esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In light of the obtained results, it is evident that, for large slope landslides (slide and slope flows), the obvious optimal cell size is 8 m. This size corresponds to an increase in AUC performance with respect to the smallest cell size (5 m), and switching to a larger size (16 m) was also shown to reduce the performance. This behaviour was mainly related to the effectiveness of the 8 m size cell to capture those landform conditions which play an important role in predisposing the slopes to slide or slope-flow failures: midslope and high ridges, together with profile curvature. Landform classification factors are the only predictors which clearly exclusively drove the 8 m models, whilst no relevant difference in the factor selection was produced for the other factors by changing the cell size. A similar quality loss in switching from 5 to 8 m was observed for the local-flow model; however, enlarging the cell size did not result in a further loss in quality, but rather in a linear increase. This can be explained in light of the non-specific conditions required for these types of small landslides, which are rather dependent, together with the slope steepness and land cover, on the association with clayey outcropping lithologies dominating the midslope drainage areas.  
It is worth noting that the best-performing models were not implemented on the higher spatial resolutions. This is similar to findings from other experimental sites affected by debris flows [2; 3], as well as similar landslide types to the ones taken into consideration here [1; 4; 5; 7]. However, refs. [4; 5] grouped all of the landslides into a single inventory, whilst [7] adopted a much coarser size for mapping units than the one adopted here. In addition, in contrast to the aforementioned cases, in this study, we observed the different influences of size changes in the three landslide typology models in the same area.  
The lower accuracy of the local-flow model was explained by the higher generation of False Positives. These actually reflect the wider diffusion of predicted unstable pixels on slopes, which do not host a LIP in the calibration inventory. It is likely that some of these FPs actually mark sites for future activations, as confirmed by the seasonal randomly diffused occurrence of this local-slope failure on larger slopes. The same model behaviour also partially affects the slope flow.
## 3 Discussion
_GGP3_ member is the principal regulatory gene in _Actiridia_ species; its overexpression augmented Asc content by 6 to 22,7-fold [13; 49]. Rice plants overexpressing _GGP_ showed a 2.5-fold increase in ascorbate and salt tolerance [28]. Also, _Arabidopsis_ and rice leaves overexpressing _GME_ increased Asc levels by \(\sim\)1.5-fold and improved drought and salt resilience [28; 29]. The manipulation of _GallDH_ in tobacco transformed plantsand enhanced Asc, growth, and shoot length under salt imposition [30], whereas _GPP_ was the only overexpressed and strongly correlated with tomato response to stresses and hormone exposures [44]. In addition, the overexpression of _GulLO_ and _MIOX_ from yeast or rat in _Stylosanthes guianensis_ and _Arabidopsis_ augmented ascorbate by 1.5 to 3.1-fold enhancing chilling, salt, and heat tolerance [31; 79]. Nevertheless, the overexpression of only one gene has generally revealed to be insufficient to reach elevated ascorbate amounts of transformed plants [6; 61]. Based on this knowledge, we recommended as a strategy, the co-overexpression of multiple steps by the combination of _GGP2_ members with one or two more indicated members to satisfactorily increase the pathway flux, thus achieving higher ascorbate contents in engineered crops. To corroborate evidence for responsiveness of genes, putative cis-acting elements were investigated. Here, at least one light-responsiveness element was found in every Asc biosynthesis genes (Figure 12 and Table S12), confirming that their expression regulation is dependent on light activation [27; 44; 80]. Moreover, cis-acting drought-responsive MBS, MYB and MYC, and specific elements hormone-responsive were all found in the promoter region of _GGP1_ and \(2\) and _GME2_, which emphasize their putative regulatory role in such conditions (Figure 12 and Table S12). MBS is a MYB-binding site induced by drought, also detected in _GME_ from _Actinidia_ spp. [61] and various genes of the L-galactose pathway in tomato [44]. Recently, the expression profile of _MYB16_ and _GallDH_ were correlated to higher Asc amounts in chilli pepper fruit [46]. Noticeably, _AceMYBS1_ activates the expression of _AceGGP3_, the main gene responsible for Asc accumulation in kiwifruit [49]. In addition, Dehydration-Responsive Elements (DRE-core) found in the promoter region of _GPP2_ and _GulLO2_ genes, targets of DREB TFs, were associated with rice tolerance to heat, salt, cold, and drought [81]. Here, LTR, TCA-motif, and TC-rich repeats low temperature elements detected in responsive genes (_GPP2_, _PMI2_ and \(3\), and _MIOX3_) evidence their role in pepper plants' resilience to chilling. Interestingly, the anaerobic-induction ARE, abundantly detected in the promotor region of _GGP_, _GME_, and _GPP_ also found in _GPP_ from kiwifruit and tomato [44; 53; 61], highlighting the major role of these members in Asc biosynthesis modulation under hypoxic conditions provoked by waterlogging.
