esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
## 4 Analysis of Cyclone Spinning Effect
### Additional Loss of Mechanical Energy  
In this paper, the solid-liquid two-phase flow downstream of the cyclone is numerically simulated, and the velocity distribution, volume concentration distribution, energy, and critical siltation position of particles in the two-phase flow are analyzed; the following conclusions are drawn.  
There is a loss of mechanical energy when the water flows through a pipe with a cyclone, and there is also a loss of mechanical energy when it passes through a pipe without a cyclone. The difference between the mechanical energy dissipated when the water flow passes through the cyclone to produce a spiral flow and the mechanical energy dissipated by the water flow at the same speed when passing through a pipe of equal length without a cyclone is the additional mechanical energy loss of the cyclone under this condition. The mechanical energy loss of the water flow through a pipe of the same length as the cyclone per unit time is \(\Delta e\), and the additional mechanical energy loss is \(\Delta E-\Delta e\), and the proportion \(\zeta\) of the additional mechanical energy loss in the mechanical energy loss can be expressed as follows:  
\[\zeta=\frac{\Delta E-\Delta e}{\Delta E} \tag{11}\]  
The proportion of additional mechanical energy loss in total mechanical energy loss \(\zeta\) is shown in Figure 10. As can be seen from Figure 10, (1) with the increase in the height of  
Figure 11: Cyclone mechanical efficiency \(\eta\).  
Figure 10: Proportion \(\zeta\) of added mechanical energy loss to total mechanical energy loss.  
the guide vane, the proportion of additional mechanical energy loss in the total mechanical energy loss when the water flow passes through the cyclone increases. Since the proportion of additional mechanical energy loss in the total mechanical energy loss increases under the same Reynolds number, it indicates that the additional mechanical energy loss increases with the increase in guide vane height. (2) With the increase in Reynolds number Re, the proportion of additional mechanical energy loss in the total mechanical energy loss increases. (3) The cyclone with smaller guide vane height \(\zeta\) increases with the increase in Reynolds number, and the cyclone with larger guide vane height \(\zeta\) is more stable.
## 4 Smart City Service and the CEI Continuum
### Open Issues and Real-World Validation  
The distributed and scalable computing model of the CEI continuum has the potential to bring numerous advantages to Smart City applications, significantly improving the efficiency and resilience of urban services. However, several challenges from key research areas remain to be addressed to fully leverage its capabilities. In particular, there are still open issues around how to manage resources across the continuum, and specifically what degree of intelligence is needed to automatically reconfigure system and application resource allocations to meet overall QoS constraints as the real-world infrastructure changes [36, 41].  
Equally, issues such as security and privacy, data management, and energy efficiency are all areas where Smart City application developers are still experimenting to understand the best approach given the heterogeneous nature of the infrastructure and devices in a typical city deployment [24, 42, 43].  
However, the area where there is still a need for significant work is that of _Real-world validation_. While there has been significant research on many of the issues underlying the successful development of the CEI, the validation of CEI continuum systems in Smart City scenarios remains a significant challenge. Despite notable advancements in frameworks and methodologies, most experimental evaluations are constrained to Proofs of Concept (PoC) or, at most, small-scale testbeds. As a result, both research and industry initiatives lack the scope required for real-world deployment and comprehensive validation [44]. To begin to address some of these concerns, we, and other researchers have begun to move towards real-world validation of initial research ideas in an attempt to move away from PoCs. The COGNIFOG project represents one such attempt to build a working platform to explore the CEI continuum in real-world, i.e., Smart City, situations.
# RNA Modification Related Diseases and Sensing Methods
## 2 RNA Modification
### N1-Methyladenosine  
Alteration of N1-methyladenosine (m1A) (Figure 1B) can be found in mRNA, tRNA, rRNA, and mitochondrial RNA (mtRNA) [24]. Three well-known m1A-writing enzyme types are tRNA Methyltransferase 10C (TRMT10C), TRNA Methyltransferase 61B (TRMT61B), and TRNA Methyltransferase 61A (TRMT61A), while AlkB Homolog 1 (ALKBH1) and AlkB Homolog 3 are two well-known Fraser enzyme types (ALKBH3). Three reader proteins, YTHDF1, YTHDF2, YTH N6-Methyladenosine RNA Binding Protein 3 (YTHDF3), and YTH Domain Containing 1 (YTHDC1), have also been identified and strictly regulate post-transcriptional regulation of mRNA and ncRNA through the combined action of a writer, eraser, and reader [25; 26; 27]. It has been reported that the key modifier that affects translation in mRNA is the 5\({}^{\prime}\) untranslated region (5\({}^{\prime}\)-UTR) [28]. Additionally, m1A is thought to prevent reverse transcription [29].
