esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Point Cloud Data Registration Method
### Methodology
#### 2.1.1 SIFT Feature Extraction  
SIFT feature extraction includes spatial feature point extraction, feature point detection, determination of the feature point direction, and determination of the feature point descriptor [39]. The main processes include:  
Assume that the coordinate of a point in the point cloud is \(P(x,y,z)\) and the spatial scale of the point cloud is \(L(x,y,z,\sigma)\). The relationship between the point coordinate and the spatial scale is as shown in Equation (1).  
\[L(x,y,z,\sigma)=G(x,y,z,\sigma)*P(x,y,z) \tag{1}\]  
where \(\sigma\) is the spatial scale factor of the point cloud; \(G(x,y,z,\sigma)\) is the three-dimensional Gaussian kernel function of the point cloud; and the calculation method is as shown in Equation (2).  
\[G(x,y,z,\sigma)=\frac{1}{\left(\sqrt{2\pi\sigma}\right)^{3}}e^{\frac{-(x^{2}+y ^{2}+z^{2})}{2\sigma^{2}}} \tag{2}\]  
When constructing a point cloud Gaussian pyramid, the Different-of-Gaussian (DoG) operators at the \(k\) scale are defined as shown in Equation (3).  
\[D(x,y,z,k^{s}\sigma)=L\!\left(x,y,z,k^{s+1}\sigma\right)-L(x,y,z,k^{s}\sigma) \tag{3}\]  
where \(s\in[0,1]\). Using the Lowe operator to linearly fit the Gaussian difference-scale space function and eliminate extreme points with strong edge effects, the remaining extreme points are the feature points. Equation (4) is used to calculate the amplitude \(m(x,y,z)\), azimuth \(\theta(x,y,z)\), and angle \(\varphi(x,y,z)\) from the feature point \((x_{i},y_{i},z_{i})\) to its neighborhood center point \((x_{c},y_{c},z_{c})\).  
\[\left\{\begin{array}{l}m(x,y,z)=\sqrt{\left(x_{i}-x_{c}\right)^{2}+\left(y_ {i}-y_{c}\right)^{2}+\left(z_{i}-z_{c}\right)^{2}}\\ \theta(x,y,z)=\tan^{-1}(\left(y_{i}-y_{c}\right)/\left(x_{i}-x_{c}\right))\\ \varphi(x,y,z)=\sin^{-1}(\left(z_{i}-z_{c}\right)/m(x,y,z))\end{array}\right. \tag{4}\]  
Based on the azimuth and angle of the neighborhood of each feature point, define the amplitude as the weight and smooth the histogram, and define the peak direction of the histogram as the feature point direction. Define a four-dimensional vector (\(\sigma\), \(m\), \(\theta\), \(\varphi\)) for any feature point, divide its neighborhood into sub-domains of \(n^{*}n^{*}n\), and count the frequency of occurrence of all neighborhood points in the sub-domain in each direction. The frequency of occurrence is the feature point descriptor.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 4 Discussion
### Dependence of the Sensitivity of Terrestrial Carbon Fluxes to Drought on Climate Conditions  
An additional finding of this study was that sensitivity of the terrestrial carbon fluxes to drought was the highest in semi-arid areas. This is consistent with the peaked hydrodynamic sensitivity over semi-arid regions found by [PERSON] et al. [45] using an enhanced vegetation index and SPEI. However, by quantifying reductions in above-ground net primary production, [PERSON] et al. [46] reported that drought sensitivity generally decreased with increasing mean annual precipitation. Our results partly agreed with the above study, since in areas with negative annual water balance, the sensitivity of terrestrial carbon fluxes to drought increased significantly with increasing annual water balance, but in areas with positive annual water balance, the sensitivity of terrestrial carbon fluxes to drought decreased with increasing annual water balance, except for ER.  
