esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Research Directions
### Knowledge Extraction  
As discussed in Section 5.2, different elements of a structure require a unique approach to quality inspection. Previous research has developed independent techniques for inspecting the quality of a single type of element, such as indoor partitions [169], reinforced concrete [217], storage tanks [218], glass facades [107], and so on. Although vision-based defect detection techniques can detect many surface defects, other sensors, such as hammering devices, are employed to detect internal defects [264]. Future research should focus on developing an integration framework that will combine these techniques into a single robotic system for a comprehensive inspection and monitoring of the built environment.  
Nevertheless, vision-based defect detection is an important research area [163]. The CNN-based deep-learning approach is commonly explored by many researchers [71; 151]. However, CNN and other deep-learning models require large training datasets to train. Various researchers created separate databases of labeled images to train CNN models. However, these databases contain only a single type of component. SDNET2018 by the Structural Defects Network (SDNET) is an open-source dataset of 56,000 annotated imagesof concrete defects developed at Utah State University [265]. Similar open-source libraries of images of different building components and construction defects can significantly advance the development of more intelligent inspection robots.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 2 Background
### Flying Ad Hoc Networks (FANETs)
Therefore, it is essential to dynamically carry out the routing process to make UAVs more autonomous and reduce the delay-time between the source node and a target node [7]. FANETs are widely used to track regions with images or videos by using various sensors. Therefore, it is incredibly critical for the QoE metrics to assure the consistency of the video streaming and also ensure adequate data transmission.
###### Abstract  
Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce the risk of landslide. In recent years, deep learning has been successfully applied to landslide susceptibility assessment due to the strong ability of fitting. However, in actual applications, the number of labeled samples is usually not sufficient for the training component. In this paper, a deep neural network model based on semi-supervised learning (SSL-DNN) for landslide susceptibility is proposed, which makes full use of a large number of spatial information (unlabeled data) with limited labeled data in the region to train the mode. Taking Jiaohe County in Jilin Province, China as an example, the landslide inventory from 2000 to 2017 was collected and 12 metrological, geographical, and human explanatory factors were compiled. Meanwhile, supervised models such as deep neural network (DNN), support vector machine (SVM), and logistic regression (LR) were implemented for comparison. Then, the landslide susceptibility was plotted and a series of evaluation tools such as class accuracy, predictive rate curves (AUC), and information gain ratio (IGR) were calculated to compare the prediction of models and factors. Experimental results indicate that the proposed SSL-DNN model (AUC = 0.898) outperformed all the comparison models. Therefore, semi-supervised deep learning could be considered as a potential approach for LSM.  
Keywords:landslide susceptibility; deep learning; semi-supervised learning; DNN; SVM; LR; AUC; IGR +
Footnote †: journal: Applied Sciences
## 3 Kinematic Analysis
### Kinematic Simulation of Mechanism Model  
To verify the kinematic equation solving, a verification based on the simulation model is conducted in the software Automatic Dynamic Analysis of Mechanical Systems (ADAMS) as shown in Figure 7. The main steps include inputting the model, adding constraints and setting the drive equations. The joint initial positions are \(\left[\begin{array}{cccc}a_{0}&\theta_{1}&\theta_{2}&\theta_{3}\end{array} \right]=\left[\begin{array}{cccc}600&61^{\circ}&20^{\circ}&30^{\circ}\end{array}\right]\), and the initial position of the end effector is (1019.7, 167.6, 777). The drive equations are given as follows.  
\[\left\{\begin{array}{l}a_{0}=300\ cos(t)+300\\ \theta_{1}=11^{\circ}cos(t)+50^{\circ}\\ \theta_{2}=16^{\circ}sin(t)+20^{\circ}\\ \theta_{3}=30^{\circ}cos(t)\end{array}\right.. \tag{13}\]  
After setting the other relative parameters, the displacement and velocity of the end point could be simulated through the software. Alternately, the end point motion information could be calculated by substituting the joint information into kinematic equations. Two sets of end point motion results are shown in Figure 8. Comparing two curves of the end point motions, it could be found that kinematic calculation results are largely in agreement with simulation results from ADAMS. The calculation of the kinematic equati  
Figure 7: Simulation model in Automatic Dynamic Analysis of Mechanical Systems (ADAMS).
