esa-sceva/satcom-chunk-collection huggingface dataset:
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 4 Results
### MapMatcher
\(N_{all}\) shows the Number of Matched Keypoint Pairs, which directly reflects the potential quality of the image alignment. RIFT2 generates dense correspondence sets (e.g., 2059 pairs in task a-b), attributed to its exhaustive keypoint sampling strategy. D2 Net and DeDoDe produce sparser matches (often <200 pairs), indicating conservative feature selection heuristics. SuperGlue achieves a high match count at the cost of increased runtime (RT). R2D2 prioritizes speed over match density, with most tasks yielding <300 pairs. RoMa's dense matching paradigm incurs high computational overhead, yet suffers from lower precision (more keypoints but fewer valid matches). MapReduce consistently achieves superior match density, exemplified by 2418 pairs (a-b), 2400 pairs (a-g), and 1829 pairs (a-h). In summary, MapReduce demonstrates a superior trade-off across all evaluation metrics. MapReduce achieves near-R2D2-level runtime efficiency while yielding more matches, rivaling or surpassing robust matching methods such as RIFT2 and SuperGlue. Although we extracted fewer feature points than RoMa, the ones we extracted have a higher probability of successful matching. This combination of speed and match quality indicates that MapReduce is both time-efficient and effective at producing rich, reliable correspondences, making it a strong choice for real-time or near real-time image matching and registration tasks. We benchmarked MapReduce Extraction (feature extraction) and MapReduce (feature matching) against state-of-the-art methods to assess their performance on historical map datasets. Quantitative results are summarized in Table 4. Let \(N_{all}\) denote the total number of matched pairs, and ROCC represent the ratio of correct correspondences. MapReduce Extraction enhances matching precision by prioritizing high-saliency keypoints during feature extraction. Despite reducing the total keypoints, MapReduce boosts both efficiency and precision by filtering out low-confidence candidates. In the feature matching stage, compared to Nearest Neighbor (NN) matching and the SuperGlue method, the proposed MapReduce demonstrates a higher matching accuracy. Under optimal experimental conditions, MapReduce's accuracy improved by approximately 30% compared to SuperGlue, further validating its outstanding performance in complex historical map data. This indicates that MapReduce not only handles heterogeneous features but also effectively addresses registration challenges such as low texture, providing a superior solution for the automated processing of historical maps.
# Acceleration Capacity and Vertical Jump Performance Relationship in Prepubertal Children
## 2 Materials and Methods
### Experimental Procedure  
The protocol was composed of two sessions. The first session was dedicated to anthropometric measurements and the control of inclusion criteria. The second session started with a standardized warm-up composed of concentric and eccentric submaximal contractions of the knee extensors, knee flexors, and plantar flexors, followed by some athletic drills and several horizontal and vertical jumps [33]. Jump and sprint physical tests described thereafter were then performed.  
All participants began with the sprint tests using photoelectric cells ([PERSON], [PERSON], Bolzano, Italy). Two maximal 20 m sprints were performed with intermediate measurements at 5 and 10 m. The two sprints were separated by two minutes of recovery. Volunteers were free to start the sprint when they wanted.  
Then, volunteers conducted vertical and horizontal jumps in a randomized order. Vertical jumps were composed of three modalities. The squat jump (S) was performed starting from a static semi-squatting position (90\({}^{\circ}\) knee angle). The countermovement jump (CMJ) started from a standing position, bending the knee until a 90\({}^{\circ}\) knee angle and then extending the knee in one continuous movement. During the SJ and CMJ, their hands were kept on their hips. Finally, the countermovement jump with arms (CMJa) corresponds to the CMJ with the instruction to use their arms during the jump. Volunteers performed two trials per modality. The vertical jump height was evaluated with an Optojump (Optojump; Microgate, Bolzano, Italy). Horizontal jumps were composed of two modalities: the bilateral broad jump and the unilateral hop test. For the broad jump, participants started in a standing position. Firstly, they bent their knees and their hips to a self-selected depth, bringing their arms behind their body. Then, they achieved full extension in a continuous way, with the instruction to go as far as possible [34]. For the hop tests, volunteers were asked to stand on one leg, then to jump horizontally as far as possible, and to land on the same leg [35]. The hop test was performed using the right and left side, without considering the side dominance. For the broad jump and the hop tests, the distance was measured from the toe in the start position to the heel at the landing position [36].  
