esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Characterizations  
The crystal structure, surface morphology, roughness, and elemental composition of CuO nanocoatings were characterized using X-ray diffraction (XRD, Bruker with 1.5406 A wavelength radiations), Scanning electron microscopy (SEM, Leo-Steros Scan 440), Atomic force microscope (AFM, Veeco Nasoscope IV Multi-Mode), and Energy dispersive spectroscopy (EDS), respectively. The Raman spectroscopy (Jobin T64000) was applied to analyze the existing phases and lattice vibrations within the coatings. Rutherford backscattering spectrometry (RBS) characterization was carried out to determine the thickness and elemental compositions of the coatings using 2 MeV He\({}^{+}\) ion with the detector at a backscattering angle of 165\({}^{\circ}\). The Spectral reflectance properties of the coatings was analyzed from diffuse reflectance spectra measurements in 0.3-2.5 \(\upmu\)m, and 2.5-25 \(\upmu\)m the wavelength ranges using UV\(-\)Vis\(-\)NIR (Varian, DRA-2500), and FTIR (Buker Tensor 27) spectrophotometers, respectively. The solar absorptance (\(\upalpha\)) is defined by the weighted integration of spectral reflectance with AM 1.5 in the solar spectrum of 0.3-2.5 \(\upmu\)m wavelength range, while the emissivity (\(\epsilon\)) is evaluated by the weighted integration of the reflectance  
Figure 1: Schematic representation of extraction, biosynthesis, and deposition of CuO nanocoating as SSA surface.  
with the perfect [PERSON]'s black body radiations. Both the absorptance and emittance (\(\alpha/\varepsilon\)) of the coatings were calculated from the diffuse reflectance spectra using Equations (1) and (2), respectively [29; 30; 31; 32; 33].  
\[\alpha=\frac{\int_{0.3\mu m}^{2.5\mu m}(1-R(\lambda))\cdot I_{\text{sol}}( \lambda)d(\lambda)}{\int_{0.3\mu m}^{2.5\mu m}I_{\text{sol}}(\lambda)d\lambda} \tag{1}\]  
where \(\lambda\) is the wavelength, \(I_{\text{sol}}\) is incident solar power, and \(R\) is the measured reflectance.  
\[\varepsilon=\frac{\int_{25\mu m}^{20\mu m}(1-R(\lambda))\cdot I_{b}(\lambda,T)d (\lambda)}{\int_{2.5\mu m}I_{b}(\lambda,T)d\lambda} \tag{2}\]  
where \(I_{b}\) (\(\lambda\),T) is emitted radiative power by the blackbody at \(\lambda\) and \(T\).
# Deep Validation of Spatial Temporal Features of Synthetic Mobility Models
###### Abstract  
This paper analyzes the most relevant spatial-temporal stochastic properties of benchmark synthetic mobility models. Each pattern suffers from various mobility flaws, as will be shown by the models' validation. A set of metrics is used to describe mobility features, such as the speed decay problem, the density wave phenomenon, the spatial node distribution, and the average neighbor percentage. These metrics have already been validated for the random waypoint mobility model (RWPMM), but they have not yet been verified for other mobility patterns that are most frequently used. For this reason, this investigation attempts to deeply validate those metrics for other mobility models, namely the Manhattan Grid mobility, the Reference Point Group mobility, the Nomadic Community mobility, the Self-Similar Least Action Walk, and SMOOTH models. Moreover, we propose a novel mobility metric named the \"node neighbors range\". The relevance of this new metric is that it proves at once the set of outcomes of previous metrics. It offers a global view of the overall range of mobile neighbors during the experimental time. The current research aims to more rigorously understand mobility features in order to conduct a precise assessment of each mobility flaw, given that this fact further impacts the performance of the whole network. These validations aim to summarize several parameters into 18,126 different scenarios with an average of 486 validated files. An exhaustive analysis with details like those found in this paper leads to a good understanding of the accurate behaviors of mobility models by displaying the ability of every pattern to deal with certain topology changes, as well as to ensure network performances. Validation results confirm the effectiveness and robustness of our novel metric.  
mobility model; mobility metric; spatial node distribution; speed decay problem; density wave phenomenon; average neighbor percentage; mobile neighbors range +
Footnote †: journal: Computer Physics Communications
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 2 Algorithm Theory and Method
### Curve Fitting of Tool Wear  
Different distributions as the alternatives are chosen to match the relation between the feature value and tool wear. Linear, exponential, and Gaussian distribution as the alternatives are chosen to calculate the parameters of distribution in this paper. More different distributions will be discussed in the future depending on the feature extracted.  
