esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods
### Multi-Channel Electronics
#### 2.3.2 Front-End Boards
Each front end uses two MAX4937s from Maxim Integrated, each integrating eight channels and designed for transmit voltages up to +/\(-\)115 V. The transmit voltages are limited by the integrated diode bridges in such a way that only +/\(-\)0.75 V is present at the output of the component, which is completely uncritical for the input amplifiers of the receiver. Followed behind the T/R switches are analog anti-aliasing filters that can be equipped with different values depending on the application. Then, two AD9674 devices from Analog Devices are used to process the analog receive signals. Each of them features eight receive channels, including a low-noise preamplifier, a voltage-controlled attenuator, a post-amplifier, programmable filters consisting of a high-pass/low-pass combination and an A/D converter. The receiver gain can be set to a maximum of 52 dB. The voltage-controlled attenuator allows a Time Gain Control (TGC) to be implemented in a range of up to 45 dB. To realize this, the attenuators are controlled by a TGC curve defined using up to 1024 supporting values, which specifies how high the amplification factor should be at which time. The TGC curve is generated centrally by the FPGA on the main board and is routed differentially to the individual front-end boards via a 12-bit digital-to-analog converter and a high-frequency output driver. The amplifiers are followed by a programmable high-pass/low-pass filter combination implementing a band-pass behavior. The maximum adjustable cut-off frequencies are mainly dependent on the sampling frequency of the following analog-to-digital converter. However, the device offers the additional option of bypassing the integrated high-pass filter. As a result, the lower cutoff frequency can be reduced to 300 kHz. After filtering, the received signals are digitized. The analog-to-digital converter is operated with a sampling rate of 80 MSPS and 12-bit of resolution. The digitized data of the 16 channels are transmitted via LVDS lines to the Spartan-6 FPGA, parallelized for internal preprocessing, extended to a 16-bit data type, sorted and then buffered in 2 \(\times\) 4 Gbit DDR3 RAMs (32 million samples per channel). Depending on the sampling rate requirements, an accumulation of the data with a factor of two or four is possible, reducing the resulting sampling rate while increasing the signal-to-noise ratio. After that, the data of all the front ends are transferred to the main board, where they are sorted again and then transferred via the PCIe interface to the PC using direct memory access (DMA).
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 2 Experimental Section
### Generalprocedure of the Reduction of Carbonyl Compounds  
The catalytic reduction of benzaldehyde was used as a model reaction. Typically, the reaction was performed in a stainless steel 40 mL Parr batch reactor. Typically, benzaldehyde (1 mmol), \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd (20 mg), and H\({}_{2}\)O (10 mL) were charged into the reactor. The reactor was purged with H\({}_{2}\) by 5 times to completely remove the air in the reactor and the reaction solution, then charged with 2.5 bar H\({}_{2}\) at room temperature. The reactor with H\({}_{2}\) was heated to 70 \({}^{\circ}\)C within 4 min, commencing the catalytic reaction. The reaction was maintained at 70 \({}^{\circ}\)C for 12 h, while the reaction mixture was mechanically stirred at 1000 rpm. The reaction was stopped by cooling the reactor to room temperature and depressurized. The reaction mixture was extracted by ethyl acetateandanalyzed by gas chromatography (GC) using ethylbenzene as the internal standard.
# High Brightness Diode Lasers Based on Beam Splitting and Polarization Combining
## 2 Materials and Methods  
Beam quality of a semiconductor laser can be described by the \(M^{2}\) factor, which is defined as  
\[M^{2}=\frac{w\times\theta}{w_{0}\times\theta_{0}}, \tag{1}\]  
where \(w\) and \(\theta\) are, respectively, the diameter of the measured beam waist and the far-field divergence. \(w_{0}\) and \(\theta_{0}\) are, respectively, the diameter of the TEM\({}_{00}\) Gaussian beam waist and the far-field divergence. The beam width was determined by the second-order moment according to ISO11146. A small \(M_{2}\) factor value is desired for high-power diode lasers. Evidently, the beam quality can be improved by reducing the beam waist when the divergence angle is constant.  