## 6 Performance Evaluation
### Simulation-Based Performance Analysis
#### 6.4.3 Simulation Results and Analysis  
Table 5 summarizes the performance results of Bio-2 FA-IoD compared to other general IoD authentication protocols under the described simulation environment.  
As shown in Table 5, Bio-2 FA-IoD achieved an average end-to-end latency of **0.45 s**. This remarkably low latency is primarily attributed to its lightweight symmetric cryptographic operations and minimal message exchanges. Compared to Protocol A, which relies on more computationally intensive ECC operations, Bio-2 FA-IoD is significantly faster (0.45 s vs. 1.2 s). Even against other lightweight symmetric or biometric/PUF schemes (Protocol B: 0.7 s, [PERSON] et al.: 0.5 s), Bio-2 FA-IoD demonstrates superior responsiveness, making it ideal for time-sensitive IoD operations.  
Bio-2 FA-IoD achieved a high packet delivery ratio (PDR) of **95%** in the simulated lossy environment. This indicates the protocol's robustness in reliably delivering authentication messages. The high PDR is a direct consequence of its efficient use of the wireless medium, reducing contention and subsequent packet drops that can occur with heavier traffic or more complex handshakes (e.g., Protocol A at 88%, Protocol B at 90%). The streamlined authentication flow minimizes the opportunities for packet loss, even in challenging radio conditions.  
Average Radio ON Time (Energy Consumption)  
The simulation results show that Bio-2 FA-IoD maintained an impressively low average radio ON time of just **1.8%**. This directly translates to minimal energy consumption, extending the operational lifespan of battery-powered ODs and drones. The protocol's design,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Performance Metric** & \begin{tabular}{c} **Bio-2 FA-IoD** \\ **(Proposed)** \\ \end{tabular} & \begin{tabular}{c} **Protocol A** \\ **(ECC-Based)** \\ \end{tabular} & \begin{tabular}{c} **Protocol B** \\ **(HMAC-Based)** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **et al.**[5] \\ **(Biometric, PUF)** \\ \end{tabular} \\ \hline \hline \begin{tabular}{l} Average end-to-end latency (s) & **0.45** & 1.2 & 0.7 & 0.5 \\ \begin{tabular}{l} Packet delivery ratio (PDR, %) \\ \end{tabular} & **95** & 88 & 90 & 93 \\ \
\begin{tabular}{l} Average radio ON time (%) \\ \end{tabular} & **1.8** & 5.5 & 3.0 & 2.0 \\
\begin{tabular}{l} Control traffic overhead (packets/h) \\ \end{tabular} & **150** & 400 & 250 & 300 \\ \hline \hline \end{tabular}  
**Note:** Bold indicates the proposed protocol for emphasis.  
\end{table}
Table 5: Simulation-based performance comparison.  
which emphasizes few messages and rapid processing, ensures that the energy-intensive radio component is active for the shortest possible duration. This is substantially lower than Protocol A (5.5%) and Protocol B (3.0%), and even slightly better than [PERSON] et al. [5] (2.0%), highlighting its energy efficiency, a critical advantage for IoD deployments.
## 2 Materials and Methods
### Study Areas
#### 2.1.2 Massaciuccoli Lake  
Massaciuccoli Lake is included in the Migliarino-San Rossoe-Massaciuccoli Regional Park, representing an area of international importance under the Ramsar Convention, being recognized as a Site of Importance of the European Community [39], and thus it is regarded as one of the most important wetlands in Italy [46]. It includes (i) a main open-water basin with an average depth of 2 m [39], (ii) a secondary excavated basin (Cava Sisa) in correspondence of a former quarry, with a maximum depth of 24 m [46] and references therein, (iii) an extensive marsh area surrounding the lake northward and eastward and consisting of several pools of different sizes, artificial canals and basins (Figure 1b). The lake waters are fed by precipitation, groundwaters and a series of natural and artificial tributaries, flowing from the mountain belt or fed by water springs (e.g., Case Rosse, Villa Spinola). The surface water network drains agricultural, industrial and urban areas upstream of the lake basin and includes Barra, Fossa Nuova and Quiesa channels (Figure 1b) [47]. The lake has a main emissary, i.e., the Burlamacca canal, that, flowing through the city of Viareggio, directly connects Massaciuccoli Lake to the sea (Figure 1b). The lake waters are affected by severe eutrophication related to water inputs from the drainage of the farmlands and treatment plants of urban wastewater that produce a dramatic reduction of macroalgae and submerged macrophytes and an excessive growth of phytoplankton [46; 48; 49]. Reed beds are only discontinuously present along the lake and channel shores [50]. Such an environment is thus characterized by variable conditions of water depth, temperature, pH, salinity and redox state (dictated by the availability of free oxygen) along both vertical and lateral profiles, also depending on the contribution of brackish waters due to the marine ingression and fresh, meteoric and runoff waters from the hinterland.  
