esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications
### Other Applications  
Apart from the applications in diffuse optics described in the previous paragraphs, there are other areas in which the use of detectors operated in TG modality is necessary. TG mode represents a possible solution, whenever, in an optical measurement, it is necessary to reduce the noise or reject a spurious signal that differs temporally from the target signal. In this paragraph we briefly present some of this kind of applications.  
Fluorescence lifetime imaging (FLIM) is a technique which measures not only the fluorescence intensity of the sample, but also the lifetime of the fluorescence signal. This further information allows the discrimination of the composition of the sample and its environment and also a better contrast with respect to the background fluorescence. TG modality can be applied to FLIM: a moving gate and a simple counter allow to measure the lifetime. This simple scheme enables the use of large area detectors [163]. Furthermore, the TG capability can be used in order to reject the excitation signal without using a spectral filter by exploiting the different timing between excitation and fluorescence signals. Similar detection architectures used for FLIM can be applied also in Raman spectroscopies [163].  
Stimulated emission depletion (STED) is a fluorescence microscopy with sub-diffraction resolution [164]. In STED microscopy, the resolution of the optical system is increased over a conventional microscope by adding a second beam to the excitation beam whit a \"doughnut\" profile which is spectrally tuned to trigger stimulated emission in the excited fluorophores. Choosing the right intensity of the second beam it is possible to quench all fluorophore emission except for a sub-diffraction region in the center of the \"doughnut\" where the second beam intensity is null. The use of a TG detector allows one to collect the fluorescence signal only when the stimulated emission effectively quenches the fluorophores. Thus, the secondary beam can operate at lower power (reducing sample damages) and in a nanosecond pulsed or CW regime (reducing system complexity) [165].  
Another important field, where TG detectors gave a breakthrough in the last decade, is in the light detection and ranging (LIDAR) applications. LIDAR systems are used, for instance, for 3D high resolution depth imaging where the time of flight of reflected light pulses is used to probe the spatial position of distant objects. Thanks to the time gating capability of the detector, it is possible to reject the reflected pulses coming from objects between the detector and the target thus improving the dynamic range of the system and its depth sensitivity [166; 167]. TG becomes important in more complex LIDAR applications where reflections of higher orders are considered, like in non-line-of-sight imaging where hidden scene are reconstructed by exploiting photons which reach the target after multiple reflections. In this case the TG mode is used for rejecting signal coming from the first reflections which are needed to shine light towards the hidden scene [168].
# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
## 3 Problem Description for the Proposed Methodology
### Multi-Objective Optimization  
A MOP has multiple conflicting objectives which need to be optimized simultaneously. Therefore, the goal is to find good trade-off solutions that represent the best possible compromises among the objectives. A MOP problem can be formulated as:  
\[\min\,f(x)=(f_{1}(x),f_{2}(x),\ldots,f_{d}(x)) \tag{1}\]  
subject to \(x\in\omega\) wherein \(\omega\) represents the decision space. \(f(x)\) consist of \(d\) objective functions. Since multi-objective optimization usually involve conflicting objectives, so there is no single solution which can optimize all objectives simultaneously. Hence, the desired solution is considered to be any possible solution which is optimal for one or more objectives. For this purpose, the concept of Paretodominance is mostly employed. Given two solutions \(x_{1},x_{2}\in\omega\), solution \(x_{1}\) Pareto dominates \(x_{2}\) or \(f(x_{1})\) Pareto dominates \(f(x_{2})\) if and only if,  
\[f_{i}(x_{1})\leq f_{i}(x_{2})\ \forall i\in\{1,\ 2,\ 3,\ \ldots,\ d\}\ \text{and}\ (x_{1})\
eq f(x_{2}) \tag{2}\]  
where \(f_{i}(x)\) is the \(i^{\text{th}}\) objective function of solution \(x\) in \(d\) dimensional space. A solution \(x^{*}\in\omega\) is denoted as Pareto optimal if and only if \(\
exists x\in\omega\) such that \(x\) Pareto dominates \(x^{*}\), that is, it is not dominated by any other solution within the decision space. The set of all Pareto optimal solutions is termed as the Pareto set and its image in the objective space is called the Pareto front. Workflow scheduling in the cloud can be seen as a MOP whose goal is to find a set of good trade-off solutions enabling the user to select the desired trade-off amongst the objectives.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Rate of Change of Voltage:  
This method involves calculating of rate of change of voltage given by:  
\[ROCOV=\ \frac{dV}{dt}. \tag{2}\]  
It is based on the fact that the reactive power imbalance causes a transient change and voltage starts to vary dynamically. Both ROCOF and ROCOV were tested with the conclusion that they can detect islanding condition within 0.125 s [55].
