esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Network Covert Channel  
Network covert channels were originally studied in [19], and two typical classes of network covert channels are timing and storage channels. The former modulates packet timing [20] and the latter embeds data inside packet headers [21]. As an example of the former, an attacker could use the data packet intervals or the arrival time of packets to convey hidden information [8].  
The detection and mitigation of network covert channels are of increasing interest in light of the research of their defects or weaknesses, which is summarized as follows.  
The detection of network covert channels: [PERSON] et al. [22] proposed a novel DomEye method based on throughput spoofing defects to detect domain name spoofing through traffic-level analysis and machine learning. This method achieves efficient detection of covert channels with a lower false alarm rate and computational overhead. [PERSON] et al. [23] proposed a detection method based on the isolation forest algorithm, which can effectively identify covert channels disguised as legitimate traffic and doped with abnormal features, and improve the detection performance of various covert channels.  
The mitigation of network covert channels: [PERSON] et al. [24] proposed a system-aware defense method that combines heuristics and machine learning strategies to effectively defend against power-based covert channel attacks through task migration and intelligent DVFS adjustment. Taking advantage of the dynamic nature of Quick UDP Internet Connection (QUIC) protocol traffic and the encapsulation capabilities of proxy services, [PERSON] et al. [25] proposed a covert communication system, QuicCourier. This method successfully achieved high-speed, highly concealed information transmission and verified its superior anti-detection performance on a large amount of real traffic.  
Few covert storage channels could rely on the features of the carrier to guarantee robustness [15] and timing covert channels are susceptible to network performance (e.g., congestion, packet loss, noise, etc.) or limited to the channel capacity [26]. Depressed by low robustness, poor concealment, and being eliminated with ease, we are eager to construct a novel covert channel with high robustness and good performance against existing detection or mitigation technologies; then, we turn towards end spreading.
# Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
## 6 Conclusions  
This paper has presented a low-cost full Correlated-Power-Noise Generator as a countermeasure against power-analysis attacks. Correlated power noise has been generated for each encryption/decryption round, with the _AddRoundKey_ module input being the plaintext for the first round and feedback for subsequent rounds. The proposal has been validated with experimental results using a first-order CPA attack on a SAKURA-G board with a SPARTAN-6 Xilinx FPGA. An unprotected AES design with a 128/256-bit key size was used for this study. The countermeasure has proven resilient against CPA attacks due to the increased difficulty in obtaining SubBytes, resulting in a 44.5% reduction in their count when using a dataset of 20,000 traces. Additionally, the number of traces needed to obtain the initial SubBytes has increased. Our approach is introduced as a cost-effective solution that may interest various applications prioritizing both area and throughput while enhancing the security of lightweight ciphers. For our secure design, the area overhead is only 29.04%, while the maximum frequency for both unprotected and secure designs remains the same, making the degradation negligible.  
Conceptualization, E.T.-S., F.E.P.-O. and A.J.A.; methodology, E.T.-S., F.E.P.-O. and A.J.A.; software, E.T.-S., F.E.P.-O. and V.Z.-G.; validation, E.T.-S., F.E.P.-O. and V.Z.-G.; formal analysis, E.T.-S., F.E.P.-O. and V.Z.-G.; investigation, E.T.-S., F.E.P.-O., V.Z.-G. and A.J.A.; resources, E.T.-S., F.E.P.-O., V.Z.-G. and A.J.A.; data curation, E.T.-S. and V.Z.-G. writing--original draft preparation,  
Figure 10: Number of revealed SubBytes versus the number of power consumption traces used.  
