esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Prediction Models Based on Machine Learning (ML)  
The prediction models were developed using the regression learner application in MATLAB (R2022a edition). Several ML algorithms, including linear regression, decision tree, support vector machine (SVM), regression tree ensemble, and Gaussian process regression (GPR) [45,46], were utilized to establish yield-strength prediction models. The input parameters for the model consisted of the chemical composition of the weld metal and heat input.  
The linear regression model predicts the value of a dependent variable by assuming a linear relationship between the independent and dependent variables. Decision-tree algorithms involve multistage decision-making processes comprising complex decision trees from simple decisions to predict the final dependent variable.  
The regression tree ensemble technique combines multiple decision trees to create a robust model using boosting and bagging methods. SVM was originally designed for binary classification problems but can be adapted for regression tasks to find a hyperplane that maximally separates data points. Various SVM models (linear, quadratic, cubic, fine, medium, and coarse Gaussian SVM) were employed for training. GPR utilizes Gaussian processes for regression modelling, employing the squared exponential, Matern 5/2, exponential, and rational quadratic kernels.  
To evaluate the performance of the developed model, a comparative assessment was conducted using various predictive models to enable a comprehensive analysis of their effectiveness.
# Effect of Calined Marble Powder and Magnetized Water on the Performance of Cement-Based Composites
## 3 Results and Discussion
### Microstructural Characteristics  
The use of scanning electron microscopy (SEM) in the determination of the physical and mechanical properties of cementitious composites is a powerful tool with which to visualize and understand micro-scale changes in material structures. R0/TW- (Ref), R20/TW-, C0/MW- (Ref), and C20/MW-coded mortars were performed by scanning electron microscopy (SEM) in this section. Figure 18 shows the SEM images of Ref cement mortars under different magnifications.  
Figure 18: The SEM images of Ref cement mortars (**a**) 5000\(\times\), (**b**) 10,000\(\times\) and (**c**) 20,000\(\times\).  
In the SEM image of the non-substituted cement mortars (reference), C-S-H gels were seen intensely at 5000\(\times\) magnification. Ettringites were very few and CH could not be seen. In Figure 18b, ettringite, and C-H and C-S-H gels were observed at 10,000\(\times\) magnification. In Figure 18c, at 20,000\(\times\) magnification, C-S-H and ettringites between 1 \(\upmu\)m and 6 \(\upmu\)m in size are intensely observed. It can be seen that CSH gels cover CH gels and ettringites in the form of a network.  
SEM images of mortar samples coded R20/TW with a 20% raw marble powder substitution instead of cement are given in Figure 19.  
In the 5000\(\times\) SEM image in Figure 19a, it is seen that the entire surface forms C-S-H gels in the form of small particles. In Figure 19b, the 10,000\(\times\) SEM image shows C-H and C-S-H gels formed in the mortar. The densities of C-S-H gels are clearly observed. It is seen that the C-H gels formed in a small section have started to be replaced by C-S-H gels. In the 20,000\(\times\) SEM image of R20/TW mortar in Figure 19c, it is clearly observed that a very small gap and dense CSH gels are formed.  
Figure 20 shows the microstructure images of C20/MW cement mortars under different magnifications.  
In the 5000\(\times\) SEM image in Figure 20a, C-S-H gels are seen in most of the surface and C-H gels are seen in the form of small pieces. In Figure 20b, the 10,000\(\times\) SEM image shows dense C-S-H gels and minor CH gels formed in the mortar.  
An SEM image analysis showed that C-S-H had better bonding with the aggregate in the samples containing calcined marble powder. This can be explained by the positive contribution of calcined marble powder to the cement matrix and acceleration of hydration processes. In the specimens containing raw marble powder, the bond between the aggregate and C-S-H was weaker and more irregular than the calcined marble powder substitute. In conclusion, the SEM structure analysis shows that cementitious composites containing calcined marble powder have a stronger binder-aggregate relationship and more homogeneous microstructure compared to the samples containing raw marble powder.
