esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Autonomous mobile robots (AMRs) play a crucial role in transportation and service tasks at hospitals, contributing to enhanced efficiency and meeting medical demands. This paper investigates the optimization problem of scheduling strategies for AMRs at smart hospitals, where the service and travel times of AMRs are stochastic. A stochastic mixed-integer programming model is formulated to minimize the total cost of the hospital by reducing the number of AMRs and travel distance while satisfying constraints such as AMR battery state of charge, AMR capacity, and time windows for medical requests. To address this objective, some properties of the solutions with time window constraints are identified. The variable neighborhood search (VNS) algorithm is adjusted by incorporating the properties of the AMR scheduling problem to solve the model. Experimental results demonstrate that VNS generates high-quality solutions. Both enhanced efficiency and the meeting of medical demands are achieved through intelligently arranging the driving routes of AMRs for both charging and service requests, resulting in substantial cost reductions for hospitals and enhanced utilization of medical resources.  
scheduling; autonomous mobile robot; time window; smart hospital; variable neighborhood search
## 5 Discussion
### Comparison Results with Baseline Deep Learning Models on Position-Dependent HAR  
To evaluate the proposed Att-ResBiGRU model, five baseline deep learning models (CNN, LSTM, bidirectional LSTM (BiLSTM), GRU, and BiGRU) were trained and tested using 5-fold cross-validation on three datasets: Opportunity, PAMAP2, and REALWORLD16.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Recognition Effectiveness**} \\ \cline{2-4}  & **Accuracy (Mean \(\pm\) std.)** & **Loss (Mean \(\pm\) std.)** & **F1-Score (Mean \(\pm\) std.)** \\ \hline Opportunity & 90.27\% (\(\pm\)0.23\%) & 0.34 (\(\pm\)0.01) & 86.69\% (\(\pm\)0.30\%) \\ PAMAP2 & 96.61\% (\(\pm\)0.26\%) & 0.12 (\(\pm\)0.01) & 96.44\% (\(\pm\)0.25\%) \\ REALWORLD16 & 96.11\% (\(\pm\)0.17\%) & 0.14 (\(\pm\)0.01) & 96.23\% (\(\pm\)0.19\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracies and F1-scores of the proposed Att-ResBiGRU model for position-independent HAR across the three HAR datasets.  
These models identify human activities based on wearable sensor data from various on-body locations specific to each dataset. For PAMAP2, this includes the hand, chest, ankle, and right upper arm. REALWORLD16 uses the back, right upper/lower arm, and left upper/lower arm. The other dataset utilizes the chest, forearm, head, shin, thigh, upper arm, and waist. Two separate experiments were conducted to evaluate the efficacy of the suggested Att-ResBiGRU model: position-dependent activity recognition, where the sensor location was specified, and position-independent recognition, where the position varied.  
Compared to the CNN, LSTM, BiLSTM, GRU, and BiGRU models, the proposed Att-ResBiGRU model achieved superior F1-scores across all sensor locations and datasets, as shown in Figure 5. The most significant performance gains were observed on the Opportunity dataset, with F1-scores over 5% higher than the next most satisfactory model. On the PAMAP2 and REALWORLD16 datasets, Att-ResBiGRU showed modest but meaningful improvements of around 2% in the F1-score over alternative models. CNN could have performed better overall, highlighting the limitations of solely using convolutional layers for sequential sensor data classification. The close scores between BiGRU and Att-ResBiGRU demonstrate the advantages of bidirectional architectures over standard LSTM and GRU for human activity recognition. However, the additional residual connections and attention mechanisms in Att-ResBiGRU led to a significant performance boost compared to BiGRU, especially on the Opportunity dataset.  
Figure 5: Comparison results of five deep learning models and the proposed Att-ResBiGRU model in position-dependent HAR: (**a**) Opportunity, (**b**) PAMAP2, and (**c**) REALWORLD16 datasets.  
