esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Camino Architecture for V2X Communication
Furthermore, it can offer communication redundancy by allowing to transfer messages over multiple technologies in an intelligent way, based on the current environmental characteristics (e.g., link quality, congestion, interference,...). Currently, the selection of the communication path is based on configuration settings. Adding an intelligent selection algorithm could further improve the performance of the communication and can be part of future studies. Moreover, the CAMINO-Core provides the logging service that enables several logging capabilities. It stores information either locally or in a remote database and can log among others transmitted or received messages, triggered events, positioning information, etc. For positioning and timing information, the CAMINO-Core is linked to a GNSS device via the GNSS service. Section 6 provides more information about the logging service.
###### Abstract  
Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and evaluate the roof damage. This study comprised three parts: training a deep learning model, detecting the roof damage using a trained model, and classifying the level of roof damage. The detection object comprised a roof outline, blue tarps, and a completely destroyed roof. The roofs were divided into three categories: without damage, with blue tarps, and completely destroyed. The F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into five levels from levels 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide a certain reference for real-time detection of roof damage after the occurrence of a typhoon.  
deep learning; aerial photo; typhoon faxai; roof damage; detection; classification 2022
## 3 Results
### Numerical Static Flow Characteristics of the Main and Additional Supply Tracts  
The determination of the static flow characteristic in the numerical approach was based on the search for a solution to the flow through the internal volume of both supply tracts of the valve based on the developed 3D model. Based on previous analyses [22], it was decided that a polyhedral mesh would be used to discretize the model in accordance with Section 2.3, which, after observing the results, ensured that the numerical method complied with the experimental method adopted as the standard to a certain degree; however, in certain areas, resulting mainly from the small degree of opening in both supply tracts, differences between the two methods are noticeable.  
The determination of the static flow characteristics based on the results obtained from Ansys Fluent, similarly to the experimental studies, was possible thanks to the fitting of the regression line based on the obtained values with a polynomial function of the 2 nd and 3 rd degree, respectively (Figure 12). The use of a polyhedral mesh ensured the optimal simulation time for each considered degree of opening in both tracts.  
Figure 11: Experimental static flow characteristics: (**a**)—main supply tract from inlet to outlet, (**b**)—main supply tract from outlet to inlet, (**c**)—additional supply tract from inlet to outlet, (**d**)—additional supply tract from outlet to inlet.  
Figure 12: Numerical static flow characteristics: (**a**)—main supply tract from inlet to outlet, (**b**)—main supply tract from outlet to inlet, (**c**)—additional supply tract from inlet to outlet, (**d**)—additional supply tract from outlet to inlet.  
Examples of the pressure distributions in key flow areas for different degrees of opening in both tracts are shown in Figures 13 and 14. In order to correctly present the pressure distribution in the additional tract, it was necessary to use flow sections in two perpendicular planes, in which the supply and outlet connections were located. The colour map in each section was limited by the minimum and maximum value of total pressure.  
The pressure distribution inside the valve chambers shown in Figures 13 and 14 shows the occurrence of local flow accelerations caused by the flow through the narrow channel, which contributes to the formation of local overpressure exceeding the given boundary conditions. The flow through the narrow channel and the Venturi effect also favour the formation of local low pressure areas (negative pressure).  
Figure 13: Pressure distribution of main tract opening: (**a,b**)—0.5 mm; (**c,d**)—1.0 mm; (**e,f**)—1.5 mm.
