esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 5 Methodology
### Experimental Setup
As it was used in other works [15; 17], 70% of the maximum traffic capacity of the network was established as a threshold to determine the saturation condition. For the unsaturated network condition, the frame arrival rate (or frame generation rate) of the nodes corresponded to a exponential distribution with a mean time of 953.6 \(\upmu\)s. These frames presented a payload of 2000 bits. Considering the ACK time, header size, and the amount of users, this configuration demands approximately a 20.54% of the maximum manageable traffic. On the other hand, the saturated configuration presented a mean time of 100 \(\upmu\)s, resulting in beyond-capacity necessities (195% of the maximum traffic). This a priori huge value was selected to keep a very demanding situation even when some of the nodes were unable to be associated. \begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline \hline Parameter & Values & Parameter & Value & Parameters & Values \\ \hline Room size & 10 m \(\times\) 10 m \(\times\) 3 m & BO & 6 & Number Backoff & 5 \\ \hline Room’s material & plaster/wood & SO & 6 & Number of slots & 16 \\ \hline Reflection coefficient & 0.75/0.4 & Frame payload & 2000 bits & Modulation & OOK \\ \hline Number of users & 4 & Frame header & 270 bits & Code & 10B8B \\ \hline Number of coordinators & 1 & ACK length & 50 bits & LED Lambertian order & 1 \\ \hline Radii & 0.5 to 4.5 m & Optical Clock & 60 MHz & aBaseSlotDuration & 60 \\ \hline Number of rays in MMCRT simulation & 500 & \begin{tabular}{p{113.8 pt}} Random time window for an association request \\ \end{tabular} & 38 slots & Simulation time & 100 s \\ \hline Reflections per ray & 3 & \
\begin{tabular}{p{113.8 pt}} MAC response wait time \\ \end{tabular} & 300 ms &
\begin{tabular}{p{113.8 pt}} Number of simulations \\ \end{tabular} & 15 \\ \hline Receiver area & 1 cm\({}^{2}\) & Backoff unit & 200 & Coordinator Tx power & 200 mW \\ \hline Receiver FOV (FWHM) & 60\({}^{\circ}\) & maxMaxBE & 3 & NodeTx power & 50 mW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of simulation parameters. This table includes information about the scenario and both PHY and MAC layers. Figure 9: DC channel gain of the Non-LOS (NLOS) link between nodes at different horizontal distances. Some parameters of IEEE 802.15.7, concretely the association parameters, were chosen according to other works [29; 30] since there is no guide or reference for them. The association request was sent randomly with a 38 slots window. This request must be completed before 300 ms, or the node assumes that it failed and repeats the operation.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 4 Results and Discussion
### Validation Strategies  
The validation step helps us find the optimum parameters for our model while preventing it from becoming overfitted. Two of the most known validation strategies are:  
1. Hold-out strategy.
2. K-fold strategy.  
In the hold-out validation [49], the dataset is divided into two non-overlapping sets of the training and testing dataset. The test dataset is held out while training the network model. It prevents overlapping and estimates the more accurate and generalized performance of an algorithm. It also reduces the computational cost because it only needs to be run once. The drawback of this procedure is that it does not use all the available data and results are highly dependent on the choice of training and testing data split.  
Cross-validation [49] is a very powerful re-sampling technique used to evaluate ML models for limited data. It consists of a single parameter 'K' referred to as the number of groups in which the dataset is divided, this method is also referred to a K-fold cross-validation. We have performed 5 cross-fold validation, so it can use all the available data for validation.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 3 Beam Focusing Effect Using Dielectric Lens for OAM Multiplexing  
### Usage of Dielectric Lens for OAM Multiplexing  
We present the beam focusing effect obtained by using a dielectric lens for OAM multiplexing to increase the transmission distance. Figure 3 shows the configuration of the transmitting and receiving antennas. The OAM mode radiates from the UCA installed on the back of the dielectric lens. The OAM mode radiated into the space is phase-modulated by the lens and reaches the reception point. When transmitting the OAM mode \(n\), we weight each element of the array antenna. Since the phase distribution in the OAM mode is circularly symmetric in the plane perpendicular to the beam traveling direction, a circular lens is used so that circular symmetry is not disturbed by phase modulation at the time of passing through the lens.  
