esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
## 3 Baseband Signal Generation METHOD
### Baseband Sequence Generation  
The COSPAS-SARSAT transmitter coding process includes: setup, receiving position information from GPS receiver, protection bit calculation, BCH generation and baseband sequence generation. The flowchart of the implementation is shown in Figure 2:  
Step 1: Set up system bits, country code, long and short messages, etc.  
Step 2: The GPS receiver receives the position information.  
Step 3: Generate protection bits.  
Step 4: Calculate the BCH checksum bits based on the codeword polynomial and the information polynomial.  
\begin{table}
\begin{tabular}{c c c} \hline
**Bit** & **The Presentation** & **Value** \\ \hline
0 & Emergency Code & 0: National use, Default 0 \\
1 & Activation Type & 1: Emergency code sign \\  & & 0: Manual activation \\
2–5 & IMO nature & 1: Automatic and manual activation \\  & & 1–9: Each represents a type of distress \\  & & nature and is set according to the \\  & & location of the distress. \\ \hline \end{tabular}
\end{table}
Table 4: The data presentation of the emergency code.  
\begin{table}
\begin{tabular}{c c c} \hline
**IMO Indication Position** & **Binary Code** & **Uses** \\ \hline
1 & 0001 & Fire \\
2 & 0010 & Crash \\
3 & 0011 & Stranded \\
4 & 0100 & Tilt/Tipping \\
5 & 0101 & Sink \\
6 & 0110 & Loss of control and drift \\
7 & 0111 & Nature of unspecified distress \\
8 & 1000 & Abandon ship \\
9 & 1001–1111 & For future rescue purposes or for other \\  & & information if needed for search purposes \\ \hline \end{tabular}
\end{table}
Table 5: The table of emergency codes recommended by IMO for the nature of distress.  
Figure 2: The baseband sequence generation flow.  
Step 5: The baseband sequence is obtained from the system bits, the message polynomial, the BCH check bits and the short message emergency code.  
Step 6: Based on the baseband sequence, transmit an emergency message.  
The baseband sequence of long messages consists of 5 integers. The 24 bits of the system bit is the first integer. The 61 bits of the data bits is the second integer. The 21 bits of the error correcting code is the third integer. The 26 bits of the emergency code is the fourth integer. The 12 bits of the additional selection codes is the fifth integer. The baseband sequence of the long messages consists of 4 integers. The 24 bits of the system bit is the first integer. The 61 bits of the data bits is the second integer. The 21 bits of the error correcting code is the third integer. The 6 bits of the emergency code is the fourth integer.
# Two-Dimensional Scanning of Circularly Polarized Beams via Array-Fed Fabry-Perot Cavity Antennas
## 4 FPCA: Array Feeding and Azimuth Scan
Specifically, in Figure 6a, we show the possibility of producing a CP beam with the maximum at \(\theta=12^{\circ}\) on the \(\phi=-60^{\circ}\) plane. The desired elevation angle is obtained by considering a 27.3 GHz working frequency, by exploiting the inherent frequency-scanning property of the LWA element pattern (see Figure 3b). For the azimuth-angle pointing direction \(\phi=-60^{\circ}\), the array theory [41] is applied to achieve the required inter-element phase difference values of \(\Delta\phi_{x}=-22.68^{\circ}\) and \(\Delta\phi_{y}=39.28^{\circ}\), as derived from (5). In a similar manner, by fixing the working frequency at 28 GHz and setting \(\Delta\phi_{x}=-105.05^{\circ}\) and \(\Delta\phi_{y}=0^{\circ}\), we ensure a perfect CP pencil-beam with the maximum on \(\phi=0^{\circ}\) at \(\theta=28^{\circ}\) (see Figure 6b). Similarly, by further increasing the frequency to 28.5 GHz, and thus considering the maximum of the element pattern at \(\theta=37^{\circ}\), a perfectly CP pencil beam can be obtained on the \(\phi=30^{\circ}\) plane imposing \(\Delta\phi_{x}=-113.14^{\circ}\) and \(\Delta\phi_{y}=-65.32^{\circ}\) (see Figure 6c). We have thus demonstrated that the proposed array-fed FPCA is able to generate a pure CP pencil beam pointing at various directions. It is worthwhile to point out that the proposed approach does not consider the mutual coupling among the sources. However, as shown in [48], and experimentally validated in [50], this effect can be taken into account and easily mitigated, keeping cross-coupling scattering coefficients well under the threshold of \(-15\) dB.
