esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mathematical Modeling
### Modeling the Path Gain Factor and the Link Budget  
The reflection of electromagnetic energy is highly dependent on the frequency, the incident angle, the material and physical composition of the surface, its roughness, etc. In this study, the Earth is assumed to be completely reflective, and its reflectance is assumed to be independent of frequency. As this assumption increases the effect of interference, it can be considered to be a Best-Case Condition for the unintentional _Source_ of interference. Using this approach, the Path Gain Factor can be expressed as follows [18]:  
\[G_{Path}=2\left|\sin\left(2\pi f\sqrt{\mu_{0}\varepsilon_{0}}h_{TX}\tan\left( \frac{z(x)}{\sqrt{x^{2}+y(x)^{2}}}\right)\right)\right| \tag{11}\]  
where the \(\left|..\right|\) operator returns the absolute value of its argument and \(\mu_{0}=4\pi\times 10^{-7}\) (F/m) and \(\varepsilon_{0}=8.854\times 10^{-12}\) (H/m) are the electrical permittivity and magnetic permeability, respectively, of the vacuum, and \(h_{TX}\) is the altitude of the _Source_. The link budget can be calculated as follows:  
\[P_{RX}=P_{TX}+G_{Path}+G+G_{TX}+20\log_{10}\left(\frac{f}{4\pi cR_{LoS}}\right) \tag{12}\]  
In which \(P_{TX}\) is the ERP of the Signal transmitter, \(G_{Path}\) is the Path Gain Factor, \(G_{TX}\) is the antenna gain of the _Source_ in the direction of the AoA assuming \(G_{TX}=0\), \(G\) is the receiver's antenna gain in the direction of the AoA, and \(P_{RX}\) is the received power, all in dBW.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 6 Description Scenario
### Experimental Metrics  
We consider five different metrics (packet loss, reconfiguration overhead, link utilization, maximum link utilization, and computational complexity) in the simulation to measure the proposed scheme. These metrics are:  
1. _Packet loss_: the ratio of the number of lost packets to the total number of sent packets;
2. _Reconfiguration network effect_: the number of elements that should be set into the switches forwarding tables; and
3. _Link utilization_: the percentage of a network's bandwidth that is currently being consumed by the SDN traffic;
4. _Maximum link utilization_: the utilization of the link that has the maximum amount of link utilization.
5. _Computational complexity_: the time in seconds that it takes to run each set of flow in QRTP and relaxed QRTP (RQRTP).
## 5 Experimental Results and Discussion
### Discussion  
The proposed model avoids manual extraction of features and is more robust in design as compared to the traditional approaches which typically unable to address difficult problems. The approach of using multiple convolutional neural networks for the same task has already been used in [46]. The idea behind this is that each network will learn different characteristics of the same input. To reach this goal, the authors of [47] apply different distortions (color, shape) to the learning set. This approach cannot be used here since the application of distortions can change their chromatin size or distribution, which is not viable for classification. Our model contain the following advantages:  
* The design of feature extraction is robust and easy to calculate.
* Perform the selection, calculation and evaluation of the relevant characteristics and their relevance for class separation at different level.
* Avoid and circumvents the difficulties occurred in delicate step related to characteristics.
## 4 Noisy Location with Local Differential Privacy
### Achieve Local Differential Privacy on Location
#### 4.2.1 Generating Noise Point  
In this paper's framework, instead of uploading the worker's real location \(l_{0}\) to the SC-server, we used the noise function to generate a noisy location \(l\) to send to the SC-server. Because the location is two-dimensional (2D), we could not use the standard Laplacian method. Instead, we used the method previously described [15; 16], which was obtained from the standard Laplacian by replacing \(|x-u|\) with \(d(x,u)\). The probability density function is given as:  
Given the parameter \(\epsilon\in R^{+}\), the actual location \(l_{0}\in R^{2}\), and any other location \(l\in R^{2}\), the probability density function (pdf) of the noise mechanism is:  
\[D_{\epsilon}(l_{0})(l)=\frac{\epsilon^{2}}{2\pi}e^{-\epsilon d(l_{0},l)} \tag{2}\]  
where \(\frac{\epsilon^{2}}{2\pi}\) is a normalization factor.  
