esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Radar Signal Simulation  
The radar equation (Equation (18)) calculates the echo amplitude, including the radar parameters shown in Table 2 and the target properties of each scenarios.  
\[\mathrm{R_{max}}=\sqrt[4]{\frac{P_{s}G^{2}\lambda^{2}A_{c}\tau}{k_{B}T_{0}F_{ N}(4\pi)^{3}L_{s}}} \tag{18}\]  
where \(\mathrm{P_{r}}\) reflected power (in Watt), \(\mathrm{P_{s}}\): power emitted (in Watt), \(\mathrm{R_{max}}\): target-radar distance (in m), G: antenna gain, \(\tau\): Pulse width, \(\mathrm{k_{B}}\): Boltzmann constant (in J/K), \(\mathrm{T_{0}}\): absolute temperature (in \({}^{\circ}\)K), \(\mathrm{F_{N}}\): background noise from the receiver, \(\mathrm{L_{s}}\): radar system losses in transmission and reception, \(\beta\): The bandwidth of the receiver, with \(\beta=\frac{1}{\tau}\), and \(A_{c}\): Apparent reflection surface of the radar antenna, with \(A_{c}=\frac{G\lambda^{2}}{4\pi}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Radar Parameters** & **Symbols** & **Values** \\ \hline RF frequency & \(f\) & 900 MHz \\ \hline IF frequency & \(f\) & 30 MHz \\ \hline Pulse repetition frequency & \(\mathit{PRF}\) & 1 kHz \\ \hline Peak transmit power & \(\mathit{Pt}\) & 280 kWatts \\ \hline Pulse width & \(\tau\) & 6 us \\ \hline Main beam gain & \(\mathit{Gt}\) & 28.5 dB \\ \hline \(-3\) dB HPBW & \(\mathit{HPBW}\) & 3.3\({}^{\circ}\) \\ \hline Up spot angle elevation & \(\mathit{El\_up}\) & 12\({}^{\circ}\) \\ \hline Scan rate & \(\mathit{Fs}\) & 72\({}^{\circ}\)/s \\ \hline Scan sector (min to max) & \(\mathit{Scan\_sec}\) & 25\({}^{\circ}\) \\ \hline Receiver bandwidth & \(\mathit{Bn}\) & 1 MHz \\ \hline Doppler filter bank & \(\mathit{Nif}\) & 32 \\ \hline Figure noise & \(\mathit{Fn}\) & 2.5 \\ \hline IF amplitude gain & \(\mathit{Aif}\) & 10 dB \\ \hline Total radar loss & \(\mathit{Loss}\) & 20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radar parameters.  
The radar receiver is modeled as shown in Figure 11 and is implemented using the set of real parameters of a search radar.  
The radar echo X(m) is composed of a useful signal s(m) and an unwanted noise b(m). All the noises here are considered additive noises:  
\[\text{X(m)}=\text{s(m)}+\text{b(m)} \tag{19}\]  
The following figure (Figure 12) illustrates the probability density function of the signal X(m) and shows that the radar echo at the output of the proposed detector follows an exponential distribution [23].  
We clearly see that the histogram of the radar echo X(m) (see Figure 12) is exponential in shape independently of the clutter model at the input of the proposed detector.
## 3 Results and Discussion
### Cheminformatics Prediction
#### 3.2.3 Drug-Likeness Prediction  
Drug likeness is considered as a complex balance of molecular properties and structure merits that show the extent of similarity between certain compounds and known drugs. The drug likeness is guided by molecule properties, including hydrophobicity, electronic distribution, hydrogen bonding, molecule weight, pharmacophore entity, bioavailability, reactivity, toxicity, and metabolic stability [60]. [PERSON]'s rule is one of the tools most used to estimate the solubility and permeability of the compounds and, thus, to predict its qualification as a drug candidate. The rule states that: poor absorption or permeation is more likely when a compound violates [PERSON] rule of 5; that it has more than 5 H-bond donors (the sum of NH and OH), molecular weight (MW\({}_{\text{t}}\)) is over 500, Log P is over 5, and more than 10 H-bond acceptors (the sum of N and O) [61]. Table 6 depicts the drug likeness properties of the compounds **(5-7) _anti_**, (5-7) _syn_**, and maprotiline **9** using a free web SwissADME server; [[http://www.swissadme.ch/](http://www.swissadme.ch/)]([http://www.swissadme.ch/](http://www.swissadme.ch/)) [62]. All tested compounds **(5-7) _anti_**, (5-7) _syn_**, and maprotiline **9** as a reference drug do not violate Lipinski rule as their values fall in the normal range and the bioavailability shows that all compounds **(5-7) _anti_** and **(5-7) _syn_** are identical to the reference drug **9**. Furthermore, these compounds **(5-7) _anti_** and **(5-7) _syn_** are in the optimal range of the physiochemical space, as shown by the colored zone of the bioavailability radar and, thus, these compounds could be considered as lead compounds.  
