esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### The End-User Layer  
This layer represents the ground station, which is typically sets of ground-based hardware and software to communicate with the drone via wireless telemetry in the hand of an admin user. This portable and universal ground station also provides a range of applications (e.g., control the launch, flight, aerial imaging, and recovery of the drone). This work considered the optimized Hata empirical propagation path loss model to bridge the gap between the ground station and the drone at the cloud layer. The novel twist on the considered Hata path loss model used in this work involves using the elevation angle instead of distance to optimize the model when propagating signals from the drone to terrestrial sensors and using LoS connectivity to improve reliability, increased end-user mobility, and reduce power consumption [35; 36]. This is a noteworthy shift from  
Figure 8: Block diagram of drone–LoRa gateway.  
Figure 7: The drone–LoRa gateway.  
what exists in models presented in the literature, as presented in related works of the previous section.  
Hata's equations are classified into urban, suburban, or rural areas; yet, here, it was considered only in rural areas since it involved farm zones [37; 38]. The distance \(d\) of the Hata propagation model is computed based on elevation angle \((\theta)\), which is a notable shift from existing propagation models. The \(\theta\) is a vital element in calculating path loss from space such as in drone scenarios. Path loss in a rural area is given by  
\[\mathrm{P_{L(urban)}}=69.55+26.16\ log\ (f)-13.82\ log\ (h_{t})-a(h_{r})+[44.9-6.55\ log \ (h_{t})]\times[log\ (d)]^{b} \tag{1}\]  
\[\mathrm{P_{L(rural)}}=\mathrm{P_{L(urban)}}-4.78\ log\ (f)^{2}+18.33\times\log(f)-40.94 \tag{2}\]  
\[d=2\ E_{r}[\cos^{-1}\left(\frac{E_{r}}{E_{r}+h_{t}}*\cos(\theta)\right)-\theta] \tag{3}\]  
where \(f\) denotes frequency (MHz), \(ht\) denotes transmitter altitude (m), which represents drone altitude, \(hr\) denotes receiver altitude (m), which represents the ground station, \(d\) denotes transmitter to receiver separation in (km), \(E_{r}\) denotes Earth's radius at 6378 km, and \(a(hr)\) denotes correction factor for effective mobile antenna altitude, which is a function of the size of the coverage area. Received signal strength (RSS) represents the strength of wireless signals as measured by the receiver, which is typically measured in units of decibels (dB) [39]. Calculating PL model and RSS are useful in monitoring system performance, network planning, and coverage to achieve perfect reception. The RSS can be calculated as follows:  
\[\mathrm{RSS}=\ \mathrm{Pt}+\mathrm{Gt}+\mathrm{Gr}-\mathrm{P_{L(rural)}}-L \tag{4}\]  
where Pt denotes transmitter power (dBm), Gt denotes transmitter antenna gain (dBi), Gr denotes receiver antenna gain (dBi), P\({}_{L}\) denotes path loss of Hata model in a rural area, and \(L\) denotes connector loss (dB).