# Recent Advances in Brillouin Optical Correlation-Domain Reflectometry
## 4 Development of Slope-Assisted BOCDR
### Other Achievements  
In addition to the detection of the extremely short hot spots using the beyond-nominal-resolution effect, a number of remarkable advances have been made in the slope-assisted BOCDR. First, by analyzing the BGS shape, the measurement sensitivity dependencies on the incident power and the spatial resolution were investigated [70]. The sensitivity was shown to be enhanced with higher incident power and/or lower spatial resolution; this result was verified through distributed temperature measurements. Then, we implemented slope-assisted BOCDR using a high-loss POF [71]. Owing to the gradual reduction in the transmitted power along the POF, the measurement sensitivities were found to depend on the sensing position. This unique effect was investigated experimentally, and a correct POF-based distributed measurement was performed by compensating this effect. We also demonstrated loss-insensitive operation by employing a special silica fiber with low bending loss [72]. We measured the coefficients of the power-change dependencies on strain and temperature, and by comparing the distributed strain and temperature measurement results using a standard silica fiber and the special fiber, showed that this configuration offers highly stable loss-insensitive operation. Another advance was made from a more practical viewpoint; we presented an example of diagnosis using a composite structure with carbon fiber-reinforced plastics [73]. A silica SMF was embedded in this composite structure during a vacuum-assisted resin transfer molding process. When the composite structure was bent, the system output agreed well with the actual compressive and tensile strain  
Figure 16: (**a**) Normalized power-change distributions along a POF measured at four temperatures. (**b**) Maximal power-changes plotted as a function of temperature. The dotted line is a linear fit. Reproduced with permission from [61]. Copyright (2016) JSAP.  
Figure 15: (**a**) Normalized power-change distributions along a silica SMF measured at four strains. (**b**) Maximal power-changes plotted as a function of strain. The dotted line is a linear fit. Reproduced with permission from [61]. Copyright (2016) JSAP.  
distributions. We were also able to detect the breakage of the embedded fiber, which indicated that the slope-assisted system is potentially beneficial for structural health monitoring. In addition, very recent advances include the enhancement of the stability and strain sensitivity using polarization-maintaining fibers [74] and the demonstration of the beyond-nominal-resolution effect with a long measurement range of over 10 km.
# Research and Design of the RF Cavity for an 11 MeV Superconducting Cyclotron
###### Abstract  
In contrast to the room temperature cyclotron, the superconducting cyclotron's high operational magnetic field and small extraction radius lead to a magnet design with a reduced radius. This limits the space available for the RF cavity in the 11 MeV superconducting cyclotron, necessitating a more compact RF cavity design. By using the transmission line theory, the complex structure of the quarter-wavelength coaxial cavity can be represented as a microwave circuit. Through relevant theoretical analytical formulas, equivalent circuit parameters can be derived. The resonant frequency of the RF cavity is then determined using the equivalent circuit method. The optimization of the RF cavity structure was achieved by creating a numerical model and conducting finite element numerical calculations on the high-frequency resonant system. The comparative results between the equivalent circuit and numerical calculations indicate that the frequency error remains within 0.1%, validating the compact RF cavity design. A multiple linear regression analysis facilitates the prediction of resonance frequency across various parameter variables. By analyzing the fitting formula, RF cavity machining error requirements are established, ensuring a prediction error within 1%, thus meeting engineering design criteria.  
superconducting cyclotron; compact resonant cavity; microwave equivalent circuit; multiple linear regression 2024 14 3549. [[https://doi.org/10.3390/app14093549](https://doi.org/10.3390/app14093549)]([https://doi.org/10.3390/app14093549](https://doi.org/10.3390/app14093549))
