esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Saprolite Thickness Needed to Remove _E. coli_ from Wastewater
## 2 Materials and Methods
### Laboratory Analyses
#### 2.4.2 Other Properties  
The disturbed soil samples collected from the horizons in the pits were used for particle size distribution and chemical analyses. These materials were air-dried, crushed, and passed through a 2-mm (No. 10) sieve. Particle size distribution was determined by the pipet method [23] using 10 to 20 g of the air-dried specimen. Each specimen was first treated with 30% H\({}_{2}\)O\({}_{2}\) and heated in a constant temperature bath to remove its organic matter. The mineral particles were then passed through a 300 mesh (0.05 mm) sieve into a 1000 mL cylinder. The sand portion that was retained by the 300 mesh sieve was oven-dried at 105 to 110 \({}^{\circ}\)C and fractionated by dry-sieving through a nest of sieves. The silt and clay fractions were determined by sampling the suspension in the 1000 mL cylinder using a 25 mL pipet [23]. The cation exchange capacity (CEC) for each of the disturbed saprolite samples collected from each pit was determined at pH 7 using a procedure for acid soils [24].  
Regression analyses were performed to assess the relationships between selected saprolite properties and the calculated saprolite thickness for removing _E. coli_ using R-Studio's (lm) function (R Studio, Boston MA) for general linear models. Variables in the regression models that had significance values of 0.05 or less were included here.
## 2 Literature Review
### QFD-Integrated Technologies  
QFD originated in Mitsubishi's Kobe shipyard in 1972, which is a process that helps businesses translate real CRs into product features, engineering characteristics, and production details, ultimately creating products that meet customer satisfaction [30]. QFD can effectively enhance design quality, reduce engineering time, optimize design and manufacturing, and decrease the number of product components. It typically includes four interrelated processes that translate CRs into technical characteristics, product features, and manufacturing processes, with the HoQ being the core for mapping the relationship between CRs and ECs [31]. By analyzing and quantifying the correlation between CRs and ECs, the results are input into the HoQ for calculation and ranking, which then guide the product design and development based on the prioritized ECs [32]. The key elements in the HoQ are customer requirements, engineering characteristics, the correlation matrix, competitive analysis, and the importance ratings of CRs and ECs [33]. Experts rate the correlation of CRs and ECs using multi-criteria decision analysis, marking it in the HoQ for calculation [34] (Figure 1).  
Recently, scholars have begun to explore the combined application of QFD with other methods, thereby optimizing the application scenarios of this method and enhancing efficiency. QFD method was applied in a scenario-driven approach for the sustainable design of products and service systems, effectively capturing the dual-layer requirements of medical experts and elderly users through the study of nursing beds, and proposed a more accurate functional requirement solution [35]. Radical Innovations (RIs) were introduced into the framework of QFD and TRIZ, supporting the RIs process by extending it across two stages of the HoQ method. The effectiveness of this innovative approach was validated by using the example of a wind power generation system [36]. The Kano model and the TOPSIS algorithm were integrated into the QFD model to measure uncertainties and behavioral risk factors in Chinese e-commerce service systems, demonstrating that  
Figure 1: Components of the HoQ.  
this integrated approach has good flexibility in dealing with rapidly changing CRs [37]. ANP and fuzzy mathematics were integrated into QFD to develop new equipment for squeezing polyethylene pipes in a real-world scenario, demonstrating that multi-criteria decision-making and fuzzy algorithms can enhance the precision of the QFD process [14].  
In summary, QFD can translate customer needs into engineering features in product development, thus potentially making it a valuable tool for SMEs to integrate CR information when screening concept sketches for PI. However, the traditional HoQ demands extensive data on CRs and ECs and is time-consuming to process, which hampers accurate decision-making with fewer experts. Moreover, the single-point values in the relationship matrix assessment fail to accurately capture the evaluators' subjective and fuzzy information.
