esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Service Efficiency Evaluation and Management Model
###### Abstract  
Numerous scholars have invested in the research of service innovation management, hoping to find a more objective and scientific service efficiency evaluation and management model so as to stride forward towards the goal of smart innovation management. In the service operating system, the multi-workstation service operation is one of the common service operation models. Some studies have pointed out: apart from a good service attitude, the service operation time of each workstation is a key factor which measures the performance of the workstation's service operation. Therefore, this paper proposed a standardized concept with a service operation efficiency evaluation index. This index is not only convenient and easy-to-use, but it also has a one-to-one mathematical relationship with the performance achievement rate. Next, the radar evaluation chart was employed to evaluate the service efficiency of each workstation. First, according to the upper confidence limit and the required value of the index, the minimum value (MV) of the index estimator was derived and marked on each radar line; at the same time, all MVs were connected to form a control block. When the point estimate of the index does not fall into the control block of the radar chart, it represents that the service operation efficiency of the workstation has not reached the required level, so it needs to be improved. Because this model can directly compare the point estimate of the index with the MV, it can judge whether the service operation efficiency reaches the required level. In this way, the advantage of simple and easy-to-use point estimate can be maintained, and the risk of misjudgment caused by sampling errors can be reduced as well, which is helpful for the service industry to move towards the goal of intelligent innovation management. This method is not only applied to the performance evaluation of the multi-workstation service operation process but also applicable to the performance evaluations of other service operations.  
multi-workstation service operation; service efficiency index; radar evaluation chart; minimum value; control block +
Footnote †: journal: Applied Sciences  
1 School of Business Administration, Guangxi University of Finance and Economics, Nanning 530003, China; [EMAIL_ADDRESS]  
2 Department of Leisure Business Management, Nan Kai University of Technology, Nantou 542021, Taiwan; 2 [EMAIL_ADDRESS]  
3 Graduate School of Technological and Vocational Education, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan 3  
4 Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan 41030, Taiwan 4  
5 Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan 5  
6 Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan 6  
7 Department of Finance, National Taichung University of Science and Technology, Taichung 40401, Taiwan 7  
Footnote 7: email: [EMAIL_ADDRESS] (K.-S.C.); [EMAIL_ADDRESS] (T.-H.H.)
## 4 Experiments and Results
### Experimental Testbed  
When establishing the test environment, a pre-determined floor area was split into squares, congruent with the hTromo robot blocks, and an overhead support frame mechanism erected, to accommodate a camera. Image data captured by this camera was post-processed, to evaluate the percentage of the test area covered by hTromo during each experiment. The complete area used for all experiments measured 196 \(\times\) 196 cm. The limits of the specified test area were adapted using an extendable metal framework, according to the assertions of each theory. The test area was further split, using white tape, into a 14 \(\times\) 14 square grid. As shown in Figure 14, every square within this grid mirrored the measurements of a single hTromo robot block.  
A shake-resistant parallelepiped was created using an aluminum extrusion profile, with a camera then attached in the center, at the top of the structure. Furthermore, the image plane was verified as being horizontal to the floor, to avoid perspective projection complications for area calculation. As such, when the camera was attached to the structure, a spirit level was used to ensure it sat parallel to the ground. The camera set-up and corresponding test area can be seen in Figure 15. Auto-focus was turned off, and a set focal length employed when recording robot tiling. The raw video recordings were subsequently post-processed, in order to assess robot performance.  
