esa-sceva/satcom-chunk-collection huggingface dataset:
# ResADM: A Transfer-Learning-Based Attack Detection Method for Cyber-Physical Systems
## 4 Experiment and Analysis
### Evaluation Indicators  
The performance metrics used to evaluate the machine-learning model have been extensively explained in previous studies [18; 19]. Here is a brief introduction to these metrics:  
Accuracy measured the overall performance of the model by calculating the ratio of correctly classified samples to the total number of samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Configuration** & **Parameter** \\ \hline \multirow{4}{*}{Hardware} & CPU & AMD 7T83 \\  & RAM & DDR4 ECC 128 GB \\  & GPU & NVIDIA GeForce RTX 3090 \\  & SSD & 2T \\ \hline \multirow{4}{*}{Software} & Operating System & Ubuntu 22.04 \\  & Python & 3.9 \\ \cline{1-1}  & LightGBM & 3.2.1 \\ \cline{1-1}  & TensorFlow & 2.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration.  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP}, \tag{7}\]  
Precision evaluated the ability of the model to correctly identify negative samples by calculating the ratio of true-negatives to the sum of true-negatives and false-positives.  
\[Precision=\frac{TP}{TP+FP}, \tag{8}\]  
Recall, also known as sensitivity or the true-positive rate, measured the model's ability to correctly identify positive samples. It was calculated as the ratio of true-positives to the sum of true-positives and false-negatives.  
\[Recall=\frac{TP}{TP+FN}, \tag{9}\]  
The F1-score was a measure of the model's balance between precision and recall. It was the harmonic mean of precision and recall and provided an overall assessment of the model's performance.  
\[F1-score=\frac{2 TP}{2 TP+FN+FP}, \tag{10}\]  
The formulas for calculating these metrics are shown in Equations (7)-(10), where \(TP\) represents true-positives, \(FP\) represents false-positives, \(TN\) represents true-negatives, and \(FN\) represents false-negatives.
## 3 Finite Element Modeling
### Acting Load  
Throughout the operation of a scroll compressor, the various compression chambers experience complex forces, with their load dynamically shifting in correlation with the spindle rotation angle during the compression phase. The gas pressure in the Nth compression chamber can be determined using the equation governing adiabatic gas processes. The specific formula for this calculation is expressed as Equation (12):  
\[p_{N}=p_{0}\Bigg{(}\frac{V_{s}}{V_{(\theta)}}\Bigg{)}^{K} \tag{12}\]  
where \(P_{0}\) is the inlet pressure, \(P_{N}\) is the pressure of the first \(N\) gas pressure of the first compression chamber, \(V_{s}\) is the initial compression chamber volume, and \(V_{N}\) is the volume of the first \(N\) volume of the first compression chamber.  
The radial force exerted by the gas within the scroll tooth is a consequence of the difference in pressure between its inner and outer sides. Given the geometrically symmetrical configuration of the scroll teeth, the application of gas pressure can be simplified as a differential pressure acting across multiple evenly spaced angular sectors, primarily impacting the inner wall surfaces. These forces correspond in magnitude to the difference in pressure between adjacent compression chambers. The specific calculation formula is Equation (13).  
\[\Delta p_{i}=p_{N}-p_{N+1} \tag{13}\]  
Throughout its operational phase, the gas pressure within individual compression chambers, together with the corresponding simplified gas forces exerted on inner wall surfaces, undergo continuous temporal fluctuations. Therefore, it becomes imperative to calculate the gas pressure exerted on the inner wall surface within each enclosed compression chamber separately, accounting for various operational scenarios. This study focuses specifically on the start of the exhaust phase, requiring calculation only at this point. At this precise moment, the spatial configuration between dynamic and static vortex discs is elucidated, as depicted in Figure 6.  
