esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Determination of Duncan-Chang Model Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**S (\%)**} & \multicolumn{4}{c}{**K**} & \multicolumn{4}{c}{**N**} \\ \cline{2-11}  & \(\mathbf{A_{1}}\) & \(\mathbf{B_{1}}\) & \(\mathbf{C_{1}}\) & \(\mathbf{D_{1}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{A_{1}}\) & \(\mathbf{B_{1}}\) & \(\mathbf{C_{1}}\) & \(\mathbf{D_{1}}\) & \(\mathbf{R^{2}}\) \\ \hline
0.0 & 42.01 & \(-\)0.15 & 56.04 & \(-\)0.0016 & 0.9992 & \(-\)0.17 & \(-\)0.24 & 0.51 & 0.00034 & 0.999 \\
0.5 & 31.10 & \(-\)0.14 & 55.48 & \(-\)0.0018 & 0.9997 & \(-\)0.16 & \(-\)0.34 & 0.51 & \(-\)0.00004 & 0.999 \\
1.0 & 19.79 & \(-\)0.14 & 55.65 & \(-\)0.0023 & 0.9998 & \(-\)0.08 & \(-\)0.46 & 0.51 & 0.00036 & 0.999 \\
2.0 & 27.38 & \(-\)0.15 & 51.43 & \(-\)0.0018 & 0.9997 & \(-\)0.14 & \(-\)0.56 & 0.55 & \(-\)0.00046 & 0.999 \\
3.0 & 25.94 & \(-\)0.12 & 48.27 & \(-\)0.0014 & 0.9998 & \(-\)0.17 & \(-\)0.42 & 0.58 & \(-\)0.00102 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Parameters of exponential functions that can reflect the relationship of K and N to FTC under different S.  
Figure 8: Variation of K and N with FTCs of different confining pressures: (a) K and (b) N.  
In conclusion, the experience formula of the five material constants contained in the tangent deformation modulus of the Duncan-Chang model were established, so the variation regular of the tangent deformation modulus with the FTC could be determined by unconsolidated undrained tests. In the triaxial unconsolidated, undrained shear tests, no volumetric strain occurred in the samples. So, material constants G, F, and D that calculate tangential Poisson's ratio in the E-umodel (initial tangent modulus - tangent Poisson's ratio model) could not be obtained, nor could the K\({}_{\text{b}}\) and m of the E-B model (initial tangent modulus -bulk modulus model) be obtained. According to the generalized Hooke's law, the value of \(\mu_{\text{t}}\) was 0.5 when the sample strain was 0. In order to ensure the stability of numerical calculation, Poisson's ratio is usually 0.49 [35].
## 1 Introduction
### Sound Quality Performance
A higher SNR implies that the earphone can produce a more accurate and detailed sound by minimizing the amount of noise and interference that can degrade the quality of the audio signal. Therefore, a high SNR is a crucial factor to consider when evaluating the performance of earphones. Fifthly, impedance is a measure of the resistance that an earphone offers to the flow of electrical current. On the other hand, the evaluative factor is similar to the power of output, which indicates the amount of power that the earphone can deliver to the listener. Both impedance and power of output are crucial factors to consider when selecting earphones, as they can significantly impact the overall sound quality and listening experience. In essence, impedance is typically measured in ohms and indicates how much power is lost as the current flows through the earphone. Low impedance indicates high quality, as it ensures that the earphone draws maximum power from the source. Finally, apart from measurement with devices, headroom refers to the ability of an earphone to generate a short burst of sound without distortion or clipping. It is a subjective measure of the ability of the earphone to provide a thrilling experience to the listener, especially in the case of action movies or music with sudden loud sounds. High-quality earphones should have good headroom to deliver a powerful and exciting listening experience. It is worth noting that the same setting was preserved in the hosting company of the data in order to ensure reproducibility. Nonetheless, as the current work would like to gain inspiration for the design, alignment of the scale may not be an important concern, along with the slight diversity of the 388 sets of designs.
