esa-sceva/satcom-chunk-collection huggingface dataset:
# Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning
## 6 Conclusions
In simpler movements, the influence of non-linear terms may not be significant, allowing even a simple feedback controller to achieve sufficient performance. However, for complex motions such as walking, deterioration in tracking performance may occur because compensating for the effects of fluid becomes challenging, requiring more accurate and sophisticated control methods. Our proposed hybrid controller utilizes the MLP neural network to consider non-linear dynamic characteristics and incorporates them into position-tracking control in a simplified manner. This method initially poses the challenge of requiring pre-collected data to create a high-performance learning model. Specifically, for underwater data learning, it is necessary to collect underwater motion data first. However, after completing the training model, high-accuracy position tracking control can be performed across various motions and environments without the need for complex operations in each specific case. The motions used in the verification are based on the motion of a radial-type quadruped walking robot. Therefore, when the ULR depicted in Figure 1 is deployed later, the learning model can be employed for high position tracking control performance in both air and water environments. Furthermore, additional control methods could be combined with the hybrid controller to maintain performance in more diverse environments, such as tidal current environments. Conceptualization, J.-H.B. and J.-Y.K. ; methodology, J.-H.B. ; software, J.-H.B. ; validation, J.-H.B. and J.-Y.K. ; formal analysis, J.-H.B. ; investigation, J.-H.B. ; resources, J.-H.B. ; data curation, J.-H.B. ; writing--original draft preparation, J.-H.B. ; writing--review and editing, J.-Y.K. ; visualization, J.-H.B. ; supervision, J.-Y.K. ; project administration, J.-Y.K. ; funding acquisition, J.-Y.K. ; All authors have read and agreed to the published version of the manuscript. The authors declare that this study received funding from the Civil-Military Technology Cooperation Program of ICMTC, Republic of Korea (Grant number: 21-CM-MU-01, Title: Development of underwater robot for removal of tangled fishing net in propellers). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. Not applicable. Data Availability Statement: Data is contained within the article. The authors declare no conflicts of interest.
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 1 Introduction
In gradient measure, [PERSON] et al. [19] utilized the joint statistics of normalized gradient magnitude and Laplacian of Gaussian (GMLOG) features to characterize image quality. The model, oriented gradients image quality assessment (OG-IQA) [20], utilizes an AdaBoosting back-propagation neural network for mapping the statistics of image relative gradient orientation to image quality. In the work of [PERSON] et al. [21], local statistical features were extracted from the gradient magnitude and phase for image quality estimation. Recently, [PERSON] et al. [22] applied the local binary pattern (LBP) operator to the wavelet sub-bands of the distorted image and calculated the histogram of LBP coefficients to form the perceptual quality features. [PERSON] et al. [23] proposed a BIQA algorithm based on multi-scale second-order statistics derived from the joint distribution of adjacent wavelet sub-bands. Although the aforementioned BIQA algorithms improved the prediction accuracy and robustness, but there is still much room for performance improvement on contrast distorted, multiply distorted, and authentically distorted images. In this paper, we propose a general-purpose BIQA algorithm by using various perceptual features extracted from the spatial and the color domains (named BIQA-SC). Based on the observation that measuring the correlation between intensity values of image points in receptive fields of different sizes could get more local details of the real-world [24; 25; 26], we generate multiple normalized feature maps (NFMs) for feature extraction. Multiple Gaussian filters of different filter sizes are utilized in image normalization procedure to produce multiple NFMs. The distribution statistics, including GGD and AGGD parameters, are first extracted from the NFMs. Then, Pearson linear correlation coefficient (PLCC) is employed to better characterize the correlation changes between neighboring pixels of the distorted images. The differences between different NFMs are also measured by using the Weibull fitting function. Furthermore, we utilize the entropy and the gradient statistics obtained from the HSV color space to improve the prediction accuracy for color distorted images. Experimental results on five benchmark databases indicate that, compared with the other competing BIQA algorithms, the proposed algorithm achieves better evaluation accuracy and robustness on a wider range of image distortion types with low computational cost.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 5 Design and Development Process
### Directional Vector  
In this step, the directional vector was calculated between the Cartesian coordinates of the control tower and airplane in order to map the airplane with respect to the tower's location. If the coordinates of the control tower are represented as C(x\({}_{1}\), y\({}_{1}\), z\({}_{1}\)) and the airplane as A(x\({}_{2}\), y\({}_{2}\), z\({}_{2}\)), the directional vector can be calculated as follows:  
\[\overrightarrow{\text{CA}}=<x_{2}-x_{1},y_{2}-y_{1},z_{2}-z_{1}> \tag{9}\]  
After calculating the directional vector, the magnitude of this vector was calculated. This magnitude was the distance between the control tower and airplane in the actual world. Later, the calculated magnitude, i.e., the control tower-airplane distance, was downscaled by multiplying it with the scaling factor calculated in Section 5.1.
