esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### TDS-1 Data Analysis at Regional Scale
#### 3.2.2 Iberian Peninsula  
SMOS L3 SM Sensitivity  
Figure 14a together with Table 3 show the derived sensitivities (slope) to soil moisture. Results have been separated depending on the incidence angle range (0\({}^{\circ}\)-30\({}^{\circ}\), 30\({}^{\circ}\)-60\({}^{\circ}\), and 60\({}^{\circ}\)-90\({}^{\circ}\)). Figure 14a shows the main results when all different corrections are applied (vegetation and roughness  
Figure 13: Scatter plot between the (**a**) Low Resolution (25 km) SMOS L3 SM and (**b**) High Resolution (1 km) SMOS L4 SM and the TDS-1 reflectivity value over the CEMADEM region from June 2016 to June 2017, with vegetation and roughness corrections applied. Each column represents the values for a specific range of incidence angles: (**left**) 0\({}^{\circ}\)-30\({}^{\circ}\); (**center**) 30\({}^{\circ}\)-60\({}^{\circ}\); and (**right**) 60\({}^{\circ}\)-90\({}^{\circ}\).  
corrections). These results have been obtained for the period ranging from June 2016 to June 2017. In general, it can be appreciated that higher values of VOD are located at the top of the graphs. This is probably due to an overcorrection of the vegetation effects. This dependence with the vegetation becomes more evident for higher incidence angles as the cosine of the incidence angle tends to zero. Without any kind of correction, the values of the slope are 6.22, 8.94, and \(-\)3.87 dB/100%, for the incidence angles from 0\({}^{\circ}\) to 30\({}^{\circ}\), from 30\({}^{\circ}\) to 60\({}^{\circ}\), and from 60\({}^{\circ}\) to 90\({}^{\circ}\), respectively. These same values after applying the vegetation and roughness correction are: 12.15, 11.73, and 22.61 dB/100%, although this last value is unreliable.  
SMOS L4 SM Sensitivity  
Similarly, Figure 14b shows the main results, when different types of corrections are applied, and for different ranges of incidence angles for SMOS L4 SM (1 km resolution). See figure caption for details. Results will be discussed at the end.
# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 2 Related Work
### Limitations of the Existing Schemes  
The summary of relevant studies shows that the IoT has grown significantly in creating and developing smart agriculture systems. It aids the farmers in monitoring soil conditions and water supply and increases productivity with the management of costs. It gathers the agricultural data and sends them to cloud databases to take the necessary actions accordingly. Numerous solutions have recently been proposed to deal with intelligent data monitoring with improved latency using machine learning techniques. However, many of the existing systems do not make it apparent how different connections are handled when there are redundant data. Moreover, efficient load balancing with the collaboration of mobile sensors is another significant research challenge [40; 41]. Although many solutions have offered security for remote monitoring, this has been at the cost of computation and complexity. As a result, we need a framework to handle the timely supply of monitoring data using some smart and intelligent behaviors.
## 3 Results and Discussion
### Multivariate Data Analysis  
#### 3.3.1 SIMCA Classification Model of GC-MS  
The GC-MS data of volatile compounds in traditional and BBL-aged maple syrup samples were analyzed and grouped using the Soft Independence Modeling of Class Analogy (SIMCA), and the class projection plot is shown in Figure 11. All the authentic BBL maple syrups were successfully discriminated from the traditional maple syrups based on their volatile composition, having an interclass distance (ICD) of 4.1. Furthermore, authentic BBL samples were also successfully differentiated from the suspicious BBL maple syrups (ICD = 2.2), and the classification pattern agreed with the GC-MS data that one of the suspicious BBL grouped with traditional samples, while the other one did not fall into either traditional or authentic sample group. Overall, the five most critical volatile compounds that have the highest impact on SIMCA model discrimination are the order of ethanol, isoamyl alcohol, isobutanol, oxal acid, and acetoin, which are found to exist in bourbon whiskey or maple sap [33; 40]. Therefore, these compounds are significant in authenticating qualified BBL maple syrups from suspicious BBL and traditional maple syrups.
