esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 3 Related Work
Although these service-differentiated routes could pass through the PU activity area, the routing metric did not consider spectrum opportunity fluctuation due to mobility, nor did it consider the effect of mobility on the stability of the built routes. In order to reduce the computational overhead at the destination, CRP uses CCC for signaling purposes. On the contrary, MORR uses the spatio-temporal opportunities that are induced by mobility to compensate the reduced channel access opportunity for routes that have to go through PU coverage. While for routes that are built out of the PU coverage, it considers a mobility-induced guard (mguard) distance in addition to the keep-out distance, which ensures adequate primary transmission protection based on the interference temperature limit (ITL) and significantly improves the throughput thereof. The mobility-assisted routing algorithm with spectrum awareness (MARSA) [20] was proposed to select relays based on both the probability that a node meets the destination and the chance of the existence of at least one available channel when they meet. MARSA, which was mainly motivated by analyzing the real-world phenomenon and trace data of human mobility habits, is based on the deduced rule that the approximately regular PU behaviors result in the approximate regularity of the mobility of the spectrum that could be available for SUs. Hence, it basically identifies a set of channels according to location and sets them as home channels. A central server is used to always update nodes about this channel. When a node comes into the area, it automatically switches to the available home channel for communication. Otherwise, it stores the information until it enters a location where a free home channel exists; only then will it transmit its data. It is important to note that this study only considers a network scenario of static PUs and mobile CRSN nodes. The scenario wherein mobile PUs and static SUs are considered [21] cannot be freely applied to the considered study. This is because, when the CRSN nodes are static in the presence of mobile PUs, the distance between the CRSN nodes (which is a prime function of accurate channel estimation when source-to-sink routing is considered) remains constant with time. This is quite different from the case where the CRSN nodes are mobile, which makes the channel estimation availability vary according to the distance of the next-hop selection, which also varies with time. Furthermore, PU protection, which is also a main concern of this work, was not considered in [21]. Table 1 presents a table of recurring symbols that have been used in developing and analyzing MROR.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 4 Case Application
### Calculation of Track Prediction Error
#### 4.1.3 Track Prediction  
Based on the aforementioned track classification results, track prediction studies are conducted for each category. Initially, each category of track data needs to be segmented by selecting 60% as the training set, 20% as the validation set, and 20% as the test set. The Adam-LSTM prediction model is chosen for this study. In this research, the LSTM model takes data from 10 track points as input and predicts data for 1 future track point. Track predictions are carried out for each category, and the track prediction effect is shown in Figure 8.  
Figure 8: Examples of comparison of the predicted value with the true value.  
Figure 7: Examples of UAV track data classification.  
Based on the provided track prediction graph, it is intuitively observed that the predicted track is very close to the actual track, indicating that the prediction model is effective.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 4 Fault Ride-Through Control Strategies
### HVRT Strategy in Pumping Condition  
Under pumping conditions, the unit behaves such as an adjustable load. An increase in grid voltage leads to a significant rise in the power consumed by the pump. Simultaneously, due to the overmodulation of the converter, there will be an overvoltage on the DC side. The correlation between pump power and speed is detailed in Equation (19).  
\[\frac{P_{1}}{P_{2}}=\left(\frac{n_{1}}{n_{2}}\right)^{3} \tag{19}\]  
where subscripts 1 and 2 represent two operating points at different speeds. \(P\) is the power consumed by the pump, and \(n\) is the unit speed, with pump power proportional to the cube of the unit speed. Therefore, a slight adjustment in speed can result in a significant power adjustment. During high voltage conditions, rapidly increasing the pump speed can also help to consume the increase in grid-side power caused by the increase in grid voltage. The rapid acceleration strategy of the pump is the same as the rotor kinetic energy control strategy shown in Figure 8. In addition, it can also be used to reduce DC overvoltage. Similar to power generation conditions, reactive power compensation and crowbar circuits are also applicable.
