esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Propagation Model between Plane and Sphere  
The schematic of the propagation model between plane and sphere is shown in Figure 1a,b: Figure 1a shows a segment of a spherical SCGH and Figure 1b is the detail propagation model of a segment SCGH. In Figure 1b, \(\theta\) and \(\varphi\) are, respectively, the horizontal and vertical flare angles of WRP to the center of SCGH which determine the curvature of SCGH segment since the size of the WRP is fixed. Moreover, they correspond to \(\angle AOB\) and \(\angle COD\), respectively. In Figure 1c,d, \(\theta_{h}\) and \(\varphi_{h}\) are the angles of the radial projection on x-z plane to \(z\) axis and the radial direction to x-z plane, respectively. The point source method is the traditional way to calculate the diffraction distribution of this model. It is especially time-consuming as its computational complexity is \(O(N^{2})\) when computing \(N\) sampling points. Unfortunately, there is not any fast calculation method so far. Therefore, we propose the PC method to accelerate the computation.  
In the PC method, the propagation calculation between an object plane and an SCGH can be summarized in two steps: the diffraction from the object plane to the WRP near the spherical surface, and the phase compensation from the WRP to the SCGH. In the first step, the diffraction propagation from the object to WRP is calculated by any conventional plane-to-plane diffraction calculation method as the universality of our method, and we choose the angular spectrum method as an example.  
Figure 1: Panels (**a**,**b**) represent for the propagation model between plane and sphere; panels (**c**,**d**) are schematics of spherical computer-generated hologram (SCGH) generation by phase compensation (PC) method.  
The second step is to compensate the phase difference due to the optical path difference between WRP and SCGH.
# Feasibility of a Helium Closed-Cycle Gas Turbine for UAV Propulsion
## 2 The Closed-Cycle UAV Propulsion System
### The Rankine Closed-Cycle Engines  
The Rankine cycle is a vapour power cycle with constant pressure heat addition and rejection process. This cycle type is famous for power generation, and with a TRL less than 6 for aircraft application as a Besler engine, was the first successfully flown Rankine aircraft. The Besler engine operated on the Rankine cycle with a two-cylinder V-type engine that generated up to 150 HP. A steam boiler powered this semi-closed engine which flew 200 feet altitude and had a reduced noise operation compared with the reciprocating engines. It had a reciprocating turbine and a small condenser for cooling the steam. However, maintaining cooling was not a smooth task for this aircraft.  
Although the first successfully flown Rankine occurred in 1933, its prospect quickly diminished due to several constraints associated with the Rankine cycle such as weight, lower efficiency compared with the competing engines and complex system integration with the airframe due to heat exchanger requirement. Additionally, its response to changes in throttle demand is relatively slow, and it has obvious cooling problems. These are among the many reasons that there are not many ongoing pieces of research that explore the potentials of steam Rankine cycle for UAVs mission, as known by the authors. However, rather than using steam, there are propositions to use organic fluid for this type of UAV altitude propulsion system [10; 15; 16]. Using organic fluid could potentially allow for high altitude operation as organic working fluids can handle colder temperature without freezing, and the heat required to power the system is incredibly low. One perceived advantage of this cycle with organic media is that a small high-pressure electric fluid pump could entirely replace its compressor as the pump work required is relatively small compared to other propulsion systems. This is because the compression process occurs in the liquid phase. Numerical analysis in reference [16] shows that high-temperature dry fluid with trans-critical cycles such as Toluene or cyclohexane could improve the system efficiency by ~8%.  
