esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SEM Test  
We have performed SEM studies on a 3D scaffold of PLA material to analyze the accuracy of the three pore size settings and the size of the printed layers. Figure 6 displays the highly variable layout and sizes of this type of 3D scaffold made with the FDM technique.  
In none of the cases is there a clear trend towards a fixed pore size. However, in the layer width, two superimposed layers are observed that correspond to the passes made by the extruder nozzle in each direction. A total 12 measurements have been carried out in different places; the average pore sizes measured for 300, 400 and 500 turned out to be 175.95 \(\pm\) 19.55 \(\upmu\)m, 238.78 \(\pm\) 19.45 \(\upmu\)m y, and 382.82 \(\pm\) 43.37 \(\upmu\)m, respectively. Contractions have been applied to the pores, whose magnitudes were close to 40% in 300 and 400 \(\upmu\)m, as well as 23% in 500 \(\upmu\)m. Similarly, the average measured layer sizes turned out to be 517.77 \(\pm\) 25.42 \(\upmu\)m, 557.68 \(\pm\) 13.35 \(\upmu\)m, and 549.37 \(\pm\) 17.45 \(\upmu\)m, respectively. The cross-section view shows an offset zone between the lower part of the 3D scaffolds and the first layer deposited, whose measurements are: 223.4 \(\pm\) 7.93 \(\upmu\)m, 247.83 \(\pm\) 1.15 \(\upmu\)m and. 238.47 \(\pm\) 9.26 \(\upmu\)m. This offset may be due to a lower contraction of the material in that lower area attached to the trap, in which the temperature gradient is not that high. Similarly, the thicknesses of each of them have been measured, applying the correction of the corresponding angle until obtaining its true magnitude, resulting in: 460.40 \(\pm\) 16.19 \(\upmu\)m, 460.40 \(\pm\) 16.19 \(\upmu\)m, 488.9 \(\pm\) 22.04 \(\upmu\)m and, 545.50 \(\pm\) 9.1 \(\upmu\)m. This last result suggests an increase in thickness as the pore size increases and, therefore, the size of the 3D scaffold increases as well.
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## 3 Optimization Procedure  
### Optimal Control Problem  
The objective of this paper is the optimization of motor energy efficiency for dynamic operation, that is for changing load torques and/or for changing speeds. To evaluate the effect of changing speeds, a speed ramp \(\omega_{\text{Rm,ref}}(t)\) in an interval \(t\in[t_{0}\quad t_{\text{f}}]\) is considered. In contrast to the efficiency optimization methods for steady state operation, the power losses will not be constant in the considered time interval. Therefore, the efficiency must be evaluated based on the overall energy losses. In addition, the speed must closely match the reference speed trajectory. The speed deviation therefore must also be considered in the objective functional, which is formulated as follows:  
\[J=\int_{t_{0}}^{t_{\text{f}}}\Bigl{(}(P_{\text{L}}(t)+q\bigl{(}\omega_{\text{ Rm}}(t)-\omega_{\text{Rm,ref}}(t)\bigr{)}^{2}\Bigr{)}dt\;. \tag{13}\]  
The variable \(q\) represents a weighting factor in \(\text{W}/(\text{rad s}^{-1})^{2}\) for specifying optimization priorities. The impact of this factor is discussed in Section 3.3.  
The optimal control problem must consider the current, flux, and speed dynamics (6)-(9), the equality constraint with respect to the torque (10), and the inequality constraints (14):  
\[\sqrt{U_{\text{Id}}^{2}+U_{\text{Iq}}^{2}}\leq U_{\text{1,max}}\qquad\qquad \qquad\qquad\sqrt{I_{\text{Id}}^{2}+I_{\text{Iq}}^{2}}\leq I_{\text{1,max}}\;, \tag{14}\]  
where \(U_{\text{1,max}}\) is the maximum length of the inverter output voltage phasor, which depends on the DC link voltage. The variable \(I_{\text{1,max}}\) represents the current limit given by the minimum of the inverter and motor current limits.  
Due to the nonlinear nature of the objective functional (13), the system of Equations (6)-(10), as well as the constraints (14), the optimization problem will be solved using a sequential programming method to find the minimum of the constrained nonlinear multivariable function.
