esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Structure and Key Technologies
### Deadweight Balance Structure
#### 2.5.2 Lateral Deadweight Balance System  
In Figure 4, although the deadweight of the \(Z\)-stage is balanced by two counterweights, the total deadweight of the \(Z\)-stage with its accessories and its mechanical balance system is transferred to the \(Y\)-stage, then to the \(X\)-stage during \(Y\) and \(X\) motions. When the \(Y\)- or \(X\)-stage moves laterally, the total center of the gravity of the moving body moves and will cause structural deformation to the degree of micrometers, which is unacceptable in the developed machine. In order to balance the deadweight of the \(X\)- and \(Y\)-stages and the balance system of the \(Z\)-stage, we developed a lateral deadweight  
Figure 4: Schematic structure of \(Z\)-stage deadweight balance system. 1: \(Z\)-stage, 2: \(Y\)-stage 3: Pulley, 4: Steel strip, 5: Support frame, 6: Outer frame (It is fixed with \(Y\) stage), 7: Counterweight, 8: Linear piezo-motor.  
balance mechanism, as schematically shown in Figure 5. Through this mechanism, their deadweight is transferred to the granite base, which has a high stiffness.  
In Figure 5, the \(X\)-stage (12) is located on the granite base (not shown in this figure for clarity) and can move only along the \(X\) direction. The \(Y\)-stage (11) together with the balanced \(Z\)-stage can move not only in the \(Y\) direction along the \(Y\)-guideway but also in the \(X\) direction together with the \(X\)-stage.  
Two horizontal arms (1) are fixed to the bottom of the outer frame (2) symmetrically and move together with the \(Y\)-stage. The lower arm (5), upper arm (4), springs and tension sensors (3), guide rod (6) and upper plate (7) are connected rigidly in the lateral direction. Thus, the movement of \(X\)- and \(Y\)-stages in the lateral direction is transferred to the upper plate (7). The upper plate, guide rod and upper arm are connected rigidly, and are also connected with the lower arm through two springs and tension sensors in each side. The tension forces of four springs are measured by four tension sensors respectively. During fabrication of the stages, the tension forces of the four springs are adjusted to be equal. In Figure 5b, the two-dimensional moving stage is composed of the upper plate, rolling bearing (8) and lower plate (9). The lower plate in each side is fixed rigidly to the granite base (not shown in this figure for clarity). The upper plates can move on the lower plates with a very low friction through the linear rolling bearing. Therefore, the total deadweight of all the moving parts is transferred to the granite base, and the stages can move in lateral direction smoothly.
# Atmospheric and Fog Effects on Ultra-Wide Band Radar Operating at Extremely High Frequencies
## Millimeter Wave Propagation in the Atmosphere  
The millimeter wave propagation model is based on a complex presentation of the refraction index:  
\[n\left(f\right)=1+N\left(f\right)\times 10^{-6} \tag{15}\]  
The complex refractivity \(N(f)=N_{0}+N^{\prime}(f)-jN^{\prime\prime}(f)\) is given in parts-per-million (ppm) [15; 16]. The propagation factor \(k_{z}\left(f\right)\) can be written in terms of the index of refraction:  
\[\begin{array}{l}k_{z}\left(f\right)=\frac{2\pi f}{c}n(f)=-j \underbrace{\frac{2\pi f}{c}N^{\prime\prime}\left(f\right)\times 10^{-6}} _{\alpha\left(f\right)}\\ +\underbrace{\frac{2\pi f}{c}\left(1+N_{0}\times 10^{-6}\right)+ \frac{2\pi f}{c}N\left(f\right)\times 10^{-6}}_{\beta\left(f\right)}\end{array} \tag{16}\]  
where \(c\) is the speed of light in a vacuum. The attenuation factor is:  
\[\alpha\left(f\right)=-\text{Im}\left\{k_{z}(f)\right\}=\frac{2\pi f}{c}N^{ \prime\prime}\left(f\right)\times 10^{-6} \tag{17}\]  
The wavenumber of the field is given by:  
\[\begin{array}{l}\beta\left(f\right)=\text{Re}\left\{k_{z}(f)\right\}\\ =\frac{2\pi f}{c}\left(1+N_{0}\times 10^{-6}\right)+\frac{2\pi f}{c}N\prime \left(f\right)\times 10^{-6}\end{array} \tag{18}\]  
The group delay at a distance \(d\) is defined via the derivative of the wavenumber:  
\[\begin{array}{l}\tau_{d}=\frac{d}{2\pi}\frac{d\beta}{df}\\ =\frac{d}{c}\left(1+N_{0}\times 10^{-6}\right)+\frac{d}{c}\left[N\prime \left(f\right)+f\frac{dN\prime}{df}\right]\times 10^{-6}\end{array} \tag{19}\]  
[PERSON]'s millimeter propagation model (MPM) is used for calculation of the atmospheric frequency response under foggy conditions [15; 16; 17; 18]. The results of this calculation are shown in Figure 1. Curves are drawn for several values of water droplet concentration. Absorption peaks are placed at 22 GHz and 183 GHz and 325 GHz, where the resonant absorption of water (H\({}_{2}\)O) occurs. Oxygen molecule (O\({}_{2}\)) absorptions are at 60 GHz and 119 GHz [24; 25; 26]. Atmospheric transmission windows are located between these frequencies, at 35 GHz (Ka-band), 94 GHz (W-band), 130 GHz and 220 GHz [12].
