esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Induction Heating and Numerical Simulations
### Numerical Solution  
For the reliable modeling of the induction heating process, the finite element method (FEM) working with real geometrical models of inductors and workpieces can advantageously be applied. The computational algorithm and methodological procedure can significantly differ according to the used computational software. In the presented study, we used two different pieces of software, FORGE NxT and ANSYS-Maxwell, to design the induction heating procedure with the highest accuracy possible.  
Forge NxT is a specialized computational software by Transvalor s.a. designed primarily to enable simulations of forming processes under cold, warm, and hot conditions. However, it is also suitable for the solving of issues related to thermomechanical processing and heat treatments. For this reason, an efficient electromagnetic module, which can favorably be applied for simulations of induction heating, is integrated within the software [46; 47; 48; 49]. The numerical simulation of the electromagnetic induction process, including simulating the generated eddy currents, using the Forge NxT software is based on the method of finite elements, i.e., Nedelec elements. The software combines the Maxwell equations with the above-presented materials equations (Equations (2)-(4)), and implements further equations characterizing the conditions in boundary regions--Neumann and Dirichlet boundary conditions. The fact that the \(\mu\) and \(\sigma\) parameters can be defined as temperature-dependent needs to be taken into account. Furthermore, magnetic permeability can be defined depending on magnetic field intensity. Finally, the numerical simulation of induction heating using the Forge NxT software processes in a loop comprising an electromagnetic simulation followed by a thermal simulation [50; 51; 52].  
ANSYS-Maxwell is a specialized software package designed for calculating low-frequency electromagnetic fields in 2D and 3D spaces. The main advantage of this software is the possibility to use an automatic adaptive mesher for static and harmonic tasks. The principle of the software lies in implementing the Maxwell equations, together with FEM. The Maxwell equations are solved depending on the particular calculated issue--electrostatics, magnetostatics, eddy currents, or transient electromagnetic issues. For the presented study, we used the 2D axisymmetric model and eddy currents solver, which is suitable for solving steady-state types of issues, as well as alternating the sinusoidal magnetic field (AC) in the frequency zone. Materials with linear, as well as non-linear, magnetization characteristics can be calculated; this software package is thus advantageous for various geometric configurations of inductor coils and magnetic circuits, from electromagnets, through various types of engines, to induction heating [46].  
Figure 2: Typical B/H characteristics for paramagnetic material (i.e., tungsten) (**a**); permeability characteristics for paramagnetic material (i.e., tungsten) (**b**).
## 4 Other Resources
### Sense of Presence  
Qualitative data, based on [PERSON] [PERSON]'s Presence Questionnaire [22], were gathered through participant feedback and observations made during the tests. Participants were asked to rate their sense of presence, immersion, and realism in the virtual environment under each interaction method (C1, C2, or C3). The feedback revealed that users felt a higher sense of presence and immersion when using their wheelchairs in C1 and C2. Eighty-five percent of participants reported that this method allowed for more natural movements and better alignment with their real-world experiences. Conversely, joystick control was frequently described as less intuitive and less immersive, detracting from the overall virtual experience. Observations corroborated these findings, as users appeared more comfortable and engaged when using natural interaction methods compared to joystick control. Notably, users exhibited 20% more positive engagement behaviors, such as exploring more of the virtual environment, when using C2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Comparison** & \begin{tabular}{c} **Mean Navigation Time** \\ **Difference (s)** \\ \end{tabular} & **t-Statistic** & _p-Value_ & \
\begin{tabular}{c} **Effect Size** \\ **(Cohen’s d)** \\ \end{tabular} &
\begin{tabular}{c} **Statistical** \\ **Power** \\ \end{tabular} \\ \hline Base Motors ON vs. Base Motors OFF & Faster by 8 s (131 vs. 139) & 4.001 & 0.0013 & 1.036 & 0.961 \\ Base Motors ON vs. With joystick & Faster by 15 s (131 vs. 146) & \(-3.939\) & 0.0015 & \(-0.988\) & 0.944 \\ Base Motors OFF vs. With Joystick & Faster by 7 s (139 vs. 146) & \(-6.046\) & \(6.547\times 10^{-6}\) & \(-1.803\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summaries the results of the statistical analysis.  
Additionally, participants noted that the haptic feedback system enhanced their sense of realism, particularly when navigating varying terrains within the virtual environment. Those using the wheelchair with C1 reported a greater sensation of resistance and support that closely mimicked real-world conditions, contributing to a more immersive experience. Specifically, 90% of participants with manual wheelchairs indicated that the haptic feedback provided a realistic simulation of different surfaces and obstacles. This was less evident with joystick control, which lacked the nuanced feedback that users experienced with their wheelchairs. Overall, the qualitative data underscore the importance of natural interaction methods and sophisticated haptic feedback systems in enhancing the sense of presence and immersion for wheelchair users in virtual reality environments.
