esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy-Based Algorithm for Supply-Chain Complexity Assessment
## 4 Entropy-Based Algorithm for Complexity Assessment  
This section summarizes theoretical findings of the previous sections for obtaining a decreased SC model on which planning and coordination of supplies can be done without loss of essential information.  
Input data of the algorithm:  
* the given number \(N\) of risk drivers,
* weight functions \(c(s)\) selected by the decision maker,
* probabilities \(p^{prime}(s)=\Pr\ (A_{f}\ ^{prime}(s))\) that the risk driver \(f\) is the direct source of the supply failure/delay in layer \(s\), which are caused by risk driver \(f\),
* probabilities \(p^{second}(s)=\Pr\ (A_{f}\ ^{second}(s))\) that the risk driver \(f\) is the source of the supply failure/delay in layer \(s\), which is a result of the indirect effect on the \(f\) by the risk drivers \(f^{\prime}\) of supply delay in layer \(s+1\); these probabilities are termed as _secondary_.
* transition probability matrices \(M^{(2)}(s)=\left(p^{(2)}_{ij}(s)\right)_{N\times N}\), \(s=0,1,2,\ldots,k\).  
**Step 1.** Using the entropy Formulas (11)-(17), calculate entropy of the layer 0:  
\[H^{*}(L_{0})=-\sum\limits_{j=1}^{N}p_{j}(0)\log\bigl{(}p_{j}(0) \bigr{)}\] \[H(0)=H^{*}(L_{0})\]  
**Step 2.** Using Formulas (2)-(9), compute the matrix  
\(\hat{M}^{(1)}(0)\), and vector \(\overline{p}(1)=(p_{1}(1),p_{2}(1),\ldots p_{N}(1))\)  
**Step 3.** Compute the corrected vector of probabilities for the layer \(L_{0}\), using Formula  
\(\overline{p}^{c}(0)=\overline{p}(1)\cdot\hat{M}^{(1)}(0)\) and corrected entropy for layer \(L_{0}\)  
\[HC^{*}(L_{0})=-\sum\limits_{j=1}^{N}p_{j}^{c}(0)\log\Bigl{(}p_{j}^{c}(0) \Bigr{)}\]  
**Step 4.** For \(s=2,3,\ldots,\) using matrix \(\hat{M}^{(1)}(s)\), vector \(\overline{p}(s)=(p_{1}(s),p_{2}(s),\ldots p_{N}(s))\) compute sequentially the corrected vectors of probabilities for the layers \(L_{s-1}\): \(\overline{p}^{c}(s-1)=\overline{p}(s)\cdot\hat{M}^{(1)}(s-1)\)  
**Step 5.** Compute \(HC^{*}(L_{s-1})=-\sum\limits_{j=1}^{N}p_{j}^{c}(s-1)\log\Bigl{(}p_{j}^{c}(s-1 )\Bigr{)}\)  
**Step 6.** Compute  
\[H(0)-H(1)=\] \[=\Bigl{(}1-\tfrac{c(1)}{c(0)}\Bigr{)}H(0)-c(1)(H^{*}(L_{1})-HC^{*}(L_ {0}))+c(1)\sum\limits_{i=1}^{N}\Biggl{(}p_{i}(1)\cdot\sum\limits_{j=1}^{N} \hat{p}^{(1)}_{ij}(0)\cdot\log\Bigl{(}\hat{p}^{(1)}_{ij}(0)\Bigr{)}\Biggr{)}\]  
**Step 7.** For \(s-1,2,\ldots,\) compute  
\[H(s-1)-H(s)=\] \[=\left(1-\frac{c(s)}{c(s-1)}\right)H(s-1)-c(s)(H^{*}(L_{s})-HC^{*}(L _{s-1}))+c(s)\underset{i=1}{\overset{N}{\sum}}\left(p_{i}(s)\cdot\underset{j=1 }{\overset{N}{\sum}}\,\theta_{ij}^{(1)}(s-1)\cdot\log\left(\theta_{ij}^{(1)}(s -1)\right)\right)\] As the stopping rule use the following rule: Stop at the cut \(s_{1}\) for which \(\frac{H(s_{1}-1)-H(s_{1})}{H(0)-H(s_{1})}<\varepsilon\) holds. Then the reduced SC model contains only the cut \((s_{1}-1)\).