## 3 Results  
### Testing  
There were 1414 images for training and testing and 341 for external validation in the final dataset. The evaluation of boundary box metrics showed a moderate overlap with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all 5-folds were 0.694 and 0.611, respectively. The mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Figure 1 illustrates the detection of the keypoints including the reference line for automatic measurements. Notably, the evaluation metrics were determined by considering varying IoU thresholds from 0.50 to 0.95 (Table 1). This means that we considered true positive cases when the overlap between the prediction and the ground truth was over 50%. Lower thresholds would lead to higher AP and AR metrics.  
The intersection over union (IoU), average precision (AP) per fold, average recall (AR) per fold, and mean average precision (mAP) and recall (mAR) over all folds of cross-validation are shown for the box (molar) and keypoint (apical, mesial, distal) detections. maxDets: a given number of maximum detections. IoU = 0.50:0.95: varying thresholds within the given range.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Boxes**} & \multicolumn{2}{c}{**Keypoints**} \\ \hline IoU = 0.50:0.95 & \multicolumn{2}{c}{IoU = 0.50:0.95} \\ \hline Fold 1 AP & 0.534 & Fold 1 AP & 0.639 \\ Fold 2 AP & 0.758 & Fold 2 AP & 0.622 \\ Fold 3 AP & 0.717 & Fold 3 AP & 0.726 \\ Fold 4 AP & 0.725 & Fold 4 AP & 0.643 \\ Fold 5 AP & 0.735 & Fold 5 AP & 0.531 \\ \hline IoU = 0.50:0.95 & \multicolumn{2}{c}{IoU = 0.50:0.95} \\ \hline Fold 1 AR & 0.642 & Fold 1 AR & 0.616 \\ Fold 2 AR & 0.614 & Fold 2 AR & 0.513 \\ Fold 3 AR & 0.664 & Fold 3 AR & 0.556 \\ Fold 4 AR & 0.546 & Fold 4 AR & 0.633 \\ Fold 5 AR & 0.589 & Fold 5 AR & 0.579 \\ \hline mAP & 0.6938 & mAP & 0.6322 \\ mAR & 0.611 & mAR & 0.5794 \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation of the training dataset.
## 4 Experiments and Analysis
### Compared Methods  
We compared LPS-Net with state-of-the-art methods, including traditional methods and deep learning methods. Traditional methods included UDCP [9], IBLA [49], and MLLE [36], and deep learning methods included UWCNN [22], Water-Net [5], PRW-Net [15], Shallownet [38], Ucolor [14], UIEC\({}^{\prime}\)2-Net [50], PUIE-Net [16], FUNIE-GAN [43], UHD-SFNet [18], HAAM-GAN [40], MFFF [19], and the latest NU2 Net [17] for underwater image enhancement.
## 4 Conclusions  
Satellite remote sensing has been an indispensable tool in the retrieval of vegetation traits. The application of traditional vegetation indices has long been the norm for translating satellite images into tangible information and subsequently using these indices as proxy to biophysical properties. The advent of contemporary satellites has made available augmented information in key spectral bands, such as ultraviolet, red edge, and thermal, and at increased numbers of bands and radiometric accuracies, opening the way for new methods of approximating biophysical traits. In the current case study conducted in a tropical private forest, we demonstrated that the exhaustive combination of the simple spectral ratio between all the Sentinel-1 and Sentinel-2 bands yields considerably higher correlation compared to nine traditional vegetation indices. The spectral ratio from bands 8 and 2 from Sentinel-2 yielded an R\({}^{2}\) = 0.70, while the best-performing popular spectral index, namely EVI, yielded R\({}^{2}\) = 0.65. When restricting the data pool to only monodominant plots, the R\({}^{2}\) was 0.78 for the band combination 7/2, a fact which can perhaps be attributed to the homogeneity of the field specimens. Overall, we claim that an investigation of the complete band combinations for small scale studies can provide a stronger correlation compared to the random application of popular vegetation indices and circumvent the assumptions on the environmental, structural and ecosystem characteristics that are made for the selection of a vegetation index.  
