esa-sceva/satcom-chunk-collection huggingface dataset:
### Retrieval of Artwork  
Many studies indicate the importance of colors in image retrieval or artwork retrieval [9; 10]. However, the classification methods of colors are complicated due to physical characteristics or luminosity and brightness. Different classification methods have different views of color characteristics and greatly affect the artwork retrieval results. Some simple color features, such as RGB features, are easily affected by light, thus causing the color information of the same artwork to become unequal during the digitizing process. Therefore, for the retrieval of digitized artwork that is heavily influenced by natural light, it is difficult to use these types of features. Besides colors, sketches also form the basis of artwork retrieval, as well as an important way of studying the characteristics of image composition and the painters' style [11]. Different from ordinary image retrieval, the separation of colors and sketches for feature extraction is meaningful for the retrieval of artwork. Paying attention to the similarity of sketches helps to retrieve artwork with different digitized versions or artwork with a fading phenomenon.  
One of the important features of our proposed system is that we allow users to search for the rare colors in the artworks, since the colors that are less used often attract more attention. Painters often use rare pigment materials and toning methods in their paintings [6]. However, in many cases, these rare color schemes do not appear on a large area of the paintings. If the retrieval method is based on common feature extraction methods of color space, these rare color schemes might be ignored. In addition, learning an image feature that does not ignore rare colors is also a challenge for artwork retrieval. Figure 1 shows some rare color examples of Japanese ukiyo-e prints.  
Figure 1: The example of rare and attractive colors in ukiyo-e prints are shown in red circles.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 4 Materials and Methods for Offline Fire Detection
### YOLO Models  
There have been numerous deep learning models developed for object detection. Models such as region-based convolutional neural networks (RCNNs) detect objects in two steps. They begin by identifying the areas of interest (bounding boxes) where an item is pre-presented. Second, they classify that specific area of interest. YOLO models are one-stage object detectors in which the model only needs to process the input image once to generate the output. YOLO models are a type of computer vision model capable of detecting objects and segmenting images in real time. The original YOLO model was the first to combine the problems of drawing bounding boxes and identifying class labels in a single end-to-end differentiable network. YOLO models have been shown to be faster, more accurate, and more efficient than alternative object identification models that use two stages or regions of interest. In 2015, [PERSON] et al. presented the first YOLO model and YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and YOLO-NAS have been released since then in an effort to keep up with the ever-changing computer vision field and improve the models' performance, accuracy, and efficiency. Each YOLO release added new features, approaches, or architectures to address some of the limitations or issues of the previous releases. For example, YOLOv3, which uses the Darknet-53 backbone, improved small object identification by using multi-scale predictions, whereas YOLOv4 combined numerical features from other models to increase the model's speed and reliability. YOLOv4 was built on a CSPDarknet-53 backbone, which is a modified version of Darknet-53 that uses cross-stage partial connections to reduce the number of parameters and improve feature reuse. It also used a PANet neck, which is a feature pyramid network that combines features from different levels of the backbone and uses attention modules to improve feature representation. Ultralytics' YOLOv5 is a PyTorch-based version of YOLO that offers improved performance and speed trade-offs over the previous versions, as well as support for image segmentation, pose estimation, and classification. In 2022, the YOLOv6 model was released, which uses a ResNet-50 backbone and provides cutting-edge performance on the COCO dataset. YOLOv7 is a 2022 model created by the designers of YOLOv4 that uses a CSPDarknet-53 backbone and incorporates a new attention mechanism and label assignment approach. It can also perform tasks such as instance segmentation and pose estimation. YOLOv8 and YOLO-NAS are the most recent models that are considered powerful additions to the YOLO model family [39].
