esa-sceva/satcom-chunk-collection huggingface dataset:
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 1 Introduction
(**a**) Power system layer; (**b**) Communication system layer. Due to the strong heterogeneity [27] in services and large difference in QoS requirements for SG NAN, the way to provide solutions for each kind of service to meet the link utilization and QoS requirements under the time-varying network resources has become one of the key issues of SG. By utilizing the advantages of SDN controllers, we design a neighbor-routing mechanism, which can balance the neighbor traffic with different service requirements in real time automatically. By considering the data size and the traffic flow, our method is capable of not only monitoring the congestion level of each node in the network, but also meeting the low delay requirement for distinguished applications. In addition, our approach increases the flexibility and reliability of the SG communication networks. In summary, our main contributions can be summarized as follows. * To meet the QoS requirements of different service flows in the SG, by focusing on the flow trends in the network, we design an SDN-based strategy to predict the future congestion status for the link and construct a virtual congestion-free topology. * We propose a queueing model and construct a long-term network utility maximization problem, which can be effectively solved by the Lyapunov optimization theory, to determine the final routing decision. * The experimental results of different algorithms show that our QCORS can effectively grasp the advantages of global network and reduce end-to-end delay and packet loss rate. The rest of this article is organized as follows. Section 2 introduces the research background and Section 3 describes the concrete implementation process of the QCORS mechanism. In Section 4, the performance of the QCORS mechanism is verified by constructing an experimental platform. Section 5 presents a summary of the paper.
# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 3 Collaborative Scheme with Global Navigation Satellite System (GNSS) and RFID Location  
In this section, we propose a collaborative location scheme using GNSS and RFID. It is a low-cost and easily deployable system in which only one RFID reader and one GNSS device are used without any reference tags. This system can achieve sufficient location accuracy for urban public infrastructure asset management in the era of smart cities.
# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 4 Computational Experiments
### Main Experimental Results  
In this section, we consider the instances commonly adopted in the previous PDSTSP literature. We also present optimal solution costs and the computation times required by the CP model discussed in Section 3 to retrieve these solutions.  
It should be noted that the optimal solution costs were only already published for the 720 small instances discussed in Section 4.2.1 and for two of the instances addressed in Section 4.2.2. For all remaining 96 instances, optimality was demonstrated here for the first time.
# Grayscale Iterative Star Spot Extraction Algorithm Based on Image Entropy
## 8 Conclusions  
To address issues such as the low star spot extraction rate and a high number of false star spots when the star tracker is affected by stray light during on-orbit operation, a grayscale iterative star spot extraction algorithm based on image entropy was proposed. Using this method, we first eliminated some background stray light through multiple iterations of grayscale transformations, enhancing the contrast of the star spot targets. Next, inner-outer star spot extraction templates were constructed, and then the extraction of star spots was carried out based on image entropy, which further suppressed the background clutter and noise. Based on the simulated and real star images, the algorithm was verified in numerical simulations and experiments. Compared to the max-mean and the max-median filtering algorithm, the detection rate of the IEGI algorithm increased by over 45%, and compared to the Top-Hat algorithm, the error rate decreased by over 50%. Compared to other star spot extraction algorithms presented in this paper, the proposed algorithm can achieve a higher number of correctly extracted star spots and a lower number of false star spots under different background noise interference conditions. Our work provides an alternative approach for star spot extraction, which should be further investigated in the future.  
