esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Several conclusions can be drawn from the studies carried out.  
First, for the lithium-ion cells, it is impossible to use the classic Peukert equation in the entire range of discharge currents as it does not correspond to experimental data for medium and very small discharge currents (Figure 3).  
Second, the generalized Peukert Equations (7) and (9) correspond well to the experimental data at any discharge currents.  
Third, among Equations (7)-(9), the most preferable ones for practical use are the generalized Peukert Equations (8) and (9). These equations approximate the experimental data with the least relative error (Table 2).  
Fourth, research has shown that, when calculating the remaining capacity of a battery, its temperature must be taken into account. The dependence of the discharge capacity on temperature is proposed in [6], which correctly describes this dependence at all temperatures, both very low and high.  
Fifth, on the basis of experimental studies, an improved analytical model by [PERSON] is proposed for calculating the remaining capacity of lithium-ion batteries under dynamic loads. The proposed model is true for any discharge current in a wide temperature range.  
Of course, the proposed analytical model requires further experimental and theoretical research, which is the topic of our further scientific studies.  
Conceptualization, N.E.G.; methodology, N.E.G., N.N.Y. and D.N.G.; software, N.E.G. and D.N.G.; validation, N.E.G., N.N.Y. and D.N.G.; formal analysis, N.E.G. and D.N.G.; investigation, N.E.G., N.N.Y. and D.N.G.; resources, N.E.G., N.N.Y. and D.N.G.; data curation, N.E.G. and N.N.Y.; writing--original draft preparation, N.E.G. and N.N.Y.; writing--review and editing, N.E.G. and D.N.G.; visualization, N.E.G. and N.N.Y.; supervision, N.E.G.; project administration, N.E.G.; and funding acquisition, N.E.G., N.N.Y. and D.N.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Polarimetric Time-Frequency DOA Estimation Using Dual Algorithms
\mathbf{\Psi}, \tag{40}\]where the rotation operation \(\mathbf{\Psi}\) is described as  
\[\mathbf{\Psi}=\text{diag}\Big{[}e^{-|2\pi\frac{d}{\lambda}\sin(\phi_{1})},...,e^{-|2 \pi\frac{d}{\lambda}\sin(\phi_{N})}\Big{]}. \tag{41}\]  
The signal eigenvectors consist of the columns of \(\mathbf{U}_{s}\) acquired approximately from the signal subspace, which is spanned by the SPTFD matrices. A transformation matrix \(\mathbf{T}\) satisfies  
\[\mathbf{U}_{\mathbf{s}}= \begin{bmatrix}\mathbf{AQ}^{[v]}\\ \mathbf{AQ}^{[h]}\end{bmatrix} \mathbf{T}. \tag{42}\]  
By utilizing the same transformation matrix \(\mathbf{T}\) to the two subarrays' steering matrices, \(\mathbf{U}_{s1}\) and \(\mathbf{U}_{s2}\) are described as  
\[\mathbf{U}_{\mathbf{s}1}= \begin{bmatrix}\mathbf{A}_{1}\mathbf{Q}^{[v]}\\ \mathbf{A}_{1}\mathbf{Q}^{[h]}\end{bmatrix} \mathbf{T}, \tag{43}\]  
\[\mathbf{U}_{\mathbf{s}2}= \begin{bmatrix}\mathbf{A}_{2}\mathbf{Q}^{[v]}\\ \mathbf{A}_{2}\mathbf{Q}^{[h]}\end{bmatrix} \mathbf{T}. \tag{44}\]  
According to the above two equations,  
\[\mathbf{U}_{\mathbf{s}2}=\mathbf{U}_{\mathbf{s}1}\mathbf{G}. \tag{45}\]  
The matrix \(\mathbf{G}\) satisfies  
\[\mathbf{G}=\mathbf{T}\mathbf{T}\mathbf{T}^{-1}, \tag{46}\]  
where the matrix \(\mathbf{G}\) eigenvalues are \(e^{-|2\pi\frac{d}{\lambda}\sin(\phi_{n})}\), \(n=1,2,\ldots,N\). For solving the matrix \(\mathbf{G}\), the overdetermined Equation (46) is solved by the least squares approach and the total least squares approach. In this paper, the least squares approach is used:  
\[\mathbf{G}=\left(\mathbf{U}_{\mathbf{s}1}^{\text{H}}\mathbf{U}_{\mathbf{s}1} \right)^{-1}\mathbf{U}_{\mathbf{s}1}^{\text{H}}\mathbf{U}_{\mathbf{s}2}. \tag{47}\]  
At the low signal-to-noise ratio, the PTF-MUSIC algorithm has a higher success rate, but the estimation error is slightly larger. On the contrary, although the estimation error of the PTF-ESPRIT algorithm is small, the success rate of estimation is slightly lower. With the increase in SNR, the performance of both is improved. Similarly, PTF-MUSIC has the advantage of a high success rate, while PTF-ESPRIT has the advantage of small error. This paper observes these characteristics and combines them. Based on the rough estimation from PTF-MUSIC, the results obtained by PTF-ESPRIT are compared with the rough estimation results. If the difference between the two is within 5 degrees, the results of PTF-ESPRIT are selected. The success rate of estimation is guaranteed, and the accuracy of estimation is improved as much as possible.
