esa-sceva/satcom-chunk-collection huggingface dataset:
# An Interactive Personalized Garment Design Recommendation System Using Intelligent Techniques
## 4 Implementation and Application
### Evaluation of the Relationship between Consumer Profile and Garment Profile  
According to Equation (1), \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), and \(\alpha_{4}\), corresponding to the weights of the style, color, fabric, and garment fit, were set to 0.45, 0.05, 0.05, and 0.45, using the analytic hierarchy process (AHP). According to Equation (2), we obtained the relationships between the style requirements and the style characteristics of the five patterns: \(r\left(SR,SC^{1}\right)=0.6721\), \(r\left(SR,SC^{2}\right)=0.6727\), \(r\left(SR,SC^{3}\right)=0.6730\), \(r\left(SR,SC^{4}\right)=0.7148\), and \(r\left(SR,SC^{5}\right)=0.7665\).  
According to Equation (3), we obtained the relationships between the color requirements and the five color characteristics in the system: \(r\left(CR,CC^{1}\right)=0.011\), \(r\left(CR,CC^{2}\right)=0.013\), \(r\left(CR,CC^{3}\right)=0.017\), \(r\left(CR,CC^{4}\right)=0.019\), and \(r\left(CR,CC^{5}\right)=0.027\).  
According to Equation (4), we obtained the relationships between the fabric requirements and the three fabrics in the system: \(r\left(MR,MC^{1}\right)=0.312\), \(r\left(MR,MC^{2}\right)=0.36\), \(r\left(MR,MC^{3}\right)=0.50\), \(r\left(MR,MC^{4}\right)=0.53\), and \(r\left(MR,MC^{5}\right)=0.59\).  
According to Equations (5) and (6), we obtained the relationships between the garment fit requirements and the garment fit characteristics of the pants in the system: \(r\left(FR,FC^{1}\right)=0\), \(r\left(FR,FC^{2}\right)=0\), \(r\left(FR,FC^{3}\right)=0.0726\), \(r\left(FR,FC^{4}\right)=0.1330\), and \(r\left(FR,FC^{5}\right)=0.1846\).  
Then, with the combinations of the five fabrics, five colors, and five size patterns, we created 125 pants profiles and evaluated them based on Equation (1). Table 6 shows the top five \(r\left(CP,\;GP^{k}\right)\) in our database \(GP\).  
Figure 6: Fuzzy membership function of the waistline position.  
If the tolerance \(\rho\) was set to 0.40, all the top five pants combinations (see Table 6) would be recommended to the consumer based on the 3D virtual try-on technology. Additionally, combination one would be recommended first due to the highest \(r\left(CP,\ GP^{k}\right)\) value. If the tolerance \(\rho\) was set to 0.80, none of the combinations at present would meet the requirements. A self-adaptation mechanism will be active until \(r\left(CP,\ GP^{k}\right)\geq\rho\).
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 4 Results
### Experimental Parameters  
The PyTorch framework was used to write the experimental source code. We trained our network on a single NVIDIA V100 GPU with 32 GB of memory. To compare the proposed method with other methods, the experiment used ResNet50 as the backbone network and the parameters of a pretrained ImageNet to initialize the network proposed in this paper. All input images were adjusted to \(288\times 144\). Zero padding and horizontal flipping with random cropping were used for data expansion, and the SGD optimizer with a parameter set to 0.9 was used to optimize the network. A total of 80 training cycles were considered.  
According to [12], the initial learning rate was set to 0.1; the learning rate decayed by 0.1 after the 30 th iteration and 0.01 after the 50 th iteration. Eight different pedestrian images, including four visible-light images and four infrared images, were randomly selected from each training batch.  
In the feature extraction stage, the first convolution block does not share parameters to extract low-level features associated with different modalities, and the remaining four convolution blocks share parameters with the attention module to obtain discriminative features. To produce a fine-scale feature matrix, the last stride of the last convolution block is set to 1. In the SGA module, we set \(m\) to 16 to obtain a larger receptive field and capture key areas.  
In Equation (6), there is a different pooling parameter \(p_{k}\) for each feature map \(\chi_{k}\). However, one can also use a shared parameter p for all feature maps, and it turns out that this simpler option achieves slightly better results than multiple parameters, as demonstrated in the experimental section of [43]. Consequently, we shall also adopt this option for our experiments. We set p = 3 in our code.
