esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussions
### Mechanical Properties
Given the following equation, we also can calculate the characteristic residual flexural strengths (\(f_{RjK}\)) and evaluate the material class [26]:  
\[f_{RjK}=f_{R,j}-1.89\sigma \tag{5}\]  
where \(f_{R,j}\) is the residual flexural strength and \(\sigma\) is the standard deviation. The flexural strength at the LOP (\(f_{L}\)) slightly increased with increasing fiber volume fraction, as shown in Figure 6. However, the increase in value of \(f_{L}\) was relatively minor compared to those of residual flexural strengths (\(f_{R,J}\)) because the value of \(f_{L}\) is more dependent on the matrix cracking rather than on the fiber bridging capability [25]. The highest flexural strengths were generally obtained near a CMOD value of 1.5 mm or 2.5 mm, and the residual flexural strengths almost linearly increased with the fiber volume fraction (Figure 6). Since the values of CMOD\({}_{1}\) (=0.5 mm) and CMOD\({}_{3}\) (=2.5 mm) are related to serviceability and ultimate limit states, respectively [26], it can be concluded that using higher amounts of hooked-end steel fibers is effective for enhancing the flexural strengths of concrete at both the serviceability and ultimate limit states, regardless of the geometry of hook ends. The characteristic flexural strength ratios (\(f_{R3K}/f_{R1K}\)) were not clearly influenced by the fiber volume fraction, because both the characteristic flexural strengths (\(f_{R1K}\) and \(f_{R3K}\)) were enhanced by increasing the fiber content, whereas the SFRCs with 1.0% of steel fibers were considered as higher material classes (Table 2) compared to those with lower fiber volume fractions, regardless of the hooked-end fiber type. In order to guarantee a ductile flexural behavior of SFRC, the post-cracking strength needs to be at least 20% higher than the initial cracking strength. At the low volume fraction of 0.37%, all types of steel fibers did not satisfy this requirement, but all tested specimens with fiber volume fractions equal to or greater than 0.6% fulfilled this requirement. For instance, the post-cracking flexural strengths of 3D, 4D, and 5D hooked-end fiber specimens with \(v_{f}\) of 0.6% were respectively 26%, 31%, and 44% higher than their initial cracking strengths. The effectiveness of adding steel fibers on guaranteeing the ductile flexural behavior of concrete increased by increasing the number of hook ends. Thus, the 5D hooked-end fiber provided the greatest enhancement in flexural strength from the initial cracking point to the peak point, i.e., 44% and 90% for the cases of \(v_{f}\) of 0.6% and 1.0%, respectively, followed by the 4D and 3D hooked-end fibers. Consequently, it can be stated that increasing the number of hook ends in hooked-end steel fibers is effective for inducing the ductile behavior of plain concrete under flexure.
The objectives of this study are as follows: (1) to accurately map hazelnut prove extension within the designated area of interest (AOI) in 2022, utilizing freely available satellite imagery; (2) to assess the effectiveness of a GEE-based approach for extracting spectral-temporal signatures from multiple platforms in the temporal dimension, enabling the accurate discrimination of a specific arboreal species; (3) to characterize the identified hazelnut cultivation areas using unsupervised algorithms based on the acquired spectral-temporal signatures, aiming to gain valuable insights into the patterns and distribution of hazelnut cultivation; and (4) to propose a reproducible workflow applicable to other perennial crops in different contexts, enabling their mapping and characterization on the territory.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 4 Results and Discussion
### Calculation of Model Accuracy  
To comprehensively evaluate the performance of the S-ANC prediction model, three unified evaluation indices, including R-squared score, mean absolute error (MAE), and mean square error (MSE) [76,77] were selected in this paper. The evaluation indexes have the following mathematical definitions:  
**R-squared Score**:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y})^{2}} \tag{23}\]  
Figure 8: Illustration of **(a)** train–test data and **(b)** prediction of grid consumption using AR regression and S-ANC.  
