esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
It is important to summarize the development of the solution for CRCP in the light of major benchmark studies done in the past to understand the adequacy of the solution. In the United States, different state agencies conducted comprehensive field investigations for evaluating the cracking characteristics of in-service CRCP sections [10; 17; 19; 49]. It was reported that the cluster crack formation and the crack randomness could not be effectively eliminated by modifying the design parameters and construction practices. The passively induced crack pattern often led to the development of cluster cracks, Y-cracks, and divided cracks. Field findings indicated that approximately 90% of all punch-outs on CRCP sections were triggered by clusters of closely spaced cracks [45].  
To eliminate the randomness in the transverse crack pattern, the active crack control method in form of shallow saw-cuts and automated tape insertions were used to induce cracks over the pre-determined locations in CRCP sections. Regular crack surveys were performed to evaluate the cracking pattern of active crack control CRCP sections. Both techniques were found quite effective in inducing more uniform crack patterns which was the original intention of the active crack control method [8; 21; 50; 51]. However, there are some demerits associated with both the mentioned techniques. The tape insertion technique causes construction disruption at every insertion point, whereas the thorough saw-cut results in concrete spalling over every cut location.  
While in the case of Belgium, the partial surface saw-cuts not only effectively fulfilled the purpose of the active crack control method but also eliminated the issue related to concrete spalling without any construction disruption [12; 22]. The concept of partial surface saw-cuts has been numerically evaluated in this study as detailed above. The overall configuration of the CRCP structure in Belgium is not quite different from that being used in the United States and other parts of the world. Therefore, the partial surface saw-cuts instead of thorough saw-cuts and automated tape insertion could be implemented for fulfilling the main purpose of the active crack control method.
## 4 Discussion
### Limitations and Further Research  
This paper has several limitations that suggest directions for future research. First, apart from the four kinds of customer-centric relationships, information quality and flow, other factors affecting participation intention may exist, such as community identification and emotional stimulation when participating in a brand community. Future research can examine their effects. Second, we only collected data in the Sina micro-blog platform, neglecting differences from other micro-blogs platforms, such as, Twitter and Tencent Weibo. Thus, we believe that additional research should collect data from different platforms. Third, we mainly conducted a cross-sectional research. However, this research may not fully capture the long-term behavior of customers in a brand community. Thus, a longitudinal research may provide additional insights into the development of user behavior. Fourth, brand types may influence customers' behavior, while due to the small sample size; we did not compare the effects of the four kinds of customer-centric relationships (customer-brand, customer-marketers, customer-platform and customer-others) on brand community participation in the current study.  
Future research can compare the effects of the four kinds of customer-centric relationships on brand community participation.  
This study is supported by the Humanity and Social Science Youth Foundation of Ministry of Education (No. 17 YJC630223), and China Postdoctoral Science Foundation (No. 2017M612299).  
The author declares no conflict of interest.
## 2 Stereo Reconstruction Algorithm for Unstructured Scenes
### SAD-FAST Feature Detection and Recombination Stereo-Matching System
#### 2.1.4 Mahalanobis Distance  
Euclidean distance treats the differences between different dimensions as equivalent, which is very likely to lead to mismatching of feature points. The Mahalanobis distance corrects this shortcoming by considering the relationship between various characteristics. In addition, cross-matching was added to the calculation, for which it is commonly understood that, after a feature point in the matching image searches for a corresponding point in the image to be matched, the corresponding point returns to search for a feature point in the matching image, and the two points derived from the two processes are observed to see if they correspond; if so, they are regarded as a pair of matching points. The formula for calculating the Mahalanobis distance between two feature points \(X\) and \(Y\) is shown below.  
\[D_{(x,y)}=\sqrt{(X\quad Y)^{T}S\ ^{1}(X\quad Y)} \tag{12}\]  
\[S=\text{cov}(X,Y)=Ef[X\quad E(X)][Y\quad E(Y)]g \tag{13}\]  
where \(S\) is the covariance matrix of the two feature points and \(E\) is the mean value. When the covariance is the identity matrix--that is, each dimension is independently distributed--the Mahalanobis distance becomes the Euclidean distance. The method substantially reduces  
Figure 5: Flow chart for the improved SAD-FAST detection algorithm.  
the risk of mismatching and demonstrates a good matching effect and real-time performance with only a subtle increase in the number of operations.