## Methods and Data
### Comparison among These Five Evapotranspiration (ET) Products
Along with \(R^{2}\), we also reported slope and intercept values to provide useful information of the degree of bias at higher ET and intercept for the magnitude of bias affecting lower comparison ET. \(R^{2}\) ranges from 0 to 1, and this represents the proportion of the total variance in the reference data that can be explained, with higher \(R^{2}\) values indicating better performance. \[R^{2}=\frac{\left(\sum_{i=1}^{n}\left(Q_{i}^{obs}-Q_{avg}^{obs}\right)\left(Q _{i}^{sim}-Q_{avg}^{sim}\right)\right)^{2}}{\sum_{i=1}^{n}\left(Q_{i}^{obs}- Q_{avg}^{obs}\right)^{2}\sum_{i=1}^{n}\left(Q_{i}^{sim}-Q_{avg}^{sim}\right)^{2}} \tag{5}\]  
Finally, the Nash-Sutcliffe efficiency (NSE) [32] was used to measure how well comparison values represent the reference data, relative to a prediction made by using the average reference value. It is a more stringent measure than \(R^{2}\). NSE ranges from \(-\infty\) to 1, with NSE = 1 being the optimal value. NSE is calculated as:  
\[\text{NSE}=1-\frac{\sum_{i=1}^{n}\left(Q_{i}^{sim}-Q_{i}^{obs}\right)^{2}}{ \sum_{i=1}^{n}\left(Q_{i}^{obs}-Q_{avg}^{obs}\right)^{2}} \tag{6}\]  
where \(Q_{i}^{sim}\) is the comparison ET (other four ET products), and \(Q_{i}^{obs}\) is the reference ET (AWAP ET) at time step \(i\), respectively, whereas \(Q_{avg}^{obs}\) and \(Q_{avg}^{sim}\) are the average reference and comparison ET values, and \(n\) is the total number of data, and subscript \(i\) represents the time step (month or year).
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### Risk Reduction Algorithm  
The rock-fall risk reduction algorithm was developed to perform the rock-fall risk reduction process. It computes the rock-fall hazard, classifying it into three levels and generating an early warning action to reduce the risk of a critical situation. The following steps show how the presented Algorithm 1 determines a rock-fall hazard level and manages it.  
```
Step 1: Inputs Read (video frames from camera) Read (weather data from sensors) Step 2: Detect the moving rocks \(\hat{\mathrm{P}}\left(\overrightarrow{\mathrm{x}}\middle|\chi_{\mathrm{T}}\,\mathrm{BG}\right)\): according to Equation (6) Step 3: Predict the rock fall event p(x): according to Equation (2) Step 4: Compute the rock fall risk \(P_{(Risk)}\) according to Equation (3) Step 5: Classify the hazard level: Classifying the hazard level in to three levels if (\(P_{(Risk)}\ \geq 1\times 10^{-3}\)) then Unacceptable level if (\(P_{(Risk)}>1\times 10^{-6}\) and \(<1\times 10^{-3}\)) then Tolerable level if (\(P_{(Risk)}\leq 1\times 10^{-6}\)) then Acceptable level Step 6: Perform the rock-fall risk reduction action Generate light and sound alarms in case of Unacceptable level (Red light+ sound) in case of Tolerable level (Yellow light) in case of Acceptable level (Green light) Save (\(x_{1}\), \(x_{2}\), \(x_{3}\), p(x)) every 30 min Step 7: Return to Step 1
```  
**Algorithm 1** To compute a rock-fall risk, classifying the risk level, and performing the rock-fall risk reduction action
## 1 Introduction
Regarding the optimization formulation, the emphasis was placed on determining the optimal parameters of the isolation period and damping ratio, while the objective function was related to the minimization of superstructure acceleration. The results show that the total acceleration of the structure was reduced when the isolator's ductility was increased. In most relevant studies, the optimization of the seismic performance of the isolation system was mainly focused on isolators' critical parameters, such as the friction coefficient, radius of curvature, etc. A literature review revealed that there is no relevant study that has focused on the combined optimization of both the key parameters (i.e., sizing optimization) and the layout (i.e., optimal placement) of the isolators installed at the base of buildings or tanks. For this reason, single and dual optimization of liquid storage tanks, isolated either by SFPB or TFPB, is investigated herein. The computationally efficient nature-inspired Cuckoo Search (CS) algorithm is used for the solution of the optimization problems. The optimized configurations are assessed by comparing the fragility curves with respect to isolators' maximum displacements when the coupled system (isolators and superstructure) is subjected to near-fault excitations utilizing an incremental dynamic analysis process. A base-isolated tank with SFPB or TFPB isolators with identical properties derived from a standard design approach is used as the reference case for comparison with the optimized designs. In the first optimization formulation, the number of isolators is kept constant and the key parameters (friction coefficients and radii of curvature) of the bearings are optimized. In the dual formulation, the optimization includes both the sizing parameters in conjunction with the optimal placement of the isolators at the tank base, eliminating redundant ones. It is proven that the proposed combined approach leads to a more cost-efficient design, with fewer isolators compared to the conventional design.