# Generation of Sparse Antennas and Scatterers Based on Optimal Current Grid Approximation
## 2 Review of Approaches for Reducing Mass and Surface of Various Antenna Structures Using the OCGA
To meet the far-field conditions, the UHF dipole antenna is placed 4 m away from the horn antenna. The characteristics of the antenna are measured using a vector network analyzer R4M in an anechoic chamber to enhance accuracy. Based on the sparse WG structures obtained from simulations, the corresponding wires in the initial WG structure are cut using simple tools. Sparse WG structure with GEET = 40% was created. When using OCGA with GEET = 40%, some free wires appeared in the sparse structure, so the modified OCGA was used to connect free wires to the WG structure. The main characteristics were measured for the sparse WG structure and compared with experimental results for the original WG structure in the operating frequency range of 0.7-1.3 GHz. In [63], it is noted that when a lightmass antenna is required while maintaining acceptable main characteristics, GEET = 40% is reasonable choice. This structure is up to 1.41 times lighter than the original WG structure and up to 7.63 times lighter than the solid structure. This is extremely important in modern devices and systems, particularly in satellite or military applications. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Antenna** & _f,_ **GHz** & **Approach for** & **Approach for** & **Current Distribution** & **Frequency for** \\  & & **Reduction** & **Characteristics** & **for Sparse Structure** & **Characteristics** \\ \hline Reflector & 3.4–3.7 & E/OCGA & NCOCGA & at the lowest frequency &
\begin{tabular}{c} at the center \\ frequencies \\ at higher frequencies \\ \end{tabular} \\  & 10–12 & E/OCGA & NCOCGA & at the lowest frequencies & at higher frequencies \\ \hline Horn & 0.7–1.3 & E/OCGA & NCOCGA & at the center and & at higher frequencies \\  & 8–12 & OCGA & COCGA & at the lowest frequencies & at higher frequencies \\  & 18–38 & E/OCGA & NCOCGA & at the lowest or & at higher frequencies \\  & 5.3–6.3 & E/OCGA & NCOCGA & at the lowest or & at lowest and \\  & 5.3–6.3 & E/OCGA & NCOCGA & center frequencies & higher frequencies \\ \hline Patch & 2–3 & E/OCGA & NCOCGA & at low and & at a particular \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The conclusions and recommendations are given after comparative analysis of the sparse WG structures in different works.
## 3 Results
### Mapping Flight Test
#### 3.2.2 Irradiance Data Assessment  
To evaluate the accuracy of AIRS irradiance data, a comparison was made between irradiance measurements on-board by AIRS, on-board by unstabilized irradiance sensor, and on-ground reference by the ASD FieldSpec Pro (Figure 6). During the first 10 min of the flight (minutes 5-15 in Figures 4 and 6), the illumination changed only slowly, allowing coarse numerical comparison of the on-ground and on-board measurements. For data during this period, the correction improved the R\({}^{2}\) value between the on-board and on-ground measurements from 0.57 to 0.81. The correction also decreased the noise and variations unexplained by the on-ground reference from \(\pm\)4.6% to \(\pm\)2.5%.  
Figure 6: (a) Irradiance during the mapping flight as measured on board by AIRS, on board by an unstabilized photodiode, and on the ground by the ASD FieldSpec Pro. As the photodiodes have only relative radiometric calibration, for the plotting purposes, their level was normalized to the FieldSpec irradiances. The illumination changes appear non-concurrently in the on-ground and on-board data, as the measurements took place in different locations. (b) Scatter plot comparing on-board irradiances to on-ground reference during minutes 5–15 when changes in illumination were slow.  
To evaluate the performance of AIRS irradiance data in image processing, three traditional reflectance factor conversion methods were tested against the AIRS method. The raw images on the 844 nm spectral band were processed to reflectance factor images and orthomosaics using the using the methodology described in Section 2.5. Figure 7 shows the produced orthomosaics produced with each method.
