esa-sceva/satcom-chunk-collection huggingface dataset:
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 3 Data Simulation and Testing
### Compensation Results  
In order to verify the performance of the algorithm, this paper records two sets of simulation data, Flight A and Flight B, as training and test sets, respectively. As shown in Table 1, in the test set Flight A, the standard deviation of the raw data is 5.5873, the BP-ANN reduces the standard deviation to 0.0792, the IR is 70.5465, and the BRF-ANN reduces the standard deviation to 0.0649 and the IR is 86.1052. In the test set Flight B, the standard deviation of the raw data is 5.5927, the BP-ANN reduces the standard deviation to 0.0828 and the IR to 69.5160, and the BRF-ANN reduces the standard deviation to 0.0651 and the IR to 85.9473. The result after compensation is shown in Figure 7.
# Enhanced Effect of Phytoextraction on Arsenic-Contaminated Soil by Microbial Reduction
## 3 Results and Discussion
### Extraction and Transport of Arsenic by the Pteris vittata  
The ability of plants to absorb and enrich heavy metals is the key to the removal of heavy metals by phytoextraction. The enrichment content of arsenic in the roots and fronds of _Pteris vittata_ is shown in Figure 4a. The content of arsenic in the fronds was significantly higher than that in the roots in all treatment groups. In the control group, arsenic content in the roots and fronds of _Pteris vittata_ was 536.19 mg/kg and 1145.16 mg/kg, respectively. After inoculation with bacteria, the arsenic content in the roots and fronds  
Figure 3: Plant height of _Pteris vittata_ in each group (**a**), biomass of root (**b**), biomass of from (**c**).  
increased by 21.27 and 18.56 percentage points, respectively. Furthermore, when bacteria and organic matter were both introduced, the rise in arsenic content in the roots and fronds increased 47.60 and 37.46 percentage points. As depicted in Figure 4b, the accumulation of arsenic in the roots and fronds was 0.58 mg and 3.60 mg, respectively, in the control group. However, in the treatment group with the addition of bacteria and organic matter, the accumulation of arsenic increased to 1.41 mg in the roots and 6.43 mg the fronds. The overall arsenic accumulation per plant increased from 4.18 mg in the blank treatment to 7.84 mg in the biofortified treatment, resulting in an increased arsenic extraction rate of 87.56 percentage points.  
The effectiveness of transferring arsenic contamination from soil to plant roots, as well as from roots to fronds, can be evaluated using the enrichment factor (bioaccumulation factor, BCF) and the translocation factor (TF). Figure 4c shows that the bioaccumulation factor (BF) for _Pteris vittata_ ranged from 4.31 to 7.03 in all treatment groups, indicating that _Pteris vittata_ is a typical arsenic hyperaccumulator and is highly efficient in removing arsenic from contaminated soil. The addition of bacteria and organic matter increased the bioaccumulation factor by 63.11 percentage points, indicating enhanced arsenic extraction by _Pteris vittata_. This may be attributed to changes in the soil's arsenic morphology under bacterial activity after inoculation, leading to the rapid uptake and accumulation of arsenic in the roots of _Pteris vittata_. On the contrary, the TF of the translocation coefficient of _Pteris vittata_ decreased slightly, from 2.16 to 1.94, suggesting that arsenic transport from roots to stems has slowed down. Although the transfer of heavy arsenic roots to fronds contributes to plant tolerance, _Pteris vittata_ may develop new detoxification mechanisms with the assistance of bacteria when exposed to high arsenic concentrations in the roots [34].
