esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Two-Dimensional Resistivity Tomography Data
Figure 8: The Combined 2-D inverted tomograms show how the resistivity variations vertically and horizontally at the ten tested locations from 1 to 10 (P-1 to P-10), according to the positions, due to change in hydrogeological conditions and lithology and soil texture. Layer-3 is a thick zone below Layer-2. This layer has different resistivity ranges. The resistive nature of this third layer (above 10 Ohm-m) is along 2-D transects of profiles 6, 5, 3, and 4 (P-6, P-5, P-3, and P-4, Figure 8), which are located in the southwest of Al-Ahsa oasis. This layer at these locations is attributed to lithological changes and as well different hydrogeological conditions. The existence of Neogene limestone outcrops has been noticed at the surface and the near-surface in this part, while at the northern part, it does not exist. For more geological details of Al-Ahsa Oasis, refer to [PERSON] et al. [40]. On the other hand, this layer has resistivities below 2 Ohm-m along with profiles 7, 1, 9, 8, 10, and 2 (P-7, P-1, P-9, P-8, P-10, and P-2, Figure 8) located in the north and east of Al-Ahsa oasis. However, and referring to Figure 8, it is noticed that this layer's resistivity values have relatively low values due to the effect of water saturation from both the Al Yuoun evaporation lake at the north of Al-Ahsa Oasis and of Al Asfar Lake at the east of the Oasis. Figure 9 shows one of the dug pits beside 2-D profile 1 (P-1, Figure 8) and shows how the water table is very shallow and the saturation affects the resistivity values to be below 2 Ohm-m.  
A low resistivity layer was mapped along some 2-D transects, for example, profiles 6, 9, and 8 (P-6, P-9, as well as P-8, Figure 8). This layer is a hardpan layer composed of marl, clay, mudstone, or shale [22]. The layer directly affects the level of soil water and thus the accumulation of salt in some locations resulting in the presence of some of the sites affected by the salt composed sabkha, especially if it is very shallow and this, in turn, increased the soil conductivity measured by EM38-MK2, as will be discussed below. Figure 10 shows some excavation and the existing hardpan layer at different depths near profile 6 (P-6, Figure 8).
## 3 Data and Methods
### Unsupervised Machine Learning Models
#### 3.3.1 k-Means  
The k-means clustering algorithm is a typical unsupervised machine learning model that is widely used for the clustering analysis [25] of non-labeled data. The advantage of k-means is that it is easy to implement and visualize the result. The number of clusters is the only parameter that needs to be specified beforehand. To build a k-means model:  
1. Predetermine the clustering number k.
2. The k-clustering prime points are randomly selected as \(\mu_{1}\), \(\mu_{2}\),..., \(\mu_{k}\).
3. All the points are assigned to the nearest centroid and clusters are formed. Calculate the distances between every point to the centroid in each cluster. \[dist(x,\mu)=\sqrt{\sum\limits_{i=1}^{n}(x_{i}-\mu_{i})^{2}}\] where \(x\) is the sample point; \(\mu\) is the center of mass of the cluster; \(n\) is the number of features in each sample point; and \(i\) is each feature of the constituent point \(x\).
4. Summarize the total distances of all clusters: \[Cluster\;\;Sum\;\;of\;Square(CSS)=\sum\limits_{j=0}^{m}\sum\limits_{i=0}^{n} (x_{i}-\mu_{i})^{2}\] \[Total\;\;Cluster\;\;Sum\;\;of\;Square=\sum\limits_{l=1}^{k}CSS_{l}\] where \(m\) is the number of samples in a cluster and \(j\) is the number of each sample.
5. Calculate the minimum quadratic error from the data point to the center of each cluster, and move the center to the point.
6. Repeat the calculation from step (c) until the total cluster sum of squares does not change or reach the maximum iteration times.
