esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 4 Case Study
### Simulation Experimental Design  
We adopt the dual closed-loop control strategy with a current inner loop and a speed outer loop. The outer-loop controller adopts the improved SMC. The four inputs of the EKF depicted in Figure 6 are \(i_{\alpha}\), \(i_{\beta}\), \(u_{\alpha}\)\({}^{*}\) and \(u_{\beta}\)\({}^{*}\). The EKF algorithm is used for the online state estimation of angular velocity \(\omega_{e}\) and position angle \(\theta_{e}\). The whole system is optimized by iterative operations using the IPSO algorithm, and the appropriate noise covariance matrix is selected to achieve the optimal EKF estimation [37].
## 4 Experimental Results  
In order to assess the performance of the proposed semi-supervised learning framework on a small labeled dataset, this section presents extensive experimental results for machining feature recognition. Furthermore, the lightweight CNN networks FeatureNetLite and MsvNetLite are compared to other DL-based machining feature recognizers to demonstrate the advantages of the proposed approaches.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Operator** & **SE** & **Stride** & **Input (\(V\times C\times H\times W\))** & **Output (\(V\times C\times H\times W\))** \\ \hline Stem Conv2D, k = 3 & - & 2 & \(12\times 3\times 64^{2}\) & \(12\times 16\times 32^{2}\) \\ DSConv2D, k = 3 & - & 1 & \(12\times 16\times 32^{2}\) & \(12\times 32\times 32^{2}\) \\ DSConv2D, k = 3 & - & 2 & \(12\times 32\times 32^{2}\) & \(12\times 64\times 16^{2}\) \\ DSConv2D, k = 3 & - & 1 & \(12\times 64\times 16^{2}\) & \(12\times 64\times 16^{2}\) \\ DSConv2D, k = 3 & - & 2 & \(12\times 64\times 16^{2}\) & \(12\times 128\times 8^{2}\) \\ DSConv2D, k = 3 & - & 1 & \(12\times 128\times 8^{2}\) & \(12\times 128\times 8^{2}\) \\ DSConv2D, k = 3 & - & 2 & \(12\times 128\times 8^{2}\) & \(12\times 256\times 4^{2}\) \\ \(5\times\) DSConv2D, k = 5 & - & 1 & \(12\times 256\times 4^{2}\) & \(12\times 256\times 4^{2}\) \\ DSConv2D, k = 5 & ✓ & 2 & \(12\times 256\times 4^{2}\) & \(12\times 512\times 2^{2}\) \\ DSConv2D, k = 5 & ✓ & 1 & \(12\times 512\times 2^{2}\) & \(12\times 512\times 2^{2}\) \\ View pooling & - & - & \(12\times 512\times 2^{2}\) & \(512\times 2^{2}\) \\ GAP2D & - & - & \(512\times 2^{2}\) & \(512\times 1^{2}\) \\ Conv2D, k = 1 & - & 1 & \(512\times 1^{2}\) & \(1280\times 1^{2}\) \\ Flatten & - & - & \(1280\times 1^{2}\) & \(1280\times 1^{2}\) \\ Linear & - & - & \(1280\times 1^{2}\) & \(24\) \\ \hline \end{tabular}
\end{table}
Table 2: Configuration details of MsvNetLite; \(k=3\) denotes convolution kernel size is set to 3, and \(SE\) denotes whether to use the Squeeze-and-Excite module. And if \(SE\) is set to ✓, it means Squeeze-and-Excite module is enabled.  
Figure 7: A detailed view of MsvNetLite.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 6 Discussion
### IoT Networks  
The development of 6G networks promises many opportunities and advancements, particularly in the IoT networks. A massive number of IoT devices and enhanced security and privacy protection are the main features of 6G IoT networks [1; 2]. Fortunately, our analysis and proposed opportunistic scheduling schemes can provide wireless network security against eavesdropping attacks by exploiting physical-layer security techniques. Further, the proposed scheme that opportunistically selects multiple transmitters can achieve improved secrecy performance with a high probability when the number of IoT devices increases.