As shown in Figure 6, in the case of scroll teeth where both the inner and outer walls are exposed to the same gas pressures, a state of equilibrium is achieved, resulting in the nullification of pressure differentials. Therefore, the focus lies on determining the angular extent of the scroll teeth where pressure differentials occur. Pressure differentials are quantified as follows: \(\Delta p_{1}=p_{2}-p_{1}=0.34\) MPa, \(\Delta p_{2}=p_{pai}-p_{2}=0.746\) MPa.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Material** & **Numerical Value** \\ \hline Modulus of elasticity (MPa) & \(1.38\times 10^{5}\) \\ Poisson’s ratio & 0.156 \\ Density (t/mm\({}^{3}\)) & \(7.28\times 10^{-9}\) \\ Coefficient of thermal expansion (m/(m\(\cdot\)K)) & \(8.2\times 10^{-6}\) \\ Heat transfer coefficient (W/(mm\(\cdot\)K)) & 0.045 \\ Specific heat capacity (mJ/(t\(\cdot\)K)) & 510 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic material parameters.
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
###### Abstract  
In order to maximize the available data rate and spectrum utilization efficiency, a high-throughput satellite communication system adopts the full spectrum reuse scheme, which will cause serious co-frequency interference. In this paper, a forward link model, considering the effects of free space loss, rainfall attenuation, and beam gain, is established, and the classical low-complexity of the zero-forcing precoding algorithm is improved in order to solve the serious co-frequency interference. Moreover, the regularized zero-forcing precoding algorithm considering the influence of system noise is studied, and a low complexity regularized zero-forcing dirty paper precoding algorithm is proposed, whose basic principle is to sort users based on the principle of channel maximum norm selection and practical application scenarios. Simulation results show that it can encode users sequentially, according to the channel conditions, to maximize the SINR (signal-to-interference-plus-noise ratio) and increase the throughput of the system.  
high-throughput satellite; full spectrum reuse; co-frequency interference; precoding; zero-forcing precoding +
Footnote †: journal: _electronics_
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 2 Theoretical Considerations
### Wells Turbine Model  
The considered air turbine is a Wells turbine which is a self-rectifying axial-flow turbine [23]. This type of air turbines permits a unidirectional pivotal motion unrelatedly to the airflow direction thanks to the blade's distinct shape [24; 25].  
The Wells turbine shown in Figure 2 may be described by the equations given in [11] where the pressure drop across the turbine rotor is defined as:  
\[dp=C_{a}K\left(1/a\right)\left[v_{x}^{2}+\left(r\omega_{r}\right)^{2}\right] \tag{6}\]where \(a\) is the cross-sectional area, \(C_{a}\) is the power coefficient, \(r\) is the mean radius, \(\omega_{r}\) is the angular velocity.  
The turbine constant \(K\) is different from Wells turbine to another and is obtained from the turbine's geometry as:  
\[K=\frac{\rho lbn}{2} \tag{7}\]  
where \(\rho\) is the air density, \(l\) is the blade chord length, \(b\) is the blade height, and \(n\) is the number of blades.  
The torque produced by the turbine will mainly depend on the turbine's mechanical characteristics and both airflow and rotational speed which is expressed as:  
\[T_{t}=rC_{t}K\left[v_{x}^{2}+\left(r\omega_{r}\right)^{2}\right] \tag{8}\]  
where \(C_{t}\) is the torque coefficient.  
The relation between the torque and the pressure drop may be defined as:  
\[T_{t}=\frac{raC_{t}}{C_{a}}dp \tag{9}\]  
The flow coefficient is obtained from the ratio of the airflow speed by rotational speed as:  
\[\phi=v_{x}\left(r\omega_{r}\right)^{-1} \tag{10}\]  
The expression of the flow rate can be defined as a function of the cross-section area and the airflow speed as:  
\[Q=av_{x} \tag{11}\]  
Lastly, the turbine efficiency may be written as:  
\[\eta_{t}=T_{t}\omega_{r}\left(dpQ\right)^{-1}=C_{t}\left(C_{a}\phi\right)^{-1} \tag{12}\]  
Figure 2: PTO of OWC combining Wells turbine and DFIG.  
The characteristic curves of the Wells turbine under steady-state operating conditions, for which no hysteretic loop is reported, are formed by the power coefficient \(C_{a}\) and the torque coefficient \(C_{t}\) versus the flow coefficient \(\phi\) depicted in Figure 3.