By properly setting focal length \(f\) of the lens considering the distance between the lens and the transmitting antenna, the beam can be narrowed by the light converging effect. For example, [PERSON] et al. [4; 18] showed that the beam spread can be reduced based on the imaging magnification at the reception point.  
Figure 3: Configuration of the Tx and Rx antennas using dielectric lens.
## 1 Introduction
### Movement of Spherical Particles within the Polymer Matrix  
The drag force arises from pressure and viscous stresses applied to the particle surface and resists the relative velocity W. Assuming the flow is steady and uniform away from the particle, \(\
abla.U_{mean}=0\), the magnitude of drag is primarily represented by the particle Reynolds number (\(R_{ep}\)), defined as: [16]  
\[R_{e_{p}}=\frac{\rho_{f}|W|d}{\mu_{f}} \tag{3}\]  
where \(d\) is the particle diameter, \(\rho_{f}\) is the continuous-phase density and \(\mu_{f}\) is the continuous-phase viscosity. The drag force for a sphere is represented by: [17]  
\[F_{D}=-3\pi d\mu_{f}W,\text{ for }R_{ep}\ \ll\ 1 \tag{4}\]  
On increasing particle Reynolds numbers, the flow changes to laminar for \(R_{ep}<22\), more towards unsteady \(R_{ep}<1000\), and then towards turbulent. At higher Reynolds numbers for \(2000<R_{ep}<300,000\), the boundary layer at the front of the particle becomes laminar with \(\theta=0^{\circ}\) separate towards \(\theta\sim 80^{\circ}\), creating a fully turbulent wake behind the particle. The drag coefficient \(C_{D}\) as the function of drag force is: [18]  
\[F_{D}=-\frac{\pi}{8}d^{2}C_{D}\rho_{f}wW \tag{5}\]  
Based on Equations (4) and (5), drag coefficients for regular isotropic particles at various Reynolds numbers for incompressible flow conditions are reported in Figure 1. The Reynold's number in the range of \(2000<R_{ep}<300,000\) indicated that \(C_{D}\) is approximately constant. The approximately constant \(C_{D}\) value is called the critical drag coefficient, assuming values of about 0.4 to 0.45. This range often falls in the \"Newton regime \"on considering constant drag coefficient for ballistics. The drag force could be normalizing by the creeping flow solution to obtain stokes correction: [19; 20]  
\[f_{Re}=\frac{F_{D}\left(R_{ep}\right)}{F_{D}\left(R_{ep}\to 0\right)}= \frac{C_{D}\left(R_{ep}\right)}{24/R_{ep}} \tag{6}\]The ratio is unity for \(R_{qp}\ll 1\) and it is proportional to \(R_{ep}\) for the Newton regime (ca. 3000 \(<R_{qp}\) < 200,000). There is no general analytical solution for intermediate Reynolds numbers; as a result, these conditions are prescribed by an empirical expression of the drag coefficient. An approximate expression is proposed for the subcritical regime [3] as below:  
\[f_{Re}=\left[1+\sqrt{\frac{0.4R_{ep}}{24}}\right]^{2}\text{ for }R_{ep}\;<\;2\; \times\;10^{5} \tag{7}\]  
A closer approximation for the sub-critical expression [4], falling within the experiments of Figure 1, is expressed as below: [21]  
\[C_{D}=\left[\frac{24}{R_{ep}}\Big{(}1+0.15R_{ep}^{0.687}\Big{)}\right]+\frac{0.42}{1+\frac{42.500}{R_{ep}^{26}}}\text{ for }R_{ep}\;<\;2\;\times\;10^{5} \tag{8}\]  
The term represented in the square brackets is known as the Schiller-Naumann [5] drag coefficient for moderate Reynolds number. This can be written in terms of the Stokes correction as: [22]  
\[f_{Re}=1+0.15R_{ep}^{0.687},\text{ for }Rep\;<\;800 \tag{9}\]
## 3 Materials and Methods
### Evaporative Cooling (EC) Systems
#### 3.1.2 Indirect EC (IEC) System  
Indirect evaporative cooling (IEC) system is a system in which heat and mass transfer take place to reduce the temperature of primary/ambient air without the addition of moisture. It works on the basis of sensible cooling [1]. It consists of two channels named wet and dry channels [34]. Primary air passes through a dry channel and secondary/process air passes through a wet channel. In the IEC system, a cooling effect is produced by a two-way thermodynamic process: (1) isenthalpic cooling in the wet channel by water evaporation, and (2) sensible heat transfer in the dry channel [1; 35]. It can reduce the temperature up to the wet-bulb temperature. Therefore, its WBE range is 50-65% [36; 37]. In the IEC system, humidity (g/kg of dry air DA) of inlet air and outlet air remains the same. However, enthalpy (kJ/kg of dry air DA) of outlet air is decreased [1]. Schematic and psychrometric representation  
Figure 4: The schematic and psychrometric representations of the EC systems for (**a**) DEC, (**b**) IEC, and (**c**) MEC configurations.  
