esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Particle Swarm Optimization for High-Dimensional Latin Hypercube Design Problem
## 5 Conclusions  
This paper proposes an improved hybrid PSO (IHPSO) to optimize the High-dimensional LHD (HLHD), which combines local and global search strategies. First, the minimum point  
Figure 10: Boxplots of different algorithms at 18 \(\times\) 80 LHD problem.  
spacing method is proposed to solve the oscillation problem in the PSO. Second, the ranked ordered value (ROV) is adopted to achieve the discretization of PSO. Third, a comprehensive learning strategy is used to ensure the diversity of the population in the evolution process. The superiority of the proposed algorithm is verified by numerical comparison with six other state-of-the-art algorithms. The advantages of the proposed method are shown as follows:  
(1) The proposed IHPSO can obtain a better HLHD than the compared algorithm at the same number of iterations. It performs better in high dimensions, and the numerical results are significantly better than the compared algorithms.  
(2) IHPSO can effectively improve the efficiency of obtaining the near-optimal HLHD. Compared with the compared algorithm, IHPSO converges faster and performs better in optimizing medium and large LHDs. IHPSO shows better local search capabilities and global search performance.  
(3) IHPSO effectively promotes convergence in the initial optimization stage and maintains the convergence performance until the end of the iteration. Even in the case of a small number of iterations, IHPSO still exhibits excellent performance. Under the same number of iterations, IHPSO can obtain HLHD with better space-filling.  
(4) IHPSO balances the local search and global search capabilities by adaptively adjusting the parameters of the PSO equation, ensuring the diversity of the population, and can quickly obtain the near-optimal HLHD.  
Although our proposed IHPSO algorithm is efficient and stable and can search for the optimal solution, it needs further optimization. IHPSO has many parameters that need to be discussed. These parameters significantly impact the algorithm's performance and will be further researched in future work.  
Conceptualization, Z.X. and F.W.; methodology, software, validation, Z.X. and D.X.; formal analysis, J.W.; resources, J.L.; writing-original draft preparation, Z.X.; writing-review and editing, N.Y.; visualization, S.X.; supervision, D.G. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China (2018 YFB1900301) National Natural Science Foundation of China (71901203 and 72204246). At the same time, the authors sincerely thank the editor and anonymous reviewers for their insightful comments that help us improve the quality of the article.  
Not applicable.  
All relevant data are included in the article.  
The authors declare no conflict of interest.
## 2 Collation and Analysis of Measured Data of Temperature and Wind
### Probability Model Fitting of Wind Speed and Direction
#### 2.3.3 Basic Wind Speed Calculation and Result Analysis
According to the Formula (6), the wind direction frequency and distribution parameters \(A\) and \(B\) of Gumbel distribution in Table 2 are fitted by Origin 2022 software, and the \(n_{f}\), \(n_{A}\), \(n_{B}\) are taken as seven orders. The results are shown in Table 3.  
