esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulations
### Link Budget Analysis
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**RRU\({}_{\text{M}}\) (3.5 GHz)**} & **RRU\({}_{\text{m}}\) (28 GHz)** \\ \hline
**Parameters** & = & **Uplink** & **Downlink** & **Uplink** & **Downlink** \\ \hline Transmit Power (dBm) & A & - & 49 & - & 36 \\ \hline Subcarrier Spacing (KHz\(/\)MHZ) & SCS/BW & 30/100 & 30/100 & 60/200 & 60/200 \\ \hline Transmit Antenna Gain (dBi) & B & 11 & 11 & 21 & 21 \\ \hline Transmitter Cable Loss (dB) & C & 2 & 2 & 0 & 0 \\ \hline Penetration Loss (dB) & D & 28 & 28 & 9.5 & 9.5 \\ \hline foliage Loss (dB) & E & 8 & 8 & 7.74 & 7.74 \\ \hline Body Block Loss (dB) & F & 4 & 4 & 8 & 8 \\ \hline Interference Margin (dB) & G & 6 & 6 & 2 & 1 \\ \hline Rain/Ice Margin (dB) & H & 0 & 0 & 3 & 3 \\ \hline Slow Fading Margin (dB) & I & 6 & 3 & 4 & 2 \\ \hline Temperature (Kelvin) & K & 290 & 290 & 290 & 290 \\ \hline Thermal Noise (dBm) & L & \(-\)129.206 & \(-\)129.206 & \(-\)126.195 & \(-\)126.195 \\ \hline Noise figure UE (dB) & M & 7 & 7 & 8 & 8 \\ \hline Demodulation threshold SNR (dB) & N & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RRU\({}_{\text{M}}\) and RRU\({}_{\text{m}}\) downlink and uplink link budget parameters.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 2 Measurement and Prediction Methods
### Test Site for Wind Field Measurement and Prediction  
The measurements were collected from the Choshi offshore wind energy test facility located about 3.5 Km offshore of Choshi city of Chiba prefecture, Japan. As shown in Figure 1, the facility consists of a 2.4 MW wind turbine (MWT92/2.4) with a rotor diameter \(\mathrm{D}=92\) m and a hub height \(z_{\mathrm{h}}=80\) m, and a meteorological tower located 285 m east from the turbine. The meteorological tower has a height of 95 m, with 3D sonic anemometers (SAT-600A of Sonic Corporation) located at 40 m, 60 m, and 80 m. In addition to this, cup anemometers and wind vanes are installed between heights 20 m and 90 m at an interval of 10 m. The tower has a platform at a height of 15 m above the mean sea level. Two LiDARs, WindCube V1 (Leosphere, Orsay, France) and a scanning Doppler LiDAR (WindCube100S), are mounted on this platform. V1 is a profiling LiDAR; therefore, it can only measure vertical profiles at the installation point. WindCube 100S is capable of performing 3D volumetric scans [17]. Except for the validation purpose, the current study primarily used WindCube100S. Note that measurements from the V1 LiDAR were already validated against measurements from a sonic anemometer [18].
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Algorithm Flow  
Based on the above theory, the pseudo-code of the GATTRP proposed in this paper is described in Algorithm 1.  
