esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
## 7 Conclusions  
Given the complex nature of a river network, it is challenging to perform good water level predictions with high accuracy and low computational cost when using the traditional EKF-based data assimilation approach. In this study, we developed a new data assimilation scheme based on the EnKF algorithm to optimize real-time water level simulation in river networks. The parameter analysis results showed that the initial sample ensemble size, its standard deviation, and the observation noise level could significantly impact the data assimilation performance. Optimized parameter settings, with a medium ensemble sample size (e.g., from 100 to 150), a relatively low observation noise level (e.g., from 0.0001 to 0.01 m), and a relatively high standard deviation (e.g., from 0.01 to 0.1 m), resulted in good data assimilation performance.  
The experimental testing conducted on a mountainous single-channel river showed that the data assimilation method based on EnKF had a similar capacity to correct simulation errors to that of the EKF-based model, but for a river network with complex branches and connecting nodes, compared to the EKF-based model, the EnKF-based data assimilation method was able to decrease simulation errors greatly by up to 49%, to reduce calculation costs by 45%, and to reduce the filtering time by up to 43%. In addition, this performance was not impacted by the key model parameter, i.e., the observation noise level.  
The experiments conducted on a real water river network showed that the EnKF-based data assimilation model forecast water levels with a high accuracy after 5 to 8 h and could make predictions up to 20 to 30 h after the data assimilation process was completed, indicating the potential application capacity of the EnKF-based assimilation model developed in this paper.  
Conceptualization, Y.C. and F.C.; methodology, Y.C.; validation, X.M. and W.C.; formal analysis, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 12002310 and 51709235, General Scientific Research Projects of Education of Zhejiang Province, grant number Y201941076, and Zhejiang Water Science and Technology Research Project, grant number RB2202. The APC was funded by General Scientific Research Projects of Education of Zhejiang Province, grant number Y201941076.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: All data, models, and code generated or used in this study are available upon request from the corresponding author.  
**Acknowledgments:** We thank Papergoing group for their writing support.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Simulated Result and Analysis
### Current Distribution  
In Figure 5a, the current distribution on the inner surface at 3.5 GHz is primarily focused on the lower left corner of the Wang-shaped feed microstrip line, while the current distribution on the outer surface is concentrated in three open-ring structures with a 0.3 mm width beside the lower left corner. In Figure 5b, the current distribution on the inner surface at 4.9 GHz is primarily centralized in the lower right corner of the Wang-shaped feed microstrip line, while the current distribution on the outer surface is primarily concentrated in the lower right corner of the open-loop structure. In Figure 5c,d, the electric field distribution on the outer surface at 3.5 GHz is primarily focused in the upper right corner of the metal rim, while the electric field distribution on the outer surface at 4.9 GHz is primarily concentrated in the right corner of the metal rim. As a result, three resonant positions constitute a wide frequency band, and the \(-10\)dB impedance bandwidth can fully cover 3.3-5 GHz. Therefore, a small variation in  
Figure 4: Simulated parameter results of the proposed miniature eight-port antenna array. (**a**) L1. (**b**) L2.  
Figure 3: The evolution of the proposed miniature eight-port antenna array. (**a**) Design evolution. (**b**) Reflection coefficients.  
the size of the structure can make a big difference in the reflection coefficients. Three open-loop structures can greatly reduce the size of the antenna and achieve wide bandwidth and better antenna performance at the same time, which is very meaningful for the prospect of 5G sub-6 GHz miniature mobile handsets.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 4 Proposed Approach  
In this section, we present our model designed for mobility analysis and the corresponding algorithm for extracting POIs. The main objective of our approach is to leverage advanced graph-based techniques to dynamically interpret and analyze user movement patterns within urban environments. We aim to provide predictive insights that are not only accurate but also rich in contextual relevance. Here, we describe the model's architecture, focusing on how we generate the mobility graph and the algorithmic strategies employed for data processing, graph formalism, and POI extraction.
