esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sensing Performance Tests and Discussions
### Refractive Index Response  
We examined the RI response using the experimental setup as described in [35]. The polarized light was coupled into the hybrid grating via the polarizer and the polarization controller, and the transmission signal was monitored and recorded by the OSA. A series of RI matching liquids (from Cargille Labs) ranging from 1.300-1.400 with an interval of 0.01 (with a maximum error of 0.0002 standardized at 589.3 nm and 25 \({}^{\circ}\)C) were used to test the device. The measured spectra of the hybrid grating for various RIs are depicted in Figure 4a. A significant red-shift in the cladding mode resonance and an increase in the coupling intensity was observed, especially in the shorter wavelength region. That is because the surrounding medium's RI modulates the effective index and coupling coefficient of the cladding mode.  
Figure 4b plots the resonant wavelengths versus the RI value. The resonant wavelength demonstrated an exponential red-shift trend with increasing RI in the range of 1.300-1.400. According to the exponential fitting result \(\lambda_{\text{EX-TFG}}=1.01\exp(n_{\text{RI}}/0.078)+1561.34\) (nm), the calculated RI sensitivity was up to \(\sim\)1224 nm/RIU (RIU stands for \"refractive index unit\") near the RI of 1.38. The comparison  
Figure 4: (a) Spectral evolution with the refractive index (RI) increasing; (b) RI responses of Bragg resonance and cladding mode resonances of the hybrid grating.  
Figure 3: (a) Spectral evolution with the temperature; (b) temperature responses of Bragg resonance and cladding mode resonance of the hybrid grating.  
results of sensitivity between different fiber grating-based RI sensors are shown in Table 1. The sensitivity of the proposed sensor was much higher than that of most grating-based refracometers. For instance, the sensitivity of an LPG with a small period was around 312.5 nm/RIU [8] and can be enhanced to 1005 nm/RIU by inscribing the LPG in a hollow eccentric fiber [9]. The TFBGs with a small tilted angle in different fibers had the sensitivity of 500-574.2 nm/RIU by tracking the cut-off wavelength [13, 14, 16, 39]. The sensitivity of SPR-assisted TFBGs with noble metals or nanowires coating reached to 510.5-651 nm/RIU by monitoring the SPR resonance wavelength. The TFBG with high RI film (ZnO) had a sensitivity of 210 nm/RIU at the RI of 1.39. The TFGs with large tilted angle showed a sensitivity of \(\sim\)340 nm/RIU by tracking the resonance wavelength of the cladding mode. For the proposed sensor, the use of an RDF for the Ex-TFG enhanced the penetration of the evanescent wave of the cladding modes into the external medium and increased the RI dependence of waveguide dispersion, which is critical in determining the sensitivity [32, 40, 41]. Meanwhile, as the RI changed, the Bragg wavelength of FBG remained unchanged, which provides the feasibility for simultaneous measurement of temperature and RI.
# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
## 4 Experiments
### Fault Prediction  
After training the satellite rotational mechanism temperature health model using motor current and temperature data from 22 November to 28 November, we used normal telemetry data from the rotational mechanism spanning 72 h, from December 1 to 3 December, as input for the model. The model's output data represent the ideal data under normal operation of the rotational mechanism. Further, using the threshold generation formula described in this paper, we established the upper and lower bounds for the data thresholds as a reference. This study conducted experiments using fault data over an 8 h time span to predict rotational mechanism faults. Figure 11 illustrates the fault detection process determined by the proposed model. The data shown in the figure start at 12:53 on 3 December, with each time step corresponding to 16 s, matching the sampling frequency of our signals. Anomaly detection on subsequent data begins from the 750 th time step, which corresponds to 16:13 on 3 December, using the threshold generation method and predictive data approach we proposed. The pink region in the figure represents the normal data range after the 750 th time step, while the red curve indicates abnormal temperature data exceeding the threshold. As time progresses, the rotational mechanism temperature data gradually exceeds the threshold range, signaling an impending fault.  