## 7 Conclusion
### Practical Implications, Limitations, and Future Research Directions  
This study's exploration of the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) unveils significant insights with practical implications for the realm of education. The framework's ability to adapt and personalize learning pathways demonstrates its potential to enhance student engagement and learning outcomes. For educators, this implies a paradigm shift toward more data-driven, student-centric approaches. In the context of technology developers, it underscores the need for designing adaptive, secure, and ethically sound educational platforms.  
The study faces limitations, primarily due to its reliance on digital platforms, which may not be universally accessible, potentially leading to a digital divide. Additionally, the complexity of implementing such frameworks in existing educational structures and the need for substantial data collection pose challenges.  
Future research should address these limitations by exploring the integration of DFDLOF in more diverse educational settings, including traditional classrooms. Longitudinal studies would help in understanding the long-term impact of such frameworks on student learning. Moreover, further research is needed to assess the scalability of DFDLOF in different socio-economic contexts, ensuring equitable access to personalized education.  
Conceptualization, C.S. and S.-Y.S.; methodology, C.S., S.-Y.S. and K.-S.S.; writing original draft preparation C.S., S.-Y.S. and K.-S.S.; supervision, S.-Y.S. and K.-S.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MISP (Ministry of Science, ICT & Future Planning), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (2023-0-00065).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Hierarchical Mobile Edge Computing Architecture Based on Context Awareness
## 3 Proposed Context-Aware Hierarchical Mobile Edge Computing Architecture
### Content Migration Operation  
MECS with either a large storage capacity or poor computation ability are selected for the content migration operation. The proposed content migration operation provides content with a low latency to the end users through the caching function and hierarchical MECS configuration. First, when receiving the content transmission request from the user, the MECS performs a content lookup process to determine whether the related content exists in its own repository. If the same content is cached in the repository, the MECS deliver the matched content, not from the central mobile cloud data center. On the other hand, if the matched content is not found, a request message is sent to the neighboring MECS.  
Figure 6 shows the content type migration request message format and example. Since the hierarchy information of the MECS is recorded in the level field of the request message, the MECS can recognize the layer information of the MECS that transmitted the request message. First, the MECS receiving the content request message checks the level field of the request message to see if the sender of the request message is of the same hierarchical level. In cases where the request message is from the same layer, the corresponding content is transmitted if the requested content exists in its own repository. If the requested content does not exist, the request message is discarded. If the MECS that transmitted the request message does not receive the content, this means that there is no content corresponding to the same layer. Therefore, the upper layer information is recorded in the level field of the request message, and the newly recorded request message is transmitted to the MECS. After receiving the content request message, the MECS checks the level field of the request message and immediately discards the content request message if the message is sent from a different hierarchy level. That is, the proposed architecture performs a content request to the MECS existing in the same hierarchy and performs a content migration request to an upper layer when there is no content in the same hierarchy.
## 6 Simulation Results
The same observations are also true for SU-network MIMO in Figure 16, where we just show two different area ratios for clarity of illustration. It also shows that decreasing the center area significantly benefits the CEU capacity and improves the CCU capacity slightly. Overall, the system capacity increases due to enlarging the cell-edge area, which directly increases the proportion of downlink multi-cell cooperation (network MIMO) transmission. With network MIMO, multiple geographically separated base stations cooperatively serve their CEUs using their antennas, acting together as a network of distributed antenna array. In fact, this network architecture not only can effectively mitigate the serious co-channel interference but also provide diversity transmission. However, it is worth mentioning that the disadvantages of cooperative transmission require additional overhead for information exchange among cooperative BSs. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Ratio between Cell-Center and Cell-Edge Area**} \\ \cline{2-4}  & 7/3 & 3 & 4 \\ \hline MMSE & \(9\times 10^{-2}\) & \(1.52\times 10^{-3}\) & \(3.5\times 10^{-3}\) \\ ZF & \(2.3\times 10^{-3}\) & \(4.5\times 10^{-3}\) & \(9\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The distribution of the value of \(\alpha\). Figure 12: Capacity comparison between different values of center-edge area ratio for MU-network MIMO with MMSE joint preprocessing. Figure 14: Per-CCU Capacity comparison between different values of center-edge area ratio for MU-network MIMO with MMSE joint preprocessing. Figure 13: Capacity comparison between different values of center-edge area ratio for MU-network MIMO with ZF joint preprocessing.
