esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced FANO Structure Based on Tip-Field-Enhancement Theory
## 4 Conclusions  
An enhanced FANO resonance structure was proposed in this paper. The methodology is based on the idea that electric charges are more likely to accumulate at the sharpest tips in a static E-field. Both simulation and experiment results showed that the Q factor of the proposed structure could be increased to up to 2.4 times compared to the original, without sacrificing too much resonance intensity. E-field and surface-current distribution also showed that the charges indeed do accumulate at tips, which enhanced the coupling between the tips. This result also proves that the law of tip-field-enhancement theory could also be applied to time-harmonic electromagnetic (EM) fields. With this method, sensitivity and accuracy could be improved in existing FANO-based micro-fluidic sensors. Tip-field-enhancement theory could also be beneficial to other fields of research, such as antenna design, where the control of charge and current distribution is required.  
Conceptualization, T.Z., B.Z. and Y.Z.; methodology, T.Z., B.Z., Y.Z. and K.S.; investigation, T.Z., B.Z. and L.W.; resources, S.L.; supervision, Y.Z.; validation, T.Z. and B.Z.; Writing--review and editing, C.S.  
This work is supported by the The National Key Research and Development Program of China under Contract No. 2018 YFB1801503, National Natural Science Foundation of China under Contract Nos. 61931006, 61921002 and 61771327.  
The authors declare no conflict of interest.
## 1 Introduction
### Innovative Contribution  
In this paper, a new operational scheduling method based on a joint day-ahead energy and reactive power market at the distribution level is presented for the coordinated VVC issue. In line with this purpose, a modified reactive power bidding structure for DGs is developed. Disco buys active and reactive power from a wholesale market and sells them through the suggested market-based model. In the joint active and reactive market, similar to decoupled reactive power market, a DG will be paid for the lost opportunity cost (LOC) if its active power output in the joint market is less than that of the energy-only market. The presented joint active/reactive power market settlement model seeks to minimize the DSO's total payments that comprise the offered energy cost of generation units and Disco, the reactive power payment function of DGs for their reactive power compensation, and the total payment for reactive power purchased by Disco from the wholesale market. Owing to the presence of control devices, such as DGs, OLTCs, and Sh.Cs, the proposed model for joint market settlement in distribution systems is a Mixed Integer Nonlinear Programming (MINLP) optimization problem. The suggested model is programmed with General Algebraic Modelling Systems (GAMS) and solved with the DICOPT solver.  
The main innovative contributions of this paper are summarized as follows:  
* A modified reactive power cost model is introduced for DGs; and
* A joint active/reactive power market in distribution systems is proposed.  
The remainder of this paper is organized as follows:  
Section 2 describes reactive power offers of a DG. Section 3, describes the decoupled day-ahead active power market. Proposed joint active/reactive power dispatch framework is introduced in Section 4. Simulation results are reported in Section 5 and, finally, conclusions of this paper are given in Section 6.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 3 Method
### Classification Models
#### 3.3.4 Neural Network  
A fully connected neural network (NN) is a basic feed-forward neural network model where each neuron is connected to all neurons in the previous layer. It consists of an input layer, hidden layers, and an output layer. The neural network can learn complex nonlinear mappings from the joint photonic and electronic feature data, thereby capturing better correlations between the data and considering information from different views. Additionally, neural networks are effective in handling high-dimensional data from the joint photonic and electronic features. This capability helps the model learn more complex features and relationships.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 4 Experimental Results and Performance Analysis
### Experimental Results  
Machine learning classifiers are implemented to classify stress and relaxed conditions using the EEG signals in response to videos used in the DEAP dataset. To obtain the results, machine learning classifiers are trained using the PSD features on two-class problems, i.e., both groups, i.e., stressed and relaxed. Each of the participants watched the  
A total of 40 videos and their EEG signals were recorded while watching the videos. These videos are presented randomly, irrespective of age, gender, and preference. While watching the videos, the participants rated the videos based on their arousal valence. The arousal and valence scores were used to label the participants for the two-class problems, such as those in the stressed and relaxed classes. PSD features from four EEG bands, delta, theta, alpha, and beta, are extracted using 32 channels.  
Four different classifiers named NB, MLP, SMO, and Linear Regression are employed for the assessment of stress levels into two states: stressed and relaxed. A 10-fold cross-validation approach is used in our study to validate results. The results obtained by implementing the different classifier algorithms are analyzed and compared based on accuracy, precision, recall, and f-measure. Their results are also evaluated and differentiated based on kappa statistics.  