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 4 Tracking Radar Design
### Radar Block Diagram and Operation
#### 4.3.5 Antenna Control Unit  
The azimuth and elevation movement of the _Tx_ antenna and _Rx_ antenna are controlled by two antenna positioning systems: antenna positioning system 1 (APS1), comprising an antenna 1 control unit (ACU) and an antenna 1 positioning unit (APU), and antenna positioning system 2 (APS2), comprising an antenna 2 control unit (ACU) and an antenna 2 positioning unit (APU). APS1 and APS2 are synchronized by the main processing unit (MPU).  
During auto-track, the error signals control the antenna 2 control unit (ACU), which in turn controls the movement of the _Tx_ Antenna through the antenna 1 control unit (ACU) in order to maintain positioning synchronization of the two antennas.
# An Overview of CubeSat Missions and Applications
## 5 CubeSats as Astronomical Exploration and Research Enabling Platforms
### Radio Interferometers  
CubeSats have also been used in missions incorporating radio interferometers. In these configurations, several CubeSats, each with an antenna that gathers radio waves, circle each other. By doing this synchronized movement, a \"virtual telescope\" is created, which is more portable, modular, and adaptable than a much bigger, ground-based radio observatory. This method works well for monitoring the Earth's ionosphere, tracking solar radio bursts, and identifying cosmic radio sources. By adding more satellites, researchers can expand the system using CubeSat interferometers, increasing the sensitivity and resolution of their studies. This method offers a cost-effective means of obtaining precise, high-quality data, opening up fascinating prospects for space radio astronomy. One upcoming mission that incorporates radio interferometers is the Sun Radio Interferometer Space Experiment (SunRISE) [228]. Developed by NASA and its partner institutions, the SunRISE project will deploy a constellation of six 6U CubeSats, as can be seen in Figure 14, to study the origins and propagation of solar radio waves associated with solar storms. Currently expected to be launched before the end of 2025 onboard ULA's Vulcan Centaur launch vehicle, the SunRISE CubeSats will capture radio waves from solar energetic particles (SEPs), which are produced during solar eruptions, using a coordinated arrangement of the six CubeSats. The CubeSats will employ radio interferometry to create a virtual radio telescope that can take high-resolution images of solar radio bursts to assist scientists in monitoring their velocity and positions. By providing insight into how SEPs travel through the heliosphere, the data collected will aid us to understand space weather and its potential impacts on satellites, communication systems, and humans in orbit. SunRISE's successful demonstration will present a major advancement in solar research, demonstrating how small, coordinated satellites can achieve complex scientific goals typically reserved for larger, more expensive missions.  
Figure 14: Sun Radio Interferometer Space Experiment (SunRISE) mission—Six 6U Cubesat constellation. Image credit by Space Dynamics Laboratory/Allison Bills [229].
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 5 Simulation Results and Discussion
### Performance Evaluation of UAV Dynamic Deployment Algorithms
When comparing the average energy consumption under different numbers of users, the RUDD algorithm can adjust the strategy in advance and optimize the energy allocation due to the prediction mechanism, thus effectively reducing the energy consumption while ensuring coverage. As a result, it has the best performance among the four algorithms, with the average energy consumption of users remaining stable at around 710 mAh. In contrast, the PPO algorithm has a relatively stable performance in terms of energy consumption, but due to the lack of the ability to adjust the strategy in advance, its energy consumption is slightly higher than that of RUDD, with an average energy consumption of about 800 mAh at about 30 users. With DDPG and Random algorithms it is difficult to effectively control energy consumption in dynamic environments since the algorithm's strategy is not reasonably limited. Both of them show an obvious decreasing trend, but the uncertainty of strategy selection leads to their energy consumption always being higher. The average user energy consumption of the DDPG and Random algorithms is reduced to 850 mAh and 825 mAh, respectively, when the number of users is 30. Therefore, when applied to offshore scenarios, the average energy consumption of users is reduced to 850 mAh and 825 mAh, respectively, when the number of users is 30.  
the RUDD algorithm can efficiently optimize energy utilization, and significantly improve the economy and durability of UAV operations.
