esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimension Inversion of Magnetic Data Based on Multi-Constraint UNet++
###### Abstract  
The three-dimension (3D) inversion of magnetic data is an effective method of recovering underground magnetic susceptibility distributions using magnetic anomaly data. The conventional regularization inversion method has good data fitting; however, its inversion model has the problem of a poor model-fitting ability due to a low depth resolution. The 3D inversion method based on deep learning can effectively improve the model-fitting accuracy, but it is difficult to guarantee the data-fitting accuracy of the inversion results. The loss function of traditional deep learning 3D inversion methods usually adopts the metric of the absolute mean squared error (MSE). In order to improve the accuracy of the data fitting, we added a forward-fitting constraint term (FFit) on the basis of the MSE. Meanwhile, in order to further improve the accuracy of the model fitting, we added the _Dice_ coefficient to the loss function. Finally, we proposed a multi-constraint deep learning 3D inversion method based on UNet++. Compared with the traditional single-constraint deep learning method, the multi-constraint deep learning method has better data-fitting and model-fitting effects. Then, we designed corresponding test models and evaluation metrics to test the effectiveness and feasibility of the method, and applied it to the actual aeromagnetic data of a test area in Suqian City, Jiangsu Province.  
3D inversion; deep learning; model and data fitting; multi-constraint; UNet++
## 3 Methodology
### Fuzzy Fault Tree Analysis
#### 3.3.4 Defuzzification  
When fuzzy ratings are incorporated into an FTA problem, the final ratings are also fuzzy numbers. To determine a clear rating, \(\widetilde{R}_{AG}\) must be converted to a crisp score, named fuzzy possibility score (\(FPS\)). Defuzzification methods include'mean-max membership', 'center of sums', 'weighted average method' [65]. In this study, due to its simplicity and ease of understanding, 'center of area' method was used to calculate the \(\widetilde{R}_{AG}\) of BEs.  
For the \(\widetilde{R}_{AG}\) = (\(c_{1}\), \(c_{2}\), \(c_{3}\)), the equation is as follows:  
\[R_{AG}=\frac{\int_{c_{1}}^{c_{2}}\frac{x-c_{1}}{c_{2}-c_{1}}xdx+\int_{c_{2}}^{ c_{3}}\frac{c_{3}-x}{c_{3}-c_{2}}xdx}{\int_{c_{1}}^{c_{2}}\frac{x-c_{1}}{c_{2}-c_{1}} dx+\int_{c_{2}}^{c_{3}}\frac{c_{3}-x}{c_{3}-c_{2}}dx}=\frac{1}{3}(c_{1}+c_{2}+c_{3}) \tag{13}\]  
where \(R_{AG}\) is the FPS of \(\widetilde{R}_{AG}\).
## Appendix A
When taking measurements by emission spectrometers based on the dataset \(I_{nmk}\), \(k=1,2\ldots,M\gg 1\) the dispersion \(\sigma_{nm}\) is calculated using the known formulas. At the same time, Equation (15) contains the dispersion of the density distribution of the actual intensity probability (Equation (3)). This means that it contains two unknown values \(\sigma_{n}\), \(\overline{I}_{n}\); to calculate these, we need to find another equation linking the experimentally measured and actual values \(\sigma_{nm},\overline{I}_{nm}\) and \(\sigma_{n},\overline{I}_{n}\), respectively. To do so, we calculate the value \(\sigma_{nm}{}^{2}\), which is, by definition, equal to:  
\[\sigma_{nm}{}^{2}=\int\limits_{I_{nN}}^{I_{max}}(x-\overline{I}_{nm})^{2}P_{ nm}(x)dx=\int\limits_{I_{nN}}^{I_{max}}(x-\overline{I}_{nm})^{2}P_{n}(x)dx+ \left[1-\int\limits_{I_{nN}}^{I_{max}}P_{n}(x)dx\right]\left(I_{max}-\overline {I}_{nm}\right)^{2}. \tag{16}\]By calculating the integrals in Equation (A8), we obtain the correlation we are looking for:  