```
Input:\(m,n,\{d_{ij}\},G(V,E)\) Output:\(\omega_{1},\omega_{2},\dots,\omega_{m},1/fit_{max}\)
1 Initialize parameters \(U_{0},U_{max},O_{min},O_{max},I_{max}\);
2 Randomly initialize \(U_{0}\) chromosomes of length \(n+m-2\);
3 foriterator from 1 to \(I_{max}\)do
4 Calculate the fitness \(\{fit_{i}\}\) of each chromosome according to Equation (2);
5 Sort chromosomes according to fitness;
6 ifpopulation size \(>U_{max}\)then
7 Eliminate the last chromosomes until the population size \(=U_{max}\);
8 end if
9 ifThere is no elite chromosome recordsthen
10 Mark the top 20% of chromosomes as elite chromosomes and record them;
11 else
12 ifThe top 20% of chromosomes are consistent with the elite chromosome records then
13 The number of iterations without valid evolution +1;
14 ifThe number of iterations exceeds the allowable rangethen
15 Take postposition chromosomes to swap with elite ones;
16
17 else
18 Update elite chromosome records;
19 Reset the number of iterations without valid evolution;
20
21 end if
22
23 end for
24 forall chromosomes in the current populationdo
25 Calculate the number of child breeding \(O_{i}\) according to Equation (7);
26 foriterator from 1 to \(O_{i}\)do
27 Select other chromosome by roulette to swap and obtain a child;
28 The child have \(P_{mut}\) probability of mutation. Mutation of flip, slide, and drift occurs in the proportion of \(P_{flip},P_{slide},and\ P_{drift}\) respectively;
29 end for
30
31 Max offspring chromosomes into current population;
32 end for
33 Decompose the chromosome with the highest fitness into \(m\) sub-segments;
34 for\(k\) from 1 to \(m\)do
35 In the \(k\)th sub-segment, sequentially obtain the values of the connected segments \(i,j\), make \(\rho_{ij}^{k}=1\), generate \(\omega_{1},\omega_{2},\dots,\omega_{m}\);
36 end
```  
**Algorithm 1**GATTRP.
## 2 Materials and Methods
### Descriptive Sensory Analysis  
Eight highly-trained panelists from UMH (Miguel Hernandez University of Elche, Orihuela, Alicante, Spain), with over 500 h of experience on wine, aged between 30 and 62 years (4 females and 4 males) evaluated the three wine samples. The panel was trained according to ISO 8586:2012 [12] and it is specialized in alcoholic beverages [13], including wine [14; 15] and beer [16]. These three wines were included in a normal session of the panel, in which two quality control parameters, evaluating the panel performance, were also included: _(i) reproducibility_, consisting in evaluating the same sample but at two different sessions, and _(ii) repeatability_, consisting in evaluating the same sample twice in the same session. To validate the panel performance the deviation of these two parameters between the samples must below 20% for all sensory descriptors. The panel worked in the same way as the official panel of the Regulatory Board of the Alicante Protected Design of Origin (wine), which is certified by the National Accreditation Agency (ENAC) under the ISO 17065 [17].  
For the analysis, 25 mL of wine were served in transparent cups and evaluated at 16-18 \({}^{\circ}\)C, in normalized sensory booths with white light. Water and unsalted crackers were provided to panelists between samples for palate cleansing.  
The attributes under evaluation were alcohol, fruity, floral, vegetable, spicy, animal, mineral and toasted notes (including oak notes) for both odor (o) and flavor (f), basic tastes (sweet, sour and bitter), tactile sensations (astringent), global attributes (aftertaste), and appearance (color intensity). Definitions of the sensory attributes and the reference materials used are described in the lexicon used and previously published by [PERSON] et al. [15]. The attribute \"minerality\" was added to this previous lexicon because it is considered essential to describe clay-_tinaja_ wines. Minerality is defined as the perception of aromatics (odor) and non-volatile minerals (flavor) commonly associated with a liquid stored in a clay-_tinaja_ for several days and resembling wet soil after a rainy day. The volatile compounds behind the odor are those resembling humidity and moisture attributes such as geosmin and regarding the minerality flavor is mainly due to the presence of increased contents of minerals such as potassium, calcium, magnesium, and sodium. The reference materials used in the training of the panel were _(i)_ distilled water store in a clay-jug (_botijo_) for 2 days: 6 (odor, o) and 8 (flavor, f); _(ii) wine B_ (Celler del Roure, Moixent, Valencia, Spain): 3 (o) and 6 (f); _(iii)_ geosmin, dipped strips using a concentration of 4000 mg L\({}^{-1}\) = 6 (o).  
A structured scale of 10 points was used to quantify the intensity of the wine attributes, where 0 represents no or extremely low intensity and 10 represents extremely high intensity.
