esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Verification of Proposed Model
#### 3.2.3 Efficiency Results  
The efficiency of the proposed model and watermarking algorithms are listed in Table 6.  
The high-efficiency of the proposed model is deduced from two perspectives, which are the time and the processed data volume. The time cost by the proposed model is much less than those of watermarking algorithms, although the watermarking algorithms have efficient performance in watermark embedding and extraction. Furthermore, unlike the algorithms used in experiments, the proposed model is running based on a few lines of code without any sample data. Hence the volume of data processed by the proposed model is tiny compared with watermarking algorithms. These advantages in time and data volume, offered by the proposed model, will definitely promote the robustness evaluation and computation in watermarking algorithms, especially for huge data volume of remote sensing images in the big data era.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multicolumn{2}{c}{**Proposed Model**} & \multicolumn{2}{c}{**Watermarking Algorithms**} \\ \cline{2-5}  & **Time** & **Data Volume** & **Time** & **Data Volume** \\ \hline Experiment 1 & 1175 s & \textless{}1 MB & 20,576 s & \(\approx\)601,000 MB \\ Experiment 2 & 3298 s & \textless{}1 MB & 67,510 s & \(\approx\)601,000 MB \\ Experiment 3 & 1790 s & \textless{}1 MB & 24,792 s & \(\approx\)601,000 MB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computation time and processed data volume cost by the proposed model and algorithms.
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
## 5 Simulation and Numerical Results  
The simulations are applied in two different phases. The first one compares the performance of IRS-OMA against IRS-NOMA in terms of several performance metrics under a single IRS-assisted setup. The second phase of the simulation addresses the impact of having more than one IRS. The distance separating the source and the destination \((d_{SD})\) is set to vary from 100 m to 150 m. It is also assumed that the distance between the source and the IRS plane \((d_{SR})\) is set as 100 m, while the minimum distance between destination and the IRS plane \((d_{RD})\) is chosen to be 5 m. The power amplifier efficiency \(\eta_{PA}\) is chosen as 0.38. The simulated channel is chosen in accordance with 3 GPP standards with deterministic characteristics as a function of the distance, as listed in Table 1.  
Figure 2: The proposed two IRS-assisted scenarios.
# Impacts of Lightning-Induced Overvoltage on a Hybrid Solar PV-Battery Energy Storage System
## 5 Simulation Results and Discussion  
In this study, the simulations were divided into three sections, namely the lightning-induced overvoltage effect analysis on the hybrid solar PV-battery energy storage system with different lightning current amplitudes (e.g., 3 kA, 19 kA, and 169 kA), the analysis of different distances of lightning strike locations (e.g., 20, 50, and 100 m), and the analysis of different cable lengths (e.g., 5, 10, and 20 m). The presence of a lightning current along the channel and in the hybrid solar PV-battery energy storage system at the point nearest the lightning strike, x = 0, was defined as outlined in Sections 5.1-5.3. The lightning current waveform of 10/350\(\upmu\)s was simulated using the Heidler function and lightning-induced overvoltage was simulated using the Rusck model. The parameters of the simulation and the recorded data for lightning-induced overvoltage are shown in Tables 8 and 9, respectively.
