esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper aims to address the issue of poor spectral consistency and the difficulty in achieving the high-precision concentration inversion of the target gas due to the influence of external environmental factors on spectra collected by SMWIR-IFTS. A multi-scale feature attention (MDISE) model is proposed in this paper. The MD module is designed to extract both global and local shallow features of the spectrum using multi-scale dilated convolutions; two series-connected 1D Inception modules are used to further extract multi-scale deep features; and incorporates the SE module after each 1D Inception module to enhance the attention to important spectral wavelengths and suppress redundant and interfering information. A target gas detection system was built in the laboratory, and the proposed model was tested on gas samples collected by the two filter channels of SMWIR-IFTS. Experimental results show that, compared to the PLSR, SVR, and standard 1D-CNN models, the _RMSE_ of the MDISE model decreased by 79.14%, 76.59%, and 69.80% for the 1 st filter channel and 81.45%, 82.65%, and 74.01% for the 2 nd filter channel, respectively. Furthermore, the average \(R^{2}\) values of the MDISE model for different concentration intervals in both channels reached 0.997 and 0.995, respectively. The MDISE model demonstrates excellent performance and effectively improves the inversion accuracy of GHG concentrations. In future research, experiments will be conducted using the SMWIR-IFTS on a broader range of gas types and in real atmospheric environments to expand the gas spectral dataset, further optimize and refine the MDISE model, and enhance its general applicability. Additionally, interdisciplinary collaboration will be pursued to integrate the MDISE model into field-deployable systems, enabling the accurate inversion of GHG concentrations in complex and dynamic detection scenarios.  
Figure 14: Visualization of the feature weights generated by (**a**) Base model; (**b**) 1D Inception model; (**c**) MD-1D Inception model; and (**d**) MDISE model.  
**Author Contributions:** Conceptualization, J.L. and S.W.; methodology, J.L., K.S. and S.W.; software, J.L. and K.S.; validation, J.L., W.Z., F.O., K.S. and S.W.; formal analysis, J.L. and K.S.; investigation, J.L., W.Z. and K.S.; resources, K.S.; data curation, J.L. and W.Z.; writing--original draft preparation, J.L.; writing--review and editing, K.S. and S.W.; visualization, J.L. and K.S.; supervision, K.S. and S.W.; project administration, S.W.; and funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the research fund of Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology (No. 2020B1212030010).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Low Cost AIP Design in 5G Flexible Antenna Phase Array System Application
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & **The Unit Cells** & **The Bandwidth** & **The Peak Gain of the Array** & **Evaluated Peak Gain of the Unit** & **The Dimensions of the Antenna** & **Material** \\  & **Structure** & **Loss** & **7.5 dB** & **10.8-14 GHz** & 2.5 dBi & \(\begin{array}{c}112\text{ mm}\times 112\\ \text{mm}\end{array}\) & **R\(\begin{array}{c}\text{R}\text{gers}\\ \text{RT4735 LZ}\end{array}\)** \\ \hline
[28] & 2\(\times\) 2 & \
\begin{tabular}{c} 238.4–309.5 \\ GHz \\ \end{tabular} &
\begin{tabular}{c} 10.1 dB at \\ 71.1 GHz \\ \end{tabular} & 8 dBi & \(\begin{array}{c}3\times 1.5\text{ mm}^{2}\end{array}\) & silicon \\ \hline
[29] & 4\(\times\) 4 & \
\begin{tabular}{c} 57.2–64.5 \\ GHz \\ \end{tabular} &
\begin{tabular}{c} 6.9 dBi at 62 \\ GHz \\ \end{tabular} & 7.5 dB & \(\begin{array}{c}14\text{ mm}\times 14\text{ mm}\\ \times 0.925\text{ mm}\end{array}\) & Rogers 5880 \\ \hline