# Fault Tolerant DHT-Based Routing in MANET
###### Abstract
In other words, existing protocols do not consider network dynamics and associated issues that limit the scope of these protocols in the adversarial environment of MANET. However, some closely related work [13; 14] consider the problem partially and solve it independently in the logical network without using physical network information. The adopted mechanisms in [13; 14] introduce huge lookup delays with extra overhead. For further detail please see Section 3. Figure 1: Address publication, lookup, and routing in DHT networks. The paper contributions are twofold, i.e., in the problem domain and the solution domain. In particular, the main contributions are summarized as under:  
* This paper explores the classical problem of fault tolerance in DHT-based MANET and defines its scope(Section 2). In particular, issues such as disrupted logical network structures, loss of index information due to AN failure or network partition, detecting and differentiating occasional node failure (i.e., node failure without causing network partitioning, where alternate paths avoiding the failing node are needed to be found) and partition detection, merging of logical structures and LID reconfiguration are addressed (for details, please see Section 2). Additionally, limitations of the existing protocols are highlighted in Section 3. * In the solution domain, a suite of distributed mechanisms is provided for the identified problems under the scalability constraints. Specifically, we consider k-hop topological information and the philosophies of detection, recovery, avoidance, and redundancy to cope with the problems. Moreover, pre-failure measures (finding critical regions in the network, dynamic replication, and partition detection) and post-failure measures (finding alternate paths avoiding the failure, merging and merging detection, and LIDs reconfigurations) are considered in a distributed manner. (Section 4 presents detailed description). The rest of the paper is structured as follow: the scope of the problem is defined in Section 2, and related work is provided in Section 3. Section 4 presents a suite of distributed solutions. Simulation and results analysis are given in Section 5. Finally, Section 6 concludes the paper.
# Optimizing Bladeless Wind Turbines: Morphological Analysis and Lock-In Range Variations
## 2 Theoretical Background  
When solid objects or bluff bodies interact with fluid flows, they experience drag and lift forces influenced by their shape and orientation within the stream [27]. The distribution of fluid velocity along the body surface can induce oscillatory movement through vortex shedding, a phenomenon elucidated by [PERSON] [28], [PERSON] [29], and [PERSON] [30], explaining the vibration caused in bodies by VIV. Historically, aerodynamic effects have been studied as a detrimental effect of wind on structures, which is typically sought to be avoided, like in [31] to avoid risk in high-rise buildings.  
[PERSON] [32] established the relation between shedding frequency and wake width, known as the Strouhal number (St = \(f\cdot\text{D}/\text{U}\), where f is the frequency, D is the diameter, and U is the fluid velocity). Following this, ref. [33] stated that the correlation between the Strouhal (St) and Reynolds (Re) numbers is such that St number consistently maintains a value of 0.2 with slight variation between a smooth surface and a rough surface, across a wide Re range (250 < Re < 2 \(\times\) 10\({}^{5}\)).  
BWT energy harvesters are pioneering designs that aim to capture wind energy without traditional rotating blades. The basic main components, outlined in [19], encompass the mast, central base, suspension and tuning system, alternator, and power storage unit.  
The inherent frequency of oscillation within a rigid structure remains constant, limiting the range of wind velocities where resonance and the resulting VIV phenomenon occur, commonly known as the \"Lock-In range\" [34]. Considering that the VIV can be approximated as a simple harmonic motion of a body with a mass (\(m\)) connected to an elastic element with a spring constant (\(k\)), the resonance frequency (\(f\)) of this oscillation can be determined using Equation (1) [35].  
\[f=1/2\ \pi\ \surd\ (k/m) \tag{1}\]  
Figure 2: Vortex Nano with a height of 0.85 m (**a**), and Vortex Atlantis with a height of 9 m (**b**).  
Ongoing BWT research mainly focuses on expanding this range through design extensions, as done in [36], where an interfering cylinder analysis is conducted. In addition, ref. [25] introduces an electro-mechanical device serving both as an energy generation (alternator) and a tuning system, aiming to broaden the Lock-In range.  
