esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Analysis of the Ride Comfort Indices
All the above also comply with the remarks made for Figure 9b. Nevertheless, both methods demonstrate that the carbody rigidization has positive effects upon the ride comfort, at high velocities. Figure 12: Ride comfort indices depending on the vehicle velocity. Figure 13: Ride comfort index \(N_{MVZ}\) depending on the carbody bending frequency: (**a**) in point \(C\); (**b**) in point \(B_{1}\); (**c**) in point \(B_{2}\). Further on, the influence of the secondary suspension damping and of the primary suspension damping upon the ride comfort indices is analyzed. To make the analysis simpler, the damping ratios corresponding to the two levels of suspension that are introduced, as follows:  
\[\zeta_{c}=\frac{4c_{zc}}{2\sqrt{4k_{zc}m_{c}}};\,\zeta_{b}=\frac{4c_{zb}}{2\sqrt {4k_{zb}m_{b}}} \tag{26}\]  
where \(\zeta_{c}\) is the damping ratio of the secondary suspension and \(\zeta_{b}\) is the damping ratio of the primary suspension. In accordance with the reference parameters of the model vehicle (see Table 4), the reference values of the suspension damping ratios are \(\zeta_{c}=0.12\) and \(\zeta_{b}=0.22\). Figure 14: Ride comfort index \(Wz\) depending on the carbody bending frequency: (**a**) in point \(C\); (**b**) in point \(B_{1}\); (**c**) in point \(B_{2}\). The diagrams in Figures 15 and 16 show the variation of the ride comfort indices due to the change of the damping ratio of the secondary suspension. The general trend in the increase of the damping ratio of the secondary suspension is to have the ride comfort indices decrease to a certain value. Once having reached this value, the ride comfort indices start going up again. Consequently, a value of the damping ratio of the secondary suspension leading to the best ride comfort for a certain velocity can be identified. This value differs among the reference points for the same velocity and the ride comfort assessment method. Figure 15: Ride comfort index \(N_{MVZ}\) depending on the damping ratio of secondary suspension: (**a**) in point \(C\); (**b**) in point \(B_{1}\); (**c**) in point \(B_{2}\). As seen in the diagrams in Figures 17 and 18, the ride comfort indices decrease continually along with the increase in the damping ratio of the primary suspension. Figure 16: Ride comfort index \(W_{z}\) depending on the damping ratio of secondary suspension: (**a**) in point \(C\); (**b**) in point \(B_{1}\); (**c**) in point \(B_{2}\). Figure 17: Ride comfort index \(N_{MVZ}\) depending on the damping ratio of primary suspension: (**a**) in point \(C\); (**b**) in point \(B_{1}\); (**c**) in point \(B_{2}\). The diagrams in Figures 15-18 also illustrate that, irrespective of the ride comfort assessment method, the ride comfort indices similarly depend on the velocity in all three reference points. Above the front bogie, the ride comfort indices increase continually, while these indices present a variation in 'N' (increase-decrease-increase) at the carbody center and above the rear bogie.
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 2 Related Work
### Time Series Models  
The ARIMA model, i.e., autoregressive integrated moving average, and ETS, i.e., exponential smoothing, are commonly utilized to forecast the exchange rate. The ARIMA models are generally used in the context of the encompassing autoregressive model and moving average model as special cases. By differencing transformation, if needed, ARIMA can deal with non-stationary data. The other time series ETS models can handle trends and seasonality due to their non-stationary nature.  
Although the time series models cannot be exactly utilized for classification, researchers utilized ARIMA and ETS as baseline models just for demonstration because these models give acceptable point approximations for the forex rate. [PERSON] et al. [18] proposed a time series model that gives very good results.