###### Abstract
The data would provide insight on how to address microwave link design flaws resulting in RFI. The RFI data would provide valuable information on efficient frequency spectrum planning, analysis, assignment, and management. The dataset would help handle the concentration of the base transceiver stations transmitting at different frequencies in a spectrum, which poses a significant cause of RFI. Finally, the data would further support research and development towards the design and development of intelligent interference-resistant transceivers. The main contributions of the paper are summarized as follows:  
1. Extensive field measurements to experimentally detect radio frequency interference in microwave links operating at the 6 GHz, 7 GHz, 8 GHz bands using a special-purpose spectrum analyzer:
2. We analyzed the actual frequency scan measurements for the horizontal and vertical polarizations at the operating frequency bands:
3. We conducted elaborate signal processing and analysis of the frequency scan measurements for the investigated links in the horizontal and vertical polarizations at the operating frequency bands:
4. Determination of the probability density functions of the frequency scans covering the normalized histogram, estimated normal distribution, normalized center lines, and the smoothed center lines:
5. Determination of the cumulative distribution functions of the frequency scans at the 6 GHz, 7 GHz, and 8 GHz bands for horizontal and vertical polarization:
6. Derivation of the statistical error characteristics of the frequency scans, including the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), relative absolute error (RAE), and mean squared error (MSE), each for the estimated normal distribution and the estimated fitness curve:
7. We conducted the denoising of the frequency scan signals and performed a comparative analysis of the scanned signals before and after the noise removal algorithm was applied. The remaining parts of this paper are organized as follows. Section 2 presents the related literature and covers the existing RFI detection and mitigation algorithms and the emerging reconfigurable intelligent surfaces (RIS). Section 3 reports the materials and methodology--measurement campaign and signal processing of the frequency scan measurements. Section 4 presents the analyzed results and valuable discussions. Finally, the conclusion to the paper and future perspectives are given in Section 5.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 5 Experimental Results
### Results of Assimilating False Negative Samples  
Compared to the deletion strategy, the assimilation strategy is more sensitive to the quality of the false negative samples. As shown in Table 3, in the English dataset, when assimilating one false negative sample, there is a relative improvement of 2.98% over the original model, and in the Mandarin dataset, a relative improvement of 4.04% is still obtained, with the final number of negative samples remaining the same. However, after assimilation of two false negative samples, both the English and Mandarin models have a negative effect on the model. That is, the assimilation strategy is only suitable for more reliable false negative samples (i.e., those with very high similarity scores), and excessive false negative samples will interfere with the model, making it difficult for the model to capture the characteristics of positive samples. This is consistent with our original assumption that the negative sample with the highest similarity will improve the model's learning ability and have a larger weight, while other samples assimilated as positive samples will have a smaller weight. Compared to the method of deleting false negative samples, the method of assimilating false negative samples can achieve higher accuracy in longer speech segments. However, this method requires higher quality for the false negative samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(K\) & \(K^{\prime}\) & \(N\) &
\begin{tabular}{c} **Mandarin** \\ _CER\%_ \\ \end{tabular} \\ \hline wav2 vec 2.0 & 100 & 100 & 0 & 27.853 \\ _FNIE_ & 101 & 100 & 1 & 27.791 \\  & 102 & 100 & 2 & 27.335 \\  & 104 & 100 & 4 & 26.755 \\ \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(K\)} & \multirow{2}{*}{\(K^{\prime}\)} & \multirow{2}{*}{\(N\)} & \multicolumn{2}{c}{**English**} \\ \cline{4-4}  & & & & **CER\%** & _WER\%_ \\ \hline wav2 vec 2.0 & 100 & 100 & 0 & 11.753 & 23.998 \\ _FNIE_ & 101 & 100 & 1 & 12.115 & 24.062 \\  & 102 & 100 & 2 & 11.805 & 23.353 \\  & 104 & 100 & 4 & 12.18 & 24.254 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of deleting different numbers of false negative samples.
## 2 Related Works
### Alphabet Languages  
Researchers have started developing OCR systems for the left to right languages since 1900 [27]. Of all the left-to-right languages English is the most explored language in developing an OCR system.  
One of the most famous OCR methods belonged to [PERSON] [3], in which a line-finding algorithm used to find the rows of text was followed by baseline fitting. Then, fixed pitch detection, chopping, and proportional word finding were implemented. In the word recognition phase, chopping joined characters was improved by chopping the blob with the worst confidence from the character classifier. In the character classifier step, features were extracted using segments of the polygonal approximation. After that, the classification was done in two steps. First, a class pruner created a shortlist of character classes that the unknown might match. Second, each feature of the unknown looked up a bit vector of prototypes of the given class that it might match, and then the actual similarity between them is computed.  
