esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Effective Moisture Diffusivity  
Based on Table 2, the independent effect of the \(MP\) and \(ST\), as well as the interaction effects of \(ST\times MP\), were significant on the \(D_{\mathit{eff}}\) for different pretreatments. The quadratic effect of the \(MP\) on the \(D_{\mathit{eff}}\) of blanched and ultrasonicated samples was significant (\(p<0.0001\)), while it was not significant in the controls. Equations (11), (17), and (23) in Table 2 show the model of the \(D_{\mathit{eff}}\) variations in relation with the independent parameters. According to the model coefficients, the influence of the \(MP\) was higher than the effect of the \(ST\) on the \(D_{\mathit{eff}}\) value of the pretreated samples. Figure 3 also reveals the interaction effects of the \(MP\) and \(ST\) on the \(D_{\mathit{eff}}\) value of (a) untreated, (b) ultrasonicated, and (c) blanched carrots. The \(D_{\mathit{eff}}\) value showed a significant (\(p<0.0001\)) rise by enhancing the \(MP\) and \(ST\). In general, a higher \(MP\) leads to higher temperatures and, hence, lower water viscosity and higher water activities. This phenomenon can result in easier diffusion of water within the product [33]. The same observations were reported in drying lemon [29] and tomato [6] slices. The impact of the \(ST\) on moisture diffusion can be discussed by the surface hardening of the samples. Due to the higher rate of moisture evaporation during the drying process, the surface of the thin slices is generally hardened faster than the thicker ones, which will inhibit the diffusion of moisture from the center to the surface of the samples [42]. The results of the current research are well in line with the reports on aloe vera gel [26], apple slices [43], and potatoes [44]. Based on the results, the use of ultrasonication and blanching pretreatments can quadratically increase the value of the \(D_{\mathit{eff}}\) compared to the control samples. The influence of the blanching was more profound on the increase in the \(D_{\mathit{eff}}\) as compared with ultrasonication. These results are consistent with the drying kinetic data, which imply that the stronger the mobility of internal water molecules, the shorter the \(D_{t}\)[45]. The formation of more pores and microchannels due to the ultrasound-induced cavitation and more enzyme degradation and tissue loosening due to blanching led to higher permeability [14]. The results of this research are in agreement with the researchers from other reports for cranberry snacks [46] and pumpkin products [47] that were pre-treated with ultrasound and blanching. Furthermore, when ultrasound waves pass through an environment, they cause successive expansion and contractions, leading to the cavitation phenomenon [39; 48]. As a result, ultrasonic waves increased the \(D_{\mathit{eff}}\) due to the small channels created inside the food which facilitated the moisture removal [49].
# Multimedia Vision for the Visually Impaired through 2D Multiarray Braille Display
## 3 Proposed eBook Reader Application for 2D Braille Display
### Components and the Design of 2D Braille Display for the VI
#### 3.4.1 The Proposed Method of Page Division for 2D Multi-Array Braille
Thus, all the contents of the book are expressed in braille appropriately by fitting in the screen. According to the conditional statement at the last part of algorithm, if the maximum cell value is exceeded, only the last word is added to the buffer and a braille page is produced, and then a next page is created; this process continues until the end of the book. Furthermore, if the contents are longer than a braille line, there might be more text contents. In such cases, the smartphone shows a long text by using the design setting of activity, but in the case of braille, the user might find it difficult to read if the page is automatically turned. Therefore, by expressing the cell line in a fixed format, a method of changing the text content with a cursor is chosen. With respect to the splitting phenomenon occurring because of insufficient horizontal cells on the screen, the next/previous text content is shown by pressing the left/right button using a cursor on the corresponding braille page, and the data can be revised to fit the braille page by using the dynamic allocation of an ArrayList in Java. Furthermore, because the part of the content being read by the user is automatically highlighted in the smartphone, a teacher, guardian, or assistant who is not disabled can easily track the activity of a VI person and provide appropriate guidance. The present study used a braille display that has a 2D multiarray display similar to that shown in Figure 11 to effectively express multimedia contents for VI users. The braille display consists of \(12\times 12\) cells in total, as shown in Figure 7a. The column (\(11\times 1\)) at the far left indicates the user's cursor position, and the contents cells (\(11\times 11\)) at the right side show the multimedia information received from the smartphone. Excluding these, the last line of the cells (\(1\times 11\)) at the bottom expresses additional information such as page and book information. As shown in Figure 7c, when the cursor points to a content in the list, a total of 11 list contents are displayed, and if there are more than 11 list contents, the list pages are managed automatically to move to the next or previous page; and the screen of the corresponding list activity is appropriately displayed. Figure 11: Example of a braille display presented in this study.