## 3 Results on ML/DL-Based Channel Estimation
### Discussion on ML/DL-Based Channel Estimation in SC-UWA Communication Systems  
Research on single-carrier UWA systems shows steady progress in tackling UWA communication challenges. Each study offers distinct approaches and insights, driving forward UWA communication advancements.  
The work in [17] presents ML-ECQP (Machine Learning-based Environment-aware Communication Channel Quality Prediction), a channel quality prediction method using LR. It relies on environmental inputs like wind speed, water temperature, air humidity, and SNR. The system operates at 23 kHz and 25 kHz over a 205 m Tx-Rx distance using BPSK modulation. Real data from Furong Lake helps optimize MAE to \(4\times 10^{-3}\) and achieve BER near \(10^{-3}\). The method boosts energy efficiency and network performance. However, it faces challenges under favorable channel conditions and suffers from limited training data. Building on the foundation of channel prediction, ref. [20] explores M-ary Spread Spectrum modulation with BPSK for single-carrier systems operating within a 4 kHz bandwidth and transmission distances of 2-3 km. The study employs advanced LSTM architectures, including BiLSTM and stacked bidirectional uni-directional LSTM (SBULSTM), optimized using the Adam optimizer and simulated channel data generated via BELHOP software. The system achieves robust channel prediction and significant BER improvements, particularly under low SNR conditions, with values close to \(10^{-2}\) for SBULSTM models.  
The work in [21] introduces ABiGRU for real-time channel prediction. The model combines Space-Time Block Coding with MMSE pre-equalization. This setup improves BER and prediction accuracy over MMSE and LSTM. The authors in [57] present AttLstmPreNet. It enhances LSTM using attention mechanisms. The model focuses on key input features and adapts well to fast time-varying UWA channels. Simulated data shows it performs better than LMS and RLS. Both models strengthen robustness and prediction accuracy in dynamic underwater environments. Finally, the study [29] introduces UACC-GAN, a GAN-based simulator for underwater acoustic channels. It generates realistic time-varying impulse responses using measured data and achieves BER performance comparable to real-world conditions via validation on the WATERMARK dataset. While it enhances simulation capabilities for system design, limitations include high data requirements and limited control over output properties.  
These studies show a clear progression in ML-based channel estimation for SC-UWA systems. Early models use environmental data and logistic regression to enhance performance, while advanced LSTM variants and attention-based networks improve prediction under low-SNR and time-varying conditions. Simulation-driven approaches like UACC-GAN further support design and validation, establishing a technically robust and adaptive communication framework.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 3 Methodology
### Isolation Forest
#### 3.2.1 Data Preprocess  
In analogy to the neural-network pre-processing, a reduced selection of variables is used. Main bearing temperature is chosen, as monitoring the status of this component is the objective of the research. Additionally, ambient temperature and rotating speed of the main shaft are selected as they determine information on the context and operating status of the main bearing. A summary table of the inputs is proposed in Table 4, completed with the ranges used to filter outliers and sensor measurement errors. No data imputation is performed for missing values, instead data are down-sampled from ten minutes to hourly resolution, reducing the amount of empty timestamps and cutting computation time. Being the main bearing a massive component the reduction in data frequency is mitigated by the large thermal inertia of the bearing, thus limiting information loss.
## 2 Materials and Methods
### Principal Component Analysis (PCA)  
We applied an unsupervised machine-learning technique to reduce the dimensionality of the database while retaining as much information as possible [52]. The database is a matrix of the dimension \(N\times p\), where \(N\) is the number of observations and \(p\) is the number of variables. In this study, variables are the monthly accumulated rain values from 2010 to 2019 and observations correspond to each station.  
The PCA can be thought of as a rotation of the axes of the original variable's coordinate system to a new orthogonal axis. This is done to align these axes with the direction of the data's highest variance [52]. In other words, each principal component corresponds to a linear combination of the original variables. The first principal component (PC1) is the component that allows us to describe the maximum variance of the data set; the second component (PC2) is the component with the second highest variance, and so on. The number of principal components that can be calculated corresponds to \(\min(N-1,p)\), but in practice, only a few components collect most of the information.  
