esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Consequently, the solution to these presets is directly related to the analysis of the distribution of users and the environment. In addition, users in an access network or nodes in a wireless sensor network are modeled by point processes, such as a Poisson field or fields with other properties. However, the numerical characteristic that describe the features of the real environment and allow them to berelated to the properties of the model are not addressed. Therefore, adequate models are required to solve these problems, taking into account the characteristics of users and the environment. Motivated by such considerations, in this paper, we propose a new model for addressing the wireless network structure in an efficient manner. More specifically, the paper considers a general approach to modeling an access network, taking into account the features of the environment, which are numerically expressed through the fractal dimension. This approach is applicable to a wide variety of situations, such as the external urban structure (buildings, streets, structures), the territory inside buildings (rooms, corridors, halls, interior walls), and open suburban area (hills, forest plantations, mountains, hills, individual buildings).... As an example, the paper provides a model of an external network for an urban structure. In our work, we are not talking about heterogeneous distribution, but about the uneven distribution of users, which most often occurs in real networks. It is also quite difficult to describe this unevenness since, in the known models, there is no numerical characteristic to take into account the nature of this irregularity. The Hurst parameter and fractal dimension also allow us to give its numerical description. The main contributions of this study are summarized as follows:  
* We propose a new model for addressing the wireless network structure in an efficient manner in which the fractal dimension, geometric fractals, Hurst parameter, and the characteristics of user's distribution territory and urban development are utilized. * We analyze the influence of the fractal properties of the environment on the structure of promising communication networks; in particular, on the structure of the Internet of Things network. * Finally, simulation results proved that the proposed model is considered as a beneficial solution for modeling mobile communication and wireless access networks, including fifth-generation networks. The rest of the paper is organized as follows. Section 2 reviews the related work. Section 3 presents the task statement. Our network model is presented in Section 4. Then, the analysis of the properties of the Internet of Things is introduced in Section 5. Finally, Section 6 concludes the paper.
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System
## Nomenclature  
\begin{tabular}{l l} \(A\) & Area (m\({}^{2}\)) \\ \(C\) & Specific heat capacity (kJ/kg\({}^{\circ}\)C) \\ \(Ee\) & Electrical power (W) \\ \(Eth\) & Thermal power (W) \\ \(F_{j,k}\) & View factor between layer \(i\) and \(j\) \\ \(E_{sun}\) & Solar irradiance (W/m\({}^{2}\)) \\ \(Gr\) & Grashof number \\ \(he_{i,ij}\) & Conductive heat exchange coefficient between components \(i\) and \(j\) (W/m\({}^{2}\)K) \\ \(hr_{i,j}\) & Radiative heat exchange coefficient between components \(i\) and \(j\) (W/m\({}^{2}\)K) \\ \(hr_{i,ij}\) & Convective heat transfer coefficient between components \(i\) and \(j\) (W/m\({}^{2}\)K) \\ Isc & Short circuit current (A) \\ \(I_{mp}\) & Maximum current (A) \\ \(L\) & Length (m) \\ \(NOCT\) & Nominal operating cell temperature \\ \(Nu\) & Nusselt number \\ \(Pr\) & Prandlt number \\ \(P_{max}\) & Maximum power (W) \\ \(PF\) & Packing factor \\ \(Ra\) & Rayleigh number \\ \(Re\) & Reynolds number \\ \(STC\) & Standard test conditions \\ \(t\) & Time step (s) \\ \(T_{i}\) & Temperature of layer \(i\) (\({}^{\circ}\)C) \\ \(T_{a}\) & Ambient temperature (\({}^{\circ}\)C) \\ \(V\) & Water velocity (m/s) \\ \(V_{OC}\) & Open circuit voltage(V) \\ \(V_{mp}\) & Maximum voltage (V) \\ \(w\) & Wind speed (m/s) \\ \multicolumn{2}{l}{_Greek symbols_} \\ \(\alpha\) & Absorptivity \\ \(\beta\) & Reference temperature coefficient for efficiency \\ \(\eta_{e}\) & Electrical efficiency (\%) \\ \(\eta_{th}\) & Thermal efficiency (\%) \\ \(\lambda\) & Thermal conductivity (W/m K) \\ \(\rho\) & Material density (kg/m\({}^{3}\)) \\ \(\sigma\) & Stefan-Boltzmann constant \\ \multicolumn{2}{l}{_Scripts_} \\ \(Abs\) & Absorber \\ \(f\) & Fluid \\ \(g\) & Glass \\ \(gr\) & Ground \\ \(PV\) & Photovoltaic \\ \multicolumn{2}{l}{_PV/T_} & Photovoltaic/thermal \\ \multicolumn{2}{l}{_Ted_} & Tedlar \\ \end{tabular}
