esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiment and Analysis
### Performance Evaluation of Multipath Routing Algorithms
#### 4.1.1 Waiting Delay  
With packet filling for a single path, as the service data packet sending rate increases, the available bandwidth margin becomes less and less, resulting in waiting delay conflicts. The experimental data in Figure 8 shows that the larger the data packet sending rate, the longer the waiting delay. When there is a demand for data package business, the algorithm first chooses a path with the least amount of business. As the rate of data package sending increases to a certain threshold, the algorithm chooses the path with less business consumption among the other shortest links as the transmission path to disperse the business packets to different neighbor nodes, decreasing the wait conflicts that occur as the rate of sending business packets increases.  
Figure 7: Simulate the network connection of inter-satellite nodes.
# Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
## 4 Deep Learning Framework for Important Trading Point Prediction
### Hybrid Convolutional Recurrent Neural Network  
The forecasting of stock performance is affected by both long-term temporal dependencies and local fluctuation features. Due to its memory blocks, the LSTM network has a strong capability of capturing the long-term memory of sequential data with a high prediction capacity for chaotic time series. Thus, we adopt LSTM to learn long-term temporal dependencies from stock data time series. The overall structure of our proposed model is shown in Figure 4. Unlike the common methods that use a \"many-to-one\" model with a sliding window of a fixed size of preceding data, this work adopts the \"many-to-many\"model; i.e., the overall historical time series is input into the model from the beginning to time \(T\left\{x_{t}\mid t=1,2,\ldots,T\right\}\), and the prediction value is the output at each time step \(\left\{y_{t}\mid t=1,2,\ldots,T\right\}\). The reason is that LSTM has the capacity to maintain long-term dependencies between the input time steps because of the memory state; thus, there is no need to preprocess the past data in a time window, as required with other classification models such as the support vector machine (SVM) and MLP [15]. In fact, preprocessing the past data in a time window with a fixed size damages the raw temporal sequence information because the information before the time window is discarded, which could even decrease the prediction accuracy. In addition, one-dimensional convolution (Conv 1D) is introduced to extract local fluctuation features, which can help to enhance the prediction performance. This particular neural network learns filters to study the mapping relationship between any input and output from the training on known patterns. The formula for forward propagation in the model is as follows:  
Given a time series of historical data for a stock \(\left\{x_{t}\mid t=1,2,\ldots,T\right\}\), for each input time step \(x_{t}\), the proposed model performs convolution on the recent region from time \(t-M\) to \(t-1\left(x_{t-M},\ldots,x_{t-3},x_{t-2},x_{t-1}\right)\) to capture local fluctuation features. The formula is given in (4):  
\[x_{j}^{l}=f\left(\sum_{i\in M_{j}}x_{i}^{l-1}*k_{ij}^{l}+b_{j}^{l}\right), \tag{4}\]  
where \(M_{j}\) denotes a selection of input maps; for easier description, it is defined as \(Conv_{t}\). The Conv 1D layer and LSTM layer are cascaded, the output of the Conv 1D layer \(Conv_{t}\) and the raw input \(X_{t}\) are concatenated to form a vector, and this joint feature is fed to the LSTM layer for learning long-term temporal dependencies. LSTM is a variant of an RNN that uses a gating mechanism to control the flow of information into or out of memory. Formally, the LSTM can be described as follows.
## 6 Discussion
### Checking Complex Inconsistencies via Template  
In previous work, we performed the checks via SPARQL queries. For this purpose, the manufacturing environment had to be modeled with ontologies in advance. For the validation step, incoming process and machine data were converted from JSON to RDF format using these ontologies. RDF creates a graph structure that can be traversed via SPARQL queries. Validation via ontologies and SPARQL has the advantage that changes can be incorporated in a simple way. In addition, the modeling languages are easy to understand. These are two properties that argue for its use in a heterogeneous, continuously changing environment. A serious disadvantage of semantic modeling in our scenario is  
Figure 14: CPU load during 2 h of operation on real manufacturing data.  
that every discrepancy to be checked must be modeled and thus known. This makes it impossible to cover unknown errors. In addition, many models (including queries and ontology) have to be adapted even for small changes.  
Therefore, we have decided to no longer perform checks of Categories 3 and 4 semantically, but by means of template matching. We receive the templates directly when changes are made. The line structure and the general process flow are relatively fixed. Thus, we can still refer to the respective process ontology when creating an automaton.  
Template matching also offers further scope for optimization. Currently, we use an accurate method to detect inconsistencies with very high probability. As the authors have indicated in [6], relevant savings are recognizable in energy consumption when small measurement errors are taken into account. The consideration and evaluation of approximate approaches will be part of future work.  
