normative class for RI 1 & A & A & A 3 & C \\ Normative class for contents of toxic metals and metalloids 1 & 1 & 1 & 1 & 1 \\ Normative roundness 1 & \textgreater{}80\% & \textgreater{}90\% & \textgreater{}90\% & \textgreater{}90\% \\ Typical intrinsic R\({}_{\text{L}}\) (mcd/m\({}^{2}\)/lx) 4 & \textless{}100 & \textless{}100 & 300–400 & \textgreater{}3000 \\ Typical maximum R\({}_{\text{L}}\) (mcd/m\({}^{2}\)/lx) in white paint & 300–350 & 350–400 & 850–1000 & \textgreater{}1800 \\ \hline Typical composition (weight percentages, as analysed): & & & & \\ \hline Silicon dioxide (SiO\({}_{2}\)) 5 & 70–75\% & 70–75\% & 30–35\% & 10–20\% \\ Titanium dioxide (TiO\({}_{2}\)) & \textless{}2\% & \textless{}2\% & 10–15\% & 30–35\% \\ Sodium oxide (Na\({}_{2}\)O) & 10–20\% & 10–20\% & \textless{}2\% & \textless{}2\% \\ Calcium oxide (CaO) & 5–10\% & 5–10\% & 20–25\% & 5–10\% \\ Magnesium oxide (MgO) & 3–5\% & 3–5\% & 10–15\% & \textless{}2\% \\ Barium oxide (BaO) & \textless{}2\% & \textless{}2\% & \textless{}2\% & 30–50\% \\ \hline \hline \end{tabular}  
* 1 Measured and classified according to standard EN 1423 [45]. 2 GB with RI reaching 2.4 are available for use in RM. Data given for the most frequently utilised RI of 1.9. 3 Excluded from classification as GB with low RI in the latest update of North American standard AASHTO M247– [46]. 4 R\({}_{\text{L}}\) measured against black background, due solely to RI. Not an official test procedure. 5 Only amorphous SiO\({}_{2}\); no crystalline silica polymorphs (publication pending).  
\end{table} 
Table 1: Selected typical properties of glass beads for road marking.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 4 Hamming Error-Correcting Code, IIR and FIR Filters
### Hamming Error-Correcting Code  
LoRa supports four error correction coding rates: CR \(\in\) [4/5, 4/6, 4/7, 4/8]. It uses (k, n) Hamming codes with k = 4 and n \(\in\) [6, 7, 8], where k represents the data length and n the codeword length as illustrated in the Figure 4.  
Figure 3: The simulated channel when noise is modeled as (**a**) AWGN Noise, and (**b**) an industrial noise.  
The parity bits from p0 to p3 are calculated as follows:  
\[\text{p0}=\text{d0}\oplus\text{d1}\oplus\text{d2}\]  
\[\text{p1}=\text{d1}\oplus\text{d2}\oplus\text{d3}\]  
\[\text{p2}=\text{d0}\oplus\text{d1}\oplus\text{d3}\]  
\[\text{p3}=\text{d0}\oplus\text{d2}\oplus\text{d3}\]  
To generate code words for code rates from 4/6 to 4/8, the following multiplication is performed, keeping only the first 4 CR bits:  
\[\begin{bmatrix}d3&d2&d1&d0\end{bmatrix}\cdot\begin{bmatrix}0&0&0&1&0&1&1&1\\ 0&0&1&0&1&1&0&1\\ 0&1&0&0&1&1&1&0\\ 1&0&0&0&1&0&1&1\end{bmatrix}mod2=\begin{bmatrix}d0&d1&d2&d3&p0&p1&p2&p3\end{bmatrix} \tag{13}\]  
Both the 4/8 and 4/7 code rate cases offer the possibility of correcting an erroneous bit. However, with the additional parity bit p3 present for the 4/8 code rate, we can avoid exchanging a correct bit in the case of an even number of erroneous bits. This is because:  
\[p3=d0\oplus d2\oplus d3=d0\oplus d1\oplus d2\oplus d3\oplus p0\oplus p1\oplus p2, \tag{14}\]  
This can act as a parity checksum. Once there is an odd number of errors, most likely only 1, p3 can be removed, and the two code rates are treated in the same way. A property of Hamming coding is that the syndrome resulting from multiplying the codeword by the parity check matrix can indicate the position of the bit to be exchanged in the codeword. To do this, the bits within the codeword are reordered to match the output of the syndrome. Again, an arbitrary order is chosen for the position of the parity bits in the syndrome calculation.  
\[S=\begin{bmatrix}p2\oplus d0\oplus d1\oplus d3&p1\oplus d1\oplus d2\oplus d3&p0 \oplus d0\oplus d1\oplus d2\end{bmatrix}, \tag{15}\]  
The bit order of the code word is changed as follows:  
\[\begin{bmatrix}p0&p1&d2&p2&d0&d3&d1\end{bmatrix}\]  
The parity check matrix can be written as follows: \(H=\begin{bmatrix}0&0&1\\ 0&1&0\\ 0&1&1\\ 1&0&0\\ 1&0&1\\ 1&1&0\\ 1&1&1\end{bmatrix}\)  
Figure 4: Bits in each codeword.  
