esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 2 Ad Hoc Separation Conops
### Ad Hoc Separation Minima Determination  
To determine the separation minima to be applied in an airspace, it is necessary to ensure that the air traffic operation is developed while maintaining safety levels in that scenario. According to the ICAO [32], there are two methods for evaluating the safety performance of a system: the reference and the absolute methods.  
Figure 1: ASMT architecture and relation to the working position of the ATCO.  
The reference method is a relative method in which the estimated performance of the proposed system is compared against the performance of a system which has already been judged to be safe [32]. If the performance of the proposed system is equal to or better than that of the reference system in all safety-related aspects, then the proposed system can also be assumed to be safe. The absolute method is based on estimating the collision risk of the system and then comparing it to a predetermined maximum tolerable collision risk value (denoted as the Target Level of Safety (TLS)).  
Generally, the absolute method has been used by the ICAO to determine whether a system is safe for specific separation minima. This is the one used for computing ADSM. Thus, the ADSM values were strategically determined based on a Collision Risk Model (CRM). This CRM, as well as the TLS, were expressly developed and estimated throughout the research on the Ad Hoc separation concept.  
Risk values were determined through Monte Carlo simulations, considering navigation and speed uncertainties. Different encounter situations between aircraft pairs were simulated, resulting from the combination of various factors influencing ADSM (aircraft models, aircraft masses, FL, AM, wind conditions, etc.), and the ADSM were obtained (an example of ADSM in NM is shown in Figure 2). The influence of these parameters on ADSM is shown in [29].
# Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images
## 8 Results And Discussions
### Stratified K-Fold
#### 8.2.2 Scenario-2  
In Scenario-2, CASME II was applied for training and testing. Again, Model-A performs best with a margin of 8.4% to the second best Model-C. An accuracy of 56.5% was obtained for Model-A, which is 12.8% higher than for the SMIC dataset in Scenario-I. Model-B and Model-C were also able to increase by 11.9% and 10.8%, respectively. This can be mainly attributed to the higher frame rate in the video recordings, which is with 200 FPS twice as high as with SMIC. Thus, the networks are capable of detecting even more minor motion information. As mentioned above, a higher frame rate can help produce higher accuracies in micro-expression recognition. Another advantage of the CASME II dataset is that the on-set frame, Apex frame, and off-set frame information is provided by the authors. As the Apex frame was known in the entire video, the pre-processing was executed easily in a correct manner.  
Even though the accuracy achieved with CASME II is higher than with SMIC, it is still not very high for micro-expression recognition. There are some reasons for this: The major problem in CASME II was the data imbalance. So, for the experiments, only three classes are appropriate: namely, happiness, disgust, and surprise. These emotions were mainly considered in order to be consistent with SMIC and SAMM. Even though the dataset has 255 samples, only 123 samples were utilized in the experiments. Thus, the low overall performance in CASME II can be attributed to insufficient and imbalanced samples.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 3 Control Strategy of the Converter
### The Effect of LC Filter  
The LC filter is shown in Figure 7a. In the grid-connected system, the transfer function from \(i_{1}(s)\) to \(i_{2}(s)\) for the LC filter is:  
\[\frac{i_{2}(s)}{i_{1}(s)}=\frac{1}{LC}\frac{1}{s^{2}+\frac{1}{LC}}=\frac{1}{LC} \frac{1}{s^{2}+\omega_{r}{}^{2}} \tag{25}\]  
\[\omega_{r}=\sqrt{\frac{1}{LC}} \tag{26}\]  
where \(\omega_{r}\) is the resonance angular frequency. The LC filter has a resonant spike at the resonant frequency, and a phase \(-180^{\circ}\) jump occurs at the same time, which causes the system to be unstable, as shown in Figure 8.  
In order to solve the problem, damping should be introduced to eliminate the pole. Adding a series resistor R to the inductor L is shown in Figure 7b. The transfer function from \(i_{1}(s)\) to \(i_{2}(s)\) for the LCR filter is:  
\[\frac{i_{2}(s)}{i_{1}(s)}=\frac{1}{LC}\frac{1}{s^{2}+2\xi\omega_{r}s+\omega_{r }{}^{2}} \tag{27}\]  
\[\xi=\frac{R}{2}\sqrt{\frac{C}{L}} \tag{28}\]  
\[\omega_{r}=\sqrt{\frac{1}{LC}} \tag{29}\]  
\(\xi\) is the damping coefficient. Compared with the transfer function of the LC filter, a damping term is introduced in the transfer function of the LCR filter. The pole is eliminated  
Figure 8: The bode waveform of LC filter and LCR filter.  