The results demonstrate significant improvements in performance using the Att-ResBiGRU model compared to standard deep learning models for position-based HAR on various datasets. Incorporating residual connections and attention components substantially enhances the modeling of sequential sensor information.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 3 Results and Discussion
### Experimental Results and Analysis of CWRU Dataset  
In this experiment, the vibration data of the drive end bearing at 12 kHz sampling frequency in the bearing data of Case Western Reserve University (CWRU) were used as experimental samples. The dataset is divided into nine fault types based on three fault locations (inner race, outer race, rolling element) and three fault sizes (0.007, 0.014, 0.021 inches). Additionally, there is a healthy state sample, resulting in a total of 10 bearing condition types. The labels are set as 0-9 to represent each type. There are 3808 training set samples and 1219 test set samples in this experimental dataset.
## 4 Conclusions  
In this work we have extended the investigation of heat turbulence modeling to a wider range of low-Prandtl numbers characteristic of liquid-metal flows. The investigation, which presents a two-equation logarithmic \(k\)-\(\Omega\)-\(k_{\theta}\)-\(\Omega_{\theta}\) model, includes forced flow studies with both low Peclet numbers, where only DNS simulations are available, and high Peclet numbers where only experimental results are available. Two approaches have been compared, a Kays model and a logarithmic \(k\)-\(\Omega\)-\(k_{\theta}\)-\(\Omega_{\theta}\) turbulence model, to be used in engineering applications with low-high Peclet numbers. Since only DNS results are available, for low-Peclet-number, in plane and channel geometries, we  
Figure 5: Cylindrical pipe tests. Nusselt number values for the simulations performed for fluids with \(Pr=0.025\) and \(Pr=0.01\) with the [PERSON] and [PERSON] [13] models. The values are compared with the [PERSON] [33] and [PERSON] [4] correlations.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \multirow{2}{*}{**Pr**} & \multirow{2}{*}{**Source**} & \multicolumn{6}{c}{**Reynolds Number**} \\ \cline{3-9}  & & **341,360** & **285,800** & **165,400** & **41,000** & **20,680** & **12,760** & **5760** \\ \hline \multirow{4}{*}{0.025} & KLW & 29.4092 & 26.1685 & 18.5608 & 9.2123 & 7.5382 & 6.6523 & 5.6329 \\  & Kays & 42.9137 & 38.1888 & 27.0105 & 12.4689 & 9.3894 & 7.9416 & 6.3323 \\  & Kirillov & 29.6295 & 26.3008 & 18.5779 & 9.1108 & 7.1690 & 6.3135 & 5.4593 \\  & Cheng & 28.7295 & 25.4008 & 17.6779 & 9.0885 & 7.1690 & 6.3135 & 5.4593 \\ \hline \multirow{4}{*}{0.01} & KLW & 15.9311 & 14.4070 & 10.8496 & 6.7213 & 6.0596 & 5.7411 & 5.3430 \\  & Kays & 22.9363 & 20.7126 & 15.4458 & 8.6137 & 7.1788 & 6.5008 & 5.7193 \\ \cline{1-1}  & [PERSON] & 16.6110 & 15.0068 & 11.2848 & 6.7221 & 5.7863 & 5.3740 & 4.9623 \\ \cline{1-1}  & [PERSON] & 15.7110 & 14.1068 & 10.6900 & 6.7221 & 5.7863 & 5.3740 & 4.9623 \\ \hline \end{tabular}
\end{table}
Table 9: Cylindrical pipe tests. Numerical comparison of the [PERSON] and [PERSON] [13] models with [PERSON] [33] and [PERSON] [4] correlations for \(Pr=0.025\) and 0.01.  
have compared them for sodium and lead's characteristic Prandtl numbers. The comparison between the numerical results obtained with the four-parameter turbulence model with DNS data and experimental correlations can be considered satisfactory for a wide range of Peclet numbers and for both the Prandtl numbers studied. On the other hand, the [PERSON] model does not seem to be able to adapt to the study of all these different cases, especially for high Peclet numbers, and a two-equation model for the heat turbulent diffusivity seems necessary.  
The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Marshall Parameters in OBC  
Table 5 provides results on the changes in the Marshall parameters for R0 and RAP mixes in OBC. Based on the results, all mixes met the minimum 8 kN stability criteria for high traffic volume roads. Overall, the stability of all RAP mixes was higher than that of R0 as well as the standard limits.  
