esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Concrete Failure Time Based on Statistical Properties of Compressive Strength
## 1 Introduction
Regarding the discreteness of the statistic, both mean value and coefficient of variation are expected to be considered in the model to evaluate the service life of concrete. Thus, a time-varying evaluation model based on reliability was proposed in this study. The failure probability and reliability index equivalently evaluate the structural safety. However, the reliability index, being very simple and with respect to function, is not integral, which is in contrast to the calculation of failure probability. Among all the methods for computing the reliability index, moment methods are used worldwide to observe the reliability index [25]. Compared with the second-moment (SM) method, the reliability evaluation based on the third-moment (TM) method is more accurate because the skewness (the third dimensionless central moment) is considered [31; 32]. Tested by MC simulation, the results evaluated by the TM reliability are more accurate than those by the SM reliability. In this paper, the concrete guaranteed rate was applied to predict the concrete failure time calculated by time-varying reliability. To testify the reliability-based computation of the concrete failure time, the equivalent design compressive strength (functioning as the ultimate concrete compressive strength) was investigated to observe the concrete failure time. The concrete failure times computed by both models agree well. MATLAB software was used to analyze the fitting distributions of datasets with the sample significance level a = 0.05. The tests of gamma distribution, normal distribution, Rayleigh distribution, and extreme value distribution were executed based on these data. It was found that the gamma distribution had the highest goodness of fit, as shown in Figure 4. Figure 5 shows the comparison of R-squares between the gamma distribution and the normal distribution of different service years. According to Figure 5, the gamma distribution proved to be more applicable to describe the compressive strength. Additionally, for a deteriorating process, the stochastic gamma process was adopted to analyze the time-varying reliability. [PERSON] [33] was the first to propose the gamma process as the first choice to describe the performance degradation process [34]. Moreover, [PERSON] modeled the stochastic process of deterioration with a gamma process, which had independent gamma-distributed increments with identical scale parameters [35]. The results indicate that a gamma process can properly model the monotonic behavior of aging. Overall, the preceding research figure shows that the gamma process is suitable to model gradual and monotonically accumulating damage over time [35; 36]. Based on these existing conclusions and the analysis in this paper, the gamma distribution is appropriate to describe the distribution of compressive strength. Figure 4: Distribution fitting of compressive strength. Figure 5: Contrast of goodness of fit for gamma distribution and normal distribution.
# A Novel Method to Mitigate the Multipath Error for BDS-2 Dam Deformation Monitoring
## 2 Materials and Methods
### Multi-Point Hemispherical Grid Model (MHGM)
When the grid point parameters are estimated using a sequential least square filter, the multipath error at \(V_{m}^{i}\) in the grids can be calculated by bilinear interpolation. \[V_{m}^{i}=\mathcal{W}_{m1}^{j}\mathcal{Q}_{m1}^{i}+\mathcal{W}_{m2}^{j} \mathcal{Q}_{m2}^{i}+\mathcal{W}_{m3}^{j}\mathcal{Q}_{m3}^{i}+\mathcal{W}_{m4 }^{j}\mathcal{Q}_{m4}^{i} \tag{3}\]  
\[\left\{ \begin{array}{l}\mathcal{W}_{m1}^{j}=\frac{(b_{2}-b)(l_{2}-l)}{(b_{2 }-b_{1})(l_{2}-l_{1})}\mathcal{W}_{m2}^{j}=\frac{(b_{2}-b)(l-l_{1})}{(b_{2}-b _{1})(l_{2}-l_{1})}\\ \mathcal{W}_{m3}^{j}=\frac{(b-b_{1})(l-l_{1})}{(b_{2}-b_{1})(l_{2}-l_{1})} \mathcal{W}_{m4}^{j}=\frac{(b-b_{1})(l_{2}-l)}{(b_{2}-b_{1})(l_{2}-l_{1})} \end{array} \right. \tag{4}\]  