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 3 Proposed Algorithm  
In this section, a cross-correlation threshold method is employed for the detection of a low-altitude signal source. An improved cross-correlation algorithm is proposed to estimate the frequency and DOA. In addition, the SNR and distance estimation are also discussed.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 3 Serious Gaming Application Developed for LegUp  
In line with the patient-centered design of the robot presented previously, a serious game based on augmented reality has been developed to help enhance their rehabilitation process. By combining two cutting-edge technologies (robot-assisted therapy and augmented reality), the system has the potential to reshape rehabilitation treatment by providing a safe and personalized rehabilitation experience that increases patient engagement.  
Given that LegUp is intended for bedridden patients, the game was designed to provide the patient with the necessary visual stimulus, taking into account psychological factors such as motivation and engagement, sense of control and autonomy, and social interaction and support [29; 30]. To deliver this visual stimulus, digital goniometers measure dynamic joint movement across two planes simultaneously, transmitting these angles to the developed application. In the application, a virtual model performs leg movements based on the received angles, displaying the holograms through a HoloLens 2 device (Figure 5). The entire scene can be also visualized by the therapist through the LegUp user interface, which features real-time streaming from the HoloLens device.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### User Association (UA)
#### 4.2.2 Data Rate
However, as the number of users increases, the interference becomes unacceptable, resulting in data rate degradation.
## 6 Experimental Setup and Results
### Numerical Results
#### 6.2.1 Dqn  
Regarding the DQN, Figure 2 shows performance in three different configurations. The performance figures were the HPA threshold over time, the number of replicas over time, and the training loss over time, respectively. The three different configurations differed by the following parameters:  
* batch_size: Minibatch size for each gradient update.
* train_freq: Number of steps between model updates.
* target_update_interval: Number of steps between target network updates.  
The values used in the three different configurations are reported in Table 2.  
Figure 2: DQN performance in the three different configurations reported in Table 2: CPU threshold in HPA, number of replicas, and training loss.  
The reason why all these configurations were tested was to find a solution that would make the DQN work. Despite this, as can be seen from Figure 2a-c, even when the training phase seemed to stabilize with a decreasing training loss (Figure 2g-i) the algorithm was unable to identify an optimal HPA threshold in all configurations. This implied a continuous oscillation of the number of replicas instantiated to serve a constant load Figure 2d-f). The reduced time interval for DQN Config 3 is to illustrate in detail the behavior of the DQN. As already mentioned above, the DQN is affected by estimates of the gradient of parameters, which can take on excessive values and make it unstable. This behavior, already observed in other contexts, also emerged in our tests relating to the environment considered in this paper. To better highlight the problems of the DQN, in Figure 3 the training loss in the three cases was compared with that of the PPO, which will be better illustrated in the next Section 6.2.2. The marked differences that emerged undoubtedly confirm the inadequacy of DQN:  
In summary, the DQN was considered unsuitable in the analyzed scenario, and the A2C and PPO algorithms will be compared from now on.
# Promising Novel Biomarkers in Cardiovascular Diseases
## 1 Introduction
### GDF-15 (Growth Differentiation Factor 15)  
GDF-15 is a dimeric protein and a member of the transforming growth factor beta (TGF-8) family. It was shown to be a useful biomarker in inflammatory processes, CVD, cancer, and kidney injury [31; 32].  
[PERSON] et al. used mice with myocardial infarction, HF and pressure overload to show that GDF-15 is important for cardiac cell repair and tissue remodelling [33]. Overall increased serum levels of GDF-15 were predictive for CVD and associated with high all-cause mortality [34].  
[PERSON] et al. also investigated the expression of GDF-15 in participants with ACS or stable angina pectoris, comparing it to the serum levels of NT-proBNP, CRP (C-reactive protein) and troponin. The results demonstrated higher GDF-15 concentrations in patients with ACS than subjects suffering from stable angina pectoris. The patient cohort with stable angina pectoris showed increased levels of GDF-15, but also CRP and NT-proBNP.  
