esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Reddyproc and MLP Interpolation Effect  
#### 3.3.1 Interpolation Precision  
Figure 6 shows the decision coefficients R\({}^{2}\) of the Reddyproc and MLP algorithms for the CO\({}_{2}\) flux fitting. The decision coefficient of the Reddyproc estimation model was larger  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline Feature & \multicolumn{3}{c}{MLR} & \multicolumn{3}{c}{CART} & \multicolumn{3}{c}{RF} & \multicolumn{3}{c}{MLP} \\ \cline{2-13} combination & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE \\ \hline combination 1 & 0.32 & 2.60 & 1.65 & 0.22 & 2.99 & 1.64 & 0.58 & 2.19 & 1.16 & 0.57 & 2.17 & 1.11 \\ combination 2 & 0.36 & 2.54 & 1.57 & 0.24 & 2.93 & 1.64 & 0.60 & 2.17 & 1.14 & 0.60 & 2.10 & 1.14 \\ combination 3 & 0.36 & 2.54 & 1.57 & 0.25 & 2.87 & 1.57 & 0.60 & 2.22 & 1.21 & 0.61 & 2.10 & 1.14 \\ combination 4 & 0.36 & 2.54 & 1.57 & 0.22 & 3.03 & 1.57 & 0.59 & 2.17 & 1.15 & 0.61 & 2.11 & 1.14 \\ combination 5 & 0.36 & 2.53 & 1.57 & 0.22 & 2.98 & 1.51 & 0.60 & 2.15 & 1.10 & 0.61 & 2.10 & 1.14 \\ combination 6 & 0.38 & 2.51 & 1.55 & 0.25 & 2.86 & 1.46 & 0.62 & 2.10 & 1.08 & 0.62 & 2.18 & 1.12 \\ Average & 0.36 & 2.54 & 1.58 & 0.23 & 2.94 & 1.57 & 0.60 & 2.17 & 1.14 & 0.61 & 2.13 & 1.13 \\ \hline \multirow{2}{*}{Feature} & \multicolumn{3}{c}{KNN} & \multicolumn{3}{c}{Adaboost} & \multicolumn{3}{c}{GBRT} & \multicolumn{3}{c}{XGBoost} \\ \cline{2-13} combination & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE \\ \hline combination 1 & 0.52 & 2.16 & 1.35 & 0.40 & 2.29 & 1.24 & 0.56 & 2.20 & 1.17 & 0.58 & 2.17 & 1.11 \\ combination 2 & 0.54 & 2.14 & 1.37 & 0.42 & 2.3 than that of the MLP estimation model, and the noise influence of the MLP model was larger than that of Reddyproc.
## 3 Comparative Experiment
### Experiment Setting
#### 3.1.2 Data Preprocessing  
Patch-Level Data Preprocessing  
In the first step, considering that the light source had a significantly large effect on the color of the microscope image and that the same sample show different colors under different light sources, the color information was less important for the sign extraction of the microscopic image [43]. Therefore, we converted the EM microscopic images into grayscale to reduce the computational workload of the network model. In the second step, we converted all image sizes into \(256\times 256\) pixels because the microscopic images were of various sizes. In the third step, the training and validation images, and their corresponding GT images were cropped into patches (\(8\times 8\) pixels), and \(105\times 1024=107,520\) patches were obtained. Based on the corresponding GT image small patches, we divided these small patches into two categories: foreground and background. The partition criterion was based on whether the area of interest in the patch comprises half of the entire patch. If so, we assigned the foreground as the label of this patch; otherwise, it was annotated as the background. Finally, we found that the \(8\times 8\) pixel patches with foreground and background were 16,554 and 90,966, respectively. During the training process, we found that the model weights were heavily biased towards negative samples owing to the imbalance between positive and negative samples. To avoid data imbalance during training, we rotated the training set image small patches by \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\), and mirrored them for data augmentation. We then further obtained 16,544 \(\times 8\) = 132,432  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Set** & **Validation Set** & **Test Set** \\ \hline _Actinophyss_ & 5 & 5 & 10 \\ _Arcella_ & 5 & 5 & 10 \\ _Aspidisca_ & 5 & 5 & 10 \\ _Codosiga_ & 5 & 5 & 10 \\ _Colpola_ & 5 & 5 & 10 \\ _Epistylylis_ & 5 & 5 & 10 \\ _Euglypha_ & 5 & 5 & 10 \\ _Paramecium_ & 5 & 5 & 10 \\ _Rotifera_ & 5 & 5 & 10 \\ _Vorticlla_ & 5 & 5 & 10 \\ _Noctiluca_ & 5 & 5 & 10 \\ _Ceratium_ & 5 & 5 & 10 \\ _Stentor_ & 5 & 5 & 10 \\ _Siprostomum_ & 5 & 5 & 10 \\ _K.Quadrala_ & 5 & 5 & 10 \\ _Euglen_ & 5 & 5 & 10 \\ _Gymnodiminium_ & 5 & 5 & 10 \\ _Gonyalux_ & 5 & 5 & 10 \\ _Placus_ & 5 & 5 & 10 \\ _Stylonychia_ & 5 & 5 & 10 \\ _Synchaeta_ & 5 & 5 & 10 \\ total & 105 & 105 & 210 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EMDS-5 experimental data setting.  
