esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Free-Space Optical Network Optimization
###### Abstract  
This paper presents a set of graph optimization problems related to free-space optical communication networks. Such laser-based wireless networks require a line of sight to enable communication, thus a visibility graph model is used herein. The main objective is to provide connectivity from a communication source point to terminal points through the use of some subset of available intermediate points. To this end, we define a handful of problems that differ mainly in the costs applied to the nodes and/or edges of the graph. These problems should be optimized with respect to cost and performance. The problems at hand are shown to be NP-hard. A generic heuristic based on a genetic algorithm is proposed, followed by a set of simulation experiments that demonstrate the performance of the suggested heuristic method on real-life scenarios. The suggested genetic algorithm is compared with the Euclidean Steiner tree method. Our simulations show that in many settings, especially in dense graphs, the genetic algorithm finds lower-cost solutions than its competitor, while it falls behind in some settings. However, the run-time performance of the genetic algorithm is considerably better in graphs with 1000 nodes or more, being more than twice faster in some settings. We conclude that the suggested heuristic improves run-time performance on large-scale graphs and can handle a wider range of related optimization problems. The simulation results suggest that the 5G urban backbone may benefit significantly from using free-space optical networks.  
urban free-space optical communication; 5G backbone optimization; genetic algorithm +
Footnote †: journal: Applied Soft. 2020, 10, 7872; doi:10.3390/app10217872
## 2 Theory
### Equivalent Circuit Parameter Calculation  
Although there are five variables and six equations in Equations (7)-(12), it does not mean that each parameter of the equivalent circuit can be obtained independently. \(X_{m}\), \(X_{r}\), and \(R_{r}/s\) form series/parallel circuits. The equivalent circuit is a series connection of resistor and reactance. The resistance is shown in Equation (2), where it can be seen that \(R_{s}\) and \(R_{r}\) are independent of each other and their solutions can be obtained separately. The reactance part is shown in Equation (3), and is equivalent to a fixed reactance and a reactance that changes with the slip rate due to the series/parallel circuit. The fixed reactance will be combined with the stator reactance as a complete reactance so that the three reactances cannot be obtained separately. This article set the distribution of reactance to a specific ratio \(\eta\), i.e.,  
\[\eta=\frac{X_{m}+X_{r}}{X_{m}+X_{s}}. \tag{30}\]  
The general \(\eta\) value was about 0.95 to 1.05. Introducing Equations (7)-(12) into \(\eta\) values  
\[\frac{\alpha_{2}}{\alpha_{2}\beta_{3}-\beta_{4}}=\frac{(X_{m}+X_{r})^{2}/R_{r} ^{2}}{X_{m}^{2}(X_{m}+X_{r})/R_{r}^{2}}=\frac{X_{m}+X_{r}}{X_{m}^{2}}=\frac{ \eta\beta_{3}}{X_{m}^{2}}. \tag{31}\]  
The excitation reactance \(X_{m}\) is  
\[X_{m}=\sqrt{\frac{\eta\beta_{3}(\alpha_{2}\beta_{3}-\beta_{4})}{\alpha_{2}}}. \tag{32}\]  
Stator reactance \(X_{s}\) is  
\[X_{s}=\beta_{3}-X_{m}. \tag{33}\]  
Rotor reactance referred to the stator side \(X_{r}\) is  
\[X_{r}=\eta(X_{m}+X_{s})-X_{m}=\eta\beta_{3}-X_{m}. \tag{34}\]Rotor resistance referred to the stator side \(R_{r}\) is  
\[R_{r}=X_{m}^{2}/\beta_{1}. \tag{35}\]  
This article uses an optimized method to estimate \(R_{s}\) as  
\[R_{s}=\frac{\sum\limits_{n=1}^{N}\left[(1+\alpha_{2}s_{n}^{2})(\beta_{0}+\beta_ {2}s_{n}^{2})\right]}{\sum\limits_{n=1}^{N}\left(1+\alpha_{2}s_{n}^{2}\right)^ {2}}. \tag{36}\]
## 4 The Verification of CGH
### Kinoform CGHs  
Figures 23-25 illustrate the flowchart of Kinoform CGH encoding, Kinoform CGHs under varying Gaussian noise coefficients (\(ef_{1}\) = 0.0-1.0), and Kinoform reconstructed images under varying Gaussian noise coefficients (\(ef_{1}\) = 0.0-1.0). Kinoforms CGHs encode information as relief-phase profiles on recording elements.  
