esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 9 Conclusions  
This paper has thoroughly examined the latest advancements in fast charging infrastructure, elucidating widely adopted strategies alongside their advantages and disadvantages. It has delved into power electronic topologies and advanced control techniques to reduce charging durations and enhance efficiency within fast charging systems. Additionally, exploring the potential impact of fast charging on the electric vehicle (EV) industryhas underscored its significance for EV adoption. This research highlights the critical need to understand current technologies and anticipate future trends to effectively navigate the evolving landscape of fast charging and its implications for the broader EV ecosystem. It aims to bring clarity to the pursuit of faster charging times, emphasising high-power fast charging as a significant focal point of EV charging technology. The review explores methods to enhance the efficiency and effectiveness of fast charging systems, providing insights into current developments in EV fast charging techniques and power electronic converters utilized in fast-charging systems. Control strategies for electric vehicles are analysed, alongside the impacts of high-power fast charging on battery systems and future developmental trends. Various fast charging methods for electric vehicles are extensively examined, including inductive charging, UFC, DCFC, Tesla superchargers, V2G integration, and battery swapping, with a summary of the associated advantages and drawbacks for each technique provided. Moreover, the essential components and considerations involved in the intricate infrastructure for DC fast charging are outlined. An analysis of high-power rear DC-DC converters, both isolated and non-isolated, utilized in on-board and off-board chargers has been conducted. These two-stage or single-stage converters efficiently convert high-voltage DC power for the fast-charging infrastructure of electric vehicle batteries. Control strategies employed for these converters are also investigated, with the primary objective being to maintain consistent performance across the entire load spectrum, minimize current fluctuations, and enhance system efficiency. The efficiency, affordability, safety, reliability, and control mechanisms of converter configurations have significantly influenced the advancement of fast charging technology for electric vehicles. Fast charging systems integrated with V2G capabilities can lower charging expenses and enable EVs to engage in grid services, extending their interaction with the electrical grid beyond conventional charging. When connected to the grid, EV batteries can serve various purposes, including grid stabilization, load management, and integrating renewable energy sources.  
This research received no external funding. The authors declare no conflict of interest.
## 4 Experimental Results
### Accuracy in Terms of Regression
It can be seen that prediction errors are limited to 4% in almost all cases and are limited to even 2% with a probability exceeding 0.95. For the 80 th percentile, the prediction errors are limited to 1%, with predictions for OVS being marginally more accurate. Figure 12 shows the CDF of the error percentages in the predictions of the FNN. For all the considered VNFs, errors in the predictions of FNN are visibly large compared to SVR and MLR (cf. Figures 10 and 11, respectively). The FNN is able to produce relative accurate predictions for the OVS function. Predictions for the firewall function are least accurate.
## 4 Limitations  
The first limitation is given by the measurement protocol used in individual datasets. The first key parameter in this topic is given by the PPG sensor LED wavelength since the light beam targets different depths of tissue. It is known that green light is able to reach 1-2 mm immediately after the epidermis layer and the infra-red (IR) light is the strongest one which can reach the deepest layers of dermis while the red wavelength is situated in the middle in terms of performance. Therefore, the PPG obtained with IR light contained the most hemodynamic information which can be obtained with this technique. The second electrical related parameter is the sampling frequency used by the processing chain. Dataset 1 used 1 kHz where a high-detail level of signal was obtained, while Dataset 2 used 125 Hz. By lowering Fs, the gradient read by FD would be affected, thus the ANC results would suffer too. The third critical parameter which also has been tested, is the cut-off frequency for filtering the PPG signal apart from noise. By lowering this frequency, the details of the PPG are again attenuated, leading to a different ANC results value.  
The second limitation is given by the number of samples and its distribution. Even if around 70% accuracy has been obtained with a number of 219 and 140 patients, respectively, it is known that more data will strength the ML model by giving a better performance. The type of samples distribution plays an advantageous role as was shown by comparing Gaussian Dataset 1 and uniform Dataset 2. Although it is intuitive to merge two or more datasets in order to increase the number of samples, it is not a winning strategy. With a different measurement protocol and a different recordings distribution it will weaken or limit the overall accuracy of the ML model.
