esa-sceva/satcom-chunk-collection huggingface dataset:
# Speed-Flow-Geometric Relationship for Urban Roads Network
## 4 Results
### Developed Models Features
The same density of 1 TCSD/km had no impact on the reduction of average travel speed (ATS) for the urban roads having the cross-section combination of no median, one lane, and low side friction [M0, NL1, SF0]; with median, two lanes, low side friction [M1, NL2, SF0]; and also with median, two lanes, and high side friction [M1, NL2, SF1]. Conversely, the same density of 1 TCSD/km could reduce the average travel speed (ATS) on the traffic flow by small amount 0.22 km/h, if the urban road had no median, two lanes, and a high side friction [M0, NL2, SF1]. Figure 6 illustrates the impact of traffic calming speed density (TCSD) on the average travel speed (ATS) for various combinations of cross-sectional features in the model for the studied urban roads segment. Figure 6: TCSD variation over five developed models. The values of average travel speed (ATS) for the urban road category [M1, NL2, SF0] remained unaffected by the variation of three geometric parameters namely traffic calming speed density (TCSD), access driveway density (AccessD) and intersection density (IntersD) (Figures 6-8). However, the values of average travel speed (ATS) for the urban road category [M0, NL1, SF1] was appreciably impacted due to the variation of traffic calming speed density (TCSD) and intersection density (IntersD). Furthermore, for every 1 TCSD/km and 1 IntersD/km, the average travel speed (ATS) was reduced by 1.28 km/h and 1.26 km/h, respectively for the traffic volume in veh/h. Under the heterogeneous traffic (pcu/h) condition for every 1 TCSD/km and 1 IntersD/km, the average travel speed (ATS) value was decreased by 1.47 km/h and 1.4 km/h, respectively. On top, the access driveway density (AccessD) parameter has the highest impact on the average travel speed (ATS) value variation for the urban road category [M1, NL2, SF1]. Figure 8: IntersD variation over the five developed models. Figure 7: AccessD variation over the five developed models.
# An Initial Dot Encoding Scheme with Significantly Improved Robustness and Numbers
## 1 Introduction
Because the decoding value is affected by the linear length of the encoded image, the decoding stability is much lower under the large field of view. [PERSON] and [PERSON] designed 8 bit encoding circular markers, which have the advantage of dotted distribution coded mark and concentric ring type coded scheme [19]. In order to keep the rotation invariance of the coded mark points, the scheme used the method of encoding circularly cyclic decoding for minimum. Although the scheme has good robustness and recognition efficiency, the number of codes is as small as Schneider codes and does not satisfy the required for large field of view. In addition, the digital symbol scheme also included numbers or other binary symbols, such as the coded scheme proposed by [PERSON] and the coded scheme proposed by [PERSON] [20]. They usually used binary and digital symbols to encode the marked points and these programs are composed of positioning and symbol [21]. The advantage is that the expansibility of the number of coded is better, but the drawback is that the identification of symbols is difficult and the recognition efficiency is low. In the case of large field of view, the more complex the encoded image, the worse the decoding stability. In summary, the above researchers have focused on the following two aspects in the study of the coded mark points: coded number and robustness. However, in order to match the suitable marked points in a large field and overcome the limitation of the coded numbers, there is an urgent need for a coded mark point that can afford both the number of codes and the rashness of the decoding. This paper introduces a dotted distribution type encoding that has initial information mark called the Initial Dot scheme. It is remarkable that a starting point is added to the coded scheme to increase the number of codes, as well as the full using dots improve the robustness of recognition. The aim of this paper is to design a new coded mark to support expansion, which can solve the limitation of the insufficient number of traditional coded points and the influence of affine transformation on robustness. In Section 2, a detailed introduction of this programme is given. A stability experiment of this scheme is introduced in Section 3. In Section 4, this paper mainly introduces the experiment of applying an Initial Dot scheme to flexible solar panels. Finally, the conclusions are in Section 5.