where \((b,l)\) is the longitude and latitude of \(V_{m}^{i}\). The longitude and latitude of the four grid point parameters \(Q_{m1}^{j},\)\(Q_{m2}^{i},\)\(Q_{m3}^{i}\) and \(Q_{m4}^{i}\) are \((b_{1},l_{1}),\)\((b_{1},l_{2}),\)\((b_{2},l_{2})\) and \((b_{2},l_{1})\), respectively. When the number of grid points is three (\(Q_{m1}^{i}\), \(Q_{m2}^{i}\), \(Q_{m3}^{i}\)), the multipath error at \(V_{in}^{j}\) can be written as  
\[\left\{ \begin{array}{l}V=(1-u-v)Q_{m1}^{i}+uQ_{m2}^{i}+vQ_{m3}^{i}\\ u=\frac{(b_{2}-b_{1})(l-l_{1})-(b-b_{1})(l_{3}-l_{1})}{(b_{2}-b_{1})(l_{2}-l_{ 3})}\\ v=\frac{(b-b_{1})(l_{2}-l_{1})-(b_{2}-b_{1})(l-l_{1})}{(b_{2}-b_{1})(l_{2}-l_{ 3})}\end{array} \right. \tag{5}\]  
Similarly, the values of \(V_{m}^{k}\), \(V_{n}^{j}\) and \(V_{n}^{k}\) parameters between stations and satellites can also be determined. The DD residuals also can be expressed as follow:  
\[DD\ residuals=\left(V_{m}^{j}-V_{m}^{k}\right)-\left(V_{n}^{j}-V_{n}^{k}\right)- \lambda\cdot\Delta\
abla N \tag{6}\]  
The normal equation corresponding to the parameters of relevant grid points is constructed by using Equation (6), and the solution of different satellites, stations and time periods can be realized by the superposition of the normal equation. Any constellation of the BDS-2 satellites can obtain the multipath correction according to the satellite altitude and azimuth.
# Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors
## 3 A Conceptual Framework of Social Media Rumor Precise Governance
### From the Perspective of Rumor  
Rumor can be divided into different categories according to its content, such as social rumors (content involving social events and false alarms, etc.), political rumors (content involving policies and political figures, etc.). Regardless of the type, false rumors have negative effects on individuals, organizations, and society. It is recommended to strengthen rational understanding of false rumors and grasp the law and trend of the recurrence and large-scale spread. In the era of new media, the rumor spreading process is greatly accelerated which can lead to an explosion of information in a short period of time and a concentrated outbreak. In comparison, refutation activities are carried out in response to the false rumors which have already been generated. Even with the highest efficiency, the rumor refutation is lagging behind the generation and dissemination of false rumors. At the same time, it should be noted that large-scale false rumors cause greater harm and deeper impact due to the wide coverage. Under limited conditions, false rumors spreading on a large scale should be monitored and tracked. Besides, timely prevention and control should be carried out to reduce the harm. Therefore, we can seize the opportunities and realize accurate rumor governance only if using big data and advanced analysis technologies to dig out the contextual and textual features of large-scale and recurring rumors and carry out effective monitoring and early warning.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### TSN Support for Automated Driving
#### 4.6.1 Temporal Requirements  
Automated Driving systems include a number of sensors that generate traffic with very diverse characteristics, e.g., regular periodic traffic and burst traffic with a periodic, sporadic, or aperiodic arrival pattern.  
The TSN standards that enable meeting the temporal constraints imposed by the traffic classes mentioned above include:  
* IEEE 802.1 Qbv, which is able to provide deterministic behavior and low latency to closed-loop control (steering, braking) and inter-processor communication. The standard also fits highly regular periodic traffic well.
* IEEE 802.1 Qbu, which in combination with the IEEE 802.3 br standard [46; 47] offers frame preemption ability to reduce the access delay time for urgent traffic (e.g., steering and braking actuation).