Crosstalk Reduction Strategies on UCPW Transmission Lines Supported by Real Application Data: Experimental Analysis of Point Capacity  
[PERSON]  
1 Department of Electrical and Electronics Engineering, Dogus University, Istanbul 34775, Turkiye;  
[EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS]  
[PERSON]  
Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Electrical and Electronics Engineering, Dogus University, Istanbul 34775, Turkiye;  
[EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS]  
Footnote 1: email: {s.t.s.t.  
have emerged as an efficient solution for high-frequency applications. Their low-loss characteristics and simplified design eliminate the need for a ground plane beneath the signal line, making them cost-effective and easier to manufacture compared to traditional coplanar waveguide (CPW) structures [15; 16; 17]. UCPW configurations allow parallel transmission lines to be positioned on the same plane in close proximity, making them ideal for RF and microwave applications. Moreover, UCPW's compact design and low radiation losses enhance its utility in high-frequency circuits, antennas, and communication systems [18].  
However, traditional methods for reducing crosstalk, such as increasing the spacing between transmission lines or using guard traces, face significant challenges in high-density PCB designs due to space and cost constraints. While CPW lines are extensively studied, the impact of integrating capacitors between ground planes in UCPW configurations remains underexplored. Addressing this gap is crucial for advancing high-frequency communication systems.  
The integration of passive components, particularly capacitors, between reference planes has been extensively studied to improve transmission line performance. Capacitors create low-impedance return current paths, which enhance signal integrity and suppress electromagnetic interference [19; 20; 21]. Decoupling capacitors are particularly effective in reducing noise and stabilizing the performance of high-frequency transmission lines [22; 23; 24; 25].  
This study investigates whether the inclusion of capacitors between ground planes in a UCPW-configured two-layer circuit board with 50 \(\Omega\) impedance can effectively reduce crosstalk and preserve signal integrity in high-frequency applications. By systematically analyzing the effects of capacitors on near-end crosstalk (S\({}_{31}\)) in UCPW transmission lines, this study provides a novel perspective on mitigating interference and enhancing performance. Building on these insights, the proposed method not only achieves significant improvements in signal integrity but also offers a practical and scalable solution for high-density electronic systems, effectively bridging a critical gap in the existing research. This correlation highlights the method's ability to improve signal integrity, ensure practical scalability, and address a critical research gap.
## 3 System Model and Problem Formulation
### Calculation Model
#### 3.3.1 Local Computing Model  
The delay for executing the bit-level task \(D_{i}^{l}\) is given by the following formula.  
\[T_{i}^{l}=\frac{D_{i}^{l}C_{i}}{f_{i}^{l}} \tag{6}\]  
Here, \(f_{i}^{l}\) represents the CPU clock frequency of the \(i\)-th M-IoT device.  
Accordingly, the energy consumption for executing the computing task locally is:  
\[E_{i}^{l}=\kappa_{i}\Big{(}f_{i}^{l}\Big{)}^{2}D_{i}^{l}C_{i} \tag{7}\]Here, \(\kappa_{i}\) represents the energy consumption coefficient related to the CPU architecture of the \(i\)-th M-IoT device, which indicates the energy consumption coefficient per cycle of the CPU of device \(i\) and depends on the effective switching capacitance of the chip architecture [10].
# ARAAC: A Rational Allocation Approach in Cloud Data Center Networks
## 2 ARAAC: A Rational Allocation Approach in Cloud-Service Networks
### Benchmark Mechanisms  
In ARAAC, the service allocation process is modeled as a game between two parties. One is the cloud-DCN provider, and the other is a set of service tenants. The cloud-DCN provider aims to maximize its collected profits by asking for competing service prices, with acceptable service levels. On the other hand, service tenants also seek to maximize their utilities, but while receiving acceptable service levels. To achieve this, ARAAC proposes a conservative selection algorithm presented in Table 1, that adopts the SBP mechanism along with the behavior-based reputation mechanism. Through such mechanisms, together, ARAAC motivates: (1) truthful revealing of price units (achieved by the SBP's pricing policy), and (2) high utilization rates for the available resources (achieved by the reputation records). To evaluate the efficiency of our model, we chose to compare ARAAC's performance with two other models as a benchmark: (1) a model that deploys the SBP mechanism [7] alone to select its set of candidate tenants for allocation, and (2) a model that deploys the _Tit-for-Tat strategy_[11] along with the SBP mechanism being deployed.