E.T.-S., F.E.P.-O. and V.Z.-G.; writing--review and editing, E.T.-S., F.E.P.-O. and A.J.A.; visualization, E.T.-S. and F.E.P.-O.; supervision, E.T.-S. and A.J.A.; project administration, A.J.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Authors want to thank projects Grant PID2020-116664 RB-I00 funded by MCIN/AEI/ 10.13039/501100011033, project of \"Programa Operativo FEDER 2014-2020 and Consejeria de Economia, Conocimiento, Empresas y Universidad de la Junta de Andalucla under Project US-1380823\", the SPIRS (Secure Platform for ICT Systems Rooted at the Silicon Manufacturing Process) Project with Grant Agreement No. 952622 under the European Union's Horizon 2020 research and innovation programme, and by the Ministry for Digital Transformation and Public Function through grant USECHIP (TSI-069100-2023-001) of PERTE Chip Chair program, funded by European Union-Next Generation EU.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Improvement of Performance Level of Steel Moment-Resisting Frames Using Tuned Mass Damper System
## 7 Results and Discussion
### The Results of the Incremental Dynamic Analysis  
In this section, the parameters of the TMD system subjected to the different earthquakes are optimized based on minimizing the relative displacement criterion presented by FEMA-356 [48]. Next, the IDA analysis is used to investigate the dynamic performance of the six-story steel frame equipped with the TMD system. The IDA analysis is calculated for the maximum base shear, maximum acceleration, maximum displacement, and maximum drift ratio of the desired structure, and its curves for near- and far-field earthquakes are plotted in Figures 8-11.  
Figure 7: Drift ratios of the stories and the time history of the displacement of the roof in the controlled and uncontrolled structure subjected to the near-field earthquakes.  
7.3.1. The Results of IDA for the Drift Ratio  
The drift ratio of the structures with and without the TMD system is shown in Figure 8. It can be concluded that the maximum drift ratio of all stories has been decreased for the structure controlled with the TMD system subjected to the FE1, NE1, and NE2 earthquakes. The drift ratio has increased for the FE2 earthquake with PGA equal to 0.9 g and 1 g. Additionally, the maximum drift ratio of the structure has increased under the FE3 earthquake in the range of 0.6g \(\leq\) PGA \(\leq\) 0.9g. A disturbance has taken place for the NE3 earthquake in the range of 0.7g \(\leq\) PGA \(\leq\) 0.9g. Therefore, it might be concluded that the TMD parameters should be evaluated for PGA values larger than 0.5 g in controlling the drift ratio.  
Figure 8: Maximum drift ratio of the frame subjected to different far- and near-field earthquakes.
## 2 Materials and Methods
### EO Composition Analysis
#### 2.4.2 Gas Chromatography-Mass Spectrometry (GC-MS)  
A Perkin-Elmer Clarus 600 gas chromatograph, coupled with a DB-1 fused-silica column as detailed above and interfaced with a Perkin-Elmer 600T mass spectrometer (software version 5.4.2.1617, Perkin-Elmer, Shelton, CT, USA), was used in GC-MS analyses. Owen programming and injector temperatures were the same as for GC-FID analyses. The carrier gas was helium at a flow rate of 30 cm/s. The split ratio was 1:40. The transfer line and ion source temperatures were 280 \({}^{\circ}\)C and 220 \({}^{\circ}\)C, respectively. Electron impact (EI) mode at 70 eV, and mass scan range of 40-300 amu with a scan time of 1 s. Component identities were established by comparing their retention indices (RI), calculated as in ISO 7609 [18], relative to C\({}_{9}\)-C\({}_{22}\)\(n\)-alkane (Sigma) indices, and with GC-MS spectra from a lab-made library, created using commercial available standards (Extrasynthese, Cymit Quimica, S.L.; Sigma-Aldrich; Fluka, Riedel-de Haen), laboratory-synthesised components, laboratory isolated compounds [19], and reference essential oils of _Thymus caespittius_[20], _Juniperus ccdrus_[21], and _C. japonica_[22], in which component identities were confirmed by RI, GC-MS, and by \({}^{13}\)C-NMR.