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 2 Development Stages of Pavement Distress Detection
### Semi-Automated Detection Stage  
Advancements in digital imaging led to semi-automated detection systems. These systems first capture pavement images using standardized cameras and then analyze them with human-assisted distress labeling. This method significantly reduces field investigation time. However, it introduces two major limitations: (1) strict requirements for image quality control and (2) high human resource demands for processing large image datasets (typically captured every 2-4 m). These factors create a bottleneck in the evaluation process, particularly for extensive highway networks covering thousands of kilometers.
## 3 Results and Discussion
### Percolation Tests
#### 3.2.1 Addition of MWCNTs  
Figure 5 and Table S3 in Supplementary Materials present the results of the adsorption of the HMs by the soil alone (reference) and by the soil enriched with MWCNTs (two different concentrations of MWCNTs were tested: 0.01 and 0.05%, _v/w_). Evaluating the results, it is possible to see that in the percolation tests, the amount of heavy metals immobilized by only the soil particles is much higher when compared to the suspension adsorption tests. In this case, the heavy metals Pb\({}^{2+}\) and Cu\({}^{2+}\) are almost totally immobilized by the soil particles alone without the need of any additive. Regarding Ni\({}^{2+}\) and Zn\({}^{2+}\), the amount of these HMs immobilized increases also when compared to the suspension adsorption tests. However, these two heavy metals still show potential to contaminate adjacent areas, when only soil is used, due to their higher mobility. The main difference between the adsorption and percolation tests are the initial conditions of the soil: for the first case, fluid-like conditions (without any soil structure) are present, while in the second case, the conditions are solid-like (with a soil structure, characterized by a low hydraulic conductivity coefficient of the order of \(10^{-9}\) m/s [53]). Thus, in the percolation tests, the soil properties have more influence on the immobilization of the HMs, mimicking closer what may happen in a real situation.  
A careful look at the results of the tests where the soil was contaminated with nickel and zinc, leads to the conclusion that the additive MWCNTs in a concentration of 0.01% (_v/w_) produced the increase of the adsorption of Ni\({}^{2+}\) and Zn\({}^{2+}\), as it was observed in [31] for a different surfactant type and concentration. This increase is more significant when a higher concentration of MWCNTs  
Figure 5: Results from the percolation tests. Immobilization of heavy metals by the soil with or without the addition of MWCNTs (two concentrations: 0.01 and 0.05% _v/w_).  
is used (0.05%, _w/w_). For this last MWCNTs concentration, it was possible to achieve almost total immobilization of these heavy metals (99.86 and 97.06 retention percentage for Ni\({}^{2+}\) and Zn\({}^{2+}\), respectively). The results reported lead us to conclude that mixing a small quantity of MWCNTs with the soil studied can efficiently decrease the mobility of the HMs, in situations similar to field conditions, indicating that this may be a valid option to be used in the immobilization of contaminated soils with similar properties to the soil studied here.
# Vectorized Numerical Algorithms to Solve Internal Problems of Computational Fluid Dynamics
## 7 Vectors of Computational Variables  
Vectors \(\mathbf{U}_{0}\) and \(\mathbf{U}_{N}\), at some generally arbitrary places, contain quantities corresponding to the boundary conditions, which makes it difficult to formalize the computational procedure with vectors of physical variables.  
