esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experiments
#### 2.2.2 Measurement of Stress Distribution  
The measurement of stress distribution on the mating surface mainly includes the following three steps: the calibration of \(P_{m}\)-\(K_{n}\), the measurement of the reflection coefficient, and the calculation of stress distribution.  
1. Calibration of \(P_{m}\)-\(K_{n}\): In order to achieve a reliable measurement of the stress distribution on the mating interface, the equipment in Figure 4 is used to calibrate the \(P_{m}\)-\(K_{n}\) relationship, and kerosene is used as the coupling agent in the calibration experiment. The calibration results are shown in Figure 8. The \(R\)-\(P_{m}\) curve was obtained by calibration experiments. Then, \(K_{c}\)-\(P_{m}\) and \(P_{m}\)-\(K_{n}\) curves were calculated according to Equations (4) and (6). Obviously, the reflection coefficient is negatively correlated with the contact stress. This is due to the fact that the increase in contact stress increases the actual contact area of the rough interface, resulting in more ultrasonic transmission. As a result, the ultrasonic amplitude reflected from the rough interface is weakened, thus reducing the reflection coefficient. On the other hand, with the increase in the actual contact area, the bearing capacity of the rough interface gradually increases; that is, the ability to resist deformation increases. Therefore, the contact stiffness of the rough interface is positively correlated with the contact stress. In addition, it can also be found that \(K_{c}>K_{n}\). This is due to the interaction between asperities. The asperities in the interface will bear the load together; the interaction behavior will reduce the interface deformation and the actual contact area. As a result, the reflection coefficient of the rough interface is weakened. According to the above analysis, the contact stiffness will decrease. Therefore, the contact stiffness obtained by Equation (4) is greater than that obtained by Equation (6), i.e., \(K_{c}>K_{n}\). The relative error of the two contact stiffnesses is about 10%. Therefore, the accuracy of the connection force can be improved by about 10% by using the proposed acoustic model.
2. Measurement of reflection coefficient: The reflection coefficient is the ratio of the amplitude of the reference signal to the reflected signal at the interface. The reference signal is the signal reflected from the inner surface of the hub when it is not assembled. The reflected signal is the signal reflected from the mating interface. According to the distribution of the reflected signal at the interface, the reflection coefficient distribution can be obtained.
3. Stress distribution on the mating interface: The reflection coefficient distribution and the relationship between contact stress and interface stiffness have been obtained through the previous experiments. The calibration results are shown in Figure 8. According to the reflection coefficient distribution and the \(P_{m}\)-\(K_{n}\) mapping relationship, the stress distribution of the mating surface can be obtained.  
Figure 8: Calibration results of contact stress interface stiffness.
## 3 Results
### Results on SMOTE and Data Augmentation  
After train-test splitting, 69.6% (\(n\) = 6680) of the training data represented the defaulter class, while 2926 (30.4%) records belonged to the non-defaulter class. Similarly, in the test set, the defaulter class constituted 2864 (69.5%) records, while 1254 (30.5%) belonged to the non-defaulter class. These distributions indicated class imbalance in both training and test subsets. Figure 3 shows the imbalance in defaulter and non-defaulter classes within the original training and test sets, with approximately 70% of records representing the defaulter class.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Distribution** & **Total (\%)** & **Percentage** \\ \hline Defaulters & 9544 & 69.54 \\ Non-defaulters & 4180 & 30.46 \\
**Total** & **13,724** & **100.00** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class distribution of defaulters and non-defaulters.  
The bar graphs in Figure 4 show the results of the balanced class distribution achieved after DA and using the SMOTE.  
Figure 4 shows that the techniques applied resulted in an expanded training set of 26,744 observations with a perfectly balanced distribution of non-defaulters (\(n\) = 13,372) and defaulters (\(n\) = 13,372). DA on the test dataset expanded the original records to 8235 observations, with 5731 (69.6%) for the defaulter class and 2504 (30.4%) belonging to the non-defaulter class.
## 2 Study Area and Datasets
### Satellite Data Collection and Preprocessing
#### 2.2.2 Sentinel-1 SAR Data  
C-band Sentinel-1 is designed by the European Space Agency (ESA) onboard the Copernicus mission. It provides images of 10-m resolution with the central frequency of 5.405 GHz. Both co- and cross-polarized backscatter are provided and the temporal revisit time of one satellite is 12 days, resulting in the repeat cycle of 6 days by the constellation. As a microwave sensor, the Sentinel-1 also provides all-weather, all-day images with global coverage and these data have been extensively used for ice sheet observations [44], land cover mapping [45], crop monitoring [46; 47], ship detection [48], flood and earthquake applications [49; 50], and soil moisture monitoring [51; 52; 41; 53; 54].  