## 4 Result and Discussion
### Analytical Measurement and Visualization without Preprocessing of Dataset
#### 4.3.1 Naive Bayes Multinomial  
Naive Bayes is a commonly used classification algorithm. Naive Bayes assumes that the occurrence of a specific feature is independent of the existence of other features. It is fast to make models and make predictions. We applied the naive Bayes algorithm for classification on that dataset of reviews and find different information on different parameters concerning TF and TF/IDF. We found the accuracy of classification of each category application, and in the statistical information found precision, recall and F1-scored these all parameters used to measure the accuracy of the dataset. In addition, bar-chart visualization of naive Bayes algorithm in which series1 shows the accuracy of the naive Bayes algorithm, series2 shows the precision, series3 shows the recall, and series4 shows the F1 score measurement as shown in Figure 8.
## 3 Results  
### Differences among Consecutive Hops in the Unilateral Hopping Task  
In the unilateral hopping task, very similar results were obtained for each leg on a group level (\(p\) > 0.114 for all between-leg comparisons). Thus, only the results averaged between the legs are presented here. Three participants for hop 7 and eighteen participants for hop 8 were removed, as they presented as outliers.  
Contact times decreased with consecutive hops (F = 136.8; \(p\) < 0.001; \(\eta_{\rm p}\)\({}^{2}\) = 0.58; large effect). A post hoc t-test showed that the decreases with adjacent hops were statistically significant (all \(p\) < 0.05) from hop 1 (240.3 \(\pm\) 31.8 ms) to hop 5 (196.1 \(\pm\) 21.7 ms), but then plateaued (193.8-194.2 ms) for hops 6-8. The main effect of the hop was also statistically significant for flight times (F = 14.4; \(p\) < 0.001; \(\eta_{\rm p}\)\({}^{2}\) = 0.15; medium effect). In this case, the changes between adjacent hops were smaller and not statistically significant. However, statistically significant differences were observed between hop 1 and hops 4-8 (\(p\) < 0.001), hop 2 and hops 5-8 (\(p\) = 0.002-0.043), as well as hop 3 and hops 7-8 (\(p\) = 0.008). As expected from contact time and flight time results, RSI statistically significantly increased with each hop (F = 78.3; \(p\) < 0.001; \(\eta_{\rm p}\)\({}^{2}\) = 0.49; large effect). Specifically, the RSI increased with each consecutive hop from hop 1 (1.04 \(\pm\) 0.17) to hop 5 (1.41 \(\pm\) 0.22), but then plateaued (1.43-1.44) for hops 6-8 (Figure 1).  
We further assessed the proportion of participants that achieved stable performance at a given hop. Namely, although group data plateaued at hop 5, it could be that hop-by-hop changes in individual participants were different. Table 1 shows the proportion of the  
Figure 1: Contact time, flight time, and reactive strength index throughout the 8 hops.  
participants that achieved stable performance (no further increase in RSI in comparison to the previous repetition), using <20%, <10%, and <5% change as cut-off criteria for stable performance. The associated data are shown in Table 1. If the least strict threshold was used, 88% of the participants reached stable performance at hops 4-5, and the number did not further increase (90-91%). When the thresholds for stable performance were stricter, the trend was similar (i.e., the proportion of the participants with stable performance increased to about hop 5); however, the final proportion of participants with stable performance was lower (68-78% with 10% threshold and 50-68% for 5% threshold).
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 6 Simulation  
### Simulation Environment and Related Settings  
(1) Setting of simulation  
In this paper, CAV-H [29] is taken as the non-cooperative reentry glide target, and 6825 trajectories are generated according to the lateral guidance logic and no-fly zone avoidance logic in the literature [30]. The IMM-UKF [31] filtering method is used to track the trajectory. The truth trajectories and filtered trajectories are set as training set and testing set, respectively, with the ratio of 4:1. A total of 1403 trajectories are used to test the prediction method. The network training environment is Pytorch-GPU framework, and Matlab software is employed to draw the final results.  
(2) Training conditions  
Each network took 100 trajectories as a batch and trained 1000 epochs in total. The optimizer uses the ADAM optimizer, and the training learning rate is 0.001 for the first 10 epochs, which is then adjusted to 0.0001.  