Linear distribution can be expressed by Equation (11).  
\[f_{Linear}(x)=ax+b \tag{11}\]  
Exponential distribution can be expressed by Equation (12).  
\[f_{Exponent}(x)=a\cdot e^{bx} \tag{12}\]  
Gaussian distribution can be expressed by Equation (13).  
\[f_{Gaussian}(x)=a\cdot e^{\left(-\left(\frac{x-b}{c}\right)^{2}\right)} \tag{13}\]  
where \(a\), \(b\), and \(c\) are estimators of the distribution parameters and can be calculated using MATLAB based on the relation between the features extracted and the tool wear curve. To compare fitting results of different distributions, so goodness of fit is evaluated by root mean square error (RMSE).  
\[RMSE=\sqrt{\frac{\sum_{n=1}^{N}di(n)^{2}}{N}} \tag{14}\]  
Figure 2: Time-dependent chart and frequency spectrums of vibration signals and intrinsic mode functions (IMFs) by ensemble empirical mode decomposition (EEMD).  
Here, \(N\) is the number of fitting points and \(di(n)\) is deviation value between actual value and estimation value at \(n\)th point. A small RMSE that means the tool wear curve fits the distribution, correspondingly. The larger RMSE is, the worse the effect of the fitting is.
# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 2 Channel Model
### Atmospheric Model
#### 2.1.2 Fog and Haze  
As a type of aerosol particle, fog droplets suspend in air and can have serious absorption effects on terahertz frequencies [18]. The attenuation caused by fog can be calculated by equation \(\gamma_{c}\) (\(f,T\)) = \(K_{l}\) (\(f,T\))\(M\)[19], with parameter \(f\) as the frequency [GHz], \(T\) as the temperature [K], and \(M\) as the density of liquid water in fog [g/m\({}^{3}\)]. Parameter \(K_{l}\) is the specific attenuation coefficient of liquid water with its dielectric constant predicted by the double Debye model. A situation of dense fog with a fog concentration of 0.5 g/m\({}^{3}\) is considered in the model, which leads to a visibility of 50 m [19].  
The diameter of haze droplets is two orders of magnitude smaller than that of fog particles. Its number density lies between 10 and 1000/cm\({}^{3}\)[18]. When foggy weather occurs, haze particles (such as PM 2.5) in the air can be adsorbed onto the surface of fog particles, and fog and haze weather finally forms. Attenuation caused by haze can be conducted from equation \(A_{s}\)(\(f\)) = \(exp(\sum_{j}k_{j}^{s}\) (\(f\))\(r\)) [18]. Parameter \(k_{j}^{s}\) is the scattering coefficient of particles of different sizes, and \(r\) refers to the radius of haze particles. In the case of heavy haze, \(r\) is approximately 0.2 \(\upmu\)m. \(k_{j}^{s}=N^{s}_{s}\)\(\sigma^{\prime}_{s}\) is particle density- and refractive index-dependent. Here, parameter \(N^{s}_{s}\) is the particle density of haze, and can be 7000/cm\({}^{3}\) for heavy haze [18]. Parameter \(\sigma^{\prime}_{s}\) is the Rayleigh scattering cross-section by the haze particle, which can be obtained when the refractive index of haze particles is ready [20]. These parameters will remain unchanged to form a static attenuation.  
\begin{table}
\begin{tabular}{c c c c c c} Parameter & **Year** & **Year** & **Rain** & **Fog and Haze** & **Wind** & **Remark** \\ Distance (km) & \(1\) & \(1\) & \(1\) & \(1\) & \\ Atmospheric pressure (Pa) & \(1.0037\times 10^{5}\) & \(1.0037\times 10^{5}\) & \(1.0037\times 10^{5}\) & \(1.0037\times 10^{5}\) & \\ Temperature (K) & 287.63 & 287.63 & 287.63 & 287.63 & \\ Water vapor density (g/m\({}^{3}\)) & 7.7 & 12.3 & 12.3 & 7.7 & \\ Rainfall rate (mm/h) & / & 25 & / & / & heavy rain \\ Fog concentration (g/m\({}^{3}\)) & / & / & 0.5 & / & dense fog and \\ Haze particle density (1/cm\({}^{3}\)) & / & / & 7000 & / & heavy haze \\ Wind speed (m/s) & / & / & / & 15 & strong wind \\ \end{tabular}
\end{table}
Table 1: Weather model parameters.