[PERSON]: Conceptualization, Methodology, Visualization, and Writing--Reviewing and Editing; [PERSON]: Data curation, Investigation, and Writing. [PERSON] and [PERSON]: Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was supported from Prince of Songkla University and the Ministry of Higher Education, Science, Research and Innovation under the Reinventing University Project (Grant Number REV64017).  
The satellite data that support the findings of this study are openly available from the Copernicus Open Access Hub at [[https://sciubb.copernicus.eu/dhus/#/home](https://sciubb.copernicus.eu/dhus/#/home)]([https://sciubb.copernicus.eu/dhus/#/home](https://sciubb.copernicus.eu/dhus/#/home)) (accessed on 25 February 2022).  
The authors would like to thank [PERSON] for collecting and providing the in situ data used in this study.  
The authors declare no conflict of interest.
# Feasible Energy Density Pushes of Li-Metal vs. Li-Ion Cells
## 5 Achieving Long Cycle Life and Long Driving Distance Range with a Reasonable Excess Lithium  
As EVs are the main drivers for battery technology developments, the most suitable use-case scenario can be created by using target driving ranges. In this section, a theoretical analysis of excess lithium factor is made by considering different EV battery capacities, different energy consumption, and driving cycles.  
Considering different EVs and their battery sizes, three different average specific energy consumption rates can be assumed: 15 kWh/100 km, 20 kWh/100 km and 25 kWh/100 km. We have chosen three different battery sizes from the study of [PERSON] et al. [39]:  
* [noitemsep,topsep=0 pt]
* Peugoeit e-208: 50 kWh
* Audi e-tron: 95 kWh
* Tesla Roaster: 200 kWh (outlook, future battery design)  
End-of-life (EoL) driving distances of EVs range from 150,000 to 300,000 kms (from 8 to 15 years) [40]. The table below gives the theoretical required cycle numbers in order to achieve 150,000 and 300,000 kms with different EV battery capacities and different energy consumption scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\text{X}_{\text{Li}}\) & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{3}{*}{Cathode thickness (\(\upmu\)m)} & 50 & 50 & 50 & 50 & 50 & 50 \\  & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\  & 150 & 150 & 150 & 150 & 150 & 150 & 150 \\ \hline \multirow{3}{*}{Anode thickness (\(\upmu\)m) (after Li deposition)} & 8.8 & 23.5 & 38.2 & 52.9 & 67.6 & 82.3 & 97.0 \\  & 17.6 & 47.0 & 76.4 & 105.8 & 135.2 & 164.6 & 194.0 \\  & 26.5 & 70.5 & 114.6 & 158.7 & 202.8 & 246.9 & 291.0 \\ \hline \multirow{3}{*}{Li foil thickness (\(\upmu\)m)} & 0 & 14.7 & 29.4 & 44.1 & 58.8 & 73.5 & 88.2 \\  & 0 & 29.4 & 58.8 & 88.2 & 117.6 & 147.0 & 176.4 \\ \cline{1-1}  & 0 & 44.1 & 88.2 & 132.3 & 176.4 & 220.5 & 264.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Required Li-metal Anode thickness for given cathode thicknesses.  
As has been represented above, small batteries with higher energy needs per 100 km will require more cycles to achieve the target driving distance. Additionally, the EV batteries are already expected to reach between 1000 and 3000 cycles.  
In Section 3 we have shown the correlation between the CE and the cycle number (Equation (8)). The amounts of excess lithium required for different cycles and different CE values are listed in the Supplementary Materials. An overview is presented below.  
If we combine the results of Tables 4 and 5, it is very clear that big capacity batteries offer the ideal EoL driving range with lower cycle numbers. These cycle numbers can be reduced even more by decreasing the energy consumption (kWh/100 km). It should be noted that this consumption increases linearly with the weight of the EV, which can be reduced by use of light weight batteries.