## 4 Results
### Dependence of the Transfer Quality on the Size of Dictionaries
The values of the correlation coefficients in the third and fourth blocks are noticeably smaller than in the first and second, respectively. It is a natural result that an increase in the number of words to which rankings need to be extrapolated leads to a deterioration in quality. 2. The size of the target vocabulary is fixed while the source is changed. This is shown in Figures 3 and 4 by moving from the first block of columns to the second and from the third to the fourth. Increasing the size of the training set of 1000 words to a complete dictionary does not lead to a noticeable improvement in the results. The quality of the ratings obtained due to the transfer can be assessed as sufficiently high. Thus, the quality of the cross-lingual transfer remains practically at the same level. This result is somewhat unexpected. A smaller amount of training data, it would seem, should lead to a deterioration in the results, which we have in the case of extrapolation within one language (Table 1). However, with a cross-lingual transfer, the situation changes, and an increase in the amount of training data does not improve the result. All the information sufficient for the qualitative transfer of ratings from one language to another is already contained in the 1000 most frequent words, and rarer words do not add useful information. Perhaps this is due to the quality of the multilingual semantic space, in which the correspondence between languages is well established for more frequent words and worse for rare ones. However, this issue requires a separate additional study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source/Target** & **EN** & **DE** & **NL** & **RU** & **HR** \\ \hline EN & - & 0.77 & 0.69 & 0.80 & 0.68 \\ DE & 0.83 & - & 0.72 & 0.81 & 0.68 \\ NL & 0.82 & 0.77 & - & 0.74 & 0.67 \\ RU & 0.79 & 0.73 & 0.61 & - & 0.67 \\ HR & 0.83 & 0.75 & 0.66 & 0.79 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [PERSON]’s correlation coefficient for MS-MiniLM model, 1000-word dictionaries. In the table, \(p\)-values for correlation coefficient values are less than 0.01. The data for the distilled multilingual BERT and multilingual BERT models on 1000-word dictionaries are presented in Appendix A (Tables A6 and A7). The results of these models, as well as in the previous experiment, turned out to be somewhat worse. For example, the MS-MiniLM model has five coefficients greater than or equal to 0.8, while the distilled multilingual BERT has none at all, and the multilingual BERT has only one. The MS-MiniLM model has no coefficient less than 0.6, while multilingual BERT has three, and distilled multilingual BERT has six such cases.
# Bit-Level Construction for Multiplicative-Repetition-Based Non-Binary Polar Codes +
## 3 Bit-Level Construction for Non-Binary Polar Codes  
In this section, we introduce the bit-level construction for MR-based non-binary polar codes and propose improved bit-level constructions based on the CD method and the MC method, respectively.
## 2 Materials and Methods
### Data Analysis  
The marker trajectories were smoothed with a 12-Hz low-pass Butterworth filter, and ground reaction force data were filtered at 60 Hz [28]. A custom-written MATLAB (MathWorks, Natick, MA, USA) script was used to convert the filtered marker trajectory and ground reaction force data into an OpenSim-suitable format. A static calibration trial was performed before the running trial in order to optimize individual segment dimensions. The angular displacement of the joints was calculated for each stride using inverse kinematics and inverse dynamics algorithm tools in OpenSim (SimTK v. 4.0.1) were then used to calculate joint moments [35]. Joint flexion is expressed through negative values, while joint extension is expressed through positive values. Kinematic and kinetic datasets were time normalized for the stance phase (0-100%) in order to use time-series comparison. Kinetic variables were normalized to body weight, and COP was adjusted based on leg length to reduce height's influence [33]. To match dynamic results with applied forces, OpenSim's Residual Reduction Algorithm (RRA) was used to conduct residual reduction on the model. RRA modifies the model's center of mass by estimating and applying external forces and moments operating on or around each pelvic axis [36].  
