esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
For a dynamic WPTS, DD coils or rectangular coils would be preferable because they allow the transfer of higher energy while the receiving coil runs over the transmitting one [41; 42]. Nevertheless, in this paper the FEM model of the circular coils has been used because it makes it possible to compare the intermediate numerical results with the available experimental measurements and to validate the model itself before using its outputs in the optimization algorithm. On the other hand, from the point of view of the optimization algorithm, the coil pair is represented by the series of the inductive parameters in different positions. Consequently, the algorithm is in no way aware of the shape of the coils or influenced by it, and could be applied without any change for the optimization of the CNs of a coil pair having any other different shape. Besides the coils, the proposed model encompasses the bottom of the car frame and the magnetic shielding system, i.e., ferrite yokes and an aluminum plate. The transmitting coil is supplied by a current at 85 kHz, as prescribed by the SAE standard [43]. The simulated device corresponds to a laboratory prototype and was sized to transfer a power of 600 W to charge a minister battery [40]. At nominal condition, the current in the transmitting coil is 5.7 A and the corresponding voltage induced across the receiving coil is approximately 90 V.
## 4 Results and Discussion
### Naive Bayes Results  
Then, the Naive Bayes model was applied to the training data sample. Table 6 presents the results obtained using the following distributions: Gaussian, Bernoulli, and Multinomial.  
Moreover, with accuracy reaching 66.62%, the specific instance of the Naive Bayes classifier, using the Multinomial distribution, stood out from the others that were analysed. The Accuracy metric, used to investigate whether the model is accurate in its classifications, obtained 66.98%. Sensitivity or recall, indicative of assertiveness frequently in the classification of the patient's clinical evolution, obtained a value of 0.9618, considering the optimum equal to 1. This was a significant result. The harmonic means between precision and recall (F1 score) resulted in 0.78966 for the model's overall quality.  
The area under the ROC curve or Area Under the ROC curve (AUC) was verified for all Naive Bayes distributions, as shown in Figure 6. The highest value reached was 0.64 for the multinomial distribution, which means a 64% chance of correctly classifying clinical evolution using the multinomial naive Bayes.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{_Naive Bayes_} \\ \hline Metrics/Distribution & Accuracy & Precision & _Recall That_ & _F1 Score_ \\ \hline _Gaussian_ & 65.13\% & 66.42\% & 0.9401 & 0.77843 \\ \hline _Bernoulli_ & 59.37\% & 66.93\% & 0.7439 & 0.70462 \\ \hline _Multinomial_ & **66.62\%** & **66.98\%** & **0.9618** & **0.78966** \\ \hline \end{tabular}
\end{table}
Table 6: Benchmark for Naive Bayes.  
Figure 5: ROC curve—Hamming.  
Figure 6: ROC curve—multinomial.
# Resolution Aspects for Near-Field Projections
###### Abstract  
This work intends to define the resolution requirements for near-field projections in a psycho-physical study design to evaluate the participants' perception under the influence of different ambient lighting levels and various viewing distances. The variation in ambient lighting and viewing distances relates to various daytime and critical distances in urban environments. The application of near-field projections increases the popularity of communication- or safety-relevant projections, such as for automated vehicles. However, previous studies in the filming industry have shown that the resolution requirements differ depending on the application. In this work, a field study design presents an experimental approach to define a perceived resolution on the street surface in the near field around the vehicle. Furthermore, the study evaluates the influence of viewing distance, ambient lighting and projection content on the perceived resolution in detail. The results reveal a significant dependency on ambient lighting (\(p<0.05\)). Furthermore, this work states that the symbol-based projection has lower resolution requirements, e.g., a viewing distance of 1 m and 3 m results in a 2 pixels per degree resolution compared to the text-based projection in the parking garage scenario. Nevertheless, in the dusk/dawn scenario, the perceived resolution can be grouped for viewing distances above 1 m for content-independent projections.  