###### Abstract  
Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m\({}^{2}\)) of 1.77, 1.36, 1.11, and \(-\)0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.  
water vapor retrievals; validation; sampling effects; MODIS; MIRS; IASI; GPS 2020, 12, 1170; doi:10.3390/rs12071170
# Development of Operator Theory in the Capacity Adjustment of Job Shop Manufacturing Systems
## 4 Capacity Control
### Decoupling Control  
In (11), there exist couplings between all workstations, which can be described as \(n\) linear equations. Solving the \(n\) linear systems:  
\[u_{j}(\cdot)=\sum\limits_{k=1}^{n}D_{jk}(w_{k})(\cdot),\qquad j=1,\ldots,n,\]is obtained. To avoid the difficult computation of an RRCF control for the MIMO system, decoupling as proposed in [29] was utilized to transfer it into multiple SISO systems. To obtain \(n\) independent SISO systems, the decoupling operators \(H\) and \(G\) as shown in Figure 6 need to satisfy the following theorem, cf. Theorem 1 in [29].  
**Theorem 2** (Decoupled RRCF Control).: _If \(G_{j}\) is linear and:_  
\[\sum_{k=1,k\
eq j}^{n}[H_{jk}(w_{j})](w_{k})+G_{j}\circ D_{jk}(w_{ k})=0 \tag{13}\] \[H_{jj}(w_{j})+G_{j}\circ D_{jj}(w_{j})=F_{j}(w_{j}), \tag{14}\]  
_then the MIMO system is decoupled and \(F_{j}\) is stable and invertible. Here, \(\mathbf{F}=(F_{1},\ldots,F_{n})\) represents the decoupling operator with \(v_{j}=F_{j}(w_{j})\)._  
Applying the latter to the job shop system, the following proposition can be concluded.  
**Proposition 1** (Decoupled RRCF Control for Job Shop System).: _Consider a plant (9) as well as decoupling parameters \(h_{j}\) with \(\frac{1}{v_{j}^{\mathrm{RMT}}}\
eq|h_{j}|<\infty\) for \(j=1,\ldots,n\) and let \(\mathbf{G}=(G_{1},\ldots,G_{n})\) be the identity operator, \(H_{jj}\) unimodular for \(j=1,\ldots,n\), such that:_  
\[\sum_{k=1,k\
eq j}^{n}[H_{jk}(w_{j})](w_{k})=-\sum_{k=1,k\
eq j}^{n}G_{j}\circ D _{jk}(w_{k}),\]  
_holds. Then:_  
\[F_{j}(w_{j})=(h_{j}-\frac{1}{v_{j}^{\mathrm{RMT}}})\cdot w_{j}-\frac{v_{j}^{ \mathrm{DMT}}n_{j}^{\mathrm{DMT}}}{v_{j}^{\mathrm{RMT}}},j=1,\ldots,n, \tag{15}\]  
_holds. Additionally, if \(n^{\mathrm{RMT}}\) is sufficient large, then \(F_{j}(w_{j})\) is stable and invertible._  
Proof.: From (13) and (14), we obtain:  
\[F_{j}(w_{j})=H_{jj}(w_{j})+G_{j}\circ D_{jj}(w_{j}).\]  
As \(\mathbf{G}=(G_{1},\ldots,G_{n})\) are the identity operators and \(H_{jj}\) unimodular operators \(H_{jj}=h_{j}\cdot w_{j}\) for \(j=1,\ldots,n\), then plant (9) has:  
\[F_{j}(w_{j}) =H_{jj}(w_{j})+D_{jj}(w_{j})\] \[=(h_{j}-\frac{1}{v_{j}^{\mathrm{RMT}}})\cdot w_{j}-\frac{v_{j}^{ \mathrm{DMT}}n_{j}^{\mathrm{DMT}}}{v_{j}^{\mathrm{RMT}}},j=1,\ldots,n.\]  
Figure 6: Decoupling control of multi-input–multi-output (MIMO) system.  