Figure 23: The flowchart of Kinoform CGH encoding.  
Figure 24: Kinoform CGHs under varying Gaussian noise coefficients (\(ef_{1}\) = 0.0–1.0) (Part).  
Figure 25: Kinoform reconstructed images under varying Gaussian noise coefficients (\(ef_{1}\) = 0.0–1.0).  
As shown in Figure 26, noise coefficients exhibit distinct impacts on image quality metrics. PSNR declines continuously from 16.59 dB to 12.43 dB (25% reduction) as noise increases from 0.1 to 1.0, with the steepest drop (10.2%) occurring at low noise (0.1-0.3), consistent with the inverse-square relationship between noise energy and SNR. At higher noise levels (0.5-1.0), the reduction narrows to 10.9% (13.95\(\rightarrow\)12.43 dB), indicating SNR saturation approaching noise dominance. In contrast, SSIM shows only a 4.0% decline (0.7551\(\rightarrow\)0.7248) across the noise range, with <1.0% reduction for coefficients \(\geq\) 0.5, highlighting its strong noise robustness.  
Mechanistic analysis reveals that PSNR sensitivity stems from its direct response to global energy variations, while SSIM stability benefits from preserved macroscopic structural features (edges, textures) under high noise. This discrepancy may be attributed to the experimental image characteristics (low texture, high contrast) and noise properties (uniform Gaussian distribution).
## Materials and Methods
### Process Feasibility Study  
The main purpose of the application of the iMBR set-up for the production of VFAs from food waste and excess sludge was the provision of the carbon source required for denitrification purposes in the WWT process. In this regard, two hypothetic scenarios were considered for the application of excess sludge and food waste as the substrate for VFAs production using the iMBR. The ultimate goal for both scenarios was to replace methanol used during denitrification with VFA permeate from the iMBR. The data used as the basis for the calculations and further comparison of different cases are based on actual average data provided by Gryaab AB (Gothenburg, Sweden) for a WWTP with processing stages presented in Figure 4. Figure 4 also considers the base scenario for the treatment of food waste for biogas production.  
The two scenarios for the integration of iMBRs into the VFAs production process using food waste and excess sludge are presented in Figures 5 and 6. In this scenarios, VFAs production from FWS and ESS in two AD MBRs working at an OLR of 3 gVS/L/day, hydraulic retention time of 10 days, and filtration flux of 7.5 L/m\({}^{2}\)/h were considered. In this regard, the economic feasibility calculations on the capital and operational expenses were made for conditions that the VFA solution obtained as permeate is fed as an external carbon source to the denitrification stage for complete replacement of the conventionally used methanol.  
Figure 4: An overview of the processing stages of the base WWTP and food waste treatment facility.  
Figure 5: An overview of the proposed scenario one.  
In order to have a general understanding of the economic feasibility of applying either of the scenarios to provide the required amounts of VFAs for methanol replacement, the following main parameters were considered in the estimation: membrane price (100 \(\upxi\)/m\({}^{2}\)), scouring gas demand per unit area of membrane per time (0.09 Nm\({}^{3}\)/m\({}^{2}\).h), specific energy demand for gas sparging (0.04 kWh/m\({}^{3}\)), membrane life (8 years), electrical energy cost (0.08 \(\upxi\)/kWh), blower energy constant (6.5), and cost of chemical cleaning solution (0.01 \(\upxi\)/m\({}^{3}\) based on NaOCl and citric acid). Considering the mentioned factors, the operation expenses for the applied MBR ranges between 0.13-0.35 euros per cubic meter of recovered permeate. The average process values are presented in the tables in Section 3.4. The prices noted for methanol and VFAs are average prices acquired in 2018 (provided by Gryaab AB and other suppliers).
## 2 Negative-Stiffness Device and Its Mechanical Model
### Parameter Impact Analysis
#### 2.3.4 Influence of Friction Coefficient, \(\mu\), of Track Surface on Negative Stiffness  
In accordance with Formula (7), the relationship between negative stiffness, \(K_{NSD}\), and displacement, \(x\), at different track surface friction coefficients, \(\mu\), is shown in Figure 12.  
Figure 11: Influence of preload of Belleville spring on negative stiffness.  
Figure 12: Influence of friction coefficient of track surface on negative stiffness.  
It can be seen from Figure 12 that changing the friction coefficient, \(\mu\), of the track surface has an insignificant effect on the negative stiffness provided by the device.