# A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations
###### Abstract  
Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on the quality of responses provided by participants specialized in these processes across the organization. Risk maturity models reported in the literature gave equal importance to participants' responses during the model development, neglecting their level of authority in the organization as well as their level of expertise in risk management processes. Unlike the existing models, this paper presents a new risk maturity model that considers the relative importance of the responses provided by the participants in the model development. It considered their authority in the organization and their level of involvement in the risk management processes for calculating the relative weights associated with the risk maturity attributes. It employed an analytic network process (ANP) to model the interdependencies among the risk maturity attributes and utilizes the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the model development. The developed model allows the construction organizations to have a more accurate and realistic view of their current performance in risk management processes. The application of the developed model was investigated by measuring the risk maturity level of an industrial partner working on civil infrastructure projects in Canada.  
risk management; risk maturity evaluation; analytic network process (ANP); fuzzy set theory; construction organizations Article
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 2 Methodology  
### Model Architecture  
The EEND-EDA (End-to-End Neural Diarization with Encoder-Decoder based Attractors) architecture introduces a flexible framework for speaker diarization that supports two distinct processing paths, as illustrated in Figure 1. The system can operate either with traditional acoustic features alone or with an enhanced representation that combines multiple feature types [33]. The architecture comprises three fundamental components:  
* A Self-Attention Encoder (SA-EEND) that leverages transformer blocks to capture long-term temporal dependencies and speaker-discriminative patterns in the input features. The encoder generates a sequence of high-dimensional embeddings (\(e\)) that encode both acoustic and speaker characteristics.
* An Encoder-Decoder Attractors module (EDA) that dynamically determines both the number of speakers and their unique representations in a unified process. This component processes encoder embeddings through an LSTM-based architecture to generate speaker-specific attractors (\(a_{s}\)). For each potential attractor, it computes an existence probability and activates only those that exceed a learned threshold. The number of speakers is implicitly determined by counting activated attractors, eliminating the need for a separate classification step.
* A dot product operation that works as a selection mechanism that effectively suppresses inactive speakers by producing near-zero values when the embedding and attractor are orthogonal. The resulting values are then processed through a sigmoid activation to produce binary speaker activity decisions.  
Figure 1: EEND-EDA architecture overview showing alternative feature configurations. The model processes input audio through parallel streams: either standalone Mel-filterbanks (\(X\in\mathbb{R}^{T\times M}\)) or a concatenated representation (\(F\in\mathbb{R}^{T\times(M+E)}\)) combining Mel-filterbanks with auxiliary features (\(B\in\mathbb{R}^{T\times E}\)). The SA-EEND encoder generates embeddings (\(e\in\mathbb{R}^{T\times D}\)), which the EDA module transforms into speaker-specific attractors (\(a_{s}\)) for final diarization output.
# Shared-Custodial Wallet for Multi-Party Crypto-Asset Management
## 3 System Architecture
### Roles in the System
#### 3.1.3. The Custody System  
The Custody System serves as the facilitator and coordinator of the platform, providing the infrastructure necessary for secure key management, transaction processing, policy enforcement and communication between parties. The Custody System holds a private key share generated through the DKG process and participates in the signing process if necessary. However, as a security measure, the Custody System cannot initiate transactions under any circumstances. The system operations are carried out by the following components:  
* Backend: Ensures connectivity to the components within and outside the custody systems by making requests, fetching and providing data.
* Database: Stores mostly nominal data records on wealth managers and clients, as well as a history of transactions and public addresses of users.
* Key Store Server: Participates in the secure generation of key shards, manages its key shard generated and engages in transaction signing without exposing private keys.  
By facilitating communication between the components above, the custody system enables a flexible and secure environment for the client and wealth manager. The functions performed by the system include:  
1. Key Management Coordination: Facilitates the coordination of the DKG protocol during wallet creation, providing the necessary infrastructure and communication channels between the platform, and the participating parties. All parties independently generate and exchange public commitments of their key shares securely (see Figure 1). The system does not access or control the private key shares of other parties.
2. Key Share Management: Securely stores and manages the controlled key shard which cannot initiate transactions.
3. User Authentication Support: Facilitates secure user authentication by providing interfaces and protocols for the Client User and Wealth Manager to authenticate themselves.
4. Communication Interface: Provides interfaces for interactions between the Client User and Wealth Manager, facilitating seamless operation and user experience (see Section 4.4).
5. Transaction Verification and Policy Enforcement: Verifies weather transactions comply with predefined policies set by the Wealth Manager.
6. System Monitoring: Monitors system activities to detect and prevent unauthorised access or fraudulent transactions, ensuring compliance with security standards and regulatory requirements.
