esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis Results of the Fatigue Strength under Operational Loads
#### 3.3.2 Influences of Residual Stress Induced by Repair Welding  
This research first simulated the temperature field during repair welding of fillet welds without defects in the traction rod brackets on the bogie frame (Figure 16). At the initial temperature of room temperature (22 \({}^{\circ}\)C), a volumetric heat source of 13 W/mm\({}^{3}\) was applied to the fillet welds for 1 s, which were then cooled to room temperature. Taking the transient temperature field as the load for structural analysis, the stress field was simulated. After calculating the residual stress distribution induced by welding, the models with residual stress were solved under the UIC standard conditions; thus, the fatigue strength of the repair-welded structure was measured.  
Figure 16: Models used in calculation of the temperature field.  
Figure 14: Maximum stress amplitudes at weld roots of type A traction rod bracket.  
Figure 15: Maximum stress amplitudes at weld roots of type B traction rod bracket.  
To verify the reliability of the repair schemes for defects in the attachment welds of existing welded structures on the type B traction rod brackets of the locomotive bogie, the fillet welds of failed traction rod brackets were repair-welded, and the results were combined with data-processing output from the test railway line, followed by stress-relief treatments after welding. Fatigue tests were conducted on repair-welded fillet welds of traction rod brackets on existing welded structures under equivalent loads on the test railway line. Test results prove that the fatigue life of the repair-welded fillet welds of traction rod brackets reached 10 million cycles. The simulations and practical fatigue tests differ in the following aspect: the welds in simulations incur residual stress, while the fillet welds of traction rod brackets in the practical fatigue tests are subjected to stress-relief treatment. The difference indicates that the residual stress induced by welding affects the fatigue strength of welds. In practice, that is, after repair welding, stress-relief treatments need to be performed to guarantee the safety of the components. Considering that fillet welds cannot be subjected to thermal treatment after repair welding, measures are suggested after welding to eliminate or reduce residual stress.
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Throughput  
Throughout is defined as the total number of data packets successfully received at sink. In this context, the number of _Ad_Msgs_ received at the sink are considered as the throughput. In clustering and data fusion based approaches, local data has a significant role in information gain of the network. Therefore, we have taken into consideration the received _D_Msgs_ at different RC/CH nodes, partially. Figure 5 shows that the number of _D_Msgs_ received by RC/CH nodes of the EDR and 3D EADC is higher compared to 3D LEACH. Meanwhile, the results show that the number of the packets decreases due to the premature death of nodes in the network of 3D LEACH. However, the EDR outperforms 3D LEACH and 3D EADC, considering the total number of _D_Msgs_ received at RC nodes throughout their network lifetime.  
Figure 6 shows the throughput of the protocols. The throughput of EDR is larger than that of 3D LEACH and 3D EADC, considering the network lifetime of those protocols. Throughout the network lifetime of 3D LEACH, the throughput of 3D LEACH is larger than the EDR and 3D EADC, because the 3D LEACH elects an average of \(p\times n\) I ( \(p=0.1\), \(n=120\)) = 12 CHs in each round, unlike the predefined 8 RCs/CHs in each round in EDR and 3D EADC.  
Figure 5: Number of local data packets sent to CH/RC nodes.  
Figure 6: Number of data packets sent to the sink from CH/RC nodes.
# Learning Optimal Time Series Combination and Pre-Processing by Smart Joins
## 4 Experimental Setup
### Experiments on Real Industrial Dataset  
The efficient management and the energy optimization of distillation units [11,12] in terms of both product quality and energy efficiency in both the petro-chemical and in the sustainable sector pose a great challenge to process and control engineers because of their complexity. The management, optimization and fault analysis of such units all require accurate process models, which in recent years have started to be generated directly from the data available in SCADA Historian databases by using machine learning methods [13, 14, 15] whose performance depends on the availability of properly pre-processed multi-variate data.  
Suppose that the system captures and stores real-time sensor-based data. In this particular case, each sensor writes values in the database only when there is a significant change in the values of data. The decision on the significance of the difference between data points is based on the scale of each feature. The aim of this data recording strategy is reducing data volume. Consequently, if a feature becomes unstable the writing frequency augments drastically.  
For machine learning applications, an alignment between features is needed. Each feature should be resampled to obtain a common desired temporal reference system previous to any feature extraction/selection algorithm application. Depending on the feature and the application system, the optimal joining method can be different.  
Figure 8 shows the initial sampling for different features. Each column represents a feature and each row an hour time window. The number of samples is counted per hour and feature, and represented by the colour.  
Figure 8: Original sampling of real industrial data from a distillation unit.  
Figure 7: Original vs. distorted Lorenz first dimension.  
