esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials And Methods
### Description of the INLAND Surface Model
#### 2.1.2 Modeling Soil Water Movement  
The soil physics module simulates processes involved in soil water movement. Richards equations are used to simulate temporal variations in soil moisture determined as a function of soil hydraulic conductivity, the soil water retention curve and soil water uptake by roots. Vertical soil water movement through interlayers \(q_{z}\) (m\({}^{3}\) s\({}^{-1}\) m\({}^{-2}\)) is calculated on the basis of [PERSON]'s law [38; 39]  
\[q_{z}=-K_{h}(\theta)\left[1+\frac{d\psi(\theta)}{dz}\right] \tag{19}\]  
where \(K_{h}\) is hydraulic conductivity (m s\({}^{-1}\)), \(\theta\) is volumetric water content (v v\({}^{-1}\)), and \(\psi\) is the pressure head (m).  
The INLAND model uses [PERSON]'s empirical formulations [39] to determine the soil moisture retention curve and soil hydraulic conductivity (relative to moisture):  
\[\psi=\psi_{s}W^{-b^{*}} \tag{20}\]  
\[K(\theta)=K_{s}W^{2b^{*}+3} \tag{21}\]  
where \(\psi_{s}\) is air entry water potential (\(m\)), \(K_{s}\) is the saturated hydraulic conductivity of soil (m s\({}^{-1}\)), and \(W\) is soil moisture calculated from ratio \(\theta\)\(\theta_{s}^{-1}\), where \(\theta_{s}\) is saturated soil water content (or porosity). Exponent \(b^{*}\) is an empirical parameter ([PERSON]'s parameter) related to the distribution of soil pore size. Variations in temperature, humidity, and water content levels in the soil are simulated with each time step for six layers of soil.  
Vegetation cover represented in the INLAND varies according to the Plant Functional Type (PFT) involved. By default, 12 PFTs are represented in the model and each PFT is described according to its structural characteristics (leaf area index, vegetation layers--lower and upper canopy, and biomass) [17; 29].  
The vertical distribution of the root system follows the formulation proposed by [40]  
\[Froot(d)_{i}=\frac{1-\beta_{i}^{d}}{1-\beta_{i}^{dtot}} \tag{22}\]  
where \(Froot\) is the root fraction (ranging from 0 to 1) between soil depth \(d\) (cm) and total soil depth \(d_{tot}\) (cm). The root distribution parameter \(\beta\) varies according to the type of plant concerned and according to different soil characteristics differentiated for the lower (\(\beta_{1}\)) and upper (\(\beta_{2}\)) canopies.
# Using Financial Instruments to Transfer the Information Security Risks
## 7 Demonstration: An Example Application
### Information Security Swaps
#### 7.3.3 Participants and Incentives  
The prospective participants, their motivation and incentives for participation in trading of ISS at ISPM or Over-the-counter (OTC)is shown in Table 15.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Fixed Specifications** & **Variable Specifications** \\ \hline Issuer & Information Security Prediction Market \\ \hline Information Security Derivative Type & Information Security Swap \\ \hline Contract ID & ISS 123 \\ \hline Underlying Event or Condition & The satellite services providing company “S” suffers a (pre-specified type of) cyber-attack during the trading period of the contract and its services are completely unavailable to its clients for continuous six hours at least. \\ \hline Trading Start Date and Time & 1 January 2016 00-00-01 CET \\ \hline Trading End Date and Time & 31 December 2016 23-59-59 CET \\ \hline Minimum Investment Required & Amount : 1000 (One Thousand) \\ \hline Maximum Investment Permitted & Amount : 50,000 (Fifty Thousand) \\ \hline Contract Trading Unit (Lot Size) & One (01) \\ \hline Transaction Fee & 0\% to the exchange(_Zero for the sake of easy calculation and demonstration_), Premium of 2\% to the counterparty. \\ \hline Payout Trigger Criteria & For the calculation of payout, the disruption of services for a continuous period of six or more hours during the contract trading period. \\ \hline Decision Criteria & At least one of the following: (i) Press release by the company “S”. (ii) A filing by the company “C” to a regulator, such as stocks trading regulator. (iii) A third party verification report. \\ \hline Payout (Return) Structure & Maximum payout of \$500,000 or the actual impact, whichever is lower. \\ \hline Pay-Off Horizon & On the day of settlement \\ \hline Settlement Date & If there is no news/report of any cyber-attack on the company “S” then the contract will automatically settle on the first business day from the last trading day) from the last date of the contract period. If any of the states \(S_{2}\), \(S_{3}\), or \(S_{4}\) occur during the trading period then the settlement would occur on the fourth business day from the date of incident reporting. \\ \hline Independent Third Party Verification Required & Yes, if the counterparties want to verify the incident. \\ \hline Eligible Investors/Traders & Only verified \\ \hline Know Your Trader/Investor Required & Yes \\ \hline Transferable Instrument & Yes, to other verified traders only \\ \hline Other Relevant Information & Nil \\ \hline \hline \end{tabular}
