esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
## Methods
### Genetic Algorithms  
GA are nature-inspired searching algorithms initially proposed by [PERSON] [26]. GA are widely used in various design, control, and optimization problems when exact analytical approaches either do not exist or are too time-consuming to be applied. Moreover, GA [27; 28] can be used for solving problems that feature an unknown, non-uniform, discontinuous, or intractably large search space. The representation of the candidate solution in GA is in the form of a population of linear strings (i.e., chromosome) of evolving values of parameters (genetic alleles). Mathematically, the chromosome could be represented as an \(n\)-dimensional vector (x\({}_{1}\), x\({}_{2}\),..., x\({}_{n}\)) where each coordinate x\({}_{i}\) of the vector--the value of a parameter to be optimized--represents a genetic allele. The objective of applying GA is to find such a chromosome that results in an optimal value of a given target--fitness function F. It is important to note that the design of fitness function F is domain-specific, and depends on the nature of the problem being solved. In GA, the quest for the best chromosome (solution), i.e., the chromosome that yields an optimal value of F, consists of the following main steps:  
1. Creating the initial population of randomly generated chromosomes;
2. Evaluating the fitness of chromosomes in the population;
3. Checking the termination criteria: good enough fitness value (of the best chromosome in the population), too long runtime, or too many generations. GA terminates if one of the criteria is satisfied;
4. Selection: selecting the mating pool of chromosomes. The size of the mating pool is a fraction (i.e., 10%) of the overall size of the population, and the selection of the chromosomes in the mating pool is fitness-proportional (roulette-wheel, tournament, elitism, etc.);
5. Reproduction: implementing crossover by swapping random fragments of randomly selected pairs (parents) of chromosomes from the mating pool. Crossover produces pairs of offspring chromosomes that are inserted into the newly growing population; and
6. Mutation: random gene(s) of newly generated offspring chromosomes are randomly modified with a given probability.  
The implementation of GA could be illustrated by the following pseudo-code:  
1. Generate initial population;
2. Evaluate population;
3. While not (Termination Criteria) do Selection; // Creating the mating pool of surviving chromosomes Reproduction; // Crossing over a randomly selected pair of survived chromosomes Mutation; // Mutating the newly produced (by crossover) offspring Evaluate population;
## 2 Physical and Mathematical Models  
The mechanical problem involves using hydraulic and geometrical parameters to solve the Laplace equation in porous media ([PERSON]'s law), as shown in Equation (1). The potential field result is the energy divided by unit weight, which is commonly known as the hydraulic head or piezometry \"\(h\)\". The thermal problem solves the heat transfer problem with the application of [PERSON]'s equation [9] (Equation (6)). As no buoyancy effect caused by the change in temperature (density changes) is considered, the mechanical problem is independent of the thermal problem, but the thermal problem is coupled with the hydraulic problem. This means that the flow conditions affect the temperature distribution, but the thermal parameters do not influence the recharge flow. The physical scheme of the problem is shown in Figure 2. It depicts a saturated aquifer of anisotropic hydraulic conductivity that is impermeable at its bottom, with second-class homogeneous conditions or a Neumann condition (Equation (3)) and a water table coincident with the ground surface. The right border is the outflow region, while the left is impermeable (no flow) due to domain symmetry (half of the river); see Equation (3). Groundwater flow is imposed by constant potentials (first-class or Dirichlet conditions) at the river bottom (\(h_{2}\)) and potentials on the right border (\(h_{1}\)); see Equation (2). The thermal boundary conditions on the top and bed of the aquifer are temperatures \(T_{1}\) and \(T_{2}\), respectively, and Dirichlet conditions (constant values). On the left and right boundaries, there are adiabatic conditions (zero heat flux); see Equations (7)-(9). The hydraulic problem, decoupled from the thermal problem, is characterized by a potential field \(h(x,y)\) resulting from the mechanical boundary conditions and the hydraulic conductivity. The flow lines go down below the streambed of the river and gradually curve until they become practically horizontal and equidistant (constant flow) beyond a certain extension. Regarding the temperature field \(T(x,y)\), there is a region in which the final temperature-depth profile develops. The extension in which the temperature-depth profile develops is called \(I_{T}\). The mathematical model is gathered in the consequent set of equations:  