\[ \begin{split}\int\limits_{I_{nN}}^{I_{max}}&(x-\overline{ I}_{mn})^{2}P_{nm}(x)dx\\ &=\sigma_{n}{}^{2}\Big{[}1-\frac{2}{\sqrt{\pi}}\Gamma\Big{(} \frac{3}{2},\frac{I_{max}-I_{n}}{\sqrt{2}\sigma_{n}}\Big{)}\Big{]}-\sqrt{ \frac{2}{\pi}}\sigma_{n}\big{(}\overline{I}_{n}-\overline{I}_{nm}\big{)}exp \Big{[}-\Big{(}\frac{I_{max}-\overline{I}_{n}}{\sqrt{2}\sigma_{n}}\Big{)}^{2} \Big{]}\\ &+\frac{\big{(}\overline{I}_{n}-\overline{I}_{nm}\big{)}^{2}}{2} \Big{[}1+erf\Big{(}-\frac{I_{max}-\overline{I}_{n}}{\sqrt{2}\sigma_{n}}\Big{)} \Big{]}\text{ when }I_{max}\geq\overline{I}_{n};\end{split} \]  
\[ \begin{split}\int\limits_{I_{nN}}^{I_{max}}&(x- \overline{I}_{nm})^{2}P_{nm}(x)dx\\ &=\frac{\sigma_{n}{}^{2}}{\sqrt{\pi}}\Big{[}\Gamma\Big{(}\frac{3} {2},\frac{I_{n}-I_{max}}{\sqrt{2}\sigma_{n}}\Big{)}\Big{]}-\sqrt{\frac{2}{\pi }}\sigma_{n}\big{(}\overline{I}_{n}-\overline{I}_{nm}\big{)}exp\Big{[}-\Big{(} \frac{I_{max}-\overline{I}_{n}}{\sqrt{2}\sigma_{n}}\Big{)}^{2}\Big{]}\\ &+\frac{(I_{n}-\overline{I}_{nm})^{2}}{2}\Big{[}1+erf\Big{(}- \frac{I_{max}-\overline{I}_{n}}{\sqrt{2}\sigma_{n}}\Big{)}\Big{]}\text{ when }I_{max}<\overline{I}_{n};\end{split} \]  
which, together with Equation (A7), gives the system of Equation (5) to calculate the unknown values \(b_{nm}\) and \(\Sigma_{n}\) with the known values \(\alpha_{nm}\) and \(\Sigma_{nm}=\frac{\sigma_{nm}}{I_{nm}}\).
## 3 Methods
### Dqn
#### 3.3.1 Double DQN  
Building on the foundational work [16], we establish the Double DQN as our baseline model. DDQN uses two neural networks, one for selecting the best action and another for evaluating the action's value. When updating the Q-values, the action selection is performed by the online network, but the Q-value for updating is estimated using the target network. A recognized limitation of the DQN algorithm is its propensity to overestimate actual rewards, resulting in exaggerated Q-values. The Double DQN technique [58] offers a solution to this problem through an alteration of the Bellman equation utilized in the Deep Q-network. This modification involves separating the processes of action selection and action evaluation, as follows:  
\[Q(s,a;\theta)=r+\gamma Q\big{(}s^{\prime},\operatorname{argmax}_{a^{\prime}} Q\big{(}s^{\prime},a^{\prime};\theta\big{)};\theta^{\prime}\big{)} \tag{1}\]  
In this approach, the primary neural network, denoted by \(\theta\), is responsible for identifying the optimal next action, \(a^{\prime}\). Meanwhile, the target network assesses the selected action to calculate the Q-value. The straightforward adjustment has been demonstrated to mitigate overestimation issues, leading to improved policy outcomes.
# Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
## 6 Conclusions  
Nowadays, critical infrastructures, such as electricity grids or industrial installations handling dangerous materials, become increasingly targeted by cyberattacks as they have been interconnected to remote monitoring communication infrastructures due to the need for 24/7 remote monitoring and control. Thus, customary SCADA systems have been enhanced to include outwards communication functionality, most often over the Internet. To protect communication various cryptographic protocols have been incorporated in the standard Modbus protocol suite.  
