esa-sceva/satcom-chunk-collection huggingface dataset:
# TF-YOLO: An Improved Incremental Network for Real-Time Object Detection
## 2 Related Work  
### Preliminaries on YOLO  
YOLO reframes object detection as a single regression problem, which obtains bounding box coordinates and class probabilities straight from image pixels. As one of other cutting-edge detectors, it has many advantages over the others [20]. Firstly, YOLO is exceedingly fast. On a Titan X GPU, its third version can run at 45 frames per seconds without any batch processing. Secondly, YOLO handles the input as a whole when making decisions [21]. Therefore, the contextual information about classes can be encoded. It is less likely to predict false positives on background. Lastly, YOLO is more applicable to unexpected inputs and new domains, owing to its good generalizable representations.  
The separate components of objects detection are unified into a single network in YOLOv3. At the beginning, the input image is divided into an \(S\times S\) grid. Then \(B\) bounding boxes and confidence score are defined in each grid cell. Each grid cell predicts \(C\) conditional probabilities, denoted as \(\mathrm{P(Class_{i}|Object)}\). If there is an object in the grid, \(\mathrm{P(Object)}=1\). Otherwise, \(\mathrm{P(Object)}=0\). The confidence score here refers to the accuracy the box predicts and the probability the objects are contained, which is defined as \(\mathrm{P(Object)}*\mathrm{IOU_{pred}^{truth}}\). Intersection-Over-Union (IOU), here, refers to the intersection area between the predicted bounding box and ground truth box, representing a fraction ranging from 0 to 1. It is noteworthy that conditional class probability (\(\mathrm{P(Class_{i}|Object)}\)) is quite different from the confidence score (\(\mathrm{P(Object)}*\mathrm{IOU_{pred}^{truth}}\)). The former is predicted in each grid, while the other is predicted in each bounding box [1]. Multiply these values in the test process, the class-specific scores in each box is defined in Equation (1). These scores encode the probability of the object appearing in the box, as well as how well the bounding box fits the object.  
\[\mathrm{P(Class_{i}|Object)}*\mathrm{P(Object)}*\mathrm{IOU_{pred}^{truth}}= \mathrm{P(Class_{i})}*\mathrm{IOU_{pred}^{truth}} \tag{1}\]
# Real-Time Simulator for Dynamic Systems on FPGA
## 4 Development of the Prototype and Results
### Methodology
The process begins with the creation of Simulink files (.mdl), in which the desired system is modeled using blocks from a specialized hardware library. From this model, the automatic generation of HDL code is performed. This HDL code is divided into two paths: one for the creation of an HDL test bench, which allows functional simulation, and the other for the generation of RTL code in HDL (.vhd). In the HDL test bench block, a thorough functional simulation analysis is performed, providing reassurance about the integrity of the generated code. On the other hand, the result obtained from the HDL RTL block is sent to the synthesis flow, where the feasibility of synthesis is determined (indicated as 'Synthesis'). In this synthesis flow, documents such as the Netlist are generated, describing the usage and interconnection of components, and the Timing Simulation is performed to validate the temporal performance of the project. If the synthesis is successful, a binary file (.bit) is generated, which can be transferred to the FPGA, allowing implementation on the hardware device. After downloading the.bit file to the FPGA, the device can be tested directly in the physical environment, where the input and output signals can be monitored to ensure that the FPGA's behavior aligns with the original project expectations. Real-time tests can be conducted using debugging and signal analysis tools, such as oscilloscopes and logic analyzers, to ensure that the system functions correctly under real operating conditions. Figure 19: Design workflow for FPGA project development using MATLAB Simulink.
## 4 The Proposed Detection and Restoration Framework
### Detection Method Based on KL Divergence  
Signal processing involves the challenge of distinguishing genuine signals from adversarial ones. Due to the inherent complexities, conventional methods often fall short. We propose an innovative solution, harnessing KL divergence and deep learning, to detect adversarial signals.  