Specifically, the stability of RAP mixes improved from 32% to 48% compared to the standard PWD (2008) value of 8 kN, indicating that WEO was an efficient rejuvenator that could effectively enhance the properties of the aged binder [38, 41, 89]. Given that CRM could improve the mechanical performance of binders [23], the addition of CRM into the mix was assumed to improve the stability. Furthermore, all mixes followed a 2-4 mm flow standard. The flow of the mixes increased with the increment of RAP content due to the use of WEO in RAP mixes, which allowed aggregates to float in the mix. In addition, the amount of WEO increased with the increment of RAP content. The result also indicated that the rejuvenator softened the aged binder and increased the workability of the asphalt mix. Moreover, the inclusion of CRM in the mix enhanced its flow value [23, 61, 91].  
Table 5 also includes the results of the volumetric properties for R0 and RAP mixes. Since the value of VIM of all mixes met the PWD standard of 3-5%, their permeability properties were considered substantial. Furthermore, it was relatively necessary to maintain the minimum 14% VMA standard set by the Asphalt Institute to adopt the binder film within the mix [90]. According to the result, the VMA values of all RAP mixes were above the minimum limit while the VFB values of all RAP mixes were within the threshold limit (70-80%). Therefore, the volumetric properties indicate that all RAP mixes were durable.
## 2 Materials and Methods
### Gene Expression Analysis with Real-Time PCR  
After 48 h of _L. paracasei_ CFS treatment, the adipocytes from 12-well plates were lysed, and a total amount of RNA was isolated using an RNeasy Mini Lipid Tissue Kit (QIAGEN GmbH). The obtained RNA quantity and quality were determined spectrophotometrically at 260 and 280 nm wavelengths using Synergy(tm) LX Multi-Mode Microplate Reader (BioTek Instruments, Inc., Santa Clara, CA, USA), equipped with Take3 Microvolume Plate (BioTek Instruments, Inc., Santa Clara, CA, USA). Then, the total RNA concentration was equilibrated to the lowest concentration among all samples (1000 ng/uL), and cDNA was synthesized by the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, Waltam, MA, USA) according to the manufacturer's instructions. cDNAs were stored at \(-20\)\({}^{\circ}\)C until use.  
Housekeeping and target-gene primer pairs were designed by \"Primer 3_4.1\" and the NCBI software program. The specification of the primer pairs was further confirmed in Primer-BLAST (NCBI), Primer 3 plus (version: 3.2.5), and SerialCloner (version 2.6.1). The predicted products' melting temperature and curves were analyzed using uMelt QuartzSM (Version: Release: 3.6.2 \"Quartz\"/5 November 2020). The primer sequences used in this experiment are listed in Table 1.  
Gene expression was evaluated by SYBR Green-based two-step detection method performed on Real-Time PCR System (PikoReal, Thermo Scientific, Waltam, MA, USA), strictly following the KAPA SYBR(r) fast qPCR master mix kit manufacturer's criteria (Qiagen Sciences, Inc., Germantown, MD, USA). All real-time PCR reactions were performed in duplicate.  
Housekeeping gene stability was evaluated separately and in combination using the Ref Finder software (20 October 2020-25 May 2023) ([[http://blooge.cn/RefFinder/](http://blooge.cn/RefFinder/)]([http://blooge.cn/RefFinder/](http://blooge.cn/RefFinder/))), accessed on 8 February 2023. The results revealed that the average value of both housekeeping genes (Ribosomal protein, large, P0, and \(\beta\)-actin) was the most stable. Therefore, gene expressions were evaluated using the modified \(2^{-\Delta\Delta\Delta{\rm C}}\) approach with multiple reference genes [47; 48]. The expression efficiency of each gene was also estimated and accounted for in the approach mentioned above. The PCR efficiency varied between 97-99%.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 4 Results
### Result of Delivering Image and Its Metadata  
The logger transmits metadata about images and videos to the Video Management System (VMS). In this process, the logger uses the HTTP-supporting curl library. Images are transferred using the POST method, and image-related metadata are formatted in JSON and also transmitted via POST. Figure 10 shows the metadata transmitted to the VMS. The object detection information includes object labels and bounding box information.  