Figure 7: Circuit of LC and LCR filters.  
by a proper \(\xi\). The low-frequency and high-frequency amplitude-frequency characteristics of the transfer function have not been changed, as shown in Figure 8.  
\(\xi\) can be increased by increase \(R\) or \(C\), or decrease \(L\) in (28). In the converter, \(C_{o}\) and \(L_{o}\) have been determined and \(L_{o}\) is much larger than \(C_{o}\). That will decrease the value of \(\xi\).  
By increasing the resistance, the damping coefficient can be effectively improved, which will increase the converter loss in practice. At the same time, adding a damping resistor on the high-voltage side will also increase the cost of the converter.
## 5 Discussion  
### Answers to Research Questions  
RQ1: Hybrid architecture superiority  
The CV-CNN-TCN-DCC model achieves 55.16% training accuracy, outperforming standalone CNN (54.82%) and ResNet (51.72%). At \(-10\) dB SNR, the hybrid shows a 44% accuracy boost over CNN (37% vs. 25%), proving that TCN integration enhances modulation classification.  
RQ2: Component contributions  
CNNs excel at local feature extraction (93.7% PAM4 accuracy at 0 dB), while TCNs capture long-range dependencies (WBFM accuracy jumps to 76.52% vs. CNN's 39.77%). Dilated convolutions in DCC further improve noise resilience (16-QAM: 49.35% vs. CLDNN's 10.6% at \(-4\) dB).  
RQ3: SNR robustness  
The hybrid dominates low-SNR conditions (\(-10\) dB: 40% vs. ResNet's 33%) while maintaining high-SNR advantages (10 dB: 75% vs. 73%). SELU activation and 256 batch size optimize performance across SNR ranges.  
RQ4: Computational efficiency  
With 484,842 parameters and 187 s/epoch training time, the model balances complexity and speed. Inference scales linearly (6.15 ms for 128 samples), making it practical for real-time deployment despite higher costs than ResNet.  
RQ5: SOTA comparison  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **CLDNN** & **SCRNN** & **ResNet** & **CNN** & **CV-CNN-TCN** & **CV-CNN-TCN-DCC** \\ \hline
**CLDNN** & 1.000 & 1.000 & 0.553 & 1.000 & 0.0000 & 0.00020 \\
**SCRNN** & 1.000 & 1.000 & 0.010 & 0.043 & \(6.77\times 10^{-8}\) & \(2.28\times 10^{-7}\) \\
**ResNet** & 0.553 & 0.010 & 1.000 & 1.000 & 0.202 & 0.352 \\
**CNN** & 1.000 & 0.043 & 1.000 & 1.000 & 0.059 & 0.112 \\
**CV-CNN-TCN** & 0.00008 & \(6.77\times 10^{-8}\) & 0.202 & 0.059 & 1.000 & 1.000 \\
**CV-CNN-TCN-DCC** & 0.00020 & \(2.28\times 10^{-7}\) & 0.352 & 0.112 & 1.000 & 1.000 \\ \hline \end{tabular}
\end{table}
Table 7: [PERSON]’s post hoc test (\(p\)-values) for model pairs. Significant values (\(p<0.05\)) are highlighted in bold.  
The model sets new benchmarks, reducing QPSK misclassifications (74.58% vs. CLDNN's 63.58% at 4 dB) and leading all metrics (F1: 0.38, MCC: 0.67 at \(-\)10 dB). Its consistent superiority across modulations and SNRs makes it a new reference for AMC.
## 2 Materials and Methods
### Synergistic Optimization of Multi-USV Encounters
#### 2.4.2 Encounter Coordination Strategy for Multiple USVs  
To solve the encounter problem in multi-USV navigation, an encounter coordination strategy is proposed. First, an encounter judgment rule is set such that when the distance between the local guidance points of each USV _Dist_ is less than or equal to a specified value, the USVs are judged to be in an encounter situation. Second, the priority of USV maneuvering is set to avoid a collision. The USVs considered to be in a mutual encounter situation are grouped, and the outboard angle value \(\varphi\) of the current USV relative to another USV is calculated, whereby the USV with the larger outboard angle value is given higher priority to avoid a collision. Finally, according to the priority level, the local guidance point of each USV is adjusted, only keeping the local guidance point position of the low-priority USV unchanged, and the current position of the other USV is incorporated into the global environment of the high-priority USV as the midpoint of the obstacle. At this time, the low-priority USV gradually decelerates to the local guidance point, and the high-priority USV maneuvers rapidly toward the global target point to ensure navigation safety. The process continues until the distance _Dist_ between the two local guidance points is greater than the critical value, at which point the encounter situation is circumvented, and the low-priority USV resumes movement from the local target point, continuing to guide the USV while gradually accelerating.  