## Materials and Methods
### Environment Model
The density of the UE locations obtained in 25 environment samples is presented in Figure 2. The \((\varphi,\theta)\) coordinate system is shown in Figure 1. Figure 2 describes the UE locations in the DOD space, as viewed from the BS. The UE distribution averaged over the environment samples is symmetric with respect to the plane \(\varphi=0\), as a result of the matching (statistical) symmetry of the environment geometry. The UE density increases towards the upper cell edge (further away from the BS) as the polar angle of incidence decreases. The RT simulation parameters were set as recommended in [16], limiting the environment interactions to up to 6 reflections, 1 diffraction, and 1 transmission. Figure 1: An example of an environment sample. The BS array is depicted in red. The cell boundaries are shown in blue. The Rx locations are shown in green.
## 2 Fundamental Concepts Related to FL
### Major Taxonies in FL  
On the client side, the data on which local models are usually trained can be either independent and identically distributed (IID) or non-IID. The major taxonomies of the FL system are demonstrated in Figure 2.  
Referring to Figure 2, there are six different types of classifications of FL systems. The data-based classification of FL has three main types [13]. In the horizontal FL (HFL), the sample space is different but the feature space is the same. In contrast, vertical FL (VFL) has the same sample space but feature space is different [2]. In hybrid FL (also known as federated transfer learning (FTL)), both feature and sample spaces are different [14]. An example of all three classifications of FL (e.g., HFL, VFL, and hybrid/FTL) is given in Figure 3.  
The communication architecture is either centralized, decentralized, and/or depending upon the application scenario [15; 16]. The AI models used in the FL setting can be linear like linear regression, and more complex non-linear like convolutional neural networks. In some cases, hybrid AI models can also be employed to achieve faster convergence [17]. There are two main branches of the FL in terms of data and resources, which is called federation. To this end, FL systems can be categorized into three key settings. In a cross-silo setting, the size of participating devices is large, but the # of clients is small [18]. In contrast, the cross-device setting has small-sized devices, but the clients can be large [19]. In the hybrid setting, the device sizes and number of clients are balanced. The updates from the server can be shared in synchronous (server shares the global model after receiving updates from all clients) and asynchronous (the server does not wait for all clients in order to share the global model) ways [20]. Lastly, the communication from the local devices is either frequent vs. less frequent [21; 22].  
These taxonomies can pave the way to clearly understand the dimensions of FL. Apart from these taxonomies, some studies have classified the FL systems based on the privacy mechanism used as well [23]. The rest of this paper discusses the significance/use of FL in the general medical domain and COVID-19 context.
# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 5 Discussion
Therefore, this study suggests that the fingerprinting positioning method should be carried out only for the dangerous areas, while most of the areas in the hospital should still use the circle-circle intersection method for indoor positioning so that the cost of localization can be reduced and the time of localization can be shortened, which is more in line with the actual needs of localization. The use of a security protection mechanism for MQTT ensures the privacy of personal sensitive medical information from being stolen. Therefore, the patient's physiological information and the medical staff's work coordination information can be protected. Although there exists popular social software, such as LINE, QQ, Skype, Facebook messenger, etc., can provide the function of the management of medical staff work coordination information exchange, the privacy issue in the medical information still needs to be taken care of. The developed functions with the proposed secured MQTT mechanism in this paper fulfill the needs for the healthcare information and medical resource management system. In order to compare the differences between the presented system and similar systems such as the ones presented in [24; 50], several key functionalities are explored: sensors, communication, indoor positioning, MQTT, and medical staff work coordination information exchange. Table 3 lists the differences. The system of [50] developed a Bluetooth-based ECG sensor, while the system presented in this paper used smart diapers and smart T-shirts to measure temperature and diaper humidity. The system of [24] did not discuss any implementation of sensors. All three systems used Bluetooth to connect sensors and the data collector (coordinator node, PDA, or Bluetooth Hub/smartphone). Only the system presented in this paper provides functionalities of indoor positioning, medical staff work coordination information exchange, and secure MQTT. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
Notes: X means \"the function is not available or not discussed\", \(\surd\) means \"the function is provided\".  
\end{table} 
Table 3: Functionalities of three different systems.
# Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Drivers
## 5 Simulation and Analysis  
To study the performance of the path planning algorithm based on the excellent driver model proposed in the paper, simulation experiments under different working conditions were carried out in MATLAB environments. The scenarios of obstacle avoidance steering and free lane-changing steering at the speed of 30 km/h and 40 km/h were simulated. In each simulation test, the aggressive and conservative types were selected as driver type, respectively. Then, the lane-changing trajectory generated by the proposed method was compared with the actual driving trajectory under the same working condition. The effectiveness of the proposed algorithm can be quantitatively evaluated by calculating the deviation between the actual value and the simulation value. The change of the error is also presented.  
