esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Load-Displacement Relationships and Initial Stiffness  
Table 2 presents the experimental results, and Figure 10 illustrates the load-displacement curves for each specimen. Specimens retrofitted with modularized steel plates showed an average maximum load of 262.6 kN, about 1.3 times the maximum load of the non-retrofitted concrete beam, CB. The initial stiffness of CB was 8.87 kN/mm, while the retrofitted specimens had an average stiffness of 10.3 kN/mm. These data suggest that the modularized steel plate significantly enhances the stiffness of the pre-existing RC beam, likely due to the added section between the steel plate and existing beam.  
In terms of steel plate connectivity, MSP-NC, without interconnected steel plates, reached a maximum load of 247.65 kN. On the other hand, MSP-C, with bolted steel plate connections, achieved a maximum load of 311.95 kN. This indicates that operation of the modularized steel plate as a cohesive unit improves its load capacity by about 25% compared to when it operates in isolation. The initial stiffness also exhibited an increase of approximately 21%. The performance based on module size was analyzed using MSP-NC  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Name** & \(\mathbf{P_{u}}\) **(kN)** & \(\mathbf{\delta_{u}}\) **(mm)** & \(\mathbf{K_{i}}\) **(kN/mm)** \\ \hline \(1\) & CB & 204.40 & 40.39 & 8.87 \\ \hline \(2\) & MSP-NC & 247.65 & 82.33 & 9.74 \\ \hline \(3\) & MSP-C & 311.95 & 116.27 & 11.86 \\ \hline \(4\) & MSP-L & 228.24 & 78.65 & 9.37 \\ \hline \(P_{u}\), maximum load of each specimen; \(\delta_{u}\), displacement when maximum load is applied; \(K_{i}\), initial stiffness. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the test results.  
Figure 8: Failure after testing for (**a**) MSP-NC, (**b**) MSP-C, and (**c**) MSP-L.  
Figure 9: Modularized steel plates after testing: (**a**) side, (**b**) bottom (MSP-C), and (**c**) bottom (MSP-L).  
and MSP-L. MSP-L, which was loaded at the steel plate connection, had a maximum load of 228.24 kN, about 85% of the maximum load of MSP-NC where the load was applied within a single module. This suggests that the performance of the modularized steel plate is influenced by the size of the module and the position of load application. Furthermore, even without specific connections between steel plates, optimizing module dimensions based on the load distribution of the RC beam can lead to more highly integrated behavior.
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 6 New Scheme of DDS Protocol  
By introducing attackers to conduct security assessment of the protocol, it is found that there are security vulnerabilities in the protocol for tampering and replay attack. In order to ensure fairness and security, the establishment of the shared key must be jointly participated in by both parties. However, in the original scheme, the positions of the  
Figure 12: Attacker model.  
Figure 13: Identity authentication result.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Original Model** & **REY-ATK** & **TAR-ATK** & **SPF-ATK** & **Original Attacker Model** \\ \hline State Space Nodes & 420 & 5830 & 680 & 420 & 10,380 \\ State Space Arcs & 1056 & 21,319 & 1854 & 1056 & 39,504 \\ Strongly & 420 & 5830 & 680 & 420 & 10,380 \\ Connected Nodes & & & & & \\ Strongly & 1056 & 21,319 & 1854 & 1056 & 39,504 \\ Connected Arcs & 1 & 2 & 2 & 1 & 6 \\ Dead Markings & 0 & 0 & 0 & 0 & 0 \\ Live transition & 0 & 0 & 0 & 0 \\ instances & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Original and attacker state space reports.  
two communicating parties are not equal, that is, the shared key is only generated by the communicating party M, and the other party S is only passively accepted, and does not play its due role in the key-establishment process. At this time, if the attacker has intercepted the shared key, S will not be able to determine whether the shared key is safe. If the key is still used to derive other related keys or perform encryption and decryption, the attacker will obtain the content of the message, resulting in information leakage. In addition, the original protocol lacks an effective method to verify key consistency, so all information generated using this shared key cannot guarantee confidentiality and security.  
In response to the security assessment results, we introduce timestamps into the original protocol and set time thresholds to ensure that data is securely verified. In addition, due to the lack of message integrity, consistency and fairness in the original protocol, the two communicating parties cannot determine whether the other party receives the key message safely. Therefore, we changed the method of generating the shared key in the original protocol, increased the key verification process and added a counter to achieve session integrity.
## 2 Related Work
### Single Object Tracking Model Based on Transformer  
Transformer stands out in object tracking after [PERSON] et al. [40] formally introduced the Transformer model into computer vision, especially in the field of human pose estimation [41] or head posed estimation [42].  
