esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The contributions of this paper can be summed up below:  
1. Based on the field-oriented control structure, a new observer structure is created to estimate the cogging torque. The proposed observer utilizes the position-based repetitive controller (PBRC) as an internal model in a model reference disturbance observer (MRDOB) to enhance the accuracy of cogging torque estimation.
2. An incremental encoder is used directly to realize the position-based delay in the PBRC, and a further practical method is introduced to realize the proposed observer with DSP memory array.
3. Online and offline compensation strategies are proposed, and the position-based forgetting factor and position-based average concept are utilized respectively to reduced noise and uncertainty of the observed information.
4. The proposed observer and compensation strategies are further verified with HIL emulation and experimental results under no load and rated load conditions. The results show that the cogging torque can be well estimated and the speed ripple can be reduced significantly.  
The cogging torque problem occurs not only in PMSM but also in a reluctance motor, such as a synchronous reluctance motor [14], or a switched reluctance motor. Different observer models paired with PBRC can be proposed to minimize the torque and speed ripple with online and offline compensation strategies.  
Figure 23: (**a**) Estimated offline cogging torque of a power meter platform under 30 rpm no load; (**b**) Speed response of the rated step load torque at 30 rpm; (**c**) Speed response with the rated load torque at 30 rpm by offline torque compensation.  
**Author Contributions:** All authors have read and agree to the published version of the manuscript. Conceptualization, M.-C.T. and C.-J.W.; methodology, M.-C.T. and C.-J.W.; software, L.-J.C. and C.-J.W.; validation, M.-C.T., L.-J.C. and C.-J.W.; formal analysis, M.-C.T., L.-J.C. and C.-J.W.; investigation, M.-C.T. and C.-J.W.; resources, M.-C.T. and L.-J.C.; data curation, L.-J.C. and C.-J.W.; writing--original draft preparation, C.-J.W.; writing--review and editing, M.-C.T. and L.-J.C., C.-J.W.; visualization, C.-J.W.; supervision, M.-C.T. and L.-J.C.; project administration, M.-C.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work is supported by the DELTA ELECTRONICS, INC, and the Ministry of Science and Technology (MOST), Taiwan, under grants numbers MOST 106-2221-E-006-251-MY3 and MOST 108-2622-8-006-014.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 7 Conclusions and Future Work  
In this paper, we have shown how random segmentation can obfuscate packet-size patterns without introducing additional noise into the packets themselves, as is the case with packet padding. The proposed approach enables network devices to send application messages through random-sized segments and pass them to the network layer for immediate transmission. Therefore, the observed traffic at and above the network layer is randomized, defending against both in-network and out-network observers (i.e., IP- and MAC-level observation). The technique has been tested on a client machine connected to a remote server, and the results demonstrate the effectiveness of our defense with a reasonable time overhead (\(<\)21%).  
For future work, we seek to make our defense accommodate the heterogeneity in the IoT environment. The adjustable parameters in our code allow the defender to choose the suitable obfuscation level to achieve sufficient randomness with fewer splitting operations. Thus, our defense system lacks an adaptive functionality to adjust its parameters based on the device traffic intensity and specific hardware. To this end, we aim to present an optimization model to enable our system to dynamically choose the optimum parameters that yield astonishingly less overhead with maximum privacy protection.  
Conceptualization, M.A., C.Z. and Y.S.; methodology, M.A. and C.Z.; software, M.A.; validation, M.A., C.Z. and Y.S.; formal analysis, M.A., C.Z. and Y.S.; investigation, M.A., C.Z. and Y.S.; resources, M.A., C.Z. and Y.S.; data curation, M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A., A.A., M.A.A., C.Z. and Y.S.; visualization, M.A.; supervision, C.Z. and Y.S.; project administration, C.Z. and Y.S.; funding acquisition, C.Z. and Y.S. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Buffer Size**} & \multicolumn{3}{c}{**Time Overhead (\%)**} \\ \cline{2-3}  & \(Rand_{(Low)}\) & \(Rand_{(High)}\) \\ \hline \(2^{15}\) Bytes & 132.53 & 128.67 \\ \(2^{16}\) Bytes & 26.1 & 26.78 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Send buffer size impact on transmission time using two obfuscation levels.  
**Funding:** This research was sponsored by the U.S. National Science Foundation (NSF) under Grant DGE-2325452.  
**Data Availability Statement:** Data are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 3 CR-WSN Characteristics and Architecture
### Classification of CR-WSNs According to Network Topology
#### 3.4.1 Flat Topology  
In this type of network, the BS directly aggregates data using routing algorithms. The BS broadcasts channel availability information to its members using a common control channel (CCC), and the CR-WS nodes hop to the channel and communicate with the BS via multihop routes. Based on the types of node deployed, the following two types of flat topologies can be considered for CR-WSNs.  