# A Review of the Vibration Arthography Technique Applied to the Knee Diagnostics
## 3 Signal Analysis
### Signal Processing
#### 3.2.1 Time Domain Analysis  
In the time domain, segmentation is commonly used to cleave the signal into smaller segments according to angular data given by the electro-goniometer. The use of segmentation to isolate flexion and extension segments has been performed by [PERSON] et al. and [PERSON] et al. [14; 27]. Results have shown that the ascending and descending phases produce different acoustic emissions amplitudes, and that features extracted from the ascending phase highlight a greater difference between healthy and OA knee joints, thus confirming the interest in segmentation [14].  
VAG signals were characterized with Form Factor, kurtosis, entropy, and skewness, as used by [PERSON] et al. [22]. The Form Factor gives information about the variability of the signal and is defined using three parameters--namely, activity, mobility, and complexity. Skewness is related to the asymmetry of the probability density function (PDF), kurtosis is the tendency of the signal to have peaks, and entropy is at its maximum for a uniform PDF [22]. These parameters provide good classification performance in discriminating between normal and abnormal subjects. [PERSON] et al. also showed that the Turns Count (TC) can be used to characterize the variability of a VAG signal [23; 24]. TC can detect the number of spikes, swings, or changes in an amplitude larger than a defined threshold [23]. Thus, the TC provides relevant information on the overall variation in a signal. According to [PERSON] et al. [23], TC yields good levels of accuracy in screening VAG signals. In addition, the amplitude and duration of the signal have shown encouraging results in differentiating OA and healthy knee joints [14]. Similarly, [PERSON] et al. [30] have estimated the variability of the VAG signal by computing the variance of the mean-squared values and the signal amplitude.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Channel Estimation
#### 3.2.4 Wideband Systems under Beam Squint  
The authors in [48] consider both the channel's frequency selectivity and the effect of beam squint to develop a twin-stage orthogonal matching pursuit (TS-OMP) CSI estimation algorithm in a single-user SISO wideband OFDM system by considering NLOS between both ends, i.e., only the cascaded BS-IRS-user channel is estimated in the downlink. The authors demonstrate that the mutual correlation function between the spatial steering vectors and the cascaded channel presents two peaks, which leads to a pair of estimated angles for a single propagation path due to the beam squint effect. One of those angles constitutes the frequency-independent angle while the other one is frequency-dependent and inserts false angle estimations. In order to reduce the influence of false angles on the CSI acquisition, the TS-OMP algorithm obtains the path angles of the cascaded channel in the first stage, while the propagation gains and delays are obtained in a second stage. A bespoke pilot design that exploits the characteristics of the mutual correlation function and the cross-entropy theory is also proposed to obtain a CSI estimation with improved performance. The NMSE is the metric employed to evaluate the system performance and comparisons are made with a benchmark approach that uses a high number of pilots. Such benchmark approach only provides a small performance gain over the low-complexity TS-OMP algorithm.
## 4 Design
### Modeling and Prediction  
After the above processes, models can be built. In this work, several ML/AI models were chosen. They are the naive Bayesian (NB) [27], RF, support vector machine (SVM) [28], GBDT, CNN, and LSTM. All of them have been used for disk failure prediction tasks, among which RF and LSTM, especially LSTM, are widely studied and have obtained significant results, as described in Section 2. To train and evaluate the models, the ratio of training and test data was set to 7:3, which is the same as in other research, including [9]. Additionally, some important parameters were set for these models, the number of estimators for RF and GBDT were 10 and 50, respectively, because there was no significant improvement when using more than 10 and 50 estimators for these.  
