esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera
## 4 Measurement Results and Analysis  
### Pixel Values According to Working Distance  
#### 4.1.1 Measured Pixel Values According to Changes in Resolution  
To verify the measurement limits of imaging equipment according to differences in resolution, lenses with the same focal length were applied, and lenses suitable for individual manufacturers were applied. As presented above, the names of imaging equipment units were written as SC (Sony camera) and CC (Canon camera) to improve understanding of the study findings and analysis values. The pixel values from the video images acquired using each imaging equipment unit, measured using the Matlab program, are shown in Figures 8 and 9. According to the physical specifications of the applied single-vision lens with a focal length of 50 mm, working distances ranging from the minimum distances of 300 mm (CC) and 400 mm (SC) to the working distances at which the pixel sizes become the same as the sizes of the virtual images are shown. A relative comparison was carried out at a working distance of 400 mm, which is the minimum distance of the SC equipment.  
Figure 8: Results of pixel measurement by each manufacturer.  
Figure 7: Indoor test setting.  
The pixel values measured using CC50 and SC50 at a working distance of 400 mm are shown in Table 3 and Figure 8. The pixel values measured while the width value increased by five times from 0.1 mm to 0.5 mm show a resulting increase by 5.5 times from 2 to 11 in the case of CC, and by 4.75 times from 4 to 19 in the case of SC, at a working distance of 400 mm. When the width value was 0.1 mm, the relevant working distance was 850 mm in the case of CC and 1370 mm in the case of SC, with an increase of 1.61 times, and when the width value was 0.3 mm, the relevant working distance increased by 1.63 times from 2450 to 4010 mm, and when the width value was 0.5 mm, the relevant working distance increased by 1.64 times from 4050 to 6650 mm. Therefore, it was confirmed that the number of pixels measured and the limitation of working distance increased dependence on the resolution of the image sensor and the size of the object. Regardless of the proportional relationship for all the variables, it was found that the number of pixels acquired exponentially decreases as the working distance increases. Additionally, the number of pixels at the minimum working distance does not proportionally increase as the physical dimensions of an object increase.
## 3 Results and Discussion  
### FEA Validation  
To ensure the accuracy of the FEA, experimental tests were performed and compared with the simulation results. Table 3 shows the validation results. As can be observed from this table, the results of FEA are in relatively good agreement with the results of the experimental tests, and the absolute value of the average deviation error is 5.6%. The parameters of flow rate, melt temperature, and holding pressure were varied to verify the  
Figure 6: Flowchart of the research method.  
results. Other parameters remained constant due to practical limitations in changing the geometry of the mold.  
According to the shear stress heat map, the area that exceeded the maximum allowable shear stress (0.2 MPa) appeared as the blush [34]. As can be concluded from Table 3, lower amounts of error were observed for simulations associated with process configurations, resulting in a smaller area of defect. Since the objective of the study is to decrease the area of defect, the closer the study gets to its goal, the lower the error amount. Figure 7 shows a comparison between the simulation results for maximum shear stress and the blush defect in an experimental test performed with the same parameter settings.
# Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images
## 2 Materials and Methods
### Deep Learning Methods
#### 2.1.2 InceptionNet, XceptionNet, ResNet  
The above-mentioned building blocks and architectures are learned lessons through months of development and experience produced in the ever-evolving domain of deep learning and encapsulate notions that have been partially tested and evaluated in earlier deep learning architectures such as ResNet [36], XceptionNet, and InceptionNet [37]. These approaches achieved state-of-the-art results in computer vision tasks because they have incorporated these blocks partially. Once combined in a structured manner by means of a controlled augmentation mechanism such as in the EfficientNets, the performance is further improved.  
ResNets are driven by the intuitive need for neural networks to grow deeper in order to understand and quantify more complex features and simultaneously compensate for the vanishing gradient issue. The authors discovered that, by adding the identity function between layers, the network can reach deeper architectures and cope with the vanishing gradient issue, since the layers where the gradients diminish rapidly gets bypassed. Since its publishing, the idea has spread around fast and is being utilized in different deep CNN architectures including EfficientNets.  