## 4 Co-Seismic Ground Deformation
### GNSS Data and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & \begin{tabular}{c} **D\({}_{East}\)** \\ **(cm)** \\ \end{tabular} & \
\begin{tabular}{c} **D\({}_{North}\)** \\ **(cm)** \\ \end{tabular} &
\begin{tabular}{c} **D\({}_{Up}\)** \\ **(cm)** \\ \end{tabular} \\ \hline ARKL & 35.1339 & 25.2689 & 4.51 \(\pm\) 0.11 & 7.85 \(\pm\) 0.14 & \(-\)15.45 \(\pm\) 0.60 \\ \hline HERA & 35.4241 & 25.1415 & \(-\)0.54 \(\pm\) 0.09 & 0.62 \(\pm\) 0.19 & 0.57 \(\pm\) 0.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GNSS stations’ coordinates and the respective vector of the co-seismic displacement. In the period following the mainshock and up to 30 April 2022, the two GNSS sites in Arkalochori and Heraklion city show increased velocity values, compared to the previous period, indicating that the post-seismic relaxation is continuous up to this date.
## 3 Results
### Spatio-Temporal Characteristics of NCP Importance from Supply and Demand Perspectives  
The spatial distributions of the importance of NCPs were mapped across both supply and demand perspectives on the QTP from 2000 to 2020 (Figure 5), at both pixel and WTU scales. At the pixel scale, from the supply perspective, the importance of NCPs was mainly concentrated in the southeastern part of the QTP, with high-value areas accounting for 7.11% of the total area. From the demand perspective, high-value NCPs were distributed in the eastern and northern parts of the QTP, covering more than 43.05% of the area. To reveal the relative balance between supply and demand, this study calculated averaged values. By comprehensively considering both NCP supply and demand, high-value areas accounted for 31.21% of the QTP, primarily located in the center and southeast. At the WTU scale, high-value NCPs from the supply perspective were mainly in the southwest, covering 43.05% of the study area. From the demand perspective, high-value regions were found in the central and eastern parts of the QTP. When linking the supply and demand perspectives, high-value areas constituted 53.14% of the study area, primarily located in the southwest of the QTP.  
Figure 4: Spatial and temporal distributions of multi-year averaged NCP1, NCP4, and NCP6 ((**a–c**) represent the supply perspective and (**d–f**) represent the demand perspective). Note: The bar chart reflects the change in time trend.  
Figure 5: Importance of NCPs at the pixel and WTU scales ((**a–c**) represent the pixel scale; (**d–f**) represent the water tower unit scale).
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 4 Experiment
### Application to Display Image Analysis  
One application of viewpoint estimation is the analysis of the display angle of objects. Among various object displays, the chair display is the most closely related to our training set. Therefore, we use it as an example to illustrate the application of our method. We downloaded chair images from the Nitori company website ([[https://www.nitori-net.jp/ec/cat/Chair/](https://www.nitori-net.jp/ec/cat/Chair/)]([https://www.nitori-net.jp/ec/cat/Chair/](https://www.nitori-net.jp/ec/cat/Chair/)), access date: 12 February 2025). We randomly chose chair products across different categories and used the first image of the product profile page. A total of 105 chair images were analyzed using viewpoint estimation with our method. The estimated viewpoints were then visualized using a frequency distribution histogram, as shown in Figure 14. From the figure, we observe that only 13 viewpoints are adopted by the company, and 44% of the images were captured from the same viewpoint, the 28 th viewpoint. This perspective corresponds to a right-side, slightly upward-angled shot of the chair. This application illustrates that we can infer the preference of the website display angles for items using our proposed viewpoint estimation method.
## 2 Materials and Methods
### Surface EMG  
sEMG was recorded using bipolar electrodes (Ambu Neuroline 720 00-S/25, electrode diameter 45 \(\times\) 22, interelectrode distance 2.5 cm, Ambu A/S, Baltorpbakken, Denmark) in four muscles: the vastus lateralis (VL), bicep femoris (BF), tibialis anterior (TA) and lateral gastrocnemius (LG). Before placing the electrodes on the muscle belly, the skin was shaved and then cleaned with an alcohol solution to minimize impedance (<5 k\(\Omega\)) following the SENIAM recommendation for surface electromyography [12]. The electrodes and modules were fixed on the thigh and shank with an elastic band to prevent motion artefacts. The wireless data synchronization unit (MuscleLab 6000, Ergotest-innovation, Porsgrunn, Norway) characteristics were as follows: a built-in radio frequency (RF) module ML6 RFM02 with 2.4 GHz and 1 mW, and a typical wireless range in open space of 20 m. The modules of the MuscleLab characteristics were as follows: RF characteristics of 2.4 GHz, 1 mW; sample rate of 1 kHz; high-pass filter of 20 Hz; bandwidth of 20-500 Hz; input signal range of \(\pm\)5 mVp-p; noise of 14 \(\upmu\)Vp-p (2.2 \(\upmu\)Vrms); and resolution of 0.33 \(\upmu\)V/bit. The root mean square (RMS) of the EMG signal (EMG\({}_{\text{RMS}}\)) was determined in ten successive stance phases for the dominant leg in the HCs (1 and 3 km/h) and for the weakest leg in the PwMS (1 km/h). Ten stance phases were selected in the middle part of the kinematic walk recording.
