esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characteristic of the Laboratory Hardware and Software Setup
In order to adapt the range of the measured voltage to the voltage input module a voltage transducer provided by Life Energy Motion Company (LEM) model CV 3-1000 was implemented with a voltage ratio of 1000:10 and frequency bandwidth to 500 kHz [38]. The obtained measurement setup allows recording simultaneously four voltage and four current waveforms with a maximum frequency of 1 MHz per channel. The investigations were focused on voltage measurements. For the measurements in the frequency range 2-150 kHz, the voltage units (instead of volts) are expressed in dB\(\upmu\)V. Thanks to the use of cables between the concentrator and individual meters, with a cross-section of 2.5 mm\({}^{2}\) and their small length (1-2 m), the impact of power supply cables on the attenuation of the PLC signal in the voltage line should be considered insignificant. The accuracy of measurements was validated using commercial spectrum analysers used in the power line communication investigation: MFA 500 Spectrum Analyser 3-500 kHz by Swemet [39] and USB-SA44B Spectrum Analyser 1 Hz-4.4 GHz by Signal Hound [40]. The validation was mainly based on the comparison of the spectrum of the simultaneously recorded signal. The frequencies and magnitudes of spectrum components were compared. The obtained results were comparable. The advantage of using an authorized solution based on the cRIO platform is the open possibility to define the acquisition process e.g., time recording of the waveform. Figure 5: Information and communications technology (ICT) functional diagram of the test stand as a local area network (LAN). Figure 6: View of 1 MHz voltage and current recorder components. Recently, a wide discussion has taken place in literature referring to the measurements method for the frequency range 2-150 kHz [9; 12; 13]. Some consideration is also provided by the informative annex in IEC 61000-4-30 [15]. One of the suggested approaches is to implement a discrete Fourier transform (DFT) defined in IEC 61000-4-7 [14] for the frequency range of 2 kHz and extend it above 2 kHz. Traditionally DFT implementation uses a rectangle data acquisition window with a width of 200 ms, corresponding to approximately 10 (12) periods of power system frequency 50 Hz (60 Hz). Consequently, spectrum resolution is 5 Hz. Before 2009, the informative annex of this standard suggested implementation of DFT in the range 2-9 kHz using DFT technique with 10 Hz resolution, realized by a time windowed signal with a window width equals to 100 ms. The current form of the annex recommends preserving 5 Hz spectrum resolution also in the range 2-9 kHz, obtained by using a 200 ms window. Additionally, a grouping concept is also proposed. For the frequency range 2-9 kHz, it is realized using 200 Hz band centred at frequencies being a multiplication of 100 Hz. The first group is centred at 2100 Hz. The grouped spectrum has a final resolution of 100 Hz.
# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 2 Single Improved Prediction Model  
In this part, two improved prediction models will be introduced. The Gaussian process (GP) is a generalization of the Gaussian probability distribution. The sparse Gaussian process regression (SPGP) model is a supervised learning model based on Bayesian theory and statistical theory. Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical learning theory. In order to obtain the optimal solution of the super-parameters, the improved grey wolf optimization algorithm was used to solve the super-parameters of the SPGP model and LSSVM. In this paper, improved SPGP model and LSSVM model are used to predict photovoltaic power, respectively.
## 4 Results and Discussion
### Performance Analysis for Subject 3  
The performance measures obtained for subject 3 are shown in Table 1.  
Table 1 shows the precision, recall, and F1 score of the proposed methods based on ANN, CNN1, CNN2, Merged CNN1 with CNN2, Modified merged CNN1 with CNN2 model for Subject 3. We notice that the five models gave very similar results.  
