esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Visual Realism and Presence
#### 2.2.3 Evaluating Presence  
How to measure presence, and which factors foster or hinder this sense, are questions that have been thoroughly examined in the literature for several decades [8; 10].  
Evaluating presence is most often done by way of questionnaires: [PERSON] et al. [8] thus recently performed a comparative analysis that showed that multiple-item forms such as the Witmer and Singer (WS) [18] and Slater-Usoh-Steed (SUS) [19] questionnaires are commonly used to provide standardized self-reported measures of presence. In this way, when evaluating virtual environments created from photographs, many researchers rely on questionnaire data to evaluate presence, using either standard forms from the literature [13] or simple single-item assessments [7; 12].  
Through the evaluation of presence, user studies have also helped to better understand the different factors that may hinder it. For instance, [PERSON] et al. [10] provide a detailed analysis of the association between presence and cybersickness in VR based on a review of existing work, in which they outline the different factors that seem to contribute to one or the other, and conclude that existing evidence points to a negative correlation between the two. Additionally, several authors discuss ways to reduce the occurrence of \"breaks in presence\" [8; 9], which are notably expected to occur when users notice inconsistencies in the perceived stimuli due to reaching the boundaries of the immersive system's capabilities [9]. In order to better understand the influence of a proposed method on viewers' sense of presence, it is thus important to anticipate these different barriers and evaluate the significance of their impact.
## 3 Proposed Configuration and Beamforming Method Eliminating CSI in the Downlink  
### Configuration in Downlink and Beam Patterns Using Analog Weight  
Figure 2 shows the configuration of the multi-beam massive MIMO for the downlink with CMA implemented at the UT. In the case of the downlink, only directed beams are used for the UTs. Figure 3 shows an example selected beam for the uplink channel, when the direction of UT1 is 30 degrees and the direction of UT2 is \(-30\) degrees. The center direction of the BS is set to 0 degree. Because the peaks of the main beams selected on the uplink channel are directed toward each UT, the beams selected on the uplink are used in the downlink also. Each UT receives the desired signals at the main lobes of the beams facing the UT, and they receive the interference signals from the sidelobes of the beams facing the other UTs. However, as shown in Figure 2, the sidelobe level of UT2 directed toward UT1 is lower than the main lobe level of UT1. Therefore, UTs can be separated in the downlink by using the selected beams of the uplink channel without any calculation in the signal processing part thanks to analog multi-beams.  
Figure 2: Configuration of multi-beam massive MIMO in downlink.
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 3 Case Studies  
The proposed strategy was verified using a modified IEEE 34-bus test distribution system [21]. As shown in Figure 2, the system has two voltage regulators and three switchable capacitor banks. Each regulator has voltage change range of \(\pm 10\%\) and 32 steps, with each step amounting to a 5/8% voltage change. Three \(6\times 100\) kVar capacitor banks are added to bus 848, 832 and 850. The voltage levels in the distribution system contain 69 kV, 24.9 kV and 4.16 kV.  
Three daily load curves used in the simulation were extracted from measurements at user side of a 10 kV distribution system. The original 5-min measurements are first aggregated into hourly ones, and the load factor for each hour is afterwards averaged using two-month data [22]. The numbers 1 to 3 in Figure 2 represent the associated load curve at each bus node. Three daily load curve used in the experiment are illustrated in Figure 3. Peak loads occur during the period of 9 am to 11 am as well as 6 pm to 9 pm. Note that the base load values refer to that defined in the original IEEE 34-bus testing system. The integrated load curve is calculated and shown in Figure 3d.  
Figure 1: Flow chart of online reactive power optimization.  
Figure 2: A Modified IEEE 34-bus system, numbers 1–3 indicate corresponding load curve for each bus.  
As for optimization method, a genetic algorithm (GA) [23; 24], which uses chromosomes instead of real parameter to imitate natural selection process, is an efficient method for the optimization of complex and non-linear problems, thus is carried out to produce optimal reactive power control strategies.  