## 7 Limitations and Future Study  
Despite the promising findings, a few limitations should be acknowledged. The dataset used was minimal, potentially affecting the generalizability of the results to broader groups. The research concentrated on a restricted array of parameters, and the inclusion of additional clinical and genetic data may boost the models' prognostic efficacy. Thus, subsequent studies need to examine more extensive datasets and assess the incorporation of DL methodologies with feature engineering strategies to augment the predictive accuracy further. Secondly, [PERSON]'s correlation technique was used to find the most relevant attributes. However, it is acknowledged that [PERSON]'s correlation has limitations; it only detects linear correlations and may miss complex, non-linear relationships that could also be predictive. Thus, future work should use more advanced feature selection methods, such as information gain, chi-square test, recursive feature elimination (RFE), and model-based selection methods, to evaluate their impact on the model performance and identify any non-linear relationships that [PERSON]'s correlation might have missed. It will provide a more comprehensive understanding of the feature space and potentially lead to improved predictive accuracy. Finally, the literature indicates that shortness of breath is one of the key features of determining lung cancer. The current study used [PERSON]'s correlation to identify the most relevant attributes, setting a threshold of 0.15 for inclusion. Shortness of breath was eliminated due to its correlation coefficient of 0.0644, which was below the required threshold. On the other hand, swallowing difficulty (0.2689) and alcohol consumption (0.2944) had correlation coefficients above the 0.15 threshold, indicating a more substantial correlation with lung cancer in the dataset. Thus, these were considered essential features and retained for the analysis. Future studies should consider other feature selection methods, such as the Information gain chi-square test or Principal Component Analysis (PCA), to confirm the outcome.  
This research received no funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: The datasets analyzed during this study are available at: [[https://www.kaggle.com/datasets/sanjoli02/lung-cancer](https://www.kaggle.com/datasets/sanjoli02/lung-cancer)]([https://www.kaggle.com/datasets/sanjoli02/lung-cancer](https://www.kaggle.com/datasets/sanjoli02/lung-cancer)) (5 January 2025) (Original Dataset). You can download the dataset freely. There is no issue related to copyright. You can find all the information at [[https://www.kaggle.com/docs/datasets](https://www.kaggle.com/docs/datasets)]([https://www.kaggle.com/docs/datasets](https://www.kaggle.com/docs/datasets)) (5 January 2025).  
The author declares no conflicts of interest.
# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
## 3 Methods and Materials  
### Research Questions and Research Design  
As previously stated, it is our objective to determine the perceived usefulness (as defined by [PERSON] [17]) of video learning analytics in small scale learning scenarios. In order to do so, our research questions are:  
1. Are data generated by students watching educational videos perceived as useful by teachers in small scale learning scenarios?
2. Is there a minimum number of video playbacks to record so that the presented data are perceived as useful by teachers to enhance the learning process? In the affirmative case, what is that minimum?  
In order to give answers to our research questions we collected LA data on a series of educational videos, we developed visualizations to make the data easier to understand for stakeholders, we distributed the information to lecturers in the different courses for which we collected data and, finally, we conducted semistructured interviews with them to gather their opinion on the utility of the information.  
