esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Assays of Cell Viability  
The cell counting kit-8 assay (CCK-8; Dojindo Molecular Technologies, Kumamoto, Japan) and live/dead viability/cytotoxicity kit for mammalian cells (Invitrogen, Carlsbad, CA, USA) were used to quantify cell proliferative ability. The CCK-8 assay is based on the activation of 1-methoxy phenazinium methylsulfate by nicotinamide adenine dinucleotide phosphate (NADP) and nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) produced by dehydrogenase in living cells. Water-soluble tetrazolium salt, 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H tetrazolium, and monosodium salt (WST-8) are reduced to orange formazan; therefore, the amount of WST-8 formazan produced by dehydrogenogenases is directly proportional to the number of viable cells.  
The live/dead assay quantifies living and dead cells by measuring their esterase activity and plasma membrane integrity. The non-fluorescent dye calcein AM is converted into a fluorescent calcein that fluoresces in the presence of esterase in living cells, producing a uniform green fluorescence. Ethidium homodimer-1 enters dead cells through damaged membranes and binds with nucleic acids to produce red fluorescence.  
The 2D- and 3D-cultured sPDLSCs were seeded at a density of 1.0 \(\times\) 10\({}^{5}\) per well on a six-well plate and subjected to live/dead assay on days 3, 5, and 7. Images were obtained with a fluorescence microscope (IX71; Olympus, Tokyo, Japan). CCK-8 assay was performed on days 1, 3, 5, and 10, and the absorbance at 450 nm was measured with a Benchmark Plus multiplate spectrophotometer (Bio-Rad, Hercules, CA, USA).
## 2 Materials and Methods
### Metrics and Best Features  
Four main metrics were used to assess the performance of each model within each database, including overall classification accuracy, sensitivity, precision, and the F1 score. These results were also compared with the confusion matrices obtained for each exercise. In this case, the results were classified into the following categories: True Positives (_TP_) are samples classified as depressive and are indeed depressive, False Positives (_FP_) are samples classified as normal but are depressive, False Negatives (_FN_) are samples of normal individuals classified as depressive, and true negatives (_TN_) are normal individuals correctly classified as normal. To characterize the signals, a statistical analysis was performed to identify the best features for accurate classification within an artificial intelligence algorithm. This was achieved through hypothesis testing and the use of the SHAP Python library [36] to gain insight into the importance of features in the classification process. However, it should be noted that the scikit-learn classification report was used to obtain the respective values, the equations normally used to calculate the mentioned metrics [37] are shown as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[\text{Precision}=\frac{TP}{TP-FP} \tag{11}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{12}\]  
\[\text{FI Score}=\frac{2\cdot Recall\cdot Precision}{Recall+Precision} \tag{13}\]
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 4 Nitrate Removal Technologies in Waterbodies
### Transformation-Based Technologies
#### 4.2.2 Chemical Nitrate Reduction
The main advantages of chemical nitrate reduction are that no waste stream or brine is generated, as well as the prospect of more sustainable treatment and the multi-pollutant removal potential. As drawbacks, it should be noted that nitrite, ammonium, and nitrogen-containing greenhouse gases are undesirable by-products of nitrate reduction, which must be almost entirely avoided due to their harmfulness. The formation of Fe\({}^{2+}\) in solution may require some additional treatment step for downstream removal. \[\mathrm{Fe}^{0}+\mathrm{NO}_{3}{}^{-}+2\;\mathrm{H}^{+}\rightarrow\mathrm{Fe }^{2+}+\mathrm{NO}_{2}{}^{-}+\mathrm{H}_{2}\mathrm{O} \tag{3}\]1 Scheme of the reactions taking place on the surface of a bimetallic catalyst. Catalytic processes for nitrate removal are viable technologies but need further research to propose an operating reactor configuration to reduce the above-mentioned inconveniences.
