esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to Tropopmi
## 3 Results and Validation
### Examples of the Total NO\({}_{2}\) Vcd of GF-5B and DQ1  
Figure 3 presents the global monthly distribution maps of the NO\({}_{2}\) VCD from GF5B (upper row) and DQ1 (middle row) in June 2022 (left panel) and December 2022 (right panel), which represent summer and winter, respectively. Both GF5B and DQ1 clearly exhibit classic seasonal patterns in the NO\({}_{2}\) distribution: during the summer, the average NO\({}_{2}\) concentration in the Northern Hemisphere exceeds that of the Southern Hemisphere, while in winter, the situation reverses, with the Southern Hemisphere displaying higher average NO\({}_{2}\) levels. This seasonal variation is clearly illustrated in the monthly average distribution maps of TROPOMI NO\({}_{2}\). Notably, stratospheric NO\({}_{2}\) is the primary contributor to this phenomenon, rather than NO\({}_{2}\) from anthropogenic sources.  
Figure 2: NO\({}_{2}\) SCD uncertainty from GF5B and DQ1. The blue line represents the distribution of deviations in SCD from the mean values of the box for all valid pixels. A Gaussian function, fitted to the histogram data, is illustrated by the black line.  
During the summer, NO\({}_{2}\) hotspots are prominent in regions such as South Dakota in the United States, eastern South Africa, Cairo in Egypt, Kuwait, Qatar, the United Arab Emirates, and Tehran in Iran. In the winter, NO\({}_{2}\) hotspots are notable in cities such as Riyadh in Saudi Arabia, Baghdad in Iraq, Tehran in Iran, the Jing-Jin-Ji region in China, and Seoul in South Korea. GF5B and DQ1 exhibited high levels of spatial consistency in both summer and winter. The global spatial distribution characteristics of NO\({}_{2}\) demonstrated by the EMI-II are consistent with earlier research results from similar instruments such as the GOME-2 [33], OMI [34], TROPOMI [35], and EMI [12], which indicates that the retrieval results from the EMI-II can characterize the spatial distribution features of NO\({}_{2}\).  
Figure 3: Monthly global NO\({}_{2}\) VCD distributions of GF5B (**top**), DQ1 (**middle**), and TROPOMI (**bottom**) products in July 2022 (**left**) and December 2022 (**right**). (**a**) GF5B EMI Monthly global NO\({}_{2}\) VCD in July 2022 (**b**) GF5B EMI Monthly global NO\({}_{2}\) VCD in December 2022 (**c**) DQ1 EMI Monthly global NO\({}_{2}\) VCD in July 2022 (**d**) DQ1 EMI Monthly global NO\({}_{2}\) VCD in December 2022 (**e**) TROPOMI Monthly global NO\({}_{2}\) VCD in July 2022 (**f**) TROPOMI Monthly global NO\({}_{2}\) VCD in December 2022. The four red square frame in subfigure (**f**) indicate the four typical study regions with notable NO\({}_{2}\) emissions, including eastern China (region 1), most parts of India (region 2), the Arabian Peninsula and Iran (region 3), and southern North America (region 4).
## 2 Materials and Methods
### Data and Processing
#### 2.2.3 Remote Sensing Image Preprocessing  
Landsat-8 was used as the source of image data in the study presented herein. In the process of image preparation, clouds and shadows usually cause interference, thus affecting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Point No.** & **Monitoring Section** & **Longitude** & **Latitude** \\ \hline
1 & Mahuang Ditch & \(106^{\circ}44^{\prime}53^{\prime\prime}\) & \(39^{\circ}22^{\prime}35^{\prime\prime}\) \\
2 & Sand Lake & \(106^{\circ}20^{\prime}52^{\prime\prime}\) & \(38^{\circ}49^{\prime}20^{\prime\prime}\) \\
3 & Pingluo Yellow River Bridge & \(106^{\circ}39^{\prime}58^{\prime\prime}\) & \(38^{\circ}48^{\prime}46^{\prime\prime}\) \\
4 & Yingu Highway Bridge & \(106^{\circ}24^{\prime}40^{\prime\prime}\) & \(38^{\circ}21^{\prime}33^{\prime\prime}\) \\
5 & Yazidang Reservoir & \(106^{\circ}32^{\prime}12^{\prime\prime}\) & \(38^{\circ}09^{\prime}07^{\prime\prime}\) \\
6 & Yesheng Highway Bridge & \(106^{\circ}12^{\prime}58^{\prime\prime}\) & \(38^{\circ}08^{\prime}14^{\prime\prime}\) \\
7 & Jinsha Bay & \(105^{\circ}55^{\prime}34^{\prime\prime}\) & \(37^{\circ}49^{\prime}51^{\prime\prime}\) \\