# Technological Properties of Tritordeum Starch
## 3 Results and Discussion
### Morphology of Starch Granules  
Microscopic observations of starch grains allow, to some extent, the determination of the botanical origin of starch, but also enable measurement of their size and shape [10].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Color Parameters [-]**} \\ \cline{2-5}  & **L*** & **a*** & **b*** & **WI** \\ \hline BaS & 83.81 \(\pm\) 0.17 a & 0.59 \(\pm\) 0.02 d & 5.79 \(\pm\) 0.08 e & 82.80 \(\pm\) 0.19 a \\ BuS & 86.17 \(\pm\) 0.03 c & 0.45 \(\pm\) 0.01 c & 5.30 \(\pm\) 0.04 d & 85.18 \(\pm\) 0.04 b \\ TrS & 84.21 \(\pm\) 0.05 b & 0.43 \(\pm\) 0.01 c & 6.51 \(\pm\) 0.03 f & 82.91 \(\pm\) 0.06 a \\ WaS & 87.37 \(\pm\) 0.00 d & 0.56 \(\pm\) 0.01 d & 1.94 \(\pm\) 0.02 b & 87.21 \(\pm\) 0.01 c \\ WdS & 88.09 \(\pm\) 0.95 e & 0.15 \(\pm\) 0.13 b & 0.96 \(\pm\) 0.58 a & 88.05 \(\pm\) 0.86 d \\ WhS & 90.00 \(\pm\) 0.02 f & \(-\)0.11 \(\pm\) 0.01 a & 2.13 \(\pm\) 0.01 c & 89.77 \(\pm\) 0.02 e \\ \hline \hline \end{tabular} Values with similar superscripts in the column do not differ significantly (_p_ < 0.05). L*—lightness, a*—color parameter ranging from green to red; b*—color parameter ranging from blue to yellow; WI—bitbitness index.  
\end{table}
Table 2: Color parameters of investigated starches.  
In addition, they make it possible to determine the presence of any damage that the granules may have suffered. Micrographs of tritordeum starch (TrS) are shown in Figure 1, whereas barley (BaS) and wheat starch (WhS) are in Figure 2. In the case of the TrS granule population, both large and small granule fractions can be observed, but the presence of small granules in TrS is much more noticeable than in the case of WhS. TrS granules have a slightly flattened spherical shape, but grains with a more elongated shape, as well as clearly flattened or lenticular shapes, can also be observed. The surface of the granules appears to be smooth, without any visible cracks, gaps, or depressions. The shape of TrS granules resembles wheat starch granules more than barley starch ones (Figures 1 and 2).  
Figure 1: Scanning electron micrographs of tritordeum starch observed at different magnifications (**A**—700\(\times\); **B**—1400\(\times\); **C**—2100\(\times\); **D**—2300\(\times\)).  
Figure 2: Scanning electron micrographs of barley (**A**—350\(\times\) and **B**—1500\(\times\)) and wheat (**C**—400\(\times\) and **D**—1500\(\times\)) starches observed at different magnifications.
## 2 Analysis and Experimental Methods of Correlating Toughness and Hydraulic Resistance
### Test Method
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Specimen** \\ **Types** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cohesive** \\ **[N/mm\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Avg.** \\ **Cohesive** \\ **Strength** \\ **[N/mm\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Elongation** \\ **[\%]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **[\%]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Toughness** \\ **[N/mm\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Toughness** \\ **[N/mm\({}^{2}\)]** \\ \end{tabular} } \\ \hline \multirow{7}{*}{Type A} & \multirow{2}{*}{5} & 0.0170 & & 1297 & & 0.0138 & \\  & 0.0175 & 0.0195 & 1002 & 1141 & 0.0110 & 0.0123 \\  & & 0.0242 & & 1125 & & 0.0122 & \\ \cline{2-7}  & \multirow{2}{*}{10} & 0.012 & & 1221 & & 0.0105 & \\  & & 0.0097 & 0.0115 & 1571 & 1400 & 0.0119 & 0.0117 & \\  & & 0.0128 & & 1398 & & 0.0129 & \\ \cline{2-7}  & \multirow{2}{*}{20} & 0.0125 & & 106 & & 0.0035 & \\  & & 0.0134 & 0.0136 & 849 & 695 & 0.0038 & 0.0033 \\  & & 0.0148 & & 1129 & & 0.0026 & \\ \hline \multirow{7}{*}{Type B} & \multirow{2}{*}{5} & 0.0236 & & 1589 & & 0.0298 & \\  & & 0.0169 & 0.0213 & 