esa-sceva/satcom-chunk-collection huggingface dataset:
# FUXYA2020: A Low-Cost Homemade Portable EDXRF Spectrometer for Cultural Heritage Applications
## 2 FUXYA2020 Design and Characteristics
### Detector  
The X-ray detector used is a complete SDD spectrometer (XGL-SPCM-DANTE-25 model) from XGLAB Bruker Nano Analytics, Milan, Italy, with 17 mm\({}^{2}\) active area, 500 \(\upmu\)m thickness and 12.5 \(\upmu\)m Be window. The energy resolution for an SDD detector is usually given as FWHM of the Mn K\(\alpha\) line (5.9 keV); for our spectrometer, the nominal energy resolution is 140 eV with peak-shaping time of 96 ns, or 125 eV with 1 \(\upmu\)s peak-shaping time. In our working conditions (0.8 \(\upmu\)s shaping time) we measured the detector resolution on a pure metal Mn target obtaining 130 eV as expected; for count rate of about 1 Mcps, we have a dead time of about 2%. In order to better detect the X-ray fluorescence emitted by the sample eliminating fluorescence from the detector itself, it is necessary to use collimators in front of the detector Be window, in addition to the internal one. We used a 2 mm lead collimator completely lined in pure aluminum (total thickness of the collimator 5 mm), so that no detectable lines are added. To allow an easy change of the collimator, whose best diameter can vary depending on the object to be studied, a cap for the detector window has been designed and 3D printed. This expedient also introduces an indirect protection to the thin Be window, but slightly increases the distance between the detector and the sample. In the geometric configuration of the FUXYA system, the angle between the X-ray tube and the detector was set at 90\({}^{\circ}\) (Figure 2) in order to better separate the Compton scattering peak from the Rayleigh scattering peak and to obtain information about light elements when necessary [45].  
Figure 1: Attenuation of X-ray tube spectra by Al (254 \(\upmu\)m), Cu, Mo, Ag and W (25.4 \(\upmu\)m) filters.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 6 Conclusions  
In this paper, we propose an adaptive solar power forecasting algorithm for highly precise solar power forecasting. We firstly present the adaptive framework of the algorithm, integrating the improved _k-means_ clustering, LARS and BPNN. ASPF is thus able to select the important variables, compensate the predictions from different methods, and update the BPNN adaptively. We also present these algorithms in details. Finally, we evaluate the proposed ASPF through the simulated experiments. We verify the validity of these algorithms, such as, improved _k-means_, LARS, BPNN and ASPF. Furthermore, we testify the effectiveness of the ASPF algorithm for different prediction models. The simulation results show that our proposed method greatly improves the accuracy of solar power, and keeps improving as more data collected. For future work, we consider to apply ASPF in other application, such as energy scheduling mentioned in Introduction.  
[PERSON] proposed the conceptualization of adaptive forecasting the solar power; [PERSON], [PERSON], [PERSON] and [PERSON] worked together to investigate the proper method to realize this adaptive solar power forecasting; [PERSON], [PERSON] and [PERSON] validated the performance in Matlab, and the test data is provided by [PERSON]; [PERSON] and [PERSON] analyzed the data and wrote the paper; [PERSON], [PERSON] and [PERSON] supervised the all the entire process.  
The authors gratefully acknowledge financial support for this project provided by the NSF of China under Grants No. 51607087 and the Fundamental Research Funds for the Central Universities of China, NO. XCA17003-06.  
The authors declare no conflict of interest.