# Flexible Vibrotactile Actuator Based on Dielectric Elastomer for Smart Handheld Devices
## 3 Experiments and Results
### Mechanical Properties of the Dielectric Layer  
We constructed an experimental setup consisting of a loadcell (Xforce P, Zwick/Roell Co., Ulm, Germany), a dielectric layer, a sample plate, a universal testing machine (UTM, Z0.5, Zwick/Roell Co., Ulm, Germany), and a personal computer (PC) to investigate the influence of Ecoflex on the mechanical properties of the dielectric layer, as shown in Figure 4. Each prepared dielectric layer was placed on the sample plate and pressed by the head of the UTM. During compression, we measured the pressing force using the loadcell and understood the position of the head using an encoder in the UTM. Subsequently, the pressing force and head position were fed to the PC. Figure 5 shows the hysteresis loops of all prepared samples after 100 repetitions (loading and unloading). The results show that the greater amount of Ecoflex used, the softer the dielectric layer became. Among the samples, the Young's modulus of pure PDMS (\(\text{dielect}_{1:0}\)) was much higher than that of the other samples.
## 4 Generative Modeling as a Distribution Approximation Process
### Practical Distortion and Perceptual Quality Measures  
In practical applications such as image quality assessment, two types of metrics focus on pixel quality and statistical properties, respectively. Distortion measures, such as mean squared error (MSE), structural similarity index (SSIM), its multiscale variant (MS-SSIM) [77], and peak signal-to-noise ratio (PSNR), require a ground-truth image for comparative analysis (see Figure 1c). Traditional video codecs are designed to efficiently remove spatial-temporal redundancies. Current coding standards, including H.264/AVC [78], H.265/HEVC [79], and H.266/VVC [80], achieve efficient compression through rate-distortion optimization, where the rate is represented by bits-per-pixel (BPP) and distortion is measured by PSNR.  
In contrast, perceptual quality measures focus on the statistical properties of images. For example, the Frechet Inception distance (FID) [81] uses the Inception Network to assess image quality without a reference image by comparing the distributions of generated and real images used to train the generator. The learned perceptual image patch similarity (LPIPS) [82] computes the distance between features in the latent space of a trained neural network and has been shown to effectively capture the underlying semantics of the data. In [83], an unsupervised perceptual information metric (PIM) is proposed using information-theoretic objectives. It is common practice, especially in the domain of LLMs, to employ a powerful model as a judge of semantic similarities. This judge can be conceptualized as a function, and the optimal rate of lossless functional compression can be determined using graph entropy [84; 85; 86].  
Learning-based compression methods not only consider pixel-wise signal quality metrics such as PSNR but also emphasize perceptual quality. These methods are increasingly outperforming standard codecs in many cases. Inspired by the information-theoretic setting [31], ref. [3] employs a GAN-based model to optimize the RD trade-off under the constraint that reconstructed samples follow the training data distribution. Source coding based on diffusion models is also gaining prominence [87; 88]. In [89], a posterior sampling-based compression (PSC) method using diffusion is proposed, with two configurations: PSC-Perception and PSC-Distortion, focusing on high perceptual quality and low-distortion reconstructions, respectively. With the increasing demand for processing longer contexts, significant research has also been devoted to the RD trade-off in text compression using large language models (LLMs) [90; 91; 92].