2013 & 1409 & 0.0359 & 0.0294 \\  & & 0.0237 & & 626 & & 0.0226 & \\ \cline{2-7}  & \multirow{2}{*}{10} & 0.0194 & & 1358 & & 0.0167 & \\  & & 0.0276 & 0.0251 & 1373 & 1369 & 0.0324 & 0.0270 \\  & & 0.0284 & & 1377 & & 0.0318 & \\ \cline{2-7}  & \multirow{2}{*}{20} & 0.0220 & & 804 & & 0.0059 & \\  & & 0.0179 & 0.0186 & 959 & 811 & 0.0051 & 0.0057 \\  & & 0.0160 & & 671 & & 0.0060 & \\ \hline \multirow{7}{*}{Type C} & \multirow{2}{*}{5} & 0.0327 & & 1602 & & 0.0379 & \\  & & 0.0282 & 0.0296 & 305 & 806 & 0.0271 & 0.0309 \\  & & 0.028 & & 512 & & 0.0276 & \\ \cline{2-7}  & \multirow{2}{*}{10} & 0.0375 & & 532 & & 0.0377 & \\  & & 0.036 & 0.0342 & 290 & 583 & 0.0260 & 0.0295 \\ \cline{2-7}  & & 0.0291 & & 928 & & 0.0247 & \\ \cline{2-7}  & \multirow{2}{*}{20} & 0.0251 & & 1881 & & 0.0392 & \\  & & 0.0143 & 0.0216 & 1817 & 1667 & 0.0336 & 0.0368 \\  & & 0.0253 & & 1304 & & 0.0377 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurement result based on specimen types (2 mm thickness).
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### RFI Power Affecting the Pixel after SAR Processing
#### 2.4.3 Discussion on GEO-TO-LEO RFI and LEO-TO-GEO RFI  
According to the derivations above, the power of GEO-to-LEO RFI and LEO-to-GEO RFI affecting the pixel after SAR processing can be given in the same form, written as  
\[P_{RFI}=\frac{P_{ti}G_{i}\sigma_{p}B_{As}C_{GaAs}{f_{ss}}^{2}T_{p}C_{aLEO}R_{ LEO}}{32\pi^{2}R_{GEO}{{}^{2}R_{LEO}}^{2}|k_{GEO}-k_{LEO}|\sin\theta_{s}} \tag{24}\]  
where \(P_{ti}\) and \(G_{i}\) are the peak transmitted power and antenna gain of RFI source SAR system. \(A_{rs}\), \(f_{ss}\), and \(\theta_{s}\) are the antenna aperture area, sampling rate and the incidence angle of the disturbed SAR. These symbols have different meanings in different cases; for example, \(P_{ti}\) represents the peak transmitted power of GEO SAR when GEO SAR is the source of interference, while it represents the peak transmitted power of LEO SAR when LEO SAR is the source of interference. Similarly, \(\theta_{s}\) represents the incidence angle of LEO SAR when GEO SAR is the source of interference, while it represents the incidence angle of GEO SAR when LEO SAR is the source of interference. Furthermore, \(G_{a}\) and \(T_{p}\) are given by Table 1 and (12), respectively.  
Differences in power calculation between GEO-to-LEO RFI and LEO-to-GEO RFI mainly lie in azimuth gain, considering spectrum aliasing and the interference zone widening caused by different range migration. On one hand, to solve the spectrum aliasing, a numerical simulation is used and a unified symbol \(G_{a}\) is used to replace the entire numerical simulation. On the other hand, in the case of LEO-to-GEO RFI, the interference zone widening caused by range migration generates a change in some intermediate variables (such as the range direction width of the interference zone and the azimuth gain of RFI), but the total energy remains unchanged. Therefore, the LEO-to-GEO RFI power can still be simplified to the same form as the GEO-to-LEO RFI. Moreover, we analyzed the RFI from
## 3 Model Establishment
### Data Preprocessing  
Due to errors in data collection, there may be some unreasonable values in the original data. These unreasonable data will decrease the prediction accuracy, which is not conducive to establishing the model to predict wind power. Besides, data preprocessing can provide a good preliminary understanding of the data, so the first step of the model is to preprocess the data.  
* Data cleaning: There are outliers in the original data. For example, there are some points in the original data, whose corresponding wind speed is equal to zero, but the corresponding wind power is smaller than zero. It is obvious that these data are unreasonable and need to be cleaned.