# Effect of Plasma Surface Modification on Print Quality of Biodegradable PLA Films
## 3 Results and Discussion
### Effect of Plasma Activation on Film Properties That Can Affect Printability
In the case of NTSS film, the changes depended significantly on the type of gas, with argon activation increasing the elongation value at break many times, while modification with oxygen plasma caused a very significant decrease in elongation at break. In conclusion, it can be stated that oxygen plasma activation has a negative effect on the tensile properties of NTSS films, while argon plasma activation has a lesser effect, and in the case of BCP films, it is even positive for both gases. In conclusion, the type of film is crucial when it comes to the effect of plasma activation on the strength properties of the modified material, and for each new substrate it would be suggested to determine such an effect. Changing the substrate color and gloss could affect the user's perception of the print and the final color of the print. If the substrate became yellowed, it would adversely affect the quality of the print and translate to the range of colors reproduced [59]. Regarding gloss, prints with higher gloss are generally perceived to be of higher quality. Table 5 summarizes the results of calculating the color change (\(\Delta E\)) of the film due to plasma activation of the film using oxygen and argon as gases and the calculated values of the gloss difference. The color changes due to plasma activation with both oxygen and argon plasma are insignificant. The \(\Delta E\) values indicate that the color change is not noticeable to the film user [59]. The NTSS film obtained a significant increase in the gloss value for the modified films under all conditions considered, while the gloss of the BCP film decreased as a result of plasma activation. Nevertheless, it should be noted that both before and after activation, regardless of the type of gas used, both materials exhibited high gloss. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Measured Parameter** & **BCP** & **NTSS** \\ \hline Tensile strength (without plasma) (MPa) & 27.54 \(\pm\)6.35 & 59.84 \(\pm\) 5.72 \\ \hline Tensile strength (100\% Ar plasma) (MPa) & 44.64 \(\pm\) 23.09 & 102.10 \(\pm\) 11.44 \\ \hline Tensile strength (100\% O\({}_{2}\) plasma) (MPa) & 41.24 \(\pm\) 16.60 & 38.51 \(\pm\) 5.72 \\ \hline Elongation at break (without plasma) (\%) & 6.68 \(\pm\) 2.80 & 16.70 \(\pm\) 17.09 \\ \hline Elongation at break (100\% Ar plasma) (\%) & 3.62 \(\pm\) 0.79 & 146.00 \(\pm\) 15.17 \\ \hline Elongation at break (100\% O\({}_{2}\) plasma) (\%) & 3.66 \(\pm\) 0.52 & 3.24 \(\pm\) 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tensile properties of the film due to plasma activation.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 2 FSO Communication Channel Effects and Their Mitigation Techniques
### FSO Communication--Spatial Profile Challenges  
The spatial displacement of the receiving light beam must first be corrected to build a stable FSO link. After beam stabilization and phase noise compensation have been accomplished, traditional error management and data decoding techniques can be used to increase the overall communication efficiency. The following are necessary methods to minimize spatial disruptions: (a) aperture averaging; (b) pointing acquisition and tracking(PAT); (c) wavefront adjustment; and (d) random focus-spot mitigation, all of which are mentioned below.
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 3 Measurement Results
### Received Signals
#### 3.2.1 Line-of-Sight Propagation
A comparison between the 433 and 868 MHz bands reveals a systematically weaker signal for the 868 MHz band. A fairly constant difference between the two frequency bands is observed, with the 868 MHz signal being about 10 dB weaker than the 433 MHz signal. This effect is caused by the differences in path loss, cable losses, and antenna gain. The Friis formula reveals a 6 dB higher path loss for the 868 MHz signal, compared to 433 MHz at the same propagation distance. Yet, the Yagi-Uda antenna 433 MHz exhibits a maximum receive gain of 9 dBi, compared to 13 dBi maximum receive gain at 868 MHz. The additional attenuation experienced at 868 MHz may be explained by the higher cable losses. Moreover, the alignment of the directional antenna plays a role: although the 868 MHz Yagi-Uda antenna provides more gain, it exhibits a narrower beam width, making it more difficult to keep the antenna aligned with the direction of the mobile user. This also explains why the difference in path loss between the two frequency bands is larger than expected.
## 5 Emerging Trends in FSO Communication
### Enhanced FSO Transceiver Design  
A roadmap for future wireless communications is expected to exploit all transmission-suitable spectrum bands, from the microwave to the optical frequencies, to support orders of magnitude faster data transfer with much lower latency than the deployed solutions nowadays. The currently under-exploited mid-infrared (mid-IR) spectrum is an essential building block for such an envisioned all-spectra wireless communication paradigm. FSO communications in the mid-IR region have recently attracted great inter est due to their intrinsic merits of low propagation loss and high tolerance of atmospheric perturbations [446].  
Meanwhile, FSO technologies have been proposed, studied, and even developed for decades by reusing the fiber-optic components in the near-IR band (1-2 \(\upmu\)m). Unfortunately, these have not been considered reliable building blocks of the modern ICT infrastructure. The main concern of these near-IR FSO technologies is that they are susceptible to the dynamic atmospheric environment. For instance, with the development of passive and active fiber components and devices, the 1.55 \(\upmu\)m band, also referred to as the telecom band, has been applied for commercial FSO communication systems. However, the 1.55 \(\upmu\)m band is sensitive to atmospheric channel perturbations caused by fog, dust, and turbulence [447].  
