esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Qualitative and Quantitative Analyses of Changes in Bacon Proteins during LTS  
To further evaluate the protein changes induced by LTS, label-free proteomic analysis was performed. In total, 2017 proteins were quantified in the bacon samples, but the proteins differed across the different stages of LTS (Figure 4A), indicating that the types and contents of proteins in bacon changed markedly during LTS.  
Figure 3: Protein oxidation in bacon during LTS. (**A**) The content of sulfhydryl groups in bacon during LTS. (**B**) The content of carbonyl groups in bacon during LTS. Results are expressed as mean \(\pm\) standard derivation. \({}^{\text{a-c}}\) Means within rows and same breed with different superscripts differ significantly (\(p\) < 0.05).  
The key differentially abundant proteins (DAPs) during LTS were further screened using the following criteria: a \(p\)-value < 0.05 and an FC \(\leq\) 0.83 or \(\geq\)1.2 with Student's \(t\)-test or the chi-squared test. As shown in the volcano plot in Figure 4B, red dots represent significantly up-regulated DAPs, blue dots represent significantly down-regulated DAPs, and gray dots represent DAPs that changed insignificantly.  
As shown in Figure 4A,B, the up-regulated DAPs in beacon during LTS comprised collagen alpha-1(I) chain preproprotein (A0A287A1S6), caveolae-associated protein 1 (A0A286 ZZ97), myristoylated alanine-rich protein kinase C substrate (A0A287 BRL8), protein S100-A11 (P31950), and cardiomyopathy-associated 5 (A0A286 ZYS8), whereas the down-regulated DAPs comprised aconitate hydratase, mitochondrial (P16276), vitamin D-binding protein (A0A287 AHK1), mannose-6-phosphate isomerase (A0A287 BNZ5), CXXC motif-containing zinc-binding protein (F1S765), and LIM zinc-binding domain-containing protein (A0A287 AYR8).
## 2 Materials and Methods
### The Neuro-Fuzzy Transformation with Minimum Entropy Principle Model
#### 2.2.3 Artificial Neural Networks  
Artificial neural networks (ANNs) are a mathematical model that is imitated from the human nervous system. There are numerous neurons to process data. Neurons transfer data to one another. An advantage of ANN is that the parameters can be learned and modified from error. A popular structure of ANN is a combination of three types, including an input layer, hidden layers, and output layer. The input layer represents input features, while the output layer represents output classes. Each layer contains a group of neurons that receive information from the other neurons in the previous layer and send the information to the other neurons in the next layer [35].  
The process of ANN is a combination of the set of input data and random weights plus the bias value. Next, the output value from the first process is transformed by a sigmoid transfer function. The output value after passing the transfer function is between 0 and 1. The ANN has self-adaptive learning, which adjusts all weight values from their error, called the backpropagation algorithm [36]. The stochastic gradient descent (SGD), among the popular weight optimization algorithms, was selected in this research to minimize the loss function, which is an error of the model. Finally, each weight value was updated by the chain rule of calculus.  
This research enhanced the ANN structure proposed by [37]. In previous research, this model has been used to predict a daily average PM\({}_{10}\) class where classes are defined according to the T-AQI. The structure of ANN is divided into two processes: the constructing an ANN model process and the decision process. In the first process, there are many ANN models and the number of models is equal to the number of classes. Each ANN model focuses on learning for each class, which includes an input layer, two hidden layers, and an output layer. For the input layer, Dataset-II and Dataset-III were combined and used as input features. The number of hidden neurons was fixed to six and three neurons in the first and second hidden layers, respectively. Finally, only one output neuron was utilized in the output layer. The initial parameters of ANN in every model were similar, including random weights for all neurons, a sigmoid transfer function for all layers, and a learning rate of \(-0.02\). In the second process, the class in each record was identified by the outputs from the ANN models by Equation (7), where _Class_ denotes the class of data and \(O_{i}\) denotes output data from ANN in model \(i\). The ANN had the same number of classes. The value of output data of each model ranged from 0 to 1 due to the sigmoid transfer function. The maximum function determined the maximum value of the output data, then the index function was used to find the index of the maximum value. Finally, the class was identified by the index value.  
\[Class=index(max(O_{i})) \tag{7}\]
## 4 Discussion
### Validation of the Models
Both models demonstrated good agreement with the measured Vs values, with the XGBoost model achieving a relatively higher \(r\) value (0.39).