# Performance Comparison of 500 MW Coal-Fired Thermal Power Plants with Final Feedwater Temperatures
## 2 Materials and Methods  
### Regenerative-Reheat Rankine Cycle  
#### 2.1.1 Typical Coal-Fired Power Plant  
A coal-fired power plant is based on the regenerative-reheat Rankine cycle. High-temperature and high-pressure steam with thermal energy is generated by the combustion of coal. This steam rotates the turbine to generate electric power. Currently, most thermal power plants raise the steam pressure above the critical pressure to increase efficiency, which is called a supercritical cycle [38].  
Figure 1 shows a block diagram of a typical coal-fired power plant. The power plant consists of pumps, low-pressure feedwater heaters (hereinafter LP heaters), a deaerator, high-pressure feedwater heaters (hereinafter HP heaters), a boiler, turbines, and condensers. Feedwater is the water supplied to the boiler as a working fluid. The feedwater system consists of feedwater pumps, high-pressure feedwater heaters, and a deaerator. As shown in Figure 1, to improve the cycle efficiency, some of the steam in the turbine is extracted to raise the temperature of the feedwater supplied to the boiler. This is called a regenerative cycle. In modern large-scale power plants, a regenerative cycle using about 7 to 8 or more feedwater heaters is applied [39]. The final feedwater temperature is the temperature of feedwater that is supplied to the boiler through the feedwater heater. In Figure 1, the temperature of point 2 is the final feedwater temperature. Feedwater heater No. 8 uses steam to heat the feedwater. The steam to heat the feedwater is supplied from the extraction of the high-pressure turbine (hereinafter HPT) as shown in Figure 1. It is well known that as the final feedwater temperature increases, the heat transfer of the boiler decreases, thereby improving the efficiency of the power plant. However, if the final feedwater temperature is too high, a two-phase flow of steam and water occurs in the economizer of the boiler, and heat transfer to the feedwater flowing inside the economizer tube is not smoothly performed, causing problems such as local overheating of the tube. In general, there is a maximum allowable final feedwater temperature suggested by boiler manufacturers in consideration of the lifetime and reliability of the boiler.  
A temperature-entropy diagram depending on the configuration of the power plant is shown in Figure 2 according to Figure 1. As shown in Figure 1, the section from point 2 to point 3 is a boiler and the section from point 4 to point 5 is a reheater. In Figure 2, section 2 to 3 and section 4 to 5 are the sections where heat is transferred through a boiler.  
Figure 1: Typical coal-fired power plant configuration.
## 4 Experiments
### RFE-based feature selection  
For the purpose of further improving the performance of the fault diagnosis, the RFE algorithm is utilized to eliminate the redundant components from the feature set and then select the optimal  
Figure 8: Two-dimensional time-frequency matrix (**left**); energy density matrix (**right**) under the normal mode.  
Figure 9: Two-dimensional time-frequency matrix (**left**); energy density matrix (**right**) under the outer-race fault.  
feature subset. With the help of cross-validation technique, the above process of feature selection is totally automatic.  
As described in Section 3.4, we finally obtain the ranking of features by training the linear SVM using the \(5\times 10=50\) training samples. The ranking of our 10 TfEn features is presented in Table 1.  
As shown in Table 1, a 5-dimensional feature vector including \(s(q)_{1}\), \(s(q)_{2}\), \(s(q)_{3}\), \(s(q)_{4}\) and \(s(q)_{10}\) is finally selected as the optimal feature subset \(TfEns^{*}\). In the optimal feature subset, the first 4 Imfs occupy the dominant, which is consistent with the general experience and intuition of fault diagnosis. While contrary to common sense, the last 3 non-stationary components also obtain quite a high ranking, and the last Imf is also selected as the optimal feature. The above-mentioned phenomenon indicates that it is not reasonable to directly abandon the non-stationary components without analysis, and it also demonstrates that it is necessary to take into account both time and frequency domains during the process of feature extraction.  
For the comparisons, another two mainstream feature selection methods, PCA [28] and kernel PCA (KPCA) [29], are also applied to select the optimal TfEn features. In order to understand the performance of these feature selection methods more intuitively, t-distributed stochastic neighbor embedding (t-SNE), a popular technique of dimensionality reduction, is applied to respectively transform the total raw features and the optimal features selected by PCA, KPCA, RFE into the 2D feature space, their corresponding scatter plots with 250 testing samples, are shown in Figure 10.  
