esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
By stacking the feature sequences, this approach mitigates the local bias of Mamba-2 for non-causal data [19], thereby enabling more accurate global modeling and water edge segmentation. The main contributions are summarized as follows:  
* We have improved the Deep Dual-branch Network by adding an Atrous branch to increase the receptive field and enhance the information complementarity between the detail branch and the context branch. Furthermore, we are the first to integrate the deep dual-branch backbone with Mamba-2, thereby enabling Mamba-2 to more effectively acquire global contextual information. * We designed a Recombine Attention Module based on convolution, which serves as a gate activation condition for Mamba-2 to enhance feature interaction and global-local information fusion from both spatial and channel dimensions. * We proposed a Hilbert curve-scanning mechanism to realize the serialization of multi-scale features, and through feature sequence stacking, we alleviated the local bias of Mamba-2 toward non-causal data. * The experimental results of four datasets show that the LDMNet network demonstrated strong applicability and edge prediction capabilities in both waterway environments and urban road environments.
## 2 Materials and Methods
### Methods
#### 2.2.1 Covolutional Neural Network (CNN)  
Convolutional neural networks (CNNs) extract local features of input data through convolutional layers, followed by pooling layers to reduce spatial dimensions of features. The CNN structure proposed in this article consists of two One-dimensional convolutional layer (Conv1D), each followed by a One-dimensional max pooling layer (MaxPooling1D) and a Dropout layer. The first Conv1D layer utilizes 64 filters with a size-1 convolutional kernel and ReLU activation function. Subsequently, the MaxPooling1D layer performs pooling with a window size of 2. The second Conv1D layer employs 64 filters with a size-3 convolutional kernel, and it uses ReLU activation.  
By utilizing different kernel sizes, the model can capture temporal information at different scales. The first convolutional layer, with a smaller kernel size, helps capture finer-grained features, while the second convolutional layer, with a larger kernel size, captures broader temporal patterns. MaxPooling layers reduce parameters count, mitigating overfitting risk while maintaining feature representativeness. The dropout layers randomly discard outputs of neurons to enhance model generalization. This combination of CNN layers provides rich feature representations for subsequent LSTM layers and attention mechanisms, enhancing the model's capability to handle series data.
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 5 Discussion  
During the design of high-speed and high-output power photodiodes, the compatible photodiode-integrated devices, the measurement setups, and applications for communication systems, there are lots of considerations and challenges that need to be noticed. They are mainly reflected as follows: (1) The efficient optical coupling and precise alignment between the photodiode and other optical components. (2) The wider bandwidth with high response for photodiodes. (3) The wider dynamic range and linearity for higher output power without small saturation or signal distortion. (4) The effects resulting from temperature and heat from high-power conditions. (5) The reliability and robustness of photodiodes, and the scalability and modularity of photodiode-integrated devices. (6) The potential limitations at the 300 GHz band, such as the coverage range with high propagation loss, the penetrating obstacles from the walls, buildings, and vegetation, and the complexity and cost of exploiting and deploying photodiodes to the MMW and THz bands. (7) The design of corresponding other components matching with photodiodes in THz links and systems. Though achieving high-speed, high-power photodiodes and photodiode-integrated devices with over 2 mW output power in the THz band has been realized, it is still a big challenge to apply photodiodes to 6G technology. Compared with existing communication systems, 6G requires ultra-high data rates (such as 200-1000 Gbit/s) and also requires the matching terahertz sources, photodiodes, and components in THz links and systems. However, considering the wider applications of photodiodes in transparent materials, including cell phone screens, smart windows, and automobile/aircraft win screens, is also a big challenge. A possible solution could be to implement the integration of photodiodes
## 6 Kiwi Bioactives in Cosmetics Applications
### Kivi Bioactives and Skin Health
#### 6.2.3 Vitamin E  
Vitamin E (a.k.a. \(\alpha\)-tocopherol) has been widely used as an additive in cosmetics and has various benefits, including anti-tumorigenic, photoprotective, and skin barrier stabilizing properties. Whether taken orally or topically it has the same effect, unlike vitamin C. Vitamin E TPGS (a water-soluble derivative of vitamin E) undergoes intracellular hydrolysis, releasing \(\alpha\)-tocopherol after crossing cell membranes, making it suitable for incorporation into personal care and cosmetics products [93]. Furthermore, vitamin E acetate serves as an antioxidant, effectively scavenging free radicals to reduce DNA damage and keratinocyte death, as a result of sunburn cell formation, while also enhancing stratum corneum hydration and diminishing skin roughness [110]. Vitamin E is classified as a water-insoluble vitamin and is known for its capacity to protect the skin from harmful oxidative stress, a leading cause of skin aging, and its excellent moisturizing properties [111].  
