esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Signal Processing-Based Integrated Multi-User Detection
## 3 Design of VSP-IMUD
### Integrated Multi-User Detection
Additionally, it is important to note that the matched filter chains are implemented in the digital domain, eliminating the need for radio frequency chains, which makes them more cost-effective compared to receiving antennas. We denote the decoded data vector using \(\mathbf{g}^{[M]}\) as \(\mathbf{x}^{[M]}\). It is important to note that two possible forms of \(\mathbf{x}^{[M]}\), namely \(\mathbf{x}^{[M]}\) and \(-\mathbf{x}^{[M]}\), may arise due to the symmetric nature of the constellation points with respect to the origin. However, only one of \(\mathbf{x}^{[M]}\) and \(-\mathbf{x}^{[M]}\) corresponds to the correct data vector sent from the \(K\) Txs. To determine the exact data vector, we use ZF to recover the data from the Tx with the largest channel gain relative to the RX, and adopt this as referential data for determining the exact data vector. In Figure 2, we illustrate the realization of VSP-IMUD using binary phase shift keying (BPSK) as an example. As the figure shows, with \(K\) Txs and \(L=2\), two data vectors \(\mathbf{x}^{[M]}\) and \(-\mathbf{x}^{[M]}\) correspond to the same MF vector \(\mathbf{g}^{[M]}\). We denote these two symbol sets as \(\Phi_{C}\) and \(-\Phi_{C}\), respectively. Since the elements in \(\Phi_{C}\) and \(-\Phi_{C}\) are opposite, we use ZF to decode the data sent from the Tx with the largest channel gain to obtain the correct symbol set. We denote this referential symbol as \(x_{B}\) (where \(B\in\{0,1,\cdots,K-1\}\)). We then compare the referential symbol \(x_{B}\) with the symbol under the same index in sets \(\Phi_{C}\) and \(-\Phi_{C}\). If there is an \(x_{B}^{*}\) in \(\Phi_{C}\) that matches \(x_{B}\), we can determine that \(\Phi_{C}\) is the correct symbol set. Otherwise, \(-\Phi_{C}\) becomes the final decoding output. In practice, due to the randomness of the Tx channels, the Euclidean distance between two rs may be very small, i.e., \(\|\mathbf{r}^{[i]}-\mathbf{r}^{[j]}\|\leq\varepsilon\) (for \(i,j\in\{1,2,\cdots,L^{K}\}\) and \(i\
eq j\)), where \(\varepsilon\) is a small positive real number. This situation can lead to incorrect outputs from the Rx structure shown in Figure 2, resulting in a high bit-error rate (BER). We refer to this condition as channel ambiguity, and we will design an ambiguity detection and elimination method in the following subsection.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Comparison with References  
The chart shown in Figure 7 presents a visual comparison of throughput (TPS) and latency (in seconds) for different system configurations, illustrating the performance of the baseline, State Channels, and Rollups from various references, including our system. Our system is competitive in terms of throughput (TPS), particularly in comparison to State Channels and Rollups. Our baseline achieves a throughput of 800 TPS, which is comparable to specific sources but significantly underperforms compared to State Channels, which scale performance to 2500 TPS, and Rollups, which scale throughput to 3500 TPS. This improvement in throughput, achieved using both State Channels and Rollups, demonstrates the scalability of our system and its ability to process large volumes of transactions effectively.  
Comparing latency (s), the chart is clear, indicating that our system has a lower latency than several other references. The latency of our system is 5 s, which serves as our baseline; this is competitive because there are other systems with latencies ranging from 5.5 to 6.2 s. The latency is minimized by using State Channels, ensuring that it takes 2 s. Even with Rollups, the latency drops to 1.5 s, surpassing most other setups. Such a tremendous decrease in latency portends that not only can our system support more transactions, but it can also process them faster, which is exceedingly efficient in real-life situations where speed and throughput are crucial.  
Compared to the data from other literature sources (including [33; 34; 35; 36; 37; 38; 39; 40], our system also performs reasonably well in terms of throughput and latency. The rollups used in our system are better than other reference sources, both in terms of TPS and latency, which shows its strength and sustainability in most instances. Our system is quite competitive, whereas the performance of other systems fluctuates, as they do not consistently record the highest throughputs and the lowest latencies as frequently as Rollups do. This makes  
Figure 7: Detailed comparison of throughput (TPS) and latency (s) across various scenarios, with data from multiple references.  
our model an ideal choice in scenarios that require high transaction volumes and short processing times.
