esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### Bench Performance Analysis (Testbed)  
Initially, the two technologies were tested on bench or testbed. In this case, the technologies were submitted to the process of sending and receiving packages from their respective responsible parties (coordinator for the LoRaMESH endnode and Gateway for the LoRaWAN). In the tests, the endnodes were kept at a distance of 2 m for their responsible and all communication was monitored and analyzed in order to understand the operation of the metrics analyzed. In this case, the RSSI was considered the indicator responsible for presenting the received signal strength, SNR (Signal to Noise Ratio) metric responsible for measuring the relationship between signal and noise, Time on Air (ToA), this measure will show the time the package stays in the air until it reaches the other device and Delay which is the time between the last message sent and the next. In addition, some parameters have been configured to analyze their relationship with the measures analyzed as the SF, in LoRa technology we have the SF 7 to 12 and the payload size (the Portuguese package size) that in this work were considered the following package sizes for the two message sending scenarios (A) From the EPC to the SMs and (B) From the SMs to the EPC, in the tabletop Table 3 are those described in more detail.  
As can be seen in the Table 3, this work presents 3 different packet size configurations for each different SF group be it 7, 8 or 9 we have the packets of 63 bytes, considering that it will be necessary to send only one packet that will already contain all the information that the EEC needs. In case of reducing the size of this packet, you have the option to use the 31 byte size packet, however, the packet will be split in half, thus making the SM have to send two packets. The same occurs in cases where there is an even larger division, which will be 21 bytes divided into 3 packages. In the case of SF bigger as 10, 11 and 12 the packets must be reduced, because the greater the distance and the SF that the endnode is present, the smaller the packet must be, otherwise the packet will not reach its destination or will have a very large loss rate due to the bandwidth being too low.  
Finally, we also analyzed packages that the EPC will send to the SMs with varying sizes of 60 bytes for packages with price information for the next 24 h as well as suggestions for plans to use the electricity to be followed, 40 bytes when for packages with prices for the next 12 h and suggestions for plans and finally 20 bytes for packages with only prices from 1 h to 6 h ahead and suggestions for plans. In this case, only one package will be sent at a time from the EPC to the end-nodes. Finally, the fixed end-node configurations consist of 4/5 coding rate (CR) and 125 MHz bandwidth (BW).
## 5 Data Processing and Simulation Experiment Setup
### Data Processing  
Due to the presence of noisy data (sudden changes in speed and acceleration) in the original CitySim dataset, as shown in the gray area of Figure 6, which may lead to anomalous vehicle trajectories, abnormal acceleration data exceeding the acceleration threshold were filtered out during the data processing. When the original data's acceleration exceeded the threshold, linear interpolation was used to denoise the two adjacent data points. Additionally, a moving average filter with a duration of 0.5 s was applied to reduce the impact of random noise. The denoised data are represented by the dashed line in Figure 6 [29; 30; 31; 32].  
Figure 5: Deep learning predictive modeling flowchart.
###### Abstract  
This paper introduces a novel framework for designing wideband antenna arrays using self-similar Eisenstein fractal geometries combined with multi-objective evolutionary optimization techniques. The approach employs multi-objective binary differential evolution (MO-BDE) for array thinning and multi-objective particle swarm optimization (MO-PSO) for optimizing amplitude excitations. This integrated methodology reduces the number of active elements while enhancing overall array performance. The optimization process targets minimizing peak side lobe levels and maximizing directivity over a broad frequency range. Two designs are explored: one optimized at a primary frequency, and another providing consistent wideband behavior. The proposed method achieves a \(37.5\%\) reduction in active elements. Design A shows an SLL reduction of \(-12\) dB at the target frequency, while Design B maintains up to \(-3\) dB SLL improvement across the bandwidth. The results confirm the efficacy of the proposed synthesis method for developing scalable, energy-efficient antenna arrays for next-generation systems.  
antenna array; fractal array; wideband; evolutionary computing; multi-objective optimization Article  
* 2025 15 5584
## 6 Conclusions  
The analysis performed has shown that current methods of attack tree analysis involve human experts performing expert analyses of attack trees and the systems being utilized by a company to determine the best method of defense for that system or company. The vast amounts of data about possible attacks against information systems collected in the form of attack trees can be seen as prospective sources of data for the automatic creation of knowledge bases for expert systems, dedicated for IT security risk analysis, thereby minimizing the knowledge base creation process's expenses and ensuring process reliability.  