Figure 1: Porta (**a**) and Massaciuccoli (**b**) lakes with the respective sampling sites and their locations (**c**) on the coastal shoreline of western Tuscany (central Italy). In (**a**), circles refer to samples collected in summer, whereas triangles identify those collected in winter. Winter samples are distinguished into waters collected from the boat (blue upward triangles) and those collected from the lakeshores (green downward triangles). The locations of the three springs are indicated by yellow circles. In (**b**), symbols and colors refer to the sample type, as follows: SPW (magenta square), TCW (orange upward triangle), LSW (green diamond), OMW (cyan circles), OSW (blue circles) and ECW (red downward triangle). See text for further explanations. Aerial photographs are from Bing Maps (© 2021 Microsoft, Redmond, WA, USA; www.bing.com/maps, Accessed on 21 October 2021) and Google Earth®
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Evaluation on Stage 2: OCSVM + Deep Autoencoder (dAE)  
The deep autoencoder (dAE) model operates as the second stage in the intrusion detection system, handling data flagged as negative by the stage 1 one-class SVM (OCSVM). The evaluation of detection stage 2, dAE, utilizes mean squared error (MSE) as the loss function, with a threshold value established at 0.0219. Figure 11 illustrates the stage 2 dAE detection model's loss function, where this research utilized a three-latent-space dAE, 40 epochs, 32 batches, and the Adam optimizer with a learning rate of 0.001 and patience of 8. The stage 2 dAE model's confusion matrix is shown in Figures 12 and 13 and its performance is depicted in Figure 14 across the CIC-IDS2017 and CSECIC-IDS2018 datasets, showcasing the effectiveness of the model. The detection model, constructed using both \"Normal\" and \"Attack\" data from these datasets, demonstrates significant performance across both datasets. The experimental results highlight its notable performance, which is comparatively higher than that of the stage 1 OCSVM.  
The dAE demonstrates improved performance metrics across precision, recall, specificity, F1-score, and accuracy for both the CIC-IDS2017 and CSECIC-IDS2018 datasets, as depicted in Figure 14. Moreover, it notably reduces false positives, enhancing its ability to differentiate between normal and attack traffic beyond the initial screening by OCSVM. With a reduced false negative rate, the dAE enhances overall detection capabilities, highlighting its role in improving the reliability and efficiency of intr  
Figure 10: Performance measures of stage 1 detection OCSVM.  
Figure 11: Detection stage 2 OCSVM + dAE model loss function.  
Figure 12: Confusion matrix of detection stage 2 OCSVM + dAE on CSECIC-IDS2018.
# High Frequency Transformers for Solid-State Transformer Applications
## 1 Introduction
To overcome these challenges, it is necessary to know the impact of high frequency operation on the transformer and the options that exist to minimize this impact, such as utilizing different topologies and materials for the magnetic material structure, windings, and conductors [17]. Another challenge to overcome is the constitution of the power converters, namely the power semiconductors that allow the operation of the SST at high power and frequency. Moreover, the development of a control solution allows for maximizing the system efficiency, but this last challenge is not addressed in this article. The main research objective of this paper is to study the operation of an HFT by considering different types of magnetic cores to achieve higher power densities, and furthermore to investigate different equivalent circuits of the transformer, such as the impact of stray parasitic capacitances. The novelty of this paper lies in demonstrating the systematic steps to design an HFT according to the desired specifications of a given project, helping students and engineers to achieve these objectives in power electronic applications. Moreover, we aim at widening the knowledge of this area of power electronics and improving the development of new topologies with high power density, an area important to the integration of renewable power sources and other applications. The paper is organized as follows. Section 2 presents the modulation of the converter system, showing the application of SST, its characteristics and design issues. Furthermore, we discuss modeling aspects of converters with SST transformers. Section 3 presents some control aspects of SST and proposes a control solution. Section 4 presents the results of simulations for a fast charger of an electric vehicle with power bidirectionality. Finally, Section 5 is dedicated to conclusions. Table 1 shows the variables and parameters used throughout the paper.