Figure 11: The mean square error variation curve of the GA-BP neural networks model.  
Figure 10: The testing error of the GA-BP neural network.  
As can be observed in Figures 12a, 13a, 14a and 15a, the longitudinal distance of the lane-changing trajectory is increased with the increased speed, and the longitudinal distance of the conservative driver at the same speed is shorter than that of the aggressive driver at same speed. This is because, in the process of obstacle avoidance, the conservative drivers usually keep a larger safety distance, and a larger steering wheel angle will be input at the early stage of the lane-changing. Therefore, the vehicle will travel to the target lane as soon as possible. Aggressive drivers often choose a smaller safety distance, so the lane-changing operation is more stable, making the terminal distance of lane changing longer.  
Figure 14: _Cont._  
Figure 13: The simulation results at the speed of 30 km/h under the obstacle avoidance conditions of the aggressive driver: (**a**) simulation trajectory and actual trajectory; and (**b**) lateral deviations between the simulated and real trajectories.
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 4 Results and Discussion
### Comparison with ISMN  
Additionally, observations from in situ stations were also utilized for product validation. Overall, 16 months of FY-3E GNOS-II data from July 2021 to October 2022 were used in this comparison. Comparison data were obtained from the ISMN networks. For each site, GNOS-II specular points located in the same ease grid of 36 km were selected, and the time-matching window is one day. For each observation technique, observations from a single day were averaged. As suggested in Section 4.1, data from the three GNSS systems have similar accuracy. Therefore, The GNOS-R soil moisture retrieved from GPS/BDS/GAL are combined for validation, to obtain more matching data. Some examples of comparing GNOS-II soil moisture time series with in situ measurements are illustrated in Figure 11. The histograms of the ubRMSE between GNOS/SMAP soil moisture and ISMN in situ observations are represented in Figure 12. The average ubRMSE of GNOS-II soil moisture estimations is 0.054 cm\({}^{3}\)/cm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SYSTEM** & **GPS** & **BDS** & **GAL** \\ \hline RMSE (cm\({}^{3}\)/cm\({}^{3}\)) & 0.0503 & 0.0497 & 0.0482 \\ Correlation Coefficient & 0.83 & 0.85 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The RMSE and correlation coefficient between GNOS-II and SMAP soil moisture.  
Figure 10: GNOS-II soil moisture vs. SMAP soil moisture. (**a**) GPS; (**b**) BDS; (**c**) GAL.  
Figure 11: GNOS/SMAP soil moisture vs. ISMN in situ observations. (**a**) ARM Medford; (**b**) ARM Ashton; (**c**) ARM Waukomis; (**d**) SCAN Phillipsburg; (**e**) TxSON CR 200-18.
# A Mathematical Model of Industrial Waste-Derived Fuel Droplet Combustion in High-Temperature Air
## Abbreviations
  
\begin{tabular}{l l} C\({}_{1,r}\) & molar concentration of species j in reactions, mol \\ \(D_{i,m}\) & diffusion coefficient of vapor in the bulk, m\({}^{2}\)/s \\ \(d_{p}\) & particle (droplet) diameter, m \\ \(D_{0,r}\) & diffusion rate coefficient for reaction \\ \(E_{r}\) & activation energy for reaction, J/(mol\(\cdot\)K) \\ \(\overset{r}{F}\) & vector of volumetric fluid / particle interaction force, N \\ \(F_{a}\) & additional acceleration, N/kg \\ \(F_{D}\) & coefficient in drag law, l/s \\ \(f_{h}\) & particle fraction which absorbs heat of reaction \\ \(f_{v,0}\) & initial mass fraction of volatiles \\ \(f_{w,0}\) & initial mass fraction of evaporating material \\ \(\overset{\rightarrow}{\delta}\) & gravity acceleration, m/s\({}^{2}\) \\ \(G_{k}\) & generation of turbulence kinetic energy related to the mean velocity \\ \(H\) & enthalpy, W \\ \(h\) & convective heat transfer coefficient, W/(m\({}^{2}\cdot\)K) \\ \(h_{fg}\) & latent heat, J/kg \\ \(h_{i}\) & sensible heat, W \\ \(H_{\text{Heac}}\) & heat released by the surface reaction, J/kg \\ \(\overset{\rightarrow}{f}\) & mass flux, kg/(m\({}^{2}\cdot\)s) \\ \(k\) & turbulent kinetic energy, m\({}^{2}\)/s\({}^{2}\) \\ \(k_{c}\) & mass transfer coefficient, m/s \\ \(k_{\infty}\) & thermal conductivity of