esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations
## 2 The Numerical Modeling
### Computational Modeling and Meshing  
A constitutive model named UBC3D-PLM [26] was used by PLAXIS (www.bentley.com/en/products/brands/plaxis accessed on 1 March 2024) to describe the liquefaction behavior of soils, which is able to accurately characterize the change in the mechanical properties and liquefaction behavior of saturated sandy soils under cyclic loading. The model mainly consists of the bulk modulus \(K\) and shear modulus \(G\) to describe the nonlinear elastic behavior of the soil (see Equations (1) and (2)).  
\[K=K_{\text{B}}^{e}P_{\text{A}}\left(\frac{p}{p_{ref}}\right)^{me} \tag{1}\]  
\[G=K_{\text{G}}^{e}P_{\text{A}}\left(\frac{p}{p_{ref}}\right)^{me} \tag{2}\]  
\(K_{\text{B}}^{e}\) and \(K_{\text{G}}^{e}\) are the bulk modulus and shear modulus at the reference stress, respectively. The parameters ne and me determine the stress dependence. The reference stress \(p_{ref}\) is usually considered to be the atmospheric pressure (100 kPa).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parts** & **Dimension/mm** & **Concrete Strength/MPa** \\ \hline Top slab & 800 & 40 \\ Negative floor middle slab & 400 & 40 \\ Negative second floor middle slab & 400 & 40 \\ Bottom slab & 1000 & 40 \\ Wall & 800 & 40 \\ Top longitudinal beam & \(900\times 1400\) & 40 \\ Negative floor longitudinal beam & \(900\times 1000\) & 40 \\ Negative second floor longitudinal beam & \(900\times 1000\) & 40 \\ Bottom longitudinal beam & \(1000\times 2000\) & 40 \\ Column & \(800\times 1200\) & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions and concrete strength classes of various parts of the subway station.  
Figure 1: Cross-section of the subway station/mm.  
The model initially employs the Mohr-Coulomb yield criterion for the primary loading phase, and utilizes a yield surface with a kinematic hardening rule for secondary loading. Upon the stress state reaching the yield surface, an immediate reversion to the elastic regime is not observed, thus enabling the prediction of plastic behavior. More specifically, strain hardening principles based on plasticity hardening are incorporated into the model. The structure of the station adopted in this article is characterized by a continuous, regular longitudinal form with an invariant transverse section, allowing for an approximate treatment as a plane strain problem, in accordance with the provisions of _The Code for Seismic Design of Urban Rail Transit Structures_. A two-dimensional computational model with dimensions of 340 m in width and 70 m in height has been established, encompassing soil layers, structures, and contact surfaces. The mesh size division should be less than one-eighth to one-tenth of the wavelength, corresponding to the highest frequency of the seismic waves. This is to prevent the mesh size from influencing the spectral characteristics of the seismic waves. The division of the model grid is illustrated in Figure 2.
## 3 Results and Discussion
### Descriptive Sensory Analysis with Trained Panel  
According to the legislation, _Fondillon_ is defined as a wine being sweet, balanced, with good structure, and having intense aroma with predominance of ripe fruit, nuts, well-integrated wood and toasted coffee, and long aftertaste [23]. The descriptive profiles of the nine _Fondillon_ samples (66 years of difference in the aging time) under analysis only showed statistically significant differences for 10 of the 22 attributes, which were used to evaluate the quality of these wines (Figure S1); these attributes were: alcohol (o, f), fruity (o, f), spicy (o, f), sweetness, sourness, astringency, and aftertaste (Table 4).  