# Durability of Superamphiphobic Polyester Fabrics in Simulated Aerodynamic Icing Conditions
## 3 Results and Discussion  
Wetting properties were the main evaluation criteria for the durability of the superamphiphobic and superhydrophobic fabrics exposed to high aerodynamic shear and multiphase flow. The water contact angle and water roll-off angle are presented for both the airfoil configuration and for the hydrophobic fabrics in the flat plate configuration. The oil contact angle and roll-off angle are presented for the superamphiphobic fabrics in the flat plate configuration. Error bars represent one standard deviation in the population of measurements taken for each point. The values shown are an average of at least three measurements at different positions. Roll-off angle values plotted as 90\({}^{\circ}\) represent the case of complete pinning where gravitational force was insufficient to overcome adhesion of the liquid droplet to the fabric surface. As a general note, the fabrics themselves were not noticeably damaged after exposure to high aerodynamic shear, or icing conditions. That observation was confirmed by SEM images featured in this section.
# Numerical Study of the Seismic Response of an Instrumented Building with Underground Stories
## 2 Analysis Approach  
This investigation used an existing high-rise building to investigate the influence of different foundation and underground story modeling approaches and their interaction with soil on the seismic response of the structure. With only the framing completed by the 2010 Mw 8.8 Maule earthquake, instrumentation was installed thereafter to collect the building's response histories to aftershocks. In the study, the building was analyzed using a high-fidelity FEM that included the geometry and materials' properties based on the structural design drawings' information. Different soil-foundation-structure interaction modeling options are considered, some including a series of distributed springs acting around the foundation and subterranean perimeter walls. A base-case where the structure is modeled with a fixed condition at the ground surface level is adopted.
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 3 Analysis and Procedure of the Development Model
### Development Model Analysis Procedure  
Figure 2 depicts the procedure for developing the deep-learning-based model. Initially, the process involved establishing baseline values for traffic volume data, detecting anomalies in the raw data, and conducting data validation and preprocessing to correct and predict the traffic volume. Subsequently, datasets were organized for anomaly detection in traffic volume data, including a training set exclusively comprising normal values, a validation set with normal values, a secondary validation set containing both normal values and anomalies, and a test set comprising both normal values and anomalies. The third step involved defining the characteristics of the data to ensure appropriate data transfer suitable for each deep learning structure before the actual implementation of the deep learning model. The fourth step addressed the challenge of gradient vanishing during data analysis by setting input sequences for traffic volume prediction to construct the deep learning model. An architecture was then developed to evaluate and validate the predictive power of the devised model.  
Figure 2: Development of the architecture for deep-learning-based models.
# A Novel Kinematic Directional Index for Industrial Serial Manipulators
## 3 System Layout for KDI Validation  
Two industrial manipulators were chosen to verify the accuracy of the KDI: an Adept Cobra 600 and an Adept Viper 650 robots (manufactured by Adept Technology, Pleasanton, CA, USA). This choice was made by looking for the most common kinematic architectures for serial robots. The first robot is a Selective Compliance Assembly Robot Arm (SCARA), while the second one is an articulated robot with six degrees of freedom (see Figure 1). In the SCARA robot, the index is used only in the horizontal plane since the vertical movements depend only on a prismatic joint whose performance does not change throughout the workspace; in the second case, the index can be useful along any direction of the workspace. However, in order to achieve comparable results, the performance of the articulated robot will also be investigated in a horizontal plane.
# Effect of Slot Opening Width on the Air-Gap Magnetic Field of a Direct Drive Permanent Magnet Motor
## 2 Analytical Model
### Air-Gap Flux Density Analysis of DDPMM with 16 Poles and 18 Slots  
Figure 10 shows the analysis of the air-gap magnetic field distribution of DDPMM with 16 poles and 18 slots and its harmonic components at different slot opening widths. In comparison with  
Figure 9: Air-gap magnetic field analysis of the DDPMM with eight poles and nine slots at different positions: (**a**) magnetic field distribution at position 1; (**b**) harmonics at position 1; (**c**) magnetic field distribution at position 2; (**d**) harmonics at position 2; (**e**) magnetic field distribution at position 3; (**f**) harmonics at position 3; (**g**) magnetic field distribution at position 4; (**h**) harmonics at position 4.  