# Airfoil Optimization Design Based on the Pivot Element Weighting Iterative Method
## 2 Airfoil Parameterization
### Airfoil Parameterization Based on the PEWI Method
When \(\alpha>0\), the condition number of \((S+\alpha E)\) is proved smaller than \(S\). It is very important to choose the appropriate \(\alpha\) value. If \(\alpha\) is too small, \((S+\alpha E)\) matrix is still ill-conditioned and \(A\) matrix calculated by Equation (11) is inaccurate. And if \(\alpha\) is too large, the convergence rate of the linear equation is very slow and the solution might be distorted. Therefore, we need choose the suitable \(\alpha\). Taking Equation (13) into (12), this equation can be obtained. \[(S+\alpha E)\mathbf{A}=\mathbf{Z}+\alpha\mathbf{A} \tag{14}\]  
Bernstein coefficient matrix \(\mathbf{A}\) can be calculated by the iterative algorithms. The iterative formula is as follows:  
\[(S+\alpha E)\mathbf{A}^{(i+1)}=\mathbf{Z}+\alpha\mathbf{A}^{(i)} \tag{15}\]  
where \(\mathbf{A}^{(i+1)}=\mathbf{A}^{(i)}+\mathbf{e}^{(i)}\), so  
\[ \begin{array}{l}(S+\alpha E)(\mathbf{A}^{(i)}+\mathbf{e}^{(i)})=\mathbf{Z}+\alpha\mathbf{A} ^{(i)}\Rightarrow\\ (S+\alpha E)\mathbf{A}^{(i)}+(S+\alpha E)\mathbf{e}^{(i)}=\mathbf{Z}+\alpha\mathbf{A}^{(i)} \Rightarrow\\ (S+\alpha E)\mathbf{e}^{(i)}=\mathbf{Z}-\mathrm{SA}^{(i)}\end{array}  \tag{16}\]  
Obviously, we can gain the optimal solution of Equation (16) as long as 2-norm of \(\mathbf{e}^{(i)}\) reaches the setting precision. The iterative process is presented in Figure 1. \(i\) is the number of iterations of 2-norm and \(j\) is the number of iterations of the PEWI method. \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the setting errors. \(\Delta\) is the step size of every iteration.
## 2 Materials and Methods
### Computational Mesh  
The mesh was an unstructured hexahedral grid generated by OpenFOAM. As shown in Figure 5a, two boxes were used to refine the railway region; a fine mesh was used along the 3/4 length of the railway. The WT region and the sliding mesh domain were set as extra-fine mesh. Figure 5b shows the mesh around the train and windbreak, as well as the corresponding layer meshes. To capture the high gradient velocity change near the wall, ten prism layers were attached to the train surface; the height of the first layer was 0.1 mm, and the average nondimensional wall distance (\(y^{+}\)) was approximately 30-40 around the geometry. In Fluent [39], a \(y^{+}\)-insensitive wall treatment was embedded into the \(\omega\) equation to blend the viscous sublayer and the logarithmic layer formulations; therefore, the shear stress in the first cell close to the wall can be determined reliably.  
Furthermore, as presented in Table 1, three different mesh resolutions--coarse mesh (22 million), medium mesh (41 million), and fine mesh (64 million)--were considered to avoid the mesh sensitivity. As shown in Figure 6, when the train was stationary in the WT region, the \(C_{p}\) values (defined in Equation (1)) along the middle cross section of the head car were compared for the different mesh resolutions. Due to the poor wind-proofing ability of the WT, the WWS and bottom pressures were greater than those of the LWS and top, respectively, resulting in sudden positive side and lift forces. The three mesh resolutions resulted in a similar phenomenon. However, in both top-corner positions, there was an obvious deviation between the results of coarse mesh and those of medium mesh and fine mesh. Results obtained from the medium and fine meshes were in closer agreement. Therefore, the finer mesh was deemed unnecessary for the simulations, and the medium mesh was used in the present work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Coarse** & **Medium** & **Fine** \\ \hline Number of volume meshes/million & 22 & 41 & 64 \\ Size of the smallest mesh on train surface/mm & 3.1 & 1–1.6 & 0.96 \\ Number of prism layers & 10 & 10 & 10 \\ First boundary layer/mm & 0.31 & 0.1–0.16 & 0.096 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the different grid resolutions.  
Figure 5: Computational mesh: (**a**) different views and (**b**) the mesh around the model and the surface layer mesh.