To draw the point \(l\), we switched the pdf defined in Equation (2) to a system of polar coordinates. Therefore, following the standard transformation formula, the pdf of the polar Laplacian is:  
\[D_{\epsilon}(r,\theta)=\frac{\epsilon^{2}}{2\pi}re^{-\epsilon r} \tag{3}\]  
where \(r\) is the distance of \(l\) from \(l_{0}\), and \(\theta\) is the angle that the line \(ll_{0}\) forms with respect to the horizontal axis of the Cartesian system.  
Basing the pdf of the polar Laplacian, we can express it as two marginals. We denote these two random variables by \(r\) (radius) and \(\theta\) (angle). The two marginals are:  
\[D_{\epsilon,R}(r)==\int\limits_{0}^{2\pi}D_{\epsilon}(r,\theta)d\theta=e^{2 }re^{-\epsilon r} \tag{4}\]  
and  
\[D_{\epsilon,\Theta}(\theta)=\int\limits_{0}^{\infty}D_{\epsilon}(r,\theta)dr= \frac{1}{2\pi} \tag{5}\]  
Hence, we generate \(\theta\) as a random number in the interval \([0,2\pi)\) with uniform distribution base on the \(D_{\epsilon,\Theta}(\theta)\) defined in (5).  
In addition, we can obtain the cumulative distribution function (cdf) of variable \(r\) with \(D_{\epsilon,R}(r)\) defined in Equation (4). The cdf is:  
\[C_{\epsilon}(r)=\int\limits_{0}^{r}D_{\epsilon,R}(\rho)d(\rho)=1-(1+\epsilon r )e^{-\epsilon r} \tag{6}\]  
Finally, we can generate \(r\) by \(C_{\epsilon}(r)\) defined in Equation (6), and  
\[r=C_{\epsilon}^{-1}(p)=-\frac{1}{\epsilon}\left(W_{-1}\left(\frac{p-1}{e} \right)+1\right) \tag{7}\]  
where \(W_{-1}\) is the Lambert W function (the \(-1\) branch).  
Therefore, we can generate the noisy point (\(r\), \(\theta\)) by following Algorithm 1.  
```
Input: privacy budget \(\epsilon\) Output: (\(r\), \(\theta\))
01. \(\theta\) = random [0, 2\(\pi\))
02. \(p\) = random [0,1)
03. \(r=C_{\epsilon}^{-1}(p)\)
04. return (\(r\), \(\theta\))
```  
**Algorithm 1** Noisy Point Generate Algorithm  
Consequently, in Algorithm 1, we first generate \(\theta\) as a random number in the interval [0, 2\(\pi\)) with uniform distribution, then we generate a random number \(p\) with uniform probability in the interval [0,1). Finally, we generate \(r\) using Equation (7).
# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
## 2 Methodology
### Closed Loop Detection and Closed-Loop Optimization Algorithm Based on Point-Line Features
\[e_{ij}\big{(}u_{i},u_{j}\big{)}=\xi_{ij}-\xi_{ij} \tag{5}\]  
Figure 3: Dictionary tree based on point-line features. The sum of the overall error functions is:  
\[\mathrm{F}(u)=\sum_{(i,j)\in\epsilon}\underbrace{e_{ij}^{T}\Omega_{ij}e_{ij}}_{F_{ ij}} \tag{6}\]  
The goal of optimization is to find the \(\mathrm{u}\) that minimizes \(F(u)\):  
\[\hat{\mathrm{u}}=\arg\min_{u}F(u) \tag{7}\]  
Then a first-order [PERSON] expansion on the error function is performed:  
\[ \begin{array}{ll}F_{ij}(u+\Delta u)&=e_{ij}(u+\Delta u)^{T}\Omega_{ij}e_{ij }(u+\Delta u)\approx\big{(}e_{ij}+J_{ij}\Delta u\big{)}^{T}\Omega_{ij}\big{(}e _{ij}+J_{ij}\Delta u\big{)}\\ &=\underbrace{e_{ij}^{T}\Omega_{ij}e_{ij}}_{c_{ij}}+2\underbrace{e_{ij}^{T} \Omega_{ij}J_{ij}}_{b_{ij}^{T}}\Delta u+\Delta u^{T}\underbrace{J_{ij}^{T} \Omega_{ij}J_{ij}}_{H_{ij}^{T}}\Delta u\\ &=c_{ij}+2b_{ij}^{T}\Delta u+\Delta u^{T}H_{ij}^{T}\Delta u\end{array}  \tag{8}\]  
Then let \(c=\sum c_{ij}\), \(b=\sum b_{ij}\), \(H=\sum H_{ij}\), so we can obtain Formula (9):  
\[F(u+\Delta u)=\sum_{(i,j)\in\epsilon}F_{ij}(u+\Delta u)\approx\sum_{(i,j)\in \epsilon}\Bigl{[}c_{ij}+2b_{ij}^{T}\Delta u+\Delta u^{T}H_{ij}\Delta u\Bigr{]} =c+2b^{T}\Delta u+\Delta u^{T}H\Delta u \tag{9}\]  
In order to find the minimum of Equation (9), we can use the following formula:  
\[\frac{\partial F}{\partial(\Delta u)}=2b+2H\Delta u \tag{10}\]  
Thus,  
\[H\Delta u=-b \tag{11}\]  
Through the above formula, the increment \(\Delta u\) of each iteration is calculated using the L-M iterative algorithm. This kind of problem can be optimized through the g2o (General Graph Optimization) library [15], which can greatly simplify the operation.
## 3 User-side Constraints
### User and Virtualization  
On the user side, virtualization has been used--mostly in enterprise environments--to provide remote access to desktops or applications, to give users access to a functionality which is too costly, insecure or inconvenient to deploy on their computer. Essentially, in user-side virtualization the application will run on a remote server while only the interface (or GUI) will run on the user's computer. Historically this model was used to virtualize the whole desktop, but it now allows applications to be run from the Internet cloud. SaaS provided through Cloud computing is one such example, as we have just mentioned. For the general public the Internet browser is used more and more extensively to act as the user interface of an application running on remote servers: mail access is common and office-style documentprocessing increasingly popular. The browser, present on all platforms, has become the most ubiquitous virtual client.  
User-side virtualization has several benefits for the user, including reduction in the complexity of managing the computers, in the resources required to support applications on the user's device, and uniform access from different locations and multiple devices.  
On this matter, user-side virtualization also reflects a move away from the smart terminal, or rather, the limits in demanding always increasing functions from the terminal. Some degree of processing is pushed back into the network, for the variety of reasons we have exposed. One challenge then is to integrate such a service with support for content delivery in a unified infrastructure.
# Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia
## 3 Materials and Methods
### Rainfall Data and Erosivity (\(R\))  
Rainfall activity (MJ mm/ha/h/yr) is one of the major factors that control soil erosion in Jeffara, south of Tunisia. Several indices have been developed in the past to quantify rainfall erosivity. In this study, we examined the performance of numerous indices with different temporal-resolution of the required rainfall inputs data. In Koutine catchment, rainfall data were measured with monthly and daily rainfall data were used to calculate the Fournier Index (\(FI\)) and half-month erosivity index (\(Mi\)).  