As shown in Figure 6, when the distance _Dist_ between USV1 and USV2\({}^{\prime}\)s local guidance point reaches the critical value, the outboard angle values are compared, and if \(\varphi_{12}<\varphi_{21}\), USV2\({}^{\prime}\)s collision avoidance priority is higher than that of USV1, whereby USV1 will slow down to the current local guidance point to avoid USV2, and USV2 will sail normally.  
Figure 6: Encounter coordination strategy for multiple USVs.
# A 28-nm CMOS Low-Power/Low-Voltage 60-GHz LNA for High-Speed Communication
## 2 LNA Design
As a result, the overall current required by the first differential stage (i.e., 5.2 mA) is almost the same as a single-ended amplifier, while guaranteeing superior robustness against common-mode noise/disturbances and ESD protection. The second stage is essential to extend the 3-dB bandwidth (\(BW_{\mathrm{3 dB}}\)) of the amplifier in order to comply with multi Gbit/s applications. The first stage is connected to the second one by means of the integrated interstage transformer, \(T_{\mathrm{INT}}\), which not only provides the interstage matching, but also biases the following stage through its center tap without requiring coupling capacitors. Indeed, series coupling capacitors are not effective due to their excessive losses at mm-wave frequencies. Flat gain and a large \(BW_{\mathrm{3 dB}}\) are achieved by properly tuning the load capacitors, \(C_{\mathrm{L1}}\) and \(C_{\mathrm{L2}}\), which are in parallel with the two integrated transformers, \(T_{\mathrm{INT}}\) and \(T_{\mathrm{OUT}}\), respectively. As the last stage, the buffer also exploits a CS pseudo-differential topology with a transformer load, \(T_{\mathrm{BUF}}\). The transformer has been designed to provide wideband 50-ohm output matching and differential-to-single-ended conversion resonating with capacitor \(C_{\mathrm{L3}}\). Moreover, the buffer provides high input impedance as the second-stage output load (i.e., \(Z_{\mathrm{DEM}}\)) to preserve the voltage gain of the two-stage amplifier, as happens when the LNA is connected to a demodulator. It is worth noting that buffer functionalities cannot be achieved by means of an output passive matching network. By choosing proper resonance frequencies of the three integrated transformers \(T_{\mathrm{INT}}\), \(T_{\mathrm{OUT}}\) and \(T_{\mathrm{BUF}}\), a \(BW_{\mathrm{3 dB}}\) of nearly 7 GHz can be guaranteed.
# Toward More Efficient Large-Scale Green Hydrogen Systems via Waste Heat Recovery and ORC
### Organic Refrigerant Cycle  
Organic refrigerant cycles (ORCs) are thermodynamic processes that use organic working fluids to transfer heat. These cycles are particularly advantageous when dealing with low-to-moderate-temperature heat sources [14]. ORCs operate similarly to the traditional Rankine cycle but uses organic fluids with lower boiling points, such as hydrocarbons or refrigerants, instead of water. ORCs can be used in various applications such as waste heat recovery, geothermal power generation, and solar thermal power.  
Choosing the appropriate working fluid depends on the application, thermodynamic properties, environmental impacts, safety, and economic concerns. Fluids like R123, R245 fa, and R245 ca are commonly selected based on their favorable characteristics.  
[PERSON] and his colleagues have surveyed R245 fa and R134 fa for the heat recovery of PEM units. For both working fluids (R\(-\)134a and R\(-\)245 fa), they noticed that increasing the thermal load decreases the net power output while the efficiency of the hybrid system decreases [15]. [PERSON] and his colleagues have reviewed the recovery of waste heat from PEM and fuel cells and discussed various applications for the system [16]. [PERSON] and his colleagues discussed PEM fuel cell thermal management solutions [17]. [PERSON] and his colleagues looked at PEM system waste heat recovery using an ORC utilizing R123, R245 fa, R134a, water, and ethanol. They found that the maximum efficiency of the recovery system is 4.73% [18].  