## 5 Experimental Tests
### Transmission Parameters of UAC Channel
The values of the coherence bandwidth estimated at the level of 0.5 of the maximum value of SCF are between 47 Hz and 79 Hz, while the range of \(B_{C}\) values calculated at the level of 0.9 is much larger, from 15.68 Hz to 62.73 Hz. Figure 11: The module of time-varying impulse response, measured using PRBS of a bandwidth equal to 1 kHz, and estimated power delay profile and space-frequency correlation function. Figure 12: The module of time-varying impulse response, measured using PRBS of a bandwidth equal to 2 kHz, and estimated power delay profile and space-frequency correlation function. Figure 13: The module of time-varying impulse response, measured using PRBS of a bandwidth equal to 4 kHz, and estimated power delay profile and space-frequency correlation function. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(B\) & \(T_{s}\) & \(\tau_{M}\) & \(\tau_{rms}\) & \(B_{cr}\) & \(T_{R}=0.5\) & \(B_{cr}\) & \(T_{R}=0.7\) \\ \hline
1 kHz & 255.00 ms & 61.64 ms & 37.54 ms & 54.90 Hz & 15.68 Hz \\
2 kHz & 127.50 ms & 62.36 ms & 21.77 ms & 47.05 Hz & 15.68 Hz \\
4 kHz & 63.75 ms & 54.12 ms & 13.54 ms & 62.75 Hz & 31.37 Hz \\
5 kHz & 51.00 ms & 20.23 ms & 9.29 ms & 78.43 Hz & 39.22 Hz \\
8 kHz & 31.88 ms & 31.88 ms & 8.15 ms & 62.73 Hz & 62.73 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Transmission parameters of UAC channel. For each DSSS signal described in Table 1, the modulation symbol duration exceeds both the maximum and rms values of the delay spread, regardless of the probe signal's bandwidth. Thus, it meets the basic condition of avoiding ISI in the time domain, which is that the duration of the transmitted symbol should be longer than the delay spread of the channel [4]. None of the transmitted signals meet the condition for avoiding the frequency-selective fading of the signal spectrum, which is that the signal bandwidth should be smaller than the channel's coherence bandwidth. However, it is difficult to meet such a condition in a channel with strong multipath propagation if the spread spectrum modulation technique is used.
## 6 Conclusions  
The proposed method of RTK GNSS-assisted terrestrial SfM photogrammetry and the associated acquisition system enable low-cost and high-quality photogrammetric surveys without requiring GCPs. By comparison to a synchronous TLS survey, a mean error of 0.3 cm (standard deviation of 4.7 cm) was obtained with a Nikon D800 Reflex camera and, respectively, a mean error of 0.2 cm (standard deviation of 3.8 cm) with a Huawei Y5 Smartphone camera. Avoiding the use of GCPs saves considerable time during the field survey as well as during data processing. This method will be particularly interesting when simplicity, portability, and autonomy are desirable. In the future, using an open RTK GNSS network would help to make this approach transposable to participatory science programs and citizen observatories.  
Conceptualization, M.J.; methodology, M.J. and M.B.; software, M.J. and S.B.; validation, M.J.; formal analysis, M.J., E.A. and S.B.; writing--original draft preparation, M.J. and S.B.; writing--review and editing, M.J. and S.B.; project administration, M.J. and C.D. All authors have read and agreed to the published version of the manuscript.  
This work is part of the \"Service National d'Observation\" DYNALIT, via the research infrastructure ILICO. The authors acknowledge financial support provided by the European program Interreg Atlantic Area, via the project AGEO (EAPA 884/2018).  
The authors declare no conflict of interest.
## 2 Numerical Simulations of the Randomly Shaped Pebbles
### DEM-Based Shape Reconstruction Method for Irregularly Shaped Pebbles
#### 2.1.2 Shape Reconstruction Method for the Irregularly Shaped Pebbles  
Figure 5 depicts the method used to construct the shape of a single pebble, utilising it as an illustrative example.  