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 1 Introduction
The influence of the boundary slip flow effect on the seal performance parameters, such as gas film opening force, end face deformation, and the stiffness-to-leakage ratio of the compliant foil face gas seal, is analyzed. The influence of different foil structure parameters on the sealing performance is also analyzed with the consideration of the slip flow effect in order to provide a theoretical basis for the matching design of the structural parameters of compliant foil face gas seals under different service conditions.
# Fractal Behavior of Size Distribution and Specific Surface Area of Blasting Fragments
## 3 Theory and Calculation Results
### Calculation of Probability of Blasting Fragmentation  
The fragmentation fractal dimension is a crucial parameter for studying the degree of rock fragmentation and fracture patterns. Because rock fragmentation exhibits self-similarity, [PERSON] et al. [28] proposed the following formula for calculating the probability of rock fragmentation:  
\[D_{\text{F}}=\frac{\log(nP_{c})}{\log\left(\frac{1}{r}\right)}, \tag{11}\]  
where \(P_{c}\) represents the probability of further rock fragmentation, \(r\) represents the fragmentation similarity ratio, and \(n\) represents the number of secondary fragmentation units.  
At quarry Q\({}_{3}\), high-speed photography using the Phantom Vee0710l camera was employed to monitor the granite-rock-blasting fragmentation process multiple times. The setup of the high-speed camera is illustrated in Figure 9. Through a comparative analysis of high-speed images of the bench surface before and after blasting, the macroscopic crack distribution on the bench surface was determined, as shown in Figure 10.  
As shown in Figure 10, under the action of explosive energy, the rock of the bench surface underwent multistage fragmentation, with each stage resulting in smaller fragments of similar shape. According to fractal models such as the Sierpinski carpet, it can be learned from the macrocrack distribution in Figure 10 that the blasting fragmentation process of granite rock underwent two or three stages of fragmentation, with each stage involving a division into smaller fragments with similar shapes and a division factor of 2. This finding implies a fragmentation similarity ratio of 0.5, and the number of secondary fragmentation units was 8. Accordingly, the average blasting fragmentation probability for each experiment was calculated using Equation (11), and the results are shown in Figure 11.  
Figure 9: Schematic of high-speed photography monitoring of the rock fragmentation process in deep-hole bench blasting.  
As shown in Figure 11, the average blasting fragmentation probabilities for the four quarries ranged from 0.44 to 0.71. Quarry Q\({}_{3}\), which consisted of granite, was excavated using three different methods (experiments #5-#16), resulting in a wide fluctuation range of fragmentation probabilities. When the bench height was the same, employing shock-reflection devices (experiments #5-#10) increased the blasting fragmentation probability. Therefore, adding a shock-reflection device at the bottom of the boreholes enhances the stress wave transmission and reflection, improving the blasting fragmentation effects.
