esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights in Potato Leaf Freezing by Infrared Thermography
## Materials and Methods
### Detection of Freezing Exotherms by DTA  
Freezing exotherms in leaves of _S. tuberosum_ were measured by the established method of differential thermal analysis (DTA; [28]). During controlled freezing treatments, leaf temperatures were monitored via copper constantan thermocouples. For detecting ice formation based on a rise in temperature due to phase-transition-heat-release, thermocouples were sandwiched between Styrofoam and the leaves. A thermocouple positioned in close vicinity to the leaves was used as a reference temperature. For the DTA-plot the differential temperature (leaf \(-\) reference temperature in K) was plotted against time. DTA was conducted 10 times on single leaves of _S. tuberosum_.
# Multi-Module Combination for Underwater Image Enhancement
## 4 Experiment
### UIEB Dataset Evaluation  
This section is evaluated for a randomly selected set of images from a dataset with reference images. Out of 100 randomly selected pairs of images, underwater images of different underwater scenes are chosen to be presented in the subjectivity assessment to go through the enhancement characteristics of different methods.  
The output depicted in Figure 5 demonstrates that the UIESS algorithm obviously improves the image brightness and improves the original color bias but introduces a slight color distortion. The MLLE algorithm successfully resolves the color cast issue and restores the image's colors effectively, but the brightness is excessively high, resulting in a loss of detail. UW-AEE handles blue-green tinted images well, but it is not as effective in terms of defogging effects and boosting contrast. EUICCCLF has the problem of color loss. With UWCNN, it can be clearly seen that the enhancement is poor. PCDE has the problem of detail loss, and BRUIE has the problem of insufficient color compensation and local darkness. Our algorithm outperforms others in processing images with varying color shades, improving brightness and visibility. However, the saturation enhancement is somewhat excessive and requires further optimization.  
Table 1 presents the evaluation results for the real-world dataset. As indicated in the table, our method outperforms all other approaches. This demonstrates that our method effectively compensates for the color absorption in underwater images while preserving more information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Metric**} \\  & **NIQE** & **UCIQE** & **UIQM** \\ \hline UW-AAE & 15.705 & 0.789 & 115.897 \\ EUICCCLF & 15.406 & 0.704 & 116.363 \\ MLLE & 12.771 & 2.475 & 114.243 \\ PCDE & 14.085 & 1.453 & 126.915 \\ UWCNN & 15.328 & 0.897 & 69.118 \\ UIESS & 16.896 & 1.059 & 99.772 \\ BRUIE & 12.970 & 0.812 & 115.0823 \\ UMMC & 12.612 & 2.532 & 124.220 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An objective assessment of real underwater benchmarks with different algorithms. The best result is in red, whereas the second-best result is in green.
## 3 The Basic of GNSS
### System Architecture
#### 3.5.2 Ground Segment  
The ground component performs key functions to coordinate GNSS activities. These functions include tracking the many satellites that make up the GNSS constellation, monitoring their navigation transmissions to determine the status of their on-board subsystems, particularly their atomic clocks, and analyzing their ephemerides. The operational control component of the ground segment sends feedback commands and data to take corrective measures on the orbital parameters and on-board atomic clocks, and, if necessary, resolve any anomalies, ensuring that all subsystems operate according to specifications. These functions are performed by a global network of ground installations operated by the main control station. The main control station (MCS), otherwise known as the Mission Control Center, is the facility where the ground control team has sole responsibility for centrally controlling the operations of the entire system, 24 h a day. The ground control segment is supported by globally distributed monitoring stations in geodetically precise locations selected to ensure optimal coverage. The monitoring stations are equipped with atomic clocks with enhanced performance and GNSS receivers with geodetic-level measurement quality. The atomic clocks are characterized by high beam intensity. The time reference of the monitoring station at MCS is coordinated with the master clock. At all monitoring stations around the world, atomic clocks provide standard signals of equal frequency as references to their GNSS receivers. The main control station regularly receives satellite data from the monitoring stations regarding the satellites' ephemerides and atomic clock readings, as well as on-board systems data, information that is analyzed to determine firstly the status and integrity of the satellites' on-board systems, and secondly to determine their exact orbital positions and whether corrective action may need to be taken to ensure that all satellites are in constant transmission with the station. In the event of an emergency, such as a satellite failure, immediate action can be taken to reposition the satellite to maintain system integrity [67]. Figure 8 shows an example of ground segment architecture.  