# Hybrid A-Star Path Planning Method Based on Hierarchical Clustering and Trichotomy
## 3 Hybrid A-Star Algorithm and Trichotomy Search
### Design of the Evaluation Function  
The evaluation function of the Hybrid A-star algorithm consists of the cumulative cost function \(g(n)\) and the heuristic cost function \(h(n)\), as depicted in Equation (10). For the cumulative cost function \(g(n)\), aiming to minimize the path length, a cumulative cost function for the path is defined. Meanwhile, to ensure path smoothness and minimize steering maneuvers, a penalty is imposed on vehicle steering within the cumulative cost function. The specific cumulative cost function is shown in Equations (11)-(13):  
\[g(n_{1})=S \tag{11}\]  
\[g(n_{2})=\sum_{0}^{i}\left|\varnothing_{i}-\varnothing_{p}\right| \tag{12}\]  
\[g(n_{i})=g(n)+\alpha g(n_{1})+\beta g(n_{2}) \tag{13}\]  
where \(S\) represents the set expansion step length; \(g(n_{1})\) considering the cumulative length of the path to ensure it is as short as possible; \(g(n_{2})\) incorporates a penalty for the change in heading angle between consecutive states to steer the vehicle smoothly; \(\varnothing_{i}\) denotes the heading angle of the new expanded node; \(\varnothing_{p}\) represents the heading angle of the parent node; \(g(n_{i})\) represents the cumulative cost for the child node; \(g(n)\) represents the cumulative cost for the parent node; and \(\alpha\) and \(\beta\) are the weighting coefficients for each term in the cost function.  
The heuristic function \(h(n)\) estimates the cost from the current point to the goal point. In the A-star algorithm, if the heuristic cost function provides a closer estimate of the  
Figure 3: Diagram of node expansion mode. (**a**) Node expansion of A-star algorithm. (**b**) Node expansion of Hybrid A-star algorithm.  
actual cost of the remaining path, it can significantly improve the search efficiency of the algorithm. A similar property exists in the Hybrid A-star algorithm. Therefore, to bring the heuristic function value closer to the actual value, the shortest collision-free distance from the endpoint to each grid point planned by the A-star algorithm is used as the heuristic function value.
## 2 Overview of Autonomous Vehicles (AVs)
### Spectrum of Autonomous Vehicles  
As mentioned earlier, the automation process in vehicles comprises several levels, each denoting varying degrees of autonomy. The National Highway Traffic Safety Administration (NHTSA) [18] has proposed a detailed six-tier categorization to encapsulate the spectrum of vehicle automation:  
* **No Automation (Level 0)**: At this level, vehicle operation is entirely under the control of the human driver, including the core functions of steering, brakes, throttle, and motive power.
* **Driver assistance (Level 1)**: At this level, while the vehicle operation continues to be largely controlled by the driver, certain driving assistance features, such as automatic braking or lane assistance, may be integrated into the vehicle's system.
* **Partial Automation (Level 2)**: At this level, the vehicle is equipped with advanced automated capabilities that can control both steering and acceleration/deceleration, but the driver must maintain active engagement with the driving environment and be ready to take control if required.
* **Conditional Automation (Level 3)**: Vehicles at this level can handle all critical driving functions under certain conditions. However, the driver must be ready to retake control when the system requests, thus maintaining an active supervisory role.
* **High Automation (Level 4)**: Vehicles at this level can perform all necessary driving tasks autonomously under specific conditions. The driver has the option to control the vehicle, but it is not a requirement, and the vehicle can operate independently when conditions permit.