[30] & 4\(\times\) 4 & \
\begin{tabular}{c} 12 GHz \\ \end{tabular} &
\begin{tabular}{c} 8.9 dBi at 12 \\ GHz \\ \end{tabular} & 10.1 dBi & N/A & RO3003 \\ \hline
[31] & 1\(\times\) 8 & \
\begin{tabular}{c} 27.2–29.2 \\ GHz \\ \end{tabular} &
\begin{tabular}{c} 10.33 dBi at \\ 29.2 GHz \\ \end{tabular} & 6 dBi & \(\begin{array}{c}130\text{ mm}\times 42\\ \text{mm}\times 0.127\text{ mm}\end{array}\) & Taconic \\ \hline
[32] & 4\(\times\) 4 & 0.8 GHz &
\begin{tabular}{c} 3.8 dBi at \\ 30.5 GHz \\ \end{tabular} & 6 dBi & \(\begin{array}{c}6.85\times 6.85\text{ cm}^{2}\end{array}\) & organic \\ \hline
[33] & 2\(\times\) 2 & N/A &
\begin{tabular}{c} 4.5 dBi at 60 \\ GHz \\ \end{tabular} & -1.5 dBi & 4.5 mm \(\times\) 3 mm & RO4003C \\ \hline
[34] & 1\(\times\) 2 & \
\begin{tabular}{c} 9.39–10.26 \\ GHz \\ \end{tabular} & N/A &
\begin{tabular}{c} 4.8 dBi \\ (Simulated) \\ \end{tabular} & \(\begin{array}{c}15\times 1\text{ mm}^{2}\end{array}\) & RO4003 \\ \hline
[35] & \begin{tabular}{c} 2\(\times\) 32 \\ 2\(\times\) 2 \\ beamformer \\ chips \\ \end{tabular} & \begin{tabular}{c} 23.5–30.5 \\ GHz \\ \end{tabular} & EIRP 46 dBm & 2–3 dBi & \
\begin{tabular}{c} 32 elements \\ (5.3 mm) \\ \end{tabular} & Megtron-6 \\  &
\begin{tabular}{c} 2\(\times\) 2 \\ (0.5 mm) \\ \end{tabular} & Megtron-6 \\ \hline
[36] & 2\(\times\) 2 & N/A & \
\begin{tabular}{c} 15 dBi at 20 \\ GHz \\ \end{tabular} & 9 dBi &
\begin{tabular}{c} 2\(\times\) 2 \\ Quad-Mode \\ Antenna Array \\ (QMA) \\ \end{tabular} & N/A \\ \hline
[37] & \
\begin{tabular}{c} Yagi–Uda \\ antenna \\ \end{tabular} &
\begin{tabular}{c} 26.86–28.87 \\ GHz \\ \end{tabular} & 6.03 dB at 26.86 GHz & 6.03 dBi & 25 mm \(\times\) 15 mm & Rogers 5880 \\ \hline
[38] & 2\(\times\) 2 × 14 & 28–30 GHz & EIRP 54 dBm & 3–4 dBi & 70 mm \(\times\) 70 mm & N/A \\ \hline
**This study** & 2\(\times\) 2 & \
\begin{tabular}{c} 26.5–29.5 \\ GHz \\ \end{tabular} & 14.4 dB at 28 & 10.6 dBi &
\begin{tabular}{c} 18 mm \(\times\) 14 mm \\ \(\times\) 0.71 mm \\ \end{tabular} & FR4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of antenna performance.
## 2 Materials and Methods  
**Reagents.** CdSe/ZnS QDs were supplied from the FINE lab. Tetrachloroauric acid trihydrate (HAuCl4.3H2O, 99.9+%), Oleylamine, and Sodium Hydroxide (NaOH) were acquired from Sigma Aldrich. Octanethiol, cyclohexane, and ethanol were provided from EMD Chemicals. Deionized water (Millipore Milli-Q grade) was handled in all experiments.  
**Synthesis of Au NPs-oil soluble.** Au NPs with various sizes were synthesized by the standard chemical reduction of HAuCl4, varying the volume of oleylamine [25]. Briefly, 3 mL of 1 wt.% HAuCl4 was added to 70 mL DI water and then heated to boiling point. A quantity of 0.75 mL oleylamine was quickly added to the boiling solution under vigorous stirring for 2 h and allowed to cool to room temperature. For transferring the solution from water-soluble to oil-soluble, 30 mL cyclohexane was added to the above solution and stirred until the solution changed to ruby red color. To control the pH condition at the ideal value, 1.5 mL of 2 M NaOH was injected quickly into the mixture and stirred for 30 min. The solution was kept undisturbed at room temperature until it separated into two layers. The upper layer, including Au NPs, was allowed to precipitate by adding EtOH and purified by cyclohexane and EtOH once, then dispersed in 60 mL cyclohexane. For diverse Au NPs sizes, the volume of oleylamine was varied at 0.75, 1.0 mL, and 1.25 mL (corresponding to \(3\times 10^{-2}\), \(4\times 10^{-2}\), and \(6\times 10^{-2}\) M, respectively) at a fixed HAuCl4 concentration.  