## 5 Results and Analysis
### Loose Coupling Solutions  
#### 5.1.1 Segment 1  
In Figure 10 the trajectories obtained with the different LC approaches are shown. The GPS/INS solution shows large errors during the above-mentioned longest GPS outage, with maximum errors reaching about 400 m. Adding GLONASS to this solution shows evident improvements relative to the baseline case. In fact the isolated GPS/GLONASS fix (circled in right part of Figure 7) allows the trajectory to stay relatively near the reference between the second and the third turns, whereas without GLONASS much larger errors occur. In addition, the LC GG/INS configuration provides better performance than the GPS-only solution during the east-west stretch (around easting values of 705,000), where LC GPS/INS error tends to grow owing to short GPS only outage (15-20 s duration). Finally, the trajectory obtained with GPS/GLONASS/INS augmented with the GNSS-derived yaw aiding as well as velocity and height pseudo-measurements shows remarkable improvements relative to the other configuration with only small disagreements with the reference trajectory.  
The contribution of various combinations of constraints to the position solution is summarized in Table 5. As can be seen, the yaw (GNSS-derived heading) provides little overall benefit. In contrast, the velocity provides a large benefit. The height constraint does not improve the RMS accuracy, but does reduce the maximum position error by about 28%.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{3}{*}{**Configuration**} & \multicolumn{6}{c}{**Position Error (m)**} \\ \cline{2-7}  & \multicolumn{2}{c}{_RMS_} & \multicolumn{3}{c}{_Maximum_} \\ \cline{2-7}  & _East_ & _North_ & _Up_ & _East_ & _North_ & _Up_ \\ \hline LC GPS/INS & 48.4 & 48.7 & 16.3 & 347.0 & 341.2 & 48.0 \\ LC GG/INS & 20.2 & 15.0 & 8.0 & 109.6 & 51.2 & 34.4 \\ LC GG/INS Y & 15.0 & 12.9 & 7.6 & 80.1 & 48.2 & 33.1 \\ LC GG/INS YV & 8.1 & 6.6 & 3.8 & 41.9 & 40.0 & 8.8 \\ LC GG/INS YVH & 7.9 & 5.6 & 3.9 & 33.9 & 20.5 & 6.3 \\ \hline \end{tabular}
\end{table}
Table 5: Position error obtained with LC configurations.  
Figure 10: Trajectories obtained with the loose coupling approach (Segment 1).
# Sequence-Information Recognition Method Based on Integrated mDTW
## 1 Introduction
### Our Contribution  
Development of the mDTW Algorithm: We developed a multidimensional Dynamic Time Warping (mDTW) algorithm specifically designed for handling sequence information, significantly enhancing accuracy in tasks such as online handwriting recognition.  
Integration with Deep-Learning Models: We proposed the integration of mDTW with modern deep-learning models such as Recurrent Neural Networks (RNNs) and Transformers, which improved sequence-data classification across multiple domains, including text recognition, action classification, and point matching.  
Hierarchical Clustering Model: We introduced a simplified hierarchical clustering model that groups characters and reduces the computational time for DTW distance calculations, making it feasible to efficiently process large handwriting datasets.
## 5 Conclusions  
In this work, we have performed a performance analysis of RIS-equipped UAV wireless communication systems under the influence of RIS-equipped UAV malicious jamming. First, we proposed the system and channel models and derived the closed-form expressions for the PDFs of both the legitimate and jamming channels. Next, we derived novel closed-form expressions for the PDF of the received SJR. Using the Gauss-Laguerre approximation,  
Figure 8: ABER versus SJR (dB) curves for a wireless communication system with varying legitimate RIS positions under 16-QAM and BPSK modulations.  
Figure 7: A comparison of the relationship between the jamming probability and ABER under BPSK and 16-QAM modulations.  
we further derived the expressions for the ABERs under BPSK and QAM modulation. We analyzed the effects of jamming probability, the location of the legitimate RIS, and different channel conditions on ABER performance through theoretical research and simulation results. Our simulations indicated that an increase in the probability of malicious jamming significantly raises the ABER. Moreover, the results show that the legitimate RIS is closer to the transmitter, so the system performs better. Furthermore, this study reveals that system performance varies under different channel conditions. Our study indicates that the system's performance is significantly influenced by channel conditions. For instance, under favorable channel conditions, the ABER for BPSK modulation was observed to be as low as \(10^{-5}\), whereas under poor channel conditions, the ABER increased to \(10^{-2}\). Additionally, we found that by reducing the distance between the transmitter and the RIS, the ABER could be improved. These findings highlight the critical impact of channel conditions and the placement of the RIS on the overall system performance. Last, when the channel conditions are poor, it is advisable to prioritize the use of BPSK modulation, and when jamming is active, minimizing the distance between the transmitter and the RIS can help optimize the system's ABER performance.  