Moreover, a CNN model was built with a two-dimensional convolutional layer, a max pool layer, a two-dimensional convolutional layer, and a dense layer; and an LSTM model was built with 3 LSTM layers, and the number of units for the LSTM layers were 64, 128, and 64. Regarding these two models, one of the most important parameters is the training epoch, as described in Section 3, as sufficient epochs can lead to a higher accuracy. However, when the number of epochs is too high, the prediction performance is decreased because of overfitting [29]. To avoid overfitting, the number of epochs was chosen based on the models' performance on the validation set. We used 20% of the training set as validation data. Figure 5 shows the change in the value of the training and validation loss functions of CNN and LSTM with the increase in epochs, where the lower the loss, the better. For CNN, as shown in Figure 5a, the training and validation loss decreased with the increase in epochs, until the epochs were around 60; after that, the validation loss was not reduced, which reflects the overfitting issue. Thus, 60 was chosen as the epoch number for the CNN. With the same logic, the number of epochs selected for LSTM was 270, as shown in Figure 5b. In addition, for these two models, the batch size was 72, the dropout rate was 0.25, and the learning rate was 0.0001. These settings were similar to other research, including [8; 9].
# Analytical Modeling for Underground Risk Assessment in Smart Cities
## 4 Approximation Schemes for Underground Risk Index
### Hierarchical Fuzzy Inference System based Approximation
#### 4.2.2 Approximation using Hierarchical Fuzzy Inference System  
At the core of Hierarchical Fuzzy Inference System (FIS), we have several basic fuzzy inference systems connected together in a tree-like structure. Related inputs are fed to bottom layer FIS and its output is given to intermediate layer FIS. Finally, root nodes generates the system overall output. For example, in Figure 3, \(FIS_{M1}\) is a basic FIS taking \(x_{1}\) and \(x_{2}\) as input and generate output as \(y_{1}\). Different fuzzy inference system models can be used but we consider using Mamdani-type fuzzy inference system in this paper [33]. \(y_{1}\) is then given as input to \(FIS_{M4}\) and so on. The final output of this hierarchical FIS system is \(y_{5}\) which is calculated at top layer (Level 0). Output of Hierarchical FIS is obtained from repeated evaluation of all basic FIS, starting from the bottom layer and working upward to the root node. For the sample Hierarchical Model given in Figure 3, we have the following evaluations  
\[y_{1}=FIS_{M1}(x_{1},x_{2}) \tag{10}\]  
i.e., in \(FIS_{M1}\) takes \(x_{1}\) and \(x_{2}\) as input and generate output as \(y_{1}\). \(y_{1}\) is then given as input to \(FIS_{M4}\) and so on. The following evaluations are performed at intermediate layers.  
\[y_{2}=FIS_{M2}(x_{3},x_{4},x_{5}) \tag{11}\]  
\[y_{3}=FIS_{M3}(x_{6},x_{7}) \tag{12}\]  
\[y_{4}=FIS_{M4}(y_{1},y_{2}) \tag{13}\]  
\[y_{5}=FIS_{M5}(y_{4},y_{3}) \tag{14}\]  
where \(y_{5}\) is the resultant output of the hierarchical FIS which is calculated at top layer, as shown in Figure 3.
## 2 Results
### Unknown or Other Mechanisms of Action
#### 2.4.1 Anti-Rhinovirus
The study concluded with no significant prevention or decrease in the severity of the infection. This can be explained by the compound's too-low concentration reaching the nasal mucosa. Moreover, the treatment formulation seemed to be an irritant when used intranasally. In 1987, a Ro 09-0410 (Nippon Roche KK, Kamakura, Japan & Roche Research Laboratory, Welyn Garden City, United Kingdom) dephosphorylated Ro 09-0415 against RV-A2 infections report study was published [82]. Ro 09-0410 inhibits RV uncoating [86]. A total of 50 mg of the drug was sprayed in 0.05 mL of nasal solution, three times per nostril and three times daily, for 5 days. This double-blind placebo-controlled study recruited 50 adults. No prevention of viral infections was observed, but there was a tendency to lower the virus shedding and the antibody response and increase the nasal secretion (0.01 \(<\)\(p\)\(<\) 0.05). Finally, in 1990, another phase 2 clinical trial was published, evaluating the effect of RV-A2-resistant at the same drug (Ro 09-0410) delivered this time in an oral solution [83]. 42 patients were recruited. The main goal of this study was to evaluate the effect of wild-type viruses versus resistant-induced viruses. Thus, no in vivo evaluation of the drug was performed. * GlaxoSmithKline (GSK) studies  
Finally, GlaxoSmithKline's laboratory is conducting two randomized, double-blind, placebo-controlled phase 1 clinical trials, which began in 2022, for its compound GSK-3923868 (GlaxoSmithKline Research & Development Limited, London, United Kingdom) against asthma and chronic obstructive pulmonary disease (COPD) during rhinovirus infections [84; 85]. The treatment began after a viral challenge. Both studies recruited 68 patients, ranging from 18 to 64 years, divided into two cohorts, where the active cohort received the drug as an inhalation powder.