Ultimately, the combination of vitamin C and vitamin E can notably enhance the efficacy of sunscreen in preventing DNA damage and inflammation, while also promoting cell renewal. Consequently, this combination has the prospect of being the main ingredient in photoprotective formulations with antiaging attributes [109].
###### Abstract  
The present research analyzed the nonlinear vibration of a CNTRC embedded in a nonlinear Winkler-Pasternak foundation in the presence of an electromagnetic actuator and mechanical impact. A higher-order shear deformation beam theory was applied to various models, as well as Euler-Bernoulli, Timoshenko, Reddy, and other beams, using a unified NSGT. The governing equations were obtained based on the extended shear and normal strain component of the von Karman theory and a Hamilton principle. The system was discretized by means of the Galerkin-Bubnov procedure, and the OAFM was applied to solve a complex nonlinear problem. The buckling and bending problems were studied analytically by using the HPM, the Galerkin method in combination with the weighted residual method, and finally, by the optimization of results for a simply supported composite beam. These results were validated by comparing our results for the linear problem with those available in literature, and a good agreement was proved. The influence of some parameters was examined. The results obtained for the extended models of the Euler-Bernoulli and Timoshenko beams were almost the same for the linear cases, but the results of the nonlinear cases were substantially different in comparison with the results obtained for the linear cases.  
carbon nanotube-reinforced beam; nonlocal beam theory; OAFM; nonlinear vibration; buckling; bending Article
## 2 Materials and Methods
### Sampling, Microbiological Analysis, and pH of the Meat Product  
The meat product was sampled at appropriate time intervals depending on the storage temperature. Three randomly selected individually packaged slices, inoculated or not, from each temperature storage condition were microbiologically analyzed for the detection and enumeration of _Listeria_ spp. other than _L. monocytogenes_ (in noninoculated slices; i.e., controls) and for the enumeration of _L. monocytogenes_ (in inoculated slices) after 0, 3, 8, 13, 15, 18, 21, 24, 27, and 29 and 0, 1, 2, 3, 4, 5, 7, 8, 9, 13, and 15 days of storage at 4 \({}^{\circ}\)C and 10 \({}^{\circ}\)C, respectively. Samples were also analyzed for total viable counts (TVCs) and lactic acid bacteria (LAB) counts during storage at 4 \({}^{\circ}\)C (at Days 0, 8, 13, 18, 24, 29) and 10 \({}^{\circ}\)C (at Days 0, 3, 7, 15). At the beginning of the storage experiment (Day 0) only samples taken from 4 \({}^{\circ}\)C were analyzed.  
The microbiological analysis of the product samples was performed according to the detailed methodology described elsewhere [24; 26]. Briefly, the presence and enumeration of _Listeria_ spp., as well as _L. monocytogenes_ counts, were determined on PALCAM agar (Biolife) incubated aerobically at 37 \({}^{\circ}\)C for 48 h, while TVC and LAB were enumerated on plate count agar (PCA; Merck) and de Man, Rogosa, and Sharp (MRS) agar (Biolife), respectively, both plating media being incubated aerobically at 30 \({}^{\circ}\)C for 72 h. Hence, three serial decimal dilutions were plated in triplicate for enumeration purposes, whereas the detection of _Listeria_ spp. was performed in duplicate plates of PALCAM agar.  