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 2 Cognitive Policy-Based Radio
In case the access cannot be granted, the PE may respond with a list of opportunities, i.e., other channels, in which opportunistic access is possible. The above requirements cannot be achieved via static definitions of vocabularies (e.g., in XML or a relational database) since adding new terms to such vocabularies would require developing new software (for interpreting XML tags or relational schema and data). On the contrary, thiskind of capability can be achieved by an ontological approach in which new terms and their definitions can be introduced dynamically. This is the consequence of bounding ontologies to a highly expressive language (such as OWL [4]) in which new terms can be defined on the fly. These definitions are then interpreted by generic inference engines that are bound to the same language (such as OWL), thus avoiding the need to rewrite the underlying software. While opportunistic access to the RF spectrum is reasonable for addressing the problem of no-interference to PUs, such an approach does not solve the problem of spectrum sharing among the radios within the same group, i.e., either PUs or the friendly secondary users (SUs). In this paper, we present parts of a policy-based solution to such cases. First, we briefly describe an architecture in which such scenarios can be investigated. Then, we describe our experiments with policy interpretation, policy-based generation of transmission opportunities, policy-based definition of rendezvous and policy-based optimization of radio parameters (knobs) based on the transmission feedback (meters). Finally, we analyze the consequences of a radio not having some of the capabilities considered in this paper.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
###### Abstract  
Innovative concrete structure maintenance now requires automated computer vision inspection. Modern edge computing devices (ECDs), such as smartphones, can serve as sensing and computational platforms and can be integrated with deep learning models to detect on-site damage. Due to the fact that ECDs have limited processing power, model sizes should be reduced to improve efficiency. This study compared and analyzed the performance of five semantic segmentation models that can be used for damage detection. These models are categorized as lightweight (ENet, CGNet, ESNet) and heavyweight (DDRNet-Slim23, DeepLabV3+ (ResNet-50)), based on the number of model parameters. All five models were trained and tested on the concrete structure dataset considering four types of damage: cracks, efflorescence, rebar exposure, and spalling. Overall, based on the performance evaluation and computational cost, CGNet outperformed the other models and was considered effective for the on-site damage detection application of ECDs.  
computer vision; edge computing device; deep learning; lightweight models; damage detection Article
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 6 Conclusions  
This paper regards the quantization parameter decision problem in the video coding as a combinatorial optimization problem. Firstly, encoding the video to obtain the target bitrate under the default configuration; secondly, the projected 360-degree panoramic video is divided according to latitude. As a result, the problem dimension in the differential evolution algorithm is obtained; finally, after several iterations, under the target bitrate, The differential evolution algorithm will obtain a set of optimal quantization parameter combinations for the current video. Under the same encoding configuration, the algorithm proposed in this paper can achieve bitrate savings of 1.86% compared to the HEVC reference software HM-16.20, and the PSNR has an improvement of 0.07 dB. At the same time, the actual bitrate generated via encoding is nearly equal to the target bitrate, and the mismatch has decreased. Since the DE algorithm has a simple concept and has good robustness, it can be widely used in optimization problems in video coding.  
**Author Contributions:** Conceptualization, X.J. and Y.C.; methodology, X.J.; software, Y.C.; validation, X.J. and Y.C.; formal analysis, X.J.; investigation, Y.C.; resources, X.J.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, X.J.; visualization, Y.C.; supervision, X.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### The Measurement of the Positioning Error  
The normalized measurement error and positioning error ratio are plotted in Figure 6 in the red and green histograms, respectively. To quantify the relative error between the estimation result and the actual distance, we define the error ratio (denoted as err) as:  
\[err=\frac{|d_{measured}-d_{real}|}{d_{real}} \tag{7}\]  
where \(d_{measured}\) and \(d_{real}\) are the distance of the calculation results of our system and the actual distance between the LED and the receivers, respectively. In Figure 6, we can see that the error ratio is mostly below 10% when the actual distances are within 2 m. In addition, the positioning error is below 0.2 m, which depends on the maximum radius of the PIN circle. According to Reference [11], the RSS positioning algorithm can achieve an accuracy of 7 cm. In the proposed indoor positioning system, we use the RSS to calculate the distance without using any lens to capture a high proportion of light. So the positioning error grows significantly with the increasing distance because of the strong power attenuation. In practice, a lens or lampshade can be fixed in front of the PINs to improve the signal-to-noise ratio (SNR), and thus the positioning accuracy of the system will be improved.
## 3 Results and Discussion
### Photocatalytic Activity Tests
#### 3.1.3 Effect of the Glycerol Initial Concentration on Photocatalytic Hydrogen Production  
Figure 4 reports the effect of glycerol concentration on the amount of hydrogen produced during 4 h of visible irradiation time.  