The raw EMG signals were filtered in the frequency range of 10-500 Hz using a bandpass fourth-order Butterworth filter [37]. The filtered EMG amplitudes were smoothed with a root mean square (RMS) with a window size of 50 ms. Next, the EMG signalwas normalized to the peak MVIC amplitude, and the dataset was then time normalized (0-100%) to the stance phase of the running trials.
## 5 Conclusions  
This paper proposed a multilayer measurement methodology with open-source tools, i.e., _iperf3_, _ping_, and _iw_, for evaluating IEEE 802.11 ad communication. IEEE 802.11 ad is one of the most promising wireless communications in the beyond 5G and 6G era. However, compared with existing Wi-Fi networks, various factors easily affect communication quality due to the high carrier frequency. It is necessary to measure various metrics of communication qualities to build an optimal network, and it is desirable to establish a simple and inexpensive measurement method for non-specialists, such as end-users, to use easily.  
To this end, this paper employed three open-source tools: _iperf3_, _ping_, and _iw_. _Iperf3_ and _ping_ mainly evaluate the IP layer indicators of throughput, jitter, packet retries, packet loss, and RTT. These indicators are considered relatively familiar to non-specialists because they are relevant to the communication quality of an application. However, they do not necessarily represent the quality of the wireless link, so indicators are also needed to understand the characteristics of the wireless link.  
On the other hand, the RSSI and Tx bitrates of the _iw_ results were used to investigate the quality of the wireless link. Although RSSI is a familiar metric for end-users, it is difficult to fully assess communication quality from only this metric. Thus, this paper proposed the use of Tx-bitrate as a supplemental indicator. Since the Tx-bitrate indicator shows the percentage of bitrate values used, end-users can intuitively understand the wireless link quality. Furthermore, two types of errors may be used as other supplemental indicators. The appearance of the errors is helpful for indicating that the wireless link quality is starting to deteriorate or has already deteriorated.  
This paper also presented three specific measurement methods and measurement results. We consider that these measurements can provide a measurement method needed by users. The next step of this study is to develop integrated applications with them.  
Conceptualization, S.K., M.T., A.H. and H.Y.; data curation, S.K.; formal analysis, S.K. and M.T.; funding acquisition, A.H. and H.Y.; investigation, S.K. and M.T.; methodology, S.K., M.T., A.H. and H.Y.; project administration, A.H. and H.Y.; resources, M.T. and H.Y.; software,S.K., M.T. and A.H.; supervision, A.H. and H.Y.; validation, S.K. and M.T.; visualization, S.K.; writing--original draft, S.K.; writing--review and editing, S.K., M.T., A.H. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Internal Affairs and Communications in Japan (JPJ000254).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
We would like to thank the anonymous reviewers for their valuable comments and feedback.  
The authors declare that they have no conflict of interest.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 3 Problem Description and Formulation
### Constraints
#### 3.3.7 Dependency and Execution Time Constraints  
In scenarios where subtasks have dependencies, the completion time of a subtask \(T_{ij}\) is determined not only by its own resource consumption, but also by the completion of preceding tasks. This constraint ensures that the completion time of \(T_{ij}\) is bounded by the completion of task \(T_{ik}\) and the delay \(\Delta t_{ik}\) that accounts for synchronization and data transfer between tasks. The following relation must hold for Equation (7):  
**Remark 2** (Fault Tolerance).: _The above fault tolerance mechanism is predicated on the system's capability for real-time fault detection and response, ensuring that when a single satellite or some of its resources fail, the remaining satellites can utilize their available resources to complete the tasks, thereby maintaining the overall system performance without significant degradation._
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 6 Results and Discussion
### Distillation Loss  
To isolate the impact of the chosen distillation loss, we compare the average return of students trained using each of the three proposed loss functions, with the same SAC teacher and a student-driven control policy. Distilling a stochastic policy (learning \(\sigma\)) and using this to collect training data will increase exploration and therefore widen the state distribution in the replay memory. If the student is trained using Equation (2) instead, the replay memory will only contain deterministic trajectories, which are not always optimal (see Table 1).  