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 3 Optimization of Design Parameters for an LSTM-Based Predictive Maintenance Model
### Design of GA for Exploring Optimal Hyperparameters of LSTM
#### 3.3.3 Crossover Operator  
The GA investigates the solution space of hyperparameters by an iterative searching process. During this procedure, crossover operator decides the direction and size of moves, which affect the effectiveness and efficiency of the GA. In general, a point crossover is a representative one that simply swaps genes of parental chromosomes according to a randomly selected crossover point. However, this approach cannot generate diverse offspring in the case of integer-valued encoding. As an alternative, we consider an arithmetic crossover operator that generates offspring via an arithmetic operation of values located in the parental chromosomes and preserves the order of genes. Specifically, two gene values, \(y_{r}^{1}\) and \(y_{r}^{2}\), corresponding to the \(r\)-th hyperparameter \((r=1,2,\cdots,m)\) in two offspring can be obtained by using \(\alpha x_{r}^{1}+(1-\alpha)x_{r}^{2}\) and \((1-\alpha)x_{r}^{1}+\alpha x_{r}^{2}\), where \(x_{r}^{1}\) and \(x_{r}^{2}\) are the \(r\)-th gene values recorded in the parental chromosomes, and \(\alpha\) is a random number generated from [\(-0.5\), \(1.5\)]. However, since these values are real values, we need to modify the equations in order to make them integers by applying a flooring function after adding \(+0.5\) to each of them, as in Equation (2).  
\[\begin{split} y_{r}^{1}&=\big{\lfloor}(\alpha x_{r} ^{1}+(1-\alpha)x_{r}^{2})+0.5\big{\rfloor}\\ y_{r}^{2}&=\big{\lfloor}((1-\alpha)x_{r}^{1}+ \alpha x_{r}^{2})+0.5\big{\rfloor}\end{split} \tag{2}\]
## 5 Results
### Statistical Significance  
To determine whether the observed performance differences were statistically significant, we performed paired \(t\)-tests on the F1 scores obtained from each fold of the five-fold cross-validation for each class and overall. The results are summarized in Table 2.  
The \(p\)-values for all comparisons (CP, UCLP, BCLP, and Overall) are less than 0.05, indicating that the improvements in F1 score achieved by the new model using SigLIP 2 are statistically significant at the 95% confidence level. The BCLP results are significant at \(p=0.05\).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Original (BiomedCLIP)**} & \multicolumn{4}{c}{**New (SigLIP 2)**} \\ \cline{2-9}
**Class** & **Acc.** & **Prec.** & **Rec.** & **F1** & **Acc.** & **Prec.** & **Rec.** & **F1** \\ \hline CP & 82.10\% & 90.00\% & 82.00\% & 86.00\% & **85.45\%** & **91.82\%** & **83.64\%** & **87.54\%** \\ UCLP & 83.10\% & 82.00\% & 82.00\% & 82.00\% & **86.36\%** & **84.55\%** & **85.45\%** & **85.00\%** \\ BCLP & 83.75\% & 75.00\% & 86.00\% & 80.00\% & **88.57\%** & **80.00\%** & **91.43\%** & **85.33\%** \\ \hline Overall & 82.76\% & - & - & - & **86.67\%** & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of classification performance.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Class/Overall** & **\(t\)-Statistic** & **\(p\)-Value** \\ \hline CP & \(-2.98\) & 0.041 \\ UCLP & \(-3.52\) & 0.024 \\ BCLP & \(-2.75\) & 0.050 \\ Overall & \(-4.12\) & 0.014 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Paired \(t\)-test results comparing F1 scores (original vs. new model).
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 3 Methodology
### Bidirectional LSTMs  
The bidirectional RNN [24] deals with the sequential data in a backward and forward direction in two different hidden layers. The thought of using bidirectional LSTMs came from a bidirectional recurrent neural network. In various fields, the BDLSTMs show better performance than LSTMs, e.g., phoneme classification and speech recognition [25; 26]. Figure 1 demonstrates the structure of the BDLSTM.  
\[h_{t}\ =\sigma_{h}(W_{xh}x_{t}+W_{hh}h_{t-1}+b_{h}) \tag{4}\]  
\[\hat{y}_{t}\ =\sigma_{h}\Big{(}W_{hy}h_{t}+b_{y}\Big{)} \tag{5}\]  
\[f_{t}=\sigma_{g}\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)} \tag{6}\]  
\[i_{t}=\sigma_{g}(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{7}\]  
\[o_{t}=\sigma_{g}(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{8}\]  
\[\widetilde{C}_{t}=\tan h(W_{C}x_{t}+U_{C}h_{t-1}+b_{C}) \tag{9}\]  
The weight matrices \(W_{f}\), \(W_{i}\), \(W_{o}\), and \(W_{C}\) draw the hidden layer input to the input cell state and gates, whereas the weight matrices that join the input cell state and three gates to the output of the previous cell state are \(U_{f}\), \(U_{i}\), \(U_{o}\), and \(U_{C}\). The gate activation function is \(\sigma_{g}\), technically which can be a sigmoid function. The bias vectors are \(b_{f}\), \(b_{i}\), \(b_{o}\), and \(b_{C}\), and \(\tan h\) is a hyperbolic tangent function. Figure 2 depicts the structure of the AttBDLSTM architecture.  