The fitted harmonic function curves for each parameter are shown in Figures 5-7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Wind Direction**} & \multirow{2}{*}{\(f(\theta)\)} & \multicolumn{4}{c}{**Gumbel Distribution**} \\ \cline{3-5}  & & **10 Years (m/s)** & **50 Years (m/s)** & **100 Years (m/s)** \\ \hline N & 2.00\% & 9.82 & 11.32 & 11.97 \\ NNE & 8.00\% & 23.82 & 28.62 & 30.69 \\ NE & 4.75\% & 10.58 & 12.35 & 13.12 \\ ENE & 4.25\% & 10.90 & 12.72 & 13.50 \\ E & 2.00\% & 10.46 & 12.33 & 13.13 \\ ESE & 5.00\% & 8.71 & 10.26 & 10.93 \\ SE & 11.00\% & 20.46 & 24.76 & 26.60 \\ SSE & 14.75\% & 6.96 & 7.91 & 8.31 \\ S & 12.25\% & 11.73 & 13.95 & 14.91 \\ SSW & 10.75\% & 23.30 & 28.15 & 30.24 \\ SW & 12.25\% & 22.62 & 27.17 & 29.13 \\ WSW & 4.50\% & 12.12 & 14.07 & 14.92 \\ W & 2.25\% & 11.31 & 13.33 & 14.20 \\ WNW & 1.75\% & 9.73 & 11.60 & 12.40 \\ NW & 2.00\% & 6.37 & 7.49 & 7.97 \\ NNW & 2.50\% & 4.57 & 5.12 & 5.36 \\ \(\Sigma\) & 1 & 24.68 & 28.54 & 30.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic wind speed in different return periods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fifth-Order Fitting** & \(A(\theta)\) & \(B(\theta)\) & \(f(\theta)\) \\ \hline c & 1.462 & 4.803 & 0.062 \\ d1 & \(-\)0.409 & \(-\)0.377 & \(-\)0.047 \\ e1 & 0.029 & \(-\)3.468 & \(-\)0.193 \\ d2 & 0.576 & 1.116 & 0.032 \\ e2 & 1.207 & 8.202 & 0.255 \\ d3 & 0.255 & 0.611 & 0.011 \\ e3 & 0.283 & 0.715 & \(-\)4.585 \\ d4 & 0.686 & 0.503 & \(-\)0.015 \\ e4 & \(-\)4.225 & 0.952 & 0.034 \\ d5 & 0.296 & 0.496 & 0.009 \\ e5 & 0.012 & 0.450 & 1.014 \\ d6 & \(-\)0.458 & \(-\)0.476 & \(-\)0.006 \\ e6 & 11.520 & 5.055 & 0.336 \\ d7 & \(-\)0.358 & 0.145 & \(-\)0.011 \\ e7 & 0.088 & \(-\)7.861 & \(-\)0.691 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Gumbel distribution parameters of harmonic function fitting.
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 6 Conclusions  
In this paper, we proposed an adaptive path construction for the mobile sink using FIS called FUZZY_RPs. After determining the required set of RPs using the k-means algorithm in the first round, the locations of these RPs are then adapted dynamically in the subsequent rounds using the FIS, which calculates the new location of each RP based on a weight value generated by each sensor node in the network. This weight value generated by a certain node determines how long the current location of the corresponding RP should be moved towards that node. The fuzzy-based calculation of the weight value for each sensor node depends on three factors: the remaining energy of the sensor node, the distance between the sensor node and its corresponding RP, and the 1-hop neighbors of the sensor node. Simulation results showed that the proposed approach outperforms the DBLkM approach, which uses fixed RPs over the whole network lifetime. For example, in terms of the network lifetime, the proposed FUZZY_RPs approach achieved an 83.76% improvement when the number of sensor nodes is 300. Future work can look into the adaptive path design for the MS by updating the locations of the RPs in environments with obstacles.  
Implementation, software, validation, writing--original draft preparation, data collection, and analysis, F.A.-Q.; supervision, writing--review and editing, project administration, O.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jordan University of Science and Technology grant number 20200241.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author.  
The authors declare no conflict of interest.  
Figure 10: Path Length.
# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 3 Materials and Methods
### Data Validation with Neural Network Models
#### 3.4.2 Convolutional Neural Network Analysis Based on Sub-Sequences  
While the MLP architecture can only process vectorial data, CNNs can take full advantage of sequential information. The CNN multilayer design allows to extract a hierarchy of representations from the data while the implemented weight sharing guarantees to limit the number of parameters with respect to a fully connected architecture. In our experiments, the model input are the nucleotide sub-sequences collected in \"SubsequencesDataset\". The CNN elaborates the input to produce a prediction about the translation speed associated with the sequence. Specifically, our CNN architecture comprises two successive 1-D convolutional layers, with average pooling employed between the layers, followed by two fully connected layers. The two-class probability distribution output of the network is produced by using a Softmax activation function on the output of the second fully-connected layer. The 1-D convolutional layers are based on 16 kernels with dimension three and a ReLU activation function, applied to each layer [43]. The optimization of the network parameters is performed by the Adam optimizer using early stopping [44]. The overall network architecture is described in Figure 6.