To generate a tracking map for the hTromo movement, an image-processing calculation was employed, which comprised three key stages. The first of these stages concerned the storage of a reference image, from which a track map would be created. Secondly, the location and form of the robot were identified in each frame. After multichannel color thresholding, the algorithm recognized the robot as three spots. The center of these spots corresponds to the center of each red color. Finally, a track map was created, by marking the green squares in accordance with spots detected on the reference image. When a track map was ready, a percentage calculation was conducted using the following formula, to assess the total area coverage achieved.  
\[\%\textit{Coverage Area}=\frac{\text{Pixels area of the robot}}{\text{Total pixels area of the testing field}}\times 100 \tag{1}\]  
\[\%\textit{Coverage Area}=\frac{\text{Pixel area of the robot}}{\text{Total pixel area of the testing field}-\text{Total pixel area of the obstacles}}\times 100 \tag{2}\]
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 3 Methodology
### Data Preprocessing
#### 3.3.2 Normalization  
The normalization process converts the various forms of a word within the data into a single standard and common form [10]. In particular, we performed orthographic normalization to unify certain Arabic letters that have more than one shape. Table 5 demonstrates the orthographic normalization that we applied. In addition, the normalization process included the removal of tashkeel, which is a diacritic added to Arabic letters. Generally, the normalization step minimizes text sparsity and its noise, as well as guarantees the consistency of the data.
# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 5 Conclusions  
The aim of the investigation was to show the potential of constraint programming solvers on optimization problems that--like the parallel drone scheduling traveling salesman problem--extend the concepts of a traditional traveling salesman problem. The new constraint programming model we propose leads--by exploiting multi-threading computation--to a very effective exact algorithm for the problem considered. The method was able to find the optimal solution for all the instances normally considered in the literature, and this was the first optimality proof for most of the instances. During the process, four of the previously best-known solution costs were also improved.  
Finally, we believe that straightforward adaptations of the model we propose could lead to state-of-the-art results for many of the several extensions proposed to the basic problem considered here, although this is beyond the scope of the present paper. From a more general perspective, we also believe that constraint programming has potential for use in effectively attacking many other optimization problems arising in different fields.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/a16010040/s1](https://www.mdpi.com/article/10.3390/a16010040/s1)]([https://www.mdpi.com/article/10.3390/a16010040/s1](https://www.mdpi.com/article/10.3390/a16010040/s1)).  
Conceptualization, R.M. and M.D.; methodology, R.M.; software, R.M.; validation, R.M. and M.D.; formal analysis, R.M. and M.D.; investigation, R.M. and M.D.; resources, R.M.; data curation, R.M.; writing--original draft preparation, R.M.; writing--review and editing, R.M. and M.D.; visualization, R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 4: Optimality gap for the different methods tested on the instances proposed in [13].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **[PERSON] et al. [13]** & **[PERSON] et al. [13]** & **[PERSON] et al. [13]** & & **CP** & \\
**Instances** & **MILP** & **B\&P** & **Matheuristic** & **Opt** & **Sec** & **Sec** \\  & **UB** & **UB** & **UB** & **Cost** & **Found** & **Opt** \\ \hline att48\_0, 80\_6\_2\_1 & 28,610.0 & _28,784.0_ & _30,708.0_ & 28,610.0 & 9.8 & 43.0 \\ berlin52\_0\_80\_6\_2\_1 & 5190.0 a & 5190.0 & _5685.0_ & 5190.0 & 4.8 & 4.9 \\ eillon10\_0\_80\_6\_2\_1 & 314.0 & _320.0_ & _365.0_ & 314.0 & 43.6 & 78.2 \\ gr120\_0\_80\_6\_2\_1 & 851.4 & _889.0_ & _851.5_ & **820.0** & 666.9 & 2232.0 \\ pr152\_0\_80\_6\_2\_1 & _65,140.0_ & _57,794.0_ & _57,794.0_ & **53,478.0** & 1401.2 & 1446.9 \\ gr29\_0\_80\_6\_2\_1 & 1664.6 & _1883.8_ & _1883.8_ & **1415.1** & 3214.2 & 115,622.3 \\ \hline \end{tabular}  
* Optimality first proven in [13].  
\end{table}
Table 8: Instances originated from TSPLIB in [13]. Optimal solution costs.