Within this context, numerous tests have been performed to analyse how the shape and surface roughness of the structure influence the aerodynamic design of BWTs.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design
### Fitness Evaluation  
Fitness value is a crucial metric that determines the quality of each chromosome. Equation (10) is selected as the fitness function for the ITGA. This fitness function represents both the data volume and the value of data received by the ground station from satellite transmission tasks, aligning with the objective of hardware resource allocation at the ground station.  
\[f(x)=\sum_{1\leq k\leq n}x_{k}\cdot p_{k}\cdot(time_{e}^{k}-time_{s}^{k}) \tag{10}\]
# Multi-Modal Sonar Mapping of Offshore Cable Lines with an Autonomous Surface Vehicle
## 2 ASV Development  
### Hardware System  
The base platform of the ASV was designed based on a catamaran to enable a differential thrust. The vehicle was integrated above the base along with various component systems, including power, electronic sensors and devices, onboard computers, and wireless communication systems, as shown in Figure 1. The detailed configuration of the installed sonar sensors is shown in Figure 2. The MBES and SSS were installed in the middle of the main hulls on the stern and bow sides, respectively. As the two sonar sensors used different frequencies and did not produce a common multiple, there was zero interference between them.  
Figure 1: Developed ASV for offshore cable line inspection.  
For the propulsion system, twin electric outboard thrusters were mounted on the stern of each hull. Three high-performance lithium batteries were installed for electrical power. Each battery module includes a battery management system. The battery capacity was determined to support the operating hours of the vehicle for one-day survey missions. Moreover, a GPS receiver and an attitude heading reference system (AHRS) were installed to acquire the real-time motion information of the vehicle. A situational awareness system (e.g., optical cameras) was installed to inform about the objects floating on the water to the users. Wireless local area networks using wireless fidelity and radio frequency devices were deployed to reciprocally communicate operating commands and the current state of the vehicle. Furthermore, wireless remote stops and onboard kill switches were configured in the system to handle emergencies. Table 1 summarizes the specifications of the developed ASV, including the payload sensors for navigation and mapping.
# Special Issue \"Multi-Agent Systems\": Editorial
## 3 MAS for Decision Support  
Interestingly enough, the category reflecting the new entry w.r.t. previous editorial is also the most represented: 4 papers out of the 11 published exploit MAS to deliver decision support.  
In [3], the authors exploit agent-based modeling and simulation to define a photovoltaic adoption prediction model based on self-reported behavior, then refined by a genetic algorithm looking at observed data. The goal is to help energy-related decision making by policy makers, by modeling and predicting households pondering whether to adopt photovoltaic energy solutions. Here, the agent abstraction is useful to model individual behavior driven by rational utility functions (such as economic savings), and the social dimension stemming from neighborhoods influencing each others' decisions.  
In [4], an MAS is used as the operational backbone of a game-theoretic approach to task allocation under strict spatio-temporal constraints, applicable to deliver decision support in many critical scenarios such as disaster relief. Here the main motivation behind usage of an MAS lies in the preference of quickness over optimality as regards convergence to useful allocations, as the targeted scenarios do not mind optimal solutions if they do not come within a reasonable time. As such, an MAS is built to run a scheduling algorithm rooted in game theory in a decentralized fashion, improving convergence time while giving away optimality.  
In [5], an MAS is proposed as a platform for instrumenting a collective of neural network based classifiers by adopting a crowdsourcing metaphor: each classifier is an agent, each classification is an opinion, and the overall prediction delivered by the system is the aggregation of the crowd's opinions. The goal is to improve prediction accuracy and transparency, by letting agents interact socially to exchange knowledge (e.g., new features), gain reciprocal trust, and change opinion when given enough evidence. The agent abstraction is then used mostly for its autonomy, and the MAS as an enabler of the sociality needed to improve transparency and accuracy through the exchange of information.  
In [6], the authors target the green coffee supply chain with an agent-based decision support system devoted to planning production scheduling in face of fluctuating and peak demand. The modeled supply chain is rather complex, with plenty of interdependencies amongst activities and variables influencing the decision process at each step. An MAS is thus used to tame this complexity,by modeling all the different tasks and processes as autonomous agents, each undergoing its own reasoning to take decisions, while interacting with others upon need.  
In spite of the heterogeneity of the application domains and the techniques adopted, all the described approaches leverage on MAS central notions to improve delivering of decision support functionalities, either by simulation [3,4] or as an operational platform [5,6].
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 3 Methodology  
We propose a three-stage model including people detection, tracking, inter-distance estimation as a total solution for social distancing monitoring and zone-based infection risk analysis. The system can be integrated and applied on all types of CCTV surveillance cameras with any resolution from VGA to Full-HD, with real-time performance.