## 3 Results  
The curve fitting of the several hysteresis loops for the three considered models is reported in Figure 1, while the coefficients obtained by the identification parameters are summarized in Table 1. As can be observed, the CPM and D-D-D models can fit hard materials and _MnZn_ ferrite well but suffer to reproduce the near saturation region of the major loop 11 in the rolling direction of the _Fe-Si_. It should be noted that a better fitting for this material has been obtained with the CPM in [32], but a higher order (up to 3) of the polynomial used to discretize (3) has been taken, bringing the number of parameters to be identified up to nine, plus the \(d\) coefficient for the reversibility. On the other hand, the J-A model is well suited for soft materials [29] but suffers to fit semi-hard and hard materials. The main reason for this is the natural sigmoid-shaped hysteresis curve coming from [PERSON]'s polynomial used for the anhysteretic magnetization [35], which is not always suitable for hard materials. Therefore, modifications to the original J-A model have been provided to overcome this issue but complicate the model and the identification procedure, with eight parameters to be identified [36].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Parameter Number 1**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{3}{*}{\(MnZn\) ferrite} & CPM & 0.049 & \(-8.73\) & 17.495 & 0.95\(\cdot 10^{-3}\) & - \\  & J-A & 3.178\(\cdot 10^{5}\) & 1.099\(\cdot 10^{-7}\) & 12.649 & 12.448 & 0.844 \\  & D-D-D & 2.74\(\cdot 10^{5}\) & 5.5 & 0.116 & - & - \\ \hline \multirow{3}{*}{_Fe-Si_ loop} & CPM & 0.044 & 69.12 & 23.212 & 4\(\cdot 10^{-4}\) & - \\  & J-A & 1.23\(\cdot 10^{6}\) & 1\(\cdot 10^{-4}\) & 47 & 66 & 0.99 \\  & D-D-D & 1.194\(\cdot 10^{6}\) & 300 & 0.07 & - & - \\ \hline \multirow{3}{*}{_NdFeB_ at \(80\;^{\circ}\)C} & CPM & 6.6\(\cdot 10^{-3}\) & 4.56\(\cdot 10^{5}\) & 1.49\(\cdot 10^{5}\) & 0.5\(\cdot 10^{-3}\) & - \\  & J-A & 1.18\(\cdot 10^{6}\) & 0.46 & 1.25\(\cdot 10^{5}\) & 5.24\(\cdot 10^{5}\) & 0.05 \\  & D-D-D & 1.13\(\cdot 10^{6}\) & 12.5 & 0.3 & - & - \\ \hline \multirow{3}{*}{_NdFeB_ at \(27\;^{\circ}\)C} & CPM & 0.016 & 1.24\(\cdot 10^{6}\) & 5.44\(\cdot 10^{4}\) & 0.2\(\cdot 10^{-4}\) & - \\  & J-A & 0.954\(\cdot 10^{6}\) & 1.1 & 1.45\(\cdot 10^{5}\) & 1\(\cdot 10^{6}\) & 1\(\cdot 10^{-6}\) \\ \cline{1-1}  & D-D-D & 0.909\(\cdot 10^{6}\) & 20 & 1.02 & - & - \\ \hline \hline \end{tabular}  
* Parameter number reads as follows: CPM: _a, b, c and d;_ J-A: \(M_{a}\), \(\alpha_{J}\), \(\alpha_{J}\) and \(\varsigma_{j}\); D-D-D: \(\gamma\), \(\alpha_{D}\) and \(\beta\).  
\end{table}
Table 1: Parameters of the considered hysteresis models for the several materials.
## 2 Motor Fault Diagnosis and Prediction
### LSTM  
Long short-term memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to model sequential data with long-range dependencies. Traditional RNNs often suffer from vanishing gradient issues when learning from extended time sequences. LSTMs address this limitation through the use of memory cells and gating mechanisms that regulate information flow [19].  
