esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Network Scalability  
This evaluation has the purpose of determining the performance of the proposed algorithm with different network parameters. The JP-CoMP clustering scheme is expected to be able to handle such network changes. Two parameters are utilized in this simulation in order to evaluate this problem: the cell size and the number of users.  
Figure 6 shows the cell size evaluation. This evaluation provides the average edge user throughput performance with different cell sizes. The result shows that the average edge user throughput performance gradually decreases with an increase in the cell size. When the cell radius is 100 m, the proposed algorithm shows 2%, 39%, 39%, and 41% average edge user throughput improvements as compared to coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering, respectively. In addition, when the cell radius is 500 m, the proposed algorithm shows 29%, 37%, 208%, and 341% average edge user throughput improvements when compared to coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering, respectively. These results demonstrate that the proposed algorithm consistently outperforms the others in all cell size ranges.  
Figure 7 presents the evaluation result, depending on the the number of users. This evaluation presents the throughput performance with different numbers of users. The results indicate that the increase in the UE number does not have significant effect to the average edge user throughput. When the UE number is 50, the proposed algorithm shows 9%, 35%, 208%, and 240% average edge user throughput improvements as compared to coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering, respectively. Accordingly, the proposed algorithm outperforms the others consistently with all numbers of UE ranges.  
These results verify that the proposed algorithm sophisticatedly optimizes the objective function properly in different networks, because the proposed algorithm utilizes distributed control of the network. This scheme allows for the utilization of different networks to determine the appropriate cooperating formation instead of a greedy choice when determining the cooperation formation.  
Figure 6: Average edge user throughput versus the cell size.
## 2 Materials and Methods
### Guided Back-Propagation  
Relying on gradients can be problematic. CNNs are highly non-linear networks and some important features may have a large contribution globally but small effects on local scales. One approach to this is changing the network in such a way that only positive contributions pass during back-propagations, with all negative values being eliminated. One such strategy is guided back-propagation [5], which modifies the gradients of the rectified linear unit (ReLU) functions by discarding all negative values during the back-propagation calculation, thereby avoiding the problem. To adapt the guided back-propagation method to semantic segmentation, the back-propagation behavior of the model has to be modified. Given an input image, a forward pass to the last layer is performed, then all activations except one are set to zero and the gradient of the output map with respect to the input image computed. Figure 2 illustrates the computation of guided back-propagation in a sample segmentation network. As expected, the new map is much sharper and more detailed than the saliency map. Although guided back-propagation maps are easier to interpret, they are neither completely faithful to the model nor truly class distinctive [31]. An example is presented in Section 3.2.  
Figure 1: Class saliency map generation for semantic segmentation, computing the class saliency map of the output score map (class ‘car’) with respect to the input image. Brighter pixels indicate partial derivatives with higher absolute values.
# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 3 AFSFusion Network Model
### AFFS Module  
The figures depicted in Figure 4 represent the number of feature channels utilized in the network model, commencing from 2 (representing the IR and visible images for fusion) and progressively increasing up to 256. Subsequently, there is a layer-by-layer reduction before eventually reverting back to a single channel that corresponds to the fused image. In the front and middle sections of the network, image features are sequentially extracted and fully fused before being passed on to the next layer. In the middle and rear sections of the network, the fused network features are reconstructed layer-by-layer before being passed on to the next layer to form the fused image. Thus, the proposed network model ensures a methodical and well-balanced execution of the three critical tasks, namely, feature extraction, feature fusion, and fused image reconstruction. Notably, the AFFSusion network is simpler to construct than previous fusion networks in terms of the overall backbone network. Our investigation results demonstrate that enhancing performance in image fusion networks does not necessitate a complex network architecture and can be achieved using a well-designed network architecture. Detailed results for our comparative experiments are presented in Section 4 for further reference.  