Given a genuine signal \(S\), an ideal classifier outputs a probability distribution \(P_{\text{C}}(S)\). Adversarial perturbation shifts this to \(P_{\text{C}}(S_{adv})\), with the divergence quantified as:  
\[D_{KL}(P_{\text{C}}(S)||P_{\text{C}}(S_{adv}))=\sum_{i}P_{\text{C}}(S)_{i} \log\frac{p_{\text{C}}(S)_{i}}{p_{\text{C}}(S_{adv})_{i}} \tag{9}\]Using an AE, signals transition to a latent space representation \(z\), followed by a classifier yielding \(Q(z|S)\). The KL divergence between this and the genuine signal distribution \(P(z|S)\) is our key metric:  
\[D[Q(z|S)\|P(z|S)]=E_{Z\sim Q}[\log Q(Z)-\log P(z|S)] \tag{10}\]  
We further refine the result by integrating the classifier parameters, \(\theta_{C}\), resulting in:  
\[D[Q(z|S,\theta_{C})\|P(z|S)]=E_{Z\sim Q}[\log Q(Z,\theta_{C})-\log P(z|S)] \tag{11}\]  
This methodology underscores the classifier's probabilistic boundary in the latent space, using KL divergence to spotlight adversarial perturbations. Using an appropriate prior distribution, typically Gaussian, enhances the method's robustness. This synergy between classifier probabilities and latent space representation empowers detection of adversarial signal examples, merging mathematical rigor with practical efficacy. The algorithmic process is outlined in Algorithm 2.  
```
Data: Training sample \(x_{i}\), testing sample \(y_{j}\), initial sample size \(N\), random sample size \(M\), classifier \(C\) Result: Classification results of the samples
1. Assume the original data follows a Gaussian distribution \(P(z)\).
2. Use \(P(z|X)\) to approximate \(P(z)\), where \(X\) is the output from the classifier \(C(x)\).
3. Introduce another distribution \(Q(z|X)\) to approximate \(P(z|X)\).
4. Calculate KL divergence: \[D[Q(z|X)\|P(z|X)]=E_{Z\sim Q}[\log Q(Z)-\log P(z|X)]\]
5. Set the optimal threshold \(t_{re}\) based on validation set.
6.for\(j=1\)to\(M\)do
7. Compute \(D[Q\|P]\) for \(y_{j}\).
8.if\(D[Q\|P]<t_{re}\)then
9\(y_{j}\) is labeled as a normal sample.
10
11 else
12\(y_{j}\) is labeled as an adversarial example.
13
14 end if
15
16 end for
```  
**Algorithm 2**Detection method based on kl divergence with classifier integration.
## 5 Discussion  
According to the experiment results, the buck and push-pull converter works in ZVS and ZCS in both the off and on states. Another two-stage converter, boost and push-pull,  
Figure 16: The test result of conducted emission in different switching conditions: (**a**) The two-stage converter in hard switching; (**b**) The two-stage converter in soft switching.  
Figure 15: The efficiencies of the converter in different input voltages for different loads: (**a**) The efficiencies for 130 W; (**b**) The efficiencies for 75 W.  
can realize ZVS but not ZCS under the same load condition (130 W), and the drain-source voltage also increases.  
Compared with a unipolar topology, this two-stage topology can operate steadily in ZVS and ZCS over a wider input voltage range from 25 V to 40 V. When the input voltage ranges from 24 V to 30 V, unipolar topologies, such as three-transistor push-pull, can realize ZVS and ZCS [12].  
Another common two-stage converter is forward and push-pull. The forward and push-pull converter can realize ZCS or zero-current transition (ZCT) using resonant components on the secondary side of the transformer. ZCS causes high current stress, and its efficiency is low. ZCT operates in quasi-resonance mode, which may result in low current stress; the efficiency is relatively higher; and the maximum efficiency is 91.47% when operating at full load [17]. Compared with forward and push-pull, the buck and push-pull converter can achieve up to 94.5% efficiency.  
However, the two-stage converter has many aspects that need to be improved. In subsequent work, the planar transformer will be considered for a further reduction in volume, and the lower ripple of the output voltage needs to be researched continuously.