Figure 8: (**a**) Private key, (**b**) public key.  
Figure 9: (**a**) Grayscale image, (**b**) transformed image by Canny edge detection.  
The transmitted images are stored as files, and the metadata are stored in the database. Metadata are data that describe other data. The metadata are described in Table 4.  
Figure 11 shows the server's received message from the logger, which includes CID (Content ID), signed hash value formatted in BASE64, hash a hash value, object detection results, and media data format. The signed hash value is used to verify the fingerprint obtained after feature extraction on the subsequently received video. CID is a unique identifier for the content.  
Figure 11: Received message.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Description** \\ \hline CID & unique ID of captured image \\ \hline Hash & hash value of a feature vector in character format \\ \hline Sign hash & digital signature of hash value \\ \hline Object detection result & information of object with bounding box \\ \hline Media type & media format like BGR \\ \hline \hline \end{tabular}
\end{table}
Table 4: Metadata.  
Figure 10: Generated metadata.
# Using Statistical Test Method to Establish a Decision Model of Performance Evaluation Matrix
## 3 Performance Evaluation Matrix  
As mentioned earlier, the performance evaluation matrix is widely used to evaluate and improve the performance levels of \(q\) service items for various service systems. In this paper, the satisfaction index is set as \(x\)-axis and the importance index is set as \(y\)-axis to form the performance evaluation matrix. Then, the horizontal line \(y=C_{Y0}\) and the vertical line \(x=Cx_{X0}\) are used to divide the performance evaluation matrix into four quadrants, where \(C_{X0}=Min\big{\{}C_{X01},C_{X02},\ldots,C_{X0q}\big{\}}\) and \(C_{Y0}=Min\big{\{}C_{Y01},C_{Y02},\ldots,C_{Y0q}\big{\}}\). These four quadrants can be defined as follows:  
\[\text{Quadrant 1}:\ Q_{1}=\big{\{}(x,y)|C_{X0}\leq x\leq 1,C_{Y0}\leq y\leq 1 \big{\}}, \tag{25}\]  
\[\text{Quadrant 2}:\ Q_{2}=\big{\{}(x,y)|0\leq x<C_{X0},C_{Y0}\leq y\leq 1 \big{\}}, \tag{26}\]  
\[\text{Quadrant 3}:\ Q_{3}=\big{\{}(x,y)|0\leq x<C_{X0},0\leq y\leq C_{Y0} \big{\}}, \tag{27}\]  
\[\text{Quadrant 4}:\ Q_{4}=\big{\{}(x,y)|C_{X0}\leq x\leq 1,0\leq y\leq C_{Y0} \big{\}}. \tag{28}\]  
Then, the performance evaluation matrix and four quadrants can be displayed in Figure 1 as follows:  
Obviously, when the evaluation coordinate points fall into Quadrant 2 and Quadrant 3, it means that the satisfaction of the service items is lower than the average satisfaction, so that the service items definitely need to be improved. Since the evaluation coordinate points falling into Quadrant 2 are more important than the ones falling into Quadrant 3, the priority of their improvement is higher. In addition, when the evaluation coordinate points fall into Quadrant 1 and Quadrant 4, it means that the satisfaction of the service items is higher than the average satisfaction, so that there is no need to make any improvement, and the current state can remain unchanged. Based on the above concept, we can define the evaluation coordinates for the \(i\)th service item as follows:  
\[(x_{i},y_{i})=\left(\hat{\theta}_{i}^{S},\hat{\theta}_{i}^{I}\right). \tag{29}\]  
Then, the evaluation rules and improvement ranking rules are established as follows:  
