esa-sceva/satcom-chunk-collection huggingface dataset:
# Concept for Generating Energy Demand in Electric Vehicles with a Model Based Approach
## 4 Simulation and Model Environment
### Vehicle Model  
The virtual vehicle model is a computer-generated, mathematical representation of a real vehicle. It combines various subsystems that represent the simulated vehicle. These include:  
* The main body, with information about the total mass and the aerodynamic behavior of the vehicle;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **OSM** & **Google** & **TomTom** & **HERE** & **Others** \\ \hline \multirow{3}{*}{**Route**} & MapQuest, Skobbler, etc. & Directions & Routing & Routing & \multirow{3}{*}{-} \\  & \multirow{2}{*}{TMC} & Traffic & API & API & \\ \hline \multirow{3}{*}{**Realtime traffic**} & \multirow{2}{*}{TMC} & Traffic & Routing & Traffic & \multirow{3}{*}{-} \\  & & API & API & API & \\ \hline \multirow{3}{*}{**Elevation**} & Open-Elevation & Elevation & Search & Routing & \multirow{3}{*}{SRTM} \\  & API & API & API & API & \\ \hline \multirow{3}{*}{**Weather**} & Open-Weather & \multirow{3}{*}{-} & Advanced-Weather & Destination-Weather & \multirow{3}{*}{SolCast} \\  & API & & API & API & \\ \hline \multirow{3}{*}{**Speed limit**} & Overpass & Geocoder & \multirow{3}{*}{-} & Routing & \multirow{3}{*}{-} \\  & API & API & API & & API \\ \hline \multirow{3}{*}{**Traffic light**} & Overpass & \multirow{3}{*}{-} & \multirow{3}{*}{-} & Advanced & \multirow{3}{*}{-} \\  & API & & & Data sets & \\ \hline \multirow{3}{*}{**Bridge/ Tunnel**} & Overpass & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} \\  & API & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Services available from various providers via application programming interfaces (Traffic Message Channel (TMC), Shuttle Radar Topography Mission (SRTM)) [20].  
Figure 1: Blackbox schematic with input and output data for the route-generating algorithm [21].  
The steering system, which contains information about the ratio between the steering wheel angle (or steering wheel torque) and the steering rack displacement;  
The suspension, with the components including the springs, dampers, bushings, and stabilizers and the kinematics of the chassis;  
The drivetrain, which contains information about the engine, energy storage, motor control unit (MCU) and the battery control unit (BCU);  
The brakes, with information about the possible deceleration of the vehicle.  
The structure of the subsystems of the virtual vehicle model is shown schematically in Figure 2.  
For the BMW i3 evaluated in this study, the following vehicle parameters in Table 2 are used. The selected reference vehicle (BMW i3) is only used to validate the functionality by using real-world measurement results. By altering the individual parameters of the components (main body, drivetrain, steering system, brakes and suspension), it is possible to represent any vehicle and use it for the simulation.
## 2 Materials and Methods
### Scanning Electron Microscopy (SEM) and Confocal Laser Scanning Microscopy (CLSM)  
Prior to inoculation with bacterial biofilm, 10 titanium discs (5 M and 5 MRS) were evaluated by SEM (Zeiss 1555 VP-FESEM, Carl Zeiss MicroImaging GmbH, Gottingen, Germany) and CLSM (Eclipse Ti-E; Nikon, Tokyo, Japan) to provide a baseline.  
In addition, the assessment of pristine discs, contaminated untreated discs and residual biofilm measurements after application of each treatment was conducted. Images were recorded using Nikon(r) Ti-E inverted motorized microscope with Nikon A1 Si(r) spectral detector confocal system in 2 modes: reflection mode with emission wavelength of 575 nm and autofluorescence with emission wavelength of 525 nm. Reflection mode showed the surface topography of the disc only, while autofluorescence mode showed fluorescence of residual biofilm on each disc surface, allowing the quantitative assessment of residual biofilm volume after treatment. A pinhole of 127 nm and objective of 4\(\times\) magnification were used during imaging. After imaging was completed, processing of the images was performed. A rectangle of 11.5 mm\({}^{2}\) was superimposed on each half of each disc, and fluorescence of each rectangle was measured and recorded by NIS-C Elements(r) software (version 5.2.1).  