# E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios
## 2 Related Work
### Application of Edge Computing in the Domain of Financial Data Protection
[11] introduced a novel attribute-based proxy re-encryption approach (COAB-PRE) that enables data privacy, controlled delegation, bilateral access control, and distributed access control capabilities for data sharing in cloud edge computing. On the other hand, [PERSON] et al. [6] developed a data integrity checking and corruption location scheme known as ICL-EDI, which focuses on efficient data integrity checking and corruption location specifically for edge data. Additionally, [PERSON] et al. [12] introduced a flexible time-ordered threshold ring signature scheme based on blockchain technology to secure collected data in edge computing scenarios, ensuring a secure and tamper-resistant environment. However, to the best of our knowledge, the existing research has not extensively addressed the topic of leakage tracking techniques for sensitive data in edge computing scenarios.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
###### Abstract  
Optimizing airflow organization is essential for ensuring the energy-efficient and secure operation of data centers. To address common airflow distribution issues in air-cooled systems, such as uneven air supply and cooling capacity imbalance, this study investigates a bidirectional airflow data center room located in a hot-summer and warm-winter region. A computational fluid dynamics (CFD) model was developed based on field-measured data to analyze the airflow distribution characteristics and evaluate the existing thermal conditions. Three optimization strategies were systematically examined: (1) Installation of rack blanking panels, (2) cold aisle containment with varying degrees of closure, and (3) combined implementations of these measures. Performance evaluation was conducted using three thermal metrics: the Return Temperature Index (RTI), Supply Heat Index (SHI), and Rack Cooling Index (RCIHI). The results demonstrate that among individual optimization strategies, rack blanking panels achieved the most significant improvement, reducing SHI by 42.61% while effectively eliminating local hotspots. For combined optimization strategies, the rack blanking panels and fully contained cold aisle containment showed optimal performance, improving cooling utilization efficiency by 88.26%. The optimal retrofit solution for this data center is the rack blanking panels with fully contained cold aisle containment. When considering budget constraints, the secondary option would be rack blanking panels with cold aisle top-only containment. These findings provide practical guidance for energy efficiency improvements in similar data center environments.  
data center; airflow organization optimization; numerical simulation; containment solution 2020  
[PERSON], [PERSON], [PERSON], X.
# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 6 Discussion  
The hybrid deep learning model proposed in this paper has achieved remarkable results in assisting cataract diagnosis. The experimental results show that the model achieves high accuracy in the recognition task of cataract retinal images by using ResNet50 as the backbone network and integrating an SE_block and prototype network classifier. The SE module enhances the ability of feature expression through the channel attention mechanism, enabling the model to capture key image information more sensitively. At the same time, the prototype network classifier performs well in its ability to generalize to new samples, which is suitable for different sample distributions and shows strong robustness. These characteristics play an important role in the processing of medical images, especially cataract images, and greatly improve the practicability of the model.  
The contribution of this study is the use of transfer learning and the combination of SE_blocks with prototype networks, which improves the adaptability of the model to new data while maintaining high accuracy, which has important implications for clinical settings. In the case of limited resources, this model can provide effective auxiliary support for physicians, thereby improving the coverage and efficiency of screening. In addition, the ablation experiments further verified the effectiveness of each module, and confirmed that both the SE_block and the prototype network significantly improved the recognition performance of the model.  
However, there are some limitations to this study. First, the diversity of the dataset is limited, and more images of patients of different ages, genders, and ethnicities need to be included in the future to ensure the generalizability of the model in a wider population. In addition, the explanatory nature of the model is also key in medical applications, especially in the process of auxiliary diagnosis; medical staff need to understand the decision-making basis of the model. Therefore, future research may consider exploring more explanatory network structures or visualization methods to help doctors better understand the diagnostic results of the model. In terms of practical application, the feasibility and stability of the model in the clinical setting still need to be further verified. In the future, the model can be deployed in practical diagnostic work by working with experts, and the model structure and parameters can be optimized based on feedback to improve its performance in complex  
Figure 10: Supplementary experiment 2—confusion matrix.  
medical scenarios. At the same time, it is also worth exploring further the applicability of SE_blocks and prototype networks in other medical imaging diagnostic tasks.