As shown in Figure 4, the proposed AFFS module in this study employs two branches for feature extraction and fusion. The first branch uses two parallel convolutional layers to extract the feature information from the two inputs, and each convolutional layer consists of convolution and activation functions. Then, three consecutive convolutional layers are used to modulate the extracted feature information. In the second branch, the two inputs are directly concatenated and then fed into convolutional layers for feature extraction. Finally, the features extracted from the two branches are added together and shuffled to enable effective interaction between them. The shuffle operation is applied at the end of the module to enhance the network's mapping and feature processing capabilities, thereby improving the degree of feature information extraction and achieving better training results. In addition, the channels of the input data are shuffled by the shuffle operation, allowing each channel to receive information from different groups, providing a powerful mechanism for feature fusion, enhancing the diversity and representativeness of features, ultimately improving the generalization performance of the model.
## 3 Proposed System Model
### Adaptive BCH Codes  
The pilot data are adaptively coded by applying BCH codes. BCH codes use special values of \(n\) and \(k\), where \(n\) is the codeword block length and is typically an integer with a value of \(2^{m}-1\) (\(m\) is an integer in the range \(3\leq m\leq 16\)). To attain a fairly short BCH code, each power of \(m\) must follow some sort of polynomial equation of degree \(k\), and \(m\) must be the multiplicative order of two modulo \(n\)[36; 37]. This range of values is available in the form of tables in order to produce any desired BCH code length [38], and \(k\) is the number of the information message bits, which is a positive integer given by \(k\geq n-m^{*}t\) (\(t\) is the number of errors that could be corrected by the code) [37]. The minimum Hamming distance for a BCH code is \(d_{min}\geq 2t+1\)[39; 40; 41]. However, only a few [\(n\), \(k\)] pair values are  
Figure 3: Successive interference cancellation (SIC) algorithm [6].  
valid [36; 42; 43]. A BCH code can correct any group of \(t\) or less errors in a block of \(n\) bits. The generator polynomial of the BCH code is defined as a function of its solution over the Galois field (GF) (\(2^{m}\)) [36; 44]. By performing the same procedure as in [39], we can determine the generator matrix, parity check matrix, and the equivalent \(d_{min}\). Using an [\(n\), \(k\), \(d\)] BCH code corresponding to the length of the pilot data, the code rates are varied efficiently along with the channel conditions. This resourcefully reduces the bandwidth consumption, since conventionally, the used BCH code rates range between (63,24) and (255,123) [45; 46]. When we have a high SNR value, fewer parity bits are used, and the information bit rate increases. When we have a low SNR value, a lower information bit rate is used, as all the parity bits are transmitted [36; 39].
## 4 Case Study
### Analysis
For instance, S1-I1 mentions component (EMU) malfunction; so, according to the _template_, all three branches for \"Algorithm Failure\", \"Input Failure\" and \"Damage or Disruption\" may be applied to elaborate S1-I1; this results in S1-I1-A1, S1-I1-A2, S1-I1-I1, S1-I2, S1-I1-I3, and S1-I1-D1. Likewise, S1-I1-I2 mentions component (transmission medium) malfunction; thus, the entire _template_ may be applied again to elaborate S1-I1-I2, thereby leading to S1-I1-I2-D1. Nonetheless, not all branches or nodes of the _template_ are always applicable to any _system_ components. _Step 6_ outputs a set of S&S requirements based on STRIDE analysis and also by negating the identified PCs. MTMT result can not only be converted into Security requirements, but may also be used to refine some identified PCs; for instance, as MTMT result mentions the possibilities of replay attacks on particular data like Modbus messages, S1-I3 can therefore be refined as \"PCS Controller receives replayed signal S1\". In the following, we discuss the 8 MD illustrated in Figure 8; note that the matrices' symbols are described in Table 1.  
Figure 6: Identifying how PCS Controller behaves due to inappropriate signals S1 and S3. Figure 8: A highly simplified version of 8-Matrix Diagram (8 MD) built for the case study. Figure 7: Example of Potential Causes (PCs) derived from the _template_. These PCs describe why S1 is (“Configuration needed”) can be inappropriate (i.e., incorrect or not provided).