The results showed that hydrogen production increased with the increase of the glycerol concentration up to 10 wt % and then decreased for 20 and 40 wt % of glycerol initial concentration. Thus, the optimum glycerol concentration is to be considered equal to 10 wt %, with hydrogen production of 4776 \(\upmu\)mol/L, as reported. The effect of the sacrificial agent initial concentration for photocatalytic hydrogen production has been extensively discussed in the literature. In particular, some studies have evidenced that this behavior follows a Langmuir-type isotherm [54; 55; 56], meaning that the photocatalytic hydrogen production rate is controlled by saturation of active centers by the adsorbed glycerol molecules [32]. However, in the present study, the interpretation of the data through a Langmuir-type isotherm does not adapt to the obtained experimental results; rather, the results show the existence of an optimum glycerol concentration. The worsening of the photocatalytic activity beyond the optimal value of the initial glycerol concentration can be attributed to the blockage of the adsorption of H\({}_{3}\)O+ on the active site's surface [32]. Moreover, the existence of a maximum value for hydrogen production as a function of initial glycerol concentration could be also attributed to the action of glycerol as a quenching agent for ions and radicals generated during the irradiation [18].  
Figure 4: Photocatalytic hydrogen production during the irradiation time at different initial glycerol concentrations. Catalyst dosage: 1.5 g/L; solution pH: 6; visible light intensity: 32 mW/cm\({}^{2}\); photocatalyst: 1.08 Cu_ZnO.
# Interplanetary Magnetic Field Bx Effect on Field-Aligned Currents in Different Local Times
## 3 Results
Blue and red lines correspond to data from the Northern and Southern Hemispheres, respectively. Error bars denote the standard error of the mean. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Local Time** & \multicolumn{4}{c}{**15-20 MLT**} & \multicolumn{4}{c}{**21-62 MLT**} & \multicolumn{4}{c}{**03-08 MLT**} & \multicolumn{4}{c}{**09-14 MLT (PIg)**} & \multicolumn{4}{c}{**09-14 MLT (PIg)**} \\ \hline
**Hemisphere** & **NH** & **SH** & **SH** & **NH** & **NH** & **NH** & **SH** & **NH** & **NH** & **SH** & **  
In the 03-08 MLT sector, the response of FACp is more pronounced during local winter than the summer, while the response of FACe is not significant. In the south, FACp is greater for IMF cone angles \(\leq\) 45\({}^{\circ}\) than for angles \(\geq\) 135\({}^{\circ}\), whereas in the NH, FACp is greater for IMF cone angles \(\geq\) 135\({}^{\circ}\) than for those \(\leq\)45\({}^{\circ}\). At 09-14 MLT, with westward electrojet (PEJw), the SH (NH) FACp shows higher values for IMF cone angles \(\leq\) 45\({}^{\circ}\) (\(\geq\)135\({}^{\circ}\)). However, FACe exhibits the opposite response to IMF Bx polarity. As shown in the bottom panel of Figure 4, the impact of the IMF cone angles on the latitudes of FACs is mainly concentrated around noon and midnight, with smaller effects on the dawn and dusk sides. The changes in MLatp and MLate are quite similar in response to IMF cone angles. In the 09-14 and 21-02 MLT sectors with westward PEJ (PEJw), NH FACs are at a higher latitude for an IMF cone angle \(\leq\) 45\({}^{\circ}\) than for an angle \(\geq\) 135\({}^{\circ}\). However, in the noon sector with eastward PEJ (PEJe), when the IMF cone angle \(\geq\) 135\({}^{\circ}\), north FACs tend to be situated at a higher latitude, whereas for an IMF cone angle \(\leq\) 45\({}^{\circ}\), south FACs shift to a higher latitude.
## 4 Application to the RG8 Test
### Results
#### 4.2.2 From 0 to 700 h: Restrained Shrinkage  
The initial period was primarily marked by heating and cooling of the concrete during hydration. Beyond 120 h, maturation continues, and the beam undergoes the effects of night-day temperature variations, which generate a near cyclic stress in the struts that is transmitted to the beam. This effect is clearly visible for the load evolution in all three systems (concrete, struts, and rebar), as shown in Figure 7.  