Interestingly, organic fluid could achieve high efficiency with mid-low temperatures (between 100-200 \({}^{\circ}\)C). Operating an organic Rankine cycle (ORC) at temperatures above 300 \({}^{\circ}\)C could be limited by thermal stability [17]. The typical efficiency for this cycle would be between 20% and 27%. One option to manage to weight issue for the Rankine cycle design is to ensure the small mass flow rate.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 1 Introduction
When we extend the navigation tasks to the multi-robot system, the coordination between a group of mobile robots becomes more complex. Similarly, we analyze the human intuition to get the inspiration. If there are a group of humans navigating in the unknown indoor environment, they would communicate and collaborate with each other to attain the goal. There are several forms of communications such as sharing the experience and observations. For a multi-robot navigation system, the robots can share the sensor observations, the training data and the relative state parameters. Inspired by the analysis of human intuition, our work provides a new insight into the multi-robot collaborative navigation task with deep reinforcement learning. By sharing the training experience and observing the states of other robots, the multi-robot system can keep the formation during group navigation. In our proposed method, we assume that the relative position values of the targets are easily acquirable for the robots via cheap localization solutions such as WiFi [12] and QR code [13]. By improving the classical deep reinforcement learning methods, the proposed algorithm can accomplish the single robot mapless navigation task with human intuition. We also extend the method into the multi-robot navigation cases with some useful training strategies, and then evaluate the performance in the simulation platform. Particularly, the contributions of this paper can be summarized as follows:  
1. We improve the classical Deep Deterministic Policy Gradient (DDPG) to address the end-to-end single robot mapless navigation problems directly through the raw lidar data inputs. 2. The Parallel Deep Deterministic Policy Gradient (PDDPG) is proposed to accomplish the multi-robot formation construct and the formation keeping during collaborative navigation. We also evaluate the proposed methods in the Gazebo simulation platform. This paper is organized as follows: Section 2 describes the deep reinforcement learning algorithms and their applications in robot navigation tasks. In Section 3, an overview of the proposed algorithm is presented. The methods for different situations and the training details are described separately. Section 4 presents the evaluations of the proposed methods in the simulated environment and analyzes the experiment results. Section 5 concludes this work.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 5 Validation, Evaluation and Obtained Results
### Best Effort
#### 5.1.2 Multiple Slices  
Since our goal is to address multiple use cases, it is relevant to test two simultaneously running slices in a best effort approach. The testing scenario addresses both network segments with 50 clients each, which are instructed to inject 1 Mbit/s per interface with Iperf3 default packet sizes in the gateway. The test also considers the previously mentioned protocols where Slice A and Slice B are using TCP and UDP, respectively, to illustrate better and differentiate each slice. Figure 16 shows a comparison of both slices' achieved bandwidth.  
As expected, the implemented solution can handle several end devices, even if their destination slices happen to be different. Because this work's proposed solution addresses two use cases, the evaluation considered two slices as well. Despite the commitment to perform exhaustive tests, it is crucial to understand that the main objective is to find the QoS implementation that would better fit the Industry 4.0 and digital farming environments.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.2.3 Lining Structure Collapse and Concrete Chipping  
Lining structure collapse and concrete chips are forms of seismic damage to tunnel structures caused by the seismic force exceeding the bearing capacity of the lining structure.  
The entrance section of the Baiyunding Tunnel is located at the interface of soft and hard rock. During an earthquake, interlayer staggering occurs, and the horizontal displacement changes greatly, resulting in lining structure collapse. The arch and waist of the entrance section of both lines of Longxi Tunnel produced lining structure collapse and concrete chipping. The reason is also that the structure is located at the interface of rock layers, and the fault movement caused by the earthquake has a tremendous shear force, which exceeds the ultimate bearing capacity of the structure. Another reason for the severe collapse of the lining of Longxi Tunnel [25] is that the surrounding rock underwent a large deformation during the construction period; the left wall was extruded; the vault severely sank and the steel arch was twisted, deformed and sheared. Although the construction companies actively remedied it, the supporting effect was poor, and the structure was not maintained and managed in time during the operating period, which allowed the deformation to be seriously damaged during the earthquake.  
The main reason for the lining structure collapsing and concrete chipping is that the tunnel is located at the interface of weak rock, or the tunnel portal section is located in the weak rock zone, and the seismic inertial force transmitted during the earthquake is large, which is an engineering geological factor. The structure of the tunnel portal section is plain concrete with low tensile and shear strength, and the bearing capacity is insufficient, resulting in lining structure collapse and concrete chipping, which are tunnel structural factors. For the hidden dangers such as large deformation of the structure during the construction period, support and reinforcement measures were not taken in time, resulting in poor structural health after the tunnel entered the operation period and insufficient resistance to the external force generated by the earthquake, which is an operation management factor.  