## 3 Experimental Results
### Dataset Creation  
The insulator dataset used in this study consists of two parts: one part includes 816 images of ice-covered transmission lines obtained from the internet, while the other part is generated through data augmentation techniques such as rotation, mirroring, and flipping, resulting in a total of 2448 images. In total, the combined dataset consists of 3264 images. The dataset includes a dataset of real transmission line icing from Qinghai Province, China, covering a total length of about 30 km and monitored through approximately 100 monitoring points placed along the network. These monitoring points are selected to ensure comprehensive coverage of critical areas and to reflect the typical environmental conditions that the transmission lines face, such as high-altitude regions prone to severe icing. The dataset was annotated using the Labellmg software for two types of targets and was randomly divided into training, validation, and test sets in an 8:1:1 ratio.
## 3 Experiments and Results
### Experiment 2: Questionnaire Survey  
#### 3.2.1 Method  
The seven subjects that participated in the first experiment participated in the second experiment on a different day. The conditions of the second experiment were as follows:  
* Visual feedback;
* Visual and haptic feedback (conventional method [14]);
* Visual and force feedback (proposed method).  
Tests on the conventional system [14] revealed that when users stepped on protrusions installed on the floor at positions corresponding to the edges of virtual stairs, which were presented via a head-mounted display, the users acquired a sense of stair-climbing. In this study, we evaluated the cross-modal effect of the visual and force feedback, and compared it with that of visual feedback alone. Figure 12 shows the experimental scenes under the conventional and visual+force conditions, respectively. To eliminate the order effect, the order of the experimental conditions was randomly decided. At the end of each task, the subjects were presented with ten questions related to the immersion experience and the intensity of the stair-climbing illusion. The immersion questions included six items based on the Immersive Virtual Environment Questionnaire (IVEQ) [20]. The questions on illusion intensity included four items. All answers were given on a five-point Likert scale. On the basis of the '0' of the neutral response to the question, we set a minus score for negative responses and a plus score for positive responses.  
Figure 11: Peak maximal voluntary contraction (MVC) values (%) in four muscles during level walking (gray bars), stair-climbing (green bars), and virtual stair-climbing (orange bars): (**a**) vastus lateralis muscle, (**b**) biceps femoris muscle, (**c**) tibialis anterior muscle, and (**d**) gastrocnemius muscle.  
Figure 12: Scenes of the subjective evaluation experiment: (**a**) conventional method; (**b**) the developed illusionary system.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 3 The Proposed Image Encryption Scheme
### The Encryption Process
#### 3.1.1. Generate a Modified Chaotic Sequence  
At this stage, the chaotic system (4) is used to generate four modified chaotic sequences: **X, Y, I, J**. The detailed steps of the algorithm are described by Algorithm 2.  
```
Input: The number of rows \(M\) and columns \(N\) of the image to be encrypted, chaotic system parameters \(\{x_{0},y_{0},a,b\}\) and a positive integer \(N_{0}\). Output: Four sequences \(\textbf{X, Y, I, J}\).
```  
**Algorithm 2** Generate the modified chaotic sequences.  
**Step 1:** Iterate the system (4) with \(x_{0}\) as the initial value for \((M+N_{0}-1)\) times to generate a sequence **x** with the length of \((M+N_{0})\). In order to improve the sensitivity of the intermediate key to the initial value, the first \(N_{0}\) values of the sequence are discarded, and the real chaotic sequence \(\textbf{x}=[x(1),\,x(2),\,\ldots,\,x(M)]^{\mathrm{T}}\) with the final length of \(M\) is obtained. Iterate the system (4) for \((N+N_{0}-1)\) times with \(y_{0}\) as the initial value, and use the same method to generate the real-valued chaotic sequence \(\textbf{y}=[y(1),y(2),\,\ldots,y(N)]\) with the final length of \(N\).  