Figure 8 shows how, depending on the feature the frequency of data availability can be constant or variable, and the quantity of samples can be very different among features. For example, the feature with id 14 changes drastically from very low frequency to high frequency only in a couple of hours. The data points for this particular feature are shown in Figure 9, where the frequency change is observable.  
In this particular case, the desired time sampling interval is selected to be 15 min.
## 4 Discussions
### The Parameter that Affects Contact Velocity  
The scatter plot of contact velocity and collision time is shown in Figure 8. Each condition presents a negative correlation between collision time and contact velocity. This pattern can be attributed to the effect of the bracing of the supporting leg, which mitigates the collision impact. The prolonged collision time corresponds with a larger deceleration of  
Figure 6: CoM height at the timing of collision (\(\blacklozenge\) means the outlier., \(\star\)\(\colon p<0.0033\). They are at 5% and 1% significance levels).  
Figure 7: CoM descent velocity at the timing of collision (\(\blacklozenge\) means the outlier., \(\star\)\(\colon p<0.0033\). They are at 5% and 1% significance levels).  
the contact part. Delaying the time to collision is crucial as it can significantly reduce the speed of impact [3]. Compared to the free condition, distributions for the elderly and young conditions show wider spreads, signifying that the mitigation motion effectively extends the collision time in both these conditions. However, due to the varying distribution of initial posture and velocity, the correlation coefficients are not particularly high.  
Contact velocity serves as a primary indicator of impact force, which, in turn, reflects the severity of the fall, as discussed in the literature [28, 29]. It was the highest in the free condition, followed by the elderly and young physical conditions, respectively, as shown in Figure 5. Considering the variation in joint torque constraints across different conditions, the action of bracing with the supporting leg appears to play a role in postponing ground contact. Consistent with findings from various studies [30, 31], the physical capabilities of an individual are key in managing motion post tripping. This trend aligns with the difference in collision time between conditions as shown in Figure 4, which can be attributed to the deceleration of the trunk due to joint torque in the supporting leg.  
On the other hand, a clear correlation exists between contact velocity and CoM descent velocity as shown in Figure 9. Since the movement pattern of upper body joints remains constant across conditions, contact velocity largely functions as CoM descent velocity. The increased impact speed results in a higher impact force [32]. Consequently, these parameters are strongly related to fall-induced injuries. Additionally, collision time influences the movement of contact parts through the motion pattern of upper limb joints. The high correlation coefficient suggests a considerable impact of CoM descent velocity, which is relatively larger than the changes in swing velocity of contact limbs induced by variations in collision timing. While some studies have employed a fixed posture, such as an outstretched hand [32], there have been reports indicating that certain types of arm movements, including elbow flexion [33] and more complex guarding motions [34], can mitigate impact forces. The effect of such a mitigation motion should be considered in future studies.
## 3 SE-WiGR Gesture Recognition Method
### Data Preprocessing  
The collected CSI signals are susceptible to noise interference due to environmental factors and equipment operation. To enhance the extraction of gesture-induced CSI variations, this study employs a three-step denoising approach. First, a Hampel filter [26] removes outliers that deviate significantly from the mean and standard deviation range. Next, a Butterworth low-pass filter [27] with cutoff frequencies of 1.5 Hz (lower) and 60 Hz (upper) eliminates noise outside the gesture signal's frequency range. Finally, sym8 wavelets are applied for data smoothing. To optimize gesture recognition, PCA is used to reduce data dimensionality while retaining key information. The number of PCA components is crucial: too few may discard essential gesture features, reducing accuracy, while too many retain redundant data, increasing complexity and risking overfitting. Through experiments and cross-validation, an optimal number of components was chosen to balance information retention and dimensionality reduction, enhancing the efficiency and generalization of the recognition model. Figure 3 demonstrates the processing results: (a) and (b) show the raw data from 30 subcarriers corresponding to Narrow and Right gesture signals, while (c) and (d) display the filtered data with principal component features extracted using PCA [28]. The comparison clearly illustrates the significant improvement in signal quality after processing.  
This study employs a sliding window approach to calculate local variance, enabling effective differentiation between inactive and motion-activated periods for precise extraction of gesture-induced signal data. The extracted signals are then processed using short-time Fourier transform (STFT) to generate Doppler shift spectrograms, as illustrated in Figure 3e,f, the red rectangular box represents the features displayed after processing the current gesture into a Doppler image. The WiFi signal's Doppler shift resulting from gesture movements can be expressed as  
\[f_{D}=-\frac{1}{\lambda}\frac{d}{dt}d(t) \tag{4}\]  
Figure 3: Example of the data processing process.  