Since the optimization problem here involves objective functions with a number of local extreme points and non-linear constraints, the initial population should be generated with consideration of covering the whole solution space. In this work, the number of members in initial population is set as 50 and point pseudo random number generator is used to generate initial population. Crossover, mutation and selection are then carried out. The involved parameters such as crossover and mutation probabilities, maximal generation number, as well as tolerance level in objective function are obtained using a trial and error scheme. GA is programmed in Matlab platform, and the power flow is calculated using a simulation tool, named the Open Distribution System Simulator (OpenDSS) [25].
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 3 Results and Discussion
### Evaluation Metrics for Experiments  
In this study, the following commonly used model evaluation metrics were adopted.  
1. Accuracy  
Accuracy refers to the proportion of correctly classified samples to the total number of samples, and it is the most straightforward performance metric. The calculation formula is:  
\[Accuracy=\frac{n_{correct}}{n_{total}} \tag{19}\]  
where \(n_{correct}\) is the number of correctly classified samples, and \(n_{total}\) is the total number of samples.  
2. Precision  
Precision measures how many of the samples predicted as positive by the model are truly positive, focusing on the accuracy of positive predictions. The formula is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{20}\]  
where \(TP\) represents the number of samples that are actually positive and predicted as positive, and \(FP\) represents the number of samples that are actually negative but predicted as positive.  
3. Recall  
Recall measures how many true positive samples are correctly predicted as positive by the model, focusing on the model's ability to identify positive samples. The calculation formula is:  
\[Recall=\frac{TP}{TP+FN} \tag{21}\]  
where \(FN\) represents the number of samples that are actually positive but predicted as negative.  
4. F1 Score  
The \(F1\) score is the harmonic mean of precision and recall, calculated by the formula:  
\[F1=\frac{2\times precision\times recall}{precision+recall} \tag{22}\]
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 2 Related Work and Definitions
### New Threat Landscape  
The great evolution of computing in recent years largely stems from the appearance of multiple and heterogeneous devices [38] that can interact with other objects and applications over the internet [39]. However, the heterogeneity and interconnectedness of these devices lead to a significant increase in the number of security attacks [40] and the threat environment is expanding in alarming proportions. This growth comes together with more complex attack scenarios and sophisticated threats. Nowadays, adversary behavior is more focused on the target and it considers multi-staged attacks that aim to persist on the host or system and cause ongoing damage [41]. Most of these attacks do not generate noise or substantial changes in the environment, making it harder to detect.  
Some of these new generation threats are denominated Advanced Persistent Threats (APTs). They perform a sophisticated type of attack characterized by establishing a persistent foothold into the target and stay undetected for a long period of time [5]. Also, there are polymorphic threats with the capability of constant modification, making the detection a complex task. Additionally, other type of threat largely exploited is the zero-day vulnerabilities. Since they explore unknown vulnerabilities of software, it is easy to stay undetected for long periods until the flaw is discovered and patched [4].
# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
They used a non-dominated sorting genetic algorithm (NSGA)-II within the CFD solver STAR-CCM+ to identify the optimal ship type. This approach mitigated the errors associated with indirect model construction; however, the complexity and time-consuming nature of the CFD coupled with additional algorithmic iterations may exacerbate this situation. The main works and contributions of this study are as follows: (1) A parameterized modeling process for ship hull spherical bow shapes has been established based on NURBS surface construction. Using the DTMB5415 ship model as an example, the free-form deformation method was employed to modify the shape of the spherical bow. Furthermore, computational fluid dynamics (CFD) simulations were conducted to collect data on ship resistance. (2) To address the issue of long computational times associated with CFD simulations, a rapid prediction model for ship resistance based on CFD simulation data was developed. A hybrid surrogate model construction framework was proposed to improve model accuracy based on prediction errors. (3) An improved whale optimization algorithm was implemented to perform optimization of the spherical bow shape based on the surrogate model, enabling the identification of the shape that minimizes ship hull resistance. The structural framework of this paper is as follows: Section 2 presents the materials and fundamental methods used. Section 3 discusses the experimental results and provides a validation of these results. Section 4 engages in a discussion of the experimental findings, analyzing the contributions and limitations of the research, and suggests directions for future work. Finally, Section 5 summarizes the work of this paper.