Vectors of computational variables \(\mathbf{W}_{i}\) are introduced (their number is one less than physical vectors \(\mathbf{U}_{i}\)). They contain only unknowns. Each computational vector \(\mathbf{W}_{i}\) contains part of the unknowns from the vector \(\mathbf{U}_{i-1}\) and part of the unknowns from the vector \(\mathbf{U}_{i}\). Obviously, any component of vectors \(\mathbf{U}_{i}\) is included in the set of vectors \(\mathbf{W}_{i}\) at most once. In this case, the known components of vectors \(\mathbf{U}_{0}\) and \(\mathbf{U}_{N}\) corresponding to boundary conditions are not included in vectors \(\mathbf{W}_{i}\).  
The common scheme for constructing computational vectors \(\mathbf{W}_{i}\) is considered. The construction with the vector \(\mathbf{W}_{N}\) is the first step. The components of the vector \(\mathbf{U}_{N}\) that do not correspond to boundary conditions are transferred to the vector \(\mathbf{W}_{N}\) to the same places. The components remaining in the vector \(\mathbf{W}_{N}\) are filled with the components of the vector \(\mathbf{U}_{N-1}\) from the same positions. Formally, this procedure is written using the index vector \(\mathbf{I}_{R}\)  
\[\mathbf{W}_{N}^{j}=\left(1-\mathbf{I}_{R}^{j}\right)\mathbf{U}_{N}^{j}+\mathbf{I}_{R}^{j}\mathbf{U }_{N-1}^{j}. \tag{8}\]  
Moving to the left from the right boundary (indices move from \(N\) to \(1\)), the vector \(\mathbf{W}_{i}\) is represented as  
\[\mathbf{W}_{i}^{j}=\left(1-\mathbf{I}_{R}^{j}\right)\mathbf{U}_{i}^{j}+\mathbf{I}_{R}^{j}\mathbf{U }_{i-1}^{j}. \tag{9}\]  
The method for generating the vector \(\mathbf{W}_{1}\) differs from the others. The still unclaimed components of the vector \(\mathbf{U}_{1}\) take their positions, and the remaining places are filled with components of vector \(\mathbf{U}_{0}\) that do not correspond to the boundary conditions. To do this, these components are taken in increasing order of their indices and placed on the free places of vector \(\mathbf{W}_{1}\) in increasing order of indices of these places. The representation of vector \(\mathbf{W}_{1}\) has the form  
\[\mathbf{W}_{1}^{j}=\left(1-\mathbf{I}_{R}^{j}\right)\mathbf{U}_{1}^{j}+\mathbf{I}_{R}^{j}\mathbf{ U}_{*}^{j}. \tag{10}\]  
The vector \(\mathbf{U}_{*}\) is obtained from the vector \(\mathbf{U}_{0}\) by rearranging the components according to the described principle. The formal representation of the vector \(\mathbf{U}_{*}\) is not essential for further presentation and is not provided here.
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 5 Results and Analysis
### Simulation Results and Analysis
The increase in delay for platoon vehicles under the priority-based algorithm, despite its prioritization, is attributed to the higher resource demand for platoon vehicles compared with general vehicles, leading to resource shortages. Since platoon vehicles transmit packets more frequently and with larger data sizes than general vehicles, the resource shortage problem intensifies as the number of platoon vehicles increases, increasing delays in the priority algorithm. However, the proposed algorithm mitigates this resource shortage to some extent by allowing resource reuse between platoon vehicles and enabling resource reuse based on SINR with general vehicles. As a result, the proposed algorithm is able to maintain stable communication performance for platoon vehicles, even as the number of platoon increases. Therefore, the proposed algorithm maintained an average delay time of 16\(\sim\)17 ms across scenarios with 1 to 5 platoons.