Step 1: A single pebble was randomly selected, and its three views were precisely captured. The Canny edge detection algorithm [24] was employed to define the outlines of these views, which were then segmented into upper and lower parts. The outcomes demonstrate that this approach effectively captured the exterior shape of the selected pebble. A high-order polynomial fitting (Equation (1)) was then applied to each segment. The length of each view was standardised to 100 units before fitting, to facilitate calculation.  
Figure 4: Distribution of the pebble dimensions (**a**) length; (**b**) width; (**c**) height.  
Figure 3: Tested pebbles.  
\[z=\sum_{i=1}^{7}a_{i}\cdot x\cdot(x-100)^{i} \tag{1}\]  
Step 2: The main and left views were filled with circular elements, with the parameters for each circle derived from Equations (2)-(4).  
\[x_{i}=100/n\cdot i(\mathrm{i}=1,2,3\ldots\mathrm{n}) \tag{2}\]  
\[y_{i}=(z_{u}+z_{l})/2 \tag{3}\]  
\[r_{i}=(z_{u}-z_{l})/2 \tag{4}\]  
Step 3: A preliminary 3D model was constructed using the information derived from the circular elements observed in the left view. In this model, the position coordinates (x, y, z) of each spherical element were defined as 0, x_left and y_left, where x_left and y_left represent the horizontal and vertical coordinates of the circular elements in the left view, respectively. The radius of each spherical element matched the radius observed in the left view. The 3D model was then replicated and scaled multiple times based on the x-coordinates of the elements in the primary view. The x-coordinates of all elements were methodically adjusted throughout this scaling process to align with the x-coordinates of the circular elements in the primary view. The radii of the ball elements were recalculated based on the outline of the main view. This procedure was repeated for the ball elements based on the outline of the upper view.  
Step 4: In light of the computational demands, simplified pebble DEM models were developed by reducing the number of spherical elements. Simulations of several pebbles were conducted to further substantiate the model's construction approach, with the outcomes illustrated in Figure 6. These findings indicate that the methodology effectively captures the pebble's shape.  
Figure 5: Methodology for constructing the shape of the pebble, DEM model.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments
### Comparison with Previous Research  
In the model comparison in Table 6, [PERSON] et al.'s SVM has an accuracy of 91.60%, indicating its competence in binary classification tasks. [PERSON] et al.'s SVM, on the other hand, reaches an amazing 97%, highlighting the potential of SVMs with refined applications. Their RF model, on the other hand, has a lower score of 78%, indicating the heterogeneity of its performance within the same research. The W-CoHOG method developed by [PERSON] et al. achieves 93.30% accuracy, confirming the usefulness of its feature extraction technique. [PERSON] et al.'s Bi-LSTM, built for sequential data, achieves 95% accuracy, demonstrating its suitability for time-series data. [PERSON] et al.'s CNN achieved 97% accuracy, demonstrating CNNs' prowess with grid-based data. Yolo V8 achieves 99% accuracy, demonstrating YOLO's object detection capabilities. Nonetheless, given the variety of methodologies used to solve the problem, the choice should be aligned with the unique needs of the task and subtleties of the dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1\_Score** \\ \hline Inception & 78 & 77 & 77 & 76 \\ \hline Xception & 77 & 67 & 77 & 72 \\ \hline VGG16 & 74 & 64 & 74 & 68 \\ \hline AlexNet & 72 & 64 & 72 & 67 \\ \hline YoloV6 & 86 & 89 & 86 & 87 \\ \hline YoloV7 & 95 & 96 & 95 & 95 \\ \hline YoloV8 & 99 & 98 & 98 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance measures of different models on the real farm dataset.