Table 6 shows the performance comparison for the different classifiers for the stressed and relaxed states. The presented results of these classifiers show that the overall SMO classifier is better than the other classifiers used, such as MLP, NB, and LR, having an accuracy of 95.65%. Similarly, precision and recall show that more relevant instances are close to the best value. From Table 6, it is evident that the SMO classifier outperforms the other classifiers. Although the difference between the accuracy of MLP and SMO is not statistically significant, it was observed that MLP took longer to train than of SMO. The kappa value is 0.69, which is close to the best value, i.e., 1, which means the classification is not by chance. F\({}_{\text{m}}\) value is 0.91, which shows that the SMO classifier provides a value closest to its best value, 1. The classification results of MLP, NB, and LR are also comparable with the SMO classifier accuracy results, which are 94.56%, 88.04%, and 92.39%, respectively.  
Figure 4: Average and standard deviation value of power spectral density of alpha band in stressed and relaxed conditions over all electrodes.  
Figure 5 shows the confusion matrices of different classifiers for the classification of two-class problems: stressed and relaxed groups whose features are extracted from the EEG signals that are recorded while watching the videos. It is noticed that the SMO classifier provides the highest classification accuracy for the individual two-class problem, i.e., stressed and relaxed groups.
# Exploring Ephemeral Features with Ground-Penetrating Radar: An Approach to Roman Military Camps
## 3 Results
### Sueros de Cepeda  
The geomagnetic survey on the western sector of this camp revealed a shallow line that essentially fits with the defensive ditch outline proposed after different aerial coverages were interpreted [7]. The objective of the GPR survey was to extract volumetric data about this feature before its excavation.  
A linear feature measuring 40 m long and showing a variable width is visible almost below the ploughing surface (0 to 30 cm), disappearing beyond 110-120 cm deep (Figure 9). At this point, a solid linear amplitude may reflect the very bottom of the ditch. The direction of GPR lines could have caused difficulties in defining the width of this feature. Moreover, the spacing between the survey lines (50 cm) also has an impact on the visualisation of structures. Even if the overall resolution is acceptable, decreasing the spacing also helps define the structures in a more detailed way. The survey was performed following the plough lines to avoid bumps and the loss of coupling between the GPR antenna and the ground. Although this was a reasonable decision in logistical terms, the limited results persuaded us to approach these archaeological features perpendicularly in the future, generating profiles by 'cutting' the trenches. Nevertheless, in other study cases, such as Sasamon, the alignment of archaeological features and survey lines did allow the adequate recording of the former. The overall size of the structures, differential soil, backfill  
Figure 8: Voxler visualisation of the clavicula gate at Alto da Raia. Arrows point to the location of the fortification and the _claicula_.  
material's characteristics, and contrast with the surrounding soil matrix could explain this phenomenon.  
No other archaeological features were detected within the survey area, meaning that recent agrarian works completely erased earth ramparts to the interior of the external ditch. These observations could be considered paradigmatical of the temporary camps in the plains of the Duero valley, where constant ploughing has eroded any topographical expression of ancient ephemeral structures. Archaeological excavations later confirmed GPR interpretations, which allowed us to characterise the traces of the site's defences (see Section 4). A V-shaped ditch was identified after removing 30 cm of agricultural soil. It was filled with distinctive, slightly darker deposits from the natural colluvial layers with more abundant rolling stones. Indeed, GPR prospection was useful to identify the precise location of the external ditch, assess the dimensions of the structure with accuracy, both wide and deep (Figure 10), and ultimately predict the labour effort needed to investigate these ephemeral traces of the Roman army at Sueros de Cepeda.  
Figure 9: Selected depth slices of the western defensive line at the Roman camp of Sueros de Cepeda. The black line shows the location of the defensive ditch according to the crop marks detected on the aerial imagery. Red line indicates the location of excavation. EPSG: 25830.