Figure 1 shows the schematic of experimental setup of BSPC. A laser beam from the diode laser was split into two beams by a triangle prism along the beam waist in slow axis, and then these two beams went through two loops to overlap on a polarization beam combiner (PBC) using high reflecting mirrors. The polarization of one beam was changed by a half-wave plate (HWP). These two beams were combined into one beam by PBC. In principle, the beam quality of a single beam would improve by 50% because the beam-waist was reduced to its half, and the power would be almost unchanged due to the combining if the initial laser beam has a high purity polarization. Hence, the brightness increases. This method is suitable for the laser source with high purity polarization and large beam waist. Additionally, this approach improves the brightness with the penalty of polarization purity. To demonstrate this method, a single emitter and a diode laser array are employed.  
In the realistic setup, the highly reflective (HR) mirrors (\(M_{1}\), \(M_{2}\), \(M_{3}\)) were optical glass coated with Au. The corresponding reflectivity was 98.2%, and the size was 30 mm \(\times\) 30 mm \(\times\) 10 mm. The HWP used was zero-order with a transmission of 98.5% at 980 nm. The precisely cut prism, manufactured from N-BK7, featured reflective coatings on the two legs and offered a clear aperture extending across the 90\({}^{\circ}\) angle between the coated surfaces. The combining efficiency of the PBC was 97.5%.  
Figure 1: Schematic diagram of BSPC setup. HR mirror: highly reflective mirror, PBC: polarization beam combiners, HWP: half-wave plate.
## 4 Simulation and Implementation  
HFSS simulation is carried out by using (_sL1_, _sL2_, _sW1_, and _sW2_) and (_r_) parameter values of the MPATSAT obtained by the trained SVR model in Section 3. 1S111 plot of MPATSAT is illustrated in Figure 3. The obtained resonance frequencies are 2.45 GHz, 4.73 GHz, and 5.21 GHz. At these frequencies, the 10 dB bandwidths are obtained as 20 MHz, 40 MHz, and 50 MHz, respectively.  
Three-dimensional realized gain diagrams of MPATSAT for 2.45 GHz, 4.73 GHz, and 5.21 GHz are shown in Figure 4a-c, respectively. The maximum realized antenna gains are obtained as 6.1 dB, 4.6 dB, and 7.21 dB.  
Figure 3: 1S11 plot of MPATSAT.  
Figure 2: Flow chart of the design procedure.  
Figure 4: Three-dimensional realized gain diagrams of MPATSAT: (**a**) for 2.45 GHz; (**b**) for 4.73 GHz; (c) for 5.21 GHz.  
Figure 5: 1Z111 plot of MPATSAT.  
Figure 6: Co-pol. (**—**) and cross-pol. (**—**) radiation diagrams of MPATSAT in y–z (\(\Phi=90^{\circ}\)) and x–z plane (\(\Phi=0^{\circ}\)): (**a**) for 2.45 GHz; (**b**) for 4.73 GHz; (**c**) for 5.21 GHz.  
Current distributions at the surface of MPATSAT for 2.45 GHz, 4.73 GHz, and 5.21 GHz are shown in Figure 7a-c, respectively. Current flows that cause the radiations in resonance frequencies can clearly be seen in Figure 7.  
Radiation efficiency is obtained as 96% for 2.45 GHz, 75% for 4.73 GHz, and 98% for 5.21 GHz. Satisfactory radiation efficiency values are acquired at the resonance frequencies.  
MPATSAT given in Figure 1a,b was implemented by using Rogers RT/duroid 5870 with relative permittivity of 2.33 and substrate thickness of 0.787 mm and antenna geometric values given above. The top and bottom views of the implemented MPATSAT are presented in Figure 8a,b, respectively. The soldering in Figure 8 is implemented by hand in a classical way. After connections are tested via a Multimeter, no connection problem was detected between the outer conductor-antenna ground and inner conductor-antenna patch. |S11| plot of the implemented MPATSAT is obtained by a network analyzer.  
Figure 7: Current distributions at the surface of MPATSAT: (**a**) for 2.45 GHz; (**b**) for 4.73 GHz; (**c**) for 5.21 GHz.  
A comparison of \(|\)S11\(|\) plots of the simulation and measurement results is given in Figure 9. It can be observed that the simulation and measurement results coincide with each other approximately. The difference between simulation and measurement is due to measurement and implementation errors. The measured maximum antenna gains at the measured resonance frequencies are 3.1 dB, 3.8 dB, and 7.1 dB, respectively.  
