esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 5 Conclusions  
In this work, we introduced a DL technique, namely Mask-RCNN with two backbones, ResNet-50 and DenseNet-41, for precise and automated segmentation of brain tumor regions from MRI images. We obtained better segmentation and classification results for DenseNet-41 based Mask-RCNN as compared to the ResNet-50 network, due to its dense connections which result in more robust image feature calculations. Comparative experimental results show that our proposed method more precisely delineates the tumor region and can serve as a new automated tool for diagnostic purposes. Moreover, as compared to state-of-the-art models, our Custom Mask-RCNN can compute deep features with effective representations of brain tumors. In future, we aim to perform classification along with segmentation of brain tumors using more challenging datasets. We also plan to evaluate the robustness of our Custom Mask-RCNN for other medical image analyses applications such as eye disease detection, finger skin recognition, skin cancer, and COVID detection. Furthermore, we aim to increase training samples and optimize hyper-parameters to further improve the accuracy of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Classification Method** & **Acc (\%)** \\ \hline [PERSON] et al. [66] & GoogLeNet and SVM & 97.10 \\ [PERSON] et al. [67] & VGG19 & 94.82 \\ [PERSON] et al. [68] & CCN based on complex networks & 95.49 \\ [PERSON] et al. [69] & GIST descriptor and ELM & 94.93 \\ BrainMRNet [70] & Attention module, Hypercolumn technique, and Residual block & 97.69 \\ Proposed method & Custom Mask-RCNN & 98.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of our method with other classification techniques.  
**Author Contributions:** Conceptualization, M.M., T.N., M.N.,and A.M.; data curation, M.M., T.N., M.N. and A.M.; formal analysis, J.R., T.M., H.-Y.K., A.H.; funding acquisition, H.-Y.K.; investigation, J.R., H.-Y.K. and A.H.; methodology, T.N., J.R. and M.N.; project administration, T.N., J.R.; resources, A.M.; software, A.M. and M.M.; supervision, J.R.; validation, T.N.; visualization, J.R.; writing--original draft, M.M., T.N., M.N., J.R.; writing--review and editing, M.M., T.N., M.N., A.M., J.R., H.-Y.K., T.M. and A.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable to this article as authors have used publicly available datasets, whose details are included in the \"experimental results and discussions\" section of this article. Please contact the authors for further requests.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
### Open-Source Switches  
Several open-source projects exist, implementing network communication systems and different types of Ethernet switches. This subsection presents these projects and identifies the best solution to use as a starting point for the development of the redundant switch.  
Private Island [14] is a Gigabit Ethernet network communication project developed by Mind Chasers Inc. It implements network communication based on FPGA technology, which is capable of performing different parallel processes and executing operations in the physical layer, like filtering addresses and ports. This implementation can be used for packet processing, being used, for example, in the Internet of Things (IoT) to read sensor values and control motors/actuators.  
NetFPGA [15] is an open-source development platform for both hardware and software implementations; it is available for use by the academic community, allowing not only researchers but also students to build and develop networking systems in hardware [16]. Several boards have been developed, namely NetFPGA Plus, NetFPGA SUME, and NetFPGA 1G, for network and high-speed communication. Both of them can be used to implement a regular Ethernet switch, with the NetFPGA Plus and the NetFPGA SUME boards being the most recent ones. Regarding software implementations, there are two Ethernet switches: the Real-Time Switch [17] and OpenFlow Switch [18]. The Real-Time Switch is an Ethernet switch based on a time-triggered mechanism that has two working modes: soft mode, where it works like a regular Ethernet switch, performing switching through best-effort approximation, and hard mode, where time triggers are defined to  
perform switching. Both modes can be applied simultaneously on the same network. The other implementation is the OpenFlow Switch, which is a switch based on flow tables. These tables have information regarding the priority and destinations of the packets. The switch and controller communicate with each other, and actions are made taking into consideration the values present in the flow tables. This implementation was expanded to the OpenFlow MPLS Switch and became capable of dealing with multiple protocols at once. To allow this, two tuples containing information about the existing protocols were added to the OpenFlow framework.  