Figure 7: Orbital parameters of satellites. Prepared on the basis of [67].
## 2 Results and Discussion
### Statistical Analysis
#### 2.3.4 Differences between Solvents in Microwave-Assisted Extraction  
To verify the differences in the fourth method, an analysis of variance was performed in conjunction with an auxiliary Friedman ANOVA test, the results of which are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Time** & & \multicolumn{2}{c}{**24 h**} & \multicolumn{4}{c}{**48 h**} & \multicolumn{4}{c}{**72 h**} & \multirow{2}{*}{**Intragroup Tests**} \\ \hline
**Solvents** & \(M\) & \(Me\) & \(SD\) & \(M\) & \(Me\) & \(SD\) & \(M\) & \(Me\) & \(SD\) & \\ \hline n-hexane & 8.29 & 0.66 & 17.25 & 5.77 & 0.35 & 13.72 & 4.74 & 0.29 & 11.88 & \(F=0.19\); \(df=2\); \\ diethyl & 11.44 & 3.92 & 17.01 & 11.87 & 5.05 & 16.29 & 12.29 & 8.07 & 15.08 & \(F=2.67\); \(df=2\); \\ ethylene & 12.17 & 5.61 & 18.48 & 12.22 & 6.23 & 17.40 & 11.98 & 6.62 & 15.95 & \(F=3.68\); \(df=2\); \\  & & & & & & & & & & \(p=0.159\) \\ ethyl & 7.52 & 1.12 & 14.11 & 6.62 & 0.73 & 12.27 & 6.38 & 0.85 & 11.53 & \(F=3.22\); \(df=2\); \\  & & & & & & & & & & \(p=0.199\) \\ methanol & 9.24 & 1.38 & 14.15 & 8.78 & 1.11 & 14.02 & 8.80 & 1.11 & 14.06 & \(F=6.00\); \(df=2\); \\  & & & & & & & & & & \(p=0.050\) \\  & & & & & & & & & & \\  & & & & & & & & & & & \\ \hline \end{tabular} \(M\)–mean. \(Me\)–median. \(F\)—the result of the analysis of variance. \(df\)—degrees of freedom. \(p\)—significance level.  
\end{table}
Table 8: Analysis of differences in the scope of the SLE method.  
The analyses performed with the parametric test did not show statistically significant differences in the mean intensity of VOCs, F (3.33) = 1.53; \(p\) = 0.224; \(\eta\)2 = 0.12. However, the nonparametric analysis, which ignored errors in the measurement of the means, showed a statistically significant effect for the differences between individual solvents in the MAE method, F = 20.01; df = 3; \(p\) < 0.001. The intensity of VOCs was significantly higher under the MAE1 condition than under the MAE3 (\(p\) = 0.007) and MAE4 (\(p\) = 0.001) conditions. Furthermore, a higher level of VOCs was observed under condition MAE2 compared to condition MAE4 (\(p\) = 0.034). However, no differences were found between the conditions MAE2 and MAE3 (\(p\) = 0.197), MAE1 and MAE2 (\(p\) = 1.000) and MAE3 and MAE4 (\(p\) = 1.000).
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
## 6 Simulations and Analyses  
In order to compare the performance of the abovementioned methods, navigation constellation simulations were carried out with the parameters of Walker 24/3/2:55\({}^{\circ}\), 22,116 km [30]. The dynamic model applied to satellite orbit was:  
1. the earth's gravitational effects of 70 \(\times\) 70,
2. the lunar, solar, and other planetary gravitational perturbations,
3. the solar radiation pressure, and
4. the other general relativistic forces.  