## 5 Experimental Section
### Analysis and Discussion
#### 5.3.3 Error Analysis  
To investigate the variables that influence the effectiveness of ERHGA, the extraction performance of triple elements was analyzed. (E1, R, E2) was used to represent three elements of the triple. When there was one element to be tested, as long as this element was correct, it was considered to be correct, regardless of the other two. When there were two elements to be tested, as long as the two elements were correct at the same time, it was considered to be correct, regardless of the remaining one.  
Table 8 shows the results of the relational triple elements on ERHGA and PRGC. From the results of E1, E2, and R, it can be seen that ERHGA has excellent extraction performance for each element, especially the extraction precision of subjects (reaching 98.9%) and the extraction precision of objects (reaching 98.2%). ERHGA and PRGC mark subjects and objects in different ways; the BIO tag scheme was used to identify the tag of each token in PRGC. ERHGA learns and fuses representations of parts for the extraction of relational triples before object extraction, and the F1 of ERHGA in object extraction was 14.1% higher than that of PRGC. The extraction performance of the subjects and the extraction performance of the objects were better than that of the relations. The reason for this was that the WebNLG dataset has 171 relations; this large number and the complex situation bring some difficulties to the identification of relations. The relation-specific object extraction module is applied to each relation in ERHGA, whereas PRGC predicts potential relations in advance. Even though PRGC believes that predicting potential relations in advance improves the efficiency of the model, and the recall rate is more important in this step, it still affects the overall performance. The performance of (R, E2) and (E1, E2) demonstrates the role of our proposed method in identifying object entity  
\begin{table}
\begin{tabular}{c c} \hline
**Different Hyperparameters.** & **WebNLG** \\ \hline Training batch size = 8, Learning rate =0.01, Weight decay = 1 \(\times\) 10\({}^{-4}\), Momentum = 0.9 & 91.0 \\ Training batch size = 8, Learning rate =0.01, Weight decay = 2 \(\times\) 10\({}^{-4}\), Momentum = 0.8 & 91.2 \\ Training batch size = 8, Learning rate =0.01, Weight decay = 2 \(\times\) 10\({}^{-4}\), Momentum = 0.9 & 92.1 \\ \hline \end{tabular} Bold marks the highest score.  
\end{table}
Table 7: _Cont._  
Figure 2: F1 scores with different numbers of relations.  
mentions. Our method incorporating subjects and relations as a priori information lessens the detection of semantically irrelevant objects in the sentence. From the results of (E1, R), (R, E2), (E1, E2), and (E1, R, E2), the F1 of (E1, R, E2) is lower than that of the other three; making the alignment between entities and relations explicit is the crux for the extraction of relational triples.
## 3 Methodology  
A WSN consists of a set of sensor nodes and a sink. When the network has been set up and initialized, all the sensor nodes broadcast their status information to the sink. The status information of each sensor node broadcasted to the sink includes a distinctive identification (ID), \(\kappa-\gamma\) cartesian coordinates, traffic load (number of data packets to send in each second to the sink), initial residual energy, and maximum communication range. The graph of the WSN \(G=(V,E)\), is built after the sink has received all the sensor nodes' status information. \(V=\{v_{1},\ldots,v_{n}\}\) is the sensor nodes set and \(E=\{e_{1},\ldots,e_{m}\}\subseteq\bigvee\times V\) is the WSN links set. Each link is the connection between two sensor nodes in the WSN. Two sensor nodes are only connected if their cartesian distance is equal to or less than the sensor nodes' maximum communication range. The list of routing tables (RTs) is computed by the sink with GA-based MSTs when the edge weight is the cartesian distance between two connected sensor nodes. The sink selects an RT in each learning round of data transmission with an LSPI and broadcasts it to all sensor nodes for data transmission. This enables the global energy consumption and network lifetime optimization of the WSN. The centralized routing protocol for energy and lifetime optimization using GA and LSPI is presented in the sequel.