A method was proposed that utilizes several tools: already existing ATTop, which performs the initial conversion of attack tree data into the unified UATS XML format, and the newly developed ATES tool, which performs further transformation into the CLIPS rules supported by the JESS expert system platform, which was chosen for the experiments due to its broad adoption.  
The experiments performed have covered data importation from attack trees from 22 different sources and allowed creating the knowledge base that could be used to assess risks of 49 cyber attacks and propose over 826 possible controls. The method has demonstrated reliability while converting attack trees of a supported type. Throughout the heuristic portion of experiments, the data conversion correctness was confirmed to be valuable by JESS and was tested on the JESS platform to verify that accidental attack tree data modification will not occur during any phase of the translation process. Still, the recent trends in attack tree representation, transformation, and generation show a large amount of variability in attack tree representation and generation. This variability leads to some difficulties in the transformation that could require further research.  
All authors contributed to investigation, methodology, software, measurements, data analysis, scientific discussions and writing the article. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research received no support which is not covered by the Author Contributions and Funding sections.  
The authors declare no conflict of interest.
## 2 Background  
### Named Data Networking  
Named data networking (NDN) is an emerging Internet paradigm. Current Internet architecture is the base line behind the design principles of NDN. Design principle of NDN reflects our understanding towards the strength and the limitations of current internet architecture [4]. Communication in NDN focuses on content rather than content container and is based on content names. NDN offers innovative and advanced way to think about the internet and making a beneficial impact on the overall performance of the network. Separation of forwarding and routing planes, built-in security, state full forwarding, and end-to-end principle are the key features of NDN architecture [5].
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
## 2 Materials and Methods
### Data Acquisition and Processing  
For the validation project of documentation and volume determination of bulk material heaps, surveying methods listed above were used. In addition, two neighbouring reference heaps were selected in which we assume the occurrence of error for points in a cloud, due to parts of the image that are too bright (overexposed) or fractions that are too soft (dust); thus, a smooth structure reduces the accuracy of the correlation, which was confirmed in the processing. For purposes of 3D model comparison, obtained by listed methods, the network of control points (CP) for terrestrial measurement and of ground control points (GCP) for aerial photogrammetry was created and surveyed. The coordinates of these points were determined in a common coordinate system ETRS 89 (European Terrestrial Reference System) by quick static method using relative GNSS measurements and transformed into the positional coordinate system S-JTSK (The Uniform Trigonometric Cadastral Network) and height system Bpv (Balt after adjustment). Both listed coordinate systems are used as a mandatory geodetic base in the Slovak Republic. Workflow diagram is shown in Figure 3. All field measurements and data acquisition GNSS, TLS, and AP were performed in one day during the shutdown of the production to prevent any changes in the morphology and size of the examined heaps.  
Figure 2: Aerial equipment: (**a**) Camera Microsoft UltracamLP; (**b**) Aircraft Tecam MMA.
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 3 Failure Type Prediction Method Using Physical Indices and Data Features  
In this section, the scheme of the proposed failure type prediction method using physical indices and data features is presented as Figure 3.  
Firstly, the original current signal is collected and the five physical indices are abstracted. Secondly, the current change rate curve is analyzed by the wavelet packet decomposition to get the energy value of each frequency band. Then, the five physical indices and energies of frequency bands are combined into a comprehensive feature vector. Finally, the random forest classifier is applied to get failure type prediction and classification results.
# A Novel Approach to Reach Impedance Matching in Wireless Power Transfer Systems
###### Abstract  
Wireless power transfer (WPT) using magnetic resonant coupling technology, came into focus promptly by virtue of its long transfer distance, and its non-radiative and high-efficiency power transfer. The impedance matching has been studied in the literature in recent years. However, there is no suitable way to reach the optimum load in reality. In this paper, a new method is proposed to maximize the power transfer ability of a given pair of coupled coils. An analytical calculation of the mutual inductance is presented accurately with respect to the angled concentric multiple-turn printed spiral coils (PSC). In addition, the experimental results were in good agreement with the circuit simulation. Finally, a WPT experiment setup working at 3 MHz resonance was established. The experiment results verified that the maximum transfer efficiency at fixed distances can be easily achieved by adjusting the angle to reach impedance matching. Compared to prior to optimization, the maximum improved efficiency was improved by \(11\%\).  