(a) Flat CR-WSN with All CR-WS Nodes (Flat CR-WSN-all)  
This is similar to traditional WSNs, where the BS usually broadcasts a query message, and the WS nodes that have data matching the query will send a response to the BS. The BS updates spectrum availability information (SAI) from the spectrum database for the particular geographic region that is maintained by the authority providing the local or central spectrum information. The BS finds the common channels between the SAI and its own sensing result, and then broadcasts a channel availability message to the CR-WS nodes in its vicinity. The CR-WS nodes again compare the channel list received from the BS to their own channel-sensing results and select the best channel to communicate with the BS.  
Figure 2 shows the flat network topology. The WS nodes usually communicate in multihop fashion by using peer nodes as relays. This network is very expensive in terms of deployment cost and energy efficiency. This type of network has a scalability issue, and suffers from the energy hole problem. Therefore, it is recommended for a small geographic region.  
(b) Flat CR-WSN with Only Few CR-WS Nodes (Flat CR-WSN-few)  
This is similar to a Flat CR-WSN-all network; however, only few nodes have CR module, unlike the previous category. All nodes do not participate in the channel-sensing and channel-selection processes. Only the nodes with CR module sense channels and send available channel information to the BS, and all other nodes just follow the BS's broadcast message for channel selection and channel switching. In this network, \"node with CR module\" deployment with proportional representation for all targeted geographical reason is difficult. However, overall hardware cost is comparatively inexpensive, because not all WS nodes require CR modules.  
Figure 2: Flat network topology with all CR-WS nodes.
## 4 Future Directions
### Build Battery-Free Sensor Nodes/Battery-Less Platforms for UIoT Networks  
In [25], the Massachusetts Institute of Technology developed battery-free sensor nodes for UIoT applications. In [24], a battery-less platform using underwater wireless power transfer was developed for UIoT applications. These applications are still in the development stage and need further improvement in the future. In this case, in order to solve the energy optimization issues in underwater, it is necessary to adapt battery-free sensor nodes or battery-less platforms in UIoT networks.
## 5 Simulation and Results
### Clustering Network and Dynamic Maintenance Function Simulation
#### 5.2.2 Simulation of Dynamic Maintenance Function Under Interference  
4. Cluster member disconnected  
After running the simulation for 5 s, interference is simulated on the cluster member node 1, causing its communication link to be interrupted. The update and maintenance process of the cluster is shown in Figure 10. After the communication of the cluster member  
Figure 9: Final network topology based on clustering algorithm.  
node 1 is interrupted, the cluster head node 5 removes the failed node from the current cluster's member list, thereby completing the update of the network topology.  
5 Cluster head disconnected  
After restoring the communication function of node 1, interference is simulated on cluster head node 7 after 5 s of simulation, causing its communication link to be interrupted. The update and maintenance process of the cluster is shown in Figure 11. After the communication of cluster head node 7 is interrupted, the backup cluster head node 15 automatically becomes the new cluster head, takes over the cluster, and reorganizes the other cluster members to join the cluster, thereby updating the entire cluster's network topology and ensuring the normal operation and connectivity of the cluster.  
Figure 11: Cluster head replacement.  
Figure 10: Cluster member disconnected.  
After restoring the communication function of node 8, the simulation continues for 5 s, and a new node is added to the scene. The cluster update and maintenance process is shown in Figure 12. After the new node is added, it selects the nearest cluster head node 9 to complete the joining operation, thereby updating the network topology.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 1 Introduction
### Problem and Approach  
Reactive programming has the potential to offer a more elegant and simpler alternative through the event-based and non-blocking I/O model, contrasting with the increased complexity in managing concurrency and system resources of the asynchronous multi-threading approach which, while it has brought significant improvements over previous models and has been the basis for many of the solutions subsequently developed, also has the potential to become the limiting factor in terms of scalability and extensibility. The main issue addressed by this research is the exploration of a migration methodology and feasibility assessment, as well as the quantification of the benefits that could be achieved. The untapped potential of reactive programming and the opportunities to optimize the performance and efficiency of current applications is the central motivation of the research, outlining a promising option to address current challenges.  
However, migrating from asynchronous multi-threading programming to reactive programming is not a universal formula for performance enhancement under any conditions and is not without challenges, featuring its particularities, limitations, and trade-offs. While it has been successfully adopted in some areas, such as real-time data stream processing or distributed application development, migrating existing applications requires a detailed evaluation of the architecture, possible infrastructure adjustments, adherence to guidelines and practices to ensure the transition, and an openness to understanding the different paradigm, demanding a shift in perspective from writing imperative code. Analysis of the benefits and limitations of reactive programming, such as identifying the use cases for which it is suitable and the appropriate strategies for dealing with complexity, remains defining and specific to each product.  