Recently, the under-exploited mid-IR spectral region, particularly the two atmospheric transmission windows at the mid-wavelength IR (MWIR, 3-5 \(\upmu\)m) and the long-wavelength IR (LWIR, 9-12 \(\upmu\)m) [448], are attracting increasing interest for FSO communications. The interest in a MWIR platform stems from the intrinsic PHY layer advantage of low absorption when propagating in the atmosphere even under adverse conditions (for instance, scattering by aerosols, rain, and snow, as well as beam broadening and scintillation by turbulence effects), robustness of the wavefront during long-distance propagation compared with the near-IR FSO, and the absence of regulations and restrictions for this range of wavelengths [446; 448; 449; 450]  
The future development of viable mid-IR FSO transceivers requires a semiconductor source to fulfill the high-bandwidth, low-energy-consumption, and small-footprint requirements. In this context, quantum cascade devices, including quantum cascade lasers (QCLs) and quantum cascade detectors (QCDs) appear as promising technological candidates to fulfill this role [446; 447; 451; 452]. Quantum cascade devices are based on inter-sub-band transitions to cover a wide spectral range from the mid-IR to THz. Particularly, directly modulated-QCLs have ultra-short carrier relaxation lifetime, leading to a high intrinsic modulation bandwidth and making the laser response over-damped, thus repressing the resonance frequency [449; 451; 452]. It is a promising candidate for solid-state FSO Txs at the MWIR and LWIR bands. Combined with advanced modulation formats (e.g., PAM and DMT) and DSP techniques, capacity-approaching transmission performance in both optical and wireless communication systems can be achieved [447].
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 5 Field Campaign
### Maximum Coverage Distance  
The assessment of the maximum coverage distance was performed based on the availability of OAI measurements as the UE moved farther from the gNB. The distance between gNB and UE was computed with the stand-alone GNSS solution estimated at both nodes during the entire duration of the field test (i.e., 120 s).  
The field results shown in Figure 5 indicate a maximum distance around 30 m between the UE and gNB with the HOP-5G mmWave positioning testbed. At 30 m of distance, the OAI UE lost the 5G NR signal synchronization, and the test was stopped before the UE returned to the starting point for the following test. The AoA estimation accuracy was analyzed based on the RSRP distribution for each beam as a function of the distance between the UE and gNB, as it is shown in Figure 5a,b. Considering the static periods within the trajectory, the average RSRP was computed for each beam angle. The result indicates an almost constant average RSRP value between beam angles \(-13.5^{\circ}\) and \(13.5^{\circ}\), while the rest of the beam angles (outside of this range) include values below 3 dB. This beam pattern was kept over the different distances. As a result, when both the transmitter and receiver are aligned, i.e., an azimuth orientation of \(0^{\circ}\), the AoA estimation accuracy is equal to \(4.6^{\circ}\) at static periods. The AoA performance degrades to \(9.5^{\circ}\) for the full trajectory, which includes dynamic and static periods. Certainly, the reduced array size limits the AoA estimation accuracy.
## 5 Conclusions
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## 3 Results  
### Effect of MLE on Morphological Attributes of Plants  
#### 3.1.1 Shoot Length  
Figure 1A shows the effect of MLE application on the shoot length of wheat facing Cd stress. Results revealed that the application of 3% MLE along with combinations of 500 \(\upmu\)M and 1000 \(\upmu\)M (under light) cadmium solutions increased the shoot length by 17.28% and 18.72% compared to control. This increase was less under shade compared to light but was still higher under Cd 500+MLE (13.7%) and Cd1000+MLE (16.3%) compared to their control under shade. More interestingly, 3% MLE with both cadmium treatments even resulted in longer shoot lengths compared to MLE 3% alone treatment (higher in light compared to shade).
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods
### Data Preprocessing  
When analyzing the volatility of non-stationary or nonlinear time series, it is crucial to employ decomposition and denoising techniques that reduce the complexity and enhance the interpretability when forecasting time series [51]. Through multiscale decomposition, the raw time series is finely divided into different frequency components, allowing us to gain independent insights into long-term trends, seasonal fluctuations, cyclical characteristics, and random noise in scallion prices [52; 53]. Once the raw data have been decomposed and denoised, recombining all the valid decomposed sequences produces a sharper and less noisy reconstructed sequence. It has been pointed out that using the reconstructed sequence as the input data for the model can effectively eliminate the interference of irrelevant information, which, in turn, significantly improves the prediction accuracy, and thus significantly improves the accuracy of prediction [54; 55].  