SOT models based on Transformers can be divided into two- and one-stream frameworks [19]. The basic difference lies in how to carry out feature extraction and relation modeling on the template image and the search region image:  
1. Two-stream framework. This framework first inputs the template and the search region into the backbone of the model and shares the weight. It then concatenates the output results to feed into the Transformer. Finally, the location of the object is predicted by classification, regression, and other methods. In recent years, [PERSON] et al. [43] proposed TransT, which fuses iterative features through stacked self-concern layers and cross-concern layers. The STARK [21] model implemented by [PERSON] et al. connects a new template with the search region in a way that automatically updates template images. [PERSON] et al. [44] proposed SwinTrack based on the total attention mechanism instead of using CNN and other neural networks. These algorithms have satisfactory tracking accuracy, but their inference efficiency is not very fast because of heavy relation modeling.
2. One-stream framework. In this framework, the template and the search region are concatenated before they are input into the backbone, and the subsequent process is similar to that of the two-stream framework. A typical example is the MixFormer [18] developed by [PERSON] et al., which introduces a mixed attention module to build an information interaction channel between the template-search image pairs. The OSTrack [19] proposed by [PERSON] et al. connects the template and the search region to bidirectional information flows to combine feature learning with interaction. [PERSON] et al. [45] built the SimTrack, which is a simplified tracking model using the Transformer as a backbone for relation modeling and feature extraction. These algorithms achieve not only high tracking accuracy but also fast inference speed, thereby balancing between performance and speed.  
However, regardless of the two-stream framework or one-stream framework, most single object tracking methods based on Transformer focuses on enhancing the performance by improving the relation modeling and feature extraction capabilities and lack targeted solutions to common object tracking cases such as object occlusion and deformation [29]. When the object is obscured or changes its appearance, the effective features of the object itself are extremely limited [30]. Target out-of-view is a special case of target disappearing, so it is difficult to re-identify the target in a short time when it reappears.
_applied sciences_  
_Article_ Dark Current Measurement and Noise Correction Method for LWIR QWIP Detection System Based on Focal-Plane Temperature_  
**[PERSON] 1,2**,2**, **[PERSON] 1,2**, **[PERSON] 1,2**, **[PERSON] 1,2**, **[PERSON] 1, **[PERSON] 2,3,* and [PERSON] 1,2,**  
* **Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 20083, China; [EMAIL_ADDRESS] (H.D.)**
* **Key Laboratory of Infrared Imaging Materials and Detectors, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 20083, China**
* **Correspondence: [EMAIL_ADDRESS] (J.X.); [EMAIL_ADDRESS] (D.S.)**  
**Abstract: The performance of long-wave infrared (LWIR) quantum well (QWIP) detection systems is seriously affected by the dark current of the detectors. Tiny variations in the focal-plane temperature of the devices cause fluctuations in the dark current, which in turn generate temporal noise. It is difficult to measure the dark current accurately after the detector assembly is packaged. To address the above problems, a QWIP dark current measurement method based on focal-plane temperature is proposed, as well as a method to reduce dark current noise. First, the response model of the LWIR QWIP detection system was established, and the dark current model was introduced. Then, the detection system components were introduced, chiller calibration experiments were carried out, and the dark current values of the QWIP at different temperatures were measured by combining the system design and parameters. Next, the dark current noise correction method was proposed, the target data were collected, and experiments were carried out to correct them. Finally, after the calculation, the temporal noise was reduced by 57.69% after the correction, which is proof of a significant effect. This method can obtain the real-time dark current value by collecting the focal-plane temperature data, and reduce the dark current temporal noise (difficult to eliminate using conventional methods), which is beneficial for promoting the application of QWIPs in LWIR remote sensing detection.**  
**Keywords: LWIR; dark current; noise correction; QWIP; temperature**
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods
### Jamming Detection and Mitigation Strategy  
Considering the risk of jamming attacks in wireless IoT networks, one of the CC1352P sensors is designated as a jamming detection node. Based on the methodology described in our paper, this sensor continuously monitors key network metrics such as retransmission rates, signal strength variations, and packet delivery success rates. When an anomalous pattern indicative of jamming is detected--such as an excessive number of failed transmissions or abrupt fluctuations in received signal strength indicator (RSSI) values--the detection node signals the gateway to initiate a mitigation protocol. To detect and mitigate jamming attacks within the proposed experimental network in the university campus, one of the CC1352P sensors is designated as a jamming detection node. This sensor continuously monitors key network metrics, such as packet retransmission rates, variations in RSSI, and abnormal energy consumption patterns, to identify potential jamming zones. Based on our methodology, detection is based on an energy model that evaluates the sensor nodes' total energy consumption, including transmission, reception, mode switching, and contention resolution (CSMA/CA). If a significant deviation in energy usage is detected--such as excessive retransmissions due to interference--the node reports an anomaly to the Laird Sentrius Gateway.  