In this paper we use a mixed research methodology combining action research with a design and creation approach [42]. Action research is used as a framework since it tries to create a system to solve an existing problem, generating a product dealing with market needs that will undergo basic pilot tests. Derived from that action research approach, it concentrates on practical issues, is based on an iterative cycle and puts its emphasis on change and collaboration with practitioners. Our iteration cycle is based on five steps. The first step is awareness: we realize we do not have an understanding of how students watch educational videos beyond the very basic. Then comes suggestion: we decide a learning analytics approach is the best approach to gather the best possible information to solve the problem. The next step is development: we implement a learning analytics solution which is tailor-made for our circumstances (but is easily applicable to other scenarios). Then comes evaluation: we go over the collected data, process them, create some visualizations, and present them to the teachers involved in the teaching and learning process our objective is to understand better. The development and evaluation steps are repeated in order to provide better results.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 3 State-of-the-Art Deep-Learning Models
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Networks** & **Backbone** & **Efficiency-Oriented Approaches** \\ \hline DDRNet [16] & ResNet & two-branch network \\ \hline STDC1-50 [11] & STDC & feature map size reduction \\  & & single branch efficient decoder \\ \hline U-HarDNet-70 [42] & DenseNet & depthwise separable convolutions \\ \hline HyperSeg [46] & EfficientNet-B1 & pointwise convolutions \\  & PSPNet & depthwise convolutions \\  & ResNet18 & small decoder size \\ \hline SwiftNetRN-18 [47] & ResNet-18 & small decoder size (lightweight decoder) \\  & MobileNet V2 & \\ \hline BiSeNet V2 [15] & VGGnet & two-branch network \\  & Xception & fast-downsampling \\  & MobileNet & \\ \hline TD4-BISE18 [48] & TDNet & Grouped convolutions \\ \hline ShelfNet18 [33] & ResNet & multi-branch network \\  & Xception & channel reduction \\  & DenseNet & \\ \hline BiSeNet [14] & Xception39 & \\  & ResNet18 & two-branch network \\ \hline SegBlocks [17] & - & block-based processing \\ \hline FasterSeg [27] & FasterSeg & multi-branch network \\ \hline ESNet [32] & ESNet & multi-branch network \\  & & pointwise convolutions (factorized convolutions) \\ \hline LEDNet [44] & ResNet & channel shuffling \\ \hline ICNet [34] & image cascade network & multi-branch network \\ \hline SQ [49] & SqueezeNet & decreased kernel size \\ \hline ERFNet [43] & ERFNet & small decoder size \\ \hline LinkNet [50] & ResNet18 & bypassing spatial information \\ \hline SS [23] & ResNet18 & two-branch network \\ \hline ContextNet [24] & ContextNet & two-branch network \\  & depthwise separable convolutions \\  & pruning \\ \hline DSNet [51] & DSNet & channel shuffling \\ \hline ESPNetv2 [37] & ESPNet & group pointwise convolutions \\  & depthwise separable convolutions \\ \hline ESSGG [38] & ERFNet & depthwise separable convolutions \\  & channel shuffling \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Common efficiency-oriented approaches used in real-time Semantic Segmentation models.
## 5 Numerical Applications and Discussion  
To validate the unique abilities of the proposed method in addressing deformation and failure, several numerical applications are conducted in this section. The time step \(\Delta t=10^{-6}\)s satisfies the stability requirement. \(C=5\times 10^{5}\) kg/m\({}^{3}\)s, \(\gamma=0.005\) for quasi-static problems based on the convergence analysis.
# Retracted: W-GPCR Routing Method for Vehicular Ad Hoc Networks
## 2 Materials and Methods  
The W-GPCR approach is a geographical location routing protocol based on weight selection. It adopts the corresponding adaptive weight parameter ratio for different scenarios to select the optimal next-hop relay node. The application layer of the source node sends data packets to the data link layer, which performs route discovery to obtain sensor data, and sends a RREQ (Route Request) to the next-hop node. The data exchange between the nodes adopts the IEEE802.11p protocol; the GPS module can be used to obtain its position coordinates and the installed IMU (inertial measurement unit) can obtain information such as the node's movement direction. The next-hop node becomes the current node to execute the same route discovery strategy as the source node, and finally sends the RREQ to the target node. After receiving the RREQ sent by the source node, the target node sends a RREP(Route Reply) according to the optimal path determined by the route discovery process.  
When selecting the next-hop node, the traditional greedy forwarding strategy only considers whether the distance between the next-hop node and the target node is smaller than the distance between the current node and the target node. Such a choice can easily cause local optimization problems, but may not be optimal on the global path. Thus, the proposed approach considers the distance between nodes, the driving direction of nodes, and the density of nodes. By calculating the weights, the optimal next-hop node is selected so that the direction of packet delivery will always converge towards the target node, thus avoiding routing loops and other problems. When the next-hop node cannot be found in the signal coverage of the node for packet delivery, the delivery of the data packet falls into the routing hole at this time. In view of such a scenario, a restricted forwarding strategy is proposed to prevent nodes from delivering data packets in the direction of routing holes again and to reduce unnecessary routing overhead.