Conceptualization, [PERSON] ([PERSON]) and G.W.; methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, D.W., and H.J.; formal analysis, [PERSON] ([PERSON]); investigation, J.Z.; resources, D.W.; data curation, G.W.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON]; visualization, [PERSON] ([PERSON]); supervision, [PERSON]; project administration, [PERSON]; funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript. This research was funded by National Science Foundation of China, grant number 62301599. The data presented in this study are available in this article. The authors declare no conflicts of interest. The authors declare no conflicts of interest.
## 4 Proposal of Static Uniform Job Assignment Algorithm
### Static Uniform Job Assignment Algorithm
#### 4.5.3 Third Step  
In the third step, the remaining jobs (\(r_{t}\)) in the second step will be assigned to workers in a greedy way, such that the increase in the maximum estimated _makespan_\(EM\) is minimized. It is noted that the remaining jobs may exist for any job type. Here, to utilize the parallel job computation using multiple threads on multiple cores for each worker as much as possible, the simultaneous assignment of multiple jobs to one worker is always considered.  
1. Find the worker whose \(E\hat{M}_{w}\) is smallest among the workers (let worker \(w\)). \[E\hat{M}_{w}=EM_{w}+R_{w,D_{w}}^{t}\] (10)  
Figure 2: Flowchart of the proposal.  
2. Assign \(\Delta x_{w}^{t}\) jobs to worker \(w\). \[\Delta x_{w}^{t}=\begin{cases}D_{w},&r_{t}>D_{w}\\ r_{t},&r_{t}\leq D_{w}\end{cases}\] (11)
3. Update the number of the remaining jobs (\(r_{t}\)), and the number of assigned jobs and _makespan_ of the worker \(w\) by: \[\begin{split}& x_{w}^{t}=x_{w}^{t}+\Delta x_{w}^{t},\\ & EM_{w}=EM_{w}+R_{w,\Delta x_{w}^{t}}^{t},\\ & r_{t}=r_{t}-\Delta x_{w}^{t}\end{split}\] (12)
4. If the number of the remaining jobs becomes zero (\(r_{t}=0\)), terminate the procedure. 5. Go to 1.
## 3 Results and Discussion
### Impact of Fermentation Time on the Sensory Profile of Dark Chocolate Samples
 & Pyrrole & 61.00 & \(2030\pm 14\) & 2041 \\ \hline
30 &
\begin{tabular}{  
The model demonstrated separation based on fermentation time, and we observed changes in the volatilome throughout the fermentation process from 48 to 120 h, with a more marked change between 96 and 120 h, as shown in Figure 4.  
