esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
If the ocean current is neglected, it will lead to an obvious deviation between the planned and practical paths. In contrast, if a real-time robust control is applied to eliminate these deviations, it will cost extra power and computational resources. Several works have been done to solve the AUV path planning problem. [PERSON] et al. [16] used the A* searching procedure to determine the optimal path with consideration to the ocean currents and their spatial variabilities. [PERSON] et al. [17] introduced a quantum-behaved particle swarm optimization algorithm for solving the optimal path planning problem of an AUV operating in environments with ocean static currents. [PERSON] et al. [18] described a novel optimum path planning strategy for long-duration operations in environments with time-varying ocean currents. [PERSON] et al. [19] presented a graph-search-based method to compute energy-optimal paths for AUVs in two-dimensional (2-D) time-varying flows. [PERSON] et al. [20] integrated data-driven ocean modeling with the stochastic dynamically orthogonal level-set optimization methodology to compute and study energy-optimal paths. [PERSON] et al. [21] predicted the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents through solving an accurate partial differential equation. [PERSON] et al. [22] presented a numerical method for minimum time heading control for the underwater vehicle moving at a fixed speed in known time-varying and two-dimensional flow fields. The MDP method is suitable for the AUV underwater path planning. The MDP seeks a globally optimal solution through a value iterative method. The optimal paths of all state points in the whole domain to the target are computed only once. This is more efficient than the traditional A* algorithm [23; 24], which has to repeat similar computations for every step. Because AUVs move underwater with a low cruising speed and a high fault tolerance, the actions of AUVs are limited, and thus, suitable for establishing the MDP model. Otherwise, computational difficulties will increase exponentially with increasing robotic actions. In our application, the ocean current is predicted from an oceanic forecast model, therefore the ocean currents can be regarded as fully observable. This information is provided to the AUV as a priori or in real-time through acoustic communications so that the parameters used in the MDP model can be updated. The fully observable MDP model has a faster convergence rate than the partially observable MDP models. The paper is organized as follows. The principle of the MDP path planning and its numerical algorithm for applications in the AUV navigation are introduced in Section 2. In Section 3, the efficiency of the MDP algorithm is examined and its performance is compared with the traditional A* algorithm. Then, the ocean currents predicted by a regional ocean model are incorporated into the MDP model to evaluate the performance of the MDP algorithm in a'real' oceanic environment. The conclusions are presented in Section 4.
## 2 Materials and Methods
### Validation of Field Partition Models  
Selection of best field partition models (i.e., testing the performance of MC algorithms) and appropriate numbers of MZ were based on BHI and BSI indexes (0-1). BHI is an external measure for genetic clustering validation proposed by [PERSON] and [PERSON] (2018) that determines how homogeneous clusters in a partition model are (higher values meaning a higher homogeneity) in terms of biologically meaningful categories called \"functional classes\" (i.e., genetic functions). In our case, surrogate environmental classes (e.g., high presence, low presence, pest absence) were generated by applying different suitability thresholds to distribution maps representative of the simulated pests. The specific number of presence levels and thresholds used to define them varied according to the case. The package \"clValid\" calculates BHI based on the following equation:  
\[BHI(C,B)=\frac{1}{K}\sum_{k=1}^{K}\frac{1}{n_{k}(n_{k}-1)}\sum_{i\
eq j\in C_ {k}}I(B(i)=B(j)), \tag{1}\]  
where \(n_{k}\) equals \(n(C_{k}\cap B)\), which is the number of annotated categories (i.e., pest levels) in statistical cluster \(C_{k}\), \(B(i)\) is the functional class containing category \(i\), and \(B(j)\) is the functional class containing category \(j\).  