[PERSON] developed an erosivity index using monthly rainfall data [40]. The \(FI\) is usually used due to the availability of monthly rainfall (Equation (2)). This index was later modified and called the Modified Fournier Index (\(MFI\)). It is defined by Equation (3). Another modification form of the Fournier index has been described in Equation (4) [41].  
\[FI=\frac{Pi^{2}}{P} \tag{2}\]  
\[MFI=\frac{\sum_{1}^{12}Pi^{2}}{P} \tag{3}\]  
\[MFI=\frac{P_{wettest}^{2}}{P} \tag{4}\]  
where \(Pi\) is monthly rainfall, \(P\) is annual rainfall and \(P_{wettest}\) is the maximum monthly rainfall (wettest month).  
In our study, the \(R\) factor was calculated using Equation (5), where \(a\) and \(b\) are regional coefficients. For Koutine catchment, \(a\) and \(b\) were taken as 1 and 0, respectively. For \(MFI\) calculation, Equation (3) was used due to the data availability of monthly and annual rainfall data [42].  
\[R=a\times MFI+b \tag{5}\]  
Given the arid climate, the rainfall in the study site is characterized by low annual mean, high irregularity (both in time and in space) and torrential form. The annual average rainfall during observed years (1969-2018) is between 234 mm in the mountains and 162 mm in the downstream.  
The study area has been equipped with climatic and weather stations for several years (Table 1). These data acquired in situ are being updated, depending on the availability of existing data at different time steps, including daily, monthly or annual. Initial quality analysis of rainfall time series focused on their duration and quantity in mm. Rainfall  
Figure 2: General methodology to apply RUSLE model and data sources.  
data were collected for more than 48 years (1969-2018) with a variable time interval (daily, monthly and annual) from eight stations located in the study region (Table 1).
## 2 Hyperspectral Imaging Configurations
### Common Sensing Modes  
There are three commonly used sensing modes in hyperspectral imaging, i.e., reflectance, transmittance and interactance, as illustrated in Figure 3. These sensing modes are mainly different in their lighting configuration for imaging the sample, and each has different implications in food quality and safety assessment. In reflectance mode (Figure 3a), the light source and the detector are placed above the sample and the measured light is diffusely reflected from the illuminated area of the sample. This sensing mode is easy to implement and has been widely used to detect quality attributes, both external and internal, of food products [16; 32; 33; 34]. However, in reflectance mode, light has limited penetration in horticultural and food products, and hence this sensing mode may not be quite effective for detecting internal quality. In transmittance mode (Figure 3b), the detector and the light source are positioned in the opposite side of the sample, and the measured light is transmitted through the whole product, carrying information about the internal features of the product. This sensing mode is well suited for evaluating the internal condition of food products, e.g., internal defects [26; 27], but it requires a high-intensity light source and a high-sensitivity detector to obtain sufficient detectable signals, and thus is relatively difficult to implement in practice. Moreover, transmittance measurements are influenced by product size and shape, and the long light pathlength in the product could complicate quality assessment. In addition, transmittance mode can be implemented in a semi-transmittance setup [35], by placing the lamp horizontally at one side of the sample, which is  
Figure 2: Diagrams of a transmission grating-based imaging spectrograph using a prism-grating-prism (PGP) component (**a**) and a reflection grating-based imaging spectrograph using an Offner configuration (**b**), where \(\lambda_{1}\) and \(\lambda_{2}\) represent different wavelengths.  
able to acquire the information inside the sample with less light power supply. In interactance mode (Figure 3c), the detector and light source are positioned in the same side of the sample with a certain source-detector separation, and a light barrier is placed between them to ensure that the detected light has gone through a minimum distance, which is conducive to assessing the properties of sublayer tissues. This sensing mode, like semi-transmittance, makes a compromise between reflectance and transmittance measurements [36; 37], but it may be unsuitable for detection of small samples.
# Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans
## 7 Field Navigation
### Global Localization  
The relative position and orientation of the mobile robot with respect to the current goal point are obtained by the Global_positioning node. This node process the data of the GPS and magnetometers of the smartphone published by the Android_sensors_driver app (see Figure 4).  