\end{table}
Table 14: ISS Specifications.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Carbohydrate Polymers
#### 4.1.2. Chitosan  
Chitosanan originates from chitin which is naturally synthesized by algae and the shell waste of crab, shrimp, and crawfish [45]. Chitosan-based hydrogels have been extensively employed to deliver probiotications owing to their unique cationic character, non-toxicity, high biocompatibility, biodegradability, bio-adhesiveness, inexpensive nature, antimicrobial, and antifungal properties [67; 75; 93]. Chitosan also has high tolerance against the deteriorative effects of calcium chelating and anti-gelling agents [67]. However, chitosan is a pH-sensitive material that tends to degrade in low pH conditions and is water-insoluble at pH > 5.4. Therefore, it is less effective in the delivery of probiotics to the gut [75]. Moreover, using chitosan as a polymer for entrapping live lactic acid bacteria (LAB) could exhibit inhibitory effects on the LAB [1]. Therefore, it is commonly applied as a coating or shell rather than a capsule matrix. Chitosan has been extensively used in combination with other biomaterials, including alginate, starch, whey protein isolate, and xanthan gum [1; 45]. Chitosan coating could enhance the porosity of alginate beads, thus, reducing leakage of encapsulated bacteria and improving the pH stability of beads [67]. Chitosan coating increased the release rate of probiotics from alginate/starch beads and enhanced the survivability of probiotics in low pH conditions [94]. The chitosan coating on alginate/whey protein  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Biomaterial** & **Characteristics and Advantages** & **Limitations** & **Remarks** & **References** \\ \hline Lipids & Natural waves, vegetable oils, adipocytes, monocytes, acids, resins & Low in polarity, excellent water barrier properties, thermally stable, and could encapsulate hydrophilic substances. & Weakly mechanical properties, chemically unstable, might negatively affect the sensory characteristics & Technique: spray chilling, spray coating,Have melting points ranging from 50-85 \({}^{\circ}\)C. & [91] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._isolates beads increased the resistance to thermal, storage, and simulated gastrointestinal environment [95]. In addition, the heat resistance of microcrystalline cellulose/Xanthan gum beads has also been enhanced by chitosan coating [32]. Encapsulation of probiotics using microcrystalline cellulose powder and alginate, or Xanthan gum followed by coating with chitosan (0.5%) as the outermost layer is effective in protecting probiotics (_L. Sporogenes_) against the baking process (90 \({}^{\circ}\)C for 15 min) in bread making.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 4 Covariance Analysis
### Prediction Model  
The prediction model is selected in alignment with previous works [29] and represents the dynamics of the lunar rover. As previously discussed in Section 4.1, a simple kinematic model was selected assuming a uniform motion such that the propagation of the rover position at epoch \(k\) is based on the estimation of its velocity at epoch \(k-1\). The state transition matrix (or propagation model) is shown in Equation (8), with \(I_{3\times 3}\) the \(3\times 3\) identity matrix and \(\Delta T\) the length of the time interval between epochs \(k-1\) and \(k\).  
\[F_{k-1|k}=\begin{bmatrix}I_{3\times 3}&I_{3\times 3}\cdot\Delta T&&\\ &I_{3\times 3}&&\\ &&1&\Delta T\\ &&&1\end{bmatrix} \tag{8}\]  
Whereas the state transition matrix assumes a linear motion profile, the motion of the rover will be non-uniform. This mismodelling is considered in the propagation step by adding additional uncertainty by means of the process noise matrix \(Q_{k-1}\), which is defined in Equation (9), where \(\sigma_{p}\), \(\sigma_{v}\), \(\sigma_{gt}\) and \(\sigma_{gt}\) are the process noises associated, respectively, with the user position, velocity, clock bias and clock drift elements in the state vector.  