wireless power transfer; optimum load; impedance matching; printed spiral coils 2019 976; doi:10.3390/app9050976
# Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds
## 3 Minimizing Resource Gap
### MRG Algorithm
Tables 3 and 4 illustrate allocation steps for Case 1, applying the default algorithm in Apache Spark and the proposed MRG algorithm, respectively. Tables 5 and 6 illustrate allocation steps for Case 2. In Case 1, the load-balanced algorithm and the proposed MRG algorithm could allocate resources to satisfy the application demands for \(AP_{1}\) and \(AP_{2}\). However, in Case 2, the load-balanced algorithm can only satisfy two application demands for one \(AP_{1}\) and one \(AP_{2}\). Resource wastage for the available but unused resources is higher for the load-balanced algorithm in this case. The proposed MRG algorithm can satisfy all the resource demands for \(AP_{1}\) and \(AP_{2}\). This is because MRG minimizes the resource gap for reducing the amount of available but unused resources. The aforementioned cases indicate that the MRG algorithm can not only reduce the resource gap in the heterogeneous resource environment but also improve resource utilization. The load-balanced algorithm performs satisfactorily in the environment with homogeneous resources but results in poor resource utilization in the environment with heterogeneous resources. The time complexity of our MRG algorithm depends on the factors of \(S\) servers and \(R\) kinds of resource capacities. For each server \(S\), the proposed algorithm calculates the \(ARG\) by Equation (2). for determining the accumulated resource gap. Then, the proposed algorithm finds out the target server \(Ts\) with the smallest \(ARG\) and allocate the resource of this server to the application. So, the most time consuming of the proposed algorithm is to find out the \(Ts\). Therefore, the time complexity of the proposed algorithm is O(\(S\times R\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Allocation** & \multicolumn{2}{c}{**Server \(S_{1}\)**} & \multicolumn{2}{c}{**Server \(S_{2}\)**} & **Allocated** \\ \cline{2-5}
**Steps** & \(A_{S_{1}R}\) & \(ARG_{S_{1}}\) & \(A_{S_{2}R}\) & \(ARG_{S_{2}}\) & \\ \hline \(AP_{1}\) & \(D_{R}\)=(1,2,3) & (4,6,8) & (4,6,8) & (1,2,3) = (3,4,5) & (8,6,4) - (1,2,3) = (7,4,1) & Server \(S_{1}\) \\ \hline \(AP_{2}\) & \(D_{R}\)=(4,3,2) & (3,4,5) & (8,6,4) & (8,6,4) - (4,3,2) = (4,3,2) & Server \(S_{2}\) \\ \hline \(AP_{1}\) & \(D_{R}\)=(1,2,3) & (3,4,5) & (2,2) + (2-2) = 0 & (4,3,2) & Server \(S_{1}\) \\ \hline \(AP_{2}\) & \(D_{R}\)=(4,3,2) & (2,2,2) & (4,3,2) & (4,3,2) = (0,0) & Server \(S_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Allocation steps for Case 2 using the MRG algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Allocation** & \multicolumn{2}{c}{**Server \(S_{1}\)**} & \multicolumn{2}{c}{**Server \(S_{2}\)**} & **Allocated** \\ \cline{2-5}
**Steps** & \(A_{S_{1}R}\) & \(ARG_{S_{1}}\) & \(A_{S_{2}R}\) & \(ARG_{S_{2}}\) & \(ARG_{S_{2}}\) \\ \hline \(AP_{2}\) & \(D_{R}\)=(4,3,2) & (4,6,8) & (4,6,8) - (4,3,2) = (0,3,6) & (8,6,4) - (4,3,2) = (4,3,2) & Server \(S_{2}\) \\ \hline \(AP_{1}
# Extension of the Discrete Electron Transport Capabilities of the Geant4-DNA Toolkit to MeV Energies
## 2 Materials and Methods
### Relativistic Corrections
\right\}, \tag{13}\]  
where \(X=log_{10}\left(\frac{\beta}{\sqrt{1-\beta^{2}}}\right)\). The other parameters of Equation (13) are provided by the corresponding tables from [61]. For liquid water, we have used \(\alpha=0.09116\); \(X_{1}=2.8004\); \(m=3.4773\); \(C=-3.5017\); \(X_{0}=0.24\). Following the criterion discussed above (see Figure 1), the density effect correction, Equations (12) and (13) is added only in the energy Regimes IV. Finally, it is well known that the Drude-based ELF, although very convenient at low-intermediate energies, exhibits undesirable asymptotic properties at high energies. Specifically, at sufficiently large energy transfers (i.e., above inner-shell thresholds), long-range screening effects vanish, so the following relation holds: \(\mathrm{Im}\left[-\frac{1}{\varepsilon(E)}\right]\approx\mathrm{Im}\left[ \varepsilon(E)\right]\equiv\varepsilon_{2}(E)\). However, \(\varepsilon_{2}(E)\) is proportional to \((1/E)df(E)/dE\), where \(df\left(E\right)/dE\) is the optical oscillator strength distribution which, for hydrogen-like systems, reduces asymptotically according to \(df(E)/dE\sim E^{-3.5}\). Therefore, for