# Gastrointestinall Tumors: Phytochemical and Drug Combinations Targeting the Hallmarks of Cancer
## 2 Anticancer Effects of Phytochemicals Used Alone and in Combination with Anticancer Drugs
### Colon Cancer
#### 2.1.2 Antitumor Properties of Anticancer Drug and Phytochemical Combinations in Colon Cancer
Interestingly, it was demonstrated that treatment of Caco-2 colon cancer cells with 1,25(OH)\({}_{2}\)D3 induced an increase in the expression of p21, p27, E-cadherin, and other adhesion proteins ((Zonula occludens-1 (ZO-1), Zonula occludens-2 (ZO-2), and vinculin)) and promoted the translocation of \(\beta\)-catenin and ZO-1 from the nucleus to the plasma membrane [89]. Regarding in vivo experiments, [PERSON] et al. [90], by using a mouse model of bacteria-driven colitis and colon cancer, when infected with Helicobacter bilis (H. bilis), showed that mice fed high vitamin D diet had a significantly lower incidence of cancer compared with mice fed maintenance diet. These findings further suggest that increased dietary vitamin D is beneficial in preventing inflammation-associated colon cancer through the suppression of inflammatory responses during the initiation of neoplasia or early-stage carcinogenesis [84]. The molecular pathways and molecular targets modulated by the combinations of phytochemicals and anticancer drugs in colon cancer models are reported in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phytochemical-Drug** & **Molecular Mechanisms** & **Molecular Targets** & **References** \\ \multicolumn{2}{c}{**Combinations**} & & \\ \hline Curcumin+5-FU & Resensitizing cancer cells to 5-FU effects & EMT markers & [47] \\ \hline Curcumin+Bevacizumab & Proliferation inhibition & Ki-67 & [48] \\ \hline Curcumin+Capecitabine & Apoptosis induction & NF-kB; STAT3 & [49] \\ \multicolumn{2}{c}{Prolification inhibition} & & \\ \hline Curcumin+Irinotecan & Apoptosis induction & GSTM5; PDI; PRDX4 & [50] \\ \multicolumn{2}{c}{Resensitizing cancer cells to irinotecan effects} & & \\ \hline Curcumin+Irinotecan & ER stress induction & BIP; CHOP & [52] \\ \multicolumn{2}{c}{Apoptosis induction} & & \\ \multicolumn{2}{c}{ROS level increase} & & \\ \hline Curcumin+Dasatinib & Proliferation inhibition & COX-2; Bcl-xL; EGFR & [53] \\ \multicolumn{2}{c}{Apoptosis induction} & & \\ \hline Curcumin+5-FU & Resensitizing cancer cells to 5-FU effects & NF-kB; CXCR4; MMP9 & [54] \\ \hline Hexahydrocurcumin+5-FU & Proliferation inhibition & COX-2 & [55] \\ \hline Resveratrol+5-FU & Apoptosis induction & EMT markers & [56] \\ \multicolumn{2}{c}{Resensitizing cancer cells to 5-FU effects} & & \\ \hline Resveratrol+5-FU & Proliferation inhibition & JNK; p38 MAPK; & [58] \\ \multicolumn{2}{c}{Reduction of migration capacity} & Caspase 3 \\ \multicolumn{2}{c}{Induction of DNA damage} & & \\ \multicolumn{2}{c}{Apoptosis induction} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Colon cancer targets.
## 3 Main Framework, Dataset, and Feature Extraction Overview
### Dataset Used in Numerical Experiments  
In this study, we utilize the artifact-free EEG database provided by the University of Bonn, which is freely available online [17]. This database has been widely used for  
Figure 6: Example of MFDM for time series with \(Q=4\) in non-divisible case. The different color represents two stages of related algorithm, i.e., stage 1: connecting the node to its neighbours (lines in green); stage 2: connecting nodes in same divided aera (lines in blue).  
Figure 7: Comprehensive framework flowchart of our algorithms.  
EEG feature extraction and classification in the literature [18]. Our dataset comprises five distinct sets, labeled \(A\), \(B\), \(C\), \(D\), and \(E\). Each set contains 100 single-channel EEG segments, each segment lasting 23.6 s and sampled at a rate of 173.61\(H_{z}\). Sets \(A\) and \(B\) consist of surface EEG recordings from healthy, awake volunteers, with Set \(A\) containing recordings taken while the volunteers had their eyes open and Set \(B\) containing recordings taken with their eyes closed. Sets \(C\) and \(D\) contain intracranial EEG signals from epileptic patients recorded during seizure-free intervals, with Set C containing recordings from the hippocampal formation in the opposite hemisphere of the brain and Set D containing recordings from the epileptogenic zone. Set E includes intracranial EEG signals recorded at sites exhibiting ictal activity, representing seizure activity. Figure 8 illustrates sample EEG segments from sets \(A\), \(B\), \(C\), \(D\), and \(E\). There are five data subsets, each containing 100 brain telecom number segments and 4097 data points. The data have been filtered according to related literature published in the last several decades (i.e., we chose to filter broadband in 0.53-40\(H_{z}\). This range covers most of the EEG frequency components associated with cognitive activity, such as \(\alpha\) waves (8-12\(H_{z}\)) and \(\beta\) waves (13-30\(H_{z}\))), and the specific state diagram of different types of EEG signals can be seen in Figure 8.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 2 Related Work
### Random Key Distribution  
In the schemes based on random key distribution, a node stores a ring of \(r\) starting keys. The rings are randomly picked from a larger pool of \(p\) starting keys. A couple of nodes can only communicate if they share a starting key. If an adversary compromises a node, he/she only obtains a part of the starting keys used in the network. The drawbacks due to a compromised node are limited. However, the connectivity in the network is reduced, since two nodes may not share any common key and be unable to communicate. Schemes based on the random key distribution can be applied both to mobile and to static networks. This kind of approach has been extended even to vehicular ad hoc networks [21].  