Figure 1: Flow diagram of the methodology. The mechanical problem is presented as follows:  
\[\mathrm{K_{x}}\frac{\partial^{2}\mathrm{h}}{\partial\mathrm{x}^{2}}+\mathrm{K_{y} }\frac{\partial^{2}\mathrm{h}}{\partial\mathrm{y}^{2}}=0 \begin{cases}\frac{ \partial\mathrm{v_{x}}}{\partial\mathrm{x}}+\frac{\partial\mathrm{v_{y}}}{ \partial\mathrm{y}}=0\\ \mathrm{v_{x}}=-\mathrm{K_{x}}\frac{\partial\mathrm{h}}{\partial\mathrm{x}} \end{cases}
## 5 Conclusions  
This paper develops theory and computing to explore the spatial response of electromagnetic flowmeters. While most flowmeters focus on measuring the overall flow rate, this paper explores the response of a flowmeter to the spatial distribution of the flow and imposed magnetic field, and develops a new dipole form of the forward map. By writing the induced boundary potential in terms of the Neumann Green's function and introducing the dipole form of the forward map we generalize previous approaches to deriving weight functions, and are able to provide a succinct intuition for the response of flowmeters to the distribution of flow within a pipe.  
A detailed definition of the Neumann Green's function is provided, along with the salient properties of the Neumann Green's function.The dipole kernel for electromagnetic flowmeters shows how previous analyses are examples of the forward map developed here.  
Reconstruction of flow distribution was implemented using a TSVD, showing that bulk flow and flow distribution can be determined, though with a quite large number of measurements. We suggest that improved accuracy of measurement of the total flow rate, and flow distribution, from relatively few measurements can be achieved by ensuring that electrodes are at nulls of the magnetic field. This is a topic of ongoing research.  
In conclusion, the authors demonstrate the potential of electromagnetic flowmeters to improve the accuracy and informativeness of flow measurements in industrial settings by examining their spatial response and presenting a new dipole form of the forward map. The methods presented in this paper can easily extend to general flows in 3-dimensional domains, making them highly applicable in a variety of contexts.  
[PERSON] and [PERSON] contributed equally to this work. [PERSON] provided initial theoretical background and methods while [PERSON] developed the novel dipole form and produced the computed examples of flow tomography. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the New Zealand Ministry of Business, Innovation & Employment SfTI Spearhead project \"Inverting Electromagnetics\" via Contract No. UOOX1208.  
The authors are grateful to [PERSON] and [PERSON] of Lincoln Agritech for assistance and leadership in this project.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EM & Electro-Magnetic \\ SVD & Singular value decomposition \\ TSVD & Truncated singular value decomposition \\ SNR & Signal to noise ratio \\ \end{tabular}
# Survey of Blockchain-Based Applications for IoT
## 6 Data Integrity and Trust
### Verifiable Credentials in Blockchain-IoT Systems  
Verifiable Credentials (VCs) are digitally signed credentials that allow entities, such as IoT devices, users, or organizations, to prove claims about their identity, capabilities, or permissions without relying on a centralized authority. Many VCs utilize DIDs and software implementations of this model must be capable of resolving DIDs. DID-based URLs serve as identifiers for various entities involved in credential management, including subjects, issuers, holders, credential status lists, cryptographic keys, and other machine-readable information linked to a verifiable credential [48].  
To support Verifiable Credential (VC) validation, a verifiable data registry (VDR) is used to store and manage DIDs, public keys, and credential metadata. A VDR can be implemented on distributed ledger technologies (DLTs) like blockchains, ensuring tamper-proof and decentralized identity records. Alternatively, it can be stored in external databases or decentralized storage solutions, allowing flexible and scalable identity management.  