near-field projections; resolution; ambient luminance; projection content; logistic regression; human-centric lighting; interactive design Article Resolution Aspects for Near-Field Projections [PERSON] and [PERSON] (2023) 13 1039 1
## 5 Results and Evaluation
### Comparison of Used Machine Learning Classifiers  
The performance of the selected classifiers can also be compared using statistical significance tests for comparing machine learning algorithms, even though the dataset used in this case study was small and training multiple copies of classifier models were not expensive or impractical. As each model was evaluated on the same dataset, [PERSON]'s test could be used to compare the classification algorithms if the models seem to predict the same way or not. [PERSON]'s test is a paired nonparametric or distribution-free statistical hypothesis test [38]. This test checks if the disagreements between two cases match. Therefore, [PERSON]'s test is a type of homogeneity test for contingency tables.  
The results of [PERSON]'s test (Table 4) show that paired classifiers with a \(p\)-value higher than 0.5 had consistently underpredicted and overpredicted the same test set. It means the classifiers have a similar error proportion on the test set. Simultaneously, the results show that they are not significant in the majority of cases.  
On the other hand, all classifiers paired with the NB model had obtained a \(p\)-value lower than 0.5. If different models make different errors, one model is more accurate than the other and most likely has a different proportion of errors on the test set. In that case, the result of the test is significant. Therefore, as it was already assumed, the NB classifier is considered not suitable for predicting student's dropout in this case.
## 3 Results
### Expression of the pltzA Gene in Mutants Defective in PHZ and/or PRN Production  
The first gene in the PHZ biosynthetic operon is _pltzA_ (EY04_26270, annotated as PHZ biosynthesis protein). Expression of _pltzA_ was studied by qPCR in _P. chloroformis_ PA23 (Wt), PA23-63 (_pltz_'), PA23-8 (_prn_'), and PA23-63-1 (_phz_' _prn_'), as well as the _gacs_, _rpoS_, and SR mutants. There was no change in _pltzA_ gene expression in the _pltz_', _prn_', or _pltz_' _prn_'. _chlororaphis_ mutants in either RMM glucose or RMM octanoic acid cultures (Figure 5). In the _gacs_, _rpoS_, and _relA/spoT_ regulatory mutants, there was a significant decrease in the expression of _pltzA_ in all three strains grown in glucose; in octanoate, _pltzA_ expression was reduced in PA23 gacS and PA23 rpoS.  
Figure 4: Growth and PHA accumulation, with substrate and nitrogen consumption by _P. chloroformis_ PA23 in RMM glucose versus RMM octanoic acid media. (**A**) Cell dry mass production; (**B**) PHA accumulation; (**C**) Residual glucose and octanoic acid concentrations; and (**D**) Residual ammonium-nitrogen concentrations. Samples for RNA isolation from RMM glucose cultures were taken at 24 h pi and from RMM octanoic acid cultures at 16 h pi.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 5 Proposed Future Security Mechanisms
### Post-Quantum Cryptography  
As quantum computers become more of a reality over time, it is important to consider the implications of these machines and their capabilities. Quantum computers pose a significant risk to classical cryptography, as approaches such as [PERSON]'s algorithm make it feasible to effectively break current public key cryptography approaches given a quantum computer with a sufficiently high number of qubits. In 1994, [PERSON] created a quantum algorithm for factoring integers and solving discrete logarithmic problems, essentially breaking two commonly used encryption algorithms, Elliptic Curve Cryptography (ECC) and RSA. Currently, the US implements AES encryption as the standard approach, replacing its predecessor DES, which is now considered insecure. Fortunately, no current quantum computer is powerful enough for this task, as the number of available qubits in current quantum computers falls far short of the required amount to effectively apply [PERSON]'s algorithm [122]. However, if advances are not made in current encryption schemes in order to protect against quantum attacks, AES may eventually be vulnerable to quantum attacks. Understanding the theory behind these claims also sheds more light on whether or not quantum computers threaten 5G operations and or its safeguards for confidentiality, integrity, and availability. Current efforts also focus on a new generation of cryptographic algorithms that aim to be robust against such quantum attacks. These new algorithms are known as post-quantum ciphers or post-quantum cryptography (PQC).