# Haptic-Enabled Hand Rehabilitation in Stroke Patients: A Scoping Review
###### Abstract
Therapists dealing with this population use strategies to improve motor behaviour to regain occupational performance. Treatment interventions such as materials-based training [14], task-related [15; 16] or task-specific training [17; 18] are common training methods for restoring function in the upper limb. Such training methods emphasize the patient's active participation, the use of goal-oriented tasks or environmental features to drive motor activity, and the execution of the entire task or components of the task under different conditions. Several studies have failed to demonstrate the superiority of one type of conventional stroke training over another [19; 20; 21; 22]. Our understanding of brain function and brain trainability is becoming more evident with identifying mirror neurons and the recent development of neuroimaging techniques. This training modality has traditionally been used in athletics in an intuitive manner [23; 24] to review or reinforce the sequence of movements that make up the action to be taken. Mental practice has been shown to be effective in reducing impairment and improving functional recovery [25]. Literature shows that mental practice is an effective intervention when it is added to physical practice [25]. Although functional imaging has shown that mental practice induces similar cortical activation patterns, such interventions' clinical efficacy in the treatment and functional recovery has yet to be demonstrated [25]. Retraining a motor task can be controlled more precisely than conventional treatment approaches by using a variety of technologies such as robots (e.g., [26; 27; 28; 29; 30; 31; 32]), virtual reality (e.g., [33; 34; 35; 36]) and sensor-based devices (e.g., [37; 38; 39]). The complex nature of the human hands and arms and the various daily activities will contribute to an approach in which specific approaches were integrated to address the diverse needs of upper limb/hand rehabilitation. Further research is needed to determine the most effective technology-assisted intervention or combination of interventions. This paper aims (i) to draw a portrait of existing haptic-enabled hand rehabilitation in stroke patients, (ii) to map the use of haptic technology to support technology-assisted therapeutic interventions, and (iii) to investigate the effects of haptic-enabled interventions on the motor and functional recovery in patients with stroke. One of the common locations to apply haptic technology to provide biofeedback is the hand (e.g., [37; 38; 39; 40; 41]). Integration of haptic technology in hand therapy plays a significant role in the interaction between the body and the objects. A better sense of touch determines the efficacy of daily life movements. Haptics can be defined as \"the perception of combined tactile and kinesthetic inputs during object manipulation and exploration\" [42]. It is expected that the haptic feedback increases motor and functional recovery. It is hypothesized that the more different technologies are combined with haptic technology, the better the therapeutic outcomes.
## 3 Results
### Comparison of All Models  
Considering all the factors discussed, it can be concluded that the three models evaluated in this study--YOLOv8-m, YOLOv9-e, and CNN--demonstrate high classification accuracy in distinguishing between broken, clean, and dirty solar panels. Each model exhibits strong performance metrics, with variations in precision, recall, specificity, and overall efficiency depending on the dataset and training methodology. These results highlight the effectiveness of deep learning-based approaches in automated solar panel inspection, providing a reliable foundation for real-world implementation.  
As demonstrated in Table 7, the YOLOv8-m model achieves higher accuracy compared to the other algorithms evaluated in this study. Its superior classification performance can be attributed to its advanced feature extraction capabilities, efficient object detection framework, and optimized training process. These factors collectively enable YOLOv8-m to accurately distinguish between broken, clean, and dirty solar panels, making it the most effective model among the three tested approaches. The detailed comparison presented in Table 7 further reinforces its superiority in accuracy and reliability for solar panel classification tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Score of Class** & **F1-Score** & **Precision** & **Recall** \\ \hline class score 0 & 91.60\% & 95.54\% & 88.23\% \\ class score 1 & 88.90\% & 88.78\% & 89.20\% \\ class score 2 & 88.22\% & 85.60\% & 91.00\% \\ all classes & 87.18\% & 87.18\% & 87.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test results for each class and overall performance in CNN k-fold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision–Recall** & **F1-Score** & **Precision** & **Recall** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline YOLOv8-m & 97.10\% & 93\% & 100\% & 100\% & 97.26\% & 100\% & 95.94\% \\ YOLOv9-e & 96.80\% & 92\% & 100\% & 100\% & 95.18\% & 100\% & 93.07\% \\ CNN k-fold & - & 87.18\% & 87.18\% & 87.18\% & 92.86\% & 89.29\% & 94.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Precision–recall, F1, precision, recall, accuracy, sensitivity, and specificity results for YOLOv8-m, YOLOv9-e, and CNN k-fold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Train & 100\% & 100\% & 100\% \\ Test & 92.86\% & 89.29\% & 94.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy, specificity, and sensitivity results for training and testing in CNN k-fold.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 4 Algorithm Design
### Genetic Manipulation  
Selection operation: Generally speaking, the high or low parent chromosomes' fitness affects the algorithm results. The better-quality chromosome fitness function is selected,and the higher probability will be saved. In this study, the parents are arranged in terms of the volume of the cargo from largest to smallest, which can improve the algorithm results.  