patches, from which 90,966 patches were randomly selected as the patches that were finally used in the training set. Details of the image patches are listed in Table 2.  
Pixel-Level Data Preprocessing  
The image was converted to grayscale and resized to \(256\times 256\) pixels for the pixel-level segmentation experiments.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Organic Seeds (mg/kg)**} & \multicolumn{3}{c}{**Conventional Seeds (mg/kg)**} \\ \cline{3-7}
**Essential Elements** & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Minimum**} & \multicolumn{1}{c}{**Maximum**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Minimum**} & \multicolumn{1}{c}{**Maximum**} \\ \hline  & Ca & 2866.142 & 2040.609 & 3890.695 & 3199.202 & 1698.206 & 6173.952 \\  & Co & 3.963 & 0.048 & 56.165 & 5.509 & 0.063 & 56.031 \\  & Cr & 0.113 & 0.047 & 0.168 & 0.249 & 0.030 & 2.168 \\  & Cu & 11.407 & 6.993 & 15.929 & 11.035 & 5.666 & 20.100 \\  & Fe & 35.296 & 5.581 & 51.619 & 41.098 & 5.957 & 71.521 \\  & K & 7685.005 & 4844.334 & 9326.960 & 7348.621 & 2999.681 & 11,155.318 \\  & Mg & 6733.650 & 4539.060 & 8592.595 & 6140.583 & 2657.309 & 10,571.919 \\  & Mn & 15.745 & 0.249 & 34.084 & 16.428 & 0.175 & 54.657 \\  & Mo & 3.204 & 0.070 & 44.722 & 4.621 & 0.032 & 44.933 \\  & Na & 1182.777 * & 667.913 & 2020.104 & 898.004 & 177.760 & 1640.311 \\  & Zn & 37.739 & 21.837 & 44.034 & 38.714 & 22.980 & 49.230 \\ \hline  & Al & 6.015 * & 0.226 & 23.654 & 8.940 & 0.326 & 21.469 \\  & B & 11.469 & 4.562 & 46.923 & 10.099 & 1.943 & 48.941 \\  & Ba & 1.210 & 0.638 & 2.373 & 1.158 & 0.225 & 2.228 \\ Potentially & Cd & 0.116 & 0.043 & 0.290 & 0.127 & 0.033 & 0.578 \\ Toxic & Li & 1.835 & 0.250 & 3.961 & 1.951 & 0.385 & 5.233 \\ Elements & Ni & 0.652 * & 0.219 & 2.104 & 0.976 & 0.105 & 2.667 \\  & Pb & 0.368 & 0.015 & 1.654 & 0.286 & 0.015 & 2.762 \\  & Sr & 6.452 & 1.768 & 13.449 & 6.394 & 1.615 & 34.778 \\  & V & 0.033 & 0.025 & 0.042 & 0.033 & 0.020 & 0.057 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Concentrations of elements (mg/kg) in organic and conventional flaxseed. Figure 1: Essential elements (mg/kg) in organic and conventional flaxseed samples. * Statistically significant difference (\(p<0.05\)).
###### Abstract  
This paper proposes an underwater communication receive model based on the spread spectrum technique in order to provide the characteristic of a low probability of interception. To do this, turbo equalization techniques employing Bahl-Cocke-Jelinek-Raviv (BCJR) decoding to improve performance through repetition are applied to offer excellent performance even at a low signal to noise ratio (SNR) of transmitted signals due to the spread spectrum technique. A turbo equalization model based on RAKE which increase signal power by summing the received signal through the multipath is proposed to compensate distorted data due to multipath channel and the performance improvements were proven by applying the threshold and weighted coefficient in the RAKE receiver model. The model was applied to covert underwater communication in a multi-sensor environment, and the efficiency of the proposed method was proven through underwater experiments.  
