esa-sceva/satcom-chunk-collection huggingface dataset:
# THz Range Low-Noise SIS Receivers for Space and Ground-Based Radio Astronomy +
###### Abstract  
We report on research in the field of low-noise receiving systems in the sub-terahertz (THz) range, carried out in recent years, aimed at developing receivers with quantum sensitivity for implementation in space and ground-based radio telescopes. Superconductor-Insulator-Superconductor (SIS) mixers based on high-quality tunnel junctions are the key elements of the most sensitive sub-THz heterodyne receivers. Motivations and physical background for technology improvement and optimization, as well as fabrication details, are described. This article presents the results of the SIS receiver developments for the 211-275 GHz and 790-950 GHz frequency ranges with a noise temperature in the double sideband (DSB) mode of approximTELY 20 K and 200 K, respectively. These designs and achievements are implemented in the development of the receiving systems for the Russian Space Agency mission \"Millimetron\", and for the ground-based APEX (Atacama Pathfinder EXperiment) telescope.  
radio astronomy; niobium-based high-quality tunnel junctions; low-noise SIS receivers; THz range quantum-limited mixers +
Footnote †: Accepted: 26 October 2021
# UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net
## 5 Conclusions  
The experience in using a spatial-temporal hybrid Petri net for modeling the attack mission of a UAV cluster has been very effective, providing many advantages over other methods as well as some difficulties that we have dealt with.  
This paper focused on representing the heterogeneous UAV cluster combat mission through spatial-temporal hybrid Petri net modeling. First, the modeling and verification of the heterogeneous UAV cluster combat mission was performed, then the dynamic and kinematic equation of the change in key attributes of the heterogeneous UAV cluster were represented, and the formal modeling of the perceptive events, control events, and their interaction event was achieved, extending the single object modeling to various objects modeling for UAVs. This modeling method can effectively represent the fusion of the physical and computational process and information exchange and coordination between each UAV node in the UAV cluster system. Throughout verification, the UPPAAL tool provides an effective framework and environment to demonstrate the reliability, accessibility, effectiveness, and real-time of the aforementioned modeling method.  
The main advantage of the spatial-temporal hybrid Petri net is the clear definition of the modeling language and of its semantics in terms of Petri nets. Using such a modeling method guarantees the possibility of using standard tools to evaluate the properties of the model such as liveness and reachability of the goal states. High expressiveness of the spatial-temporal hybrid Petri net allows for effectively capturing and dealing with most of the situations encountered in the research of a UAV cluster.  
Although we proposed a modeling method to describe the scenario of the attack mission of a UAV cluster, in order to have a clear specification of the behavior of the UAV cluster, it may still be very difficult to design a model when more and more factors are  
Figure 6: The validation result of the attributes of the example.  
considered and the dependency across each other becomes more complex. There is no automatic tool to support the conversion between the behavior model proposed in this paper and the simulation verification model, and further research is needed to simplify the connection process between the formal characterization model and the simulation model.  
Methodology, X.W. and N.L.; Validation, N.L. and P.H.; Writing--original draft preparation, X.W. and N.L.; Writing--review and editing, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research has been supported the Project of National Defense Basic Research Program, the National Defense Science and Technology Key Laboratory Fund Under Grant No. 6142219200205, the Open Fund of CETC Key Laboratory of Data Link Technology Under Grant No. CLDL-20202208_2, the Fundamental Research Funds for the Central Universities (G2022 KY0603) and the National Natural Science Foundation of China under grant No.62203361.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Raman Spectroscopy  
The Raman spectra were obtained with an integrated confocal micro-Raman system with a LabRam Aramis (Horiba Jobin Yvon, France) 460 mm spectrometer equipped with a confocal microscope. The light source was a laser emitting green light diode at 532 nm with 50 mW power. Data were smoothed (with a Savitzky-Golay method, 15 points, cubic interpolant) and the baseline (approximated with a cubic polynomial) subtracted and normalized.