Figure 8: General steps of IPFS file storage and retrieval.  
Examples of verifiable data registries include trusted databases, decentralized databases, government ID databases, and distributed ledgers. Often, more than one type of verifiable data registry is used within an ecosystem, providing redundancy and interoperability across different identity management systems [48].  
A Verifiable Credential (VC) ecosystem operates within a trust triangle consisting of three primary entities, the issuer, holder, and verifier, along with a verifiable data registry, such as a blockchain [49]. Figure 9 below shows the relationship between the three components below and the VRD:  
1. The issuer is responsible for generating and assigning credentials that confirm specific information about a subject;
2. The holder securely stores these credentials and presents them to a verifier;
3. The verifier validates the claims against a verifiable data registry to ensure authenticity.  
Smart contracts play a critical role in the issuance, verification, and revocation of Verifiable Credentials (VCs) by automating credential management on decentralized networks [32; 49].
## 3 Preliminaries
### Additive Homomorphic Encryption (Additive HE)
\(Eval(c_{1},c_{2})\to c_{1}\oplus c_{2}\): For two ciphertexts \(c_{1}\) and \(c_{2}\), whose corresponding plaintexts are \(m_{1},m_{2}\), where \[c_{1} = Enc(m_{1})=(A_{1},B_{1})=(g^{\sigma_{1}}\ mod\ N^{2},\ g^{\tau_{1} }(1+m_{1}N)\ mod\ N^{2})\] (1) \[c_{2} = Enc(m_{2})=(A_{2},B_{2})=(g^{\sigma_{2}}\ mod\ N^{2},\ g^{\tau_{2} }(1+m_{2}N)\ mod\ N^{2}),\] (2) the additive homomorphism property is described as follows: \[c_{1}\oplus c_{2} = Enc(m_{1})\oplus Enc(m_{2})\] (3) \[= (g^{\sigma_{1}}\cdot g^{\sigma_{2}},g^{\tau_{1}}(1+m_{1}N)\cdot g ^{\tau_{2}}(1+m_{2}N))\ mod\ N^{2}\] \[= (g^{a(r_{1}+r_{2})},g^{r_{1}+r_{2}}(1+(m_{1}+m_{2})N))\ mod\ N^{2}\] \[\rightarrow Enc(m_{1}+m_{2},pk).\]  
In particular, given a constant \(\gamma\), we have \(Eval(c_{1},\gamma)\to c_{1}^{\gamma}\), where  
\[c_{1}^{\gamma} = (g^{\sigma\gamma_{1}},\ g^{\gamma\tau_{1}}(1+m_{1}N)^{\gamma})\ mod \ N^{2} \tag{4}\] \[= (g^{\sigma\gamma_{1}},g^{\gamma\tau_{1}}(1+\gamma m_{1}N))mod\ N^ {2}\] \[\rightarrow Enc(\gamma m_{1},pk).\]
# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 3 Method  
### Anomalous Volcanic Temperatures  
Volcanic activity is typically marked by the transfer of heat to the Earth's surface and thermal emissions from the erupted products. Thus, an evident indicator of volcanic activity is the high surface temperature compared to the ambient temperature of the ground. Hence, identifying volcanic thermal activity in satellite images is relatively simple; it is sufficient to distinguish the pixels that exhibit thermal emissions directly associated with volcano activity from neighboring pixels that do not [(41)]. This approach allows not only for the detection of volcanic unrest based on the occurrence of thermal anomalies at a specific time (e.g., [(17)]), but also for the use of thermal data to retrieve such parameters as the effusion rate of lava flows (e.g., [(42; 43; 44)]) and the areal coverage of hot volcanic deposits (e.g., [(45)]). As a result, thermal satellite observations are an essential component of volcano monitoring and a valuable tool for identifying numerous volcanic features based on their thermal characteristics (e.g., [(46)]). However, the task of distinguishing volcanic thermal features with respect to the background is made difficult not only by the characteristics of the target and atmospheric conditions (optical sensors are unable to make measurements through the cloud cover) but also by the spatial, temporal, spectral and radiometric resolution at which the remote sensing data are captured and whether the measurements are made during the day or at night (an additional contribution from reflected solar energy is present during the day). Such complications can hamper the detection of subtle temperature anomalies by using low-spatial-resolution sensors or introduce spurious data into time series not attributable to active volcanism (e.g., [47]).