## 3 Classification of MPPT
### Fuzzy Logic Based MPPT  
The advantage of fuzzy logic is without dealing with nonlinearity and without precise mathematical model, it can work with inaccurate input. Fuzzification, rule base table and defuzzification, these are the steps involve in fuzzy logic. The input for fuzzy logic are error signal and change in \(CE\) which is given by Equations (6) and (7) and output is duty cycle.  
\[E(k)=\frac{P_{ph}(k)-P_{ph}(k-1)}{i_{ph}(k)-i_{ph}(k-1)} \tag{6}\]  
\[CE(k)=E(k)-E(k-1) \tag{7}\]  
The accuracy of fuzzy logic depends on the number of fuzzy level involved in algorithm. In algorithm, first \(E(k)\) and \(CE(k)\) is calculated. Based on these values, a rule based table is formed. The last stage in fuzzy algorithm is defuzzification in which linguistic variable is converter to numerical value with the help of membership function.  
Figure 6: Flowchart of incremental conductance (INC) method.
## 4 Antennas for Beam Steering and Beamforming
### Beam-Steering/Beamforming Techniques and Their Possible Features for CubeSat Communications
The method involves electronically adjusting the surface properties to direct beams dynamically. The challenges with this technique include the need for sophisticated control algorithms and potential signal degradation. Nevertheless, the outcomes indicate improved beam steering flexibility and efficiency, significantly impacting future wireless communication technologies. Metasurfaces and reconfigurable antennas offer precise beamforming and beamsteering without mechanical movement, making them ideal for modern communication systems. Various designs, including reflective, tunable, and Huygens' metasurfaces, enable wide-angle and adaptive beam control, benefiting CubeSats, UAVs, and optical systems. While these structures improve the compactness and efficiency, challenges like the fabrication complexity and bandwidth limitations remain. Advances in tunable materials and electronic control continue to enhance their adaptability, reinforcing their potential for next-generation wireless and satellite communications. In addition, metasurfaces and reconfigurable structures outperform traditional beam-steering methods like mechanical steering and phased arrays by offering compactness, low power use, and real-time beam control without moving parts. Unlike phased arrays, which require complex phase shifters, metasurfaces use subwavelength elements for precise wavefront control, reducing the system's complexity and cost. Reconfigurable designs further enhance adaptability to changing environments with minimal hardware modifications. These advantages make them ideal for CubeSats, UAVs, and next-generation wireless networks, for which the size, weight, and energy efficiency are crucial. As a vital part of any communication system, antennas should be fully understood so that all of their requirements and characteristics can be used for CubeSat and satellite communications. The following section illustrates more passive antennas utilized for beam steering in CubeSat communications. A brief of some metasurface and reconfigurable antennas are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Efficiency (\%)**} & \multicolumn{2}{c}{**Dimensions (mm**} & \multirow{2}{*}{**Technique**} & \multirow{2}{*}{**f\({}_{t}\) (GHz)**} & \multirow{2}{*}{**Steering Range (\({}^{\circ}\))**} \\  & & & \(\times\) mm\({}^{2}\)) & & & \\ \hline
[80] & N/A & N/A & N/A & metasurface & 12.5 & 10–23 \\
[81] & \textless{}10 & \textless{}90 & 120 \textgreater{
## 4 Discussion
### Potential Risks Related to the Occurrence of Mycotoxins in Compost  
The motivation behind our study was that, if mycotoxins are actually produced during composting, these harmful compounds could pose a health risk to not only wildlife or domestic animals [13] but also farmers and workers in composting facilities [10; 20; 21] or compost consumers, e.g., gardeners or florists. Therefore, in addition to examining the potential of kitchen and garden waste to form mycotoxins during composting in passively aerated conditions, we were also interested in screening for mycotoxin contamination in compost products that are provided to the local market by biowaste composting plants and garden centres. This additional survey found only trace amounts of ZEN and AFB1 (\(<\)5 \(\mathrm{\SIUnitSymbolMicro kg}^{-1}\)) in the mature compost obtained from the composting plants and no mycotoxins in the commercial compost products purchased from the garden centres (Supplementary Table S1). These findings indicate that there is little or no mycotoxin contamination of compost products from these sites.  