In order to ensure that the acquisition time of remotely sensed data is synchronized with the field observations, the Sentinel-1 data on 15 September 2019 were selected (Table 1) and freely downloaded through the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/))). The incidence angle of the acquired Sentinel-1 data is also about 40\({}^{\circ}\). Sentinel-1 data were preprocessed including radiometric correction, terrain correction, speckle noise filtering using Lee filter and projection transformation to reduce the effect of local spatial heterogeneity.
## 3 Results and Discussion
### Selection of HDEs
#### 3.1.2 Interaction of the 4:1 HDEs and Selected Steroids Based on Computational Methods  
The most stable structure of the 4:1 HDEs (Figure 3) shows that two menthol molecules form hydrogen bonds with the carboxylic group of lauric acid, while the alkyl chain of lauric acid interacts via van der Waals interactions with the hydrophobic moieties of the surrounding menthols.  
Among the many starting geometries of the molecular complexes of each steroid hormone with 4:1 HDEs that were used as input for the computational work, two significant modes of binding of the hormone with HDEs (Figure 4) are obtained. The optimized  
Figure 2: The spectra \({}^{1}\)H NMR for (**A**) menthol, (**B**) lauric acid, and (**C**) for synthesized HDE.  
geometry of binding mode 1 of each complex reveals the hydroxyl/carbonyl group of the hormone situated within the polar pocket of HDEs and forming a hydrogen bond with one of the menthol molecules, while the hydrophobic moieties are in close contact with the hydrophobic region of HDEs. Furthermore, for binding mode 2, the hormone molecules are located in the hydrophobic pocket composed of the alkyl chain of lauric acid and the hydrophobic moieties of menthol molecules, while the polar groups are in close proximity to the hydroxyl groups of the menthol molecules. PM3 estimates of the interaction energies are listed in Table 3 and show that both TES and PRO interact more strongly with HDEs than BES in both binding modes. The lower interaction energy in the case of the BES complex might be a direct result of the presence of an aromatic ring in BES, which reduces its ability to adapt to a more favorable conformation. The interaction strength of BES with HDEs is nearly the same for both binding modes, whereas TES and PRO binding mode 2 is more favorable than binding mode 1 by 1.9 and 2.5 kcal-mol\({}^{-1}\), respectively.  
Figure 4: PM3-optimized structures of the studied steroid hormones (in grey) with HDEs (in green).  
Figure 3: PM3-optimized structure of 4:1 HDEs.
## 3 Results and Discussion
### Hardness-TPA and Sensory Assessment of Marinated Meat  
For two weeks of marinating, the highest texture rating in sensory analysis was regularly attributed to marinades made using the German method; with the exception of the 14 th day in the case of marinades made from headed herring (Figure 8A). G-whole marinades scored highest in texture, with scores ranging from 4.79 to 4.93 after 4-14 days of marinating. D-fillets received the lowest marks of 4.20-4.43 for texture. German marinades needed only 4-7 days to achieve maximum marks for texture, while Danish marinades needed at least twice as long. Texture profile analysis (TPA) showed that the hardness-TPA of the meat of G-whole and G-headed marinades averaged 6.5 N, while the hardness of Gs marinades was significantly higher, at 9.6 N (Figure 8B). On the other hand, in the case of the Danish method, the hardness-TPA of the D-headed and D-whole marinades increased to from the fourth and decreased by the tenth day, obtaining higher values than the German G-headed and G-whole marinades (except D-headed after the seventh day). This could have been due to the lower activity of cathepsins L and D in the Danish marinades than German marinades. Cathepsin L hydrolyzes collagen, which has a greater impact on meat hardness than muscle proteins [37], which are mainly hydrolyzed by cathepsin D [38]. It was observed that cathepsin L activity positively correlated with texture in marinades obtained from herring with intestines (G-headed 0.847, D-headed 0.861, D-whole 0.779), contrary to a negative correlation in marinades from fillets (G-fillets \(-\)0.709, D-fillets \(-\)0.772). Although both marinating methods had a positive effect of marinating with digestive proteases on improving meat texture, significant hardness-TPA correlations with texture in sensory analysis were only for G-whole (0.620) and D-whole (0.568) marinades (Figure 3E,F).  
