esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Using waste PET fibers in concrete may reduce the adverse environmental effect of plastic bottle waste. This paper aims to assess the unreported aspects of using PET fibers in the mechanical properties of concrete. This may help engineers and stakeholders understand the effect of PET fibers and increase their implementation to enhance the mechanical properties and structural responses of concrete. Accordingly, an accurate and comprehensive dataset collected from the available literature and an artificial neural network were utilized to perform a generalization analysis. It was shown that the network is able to predict the results with a MSE error of 7.11 MPa and correlation coefficient of 98%. It can be concluded that the results of the proposed network may be used to understand the response of PFRC. Moreover, it was shown that the most important factors on the behavior of PFRC are PET fiber content, fiber aspect ratio, and fiber shape.  
Generalization results indicate that an increase in the aspect ratio has an adverse impact on the compressive strength of concrete. This negative impact is more dominant in higher amounts of fiber content, which is due to the formation of weak regions in the concrete mass. Moreover, by comparing the generalized results of the ANN model, it was concluded that PET fiber shapes have an undeniable effect on the compressive strength of PFRC. This effect mainly depends on the bond strength between PET fibers and the concrete matrix. However, the mechanical properties of PET fiber, i.e., the tensile strength and modulus of elasticity of PET fibers, play an important role in the compressive strength of PFRC. It was demonstrated that the tensile strength of PET fibers is dominant in fibers with a higher bond strength, while the modulus of elasticity of PET fiber affects the compressive strength of FRC containing smooth PET fibers.  
Figure 7: Variations of the 28-day compressive strength for different PET fibers, based on tensile strength, modulus of elasticity, and fiber shapes. (**a**) String shape, (**b**) Irregular shape, (**c**) Deformed shape, and (**d**) Embessed Shape.  
**Author Contributions:** Conceptualization, S.R., M.J.M., K.D., and V.F.; methodology, S.R. and M.J.M.; software, M.J.M.; validation, S.R. and M.J.M.; formal analysis, S.R., M.J.M., H.D., K.D., and V.F.; investigation, S.R., M.J.M., H.D., K.D., and V.F.; resources, M.J.M., H.D., V.F., and M.K.; data curation, S.R., H.D., K.D., and V.F.; writing--original draft preparation, S.R., M.J.M., and V.F.; writing--review and editing, M.J.M. and V.F.; visualization, S.R., M.J.M., K.D., and and V.F.; supervision, M.K.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Implementation and Evaluation
### Complexity Analysis for the Water Cycle Detection Approach
#### 5.1.2 Evaluation of the Water Cycle Approach
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Accuracy** & **\#Features after Selection** & **Feature Sequence** \\ \hline WC + DT & 100 & 1 & 17 \\ WC + SVM & 99.0356 & 15 & 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 18 \\ WC + KNN & 98.92145 & 16 & 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18 \\ WC + NB & 80.98 & 12 & 1, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 18 \\ WC + DL & 100 & 1 & 17 \\ POS + DT & 99.4278 & 10 & 1, 5, 6, 7, 9, 10, 11, 13, 16, 17 \\ PSO + SVM & 98.9156 & 15 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 18 \\ POS + KNN & 98.6 & 14 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18 \\ POS + NB & 77.1 & 13 & 1, 2, 4, 5, 6, 7, 8, 9, 10, 13, 15, 16, 18 \\ POS + DL & 97.6891 & 8 & 1, 4, 5, 6, 8, 10, 15, 16 \\ SA + DT & 99.3471 & 7 & 2, 3, 5, 7, 10, 15, 17 \\ SA + SVM & 98.267 & 9 & 1, 2, 4, 6, 7, 9, 10, 15, 16 \\ SA + KNN & 98.599 & 15 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18 \\ SA + NB & 58.9 & 14 & 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 18 \\ SA + DL & 97.6913 & 7 & 7, 8, 10, 11, 13, 15, 16 \\ HS + DT & 99.3594 & 8 & 5, 7, 8, 10, 12, 14, 15, 17 \\ HS + SVM & 98.183 & 10 & 5, 6, 7, 8, 9, 10, 12, 13, 14, 16 \\ HS + KNN & 98.527 & 13 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 16, 18 \\ HS + NB & 78.6 & 13 & 1, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 18 \\ HS + DL & 89.9296 & 10 & 1, 4, 5, 8, 10, 11, 13, 14, 15, 18 \\ GA + DT & 99.5794 & 10 & 1, 3, 4, 5, 6, 7, 8, 10, 11, 17 \\ GA + SVM & 98.1789 & 13 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 15, 16, 18 \\ GA + KNN & 98.714 & 12 & 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15 \\ GA + NB & 68.92 & 16 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18 \\ GA + DL & 97.6993 & 11 & 1, 4, 5, 6, 8, 11, 12, 13, 15, 16, 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of five classifier algorithms using various feature selection techniques. \begin{table}
\begin{tabular}{c c} \hline \hline
**Techniques** & **Average Ranking** \\ \hline WCA + NB & 9.71 \\ WCA + KNN & 6.68 \\ WCA + SVM & 6.51 \\ WCA + DL & 5.99 \\ WC + DT & 5.94 \\ Friedman test (\(p\)-value) & 0.00 \\ Iman–Davenport (\(p\)-value) & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average ranking of Friedman test for WC with machine learning techniques.