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 4 Multi-Agent Communication and Networking  
A fundamental component of any multi-agent system is the communication mechanism between robots. In many applications of interest, the system operates without any pre-existing wireless infrastructure; the agents themselves must wirelessly share and disseminate information to coordinate their activity, a model of communication known as Mobile Ad-Hoc Networking. One of the major challenges in Mobile Ad-Hoc Networks (MANETs) is routing; the \"nodes\" (i.e., agents) in the network must collectively find communication paths to relay messages from a \"source\" agent (the point of origin) to a destination. Since the agents are mobile, the routes change rapidly with time. This imposes another computational burden on the system, that of recognizing any changes to the network topology and updating the routing tables at each robot. Routing instability also has safety implications if the agents rely on the network to broadcast their positions and headings. Messages dropped en-route could lead to mid-air collisions.  
A second challenge in ad-hoc networking is interference management. In wireless networks, all users share the same communication medium, which imposes fundamental limits on the amount of data the network can support. An important result in wireless network theory is that the data throughput is subject to sub-linear scaling (with respect to the number of agents) for almost all fading regimes; the data rate per agent goes to zero as the number of robots in the system grows to infinity [36]. The communication problem is thus inseparable from the control problem discussed in Section 3; any system of agents that relies on \"raw\" uncompressed data to coordinate its activity is fundamentally unscaleable. The data must be compressed in a way that preserves all of the information needed for coordination. In other words, the data compression scheme must conform to the locally interactive structure of the global state space.  
The following sections provide an overview of some important routing protocols for wireless ad-hoc networks. One class of protocols based on the idea of clustering is especially pertinent to multi-robot networks and will be discussed in some detail.
# Vehicle Interior Noise Prediction Based on Elman Neural Network
## 5 Prediction and Optimization of Noise in the Vehicle  
### Acoustic Sensitivity Analysis of Panels  
In neural network prediction, some unimportant input variables will not only increase the size and complexity of the network but also reduce the prediction accuracy of the model. Therefore, the selection of input variables is a key step in the application of neural network methods. This article performs sensitivity analysis to filter the input variables. Sensitivity indicates the effect of the input variables on the output variables. Given that the body panels contribute differently to the sound pressure level at any position in a vehicle, before training the Elman neural network, a sensitivity analysis of panel thickness should be performed, those panels with a large impact on the output variables should be screened out, and the thickness of these panels should be taken as the input variables of the Elman neural network.  
All 36 panels surrounding the sound cavity in the vehicle were labeled, and Hyperstudy was used to analyze the sensitivity of the first-order global modal, the body mass, and the maximum sound pressure level of the target node in a vehicle. Given the large number of variables, the four-resolution partial factor method was selected for the DOE design to avoid confusing the main effect for the second-order cross effect and to take calculation economy into account. Panel sensitivity was outputted using Hypergraph as shown in Figure 10.  
As shown in the figure, panels with high response and negative sensitivity are obtained. The B-pillar inner panel is determined via side impact simulation analysis, and the side wall, B-pillar inner panel, C-pillar inner panel, middle bottom panel, rear bottom panel, tail-light inner panel, A-pillar inner panel, and roof are identified as panels that require optimization. The value ranges of the panels are shown in Table 3.  