The procedure to calculate the principal components is as follows [52]:  
1. Center the variables by subtracting from each the value of its mean.
2. Generate the variances and covariances matrix.
3. Determine the eigenvector and eigenvalue of the matrix found in step 2.
4. Find the contribution of each variable (loadings) to the principal component by multiplying the transposed eigenvector matrix with the transposed matrix of the centered data.
## 4 Multi-Objective Coverage Optimization Designs
### Encoding Rule  
Encoding is a mapping from the problem space to the solution space, but memetic algorithm cannot directly process the solution data of the solution space, so before searching, the variables of the solution space must be mapped into the data structures of evolutionary space, namely chromosomes. In MOCOAMA, when the sink divides the target area into \(M\times N\) cells, each chromosome represents a deployment solution of WSNs with the form of an \(M\times N\) matrix given in Equation (13):  
\[\mathbf{x}=\left[\begin{array}{cccc}\mathbf{X}_{1,i}&\mathbf{X}_{1,2}& \cdots&\mathbf{X}_{1,N}\\ \mathbf{X}_{2,i}&\mathbf{X}_{2,2}&\cdots&\mathbf{X}_{2,N}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{X}_{M,i}&\mathbf{X}_{M,i}&\cdots&\mathbf{X}_{M,N}\end{array}\right] \tag{13}\]  
In each chromosome \(\mathbf{x}\), allele \(X_{i,j}\) denotes one of the \(n\) sensor nodes covering the virtual cell of \((i,j)\) where \(X_{i,j}\in[0,n]\) ; if \(X_{i,j}=0\), the virtual cell of \((i,j)\) is not covered by any sensor node. The sensor nodes in one cell have one of three states: \"sleeping\" state, \"detecting\" state and \"working\" state; the \"working\" state is the state of activation, and other two states are the states of inactivation. Sensor nodes covering one cell update their remaining energy in real time, and the sink chooses one sensor node as the working node which has shorter distance from the center of the virtual cell or has more residual energy and has a priority right to being the working node. After collecting the information on locations and residual energy of all nodes covering each cell, the sink computes the probability of being a working node for each sensor node. The node with greater probability will be elected as the working node covering one cell, or a node will be randomly selected as the working node from those nodes with the same probability, and other nodes will turn into being the sleeping state from the detecting state. In order to balance the energy consumption of each node, re-election of the working node covering one cell will be requested under the control of the sink when the remaining energy of the working node is less than the average energy of sensor nodes covering one cell.  
In MOCOAMA, only one of the sensor nodes is elected in its virtual cell; one virtual cell is covered at least one sensor node at the different time unless it cannot be covered by any sensor node. Therefore, from each chromosome, given the WSNs of \(T=\left\{t_{1},t_{2},\ldots,t_{s}\right\},\) we can get some sets of sensor nodes in working state and one set of sensor nodes can be described as:  
\[\Psi=\bigcup_{i=1,j=1}^{n\times t_{i},j=N}\left\{X_{i,j}\right\}=\bigcup_{i=1 }^{n}\gamma_{i}t_{i} \tag{14}\]  
where \(\gamma_{i}=1\) if the sensor node \(i\) is in working state and \(\gamma_{i}=0\) if otherwise.  