* IEEE 802.1 Qcr, which can handle the traffic bursts generated by radars and LiDARs by applying token-bucket shaping on a per-flow basis.
* IEEE 802.1 Qav, which implements the Credit-Based Shaper to effectively deal with burst traffic.
## 4 Conclusions  
In this work, we used LIPIT to launch \(D_{\rm p}\) = 3.7 and 7.6 \(\upmu\)m microspheres in \(P\) = 76 and 760 Torr and quantified \(C_{D}\) across 5 \(<\)\(Re\) \(<\) 300 and 0.3 \(<\)\(M\) \(<\) 2.2. While prior methods of accessing the 1.2 \(<\)\(M\) \(<\) 4 and \(Re\) \(<\) 300 experimental space accessed the transitional-flow regime, our study broadened the range of flow regimes by accessing the transitional-flow and slip-flow regimes. We illustrated the diminishing accuracy of the Carlson-Hoglund model with increasing rarefaction in 1.2 \(<\)\(M\) \(<\) 4 and \(Re\) \(<\) 300 conditions. The diminishing accuracy was attributed to the suppression of drag-inducing flow characteristics in increasingly rarefied conditions. The results of this work will be useful to the fields of micro-ballistic mechanical characterization and projectile gene and drug delivery, as the Carlson-Hoglund model may be more accurate in modeling \(C_{D}\) for \(M\) \(>\) 1.2 and \(Re\) \(<\) 300 in the continuum and slip-flow regimes than in the transitional-flow and free-molecule flow regimes. The results also attest to the accuracy of the Carlson-Hoglund model, and the standard drag curve in modeling \(C_{D}\) for \(M\) \(<\) 0.8 and \(Re\) \(<\) 300. These models may be used to estimate the velocity and energy of a microsphere at any point during its flight, including the moment of impact, which is critical for applications such as micro-ballistic mechanical characterization and projectile gene and drug delivery.  
Conceptualization, N.F., D.S. and J.-H.L.; methodology, J.-H.L.; software, J.-H.L.; validation, N.F. and J.-H.L.; formal analysis, N.F. and J.-H.L.; investigation, N.F., D.S. and J.-H.L.; resources, J.-H.L.; data curation, N.F.; writing--original draft preparation, N.F.; writing--review and editing, D.S. and J.-H.L.; visualization, N.F. and J.-H.L.; supervision, J.-H.L.; project administration, J.-H.L.; funding acquisition, J.-H.L. All authors have read and agreed to the published version of the manuscript.  
Figure 4: Drag coefficient, \(C_{D}\), vs. Reynolds number, \(Re\), at 1.2 \(<\)\(M\) \(<\) 4 for the three supersonic experimental conditions are displayed with the data of [PERSON] [12], [PERSON] [13], [PERSON] [14], and [PERSON] [15]. The shaded region indicates the Carlson-Hoglund model’s range for 1.2 \(<\)\(M\) \(<\) 4. Data points are colored according to the \(M\)/\(\sqrt{Re}\) flow regime metric from 0.1 to 0.7, where increasing values correspond to increasingly rarefied conditions. Data points in black with \(M\)/\(\sqrt{Re}\) \(<\) 0.1 belong to the slip-flow regime.  
**Funding:** This research was funded by the Army Research Laboratory, Contract No. W911 NF1920108 (Program Manager: [PERSON]).  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 4 Discussion
The driving simulation environment, while highly controlled, cannot perfectly replicate the psychological stress and environmental conditions of real-world driving scenarios. Future research should address these limitations through expanded driver samples, diverse geographical locations, and enhanced integration of simulation with naturalistic driving studies. In conclusion, our research contributes to the growing body of evidence supporting data-driven approaches to traffic safety, with particular relevance for the challenging context of mountain roads. By combining behavioral pattern analysis with advanced predictive modeling, we have developed tools that can meaningfully contribute to reducing accident risks and enhancing road safety in complex driving environments. The FP-Growth algorithm's effectiveness in identifying association rules between driving behaviors and conflict types offers a promising methodological approach for future traffic safety research, while the LSTM neural network provides a robust framework for predictive modeling of safety-critical events. Together, these methodological advances offer significant potential for improving road safety management and reducing the occurrence of traffic conflicts in mountain road environments.