## 3 Results and Discussion
### Environmental Analysis  
In this section, we discuss the environmental implications of Egypt's reliance on fossil fuel electricity generation and the potential benefits of implementing the proposed renewable energy system. Fossil fuel electricity generation in Egypt has significant environmental consequences, particularly in terms of greenhouse gas emissions (GHGs). The current CO2 emission factor (EF) for Egypt's power system is reported as 431 g CO2/kWh emitted.  
The results highlight the potential reduction, or mitigation, in GHG emissions that could result from implementing the proposed energy system. By transitioning to renewable  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Charging Station** & **Sessions per Year** & **Annual Energy Served (kWh)** & **Energy per Session (kWh)** & **Sessions per Day** \\ \hline Deferrable EV charger & 10,072 & 65,343 & 6.49 & 27.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: EV charging station results.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**System Architecture** & **PV/Wind/Grid** \\ \hline LCOE (USD/kWh) & 0.042 \\ \hline NPC of HRES (MUSD) & 3.0 \\ \hline NPC of the base system (MUSD) & 4.4 \\ \hline Utility bill savings (kUSD/yr) & 67.2 \\ \hline Net present utility bill savings (MUSD) & 2.54 \\ \hline Payback time & 11.8 years \\ \hline \hline \end{tabular}
\end{table}
Table 5: The economic results of the proposed system configurations.  
energy sources, like solar and wind power, the system could substantially decrease CO2 emissions. Specifically, the estimated reduction in environmental emissions could reach up to 510 tons of CO2 per year. Additionally, the results emphasize the potential impact on fossil fuel consumption. It is indicated that adopting the proposed renewable energy system could lead to a decrease in fossil fuel usage by 248 tons of oil equivalent (toe) per year. This reduction in fossil fuel consumption is significant for reducing reliance on non-renewable energy sources and mitigating associated environmental impacts, such as air pollution and carbon emissions.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 2 Materials and Methods
### Field Experiment and Data Measurement  
#### 2.2.1 Sensor System  
To measure the field data, a 78 kW class tractor (S07, TYM Co., Ltd., Gongju, Korea), which is widely used in Korea, was used in this study. The dimensions of the tractor are \(4225\times 2140\times 2830\) mm (length \(\times\) width \(\times\) height), and the weight is 3985 kg. The rated engine torque at a rated speed of 2300 rpm is 324 Nm.  
Figure 1: Schematic diagram of a tractor power transmission system with prediction target axles.  
Figure 2 shows the tractor and load measurement system. The tractor is equipped with an engine (D34P, Doosan Infracore Co., Ltd., Seoul, Korea) capable of CAN communication. The major parameters of the engine are communicated from the engine control unit (ECU) via CANbus J1939 communication. Among them, CAN message data, including engine torque and engine speed, were measured during a field experiment with an accuracy of 1.0%. The tillage depth was calculated through a potentiometer, with an accuracy of 0.1%, built into the lift arm of the three-point hitch. The output value of the potentiometer was calibrated through 10 repeated measurements, and it showed an R\({}^{2}\) of 0.99 (Tillage depth = \(-0.2342\times\) potentiometer value + 32.857). The travel speed was measured using GPS (18 \(\times\) 5 Hz, Garmin, Olathe, KS, USA) with an accuracy of 5.0%, and the wheel rotational speed was measured using a proximity sensor (Autonics, PRDCMT30-25 DO, Seoul, Korea) with hysteresis of the maximum 10% of sensing distance by installing a separate gear jig between the axle case and the wheel. The axle torque was measured using two front torquemeters (Manner Sensortelemetrie GmbH, MW 15 kNm Fu PCM16, Spaichingen, Germany) and two rear torquemeters (Manner Sensortelemetrie GmbH, MW 30 kNm Fu PCM16, Spaichingen, Germany). The linearity deviation of torquemeters is 0.2%. A data acquisition system (IMC, CRONOS compact CRC-400-11, Berlin, Germany) was used to measure field data from each sensor data.