In [22], [PERSON] and [PERSON] presented an approach hereafter called \(EG\). Before deployment, a pool of \(p\) keys is produced and, for each node, a ring of \(r\) keys is randomly selected from the pool and distributed to that node. After deployment, every node verifies if it shares keys with the nodes within its communication range. The configuration of \(p\) and \(r\) affects the performance of the network. If the magnitude of \(r\) and of \(p\) is close, the nodes can establish common keys with the majority of the other nodes. However, if a node is compromised, a large part of the WSN is also compromised. If \(r\) is too small with respect to \(p\), the nodes cannot communicate with all the nodes in their communication range, but an adversary that has compromised a node can achieve a low percentage of pairwise keys.  
An evolution of EG is \(q\)-composite random key predistribution [23]. With this scheme, a link between a pair of nodes can be established only if they have at least \(q\) shared keys. Their pairwise key is computed by a hash function executed on the concatenation of the shared keys. In case of a small quantity of captured nodes, the \(q\)-composite approach provides a higher level of security than EG. However, EG is less vulnerable when several nodes have been compromised. According to the analysis proposed in [23], \(q\) is set to 2. In the following, \(q\)-composite is called 2C.  
In [24], the authors propose \(q\)-\(s\)-Composite, a further evolution of EG and \(q\)-composite. In this scheme there are a minimum (\(q\)) and a maximum (\(s\)) quantity of initial keys used to establishment a pairwise key. A new organization of the storage allows to increase the quantity of initial keys per node with the same memory overhead. According to the analysis proposed in [24], \(q\) is set to 1 and \(s\) to 5. In the following, the \(q\)-\(s\)-Composite is called 1-5C.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 2 Related Work
The contribution can be summarized as follows. Firstly, we establish a re-encoding model in the multi-hop D2D communication network, which can allocate the computing overhead of the source node to other destination nodes to achieve load balancing. Secondly, we design some algorithms for D2D networks, which can effectively improve the Successful Decoding Rate (SDR). Finally, we built a testbed to evaluate the efficiency of the proposed RLNC-based video transmission scheme. The experimental results show that in addition to guaranteeing the video transmission quality, the proposed scheme also has lower coding latency and balanced computational overhead.
# Filter Pruning via Attention Consistency on Feature Maps
## 5 Ablation Study  
The detailed ablation experiments are performed to demonstrate the effectiveness of pruning the feature maps with low attention consistency. For simplicity, only the performance of ResNet-56 on the CIFAR-10 dataset is discussed. The following four different comparison methods at the same pruning ratio are proposed to prove that the filter pruned by FPAC is reasonable: (1) Baseline: the original ResNet-56 network and it maintains the original number of filters; (2) Edge: filters that generate feature maps with lower and higher attention consistency are pruned; (3) Random: with the same number of filters removed from each layer of FPAC, several filters are selected randomly to prune; (4) Reverse: filters that generate feature maps with higher attention consistency are pruned. In all these experiments, the pruning rate is set to 47.4%, and the parameters rate is set to 42.8%. Table 3 presents the corresponding top-1 accuracy of FPAC for the comparison methods. All five methods present higher classification accuracy. It shows that the filters of ResNet-56 are indeed redundant on the CIFAR-10 dataset. Due to the existence of redundancy, after fine-tuning, there is no significant decrease in these methods. It is seen that the performance of FPAC surpasses the baseline and all other comparison methods. It is also shown that FPAC retains important filters. Note that although part of the feature maps with lower or higher attention consistency is removed, the retained feature maps with lower attention consistency contain rich information. It is also seen that Edge performs well and Random is also better than Reverse. The latter is because significant filters are pruned by Reverse which further demonstrates the effectiveness of FPAC.