## Materials and Methods  
Synthesis of 5-amino-N-tert-butyl-4-(3-(1-methylpyrazole-4-carboxamido)phenyl)-2- (methylthio)thieno[2,3-d] pyrimidine-6-carboxylic Acid amide (TP4/2)  
The synthesis of TP4/2 (Figure S4) was carried out using the acylation of 5-amino-4-(3-aminophen)-\(N\)-(_tert_-butyl)-2-(methylthio)thieno[2,3-d]pyrimidine-6-carboxamide, which was obtained according to the method of [PERSON] and [PERSON] [85]. For this, 1.0 equivalent of this compound was dissolved in dry \(N\),\(N\)-dimethylformamide and mixed with 1.1 equivalents of 1-methyl-\(1H\)-pyrazol-4-carboxylic acid in the presence of \(N\),\(N\)-diisopropylthymamine (1.2 equivalents), and then 1.1 equivalents of 1-[bis(dimethylamino)methylene]-\(1H\)-1,2,3-triazole0[4,5-b]pyridinium3-oxide hexafluorophosphate (HATU) were added and stirring was carried out at room temperature for 5 h. Previously, 1-methyl-\(1H\)-pyrazole-4-carboxylic acid was obtained via alkaline hydrolysis of the corresponding ethyl ester, which was synthesized according to the method in [86]. After removal of solvent under reduced pressure, the residue was washed with water, and the crude material was filtered off. The target product was purified by column chromatography and characterized by NMR and high-resolution mass spectrometry. The NMR spectra were obtained using a \"Bruker Avance III 400\" spectrometer (400.13 MHz for \({}^{1}\)H and 100.61 MHz for \({}^{13}\)C) (\"[PERSON]\", Hamburg, Germany) in DMSO-\(d_{6}\) and were referenced to residual solvent proton signal (\(\delta\)H = 2.50) and solvent carbon signal (\(\delta\)C = 39.5). The high-resolution mass spectra were recorded using a \"Bruker microTOF\" spectrometer (\"[PERSON]\", Hamburg, Germany).  
The compound TP4 (C\({}_{23}\)H\({}_{25}\)N\({}_{7}\)O\({}_{2}\)S\({}_{2}\)) is yellow solid with the melting temperature 179-181 \({}^{\circ}\)C; the \({}^{1}\)H-NMR spectrum (DMSO-\(d_{6}\)), \(\delta\), ppm (J, Hz): 10.08 (1H, s, 3'-NH); 8.35 (1H, s, 3'-H); 8.05 (1H, s, 5'-H); 7.94-8.02 (2H, m, 2'-H and 6'-H); 7.56 (1H, t, \(J\) = 7.8, 5'-H); 7.35 (1H, d, \(J\) = 7.8, 4'-H); 6.98 (1H, s, NH-\(t\)-Bu); 6.14 (2H, s, NH\({}_{2}\)); 3.91 (3H, s, N-CH\({}_{3}\)); 2.62 (3H, s, SCH\({}_{3}\)); 1.37 (9H, s, C(CH\({}_{3}\))\({}_{3}\)); and the \({}^{13}\)C-NMR spectrum (DMSO-\(d_{6}\)), \(\delta\), ppm: 168.8; 167.8; 165.2; 162.7; 161.2; 144.7; 140.0; 139.4; 137.0; 133.3; 129.7; 124.0; 122.1; 120.5; 118.7; 97.7; 51.9 (C(CH\({}_{3}\)))\({}_{3}\)); 29.2 (C(CH\({}_{3}\))\({}_{3}\)); 14.3 (SCH\({}_{3}\)). According to high-resolution mass spectrometry (ESI-TOF), the found value was 518.1422 (calculated for [M + Na\({}^{+}\)]: 518.1403) (Figure S4).