As a baseline, we start by comparing the MSE-based loss originally proposed by [PERSON] et al. (2018) between mean actions \(\mu\) to our Huber-based loss functions ((2) and (4)), as well as an analogous MSE loss with both \(\mu\) and \(\sigma\). The results of this are shown in Figure 6. The students trained using our baseline Huber-based loss converge much more quickly and obtain an average return that is 18% higher on average. This confirms the benefit in the context of distillation of the Huber loss being less sensitive to outliers and having a smoother slope for larger values. However, it is also notable that learning the state-dependent value of \(\sigma\) through an auxiliary MSE or Huber loss does not yield any noticeable benefit; instead, this results in a comparable average return to when only the mean action is distilled in this experiment.  
Figure 6: The average return obtained using either an MSE or Huber-based distillation loss.  
Looking at Figure 7, we do see that our proposed loss based on the KL-divergence (Equation (8)) to transfer the action distribution performs significantly better than those based on a Huber loss. This does align with our hypothesis that the loss landscape when shaping the probability distribution of the student to match that of the teacher more closely is smoother than learning the two concrete values independently, leading to a better optimization. Learning these values separately, as was the case in Figure 6, precisely enough to accurately model the distribution might also require more capacity, resulting in this approach suffering more heavily from the limited capacity of the student. We test this conclusion more extensively in Section 6.4 by comparing these results with different student sizes.
## 6 Experimental Results  
In order to evaluate our algorithm, a number of experiments was designed and executed. In this section, all of the steps of the algorithm are examined in terms of performance. Moreover, the use of the compressed sparse row format instead of the full sparse conflict matrix is analyzed. Two other issues that are discussed are the improvement that the greedy steepest descent algorithm adds to the genetic algorithm and the impact of the tournament selection size as the population grows, in the performance of the algorithm. In the end, the quality results obtained are shown. From the thirteen datasets, in the speedup figures, only four are presented, so that the figures remain easily readable. However, special attention was given in the selection of the datasets to be presented. The datasets chosen are _pur-s-93_,due to its large size and the special design options that it requires, _car-s-91_, as the largest from the remaining ones, _hec-s-92_, as the smallest one, but with the highest conflict density, and _Ise-f-91_, as a medium-size dataset with small conflict density. All of the experiments were conducted in work stations, containing one NVIDIA graphics card GTX580 with 512 cores. The CPUs used had an Intel i7 processor at 3.40 GHz with 16 GB RAM and a solid state disk. It should be mentioned that the serial approach discussed here is single threaded. Given that evolutionary algorithms are stochastic processes, many runs of the algorithm with different parameters were done, and parameters that gave good results were chosen. Only for the speedup section, it should be mentioned that the value of 50% was chosen as a crossover probability in order not to help the GPU achieve better time and, thus, be unfair in the comparison with the CPU, due to divergent execution. Furthermore, it should be added that the selected values are not necessarily the best for all of the datasets, and of course, all of the results depend on the initial seed of the random generator. Parameter values were chosen after a parameter sweep that was performed. Therefore, the crossover probability has been tested for values ranging from a 10% to 95%, mutation probability for values ranging from 0.1% to 2% and tournament selection size assumed values: pop/64, pop/32, pop/16 and pop/8. Runs showed that value pop/32 struck a balance between good results and fast execution times. Regarding the _curand_ library, the default pseudorandom generator, XORWOW, and the default ordering were used.