8 & Xiangshan Lake & \(105^{\circ}12^{\prime}01^{\prime\prime}\) & \(37^{\circ}29^{\prime}25^{\prime\prime}\) \\
9 & Zhongwei Xiaheyan & \(105^{\circ}07^{\prime}27^{\prime\prime}\) & \(37^{\circ}29^{\prime}11^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Monitoring section information of the Yellow River basin in Ningxia.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Wave Band** & **Wavelength Range/\(\upmu\)** & **Spatial Resolution/m** \\ \hline OLI & B1-Coastal & 0.43–0.45 & 30 \\ OLI & B2-Blue & 0.45–0.51 & 30 \\ OLI & B3-Green & 0.53–0.59 & 30 \\ OLI & B4-Red & 0.64–0.67 & 30 \\ OLI & B5-NIR & 0.85–0.88 & 30 \\ OLI & B6-SWIR1 & 1.57–1.65 & 30 \\ OLI & B7-SWIR2 & 2.11–2.29 & 30 \\ OLI & B8-PAN & 0.50–0.68 & 15 \\ OLI & B9-Cirrus & 1.36–1.38 & 30 \\ TIRS & B10-TIRS1 & 10.60–11.19 & 100 \\ TIRS & B11-TIRS2 & 11.50–12.51 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landsat 8 band introduction.  
the observation and analysis of surface features [30]. In order to eliminate the effects of light conditions at different times and places, the digital meter values must be converted to radiation values. In addition, atmospheric absorption and scattering can cause energy loss and color changes in the image, further reducing the accuracy of the image. Therefore, radiometric calibration and atmospheric correction pre-processing steps are adopted to improve image quality. For atmospheric correction, the FLAASH method in ENVI 5.6 software was used in the present study to select an appropriate atmospheric model and aerosol model, and the 2-band (K-T) method was used for aerosol detection. This method estimates the aerosol optical thickness (AOT) and particle size distribution by analyzing the difference in radiation brightness between the short-wave infrared band (K band) and the thermal infrared band (T band), so as to realize atmospheric correction processing.
## 5 Performance Evaluation  
In this section, simulations are conducted based on Matlab/TrueTime [32] to evaluate the performance of the proposed sampling scheme (denoted FTT), in comparison with the conventional non-adaptive time-triggered sampling scheme (denoted TT). For simplicity, suppose all control loops in the WCS have the same settings. The controlled process is a DC motor modeled by:  
\[G(s)=\frac{1}{0.5s^{2}+6s+10}\]  
The DC motor is a physical component widely used in control systems. Details on its modeling can be found in [33]. The controller (in the control loop) for the DC motor uses the PID control algorithm, implemented as follows [3]:  
**Procedure** PID controller {  
**Input**: \(r(k)\), \(y(k)\), \(h\)  
//\(r(k)\): reference input (desired system output) at \(k\)-th sampling instant  
//\(y(k)\): measured system output at \(k\)-th sampling instant  
\(err(k)=r(k)-y(k)\);  
\(P(k)=100*err(k)\);  
\(I(k)=I(k\)-1) + 200*\(h*(err(k)+err(k\)-1))/2;  
\(D(k)=2*(err(k)-err(k-1))/h\);  
\(u(k)=P(k)+I(k)+D(k)\);  
**Output**: \(u\)  
//\(u(k)\): control command corresponding to the \(k\)-th sampling  
It is worth noting that the above program is used in each controller within the control loops, which should not be confused with the sampling period adaptation module (see Section 4.1) in each sensor, although both of them use the PID control algorithm. Both the controlled process and the controller design are kept as common as possible to reflect the wide applicability of the proposed approach. The default sampling period is 10 ms, and the maximum allowable sampling period is \(h_{max}=30\) ms. The reference input follows a square wave with a period of 4s. The data rate of ZigBee is 250 Kbps. The sizes of all data packets are 32 bytes. Since the flexible time-triggered sampling scheme is implemented in each control loop separately, the parameters \(K_{P}\), \(K_{I}\), and \(K_{D}\) in (1) can be different from one loop to another. For simplicity, the same parameters are used in all sensors: \(K_{P}=0.007\), \(K_{I}=0.006\), \(K_{D}=0.003\), \(\rho_{r}=10\%\), \(\lambda=0.7\), and \(T_{SP4}=500\) ms.  