As shown in Figure 10, we can see that the scatters of features selected by RFE achieve the best resolution under 5 modes. Using these RFE-based optimal features, the difference of TfEn between different fault modes are further highlighted.
## 4 Experiment and Results
### Results and Discussions  
#### 4.2.1 Impact of Channel Selection on Model Performance  
Table 3 presents the performance of the ADLCNN model across eight channel combinations for predicting cumulative departure delay levels. Notably, the Chanel_combi7 combination achieves a prediction accuracy of 90.82%, with superior precision, recall, and F1 scores compared to other combinations. Hence, this combination is selected for subsequent predictions. 8 channels: inbound flight volume channel, inbound flight average flight altitude channel, inbound flight average flight speed channel, departing flight volume channel, departing flight average flight speed channel, mild convective weather channel, moderate convective weather channel, and strong convective weather channel.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 2 Materials and Methods
### Data Sets  
#### 2.2.1 Measured Chl-a Concentration Data  
The data in this paper were provided by the national research team of \"River and Lake Wetland Water Environment Protection and Restoration\" of Inner Mongolia Agricultural University. This team has long been committed to the scientific research of Wuliangsu Lake, so the data sources are authentic and trustworthy. The data were collected at 19 sampling locations in Wuliangsu Lake. Wuliangsu Lake enters the freezing period from the end of October to the first half of November every year, and thaws in March of the next year; the freezing period lasts five months [21; 22]. Therefore, the sampling time is concentrated in the period from June to September, and the sampling time is fixed at the middle and the end of every month. The sampling is conducted by boat, and the depth of sampling is vertically downward from the surface of the water to a depth of 0.5 m. Between 2015 and 2018, 92 Chl-a concentration measurements were sampled. Table 1 presents the internationally recognized \"Chl-a Concentration Hierarchy\", which was used to assess the degree of eutrophication at the selected sample sites, and the results are shown in Table 2. In this paper, all sampling dates were divided into a training set and test set according to the ratio of 8.2, and 73 measurements were used to train the model. After the training was completed, 19 measurements were used to test and evaluate the model efficiency.  
Figure 1: Geographic location of Wuliangsu Lake and sampling locations.
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Literature Analysis
#### 4.2.1 Diversity of Application Areas  
Table 4 shows a possible mapping between the different application areas of TSN and the algorithms presented in Tables 12 and 13. For example, [108] suggested a routing and scheduling scheme for industrial IoT in the field of underground mining. However, besides industrial automation, the largest number of approaches was developed in the automotive field. Although TSN scheduling research in the automotive started early (e.g., [34] published in 2016), the work continued to evolve. Works from later years treated important aspects such as security and authentication [109], functional safety with regards to Automotive Safety Integrity Level (ASIL) standard [110], reliability [106], fault tolerance [110], and cross-domain optimization [59]. We believe the reason resides in the fact that the  
automotive network topology is well established and pretty static concerning the streams. Therefore, researchers focus more on guaranteeing the good functioning of the network rather than agility of communication. However, we expect this view to change since future vehicles are meant to have more communication with the outside world (i.e., other vehicles or infrastructure) especially in the context of autonomous driving. In addition, the in-car services are expanding with regards to infotainment, for example.
## 3 Challenges in Terahertz-Based FSO Communication
### Atmospheric Absorption
#### 3.1.1 Mitigation Strategies  
1. Photonic crystal fibers: hybrid photonic crystal fibers (PCFs) confine the optical field to low-loss regions, minimizing its interaction with water vapor and reducing (f) [7; 8];
2. Frequency selection: operating at THz frequencies with minimal water vapor absorption peaks, such as 0.3 THz, significantly reduces the losses [8; 15];
3. Adaptive beamforming: dynamically steering the beam to optimal transmission paths in response to weather variations helps mitigate attenuation [37; 57; 58];
4. Low-loss materials: advanced materials like metamaterials and dielectric polymers are engineered to enable efficient wave guiding and minimal absorption.