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 3 Background
### Malicious Behavior in Vishing Apps
#### 3.1.1 Call Redirection  
Android provides APIs for Call Redirection, enabling developers to create applications that cancel outgoing calls and automatically connect to a different phone number. Therefore, attackers develop vishing applications to cancel a victim's requested outgoing call and redirect it to an attacker. To implement Call Redirection, attackers employ different APIs based on the target Android API level. We detail different Android APIs corresponding to API levels in Section 3.2. Additionally, vishing applications combine Display Overlay with Call Redirection to disguise the redirected outgoing number.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 2 Object Detection with Frame Subtraction  
A current frame is subtracted from a past frame at a constant interval. A thresholding step follows to generate a binary image as:  
\[\begin{array}{c}I_{T}(m,n;k)=\left\{\begin{array}{cc}1,&if|I(m,n;k)-I(m,n;k-k _{d})|>\theta_{T}\\ 0,&otherwise\\ m=1,\ldots,M,\ n=1,\ldots,N,\end{array}\right\},\end{array} \tag{1}\]  
where \(I(m,n;k)\) and \(I(m,n;k-k_{d})\) are the \(k\)th frame and the (\(k-k_{d}\))th frame, respectively, \(k_{d}\) is a constant interval for frame subtraction, \(\theta_{T}\) is a thresholding value, and \(M\) and \(N\) are pixel sizes in \(x\) and \(y\) directions, respectively. After this, a morphological filter, dilation is applied to the binary image to enlarge the segmented regions. The dilation operation is defined as [26]:  
\[I_{D}=\{|D_{l}\cap I_{T}\
eq\varphi\}, \tag{2}\]  
\[D_{l}=\{d+|ld\in D\}, \tag{3}\]  
where \(D\) is the structuring element for dilation, and \(l\) denotes an integer value less than the image size. All alternative regions are considered candidate target regions. At last stage of detection, false target regions are removed as:  
\[O_{i}(m,n)=\left\{\begin{array}{cc}1,&\theta_{S}\leq Size\{O_{i}\}\leq \theta_{f}\\ 0,&otherwise\end{array}\right\}, \tag{4}\]  
where \(O_{i}\) is the \(i\)-th region, \(\theta_{s}\) and \(\theta_{f}\) are the minimum and the maximum size of region, respectively; they are determined based on the true size of the target. The center of each target region is considered a measured position for target tracking in the next section. Figure 2 is the block diagram of moving object detection.  
Figure 1: Illustration of aerial surveillance with a small unmanned aerial vehicle (SUAV).  
Figure 2: Block diagram of moving object detection.
## 1 Introduction
### Background  
The most widely used crop water balance technique for operational monitoring is the Food and Agriculture Organization (FAO) water balance algorithm by [PERSON] and [PERSON] [10] that produces the crop water requirement satisfaction index (WRSI). The WRSI shows the relative relationship (ratio/percent) between the supply (from rainfall and existing soil moisture) and demand (crop demand to meet its physiological needs) using observed data from the beginning of the crop season (planting) until a specified date throughout the growing season.  
The Famine Early Warning System Network (FEWS NET) demonstrated a regional implementation of the FAO WRSI in a grid-cell modeling environment [7]. Furthermore, [PERSON] and [PERSON] [8] enhanced the geospatial model by introducing the concept of maximum allowable depletion (MAD) and soil water stress factor, commonly used in irrigation engineering, for better estimation of ETa as a function of soil water content in an operational setup ([[http://earlywarning.usgs.gov/adds/](http://earlywarning.usgs.gov/adds/)]([http://earlywarning.usgs.gov/adds/](http://earlywarning.usgs.gov/adds/))). The WRSI crop water balance index has been demonstrated to be a reliable tool for crop performance monitoring of large areas in an operational manner.  
However, the use of crop coefficients (Kc) in the model has certain limitations: 1) Kc factors are crop specific and require a prior knowledge of the crop planted in the area, 2) Kc factors are spatially specific since they are influenced by local climate, soils, and other geophysical conditions, and 3) the use of the Kc model requires knowledge (or assumption) of the crop calendar such as the start of season (SOS) and length of growing period (EOS) in each of the four crop developmental stages (initial, vegetative, mature, and senescence).  