Additionally, the results of sensory analysis showed that a bitter taste appeared in the D-whole and D-headed marinades after 4 days of ripening, the intensity of which increased up to the tenth day of marinating from 1.0 to 4.0 points and from 0.5 to 2.0 points, respectively. The bitter taste was not present in D-fillets marinades, indicating that it was caused by the intestines of the fish. In the case of the German method, the bitter taste also appeared, but only after 10-14 days of marinating in G-whole and G-headed marinades, obtaining 1.0 and 0.5 points, respectively. The bitter taste could have come from blood and/or from the gastrointestinal tract and/or from bitter peptides, which are mainly released by chymotrypsin [39]. Chymotrypsin activity after 10-14 days of marinating was highest in only the D-whole and D-headed marinades, then was significantly lower in G-whole and G-headed marinades, and was lowest in fillets marinades (Figure 5C). In turn, carboxopeptidase-A, which is known to reduce bitter taste [40], was most active in G-whole marinades (Figure 5D), which likely protected these marinades from the rapid appearance of bitter taste.
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 4 Experimental Set-Up
### Classifier Performance and Tests of Statistical Significance  
The geometric mean (\(g\)-_mean_) is the most widely used method to evaluate the performance of classifiers and it is a well recognized tool for the evaluation in the class imbalance context; it is defined as follows [17]:  
\[g-mean=\sqrt[]{\prod_{j=1}^{I}acc_{j}} \tag{5}\]  
where \(acc\) is the accuracy by class, and \(J\) is the number of classes in the dataset.  
In order to strengthen the results analysis, a non-parametric statistical test was additionally achieved. Friedman test is a non-parametric method in which the first step is to rank the algorithms for each dataset separately; the best performing algorithm should have rank 1, the second best rank 2, etc. In the case of ties, average ranks are computed. Friedman test uses the average rankings to calculate Friedman statistic, which can be computed as follows:  
\[\chi_{F}^{2}=\frac{12N}{K(K+1)}(\sum_{j}R_{j}^{2}-\frac{K(K+1)^{2}}{4}) \tag{6}\]  
\(K\) denotes the number of methods, \(N\) the number of data sets and \(R_{j}\) the average rank of method \(j\) on all datasets.  
On the other hand, [PERSON] and [PERSON] [71] demonstrated that \(\chi_{F}^{2}\) has a conservative behavior. They proposed Equation (7) according to \(F-\)distribution with \(K-1\) and \((K-1)(N-1)\) Degrees of Freedom, as a better statistic:  
\[F_{F}=\frac{(N-1)\chi_{F}^{2}}{N(K-1)-\chi_{F}^{2}} \tag{7}\]  
[PERSON] and Iman-Davenport tests were used in this investigation, by using the level of confidence \(\gamma=0.05\) and KEEL software [72].  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Name** & **Layer** & **Layer** & **Layer** & **Layer** & **Layer** & **Layer** & **Layer** & **Layer** & **Epoch** & **Size** \\  & **Int** & **1** & **2** & **3** & **4** & **5** & **6** & **Output** & **of Batch** \\ \hline Salinas & 20/Relu & 60/Relu & 60/Relu & 60/Relu & 60/Relu & 60/Relu & 60/Relu & 17/SoftMax & 500 & 1000 \\ Indian & 224/Relu & 60/Relu & 60/Relu & 60/Relu & 60/Relu & – & – & 17/SoftMax & 500 & 100 \\ PaviaU & 103/Relu & 40/Relu & 40/Relu & 40/Relu & 40/Relu & 40/Relu & – & 10/SoftMax & 500 & 1000 \\ Pavia & 102/Relu & 40/Relu & 40/Relu & 40/Relu & 40/Relu & 40/Relu & 40/Relu & 10/SoftMax & 250 & 1000 \\ Botswana & 145/Relu & 30/Relu & 30/Relu & 30/Relu & 30/Relu & 30/Relu & 30/Relu & 15/SoftMax & 250 & 1000 \\ KSC & 176/Relu & 60/Relu & 60/Relu & 60/Relu & 60/Relu & 60/Relu & 60/Relu & 14/SoftMax & 250 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Neural networks’ parameters and specification used in the experimentation stage. The first column shows the dataset name. In the following columns: the number of hidden layers and its parameter (number of hidden neurons and activation function used in the respective layer). Layer output indicates the number of output neurons and the activation function to these nodes. Epoch is the number of epochs employed in the training. Size of Batch is the portion of samples used for the deep neural networks in each epoch.