## 3 Results
### Online Test  
The temperatures and the NMR spin-spin relaxation rates of the weak black liquor were measured continuously using an online TD-NMR system in a pulp mill for seven days. The temperature correction in Equation (6) was applied to these data. The reference temperature Tref was 60 \({}^{\circ}\)C. A moving average was also used for these data sets to smooth out the short-term fluctuations. The curves are shown in Figure 4a,b, where we observed that T and R2 are changing, especially at the beginning and after the fourth day of the online test. A demonstration of the temperature correction at the beginning of the online test is given in Figure 4c. The deviation in the R2 value in Figure 4c was mainly caused by the temperature change. In Figure 4c, no moving average was used. Then, using the temperature-corrected R2 TC values, the estimation of the SC was performed using Equation (7). A one-hour moving average was applied to the SC values, and the SD values were calculated within each hour using the moving data. The resulting SCs and SDs of the weak black liquor are given in Figure 5.  
Figure 4: Data measured for the weak black liquor in the online test: (**a**) Sample temperature; (**b**) Measured R2 values and values of the R2 TC corrected to Tref = 60 \({}^{\circ}\)C, after applying a one-hour moving average; (**c**) Temperature correction at the beginning of the test (dotted rectangle in Figure 4b): measured R2 (squares), values of the R2 TC corrected to Tref = 60 \({}^{\circ}\)C (circles), and the sample temperature T (triangles).  
Figure 5: Estimated solids content (SC) shown by a black line, and standard deviations (SD) shown by the grey lines. Average SCs and SDs were estimated within one hour, using moving data.
## 3 Analysis of Edge Method Accuracy
### Random Noise  
In this section, Gaussian, gamma, uniform, and speckle noise, which are four typical noises, are mixed and added into ideal edge images as noise simulation sources in RS images. Different noise levels are controlled by modifying the variance and mean of noise. Moreover, the parameters of added noise are shown in Table 2. The BNDE images with four angles of 3, 10, 25, and 40 deg, 400 \(\times\) 400 pixels, and 300 dpi are determined for analysis, and the SNR of edge images is controlled to be 3.9-12.6 dB by adjusting different noise parameters. The OMNI-sin edge method is used to measure the MTF of edge images with different angles and different SNRs in Figure 4, and the simulation results are shown in Figure 15.  
Figure 15a-d shows that MTF curves will fluctuate to some extent as long as there is noise in the edge image. When the SNR of the edge image is greater than 10 dB, both the noisy fluctuant MTF and the noiseless smooth MTF still have high consistency, and the measured results are still valid. When the SNR of the edge image is lower than 4.8 dB, the possibility of aliased MTF is greatly increased. In this case, MTF deviates from the trend line of noiseless MTF in all frequency ranges, so the confidence of the measured MTF is  
Figure 15: MTF for some tilt angle BNDE images with different SNR (\(\infty\), 12.6, 10.0, 4.8, 4.2, 3.9) using OMNI-sin edge method: (**a**) when edge angle is 3 deg, (**b**) when edge angle is 10 deg, (**c**) when edge angle is 25 deg, (**d**) when edge angle is 40 deg.  
extraordinarily low. The larger the edge angle, the more obvious the influence of noise. In this study, only the upper and lower limits of two thresholds are preliminarily determined, but the influence of the signal-to-noise ratio between 4.8 and 10 dB needs further study.
## 7 Conclusions
### Limitations and Future Research  
There are several limitations in this study. Firstly, it was conducted in China, a developing economy where Industry 4.0 is still emerging. Future research could explore more-developed and less-developed economies to compare technological competencies among firms and suppliers. Secondly, the focus was exclusively on the manufacturing industry, potentially overlooking critical success factors relevant to the service industry. A comparative analysis between manufacturing and service sectors would be insightful. Thirdly, the study only examined a limited number of factors, leaving room for exploration of additional variables as Industry 4.0 technologies continue to evolve. Lastly, the amalgamation of diverse research types, periods, and regions in the study's tables poses a limitation, potentially impacting data consistency and reliability. Further efforts to ensure the coherence and comparability of the findings are necessary to address biases stemming from dataset heterogeneity.  