ONetSwitch is a platform that started as a crowdfunding project on Kickstarter [19]. The goal was to create an open-source software-defined network (SDN) [20]. Multiple boards, based on FPGAs, were developed: ONetCard, ONetSwitch20, ONetSwitch30, and ONetSwitch45. In addition to these hardware boards, open-source software was also developed; this is how the REF Switch [21] project emerged. The REF Switch is a basic Ethernet switch that works at speeds of 10/100/100 Mbps; it can parse the MAC addresses and EtherType from a packet header and forward packets using a MAC address table that is capable of learning and mapping new addresses. This implementation also has CPU control and communication interfaces.
# Full-Parallax Multiview Generation with High-Speed Wide-Angle Dual-Axis Scanning Optics
## 7 Conclusions  
We implemented a full-parallax multiview system with spherical symmetry and fabricated a high-speed wide-angle dual-axis scanner. This scanner comprises two single-axis scanners, a resonance scanner and galvanometer scanner, connected by high-NA scanning optics. Additionally, we designed the scanning optics to reduce the pincushion error due to mismatch of the common plane of each scanner, such that the image plane is eventually located in the ball lens. The proposed system has a field of view of 40 degrees and generates 400 SVZs, 20 horizontally and 20 vertically with 20 fps. Specifically, when the viewing distance increases, distortion, shown in the experimental results herein, appears. Although the horizontal viewing angle of the proposed system is not 360 degrees, it does not show distortion in the specific viewing distance range. In addition, the proposed system has a capability of changing the SVZ width by adjusting the aperture size in the Fourier domain. In a future study, we will perform experiments to analyze human vision perception according to the change in SVZ width and interval between adjacent SVZs in multiview displays. The WDF analysis on VZ and the proposed system can help evaluate the characteristics of multiview systems.  
Conceptualization, D.H. and J.H.; methodology, D.H. and J.H.; software, G.C.; formal analysis, D.H., S.L. and J.H.; validation, D.H. and G.L.; writing--original draft preparation, D.H.; writing--review and editing, D.H., S.L. and J.H.; supervision, J.H.; All authors have read and agreed to the published version of the manuscript.  
This work was funded by Samsung Research Funding and Incubation Center of Samsung Electronics under Project Number SRFC-IT301052.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Experimental Results
### Results Using the Dataset with Pathologies  
#### 5.1.1 Results without Feature Selection  
In the scenario with pathologies, Table 2 summarizes the results of the algorithms with and without feature selection (No-FS). The average accuracy rate is 0.6953, and the standard deviation is 0.0297, with DT, ADB, BNB, and GNB being below this average. The algorithms with the best results are GB and RFC, with a mean that is close to 0.74.  
Figure 2 shows the impact of the different variables of the RFC model. The age variable is in first place, in the case of younger people, represented by the lowest values (blue), which are mainly found in the left part of the graph, indicating a lower risk of being overweight/obese. In contrast, the higher values of this variable, the older people (red), have a higher risk of suffering from these health problems. In the case of sex, being male has a higher probability of being classified in the overweight/obesity class. Third, the first variable associated with the type of pathology, _apnea_, appears. The variable apnea has all the blue points very close to the zero point, while the red points extend along the positive part of the axis. This indicates that in cases of suffering from this pathology it is likely to suffer from overweight/obesity, but otherwise, it does not have a significant impact on the prediction.