All the authors contributed to this study. Conceptualization, Q.Z. and J.L.; investigation, Q.Z. and D.Z.; methodology, Q.Z. and J.F.; resources, J.L. and D.Z.; software, Q.Z. and J.L.; validation, Q.Z. and J.L.; funding acquisition, Q.Z. and J.L.; writing--original draft preparation, Q.Z.; writing--review and editing, Q.Z. and J.L.; visualization, D.Z. and J.F.; supervision, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Basic Research Plan in Shaanxi province of China under Grant No. 2022 JQ-706.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 CubeSats and Inter-Satellite Links (ISLs)
Corp.)} & LEO-GND & \multirow{4}{*}{1.064 \(\upmu\)m} & IMDD & \multirow{4}{*}{50/100 Mbps} \\ \cline{1-1} \cline{5-5}  & & & LEO-GND & & & \\ \hline \multirow{4}{*}{[50]} & \multirow{4}{*}{2019} & \multirow{4}{*}{Starlink/Space-X (USA)} & - & - & - & 1 Gbps \\ \cline{1-1} \cline{5-5}  & & & REISSAT/VSOTA (NICT) & LEO-GND & \multirow{4}{*}{0.98/1.5 \(\upmu\)m} & IMDD & \multirow{4}{*}{–1 kbps} \\ \cline{1-1} \cline{5-5}  & & & SOLISS (Sony) & LEO-GND & & \\ \hline \multirow{4}{*}{[52]} & \multirow{4}{*}{2019} & \multirow{4}{*}{EDBS-C (ESA)} & GEO-LEO & \multirow{4}{*}{1.06 \(\upmu\)m} & BPSK & \multirow{4}{*}{–1.8 Gbps} \\ \cline{1-1} \cline{5-5}  & & & OPS-SAT (TU Graz) & LEO-GND & & & PPM & 2 kbps (uplink) \\ \hline \multirow{4}{*}{[13]} & \multirow{4}{*}{2020} & \multirow{4}{*}{JDRS (JAXA)} & GEO-GND & \multirow{4}{*}{1.5 \(\upmu\)m} & BPSK & \multirow{4}{*}{1.8 Gbps} \\ \cline{1-1} \cline{5-5}  & & & LEO-GND & & & \\ \hline \multirow{4}{*}{[54]} & \multirow{4}{*}{2020} & \multirow{4}{*}{ALG-3 (JAXA)} & GEO-LEO & \multirow{4}{*}{1.5 \(\upmu\)m} & BPSK & \multirow{4}{*}{1.8 Gbps} \\ \cline{1-1} \cline{5-5}  & & & LEO-GND & & & & \\ \hline \multirow{4}{*}{[47]} & \multirow{4}{*}{2020} & \multirow{4}{*}{OSRISV3, v4 (DLR)} & LEO-GND & \multirow{4}{*}{1.5 \(\upmu\)m} & IMDD & \multirow{4}{*}{10 Gbps} \\ \cline{1-1} \cline{5-5}  & & & LEO-4(JAXA) & GEO-LEO & & & \\ \hline \multirow{4}{*}{[55]} & \multirow{4}{*}{2021} & \multirow{4}{*}{LCRD (NASA GSFC, USA)} & GEO-LEO, & \multirow{4}{*}{1.5 \(\upmu\)m} & BPSK/PPM & \multirow{4}{*}{2.8 Gbps/622 Mbps} \\ \cline{1-1} \cline{5-5}  & & & GEO-GND & & & & \\ \hline \multirow{4}{*}{[57]} & \multirow{4}{*}{2021} & \multirow{4}{*}{DSOC (NASA JPL, USA)} & Deep space-GND & - & PPM & 264 Mbps \\ \cline{1-1} \cline{5-5}  & & & & LES-9/HICAL (NICT) & - & & & \\ \hline \multirow{4}{*}{[58]} & \multirow{4}{*}{2022} & \multirow{4}{duction costs, making them particularly suitable for technology demonstration, scientific discovery, and educational purposes. Additionally, their compatibility with secondary launch opportunities allows for more frequent access to space, further accelerating the pace of exploration and innovation. However, CubeSat's are currently limited by their size and weight constraints, impacting their power generation, processing capabilities, and payload capacity. Thus, this has imposed a strict requirement in terms of the size, weight, and power (SWaP) when it comes to design of laser communication systems for space.
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 2 Related Work
### Hole-Filling  
According to the work from [PERSON] et al. [16], the hole-filling methods based on a triangular mesh can be classified into two groups: surface-based methods and volume-based methods. In most cases, the former operates directly on the given mesh and follows the same stages: hole detection, initial filling and refinement [4]. The latter converts the input mesh into a signed distance function over a volumetric grid to fill holes and then extracts a completed mesh from a zero-level set of the distance function [17]. [PERSON] et al. [6] described an approach to fill holes in unstructured triangular meshes by interpolating the shape and density of the surrounding mesh by taking into account the area and angle of triangles. [PERSON] et al. [18] proposed an advancing front hole-filling technique which generates an initial closure of the detected holes and then modified them by estimating the appropriate normal vectors, finally repositioned the 3D coordinates of each new vertex through the resolution of Poisson equations. In recent work, [PERSON] et al. [19] devised a novel multi-scale geometry detail recovery algorithm for 3D surfaces based on Empirical Mode Decomposition (EMD). In the case of complex holes, they are first partitioned into sub-holes by feature curves extended from the existing parts, and then typical hole-filling methods can be performed on these sub-holes [20; 21]. In cases of complex holes, [PERSON] et al. [22] created a set of contour curves by shortening the flow of the boundary edges of the holes and then applied the Delaunay triangulation. There are some other non-polygonal methods which apply implicit functions to complete mesh surface, such as Radial Basis Functions (RBFs) [23], Moving Least Squares (MLS) [24], and Bezier surface [25]. To recover the missing geometry details [19], texture synthesis-based methods [26; 27], exampled-based methods [28; 29], context-based methods [17; 30] have also been reported to fill holes while preserving features. For volume-based methods, [PERSON] et al. [31] applied a diffusion process to fill holes, while [PERSON] et al. [32] constructed an inside outside volume using an octree grid and reconstructed a surface by contouring. [PERSON] et al. [33] applied a voxel-based method to simplify and repair mesh models by adopting open and close morphological operators. [PERSON] et al. [34] introduced an oriented voxel global diffusion method to fill holes in complex surfaces. [PERSON] et al. [35] employed a Poisson reconstruction step to generate an implicit function for completing the missing parts. [PERSON] et al. [36] presented an adaptive, multigrid algorithm for mesh completion in general and complex cases by using the Bi-Harmonic fields. The main advantage of volume-based methods is its robustness in resolving geometric errors, and the drawback is the loss of geometric detail. For more works, please refer to these two survey papers [3,4]. Based on the above analysis, fast hole-filling, especially for different sizes of holes, is still a challenge.