Keywords: underwater communication; low probability of interception; SNR; RAKE receiver; turbo equalization +
Footnote †: journal: Applied Sciences
## 3 Monitoring Results and Analysis
### Contact Pressure between the Primary and Secondary Supports  
Figure 23 has shown the time-history curve of the contact pressures between the primary and secondary supports. For the seven monitoring points at the tunnel vault, the change laws of the contact pressures were closely related to the construction processes. After the concrete of the vault's secondary support has been poured, the contact pressures increased sharply with the rapid increase of the concrete's strength and stiffness. The existence of the model board trolleys improved the rigidity of the secondary support, when they were detached, the contact pressures decreased slightly due to the reduction in the rigidity. As the strength of the concrete continued to slowly increase, the contact pressures slowly increased and finally reached a stable state. In general, the change laws of concrete pressures at the above-mentioned monitoring points could be summed up as \"sharp increase-slight decrease-slow increase-basically stable\". For the five monitoring points at the bottom of the tunnel, the change laws of the contact pressures were similar to that of the monitoring points of the tunnel's vault, but there were some differences. Within five days after the concrete of the vault's secondary support has been poured, the contact pressures of the inverted arch increased by 2.3% to 10.2%. This was because the loads on the upper part of the tunnel were transferred down to the inverted arch after the secondary support was closed. In general, the development of the contact pressures were mainly concentrated within the first seven days after the installation of the secondary supports of the vault and the inverted arch, which could reach more than 52% of the final values.  
The final distribution of the contact pressures between the primary and secondary supports have been plotted and shown in Figure 24. Overall, the contact pressures had a tendency to gradually decrease from the upper part to the lower part of the tunnel. The maximum value appeared at the crown, reaching 135.1 kPa, followed by the left haunch, with a value of 115.6 kPa. Compared with the  
Figure 23: Time history plots of the contact pressures between the primary and secondary supports.  
Figure 22: Final distribution of the axial forces at the end of the anchor bolts.  
contact pressure between the surrounding rock and the primary supports, it can be seen that the contact pressure between the primary and secondary supports was much lower than the contact pressure between the surrounding rock and the primary supports. Moreover, the maximum contact pressure between the primary and secondary supports was only 32.9% of the maximum contact pressure between the surrounding rock and the primary supports. Therefore, for super-span tunnels, the main function of the secondary support is as a safety reserve.
# Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming
## 3 Results
### Changes in the Sporadic E Layer During the 2008/2009 Winter SSW
Furthermore, the authors of reference [26] provided initial direct evidence of a correlation between sporadic E layers and planetary waves. They observed disturbances in the intensity parameters of sporadic E layers exhibiting a 7-day  
Figure 4: Daily average foEs wavelet spectra (frequency range: 0.1–1) from 9 January to 28 February 2009, for Okinawa, Yamagawa, and Kokubunji. Vertical white dashed lines represent the transition from easterly to westerly winds, and horizontal white dashed lines correspond to the peak values within the enclosed structures of the graphs. The y-coordinates corresponding to the horizontal white dashed lines in (**a–c**) are 6 days, 6 days, and 6.5 days, respectively. Figure 5: Daily average foEs wavelet spectra (frequency range: 0.05–0.1) from 9 January to 28 February 2009, for Okinawa, Yamagawa, and Kokubunji. Vertical white dashed lines indicate the transition from easterly to westerly winds, and horizontal white dashed lines correspond to the peak values within the enclosed structures of the graphs. The y-coordinates corresponding to the horizontal white dashed lines in (**a–c**) are 18 days, 16 days, and 17.5 days, respectively. cycle, which aligned with the occurrence of westward-propagating planetary wave events also characterized by a 7-day period. SSW events are closely linked to planetary wave activity. Initially, reference [18] posited that the vertical propagation of planetary waves from the troposphere, along with their interaction with stratospheric vortices, plays a crucial role in triggering SSW events, a theory that has gained broad acceptance. The findings from numerical simulations referenced in [30] provided further support for this hypothesis. Additionally, the authors of reference [31] utilized observational data to demonstrate that quasi-16-day planetary waves were instrumental in the development of the 2012-2013 SSW event. Reference [32] suggests that SSW events intensify the activity of quasi-six-day planetary waves. As a result, planetary wave activity during SSW periods is more frequent and intense compared to non-SSW periods. The following section will analyze the correlations between fluctuations in the wind field and variations in the sporadic E layer.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 2 Mathematical Models of the System Components
### Fuel Cell  
A fuel cell (\(FC\)) uses the chemical energy of hydrogen or another fuel to produce electricity. There are various types of \(FCs\) available in the market. The so-called proton exchange membrane fuel cell (PEMFC) is the most frequently used. The advantages of PEMFC include high-power density, low operating temperature, small size, and good performance at start up and shut down. For this reason, PEMFC was chosen for this project. The hourly hydrogen consumption can be expressed as follows [9]:  
\[q_{H_{2},con}=\frac{P_{FC}}{E_{low,H_{2}}\eta_{therm}U_{f}\eta_{FC}} \tag{7}\]  
where \(P_{FC}\) denotes the output power supplied by the \(FC\), \(E_{low,H_{2}}=33.35\) kWh/kg assumes the lower heating value of the hydrogen, \(\eta_{therm}=0.98\) is the thermodynamic efficiency at 289 \(K\), while \(U_{f}\) is the fuel utilization coefficient, namely, the ratio between the mass of fuel entering the \(FC\) and the mass of fuel reacting in the \(FC\). Finally, \(\eta_{FC}\) denotes the \(FC\) efficiency.