(3) Definition of errors  
In this paper, two types of errors commonly used in trajectory prediction are selected: average error of spatial distance (AESD) and maximum error of spatial distance (MESD). The corresponding definitions are expressed as follows:  
AESD is the average spatial distance between truth trajectories and predicted trajectories, such as  
\[MESD=\sum_{i}^{N}\sum_{k}^{L_{p}}\sqrt{\left(X_{pk}^{i}-X_{tk}^{i}\right)^{2}+ \left(Y_{pk}^{i}-Y_{tk}^{i}\right)^{2}+\left(Z_{pk}^{i}-Z_{tk}^{i}\right)^{2} }/N \tag{23}\]  
MESD is the maximum spatial distance between truth trajectories and predicted trajectories, such as  
\[MESD=\sum_{i}^{N}\max_{k}^{L_{p}}\sqrt{\left(X_{pk}^{i}-X_{tk}^{i}\right)^{2}+ \left(Y_{pk}^{i}-Y_{tk}^{i}\right)^{2}+\left(Z_{pk}^{i}-Z_{tk}^{i}\right)^{2} }/N \tag{24}\]  
where \(N\) represents the number of the testing dataset trajectories; \(L_{p}\) represents the length of the predicted temporal sequence; \(\left[X_{pk}^{i},Y_{pk}^{i},Z_{pk}^{i}\right]\) represents the predicted position at the time \(k\) of the \(i\)-th test trajectory, and \(\left[X_{tk}^{i},Y_{tk}^{i},Z_{tk}^{i}\right]\) represents the true position.
# Theoretical Study on a Cable-Bracing Inerter System for Seismic Mitigation
## 3 Parametric Study
### Multi-Objective \(H_{2}\) Norm Optimum Design
#### 3.3.2 Earthquake Excitation  
Figures 10 and 11 show the SDOF structures' acceleration and displacement responses. The peak value and RMS value of the displacement and acceleration responses were chosen to evaluate the vibration mitigation performance of CBIS. These values are both important controlling indices in structural vibration control. The peak value reflects the dynamic response at a certain instant, whereas the RMS value indicates the vibration energy and reflects the responses over an entire period.  
Figure 10: _Cont._  
Figure 9: Displacement amplification factors.  
The vibration reduction effect is defined as:  
\[\text{Reduction effect }=\frac{\text{Response of uncontrolled structure}-\text{Response of controlled structure}}{\text{Response of uncontrolled structure}}\times 100\%. \tag{31}\]  
The results are listed in Tables 2 and 3. The reduction effects were favorable under the El Centro wave, Tohoku wave, Kobe record and Chi-chi record. The best vibration control effects for the peak and RMS values of the acceleration responses were 52.08% and 45.71% (marked in bold), respectively. The values for the displacement responses were 55.56% and 52.50%, respectively.  
Figure 11: Displacement response time histories for two test frame roofs during (**a**) the El Centro wave, (**b**) the Tohoku wave recorded by Tohoku University, (**c**) the Kobe record and (**d**) the Chi–chi record.  
Figure 10: Acceleration response time histories for two test frame roofs during (**a**) the El Centro wave, (**b**) the Tohoku wave recorded by Tohoku University, (**c**) the Kobe record and (**d**) the Chi–chi record.
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 4 Results
### Minimizing Noise  
In order to minimize the impact of discrepant data on the variation of the \(p\)-value, two noise filters were applied. They consisted of applying a rolling average, with sliding windows of 1, 3, 7, or 15 days, followed by the application of a LOWESS filter with _frac_ = 0.15. The impact of those filters can be observed in Figures 9-11.  
Figures 9 and 10 show a portion of the VCP1 signal, smoothed with rolling averages of 1- and 3-day windows, respectively, and then a LOWESS filter with a fraction of 15%. Analysing Figures 9 and 10, it is possible to observe some stability in the mean value of VCP1 throughout most of the time window. However, towards the end of the window, there is a sudden increase. Looking at  
In Figure 10, it is also possible to see that before the final increase in the VCP1 mean, the \(p\)-value reacts twice, rising above the threshold value of 0.05 twice in a short time span. This is the same signal as in Figure 9. The figures show that the width of the rolling window can have a significant impact in the results and possible alarms generated, in cases where the \(p\)-value is used to generate an alarm when it rises above a certain threshold.  
This same \(p\)-value behaviour can also be observed in Figure 11, which shows the vibration of VCP3 with a rolling average of 15 days. In this case, before a negative peak in the mean value of the variable is observed, it is possible to see a significant increase in the \(p\)-value followed by a sudden drop. The \(p\)-value reacted to a spike in vibration that lasted several days, and at the end of May it also rose above 0.05, reacting to a consistent increase in vibration levels.  
