esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Supervised Support Vector Machine Classification  
The main land cover (LC) types present in the study area were obtained by a supervised classification employing the Support Vector Machine (SVM) algorithm using a Gaussian kernel [53] on the LANDSAT 8 spectral bands calibrated in surface reflectance normalized for the effects of topography. The classification was applied for each date, taking into account a land cover typology considering the physical nature of the material covering the ground. This typology gathers a set of seven classes: GV, which corresponds to photosynthetically active \"green\" vegetation; NPV, which is assimilated here to senescent or non-photosynthetically active vegetation; SOIL for bare soil; SNOW for snow cover; ROCK for outcropping rock; URBAN for built-up areas; and NA for unclassified artifacts. The training data used to calibrate the model, 237 samples, come either from a photo-interpretation procedure or from field surveys. The classifications for each period are then combined to obtain a final classification that provides information on the succession of physical states in which the land cover is found at different times of the year. We consider that this type of classification is of interest for the mapping of ES because it provides bio-geophysical-based qualitative information on the land cover, which an LC classification defined according to a more traditional nomenclature does not directly indicate. It differs, however, from a spectral unmixing method for which a sub-pixel proportion of the material can be estimated [54].  
K-fold cross-validation with 10 folds repeated 10 times was performed to estimate the accuracy of the model (Table 1).
## 4 Discussion
### Evaluation of Merged Satellite Precipitation  
Fusion methods based on different datasets or different members of the same model could be used to effectively reduce estimation uncertainty in meteorology and hydrology [113]. Typically, merged datasets contain information about multiple members and possess superior data quality than any single dataset [114; 115]. The basic data commonly used for precipitation data fusion are mostly raw satellite data with low precision and rough resolution, which has a great impact on the quality and application of the final fusion data. In this paper, the precipitation data obtained after downscaling and calibration are selected, which is also an essential factor for obtaining the final high-quality precipitation data.  
On this basis, the AHP and EW method are combined to merge different satellite precipitation data. This method not only incorporates experts' experience and prior knowledge, but also includes the correlation between data information and evaluation indicators. The use of this method is critical to obtain more reasonable results (Figure 12).  
Figure 12: Taylor diagrams for daily precipitation of Rain gauge observation, original satellite (i.e., CMORPH, IMERG and ERA5) and calibration during 2010–2014: O represents the original satellite data, C means the calibrated precipitation data and RGS denotes the rain gauge data.
## 2 Optimized Link State Routing Protocol
### Multi-Point Relays Selection Process  
For node A to call node B its neighbor, node B should be able to hear node A (i.e., a link exists between an OLSR interface on node A and an OLSR interface on B). This concerns 1-hop neighbor nodes. The node A 2-hop neighbors represent the nodes heard by the node A neighbors.  
MPRs are selected by a node from its one-hop neighbors with \"symmetric,\" meaning bi-directional, links. Consequently, routing via an MPR inherently mitigates issues related to data packet transfer over unidirectional links, such as the absence of link-layer acknowledgments for data packets at each hop. Indeed, a node N\({}_{\text{i}}\) initially designates as MPRs all its neighbors that are the sole neighbors of a two-hop node from N\({}_{\text{i}}\). It then chooses an MPR neighbor with the highest count of covered two-hop nodes. This step is repeated iteratively until all two-hop nodes are covered. Finally, any MPR node N\({}_{\text{i}}\) that the MPR set, excluding N\({}_{\text{j}}\), covers all two-hop nodes is discarded from the final selection.
## 1 Introduction: On Noise-Based Logic  
The Einstein-Podolsky-Rosen paradox [1] points to a deep feature of quantum physics leading to the notion of entanglement. In this paper, we address a similar problem in noise-based logic (NBL) [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15], which is a classical physical scheme where noise-bits carry the same high-dimensional Hilbert space as qubits in quantum computing. We explore some of the similarities and differences between the entanglements and their measurements in these systems and propose relevant data search applications for NBL.  