The pharmacokinetic parameters revealed that the tested compounds **5 _anti_**, **5 _syn_**, **6 _anti_**, and **6 _syn_** are similar to the reference drug maprotiline **9**, since they are highly absorbed through the gastrointestinal tract (GI) after oral intake, and able to penetrate through the blood-brain barrier (BBB), and could be effluxed by P-glycoprotein (P-gp), while compounds **7 _anti_** and **7 _syn_** are low absorbed through GI. This may be attributed to the molar refractivity (MR) of **7 _anti_** and **7 _syn_**, since it is higher than the MR of reference drug **9** (MR: 109.04 for **7 _anti_** and **7 _syn_** versus 88.32 for reference drug **9**), and even higher than MR values of other tested compounds.  
The Brenk and pan assay interference compounds (PAINS) structural alerts have been used in medicinal chemistry to predict unstable, reactive, toxic fragments present in the structure [63; 64]. All compounds **(5-7) _anti_** and **(5-7) _syn_** have zero alerts in Brenk and PAINS descriptors, providing other promising indicators to be drug candidates.
## 6 The Correlation between Planning Investment Intensity and Level of Development
### Planning Research Correlation Analysis between Investment Intensity and HDI  
The number of publications on planning research in a certain country to a large extent represents the country's investment in the field of planning research. Due to the huge population difference among countries, it is unfair to only use the total literature quantity of a country to represent the country's investment in planning research field. This is because some large developing countries with large populations have a huge quantity due to their agglomeration advantages, while some small developed countries with small populations have a relatively small literature quantity. Therefore, '**planning research intensity**' is introduced to evaluated in per capita value. It could fairly reflect the intensity of investment in planning research.  
A correlation analysis is designed to figure out the relationship between planning investment intensity and level of development. The selected countries in this study are major powers in the sense of economy and politics. The correlation analysis of the per capita amount of planning research literature in these countries and its HDI index shows that the correlation coefficient is 0.71, which is a strong correlation (Figure 5).  
The correlation analysis outcome could be explained as the more research investment in planning and related fields, the higher the country's HDI would be. Thus, the amount of research is crucial. Strong correlation can also be interpreted as, in a sense, the more developed the society is, the more attention it pays to planning research, as basis for better economy and development, to some extent forming a virtuous circle.
# Analysing Mouse Skin Cell Behaviour under a Non-Thermal kHz Plasma Jet
## 3 Results  
### Gaseous Plasma Characteristics  
Broad-range spectra (Figure 2a) confirmed the presence of the usual species for an APP generated within argon gas; excited argon species from the ionisation of the working gas, and the reactive species of nitrogen (second positive band--C3\({}^{2}\Pi_{\text{u}}\)\(\rightarrow\) B3\({}^{2}\Pi_{\text{g}}\)), along with OH groups coming from the ambient air filled with water vapour. Given that the OH peak did not overlap with other lines, it was used to determine plasma gas temperature with LIFbase Database and spectral simulation programme (Version 2.1) [12]. The temperature measured was 318 K while plasma was in contact with a water target as described in Section 2.2.  
Spatially-resolved spectra were measured at the exit of the glass tube and the plasma plume's end; approximately 1 cm apart. This distance was also the length of the used plasma plume under all experimental conditions. The spectra exhibited no specific difference when the intensity was normalised to 1 (presented in the supplementary file). Intensity normalisation allowed spectra intensity comparison, and did not influence the line intensity ratio within each recorded spectrum. Based on this result, we were confident that excited species from the gas phase delivered to the cell medium were similar at each distance used for direct cell treatment.  
