esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to an Octagonal Viscoelastic Bar
### Forcing function \(F_{02}\)  
Here the force distribution \(\mathbf{F}_{\varpi}\) and the time amplification factor \(f(t)\) of the previous case were considered. In this case the presence of the two identical Flexural modes generates some numerical instability to the proposed formulation. These instabilities originate some unexpected peaks in the FRF at very low frequencies and in turn a non zero time transient response before the first arrival.  
In order to overcome this problem, an octagonal stretched cross-section characterized by two flexural modes with a slightly different behavior, was adopted. The non symmetric cross-section was obtained by simply scaling the \(y\)-coordinates of the mesh nodes by a constant factor while keeping constant their \(x\)-coordinates. Using and a sampling frequency of 500 kHz and a total duration \(T=5\,\mathrm{ms}\), the frequency response normalized by the force spectrum for the \(x\)-dof of node 13 is represented in Figure 10(a) for three different distances source-receiver (1, 3 and 5 meters, respectively). The results have been carried out considering an octagonal stretched cross-section with node coordinates \(x_{g}=x\) and \(y_{s}=y\times 1.000001\), where \(x\) and \(y\) are the coordinates of the nodes for the symmetric octagonal cross section, while \(x_{\rm S}\) and \(y_{\rm S}\) are the nodes coordinates of the modified cross-section. Even if for the slight modification considered the two flexural modes were very alike, the change was sufficient to remove all the formulation instabilities. By calculating the FRF and the time transient response for each mode at a time, it was verified that only the F\({}_{\rm Y}\)(1,1) mode was excited among the four fundamental modes. This mode, in fact, presenting mainly bending around the \(y\)-axis was strongly coupled to the considered forcing vector.  
In order to consider the response and collect the information (wavenumber, attenuation, phase and energy velocity) related to each separate mode, a sorting procedure similar to the one proposed in Reference [8] was implemented. Next, in the post-processing phase it was possible to compute Equation(29) for a single mode. As it can been seen in Figure 10(a) the FRFs have smooth behavior up to around 100 kHz where one of the F(1,2) modes is excited. It can be seen that the proposed formulation well captures the damped behavior of the F\({}_{\rm Y}\)(1,1) mode for an increased traveled distance. The dispersive behavior of the F\({}_{\rm Y}\)(1,1) wave is slightly visible from the time-transient responses of Figure 10(b). This is due to the fact that the narrow energy content of the forcing function at around its central frequency of 30 kHz generates frequencies within the dispersive F\({}_{\rm Y}\)(1,1) wave with similar energy velocity.  
Figure 10: Response for the \(x\)-dof of node 13 at different distances source-receiver \(d\)=z\({}_{\rm R}\)-z\({}_{\rm S}\). (a) absolute value of the scaled frequency response function, (b) time-transient response.
## 2 Materials and Methods
### Data
#### 2.2.3 Cropland Mask  
MODIS data were masked by rained cropland and irrigated cropland in order to exclude interference from non-cultivated areas (i.e., cover, forest, shrubland, grassland, vegetation, wetlands, and others). The cropland mask for the study area was generated using the Global Land Cover with Fine Classification System at 30 m (GLC_FCS30 dataset [42], and the original spatial resolution (30 m) was resampled to 250 m for the years 2005, 2010, 2015, and 2020. The masked images are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Category Code** & **Abbreviations** & **Training Set** & **Validation Set** & **Test Set** & **Total** \\ \hline Summer corn & SU & 499 & 166 & 167 & 832 \\ \hline Rice & RI & 274 & 91 & 92 & 457 \\ \hline Spring corn & SP & 241 & 80 & 81 & 402 \\ \hline Other & OT & 764 & 255 & 255 & 1274 \\ \hline Winter wheat-Other & WT-OT & 281 & 94 & 94 & 469 \\ \hline Winter wheat-Rice & WT-RI & 2221 & 741 & 741 & 3703 \\ \hline Winter wheat-Spring corn & WT-SP & 473 & 158 & 158 & 789 \\ \hline Winter wheat-Summer corn & WT-SU & 2894 & 965 & 965 & 4824 \\ \hline Total & & 7647 & 2550 & 2553 & 12,750 \\ \hline \end{tabular}
\end{table}
Table 1: The classification was conducted using a total of eight categories in this study. The first seven categories corresponded to wheat, corn, and rice, while the remaining category encompassed other crops.  