## 3 Results and Discussion
### Wind Turbine Thrust and Power Characteristics
The output power of the wind turbine under dynamic yaw is shown in Figure 10. The blade power is nearly cosine-distributed with respect to yaw fluctuations. Smaller values of output power appeared at \(t=30\) s and \(t=42\) s, and larger values were found at \(t=24\) s and \(t=48\) s. The output power gradually dropped with the increases in yaw angle, as shown in Figure 3, when paired with the relationship between yaw angle and time. At maximum yaw, the output power was at its lowest, and as the yaw angle decreased, it steadily rose. When facing the direction of the incoming wind, the wind turbine produced the most electricity. The same conclusion was reached in [13] in a study of power output at a fixed yaw angle. At the same time, under the influence of wind turbine rotation and wake flow, the induced speed of the blades at the same yaw angle position differed between positive and negative yaws, resulting in the minimum output power of the wind turbine at positive yaw \(t=30\) s (\(\theta=30^{\circ}\)), which was reduced by 25.58%. The maximum power was found at zero yaw (\(\theta=0^{\circ}\)), where the average value within a yaw cycle was 194.91 Pa.  
Figure 9: Comparison of axial thrusts of single blades at the same yaw position.
## 3 Simulation Results and Discussion
### Reconfiguration without DGs
#### 3.1.3 Standard IEEE 119-Bus Distribution System  
Figure 7 shows the standard IEEE 119-bus distribution systems. The power loss and minimum voltages are 1296.57 kW and 0.8688 pu, respectively, at the initial configuration. As seen in Table 3, all the three algorithms have given power loss of 853.6, 853.6, and 854.03 kW after system reconfiguration, respectively. Regarding SSOE and MSSOE, it can be noticed that the computation time is reduced from 4.0328 to 2.5256 s. Refer to the voltage profile, the performance is increased considerably with reconfiguration. Figure 8 illustrates the voltage profile before and after the reconfiguration.
## 2 Materials and Experimental Procedures  
The process of preparing the coating is as follows: initially, the carbon fiber preform is infiltrated with phenolic resin and pyrolysis to create a carbon fiber-reinforced carbon aerogel composite material. Pyrolysis plays a crucial role in the process of decomposing the phenolic resin, which promotes the cross-linking and carbonization of the polymer chains. This results in the formation of a porous carbon aerogel structure, which enhances the composite's thermal resistance and mechanical properties. The pyrolysis step is essential for converting the resin into a stable, porous carbon matrix that reinforces the carbon fibers.  
Subsequently, this material is coated by infiltrating liquid silicon on the surface. Based on the energy spectrum analysis and process, the compositions of different regions of the material can be roughly inferred. The gradient coating can be divided into a surface layer and a transition layer according to the composition. The surface layer consists of 64 vol% SiC, 16 vol% carbon fibers, and 20 vol% pores. The transition layer includes 20 vol% pores and 80 vol% composite components, where the composite components contain 16 vol%carbon fibers, 27 vol% pyrolytic carbon, 32 vol% Si, and 25 vol% SiC. Finally, the substrate is a porous pyrolytic carbon with approximately 40% porosity. Figure 1 shows the SEM image and energy spectrum of the sample cross-section. The thickness of the SiC coating and its transition region is about 400 \(\upmu\)m, with the surface layer being about 40 \(\upmu\)m thick. Figure 2 shows the SEM images of different regions of the sample including the surface layer (40 \(\upmu\)m), transition layer (400 \(\upmu\)m), and substrate (below 400 \(\upmu\)m).  