## 4 Conclusions  
This paper designed and presented an embedded power allocation system for optical wireless communication channels, including fog, Rayleigh, and Rician fading channels, as well as the Gamma-Gamma channel model. This system can enable real-time optimal power management in noisy, channel fading, and fog communication channels, increase the data transmission rate, and reduce interference in optical wireless communication. The embedded power allocation system for the Rician fading wireless communication channel has the highest accuracy, about 99%. The accuracy of the proposed system for standard and Rayleigh fading channels is 97%, and for fog-affected channels, it is 96%. Also, the maximum data transmission rate for BPSK, QPSK, 16 QAM, and 64 QAM modulations is 2.8 Gbps, 5.6 Gbps, 11.2 Gbps, and 16.81 Gbps, respectively. The data transmission rate is increased by installing the designed embedded power allocation system in the transmitter of optical wireless communication systems, and power consumption is optimized. Due to the optimal management of the power of the communication channels, this system also improves interference. This system can be developed and expanded for other modulations with other optical wireless communication channel conditions.  
M.A. designed and performed simulations, analyzed data, and drafted this manuscript. [PERSON] supervised, verified, reviewed, and edited this manuscript. [PERSON] verified and edited this manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shahid Rajaee Teacher Training University under grant number 5973.84.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This work was supported by the Nano-photonics and Optoelectronics Research Laboratory (NORLab), Shahid Rajaee Teacher Training University, and ICT Research Institute (IRAN Telecommunication Research Center (ITRC)) under Project No 14403.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Modulation Scheme** & **Min Power (mW)** & **Max Power (mW)** & **Accuracy \%** \\ \hline BPSK & 0.0093 & 4.96 & 98.50 \\ \hline QPSK & 0.0099 & 4.96 & 99.98 \\ \hline
16 QAM & 0.0082 & 4.98 & 98.68 \\ \hline
64 QAM & 0.0100 & 4.96 & 99.82 \\ \hline \end{tabular}
\end{table}
Table 10: Minimum and maximum values and accuracy of embedded power allocation system for OWC channel affected by strong Rician fading.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### O-AODV Topologies and Testbed Design
#### 4.1.3 Experimental Topologies  
In this section, we describe the topologies that we used in our experiments. For preliminary testing, we used a simple 10-node linear topology (as shown in Figure 3 by transmitting 100 packets from the source and reduced Tx power up to \(-40\) dB (to make the testbed manageable) to compare the proposed protocol's efficiency to the AODV and mesh (flooding) protocols. Furthermore, to test the proposed protocol's efficacy, we experimented with a multipath simple topology with 6 nodes and with more complex/partial mesh scenario with 10 nodes, as depicted in Figures 4 and 5, with the same configuration as discussed for the linear topology.
# Fractal Behavior of Size Distribution and Specific Surface Area of Blasting Fragments
## 3 Theory and Calculation Results
### Calculation of Fractal Dimensions of Particle Size Distribution  
According to the image processing results for the shape parameters presented in Section 2.3, the power-law fractal distribution curves of the minimum enclosing cubic dimensions (\(L\), \(I\), \(S\)) of the blasting fragments were obtained for each experiment, as shown in Figure 6.  
For small particles, size measurement through image processing is relatively inaccurate, and the number of large particles in the muck piles is limited. Consequently, data deviations are observed at both ends in Figure 6, which can adversely affect the fitting of the power-law fractal distribution curve. Therefore, data with particle sizes greater than 5 mm but less than 900 mm were employed for fitting in this study. The results are presented in Table 3.  