# Research on Magnetic Coupling Flywheel Energy Storage Device for Vehicles
## 2 The Structure and Function of the Device  
### Structural Features of the Device  
The magnetic coupling flywheel energy storage device fully considers the limited space inside the vehicle, adopts the compact design concept, and realizes the advantages of simple structure as well as convenient disassembly and maintenance. Using the magnetic effect, without any external energy, the device belongs to a kind of green energy-saving environmental protection equipment [22]. The schematic diagram of the layout is shown in Figure 1.  
Figure 1: Layout diagram.  
The magnetic coupling mechanism is the core component of the device; it is mainly composed of three parts: driving shaft, driven shaft, and magnetic ring. The structure is shown in Figure 2.  
Figure 3: Three working positions of the magnetic ring.  
Figure 2: Structural model diagram.  
drives the flywheel to rotate and realize energy storage. When the driven shaft and the driving shaft rotate at the same speed, the energy storage ends, and then the magnetic ring returns to the left end.  
After braking, when the car starts again or accelerates, the magnetic ring begins to move to the right, and the driven shaft is gradually magnetized with the change of the position of the magnetic ring. The power of the flywheel is transmitted to the wheels through the magnetic coupling device's driving shaft and driven shaft and the vehicle's transmission shaft, resulting in energy release. Until the two shafts rotate at the same speed, the energy release ends, and the magnetic ring returns to the left end.  
When the magnetic ring is in the contact position of two shafts, the transfer torque is the largest. So, the moving range of the magnetic ring is between the left end of the driving shaft and the contact position of two shafts. The closer the magnetic ring is to the contact position of two shafts, the greater the transfer torque. The farther the magnetic ring is from the contact position of two shafts, the smaller the transfer torque.
## 4 Empirical Insights from Sectorial and Regional Studies
### AI Adoption and Sustainable Performance in Developing Economies: Evidence from Pakistani SMEs  
A comprehensive study by [PERSON] et al. [118] provides empirical evidence on how AI adoption influences the sustainable performance of SMEs in Pakistan. Using a hybrid methodological approach that combines partial least squares structural equation modeling (PLS-SEM) and artificial neural networks, the study analyzed data from 305 SMEs in sectors such as retail, agriculture, manufacturing and services. The research adopts a TOE-DOI perspective, evaluating factors such as top management support, employee capability, vendor support, and environmental pressures. In particular, top management support (\(\beta=0.27\)), employee capacity (\(\beta=0.25\)), and customer pressure (\(\beta=0.16\)) were found to have significant positive effects on AI adoption, while the availability of financial resources did not show a significant effect. From a DOI lens, perceived relative advantage (\(\beta=0.18\)) emerged as a key enabler, while perceived complexity also positively influenced adoption (\(\beta=0.12\)), contrary to common assumptions. Importantly, the study demonstrates that AI adoption improves the economic (\(\beta=0.64\)), social (\(\beta=0.57\)), and environmental performance (\(\beta=0.56\)) of SMEs--quantitative evidence that reinforces AI's role as a transformative tool for sustainability.  
These findings substantiate the challenge-solution framework proposed in this article by confirming the importance of organizational readiness, capability building, and vendor ecosystems in supporting scalable AI implementation across SMEs in developing economies.
# Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function
## 6 Conclusions  
The proposal of this paper consists of fitting WT power curves to Weibull CDF expressions. Shape and scale parameters have been deduced for a given group of WTs, and results have been compared with those obtained by means of several logistic functions proposed in the literature. This article concludes that the proposed function can be used in achieving accuracy in the wind power curve fitting process with a comparatively less complex approach. The proposed function shows comparable accuracy with three and four parameter logistic functions. Further investigation into Weibull CDFs, parameter estimation techniques and corresponding improvement in curve fitting accuracy can motivate the use of the proposed function. Further, a software package has been included in Appendix A, which makes it easy to obtain Weibull functions for fitting WT power curves.  
Conceptualization, N.B. and A.F. Methodology, N.B. and A.F. Software, N.B. Validation, N.B., A.F. and D.V. Formal analysis, D.V. Investigation, A.F. Resources, N.B. and A.F. Data curation, N.B. and A.F. Writing, original draft preparation, A.F. and N.D. Writing, review and editing, D.V. Visualization, N.B. Supervision, A.F. and D.V. Project administration, A.F.  
This research received no external funding.  