In summary, the causes of seismic damage in the portal section of highway tunnels can be identified. The engineering geological factors include the characteristics of the surrounding rock, the characteristics of the side slope and the engineering environment. Tunnel structural factors include tunnel portal forms and supporting structures. The operation management factor is the technical condition of the structure. On this basis, the FTA model of seismic damage at the portal section of highway tunnels is established, as shown in Figure 5.  
Analysis of the Factors Influencing the Seismic Damage in the Tunnel Body Section of Highway Tunnels
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 4 CS-FL Method
### Training Phase: Hybrid Encryption and SCI Incentive Method
#### 4.3.2 SCI Incentive Mechanism
```
Input: ZS set \(M\), EN set \(N\), selected number \(K\), total training rounds \(R\); Output: global model \(\bar{w}_{i}\);
1 initialize \(\bar{w}_{0}\), \(\overline{SCI}_{0}=\left\{0.5\right\}_{1\times N}\);
2 for each round \(i=1,2,\cdots,R\)do
3 for\(n\in N\)do
4 receive \(\bar{P}_{task}\leftarrow\left\langle m\ |\ i,\bar{w}_{i-1},Q,|D_{test}|\right\rangle\); // Probe
5 test on \(D_{n,i}^{test}\) and compute indexes;
6 send \(\bar{P}_{reply}\leftarrow\left\langle\operatorname{ID}_{n}\ |\ NQI_{n},CQI_{n},t_{n}^{com}\right\rangle\) to ZS \(m\);
7 for\(m\in M\)do
8 compute CRI with \(\bar{P}_{reply}\) and timestamp \(t_{n}^{com_{2}}\);
9 retrieve \(\overline{SCI}_{i-1}\) and update \(\overline{SCI}_{m}\), \(View_{m}\);
10 broadcast \(\bar{B}_{m,1}\leftarrow\left\langle m\ |\ View_{m},\overline{NQI}_{m}, \overline{CRI}_{m},\overline{SCI}_{m}\right\rangle\);
11 Leader \(L\) updates \(\overline{View}\), \(\overline{NQI}\), \(\overline{CRI}\), \(\overline{SCI}_{i}\) on the ledger Zone-Chain; // Selection
12\(S_{i}\leftarrow\operatorname{GNSS}(K)\); // See Algorithm 1
13 Leader \(L\) broadcasts \(S_{i}\);
14 Each ZS sends \(R_{1}\leftarrow\left\langle m\ |\ S_{i},PubKey_{m}\right\rangle\) to selected ENs and sends \(R_{2}\leftarrow\left\langle m\ |\ S_{i}\right\rangle\) to unselected EN; // Training
15 for\(n\in S_{i}\)do
16 train on \(D_{n}\) and obtain \(w_{n,i}\), \(\bigtriangledown S_{n,i}\);
17 generate \(Encryparam_{n}=\operatorname{AES}(LocalKey_{n},\bigtriangledown S_{n,i})\) and \(EncrypKey_{m,n}=\operatorname{RSA}(PubKey_{m},LocalKey_{n})\);
18 send \(T_{n}\leftarrow\left\langle\operatorname{ID}_{n}\ |\ Encryparam_{n}, Encryparam_{n},n_{n}^{com_{3}}\right\rangle\) to ZS;
19 for\(m\in M\)do
20 record timestamp \(t_{n}^{com_{4}}\) and compute \(t_{n}^{\prime}\);
21 get \(\overline{LocalKey_{n}}=\operatorname{RSA}(PriKey_{m},\overline{Encryparam _{n})}\) and \(\overline{\bigtriangledown S_{n,i}}=\operatorname{AES}(LocalKey_{n},\overline{ Encryparam_{n})}\);
22 send \(\bar{B}_{m,2}\leftarrow\left\langle m\ |\ \overline{\bigtriangledown S_{n,i}},t_{n}^{\prime}\right\rangle\) to Leader \(L\);
23 Leader \(L\) aggregate local models to obtain \(\bar{w}_{i}\);
24\(\overline{SCI}^{\prime}_{i}\leftarrow\operatorname{SIM}(\bar{w}_{i},\overline {w_{n,i}})\); // See Section 4.3.2
25 Leader \(L\) broadcasts \(block_{i}\leftarrow\left\langle L\ |\ \bar{w}_{i},\overline{SCI}^{\prime}_{i}\right\rangle\);
```  
**Algorithm 2**CS-FL Algorithm
## 4 Simulation Analysis
### Simulation Environment  
Our simulation environment for the R-EERP, S-EERP and LEACH protocols is an area of 200 m by 200 m. All nodes are immobile. The base station is fixed and located far from the sensors. The initial energy of all of the nodes is 1.2 J. The energy consumption of a node that sends a packet is calculated with Formula (2) and the energy consumption of a node that receives a packet is calculated with Formula (3). The size of packets is assumed to be 525 bytes. Table 2 summarizes the simulation parameters [8].  