## 2 Proposed Localization System
### Offline Phase  
In the offline phase, the area to be localized is divided into several grids, and the received signal strength (RSS) values from various APs are collected for each grid point to build a comprehensive fingerprint database [15]. The goal is to capture representative RSS values across multiple locations to form a reliable reference that can be used for position estimation during the online phase.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 6 Challenges and Perspectives
### Study of Spectrum Sensing under Small-Sample Conditions  
At present, research on spectrum sensing based on deep learning mainly involves supervised learning and the basic requirements for detection accuracy in the presence of a large amount of labeled data. However, in many practical applications, obtaining large amounts of labeled data is expensive and impractical. The current main research idea is to increase the quantitative diversity of datasets through data augmentation techniques to improve model performance in general. For example, in [55] high-fidelity data are generated to enhance the training samples through the data enhancement technique of DCGAN. Another idea is to introduce transfer learning to adapt models trained on large datasets to new models that can adapt to small samples. In Ref. [94], the authors aim to obtain a new model by transferring learning from a pre-trained model with good results. The combination of semi-supervised and self-supervised learning is also a future research trend. Semi-supervised learning, for example, can be modeled using both labeled and unlabeled data, which can significantly reduce the cost of collecting the training set. In [115], the semi-supervised learning method is combined with DNN, and the training phase of its model is based on semi-supervised classification, which achieves good results and provides a new research idea for spectrum sensing under small samples. However, there is relatively little research in this area, and further exploration is needed.
# Superior Control of Spacecraft Re-Entry Trajectory
## 1 Introduction
Therefore, in order to avoid out-of-control and landing point deviation caused by severe aerodynamic ablation [11] and to reduce aerodynamic heating, overload, and dynamic pressure [12; 13; 14]--in response to the severe aerodynamic heating environment caused by a re-entry speed of 10.8 km/s, in order to reduce the cost of thermal protection systems and other overall design schemes [15; 16; 17], such as more fuel for braking and orbit-changing engines, and reduce the payload--it is necessary to optimize the design of the return re-entry trajectory [18; 19; 20]. For the lunar landing return re-entry scenario with a re-entry speed of 10.8 km/s [21], this article uses C++ to perform programming simulation and optimizes several lunar landing return re-entry trajectories and their parameters using two algorithms. The performance indexes of the optimized design are maximum payload. They also reduce the mass of the thermal protection system and reduce fuel consumption of the orbit maneuvering engine. This article organizes the re-entry trajectories obtained from the simulations of the two algorithms and conducts a detailed analysis using a representative trajectory for each. The analysis results show that both algorithms can optimize the available re-entry trajectory, but their performance in terms of optimization performance indexes is different. The innovative aspects of this article are as follows:  
1. For the first time, [PERSON]'s superior control theory was refined, and the theoretical model of superior control was written in detail;
2. A simulation algorithm of [PERSON]'s superior control theory was designed for the first time;
3. For the first time, superior control was applied to the field of aerospace re-entry trajectory optimization for analysis.
## 3 Experimental Dataset and Results
### Results and Discussions
Since the experimental data was not using the first language of all speakers, the experimental result would be adversely affected. It could be inferred that the accuracy of lip-reading recognition result would be higher if standard announcer's pronunciation videos were used. In addition, it was difficult to put it into practical applications. Therefore, the dataset in this paper was closer to the actual application and it had high academic research value. As a whole, the proposed model effectively improved recognition accuracy. It could be concluded that the proposed model was stronger than the general CNN-LSTM structure in the performance of these ten independent digital pronunciations. Figure 9: Visualization of the attention mechanism.