# Random Routing Algorithm for Enhancing the Cybersecurity of LEO Satellite Networks
## 4 Simulation Results and Discussion
of the algorithm used for routing that would mean an additional probabilistic cost to the attacker. In Figure 4, we show the histogram plot of the routing algorithm with respect to the detectability/MaxUp. The adversary would not fully occupy the bandwidth of one particular uplink as it would be easily detectable. The MaxUp is normalized to the bandwidth of one uplink, so it will distribute such attacks among multiple uplinks. It is expected that, due to increased uncertainty in the routing algorithm, the detectability of the algorithm will slightly degrade. This can be noticed in the histogram plots. The average median values of the Maxup are shown in Table 3 for all the algorithms. As it can be noted, the k-SP provides the best MaxUp results, however, at the same time, we noted that its average and median costs are not better compared to the k-RAND algorithm. We note that the k-RAND provides the lower median detectability, but it is better than k-DS. At the same time, we note that k-RAND has the highest probability of the cost of attack exceeding the average cost of attack compared to all the algorithms. The CDF of the MaxUp is shown in Figure 5. As the CDF plot shows that we have obtained moderate results in the case of the detectability of the algorithm. It is seen that the k-SP is better in terms of detectability, but we have already seen that the proposed algorithm outperforms k-SP in terms of adversary cost. Due to the increased complexity of our algorithm, the detectability is slightly degraded, i.e., if an LSN has an algorithm that produces more predictable routing, a specific link would be more easily congested. On the other hand, for the same reason, it would be easier to detect an attack because a pattern in the routing would stand out more easily. Figure 4: Histogram plot of the attackers’ detectability/MaxUp of launching a DDoS Attack on an LEO satellite network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Average** & **Median** \\ \hline k-SP & 0.1618 & 0.1050 \\ \hline k-DG & 0.0905 & 0.0500 \\ \hline k-DS & 0.0684 & 0.0350 \\ \hline k-LO & 0.0775 & 0.0475 \\ \hline k-RAND & 0.0690 & 0.0450 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MaxUp Statistics.
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 1 Introduction
Besides, the problem of fast objects recognition is also very important in monitoring the electromagnetic environment where signals generated by different types of emitters (radars, jammers) are in many situations noisy, misshaped or changing in relation to the weather condition, task and application, thus the FSRF network (FSRFNet) may also be directly applied in recognition and identification emitter signals [34; 35; 36; 37]. Main contributions of this work are summarized as follows: (1) We propose a simple and effective attention block (FSRF) that can be widely applied to boost representation power of CNNs; (2) We validate the effectiveness of the FSRF block through extensive ablation studies; (3) We demonstrate that the FSRFNet outperforms previous state-of-the-art models on datasets of different sizes, and successfully embed an FSRF block into lightweight models (e.g., ShuffleNetV2 [38] and MobileNetV2 [39]).
# Recent Advances and Challenges in the Seismo-Electromagnetic Study: A Brief Review
## 2 Seismo-Electromagnetic Phenomena in the Lithosphere
[13], the above results added to the evidence that there is a legitimate electromagnetic signal prior to medium-to-large earthquakes. However, more work needs to be conducted to refine the algorithms and to characterize and remove the known noise sources. In China, a mobile geomagnetic monitoring network has been established in the last 20 years, covering most of the country's potential earthquake zones, and the geomagnetic anomaly provides very important support for the prediction of earthquake location [60]. Some research about the pre-seismic anomaly characteristics of geomagnetic changes was reported. For example, the amplitude ratio of geomagnetic harmonic waves was significantly enhanced before the Ms5.5 Eryuan earthquake in 2013 [61], and the geomagnetic and geoelectric data show abnormal variations before the Wenchuan earthquake [33]. There were also some reports about the apparent resistivity changes before and after earthquakes [62; 63; 64; 65]. The apparent resistivity change revealed by magnetotelluric (MT) measurements is a well-accepted technique, and some tests on the relation between earthquakes and apparent resistivity changes were revealed by repeated MT methods [66; 67]. Further rigorous analysis based on continuous MT observation is required to ensure the reliability of the results. On the other hand, some researchers claim that there were no electromagnetic changes prior to earthquakes. For example, [PERSON] et al. [68] analyzed the geomagnetic data associated with the 2004 Parkfield Earthquake (September 28, Mw 6.0, depth 8 km) by several methods, and they found no clear change of magnetic fields before the main shock. At present, there are still active debates in the geophysical community on seismo-electromagnetic phenomena [69; 70; 71].