Noise-based logic (NBL) [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15] is a classical physical computation system using orthogonal stochastic processes to carry and process the logic information. Even though the creation of NBL was originally inspired by the stochastic nature of neural signals [2; 3; 4; 5] and there are some brain-inspired NBL schemes [6; 7] that may help in understanding the functioning of the biological brain, the developed NBL schemes and their methods reach far beyond the brain-mimicking efforts [8; 9; 10; 11; 12; 13; 14; 15]. Similarly, there is some resemblance between NBL and quantum computing including some exponentially large superpositions that can be created by polynomial complexity [8; 9; 10; 11; 12; 13; 14; 15]. However, the NBL schemes and their modes of operations are substantially different, even if some of the computational goals may be the same. While the explorations of NBL are still taking place, it is already clear that in certain demonstrated cases, the robustness and controllability of classical physical systems and information offer strong advantages [14; 15] over quantum informatics. Some NBL schemes can offer solutions that either reach beyond the abilities of quantum informatics or offer alternative means with identical potential and less cost, similarly to recent achievements by [PERSON] in classical computing [16; 17]. The present paper is an additional new example of this fact.  
Note that the exploration of classical physical stochastic processes for information technology also yielded very similar positive surprises regarding quantum encryption. The classical statistical physical key exchange scheme, the KLJN system [18], offers the same level of security and it is the only currently known unconditionally secure communication scheme that can be integrated on a chip.
## 2 Influencing Factors of Permeability Coefficient  
### Porosity  
Porosity is one of the key parameters governing the permeability coefficient of coarse-grained soils, exhibiting a distinctly nonlinear influence. The classical Kozeny-Carman equation establishes a positive correlation between permeability (\(k\)) and porosity (\(n\)), ex pressed as Equation (1). Experimental studies indicate that the permeability coefficient of uniform coarse sand can increase by two to three orders of magnitude when porosity rises from 0.25 to 0.35.  
\[k=\frac{n^{3}}{\left(1-n\right)^{2}} \tag{1}\]  
However, significant deviations from this theoretical relationship often occur in practical engineering applications due to spatial heterogeneity in pore structures, such as localized pore clustering and the formation of preferential flow channels. Fractal theory studies reveal that the permeability coefficient can increase by 40-60% when the fractal dimension (\(D_{J}\)) rises by 0.1, reflecting enhanced pore connectivity at the same porosity. This effect becomes particularly pronounced under high compaction conditions (\(n<0.2\)), where intensified grain-to-grain contact sharply increases pore tortuosity, reducing the sensitivity of permeability to porosity changes by over 80%. Under such conditions, the evolution of the contact network topology induced by gradation reconstruction becomes the dominant factor controlling permeability.
## 1 Introduction  
The interest in DC-DC types of forward converters is reflected in the plethora of publications on the subject, including academic research, industry, and manufacturer publications [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. The multi-output single-switch forward converter, a member of the DC-DC converters family, which is single-input multiple-output (SIMO), also offers isolated topology. Single-switch multi-output forward converters are used from 50 W to 300 W. In short, multi-output forward converters are an option for lower powers [12]. Some of the advantages of forward converters are galvanic isolation between the input and output, their simple structure and their low cost [13].  