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 6 Experimental Evaluation
### System Operation Data Analysis  
The video conference system consists of a login interface and a main interface. The main interface includes functions such as site layout, classical encrypted communication, and quantum encrypted communication. QKD equipment generates quantum keys in real-time, and the quantum key rate is about 3.64 kbps. According to the key reading speed of 3 kb/s,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Color Depth** & **160 \(\times\) 120** & **320 \(\times\) 240** & **640 \(\times\) 480** & **800 \(\times\) 600** & **1024 \(\times\) 768** \\ \hline
3 & 7.03 & 28.13 & 112.5 & 175.78 & 288 \\
8 & 18.75 & 75 & 300 & 468.75 & 768 \\
16 & 37.5 & 150 & 600 & 937.5 & 1536 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image size in storage space (unit: KB).  
Figure 10: The quantum key rate.  
the two sites are stored in their respective quantum key management servers. At the same time, the main site writes the key to the video conference system server. After the video conferencing system is started, the classic encryption is used by default, and the user of the main conference site switches to use quantum encryption. After running for 300 s, the total key consumption is shown in Figure 11 and Table 3. The key adopts the classical key + quantum key, so the key consumption increases with time. In 300 s of operation, user1 and user2 use about 62.74 KB keys per second, while the reading speed of quantum keys is 3 kb/s, so the algorithm of \"one at a time + AES\" can ensure the normal operation of the system.
# Stability Conditions in Lignite Open Pits from Romania, Case Study: Olteu Open Pit
## 3 Results and Discussion
### Analysis of the Stability of In Situ Slopes
The slope in L2 section was stable in December 2021, and the analyses performed indicate a stability reserve of 22% under no external stress, equilibrium limit under the conditions of the manifestation of pore water pressure, and instability under the conditions of the simultaneous effect of water and vibrations (shocks). * The other sections, namely cross sections P0-P2, P4 and P5, present the hypothetical position of the lateral slopes at the end of the four quarters of 2022 (as they materialize). * The stability factor depends on the height, inclination, and structure of the slope, a significant influence on the physical state (static and dynamic) coming from the weight of the clay layer in the structure of the slope. As for the sliding surface, in conditions where the thickness of the clay layer on the upper part exceeds 9-10 m, it is transmitted only through this layer and, as a rule, the stability coefficient is sub-unitary, indicating clay instability phenomena. Under the conditions of a clay layer of less thickness, the sliding surface is transmitted either through the layers of clay and sandy marl or through the entire step. * Stability is ensured for slopes between 18 and 26 m high, and slope angles of up to 60\({}^{\circ}\), in conditions of no external stress. For all other cases, namely the presence of water and seismic shocks (except for the slope in section L2, III quarter), the stability factor is sub-unitary or approaches the equilibrium limit. Considering the advancement of work fronts in areas with an ascending slope, in most cases, it is necessary to build a system of steps, which can ensure, on the one hand, the technical possibility of excavation (considering the fact that the theoretical excavation height of the ERC 1400-30/7 bucket wheel excavator is of maximum 30 m), and on the other hand, maintaining stability conditions. Such situations are recorded in the case of cross-sections P5, P1 and P0, corresponding to the stage of the excavation works in the I, II, and III quarters of 2022, where the distance between the bed of the first lignite layer (open-pit base) and the surface of the land is 40, 41, and, respectively, 38 m. For these cases, the stability of the designed (forecasted) slopes (with the geometry resulting from the provided sections) was analyzed, and the general conclusion is that such slopes would be unstable in all the three calculation hypotheses of the stability factor.