In summary, the pictures show the following:  
1. The impact of short spikes on the \(p\)-value when no noise-filtering methods are used. The \(p\)-value responds to the spikes and can generate true or false positives, with more data being necessary to determine which is the case.
2. When applying noise-filtering methods such as a rolling average and LOWESS filter, the impact of short spikes is minimized. The size of the rolling window has a direct impact on the number of times the \(p\)-value crosses the significance threshold defined.
## 3 Results
### System Performance Evaluation
#### 3.1.3. Suppression Accuracy and Coverage  
Following fire detection and ventilation activation, the suppression module initiates targeted agent discharge to extinguish the source of ignition. This module comprises a pressurized reservoir of residue-free clean extinguishing agent coupled with a motorized 360\({}^{\circ}\) rotating nozzle, which is capable of precise angular positioning based on sensor-determined fire location.  
During testing, the suppression system exhibited high spatial accuracy in agent deployment. The nozzle, driven by a dual-axis stepper motor mechanism, achieved target alignment within \(\pm 5^{\circ}\) of the designated fire zone. The residue-free clean agent was discharged in a conical spray pattern with a coverage radius of approximately 2.5 m, effectively suppressing fire loads within micro-zoned compartments up to 40 m\({}^{3}\) in volume.  
The mean suppression time--defined as the interval from nozzle discharge initiation to complete flame extinguishment--was recorded at 13.2 s, with all tests achieving full suppression without re-ignition. Thermal imaging confirmed a rapid decrease in surface temperatures following discharge, typically dropping below 80 \({}^{\circ}\)C within 20 s. No water or conductive residue remained, confirming the system's suitability for electronics-sensitive environments such as control rooms and data facilities.  
Furthermore, the suppression system operated autonomously with zero false activations during non-fire trials, thanks to its edge AI-based classification logic. The use of a residue-free clean extinguishing agent also complied with relevant environmental and safety standards (e.g., UL 2127 [58], ISO 14520 [59]), providing a non-toxic, ozone-safe, and rapidly vaporizing solution suitable for enclosed environments.  
Combined with precise nozzle orientation and rapid deployment timing, these results confirm that the proposed suppression module delivers effective, localized extinguishment with minimal collateral impact--an essential requirement in sensitive environments or high-occupancy zones.  
The spatial coverage and directional control of the residue-free clean extinguishing agent nozzle are illustrated in Figure 7. The nozzle is centrally mounted and supports 360\({}^{\circ}\) horizontal rotation, enabling targeted discharge across multiple fire zones. Each directional spray path maintains a consistent coverage radius of approximately 2.5 m, ensuring complete extinguishment within micro-zoned compartments. This configuration facilitates zone-specific suppression, minimizes overspray, and promotes efficient agent usage while reducing the risk of damage to adjacent assets or surfaces.
## 3 Results
### CBH Estimations  
The results of the CBH estimations are presented in Figure 23. The left boxplot shows the statistical distribution of the heights obtained with the stereographic method, while the right boxplot shows the statistical distribution of a weather station located in Arrecife, Lanzarote, which belongs to the network of the University of Wyoming.  
There are several reasons for the apparent mismatch in the data. First, the weather station is located 30 km south of the position of the cameras, which undoubtedly has a significant effect taking into account how the atmospheric conditions develop in the region (with the thermal inversion steadily rising its level from the Sahara Desert). Second, the data of the weather station is obtained using a punctual measurement such as LIDAR, while the CBH estimations of the cameras cover a larger area of the sky (mainly the central part of the fish-eye image, since the distortion on the borders makes it almost impossible to compute the height). Finally, the temporal resolution of the weather station data is up to 1 hour, while the estimation of the CBH by the stereographic approach is done every minute. Likely the most important conclusion here is that the estimations made by the systems are coherent with the previous knowledge of the atmospheric conditions, with a stable thermal inversion ranging from 600 to 2000 m depending on the season of the year, which prevents clouds to rise over a certain height.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 6 Performance Evaluation and Discussions
### End-to-End Delay Results
#### 6.3.2 Uniform Node Distribution Results  
This effect is not observed to the same extent in the uniformly distributed network with dropping attacks. This can be attributed to the decreased number of packets being dropped by the attack in the uniform network compared to the random network. The inherent redundancy in node placement within the uniform network may offer alternative routes for packets to bypass attackers, mitigating the impact of dropping attacks on end-to-end delay.  