## 2 EM Algorithm for Maximum Likelihood Reconstruction in Emission Tomography
It has been pointed out in [23, 30] that formula (15) can also be explained by the Bayes conditional probability formula. This EM algorithm possesses the following properties making it attractive for emission tomography; they are:1. If the initial \(\mathbf{x}^{(0)}\geq 0\) then \(\mathbf{x}^{(k)}\geq 0\) for all \(k\geq 1\); _i.e._, it automatically satisfies the non-negativity constraint on \(\mathbf{x}\). 2. The algorithm is easy to implement as it only involves forward- and back-projections. 3. The updating formula in Equation (15) increases the incomplete data log-likelihood: \(l(\mathbf{x}^{(k+1)})\geq l(\mathbf{x}^{(k)})\), where equality holds only when the iteration has converged. 4. \(\mathbf{x}^{(k)}\) satisfies \(\sum_{i}\mu_{i}^{(k)}=\sum_{i}y_{i}\), where \(\mu_{i}^{(k)}\) is \(\mu_{i}\) with \(\mathbf{x}=\mathbf{x}^{(k)}\). Thus the \(\mathbf{x}\) estimate at any iteration satisfies that the total expected and the total observed counts are equal. The above EM is easy to implement and possesses some attractive properties on the reconstructions. This algorithm, however, is restricted only to emission tomography with Poisson distributed measurements. It cannot be easily extended to other reconstruction tasks. For example, application of the EM algorithm to transmission tomography does not lead to an exact updating formula due to the fact that its M-step does not produce a closed-form solution; see [29]. Another limitation is that this EM algorithm can only be used for maximum likelihood reconstructions, and its application to the MPL reconstruction will not in general result in closed-form updating formula. To rectify this problem, [PERSON] [31] developed a one-step-late (OSL) algorithm for the MPL reconstruction by replacing \(\mathbf{x}\) in the derivative of the penalty function by its current estimate \(\mathbf{x}^{(k)}\), and therefore an \"exact\" solution can still be accomplished. But this method suffers from the deficiencies that (i) the algorithm may be non-convergent; and (ii) some estimates may be negative. [PERSON] [32] reproduced the EM updating formula using a totally different argument. In his derivation, there is no missing data and hence no E-step. Although the algorithm is named \"modified EM\", it is not a real EM. In fact, this algorithm belongs to a more general class called the optimization transfer algorithms, since the Poisson log-likelihood optimization problem is transferred to a simpler optimization in each iteration. We will summarize the optimization transfer algorithms in the Section 4.
###### Abstract  
The numerical evaluation of elastic guided wave (EGW) phenomena is an important stage in the development and configuration of ultrasonic-based non-destructive testing/structural health monitoring (NDT/SHM) systems. To reduce the computational costs, which are typical for EGW simulations in laminated composite structures, and to make the corresponding parametric analysis possible, the latter could be treated by employing an effective single-layer model with homogenized anisotropic material properties. The present study investigates the applicability of such an approach to simulate EGW excitation, propagation, scattering, and sensing in laminate composite structures, which are among the typical problems for ultrasonic-based NDT/SHM. To this end, two homogenized models have been implemented: the well-known static long-wave homogenization approach and the advanced Lamb wave homogenization method, where the effect of angular and frequency dispersion of EGWs is taken into account. To illustrate their performance, in-plane elastic guided wave excitation and sensing with surface-mounted piezoelectric transducers as well as wave scattering by a T-shaped stringer in cross-ply symmetric anisotropic laminates are examined by employing a recently developed semi-analytical hybrid approach. The limits of the applicability of both homogenized models are demonstrated and discussed via the comparison with the multi-layered model. The general conclusion from the obtained results is that only a qualitative, although computationally efficient, description of the EGW phenomena in the considered composites can be achieved using single-layer models.  
Keywords:guided waves; anisotropy; laminate; homogenization; piezoelectric transducer; stringer; sensing +
Footnote †: journal: Applied Physics Letters
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 4 Gabor-Based Speech Reconstruction Network
### Model II: GaborFea2 Speech  
Our proposed GaborFea2 Speech model uses Gabor Lip Features (as detailed in Section 3.3) as inputs. Instead of the original 28 \(\times\) 28 pixel Gabor lip images, seven key visual features (width, height, mass, area, Xpos, Ypos, and orientation) are used to represent lip dynamics. This reduction in input data dimensionality, from 782 to 7, is accompanied by an enhancement of the feature set through the incorporation of three-time derivatives, effectively extending the Gabor features. This extended Gabor feature set serves as a direct visual representation of lip changes during speech, eliminating the need for complex CNN layers. The preprocessed Gabor features are directly fed into a 1-layer LSTM sequence network to generate the corresponding audio spectrogram. To the best knowledge of the authors, this is the first attempt to use Gabor Features for speech reconstruction, and this approach offers advantages in terms of lightweight structure and speed of training. Moreover, the objective is to harness the distinctness of Gabor feature visual attributes across different speakers by developing a multi-speaker speech reconstruction model based on Gabor Features, thereby achieving greater speaker independence.  