As \(h_{j}\
eq\frac{1}{v_{j}^{RMT}}\), it is a linear operator. Additionally, considering \(n^{RMT}\) is sufficient large, \(F_{j}\) is invertible. Its norm is:  
\[\|F_{j}\|=|h_{j}-\frac{1}{v_{j}^{RMT}}|.\]  
As \(h_{j}<\infty\) and the production rate of RMTs \(v_{j}^{RMT}\) is a positive constant, hence, \(\|F_{j}\|<\infty\). Thus, from Definition 1, this operator is stable, showing the assertion.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### Stringent Response-Related Molecules
#### 3.6.1 (P)ppGpp  
PpGpp and pppGpp, collectively referred to as (p)ppGpp, are pyrophosphate esterified to the 3' carbon of ribose. Mtb possesses a gene named _rel\({}_{M\text{th}}\) (Rv2583c)_, which reacts to starvation by generating intracellular (p)ppGpp [48]. Rel\({}_{M\text{th}}\) catalyzes the transfer of pyrophosphate from ATP to GDP or GTP to synthesize ppGpp and ppGpp through its synthetase domain. Rel\({}_{M\text{th}}\) also catalyzes the hydrolysis of (p)ppGpp into PPi and GDP or GTP via its hydrolase domain [48]. [PERSON] et al. observed that the mutation of the _rel\({}_{M\text{th}}\)_ synthetase domain in Mtb resulted in a (p)ppGpp synthetase defect. Slow growth and delayed biofilm and pellicle formation were observed in this strain [32]. In short, stress and starvation stimulate the synthesis of Rel\({}_{M\text{th}}\) to catalyze the production of (p)ppGpp, which performs a series of cellular activities including biofilm formation.
## 4 Experiments, Results, and Analysis
### Typical Example  
To demonstrate a normative scenario, consider a composite signal generated by adding the signals described in Table 7. Figure 13 compares the CDF of the composite signal's voltage levels, as estimated by the DNN [3], with the ground truth, showing that the two distributions closely align. Additionally, we examine three scenarios detailing the probabilities of surpassing specific voltage levels. For instance, the estimated distribution indicates that 1% of the voltage levels exceed 1.26 V. Such insights are valuable when determining the DAC's gain. For example, if the DAC's operational range is \(\alpha\) V and \(G\) is the gain, then setting \(G=\frac{\alpha}{1.26}\) ensures that only 1% of the signal samples will be clipped [3].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(a\) & \(b\) & \(s\) & **Overall** \\ \hline LR & 0.4926 & 0.4484 & 0.6994 & 0.5468 \\ K-NR & 0.4795 & 0.5297 & 0.4447 & 0.4846 \\ DTR & 0.5578 & 0.7218 & 0.9400 & 0.7398 \\ RFR & 0.7651 & 0.8527 & 0.9745 & 0.8641 \\ GBRT & 0.8381 & 0.8982 & 0.9928 & 0.9097 \\ SVR & 0.5908 & 0.6814 & 0.9142 & 0.7288 \\ S-DNN & 0.8311 & 0.9548 & 0.9952 & 0.9270 \\ DNN & 0.9358 & 0.9664 & 0.9985 & 0.9669 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of \(R^{2}\) scores obtained using different multi-output regression techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modulation Order** & **Data Rate (bps)** & **Power Level (dBm)** & **Normalized Frequency (kHz)** \\ \hline
64 & 6753 & \(-\)23.65 & \(-\)9 \\
32 & 5566 & \(-\)23.05 & \(-\)44 \\
4 & 1460 & \(-\)26.23 & \(-\)21 \\
8 & 5518 & \(-\)22.09 & 37 \\
16 & 9751 & \(-\)10.05 & 28 \\
256 & 7993 & \(-\)5.43 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Features of six component signals used to generate a composite signal.