A second evaluation based on BSI measures was performed in cases where more than one field partition model shared the highest BHI values. BSI tests clustering consistency for observations with similar biological functionality (higher values meaning higher stability) ([PERSON] and [PERSON], 2018). To do so, new clustering essays are developed by removing one sample at a time (from the clustered data set) and cluster membership of observations with similar functional annotation is compared with cluster memberships observed during essays based on all available samples. BSI can also be calculated by the \"clValid\" package through the following equation:  
\[BSI(C,B)=\frac{1}{F}\sum_{k=1}^{F}\frac{1}{n(B_{k})(n(B_{k})-1)M}\sum_{l=1}^{M }\sum_{i\
eq j\in B_{k}}\frac{n\left(C^{i,0}\cap C^{j,l}\right)}{n\left(C^{i, 0}\right)}, \tag{2}\]  
where \(F\) is the total number of functional classes, \(C^{i,0}\) is the statistical cluster containing observation \(i\), and \(C^{j,l}\) is the statistical cluster containing observation \(j\) when column \(l\) is removed.  
After the best partition models were selected (i.e., MC algorithms and number of MZ that maximized BHI/BSI values), cluster membership numbers were interpolated within the experimental orchard using the IDW algorithm included in GRASS GIS 7 (function \"v.surf\"). Maps resulting from these interpolations were reclassified to eliminate decimal values and produce management zones containing unique cluster membership numbers.
# Weather Radar in Complex Orography
## 2 Practical Applications, Needs and Requirements
### The Scales of Interest
Figure 3: The skill of ensemble forecasting in the nowcasting time frame of three different methods, that is, a stochastic Lagrangian extrapolation of radar fields [88], a state-of-the-art numerical weather prediction model in ensemble mode (COSMO-E), and a Bayesian blending of the previous two [85]. The analysis was conducted as a function of the spatial scale and for different rain rate thresholds by pooling data of four events for a total of 48 hours of precipitation. The fraction skill score, FSS, for precipitation \(>\)2 mm/h and a spatial scale of 32 km is shown here. This is the average of the four events shown in Figure 12 in [85], ©American Meteorological Society; used with permission.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 3 Hash Index-Based Skyline Query Processing
### Hash Index for Skyline
Given the data points belonging to 2-D data space [0, 1]\({}^{2}\) as shown in Figure 1a, and assuming the number of partitions (np) of each dimension is four, each dimension is divided into regular intervals and marked in an ordered manner starting from zero (e.g., 0, 1, 2, 3) as shown in Figure 2. Thus, given the data point \(G=\{0.6,\ 0.1\}\), we obtain values of two for the horizontal axis and zero for the vertical axis. On the other hand, if the data point is on a partition boundary, the higher order is applied (e.g., 0.5 obtains two). The hash function computes the order along each axis for the given data, i.e., the number of dimensions, and generates GLAD based on this order. In this case, GLAD is obtained by converting the order of each dimension into bits and combining them in sequence. The number of digits representing all intervals in order needs to be the minimum number of bits that can represent them. The required number of minimum digits can be obtained by taking \(log_{2}(np)\) and performing a subsequent decimal point round-up. Therefore, if np is four, as shown in this example, two bits are required to indicate the marked intervals of each dimension. Because data point G is located in the third interval along the horizontal axis (bit value 10), and in the first interval along the vertical axis (bit value 00), the result of their combination in GLAD is eight (1000). Figure 2 shows the partition results for the data space used in this example as well as the GLAD assigned to each partition. The generated GLADs were not duplicated, and were instead obtained by combining the interval order corresponding to each partition in a bitwise sequence. Furthermore, the generated GLADs have the column major-order characteristic for the data space when the bit is changed to decimal, allowing GLADs to be more easily managed. HashGrid uses the GLAD obtained to store the data in the appropriate hash index. Once the hashing and storing of all data is complete, only the hash index with the bucket is stored in the index storage space on the disk according to the order of GLAD, thereby minimizing the use of unnecessary index storage space. Because of these features, HashGrid is very robust against frequent insertion and deletion, unlike index structures used in previous studies, and can also prune the data space because the clustering of the given data is based on the lattice region. Moreover, dividing the data into a uniformly sized lattice space minimizes the reduction in the efficiency of data space pruning resulting from partition overlap.