\[\hat{y}_{t}\ =\sigma(\leftharpoonright_{h},\leftharpoonright_{h}) \tag{10}\]  
\(\leftharpoonright_{h}\) and \(\leftharpoonright_{h}\) are backward and forward layer output sequences computed by utilizing positive sequence inputs from time \(T-n\) to \(T-1\) and vice versa, respectively. Equations (4)-(9) compute both backward and forward layers outputs by updating these LSTM equations. \(\hat{Y}_{T}\) is an output vector, where to combine the two output sequences, \(\sigma\) function is used. The final output of the BDLSTM is also demonstrated by a vector the same as the LSTM output, \(\hat{Y}_{T}=[y_{T},\ldots,y_{T+n}]\), where the last element \(y_{T}\) is the predicted speed of the next time iteration, and so on.
## 4 Experimental Results
### Fine-Tuning Evaluation Protocol
method performed better than ENet on three of the four datasets (i.e., 96.25% vs. 93.47% on Artlantis, 94.31% vs. 90.83% on Autodesk, and 93.06% vs. 91.39% on Corona), while ENet performed better on VRay (92.64% for ours vs. 94.58% for ENet). to the second-best self-supervised method of MoCo-V2, i.e., 83.22% (ours) vs. 72.06% (MoCo-V2) and 71.50% (ours) vs. 57.33% (MoCo-V2), respectively, for the two fine-tuning scenarios. In addition, although the supervised and adapted ENet clearly outperformed our self-supervised method when the fine-tuning was carried out on the whole training set, the performances were comparable under the fine-tuning on 20% of data (ours being even slightly better, i.e., 71.50% for ours vs. 71.06% for ENet). This might be an indication of the importance and advantage of a properly designed forensics-oriented pretext task for a self-supervised learning method, in particular under a data scarcity scenario with a very challenging forensic problem.
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
###### Abstract
In practice, migration to VPAC also supposes overcoming a number of challenges regarding time synchronization, redundancy, and cybersecurity, especially since cloud-based systems open their systems to new types of vulnerabilities. But even now, the VPAC systems have a very important fail-safe--the electromechanical relays. Even if there is a complete failure of the digital system, the electromechanical devices will be able to provide protection in a very reliable manner, as they are independent of software and digital communications. These devices, together with protocols for manual intervention, ensure safety and stability in the worst-case scenario. The transformation from electromechanical systems to VPAC can therefore turn power systems into more intelligent and adaptive networks. Much ongoing research is required, along with standardization like IEC 61850, and the integration of advanced technologies in overcoming the present challenges and assuring the reliable and secure operation of modern electrical grids.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 3 Experimental Set-Up  
Figure 2 shows a diagram of the experiment set-up employed in this work to demonstrate the iRO concept.  
Two instruments collect GNSS signals from two antennas both: a hemispherical antenna pointing to Zenith and a directive antenna (3 dB main beam of 20\({}^{\circ}\)\(\times\) 40\({}^{\circ}\)) pointing towards the RO transmitter. The RO antenna was pointed to Azimuth \(+\)145\({}^{\circ}\) and \(+\)15\({}^{\circ}\) elevation, covering an elevation range spanning from \(-\)5\({}^{\circ}\) to \(+\)35\({}^{\circ}\). The antennas were placed on top of a tower in Cap de Sant Sebastia in Llafranc (N 41\({}^{\circ}\)53\({}^{\prime}\)49.61786, E 3\({}^{\circ}\)12\({}^{\prime}\)10.15863) at an altitude of 153 m with direct views to the Mediterranean sea. The first instrument is a multi-port GNSS receiver (JAVAD DeltaQ Quattro-G3D board) capable of generating independent GNSS observables (pseudo-ranges and phases) for all antenna inputs. The objective of using this receiver is to obtain standard RINEX observables that can serve as comparison to the data collected by the second receiver. This second instrument is the SPIRrecording receiver [10]. It collects raw data (IF samples) from the RO antenna. The signal from the Zenith antenna is used to obtain precise GPS time-stamps for the recorded data.  
The experiment was carried out on 27 May 2016 from 14 h until 17 h, local time. Figure 3 shows the visible satellites during that interval, and the approximate 3 dB antenna beam of the directive RO-antenna. The azimuth pointing of the antenna was selected so that the setting radio-occultation of PRN24 fell well within the main beam, as well as a a significant part of the satellite's arch before the RO event.  