Cross-regulation is a technique for achieving desired output voltages in multiple-output DC-DC power supplies at desired output voltages. There are many research articles that touch upon the issue of cross-regulation in SIMO converters [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]. In some of these studies, the desired output voltages were achieved by developing a new strategy [14, 16, 17, 20, 21, 22, 23, 28]. In others, the desired output voltages were achieved by changing diodes or transformer changes, etc. [15, 18, 19, 26]. Frankly, since there is not much research on a multi-output forward converter cross-regulation, we can consider the multi-output flyback converter as a suitable candidate for investigating cross-regulation, due to its low power. Therefore, we will examine and discuss flyback converter cross-regulation issues more meticulously. For instance, in a dual-output 40 W flyback converter [24], the current is limited by being shifted, thus 0.2% cross-regulation is observed. It is used for digital control. Thus, this approach increases additional costs. In another study [25], a flyback converter containing multi-output windings with a synchronous rectification (SR) function to achieve good cross-regulation was utilized, with a cascade structure created by recruiting a few MOSFETs. In addition, 2.26% cross-regulation was achieved, and 87% maximum efficiency was observed. However, there is a problem with most of these cross-regulation techniques. The use of lots of MOSFETs and their controls in the circuit are macro problems. Additionally, the use of additional drivers for controls will increase costs. In a similar study [29], by utilizing PSR control, the relative output voltage deviation was relatively reduced, to below 5%. In another study [30], the output voltage variation ratio was reduced from 17% to 8% with a stacked output flyback topology design. In a similar study [31], cross-regulation up to 0.294% was achieved under same loading. By not using DSP in this article, the authors have avoided the control complexity of the circuit to be designed. In other words, a simple analogue control method of a designed low-power converter is demonstrated.
## 5 Position and Opportunities for Further Research
### Timing Predictability of Safety-Critical CPSs
Regarding the sub-question of the second research question, several existing timing predictability verification techniques for the computation and communication parts have already been integrated to the existing safety processes. For example, the classical safety analysis Failure Mode and Effects Analysis (FMEA) [53] includes such failures modes as late or early relating to the timing of inputs. However, to the best of our knowledge, the integration of timing predictability of inputs arrival order and inputs ready times to the existing safety analyses is yet to be done. The answer to the third research question is similar to the answer of the second research question. The existing execution frameworks and tools support predictable runtime environments in the computation and communication parts, as discussed in Section 3.1. However, these frameworks and underlying techniques need to be extended to provide upper bounds on the input ready times and to enforce the inputs ready order.
## Materials and Methods
### The Models: NOAH  
GLDAS contains the main outputs of the simulations carried out using the NOAH LSM. NOAH is an acronym deriving from the initial letters of the four agencies who collaborate for developing the model: National Centers for Environmental Prediction (NCEP), Oregon State University (OSU), the Air Force, and the Hydrology Research Laboratory at the National Weather Service. The NOAH is a well-known second generation LSM, incorporated in the WRF (Weather Research and Forecasting) mesoscale atmospheric model, and was evolved from the Oregon State University (OSU) model created in 1980s [40]. It can simulate water and energy fluxes, soil and land surface temperature, snow depth and snow water equivalent. The standard model configuration commonly has four soil layers and one vegetation canopy layer and includes a detailed snow parameterization. For further details about NOAH configuration, please refer to Reference [41]. We underline here that we did not run NOAH, but we just extracted the 3-hourly simulation outputs from GLDAS.
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
## 2 Preliminaries
### UV Node Communication Radius  
The scattering and absorption of gas molecules and aerosol particles in the atmosphere make the UV signal seriously attenuated. After a large number of experimental tests, [PERSON] et al. proposed a path loss model for UV communication [12]. In short-distance communication of hundreds of meters, the simplified expression of path loss is as [12]  
\[L=\xi r^{\alpha} \tag{1}\]  
where \(L\) is the power attenuation, \(\xi\) is the path loss factor, \(\alpha\) is the path loss exponent and \(r\) is the communication radius. On the basis of [PERSON], we came to similar experimental conclusions after many Monte Carlo simulation experiments when \(\Phi_{1}\) and \(\Phi_{2}\) are fixed at \(17^{\circ}\) and \(30^{\circ}\), respectively. Figure 3 shows the experimental results under the conditions of different transmit and receive elevation angles and a step length of \(10^{\circ}\).  