The eighth-order Runge-Kutta method was employed for orbit integration. The IERS96 model was adopted for the Earth orientation parameters [31]. Besides, the TDMA mode was adopted in ISL with measurement frames and data transmitting frames. The total error of Ka-band ISL PR was 0.5 m (1\(\sigma\)). To avoid ground atmospheric disturbance, the ISLs with a vertical distance of less than 1000 km to the Earth surface were not considered. Eight GASs that are located in Xiamen, Kashi, Beijing, Lhasa, Sanya, Urumqi, Jiamusi, and Xi'an in China were set up in the simulation. With a minimum  
For each method, the orbit determination errors tended to oscillate steadily after poor initial results. However, differences can be observed among the four algorithms. To have quantitative comparisons, the root mean square (RMS) of different errors for all the satellites in the constellation was calculated for the stable section. The average RMSs were then obtained by averaging the data from all the satellites and are plotted in Figure 5. It is worth pointing out that the average RMS of position error for the WCCEKF, ICEKF, IMCEKF, and BEKF algorithms was around 1.6 m, 4.5 m, 2.9 m, and 1.9 m, respectively.  
Figure 4: Orbit determination errors of SV-01 by the balanced extended Kalman filter (BEKF) algorithm. (**a**) the position error and radial error; (**b**) along track error and cross track error.  
Figure 5: Average RMS of orbit errors.  
Figure 3: Orbit determination errors of SV-01 by the increased measurement covariance extended Kalman filter (IMCEKF) algorithm. (**a**) the position error and radial error; (**b**) along track error and cross track error.  
Next, the computation amounts for the different methods are summarized in Table 1 and visualized in Figure 6.  
The WCCEKF algorithm yielded optimal results with the highest precision. Among the three distributed orbit determination algorithms (ICEKF, IMCEKF, and BEKF), the highest orbit estimation precision was observed in the method of BEKF. When it comes to computation amounts for the four methods in Table 1 and Figure 6, the largest calculation amount was required in the WNCEKF algorithm, which achieved the best orbit accuracy. In the methods of ICEKF, IMCEKF, and BEKF with sub-optimal distributed orbit determination, significant reductions of the computation amount were observed when compared to the method of WNCEKF.  
Finally, the performances of the WCCEKF, ICEKF, IMCEKF, and BEKF algorithms are summarized and provided in Table 2, considering different aspects.
## 3 Experimental Design
### Attack Experiment Design
#### 3.3.1 Geometric Attack Experiment  
Geometric attacks mainly include translation, rotation, scaling, coordinate transformation, compression, and cropping attacks. The attacking intensities and modes are shown in Table 2 and Figure 4 In the coordinate transformation attack, both ZC and SZ datasets are converted into the Xian80 and Beijing54 coordinate systems, of which both are commonly used in China, respectively. Four levels of compression attack experiments, of which 78% is the threshold for constructing the current scheme. For cropping attacks, several cropping modes as described in [24] are applied to facilitate comparisons. This study designed six cropping patterns for attacking experiments (Figure 4) and extracted watermark information from the remaining vector data. Both NC and BER values are calculated to assess the robustness against attacks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attack Types** & **Range of Magnitude** \\ \hline Translation & 0–100 on x-axis or y-axis \\ Rotation & \(0^{\circ}-360^{\circ}\) \\ Scaling & \(-500\%\)–\(500\%\) \\ CST & Transform to Beijing54 and Xian80 \\ Compression & Compression ratios are 30\%, 50\%, 78\% and 88\% \\ Cropping & Multiple cropping patterns show in Figure 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Magnitudes of attacks. CST: Coordinate System Transformation.  
Figure 4: Different cropping patterns. Pattern 1 cropped the lower half of the original maps by approximately 1/2. Pattern 2 cropped the right half of the original maps by approximately 1/2. Pattern 3 left approximately 1/4 of the upper left corner of the original maps. Pattern 4 left approximately 1/4 of the first half of the original maps. Pattern 5 left approximately 1/4 of the right half of the original maps. Pattern 6 left approximately 1/8 of the upper right of the original maps.
# An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments
## 7 Discussion and Future Work  
Object detection and tracking will undoubtedly play a crucial role in creating contextual awareness and perception of autonomous driving for higher levels of autonomy. We plan to extend the contribution of this work in the following directions:  
* Implementing the 3 GPP Enhanced Sensors use case scenario by evolving the contribution of this article. One of the major ingredients in this case will involve additional sensors to be deployed on the drone. This activity will allow us to investigate the role of 5G C-V2X capabilities (including side-ling, PC 5), etc. for meeting the ITS service quality requirements.
