esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 5 Applications of Photonic Neural Networks for Equalizing Optical Communication Signals
\begin{table}
\begin{tabular}{c c} \hline \hline
**Challenge** & **Solution** \\ \hline Photonic neural network devices with high insertion loss. & Integrating optical amplifiers (e.g., erbium-doped waveguide or Raman amplifiers) with the photonic neural networks. \\ \hline Optical systems often rely heavily on linear operations, which may not capture the nonlinearities present in the data. & Implement nonlinear activation functions directly in the optical domain with nonlinear waveguides or saturable absorber materials. \\ \hline Photonic neural network structures with insufficient layers, neurons, and lack of flexibility can lead to poor accuracy performance. & Design deeper photonic neural networks using multi-layer interferometers and photonic integrated circuits for better feature extraction and error correction. \\ \hline Lack of temporal feedback in feedforward photonic neural networks with training in the electronic domain limits their ability to handle dynamic systems. & Develop feedback-enabled photonic neural networks using delay-line reservoirs or integrated optical loops to capture time-dependent signals. \\ \hline Limited scalability and parallelism in current photonic neural networks can restrict their ability to handle signals with high data rates. & Design WDM-compatible photonic neural networks for ultra-high-speed systems. \\ \hline Hardware imperfections, noise, and fabrication variabilities can degrade accuracy in photonic neural networks. & Utilize advanced fabrication technologies like complementary metal–oxide–semiconductor (CMOS)-compatible photonics to ensure high precision and scalability of optical components. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of challenges and solutions to improve the performance of the future photonic neural networks. causes pulse broadening, which leads to intersymbol interference [145]. Consequently, chromatic dispersion is the predominant effect when recovering distorted signals employing photonic neural networks, correcting phase shifts and delays in the time domain. On the other hand, nonlinear compensation requires more complex photonic neural networks due to the higher-order interactions in the impairments.
# Strategies for Reducing Sodium Intake in Bakery Products, a Review
## 2 The Physiological Role of Sodium Chloride and Legislative Recommendations on Its Consumption  
Sodium is among the top six elements in the earth's crust, comprising 2.83% sodium in all its forms. The most important sodium salts found in nature are sodium chloride, sodium carbonate, sodium borate, sodium nitrate and sodium sulfate [14]. To obtain sodium chloride, the weight of sodium must be multiplied by 2.54. Regulating the levels of sodium and chloride in the human body is an important biological process, maintained by multiple mechanisms that work to control them. In the human body, sodium and  
Figure 1: Strategies of salt reduction in bakery products.  
chloride are the major constituents of the extracellular fluid, participating in maintaining the electrochemical gradient between the extracellular space and the cytoplasm. In the absence of this gradient, life is not possible. Their presence in the human body maintains the membrane potential that is absolutely necessary in the transmission of nerve impulses, muscle contraction and therefore cardiac function. Absorption of sodium in the small intestine promotes the assimilation of amino acids, glucose and water [1].  
Sodium is a colorless crystalline compound found naturally in many foodstuffs and its most widely used form is sodium chloride, also named table salt. Salt has an important role in the human history. The ancient populations used salt to preserve food, salt being for them so precious that it could be exchanged for gold. Until the end of antiquity, salt was, along with amber and tin, one of the main currencies. In American history, salt has been a vital element for survival and during the Civil War it was used not only for foodstuffs but also for tanning the skin, dyeing clothes and preserving rations [15]. The need for permanent salt intake is one of the physiological needs to which man is exposed. Blood sodium concentration is an important homeostatic parameter that controls extracellular fluid volume and blood tonicity [11].  
Nowadays salt is used as a spice and preservative in processed foods, being used in various forms such as sodium nitrite, sodium benzoate, monosodium glutamate and baking soda. It is present in processed foods such as soy sauce, canned meat and vegetables, soups, processed meat and almost any food with a long shelf life [15].