In summary, both concepts have their advantages and disadvantages. If changes in the checking process of complex inconsistencies are necessary, the adaptation of the template is easier than revising ontology and SPARQL queries. If the process changes, complex changes in the automaton are necessary. The GreenCC, on the other hand, remains unaffected. The stream handling controls the message grouping; at the end, a check is performed.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 6 Discussion
In future work, there are various parameters that can be alternated and evaluated, including different constellation designs such as pure TLE-based satellites, variations in the orbits, number of planes and satellites, adjustments to angles of elevation, and modifications to satellite altitudes. Additionally, the exploration of advanced mechanisms could be valuable for optimization purposes. Examples of such mechanisms involve the implementation of algorithms for routing, queuing, and traffic management, as well as the integration of congestion control mechanisms or techniques like the \"_store-and-forward_\". Furthermore, the implementation of DTN and protocols such as the NDP (Neighbor Discovery Protocol), specifically tailored for satellite infrastructures, could be considered as part of future research endeavors. Moreover, in our experiments we utilize \"Ships\" as relays which operate as the rest of the ground stations, establishing or enhancing the communication between sender(s) and receiver(s). Their utilization should be investigated in detail regarding the technical specifications and applicability of such a proposal within the context of satellite-based communication systems. To conclude, while the advantages of inter-satellite links are apparent, there is a tradeoff conserning the cost and complexity of satellites. The implementation and maintenance of inter-satellite links contribute to the overall complexity and cost associated with satellite systems, aspects that were not considered or analyzed in the present work but need to be carefully evaluated in the context of any satellite communication deployment. Finally, the extension of satellite or -in general- space utilization opens up numerous novel research areas. Ships, drones, UAVs, and any \"smart\" devices with suitable characteristics can play vital role in various scenarios. For instance, the deployment of micro/nano satellites or drones with a designated purpose can influence the communication resilience and security in smart-city and IoT scenarios. These devices can serve as relays, contributing to communication continuity or enhancing security in situations where communication infrastructures are jammed, compromised, or under attack. Equipped with specific and secure instructions, these satellites or drones can act as connection bridges, offering temporary solutions to potential issues, such as addressing a security breach in the communication infrastructure.
## 3 Application to Experimental Data
### Evaluation
#### 3.4.3 Influence of Weights  
In the previous experiments, the individual sensors' hits were weighted by factors \(w_{d}\) in Equation (2) to take into account the local SNR. This experiment assesses the benefit of these weights over an unweighted approach (\(w_{d}=1\)) in which the densities \(\hat{f}_{S}\left(\mathbf{p}\right)\) are only influenced by the spatial proximity of neighboring hits. This setting represents inspection results for which no measure of confidence is available. In both experimental cases, the _product_ fusion rule was chosen and the kernel size was set to the value suggested by Equation (4). Figure 13 illustrates the respective detection performances. According to the results, the unweighted variant never surpasses the proposed weighted  
Figure 12: Evaluation of different kernel sizes, for the _product_ fusion rule. For each groove and fusion method, the AUC-PR-0.5 is shown in shades of gray.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Relative Kernel Size**} \\ \cline{3-6}  & & **0.25** & **0.5** & **1.0** & **1.5** & **2** \\ \hline \multirow{3}{*}{ET} & \(h_{x}\) & 0.05 & 0.1 & **0.2** & 0.3 & 0.4 \\  & \(h_{y}\) & 0.346 & 0.6 & **0.6** & 0.6 & 0.6 \\  & \(h_{x}\) & 0.05 & 0.1 & **0.2** & 0.3 & 0.4 \\ \cline{1-1}  & \(h_{y}\) & 0.345 & 0.6 & **0.6** & 0.6 & 0.6 \\ \cline{1-1}  & \(h_{x}\) & 0.186 & 0.373 & **0.746** & 0.746 & 0.746 \\ \cline{1-1}  & \(h_{y}\) & 0.05 & 0.1 & **0.2** & 0.3 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kernel sizes used in the experiment. All sizes are in mm. _Relative kernel size_ denotes the fraction of \(\hat{u}=0.2\) mm that was used to compute the kernel sizes according to Equation (4). That is, a range of smaller and larger kernels compared to the default size (relative kernel size = 1, bold faced column) were assessed. To prevent unrealistically large kernels due to the disproportionate spatial sampling distances in our data, kernel sizes were limited to 0.6 mm for ET and MFL, and to 0.75 mm for TT (gray shaded table cells).  
density estimation at any groove. Interestingly, although the unweighted method does not take into account the local SNR and therefore is not influenced by defect depth, it is clearly observed that most of the deeper grooves (e.g., nr. 1-5) are more reliably found than the shallower grooves (e.g., nr. 9-15). This is because during the first stage of individual detection before fusion, only parts of the shallower grooves might be retained whereas deeper grooves are completely preserved. Therefore, the weighted approach should be favored over the unweighted method if possible. Otherwise, much effort should be spent on high-quality registration to make the sole feature of spatial proximity of hits across different sensors a reliable indicator of defect presence. Yet, even without weights, _product_-fusion still outperforms any individual method in our evaluation for grooves shallower than 43 \(\upmu\)m (groove nr. 9).