[PERSON] was supported by the R&D project work undertaken under the Visvesvaraya PhD Scheme of the Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Vestas V80** & **Siemens 82** & **REpower 82** & **Nordex N90** & **Siemens 107** & **Vestas V164** \\ \hline
**RMSE** & 18.1487 & 10.8834 & 11.2334 & 25.9851 & 19.8567 & 43.2761 \\
**MAE** & 10.66565 & 6.9250 & 6.8415 & 14.06718 & 9.2265 & 21.4085 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the RMSE and MAE values for the best case parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Vestas V80** & **Siemens 82** & **REpower 82** & **Nordex N90** & **Siemens 107** & **Vestas V164** \\ \hline \(k\) & 4.57 & 4.13 & 4.08 & 4.36 & 4.54 & 4.53 \\ \(C\) & 9.29 & 10.11 & 9.55 & 9.43 & 9.80 & 9.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best case values of parameters ‘\(k\)’ and ‘C’ for several WTs.
## 7 Simulations and Results
### Unallocated Patients with DTA  
Figure 11 shows a comparison of actual (black) data compared to ARIMA (green), Prophet (yellow) and GRNN (purple) predictions for the Unallocated Patients with DTA indicator. This indicator is perhaps the most challenging to predict in this study, as it includes a native and \"variable\" seasonal factor in that typically the decisions to admit patients are made in specific periods of the day, i.e., at the beginning of the doctors' shifts, after their first meetings with patients. As doctors' shifts commence at different times (unknown by the models), the lag factor of the seasonality is not constant, making the indicator somewhat unpredictable. This is the main reason residuals are typically greater than for other indicators.  
Looking at the residuals charts at the bottom of Figures 12-14, the unpredictability mentioned can be clearly be observed by the high residuals in the pre-COVID phase for  
Figure 11: Comparison of Actuals, ARIMA, Prophet and GRNN, February 2020–June 2020.  
Figure 10: Comparison of Actual and GRNN with residual distribution chart, January 2020–November 2020.  
Figure 9: Comparison of Actual and Prophet with residual distribution chart, January 2020–November 2020.  
all three analysed models (ARIMA, Prophet and GRNN). During the lockdown affected period, as expected only GRNN predicts values reliably. In the post-lockdown learning period, all three models are able to reliably predict Unallocated Patients with DTA similarly, until October, when data change behaviour again, and ARIMA seems to explain the variables better.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 3 Advances in Physical Modeling Method
The next step was to develop scale factors, which enable the parameters at the prototype scale (such as displacements, strains, stresses, and forces) to be calculated from model-scale observations. Physical modeling has been successfully employed for many decades [34], enabling visualization of the problem, as well as validation and verification of numerical modeling [35]. Several 1-g physical models have been presented, and many useful results have been obtained, examining soil-structure interaction [36; 37; 38; 39; 40; 41], failure models and mechanisms [21; 22; 23], ground movements, structure stability and collapse, arching effect [28; 29; 42], ground displacements [43; 44; 45; 46; 47], reinforcement effect [48; 49], and pile behaviors [50; 51]. Overall, 1-g models are often employed for soft ground tunneling, mining, geomorphology, and soil-structure interaction. Centrifuge tests were conducted by [52; 53] on shafts to simulate the half excavation profile (i.e., the plan shape of the excavation was semicircular). The purpose of these soil models was to analyze the subsurface earth movements caused by the construction. The excavation was driven up against a poly-methyl methacrylate (PMMA) window in huge rectangular boxes. The researchers used physical models in order to comprehend the processes occurring during excavation in the soil. A series of physical model case studies and their results are described in this paper for better understanding of the deformation induced by the shaft construction. A widespread review of the literature on shafts conducted by [54] revealed that only eighteen case studies on circular shaft construction were published between 1980 and 2016. In 2017, further case studies were reported [54; 55]. The majority of these case studies were directed at surface settlement, with only three cases presenting subsurface soil displacements and one example reporting surface horizontal displacement.
# Rapid 3D-Imaging of Semiconductor Chips Using THz Time-of-Flight Technique
## 2 Experimental Setup  
A schematic and a photograph of the THz TOF imaging equipment are illustrated in Figure 1a,b, respectively. For the rapid THz-TDS system, we used a commercialized OSCAT (TERA-OSCAT, MenloSystems GmbH), which allows for a speed of 100-200 Hz/pixel. Therefore, a measurement time of less than 10 s is allowed for each image, with a pixel number of 32 \(\times\) 32 pixels. We incorporated a 2D galvano scanner (Hurryscan25, SCANLAB GmbH), enabling us to obtain the images with a speed that is limited by the OSCAT THz-TDS system. The THz signal (with a beam size of 2 cm after the collimation lens) was focused using either a customized f-theta lens (Teflon) or a convex lens, both with a focal length of 5 cm and a diameter of 3.8 cm. The reflected THz signal was collected using a 50:50 beamsplitter (Tydex Inc., St. Petersburg, Russia), which was fed into the THz receiving antenna (denoted by Rx). The current signal from the antenna was amplified using a fast current preamplifier (DLPCA-100; FEMTO Messtechnik GmbH) and digitized with a conventional data acquisition card. The collected data contain the phase-sensitive THz amplitudes as a function of the 3D parameters such as the time delay, \(x\)-axis, and \(y\)-axis. They are recorded as a single binary file, which can be reconstructed using home-built analyzing software.  
