esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Works  
In this work, an enhanced approach as a wrapper feature selection that combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) is introduced. The main idea is to enhance the performance of the WOA exploitation process by improving the worst half in the population based on the SCA algorithm at every iteration. In addition, to enhance the population diversity and increase the exploratory behaviour of WOA, chaotic sequence number generated by logistic chaotic map is employed to balance between two updating mechanisms (i.e., spiral-path and shrinking-circles path) inside the WOA.  
The performance of the proposed algorithm was examined on educational data come from two different schools. Five different classifiers have been examined (i.e., k-NN, DT, LDA, NB, and LB). The performance of LDA outperforms other classifiers with respect to the AUC value. The performance of EWOA with V4 TF (EWOA-V4) shows an outstanding performance compared to other algorithms in the literature.  
The limitation of this work is the availability of students' performance datasets, where few datasets are available for research. Another limitation of this work is that the proposed enhanced WOA has only been tested in the SPP domain. In addition, the parameters of the algorithms were set based on small simulations and common settings in the literature. In future works, we will examine the performance of the proposed approach in multi-objective optimization problems and more complex data such as medical and biological datasets. We will also conduct extensive experiments to determine the most appropriate values of common and internal parameters for the enhanced WOA as well as other utilized algorithms.  
Conceptualization, T.T., M.M. and H.T.; Methodology, T.T., A.Z., S.A.A., M.M., H.C., A.A., H.T., S.M. and A.S.; Data curation, T.T. and H.T.; software, T.T., M.M. and H.T.; investigation, T.T. and H.T.; Validation, T.T., A.Z., M.M., H.T., S.M. and A.S.; Writing original draft preparation, T.T., A.Z., S.A.A., M.M., H.C., A.A, H.T. and S.M.; Writing review and editing, A.Z., S.A.A., H.C., A.A., S.M. and A.S.; Supervision, T.T., M.M. and H.T.; funding acquisition, H.T. and A.Z. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/114), Taif University, Taif, Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge Taif University Researchers Supporting Project Number (TURSP-2020/114), Taif University, Taif, Saudi Arabia.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **MLP-Adam [14]** & **BTLBO-LDA [15]** & **Our Approach** \\ \hline Data1 & 0.8201 & - & **0.9157** \\ Data2 & 0.6052 & 0.6323 & **0.6557** \\ \hline \hline \end{tabular}
\end{table}
Table 19: Comparison of the proposed approach with similar studies in terms of AUC.
## 3 Result and Discussion
### SERS Selectivity of the Positively Charged Au NPs-Embedded MIHs  
To investigate the SERS selectivity of the positively charged Au NPs-embedded MIHs, both the MIHs and the positively charged Au NPs-embedded NIHs were used to adsorb New Red from an aqueous solution of New Red at a concentration of 8.18\(\times 10^{-5}\) M, followed by SERS measurements using the two swelled hydrogels as substrates. Figure 4a presents the SERS spectrum of New Red solid powders. Figure 4b shows the spectrum obtained using the Au NPs-embedded MIHs as a substrate. Strong characteristic signals of New Red at 1572 cm\({}^{-1}\), 1513 cm\({}^{-1}\), 1439 cm\({}^{-1}\), 1359 cm\({}^{-1}\), 1238 cm\({}^{-1}\), and 1144 cm\({}^{-1}\) were clearly observed. By contrast, Figure 4c displays the SERS spectrum using Au NPs-embedded NIHs as a substrate, and no notable signal of New Red could be detected. These results demonstrate that the selectivity of the MIHs towards the detection of New Red was quite satisfactory.  
Figure 4: Surface-enhanced Raman spectroscopy (SERS) spectra of New Red solid powders (**a**), MIHs absorbing \(8.18\times 10^{-5}\) M of New Red (**b**), and NIHs absorbing \(8.18\times 10^{-5}\) M of New Red (**c**); (Inset: The enlarged spectrum of c).  
Figure 3: High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images (**a**,**b**) and compositional maps of gold (**c**,**d**), nitrogen (**e**), oxygen (**f**), and carbon (**g**) of the positively charged Au NPs-embedded MIHs.
# Analysing Social Media Discourse on Electric Vehicles with Machine Learning
## 3 Materials and Methods
### Evaluation  
Precision, recall, accuracy, and F1 score are important metrics used to evaluate the performance of the model in classification problems, and each of them analyses the success of the model in different aspects. These metrics allow for a more comprehensive assessment of not only the accuracy of the model but also its errors and shortcomings [64].  