## 3 Results
### Monitoring of Marsh Shores and Coastal Wetlands
Thermal surveys of shallow waters and wetlands near the mouth of the Masrik River made it possible to clearly distinguish streams of riverine waters with lower surface temperature, flowing through thick cattail marshes, and using the temperature gradient to identify where it mixed with the lake waters (Figure 11). Figure 11: Structure of coastal wetlands in the mouth of Masrik river by thermal (**a**) and optical (**b**) UAV imagery. Circles with figures marked by specific thermal signatures of the following objects and phenomena: 1—mixing of riverine and lacustrine waters, 2—living tree vegetation, 3—decay of dead submerged vegetation, and 4—small path of river flow undetected in optical imagery. This result is well consistent with time series analysis of multispectral high-resolution PlanetScope satellite imagery. Even in a period of intense algal blooming, the area influenced by the mouth of the Masrik River is distinguished by a characteristic plume of turbid surface waters with low primary productivity (according to SABI values spatial distribution, see Figure 10).
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 4 OBFNs with Phase Shifter Architectures
### Polarization-Modulated Phase Shifter Architectures  
One phase shifter architecture for the OBFN receiver is presented in Figure 17a, which is a polarization-modulated phase shifter architecture that is composed of a laser diode (LD), a polarization division-multiplexing MZM, a PBS, an optical band-pass filter (OBPF), a \(1\times N\) splitter, \(N\) polarization controllers (PCs), \(N\) polarizers, and \(N\) PDs [97]. Via carrier-suppressed double-sideband modulation at the MZM, sidebands corresponding to the RF signal and local oscillator (LO) signal with orthogonal polarization directions are produced, separately. Then, the upper sidebands of the RF signal and LO signal, extracted by the OBPF, beats at the PD and down-converts into an intermediate frequency (IF) signal in this OBFN receiver architecture. The phase of each IF signal is controlled by the corresponding PC which adjusts the light polarization direction and further tunes the phase difference produced at the polarizer [98]. A similar architecture is presented in Figure 17b, in which the laser light is oriented at an angle of \(45^{\circ}\) originally and is modulated by a polarization modulator [93]. The functions of the tunable PC, polarizer and OBPF are same as those applied in the architecture before. The phase of the RF signal at one antenna can be given by Equation (15) [25], where \(\phi_{i}\) is the phase of one antenna in the PAAs, and \(\alpha_{i}\) is the polarization angle between one principal axis of the PolM and the polarization direction aligned by a PC. The phase induced by the phase shifter can vary in a range between 0 and \(2\pi\), if \(\alpha_{i}\) changes from 0 to \(\pi\). These architectures both have a key advantage in that the amplitude of the RF signal in each antenna remains unchanged when tuning the phase, since the PC, PBS and polarizer will not influence the magnitude of a circularly polarized optical signal [93]. However, the tuning speed of the PC may be too low to meet the demand of beam sweeping for PAAs. One solution to improve the tuning speed is to use the other PolM to change the phase through the electric control of its DC voltage [99].  
\[\phi_{i}=\frac{\pi}{2}+2\alpha_{i} \tag{15}\]  
Figure 16: Timeline of phase shifter architectures proposed in the past [16; 91; 92; 93; 94; 95; 96].  
Figure 17: Schematic diagrams of OBFNs based on polarization-induced phase shifter using (**a**) polarization division-multiplexing MZM (reprinted with permission from [97] © The Optical Society), and (**b**) PolM (reprinted with permission from [93] © The Optical Society).