* Missing data filling: After data cleaning is completed, there will be gaps in the data. It is necessary to fill these missing values, because if the filling of missing values is reasonable, the prediction accuracy of the model can be improved. The k-Nearest Neighbor (KNN) algorithm is adopted to find the nearest neighbor in the time series to fill the missing values in the data.
* Data normalization: The purpose of data normalization is to reduce the influence of order of magnitude on the model, accelerate the training speed of the model, and make the model more accurate than before. Min-max normalization maps data between zero and one, whose transformation formula is described by Equation (5): \[x=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}\] (5)  
Data cleaning, filling of missing data, and data normalization constitute data preprocessing. The three steps above are significant for improving the prediction accuracy of the model.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 4 Adaptive GLAN Using Reinforcement Learning
### System Environment  
An agent performs learning and continuously interacts with the environment, so the environment must be configured well. In this system, the FR angle changes as the episode progresses and determines the network coverage range. In the proposed system, it is initially planned to train using 100 episodes of 100-step functions. This subsection provides a detailed description of the state, action, and reward.
###### Abstract  
Reconfigurable intelligent surfaces (RISs), which consist of numerous passive reflecting elements, have emerged as a prominent technology to enhance energy and spectral efficiency for future wireless networks. RISs have the capability to intelligently reconfigure the incident wave, reflecting it towards the intended target without requiring energy for signal processing. Consequently, they have become a promising solution to support the demand for high-throughput satellite communication (SatCom) and enhanced coverage for areas inaccessible to terrestrial networks. This paper presents an asymptotic analysis of an RIS-assisted SatCom system. In this system, an unmanned aerial vehicle equipped with an RIS operates as a mobile reflector between a satellite and users. In particular, a passive beamformer is designed with the aim of asymptotically attaining optimal performance, considering the limitations imposed by practical SatCom systems. Moreover, the closed-form expressions for the ergodic achievable rate and outage probability are derived considering the proposed passive beamforming technique. Furthermore, we extend the system model to a multicast system and asymptotically analyze the optimality of the proposed scheme, leveraging the derived asymptotic results in the unicast system. The results of the simulations confirm that our analyses can precisely and analytically assess the performance of the RIS-assisted SatCom system, confirming the asymptotic optimality of the proposed scheme.  
satellite communication; reconfigurable intelligent surface (RIS); passive beamforming; outage probability  
**MSC:** 94A12
# Topology Optimisation in Structural Steel Design for Additive Manufacturing
## 4 Approaches, Methods, Criteria, and Software in Topology Optimisation
### Practical Methodologies  
Unsurprisingly, the literature devoted to TO is profuse in depicting theoretical approaches and methods, as well as in validating it with well-known or trivial cases, but much scarcer in providing comprehensive and holistic methodologies (which contain practical aspects) for implementing a TO strategy into the Engineering design. This is related not only to researchers' tendency to publish their work and industrial practitioners and developers not doing so, but also to the fact that TO is still in a stage of developing and stabilising methods before a universal application by non-experts. Moreover, Topology Optimisation is still much more complex than the regular structural analysis and design in most engineering fields, requiring a significant time, studying and computational resources that limit the number of large-scale projects currently being developed.  
One further issue lies in TO objectives. While academic developments must seek assurance of optimisation to the theoretical extrema, industrial applications are usually comfortable with optimisation to a certain pre-defined threshold and value reliability, predictability, reproducibility and, frequently, computing efficiency above all. However, avoiding local extrema is a common goal.  
Nevertheless, some examples were found in a recently published literature, which can be referred to as interesting examples for conceiving case-specific methodologies for the systematic application of TO in structural steel design. Table 4 summarises those findings.  
\begin{table}
\begin{tabular}{c c} \hline
**OC Methods** & **References** \\ \hline Discretised, Continuum-type Optimality Criteria technique (DCOC) & [119; 325] \\ Continuum-based Optimality Criteria (COC) & [119; 326] \\ Iterative COC & [119; 327] \\ Design Optimisation Tools (DOT) & [123] \\ Sparse Nonlinear Optimiser (SNOPT) & [328] \\ Interior Point Optimiser (TOPT) & [329] \\ Convex Linearisation Method (CONLIN) & [42; 330; 331] \\ Method of Moving Asymptotes (MMA) & [30; 332] \\ Globally Convergent Method of Moving Asymptotes (GCMMA) & [333] \\ \hline \end{tabular}
\end{table}
Table 3: Frequently used optimality criteria methods.