## 4 Experiments
### Performance and Result Analysis
Whereas, PCAN's learning efficiency for stitched features was low, with the reasoning speed of the network being 0.20 s per epoch. The emotion recognition accuracy improved by only 0.59% compared to the cascade attention-based model, and by 0.31% compared to Random Forest. These results demonstrate that parallel-connected attention aggregation networks exhibit relatively weak fusion capabilities for concatenated features. When concatenated features are introduced into the network, the network fails to fully extract and leverage the benefits of both video signal data and PPG signal features using local and global attention, consequently resulting in only a marginal improvement in emotion recognition accuracy. Therefore, we mainly consider the PCAN Add method. When employing the PCAN (Add) approach, the multi-scale attention module within the parallel-connected attention aggregation network effectively manages the dependency correlations between the dual-modal data. As a result, the accuracy of emotion recognition reaches 95.75%, which is a 1.79% improvement over Random Forest classification methods and a 2.07% advancement over single-mode video data. The ROC curve in Figure 13 reveals that the average AUC area of the PCAN (Add) method reached 0.97, indicating that the model exhibits excellent performance in recognizing learner learning emotions on the self-collected dataset. The experimental outcomes indicate that PCAN networks are more compatible with feature stacking and integration techniques and they can mutually reinforce the benefits of dual-modal data, leading to higher recognition accuracy. Overall, the emotion recognition accuracy is enhanced using fusion methods with dual-modal data compared to single-modal data. PCAN network aligns well with the superposition and integration of diverse modal features. This results in the complementary advantages of integrating different modes of data, confirming the viability and efficacy of the multimodal data fusion approach. Figure 13: ROC curve of PCAN (Add).
## 3 Required Performance and Resistance Capacity Considering Environmental Factors
### Required Performance Based on Traffic Conditions
Table 8 and Figure 2 present the calculated lateral load distribution coefficients of the sample bridge via finite element analysis and the results with them. The heavy vehicle distribution proportion per lane was set to 15:85 as recommended by the Korea Highway Bridge Specifications [6]. Based on the traffic characteristics suggested above, the annual extreme load applied to the sample bridge was analyzed (Table 9). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Unit Mass (kg/m3)** \\ \hline Reinforced concrete & 2500 \\ Pre-stressed concrete & 2500 \\ concrete & 2350 \\ Asphalt pavement & 2300 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Unit masses of construction materials. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Distribution** & **Bias Factor** & **COV** \\ \hline Precast members & Normal & 1.03 & 0.08 \\ Cast-in-place members & Normal & 1.05 & 0.10 \\ Wearing surface & Normal & 1.00 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bias factors and coefficients of variance (COVs) of the dead load. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Vehicle** & **Mode** & \begin{tabular}{c} **Distribution** \\ **Type** \\ \end{tabular} & \multicolumn{2}{c}{**Coefficients**} & \begin{tabular}{c} **Min.** \\ **(Tons)** \\ \end{tabular} & \
\begin{tabular}{c} **Max.** \\ **(Tons)** \\ \end{tabular} &
\begin{tabular}{c} **Correction** \\ **Coefficients** \\ \end{tabular} \\ \hline P & 1 & L-N & 0.398 & 0.317 & 0.7 & 5.0 & 1.0000 \\ \hline B & 1 & Normal & 4.089 & 1.020 & 1.4 & 17.1 & 0.0980 \\  & 2 & Normal & 11.552 & 1.542 & 4.0 & 24.0 & 0.9020 \\ \hline T & 1 & L-N & 1.338 & 0.620 & 1.25 & 24.1 & 0.7330 \\  & 2 & L-N & 2.721 & 0.221 & 1.25 & 40.0 & 0.2945 \\ \hline TT & 1 & L-N & 2.467 & 0.178 & 7.3 & 41.3 & 0.2190 \\  & 2 & L-N & 3.253 & 0.203 & 7.3 & 62 & 0.7818 \\ \hline ST & 1 & Normal & 18.541 & 3.000 & 11.3 & 63.4 & 0.2600 \\  & 2 & L-N & 3.420 & 0.225 & 59.7 & 90 & 0.7421 \\ \hline \hline \end{tabular}  
* P, mobile cars; B, buses; T, mid-size trucks; TT, heavy trucks; ST, semi-trailers.  
\end{table} 
Table 6: Probabilistic characteristics of the proposed vehicle weight model 1. Figure 2: Lateral load distribution coefficients of the PSC-I girder bridge for (**a**) lane 1 and (**b**) lane 2.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 4 Experiments
### Computational Efficiency  
In this section, we evaluate the average processing time of the proposed method. The experiments were conducted on a PC equipped with an Intel i7-1360P CPU and 64 GB of RAM, without any GPU acceleration.  