# Detection of Waste Containers Using Computer Vision
## 4 Conclusions  
This study focused on the need for a different solution for waste detection and a means to accomplish it using computer vision techniques. This work evaluated different approaches to object detection and classification towards the differentiation of waste container types. Several experiments were performed using feature-based methods and convolutional neural networks.  
Figure 12: The mAP plotted over average loss and number of iterations.  
Figure 13: The average loss per number of iterations.  
Regarding VLAD, the implementation will no longer be used as it does not fit the problem of the detection itself. A possible use for this tool would be having a VLAD with images of waste containers at different locations, having those locations be associated with the images, and finding the location of a queried picture that encompasses waste containers. If this kind of scenario is required, then this technique can be revisited. However, in the meantime, there is no use for it considering the results obtained with other implementations.  
Concerning CNNs, the detection and classification of the containers peaked at an mAP of 90%. The work with CNNs will be continued, since the achieved results are remarkable given that the results were obtained with constraints on the amount of data, the computational power, and the computation time. These constraints have had a great influence on the acquired results, and this was evident during the implementation of YOLOv3.  
The viability of a computer vision approach to replace the current method of detection of waste containers seems promising, although further research and testing needs to be done to make sure the results obtained were not a result of an ad-hoc methodology. As mentioned in the Introduction, if this method produces good and repeatable results, a transfer of target concerning the detection of objects can be initiated.  
Future work aims to have a larger dataset with more types of waste containers, as well as a better hardware setup to generate results faster. Since the final goal is to implement this technology in a mobile platform, the created program can complement CNNs. This can involve the creation of a database with geo-locations associated with the detection of waste containers. This information can then be used to confirm a detection or to identify false positives. This process will then generate data that can be used to train CNNs to increase their accuracy.  
All authors contributed to the research, the tests, and the performance evaluations that were presented in this work. All authors contributed to the writing of the paper.  
This work was funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UID/EEA/50008/2019.  
The authors would like to acknowledge the companies EVOX Technologies and InspiringSci, Lda for their interest in and valuable contributions to the successful development of this work.  
The authors declare no conflict of interest.
## 4 Discussion  
Based on the analysis of contact pressure and the stress distribution characteristics of the casing under varying casing parameter combinations presented in this study, it was observed that optimal performance was achieved with scheme 2 (\(\alpha\) = \(80^{\circ}\), \(\beta\) = \(70^{\circ}\)) and scheme 3 (\(\alpha\) = \(85^{\circ}\), \(\beta\) = \(70^{\circ}\)). Both of these parameter combinations have their own advantages. The following key findings were obtained:  
(1) When the optimized combination of structural parameters is determined to be as in scheme 2 (\(\alpha\) = \(80^{\circ}\), \(\beta\) = \(70^{\circ}\)), which will achieve the highest peak contact pressure and the lowest contact pressure standard deviation, a highly reliable anchoring performance is ensured along with excellent uniformity in contact pressure distribution.  
(2) When the optimized combination of structural parameters is selected as in scheme 3 (\(\alpha\) = \(85^{\circ}\), \(\beta\) = \(70^{\circ}\)), which will exhibit relatively high peak contact pressure while minimizing casing stress, a reliable anchoring force is ensured with minimal casing damage during setting.  
(3) Consequently, the slip tooth apex angle of \(80^{\circ}\) or \(85^{\circ}\) and tooth inclination angle of \(70^{\circ}\) were identified as the preferred structural parameters for the DBS slip design.  