# Optimization of Composite Material Repair Patch Shape Based on Strength Analysis
## 5 Tensile Test of Composite Laminate Panels Repaired with Different Patch Shapes
### Static Tensile Experiment of Rounded Square Patch Repair Component
According to the displacement graph in the z-direction, the patch of R3 is elevated by approximately 0.2 mm compared to the base plate. The ultimate tensile strength of R3 is determined to be 365 MPa, the highest among the six specimens. However, the repair quality of specimen R3 is similar to that of R2. Figure 44: Average displacement of the entire board and the patch in the y-direction of sample R3. Figure 36.0 Average displacement of the entire board and the patch in the z-direction of sample R3. Due to external disturbances that affected the recording equipment during the experiment, DIC data for specimen R4 were lost. Therefore, the DIC test data for this group consists of five sets, still meeting the requirements of the experimental standards. Figures 46-48 show the comparative graphs of the average displacement in three directions for specimen R5. The DIC experimental data for R5 retained data after the occurrence of damage, displaying the displacement of the patch and the entire plate in three directions after damage failure. Before experiencing tensile failure, the displacements of the patch and the plate in the x and y-directions are coordinated well. However, in the z-direction, the patch is elevated by more than 0.6 mm compared to the base plate, indicating a significant excess of adhesive in the repair of this plate. The repair quality of this plate is better than R2 and R3 specimens. It can be observed that after surpassing the ultimate strength, the displacement of the patch deviates significantly from that of the entire plate in all three directions. Before reaching the ultimate strength, the coordination of displacement between the patch and the entire plate is relatively good in all three directions. The adhesive quality of the patch for this specimen is similar to that of R3. The adhesive quality is slightly inferior to that of R1. It can be concluded that although the square patch exhibits relatively poorer strength recovery, its repair method demonstrates higher reliability compared to the circular patch specimens. The repair quality is more consistent and significantly superior to that of the circular patch specimens. The ranking of specimens based on the strength of the repaired specimens using square patches is as follows: R3, R1, R6, R2, R5, and R4. The openings in the average displacement curves along the x-direction are all very small and thus not sortable. By the magnitude of the opening in the average displacement curve along the y-direction, the quality of repair can be judged from best to worst, as follows: R1, R6, R5, R3, and R2. By examining the displacement curves along the z-direction, it is primarily evident that specimen R6 exhibits a lack of adhesive, resulting in the patch being lower than the base plate, whereas patches in other specimens are generally elevated by around 0.2-0.7 mm compared to the base plate.
## 3 Experimental Results
### Shear Behavior
#### 3.4.3 Size Effects on Shear Behavior
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Notation**} & \multicolumn{3}{c}{**Section (mm)**} & \multirow{2}{*}{\(\mathbf{a}\)/\(\mathbf{d}\)} & \multirow{2}{*}{\(\mathbf{\rho}\)} & \multirow{2}{*}{\(\mathbf{f_{c}}\)} & \multirow{2}{*}{\(\mathbf{V_{u,exp}}\)} & \multirow{2}{*}{\(\mathbf{V_{u,com}}\)} & \multirow{2}{*}{\(\mathbf{V_{u,exp}}\)/\(\mathbf{V_{u,com}}\)} & \multicolumn{2}{c}{\(\mathbf{v_{u}}\)} \\ \cline{3-3} \cline{5-10}  & & & & & (\%) & & (MPa) & (KN) & (KN) & **(MPa)** & **Average** \\ \hline NB60 & & & & & & 30.17 & 46.39 & 54.45 & 0.85 & 1.03 & \\ HB60 & & & & & 32.03 & 51.05 & 55.55 & 0.92 & 1.13 & \\ MB60 & & & & & 33.33 & 55.10 & 56.29 & 0.98 & 1.22 & \\ LB60 & & & & & 31.21 & 49.45 & 55.07 & 0.90 & 1.10 & \\ NS60 & & & & & 29.86 & 51.55 & 40.65 & 1.27 & 2.29 & \\ HS60 & & & & & 33.56 & 43.90 & 42.27 & 1.04 & 1.95 & \\ MS60 & & & & & 33.62 & 36.30 & 42.29 & 0.86 & 1.61 & \\ LS60 & & & & & 31.55 & 37.35 & 41.41 & 0.90 & 1.66 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of the shear strength of the RC beams using a natural and recycled aggregate concrete, without web reinforcement. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{3}{c}{**Section (mm)**} & \multirow{2}{*}{\(\mathbf{f_{c}}\)} & \multirow{2}{*}{\(\mathbf{V_{u,exp}}\)} & \multirow{2}{*}{\(\mathbf{V_{u,com}}\)} & \multirow{2}{*}{\(\mathbf{V_{u,exp}}\)/\(\mathbf{V_{u,com}}\)} & \multicolumn{2}{c}{\(\mathbf{v_{u}}\)} \\ \cline{3-3} \cline{5-10}  & & & & & (MPa) & (KN) & (KN) & **(MPa)** & **Average** \\ \hline A1 & 400 & 930 & 1000 & 29.2 & 358.4 & 313.71 & 1.14 & 0.84 & \\ A2 & & & & 25.5 & 328.4 & 300.10 & 1.09 & 0.81 & \\ \hline B1 & & & & 27.2 & 104.2 & 91.09 & 1.14 & 0.98 & \\ B2 & 200 & 465 & 500 & 24.9 & 87.3 & 88.44 & 0.99 & 0.95 & 0.99 \\ B3 & & & & 32.1 & 85.3 & 96.26 & 0.89 & 1.04 & \\ \hline C1 & & & & 25.6 & 22.5 & 26.50 & 0.85 & 1.14 & \\ C2 & & & & 25.6 & 24.0 & 26.50 & 0.91 & 1.14 & \\ C3 & 100 & 232 & 250 & 27.5 & 27.5 & 27.14 & 1.01 & 1.17 & \\ C4 & & & & 20.8 & 22.5 & 24.72 & 0.91 & 1.07 & \\ C5 & & & & 22.4 & 27.0 & 25.34 & 1.07 & 1.09 & \\ C6 & & & & 28.8 & 27.5 & 27.56 & 1.00 & 1.19 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of the shear strength of the RC beam using natural aggregate concrete as given in Taylor [23].
###### Abstract  
The odors and emanations released from the human body can provide important information about the health status of individuals and the presence or absence of diseases. Since these components often emanate from the body surface in very small quantities, a simple sampling and sensitive analytical method is required. In this study, we developed a non-invasive analytical method for the measurement of the body odor component 2-nonenal by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry by selective ion monitoring. Using a StableFlex PDMS/DVB fiber, 2-nonenal was efficiently extracted and enriched by fiber exposition at 50 \({}^{\circ}\)C for 45 min and was separated within 10 min using a DB\(-\)1 capillary column. Body odor sample was easily collected by gauze wiping. The limit of detection of 2-nonenal collected in gauze was 22 pg (S/N = 3), and the linearity was obtained in the range of 1-50 ng with a correlation coefficient of 0.991. The method successfully analyzed 2-nonenal in skin emissions and secretions and was applied to the analysis of body odor changes in various lifestyles, including the use of cosmetics, food intake, cigarette smoking, and stress load.  
b odor; 2-nonenal; wiping method; pasting method; solid-phase microextraction (HS-SPME); gas chromatography-mass spectrometry (GC-MS) Article  
Hedspace 2021
method of skin emissions and secretions consists of placing the palm of the hand in a PVF bag and collecting the generated gas [1], but this method has drawbacks, including a possible lack of airtightness and questions about quantitative sampling. Body odor compounds have also been analyzed by gas chromatography (GC) after headspace sampling or passive flux sampling [1; 17; 18], but these methods have problems with cost and storage. The SPME, using a fused-silica fiber externally coated with an appropriate stationary phase, is an effective sample preparation technique for integrating several operations such as sample collection, extraction, analyte enrichment, and isolation from sample matrices and is easily coupled with GC [4; 19; 20; 21]. It is fast, solvent-free, and cost-effective and can improve the detection limits.  
2-Nonenal is produced by reactions between fatty acids and lipid peroxides and is involved in the intake of fats and foods rich in fatty acids and in the peroxidation of lipids [1]. Thus, this production and intake of this compound are associated with the generation of active oxygen species [2]. Drinking, smoking, stress, and other lifestyle factors may therefore affect 2-nonenal secretion and alter body odor. Therefore, the effects of internal and external factors such as dietary intake, cosmetic use, smoking, and stress on body odor changes were analyzed using the method developed in this study.  