such systems, \(\mathrm{Im}\left[-\frac{1}{\varepsilon(E)}\right]\) and \(\varepsilon_{2}(E)\) should decrease asymptotically as \(E^{-4.5}\)[46]. However, the Drude-based ELF of Equation (1) used in the present model decreases by \(E^{-3}\), as can be trivially deduced from the analytic properties of the Drude function [46]. Although water is not hydrogen-like, it is expected according to the above reasoning that the Drude-type ELF may still overestimate the \"true\" ELF of liquid water at sufficiently high \(E\). For example, the somewhat higher I-value commonly obtained by this methodology may be traced to the above overestimation. In an effort to minimize this effect, we have applied an ad hoc asymptotic correction to the ELF at energy transfers above 50 keV (i.e., incident electron energies above \(\sim\)100 keV) which yields a steeper reduction to the ELF and improves agreement with the SP data of the ICRU Report 90 [59] (see below). Following the criterion discussed above (see Figure 1), the asymptotic correction is added in the energy Regimes III and IV.
## 6 GA-Based Slot Assignment Algorithm
### Encoding
#### 6.1.1 Chromosome Structure  
An example coding chromosome representing a schedule is shown in Figure 7. Each individual has two chromosomes corresponding to a sequence of node numbers and a sequence of slot types. Both chromosomes have \(N_{QF}\) QFs which consist of \(N_{all}\) slots as shown in Figure 4, respectively.  
As the first chromosome, a sequence of node numbers represents transmission orders from a central node to nodes. In this paper, we substitute a simple number like \(1,2,3,\dots\) as a node number for (Group number, node number) in order to simplify the notation.  
As the second chromosome, a sequence of slot types represents kinds of packets that a central node sends to a node at the slot. We define three slot types in this paper. First type \"1\" represents that a central node transmits an original polling request packet for periodic data collection. Second one \"2\" represents that a central node sends the retry packet of the original packet to an end node. Final one \"3\" means that a central node can transmit a query for an unpredictable on-demand collection to any node at the slot.  
For example, Figure 7 shows that a central node transmits a request packet to collect periodical application data from node 1 at slot 1, and a retry request packet to node 1 at slot 8 if the central node did not receive the data corresponding to the previous request to node 1. If the central node received a reply from node 1 by slot 8, the central node does not transmit the retry packet to node 1 at slot 8. In addition, a central node may transmit a polling query to any node for an unpredictable on-demand  
Figure 6: Flowchart of our Genetic Algorithm (GA)-based slot assignment algorithm.  
collection at the slot 4 but a central node never transmits a request packet to node 4 for a periodic data collection at the slot.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 4 The Proposed Protocol
### Data Transmission Stage  
At this period, TDMA operates in the communication process to guarantee no interference or data collision during data transmission. The coordinator assigns TDMA-based time slots to the relay nodes and the in-body sensors. The in-body sensor transmits the collected data to the selected relay node in the scheduled period. Then, the relay node forwards the aggregated data to the coordinator as mentioned in Section 3.6.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 3 Methods
### Notation  
The following notation is used throughout the paper. Uppercase letters denote spatial coordinate systems, such as the eye-display coordinate system E. Lowercase letters denote scalar values, such as the focal length \(f_{u}\). Both 3D and 2D points are represented by homogeneous vectors. Vectors are denoted by lowercase bold letters with a superscript denoting the reference coordinate system (e.g., a 3D point in eye-display coordinates \(\mathbf{v}^{\mathrm{E}}\) or a 2D image point in virtual screen coordinates \(\mathbf{i}^{\mathrm{F}}\)). Vectors can also be expressed in component form, with a bold subscript indicating the correspondence (e.g., \(\mathbf{v}^{\mathrm{E}}=(x_{\mathrm{v}},y_{\mathrm{v}},z_{\mathrm{v}},1)^{ \mathrm{E}}\)). Matrices are denoted by uppercase typewriter letters (e.g., the intrinsic camera matrix of the eye-display \({}^{\mathrm{F}}\)K).  
