esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Application
### Experiment with Speckle Noise
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**MethodsParameters**} & \multicolumn{2}{c}{\(\sigma\) = 0.01} & \multicolumn{2}{c}{\(\sigma\) = 0.05} & \multicolumn{2}{c}{\(\sigma\) = 0.1} & \multicolumn{2}{c}{\(\sigma\) = 0.2} \\ \cline{2-9}  & **PSNR** & **r** & **PSNR** & **r** & **PSNR** & **r** & **PSNR** & **r** \\ \hline Noised & 25.68 & 0.9637 & 18.83 & 0.8520 & 15.99 & 0.7526 & 13.25 & 0.6226 \\ Median & 29.44 & 0.9839 & 25.82 & 0.9634 & 23.66 & 0.9410 & 21.27 & 0.9030 \\ Wiener & 31.49 & 0.9899 & 24.92 & 0.9553 & 22.22 & 0.9180 & 19.60 & 0.8547 \\ WDF & 28.76 & 0.9810 & 26.98 & 0.9713 & 25.40 & 0.9586 & 23.29 & 0.9328 \\ Bilateral & 31.51 & 0.9900 & 22.05 & 0.9180 & 17.75 & 0.8100 & 14.19 & 0.6610 \\ BM3D & 33.40 & 0.9935 & 26.79 & 0.9704 & 19.79 & 0.8683 & 15.08 & 0.6978 \\ DTCWT & 31.88 & 0.9908 & 25.83 & 0.9633 & 23.65 & 0.9392 & 21.28 & 0.8954 \\ IFOA-DTCWT-BF & 29.14 & 0.9829 & 28.41 & 0.9796 & 27.39 & 0.9749 & 25.55 & 0.9633 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the six methods (Lena with Speckle noise). Figure 17: Denoising results of Lena with Speckle noise using IFOA-DTCWT-BF. (**A**) Lena of noise \(\sigma\) = 0.01; (**B**) Lena of noise \(\sigma\) = 0.05; (**C**) Lena of noise \(\sigma\) = 0.1; (**D**) Lena of noise \(\sigma\) = 0.2; (**E**) Denoised image of \(\sigma\) = 0.01; (**F**) Denoised image of \(\sigma\) = 0.05; (**G**) Denoised image of \(\sigma\) = 0.1; (**H**) Denoised image of \(\sigma\) = 0.2. Figure 18: Denoising results of Columbia with Speckle noise using IFOA-DTCWT-BF. (**A**) Columbia of noise \(\sigma\) = 0.01; (**B**) Columbia of noise \(\sigma\) = 0.05; (**C**) Columbia of noise \(\sigma\) = 0.1; (**D**) Columbia of noise \(\sigma\) = 0.2; (**E**) Denoised image of \(\sigma\) = 0.01; (**F**) Denoised image of \(\sigma\) = 0.05; (**G**) Denoised image of \(\sigma\) = 0.1; (**H**) Denoised image of \(\sigma\) = 0.2.
## 4 Discussion and Conclusions  
This study explored the potential of using Mindlin plate theory for GWT to map corrosion defects in plates. The findings demonstrated that the phase velocity of the \(A_{0}\) mode in the Mindlin model closely aligns with the true velocity, a critical factor for accurate thickness mapping. In 2-D scattering studies involving small defects, the Mindlin model effectively predicted scattering from one-sided smooth defects, including reflections, with calculations performed using the FDM. This capability is a distinct advantage over acoustic models, allowing for higher-resolution imaging at comparable computational costs. Additionally, the 3-D investigation using the PsM demonstrated the model's ability to simulate wavefields for more complex defect geometries. The Mindlin model showed excellent agreement with FEM simulations for smoothly shaped defects, though some discrepancies were observed for sharp-edged defects due to abrupt reflections. Overall, the Mindlin model performed well in predicting reflection and transmission coefficients, particularly for small and smooth defects. This makes it a promising tool for inverse modeling to improve accuracy in corrosion mapping. Future research should focus on  
Figure 11: Polar plot of the scattered wave-field of the \(A_{0}\) mode interacting with the combined defects at 50 kHz. Signal propagation is from right to left. Figure 12: Comparison of reflection and transmission from the defect in two-dimensional wave propagation. Amplitudes are normalized by the sum of the maximum amplitudes of reflected and transmitted waves. expanding the analysis across different frequencies and defect shapes for even more robust defect imaging. Conceptualization, M.L. and M.R. ; methodology, M.L. ; simulations and data acquisition, M.L. ; writing--original draft preparation, M.L. ; writing--review and editing, M.L. and M.R. ; project administration and funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript. This research was funded by the Estonian Research Council, grant PRG737. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest. The central difference for the derivative of a function \(w\) with respect to \(x\) for all the nodal points inside the plate is  
\[\frac{\partial w(x,y,t)}{\partial x}=\frac{1}{2\Delta x}[w(x+\Delta x,y,t)-w (x-\Delta x,y,t)], \tag{1}\]  
where \(\Delta x\) represents the distance between grid points.