The research directs its attention on the migration process from the asynchronous approach towards the reactive paradigm, following closely the steps to achieve the transition in a controlled and efficient way based on the purpose-built application. The step-by-step process aims at investigating some aspects of interest, trying to clarify some technical details, but also capturing some consequences of the adoption of this new paradigm, and assessing its effectiveness in enhancing system performance.
## 3 Results
### The Results and Analysis for Tool Wear
By incorporating the fitted parameters into Equation (1), the wear width, \(b\), of the rake face can be articulated as follows:  
\[b=6.68\times 10^{-8}n^{1.043}v_{\rm f}^{0.505}a_{\rm P}^{0.152} \tag{8}\]  
According to Equation (8), the exponents \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{3}\) corresponding to spindle speed, feed rate, and axial milling depth are all positive values that adhere to the relationship \(\alpha_{1}>\alpha_{2}>\alpha_{3}\). The result is consistent with the previous analysis of variance and was anticipated, as it aligns with established expectations regarding the influence of these parameters on tool wear. This finding suggests that spindle speed exerts the most pronounced influence on the wear width of rake face, followed by feed rate; in contrast, axial milling depth has a comparatively minor effect. As illustrated in Figure 6, the correlation between variations in these factors and the extent of wear on the rake face is clearly delineated. The primary factors influencing the wear of the milling tool are spindle speed and feed rate. This is because as the spindle speed increases, the cutting speed also rises, leading to an accelerated relative motion between the tool and the workpiece, thereby exacerbating tool wear. Additionally, an increase in feed rate results in higher contact stress between the tool and the workpiece, causing a rapid rise in milling forces, which further accelerates tool wear. While augmenting axial milling depth may also enhance material removal rates per unit time, its effect on leading edge wear is significantly less pronounced compared to that of feed rate. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Factors** & **Squared Deviations** & **F** & **Fcritical value** & **Significance** \\ \hline \multirow{3}{*}{S/N (\(\eta\))} & \(n\) & 5.276 & 83.746 & 19 & * \\  & \(v_{\rm f}\) & 3.903 & 61.952 & 19 & * \\  & \(a_{\rm P}\) & 0.226 & 3.587 & 19 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of variance for S/N (\(\eta\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**C\({}_{\rm F}\)** & \(\alpha_{1}\) & \(\alpha_{2}\) & \(\alpha_{3}\) & **R\({}^{2}\)** \\ \hline
6.68 \(\times\) 10\({}^{-8}\) & 1.043 & 0.505 & 0.152 & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The fitting results of tool wear. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **K\({}_{\rm i}\)** & **_n_ (r/min) & **\(v_{\rm f}\) (mm/min)** & **\(a_{\rm P}\) (mm)** \\ \hline \multirow{4}{*}{S/N (\(\eta\))} & \(K_{1}\) & 12.394 & 12.313 & 11.613 \\  & \(K_{2}\) & 11.389 & 11.263 & 11.463 \\  & \(K_{3}\) & 10.520 & 10.727 & 11.228 \\  & Range & 1.874 & 1.586 & 0.385 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Range Analysis of S/N (\(\eta\)).
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 3 System Model  
The main reliable wireless technologies implementation of the vehicular networks is DSRC. Due to vehicle mobility and network topology changes, communication between vehicles is sometimes not possible by direct transmission according to DSRC. We focus on a two adjacent RSUs and vehicles scenario where vehicle \(S\) attempts to transmit a message to vehicle \(D\), as shown in Figure 1. The road is a two-lane unidirectional road, and RSUs (such as \(RSU_{a}\) and \(RSU_{b}\)) are deployed along the road. Vehicles on the road have uninterrupted traffic in a free flow state. When communicating between the source vehicle \(S\) and the destination vehicle \(D\), we assume they are in two adjacent different areas. DSRC provides two communication modes in the networks: Vehicle-to-Infrastructure (V2I) communication and Vehicle-to-Vehicle (V2V) communication. Cloud-based services and vehicular networks are combined to form a VC. It provides powerful computing power, also known as vehicular cloud computing. Some complex calculations can depend on VC to realize the computation in this V2I transmission mode, and the V2V transmission mode ensures a transmission between the source vehicle and destination vehicle.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Analysis and Selection of Transformation Basis
#### 4.3.3 Selection of Transform Bases in Signal Reconstruction Experiments  
In signal reconstruction experiments, signal reconstruction experiments are performed on signals \(S_{1}\) and \(S_{3}\) with different transform bases. And the observation matrix used is the same as the ABA optimized observation matrix. The experimental results are shown in Table 3.  
It can be seen from Table 3 that DHT and FFT reconstruction are successful. Furthermore, both signal reconstruction errors are superior to DHT transform bases when using FFT transform bases. Therefore, the FFT transform base is more suitable for signal reconstruction. In this paper, we select the FFT transform basis as the signal sparse tool.