The 6 DoF transformations from one coordinate system to another are so defined. Given two coordinate systems A and B, the transformation from A to B is defined as \({}^{\mathrm{B}}_{\mathrm{A}}\mathrm{T}=(^{\mathrm{B}}_{\mathrm{A}}\mathrm{R} _{\mathrm{V}_{\mathrm{A}}}\mathrm{t})\) where \({}^{\mathrm{B}}_{\mathrm{A}}\mathrm{R}\) is the rotation matrix and \({}^{\mathrm{B}}_{\mathrm{A}}\mathrm{t}\) is the translation vector. Therefore, we have:  
\[\mathbf{v}^{\mathrm{B}}={}^{\mathrm{B}}_{\mathrm{A}}\mathbf{b}\mathbf{v}^{ \mathrm{A}}+{}^{\mathrm{B}}_{\mathrm{A}}\mathrm{t} \tag{1}\]
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 4 Simulations  
In this section, we first simulate and analyze the variation in the false alarm rate and detection probability with accumulation time for several specific false alarm probabilities in radar operation. Then, the results acquired using the proposed method in multi-target tracking simulations are presented. We apply the proposed method to the PHD filter and LGM-PHD filter proposed by [PERSON] in [35], and compare them with the basic GM-PHD filter [19] and the LGM-PHD filter [35]. Simulations are designed using the multi-target constant velocity (CV) motion model and a constant acceleration (CA) motion model in dense scenarios.
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 3 Methods
### Shape-Awareness Regularization Loss  
We attempt to investigate the method for considering prior shape information to provide extra performance gains. Instead of using the complex techniques that require computationally expensive optimizations, we resort to the distance transform maps (DTMs), which have been investigated for introducing shape information for segmentation tasks [41; 42]. In their studies, the segmentation labels are clean and provide accurate supervision information. However, in our study, the labels are noisy and the supervision signals are confusing. If directly applying the DTMs as the learning targets, as with the representative works [41; 42], it will lead to unstable training or severe overfitting problems. There are no related studies to apply DTMs for the tasks of learning with noisy labels. In order to benefit from the shape information and exclude the negative effect of noisy labels, we propose shape-awareness regularization loss as the auxiliary loss, which is very different from previous studies.  
Inspired by the studies of [41; 42], our regularization loss \(L_{R}\) is formulated as:  
\[L_{R}=\frac{1}{|\Omega|}\sum_{\Omega}(P-G)^{2}\circ(G_{DTM}^{2}+P_{DTM}^{2}), \tag{2}\]  
where \(G\) denotes the original labels and contains noise. \(P\) means the outputs of predictions. \(G_{DTM}\) and \(P_{DTM}\) denote the DTMs of original labels \(G\) and predictions \(P\). Let \(\Omega\) denote the grid on which the slice is defined. Formally, the \(DTM\) of \(G\) is written as:  
\[G_{DTM}=\begin{cases}\inf_{y\in\partial G}||v-y||_{2},&v\in G_{in};\\ 0,&others\end{cases}, \tag{3}\]  
where \(||v-y||_{2}\) is the Euclidian distance between voxels \(v\) and \(y\), and \(G_{in}\) denotes the inside of the object. \(G_{DTM}\) computes the distance transformation of the foreground and the presentation for \(P_{DTM}\) is similar. We further normalize the \(G_{DTM}\) to be in the range \((-1,1)\) by dividing the maximum value. As an implicit shape representation, DTMs embed contours in a higher dimensional space. With the aid of slice-level label-quality awareness, the regularization loss is useful for boosting the final segmentation performance.