## 4 Results and Discussion
A broader comparative study examined the LCCAand LCA of SAPL, CIPPRT, sliplining, and pipe-bursting methods, providing a holistic view of sustainability in utility management. [PERSON] et al. [89] detailed an LCCA methodology comparing CIPPRT lining alternatives, revealing significant cost savings. Pre-engineered rubber gaskets ($3192.42) were more cost-effective than hydrophilic adhesive seals ($6185.37). [PERSON] [33] reviewed emissions from steam-cured CIPPRT, assessing concerns about air quality and water contamination. Existing studies lacked robust methodologies to capture exposure levels. In 2020, [PERSON] [55] introduced a machine learning framework aimed at forecasting construction and environmental expenses associated with several trenchless techniques, such as spray-applied cementitious pipe linings, CIPPRT utilizing polyester resin, and sliplining, with respect to high-density polyethylene pipes. The model utilized actual construction and environmental cost data for its analysis. Among the evaluated methods, CIPPRT was identified as having the highest environmental costs for culverts with diameters ranging from 30 to 108 inches. Notably, for diameters between 84 and 108 inches, the environmental costs associated with CIPPRT were found to be double those of spray-applied cementitious pipe linings. [PERSON] and [PERSON] [18] compared the cost allocation of OCCM and trenchless methods. Trenchless methods prioritize labor and material costs, while OCCM allocates a higher proportion to social costs. Table 3 summarizes all the studies relevant to cost prediction or estimation based on analytical and machine learning tools.
## 2 Proposed Vehicle Detection and Tracking System
### Headlight and Taillight Classification  
After executing the bright blob segmentation process, the contours of bright blobs are retrieved and the location of each bright blob is determined by the position of the bounding box surrounding the contour. The position of a bounding box is defined by the left, top, right, and bottom coordinates. In this section, to remove the nonvehicle illuminant objects and identify vehicle lights, the pre-trained classifier is applied to the extracted bright blobs. First, the position of the bounding box surrounding the extracted bright blob is used to extract a subimage from the input RGB image (see Figure 7). Then, the combined features of the bright blob are calculated by using the feature extraction process presented in Section 2.1. Finally, the pre-trained classifier is used to assign a class label (headlight, taillight, or nuisance) to the combined features. The result of the classification process applied to an input RGB image is depicted in Figure 8. In Figure 8, headlights and taillights are marked by yellow and white rectangles, respectively.
## 3 Comprehensive Data Workflow
### Data Augmentation  
The process of data augmentation has been shown to enhance the quantity and quality of training data by applying various transformations to an existing dataset, thereby generating new, meaningful data instances. In NLP, particularly for underserved languages such as the Moroccan dialect, challenges arise due to the limited availability of datasets. This scarcity has resulted in a lack of focus on data augmentation in this area, as no standardized approach exists. The three stages below outline the data enrichment techniques applied in this study [16; 17; 18]:  
* Easy Data Augmentation (EDA): This approach is based on four straightforward but effective operations: synonym replacement, random insertion, random permutation, and random deletion. EDA produces particularly convincing results for small datasets.
* Hierarchical Data Augmentation (HAD): This technique employs an attention mechanism to distil important content from hierarchical text into summaries, Thesauruses, translation, and transformers. Experiments reveal that HAD is a promising technique compared to EDA.
* Keyword-Driven Data Augmentation (KDA): This method retrieves keywords based on category labels and completes them accordingly.  
Maintaining contextual integrity is particularly critical when dealing with administrative data, where preserving each word is essential to retain meaning. In this regard, research confirms that transformer models used for data augmentation yield satisfactory results in maintaining textual coherence.  
Therefore, we adopted a novel data augmentation method: 'AugGPT', which leverages ChatGPT to generate auxiliary samples for text classification. While AugGPT was not the only possible solution, it proved to be the most effective. This solution was the most efficient as it saved time and energy, and generated correct sentences in the Moroccan dialect, since the latest version of ChatGPT masters the Moroccan dialect. An alternative approach considered was back-translation, where Moroccan dialect text would first be translated into French or English and then back into Moroccan dialect. However, this method is more effective for longer sentences, whereas our dataset consisted primarily of short sentences, which tended to retain the same words after back-translation. Moreover, it is more time-consuming, and not free of charge.  