1. If \((x_{i},y_{i})\in Q_{1}\cup Q_{4}\), then service item \(i\) does not need to make any improvement.
2. If \((x_{i},y_{i})\in Q_{2}\cup Q_{3}\), then service item \(i\) needs to be improved.
3. If \((x_{i},y_{i})\in Q_{2}\), then the service item \(i\) is ranked the first priority of improvement.
4. If \((x_{i},y_{i})\in Q_{3}\), then the service item \(i\) is ranked the second priority of improvement.  
Figure 1: Performance evaluation matrix and four quadrants.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 1 Introduction
Whereas MSA-GRU does not filter out uninformative time steps, TSA-GRU introduces a sparsity threshold mechanism to filter out the uninformative time steps in a cognitively efficient manner and from a perspective of interpretability. Unlike traditional probabilistic models or recurrent architectures that do not handle long-range dependencies well, TSA-GRU is a combined TSA model with a GRU-based sequential model that captures both short- and long-range dependencies in learner behavior. Over the other models, it renders a dense and robust representation of learner behavior while reducing noise and redundancy with regard to all of the learners' evidence. Some experimental evidence indicates that TSA-GRU outperforms most prior knowledge tracing models, including MSA-GRU, BKT, PFA, and TSA alone, with an accuracy of 95.60% and a test loss of 0.0209. The model also converged considerably faster than other models to reach its optimal performance in five epochs, suggesting that TSA-GRU is an efficient learner behavior representation and can mitigate the risks of overfitting or underfitting models to learner behaviors. Overall, this evidence supports the contention that TSA-GRU will provide both advances in predicting better student performance outcomes and act as an exemplar platform for adaptive, data-driven, and personalized approaches for large-scale online educational environments. This work makes three primary contributions to the field of learner behavior analytics in MOOCs:  
* We introduce a next-generation learner model, TSA-GRU, a novel deep learning module that uses the combined output of TSA and a GRU, each focusing on context-aware, enriched embeddings of student interactions. * In parallel, such embeddings are concatenated with a sequential modeling procedure, which helps capture short- and long-term dependencies in student behavior and hence supports learning outcome and response time prediction. * In our current procedure, the embedding originating from TSA has been merged with GRU-based sequential modeling. This allows it to capture short-term fluctuations and long-term temporal dependencies in student behavior. All of this translates into better performance in predicting learning outcomes and the time needed to respond. * Finally, extensive experiments and validation on large-scale educational datasets have proved the superiority of TSA-GRU over existing knowledge tracing models. These experiments show that TSA-GRU is capable of being put to real-world use in adaptive learning systems. The structure of the paper is as follows: Section 2 discusses related work on knowledge tracing and online learning analytics; Section 3 presents the architecture and method of the TSA-GRU module; Section 4 provides experimental results and compares the models; Section 5 discusses how to interpret the findings according to their impact; Section 6 gives a preview of implications and future works; and Section 7 concludes the paper.
## 4 Results and Discussion
### Evaluation of Susceptibility Maps  
Using the models' outputs, the distribution maps of landslide susceptibilities were constructed for the Nam Dan commune in favor of proper land-use planning for the development of infrastructure across the area. The distribution maps of landslide susceptibility were prepared to show very low, low, moderate, high, and very high susceptibilities to landslide occurrence within the commune (Figure 7). In the map produced using the ABHP model, approximately 5% and 15% of the land area fall in the very high and high susceptibility zones, respectively. In the map generated by the BHP model, approximately 12% of the Nam Dan commune is located in the high susceptibility zone and 5% in the very high susceptibility zone. In the map generated by the Dagging-HP model, 17% of the land area is affected by high susceptibility and 10% by very high susceptibility. In the map generated by the Decorate-HP model, 65% of the land area is hit by high susceptibility and 15% by very high susceptibility. The RABHP model locates approximately 27% of the land area in the high susceptibility zone and 10% in the very high susceptibility zone. In the map generated by the HP model, approximately 50% of the land area is in the high susceptibility zone and 12% in the very high susceptibility zone (Figure 8a).  
Figure 6: The AUC values derived from the models over (**a**) the training phase and (**b**) the validation phase.  
The frequency ratio analysis of the susceptibility maps revealed that the map derived from the ABHP model was the most trustworthy map in comparison with the other maps derived from the other five models. The percentage and frequency of landslide pixels in the ABHP maps for the high and very high susceptibility classes were higher than the other models (Figure 8b,c).  
Figure 7: Landslide susceptibility maps (LSM) generated for the Nam Dan commune.