We provide the confusion matrix for the proposed methods in Figure 10. The diagonal elements demonstrate the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix, the better, showing many correct predictions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{ANN} & Left hand & 81\% & 87\% & 84\% \\  & Right hand & 73\% & 80\% & 76\% \\  & Feet & 94\% & 94\% & 94\% \\  & Tongue & 80\% & 71\% & 75\% \\  & Left hand & 71\% & 83\% & 77\% \\  & Right hand & 83\% & 91\% & 87\% \\  & Feet & 100\% & 80\% & 89\% \\  & Tongue & 75\% & 67\% & 71\% \\  & Left hand & 50\% & 58\% & 54\% \\  & Right hand & 84\% & 73\% & 78\% \\ CNN2 & Feet & 81\% & 87\% & 84\% \\  & Tongue & 78\% & 78\% & 78\% \\  & Left hand & 90\% & 75\% & 82\% \\ Merged CNN1 with & Right hand & 84\% & 95\% & 89\% \\ CNN2 & Feet & 92\% & 73\% & 81\% \\  & Tongue & 64\% & 78\% & 70\% \\  & Left hand & 82\% & 75\% & 78\% \\ Modified Merged & Right hand & 91\% & 91\% & 91\% \\ CNN1 with CNN2 & Feet & 82\% & 93\% & 87\% \\  & Tongue & 75\% & 67\% & 71\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification Report for the proposed methods based on (a) ANN, (b) CNN1, (c) CNN2, (d) Merged CNN1 with CNN2, (e) Modified merged CNN1 with CNN2 model (Subject 3).  
Figure 10: Confusion matrices of classification accuracy for the proposed methods based on (**a**) ANN, (**b**) CNN1, (**c**) CNN2, (**d**) Merged CNN1 with CNN2, (**e**) Modified merged CNN1 with CNN2 model (Subject 3).
# Spam Classification Based on Signed Network Analysis
## 5 Performance Evaluation  
### Settings  
In this section, we investigate the performance of the proposed scheme by using the same Twitter dataset described in Table 1. We measure accuracy, precision, recall, false positive rate, and F1 score as performance metrics. For comparison, we implement the following four spam detection methods: SybleRank [10], NFS [17], CatchSync [16], and TSP-Filtering [29]. We use C4.5 [38] in Weka [39] as the classifier (note that it is known as J48 in Weka). The batch size is set as 100, and 10-fold cross-validation is used. Surprise measurements for triad types in Figures 1 and 2 are used as the main features for the classifier:  
* Balance: using only the balance feature;
* Status: using only the status feature;
* Balance and Status: using both the balance and status features.  
Table 4 shows the information gain values in the decision tree when both the balance and status features are used. In the table, BT and ST refer to the triad types in Figures 1 and 2, respectively. These results are sorted by gain value.  
Figure 7: Generative surprise and receptive surprise of spammers’ ego-networks and non-spammers’ ego-networks. Spammers and non-spammers show different patterns of status surprise values. Similarly to the results of balance surprise, the variance of the status surprise values of non-spammers is bigger than that of spammers. Such a difference appears more clearly in the receptive surprise result.
## 4 Experiment Setup
### Competing Methods  
In order to evaluate the effectiveness of our model, the comparable baseline models selected in this paper are trained with the Valentini dataset, and all reference methods are as follows:  
* Wiener filtering [3]: A traditional speech enhancement algorithm.
* SEGAN [4]: A groundbreaking model of U-Net with GAN training in time-domain.
* Wave-U-Net [13]: A time-domain U-Net enhancement model.
* Attention-Wave-U-Net [14]: A time-domain U-Net with local self-attention.
* DEMUCS (causal) [15]: A U-Net model with a unidirectional LSTM.
* DSEGAN [6]: Multiple U-Net model with GAN training in time-domain.
* SASEGAN [7]: Time-domain U-Net with GAN training combined with self-attention.
* Sinc-SEGAN [9]: Sinc convolution was added to the U-Net based generator.
# A New Multi-Axial Flux Pm Motor-Generator System for Flywheel Energy Storage
## 3 Results
### Magnetic Analyses  
Magnetic analyses of the designs have been performed with the ANSYS Maxwell program. Firstly, magnetic analysis of the 2p = 2 pole high-speed stage was obtained. The 3D Maxwell model of the 2p = 2 pole AFPM motor is given in Figure 5.  
The design parameters of the 2p = 2 pole AFPM motor are shown in Figure 5. Magnetic analyses were performed with the ANSYS Maxwell program by iteration, the conventional optimization method. As a result of the magnetic analysis, the L\({}_{\text{h}}\), L\({}_{\text{w}}\), M\({}_{\text{d}}\), and M\({}_{\text{h}}\) values were 12 mm, 2.5 mm, 5\({}^{\circ}\), and 7 mm, respectively [27]. The ratio of the total foot width to the circumference was obtained as \(\sigma\) = 0.377 for the L\({}_{\text{w}}\) parameter. The ratio (\(\sigma\)) should not exceed the 0.4 value since the increase in the L\({}_{\text{w}}\) parameter causes a decrease in the number of turns.  