The pH of the meat product was measured in triplicate by using a digital pH meter (WTW, pH 526, Weilheim, Germany), according to the ISO 2917 reference method [27], after the microbiological analysis was concluded. In this way, the accurate recording of pH changes for the meat product throughout its storage was achieved.
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
## 4 Experimental Results and Discussion
### Speed Analysis  
For the compression circuits, processing speed is one of the critical performances, especially when they are applied in some high-speed vision systems such as high-speed object tracking, motion analysis, etc. In terms of our proposed vector quantizer, the clock cycles to encode one individual pixel-block can be defined as Equation (6), where \(d\), \(N\), and \(k\) are the pixel-block size, the codebook size and the number of sub-blocks, respectively. The number '7' represents the depth of the shift register in Figure 4, which is equal to the sum of 1 stage in SDU (shown in Figure 2), 5 stages in the RCBAT (shown in Figure 3) and 1 stage in WTA circuits (shown in Figure 4).  
In addition, since updating a weight vector will additionally take \(\lceil d/16\rceil+3\) clock cycles, the learning time for each pixel-block can be calculated by Equation (7).  
\[T_{encoding}=\lceil d/16\rceil\times N/k+7 \tag{6}\]  
\[T_{learning}=\lceil d/16\rceil\times(N/k+1)+10 \tag{7}\]  
As a matter of fact, the compression speed of vector quantizer is determined by the encoding time. In our design, if the size of pixel-block is defined as \(8\times 8\), the clock cycles to encode each pixel-block can be calculated as 39 according to Equation (6). Hence, the clock cycles to encode a frame of the grayscale 512 \(\times\) 512-pixel image will be 159,744, and the corresponding time can be calculated as 2 ms with a maximum frequency of 79.8 MHz. The short encoding time makes our vector quantizer capable of compressing the high-speed image sequence with a frame-rate of 500 frames/s. Moreover, if our proposed vector quantizer is fabricated by advanced CMOS (Complementary Metal Oxide Semiconductor)process, a higher work frequency can be reached, and thus a faster compression speed will be obtained accordingly.  
\[MCPS=d\times\frac{N}{T_{encoding}} \tag{8}\]  
Furthermore, the million connections per second (MCPS) [16], which is a common metric to evaluate the performance of SOM, is also calculated according to Equation (8) and then plotted in Figure 8. Likewise, in Equation (8), \(d\) and \(N\) represent the pixel-block size and the codebook size, respectively. The results indicate that the value of MCPS reaches 28,494 when the size of pixel-block is \(8\times 8\) (compression ratio is 64) and the sub-block number is 32.  
Figure 8: MCPS (million connections per second) of the proposed vector quantizer with different compression ratio and parallel block number.
# A High-Level Representation of the Navigation Behavior of Website Visitors
## 3 Representation of Sessions with Rules
### Selection of Rules to Visualize  
We used the rules identified in Section 2 to represent sessions. In each session or group of sessions, we only used the rules that belong to the same class of visitors, according to Table 5. For example, a session that belongs to the class of visitors \"Made payment\" is represented only with the nine rules found in the sessions of the same class of visitors. In Figure 4, we show an example of three representations of a session: (1) the original session, (2) the session we obtained by replacing in the original session frequently occurring sub-sequences with rules (we called it a shrinked session), and (3) the session we obtained by stripping off any symbol but a rule in a shrinked session (we called it a stripped session).  
The rules represent the behavior that is common among visitors from the same class. Conversely, pages that do not form rules represent uncommon behavior. To determine which analysis is worth conducting, we obtained statistics about sessions represented with rules. These statistics would help a marketing or information technology expert to determine questions of interest. We computed statistics on the three representations exemplified in Figure 4: original session, shrinked session, and stripped session. We computed the length of each representation and the reduction rate with respect to the length of the original session. Using the example in Figure 4, the length of the original session is 12, the length of the shrinked session is 5, and the length of the stripped session is 3.  