Figure 4: GLC_FCS30 datasets masked by the rained cropland and irrigated cropland in North China and resampled to 250 m in (**a**) 2005, (**b**) 2010, (**c**) 2015, and (**d**) 2020.
# Compact Bistatic Iterative Passive Radar Based on Terrestrial Digital Video Broadcasting Signals
## 3 Proposal Description
### Iterative Algorithm
Subsequently, the corresponding distances, \(\bar{\mathbf{v}}\), are recalculated based on the modified coordinates [step 14], and the total error for each combination is evaluated [step 15]. The combination producing the smallest change is selected as the updated values for the estimated distances [steps 18-22] because it is the safer one. Finally, the total error is computed with these updated distances in step 23, and the stop condition is checked using this new total error, \(\mathbf{\epsilon}_{T}\). Upon completion, the algorithm outputs the final coordinate estimates, derived from the updated distances. For example, the first column in \(\mathbf{M}\) is represented in the algorithm by \(\mathbf{m}=\mathbf{M}[:,1]\) and shows how many of the three distances are changed. This first column in \(\mathbf{M}\) indicates that only the first distance is modified. \(\mu\) is the update factor. Notably, larger values of \(\mu\) converge more rapidly; however, since these values are produced during the updating step, the granularity and resolution decrease, and a highly probable erroneous estimation is given. For example, if the initial error is of 1.5 m, an update of 0.1 m or 0.3 m will produce zero-error, but an update of 0.2 will leave a remainder error. On the contrary, smaller values will produce a slower convergence but better resolution. If we denote the remainder error \(\delta_{i}\), which is essentially the non-divisible part of the error with respect to \(\mu\), and recall our on the analysis of \(\epsilon\) in the previous sections, the total error of the algorithm will be proportional to \(\delta_{i}^{2}\). Eventually, there is a trade-off between speed and resolution. It worth nothing to indicate that the proposal can be applied to the DVB-T2 standard, only replacing the structure of the frame and the pilots for the channel estimation process.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 4 Experiments and Analysis
### Evaluation Metrics  
Evaluation metrics for image segmentation measure how well the segmented output matches the ground truth. In this paper, we used different metrics to evaluate network performance, including Accuracy (_AC_ ), Jaccard Index (_IJ_), Dice Coefficient (_DC_), Sensitivity (_SE_), and Specificity (_SP_), which are calculated as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Input shape & \(256\times 256\times 3\) \\ Batch Size & 32 \\ Epochs & 200 \\ Learning Rate & \(1\times 10^{-4}\) \\ Optimizer & Adam \\ Loss Function & Binary Cross Entropy \\ Callbacks & Early Stopping \\  & Reduce LR On Plateau \\ \hline \hline \end{tabular}
\end{table}
Table 1: The used hyperparameters in our experiments.  
\[AC=\frac{TP+TN}{TP+TN+FP+FN}\,, \tag{6}\] \[DC=\frac{2\times TP}{2\times(TP+FN+FP)}\,,\] (7) \[JI=\frac{TP}{TP+FN+FP}\,,\] (8) \[SE=\frac{TP}{TP+FN}\,,\] (9) \[SP=\frac{TN}{TN+FP}\,, \tag{10}\]  
where  
* _TP_ (True Positive): Pixels that are correctly identified as part of the object or region of interest.
* _TN_ (True Negative): Pixels that are correctly identified as not being part of the object or region of interest.
* _FP_ (False Positive): Pixels that are incorrectly labeled as part of the object or region of interest when they are not.
* _FN_ (False Negative): Pixels that are incorrectly labeled as not part of the object or region of interest when they are.
## 2 Principle
### Geometric Constraints for Phase Unwrapping  
[PERSON] et al. [25] have recently proposed a pixel-wise phase unwrapping method based on geometric constraints of the fringe projection system. The main idea is to create the minimum phase map \(\Phi_{min}\) at the closest depth plane z\({}_{min}\) of the measured volume. Then phase unwrapping can be performed with reference to minimum phase map \(\Phi_{min}\). The details of this method have been described in [25]. The following briefly introduces the main idea of this method.  