## 2 Experimental Testing
### Experimental Result
#### 2.2.3 Temperature Variation  
If the temperature changes when the lining is not limited by the outside and freely stretched, then the lining does not produce thermal stress; however, in the actual project of SSL repair, port is considered to have fixed end constraints due to welding. In the test, the end was also constrained by the fixed end; thus, the liner could not expand and contract freely when the temperature changed, and thermal stress was generated in the pipeline. The axial elastic modulus of SSL is sufficient, and the temperature change in a small range will also cause greater temperature stress. The temperature stress will make the welding part and the port connection part produce more significant local deformation or even damage, thus the thermal stress analysis of the lining is a necessary process. Figure 9 shows the temperature-change curve in the sealed cavity due to work done to the outside during the negative pressure test of groups T1-T5. During the test, the temperature decreased by 1.4, 0.7, 2.5, 0.5 and 2.5 \({}^{\circ}\)C, respectively.  
\[\sigma_{z}=\frac{p}{A}=aE_{z}\Delta T \tag{1}\]  
Figure 8: Reduction of the flow section in several buckling modes.  
Figure 9: Temperature curve of lining during test.  
Based on Equation (1), the change value of axial stress caused by temperature drop is 3.276, 1.638, 5.85, 1.17 and 5.85 MPa, respectively.
# Prediction of Tunnel Blasting Vibration Velocity Considering the Influence of Free Surface
## 3 Effect of Free Surface on Tunnel Blast Vibration
### Analysis of Test Data  
The experimental test results are summarized in Table 2, while a box-line diagram is drawn, as shown in Figure 4. According to Table 2 and Figure 4, the peak distribution of vibration velocity varied with the number of blastholes. The average vibration velocity is very near when the free surface and burden are the same, and the average vibration velocities of groups B and C are 0.64 cm/s and 0.55 cm/s, respectively, according to the analysis of the results for a total of 48 groups of data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Points** & \multicolumn{5}{c}{**Vibration Velocity of Measuring Point/(cm/s)**} & **Distance/m** \\ \cline{2-7}
**Number** & **A** & **B** & **C** & **D** & **E** & **F** & \\ \hline
1 & 2.37 & 1.36 & 1.13 & 1.99 & 1.61 & 1.00 & 11.20 \\
2 & 1.73 & 0.83 & 0.65 & 1.49 & 1.00 & 0.56 & 20.30 \\
3 & 0.91 & 0.64 & 0.55 & 0.62 & 0.51 & 0.41 & 39.30 \\
4 & 0.87 & 0.56 & 0.48 & 0.56 & 0.45 & 0.26 & 50.50 \\
5 & 0.64 & 0.42 & 0.31 & 0.42 & 0.31 & 0.22 & 64.20 \\
6 & 0.33 & 0.23 & 0.18 & 0.19 & 0.15 & 0.11 & 79.80 \\
7 & 0.27 & 0.17 & 0.15 & 0.18 & 0.17 & 0.10 & 110.80 \\
8 & 0.12 & 0.06 & 0.03 & 0.10 & 0.05 & 0.03 & 140.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test data statistics.  
Figure 3: Detonation sequence and blasting free surface change.  
However, the propagation of the vibration waves is greatly impacted by changes in the number of free surfaces and burdens. The most significant aspect is that the vibration velocity decreases as the number of free surfaces increases. When the number of free surfaces increases from one to four, the average vibration velocity of group A decreases from 0.91 cm/s to 0.41 cm/s in group F. Groups D and E have the same burden, and the average vibration speed decreases from 0.62 cm/s to 0.51 cm/s when the number of free surfaces increases by one.  
Meanwhile, the burden also affects the propagation of the vibration velocity. Despite the same number of free surfaces in groups C and D, the average vibration velocity rises from 0.55 cm/s to 0.62 cm/s.