Although sensitivity of the terrestrial carbon fluxes generally increased with increasing annual water balance, and then decreased with increasing annual water balance, there was large heterogeneity in different climate conditions, especially in humid regions (indicated by the error bars in Figure 7). This may be attributed to the different climatic constraints in these areas. For instance, in the temperate humid regions of Asia, radiation played a more important role in explaining productivity variability [3], while in the temperate humid regions of Oceania, water was the most important limiting factor [3]. It is important to note that different terrestrial ecosystem growth controlled by different climatic constraints may respond differently during wet or drought conditions.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 2 Basic Principles  
Employing a dual-antenna configuration, the LuTan-1 system acquires sea surface backscatter data, creating two complex images, and then processes them to obtain an interferogram image.  
\[I=s_{1}(r_{1})s_{2}^{*}(r_{2})=|s_{1}(r_{1})s_{2}(r_{2})|\exp[j(\varphi_{A1}- \varphi_{A2})]. \tag{1}\]  
where \(s\) denotes the complex SAR image data, \(\varphi\) is the phase, and \(r\) is the slant range from the SAR platform to the scatterer on the sea surface. The subscripts 1 and 2 correspond to the dual-receiver antenna configuration of the LuTan-1 SAR satellite system. The asterisk (*) indicates complex conjugation. Given that the LuTan-1 system operates as a hybrid baseline interferometric SAR system, the interferometric phase \(\phi\) delineated in (1) comprises the along-track baseline component that is sensitive to velocity and the cross-track baseline component that is sensitive to the sea surface height. The hybrid baseline interferometric phase is expressed as follows:  
\[\phi=\phi_{ATI}+\phi_{XTI}+\phi_{error}=\phi_{d}+\phi_{h}+\phi_{flat}+\phi_{ error} \tag{2}\]  
where \(\phi_{d}\) represents the phase component induced by the ocean surface Doppler velocity, which is caused by the along-track baseline component; in addition to the contribution of the sea surface current velocity, this term also incorporates the effects of the Bragg phase velocity, the long-wave orbital velocity, etc. \(\phi_{flat}\) and \(\phi_{h}\) represent the flat Earth phase and topography phase, respectively, caused by the cross-track baseline components. \(\phi_{error}\) contains all errors. To achieve velocity retrieval, the velocity-independent phase components should be removed.
# Formal Modeling of IoT-Based Distribution Management System for Smart Grids
## 3 Problem Statement and System Model
### Supervisory Control and Data Acquisition (SCADA) System  
A smart grid is made up of several micro subsystems that work together to share connectivity and security components. It is the core component of the substation's control center, which is not only a monitoring system but also provides communication links. It is used for automating the distribution network of a medium voltage substation for intelligent remote controlling. This controlling and monitoring infrastructure provides benefits to  
Figure 5: Possible states of the IED during the fault.  
the power utilities by enhancing electric supply maintainability and lowering the cost of operation [10]. The essential features of SCADA are gathering data, presentation and monitoring, supervisory control, and notifying alarms as shown in Figure 6. It includes both hardware and software, having primary components such as human-machine interfaces (HMIs), programmable logic controllers (PLCs), data collection servers, and remote terminal units (RTUs).
## 3 Results and Discussions
In our results, SOI flattens the electronic band of (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\) in the vicinity of the band edges (VBM and CBM) because dE/dk decreases near these edges. We expect that this is related to the change in the electronic effective mass near the VBM and CBM, although it was not quantitatively analysed. For Bi\({}_{2}\)Te\({}_{3}\), the SOI calculations accompanied an increase in the energy level of VBM (\(-\)0.061 eV) and a decrease in that of CBM (0.068 eV), resulting in a slight decrease in energy gaps (E\({}_{\rm gap}^{\rm i}\) = 0.129 eV, E\({}_{\rm gap}^{\rm d}\) = 0.135 eV). For Bi\({}_{2}\), the SOI calculation increases both VBM (0.113 eV) and CBM (-0.048 eV), leading to a small increase in the energy gap values (E\({}_{\rm gap}^{\rm i}\) = \(-\)0.161 eV, E\({}_{\rm gap}^{\rm d}\) = 0.079 eV). In