Therefore, three advanced denoising techniques, SSA, EMD, and VMD, are used in this study in order to accurately remove the noise components from the original price series and recombine the remaining valid information into a new input series. For detailed techniques and principles of SSA, EMD, and VMD, please refer to Appendix A. In the SSA method, we specifically select the top three decomposed sequences among the largest components with contribution rates over 99.9% to be accumulated in order to form the denoised reconstructed sequences. For the EMD method, since the intrinsic mode functions with the highest frequencies tend to carry the most noise, we choose to accumulate the residual terms and all modes except the highest frequency modes to obtain the denoised reconstructed sequence. As for the VMD, we video the highest-frequency modes as noise and choose only the other modes to be combined to obtain a pure denoised sequence.
## 2 Materials and Methods
### Training, Testing, and Results of the ML Model
This suggests that MLR struggles to generalize effectively in scenarios with high-accuracy requirements or when the target variable exhibits significant variability. Although the models trained with ML algorithms showed high performance in the test data, they can contain high errors in real data outside the dataset. For this reason, the four ML algorithms used in the study were tested with nine GNSS data from stations other than the dataset (Figure 8). These data had a campaign type measure and were measured at one-year intervals. Table 4 and Figure 8 demonstrate that the RF, KNN, and SVM models delivered the most accurate predictions across all sites for both the E and N components. The residuals for these three models in all components remained below 0.3 mm/year. In contrast, the MLR model achieved high accuracy (residuals \(<\) 0.2 mm/year) when predicting reference values below 2 mm/year in both components but struggled to maintain accuracy for higher values, indicating its limitations in handling larger target ranges. Using the developed machine learning model, velocity accuracies were predicted for varying position accuracies and temporal intervals between observations, which were tailored for tectonic-focused GNSS studies (Figure 9). Given that fault slip rates in many tectonic zones are on the order of a few millimeters per year, the prediction range for position accuracies was constrained between \(\pm\)1.5 mm and \(\pm\)5 mm. The lower limit corresponds to the highest position accuracy observed in the dataset, while the upper limit reflects a realistic threshold for tectonic applications. Notably, the model remains applicable for position accuracies exceeding \(\pm\)5 mm, although such cases were not explicitly included in the training data.
## 3 Data and Methods
### Establishment of the Basic Database
#### 3.3.4 Weather and Meteorological Data  
Meteorological data were obtained from the Atmospheric Assimilation Driving Set (CMADS V1.0) supplied by the National Tibetan Plateau Scientific Data Center, covering the period from 2008 to 2016. CMADS (China Meteorological Assimilation Driving Datasets) is among the most extensively utilized datasets in the SWAT model (version10.6). It primarily offers standard meteorological parameters, including temperature, precipitation, pressure, humidity, wind speed, and soil moisture data [30]. The meteorological data from 104 stations in the study area were delineated in ARCGIS, and further modifications ensured that the accuracy and operational criteria of the SWAT model (version10.6) were largely satisfied.  
In conclusion, the acquired elevation data, soil type data, land use data, and meteorological data were incorporated into the SWAT model (version10.6), and the research region was segmented into 29 sub-basins to finalize the building of hydrological response units (HRUs). Each Hydrologic Response Unit (HRU) serves as a centralized representation of elevation, soil type, land use, and meteorological data within various segments of the study area, with each sub-basin comprising multiple HRUs to collectively simulate the intricate hydrological processes of the region from 2010 to 2018 [31].
## 3 Experimental Content and Methodology
### Monitoring Points
#### 3.1.3 Displacement Measuring Points  
A 5G10X-series linear displacement gauge is selected to measure the vertical relative displacement, longitudinal relative displacement and deflection of the tested beam, and the DH3820 collector is selected to collect the displacement data, and the sampling frequency is set to 0.01 Hz to ensure that the sampling is more than once every 30 min. The layout section of the displacement measuring points is the same as that of the strain measuring points, as shown in Figure 5. The installation method of the LVDT (Linear Variable Differential Transformer) displacement meter between the structural layers is as follows: To measure the relative displacement between two layers, the base of the LVDT is fixed to steel and attached to one layer with strong structural adhesive, while its top bar is fixed on another  
Figure 4: The layout of the fiber Bragg grating (FBG) strain sensors (Unit: mm).  
Figure 3: The layout of the temperature gauges (Unit: mm).  
steel angle sand attached to the other layer. Relative displacements between the track slab and CA mortar layer, the track bed and box beam, the track slab and track bed and the CA mortar layer and track bed were measured.  
The deflection of the bridge structure under the action of temperature is measured by the LVDT displacement gauge. Displacement measuring points are arranged at the support sections of the beam, the 1/4 span section, the 3/4 span section and at the mid-span section of both sides of the concrete box girder transverse section.