Participants performed two trials for each test with 15 s between each trial and 1 min between jump modalities. Maintaining stability for two seconds was required after landing for acceptance of the jump.  
The best performance was retained for the analysis of sprint and jump modalities. For the hop test, no difference was observed between the right and left sides (\(p\) = 0.454). Accordingly, the average between the right and left sides was used for the analysis.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 3 Opportunities of NR-U for Industry 4.0
### Reliability  
Cellular technology is the most reliable communication technology in wireless technology including WiFi. Hence, the ability to operate a private network in the newly available broadband unlicensed spectrum (3.5/5/6/60 GHz) under cellular technology will offer vastly more reliable means of communication for the industry. Additionally, connectivity between the access points is always coordinated in the cellular system, meaning that there will be a seamless connection as a system transfers from one access point to another. Mission-critical technologies that require continuous, reliable connections, such as safety communications and warning systems, will be supported with extremely trustworthy links.
## 2 Description of the Problem  
### Power Generation  
The obtainable power \(P\) of a hydropower plant can be estimated in terms of the net head, \(H_{t}\), and the water flow rate, \(Q\), as  
\[P=\rho gQh_{t}\eta, \tag{1}\]  
where \(\rho\) is the density of the water, \(g\) is the acceleration of gravity, and \(\eta\) is the overall efficiency of the generation equipment. The net head, \(H_{t}\), represents the net height of the water at the entrance of the turbine, which can be written as the gross height, \(H_{g}\) minus the friction loss \(h_{fric}\):  
\[h_{t}=H_{g}-h_{fric}, \tag{2}\]  
Considering an action turbine, in which the energy to be transformed in the turbine is entirely kinetic (through the formation of a jet), it can be written that  
\[h_{t}=\frac{1}{2g}v_{jet}^{2} \tag{3}\]  
The velocity of the jet, \(v_{jet}\), can be written in terms of the water flow rate \(Q\) and the nozzle injector section area, \(S_{noz}\), and after substituting the expression for \(h_{t}\) can be rewritten as  
\[h_{t}=\frac{Q^{2}}{2 gC_{D}S_{noz}^{2}}, \tag{4}\]  
where a discharge coefficient \(C_{D}\) has been introduced to model the formation of a _vena contracta_ in the jet. Finally, a simple approximation for the height loss term from [25] as:  
\[h_{fric}\approx k_{p}\frac{L_{p}}{D_{p}^{5}}Q^{2} \tag{5}\]  
Figure 2: Bending in a real MHPP penstock length.  
with \(k_{p}\) being a constant of the pipe material and \(D_{p}\) its internal diameter. Now, substituting this last expression, together with Equation (4) in Equation (2), an expression for the water flow rate that will feed the plant can be obtained:  
\[Q=\left[\frac{H_{g}}{\frac{1}{2 gC_{D}^{2}S_{\text{noz}}}+k_{p}\frac{L_{p}}{D_{p} ^{5}}}\right]^{\frac{1}{2}} \tag{6}\]  
Finally, introducing Equation (6) in Equation (4), and the resultant expression in Equation (1), it yields:  
\[P=\frac{\eta\rho}{2C_{D}^{2}S_{\text{noz}}^{2}}\left[\frac{H_{g}}{\frac{1}{2 gC_ {D}^{2}S_{\text{noz}}^{2}}+k_{p}\frac{L_{p}}{D_{p}^{5}}}\right]^{\frac{3}{2}} \tag{7}\]  
It is relevant to note that this expression allows for estimating the power generated by any hydropower plant on the basis of the gross head \(H_{g}\), and penstock length and diameter, \(L_{p}\) and \(D_{p}\). These three variables, indeed, are determined by the spatial layout of the plant over the terrain.