7. Transaction Confirmation: Sends transaction updates, transaction confirmations and other notifications to the users.
## 6 Isolated Empirical Studies on ML-based xApp Vulnerabilities
### Empirical Validation of Synergy Coefficients  
Using the DAWN dataset's adversarial attack traces [11], we simulated 1000 attack iterations to derive \(\alpha\) and \(\beta\) values:  
The simulations were conducted on an O-RAN-compliant testbed comprising 10 distributed nodes equipped with Intel Xeon E5-2678v3 CPUs and 64 GB RAM, emulating RIC and xApp components. The DAWN dataset's adversarial traces were injected into Kubernetes-managed containers running on OpenShift 4.12, which hosted the xApps. Network slicing configurations included enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communication  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Attack Pair** & \(\alpha\) & \(\beta\) \\ \hline Data Poisoning + Model Inference & 0.52 & 0.31 \\ Evasion + Resource Manipulation & 0.48 & 0.29 \\ xApp Compromise + DoS & 0.61 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Synergy coefficients from DAWN dataset simulations.  
(mMTC) slices, utilizing 5G NR numerology with Subcarrier Spacing (SCS) of 30 kHz and Time Division Duplexing (TDD) frame structures. For each attack pair, such as data poisoning combined with model inference attacks, 1000 iterations were executed with randomized adversarial perturbations. The DAWN dataset's poisoned samples, representing 15% of the data, were dynamically injected into FL training rounds, while E2 interface exploits were simulated using Metasploit modules specifically tailored for O-RAN architectures. The synergy coefficients \(\alpha\) and \(\beta\) were derived through linear regression by correlating the observed attack impact metrics, such as accuracy degradation and latency increase, with the component overlap ratios \(\delta(v_{i},v_{j})\). The synergy coefficients presented in Table 6 illustrate that attacks exploiting shared components, such as the combination of xApp compromise and denial-of-service (DoS) attacks with a component overlap ratio of \(\delta=0.85\), demonstrate notably stronger interactions, quantified by a synergy coefficient of \(\alpha=0.61\). This interaction amplifies the combined impact of these attacks by 35% compared to scenarios where each exploit is executed independently. These findings underscore the importance of implementing micro-segmentation strategies, as discussed in Section 5.1.3, to effectively isolate critical components within the RAN Intelligent Controller (RIC) and thereby mitigate the risk of cross-layer attack chains exploiting such synergies.
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
###### Abstract  
Recent years have witnessed the fast development of the Financial Internet of Things (FloT), which integrates the Internet of Things (IoT) into financial activities. At the same time, the FloT is facing an increasing number of stealthy network attacks. Long short-term memory (LSTM) can be used as an anomaly-detecting method to perceive such attacks since it specializes in discovering anomaly behaviors through the time correlation in FloT traffic. However, current LSTM-based anomaly detection schemes have not considered the specific correlations among the features of the whole traffic. In addition, current schemes are usually trained based on local traffic with rare cooperation among different detecting nodes, leading to the result that current schemes usually suffer from insufficient adaptability and low coordination. In this paper, we propose a feature-attended federated LSTM (FAF-LSTM) for FloT to address the above issues. FAF-LSTM combines feature-attended LSTM and federated learning to make full use of the deep correlation in data and enhance the accuracy of the trained model via cooperation among different detecting nodes. In FAF-LSTM, the features are grouped so that the model can learn the time-spatial correlation inner the flows of each group as well as their impact on the output. Meanwhile, the parameter aggregation is optimized based on feature correlation analysis. Simulations are conducted to verify the effect of FAF-LSTM. The results show that FAF-LSTM has good performance in anomaly detection. Compared with independently trained LSTM and traditional federated learning-based LSTM, FAF-LSTM can improve the detection accuracy by up to 39.22% and 334.36%, respectively.  
feature-attended; anomaly detection; long short-term memory; federated learning; Financial Internet of Things +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Overall Design of the Communication Channel Reconstruction
### Data Preparation  
The digital relay devices at each substation gather current signal obtained by current transducers and convert measured signals to digital sampled or phasor values by quantizing and encoding. The WSN data packet frame of Medium Access Control (MAC) layer launched by application layer are shown in Figure 4. The frame control field is two octets in length followed by sequence number and addressing field. The data payload has variable length and contains information on the basis of protection requirements. The digital sampled values in application layer is passed to the MAC layer as data payload. The addressing field in MAC layer is filled with a 16-bit short address, which is assigned uniquely to every node. The last field is the Frame Check Sequence (FCS), which contains a 16-bit Cyclic Redundancy Check (CRC) for transmission-error checking.  
