esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 5 Discussions
### Case Study Showing an Application of the Model for Monitoring the Health of a Road Network
Using the same distress information from Figure 18, a histogram highlighting the frequency of distresses along the road section was plotted and this is visualized in Figure 19. For the road section under analysis in Figure 19, most of the intervals have damaged areas with distressed of 0 to 5% with only a few intervals have sections where most of the road has suffered damage. Similar representations can thus be made for each road section that is surveyed over a network. After this, comparisons could be made between them to see which roads have more distressed sections and therefore which should be prioritized for interventions. This will lead to a further channel for monitoring the health of road networks and optimizing maintenance activities. For the methodology proposed, the computational power would be relatively high at the point of model creation as dictated by the deep learning methodologies developed as illustrated in Figure 3 and the computational power needed to run the Tensorflow model. Once the model has  
Figure 18: Level of the distress on the road across the test section. been validated for a given network, an application can then easily record the points of interest, their location and the percentage of damage incurred on the pavement. This is as a result of the application producing grouped georeferenced data on the damaged points and sections with the definitions of severity already defined within the model. This data can essentially be put in the form on a logged CSV file which is not computationally difficult to interpret. Whilst it should be noted that this could produce a very large file based on the size of the network, the data can be manipulated within a simple statistical programming environment to produce figures such as Figures 18 and 19 which visually depict the points of interest along the network and thus the points for maintenance. The data analysis can be more streamlined to highlight other practical examinations considering other environmental factors and testing on larger networks. The case study presented and illustrated in Figures 18 and 19 represents a snapshot of what is possible.
## 4 Results  
To investigate the performance of the designed control schemes, the standard IEEE nine-bus three-generator system is considered as test model including three wind farms with 25% penetration. Simulation results show a comparison between the proposed methods and conventional ones. Actually, four cases are considered in simulation results. The first case represents the traditional methods consisting of the small power system without any controllers. The second and third cases are treated as the proposed cases in which the proposed governor controller for the conventional generators only, and both the designed governor and blade pitch controllers are implemented, respectively. The last case discusses the performance of the system under faulty operating conditions. Figure 9 shows the wind speed, which is modeled by random functions. The cut-in, rated, and cut-out wind speeds are considered as 5 m/s, 12.5 m/s and 25 m/s respectively. The simulation is performed for 150 s.  
Figure 8: (**a**) Singular value plot of frequency control loop. (**b**) Singular value plot of the pitch angle control loop.  
Figure 9: Wind speed.
## 2 Models and Methods
### Models
#### 2.2.1 Lumped Parameter Models of the Agricultural Tractor  
In order to investigate the vertical vibration of the operator's seat, it is possible to model different tractors using lumped parameter models (see Figure 1) in the longitudinal plane, namely:  
* A 3-DoFs tractor with seat suspension (see Figure 1a);
* A 4-DoFs tractor with seat and front wheel suspension systems (see Figure 1b);
* A 6-DoFs tractor with seat, front wheel, and cabin suspension systems (see Figure 1c).  
Figure 1: Lumped parameter models of different agricultural tractors: (**a**) a tractor with seat suspension; (**b**) a tractor with seat and front wheel suspension systems; (**c**) a tractor with seat, front wheel and cabin suspension systems.  
The tractor modeled in Figure 1c accounted for two lumped masses and two rigid bodies. The lumped mass \(m_{1}\) represents the mass of the operator, including that of the seat, and mass \(m_{3}\) represented the mass of the front axle. Conversely, the tractor frame and the cabin were modeled as rigid bodies characterized by masses \(m_{2}\) and \(m_{4}\), and moments of inertia \(I_{2}\) and \(I_{4}\), respectively.  