## 3 Results
### SIF Data Accuracy Improvement Analysis
#### 3.3.2 Analysis of Linear-Based Accuracy Improvement  
From Figure 8, it can be seen that the R\({}^{2}\) fitting values after improvement (based on the linear improvement method) at each site are sorted by underlying surface conditions as follows: pasture grassland area (Figure 8b) > cultivated and farmland area (Figure 8c) > desert vegetation area (Figure 8a), with the corresponding values are 0.87, 0.85 and 0.75,  
Figure 7: Linear fitting graph of 2020 GPP data and RTSIF corresponding station data for each station after improving based on the canopy method. (**a**) Ulastai Station, pasture and grassland area. (**b**) Ulan Usu Station, cultivate land and farmland area.  
respectively. All values were greater than 0.70 and close to the optimal value, indicating that 70% of the dependent variable variability in the underestimated desert vegetation area can be explained by the linear method. In addition, compared with before the improvement, the R\({}^{2}\) fitting values of the cultivated and farmland area, the pasture grassland area, and the desert vegetation area increased by 0.01, 0.02, and 0.13, respectively, showing a significant overall improvement effect.  
In addition, the overall improvement ranges of the MB, RMSE, and SD at each site ranged from 0.0001 to 0.0011, 0.0003 to 0.0146, and 0.0126 to 0.0799, respectively. Various fitting parameters showed different degrees of enhancement, with the greatest improvement observed in the desert vegetation area, and relatively minor improvements in the cultivated and farmland area and the pasture grassland area, the enhanced SIF data can further reflect the characteristics of GPP changes.  
Overall, compared to before the improvements, SIF data are now more closely aligned with the measured GPP data. The parameters in the underestimated desert vegetation area showed the greatest overall improvement due to the linear method, whereas the cultivated and farmland area and the pasture grassland area exhibited a relatively smaller overall improvement. This suggests that while the linear method is applicable across different ecosystems, there are limitations when applying this method to improve the accuracy of SIF data in arid regions.
## 8 Current Challenges in the Development of 3D-Printed Prostheses
### Material Optimization  
One of the main challenges in the manufacture of robotic prostheses with 3D printing is the selection and development of materials that meet the requirements of durability, flexibility, bio-compatibility, and resistance. Although the most commonly used materials, such as PLA and ABS, are accessible and easy to print, they have limitations in terms of mechanical strength and long-term bio-compatibility. According to [13], 3D-printed materials do not always withstand prolonged use, which can lead to deformation or structural failure in prostheses.  
The search for new, more robust, and flexible materials is an area of active research. Advanced polymers and hybrid composites, such as those that combine plastics with carbon fibers or metal materials, offer higher levels of durability and strength. However, these materials are often more expensive and complex to process, making them difficult to use in affordable prosthetics [14].
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 2 Methodology  
The multi-source information fusion diagnosis method proposed in this paper is shown in Figure 1. The sensor data are divided into gas path parameters and vibration parameters according to the information sources. At present, the information fusion methods are divided into data layer, feature layer, and decision layer. In terms of data and features, it is difficult to directly integrate the information types and qualities of different aero engine subsystems. For instance, the gas path parameters and vibration signals differ considerably in data density and format. Thus, this paper adopts decision-level fusion. The frame of proposed information fusion method is shown as Figure 1. The fault features of each subsystem are obtained by the response fault feature extraction method based on the original data. According to the fault criteria of various systems, corresponding fault decisions are obtained. Finally, the decision fusion algorithm is used to fuse the decision level and output the diagnosis result after fusion.