## 3 Proposed Method  
YOLOv3 is a state-of-the-art single-pass fast object detection network. It processes a complete image at once by implicitly using contextual information about shape, size, and structure. YOLOv3 network is an amalgam of residual and feature pyramid networks which significantly benefits detection and classification-related tasks. Nonetheless, it still lags in terms of detection accuracy of smaller objects with reference to image size. For COCO dataset [26], it attained 31% mAP [27] with an inference time of 29 ms equivalent to 34.5 frames per second, which is a real-time performance. But due to its low mAP, it cannot be used as a real-time object detector. Furthermore, the COCO dataset includes objects of larger size that cover typically 50-70% of the image area. On the other hand, traffic signs appear much smaller in an image, covering only 2-5% of an image. In YOLOv3, K-means clustering is used to determine best-fit anchor boxes for a particular data set.  
Furthermore, the random selection of initial K-means centroids in YOLOv3 sometimes results in good accuracy and at times it is worse. Hence, multiple executions are required to determine the optimal values. Anchor boxes' size and scale have a major effect on Average Precision. Unlike Faster RCNN, it is not trained for a region of interest (ROIs). Rather the network fits the chosen anchor boxes over the objects to be detected in different segments of the image. This anchor box fitting over an object takes a long time with anchor boxes of improper sizes and scales. Besides, sometimes it misses out on true detections and outputs much false detection, leading to lower Average Precision values.  
To manage smaller object detection with higher accuracy and lower false detections due to improper bounding box placement, we propose an improved YOLOv3 network along with the anchor box selection method. The block diagram of the proposed method is shown in Figure 1. There are two improvements in the YOLOv3 network; the network layers pruning and replacement of the default anchor box algorithm with the regression-based anchor box selection. In addition, the input image is divided into patches of 400 \(\times\) 400 pixels and passed through the improved network for traffic sign detection. The output patches are consolidated into the original input image size and redundant detections were removed through a non-maximum suppression block.
## 2 Fundamentals of Shape-Memory Polymers (SMPs) and Shape-Memory Polymer Nanocomposites (SMPNCs)
### Activation Techniques for SMPs and SMPCs  
SMPs can be activated by several methods, broadly classified as heat, electricity, light, magnetism, moisture, pH, and so on [11; 19; 42; 43]. The SME of thermally-responsive SMPs is immediately activated by the application of Joule heating, which is generated by a heat source such as hot gas or hot water. However, direct thermal actuation may not always be convenient for applications requiring remote or spatial activation. To address this problem, several functional additives, such as nanofillers and photochemical groups, are incorporated into the SMP to facilitate the activation of SMEs using diverse stimuli such as electricity, light, magnetism, and moisture [10; 44].  
SMPs induced using these techniques are inherently activated by Joule heating, which is generated through various indirect means. For example, when magnetic NPs are included in SMP nanocomposites and subjected to an external magnetic field, the composites are activated through inductive Joule heating in an alternating magnetic field. This process converts the electromagnetic energy from the high-frequency external field into heat energy. Similarly, light-activated groups can convert light energy into heat energy to trigger the activation of polymers for shape recovery. In the case of water- or solvent-driven SMPs, solvent molecules permeate the polymer sample. When the polymer is submerged in solution, the solvent molecules act as a plasticizer, reducing the transition temperature and helping the polymer recover its original form. This is an example of actuation indirectly caused by thermal changes. Specifically, the development of intrinsically light-induced SMPs involves the incorporation of reversible photoreactive molecular switches. This method of stimulation is not influenced by temperature, distinguishing it from the indirect actuation of thermally-responsive SMEs.
# Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
## 3 Calculation Method and Verification
### Fan External Characteristic Test  
According to the regulations and requirements of 'Fans--Performance testing using standardized airways (ISO 5801:2017)' [13], the air performance of the small diagonal flow fan in this paper was tested with the air flow test bench. Figure 5 shows the air test rig device diagram and field test picture. To reduce the test error and ensure the accuracy of test results, the mixed-flow fan was operated at 220 V standard voltage for 30 min and  
Figure 4: Grid independence verification.  
tested when the performance was stable. Parameter sensors such as pressure, temperature, and humidity weeere arranged inside the test pipe, which were imported into the computer and converted into relevant air parameters after formula calculation. In the test process, it is necessary to control the outlet diameter of the air duct by replacing the orifice plate to realize the air performance test of the fan under different flow conditions.  