Figure 1c shows a representative THz TOF signal (A-scan) where the focused THz beam is reflected on one of the encapsulated IC chips. The signal-to-noise ratio reached 20 dB at a rate of 100 Hz. Clearly, we can identify the multiple pulsed signals reflected from the front of the sample, the IC chip, and the back plane of the sample, which will be shown in more detail later. We note that the thickness of the beamsplitter was 5 mm; hence, there was no noticeable multiple reflection effect from the beamsplitter. Here, the time delay can be converted into depth information, in which 1 ps corresponds to 0.15/n mm, where n is the refractive index of the materials. The refractive index of the encapsulating material was about 2.0 for all chips used in the experiment.
## 3 Results
### In Vitro Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Food Sample** & **Origin of Samples** & \
\begin{tabular}{c} **FRAP Assay** \\ **(mmol Fe\({}^{2+}\)/g)** \\ \end{tabular} &
\begin{tabular}{c} **Folin-Cioalteu** \\ **(mg CA/g)** \\ \end{tabular} & **Study** \\ \hline Blueberries & Poland & - & 0.52 & [PERSON] et al. [29] \\ Turkey & - & 1.58-27.84 & [PERSON] [30] \\ Turkey & - & 2.65-3.67 & [PERSON] et al. [31] \\ Italy & 1.98-10.29 & - & [PERSON] et al. [32] \\ Brazil & 0.25-0.31 & 19.22-34.56 & [PERSON] et al. [33] \\ Australia & 0.37 & 2.93 & [PERSON] et al. [34] \\ \hline Cranberries & Poland & - & 1.20-3.15 & [PERSON] et al. [29] \\ Poland & 1.02 & - & [PERSON] et al. [35] \\ Hawaii & 0.45 & - & [PERSON] et al. [36] \\ USA & - & 1.60 & [PERSON] et al. [37] \\ Poland & - & 2.5 & [PERSON] et al.[38] \\ \hline Pomegranates & Spain & 61.28 & 40.74 & [PERSON] et al. [39] \\ Croatia & 10.03-17.66 & 57.66-105.99 & [PERSON] et al. [40] \\ Iran & - & 22.61 & [PERSON] et al. [41] \\ \hline Grapefruits & Cina & 0.93-1.76 & - & [PERSON] et al. [42] \\ China & 23.46 & - & [PERSON] et al. [43] \\ Poland & 0.46 & 0.05 & [PERSON] et al. [44] \\ \hline Red peppers & Brazil & 0.004 & 1.19 & [PERSON] et al. [45] \\ Iran & 0.5 & - & [PERSON] et al. [46] \\ Thailand & 0.28 & 3.21 & [PERSON] et al. [47] \\ Malaysia & 0.003 & 47.06 & [PERSON] et al. [48] \\ \hline Almonds & Italy & 0.041 mmol Fe\({}^{2+}\)/g & - & [PERSON] et al. [49] \\ California & 36.8-88.8 mmol Fe\({}^{2+}\)/g & 0.58–1.59 & [PERSON] et al. [50] \\ China & - & 8 & [PERSON] et al. [51] \\ Italy & - & 33.420 & [PERSON] et al. [52] \\ Italy & & 21.43–141.06 & [PERSON] et al. [53] \\ \hline Mountain tea & Greece & 0.03-0.11 & 2.37–4.56 & [PERSON] et al. [54] \\ Greece & 0.13 & 18.63 & [PERSON] et al. [55] \\ Spain & & 102.54 & [PERSON] et al. [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean concentrations of total phenolic content and total antioxidant activity of blueberries, cranberries, posergranates, grapefruits, red peppers, almonds, and mountain tea reported in literature. Figure 2: Mean and SD values of total phenolic content of different food samples as determined by the Folin-Cioalteau assay. Statistically significant differences are portrayed with superscripts (different letters).
## 5 Discussion
### Future Directions
#### 5.2.2 Modular and Scalable Design  
The proposed STPC has already been developed with a modular architecture, allowing for flexible capacity scaling. Multiple units can be paralleled to support larger loads or to provide redundancy, making the system adaptable to a wide range of applications in the low-voltage distribution network [30].