The efficiency of a typical with no heat recovery modules, PEM hydrogen production is in the range of 60 to 70% when considering the lower heating value of hydrogen [19]. In this research, a 20 MW plant is modeled to evaluate its efficiency after adopting ORC and waste-to-heat modules.  
As the efficiency of the PEM module has increased, less attention has been focused on the desired heat from the modules. In typical PEM systems, the heat dissipates to the surroundings. In large-scale applications, the amount of wasted heat becomes more viable and cost-effective for recovery and use. In this design, the extra wasted energy is recovered through water flow and then passed through the ORC to enhance the efficiency of the system by generating electricity via a turbine. Moreover, the water returns to PEM units at ambient temperature, so the water for cooling the system is within a closed loop.  
Figure 3 illustrates the organic refrigerant cycle module. The module includes a pump, an evaporator, a turbine, and a condenser. An organic working fluid is heated in a boiler until it is converted to vapor. Then, the vapor enters the turbine, where it is expanded and  
Figure 2: PEM electrolysis process [13].  
produces work. The working fluid from the turbine passes through a pump to be condensed. At this stage, the fluid's pressure rises, making it ready to enter the evaporator again.
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 3 Experimental Test of Output Torque for a PMSM
### Comparison with Simulated Results  
In order to compare with the simulated torque by the FEM, we take the output torque vector at the moment that the torque component on the OX axis reaches the maximum value at the first time as an example. This is the vector corresponding to the output torque at the starting time of the rotor. The torque components on the OX, OY and OZ axis of the coordinate system OXYZ at the moment are represented by \(Torque_{X}\), \(Torque_{Y}\) and \(Torque_{Z}\), respectively. Thus, the output torque vector \(\mathbf{Torque}_{output}\) and its amplitude \(\left|\mathbf{Torque}_{output}\right|\) can be respectively written as:  
\[\mathbf{Torque}_{output}=(Torque_{X},Torque_{Y},Torque_{Z}) \tag{17}\]  
\[\left|\mathbf{Torque}_{output}\right|=\sqrt{Torque_{X}^{2}+Torque_{Y}^{2}+Torque _{Z}^{2}} \tag{18}\]  
Two motion cases are considered in the comparison with simulated results. In the first case, the initial rotor option is stationary, and the total current of coils increased from 1.5 to 5.42 A. In the second case, the initial rotor position rotates around the OZ axis from 0 to 180 degrees and the total current of coils is fixed 4 A. In this case, each coil is energized by unit current. The current directions of No. 2 coil and No. 20 coil are positive as the same as shown in Figure 2, while the current directions of No. 8 coil and No. 20 coil are negative, which are opposite to that in Figure 2. The experimental results of the two cases are shown in Figure 15.  
Figure 14: Components of the output torque vector.  
Figure 13: Components of the angular acceleration vector.  
Taking the simulation torque as a reference, we define the measurement error \(\Delta\) as:  
\[\Delta=\frac{\left\|\mathbf{Torque}_{output}\right|-\left|\mathbf{Torque}_{sim} \right\|}{\left|\mathbf{Torque}_{sim}\right|} \tag{19}\]  
where \(\mathbf{Torque}_{sim}\) denotes the simulation torque. The measurement errors of the two motion cases are shown in Tables 2 and 3.  
The output torque measured by the MEMS gyroscope sensor has a wide range of error from 9.48% to 53.54% in the coordinate system OXYZ.
## 3 Uncertainty Modeling of WT/PV  
The output power of WT and PV are greatly affected by weather conditions such as wind speed, solar radiation and environment temperature. In order to make the optimization result more accurate, the microgrid optimization model should take the influence of uncertain factors into consideration.  
Figure 1: The structure diagram of the microgrid.  