of the IEC system is shown in Figure 4b. Further insights into the IEC system are governed by Equations (4)-(7) given in the literature [3].  
\[\left(DBT\right)_{outlet} \geq\left(WBT\right)_{inlet} \tag{4}\] \[\left(RH\right)_{outlet} >\left(RH\right)_{inlet}\] (5) \[w_{outlet} =w_{inlet}\] (6) \[h_{outlet} <h_{inlet} \tag{7}\]  
where \(w\) and \(h\) represent the humidity ratio (kg/kg) and enthalpy of air (kJ/kg), and subscripts outlet and inlet represent outlet and inlet air conditions, respectively.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 2 Materials and Methods
### Materials
#### 2.2.3 IMERG Satellite Precipitation Dataset  
GPM is an international project that launched on 27 February 2014 by a joint effort of NASA and JAXA. It was designed to provide a new generation of global precipitation observations. IMERG is the level 3 multi-satellite precipitation algorithm of GPM, which combined precipitation information obtained from all constellation microwave sensors, infrared-based observations and monthly gauge precipitation data [2]. IMERG employs the 2014 version of the Goddard Profiling Algorithm (GPROF2014) to compute precipitation estimates from all passive microwave (PMW) sensors onboard GPM satellites constellation which was one of the significant improvements compared with TMPA (GPROF2010) [16; 30]. The GPM-3 IMERGM 'Final' V05B product (hereafter referred as IMERG), which estimated global monthly precipitation at spatial resolution of 0.1\({}^{\circ}\), were used in this study. Since IMERG data are available from March 2014, the first year with full months (i.e., 2015) was selected. IMERG data could be downloaded at [[http://pmm.nasa.gov/data-access/downloads/gpm](http://pmm.nasa.gov/data-access/downloads/gpm)]([http://pmm.nasa.gov/data-access/downloads/gpm](http://pmm.nasa.gov/data-access/downloads/gpm)). Furthermore, annual IMERG precipitation was obtained by summing up monthly precipitation in the year.
# Optimal Pricing in a Rented 5G Infrastructure Scenario with Sticky Customers
## 2 Literature Review
Bandwidth, in addition to pricing, was considered as the strategic leverage in [32], where a Stackelberg game framework was adopted for spectrum pricing and spectrum sharing, with bandwidth pricing control for the leader (the spectrum licensee) and bandwidth optimization for the followers (i.e., mobile network operators). Volume, rather than bandwidth, was considered as the strategic leverage in [33], where a Cournot equilibrium was found for the competition between owner and renter. The optimal price was shown to be a weighted average of the customer's willingness to pay and the operators' costs. In summary, the literature on the direct competition between MNOs and MVNOs for end customers has considered only the quality of service as a parameter in addition to price to move customers' choices. No studies have considered different factors that may influence these choices. A research gap exists in the inclusion of these factors. Our study aims to include these factors by making the demand function only partially respond to prices, i.e., removing the winner-takes-all approach in the literature where customers all prefer the operator offering the lowest price.
