esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We propose the concept of high-power THz radiation sources based on five-fold frequency multiplication in gyrotrons intended for plasma applications. The efficient excitation at the 5 th cyclotron harmonic is due to the specific property of the eigenmodes of cylindrical waveguides, as a result of which, the conditions of simultaneous electrodynamic resonance at two selected TE modes are satisfied asymptotically with very high accuracy. Previously, we have verified this principle in experiments with a low-frequency kilowatt-level gyrotron in which, due to the low-density spectrum, the operating mode is excited with no competition from parasitic oscillations. The novel concept is a development of this idea applied to the systems with a denser spectrum, which is inevitable in higher frequency and power devices. Simulations within the averaged time-domain model demonstrate that, despite the mode competition, it is possible to excite Watt-level 1.25 THz 5 th cyclotron harmonic in a recently developed sub-MW 0.25 THz gyrotron with TE\({}_{19.8}\) operating mode. The obtained results open a possibility for implementation of radiation sources with output power/frequency combination, practically inaccessible using other THz generation methods and highly sought for a number of applications, including high-resolution molecular spectroscopy.  
f  
frequency multiplication; high-order cyclotron harmonics; gyrorotrons; high-power THz radiation
## 4 Results and Discussion
### Impact of the Number of Users and the Pilot Reuse Factor per Access Point on Spectral Efficiency
The proposed estimator obtained the average UL SE = 17.87 bit/s/Hz at N = 4 and M = 80, whereas the MMSE estimator reached the average UL SE = 16.977 bit/s/Hz; i.e., the proposed estimator outperformed the other estimator by 5.27%. The increment of the pilot reuse factor results in a decrease in average UL SE for both the existing and proposed estimators. The proposed estimator has better performance as compared to the existing estimator when the number of APs increases for different numbers of UE. The pilot length is 20 since w = 2, K = 10, and N = 6. The maximum average UL SE achieved for the proposed method is 17.88 bit/s/Hz at N = 4 and M = 40, whereas for the existing one, it is 17.35 bit/s/Hz at N = 4 and M = 80. Figure 6: Spectral efficiency versus the number of antennas per UE for fully centralized processing with the MMSE combiner at K = 20, L = 4, \(\tau_{\rm c}\) = 200, and w = 1.
## 3 Results
### Experiment and Analysis
#### 3.2.2 Verify the Accuracy of LFCTEM by Comparison
Because FITEV considers the monitoring factor and the content change factor, it can protect against malicious side devices to a certain extent, and, thus, can guarantee a relatively low error detection rate when the proportion of malicious edge devices is high (40%). However, DTEM does not take corresponding measures, resulting in an error detection rate of 47.60% when the proportion of malicious edge devices reaches 50%, while the error detection rate of malicious edge devices of LFCTEM decreases by 16.20%. As can be seen from the figure, LFCTEM significantly outperforms the other two models.
# Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data
## 5 Evaluation and Results  
As we have already commented, part of this work has been carried out for a competition in the MediaEval 2018 workshop, in which a total of nine teams participated. In this section, we will compare not only the results of the different methods proposed in this paper but we will also compare them with the results of all the other participants of the workshop. However, since this is an extension of the work done for the competition and we do not have access to the ground truth of the test set, we had to divide the training set into training and test sets, as explained in Section 3. Therefore, the results given for our models will be tested on a different set as the ones for the competition. To make the comparison as fair as possible, we created a validation set from our training set, to validate the models and tune the hyperparameters. The test set was only used to provide the final results for the paper.  
In addition, in order to have an understandable baseline to compare the results, we asked four people to perform the task on a subset of 50 images. These persons were external to the project but had knowledge about artificial intelligence and computer vision. They received a verbal explanation of the task in the lines of the explanation given by the challenge organizers and they were not given any examples before starting the annotation.  