In the LSTM structure, \(f_{t}\), \(i_{t}\), and \(o_{t}\) are three gates designed to control the flow of information. \(f_{t}\) controls the information of the memory cells from \(t-1\) to time \(t\). \(i_{t}\) controls the input of information into the memory cells at time \(t\). In the end, \(o_{t}\) controls the information of the memory cells at time \(t\) in the hidden state of \(h_{t}\). These are shown in the following equation:  
\[f_{t}=\sigma\Big{(}w_{f_{t}}C_{t-1}+w_{f_{t}}h_{t-1}+w_{f_{x}}x_{t}+b_{f}\Big{)} \tag{1}\]  
\[i_{t}=\sigma\big{(}w_{i_{t}}C_{t-1}+w_{i_{t}}h_{t-1}+w_{i_{x}}x_{t}+b_{i}\big{)} \tag{2}\]  
\[o_{t}=\sigma\big{(}w_{o_{t}}C_{t-1}+w_{o_{t}}h_{t-1}+w_{o_{t}}x_{t}+b_{o}\big{)} \tag{3}\]  
where \(w_{fc}\), \(w_{h}\), and \(w_{oh}\) are the weight matrices between gate \(f_{t}\) and memory cell \(C_{t-1}\), and \(b_{f}\) is the bias of the gate \(f_{t}\). Other weight matrices are derived as follows: \(C_{t}\) and \(C_{t-1}\) represent the values of memory cells at time \(t\) and time \(t-1\), \(b_{f}\), \(b_{i}\), and \(b_{o}\) represent the biases, and \(\sigma\) is the activation function. The structure of the unit of the long short-term memory network is shown in Figure 1.  
Figure 1: Structure of long short-term memory hidden unit.  
In predictive maintenance contexts, LSTMs are highly effective for modeling time series data such as vibration levels, current amplitudes, or frequency domain features. Their strength lies in capturing nonlinear temporal trends and forecasting future signal evolution based on past behavior. This makes them well-suited for predicting gradual degradation processes that precede fault events.
## 3 Results and Discussion  
### Mass Spectrum of FITC-Mel  
FITC-Mel was examined using ESI-MS in positive- and negative-ion modes. The molecular weights of FITC and melamine are 389.38 and 126.12, respectively. Figure 2 shows that FITC-Mel has three hydroxyl and two amino groups. In positive-ion mode, the molecular ion peak should appear at 516.5 (M + H)\({}^{+}\) or 517.5 (M + 2H)\({}^{2+}\), but we did not observe this molecular ion peak. In negative-ion mode, the molecular ion peak should appear at 514.5 (M \(-\) H)\({}^{-}\), 513.5 (M \(-\) 2H)\({}^{2-}\), or 512.5 (M \(-\) 3H)\({}^{3-}\). Figure 2 shows a molecular ion peak at 512.8, corresponding to FITC-Mel and indicating successful combination of FITC and melamine. In the previous literature [48], a commercial fluorescent, 5-(4,6-dichlorortiazinyl) amino fluorescein (DTAF) was used as fluorescent label and structural analog to monitor melamine by performing directly competitive adsorption. However, there is a relatively large difference in structure between DTAF and melamine, which restricts the sensitivity and selectivity of the fluorescent assay. In this work, we prepared FITC-Mel as a novel fluorescent label by combining FITC and melamine using chemical modification. The structure of FITC-Mel has a melamine molecule which will endows it high similarity and provides the fluorescent MIP sensor developed in this paper with good recognition and sensitivity.