English OCR systems can perform with the accuracy of about 100% in recognizing of typed and digital texts. However, recognition of handwritten texts is still challenging and considered as one of the trending topics for several researchers in this field. In 2009, an approach based on training a BLSTM network on unsegmented sequence data was introduced for recognizing unconstrained English handwritten text [22]. Word recognition accuracy on online and offline data was 79.7% and 74.1%. In another research [23], the authors suggested an approach for online recognition of English handwritten whiteboard notes. In this work, the BLSTM with a Connectionist Temporal Classification (CTC) output layer was applied to unsegmented labels directly. By having a 74.0%-word recognition rate, this model showed an improvement over the previously developed Hidden Markov Model (HMM)-based recognition system with a recognition rate of 65.4%.  
Researchers presented an LSTM-based model for multilingual OCR systems that covered English, French, and German without any language model or dictionary correction [28]. Possessing only 1% recognition error showed its extraordinary performance. In another work [19], scientists used an RNN-LSTM model to recognize French words with 99% accuracy. The utilized data set involved the top 5000 French words in six fonts with the size 14.
## 3 Results
The results summarized in Figure 6 show that Unicone has significantly higher hardness (approximately 4 GPa) than other three instruments (approximately 3 GPa), which show very similar hardness. It should be noted that 4 GPa is a typical value for a commercial austenitic NiTi superelastic alloy. Unicone Plus and Reciproc Blue exhibit a very small but observable decrease of hardness between cross-sections (2 and 6.4 mm from the apex), which is not observable for WaveOne Gold and Unicone. Time-dependent plastic deformation, known as creep, was evaluated using a time period of 120 s. Sets of normalized creep curves for all three instruments are presented in Figure 6 for comparison. The normalization was performed by subtracting the initial creep depth measured at the beginning of the dwell period, when the normal force was kept constant. Despite the natural spread of creep curves, Figure 7 provides a qualitative comparison of the tendency of the material to deform under constant indentation load. Comparing the penetration depth, one can see significantly lower tendency to creep for Unicone in comparison to other three samples. Minor differences are also observable between other samples, where WaveOne Gold shows a lower tendency to creep than Unicone Plus and Reciproc Blue. Figure 5: Micrographs of the side walls of the instruments, intensity (left) and color (right) representation, with chosen roughness profile: (**a**) Unicone, (**b**) WaveOne Gold, (**c**) Unicone Plus and (**d**) Reciproc blue.
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
## 5 Discussion  
The experimental results showed that the proposed method was able to extract the respiratory motion from the CBCT images with a minimal difference compared to the ground-truth signal. The work presented in this paper can be compared to the works presented in [20; 21; 26]. In [20], the average phase shift was reported as \(1.68\) projections when compared to signal extracted based on diaphragm position, and \(1.78\) projections when compared to signal extracted based on internal markers. The work presented in [20] involved several steps which may increase the method complexity. In our previous work in [26], the average phase shift did not exceed \(1.5\) projections for tests performed on regular and irregular phantom datasets. However, that method was only applied to phantom datasets. In [21], the average phase shift was \(3.8\) projections when compared to signal extracted based on diaphragm position, and \(3.59\) projections when compared to signal extracted based on internal markers using the non-linear dimensionality reduction method which was the best performing method. Although applied to clinical datasets, the average phase shift was relatively high. Furthermore, no 4D CBCT reconstruction results were provided. All in all, compared to [20; 21; 26], the extracted respiratory signal  
Figure 8: Edge profile along diaphragm of reconstructed slice of phantom dataset 2.  
using the proposed method in this paper achieved lower phase shift than similar results in the literature.  
Moreover, the reconstructed 4D CBCT images considering the respiratory signal extracted using the proposed method showed clear images with sharp edges compared to the ground-truth based images. The reconstructed 4D CBCT images are affected by streaking artifacts that are caused by the reconstruction process wherein only un-der-sampled projections are used for the reconstruction of each phase. These artifacts have nothing to do with the phase sorting process. Several studies have been proposed in the literature to minimize the streaking artifacts and improve the overall quality of the reconstructed 4D CBCT images. These solutions revolve around using motion compensation and modeling methods [34, 35], deep learning [36, 37, 38] or projection oversampling through in-between projection interpolation [39, 40].  