###### Abstract  
Magnetometers are important sensors with applications in the attitude determination and control systems of satellites. CubeSats have certain limitations related to power, mass, and volume. Due to this, CubeSat magnetometers are not separated from other electrical circuits inside the satellite. Thus, it is important to calibrate the magnetometer, simulating operating conditions while the satellite is running before the launch. However, due to the limited facilities, not every CubeSat is able to calibrate its magnetometers properly on the ground. This study focuses on the calibration of on-orbit magnetometer data observed by BIRDS-3 CubeSats with a genetic algorithm. High oscillations in the total magnetic field were found in the on-orbit magnetic field data measured by magnetometers inside BIRDS-3 CubeSats. Nine unknowns, scaling factors, non-orthogonal angles, and offsets are identified with the genetic algorithm. This paper discusses the factors that affect the high oscillations in the measured total magnetic field data. For the calibration, we used magnetic field data similar to those of a model magnetic field, as the deviation is smaller. This paper presents the accuracy of determining unknowns using the genetic algorithm, as well as the interchangeability of the answers with additional orbit data from the same satellite. This method can be used in the future to calibrate magnetometers inside CubeSats before or after launch.  
CubeSats; magnetometer; on-orbit data; calibration; genetic algorithm +
Footnote †: journal: Applied Sciences
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 2: What Are the Features of Blockchain That Could Benefit Education?
#### 4.2.4 Transferring Value among Peers  
Blockchain facilitates the reliable transfer of value among participants using tokens, which have different properties (or may not even exist) depending on the type of blockchain used.  
Tokens in the education field are introduced with different purposes. For example, they can be used as a reward or financial bonus for students' academic achievements [15; 36], as reputation or nominal prestige [28], or as learning units EduCTX [39].
## 1 Introduction
To address these gaps, this paper introduces a novel cross-modal behavioral intelligence framework, driven by rough set theory, which uniquely integrates spatiotemporal perception and object semantics, for ship bridge environments. Our approach distinguishes itself from prior work in regard to the following three key aspects:  
(1) Original dataset construction. We present the first publicly available ship crew behavior dataset, the SCA. This dataset comprises 4788 annotated action sequence samples, captured from video surveillance systems within the ship bridge of passenger vessels operated by Zhoushan Haihua Passenger Transport Co., Ltd. During navigation, these vessels maintain a bridge team consisting of one helmsman, and two lookouts. The SCA dataset covers seven behavior categories: sitting, standing, walking, driving, lying, playingon a phone, and smoking. Each behavior sample contains a minimum of eight consecutive frames to ensure adequate temporal context for action recognition. Additionally, we provide a specialized subset of 3400 images, with detailed object-level annotations (humans, phones, cigarettes), to support feature object detection tasks. By combining comprehensive behavior sequences with precise object annotations in authentic maritime operational contexts, the SCA dataset addresses a significant research gap and enables reproducible studies in regard to maritime computer vision applications. (2) Hierarchical recognition architecture with enhanced spatiotemporal modeling. We propose a lightweight framework that innovatively combines ByteTrack multi-object tracking with an improved Temporal Segment Network (TSM). Our novel integration of EfficientNet-B3, as the backbone network for computational efficiency, and Coordinate Attention (CA) mechanisms, for spatiotemporal feature enhancement, achieves a Top-1 accuracy of 82.1%, based on the SCA dataset, surpassing existing methods, in complex ship environments. (3) Rough set-driven decision optimization. Departing from conventional unimodal approaches, we introduce a feature object-assisted method that establishes spatiotemporal association rules between personnel and objects via rough set theory. The proposed joint reasoning engine and multi-source fusion strategy increase the Accuracy of Focused Anomaly Recognition (AFAR) by 7.07% and the Comprehensive Classification Accuracy (CCA) by 2.2% above the baseline methods.