## 2 Project Background  
### Project Overview  
Section 2 of Hangzhou Metro Line 7, comprising one station and three sections, is situated on Wangliang Street, Shangcheng District, Hangzhou City, Zhejiang Province. The small-mileage end of the foundation pit of the station serves as the shield initiation well for the new Line 7 and has an excavation depth of 16.7 m, as illustrated in Figure 1. This pit is near Hangzhou Metro Line 1, which has been operational for five and a half years, with a diaphragm wall 21.35 m from the Metro Line 1 tunnel. The foundation pit's retaining structure features an 800-mm-thick diaphragm wall extending 33.3-m deep. The support system consists of a five-channel internal support system, with the first and third channels reinforced by concrete and the remaining supported by steel. The foundation pit was segmented into five steps, with each step 50 cm below the corresponding support elevation. To mitigate disturbances to the operational subway line, the excavation employed a segmented approach. Specifically, a sealing wall was added at the shield opening well and the standard section of the station, effectively dividing the large foundation pit of the station into foundation pits. This strategy aimed to minimize the deformation of the existing tunnel caused by the excavation of the standard section of the station. This study focuses solely on the impact of pit excavation and over-crossing tunneling on the existing line in conjunction with the site conditions.  
The spacing between the existing Line 1 and the new Line 7 near the station was approximately 15.8 m, with the new Line 7 at a horizontal distance of 16 m and a vertical distance of only 1.95 m. Line 7 was oblique to Line 1 at an angle of 80\({}^{\circ}\), as shown in Figure 2. Both tunnels were constructed using the shield construction method. The  
Figure 1: Profile view of the project for the new tunnel of Line 7.  
segments were made of C50 material with an inner diameter of 5500 mm, an outer diameter of 6200 mm, and a segment thickness of 350 mm. The segments were joined with bent bolts, and each ring shield segment was divided into six pieces, installed by the erector of the shield machine.  
In this scenario, the existing tunnel experienced multiple construction disturbances. These primarily included the foundation pit excavation and structural construction at the shield initiation well of the Chengzhan station, along with the upper sections of the right-line shield for the new tunnel and the upper section of the left-line shield for the new tunnel.
## 3 Methods
### Software Design and Data Structure  
We chose to implement the workflow as a server application accessible by a web app because this allows a deployment as a true web app, as well as locally. Furthermore, the incorporation of Docker effectively assures platform independency, as it can be installed and run on basically all modern operating systems including Windows, Mac, and Linux.  
Regarding our data structure, apart from different representations of page images, we focus on PageRank [11] as the main carrier of information. This allows for a modular integration of the main submodules of OCR4 all and sets up easy to fulfill requirements regarding interfaces, ensuring a reasonably straightforward addition of new submodules or replacement of existing ones. Additionally, defining a unified interface for all tools and modules enables the usage of a comprehensive post-processing functionality. Another positive side effect of this approach is that submodules developed by us for OCR4 all can be integrated analogously into other OCR workflows that use PageXML.  
PageXML requires one XML file per page, which can store a wide variety of information, most importantly:  
* A page can comprise an arbitrary number of regions whose reading order can be specified.
* Among others, a region can store its enclosing polygon and type.
* There are main types like image, text, or music. Text regions can be further classified into sub-types like running text, heading, page number, marginalia, etc.
* A region can contain an arbitrary number of text lines.
* Each line stores its enclosing polygon, as well as an arbitrary number of text elements, which may contain GT, various OCR outputs, normalized texts, etc.