The limitations of this work include the limited number of datasets used to test the proposed method. As a future work, it is recommended to test the proposed method on a larger number of datasets to get more reliable findings. Moreover, different optical flow methods can be tested for motion estimation. In addition, other dimensionality reduction methods can be explored for the breathing motion extraction from the optical flow vectors
## 5 Experimental Result and Analysis
### Effect of Joint Optimization Coefficient  
In the joint optimization framework, the total loss is a weighted combination of the classification and regression losses:  
\[\zeta=\alpha\zeta\mathbf{class}+(1-\alpha)\zeta_{reg} \tag{37}\]  
where \(\alpha\in[0,1]\) controls the trade-off between the two tasks. Proper selection of \(\alpha\) is crucial for balancing the contributions of both tasks, ensuring neither dominates the optimization process. This experiment investigates the effect of different \(\alpha\) values on multi-task performance.  
To assess the influence of the joint optimization coefficient (\(\alpha\)) on the performance of the proposed multi-task learning framework, we conduct experiments with \(\alpha\) values ranging from 0.1 to 1.0. This coefficient determines the relative weighting of the classification and regression losses during training. Appropriate calibration of \(\alpha\) enables the model to jointly learn discrete classification and continuous regression tasks, thereby enhancing the quality of the learned feature representations. The goal is to identify an optimal \(\alpha\) that achieves the best overall performance across various datasets. The experimental results are presented in Figure 4.  
As shown in Figure 4, the relationship between \(\alpha\) and classification accuracy exhibits a non-linear trend. Accuracy improves significantly as \(\alpha\) increases from 0.1 to 0.6, highlighting the benefits of incorporating regression for improved generalization and feature learning. The Geiser dataset [17] achieves its highest accuracy of 92.454% at \(\alpha=0.6\), while the Miao (\(\eta=1\)) dataset [18] reaches 86.64% under the same configuration. Similarly, the Miao (\(\eta=2\)) and Miao (\(\eta=4\)) datasets [18] attain peak accuracies of 92.33% and 94.75%, respectively, at \(\alpha=0.6\). However, further increasing \(\alpha\) leads to a slight degradation in performance, suggesting that excessive emphasis on reg  
Figure 3: Effect of transformer layers on detection accuracy across multiple steganography methods.  
classification objective. These findings emphasize the importance of balancing both tasks, with \(\alpha=0.6\) identified as the optimal setting for maximizing detection performance.
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 2 Related Work
### Multi-branch Network in Image Restoration Task  
The multi-branch network architecture has been widely used in many deep-learning-based algorithms, while there have been still a few attempts for image restoration. Different branches are usually designed as different architectures for specific tasks. [PERSON] et al. [29] proposed a deep guided network for image deblurring tasks, which includes an image deblurring branch and a scene depth feature extraction branch. The image deblurring branch is guided by the scene depth feature extraction branch to restore a clear image. The image restoration task usually contains two parts of information, namely image structure and details. Combining these features, [PERSON] et al. [30] proposed a parallel convolutional neural network for image restoration tasks. The network includes two parallel branches to jointly estimate the image structure and detail information, and restore them in an end-to-end manner. Therefore, combining certain characteristics of the image itself can help to improve the quality of restoration.  
From the view of signal processing, the global uniform blur is a linear shift invariant processing, while, in the local non-uniform blur, the blur kernel will vary with respect to spatial position. Therefore, different network mechanisms should be considered to deal with the two types of blur separately.  
In this paper, a complex blur image is modeled from a novel perspective, and a disentangling network of the complex blur image is established from the perspective of the global uniform blur of the image background and the local blur of the foreground. Different network branches trained with task-specific datasets to disentangle the global and local blur features and adaptively restore a clear image. Different from other multi-branch networks, in our model, two branches are designed to the same architecture. We show that the disentangling function can be achieved via a merely data-driven manner by a phased training strategy.