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 4 Experiments and Discussion
### Performance Evaluation Metrics and Experimental Settings
#### 4.1.2 Training Process and Training Dataset of MultiRes-RCF  
In the experiments, we implemented our proposed Multi-RCF network using Keras (Tensorflow as the backend) on a GPU workstation equipped with NVIDIA RTX 2080 Ti GPU (11 G memory and 4352 cores) and an Intel i7-9700K CPU. The training process of each models adopted the same dataset as [9; 23]. This dataset combines the existing WHU building dataset [30] and the method of manually drawing robust edge images, including a total of 3166 pairs of training samples. The construction of this training dataset can be divided into two steps: First, modify the WHU building dataset to meet the needs of our model; then, draw the training samples manually with the false edges removed.  
As edge pixels occupy a small proportion in the training samples, an \(\alpha\)-balanced variant of focal loss [31] was taken as the activation function of MultiRes-RCF. Due to the limitation of the GPU memory size, the batch size was set to four, and the epochs was set to 100 during the training of the model. As shown in Figure 5, the training loss curve distribution and the validation loss after each epoch are plotted.  
We took the following three evaluation metrics to compare the complexity of each model: the number of parameters, the size of the storage space consumed, and the Floating Point Operations Per Second (FLOPs). The results are shown in Table 1:  
As can be seen from the table above, our MultiRes-RCF significantly reduced the number of parameters, storage size, and FLOPs compared to the existing models, and had certain potential advantages in further lightweighting.
## 1 Introduction
proposed the arrival and departure route optimization method based on A* algorithm by considering the influence of dangerous weather [20]. [PERSON] et al. proposed the terminal area route network optimization method based on simulated annealing algorithm by referring to the Standard Instrument Departure (SID) and Standard Instrument Arrival (STAR). The above scholars all adopted the route network optimization sequence principle proposed by [PERSON] [21]. [PERSON] et al. designed the dynamic terminal area route optimization method when studying the route network of London multi-airport terminals, and established the route network priority evaluation mode based on the analytic hierarchy process [22]. In addition, [PERSON] et al. proposed the terminal arrival and departure fix optimization model in combination with the airspace structure optimization of the terminal area, and the results showed that the optimized fixes can effectively reduce fuel consumption and environmental impact [23]. Then, [PERSON] et al. found a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, and modelled the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process [24]. [PERSON] proposed a framework for the design of dynamic arrival and departure routes in MAS Terminal Maneuvering Areas and used the AirTOp fast-time simulation model to prove the advantages in distance, time, fuel burn, and controller workload [25]. At present, the scholars' research on route network planning of terminal area mainly focuses on the single optimization goal of the shortest route or the minimum fuel consumption or noise impact. Most of them start from the micro perspective such as the local horizontal or vertical profile of a single route, but lack of solving the environmental impact of the route network in the terminal area from the macro level. In addition, in the existing priority evaluation mode of route network, the number of evaluation indexes is small and it is difficult to provide scientific decision-making. In order to develop the safe, efficient environmentally and friendly route network, this paper proposes a terminal area route network optimization method for reduction of air emissions and noise. This paper establishes a route network optimization model by considering fuel consumption, air emissions, aviation noise factors and operation efficiency, uses non-dominated sorting genetic algorithm with elitist strategy to search the optimal results, and then compares with before optimization route network terminal area for analysis.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 7 Discussion
### Decentralization Challenges in Consortium-Based Blockchain DNS  
While the proposed architecture aims to enhance decentralization, the introduction of a main chain for coordination raises potential centralization concerns. This mirrors observations from systems like Ethereum Name Service, where significant portions of the infrastructure are hosted by a few dominant cloud providers. To mitigate such risks, our consortium blockchain design leverages distributed governance across multiple trusted  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Query Sending Rate (TPS)** & **ODD** & **DNSSEC** & **HARD-DNS** & **DepenDNS** & **Our Solution** \\ \hline
40 & 535 & 530 & 570 & 620 & 460 \\
80 & 540 & 555 & 615 & 715 & 516 \\
120 & 555 & 605 & 645 & 890 & 572 \\
160 & 560 & 600 & 790 & 950 & 628 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the DNS resolution latency (ms) under varying query sending rates.  
stakeholders, each maintaining independent control over their Fabric peers and chaincode. Unlike public chains, where economic incentives may concentrate influence, our approach ensures policy diversity through governance voting mechanisms, cross-organizational endorsement policies, and geographically distributed node deployment. Nonetheless, we acknowledge that consortium blockchains may still centralize decision making at the governance layer. Future work will explore integrating decentralized governance protocols and dynamic membership models to further reduce reliance on any single coordinating entity.