## 3 Results and Discussion
### Development of the Calibration Model
Scattering effects are primarily related to textural inhomogeneity due to variations in protein fiber arrangements and to the presence of intramuscular fat and connective tissue [30]. Figure 2: Plots for measured (Ref) versus estimated (NIR) values for moisture, ash, protein, and fat content of traditional sausages. Dashed lines represent the reference lines for the expected behavior. Solid lines are the actual regression lines. In this study, 39 samples were used for the development of the calibration model and many of these samples were produced in the same butcher shops. However, the great variability between production batches [6] allowed the use of these samples in the development of the calibration model. Additionally, the calibration model was initially constructed with 33 samples, as reported in the study of [PERSON] et al. [32]. The additional 6 samples, used in the construction of this model were purchased from completely different butcher shops than the ones used for the first calibration model. It was observed that there were little differences in the calibration statistics between the two models indicating that the possibility to further improve the model was limited. This finding is attributed to the fact that the same level of variation was maintained. In any case, analysis of a greater number of samples results in better calibration statistics. However, the acceptable level of accuracy defines the number of analyzed samples since it is also associated with increased analytical costs.
# Practical Performance Analysis of Interference in DSS System
## 4 DSS Interference and Solutions
### Rate Matching
Through theoretical analysis, it is known that DSS cells will cause inter-standard interference due to resource conflict, which needs to be avoided by rate matching. At the same time, the interference of the DSS cell by the neighbor base station can also be solved by rate matching. Figure 9: Schematic diagram of rate matching. We simulated and analyzed the interference caused by the LTE to NR downlink caused by the rate matching scheme, and the results are shown in Figure 10.  
In Figure 10, Normal is the throughput when NR is not interfered, Interference-CRS is the throughput when NR is interfered by LTE CRS, and Rate match-CRS is the throughput after rate matching is used when NR is interfered. It is obvious that when NR is interfered by LTE-CRS, its throughput will be reduced up to 56%, which shows that the interference of LTE to NR is very serious. After the CRS rate-matching scheme is adopted, the rate of NR will be reduced by about 25% compared with that without interference, but the throughput of NR will be increased by about 60% when it is interfered. It can be seen that although the rate-matching scheme will bring a certain amount of resource overhead, it can ensure that the NR throughput will not drop significantly when the interference is heavy, and ensure the user's network experience, which is an effective interference mitigation solution. We compared the two schemes of rate matching and buffer setting, and the simulation results are shown in Figure 8:0  
It can be seen in Figure 11 that the effects of the buffer setting solution and the rate matching solution are different. The performance improvement effect of the buffer setting solution is inconsistent at each point: the farther the distance from the observed base station,  
Figure 8:1 Simulation of rate matching. Figure 8:2 Simulation of rate matching. the better the interference mitigation effect, up to 99%. The closer the distance, the less the improvement effect, down to 3%. Instead, the rate-matching solution can steadily improve the interference mitigation performance at all locations, about 60%. In summary, both the rate-matching scheme and the buffer setting scheme can theoretically reduce the neighbor interference. However, the effectiveness of the theoretical analysis and the advantages and disadvantages of the two solutions need to be verified by field tests.
## 3 Results
### Data Stability  
Figure 22 shows the two-weeks sampling of the sleep rhythm of three persons. We chose these three persons randomly from the total sample of 50 to evidence the data behavior in the sampling period stability. The visual statistical analysis (Figures 23-25) shows that the sleep rhythm is close to be identified as Gaussian distribution. We observe how the three figures have key characteristics of a Gaussian distribution. Figure 23 shows a histogram, which follows a Gaussian distribution with the data obtained. Figure 24 shows a box plot, which also shows the characteristics for a Gaussian distribution; it is symmetrical and has a median centered. Finally, Figure 25 shows a Q-Q plot, which
## 6 Conclusions and Future Work  
Among all the fault diagnosis methods based on the dependency model, the sequential diagnosis strategy-based method is only available for single fault diagnosis, while the method based on the maximum posterior probability can only provide an optimal solution which is easy to be trapped in the local optimal search. The proposed method can overcome the aforementioned problems according to the following strategies:  
* The proposed method is designed for multiple faults diagnosis. The potential multiple faults are modeled by formulating a multiple fault dependency matrix based on the test results and the dependency matrix;
* Each multiple fault type is assigned a possibility based on the prior probability of the original fault status. The possibility value of each fault type provides a basis of ranking and judging the failure for the following diagnosis and maintenance work.  