# Layer-Wise Compressive Training for Convolutional Neural Networks
## 5 Results
### Performance
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**CIFAR100**} & \multicolumn{2}{c}{**AlexNet (44.01\%)**} & \multicolumn{2}{c}{**VGG19\({}_{bn}\) (71.95\%)**} & \multicolumn{2}{c}{**ResNet110 (71.14\%)**} \\ \cline{2-6}  & **Accuracy** & **Acc.Loss** & **Accuracy** & **Acc.Loss** & **Accuracy** & **Acc.Loss** \\ \hline OUR & 43.47\% & 0.54\% & 71.62\% & 0.33\% & 70.80\% & 0.34\% \\ \hline Compression Rate & 26.4\(\times\) & & 6.5\(\times\) & & 7.3\(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Validation accuracy results of compressive training on CNNs trained on CIFAR100. For each CNN model, the accuracy loss (_Acc.Loss_) is referred to the baseline accuracy reported in parentheses. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Input Shape**} & \multicolumn{2}{c}{**FP**} & \multicolumn{2}{c}{**CM**} \\ \cline{3-5}  & & **Sparsity** & **Width** & **Sparsity** & **Width** \\  & & **[\%]** & **[Bit]** & **[\%]** & **[Bit]** \\ \hline Conv1 & (16,3,3,3) & 0 & 32 & 41 & 32 \\ Conv2 & (16,16,3,3) & 0 & 32 & 18 & 2 \\ Conv3 & (16,16,3,3) & 0 & 32 & 45 & 32 \\ Conv4 & (16,16,3,3) & 0 & 32 & 23 & 2 \\ Conv5 & (16,16,3,3) & 0 & 32 & 24 & 2 \\ Conv6 & (16,16,3,3) & 0 & 32 & 31 & 2 \\ Conv7 & (16,16,3,3) & 0 & 32 & 37 & 2 \\ Conv8 & (32,16,3,3) & 0 & 32 & 32 & 2 \\ Conv9 & (32,32,3,3) & 0 & 32 & 45 & 2 \\ Conv10 & (32,16,1,1) & 0 & 32 & 29 & 32 \\ Conv11 & (32,32,3,3) & 0 & 32 & 44 & 2 \\ Conv12 & (32,32,3,3) & 0 & 32 & 50 & 2 \\ Conv13 & (32,32,3,3) & 0 & 32 & 53 & 2 \\ Conv14 & (32,32,3,3) & 0 & 32 & 62 & 2 \\ Conv15 & (64,32,3,3) & 0 & 32 & 44 & 2 \\ Conv16 & (64,64,3,3) & 0 & 32 & 58 & 2 \\ Conv17 & (64,32,1,1) & 0 & 32 & 50 & 32 \\ Conv18 & (64,64,3,3) & 0 & 32 & 70 & 2 \\ Conv19 & (64,64,3,3) & 0 & 32 & 87 & 2 \\ Conv20 & (64,64,3,3) & 0 & 32 & 90 & 2 \\ Conv21 & (64,64,3,3) & 0 & 32 & 94 & 2 \\ Fc1 & (10,64) & 0 & 32 & 39 & 32 \\ \hline Final Sparsity & \multicolumn{2}{c}{0.00\%} & \multicolumn{2}{c}{68.16\%} \\ \hline Compression Rate & \multicolumn{2}{c}{- \(\times\)} & \multicolumn{2}{c}{6.1\(\times\)} \\ \hline Accuracy & \multicolumn{2}{c}{93.02\%} & \multicolumn{2}{c}{92.47\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Layer-wise sparsity and width before (Full Precision) and after the compressive greedy technique (Compressed Model) application on ResNet20 architecture (CIFAR10). Considering that convolutional layers have input shape \((n,c_{in},k_{in},k_{w})\), and fully-connected layers have input shape \((n,c_{in})\), where \(k_{h}\) and \(k_{w}\) are respectively the height and width of input planes in pixels, while \(c_{in}\) denotes the number of input channels and \(n\) the batch size.
# RECRP: An Underwater Reliable Energy-Efficient Cross-Layer Routing Protocol +
## 5 Conclusions  
RECRP is an energy-efficient routing protocol, and it is suitable for long-term running applications such as underwater pollution detection and equipment monitoring (where real-time requirement is not desired). RECRP achieves a good performance for several reasons. Without extra hardware, we use RSSI and relative speed as double insurance to estimate the distance conservatively. As a single-copy routing protocol, we are able to achieve the lower overhead without redundant packets. Our max-min method not only dynamically changes power and frequency but also takes the two-hop forwarding capabilities into account to achieve energy efficiency. With the surface-to-bottom routing establishment mechanism, RECRP can handle communication voids. The simulation results demonstrated that RECRP significantly reduced the energy cost, packet loss ratio, and end-to-end delay compared with DBR and HH-VBF.  
