esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Database and Methods  
### System Model  
A database of 1476 experimental measurements has been incorporated in this paper, focusing on the fiber-reinforced polymer (FRP) confined concrete compressive strength, \(f_{cc}\), as a function of specimen geometric parameters and mechanical properties of FRP and concrete [20]. In a similar work, \(f_{cc}\) has been predicted by ML methods with satisfying accuracy based on 780 literature results [21]. Our approach (Figure 1) integrates feature engineering concepts for effective data curation and novel ML algorithms to decide on the mapping between input parameters and the output variable, along with fundamental concepts to argue the results.  
More specifically, the experimental database is created by tabulating eight independent input parameters that can be used to evaluate the compressive strength of the material. Feature engineering concepts are exploited before data enters the ML flow. After extensive testing, all eight inputs are examined on their correlation with each other, and their partial effect on the output is evaluated. Care is also taken on overfitting prevention. Overfitting is a common problem in supervised ML, where a model becomes too closely tailored to training data, resulting in poor generalization to new, unseen data [22]. To address this issue, it is important to carefully monitor the model's fit during training and use techniques such as regularization and the chi-squared test to prevent overfitting. The pre-processing stage also involves dimensionality reduction techniques, such as the Principal Component Analysis (PCA). This method linearly transforms the given inputs to a smaller number of new inputs, which, oftentimes, produce almost similar accuracy to the original inputs when fed to an ML model, but with less computational burden.  
A total of 12 different ML algorithms are evaluated on their performance for the specific dataset. These algorithms are divided into five categories: based on their architecture, linear-based, kernel-based, instance selection, tree-based, and neural network-based. Apart from selecting the optimal choice for compressive strength prediction, this detailed investigation  
Figure 1: ML approach to predict concrete compressive strength from experimental data.  
is an additional tool for overfitting prevention, as conclusions on their applications are drawn upon their different behavior on data.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing  
This section aims at summarizing past and current applications of remote sensing data and image processing techniques to Li exploration at first, and then highlights the main weaknesses associated with each methodology already proposed.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 2 Mathematical Models of the System Components
### Power Balance  
Power balance is the state of equality between the produced energy and the load demand. More exactly, at each time step, the total possible power generation should never fall short of the power consumption. The weather data collection for feasibility extended over one year to facilitate system analysis and to allow for scheduling the operation of the whole system. The power balance equation is expressed as follows:  
\[P_{PV}+P_{WT}+P_{Bat}+P_{DG}+P_{FC}+P_{EL}=P_{Load} \tag{10}\]
# Unsupervised Machine Learning Techniques for Detecting PLC Process Control Anomalies
## 5 Results and Discussions
### Performance Evaluation
#### 5.2.2 Performance of OCNN  
OCNN's performance on all metrics is similar to OCSVM. OCNN's recall value on test set 1 is high at 91%, whereas the recall values on test sets 2 and 3 are similar at 88%. A closer observation of Figure 7 reveals that in all test sets, TP prediction scores by OCNN are close to the decision boundary signifying low confidence of the positive class prediction. Over all test cases, about 75% of the correctly predicted positive class have scores closer to the decision boundary, which shows that OCNN has potential instabilities similar to OCSVM.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Accuracy** & & **Precision** & & **Recall** & & **F1-Score** & \\
**Dataset** & **OCSVM** & **[**13**]** & **OCSVM** & **[**13**]** & **OCSVM** & **[**13**]** & **OCSVM** & **[**13**]** \\ \hline Training set & 0.96 & 0.96 * & 1.00 & 1.00 * & 0.96 & 0.96 * & 0.98 & 0.98 * \\ Test set 1 & 0.90 & 0.78 * & 0.89 & 1.00 * & 0.90 & 0.78 * & 0.89 & 0.88 * \\ Test set 2 & 0  
In addition, OCNN misclassifies more than 25% of anomalies as normal instances, which is undesirable, especially in ICS anomaly detection. However, the TN and FP scores in all test sets are high, showing OCNN's robustness in detecting outliers. OCNN misclassifies several anomalies as normal instances, especially in test sets 2-5, leading to high false positive rates. Figure 7 shows a histogram of normalized FP, FN, TP, and TN decision scores made by OCNN on test sets 1-5.  