## 2 Theory Introduction
### Time Domain Representation  
The mathematical model of phase jitter (expression in time domain) caused by various phase noises in Figure 4 is shown in Equation (8). This formula can be used to simply calculate the phase jitter of the signal corresponding to a given phase noise sequence. The resulting phase jitter sum is actually approximately equal to the RMS value of the phase jitter noise sequence exhibited in the time domain of this signal  
\[J=\frac{\sqrt{2\cdot\int_{f_{L}}^{f_{H}}L_{\phi}(f)\cdot\mathrm{d}f}}{2\cdot\pi} \tag{8}\]  
\[J_{UI}=\sqrt{J_{WPM}^{2}+J_{FPM}^{2}+J_{WFM}^{2}+J_{FFM}^{2}+J_{RWFM}^{2}} \tag{9}\]  
Among Formula (9), \(L(f)\) is the SSB phase noise curve (dBc/Hz); \(J\) is the phase jitter in the frequency range \(f_{L}\sim f_{H}\), unit: cycle; and \(J_{UI}\) is the sum of the phase jitter in the spectral range of interest, unit: cycle.  
Table 1 provided a set of phase noise data from test, and Figure 5 interpreted in visual. Moreover, Table 2 demonstrated the phase noise results by using segment method.  
\begin{table}
\begin{tabular}{c c} \hline
**Frequency Offset** & **Phase Noise** \\ \hline
1 Hz & \(\leq\)\(-\)34 dBc/Hz \\
10 Hz & \(\leq\)\(-\)51 dBc/Hz \\
100 Hz & \(\leq\)\(-\)63 dBc/Hz \\
1 KHz & \(\leq\)\(-\)75 dBc/Hz \\
10 KHz & \(\leq\)\(-\)95 dBc/Hz \\
100 KHz & \(\leq\)\(-\)101 dBc/Hz \\
1 MHz & \(\leq\)\(-\)120 dBc/Hz \\ \hline \end{tabular}
\end{table}
Table 1: A set of typical phase noise measurements.  
Figure 4: Phase noise power spectral density. (Power law spectrum model).
## 2 GPS Attitude Baseline Determination
### Fixed Solution  
In the fixed solution, the final baseline parameters \(\Delta\mathbf{x}_{b}^{e}\) are estimated. Once the ambiguities have been fixed successfully, they can generally be held fixed for the further epochs, as long as no signal interruption or cycle slip occurs. To ensure that the ambiguities are held fixed correctly, they are resolved independently, epoch by epoch, until the same ambiguity resolution has been determined in ten consecutive epochs. The risk that the same incorrect ambiguity resolution is found in ten consecutive epochs during dynamic applications is negligible.
## 7 Conclusions and Future Prospects  
MESs have, in recent years, fuelled a range of investigators from different fields, including engineering, material sciences, and microbiology, to delve into the construction and application of these devices for the production of biocommodities including biofuels, as well as other applications such as the production of power or bioremediation of environments. These systems utilise the microbial metabolism of EAMs that can be harnessed with the presence of electrodes, which they can use to accept or donate electrons. EAMs that are capable of using electrodes as electron acceptors are known as electricigens and are capable of producing low amounts of electrical current in doing so. Furthermore, electrotrophs--EAMs that can use electrodes as electron donors--can be utilised for the production of compounds including biofuels and biogas. The understanding of the taxonomic range of EAMs, and the exact mechanisms used for EET, continue to expand, allowing for more opportunities to tailor the efficiency of EET mechanisms and the desired outcomes for different MES designs. Furthermore, by testing different materials and designs of the components within MESs, such as the anode, cathode and other membranes or filters, these devices continue to be specialised and improved for their individual aims. This review has outlined the basic components and designs of MECs, including MFCs for power generation and MECs for compound production, as well as the microbial taxa and EET mechanisms that are exploited within these systems.  