**Fabrication of Si Schottky junction solar cells.** For uncomplicated and reliable devices, the solar cells were fabricated on a 525 um single polished n-type 1-10 \(\Omega\)-cm Si wafer. For the back and front contact of the devices, 100 nm of titanium (Ti) and 5 nm of Au were deposited at 0.2 A/s by thermal evaporation, respectively. Ti layer works as back contact, and Au film is responsible for the Schottky junction and oxidation barrier. Silver (Ag) front contact was deposited with a thickness of 100 nm using the grid pattern. For examining the effect of the Au/QDs layer on the devices, the synthesized Au NPs colloids and CdSe/ZnS QDs were coated layer-by-layer on the surface of solar cells by the spin coater as described in Figure 1.  
**Characterization.** UV-Vis absorption spectrum was performed using an EMC-11-UV spectrophotometer (ranging from 300 nm to 1000 nm). Transmission electron microscopy (TEM) images were presented using TALOS F200X (S)TEM. PL spectra of samples were measured by a FluoTime300/MicroTime100 PicaQuant spectrometer (PicoQuant, 12489 Berlin, Germany) equipped with a dichroic filter and a 405 nm laser as the excitation source. For investigating the electrical properties of photovoltaic devices, the Keithley 4200-SCS system and IPCE PEC-S20 system were applied.
## 4 Discussion
For future reference, the improvement of georeferencing solutions via MLS strip adjustment process needs more automation. The corrections to the offsets and orientations need to be made to the trajectory data. The improvements in georeferencing could also include improvements due to abrupt jumps and other errors in the data. [PERSON] and [PERSON] [43] have discussed the use of three post-processing techniques, such as denoising, auto regression modeling and smoothing. Denoising is noise reduction done directly to the sensor measurements. Auto regression modeling reduces noise in the data fusion step, and forward and backward Kalman filters are example of this method type and they have already been implemented in the typical GNSS/IMU integration. Also, numerical smoothing on the post-processed trajectories can be performed.
## 5 Results  
### Range Offset from Corner Reflectors  
The determined range offset from L0 data is depicted in Figure 6 (blue) for both satellites (S-1A: left and S-1B: right). In addition, the range offset from the L1 products are also displayed (black), which were already presented in previous studies [14; 15; 16; 17]. An averaged range offset of \(-0.99\) m  
Figure 5: Height corrected zenith path delay from the permanent GNSS network EUREF for two stations (OBE4: Oberpfaffenhofen, Germany; PFA2: Bregenz, Austria) nearby the DLR calibration field covering an observation period of one month for S-1A acquisitions during summer 2015 **(a)** and for S-1B early in 2017 **(b)**.  
remains for the S-1A L0 products and of \(-1.05\) m for the S-1B ones. These results match the offset between the spacecraft's center of gravity and SAR antenna phase center, which is the order of 1 m and not considered for the L0 evaluation. After compensating for these offsets, the remaining standard deviation is a measure for the pixel localization in range, which is 12 cm for S-1A and 7 cm for S-1B.  
The range offsets derived from L1 products are smaller than the ones derived from L0 products. There is one exception though: the S-1B Stripmap beam S1 shows a larger offset with 1.6 m and indicates an inaccurate set of SAR processing parameters for this beam. The difference between L0 and L1 products is on average 1.3 m for S-1A and of 1.2 m for S-1B (excluding the S1 beam results). Furthermore, the variation of the range offset for L1 products is nearly identical to the one derived from L0 products. Although no explicit indication is found in the L1 product annotation files, the small values of the L1 derived range offsets evidence that the distance between spacecraft's center of gravity and SAR antenna phase center is already considered during SAR data processing.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 3 Research Method  
### Ship-ice Interaction and Material Parameters  
We determine the actual ice conditions and loads based on full-scale ice measurements and visual observations of the prevailing ice conditions conducted on the Polar Class 5 classified polar research ship S.A. Agulhas II on a voyage in the Antarctic Ocean in the period November 2013-February 2014 [40]. For the purpose of ice load measurements, the ship's hull was instrumented in accordance with Figure 2 with strain gauges at the bow (frames 134 and 134.5), bow shoulder (frames 112 and 113),and stern shoulder (frames 39.5, 40, 40.5, and 41) [40]. The prevailing ice conditions were measured using a stereo camera.  
The full-scale data describes the ship-ice interaction process and the prevailing operating conditions in terms of [40]:  
* Ship parameters including hull particulars: Length, beam, draft, displacement, hull angles.
* Impact parameters including impact location and speed.
* Environmental parameters including ice floe size, ice thickness and air temperature.  
From the full-scale data, we extract two data series for two separate operating days representing different types of ice conditions and operating modes:  
* Data set A. Measurements from 10.12.2013 representing continuous operation in ice as well as specific manoeuvres such as ramming and reversing in ice. An extract of data set A is presented in Figure 3.