## 3 Results and Discussion
### Characterization of AuNPs
#### 3.2.6 FT-IR Spectroscopy  
The functional groups in plant aqueous extract, as well as in plant-mediated AuNPs, were identified by Fourier transform infrared spectroscopy (FT-IR). As shown, the plant aqueous extract has nine peaks at wavenumbers of 3419, 2077, 1633, 1408, 1309, 1102, 1016, 700, and 515 cm\({}^{-1}\). These peaks are shifted after reacting with gold to produce AuNPs, in addition to the appearance of new peaks (Figure 4B and compiled assignments in the chart of phytosynthesized AuNPs in Table 2). The many functional groups present in plant aqueous extracts, such as amines, carbohydrates, proteins, and amino acids, can be involved in the reduction, capping, and stabilization of the as-formed AuNPs (based on the modification of their specific bands: shift or disappearance/appearance of new bands) [51] through the interaction of metal with various metabolites. This can be identified through the shift of some peaks or the changes in their relative intensities. Herein, changeswere observed in the peaks at 3419, 1633, 1408, 1102, 1016, 700, and 515 cm\({}^{-1}\), which were shifted to 3423, 1620, 1417, 1128 1047, 780, and 513 cm\({}^{-1}\), respectively (with decreases in their relative intensities). This is consistent with previous observations [75; 76]. It is noticeable that in the case of citrate-mediated synthesis of AuNPs, the FT-IR spectra show a much simpler profile with two main peaks corresponding to C=O symmetric stretching (at 1635 cm\({}^{-1}\)) and C-H stretching vibration (at 1381 cm\({}^{-1}\)). With the more complex composition of the ginger extract, many other functional groups representing the capping molecules that stabilize the AuNPs were also identified.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vibration** & **Plant Extract** & **AuNPs** & **References** \\ \hline N-H/O-H stretching (overlapped) & 3419 & 3455 & [77] \\ C-H stretching of the aliphatic hydrocarbons & & 2927, 2855 & [78] \\ Aromatic compounds overlapped with \(\
u\)(C-O) & 2076 & & [71; 79] \\ C=N, C=O of amide and N-H of the primary amine stretching (overlapped) & 1635 & 1620, 1584 & [78] \\ O-H, N-H of the secondary amine and C-H binding (overlapped) & 1409 & 1417 & [78] \\ \(\
u\)(C-O-C) carbohydrate, C-O stretching (overlapped) & 1097 & 1128 & [80] \\ C-N stretching & 1049 & 1047 & [81] \\ Aromatic C-H in plan bend & & 879, 838 & [82; 83] \\ C-H Out of plane bending & & 780, 698 & [77] \\ C-S, C=S (overlapped) & 529 & 513 & [84] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assignment of peaks of plant aqueous extract and as-formed AuNPs.  
Figure 4: Characterization of materials. (**A**) UV-Vis spectrum of AuNPs (before and after purification); (**B**): FT-IR spectra of plant extract (aqueous extraction) and as-formed AuNPs.
## 3 Simulation and Analysis
### Two Satellites under Synchronous Scenarios
#### 3.1.2 Results in Downlink and Analysis
All of the ranging errors in downlink versus different PWDFs and CNRs are illustrated in Figure 9. We note that, when the CNR is 50 dB and the PWDF exceeds 20 dB, the I branch cannot work normally. In addition, when the CNR is 60 dB and the PWDF exceeds 25 dB, the phenomenon is similar to that noted previously. Therefore, it is essential to select a suitable PWDF under various CNRs, or the communication performance cannot be guaranteed. With the assistance of the Doppler frequency shift and the code phase acquired from the I branch, the Q branch can be converted to tracking directly, which uses the joint Costas loop and Gardner timing loop to recover local carrier and to realize bit synchronization. Figure 10 shows the BER of the Q branch in downlink versus CNR and PWDF. Both the simulated and the theoretical BERs in downlink versus different PWDFs and CNRs are illustrated in Figure 10. The theoretical BERs are calculated by \((1/2)\cdot erfc\big{(}\sqrt{SNR}\big{)}\), where \(erfc(\cdot)\) denotes the complementary error function and SNR is transformed from CNR according to the baseband bandwidth. Empirically speaking, the evaluation standard of communication performance, i.e., BER, can be adopted as \(10^{-5}\). When the CNR is 80 dB, the BER cannot achieve the standard until the PWDF reaches 5 dB. In addition, it can be seen that increasing \(\alpha\) is an efficient but limited means to improve BER. A prominent improvement is achieved when the factor \(\alpha\) increases from 0 to 10 dB, but the rate of improvement slows as it increases beyond 15 dB. In reality, a trade-off exists between the ranging error in the I branch and the BER in the Q branch, thus a reasonable PWDF in downlink should be chosen to balance the performance of the two links. As a suggestion, when the PWDF is selected in the range from 10 to 20 dB, both ranging error and BER behave better and the system performs efficiently. Figure 9: Ranging error in downlink under different CNRs and PWDFs.