In order to compare the error between the predicted value of the calculation model and the true value of the test, the calculation results are compared with the experimental values to obtain the flow-static pressure-total pressure efficiency performance curve shown in Figure 6.  
Figure 5: Air performance test. (**a**) Test device diagram (**b**) Field test diagram.  
Figure 6: Comparison of experimental and predicted values.  
It can be seen from the external characteristic curve of Figure 6 that the predicted value is close to the experimental value, and their changing trends are basically consistent. Under the condition of a small flow rate, due to the influence of turbulence and boundary layer separation, there is still a certain deviation between the simulated and experimental values. However, this paper studies the operating performance of the diagonal flow fan under standard operating conditions. Under the design conditions, the error between the experimental value and the calculated value of the diagonal flow fan is basically controlled to within 3%, which is significantly less than the engineering calculation error requirements [14]. Therefore, the numerical simulation method is considered to be more reliable and can be used for subsequent optimization calculations.
## 8 Introduction to Proprietary and Standards-based Solutions
### Standards-Based Technologies
#### 8.2.8 IEEE 802.15.4k  
This standard is developed by the IEEE 802.15.4k Task Group (TG4k) for low-energy critical infrastructure monitoring (LECIM) applications. It works in unlicensed sub-GHz and 2.4 GHz ISM bands with multiple discrete channel bandwidths ranging from 100 kHz to 1 MHz [6]. It uses direct sequence spread spectrum (DS-SS) and FSK at the physical layer [40]. The MAC is based on carrier sense multiple access/collision avoidance (CSMA/CA) and works with three different approaches, viz. without priority channel access (PCA), CSMA, and ALOHA with PCA. With PCA, the end devices and base stations can prioritize their traffic in accessing the medium. It supports star topology and has the capability to exchange messages asynchronously. It supports the peak data rate of up to 128 kbps and maximum coverage of 5 km.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 2 Data and Methods
### Ancillary Data  
Ancillary data were required to both develop the regression models and validate the results.  
The volumes of gas flared in Nigeria (Figure 2) were derived from three different official datasets, made available online by: (i) Nigerian National Petroleum Corporation (NNPC), providing information from 2000 to 2016 [29]; (ii) Organization of the Petroleum Exporting Countries (OPEC), from 2000 to 2014 [30]; and (iii) Energy Information Administration (EIA), whose data cover the period 2005-2014 [31]. NNPC, OPEC and EIA are international organizations providing monthly or annual statistical bulletins, containing useful information on the oil and gas activities of their members as well as most of the worldwide oil-producing countries. Such data, of which they do not guarantee a 100% accuracy, reflect general analysis and trend of activities that characterize the industry within each country for a specific year. It is worth noting that the NNPC and EIA data were originally provided in mscf (million cubic feet) and bscf (billion cubic feet), respectively. In this study they were converted in Billion Cubic Meters--BCM (1 cubic feet = 0.0283168 cubic meter) to make possible the comparison among all official data. In detail, as shown later, OPEC and EIA were used to develop the regression models, while the NNPC records, offering the longest temporal range, were employed to validate the estimates of gas flared volumes.  
Moreover, results obtained by another independent, MODIS-based, algorithm developed by [12] (i.e., MODET-MODIS Flare Detection Technique and MOVET-MODIS Flare Volume Estimation Technique) were also used for assessing the reliability of the RST-FLARE achievements, in terms of flaring sites localization and gas flared volumes estimation.  
Besides, satellite products provided by NOAA's Earth Observation Group (EOG), applying the VIIRS Nightfire algorithm--VNF [22], have been also exploited. Such data are made available as of November 2017 by SkyTruth, which has built an interactive map showing individual flaring locations  
Figure 2: Annual values of gas flared volumes (in BCM) for Niger Delta region provided by NNPC (grey line), OPEC (blue line), EIA (pink line) and VNF (green line).  
as well as annual estimates of gas flaring volume over virtually any area of interest, for years 2012 through 2016 ([[https://viirs.skytruth.org/apps/heatmap/flarevolume.html](https://viirs.skytruth.org/apps/heatmap/flarevolume.html)]([https://viirs.skytruth.org/apps/heatmap/flarevolume.html](https://viirs.skytruth.org/apps/heatmap/flarevolume.html))). In this paper, the data referred to Niger Delta were extracted and used (green line in Figure 2).