Table 3 shows the comparison of the antenna designed by the proposed method with several triple-band antennas given in the literature [55; 56; 57; 58; 59; 60; 61; 62]. In terms of antenna gains, MPATSAT gives relatively better results. It should be noted that these antennas given in the literature were not designed with soft computation techniques.  
Figure 8: Implemented MPATSAT: (**a**) Top view; (**b**) Bottom view.  
Figure 9: Comparison of \(|\)S11\(|\) plots of the simulation and the measurement.
## 2 Mathematical Models  
### Elevation Angle Function  
Since the radius of the Moon is small, approximately 1738 km on average, and the distance from the satellite to the Moon can reach more than 350,000 km, the Moon is abstracted as a point for analysis in the following model.  
Due to the ionospheric effect, tropospheric effect, etc., the satellite must first be considered under the condition of meeting a certain elevation angle with the station in order to achieve a good positioning effect. The elevation angle criterion is thus given as follows:\[V(t)=\frac{\Delta r\cdot r_{s}}{\|\Delta r\|\cdot\|r_{s}\|}>\sin\theta_{0} \tag{1}\]  
where \(\Delta r=r_{m}-r_{s}\) and \(r_{s}\) represent the coordinate vectors of the satellite and \(r_{m}\) represents the coordinate vector of the Moon. The calculation of the satellite visibility at a certain elevation angle can also be approximated as the problem of solving \(V(t)-\chi=0\), where \(\chi\) is the cosine of elevation angle threshold \(\theta_{0}\). The solution to this equation can be used for the time points of the satellite's rise and set with respect to the Moon.
## 2 Materials and Methods  
M-type hexaferrites with chemical formulae of SrFe\({}_{12-2x}\)Zn\({}_{x}\)Ti\({}_{x}\)O\({}_{19}\), SrFe\({}_{12-2x}\)Mn\({}_{x}\)Ti\({}_{x}\)O\({}_{19}\) (\(x\) = 0, 0.5, 1.0, 1.5, 2.0), and SrFe\({}_{12-2x}\)Zn\({}_{x}\)Zr\({}_{x}\)O\({}_{19}\) (\(x\) = 0, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1 1.5, 2.0) were synthesized via conventional solid-state reaction processes. Precursor powders of SrCO\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), MnCO\({}_{3}\), ZnO, ZrO\({}_{2}\) (all Kojundo Chemical, Saitama, Japan, 99.9%), and TiO\({}_{2}\) (Sigma-Aldrich, 99.9%) were weighed based on the cation ratios of the formulae. The powders were ball-mixed in water, dried, and calcined in air at 1200 \({}^{\circ}\)C for 4 h. The calcined samples were ball-milled and calcined again at 1250 \({}^{\circ}\)C for 2 h in air to ensure compositional homogeneity. Subsequently, the samples were ground again to fine powders. The hexaferrite powder and epoxy binder (YD014, Kukdo Chemical, Kunshan, Korea) were mixed at a 9:1 wt% ratio, pressed into toroidal-shaped (inner and outer diameters of 3.04 and 7.00 mm, respectively) and disk-shaped (diameter = 15 mm) green compacts, and finally cured at 180 \({}^{\circ}\)C for 20 min in air. The volume fraction of the epoxy in the final sample was \(\sim\)50% [6; 7].  
X-ray diffraction (XRD, D8 Advance, Bruker AXS GmbH, Germany) with a Cu K\({}_{\alpha}\) radiation source (\(\lambda\) = 0.154056 nm) and field-emission scanning electron microscopy (SEM, JSM-7610F, JEOL, Japan) analyses were performed on second calcined hexaferrite powders for crystalline phase analysis and microstructural observation, respectively. Magnetic properties on the disk-shaped composites were performed by a B-H tracer. The complex permittivity and permeability spectra (100 MHz-18 GHz) of the toroidal-shaped composite samples were measured using a vector network analyzer (E5063A, Keysight, Santa Rosa, CA, USA) with an airline kit (85051 BR03) and N1500A software.
## 3 Results and Analysis
### Grad-CAM Results  
Grad-CAM is used to gain more insights into how each of the models made their predictions. Figure 7 shows the heatmaps of interlayer distress samples. The warm colors indicate the areas with the greatest impact on prediction, while cooler colors indicate little to no contribution to the prediction.  