Figure 9, the agglomerated magnetic field region in Figure 10 is wider, the harmonic content in the air-gap magnetic field is lesser, and its waveform is relatively closer to the sinusoidal. In addition, the harmonics in Figure 10 are mainly composed of harmonics below the seventh harmonic.  
Figure 10a,b shows the magnetic field distribution at position 1. No serious fluctuation occurred in the concave section of the magnetic field, and the fundamental amplitude was increased compared with that in Figure 9. Furthermore, the harmonics mainly included the fifth harmonics when the slot opening width was less than 8 mm, and the main harmonic was the third harmonic when the width of the slot opening was greater than 8 mm. However, the amplitude of each harmonic was smaller than that of the harmonic in Figure 9 at the same conditions.  
The magnetic field distributions in Figure 10c,f show the same trend as that in Figure 9 in terms of the width and depth of the concaved magnetic field. However, the fundamental value increased, the amplitudes of the second and third harmonics decreased, and the total harmonics were suppressed. Figure 10g,h show that the magnetic field evidently changed when the slot opening width was between 2 mm and 6 mm. This was mainly affected by the fifth harmonic.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Full Wave Analysis Using EM Simulator  
In this section, the simulation results of design 6 are verified through full-wave electromagnetic simulation using the commercially available EM solver CST Microwave Studio. The optimized amplitude obtained using different metaheuristic algorithms in design 6 for PSLL minimization and presented in Table 8 are taken as input to simulate a 10-element inset-feed patch antenna array in the EM simulator. The dimensions of the patch antenna are presented in Figures 22 and 23 shows the geometrical representation of the antenna array.  
Figure 21: Array pattern of a 14-element CAA for design 11.  
Figure 22: Inset Feed Patch Antenna.  
Figure 24 displays the normalized array pattern with respect to the azimuthal angle for ULA, DA, STO, DBO, and the proposed MAOA. The simulation results indicate that there is a strong agreement between the radiation characteristics obtained using the EM simulator and the MATLAB software. The proposed MAOA presents the lowest PSLL in comparison to other optimization algorithms and finds applications in wireless applications.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 3 Model Construction
### Objective Function  
The primary objective of adjusting the train timetable is to efficiently restore the normal train service and align with the scheduled timetable. Therefore, the goal is to minimize the sum of discrepancies for all trains at each physical station between the scheduled timetable and the rescheduled timetable. The objective function is represented by Equation (1).  
\[\min W=G_{\text{deviation}}=\sum_{k\in K}\sum_{i\in I^{\prime}}\Bigl{(}\left| \overline{G}_{k,i}^{arr}-G_{k,i}^{arr}\right|+\left|\overline{G}_{k,i}^{dep}-G _{k,i}^{dep}\right|\Bigr{)} \tag{1}\]
# A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
## 3 Proposed Method  
In this section, we describe the proposed method that:  
1. classifies on-spot tweets from Twitter data by incorporating clustering and BERT, and
2. adds rating information to on-spot judged tweets  
In this section, we firstly, introduce the procedures involved in realization of the proposed method and further discuss its inner processes at each stage.  
The proposed method incorporates location clustering and classification techniques. The outline of the procedures involved, consists of a series of stages as observed in Figure 2.  