The VegaET modeling approach presented in this paper removes the above constraints by the inclusion of the LSP parameter, which describes the seasonal progression of vegetation growth and development. LSP can be observed by spaceborne sensors and is a key biogeophysical parameter that links the water and carbon cycles with anthropogenic activities, providing an important approach to change detection in terrestrial ecosystems (e.g., [11]; [12]; [13]; [14]).  
This paper will discuss the formulation of the LSP-based Kc parameter function in the VegaET model and demonstrate its application using two years of daily meteorological data in the conterminous United States. To differentiate the LSP-based Kc from the commonly used \"crop coefficient,\" the designation Kcp (the \"p\" denoting the phenology-based coefficient) is used in this study.
## 3 Numerical Modelling and Validation  
### Numerical Model Establishment  
This study uses COMSOL Multiphysics 6.2 to develop a multi-fracture propagation model with heterogeneous characteristics to simulate the fracture propagation process. Specifically, the model is used to investigate the effects of pumping rate, compressive strength, in situ stress difference, and segment length on multi-fracture propagation. Furthermore, it investigates the influence of stress shadowing on fracture propagation, providing a theoretical foundation for field hydraulic fracturing operations.  
The multi-cluster fracture propagation model adopts a sequential fracturing method. The 2D geometric model is shown in Figure 2. The model includes 41,430 domain elements and 502 boundary elements. Mesh sensitivity analysis showed a small variation in fracture length when reducing element count from 41k to 13k. The model assumes plane strain deformation, with the material being linear elastic, homogeneous, and isotropic. The fluid is modeled as a Newtonian laminar flow obeying the cubic law, while gravitational effects are neglected. In the model, the formation properties follow a Weibull distribution. Three fracture clusters are initialized with the same length \(a\), and the spacing between clusters is set to \(d\). The model dimensions are defined by length \(L\) and height \(H\). The heterogeneity coefficient \(m\) was set to 10. The model parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter Name** & **Symbol** & **Value** & **Unit** & **Parameter Name** & **Symbol** & **Value** & **Unit** \\ \hline Heterogeneity Coefficient & \(m\) & 10 & / & Initial Permeability & \(k_{0}\) & \(5\times 10^{-15}\) & m\({}^{2}\) \\ Compressive Strength & \(f_{c0}\) & 40 & MPa & Pumping rate & \(q\) & 40 & m\({}^{3}\)/h \\ Poisson’s Ratio & \(v\) & 0.2 & / & Injection Pressure & \(p\) & 25 & Mpa \\ Internal Friction Angle & \(\varphi\) & 40 & \(\circ\) & Fluid Density & \(\rho_{w}\) & \(1\times 10^{3}\) & kg/m\({}^{3}\) \\ Initial Porosity & \(\Phi_{0}\) & 0.1 & / & Maximum Principal Stress & \(\sigma_{1}\) & 11 & Mpa \\ Residual Porosity & \(\varphi_{r}\) & 0.001 & / & Biot Coefficient & \(a\) & 0.5 & / \\ Model Length & \(L\) & 140 & m & Fluid Viscosity & \(\mu_{w}\) & \(1\times 10^{-3}\) & Pa-s \\ Model Width & \(H\) & 140 & m & Porosity of Matrix Region & \(n_{r}\) & 0.1 & / \\ Initial Hydraulic Fracture Length & \(a\) & 1 & m & Minimum horizontal stress & \(\sigma_{2}\) & 6 & Mpa \\ Segment length & \(d\) & 30 & m & & & \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 2: The geometric model.
# Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis
## 2 Methodology
### Neural Network Setup  
#### 2.3.1 Supervised Learning Case  
The tuning of neural network hyperparameters for pre-stack seismic data was started from scratch, due to the absence of a predefined set of parameters regarding the number of features, sequence length, or the volume of examples designated for training and validation. This tuning process led to the adoption of the following architecture for supervised learning: three layers of LSTM units, each comprising 1024 units, with a dropout rate of 0.75. This  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time (ms)** & **Well-1** & **Well-2** & **Well-3** & **Well-4** & **Well-5** \\ \hline Start & 2375 & 2408 & 2402 & 2582 & 2640 \\ End & 2584 & 2648 & 2691 & 2831 & 3013 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time interval from which the examples were extracted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Labeled** & **Unlabeled** \\ \hline Pre-stack Seismic & 10,800 training & 98,280 training \\ (164,100 Full) & 2100 test (well) & 52,920 test \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pre-stack seismic dataset.  