Further data collection on the satellite's rotation mechanism confirmed that the turntable indeed experienced a temperature anomaly, consistent with our experimental predictions. Overall, the experimental results demonstrate that the temperature of the satellite's rotation mechanism is not only affected by factors such as orbital position, satellite attitude, and solar irradiation but that it also has a strong correlation with the motor current of the rotation mechanism. The results also show that the proposed method of predicting turntable temperature anomalies using motor current data is highly reliable, providing an effective approach for fault prediction in satellite rotation mechanisms like the turntable.  
Figure 10: Comparison of predicted data and true data.
# Modeling and Resolving Uncertainty in DIKWP Model
## 4 Problem Processing
### Crossing-Space Processing of DIKWP
#### 4.4.3 Feedback from Semantic Space to Concept Space and Cognitive Space  
* **Definition:** Feedback from the external world to semantic expression is transmitted through the Semantic Space, thereby influencing Concept Space and Cognitive Space, forming a closed-loop process of cognitive updating and learning. \[T_{SemA\to ConC}:SemA\to ConC\] (33) \[T_{SemA\to ConN}:SemA\to ConN\] (34) Equations (33) and (34), respectively, represent the feedback process from semantic expression to concept updating and cognitive updating, achieving dynamic adjustments and learning of internal understanding and concepts in response to external feedback.
* **Application:** For instance, when a financial company faces penalties, it generates new semantic content and expressions regarding the regulatory boundaries of the legislation. The penalties prompt the company to develop new conceptual attributes regarding the legislation and to perform corresponding operations on its previously vague Concept Space. As the Concept Space changes, the mapping function \(T_{ConC\to ConN}\) from Concept Space to Cognitive Space varies accordingly, resulting in new cognition that is reflected in concrete actions. This refers to the process of handling the \"4-N\" problems under the acceptance of external feedback and Purpose-Driven circumstances, as outlined in the definition: this process constitutes a closed-loop cognitive updating and learning process.  
In summary, we integrate the mapping and feedback processes to form a dynamic and interactive DIKWP model framework, where Cognitive Space, Concept Space, and Semantic Space interact and influence each other, presenting the complete process from subjective understanding to objective semantic processing.
## 4 Different Manners of Production of Green Hydrogen and Derivatives
### Process of Production of Green Ammonia  
The Magallanes region, in southern Chile, has become a strategic pole for the production of green ammonia (NH\({}_{3}\)), an essential compound for fertilizers and clean fuels, manufactured without carbon emissions. The process integrates renewable energy and green hydrogen into several key stages [36; 37; 38; 39; 40; 41] (Figure 7):  
* Wind energy generation: Magallanes has one of the most powerful wind resources in the world, with constant and high speed winds. The Great wind farms will generate 100% renewable electricity to feed the process.  
Figure 6: Schematic process of GH2 production considered for the Magallanes region.  
* Green hydrogen production (H\({}_{2}\)): Wind energy drives electrolyzing water molecules (H\({}_{2}\)O) into hydrogen (H\({}_{2}\)) and oxygen (O\({}_{2}\)) by electrolysis. Water can come from the public network, wells, or desalination plants, ensuring a sustainable supply.
* Nitrogen (N\({}_{2}\)) extraction: The nitrogen needed to synthesize ammonia is extracted from the air using a process called cryogenic separation or adsorption (PSA), which filters and purifies atmospheric N\({}_{2}\) (air contains 78% nitrogen).
* Green ammonia synthesis (modified Haber-Bosch): Green hydrogen and nitrogen are combined in a synthesis reactor under high pressures and temperatures (using iron catalysts), following the traditional Haber-Bosch process, but with two key differences: (i) The energy comes from renewable sources (wind power), and (ii) No CO\({}_{2}\) is emitted, unlike conventional ammonia (which uses natural gas).
* Storage and export: Liquid ammonia (NH\({}_{3}\)) is stored in cryogenic tanks and transported by specialized vessels to international markets. Its application includes (i) green agricultural fertilizers (reducing the carbon footprint of the sector); (ii) decarbonized marine fuel (alternative to diesel in ships); and (iii) using it as an energy vector to transport green hydrogen (NH\({}_{3}\) is easier to handle than pure H\({}_{2}\)).