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 3 Preliminaries
### Channel Impulse Response  
Since the frequency interval of all subcarriers is essentially identical (Since Intel 5300 adopts a technique called carrier grouping to reduce CSI storage space, it only picks around half of the subcarriers (30 out of 56). According to the standard description [41], although the selection scheme may cause a slightly uneven distribution of subcarriers, the frequency interval after the process can be approximated as double the original one. It is typically 312.5 KHz for most commodity WiFi, except 802.11 ax.), we can convert CSI to CIR through the inverse Fast Fourier Transform. After transformation, the frequency index of subcarriers is converted to the time index that indicates signal arrival delay. In indoor environments with severe multipath effects, copies of the original signal can travel along different paths to reach the receiver, resulting in a superposition in CIR. If we use \(a_{i}\delta(\tau-\tau_{i})\) to approximate the CIR of a single path and \(n(t)\) to represent the additive white noise, the composed CIR can be denoted as  
\[h(\tau)=\sum_{n=1}^{N}a_{i}\delta(\tau-\tau_{i})+n(t). \tag{2}\]  
where \(N\), \(a_{i}\), and \(\tau_{i}\) denote the number of paths, power attenuation, and time delay of the \(i\)-th path, respectively. \(\delta(\tau)\) represents the unit impulse function.  
In an ideal case, the signal traversing the LOS path incurs the least attenuation and time delay, leading to a strong and stable energy peak in the CIR. In contrast, in the presence of obstacles, the signal may experience increased propagation distance and reflections, causing the energy peak to shift and incline to the right side (higher time index). Researchers have explored exploiting this difference to identify LOS/NLOS scenarios in wireless communications [24; 30; 39]. However, this observation may not be significant for commodity WiFi.  
We compare two of the CIR samples obtained under LOS/NLOS paths (Figure 1). The experimental settings are as follows: Two PC equipped with Intel 5300 are placed on either side of the table (3.2 m away) in a conference room. Stacks of books and a monitor screen are placed in the middle to simulate the NLOS environment. We ask one of the devices to inject packets at a fixed frequency and the other to receive. iFFT of 30-point is applied to obtain CIR for each packet.  
It shows that although the time index of peak energy does change under two channel states (from the 7 th to the 9 th), the main components still reside in the same region (between the 7 th and 10 th). Moreover, the envelope of the two CIR is almost the same, providing no helpful information for LOS/NLOS identification. Such a result reveals the poor resolution of CIR. For a 20 MHz WiFi channel, the time resolution can be estimated by its inverse, which is 50 ns. That means the signal of different paths may fall in the same time bin if their difference is less than 50 ns. As a result, only paths separated by more than 15 m can be noticable [42].
## 3 Sidebands and Their Correlation Analysis
### Evolution of the Correlation Plots
As a consequence, Panels a and j present a very similar distribution. Similarly, the evolution of the distribution from Panels a (f) to e (j) suggests a change in phase of \(\approx\pi\) rad. This is consistent with the observation that the phase difference between the two panels is 2.76 rad. The exact value of \(\Delta\varphi_{7,8,9}^{(a)}\) can be extracted from the calibration of the evolution of the correlation coefficient as a function of the delay introduced. The evolution of the correlation plots follows the pattern and periodicity as expected; however, it is important to establish that the phase scan did not affect the amplitudes of the individual harmonics. Figure 1: Undulator configuration for the three harmonics’ generation. Each harmonic is generated using a pair of undulators. The harmonics H\({}_{9}\), H\({}_{8}\), and H\({}_{7}\) are generated by the undulators U\({}_{1}\) and U\({}_{2}\), U\({}_{3}\) and U\({}_{4}\), and U\({}_{5}\) and U\({}_{6}\), respectively. The phase shifters used to conduct a phase scan are indicated in red. Figure 2: Experimental correlation plots for a phase scan using PS\({}_{4}\) in the three-harmonic case. (**a–j**) Evolution of the correlation plots of the oscillating components of the sidebands (\(P_{7,8}\), \(P_{8,9}\)) for increasing values of the delay \(\tau_{54}\) introduced by the phase shifter PS\({}_{4}\). The delay \(\tau_{54}\) introduced by the phase shifter PS\({}_{4}\) affects the phase of the harmonic H\({}_{7}\). The color scale indicates the density of single-shot experimental points normalized to unity for each panel. In Figure 3a, the energy spectrum of the harmonics is presented for different phases in a phase scan without the NIR dressing field. Panels b-d display the distribution of the shot-to-shot amplitudes of the individual harmonics for a fixed delay of PS\({}_{2}\) and PS\({}_{4}\). The amplitudes are evaluated as the square root of the area under the corresponding peaks. For the reconstruction, it is sufficient to consider the relative amplitudes of the harmonics, and hence, we normalized the distributions of the harmonics with the mean value of the H\({}_{8}\) amplitude. The mean (\(\mu\)) and the standard deviation (\(\sigma\)) of the harmonic amplitudes are displayed in the panels showing the distribution of the harmonics H\({}_{9}\) (b), H\({}_{8}\) (c), and H\({}_{7}\) (d). The evolution of the correlation plots in a phase scan and a negligible effect of phase variation on the harmonic amplitudes suggest the possibility of a more advanced complex attosecond waveform synthesis.