## Materials and Methods
### Evaluation of the MIBFT
#### 2.6.2 Error Metrics  
For each trial in the cadaveric experiment, manual adjustment of the bone pose followed by single-plane 3D/2D image registration [40] was only performed for the first image frame (\(k\) = 1). The MIBFT was then executed to determine the bone pose parameters in the following successive image frames (\(k\) = 2, 3,..., N). The pose parameters in the first and last image frames were discarded for the error analysis, because they were not included in MIBFT analysis due to the lack of both a preceding and succeeding frame. A 64-bit personal computer with an Intel CPU (Core i7-9700K, 3.6 GHz), an NVIDIA graphics card (GeForce RTX 2080), and 16 GB of RAM were used for all numerical computations.  
The bone pose errors were quantified as the deviations of the MIBFT-determined pose parameters from the standard references. The absolute errors at each registration stage and for different tibiofemoral flexion speeds were analyzed. For each trial, the bias (i.e., mean of the errors), precision (i.e., standard deviation (SD) of the errors), and root-mean-squared errors (Rms errors) across all image frames were computed. Descriptive statistics of these error metrics over all trials were obtained. To express the overall positioning error of a bone, a commonly used metric, the mean target registration error (mTEE) [58], was also quantified for each image frame. The agreement between the MIBFT-determined tibiofemoral angles and translations and those obtained from the standard references were quantified by limits-of-agreement (LoA) plots and analysis [59].
###### Abstract  
Human immunodeficiency virus (HIV)-infected individuals display an enhanced production of reactive oxygen species (ROS). This reduction of antioxidant capacity in host tissues has been related to the decrease in total levels of ROS scavengers such as glutathione (GSH). Prevention of opportunistic infections due to a weakened immune system is becoming a key strategy along with HIV elimination. Research in these directions is clearly warranted, especially a combination of antiretroviral and antioxidants to ameliorate oxidative stress, improve intracellular uptake and target viral reservoirs. Hence, we aimed to formulate liposomes loaded with the antiretroviral drug efavirenz (EFA) in the presence of glutathione, as these carriers can be engineered to enhance the ability to reach the target reservoirs. The goal of the present work was to investigate the intracellular uptake of EFA-loaded liposome (with and without GSH) by human monocytic leukemia cells (THP-1 cells) and examine cell viability and ROS scavenging activity. Results obtained provided significant data as follows: (i) treatment with EFA and GSH combination could enhance the uptake and reduce cytotoxicity; (ii) encapsulation of EFA into liposomes increased its levels in the macrophages, which was further enhanced in the presence of GSH; (iii) delivery of EFA in the presence of GSH quenched the intracellular ROS, which was significantly higher when delivered via liposomes. Data revealed that a combination of EFA and GSH encompasses advantages; hence, GSH supplementation could be a safe and cost-effective treatment to slow the development of HIV infection and produce an immune-enhancing effect.  