In this study, we developed an efficient sampling method for the collection of body odor and an HS-SPME coupled with GC-mass spectrometry (GC-MS) for the selective and sensitive analysis of 2-nonenal in skin emissions and secretions. The method was applied to the body odor analysis in various lifestyles.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 2 Methodology
### Leak Detection System
\begin{split} E_{3}=\sum_{i}\biggl{(}\frac{W_{4}}{1000\times \Delta t}\times\left(P_{i}^{A}-P_{i,prev}^{A}\right)\biggr{)}^{2}\ +\ \sum_{i}\bigl{(}W_{5}\times\left(FPDC_{i}-FPDC_{i,prev}\right)\bigr{)}^{2}\\ +\sum_{i}\biggl{(}\frac{W_{6}}{BMC_{a,i}}\times\left(BMC_{i}-BMC _{i,prev}\right)\biggr{)}^{2}\end{split}  \tag{6}\]  
where \(E_{3}\) is the penalty associated with the errors in the rate of change of the modelled pressure, the friction correction coefficient, and the bulk modulus correction coefficient \(BMC\). \(BMC\) is a variable to be optimized and is constrained by \(BMC_{a,i}\), which is the maximum allowed bulk modulus correction in the simulation for pipe segment \(i\); \(\Delta t\) is time step and subscript _prev_ represents the value at the previous time step; \(W_{4}\), \(W_{5}\), and \(W_{6}\) are the corresponding tuning weights:  
\[E_{4}=\sum_{i}(W_{7}\times DF_{i})^{2} \tag{7}\]  
where \(E_{4}\) is the penalty associated with the error that cannot be explained by the above-mentioned uncertainties and thus may be induced by a leak, and \(W_{7}\) is the corresponding tuning weight. \(DF_{i}\) is defined as the \"diagnostic flow\" in pipeline segment \(i\); if negative, indicates that fluid is being removed from the modelled pipeline [15]. Leak detection is often based on a volume imbalance, which is calculated by integrating \(DF\) over a period of time, or time window. Volume imbalance is typically monitored in different time windows (e.g., 5 min, 1 h, and 24 h), with smaller windows targeted to detect large leaks quicker and larger windows targeted to detect smaller leaks [17]. This is because in a longer time window, the volume imbalance caused by a small leak steadily increases and becomes distinguishable from the volume imbalance caused by uncertainties. In this study, volume imbalance in a one-hour time window was used to quantify leak detectability. A dimensionless volume imbalance (\(DVB\)) was defined as volume imbalance divided by the volume of fluid leaked out over 1 h. This allowed different testing scenarios with different sizes of leaks to be compared.
## 3 Application to Field Data  
### Geological Background  
The geotectonic structure of Wuhan City straddles two major primary tectonic units: the Yangtze landmass and the Qinling-Dabie orogenic belt. It is bounded by the Xiangfan-Guangji Fault, the southern side is the southeastern Eurasian Fold Belt of the lower Yangzte landmass, and the northern side is the Qinling arc-basin system of the Qinling-Dabie orogenic belt. Wuhan is dominated by a denudation landform and an alluvial lacustrine plain landform, the surface is widely distributed within a Quaternary loose accumulation layer, and the underlying bedrock is dominated by four sets of carbonate rocks.  
The karst area in Wuhan is mainly distributed in strips in the Daye stratigraphic community of the lower Yangzte stratigraphic division in the southern Yangzte stratigraphic region, with hidden karst as the main form. The karst forms are mainly karst gaps, karst pores, and small-scale karst caves, with filling karst caves as the main form.  
The main soluble rocks are dolomitic limestone and limestone intercalated with dolomite of the Jialing River Formation in the middle and lower Triassic, and limestone of Daye Formation in the lower Triassic. Meanwhile, there is also chert nodule limestone of the Qixia Formation in the lower Permian, thick layered dolomite and limestone of the Chuanshan Formation and Huanglong Formation in the upper Carboniferous, and claystone intercalated with limestone of the Hezhou Formation in the lower Permian. Among them, the highest degree of karst development is found in the Huanglong Formation and Chuanshan Formation in the upper Carboniferous.