where \(\lambda\) is the signal wavelength, and \(d(t)\) is the transmit path length. The Doppler spectrogram [29] primarily displays the signal characteristics in the frequency domain, and the Doppler spectrum of CSI is obtained by video analysis:  
\[H(f,t)=h_{\rm s}(f)+\sum\
olimits_{k\in\mathbb{D}}a_{k}(t)B\big{(}f_{D_{k}}(t) \big{)} \tag{5}\]
## 6 Testing and Validation
The highest response times under a high load (1000 users) are observed in the first architecture, with an average of 5222.49 ms and a minimum of 218.75 ms. These values demonstrate the limitations of the first architecture as user numbers increase. In contrast, architectures two and three, which exploit edge devices, show significantly lower average response times. For example, architecture two manages a minimum response time of 39.34 ms even under a load of 1000 users. Furthermore, in the one-user scenario, architecture two achieves the lowest average response time of 48.00 ms, and the extreme values remain very low, with a minimum of 36.72 ms and a maximum of 62.64 ms. So, the data show that architectures two and three consistently outperform architecture one in terms of stability, as evidenced by the lower range between the minimum and maximum response times under varying loads. The highlighted values serve as benchmarks for evaluating the scalability and efficiency of the architectures. \begin{table}
\begin{tabular}{l c c c} \hline
**Simulation** & **Average Response Time (ms)** & **Min (ms)** & **Max (ms)** \\ \hline
1 Arch0\_1 & 297.66 & 44.12 & 621.06 \\ \hline
1 Arch1\_10 & 329.92 & 43.76 & 673.30 \\ \hline
1 Arch10\_100 & 800.68 & 73.04 & 1819.52 \\ \hline
1 Arch100\_1000 & **5222.49** & **218.75** & 17,223.80 \\ \hline
2 Arch0\_1 & **48.00** & **36.72** & **62.64** \\ \hline
2 Arch1\_10 & 76.27 & 36.70 & 269.22 \\ \hline
2 Arch10\_100 & 409.71 & 34.53 & 2131.46 \\ \hline
2 Arch100\_1000 & 4051.75 & **39.34** & **27,742.37** \\ \hline
3 Arch0\_1 & 47.61 & **35.30** & 64.73 \\ \hline
3 Arch1\_10 & 74.46 & **33.75** & 269.02 \\ \hline
3 Arch10\_100 & 353.02 & 37.72 & 1886.20 \\ \hline
3 Arch100\_1000 & **3423.89** & 37.59 & 23,583.14 \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation results: average, minimum, and maximum response times of the three architectures in the different designated scenarios. The critical values for experiment replication are highlighted in bold.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 1 Introduction  
In recent years, volumetric capture for real-time 3D animation has been focus of research. 3D animation can be represented in the form of dynamic point-clouds or mesh data. Volumetric capture is the creation of dynamic 3D animation data through the capturing of an object with multiple cameras from all directions. Several research groups working on multi-view video-based 3D reconstruction that offer volumetric capture systems worldwide include Mixed Reality Capture Studio [1], si [2] and 4D Views [3]. The complete workflow for volume video production based on RGB-D sensors is described in [4]. In [5], the authors proposed a method based on spatial-temporal integration for surface reconstruction using 68 RGB cameras. Their system took 20 min/frame processing time to generate upto three-million faces mesh. [PERSON] et al. [6] presented an approach focused on improving surface detail by maximizing photo-temporal consistency. [PERSON] et al. [7] exploited a complex dynamic lighting system that enabled controllable light and acquisition at a dynamic shape capture pipeline of 240 frames/sec using eight 1024 \(\times\) 1024 pixel resolution cameras. High quality processing requires processing times of 65 min/frame, however implementations based on a graphic processing unit (GPU) reduced this to achieve 15 min/frame. The use of real-time 3D animation data is widespread in a variety of applications (e.g., game, military, autonomous driving). A large number of resources are used in the process of sending motion data using a raw point-cloud. In general, a dynamic point-cloud has about 10,000 points per frame. In an uncompressed situation, the total bandwidth of 30 fps is 3.6 Gbps [8]. In various research articles, point-cloud simplification is discussed but dynamic point-cloud compression is ignored. There are some volumetric capture studies,such as MS Holopotation, where data acquired in mesh form are studied [9]. In 8i [10], data obtained in the point-cloud form is studied.  
In this paper, to reduce the size of continuous point-clouds, we propose a method to convert overlapping vertices between frames into a reusable format, and evaluate the phenomena and results that appear in the conversion process.