# Research on Predicting the Mechanical Characteristics of Deep-Sea Mining Transportation Pipelines
## 4 Construction of the Predictive Model for Pipeline Mechanical Characteristics  
### Construction Method of Pipeline Mechanical Properties Prediction Model  
#### 4.1.1 Integrated Simulation Platform  
To build a predictive model, substantial and comprehensive data are required. This paper uses the dynamic simulation results of multiple working conditions from orthogonal experiment combinations as simulation sensor data for training the machine learning algorithm model. An online simulation platform that enables interaction between OrcaFlex 11.0 and Matlab R2022a has been established. The interaction flow between the platforms is illustrated in Figure 10. Upon completion of the simulation analysis model, a connection is made to Matlab through the OrcFxAPI module of OrcaFlex, where the setting and execution of parameters for multiple simulation analysis conditions in OrcaFlex are completed in Matlab. OrcaFlex performs static and dynamic analyses of the system, with the simulation sensor data outputs exported to Matlab to complete the training and testing of the machine learning algorithm model, thus constructing a prediction model with relatively high accuracy.  
Figure 10: Online simulation platform with OrcaFlex and Matlab interaction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Time (s)** & **Lifting Hard Pipe Top Strain (\%)** & **Lifting Hard Pipe Top Effective Tension (kN)** \\ \hline
0.1 & 0.2019 & 1478.5228 \\
0.2 & 0.2017 & 1476.5988 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) \\
25.0 & 0.2016 & 1476.2897 \\
25.1 & 0.2019 & 1478.6173 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) \\
49.9 & 0.2027 & 1484.0249 \\
50.0 & 0.2020 & 1479.2711 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Time history data for mechanical parameters of mining rigid pipe in test condition 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulation Time (s)** & **Strain at 107.5 m (\%)** & **Effective** **Tension at 107.5 m (kN)** & **Strain at 132.5 m (\%)** & **Bending** **Radius at 132.5 m (m)** & **Bending** **Moment at 132.5 m (kNm)** \\ \hline
0.1 & 0.0341 & 27.5893 & 0.0341 & 7.5405 & 2.7796 \\
0.2 & 0.0333 & 27.5856 & 0.0339 & 7.5388 & 2.7802 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
25.0 & 0.0332 & 27.5181 & 0.0340 & 7.5317 & 2.7828 \\
25.1 & 0.0331 & 27.5166 & 0.0341 & 7.5318 & 2.7826 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
49.9 & 0.0333 & 27.5983 & 0.0341 & 7.5610 & 2.7721 \\
50.0 & 0.0334 & 27.5984 & 0.0343 & 7.5615 & 2.7720 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Time history data for mechanical parameters of flexible hose in test condition 1.
## 2 Methodology and Network Design  
This section presents the methodology followed in this study, including the signalling system requirements and the protocol stack perspective.
## 2 Environment Description and Problem Formulation  
### Environment Description  
In an uneven environment, the working environment of a mobile robot is a 2D-H grid map. The grid of the map is divided into grids occupied by obstacles, grids with height information, and free grids.  
In the 2D-H map, the obstacle grid is considered impassable, the free grid is considered passable, and the height grid affects the quality of trajectories planned by robots. The height grid is modeled by a normalized method, as shown in Equation (1). \(H(i)\) is the color intensity value of the \(i\)-th height grid cell, and the color becomes darker as the value of \(H(i)\)increases, \(H(i)\in[0,1]\). \(h\) is a height function when \(h(i)>0\) and \(H\) represents the intensity value of green, otherwise \(H\) represents the intensity value of blue.  
\[H(i)=\left\{\begin{array}{ll}\frac{\max(h)-h(i)}{\max(h)},&h(i)>0\\ \frac{\min(h)-h(i)}{\min(h)},&h(i)<0\end{array}\right. \tag{1}\]  
Figure 1 shows the modelling process of the 2D-H map. The uneven environment here is simulated with the peaks function, a probability density function of a binary Gaussian distribution. Researchers can design other functions to simulate an uneven environment according to different requirements. Figure 1a shows the three-dimensional model of the mountain function and the contour mapping on the 2D plane. Figure 1b shows the 2D-H map generated by the peak function and obstacles, where black grid cells represent obstacles, blue cells represent concave areas, and green cells represent raised areas. Here the height information in the map and the location of static obstacles are known.