the gas, W/(m\(\cdot\)K) \\ \(m_{p}\) & particle (droplet) mass, kg \\ \(m_{p,0}\) & initial particle (droplet) mass, kg \\ \(M_{\varpi,i}\) & molecular weight of species i, kg/mol\(\cdot\)K \\ \(N_{i}\) & molar flux of vapor, kg\(\cdot\)mol/(m\({}^{2}\cdot\)s) \\ \(N_{r}\) & apparent order of reaction \\ Nu & Nusselt number \\ \(p\) & gas local absolute pressure, Pa \\ \(p_{\pi}\) & bulk partial pressure of the gas phase species, Pa \\ Pr & Prandtl number \\ \(p_{r,d}\) & bulk partial pressure of the diffusion-limited species, Pa \\ \(p_{\text{sat}}\) & saturated vapor pressure, Pa \\ \(Q\) & heat of combustion, J/kg \\ \(Q^{q}_{s,V}\) & higher heating value, J/kg \\ \(r\) & droplet radius in spherical coordinates, m \\ \(R\) & the universal gas constant, J/(K\(\cdot\)mol) \\ \(R_{p}\) & rate of production, units vary \\ Re & relative Reynolds number \\ \(\overline{R}_{j,r}\) & rate of particle surface species depletion, kg/s \\ \(R_{j,r}\) & rate of particle surface species reaction per unit area, kg/(m\({}^{2}\cdot\)s) \\ \(R_{\text{kin},r}\) & kinetic rate of reaction, units vary \\ Sc & Schmidt number \\ Sh & Sherwood number \\ \(t\) & time, s \\ \(T_{bp}\) & boiling point, K \\ \(T_{d}\) & CLF ignition temperature, K \\ \(t_{d}\) & ignition delay time, s \\ \(T_{g}\) & oxidizer temperature, K \\ \(t_{gf}\) & gas-phase ignition-delay time, s \\ \(h_{bg}\) & heterogeneous ignition-delay time of CLF, s \\ \(T_{p}\) & droplet temperature, K \\ \(T_{p,0}\) & initial particle (droplet) temperature, K \\ \(\text{Yap}\) & vaporization temperature, K \\ \(T_{\infty}\) & gas temperature,
# A Survey on Troll Detection
## 3 Troll Detection Methods  
Several research directions have been considered by researchers in order to solve the troll problem.  
The first to be taken into consideration was to automatically analyse online contents through a natural language processing (NLP) approach. A rudimentary NLP technique involves the calculation of the negative words contained in a given post, in order to measure the comment's degree of hostility. Another approach is based on the subdivision of the content of the post in n-grams, i.e., sequences of n elements included in the text (in this case, words or characters, but also emoticons). These elements are compared to other well-known n-grams or they are used to create statistical models aimed at identifying trolls [29]. More sophisticated methods have been developed making progress in this direction; they try to spot trolls using sentiment analysis techniques, i.e., by understanding and measuring the sentiment of the text [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. In fact, by attributing a certain emotion to the words in a sentence, it is possible to evaluate the predominant sentiment.  
Emotions such as anger or rage are clues for detecting a troll's comment [22]. According to the authors of [29; 33; 34], the information acquired from single comments is not enough to perform a correct analysis and, consequently, they try to integrate methods to verify the consistency of the text according to other comments and their topic. A second research direction involves the Social Network Analysis (SNA) of the communities in order to identify possible trolls [9; 28; 35] Other analyses on data from users [36; 37] are carried out, in order to identify users with antisocial behaviours within a community.  
In general, the SNA approach makes it possible to extract the information needed to assess the attitude of a user.  
Finally, another approach is to combine all the previous ones by identifying heterogeneous groups of features to feed more complex machine learning models. In practice, features of trolls and legitimate users are collected, through the analysis of: writing style, sentiment, behaviours, social interactions, linked media and publication time. A machine learning approach is finally used to identify trolls with very high accuracy [12; 38; 39].  
A useful classification of troll detection methods is based on the type of information they use for detection. In particular, we identify four main types of information: posts, discussion threads, user behaviours and community relationships. A detailed analysis of the four methods is reported below.