* The reduction rate of the shrinked session is equal to \(1\) \(-\) (length of the shrinked session/length of the original session); that is, \(1-(5/12)=0.58\). The length of the session is reduced by 0.58 (58%) when it is expressed with rules and pages that do not form a rule.
* The reduction rate of the stripped session is equal to \(1\) \(-\) (length of the stripped session/length of the original session); that is, \(1-(3/12)=0.75\). The length of the session is reduced by 0.75 (75%) when it is expressed only with rules.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rule Name** & **Rule Length** & **Made Payment** & **Started Payment** & **Other Conversion** \\ \hline Go to control panel & 4 & X & X & X \\ Pay via control panel & 7 & X & & \\ Pay and modify product & 5 & X & & \\ Consult availability and pay & 8 & X & & \\ Modify product and pay & 8 & X & & \\ Start payment & 4 & X & & \\ Pay for a service & 8 & X & & \\ Make invoice & 4 & X & & \\ Login and modify product information & 4 & X & & X \\ Modify product information & 3 & & X & X \\ Consult payment details & 3 & & X & \\ Make payments query & 3 & & X & \\ Modify product information and start payment & 3 & & X & \\ Consult availability & 3 & & & X \\ \hline \hline \end{tabular}
\end{table}
Table 5: Name of the rules found in each class of visitors. The rule length indicates the number of pages that form each rule. An “X” indicates that the rule was found in that class of visitors.
# Design of an Optically Transparent Microwave Absorber Based on Coding Metasurface
## 1 Introduction
This design also has polarization insensitivity and angular stability, with promising applications in flat panel displays, low radiation glass, special functional window coatings, etc.
## 2 Subgrid Stress Tensor Equation Closure and Calibration
### DNS Database and Filtering  
To calibrate the coefficients in the formulated models, a DNS dataset that was representative of typical subgrid-scale turbulence was required. We used a computation of forced isotropic turbulence [24] from the Johns Hopkins Turbulence Database [25] for this purpose. This dataset was originally generated with a pseudo-spectral algorithm in a periodic domain of size \(L\times L\times L\) with uniform grid of \(1024^{3}\) points, \(L=2\pi\). In this flow, the Reynolds number, with respect to the Taylor microscale, is equal to 433; the Kolmogorov microscale is equal to 0.46 \(h\), where \(h\) is grid spacing, the integral length scale is equal to 0.217 \(L\); the integral time scale is equal to 0.2 \(T\), where \(T\) is simulation period after attaining a statistically steady state. All the numbers are nondimensional.  
We extracted 11 snapshots separated by 0.1 \(T\) and filtered them using a box filter of different sizes, \(\Delta\). Three filter sizes were considered: \(\Delta=64\)\(h\), \(\Delta=128\)\(h\) and \(\Delta=256\)\(h\). In Figure 1, their positions on a radial kinetic energy spectrum are shown. While \(\Delta=64\)\(h\) is close to the bottom part of the inertial interval, two larger filters are significantly inside it, which corresponds to a correct LES.  
To process the filtered data effectively, we extracted an \"LES grid\" from each filtered field with the spatial step \(h_{LES}=\Delta/4\). The derivatives of filtered fields on the RHS of (7) and (8) were calculated by a central difference formula on LES grids, for example:  
\[\left(\frac{\partial\overline{u}}{\partial x}\right)_{\alpha,\beta,\gamma} \approx\frac{\overline{u}_{\alpha+1,\beta,\gamma}-\overline{u}_{\alpha-1,\beta,\gamma}}{2h_{LES}}. \tag{10}\]  
Figure 1: Radial kinetic energy spectrum in DNS and different filter cut-off scales.  
A comparison of spatially averaged, scale-to-scale energy flux, \(P=-\overline{u_{i}^{\prime}u_{j}^{\prime}}S_{ij}\), calculated first using finite differences (10) and after that employing \"exact\" values of \(\partial\overline{u}_{i}/\partial x_{j}\), showed that the error induced by the finite spatial step was within 3%.