\[Q_{k-1}=\begin{bmatrix}I_{3\times 3}\cdot\sigma_{p}^{2}&&\\ &I_{3\times 3}\cdot\sigma_{v}^{2}&&\\ &&\sigma_{gt}&\\ &&\sigma_{gt}\end{bmatrix} \tag{9}\]  
To account for the length of the propagation time-step (\(\Delta T\)), the process noise transition matrix \(\Gamma_{k-1|k}\), as defined in Equation (10), is used.  
\[\Gamma_{k-1|k}=\begin{bmatrix}I_{3\times 3}\cdot\Delta T&&&&\\ &I_{3\times 3}\cdot\Delta T&\\ &&\Delta T&\\ &&&\Delta T\end{bmatrix} \tag{10}\]  
In this contribution, however, the propagation time-step is considered to be 1 s, such that \(\Gamma_{k-1|k}=I_{8\times 8}\).  
The definition of the process noise plays an important role in the final accuracy that can be achieved. Therefore, this paper performs a sensitivity study using different process noise values to assess the impact on the attainable position accuracy.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 4 Derivation of Offshore Dynamic Topography
### Estimation of Dynamic Bias Uncertainties at the Tide Gauge Locations
It was demonstrated that the DT could differ by up to 8-9 cm between two nearby sheltered locations. According to the [15] and [30] approach, TG\({}_{4}\) data would represent the DT at the harbor. In predicting offshore DB, the described potential offsets should be considered. Hence, the second uncertainty component (\(\sigma_{2}\)) was estimated by first comparing mean DB values at neighboring TG stations. These comparisons yielded \(N\) discrepancy estimates \(\overline{dDB}\) for each mean DB value (i.e., mean discrepancies). The second uncertainty component was then estimated in the DB spatial domain as:  
\[\sigma_{2}=\sqrt{\frac{1}{N-1}\sum_{m=1}^{N}\left[\overline{dDB}_{m}(\varphi^{ TG},\lambda^{TG})-\frac{1}{N}\sum_{m=1}^{N}\overline{dDB}_{m}(\varphi^{TG}, \lambda^{TG})\right]^{2}}, \tag{11}\]  
It was determined that comparisons with mean DB estimates at the nearest three TG stations (by also considering the distribution of TG stations) provided satisfactory results (i.e., \(N=3\)). Note that the second uncertainty component was calculated separately for each campaign (e.g., for campaign C1, precisely the values shown in Figure 4a were used), whereby the mean DB values were estimated over an extended period. For example, the 9-h length of campaign C5 may not have been sufficient for calculations, which were therefore performed over a 96-h period instead (cf. Table 1). In studies investigating extensive periods, the second uncertainty component should also be estimated as a moving window (as is done for the first component), because offsets caused by marine processes are not likely to reoccur often (for instance, the TG\({}_{14}\) example). Here, this is emulated by a separate estimation for each campaign. It is assumed that the first (temporal domain) and second (spatial domain) uncertainty components are (generally) independent variables. The final uncertainty estimates for DB were, thus, calculated as:  
\[\sigma_{DB}\Big{(}t^{H}\Big{)}=\sqrt{\left[\sigma_{1}(t^{H})\right]^{2}+\left[ \sigma_{2}\right]^{2}}, \tag{12}\]  
where \(t^{H}\) denotes hourly temporal resolution. The resulting DB uncertainties for campaign C1 are shown in Figure 5a. Notice how the estimates at TG\({}_{9}\) increase significantly during  
Figure 4: The mean predicted DB (**left**) and standard deviation estimates of predicted DB (**right**) for campaigns C1 (**a**,**b**), C2 (**c**,**d**), and C6 (**e**,**f**). The colored circles show, correspondingly, the mean DB values and standard deviation estimates of the DB values determined at the locations of TG stations. the second half of the campaign, which coincides with the period of strongest storm winds (as discussed earlier). The uncertainty estimates are summarized for all campaigns in Figure 5b by averaging. These (hourly resolution) results were next employed in predicting offshore DB.
## 5 Experimental Verification
### Comparison between the Proposed and Traditional Data Transfer Method  
Figure 13 shows the comparison results between the proposed and traditional data transfer as mentioned in Figure 7. Figure 7a shows the proposed data transfer circuit while Figure 7b shows the traditional data transfer circuit. Channel 1 indicates the output voltage \(U_{L}\), channel 4 indicates the output voltage of the amplifier, and channel 2 indicates the output data.  