Figure 4 illustrates the phase unwrapping method using the minimum phase map \(\Phi_{min}\). If the wrapped phase \(\phi\) is less than \(\Phi_{min}\), we need to add \(k\) times of \(2\pi\) to the wrapped phase \(\phi\) to obtain the absolute phase \(\Phi\). The fringe order \(k\) can be computed as:  
\[k(x,y)=ceil\left(\frac{\Phi_{min}-\phi}{2\pi}\right) \tag{8}\]  
where function _ceil()_ returns the closest upper integer value. It should be noted that the above equation must satisfy the following condition:  
\[0\leq\Phi-\Phi_{min}<2\pi \tag{9}\]  
Its physics signification is that the measured objects should be close to the depth plane z\({}_{min}\) and within \(2\pi\) in phase domain. In other words, the maximum depth range should be less than \(2\pi\) changes  
Figure 3: Same rows as in Figure 2. (**a**) Phase-shift patterns; (**b**) coded pattern.  
Figure 2: Projected patterns. (**a–c**) phase-shift patterns; (**d**) coded pattern.  
which will limit the applications of this method. For example, at point A,\(\Phi-\Phi_{min}<2\pi\), and wrapped phase \(\phi\) is correctly unwrapped for the absolute phase \(\Phi^{\prime}\)= \(\Phi\); at point B, \(\Phi-\Phi_{min}>2\pi\), but wrapped phase \(\phi\) is wrongly unwrapped for the absolute phase \(\Phi^{\prime}\
eq\Phi\).
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 1 Introduction
Cluster analysis of maps obtained both by soil survey, such as proximal soil sensing, and by plant water status, such as NDVI, allowed to identify functional homogeneous zones (fHZs), corresponding to areas where soil and plant performance showed limited variability [37; 38]. Soil and crop indices have been mainly tested in vineyards in experiments focused on zoning and precision irrigation management [19; 39; 40; 41; 42] while few studies have been conducted in orchards [43; 44; 45]. To our knowledge, there is only one study in which the use of proximal and remote sensing techniques was used to separate management zones within an irrigated olive orchard [46]. Moreover, there is little information on how the interaction between irrigation, tree vigour, and soil properties affects yield and tree growth in olive. Since water status is one of the most important factors determining plant performance, we manipulated soil water availability to better define how remote and proximal sensing can help to manage irrigation. The objectives of this study were: (i) to evaluate the effect of different irrigation regimes on tree productivity and growth in different management zones identified using remote and proximal sensing techniques; (ii) to discriminate the effect of tree vigour (NDVI) and soil features derived by proximal sensing on fruit yield and vegetative growth under different conditions of soil water availability.
## 3 Results  
This section evaluates the a priori model for the BDS-3 MEO satellite body from a different modeling strategy. The accuracy of the satellite orbits and clock offsets during modeling served as an indirect measure of the quality of the estimated optical parameters. Subsequently, these parameters were employed as a priori models for the satellite body, with the ECOM model compensating for the solar panels and residual SRP. Finally, the accuracy of the orbits and clock offsets estimated with the a priori model was assessed by SLR residual, day boundary difference (DBD), and overlapping Allan deviation (OADEV).
## 3 Methodology
### Modeling
#### 3.4.4 CatBoost  
CatBoost, also known as category boost, is an ensemble learning algorithm developed in 2017. It was designed for gradient-boosting decision trees and offers enhancements to handle categorical variables effectively. One key feature of CatBoost is its automatic handling of categorical features, which eliminates the need for manual encoding. It utilizes a combination of one-hot and target encoding techniques for categorical variables with high cardinality.  
The pseudocode in Algorithm 4 represents the steps used for constructing the CatBoost model in this research. The essential hyperparameters utilized in this process included the number of boosting iterations, which determined the number of trees in the ensemble and influenced the complexity and learning capacity of the model. The learning rate controlled the step size for updating the weights of each tree during the boosting process. The maximum tree depth governed the complexity and depth of the individual decision trees, affecting their ability to capture intricate patterns in the data. Lastly, the \(L2\) regularization strength regulated the penalties applied to the leaf weights, aiding in preventing overfitting and enhancing the generalization capabilities of the model [38; 39; 40; 41].