As shown in Figure 7, these models focus on a small area of interlayer debonding and interlayer water seepage samples, while focusing on a large area of interlayer loosening samples. For the interlayer debonding and interlayer water seepage, there is a polarity reversal between the two distresses, which is enough for the model to distinguish the two distresses by focusing on the small lower part of the distress samples. Compared with interlayer debonding and interlayer water seepage, interlayer loosening has more complex texture features. Thus, the model needs to extract more information to distinguish the distress. As the heatmaps of interlayer loosening shown in Figure 7, simpler models focus on multiple smaller areas representing local features. In contrast, more complex models focus on larger areas representing high-level semantic features.
## 5 Conclusions and Discussion
When the monitored temperature is added in the features set (TC-S2), the average SMAPE is significantly lower (29.9%), and there are also improvements for the best cases from 14.9% to 11.2%. When more features that correlate energy consumption are available, such as the temperature, the prediction accuracy is higher. The improvements brought by additional weather data can also be observed in the case of the medium-scale prosumer when temperature and humidity data were considered in two test scenarios (TC-M2 and TC-M3). The best prediction day has a lower error for both test scenarios, but a significant improvement is shown for the worst predicted day error that decreases consistently. As future work, we plan to combine the black-box machine learning models with the white-box models provided as digital twins of energy assets. We will move toward the direction of physical informed machine learning by developing a hybrid solution that integrates physical realities and data model predictions to capture finer-grained details and provide more precise predictions. Digital twins' models of the energy assets will be used to model the physical features and to conduct data-driven simulations for better understanding their fine-grained energy behavior, operational constraints, and optimal integration/usage with multi-energy services. Moreover, such models will allow the generation of data with better quality that will cover non-optimal situations and will complement the monitored data. For the black-box models, we will implement deep neural network models that can be used on top of the big data-processing infrastructure, while considering the comprehensive modeling of demand and supply provided by the digital twins. Lambda architectures could be used to dispatch heterogenous monitored data to both a batch layer managing historical datasets and a speed layer for managing closer to the real-time streams of data. Finally, the coupling and association of energy data with other non-energy vector data, such as social data or cross-sectors' data, can be investigated, with a view of generating more accurate predictions. Conceptualization, M.A. and L.T. ; methodology, T.C. and I.A. ; software, L.T. ; validation, M.A. and L.T. ; formal analysis, M.A. ; investigation, I.A. and L.T. ; writing--original draft preparation, T.C., M.A. and L.T. ; writing--review and editing, I.A. and T.C. ; visualization, I.A. and L.T. ; supervision, T.C. ; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript. This research was funded by European Commission as part of the H2020 Framework Programme, grant number 957816 and was partially supported by a grant of the Romanian Ministry of Education and Research, CNCS/CCCDI-UEFISCDI, project number PN-III-P3-3.6-H2020-2020-0031. Not applicable. **Acknowledgments:** The authors thank Eng. [PERSON] for helping with the experiments for evaluating the prediction model.
# Safety Evaluation Method and Management Strategy for Aviation Flight Plans
## 3 Method
### Safety Evaluation of Flight Plans  
#### 3.2.1 Generation of Aircraft-Accessible Area  
The concept of four-dimensional tracking operations emphasizes the importance of time and places higher demands on the temporal dimension. In addition, due to the changing environment during the flight, the optimal management of the flight plan needs to take into account the pilot's intentions, the airspace environment, bad weather, and aircraft's performance. Based on this, the concept of aircraft-accessible area that is proposed in this paper describes the spatial area that can be reached by an aircraft at a given moment during the flight between two waypoints; the specific calculations for this are described in detail in [22]. Accessible areas can assist with aircraft conflict detection and flight plan safety assessment, they can provide quantitative decision support for air traffic control centers and are of great significance to aviation safety in the context of next-generation air traffic management systems. Figure 5 shows the accessible flight area for aircrafts A and B (O\({}_{\text{A}}\) and O\({}_{\text{B}}\) indicate the start waypoints of the flight paths for the two aircraft, while D\({}_{\text{A}}\) and D\({}_{\text{B}}\) indicate the end waypoints of the flight paths). This area depicts the entire range of space that the aircraft can reach throughout the course of the flight.  
Figure 4: Weather-influenced airspace (typhoon near Okinawa, Japan).