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 2 Materials and Methods
### Data Analysis
#### 2.2.3 The Occurrence Rate of Es and IVN  
In this analysis, S4 index profiles of 2014 were utilized to compute the occurrence rate of Es layers. These profiles were categorized into four seasons and 24 local time regions. Subsequently, the data within each region were further divided into a sliding window of 5\({}^{\circ}\) latitude by 5 km altitude. Within this window, events with a maximum S4 index greater than 0.3 were identified as Es layers cases. The ratio of the number of Es layer cases to the total number of occultation profiles was designated as the Es occurrence rate (EsOR) [35].  
Similarly, the ratio of the number of ion vertical velocity null (IVN) to the total number of grid points of simulation at a fixed height and local time was also calculated and described as the occurrence rate of IVN.
## 4 Proposed Data Collection Algorithms
### Probabilistic Neighborhood Covering Set-Based Greedy Heuristic Algorithm (PNCS-GHA)
#### 4.2.2 AUV Trajectory Planning Phase  
Once the PNCS has been constructed, we need to determine a path for the AUV to traverse all the PNCS nodes. This is a typical NP-hard problem and is quite similar to TSP. At present, some classic heuristic strategies have been proposed to solve TSP, such as Nearest-neighbor Heuristic, Heuristics of Christofides, Minimum Spanning Tree, and Random Insertion [30]. Here, in order to reduce the excessive complexity of 3D path planning and shorten the path length, we employ the Nearest-neighbor Heuristic, which is easily and efficiently extended to 3D space, for the AUV to traverse every node in the constructed PNCS. Algorithm 2 presents the main procedure of the PNCS-based Nearest-neighbor Heuristic trajectory planning algorithm. Firstly, the visit status of each PNCS node is set to be false, represents that this node hasn't been visited by the AUV. Next, among those PNCS nodes whose visit statuses are false, the nearest node of the AUV is greedily selected as the next stay point for data collection, and the visit status of this node is set to be true. Then, the greedy heuristic procedure is repeated until all the PNCS nodes have been visited.  
```
0: The constructed probabilistic neighborhood covering set \(S\); the locations of nodes in \(S\);
0: The traveling path of the AUV, \(P\);
1:\(m=|S|\);
2:for\(i=1\) to \(m\)do
3:\(visit(i)=false\);
4:\(d_{0}(i)=\) distance from \(S(i)\) to the surface control center;
5:endfor
6:\(curr=\) {arg min \(\{d_{0}(i)|i=1,2,...,m\}\)};
7:\(visit(curr)=true\);
8:\(visitNum=1\);
9:\(P=\{S(curr)\}\);
10:while\(visitNum<m\)do
11:for\(i=1\) to \(m\)do
12:if\(visit(i)=false\)then
13:\(d_{curr}(i)=\) distance from \(S(i)\) to \(S(curr)\);
14:else
15:\(d_{curr}(i)=\infty\);
16:endif
17:endfor
18:\(curr=\) {arg min \(\{d_{curr}(i)|i=1,2,...,m\}\)};
19:\(visit(curr)=true\);
20:\(visitNum=visitNum+1\);
21:\(P=\{P;S(curr)\}\);
22:endwhile
```  
**Algorithm 2** Planning the AUV trajectory in PNCS-GHA
## 4 Results
Figure 4: Kinetic curves of the release of dioxidine (1) and gentamicin sulfate (2) from the biopolymer matrices: Ca-alginate (a), gelatin (b), chitosan (c). Such property of this system slowed the release of antibacterial substance dioxide and metal ions reducing the whole antibacterial activity of the hybrid nanosystem. Figure 5: Inhibition zones (IZ) of bacteria strains growth for paper disks impregnated with aqueous solutions of dioxide-based samples (dioxidine, 0.3 mass%; Ag, 0.0015 mass%; Cu, 0.006 mass%). Figure 6: Inhibition zones (IZ) of bacteria strains growth for paper disks impregnated with aqueous solutions of samples based on gentamicin sulfate (gentamicin sulfate, 0.3 mass%; Ag, 0.0015 mass%; Cu, 0.006 mass%). Figure 7: Inhibition zones (IZ) of bacteria strains growth for biopolymer matrices (Ca-alginate (**a**), gelatin (**b**), chitosan (**c**)) impregnated with individual antibacterial drugs, metal nanoparticles, and their hybrid nanocomposites. It should be concluded that modifying gentamicin sulfate with metal nanoparticles that are active against pathogenic microorganisms also resulted in an increase of its antibacterial activity Figures 6 and 7 (Tables A2 and A3). The incorporation of hybrid composites of gentamicin sulfate with metal nanoparticles into the Ca-alginate and gelatin biopolymer matrices did not change the observed pattern, and their activity was higher in comparison to the activity of individual metal nanoparticles or individual gentamicin sulfate particles incorporated into the similar biopolymer matrices. Thus, a synergistic increase in antibacterial activity was observed for hybrid nanosystems of dioxidine and gentamicin sulfate with silver and copper nanoparticles when compared with the effect of individual components against _E. coli 52_ and _S. aureus 144_ strains. In the case of chitosan biopolymer matrix, the antibacterial activity for hybrid nanosystems of gentamicin sulfate with metal nanoparticles was decreased. This effect was similar with the data observed for dioxidine containing chitosan-based systems and probably had a similar nature.