The attribute \"aftertaste\" is defined as a pleasant sensation, and it is considered by most of the experts and consumers as one of the most relevant; besides, it was significantly affected by the \"_solera_\" factor (Table 4). It was found that the sample with the longest aftertaste was that of the _solera_ 1960. While the samples of _solera_s 1950 and 1987 were on the opposite side (shortest aftertaste, indicating perhaps a less complex organoleptic profile. Positive and significant correlations were found among aftertaste and (i) sweetness (R\({}^{2}\) = 0.462; \(p\) < 0.05) and (ii) fruity flavor (R\({}^{2}\) = 0.467; \(p\) < 0.05).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multirow{2}{*}{**ANOVA**\({}^{\dagger}\)} & \multicolumn{8}{c}{_Fondillon solera_} \\ \cline{2-10}  & & **1930** & **1944** & **1950** & **1960** & **1969** & **1975** & **1980** & **1987** & **1996** \\ \hline \hline \multicolumn{10}{c}{**Tukey Multiple Range Test**\({}^{\ddagger}\)} \\ \hline \hline \multicolumn{10}{c}{**Appearance**} \\ Color & NS & 5.2 & 5.2 & 5.2 & 5.0 & 5.2 & 5.7 & 5.8 & 5.3 & 5.7 \\
**Odor** & * & 6.0 a & 5.0 ab & 3.7 b & 4.5 ab & 4.2 ab & 4.5 ab & 5.0 ab & 4.0 ab & 3.7 b \\ Alcohol & ** & 7.0 a & 5.2 ab & 3.3 b & 5.0 ab & 4.7 ab & 4.8 ab & 5.2 ab & 4.5 b & 3.7 b \\ Fruity & ** & 1.3 & 1.5 & 1.5 & 1.7 & 1.7 & 1.7 & 1.8 & 1.7 & 1.5 \\ Mediterranean & NS & 2.0 & 1.8 & 2.0 & 1.8 & 1.5 & 1.8 & 1.7 & 1.7 & 1.7 \\ forest & ** & 4.2 a & 3.5 ab & 2.0 b & 3.7 ab & 3.3 ab & 3.3 ab & 3.8 a & 3.5 ab & 3.0 ab \\ Animal & NS & 1.5 & 2.2 & 1.0 & 1.7 & 1.7 & 2.2 & 2.0 & 1.7 & 1.3 \\ Toasted & NS & 6.7 & 5.7 & 4.0 & 4.2 & 5.0 & 4.7 & 5.3 & 4.7 & 4.2 \\ Chemical & NS & 0.8 & 2.5 & 2.5 & 2.7 & 2.3 & 2.7 & 3.5 & 2.0 & 1.3 \\
**Basic taste** & * & 3.0 ab & 5.0 a & 2.7 b & 3.8 ab & 3.7 ab & 3.7 ab & 3.2 ab & 3.0 ab & 2.7 b \\ Sourness & ** & 4.5 a & 3.5 abc & 2.5 c & 3.5 abc & 3.0 bc & 3.8 abc & 4.2 ab & 3.3 abc & 2.8 bc \\ Bitterness & NS & 1.5 & 1.3 & 2.2 & 2.0 & 2.5 & 2.5 & 1.8 & 1.8 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive sensory analysis of wines aged in _Fondillon_ wines as affected by the \"_solera_\" factor.
# Application of Wavelet Transform and Fractal Analysis for
## 2 Materials and Methods
### Preparing pH-Metry Measurements  
Statistical analysis (maximal, minimal, and mean values) was performed for the collected pH courses--the results are shown in Table 2. The maximum pH value for the healthy patient group was 9.1005, whereas for the GERD patients it was 9.0414. The mean pH values were: 6.0928 for the healthy patients and 4.5769 for the GERD patients. This significant drop in pH values for unhealthy patients was expected since GERD patients tend to have lower pH results. The mean standard deviation in the case of the healthy pH courses was 0.99225, whereas the GERD patients' pH courses had a mean standard deviation of 1.7581. Negative minimal values of pH can be registered in esophageal pH courses since the scale is logarithmic. The minimal values for the healthy and sick patients in this case were also negative, but the healthy patients had a higher (and closer to 0) mean value of the minimal pH than the GERD patients.  