## 3 Results
### Preliminary Verification of the FE Film Response
#### 3.1.2 Transducers with a Polymer Coating  
HS03 transducers with different coatings were fabricated and the pulse-echo response in air compared before and after coating deposition. The purpose was to estimate the effect of the coating on the resonant response of the FE film: the thicker the coating, the larger the frequency shift and the lower the coating uniformity the larger the reduction in the resonance Q-factor. The measured \(SNS\) in pulse-echo mode in air, using the same equipment as in the previous section is shown in Figure 5. The frequency shift of the \(\lambda\)/4 thickness resonance of the transducer operated in air in pulse-echo mode is shown in Table 3.  
Figure 5: Sensitivity (\(SNS\)) vs. frequency for flat HS03 transducers with different coatings in pulse-echo mode in air.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies  
Carbon flux-assimilation inversion is a technique that integrates atmospheric carbon-assimilation models, observational data, and statistical tools to estimate the carbon dioxide absorption by ecosystems. This method enhances our understanding of carbon exchange on Earth's surface and is crucial for global carbon-cycle research. It employs an atmospheric transport model as the forward model and continuously refines prior carbon fluxes using a Bayesian and data-assimilation-based optimization algorithm by comparing simulated and observed CO\({}_{2}\) concentrations. Carbon flux-assimilation inversion leverages various technical methods, effectively capturing both anthropogenic CO\({}_{2}\) emissions and natural variations in land-air CO\({}_{2}\) fluxes, making it a prominent approach in carbon-cycle research.  
Building and applying an atmospheric CO\({}_{2}\) carbon-assimilation system involves several key aspects, including selecting assimilation algorithms, designing assimilation frameworks, processing observational data, and weighting prior fluxes. Due to the complexity and breadth of the global carbon-assimilation system, providing a comprehensive overview in a brief space is challenging. Consequently, this chapter offers an in-depth introduction to the historical development, primary research methods, notable achievements, and recent advancements in atmospheric carbon-assimilation systems.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 4 Numerical Results
### FR2 Operation Mode  
In a similar way to the previous results, Figures 8-10 illustrate the comparison of the different decoding percentages of the DL PHY channels of the three considered algorithms for the FR2 operation mode. We can verify that the designed BSAE algorithm resulted in higher channel-decoding percentages compared to the beam pair selection algorithm based on RSRP maximization. Additionally, the BSAE algorithm achieved 100% throughput for values higher than \(E_{b}/N_{0}=-24\) dB, which corresponds to a 4 dB performance gain. When comparing the decoding throughputs of the proposed and an optimal beam pair determination algorithm, it is still possible to observe similar performances.  
Figure 8: Decoding percentage of PBCH for FR2.  
Figure 9: Decoding percentage of PDCCH for FR2.  
The respective performance results in terms of BER are presented in Figure 11, where the proposed BSAE algorithm presents a clear improvement over beam pair selection based on the RSRP maximization algorithm. The performance gain is around 15 dB at the BER target of \(10^{-3}\).