The seat suspension was composed by a viscoelastic element with stiffness \(k_{1}\) and damping coefficient \(c_{1}\). The cabin was suspended to the tractor frame by two viscoelastic elements characterized by stiffnesses \(k_{5}\) and \(k_{6}\), and damping coefficients \(c_{5}\) and \(c_{6}\), respectively. The rear axle was rigidly connected to the tractor's frame. The elasticity and the damping of the rear wheels were modeled by a stiffness constant \(k_{2}\) and a damping coefficient \(c_{2}\). The tractor frame was suspended to the front axle by a viscoelastic element composed of a spring \(k_{3}\) and a damper \(c_{3}\). The elasticity and the damping of the front wheels were modeled by a stiffness constant \(k_{4}\) and a damping coefficient \(c_{4}\).  
In Figure 1c the DoFs of the model are also shown. For the seat and the front axle, the DoFs \(z_{1}\) and \(z_{3}\) represent the vertical displacements of the two masses. The DoFs \(z_{2}\) and \(z_{4}\) represent the vertical displacements of the CoGs of the tractor frame and the cabin, and the DoFs \(\theta_{2}\) and \(\theta_{4}\) represent the respective rotations. Moreover, the DoFs \(z_{F}\) and \(z_{R}\) represent the vertical displacements of the contact points between an irregular soil and the front and rear wheels. Finally, the points \(A_{i}\) and \(P_{i}\) denote, respectively, the connection points between the tractor and the front and rear three-point linkages [25; 31; 32].  
The models in Figure 1a,b can be easily obtained by the model in Figure 1c by considering the absence of the cabin suspension and the axle suspension.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 2 5G Testbed Overview
### Adc/dac  
The selection of both DAC and ADC was based on the criteria of speed, bit resolution together with VC707 FPGA board compatibility. According to the _Xilinx_ documentation, the FMC 230 (DAC) [14] and FMC 126 (ADC) [15] from 4 DSP are the only converters available in the market that met such requirements. Table 1 summarizes the main converters features, where it can be noticed that the maximum bandwidth of the proposed SDR is restricted to 2.5 GHz.  
\begin{table}
\begin{tabular}{c c c} \hline
**Features** & **FMC 126** & **FMC 230** \\ \hline Bit resolution & 10 & 14 \\ Max. sampling rate & 5 GSPS & 5.7 GSPS \\ Maximum signal bandwidth & 2.5 GHz & 2.85 GHz \\ Number of channels & 4 & 2 \\ Connector FMC type & HPC & HPC \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the main FMC converts features [16; 17].  
Figure 3: VC707 FPGA with both FMC HPC used by ADC/DAC daughterboards.  
The FMC126 is a four channel 1.25 GSPS 10-bit resolution ADC daughter board compliant to the mentioned HPC VITA 57.1 standard. Its design is based on four EV10 AQ190 Quad 1.25 GSPS ADC chip-sets [18], which are characterized by Double Data Rate (DDR) Low Voltage Differential Signal (LVDS) outputs [16]. Moreover, each core can operate either individually, in pairs or even all interleaved together, which in the end enables sample rates of 1.25, 2.5, and 5 GSPS, respectively. To achieve the gigabit rates reported on this work, the ADC had its reference sample clock configured to 2.5 GHz, which can be locked by either an external clock source or an internal IC clock circuit.  
The FMC230 is a two channel 14-bit DAC daughter board, which is controlled through a single Serial Peripheral Interface (SPI) communication bus [17]. Such board design is based on two AD9129 single-channel 14-bit 5.7 GSPS DAC with DDR LVDS inputs [19].  
Since such boards are intended to work at gigabit rates, special attention was taken in their clock distribution. The clock distribution is carried out by a AD9517 chip from Analog Devices (AD) [20], which by default has an internal PLL and a 2500 MHz internal VCO. However, as discussed in Section 2.3, the VCO can also be provided externally.  
As in FMC126, the FMC230 sample clock is provided by the mentioned AD9517, but its internal PLL reference is configured at 30.72 MHz rather than 100 MHz, which in the end achieves an internal VCO locked at 2457.60 MHz (rather than the 2500 MHz).