Thus, the 'AugGPT' approach allowed us to augment the dataset in a way that introduced variation while preserving semantic coherence. Through detailed and focused prompting, ChatGPT successfully generated a dataset of over 10,000 sentences from an initial 3000-line dataset.  
Large language models such as GPT offer new opportunities for generating human-like text. Their large parameter space enables them to store vast amounts of linguistic information, while extensive pre-training allows them to encode factual knowledge for language generation--even in highly specific domains and languages, such as Moroccan dialect text generation for Public Administration papers [19].
## 2 Related Work
### Research Methodology in Transport Research  
#### 2.3.1 Work on Testbeds  
The development of vehicular testbeds with V2X communications [17] has played a significant part in the development of ITS. The authors in [18] developed a vehicular testbed to explore the communication dynamics of vehicular systems to understand how to provide seamless connectivity in these highly mobile environments by analysing the network dwell time. The results showed that network connectivity was related to the frequency and length of the beacon from the roadside units (RSUs) as well as the velocity of the vehicle. This testbed was later used to study service migration in vehicular networks [19]. Co-operative intelligent transport systems (C-ITS) allow road users and traffic managers to share information and use it to coordinate their actions. C-ITS works in association with the communication between systems where one vehicle interacts with another vehicle or with the road infrastructure through a VANET network, leading to better transport safety systems [20].
# A Novel Approach of Modelling and Predicting Track Cycling
## 5 Conclusions  
A novel approach of modelling time trial track cycling to predict sprint performance is presented. It combines a physiological model for the athlete's instantaneous power output with a physical model that returns the transient power required for cycling. A comparison of the power output and velocity data predicted by the model with actual measurement values confirmed its validity.  
Models accurately replicating a known performance using known power output-time characteristics exist. Our contribution expands this knowledge by achieving a comparable accuracy with generalised maximal power output and fatigue characteristics measured beforehand, independent of the actual run or setup. By varying the gear ratio and calculating the time-dependent power output and cycling velocity for several runs, we demonstrated experimentally that our method is capable of predicting time trial results with good accuracy.  
This combined model provides a useful means to assess and optimise the performance of individual athletes on a theoretical basis. It enables users to study the isolated effect of single parameter changes, translate improvements made by training and technical optimisation into increases in performance, and optimise multiple other variables in a way that the mean velocity during the effort is maximised and run-time reduced.  
In this first attempt, only factors of fundamental importance were considered. During the track tests in the velodrome, we experienced that the impact of unsteady start performance on the results of each run was especially severe, rendering any further sophistication of the physical or physiological model unnecessary for training purposes in time-trial track cycling. Instead, the method was deliberately kept as simple as possible to promote its application in practice. All input values necessary to calibrate the model can be measured in standard performance tests or derived by the analysis of training data.  
**Author Contributions:** Conceptualisation, A.K.D., methodology A.K.D. (physiological model, parameter estimation) and R.G. (physical model, implementation), investigation, data curation and visualisation A.K.D., writing--original draft preparation, A.K.D. and R.G., writing--review and editing, R.G. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by BMI (Federal Ministry of the Interior and Community, Germany) as a part of the project (AD-5-17).  
All procedures were approved by the institute's ethics committee (Institute for Applied Training Science, Leipzig, Germany; ER_2020.18.12_11) and conducted in accordance with the Declaration of Helsinki.  
All participants provided their written informed consent to participate in this study.  
