esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network-Enhanced Fault Diagnosis of Robot Joints
## 3 Diagnosis Framework
### Multi-Feature Fusion for Optimal Fault Diagnosis
Inspired by the dual feature and [19], the acceleration of the end-effector in the three directions, _xyz_, is added as the third feature. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sampling Number** & **Number of Epochs** & **Layers** & **Accuracy (\%)** & **Cross-Entropy Loss** \\ \hline
500 & 1000 & 6-13-13-7 & 89.21 & 0.7011 \\
1000 & 800 & 6-13-13-7 & 93.92 & 0.6425 \\
1500 & 800 & 6-13-13-7 & 95.99 & 0.6312 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Network training results of joint diagnosis for robots. Size of joint angle error = 0.1\({}^{\circ}\) (dual features). Figure 9: Process of data processing for two features. From the perspective of the robotic arm, it is assumed that the change in the actuator orientation and position predestines the acceleration of the actuator in the _xyz_ directions to be different as well, which again increases the characteristics of each labelled data, which makes the neural network better for classification. The acceleration can be obtained from the second-order derivative of the three-direction _xyz_ position change between each sample, then transformed into a \(1\times 3\) matrix, and the position and direction features are spliced and fused, so that each set of data is a \(1\times 9\) matrix, and the number of neurons in the input layer is nine. Each set of data consists of nine numbers containing the three features of the actuator. The classification ability for neural network of the database built by three features is shown in Figure 10.  
When the injection fault is 0.1 degrees, it can be seen that compared with the two-feature database, the neural network requires a smaller database and the accuracy is also improved. When injected fault is 0.5 or 1 degree, the accuracy is also better. But the overall accuracy remains at about the same order of magnitude, and there is no particularly significant change, indicating that the acceleration features to a certain extent can differentiate between the different trajectories, but the ability to differentiate is limited. It can be noticed that, in the case of multiple features, although the accuracy is improved, the corresponding cross-entropy loss is also more than before. The possible reason for this is that the model is very confident in all the predictions, i.e., the probability of prediction is very close to 1 for the correct category and very close to 0 for the other categories. Then, even if the predictions are mostly correct, a single incorrect prediction may lead to a very high cross-entropy loss. To fully evaluate the performance of a neural network as a classifier, it is not sufficient to rely only on accuracy and cross-entropy loss, only an initial judgement is made here. The performance of the network will be further evaluated in the next section, in conjunction with other metrics.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 4 The Use of EPR in Passive Tumor Targeting with Nanomedicines  
Nanomedicine is broadly defined as the biomedical and clinical applications of rationally designed nanoscale materials, typically ranging from 1 to 100 nm in size. These nanoparticles exhibit unique optical, magnetic, and biological properties [123]. The application of nanoparticles as carriers for therapeutic agents in the medical field requires strategies for encapsulation, conjugation, or surface modification. Macromolecular carriers, such as proteins, liposomes, polymers, and micelles, can be loaded with various small-molecule therapeutic agents, including chemotherapeutic drugs, peptides, antibodies, and nucleic acid-based drugs, to achieve selective tumor delivery and toxicity of anticancer drugs [110; 123]. Nanocarriers are used to improve the solubility and bioavailability of drugs within living organisms, potentially leading to greater therapeutic efficacy in diseased cells compared to the administration of free drugs [123].  
Long-circulating pharmaceutical nanocarriers, such as liposomes, micelles, and polymeric nanoparticles, can accumulate in various pathological areas with vascular abnormalities via the enhanced permeability and retention (EPR) phenomenon during passive tumor targeting [124]. This phenomenon has also been observed in various molecules, including natural and synthetic polymers with molecular sizes greater than 7-8 nm [110]. The accumulation of nanocarriers in the human body, and subsequently within solid tumors, certainly occurs due to the pathophysiological characteristics of the tumor's vascular network, but the extent of this accumulation varies significantly between patients and tumor types [125].