It should be emphasized that:  
* 3 main cracks are observed during the experiment. The calculation exhibits an initial cracking zone in the beam center at 76 h, as shown in Figure 4c, which is consistent with the first experimental crack appearing at 71 h. At 103 h, the cracking zone extends on both sides at the same time, while the experimental cracks appear on the left side at 167 h and then on the right side at 239 h. It can thus be concluded that the calculation indeed provides for these two cracking phases (left and right of the central zone) but prematurely with respect to the experiment. In the case of the present loading (tension + bending), as demonstrated in [7] for multi-fiber beams, the damage is diffuse and the resultant mesh is insensitive. This diffuse characteristic and the loading mesh symmetry are probably at the origin of the simultaneous appearance of the left and right zones.
* Figure 7a,b display the global responses of all 3 systems (struts, concrete, and rebar). In the central section, once the concrete is cracked, the rebar and struts are the main active systems. In the left section outside the cracking zone, the main active systems are the struts and concrete, as the load supported by the rebar is close to zero. After 120 h, the mean values of these loads are practically constant, and the small cyclic evolution that appears is due to the cyclic day-night temperature variations, which generate a quasi-cyclic load in the struts that is subsequently transmitted to the beam.
* This limited evolution does not produce many changes inside the beam, hence the calculated damage forecast is nearly the same at 700 h (Figure 7c) as that obtained after the 2 nd-3 rd cracking stage at 103 h, as shown in Figure 4c.  
Figure 7: 0–700 h evolution: (**a**) global forces supported by the 3 systems in the left section, 1 m from the beam center, outside the cracking zone (dashed line: experiment—solid line: calculation); (**b**) global forces supported by the 3 systems in the central section (inside the cracking zone); and (**c**) cracks observed and tensile damage contour at the end of the process (700 h).
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 4 QBAIoT Performance Evaluation
We can notice that with the self-configuring capability, the slots of the streaming and NRT traffics are reallocated to the remaining QoS classes immediately. Consequently, the PDRs of RTMC and RTNMC traffics undergo a small improvement as more slots are allocated to their QoS classes. Figure 19: Delay comparison for QBAIoT and IEEE 802.15.4 with different configurations. After comparing the performance of QBAIoT with the standard slotted CSMA/CA according to multiple scenarios and evaluating the importance of the self-configuring capability, it is crucial to compare the performance of our proposed method with other QoS based access methods such as SDA-CSMA/CA [30]. SDA-CSMA/CA and QBAIoT aims to offer a certain service level in an IEEE 802.15.4 environment using the slotted CSMA/CA process for accessing the channel. Each approach has its own parameters and is based on a certain configuration for BO/SO. For instance, SDA-CSMA/CA uses an SO equal to 7 and a BO equal to 8. The SDA-CSMA/CA approach does not depend on Superframe Duration, unlike QBAIoT where BO and SO are set to 2. For the comparison, we consider the same environment characteristics as for the simulations conducted for SDA-CSMA/CA. The latter is limited to three QoS classes, therefore we specify only RTMC, RTNMC, and streaming QoS classes when we compare QBAIoT to SDA-CSMA/CA. Concerning the slots configuration, we consider the 7/6/3 pattern. Finally, for the data generation interval, we use an interval equal to 0.25 s. The SDA-CSMA/CA and QBAIoT comparison is conducted while using two then three objects per QoS class. The results are shown in Figure 22.  
Figure 21: PDR evaluation using QBAIoT with and without self-configuring. Figure 20: Delay evaluation using QBAIoT with and without self-configuring. Figure 22 demonstrates that QBAIoT offers better delays for all QoS classes while considering two nodes per QoS class. Furthermore, when we consider three nodes per QoS class, the delay experienced by critical traffic (RTMC and RTNMC) is better while using QBAIoT compared to SDA-CSMA/CA. As for the streaming traffic, we observe a more important delay with QBAIoT due to the minimal slots allocation but without any QoS degradation because the delay QoS parameter is not critical for such traffic.