Mean Absolute Error:  
\[MAE=\frac{\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|}{n} \tag{24}\]  
Mean Squared Error:  
\[MSE=\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n} \tag{25}\]  
where \(y_{i}\) is the output vector using the S-MPC; \(\hat{y}_{i}\) represents the predicted value of the output vector by employing the S-ANC; \(\hat{y}\) represents the average value of output vector; n represents the total number of samples.  
In this study, we assessed the performance of our predictive model using various accuracy metrics. The R-squared error, which measures the proportion of the variance in the dependent variable that can be predicted from the independent variables, was 0.951. This suggests that our model accounted for approximately 95.1% of the data variance, indicating a strong predictive ability. In addition, we determined that the MAE was 0.571. The MAE is the mean absolute difference between observed (from the MPC) and predicted values. A smaller MAE indicates that the predicted and observed values correspond more closely. In our case, the relatively low MAE indicates that our model's predictions deviated from the true values, on average, by approximately 0.571%. Likewise, we determined the MSE to be 0.571. The MSE measures the average squared deviation between predicted and observed values. Similarly to the MAE, a lower MSE indicates greater precision. The MSE value of our model indicated that the squared differences between the predicted and observed values were, on average, 0.571 units. Overall, the results show that our predictive model was effective. The relatively low MAE and MSE values of 0.571 indicated precise predictions, with minimal deviations from the actual values.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Sensor-Based Modality
#### 3.4.2 Motion-Based  
[PERSON] et al. [165] designed a low-cost smart glove equipped with a gyroscope/ accelerometer and vibration motors. Using _finger motion tracking_ and _hand motion tracking_, hand and finger motions can be detected through the glove, including angular movements of the arm and joints. Using the same concept, [PERSON] et al. [166] proposed another smart glove capable of multi-dimensional motion recognition of gestures to achieve haptic feedback. The glove is equipped with piezoelectric mechanical stimulators that provide feedback when interacting with a virtual object. In another research paper [167], the authors used Microsoft's Hololens HMD, exploiting the _head movement tracking_ and _head gaze detection_ methods. They introduced a novel mixed reality system for nondestructive evaluation (NDE) training, for which, after a user study, they concluded that such systems are preferred for NDE training. In [168], [PERSON] et al. presented a Kalman filter for head-motion prediction for cloud-based volumetric video streaming. Practically, server-side rendering systems, although they can provide high-resolution 3D content in any device with an acceptable internet connection speed, suffer from interaction latency. Although the research results were promising, the authors stressed that more research is necessary to examine several shortcomings, including predicting spherical quantities and more accurate predictions of head orientations.  
Figure 5: Displaying all categories of the sensor-based modality.
## 3 Pretreatment Methods in the Chain of Biogas Production
### Chemical Pretreatments
#### Alkaline Hydrogen Peroxide  
The alkaline reagent can be combined with hydrogen peroxide. The use of H\({}_{2}\)O\({}_{2}\) improves the enzymatic digestibility in a wide range of lignocellulosic biomass with the main intention of improving the enzymatic hydrolysis yields [120]. H\({}_{2}\)O\({}_{2}\) is used as a bleaching agent of lignocellulosic biomass. During the pretreatment, many free radicals are released but has the advantage of not leaving residues in the biomass because it degrades into oxygen and water and hardly forms secondary products. Thus, an oxidative fragmentation occurs, as well as lignin removal from the lignocellulosic matrix by attacking lignin side chains [121]. Alkaline hydrogen peroxide improves the performance in terms of delignification and enzymatic digestibility than single alkali reagent [122]. However, this method is limited due to the need to maintain constant pH during the process to avoid hemicellulose elimination, and is one of the most important parameter to achieve process efficiency [117].  
Alkaline hydrogen peroxide is a delignification process, but not only improves the depolymerization of lignin, but also has the advantage of requiring mild conditions of temperature and pressure [123], which reduces the formation of inhibitors [124]. [PERSON] et al. [125] studied the operating conditions of alkaline hydrogen peroxide pretreatment of sugarcane bagasse and reported optimum pretreatment conditions were 0.3% hydrogen peroxide concentration, 100 \({}^{\circ}\)C for 4.6 h. [PERSON] et al. [126] confirmed the use of low concentration of oxidant at room temperature significantly improved the anaerobic treatability of olive mill residues and reported methane yield up to 0.328 L/g of COD (chemical oxygen demand) removed. Similarly, [PERSON] et al. [127] reported 49% increase in methane yield over untreated substrate of _Miscanthus floridulus_ by using 0.8% of H\({}_{2}\)O\({}_{2}\).  