Following CLSM assessment, discs were dehydrated with graded ethanol solutions (50%, 70%, 80%, 90% and 100% ethanol solutions for 15 min each) and then dried in a critical point dryer. Discs were coated with platinum in preparation for SEM for qualitative analysis of surface changes. SEM assessment for surface alterations was performed at 1000\(\times\) and 5000\(\times\) magnifications using SE2 (WD: 20 mm, 3 KV) detector of Zeiss(r) 1555 VP-FESEM for both halves of each disc.
# Research on Dynamic Modeling and Transition Flight Strategy of VTOL UAV
## 5 Flight Strategy Calculation Case
### Slow Balancing State  
In the hover state, the forward flight speed is obtained by slowly adjusting the lift duct deflection angle, duct throttle and lift fan throttle, and the attitude pitch angle is chosen to be equal to the cruising pitch angle, which has value of 2 degrees, and its balance state is shown in the Figure 10.  
Similarly, the attitude angle \(\theta_{safe}=7.21^{\circ}\) at the end of the transition window is taken as the pitching attitude angle in the equilibrium state, and its balanced state is shown in the Figure 11.  
Calculation results show that a certain forward speed is observed when duct is deflected slowly during transition from hovering to acceleration. Since aerodynamic force increases as speed increases, which causes increase of lift, the lift fan power in vertical direction decreases. Thrust is gradually reduced as the duct angle gradually shrinks to \(0^{\circ}\). There exist two kinds of transition ways of pitch angle. When transition is performed with small pitching angle, the deflection angle of duct reduce  
Figure 10: Lift fan and lift duct state curve when pitch angle is \(2^{\circ}\).  
to zero, and the speed of UAV meet requirement of transition. When transition is performed at large pitching angle and the deflection angle reduce to zero, the speed of UAV has not met the need of transition terminal window, so duct throttle should be enlarged to get better secure speed for horizontal flight.  
In the process of slow balancing, by comparing different transition attitude angles, it can be seen that smooth transition to flat flight with small pitch angle can be observed, but the transition time is longer. The transition time of transition with large pitch angle is shorter, but the control parameters of the transition process change greatly, which is not stable compared with the transition of small pitch angle.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 3 Robotic Technologies Used in Railway Track Inspection and Maintenance
### Robotic Inspection Technologies in Railway
#### 3.1.2 Tunnel and Bridge Inspection  
Tunnels and bridges are essential components of the infrastructure of the railway network. The inspection of bridges poses fatal risks for the operator from ergonomics and workplace perspectives. Tunnels are usually dark and enclosed, lacking a cellular signal or GPS. Apart from the track inspection in the bridge and tunnel, a particular inspection method is required to inspect structural integrity.  
An unmanned aerial vehicle (UAV) can navigate in remote places both with and without the help of a human. [PERSON] et al. proposed a framework for autonomous bridge  
Figure 6: Autonomous track and S&C inspection robot: (**a**) Railpod [58]; (**b**) robot with ACFM sensor [59]; (**c**) AutoScan [48]; (**d**) RII1005 [22]; (**e**) Felix [60].  
inspection using a UAV equipped with LiDAR, GPS, IMU, and a camera [61], as shown in Figure 7a. Initially, a map of the test area was created with the help of a human operator, and then the UAV localized and navigated between different waypoints on a predefined map and acquired inspection data which were processed later. [PERSON] et al. also demonstrated automatic data acquisition for bridge bottom inspection using a tethered UAV [62], which is shown in Figure 7b. [PERSON] et al. developed an autonomous bridge bottom inspection robot with an unmanned ground vehicle (UGV) [63], as shown in Figure 7c.  
[PERSON] et al. designed a prototype of a steel bridge inspection robot, a combination of a rail kart and a manipulator, to discover research challenges [64]. Next, [PERSON] and [PERSON] designed and experimented with a robot with a magnetic wheel for steel bridge inspection. Magnetic wheels provide two advantages: they can climb vertical steel structures and create magnetic fields for sensing [65]. A specially designed robotic system with a guide rail was proposed, which came with a user interface for controlling it from a long distance [66].  