OCNN has good performance on all metrics on test sets 1, 2, and 3, similar to OCSVM. OCNN learns the TLIGHT system's normal behavior well by having a training recall of 97%. However, OCNN's performance on test sets 4 and 5 is low with F1-scores of 79% and 68% respectively. Table 6 shows OCNN's ability to detect changes in the light signals behavior of the TLIGHT system and an inability to detect timing bits errors. OCSVM and OCNN have similar performance because the OCNN objective function is developed as an improvement upon the OCSVM optimization problem in Equation (3). Table 6 summarizes OCNN's performance on the five test sets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Training set & 0.97 & 1.00 & 0.97 & 0.99 \\ Test set 1 & 0.91 & 0.90 & 0.91 & 0.90 \\ Test set 2 & 0.88 & 0.81 & 0.88 & 0.84 \\ Test set 3 & 0.88 & 0.87 & 0.88 & 0.86 \\ Test set 4 & 0.81 & 0.82 & 0.81 & 0.79 \\ Test set 5 & 0.70 & 0.79 & 0.70 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of evaluation results for OCNN.  
Figure 7: OCNN results of the normalized TP, TN, FP, and FN values on test sets 1–5.
# Effect of Pole Head Profile on Torque Performance of a Disc-Type Multipole Magnetorheological Brake
###### Abstract  
A magnetorheological brake (MRB) works on the shearing effect of MR fluid in the magnetic field to develop resistance between the stator and the rotor, which are relative moving surfaces. Researchers achieved high torque volume ratios in magnetorheological fluid braking systems by adding multiple layers and using multipole approaches. Even though several designs already exist, there is a need for innovative ideas and advanced structures of MRBs for practical engineering applications. The number of magnetic poles, pole geometry and placement have a significant effect on the performance of multipole MR brakes. This research focused on the effect of the pole head geometry in a multipole dual disc compact MR brake to understand the relationship between the geometry and its magnetic flux flow. The magnetic field generated by the electromagnetic coil's orthogonal travel through pole heads, rotor and MR fluid layers results in friction to fulfill the action of braking that achieves maximum brake torque. This flow depends on the magnetic pole head area and corresponding core dimensions. This study considered polygon, square, hexagon, trapezoid and ellipse pole head profiles in place of the conventional circular pole and the best possible core dimensions were used for each geometry. To study the brake's performance, COMSOL Multiphysics software (version 5.5) commonly used for 3D electromagnetic modeling was used to simulate and examine the magnetic flux intensity for various pole head configurations. Results indicate an improvement compared to previous works, considering the same dimensional limitations and the flux leakage between the adjacent poles being much less. The hexagonal and elliptical pole heads had better magnetic field area, magnetic intensity distribution and higher magnetic flux lines piercing through the MR fluid perpendicularly than the circular and square pole heads. An increment of 3.88% for braking torque performance was obtained, and the braking torque was significantly enhanced.  
MR brake; magnetic flux; pole head profiles; COMSOL; braking torque +
Footnote †: journal: Applied Sciences
## 3 Traffic Data Sensing and Gathering
### Wireless Sensor Networks  
Offering high efficiency and accuracy in tracking different events, wireless sensors have already been deployed widely in various environments for the purposes of data collection and monitoring [21; 22; 23; 24; 25]. The examples of such implementations are the real-time control of traffic lights [26; 27; 28] and their adaptation according to the road congestion levels [29] as well as urban parking management [30]. However, the deployment of WSNs in the road environment faces further challenges in addition to the well-known issues [31]. These drawbacks require a careful choice of an appropriate routing protocol. Among said challenges, there is a need to offer fast and reliable Medium Access Control (MAC) functionality and data forwarding mechanisms [32]. An example of the WSN deployment for traffic monitoring is illustrated in Figure 3.  