Figure 10: Panel sensitivity: (**a**) first order bending modal sensitivity. (**b**) First order torsional modal sensitivity. (**c**) Body mass sensitivity. (**d**) Maximum sound pressure level sensitivity.
## 4 Discussion
Although REWD and REWI are considered as useful parameters for distinguishing sea ice from water, simple thresholding based on just one parameter was shown to be insufficient for effectively monitoring sea ice. The results of DT- and RF-based approaches are presented in Figure 11c,d, respectively. The false sea ice monitoring of DT- and RF-based methods mainly appear around the sea ice edge areas with a relatively low SIC. The area with a low SIC may be affected by ocean winds, which results in a rougher surface. Then, the sea ice is wrongly identified as sea water. The effects of ocean winds on low SIC have not been analyzed in this study.
## 3 Results and Discussion
[27] reported that 20 mm displacement can be determined with 6 mm RMSE using the PPP approach based on a 3 h observation period by using low-cost GNSS equipment. Remarkably, similar results were obtained in this study despite the use of low-cost GNSS equipment, demonstrating comparable performance to geodetic GNSS equipment.
# Modeling the Energy Performance of LoRaWAN
## 3 LoRaWAN Overview
### LoRaWAN General Overview  
LoRaWAN is a wireless communication technology designed to achieve long range while consuming low power, based on a one-hop radio system. This approach allows overcoming some important issues related with deployment complexity and low energy routing protocols, among others [18]. On the other hand, LoRaWAN exhibits communication constraints, which may limit the suitability of this technology for present and future IoT applications.  
A LoRaWAN network is based on a star-of-stars topology composed of three basic elements: end-devices, gateways and a central network server (see Figure 1a) [27]. End-devices, which may correspond to e.g., sensors or actuators, communicate with the network server through one or more gateways, while the network server sends messages to end-devices through a specific gateway. End-devices use the LoRa physical layer to exchange messages with the gateway, while the gateway and the network server communicate over an IP-based protocol stack (Figure 1b).  
LoRaWAN provides end-to-end encryption and data integrity. Upper layer payloads are encrypted by means of an Application Session Key, which provides confidentiality. On the other hand, a Network Session Key is used to provide data integrity over the ciphertext payload and a subset of header fields [27].  
LoRaWAN specification defines three functionality classes: Class A, Class B and Class C. Class A must be implemented by all LoRaWAN devices, and is also known as basic LoRaWAN. Class A allows bidirectional communication between an end-device and the network server, which is scheduled by the end-device based on its needs. In this functionality class, downlink transmission (i.e., from the network server to the end-device) can only occur after an uplink transmission (i.e., from the end-device to the network server).  
Class B is based on class A; however, it supports additional downlink transmission opportunities at prescheduled times. On the other hand, Class C allows downlink transmission at any time, except  
Figure 1: LoRaWAN (**a**) system and (**b**) protocol architecture.  
when the end-device is transmitting. Class C end-devices consume greater power to operate, but offer the lowest latency, compared with Class A and Class B end-devices. Both Class B and Class C are optional, and are in consequence not typically supported by LoRaWAN devices.  
The study presented in this paper focuses on Class A LoRaWAN operation. The next two subsections present physical layer and MAC layer details for this LoRaWAN mode.
# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 2 Related Work  
The increasing availability of user-generated content from social media has motivated a growing number of reviews that examine the role of natural language processing in transportation research. These reviews often focus on specific analytical techniques, data sources, or application domains, offering diverse but often fragmented perspectives on the state of the field.  
A number of studies have reviewed the use of sentiment analysis in transportation. For instance, [PERSON] et al. [6] conducted a systematic mapping of sentiment analysis applications in transportation, highlighting dominant research themes, such as traffic monitoring, ride-hailing services, and public transport satisfaction. Their review also noted a concentration of studies in a few geographic regions and a reliance on Twitter as the primary data source.  
[PERSON] [7] presented a bibliometric and thematic analysis of sentiment analysis across public service domains, including transportation, emphasizing the growth of research after 2015 and the increasing role of machine learning models. However, that study adopts a broad and multi-sectoral perspective, covering areas such as governance, health, infrastructure, and mobility, with the overarching goal of supporting innovative society initiatives. In contrast, the present review is domain-specific and focuses exclusively on the intersection of sentiment analysis, topic modeling, and transportation research.  