Figure 13a shows that data transfer with the proposed method is stable, while Figure 13b shows that data transfer with a traditional circuit failed. The amplitude of channel 4 shows that the amplifier output of the proposed method is larger than that of the traditional circuit, thus the data transfer  
Figure 11: Detailed power and data transfer circuit of the experimental prototype: (**a**) detailed power transfer circuit; (**b**) detailed data transfer circuit.  
Figure 12: The monitored interference of data transfer to power transfer: (**a**) with data transfer; (**b**) without data transfer.  
capacity of the proposed method is larger than that of the traditional circuit. These results verify the modeling of the transfer function in Section 3 and the Bode plot analysis shown in Figure 10.
## 4 Results and Discussion
### Influence on and Absolute Error Analysis \(f^{\prime}(\eta)\)  
The results of the LMS-BPNN are examined using MATLAB software to explore the effects of changing the Casson fluid parameter (\(\beta\)), magnetic field parameter (\(M\)), and suction/injection effects (\(S\)) on \(f^{\prime}(\eta)\) with absolute errors are shown in Figure 5. The absolute error analysis is conducted between numerical values and predicted values as discussed in [42; 43]. Figure 5a depicts the effects of the Casson fluid parameter (\(\beta\)) on velocity profiles (\(f^{\prime}(\eta)\)) with an absolute error \(10^{-8}\to 10^{-4}\) as shown in Figure 5b. While Figure 5c shows the impacts of \(M\) on \(f^{\prime}(\eta)\) with absolute error \(10^{-7}\to 10^{-4}\) as depicted in Figure 5d. Similarly, the influence of porosity is depicted in Figure 5e and its absolute error (\(10^{-9}\to 10^{-3}\)) is shown in Figure 5f. The following consequences of pertained parameters on the velocity profile are observed.
* The velocity decreases as we increase the Casson fluid parameters. This effect is examined due to the shear thickening of ternary hybrid nanoparticles. It is concluded that Casson fluid parameter affect the flow behaviour, possibly interacting with the ternary hybrid nanoparticles and affecting the system's overall shear thickening behaviour  
Figure 6: Comparison of LMS–BPNN for \(\theta(0)\) with numerical data of Problem. (**a**) Variation of \(\phi_{1}=\phi_{2}=\phi_{3}\) on \(\theta(0)\). (**b**) Error analysis. (**c**) Variation of \(Rd\) on \(\theta(0)\). (**d**) Error analysis.  
based on the observation that the velocity decreases as the Casson fluid parameters are increased.
* It is also investigated that with the enhancement in magnetic parameters the fluid velocity increases. When a magnetic field is present, it can cause Lorentz forces that can cause the fluid to move more quickly, which has an impact on magnetohydrodynamics (MHD). The Lorentz forces grow greater as the magnetic parameters are increased, increasing the fluid velocity.
* Porosity effects also enhance the velocity of the fluid. It is examined that when we increase the porosity the fluid velocity increases.
# A Shearlets-Based Method for Rain Removal from Single Images
## 4 The Proposed Method  
### The Proposed Optimization Model  
This subsection describes the proposed convex model with three sparse prior regularizers and some non-negative constraints as follows:  
1. The sparse constraint of the Shearets coefficients of the background layer in the rain drops' direction. In fact, the Shearlets decomposition coefficients of the background layer in high frequency can be approximately considered (in the rain drops' direction) as sparse (see Figure 1) due to the intensity distribution for multilevel coefficients. In order to describe this sparse regularizer, the \(\ell_{1}\) norm of multilevel coefficients in different scales, along the direction of the rain-free layer is used, which also reflects the discontinuity of rain streaks, specifically \[Reg_{1}(b)=\|S_{r}b\|_{1}=\|S_{r}(o-r)\|_{1};\] (9) where \(S_{r}b\) is the Shearlets transform of the rain-free layer in the rain drops' direction.
2. The sparse constraint of the Shearlets coefficients of the rain streaks across the rain drops' direction. In the real scenes of rainfall, the shape of rain streaks is a stretched ellipse in a specific direction reflected in pixels, therefore the directional multilevel transform is more sensitive than UTV in detecting the directional singularities in images. From Figure 2, it shows that the Shearlets coefficient in scale 2 across the rain drops' direction of the rain streak has sparse structure. Similarly, \[Reg_{2}(r)=\|S_{b}r\|_{1}.\] (10)  
Figure 3: The sub-band of Shearlets in the frequency domain.  
where \(S_{b}r\) is the Shearlets transform of the rain streak across the rain drops' direction.