In this context, it is worth noting that the anticipated wide and dense networks of wireless sensors on the roads require utilization of data aggregation techniques to combat the high redundancy and correlation within the transmitted information. To reduce this data traffic redundancy, the optimized placement of wireless sensors alongside roads should be investigated. In addition, the tradeoff between the number of deployed sensors and the road events detection accuracy needs to be characterized.
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 5 Practical Applications  
This study points out new insights about accumulated weekly training and recovery status to sub-elite football players according to age group, training day, weekly microcycle and playing positions. Hence, there were several practical applications of the present training data. First, reporting accumulative training load could enhance the ecological validity more so than constrained tasks [32; 75]. Second, the researchers and practitioners should consider age-related differences to design training programs. Similarly, periodization strategies must focus on a short- and mid-term implementation given the high intra-week variation and the low inter-week variation. Finally, the positional role did not seem to be a main factor affecting the weekly training load performed by young sub-elite football players.
# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 3 Results
### Triple Collocation Analysis
#### 3.2.1 Identifying Temporal Threshold  
To identify the optimal temporal collocation thresholds, the results of matchups between individual datasets are presented in Table 3.  
The matchups between CYGNSS and SAR show peak performance at a time threshold of +/ -1.5-2 h interval with a maxima R value (0.550) after 1.5 h and minima RMSD (5.861) after two hours. We therefore select the 2 h threshold as it has a higher number of coincidental measurements.  
These values show the relatively weak performance of CYGNSS compared to SAR at speeds above 15 m/s. There are no previous values in the literature to compare this to. Other studies compared the earlier NOAA v1.1 product to other radiometers and observed correlations of approximately 0.75 [19]. The further drop in performance may be due to excluding winds less than 15 m/s. There is a linearly increasing number of measurements because CYGNSS constellation lies at an orbit around the tropics of +/ -35 degrees, which is almost perpendicular to the polar orbit of SAR.  
A majority of SAR-SMAP matchups occur within the first 30 min, as both missions are in sun-synchronous polar orbits with a local time of ascending node of approximately 18:00 [53; 54]. There are benefits of a 1 h threshold that leads to a slightly reduced RMSD and 20% increased number of measurements. There is no discernible change in Pearson correlation between SAR and SMAP, as a majority of the collocated measurements are already in the first 30 min interval.  
Note that the measurements within 30 min of CYGNSS-SAR had a higher RMSD than at the 2 h threshold. This suggested that the noise in NOAA CYGNSS wind retrievals needs smoothing from measurements over a total 4 h window to increase their accuracy. The spread of CYGNSS values for the timeseries of TC Jasper (Figure 3) also supports this finding.
## 2 ADCS System Model
### Attitude Control Algorithm  
Detumbling.- To maintain safety conditions and reach good initial conditions to apply the nadir controller, detumbling control is applied to try to achieve these requirements. For this purpose, the B-dot control is applied:  
\[{}^{B}\mathbf{m}=-C^{B}\dot{\mathbf{B}}_{\text{Earth}},\quad C>0 \tag{24}\]  
Nadir control.- In order to reach \([BR]\to\mathbf{I}_{3\times 3}\), the following control law is proposed for nadir control:  
\[{}^{B}\mathbf{m}=-\frac{1}{\|\mathbf{B}_{\text{Earth}}\|}\,^{B}\mathbf{B}_{\text{Earth}} \times(K_{P}\mathbf{q}_{v}+K_{D}\,^{B}\mathbf{\omega}_{B/\mathcal{R}}),\quad K_{P}>0, K_{D}>0 \tag{25}\]  
where the DCM matrix associated with \(\mathbf{q}_{v}\) is \([BR]\). The purpose of nadir control is to make the magnetic dipole go to zero. For this to be achieved, the states of the system, which are the quaternions and the angular rates, need to reach zero. This means that the body frame (\(\mathcal{B}\)) is perfectly aligned with the reference frame (\(\mathcal{R}\)). Notice that, if it is assumed that \(\mathbf{B}_{\text{Earth}}\) is periodic, then the proposed control guarantees \(\mathbf{q}_{v}\to\mathbf{0}\) with the next four asymptotically stable local equilibrium points \(\{\hat{r}_{1}=\pm\hat{r}_{i},\hat{r}_{3}=\pm\hat{h}_{i}\}\)[42].