There are few reports on the content of mycotoxins in compost. The most recent study screened 28 different compost piles in Canada for the presence of OTA, T-2 toxin, and ZEN [13]. Although those authors found mycotoxins in 86% of the analysed samples, and the contents often exceeded the regulatory limits for mycotoxin contamination of animal feed (i.e., OTA 5 \(\mathrm{\SIUnitSymbolMicro kg}^{-1}\), T-2 toxin 100 \(\mathrm{\SIUnitSymbolMicro kg}^{-1}\)), they found the highest contents of mycotoxins only in compost from agricultural piles of decomposing animal feed and bedding and residential compost piles located in backyards. In contrast, they did not find T-2 toxin and OTA in compost collected from large-scale industrial piles of decomposing food waste, and they reported ZEN contents of 0.54-6.32 \(\mathrm{\SIUnitSymbolMicro kg}^{-1}\) with a mean of 3.43 \(\mathrm{\SIUnitSymbolMicro kg}^{-1}\) in these samples [13]. Those findings are in full agreement with our results (Supplementary Table S1). Thus, based on our findings on composting biowaste and on our survey of the local compost market, as well as the findings [PERSON] et al. [13], we believe that the environmental and health risks related to the occurrence of mycotoxins in biowaste compost produced in large-scale installations are likely to be minimal. However, further research should investigate the factors leading to the production of mycotoxins during small-scale composting, and more screenings of agricultural and residential compost piles are necessary to understand the potential threat of mycotoxin contamination resulting from the organic recycling of biowaste.
# Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration
## 4 Integrating 5G with Real-Time Digital Twins
By utilizing 5G-powered digital twins, urban areas can consistently track the status of roads, bridges, and structures [9; 10]. By examining patterns in sensor data, the digital twin can foresee possible failures, arrange maintenance in advance, and avert expensive repairs or disastrous incidents [62; 67]. This forecasting ability not only prolongs the duration of infrastructure but also improves public safety and lowers long-term operational expenses [36; 37; 55; 71]. Despite its vast potential, the widespread adoption of 5G-powered digital twins comes with significant technical and operational challenges. Deploying high-performance computing resources, secure network infrastructure, and interoperable digital twin frameworks demands substantial investment [69, 70, 71]. Since real-time digital twins rely on sensitive urban and personal data, ensuring robust cybersecurity measures, encryption protocols, and compliance with data privacy regulations is crucial. Overcoming these challenges is essential to unlocking the full potential of digital twins and realizing the vision of truly intelligent self-optimizing cities [69, 70]. By enabling seamless data-driven urban management, the synergy between 5G and digital twins is set to redefine the future of smart cities, enhancing efficiency, sustainability, and quality of life for millions of urban residents.
# Investigation of Spatial Chirp Induced by Misalignments in a Parallel Grating Pair Pulse Stretcher
## 4 Experimental results
### Temporal Chirp Measurement of the Stretched Pulse  
Since the incident angle of the pulses on the first grating and the vertical distance between the grating pair may deviate to their designed values, which will only affect the temporal chirp but not affect the spatial chirp of the stretched pulse, the temporal chirp offset to the designed value may still exist, even for a spatial chirp well-adjusted stretcher. In order to examine the performance of the stretcher, we have measured the actual temporal chirp of the stretched pulse with the time-delayed spectral interferometer. By making the stretched ps chirped pulse interfere with the unstretched fs pulse in the spectral domain, we can get the point-in-time corresponding to the wavelength in the center of the interference fringe. When setting the fs pulse with different delay times, the time corresponding to each wavelength of the chirped pulse can be obtained. Performing a linear fitting on the measured data, the linear chirp coefficient can be extracted. More theory and details about the method can be found in [26].  