* Data set B. Measurements from 20.12.2013 representing mainly continuous operation in ice. An extract of data set A is presented in Figure 4.  
Figure 3: Extract from data set A, showing how the measured ice floe diameter, ice thickness and speed vary during a selected period of 14 h.  
Figure 2: Location of strain gauges on the S.A. Agulhas II. (The figure was determined based on Suominen & Kujala [40]).  
The specified ice floe diameters were determined by visual observations from the ship carried out every 10 or 15 min [41]. Because the measurements were carried out by visual observation, and because the size of individual ice floes were specified by categories of diameter range (e.g., 0-20 m, 20-100 m, 100-500 m), the diameter values specified in Figures 3 and 4 are subject to significant uncertainty.  
As mentioned in Sec. 2.5, ice crushing strength \(\sigma_{cr}\) is an important factor for estimating ice loading on ships. In order to enable a direct comparison between the measured and calculated ice loads, accounting for the fact that \(\sigma_{cr}\) depends on the prevailing environmental conditions, we calculate \(\sigma_{cr}\) as function of ice salinity, brine volume, ice temperature, ice density and indentation rate in accordance with an approach proposed by [PERSON], et al. [42]. This approach assumes among others that ice crushing strength is proportional to the compressive strength of ice as suggested by [PERSON] [31].
###### Abstract  
Stable isotope analysis is a powerful tool for inferring and quantifying transformation processes, but its effectiveness relies on understanding the magnitude and variability of isotopic fractionation associated with specific reactions. Potassium permanganate (KMnO4) is widely used as an efficient oxidant for the degradation of trichloroethylene (TCE); however, the influence of environmental factors on the isotope fractionation during this process remains unclear. In this study, compound-specific isotope analysis (CSIA) was conducted to investigate the variability in carbon isotope effects during the KMnO4-mediated degradation of TCE under varying conditions, including initial concentrations of KMnO4 and TCE, the presence of humic acid (HA), pH levels, and inorganic ions. The results showed that the overall carbon isotope enrichment factors (\(\varepsilon\)) of TCE ranged from \(-26.5\pm 0.5\)% to \(-22.8\pm 0.9\)%, indicating relatively small variations across conditions. At low KMnO4/TCE molar ratio (n(KMnO4)/n(TCE)), incomplete oxidation and/or MnO2-mediated oxidation of TCE likely resulted in smaller \(\varepsilon\). For dense, non-aqueous phase liquid (DNAPL) TCE, which represents extremely high concentrations, the \(\varepsilon\) value was \(-13.0\pm 1.7\)% during KMnO4 oxidation. This may be attributed to the slow dissolution of isotopically light TCE from the DNAPL phase, altering the \(\delta^{13}\)C signature of the reacted TCE and resulting in a significantly larger \(\varepsilon\) value than observed for dissolved-phase TCE oxidation. The \(\varepsilon\) values increased with rising pH, probably due to the decrease in oxidation potential (E0) of KMnO4 from pH -2 to -12, as well as the emergence of different degradation pathways and intermediates under varying pH conditions. Both SO4\({}^{2-}\) and NO3\({}^{-}\) slightly influenced the \(\varepsilon\) values, potentially due to the formation of H2 SO4 and HNO3 at lower pH, which may act as auxiliary oxidants and contribute to TCE degradation. A high concentration (50 mM) of HA led to a decrease in \(\varepsilon\) values, likely due to competitive interactions between HA and TCE for KMnO4, which reduced the effective oxidation of TCE. Overall, the carbon isotope enrichment factors for KMnO4-mediated TCE degradation are relatively stable, although certain environmental conditions can exert minor influences. These findings highlight the need for caution when applying quantitative assessment based on CSIA for KMnO4 oxidation of TCE.  
**Keywords:** carbon isotope effect; trichloroethylene; pH; humic acid; potassium permanganate
## 3 Simulation Analysis of PD Signal Denoising
### AO-VMD Simulation  
Subsequently, preprocessing is needed on the noisy PD signal to remove some noise and allow the characteristics of the PD signal. Common preprocessing techniques for PD signals include Fast Fourier Transform(FFT), Short-Time Fourier Transform(STFT) [31], and EMD [32]. However, the effectiveness of these preprocessing methods is generally suboptimal, as illustrated in Figure 4:  
Among them, FFT has almost no denoising results, while SFTF filters out both useful signals and noise. The EMD algorithm has a certain effect in the range of useful signals, such as around the sampling point 200 and 1200, but it is not enough to show the characteristics of the original signal.  