# Coordination and Private Information Revelation
## 3 The Model
* Following a history where, in Stage 1, agent A revealed her type to be \(\theta\), let \(q_{R}(\theta)\) denote a mixed strategy of agent B where he plays \"Adopt B\" with probability \(q_{R}(\theta)\). * Similarly, following a history where agent A played \"Not Reveal \(\theta\)\" in Stage 1, \(q_{NR}\) denotes a mixed strategy of agent B, where he plays \"Adopt B\" with probability \(q_{NR}\). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Switch to \(A\)** & **Adopt \(B\)** \\ \hline Adopt \(A\) & \(\theta+c,c\) & \(\theta,b\) \\ Switch to \(B\) & \(0,0\) & \(c,b+c\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Payoff matrix for the one-shot game. The belief of agent B at the coordination stage, i.e., Stage 2, is a mapping \(\bar{B}\) from the message sent by agent A in the revelation stage to a probability distribution over agent A's type space \([\theta_{l},\theta_{h}]\), i.e., \(\bar{B}:M\rightarrow\Delta[\theta_{l},\theta_{h}]\)5. Footnote 5: Given that A can only mix between the true type and no announcement, note that a rational belief for B should put zero mass on all types other than what was announced. Pooling among different types can only occur if “no announcement” realizes. We thank an anonymous reviewer of this journal for pointing this out. Finally, agent A's strategy in Stage 2 is said to be a _cut-off strategy_ iff there is some \(\hat{\theta}\in[\theta_{l},\theta_{h}]\) such that she adopts A iff \(\theta\geq\hat{\theta}\). Given these notations on strategies and beliefs, the perfect Bayesian equilibria of this game can be defined in a routine fashion. We say that a perfect Bayesian equilibrium is efficient if the outcome involves both agents choosing \(A\) when \(\theta\geq b\), and both agents choosing \(B\) otherwise.
## 2 Materials and Methods
### Aligner Fabrication  
Sistry clear aligners were divided into six groups of ten aligners each, based on material types and activation amounts. uDesign 6.0 software (uLab Systems Inc., Memphis, TN, USA) was used to design aligners with 0.25 mm and 0.50 mm activations for lingual bodily movement of tooth 21 (Figure 2).  
For TFM, the corresponding resin models were 3D printed with SprintRay Pro 95 (SprintRay Inc., Los Angeles, CA, USA) in vertical orientation in 100 \(\upmu\)m layers. Next, aligners were fabricated using 0.76 mm thick sheets of ATMOS (American Orthodontics, Sheboygan, WI, USA) and Zendura FLX (Bay Materials LLC, Fremont, CA, USA) materials through the thermoforming process with a Biostar machine (Scheu Dental GmbH, Iserlohn, Germany) according to the manufacturers' instructions (Code 123 and Code 162, respectively). The aligners were trimmed flush with the gingival margins. ATMOS is advertised as a single-layer PET-G polymer. Zendura FLX is a multi-layer TPU plastic that consists of two hard outer shells and an inner elastomeric layer, the exact composition of which is proprietary.  
Figure 1: In vitro experimental apparatus. (**A**) Hardware setup. (**B**) Multi-axis force/moment transducer. (**C**) Software setup for data collection and visualization.  
Figure 2: The maxillary arch setup created on uDesign 6.0.  
For DPA (Figure 3), uDesign 6.0 was used to generate aligner files with 0.50 mm thickness and 0.05 mm offset from tooth surfaces, with edges extending to the gingival margins. The thickness was set to 0.50 mm to represent the post-thermoforming thickness of TFM, and the offset was set to 0.05 mm to allow ease of application [12; 13]. The aligner files were imported into Uniz Maker (Uniz Technology LLC, San Diego, CA, USA) for 3D printing preparation and subsequently 3D printed with SprintRay Pro 95 using TC-85 resin (Graphy Inc., Seoul, Republic of Korea) in 100 um build layers. Afterwards, the aligners were detached from the build platform and cleaned of residual resin using centrifugation for 6 min. The aligners were post-cured using Tera Harz Cure (Graphy Inc., Seoul, Republic of Korea) with UV light under nitrogen gas for 14 min.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 6 System Implementation
### System Function Comparison
#### 6.5.2 Outsourcing Storage  
Cloud storage services in uncontrolled environments face challenges such as uncontrollability and high risks of data leakage. Existing data cleansing methods have coarse granularity and poor practicality, while current storage methods incur high computational  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Access Control** & **Trust Evaluation** & **Dynamic Authorization** \\ \hline \multirow{3}{*}{[16]} & \multirow{3}{*}{\(\mathsf{x}\)} & Support the dynamical & \multirow{3}{*}{\(\mathsf{x}\)} \\  & & trustworthiness evaluation of users & \\  & & and platforms by introducing & \\  & & multi-dimensional attribute sets & \\ \hline \multirow{3}{*}{[17]} & Support attribute-based efficient & \multirow{3}{*}{\(\mathsf{x}\)} & \multirow{3}{*}{\(\mathsf{x}\)} \\  & and dynamic access control for & & \\  & multiple datasets & & \\ \hline \multirow{3}{*}{[18]} & Support the access control with & \multirow{3}{*}{\(\mathsf{x}\)} & Support the witness of the \\  & provable credibility & & \\  & & & sharing by blockchain \\ \hline \multirow{3}{*}{ours} & Support fine-grained access control & \multirow{3}{*}{\(\mathsf{Support}\) continuous dynamic trust} & Support fine-grained \\  & based on different time, location, & assessment of multi-dimensional & dynamic authorization for \\  & and other attributes; & trust evaluation factors for data & data assets throughout \\  & & assets and users & their lifecycle \\ \hline \hline \end{tabular} Note.: “\(\mathsf{x}\)’ represents that this security feature is not available.  