liposomes; efavirenz; glutathione; HIV infection; THP-1 cells; oxidative stress Article  
Liposomes as Carriers for the Delivery of Efavirenz in Combination with Glutathione--An Approach to Combat Opportunistic Infections
## 2 Enhancing Food Production through RS Applications in Agriculture
### Identifying and Estimating Crop Area
The spatial heterogeneity and spectral mixture of these fields with croplands could result in the inaccurate classification of croplands from the analysis of RS data in these regions. In addition, there may be a scarcity of field samples to train the classifier in the large and heterogeneous landscapes of ASA regions, particularly in insecure and fragile regions. For example, in the spatial distribution of the training data used in the global MODIS land cover classification, Iraq and Syria were excluded [64]. For crop classification, single date and multi-temporal (time-series) imagery can be used to estimate and classify crop area. Due to inter-annual spatio-temporal variation in cropping area in ASA regions due to significant climatic variability and crop rotation practices, time-series RS data can be used with several benefits. At different phenological stages, time-series data can be analysed selectively to provide more useful vegetation spectral information relative to single date imagery [65; 66]. In addition, temporal analysis aids in the discrimination of various crop classes based on differences in their growth patterns. Furthermore, multi-temporal data increase the information of the imagery as the sun angle changes with the season, which affects vegetation surface reflectance [67]. Lastly, time-series data provide a larger number of predictor variables applicable to machine learning classification approaches, thus, potentially increasing classification accuracy [68; 69]. Several studies have demonstrated that RS approaches can be used to produce reliable crop area estimates and crop type classification [39; 70; 71; 72]. In the ASA region of Arizona, nine major crop types were able to be classified using a RS approach with an overall accuracy of \(>\)85% [41]. In Turkey, overall accuracies above 90% were achieved between cropland area estimates using RS data and official statistics [73]. In addition, RS can provide a powerful solution in some ASA regions where access is restricted due to security issues and remoteness. In Northern Bosnia, Witmer [45] managed to identify abundant agricultural land before, during and after the 1992-1995 war using Landsat imagery with an overall accuracy of 82.5%. Similarly, due to the 1991-1994 Nagorno Karabakh conflict between Armenia and Azerbaijan high farmland abandonment rates of more than 60% were found in the conflict zone using multi-temporal Landsat imagery [46]. Therefore, RS can provide timely information on crop area and crop types in various circumstances where traditional approaches are not feasible, and such information can help reshape policy decisions and enhance food security [39; 46].
# Advancing Knowledge on Cyanobacterial Blooms in Freshwaters
## 1 Introduction  
Cyanobacterial blooms constitute a water quality problem that has been widely acknowledged to cause negative effects on the use, safety, and sustainability of drinking and recreational waters supplies and fisheries. Water eutrophication combined with relative high water temperature promote cyanobacterial growth and water bloom formation. The cyanobacterial bloom season may be of extended or even continuous duration throughout the year, especially in freshwaters experiencing external and internal conditions, such as high temperatures and irradiance, thermal stratification, long water replacement times, high phosphorus and nitrogen concentrations, and low zooplankton grazing [1] or low viral attack [2]. Evidence for this can be obtained from lakes in the Mediterranean region [3, 4]. In a human and climatically changing world, cyanobacterial blooms seem to have increased in frequency and longevity globally, and are likely to expand and thrive in water resources [1, 5, 6]. Moreover, the successful dispersal and colonization of new aquatic habitats or newly formed systems by airborne bloom-forming cyanobacteria, such as the cosmopolitan potentially toxic _Microcytsis_, seem to facilitate their global expansion and are of immediate ecological, economic, and health-related interest [7, 8]. Of most concern are the cyanotoxins along with the mechanisms that induce their release and fate in the aquatic environment. These secondary metabolites pose a potential hazard for human health and agricultural and aquaculture products directed for animal and human consumption, therefore the strict and reliable control of cyanotoxins is crucial for assessing risk [6]. In this direction,a deeper understanding of the mechanisms that determine cyanobacterial bloom structure and toxin production become a target for managing practices [5; 6].  