```
1:Training dataset: \(D\)\(iterations\): [100, 200, 300]
2:learning_rate: [0.01, 0.1, 0.5]
3:depth: [4; 6; 8]
4:l2_leaf_reg: [1; 3; 5]
5:random_state: [42; 123]
6:Ensemble prediction: \(E(X)\)
7:procedureCatBoostClassifier(\(D\), iterations, learning_rate, depth, l2_leaf_reg, random_state)
8: Initialize an empty ensemble \(E(X)\gets 0\)
9:for\(i=1\) to \(N\)do
10: Train a decision tree \(T_{i}\) on \(D\) with the given parameters
11:for each sample \(x\) in \(D\)do
12: Compute the prediction of \(T_{i}\) on \(x\): \(p_{i}(x)\)
13: Update the ensemble prediction: \(E(X)\gets E(X)+\eta\cdot p_{i}(x)\)
14:endfor
15:endfor
16:return\(E(X)\)
17:endprocedure
18:procedureCatBoostPrediction(\(X\))
19: Ensemble prediction \(E(X)\gets 0\)
20:for each decision tree \(T_{i}\)do
21: Make prediction using \(T_{i}\) on input \(X\)
22: Update \(E(X)\) by adding the prediction
23:endfor
24:return\(E(X)\)
25:endprocedure
```  
**Algorithm 4** The category boost (CatBoost) pseudocode
## 3 Simulation and Analysis of Structural Behavior
### Input Parameters for Models
#### 3.1.3 MBS Model  
Recognizing that the structural influence of the joint is not only concentrated at the joint position but also on the segment within the joint influence area, the MBS model is proposed to reduce the stiffness of the segment within a certain range near the joint.  
As shown in Figure 11, for the segment in the vicinity of the joint, the inner surface plate is not fully utilized because of openings and hand holes. The internal surface plate near the joint is divided into two zones to consider stress transmission from the joint: Zone I refers to stress transmission from circumferential joint plates, while Zone II refers to stress transmission from separation boxes. As the distance from the joint increases, the engagement percentage of the internal surface plate increases.  
\[\int_{-l}^{l}\frac{M(x)}{EI(x)}dx=\frac{M_{0}}{E\eta l_{0}}2l. \tag{2}\]  
\[\rho=\frac{2l}{l_{0}}\Bigg{/}\int_{-l}^{l}\frac{dx}{I(x)}. \tag{3}\]  
In Equations (2) and (3), \(I(x)\) represents the variation of the moment of inertia along the segment, and the bending moment \(M(x)\) is assumed to be constant (bending moment \(M_{0}\)). Considering the deformation within a certain distance \(l\) from the joint, the stiffness reduction factor \(\rho\) can be obtained. Therefore, for segments near joints, with a length of the influence area set to 450 mm (the thickness of the segment [41; 42]), the reduction factor is determined to be 0.82.  
For other parameters used in the MBS model, the segment parameters are consistent with those used in the ESIR model, while the joint parameters are consistent with those used in the BS model.
# Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats
## 6 Experimental Results and Discussion
### Visualization  
In this section, we are making some visualization of our proposed system. Figure 9 shows the distribution of the NSL-KDD dataset [14] across the different labels, where the \(x\)-axis is the label category, which is either the normal or attack category (DOS, R2L, U2R, or Probe), and the \(y\)-axis is the count of samples in each category in the dataset. It is observed that the dataset is somewhat balanced between normal and attack, while the attack categories are not balanced, where DOS presents the most commonly observed category in the dataset, and U2R is the least observed one. In Figure 10, for binary classification between normal and attacks, which are referred to as attack_flag in the figure, all the features have a positive correlation except for protocol_type. Count, duration, src_bytes, and dst_bytes positively correlate with attack_flag in descending order. The binary classification between DOS and the others is called dos_flag in the figure. Count and srv_count have a positive correlation with dos_flag in descending order. Duration, src_bytes, dst_bytes, and protocol_type negatively correlate with dos_flag. The binary classification between Probe and others is referred to as probe_flag in the figure. protocol_type, count, and srv_count have a negative correlation with probe_flag. Duration, src_bytes, and dst_bytes have a positive correlation with probe_flag. For binary classification between U2R and others, this is referred to as u2r_flag in the figure. All of the features except for protocol_type have a negative correlation with u2r_flag. For binary classification between R2L and others, this is referred to as r2l_flag in the figure. protocol_type, count, and srv_count have a negative correlation with r2l_flag. Duration, src_bytes, and dst_bytes has a positive correlation with r2l_flag. For multi-classification between normal, Probe, DOS, U2R, and R2L are called attack_map. The protocol_type and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metric** & **Description** & **Formula** \\ \hline Detection Rate/Precision & The proportion of true positives among projected positives (or) & TP/(TP + FP) \\  & proportion of test data flagged as an attack that is an attack. & \\ \hline Accuracy & To establish the total accuracy, take a measurement. It is the percentage of accurately predicted values over the whole dataset. & TP + TN/(TP + FP+ FN +TN) \\ \hline False Alarm Rate & The false-positive rate (FPR), also known as the false alarm rate & \\  & (FAR), is the percentage of legitimate packets mistakenly identified & FP/(FP + TN) \\ \hline True Positive Rate Sensitivity/Recall & The proportion of attack classes successfully detected (or) the & \\  & percentage of true positives projected as positives. & TP/(TP + FN) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: IDS Performance Measures [58].