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 6 Conclusions  
In this paper, we propose a novel wavelet-based, blur-aware decoupled network (WBDNet). Through wavelet transform, we decompose the deblurring task into two sub-tasks: structure restoration and detail enhancement. For the low-frequency domain, we design a multi-scale progressive fusion (MSPF) module that effectively integrates multi-frame structural information through a multi-scale feature fusion strategy and structure feature fusion block. For the high-frequency domain, we introduce a blur-aware detail enhancement (BADE) module that combines a blur-aware attention block utilizing sharpness priors and deformable convolution alignment based on optical flow residuals to enhance detail reconstruction. Experimental results on benchmark datasets demonstrate that our proposed method achieves excellent performance in both objective metrics and subjective visual quality.  
Conceptualization, H.W.; methodology, H.W.; software, H.W.; validation, H.W.; formal analysis, R.C.; investigation, H.W.; resources, R.C.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, R.C. and P.P.; visualization, H.W.; supervision, R.C.; project administration, R.C.; funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript.  
This research project was financially supported by Faculty of Informatics, Mahasarakham University. The APC was funded by Mahasarakham University, Thailand.  
Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**BADE Module**} \\ \hline BAAB & & ✓ & ✓ \\ DConv Align & ✓ & & ✓ \\ \hline PSNR & 33.06 & 30.73 & 34.87 \\ SSIM & 0.9616 & 0.9085 & 0.9729 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study for BADE module design on the GoPro dataset.  
Figure 9: Feature map visualization of ablation on blur-aware attention block (BAAB).
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 3 Experimental Setup of A-RoF Based DAS
### Peak Point Optimization  
The following equation describes the transfer function of a _MZM_ with driving voltages:  
\[S_{MZM}=\frac{E_{out}}{E_{IN}}=\cos\left(\frac{V(t)\varnothing}{2}\right)\cos (\omega_{LO}t) \tag{1}\]  
Here, \(E_{out}\) and \(E_{IN}\) represents the _MZM_ output and input electromagnetic field, respectively, while \(\varnothing V(t)\) represents the phase difference between the branches of _MZM_, which is given as:  
\[V(t)\varnothing=\frac{V(t)}{V_{\pi}}\pi \tag{2}\]  
where \(V(t)\) represents the driven voltage and \(V_{\pi}\) represents the half-wave voltage of the MZM. The biased voltage must be set to a value equal to \(0.5V_{\pi}+nV_{\pi}\) to function in the quadrature point of the MZM.  
Figure 4 shows the MZM peak point optimization performance. The performance of the 5G and 6G signals varied significantly as the bias voltage moved through theinterference cycle (from null to peak). To marginally increase the performance of the 6G signal at the expense of the 5G signal EVM, where we placed the MZM in the experiment at the quadrature point. The simultaneous transmission of both the 5G and 6G signal degrades the SNR of the 6G signal, which leads to an overall increase in the EVM of the system. The 6G RAU has a THz receiver that consists of a horn antenna constituting DAS. The ainfoinc horn antenna has a directional gain of approximately 20 dBi, allowing it to transmit the signal to the THz receiver through the 3 m long wireless channel. The emitted signal from the horn antenna is received from the other side of the horn antenna connected with the Vector Signal Analyzer through a frequency mixer chain which already has an inbuilt downconverter to bring the signal to its IF band for post-processing. This RF mixer LO setup can be switched between 5G RAU and 6G RAU as required. In post-processing, offset recompenses are then achieved, which includes synchronization in the time and frequency domain. To ensure tight time synchronization, a mechanism developed in [34] was employed that relies on fast detection via the first path of arrival in the channel impulse response. Table 1 summarises the values of the different components that were employed in this study.  