Figure 7: Dataset configuration for semi-supervised learning. Emphasizing the selection of gathers employed for generating unlabeled data.  
architecture was further complemented by a dense layer with 1024 units, followed by an additional layer with a single unit. The model was compiled using the loss function of the mean absolute error and the Adam optimizer, an efficient stochastic optimization method for deep learning problems.  
Additionally, the hyperparameters were determined by the linear-epoch gradual-warm-up (LEGW) method (Section 2.8). In the case of supervised learning, it was determined that employing a batch size of 32, 1 warm-up epoch, and a learning rate of 0.001, all scaled by a factor of 2 except for the warm-up epoch, produced the most favorable loss values.
###### Abstract  
Volcanic eruptions cause pyroclastic flows, which can destroy plantations and settlements. We used image data from Landsat 7 Bands 7, 4 and 2 and Landsat 8 Bands 7, 5 and 3 to observe and analyze the distribution of pyroclastic flow deposits for two volcanos, Mount Sinabung and Merapi, over a period of 10 years (2001-2017). The satellite data are used in conjunction with an artificial neural network method to produce maps of pyroclastic precipitation for Landsat 7 and 8, then we calculated the pyroclastic precipitation area using an artificial neural network method after dividing the images into four classes based on color. Red, green, blue and yellow were used to indicate pyroclastic deposits, vegetation and forest, water and cloud, and farmland, respectively. The area affected by a volcanic eruption was deduced from the neural network processing, including calculating the area of pyroclastic deposits. The main differences between the pyroclastic flow deposits of Mount Sinabung and Mount Merapi are: the sediment deposits of the pyroclastic flows of Mount Sinabung tend to widen, whereas those of Merapi elongated; the direction of pyroclastic flow differed; and the area affected by an eruption was greater for Mount Merapi than Mount Sinabung because the VEI (Volcanic Explosivity Index) during the last 10 years of Mount Merapi was larger than Mount Sinabung.  
Sinabung eruption; Merapi eruption; pyroclastic flow deposits; Landsat imagery; artificial neural network
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 5 Experimental Results and Discussion
### Comparison of Proposed Technique with Existing Techniques  
The performance of the proposed technique is compared with other traditional machine learning algorithms i.e., Naive Bayes ([65]), Random Forest ([66]) and k-NN ([67]).
# Precise Channel Estimation Approach for a mmWave MIMO System
## 7 Simulation Results and Discussion
### Simulation Environment  
In this section, we interpret the results of a comparison between the proposed Ex-ADMM algorithm and the TSSR, OMP, VAMP and ADMM algorithms, in terms of ASE, NMSE, the convergence of Ex-ADMM with respect to ADMM and SVT, and the effect of NMSE on the proposed Ex-ADMM, TSSR, OMP, VAMP and ADMM algorithms for multiple paths. An average of 100 independent iterations and 100 Monte Carlo realizations were considered for the simulations [48]. Three different training symbol lengths were considered for training purposes, i.e., T = 400, 800 and 1200.
# Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System
## 2 Proposed AIoT System with Compressed Sensing and Pose Estimation
### Pose Estimation with a PoseNet Model
We define the Expected-OKS for the \(pk\)-th keypoint as:  
\[s_{psj,psk}=E\Big{\{}OKS_{psj,psk}\Big{\}}=p_{k}\Big{(}y_{psj,psk}\Big{)}\int_{ x\in\mathcal{D}_{R}\big{(}y_{psj,psk}\big{)}}\hat{h}_{psk}(x)\exp\left(-\frac{ \Big{(}x-y_{psj,psk}\Big{)}^{2}}{2\lambda_{psj}^{2}\kappa_{psk}^{2}}\right)dx \tag{7}\]  
where \(\hat{h}_{psk}(x)\) is the Hough score maps normalized to \(\mathcal{D}_{R}\big{(}y_{psj,psk}\big{)}\). The Expected-OKS keypoint level score is the confidence that a keypoint exists multiplied by the OKS localization accuracy confidence. The pose score is calculated as the instance-level score \(s_{psj}^{h}=(1/K)\sum_{k}s_{psj,psk}\), followed by non-maximum suppression (NMS). \[s_{psj}=(1/K)\sum_{k=1:k}s_{psj,psk}\Big{[}\Big{\|}y_{psj,psk}-y_{psj^{\prime},psk}\Big{\|}>r,\text{ for every }psj^{\prime}<psj\Big{]} \tag{8}\]  
where \(r=10\) is the NMS radius. The PoseNet model has 1,180,147 parameters calculated by \(N_{\text{Conv 2D}}=\{(f_{w}\times f_{h}\times f_{h})+1\}\times f_{i}\) where \(f_{w}\) is the width of a filter, \(f_{h}\) is the height of a filter, \(f_{n}\) is the number of filters in the previous layer, and \(f_{i}\) is the number of filters.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 4 Meteorological Modeling
### Data Assimilation and Numerical Simulations  
Data assimilation was performed using a 3-Dimensional Variational (3D-Var) technique with the WRF Data Assimilation (WRFDA) system [50] in version 3.9.1. The 3D-Var scheme aims to improve initial conditions by minimizing a penalty or cost function that reduces the misfit between the background forecast and observations [51]. Several experiments that assimilated different datasets were carried out to assess the benefit of data assimilation for the nowcasting of severe convective events that can impact aviation operations. Radar reflectivity, temperature observations from in situ weather stations, and Global Navigation Satellite System (GNSS)-derived data were assimilated every 3 h within a 6 h assimilation window, whereas lightning data were assimilated every 15 min through a nudging technique [52] that increased model instability by adding water vapor when flashes were observed. The results, widely discussed in [46], show that the assimilation of lightning data plays a key role in improving forecast skill. Indeed, the numerical experiment with radar and lightning data was able to produce reliable forecasts at high spatial and temporal resolution that are suitable for ATM purposes.