Pre-processing is an essential step for GaborFea2 Speech. In each 3-second Grid video, the Gabor features input matrix has dimensions \(F_{n}*L_{f}\) (\(7\times 75\)), where \(F_{n}\) represents the feature dimensions and \(L_{f}\) is the frame length. The output is a spectrogram with  
\begin{table}
\begin{tabular}{c c} \hline
**Layers** & **Size** \\ \hline Input Layer & (None, 3, 28, 28, 5) \\ \hline Conv3D (32) & \\ BatchNormalization () & \\ LeakyReLU & \\ MaxPooling3D (2, 2, 1) & \\ Dropout (0.25) & (None, 16, 40, 80, 5) \\ \hline Reshape & (None, 5, 12,800) \\ \hline LSTM (512) & \\ BatchNormalization () & \\ ELU (alpha = 1.0) & \\ Dropout (0.25) & (None, 5, 512) \\ \hline Flatten & (None, 2560) \\ \hline Dense (2048) & \\ BatchNormalization () & \\ ELU (alpha = 1.0) & \\ Dropout (0.4) & (None, 2048) \\ \hline Output Dense (832) & \\ Activation (sigmoid = 0.05) & (None, 832) \\ \hline \end{tabular}
\end{table}
Table 2: Structure of GaborCNN2 Speech Network.  
dimensions \(S_{n}*L_{s}\) (32 \(\times\) 390), where \(S_{n}\) represents the encoded spectrogram components and \(L_{s}\) denotes the total spectrogram time frequency. When reconstructing speech using Gabor features, the main challenge is the mismatch between the shorter input length of 75 and the longer output length of 390, which can result in inaccurate predictions and information loss. Moreover, the lower input feature dimension of 7 as compared to the desired output dimension of 32 may result in reduced accuracy and difficulties in capturing complex relationships in the data. We experimented with pre-processing to investigate different input sequence lengths and feature dimensionalities.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 4 Discussion
Hence, something anomalous had affected the vertical polarization. No anomalies were present for the following nine clear days: 6-7 January, 11 February 2015; 29 September, 3 and 15 October 2016; 21 April, 9 October 2017 and 22 November 2017 (note that on 21 April 2017, the standard deviation of \(Z^{dr}\) was as large as 1.6 dB). To summarize, nine (out of eleven) clear sky days shows good reproducibility of what had been statistically learnt from the training data set of five clear sky days. Future research directions include the analysis of days characterized by heavy rain and/or very strong winds; these events should obviously be accompanied by an investigation regarding the stability and reproducibility of the Doppler and polarimetric signatures of the BS at a sub-daily temporal scale. However, it may be pointed out, just as an example, that during the last three hours of heavy rain (37 samples) that occurred on 16 June 2016, the median values of the horizontal and vertical reflectivity were as low as 77.5 and 75.0 dBZ (two alerts). A third alert was associated to the median of \(\rho^{HV}\), which was as low as 0.9872. The standard deviation (36 degrees of freedom) of \(Z^{dr}\) was also anomalously large (1.5 dB). It has been concluded that BS signals are remarkably weaker and less correlated during heavy rain.