This article chooses the on-off keying (OOK) modulation method, and the bit error rate (\(P_{e}\)) expression is [13]  
\[P_{e}=\frac{1}{2}\exp(-\lambda_{s-\text{OOK}}) \tag{2}\]  
\(\lambda_{s-\text{oOK}}\), which is the arrival rate of photons per cycle, is expressed as [14]  
\[\lambda_{s-\text{OOK}}=\frac{\eta P_{t}}{LR_{b}hc/\lambda} \tag{3}\]  
By substituting Equations (1) and (3) into Equation (2), we get  
\[r_{\text{OOK}}=\sqrt[*]{-\frac{\eta\lambda P_{t}}{hc_{e}^{\xi}R_{b}\ln(2P_{e} )}} \tag{4}\]  
Figure 3: (**a**) Path loss factor. (**b**) Path loss exponent.  
Figure 2: (**a**) NLOS-a, (**b**) NLOS-b, (**c**) NLOS-c.
## 2 Methodology  
The LCI model is developed using actual and local data from two-year monitoring study of the compositing unit. Material flow analysis (MFA) software STAN is utilized to fill in missing and not-accessible data, while the LCA software EASETECH is used as a tool for the elemental pathway of C and N in the process. The case study facility is in Chania on the island of Crete (Greece). The compositing unit is part of the integrated \"Mechanical Recycling and Composting Facility--Landfill\" of Chania. It serves 156,585 inhabitants [25] and annually treats approximately 91,500 Mg of urban solid waste [26]. The OFMSW is collected from the mechanical recycling facility, and the process is classified as a simplified pretreatment method [27]. Briefly, the comingled waste is fed in the mechanical sorting system, passing throw a bag opener and an automatic rotary sieve (trommel) with a 70 mm diameter mess. A conveyor then drops the undersized material to a magnet for ferrous metals removal, and the remaining is obtained as OFMSW. The oversized material exited from the trommel is driven for recyclables recovery in the facility, while the rejects are disposed of in the nearby landfill.  
The system boundary for this study is shown in Figure 1. It includes the compositing subsystems (aerobic compositing tank, refinery unit, open windows-maturation), which act as the operational processes after the wastes are delivered to the compositing plant. The methodology is based on an in-depth analysis of all of the consisting fragments of OFMSW. The waste fragments are comprehensively characterized throughout the subprocesses until their degradation to greenhouse gas (GHG) emissions released to the environment, rejects disposed of to the landfill, or CLO production. Initially, every subprocess and flow are recognized and recorded, while the monitoring period is two years (2018-2019).  
The greenhouse gases (GHG) are also considered and studied as C and N transformations along with the main waste flows. The energy is calculated in the form of electricity, fuel in diesel consumed, and the water consumed in the subprocesses. The green waste (GW) consists of tree branches collected from the municipality bulky collection system. It is shredded in the facility and used as a bulking agent in a ratio of 1:4 by volume. The functional unit is 1 Mg of wet mass OFMSW mixed with green waste entering to the compositing unit.
## 3 Materials and Methods
### Methods
#### 3.2.2 Haversine Formula for Proximity Calculation  
The distance calculation between the user's location and shopping centers is performed using the Haversine formula, which estimates the distance between two points on the surface of a sphere based on their geographic coordinates (latitude and longitude). Since the Earth is approximately spherical, this formula provides an accurate estimation of the distance in ms [18]. The Haversine formula is defined by Equation (1).  
\[d=2r\cdot\arcsin\Bigg{(}\sqrt{\sin^{2}\bigg{(}\frac{\Delta\varphi}{2}\bigg{)}+ \cos(\varphi_{1})\cdot\cos(\varphi_{2})\cdot\sin^{2}\bigg{(}\frac{\Delta \lambda}{2}\bigg{)}}\Bigg{)}. \tag{1}\]  
The following notations are used:  
Figure 2: Development tools and environment elements.  
\(d\) is the distance between the two points.
* \(r\) is the Earth's radius (approximately 6371 km).
* \(\varphi_{1}\) and \(\varphi_{2}\) are the latitudes of the two points in radians.