# A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G
## 7 Conclusions  
In this paper, the transmission of 100 Gbits/s OFDM signal over a 2 GHz bandwidth at 28 GHz mm-wave RoF link has been demonstrated in Optisystem 16. This paper analyzes and compares the performance of three modulation schemes (16-PSK, 16-QAM, and 64-QAM) for optical coherent and direct detection techniques in terms of bit error rate and quality factor. In this paper, the performance of the system is reported for a transmission distance of up to 35 km and two simulation cases; when DPS compensation is used and a second case where DSP compensation is not used. Results in this work show that the OFDM-based RoF fronthaul system employing DSP compensations gives better system performance when implemented in the coherent receiver system. The results in this work again show that optimal throughput performance in terms of QF and BER is achieved when 64-QAM modulation is used. Although results in this work have shown some unacceptable values for three modulation schemes, the feasibility of transmitting a 100 Gbits/s data rate over a short distance has been achieved in this work.  
Conceptualization, J.D.G., R.A., K.A.-P.A., and K.A.-B.O.; methodology, J.D.G., R.A., K.A.-P.A., and K.A.-B.O.; Software, R.A.; validation, J.D.G., R.A., K.A.-P.A., and K.A.-B.O.; formal analysis, J.D.G., R.A., K.A.-P.A., and K.A.-B.O.; investigation, J.D.G., and R.A.; resources, R.A., and J.D.G.; data curation, J.D.G., and R.A.; writing--original draft presentation, R.A.; writing--review and editing, J.D.G., R.A., K.A.-P.A., and K.A.-B.O.; visualization, R.A.; supervision, J.D.G., K.A.-P.A., and K.A.-B.O.; project administration, J.D.G., R.A., K.A.-P.A., and K.A.-B.O.; funding acquisition, J.D.G., R.A., K.A.-P.A., and K.A.-B.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by J.D.G., R.A., K.A.-P.A., and K.A.-B.O.  
Not applicable.  
The authors declare no conflict of interest.
# Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors
## 4 Results and Discussion
### Reproducibility of the Fiber Bragg Gratings
In addition, the reduction in the reflected signal power for the F-doped FBG sensors was just under 0.45 dBm over the temperature range of 25-335 \({}^{\circ}\)C. Figure 10c shows the temperature sensitivity of the FBG sensors under a wide range of temperatures varying from 25 \({}^{\circ}\)C to 335 \({}^{\circ}\)C. Both the IR FS laser and the UV FS laser-inscribed FBG sensors showed excellent temperature sensitivity. For the IR FS laser-fabricated F-doped FBG sensor, the highest temperature sensitivity of 14.4 pm/\({}^{\circ}\)C was evident at 310 \({}^{\circ}\)C, a value similar to those of the IR FS laser-fabricated Ge-doped FBG sensor and the UV FS laser-inscribed Ge-doped FBG sensors. From the experimental results, we can infer that the wavelength of the femtosecond laser beam has a significant impact on the fabrication of quality FBG sensors. We also compared the performance of the IR FS laser-inscribed FBG sensor with other reported research works, a summary of which is shown in Table 1. Compared with other radiation-resistant FBG sensors, our F-doped FBG sensor shows superior results from all perspectives. Thus, our IR FS laser-fabricated F-doped FBG sensor shows promise as a radiation-resistant sensor, especially in the atomic energy industry.