# Data Structure and Management Protocol to Enhance Name Resolving in Named Data Networking
## 4 Experimental Setup and Results
### Throughput Performance  
This experiment tested the local insertion operation of 100,000 new entries in TLV encoded format into the previously \"1 million\" populated tries. In this experiment, ECHT processes the entries and makes replies without any conversion, while Component/Character tries need to TLV decode the entries before processing them and encode the replies before sending them to the caller. The results showed that the insertion throughput is more than 37 times faster in ECHT than in the other tries, which is around 336 names/ms for ECHT and 9 names/ms for the other tries, as detailed in Table 4. The results also indicated that the trie type is of less significance when TLV encoding/decoding is involved, i.e., the conversion is way more expensive than the actual entry addition.
## 2 Methodology
### Data Cleaning and Preprocessing
(**a**) Density; (**b**) W/C ratio; (**c**) cement content; (**d**) S/C ratio; (**e**) Cag/Fag ratio; (**f**) porosity; (**g**) FA/C ratio. strength and thermal conductivity, suggesting an inherent trade-off between mechanical performance and thermal insulation. The correlation matrix also highlights other key relationships: the W/C strongly correlates positively with porosity [75]. Additionally, the FA and the Cag/Fag significantly influence porosity [76; 77]. The research has shown that increasing the fly ash content leads to a 5-8% increase in porosity. The phenomenon is mainly attributed to the micro-filler effect of fly ash particles and the changes in the microstructure caused by the pozzolanic reaction. Furthermore, reducing the Cag/Fag by increasing the proportion of fine aggregates effectively reduces porosity, as fine aggregates fill the voids between coarse aggregates, leading to a more compact particle packing structure [78; 79]. The FA/C is negatively correlated with early strength. On the contrary, it has a positive effect on long-term strength. Specifically, it enhances long-term flexural strength and maintains a lower thermal conductivity [80]. Many studies suffer from methodological limitations, such as using a single train-test split, focusing only on compressive strength, or excluding standardized ratio-based inputs. These gaps can lead to biased results or models that are not generalizable. In this study, we apply 5-fold cross-validation to improve reliability. We also predict three key performance indicators--compressive strength, flexural strength, and thermal conductivity--under the same framework. In addition, we include dimensionless input variables, such as W/C and FA/C, which improve the consistency of feature scaling and broaden the applicability of the model. These improvements help overcome the shortcomings seen in earlier work and provide a more complete view of foam concrete behavior. Figure 4 presents a dual analysis of the research corpus, illustrating (a) the temporal evolution of publication volume across years and (b) the categorical distribution of studies by their primary investigated parameters, revealing emerging trends and methodological focus areas in foam concrete research. Figure 3: The Pearson correlation heatmap between the input variables and predicted performance indicators.
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 5 Offline Experiments
### Discussion of Offline Experiments
#### 5.4.1 The Effect of Input Features on Prediction Performance  
In our framework, the effective feature of the prediction of short- and long-term ad discontinuation is different. In the short-term model under the single-task setting, the text and image features that make up the ad creative contribute to the prediction. The short-term discontinuation (i.e., cut-out) may be caused by many users who are not immediately interested in the ad creatives. Since the text and image affect the user's impression of the ad creative, this result reflects characteristics of the short-term discontinuation. In the long-term model, the time-series feature contribute to the prediction. The long-term  
\begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**Short-Term**} & \multicolumn{2}{c}{**Long-Term**} \\ \cline{2-5}  & **3 Days** & **7 Days** & **30 Days** & **90 Days** \\ \hline Classification framework & 0.3208 & 0.3106 & 0.1098 & 0.0980 \\ Regression framework & 0.3813 & 0.2852 & 0.0969 & 0.0735 \\ \hline
**Our framework** & **0.7988** & **0.7796** & **0.7287** & **0.7004** \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of the F1 score in discontinuation prediction after \(N\) days using the binary classification framework and regression framework.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Concordance Index**} \\ \cline{2-4}  & **without** & \(\mathbf{\ell^{\text{multi}}_{\text{imp}}}\) & \(\mathbf{\ell^{\text{multi}}_{\text{CTR}}}\) \\ \hline Short-term & 0.8700 & 0.8884 & **0.8958** \\ \hline Long-term & 0.9228 & 0.9297 & **0.9390** \\ \hline Overall & 0.7915 & 0.8060 & **0.8127** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of prediction performance by proposed CTR-weighted loss in the multi-task model. We compared the vanilla model (without any special loss), \(\ell^{\text{multi}}_{\text{imp}}\), and \(\ell^{\text{multi}}_{\text{CTR}}\).  
discontinuation (i.e., wear-out) is caused by the user getting fatigued, as described in Section 3. This is due to the fact that users have been repeatedly interacting with similar or the same ads, and thus the CPA has been decreasing. Since the time-series feature captures the user's fatigue by change of the ad performance, this result reflects characteristics of the long-term discontinuation. As a result, we confirmed that each input feature contributes to the prediction of both cut-out and wear-out appropriately.