## 4 Experiments and Results
### Single-Difference Residual Analysis
The spatial-domain correlation of multipath can be used to model the three-dimensional environment. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Oblblbl**} & \multicolumn{4}{c}{**DOY 303-303**} & \multicolumn{4}{c}{**DOY 296-303**} & \multicolumn{4}{c}{**DOY 297-304**} \\ \cline{2-13}  & **Time Shift** & **Correlation** & **RMS (sme)** & **RMS (sme)** & **RMS (sme)** & **Crossification** & **RMS (sme)** & **RMS (sme)** & **Crossification** & **RMS (sme)** & **RMS (sme)** \\ \hline GEO C01 & 234 & 0.977 & 1.55 & 0.86 & 1645 & 0.963 & 1.54 & 0.75 & 1642 & 0.982 & 1.55 & 0.27 \\ MBG-C11 & 237 & 0.907 & 1.69 & – & 1694 & 0.848 & 1.69 & 0.91 & 1696 & 0.948 & 1.37 & 0.44 \\ MBG-C3 & 227 & 0.979 & 2.81 & 1.46 & 1603 & 0.928 & 2.81 & 1.05 & 1986 & 0.927 & 2.77 & 1.04 \\ MBG-C3 & - & -0.406 & 1.89 & — & 1696 & 0.948 & 1.89 & 0.61 & 1698 & 0.986 & 1.71 & 0.8 \\ \hline \end{tabular} Note: “Correlation coefficient” is calculated by aligning the time shift of the ORT. “MEO-2” means the BDS-2 MEO satellites. “MEO-3” means the BDS-3 MEO satellites. “RMS_M_” means the mitigated series by the ASF method.  
\end{table} 
Table 1: The statistics of multipath mitigation for C01/C11/C13/C29. Figure 6: RMS of the original single-difference series and their series with multipath mitigation for the DOY 303 observed satellite. Figure 7: (**a**) SD-MHM model established by DOY 296–302; and (**b**) the distribution of DOY303 single-difference residuals. To explore and analyze the frequency domain characteristics of the series after mitigating the multipath, the DOY 303 C29 satellite is taken as an example, as shown in Figure 8. Figure 8a shows the original single-difference residuals series, the ASF and SD-MHM multipath models, and their mitigated series. In terms of the multipath model trend, both are almost the same, and the series fluctuates at 0 after the mitigation. In addition, it is necessary to analyze these two methods to mitigate the characteristics of the series after multipath in the frequency domain. For the power spectral density shown in Figure 8b, the low-frequency signal dominates the signal component of the original series. After the mitigation by ASF and SD-MHM, both can effectively mitigate the low-frequency multipath. Moreover, the low-frequency power of the series after SD-MHM correction is lower than that of ASF, and the mitigation effect on low-frequency multipath is better than ASF.
# The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment
## 2 Materials and Methods
### FireBIRD Active Fire Detection and Analysis at Subpixel Level
#### 2.2.2 FRP Derivation  
As described above, two different approaches are performed for the derivation of the FRP. First, the MWIR method by [PERSON] [5] is used, which represents a straightforward approach by exclusively using the MWIR information (Equation (4)):  
\[FRP_{MWIR}=\alpha_{MWIR}(L_{f,MWIR}-L_{bg,MWIR}) \tag{4}\]  
\(L_{f,MWIR}\) and \(L_{bg,MWIR}\) are the MWIR radiances of the fire as well as the background pixels, respectively. \(\alpha_{f,MWIR}\) is a constant depending on the MWIR channel spectral response function of the corresponding sensor.  
The second approach follows the bi-spectral method of [PERSON] [17]. The following system of nonlinear Equation ((5)) has to be solved in order to derive the subpixel temperature of the fire (\(T_{t}\)) and the fraction of the pixel covered by fire \(p\).  
\[\begin{split} L_{1}(T_{1})&=pL_{1}(T_{t})+(1-p)L_{ 1}(T_{b})\\ L_{2}(T_{2})&=pL_{2}(T_{t})+(1-p)L_{2}(T_{b})\end{split} \tag{5}\]  
MWIR and LWIR surface radiances of the potential fire pixels are used as an input for these equations. They can be solved for the two unknowns (\(T_{t}\) and \(p\)), given that the background temperature \(T_{b}\) is known. Using the MWIR/LWIR approach, the FRP is derived as the difference of the power of the subpixel temperature \(T_{t}\) and the background temperature \(T_{b}\) using Equation (6). The multiplication of the pixel size and \(p\) yields \(A_{t}\), which represents the subpixel area of the active fire (in m\({}^{2}\)).  
\[FRP=\epsilon\sigma(T_{t}^{4}-T_{b}^{4})A_{t} \tag{6}\]  
\(\sigma\) is the Stefan-Boltzmann constant \(W/(m^{2}K^{4})\). [PERSON] et al. [7] showed that there is a good concordance between the results of the methodology described here and the results based on the approach of [PERSON] et al. [28]. Figure 2 gives a visual impression of the processing steps.