## 2 Long Hole Blasting Vibration Test  
Long hole blasting exerts a significant impact on surface structures, necessitating an evaluation and analysis of blasting vibration test data to ensure compliance with relevant national standards regarding blasting vibration hazards. This study focuses on the Hongling Lead-Zinc Mine in Chifeng. The mining method is the stage open stoping method in the room stopes, and the pillar stopes utilize the caving method. Currently, mining operations span from the production midsection at 805 m to 905 m. Mining activities for stopes above 905 m have been completed. During the overall collapse blasting of mine pillars, noticeable surface vibrations occur, profoundly affecting buildings in the mine.  
To mitigate the adverse effects of blasting vibrations, tests were conducted at strategic locations on the surface to analyze the impact of the blasting on nearby buildings. The blasting vibration velocity tests were performed using the Mini-Blast I type blasting vibration tester, which comprises sensors, blasting vibration measurement, and microcomputers, among other components (Figure 1). Six measurement points were strategically positioned around the building on the ground surface within the impact area. These points were arranged in a straight line, aligning with the surface testing points and the distribution of long hole blasting cores, as depicted in Figure 2. A total of 138 sets of valid data were collected from nine ground-blasting vibration tests, encompassing 46 sets of valid data related to vertical particle vibration velocity (Table 1). The recorded parameters commonly include the charge weight, the number of delays, the horizontal distance and elevation difference between the blast point and the measurement point, and the peak particle velocity in the vertical direction.  
Figure 1: Blasting vibration monitoring system.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Image Classification  
The LULC classifications were computed using a supervised Support Vector Machine (SVM) classifier. The SVM is one of the most effective classifiers in remote sensing studies producing high accuracies in the classification of satellite imageries [31; 32]. The SVM is a non-parametric machine learning classifier based on statistical models developed by [PERSON] in the 1960s [33]. It seeks to define an optimum hyperplane to separate two classes using the training samples and then validate its generalization ability [34]. The classifier generates a decision boundary with large margins, with the smallest distance between the samples to the separation boundary [35]. It was also facilitated by kernel functions which could be applied to solve optimization problems, especially in high-dimensional spaces [34]. When using the SVM classifier, the training samples (70% of all classes in this study) were mapped onto a new feature space using a kernel function, and the SVM produced a large margin separation between the training samples for all the classes in a new feature space. With labeled sequences \((x_{1},y_{1}),\ldots,(x_{m},y_{m})\) where \(x\) stands for the covariates and \(y\in\{-1,1\}\) denotes the response, a kernel function (\(k\)) was used by SVM as illustrated in the following equation:  
\[f(x)=\sum_{i=1}^{m}a_{i}k(x_{i},x)+b, \tag{1}\]  
where \(a_{i}\) and \(b\) coefficients are estimated through minimizing the function:  
\[\sum_{i,j=1}^{m}a_{i}a_{j}k\big{(}x_{i},x_{j}\big{)}+C\sum_{i=1}^{m}\varsigma_ {i}, \tag{2}\]  
which is subject to:  
\[y_{i}f(x_{i})\geq 1-\varsigma_{i}, \tag{3}\]  
where \(C\) stands for the penalty parameter of misclassification and \(\varsigma_{i}\) measures the degree of misclassification of \(x_{i}\)[36]. The training vectors (\(x\)) are mapped onto a higher or infinite dimensional space using the function \(f(x)\). The SVM classifier determines a linear hyperplane, separating the training samples a with maximized margin that is in the higher dimensional space [35]. In non-linear data, the dataset is transformed into a higher dimensional feature space using kernel-based functions such as radial basis, linear, sigmoid and polynomial [33]. In previous remote sensing studies, the radial basis was regarded as the best kernel function for classifying and mapping LULC features [37; 38]. The radial basis requires tuning the cost and gamma parameters, which affects classification accuracies [32]. The best parameters were chosen using a 10-fold cross-validation method. The best cost and gamma parameters used in this study differed for years.