Accuracy shows how accurately the model predicts across all data, that is, the ratio of correct predictions to total predictions [65].  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{6}\]  
Precision indicates how many of the samples that the model predicts as positive are positive, that is, the accuracy of the model's predictions to the positive class [65].  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
Recall indicates how many of the truly positive examples the model correctly predicts, that is, how well it captures the entire positive class [66].  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
The F1 score is the harmonic mean of precision and recall. This metric attempts to balance both the rate of correct positive predictions (precision) and the rate at which the positive class is correctly identified (recall). In particular, it is used when a trade-off between precision and recall is desired [66; 67].  
\[\text{F1}=\frac{2*\text{Recall}*\text{Precision}}{\text{Precision}+\text{ Recall}} \tag{9}\]  
During model training, hyperparameter optimisation was performed manually by evaluating different configurations of learning rate, batch size, and number of epochs to maximise model performance. The training and testing sets were split using an 80/20 ratio. Standard evaluation metrics including precision, recall, accuracy, and F1 score were used to assess the model's classification performance. These metrics provided a comprehensive understanding of the model's ability to distinguish between sentiment categories.
###### Abstract  
Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model--namely, combine-all in semi-supervised learning (C\(\
eq\)GL)--to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging-segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured \(L_{2}\) similarity (SL\({}_{2}\)SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (C\(\
eq\)GL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly.  
a +
Footnote †: journal: Medical Imaging  
1 Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA 1
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
## 3 Testing Results  
During the on-site evaluation, it was noticed that not all vehicles would pass over the P-WIMs contacting their entire top surfaces due to the vehicle wandering. Having contact with part of the tire causes the drop of the electrical output generated from P-WIMs. As shown in Figure 4d, the engineering epoxy has a darker color than the pavement surface. Some drivers noticed it would steer rather than driving directly over the P-WIMs. While the actual contacting area is unknown, it is difficult to calculate the axle loading with the generated voltage. Therefore it is necessary to filter the data and keep only the voltage generated from full contacting. Since the axle loading of the trailer trucks' first axle is almost the same, the peak value of the voltage generated from the first axle is selected as the reference to filter the measured data. Figure 5 shows a sample voltage output from the installed P-WIM and an instruction of finding the reference peak voltage. As is known, the voltage generated from piezoelectric material always has the positive part and a negative part. The applied force is mostly characterized by the positive part. To present the electrical output concisely, the negative part of the electric output is removed in the analysis. The curve of the voltage output over time presents a typical axle configuration of 5-axles trailer truck which is widely used in the United States. The data was measured on 25 October 2012, from P-WIM #1. Firstly, the maximum values of the peak voltage output of the first axle are found from each set of data measured from each P-WIMs as the reference peak voltage value. Data measured from P-WIMs #1 to 4 are used in data analysis. The voltage data are excluded from the analysis if the peak value of the first axle is less than 85% of the reference peak voltage value.
###### Abstract  
The purpose of this systematic review was to assess the postoperative facial swelling (FS) and occlusion attained with clear aligner therapy (CAT) and fixed orthodontic therapy (OT) after orthognathic surgery (OS). The focused question was \"Is there a difference in postoperative FS and occlusion attained with CAT and fixed OT after OS?\" Indexed databases were searched up to and including October 2023 using different keywords. Original clinical studies that compared postoperative FS and occlusion attained with CAT and fixed OT after OS were included. Risk of bias (RoB) and quality of evidence were also assessed. Four retrospective studies were included. One study reported no difference in postoperative FS among patients that underwent CAT or fixed OT after OS. In one study, patients treated with CAT demonstrated less FS in the first post-surgical week than individuals that underwent fixed OT. In one study, postoperative occlusion was comparable among patients that underwent CAT or fixed OT, whereas in another, postoperative occlusion was better in patients that underwent fixed OT than CAT. All studies had a moderate RoB and none of the investigations were based on power-adjusted data. Based upon the sparsity and questionable quality of available evidence, there is a need to render a firm conclusion. However, it seems that CAT and fixed OT are comparable in relation to postoperative occlusion and FS after OS.  