# Nonlinear Dynamics of an Internally Resonant Base-Isolated Beam under Turbulent Wind Flow
## 2 Model
### Modal Proprieties  
First, free dynamics of the undamped and unloaded beam, constrained at ground by the elastic spring \(\kappa\), are addressed, to evaluate the natural frequencies and modes. To this aim, by letting \(v(s,t)=\varphi(s)e^{i\omega t}\) and substituting it in Equations (3) and (4), the following boundary value problem is derived, when retaining linear terms and neglecting structural and aerodynamic damping:  
\[\begin{split}&\varphi^{\prime\prime\prime\prime}-\omega^{2} \varphi=0\\ &\varphi^{\prime}_{A}=0\\ &\varphi^{\prime\prime\prime}_{A}+\kappa\varphi_{A}=0\\ &-\varphi^{\prime\prime\prime}_{B}=0\\ &\varphi^{\prime\prime}_{B}=0\end{split} \tag{5}\]  
with \((\omega,\varphi(s))\) a real eigenpair. The solution reads:  
\[\varphi(s)=c_{1}\sin(\alpha s)+c_{2}\cos(\alpha s)+c_{3}\sinh(\alpha s)+c_{4} \cosh(\alpha s) \tag{6}\]  
with \(\alpha:=\sqrt{\omega}\) a root of the transcendental characteristic equation:  
\[\frac{\alpha^{3}\tan(\alpha)}{\kappa}+\frac{\alpha^{3}\tanh(\alpha)}{\kappa}= \sec(\alpha)\operatorname{sech}(\alpha)+1 \tag{7}\]  
The system natural frequencies, therefore, depend on the stiffness \(\kappa\) of the base elastic spring. Aimed to investigate possible internal resonances, the ratio \(\omega_{2}/\omega_{1}\) between the second and the first root of Equation (7) is evaluated by varying the stiffness \(\kappa\), see Figure 2. It is observed that, for \(\kappa=6.5\), a 1:3 internal resonance occurs.  
The relevant frequencies \(\omega_{1}\) and \(\omega_{2}\) of the two first natural modes, and the coefficients of the associated modal shapes \(\varphi_{1}(s)\) and \(\varphi_{2}(s)\), normalized according to \(\varphi(1)=1\), are reported in Table 1, for both the uncontrolled (\(\kappa\rightarrow\infty\)) and controlled (\(\kappa=6.5\)) systems. The  
Figure 2: The ratio between second and first natural frequency _vs_ the elastic spring stiffness. Red marker denotes the internally resonant case.  
modal shapes are also shown in Figure 3. They show that the chosen spring stiffness allows quite large (but again acceptable) displacements at the base, according which the isolation system is effective (in the linear field). Therefore, this particular internally resonant case deserves to be analyzed.
## 7 Edge Intelligence and Networking for IoV
### Edge Network Intelligence for IoV
#### 7.3.4 Security and Privacy for IIoV  
Security and privacy concerns are still challenging for NDN-based IoV networks [2]. In a non-trustable distributed environment, blockchain technology is being used to establish a creditworthy ecosystem among independent participants. IoV networks can leverage the potential of blockchain technology in IoV environments. Researchers discuss different solutions to utilize blockchain technology. For example, [120] proposes blockchain-based security architecture for NDN-based IoV networks using consensus algorithms and performance evaluations of its effectiveness in key management, cache positioning defense, and access control. In [121], the authors also propose a protocol for secure and privacy-friendly service provisioning at the mobile edge that provides a security service between the consumers and producers based on the NDN paradigm. In [122], the researchers apply communication sequential processes (CSP) modeling and verification tools for NDN-based IoV networks to verify vital properties, including deadlock freedom, data availability, PIT deletion faking, and CS caching pollution. The model fails to ensure the security of data in the presence of an intruder, so the authors utilized blockchain to guarantee the security of NDN-based IoV. Recently, machine-learning-based security and privacy solutions have been introduced for intrusion detection in IoT networks to reduce smart vehicle accidents and to detect malicious attacks in vehicular networks. RNN-based solutions might help to detect malicious attacks in IoV. For example, [123] proposes a hybrid deep learning (DL) model based on LSTM and GRU-based solutions for cyber-attack detection in IoV.