## 2 Material and Methods
### Image Acquisition  
ECG-gated computed tomography (CT) images from four patients who underwent LAA occlusion were considered in this study. Each acquisition comprised 10 phases of the cardiac cycle (from 0% to 90%), for a total of 40 volumetric dataset. Patients were selected as clinical cases of relevant interest from specialist doctors to cover the four different LAA shape groups (CW, CF, WS and CS). CT images were acquired with a 320-detector scanner (Toshiba Aquilion One, Toshiba, Japan), using contrast medium. Images were characterized by a dimension of 512 \(\times\) 512 pixels with a bit depth of 16 and a spatial resolution of 0.495 mm\({}^{3}\).
###### Abstract  
This study provides an automatic shipping-route construction method using functional data analysis (FDA), which analyzes information about curves, such as multiple data points over time. The proposed approach includes two steps: outlier detection and shipping-route construction. This study uses automatic-identification system (AIS) data for the experiments. The effectiveness of the proposed method is demonstrated through case studies, wherein our approach is compared with the Mahalanobis distance method for trajectory-outlier detection, and the performance of vessel trajectory reconstruction is compared with that of a density-based approach. The proposed method improves understanding of vessel-movement dynamics, thereby improving maritime monitoring and security.  
map construction; shipping-route construction; functional data analysis; data depth 202020100881 00.3390/app10030881 24 December 2019 January 202021 January 2020228 January 2020224 December 2019 January 2020228 January 202024 December 202021 January 202024 December 202021 January 2020  
equipment continuously reports vessel information, ranging from vessel heading course, speed, and position to maritime mobile satellite identity (MMSI) [6]. This study applies functional data analysis (FDA) to construct shipping routes from the AIS data. FDA is a method of understanding multiple data points over time as a single block of a curve [7]. Classical statistics is the statistical analysis of measurements made up of single numbers. However, in multivariate statistics, different values are derived for each subject and unit. As data collection by various methods becomes complicated, it is reasonable to observe the collected data in one continuum. In short, the aim of this study is to generate shipping routes from AIS data using FDA, which strengthens maritime safety.  
Following a review of related work (Section 2), Section 3 precisely presents the proposed map-construction method based on FDA. Section 4 then presents an experimental evaluation and comparisons of the proposed method. Finally, the paper concludes with a discussion of the limitations of the approach, and future work (Section 5).
## 4 Integration into Airspace (U-Space)
The airspace in which aircrafts fly can be classified as controlled airspace in which air traffic control (ATC) services such as clearance and traffic information. However, in uncontrolled airspace, there are no such services for manned aircraft. With the integration of U-Space in the future, when the member states designate a volume of airspace as U-space airspace, there will be a restriction: both for drone operators, to use U-space services to fly in that airspace; and for manned aircraft operators, to make available their position at regular intervals. Figure 3: Classification of airspace adapted from the CORUS project [26]. The VLL airspace is divided into X, Y, Z volumes: X (low risk), Y (higher risk—access only with approved operation plan and specific technical requirements per volume) and Z (highest risk—access only with approved operation plan being Zu under U-space and Za the ATC controlled airspace). Based on the evaluation conducted by EASA of existing U-space services and their maturity, the following U-space services are considered necessary and mandatory to ensure safe and efficient operations in each U-space airspace implementation: network identification, geoawareness, traffic information, and drone flight authorization. In addition, member states may decide that additional U-space services are needed to support safe and efficient drone operations in specific volumes of U-space airspace implementation. They can decide to mandate them based on their risk assessment.