Maxwell model of the 2p = 12 pole motor-generator set designed for FESS is given in Figure 6. For the 2p = 12 pole AFPM motor-generator model; L\({}_{\text{w}}\), L\({}_{\text{h}}\), and magnet  
Figure 4: Multi-section coil launcher model [26].  
Figure 5: Maxwell model of the 2p = 2 pole AFPM motor.  
placement angle M\({}_{\text{P}}\) values were found as 5.5 mm, 12 mm, and 30\({}^{\circ}\), respectively. The design parameters L\({}_{\text{w}}\) and L\({}_{\text{h}}\) were used to determine the saturation points of the ferromagnetic core. As the ferromagnetic core becomes saturated, the magnetic flux density in the air gap decreases, negatively affecting machine performance. As a result of the analysis of the magnet placement angle, an improvement of 60% was obtained in the cogging torque for the determined 30\({}^{\circ}\). In addition, the winding structure within the slots is segmented into three parts: A, B, and C, as illustrated in Figure 6. Windings A and B (toroidal winding type) are identical, each consisting of 40 turns of 0.5 mm cross-section conductor wire. Winding C (trapezoidalwinding type), on the other hand, is composed of 5 turns of the 8 \(\times\) 0.5 mm conductor wire.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 3 Method
### The Framework of Flame-SeaFormer
#### 3.2.3 Dynamic Flame Parameter Inversion of Time-Series Combustion Images  
The dynamic flame parameter inversion of time-series combustion images involves calculating the flame height, width, and area change rates based on a sequence of consecutive images denoted as \(I_{seq}=\{I_{1},I_{2},\ldots,I_{num}\}\), where \(num\) is length of the image sequence. For each image \(I_{i}\) within this sequence, the flame contour \(C_{flame}^{i}\) is extracted using the previously described method. Here, \(C_{flame}^{i}(t)\) represents the set of contour points of the flame object in the \(i\)-th image at time \(t\).  
For consecutive time points \(t_{1}\) and \(t_{2}\), the change amounts of the flame's height, width, and area are expressed as  
\[\Delta H=H_{flame}(t_{2})-H_{flame}(t_{1}) \tag{27}\]  
\[\Delta W=W_{flame}(t_{2})-W_{flame}(t_{1}) \tag{28}\]  
\[\Delta A=A_{flame}(t_{2})-A_{flame}(t_{1}) \tag{29}\]where \(H_{flame}(t)\), \(W_{flame}(t)\), and \(A_{flame}(t)\) denote the height, width, and area of the flame object in fire images at time \(t\), respectively. Then, the dynamic flame parameters are obtained as follows:  
\[\frac{d}{dt}H_{flame}(t)=\lim_{\Delta t\to 0}\frac{H_{flame}(t+\Delta t)-H_{flame}(t)}{ \Delta t}\approx\frac{\Delta H}{t_{2}-t_{1}} \tag{30}\]  
\[\frac{d}{dt}W_{flame}(t)=\lim_{\Delta t\to 0}\frac{W_{flame}(t+\Delta t)-W_{flame}(t)}{ \Delta t}\approx\frac{\Delta W}{t_{2}-t_{1}} \tag{31}\]  
\[\frac{d}{dt}A_{flame}(t)=\lim_{\Delta t\to 0}\frac{A_{flame}(t+\Delta t)-A_{flame}(t)}{ \Delta t}\approx\frac{\Delta A}{t_{2}-t_{1}} \tag{32}\]  
where \(\frac{d}{dt}H_{flame}(t)\), \(\frac{d}{dt}W_{flame}(t)\), \(\frac{d}{dt}A_{flame}(t)\) are the change rates of the flame height, width, and area at time \(t\), respectively. \(\lim_{\Delta t\to 0}\) indicates the limit at which the time interval \(\Delta t\) approaches 0.  
Therefore, the dynamic flame parameters are approximated by computing the differences and proportions of the flame's height, width, and area between two adjacent frames. According to these parameters, the dynamic behavior of the flame is analyzed within a sequence of successive images.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Performance Evaluation Parameters
#### 4.1.2 Packet Delivery Ratio (PDR)  
In the proposed MIDS algorithm, PDR is anticipated based on the data packets produced and packets got according to the record of each node. It has a quantitative relationship between the data packets produced by the source node and those received at the destination node.  