However, because of the limited computational and storage capabilities of the customarily used SCADA sensors and embedded devices, SCADA systems are vulnerable to numerous attacks since the crypto key sizes that can ensure a sufficient level of security are primitively large for SCADA devices. Elliptic Curve based cryptography can offer the same security levels with the conventional crypto algorithms deployed in various Modbus implementations using much smaller keys. Based on this fact, in this paper we presented a portable modified version of a commonly deployed SCADA protocol, the Modbus TCP protocol, using Elliptic Curve cryptography library modules in a way that does not interfere with Modbus functionality. The experimental evaluation of the library showed that it imposes almost no overhead on Modbus making it an attractive solution for upgrading Modbus into the EC based crypto domain gaining the corresponding benefits in security.  
Our future work plans include the implementation the of ECDH (Elliptic-curve Diffie-Hellman) and the ECDSA (Elliptic Curve Digital Signature Algorithm) protocols as well as the comparison of ECC, ECDH and ECDSA for resource limited devices used in SCADA systems in order to determine the most efficient approaches with respect to calculation times. Finally, an equally important future work goal is the case study of how to counteract an attacker who could steal and use the private key for Elliptic Curve cryptosystems for malicious actions, assuming the role of an authenticated entity among the devices supporting a SCADA architecture.  
Figure 9: Comparison of Modbus and Modbus with encryption.  
**Author Contributions:** Methodology, A.I. and Y.C.S.; Software, D.C.; Writing--original draft, D.C., A.I., Y.C.S. and C.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A New Observation in Spiral and Curl Antenna Configurations above the Ground Plane
###### Abstract  
We report here that spiral and curl antennas are essentially the same when using the moment method. This aids in the understanding and design of the two antennas, and serves as the groundwork for new ideas regarding antennas. First, the spiral antenna height above the ground plane was gradually reduced, and at each height, the spiral configuration parameters were optimized for an axial ratio of less than 0.1 dB. It was found that the spiral antenna had configuration parameters almost the same as those for a curl antenna at a height of 0.15 wavelengths. Next, the curl antenna was analyzed with a reduced height. The antenna, at a height of 0.10 wavelengths, showed a 3 dB axial ratio bandwidth of 3%, with a VSWR of less than two. The analysis results are verified with experimental results.  
spiral antenna; curl antenna; low-profile antenna; circularly polarized antenna +
Footnote †: journal: 25 March 2022  
Accepted: 21 April 2022  
Published: 23 April 2022
## 3 Case Study--Results  
In order to investigate the influence of fouling build-up on the dynamic behavior of the HENs and on the quality of their control, two cases (the real-life HENs coupled with a CDU plant--see Figures 2 and 3) were considered. Fractional distillation of crude oil is a highly energy-intensive process that requires the crude to be heated from ambient temperature to around 370\({}^{\circ}\)C. The required heat is provided through a set of HEs in which heat from the distillation products and pump-around streams of the distillation columns is recovered, and a furnace fuelled by heavy fuel oil. The crude is pumped through the first part of the HEN to a desalting unit where it is washed with water to remove inorganic water-soluble impurities. After that, the crude flows through the second HEN part, and further to the furnace where it is heated up to the temperature needed for entering the fractional distillation column.  
Using operational data available from the period of three years of continuous HEN operation, exchanger characteristics were studied at different stages of fouling build-up, that is, after 1, 2 and 3 years (passed from operation start-up when HE surfaces had been clean).
## 4 Results and Discussion
### Validation of EFTs by Torque Margin Evaluation  
In this section, the evaluated EFTs were reverified by the torque margin analysis. From the deployment test results, for the case T01, the deployment of dummy panels was successful but for the case T02, finally, it failed to deploy because the initial driving torque was not properly chosen as mentioned in Section 3. To investigate this phenomenon in detail, the torque margin was evaluated as  
\[\mathit{Torque\ Margin}=(\frac{T_{D}}{T_{R}}-1)\times 100(\%), \tag{7}\]  
where \(T_{D}\) and \(T_{R}\) are the driving torque and the resistance torque, respectively [36]. Here, \(T_{D}\) is the driving torque value at the deployment angle \(90^{\circ}\) for joint 1 and at the deployment angle \(180^{\circ}\) for joints 2 and 3. Also, \(T_{R}\) is the resistance torque value that was replaced by the EFT values.  