For our experiments, it is considered that there could be different amounts of methane gas leakage in different areas. For this reason, it is assumed that the critical threshold value for the methane gas is 70 units and the base threshold value is 10 units. Therefore, (i) the data above 70 units will directly be transmitted to the sink without being kept waiting at cluster heads; (ii) the data below 10 units will be ignored; (iii) the data between 10 and 70 units will be sent to cluster heads when sensed; (iv) a compression/filtering operation will be performed on the data which is received at the cluster heads and in this way sending of duplicated data to the sink node will be prevented. Methane gas level is randomly determined between 5 and 87.5 in each 5 seconds during the simulation. The cluster heads are changed at each 10 events and the simulation ends after 1,000 rounds.
## 1 Introduction
For each studied mapping, we compare the obtained simulation results of our proposed message-level performance models against detailed transaction-level models and measurements on a real prototype. We also compare our different computation models. The evaluation metrics were accuracy of the estimations and analysis time. Moreover, scalability of the analysis approach was evaluated with respect to different use-cases and different mappings. This leads to the following contributions:  
1. A transaction level model proposed and evaluated as a compromise between the detailed cycle accurate model from from [5] and the abstract message level model from [6]. 2. Systematic addressing data dependency by applying Kernel Density Estimation on measured characterization data. 3. An exhaustive evaluation combining our new advanced computation model and previous computation models from [2] with our new hybrid transaction level communication model and previous communication models from [5,6]. The paper is organized as follows. In the next section, we compare to existing modeling approaches for calibration of system-level models. In Section 3, we provide an overview of the main concepts associated with our approach. Section 4 presents our modeling workflow and the contributions to communication and computation modeling. In Section 5, we present our use-cases, the experimental setup, our comparison results and finally a discussion of these results. The paper closes with an outlook on future work and a summary.
## 3 Results
### Challenges and Optimisations
#### 3.2.1 Sentinel-2 Product Selection Based on Cloud Criteria  
A number of applications that make use of satellite imagery, such as land cover classification, crop identification or crop growth monitoring, are founded on cloud-free optical images for operation.  
The degree of cloud coverage--referred to as the cloud fraction--is measured as the ratio of the area covered by the cloud (irrespective of cloud type) to the total observed area. The global cloud cover fraction can be estimated by the analysis of satellite images or by using synoptic reports taken from ground-based weather stations. The average global cloud cover fraction has been calculated--based on data retrieved from 12 satellite image datasets--to be 68% for clouds of optical depth greater than 0.1, increasing to 72% when sub-visible cirrus clouds are also considered [62]. The global cloud fraction as determined by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor from the Terra and Aqua satellites is 67%, with lower cloud coverage in land areas (55%) than over the ocean (72%) [63].  
The creation of a minimum cloud-free mosaic for Sentinel-2 optical images is a challenge for areas subject to significant cloud cover, necessitating the strategic selection of tiles. The least cloud-affected tile must be chosen among all the tiles covering a particular ROI within the given time window. Ideally, the cloud fraction should be assessed during the initial selection of the tiles to avoid unnecessary downloads and processing. Sentinel-2 products contain information on the degree of cloud cover evaluated over the entire tile. However, the cloud cover for a particular ROI cannot be determined from tile information alone. In instances where the ROI is cloud-free while the rest of the tile is not, the latter will not be selected if only the average cloud cover statistics are considered. As an example,  
Figure 9: Collocated Sentinel-1 and Sentinel-2 patch: (**a**) False colour composite of a Sentinel-1 patch (red channel as VV, green channel as VH and blue channel as VV/VH), (**b**) RGB colour composite of a Sentinel-2 patch.  
the tile captured on 21 July 2020 is 19.24% cloud-covered, as shown in Figure 10 for a group of farms in Scotland; however, closer inspection shows significant cloud cover over the ROI (Figure 10a). For a tile captured two days later, on 23 July 2020, the cloud coverage is 90.83% but the ROI is cloud-free (Figure 10b). Therefore, accurate per-pixel metadata are required to guide the tile selection process based on cloud coverage.  