Upon detecting a jamming event, the gateway initiates a mitigation strategy by dynamically reconfiguring the network. If LoRa communication is disrupted, affected sensors automatically switch to BLE, leveraging the short-range connectivity of the LPSTK-CC1352R sensors to relay data to the gateway. Conversely, if BLE channels are jammed, the system prioritizes LoRa for long-range transmission, bypassing localized interference sources. Additionally, a network resilience mechanism is activated, where unaffected sensors in the vicinity of the jammed area are designated as relay nodes, rerouting messages through alternative paths to ensure data integrity.  
Energy-aware routing plays a crucial role in maintaining network efficiency during jamming events. Using the proposed energy model, the network dynamically adjusts node operation modes. Low-energy nodes in jammed regions transition to passive mode to conserve power, while high-energy nodes take over retransmission tasks, ensuring continuous data flow without excessive battery depletion. This adaptive strategy enhances the robustness of the wireless sensor network, maintaining reliable multi-protocol communication across the heterogeneous LoRa-BLE architecture deployed in the university campus environment.
# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular Using Machine Learning
## 3 Results
### Assessment of Performance Level of Automated Segmentation of the Retinal Vascular Leakage Area  
The retinal vascular leakage areas in six patients before treatment were manually or automatically segmented. The mean precision was 0.434 \(\pm\) 0.164 (0.270-0.652), the mean recall was 0.529 \(\pm\) 0.147 (0.340-0.767), and the mean Dice coefficient was 0.467 \(\pm\) 0.138 (0.301-0.664). Figure 3 demonstrates a representative comparison between the manual and automatic segmented vascular leakage area.  
Figure 2: Comparison of manual and automated segmentation of retinal vessels in a representative case ([PERSON]’s uveitis). Two FA images (early-phase and late-phase UWF FA images) of six patients before treatment were used for segmentation of retinal vessels. True positive: white. False negative: blue. False positive: red.
## 3 Results
### Classification of Perceived Pain Intensity Levels
#### 3.6.2 Classifications Involving Intensity Level 5  
Classifications involving intensity level 5 (such as comparisons between levels 5 and 6, 5, and 7, or the three-level classification of levels 5, 6, and 7) were conducted using the same methodology applied for classifying intensities 6 and 7. The limited sample size of 17 participants for intensity level 5 posed significant challenges in achieving robust outcomes, despite employing SMOTE-based oversampling and repeated nested CV. Consequently, the results for intensity level 5 are considered supplementary and are detailed in the Supplementary Materials. Below is a brief summary of these findings.  
Similarly to the classification of levels 6 and 7, we performed a PFI analysis to identify important features, selecting the three top-ranked features from the PHY_12 and PHY_12 + VAL_ARO feature sets for each classification task (Figures S7-S9). In the classification tasks involving intensity level 5, SCR, SCL, PTT, and SDNN were identified as the top-ranked features.  
Next, we performed classification tasks involving intensity level 5 using three input datasets: VAL_ARO, PHY_Sel, and Combined_Sel. In the two-class tasks involving levels 5 and 6, and the three-class task of 5 vs. 6 vs. 7, combining valence-arousal scores with physiological signals, such as PTT and SCL, consistently outperformed the other datasets (Tables S3 and S4, Figures S10 and S11). Confusion matrices demonstrate that integrating self-ratings with physiological metrics reduces misclassification rates. The highest F1 score in the 5 vs. 6 task was 0.705, indicating a promising performance level. In contrast, the three-class classification achieved a maximum F1 score of 0.506, with level 6 posing particular classification challenges.  
However, in the 5 vs. 7 task, using valence-arousal scores alone achieved the highest performance: approximately 80% and >90% for levels 5 and 7 (Table S5, Figure S12). These findings suggest that physiological data add limited value for well-separated intensity levels. However, the small sample size for level 5 highlights the need for cautious interpretation and further validation using larger datasets. We observed that, unlike in the 6 vs. 7 classification, tasks involving intensity 5 produced a training-test F1 gap exceeding 0.1 when PHY_Sel was used. In contrast, VAL_ARO and Combined_Sel maintained differences below 0.1, with most \(<\) 0.05.