All the results provided in this section will be given in terms of F1-Score, the harmonic mean of precision and recall. For the human annotators, we will give the results as the average of their F1-Score. It is important to note that the second task, the classification between passable and not passable roads, depends on the first task since if an image has been classified as not containing evidence of road passability, it will not be considered for the second task. Therefore, a false negative detection in the first task (an image wrongly classified as not containing evidence of road passability) will also count as a false negative in the second task, regardless of its ground truth. At the same time, a false positive in the first task (an image wrongly classified as containing evidence of road passability) will also count as a false positive for the second task. Due to this error propagation, the performance of the second task cannot be higher than the performance of the first task.  
We will evaluate the results of the proposed models in this paper in the same order that we have presented them in the previous section.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Frozen Soil
#### 2.3.2 Surface Deformation
The authors identified X-band backscatter time series as a useful tool complementing optical remote sensing and in situ monitoring for rapid analysis of tundra permafrost erosion along riverbanks and coastal areas. [PERSON] et al. (2016) used Persistent Scatterer Interferometry (PSI) to map and quantify permafrost thaw subsidence in the Qinghai-Tibet Plateau. According to the authors, the PSI approach is less affected by temporal or geometric decorrelation, while their results indicated that permafrost areas near gullies are more vulnerable to gradual thawing and degradation.
## 1 Introduction
[19] utilized football players' movement and physiological data to predict their performance during training. The data, extracted through wearable sensors, were analyzed using multivariate regression models, achieving over 90% prediction accuracy. Similarly, [PERSON] et al. [18] conducted a comparative study to forecast player performance across different positions using multiple regression, neural networks, extreme gradient boosting (XGBoost), and support vector machines. Their results demonstrated that multiple regression achieved the highest accuracy, with R\({}^{2}\) scores ranging between 0.86 and 0.93. Additionally, [PERSON] and [PERSON] [20] examined the application of ML modelsin football performance quantification and strategic planning. By analyzing data from Europe's top five leagues in the 2018 season, they implemented logistic regression, random forest, and XGBoost algorithms. Their study found that the XGBoost model outperformed other methods with an AUC score of 0.8, significantly enhancing the understanding of performance metrics, real-time strategies, and player contributions to team success. Although these studies have contributed significantly to football analytics, the effectiveness of different training methodologies remains an understudied area that requires further investigation [13]. In addition, the inclusion of theoretical training before the application, which is absent in other football applied studies, was considered crucial to increase the efficiency of the studies. We also ensured compatibility with the ecological environment, a key aspect in football education, by mirroring the real-world scenario where coaches explain objectives before matches and training practices. In this paper, we analyzed the effects of three distinct training types on football players using machine learning algorithms. Our goal was to determine the distinguishing effects of these training methods and identify key performance indicators influenced by training variations. To achieve this, we collected pre- and post-training measurements from players and employed four machine-learning algorithms to evaluate the impact of each training method. Additionally, this study aimed to investigate how machine learning could be used to analyze and predict the physical parameters of football players, thereby providing deeper insights into training efficiency and player development, which would be one of the first studies investigating the distinction between single- and combined training procedures using machine learning. Furthermore, we analyzed the distinguishing factors between the training methods upon completion. Finally, we supported our findings with statistical analyses to validate the results. In summary, our study aimed to  
(1) Compare the effectiveness of single- and double-training procedures on the physical performance of football players;  
(2) Evaluate the predictive value of machine learning models in distinguishing training procedures; and  
(3) Investigate the factors that influence the players before and after training procedures.