# Hyperlipidemic Rabbit Models for Anti-Atherosclerotic Drug Development
## 2 Hyperlipidemic Rabbit Models
### Diet-Induced Hyperlipidemic Rabbits
#### 2.1.2 Casein-Fed Rabbits  
A cholesterol-free, casein-enriched diet can also induce hypercholesterolemia and atherosclerosis in rabbits. In general, hypercholesterolemiaia is induced in rabbits by feeding them a semi-purified diet enriched in 27% casein, and plasma TC levels are increased up to 300-800 mg/dL and accompanied by aortic atherosclerosis [16; 17]. This model is seldom used; however, the possible mechanism for hypercholesterolemiaia is considered as being caused by decreased bile acids synthesis and fecal sterol excretion, which leads to increased hepatic cholesterol, followed by down-regulation of LDLR [18; 19; 20]. The major elevated lipoproteins in casein-fed rabbits are LDLs, which is different from \(\beta\)-VLDLs present in cholesterol-fed rabbits. [PERSON] et al. compared casein-fed rabbits with cholesterol-fed rabbits and found that even though with similar high hypercholesterolemia (\(\approx\)500 mg/dL), casein-fed rabbits developed significantly less aortic atherosclerosis than cholesterol-fed rabbits [17].
# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
###### Abstract  
Automated segmentation of diabetic retinopathy (DR) lesions is crucial for assessing DR severity and diagnosis. Most previous segmentation methods overlook the detrimental impact of texture information bias, resulting in suboptimal segmentation results. Additionally, the role of lesion shape is not thoroughly considered. In this paper, we propose a lightweight frequency recalibration network (LFRC-Net) for simultaneous multi-lesion DR segmentation, which integrates a frequency recalibration module into the bottleneck layers of the encoder to analyze texture information and shape features together. The module utilizes a Gaussian pyramid to generate features at different scales, constructs a Laplacian pyramid using a difference of Gaussian filter, and then analyzes object features in different frequency domains with the Laplacian pyramid. The high-frequency component handles texture information, while the low-frequency area focuses on learning the shape features of DR lesions. By adaptively recalibrating these frequency representations, our method can differentiate the objects of interest. In the decoder, we introduce a residual attention module (RAM) to enhance lesion feature extraction and efficiently suppress irrelevant information. We evaluate the proposed model's segmentation performance on two public datasets, IDRiD and DDR, and a private dataset, an ultra-wide-field fundus images dataset. Extensive comparative experiments and ablation studies are conducted across multiple datasets. With minimal model parameters, our approach achieves an mAP_PR of 60.51%, 34.83%, and 14.35% for the segmentation of EX, HE, and MA on the DDR dataset and also obtains excellent results for EX and SE on the IDRiD dataset, which validates the effectiveness of our network.  
diabetic retinopathy; segmentation; LFRC-Net; RAM +
Footnote †: journal: Applied Sciences
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 4 Error Recovery in FTT-CAN
### Error Detection and Retransmission Scheduling Mechanisms  
In FTT-CAN networks, the Master node is responsible for all the scheduling decisions concerning the TT traffic. Moreover, CAN is a broadcast bus, thus all of the nodes are potential listeners of all messages. The combination of these two characteristics enables the deployment of a simple but effective error detection mechanism. The Master is configured in promiscuous mode and, after sending the TM, listens to the bus, identifying the messages actually transmitted, as shown in Figure 3.  
The \"Bus Error Detector\" block compares the list of scheduled messages with the messages received in that EC, thus identifying eventual errors and omissions. The IDs of such messages are then put in the \"Error Server Queue\". At the end of the EC the Master schedules the traffic for the next EC, when considering both the active TT messages and the messages affected by errors, contained in the \"Error Server Queue\", as shown in Figure 3. Any server policy can be used to manage the error queue, provided that it is compatible with the traffic scheduler. In the remainder of this section, we assume that the server has maximum priority, to maximize responsiveness to errors, and that it has enough  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment** & **BER** \\ \hline Benign & \(3.0\times 10^{-11}\) \\ Normal & \(3.1\times 10^{-9}\) \\ Aggressive & \(2.6\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bit Error Rate (BER) measurements in Controller Area Network (CAN) [18].  
capacity to accommodate all the retransmission requests with a desired probability. Server design is addressed in the end of the section.  