**Step 2:** Use Equations (6), (7), (8) and (9) to obtain the sequences \(\textbf{X}=[X(1),\,X(2),\,\ldots,\,X(M)]^{\mathrm{T}}\), \(\textbf{I}=[I(1),\,I(2),\,\ldots,\,I(M)]^{\mathrm{T}}\), \(\textbf{Y}=[Y(1),\,Y(2),\,\ldots,\,Y(N)]\) and \(\textbf{J}=[J(1),\,J(2),\,\ldots,\,J(N)]\), respectively.  
\[\textbf{X}=\mathrm{mod}(\mathrm{floor}(\textbf{x}\times 10^{6}),\,256), \tag{6}\]  
\[\textbf{Y}=\mathrm{mod}(\mathrm{floor}(\textbf{y}\times 10^{6}),\,256), \tag{7}\]  
\[[\,\cdot,\,\textbf{I}]=\mathrm{sort}(\textbf{x}), \tag{8}\]  
\[[\,\cdot,\,\textbf{J}]=\mathrm{sort}(\textbf{y}). \tag{9}\]  
Here, \(\mathrm{floor}(x)\) is a function of rounding \(x\), and the returned function value is the largest integer less than or equal to \(x\); \(\mathrm{mod}(x,\,256)\) is the remainder obtained by dividing \(x\) by \(256\); \([\,\cdot,\,\textbf{I}]=\mathrm{sort}(\textbf{x})\) is to arrange the vector **X** in ascending order, and then get the position number of each element in the ordered sequence \(\textbf{X}^{\prime}\) in the original sequence **X**. These position number values \(\{I\,(1),\,I\,(2),\,\ldots,\,I\,(M)\}\) constitute the sequence **I**, where the value of the element \(I(i)\) is the position value of the \(i\)-th element in the ordered sequence \(\textbf{X}^{\prime}\), \(I(i)\)\(\in\)\([1,\,2,\,\ldots,\,M]\). The meaning of **J** is similar to **I**. Here, \(i=1,\,2,\,\ldots,\,M,j=1,\,2,\,\ldots,\,N\).
## 4 Simulations
### Objective Assessment  
To evaluate the performance of the proposed method quantitatively, we measured the detection rate. We conducted the evaluation in four different scenarios: cases where the front view was obstructed by the wiper, cases where the road area was reflected by rainwater, cases where there were other lines present in the road area, and cases where the road area was overexposed by multiple light sources. In each scenario, we evaluated the detection of both left and right lanes, resulting in a total of 400 images (100 images for each scenario). The evaluation method involved comparing the detected lanes with the actual lanes. If the detected lanes closely matched the actual lanes, they were considered true; otherwise, they were considered false. The evaluation criteria are the same as in Figure 21. We separately evaluated the left and right lanes and then combined them to assess if they correctly represented the direction of the actual vehicle. The evaluation results are presented in Table 1. In the results, compared to the U-Net-based method, about 34% in the left-lane case and about 30% in the right-lane case were observed. In the case of DSUNet, there is little difference compared to the proposed method in the score, and in  
Figure 19: Experimental results in the case where there are obstructions such as lines other than lane markings in the road area: (**a**) conventional; (**b**) DSUNet; (**c**) U-Net ConvLSTM; and (**d**) proposed method.  
some cases, even better results can be observed. However, the proposed method is more suitable for the goal of detecting only the lanes present in the driving road area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Left Lane** & **Right Lane** \\  & **(Conv/DSUNet/LSTM/Prop)** & **(Conv/DSUNet/LSTM/Prop)** \\ \hline Wiper & 0.49/0.72/0.57/0.65 & 0.40/0.63/0.54/0.69 \\ Rainy surface & 0.33/0.51/0.50/0.54 & 0.26/0.70/0.74/0.73 \\ Overexpression & 0.64/0.82/0.55/0.94 & 0.61/0.88/0.50/0.93 \\ Guideline & 0.35/0.86/0.59/0.84 & 0.03/0.75/0.65/0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of lane-detection rate.  
Figure 20: Experimental results in the case where the road arear is overexposed by headlights from vehicles and other factors during nighttime: (**a**) conventional; (**b**) DSUNet; (**c**) U-Net ConvLSTM; and (**d**) proposed method.