The highest discriminant compounds with a VIP \(\geq\) 1 and \(p\) < 0.05 were 2-pentanol, acetoin, 2,3,5,6-tetramethylpyrazine, benzyl alcohol, 2,3,5-trimethylprazine, ethyl acetate, 1-mwthoxy-2-propyl acetate, limonene, acetic acid, acetaldehyde, 3,5-dimethyl-2-ethylprazine, 3-methylbutanoic acid, 2,6-dimethylpyrazine, 2-phenethylprazine and 2,3-butanediol (Isomer A and B). The VIP values are shown in Table S2 in the Supplementary Materials. Figure 5 shows four heat maps of VOCs with a VIP \(\geq\) 1 and \(p\) < 0.05 for chocolates made from cocoa beans fermented for 48, 72, 96, and 120 h. VOCs were ordered from the highest VIP (2-pentanol) to the lowest VIP (2-phenethyl acetate). In this figure, we can see compounds, like 2,6-dimethylpyrazine, 2,3,5-trimethylpyrazine, 3,5-dimethyl-2-ethylpyrazine, 2,3,5,6-tetramethylpyrazine, and acetoin, which are associated with the fine-flavor characteristics such as nuttiness, chocolate, and caramel. Additionally, acetaldehyde, 2-pentanol, ethyl acetate, 1-methoxy-2-propylacetate, and propyl acetate are associated with fruity notes, while benzyl alcohol is associated with floral notes. Therefore, their presence was considered fundamental to the aromatic properties of chocolates. By contrast, compounds such as 3-methyl butanoic acid were found to exhibit undesirable characteristics. Volatile compounds 1-methoxy-2-propyl acetate, ethyl acetate, and 2,3,5-trimethylpyrazine were predominantly found in chocolates made from cocoa beans fermented for 96 h. Pyrazines and acetaldehyde were found predominantly in chocolates of 120 h fermentation. Chocolates from agroclimatic clusters C1, C4, C7, C8, and C9 from 96 h of fermentation showed high amounts for compounds 2,3,5,6-tetramethylpyrazine and 2,3,5-trimethylpyrazine. This observation could be correlated with the results obtained for nuttiness (Figure 3), which was higher in clusters C1, C8, and C9, followed by C7, as these compounds are directly associated with the nuttiness attribute. Figure 4: Partial least square discriminant analysis (OSC-PLS-DA) score from volatile profile data of the chocolate samples produced from 48 (yellow points), 72 (blue points), 96 (purple points), and 120 h (red points) fermented beans from nine regions of Arauca. Permutation test for the sample dataset [n = 100; R2 = (0.0, 0.133), Q2 = (0.0, \(-\)0.715)]. The esters 1-methoxy-2-propyl acetate, ethyl acetate, and 2-phenethyl acetate showed the highest amounts in chocolates made with beans fermented for 96 h, as did pentanol. Conversely, at 120 h, the lowest values were observed for the nine agroclimatic clusters, which could correlate with the decrease in the fruitiness attribute observed for most of the agroclimatic clusters (Figure 3).
# A Review of Transformer-Based Approaches for Image Captioning
## 7 Challenges and Future Directions
### All-Transformer Design  
Many of the current captioning systems mainly employ the transformer for the encoding and decoding functions. For a number of models, the visual feature extraction is still carried out based on a CNN of one form or another. A recent line of research is in the use of transformers as well for the feature extraction, thus showing that the reliance on CNNs and explicit object detectors may not be necessary. [PERSON] et al. [99] experimented with applying a standard transformer directly to images instead of using a feature extractor, e.g., Faster-RCNN. They came up with the Vision Transformer (ViT). In their work, an image is split into patches and then passed through a linear embedding whose output is then used as input to a transformer. The image patches are treated as tokens and the transformer is then trained on an image classification task in a supervised fashion.  
Their vision transformer matches or exceeds the state-of-the-art on a number of image classification datasets while requiring less computational resources. Since ViT, a number of other transformer-based feature extractors have been developed, e.g., CoAtNet [104], CoSwin [107], CLIP-ViT [53] and DaViT [118]. These have become the basis of the current state-of-the-art models ([103; 106; 110; 111; 112]).  
[PERSON] et al. [146] improve on ViT and achieve competitive results in image classification on ImageNet while requiring even less computational resources than [99] and without the use of external datasets. [PERSON] et al. [121] and [PERSON] et al. [140] applied transformers to the task of object detection and achieved competitive results. [PERSON] et al. [90] also explored the idea of an image transformer.  
A continuing research area still remains in effectively applying the vision transformer to other computer vision tasks. Another active research area is in improving self-supervised pre-training methods for the vision transformer so as to achieve comparable performance with large-scale supervised pre-training. The transformer architecture was born in the language domain and although it has been used in computer vision tasks, it is by its nature very suited to NLP tasks. There is a need for further research in developing a more refined vision transformer which is well adapted to the nature of images and other computer vision tasks. Improvements in these areas would make possible the design of image captioning systems that are entirely built on transformers.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods  
Even though the traditional cross-layer methods can greatly improve the routing performance, considering their limited capability for dealing with multiple constraints and high calculation complexity, the wide application of traditional cross-layer method is limited, so intelligent algorithm-based methods have been proposed to extend the application range of cross-layer methods.  