Figure 4: **(a)** MZM measured transmission power in the optical domain concerning bias voltage and (b) EVM with and without 6G signal for varying bias voltages.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 3 Methodology
### GridMask-Mosaic Data Augmentation
\[t=2r-r^{2} \tag{2}\]  
where \(d\) determines the size of a single deleted region, and when \(r\) remains unchanged, the relationship between the side lengths \(l\) and \(d\) of a single deleted region is shown in Equation (3). \[l=d\times r \tag{3}\]  
For \(d\), the larger the \(d\), the larger the \(l\), and during training, the proportions are kept unchanged. In addition, randomness is added to expand the diversity of the image, and the value of \(d\) is shown in Equation (4). \[d=random(d_{min},d_{max}) \tag{4}\]  
When \(d\) is small, the probability of experimental failure can be reduced, but when it is too small, it will lead to poor experimental results and cannot effectively improve the robustness of the model. The masks of \(r\) and d can be determined by moving \(\delta_{x}\) and \(\delta_{y}\), and the values of \(\delta_{x}\) and \(\delta_{y}\) are shown in Equations (5) and (6), respectively. \[\delta_{x}=random(0,d-1) \tag{5}\]  
\[\delta_{y}=random(0,d-1) \tag{6}\]
## 2 Materials and Methods
### UAV Photogrammetric Survey  
The technique of photogrammetric survey of rock faces by means of unmanned aerial vehicles (UAV) is used to:  
* solve the problems related to lack of stable points where to establish the laser scanner (for instance, at the base of a rock cliff);
* integrate the point cloud from laser scanner in those sectors characterized by no data, due to the relative position between laser and rock cliff;
* survey areas not accessible by land (sea cliffs with no or limited beach, islands, etc.).  
The photogrammetric survey was carried out for the case studies here presented by using the UAV ITALDRON 4 HESPRO (Figure 2).  
As a mandatory condition to properly use UAV, there is the need to geo-reference a number of points (ground control points--GCPs) to treat in a metric system the images, and to correctly roto-translate the point cloud. At this goal, we used a GPS (GPS LEICA GS14) and a Total Station (TRIMBLE S6).  
Choice of the GCPs must be carefully planned: preferentially, all selected points should be materialized with well-visible targets located in proximity of areas with a chromatic variation, at different elevations, in sectors well distributed over the whole area to be surveyed, far from the shadow areas by trees and other objects.  
To increase the quality of the survey, it is preferable to perform a single flight, or, at least, the flight must be carried out in the same part of the day; further, the overlap among adjacent images must never be below 80-85%.  
Following the flight, the delicate stage of image elaboration starts, by checking a variety of characteristics, namely:  
(1) correct frame of each single snapshot (the optical axis must be perpendicular to the rock face as much as possible);  
(2) quality of the image (out of focus photographs must be deleted);  
(3) exposure: over- or under-exposed photographs must be deleted, or treated with dedicated software. At this aim, it is better to shoot in raw modality in order to be able to balance the exposure during the post-processing phase;  
(4) sequential order of the photograph strips.  
Clear pictures with high depth of field are able to provide a good point cloud, whereas a good selection of GCPs allows to reduce significantly the metric errors in the survey. After having properly treated the pictures, the following steps follow (as already described in the previous section for laser scanner surveys, to which readers are referred for details): photographs alignment, building of the dense point cloud, realization of the mesh and of the texturized mesh. These elaborations require the use of commercial software such as Agisoft, Pix4D, ContextCapture, etc.
# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
## 3 Problem Modeling
### Routing Algorithm
* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Community detection based on girvan newman algorithm and link analysis of social media. In Proceedings of the Digital Connectivity-Social Impact: 51 st Annual Convention of the Computer Society of India, CSI 2016, Colindero, India, 8-9 December 2016; pp. 223-234. * [PERSON] et al. (1973) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Time bounds for selection. _J. Comput. Syst. Sci._**1973**, \(7\), 448-461. [CrossRef]
* [PERSON] et al. (2001) [PERSON]; [PERSON]; [PERSON] Virtual private network issues using satellite based networks. In Proceedings of the 2001 MILCOM Proceedings Communications for Network-Centric Operations: Creating the Information Force (Cat. No. 01 CH37277), McLean, VA, USA, 28-31 October 2001; pp. 785-789. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] STSR: A Satellite-Tailored Segment Routing Method for Efficient Space Communication. In Proceedings of the 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, 21-24 April 2024; pp. 1-6. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Clustered Multi-Criteria Routing Algorithm for Mega Low Earth Orbit Satellite Constellations. _IEEE Trans. Veh. Technol._**2024**, _73_, 13790-13803. [CrossRef]

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## 5 Results and Discussion  
This section presents the results obtained when solving the proposed optimization problem, which aims to reduce operating costs, energy losses, and \(CO_{2}\) emissions through the coordinated management of active and reactive power from WTs. The simulations were conducted using MATLAB R2024a software on a computer equipped with an Intel(R) Core(TM) i9-14900 HX processor at 2.20 GHz, 32 GB of RAM, and a 64-bit version of Windows 11. Each metaheuristic technique was executed 100 times for every objective function in both operating modes. The effectiveness of each method was evaluated by analyzing the best solution, the average solution, the standard deviation, and the average processing time. Subsequently, the best-performing strategy was implemented in the 69-node MG with seven different combinations of generation and demand profiles. Each combination was evaluated under both grid-connected and islanded conditions while considering all the objective functions.
###### Abstract  
The Beidou Global System (BDS-3) innovatively achieves autonomous navigation using inter-satellite links (ISL) across the entire constellation, but it still faces challenges such as the limitations of the prior constraint orbital accuracy and the overall constellation rotation. The gradual availability of satellite laser ranging (SLR) data, with advantages of high precision and no ambiguous parameters, can provide new ideas for solving the current problem. This work firstly deduces the mathematical model for orbit determination by combining inter-satellite links and the introduced satellite laser ranging observations, then designs orbit determination experiments with different prior orbit constraints and different observation data, and finally evaluates the impacts of the prior orbits and the introduction of SLR observations from two dimensions: orbit accuracy and constellation rotation. The experimental results using one month of measured data show the following: (1) There is good consistency among different days, and the accuracy of the prior orbits affects the performance of the orbit determination and the consistency. Compared with broadcast ephemerides, using precise ephemerides as prior constraints significantly improves the consistency, and the orbit accuracy can be increased by about 75%. (2) The type of observation data affects the performance of the orbit determination. Introducing SLR observations can improve the orbit accuracy by approximately 13% to 26%. (3) Regardless of whether broadcast ephemerides or precise ephemerides are used as prior constraints, the constellation translation and rotation still exist after introducing SLR observations. Among the translation parameters, TX is the largest, followed by TY, and TZ is the smallest; all three rotation parameters (RX, RY, and RZ) show relatively large values, which may be related to the limited number of available satellite laser ranging stations during this period. (4) After considering the constellation translation and rotation, the orbit accuracy under different prior constraints remains at the same level. The statistical root mean square error (RMSE) indicates that the orbit accuracy of inclined geosynchronous orbit (ICSO) satellites in three directions is better than 20 cm, while the accuracy of medium earth orbit (MEO) satellites in along-track, cross-track, and radial directions is better than 10 cm, 8 cm, and 5 cm, respectively.  
inter-satellite link; satellite laser ranging; BDS-3; orbit determination; priori orbit accuracy; constellation translation and rotation
## 3 Results
### Multivariate Analysis  
After spectra were preprocessed and the characteristic wavelength was selected, the PLS model and ELM model were established for 550-950 nm, 1050-1640 nm, full spectrum and characteristic wavelength, respectively. The maximum number of principal components in PLS was set to 25, the calibration set data was used to find the optimal number of principal components, and the model obtained by it was used to predict the prediction set data. The maximum number of hidden layer nodes of ELM was set to 200. In particular, for the characteristic wavelength, since the number of variables was reduced, the maximum number of hidden layer nodes of the ELM was set to 100. The final evaluation data of the regression model is shown in Table 3.  