## 2 Materials and Methods
### Segmentation Score Maps  
Segmentation score maps are a widely used means of evaluating a model's performance. For segmentation networks, different measurements, like intersection over union (IoU) or pixel accuracy, exist. However, here we are interested in highlighting important pixels which contribute more to final predictions. A segmentation network generates a 2D score map for each class at the output layer as the result of the training process. The most commonly used loss function for this training is pixel-wise cross entropy loss and the target is the segmentation ground truth. Thus the ideal case is when these score maps are equal to the ground truth maps. It should be pointed out that there is an important difference  
Figure 4: Guided Grad-CAM for semantic segmentation.  
Figure 3: Grad-CAM method for a segmentation network.  
between saliency and score maps. Saliency maps only take the pixels belonging to the chosen class into account, while score maps also consider pixels in the vicinity of the actual object pixels and the general context. True, it can be dependent on the architecture and use case: one example is context-aware architecture, but it also might occur in a biased model. Generally, not all features used in a network's decision-making process are presented in the final layer because some of them might be removed. On the other hand, the network can be sensitive (for a class) to some features of the input image, but a different class wins the final pixel. In that case those passed over features might be interesting and worth studying.
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 1 Introduction
### Brief Review of Quantum Computing Essentials
#### 1.1.1 Qubit Representation and Manipulation  
In quantum computing, qubits are the units of information that can be manipulated. Using the Dirac notation, a single qubit can be described by two complex probability amplitudes  
\[|q\rangle=a|0\rangle+b|1\rangle\ ;\ |a|^{2}+|b|^{2}=1 \tag{1}\]  
such that \(|a|^{2}\) is the probability of finding the qubit in state \(|0\rangle\) and \(|b|^{2}\) is the probability of finding the qubit in state \(|1\rangle\). We can compose a system with two qubits by taking the tensor product of the qubits' state vectors  
\[|q_{0}|q_{1}\rangle=|q_{0}\rangle\otimes|q_{1}\rangle=a_{0}a_{1}|00\rangle+a_ {0}b_{1}|01\rangle+b_{0}a_{1}|10\rangle+b_{0}b_{1}|11\rangle \tag{2}\]  
showing that in this case 4 complex amplitudes define the quantum state \(|q_{0}|q_{1}\rangle\) where \(|00\rangle\), \(|01\rangle\), \(|10\rangle\), and \(|11\rangle\) represent the four computational basis states. This concept can be extended to \(n\) qubits, such that the quantum wave function, \(|\Psi\rangle\) is defined by \(2^{n}\) complex amplitudes  
\[|\Psi\rangle=\sum_{k=0}^{2^{n}-1}C_{k}|k\rangle \tag{3}\]  
where \(|k\rangle\) represent the \(2^{n}\) computational basis states, with the integer value of \(k\) obtained from the binary representation of the \(n\) qubits. Another way of interpreting this is that a system of \(n\) qubits forms a \(2^{n}\)-dimensional Hilbert space. In quantum mechanics, operations performed on the quantum state are represented by unitary Hermitian matrices. In the quantum circuit model, discussed in next section, these unitary operations are performed by quantum gates.
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
###### Abstract  
Non-geographic routing protocols are inefficient when applied to large-scale mobile networks composed of hundreds of nodes. On the other hand, geographic routing protocols have the disadvantage of needing a location sensor. The goal is to address the challenges of efficient content retrieval and routing scalability in NDN-based networks by leveraging the benefits of both NDN and Bloom Filter technologies. In this article we propose a routing protocol for mobile networks, which is scalable to networks composed of hundreds of nodes. The protocol does not require any localization equipment and is adapted for devices with limited memory and/or processing resources. This goal is achieved through the use of Bloom Filters to efficiently store and spread topological information. In the methodology followed, nodes do not forward messages with topological information to other nodes. To make the process efficient, each node aggregates the topological information it receives from its direct neighbors with its own and only the result of this operation is transmitted to the remaining nodes. Several simulations were carried out in the Qualnet network simulator in order to validate the algorithm proposed by the Hybrid Routing Algorithm with NDNs (HRAN). The obtained results were compared with other non-geographic protocols for mobile networks. HRAN seems to be a routing protocol designed for MANETs, utilizing Bloom Filters to manage topological information. A Bloom Filter is a data structure used to test whether an element is a member of a set. It uses a bit array and multiple hash functions to determine if an element is present in the set. This type of data structure allows storing a large amount of binary information in an efficient way, reducing the resources required by the routing protocol.  