## 2 Configuration  
Figure 1 shows a schematic diagram of the five-degree-of-freedom measurement system, which consists of a fixed unit and a moving unit. The output beam from a stabilized single-frequency He-Ne laser is a linearly polarized light in the vertical direction. The light is transformed into a circularly polarized light by a quarter wave plate (QWP1), the fast axis of which is set at 45\({}^{\circ}\) with the  
polarization direction of incident beam. Then, the circularly polarized light is divided into two beams, p-polarized light and s-polarized light, by a polarization beam splitter (PBS1).  
The s-polarized light is divided into two beams by the beam splitter (BS1). The reflected light is reflected by mirror (M1) after passing through HWP, PBS3, and QWP2. The reflected light is transformed into s-polarized light through QWP2 twice, and reflected by PBS3. Finally, after passing through L1, the beam is received by complementary metal oxide semiconductor (CMOS3) for measuring pitch and yaw. Section 2.1 is the measuring principle of pitch and yaw.  
After passing through RR1, the p-polarized light transmitted by PBS1 is transmitted by BS2, and finally received by CMOS5 camera for measuring straightness error. Section 2.2 is the measuring principle of the straightness error.  
The p-polarized light transmitted by PBS1 is reflected by RR1, BS2, M2, and RR2 in turn, and the light is finally received by the CMOS1 camera after passing through PBS2. Compared with the p-polarized light transmitted by PBS1, the s-polarized light transmitted by BS1 is reflected by PBS2 through the opposite path and finally received by CMOS2 camera. The change of the two beams is used to measure the roll. Section 2.3 is the measuring principle of roll.  
The s-polarized light transmitted by BS1 is reflected by PBS2, RR2, and M2 in turn, then transmitted through BS2 and L2. Finally, the beam is received by CMOS4 for measuring beam drift. Section 2.4 is the measuring principle of beam drift.
###### Abstract  
Powder metallurgy-based metal matrix composites (MMCs) are widely chosen and used for the development of components in the fields spanning aerospace, automotive and even electronic components. Engineered MMCs are known to offer a high strength-to-weight (\(\sigma/\rho\)) ratio. In this research study, we synthesized cylindrical sintered samples of a ceramic particle-reinforced aluminum metal matrix using the technique of powder metallurgy. The samples for the purpose of testing, examination and analysis were made by mixing aluminum powder with powders of silicon carbide and aluminum oxide or alumina. Four varieties of aluminum composite were synthesized for a different volume percent of the ceramic particle reinforcement. The hybrid composite contained 2 vol.% and 7 vol.% of silicon carbide and 3 vol.% and 8 vol.% of alumina with aluminum as the chosen metal matrix. Homogeneous mixtures of the chosen powders were prepared using conventional ball milling. The homogeneous powder mixture was then cold compacted and subsequently sintered in a tubular furnace in an atmosphere of argon gas. Five different sintering conditions (combinations of temperature and sintering time) were chosen for the purpose of this study. The density and hardness of each sintered specimen were carefully evaluated. Cold compression tests were carried out for the purpose of determining the compressive strength of the engineered MMC. The sintered density and hardness of the aluminum MMCs varied with the addition of ceramic particle reinforcements. An increase in the volume fraction of the alumina particles to the Al/SiC mixture reduced the density, hardness and compressive strength. The sintering condition was optimized for the aluminum MMCs based on the hardness, densification parameter and cold compressive strength. The proposed powder metallurgy-based route for the fabrication of the aluminum matrix composite revealed a noticeable improvement in the physical and mechanical properties when compared one-on-one with commercially pure aluminum.  