\end{table}
Table 1: Comparison of security features in multi-party sharing.  
overhead and low efficiency. To address these issues, a novel method for cloud data cleansing and secure storage based on multi-dimensional equilibrium is proposed. For multi-source heterogeneous data, an efficient data transformation process and mapping rules are designed. By combining data attributes, the sensitivity of data is quantitatively assessed, and different weights are assigned to data sensitivity based on data categories. A combination of coarse-grained filtering and fine-grained control is used to achieve data deduplication. Based on data adaptive partitioning and lightweight encryption, a fragmented encryption storage scheme is designed to dynamically adjust the redundancy, dispersion, and security of data storage for different security levels. By constructing efficient retrieval and rapid recovery/reconstruction mechanisms for fragmented data using distributed multi-hash techniques, fast and reliable recovery and reconstruction of cloud data are achieved. From the comparison of security features provided in Table 2, it can be seen that the proposed approach optimally balances data storage security and efficiency, improving the extraction efficiency and recovery reliability of distributed secure storage in uncontrolled scenarios while ensuring data security.
## 3 Results and Discussion
### Case Study 1: Separating the Effects of Biomass from the Effects of \(D_{eff}\) Change
Tracing the signal due to this change in \(D_{eff}\) across all Chl \(a\) concentrations in this range in Figure 6B shows that there is a size related signal between 550 and 600 nm, but it is of an order of magnitude less than in the previous example, and so does not show potential for detection by satellite radiometry. This is illustrated further in the lower panel (C), showing the location of this signal but also that it is almost all attributable to biomass--as shown by the \(R_{rs}\phi\) representing \(D_{eff}\) 7 at 7.1 mg/m\({}^{3}\) i.e., what the higher biomass \(R_{rs}\) would look like without the increase in effective diameter as the assemblage changes. It can be seen quite clearly from these spectra that a difference in the blue due only to this \(\delta D_{eff}\), with any variability \(a_{gd}(\lambda)\), would not be detectable by any means. It should be noted that the spectral locations of maximum \(\delta D_{eff}\) features are a direct consequence of the spectral nature of the IOPs used in the modelling, and that both of these examples use the same Chl _a_-carotenoid refractive indices to generate the phytoplankton IOPs. In other words,  
Figure 5: A simulated transition from 6 to 16 \(D_{eff}\) with biomass 1 to 11 mg/m\({}^{3}\). Intermediate values of \(D_{eff}\) and Chl \(a\) are simply linearly interpolated. The lines highlight 435 nm and 570 nm, regions of maximum size signal, which are (at 435 nm) and are not (at 570 nm) sensitive to the effects of additional optical constituents. as phytoplankton IOPs are adjusted to represent pigment differences, the spectral character of the assemblage change will vary. A slight migration in the exact location of the maximum available \(\delta D_{eff}\) signal is observable with different ranges of \(D_{eff}\), although within the Chl _a_-carotenoid group it remains between 550 and 600 nm for any difference in \(D_{eff}\) between 1 and 40 \(\upmu\)m. (This is discussed in more detail later with respect to Figure 9, and an additional figure is shown in Appendix.)