* **Full Automation (Level 5)**: This level represents the epitome of vehicular automation, where the vehicle is capable of executing all necessary driving functions throughout an entire journey, under all driving conditions, without any form of human intervention.
## 7 Case Study: Position Control of a Rotor by PII\({}^{1/2}\)DD\({}^{1/2}\) Control
### Comparison of PID and PII\({}^{1/2}\)DD\({}^{1/2}\) Control in Frequency Domain and Time Domain
It is possible to note that:  
* the influence on the frequency response of \(\rho\) for \(1<\rho<10\) is moderate; therefore, in the example of Section 7.1, a value in the middle of this range was selected;
* the PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{H}\) with \(\rho=1\) does not correspond to the PID, even if its corner frequencies are paired two by two and correspond to the ones of the PID (\(\omega^{{}^{\prime}}_{c1}=\omega^{{}^{\prime}}_{c2}=\omega_{c1}\); \(\omega^{{}^{\prime}}_{c3}=\omega^{{}^{\prime}}_{c4}=\omega_{c2}\)), and consequently the asymptotic bode plots are the same (the \(-10\) dB/dec and \(+10\) dB/dec sections have null length). The second point may seem counterintuitive, as it comes from the fact that, as indicated by the following equation,  
\[\left(1+\frac{s^{1/2}}{\omega_{c}^{1/2}}\right)\left(1+\frac{s^{1/2}}{\omega_ {c}^{1/2}}\right)\
eq\left(1+\frac{s}{\omega_{c}}\right) \tag{25}\]  
two half-zero terms with the same corner frequency \(\omega_{c}\) do not have the same transfer function and frequency response of a zero term with corner frequency \(\omega_{c}\), even if the asymptotic Bode plots are the same, with a change of slope from \(0\) dB/dec to \(+20\) dB/dec in \(\omega_{c}\). For example, Figure 9 shows the Bode plots of (i) a half-zero term with corner frequency of \(100\) rad/s (blue); (ii) two half-zero terms with corner frequency of \(100\) rad/s  
Figure 8: Influence of the ratio \(\rho\) of the controller Bode plots: PID (blue) and PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{H}\) (red), for \(\rho=1\), \(4\), \(10\), \(50\), \(200\), \(\rho_{max}=355.1\). (red); (iii) a zero term with corner frequency of 100 rad/s (yellow). It is possible to note that the first and third plots have the same asymptotic trends for magnitude and phase, but the plot of the zero term is closer to the asymptotic plots than the one of the two half-zeros. This explains why the gain of the PII\({}^{1/2}\)DD\({}^{1/2}\) with \(\rho=1\) is higher than the one of the PID in Figure 8.  
Case Study: Position Control of a Rotor by PII\({}^{1/2}\)DD\({}^{1/2}\) Control in Discrete Time
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 2 Materials and Methods
### Experiment Design
Data retrieval between the UAS mini laptop was scheduled before the UAS flight using the unique PakBus address associated with the CR1000X datalogger of each sensor node station. While the UAS was in the line of sight of a sensor node station, the UAS data ferry transceiver radio (which was connected to the mini laptop) would contact and establish wireless communication with the nearby sensor node station secondary radio. The UAS was scheduled to take one minute to fly from the start point to station 'A' where it would establish wireless communication with the sensor node station secondary radio and download data within a minute before moving to the next station (station 'B'), where it would repeat the contact, wireless communication, and data retrieval protocol for each node station until the UAS returned to the start point (Figure 4a). Given the time required for the UAS to fly from one node station to another (approximately 1 min each) and the time required to retrieve data from a sensor node station when the UAS maneuvers over the station (approximately 1 min), the entire data collection by the UAS data ferry from start to finish took 9 min in 2020 with four node stations and 11 min in 2021 with five node stations. Figure 6: (a) Sensor node station comprising of infrared radiometer sensor sensing the crop canopy; (b) the layout of the datalogger along with the secondary radio and the power battery source.
## 5 Discussion
### Implications for Indoor-Outdoor Campus Navigation App Design
#### 5.4.1 Landmarks and Additional Route and Location Information  
Landmarks play an essential role in wayfinding and navigation tasks [66] and can aid in the acquisition of spatial knowledge without much workload [71]. Landmarks can be used by wayfinders as a point of reference for the identification of directions and for making route decisions which can reduce confusion and hesitation [66]. Therefore, navigation systems should use physical landmarks as a directional aid in wayfinding directions because they help with orientation [73,74]. Previous research has provided different landmark suggestions. According to [PERSON] et al. [15], a global landmark in the area does not help navigation if they are not shown on the map as a navigation clue. According to [PERSON] et al. [70], AR displays are useful for supporting individuals in acquiring landmark and route knowledge. In the present study, participants who focused on landmarks and the surrounding environment were more confident navigating in the wayfinding task.  
