esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MAPPO-Based Joint Caching and Computation Offloading Scheme
### Complexity Analysis  
The computational complexity, associated with the MAPPO algorithm, primarily consists of two parts. On the one hand, the computational complexity generated by the \(i\)-th layer of the MLP can be expressed as \(\mathcal{O}(C_{i-1}C_{i}+C_{i}C_{i+1})\), where \(C_{i}\) represents the number of neurons in the \(i\)-th layer. Denoting the number of layers of one MLP as \(I\), we can calculate the computational complexity of an MLP as \(\mathcal{O}(\sum_{i=2}^{I-1}C_{i-1}C_{i}+C_{i}C_{i+1})\). On the other hand, the complexity generated by the attention module can be expressed as \(\mathcal{O}(I^{2}W)\), in which \(W\) denotes the length of feature values output from the state encoders [54]. In our framework, the actor networks are composed of a single MLP, while the critic networks consist of one MLP for value output and two encoders designed for distinct agent types. Therefore, the computational complexity of the training algorithm for all Me episodes is calculated as  
\[\mathcal{O}\bigg{(}\text{Mte}\bigg{(}\text{Pec}\cdot I^{2}W+\text{Epl}\sum_{i =2}^{I-1}(C_{i-1}C_{i}+C_{i}C_{i+1})\bigg{)}\bigg{)} \tag{62}\]
# A Star-Identification Algorithm Based on Global Multi-Triangle Voting
## 2 Algorithm Description
### Onboard Database Generation  
Figure 2 shows the database structure in GMTV, which consists of two parts, namely, the reference catalog and the triangle unit database, including the unit index table and lookup table, and occupies a 1.5 MB memory size.  
In this paper, the SAO J2000 catalog is selected as a basic catalog to build a database for the algorithm. After discarding the variable stars and binary stars from catalog stars whose magnitude value is down to 5.5 Mv, the reference catalog and unit index table of the database are first generated, and then feature values of each unit are calculated and stored in the corresponding index position of the lookup table. Considering the memory size and redundant matching probability, we only choose some triangle units as vote units in the GMTV database. Detailed steps about building the reference catalog and lookup table are described as follows: the whole celestial sphere is scanned uniformly with 0.5\({}^{{}^{\circ}}\) steps, namely, the right ascension angle of FOV is varied from 0\({}^{\circ}\) to 360\({}^{\circ}\) in steps of 0.5 degrees, and the declination angle of the FOV from \(-90^{\circ}\) to \(+90^{\circ}\) in steps of 0.5 degrees, and for each FOV at a different orientation, the brightest \(\alpha\) detected stars in the FOV are selected as guide stars and added to the reference catalog (\(\alpha=8\) in this paper). After determining the guide stars in an FOV, we build triangle units by choosing any three stars from \(\alpha\) guide stars, so there are C\({}_{\text{N}}^{3}\) triangle units. During the above process, the indexes of the three stars in the SAO for each triangle unit are stored as a group in the unit index table, and the feature values of the unit are stored in the 2D lookup table; the process above, for example, is illustrated in Figure 3.  
In Figure 3, a triangle unit whose unit index is K consists of three catalog stars, whose star index is Star-1, Star-2, and Star-3 in the SAO catalog, and its feature value is \(pca_{k}^{1}\) and \(pca_{k}^{2}\). Thus, Star-1, Star-2, and Star-3 are stored in the Kth row of the unit index table, and unit index K is stored in the position of the lookup table where the row index is \(pca_{k}^{1}\) and the column index is \(pca_{k}^{2}\).  
Figure 2: Database structure.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 3 System Model  
### Communication System  
A digital communication system consists of a transmitter and a receiver with the goal of reliably transmitting information over a noisy channel, as seen in Figure 1.  
Transmitter and receiver comprise several building blocks to compress the source information (source encoder, source decoder), overcome the distortion and noise introduced by the channel by increasing redundancy (channel encoder, channel decoder), and to convert the signal to a more robust representation (mapper, demapper). In this work, we focus on mapper and demapper blocks. At the transmitter side, multiple information bits \(\mathbf{b}\) are commonly mapped to a complex symbol \(x\) that is transmitted over the channel. The received symbol \(y\) is then demapped to bits \(\mathbf{\hat{b}}\) at receiver side. In the case of four bits per symbol, the 16-quadrature amplitude modulation (QAM) constellation is widely used in conventional systems, where the constellation points are arranged in a square grid with equal vertical and horizontal spacing.  