addition, for BiTe, the SOI calculations decrease VBM (-0.064 eV) and increase CBM (0.053 eV), eventually opening E\({}_{\rm gap}^{\rm i}\) (0.117 eV), unlike closed gaps from calculations without SOI (\(-\)0.04 eV). Note that the electric transport type of BiTe changes to a semiconductor of calculations with SOI unlike semimetals of the calculations without SOI. For the other phases except for BiTe, with SOI, the electric transport types do not change, although E\({}_{\rm VBM}\), k\({}_{\rm VBM}\), E\({}_{\rm CBM}\), k\({}_{\rm CBM}\), E\({}_{\rm gap}^{\rm i}\), and k\({}_{\rm gap}^{\rm i}\) change slightly compared to the case without SOI. In addition, note that with SOI, LDA and PBE give the same electric transport types except for Bi\({}_{2}\)Te\({}_{3}\) and BiTe unlike the results of the band structures from the calculations without SOI, although the detailed values (E\({}_{\rm VBM}\), kV\({}_{\rm VBM}\), E\({}_{\rm CBM}\), k\({}_{\rm CBM}\), E\({}_{\rm gap}^{\rm i}\), and k\({}_{\rm gap}^{\rm i}\)) changed (see Figures 4-6 and Tables 2 and 3). In detail, LDA with SOI changes the electric transport types of Bi\({}_{8}\)Te\({}_{9}\) and BiTe from semimetals to semiconductors owing to the decrease in VBM (\(-\)0.005 eV for Bi\({}_{8}\)Te\({}_{9}\), \(-\)0.062 eV for BiTe) and the increase in CBM (\(-\)0.002 eV for Bi\({}_{8}\)Te\({}_{9}\), 0.048 eV for BiTe). Note that although it is not possible to explicitly state the semiconductor type, DFT calculations can determine possible electric transport types. Therefore, for the (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\) homologous series, only three phases (Bi\({}_{2}\)Te\({}_{3}\), Bi\({}_{8}\)Te\({}_{9}\), BiTe) can be semiconductors.
## 2 Applications  
### The Stretched Exponential  
The stretched exponential function arises in different mathematical contexts, for instance as Levy symmetric alpha-stable distribution, or as the complement of the cumulative Weibull distribution.  
In physics, the stretched exponential function is routinely employed to describe relaxation in glasses, in glass-forming liquids, and in other disordered materials. The earliest known use is by [PERSON] in 1854 who investigated charge creep in a Leiden jar. He was followed by his son [PERSON] in 1863 who used the stretched exponential to describe torsional relaxation in glass wires, thereby improving previous studies by [PERSON] (1841) and his father (1847). In the modern literature, these early accomplishments are often confounded, and a majority of references to [PERSON]'s Annalen der Physik und Chemie is incorrect [3].  
In 1993, [PERSON] _et al._[4] listed stretching exponents for over 70 materials, obtained by viscoelastic, calorimetric, dielectric, optical, and other linear response measurements. Other important compilations, though tinted by highly personal theoretical views, include a review by [PERSON] [5], and a book by [PERSON] [6]. As of 2011, the [PERSON] review has been cited over 1100 times, indicating a huge increase in the use of the stretched exponential function for describing relaxation phenomena. In the meantime, it has also become clear that non-exponential relaxation is not limited to supercooled glass-forming materials but that it also occurs in normal liquids [7; 8; 9].  
Other physical applications of the stretched exponential function are the time dependence of luminescence or fluorescence decays [10], and the concentration dependence of diffusion coefficients and viscosities [11]. In most applications, the exponent is restricted to values \(\beta\leq 1\). However, in recent years some uses of the \"compressed\" or \"squeezed\" exponential function with \(1<\beta<2\) have been proposed, mostly in protein kinetics [12; 13; 14], but also in magnetism [15]. Outside physics, the stretched exponential function has been found to provide a good fit to various socio-economic statistics, like urban agglomeration sizes, currency exchange rate variations, or the \"success\" of scientists, musicians, and Hollywood blockbusters [16; 17; 18].  
Hardly ever the stretched exponential is the full story. It usually describes a scaling regime that extends over a finite, albeit logarithmically large, time range. Mode-coupling theory has provided one physical way to connect a long-time Kohlrausch asymptote to a short-time regime that satisfies all conservation laws [19; 20]. On the other hand, a stretched exponential function can just be the short-time limit of a Mittag-Leffler function that leads over towards a power-law at long times [21; 22].
