esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The HF acid interacts with the glassy parts of the ceramic, creating a porous surface and increasing the surface roughness. It provides microretention and increases the bond strength [4; 7; 14; 20; 21]. On the other hand, the pretreatment for polycrystalline ceramics, composites, and nanoceramic resins include sandblasting. They are usually sandblasted with aluminum oxide of 50 um, between 1.5 bar if it has an organic matrix like composite, and 2 bar if it is a polycrystalline ceramic. Regarding the exposure time, the size and the distance to which the particles are applied in sandblasting, and their influence on the material, controversy exists [22; 23]. RMC have been recently introduced, and little information exists on the surface treatment for bonding cementation [9]. Furthermore, their composition varies substantially [1], which makes the problem even more difficult. In addition, few manufacturers provide information on whether RMC should be etched or not [1]. Recently, plasma has been introduced in dentistry for surface treatment. It has been used as an alternative or additional procedure for adhesion enhancement [24; 25]. Several materials have been investigated, including polymers, alloys, or ceramics, especially zirconia, to which bonding is more difficult to achieve [26; 27; 28]. Plasma enhances adhesion by producing carboxyl groups and the surface becomes hydrophilic with an improvement of wettability [26; 28; 29; 30; 31]. The interaction of plasma with surfaces is greatly affected by the type of gas used, the exposure time, and the type of material on which it is applied [26; 31]. Depending on these factors, four effects can be described that can be created on the surfaces of the materials: cleaning, activation, etching, and coating [32]. Studies on surface treatment in new ceramic materials are scarce. Likewise, studies on the effect of plasma on the surface of restorative materials are also scarce. Therefore, the objective of the present study was to evaluate and compare the effect of two surface treatment by low-pressure plasma on the roughness and wettability of leucite-reinforced feldspar and resin matrix ceramics. The null hypothesis tested was that the surface treatment by plasma would not affect the surface of the materials tested.
## 4 Discussion  
The results presented in Figure 3 indicate that the variations in velocity with the ID model increase with the acceleration exponent \(\delta\). Further, Figure 4 illustrates that the variations in velocity with the proposed model increase with the time headway. Figure 5 shows that the variations in flow with the ID model increase over time as \(\delta\) increases. Similarly, the results for the proposed model presented in Figure 6 indicate that the variations in flow over time increase with a larger time headway.  
Figures 7 and 8 present the vehicle trajectories in time and space with the ID and proposed models, respectively, and the results are summarized in Tables 2 and 3. Table 2 indicates that as \(\delta\) increases, the distance traveled by the 1 st and 15 th vehicles increases, while that traveled by the 30 th vehicle decreases. However, the distance traveled by the 50 th vehicle is approximately the same. Table 3 shows that a decrease in the time headway decreases the distance traveled by the 1 st vehicle, while the distance traveled by the 15 th vehicle increases between 0.6 s and 1.5 s, decreases between 1.5 s and 2.0 s, and then increases. Between 0.6 s and 1.0 s, the distance traveled by the 30 th vehicle increases and then decreases between 1.0 s and 2.2 s. The distance traveled by the 50 th vehicle decreases between 0.6 s and 1.0 s and then increases. These results show that the vehicle positions with the ID model obtained using an arbitrary constant are not based on real traffic conditions. Conversely, the positions according to the proposed model are based on the time headway. This results in a smooth flow with the platoon of vehicles which is more realistic than the ID model.  
Figure 9 shows that as \(\delta\) increases, the ID model density increases over time so there is congestion with a large \(\delta\). Moreover, Figure 11 indicates that the flow increases with \(\delta\). Figure 10 shows that the density with the proposed model is small for a small time headway and vice versa. In addition, Figure 12 indicates that the changes in flow are proportional to the time headway.  
Overall, the results given indicate that the traffic behavior with the proposed model is more realistic than with the ID model. In particular, the flow and velocity are smoother, and the variations in flow and density over time are smaller. The ID model can produce unrealistic traffic behavior such as the significant congestion shown in Figure 9c. This is because the ID model employs an arbitrary constant whereas in the proposed model this constant is replaced with a variable based on the time headway. The results presentedhighlight the importance of considering real traffic parameters in traffic flow models to accurately characterize traffic behavior.
# Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
## 4 Dataset  
**UA-Detrac.** Deep learning object detectors require large datasets for efficient training. An example of such a dataset gathered for traffic monitoring is the UA-Detrac dataset [2]. The data were collected from 24 different locations in China, and they were made accessible for research purposes. The videos are recorded at 25 frames per seconds (fps) with a resolution of 960 \(\times\) 540 pixels. Altogether, there are 8250 vehicles manually annotated with bounding boxes present over more than 140,000 frames summing up to a total of 1.21 million annotations. The dataset is split by the authors into 60 recordings in the training set, and 40 recordings in the test set. It consists of videos recorded from various viewpoints, different weather conditions, and moderate to dense traffic with many occlusions. Some of the videos can be considered to be very challenging, because of the dense traffic flow on many road-lanes, or the low level of illumination. Each vehicle is classified into one of the follwoing categories: car, bus, van, and others, where the last class includes mainly trucks. Each recording is also given one of the weather labels: cloudy, night, sunny, and rainy, and also contains \"ignore\" regions where vehicles are not annotated, usually because of their low resolution.  
**Our dataset.** Even though the test data in the UA-Detrac dataset were recorded in different places than the training data, it cannot be stated that those sets come from totally different distributions. Firstly, the recordings were done using the same camera. Additionally, the viewpoints and intersections are similar between some of the recordings in the test and the training sets. That is why it is very important to test the trained model on another dataset, which can reflect its performance in a real-world scenario. For this purpose, data gathered from our camera located at the proximity of a road curve, which recorded vehicles from an unusual perspective, was used. The data were recorded by an AXIS Q1615 Mk II network camera with resolution of 1280 \(\times\) 720 pix and at 25 fps. For this camera, video streams are collected in 30-min intervals. For automatic fine-tuning of the model, an unlabelled stream of data from five days recorded during daylight is used. For the testing, data from another four days is used, and in each video, the frames are manually annotated every 30 s. This interval represents the average time needed to drive through the visible road section, thus it allows for an adequate variety of captured vehicles. In total, there are 892 images annotated for testing.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 3 Requirements of Internet of Drones (IoD)
### Communication Requirements
#### 3.1.3 Transmission of HD Image/Video  
Networks should be able to provide a high uplink (from the drone to the base station) data rate for drones to permit the transmission of HD image/video. The data rate required is calculated mainly by picture/video size and quality.  
In the future, the demand for higher resolution images/videos in vertical industries needing 4K/8K HD video support would need a higher Gbps-level data rate. The 5G networks are well equipped to support such services with a data rate requirement of multi-Gbps. Transmission of HD image/video will dramatically extend the drones' application scenarios including energy and power line inspection, agricultural exploration, control and rescue, entertainment and monitoring. With high transmission rates, drones linked to networks are able to transmit HD images/videos to enable the immersive experience of augmented reality and virtual reality [19].
# Quantum Calibration of Photon-Number-Resolving Detectors Based on Multi-Pixel Photon Counters
## 3 Results
### Povm
The parameter \(P_{exp}\) represents the probability of the \(n\) output result measured by MPPC in the experiment, while \(P_{re}\) represents the probability of the corresponding output result obtained by the reconstructed POVM elements under the same average number of incident photons. The calculated results show that the values of fidelity \(F\) are all larger than 99.96 % in the interval of 0.5-250 incident photons. Thus, the reconstructed POVM elements accurately characterizes the MPPC. According to Figure 5, the 1-click peak was achieved at 15.9 incident photons per pulse at 791 nm in Figure 5a while 3.6 incident photons per pulse at 523 nm in Figure 5b, showing the difference on the detection efficiency. Therefore, the detection efficiency of MPPC at 523 nm is significantly higher than that at 791 nm. The uncertainties of the reconstructed POVM elements and the probability distributions of incident photons are mainly caused by the laser source and the powermeter to measure the laser power before attenuation. Moreover, the intensities stability of the laser pulse would also destroy the Poisson distribution of the incident photon pulses. To visualize the quantum performance of the MPPC, we plotted cross section of the Wigner function based on reconstructed POVM [43] in Figure 6. At 523 nm, for one click POVM, the origin of the Wigner function is negative (-0.032), which indicates that MPPC is a fundamental quantum detector. However, for 791 nm, Wigner function is positive at the origin, which means that the quantum characteristics of MPPC is not as obvious as that of MPPC at 523 nm. We ascribe this situation to noise and low detection efficiency of MPPC at this wavelength. For more click POVMs, the origin of the Wigner function is flattened, which is caused by fast decoherence [44]. Figure 5: Probability distribution of incident photons at (**a**) 791 nm; (**b**) 523 nm. Black, red, green and blue discrete points respectively represent the probability distribution of 0, 1, 2 and \(\geq\)3 photons detected by MPPC in the experiment. The corresponding solid lines represent the probability distribution calculated according to the reconstructed POVM elements.