Intensities of OH (309 nm), N2 (391 nm) and Ar (696 nm) were monitored throughout one period by time-resolved spectroscopy. These data were paired with I-V characteristics in Figure 2b. Our power source operated at an average maximum value of 3.4 kV and  
Figure 2: Broad-range emission spectrum of Ar plasma operated above a non-grounded water target (**a**) and time-resolved optical emission spectroscopy (OES) during one period (**b**).  
2.8 mA at chosen discharge parameters. One period lasted for 50 us, where maximum voltage values were fixed at 0 and 50 us. All monitored plasma species exhibited similar behaviour. Their excitation intensity increased as the voltage dropped, and later decreased as the voltage reached its minimum value. A subsequent increase in the intensity started when voltage rose. However, Ar time-intensity more than doubled compared to the intensities of OH and N2. This behaviour is connected to plasma 'bullet' streamer and filament formation and propagation, which ICCD images highlight in Figure 3[18; 19; 20]. Plasma streamer formation starts around 12 us when the voltage changes from positive to negative, and propagates until the voltage reaches its minimum value. Then, the ionisation shifts and at around 37 us filaments start to appear, gaining their maximum intensity soon after. Time-resolved OES indicated that Ar plays the most important role in filament formation, with OH and N2 in secondary roles.  
Figure 3: Plasma ionisation process. Propagation of a plasma bullet type of ionisation (**left**), from 12 us to 25 us, and formation of filaments (**right**), from 37 us to 50 us.
## 4 Experimental Results
### Classification Results
#### 4.2.1 Baseline Classification Results  
ResNet-18, ResNet-34, DenseNet-121, DenseNet-169, MobileNet-V2, and ShuffleNet-V2 are selected as backbones for the baseline experiment. The data augmentation technique was also implemented for training the baseline models; experimental results are shown in Table 4. In addition, the initial fully connected layer for each backbone model was replaced by three fully connected layers with dropout and batch-normalization layers. The three fully connected layers are presented in Figure 7. Regarding the dataset, all cases in the training set were used for each CNN model. Next, 400 images were randomly selected from the training set, comprising 200 benign and 200 malignant images. All hyper-parameters are listed in Table 2. The MobileNet-V2 backbone achieved the best results in the baseline experiment with a 0.63 accuracy, 0.62 F1-Score, 0.62 AUC, 0.76 PPV, 0.59 NPV, 0.50 sensitivity, and 0.78 specificity.
# Influence of Fatigue and Defensive Pressure on Three-Point Jump-Shot Kinematics in Basketball
## 2 Materials and Methods
### Measuring Devices  
For the purpose of measuring body weight and body height, a portable stadiometer (SECA 213) and a scale (TANITA BC545-n) were used. To analyze specific kinematic variables of jump shots, the MNV BIOMECH Awinda inertial system (Xsens Technologies B.V., Enschede, The Netherlands) was utilized. The player wore a full-body suit with 17 wireless motion trackers, sampling at 60 Hz, to enable comprehensive 3D motion analysis. Movement data were processed using the MVN Studio BIOMECH software (Xsens Technologies B.V., Enschede, The Netherlands). Sensor calibration was conducted in an N-pose, and if the calibration was not deemed successful, it was repeated. After successful calibration and prior to measuring or executing each shot, the subject was asked to raise their arms to approximately 90\({}^{\circ}\) and 180\({}^{\circ}\) to verify correct system operation, sensor positioning, and calibration accuracy. The Xsens system was used in previous studies for different kinematic data in the field of basketball [6]. Furthermore, the reliability and validity for evaluating angular velocity and other kinematic parameters in different basketball techniques was determined [26]. The OptoJump Next system (Microgate, Italy) was used to determine the height of each jump shot. The Witty timing system (Microgate, Italy) was used for measuring time on the 300 m running test. For tracking fatigue, the Polar H10 sensor with connection to a mobile app was used. Using systems like Xsens and OptoJump for sports diagnostics has distinct advantages compared to alternative devices due to their precision, versatility, and the richness of data they provide. Specifically, Xsens provides detailed 3D kinematic data, capturing complex movement patterns with high accuracy. Unlike optical systems that require a controlled environment and cameras, Xsens uses inertial sensors that allow testing in any location, whether indoors or outdoors; it allows for precise tracking of joint angles, velocity, and acceleration in real-time, and the system is wireless so athletes can perform movement freely. The OptoJump system requires minimal setup and does not interfere with an athlete's natural movement, which makes it preferable for tests requiring real-time analysis in training environments. Also, this system enables high-precision analysis of vertical jump performance. The reliability and validity of photocells were proved and used by previous studies [27; 28]; additionally, the OptoJump system was used in previous studies and testing protocols, and it demonstrated strong concurrent validity and excellent test-retest reliability for the estimation of vertical jump height [29; 30].The [PERSON] shooting machine was used to standardize the speed of the pass (8 m/s) and the time between the shots (6 s).