In Figure 6 PDR is plotted on the \"X-pivot\" and rounds are plotted on the \"Y-pivot\". Toward the beginning when the sensors are completely dynamic and in their most elevated energy structure, PDR is higher up to the 500 th round. After that, as the energy utilization begins expanding, the nodes turn out to be dead which slows the procedure of packet conveyance. Be that as it may, it does not fully disappear up to the 35,000 th round. At the point when the all-out energy of the network dips under the edge esteem, the PDR becomes \"0\".  
Figure 5: Alive nodes of the network vs. rounds.
## 5 Evaluation
### Experimental Results  
#### 5.2.1 Effectiveness of Inter-Class Closeness  
In this experiment, we evaluated the effectiveness of inter-class closeness as a metric for assessing ambiguity. Therefore, we compared the KNN classifier's accuracy on malware sets satisfying the inter-class closeness (i.e., inliers) and not satisfying them (i.e., outliers).  
As shown in Table 1, the classifier was able to classify 1819 out of 1941. Specifically, for each group A, B, C, and D, the classifier was able to classify with an accuracy of 96.4%, 74.1%, 82.4%, 80.6%, and 94.4%, respectively. Groups B, C, and D showed a relatively low accuracy, presumably due to the low number of samples. Importantly, every accuracy with the inliers from each group outperformed those of the outliers by a large portion, which demonstrates the effectiveness of our inter-class closeness metric.  
We were also able to confirm that postponing the classification of outliers for further inspections is effective. As shown in the accuracy results for the inliers, the machine can perform the classification with a significant accuracy of 96.6% by classifying only with the inliers and postponing the outliers.
## 3 Results and Discussion
### Multivariate Analysis of Coffee Powders and Brews
Since the HS-SPME extraction conditions herein used were different for the two matrices, aiming to have the perspective of the consumer when drinking an espresso coffee,  
Figure 4: Heatmap representation corresponding to the 390 volatile compounds of the 8 capsule-coffee powders (**a**) and respective espresso brews (**b**) under study, distributed by chemical families, considering the GC \(\times\) GC peak areas after mean-centering the data for each variable and dividing by the standard deviation (autoscaling). The relative content of each compound is illustrated through a chromatic scale (from dark blue, low values, to dark red, high values). Detail GC \(\times\) GC peak areas data were reported in Table S2. and the perspective of the overall volatile composition of coffee powder, it is not possible to make a direct comparison between the GC \(\times\) GC peak areas. However, it was possible to make a relative comparison among the volatile profiles of each type of capsule-coffee blend (Figure 4): (i) Blend Dec and Blend 1 revealed a similar volatile profile among their coffee powders and respective espresso brews, except for aldehydes, furan and sulfur compounds, ketones, and pyrazines which were less intense in the powder, (ii) Blend 2 and mainly Blends 3 and Sup were less intense in espresso brews, (iii) Blend 4 exhibited a higher volatiles' intensity in the brews than in the respective powder, and (iv) Blends 5 and 6, only differing in their origins (Tanzania and Ethiopia, respectively) had a more intense volatile profile in the powder when compared to the brew, in particular for the furan compounds, ketones, pyridines, pyrroles, and terpenic compounds. The different matrix structures may explain the distinct extractability of the volatile compounds of the different blends [12,71]. Nevertheless, these results revealed that the variations in espresso coffee brews were not so perceptible when analyzing coffee powders, and the differences determined in coffee powders were not always reflected in the espresso brew. The aqueous nature of the extraction process hinders the passage of coffee powder hydrophobic compounds to the brew. Moreover, the non- or low-water soluble volatile compounds tend to migrate in a higher extent in the brew to the headspace and are perceived by the consumer [63]. As a consequence, only a few compounds relevant to the aroma of the brews are extracted from the solid matrix and perceived in the brew in the same proportion.