Figure 2 outlines the procedures involved in the realization of the proposed method. In stage A, tweets are collected from the Internet by specifying the keywords \"ngorongoro\" and \"serengeti\", which may appear anywhere in the tweet, by using an accredited Twitter API ([[https://developer.twitter.com/en/products/twitter-api](https://developer.twitter.com/en/products/twitter-api)]([https://developer.twitter.com/en/products/twitter-api](https://developer.twitter.com/en/products/twitter-api)), accessed on 15 January 2022). In stage B, we cluster the collected tweets by location. A K-means algorithm, which is a vector quantization algorithm introduced by [PERSON] et al. [19] is applied to tweets' location information to automatically partition them into clusters K, by calculating the nearest mean from cluster centroid. Tweets located within the target spot estimated boundaries are retained. Since the target spot boundaries are not explicitly specified, we decide our target spot boundaries with the help of Google maps ([[https://maps.google.com/](https://maps.google.com/)]([https://maps.google.com/](https://maps.google.com/)), accessed on 15 January 2022) which highlights the East, West, North and South boundaries of the target spot as follows;  
\begin{tabular}{l l} \(\bullet\) & East = 2\({}^{\circ}\)24\({}^{\prime}\)13.5\({}^{\prime\prime}\) S 35\({}^{\circ}\)16\({}^{\prime}\)03.4\({}^{\prime\prime}\) E \\ \(\bullet\) & West = 2\({}^{\circ}\)11\({}^{\prime}\)27.2\({}^{\prime\prime}\) S 34\({}^{\circ}\)07\({}^{\circ}\)58.8\({}^{\prime\prime}\) E \\ \(\bullet\) & North = 1\({}^{\circ}\)26\({}^{\prime}\)33.6\({}^{\prime\prime}\) S 34\({}^{\circ}\)48\({}^{\prime}\)45.0\({}^{\prime\prime}\) E \\ \(\bullet\) & South = 3\({}^{\circ}\)11\({}^{\prime}\)02.6\({}^{\prime\prime}\) S 34\({}^{\circ}\)38\({}^{\prime}\)08.2\({}^{\prime\prime}\) E \\ \end{tabular}  
In stage C, we manual annotate location clustered tweets as either on-spot or not. We also assign sentiment score to the tweets. To accomplish this task, we use three annotators. The details of annotation task is discussed in details in later part of this article.  
In stage D and E, we trained our classifier to predict tweets and the sentiment score assigned to them and further evaluate the model performance. We adopt a pre-trained BERT neural network model for this task. In stage F, we map selected and rated tweets as touristic information in the designed system (PSRS).
###### Abstract  
The study of the changes in stress and deformation of frozen walls during excavation has always been a hot topic in underground freezing engineering, and the size of the plastic zone is an important basis for evaluating the stability of frozen walls. In response to the shortcomings in the current design of horizontal frozen walls, based on the internal excavation unloading conditions of the frozen wall in artificial ground freezing, an elastoplastic mechanical model for the interaction between a circular horizontal freezing wall and unfrozen soil mass is established under nonuniform loads to obtain the corresponding solutions for stress and displacement in the system. In this study, considering the shear stress of the plastic zone, the method for solving the traditional plastic zone contour equation is modified; consequently, the modified solution of the contour equation of the plastic zone for the frozen wall is obtained. Using this theoretical solution, the influence of the external load \(p_{0}\) and the lateral pressure coefficient \(\lambda\) on the contour line of plastic zone and tensile stress zone are analyzed by combining the project case, the calculation results show that: the \(\lambda=0.485\) is the critical point where the inner edge of the frozen wall just happens to have tensile stress. When \(\lambda<0.485\), the tensile stress zone is inevitable in the inner edge of the frozen wall vertical direction, and its range is only related to \(\lambda\) and increases with the decrease of \(\lambda\). The \(p_{0}\) only affects the magnitude of tensile stress in the region, but does not affect its range. At this time, the frozen wall compression plastic zone contour evolves from crescent shaped to ear shaped with the increase of \(p_{0}\). When \(0.485<\lambda<0.61\), there will be no tensile stress zone, the frozen wall compression plastic zone contour also evolves from crescent shaped to ear shaped with the increase of \(p_{0}\). When \(\lambda>0.61\), there will be also no tensile stress zone, with the increase of \(p_{0}\). the compression plastic zone contour evolves from the crescent shaped in the horizontal direction to the elliptical shaped, and there is no ear-shaped plastic zone in the whole evolution process. Based on our results, we show that our method can be used to provide a theoretical basis for the stability evaluation and parameter (thickness) design calculation of horizontal frozen walls under nonuniform loads.  