# Green Production and Biotechnological Applications of Cell Wall Lytic Enzymes
## 3 Degradation of Lignocellulose by CWLEs  
Arrays of CWLEs are secreted by microbes to efficiently hydrolyze the variety of polysaccharides in plant cell walls into simple sugars [15]. Ligninases, hemicellulases, and pectinases, together with cellulolytic enzymes, are required to efficiently hydrolyze the lignocellulosic biomass. Hemicellulose and pectin are branched polysaccharides with highly heterogeneous compositions; therefore, the usage of enzymatic blends with different substrate specificities is mandatory for their efficient reconstruction. Substrate specificity is defined by the presence of specific and conserved domains or modules, such  
Figure 1: Schematic representation of plant primary and secondary cell walls. In the primary cell wall, cellulose chains are organized in fibrillar structures (white) that engage hemicellulose (orange, yellow, and green) through hydrogen bonds. This assembly is surrounded by a complex matrix of pectin (turquoise). In the secondary cell wall, pectin decreases in favor of a higher content of lignin (brownish dots) that adsorbs to cellulose fibers and confers recalcitance to enzymatic hydrolysis. Structures of arabinoxylan and substituted homogalacturonan are reported as representative classes of hemicellulose and pectin, respectively.  
as the glycosyl-hydrolytic (GH) domain and the carbohydrate binding module (CBM). In Table 1 the most common families of CWLEs are reported in accordance with the CAZy database. Bioinformatic analyses of conserved domains may be helpful to predict the substrate specificity of CWLEs with unknown functions based on sequence homology [16]. It is worth noting that the CBM is considered as an auxiliary module that is mainly conserved in cellobiohydrolases and multienzyme complexes (i.e., cellulosomes) [3]. On the other hand, some CWLEs are multifunctional enzymes that contain catalytic domains belonging to different GH families, as in the case of XynA from _Therntotoga neapolitana_[17] and CelB from _Caldicellulosiruptor saccharolyticus_[18].
# Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
## 5 Evaluation
### Automatic Recognition
#### 5.2.3 Used Features and Model  
As a feature for emotion recognition and speaker identification, the spectrogram was generated for each original and converted audio file. We utilized the CRNN model, which is a combination of a convolutional neural network (CNN) and long short-term memory (LSTM), as demonstrated in Figure 9.  
The spectrogram was segmented after being generated for each audio file. These segments, with a size of \(S_{s}\times 512\), were used as an input for the proposed model, where \(S_{s}\) represents the segment size in frames and 512 is the number of frequency points output by the short-time FFT.  
The proposed model contains three convolutional layers: the first one includes 16 kernels of dimension size 12 \(\times\) 16, the second one includes 24 kernels of dimension size 8 \(\times\) 12, and the third one consists of 32 kernels of dimension size 5 \(\times\) 7. These convolutional layers were applied with a one-pixel stride. Each layer is followed by an exponential linear unit (ELU) to help the CNNs train much faster. The ELU in each layer is attached by a max-pooling layer (2 \(\times\) 2) with a stride of two. Bidirectional-LSTM (BLSTM) layers follow these layers with 128 units in addition to a dropout layer with a dropout ratio of 40% to prevent overfitting. Finally, a fully connected layer (FC) follows with several neurons equal to the number of emotions or speakers. At the end of the FC layer, a sigmoid function was used to make classification outputs in the form of prediction accuracy for different emotions or speakers.  
We used a probabilistic evaluation for system prediction using a mean prediction-based reasoning approach for outcome prediction calculation. There were various numbers of segments in each audio file. To determine the correctness of the entire audio recording, the accuracy of each segment was calculated. If the model correctly predicted more than 50% of the audio files, the accuracy was deemed acceptable. Figure 10 presents an example of one speech file segmented into four segments (S1, S2, S3, and S4). Each segment is used as an input for the proposed model and the outcome for each segment is shown in Figure 10. In the final step, the average of all segments for one file is calculated. The maximum average is the predicted emotion.  
As an optimizer, the Adam adaptive gradient descent algorithm [47] was utilized for training and the learning rate was set at 0.001. A softmax layer was applied following training to achieve a prediction accuracy output for various emotions or speakers. The training procedure was executed ten times for a maximum of 200 epochs, with 128 samples per batch, using Google Colab Pro [48] and the following settings: hardware accelerator: GPU; runtime structure: High-RAM. This model was developed using TensorFlow [49] and Keras [50] as the front-end system.  