## 7 Results
### Clinical results  
A clinical research group from the University of Wisconsin has independently evaluated the colon CAD system performance in a large trial of asymptomatic patients in a screening population. All patents underwent cathartic preparation and were administered oral tagging agent. The data consisted of CTC volumes corresponding to \(3106\) adults, which, to our knowledge, is the largest patient cohort ever used in an evaluation of a colon CAD system. There were \(607\) polyps \(\geq 6\)mm in this data set. This data set was also independent of the data used to train the CAD system.  
As reported in [9], the per-polyp sensitivity was 90.1% (547/607) for all polyps greater than 6 mm in size. Detection of polyps \(\geq\)10 mm had sensitivity of 96% (168/175). 100% (13/13) cancers were found by the CAD system. Per-patient sensitivity, which measures whether a patient has at least one polyp, was 93.8% (350/373) for polyps \(\geq\)6 mm and 96.5% (137/142) for polyps \(\geq\)10 mm. Also of note, the CAD found 15 additional polyps that had been missed by expert readers. The CAD system generated an average of \(4.7\) false positives per volume in average, with a median of \(3\).  
\begin{table}
\begin{tabular}{c c} \hline Cause of FPs & Percentage \\ \hline Tagged stool & 31.2\% \\ Ileococcal valve & 18.8\% \\ Thick fold & 18.2\% \\ Outside of large intestine & 10.3\% \\ Rectal catheter or balloon & 8.3\% \\ Other & 13.2\% \\ \hline \end{tabular}
\end{table}
Table 4: Sources of false positives.  
\begin{table}
\begin{tabular}{c c c} \hline Polyp diameter & Sensitivity & False positives \\ \hline \(6\)mm+ & 90\% & 4.5 \\ \(10\)mm+ & 95\% & 4.5 \\ \hline \end{tabular}
\end{table}
Table 3: Evaluation of our CAD on \(202\) patients at the specified operating point.  
This group also analyzed the causes of CAD errors [50]. Table 4 illustrates the percentage of different causes of false positives. It shows that a large number comes from tagged stool (\(31.2\%\)). Other frequent sources of false positives include the ICV, thick folds, CAD marks outside of the colon, and false detections on the rectal catheter of balloon used to insufflate the colon. The major source of false negatives was liquid contrast agents.  
The overall conclusion from this study was that the CAD demonstrated robust performance, with high sensitivity and low false positive rate, and provided valuable complementary information to the _expert_ radiologists interpreting the CTC data.
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
###### Abstract  
This paper showcases current developments in the use of carbon nanotube (CNT) and nanoparticle-based materials for electromagnetic radiation shielding. Electromagnetic radiation involves different types of radiation covering a wide spectrum of frequencies. Due to their good electrical conductivity, small diameter, and light weight, individual CNTs are good candidates for shielding radio and microwaves. CNTs can be organized into macroscale forms by dispersing them in polymers or by wrapping CNT strands into fabrics or yarn. Magnetic nanoparticles can also be incorporated into the CNT fabric to provide excellent shielding of electromagnetic waves. However, for shielding higher-frequency X-ray and gamma ray radiation, the situation is reversed. Carbon's low atomic number means that CNTs alone are less effective than metals. Thus, different nanoparticles such as tungsten are added to the CNT materials to provide improved shielding of photons. The goal is to achieve a desired combination of light weight, flexibility, safety, and multifunctionality for use in shielding spacecraft, satellites, nuclear reactors, and medical garments and to support lunar colonization. Future research should investigate the effect of the size, shape, and configuration of nanoparticles on radiation shielding. Developing large-scale low-cost methods for the continuous manufacturing of lightweight multifunctional nanoparticle-based materials is also needed.  
electromagnetic radiation; shielding; nanoparticles; carbon nanotubes; composite materials +
Footnote †: journal: Applied sciences