The datasets generated and/or analysed during the current study can be obtained from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 5 Mathematical Models
### Models at the Meso Scale
#### 5.2.1 Monte Carlo-Based Methods
All the steps in the Markov Chain can be computed easily, if the value of \(E(\omega)\) is easily computable, and, most importantly, the value of \(\Delta E=E(\omega)-E(\omega^{*})\). In many settings, \(E\) is extremely simple to compute; often, it is independent of \(|\Omega|\) (the energy could reflect just the influence that neighboring particles exert on each other). Finally, thanks to the use of the Metropolis algorithm, a sample \(\{\omega_{i}\}_{i=1,\ldots,M}\) of \(\Omega\) can be generated that can be used to compute an approximation (see Equation (68)) \(\tilde{E}\) of \(\langle E\rangle\) by the following  
\[\tilde{E}=\frac{1}{M}\sum_{i=1}^{M}E(\omega_{i}). \tag{73}\]  
Moreover, since naturally physical systems _\"seek\"_ the state of minimal value for their energy, the succession of states \(\{\omega_{i}\}_{i=1,\ldots,M}\)_\"converges\"_ toward the equilibrium state characterizes the minimum energy state. The generalization of the Metropolis algorithm is due to Hastings [115]. To generalize the Metropolis method for a given distribution \(\pi\), a Markov Chain \(P=\left(p_{i,j}\right)_{i,j=1,\ldots,N}\) should be defined with \(\pi\) as its stationary distribution. Hastings assumes that \(p_{i,j}\) has the form  
\[p_{i,j}=\left\{ \begin{array}{rl}q_{i,j}\alpha_{i,j}&\text{if }i\
eq j\\ 1-\sum_{k\
eq i}p_{i,k}&\text{if }i=j\end{array} \right., \tag{74}\]  
where \(Q=\left(q_{i,j}\right)_{i,j=1,\ldots,N}\) is the proposed transition matrix of an arbitrary Markov Chain on a finite state space \(S\), and \(\alpha_{i,j}\) is given by  
\[\alpha_{i,j}=\frac{s_{i,j}}{1+\frac{\pi_{i}}{\pi_{j}}\frac{\alpha_{i,j}}{q_{ i,j}}}, \tag{75}\]  
where \(s_{i,j}\) is a symmetric function of \(i\) and \(j\) and is chosen so that \(0\leq\alpha_{i,j}\leq 1\) for all \(i,j=1,\ldots,N\). For such choices for \(s_{i,j}\), the matrix \(P\) satisfies the following reversibility condition  
\[\pi_{i}p_{i,j}=\pi_{j}p_{j,i}. \tag{76}\]  
From Equation (76), it follows that  
\[\sum_{i=1,\ldots,N}\pi_{i}p_{i,j}=\pi_{j},\text{for all }j=1,\ldots,N, \tag{77}\]  
and hence that \(\pi\) is a stationary distribution of \(P\). The following two examples for \(S=\left(s_{i,j}\right)_{i,j=1,\ldots,N}\) are given:  
\[S^{(M)} =\left(s_{i,j}^{(M)}\right)_{i,j=1,\ldots,N},\quad\text{where }s_{i,j}^{(M)}= \left\{ \begin{array}{ll}1+\frac{\pi_{i}}{\pi_{j}}\frac{q_{i,j}}{q_{i,j}}& \left(\text{if }\frac{\pi_{i}}{\pi_{j}}\frac{q_{i,j}}{q_{i,j}}\geq 1\right)\\ 1+\frac{\pi_{i}}{\pi_{j}}\frac{q_{i,j}}{q_{i,j}}&\left(\text{if }\frac{\pi_{i}}{\pi_{j}}\frac{q_{i,j}}{q_{i,j}} <1\right)\end{array} \right. \tag{78}\] \[S^{(B)} =\left(s_{i,j}^{(B)}\right)_{i,j=1,\ldots,N},\quad\text{where }s_{i,j}^{(B)}=1.\]  
With \(q_{i,j}=q_{j,i}\) and \(S\) defined as \(S^{(M)}\), a method devised by Metropolis et al. is obtained. If \(q_{i,j}=q_{j,i}\) and \(S\) is defined as \(S^{(B)}\), another classical MCMC method from Barker [116] is recalled. In Section 6.2.1, the algorithm implementing a Metropolis-Hastings method is described.