The findings of this study provide critical insights into the optimization of dissolvable ball seat (DBS) slip systems, with broader implications for hydraulic fracturing operations in both local and global contexts. The identified optimal parameter combinations (\(\alpha\) = \(80^{\circ}\) or \(85^{\circ}\), \(\beta\) = \(70^{\circ}\)) not only enhance anchoring reliability and pressure distribution uniformity, but also minimize casing damage, a common challenge in unconventional reservoir stimulation.  
From a fundamental perspective, this work advances the mechanical understanding of slip-casing interactions, providing a validated framework for stress distribution analysis. The methodology--combining finite element modeling with experimental verification--can be adapted to other downhole tool designs, reinforcing the interdisciplinary relevance for materials science and mechanical engineering researchers.  
Future work should explore the on-site performance and long-term dissolution behavior in different geological environments to further verify the robustness of the design. Moreover, the processing quality of the seal ring of DBS may affect its performance. An in-depth study can be conducted on the influence of the surface quality of the metal ring on the sealing performance to obtain its influence law.  
Figure 15: Average contact pressure between casing and slips.
# A Traffic-Based Method to Predict and Map Urban Air Quality
## 2 Materials and Methods
### Modeling
#### 2.4.3 Clustering Analysis  
Once the different models were built from the methods described in Section 2.4.1, an unsupervised learning (clustering method) was performed in order to identify the model that generalizes the best. The popular iterative distance-based clustering k-Means was chosen. The Euclidean distance was selected as metric to assess the performance of the algorithm. First, the desired number of clusters, which is the \(k\) value, is specified. Here, \(k\) = 2, because two classes of pollution are expected: low (below the median value) vs. high (above the median value) concentration of PM\({}_{2.5}\). Second, the algorithm chooses \(k\) points at random as cluster centers. Third, all the instances of the dataset are assigned to their closest cluster center. Fourth, the centroid (or mean) of all the instances in each cluster is calculated, which transforms these centroids into new cluster centers. Then, the algorithm goes back to the beginning and carries on until the cluster centers do not change. In other words, this algorithm searches for a minimization of the total squared distance from the instances to their cluster centers. The best model is the one that gets the minimum distance or error. The variables used to perform this clustering analysis were traffic and time of day for the whole city measurements and the PM\({}_{2.5}\) prediction made by each model.
## 5 Results and Discussion
### Vibration Synchronization and Control Synchronization
#### 5.1.1 Numerical Results
In particular, the better the symmetry \(\eta=m_{3}/m_{4}\), the closer the absolution phase difference is to the limit value \(180^{\circ}\) or \(0^{\circ}\).
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 3 Materials, Methods and Field Tests in a Greenhouse  
The experiments were conducted in a research center Hoogstraten (PCH) situated near the province of Antwerp, Belgium [46]. It is one of the most innovative research centers in horticultural, managing 166 trials in the greenhouse over the span of one year [47]. PCH has open-air and greenhouse-based cropping systems mainly for strawberries, bell peppers, and tomatoes. Our experiments were conducted over tomato crop which combines practical research of WSNs deployment, with directly applicable results on the crops. The deployment system architecture is shown in Figure 2. PCH has divided the area into several compartments, with an individual size range of \(500\,\mathrm{m}^{2}\) to \(1000\,\mathrm{m}^{2}\) and a height of \(8\,\mathrm{m}\).  
The first research step was a series of qualitative visits to understand the topology of the greenhouse, crop cycle, growth of the plants, relevant climate parameters to be monitored as part of requirement and possible interference for link connectivity. We defined different phases for the study and monitoring of the crop. This was done with an objective to analyze the different challenges at each stage of deployment such as the variation of sensor values at a different gradient, optimal network coverage, and wireless connectivity that might be relevant for final WSN deployment. The following part of this section reveals the sequential steps that we followed and equally propose this as the potential roadmap for the WSN deployment in the greenhouses.