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 3 Critical Influence Factors  
The proposed ADDR index is calculated in the frequency domain, and its basis is to obtain an accurate DUT output spectrum. Therefore, it is of great significance to ensure all the critical factors are controlled. They are leakage, windowing, number of sampling points, step size of the moving threshold and span of FFT bins of the specified signal.
## 3 Empirical Results and Analysis
### Establishment of Prediction Equations of Peak Ground Motion
#### 3.1.2 EPA Prediction Equation  
Effective peak acceleration (EPA) is a measure of peak acceleration with average significance, calculated using the acceleration response spectrum. It has the unit of gal and is calculated based on the following formula:  
\[\text{EPA}=R_{a}/2.5 \tag{4}\]  
where \(Ra\) represents the smoothed average of the 5% damped acceleration response spectrum within the frequency range of 2 to 10 Hz, divided by 2.5 to obtain the EPA [2].  
Due to the influence of high-frequency components and various complex factors, PGA is highly unstable, as it is controlled by high-frequency ground motion. The seismic response of a structure does not solely depend on individual PGA values. For example, decreasing the peak values that are above the structural natural frequency does not substantially affect the structural response because the response is influenced by the contributions of all harmonic components within a certain frequency band [20,21]. The use of response spectra for the measurement of the seismic response of structures is an appropriate and broadly accepted method. Considering the typical natural frequency range of general building structures, the frequency range of 2 to 10 Hz is commonly selected. Dividing the average value of the smoothed response spectra with a damping of 5% by 2.5 yields the equivalent value of EPA [2]. In this research, Model 3 was utilized to fit the EPA prediction equation. The coefficients are presented in Table 5, and the EPA attenuation curves shown in Figure 3 suggest that the vertical EPA exhibits a slightly slower decay rate compared to the horizontal EPA.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **A + \(\sigma\)** & **B** & **C + \(\sigma\)** & **Adjusted R-Square** \\ \hline Horizontal EPA & 5.415 \(\pm\) 0.167 & / & \(-\)1.818 \(\pm\) 0.0826 & 0.992 \\ \hline Vertical EPA & 4.797 \(\pm\) 0.165 & / & \(-\)1.644 \(\pm\) 0.0854 & 0.995 \\ \hline \end{tabular}
\end{table}
Table 5: Coefficients of EPA prediction equations.  
Figure 2: (**a**) Fitted attenuation curves for EW and NS PGA values; (**b**) fitted attenuation curves for horizontal and vertical PGA values.
## 1 Introduction
Therefore, this paper uses SIF and NDVI data to estimate the vegetation phenology in Northeastern China from 2001 to 2020, and focuses on the following issues: (1) the spatiotemporal distribution of vegetation phenology in the context of climate change; (2) the change patterns of different vegetation types; and (3) the main driving forces of different vegetation phenology changes. This paper has a positive significance in monitoring the potential of vegetation for photosynthetic phenology and exploring the environmental impact mechanism of vegetation phenology through SIF.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Development of Textile Materials
#### 3.3.4 Energy-Harvesting Textile Materials  
It is difficult to charge smart clothes every day. Lithium rechargeable batteries are unable to power sensors continuously due to their weak mechanical stability [99]. Smart clothes can extract the energy generated by a human body. Georgia Tech researchers have formed energy-harvesting yarns that are woven into textiles. Static electricity was used, which was generated by the friction of two materials. The fabric sewn into socks, jumpers and other clothes is capable of harvesting energy from the body movement to power a sensor. Infineon, the semiconductor company, is currently working on recovering energy for body movement to feed Mp3 players integrated in a jacket with the use of piezoelectric materials [100]. University of Bolton has developed a technology with the use of piezoelectric polymer substrate and photovoltaic coating system creating a film or fibre structure to harvest energy from the sun, rain, wind and wave [101]. Energy-harvesting film  
Figure 13: Working principle of self-cleaning fabric.  
in textiles uses printing process for gathering energy through active printed inks [102; 103]. Mechanical energy in the form of vibration or motion is converted into an electric current by flexible fibre-coated zinc oxide nanowires. A cost-efficient and scalable method was developed by scientists of RMIT University to fabricate textiles embedded with energy storage devices enabled by graphene supercapacitors. This method is capable of producing a waterproof smart textile patch. The capacitor was connected to a solar cell, which can deliver an efficient, long-lasting and self-powering smart fabric [104; 105].