###### Abstract
Compared with these growth methods, Mist-CVD has the advantages of being vacuum-free, a simpler process, and low-cost [24; 25; 26; 27; 28]. It is expected to become a mainstream method for growing Ga\({}_{2}\)O\({}_{3}\) film epitaxially on other semiconductor films and to push down the cost of growing epitaxial Ga\({}_{2}\)O\({}_{3}\) films. In this work, Mist-CVD was used to grow Ga\({}_{2}\)O\({}_{3}\) films on sapphire. We prepared a heterojunction PD with a structure of sapphire/\(\beta\)-Ga\({}_{2}\)O\({}_{3}\)/Indium Zinc Oxide (IZO)/CsPbBr\({}_{3}\). After the epitaxial growth of the \(\beta\)-Ga\({}_{2}\)O\({}_{3}\), IZO electrodes and CsPbBr\({}_{3}\) films were deposited onto it. It was shown that the response of the heterojunction PD in the deep UV wavelength region was clearly enhanced compared to its perovskite-only counterpart. The device, which has a sapphire/\(\beta\)-Ga\({}_{2}\)O\({}_{3}\)/IZO/CsPbBr\({}_{3}\) structure, reached a D* of 1.04 \(\times\) 10\({}^{12}\) Jones, which was illuminated by a 254 nm light at an intensity of 500 \(\mu\)W/cm\({}^{2}\) at a 5 V bias.
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 1 Introduction
The bit error rate (BER) and signal-to-noise ratio (SNR) determine the quality of data transmission within a specific distance and the number of users. The acceptable number of BER should be \(10^{-9}\). If the BER is higher than \(10^{-9}\), the number of bit losses during transmission will be high [16; 17; 18]. A new code, flexible double weight (FDW), with a new polarization encoding technique, was designed to support a greater number of users, high throughput, and low power consumption.
## 5 Performance Analysis
### Link Budget  
The link budget is evaluated with Equation (3), it is basically the sum of the transmitter power and receiver sensitivity. Five bandwidths 41.7 kHz, 62.5 kHz, 125 kHz, 250 kHz, and 500 kHz are considered for the SF 7-12 at different transmitting powers while calculating the link budget. The transmitting powers considered are 2 dB, 5 dB, 8 dB, 11 dB, and 14 dB. Table 11 represents the link budget for each transmitter and the plots are illustrated in Figure 17. At a particular transmission power, the link budget for any bandwidth decreases as the spreading factor increases. For a transmission power of 2 dB, BW 41.7 kHz the link budget is -140.79 dB at SF 7, the link budget decreases to \(-\)1145.79 dB as SF increases to 8, and at SF 12 the least link budget of \(-\)1165.79 bB is observed. If the BW is increased, then the link budget also increases, at 2 bB transmission power, SF 7 the link budget increases from \(-\)1140.79 dB to \(-\)1139.04 dB as the BW increases from 41.7 kHz to 62.5 kHz, and the overall highest link budget is observed at a transmission power of 14 dB for a SF 7 and BW 500.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**SF 7**} & \multicolumn{8}{c}{**SF 8**} \\ \hline  & BW41.7 & BW62.5 & BW125 & BW250 & BW500 & & BW41.7 & BW62.5 & BW125 & BW250 & BW500 \\ CR 1 & 1824 & 2734 & 5469 & 10938 & 21875 & CR 1 & 1043 & 1563 & 3125 & 6250 & 12500 \\ CR 2 & 1520 & 2279 & 4557 & 9115 & 18229 & CR 2 & 869 & 1302 & 2604 & 5208 & 10417 \\ CR 3 & 1303 & 1953 & 3906 & 7813 & 15625 & CR 3 & 745 & 1116 & 2232 & 4464 & 8929 \\ CR 4 & 1140 & 1709 & 3418 & 6836 & 13672 & CR 4 & 652 & 977 & 1953 & 3906 & 7813 \\  & \multicolumn{4}{c}{SF 9} & \multicolumn{8}{c}{SF 10} \\  & BW41.7 & BW62.5 & BW125 & BW250 & BW500 & & BW41.7 & BW62.5 & BW125 & BW250 & BW500 \\ CR 1 & 586 & 879 & 1758 & 3516 & 7031 & CR 1 & 326 & 488 & 977 & 1953 & 3906 \\ CR 2 & 489 & 732