Figure 9: Proposed CRNN model.
## 4 Risk Analysis for Current Protocols
### OneNet
#### 4.5.3 Packet Sniffing  
If OneNet devices are already under \"Secure mode,\" their communications are encrypted and the content cannot be seen by any packet analyzer [38]. However, as we posited before, an attacker can spoof a fake HID and potentially capture certificate information. After this step, the attacker can imitate a legitimate OneNet device and further capture traffic from the existing network.
## 3 Data and Methods
### Data Sets
The thematic accuracy of the map is higher at aggregated levels; thus, leaving the classification at the level of forests, shrublands and grasslands results in a higher class confidence than more specific class labels. In this study, the detailed land cover classes were aggregated into three categories: tree savanna, bush savanna, and grassland savanna (Figure 2). Tree savanna is defined as a tree-dominated savanna with tree canopy cover more than 15% and canopy height more than 5 m, bush savanna is defined as a shrub-dominated savanna type with shrub canopy cover greater than 15% and canopy height less than 5 m with no or sparse tree layer, and grassland savanna is defined as a grass-dominated savanna type with herbaceous cover greater than 15% and tree and shrub canopy cover less than 20%. Tree savanna is mainly distributed in the northern half of the study area, bush savanna is largely distributed in the southeast, and grassland savanna is distributed in the southwest and in the upper reach of Zambezi River (Figure 2). The size of grid is resampled to 0.08 by 0.08 degree to match the spatial resolution of the GIMMS3g NDVI data set.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 4 Conclusions  
In this study, the rate and propagation equations that characterize EDFA performance pumped at both 980 nm and 1480 nm in the forward direction are solved numerically, and the obtained results are displayed graphically. The main EDFA parameters are investigated in the temperature range from \(-40\)\({}^{\circ}\)C to \(+80\)\({}^{\circ}\)C. A 1480-nm pumping wavelength is chosen for its lower neglected ESA and its effective performance as compared to other pumping schemes. The obtained results showed a small  
Figure 10: Saturation pump power variation with temperature.  
Figure 9: Gain versus input pump power.  
but effective variation of the emission cross-section with temperature. The effect of the Boltzmann factor and its variation with temperature is taken into consideration, and is found to be effective only in the case of 1480 nm. In general, the obtained results imply a better gain performance at higher temperatures. The maximum gain is found to be decreased linearly for temperatures greater than \(-20\)\({}^{\circ}\)C. Regarding the optimum fiber length, its variation with temperature shows a maximum value at a certain temperature while fixing either the input signal power or pump power. The value of this temperature decreases with the increase of the input signal power and the pump power. The saturation signal power increases, and the saturation pump power decreases with the temperature increase.  
Conceptualization, I.K. and M.H.A.; Methodology, J.A.B.; Software, J.A.B.; Validation, J.A.B., I.K. and M.H.A.; Formal Analysis, J.A.B.; Investigation, J.A.B.; Resources, I.K.; Data Curation, J.A.B.; Writing-Original Draft Preparation, J.A.B.; Writing-Review & Editing, I.K., M.H.A. and M.A.E-O; Visualization, J.A.B. and I.K.; Supervision, I.K., M.H.A. and M.A.E-O.; Project Administration, M.H.A. and M.A.E-O. This research received no external funding.  
The authors declare no conflict of interest.
## 7 Conclusions  
1. Stable ambient conditions are important for infrared measurements. The climate, the objects around the measured body, and all other influencing factors should not change during the measurement. Thus, it is a big challenge to implement thermographic measurements in real practice at dairy farms, where it is very difficult to ensure stable ambient conditions with an unchanging measurement methodology.
2. The ideal measurement conditions are stable weather, cloudy sky before and during the measurement (when measuring in the open air), no direct sunlight during and before the measurement, no precipitation, dry thermally available surfaces of the measured object, no wind, no drafts, no sources of interfering radiation in the vicinity of the measured object and in between the object and the thermal camera. If possible, it seems ideal to perform thermographic measurements in the interior of the breeding facility, where the influence of outdoor weather conditions can be avoided.