Conceptualization, M.Y. and X.W.; Methodology, M.Y. and X.W.; Writing--Original Draft Preparation, M.Y.; Writing--Review & Editing, X.W. and Y.L.; Supervision, J.C., J.L. and X.W.  
This work was supported by the National Natural Science Foundation of China (Grants No.61631008), the Fundamental Research Funds for the Central Universities 2017 TD-18, and the State Key Laboratory of Robotics No.2015-O06.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Vortex Characterization  
As detailed in Section 1, previous computational studies have primarily focused on boundary layer stabilization using free-stream vortices. The Batchelor vortex [13] serves as the model for an idealized free-stream vortex, with its velocity profile defined by  
\[V_{\theta}(r,\theta)=\frac{\Gamma}{2\pi r}\bigg{(}1-\exp(-\frac{r^{2}}{R_{c}^{2} })\bigg{)}. \tag{3}\]  
In this formula, \(V_{\theta}\) [m/s] represents the tangential velocity component, corresponding to the V component in a horizontal profile or the W component in a vertical profile. \(\Gamma\) [m\({}^{2}\)/s] denotes the circulation, and \(R_{c}\) [m] signifies the vortex radius. When analyzing realistic vortices, a method must be developed to compare these realistic vortices with their idealized counterparts. The generated vortex velocity profiles are fitted along the average of horizontal and vertical lines through the vortex center to achieve this comparison using a non-linear least squares method. Although the position of maximum velocity remains consistent across both profiles, there is often a noticeable asymmetry in the vortex, depending on the specific case. This asymmetry arises from the influence of the bottom wall and the presence of the other vortex within the pair. The fitted Batchelor vortex used for comparison is based on the average of both profiles, accounting for these asymmetrical influences.  
To quantify the asymmetry present in a vortex pair, a measure of asymmetry was developed for comparative analysis across different cases. By examining the velocity profiles of vortices in both horizontal and vertical directions, distinct peaks were observed, indicating the maximum or minimum speeds of the vortex in each direction. To assess asymmetry, the ratio of the horizontal to vertical distances between these peaks was calculated, defining the aspect ratio. Although this method primarily considers the horizontal and vertical planes, it provides a sufficient estimation of asymmetry as the primary factor influencing asymmetry is the horizontal (bottom) wall. An aspect ratio of one implies that the peaks of the vortex velocity profiles in the horizontal and vertical directions are equidistant from the vortex center.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 3 Results
### Built Environmental Changes
#### 3.3.3 Impact on Evacuation Conditions  
The impact of fire on evacuation conditions occurs primarily through two mechanisms: the presence of high-temperature zones affecting evacuation environments and the obstruction of escape routes.  
Initially, the centroid distance between the ignition point and the high-temperature zone center gradually increases, accelerating significantly beyond 180 min. For centrally positioned fires, the centroid of high-temperature zones consistently remains closer to the ignition point (approximately 8.06 m at 150 min). At 580 min, the centroid distance for peripheral fires extends to 154.5 m, notably exceeding the central type by 74.56 m (Figure 6d). The perimeter growth of high-temperature zone of central fires is always greater. Consequently, while central fires affect more widely, peripheral fires affect more areas away from ignition.  
The road sections rendered impassable due to fire account for 66.39% of the total, while the length ratio of blocked roads, specifically those of main roads that are wider than 5 m (Figure 6j), reaches 85.36% (Figure 6f). This development parallels the expansion pattern of high-temperature zones, with both scenarios displaying accelerated road blockage ratios beyond 200 min, and central-type fires consistently exhibiting more rapid development (Figure 6d). The centrally situated fires, coupled with their proximity to main roads, affects primary evacuation routes more severely.  
Additionally, the number of dead-end roads, significantly influencing evacuation, steadily rises as the fire expands, accompanied by a concurrent reduction in three-way and four-way intersections, with three-way intersections declining more rapidly after 200 min. Centrally located fires exhibit the highest increase in dead-end roads within 100 m (three roads every 10 min), markedly surpassing the rate of building destruction. Conversely, peripheral fires experience peak growth rates (five roads every 10 min) between 200 and 350 m, culminating in a greater total number of dead-ends (Figure 6g). Thus, evacuation routes progressively diminish in alternative branches and increase in dead-ends over the duration of a fire, with central fires exerting a more immediate impact, while peripheral fires present a gradually intensifying accumulation of effects beyond 300 min.  