To demonstrate the independent diagnostic value of GDF-15, a backward cox regression model was performed and NT-proBNP, troponin, CRP and GDF-15 were all shown to be independently predictive of cardiovascular mortality [35].  
Patients with advanced stages of HF showed higher concentrations of GDF-15 and individuals with New York Heart Association (NYHA) class III-IV had higher serum concentrations of GDF-15 than those with less symptoms [36]. In comparison, patients with asymptomatic stage B (ACCF/AHA class) HF showed higher levels of GDF-15 than those in stage A. In summary, GDF-15 could be a predictive biomarker for patients who do not have any limiting symptoms of HF yet [37].  
Further correlation of GDF-15 with left ventricular ejection fraction was observed in patients with HF with reduced ejection fraction (HFrEF, LVEF < 40%). These individuals demonstrated increased concentrations of GDF-15, compared to those with HFmrEF (heart failure with mid-range reduced ejection fraction LVEF \(\geq\) 40-50%) or HFpEF (heart failure with preserved ejection fraction, LVEF > 50%) [36].  
A main predisposing factor for developing type 2 diabetes and, consequently, CVD, is obesity [38]. [PERSON] et al. found that the GDF-15 serum levels were higher in obese patients. Factors such as age, height, blood pressure and levels of creatinine, triglycerides, glucose, insulin and HbA1c influence GDF-15 concentrations [39]. Furthermore, [PERSON] et al. showed that women with type 2 diabetes and obesity had higher GDF-15 levels compared to those with normal HbA1C and/or BMI within normal range [40].
## 4 Verification and Validation
### Functional Tests  
During functional tests the satellite is checked against its software behavior to see if the satellite is able to perform all programmed tasks successfully. Particularly, functional tests focus on the basic tasks that guarantee the satellite's survival, such as communications, power distribution, and fault management. However, there are many other tasks, including orbit positioning and attitude control, which can hardly be quantified or even tested, because there is no suitable environment to perfectly emulate space conditions on Earth. Ideally, the satellite should have a complete verification of orbit operations. A hardware-in-the-loop simulation platform can emulate both virtual and physical in-orbit conditions, and can expand the currently available set of simulation scenarios.  
Functional tests are performed before and after every major test, in order to check the evolution of the satellite during the test campaign. If an anomaly is found during a functional test, the campaign must be stopped until engineers figure out the cause and fix the problem. Some tests may need to be repeated to check for the full functionality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Uplink** & **Downlink** \\ \hline Frequency [MHz] & 435.8 & 435.8 \\ Wavelength [m] & 0.6884 & 0.6884 \\ Transmission power [W] & 75 & 1 \\ Transmission power [dBW] & 18.75 & 0.00 \\ Transmitter antenna gain [dBi] & 14.00 & 0.00 \\ Effective isotropic radiated power (EIRP) [dBW] & 32.75 & 0.00 \\ Orbit altitude [km] & 350 & 350 \\ Minimum elevation [deg] & 1 & 1 \\ Distance between antennas [km] & 2140 & 2140 \\ Free-space path loss [dB] & \(-\)151.84 & \(-\)151.84 \\ System losses [dB] & \(-\)7.00 & \(-\)7.00 \\ Receiver antenna gain [dBi] & 0.00 & 14.00 \\ Carrier reception power [dBW] & \(-\)126.10 & \(-\)144.80 \\ Noise temperature [K] & 234 & 300 \\ Bandwidth [kHz] & 500 & 500 \\ Carrier-to-noise ratio [dB] & 21.83 & 2.00 \\ Data rate [pps] & 115,200 & 115,200 \\ Signal-to-noise ratio per bit (Eb/N0) [dB] & 28.21 & 8.38 \\ Required Eb/N0 [dB] & 7.80 & 7.80 \\ Link budget margin [dB] & 20.41 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SE01’s link budget.