Data payload consists of protection information field, three-phase current and synchronization timestamp. The protection information field is two octets in length and contains logic and control information of the local side. In phasor-based differential protection, amplitude and angle are, respectively, quantified as 16-bit data, so each phase current is four octets in length. In sampled-value-based differential protection, each phase current can be expressed by a four-byte float-type number. In addition, the last subfield of data payload is the synchronization timestamp represented by a 64-bit unsigned fixed point number of which accuracy is about 200 picoseconds.  
The MAC data frame is passed to the physical layer (PHY), which becomes the PHY payload. The PHY is prefixed with four-byte preamble (at 2.45 GHz by O-QPSK modulation), a one-byte start-of-frame delimiter (SFD) for symbol synchronization, and a one-byte frame length/reserved.
# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 5 Analysis of the Experimental Results
### The Analysis of Comparative Experiment and Model Stability  
In order to further prove the superiority of the kilometer post detection model established in this paper, in this section, we compare it with other commonly used detection networks based on deep learning, as shown in Figure 8. The abscissa represents the Intersection over Union (IoU) [39] and the ordinate represents Average Precision (AP). The IoU parameter indicates the coincidence degree of the target box and the label box, which is generally set as 0.5. The relationship between AP and IoU is usually used to determine the model stability of object detection algorithms.  
As can be seen from Figure 8, our model has obvious advantages over Faster R-CNN [24], SSD [26] and YOLOv3 [40]. When IoU was 0.5, the AP of our model was 2.12% and 2.04% higher than that of Faster R-CNN and SSD, respectively. The AP of our model was 13.35% higher than that of Faster R-CNN (IoU = 0.6). Moreover, with higher IoU (0.7), the AP of our model was 26.09% and 16.6% higher than that of Faster R-CNN and YOLOv3, respectively. As the model in this paper was obtained by simplifying the design of reference [31], the overall accuracy does not have obvious advantages compared with the ZRNet model. However, because of the simplification of the model setting, the detection efficiency is improved, i.e., the detection time is reduced, as shown in Table 5.  
In Table 5, IoU is taken as 0.5. From the results of the comparison, it can be seen that our model has the highest detection accuracy and efficiency. For example, compared with Faster R-CNN and ZRNet models, the accuracy of our model was 2.12% and 1.79% higher, and the detection time was 169.89 ms and 14.26 ms less, respectively. To sum up, our model is superior to other models in terms of detection accuracy and efficiency. This further proves the superiority of the kilometer post detection model established in this paper.  
Furthermore, the model stability was analyzed. From Figure 8, we can see that when the IoU parameter was 0.5, the AP of our model reached 98.92%. When the IoU parameter was 0.7, the AP still reached 77.78%. This shows that the model stability is good. Even if the higher IoU parameter was  
Figure 8: AP curves of different detection algorithms and our approach.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Different Model Structure** & **Accuracy** & **Time** \\ \hline SSD [26] & 96.88\% & 43.89 ms \\ Faster R-CNN [24] & 96.80\% & 205.32 ms \\ YOLOv3 [40] & 97.04\% & 56.88 ms \\ ZRNet model in [31] & 97.13\% & 49.69 ms \\ Our model in this paper & 98.92\% & 35.43 ms \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of comparative experiment.  
used as the criterion to determine whether the detection was correct or not, the detection accuracy of our model remained high.
## 3 Proposed Method
### EIG (Exposure Image Generator)
In EIG's tone mapping, we focus on detail features and base features to effectively address the retention of detail information and the alignment of base tones. First, the image is converted into luminance component and color component. Then, the luminance components are converted to logarithmic domain, and the bilateral filter decomposes the logarithmic domain image into base and detail layers. The base layer describes the luminance information of the image, and the detail layer describes the texture and edge information of the image. The base layer is extracted using the bilateral filter, and the base layer is subtracted from the logarithmic luminance of the original image to produce the detail layer. The noise in the color component is attenuated by Gaussian blur, and the final exposure-corrected image is obtained by recombining the luminance and color components. The same spatial uniform tone mapping function \(T(x,\mu)\), with different parameters, is used to attenuate the image details and adjust the contrast of the base layer. Using Equation (1) for the linear intensity of the base layer for uniform tone mapping, the value of \(\mu_{1}\) is approximately 40. The logarithmic intensity of the detail is attenuated based on the luminance of the base layer, and the attenuation process is linear with a luminance of 0.5 times the maximum pixel, which will have half of the high frequencies blocked. Since the confidence of the detail decreases at dark values, using Equation (1) and using a different \(\mu_{2}(\mu_{2}=700)\), the peaks of the lower intensities are reduced. The noise in the color component is attenuated by standard Gaussian blurring. The color component is then processed, and the base and detail layers are combined with the color component to obtain the exposure-corrected image after tone mapping.