# Full Life Cycle Evaluation of Stability Pile in High Slope with Multi-Layer Weak Interlayers
## 5 Result
### The Effect of the Pile Length (\(L_{\text{p}}\))  
The \(F\)s in Model 1 for the piles of \(L_{\text{p}}\)s of 18 m and 20 m were 1.33 and 1.34, i.e., both lower than 1.35, while those with lengths greater than or equal to 22 m increased from 1.39 to 1.43 over the pile length increase (Figure 7). The \(F\) in Model 2 of the pile of \(L_{\text{p}}\) of 18 m was 1.347, <1.35. Then, \(F\) increased from 1.36 to 1.42 as the pile length increased from 20 m to 40 m (Figure 7). These results suggest that the stability of rock and soil improved with a lengthening of the pile.  
Figure 8 shows the changes in the maximum principal stress and the maximum shear stress over \(L_{\text{p}}\) in Model 1 and Model 2 under the 1.35 state. For Model 1, both the maximum compressive stress and the maximum shear stress decreased initially and then increased slightly as \(L_{\text{p}}\) lengthened. In Model 2, both the maximum compressive stress and the maximum shear stress slightly increased or decreased with a lengthening of \(L_{\text{p}}\). In general, the change of the two stresses vs. \(L_{\text{p}}\) was not significant, especially when \(L_{\text{p}}\) was longer than 26 m and the locations of high-stress areas were almost the same.  
Figure 8: The correlation between the maximum compressive stress and pile length and between the maximum shear stress and pile length in Model 1 and Model 2 under the 1.35 state.  
Figure 7: The correlation between safety factors and pile length. The horizontal dotted line represents a safety factor (\(F\)) of 1.35 and the vertical dotted lines represent the critical pile lengths.  
Under the limit equilibrium state, the plastic zone in Model 1 (Figure 9a-d) was mainly distributed in the high slope in front of the pile if the pile was shorter than or equal to 20 m, demonstrating that S1, S2, and S3 tended to slide, indicating that the pile did not operate as it should have. When the pile length was 22 m, the plastic zone extended through the bottom of the pile, illustrating that the pile was still too short and that the entire slope may slide as a whole. When the \(L_{\text{P}}\) was 26 m, the plastic zone intersected with the pile, meaning that the pile actively resisted sliding, thereby increasing slope stability. As the pile lengthened and reached or exceeded 28 m, the plastic zone distribution remained the same as that with an \(L_{\text{P}}\) of 26 m, suggesting that the effect of resisting sliding had reached its limit.  
In Model 2 (Figure 9e,f), the plastic zone of the pile back interlinked with that of the pile front and through the bottom of the pile as long as the \(L_{\text{P}}\) was shorter than 26 m. As analyzed above, the pile and the slope could slide together as a whole. Only when the \(L_{\text{P}}\) reached 30 m did the plastic zone get cut off, resulting in two distinct plastic zones at the pile back and pile front.
# Interferometric Wavefront Sensing System Based on Deep Learning
## 2 Method
### Wavefront Analysis Neural Network
#### 2.2.1 Models
To reduce the influence of noise, we use the 3\(\lx@math@degree\)3 Gaussian kernel with a sigma of 1 to filter the restored wavefront. After that, we can get a wavefront distribution, which is conducive to the subsequent aberration analysis. The role of net2 is to make a further analysis based on the true phase map and fit the coefficients of the Zernike polynomial required for wavefront reconstruction and correction. It adopts the structure of a deep convolutional neural network as a whole, and five residual blocks [21] are added to accelerate the learning process and improve the fitting accuracy. Finally, two fully connected layers are used, and we directly output the first 36 Zernike coefficients.
## 2 Materials and Methods
### Ground Penetrating Radar (GPR)  
Ground Penetrating Radar is a non-destructive geophysical tool that uses high-frequency electromagnetic (EM) waves in the 10-1000 MHz range to survey subsurface structures. The propagation of an EMI signal depends on the frequency of the transmitted signal and the electrical properties (electrical conductivity, dielectric permittivity, and magnetic permeability) of the substrate.  