###### Abstract  
Due to concerns regarding fossil greenhouse gas emissions, biogenic material such as forest residues is viewed nowadays as a valuable source of carbon atoms to produce syngas that can be used to synthesise biofuels such as methanol. A great challenge in using gasified biomass for methanol production is the large excess of carbon in the syngas, as compared to the H\({}_{2}\) content. The water-gas shift (WGS) reaction is often used to add H\({}_{2}\) and balance the syngas. CO\({}_{2}\) is also produced by this reaction. Some of the CO\({}_{2}\) has to be removed from the gaseous mixture, thus decreasing the process carbon yield and maintaining CO\({}_{2}\) emissions. The WGS reaction also decreases the overall process heat output. This paper demonstrates the usefulness of using an extra source of renewable H\({}_{2}\) from steam electrolysis instead of relying on the WGS reaction, for a much higher performance of syngas production from gasification of wood in a simple system with a fixed-bed gasifier. A commercial process simulation software is employed to predict that this approach will be more efficient (overall energy efficiency of about 67%) and productive (carbon conversion yield of about 75%) than relying on the WGS reaction. The outlook for this process that includes the use of the solid oxide electrolyser technology appears to be very promising because the electrolyser has the dual function of providing all of the supplemental H\({}_{2}\) required for syngas balancing and all the O\({}_{2}\) required for the production of a suitable hot raw syngas. This process is conducive to biomethanol production in dispersed, small plants using local biomass for end-users from the same geographical area, thus contributing to regional sustainability.  
biomass gasification; methanol production; solid oxide electrolysis; regional sustainability; electrification; carbon yield +
Footnote †: journal: Applied Sci.  
1
# Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
## 5 Experimental Verification
### Case 2: Bearing Inner Race Fault Detection  
In case 2, the REBs with an outer race defect were mounted on the test rig. The measured test data with inner race defect are used to further investigate the superiority of FISLRA in detecting the bearing fault. The measured test data and their envelope spectrum are illustrated in Figure 16. But the amplitude spectrum and the envelope spectrum do not offer any powerful fault information.  
Then, FISLRA is performed on the raw signal to provide basic fault information for bearing condition monitoring and the results are shown in Figure 17. Repetitive transients are successfully extracted, which verify the utility of FISLRA in recovering fault features. Moreover, the envelope analysis is performed for the extracted fault features, and FCF and its multiplier are very distinct in the envelope spectrum, which indicates the inner race of the rolling bearing is faulty. For comparative analysis, the results obtained by TSRM are displayed in Figure 18. Similar to case 1, the results of fault feature extraction obtained by TSRM are unsatisfactory. The above analysis based on experimental data shows that FISLRA is an effective tool for condition monitoring of the bearing. In addition, comparative analysis also proves the superiority of the FISLRA over TSLRM in the extraction of fault features.  
Figure 16: (**a**) Measured test data; (**b**) envelope spectrum.  
Figure 15: Fault features extracted by the FISLRA. (**a**) Amplitude spectrum; (**b**) envelope spectrum.
###### Abstract  
Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation canopy height is essential for advancing topographic and ecological research. The Terrestrial Ecosystem Carbon Inventory Satellite (referred to as TECIS hereafter) offers unprecedented capabilities for the large-scale, high-precision extraction of ground elevation and vegetation canopy height. Using the Northeast China Tiger and Leopard National Park as our study area, we first processed TECIS data to derive topographic and canopy height profiles. Subsequently, the accuracy of TECIS-derived ground and canopy height estimates was validated using onboard light detection and ranging (LiDAR) measurements. Finally, we systematically evaluated the influence of multiple factors on estimation accuracy. Our analysis revealed that TECIS-derived ground and canopy height estimates exhibited mean errors of 0.7 m and \(-\)0.35 m, respectively, with corresponding root mean square error (RMSE) values of 3.83 m and 2.70 m. Furthermore, slope gradient, vegetation coverage, and forest composition emerged as the dominant factors influencing canopy height estimation accuracy. These findings provide a scientific basis for optimizing the screening and application of TECIS data in global forest carbon monitoring.  