Cracks are common in tunnel walls. [PERSON] et al. presented tunnel wall surface crack detection and classification techniques based on a camera [67]. A semi-supervised computer vision-based robotic system, named ROBO-SPECT, with multiple degree-of-freedom was designed by [PERSON] et al. for use on a road tunnel [13]. As shown in Figure 8a, with the help of a robotic arm, the robot was able to inspect the crack on the surface wall with an ultrasonic sensor. [PERSON] et al. used deep learning-based semantic segmentation of images to detect cracks on the surface [68]. Moving tunnel inspection (MTI-200a), shown in Figure 8b, was used to acquire data which were later analyzed using a deep learning algorithm.  
Figure 8: Tunnel inspection robot: (**a**) ROBO-SPECT [11]; (**b**) MTI-200a tunnel inspection robot [68].  
Figure 7: Bridge inspection robot: (**a**) UAV with multiple sensors [61]; (**b**) UAV for bridge bottom inspection [62]; (**c**) UGV for bridge bottom inspection [63].
## 2 Digital Twin Technology for V2G
### Real-Time Data Integration and Processing  
The effectiveness of digital twins fundamentally depends upon data integration mechanisms that can process heterogeneous data streams while maintaining temporal consistency and ensuring quality. For EV digital twins, this involves managing data from multiple sources, including onboard vehicle sensors, charging infrastructure systems, grid monitoring devices, weather stations, and traffic management systems.  
The data integration framework must address several critical challenges: temporal synchronization between different data sources operating at varying sampling rates, data quality assurance through validation and filtering algorithms, and scalability to accommodate large-scale fleet operations. Advanced filtering techniques, including Kalman filters and machine learning-based anomaly detection, can be essential for maintaining data integrity in noisy operational environments.  
On the other hand, adaptive model calibration represents a crucial aspect of real-time data integration, where model parameters can be continuously adjusted based on observed vehicle behaviour. This process involves comparing predicted vs. actual performance metrics and implementing feedback mechanisms to minimize prediction errors. The optimization of demand-response and load balancing in smart EV charging networks using AI-integrated blockchain frameworks has demonstrated significant improvements in handling these complex calibration processes [35]. The calibration algorithms must be computationally efficient to enable real-time operation while maintaining model accuracy across diverse operating conditions.  
In the specific case considered here, a straightforward implementation can employ Extended Kalman Filters for state estimation, particularly for battery state-of-charge estimation where sensor accuracy is compromised during dynamic driving conditions. Battery SoC sensors are notoriously imprecise during acceleration and braking events when current fluctuations affect measurement accuracy. The Extended Kalman Filter addresses this limitation by combining the validated multi-physics model predictions with real sensor data, weighing physical model predictions more heavily than unreliable sensor readings during transient conditions, then continuously adjusting the state estimate as conditions stabilize. This model-sensor fusion approach provides accurate and stable SoC estimation, which is crucial for determining optimal timing for charging and discharging energy to the grid in V2G applications.