Statistical analysis of some of the total DeMeester counts for the healthy and GERD patients also delivered some interesting details. They are shown in Table 3. The mean number of reflux episodes in the healthy pH courses was 36.75, whereas in the GERD patients it was 104.88--more than twice the number. The maximum number of reflux episodes for a healthy patient was 79, whereas for an unhealthy patient it was 235; the minimal number of reflux episodes for a healthy individual was 4, whereas for a sick patient the minimal number of reflux episodes was 35. Such data show a significant difference in the number of reflux episodes, which again is expected on the basis of the DeMeester criteria. Some GERD patients experience a higher number of episodes (even up to 10 in an hour) where their esophageal pH changes rapidly, whereas some patients have only 1-3 episodes per hour, but their duration is significantly longer and more steady. A graphical interpretation of the relevant data is shown in Figure 1. However, it should be noted that these are only aggregate statistics as the diagnosis of gastroesophageal reflux disease based on the total DeMeester count is carried out individually and is very individual. Therefore, it is very important that the diagnostics based on this method are  
\begin{table}
\begin{tabular}{c c c} \hline
**Statistic** & **Health Patient pH** & **GERD Patient pH** \\ \hline max pH & 9.10050 & 9.04140 \\ \hline mean value of max pH & 8.25210 & 8.15120 \\ \hline min pH & \(-2.44650\) & \(-2.49570\) \\ \hline mean value of min pH & \(-0.61099\) & \(-1.01580\) \\ \hline mean pH value & 6.09280 & 4.57690 \\ \hline median pH value & 6.24060 & 4.88860 \\ \hline mean standard deviation of pH & 0.99225 & 1.75810 \\ values & & \\ \hline \end{tabular}
\end{table}
Table 2: Statistical analysis of esophageal pH measurements for healthy and GERD patients.  
carried out by a specialist gastroenterologist who is able to properly interpret the pH-metry records, confronting the result of the total DeMeester count calculation with the pH course.
###### Abstract  
This study aimed to assess the safety characteristics of organic high-protein bars (HPB) during storage at ambient and refrigerated temperatures based on selected microbiological and chemical indicators. After production, the total number of microorganisms ranged from 3.90-4.26 log CFU/g. The _Enterobacteriaceae_ family was present at 2.81-3.32 log CFU/g, and the count of yeasts and moulds was 2.61-3.99 log CFU/g. No _Salmonella_ sp. was found in 25 g of the product. _Bacillus cereus_ was present in samples B1 and B2. _Staphylococcus aureus_ was presented in samples below the detection limit (<2 log CFU/g). During the storage of products, the number of microorganisms varied. After production and storage, in all samples of HPB, the amount of mycotoxins was below the detection limit. The presence of histamine and tryptamine was not found in the HPB throughout the study period. Regarding TBARS, it can be concluded that the use of prunes and oat flakes (B2 bar composition) in the production of organic bars, and refrigerated storage, reduces the degree of fat oxidation. Among the tested variants, the composition of the B3 bar seemed to be the safest and worth further research, mainly due to the lower frequency of undesirable microorganisms. The protective antioxidative effect of prunes and oat flakes in bars stored at 22 \({}^{\circ}\)C indicates the value of the composition of bar B2. The appropriate composition modifications and the use of heat treatment proved to be effective in improving the safety characteristics of HPB. Relying on the results it is possible to store HPB for at least 3 months. Next to standard safety parameters, the unique and effective to increase the safety of HPB is controlling the presence of _B. cereus_ and other low water activity (aw) resistant microorganisms.  