horizontal freezing method construction; circular frozen wall; nonuniform load; unload model; elastoplastic analysis 2023
# Crop Prediction Model Using Machine Learning Algorithms
## 4 Crop Analysis and Prediction Benefits and Challenges  
As stated earlier, while machine learning is being exploited in multiple fields, it remains an active area of research and a challenging one in the agricultural domain. This section summarizes the main benefits and challenges ML faces when used in crop analysis and predictions based on recent research [46; 47; 48; 49; 50]. Figure 2 shows AI-based crop analysis and prediction phases.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
###### Abstract  
Radio frequency energy harvesting is one of the new renewable sources that faces some technical challenges, which limit its performance. This study presents two scenarios to enhance the harvested power. The first scenario introduces a quad-band voltage multiplier circuit with a single receiving antenna and four band-pass filters of elliptic type. In this scenario, four frequencies of the Global System for Mobile communications, Universal Mobile Telecommunications System, and Wireless Fidelity frequency bands have been considered for the study. The second scenario proposes a quad-band voltage multiplier circuit with four receiving antennas at the same frequency bands as the first scenario. High conversion efficiencies were achieved for the two scenarios. The proposed quad-band system developed a harvested power level, sufficient for powering up low power micro-devices with no need for an external power supply.  
RF energy harvesting; voltage doubler; non-linearity; quad-band; impedance matching +
Footnote †: journal: Applied Optics and Photonics
## 3 Results
### Impact of Pleiades Band Designations on Water Reflectance and Turbidity Retrievals  
#### 3.3.1 Sensor-to-Sensor Band Differences Based on the Relative Spectral Responses  
Compared to OLI and MODIS, the specifications of Pleiades bands do affect the water reflectance and turbidity retrievals. The Pleiades sensor has three visible and one NIR spectral bands and no SWIR band, so that only OLI and MODIS data can be corrected for atmospheric effects using the SWIR or NIR-SWIR algorithms.  
The next step was to determine how the designation of Pleiades, OLI and MODIS bands (bandwidth, spectral resolution) affects the relationship between the water reflectance (\(\rho\), dimensionless) and water turbidity (NTU) in the Gironde Estuary. For that, the relationships between the water reflectance values in the respective Green, Red and NIR spectral bands of these sensors were established, considering the differences in terms of spectral band sensitivity and widths. The field hyperspectral water reflectance measurements carried out in the Gironde Estuary in 2012 and 2013 [43] and the relative spectral responses (RSRs) (Figure 3) of Pleiades-1B, OLI and MODIS-A sensors were used in this purpose. The obtained relationships between the Green, Red and NIR spectral bands show that Pleiades reflectance match well with the corresponding OLI and MODIS bands, with simple linear relationships having a slope close to 1 and almost negligible intercepts in the Red and Green spectral bands (Figure 9, Table 3). By opposition a significant offset is systematically obtained in the NIR. The root mean square error (RMSE) is the minimum for the Green and Red bands (less than 0.010) and slightly higher in the NIR (\(\sim\)0.012). The mean absolute percentage error (MAPE) is systematically less than 7% in the Green and Red bands and up to 18% in the NIR.  
In Green and Red bands, the reflectance sensed by Pleiades is slightly lower than that of MODIS and OLI. The lowest difference is observed between the OLI and Pleiades Red bands (1.5%). In the NIR band, the Pleiades water reflectance is a bit larger than that of MODIS and OLI. The bandwidth of  
Figure 9: Relationships between water reflectance in the green, red and NIR spectral bands of Pleiades-1B and L8-OLI sensors (**a–c**), Pleiades-1B and MODIS-A (**d–f**). The red dashed lines are 1:1 lines and the oranges lines are the best Ordinary Least Squares linear fits.  
Pleiades is overall larger than OLI and MODIS, especially in the NIR band (Figures 3 and 9), where MAPE starts to become significant (12-18%), so sensor specific calibrations are needed (see Figure 10 and Table 4 below).