# Visual Ranging Based on Object Detection Bounding Box Optimization
## 4 Experimental Results and Analysis  
### Object Detection  
Object detection was carried out by YOLOv5s. YOLOv5s was used for training in the experimental environment of a cloud server with AMD EPYC 7601 CPU and 3070-8G GPU, and the epochs parameter set to 40. Figure 5 shows the training results before and after data augmentation. The results show that the model with data augmentation has higher accuracy. The mAP value is increased from 77.1% to 99.5%, and the effectiveness of the data augmentation method in improving accuracy is verified.  
To test the performance of the trained model, experiments were conducted on the test set, and the final results are shown in Table 2. The results in Table 2 indicate that the detection model performs well in the detection task and also provides a solid foundation for the two new methods proposed in this paper.  
Figure 5: Comparison of P–R curves and [EMAIL_ADDRESS] values before and after data augmentation.
## 3 Proposed Method
### Albedo Recovery
#### 3.2.3 Implementation Details  
Since the ground truth of albedo, normal, and light coefficients of real facial images is not available, synthetic data are firstly used in this paper. Following the definition of 3 DMM [46], the shape and the albedo of a face can be represented as  
\[S=\overline{S}+\alpha_{id}\,b_{id}^{s}+\alpha_{exp}\,b_{exp}^{s}, \tag{8}\]  
\[A=\overline{A}+\beta_{id}\,b_{id}^{a}, \tag{9}\]  
where \(S\) and \(A\) are 3D face shape and albedo; \(\overline{S}\) is the mean shape; \(\overline{A}\) is the mean albedo; \(b_{id}^{s}\) and \(b_{id}^{a}\) are, respectively, the identity-related shape bases and albedo bases; \(b_{id}^{s}\) is the expression-related shape bases; and \(\alpha_{id},\alpha_{exp}\), and \(\beta_{id}\) are corresponding shape parameters, expression parameters, and albedo parameters.  
[PERSON] et al. [51] provided a method to flatten a 3D shape into 2D embedding and re-sample a 3 DMM (including \(\overline{S}\), \(\overline{A}\), \(b_{id}^{s}\), \(b_{exp}^{a}\), and \(b_{id}^{a}\)) from the initial 3 DMM over a uniform grid of size \(H\times W\) in this flattened space. The re-sampled 3 DMM has \(n=HW\) vertices and each vertex has an associated UV coordinate. In our work, \(b_{id}^{s}\) and \(b_{id}^{a}\) of initial 3 DMM come from BFM 2009 [52] and \(b_{exp}^{s}\) comes from FaceWarehouse [53]. Using the method in [51], we obtain a re-sampled 3 DMM with \(n=256^{2}\) = 65,536 vertices. Then, we remove the vertices in the neck area resulting in a 3D face of 43,867 vertices and UV map of \(256\times 256\) with 43,867 valid pixels. We use the re-sampled 3 DMM to synthesize 2330 subjects with a total of 150,704 facial images.  
In view of the difference in data distribution between synthetic images and real images, real training data are necessary. We used real data from the CelebA database [54], including a total of 202,599 facial images of 10,177 subjects, and adopted a two-step training strategy to solve the problem of unavailability of real data labels. (i) We first trained the network on synthetic data. The pre-trained network was applied to real data from CelebA to obtain their albedo, normal, and SH illumination coefficients, which were taken as \"pseudo ground truth\" of the real data. (ii) We then fine-tuned the network with the combined set of synthetic data with \"golden ground truth\" and real data with \"pseudo ground truth\". For both steps, we applied Equation (7) to supervise the learning process and set \(\lambda_{recon}\), \(\lambda_{I}\), \(\lambda_{a}\), and \(\lambda_{n}\) as 1.0 and \(\lambda_{f}\) as 0.5. Optimization was done by Adaptive Moment Estimation (Adam) with mini-batch of 32 samples, and the exponential decay rate for the first-moment estimates and second-moment estimates were set as 0.5 and 0.999, respectively. When we trained the network with only synthetic data, the learning rate was initialized as \(10^{-3}\) and then decreased by factor of 10 after every four epochs. When training the network with combination of synthetic and real data, we set the learning rate to \(10^{-4}\).