A GPR survey provides an image of the subsoil along the study profile [29]. This tool can be applied in both natural and urban environments and can generate high-resolution images of features such as shallow soils and geological structures, as well as detecting buried and underground cavities, the water table, and archaeological artefacts. This type of radar emits short pulses at frequencies of between 10 and 1000 MHz, which are transmitted into the subsoil, where the propagation velocity (\(v\)) is controlled by the relative permittivity (dielectric constant, \(\varepsilon_{r}^{\prime}\)) and conductivity (\(\sigma\)) of the substrate (see, e.g., [29, 30, 37, 50]).  
For low-loss geological materials, the electromagnetic field is propagated at a phase velocity of  
\[v=\frac{C}{\sqrt{\varepsilon_{r}^{\prime}}} \tag{1}\]  
where \(c\) = the propagation velocity of electromagnetic waves in a vacuum (\(c\cong 0.3\) m/ns) and \(\varepsilon_{r}^{\prime}\) = the dielectric constant of the substrate [38].  
The most important determinants of the propagation of the electromagnetic wave through a given medium are its velocity and attenuation. When these waves travel through conductive material, the electromagnetic energy is lost in the form of heat through the electric current. This loss of energy is the attenuation that provokes a reduction in the depth of the penetration of the radar waves.  
In rocks of high resistivity (\(>10^{2}\) ohm.m), the propagation velocity of the pulse is controlled primarily by \(\varepsilon_{r}^{\prime}\). Water has a dielectric constant of 80, while most dry geological materials have a dielectric constant of 4-8. Given this, the water content of the material has a strong influence on the propagation of a radar pulse [37, 38].  
The wavelength, \(\lambda\) (m), is given by  
\[\lambda=\frac{c}{f\sqrt{\varepsilon_{r}^{\prime}}} \tag{2}\]  
where \(f\) = the central frequency of the antennas.  
The attenuation constant (\(\alpha\)) is normally expressed as  
\[\alpha=1.636\times 10^{3}\frac{\sigma}{\sqrt{\varepsilon_{r}^{\prime}}} \tag{3}\]  
in dB/m, where \(\sigma\) = the electrical conductivity (mS.m\({}^{-1}\)) [38].
## 3 Leaky Lamb Wave Radiation from a Waveguide Plate with Finite Width
### Leaky Radiation Beam Patterns and Beam Profiles  
Beam profiles of the A(0,\(n\)) mode for \(n=0\), 1, 2, and 3 are shown in Figures 5-8, respectively. Figures 5a, 6a, 7a and 8a and Figures 5b, 6b, 7b and 8b represent the vertical and lateral beam profile results obtained from the RSI model, respectively.  
The first trend identified in Figures 5a, 6a, 7a and 8a is that the radiation angles of the vertical beam show no big differences with changes in the width mode because the phase velocities at 1.0 MHz are close to each other; the radiation angles are calculated as 34.0\({}^{\circ}\)-35.5\({}^{\circ}\) with respect to the \(z\) axis.  
The second trend is that the leaky radiation beam pattern of the A(0,0) mode has a single main lobe in the far-field as shown in Figure 5b, but the others have split ones as shown in Figures 6b, 7b and 8b, In other words, the A(0,\(n\)) mode Lamb waves above \(n=1\) induce beam splitting on the lateral plane. It is acoustically clear that the beam splitting is caused by the sinusoidal profiles in the width direction on the aperture. Also, the splitting angle increases as the order increases.  
Figure 8: Radiation patterns and beam profiles of leaky A(0,3) mode Lamb wave, (**a**) vertical beam profile, and (**b**) lateral beam profile.  
Figure 6: Radiation patterns and beam profiles of leaky A(0,1) mode Lamb wave, (**a**) vertical beam profile, and (**b**) lateral beam profile.  
Figure 7: Radiation patterns and beam profiles of leaky A(0,2) mode Lamb wave, (**a**) vertical beam profile, and (**b**) lateral beam profile.  