The fire-induced damage in historical districts exhibits spatial segmentation and wind-driven characteristics. Centrally originated fires exert a more immediate and direct impact on the built environment, whereas peripherally initiated fires display a pronounced delayed accumulation effect.
# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
###### Abstract  
Researchers encounter substantial challenges in medical image classification, mainly due to limited image resolution and low signal-to-noise ratios. This situation makes it difficult for deep learning algorithms to identify abnormal regions based solely on image content accurately. This paper proposes ResGDANet (Residual Group Dual-Channel Attention Network), an enhanced architecture that builds upon ResGANet by incorporating two novel modules: a Dual-Channel Attention Fusion (DCAF) module and a Retention-Memory Transformer (RMT) module. The DCAF module utilizes a dual-path architecture that integrates global average pooling and max pooling operations, effectively enhancing local feature representation through the fusion of channel-wise and spatial attention mechanisms. The RMT module enhances rotation-invariant feature extraction by integrating the retention mechanism from Retentive Networks and the global context modeling capabilities of Vision Transformers. Extensive experiments on the COVID19-CT and ISIC2018 datasets demonstrate the superiority of ResGDANet, achieving classification accuracies of 83.74% and 81.73% respectively, outperforming state-of-the-art models including ResGANet, GvT, and SENet. Ablation studies and visualization analyses further validate the efficacy of the proposed attention module, showing notable enhancements in feature representation capability and classification accuracy. By introducing a more robust and precise classification framework, this research contributes importantly to the progress in medical image analysis.  
medical image classification; deep learning; attention mechanism; rotation-invariant feature; feature fusion Article
## 1 Introduction  
The landscape of software development is undergoing a rapid transformation, driven by agile methodologies, cloud-native architectures, and continuous delivery pipelines. While these advancements promise increased efficiency and innovation, they concurrently introduce unprecedented security challenges. Traditional security approaches, often implemented as an afterthought or through fragmented, manual processes, are proving inadequate against an increasingly sophisticated and dynamic cyber threat environment [1, 2]. The reactive nature of finding and fixing vulnerabilities late in the development cycle leads to significant costs, delays, and persistent risks [3]. There is a pressing need for a paradigm shift, one that integrates security holistically from inception, fosters continuous vigilance, and leverages intelligent automation to keep pace with evolving threats.  
Current efforts to enhance software security often focus on individual components, such as adopting DevSecOps for faster integration of security, implementing Zero Trust(ZT) principles for stricter access control, or applying risk management frameworks like the NIST Risk Management Framework (RMF) for structured security governance [4; 5; 6]. While each of these methodologies offers distinct benefits, multivocal literature reviews show that their isolated application can lead to gaps, inefficiencies, and a lack of cohesive defense [7]. The true potential for robust and adaptive software security lies not in these individual pillars, but in their synergistic integration, empowered by advanced technologies like Artificial Intelligence (AI) [8].  
This paper introduces the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D), a novel and comprehensive framework designed to address these critical challenges. AZTRM-D unifies DevSecOps, the NIST RMF, and the ZT model, all underpinned and significantly enhanced by AI. The framework fundamentally redefines secure software development by weaving security into every phase of the Secure Software and System Development Life Cycle (S-SDLC). Unlike fragmented solutions, AZTRM-D leverages AI to provide continuous threat intelligence, automate vital security controls, enable real-time system oversight, and facilitate dynamic policy adaptation. This AI-powered automation extends across the entire life cycle, from promoting secure coding practices and identifying vulnerabilities during development to continuous monitoring and threat response in operational environments. The result is a proactive, adaptive, and highly resilient framework that not only mitigates risks but also accelerates the delivery of inherently secure software, preparing organizations for the complexities of the modern digital frontier. The subsequent sections detail the architectural components of AZTRM-D, its operational methodology, and how its integrated approach fortified by AI offers a superior path to achieving enduring software security.