## 5 Discussion
### Comparison with the Result Derived from Statistical Yearbook  
In order to verify the rationality of this method, the average completed building floor space per unit area (ACBFS) in each county derived from the Beijing Statistical Yearbook (BSY) [(41; 42; 43; 44; 45; 46; 47; 48; 49; 50)] of all counties in Beijing between 2000 and 2009 was employed as an inspection standard for comparison with the average building height expansion per unit area (ABHE) in each county of Beijing. Since it is difficult to obtain the ACBFS data of Hebei and Tianjin, the following analyses only take some counties in Beijing as examples. Table 5 shows the real ACBFS in each county of Beijing, which is dimensionless.  
Figure 12 is the scatter plot of statistical ABHE versus real ACBFS. It shows the variation trend of the coefficient of determination (R\({}^{2}\)). With the increase in the elevation change exclusion threshold, the R\({}^{2}\) value gradually increases until it reaches the peak value of 0.9436, when the exclusion threshold goes to (\(-\)14 m, 14 m). After that, it began to decline gradually. It proves that 0.9436 is the best correlation coefficient value. Accordingly, the second exclusion method in Section 3.4 with a 14-m threshold is proved to be more objective in this study area.  
Figure 12: The fit curve between statistical results and real situations. (**a**) Fitting analysis result of the Statistical ABHE (based on the first non-building exclusion method) and the real ACBFS; (**b**) Fitting analysis result of the Statistical ABHE (based on the second non-building exclusion method and the exclusion threshold is 7 m) and the real ACBFS; (**c**) Fitting analysis result of the Statistical ABHE (based on the second non-building exclusion method and the exclusion threshold is 11 m) and the real ACBFS; (**d**) Fitting analysis result of the Statistical ABHE (based on the second non-building exclusion method and the exclusion threshold is 14 m) and the real ACBFS; (**e**) Fitting analysis result of the Statistical ABHE (based on the second non-building exclusion method and the exclusion threshold is 17 m) and the real ACBFS; (**f**) Fitting analysis result of the Statistical ABHE (based on the second non-building exclusion method and the exclusion threshold is 20 m) and the real ACBFS.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Tongzhou** & **Daxing** & **Chaoyang** & **Haidian** & **Dongcheng** & **Xicheng** & **Xuanwu** & **ChongwenFengtai** & **Shijing** & **Shunyi** \\ \hline Real & & & & & & & & & & \\ ACBFS & 2.52 & 2.20 & 22.57 & 11.74 & 40.96 & 45.13 & 52.17 & 46.10 & 12.69 & 11.96 & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The values of real ACBFS in each county of Beijing.
# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
## 2 Materials and Methods
### Data Collection
#### 2.3.1 Scientific Papers  
To empirically support our proposed framework, this study acquired a corpus of scientific publication abstracts focused on the field of smart cities. These abstracts were sourced from the WoS database by conducting a search query formatted as \"'smart cit\" (Title) OR'smart cit\" (author keywords)\", accounting for various endings like -y, -ies, -itizen, or -itizens, for the time span from 1999 to 2023, as of July 2023. The search yielded 14,980 results from its core collection. Initially, we refined the results by document types and retained only articles, proceedings papers, book chapters, and review articles. Subsequently, we focused on documents in the English language, leaving 13,810 documents. As some of the documents lacked registered abstracts in the database, we ended up with 13,473 abstracts for analysis. The gathered data offers valuable insights into the changing patterns of smart city research. Figure 3 represents the recent rise in interest in the field of smart cities. Notably, research in this domain witnessed significant acceleration, starting from the beginning of the 2010s. However, there was a period of stabilization in smart city research from 2018 to the present day. Despite these variations, smart city research continues to be of great interest to the scientific community.
## 3 Application to a Case Study  
SHM assessment based on actual monitoring data is a straightforward way to verify the effectiveness of the method for an already built offshore platform. However, for an offshore platform under construction, measured structural responses are not available yet, and the numerical analysis is therefore an alternative [11; 32]. The Ansys 17.0 software was used for finite-element analysis to analyze the overall performance of the structure.