## 2 Materials and Methods
### Zebris FDM Gait Analysis System  
IGA was conducted in the gait laboratory using the Zebris FDM system, which consists of two floor-integrated pressure plates. These plates measure force distribution under the feet to assess static and dynamic loads as well as individual gait parameters. The system is connected to a standard PC and operated via dedicated analysis software (Zebris Medical GmbH, Isony, Germany). Trained medical staff performed the measurements.  
Two integrated cameras recorded the subject from the front/back and side during walking. Although the system allows additional kinematic data collection via motion markers and muscle activity via EMG, these features are not yet used for the evaluation of the rehabilitation program and could not be used in this study. Only the pressure distribution data were analyzed. Participants walked over the plates several times to capture approximately ten valid gait cycles. Care was taken to ensure full contact with the plates, which sometimes required repeated trials. The process typically lasted no more than ten minutes.  
The following clinical and gait parameters (predictors of the GMFM-66) were further analyzed statistically:  
- Age in years;  
- Height in cm;  
- Weight in kg;  
- CP subtype;  
- Mean and standard deviation of speed in km/h;  
- Mean stance phase in % and side difference in stance phase in %;  
- Average step width in cm and side difference in step width in %;  
- Average step length in cm and side difference in step length in %;  
- Average step time in seconds and side difference in step time in %;  
- Average foot rotation in degree angle and side difference in foot rotation in %;  
- Average length of gait line (LOGT = the length of the rolling surface of the foot) and side difference in LOGT in %;  
- Average single support line (SSL = the length of the rolling surface of the foot where the contralateral foot is in the swing phase) and side difference in SSL in %.
## 3 Analysis and Discussion of Results
### Multivariate Regression Analyses  
By developing the multiple regression fitting and based on the information obtained from the ANOVA analysis, the first- and second-degree multiple regression models (Equations (22) and (23), respectively), shown in Table 5, are able to represent the model in question for the set of sampled values. In addition, the linear effects and interactions of all factors must contribute greatly to explaining the experimental model.  
The ANOVA test of the model indicates that the model presented in Equation (22) is adequate to represent the UCS under the range of established parameters, and although there is no lack of fit of the model, and the value of R\({}^{2}\) (0.6788) validates it (\(v_{p}\) and \(\rho_{dny}\) do not an effect in the response for the single multiple model), the effect of the other variables does not exceed the fit of the univariate models shown in Equations (18) and (20). Additionally, the \(p\)-value of Equation (22) indicates that the model is statistically significant.  
On the other hand, the ANOVA test of the second degree model (Equation (23)) indicates that the model is also adequate to represent the response variable as a function of the independent variables, and the goodness-of-fit indicator R\({}^{2}\) (0.8447) validates this. The predicted and experimental UCS  
Figure 4: Relationship between UCS and independent variables: P-wave velocity and porosity for analytical models (**a,b**) linear, (**c,d**) exponential, (**e,f**) potential and (**g,h**) quadratic.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of** & \multicolumn{2}{c}{**Analytical Model**} & **Var.** & \(p\)**-Value** & **F-Value** & **R\({}^{2}\)** & **Equation** \\
**Fitting** & & & & & & & \\ \hline Single & \(\sigma_{\text{c}}\big{(}\text{v}_{p},\rho_{dny},n\big{)}=119.3575-7.1138n\) & Model & 0.000 & 57.06 & 0.6788 & (22) \\ multiple & (dem Equation (15)) & n & 0.000 & 57.06 & & \\ \hline  & & Model & 0.000 & 20.01 & 0.8447 & (23) \\  & \(\sigma_{\text{c}}\big{(}\text{v}_{p},\rho_{dny},n\big{)}=-13648+2407\text{v}_{p}\) & \(v_{p}\) & 0.003 & 11.58 & \\ Quadratic & \(+5623\rho_{dny}+322n\) & \(\rho_{dny}\) & 0.003 & 11.09 & & \\ multiple & \(-982\text{v}_{p}\rho_{dny}-10\text{v}_{p}n\) & 0.008 & 8.66 & & \\  & \(-115\rho_{dny}n\) & \(\text{v}_{p},\rho_{dny}\) & 0.003 & 11.18 & & \\  & & \(\rho_{dny},n\) & 0.009 & 8.11 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the multivariate analytical model.  
values are compared in Figure 5, where it is shown that the graphs of observed vs. predicted data generally follow the 1:1-line and the p value of the normality test of residuals are greater than the level of significance (value \(p>0.05\)), indicating a good fit of the models presented in the Table 5.