## 4 Discussion
(2022) [24], which included data from 188 patients, reported an average positive culture rate of 76% with significant reductions in VAS scores post-operatively. The review also highlighted the relatively low complication rates associated with endoscopic procedures, further cementing their role as a viable and effective alternative to traditional open surgery. Similarly, [PERSON] et al. (2021) [21] performed a systematic review of 14 studies involving 342 participants, reporting microorganism identification rates ranging from 54.2% to 90.4% and significant improvements in pain and functional status following endoscopic procedures. These reviews collectively emphasize the advantages of minimally invasive techniques, including reduced surgical trauma, lower blood loss, faster recovery times, and effective infection control, making endoscopic approaches particularly attractive for high-risk patients who may not tolerate more invasive surgical options. The endoscopic method, although it offers many advantages, is not without its limitations. First and foremost, it requires the surgeon to have appropriate experience and advanced technical skills. Acquiring such competencies can be time-consuming, which means this technique is associated with a relatively long learning curve. Therefore, before opting for the endoscopic approach, the surgeon should not be bold and realistically assess their skills and experience in performing such procedures. Additionally, the patient's anatomical conditions may pose a significant challenge. Limited space and difficult access to neural structures can potentially reduce the effectiveness of the procedure. Of course, there are key limitations to this study, including the retrospective design; for instance, the case series relied on retrospective data, which may introduce biases. Sample size, which consists of a limited number of cases from a single institution, reduces generalization. Furthermore, heterogeneity, as demonstrated by variability in patient conditions, techniques, and follow-up durations, limits direct comparability. Last is the lack of randomized comparisons--particularly, a control group treated with open surgery for direct efficacy comparison. No prior power analysis was conducted to ensure the adequacy of the sample size. By addressing these limitations in future studies, the evidence for the spinal endoscopy role in infection management can be significantly strengthened.
## 2 WBANs for Life and Safety Critical Applications
### Applications of WBANs
#### 2.2.2 WBANs for Mobile Workforce Safety and Health Management  
With the expansion and emergence of large mega construction projects, the safety and health of workers is also becoming a serious concern worldwide. For instance, according to the World Health Organization (WHO), the number of deaths due to work-related accidents or diseases, remains unacceptably high at around 2.3 million per year [23].  
In this context, it is expected that WBAN technology will enhance the safety and health of workers, for example, by enabling the remote monitoring of workers in unhealthy environments [18] as well as their activities [15] and physiological load [19], the tracking of mobile workers' positions to notify them about possible hazards (e.g., approaching trains [16], _etc._), the protection of construction workers against carbon monoxide poisoning [17], and many others applications.  
To that end, each worker will be fitted with a set of wearable sensing devices to continuously monitor their: (i) _physiological parameters_ (e.g., energy expenditure, heart pulse rate, stress level, _etc._); (ii) _location_ (e.g., GPS position, relative proximity to hazards, _etc._); (iii) _body movement_ (e.g., body posture and orientation, activity, fall detection, _etc._); and (iv) _surrounding environment_ (e.g., toxic gases, CO\({}_{2}\) or CO, heat, _etc._). All the collected information are then processed locally  
Figure 2: WBANs for Rescue and Emergency Management Application Use-Case.  
and/or remotely to better anticipate and protect the deployed mobile workforces from accidents and hazards, thus reducing injuries and fatalities and improving working conditions.  
Two typical application scenarios for mobile workforce safety and health management are shown in Figures 3 and 4, respectively. The first scenario illustrates a safety-critical track warning system [22] where base stations are deployed along a railway to detect approaching trains, and to notify trackside railway workers through wearable and real-time communication devices, thus improving their safety. The second application scenario illustrates a safety and health management system for construction workers, where each worker is fitted with wearable sensing and communication devices to protect them from the presence of potentials hazards and to better anticipate health-related issues.
# Antimicrobial Volatiles of the Insect Pathogen _Metarhizium brunneum_
## 2 Materials and Methods
### Collection and Identification M. brunneum VOCs  
Headspace VOCs were collected from the above treatments using a 50/30 mm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) SPME fibre (Supelco, Bellefonte, PA, USA) and analysed using an Agilent 6890N Gas Chromatograph equipped with an HP-5 MS fused capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness), which was interfaced directly with an Agilent 5975 mass spectrometer. Helium was used as the carrier gas with a constant flow of 1.0 mL/min. Immediately after collecting the VOCs, the SPME needle was inserted manually into the injection port (230 \({}^{\circ}\)C; splitless mode) of the GC-MS for thermal desorption and held for 2 min. After desorption, the oven was held at 40 \({}^{\circ}\)C for 2 min, then the temperature was raised to 200 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min. Finally, the temperature was raised to 270 \({}^{\circ}\)C at a rate of 8 \({}^{\circ}\)C/min and held at 270 \({}^{\circ}\)C for 10 min. Mass spectra were scanned repeatedly over 35-650 amu. Ionisation was performed in electron impact (EI) mode at 70 eV. A blank run was performed after each analysis to confirm that no residual compound was polluting the fibre or column. Total ion current (TIC) chromatograms were integrated without any correction for co-elution and results were expressed as the percent of the total peak area. All peaks were identified from their mass spectra via comparison with spectra in Wiley Registry (9 th edition) and NIST11 (National Institute of Standards and Technology, Gaithersburg, MD, USA) libraries. Identifications were confirmed by comparing the retention time, molecular ions and fragmentation pattern with authentic standard samples. The peaks observed in the control (blank media without fungus) were excluded from the samples during the sample analysis. All reference compounds used for identification were purchased from Sigma Aldrich (Poole, UK), except methyl 2-ethylhexanoate, which was synthesized by reacting 2-ethylhexanoic acid with methanol in the presence of concentrated sulphuric acid (catalyst), as outlined by [33].