On the other hand, [PERSON] et al. [128] evaluated the H\({}_{2}\)O\({}_{2}\) recycling strategy and tested the efficiency of recycled H\({}_{2}\)O\({}_{2}\) in five successive reuse cycles and reported a decrease in efficiency at each cycle of reuse than the previous.  
Methane yield from different residues after pretreatment by acid, alkali, organosolv, and H\({}_{2}\)O\({}_{2}\) treatment is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Substrates** & **Pretreatment Conditions** & \begin{tabular}{c} **Methane Yield** \\ **(Untreated)** \\ \end{tabular} & \
\begin{tabular}{c} **Methane Yield (After** \\ **Preteamen0)** \\ \end{tabular} &
\begin{tabular}{c} **Reaction** \\ **System** \\ \end{tabular} & **References** \\ \hline \multirow{8}{*}{Corn Straw} & Sulfuric acid 2\% \(\
icefrac{{}}{}\)\(\
ice
## 4 Characteristics of Microseismic Events in the Tunnel  
### Microseismic Monitoring System of the #2 Diversion Tunnel  
The deformation and failure caused by the excavation of underground caverns was essentially the evolution process of rock micro-fractures from initiation, growth, propagation, aggregation, and penetration, to macroscopic failure. Therefore, the aggregation and evolution characteristics of micro-fractures in the rock mass are often used as the precursor of macroscopic instability failure of the rock mass. Microseismic monitoring, as a 3D monitoring method, originated abroad when the first seismic station was established in the Ruhr coalfield in Germany in 1908 to monitor seismic events induced by mining [32]. The microseismic monitoring system can collect micro-fracture information inside the rock mass in real time for assessing the stability of the engineering rock mass. In response to the randomness and multiplicity of deformation, rock bursts, and other issues caused by the excavation and unloading of deep tunnels, the diversion tunnel project in the Xulong Hydropower Station was equipped with the high-sensitivity microseismic monitoring system produced by Canadian ESG, which could provide real-time monitoring. It mainly consists of the acceleration sensor, Paladin digital signal acquisition system, and Hyperion digital signal processing system. Figure 8 shows the network topology of the microseismic monitoring system.  
Figure 7: Acoustic emissions during the tunnel failure.  
The microseismic monitoring system of the #2 diversion tunnel implemented 24 h monitoring. In particular, the microseismic sensor converted the received microseismic signals into electrical signals, which were transmitted to the Paladin digital signal acquisition system through the cable and converted into digital signals with a 24-bit ADC. Finally, the digital signals were recorded and saved automatically by the Hyperion digital signal processing system. Due to the long transmission distance from the Paladin acquisition substation to the Hyperion host, they were connected by optical fiber in order to ensure the stability and integrity of the transmitted signals, and the conversion between digital signals and optical signals was completed by using an optical-to-electrical (O/E) converter. The data collected onsite could be shared through wireless network transmission, so that the Xulong camp office and the calculation and analysis center in Chengdu could download the microseismic data in real time, thus conducting risk warning and stability evaluation for the excavation of the #2 diversion tunnel based on calculation, analysis, and research.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
## 2 Materials and Methods
### Data Analysis Method  
As a result of this research, we obtained measurements based on the relationship between the apparent permittivity readings obtained with the TDR sensors and the mass moisture content of the material of the tested samples. The uncertainty of the measurements  
Figure 3: S2 non-invasive TDR sensor [42]. (**a**) Isometric view, (**b**) view from the bottom.  
was assessed with the use of appropriate regression models. A polynomial relationship was used for the calibration based on the formula:  
\[\hat{w}=\beta_{1}\cdot\epsilon^{2}+\beta_{2}\cdot\epsilon+\beta_{3}+\in \tag{1}\]  
where \(\hat{w}\) is the mass moisture value estimated using a polynomial model [%mass], \(\epsilon\) is the apparent permittivity measured using TDR, \(\epsilon\) is the random error of normal distribution, \(\beta_{1-3}\) are the model estimators.  