# Review on Application of Quaternary Ammonium Salts for Gas Hydrate Inhibition
## 3 Gas Hydrate Inhibition
### Quaternary Ammonium Salts
In general, ILs with higher electrical conductivity show higher thermodynamic hydrate inhibition effects [43; 56]. Recently, [PERSON] et al. [57] have studied on the effects of QAS and IL, BMIM-BF\({}_{4}\) on the thermodynamic stability via experiment (isochoric pressure-search method) and modeling. The van der Waals-Platteeuw (vdWP) theory is the main reference in calculating the chemical potential of water in hydrate phase [76]. On the other hand, the Peng-Robinson (PR) equation of state [77; 78] is referred to calculate the fugacity in the gas phase. The NRTL activity coefficient model [79; 80] is also used to investigate the water activity in the aqueous phase. It was found that the inhibition performance of methane hydrate is enhanced when both QAS and IL is combined, in which, it shows average temperature depression value of 2.7 K, when compared to its individual performances, with 0.8 K and 1.0 K for 25 wt% of TEACI and 25 wt% of BMIM-BF\({}_{4}\), respectively.
## 2 The Wustenrot Pilot Site
### Settlement Heat Demand  
The Plus-Energy settlement \"Vordere Viehweide\" spreads over 25,000 m\({}^{2}\) of land area, without the agrothermal collector field. Twenty-three buildings complying with the German KfW-55 standard are connected to the low-temperature DHC network. KfW-55 requires each building's annual demand of primary energy to be equal or less than 55% of the demand of a new building with the same geometry, according to German energy-saving regulations. In addition, stricter regulations are put directly on the properties of the building's thermal envelope. This results in a building-specific energy demand of 55 kWh/m\({}^{2}\). The settlement's estimated annual heat demand for space heating and DHW (domestic hot water) is 340 MWh, resulting in 255 MWh of heat demand on the network side when assuming an average heat pump COP (coefficient of performance) of 4. Approximately 60% of provided energy is used for space heating, and the remaining 40% is used for DHW heating. The houses are equipped with underfloor heating systems, resulting in low supply temperatures. The decentralized heat pumps increase the low-temperature DHC network's temperature to temperatures between 30 and 40 \({}^{\circ}\)C for space heating, with an estimated return temperature from the underfloor heating system of 6 K to 10 K lower than the supply temperature. The prepared DHW temperature is aimed to be around 50-55 \({}^{\circ}\)C [22].
## 4 Pheromone-Based Alarm Delivery Concept
### Assessment of the Advantage of Using the Backbone in the Pheromone Trail  
Taking into account the distribution of pheromones over the ground sensor nodes presented in Section 4.3, and the proposal of using the central \"backbone\" part of the trail as a limiting border to the flooding of an alarm message that occurs inside a trail, a fourth set of simulations was performed to assess the effectiveness of this strategy. In this fourth set, the basic conditions presented in the first simulation sets were maintained, but the pheromone trail has no backbone, such that the alarm forwarding is performed as illustrated in Figure 7a. The performed simulation had a scenario with an area of 10 km \(\times\) 10 km, in which four UAVs were patrolling the area, and there were the same increasing numbers of threats. Results for cost in terms of messages sent were compared to those of the corresponding setups presented in the previous experiments, where the pheromone trail has a backbone, as shown in Figure 16.  
From the results in Figure 16a, which uses a semi-log scale, it is possible to compare both versions of the pheromone strategy with the reference solutions (flooding and optimal) and also to observe that they have a similar behavior, being much less costly than the flooding based one, and not too far from the optimal solution. A more detailed analysis between them is provided in Figure 16b, where a linear scale is adopted. The figure shows that the usage of the backbone, which was defined as having half of the width of the whole trail, provided a reduction of in average 30% in the number of messages sent for each number of threats. These results are consistent with those presented in Section 5.3, particularly the ones presented in Figure 13, which show that the trail-follow mechanism contributes with approximately 50% of the amount of messages required by the entire strategy, unless for the variation with seven threats, in which the trail-follow mechanism requires a higher percentage (Figure 13), as was discussed above. By using a backbone layer with half of the width of the trail, it is expected that the number of messages consumed by the trail-follow mechanism drops to half of the number of messages required by this mechanism in a trail without backbone.  
As a result, the total number of messages sent by the entire pheromone strategy (trail-search and trail-follow) is expected to be reduced by in average 25%. Thus, considering the case for seven threats, in which the trail-follow mechanism requires relatively more messages, the achieved savings of approximately 30% is a reasonable result.