## 5 Conclusions  
This article presents a qualitative comparison between the BESO and SESO methods, revealing that both are effective in generating optimal topologies for three-dimensional structural systems, considering compliance minimization and the influence of the structure's self-weight. The analyzed examples demonstrate that these methods produce configurations that meet structural efficiency requirements, remaining robust even in the face of variations in boundary conditions and design parameters. Furthermore, the incorporation of the structure's self-weight into both methods, along with reliability analyses, represents a significant contribution to the field of optimal topology for structural systems. Notably, SESO exhibits a lower computational cost in all examples, while BESO results in lower compliance. These approaches provide engineers with an advanced and reliable tool for the automated design of structures by distinguishing between tensile (blue) and compression (red) regions, ensuring greater efficiency in practical applications.  
During the investigation, it was found that the sensitivity of the von Mises stress derivatives with respect to the x and z directions produces results similar to those presented in the literature for tensile and compression states that use principal stresses. However, the authors are developing a mathematical and geometric relationship to demonstrate this correspondence for a future publication.  
Figure 14: Graph—objective function by reliability index.  
Figure 15: The P*: Ideal g-function approximation point.  
**Author Contributions:** The authors have been working together for over nine years, and the tasks in this article were developed as follows: (1) H.L.S.--Implemented BESO-3D and SESO-3D programs in MATLAB code and responsible for writing and structuring the article. (2) V.S.A.--Helped with the implementation of the SESO-3D codes and responsible for reviewing the article, participated in the data analysis of the numerical examples in the article. (3) F.d.A.d.N.--Contributions to the preparation of the article: reading, review, and suggestions for examples. (4) S.Z.A.--Implemented self-weight for SESO. (5) M.M.d.S.--Participated in reading and reviewing the article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Instituto Federal de Educacao Ciencia e Tecnologia de Minas Gerais-IFMG, CNPq (National Council of Scientific and Technological Development) under Grant Number 306721/2023-6 and 316771/2023-6.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors are grateful to the Instituto Federal de Educacao Ciencia e Tecnologia de Minas Gerais-IFMG, CNPq (National Council of Scientific and Technological Development) under Grant Number 306721/2023-6 and 316771/2023-6.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 System Model and Problem Formulation
### Problem Formulation  
To share subchannel resources between the CUs and DPs, it is important to analyze the transmission power management method. To apply the power management method while generating a D2D connections, the general form of transmit powers for CU \(n\) and DP \(m\) can be derived as follows [23]:  
\[P_{n}=\ P_{n}^{max}.\ \frac{P_{N}}{l_{n,B}^{-\alpha}} \tag{8}\]  
and  
\[P_{m_{t}}=\ P_{m_{t}}^{max}.\ \frac{P_{N}}{l_{m_{t},m_{t}}^  
and non-line of sight (NLOS) communication environments. Therefore, the PL model is expressed as [25]:  
\[PL_{DP}=57+23.5 log_{10}(l)+23 log_{10}\bigg{(}\frac{f}{5}\bigg{)}, \tag{12}\]  
where \(l\) denotes the distance between the transmitter and receiver in meters (30 m \(<l<\) 1.5 km) and \(f\) is the carrier frequency in GHz (2 GHz \(<f<\) 6 GHz).