In order to consider the predictive error of renewable energies, the scenario-based representation is used [26]. In this case, a scenario is a sequence of hourly output power values of WT and PV. In this paper, the MCS method is adopted to generate multiple scenarios that represent these uncertain parameters based on the corresponding probability distribution functions [27; 28]. Forecast values of wind speed, solar radiation and temperature of the next day should be obtained in order to generate scenarios. It is assumed that the wind speed, solar radiation intensity and temperature prediction error obey normal distribution with mean value \(\mu\) and variance \(\sigma\), where the values of \(\mu\) and \(\sigma\) can be calculated from historical data. The probability density function of the forecast error is:  
\[f_{\triangle P_{E}}(\triangle p_{E})=\frac{1}{\sqrt{2\pi}\sigma_{E}}exp(- \triangle\ p_{E}^{2}/2\sigma_{E}^{2}). \tag{5}\]  
The final forecast value can be calculated by Equation (6):  
\[P=P_{B}+\triangle P_{E}, \tag{6}\]  
where \(P_{B}\) is the original forecast value.  
It is worth noting that, in this paper, we suppose that the uncertainties related to WT and PV are independent [28]. Although increasing the number of scenarios can make the accuracy of the model better, a large number of scenarios will hinder the establishment of the model in the stochastic optimization method. Therefore, a suitable scenario reduction technique must be used to reduce the number of available scenarios to a representative set. The backward scenario reduction technique [26] is utilized in this study.
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 2 Fuzzy Methodologies
### Fuzzy Inductive Reasoning (FIR)
#### 2.1.3 Fuzzy Forecasting  
In the forecasting phase, future qualitative output states can be predicted using the model by means of the inference engine [23]. The FIR inference engine is based on a variant of the k-nearest neighbour algorithm. Using the KNN fuzzy inference algorithm, the k pattern rules with the smallest distance measure are selected from the pattern rule base that is part of the FIR model, and a distance-weighted average of their fuzzy components (class, membership, and side values) is computed as shown in Figure 4. The forecast of the output variable is obtained as a weighted average of the potential conclusions of the k pattern rules with smallest distances. The qualitative simulation process predicts an entire qualitative triple, from which a quantitative value can be obtained whenever needed by means of the FIR defuzzification process (inverse process of fuzzification).  
For a deeper and more detailed insight into the FIR methodology, the reader is referred to [23; 24; 25; 26].
# Development of Zirconium-Based Alloys with Low Elastic Modulus for Dental Implant Materials
## 3 Results and Discussion
### Mechanical Properties  
To investigate the mechanical properties, the stress-strain curve of Zr-7 Cu-xSn alloys through a compression test is shown in Figure 4. The yield strength and compressive strength increase and the ductility decrease as the Sn content increases. This can be seen as a solid solution strengthening effect in the Zr-7 Cu-xSn ternary alloys. As already discussed in the microstructural observation, the Zr\({}_{2}\)Cu intermetallic phase grows and changes from dendritic structure to large plate phase as shown in Figure 2d. This Zr\({}_{2}\)Cu intermetallic phase is a harder and more brittle phase than the matrix \(\alpha\)-Zr. In addition to solid solution strengthening, the strength of ternary alloys increased with the Zr\({}_{2}\)Cu phase. Therefore, it can be inferred that the yield strength and plastic deformation of the Zr-7 Cu-xSn alloy are affected by Sn. In addition, the elastic modulus, hardness and elastic energy increase with Sn content. These mechanical test results are shown in Table 2.  
As shown in Figure 2 the secondary phase Zr\({}_{2}\)Cu intermetallic compound is uniformly distributed throughout, resulting in a secondary phase strengthening effect. However, the Zr\({}_{2}\)Cu phase increases in size, leading to a decrease in ductility. Therefore, in order to obtain an ideal Zr-7 Cu-xSn alloy, the Sn content must be within 15%. The yield strength of Zr-7 Cu-xSn alloys is in the range of 610-970 MPa, and compressive strength is in the range of 860-1200 MPa, which is higher than most metallic biomaterials such as Ti alloys [29]. The elastic modulus of Zr-7 Cu-xSn alloys is in the range of 14-18 GPa. This is similar to the elastic modulus value of human bones (15-30 GPa) and much lower than that of Ti biomaterials being studied [30]. Therefore, zirconium-based alloys are well suited as implant materials to neglect the stress-shielding effect.  
Figure 3: Scanning electron microscopy (SEM) micrographs of Zirconium-base alloys; Zr–7 Cu–xSn (x = 1, 5, 10, 15 wt%): (**a**) Zr–7 Cu–1 Sn, (**b**) Zr–7 Cu–5 Sn, (**c**) Zr–7 Cu–10 Sn, (**d**) Zr–7 Cu–15 Sn.