# 3D Ear Normalization and Recognition Based on Local Surface Variation
###### Abstract  
Most existing ICP (Iterative Closet Point)-based 3D ear recognition approaches resort to the coarse-to-fine ICP algorithms to match 3D ear models. With such an approach, the gallery-probe pairs are coarsely aligned based on a few local feature points and then finely matched using the original ear point cloud. However, such an approach ignores the fact that not all the points in the coarsely segmented ear data make positive contributions to recognition. As such, the coarsely segmented ear data which contains a lot of redundant and noisy data could lead to a mismatch in the recognition scenario. Additionally, the fine ICP matching can easily trap in local minima without the constraint of local features. In this paper, an efficient and fully automatic 3D ear recognition system is proposed to address these issues. The system describes the 3D ear surface with a local feature--the Local Surface Variation (LSV), which is responsive to the concave and convex areas of the surface. Instead of being used to extract discrete key points, the LSV descriptor is utilized to eliminate redundancy flat non-ear data and get normalized and refined ear data. At the stage of recognition, only one-step modified iterative closest points using local surface variation (ICP-LSV) algorithm is proposed, which provides additional local feature information to the procedure of ear recognition to enhance both the matching accuracy and computational efficiency. On an Inter\({}^{\text{2E}}\)Xeon\({}^{\text{6W}}\)3550, 3.07 GHz work station (DELL T3500, Beijing, China), the authors were able to extract features from a probe ear in 2.32 s match the ear with a gallery ear in 0.10 s using the method outlined in this paper. The proposed algorithm achieves rank-one recognition rate of 100% on the Chinese Academy of Sciences' Institute of Automation 3D Face database (CASIA-3D FaceV1, CASIA, Beijing, China, 2004) and 98.55% with 2.3% equal error rate (EER) on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2, University of Notre Dame, South Bend, IN, USA, between 2003 and 2005).  
biometrics; 3D ear recognition; ICP algorithm; surface variation; ear normalization
## 4 Numerical Simulations
### Low-Earth Orbit Case  
The dynamic system for the low-Earth-orbit propagation is given per Equation (25). The elements of the nominal orbit are illustrated in Table 3, and the propagation time is set to be one nominal orbit period. The initial covariance is taken to be diagonal with standard deviations of 1.3 km in position \(r_{x}\), 0.5 km in position \(r_{y}\), 2.5 m/s in velocity \(v_{x}\), and 5 m/s in velocity \(v_{y}\), that is:  
\[P_{0}=diag\Big{(}\Big{[}0.5^{2}\ \text{km}^{2},2.5^{2}\ \text{km}^{2},2.5^{2} \times 10^{-6}\ \text{km}^{2}/\text{s}^{2},5^{2}\times 10^{-6}\ \text{km}^{2}/\text{s}^{2}\Big{]}\Big{)} \tag{31}\]  
Similarly to the previous example, the performance of AGMM-VSG is verified by comparing it with fifth-order CR. The parameters involved in AGMM-VSG are set to be the same as the previous HEO case, and the results are shown in Figures 6 and 7. From Figures 6 and 7, it is seen that both implementations of the AGMM-VSG approach enable a better representation of the curvature exhibited by the Monte Carlo samples.  
Additionally, 25 components are used to represent the final PDF, and only 100 evaluations are requested. The LAMs of AGMM, AGMM-VSG, and fifth-order CR are also normalized to provide a clear relative measure. The results are illustrated in Figure 8. It is easy to observe that the fifth-order CR is outperformed by both AGMM and AGMM-VSG.  
With the validation of the two numerical simulations validation, the advantages of the AGMM-VSG method in accuracy and efficiency can be recapitulated. The proposed AGMM-VSG could represent the distribution as well as AGMM, and is better than the fifth-order CR method, while it is much more efficient than the AGMM.  
Figure 6: PDF contours using AGMM-VSG with Monte Carlo samples of LEO case.  
Figure 7: PDF contours using fifth-order CR with Monte Carlo samples of LEO case.
## 4 Results and Discussions
### Varying of the Supply Voltage and Phase Resistances  
The DSMC-based SMO speed responses are shown in Figure 21a,b, when the supply voltage varies from 250 V to 200 V at 0.02 s and 200 V to 250 V at 0.04 s. The estimated speed responses do not change rapidly due to the proposed sliding mode controller and observer switching surface properties.  