The results in Table 3 show that the full spectrum had a relatively good predictive effect on the content of N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O. The ELM model had a stronger nonlinear fitting ability, and had a better prediction performance for different nutrients than the PLS model. For example, with the full spectrum, the \(R_{p}^{2}\) of ELM was 0.945, while of PLS it was 0.853. Spectra of 1050-1640 nm had better predictive ability than those of 550-950 nm. For the three contents of N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O, although there were generally good fitting effects, the highest \(R_{p}^{2}\) of N content was 0.989, while the highest \(R_{p}^{2}\) values of P\({}_{2}\)O\({}_{5}\) and K\({}_{2}\)O were 0.963 and 0.981, respectively. In general, the predictive ability of Vis/NIR spectroscopy for P\({}_{2}\)O\({}_{5}\) was slightly worse than that of N and K\({}_{2}\)O. The model established based on the characteristic wavelength was basically the same as the full-spectrum model in terms of performance, and the gap was very small. Even the \(R_{p}^{2}\) of the characteristic wavelength was higher than the full spectrum in terms of P\({}_{2}\)O\({}_{5}\). Therefore, the characteristic wavelength selected by CARS can well represent the original spectrum. The characteristic wavelength greatly reduces the number of spectral variables and model calculations while retaining effective information.  
For N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O, the models with the highest RPD were selected to predict the prediction set, and a scatter plot of the experimental value and predicted value of each nutrient content was drawn. The result is shown in Figure 5.  
It can be seen from Figure 5 that the correlation between the experimental and predicted values of the N content and K\({}_{2}\)O content in the prediction set was relatively good, and the prediction deviation of the nutrient content was basically within 5%, while the P\({}_{2}\)O\({}_{5}\) content was not so ideal, with a maximum of 5-10% content prediction deviation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Nutrient** & **550–950 nm** & **1050–1640 nm** & **Full Spectrum** \\ \hline N & 44 & 117 & 161 \\ P\({}_{2}\)O\({}_{5}\) & 32 & 197 & 229 \\ K\({}_{2}\)O & 59 & 102 & 161 \\ \hline \end{tabular}
\end{table}
Table 2: The result of characteristic wavelength selection.
## 4 HydroSAR Product Portfolio and Product Algorithms
### Intermediary Data Products and Visual Aids
#### 4.2.3 RTC30-Color  
To ease the uptake of SAR data by end user organizations in the HKH, the HydroSAR team was asked to develop a product that is easy to interpret by analysts mostly familiar with optical data. In response to this request, we developed a simplified polarimetric decomposition concept based on dual-pol Sentinel-1 RTC-30 data, resulting in RGB-formatted image products which approximate the look and feel of visual band data.  
This approach decomposes the co- and cross-pol signals into a simple bounce (polarized) with some volume scattering, and volume (depolarized) scattering and a simple bounce with extremely low volume-scattering components. These are assigned to the red, green, and blue color channels of an RGB composite according to  
\[\begin{split} R&=a(VV-3 HV)^{0.5}\quad\text{when}\ HV>k\\ G&=b(HV)^{0.5}\\ B&=c(VV)^{0.5}\quad\quad\quad\quad\quad\text{when}\ HV\leq k \end{split} \tag{3}\]  
where \(a\), \(b\), and \(c\) are empirically determined to create a histogram-balanced image and \(k\) is set near the image noise floor. In the case where the volume-to-simple-scattering ratio is larger than expected for typical vegetation, such as in glaciated areas or some forest types, a teal color (green + blue) is used.  
The convenience of the RGB decomposition in Equation (3) for monitoring the surface water extent stems from the fact that the blue channel is predominately associated with  
Figure 5: Example of HydroSAR flood-relevant data products for an area near the Ganges River in Bihar, India. (**a**) RTC30 product. Red shaded regions correspond to the permanent water extent in this region. (**b**) RTC30-Color image, with surface water shown in dark blue colors. (**c**) HYDRO30 surface water extent product on top of an RTC30 image. (**d**) WD30 water depth product on top of RTC30.  
surface water, providing an easy-to-understand visual aid for analysts. Figure 5b shows an RGB composite product for an area near the Ganges River in Bihar, India. In this example, water areas can be identified in blue, while vegetated areas are shown in shades of green and urban environments appear in red and orange hues.