## 2 Materials and Methods
### Methods  
Thermogravimetry (TGA) was performed using a NETZSCH 209 F1 Libra gravimetric analyzer (Selb, Germany). Samples of 5 \(\pm\) 0.2 mg were placed in Al\({}_{2}\)O\({}_{3}\) crucibles. Measurements were conducted under nitrogen and air (flow of 20 mL/min) in the range of 30-800 \({}^{\circ}\)C and 10 \({}^{\circ}\)C/min heating rate.  
\({}^{1}\)H and \({}^{29}\)Si nuclear magnetic resonance (NMR) spectra were recorded at 25 \({}^{\circ}\)C on Bruker Ascend 400 and Ultra Shield 300 (Billerica, MA, USA) spectrometers using CDCl\({}_{3}\) as a solvent. Chemical shifts are reported in ppm regarding the residual solvent (CHCl\({}_{3}\)) peak for \({}^{1}\)H and \({}^{13}\)C.  
Fourier-transform infrared (FT-IR) spectra were recorded on a Nicolet iS50 Fourier-transform spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a diamond ATR unit with a resolution of 0.09 cm\({}^{-1}\).  
Particle size distribution of pollution particles was determined by a Mastersizer 3000 (Malvern Instruments Co. Ltd., Malvern, UK) using the laser diffraction method for powder substances using the Aero-S dry dispersion system and, for liquids, Hydro-UV wet dispersion. The analyses were conducted in isopropyl alcohol at room temperature. Five independent measurements in three series were taken for each sample.  
A digital light microscope Keyence VHX 7000 (Keyence International, Mechelen, Belgium) with 100-\(\times\)1000 VH-Z100T lens was used to examine the obtained material. All images were recorded with a VHX 7020 camera.  
Sample surface morphology and chemical composition (SEM-EDS) were analyzed using a Phenom XL (Thermo Fisher Scientific) scanning electron microscope with resolution <20 nm.  
X-ray diffraction studies were carried out on a Bruker D8 Eco Advance X-ray (Mannheim, Germany) diffractometer to determine the phase structure of the samples. The diffractometer works in geometry 0-20 with a Cu-K\(\alpha\) lamp (\(\lambda\) = 0.15406 nm). The following parameters were used for measurements: radiation 40 kV and 25 mA, angular range 20 15-70\({}^{\circ}\), step width 0.01\({}^{\circ}\), and step time 3 s.
# Towards NGGM: Laser Tracking Instrument for the Next Generation of Gravity Missions
## 4 Instrument Options and Next Steps
### Optical Signal Transmission  
Adding pseudo-random noise codes to the laser light as a weak phase modulation via an electro-optical modulator (EOM) allowed to measure the absolute range between the S/C to a cm accuracy, the offset between their on-board clocks to us accuracy and the bi-directional transmission of data with a rate of several 10 kbit/s. While not strictly required for the core NGGM function, this would provide additional flexibility, robustness and redundancy in operation and serve as a technology demonstration for LISA (where these functions are required) and other missions. Those functions and their performance have been demonstrated in laboratory [32], and are under development for LISA right now.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 2 Challenges for IoT Applications and IEEE 802.11 ah  
In order to visualize the challenges within IoT communications, we can distinguish the typical requirements such as large number of autonomous devices sending traffic (simultaneously or in deferred times), low power consumption and long sleep time. In this section we provide an overview of the mechanism used by IEEE 802.11 ah to tackle these challenges.