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Routing Protocol** & **Objective** & **RL Technique** & **Control Technique** & **Drawback** \\ \hline RLBR [41] & Search for optimal paths taking into consideration of hop count, link distance, and residual energy. & Q-learning & Distributed & Slow convergence to the optimal routing paths. \\ \hline RL2 TO [42] & Search for optimal paths taking into consideration of hop count, link distance, and residual energy. & Q-learning & Distributed & Slow convergence to the optimal routing paths. \\ \hline RL-based routing protocol [43] & Knows the next forwarder with Q-learning by using the inverse of the distance between connected sensor nodes. & Q-learning & Distributed & Increased number of episodes to learn the network. \\ \hline EBR-RL [45] & Learns the optimal routing path using hop count and the residual energy of sensor nodes to maximize the network lifetime. & Q-learning & Distributed & Slow convergence to the optimal routing paths. \\ \hline LACQRP [9] & Learn the optimal MST that maximizes the network lifetime. & Q-learning & Centralized & Computational complexity increases exponentially with the number of sensor nodes. \\ \hline CRPLOGARL [13] & Learn the optimal or near-optimal MST that maximizes the network’s lifetime. & Q-learning & Centralized & Slow convergence to the optimal or near-optimal MST. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 2 Design of the Micro-Phasor Measurement System  
### Signal Measuring Circuit  
To measure phasor of distribution transformers, the 110/220 V signal was directly connected to the signal measuring circuit, an isolation transformer was adopted to isolate the signal of primary and secondary sides. A fuse F1 is designed as overcurrent protection at the input and a surge absorber VR1 is also designed in this circuit to absorb instantaneous surge that occurred at the signal input to protect the circuit from being damaged. In addition, to reduce noises in the circuit, the voltage signal that passed the isolation transformer is subsequently filtered through a low-pass filter, consisting of a resistor R3 and capacitor C1. Finally, a Zener diode D1 prevented excessive voltage signal from flowing directly into back-end circuits and burning the integrated circuit. The designed voltage signal isolating circuit is shown in Figure 1.
# On the Vaneless Space Vortex Structures in a Kaplan Turbine Model Operating at Speed No Load
## 2 Experimental Setup
### Measurement Program  
Several combinations of guide vane angles, \(\alpha\), and runner blade angles, \(\beta\), were selected as presented in Table 1. It must be noted that \(\beta\)\(=-17^{0}\) and \(\beta\)\(=10^{0}\) correspond to closed and open runner positions, respectively. Hence, the selected angles covered all the possible opening range in SNL conditions and they corresponded to the same cam curve with a speed factor, \(N_{11}\), of:  
\[N_{11}=\frac{nD}{H^{0.5}}=127.1 \tag{1}\]  
where \(H\) is the head and \(n\) is the rotational speed. As shown in Table 1, the turbine was tested at eight different discharges without cavitation. Moreover, three conditions were also tested with cavitation. A sufficient time was waited for each test to reach stable and steady conditions prior to taking continuous measurements of approximately 5 min. The corresponding discharge factor, \(Q_{11}\), and Thoma number, \(\sigma\), have been calculated using Equations (2) and (3):  
\[Q_{11}=\frac{Q}{D^{2}H^{0.5}} \tag{2}\]  
\[\sigma=\frac{NPSH}{H} \tag{3}\]  
\[NPSH=\frac{P_{2}-P_{va}}{g\rho_{2}}+\frac{V_{2}^{2}}{2g}-(z_{r}-z_{2}) \tag{4}\]  
where \(Q\), \(NPSH\), \(P_{va}\), \(g\) and \(z_{r}\) are the discharge, the net positive suction head, the vapor pressure, the gravity and the height of the runner, respectively, and \(P_{2}\), \(V_{2}\), \(\rho_{2}\) and \(z_{2}\) are the reference pressure, flow velocity, density of the water and height at Section 2, respectively, as indicated in Figure 1. All the signals were sampled at 5000 Hz and recorded simultaneously. The signals of the on-board sensors were extracted during the turbine rotation through a telemetry system.  
Figure 1: Schematic of the test stand with Section A-A and heights \(Z_{2}\) and \(Z_{r}\) indicated **(top)**. Schematic of the vaneless space with dimensions (B\({}_{0}\), V\({}_{0}\) and D) **(bottom left)**. Top view of section A-A **(bottom right)**.  
Compressed air or vacuum were applied on the water free surface in the downstream tank to change the NPSH. Then, a manometer and a mass flow meter were used to measure \(P_{2}\) and \(V_{2}\), respectively.