As shown in Figure 6 and Table 3, the coefficient of determination (\(R^{2}\)) for fitting the power-law fractal distribution of blasting fragments via the particle size-frequency method for all four quarries was greater than 0.94, indicating a good fitting effect. The fractal dimensions of the particle size distribution obtained through three characteristic dimensions for each of the four quarries were all less than 2. Additionally, significant differences existed in the fractal dimensions of the particle size distribution obtained using different characteristic dimensions, indicating that the characteristic dimensions significantly affected the fractal dimensions of the particle size distribution. This result is consistent with the findings of [PERSON] et al. (2018) concerning the fractal characteristics of granite rock fragments resulting from blasting experiments. Furthermore, quarry Q\({}_{4}\) exhibited a significantly larger fractal dimension of the size distribution for limestone blasting fragments. We attribute this to the use of mixed-porous granular ammonium nitrate explosives in test #17. In this case, the coupled explosive charging construction enhanced the fragmentation under the influence of blast loads.
## 5 Results and Comparison with Other Models
### Comparison with State-of-the-Art Works  
Finally, the proposed models are compared with some state-of-the-art approaches. We focused on the ability of generalization, contrasting the results of internal validation with those of external validation.  
In this regard, some recent approaches for colorizing detection using deep learning have emerged in the literature. For example, RecDeNet (Recolored Detection Network) uses three feature extraction blocks and a feature fusion module based on the CNNS. According to the reported results [25] (p.14), the internal accuracy is 87.4% and for external validation it is 76.7%. To converting accuracy into HTER, we assume that the confusion matrix has TN = TP and FN = FP, so that, for example, if acc = 87% with 100 true examples and 100 negative examples, then TN = TP = 87 and FN = FP = 13, providing an HTER of 13%. Therefore, for [25], we use an HTER of about 12.6% for internal validation and 22.3% for external validation.  
As mentioned above, WISERNet reported HTER values of 0.95% for internal validation and 22.55% for external validation [14] (p.736). A modified WISERNet (referred to here as  
Figure 11: Time comparison between the custom model and transfer-learning-based model: (**a**) training time; (**b**) inference time.  
Figure 10: Performance evaluation of the custom model and transfer-learning-based model: HTER (the lower the better).  
WISERNet II) also reports generalization problems, with an HTER of 0.89% for internal validation and 31.70% for external validation [15] (p.129). For progressive training using WISERNet (referred to here as WISERNet III), the reported HTER is reduced to 4.74% for external validation [26]. Table 4 shows the comparison results in terms of HTER for both internal and external validation.  
It should be noted that not all works have used the same datasets. Therefore, the HTER values (internal or external) are only comparable within the works with the same datasets. For example, the best performance of the WISERNET networks has been provided by WiserNet III; while the best performance in our proposed networks corresponds to the VGG-16-based model. However, the HTER's difference can be used to compare the results in Table 4, whose lowest value corresponds to our VGG-16-based model followed by WISERNet III. Therefore, the transfer-learning-based model outperforms the state-of-the-art approaches.
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.2 DL for Predicting Ocean Warming Trends and Associated Extreme Weather Events
DL techniques leverage these diverse data sources to analyze complex spatiotemporal patterns, with models such as CNNs, U-Nets, and LSTMs demonstrating strong effectiveness in predicting MHWs [146; 147; 148]. [PERSON] et al. [149] utilized a 3D U-Net model to analyze MHW events in the South China Sea. The model integrated SST, SSH anomalies, and sea surface wind data to predict MHW frequency, duration, and intensity. The predictions of the model closely matched observed data, demonstrating its accuracy and effectiveness. [PERSON] et al. [150] improved the U-Net architecture by integrating the Convolutional Block Attention Module, which refines feature representations across both channel and spatial dimensions. This enhancement allowed the model to capture relationships within the data, leading to a significant boost for MHW prediction. Based on LSTM, [PERSON] et al. [151] developed a model to predict MHWs across 13 coastal cities in China. The model utilized input data, including SST from OISST, as well as sea surface pressure and wind speed from ERA5, achieving strong performance with RMSE values ranging from 0.2 \({}^{\circ}\)C to 0.3 \({}^{\circ}\)C. To enhance interpretability, they employed the Expected Gradients method to assess the contributions of each input variable, providing valuable insights into the model's decision-making process. LSTM models effectively capture temporal dependencies, and hybrid architectures, such as CNN-LSTM, further enhance MHW prediction by integrating spatial and temporal encoders. [PERSON] et al. [152] employed two models to analyze MHWs in the South China Sea, including a U-Net for predicting MHW intensity and a ConvLSTM for estimating occurrence probability. Their models demonstrated high performance, achieving forecast accuracy of 0.92, 0.89, 0.88, and 0.87 for lead times of 1, 3, 5, and 7 days, respectively. [PERSON] et al. [153] proposed a two-branch network combining spatial encoders (2D CNNs, ViTs, and Transformer variants) with temporal encoders (LSTMs and Transformers). Through experiments conducted at 16 locations around the Korean Peninsula using multi-modal SST datasets, they identified self-attention-based encoders as the most effective and achieved the lowest RMSE of 1.13 \({}^{\circ}\)C for 7-day predictions.