Figure 3: Visible GPS satellites at the experiment location and time. The approximate 3 dB main beam of the RO antenna has been plotted using a blue line. The dashed line corresponds to the part of the beam below the horizon. An azimuth mask (45\({}^{\circ}\)–180\({}^{\circ}\)) has been applied to the visible satellites.  
Figure 2: Diagram of the measurement setup.
# Bivolarization of Grape Stalks as Animal Feed by Solid State Fermentation Using White-Rot Fungi
###### Abstract  
This work aimed to evaluate the potential of three fungi strains, _Lentiumal edodes_, _Pleurrotus erespigii_, and _Pleurrotus citrimpiletatus_, to degrade lignin and enhance the nutritive value of grape stalks (GS). The GS was inoculated with the fungi and incubated under solid-state fermentation at 28 \({}^{\circ}\)C and 85% relative humidity for 7, 14, 21, 28, 35, and 42 days, in an incubation chamber. The influence of the treatments was evaluated by analyzing the potential modifications in the chemical composition, _in vitro_ organic matter digestibility (IVOMD) and enzymatic kinetics. An increase (\(p<0.001\)) in the crude protein content was observed in the GS treated with _L. edodes_ and _P. citrimpiletatus_ at 42 days of incubation (50 and 75%, respectively). The treatment performed with _L. edodes_ decreased (\(p<0.001\)) lignin content by 52%, and led to higher (\(p<0.001\)) IVOMD values at 42 days of incubation. By contrast, _P. errespigii_ did not affect lignin content and IVOMD. A higher activity of all enzymes was also detected for the treatment with _L. edodes_. Results indicated that _L. edodes_ has a great potential to enhance the nutritive value of GS as an animal feed, due to its lignin degradation selectivity.  
wine by-products; grape stalks; solid state fermentation; white-rot fungi; _in vitro_ digestibility; enzymatic activities +
Footnote †: journal: _Sci._  
_Applied_  
_sciences_
## 4 Results  
### Modelling of Rainfall Infiltration  
The 1D numerical models for the soil infiltration ran for each of the soil columns representatives of the different rainfall inputs and the various lithologies involved. Using the lab-derived parameters and the physical conditions of the considered soils, the degree of saturation of the covers was attributed considering its change over time and simulating the increases over time and the decreased over-depth of water content.  
As exemplified in Figure 5, the results of the modelling by Hydrus-1D, demonstrate that the saturation of the soils increased over time, simultaneously with the progress of the rains, until 16 August 2018 (end of T3), i.e., when the rainfall ceased and the Montecilfone earthquake occurred. At the rainfall peak, the degree of saturation no longer increased due to the absence of rainfall intake. The saturation decreases in the most surficial layers of the soil covers due to the infiltration process that progresses and simultaneously increases the saturation of the deeper layers.  
Figure 6 shows the degree of saturation evolution as a function of time and depth, spanning from the beginning of the rainfall event to the day after the Montecilfone earthquake when the rainfall ended.  
At a depth of 20 cm b.g.l., the degree of saturation increased until the 16 of August and decreased from the next day, thus showing a reasonably direct correspondence with the pluviometric input suffered by the study area. Considering a depth of 40 cm b.g.l., the degree of saturation began to increase after the second day of rain, while the previous days did not present surficial inflow. This step also corresponds to the end of the rainfall infiltration into the soil covers. Due to infiltration processes, the degree of saturation at this depth continued to increase even after the end of the pluviometric input.  
Finally, at a depth of 60 cm b.g.l., the degree of saturation slightly increased in limited portions of the study area, where the most permeable lithotechnical units crop out, only beginning from the day immediately following the end of the rains. At the end of the third rainy day, the Montecilfone earthquake occurred, shaking the soil covers already saturated (with different saturation degrees) by the previous rainfall.
## 3 Results and Discussion  
Monitoring and measuring the energy use for heating the rooms started on 15 September 2017 and continued for one year.  
The first day that room A (lime facade) required heating was 17 September 2017. The average outdoor temperature that day was 11.6 \({}^{\circ}\)C. The first day that room B (hemp-lime facade) required heating was 4 October 2017, almost two weeks later. The average outdoor temperature on that day was 10.3 \({}^{\circ}\)C. Even though room A required more heating than room B, the average room temperature  
Figure 8: Applying a lime render to one section of the hemp-lime facade.  
Figure 7: Applying the hemp-lime onto the wall surface.  
Figure 9: The finished test facades, summer 2017. To the left facade A (lime), to the right facade B (hemp-lime).  
during the first month of monitoring was somewhat lower in room A, 20.1 \({}^{\circ}\)C, compared to 20.5 \({}^{\circ}\)C in room B.  