# Crushing Analysis and Optimization of Adjacent Variable Thickness Hexagonal Tubes
## 2 Materials and Methods  
### Description of Models  
An adjacent variable thickness hexagonal tube (AVTHT) can be obtained by changing the adjacent wall thickness and maintaining the separated wall thickness consistent with a traditional hexagonal tube. To maintain the integrity of the structures and high machining accuracy, we manufactured the specimens (Figure 1b) using the wire-cut electrical discharge machining (WEDM) technique with aluminum alloy AA6061O blocks. WEDM is that the specimen must be pre-drilled with a hole to form the internal profile of the cross-sectional shape, and then cut through the specimen with wire according to the pre-programmed profile shape of the TW tube [23]. The surface roughness of these tubes is fairly small, and the crushing behavior is not affected by welding seams.  
As shown in Figure 1c, a specimen tube consists of two types of wall plates with thicknesses _t1_ and _t2_, and wall plates with thicknesses _t1_ and _t2_ are arranged at intervals.  
Figure 1: Location and geometrical details of the AVTHTs: (**a**) the installation location; (**b**) specimens; (**c**) the plate thickness configuration; (**d**) cross section; (**e**) thickness configuration of cross section.  
The two parameters (i.e., \(t1\) and \(t2\)) range from 1.4-2.0 and 2.0-2.6, with the interval of 0.2 separately, and \(t2=4-t1\). Figure 1d,e shows the cross section, and the center lines of the cross section constitute a regular hexagon with a side length of 40 mm. The length of the tube specimens was maintained constant to 180 mm. The dimensions of AVTHT are determined by the space of the installation location in a certain subway vehicle (Figure 1a). Herein, we nominated the specimen with its sectional dimension. If a specimen was set with plate thicknesses of 1.8 mm and 2.2 mm, we named the structure \"S1822.\"  
For the study conditions, as shown in Figure 2, we fixed the specimen tube on a stationary plate, and drove another plate to axially or obliquely compress the tube. Considering that, during the crushing process, EASs were usually subjected to various oblique loadings, we defined three loading patterns for oblique loadings. Pattern 1 (Figure 2b) is loaded along a side with a thinner plate (thickness = \(t1\)), and the loading angle is \(\alpha>0\); pattern 2 (Figure 2c) is loaded along a side with a thicker plate (thickness = \(t2\)) and the loading angle is \(\beta>0\); pattern 3 (Figure 2d) means that loading is along a hexagon corner with \(\theta>0\).
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 3 Factors Affecting Dust Generations  
Dust emissions from poultry farms are affected by various factors and changes according to variable climatic conditions, applied management practices, the number of birds, and housing types. Various researchers have explained many factors that cause PM emission, as shown in Figure 2[9, 24, 36, 37, 38, 39].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sources** & **PM Type** & **PM Constitute** & **References** \\ \hline \multirow{3}{*}{Broilers} & \multirow{3}{*}{TSP} & Features, skin, bacteria, fungus, fecal matter, spilled feed, mold spores, and bedding fragments & [16] \\ \cline{3-4}  & & & \\ \hline \multirow{3}{*}{Broilers} & PM\({}_{2.5}\) & 72.1\% manure, 21.3\% feathers, 5.8\% & \multirow{3}{*}{[17]} \\  & PM\({}_{10}\) & wood shaving, and 0.7\% ambient PM & \\  & PM\({}_{10}\) & 95.6\% manure and 4.4\% feathers & \\ \hline \multirow{3}{*}{Layers} & PM\({}_{2.5}\) & 63.7\% manure and 36.3\% feathers & \multirow{3}{*}{[17]} \\  & PM\({}_{10}\) & 69.6\% manure, 30.0\% feathers, and 0.4\% & \\ \cline{1-1}  & & ambient PM & \\ \hline \multirow{3}{*}{Layers} & PM\({}_{2.5}\) & 54.2\% manure, 23.2\% feed, 17.0\% & \multirow{3}{*}{[18]} \\  & PM\({}_{10}\) & feathers, and 5.5\% ambient PM & \\ \cline{1-1}  & & 85.5\% manure and 14.5\% feathers & \\ \hline \multirow{3}{*}{Turkey} & PM\({}_{2.5}\) & 39.1\% feathers, 34.8\% manure, 26.1\% & \multirow{3}{*}{[25]} \\  & PM\({}_{2.5}\) & wood shavings, and 0.1\% ambient PM & \\ \cline{1-1}  & PM\({}_{10}\) & 