The third trend is that the odd order of the width mode has no vertical beam profile at \(y=0\) (see Figures 6b and 8b) due to the anti-symmetric profile in the width direction, which has a node point at the center of the plate width. As a result, the lowest-order width mode of a Lamb wave in a plate strip provides a single main beam similar to that of a conventional immersion probe, and thus it can be best to solely use the leaky radiation beam of the lowest-order width mode in the waveguide sensor for immersion inspection.
## 5 Challenges
### Real-Time Processing and Energy Efficiency
LSH is widely adopted to identify abnormal measurements or those deviating from the hash values; this can be applied on top of BLE to ensure only authorized devices are accepted to retrieve the data [251]. These approaches enable IoMT systems to satisfy the dual demands of timely data processing and energy efficiency, ensuring reliable, scalable, and effective smart healthcare solutions. Table 14 compares DL models' performance, concentrating on the accuracy, computational overhead, model size to treat, the spread of processing, and generalization ability. When calculating the overhead performance, we adopted floating-point operations per second (FLOPs) to represent the number of floating-point arithmetic operations a model needs to perform during training. FLOPs are commonly used to assess the efficiency of deep learning models. Based on our findings, GANs have the highest overhead, requiring high-end GPUs for clinical text processing, which explains why GANs are widely used in cloud computing rather than local devices. DNNs and RNNs have moderate to high FLOPs, making them ideal for predictive modeling and real-time monitoring. Their speed is relatively slow compared to the model size they are processing. Therefore, they are effective for local applications with small model sizes. Another finding is that the latest DL model, MobileNetV3, is highly efficient for real-time health monitoring, with an inference time of only 10 ms. According to a study by [254; 255], using MobileNetV3 not only shortens the time but also improves the accuracy rate by around 1.2% compared to other CNN models such as Visual Geometry Group (VGG). However, MobileNetV3's accuracy rate suddenly drops when dealing with complex image scenarios, and it is sensitive to small datasets, and so may not be optimal for extremely high-resolution image processing due to its design focus on lightweight operations. Therefore, MobileNetV3 is less suitable for tasks requiring detailed feature extraction in large images [255]. To ensure a high accuracy rate with stable performance, VGG-16 is preferred over MobileNetV3 [256]. For mobile personal devices, such as smartwatches, with limited memory, MobileNetV3 is preferred over VGG-16 due to its smaller model size and efficiency. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**DL Model** & **Application in IoMT** & **Dataset** & **Accuracy (\%)** & **Overhead (FLOPS)** & **Model Size (MB)** & **Inference Time (ms)** & **Generalization Ability** \\ \hline Convolutional Neural Networks & Image analysis (e.g., classification, segmentation, detection) & Chest X-ray & Accuracy: COVID-19 CT & & & & \\ Neural Networks (CNN) [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62
# Two Scenario-Based Heuristics for Stochastic Shift Design Problem with Task-Based Demand
## 3 Solution Approach
### Two-Stage Heuristic  
In this subsection, we propose a scenario-based two-stage heuristic algorithm (TSH) to solve the previously mentioned stochastic problem. In the first phase, we will propose an algorithm to modify the shift plan designed and generate the set of modified shift plans. The algorithm process is roughly as follows. First, generate an initial shift plan via M3, and then generate a possible scenario by perturbing the start time of each task in the original set of tasks. Suppose the initial shift plan cannot provide sufficient workforce for all tasks in this scenario. In that case, the tasks with insufficient workforce and the initial shift plan are considered as a new set of tasks, and this new set of tasks is used as input to M3 to find a new shift plan (this new shift plan is called the revised shift plan). Then generate a new scenario again, and if the revised shift plan still cannot provide all tasks with a sufficient workforce, revise the shift plan again and repeat the process until there is a revised shift plan that continuously passes the test for a certain number of scenarios (the revised shift plan can provide enough labor for all tasks in any one of these scenarios). Record each revised shift plan in the process to generate a revised shift plan set and terminate the first stage of the algorithm. In the second phase, we will develop a search algorithm. This bisection method combines the Monte Carlo method for searching the revised shift plan set generated in the first phase for a shift plan that meets the probability constraint (A9) and performs relatively well in cost.