Figure 20: (**a**) Estimated and actual speeds when there are increments in the moment of inertia, (**b**) Estimated and actual speeds when there are decrements of the moment of inertia, and (**c**) Speed tracking error for change of the moment of inertia.  
In the same way, the estimated speeds shown in Figure 21c are not much deviated with actual speed when the phase resistances are varied from 0.86 ohms to 0.43 ohms at 0.02 s and 0.43 ohms to 0.86 ohms at 0.04 s. Figure 21d shows the speed tracking error with the variations of the phase resistance.  
Figure 21: **(a)** Regulated main phase voltages, (**b**) estimated and actual speeds when changes in the supply voltage, (**c**) Estimated and actual speeds when changes in phase resistances, and (**d**) speed tracking error when the phase resistances are varied.
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
###### Abstract  
This article introduces a Mixed-Integer Linear Programming model for cost optimization in multi-product multi-line production scheduling. This model considers discrete time windows and includes realistic constraints. The NP completeness of the problem is proven. A novel scheme based on embedding bounds is applied to speed up convergence. The model is tested on 16 input configurations of a real case study from the top Uruguayan grain production facility. The numerical results show that the model significantly improves the outcome of the current ad hoc heuristic planning, reducing on average 10% the overall production costs; and that the introduction of the embedded bounds-based scheme reduces significantly the elapsed time, on average by 22%.  
Keywords:grain facility optimization; multi-product multi-line scheduling; Mixed-Integer Linear Programming; theoretical bounds analysis +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 4 Results
### Comparison of Running Time  
The runtimes of the proposed method and ORB-SLAM3 were compared under the same environment configuration, as shown in Table 6, using the KITTI SLAM Evaluation 2012 dataset. Although the runtime of our method is slightly higher than that of ORB-SLAM3, primarily due to the detection of privacy-sensitive objects and their subsequent repair to ensure confidentiality, the increase is considered acceptable. Enhancing privacy protection in visual SLAM is a key contribution of this research. Furthermore, this modest runtime increase still meets the real-time requirements of SLAM.  
In practical applications, the improved privacy protection significantly supports the broader adoption of visual SLAM in autonomous driving and augmented reality scenarios, which is crucial for the further development and deployment of the technology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence** & **Number of Images** & **Proposed Method (s)** & **ORB-SLAM3 (s)** \\ \hline KITTI-SE-00 & 4541 & 604 & 551 \\ KITTI-SE-02 & 4661 & 635 & 580 \\ KITTI-SE-03 & 801 & 111 & 101 \\ KITTI-SE-04 & 271 & 40 & 37 \\ KITTI-SE-05 & 2761 & 270 & 338 \\ KITTI-SE-06 & 1101 & 150 & 137 \\ KITTI-SE-07 & 1101 & 150 & 137 \\ KITTI-SE-08 & 4071 & 542 & 495 \\ KITTI-SE-09 & 1591 & 216 & 197 \\ KITTI-SE-10 & 1201 & 164 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of running time.  
Figure 9: Comparison of pose trajectories between the ground truth and privacy-preserved ORB-SLAM3 in some sequences of the KITTI SLAM Evaluation 2012 dataset. (**a,c**) Comparison of the trajectories of sequence **SE-00** and **SE-05**. (**b,d**) The absolute pose errors of sequence SE-00 and SE-05, respectively.
## 3 Aerial Images Capture and Data Processing
### Image Data Network Sharing  
The volume of data had the potential to create bottlenecks that would slow down performance in the computer and at the interface with the GIS software, so we needed to publish on the web. Managers used a web browser or mobile phone to view aerial photos of big-data results. The web platform mainly enabled basic data input, grid data management, grid member management, daily operational supervision of grid personnel, and statistical analysis of events in the grid and of management functions in the background. Based on high-resolution aerial images of the GIS visual integrated development environment, we were able to effectively improve the ability to manage integrated grid monitoring data and provide an effective online technology for regulating unauthorized construction.