* Investigating the potential of Federated Learning for achieving the objectives of level 5 autonomous driving. For this, we plan to deploy the learning instances at the three levels i.e., vehicle, smart edges (roadside units), and backends of the OEMs, mobile network providers, and ITS service providers. It should be highlighted that the envisioned ecosystem with the engagement of aforementioned stakeholders, federated learning is expected to be a natural fit.
* We plan to evolve the contributed approach to further classify vehicles based on their types and cluster them along with their counts, which can further be analyzed for the use-cases of level 5 autonomous driving.
* Owing to the fact that contribution aligns well with other verticals and application domains, we also plan to extend the proposed methodology in the field of agriculture, to provide detailed analysis on various factors related to plant health status, productivity, and disease attacks. This analysis will guide the agricultural to improve crop productivity and take early preventive measures on disease attacks.
## 5 Conclusions  
This paper proposes the novel PCA-LSTM method for the productivity prediction of cutter suction dredger, wherein the deep learning process makes good use of the real-time operational monitoring data. The PCA method based on mechanism and knowledge is proposed to analyze the multiple parameters and select relevant variables from the operation process. Then the results are used as input into LSTM model to obtain the target prediction. This approach is also successfully validated by comparison against other methods on a real-world case in China. The productivity of cutter suction dredger is influenced by many correlated factors such as the soil characteristics, cutter parameters, mud pump  
\begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **R\({}^{2}\)** & **RMSE** \\ \hline Proposed PCA-LSTM & 0.0424 & 0.9999 & 0.0925 \\ Traditional PCA-LSTM & 0.3063 & 0.9863 & 0.4054 \\ LSTM & 0.3352 & 0.9828 & 0.5010 \\ \hline \end{tabular}
\end{table}
Table 2: Results analysis of the comparative study.  
Figure 13: The testing result of comparative study.  
performance, and pipeline layout. Thus, the mud concentration should be stabilized at a suitable value by comprehensive adjustment to improve its efficiency and productivity.  
However, this is still a workable extension of deep learning application in the productivity prediction of cutter suction dredger. In the future, we will further construct the dynamical predictive models according to the changing working condition. When the operational parameters change dynamically under different conditions, the generated data should be classified into status space to study how the operation influences the dredging performance. Additionally, considering the sensors distance in the system, more factors on time-delay should be put up to improve the prediction accuracy.  
Conceptualization, K.Y.; methodology, K.Y.; software, K.Y.; validation, K.Y.; formal analysis, K.Y. and T.X.; data curation, K.Y., J.-L.Y. and B.W.; writing--original draft preparation, K.Y.; writing--review and editing, K.Y. and J.-L.Y.; visualization, K.Y.; supervision, S.-D.F.; funding acquisition, S.-D.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant: 51679178 and 52071240.  
Not applicable.  
The authors declare no conflict of interest.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 3 Methods
### Visual Module  
To learn image representations appropriate for this dataset, we specifically fine-tuned on the images in the target dataset using the weights of the ViT [64] model that have already been trained on ImageNet. We obtained four variants of the Vision Transformer by assigning various values to the block numbers and token dimensions: ViT-T, ViT-S, ViT-B, and ViT-L, where T, S, B, and L correspond to the number of parameters (i.e., tiny, small, base, and big). As the ViT-B has a reasonable balance between performance and model size, it serves as the foundation of our methodology there. The ViT separates the input image into various patches, projects each patch into a fixed-length vector, and then, passes the vector to the Transformer. As with the original Transformer, the ensuing encoder operations are identical. However, in order to account for the classification of the images, a specific token (\(CLS\)) is added to the input sequence, and the output corresponding to the token is the final category prediction.  
As shown in Figure 4, each image will produce 196 patches when divided into groups of patches of a specific size; this means that the input sequence length is 196 and the dimension of each patch is 768. The addition of the \(CLS\) token and position embedding is required prior to entering the Transformer encoder. Position embedding is a straight add-on token, and the token is concatenated with the token created from the previous image. The shape after splicing the \(CLS\) token is currently (197, 768), so the shape of the position embedding is also (197, 768). Following feature extraction by the Multi-Layer  
Figure 3: The proposed audio module.  