The torque margin calculation results for all joints are summarized in Table 5. If the torque margin value was negative, it means that deployment of a panel at the corresponding joints will not be achieved. For the first test cases of T01, all torque margin values were positive, but for the worst cases of T02, there were negative torque margin values for joint-1. As seen in Figure 13, the T02 case failed to deploy the panels at joint-1. Hence, this torque margin calculation results were consistent with the deployment test results of the dummy solar panels.  
Through this evaluation, it can be predicted that the worst case cannot be deployed in advance. Therefore, the EFT estimation method developed in this work was verified with excellent reliability for the design of torsional spring stiffness of hinges as well as the deployment of various satellite appendages.  
Figure 11: Comparison of the time history of angular displacements after EFT and test frames consideration for test cases: (**a**) T01, (**b**) T02.  
Figure 12: Comparison of the rotation angle history for T01 case.  
Figure 13: Comparison of the rotation angle history for T02 case.
## 3 Problem Statement
### Storage Model  
Data in Hyperledger have four parts: world state, block index, ledger data, and historical state index. Among these, the world state and ledger data are the most important components used in Hyperledger, which together form the blockchain. The data-storage structure of Hyperledger consists of a state database, a file system, and a historical database. The Fabric ledger architecture is shown in Figure 1.  
The current values of all state data on the blockchain are stored in the state database, usually using databases such as LevelDB or CouchDB. The current value of an account status can be accessed directly through the world state, and the state data are stored as key-value pairs. The file system stores all the block data in the blockchain network. The blocks are linked with hash pointers, while the data information generated by the world state is recorded in the blocks. In Hyperledger, the peer nodes on the same channel store complete copies of the blockchain data. In the Hyperledger storage structure, the transaction data in the block are the main reason for the expansion of the blockchain storage capacity. Considering that the nodes require transaction verification functions, all nodes must store all the state data. Our solution uses block as the basic data unit to divide the data within the network.
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 7 Wear Rate Variability Model
### Optimization Procedure and Model Candidates  
The procedure for optimizing the wear rate variability model parameters \(\mathbf{\theta}\) is described by the flowchart given in Figure 15. Firstly, the model parameters are initialized. The wear rate standard deviation \(w_{\sigma,j}\) is then calculated for \(j\)th modeling point, by using a nominated variability model for the current operating parameters (Block 1 in Figure 15). To account for the influence of different number of repetitive closing cycles \(n_{j}\) on wear rate variability for different operating points \(j=1\),..., \(N\) (see Appendix A, Figure 2 and Section 2), the modeled standard deviation is corrected as (Block 2)  
\[w^{\prime}_{\sigma,j}=w_{\sigma,j}/\sqrt{n_{j}}, \tag{18}\]before inputting it to the NLL cost function (17) (Block 3). After iterating over all modeling points to obtain the NLL cost (17) for the current set of wear rate variability model parameters (inner loop in Figure 15), the parameter optimization algorithm is executed to provide a new set of model parameters aiming to further reduce NLL (outer loop in Figure 15). Matlab function _fminsearch_(.) is used to perform the model parameters optimization, which terminates the search when the cost improvement ceases. The determined standard deviation \(w_{\sigma,j}\) can readily be used to obtain 95% wear rate CI for \(j\)th operating point: \(w_{\mu j}\pm 2w^{\prime}_{\sigma,j}=w_{\mu,j}\pm 2w_{\sigma,j}/\sqrt{n_{j}}\).  