## 3 Accelerated Structural Topology Optimization  
Topology optimization (TO) aims at establishing the optimal material distribution over the design domain with reference to performance objectives (e.g., compliance) [17], subject to predefined loading and boundary conditions. Its application spectrum extends over several fields, including implants manufacturing [18], aerospace engineering [19], architectural engineering [20], design of materials [21], fluid mechanics [22], structural engineering [4] and others.  
Several approaches have been proposed for solving TO problems, the main ones being [17] (i) Level-Set method, (ii) Density method, (iii) Phase field method, (iv) Topological derivative method and (v) Evolutionary method. SIMP is the most well-known variant of the density method, which was proposed in the 1990s [23; 24; 25]. The general formulation of a TO problem is summarized below:  
\[\begin{split}&\text{\emph{Minimize}}\quad F(x)\\ &\text{\emph{subject}}\quad\text{\emph{to}}\ :\\ &\quad\quad\quad\quad\quad\quad\quad\  
\(x_{i}\in(0,1]\). \(x_{i}\) correspond to the density values of each FE: \(x_{i}=0\) indicates no material over the \(i\)th FE domain, while \(x_{i}=1\) indicates that the \(i\)th FE domain is filled with material. Therefore, Equation (5) can be written as follows:  
\[\begin{split} Minimize& C(x)=U(x)^{T}\times P\\ subject&\quad\text{to}:\\ & K(x)\times U(x)=P\\ &\frac{V(x)}{V_{0}}=V_{t}\\ & 0<x\leq 1\end{split} \tag{6}\]  
where \(C(x)\) is the compliance for the current material distribution \(x\), and \(V(x)\), \(V_{0}\) and \(V_{t}\), respectively, denote the volume values corresponding to the current and initial density vectors \(x\) and \(x_{0}\), as well as the target volume value of the optimized domain. In SIMP, the Young modulus \(E\) is associated via a power law to the density value of each FE as follows:  
\[E_{x}(x_{i})=x_{i}^{p}E_{0}\iff K_{x}(x_{i})=x_{i}^{p}K^{0} \tag{7}\]  
where the penalization parameter \(p\) is usually set equal to \(p=3\). Thus, compliance can be expressed as follows:  
\[\begin{split} C(x)=U(x)^{T}\times P\iff\\ C(x)=U^{T}(x)\times K(x)\times U(x)\iff\\ C(x)=\sum_{i=1}^{n}x_{i}^{p}U_{i}^{T}K_{i}^{0}U_{i}\end{split} \tag{8}\]  
Accordingly, the formulation of Equation (6) can be expressed as  
\[\begin{split} Minimize& C(x)=\sum_{i=1}^{n}x_{i}^{p}U_{i}^{T}K_{ i}^{0}U_{i}\\ subject&\quad\text{to}:\\ & K(x)*U(x)=P\\ &\frac{V(x)}{V_{0}}=V_{t}\\ & 0<x\leq 1\end{split} \tag{9}\]  
In the literature, various search algorithms have been used in conjunction with SIMP for solving the optimization problem of Equation (9). The most commonly employed ones are the Optimality Criteria (OC) algorithm and the Method of Moving Asymptotes (MMA).
# A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
## 2 RF Signals in Soil
### Propagation Models
This makes them unsuitable for scenarios where estimation of attenuation of RF signals is required in situ (e.g., prior to geophysical surveys such as Ground Penetrating Radar (GPR) for utility location). In addition to these drawbacks, there is a large discrepancy between the estimated attenuation of EM signals determined from these models. The following section describes the methodology used for the field trials and the proposed method utilising in situ TDR measurements to determine the EM properties of the soil.