## 3 Methodology
### Loss Function
#### 3.3.4 Directional Difference Knowledge Representation Loss  
The Cosine Loss, \(L_{cos}\), is derived from the knowledge representation of the differences in propagation direction and is primarily used to further optimize the directional differences between the upgoing and downgoing wavefields. The cosine loss is calculated as follows:  
\[L_{cos}=\frac{1}{N}{\sum_{i=0}^{N-1}}{||cos(u_{i},d_{i})||_{2}}, \tag{17}\]  
where \(N\) is the number of receivers, \(u_{i}\) and \(d_{i}\) represent the values of the predicted upgoing wavefield, \(\widetilde{U}\), and downgoing wavefield, \(\widetilde{D}\), at the i-th receiver from the autoencoder, respectively, and \(||\cdot||_{2}\) denotes the L2 norm of a vector. The introduction of cosine loss aims to create distinct differences between the upgoing and downgoing wavefields, reduces aliasing phenomena in wavefield separation, and ensures that the separated wavefields conform better to actual physical laws.
# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 2 Input Variables
### Exogenous Input Variables  
Because of the potential relationship between weather conditions and urban transit ridership, weather variables were defined as the exogenous input variables in this paper. The weather data were provided by the Taiwan Environmental Protection Administration (Taiwan EPA) [27]. An extensive review of the previous literature [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] showed that temperature (\(v_{Temp}\)), rainfall (\(v_{Rain}\)), relative humidity (\(v_{Hum}\)), wind speed (\(v_{Wind}\)), and snow (\(v_{Snow}\)) were the most commonly used weather variables. Because Taipei almost never sees snow, \(v_{Temp}\), \(v_{Rain}\), \(v_{Hum}\), and \(v_{Wind}\) were selected as the four weather variables in the study. The weather data were available from the Zhongshan meteorological monitoring station, which is the nearest observation station to Taipei Main Station. The weather data were hourly, which has the same time interval as the endogenous input variables described in Section 2.2.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 7 Experimental Evaluation
### Detection of Sybil Nodes in the Static Scenario  
In the same experimental environment, 20 sets of different nodes are randomly arranged at positions of 2, 3, 4 and 5 m and the angles of [30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\)] for Sybil attack detection. According to our Sybil attack model for multiple experiments, we set 6.3\({}^{\circ}\) degrees as the AoA threshold, 1.3 dB as the RSSI threshold and 1.4 as the amplitude threshold. Because we only use one AP as a detection node, the RSSI-based Sybil attack detection efficiency is 76.5%; whereas our detection system based on CSI has a detection rate of 100%. The experimental results have a relatively high accuracy, due to the large differences between the angles of different locations, and the test dataset is relatively small. However, this proves that the accuracy of our measurement is much higher than RSSI. For further research, we assume that we can detect the nodes in the error range of AoA, RSSI and CSI amplitude based on the above ideal environment.  
In order to further explore, we use MATLAB to randomly generate a large number of nodes with AoA, RSSI and CSI amplitude. We set 500 WiFi clients randomly and simulate Sybil attacks 200 times. We can detect whether the Sybil attacks occur accurately or not as the Sybil nodes increase. Figure 11a shows the detection rate of the virtual Sybil nodes generated by Sybil clients. We can see that our algorithm can achieve an average detection efficiency of 98.5% for the Sybil nodes, and the RSSI detection method can only achieve 79.8%. We define that the identities of multiple fake Sybil nodes in spoofing attack are all the real clients existing in the network. With the number of spoofing clients' Sybil nodes increasing, Figure 11b shows the probability that each node is detected by a spoofing attack. When the spoofing nodes are four, our detection efficiency is 94.2%. With the increase in spoofing at the same time, our accuracy is gradually declining. When a client is virtual out of 40 Sybil nodes, we cannot accurately distinguish every node generated by clients of spoofing attacks. However, we can still know that some nodes are being attacked. Spoofing attack detection needs to detect all fake nodes, which is a factor that limits the detection rate. Therefore, as long as there is a node that has not been accurately detected, we think that the entire test is a failure.  