# A Survey of NFC Sensors Based on Energy Harvesting for IoT Applications
## 4 Conclusions  
We recently witnessed the rapid deployment of NFC technology driven by contactless payment applications. Although NFC technology was developed over a decade ago, it was not until its massive incorporation into mobile phones that it became popular. This expansion led to the emergence of passive NFC sensors using the energy-harvesting possibilities provided by this technology. In this paper, we reviewed recent studies found in the literature. We also addressed the design of labels based on energy harvesting, as well as several aspects that can limit the transfer of power between the tag and the reader. The inductance (and the corresponding capacitance) chosen in the tag design has an important role in the energy harvesting and the loading effects between the tag and reader. This interest in passive NFC sensors led manufacturers of integrated circuits to present several ICs with energy harvesting, thus demonstrating the potential market for this technology. We also reviewed some of these ICs and highlighted their main characteristics. A review of the state of the art in batteryless NFC sensors revealed great interest in these sensors for food monitoring and wearable biomedical applications. In these applications, it is essential to eliminate potentially dangerous batteries due to their toxicity and high costs. Compared with other types of wireless sensor technology, such as UHF RFID, an inductive link at the 13.56-MHz band is more insensitive, where the body introduces high losses that limit the read range. Another advantage of NFC devices over UHF devices is the fast ROI. This is because a specific reader it is not required, since a smartphone can often be used as a reader. The data can then be uploaded to cloud database services. The ease with which NFC technology is used makes it ideal for use by elderly people in telemedicine and electronic health applications. The greater privacy and security of NFC communications compared to UHF RFID is another point to consider in biomedical and telemedicine applications. If a specific sensor must be designed because one is not commercially available, integrating NFC electronics within the ASIC and the sensor signal conditioning is justified. In other cases, standard commercially available NFC ICs were used in the NFC-based designs found in the literature. These ICs are often based on the standard ISO 15693 because higher communication distances are obtained compared with the IC under the standard ISO 14443.  
**Author Contributions:** Investigation and writing--original draft preparation, A.L. Review and editing, R.V. and D.G.  
Figure 14: Comparison of real volumetric water content (VWC) and that obtained with the sensor [71].  
Funding: This research was funded by the Spanish Government under Projects TEC2015-67883-R and BES-2016-077291 and H2020 Grant Agreement 645771-EMERGENT.  
The authors declare no conflicts of interest.
## 4 Experiment and Discuss  
### Experimental Environment and Setup  
In this paper, a data acquisition test bench was used to collect the vibration signals in the actual environment. Through destructive experiments, the faulty main reducers with some particular failure modes such as tooth surface knock, tooth surface wear, and tooth surface bonding were obtained to ensure the repeatability of the collected signals. The test bench is shown in Figure 5a, and the sensors installment is shown in Figure 5b. In order to collect the vibration signal during the running of the main reducer more accurately, two acceleration sensors are placed in the lateral direction and the longitudinal direction of the main reducer as shown in Figure 6.  
The rotating speed of the test bench is set to 800 rpm (round per minute) at a sampling frequency of 1024 Hz. To ensure that the valuable fault information is not lost during the sampling process, the sampling frequency must be higher than the mesh frequency of the gear pair inside the main reducer. The vibration signals collected within 2 s after the motor begins working and the signals collected within 2 s before the termination of motor operation are discarded. That is, delay 2 s to start the sampling so as to sample in the most stable state of the motor operation.  
According to prior knowledge, this paper mainly focuses on the six most common single failure modes of the main reducer. In order to build an intelligent simultaneous fault diagnosis framework of the main reducer, since it is infeasible to simulate all possible simultaneous failure modes, the five most common simultaneous failure modes are selected in this study. The description of several failure modes is in Table 1.  
Figure 5: The test bench.  
Figure 6: The process of data acquisition.  