## 4 Feeder Selection with Reinforcement Learning  
Central to the concept of RL is the notion of action rewards, which needs to be formulated for the problem context. Considering that \(N\) links are available to forward packets, the VNF emits periodic requests at regular intervals (\(dt\) seconds) to the switch to evaluate those links through a request/reply packet exchange, the time difference between which indicates the response time \(d_{i}^{dt}\) of each link \(i\), \(i=1,2,\ldots,N\) at the interval \(dt\). The difference between the number of request departures and reply arrivals indicates the packet loss rates on the links. The packet loss rate, \(pkt\_loss\_rate_{i}^{mt}\) is measured  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Entities** & **Description** \\ \hline Automaton & \(\{\phi\), \(\lambda\), \(X\), \(Pr\), \(G\), \(A\}\) \\ Input & \(X\in\{0,1\}\) \\ Internal state-\(\phi\) & \(\{\phi_{1}\), \(\phi_{2}\), \(\ldots\), \(\phi_{s}\}\) \\ Output-\(\lambda\) & \(\{\lambda_{1}\), \(\lambda_{2}\), \(\ldots\), \(\lambda_{r}\}\) \\ \(Pr\) & state probability vector at each instant \(t\) \\ Output function & \(G:\phi\rightarrow\lambda\) \\ algorithm **A** & computes \(\phi(t+1)\) from \(\phi(t)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Entities and descriptions.  
on the links at an interval of \(mt\) seconds. Algorithms 1 and 2 indicate the proposed procedure for updating the delay and loss metrics by separate threads. The moving exponential average values of both metrics are combined to produce the expected link cost, \(cost_{i}^{ft}\) at each flow modification interval \(ft\) by another thread as described in Equation (1).  
\[cost_{i}^{ft}=k\,pkt\_loss\_rate_{i}^{ft}+(1-pkt\_loss\_rate_{i}^{ft_{1}})\ d_{i}^{ft}\,\ \forall i=1\,\dots\,N \tag{1}\]  
where \(k\) is an arbitrary penalty constant, e.g., it may include the retransmission time [24]. At each interval \(ft\), the average cost, \(a\_cost_{i}^{ft}\) is updated via exponential smoothing with the hyperparameter \(\alpha\), \(0\leq\alpha\leq 1\) as given in Equation (2).  
\[a\_cost_{i}^{ft}\leftarrow\alpha\,cost_{i}^{ft}+(1-\alpha)\,a\_cost_{i}^{ft-1} \,\ \forall i=1\,\dots\,N \tag{2}\]  
This cost (i.e., a negative reward) is used to modify the routing policy of the feeder links as explained in the next sections.  
```
1:while true do
2:for\(i=1\) to \(N\)do
3:\(d_{i}^{dt}\leftarrow\alpha\ \bar{d}_{i}^{dt}+(1-\alpha)\ d_{i}^{dt-1}\)
4:endfor
5:Pause for \(dt\) seconds
6:
7:endwhile
8:
```  
**Algorithm 1** Delay Computation  
```
1:while true do
2:for\(i=1\) to \(N\)do
3:for\(i=1\) to \(N\)do
4:\(pkt\_loss_{i}^{mt}\gets tx\_pkt_{i}^{mt}-rx\_pkt_{i}^{mt}\)
5:\(avgpkt\_loss_{i}^{mt}\leftarrow\alpha\,pkt\_loss_{i}^{mt}+(1-\alpha)\,avgpkt\_ loss_{i}^{mt-1}\)
6:\(avggtx\_pkt_{i}^{mt}\leftarrow\alpha\,tx\_pkt_{i}^{mt}+(1-\alpha)\,avgtx\_pkt_{i}^{ mt-1}\)
7:\(pkt\_loss\_rate_{i}^{mt}\leftarrow\frac{avgpkt\_loss_{i}^{mt}}{avggtx\_pkt_{i}^{mt}}\)
8:endfor
9:Pause for \(mt\) seconds
10:endwhile
11:
```  
**Algorithm 2** Packet-loss Computation
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
###### Abstract  
An efficient location registration scheme is essential to continuously accommodate the increasing number of mobile subscribers and to offer a variety of multimedia services with good quality. The objective of this study was to analyze the optimal size for the location area of a distance-based registration (DBR) scheme by varying the number of location areas on a cell-by-cell basis, not on a ring-by-ring basis. Using our proposed cell-by-cell distance-based registration scheme with a random walk mobility model, a variety of circumstances were analyzed to obtain the optimal number of cells for location area for minimizing the total signaling cost on radio channels. Analysis results showed that the optimal number of cells for location area was between 4 and 7 in most cases. Our cell-by-cell distance-based location registration scheme had less signaling cost than an optimal ring-by-ring distance-based location registration scheme with an optimal distance threshold of 2 (the optimal number of cells for location area was 7). Therefore, when DBR is adopted, it must be implemented with an LA increasing on a cell-by-cell basis to achieve optimal performance.  