clear aligner therapy; clear aligners; complications; edema; facial swelling; fixed orthodontic therapy; orthodontics; orthognathic surgery; tooth movement
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 3 Experimental Verification
### Experimental Design and Data Processing
#### 3.2.3 Split of the Measured and Unmeasured Vibration Response Points  
In this experiment, the 8 th group was taken as the test set, and the other 14 groups as the training set. There were 18 vibration response measured points. \(n_{1}\) measured vibration response multi-point  
Figure 3: (**a**) Acoustic and vibration experimental devices. (**b**) Acoustic excitation. (**c**) Vibration excitation source. (**d**) Vibration response point layout diagram. (Reprinted from International Journal of Applied Electromagnetics and Mechanics, Vol 59, [PERSON] et al., Transfer functions and loads identification based response prediction in situation of unknown uncorrelated multi-source loads, 1029–1039, No.3, Copyright (2020), with permission from IOS Press).  
data were chosen as the input and \(n_{2}\) unmeasured response points' data (\(n_{1}+n_{2}=n\)) were chosen as the output to train the MIMO LS-SVR model, where \(n\) denotes the total number of points' responses used in the model. As discussed above, the number of measured points responses should be less than or equal to the training set sample. To satisfy the constraint of the applicable scope, \(n\) was 18. When multiple responses were predicted at the same time, the number of measured points was 16 and the number of unmeasured points was 2.
## 5 Conclusions  
Reflex control is evaluated on calf muscles during upright standing push-recovery using the ankle joint strategy in this paper. A time-increasing search method, in which the reflex gains can be obtained through a simple regression algorithm, is proposed to test the feasibility of the reflex model for fitting real muscle activations. The experimental results show that the reflex model is able to well fit the forepart and rest part of calf muscle activation with different gains. Under the reflex control mechanism, the ankle joint forms a spring-damping-like system to resist external disturbance. This simple and effective control mechanism causes the ankle joint dynamic properties to become suitable for environmental interaction. This study also investigates the human internal control mechanism underlying the push-recovery movement. The two-piecewise results indicate that controlling ankle joint robotic devices with constant gains may be insufficient to assist the human upright standing push-recovery motion. The obtained results will be applied in the control of an exoskeleton device to assist human upright standing balance in the near future.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **10 Hz** & **7 Hz** & **6 Hz** & **5 Hz** & **4 Hz** & **3 Hz** & **2 Hz** & **1 Hz** \\ \hline \(p_{a}\) & 4.06 & 4.35 & 4.48 & 4.66 & 4.93 & 5.35 & 5.84 & 5.33 \\ \(d_{a}\) & 0.38 & 0.42 & 0.44 & 0.46 & 0.48 & 0.48 & 0.39 & 0.19 \\
**VAF** & 0.74 & 0.84 & 0.86 & 0.88 & 0.91 & 0.95 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Variation in reflex gains and reflex prediction VAF values of subject B with different low-pass cutoff frequencies.  
Figure 17: Muscle activations calculated with different low-pass cutoff frequencies. Results with 10, 5, 3 and 1 Hz are plotted to make the graph easy to read.  
**Author Contributions:** [PERSON] provided the conceptualization, methodology and original draft preparation of this manuscript; [PERSON] wrote the software together with [PERSON] and took charge of data curation; [PERSON] support the development of the software and revised the manuscript in English together with [PERSON] and [PERSON]. [PERSON] provided the conceptualization together with [PERSON]; [PERSON] provided funding support and reviewed the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 61603284 and 51575412.  
**Acknowledgments:** The authors thanks for the kind help of the five volunteers.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Problem Formulation and Scheduling Algorithm
### Scheduling Algorithm
#### 4.10.1 Mode 1  
The HEMS operates in this mode if any one or more than one non-thermal appliances are activated. In this case, the optimization problem is formulated as an MILP as follows:  
\[\min_{\mathbf{u},\mathbf{v},\mathbf{w}}J_{\mathcal{P}}+J_{\mathcal{Q}}+J_{ \mathrm{s}}+\sum_{k\in\mathcal{N}}(\beta_{\mathbf{w}}(\mathbf{w}_{k})+\sum_{ i\in\mathcal{P}}\alpha_{\mathbf{v},i}(\mathbf{v}_{k,i})), \tag{14}\]  
subject to  
\[\mathbf{u}_{\min,i}\leq\mathbf{u}_{k,i}\leq\mathbf{u}_{\max,i} \ \ \ k\in\mathcal{N},i\in\mathcal{P} \tag{15a}\] \[\sum_{k=b_{j}}^{e_{j}}\psi_{k,j}=\Omega_{j},\ \ \ j\in\mathcal{Q},\ \ \psi=\{0,1\}\] (15b) \[\mathbf{y}_{k,\min,i}\leq\mathbf{y}_{k,i}+\mathbf{v}_{k,i}\ \ \ k\in\mathcal{N},i\in\mathcal{P}\] (15c) \[\mathbf{y}_{k,\max,i}\geq\mathbf{y}_{k,i}-\mathbf{v}_{k,i}\ \ \ k\in\mathcal{N},i\in\mathcal{P}\] (15d) \[\sum_{i\in\mathcal{P}}\mathbf{u}_{k,i}+\sum_{j\in\mathcal{Q}}P_{k,j}\psi_{k,j}\leq C_{k}-\mathbf{w}_{k}\ \ \ k\in\mathcal{N},i\in\mathcal{P},j\in \mathcal{Q}. \tag{15e}\]  
Here, \(\alpha_{\mathbf{v}}(\mathbf{v})\geq 0\) and \(\beta_{\mathbf{w}}(\mathbf{w})\geq 0\) are convex penalty cost functions associated with the slack variables \(\mathbf{v}\) and \(\mathbf{w}\). It should be noted that the value of \(\beta_{\mathbf{w}}(\mathbf{w})\) is time dependent and is specified by the power utility depending upon the power-grid operation and stability. This information is shared with HEMS dynamically on a real-time basis.