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 5 Experiments and Analysis of Results
### Experimental Results and Analysis
#### 5.3.2 Ablation Experiments  
To verify the advanced nature of the FPK-Net model, this section compares the 2 methods \(CNN+LSTM+\text{Attention}\) and the fused prior knowledge (FPK-Net model) through ablation experiments, where the prediction time step is varied from 50 to 150 min, and the prediction results are shown in Figure 8 below.  
From the above Table 2, it can be seen that:  
1. After adding the Hurst module into the LSTM network, the change trend of the four measures on the two models was consistent. Along with a gradual increase in the size of the prediction step, all three error indicators decreased to a minimum value and then increased while the absolute coefficient of correction increased to a maximum value and then decreased, and the prediction accuracy gradually increased. The error curve presented a concave function while the absolute coefficient of correction presented a convex function.
2. When the prediction time step reached 130 min, the prediction accuracy reached its peak, and the error was the lowest. At this time, the FPK-Net model had the best prediction effect, with an MAE of 0.369 and MSE of 0.259. When the prediction time step exceeded 130 min and continued to increase, the performance of both models decreased.
3. From Figure 6 above, it can be seen that the performance of the FPK-Net model proposed in this paper was always better than that of the baseline model, regardless of the time step. In particular, the FPK-Net model, which incorporates prior knowledge, showed the most significant improvement when the step size reached 70 min, with a 1.9% reduction in the MAE measure and a 2.1% improvement in the \(R_{adapted}^{2}\) measure.  
Figure 8: Performance comparison using different values for the sliding window. (**a**) Performance (\(R_{adapted}^{2}\)) comparison using different values for the sliding window. (**b**) Performance (\(RMSE\)) comparison using different values for the sliding window. (**c**) Performance (\(MAE\)) comparison using different values for the sliding window. (**d**) Performance (\(MSE\)) comparison using different values for the sliding window.
## 3 Results
### Comparing the Results of the Amplitude Method with D-RAP
Here we considered also the solar zenith angle (SZA) of the station at the observation time (peak time of the flare or after the fade-out) because according to previous results, the SZA plays an important role in the caused changes in the parameters derived from the ionograms (e.g., [5,6]). Figures 6 and 7 present that the observed values (red symbols) are almost always larger than the modeled ones (except in two cases at 4 MHz, Figure 7). Consequently, the model underestimates the absorption that occurred during and after increased X-Ray flux events. Furthermore, a slightly increasing trend with the X-Ray*cos (SZA) can be recognized, thus during the more intense flares the caused changes seem to be larger. Figure 6: Comparison of the modeled and measured values at 2.5 MHz (y-axis) with the X-Ray flux taking into account also the solar zenith angle of the Digisonde stations at the peak time of the flare or after the fade-out (x-axis). Figure 7: Comparison of the modeled and measured values at 4 MHz (y-axis) with the X-Ray flux taking into account also the solar zenith angle of the Digisonde stations at the peak time of the flare or after the fade-out (x-axis).