## 4 Discussion
### Comparison with Previous Investigations
In other words, to take into account the finite accuracy of the setup verification pipeline (accuracy of PPS, PVS, registration software, kV images and DRRs resolution, etc. ), the proposed protocol could be modified by defining thresholds below which no pre-corrections are applied (e.g., 1 mm for translations and 0.5 degrees for translations). As long as the algorithm used is based on averaging procedures, such thresholds should take into account a combination of the average values of shifts as well as their SD. As a matter of fact, high values of SD mean high dispersion. As per the definition, this strategy is particularly successful if the systematic component markedly prevails over the random component of the error, that is, if SD is low. Analyzing the SD values of first fractions could therefore be useful to helping us understand whether to apply or not the pre-correction strategy. Alternatively, damping pre-correction based on population statistics is frequent and can prevent situations in which the strategy not only has no advantages, but also leads to an overall worsening.
## 2 Materials and Methods
### Equipment and Diagnostic Techniques
#### 2.2.2 Elastography  
Real-time elastography was performed after the conventional ultrasound evaluation, in the same examination session, using a HITACHI PREIRUS machine (Hitachi Medical Corporation, Tokyo, Japan) equipped with real-time elastography software, using a 5-18 MHz linear multifrequency probe positioned perpendicular to the skin during compression.  
The qualitative SE (standard blue-red-green color map) was determined for each nodule. A Ueno score of 1 to 5 was assigned for each nodule based on the color balance of green and blue inside the tumor and the surrounding area. At least two measurements were performed for each solid nodule, in radial and anti-radial sections. All ultrasounds were performed by the same operator prior to any surgery, biopsy or fine-needle aspiration.  
The semiquantitative technique used was the Fat-to-Lesion Ratio (FLR) automatically computed by the machine. After placing the Region of Interest (ROI) around the entire solid lesion, the machine automatically generates a second ROI placed in the surrounding pre-glandular subcutaneous fat, resulting in a stiffness ratio called the FLR score being attributed to each nodule. A FLR ratio higher than 4.5 was considered indicative of malignancy [19].  
Multiple previous studies described excellent diagnostic quality for elastography, when used alone (Se, 100 %; Sp, 73.8%; Acc, 80%) and when used in conjunction with conventional US evaluation (Se, 88.5%; Sp, 78.6%; Acc, 80.9%) [14]. Other studies show comparable results with sensitivity ranging between 88% and 93% and specificity ranging between 83% and 95.7%, with an accuracy of 93.9 [18; 24; 28; 29; 30; 28; 31].  
Both qualitative evaluation (color code Tsukuba diagnostic performance: Se, 86.5%; Sp, 89.8%; and Acc, 88.3%) [32] and the semiquantitative procedure (FLR, Fat-Lesion Ratio, diagnostic performance: Se, 88%; Sp, 83%; and Acc, 85%) have noteworthy diagnostic value in discriminating benign from malignant lesions [33; 34; 35].
# Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks
## 2 System Model
### Pilot Transmission and Channel Estimation  
In the proposed system scenario, a set of orthogonal pilot sequences of length \(\tau_{p}\) is used to obtain the channel statistics. Since the number of user nodes \(K\) is more than the number of orthogonal pilot symbols \(\tau_{p}\), that is, \(\tau_{p}<K\), each user cannot be provided with a unique pilot. Thus, the pilots are shared among the user nodes. The nodes that share the same pilots are referred to as co-pilot nodes. Let us define \(U_{t}\) representing a set of nodes allocated pilot \(t\). Also, \(t_{k}\in\{1,2,\ldots.\tau_{p}\}\) denotes the index of pilots allotted to node \(k\). Each user node \(k\) transmits the pilot \(t\) with pilot transmit power \(\eta_{p}\). The signal received in this phase by the \(l\)th AP is given by  
\[y_{tl}^{p}=\ \sum_{i=1}^{K}\sqrt{\tau_{p}\eta_{p}}\ h_{il}\ +\ n_{tl} \tag{5}\]  
Figure 1: Proposed communication scenario showing intended AP-node transmission affected by interfering nodes and interfering APs.  