For evaluation of a communication system, abstract channel models are used to mimic the characteristics of a real channel. The most basic model is the additive white Gaussian noise (AWGN) channel, where uncorrelated Gaussian noise \(n\sim\mathcal{CN}(0,\sigma^{2})\) with noise power \(N_{0}=\sigma^{2}\) is added to the input message \(x\) to obtain \(y=x+n\). Here, the SNR determines the ratio between the average signal power \(E_{s}=\mathrm{E}\Big{[}|x|^{2}\Big{]}\) and the noise power. In our case, where we focus on \(m=4\) bit per complex-valued symbol, the SNR corresponds to the energy per bit \(E_{b}=E_{s}/m\) per noise power spectral-density and is defined as  
\[SNR=\frac{E_{b}}{N_{0}}. \tag{1}\]  
To measure the final reliability of a communication chain (after the decoder), hard-decided BER, as well as BLER, can be used, giving either the amount of incorrectly predicted bits or incorrectly predicted bit block sequences. However, as today's communication chains usually rely on practical BMD receivers, which process soft bit estimates, i.e., logarithmic likelihood ratio (LLRs), the optimal BMD-based metric for the output of the demapper is bitwise mutual information (MI) [32] which refers to the average amount of information one gains in comparison with the originally transmitted bits when observing the received symbols. The bitwise MI is given as  
\[\mathrm{BMI}:=H(\mathbf{B})-\sum_{j=1}^{m}H(B_{j}|Y) \tag{2}\]  
where \(\mathbf{B}\) denotes the binary random variable providing the input bits \(\mathbf{b}\) of length \(m\), \(H\) denotes the entropy, and \(Y\) denotes the random variable associated with the received symbol \(y\). We estimate the bitwise MI in the following via Monte Carlo simulation of large numbers of bits.  
Figure 1: Digital communication chain.
# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 5 Results and Discussion  
### Single Class Comparison Experiments  
The experiments first compared the effect of the percentage of hard examples and the number of iterations in the HEM process on the detection performance of RetinaNet. ResNet-50 is used as the feature extraction network, and focal loss is used as the classification loss function. We treat all objects as a single class to verify the performance of the method and use AP as an evaluation criterion.  
The experimental results are shown in Table 3. The percentages of hard examples were selected as 5%, 10%, and 20% for comparison, where 0% indicates the performance of the trained base model when no HEM is performed. The number of iterations of training data after each HEM is selected as 10, 20 and 30 times for comparison. In terms of the percentage of hard examples, it is not the case that more hard examples being selected is better, and too few selections do not achieve the meaning of HEM. In a limited dataset, the proportion of hard examples is small, and there is no clear threshold for the samples selected according to the loss ranking. According to the experimental results, in the terahertz image object detection dataset, a better result can be achieved when the percentage of hard examples is 10%. In terms of the number of iterations, too many and too few iterations do not give the best performance for the model. According to the experimental results, the number of iterations (20) achieve the best results on different percentages of hard examples. Selecting hard examples to enhance the training model is a process of model enhancement and model overfitting equilibrium, and the number of iterations changes according to the number of new samples.  
Overall, hard example mining works effectively on the single-class terahertz image object detection dataset, improving the AP of the model by 1.6% on top of the basic detector.
## 3 The Proposed Method
### Novel Enhancements for the Tracker Module  
To improve the accuracy of the framework, objects near the two sides of the FoV are truncated as they may not be fully visible while entering or exiting the FoV. In the mentioned zones, the detector module often has difficulties in successfully detecting and classifying nodes. To resolve the above problem, three detection zones are defined in the FoV (Figure 4).  
Figure 3: Motion detection technique.  
Thus, 15% of the frame width on either end is the area in which the framework detects and tracks nodes, but its corresponding prediction scores are not considered in the average score. The classifier module only receives the prediction score if the node occupies between 15% and 85% of the frame width; otherwise, the tracker will not pass the score to the classifier. To determine where the node position is within the three mentioned zones, the center point of the bounding box around the node is utilized.  