Figure 7 is the optical path employed in the temporal chirp measurement experiment. First, adjust the delay at a proper position at which the time delay \(\Delta t\) between the fs beam and the ps beam is assumed to be zero. The first interferogram at \(\Delta t=0\) ps obtained in one single pulse is the top one in Figure 8a. Then, move the delayer to make the time delay \(\Delta t\) to be 20 ps between two adjacent measurements, and we can achieve more interferograms, as shown in Figure 8a, in which the spatial dimension has been compressed to show more interference patterns. Figure 8b is the interference fringe corresponding to a line of pixels in an interferogram. According to the theory, the spectrum \(\lambda_{0}\) corresponding to the center of the widest fringe is the very point we need. First, we can find out the left fringe center \(\lambda_{\mathrm{L}}\) and the right fringe center \(\lambda_{\mathrm{R}}\) of \(\lambda_{0}\), which are easier to be identified due to their narrower width, as shown in the partial enlarged drawing in Figure 8b. Then, \(\lambda_{0}\) can be calculated by the equation \(\lambda_{0}=(\lambda_{\mathrm{L}}+\lambda_{\mathrm{R}})\div 2\). With this method, we can find out all the points for interferograms with different time delays. Figure 8c is the spectrum against the time of the stretched chirped pulse. Performing a linear fitting on the measurement data, we have obtained the linear relationship between the wavelength and time, which can be expressed as \(\lambda=0.1124\times t+786.2\). For the pulses with a band width of 42 nm, its corresponding stretched pulse width is about 373.7 ps, which is very close to its designed value 374.1 ps given in Section 2.  
Figure 7: The optical path employed in the temporal chirp experiment.
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 3 Fault Handling Method and Theoretical Discussion
### Time (\(t_{4}<t<t_{5}\))  
At time \(t_{4}\), the buffer capacitor is rapidly charged to the activation voltage of the metal oxide varistor (MOV) under the dual impact of the system voltage and the induced voltage from the current-limiting inductor. The fault current surges into the MOV and begins to attenuate. By the time \(t_{5}\) is reached, the fault current from the non-faulted side has been completely dissipated in the MOV. As shown in Figure 3e, the induced voltage from the current-limiting inductor \(L_{52}\) on the faulted side forms a loop with diode D\({}_{5}\) and the discharge resistor \(R_{0}\) to dissipate the energy stored in the inductor. According to Equation (6), the fault current at time \(t_{4}\) can be determined as \(i_{dc}(t_{4})=I_{4}\). Assuming that the voltage across the MOV stabilizes at \(U_{mov}\) during operation, according to [PERSON]'s voltage law, the current flowing through the MOV is:  
\[i_{mov}=i_{dc}=I_{4}e^{-(t-t_{4})/\tau_{3}}+\frac{U_{dc}-U_{mov}}{R_{line}+R_ {F}}(1-e^{-(t-t_{4})/\tau_{3}}) \tag{7}\]  
The fault isolation time \(\Delta t\) is obtained as:  
\[\Delta t=t-t_{4}=-\tau_{3}\ln\frac{\frac{U_{mov}-U_{dc}}{R_{line}+R_{F}}}{ \frac{U_{dc}-U_{2}}{R_{line}+R_{F}}-I_{4}} \tag{8}\]At this point, the energy absorbed by the metal oxide varistor (MOV) is:  
\[W=\frac{1}{2}(L_{s1}+L_{1})i_{dc}^{2} \tag{9}\]  
In the equation, \(\tau_{3}=L_{\Sigma}/(R_{line}+R_{F})\). Without the introduction of the discharge branch, \(L_{\Sigma}=L_{\Sigma 1}\); after introducing the discharge branch, \(L_{\Sigma}=L_{line}+L_{1}+L_{s1}\). From the analysis of Equations (8) and (9), it is evident that the smaller the current-limiting reactance value, the faster the fault current decreases, resulting in a shorter fault isolation time \(\Delta t\), and consequently, the energy absorbed by the MOV decreases.