In this simulation, the VMD algorithm is used to carry out preprocessing. To enhance the effectiveness of the VMD preprocessing, the AO algorithm is employed for calibrating parameters critical to the VMD process. Through a series of iterations, the AO algorithm refines a set of parameters that minimize the objective function. In each cycle, the solution space is progressively explored and exploited, facilitating convergence towards a global optimum. This process ultimately determines the optimal parameter configuration for the VMD algorithm. Figure 5 depicts the entire parameter optimization process executed by the AO algorithm, where the inflection points on the curve represent the optimal parameters for VMD.  
Figure 4: FFT, STFT, and EMD denoising on noisy PD signal.  
The PD signal reconstructed based on the kurtosis showed in Table 4 criterion is displayed in Figure 8:  
Figure 8: PD signal after AO-VMD.  
Figure 7: Waterfall IMFs after VMD.  
The preprocessing results demonstrate that VMD preprocessing, in contrast to other methods, is capable of eliminating a substantial portion of noise, thereby accentuating the intrinsic characteristics of the PD signal. However, residual noise still exists in the time domain, which may lead to misjudgment when extracting information such as the start and end points of the original signal. Therefore, further precise denoising is necessary.
## 3 Results and Discussion
### MD Simulation of CH\({}_{4}\) Desorption and Diffusion in Coalbed
#### 3.2.1 Adsorption Isotherms  
The adsorption isotherms of CH\({}_{4}\) on coal models with varying pore sizes were tested using the GCMC method. Two sets of calculations were performed. In the first set, the adsorption of pure CH\({}_{4}\) was considered. In the second set, a mixture of CH\({}_{4}\) and H\({}_{2}\)O, in which the fugacity of both CH\({}_{4}\) and H\({}_{2}\)O are equal to the values marked on the x-axis, was loaded into the cell. Isotherms generated by these two sets of calculations are shown in Figure 4a,b, respectively.  
Figure 4a suggests that the adsorption amount increases as the pore size and pressure become larger. However, the adsorption amount of the coalbed structure with 100  
Figure 4: Adsorption isotherms of (**a**) CH\({}_{4}\) and (**b**) CH\({}_{4}\) and H\({}_{2}\)O in the coalbed model. Blue, red, and black lines refer to the models with 100 Å, 60 Å, and 20 Å pore sizes, respectively. For convenience, the adsorption amounts are measured in units of both the number of molecules per cell and mmol/g (by assuming that the coal has a specific surface area of 50 m\({}^{2}\)/g). In (**a**), CH\({}_{4}\) is the only adsorbate. In (**b**), both CH\({}_{4}\) (solid lines) and H\({}_{2}\)O (dashed lines) with equal fugacity are adsorbed in the cell.  
Figure 3: CH\({}_{4}\) and H\({}_{2}\)O co-adsorption geometries and energies. Panel (**a**–**c**) show different adsorption structures and energies of H\({}_{2}\)O with the presence of a CH\({}_{4}\) molecule. Panel (**d**–**f**) show the adsorption structures and energies of CH\({}_{4}\) with the presence of an H\({}_{2}\)O molecule. For each structure, a top view and a side view are given.  
than five times of that with the 20 A pore size. A more careful analysis shows that, under 15 MPa, 300 adsorbed and 1079 free CH\({}_{4}\) molecules are in the 100 A pore size model, if we consider CH\({}_{4}\) molecules that are within 5 A of the coal surface to be adsorbed. While in the 60 A and 20 A pore size models, the adsorbed/free CH\({}_{4}\) molecules are 392/820 and 498/225, respectively. Therefore, the number of free CH\({}_{4}\) is strictly proportional to the pore size, while at the same time a larger pore size promotes the desorption of CH\({}_{4}\) from the surface when the pressure is kept constant.  
When a mixture of CH\({}_{4}\) and H\({}_{2}\)O was considered, the adsorption amounts of CH\({}_{4}\) and H\({}_{2}\)O as functions of pore sizes and pressures are shown in Figure 4b. As represented by the solid curves, the adsorption amount of CH\({}_{4}\) increases as the pressure becomes higher in the models with 100 A and 60 A pore sizes, while in the 20 A model, the adsorption amount of CH\({}_{4}\) slightly decreases as the pressures changes from 10 MPa to 15 MPa. Therefore, in the small pore size case, increasing the pressure of H\({}_{2}\)O contributes to the desorption of CH\({}_{4}\) from the coal surface; however, this strategy does not apply to cases where the pore sizes are large.