## 2 Short Literature Review  
Several studies have addressed the issue of applying ANN techniques to predict the tribological performance of tribopairs. As early as 1996, ANNs were first introduced to assist in this issue, when [PERSON] et al. [32] applied the technique to analyze the hardness and abrasive wear resistance of TiN/NbN PVD-deposited multilayers, and correlated them to the specific composition of the thin films. [PERSON] et al. [33] showed the feasibility of using neural networks to predict life data for different material/mechanical systems (rub shoe, pin-on-disk, and four-ball rigs), via the proper selection of input variables that influenced the tribological behavior of each particular system. [PERSON] and [PERSON] [34] used ANNs to describe the friction and wear behavior of various surface-treated structural steels fretted against bearing steel. The surface treatment applied comprised liquid nitriding, boriding, as well as MoS2 spraying, whilst 35 pairs of friction and wear experimental datasets were used to develop the ANN. Similarly, the wear mechanisms of glass fiber reinforced epoxy were investigated via pin-on-roller tests against hardened steel and the experimental data were used to identify the areas of mild, severe, and ultra-severe wear on a \"normalized force\"-\"normalized velocity\" chart produced with the aid of neural networking algorithms [35]. More recently, several works have emerged on the use of ANNs for the prediction of the wear of composite metallic matrix materials, based on the combined experimental investigation of the weight losses during pin-on-disk sliding and the percentage of the reinforcing constituent of the material [36; 37; 38; 39]. [PERSON] et al. [39] developed an ANN architecture for modeling the wear of polyamide 66 during dry sliding, introducing the applied load, the load ratio, the number of surface cracks, the lubricant's thickness, and the linear wear rate of the material under study as input data. [PERSON] et al. [40] combined the Taguchi method and ANN to optimize the percentage of particulate reinforcement of 2024 aluminum alloys that could ensure a minimum wear rate during dry sliding block-on-disc tests. The experimental data were used to develop a multiple linear regression model and an ANN model, where the latter proved to be more efficient. In 2018, [PERSON] et al. [41], focusing on the effects of prior heat treatment on the tribological performance of AISI A8 cold working tool steel, developed wear mechanism maps by applying K means clustering and neural networks. Detailed and in-depth state-of-the-art works can be found in References [41; 42; 43; 44; 45; 46; 47; 48].
## 4 Discussion
Next, the workpiece frequency response with the TMDs arranged with a regular pitch (TMD mounting arrangement in Figure 9b) and an irregular pitch (TMD mounting arrangement in Figure 12c) in the circumferential direction along with the workpiece frequency response without the TMDs are presented in Figure 20. It can be seen from Figure 20 that the peak of the natural frequency of the (3,1) mode related to the chatter mode in the case of the TMDs arranged with a regular pitch is almost the same as that of the original workpiece without the TMDs. In contrast, the peak becomes lower and the modal damping ratio is higher in the case of the TMDs arranged with an irregular pitch compared to that of the TMDs arranged with a regular pitch. The present study demonstrated the chatter suppression effect in specific patterns with the irregular circumferential installation pitch of the TMDs. Based on the experimental results, an analytical model will be developed to determine the optimum arrangement and tuning parameters of the TMDs to further enhance the chatter suppression effect, by considering the amount of imbalance.
###### Abstract  
Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators' posture.  
low-light image enhancement; strong light judgment; light decomposition; Zero-DCE; electric power operation environment
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)
#### 3.3.1 Combination of Audio and Video Features  
Several studies integrate audio and video features to build richer representations of classroom dynamics. While audio captures elements such as speech content, turn-taking, and prosody, video provides complementary signals, including facial expressions, gaze, and posture, offering insights that are otherwise inaccessible through audio alone.  
Some works use video primarily as a tool to support the labeling or segmentation of audio data. For example, [PERSON] and [PERSON] [57] combine diarized audio with video labels to analyze teaching assistant interventions during collaborative problem-solving, allowing for detailed mapping of intervention timing and response. Similarly, refs. [56; 60; 79; 80] use video to segment turn-taking episodes in engineering classrooms, contextualizing audio-based interaction markers with visual cues.  
Other studies extract concrete video features that are combined with audio in multimodal machine learning models. For instance, [PERSON] et al. [48] use face count and facial emotion recognition to estimate classroom climate alongside acoustic features. [PERSON] et al. [53] incorporate facial action units (FAUs), eye gaze, and body pose to detect confusion  
Figure 7: Distribution of analyzed papers that uses a multimodal approach (RQ3).  
and conflict during pair collaboration. Likewise, [PERSON] et al. [9] fuse audio features with pose estimation data to identify teaching methodologies, while [PERSON] et al. [81] include face detection and actions like note-taking to assess student engagement.  
Overall, the combination of audio and video data enables a multidimensional view of classroom interactions, supporting the identification of affective states, collaborative dynamics, and pedagogical patterns that would be challenging to infer from a single modality.