The main computational cost of the proposed algorithm lies in the projection and matching modules. Enabling the OAGS mechanism introduces additional overhead due to the evaluation of matching inconsistency. Table 6 reports the average processing time of each submodule in single-threaded implementation. On the testing platform, the system runs at approximately 14 Hz with OAGS enabled and around 30 Hz without it.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**KF (Outdoor)**} & \multicolumn{3}{c}{**KF+MC (Outdoor)**} \\ \cline{2-7}  & **0.2 s** & **1 s** & **10 s** & **0.2 s** & **1 s** & **10 s** \\ \hline
2012-02-02 & 1.40 & 1.32 & 5.61 & 1.34 & 1.36 & 1.30 \\
2012-02-04 & 1.71 & 1.71 & 6.07 & 1.86 & 1.85 & 1.58 \\
2012-02-05 & 1.73 & 1.69 & 6.07 & 1.73 & 1.73 & 1.85 \\
2012-02-19 & 1.94 & 1.79 & 7.12 & 1.83 & 1.79 & 1.99 \\
2012-03-17 & 2.66 & 2.51 & 6.72 & 2.44 & 2.53 & 2.85 \\
2012-03-25 & 4.72 & 4.58 & 9.36 & 4.52 & 4.63 & 5.01 \\
2012-04-29 & 1.19 & 1.15 & 5.54 & 1.21 & 1.28 & 1.09 \\
2012-05-11 & 2.59 & 2.52 & 7.29 & 2.55 & 2.54 & 2.83 \\
2012-06-15 & 1.73 & 1.56 & 6.27 & 1.65 & 1.78 & 2.04 \\
2012-08-04 & 1.96 & 1.81 & 5.98 & 1.87 & 1.91 & 1.87 \\
2012-10-28 & 1.61 & 1.33 & 21.39 & 1.35 & 1.33 & 1.34 \\
2012-11-17 & 1.66 & 1.57 & 6.44 & 1.54 & 1.68 & 1.88 \\ hkust-seq00 & 1.70 & 1.79 & 5.54 & 1.89 & 1.92 & 2.02 \\ hkust-seq01 & 3.47 & 3.40 & 7.14 & 3.42 & 3.35 & 3.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of localization accuracy (APE, m) with and without MC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **OAGS Disabled** & **OAGS Enabled** \\ \hline
2012-02-02 & 2.27 & 1.63 \\
2012-02-04 & 2.20 & 1.47 \\
2012-02-05 & 2.18 & 1.73 \\
2012-02-19 & 2.17 & 1.83 \\
2012-03-17 & 4.14 & 2.77 \\
2012-03-25 & 4.90 & 5.89 \\
2012-04-29 & 1.75 & 1.07 \\
2012-05-11 & 2.74 & 2.72 \\
2012-
## 4 Discussion
### Effect of Glucosinolates Level on Plant Hormone Signal Transduction  
It is well appreciated that plant hormones are crucial for the biosynthesis of glucosionolate and appropriate concentrations of plant hormones that result in the accumulation of GSLs in cruciferous vegetables [35; 36; 37]. Previous studies have shown that the accumulation of glucobrassicin and total GSLs increased in the broccoli (_B. oleracea_ L. var. _italica_) suspension cultured cells after jasmonic acid (JA) treatment. Moreover, JA significantly increased the accumulation of total aliphatic GSLs and glucobrassicin in broccoli (_B. oleracea_ L. var. _italica_) sprouts [38]. It has been reported that after the methyl jasmonate (MeJA) treatment, the content of total GSLs, glucobrassicin, gluconasturtin and neoglucobrassicin in the floret and flower bulb increased in broccoli (_B. oleracea_ L. var. _italica_) [39; 40]. In this study, we found some DEGs related to JA and MeJA between the high and low RAA content pools. Among them, _TIF7_ and _TIF9_ were upregulated in the high RAA content pool of stalk, and _TIF10_ was upregulated in the high RAA content pool of floret (Table S10). They belong to the _TIFY_ family, which plays a crucial role in cross-talk between Jasmonic acid and other phytohormones pathways [41]. Moreover, _JAR1_, which encodes a jasmonate-amido synthetase, was upregulated in the high RAA content pool of stalk (Table S10).  