## 2 Proposed Software Architecture
### Software Architecture Instantiation
#### 2.3.1 Indoor People Location Service
The characteristics of the data were: (i) ssid: This is a string value and defines the SSID of the networks in the database; (ii) bssid: this is an integer value corresponding to the network's ID in the database table; (iii) rssi: this is an integer value and is the received signal strength mapped as a fingerprint. Consequently, the target was the departments' identifiers (IDs), where the fingerprints were mapped, with 36 in total. The results were compared using the accuracy metric of the models generated with J48 and KNN using 10-fold cross-validation. However, no significant differences were found on average, with 86% and 85% accuracy, respectively. Given the computational complexity of KNN, which scales linearly with the dataset size \(O(n\cdot d)\), where \(n\) is the number of instances and \(d\) is the number of dimensions, its performance could degrade with larger datasets due to increased resource consumption and response time. In contrast, J48, which generates a rule-based model during training, has an inference complexity of \(O(h)\), where \(h\) is the tree height, making it more scalable and efficient for real-time applications. Therefore, we selected the model generated with J48 as the classifier of the fingerprint method, not only because its classification accuracy was slightly higher but also due to its superior scalability and efficiency in handling larger datasets. Furthermore, a tree model is an if-then type structure where the criteria do not involve operations with the data, so it is not sensitive to aspects such as the distribution or data types. Regarding the trace phase, when the algorithm identifies the location, it is returned to the user and shown in the application's main graphical user interface (see Figure 6). The process of requesting the user's location is carried out every 15 min and is saved in the database.
## 5 Results and Discussion
### Structure 1  
The two-bar structure was tested to see the validity of the proposed technique. From this manner, the proposed approach was applied to the structure, then its displacement results were compared with the results of SM and non-linear methods. Table 2 shows that there was a noticeable inconsistency between the displacements obtained from SM and that from geometrically non-linear methods. For this reason, it can be said that the structure behaves non-linearly.  
Figure 5: The Step-by-step enhancement of the stiffness of Structure 1.  
Figure 6: The effect of the number of iterations when the current approach is applied.  
Table 2 displays that the results of SM are significantly far from those of geometrically non-linear methods ([PERSON] et al., 1984; [PERSON] 1998), in which their discrepancy is about 228%, whereas the dissimilarity of the current study was only 16% that is reasonably close to the exact methods.
## 3 Results and Discussion
### Analysis of Strain Distribution
#### 3.2.2 Principal Strains  
Figure 8 shows the distribution of the three principal strains in the workpiece for each pass. The radial tensile strains in the first, second and third passes were larger in the area where the forward pass began. Tensile strain was generated because of the clamp constraint and the radial feed of the roller in the area. An overall trend of radial strain can be clearly observed in the fourth and fifth passes. In the area gradually loaded by the roller, compressive strain in the workpiece was presented in the radial direction because the material in this area was compressed in the radial direction by the movement of the roller relative to the workpiece. In the area immediately before the position at which the workpiece was unloaded, tensile strain was generated in the radial direction by the movement of the roller relative to the workpiece.  
With the increasing number of passes, tangential tensile strain was produced in the corners and in the area where the workpiece was gradually loaded. During the spinning process, these two areas were stretched in the tangential direction by the roller. Compressive strain was generated in the area before the position of roller unloading because the material was compressed by the roller, and the material undeformed at corners.  
Overall, the workpiece presented tensile stress in the axial direction. However, compressive strain appeared in the area where the forward pass began because the back of the roller restricted the flow of metal at the beginning of the forward pass, and material in this area was compressed in the axial direction. During the backward pass, material in this area was clearly compressed by the roller.  
Figure 7: Equivalent strain in the workpiece for each pass of the non-circular spinning process.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 6 Simulation Evaluation
### Impact of System Parameters  
In Figures 11 and 12, we explore the impacts of system parameters on DTEM regarding antennas number equipped at relay satellites and the maximum data acquisition rate of user satellites.  
In Figure 11, we evaluate the effect of antenna number on system utility. It can be observed that the system utility increases as the number of antennas \(K\) increases under different three weights \(V\). This can be explained by the fact that an increase in \(K\) implies an increase in the number of transmission channels which can improve transmission capacity. Accordingly, the network will achieve a higher system utility. Moreover, we can observe that the increasing trend of system utility stops for a certain value of \(K\) due to the bottleneck of the harvested energy at user satellites.  