The thermal shock test for the coating samples was performed using a plasma ablation device (see Figure 3). Direct contact of the plasma with the sample surface can cause severe oxidation and ablation; therefore, heat was transferred through thermal conduction using a graphite sheet placed on the sample surface. The graphite sheet, with a 5 mm  
Figure 1: Microstructure and element distribution of the cross-section of the SiC coating.  
Figure 2: SEM images of different regions of the sample including the surface layer, transition layer, and substrate.  
thickness, was heated from room temperature to 2173 K within 30-60 s by the plasma flame. Once at the target temperature, the graphite sheet was continuously ablated for 10 s, completing one thermal shock cycle. For multiple cycles, the next test was conducted after the sample cooled below 298 K. An \(\chi\)-ray stress measurement device (Model: DS-21L) was used to assess the residual stress of the SiC coating. The measurement parameters were Cu K\(\alpha\) radiation, a tube voltage of 20 kV, and a tube current of 20 mA. Residual stress was evaluated through diffraction measurements of SiC (101) and (220) planes.
## 3 Results
### Tunnel Traffic Condition Prediction  
Based on the variable selection results in Section 3.1, after normalization, the variables were input into both the BP neural network and random forest model for prediction. This paper uses a linear function transformation method to convert the input data into data between 0 and 1:  
\[x_{ij}=\frac{x_{ij}-x_{i,min}}{x_{i,max}-x_{i,min}} \tag{18}\]  
where \(x_{ij}\) is the normalized value of the \(j\)-th sample of the \(i\)-th variable, \(x_{ij}\) is the actual input value of the \(j\)-th sample of the \(i\)-th variable, \(x_{i,max}\) is the maximum value of the variable samples, and \(x_{i,min}\) is the minimum value of the variable samples.  
* BP neural network  
The BP neural network model in this paper was trained based on the PyTorch deep learning framework. The training process was set with a learning rate of \(1\times 10^{-3},3\) hidden layers with 12 neurons in each. The loss function during the training phase was calculated using the accuracy formula. The activation functions used were the sigmoid and SoftMax functions, and the optimizer used was Adam. The training was carried out for 30,000 iterations (epochs). The model was run five times, and the results are shown in Table 6:  
As can be seen from the table, the accuracy of the BP neural network model for predicting traffic flow conditions is about 82%, and the prediction results are relatively accurate. The time for completing one model training is around 10 s, which is short. Therefore, the BP neural network model has good robustness for predicting traffic flow conditions when accidents occur.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy (\%)** & & **Mean** & **Std.** \\ \hline
83.29 & 82.25 & 82.29 & 80.61 & 84.28 & 82.94 & 1.31 \\ \hline \end{tabular}
\end{table}
Table 6: Accuracy of BP neural network.  
Figure 8: Test dataset predictions.  
Random-forest model  
The random forest model has good predictive performance, so in the actual operation of the model, in order to save time and improve efficiency, it is often not necessary to adjust all parameters to ensure high model accuracy. To improve the running efficiency, this article mainly selects two main parameters for adjustment, namely, the number of decision trees (n_estimators) and the maximum depth of decision trees (max_depth). The parameter adjustment mainly uses the GridSearchCV class in the sklearn.model_selection library in Python, that is, grid search. The main function of this class is to automatically adjust the parameters. As long as the range of parameter values is given, the parameter values corresponding to the optimal result can be obtained. At the same time, this class can also perform cross-validation for each parameter combination. Through grid search and cross-validation, the optimal values of n_estimators and max_depth of the model can be obtained. Through experiments, the optimal number of decision trees for the random forest model in this article is 200, and the optimal maximum depth of decision trees is 10. The model was run five times, and the results are shown in Table 7:
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 2 Related Works
We rely only on application-level throughput measurements; therefore, the proposed solution does not require modifications to network infrastructure or the TCP stack. Consequently, our work shows the impact of the prediction on video QoE without the support of any other information, which is hard to gather and requires profound changes in a DASH system. Contrary to the majority of the works, which aim at either short or long time horizons, our prediction span is 4 s, which is suitable for a DASH system.