metal matrix composite; powder metallurgy; densification; compaction load; hardness +
Footnote †: journal: Applied Sciences
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 4 Results
### Relative Power (\(R_{SQI}\)) Analysis  
As shown in Table 3, in the lowest CF range (0-0.2), the \(R_{SQI}\) average is 0.52 with an SD of 0.25, indicating moderate signal quality with significant variability. The median of 0.64 suggests that most of the values are above average, but the wide range from 0.09 to 0.82 indicates inconsistencies among the data records due to irregular contact. In the intermediate CF range (0.2-0.4), the average \(R_{SQI}\) increases to 0.55 with an SD of 0.26. The median of 0.67 indicates improved signal quality, though the broad range (0.07 to 0.88) still shows variability. The optimal CF range (0.4-0.6) has the highest average \(R_{SQI}\) of 0.56 with an SD of 0.26. The median of 0.69 suggests consistently good signal quality despite some variability. In the high CF range (0.6-0.8), the average \(R_{SQI}\) decreases slightly to 0.54 with a SD of 0.24. The median of 0.62 and the range from 0.12 to 0.90 indicate that this SQI can reflect the excessive pressure affecting the signal quality, although it remains relatively high. In the very high CF range (0.8-1.0), the average \(R_{SQI}\) is the lowest at 0.51, with an SD of 0.22. The median of 0.57 indicates a significant decrease in the signal quality due to excessive pressure, with a range from 0.18 to 0.91 showing a reduced useful signal proportion. From a practical perspective, the CF range of 0.4-0.6 provides the best signal quality, balancing the useful signal proportion against noise.
# New Approximation Methods Based on Fuzzy Transform for Solving SODEs: II
## Appendix A Algorithms  
In this appendix, algorithms of approximation methods based on FzT and NIM for Sections 4.1-4.3 are explained with details. A pseudocode is used to describe the algorithms and simplified code that is easy to read. This pseudocode specifies the form of the input to be supplied and the form of the desired output. As a consequence, a stopping technique independent of the numerical technique is incorporated into each algorithm to avoid infinite loops. Two punctuation symbols are used in the algorithms, a period (.) indicates the termination of a step and a semicolon (\(i\)) separates tasks within a step. In algorithms with help of MATLAB software, the definite integral is specified by integral (function,upper limits, lower limits). The steps in the algorithms follow the rules of structured program construction. They have been arranged so that there should be minimal difficulty translating pseudocode into any programming language suitable for scientific applications. To approximate the solution of SODEs (16) at (\(N+1\)) equally spaced numbers in the interval \([a,b]\), proceed as follows.  
```