biogenic amines; fat oxidation; food safety; mycotoxin; organic bar; meal replacement 2022
## 3 Results and Discussions
### Gene Expression of APP, MAPT, and Caspase-3 under Mixed Chemical Minerals Stress in Rat Brain  
The expressions of APP, MAPT, and Caspase-3 genes under mixed chemical minerals were measured by qRT-PCR. The treated cells exhibited a significant increase in tested genes compared with the control (G1 and G2). Heavy metal treatment increased the gene expression of APP, MAPT, and Caspase-3 genes, producing a change of up to 2.1-3.81 fold (G3; Figure 5). Treatment with wheat and barley grasses (250 mg kg\({}^{-1}\), G4, and G6) decreased the harmful effects of HMs, and the decrease in these gene expressions ranged from 41 to 59%. Otherwise, treatment with 500 mg kg\({}^{-1}\) of wheat (G5) and barley grasses (G7) decreased the mRNA level for the tested genes by a ratio of 65-84%. G5 treatment showed the best downregulation in the gene expression of APP, MAPT, and Caspase3 with a decrease in the mRNA level of up to 84%. Amyloid-\(\beta\) precursor protein (App) changes in the gene expression have been associated with Alzheimer's disease. Previous research has suggested that the protein products of this gene may play a role in copper homeostasis [100]. The RT-PCR analysis showed the induction of App in brain tissues with mixed chemical minerals treatment (G3). This upregulation was downregulated by wheat and barley grasses. HMs are extremely toxic to the neuron system and consequently induce Alzheimer's disease. Oxidative stress-related apoptosis is concerned with the accumulation of amyloid beta-protein, which is a metabolic product of amyloid precursor protein (APP). [PERSON] et al. [101] showed up-regulated Alzheimer's disease-related genes (APP) in monkey brains following HMs exposure. In particular, snippets of the \(\beta\)-amyloid precursor protein (APP) resulted in aggregated \(\beta\)-amyloid (A\(\beta\)) peptides in Alzheimer's disease. Exposure to Pb early in life results in gene changes through the hypomethyllation of the APP gene, which is a gene responsive to Pb. Hypomethylation results in an overexpression of the APP gene and increases APP production [102]. Tau proteins are mainly expressed in the brain and especially in the neurons. The microtubule-associated protein tau (MAPT) hyperphosphorylation has also been implicated in Alzheimer's disease and other neurological diseases. There is a hypothesis that an MAPT expression reduction would lead to depolymerizing neurofibrillary tangles, and could be involved in Alzheimer's disease and other tauopathies treatment. The authors of [103-106] indicated that HMs promotes tau upregulation and neuronal cell death, whereas wheat and barley grasses downregulate MAPT gene expressions in the brain tissue. The co-administration of wheat and barley grasses with HMs significantly reduced the elevated levels of caspase-3 observed in the HMs-treated group (downregulation of caspase-3 gene expression). Apoptosis was as
## 5 Results and Discussion
### Optimum Number of Control Points  
The force forms of ordinary cables and crossing cables are different, resulting in a complex distribution of cable pre-tension force along the deck. This requires more control points to enable the curve to accurately represent the distribution of cable forces. 4, 5 and 6 control points were used for optimization, and the influence of the number of control points on the optimization results was analyzed. The influence of the number of control points on the vertical displacement of the deck is shown in Figure 9.  
As can be seen from Figure 9, under dead load case, the maximum displacement of the deck under 4, 5 and 6 control points are 0.019 m, 0.017 m and 0.014 m, respectively. Due to the presence of a long girder section without cables in the side span, the deck displacement from the end of the side span to the auxiliary pier zone is relatively large. The difference in displacement of the deck between the dead load + live load case (LC 1) and the dead load case (LC 0) is the displacement of the deck caused by the live load. When the control points are 4, 5, and 6, the maximum deflection of the deck caused by live load is 1.603 m, 1.581 m, and 1.574 m, respectively. These results indicate that as the number of control points increases, the vertical displacement of the deck gradually decreases. However, when the number of control points increased to 5, the decrease in displacement of the deck was relatively small. Therefore, 5 control points are sufficient to control the pre-tension force distribution of the current bridge.