## 3 Performance Evaluations
### Fiberless Channel
#### 3.1.2 Radar System  
When considering radar systems with spread spectrum waveforms, the signal chip duration is a crucial factor that determines the range resolution, which is related linearly, as follows [1]:  
\[\Delta R=cT_{c}/2, \tag{3}\]  
where \(c\) is the speed of light and \(T_{c}\) is the chip duration. Another significant parameter is the pulse repetition interval (PRI), or, equivalently, the radar waveform duration. This waveform consists of \(N\) bits with duration \(T=NT_{b}\). The parameter \(T\) plays a vital role in controlling the maximum detection range [1]. That is,  
\[R_{\max}=cT/2. \tag{4}\]  
To illustrate these concepts, we consider a spread spectrum signal with \(N=400\) bits, PN code of length 7, and radar waveform duration of 8 microseconds (\(T=8\)\(\upmu\)s). These parameters result in \(R_{\max}=1200\) m and \(\Delta R=43\) cm, which allow for the radar system to distinguish between targets that are at least 43 cm apart. The parameters of the transmitted radar signal are summarized in Table 1.  
Figure 7 shows the correlator output when there is a target at 250 m. The results are displayed first for noiseless data, where a clear peak appears at the correct distance with 18 dB PSL ratio. The second part of Figure 7 shows how well the radar system could perform under less-than-ideal conditions. In particular, noise is added to the transmitted signal so that \(E_{b}/N_{0}=-20\) and \(-40\) dB. With this added challenge, the system can still detect the target accurately with a 13.6 and 4.5 dB PSL ratio, respectively.  
Additionally, we conducted simulations with two targets positioned at intervals of 21.5, 43, and 215 cm under an \(E_{b}/N_{0}\) of \(-20\) dB. The first target is located at a distance of 250 m. The resulting cross-correlation function between the transmitted and received waveforms is illustrated in Figure 8. Notably, the figure demonstrates that the resolution limit, theoretically calculated at 43 cm in this case, enables the clear resolution of the two targets as long as they maintain this separation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Signal Parameter** & **Value** \\ \hline Center frequency (\(f_{c}\)) & 28 GHz \\ Bandwidth (BW) & 700 MHz \\ Pulse repetition interval (PRI) & 8 \(\upmu\)s \\ Maximum unambiguous range (\(R_{\max}\)) & 1200 m \\ Range resolution (\(\Delta R\)) & 43 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The transmitted signal parameters.  
Figure 8: The cross-correlation function when two targets are present in the scene at \(E_{b}/N_{0}=-20\) dB.  
Figure 7: The output of radar correlator for noiseless data and for noise levels of \(E_{b}/N_{0}=-20\) and \(-40\) dB.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Terahertz Antennas for 6G Applications
#### 3.7.5 THz Antenna Challenges  
Despite all the advantages mentioned above, THz antennas face several significant challenges [139], including limited resonant frequencies, a lack of optimal substrate materials, and constraints imposed by low-cost manufacturing processes. There is also a \"problem\" due to very high path loss and molecular absorption loss that lies in the nature of the specific frequency band.  
Another challenging issue is packaging. So far, metal packaging with coaxial or waveguide ports has been used for high-frequency components. Although the loss of metal housing can be reasonable, even at THz frequencies, it is obviously too big and too heavy to use in THz mobile handsets for future WLAN and WPAN systems. The application of current packaging techniques has not been studied in-depth above the frequency band of 100 GHz, and thus, some breakthrough for the THz integrated circuits is needed (ref. [140]).
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 4 Quantification of Total MC after Base-Catalyzed Deconjugation
### Miles' Approach of Based-Catalyzed Deconjugation
#### 4.2.3 Application to Natural Samples  
To our knowledge, [PERSON] et al. [59] reported the only application of basic pH deconjugation to natural samples, namely, cyanobacterial bloom samples and mallard duck (_Anas platyrhnchos_) liver. Two samples of lyophilized bloom material (10 mg each) and two samples of duck liver (50 mg each) were extracted directly into vials with 1 mL of MeOH, 500 \(\upmu\)L of Na\({}_{2}\)CO\({}_{3}\) (0.2 M), and 50 \(\upmu\)L of DMSO. The vials were then placed on a rotary mixer at around 20 rpm for 10 days. After a centrifugation step at 14,800 rpm for 5 min, the supernatants were centrifugally filtered (0.45 \(\upmu\)m) for 20 min before analysis via multi-hapten ELISA. The total MCs analyzed in this study by oxidation and deconjugation counted three times the free MCs analyzed by Adda ELISA in the same samples. Moreover, the analysis following deconjugation at basic pH allowed the detection of 2.5 times more MCs than an extraction performed by deconjugation at neutral pH. When comparing the results between the two methods for the extraction and analysis of the total MCs, depending on the matrix, one or the other method can detect more MCs. For the samples of lyophilized bloom material, the MMPB LC-MS/MS method yielded almost twice as many total MCs (840 vs. 430 \(\upmu\)g/g) than the deconjugation/ELISA multi-hapten method. Conversely, in the two samples of mallard duck liver, the deconjugation/ELISA multi-hapten method detected 26% more MCs than the MMPB LC-MS/MS method.