## 1 Introduction
In this paper, we propose a joint transmit power and power-splitting optimization scheme for SWIPT in D2D-enabled cellular networks, addressing the limitations of [15] by incorporating power-splitting SWIPT and energy signal transmission, while ensuring that the CU's performance remains at the same level as in scenarios without D2D communications. Our approach utilizes the concept of 'energy cooperation', where energy signal transmission assists energy harvesting at the CU without disrupting its information decoding. This harvested energy can then support higher transmit power in D2D communications, improving the overall performance and expanding the energy-rate region. By leveraging energy cooperation, the proposed scheme enhances the harvested energy and data rate performance of D2D communications. The main contributions of this study are as follows:  
* In considering power-splitting SWIPT and energy signal transmission, we formulate a joint optimization problem for the transmit power and power-splitting ratio, subject to the constraint that the CU's harvested energy and data rate are maintained at the same level as in scenarios without D2D communications. The formulated problem is inherently nonconvex and thus challenging to solve directly. However, by leveraging the monotonically increasing property of logarithmic functions, we transform the original problem into an equivalent convex problem. As a result, the optimal solution is derived in closed form. * The optimal D2D performance is analyzed to obtain insights into the effectiveness of the proposed scheme. First, we obtain a sufficient condition for achieving optimal D2D performance without energy signal transmission. Second, a sufficient condition is derived under which D2D communication is not feasible with the baseline (BL) scheme but becomes feasible with the proposed scheme. These conditions provide valuable insights into the scenarios where the proposed scheme significantly enhances D2D communication performance. * Numerical results are provided to demonstrate that the proposed scheme significantly surpasses the performance of the baseline scheme.
## 3 Literature Review
### Detailed Analysis
In order to integrate technical and management aspects, several frameworks insist on the necessity of including a simulation-based evaluation of the models in order to retrieve the necessary KPI for management-oriented decisions. In this way, the framework presented in [36; 37] was mostly focused on the technical aspects of the transformation. Figure 1: Conceptual scheme of Industry 4.0 evolution/revolution. Reprinted with permission from [32]. Copyright 2020 Taylor & Francis. The framework was denoted as a development framework (Figure 2) and illustrates an implementation loop based on the evaluation of simulation campaigns. The models and simulation technologies that are listed in this framework encompass only information-oriented technologies, which is a partial view when considering the whole set of available Industry 4.0 technologies. In the same way, the logical model presented in [38] clearly exhibited the need for simulation studies during the implementation process, and placed them at the core of the process. The notion of digital twins [39] is referenced here as a way to connect these simulation studies with actual data coming from the system. Another aspect of the methodology deals with the definition of enhancement levers. The authors of [40] designed an approach, SME-oriented, that incorporated an initial analysis of brakes and levers that can be used to remove those breaks (Figure 3). These levers of acceleration are meant to become the key factors in the whole approach, driving changes through the definition of appropriate indicators. The rest of this approach is SME-specific and not detailed at sufficient a level for being a generic enough methodology. Finally, the framework presented in [41] exhibited an interesting decomposition of the framework into four phases, namely Conceptual, Preparation, Implementation, and Completion. This framework was actually designed as a methodological framework for practitioners, focused on the integration of advanced concepts of Lean and Blue Ocean Manufacturing (Lean-BOM). As a matter of fact, this framework cannot be used as is for other implementations, as some of the steps are very specific (Gemba Walk for example). As a synthesis of this analysis, the five best practices that were especially highlighted by these 12 frameworks are:  
Figure 2: Smart manufacturing system development framework. Reprinted with permission from [37]. Copyright 2019 SAI. 1. An iterative and step-by-step transformation of the manufacturing systems;
2. Decomposition of the framework into four phases;
3. Integration of the management layer at different stages of the framework;
4. Mandatory definition of levers of enhancement;
5. Importance of a specific phase of simulation-based evaluation. The next section introduces the IPSI framework, incorporating all these features with the objective of becoming an actual methodological framework for practitioners aiming at implementing new Industry 4.0 technologies in industry.