## 2 Materials and Methods
### Signal Processing and Analysis  
It is known that structural damping is generally difficult to identify and even more difficult to model. The literature offers a number of parameters to quantify damping, depending on the specific sector of application, e.g., _damping ratio, logarithmic decrement, loss factor, quality factor, decay constant_. In practice, they carry very similar information, being interrelated through analytical formulations [30]. In the field of sound and vibration, the damping properties are commonly determined in the Frequency domain. In particular, from the EMA/modal identification, modal coefficients related to the several identified mode shapes are obtained; from these ones, a damping ratio per mode shape can be defined. Indeed, in this study, the damping ratio (\(\zeta\)) is chosen to quantitatively assess the structural damping associated with each vibration mode.  
For all the tests, the FRFs between response and reference signals, acceleration and force respectively, are estimated. Then, the modal parameters of the specimens, i.e., mode shapes \(X_{i}\), natural frequencies \(f_{i}\), and damping ratios \(\zeta_{i}\) (\(i\) = 1, 2... ) are computed.  
Numerous data processing are performed for each of the five experimental campaign (impact test and four shaker tests), in order to get reliable and robust results. Indeed, it is well known that EMA results might prove significantly sensitive to the frequency band and/or the FRF set chosen for the analysis, as well as to the values set up for certain parameters of EMA algorithms. In particular: algorithms in the time and frequency domains are used; different combinations of FRF sets are selected as the basis for the modal parameter computations; different bandwidths are defined (e.g., 5-1000 Hz, 5-200 Hz, 200-1000 Hz).
## 2 Materials, Modeling and Characterization
### Characterization  
The validity of the previous models, especially of the MCP model (Equation (5)), is assessed against absorption measurements carried out using a small impedance tube mounted vertically and located in a semi-anechoic room, as shown in Figure 4, for the testing of the unbacked and backed porous absorbers. The absorption is measured using the two-microphone transfer function method following the normative ISO-10534-2 [16]. It uses a Kundt tube that is 800 mm long with a radius of \(R=15\) mm and 10 mm-thick walls made of steel. Its base is connected to a loudspeaker that generates a pressure field composed of plane waves up to the first duct cut-on frequency, \(f_{\text{cut-on}}=1.84c_{0}/(2\pi\ R)=6700\) Hz. At the top of the tube is an open termination onto which the circular porous samples are inserted (Figure 4 left). The samples can be backed by a closed cavity with a varying depth (Figure 4 right). The piston on top of the closed cavity may be removed to achieve an open cavity with a depth of 152 mm. Therefore, three configurations can be tested: unbacked, backed by closed cavities with a varying depth or backed by an open cavity with a fixed depth.  
A white noise signal drives the loudspeaker between 80 Hz and 7 kHz. According to ISO-10534-2, a transfer function \(H_{12}\) is evaluated between a pair of \(1/4\)\" condenser microphones, flush-mounted on a tube wall, separated by a distance of \(d_{m}=50\) mm and located at a distance of \(e_{m}=67\) mm apart from the tube termination. The distance between the microphones is lower than the smallest half-wavelength at the duct cut-on frequency. Moreover, above 80 Hz, the smallest detectable phase difference over an acoustic wavelength is greater than the phase mismatch between the microphones. During the measurement, the output signals from the microphones are acquired using the OROS (type OR38) multi-channel system over a bandwidth of 80 Hz-6.7 kHz, at a sampling rate of 12.8 kHz and with a spectral resolution of 1.56 Hz, triggered by the generation of the drive signal. The reflection coefficient over the tested sample is then calculated as  
\[r=\text{e}^{-2\text{i}k_{0}(d_{m}+e_{m})}\frac{H_{12}-\text{e}^{-\text{i}k_{0 }d_{m}}}{\text{e}^{\text{i}k_{0}d_{m}}-H_{12}}, \tag{10}\]  
from which the absorption coefficient \(1-\left|r\right|^{2}\) is readily obtained.  
Near-field measurements of the sound field radiated by the unbacked samples are performed using a calibrated pressure-velocity (p-v) probe [17] located at a short stand-off distance (10 mm) from the porous samples, as shown in Figure 4 left. This distance avoids the occurrence of pressure or velocity nodes below 6.7 kHz while keeping a signal-to-noise ratio greater than 20 dB. The p-v probe enables collocated measurements of both the pressure and the three components of the radiated acoustic velocity. It can serve to estimate the radiation impedance of the samples. It can be displaced along a diameter of the samples to evaluate the directivity of the radiated intensity.