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 3 Our Method
### SLAM Framework  
Our SLAM process is made up of three main components: tracking, local mapping, and loop closing detection. Tracking is the front end, which needs to run in real time. Local mapping and loop closing detection belong to the back-end. The main purpose is to optimize the camera pose and the generated map points obtained by the tracking module to ensure the accuracy of SLAM. Our SLAM uses a keyframe mechanism and a bag-of-words model to accelerate feature matching and perform closed loop detection.  
The tracking module processes image information and applies adaptive feature extraction algorithms to obtain feature points. In addition, it matches the feature points and calculates the pose of the present frame relative to the active map in real time based on the obtained matching relation, minimizing reprojection errors in matching map features. It decides when to insert new keyframes. The calculation of pose by feature matching can be divided into three methods. One is to anticipate the pose of the current camera based on the previously predicted inter-frame camera motion and search for features in a small range for matching. The second is to track the features between two adjacent frames without motion prior. The third is to track the feature points between the reference key frame and the present frame and use the bag-of-words model to accelerate feature matching.  
The local mapping module adds key frames and points to active maps, applies a strict point culling strategy so that only high-quality map points are retained, and performs local optimization, that is, using visual BA to optimize map points on local maps. The local mapping module is also responsible for eliminating redundant key frames.  
The loop closing detection module detects similar key frames to the present key frame between the working map and the entire atlas. If similar key frames are found on the working map, a closed-loop correction is performed. If the two are not on the same map, map fusion is performed. Redundant key frames are removed after updating the connection relationship of the covisibility graph. Finally, global BA optimization is carried out.
## 3 Results and Analysis
### Path-Tracking Deviation Pattern Identification Test
#### 3.1.2 Performance Testing of Models
All the above performance tests show that the DBN's excellent extraction ability for hidden features, generalization ability, and extremely fast prediction speed can adapt to the application scenario of the high-frequency and complex environment of a rice transl  
Figure 13: Effects of DBN, RNN, and LSTM on Macro-F1 at different ablation levels. \begin{table}
\begin{tabular}{c c c c c} \hline
**Indicator** & **Model** & **DBN** & **RNN** & **LSTM** \\ \hline \multirow{4}{*}{MAE} & I & 0.0236 & 0.0967 & 0.0758 \\  & II & 0.0038 & 0.0235 & 0.0186 \\  & III & 0.0274 & 0.1694 & 0.0657 \\ \hline \multirow{4}{*}{Macro-F1/\%} & I & 98.56 & 93.57 & 94.58 \\  & II & 99.85 & 96.96 & 98.50 \\  & III & 95.34 & 90.36 & 93.67 \\ \hline \multirow{4}{*}{Accuracy/\%} & I & 99.02 & 92.46 & 92.35 \\  & II & 99.58 & 96.81 & 98.34 \\ \cline{1-1}  & III & 97.44 & 89.42 & 92.66 \\ \hline \end{tabular}
\end{table} 
Table 14: Test results of DBN, RNN, and LSTM.
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 3 Results and Discussion
### HSI Analyses  
#### 3.2.1 Average Raw and Pre-Processed Reflectance Spectra  
The raw and preprocessed reflectance spectra of samples acquired in the NIR range to build the sand/plastic/other materials model are reported in Figure 5, while those ones acquired in the NIR range to realize sand/plastic and polymer types (i.e., PP, PE, PS, PVC, and PET) models are shown in Figures 6 and 7, respectively.  
Two specific spectrum regions are identified by analyzing the average spectra of the materials under investigation: one absorption zone is between 1100 and around 1200 nm, and another one is between 1400 and 1550 nm. These wavelength intervals offer valuable information about the molecular properties and composition of the materials under investigation.  