In relation to topic modeling, fewer reviews have been published. [PERSON] and [PERSON] [8] performed a topic modeling-based bibliometric analysis of transportation research more broadly, identifying evolving research areas within the field. Although their work did not focus explicitly on social media or text mining, it exemplifies how topic modeling can be applied as a meta-analytical tool in transport studies.  
A comprehensive technical overview of topic modeling techniques was also provided by [PERSON] and [PERSON] [9], who surveyed over 300 publications and presented a hierarchical classification of methods. Their review included algorithmic insights, evaluation metrics, and applications in multiple fields, including social networks, which are increasingly relevant in transportation research.  
However, existing reviews typically address sentiment analysis and topic modeling in isolation. There is a lack of integrated syntheses that examine how these two complementary techniques are used together in transportation research. Moreover, few works provide comparative analyses of methodologies, performance metrics, data heterogeneity, and platform biases. This review aims to fill that gap by offering a focused and comparative synthesis of SA and TM applications in transportation studies.
## 5 Heat Transfer around an Oscillating Cylinder in Non-Newtonian Fluid Flow
### Effects of the Oscillating Amplitude  
In the current simulations, the cylinder oscillates in the frequency of the vortex shedding, so the value of the non-dimensional oscillating frequency (\(f^{*}\)) is 1.0. The Reynolds number and the Prantdl number defined in Equation (26) are 100 and 1.0, respectively. Three indexes (\(n=0.6,1.0\), and 1.4) are numerically computed to include shear-thinning, Newtonian, and shear-thickening fluid. The Strouhal numbers defined in Equation (27) for these cases have already been given in Table 2 above. Three non-dimensional oscillating amplitudes (\(H^{*}\)) 0.25, 0.5, and 1.0 are considered to identify the effects of the amplitude. The time-averaged Nusselt numbers and their amplitudes (\(\Delta Nu\)) from the present computations are presented in Table 5, where the amplitudes for \(H_{0}=0.25\) are negligible (less than 1.0%) and thus not presented. The results show that the time-averaged Nusselt number is much higher in the shear-thinning (\(n<1.0\)) fluid than in the shear-thickening (\(n>1.0\)) fluid, which agrees with that in the previous steady cases. It also shows that the increasing oscillating amplitude enhances the heat convection effects, and the oscillation of the averaged Nusselt number tends to be more remarkable with increasing \(H_{0}\). Figure 9 presents time histories of the averaged Nusselt numbers for \(H=1.0\) in two periods. The averaged Nusselt number has a frequency of \(2f\). The snapshots of the temperature fields for \(n=0.6\) and \(H_{0}=1.0\) in a period are further presented in Figure 10.  
Figure 9: Time histories of the averaged Nusselt number for Re = 100, Pr = 1.0, and \(H^{*}=1.0\).
# Enhancing biogas Plant Efficiency for the Production of Electrical and Thermal Energy
## 2 Materials and Methods  
The basic process of biogas plants is usually carried out in the following stages [8]:  
1. Preparation of raw material for processing;
2. Anaerobic digestion;
3. Storage, transport, and use of boiled liquid;
4. Storage, purification, and use of biogas.  
The purpose of a continuous-flow biogas plant above all represents the production, treatment, storage, and use of biogas, and as a result, electricity and heat are generated [17; 44; 45].  
Figure 1 shows the continuous-flow plant scheme, and Table 1 shows the yields of biogas from manure [46].  
Figure 1: Scheme of a continuous-flow plant.  
Figure 2 shows the average daily inflow of liquid manure at average organic matter content by weight of 11%.  
The research in this paper will be based on a biogas plant with a continuous flow for a power plant.  
The technological parts of the plant are the following facilities: manure storage tank, pumping station, condensate pit, digester, lagoon, CHP unit in container, separator, gas torch and blower, and others (fluid pipes, gas pipes, etc.).  
The following manure is used for the biogas plant:  
* Animal flour with a dry matter content of 92-98%;
* Corn silage with a dry matter content of 30-35%;
* Molasses with a dry matter content of 84-88%.  
The projected daily biogas production is 5688 m\({}^{3}\) per 41.4 t of substrate.  