Several wear rate variability model candidates are nominated: (i) exponential, (ii) linear, and (iii) quadratic models taking the expected wear rate \(w_{\mu}\) as the only input (see Figure 10), as well as (iv) linear model with all operating parameters as inputs (\(T_{d}\), \(\omega_{0},M_{z}\), \(t_{2}\)):  
\[w_{\sigma}=p_{1}e^{p_{2}w_{\mu}}, \tag{19}\]  
\[w_{\sigma}=p_{1}+p_{2}w_{\mu}, \tag{20}\]  
\[w_{\sigma}=p_{1}+p_{2}w_{\mu}+p_{2}w_{\mu}^{2}, \tag{21}\]  
\[w_{\sigma}=p_{1}+p_{2}T_{d}+p_{3}\omega_{0}+p_{4}M_{z}+p_{5}t_{2}. \tag{22}\]
## 1 Introduction  
One of the aims of structures design is the minimization of mass and maximization of material usage efficiency, thus a lightweight design has always been a core focus in the engineering field. Generally, implementation of lightweight design leads to high complexity of geometries and thus it has always been hindered by traditional manufacturing technologies. Nature provides several examples of lightweight and functional structures, such as plants' branched shapes, bone tissues, and honeycomb patterns [1]. Generally, human structure design leads to simplified geometries, since it is constrained by manufacturing and assembly feasibility. Concurrent engineering approaches based on Computer-aided technologies (CAX) involve design and simulation iterations to achieve structural and functional targets and subsequent redesign for industrialization, introducing process constraints. Additive Manufacturing (AM) implementation allows for a rethink of features, shapes and geometries in order to exploit the functional design and thus make the design driven by engineering specifics instead of production constraints. Currently, the main strategies for the design of optimized lightweight components to be produced by Additive Manufacturing are cellular structures design, or rather Latticing, and Topology Optimization (TO). While the former can be considered as an Expertise-driven process, the latter can be structured as a mathematically driven process [2], since it can be linked to a numerical function optimization problem. A consolidated and effective approach in advanced settings is based on a combination of results of different kinds of optimization in sequential steps for the design of lightweight structures [3]. TO allows us to obtain improved solutions for structures with non-conventional shapes, but just Additive Manufacturing has actually bridged the gap between design and manufacturing, allowing us to unlock its potential for practical applications [4; 5]. Indeed, a shape with an organic and branched aspect computed through TO is a good way to implement a functional design, exploiting the design freedom induced by AM [6]. Moreover, due to its capability to understand the optimal distribution of the material in advance, it is an interesting method, which is applicable from the earliest design step to cut down the design time.  
The rest of the work is structured as follows. The next subsections provide a synthesis of the state of the art about TO techniques and the workflow for its implementation. Moreover, current TO issues and challenges are summarized and the need for assessment approaches to select design tools for TO is introduced. Section 2 describes the assessment method and the Key Performance Indicators (KPIs) matrices it is based upon. Section 3 concerns the TO of the automotive Case Study and the application of the proposed method to perform the design tools selection phase. For this purpose, a comparison of four different Tools Systems (TSs) is performed and, finally, the conclusions discuss the proposed approach. Table 1 reports the list of abbreviations and acronyms used in the paper.
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 3 Mathematical Models
### Least Squares Solution  
The combined adjustment model (Gauss-Helmert adjustment model) was used, since the objective function includes inseparable observations and parameters, and each function includes more than one observation. Details on the implementation of the Gauss-Helmert adjustment model can be found in [43]. Therefore, only the quantities of the adjustment will be discussed herein.  
First, the VLP-16 provides two observations for each point: a range and a horizontal angle. For unknown parameters, three APs were considered for each laser as aforementioned, and six rigid body transformation parameters for each scan must be included to combine all scanner coordinate systems into a reference coordinate system. Lastly, a unit length condition must be constrained to the equation for each plane. For the network constraint, according to [45], either the ordinary minimum or the inner constraint for the datum definition has no opposing impact on the accuracy of self-calibration. Since the scale is defined by range observations, the ordinary minimum constraint, which fixes the EOPs of the first scan to define the datum as the reference coordinate system, was chosen in this study. In addition, not all 16 laser angular offsets can be estimated simultaneously, because a certain amount of angular offset for every laser can be compensated by sensor orientation, causing a problem when defining the scanner space. Therefore, horizontal and vertical angular offsets for one laser are held fixed. Assuming that we have \(i\) points located on \(k\) planes from \(n\) lasers in \(j\) scans in the adjustment, the least squares solution can be summarized as in Table 5.