# A Study on ML-Based Sleep Score Model Using Lifelog Data
## 3 Results  
### Second Step: Intermediate Sleep Score  
Figure 8 shows the distribution of the final sleep habit score calculated in this study, which is the sum of the first-generated (primary sleep habit) score and the intermediate sleep habit score. It is evenly distributed with an approximately normal distribution with a mean of 850. This is similar to the characteristics of a general scorecard in which scores are concentrated in the middle (average). It can be confirmed that the distribution of the calculated scores is close to a normal distribution, so that the data are not concentrated in a specific score range and are almost symmetrically distributed with no skew. This suggests that the score was well calculated without distortion. In addition, since it is an approximately normal distribution, it is possible to estimate the population by comparing various groups through inferential statistics, and it becomes possible to derive several kinds of statistical tests. Finally, it makes it easy to use and interpret scores.  
Figure 7: Summary graph of the process of CV stacking in each model.  
Statistical values of sleep characteristics for each sleep state are as follows.  
1. For sleep midpoint between 02:00 a.m. and 04:00 a.m. on weekdays, good sleep was 47.12%, bad sleep 12.25%, and intermediate sleep 21.39%.
2. For weekend sleep time between 10:00 p.m. and 12:00 a.m., good sleep was 43.25%, bad sleep 22.50%, and intermediate sleep 29.91%.
3. For steps within 2 h, good sleep averaged 813 steps, bad sleep 35, and moderate sleep 156.
4. For the SRI (Sleep Regularity Index) index (2 days), mean values were 87.21 for good sleep, 73.26 for bad sleep, and 80.58 for intermediate sleep.  
Figure 9 shows the sleep state probabilities for each section for good and bad sleep states.  
Figure 9 shows the following:  
* The higher the SRI value, the higher the probability of a good sleep state.
* The higher the gait (step) counts within the first 2 h before sleep, the higher the probability of a good sleep state.
* The greater the weekly total time variability, the higher the probability of a bad sleep state.  
Figure 8: Sleep habit score distribution for all data.  
Figure 9: (a) Sleep state probabilities for SRI interval; (b) sleep state probabilities for STEP_INFO_2 interval; (c) sleep state probabilities for WEEKLY_TST_VAR interval.  
Overall, good sleep was mainly distributed in the range 1400-1600 points, intermediate sleep was distributed over 700-900 points, and bad sleep was mainly distributed over less than 700 points (Refer to Table 7). According to the method proposed in this study, the higher the sleep habit score, the more data classified as good sleep state, while the lower the score, the worse the sleep state. Therefore, it is expected that good sleep guides can be elaborated according to the proposed sleep habit score.
# PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection
## 4 Experiment
### Ablation Experiment
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Exp** & **HardSwish** & **Wise-IOU v3** & **RFB** & **CARAFE** & **PConv** & **Params/M** & _GFLOPs/G_ & _mAP/\%_ & **Inference Time/ms** \\ \hline exp1 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 37.21 & 105.2 & 75.4 & 35.08 \\ exp2 & \(\check{\check{\vee}}\) & \(\times\) & \(\times\) & \(\times\) & 37.21 & 105.2 & 78.1 & 31.49 \\ exp3 & \(\times\) & \(\check{\check{\vee}}\) & \(\times\) & \(\times\) & \(\times\) & 37.21 & 105.2 & 79.1 & 32.08 \\ exp4 & \(\times\) & \(\times\) & \(\check{\vee}\) & \(\times\) & \(\times\) & 33.94 & 102.5 & 80.6 & 33.31 \\ exp5 & \(\times\) & \(\times\) & \(\times\) & \(\check{\vee}\) & \(\times\) & 37.87 & 106.5 & 79.8 & 34.65 \\ exp6 & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\vee}\) & \(\times\) & 34.6 & 103.8 & 81.1 & 30.62 \\ exp7 & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & **30.5** & **83.6** & **83** & **28.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of ablation experiments. According to the loss curve chart above, it is evident that the PRC-Light YOLO model exhibits smaller loss values after convergence compared to other models. Specifically, experiments 3, 6 and 7 employ the Wise-IOU bounding box loss function, which dynamically adjusts the emphasis on different input regions over time. For anchor boxes with lower outliers, a smaller gradient boost is allocated, thereby directing the boundary regression towards normal-quality anchor boxes. This adjustment enhances the convergence speed of the network model. Figure 9 demonstrates that the PRC-Light YOLO model achieves lower loss values, thereby delivering superior performance in fabric defect detection. According to Figure 10, it can be obtained that the _mAP_ value of PRC-Light YOLO is increased from 75.4% at the beginning to 83%, and the exp  
Figure 8: Change curve of _mAP_ value. Figure 9: Loss Function. The loss function of the model consists of three parts, (**a**), (**b**) and (**c**), respectively, show the change curves of the BoxLoss, ClsLoss and ObjLoss for YOLOv7 and PRC-Light YOLO. (**d**) shows the total loss function variation curve. that the PRC-Light YOLO has improved the effect of defect detection. The _AP_ values of threading, yard defects, stains and holes increased by 10.2%, 6.9%, 2.4% and 10.9%, respectively, with the most noticeable improvement in the detection of hanging warp and holes, and the _AP_ values of the four categories of defects all are improved.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 1 Introduction
More specifically, the contributions in this article can be summarised as follows:  
* We review the 5G enhancements beyond 3 GPP Release 15 (Rel-15) that are most relevant for CAM contexts;
* We identify a set of CAM use cases that target the CAM challenges in cross-border contexts;
* We identify the main communication requirements of the use cases and justify the need for features beyond 3 GPP Rel-15 to implement them;
* We propose an implementation, matching the identified use cases and the relevance of 5G features beyond Rel-15, to realise the use cases while satisfying their performance expectations;
* We identify some relevant open issues. Previous works in the literature do not address the challenges for highly automated driving in cross-border contexts and the new features planned for future 3 GPP releases and their relevance there. References [4; 6] gave first glimpses of the possibilities of 5G technologies for connected cars, but only addressed up to 3 GPP Rel-15. Reference [2] discussed some of the recent technological advances and standardisation developments in cellular vehicle-to-everything (C-V2X) communications, but they did not match them to specific use cases. Reference [1] reviewed the current standardisation efforts focusing on MAC and PHY layers and not on vertical applications. Reference [11] described the general 3 GPP Release 16 (Rel-16) features and provided an outlook towards Release 17 (Rel-17) and beyond, but without discussing their applications in vehicular communications and their impacts on autonomous driving. Therefore, the main novelty of this paper is its showing the limitations of the currently available technologies and the possible solutions exploiting planned 3 GPP Releases in the context of CAM in cross-border corridors. The insights presented here are valuable not only for the European Union, but also for any 5G cross-border deployment around the world.