## Results and Discussion
### Vibration Spectroscopy
#### 3.2.2 FT NIR  
The average normalized FT NIR spectra of purified basidiocarps are represented in Figure 1b. The spectra of protein-rich samples demonstrated two shoulders at 4860 cm\({}^{-1}\) (amide A/II) and 4610 cm\({}^{-1}\) (amide B/II) assigned to the combinations of NH stretching and bending modes characteristic for proteins [43; 44; 45]. Other protein bands were found near 6600 cm\({}^{-1}\) (amide A + 1 st overtone of amide I), 6471 cm\({}^{-1}\) (1 st overtone of amide A) and 6330 cm\({}^{-1}\) (amide A + 1 st overtone of amide II). The bands of polysaccharide vibrations were found near 4300 cm\({}^{-1}\) (CH\({}_{2}\) stretch/bend combination), 4400 cm\({}^{-1}\) (OH/CO stretch combination), 4770-4790 cm\({}^{-1}\) (OH stretch/bend combination), 6310 cm\({}^{-1}\) (CH stretch 1 st overtone) and 6980 cm\({}^{-1}\) (OH stretch 1 st overtone) [46; 47]. Polysaccharides and proteins contributed to the regions of 4400-5000 cm\({}^{-1}\) (CH, OH and NH stretch/bend combinations) and 5400-6000 cm\({}^{-1}\) (CH\({}_{2}\) and CH\({}_{3}\) stretch 1 st overtones) in a different manner.
## 2 Results and Discussion
### Optimization of the Extraction Procedure
#### 2.2.2 Extraction Temperature  
Effect of temperature on the extraction efficiency of HS-SPME sampling method has been well demonstrated. Rising sample temperature helps analytes to be released from the matrix and speeds up their diffusion into the headspace. Consequently, the analyte distribution coefficient (headspace/sample) increases at high temperatures. On the contrary, the distribution coefficient of analyte between fiber coating/headspace decreases. Therefore, a balance point between these two opposite effects should be obtained by optimizing extraction temperature. In this study, the effect of sample temperature was studied over the range of 20-90 \({}^{\circ}\)C, while other conditions were kept constant. The average peak areas (for three replicated experiment) vs. extraction temperature is depicted in Figure 4. As seen, with rising extraction temperature up to 60 \({}^{\circ}\)C, peak areas increase and, after that, start to decline slightly. Therefore, 60 \({}^{\circ}\)C was selected as the best temperature for the HS-SPME sampling of MTBE in solid samples.  
Figure 4: Extraction temperature vs. extraction efficiency of MTBE (analyte concentration: 2 \(\upmu\)g\(\cdot\)g\({}^{-1}\), extraction time: 15 min, desorption condition: 2 min at 250 \({}^{\circ}\)C).  
Figure 3: Effect of extraction time on the extraction efficiency of the HS-SPME–GC method for analysis of MTBE in solid samples. (Analyte concentration: 2 \(\upmu\)g\(\cdot\)g\({}^{-1}\), extraction temperature: 30 \({}^{\circ}\)C, desorption condition: 2 min at 250 \({}^{\circ}\)C.)
## 3 Results
### Crop Mapping  
Crop mapping was performed based on field data combined with a detailed study of the chlorophyll response (_NDVI_) for each of crop type. The crops determined in the region were cereals, other crops followed by cereals, vegetables (onions, potatoes, tomatoes), olive trees, and fruit trees. In order to calculate the areas of each class, pixel size was used. Therefore, after obtaining the classes, the area of each class was obtained by multiplying the number of pixels and the pixel size (10 m \(\times\) 10 m). For the crop year 2018-2019, the areas determined for the cereals represented the largest class, with an area of 8870.43 ha, followed by olive trees with an area of 4323.18 ha. Classes of other crops followed by cereals and vegetables represented 1661.45 ha and 1530.06 ha, respectively. The least represented class was that of fruit trees, with only 661.05 ha (Figure 9).