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 5 Discussions
### Impact of Grouping Strategy  
The number of groups in the HiGoReg framework plays a critical role in balancing computational efficiency. By partitioning the dataset into smaller groups, the algorithm operates on lower-dimensional matrices, significantly reducing the computational cost associated with matrix inversion. However, this benefit does not scale indefinitely with increased group numbers. As the number of groups becomes excessively large, two key issues emerge. First, more frequent matrix operations--such as inversions and updates--accumulate computational overhead, offsetting the gains from operating on smaller matrices. Second, overly fine grouping results in fewer observations per group, which can degrade the robustness of parameter estimation and slow down convergence due to instability in iterative optimization. Empirical evidence from both simulated and real-world datasets supports this trade-off. As shown in Table 6, maintaining approximately 100 observations per group consistently provides an effective balance, yielding high computational efficiency without sacrificing registration accuracy.
## 4 Results and Discussion
### Distance Estimation  
A scene consisting of four people from the UTI dataset is considered in Figure 7 to show how the distance between people contributes to the threat level. The distance between people is given by the distance activity matrix shown beside each frame in Figure 7. Each element (square) in the activity matrix denotes the proximity between the person IDs corresponding to the row and column indices. The color changes to a warmer shade (yellow) when the people are closer, and it becomes a colder shade (blue) when they are farther away.  
Considering the frames in Figure 7, the person ID 2 and 3 can be observed to be closer in the second frame than in the first frame. This causes a higher contribution to the threat level between them in the second frame and a lower contribution to the threat level in the first frame. This is seen in the distance activity matrix by the blue shade turning  
Figure 6: Results of people detection. (**Top row**)—cases where the people detection model is successful. (**Bottom row**)—instances where people detection is erroneous. Undetected people are marked by the purple oval. The green bounding box (marked by the purple arrow) does not span the full person.  
Figure 7: Distance estimation results.  
to cyan, indicating closer proximity between those persons. The reader's attention is drawn to the threat level shown in each frame. As it can be observed, when the distance activity matrix lightens up, the threat level has also risen.  
The errors in people detection (Figure 6) can propagate to the distance estimation. While people going fully undetected is usually handled by interpolation, predicted bounding boxes becoming cropped due to the occlusion of feet leads to a faulty prediction for where the person's feet are. Therefore, the calculation of distance between people becomes erroneous.
## 2 Rock Tunnel Deformation Monitoring Scheme
### Data Transmission via the WSN  
Before any WTDS nodes in the tunnel are switched on, a gateway must be deployed in the tunnel to transmit the monitoring data properly. The gateway should be installed at the tunnel entrance for a typical rock tunnel site to ensure a stable open-air mobile 4G signal source. The stable data transmission coverage area of the gateway can reach up to 300 m. In most cases, there are more than 300 m between the gateway location (next to the tunnel entrance) and the sensor location (nearby the tunnel face). Therefore, it is necessary to install relay sensor nodes to transmit the data.  
A typical WSN in the rock tunnel consists of the WTDS nodes, the relay devices, the gateway, and the cloud server. The monitoring data obtained from the sensors are transmitted to the gateway at the entrance through wireless relay devices. Then, the monitoring data are sent to the client through mobile operators and cloud servers. The monitoring frequency can be adjusted according to the site requirements for up to 1 min per reading.
## 2 Proposed Methodology  
This section proposes a methodology that combines waveform imaging with deep learning to monitor the health condition of welding electrode tips in a spot welding system. The proposed methodology consists of three steps: (1) waveform imaging, (2) unsupervised feature extraction by CNNs, and (3) welding electrode tip condition assessment. The overall procedure is shown in Figure 2. The condition monitoring signals can be selected with the criteria of (1) ease of data acquisition, (2) correlation with welding electrode condition, and (3) robustness of the data. In the area of nondestructive testing, 1D waveforms (e.g., dynamic resistance, electrode displacement, electrode force, and ultrasonic signals) are commonly employed. First, the 1D waveforms are converted into 2D images. As discussed in the previous section, several waveform imaging methods exist. This study proposes a novel imaging method that is modified from the original RP imaging method. The proposed RP imaging method is devised to improve sensitivity to weak changes in a nonstationary signal and to enhance robustness to external noise. Second, CNNs are used for unsupervised feature engineering. Next, a pre-trained CNN is used to develop activation maps. Finally, a health indicator that assesses the condition of the welding electrode tips is built using the Mahalanobis distance (MD) that is calculated using the outputs from the activation maps.  