It is possible that an object detected in the preceding frames is not detected in the current one. This scenario happens when the detector module cannot identify the present object in the current frame or simply because the object left the camera FoV. To resolve the uncertainty of whether the object reappears in the FoV or not, a transitional phase is designed to preserve object data for a certain number of object absences. Considering that the object may reappear in the subsequent frames in the mentioned phase, OpenCV Median Flow tracker is applied to increase tracking performance. Because the joists move slowly, the Median Flow tracker can effectively update the bounding box information during the frames in which the detector has difficulty finding the object. If an object is absent for more than a specific number of frames, the object is considered to be outside the FoV, and its corresponding detection data is removed from the tracker memory.
## 2 Materials and Methods  
The nodal equilibrium equation commonly used in the stiffness matrix method [48] can be presented as  
\[[K]\cdot[\delta]=\{f\}, \tag{1}\]  
where \([K]\) is the stiffness matrix; \([\delta]\) is the displacements vector, which includes the horizontal, vertical and rotational displacements; and \(\{f\}\) is the external force vector, which includes the horizontal forces, vertical forces and moments.  
In a 2D analysis, for a six-degrees-of-freedom beam element (one horizontal deflection \(u\), a vertical deflection \(v\), and a rotation \(w\) at the initial and final nodes) of length \(L\) and constant cross-section, the common Timoshenko's stiffness matrix is shown in Equation (2):  
\[[K]=\left[\begin{array}{cccccc}\frac{EA}{L}&0&0&-\frac{EA}{L}&0&0\\ 0&\frac{12 EI}{L^{3}(1+\varnothing)}&\frac{6 EI}{L^{2}(1+\varnothing)}&0&-\frac{ 12 EI}{L^{3}(1+\varnothing)}&\frac{6 EI}{L^{2}(1+\varnothing)}\\ 0&\frac{6 EI}{L^{2}(1+\varnothing)}&\frac{EI(1+\varnothing)}{L(1+\varnothing)}& 0&-\frac{6 EI}{L^{2}(1+\varnothing)}&\frac{EI(1-\varnothing)}{L(1+\varnothing)} \\ -\frac{EA}{L}&0&0&\frac{EA}{L}&0&0\\ 0&-\frac{12 EI}{L^{3}(1+\varnothing)}&-\frac{6 EI}{L^{2}(1+\varnothing)}&0&\frac {12 EI}{L^{3}(1+\varnothing)}&-\frac{6 EI}{L^{2}(1+\varnothing)}\\ 0&\frac{6 EI}{L^{2}(1+\varnothing)}&\frac{EI(2-\varnothing)}{L(1+\varnothing)}& 0&-\frac{6 EI}{L^{2}(1+\varnothing)}&\frac{EI(4+\varnothing)}{L(1+\varnothing)} \end{array}\right] \tag{2}\]  
This stiffness matrix \([K]\) includes information on the axial stiffness \(EA\) and the flexural stiffness \(EI\), with \(E\), \(I\), and \(A\) being the Young's modulus, inertia and area, respectively. A coefficient \(\mathcal{O}\) (shear parameter) can be found in some elements of Equation (2). This parameter is as follows:  
\[\varnothing=\frac{12 EI}{GA_{v}L^{2}} \tag{3}\]  
where \(A_{v}\) is the shear area and \(G\) is the shear modulus written as  
\[G=\frac{E}{2(1+v)} \tag{4}\]  
where the constant \(v\) is [PERSON]'s ratio.  
As explained in [PERSON] (2007), [PERSON]'s stiffness matrix enables the calculation of vertical deformations due to shear. Nevertheless, this matrix only considers bending rotations in the system of equations, and commercial structural programs based on this formulation systematically neglect shear rotations. In fact, despite what could be expected, in isostatic structures, the same rotation diagrams are obtained independent of whether the shear effects are considered or not. To show the inability of these programs to calculate the total value of the rotations in these types of structures, an illustrative example is presented in the following section.