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Modes Control
#### 3.2.5 Bidirectional Flow Charging  
Bidirectional power flow is supported by cutting-edge fast charging infrastructure, allowing energy to be transmitted to and from the EV's battery. By employing this capability, EVs receive grid-based charging and discharge energy back into the grid or supply power to other consuming devices [130]. Vehicle-to-grid applications, where EVs can help to stabilise the grid and offer grid services, may be supported via bidirectional charging [131]. Different charging modes are implemented in sophisticated DC fast charging infrastructures, giving EV owners and grid operators flexibility and optimisation options based on battery state, grid limitations, and user choices; it also enables efficient charging [92]. EVs may be charged quickly while preserving battery health, grid stability, and economical power usage by utilising the correct charging mode. Furthermore, intelligent charging algorithms and communication protocols that enable real-time monitoring, control, and coordination of the charging process are frequently used in conjunction with charging modes [132]. These characteristics facilitate grid integration, optimise power distribution, and guarantee secure and dependable charging operations. Implementing charging modes within advanced DC fast charging infrastructure enhances the user experience for electric vehicle owners during charging sessions. Additionally, it promotes optimal energy utilization and contributes to the broader adoption of EVs as a sustainable transportation option [133].
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
###### Abstract  
Most networks strive to provide good security and an acceptable level of performance. Quality of service (QoS) plays an important role in the performance of a network. Mobile ad hoc networks (MANETs) are a decentralized and self-configuring type of wireless network. MANETs are generally challenging and the provision of security and QoS becomes a huge challenge. Many researchers in literature have proposed parallel mechanisms that investigate either security or QoS. This paper presents a security framework that is QoS-aware in MANETs using a network protocol called optimized link state routing protocol (OLSR). Security and QoS targets may not necessarily be similar but this framework seeks to bridge the gap for the provision of an optimal functioning MANET. The framework is evaluated for throughput, jitter, and delay against a sinkhole attack presented in the network. The contributions of this paper are (a) implementation of a sinkhole attack using OLSR, (b) the design and implementation of a lightweight-intrusion detection system using OLSR, and (c) a framework that removes fake routes and bandwidth optimization. The simulation results revealed that the QoS-aware framework increased the performance of the network by more than 70% efficiency in terms of network throughput. Delay and jitter levels were reduced by close to 85% as compared to when the network was under attack.  
Keywords:routing protocols; MANETs; sinkhole attack; QoS in MANET +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON] Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks. _Tidecom_ **2022**, 3, 407-432. [[https://doi.org/10.3300/telecom](https://doi.org/10.3300/telecom)]([https://doi.org/10.3300/telecom](https://doi.org/10.3300/telecom))
## 4 Discussion  
It is necessary to introduce some additional points to mention about this research method and some points to be supplemented in the future. First, WindSim, a commercial CFD software, was used in consideration of the fact that there was land on the upwind side and that there were more than 100 wind turbines and the turbines were closely spaced. Additionally, WRF, which has excellent predictability reflecting meteorological factors and is evaluated as the most commonly used numerical weather prediction model in the world, was judged to be suitable for this study. Second, most of the actual temperature data that can determine thermal atmospheric stability among the two-year offshore LiDAR observation data were missing. Fortunately, observation data for one month was secured, so it could be supplemented and verified with ERA5 reanalysis data, but actual observation data could further improve reliability. Third, along with the Richardson number, the Monin-Obukhov length, which is known as a representative method for determining thermal atmospheric stability, could not be calculated. It is a method to determine whether the turbulent momentum is due to the shear effect or the buoyancy effect through the mechanical variable of friction velocity, the natural variable of buoyancy, and the advection of heat flux. Since there was no flux sensor, the Monin-Obukhov length could not be calculated. There is a method of using reanalysis data, such as ERA5 or MERRA2, but it was not used because verification with actual data was not possible. Fourth, since it is an offshore wind farm, thermal stability is judged to have a strong correlation with sea surface temperature, as well as air temperature. In the future, by securing sea surface temperature data, it is intended to analyze the changes in REWS and HHWS according to the difference in sea surface temperature compared to the 20-30 average years.
## 3 Empirical Results
### Exploratory Data Analysis
#### 3.1.1 VEN--Real-Time Analysis  
Figure 5 shows a periodogram, which identifies dominant cycles in the time series, for \(t=60\), that is a measure every hour the period is equal to 15, which means that the frequency is \(1/15\), implying that it takes 15 min periods to come up with a cycle.  