# Effect of the Surface Roughness on the Shear Strength of Granular Materials in Ring Shear Tests
## 2 Materials and Methods
### Methods
#### 2.2.2 Surface Roughness in the Ring Shear Box  
The annular-like shape of the ring shear box has an inner diameter of 110 mm, an outer diameter of 250 mm, and a height of 75 mm (Figure 2). The width between the inner and outer sides of the annular-shaped shear box is 70 mm. The height of the materials in the ring shear box can be varied from 70 to 80 mm, but a fixed height of 75 mm was applied in the tests. In this context, a wide range of large particle sizes (e.g., 0.075-10 mm) can be accommodated within the box. To minimize the slip effect, surface roughness is required for many types of instruments when investigating an engineering problem. In some tests, a thick sand cover with a rough surface texture was used for clayey to sandy soils. In this study, two types of ring shear boxes with smooth (classic) and rough (serrated) surfaces were made and placed inside the inner and outer sidewalls of the ring shear box to examine the frictional and fragmentation effects during shearing. No roughness was considered for the classic ring shear box except at the top and bottom. In the upper and lower plates, a combination of porous stone and 12 saw-like surfaces was used. The rough surfaces in the second ring shear box were located inside the inner and outer sides of the hollow cylinder box (Figure 2b-d). The dimensions of the rough surfaces were fixed. The thickness, width, and height were 5 mm, 3 mm, and 22 mm, respectively, for the lower ring, and 5 mm, 3 mm, and 26 mm, respectively, for the upper ring. In total, there were 32 inner rough surfaces and 72 outer rough surfaces. Because of the dimensions of the rough surfaces, large particle sizes were well sheared at the inner and outer perimeters of the shear box. In particular, the roughness was considered capable of directly affecting the gravelly soil tested (mean grain size of 6 mm).  
Figure 2: Ring shear box: (**a**) Smooth surface; (**b**) rough surfaces; (**c**) schematic view; and (**d**) inside view with inner and outer rough surfaces.
## 3 The Proposed Octree-Hiding Scheme
### The Extraction Process
#### 3.1.2 Non-Blind Approach  
No Error Correction  
The semi-blind extraction method might fail to extract the secret data with acceptable quality due to some statistical differences between the host and carrier video segments that can be imperceptible by visual means. To ensure extraction with the best extraction quality, the sender must send all of the parameters that are essential for the extraction process, such as the output of the Octree segmentation. However, this approach will maximize the communication cost, which can be impractical for large-sized video segments. Table 1 lists all of the parameters needed to extract the secret message using the non-blind approach.  
The structure information of the Octree can be simplified using the Z-order sorting method. Figure 5 shows the implementation of the Z-order method on a quad-tree. The Z-order sorting method works by analyzing the quad-tree blocks level by level in a Z-order manner (top-left block \(\rightarrow\) top-right block \(\rightarrow\) bottom-left block \(\rightarrow\) bottom-right block). The quad-tree shown in Figure 5 was described efficiently by a tree structure. The tree structure shows three levels, which represent the segmentation levels in the quad-tree. The first level has a value of 1, which means that initially the input image is divided into four non-overlapping blocks. The second level of the tree structure shown in Figure 5 represents the splitting procedure in the second level. The second level had a sequence of [0 1 1 1], which simply means the following commands:  
1. Do not split the top-left block.
2. Split the top-right block.
3. Split the bottom-left block.
4. Split the bottom-right block.  
The third level is represented in the same way that level 2 was represented. As shown in Figure 5, the third level shows the segmentation procedure on the blocks that were segmented in level 2. Blocks that were not segmented in level 2 will not have a child structure in level 3. At the end, all ones and zeros are packed in one vector in order starting from the first tree level and ending with the last tree level as shown in Figure 5. In the non-blind approach, the sender can only send the resultant vector as the quad-tree structure representative where the receiver can easily generate the quad-tree structure from the sent vector. Similarly, the Octree can be also represented by the same z-order method described in Figure 5. However, instead of having four child nodes for each parent, the Octree structure will have eight child nodes for each parent.  
Figure 5: Simplifying the information to be sent of the quad-tree structure using the Z-order method.