The parameters defining the measurement uncertainty of the methods are the mean-square error (RMSE) and the coefficient of determination R\({}^{2}\), which allows for the assessment of the regression model adaptation and the quality of the model fit in relation to the measured data.  
Additionally, an extended assessment of the measurement uncertainty (standard and extended uncertainty) of the invasive FP/mts TDR sensors and the non-invasive S1 and S2 sensors was performed based on the GUM guidelines [48].  
The uncertainty assessment included the complex uncertainty assessment, which is a combination of two types of uncertainty, i.e., type A, that is statistical uncertainty depending on the quality of the adopted model fit, and type B, which depends on the uncertainty and resolution of the individual device. With a complex standard uncertainty, the expanded measurement uncertainty was estimated [43].  
For the applied measurement methods, type B uncertainties were much lower than type A uncertainties and were ignored in the calculations; therefore, the following elements were taken into account for the estimation of uncertainty: the estimators \(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\) and the relative permeability (\(\epsilon\)):  
\[w=f(\beta_{1},\beta_{2},\beta_{3},\epsilon) \tag{2}\]  
The composite standard uncertainty of the measurement mentioned above, which included both type A and type B uncertainties, can be described by the formula:  
\[u_{C}(w)=\sqrt{\left(\frac{\partial w}{\partial\epsilon}u(\epsilon)\right)^{2 }+\sum_{i=0}^{2}\left(\frac{\partial w}{\partial\beta_{i}}u(\beta_{i})\right)^ {2}+2\sum_{i=0}^{2}\sum_{j=i+1}^{2}\frac{\partial w}{\partial\beta_{i}}\frac{ \partial w}{\partial\beta_{j}}u\left(\beta_{i},\beta_{j}\right)} \tag{3}\]  
The expanded uncertainty can be described with the following formula [47].  
\[U(w)=k_{p}\cdot u_{c}(w) \tag{4}\]  
where \(k_{p}\) is the coverage factor that depends on the number of degrees of freedom, whose value is approximately 2.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 3 5G New Radio Hybrid MIMO-Based Fibre-Wireless System
### Signal Generation and Utilization  
A Mach-Zehnder modulator (MZM) modulates a 1310 nm wavelength to drive a 1310 nm distributed feedback laser. The RF1 signal is applied to one of the MZM's arms by the first vector signal generator (VSG1). At 20 GHz, RF1 is a 5G NR signal. The 3 GHz flexible waveforms' (OFDM, GFDM, F-OFDM) signals from the 5G transceiver correspond to RF2. The biasing voltages VBIAS1 and VBIAS2 are chosen in such a way that the observed spectrum is free of the high peak to average power ratio and high-frequency peaks in the measured frequency selection.
## 6 Evaluation and Discussion
### Discussion
However, our proposed protocol provides the messages with integrity protection with a digital signature and hash algorithm. That is crucial because it will avoid the adversary from altering the messages and injecting malicious code during transmission.
# Numerical Study of Mixed Convection and Heat Transfer in Arc-Shaped Cavity with Inner Heat Sources
## 2 Physical Model and Numerical Method  
### Arc-Shaped Cavity Model  
The model considered in this study is an arc-shaped cavity with free opening and closing of the opening and containing an internal heat source. Figure 1 is a schematic diagram of the two-dimensional arc-shaped cavity model. The origin of the coordinatesis set at the midpoint of the bottom edge, and the height of the arc-shaped cavity is \(H\) (\(H\) = 49 m). The length of the bottom side is \(L\) (\(L\)/\(H\) = 2.76). The height of the entrance on both sides is \(h_{1}\) (\(h_{1}\)/\(H\) = 0.12). The width of the export on the top side is \(d\) (\(d\)/\(H\) = 0.34). The heat source is an isosceles trapezoid, the heat source height is \(h_{2}\) (\(h_{2}\)/\(H\) = 0.28), the top bottom length is \(l_{1}\) and the bottom length is \(l_{2}\) (\(l_{1}\)/\(l_{2}\) = 0.22). The two heat sources are symmetrically distributed. The monitoring point in the arc cavity is located near the heat source, and the dimensionless coordinates are P6(\(-\)0.37, 0.34) and P6*(0.37, 0.34). The arc wall surface is set at a fixed temperature to reflect the ambient temperature.