According to Equations (13) and (14), we can get one of the decision solutions of deploying WSNs given by:  
\[\Gamma=\bigcup_{i=1}^{n}\gamma_{i} \tag{15}\]
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
## 4 Multi-Scenario Physics-Based Tsunami Hazard Analysis: Data and Methods  
Highly destructive tsunamis were reported at some locations along the Mediterranean Sea coastline. However, only a few have been identified as affecting Alexandria Governate on Egypt's north shore [1; 53]. The most hazardous tsunami events known to affect Alexandria originated at the Hellenic and Cyprian Arcs, since they are defined as tsunamigenic sources or capable of generating tsunamis. Some of the tsunamigenic earthquakes generated along these well-known seismogenic sources (e.g., 365, West Crete tsunami Mw = 8.3, 1222, Cyprus tsunami Mw = 7.5, 1303, East Crete Mw = 8.5) were documented in historical diaries and geomorphological and geological records for certain locations along the Egyptian coastline. Previous studies [3; 9; 13] provide evidence of these events. Those tsunamiine earthquakes caused destruction and fatalities in Alexandria (e.g., [13]). In addition, recent tsunami hazard studies, based on either probabilistic assessment (2475-year return period) or numerical simulations (e.g., TSUMAPS-NEAM [54; 55], indicate that a  
Figure 9: Maxima MCSI spectral acceleration values (95 th percentile) at the periods T = 0.5 s and T = 1.0 s for the studied sites.  
maximum tsunami wave amplitude of 6 m along the coast could be expected. More recent tsunami events (e.g., 2 May 2020, South Crete Mw = 6.7 [3]; the Izmir-Samos in 30 October 2020, Mw = 6.6 [56], and Bodrum-Kos on 20 July 2017 with Mw = 6.6 [57]) include small local tsunamis that might motivate us to evaluate existing early warning systems, emergency and evacuation plans, and tsunami hazards and their associated risks in order to achieve the desirable, proper, and sustainable management of the Mediterranean coastline.  
In this study, we generated inundation zone maps utilizing the most recent tsunami hazard maps, which were created using a multi-scenario approach along the shoreline of Alexandria at various points of interest [3]. We considered different Coefficients values, of 0.015, 0.03, 0.05, and 0.08, corresponding to the seabed, open land, sparsely populated areas, moderately populated regions, and densely populated zones, respectively. To evaluate the average and maximum inundation depths for each location, we developed an empirical model based on the tsunami wave height maps presented by [PERSON] et al. [3]. This model employs interpolation techniques to produce maps of average and maximum inundation depths on a 30 \(\times\) 30 m grid. The assignment of coefficient values was informed by satellite imagery and data from Google Earth. GIS data processing and analysis were carried out using Quantum GIS (QGIS 3.34.11 [15]).
## 1 Introduction
Although the PPE schemes are reported in [14; 15; 19; 23], the experimental comparison with conventional PPE schemes using a longer MMF and higher data rate needs to be investigated. Moreover, addressing the lower convergence rate to achieve the optimal design of PPE coefficients is crucial [19; 23]. This research gap limits the feasible use of a pre-equalizer on the transmitter side due to high complexity. In MIMO signal processing, the singular value decomposition (SVD) technique is well established, and it has been proven to be advantageous as the MIMO channel is transformed into several parallel transmission layers [24; 25; 26; 27]. The advantages of an SDM-based MIMO model deploying an MMF channel with HOM formats have not been characterized in depth. Therefore, this work focuses on low-cost SDM-based IM/DD systems for short-distance transmission. Against this background, the novelty of this work is the enhancement of the transmission quality and the bit-error rate (BER) performance using the proposed transceiver-joint equalization scheme over an optical MIMO system with an MMF transmission link. This novel joint-PPE scheme mitigates the impairments originating from a 1.4 km long MMF channel. The performance benefits of the joint-PPE scheme are yielded by optimally designing both of the PPE coefficients simultaneously using a numerical optimization solver. The performance analysis of the transceiver-joint equalization is also conducted with a SVD-based MIMO system incorporating the HOM format. The analytical model-based simulations and the experimental setup verify the benefits of the provided joint-PPE scheme. Additionally, the supremacy of the joint-PPE scheme is established over the conventional ZF-PPE and MMSE-based post-equalization only (PE-only). Hereinafter, this paper is organized as follows: Section 1 describes the system model in the electrical domain. An analytical description of the provided system model and the quality criteria are also included in this section. Furthermore, the optimization and design of the joint-PPE scheme are presented in the Section 3, and the solver using the interior-point algorithm is explained in detail. In the Section 4, the simulation and experimental results are discussed and compared with other equalization techniques. Finally, the conclusion is outlined in the Section 5. The literature with a similar overlap in this area is reported in the Section 6.