The uniform node distribution, while demonstrating resilience against individual dropping and sinkhole attacks, exhibits a significant increase in E2E delay when subjected to the combined attack. This unexpected behavior can be attributed to the interplay between the network topology, attacker mobility, and the combined attack's functionalities.  
The presence of the combined sinkholes with dropping in the combined attack poses a significant threat to the uniform network's resilience. Strategically placed sinkholes, due to the network's inherent structure, can attract and discard a larger portion of packets compared to the random network. This effectively creates bottlenecks and increases E2E delay for packets that need to navigate around the sinkhole.  
Overall, the combined attack's ability to disrupt both data transmission and routing exploits the uniform network's redundancy in a way that individual attacks cannot, leading to a noticeable increase in E2E delay.
## 4 Experiment
### Comparison with Existing Technology
The CMC curves are shown in Figure 7.  
Finally, the recognition results of the test images in the DuKeMTMC and Market-1501 datasets were visualized and displayed, as shown in Figures 8 and 9. The first column is the query image, which tested pedestrian images with occlusion, severe lighting changes, and low resolution. Following closely are the returned matching results, where the correctly matched images are marked with green dashed boxes. The images that detect errors are all closer in appearance to the query images. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Way** & **Market-1501** & **DuKeMTMC** & \multicolumn{2}{c}{**CUHK03**} & \multicolumn{2}{c}{**Effectiveness**} \\ \hline  & & mAP & Rank-1 & mAP & Rank-1 & mAP & Rank-1 & \\ PCB & Strong supervision & 81.6 & 93.8 & 69.2 & 83.3 & 57.5 & 63.7 & More Complicated \\ PCB + RPP [34] & Strong supervision & 80.9 & 93.3 & 68.1 & 82.9 & - & - & More Complicated \\ MGN & Strong supervision & 86.9 & 95.7 & 78.4 & 88.7 & 68.0 & 67.4 & Better, more complicated \\ SVDNet [52] & Strong supervision & 62.1 & 82.3 & 56.8 & 76.7 & - & - & More Complicated \\  & & & & & & & & Multi-scale and \\ MSMG-Net [53] & Strong supervision & 88.6 & 96.3 & 80.1 & 89.9 & - & - & multi-granularity \\  & & & & & & & & supervision, difficult \\  & & & & & & & & to deploy \\ MSAN [54] & Strong supervision & 88.6 & 96.1 & - & - & 68.6 & 72.2 & More Complicated \\ CAMEL & Unsupervised & 26.3 & 54.5 & - & - & 31.9 & 39.4 & Worse \\ PAUL & Unsupervised & 40.1 & 68.5 & 53.2 & 72.0 & - & - & Worse \\ OIMI [56] & Unsupervised & 13.5 & 33.7 & 43.8 & 51.1 & - & - & Poor \\ DAL [57] & Unsupervised & 23.0 & 49.3 & - & - & - & - & Poor \\ DBC [58] & Unsupervised & 43.8 & 64.3 & 66.1 & 75.2 & - & - & Worse \\ UTAL [59] & Unsupervised & 35.2 & 49.9 & 36.6 & 48.3 & - & - & Worse \\ CV-MIML & Weakly supervised & - & - & 59.53 & 78.5 & - & - & Better \\ WSDDN [60] & Weakly supervised & 47.1 & 59.2 & 60.7 & 65.4 & - & - & Better \\ HSLR [61] & Weakly supervised & 35.8 & 56.4 & 54.7 & 61.7 & - & - & Worse \\ SSLR [61] & Weakly supervised & 31.2 & 51.9 & 50.0 & 56.3 & - & - & Worse \\ OURS & Weakly supervised & 65.4 & 86.6 & 59.6 & 79.3 & 49.8 & 61.2 & Better, lower cost, easier \\  & & & & & & & to deploy \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experiments. Figure 8: DuKeMTMC partial retrieval visualization results chart. Figure 7: CMC curves for Market-1501 dataset. Figure 9: Market-1501 partial retrieval visualization results chart.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 2 Theoretical Analyses of Typical Source Localization Methods  
This section briefly analyzes the typical passive source location methods: DOA, Doppler frequency of single-satellite, TDOA, and FDOA of multi-satellites.