Appropriate concentrations of auxins such as indole-3-acetic acid (IAA), indolebutyric acid (IBA) and naphthylacetic acid (NAA) increase the accumulation of glucosinolates. Studies have shown that 0.1 mg/L IAA, IBA and NAA treatment increase the accumulation of glucoraphanin, glucoerucin, glucobrassicin, gluconasturtin, and indole GSLs in the broccoli (_Brassica oleracea_ L. var. _capitata_ L.) hairy root, respectively [42]. In our study, we also identified some DEGs related to auxins. Among them, _IAA5_, _IAA29_, _IAA30_, _IAA32_, _GH3-2_, _GH3-3_ and _SAU20_ were upregulated in the high RAA content pool of stalk, _IAA7_ and _SAU50_ were upregulated in the high RAA content pool of floret (Table S10). _IAA_, _GH3_ and _SAU_ families, which belong to early auxin-responsive genes, are  
Figure 8: Schematic biosynthetic pathway of aliphatic glucosinolates and involved genes. Different background colors represent chain elongation machinery, biosynthesis of core glucosinolate structure and secondary modifications from top to bottom. Genes named in red were analyzed in this study. The green uppercase letters L, S, F behind the gene represent DEGs obtained in leaf, stalk and floret, respectively.  
specifically induced by auxin within minutes [43; 44]. Therefore, plant hormones increase the accumulation of glucosinolates; in turn, the glucosinolate levels also upregulated the expression of phytohormone-related genes.
## 3 Results  
In total, 270 publications were identified from the literature search, including 57 from Medline, 112 from EMBASE, and 101 from Web of Science; 101 of these were duplicates and were removed. Hence, 169 unique articles were identified, and after reviewing titles and abstracts, 18 were selected for full-text review. Three of these articles met the inclusion criteria and were finally included in this review. Figure 1 shows the PRISMA flowchart of the study selection process.  
Figure 1: PRISMA flowchart of the study selection process.
# Variability of Diffuse Daylight Due to the Diversity of Cloud Arrays
## 2 Sky Luminance Simulations  
The sky luminance patterns and corresponding illuminances on a few selected surfaces were modeled by a computational tool UniSky [8]. This tool allows us to simulate arrays of regularly or randomly distributed spherical clouds with specified dimensions and altitudes, as well as various aerosol conditions [7].  
Since we were primarily interested in the influence of clouds, we assumed fixed aerosol characteristics in computations. In particular, the single scattering albedo of aerosol particles was considered to be 0.93 and their asymmetry parameter was 0.64. These could be representative values for aerosol particles in an urban environment [9]. To simulate a moderately polluted environment, we set the aerosol optical thickness of the atmosphere to 0.3 (at 550 nm).  
The UniSky simulator solves the radiative transfer problem in the atmosphere up to the second order of scattering, therefore also the Earth's albedo has to be specified. The value 0.15 was chosen because it appears to be typical for urban regions (see [10] and references there). However, this parameter is not important for our purposes.  
The main focus was on clouds configuration in the simulations. We concentrated on low-layer clouds (the cloud base up to 2 km above the ground) because their diversity can lead to greater diffuse light fluctuation than higher-layer clouds. Cumulus clouds, forming a low-level broken cloud array, obviously have the geometrical thickness of several hundreds of meters [11; 12]. Therefore, we modelled them by the spherical clouds with a radius of 0.25 and 0.5 km. The cloud base altitude was considered to be 0.5 km and 1 km for the both cloud sizes. For each cloud type (specified by its radius and altitude), various random distributions of clouds in the sky were simulated at three different cloud fractions: 0.1, 0.3, and 0.5. The cloud fraction is defined here as the ratio of a horizontal cloud area to the total area of a selected zone [7].  
Cumulus clouds are optically dense, with an average extinction coefficient equal to 30 km\({}^{-1}\) or greater [13; 14]. This provides an optical thickness of at least 15 for the smaller clouds, although the UniSky simulator allows for the maximum value of 10. A cloud albedo of such optically thick clouds can be approximately calculated (see, e.g., [14]) and for the thickness of 15 we obtain the cloud albedo equal to 0.64. We held this value fixed in the simulations.  