Biogas Plant Components  
The biogas plant consists of the following 13 installations, each playing a crucial role in the production process:  
* Manure storage tank: a facility for storing raw manure before processing;
* Pumping station: used for transferring manure and other substrates to the digester;  
\end{table}
Table 1: Yields of biogas from manure of domestic animals.  
Figure 2: Average daily inflow of liquid manure.  
* Condensate pit collects and drains condensate formed during the biogas production process;
* Digester: the main unit where anaerobic digestion takes place;
* Lagoon: used for storing digested material (digestate) after the fermentation process;
* CHP unit (combined heat and power): generates electricity and heat from biogas;
* Separator: separates solid and liquid fractions of the digestate;
* Gas torch: burns excess biogas that cannot be used or stored;
* Blower: maintains the pressure of biogas within the system;
* Gas pipes: transport biogas from the digester to storage or the CHP unit;
* Fluid pipes: transport substrates and digestate between different units;
* Heaters: installed within the digester to maintain optimal temperature;
* Control unit: monitors and controls the entire biogas production process.  
This detailed description of the components has been included to provide a clear understanding of the biogas plant's infrastructure. Biogas composition: methane CH\({}_{4}\) ca. 50-55%, carbon dioxide CO\({}_{2}\) approx. 45-50%, nitrogen N\({}_{2}\) ca. 0-3%, hydrogen H\({}_{2}\) ca. 0-1%, oxygen O\({}_{2}\) approx. 0-1%, hydrogen sulfide H\({}_{2}\)S ca. 0-2%.
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
## 2 Vehicle Drift Motion Mechanism Analysis
### Vehicle Dynamics Model
#### 2.1.2 Tire Force  
The UniTire model is a nonlinear and non-steady-state tire model for vehicle dynamics simulation and control to describe tire properties accurately under complex conditions involving the large lateral slip, the large longitudinal slip and the camber in [22; 23; 24; 25], which can be used to describe tire properties in the studied conditions. In this section, some inferences based on the tire model are described to simplify the drift motion mechanism analysis and the whole equations of UniTire are not introduced.  
The longitudinal and lateral slip ratios at each tire are defined as:  
\[S_{xi}=\frac{\omega_{i}r_{ei}-v_{xi}}{\omega_{i}r_{ei}},\;S_{yi}=-\frac{v_{yi }}{\omega_{i}r_{ei}}=(S_{xi}-1)\tan\alpha_{i}\]  
where \(\tan\alpha_{i}=v_{yi}/v_{xi}\). The corresponding velocities along the wheel's longitudinal and lateral axes of the front and rear wheels are given by:  
\[v_{xf}=v_{x}\cos\delta_{f}+\left(v_{y}+l_{f}\gamma\right)\sin\delta_{f},\;v_{ xr}=v_{x}v_{yf}=-v_{x}\sin\delta_{f}+\left(v_{y}+l_{f}\gamma\right)\cos\delta_{f},\;v_{ yr}=v_{y}-l_{r}\gamma\]  
The normalized longitudinal, lateral, and combined slip ratios at each tire are defined as:  
\[\phi_{xi}=\frac{K_{xi}S_{xi}}{\mu_{xi}F_{zi}},\;\phi_{yi}=\frac{K_{yi}S_{yi}} {\mu_{yi}F_{zi}},\;\phi_{i}=\sqrt{\phi_{xi}{}^{2}+\phi_{yi}{}^{2}}\]  
The normalized longitudinal and lateral forces on each tire are described as:  
\[\overline{F_{xi}}=\frac{F_{xi}}{\mu_{xi}F_{zi}}=\overline{F_{i}}\frac{\phi_{xi }}{\phi_{i}},\;\overline{F_{yi}}=\frac{F_{yi}}{\mu_{yi}F_{zi}}=\overline{F_{ i}}\frac{\phi_{yi}}{\phi_{i}}\]  
The utilization of friction coefficient at each tire stays the same at the ultimate value in drift conditions, so that force on each tire reaches its maximum. Combined with UniTire, the longitudinal, lateral and resultant forces on each tire are derived as:  
\[F_{xi}=\frac{K_{xi}S_{xi}}{K_{yi}S_{yi}}F_{yi},\;F_{i}=\sqrt{F_{xi}{}^{2}+F_{ y}{}^{2}}=\mu_{i}F_{zi} \tag{3}\]