With a coding rate of \(4/6\), only a maximum of two errors can be detected. Error detection can be performed using the following syndrome:  
\[S=\big{[}p1\oplus d1\oplus d2\oplus d3\quad p0\oplus d0\oplus d1\oplus d2\big{]}, \tag{16}\]
###### Abstract  
Due to threats to human safety, remotely controlled manipulators are more and more often used to carry out rescue tasks in hazardous zones. To ensure high efficiency and productivity of their work, intuitive control systems are necessary, e.g., master-slave and drive systems that maximize the speed of working movements by copying the movements of the operator's hands and are adapted to human perception and capabilities. Proper design of manipulator drive and control systems, therefore, requires knowledge of the acceleration and velocity of hand movements as signals controlling manipulators. This paper presents the results of tests of speed and acceleration in the implementation of the hand when making precise movements and moving objects over short distances (0.4-0.5 m) and during relatively long-distance reaching movements (0.73-0.93 m). Research has shown that, at short distances, the hand movements do not reach the maximum speed, while at longer distances, there is a period of constant maximal speed. In addition, studies have shown that the maximum speed of manipulation movements (longitudinal, lateral, and vertical) does not depend on the direction of movement. Moreover, precise movements were performed at a much slower velocity than reaching movements.
# Dynamics Investigation on Axial-Groove Gas Bearing-Rotor System with Rod-Fastened Structure
## 4 Results and Discussions
### Comparison of the Rod-Fastened rotor and Continuous Integral Rotor  
The orbits of the rod-fastened rotor and continuous integral rotor are compared by using the same parameter values. When \(\omega\) = 1500 rad/s, Figure 3(a) shows the orbits comparison of the rod-fastened rotor and continuous integral rotor at the left bearing station, and Figure 3(b) shows the spectra of the continuous integral rotor. The dynamic response is period-doubling motion in the continuous integral rotor system, whereas, the orbit of the rod-fastened rotor is periodic motion. For the model of the gas bearing-rotor system in this report, when \(\omega\) = 1500 rad/s, the unbalance responses of the rod-fastened rotor are more stable than the continuous integral rotor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \multicolumn{3}{c}{Structure parameters parameter values of the three-axial-groove gas bearing} \\ Radius of bearing, \(R_{b}\) & 0.005 & m \\ Bearing width, \(B_{b}\) & 0.01 & m \\ Width-to-diameter ratio, \(B_{b}/2R_{b}\) & 1 & \\ Radial clearance, \(c\) & \(5\times 10^{-6}\) & m \\ Position angle of pad, \(\alpha_{0}\) & 10 & deg \\ Groove width angle, \(\alpha\) & 5 & deg \\ Arc bushing angle, \(\beta\) & 115 & deg \\ Structure parameters parameter values of the rod-fastened rotor & Radius of shaft, \(R\) & 0.005 & m \\ Length of the left and right segments, \(l_{1}\) = \(l_{2}\) & 0.2 & m \\ Radius of disk, \(R_{d}\) & 0.01 & m \\ Width of disk, \(B_{d}\) & 0.02 & m \\ Mass eccentricities of the disk, \(e_{d1}\) = \(e_{d2}\) & \(1\times 10^{-6}\) & m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure parameter values of the rod-fastened rotor-gas bearing system.
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
## 3 Properties of NSPs
### Water-Binding Capacity  
NSPs show hydrophilic properties so that they can bind water. The water-binding capacity results from the interaction of water with NSP molecules through the formation of hydrogen bonds or dipole interactions [132; 133].  
AXs show a strong tendency to bind water (Table 2), especially the water-soluble ones [70; 72; 134]. The addition of water-soluble AXs in the amount of 0.5-1.3% to wheat flour led to the binding of 11-12% more water compared to the flour without the addition of these polysaccharides [134]. Water-soluble AXs derived from wheat flour are able to bind an average of 0.38 g water/g d.b. [70], whereas AXs derived from rye grains are able to bind 1.58 g water/g [72]. According to other literature data, it has also been shown that AXs from wheat flour is able to bind even 15 g of water/g [69]. Cellulose-rich AX from wheat bran is able to bind from 13.3 g to 16.13 g of water [73].  
Depending on the extraction method (Table 2), the amount of water bound by \(\beta\)-glucans from barley grain ranged from 2.91 g/g d.b. using enzymatic extraction to 3.79 g/g d.b. using hot water extraction [74]. Other literature studies reported that the water binding capacity of \(\beta\)-glucans from barley grain was 6.14-6.74 g water per 1 g of \(\beta\)-glucans [66]. The significant differences in the obtained results were probably due to the use of other barley grain varieties.  
It must be noted that determined values of water-binding capacity are proximate and strongly affected by methods of NSPs extraction, but also by differences in methods used for the determination of this parameter.