The last day that the rooms needed energy for heating was 9 May 2018. The average temperature that day was 17.8 \({}^{\circ}\)C. Both rooms required some energy for space heating that day: room A used 0.013 kWh while room B used 0.009 kWh.  
The period for analysing space heating of the rooms was set to 15 September - 15 May during which heating was required. This was a total of eight months (243 days). No active cooling system was available, and therefore active cooling could not be monitored and analysed.  
During the above-mentioned heating period (15 September-15 May), the average outside temperature (T\({}_{\text{outside}}\)) was 5.8 \({}^{\circ}\)C. As the heating system kept the temperature in the interval 19-22 \({}^{\circ}\)C, the average room temperature (\(\overline{T}_{room}\)) that the heating system was trying to accomplish was therefore 20.5 \({}^{\circ}\)C.
# Structural Tunable Plasma Photonic Crystals in Dielectric Barrier Discharge
## 3 Results and Discussion
Thus, by changing the lattice constant, the plasma photonic crystals can be used to selectively control the propagation of EM waves of different frequencies. Next, we examined the angular reorientation of the plasma photonic crystals. As shown in Figure 6a,b, the plasma crystals rotate \(\frac{\pi}{6}\) when slightly increasing the applied voltage, while the  
Figure 5: Plasma photonic crystals with reduced lattice constant when increasing of the applied voltage (**a–c**), the corresponding pair correlation functions (**d–f**), the voltage-current waveforms (**g–i**), and the corresponding band diagrams (**j–l**). The discharge parameters are: (**a**) \(U=4.8\) kV, \(p=14\) W (**b**) \(U=5.8\) kV, \(p=30\) W (**c**) \(U=6.0\) kV, \(p=34\) W. \(f=53\) kHz, \(p=230\) torr, \(d=2.0\) mm, \(t=40\) ms. The working gas is 40\(\%\) Ar and 60\(\%\) air. The red short lines at the bottom of (**d–f**) indicate the positions of \(\rho_{\rm r}\) peaks for ideal triangular lattices. The electron density is set as \(10^{15}\) cm\({}^{-3}\)[36; 38; 39; 40], \(\
u_{\rm m}=60\) GHz. other structural parameters are nearly invariant. Figure 6c,d present the voltage-current waveforms corresponding to different plasma structures. One can see that a group of discharge spikes are produced, which occur at both of the falling and rising edge of the applied voltage (passing through the zero-crossing point of the ac voltage waveform). The time intervals of the discharges are about 6.0 us. The discharges occurring at the falling edge of the applied voltage are mainly dominated by the fields of surface charges, while the discharges occurring at the rising edge are initiated by the applied voltage [37]. The voltage-current waveforms change little when the orientation of the plasma has been changed. To some extent, the changes of the crystal orientation are equivalent to varying the incident angle of the EM waves, which may have significant influence on the photonic band diagrams. It was proven in previous studies that a blue shift of the photonic bands as well as a narrowed band gap is achieved when increasing the incident angle of the EM waves [42]. Thus, through modulating the orientation of the plasma structures, we suggest an alternative way to control the reflection and transmission behavior of the EM waves.
## 2 TFR Techniques  
### Short-Time Fourier Transform  
Without loss of generality, it is supposed that an integrable multi-component signal can be expressed as:  
\[s(t)=\sum_{k=1}^{n}s_{k}(t), \tag{1}\]  
\[s_{k}(t)=A_{k}(t)e^{i\phi_{k}(t)}, \tag{2}\]  
where \(A_{k}(t)\) and \(\phi_{k}(t)\) are the amplitude and phase function of \(s_{k}(t)\), respectively. In order to extract local time-frequency characteristics \(A_{k}(t)\) and \(\phi_{k}(t)\), short-time Fourier transform (STFT) has been utilized broadly in various scientific fields. The STFT of a signal \(s(t)\) is defined as [36]  
\[F_{s}^{g}(t,\omega)=\int_{R}s(\tau)g^{*}(\tau-t)e^{-i\omega\tau}\mathrm{d}\tau, \tag{3}\]  
where \(g(t)\) is the window function, \(g^{*}(t)\) is the complex conjugate of \(g(t)\). The parameters \(t\) and \(\omega\) are the time and frequency variables, respectively. Afterward, the time-frequency spectrogram \(P_{s}^{g}(t,\omega)\) is then defined as  
\[P_{s}^{g}(t,\omega)=\left|F_{s}^{g}(t,\omega)\right|^{2}. \tag{4}\]