INPUT: \(f(t,x,y);g(t,x,y)\): endpoints \(a,b\): integer \(N\): initial condition \(y_{1}\): m. Step 1 Set \(h=(b-a)/N;X_{1}=x_{1};Y_{1}=y_{1};t_{1}=e;k=1,\ldots,N+1;t_{k}=a+(k-1)\). Step 2 Define the generalized uniform fuzzy partitions as \(\mathbb{B}_{1}(t)=\left(\frac{\sqrt{\pi}(m+1)}{2(m+1)}\right)\frac{1}{2} \left(1+\cos\left(\pi\frac{t-(b)}{B}\right)\right)^{m}\). Step 3 for \(k=1\) to \(N\)do Steps 04-15. Step 04 \(F(k)=\operatorname{integral}(f(t,x(k),X(k),Y(k))\mathbb{B}_{k}(t),t(k-1),t(k+1))/ \operatorname{integral}(\mathbb{B}_{k}(t),t(k-1),t(k+1))\). Step 05 \(G(k)=\operatorname{integral}(g(t,X(k),Y(k))\mathbb{B}_{k}(t),t(k-1),t(k+1))/ \operatorname{integral}(\mathbb{B}_{k}(t),t(k-1),t(k+1))\). Step 06 \(Xatar(k+1)=X(k)+H(k)/2\). Step 07 \(Ystar(k+1)=Y(k)+H(k)/2\). Step 08 \(Fstar(k+1)=\operatorname{integral}(f(t,Xstar(k+1),Ystar(k+1))\mathbb{B}_{k+1}(t ),t(k-1),t(k+1))/\operatorname{integral}(\mathbb{B}_{k+1}(t),t(k-1),t(k+1))\). Step 09 \(Gstar(k+1)=\operatorname{integral}(g(t,Xstar(k+1),Ystar(k+1))\mathbb{B}_{k+1}(t ),t(k-1),t(k+1))/\operatorname{integral}(\mathbb{B}_{k+1}(t),t(k-1),t(k+1))\). Step 10 \(Xstar(2k+1)=Xstar(k+1)+Hstar(k+1)/2\). Step 11 \(Ystar(k+1)=Ystar(k+1)+Nstar(k+1)/2\). Step 12 \(Fstar(2k+1)=\operatorname{integral}(f(t,Xstar(2k+1),Ystar(2k+1))\mathbb{B}_{k+1}(t ),t(k-1),t(k+1))/\operatorname{integral}(\mathbb{B}_{k+1}(t),t(k-1),t(k+1))\). Step 13 \(Gstar(2k+1)=\operatorname{integral}(g(t,Xstar(2k+1),Ystar(2k+1))\mathbb{B}_{k+1}(t ),t(k-1),t(k+1))/\operatorname{integral}(\mathbb{B}_{k+1}(t),t(k-1),t(k+1))\). Step 14 \(X(k+1)=X(k)+H(F(k)+Fstar2(k+1))/2\). Step 15 \(Y(k+1)=Y(k)+H(G(k)+Gstar(2k+1))/2\). end.  OUTPUT: Approximation \(X\) and \(Y\) to \(x\) and \(y\), respectively at the (\(N+1\)) values of \(t\).
```  
**Algorithm 1** One-step algorithm for system of ODEs.
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 2 System Model and Problem Formulation
### Theoretical Foundations  
#### 2.2.1 Rate-Distortion Function of Mixed Source  
Given the mixed source in Figure 1, the probability of generating a source sequence \(x^{m}\) can be expressed as  
\[P_{X^{m}}(x^{m})=\alpha_{1}\,P_{X_{1}^{m}}(x^{m})\ +\ \alpha_{2}\,P_{X_{2}^{m}}(x^{m}), \tag{1}\]  
where the prior probabilities satisfy \(\alpha_{1},\alpha_{2}\in(0,1)\), \(\alpha_{1}+\alpha_{2}=1\). Even though \(X_{1}^{m}\) and \(X_{2}^{m}\) are i.i.d. sequences, the mixed source sequence \(X^{m}\) is not an i.i.d. sequence. For mixed source coding, a rate-distortion pair \((R,D)\) is said to be achievable if there exists a sequence of \((2^{mR},m)\) source coding \((f_{m},g_{m})\) that satisfies  
\[\limsup_{m\rightarrow\infty}\frac{1}{m}\mathbb{E}[d(X^{m},g_{m}(f_{m}(X^{m})) )]\leq D, \tag{2}\]  
where \(d(\cdot,\cdot)\) denotes the distortion measure and mean squared error (MSE) is adopted in this paper.  
Based on information spectrum methods [29], given a distortion constraint \(D\), the infimum achievable rate of the mixed source coding is  
\[R(D)=\max\{R_{1}(D),R_{2}(D)\}, \tag{3}\]  
where \(R_{i}(D)\) for \(i\in\{1,2\}\) is the rate-distortion function [30] with respect to the i.i.d. source sequence \(X_{i}^{m}\), given by  
\[R_{i}(D)=\min_{Y_{i}:\mathbb{E}[d(X_{i},Y_{i})]\leq D}I(X_{i};Y_{i}). \tag{4}\]  
This result indicates that the rate-distortion relationship of the mixed source coding is determined by the rate-distortion functions of individual i.i.d. subsources. As a result,  
Figure 1: The BCOC model.  