## 4 Discussion  
This study presents a mathematical approach to securing Split Learning using Homomorphic Encryption and Zero-Knowledge Proofs, focusing on ensuring data privacy and verifying computational accuracy in decentralized learning environments. This framework leverages the power of HE for performing computations on encrypted data and ZKP for non-disclosive verification, extending the boundaries of cryptographic applications in collaborative AI. HE enables arithmetic operations on encrypted data, making it possible to carry out complex calculations without exposing raw data. Specifically, the CKKS and BFV encryption schemes were chosen for their capability to support addition and multiplication directly on encrypted values, aligning with the mathematical requirements of neural network training. This approach ensures that client data remains fully encrypted during processing by the server, a significant advancement over traditional privacy-preserving methods. The computations on encrypted data are supported by the properties of HE schemes, which allow homomorphic addition and multiplication--an essential feature for maintaining accuracy in Split Learning while protecting sensitive information. ZKP is used to ensure the integrity of computations, allowing clients to generate proof that their local computations are accurate without revealing the data. By constructing proofs based on the algebraic properties of network operations, this method provides a mathematical guarantee against unauthorized interference and manipulation, such as backdoor attacks. ZKP ensures that no party can manipulate the model undetected, adding a critical layer of security within the Split Learning framework.  
Traditional privacy mechanisms, such as Differential Privacy (DP), add noise to protect data contributions, but this can compromise model accuracy. Our HE-based approach avoids this by simultaneously preserving privacy and computational accuracy. These results demonstrate that HE and ZKP preserve data integrity without negatively impacting model accuracy.  
This paper also underscores the power of mathematical rigor in cryptography, demonstrating that HE and ZKP can establish robust and mathematically precise safeguards in Split Learning.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 6 Solving VRPTW with the Hyper-Heuristic Approach
### Evolved Heuristics in Dynamic VRPTW  
Dynamic conditions for VRPTW are simulated in the same way as for the CVRP, where a given percentage of orders (5, 25 and 50 percent) is not known at the beginning, but revealed at a certain point in time. The dynamic scenario is simulated on the VRPTW test case with 200 customers, based on [PERSON]'s type \"RC\" example as outlined in [40; 42]. Following the same recommendations, the number of vehicles is set to 15. Apart from the GP-evolved dispatching rules, we employ the \"earliest first\" rule (EF), which selects the customer whose servicing can be started the soonest (also taking into account the travel time to that customer), and the \"most urgent first\" rule (UF), which selects the customer whose window closing time (due date) is most urgent. The results for the GP priority functions in terms of total travelled distance are shown in Figure 5 and Table 8 along with the EF and UF heuristics.  
It can easily be seen that the GP-evolved priority functions manage to encapsulate the dynamic changes and adapt to new information. In this particular case, the \"nearest neighbour\" heuristic can also produce competitive travel distance, but at the same time incurs a number of unserviced orders (up to 26 for the most dynamic scenario), which makes it inappropriate in this setting.  
Figure 4: Test results on VRPTW instances with different number of customers.
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
###### Abstract  
The technology of wireless sensor networks (WSNs) is developing rapidly, and it has been applied in diverse fields, such as medicine, environmental control, climate prediction, monitoring, etc. Location is one of the critical fields in WSNs. Time difference of arrival (TDOA) has been widely used to locate targets because it has a simple model, and it is easy to implement. Aiming at the problems of large deviation and low accuracy of the nonlinear equation solution for TDOA, many metaheuristic algorithms have been proposed to address the problems. By analyzing the available literature, it can be seen that the swarm intelligence metaheuristic has achieved remarkable results in this domain. The aim of this paper is to achieve further improvements in solving the localization problem by TDOA. To achieve this goal, we proposed a hybrid bald eagle search (HBES) algorithm, which can improve the performance of the bald eagle search (BES) algorithm by using strategies such as chaotic mapping, Levy flight, and opposition-based learning. To evaluate the performance of HBES, we compared HBES with particle swarm algorithm, butterfly optimization algorithm, COOT algorithm, Grey Wolf algorithm, and sine cosine algorithm based on 23 test functions. The comparison results show that the proposed algorithm has better search performance than other reputable metaheuristic algorithms. Additionally, the HBES algorithm was used to solve the TDOA location problem by simulating the deployment of different quantities of base stations in a noise situation. The results show that the proposed method can obtain more consistent and precise locations of unknown target nodes in the TDOA localization than that of others.  