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 2 Problem Statement and Related Work
### Related Problems in the Literature
#### 2.2.2 Related Technician Routing Problems  
TRPTW can be observed as a simple case of many technician routing problems. A large diversity of technician routing problems was studied, with many variants of additional constraints [30]. The technician routing and scheduling problem (TRSP) and workforce scheduling and routing problem (WSRP) denote such extensions of TRPTW [31; 32].  
A rich technician routing problem was proposed for the ROADEF Challenge 2007, organized by the French Operations Research society (ROADEF) [18]. This technician routing problem optimizes the daily assignment of technicians into teams, of teams to tasks and of teams to daily routes in a multi-period problem; site-dependency is implied by the teams composition. There are also precedence constraints among interventions; for instance, some interventions must be preceded by the delivery of some equipment. The TRSP considered by [31; 32] considers a single period version of the ROADEF Challenge 2007 by analyzing the impact of team building optimization. Recently, the VeRoLog Solver Challenge (VSC) 2018-2019 proposed a rich technician routing and scheduling problem with truck routing and team building optimization [19]. VSC differs from the ROADEF Challenge 2007 with additional scheduling constraints for technicians. For instance, a single technician may perform at most one tour per day and must not perform tours on more than five consecutive days.  
In the TRSP considered by [33], vehicles are equivalent to technicians with no team building optimization, which defines site-dependencies. This TRSP includes additional request requirements for spare parts and tools that can be seen as non-renewable resources. Technicians collect tools and spare parts from a central depot at any time during the execution of their routes. Recently, an extension was provided considering a mix of conventional and electric vehicles, which requires considering recharging constraints [34].  
Multi-period and dynamic technician routing problems consider many variants of scheduling constraints. The technician routing problem with experience-based service times addresses the possibility that technicians learn and improve their skills with an impact on performance [35]. Dynamic versions correspond to realistic situations where new requests, including emergency interventions, arise during the intervention tour and requires re-optimization [36; 37]. Urgent interventions may induce considering the minimization of service disruption over a time horizon, which is different from standard VRPTW objective function [38].
## 3 Results  
### High-Throughput Sequencing and Analysis of Wax Gourd Small RNAs  
To obtain a comprehensive knowledge of the small RNAs in wax gourd, five independent small RNA libraries from leaf, root, stem, flower, and fruit of wax gourd B227 were subjected for high-throughput sequencing. A total of 18,675,941, 20,161,705, 19,947,266, 23,155,945 and 16,085,205 clean reads were generated from leaf, root, stem, flower and fruit of B227, respectively (Table 1). After removing the low-quality reads, 3\({}^{\prime}\) adapter, insert, 5\({}^{\prime}\) adapter, polyA and reads length \(<\) 15 nt or \(>\) 35 nt, 18,284,736, 19,813,111, 19,551,447, 22,821,341 and 15,843,596 clean tags were respectively obtained from each tissue (Table 1). As shown in Figure 1, the 24 nt long small RNAs were the most abundant small RNAs in all five tissues. Except for root, the length of small RNAs is mainly distributed from 20-25 nt. In the root, the length of small RNAs presented nearly an even distribution from 16-35 nt (Figure 1), implying tissue specificity of small RNAs in wax gourd.  
Figure 1: Length distribution of small RNAs. Y-axis represents percentage of total reads for related small RNAs. X-axis represents length of small RNAs. Five libraries are represented by different colors.