## 3 Results
### Agro-Climatic Model Results in 2000 and 2050  
#### 3.2.1 Comparison of DOWKI Distributions between 2000 and 2050  
Using ARPEGE-Climat, two event sets of 400 years (2000 climate and 2050 climate) were computed. The first issue was to determine whether these distributions were significantly different. The numerous repeats in each target year allowed us to use a statistical test to answer this first question.  
We compared the distributions of the annual average national scale DOWKI values with a Wilcoxon-Mann-Whitney non-parametric test commonly used to compare medians of two samples that do not follow a Gaussian distribution. The test rejected the null hypothesis that the two distributions were samples from continuous distributions with equal medians. The \(p\)-value was equal to 0.027.
# Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems
## 3 System Model
### Construction of 2-BPSK Codebook  
[PERSON] employs a covert communication strategy with [PERSON] by manipulating the reflection coefficient of the IRS, adjusting both its amplitude and phase. [PERSON] transmits a message \(W\) uniformly distributed in the range \(\llbracket 1,M\rrbracket\) to [PERSON] by encoding it into a codeword \(\mathbf{c}^{n}=\left[c_{1},c_{2},\dots,c_{n}\right]\) with a blocklength of \(n\). In this work, we adopt the 2-BPSK codebook, proposed in [8], instead of the conventional Gaussian codebook or BPSK codebook. To construct the 2-BPSK codebook, we need to construct a BPSK codebook first.  
In the BPSK codebook, each symbol \(c_{i}\) exhibits a consistent amplitude \(\beta\) and two potential phases, denoted as \(\tilde{\theta}_{1}\) and \(\tilde{\theta}_{1}+\pi\). Specifically, the expression for each symbol \(c_{i}\) can be either \((\beta,\tilde{\theta}_{1})\) or \((\beta,\tilde{\theta}_{1}+\pi)\). The codeword \(\mathbf{c}^{n}\) is independently and randomly generated based on the distribution \(P_{B}^{n}(\mathbf{c}^{n})=\prod_{i=1}^{n}P_{B}(c_{i})\), where \(P_{B}(c_{i}=(\beta,\tilde{\theta}_{1}))=P_{B}(c_{i}=(\beta,\tilde{\theta}_{1 }+\pi))=1/2\). The 2-BPSK codebook is constructed by reflecting the BPSK codebook. By introducing a fixed phase angle \(\tilde{\theta}\) to each symbol \(c_{i}\), Alice can create another BPSK codebook with varying phase angles. In other words, Alice transmits each codeword in BPSK with an additional phase angle \(\tilde{\theta}\), and each symbol \(c_{i}\) can be expressed as either \((\beta,\tilde{\theta}_{1}+\tilde{\theta})\) or \((\beta,\tilde{\theta}_{1}+\tilde{\theta}+\pi)\). For the sake of simplicity in notation, we redefine the symbols \((\beta,\tilde{\theta}_{1}+\pi)\) and \((\beta,\tilde{\theta}_{1}+\tilde{\theta}+\pi)\) as \((-\beta,\tilde{\theta}_{1})\) and \((-\beta,\tilde{\theta}_{1}+\tilde{\theta})\).  
Assuming that [PERSON] may optionally append an additional phase angle \(\tilde{\theta}\) or choose not to perform this operation, we can obtain two independent BPSK codebooks. By combining these two codebooks, we can construct a 2-BPSK codebook. In the 2-BPSK codebook, each codeword can be represented as either \((\beta,\tilde{\theta}_{1})\) and \((-\beta,\tilde{\theta}_{1})\) or \((\beta,\tilde{\theta}_{2})\) and \((-\beta,\tilde{\theta}_{2})\), where \(\tilde{\theta}_{2}=\tilde{\theta}_{1}+\tilde{\theta}\). It is assumed that the codebook is revealed to [PERSON], including the value of amplitude gain \(\beta\) and the set of all possible angles, i.e., \(\{\tilde{\theta}_{1},\tilde{\theta}_{2}\}\).
# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 3 Theoretical Framework of the IoT OS Platform
### Structured Organization Mechanism for IIoT
#### 3.1.1 Perception Layer  
The perception layer encompasses entities within IIoT scenarios that receive or transmit data. The most common objects at the perception layer in industrial scenarios are sensors that convert physical information into data for upward transmission and actuators that receive data and convert it into physical actions. In the context of information transformation in the industrial sector, most target enterprises have already achieved some level of digitalization, and do not need to deploy a new set of sensors or actuators [10]. Thus, the upper layer must have the capability to access and integrate with the existing digital achievements of target enterprises. Therefore, the perception layer in IIoT scenarios should also include databases, edge computing devices, informatization systems, and other entities commonly found in industrial informatization scenarios [10].
## 3 Complementary Model Description
### Active Device Losses in the Converter  
The losses of active components are partially frequency-dependent. These are called switching losses. There are a lot of complex models able to describe switching losses [8; 20; 21; 22]. For silicon MOSFET power devices operating under a very fast switching transition, a simple one is to consider the switching losses with respect to the stored energy into parasitic capacitor Loss. Since Loss is a nonlinear capacitor, special care must be paid to estimate the energy stored in the Loss. From MOSFET manufacturer datasheets, one can derive the total energy stored in Loss, using Equation (9). Considering that each time the transistor switches ON, the entire energy stored into Loss is dissipated through the channel of the device [20; 22], then switching losses can be derived thanks to Equation (9) below. This switching loss modelling technique is valid only if the MOSFET is switching very fast. The transistor used to make the test bench being (SIS862 DN-T1-GE3), the stored energy in Loss can be extracted from the datasheet according to the curve pasted in Figure 11. Parameters in Equation (12) are derived to fit Loss versus Vds. Transistor gate drivers are also dissipating the loss function of the switching frequency. In the transistor datasheet, the stored charge can be easily derived from tables and abacus. For hard switching operation, both losses must be taken into consideration. For soft switching, only driver losses must be considered.  
Additional losses must be taken into consideration. When MOSFET-MOSFET switching transitions are considered, a short deadtime is nonetheless necessary to avoid short circuit operation [20]. At each switching time, a dead time of 100 ns is applied between off and on. This dead Time is necessary to prevent cross commutation in hard switch and allows ZVS switching but causes body diode conduction [23; 24] making extra losses. These losses are considered in Equation (11).  
\[E_{C_{oss}}=\int_{0}^{V_{max}}C_{oss}(v_{DS})v_{DS}\,dv_{DS} \tag{9}\]  
\[E_{C_{iss}}=Q_{iss}\times v_{GS} \tag{10}\]  
\[E_{body}=t_{dt}\times V_{drop}\times I_{sw} \tag{11}\]  
\[C_{oss}(V_{DS})=760-V_{DS}\frac{760-400}{760}+\exp^{-(V_{DS}*0.17-6.7)} \tag{12}\]  
Figure 11: \(C_{oss}\) of the transistor SIS862 DN-T1-GE3 versus Drain-source Voltage from Equation (12) in red and from component datasheet [25] in bleu.  
Equation (12) is a mathematical representation of the non-linear capacitor \(C_{oss}\), following its evolution with respect to Drain Source voltage across the MOSFET. It is illustrated in Figure 11 below.
## 4 Method
### Data Generation for Oscillation Type  
The main network structure of data generation is the original generative adversarial network. As shown in Figure 2, the discriminator consists of three fully connected layers, and leakyRelu is used as the activation function after calculation for each layer of the first two layers. The activation function of the last fully connected layer is sigmoid. The generator is also composed of three full connection layers, and relu is used as the activation function after calculation of each of the first two layers. The activation function of the last full connection layer is tanh.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notations** & **Meanings** \\ \hline \(y_{2}\) & the turning point judgment result \\ \hline \(t\) & the number of updates \\ \hline \(\theta_{t}\) & the update step of Adam \(t\) \\ \hline \(\theta_{t-1}\) & the step of the previous iteration \\ \hline \(\alpha\) & the learning rate \\ \hline \(\hat{m}_{t}\) & the momentum \\ \hline \(\sqrt{\hat{v}_{t}}\) & the exponential moving average of the gradient \\ \hline \(\varepsilon\) & a constant \\ \hline \(K(e^{(-\Delta x)})\) & a decreasing function of the change of sample eigenvalues \\ \hline \(y_{3}\) & is the judgment result of pulse position \\ \hline \(\Delta t\) & the length of the pulse \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Main network structure.  
As shown in Figure 1a, for oscillation types data, this paper selects the momentum optimization method as the optimizer. Compared with Adam that is widely used in various projects, momentum has a strong ability in preserving the fluctuation of oscillation type data. In addition, its convergence speed is higher than SGD.