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
###### Abstract  
NoSQL databases were created with the primary goal of addressing the shortcomings in the efficiency of relational databases, and can be of four types: document, column, key-value, and graph databases. Graph databases can store data and relationships efficiently, and have a flexible and easy-to-understand data schema. In this paper, we perform an experimental evaluation of the four most popular graph databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph. Database performance is evaluated using the Linked Data Benchmark Council's Social Network Benchmark (LDBC SNB). In the experiments, we analyze the execution time of the queries, the loading time of the nodes and the RAM and CPU usage for each database. In our analysis, Neo4j was the graph database with the best performance across all metrics.  
benchmark; graph databases; LDBC SNB; NoSQL databases; open-source tools; JanusGraph; Nebula Graph; Neo4j; TigerGraph +
Footnote †: journal: Applied  
0000-0001-5000-0001-5000]Mobile-  
We selected the four top-ranked graph databases, JanusGraph, Nebula Graph, Neo4j, and TigerGraph. For each graph database, the LDBC SNB benchmark evaluates query execution time, node load time, memory, and CPU usage.  
The main contributions of this work are as follows:  
* Revealing the strengths and weaknesses of the top four NoSQL graph databases according to DB-Engines ranking;
* Experimental evaluation of top-ranked NoSQL graph databases using a standard benchmark;
* Identifying the best NoSQL graph database in terms of query execution time, node load time, RAM and CPU usage;
* Limitations in the practical use of NoSQL graph databases.  
The remainder of this paper is structured as follows. Section 2 introduces the related work. Section 3 presents the four NoSQL graph databases. Section 4 describes the SNB benchmark of the LDBC. The experimental results are discussed in Section 5. Finally, Section 6 presents the conclusions and offers suggestions for future work.
## 3 Experiments and Results
### Experimental Results
#### 3.2.2 False Alarm Experimental Results  
In this section, two experiments were performed under the non-spoofing condition. The first experiment was conducted on 20 October 2022. Additionally, the second experiment was conducted on 25 October 2022. The calculated value of DOA is obtained according to the airspace information, and the expected value of DOA is obtained according to the ephemeris information. In the experiments, the difference between the two groups of values was compared, and the SSE test metric and the false alarm rate were calculated. Figures 14 and 15 show the comparison information of the expected and calculated values of the elevation angle and azimuth angle of G04 and G09 in the first experiment.  
Tables 2 and 3 show more satellite elevation and azimuth error statistics for the first experiment.  