## 4 Experiments
### Analysis on the Length of Key Frame Sequence  
In this part, we analyze the effects of different lengths of selected key frame sequences. How key frame length affects the action recognition performance is explored within the context of the NTU RGB+D 60 dataset. According to the key frame selection and refinement method in this paper, this section validates the effect of key frame length on classification performance using the joint modality under the X-sub and X-view split criteria. The accuracy of the action recognition task as it relates to key frame length is shown in Figure 4. For the X-sub setting, when the number of frames is less than 70, the recognition accuracy is highly sensitive to sequence length. Between 50 and 70 frames, the model's recognition accuracy significantly increases from 89.2% to 90.2%. This phenomenon may be due to an insufficient number of key frames where the model cannot capture enough motion information, leading to improved recognition accuracy as the number of key frames increases. When the frame length reaches 70, the accuracy does not rise significantly anymore but instead shows a certain decrease (at 80 frames), which could be mainly attributed to the inclusion of too many key frames, leading to redundant or noisy frames in the optimized skeleton sequence, thereby hindering the performance of the model. As the key frame length increases to 90, the task's accuracy experiences a slight increase but is almost indistinguishable from when the length is 70 frames. Therefore, for the X-sub setting, we select a key frame length of 70 to maximally include useful motion information while ensuring recognition accuracy and considering the computational efficiency of the model. Similarly, for the X-view setting, we choose a key frame length of 80. Considering that the average length of salient pose frames across all samples is approximately equal to 12, we set \(T_{\text{init}}\) = 20.
## 3 Results
### Detailed Analysis of the Typical Cycle of a Repetition for a 10 kg Load
#### 3.1.1 Evaluation of the Cycle of One Repetition--Initial and Final  
For an initial repetition, the evolution of the force for a cycle of one repetition is presented in Figure 5a. The second repetition (R2) was analyzed, being more relevant than the first one; in this case, considering that the number of repetitions was high, it can be considered that this was the relevant initial repetition. There was an interesting physiognomy of R2, with a concentric phase with reduced oscillations around the trend and an eccentric phase with a long bearing around the reference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Repetition** & **Fmax (kN)** & **t (s)** & **Repetition** & **Fmax (kN)** & **t (s)** \\ \hline R1 & 0.127 & 5.64 & R13 & 0.131 & 25.08 \\ R2 & 0.139 & 6.84 & R14 & 0.128 & 26.84 \\ R3 & 0.134 & 8.62 & R15 & 0.127 & 28.70 \\ R4 & 0.127 & 9.78 & R16 & 0.125 & 30.78 \\ R5 & 0.128 & 11.70 & R17 & 0.125 & 33.08 \\ R6 & 0.136 & 13.32 & R18 & 0.121 & 34.92 \\ R7 & 0.142 & 14.84 & R19 & 0.124 & 37.51 \\ R8 & 0.136 & 16.39 & R20 & 0.127 & 40.33 \\ R9 & 0.134 & 18.03 & R21 & 0.124 & 42.41 \\ R10 & 0.128 & 19.73 & R22 & 0.13 & 44.96 \\ R11 & 0.138 & 21.25 & R23 & 0.129 & 47.8 \\ R12 & 0.133 & 23.17 & R24 & 0.138 & 51.42 \\ Fmax (kN) & & & 0.13 & & \\
1 RM (kN) & & & 0.242 & & \\ \%1 RM & & & 54\% & & \\ \hline \hline \end{tabular} \({}^{1}\)RM—maximum one-repetition, %1 RM—relative intensity, Fmax—maximum force.  
\end{table}
Table 2: Maximum forces per repetition—10 kg load.  
Figure 4: Evolution of maximum forces per cycle—10 kg load.  
It can also be observed in Figure 5a that the ascending time (t\({}_{\text{a}}\)) and the descending time (t\({}_{\text{d}}\)) had very close values. The duration of R2 was 1.72 s, with t\({}_{\text{a}}\) being 0.81 s.  
For a final repetition (R24), the evolution of the force for a cycle of one repetition is presented in Figure 5b. In this case, interesting mechanisms were observed. First, the duration of R24 was 4.38 s, with t\({}_{\text{a}}\) being 0.41. A short t\({}_{\text{d}}\) of 0.24 s was followed by a holding time (t\({}_{\text{h}}\)) of 3.34 s. A sudden decrease of 0.39 s ended the final repetition cycle.  
This result, observed and measured at R24 whereby the subjects maintained muscle contraction for a longer period, was surprising because there is energy consumption and the natural tendency would have been exactly the opposite. The explanation lies in the psychological pressure related to the failure to add a new repetition to the MNR set. However, there was an interesting implication of maintaining the muscle contraction in the proximal area of the maximum force (i.e., 85-90%) related to the execution of the exercise through which the MNR strategy determines this surprising behavior.  
The amplitude of the oscillations on this level was higher, which confirms the effect of the subject's fatigue, close to the exhaustion of the set.