Rather than investing in deeper architectures, the authors of InceptionNet prioritized the importance of creating wider approaches, meaning filters with multiple sizes, and leveraged their options between these two dimensions in order to capture salient patterns in the image that appears in different sizes. The initial version V1 was improved in terms of accuracy and speed by adding an auxiliary classifier during the training process, factorizing convolution operations and placing them at a wider grid. By further improvement of the initial proposal, the InceptionNet is now transformed in its fourth version. A combined approach of Resnet and Inception is proposed by the enhancement with residual blocks (Inception-ResNet). Moving a step forward, an extreme version of the InceptionNet, called XceptionNet, managed to achieve even better results, inspired by the inverse sequence of operation in the depthwise convolution (firstly proposed in Inception Net) and the removal of nonlinearity between convolutional layers.  
In order to select the best performing DCNN architectures, multiple tests were performed with the two datasets and each of the above-mentioned approaches. The results verified the superiority of EfficientNets over the other approaches. Due to these preliminary tests, the ensemble scheme proposed later in this paper consists only of different ranks of EfficientNet.
## 3 Results and Discussion
### Performance Tests of the Membranes for the Removal of Emulsified Oil
#### 3.5.3 Performance Tests with Different Oils  
The performance of the new titanate nanofiber/carbon nanofiber (TNF/CNF) membrane was assisted using different oils in water emulsion in the feed which are vegetable oil, gasoline, engine oil, n-octane, and n-hexane. As represented in Figure 10a, the oil rejection rate exceeds 99% for the different types of oil emulsion. The oil concentration in  
Figure 8: (**a**) Two phase oil and water mixture prior to sonication process. (**b**) One phase of oil in water emulsion after sonication process.  
Figure 9: Oil in water emulsion before and after filtration treatment with TNF/CNF membrane. (**a**) Red dyed oil in water emulsion in the feed. (**b**) Microscopic image of oil droplet in the feed prior to filtration. (**c**) The permanent solution after using TNF/CNF membrane. (**d**) Microscopic image of the permanent solution after using TNF/CNF membrane angle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mean Size (\(\upmu\)m)** & \(\mathbf{d_{10}}\) (\(\upmu\)m) & \(\mathbf{d_{50}}\) (\(\upmu\)m) & \(\mathbf{d_{90}}\) (\(\upmu\)m) \\ \hline
3.35 & 2.04 & 2.83 & 5.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Size of oil droplet in the prepared emulsion.  
the permeate was less than US EPA discharge limit (42 mg/L) as presented in Figure 10b. The oil rejection rate for the different types of oil ranges between 1520 LMH/bar and 1600 LMH/bar as represented in Figure 10a.  
Table 3 presents some reported data from literature of different membranes and their performance for oil/water separation. TNF/CNF membranes present a high rejection rate and water flux while having low operating pressure relatively.
## 3 Methodology
### Spectrum Modeling for Discrete-Sampled Recordings  
With the analysis result of BFRS, the next step is representing the bump feature based on the Fourier transform and related spectrum. The Fourier transform provides insights into the frequency aspects of signals in the time or spatial domain. It shifts a signal from its inherent domain, typically time or space, to a depiction in the frequency domain. This shift involves breaking down a series of data points into its individual frequency components. The FFT is a computational technique for determining the discrete Fourier transform (DFT) of a dataset or its reverse, as denoted in (10), where \(D(k)\) is the output sequence in the frequency domain, \(acc(n)\) is the input acceleration sequence in the time domain, \(N\) is the number of sample points, and \(k\) is the frequency index, ranging from 0 to \(N-1\).  
\[D(k)=\sum_{n=0}^{N-1}Acc(n)e^{-j(2\pi/n)kn} \tag{10}\]  
Analyzing the spectral components focuses on understanding the frequencies present in sampled datasets, and this mathematical equation switches a signal from its original time or spatial representation to a version defined by frequency. According to (10), the spectrum \(S\)f is computed using sliding window w with overlap rate \(e\) that moves along the time dimension and computes FFT in a short time period. In this article, the spectrum is applied to represent the bump feature from spatially transformed \(Acc_{trans}\), as denoted in (11).  