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Year** & **Aim** & **Experimental Evaluation** & **Assessment Metrics** \\ \hline P28 [89] & 2021 & The main goal of this approach is to shorten the size of the selected feature vector by combining TLBO and SSA techniques, which can also increase the classifier’s predictability. & A total of 651 breast cancer screenings were produced by the hybrid approach, and the outputs demonstrate that TLBO-SSA performs better than the TLBO. Once more, the strength of this metaheuristic approach was evaluated by taking a UCI dataset. The TLBO-SSA result demonstrated its superiority when compared to GA. \\ \hline P29 [90] & 2020 & In order to increase the initial HHO’s effectiveness for collecting chemical descriptors and chemical composites, this work combined HHO, CS, and chaotic maps. & Some UCI datasets and two chemical datasets are considered to validate the presented solution. Comprehensive experimental and computational analysis showed that the proposed approach has achieved many desired solutions over other competing solutions. \\ \hline P30 [91] & 2021 & This article proposes a hybrid optimal strategy that includes SCA in HHO. By adjusting the candidate solutions in a complex manner, SCA attempts to tackle ineffective HHO identification and to prevent stagnation situations in HHO. & With 16 datasets including more than 15,000 in attributes and the CEC’17 computational optimising trials, this recommended approach was evaluated and contrasted with SCA, HHO, and other existing methods. The detailed evaluations of experiments and statistics showed that the suggested HHO hybrid variant produced effective results without extra computational cost. \\ \hline P31 [92] & 2021 & This article suggests a hybrid GWO-HHO-based FS technique. & In comparison to GWO, PSO, HHO, and GA, the accuracy of the suggested hybrid approach was tested and evaluated on 18 UCI datasets. The approach performed better than the GWO. \\ \hline P32 [93] & 2017 & This research suggests a new AC-ABC hybrid technique that incorporates the FS characteristics of ACO and ABC. By employing hybridization, the stagnation behavior of the ants is removed, and lengthy global searches for the original solutions by the employed bees. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._The search methods that have been fused together in each metaheuristic approach, the details of the corresponding fitness/objective function along with the respective means of hybridization which have been used in each approach are given in Table 3.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 3 Candidate Waveforms
### Orthogonal Frequency Division Multiplexing
The high amplitude of OFDM's spectrum outside the allocated bandwidth is due to the sharp transitions of the rectangular pulse used in the signal generation, whose spectrum is a sinc. This way, the PSD of OFDM signal is a superposition sum of sinc shaped spectra, each one associated to a sub-carrier and centered in the corresponding frequency. These lateral lobes, of considerable amplitude add together giving rise to considerable OOB emissions [4; 34], producing a decrease in the spectral efficiency of the system since they may cause interference on any adjacent channel. Besides the restricted spectral efficiency, time domain transmitted signals in an OFDM system can have high peak values since the instantaneous amplitude of each sub-carrier that form the OFDM symbol is added by the IFFT operation. Therefore, OFDM systems are also conditioned by the ratio of high peak power vs. average power ratio, i.e., PAPR, which grows proportionally to the number of sub-carriers employed on the transmission and is usually evaluated through its complementary cumulative distribution function (CCDF) [25]. Such a high PAPR demands that the power amplifier must operate with a large output backoff in order to amplify the signal within its linear range and avoid amplifier's saturation (which can lead to signal distortion), but that decreases amplifier power efficiency [27]. As can be concluded, OFDM does not fully meet some of the requirements for future wireless communication and further enhancements in this field can be made. This brought the need for the development of new techniques as alternatives to CP-OFDM, with greater spectral and power efficiency. Within the context of 5G and further generations of wireless communications, many alternative waveforms have been the subject of many recent studies [4; 11; 12; 34; 35; 36; 37; 38; 39; 40], with several techniques being proposed as Generalized Frequency Division Multiplexing (GFDM) [41; 42; 43], Filtered-OFDM (F-OFDM) [44; 45], the Time Interleaved Block Windowed Burst Orthogonal Frequency Division Multiplexing (TIBWB-OFDM) technique [46; 47; 48] and its variant with windowing time overlapping (WTO) [49; 50].
## 4 Results
### Spatial Concentration and Distribution of Vulnerable Populations
#### 4.3.2 Analysis of Daytime Population Data  
In addition to the residential population, we analyzed daytime population data to identify areas where vulnerable groups are concentrated throughout Gangseo-gu. This analysis included population density evaluations by time zone, allowing us to identify areas with significant population shifts during the day. By analyzing the density of specific age groups, particularly older adults and children under 14 years old, we identified the times and areas where these populations are most concentrated. For instance, Figure 8 (left) shows the concentration of the older adult population, while Figure 8 (right) illustrates the distribution of children under 14 years old.  