Based on the test bench of the main reducer, the collected data contains 500 normal samples, 3000 single failure sample, and 2500 simultaneous failure samples. The assignment of experimental parameters is in Table 2, where the number of sampling points per sample is 2048. All simulation experiments are performed by Matlab 7.0 on a computer with a CPU of 3.4 GHz and 4.0 GB of memory.
## 2 UWA Communication by Generalized Sinusoidal Frequency Modulation
### Orthogonality  
The orthogonality between the waveforms representing the symbol plays an important role in the theoretical background of communication performance. The orthogonality of these waveforms determines the performance of the communication system. To determine the relation of each waveform, the AF is used, and its expression is shown in Equation (6).  
\[\chi_{i,j}(\tau,\eta)=\sqrt{\eta}\int_{-\infty}^{\infty}s_{i}(t)s_{j}^{\prime}( \eta(t+\tau))dt. \tag{6}\]  
\(\eta\) is the Doppler scaling factor, and \(\tau\) is the time delay.  
Figure 3a represents the auto-AF of the FT, and Figure 3b-d represent the cross-AF of the FT/FF, RT and RT/FF with FT, respectively. In the auto-AF and the cross-AF of Figure 3, the time delay \(\tau\) range is \(-2\leq\tau\leq 2\)\(s\), and the Doppler scaling factor \(\eta\) range is \(-2\)\(m/s\leq\eta\leq\ 2\)\(m/s\). In Figure 3a, the auto-AF has a high main level and low sidelobe level; on the other hand, even when using the same bandwidth and parameter, the cross-AF has a low correlation level.  
Furthermore, the orthogonality of the four types of GSFM waveforms can also be represented in the correlation function. To determine the correlation of each waveform, we use Equation (7) to Equation (9). Equation (7) represents the basic definition of the correlation function. By the Wiener-Khintchine Theorem, Equation (7) can be represented as Equations (8) and (9).  
\[R_{ij}(\tau)=\int_{-\infty}^{\infty}s_{i}(t)s_{j}(t+\tau)dt, \tag{7}\]  
Figure 3: **(a)** Auto-AF of the FT, **(b)** cross-AF of the FT/FF and FT, **(c)** cross-AF of the RT and FT and **(d)** cross-AF of the RT/FF and FT.  
\[S_{i}(f)=\sum_{t=-\infty}^{\infty}s_{i}(t)e^{-i\omega t}dt, \tag{8}\]  
\[R_{ij}(\tau)=F^{-1}\Big{[}S_{i}(f)S_{j}^{*}(f)\Big{]}. \tag{9}\]  
\(F^{-1}[\cdot]\) is the inverse Fourier transform of \([\cdot]\), and \(S_{i}(f)\) is the spectrum of \(s_{i}(t)\). In Equation (7), the spectrum of the GSFM waveform is represented by Equation (10) [16].  
\[S_{GSFM}(f)=\bigg{|}\sqrt{T}\sum_{n=-\infty}^{\infty}\mathcal{J}_{n}^{\text{L} \infty}\Bigg{\{}\frac{\widetilde{a_{m}}\Delta f}{2};\frac{\widetilde{b_{m}} \Delta f}{2}\Bigg{\}}\times\text{sin}\cbigg{[}\pi T\Big{(}f-f_{c}+\frac{a_{0} \Delta f}{4}-\frac{n}{T}\Big{)}\bigg{]}\bigg{|}. \tag{10}\]  
Figure 4 represents the correlation of the FT. Figure 4 represents the ACF, Figure 4-d represent the cross-correlation function of the FT/FF, RT and RT/FF with FT, respectively.
## 3 Case Study
### Reliability Assessment
The unreliability of Load 1 and Load 8, which are the most and least important loads in the local framework of FREEDM system, are evaluated in each case. For Load 1, the growth of failure rates of IEM and communication components significantly increase the unreliability. However, the effect is not obvious with the IFM devices. As for Load 8, its unreliability stays steady without obvious changes as the failure rate of any of the three devices increases. It is because the high unreliability of Load 8 is mainly caused by lack of power, which is highly related to DGs, especially the renewable DGs and the generators. It should be point out that techniques of communication and intelligent control are the basis of smart grid establishment and are of key importance during the actual operation. Reliability of these parts should be guaranteed for a global reliability. Reliability evaluating method and procedure for these components also deserve further research.