\[Sf=\text{SpectrumAnalysis}(Acc_{trans},l_{w},o_{w},H_{bfrs}) \tag{11}\]  
Since the abnormal change in BFRS is recorded twice by the front wheel and back wheel of vehicles, as depicted in Figure 2, the sliding window length \(l_{w}\) of the BFRS to generate spectrum is set as 170, which can cover the average abnormal change under the speed condition \(v\in[1,60]\) km/h according to (4). Moreover, the frame duration is set as 50, the hop duration is set as 5, and the specific frequency \(H_{bfrs}\) of BFRS ranges from 3 to 50 Hz, based on the BFRS modeling result in Section 3.2. Assumption that the original acceleration recording is depicted in Figure 4a-c, the spectrum computed based on FFT is depicted in Figure 4d-f. It can be observed that abnormal changes in the original recordings are distributed along the time direction from the spectrum view, and the feature of the abnormal change can be represented by frequency domain. The heavier changes in the acceleration, the brighter (represented by the yellow color, in respect to the blue color) the color in the frequency domain. Moreover, compared with one-dimensional changes in the original acceleration recording in the upper row, abnormal changes in the low row are depicted just like the magnifier in the frequency domain, and the directional non-homogenous feature can be more clearly represented.
## 2 Results
### RNA Profiling of CD163+ Monocytes
#### 2.2.3 Gene Interaction Network Analysis  
To further understand the potential functional associations between the genes in the curated list of GOIs, interactional PPI networks were created for up-regulated GOIs (A), down-regulated GOIs (B), and all GOIs (C), as shown in Figure 4. Within the 46 up-regulated genes from the CD163+ monocytes in D\({}^{+\text{Comps}}\), the search tool for the retrieval of interacting genes (STRING) identified a network of 8 nodes and 8 edges including _RELT_, _TRAF1_, _NFKB2_, _NFKBID_, _CD5_, _CD7_, _ZAP70_, and _MAPK13_, which contained interconnected genes relating to T cell function such as _RELT_, _NFKBID_, _CD5_, _CD7_, and _ZAP70_ (Figure 4A). _CD5_ was the 'hub' gene, with 3 edges connected to 3 different nodes, and _CD7_ was the most up-regulated gene by 5.1-fold. Within the 29 down-regulated genes, STRING identified a network of 10 genes and edges including _CALM2_, _CYP1B1_, _PTGS2_, _CD244_, _CD86_, _TLR4_, _CD1C_, _PPBP_, _CX3 CR1_, and _CD200R1_ (Figure 4B). _CD86_ and _TLR4_ were the 'hub-genes', each with 6 edges connected to 6 nodes. _CD200R1_ was the top down-regulated gene by \(-3.7\)-fold. This network of down-regulated genes demonstrated a connection between monocyte activation genes _CD86_, _TLR4_, and _CD244_, and the gene associated with monocyte migration, _CX3 CR1_.  
Figure 3: A volcano plot illustrating the selected 75 ‘genes of interest (absolute FC \(\geq\) 1.5 and FC \(\leq\)\(-\)1.5, BHp \(<\) 0.05).  
Figure 4: Gene interactions network illustrating the largest connected network of genes in (**A**) up-regulated genes, (**B**) down-regulated genes, and (**C**) combined up- and down-regulated genes in individuals with long-term diabetes and complications (D\({}^{+\text{Comps}}\)) compared to those without complications (D\({}^{-\text{Comps}}\)). Genes were considered significantly up- or down-regulated if the fold change was \(\geq\)1.5, and significance determined at BHp < 0.05. The color of the circular node indicates the intensity of the fold change, confirmed by the legend below. The sectioned multicolor outline of each node indicates the involvement of respective genes with the top five most enriched GO categories including pathways in the Kyoto Encyclopaedia of Genes and Genomes (KEGG).  