In this work, two typical scenarios featuring workload variations are examined, respectively, i.e., system reconfiguration and radio interference. While the bandwidth demands of all control loops can be regulated through sampling period adaptation, the bandwidth demand of an interfering node cannot be intentionally changed by the system.
## 5 Conclusions  
The proposed DFT-LS-WIENER estimate performs better than L.S., LS-DFT, MMSE, and MMSE-DFT channel estimation algorithms in terms of BER performance for the channel estimation of MIMO-OFDM. When the number of subcarriers increases, the bit error rate is reduced. As the number of channel taps increases, the bit error rate also increases. As the cyclic prefix length increases, the bit error rate is reduced considerably. When the pilot frequency is increased, the bit error rate is also increased. Including the DFT technique in L.S. and MMSE improves the bit error rate performance. The computational complexing and time complexity of the DFT-LS-WIENER algorithm depends on the number of transmitting and receiving antennas in the system. If the number of transmitting antennae is N and the number of receiving antennae is M, then its time and computational is N*M. The accuracy of the proposed technique is high compared to the L.S., MMSE, LS-DFT, and MMSE-DFT channel estimation algorithms. Compared to the proposed approach, the efficiency of the introduced method attained 85%, 79.5%, 84.9%, and 84.1% compared with L.S., MMSE, LS-DFT, and MMSE-DFT. The proposed DFT-LS-WIENER algorithm is less complex and very easy to implement. The demerits include introducing the Wiener model in channel estimation, increasing the cost of implementation, and extra circuitry required for implementing the Wiener filter model. Our future works include implementing other adaptive filters such as the Kalman and Kernal filters in the channel estimation algorithms. Further, there will be minimal hardware implementation using the proposed methodology, and it will be simultaneously carried out without compromising the proposed methodology's performance.  
Conceptualization: D.S. and S.P.; methodology: S.P.; validation: R.S., F.H., and O.I.K.; formal analysis: G.M.A. and S.P.; investigation: O.I.K. and D.S.; resources: O.I.K., S.P., and F.H.; data curation: S.P. and R.S.; writing--original draft preparation: S.P. and O.I.K.; writing--review and editing: G.M.A. and O.I.K.; visualization: S.P. and D.S.; supervision: D.S., R.S., and O.I.K.; project administration: S.P., R.S., and F.H.; funding acquisition: F.H. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R236), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The study did not report any data.  
The author has no conflict of interest.
# Mdp1 _sciences_
## 3 Results & Discussion
### Detection Efficiency and Curtailment Margin Prediction for Different Methods  
After discussing the fundamental differences between a forecasting method (KF4) based exclusively on data from the target site and a representative method using exogenous data (KF2), it is interesting to inspect the performance of the different methods introduced in this work. Apart from the true-positive rate (or _detection efficiency_), already introduced in Section 2.2, it is convenient to also keep track of the false-positive rate, as the latter is an indicative measure of the energy cost associated with forecast-based curtailment (FBC).  
Figure 9 shows the true-positive (TP) and false-positive (FP) rates for all six KF-based methods introduced in Table 2, as well as the three ensemble methods (see Section 2.3) combining the predicted incompliance events from certain combinations of exogenous methods. It can be seen (Figure 9a) that all exogenous methods outperform the local method KF4 by far, with KF5 and KF6 showing both the highest detection efficiency for all individual methods and the slowest decay with \(a\), exhibiting an almost constant value of nearly 40%. Interestingly, KF6, the method assimilating exogenous data from only the upstream tower T9 but for two previous time steps, has a much lower false-positive rate (\(\sim\)0.1 for KF6 vs. \(\sim\)0.3 for KF5, Figure 9b), with KF6 exhibiting the highest performance ratio (=true/false positives) of all methods at large _a_-values, being only second to KF2 for small _a_-values. (Not shown for brevity.)  
Combining all exogenous methods into an ensemble approach, a further boost to the true-positive rate is obtained. All three ensemble methods achieve a detection rate of around 50%, with KF12356 being the top-ranking method in term of its efficiency for detecting incompliance events. In other words, nearly half of all incompliance events can be detected two time steps ahead, as required by the FBC method. This is quite encouraging. It terms of the cost-benefit ratio, the highest-ranking method is KF1236, given its relatively low false-positive rate (see Figure 9b). It should be noted that, in the context of the FBC method, given the quite low false-positive rates, the overall energy penalty is quite small to begin with, and variations in the true/false positive ratio, therefore, have a small impact on the overall performance of the FBC method.  