In FTT-CAN, message's periods and deadlines are expressed as integer multiples of LEC. As such, LEC is obtained by calculating the Greatest Common Divisor of these message attributes. However, as the rescheduling mechanism herein proposed implies that message retransmissions are possible, at best, in the EC that follows the error, LEC has to be halved when it matches the period or deadline of the fastest(s) message(s).
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 1 Introduction
In a junction area, the location and direction of a vehicle are essential when the vehicles' signal coverage extends to a junction area because the selected Next Forwarding Vehicle (NFV) might change its direction of navigation, which could lead to a video packet drop, thereby affecting the quality of the video streaming. Therefore, there is a need to explore the characteristics of the junction area including vehicles that have exited the junction, vehicles in the junction and vehicles before the junction. For the video data, the video frames forwarded are split into three different frames, namely the I-frame, P-frame, and B-frame, which are forwarded via a multipath setting. The I-frames are forwarded through a dedicated path, while both P and B frames are forwarded through another path. This idea enables a higher priority for I-frames, which are the most important frames as they interpret both P and B frames. In this paper, we proposed a Junction-Aware scheme for Multipath Video Streaming (JA-MVS) considering different points at the junction area in order to avoid or minimize video packet error or drop. Further, mathematical formulations have been adapted to estimate the suitability of a node for data packet delivery. Precisely, the contributions of this study are highlighted as follows:  
1. An enhanced vehicle selection considering the different points in the junction area in order to minimize packet drop due to changes of vehicle direction in the junction area. 2. Improved vehicle selection based on link quality calculation, considering the signal to interference plus noise ratio (SINR), in order not to select vehicles with high noise due to obstructing objects in the urban environment. 3. The simulation and performance evaluation of the proposed scheme. The remaining portions of the paper are organized as follows. In Section 2, a comprehensive review of the related literature is discussed. Section 3 presents the proposed JA-MVS scheme. Section 4 presents the implementation and simulation results with their analysis; and finally, Section 5 concludes the study. The related works based on video streaming in vehicular communication considering regular street and junction area video data forwarding are discussed in the next section.
# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Discussion  
Based on the analysis of the related works, as organized in the previous subsections and the related tables, a number of findings can be summarized in the following lines:  
* Most of the related works examined in this review paper concern ontology-based feature selection for text document classification, with the majority of them being Web-related.
* Most of the approaches utilize generic lexicons (either just the lexicon or in combination with domain ontologies), with the majority of them utilizing WordNet.
* For the task of feature selection, most of the approaches are based on the TFIDF method and filtering.
* SVM is the most common classification method, with KNN to follow.
# The Pin Structure Design of LaVai-1.5-1400 Microwave Vertical Tubular Furnace Based on COMSOL
## 1 Introduction
According to the principle of micro-perturbation on the cavity wall, when a certain part of the cavity wall is pushed inward (decreasing the overall volume), if the magnetic field intensity at the perturbed location is high, the resonant frequency of the resonant cavity will increase. Conversely, if the electric field intensity is high, the resonant frequency will decrease. Therefore, we can adjust the structure of the pins to reduce the volume of the resonant cavity, create micro-perturbations, and affect the resonant state of the cavity. In this study, the pin structure design of the LaVAi-1.5-1400 microwave vertical tubular furnace was based on COMSOL Multiphysics, which is a general engineering simulation software platform. COMSOL Multiphysics is based on the finite element method to realize the simulation of real physical phenomena by solving partial differential equations (single field) or systems of partial differential equations (multi-field), and researchers have used COMSOL Multiphysics to simulate the microwave heating process of charcoal [19] and graphite [20]. The information obtained from this study, such as the electric field distribution within the microwave heating cavity and the temperature of the composite material, helps to further understand the microwave heating process of Al\({}_{2}\)O\({}_{3}\)/SiC composite materials. The completed pin structure design can effectively improve the microwave coupling of Al\({}_{2}\)O\({}_{3}\)/SiC composite materials, potentially reducing the actual heating time and economic costs.