# Dynamic Response of Slender Vertical Structures Subjected to Thunderstorm Outflows
## 4 Comparison between Thunderstorm and Synoptic Dynamic Response
### Gust Response Factor Ratio \(R_{g}\)  
The gust factor of the response is derived according to Section 2.3 following the two different approaches for the turbulence intensity outlined in Section 3.2. The integral length scale is instead estimated as for the synoptic case (Appendix A, Equation (A5)).  
Figure 10: Velocity profile ratio \(R_{a}\) evaluated for the thunderstorm and synoptic event: (**a**) lighting pole; (**b**) telecommunications tower.  
Figure 9: Drag coefficient of the telecommunications tower associated with the synoptic wind.  
Figure 11 plots the gust response factor ratio for the lighting pole (Figure 11a) and the telecommunications tower (Figure 11b), evaluated according to the two different models of the turbulence intensity; i.e., case (a) \(\theta_{T}(z)=1\) varying the roughness length (Section 3.2) and case (b) assuming the vertical profile in Equation (A3) (Appendix A).  
Figure 11 shows that when moving from case (a) to case (b), \(R_{G}\) tends to increase for the low-rise structure (Figure 9a), while a reduction is observed for the taller one (Figure 9b), except for low values of \(z_{m}\). In this latter case, the dependence on \(z_{0}\) is more marked because of the greater height of the structure. Moreover, the pole \(R_{G}\) in case (a) tends to decrease with \(z_{m}\) (Figure 11a), while it increases upon increasing \(z_{m}\) in case (b) (solid lines). The opposite behavior is instead observed for the tower (Figure 9b).  
It is important to notice that for both structures, the gust response ratio is always lower than unity. This is due to the transient dynamic effects induced by the nonstationary loading of the thunderstorm that mitigates the response [13]. The only exception is observed for the tower when the turbulence intensity of the thunderstorm is assumed to be constant with the height, but variable with the roughness length for higher values of \(z_{0}\) and \(z_{m}\). This is due to the combination of a lower turbulence intensity of the synoptic wind (estimated at the equivalent height), and a larger value of the resonance factor (Equation (26)) as a result of a larger vertical mean speed profile.  
Finally, it can be observed that for the lighting pole--i.e., the low-rise structure--the gust response factor results are weakly dependent on both the roughness length and the height of the nose of the vertical profile. Therefore, future studies may investigate whether this behavior occurs after introducing a relation between \(z_{0}\) and \(z_{m}\) in the modeling of the vertical profile.
# Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning +
## 3 Materials and Methods
### Analytical Methods  
The analysis of persistence (global and local) is conducted using two types of analytical methods: descriptive models and predictive models. On the one hand, descriptive models are used to analyze the prevalence of the local persistence and its evolution over time, and to analyze the relationship between local persistence and other variables about students' behavior and performance. On the other hand, predictive models are used to analyze the possible accuracy when predicting global persistence and students' performance based on local persistence.  
For both analytical methods, R is used, and libraries _dplyr_ and _ggplot2_ are used for cleaning data and representing information, respectively. For the predictive models, _caret_ library is also used to develop the models. Particularly, four well-known algorithms are used: Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM) and Decision Trees (DT). These algorithms are used for two classification tasks: predicting the global persistence and whether the average grade of exercises is above a threshold (50% in this case, as it is a typical passing rate).  
In order to select the best parameters for the models and evaluate the performance, 10-fold cross-validation is used. The metric used to evaluate the models is Area Under the Curve (AUC). This metric indicates the area of the Receive Operating Characteristic (ROC) curve, which shows the relationship between true positive and false positive rates using several threshold settings [6]. Predictive models work better when AUC is higher as AUC means the probability of ranking (i.e., give a probability of the positive class) a randomly positive instance higher than a randomly negative one [39]. AUC is used as is it a widely used metric, generally appropriate for classification problems involving students behaviors [40] and avoids some issues other metrics present in imbalanced datasets [41] (e.g., accuracy can be high even when the model is not good in imbalanced datasets).