Interestingly, our analysis of the daytime population revealed a different pattern from that of the residential population. During the day, the population tended to concentrate more on the outskirts of Gangseo-gu, contrasting with the more centralized residential patterns. This discrepancy indicates the necessity of considering both the daytime population and the residential population when assessing the vulnerability of these groups to environmental hazards, such as fine dust. This comprehensive analysis of both resident and daytime population data underscores the importance of incorporating dynamic population shifts into planning and intervention strategies. By understanding where and when vulnerable populations are most concentrated, more effective and targeted measures can be developed to protect them from exposure to fine dust and other environmental risks.  
Figure 7: Residential distribution of children under 14 years old around Sinbanghwa-Magok and Yeomchang-Jungmi Stations.
## 1 Introduction
If the feedback signal lags behind, the control system is difficult to achieve a high performance [20]. The purpose of introducing additional signal is reducing the phase lag. Low pass filter (LPF) and high pass filter (HPF) can form a filter to utilize the phase advance information [21]. However, it is difficult for any input signal to compensate for the phase lag of LPF. A phase-lag-free LPF has been proposed using higher-order signal in [22]. This method can suppress noise without obvious phase lag. Although the common data fusion method can effectively suppress the gyro drift, the measurement accuracy is not high due to the influence of maneuver. The application of GPS and magnetometer can suppress the maneuvering acceleration interference effectively. However, GPS is not suitable for suspended platform without movement since it is necessary to establish the correction quantity related to the inertial attitude [23]. The measurement accuracy of magnetometer is not high due to electromagnetic interference [12]. In addition, signal filtering will lead to phase lag inevitably. Compensating the phase lag is also effective to improve the performance of the control system. Optoelectronic platforms adopt vibration isolators to isolate the interference from the carrier, such as attitude motion and vibration [24]. The LOS stability control accuracy of optoelectronic platform can be improved as long as the attitude estimation based on multi-sensor fusion is applied. The contributions of this study are as follows:  
(a) An adaptive threshold criterion is designed based on the accelerometer data in the sliding window. The threshold is determined online to judge whether the influence of maneuvering acceleration can be ignored;(b) Data fusion based on gyro and accelerometer (DFBGA) and suppressing maneuvering acceleration disturbance method (SLAIM) are designed in attitude estimation. Based on adaptive threshold criterion, DFBGA is applied if the influence of maneuvering acceleration can be ignored. SLAIM is applied if the influence of maneuvering acceleration cannot be ignored. It is also adopted in the estimation of yaw attitude because it cannot be measured by accelerometer;  
(c) In order to compensate for the phase lag error introduced in the above attitude estimation process based on Kalman filter, a phase-lag-free LPF is proposed to revise the phase lag by applying the higher order signal. This paper is organized as follows. The problem formulation is introduced in Section 2. The attitude estimation method design is proposed in Section 3. Simulations and experiments are described in Section 4. At last, Section 5 concludes the paper.
## 3 Results and Discussion
### Estimation of the Level of Potentially Toxic Elements Pollution
#### 3.2.2 Igeo  
Obtained Igeo values are presented in Table 3.  
According to previous literature [40], the Igeo values were interpreted as follows:  
Igeo 0 indicates no contamination; 0 \(<\) Igeo \(\leq\) 1 no/moderate contamination; 1 \(<\) Igeo \(\leq\) 2 moderate contamination; 2 \(<\) Igeo \(\leq\) 3 moderate/strong contamination; 3 \(<\) Igeo \(\leq\) 4 strong contamination; 4 \(<\) Igeo \(\leq\) 5 strong/extreme contamination; and Igeo \(>\) 5 extreme contamination.  
In our case, all values were found to be lower than 0, with the only exception of copper (Cu), indicating a very moderate contamination by Cu for all the investigated samples in all sampling sites. This occurrence can be justified, in agreement with [41, 42], considering that this metal is typically employed as a soil defense product, the soil texture and high pH--all factors that can contribute to Cu accumulation.