where, \(n_{tl}\) is the receiver noise such that \(n_{tl}\sim\mathcal{N}(0,\sigma^{2}I_{N})\). The user nodes in the set \(U_{t_{k}}\) transmit the pilot \(t_{k}\) which is received by the \(l\)th AP  
\[y_{t_{k}l}^{p}=\sum\limits_{i\in U_{t_{k}}}\sqrt{\tau_{p}\eta_{p}}\;{{}_{h}}_{ l}\;+\;{{}_{n_{l}l}} \tag{6}\]  
In order to obtain the channel estimates \(\hat{h}_{kl}\), minimum mean square error (MMSE) estimation method is used [21]  
\[\hat{h}_{kl}=\sqrt{\tau_{p}\eta_{p}}R_{kl}\sigma_{t_{kl}}^{-1}\;y_{t_{k}l}^{p} \tag{7}\]  
where \(R_{kl}\) is the spatial correlation matrix which is equal to \(E\{h_{kl}h_{kl}^{H}\}\), \(\sigma_{t_{k}l}=E\left\{y_{t_{k}l}^{p}(y_{t_{k}l}^{p})^{H}\right\}=\sum_{i\in U _{t_{k}}}\tau_{p}\eta_{p}R_{i}+\sigma^{2}I_{N}\) is the correlation matrix of the received signal.
## 5 Results
### Knee Valgus Angle  
#### 5.2.1 Within-Participant Cross-Validation  
Table 3 shows the mean MAE and RMSE of knee valgus angles for all movements in within-participant cross-validation. Table 3 indicates that both the MAE and RMSE are smaller with Feature B, suggesting that when estimating knee valgus angles using the user's data, it is better to use instantaneous values and the past 20 samples of instantaneous values as features.  
To confirm the estimation accuracy of each machine learning algorithm, we compared the knee valgus angle estimation results using instantaneous values and the past 20 samples of instantaneous values as features. First, Figure 13 shows the within-participant cross-validation results for running for participant C using the RFR and LSVR. In the analysis of Figure 13a,b, the LSVR demonstrated a higher accuracy for larger knee valgus angles. However, since the knee valgus angle is only about 2\({}^{\circ}\) at its maximum, which is not large enough to cause an ACL injury, it is difficult to determine which algorithm is more suitable based on this result.  
Next, the results of the internal cross-validation for the triple jumps of participant B using both the Random Forest Regressor (RFR) and Linear Support Vector Regressor (LSVR) are shown in Figure 14. In the analysis of the maximum values from Figure 14a,b across machine learning algorithms, the RFR exhibited a superior estimation accuracy for instances when the knee valgus angle was large. Therefore, for movements like jumping, which involve impacts on the knee joint, the RFR was more suitable for estimating the knee valgus angle.  
Lastly, the internal cross-validation results for the cutting movement of participant B using both the RFR and LSVR are displayed in Figure 15. In the analysis of Figure 15a,b for each algorithm, the RFR again showed a higher estimation accuracy when the knee valgus angles were larger. Thus, for activities like cutting, where the knee valgus angle changes significantly, the RFR was also found to be more suitable for estimating the knee valgus angle.  
From the results of these three types of movements, it is suggested that when using the user's own data to estimate knee valgus angles, the Random Forest Regressor algorithm and features comprising the user's instant values and the most recent 20 sample values provide better accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**RFR**} & \multicolumn{2}{c}{**LSVR**} \\ \hline  & **Feature A** & **Feature B** & **Feature A** & **Feature B** \\ \hline MAE [\({}^{\circ}\)] & 1.11 & 0.81 & 1.49 & 1.33 \\ RMSE [\({}^{\circ}\)] & 1.50 & 1.01 & 1.76 & 1.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimation errors of knee valgus angle in within-participant cross-validation.  
Figure 13: Within-participant cross-validation results for running for participant C.