Figures 6 and 7, respectively show the multiple histograms and scatter diagrams for independent variables. Figure 8 shows a matrix of scatter diagrams showing the relationship between GHI and each independent variable, all the variables except for AirTC_Avg appear to be not linearly related to GHI.
## 3 Results  
### Simulation Verification with ADAMS and MATLAB/Simulink  
To verify the effectiveness of the enhanced CPG algorithm combined with the biological modeling mechanism in unstructured complex terrains, a simulation model was built using the multi-body dynamics co-simulation platform of ADAMS 2020 and MATLAB/Simulink R2024b. Communication between ADAMS_sub and MATLAB/Simulink is achieved through a module, allowing Simulink output signals to be transmitted to regulate robot motion. The simulation control flow is shown in Figure 8 (where \"LF\" stands for the robot's left front leg, \"LB\" for the left back leg, \"RF\" for the right front leg, \"RB\" for the right back leg, and \"cm\" for the robot's center of mass). The robot's CPG control network model is illustrated in Figure 9. Decentralized CPG units generate rhythmic signals through internal oscillation mechanisms, which are then coupled via a weighted matrix to coordinate multi-joint movements. After processing by integration and adjustment links, eight-channel control signals are output to drive the hip, knee, and lateral swing joints. Adaptive gaits are thereby realized through self-organized neural dynamics and environmental feedback modulation.
## 3 Results
### Parameters of Kinematics and Kinetics in the NC and LC  
Tables 4 and 5 show the difference between the peak angles and angular displacements of the two groups. At peak joint angles, the LC group showed greater dorsiflexion during the TS than the NC group (i.e., pAA-2, \(p\)-value = 0.041). Similarly, the LC group exhibited more ankle joint flexion during the TS (i.e., AAD-1, \(p\)-value = 0.025). There was no statistical significance found in the other parameters. Tables 6 and 7 show the mean joint moment and joint power. The mean knee positive power of the LC group was lower than that of the NC group during the LR (i.e., mKPP-0, \(p\)-value 0.014), and the mean ankle extensor moment (i.e., mAEM, \(p\)-value = 0.014) after the LR was lower than that of the NC group. In addition, the mean hip positive power (i.e., mHPP-2, \(p\)-value = 0.035) during the PSw was lower than that of the NC group. There were differences between the two groups in other parameters except for mKPP-0, mAEM, and mHPP-2 as well, but there was no statistical significance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Group** & **mHEM** & **mHFM** & **mKFM** & **mKEM** & **mAFM** & **mAEM** & **mSM** \\ \hline NC & \(-\)0.48 \(\pm\) 0.10 & 0.22 \(\pm\) 0.05 & 0.19 \(\pm\) 0.06 & \(-\)0.32 \(\pm\) 0.11 & 0.10 \(\pm\) 0.03 & \(-\)0.70 \(\pm\) 0.07 & 0.94 \(\pm\) 0.18 \\ LC & \(-\)0.43 \(\pm\) 0.10 & 0.20 \(\pm\) 0.05 & 0.17 \(\pm\) 0.07 & \(-\)0.29 \(\pm\) 0.11 & 0.09 \(\pm\) 0.03 & \(-\)0.64 \(\pm\) 0.09 \(\dagger\) & 0.88 \(\pm\) 0.18 \\ \hline \hline \end{tabular} Mean \(\pm\) standard deviation; NC: normal cognitive function group; LC: lower cognitive function group; \(\dagger\) indicates statistical significance via the Mann–Whitney-U test, \(p\)-value \(<\) 0.05; mHEM: mean hip extensor moment; mAFM: mean hip relaxor moment; mAFM: mean knee extensor moment; mAFM: mean ankle flexor moment; mASM: mean support moment.  