## 2 Development of a Numerical Model
### Equation for Dopant Concentration Distribution  
During laser annealing, the implanted dopants are activated, and simultaneously diffuse in silicon. Severe diffusion in the junction area can induce the detrimental short channel effect, which significantly increases the leakage current in the junction area.  
In this study, an investigation of the dopant concentration distribution is also conducted to understand the aspect of dopant diffusion during laser annealing based on the temperature determined by a simulation. For this analysis, it is assumed that the dopant implanted into the silicon wafer is phosphorus and that it is implanted at 30 keV with a dose of \(1\times 10^{15}\) cm\({}^{-2}\).  
According to earlier work [34], the dopant concentration distribution can be expressed as:  
\[N(x,t)=\frac{Q}{\sqrt{2\pi(\sigma^{2}+2 Dt)}}\exp\left(-\frac{(x-R_{P})^{2}}{2 (\sigma^{2}+2 Dt)}\right) \tag{9}\]  
where \(N(x,t)\) is the dopant concentration at a depth of \(x\) (nm) and annealing time \(t\) (msec), and \(Q\) is the dose (cm\({}^{-2}\)). \(R_{P}\) is the projected range of the implanted dopant (A), \(\sigma\) is the standard deviation in the projected range (A), and \(D\) is the diffusivity of the dopant (cm\({}^{2}\)/s).  
In this study, the annealing time, defined as the beam spot size divided by the scan speed, was 1 msec, and the values of \(R_{P}\) and \(\sigma\) were adopted from work by [PERSON] et al. [35] for the case of the phosphorus and 30 keV of energy. The adopted values for \(R_{P}\) and \(\sigma\) were 37.2 nm and 12.6 nm, respectively.  
In Equation (9), the diffusivity, \(D\), can be expressed as follows [36]:  
\[D=D_{0}\exp\left(\frac{-E_{a}}{k_{B}T}\right) \tag{10}\]  
where \(D_{0}\) is the pre-exponential factor (1.44 cm\({}^{2}\)/s), \(E_{a}\) is the activation energy (2.69 eV) and \(k_{B}\) is the Boltzmann constant (\(8.617\times 10^{-5}\) eV/K). \(T\) is the temperature taken from the simulation results of this study. Based on Equation (9) and the temperature data taken  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Value** & **Reference** \\ \hline \(k\) (W/cm\(\cdot\)K) & \(4.19\times 364\) (\(T^{-1.226}\)) for \(T<1200\) K & \\ \(k\) (W/cm\(\cdot\)K) & \(9\times T^{-0.502}\) for \(1200\) K \(<T<1600\) K & [32] \\  & 0.221 for \(1600\) K \(<T\) & \\ \(C_{p}\)(J/g\(\cdot\)K) & \(0.695\times\exp\left(2.375\times 10^{-4}\ T\right)\) & [32] \\ \(\rho\) (g/cm\({}^{2}\)) & 2.3 & \\ \(R\) & 0.3 for \(\lambda=10.6\)\(\upmu\)m & [33] \\ \hline \end{tabular}
\end{table}
Table 1: Optical and thermo-physical properties of silicon.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Test No.** & **Scan Pattern** & **Overlap Ratio (\%)** & **Laser Power (W)** & **Scan Speed (mm/s)** \\ \hline
1 & Zig-zag & 0 & \\
2 & 50 & 105 & 400 \\
3 & Bidirectional & 50 & \\ \hline \end{tabular}
\end{table}
Table 2: Process conditions used in the simulation.  
from the simulation results, dopant concentration distribution during the laser annealing process is estimated.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 4 Results  
### Field Measurements  
The time series of LAINet at each ESU showed similar temporal patterns (Figure 3). The study area is characterized by a single-growing season. The LAI grows rapidly in the growing period from approximately 0.5 on DOY 152 to 4-5 at the peak of the growing period occurring on DOY 211. After DOY 211, the maize shrinks and matures, and LAI decreases gradually. Some spurious fluctuations are observed mainly close to the peak of the growing season.