###### Abstract
The paper also presents a novel approach of using data preprocessing techniques such as \"CutMix\" and'mixup' and making changes to the model's parameters and structure to improve the accuracy of FaceNet. This research is a significant step forward in the field of face recognition technology and its application in the era of mask wear. To further guide our research, we introduce the following _research questions_. 1. How can facial recognition algorithms, particularly FaceNet, be improved to accurately recognize faces that are partially or fully obscured by masks? 2. What techniques can be used to effectively utilize data from non-occluded regions of masked faces to improve recognition performance? 3. What approaches and models are most appropriate for recognizing masked faces? 4. How can recognition performance be improved in masked face datasets, including in scenarios where both training and test images are masked? 5. What solutions can be implemented to overcome the decline in recognition performance when faces are concealed? The remainder of the paper is organized as follows. Section 2 presents an overview of the state-of-the-art. Section 3 discusses the methods for masked face detection, describes the construction of the datasets, explains the image augmentation methods, and describes the proposed method. Section 4 presents the results of experiments and presents the results of the ablation study. Section 5 provides answers to research questions and discusses the limitations of the study. Finally, Section 6 presents the conclusions.
## 4 Experimental Results and Analysis
### Attention Ablation Experiment  
Since the localization module of this model incorporates the attention mechanism, a comparison is made between the training results of the standard YOLO-V5 model and those of the model after each attention module is added, in order to validate the effectiveness of the proposed improved algorithm. The results of the ablation experiment are shown in Table 2.  
By comparing the performance metrics of different network models, the following conclusions can be drawn. The model proposed in this paper performs the best in terms of miss rate and false positive rate, with values of 4.2% and 3.6%, respectively, and also achieves a high accuracy of 87.5%. In comparison, YOLO-V5+CBAM slightly outperforms the basic YOLO-V5 in terms of miss rate, but has a lower false positive rate and accuracy. YOLO-V5+SE performs best in terms of accuracy, but has a relatively higher miss rate and false positive rate. The YOLO-V5+CA model has a relatively higher miss and false positive rates and lower accuracy. Considering the overall performance of the models, selecting the model that best suits the specific task requirements is crucial.
# A Cost-Optimized Model for Dynamic Port Hinterland
## 4 Experimental Design and Results
### Results: Representing the \(H(x,y)|_{(p_{n},\rho_{n})}\)
#### 4.2.3 Regional Competition Analysis  
In the Central Plateau region, particularly Madrid, inter-port competition demonstrates dynamic economic efficiency, with competitive advantages fluctuating according to cargo types and seasonal variations.  
In the Mediterranean Corridor, Valencia and Barcelona's port competition is characterized by dynamic boundaries defined by transport costs, service offerings, and infrastructure capabilities, with rail connectivity significantly influencing their respective economic influences.  
In the southern region, while Algeciras maintains overall dominance, interesting competitive dynamics emerge with Valencia for specific cargo types and origins. The analysis shows that product characteristics strongly influence port selection in this region, with time-sensitive goods following distinctly different patterns from standard containerized cargo.
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
## 4 Discussion of Simulation Results  
We select aircraft carrier targets and airplane targets, respectively, to carry out a SAR image simulation. The material property parameters can be flexibly configured according to the actual situation. In order to fully verify the validity of the method in this paper, the single and multiple scattering simulation results of the aircraft carrier target are compared, which are used to verify the reliability of the multiple backscatter field computation method proposed in Section 2.2 of this paper. The simulation results are compared with the real SAR images to verify the fidelity of the simulation results. The simulation test is carried out for the composite scenario of the multi-ships and the sea surface, and the simulation time consumption is given, which verifies the high efficiency of the proposed method. In order to further validate the generality of the proposed method, the simulation test of a small airplane target is added and compared with its real SAR image. After the above comprehensive testing and analysis, the effectiveness of the proposed method can be fully verified.