## 2 Related Work
### The Methods Based on Hand-Crafted Features  
In early work, researchers have mainly the used threshold method [1], region growing method [2] and methods based on machine learning [3; 27; 28] for the liver and tumor  
Figure 1: (**a**) Images of slice of liver and tumor region from CT; (**b,c**) show two-dimensional and three-dimensional liver and tumor information, respectively, where the red portion represents the normal liver region, and the green portion represents the tumor region; and (**d**) is the HU values histogram distribution of the liver and tumor from CT, in which the red portion represents the liver, the green portion represents the tumor, and the shaded portion indicates that the HU values of the liver and tumor have a large number of overlapping areas.  
segmentation. The threshold method mainly determines whether the brightness value in the medical images is greater than a threshold value to determine whether the target pixel belongs to the foreground or the background. However, the threshold method is often not effective for images with many overlapping areas. Different region growing methods are also common in the liver and tumor segmentation tasks. By selecting seed points, the region growing method gradually gathers pixels similar to the seed points into a larger area. For instance, [PERSON] et al. [2] proposed a 2D region growing method to segment tumors empirically. However, the region growing method needs to manually select the seed points and cannot complete the task of automatic segmentation. Machine learning-based approaches have also been used to segment the tumor in medical images. For instance, [PERSON] et al. [3] proposed a method to segment tumors by using a support vector machine (SVM) classifier. Similar to this method, [PERSON] et al. [28] proposed texture feature vectors to train SVMs and segment the liver and tumor. In addition, [PERSON] et al. [29] proposed a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques that could achieve the automated sub-segmentation of the different dense tissue from CT. This method can be readily integrated into clinical research environments. [PERSON] et al. [27] proposed a fast-moving algorithm to generate an initial region and train a separate feedforward network for classifying the tumor. The level set method is also widely used by researchers. The advantages of the numerical calculation of curves and surfaces provide an excellent solution for the segmentation of medical images [30]. For example, [PERSON] et al. [31] proposed a multiresolution 3D level set method with an adaptive curvature skill to classify tumors. Although there are many excellent traditional methods for the segmentation of the liver and tumor, the disadvantages of relying on hand-crafted features and insufficient segmentation accuracy cause the traditional method to be difficult in the task of automatic liver and tumor segmentation.
# Deep-Feature-Based Approach to Marine Debris Classification
## 2 Materials and Methods
### Machine Learning Classifiers
#### 2.3.1 Random Forests  
The random forest (RF) classifier [57] comprises numerous decision trees that operate as an ensemble. The bagging technique combined with randomized feature selection is used to build large collection of de-correlated decision trees. Let \(n_{t}\) be the number of data instances in the training set \(\mathcal{D}_{t}\), and \(m\) the number of features given for every data instance. Each decision tree in ensemble is trained on new dataset built by randomly sampling \(n_{t}\) instances (with replacement) from \(\mathcal{D}_{t}\). During the tree growing, at each node subset of \(m^{\prime}\ll m\) features is chosen at random, and the best feature (splitter) among them is used to split the node. At inference, each decision tree in ensemble votes for the preferred class, and RF outputs the class with majority votes as the final prediction.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Advantages of Multi-Connectivity
#### 3.4.3 Robust Mobility  
With MC (or DC), UE is connected to both BSs at the same time. This allows for a simultaneous control and/or user plan connectivity over two independent radio channels. DC can help reduce the interruption times during the handovers along with the amount of control signaling required. The control signaling is either already established on the secondary BS or can be moved along easily since UE has UL and DL with the primary BS. MC can help offload the overhead signaling from the core network to the radio access network (RAN) due to the existing secondary node connection.