## 3 Experiments
### Evaluation Metrics  
To comprehensively assess the performance of the proposed model in image reconstruction, uncertainty quantification, and DEM super-resolution quality evaluation, we employ a series of metrics that capture both quantitative and qualitative performance. For reconstruction accuracy, PSNR and SSIM are used. PSNR quantifies pixel-wise error between the reconstructed and original images via the Mean Squared Error (MSE), while SSIM preserves local structural features and curvature information to ensure visual consistency. The combination of these metrics effectively evaluates both numerical error and structural fidelity. The relevant formulas are defined as follows:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|I_{i}-K_{i}\right| \tag{17}\]  
\[PSNR=10 log_{10}(\frac{1^{2}}{MSE}) \tag{18}\]  
where \(I_{i}\) represents the \(i\)-th pixel value of the original image, \(K_{i}\) is the corresponding pixel value of the reconstructed image, and N is the total number of pixels.  
The quality assessment of DEM super-resolution integrates rigorous geometric validation and probabilistic uncertainty analysis. The RMSE and MAE systematically evaluate elevation reconstruction fidelity, ensuring numerical accuracy in terrain feature recovery while maintaining geomorphological authenticity. Concurrently, a tripartite uncertainty quantification framework--comprising predictive variance (Var), information entropy (H), and expected calibration error (ECE) [53]--objectively characterizes model reliability. Specifically, to evaluate model sensitivity under varying input conditions, we compute the pixel-wise variance across Monte Carlo dropout samples:  
\[Var=\frac{1}{N-1}\sum_{i=1}^{1}\left(x_{i}-\mu\right)^{2} \tag{19}\]  
Here, \(N\) represents the total number of pixels, and \(x_{i}\) is the observed value of the \(i\)-th data point. \(\mu\) is the overall mean.  
The Shannon entropy, H, quantifies total uncertainty by measuring information content in prediction distributions:  
\[H=-\sum_{i}p_{i}log(p_{i}) \tag{20}\]  
where \(P_{i}\) is the \(i\)-th prediction, while \(\mu\) denotes the mean of all predictions. \(P_{i}\) is the \(i\)-th probability value in the predictive distribution.  
ECE assesses confidence-accuracy alignment through a bin-wise comparison:  
\[ECE=\sum_{m=1}^{M}\frac{\left|\begin{array}{c}B_{m}\\ N\end{array}\right|\;acc(B_{m})-conf(B_{m})\;\mid \tag{21}\]  
where \(B_{m}\) represents the m-th probability bin, with \(\left|B_{m}\right|\) indicating the number of samples in that bin; \(acc(B_{m})\) represents the accuracy within the bin; and \(conf(B_{m})\) represents the average confidence of samples in that bin.  
The comprehensive application of these metrics not only provides a clear and complete evaluation of the model performance in complex tasks but also significantly enhances the credibility and practical applicability of the research findings.
# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
## 2 Materials and Methods  
### Algorithm Framework  
The qualitative imaging law of the riverine water region in an image is the basis of the algorithm designed in this paper. The overall flowchart of the proposed algorithm is shown in Figure 1.  
First of all, the input image needs to be pre-processed, including image down-sampling and image blurring operation. The pre-processing operations will be discussed in Section 3.1. Secondly, the improved LBP feature and local hue variance are calculated in parallel. Then the water region with and without shadow are both obtained by threshold method. The two parts are fused as the major water region. Finally, the image morphological operation is carried out on the candidate region of the water region. After obtaining the results of the morphological processing, the largest connected domain is taken as the final water region. Judging the maximum connected domain is also an important task. It is based on the common sense that the water area often occupies a main and large part of the image, which helps to eliminate the pseudo-water patches whose features are similar to those of water patches.