# Resonant Asymmetrical Half-Bridge Flyback Converter
## 2 Proposed Circuit Configuration
### State 3 (\(t_{2}-t_{3}\))  
During this interval, \(Q_{1}\) and \(Q_{3}\) remain off, and \(Q_{2}\) is turned on. The freewheeling current \(i_{Lr}\) charges \(C_{r}\), \(i_{Lr}\) is demagnetized and drops. \(i_{Lr}\) charges \(C_{oss1}\) to \(V_{in}\), while \(C_{oss2}\) is discharged to zero. \(i_{Lr}\) and \(i_{N2}\) flow through the body diodes of \(Q_{2}\) and \(Q_{3}\), so \(v_{ds2}\) and \(v_{ds3}\) are 0, respectively. At this time, \(Q_{2}\) and \(Q_{3}\) are turned on with the ZVS function. However, in order to avoid triggering \(Q_{3}\) by mistake, the SR control IC turns on \(Q_{3}\) after a short time delay. \(i_{Lm}\) transmits energy through the winding \(N_{1}\) to the winding \(N_{2}\) to the output terminal. When \(v_{ds3}\) falls to zero, it enters state 4.  
\[(L_{m}+L_{r})\cdot\frac{di_{Lr}}{dt}=-v_{Cr}, \tag{22}\]  
\[L_{m}\cdot\frac{di_{Lm}}{dt}=-n\cdot V_{O}, \tag{23}\]  
\[L_{r}\cdot\frac{di_{Lr}}{dt}=n\cdot V_{O}-v_{Cr}, \tag{24}\]  
\[C_{r}\cdot\frac{dv_{Cr}}{dt}=i_{Lr}, \tag{25}\]  
\[i_{Lr}(t)=\frac{n\cdot V_{O}-v_{Cr}(t_{2})}{Z_{3}}\cdot\sin(\omega_{r3}(t-t_{ 2}))+i_{Lr}(t_{2})\cdot\cos(\omega_{r3}(t-t_{2})), \tag{26}\]  
\[v_{Cr}(t)=n\cdot V_{O}-(n\cdot V_{O}-v_{Cr}(t_{2}))\cdot\cos(\omega_{r3}(t-t_ {2}))+i_{Lr}(t_{2})\cdot Z_{3}\cdot\cos(\omega_{r3}(t-t_{2})), \tag{27}\]
## 5 Numerical Results
### Anomaly Detection
It can be observed that the abnormal probability of \(\times\)70 is about 40% in the first time window, increases to about 55% in the second window, and eventually becomes greater than 90% in the third time window. This way, the anomaly can be monitored in a relatively high frequency without being subject to noise.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## ZigBee (IEEE 802.15.4)
### Z-Wave (IEEE 802.15.4)  
Z-Wave is a proprietary communications standard intended for remote control of applications in residential and commercial areas. In Europe, it operate on 868 MHz with a data rate of 9.6 kbps, and on 2.4 GHz with a data rate up to 200 kbps. Range-wise, Z-Wave typically has around 30 m indoor range, and up to 100 m outdoors. Z-Wave is short range, low data rate, and low cost alternative. Z-Wave can also be organized as mesh network, increasing the range [86]. Similar to ZigBee, Z-Wave also uses AES-128 encryption standard to maintain a high level of security in the network [92].
## 5 Proposed Hybrid V2X Technology Selection Algorithm
### KPIs  
To evaluate the performance of the proposed hybrid V2X technology selection algorithm, the following KPIs are considered:  
1. One-way end-to-end hybrid CAM latency: This is the one-way end-to-end latency of the hybrid CAMs i.e., intelligently sent CAMs. This parameter gives an overview of the performance improvement with the use of intelligent hybrid V2X technology selection algorithm.
2. Packet Delivery Rate (PDR): It is measured as the ratio of the number of CAMs delivered to the total number of CAMs sent from source node to the destination node in the network.
3. Reliability: Among the received CAMs, reliability refers to the proportion of CAMs that exhibit a one-way end-to-end latency below the specified latency threshold, represented as \(L_{thr}\).