Figure 12 reveals the impact of parameter \(\varphi_{\max}\) (i.e., the maximum data acquisition rate of user satellites) on system utility. From the figure, we can see that the system utility of DTEM increases with the increase in \(\varphi_{\max}\), which corresponds to the definition of system  
Figure 11: The utility of RSNs versus \(K\).  
Figure 10: The dynamics of energy queue with different \(V\).  
utility. However, as \(\varphi_{\max}\) further increases, the system utility increases very slowly under different three weights \(V\). This is because the transmission capacity and energy resources of user satellites become the bottleneck.
## 6 Experiments
### Performance Comparison Results
The EB-FJ approach exhibits the worst performance in terms of renewable energy utilization compared to the others. This is because it does not explicitly factor in renewable energy consumption, focusing instead on energy efficiency influenced by the architectural characteristics of the deployed GPU devices. Figure 6(d)-f display the renewable energy utilization in Region 2. The trends in these results are similar to those in Figure 6(a)-c, with the absolute amount of renewable energy usage increasing due to a rise in renewable generation. Figure 6(g)-i present the renewable energy utilization in Region 3. In this case too, our RA-FS approach demonstrates the most efficient renewable utilization, averaging 55%, compared to 36% for the DP-CTR approach and 27% for the EB-FJ approach. Figure 6: Comparison of Average Epoch Completion Time and DJR Violation Ratio. In summary, across all experimental scenarios, the RA-FS approach markedly enhances the performance of renewable energy utilization, achieving an average improvement of about 63% compared to competing methods. This outcome suggests that the RA-FS approach holds significant potential for energy cost savings over its competitors. The experimental results confirm that our proposed approach is a promising candidate for creating renewable-aware and energy-efficient DL job processing clusters equipped with modern GPU devices.
# Fusion Classification Method Based on Audiovisual Information Processing
###### Abstract  
In the presence of external interference, multimodal target classification plays a crucial role. Traditional single-modal classification systems are limited by the singularity of data representation and their sensitivity to environmental conditions, making it challenging to meet the robustness requirements for target classification under external disturbances. This paper addresses the inadequacies of single-modal target classification by proposing a target classification algorithm based on audiovisual fusion. The innovative contributions of this work are as follows. (1) To resolve the issue of the lack of correlation between audio signals and image signals, we introduce a method that converts audio signals into spectrograms and fuses them with target images. The advantage of this method is that the spectrogram can fully utilize the effective information in the audio, ensuring stability, while also effectively addressing the challenge of fusing one-dimensional time series audio signals with two-dimensional discrete image signals. (2) We propose a convolutional extraction and modal fusion network framework that incorporates an attention mechanism module during the fusion process, ensuring the stability and robustness of the fused data for audiovisual target classification. Validation was conducted on both a custom dataset and the YouTube-8M dataset. The experimental results indicate that the proposed method demonstrates improvements in accuracy of 2.9%, 2.4%, 1.2%, and 0.9% compared to other multimodal fusion target classification methods on the custom dataset. This demonstrates the effectiveness of the proposed multimodal fusion recognition approach and fully validates the theoretical rationale behind our method.  
multimodal classification; audiovisual fusion; sound spectrum; modal fusion network framework +
Footnote †: journal: Applied sciences
## 4 Optimal Design of ECTMD for Damped Structure
The optimization objective function based on \(H_{\infty}\) optimization method is written as Equation (5) [41; 42]. It should be noted the objective function is aimed at minimizing the displacement response of the primary structure. The objective function targeted to minimize the acceleration of the primary structure can be found in reference [43]. \[\min_{r,\zeta}\,\max_{\omega}DMF(\omega) \tag{5}\]  
Equation (5) is solved through a numerical optimization and the optimal parameters of the TMD are achieved. The optimal tuning ratio and damping ratio of a TMD for the damped structure from the numerical optimization are shown in Figure 8 along with the optimal parameters obtained from Equations (1) and (2). The mass ratio of TMD \(\mu\) is varied from 0.01 to 0.1 with an interval of 0.01. It can be observed from Figure 8 that the optimal parameters obtained by the numerical optimization have the same values with the results calculated from Equations (1) and (2) when it is designed for an undamped structure. When the damping ratio of the controlled structure is increased, the optimal tuning ratio of TMD is lower than the value for the undamped structure, and the optimal damping ratio of TMD will increase slightly. The optimal parameters of a TMD for different mass ratios and various damping ratios of primary structures are listed in Table 3.