Keywords:routing protocols; bloom filters; hybrid routing algorithm with NDNs (HRAN); binary information; mobile networks +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Optimal Configurations of Mueller Polarimeter for Gaussian-Poisson Mixed Noise
## 2 Materials and Methods
### Instrument Matrix Optimization Theory for Gaussian-Poisson Mixed Noise
Therefore, if the polarization states corresponding to the instrument matrix form a polyhedron on the Poincare sphere, where any polarization state has a polarization state symmetric about the center of the Poincare sphere and the EWV of the instrument matrix is minimized, it must be the optimal instrument matrix. In this way, we can use the optimization method of geometric simplification to first constrain the polarization states corresponding to the instrument matrix so that each polarization state has a polarization state orthogonal to it. While doing so, the number of optimized parameters can be halved. Then, the genetic algorithm is used to find the minimum EWV to achieve the purpose of optimizing the instrument matrix. It is worth noting that the above method can be used to optimize the optimal frame for N-dimensional instrument matrices (N is even), except for N = 4. For these optimal frames, both the sum of the estimation variance is optimal and the estimation variance of each Mueller matrix element is optimal, and independent of the sample [19]. For the case of N = 4, only two tetrahedrons with specific orientations have this property [9], the projections of these two tetrahedra at \(S_{1}S_{2}\), \(S_{1}S_{3}\), and \(S_{2}S_{3}\) planes are all squares. The instrument matrix corresponding to these two special tetrahedra is:  
\[ \begin{bmatrix}1&1&1&1\\ 1/\sqrt{3}&-1/\sqrt{3}&-1/\sqrt{3}&1/\sqrt{3}\\ 1/\sqrt{3}&1/\sqrt{3}&-1/\sqrt{3}&-1/\sqrt{3}\\ 1/\sqrt{3}&-1/\sqrt{3}&1/\sqrt{3}&-1/\sqrt{3}\\ 1/\sqrt{3}&-1/\sqrt{3}&1/\sqrt{3}&-1/\sqrt{3}\\ \end{bmatrix}\begin{bmatrix}1&1&1&1\\ -1/\sqrt{3}&1/\sqrt{3}&-1/\sqrt{3}\\ -1/\sqrt{3}&-1/\sqrt{3}&1/\sqrt{3}&1/\sqrt{3}\\ -1/\sqrt{3}&1/\sqrt{3}&-1/\sqrt{3}&1/\sqrt{3}\\ \end{bmatrix}  \tag{6}\]  
In summary, the optimization method for Gaussian-Poisson mixed noise that is proposed in this paper can be summarized as the following steps:  
1. Determine the geometric relationship according to the selected system structure. 2. Choose the number N of illumination/analysis polarization states to be optimized. 3. If N = 4, the tetrahedron formed by the corresponding point of the illumination/analysis polarization states on the Poincare sphere is constrained to be two tetrahedrons with special orientation in combination with the geometric relationship, so as to simplify the number of parameters that need to be optimized. 4. If N > 4, the corresponding points of the illumination/analysis polarization states on the Poincare sphere are constrained to be pairs of orthogonal polarization states in combination with the geometric relationship, so as to simplify the number of parameters that need to be optimized. 5. The optimal configuration is obtained by searching the minimum EWV of the instrument matrix corresponding to the simplified parameters using the global optimization algorithm (i.e., genetic algorithm). The optimization process is shown in Figure 1.  
Figure 1: Optimization process of proposed method.
# Search-Space Reduction for S-Boxes Resilient to Power Attacks
## 3 Our Contributions: Reduction of the S-Boxes Search-Space into a Hamming Weight Class SEARCH-Space
### Generation of HW Equivalent S-Boxes. ESboxG Algorithm  
We present an equivalent S-box Generator (ESboxG) (Algorithm 1) to generate S-boxes belonging to a class by permuting elements of \(C(F_{a})_{k}\) sets.  