* \(\Delta\varphi\) is the difference in latitudes, \(\varphi_{2}-\varphi_{1}\).
* \(\Delta\lambda\) is the difference in longitudes, \(\lambda_{2}-\lambda_{1}\).  
Although the Haversine formula provides an accurate estimation of the distance between two points on the Earth's surface, it has significant limitations when applied in practical contexts involving real road networks. For instance, two points separated by a river without a connecting bridge may have a direct distance of 200 m according to the formula, but the actual distance via the road network could be considerably greater due to the necessity of using alternative routes. This scenario illustrates that the formula does not account for physical or topological constraints of the terrain, which can lead to errors in determining whether a user is within the specified proximity range. Therefore, future implementations could benefit from integrating models based on actual road networks to complement or replace calculations that rely solely on geographic coordinates.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 3 System Model
### Proposed Stackelberg Security-Based Game Theory Algorithm for Securing IoMT Networks Using UAVs  
This subsection presents our proposed Stackelberg security-based game theory algorithm for securing IoMT networks using UAVs. Our algorithm is designed to detect and  
Figure 3: Routing strategy with a black hole in UAV network scenario.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Destination** & **Next Hop** & **Metric** & **RREQ-A** & **RREP-A** & **RREQ-F** & **RREP-F** & **Fake-E** \\ \hline UAV-A & - & 0 & - & - & Yes & Yes & No \\ UAV-B & UAV-D & 2 & No & Yes & No & Yes & Yes \\ UAV-C & UAV-B & 2 & No & Yes & No & Yes & Yes \\ UAV-D & UAV-F & 2 & No & Yes & No & Yes & Yes \\ UAV-E & - & 0 & Yes & Yes & Yes & No \\ UAV-F & - & 0 & Yes & Yes & - & - & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Global routing table for UAV ad hoc network (A, E, F).  
recover data affected by black hole attacks, a common type of attack in IoMT networks. The Stackelberg game theory model is a powerful tool for analyzing decision-making when multiple parties have conflicting interests. In our proposed scheme, the UAV network is modeled as a game competition between the attacker, who aims to disrupt communication, and the defender, who aims to protect the network. We utilize the strong Stackelberg equilibrium (SSE) to formulate strategies that protect the system against attacks. Our proposed algorithm consists of two main components: the Stackelberg game theory scheme and the critical features of the UAV-assisted Stackelberg game model. In the following sections, we describe each component in detail.
## 3 Validation of the Numerical Model  
Validation of the current numerical model was carried out by comparing the numerical model employed with the experimental study available in [7]. As there was no data available for the geometry used in the current work, the experimental results accessible in [8] were utilized for validation purposes.  
The numerical model present above was employed to compute the erosion rate in the channel used by [8] with dimensions \(6\,\mathrm{m}\times 2\,\mathrm{m}\times 0.05\,\mathrm{m}\), as displayed in Figure 3. The diameter of the particles used for the validation work was \(165\,\mathrm{\SIUnitSymbolMicro m}\) and density was \(2680\,\mathrm{kg/m^{3}}\). A uniform volume concentration of the particles, i.e., \(8.41\%\), was imposed at the inlet boundary. While an average velocity at the inlet of the fluid domain was enforced with a value of \(1.66\,\mathrm{m/s}\). The same geometry (Figure 3) and other properties mentioned above used by [7] were implemented for the numerical model discussed above. The comparison of the numerical and experimental results is shown in Figures 4 and 5. Figures 4 and 5 show a comparison of experimental results and results produced by the current numerical model comparing particles and velocity profile concentration. The comparison shows that both numerical and reported experimental results [7] were in close agreement, and the present numerical model could predict erosion rate. The same validation procedure has also been adopted in the literature by [14].  
Figure 3: Cont.  
Figure 4: Comparison of numerical and experimental results for the concentration of particles.  
Figure 3: (**a**) geometrical model and boundary locations (**b**) mesh of the computational model.