Perceptron (MLP) and encoding by the multi-head self-attention module, the output (\(CLS\) token) is regarded as the feature (visual embedding) of the image. The visual embedding is mapped to the label for classification to obtain the visual_result vector.  
In the vision module, we are able to extract more-significant and -expressive features from images by utilizing the ViT model and the transfer learning method. Such feature representations can aid in improving our comprehension of the information contained in images and can deliver more-precise data for the upcoming scene classification tasks.
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Model** & **R\({}^{2}\)** & **MAE** & **RMSE** & **MSE** \\ \hline [PERSON] et al. [7] & Support vector machines & 0.9400 & 3.6000 & 0.0700 & - \\ \hline [PERSON] [38] & 6-degree polynomial & 0.9500–0.9800 & - & - & - \\ \hline [PERSON] [39] & 6-degree polynomial & 0.9000–0.9700 & - & - & - \\ \hline [PERSON] et al. [19] & Neuro-adaptive fuzzy logic & - & 2.0198 & - & 5.2017 \\ \hline [PERSON] and [PERSON] [16] & Neural Networks and Matching Progress & - & - & 0.0355 & - \\ \hline [PERSON] and [PERSON] [14] & Artificial neural networks & - & - & 0.0552 & - \\ \hline [PERSON] et al. [11] & Artificial neural networks & - & - & - & 0.0043 \\ \hline [PERSON] [15] & Third-degree polynomials & 0.9800 & - & - & - \\ \hline [PERSON] et al. [8] & Fuzzy model & 0.9400 & - & - & - \\ \hline \multirow{4}{*}{Present study} & Logistics Model & 0.9848 & 0.0278 & 0.0506 & 0.0026 \\ \cline{2-6}  & [PERSON] model & 0.9844 & 0.0321 & 0.0512 & 0.0026 \\ \cline{1-1} \cline{2-6}  & Proposed model & 0.9821 & 0.0278 & 0.0506 & 0.0026 \\ \cline{1-1} \cline{2-6}  & Gompertz model & 0.9832 & 0.0311 & 0.0531 & 0.0028 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mathematical models applied to project planning and control. project completion times and enhancing schedule stability. This enhancement in predictability and time control is associated with the logic of sigmoidal models, which represent the cumulative behavior of growth processes with defined phases of acceleration, maturity, and deceleration. The FSM endeavors to standardize and optimize project execution processes, aiming to ensure consistency and predictability in outcomes. In contrast, sigmoidal models adopt a quantitative and continuous approach, elucidating the dynamics of project progress over the entire life cycle.
# Jet Noise in Airframe Integration and Shielding
### Hybrid Wing Body Concept  
One of the approaches to reduce engine noise by changing propulsion system orientation, is to benefit from configurations, such as in the over-the-wing engine mount in the hybrid wing body (HWB) concept [19]. Early assessments of the HWB noted limited potential for noise reduction, specifically a Boeing version of an HWB called the \"blended wing body\" of [PERSON] et al. [20]. This configuration had the engine exhaust positioned aft of the trailing edge, making shielding of the aft-radiated engine noise sources impossible, unless the engines were moved at least some limited distance upstream on the airframe [21]. Also, various future aircraft design concepts consider over-the-wing engine configurations, in which the wing or any other airframe structures can be used as a shield against engine noise (i.e., [22]). For example, a hybrid wing body configuration under NASA's N + 2 test [23] enhances the design by moving the engine nacelles forward of the trailing edge to shield the aft turbomachinery and jet noise.  
The HWB design has attracted many researchers in the past years to move towards the noise reduction goals of NASA and FAA [24]. From the jet noise reduction perspective, this design mainly relies on the jet shielding [25; 26] enhanced by the aft body section, as illustrated in Figure 4. The focus of these experiments was to investigate the shielding effect on the exhaust jet and prospective acoustic mitigation of the aft-body section.  
Figure 3: Schematics of jet flow and acoustic wave interactions with aerodynamic surfaces.