2020 IEEE/RSJ International Conference on Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 10083, China; [EMAIL_ADDRESS]
## 6 Application of the Optimization Techniques: Routing Protocols
### Geographical Routing Protocols
#### 6.2.2 GAF (Geographic Adaptive Fidelity)  
This protocol aims at optimizing the performance of wireless sensor networks by identifying equivalent nodes with respect to forwarding packets [19]. Two nodes are considered to be equivalent when they maintain the same set of neighbor nodes and so they can belong to the same communication routes. Source and destination in the application are excluded from this characterization. To identify equivalent nodes, their positions are necessary. Additionally, a virtual grid is constructed. This grid is formed by cells whose size allows to state that all the nodes in one cell can directly communicate with the nodes belonging to adjacent cells and _vice versa_. In this way, the nodes in a cell are equivalent.  
Nodes identify equivalent nodes by the periodic exchange of discovery messages with the nodes in their cells. With the information contained in these messages, the nodes negotiate which one is going to support the communications. The other nodes will stay powered off. With this procedure, the routing fidelity is kept, that is, there is uninterrupted connectivity between communicating nodes. However, the elected node periodically rotates for fair energy consumption. To do so, the nodes wake up periodically.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
The transmitter interfaces with the FHSS modulator for input and communicates over the wireless channel to the receiver. 5. Receiver: On the receiving end, the receiver captures the signal from the wireless channel. It must be synchronized with the transmitter to correctly follow the frequency hopping sequence. The receiver demodulates the signal using the same frequency sequence \(\{f_{o}\}\), extracting the transmitted data. The receiver interfaces with the synchronization mechanism to maintain alignment with the transmitter's frequency hopping pattern. 6. Synchronization Mechanism: The synchronization mechanism is critical for maintaining alignment between the transmitter and receiver. It ensures that both ends use the same frequency \(f_{o}\) at any given time \(t\). Synchronization information, such as initial timing parameters and any required updates, is communicated through a secure channel or embedded within the communication protocol. The synchronization mechanism interfaces with both the frequency sequence generator (to obtain or verify the frequency sequence) and the receiver (to adjust timing and sequence alignment). 7. Secure Key Distribution: The secure key distribution component facilitates the exchange of keys or synchronization information necessary for secure communication. It interfaces with the synchronization mechanism to provide the required cryptographic keys or parameters. Secure channels, possibly using public-key cryptography, are employed to prevent interception or tampering during key exchange. The data flow begins with the RPU generating true random numbers, which are converted into a frequency sequence by the frequency sequence generator. This sequence is fed into the FHSS modulator, along with the data to be transmitted. The FHSS modulator outputs a frequency-hopped, modulated signal to the transmitter. The transmitter broadcasts the signal over the wireless channel to the receiver. The receiver, synchronized via the synchronization mechanism, captures and demodulates the signal using the same frequency sequence to retrieve the data. Throughout this process, the synchronization mechanism and secure key distribution ensure that both ends maintain alignment and that the communication remains secure. Implementing dynamic control architectures and robust communication protocols is essential for swarm unmanned aerial vehicles to function effectively [29]. Let \(S(t)\) represent the transmitted signal. In a traditional FHSS system, the carrier frequency at time \(t\) is determined by a sequence \(\{f_{o}\}\), where \(o=1,2,\ldots,N\) and \(N\) is the total number of hops in the sequence. The frequency-hopped signal can be expressed as:  
\[S(t)=\sum_{o=1}^{N}A_{o}\cos(2\pi f_{o}t+\phi_{o})\cdot\mathbf{1}_{[t,t_{o+1})} (t)\]  
Here, \(A_{o}\) represents the amplitude, \(\phi_{o}\) the phase, and \(\mathbf{1}_{[t,t_{o},t_{o+1})}(t)\) is an indicator function defining the time interval for each hop.