###### Abstract
The low end-to-end delay in AR/VR devices arises due to the steps involved in sensor sampling, data processing and fusion, image rendering and encoding, transmission and decoding, and displaying of each frame. These are some of the main causes for the occurrence of the motion-to-photon (MTP) latency, as shown in Figure 1. Therefore, innovative network designs, technologies, methods, and devices must be considered to improve the traditional AR/VR systems by providing an immersive seamless VR experience with extremely low end-to-end latency. However, it is required to keep the MTP latency under 20 ms as the VR cloud services are extremely sensitive to low latency, which may cause dizziness for the users. It is challenging to maintain a low MTP latency as VR terminals have to undergo a serial process of motion capture, logic computing, picture rendering, and screen display. Hence, despite the extensive advances in AR/VR technologies, MTP latency [14] acts as the greatest barrier by holding back AR/VR adoption from proving a fully immersive experience and offers limitations in the field of view due to occlusion, as well as poor display quality. A very minimal MTP latency of less than 7 ms for head-mounted display (HMD) users is required to avoid having an unrealistic experience of the virtual world [15]. It has been proven [16] that a latency of 5 ms or less for both AR and VR should be experienced to have a decent effect on users without causing any motion sickness. When the data from the HMDs fail to register spatially and temporally with the user's view of the surroundings, an effect called misregistration occurs. This phenomenon is a result of a mismatch in measuring the time consistency in tracking data displayed on the HMD considering the location and orientation of the controllers [17]. Figure 1 presents a brief description of the various types of errors arising from MTP latency in AR/VR devices. Three types of major errors, namely alignment, tracking, and rendering error, arising from MTP in HMDs along with their quantity and the technique needed to mitigate these errors are mentioned in this figure. Some hardware devices, such as optical see-through (OST) virtual headsets and simulator systems, are commonly used to translate the virtual content into the real world. However, they can also be affected by latency in the form of misregistration. Several techniques, such as time warping [17] or post-render warping [18], have been proven to improve the average quality, as well as the consistency of latency on both the display and the rendering engine. With the unprecedented growth of VR HMDs following the announcement of the Meta Oculus [15], there still exists a gap between the end-to-end delay and a reliable wireless connection in AR/VR technology. As the latency measured in the Oculus Quest 2 is around 50-70 ms [19], people usually resort to Air Link, which is an external USB cable, to minimize the latency by 10 ms. The latency for hand gestures while playing games, even after using Air Link, remains at 29 ms, making the delay seem pretty high.
## 2 Traditional Canny Algorithm and Its Improvement
### Improved Canny Algorithm
#### 2.2.1 Bilateral Filtering  
Bilateral filtering is a non-linear filtering method [15]. It combines the information of spatial neighborhood and gray value similarity and can retain edge information while removing noise. This method is particularly suitable for noise reduction scenarios where edges need to be retained. The bilateral filter is composed of two Gaussian filters. One is used to calculate the weight of the spatial proximity of the image, and the other is used to calculate the weight of the similarity of the image pixel values [16]. The expression of the bilateral filter is shown in Equation (1).  
\[\mathrm{B(i,j,k,l)}=\exp\left(-\frac{\left(\mathrm{i-k}\right)^{2}+\left( \mathrm{j-l}\right)^{2}}{2\sigma_{\mathrm{s}}^{2}}\right)\times\exp\left(- \frac{\left|\mathrm{M(i,j)}-\mathrm{M(k,l)}\right|^{2}}{2\sigma_{\mathrm{r}}^{ 2}}\right) \tag{1}\]  
In the equation, \(\mathrm{(i,j)}\) and \(\mathrm{(k,l)}\) denote the coordinates of the center pixel and neighboring pixels, respectively; \(\mathrm{M(i,j)}\) and \(\mathrm{M(k,l)}\) denote their pixel values, respectively; \(\sigma_{\mathrm{s}}\) and \(\sigma_{\mathrm{r}}\) are the standard deviations of the null and value domains, respectively; and \(\exp\) represents the exponential function with the natural logarithm \(\mathrm{e}\) as the base.