# Photonics-Based Microwave Image-Reject Mixer
## 4 Photonics-Based Microwave IRM Applications
With a programmable multi-channel filter to select the desired signal, and with splitting of LO pairs into multiple channels, simultaneous image-reject down-conversion and channelization are implemented with the help of PDs and low-frequency electrical 90\({}^{\circ}\) hybrids. For signal transmitting, a signal OFC is injected into a Mach-Zehnder modulator (MZM) to modulate electrical baseband signals. Then, the modulated signal is combined with LO OFC and injected into a programmable multi-channel filter where desired optical components are selected. With a PD to perform optical heterodyne, frequency up-conversion to the required RF band is realized. A conceptual microwave photonic RF front-end covering S, X, K, Ku and Ka bands is successfully implemented. Simultaneous channelization, with downconversion for signal receiving, and frequency up-conversion for signal emitting, is demonstrated. Furthermore, based on this scheme, demonstration of a multi-channel microwave photonic satellite repeater has been established. Figure 9: Scheme of a photonics-assisted electronic warfare scanning receiver using a photonics-based microwave mixer incorporating a 90\({}^{\circ}\) optical hybrid [34]. Another application of photonics-based microwave IRM is a wideband or multi-band radar system [39]. Generally, the key challenge associated with realization of multi-band radar systems is bandwidth limitation of the electronic components. Microwave photonics provide a promising solution due to the advantages brought by photonics, including higher carrier frequency and large bandwidth spectrum and so on [40; 41; 42]. Previously, a dual-band linear frequency-modulated continuous wave (LFMCW) radar receiver was built based on a photonic IRM [28]. Integrated up- and down-chirp linear frequency-modulated (LFM) waveforms located in two different bands were used as transmitted signals. Without photonics-based microwave IRM, the image component of beat frequency from the up- and down-chirp bands would overlap with each other, resulting in false targets. A photonic IRM scheme offers the possibility of independent target detection, and allows the sharing of hardware resources and joint dechirp processing of dual bands. The image components can be further suppressed using subsequent digital I/Q imbalance compensation. In addition to the above microwave applications, an all-fiber continuous-wave coherent Doppler LiDAR was reported, based on photonic IRMs [43].
## 1 Introduction
Most of the FEL facilities adopt afterwards to generate intense THz radiation by using a compressed electron bunch with the duration shorter than one THz period; therefore, no high gain process is expected and the radiation power is usually limited to a maximal pulse energy of hundreds \(\mu\)J (200 \(\mu\)J [21] and 100 \(\mu\)J [33]). Powerful narrow-band THz pulses can be produced by using a sub-picosecond (ps) scale pulse train, which can be generated either by directly illuminating the photocathode with a train of laser pulses [34; 35; 36] or manipulating the electron beam at relativistic energy (including exchanging transverse modulation to longitudinal distribution [37; 38], direct modulating the drive laser [39], converting wakefield induced energy modulation to density bunching [40], two wavelengths of energy modulation in two separated undulator sections [41; 42], slice energy spread modulation [43], and laser-based density modulation [44; 45]). Comparing with the former method with the laser pulse train, the electron beam manipulation method has the advantage of more flexible tunability in the output frequency and can exclude the negative effects from the space charge force, which may smear out the longitudinal structures at low beam energy. Previously, simulation studies on THz FEL using a laser modulator and a bunch compressor have been performed, which use an assumed THz seed laser [45]. In this paper, a dedicated compact light source based on electron beam manipulation is proposed for generating mJ-level high energy radiation pulses with a tunable frequency from 0.1 THz to 60 THz. In this proposal, the pulse shaping and chirped frequency beating techniques [20; 46] are utilized, and two cogenetic chirped laser pulses are employed to form optical heterodyning, thus obtaining the quasi-sinusoidal optical signal at THz wavelength. This optical pulse helps to form coherent bunching at THz frequency in the electron beam, which can be used for generating intense THz radiation in the following undulators. To achieve THz radiation with higher peak power and pulse energy, an electron beam with long pulse length and undulator taper are utilized to get longer interaction length and keep lasing after saturation. To achieve a radiation at full THz range, an initial ultra-fast and high-power laser is utilized to achieve a longer time delay and harmonic lasing is also used to furtherly increase upper limit of THz frequency. In this paper, the detail descriptions of the basic principle of this proposal and start-to-end simulations with realistic parametersand three-dimensional effects are presented. This proposal can be easily realized by using a compact accelerator facility with the electron beam energy of tens of MeV and mature accelerator and laser technologies.