location registration; distance-based registration; cell-by-cell location area; semi-Markov process; optimal location area +
Footnote †: journal: Applied  
15 June 2021  
Accepted: 22 July 2021  
Published: 24 July 2021
## 2 Results and Discussion
### Real Samples Analysis
Terpenes (such as a-pinene and limonene) were also detected. These compounds have been previously detected in the respective nuts as well as in almond drink [5; 8; 9]. Pyrazines and furfurals that were detected at the samples can be attributed to Strecker reaction due to the thermal treatment, while furans can be attributed to Maillard reaction [5]. Differences among the samples can be attributed both to the different nuts used for the drinks' production and to the different parameters of the drinks' production. Compounds such as styrene were not previously reported in these kinds of products, and therefore migration of the packaging may have occurred [26]. Since commercial samples were analyzed, addition of flavor enhancers may be another reason for the variation of type and concentration of some volatiles. Figure 6 shows a representative chromatogram obtained from walnut drink analysis. These compounds form the total volatile profile of these products containing not only chemical compounds that form the product's flavor, but also other volatiles that are present in such drinks, since extraction was performed at 60 \({}^{\circ}\)C.  
In order to evaluate the chemical compounds that are responsible for the product's flavor, extraction was performed at lower temperature. An extraction temperature of 37 \({}^{\circ}\)C may better simulate flavor release from the mouth [27]. For Almond Sample 1, analysis was also performed after extracting the volatile compounds at lower temperature (37 \({}^{\circ}\)C) instead of the optimum temperature that was finally chosen (60 \({}^{\circ}\)C). The compounds that were detected after extraction at this temperature are also shown in Table 3. Twenty-six volatile compounds could be detected at this extraction temperature; most of them were aldehydes and ketones. As a result, by reducing extraction temperature, the volatile compounds of nut derived beverages, that are responsible for the drink's flavor, can be determined with this method.
# The Impact of Distributed Energy Storage on Distribution and Transmission Networks' Power Quality
###### Abstract  
This study investigates the effect of distributed Energy Storage Systems (ESSs) on the power quality of distribution and transmission networks. More specifically, this project aims to assess the impact of distributed ESS integration on power quality improvement in certain network topologies compared to typical centralized ESS architecture. Furthermore, an assessment is made to see if the network topology in which an ESS position supports its ability to restore node voltage magnitude within acceptable ranges. The power quality of a benchmark interconnected distribution and transmission network was determined using NEPLAN software. Following that, twelve variants of the benchmark were modeled, each with a different ESS integration architecture and (or) topology. Their power quality performance was compared to that of a benchmark network in addition to several cross analyses to determine the relative impact on power quality within the context of their respective ESS integration methodologies. The findings of this study buttress the understanding that the distributed ESS integration architecture within the distribution network topology, where the majority of consumer loads are connected, provides the strongest case for voltage magnitude power quality compensation, as required by the UK Electrical System Grid Code's 5% rated node voltage compliance processes regulation.  
energy storage; NEPLAN; distribution network; transmission network; battery energy storage system +
Footnote †: journal: Applied Sciences