In theory, a fault can be detected in a timely manner after it occurs. In fact, the time duration between when a fault occurs and when it is detected is based on the working principle and time delay of the sensors. The rationality and effectiveness of the proposed method are verified by the illustrative experiment and a case study. The experiment result, which is also compared with some other methods, shows some of the superiorities of the proposed method.  
Possible future work may focus on the following. Firstly, the proposed method mainly focuses on the 'abrupt faults', which can be regarded as 'hard faults'. It should be noted that if an incipient fault like engine cracks or bearing cracks, which is a'soft fault' changing with time, can be detected by a test for modeling the dependency model, then the proposed method is also available for the incipient faults. Otherwise, the proposed method should be extended for addressing incipient faults, which is a good direction for the following work. Secondly, the proposed method can be used to perform online fault diagnosis by programming in association with specific problems in practical engineering. Thirdly, the current method mainly focuses on the known fault types modeled in the dependency matrix, how to address the potential unknown fault types, which may be masked by the control law and cause misclassification, which is another open issue. Fourthly, how to address the dependent fault states based on the dependency matrix needs further study.  
Conceptualization, X.L., D.Z., L.M. and Y.T.; Formal analysis, X.L., L.M., and Y.T.; Methodology, X.L., L.M., and Y.T.; Project administration, D.Z.; Resources, D.Z.; Software, X.L.; Supervision, D.Z.; Validation, X.L. and L.M.; Writing--original draft, X.L. and Y.T.; Writing--review and editing, X.L., L.M., and Y.T.  
This research was partially funded by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under grant number CX201705.  
The authors greatly appreciate the editor's encouragement and the four anonymous reviewers' valuable comments and suggestions to improve this paper.  
The authors declare no conflict of interest.
## 3 Proposed Method
### Space-Spectrum Adaptive Multi-Manifold Analysis
Combining spectral and spatial information of HSIs for feature extraction could further improve the performance of dimensionality reduction. Therefore, this study proposed a dimensionality reduction algorithm based on SSAMMA. The algorithm extracted feature information from HSIs in both spatial and spectral dimensions and gradually obtained the intra- and inter-manifold scatter matrixes of each submanifold \(\mathrm{M}_{r}\). By combining spatial and spectral information, the multi-manifold structure in hyperspectral data could be effectively revealed. The intra-manifold scatter matrix of submanifold \(\mathrm{M}_{r}\) could be represented by Equation (21), and the inter-manifold scatter matrix could be represented by Equation (22). \[\max_{\mathrm{V}_{1},\mathrm{V}_{2},\cdots,\mathrm{V}_{c}}J(\mathrm{V}_{1}, \mathrm{V}_{2},\cdots,\mathrm{V}_{c}) \tag{10}\]3  
Combining the two equations, we can find Equation (23). \[\max_{\mathrm{V}_{1},\mathrm{V}_{2},\cdots,\mathrm{V}_{c}}J(\mathrm{V}_{1}, \mathrm{V}_{2},\cdots,\mathrm{V}_{c}) \tag{10}\]4  
where \(\alpha\) and \(\beta\) are parameters that balance spectral and spatial information, \(\lambda_{r}\) is the eigenvalue in Equation (23), and the \(d\) eigenvectors corresponding to the d eigenvalues arranged from large to small can be obtained. The best projection matrix is composed of these eigenvectors, that is, \(\mathrm{V}_{r}=[\mathrm{V}_{r1},\mathrm{V}_{r2},\cdots,\mathrm{V}_{rd}]\). For \(c\) sub-manifolds, \(c\) projection matrices \(\mathrm{V}_{1},\mathrm{V}_{2},\cdots,\mathrm{V}_{c}\) can, in turn, be obtained. If the low-dimensional embedding of X in each sub-manifold is \(\mathrm{Y}_{1}=\mathrm{V}_{1}{}^{T}\mathrm{X}\), \(\mathrm{Y}_{2}=\mathrm{V}_{2}{}^{T}\mathrm{X}\)\(\cdot\cdot\cdot\), \(\mathrm{Y_{c}}\) = \(\mathrm{V_{c}}^{T}\mathrm{X}\), the final low-dimensional embedding of fusing different sub-manifolds can be obtained, and the final low-dimensional embedding \(\mathrm{Y}\) can be represented by \(\mathrm{Y}=[\mathrm{Y_{1}},\mathrm{Y_{2}},\cdots\mathrm{Y_{c}}]\).