# Automated Clinical Impression Generation for Medical Signal Data Searches
## 3 Experiments
### Implemented Models and Details  
The proposed model in this work and the baseline models were implemented in PyTorch version 1.12.1 [21] using the ADAM optimizer [22] with a learning rate of 0.0001 and a batch size of 32, except for the CNN-LSTM model, which had memory issues. Additionally, each model was trained for 40 epochs and the best epoch was selected based on the BLEU [23] score measured on the validation set after training. The implemented models and their details are described as follows.  
* **Ours:** Two branches of convolutional neural networks (CNNs) are utilized to process EEG signals divided into 30 s segments. The proposed architecture is adapted from DeepSleepNet, but modified to accommodate multi-channel inputs. Detailed specifications of the CNN encoder, including layer sequences and hyper-parameters, are presented in Table 1. Alongside this, a transformer is incorporated into the system. Crucial hyper-parameters for the transformer, including the dimension of the embedding layer, the number of heads, and the quantity of encoder and decoder layers, are delineated in Table 2. We adhere to the hyper-parameter values specified in the original DeepSleepNet [11] and transformer model [12]. Implementations using the DeepSleepNet Encoder also follow these same hyper-parameter values, except for the number of layers, which are indicated in Table 2. The number of layers is selected using a grid search from 1 to 6.
* **CNN-LSTM:** In [24; 25], text generators are introduced that exploit domain knowledge given by doctors or retrieved sentences from a database. Their models are based on CNNs and LSTMs for embedding input signals and sequence-to-sequence modeling, respectively. Since a text generator is considered given an EEG signal without additional domain knowledge written in sentences, a sequence-to-sequence model is implemented similar to [25]. The batch size for this implementation is 4, as it does not compress input signals enough and it causes memory issues. Note that the best epoch is selected based on the BLEU score using a validation set to ensure a fair comparison.
* **DE-LSTM:** Given that the CNN-LSTM model previously mentioned does not compress input signals into the feature space adequately, leading to memory issues, the DeepSleepNet Encoder (DE) is employed as the signal encoder in the model proposed in by this research, effectively supplanting the CNN component.
* **DE-LSTM-ATTENTION:** The attention mechanism, as proposed by [PERSON] et al. [26], is a powerful tool for capturing the relationship between input and output in a neural network. In this research, Bahdanau attention is employed to understand the correlation between segmented EEG signals and the tokens generated from them. This attention mechanism is integrated into the DE-LSTM model, which is named DE-LSTM-Attention in this section.
## 1 Introduction  
Data breaches represent increasingly prevalent and consequential organisational crises, with significant implications for both technical infrastructure and human capital management [1]. Whilst substantial scholarly attention has focused on technical remediation strategies and corporate communication approaches following breach incidents, comparatively limited research has examined the distinctive role of employees as active participants in post-breach knowledge governance processes [1, 2]. This research gap is particularly significant given the unique positioning of employees as both potential victims of data compromises and simultaneous organisational representatives tasked with maintaining stakeholder relationships during periods of informational uncertainty.  
The present study addresses this theoretical and empirical gap by examining how employees navigate knowledge sovereignty challenges following organisational data breaches. Drawing on [3] image restoration theory whilst extending its application to distributed agency contexts, we explore how employees actively influence knowledge governance following cybersecurity incidents. In doing so, this investigation contributes to emergent theoretical perspectives on employee-driven information governance [4] whilst simultaneously extending crisis communication frameworks to incorporate distributed agency dynamics [5].  
Although many organisations implement cybersecurity frameworks such as ISO/IEC 27001 (Information security, cybersecurity and privacy protection--Information security management systems--Requirements, Standard's Number 3, 2024) [6], The NIST Cybersecurity Framework (CSF) 2.0, published by the National Institute of Standards and Technology (NIST) in Washington D.C., USA, on 26 February 2024, provides voluntary guidance for organizations to better manage and reduce cybersecurity risks or CIS Controls, technical compliance alone does not guarantee immunity from breaches. Organisational responses, including employee engagement in information governance, remain critical in mitigating reputational risks after incidents through the European Union Agency for Cybersecurity. This role of employees in shaping post-breach organisational narratives is still under-explored in relation to cybersecurity policies, despite the growing adoption of procedural frameworks such as ISO 27001.  
This paper is structured as follows: First, we establish the theoretical foundations of knowledge sovereignty and information governance disruption within post-breach organisational contexts. Next, we outline our methodological approach, detailing data collection and analytical procedures. Subsequently, we present empirical findings organised around key dimensions of employee knowledge sovereignty practices. Finally, we discuss theoretical contributions and practical implications, concluding with limitations and directions for future research. The context of the companies that participated in the research comprises organisations located in Colombia.