Within all 75 GOIs, STRING identified the largest network of 26 genes (Figure 4C). The 'hub-genes' with the most connections included _TLR4_ with 10 edges and _CD86_ with 9 edges. _CD7_ was the most up-regulated gene, and _CD200R1_ the most down-regulated.
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 5 Sensing Technologies
### Metal Oxide Semiconductors
Over the past 10 years, the use of nanostructures as metal oxide semiconductors to sense gases has become a topic of interest of some research groups, with reports of ameliorated sensing characteristics [22; 65; 66; 118; 119]. Response time and energy consumption are also topics of research, where the most part of commercial solutions have response times of seconds, sometimes reaching a few minutes; the energy consumption of these devices can reach the order of 500 milliwatts (mW) or  
Figure 3: Illustration of metal oxide semiconductor gas sensors, where: (**a**) is a tubular metal oxide semiconductor (MOS) sensor, and (**b**) is a wafer MOS sensor. even higher, due to the necessity of the heater. As the miniaturization of the devices has shown better response times, of the order of seconds to milliseconds, and power consumption of 80 mW, as reported by the [PERSON] et al. [124], it has a good potential to be used integrated in an IoT solution. Although no commercial solutions were found employing nanomaterials or microelectromechanical systems (MEMS), this has shown to be a promising technology to future products, allowing the development of embedded systems to measure gas concentrations in diverse scenarios, with lower power consumption than the traditional MOS transducers [125].
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 5 Calculation Algorithm Possibilities in Commercial Treatment Planning Systems
### Eclipse System  
In the Eclipse system (Varian Medical Systems, Palo Alto, CA, USA), several algorithms have been employed. A pencil beam convolution/superposition algorithm is used, with an equivalent path-length-based method (EPL) for inhomogeneity correction (Eclipse/Mod-Batho) [96] and another one is used with a more advanced correction scheme, taking into account the density distribution in 3D (Eclipse/ETAR) [87; 97]. A third algorithm, the AAA, considers the heterogeneities by correcting the photon scatter kernel according to the 3D anisotropic changes in electron density in the neighborhood of the point of interaction. The last algorithm implemented in the Eclipse system is AXB: Acturos XB [98]. This algorithm solves the LBTE numerically and shows good agreement with MC-based validation algorithms [99].  
Regarding electron beam dose calculations, Eclipse is equipped with the generalized Gaussian pencil beam (GGPB) and electron Monte Carlo (eMC) algorithms. The GGPB incorporates a three-dimensional heterogeneity correction published by [PERSON] [100] and improved modeling of radial dose profiles by including large-angle scatter via summing a few Gaussian functions [101].  
The eMC algorithm consists of the transport and the electron beam source models. The first model transports electrons and calculates the dose delivered along the particle tracks, whereas the second one describes the electrons and photons that emerge from the LINAC treatment head [102].  
The algorithms used in Eclipse for protons are the proton convolution superposition (PCS) and Acturos proton therapy (APT) dose algorithms. The PCS engine is a fluence-based dose model that computes the dose as a convolution of the proton fluence and a dose kernel hardcoded in the system [103; 104]. APT is a fast MC module, with simplified radiation transport, benchmarked with MCNPX 2.7a [105]. It provides fast and robust dose calculations for proton therapy by neglecting minor physics processes in order to reduce the computational run-time. APT classifies radiation transport as slowing down interactions with atomic electrons, elastic nuclear Coulomb scattering, elastic nuclear strong-force scattering or non-elastic nuclear reactions.
## 5 MPPT Algorithms
### Deep Q-Network (DQN)
#### 5.2.2 Simulation Setup  
The training of the Deep Q-Network (DQN) agent was conducted in the MATLAB/Simulink environment using the Reinforcement Learning Toolbox [34]. A stochastic training strategy was adopted, where the irradiance and temperature applied to each of the three PV panel segments were randomly varied at the beginning of each episode. This approach ensured generalization of the learned policy across a wide range of environmental conditions, including STC and PSC.  
Table 10 summarizes the main parameters used to configure the agent and the training process.
# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 5 Experimental Results and Discussion
### Grad-CaM Visualization of CT-Scans  
Trusting a computer-aided architecture for predicting whether a patient suffers from a disease without proper explanation or reasoning should not be done. DL models have provided several breakthroughs in varied domains of applications. However, when these systems fail to provide an accurate result, it is difficult to say why this has happened. The reason being that DL-based models cannot be segregated into smaller intuitive units, making it difficult to understand. Before adapting these intelligent systems to our daily lives, we need an understanding of the models as to why they predict what they predict. The transparency and the ability to understand the models are useful in many ways. It helps to decode the feature extraction ability, which, in turn, highlights the discriminating features among categories that may not be visible to human eyes. In such cases, Grad-CaM visualization can be used to find the cause of wrong classification.  
When the use case of a computer-aided framework is so sensitive, visual inspection is truly required. To create such visualizations, Grad-CaMS [60] or gradient-weighted class activation mapping can be utilized. It uses the gradients of any target concept flowing into the final convolutional layer to produce a coarse localization map, highlighting the important regions in the image for predicting the concept. To obtain the class discriminative localization map of width u and height v for any class c, at first, the gradient of the score for the class c is computed. These gradients flowing back are global average-pooled to obtain the neurons' importance weights for the target class. After calculating the importance for the target class c, a weighted combination of activation maps is performed followed by ReLU activation. This results in a coarse heatmap of the same size as that of the convolutional feature maps. ReLU is applied to see the positive gradient have influence on the class of interest. As mentioned earlier, three DL-based models are used as base learners in this study. We tried to generate the Grad-CaM images to highlight the most important areas of an image, which influence the prediction made by the base learners. Figure 9 shows the produced Grad-cam images along with the original CT-scan images on the left.  
Figure 9: Grad-CaM visualizations generated from the base learners highlighting the region of interest.
## 3 Experimental and Numerical Simulation Setup  
### Experimental Setups  
The wind tunnel experiment is carried out at TJ-3 Wind Tunnel of the State Key Laboratory for Disaster Reduction in Civil Engineering at Tongji University in China. The segmental model is 4.5 m long and 1.7 m high (shown in Figure 1), the girder model width is 1.025 m, and the pylon model width is 1.08 m at the top and 1.49 m at the bottom. The scale ratio of 1:40 is applied to this experiment to improve the accuracy of the test model and the results. The wind attack angle is zero, and the inflow wind speed is 8.0 m/s and set to be uniform. Figure 1b shows the steel box girder section and the cross section of the pylon at the bridge deck elevation.  
The experiment was conducted under two conditions. C1 represents the bridge section with guard trails, while C2 represents the section without any ancillary facility. The segmental model in the wind tunnel test is shown in Figure 2.  
Figure 1: The size of Sutong Bridge. (**a**) Segmental model region. (**b**) Cross sections of the girder and the pylon column (unit: mm). (**c**) The profile image of the bridge (unit: mm).
## 3 Progress in Grating Fabrication Technology
### Holographic Exposure
It was shown that the changing distances between the collimator and the cylindrical lens may modify the tuning range, and it is simple to achieve a greater tuning range, which means the variable-line-spacing grating has promising application possibilities [71]. Tsinghua University, Beijing's [PERSON], [PERSON], et al. created a polarization-independent grating in 2020 utilizing computer codes based on precise electromagnetic theory. In addition, holographic exposure paired with ion-beam etching technology was used to create a 1300 lines/mm spectrum beam-combining multilayer dielectric reflection grating. Its center wavelength was 1065 nm, and the diffraction efficiency of the \(-\)1 st Littrow angle and TE polarization grating in the bandwidth of 30 nm was more than 97% [46]. The holographic lithography method also has its drawbacks. It can only be produced with photoresist. The produced grating has weak durability, and the surface relief structure is prone to impurities, which cannot be cleaned, resulting in a diffraction grating performance that is significantly reduced. In addition, the production of large-sized gratings is limited, and the splicing technology needs to be combined, which also makes for considerable difficulties in the production of large-sized gratings.