3. The sparse constraint of rain streaks (see Figure 1). In general, the rain streak is sparse when the rain is not heavy, therefore its sparsity can be described by \(\ell_{0}\) norm, which represents the number of nonzero elements. Here, the \(\ell_{1}\) is utilized to replace the \(\ell_{0}\) norm due to its non-convexity, thus we have the following sparse regularizer of rain streaks \[R\varepsilon_{3}(r)=\|r\|_{1}.\] (11)
4. Some non-negative constraints of \(r\) and \(b\). For the rain streaks removal problem, the pixel of the rain streaks layer \(r\) and the background layer \(b\) are non-negative, therefore the following constraints hold \[Con_{1}(r,b):r\geq 0;b\geq 0.\] (12)  
By analyzing the property of multi-direction Shearlets transform of rain streaks and background layers, the corresponding sparse regularizers are obtained. With constraints of \(r\) and \(b\), the optimization model for solving single image rain removal problem can be given as follows:  
\[\min_{s}\quad\gamma_{1}\|S_{r}(o-r)\|_{1}+\gamma_{2}\|S_{b}r\|_{1}+\gamma_{3} \|r\|_{1},\quad s.t.\quad r\geq 0;b\geq 0; \tag{13}\]  
where \(\gamma_{i},i=1,2,3\) are positive regularization parameters. The convexity of (13) guarantees its solver to be global optimal.
## 3 Results  
### Effects of Illumination on the Estimation of LAI  
#### 3.1.1 The Impact of Variable Illumination on CIs  
To analyze the impact of variable illumination on CIs from RGB images, Retinex correction was implemented on UAV images collected at five growing stages. We then analyzed changes in the SDs of original CIs and Retinex-corrected CIs. Figure 3 presents changes in the SDs of 25 CIs at five growth stages. There was a significant reduction in the SDs of CIs in the images of each growth stage after undergoing Retinex correction in comparison to the original images, indicating that Retinex correction reduced the diversity of data. The results demonstrated that illumination conditions have a significant influence on CIs. For example, the SD of the C1 for different growth stages showed a noticeable decrease (Figure 3). The decrease in SD for C1 suggests that variation in illumination conditions during the growth stages can lead to anomalous data and thus can have a negative impact on estimating crop phenotypes using CIs. Simultaneously, the SD of CIs also exhibited variation across different growth stages. Specifically, there was relatively low variability in CI changes at the tillering and grouting stages, while there was greater variability during the elongation, booting, and heading stages. Only C6, C18, and C21 exhibited relatively small variations during the entire growth period of early rice (Figure 3). The variances of other CIs such as C8 showed relatively small changes only at the tillering and elongation stages.  
Figure 3: Difference in color index SD between original CIs and Retinex-corrected CIs. T, E, B, H, and G represent tillering stage, elongation stage, booting stage, heading stage, and grouting stage, respectively.
## 2 Literature Review
### Machine Learning for Network Optimisation  
In [1], a survey of various research works on the opportunities, solutions, challenges, and roles of machine learning applications in 5G network slicing and orchestration is presented. It emphasises the need for efficiently selecting the right machine learning algorithm to enable intelligent and self-organising wireless systems.  
[PERSON] et al. [7] proposed an RL-based approach for joint slice admission control and network resource allocation. Their results show that RL outperforms conventional methods  
Figure 1: 5G network infrastructure—sub-slice network.  
in terms of resource utilisation in dynamic, unknown environments, slice admission rate, and network revenue. Authors in [8] also proposed an RL-based framework for adaptive 5G network resource allocation in sliced radio access networks.  
Long short-term memory (LSTM) networks were utilised in a study in [9] for predicting mobile traffic demands in 5G networks. The performance of LSTM models was evaluated on real-world mobile traffic datasets. The simulation result discussed in the study shows that LSTM outperforms traditional time series forecasting models like ARIMA. A deep-learning-based approach was also proposed in [10] in a study to proactively allocate network resources in sliced networks using traffic prediction models.  
Despite the substantial progress achieved, the quest for an efficacious use of limited physical resources in mobile networks still exists, which drives the continuous research and innovation in the space. Our proposed machine-learning-based framework aims to contribute by combining a series of supervised learning techniques for intelligent resource allocation and dynamic network slicing as well as reinforcement learning for traffic prediction and proactive scaling.