Conceptualization, Z.N. and L.Z.; methodology, Z.N.; software, H.Z. and B.Z.; validation, H.Z. and B.Z.; formal analysis, H.Z.; investigation, H.Z.; resources, B.Z.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, Z.N. and B.Z.; visualization, B.Z.; supervision, Z.N. and L.Z.; project administration, L.Z.; funding acquisition, H.Z. and Z.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under grant number 2020 YFB173200, and by the National Natural Science Foundation of China under grant number 72188101.  
Not applicable.  
Not applicable
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 4 Subtypes of Sphingolipidoses
### GM2 Gangliosidosis: Tay Sachs Disease and Sandhoff Disease
#### 4.4.2 Retinal Manifestations
While FA and FAF are not as commonly employed as fundus photography, they have offered important findings regarding the retinal alterations linked to TSD and SD. Late-phase FA images show areas of hyper-fluorescence, corresponding to areas of retinal degeneration around the fovea [121]. An additional examination in these conditions is the study of the ERG and VEPs. [PERSON] et al. compared the retinas of mice with GM1 gangliosidosis and SD to healthy controls, finding a 44% and 40% increase in total gangliosides, respectively, along with higher levels of specific gangliosides associated with each disorder. Interestingly, the ERG were normal in all mice, with no significant differences between GM1, SD and controls, while VEPs were reduced in mice with these storage disorders [19].
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 2 Background
### Image Recognition Classifiers  
In this research, we employed six distinct image classification network architectures: convolutional neural network (CNN) [41], deep neural network (DNN) [46], VGG16 [47], ResNet50 [48], EfficientNet [49; 50], and WideResNet [51]. Figure 1 illustrates the architectural layout of the CNN. A convolutional neural network (CNN) comprises three convolutional layers and a single fully connected layer. Each convolutional layer is succeeded by a pooling layer, designed to diminish feature dimensionality and distill essential information. To mitigate overfitting, we integrated a Dropout layer with a dropout rate of 0.2 following the last convolutional layer. Subsequently, a fully connected layer was established, and the final classification outcome was produced via a softmax activation function. Figure 2 depicts the architectural layout of the DNN. A deep neural network (DNN) is composed of an input layer, an output layer, and two hidden layers. Considering the generalization limitations of CNNs and DNNs, the primary evaluation was performed on smaller datasets, specifically MNIST and CIFAR-10.  
Furthermore, both CNN and DNN employ ReLU (rectified linear unit) as their activation function. The rationale behind selecting ReLU is its ability to expedite the training process and mitigate the issue of gradient vanishing, thus facilitating a more stable and efficient learning experience.  
The VGG16 network has garnered significant attention within the realm of image classification tasks, owing to its profound architecture and exceptional performance. This network is composed of 16 convolutional layers and fully connected layers, which progressively distill advanced image features through the iterative application of \(3\times 3\) convolutional kernels and \(2\times 2\) pooling layers. On the other hand, the ResNet50 network addresses the gradient vanishing issue inherent in deep network training by incorporatingresidual connections. This network, comprising 50 convolutional layers, employs skip connections to bypass the input directly to subsequent layers, thereby enabling the network to capture deeper representations.  
To ascertain the efficacy of this approach within modern architectural frameworks, we chose two distinct models for evaluation: EfficientNet and WideResNet. The EfficientNet model employs a set of composite coefficients to dynamically adjust the network's width, depth, and resolution, thus enhancing the model's accuracy without escalating computational demands. Conversely, the WideResNet model boosts performance by broadening the network's architecture and incorporating Dropout layers to mitigate overfitting. This model augments its capacity by expanding the number of channels within the convolutional layers, all the while preserving computational efficiency.