51.9\% manure, 25.1\% feathers, and & \\ \cline{1-1}  & & 22.9\% wood shavings & \\ \hline \multirow{3}{*}{Broilers} & \multirow{3}{*}{TSP} & 50\% excreta, 30\% litter, 15\% feed, and 5\% feathers & \\ \cline{1-1}  & & 95\% organic composition like a feather, & \multirow{3}{*}{[18]} \\ \cline{1-1}  & & feeds, urine mineral crystal, manure, & \\ \cline{1-1}  & & and bedding materials & \\ \hline \multirow{3}{*}{Poultry} & \multirow{3}{*}{TSP} & Organic and inorganic particles: excreta, & \multirow{3}{*}{[33]} \\ \cline{1-1}  & & feathers, mites, dander, bacteria, fungi, & \\ \cline{1-1}  & & fungal spores, and endotoxins & \\ \hline \multirow{3}{*}{Poultry} & TSP & Bedding materials and floor & [34] \\ \cline{1-1}  & & & \\ \hline \multirow{3}{*}{Poultry} & TSP & Feed, excreta, hair, and dander & [35] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particulate matter composition varies with different housing systems.
# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 3 Measurement Results and Analysis  
A total of four data sets (two scenarios for each, running and cycling) were collected and analyzed separately. The data set were normalized to their corresponding mean values before they were processed. The data accuracy is limited by the sampling of the RSSI every 4 ms with step size of 0.5 dB. In this section, the time and frequency dynamic characteristics of the measurement results are presented using the time series, auto-correlation functions (ACF) and power spectral density (PSD).
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 2 Related Work
### Related Research
Notably, this framework incorporates three key modules, SCINet, RFAConv, and ASFF, into the YOLO11 framework to enhance detection performance in low-light maritime scenarios. SCINet, initially proposed by [PERSON] et al. [27], was designed for self-calibrated enhancement of low-illumination images. It has since been widely adopted in low-light object detection tasks. For instance, [PERSON] [28] integrated the Self-Calibrated Illumination Network (SCINet) into the early layers of the YOLOv8 backbone, effectively improving image contrast and revealing fine-grained details in dim environments. The residual mapping mechanism within SCINet facilitates the restoration of contrast and texture. However, its original architecture was tailored for static scenes and lacks the capability to model multi-scale target information, limiting its applicability in dynamic or complex environments. RFAConv, proposed by [PERSON] et al. [29], is a convolutional module designed to enhance spatial perceptual capacity. In the context of small-object detection in aerial remote sensing imagery, [PERSON] et al. [30] proposed an improved YOLO-RLDW framework by combining RFAConv with the C2f feature fusion module, thereby forming the C2f-RFA block. This integration significantly boosted the detection of multi-scale aerial targets in cluttered backgrounds, enhancing both robustness and accuracy. ASFF, introduced by [PERSON] et al. [31], addresses the inconsistency of multi-scale feature representations by spatially filtering conflicting information. This mechanism improves the scale-invariance of features across different detection layers. Building upon this, [PERSON] et al. [32] developed ASFF-YOLOv5 by embedding a receptive-field-aware ASFF strategy, which demonstrated improved performance in multi-scale fusion and small object detection. However, its performance under low-light conditions remains constrained, particularly in aligning blurred contours and extracting low-contrast edge information--an inherent bottleneck in dim scenes. In light of the limitations in scene adaptability and architectural integration of these modules in their original implementations, this paper proposes a unified integration of SCINet, RFAConv, and ASFF within the YOLO11 framework. This fusion strategy simultaneously preserves model robustness and significantly enhances the detection of maritime targets with low texture and indistinct contours. Empirical results confirm the effectiveness of this structural design, demonstrating its superior performance in low-illumination environments.