This Special Issue, entitled \"Advancing Knowledge on Cyanobacterial Blooms in Freshwaters\", includes 11 research papers [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19] and aims to bring together the recent research of multi- and interdisciplinary approaches from the field to the laboratory and back again, driven by working hypotheses based on any aspect from ecological theory to applied research on mitigating cyanobacterial blooms.
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 4 Experiment Studies and Result Analysis  
In this section, experimental verification of the force compensation framework is performed. We conduct the uncertainties analysis of the force sensor during the dynamic palpation procedure and use the neural-network-based method to model the inertial force. Then the inertial force can be online estimated and canceled during the robotic palpation. The corrected force for the palpation is used to analyze the mechanical properties of the soft tissue, and thus can be used to detect the hard inclusions inside the soft tissue quantitatively. Static robotic palpation experiments are performed as well to verify the efficacy of the proposed approach.
# Slicing on the Road: Enabling the Automotive Vertical through 5G Network Softwarization
## 6 Proof-of-Concept Experimental Evaluation
### Reference Scenario 2: Load-Aware Slice Handover Decision  
Figure 6(a) presents the reference topology for the second investigated scenario intended to show the effectiveness of slicing federation under a multiple SDN controller setup. The target vehicle moves from eNodeB A1 towards eNodeB A2 and stops between eNodeB B1 and A2. When federation is not in  
Figure 6: Experiment metrics measured when the vehicle travels outside the reach of eNodeB A1 towards A2 through the coverage B1 and A2 for Case 1 of no slicing federation and Case 2 with federation.  
place (Case 1) between the two operators, MNO A and MNO B, the controller tracking the vehicle has no knowledge about the availability of slice connectivity options providing higher quality, i.e., the less loaded eNodeB B1. Thus, following a traditional RSSI-based handover, the vehicle ends up associating with A2, contributing to a further loaded eNodeB at risk of service degradation. Differently, when the federated network slicing concept is in place (Case 2), knowing that A2 is overloaded, the SDN controller does not trigger the handover to A2. Through this load-aware slice handover strategy, slice-specified quality levels, such as those affected by base station load, can be provided. In this use case, the controller for MNO A, through the federated information exchange with its peer from MNO B, discovers the availability of resources provided by MNO B in the area where the vehicle moves. Despite being in range (and eventually better RSSI) of A2, after estimating due to the load effect better performance could be achieved if the vehicle keeps attached to B1, the handover is delayed until the condition changes. For the sake of simplicity, we considered unidirectional video streaming traffic from the network to the vehicles and emulated the background traffic between A2 and the switch by defining the delay between them as 5 ms.  
Figure 6(b) presents the obtained results in terms of latency for two cases. For Case 1 of no slice federation, latency significantly increases when the vehicle handoffs towards A2, at time instant 120 s. On the other hand, slice federation (Case 2) yields lower and stable latency and jitter values when the vehicle is kept attached to B1.
## 3 Results
### Analysis of Sustainable Displacements in Valencia
#### 3.2.5 Public Transport Network Analysis  
Central neighborhoods exhibit high accessibility to public transport, whereas suburban areas show varied levels, emphasizing the need for targeted improvements (see Figure 3). This underscores the importance of decentralized transportation planning to address specific neighborhood disparities. The results by municipalities highlight the transportation landscape beyond Valencia. Some municipalities boost high accessibility, while others, like Canet d'En Berenguer and Domeno, lack adequate options (see Figure 3). These disparities emphasize the necessity for a comprehensive strategy.  
Figure 3: Accessibility to public transport by municipalities and neighborhoods.  
Critical implications and recommendations:
* Decentralized accessibility: Suburban disparities necessitate decentralized transportation planning for precise solutions.
* Municipal strategies: Tailored strategies for municipalities lacking accessibility are crucial for practical improvements.
* Sustainable mobility promotion: Initiatives promoting alternative transportation are vital for overall decarbonization efforts.
* Infrastructure investment: Targeted investments, expanded routes, and improved frequency enhance the public transportation network.  