Crossover operation: During the crossover operation, as the distribution center is the start and end point of the distribution path, the distribution center gene \(0\) is removed before and after the chromosome before the crossover operation to avoid losing the distribution center during the crossover operation. The crossover uses a local mapping method to perform a two-by-two swap of randomly generated segments, followed by the replacement of duplicate genes. Moreover, the two parts of the crossover operation are independent of each other, as shown in Figure 5.  
Step 1: Randomly select two paternal chromosomes named \(F_{1}\) and \(F_{2}\), select two random numbers from the range [1, \(N\)], and choose the segment of the random number interval [begin, end] corresponding to the beginning to the ending of the chromosome for a two-by-two crossover swap.  
Step 2: Delete the genes that duplicate the original segment of the new chromosome, and retain the gene segments that are unduplicated.  
Step 3: Supplement the deleted gene fragments with unduplicated genes, with the newly supplemented genes arranged in random order backward and forward.  
Step 4: Return the new offspring.  
Mutation manipulation: Mutation manipulation is the random selection of one or more genes to produce a mutation after the chromosomes are produced by selection and crossover manipulation. First, a certain probability is given, \(P_{m}\). Second, two genes are randomly selected to be swapped in the chromosome to produce a new offspring. The mutation swap does not produce duplicate genes and therefore does not require the operation of deletion  
Figure 5: Crossover operations.  
and addition of duplicate genes. The detail of mutation manipulation operation is shown in the Figure 6.
## 1 Introduction
In these cases IP-WSNs based on a mesh approach can enhance the communication scenario significantly, whereby individual sensor nodes can act as routers or fully functional devices, which is discussed in the route over routing issue in 6 LoWPAN [8, 9]. Moreover SPMIPv6 based IP-WSNs facilitate node to node seamless communication in different cases of the individual sensor node and sensor network mobility scenarios. The main contributions of this paper are as follows:  
1. A novel IP-WSN architecture solely based on PMIPv6 and named SPMIPv6 is proposed for energy efficient mobility of individual sensor nodes or a group of sensor nodes. To the best of our knowledge this is the first paper reporting on a sensor network based localized mobility management protocol in an IP-WSN domain with special consideration to energy efficiency. 2. We address the applicability of mobility issues of individual or group of IP sensor nodes in a patient care scenario. 3. We propose functional architecture, and the respective message formats, sequence diagram, network model and evaluate the performance of the proposed protocol architecture. 4. Mathematical analysis and simulation experiments are conducted to show the effectiveness of the proposed scheme. Our simulation results show that the proposed scheme effectively reduces the signaling cost and mobility cost in terms of the number of IP-WSNs and hops as compared to MIPv6 and PMIPv6, respectively. There is also a significant improvement in energy consumption for data payload and IP-WSN node density in the network. The rest of the paper is organized as follows. Section 2 reviews background data related to PMIPv6 and 6 LoWPAN. The proposed SPMIPv6 protocol architecture, along with its mobility scenarios, sequence diagram and message formats are presented in Section 3. Section 4 describes performance analysis and evaluation, Section 5 shows the experimental results, and finally Section 6 concludes the paper.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 2 Related Work
### Position of This Research  
As mentioned in Section 2.1, existing studies mainly improved the MPR selection or the node willingness in OLSR networks. They extended the MPR selection algorithm in dense networks, changed the node willingness to preserve its energy in dense networks, change the node willingness based on the battery life and reduce the MPR redundancy using shared sets. However, changing the node willingness based on the HELLO messages received has not been proposed. The position of this present research relative to existing studies is considering mobile dense networks and minimizing the MPR ratio.