## 2 MIMO Antenna Design Approaches
### Channel Capacity Loss (CCL)  
The CCL is defined as the maximum limit up to which the information can be transmitted with almost zero loss in the communication channel. The predefined CCL value for a given MIMO system is <0.4 bits/s/Hz.  
The expression of CCL using S-parameters is given by Equation (3) [14]:  
\[\text{CCL}=-log2 det(\theta^{\mu}) \tag{3}\]  
where  
\[\theta^{\mu}=\begin{bmatrix}\xi_{11}&\xi_{12}\\ \xi_{21}&\xi_{22}\end{bmatrix}\]  
and  
\[\begin{array}{rl}\xi_{11}=&1-\left[\left|S_{11}\right|^{2}+\left|S_{12} \right|^{2}\right]\\ \xi_{12}=&-\left[S_{11}^{*}S_{12}+S_{12}^{*}S_{12}\right]\\ \xi_{21}=&-\left[S_{22}^{*}S_{21}+S_{12}^{*}S_{21}\right]\\ \xi_{22}=&1-\left[\left|S_{22}\right|^{2}+\left|S_{21}\right|^{2}\right]\end{array}\]
###### Abstract  
Accretionary wedge earthquakes usually occur in the overriding crust close to the trench or above the cold nose of the mantle wedge. However, the mechanism and temperature properties related to the slab dip angle remain poorly understood. Based on 3D thermal models to estimate the subduction wedge plate temperature and structure, we investigate the distribution of wedge earthquakes in Alaska, which has a varying slab dip angle along the trench. The horizontal distance of wedge-earthquake hypocenters significantly increases from the Aleutian Islands to south-central Alaska due to a transition from steep subduction to flat subduction. Slab dehydration inside the subducted Pacific plate indicates a simultaneous change in the distances between the intraslab metamorphic fronts and the Alaskan Trench at various depths, which is associated with the flattening of the Pacific plate eastward along the strike. The across-arc width of the wedge-earthquake source zone is consistent with the across-arc width of the surface high topography above the fully dehydrated megathrust, and the fluid upwelling spontaneously influences wedge seismotectonics and orogenesis.  
wedge earthquakes; thermal regime; subduction process +
Footnote †: journal: Applied Sciences  
0000-0002-4000-0000-0000-0000-0000-0000-0000-00000-0000-0000-0000-0000-00000-000  
convection-controlled heterogeneous thermal structures and the nonuniform generation and migration of magmas [5].  
According to observations, the distances of wedge-earthquake hypocenters from the trench are significantly different between the Aleutian Islands and south-central Alaska [6]. The Aleutian Islands are characterized by steep subduction, while south-central Alaska features quasi-flat subduction due to the presence of the Yakutat Block [7]. Slab dehydration occurring inside the incoming plate usually exhibits a variation in intraslab metamorphic front depths and loci separated from the convergent trench, as other subduction zones show and are commonly acknowledged [8]. Associated with the late flattening of the Pacific from west to east, the change in the slab dip angle and clustered zones of wedge earthquakes is noticeable, while the details are still elusive. It seems that the underlying thermally controlled hydrated Pacific plate plays a key role in determining the distribution of wedge earthquakes. However, the hydrothermal state of the megathrusts underlying the accretionary/mantle wedge itself is enigmatic. This becomes a primary issue to clarify.  