These findings provide insights into the current state and offer a strategic advantage. The analysis allows for pinpointing specific zones that require precise improvements, enabling more effective and accurate interventions to enhance overall transportation accessibility in Valencia. The implementation of these strategies will fortify Valencia's commitment to sustainable urban mobility, significantly contributing to broader decarbonization objectives.
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 3 Construction of Prediction Model
### TCN Model  
TCN is a model that uses a convolutional structure to solve time-series problems and mainly consists of dilated causal convolution and residual modules [22]. TCN uses causal convolution to ensure that there is no information leakage. Suppose that the model input sequence is {x0, x1... x-1, x1}, the expected prediction output is {y0, y1... y-1, y1}. In causal convolution, the output predicted value \(\hat{y}_{t}\) at time \(t\) is only determined by {x0, x1... x-1, x1}, independent of {x+1, x+2... }. To capture long-timescale dependence in the time-series, dilated convolution is introduced into the causal convolution to become the dilated causal convolution that can realize the exponential increase of the receptive field, and the structure is shown in Figure 3.  
For a one-dimensional input sequence \(\mathbf{x}\in\mathbb{R}^{n}\) and a filter f: {0,1,..., k - 1} \(\rightarrow\) R, the dilated convolution operation F on the sequence element s is defined as follows:  
\[F(s)=\sum_{i=0}^{k-1}f(i)\cdot\mathbf{x}_{s-d\cdot i} \tag{9}\]  
where \(k\) is the size of the convolution kernel, \(d\) is the dilation factor, and \(\mathbf{x}_{s-d\cdot i}\) represents the element of the historical moment in the input sequence.  
The larger the dilation factor d, the larger the input range, thus increasing the receptive field of the convolutional network. As shown in Figure 2, the dilated causal convolution is one with dilation factor \(d\) = {1,2,4}, convolution kernel size \(k\) = 2, and final receptive field size 8.  
With the superposition of dilated causal convolutions, the depth of the network will be deepened, which may lead to the problem of gradient disappearance in the model. Therefore, a residual connection is introduced in the output layer of TCN to fuse the input \(x\) into the output of the convolutional network, which can be expressed as follows:  
\[o=\text{Activation}(x+F(x)) \tag{10}\]  
where Activation (\(\cdot\)) is the activation function and \(F(x)\) represents the output of the convolutional layer.  
After the introduction of a residual connection in TCN, as shown in Figure 4, a residual connection can make the deeper network work properly, which can improve the  
Figure 3: Structure of dilated causal convolutions.  
performance of the model [33]. The WeightNorm layer normalizes the weight of the output of the convolutional layer to speed up the calculation [34]. Meanwhile, to avoid overfitting, a Dropout layer is added after each dilated causal convolution.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 5 Novel DSP Techniques for MIMO Systems
### Digital Back-Propagation Algorithm (DBP)
The experiment setup is shown in Figure 18, where a 5 channel 50-GHz-spaced WDM system with 28 Gbaud 16-QAM modulation format is utilized. Ten fiber spans with different length are cascaded. At the receiver side, the signal is sampled and then processed in offline. Figure 16: Q factor vs. launched power per channel in three different cases. (**a**) low XT and DMD free; (**b**) high XT and DMD free; (**c**) low XT and high DMD with GD-managed. Figure 17: The architecture of DNN to implement DBP and PSP derotation. Figure 18: Experiment setup for PDM-WDM system. The Q factors of the center channel is shown in Figure 19a, it can be found that the DNN based DBP outperforms traditional DBP even in 1 step per span (StPS). Figure 19b shows the \"M\"-shaped amplitude responses of the filter, which exhibits some high-pass feature at the later staged in DNN. This phenomenon can be attributed to that the \"M\"-shaped filter tries to compensate an additional undesired term with a \"(\(\cap\))\" shaped spectrum. The optimized nonlinear coefficients with a \"U\" shape are shown in Figure 19c, which suggests that the nonlinear-phase derotation is larger in the middle stages than the beginning and end of the DBP. Figure 19d shows the optimized rotation angles for PSP, which agree with the theoretical expectation.