time difference of arrival; bald eagle search algorithm; location; chaos mapping; opposition-based learning +
Footnote †: journal: Applied Sciences
## 3 Occurrence, Distribution and Environmental Impacts of MPs
### MPs in Soil  
Although it was generally believed that the majority of MPs is distributed in aquatic environments, some recent studies indicate that larger amounts of MPs are present in terrestrial ecosystems. It is estimated that the annual amount of MPs deposited in the soil is two to three times higher than the amount of MPs found in seas and oceans. The main sources of MPs in the soil are improper waste management and agriculture. MPs in agriculture originate from artificial fertilizers, mulching, plastic packaging, nets, protective covers, greenhouses, seed treatment with polymer coatings, using wastewater and dried sludge to irrigate or supplement the soil, etc. [36].  
The accumulation of MP particles in the soil disturbs the soil structure and modifies its physicochemical properties by increasing porosity, changing acidity and alkalinity (pH), changing the aggregate structure, influencing diffusion of moisture and water holding capacity and affecting greenhouse gas emissions [36, 37]. According to [PERSON] et al. (2021) [38], MP fragments increase soil pH, which may be caused by increased soil permeability and porosity. MP type and soil type condition pH changes [39]. The vegetation can potentially influence the effects of MPs on the pH of the soil by moderating the effects of MPs on the soil pH compared to unplanted ground [38, 39]. Furthermore, plastic materials affect the enzymatic activity of the soil, which is an indicator of soil fertility and plays a role in nutrient cycling. The mobility of nutrients is disturbed by MPs, and the availability of nutrients is reduced [40]. The microbiota found on the surface of MP particles can change the basic ecological functions of the soil and biochemical processes and adversely affect the microbiological diversity of the soil [14, 36]. The influence of MPs on symbiotic fungi in the soil, soil loosening organisms and plant pollinators can impair the functionality of the entire ecosystem [41]. Microorganisms in the soil or in symbiosis with plant roots have the ability to decompose MPs and thus enable soil bioremediation [36].  
MPs in the soil make sowing and plant growth difficult. Large quantities of MPs can fill and block the pores and reduce the infiltration capacity of the soil, which will disrupt the cycle of nutrients in the soil, i.e., the transport of fertilizers and water between the soil and plants, as well as change the microbial structure. A reduction in soil-plant nutrient transfer can cause plant malnutrition, reducing crop growth and yields [42]. The frequent application of plastic mulches in agriculture can result in significant environmental pollution due to the progressive addition of plastic residues to agricultural soils [36].
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
###### Abstract  
Electrosprayed polycaprolactone (PCL) microparticles are widely used in medical tissue engineering, drug control release delivery, and food packaging due to their prominent structures and properties. In electrospraying, the selection of a suitable solvent system as the carrier of PCL is fundamental and a prerequisite for the stabilization of electrospraying, and the control of morphology and structure of electrospray particles. The latter is not only critical for diversifying the characteristics of electrosprayed particles and achieving improvement in their properties, but also promotes the efficiency of the process and deepens the applications of electrosprayed particles in various fields. In order to make it systematic and more accessible, this review mainly concludes the effects of different solution properties on the operating parameters in electrospraying on the formation of Taylor cone and the final structure as well as the morphology. Meanwhile, correlations between operating parameters and electrospraying stages are summarized as well. Finally, this review provides detailed guidance on the selection of a suitable solvent system regarding the desired morphology, structure, and applications of PCL particles.  
polycaprolactone microparticle; electrospraying; solvent; operating parameters; Taylor cone; structure; morphology +
Footnote †: journal: Applied Sciences
## 5 Simulation and Results  
In this section, we carried out various simulations to verify the effectiveness of our proposed algorithm in LEO beam hopping scenario. In this paper, there is a mega-constellation of Ka-band LEO satellites deployed at 1050 km and the cell illuminated by each beam is a regular hexagon with a side length of 40 km. In order to form a complete beam hopping map, the following two reasonable assumptions should be accepted due to the duration of a transmission cycle is short enough: (1) The position of the LEO satellite relative to the terrestrial cell is static during a beam hopping period. (2) The channel state is constant within one beam hopping transmission period. Above the orbit of the LEO system, there is a GEO high throughput satellite sharing the same spectrum, and some GEO ground stations are scattered in the footprint of LEO satellites.