Among various subduction zones, Alaska is typical because of its notably increasing dip angles along the trench, which present a good chance to study the variation in the subducted plate thermal state and distribution of wedge earthquakes along the trench. The contrasting topographies of the Pacific plate beneath south-central and southwest Alaska depend on the effects of variable factors, including potential fluid upwelling [6]. Therefore, in this paper, we use a 3D modeling method to investigate the distributed wedge-earthquake distribution underlain by particular hydrothermal state variables along the strike beneath trendward Alaska.
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 2 Materials and Methods  
### Materials  
PHA-rich biomasses from a mixed microbial culture (MMC) selected under least and famine conditions in a Sequencing Batch Reactor (S-SBR) using volatile fatty acids (VFAs) as substrates were kindly supplied by the Department of Biotechnology of Universita di Verona (Prof. [PERSON]). The PHA-rich biomass was obtained with VFAs investigating different ratios of acetic acid (HAc) to propionic acid (HPr). In particular, the following proportions were studied: 100:0 HAc:HPr; 60:40 HAc:HPr; 50:50 HAc:HPr; 30:70 HAc:HPr; 0:100 HAc:HPr. PHA samples were obtained by extraction in chloroform and recovered after filtration and drying [36].  
As reported in a previous work of ours, the extracted PHA samples resulted to be complex blends of poly(hydroxybutyrate-co-hydroxyalerate) (PHBV), where different phases, having a well-defined ratio between hydroxybutyrate (HB) and hydroxyvalerate (HV), were present. To investigate the properties of a well-defined PHBV phase, a solvent/non-solvent fractionation was also carried out for an extracted PHA batch [37].  
The PHAs samples were then characterized, and the nanomechanical properties were tested by AFM.  
As a benchmark for AFM testing, polylactic acid (PLA) IngeoTM 4043D was supplied by NatureWorks LLC.
## 4 Numerical Simulation
### Individual Accessory Parameter Sensitivity Analysis
#### 4.4.2 The Mass of the Clump Weight and the Installation Position  
Installing clump weights on the catenary increases the vertical component and the overall tension of the line, thereby increasing the mooring system recovery. In order to obtain the influence of the parameter change of the clump weight on the performance of the mooring system, the optimization effect of the mass of the clump weight on the mooring system is first considered. The starting point of the installation position of the clump weight is located at the contact point between the mooring and the seabed, and the assembly length is 50 m, the mass of the clump weights is 500 kg/m, 700 kg/m, 900 kg/m; Secondly, considering the influence of the installation position of the clump weight on the overall response of the floating wind turbine, the mass of the clump weight is 900 kg/m. The starting points of the mooring installation clump weight are set at the touchdown point, 50 m after the touchdown point, and 100 m after the touchdown point, and keep the assembly length at 50 m.SIMA was used to simulate the motion response of the floating wind turbine under extreme sea conditions under different parameters of the clump weight block for 3 h, and the motion response and tension statistical results of the floating wind turbine with different clump weight parameters were obtained.  
Figure 7 shows the motion response and tension of the floating wind turbine under different clump weight parameters. The triangle marks represent the maximum value of the response statistics, the circle marks represent the mean, and the error bars represent the standard deviation. It can be seen from Figure 7 that when the floating wind turbine mooring system installs the clump weight alone. Determining the installation position of the clump weight, and increasing the mass of the clump weight can reduce the maximum and average value of the surge and pitch, while the mean value of heave decreases and the maximum value increases. In (a) of Figure 8, it can be seen that the maximum tension value of the No. 2 mooring line decreases, the mean value does not change much, but the oscillation amplitude decreases; When the mass of the clump weight is determined, as the installation position moves backward, the maximum and mean value of the heave, pitch and pitch of the floating wind turbine increase, while the maximum value of the heave decreases and the mean value increases. In (b) of Figure 8, it can be seen that the average value of the tension of the No. 2 mooring line changes very little, and the maximum value and the amplitude are both increasing.  
Figure 7: Comparison diagram of overall response of wind turbine with separate accessories. (**a**) Surge. (**b**) Heave. (**c**) Pitch. (**d**) Tension.