3. The surface of the object to be measured should ideally be of high and accurately known emissivity, with ambient temperature and relative humidity being measured, at a suitable distance from the object of measurement, with a fixed and suitably chosen scale. From the experience of practical measurements, it appears that a major obstacle to meeting these requirements is the variable cleanliness of the legs of dairy cows.
4. Many methods are used for IRT image processing, ranging from basic histogram adjustment and linear filters to very advanced segmentation algorithms. Image adjustments are implemented in both the spatial and frequency domains, with Fourier and cosine transformations being common methods used to convert the image to a signal. The most advanced and nowadays increasingly applied technique uses deep neural networks, especially convolutional neural networks, allowing automatic classification of observed objects in the image. These methods represent modern approaches to automation and algorithmization of thermographic measurements for the detection of diseases in dairy cows, including lameness and hoof diseases, implementable within precision agriculture methods.  
Conceptualization, P.K., P.B., P.C. and V.V.; methodology, M.H., R.B., P.B., P.C. and V.V.; validation, R.B., R.S. and V.V.; formal analysis, P.B. and P.C.; investigation, V.V. and R.K.; resources, L.S.; data curation, L.S.; writing--original draft preparation, P.K., M.H., R.B., T.Z., P.C. and V.V.; writing--review and editing, P.K., P.B., R.S., T.Z., P.C. and V.V.; supervision, P.B. and L.S.; funding acquisition, P.B. All authors have read and agreed to the published version of the manuscript.  
This work is based on the results achieved within project TM02000027 \"Research and development of smart technologies for cattle and pig breeding based on advanced computing\" of the Technology Agency of the Czech Republic.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 2 Background
### Low-Energy Adaptive Clustering Hierarchy (LEACH)
There would be an equal chance for both high-energy sensor nodes and low-energy sensor nodes to attain the tag of CH. If the CH is picked as the sensor node with the least energy, it will expire fast, the network's resilience is harmed, and the network's lifespan is reduced. 2. LEACH does not guarantee the position and quantity of CHs in each round. In a basic LEACH, cluster formation is random, resulting in an unbalanced distribution of clusters in the network. 3. CH's position in some clusters may be in the center of the clusters, while in others, the CH's location may be towards the cluster's edges. As a result, intra-cluster communication in this circumstance consumes more energy and reduces the sensor network's overall performance. CH and BS communicate via the LEACH protocol using Single-Hop communication, as remote CHs consume more energy than CHs close to the BS. Thus, energy is dissipated unevenly, reducing the sensor network's lifespan.
## 2 Protein Extraction
### Novel Methods
#### 2.5.4 Supercritical Fluid Extraction  
Supercritical Fluid Extraction (SFE) is an alternative extraction technique that produces extracts with very low contents of polar impurities [93]. It is a green technology, since a concentration step can most often be skipped after the extraction process [94]. A fluid becomes supercritical if subjected to temperature and pressure conditions above its critical point values (P\({}_{\text{G}}\); T\({}_{\text{c}}\)). Experimental studies using SFE are usually limited to the region of P\({}_{\text{c}}\) < P \(\leq\) 6P\({}_{\text{c}}\) and T\({}_{\text{c}}\) < T \(\leq\) 1.4T\({}_{\text{c}}\)[95]. Under supercritical conditions, some of the properties of the fluid become indistinguishable from those of its gaseous state, with a density similar to that of a liquid, but with diffusivity and viscosity matching those of a gas. This makes supercritical fluids capable of a faster and deeper penetration into the solid particles of the matrix to be processed [93]. The fluid must be chosen carefully, especially when dealing with thermolabile compounds. Carbon dioxide, with T\({}_{\text{C}}\) and P\({}_{\text{C}}\) values of 31.1 \({}^{\circ}\)C and 73.9 bar, is by far the most used supercritical solvent [96]. In addition to being ideal for the extraction of thermolabile compounds, it has low viscosity, low surface tension, high diffusivity, is non-toxic, non-flammable, widely available and chemically inert under the conditions relevant for the extraction process. The fact that it is gaseous at normal pressure and temperature eliminates the need for a solvent evaporation step after extraction [96]. The greatest limitation of supercritical CO\({}_{2}\) is that it is not suitable for extraction of polar compounds [97].  