## 2 Methodology
### Impact Damage Detection Stage  
In order to simultaneously monitor energy and position, a multi-task CNN is used to solve the problem. Compared with single-task learning, multi-task learning has the following advantages: (1) Multiple tasks in a model can reduce memory consumption; (2) the forward calculation results of multiple tasks can be obtained at one time to improve the calculation efficiency; (3) associated tasks can improve each other's performance by sharing information and complementing each other.  
The structure of the multi-task 2D-CNN proposed in this paper is shown in Figure 3. All parameters of the convolution layer and pooling layer used in this model can be clearly found in Figure 3. The model consists of two classification tasks. One is to detect the location of impact damage and the other is to detect the energy of impact damage. The classification task of damage location and the classification task of damage energy share the same weight components. In Figure 3, two tasks share two convolution layers and one pooling layer. The classification task of damage location and the classification task of damage energy have the same structure. In this paper, a total of 16 damage locations and 3 different levels of impact energy are included. The number of neurons in the full connection layer of the damage location task is the same as that of the damage location. The number of neurons in a fully connected impact energy task is the same as the number of impact severity categories. For classification tasks, the softmax activation function is generally used as the activation function of the fully connected layer. In addition, a drop mechanism is added to both full connection layers to prevent overfitting and improve robustness. The value is set to 0.5.  
The back-propagation algorithm is used to update the parameters of the convolution neural network to minimize the loss function for the purpose of classification and recognition. The cross-entropy loss function is often used in classification problems and the mathematical formula is shown as Formula (2):  
\[J=\frac{-1}{N}\sum_{i=1}^{N}\sum_{j=1}^{K}y_{j}^{i}\log(\hat{y}_{j}^{i}) \tag{2}\]  
In Formula (2), when the \(i^{th}\) sample belongs to class \(j\), \(y_{j}^{i}\) is equal to 1. Otherwise, \(y_{j}^{i}\) is equal to 0. In addition, \(\hat{y}_{j}^{i}\) represents the probability that the \(i^{th}\) sample is the \(j^{th}\) class, \(N\) is the total number of samples, and \(K\) is the total number of classes.  
The loss functions of both tasks are multi-category cross-entropy loss functions, and the losses of both tasks are added up as the loss functions of the whole convolutional neural network, as shown in Formula (3).  
\[Loss=Loss\_location+Loss\_energy \tag{3}\]  
_Loss_location_ is the cross-entropic loss of impact damage location task calculated according to Formula (3). \(Loss\_energy\) is the cross-entropic loss of impact damage energy task according to Formula (3).
## 2 Quantum Turing Machines
### Transitions of a QTM  
Given a quantum configuration \(\phi\) of a QTM \(M\) (see Section 2.7 for the formal definition), the main idea that is introduced by [PERSON] [10] is that the machine evolves into another quantum configuration \(|\psi\rangle=U|\phi\rangle\), where \(U\) is a unitary operator on the Hilbert space of the configurations of \(M\). By linearity and continuity, this also means that, if \(\mathcal{C}\) is the set of configurations on which we define the Hilbert space of quantum configuration \(\ell^{2}(\mathcal{C})\) of \(M\), the operator \(U\), and then the behaviour of \(M\), is completely determined by the value of \(U\) on the elements of \(\mathcal{C}\). [PERSON] [12] refines this point by assuming that, as in a classical TM, for every \(C\in\mathcal{C}\), the transition from \(|C\rangle\) to a new configuration \(U|C\rangle\) only depends on the current state of \(M\) and on the current symbol of \(C\), and that \(U|C\rangle\) is formed of configurations obtained by replacing the current symbol \(u\) of \(C\) with a new one, and by moving the tape head to the left or the right. Therefore, if we denote by \(\mathbb{D}=\{L,R\}\) the set of the possible movements on the tape (where \(L\) stands for left and \(R\) for right), and \(q\) is the current state of \(M\), we have  
\[U(|C\rangle)=\sum_{p,v,d}\delta(q,u)(p,v,d)\;|C_{p,v,d}\rangle,\]  
where \(p\) ranges over the states of \(M\), \(v\) ranges over its tape alphabet, \(\delta(q,u)(p,v,d)\in\mathbb{C}\), and \(C_{p,v,d}\) is the new configuration obtained from \(C\) by changing the current state from \(q\) to \(p\), by replacing the current symbol \(u\) with \(v\), and by moving the tape head in the direction \(d\).