# Continual Learning for Table Detection in Document Images
## 2 Related Work
### Table Detection with Deep Neural Networks  
Research in table analysis has experienced a remarkable improvement with the introduction of neural networks [6; 10; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. [PERSON] et al. have thoroughly surveyed these studies in [8], where they also covered the heuristic detectors.  
[PERSON] et al. [38] applied Convolutional Neural Networks (CNNs) to retrieve spatial features from a document and merged them with PDF meta-data to detect tables. [PERSON] et al. [10] formulated table detection as object detection and used a generic detector, Faster R-CNN [9], to detect tables in document images. In a similar line of work, ref. [29] incorporates deformable networks [39] to tackle varied tabular layouts. [PERSON] et al. [40] exploited Mask R-CNN [41] to improve overall performance table, figure, and formula detection in document images. Cascade Mask R-CNN [42] has also been employed to enhance the performance of table detection with a conventional backbone network [13] and recursive feature pyramid networks [14]. The recently proposed Hybrid Task Cascade network [11] was merged with deformable convolutions to produce impressive table detection results [12].  
In addition to object-detection-based methods, earlier methods leveraged the concepts of Fully Convolutional Networks (FCN) [43; 44] and Graph Neural Networks (GNN) [37; 45] to resolve table detection in document images. It is essential to emphasize that all prior works in the field of table detection heavily rely on dataset-specific training to yield satisfactory results.
# Review of Photodetectors for Space Lidars
## 3 Space Lidar Detectors from Short-Wave to Mid-Wave Infrared
### Radiation Damage and Annealing of HgCdTe APD Arrays  
The HDVIP APD arrays were tested with gamma rays up to 100 krad(Si). The APD arrays without the ROIC were tested with protons to 7.5 \(\times\) 10\({}^{11}\) protons/cm\({}^{2}\) fluence (100 krad(Si) ionization dose) [62]. The APD arrays with the ROIC were tested with protons to 30 krad(Si). These proton fluence and ionization doses are many times the likely space radiation exposure for lidar detectors. There were no measurable changes in the APD performance from the gamma-ray irradiation. The major effect of proton irradiation was the increase in the dark current at a rate of about 3 \(\times\) 10\({}^{-22}\) A/proton/cm\({}^{2}\), which started to rise above the noise floor at about 10\({}^{10}\) protons/cm\({}^{2}\) (1-2 krad(Si)). Transient protons produced large output pulses equivalent to tens of thousands of photons, which saturated the electronic circuit, for a few \(\upmu\)s. It was also found that the dark current increase due to radiation increased by 2-3 orders of magnitude after the APDs were warmed up to room temperature and cooled down again. However, heating the APD arrays at 80 \({}^{\circ}\)C for several hours completely annealed the radiation damage and brought the dark current back to the original values. The HgCdTe APD arrays from Leonardo UK were also tested with gamma rays and protons [55]. There were no measurable changes in the APD performance after 29 krad(Si) of gamma-ray irradiation. There were no changes in APD responsivity up to 5.9 \(\times\) 10\({}^{10}\)/cm\({}^{2}\) proton fluence. The dark current of the Leonardo UK devices due to proton irradiation was measured by NASA GSFC with the APD array wire-bonded to a ROIC with CTIAs [63]. The dark current from radiation damage also went up significantly after warming up the device to room temperature and cooling it back down again. The dark current returned to the previous level after heating the APD to 70 \({}^{\circ}\)C for several hours. Based on these test results, the HgCdTe APD arrays can be used in a typical space radiation environment for a multi-year mission. The APD arrays should be cooled continuously and not be warmed up unless necessary. There should be provisions to heat the APD array to 70-80 \({}^{\circ}\)C for a few hours when the dark current becomes high.