The first range is linked to the second harmonic of C-H stretching vibrations, while the second interval is associated with the first harmonics of O-H and N-H stretching vibrations, as well as combination bands of C-H [46; 47].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Feee Verde Sand**} & \multicolumn{2}{c}{**Capo Portiere Sand**} & \multicolumn{2}{c}{**Sabaudia Sand**} \\  & \multicolumn{2}{c}{**Mass (\%)**} & \multicolumn{2}{c}{**Mass (\%)**} & \multicolumn{2}{c}{**Mass (\%)**} \\ \hline
**Element** & Mean & Std. Dev. & Mean & Std. Dev. & Mean & Std. Dev. \\ \hline
**Chlorine (Cl)** & 60.34 & 13.32 & 82.52 & 7.70 & 74.39 & 11.12 \\ \hline
**Calcium (Ca)** & 25.51 & 17.21 & 7.87 & 4.30 & 12.31 & 12.83 \\ \hline
**Iron (Fe)** & 6.39 & 3.34 & 6.44 & 3.90 & 0.88 & 0.97 \\ \hline
**Silicon (Si)** & 5.98 & 3.70 & 1.66 & 0.38 & 9.46 & 3.91 \\
**Titanium (Ti)** & 0.49 & 0.28 & 0.50 & 0.53 & 0.06 & 0.04 \\ \hline
**Aluminum (Al)** & 0.39 & 0.18 & 0.12 & 0.05 & 0.34 & 0.28 \\ \hline
**Potassium (K)** & 0.37 & 0.58 & 0.41 & 0.51 & 2.05 & 2.35 \\ \hline
**Manganese (Mn)** & 0.25 & 0.24 & 0.15 & 0.09 & 0.11 & 0.13 \\
**Strontium (Sr)** & 0.15 & 0.12 & 0.09 & 0.03 & 0.32 & 0.18 \\ \hline
**Chromium (Cr)** & 0.05 & 0.06 & 0.17 & 0.24 & 0.04 & 0.03 \\ \hline
**Nickel (Ni)** & 0.04 & 0.07 & 0.02 & 0.02 & 0.02 & 0.04 \\ \hline
**Zinc (Zn)** & 0.03 & 0.02 & 0.03 & 0.02 & 0.02 & 0.02 \\ \hline
**Phosphorus (P)** & 0.00 & 0.01 & 0.02 & 0.01 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass percentage composition (mean and standard deviation, Std. Dev.) of various elements found in the samples from Fece Verde, Capo Portiere, and Sabaudia. It should be noted that in micro-XRF analysis, a situation where the standard deviation is higher than the mean value generally indicates a high level of variability or inhomogeneity in the measured data, a phenomenon that is particularly pronounced for trace elements.  
Figure 5: Raw (**a**) and pre-processed reflectance (**b**) of the plastic, sand, and other material samples in the NIR range.  
Figure 6: Raw (**a**) and pre-processed reflectance (**b**) of the plastic and sand samples in the NIR range.
# An FPGA Implementation of a Convolutional Auto-Encoder
## 2 FPGA Implementation of the CAE  
In essence, CAE is a multilayer CNN including convolution layers and pooling layers. The model of CAE used is shown in Figure 1, where the image compression is taken as an example. The convolution layers are mainly used to extract the features of inputs and the neurons in these layers share the weight parameters. The pooling layers are used to retain the main features and reduce the parameters, which also prevent overfitting and improve the generalization ability of the model. The upsampling process is performed to obtain the image in original resolution, which can be considered the inverse process of pooling.  
The model is divided into two parts: an encoder and a decoder. The encoder contains three convolutional layers and three pooling layers. The ReLU function and the maximum pooling operation are used. The decoder, which performs the reverse operation of the encoder, consists of four  
Figure 1: Classical model of a convolutional auto-encoder (CAE).  
convolution layers and three upsampling layers. The ReLU function and the sigmoid function are used in the first three and the last convolution layers, respectively.  
From Figure 1, it can be seen that the layers of CAE have a certain regularity and periodicity, so we implement their functions by proposing the periodic layer-multiplexing framework based on FPGA. The hardware framework and the relative key techniques are described as follows.