It was also found from qualitative data that most participants faced challenges related to route and location. Participants in both groups (Google Maps and MazeMap) reported issues related to missing or no information provided (see Figure 2). Some of the participants' suggestions to improve the navigation app included: \"Provide information about access to rooms\", \"Maybe show which doors I have access or not\", \"Clearly marked elevators\", \"Building/door opening hours\", \"Info telling me where public access doors are\", \"Clearly marked entrances, including which are for common use and which require special access\", \"Providing options of accessible entrance (updates)\". Previous research also shows that route and location information is important and can improve navigation aid for users [59]. [PERSON] et al. [75] found that floor identification is important where a user can travel between floors, and it is crucial to provide floor numbers in multi-floor structures.  
According to [PERSON] et al. [76], the surrounding information, such as elevator locations, access information, and floor changes, is useful to aid in the orientation and mobility of users and increase confidence during navigation by confirming their location. [PERSON] [59] concluded that navigation aids could be improved for users by designing predictive clues and rhythm in mobile-based navigation applications. [PERSON] et al. [75] suggested that sensor-based 3D localization strategies should be used for localizing a person traveling through elevators, stairs, or escalators. [PERSON] et al. [77] suggested adding landmark objects into real-world environments in order to enhance the user experience of indoor navigation systems.
## 3 Methods
### Spectral Angle Mapper  
#### 3.2.1 Principle of the Spectral Angle Mapper  
The spectral angle mapper (SAM) treats the \(n\) bands in an image as an n-dimensional space, where each band is treated as a vector, and the value of each band is simply the length of the vector corresponding to that band. The SAM first generates a standard reference spectrum using the mean value of \(k\) known feature samples as a standard, which is then matched to the original spectrum of the image element. The image is classified by calculating the generalized angle \(\theta\) between the standard reference spectrum and the original spectrum of the image element (Figure 5). The smaller the angle \(\theta\), the more likely the image element belongs to a known feature class. Since the angle between two vectors is independent of the vector length and is unaffected by factors such as solar illumination [29], SAM can suppress shadowing effects and highlight the reflective properties of the target; it is a simple, fast, and effective classification method.  
Figure 4: Sample selection results.  
Figure 5: Principle of the spectral angle mapper.
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 4 Experiments
### The Effect of a Hybrid Loss Function  
We conducted experiments to evaluate the model's performance under different loss function settings. We first evaluated the performance of using BCE and FL individually and then fused the two loss functions in the ratio of 1:1. The results are reported in Table 3. We observe that the model with FL outperformed the model with BCE, validating the efficacy of FL in the case of imbalanced data distribution. In addition, the model with a hybrid loss function performed the best, with an F1 of 0.9275. The result demonstrates the effectiveness of the fusion strategy in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epochs** & **Batch Size** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{2}{*}{6} & 32 & 0.8525 & 0.8478 & 0.8501 \\  & 16 & 0.8495 & 0.8364 & 0.8429 \\ \hline \multirow{2}{*}{8} & 32 & 0.8654 & 0.8701 & 0.8677 \\  & 16 & 0.8643 & 0.8618 & 0.8630 \\ \hline \multirow{2}{*}{10} & 32 & **0.8987** & 0.8932 & **0.8959** \\  & 16 & 0.8903 & 0.8827 & 0.8865 \\ \hline \multirow{2}{*}{12} & 32 & 0.8848 & 0.8956 & 0.8902 \\  & 16 & 0.8817 & 0.8893 & 0.8855 \\ \hline \multirow{2}{*}{14} & 32 & 0.8902 & **0.9012** & 0.8957 \\  & 16 & 0.8949 & 0.8878 & 0.8913 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of SentiBERT-BiLSTM-CNN under different hyper-parameter settings.
## 4 Discussion of Academic Literature and Council Uses
### Infrastructure  
For key infrastructure tasks, councils are increasingly relying on drones, including for inspections of road conditions, dams, and cemeteries. Beyond these uses, drones offer significant potential in improving last-mile logistics. Their use in reducing carbon emissions and alleviating road congestion, especially in last-mile delivery scenarios, is particularly notable. The integration of truck-drone delivery modes offers an effective solution for real-time requests, combining the advantages of road and aerial transport. Furthermore, drones are invaluable in transport analysis, such as for analysing pedestrian and bicycle volume data and for vehicle identification from aerial footage. A significant application is the use of drone imagery over time, combined with deep learning models, which enables local authorities to monitor the deterioration of road surfaces. This capability is crucial for timely maintenance and improving road safety.