Figures 16a and 16b, respectively, show the SSE test metric and the judgment information of the receiver state in two different experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Maximum (\({}^{\circ}\))** & **Average (\({}^{\circ}\))** & **Std (\({}^{\circ}\))** \\ \hline G04 & 1.98 & 0.3838 & 0.3191 \\ G09 & 3.57 & 0.8474 & 0.5805 \\ G16 & 4.11 & 0.3901 & 0.4806 \\ G26 & 2.88 & 0.8492 & 0.5970 \\ G27 & 3.19 & 0.9448 & 0.5836 \\ G31 & 4.32 & 0.9099 & 0.6795 \\ \hline \end{tabular}
\end{table}
Table 2: Elevation angle error statistics.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Maximum (\({}^{\circ}\))** & **Average (\({}^{\circ}\))** & **Std (\({}^{\circ}\))** \\ \hline G04 & 2.48 & 0.6740 & 0.4353 \\ G09 & 2
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 7 Future Research Directions
### Synergy of UAVs and IoT Systems  
The Internet of UAVs (IoUAVs) is the dynamic integration of current IoT and UAVs. IoUAVs is a promising arrangement for creating the future IoT environment in which people, UAVs, and IoT gadgets are all harmoniously connected. This allows omnipresent data sharing and fine-granularity coordination among a swarm of UAVs due to unique features such as quick sending, simple programmability, controllable mobility, and flexibility. One of the technological contradictions is that while there are numerous benefits in IoUAVs application that arise from linking everything that can be connected, these operations necessitate significant energy capacity. UAVs are limited by their size, weight, and power (SWAP). SWAP limits have a direct impact on each UAV's maximum operating altitude, communication, coverage, computation, and endurance capabilities, and IoUAVs are no exception. As a result, there is an urgent need to develop this aspect of IoUAVs to provide seamless mobility and connectivity. In [129; 130; 131], the authors employed a UAV that was dispatched to collect data from IoT devices under stringent time limitations. The total number of IoT devices was maximized by optimizing the UAV trajectory and wireless resource allocation simultaneously. They proposed a UAV trajectory planning algorithm that addresses mixed-integer nonconvex and difficult issues.
## Experimental Study on Dynamic Compression Mechanical Properties of Aluminum Honeycomb Structures
###### Abstract  
In this paper, dynamic compression tests are developed to investigate the dynamic compression mechanical properties of the aluminum honeycomb structures at different strain rates, especially at the high strain rates. The difficulties at the high strain rates exist due to the large deformation, the low wave resistance and the size effect of the honeycomb structures. The Split Hopkinson Pressure Bar (SPHB) test method is carried out and special measures such as the adoption of waveform shaper, the size optimization of the impact bar and the specimen, and employment of the semiconductor strain gauge, etc. are taken to overcome the difficulties. It is discovered that the dynamic compression mechanical properties possess a stress hardening effect at a high strain rate from \(1.3\times 10^{3}\) s\({}^{-1}\) to \(2.0\times 10^{3}\) s\({}^{-1}\), but then a stress softening effect at a high strain rate of \(4.6\times 10^{3}\) s\({}^{-1}\). It is also discovered that the yield strength and the average plateau stress at the strain rate of \(2.0\times 10^{3}\) s\({}^{-1}\) is higher than that at the strain rate of \(1.3\times 10^{3}\) s\({}^{-1}\). However, the yield strength and the average plateau stress at the strain rate of \(4.6\times 10^{3}\) s\({}^{-1}\) is lower than that at the strain rate of \(2.0\times 10^{3}\) s\({}^{-1}\) and \(1.3\times 10^{3}\) s\({}^{-1}\), but higher than that at a quasi-static state. This indicates that the aluminum honeycomb structure is sensitive to the strain rate. Additionally, the damage mode of the aluminum honeycomb structure is plastic buckling, collapse and folding of the cell wall, which is carried out using dynamic compression tests. The folding length of the cell wall at a higher strain rate is found to be longer than that at a lower strain rate. The test results can also be used as the stress-strain curves of the honeycomb constitutive model at the high strain rates to carry out the numerical simulation of high-speed impact.  
aluminum honeycomb structure; dynamic compression mechanical properties; high strain rate; damage mode +
Footnote †: journal: Applied Soft. 2020, 10, 1188; doi:10.3390/app10031188
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 3 Results and Analysis
### Analysis of PPP Positioning Accuracy  
#### 3.2.1 IF123 and IF1213  
The time series of positioning errors for IF123-0 and IF1213-0 PPP at AREG station are presented in Figure 8, and the inset in each figure is the time series during the first 1 h.  