## 4 Experiments and Results
### Ablation Study
In DanceTrack, ReTrackVLM performs well, effectively handling the uniform motion patterns of dancers from t = 1 to t = 200. However, with t = N, trajectory intersections pose challenges, as rapid dancer crossings lead to IDSws as well as fast changes in appearances due to short distances between the dancers and the camera. This indicates a need for improvement in managing synchronized movements and abrupt trajectory overlaps. For MOT15, which features a moderately dense urban setting, ReTrackVLM effectively tracks both static and dynamic individuals across frames. While track consistency is generally strong, a slight dip in the number of active tracks in the metric plot suggests possible track termination due to occlusions. MOT16 and MOT17 highlight both strengths and limitations of the framework. In MOT16, the tracker performs reliably under static camera conditions, maintaining consistent track identities from t = 1 to t = 200. However, in MOT17, which involves moving cameras and frequent occlusions, the framework experiences increased IDSws. By t = N, there are clear signs of frequent IDSw and partial track losses, suggesting a need for enhanced appearance-based association and occlusion-handling mechanisms. In MOT20, one of the most densely populated datasets, ReTrackVLM struggles with trajectory overlaps and significant occlusions. The tracking metrics for MOT20 reveal a steady increase in the \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset/VLM** & **MOTA**\(\uparrow\) & **MOTP**\(\uparrow\) & **IDSw**\(\downarrow\) & **FM**\(\downarrow\) & **MT**\(\uparrow\) & **PT**\(\uparrow\) & **ML**\(\downarrow\) \\ \hline \multicolumn{8}{c}{**MOT16/17**} \\ \hline OpenCLIP & 52.5 & 54.8 & 1712 & 1687 & 257 & 107 & 488 \\ Florence-2 & 54.2 & 54.7 & 1633 & 1654 & 283 & 113 & 542 \\ DETR2 & 55.8 & 58.9 & 1574 & 1699 & 272 & 97 & 512 \\ CLIP & 56.7 & 58.6 & 1607 & 1545 & 302 & 122 & 447 \\ \hline \multicolumn{8}{c}{**MOT20**} \\ \hline OpenCLIP & 49.5 & 53.3 & 2741 & 2903 & 346 & 202 & 392 \\ Florence-2 & 52.3 & 51.9 & 2344 & 2742 & 382 & 236 & 376 \\ DETR2 & 53.7 & 57.1 & 1914 & 2458 & 397 & 275 & 348 \\ CLIP & 55.4 & 56.8 & 1826 & 2217 & 391 & 257 & 361 \\ \hline \multicolumn{8}{c}{**DanceTrack**} \\ \hline OpenCLIP & 52.1 & 57.2 & 1423 & 1850 & 401 & 33 & 7 \\ Florence-2 & 54.6 & 59.4 & 1417 & 1788 & 442 & 30 & 7 \\ DETR2 & 57.9 & 62.4 & 1474 & 1762 & 439 & 47 & 6 \\ CLIP & 61.8 & 66.3 & 1508 & 1733 & 473 & 41 & 4 \\ \hline \multicolumn{8}{c}{**WildTrack**} \\ \hline OpenCLIP & 60.2 & 63.4 & 883 & 1023 & 364 & 52 & 253 \\ Florence-2 & 62.3 & 65.7 & 809 & 1007 & 381 & 48 & 238 \\ DETR2 & 64.8 & 71.2 & 803 & 977 & 404 & 57 & 267 \\ CLIP & 69.9 & 73.5 & 864 & 943 & 397 & 61 & 220 \\ \hline \hline \end{tabular} The values represent the zero-shot ReID performance metrics for various visual language models with no fine-tuned YOLOv8 in the DeepSort framework.  
\end{table} 
Table 4: _Cont._number of tracks over time, indicating the dynamic influx of new pedestrians but also hinting at fragmented tracking due to occlusion challenges.