## 3 The Method Presented in This Paper
### Cold Wave Recognition Process
#### 3.1.3 Classification Optimization Approach Based on Continuous Cold Wave Events  
The cold wave event process exhibits persistent characteristics, and its impact on wind turbines typically lasts for several hours. For short cold waves with a duration of two hours or less, their occurrence can be categorized into two cases:  
* Transient fluctuations within a long cold wave: In the case of a prolonged cold wave, the temperature may temporarily rise, but the wind turbine continues to be affected by the cold wave.
* Isolated short-term cold wave: This refers to a brief cold wave event that appears sporadically in a time series that has not experienced a cold wave for a long period. These short-duration cold waves have a relatively small impact on wind power and can be treated as normal weather conditions.  
Figure 6: Gans structure.  
Given the particularities of short cold wave events, continuous optimization of the cold wave occurrence judgment is necessary to improve the accuracy of future cold wave forecasting. The first optimization step merges short-term fluctuations, which may cause misjudgments, into the ongoing cold wave period. The secondary optimization step combines isolated short-term cold wave events with normal weather, thereby avoiding misclassification and improving the classification results. The specific method for this optimization process is shown in Figure 7.
# Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems
## 3 Establishment of a Task Allocation Model
### Solving the Task Allocation Evaluation Factor Model
#### 3.2.1 Solution of the Speed Evaluation Factor  
At a certain moment, agent \(i\) and target \(g\) are, respectively, in the positions shown in Figure 1. The black triangle represents agent \(i\), and the red circle represents target \(g\). The speed of agent \(i\) is \(v_{i}\), \(v_{i}=\left|\mathbf{v}_{i}\right|\), and the angle between the moving directions of agent \(i\) and target \(g\) is \(\alpha_{ig}\). When agent \(i\) selects target \(g\) as the target, it will ultimately generate a speed that tends to be consistent with the direction of target \(g\). Therefore, the speed that agent \(i\) requires to change is as follows:  
\[v_{\mathbf{q}}=v_{i}\sin\alpha_{ig} \tag{6}\]  
\[v_{\mathbf{p}}=\left\{\begin{array}{ll}v_{i}\cos\alpha_{ig},&0\leq\alpha_{ig} \leq 90^{\circ}\\ -v_{i}\cos\alpha_{ig},&90^{\circ}\leq\alpha_{ig}\leq 180^{\circ}\end{array}\right. \tag{7}\]  
Figure 1: Schematic diagram of the position of the agent and target at a certain moment.  
We can design a fuzzy logic inference system using \(v_{q}\) and \(v_{p}\) as the input variables for solving the speed efficiency factor, and the speed efficiency factor \(P_{v}\) as the output variable. The domain of each variable is set as follows:  
\[v_{q} \in\left\{0,\frac{1}{4}v_{i},\frac{1}{2}v_{i},\frac{3}{4}v_{i},v_{i}\right\}\] \[v_{p} \in\left\{-v_{i},-\frac{3}{4}v_{i},-\frac{1}{2}v_{i},-\frac{1}{4} v_{i},0,\frac{1}{4}v_{i},\frac{1}{2}v_{i},\frac{3}{4}v_{i},v_{i}\right\}\] \[P_{v} \in\left\{-v_{i},-\frac{3}{4}v_{i},-\frac{1}{2}v_{i},-\frac{1}{4} v_{i},0,\frac{1}{4}v_{i},\frac{1}{2}v_{i},\frac{3}{4}v_{i},v_{i}\right\}\]  
We can define fuzzy set language variables and select appropriate fuzzy word sets for each variable, as show in Table 1.  
The fuzzy language variables corresponding to \(v_{q}\) include z0, ps, pm, and pb. The fuzzy language variables corresponding to \(v_{p}\) include nb, nm, ns, z0, ps, pm, and pb. The fuzzy language variables corresponding to \(P_{v}\) include nb, nm, ns, z0, ps, pm, and pb.  
Next, we can determine the fuzzy rules of inference. The following fuzzy rules of inference are designed according to the constraints of the agents and other actual conditions, combined with existing experience. They are listed in Table 2.