# Overview and Perspectives for Vehicle-Integrated Photovoltaics
## 2 Results and Discussions
### Examination and Outlook of PV Technologies for PV-Powered Vehicles  
#### 2.2.1 Review of the PV Technologies and Their Performance  
A large number of PV cell concepts and technologies exist. The evolution of cell efficiency in research was presented by NREL [68].  
GaAs single-junction cells and multijunction cells have the best efficiencies but also highest costs. Crystalline Si-based cells present a good reliability and the best compromise between performance and price. Thin-film technologies have lower efficiencies but a better flexibility and lower weight. Lastly, emerging PV technologies such as perovskite cells present high promise to simultaneously achieve high efficiency, low price, ultralow weight, and flexibility. Today, this technology still has limited trustworthiness, longevity, and yield compared to large-scale crystalline silicon cell-based modules (Figure 4).  
The main commercial PV technologies are c-Si-based with PERx cells for example. The ITRPV 2021 roadmap estimated the market to be split across PERx/TOPCON, SHJ, IBC, and tandem cells in 2031 [70]. Interconnection technologies using cut cells have numerous advantageous for EV applications. The cells can embrace a curved shape. In addition, smaller cells allow higher parallel sections of cells in series to be established for the same module voltage. As the voltage follows a logarithmic relationship with the irradiance while the current is proportional to the irradiance, such architectures would help to reduce the impact of partial shadowing. A large number of main commercial PV modules are not adapted for integration into EVs as they are planar, nonflexible, and heavy [71].
# Thermal Strain-Based Simplified Prediction of Thermal Deformation Caused by Flame Bending
## 2 Prediction of Thermal Deformation
### Residual Strain  
In general, the residual strain can be considered as a source of deformations and residual stresses resulting from heat processes such as welding, cutting, heating, and bending. Thus, if the residual strain is known, the deformation and residual stress can be estimated using simplified analysis instead of 3D thermo-elasto-plastic analysis. In this way, the computation time can be greatly reduced. Therefore, in the SDB method, it is very important to determine the residual strain. The process of generating residual strain can be divided into two stages: a heating phase that undergoes heating and a cooling cycle that cools down from high temperature to room temperature. Here, it is assumed that the plastic stress and strain of the material do not occur when the material is tensile from heating but are caused by the shrinkage of the material during the cooling cycle [10]. Thus, the deformation of the plate mainly occurs during the cooling phase. The analysis is performed assuming that the thermal strain is the initial strain of the HAZ, and that the surrounding area is an element that constrains the HAZ. Equation (1) shows that the total strain (\(\varepsilon^{total}\)) through the process of flame bending (or line heating) is mainly divided into four parts: plastic strain (\(\varepsilon^{p}\)), elastic strain (\(\varepsilon^{e}\)), thermal strain (\(\varepsilon^{th}\)), and phase transformation strain (\(\varepsilon^{ph}\)).  
\[\varepsilon^{total}=\ \varepsilon^{p}+\ \varepsilon^{e}+\varepsilon^{th}+ \varepsilon^{ph} \tag{1}\]  
Since the deformation and the stress are produced by the total strain and elastic strain, residual strain in flame bending includes plastic, thermal, and phase transformation [8; 9]. Considering that the distortion and the residual stress are produced by the residual strain, the inherent strain can be expressed by Equation (2) [6]. This relationship is shown in Figure 1.  
The effect of the previous heating lines is so great that it is impossible to accurately predict the thermal deformation by elastic analysis based on improved inherent strain as shown in Figure 1. Therefore, the elastic-plastic analysis based on thermal strain is presented [8; 10]. If the thermal strain (including phase transformation strain) replaces the improved inherent strain as the thermal expansion coefficient in the SDB method, it can reflect the plastic strain generated by previous heating lines. Thus, the elastic-plastic analysis based on thermal strain should be more accurate in multiple heating-line systems.  
\[\varepsilon^{*}=\ \varepsilon^{total}-\varepsilon^{e}=\ \varepsilon^{p}+ \varepsilon^{th}+\varepsilon^{ph} \tag{2}\]