## 2 Methods and Materials  
### Bayesian Estimation  
In this study, Bayesian estimation was applied to an image reconstruction method [12] using BNCT-SPECT [13]. The projection equation is represented by Equation (2). Let \(f(x,y)\) represent the concentration of a radioactive isotope at the position \((x,y)\) in a cross-section of the subject. The projection data \(g(X,\,\theta)\) in the \(X\theta\) coordinate system, which is rotated by an angle \(\theta\), is obtained by summing the values at coordinates \((x,y)\) of the subject along the Y-axis to the corresponding projection data position \((X,\,\theta)\). Specifically, suppose \(R\big{(}x_{i},y_{j},X_{k}\theta_{m}\big{)}\) denotes the probability that pixel \(\big{(}x_{i},y_{j}\big{)}\) of the subject is detected by the detector at \((X_{k},\theta_{m})\)[14].  
\[g(X,\,\theta)=\sum_{i}\sum_{j}R\big{(}x_{i},\,y_{j},X_{k}\theta_{m}\big{)}f \big{(}x_{i},y_{j}\big{)} \tag{2}\]  
In this study, the subject's pixels were set to \(n\times n\), and a line detector consisting of \(n\) detectors performed \(m\) projections while rotating. By converting the pixels and projection data into one-dimensional vectors, the projection equation can be rewritten as in Equation (3).  
\[A_{i}=\sum_{j}R_{ij}\cdot N_{j} \tag{3}\]  
Let \(A_{i}\) denote the measured value, \(N_{j}\) the true source intensity at the pixel of the subject, and \(R_{ij}\) the detection probability, which is the response function matrix. Based on the previously mentioned conditions, such as the pixel size of the subject and the number of projections, \(i=1,2,\,\ldots,\,n\times m\), and \(j=1,2,\ldots,\,n^{2}\).  
Using Bayesian estimation, and considering the corrected estimated source intensity as \(est_{j}^{(k)}\), the sum of the elements of the corrected estimated source intensity vector \(\{est_{j}^{(k)}\}\) is equal to one.  
\[\sum_{j}est_{j}^{(k)}=1 \tag{4}\]  
The revised estimated source intensity \(est_{j}^{(k+1)}\) is represented by Equation (5)  
\[est_{j}^{(k+1)}(i)=A^{\prime}{}_{i}\cdot\frac{est_{j}^{(k)}\cdot R^{\prime}{}_ {i,j}}{\sum_{j}est_{j}^{(k)}\cdot R^{\prime}{}_{i,j}}, \tag{5}\]  
where \(est_{j}^{(k+1)}(i)\) is the estimated source intensity for the measured projection data \(A_{i}\) in the \(k\)-th iteration. By summing approximately \(i\) for \(Ai\), the estimated source intensity of all the \(n\times n\) pixels can be obtained using (6).  
\[est_{j}^{(k+1)}=\sum_{i}A^{\prime}{}_{i}\cdot\frac{est_{j}^{(k)}\cdot R^{ \prime}{}_{i,j}}{\sum_{j}est_{j}^{(k)}\cdot R^{\prime}{}_{i,j}} \tag{6}\]  
This procedure is referred to as the spectral Bayesian estimation method, in which Bayesian estimation is conducted after all the measurements are completed.