Before starting navigation, it is necessary to calibrate the magnetometers of the smartphone, since their measurements can be affected by the environment and by the mobile robot itself. Calibration consists of turning the robot on spot during one full turn on horizontal plane with lateral motor speed of the same magnitude but different sign. Then, the magnetic measurements on the \(X\) and \(Y\) axes, \(B_{x}\) and \(B_{y}\), respectively, are adjusted to resemble a circumference (see Figure 14).  
Afterwards, the orientation of the vehicle with respect to the north, i.e., the yaw, can be obtained during navigation as:  
\[yaw=arctan\left(\frac{B_{x}}{B_{y}}\right), \tag{10}\]  
and the relative orientation of the vehicle with respect to the current goal point can be deduced.  
When the distance between the current position of Andabata (given by its GPS coordinates) and the goal point is smaller than 10 m (that corresponds to the GPS horizontal resolution), the goal point is considered already visited and the next goal point from the mission list becomes the current goal point.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**3D Point** & **Map** & **Forced** & **Map** & **Standard** & **Map** & **Standard** \\
**Cloud** & **Nodes** & **Spacing** & **Age (s)** & **Deviation (s)** & **Interval (s)** & **Deviation (s)** \\ \hline \multirow{4}{*}{previous} & 1 & 0 & 14.20 & 1.31 & 12.56 & 0.48 \\  & & 0 & 18.21 & 1.90 & 8.27 & 5.76 \\  & 2 & 1 & 15.30 & 1.08 & 7.45 & 1.37 \\  & 2 & 15.73 & 1.62 & 11.37 & 0.50 \\ \hline \multirow{4}{*}{current} & & 0 & 27.34 & 3.41 & 6.60 & 6.09 \\  & 3 & 1 & 20.21 & 0.45 & 7.55 & 0.77 \\  & 2 & 23.95 & 2.11 & 11.13 & 2.30 \\ \hline \multirow{4}{*}{current} & 1 & 0 & 13.81 & 0.68 & 14.91 & 0.98 \\  & 2 & 0 & 14.83 & 1.79 & 8.36 & 4.50 \\  & 2 & 15.21 & 1.33 & 8.13 & 2.20 \\  & 2 & 12.06 & 1.58 & 11.70 & 2.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between different multithreaded strategies.
# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 2 Materials and Methods
### Methodology
#### 2.3.4 The Pixel Scale  
In order to compare precipitation products with different spatial resolutions, previous studies have employed interpolation techniques to match ground observations onto grids for pixel-by-pixel validation [63; 64]. However, due to limited station coverage and their dispersed locations within the Bouregreg basin, using station interpolation data alone may not accurately represent the actual precipitation conditions [65; 66]. Therefore, precipitation estimates of the pixel corresponding to the gauge station location were extracted, enabling a pixel-to-point evaluation [67; 68]. Moreover, previous studies have compared pixel-to  
\begin{table}
\begin{tabular}{c c} \hline \hline \(R^{2}=1-\frac{\sum_{i=1}^{N}\left(X_{i}-Y_{i}\right)^{2}}{\sum_{i=1}^{N}\left(X _{i}-\overline{X}\right)^{2}}\) & \(Bias=\frac{1}{N}\sum_{i=1}^{N}Y_{i}-X_{i}\) \\ \hline \(RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(X_{i}-Y_{i}\right)^{2}}\) & \(Pearson\ Corr=\frac{\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)\left(Y_{i}- \overline{Y}\right)}{\sqrt{\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)^{2}} \times\sqrt{\sum_{i=1}^{N}\left(Y_{i}-\overline{Y}\right)^{2}}}\) \\ \hline \(MAE=\frac{1}{N}\sum_{i=1}^{N}\mid X_{i}-Y_{i}\mid\) & \(Spearman\ Corr=1-\frac{\left(r_{i}-s_{i}\right)^{2}}{N^{3}-N}\) \\ \hline \hline \end{tabular} Where \(X_{i}\): gauge observation; \(Y_{i}\): satellite precipitation estimation; \(\overline{X}\): mean gauge observation; \(\overline{Y}\): satellite mean precipitation estimation; \(r_{i}\) and \(s_{i}\) indicate the sequence numbers of the product and reference records, respectively, after sorting them in ascending order based on precipitation amount; and \(N\): the sample size.  