In the proposed framework, Sentinel-2 Level-2A scene classification masks (cloud medium probability, cloud high probability, thin cirrus) are downloaded to determine the exact cloud-free area over the ROI. The resultant outputs inform the selection of tiles with minimum cloud coverage, and thus situations such as the one presented in Figure 10 are avoided. In addition, other scene classification masks can be used for semantic enrichment, providing additional information on the presence of vegetation, snow, water, cloud shadows, etc.
## 3 Results and Discussion
### Degradation of Methylene Blue
The reproducibility of the process has also been studied, and the first step is to compare the reproducibility of different electrodes prepared by procedure A. Figure 8 shows the discolouration efficiency for five electrodes prepared under the same conditions. The amount of material deposited in each coating was calculated by weight difference between the uncoated and coated electrode, and the values obtained indicate a certain dispersion in the quantity of iron oxide nanoparticles deposited, which are between 17 and 30.3 mg. The time required for these electrodes under working conditions, to facilitate the total discolouration of the MB solution is about 47 \(\pm\) 7 min, indicating that despite the dispersion in the amount of nanoparticles deposited on the RVC the fluctuation in the time for the total discolouration of the dye present a low deviation. Figure 6: Effect of applied current on 20 mg:L\({}^{-1}\) MB degradation by electrofenton using RVC dip coated as cathode. Figure 7: Influence of temperature on the electrofenton process (RVC/A). Applied current 50 mA. It is also very important to know how many times we can use an electrode without losing its capacity and efficiency. Figure 9 shows the reuse of an electrode during four cycles. To carry out this experiment, the electrode was washed with abundant water after each process and dried in an oven overnight at 80 \({}^{\circ}\)C. Repeating the process in each cycle. The results shown a slight decrease in the degradation effectiveness in the second run, decreasing from 100% to 90%, but this value remains constant at least until the fourth cycle of electrode use.
## 4 Mechanism Analyses
### Pore Structure Analysis  
Figures 9 and 10 illustrate the pore distribution patterns of samples EP80S2 and EP100S1. Based on the size of the pores in concrete, pores are generally categorized as micropores (diameter < 10 nm), mesopores (10-50 nm), capillary pores (50-1000 nm), and macropores (>1000 nm).  
Figure 8: Apparent viscosity of 3 DPFC samples.  
It is evident from Figure 9 that the distribution of micropores in samples EP100S1 and EP80S2 is essentially similar, possibly due to the fact that they share the same cementitious material system. However, sample EP100S1 evidently contains more mesopores and capillary pores, likely due to its increased content of expanded perlite and decreased foam content [31; 41]. It is worth noting that the mercury intrusion porosimetry (MIP) method cannot accurately capture pores typically larger than 10,000 nm, so even if these pores collapse under compressive pressure, they cannot be captured with MIP. However, within the pore size range that can be measured by MIP, smaller pores merge under compressive pressure, forming larger pores. The corresponding differential volume curve obtained from the compressed sample reveals a significant increase in the peak and a rightward shift for sample EP80S2, indicating that it contains more macropores. As depicted in Figure 10, sample EP80S2 exhibits a higher total porosity. In a similar density range, the number of macropores and total porosity of sample EP100S1 decreases compared to sample EP80S2, a reduction associated with the lower foam content in EP100S1 [26]. It should be noted that the 3 DPFCs' macropores contents and total porosity are closely related to their mechanical properties. Generally, the compressive strength of the 3 DPFCs decreases with an increase in porosity, which is consistent with the results of compressive strength tests.  
Figure 10: Cumulative pore distribution of 3 DPFC.  
Figure 9: Pore distribution of 3 DPFC.