## 2 Related Work
The K-means clustering algorithm employed the elbow method to intelligently predict the number of clusters in extensive applications. Eventually. Heuristics were applied to achieve the optimal cost for real benchmark applications and synthetic instances. The authors of [28] presented RAMAN, a method inspired by Reinforcement Learning (RL), for 2D NoC-based application mapping. RAMAN is an enhanced Q-learning algorithm influenced by RL that aims to achieve mapping solutions with the lowest possible and optimized communication cost. The results of this method show tremendous potential in terms of lower complexity and cost while tackling application mapping problems. In [29], linear programming was employed to mathematically model the mapping problem in NoC. Various constraints related to communication capacity and power budget were incorporated. Finally, simulated annealing integrated with GA was implemented to take into account and consider the constraints while finding the optimum solution. The run-time application mapping technique presented in [30] aimed to balance the load on the overall network when implementing NoC for a Cube-Tree Hybrid (CTH) topology. The authors exploited the low network diameter of this topology with its high scalability, resulting in reduced mapping overhead. A significant reduction in execution time was observed during experimentation. A hybrid task mapping HyDra was proposed in [31] that incorporates design time mapping and runtime reconfiguration with the aim of reducing communication energy. At design time, multiple mapping solutions are produced to reduce latency and energy. As the applications arrive at runtime, the design time mappings are either used according to the applications or reconfigured based on the requirements. While many such algorithms have been developed, they may suffer from suboptimal performance along with inefficient convergence and complexity. To tackle these challenges, the authors of [32] used Grey Wolf optimization (GWO), which starts with cluster-based initial mapping followed by a modified GWO heuristic algorithm with polynomial regression. The modified algorithm enhances runtime efficiency and optimizes the overall mapping quality. In [33], the authors discussed various real-time application mapping techniques for complex multicore platforms. They categorized these techniques by emphasizing their optimization goals, such as communication cost and energy consumption in NoC-based systems. Future challenges, trends, and simulation tools in this area were presented as well. Application mapping in both 2D and 3D remains a complex challenge that requires further development of efficient algorithms. To address the outstanding challenges, a neural mapping model with a reinforcement learning (RL) approach (NeurMap3D) was presented in [34] to develop application-specific 3D NoCs. In addition, a neural congestion-aware mechanism was presented to address application mapping issues via placement and mapping and to incorporate TSV placement and load balancing in NoC.
## 4 Comparison Results
### Analyzing Local Metrics on Real-World Datasets
#### 4.3.5 Distance to the Nearest Training Data  
The distance to the nearest data point \(N\) (Equation (7)) indicates how far the inverse projection for a given pixel is from the actual data distribution. If an inverse projection is significantly far from this data distribution, t likely corresponds to a point where the classifier will have generalization difficulties. Ideally, a decision map should not contain points that are far away from the actual training or testing points, and the pixels close to these points in the map should actually also be close to the respective data points.  
Figure 8 (right) shows the distance \(N\) for the three decision-map methods. Data points are marked as white dots. For DV, the pixels near the data points exhibit low distance values, as expected. However, this pattern is less evident for the DBM and SDBM. In the SDBM representation of the FashionMNIST dataset, a zone on the right-hand side displays  
high distance values, even though a data point cluster is projected there. For the DBM, an entire region in the HAR dataset map also shows very high distance values. Unlike the SDBM's case, this is an NDZ, which also has low \(S\) values and low \(B\) values in the entire zone (see Figrues 7 and 8 (left), respectively). This high-value region, which is considerably distant from the data distribution, indicates that the inverse projections in this zone are unlikely to align with user expectations based on the class labels. This might be due to the square shape of the 2D map. Depending on the distribution of the data, the inverse projection is unlikely to uniformly populate the square region. In this case, the inverse projection has to extrapolate certain pixels that correspond to locations further away from the training data. This scenario underscores the value of the distance \(N\) in offering insights into potential issues.  
Another interesting observation is that the pattern of the \(N\) metric is prone to outliers. For instance, we can see some ripple-like patterns in HAR with the DBM and DV. Although these patterns appear slightly different in each case, they are consistently caused by the presence of outliers. Other NDZs not showing high \(N\) values might be caused by the same issue.  
Furthermore, a closer examination of the data point locations (white dots) reveals that not all the close decision-map pixels correspond to data _samples_ that are close to each other. That is, the 2D distance we can see on the map is not the same as the high-dimensional distance we have in the data space. In other words, decision-map pixels equally (and very) close to data points actually depict points at various distances from such data. This can lead to interpretation problems of the decision maps created by _all_ three methods, more so for the DBM and SDBM, where this pattern is more visible, and less so for DV.