## 4 Optimization Design
### Sensitivity Analysis of Design Variables  
As illustrated in Figure 13, the sensitivity analysis quantifies the relative impact of five key design parameters on two performance objectives: the robustness metric of vertical acceleration (\(\mu_{\text{RMS}}+3\sigma_{\text{RMS}}\)) and the maximum side force (\(F_{cs}\)). Among these, the crank-arm length (\(L_{1}\)) demonstrated the highest sensitivity in both categories, with indices of 31.8% for acceleration robustness and 43.1% for the side force. The orifice diameter (\(D_{z}\)) also exerted significant influence, contributing 25.8% and 13.9% to the two metrics, respectively. Conversely, the check valve diameter (\(D_{d}\)) and the connecting rod length (\(L_{3}\)) exhibited relatively low sensitivities of 10.7% and 10.4% for robustness, and 1.3% and 30.0% for the side force, respectively. The value of \(n\) had a moderate impact, with indices of 21.2% for robustness and 11.7% for the side force.
# A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection
## 4 Case Study
### Ranking of Suppliers  
After the determination of criteria weights, the experts were requested to evaluate the performance of each supplier based on each resilience criterion. By using the scoring scale, a decision matrix was created. Table 13 shows the decision matrix on suppliers obtained from the experts' judgments with the root mean squares (RSS) for each supplier. Then, a standardized decision matrix was formed in Table 14. Table 15 illustrates the weighted standardized decision matrix by multiplying the criteria weights generated by BWM with the standardized decision matrix. After obtaining the weighted standardized decision matrix, the PIS and NIS were identified by applying TOPSIS. Lastly, the suppliers were ranked based on the relative closeness coefficients, F. Table 16 presents the results of the \(\mathrm{d}_{\mathrm{i}}^{+},\mathrm{d}_{\mathrm{i}}^{-}\), sum of \(\mathrm{d}_{\mathrm{i}}^{+}\) and \(\mathrm{d}_{\mathrm{i}}^{-}\), \(\mathrm{F}_{\mathrm{i}}\) and rank of the suppliers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**C** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **RSS** \\ \hline S1 & 3.8 & 2.8 & 2.6 & 3.2 & 3.2 & 2.8 & 2.0 & 7.8333 \\ S2 & 3.8 & 4.2 & 4.2 & 4.2 & 4.4 & 4.4 & 5.0 & 11.4490 \\ S3 & 4.2 & 2.6 & 4.0 & 3.6 & 4.0 & 4.2 & 4.3 & 10.2708 \\ S4 & 3.0 & 2.4 & 3.6 & 3.2 & 3.6 & 3.6 & 3.3 & 8.6470 \\ S5 & 3.8 & 3.6 & 4.0 & 4.2 & 3.8 & 4.0 & 4.0 & 10.3673 \\ S6 & 3.4 & 2.8 & 2.6 & 3.0 & 3.4 & 2.6 & 2.3 & 7.6662 \\ S7 & 3.8 & 4.4 & 3.6 & 3.4 & 4.0 & 3.6 & 3.0 & 9.8122 \\ S8 & 4.0 & 3.6 & 3.0 & 3.2 & 4.0 & 3.6 & 2.7 & 9.1897 \\ S9 & 3.8 & 3.2 & 3.0 & 3.6 & 4.2 & 3.8 & 3.3 & 9.4663 \\ S10 & 4.2 & 3.6 & 4.0 & 4.2 & 3.6 & 3.6 & 3.7 & 10.1907 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Average experts’ opinions on suppliers for TOPSIS (Phase 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**C** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** \\ \hline GRG & 0.5133 & 0.5800 & 0.8667 & 0.6667 & 0.7133 & 0.7000 & 0.7667 \\ Rank & 7 & 6 & 1 & 5 & 3 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Grey relational grade and rank for each criterion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**C** & **Weights** \\ \hline C1 & & 0.0367 \\ C2 & & 0.0643 \\ C3 & & 0.4225 \\ C4 & & 0.0735 \\ C5 & & 0.1286 \\ C6 & & 0.1029 \\ C7 & & 0.1715 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Consistency test.