# EGMStream Webapp: EGMS Data Downstream Solution
## 4 Discussion
The definition of a customized AoI significantly reduces the time required for data conversion, eliminating the need for additional manual clipping or filtering operations and allowing more efficient handling of large datasets. The inclusion of the GeoJSON format, in addition to shapefile and geopackage (already present in [48]), as an output option represents a significant improvement that enhances the application compatibility with real-time analysis and visualization platforms. This format facilitates faster and more efficient data transfer, even in bandwidth-constrained environments, thanks to its lightweight nature and JSON-based structure. GeoJSON is particularly valued for its ability to integrate spatial data with web applications and API (Application Programming Interface) services, enabling the immediate and interactive visualization of information. The EGMStream webapp significantly simplifies the integration and management of converted data within GIS or WebGIS platforms, providing a robust starting point for detailed spatial and temporal analyses. The ability to generate deformation maps and time series not only facilitates the mapping of potentially risky areas but also enables continuous monitoring. These capabilities are essential for supporting urban planning, risk assessment, and civil protection measures. Moreover, the streamlined workflow, server-side computation, and automated delivery of results make the EGMStream webapp a powerful tool for researchers, professionals, and public entities involved in geospatial analysis and geohazard monitoring.
###### Abstract  
The present study is aimed at the development of compositional simulation models of the co-injection of CO2 and CH4 during the water-alternating-gas (WAG) process in order to assess the efficiency of carbon capture and storage in combination with enhanced oil recovery (CCS-EOR). The co-injection of CO2 and CH4 occupies more reservoir pore volume and causes higher reservoir pressure than CO2 WAG, thus leading to an enhanced early EOR performance. However, the overall EOR performance of the co-injection method becomes lower than that of CO2 WAG due to the reduced miscibility and sweep efficiency upon further CH4 addition. The decrease in gas displacement and sweep efficiency weaken the hysteresis effects upon the residual trapping mechanism. However, the solubility trapping mechanism takes effect because the co-injection generates higher average reservoir pressure than does the CO2 WAG. The index of global warming potential (GWP) in a mole unit is employed to quantify the carbon storage effects of CO2 and co-injection WAG cases. According to the index, 1 mole of CH4 sequestration has the same effects as that of 10 moles of CO2 for global warming mitigation. In conclusion, the carbon storage effects are enhanced as CH4 concentration in the WAG increases.  
carbon capture and storage associated with enhanced oil recovery (CCS-EOR); CH4 water alternating gas (WAG); global warming potential (GWP) +
Footnote †: journal: Applied Sciences
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
###### Abstract  
In this paper, a device-to-device (D2D) communications multiple-metric scenario for resource allocation and pairing is studied. To improve the D2D connection stability, a fuzzy method based on the data rate and battery levels of potential D2D pairs is proposed in such a way that the set of D2D transmitters is considered as a fuzzy set. A single cell scenario with a cellular user and some co-channel D2D nodes consists of D2D receivers, and transmitters are taken into account. Furthermore, a stable fuzzy pairing criterion is proposed for the selection of the best D2D transmitter. The proposed method is compared with three other pairing methods named the maximum sum rate, constant pairing method, and random pairing method. The simulation results show that the mentioned pairing method outperforms the other three methods in terms of stability and fairness criteria and follows the maximum sum-rate method from the sum-rate criteria point of view.  
device to device communications; fuzzy pairing method; \(t\)-norms; einstein sum Article A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D  
Pairing in D2D Communications  
[PERSON], [PERSON], [PERSON], [PERSON], M. Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications. _Appl. Sci._**2022**, _12_, 1343. [[https://doi.org/10.3390/app12031343](https://doi.org/10.3390/app12031343)]([https://doi.org/10.3390/app12031343](https://doi.org/10.3390/app12031343))  
Academic Editor: [PERSON]  
Received: 14 November 2021  
Accepted: 21 January 2022  
Published: 27 January 2022  
**Published:** **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.