## 4 Experiment and Results
In conclusion, LFA, showed relatively higher accuracy than the other dimensionality reduction algorithms. Especially using SVM, LFA showed the highest accuracy at 98.9% among the measured performances. We suggest the LFA algorithm that reduces the size of image through the analysis of line types. The suggested algorithm showed the reduction of computation by the size reduction of the image with the use of EMNIST in KNN and SVM environments, along with higher performance than the existing model in accuracy. We consider the reason for these experimental results as the result of strong classification of characteristics. In the LFA algorithm, the lines for the shapes of objects existing in the input images are converted to aggregated data. This property maintains the characteristics of shapes while classifying strong characteristics effectively in reducing data sensitive to shapes. Letters are one  
Figure 11: Accuracy measurement data using machine learning. (**a**) Accuracy measurement data using k-nearest neighbors. (**b**) Accuracy measurement data using a support vector machine. Figure 10: Capacities of the processed data (kb). of data sensitive to shapes. In recognizing letters, colors are classified as insignificant characteristics, and it classifies based only on the shapes of letters. If letter \"O\" is converted through the LFA algorithm, the aggregated values on the forms of the curve and diagonal line are greatly enhanced, and other forms show a low aggregate. This factor functions as a big advantage in classifying shapes. The distribution of aggregated values includes different shape characteristics. For these reasons, in the reduction of sizes of cursive letter images, the linear characteristics of the relevant letters are highlighted, and the sizes are effectively reduced without loss of characteristics. Thanks to this process, the high computation of the existing learning model is decreased. In addition, this study measured precision, recall, accuracy, and the receiver operating characteristic (ROC) curve to evaluate the classification performance [45; 46]. Figures 12 and 13 show precision and recall, and ROC is shown in Figure 14. After examining each performance evaluation, it is noted that LFA shows high accuracy, precision, and recall, and it is found that its ROC curve indicates a good performance. Figures 12 and 13 show low performance at some classes (6, 8, and 16). However, the other algorithms also show low classification performance of those classes. When reviewing  
Figure 14: Receiver operating characteristic (ROC) curve by the feature extraction algorithm. (**a**) ROC curve using k-nearest neighbors. (**b**) ROC curve using a support vector machine. Figure 13: Results of precision and recall evaluation by the feature extraction algorithm in a support vector machine (SVM). (**a**) Precision measurement data using SVM. (**b**) Recall measurement data using SVM.
# Internet of Things (IoT) and the Energy Sector
## 5 Challenges of Applying IoT
### Energy Consumption  
In the energy systems, the major effort of IoT platforms are saving the energy. In energy systems to enable communication using IoT, massive number of IoT devices transmit data. To run the IoT system and transmit huge amount of data generated from the IoT devices considerable amount of energy is needed [128]. Therefore, the energy consumption of IoT systems remains as an important challenge. However, various approaches have tried to reduce the power consumption of IoT systems. For example, by setting the sensors to go to sleep mode and just work when necessary. Designing efficient communication protocols which allow distributed computing techniques that enables energy efficient communications has been studied greatly. Applying radio optimization techniques such as modulation optimization and cooperative communication has been considered as a solution. Moreover, energy-efficient routing techniques such as cluster architectures and employing multi-path routing techniques was understood as another solution [129; 130; 131].  
Figure 5: Applications of IoT in an integrated smart energy system.
### Related Work
#### 1.2.2 Obtaining 3D Poses from Videos  
Recently, pose estimation has become a very interesting and successful area of research, with many applications. Obtaining 3D poses is possible with a calibrated system of more than one camera [21]. Commercial systems that can extract poses using 2 cameras (among other things) includes also kinect, which was used in other work [22] to obtain the 3D poses and with it simulate IMU signals. Using a single camera is obviously more challenging. Works in this area include [23; 24; 25; 26; 27; 28]. Some works [24; 27] predict the 2D joints and then fit the 3D skeleton using different strategies, others [23; 25; 26] are trained to predict 3D joints directly from the image. Even more interestingly, some methods predict both 2D and 3D keypoints [28].  
Figure 1: The steps involved in applying our overall method. In step 1 we record video and sensor data for a broad set of generic motions typical for a given domain. Using this data we train models to map the videos onto the corresponding sensor data. Step 2 consists of acquiring for each new recognition task a set of labeled videos of the respective activities from online repositories. The videos are then fed to the regression models which generate their simulated sensor data. Since the labels for the sensor data are identical to the video labels, we now have labeled training sensor data, which we use to train a recognition model for the required activities (step 3). The final step is 4, where we use the trained model to recognize those activities from real sensor data in the end application.  
While methods have achieved impressive results, the field is still evolving and the task itself is far from easy due to occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D [29]. Thus, for the generation of IMU sensor data going directly from 2D poses to IMU, values as proposed by this work is a promising alternative that needs to be investigated.