As seen in Figure 8, the filtering of both dcb-corr and non-corr is very smooth after convergence and can provide stable cm-level positioning. However, the accuracy of dcb-corr is significantly higher than that of non-corr in the first few tens of epochs, as illustrated in the inset. This means that the DCB correction improves the positioning accuracy for PPP only at the beginning of the filtering.  
The average convergence times for different IF combinations are plotted in Figure 9. It can be seen that both IF123 and IF1213, after correcting the DCB, speed up the convergence of the filtering, and the convergence time between each frequency combination is not much different.  
The average positioning accuracies of the IF123 and IF1213 models in all-day solutions and after convergence are shown in Tables 5 and 6, respectively. It is noted that the convergence condition is that the standard deviation of each coordinate component is smaller than 10 cm and keeps the convergence for at least 20 epochs. The time corresponding to the first epoch of convergence is denoted as the convergence time.  
Figure 8: Positioning errors of IF123-0 and IF1213-0 at AREG station; 24-h time series.  
Figure 9: Average convergence time of IF123 and IF1213.  
The IF123 and IF1213 dcb-corr have comparable accuracy, with an average positioning accuracy of 2-4 cm in the horizontal components and approximately 5 cm in the vertical component. Compared with non-corr, we find that the accuracy improvement of dcb-corr is 44-67% in the horizontal components and 45-60% in the vertical component. The average convergence times are 26.1 min and 26.9 min for the IF123 and IF1213 with dcb-corr, respectively. The improvement rates compared with the non-corr are 6.79% and 2.54%, respectively. In addition, both IF123 and IF1213 provided stable cm-level positioning after convergence. The positioning accuracy reached 1 cm in the N component, 2 cm in the E component and 2.5 cm in the U component, respectively. The maximum difference between the average positioning accuracy after convergence of the two IF models, non-corr and dcb-corr schemes, was less than 1 cm.
## 4 Experiment Results and Discussion
### Performance Evaluation and Discussion
#### 4.4.3. FAT with PGD Samples Results
In contrast, Client 3 (Figure 12c) predominantly classified inputs as severe when evaluating its local model on a normal dataset. Client 4's (Figure 12d) results were akin to those of Client 3, with a notable ability to correctly predict 60.08% of very unhealthy classes. However, accuracy for good, moderate, unhealthy for sensitive groups (USG), and unhealthy classes was considerably lower. Consequently, when these four local model gradients were aggregated to formulate a global model, the resultant accuracy of the new global model diminished, as evident in Figure 13.  
Figure 13 delineates the confusion matrix of the final global model after 30 rounds of federated adversarial training (FAT) in the fl-pgd-50 scenario. Due to the influence exerted by Clients 3 and 4, 53.35% of the 1224 normal images were classified as having a severe AQI, whereas the actual correct classification was only 160 images; the other 493 images were misclassified as having a severe AQI. This outcome underscores the success of the attacks in shaping the final global model, which demonstrated a tendency to misclassify into a specific class, namely the severe class in this instance. Figure 11: Accuracy trends for each client and server in fl-pgd-50 scenario. Figure 12: Confusion matrices of fl-pgd 50 scenario: (a) Client 1; (b) Client 2; (c) Client 3; and (d) Client 4. Figure 13: Confusion matrix of global model in fl-pgd-50 scenario.
# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 3 Materials and Methods
### Week Learners
#### 3.1.3 Support-Vector Machine  
The focus of an SVM classifier is to find a hyperplane that can separate two classes of samples. For samples with two-dimensional feature vectors, the hyperplane is a line. For the sample sets with 3D feature vectors, the hyperplane is a surface. Therefore, the dimension of the hyperplane is always 1 less than the dimension of the feature vector.  
As shown in Figure 1, the margin refers to the sum of vertical distances from sample points of different classes on both sides of the hyperplane to the hyperplane. The process of finding the desired hyperplane is the process of finding the maximum margin. The reason it is necessary to maximize the margin is to enhance robustness and minimize the error rate of the classification. The two sample points that determine the maximum margin are called support vectors, which is also the origin of the name of support-vector machine.