All the computations were performed at five solar zenith angles: 0\({}^{\circ}\), 20\({}^{\circ}\), 40\({}^{\circ}\), 60\({}^{\circ}\), and 80\({}^{\circ}\). The sun was always positioned in the south (the solar azimuth angle = 180\({}^{\circ}\)).  
We calculated a diffuse illuminance on the horizontal plane as well as four vertical surfaces to demonstrate the possible fluctuations of diffuse light. In particular, the vertical planes oriented exactly to the north, east, south and west. These illuminances could illustrate how different distributions of clouds in the sky affect the sky luminance pattern.
## 2 Materials and Methods
### Experimental Plan
#### 2.2.2 SEM Experiment  
In this study, a scanning electron microscope (SEM) is adopted for the analysis of the soil's microstructure. After the consolidation compression experiment, soil samples from various experimental conditions are used as SEM samples.  
The preparation of the SEM sample is the basis of capturing SEM images. To ensure that the scanning image can accurately reflect the true morphology of the sample, certain requirements should be met in the preparation of SEM samples: the disturbance and damage of the samples should be minimized, the shrinkage deformation of the sample should be minimized in the drying process, uniformity should be ensured as much as possible when spraying the conductive medium and the thickness of the samples should be appropriate [36,37].  
The process of preparation is as follows:  
1. The samples after different consolidation times are reserved and dried. The horizontal section of the sample is selected as the observation surface. When the sample reaches a semi-solid state, the core of the sample is selected, and a soil strip with a length of about 5 cm and a cross-section of about 1 cm \(\times\) 1 cm is cut out and dried.
2. To ensure that the observation section is not disturbed, cut and grind the sample into a microscopic sample of 5 mm \(\times\) 5 mm \(\times\) 2 mm, and then use an ear-washing ball to blow off loose soil particles on the observation section.
3. Due to the poor conductivity of soft clay, a gold film of 20-50 nm should be sprayed on the surface of the sample as a conductive material before scanning to assure the quality of microscopic images.  
Furthermore, the selection of the amplification factor has a non-negligible effect on the accuracy of the SEM images. In addition, differences in the microstructure of soil samples from various areas may have existed [38,39]. Therefore, to ensure the accuracy of the SEM images, an appropriate magnification factor must be adjusted, and many images of the soil samples must be collected. Considering the small size of clay particles in soft soils, combined with the research results of the previous literature [40], after careful consideration, the magnification in this study is set as 8000 times, and the number of SEM images of each soil sample is set as 20.
# An Earthquake Forecast Model Based on Multi-Station PCA Algorithm
## 4 PCA Extraction for Single Station
### PCA Algorithm of Multiple Stations
\[E_{s}^{\prime}=\frac{10^{4.8+1.5M}}{R^{2}},E_{s}=\sum_{1 day}E_{s}^{\prime} \tag{6}\]  
The two-stations method is used to extract the potential relationship between multiple stations, which is different from the single station method and expands the number of available points. The first principal component and the second principal component are features of two stations, and they are located at the center of the two stations. Figure 6 is an example of the first principal component feature. This area of this example is \(98^{\circ}\) E to\(107^{\circ}\) E, \(22^{\circ}\) N to \(34^{\circ}\) N, the time is from 12 September 2020 to 9 October 2020, and we use the last day, 9 October 2020, to represent the time. Each virtual station in this image is the geometric center of a couple of two stations, and its color is related to the first principal component. The geometric center of all points that are above 0.6 will be regarded as the potential earthquake center, and this is our future method to locate the epicenter, while it is not used in this work. In summary, the two-stations method works on two stations; we use the sliding method, whose window is 27 days, to cut data, stack two vectors to compute the covariance matrix, and use a PCA method to extract the features. Its output is two eigenvalues, named the first principal component and the second principal component, and their eigenvectors. The eigenvalues will be used as features in our model. The corresponding position of features is the center of two stations, which is called the virtual station. For an area that contains \(K\) stations, the number of virtual stations is \(K(K-1)\). Figure 5: Multi-PCA features; red line means earthquake. The stations used in this figure, WCFZ, JZGFZ, and QCFS, are shown in Figure 2. (**a-c**) shows the first, second, and third principal component. Figure 6: Multi-PCA map. The red star is the earthquake, and the black star is the predicted epicenter. Green points are virtual stations, which are located at the center of two stations.