Figure 9: Performance of all forecast methods for the detection of incompliance events two time steps (\(2\times 10\) min) into the future. **Left**: True-positive rate vs. the critical slope \(a\). **Right**: False-positive rate vs. the critical slope \(a\).
## 2 Materials and Methods
### Calibration of RF Setup  
The RF setup was specifically tuned to treat samples with an RF frequency of 27.12 \(\pm\) 0.5 MHz at the sample load. The calibration to this predetermined frequency was achieved by modifying the following design specifications: (i) position of the primary coil (inductor) on the core of the secondary coil to transfer maximum energy to the load, (ii) the selection of a suitable capacitor to build the LC circuit, (iii) enlarging the surface area of the hot electrode terminal by adding pieces of aluminium foil to the main electrode (copper coin) to achieve the required frequency of oscillation, and (iv) the adjustment of the positive voltage bias at the base-emitter terminal of the NPN transistor to achieve immediate oscillations at the load.  
Figure 1: Principal scheme of the RF setup for lab-scale microbiological applications.  
Figure 2: Picture of the RF setup for lab-scale microbiological applications.  
The RF setup was calibrated in a series of trials using vials containing 3 mL of one of two nutrient broths commonly used in microbiological experiments, i.e., Trypic Soy Broth (TSB, VWR International, Leuven, Belgium) or Brain-Heart Infusion broth (BHI, VWR International, Leuven, Belgium). For each trial, a broth-containing vial was treated for a period of 30 min, during which the evolution of the core temperature of the samples and the RF parameters (i.e., voltage (V), current (A), and frequency (MHz)) were recorded every minute. For each nutrient broth, at least 3 independent replicates were performed. The aim of the calibration was to achieve repeatable frequency stability and temperature profiles among treatments.
# Analysis of Fatigue Performance of Spot-Welded Steel T-Profiles Under Cyclic Torsional Loading
## 3 Results and Discussion
### Influence of Thickness  
Second, an analysis was conducted to determine the impact of the thickness of the components of the assembly on the spot-welded box's torsional fatigue life. This size was increased from 1.0 mm to 2.0 mm in increments of 0.5 mm. Figure 9 presents the obtained \(N_{f}\) values, with 9693, 37,290 and \(10^{6}\) cycles, respectively. Figure 10 displays the cracks observed at the end of the static analysis and after \(10^{6}\) cycles for the assembly with a thickness of 2.0 mm. Notably, these cracks are identical, indicating that they did not propagate further during the fatigue analysis, i.e., no fatigue failure was detected. Small cracks had formed prior to fatigue failure at various points across Parts 1-3, contrasting with the absence of crack formation at the location observed for a thickness of 1.5 mm (see Figure 8). In this investigation, a fatigue life exceeding \(10^{6}\) cycles was regarded as reaching the run-out threshold. This finding indicated that by increasing the thickness, fatigue failure in such a structure subjected to torsional loading may be eliminated. On the other hand, when \(t=1.0\) mm was used in the parts of the assembly, the \(N_{f}\) decreased by more than 3.8 times when compared to that of \(t=1.5\) mm. The structure became weaker in that case, and the applied torque of \(\pm 480\) Nm. caused the cracks to propagate quickly (the average \(da/dN\) increased more than 3 times from 3.02 \(\times\)\(10^{-4}\) to 9.63 \(\times\)\(10^{-4}\) mm/cycle), resulting in a reduced fatigue life. Figure 11 presents the resulting crack patterns after \(N_{f}\) is reached for \(t=1.0\) mm. It was observed that multiple cracks propagated in different parts of the assembly, completely different than that of \(t=1.5\) mm (see Figure 8). It was concluded that the thickness affected the failure pattern of the assembly subjected to torsional fatigue significantly.  
Figure 8: The depiction of the cracks that spread in Part 1 of the assembly for different diameters of spot welds at \(N=400\),000.  
Figure 11: The depiction of the cracks that spread in the assembly for \(t=1.0\) mm at \(N=N_{f}\).  
Figure 10: The depiction of the cracks that spread in the assembly for \(t=2.0\) mm at \(N=0\) and \(10^{6}\) cycles.  
Figure 9: \(N_{f}\) and \(da/dN\) values obtained for different thicknesses of the parts.