## 2 Materials and Methods
### DLS Model  
The dense V-Net [28] was constructed as the DLS model in this research according to the previous study [19]. The architecture of the revised model is illustrated in Figure S1. The hyperparameters of the DLS model are included in Table S2. A Laplacian of Gaussian (LoG) filter was applied to all treatment planning CT images to enhance the edges and reduce image noise. The input CT image size and output tumor region image size for the DLS model was 40 \(\times\) 40 \(\times\) 40 pixels. The model outputs were binarized with a threshold of 0.5 to obtain segmented tumor regions. The dilated convolution and spatial prior in the dense V-net were removed since they did not generate significant benefit to the performance [28]. To reduce the calculation time and the number of parameters, a bottleneck structure [29] was used in the densely connected layers. Except for the final layer, the activation function was a Scaled Exponential Linear Unit (SELU) [30], which is the best activation function in [18]. SELU was defined as  
\[SELU(x)=\mathbf{s}(\mathbf{\mathrm{max}}(\mathbf{0},\mathbf{x})+\mathbf{\mathrm{min}}(\mathbf{0},\bm {\alpha}(\mathbf{\varepsilon}^{x}-\mathbf{1}))) \tag{1}\]  
where \(\mathbf{s}\) and \(\mathbf{\alpha}\) were the constant values, which were set as \(\mathbf{\alpha}=\mathbf{1.6733}\) and \(\mathbf{s}=\mathbf{1.0507}\), respectively, based on a past study [30].
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation
### Performance Evaluation
#### 4.2.6 Handoff Delay of PUTPOSH and Hybrid (DSA) Schemes  
A DSA scheme applies proactive spectrum sensing and reactive handoff action jointly; firstly, the target channel selection is prepared beforehand or during CRU data transmission and secondly, the spectrum handoff is performed after the handoff triggering event. In PUTPOSH, a hybrid solution is referred to as \"adaptive\", if the decisions of channel selection and handoff are made by continuously monitoring the arrival and departure patterns of the PU. When PU moves quite regularly, a CRU may adapt to a reactive handoff strategy. While in the case of rare PU movements, a proactive handoff solution is preferred by the corresponding CRU. Figure 11 shows the comparison of the PUTPOSH scheme with a hybrid approach (DSA) in terms of handoff delay. The graph in Figure 11 shows that the DSA has more handoff delay than the PUTPOSH scheme because sensing is achieved prior to the arrival of the PU whereas the handoff action is performed after the PU's arrival. Therefore the cumulative handoff delay of the DSA remains high as compared to PUTPOSH. The queuing procedure in PUTPOSH adequately reduces the handoff delay because of its adoptive environment, and thus the cumulative delay becomes lower than the DSA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Comparison** & **PU Arrival** & **Reactive** & **Proactive** & **Hybrid** & **Proposed** & **Results (\%) (Proposed** \\
**Parameters** & **Rate (Per Slot)** & **Handoff** & **Handoff** & **Handoff** & **Handoff** & **(Handoff)** \\ \hline Overall & 0.03 & 9.9 & 10.4 & 9.9 & 9.9 & Has 12.6\% superior rate \\ Service Time & 0.07 & 12.3 & 10.9 & 10.9 & 10.9 & than reactive, \\ Number of & 4 & 45 & 45 & 40 & 35 & 28.57\% than proactive \\ Handoffs & 4 & 45 & 45 & 40 & 35 & and reactive and 14.28\% \\ Channel & 4 & 0.4 (static) & 0.5 (Proposed—dynamic) & 20\% than existing \\ Utilization & 4 & 0.4 (static) & 0.5 (Proposed—dynamic) & 20\% than existing \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of PUTPOSH and existing schemes based on overall service time, number of handoffs and channel utilization.  
Figure 10: Throughput analysis of the PUTPOSH approach w.r.t. the function of PU arrivals.
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 2 Preliminaries
### Autoencoder  
An autoencoder (AE) consists of an input layer, an encoding layer and a decoding layer. The unsupervised learning method is utilized to extract and represent high-dimensional features of wind power data [31; 32; 33]. More specifically, AE seeks a set of optimal connection weights by minimizing the reconstruction error between the original input and output. The input vector \(X\in R^{d}\) is input to the hidden layer through the encoder E to generate a potential abstract feature mapping \(Z\in R^{d}\) as in Equation (3) [34; 35]. Then, decoder D maps the potential variable Z to the reconstructed output vector \(\hat{X}\) as in Equation (4), which is the same size as \(X\).  
\[Z=f(W_{1}X+b_{1}) \tag{3}\]  
\[\hat{X}=\sigma(W_{2}Z+b_{2}) \tag{4}\]  
where \(W_{1}\) and \(W_{2}\) denote the weight matrices, \(f\) and \(\sigma\) denote the activation functions and \(b_{1}\) and \(b_{2}\) denote the biases of the encoding layer and the decoding layer.  
Figure 1: The schematic diagram of discrete wavelet transform decomposition.