# Associating Images with Sentences Using Recurrent Canonical Correlation Analysis
## 4 Experimental Results
### Analysis of the Number of Time Steps
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Image Annotation**} & \multicolumn{3}{c}{**Image Retrieval**} & \multirow{2}{*}{**Sum**} \\ \cline{2-2} \cline{4-9}  & **R@1** & **R@5** & **R@10** & **Med \(r\)** & **R@1** & **R@5** & **R@10** & **Med \(r\)** & \\ \hline STD (bi-skip) [49] & 32.7 & 67.3 & 79.6 & 3 & 24.2 & 57.1 & 73.2 & 4 & 334.1 \\ STD \({}^{*}\)[49] & 33.8 & 67.7 & 82.1 & 3 & 25.9 & 60.0 & 74.6 & 4 & 344.1 \\ m-RNN [45] & 41.0 & 73.0 & 83.5 & 2 & 29.0 & 42.2 & 77.0 & 3 & 345.7 \\ FV (HGLMM) [4] & 37.7 & 66.6 & 79.1 & 3 & 24.9 & 58.8 & 76.5 & 4 & 343.6 \\ FV \({}^{*}\)[4] & 39.4 & 67.9 & 80.9 & 2 & 25.1 & 59.8 & 76.6 & 4 & 349.7 \\ DVSA [10] & 38.4 & 69.9 & 80.5 & 1 & 27.4 & 60.2 & 74.8 & 3 & 351.2 \\ MNLM [7] & 43.4 & 75.7 & 85.8 & 2 & 31.0 & 66.7 & 79.9 & 3 & 382.5 \\ m-CNN-st [8] & 38.3 & 69.6 & 81.0 & 2 & 27.4 & 63.4 & 79.5 & 3 & 359.2 \\ m-CNN \({}^{*}\)[8] & 42.8 & 73.1 & 84.1 & 2 & 32.6 & 68.6 & 82.8 & 3 & 384.0 \\ \hline
**Ours**: & & & & & & & & & \\ RCCA-nc & 37.4 & 70.3 & 81.5 & 2 & 29.7 & 65.3 & 79.8 & 3 & 364.0 \\ RCCA-na & 40.7 & 71.0 & 84.6 & 2 & 32.9 & 68.8 & 81.0 & 3 & 379.0 \\ RCCA-fc & 41.9 & 73.5 & 84.1 & 2 & 33.4 & 68.1 & 81.8 & 3 & 382.8 \\ RCCA-lc & 42.3 & 77.8 & 87.9 & 2 & 34.3 & 68.4 & 81.0 & 3 & 391.7 \\ RCCA-ac & 44.9 & 79.6 & 87.7 & 2 & 35.8 & 71.2 & 83.3 & 2 & 402.5 \\ RCCA \({}^{*}\) & **49.4** & **80.1** & **89.5** & 2 & **37.9** & **73.5** & **84.9** & **2** & **415.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of image annotation and retrieval on the Microsoft COCO dataset (\({}^{*}\) indicates ensemble or multi-model methods).
## 1 Introduction
However, to the best of our knowledge, no studies have been conducted to detect the velocity anomalies of glaciers using the DDInSAR technique and to investigate the mechanism of the anomalies through combined analysis with the geophysical exploration data. In this study, the DDInSAR technique was applied to the Campbell Glacier, East Antarctica, to analyze the velocity anomalies. The existence of basal water in the form of subglacial lake or water film was proposed by analyzing seasonal variation of the anomalies observed by a series of COSMO-SkyMED one-day tandem interferometric SAR (InSAR) pairs and a high-resolution bed topography obtained from a helicopter-borne field survey using Ice Penetrating Radar (IPR). Section 2 describes the study area and data used for this study. Section 3 presents the velocity anomalies found from the DDInSAR and the IPR profiles. Section 4 discusses the cause of the anomalies, while Section 5 concludes this study.