## 2 Dual-Port MIMO Antenna
### Radiation Pattern  
Figure 14a-d display the 3D radiation patterns pertaining to 4, 6.125, 9.185, and 10.5 GHz. The plots indicate nearly omnidirectional radiation with the gain ranging from 2.7 dB to a peak of 6.3 dB.  
A comprehensive comparison between the reported designs and the presented UWB MIMO antenna with emphasis on diversity metrics, bandwidth, and gain is presented in Table 2. Additionally, the computed diversity parameters of the presented UWB antenna are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref No/Published Year** & **Size (mm\({}^{3}\))** & **UWB Bandwidth (GHz)** & **Isolation (dB)** & **ECC** & **DG (dB)** & **TARC (dB)** & **MEG (dB)** & **Peak Gain** & **No. of Ports** \\ \hline
[28]/2023 & 47 \(\times\) 38 \(\times\) 0.8 & 2.9–11.0 & 20 & 0.003 & 9.99 & \(<\)0 & \(\approx\)1 & 4.1 & 2 \\ \hline
[29]/2022 & 30 \(\times\) 18 \(\times\)
## 4 Measured Results and Discussion
The slight difference between the TE mode and TM mode can be explained by the working principle of the absorber, discussed in the last section. For the TE mode, when the incident angle increases, the change of the incident \(H\)-field is significant, while that of the incident \(E\)-field is small. As the DR mode excited at the lower frequency band is more sensitive to the changing of the \(H\)-field, the absorptivity will be much more sensitive to the large incident angle for TE mode, Despite the slight decrease in absorptivity, the absorber exhibits good performance for both the TE mode and TM mode with different incident angles.
## 3 The Experimental Campaign  
### Materials  
The selection of masonry units to be used in tests was based on common European building practices. Due to the vast variety of available blocks (solid, horizontally, or vertically perforated, of various geometries of the perforations, and a wide range of mechanical properties), the decision was made to start the experimental program using solid clay bricks. Another reason for this selection was the long European tradition of using ceramic blocks, also reflected in a significant portion of heritage buildings on the continent. A solid clay brick typically used in Greece, one of the most earthquake-prone countries in Europe, was chosen (Figure 1). The brick is 210 mm long, 100 mm wide, and 50 mm thick (nominal values), whereas the mean compressive strength of the brick, fbc, is equal to 36.7 MPa.  
To reach results representative of low-strength masonry and, hence, conservative values of bearing capacity of the anchors, a low-strength mortar (mean compressive strength, fmc = 3.20 MPa, Table 1a) was used in most specimens. Nonetheless, to evaluate the effect of mortar strength on the anchor behavior, a strong mortar (mean compressive strength, fmc = 20.4 MPa, Table 1b) was used in a few cases. The mortar compressive strengths were measured by testing conventional specimens taken during the construction of masonry wallettes. The wallettes were constructed in batches, due to space limitations. Despite the use of the same mix proportions for the mortar, there were differences in the compressive strength of the mortar among batches of wallettes. The respective measured fmc values are listed in Table 1a,b, and accounted for in Section 5.3.1, where the experimental results per test series are presented. It should also be noted that there are inevitable differences between the strength measured on conventional mortar specimens and that in bed joints or in vertical joints of a real wall.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**(a)**} \\ \hline \multicolumn{1}{c}{**Construction**} & \multicolumn{1}{c}{**Phase 1**} & \multicolumn{1}{c}{**Phase 2.1**} & \multicolumn{1}{c}{**Phase 2.2**} & \multicolumn{1}{c}{**Phase 2.3**} & \multicolumn{1}{c}{**Phase 3.1**} & \multicolumn{1}{c}{**Phase 3.2**} \\ \multicolumn{1}{c}{**Phase**} & & & & & & \\ \hline fmc, min (MPa) & - & 2.81 & 1.88 & 1.75 & 2.01 & 2.25 \\ fmc, max (MPa) & - & 7.63 & 3.48 & 2.72 & 5.12 & 3.54 \\ fmc, mean (MPa) & 3.42 & 4.53 & 2.84 & 2.29 & 3.41 & 2.89 \\ CoV (\%) & 16.91 & 36.27 & 11.79 & 13.45 & 26.92 & 13.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: (a) Mean values of compressive strength of weak mortar per batch of wallettes (construction phases). (b) Mean values of compressive strength of strong mortar per batch of wallettes (construction phases).  
Figure 1: Solid clay brick, fbc = 36.7 MPa.