Future prospects for research into MFCs should include further investigation into the feasibility of scaling-up differing MFC designs relative to their practical applications and the resulting material requirements, while continued investigation of the microbiological aspects of MFC operation both in the context of electricigens and eletrotrophs may further expand avenues for the optimisation of these devices through biological in addition to engineering-based approaches.  
Conceptualization, A.E.F., L.S., E.R.M. and G.D.; Writing-Original Draft Preparation, E.R.M. and G.D.; Writing-Review & Editing, A.E.F., L.S., E.R.M. and G.D.; Visualization, E.R.M., L.S. and G.D.; Supervision, A.E.F.; Project Administration, A.E.F., L.S., E.R.M. and G.D.; Funding Acquisition, A.E.F. and E.R.M.  
The authors [PERSON]. & [PERSON], acknowledges the financial support of an Australian Postgraduate Award at La Trobe University. The author [PERSON] acknowledges additional financial support from industry collaborators Western Water.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Plant Sampling Data  
Sampling of basil took place over two consecutive days (8-9 August 2024). Within the area, ten rows of basil were sampled, and for each row, four sampling points were collected, totalling 40 samples. Each sample is approximately 10 m apart. For each sampling point, GPS coordinates were recorded using a mobile device. Although the positional accuracy of the smartphone GPS is lower compared to that of professional surveying instruments, it was sufficient for the purpose of sample georeferencing, given the spatial separation between points. No high-precision differential correction was applied, as the required spatial resolution was compatible with the scale of sampling and remote sensing analysis performed. Additionally, height and biomass were measured.  
Sampling started on 8 August 2024. The tools used for each measurement included a wheel metre (Figure 3a), a mobile device for GPS positioning, and a cardboard frame measuring 50 cm \(\times\) 50 cm in size (Figure 3b). The protocol involves the following steps: We positioned the frame above the plants in the row to define a surface area of 0.25 m\({}^{2}\). Then, we selected three random plants to measure their height in centimetres. The day after, the sampling consisted of collecting the biomass of basil plants from the sampling points established before thanks to GPS coordinates. Once positioned at the same points, the frame was placed, and the biomass of the basil plants within the 0.25 m\({}^{2}\) area was obtained manually. Cutting was performed using a knife blade, and the yield from each sampling point was collected in a plastic bag, numbered with the progressive sample reference. The cutting process aimed to replicate the mechanical harvesting typically performed by industrial harvesters for aromatic plants, which is why the cut was made approximately 4-6 cm above the cutting point from the previous harvest. Subsequently, the samples were weighed individually.  
Figure 2: Workflow of the proposed methodology from the acquisition of field data (both parameters measured on the ground and remotely) to the analysis of data using machine learning techniques (blue dots represents training predictions, while orange dots represents testing predictions). The aim is to provide a support tool for farmers.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
###### Abstract  
Traditional visual simultaneous localization and mapping (SLAM) systems typically generate sparse or semi-dense point cloud maps, which are insufficient for effective navigation and path planning. Constructing navigation maps through dense depth estimation generally entails high computational costs, and depth estimation is prone to errors in weakly textured regions such as road surfaces. Furthermore, traditional visual SLAM methods rely on local relative coordinate systems, making it extremely challenging to merge mapping results from different coordinate frames in navigation systems lacking global positioning constraints. To address these limitations, this paper presents a multi-submap fusion mapping method based on semantic ground fitting and incorporates global navigation satellite system (GNSS) to provide global positioning information via occupancy grid maps. The method emphasizes the integration of low-cost sensors into a unified system, aiming to create an accurate and real-time mapping solution that is cost-effective and highly applicable. Simultaneously, a multi-submap management mechanism is introduced to dynamically store and load maps, updating only the submaps surrounding the vehicle. This ensures real-time map updates while minimizing computational and storage resource consumption. Extensive testing of the proposed method in real-world scenarios, using a self-built experimental platform, demonstrates that the generated grid map meets the accuracy requirements for navigation tasks.  
visual SLAM; map for navigation; GNSS-assisted mapping; AGV visual navigation Article  
[PERSON], [PERSON], [PERSON], [PERSON], H. Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration. _Appl. Sci._**2025**, 15, 3725. [[https://doi.org/10.3390/app15073725](https://doi.org/10.3390/app15073725)]([https://doi.org/10.3390/app15073725](https://doi.org/10.3390/app15073725))  
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