\end{table} 
Table 6: Mean joint moments in the NC and the LC. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Group** & **pHA-1** & **pHA-2** & **pHA-3** & **pKA-1** & **pKA-2** & **pKA-3** & **pAA-1** & **pAA-2** & **pAA-3** & **pAA-4** \\ \hline NC & \(35.8\pm 5.3\) & \(\begin{array}{c}-9.4\pm\\ 4.8\end{array}\) & \(38.2\pm 5.5\) & \(28.5\pm 6.3\) & \(11.0\pm 6.8\) & \(76.3\pm 3.6\) & \(3.1\pm 4.0\) & \(\begin{array}{c}26.7\pm\\ 2.9\end{array}\) & \(-1.0\pm 6.0\) & \(12.3\pm 3.3\) \\ LC & \(35.6\pm 4.5\) & \(\begin{array}{c}-9.4\pm\\ 4.9\end{array}\) & \(38.6\pm 5.3\) & \(27.6\pm 6.0\) & \(10.0\pm 7.5\) & \(76.5\pm 4.3\) & \(2.3\pm 2.5\) & \(\begin{array}{c}27.7\pm\\ 3.1\end{array}\) & \(-0.1\pm 4.9\) & \(12.1\pm 3.3\) \\ \hline \hline \end{tabular} Mean \(\pm\) standard deviation. NC: normal cognitive function group; LC: lower cognitive function group; \(\dagger\) indicates statistical significance via Mann–Whitney-U test, \(p\)-value \(<\) 0.05; pHA: peak hip joint angle; pKA: peak knee joint angle; pAA: peak ankle angle.  
\end{table} 
Table 4: Peak joint angle parameters in both groups.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 4 Experiment and Data Analysis
### Data Analysis  
The experimental results, as shown in Figures 6 and 7, exhibit a significant contrast in outcomes. When the TSN functionality of the interval layer device remains inactivated, the departure time of time-sensitive packets from the network card interface becomes uncertain. These packets may enter the TSN switch within time slot T0, T1, or T2. When time-sensitive packets fall within time slot T1, the time-sensitive flow encounters a negligible queuing delay in the output queue Q5 of the switch. It is instantaneously dispatched, resulting in a 50 us delay for time-sensitive flow. Correspondingly, when time-sensitive packets fall within time slot T0, the time-sensitive flow is buffered in the output queue Q5 for an additional period. It waits for the arrival of time slot T1, at which point the corresponding door for queue Q5 opens, allowing the packet to be transmitted. In this scenario, the maximum delay for the packet is 250 us, which is 200 us longer than the delay for packets falling within time slot T1. When a time-sensitive packet falls within time slot T2, it is buffered in queue Q5 for a more extended period. It is only forwarded when time slot T1 of the next cyclic period arrives. The maximum delay for such packets is approximately 1100 us. Consequently, when the interval layer device lacks TSN capabilities, both the delay and jitter of time-sensitive flows are relatively high.  
As illustrated in Figure 7, upon activating the TSN functionality in the interval layer device, the set time-sensitive message is sent just in time slot T1 of the GCL table of the TSN switch. Consequently, its delay is significantly reduced to approximately 60 us and its stable delay jitter is around 140 us when configured with Qbv scheduling of TSN switches. In addition, when TSN is enabled, the packet loss rate can be reduced from 0.11% to 0%, so network transmission is more reliable. More relevant data can be found in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Status** & **Packet Loss Rate** & **Min** & **Max** & **Jitter** & **Avg** & **Std Dev** \\ \hline TSN OFF & 0.11\% & 30.65 us & 1133.59 us & 1102.94 us & 454.18 us & 342.85 us \\ TSN ON & 0\% & 14.34 us & 158.21 μs & 143.87 μs & 60.25 μs & 29.37 μs \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical properties of TSN on and off.  
Figure 6: Latency and jitter without TSN.  
Figure 7: Latency and jitter with TSN.
## 3 Results and Discussion
### Results of the Analysis of the Window Size with GRU-Based Training  
The experiment conducted on 20 ms, 25 ms, 30 ms, and 35 ms of speech segments gave the maximum performance accuracies of 97.60%, 96.38%, 96.60%, and 97.12%, respectively, for male speakers, and the maximum performance accuracies of 95.30%, 95.09%, 95.03%, and 95.63%, respectively, for female speakers. According to the results, the effect of window size on the accuracy was insignificant. However, a window size of 20 ms performed slightly better than the others. Actually, performance of the window size depended on the nature of the speech signal.