The complexity of this algorithm is determined by the permutations it performs within the subsets \(C(F_{a})_{k}\) (Lines 5-10), in particular by the values of the two parameters \((nss,mtnos)\). Three possible cases of different complexity are highlighted:  
* The maximum complexity is reached when all elements of all sub-assemblies are exchanged (maximum values of \(nss\) and \(mnos\)).
* The complexity can be reduced by exchanging only elements of a single subset \(C(F_{a})_{k},(nss=1)\).
* The minimum complexity is reached when only two elements are permuted within a single subset \((nss=1,mnos=2)\).  
**Proposition 4** (Necessary condition of belonging to the same class).: _If two S-boxes \(F_{a}(x)\) and \(F_{b}(x)\) belong to the same class, then \(C(F_{a})_{0}=C(F_{b})_{0}\) and \(C(F_{a})_{n}=C(F_{b})_{n}\), or equivalently: \(F_{a}^{-1}(0)=F_{b}^{-1}(0)\) and \(F_{a}^{-1}(2^{n}-1)=F_{b}^{-1}(2^{n}-1),\forall\ F_{b}\in<F_{a}>\)._  
Proof.: The proof is straightforward and is essentially based on two conditions:  
1. The S-boxes of a class are generated by permuting the elements inside the sets \(C(F_{a})_{k},\ k=1,\ldots n-1\)
2. The sets \(C(F_{a})_{0}\) and \(C(F_{a})_{n}\) have a single element. By Condition 2, for each of the sets \(C(F_{a})_{0}\) and \(C(F_{a})_{n}\), it is not possible to permute elements of equal weight within the same class. If an element of one of these sets is permuted, it will necessarily be permuted with an element of a different weight, which immediately leads to another HW class through Condition 1.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 2 Related Works  
In recent years, researchers have paid close attention to the fusion of multimodal data for remote sensing scene classification. This section, divided into three parts, provides a brief overview of related work in the fields of audiovisual learning, remote sensing scene classification, and multimodal fusion in remote sensing.
# Analysis of Selected IT Tools for Environmental Product Assessment
## 2 Literature Review
### Methods for Environmental Assessment of the Product
#### 2.2.3 Semi-Quantitative Methods  
The last group are semi-quantitative methods. These methods combine elements of both quantitative and qualitative approaches, considering both numerical and descriptive data.  
The first one, ERPA (environmentally responsible product assessment), is a method based on a matrix with a dimension of five by five, where one dimension covers the different stages of a product's life cycle, and the other dimension relates to environmental categories. Each element of the matrix, i.e., each cell, is given an environmental performance rating on a scale of zero to four, where four means a high environmental performance. In this way, the ERPA method allows for a systematic and balanced assessment of environmental impact at each stage of the product life cycle [39].  
Another proposal for a semi-quantitative method is the MECO (materials, energy, chemicals, and others) matrix, which consists of five columns that include the stages of a product's life cycle and four rows (referring to the name of the matrix) designating materials, energy, chemicals, and others. By using this method, areas in the life cycle that are harmful in terms of particular environmental impacts are indicated. The method focuses on the cause of the problem [40].  
Another method is the environmental quality function deployment (EQFD) matrix. It is based on the QFD method extended to include environmental aspects. It involves creating a matrix called a quality house that integrates customer requirements with environmental criteria. This ensures that products are designed to minimize negative environmental impacts at every stage of their life cycle. EQFD helps project teams identify areas for environmental improvement and prioritize activities in the context of customer requirements [41].  
SLCA (streamlined life cycle assessment), on the other hand, is a simplified version of the LCA method, which is used to identify \"hot spots.\" It is used when comparing the environmental impact of different products in situations where detailed environmental impact information is not required, and one wants to reduce the time or resources required for analysis. The method is useful in the early stages of product design, where a quick assessment can lead to more sustainable design decisions and the implementation of eco-friendly innovations burdened with less labor [42; 43].  
Lastly, the ecodesign checklist method is a simple and effective method that allows for quick and efficient evaluation while taking into account environmental impacts during a product's life cycle. The method is based on a set of scores used to evaluate a product from an environmental perspective. By using it, designers can easily identify areas for improvement and implement sustainable design practices [44].