\end{table}
Table 4: Summary of regression metric formulas.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Rainfall Event**} & **Intensity in mm/d** \\ \hline light rain & [0, 1[ \\ moderate rain & [1, 5[ \\ medium rain & [5, 20[ \\ heavy rain & [20, 40[ \\ extreme rain & \(\geq\)40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ranking of rainfall events based on their daily intensity. [a, b]: The interval includes values greater than or equal to a and strictly less than b.  
pixel and pixel-to-point interpolation techniques and have found that they lead to similar conclusions [69; 70].
# Biohydrogen from Microalgae: Production and Applications
## 4 Genetic Engineering Approaches  
### Random Mutagenesis  
The approaches described in the previous paragraph act in a transitory way, since it is not possible to manage microalgal culture by applying stress conditions permanently. Indeed, depriving a cell culture of the optimal growth conditions for a long time leads to cellular suffering that can culminate in cell death. In this way, only a discontinuous hydrogen production is possible, alternating stress and recovery phases to avoid permanent damage to the cells. One strategy to overcome this limitation concerns the selection of organisms with a higher aptitude to withstand these stress conditions; another one could be the selection of organisms which are less susceptible to the conditions that usually hinder hydrogen evolution in photosynthetic green microalgae.  
Physical and chemical treatments induce transmissible genomic mutations that favor the appearance of new traits in an organism. These treatments are not oriented towards a specific target and it is necessary to take into account an expensive screening phase among the mutants produced to select the ones with characteristics of interest. However, a recent study demonstrated that _Chlamydomonas reinhardtii_ mutants obtained via atmospheric and room temperature plasma (ARTP) showed a lighter green coloration, compared to wild types, indicative of lower chlorophyll content. The lower chlorophyll content is associated with better photosynthetic performance (probably due to the improved light transmit tance and the consequent increased solar energy conversion efficiency), as confirmed by transcriptomic analyses, with consequent benefits in hydrogen production [40].
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description
### Channel Model
#### 2.2.2 Pointing Errors  
The model assumes a circular detection aperture of radius \(\omega\) and a Gaussian spatial intensity profile of a beam waist radius on the received plane. The misalignment-fading model derived in [21] offers a tractable probability density function (pdf) for describing the behaviour of the pointing error loss, which takes into account the detector size, beam width and jitter variance as:  
\[h_{e}=A_{0}\exp(\frac{-2r_{d}^{2}}{w_{b}^{2}}), \tag{7}\]  
where \(r_{d}\) is the radial displacement between the beam and the detector centre. The symbol of \(A_{0}\) is the fraction of received power at a zero radial distance, which can be expressed as:  
\[A_{0}=\big{(}\text{erf}(v_{p})\big{)}^{2}, \tag{8}\]  
where \(\text{erf}(.)\) denotes the error function and \(v_{p}\) can be defined as:  
\[v_{p}=\sqrt{\pi/2}\frac{\omega}{w_{L}}, \tag{9}\]  
where \(w_{L}\) is the beam waist radius and the term of \(\frac{w_{L}}{\omega}\) is identified as the beam waist normalized by the radius \(\omega\) of the receiver aperture. The \(w_{b}\) term represents the equivalent beam waist, which can be determined as:  
\[w_{b}^{2}=\frac{\sqrt{\pi}w_{L}^{2}\text{erf}(v_{p})}{2v_{p}\exp(-v_{p}^{2})}. \tag{10}\]