The intelligent algorithm-based methods for cross-layer routing protocol design are still a new research area for underwater acoustic sensor networks. In recent years, many researchers have devoted their efforts to combine the traditional cross-layer design method and the intelligent algorithms together to find an effective approach for cross-layer routing protocol design. There are many excellent intelligent algorithm-based routing protocols have been proposed for terrestrial wireless sensor networks; however, only the fuzzy logic-based routing protocols have been applied in underwater acoustic sensor networks. Therefore, the review on these intelligent algorithm-based cross-layer design routing protocols should open our minds and provide more research methods for underwater acoustic sensor network routing protocol design.
## 3 Proposed Method
### Data Analysis
* Plane model estimation: Randomly select samples from the collected point cloud and estimate a plane model. The plane model, representing the ground, is estimated using the 3D RANSAC algorithm. * Ground point identification: Identify points that fit the estimated plane model to form a consensus set. During this process, a distance threshold is set to distinguish ground points from non-ground points. * Ground data filtering: Filter out points that do not belong to the consensus set, thus obtaining a clean point cloud containing only ground points. * Post-processing: Perform additional preprocessing tasks using the filtered ground data. For instance, normalize the height information for the ground or separate obstacle data on the ground. The RANSAC algorithm does not re-predict the model from the sum of the current errors but instead creates an approximate model directly from the predicted model. This enables high-speed processing because it reduces the time required to compare the errors related to the model predicted from a small number of observation points. However, the algorithm is sensitive to noise because the errors are not compensated. Unlike the least squares method, which uses the entire dataset, the RANSAC method estimates the model parameters by applying different observation values to each subset of the data. For the  
Figure 3: The 3D point cloud data for the aircraft and jetbridge on the apron (no ground filter). subsampled data, the region of each 3D LiDAR channel is divided by \(\lambda_{k}\), and \(\lambda_{0}=0.2\) m is defined. Furthermore, \(\lambda_{k}\) can be calculated using Equations (1)-(3):  
\[a_{0}=\tan^{-1}(\frac{\lambda_{k}}{(h_{offset}+400\;\mathrm{mm})}) \tag{1}\]  
\[\lambda_{k}=h\cdot\tan(a_{0}+k\cdot\eta\cdot\Delta a),\;\{k:1,\ldots\ldots,N\} \tag{2}\]  
\[N=\frac{a_{N}-a_{0}}{\eta\cdot\Delta a},\;\eta=2,\;\Delta a=0.2 \tag{3}\]  
In conclusion, the 3D RANSAC algorithm is a powerful tool for filtering ground noise in 3D LiDAR data, as shown in Figure 4. This technique significantly enhances the path planning and environmental awareness capabilities of autonomous vehicles by ensuring that only relevant ground data are processed. [22].
## 6 Contrastive Methods
### Transformer-Based Models  
Existing reconstruction-methods frequently fail to handle sufficient temporal context and inadequate representation of normal patterns, hindering their effectiveness in identifying abnormal behaviors. The TRL-CPC [60] explores the integration of contrastive learning into time series analysis. In TRL-CPC, the context vectors represent observation windows in MTS. The latent representations of subsequent time steps are obtained through nonlinear transformations of these context vectors are contrasted with the latent representations of the encoder for multivariate time series to maximize positive density. RESIST [61] relies on a robust loss function based on contrastive learning to train a Transformer that models the expected behavior of network activity, without the need for an anomaly-free training subset. It automatically mitigates the impact of atypical corrupted training data to reduce their influence on training optimization. DCdetector [13] employs a contrastive learning framework to learn representations of time series data. By utilizing a dual-attention mechanism, it obtains representations of the input time series from two branches (the in-patch branch and the patch-wise branch). The contrastive loss function is defined by measuring the similarity of representations from these two different perspectives, thus acquiring permutation-invariant representations with superior discriminative capabilities.  
Recent research has begun to combine reconstruction and contrastive learning to create a comprehensive integrated anomaly score that can capture deviations from expected patterns and effectively identify subtle anomalies. SimAD [62] combines a sophisticated feature extractor adept at handling extended time windows, integrates normal behavior patterns comprehensively using the EmbedPatch encoder, and introduces an innovative ContrastFusion module aimed at highlighting distribution differences between normal and abnormal data to enhance the robustness of AD. RESTAD [63] implements a Radial Basis Function (RBF) neuron layer in the Transformer model to represent a non-parametric density in the latent space. A high output from the RBF indicates similarity with normal training data. Additionally, RESTAD merges RBF similarity scores with reconstruction errors to enhance anomaly detection sensitivity.