Section 2.1 describes the process of waveform imaging using the proposed RP method. Section 2.2 outlines the proposed approach for unsupervised feature extraction using pre-trained CNNs. Section 2.3 shows welding electrode tip condition assessment with MD.
## 6 Adjacent Channel Interference  
After detecting the channels on which the target public APs work, adjacent channel interference will be used to protect the user's input sensitive information. However, how does the safe wireless transmitter change when the number of target public APs is different? Which channel should the safe wireless transmitter be switched to so that each public AP will be interfered with? Then, we will give the details of the adjacent channel interference protection method.
# Binary Starling Murmutation Optimizer Algorithm to Select Effective Features from Medical Data
## 5 Experimental Evaluation
### Statistical Analysis  
To compare the algorithms fairly and to choose the best transfer function for mapping the continuous solutions to binary ones, [PERSON]'s statistical test was used to rank the algorithms. Table 7 shows the results of [PERSON]'s test according to the fitness values of the algorithms in which the Threshold-BSMO is a great variant to select the effect features from Diabetes, Heart, Hepatic, and Coronavirus diseases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{**Medical Problems**} \\ \cline{2-5}  & **Diabetes (Rank)** & **Heart (Rank)** & **Hepatics (Rank)** & **COVID-19 (Rank)** \\ \hline ACO & 10.37(11) & 8.67 (8) & 9.23 (8) & 9.70 (11) \\ BBA & 10.37 (11) & 8.67 (8) & 9.23 (8) & 9.70 (11) \\ bCWO & 2.80 (2) & 2.17 (2) & 3.07 (2) & 2.27 (2) \\ BWOA & 10.40 (12) & 11.23 (12) & 9.23 (8) & 8.70 (9) \\ S1-BSMO & 5.53 (4) & 8.57 (7) & 11.87 (11) & 7.80 (7) \\ S2-BSMO & 7.43 (8) & 9.30 (9) & 8.87 (7) & 8.97 (10) \\ S3-BSMO & 9.47 (10) & 10.73 (11) & 9.27 (9) & 10.07 (12) \\ S4-BSMO & 9.27 (9) & 10.37 (10) & 9.43 (10) & 8.40 (8) \\ V1-BSMO & 6.13 (5) & 5.67 (6) & 4.87 (6) & 5.53 (3) \\ V2-BSMO & 6.27 (6) & 5.13 (5) & 4.40 (4) & 6.73 (6) \\ V3-BSMO & 6.07 (7) & 3.90 (3) & 4.60 (5) & 6.20 (5) \\ V4-BSMO & 4.67 (3) & 4.80 (4) & 4.20 (3) & 5.60 (4) \\ Threshold-BSMO & 1.60 (1) & 1.80 (1) & 2.73 (1) & 1.33 (1) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Friedman test.  
Figure 4: Convergence comparison of the BSMO and comparative algorithms.
## 3 Algorithm Description and Process
### Problem Description
This process retains the layout of the uneven cluster (_i.e._, the number of cluster head nodes in an area close to the sink node increases, whereas the communication radius of the cluster head node in that area decreases), increases the node number within the scope of the sink node, improves the network reliability and balances network energy consumption. Finally, for each in-cluster node, the cluster head node calculates some corresponding \(CRR\) rates by supposing that the in-cluster node is deployed on each position within the maximum communication radius of its basic nodes and selects the best position for every in-cluster node by the priority of the hop of the basic node of the in-cluster node on the basis of decreasing the network coverage rate. This process decreases the hop number of the in-cluster nodes, increases the chances of the nodes near the sink node, decreases the energy consumption of deployment and improves the total coverage rate. The detailed description is presented as follows.