# Stability of Running Stride Biomechanical Parameters during Half-Marathon Race
###### Abstract  
This study explores the stability of biomechanical parameters of the running stride of male trained athletes during a half-marathon competition. Using a field-based descriptive design, eight male athletes from a local training group were monitored throughout an official half-marathon race under identical conditions, assessing biomechanical parameters including ground contact time (GCT), leg spring stiffness (LSS), vertical oscillation (VO), and stride length (SL) recorded via the Stryd Summit Power Meter. A repeated measures analysis of variance (RMANOVA) was conducted to detect significant changes in biomechanical parameters as the race progressed. Results demonstrated minimal changes in all parameters, with no significant differences observed for GCT (\(F=0.96,p=0.38\)), VO (\(F=0.23,p=0.87\)), and SL (\(F=1.07,p=0.35\)), and a small (\(\eta^{2}=0.004\)) yet statistically significant difference in LSS (\(F=5.52,p=0.03\)) between the first and second segments, indicating that athletes were able to maintain stable biomechanical parameters throughout the race. The conclusion highlights the need for personalized training programs tailored to the unique biomechanical adaptations and demands of endurance running.  
athletes; competition; Stryd; long-distance running Article Stability of Running Stride Biomechanical Parameters during Half-Marathon Race
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 6 Conclusions  
For random access in 5G and B5G networks, users have the opportunity to receive pilot signals from a number of neighboring TRxPs. This paper proposes and tests the feasibility of recent machine learning approaches, in particular RL, to solve a random access network control problem. If we allow users to have stored knowledge (through training) in terms of the service points that show better performance, they can opportunistically choose the optimal access point, which helps optimize their expected RA performance. We conducted an experiment using ns-3 to prove the efficacy of our proposed RL method in a dynamic network scenario with channel conditions varying over time.  
There are significant implications of applying the reinforcement learning method for wireless networking. For example, it allows us to estimate the random access delay that occurs at a given TRxP without requiring an exact model of the random access environment since users learn about the environment through their own experience. Another benefit for future networks is that users can intelligently determine the optimal attachment points in such a way that their QoS can be met without requiring a central agent to calculate their expectations and provide resources accordingly.  
The next step after random access is a data exchange. Such a learning algorithm, for example, reduces the congestion that can occur at the would-be overloaded access points. Hence, access request load is shared among the access points. Learning algorithms will determine the load-sharing strategy without requiring a model. In this way, efficient resource distribution and allocation are realized throughout the whole coverage area.  
The potential drawback of learning algorithms is the overhead. Space complexity is minimal. However, despite a slight training time penalty, the deep RL algorithm increases the overall long-term reward values for the users. We further analyze such learning algorithms to achieve better convergence and performance results in fewer iterations, and also reduce the associated computational overhead, which is left as an avenue for our future work.  
Conceptualization, Y.Z.B. and Y.-J.C.; formal analysis, Y.Z.B.; funding acquisition, Y.-J.C.; methodology, Y.Z.B. and Y.-J.C.; project administration, Y.-J.C.; resources, Y.-J.C.; supervision, Y.-J.C.; validation, Y.Z.B.; writing--original draft, Y.Z.B.; writing--review & editing, Y.Z.B. and Y.-J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1008530).  
The authors declare no conflict of interest.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Miniaturization
#### 2.2.6 Modification of Ground Plane  
In-body antennas can also be miniaturized by modifying their ground plane. Generally, the model of an in-body antenna considers infinite ground plane. In practice, the ground plane is designed as finite. For large-scale miniaturization, the size of the ground plane is further reduced in a way where, at times, it is slightly larger than the dimensions of the patch [32]. Refs. [37; 38; 39] present an analysis of truncated ground planes. Such miniaturization can be achieved by introducing a slot in the ground plane, which can alter the return path of the current to slow down the current flow. This causes a phase shift of the displacement of the current from one edge of the slot to the other, resulting in a smaller antenna size [40]. However, such in-body antenna has a reduced polarization concentration, and reducing the size of the ground plane can also affect input impedance. Furthermore, the edge diffraction due to ground plane modification can generate significant back lobe radiation, resulting in a reduction in front-to-back ratio [32].