Figure 11: Different attacks’ Sybil node detection rate. (**a**) Sybil attacks’ Sybil node detection rate; (**b**) spoofing attacks’ Sybil node detection rate.
## 9 Achievable Regions
### Maximum of Network Throughput  
As network throughput is the major parameter to determine the efficiency of the network. To improve the network throughput same linear programming approach is used as discussed in energy minimization. The related expression is as follows in Equation (18):  
\[\sum\
olimits_{n=0}^{max}Maximum\;(\textbf{Thr}\;(\textbf{n}))\;\forall n\; \in\;n_{\max} \tag{18}\]  
The following constraints are as follows:  
\[\text{C}_{1}:\;E_{st},\;E_{end}\leq E_{I} \tag{19}\]  
\[\text{C}_{2}:\;N_{L}\;\leq\;N_{l}^{min} \tag{20}\]  
Figure 10: Possible feasible region of throughput maximization.  
\[\text{C}_{3}:\;\text{C}_{r}\;\leq\;C_{max} \tag{21}\]  
\[\text{C}_{4}:\;D(n_{i},\;l_{1})\;\leq\;D\big{(}n_{j},\;l_{2}\big{)} \tag{22}\]  
\[\text{C}_{5}:Min\sum_{n=1}^{max}B_{frw}^{n} \tag{23}\]  
Equation (19) deals the required energy to start the transmission, and consumption should be less than the initial amount of energy of the node and Equation (20) discussed the next layer node that has the minimum energy consumption. Equation (21) is about the transmission range with its maximum transmission range of the node. Equation (22) shows that the distance between two nodes '_i_' and '_j_' is minimum for successful communication. The proposed scheme of calculation of the maximum throughput is the bandwidth assigned for the next forwarder node in the case of empty regions and is in Equation (23) such that '_B_frw_'_n_' and for non-forwarding node is '_B_(_N-frw_'_'. The overall bandwidth is calculated for the aforementioned equations are below where bandwidth is assigned for 150-300 KHz as from [17].  
\[50\;\leq\;B_{frw}^{n}\;\leq\;100 \tag{24}\]  
\[200\;\leq\;B_{N-frw}^{n}\;\leq\;300 \tag{25}\]  
\[250\;\leq\;B_{frw}^{n}\;+\;B_{N-frw}^{n}\;\leq\;350 \tag{26}\]  
The boundary of the feasible region can be plotted in Figure 11 from the above equations are:  
\[P1\;(50,200)=250\;\text{K Hz}\]  
\[P2\;(100,200)=300\;\text{K Hz}\]  
\[P3\;(50,300)=350\;\text{K Hz}\]  
\[P4\;(250,300)=550\;\text{K Hz}\]  
Hence by selecting any values from these plotting points obtain the maximized throughput.
## 1 Introduction
Additionally, recently, machine learning techniques have been extended to other fields such as the smart optimization of the friction-drilling process and abrasive waterjet machining [29; 30]. In most papers on bearing failure diagnosis, a pitting or a hole on the outer raceway has been treated as a fault factor. The existing fault diagnosis methods have detected the holes with good accuracy but lack the detection of scratch failures. Thus, the drawbacks of validating faults other than pitting were evaluated in the present method proposed. The versatility of a fault diagnosis method is important from a practical standpoint. The present paper aims to illustrate the potential of a proposed convolutional neural network (CNN)-based method in bearing fault diagnosis (scratch detection and scratch traceability analysis). To avoid a low detection percentage and overlapping effects, both the hidden layer and pooling layer are optimized, and epochs are performed. Comparing the general CNN techniques, in the proposed CNN method, an auto-tuning parameter optimization function is added to tune the hidden layer and the pooling layer. This makes the system able to reduce the effect of overlapping and increases the accuracy rate. A detailed analysis is discussed in Section 4. In a detailed analysis of scratch traceability, concentrated scratches are considered faults. Scratches are more likely to occur at sites compared with pitting, but they have seldom been considered. Finally, for validation, a scratch is induced away from the center (offset to the outer raceway of the bearing) and diagnosed using the proposed CNN method. The entire dataset used to perform CNN is given after performing an FFT analysis using the load current of the stator. Promising results are obtained and compared with other machine learning algorithms to enhance the performance of the proposed method.