The addition of an organic modifier, such as ethanol or methanol, can greatly improve the extraction efficiency with supercritical CO\({}_{2}\)[96]. Other possible supercritical solvents, such as water, methanol, ethanol, acetone, chloroform, ethyl acetate and toluene, are usually avoided when extracting bioactive compounds, since their T\({}_{\text{c}}\) value is above 200 \({}^{\circ}\)C [80].  
The SFE technology has been used for processing seaweeds and other plant-based materials mainly to extract non-polar and small-size molecular components, such as pigments (chlorophylls, carotenoids) and some lipids (e.g., sterols) that are valuable as food and pharmaceutical ingredients. Proteins, polysaccharides, long-chain fatty acids and phlorotannins have also been recovered from seaweeds under PLE using subcritical solvents, and/or subcritical water and organic modifiers (SWE) [98]. Therefore, SFE may be a first processing step in seaweed biorefinery strategy [85] wherein the protein extraction step, from the first-extracted biomass, could be done either by PLE, SWE or conventional methods, after optimizing the operating conditions to increase the selectivity, as recommended for biorefineries of other plant-based resources [98].
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments
### Data Description
#### 3.1.2 Wind Speed and Power  
It can be seen from the power-wind speed curve (Figure 6) that the wind speed fluctuates strongly. There are not only long-term fluctuations in the data, but also unstable random fluctuations in the short term, which are manifested as sawtooth jitters in the power curve. When the wind speed is low and fluctuates frequently, the power does not change frequently, and the change is delayed and gentle due to the primary inertia of the wind turbine. Where the wind speed is greater than the rated wind speed and changes frequently, the power control keeps the output fluctuating around the rated power.  
Under ideal operating conditions, the energy captured by the wind turbine can be modeled by Equation (13), which is the theoretical power value indicated in red in Figure 7. It can be seen that the theoretically available power is mainly positively related to the air density, the overflow area, and the wind speed cubic, in addition to the wind energy utilization factor described in Equations (14) and (15). The overflow area is fixed for the equipment, and the wind speed and direction records we collected are the main reference data for short-term predictions, since they contain seasonal variation characteristics and  
Figure 5: Diagram of monthly distribution of wind speed.  
Figure 6: Power–wind speed curve.  
incoming flow characteristics in different directions, from which the machine-learning model can build a mapping to the power output at the next moment.  
The wind energy that can be captured by wind turbines is as follows:  
\[P_{wtb}=\frac{1}{2}\rho C_{p}(\lambda,\beta)\pi R^{2}V_{w}^{3} \tag{13}\]  
The wind energy utilization coefficient is expressed as:  
\[C_{p}(\lambda,\beta) =0.5(\Gamma-0.022\beta^{2}-5.6)e^{-0.17\Gamma} \tag{14}\] \[\lambda =\frac{\omega_{wtb}R}{V_{w}},\ \Gamma=\frac{R}{\lambda}\cdot\frac{3600}{1609} \tag{15}\]  
\(P_{wtb}\) is the output power of the wind turbine (w); \(\rho\) is the air density (kg/m\({}^{3}\)); \(C_{p}\) is the power coefficient; \(\lambda\) is the tip speed ratio; \(R\) is the blade radius (m); \(V_{w}\) is the wind speed (m/s); \(\omega_{wtb}\) is the angular velocity of the wind turbine (rad/s); \(\beta\) is the blade pitch angle (\({}^{\circ}\)).  
Figure 7 shows the scatter plot of the theoretical and actual power of the wind turbine corresponding to the wind speed. The red dots represent the theoretical power output of the wind turbine and the blue dots represent the actual power. According to the theoretical power calculation method for the wind turbine described before, wind speed is one of the most important direct influencing factors of power. The continuous motion of the atmospheric system is inertial, and the overall wind speed conditions around the turbine are easy to measure, so the power output can be predicted ahead of time in a short period of time based on continuous observation of the local wind speed.