two separate sets of encoders and decoders are employed to compress the subsources respectively as illustrated in Figure 1. Furthermore, the reconstructed sequence, either \(Y_{1}^{m}\) or \(Y_{2}^{m}\), is an i.i.d. sequence generated with respect to the marginal probability distribution \(P_{Y_{1}}\) or \(P_{Y_{2}}\). It can be straightforward to prove that the rate-distortion function of mixed source has an alternative expression as  
\[R(D)=\min_{P_{Y_{1}},Y_{1},P_{Y_{2}},X_{2}} \max\{I(X_{1};Y_{1}),I(X_{2};Y_{2})\}\] (5) subject to \[\mathbb{E}[d(X_{1},Y_{1})]\leq D,\] \[\mathbb{E}[d(X_{2},Y_{2})]\leq D.\]
# Effect of Particle Size on Composet Analysis by Portable X-ray Fluorescence
## 3 Results and Discussion
### Stability and Performance of the pXRF Analyzer  
The concentrations determined with pXRF in the certified reference material (CRM) SdAR-M2 are shown in Table 3. During the development of the experiments, this sediment sample was analyzed 21 times (12 and 9) in two periods of time more than one year apart.  
The concentrations of the elements Cd, Cr, Cu, Mn, Ni, Sr, Zn, and Fe in the CRM were of the same order of magnitude as they were in the compost samples.  
The average recoveries for the elements As, Cr, Cu, Pb, Rb, Sb, Sr, Th, Zn, Fe, and Ca were \(\pm\)10% of the certified values. The average recoveries for the elements Ba, Mn, S, Th, K, and Si were between \(\pm\)10% and \(\pm\)20% of the certified values. Only for the elements Ag, Ni, and Ti were the recoveries greater than \(\pm\)20% of the certified values, and in the case of Ag and Ni, this deviation was due to the proximity of their concentrations in the CRM to the detection limit of the instrument. The USEPA method for soil and sediment analysis by pXRF states that the measured value for the analyte in the CRM samples should be within \(\pm\)20% of the true value for the check to be acceptable [21].  
Similarly, the average of the absolute relative deviations (RDs) was less than 10% for the elements Cu, Pb, Rb, Sb, Sr, Zn, Fe, and Ca, indicating their high instrument stability. The mean RD was between 10 and 20% for the elements Ba, Cr, Mn, S, and K, indicating the acceptable instrument stability of these elements, while the RD was greater than 20% for Ag, Cd, Ni, Th (these four elements had values close to the detection limit), Ti, and Si. It is worth noting here that Ti and Si are two elements rarely considered in compost studies, because they are nonessential nutrients for plants and are nontoxic.
# Design of a Compact Ultra-Wideband Microstrip Bandpass Filter
## 4 Measurement and Simulation Results
[17], although the structure coated with various oxides achieves a smaller size and lower insertion loss, the bandwidth is relatively narrow. In Ref. [20], a filter with a 3 dB FBW of 58% and low insertion loss is realized by a multilayer structure, but its size is too  
Figure 11: Group delay of the proposed UWB filter. large, and the design structure is complex. Refs. [21; 29] are filters with MPCB structure, which have advantages in performance of stopband, but the insertion loss and return loss in passband are relatively poor. The proposed filter has a compact structure, a relatively small volume, a large fractional bandwidth, narrow roll-off bandwidth, excellent in-band reflection characteristics and competitive insertion loss. Compared with other works, it can be found that the cost generally comes from two aspects: materials and processing methods. In comparison with single-layer designs, the multilayer structures have more dielectric substrates and adhesive materials, and the cost will inevitably increase. However, the corresponding multilayer structures also improve their performances, which are mainly reflected in the reduction of insertion loss and return loss in this work. Compared with other LCP or LTCC methods, the cost of MPCB is mainly lower in the processing method. In the works with the same MPCB structure, the Rogers RO4350 dielectric substrate used in this paper is cheaper and the etching pattern is simpler, with only two blind holes, so the overall processing cost is relatively low. The application scenarios of this work are radio positioning, satellites and radar systems. In these application scenarios, the electronic devices we use are required to integrate a variety of functional elements in a limited space, which requires the integrated electronic devices to have miniaturization characteristics. All kinds of multilayer structures can reduce the design difficulty and meet the requirements of reducing the size. However, the design based on MPCB will make the location design of vias and material selection of dielectric substrates more extensive, contributing to improving diversification and novelty of design in the future. At the same time, this MPCB method is well-developed now, leading to low cost and easy integration with other contemporary electronic devices, which has great potential in the 5G era.