esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Implications to Bilingual EHRs  
The main purpose of EHR is to support patient care and administrative tasks related to treatment as a repository of clinical data. Therefore, EHR is not optimized for accurate retrieval of many data. Eventually, in the process of using EHR for research purposes, problems such as low accessibility, poor performance, and lack of data analysis functions arise. In particular, clinicians often use free text when recording clinical findings in EHR. Natural language processing is a representative method of extracting meaningful data from documents recorded as free statements. Attempts are being made to extract drug prescriptions, problem lists, and comprehensive clinical information from clinical documents recorded in the form of free statements using natural language processing or to use them for document classification and retrieval [47; 48; 49]. However, although current research on medical images or bio-signals has progressed considerably, research on analyzing textual medical data is insignificant. In particular, studies composed of multiple languages are not common [50]. Korean medical institutes face additional difficulties in natural language processing, as EHR contains both Korean and English. This study provides a meaningful basis for extracting insights from the clinical data warehouse, mapping documents to a standardized terminology system, and classifying bilingual EHR documents.
## 1 Introduction
In addition, both the method based on modifying the Ergun equation and the method based on dimensional modeling have characteristics, advantages, and disadvantages, while few studies have compared these two methods. Based on these problems, we have carried out this study. The innovations of this study lie in the following: (a) the flow behaviors of the negative pressure conical fluidized bed are studied, (b) the Ergun equation modifying method and the dimensional analysis method are both used and compared, and (c) this study proposes the inference that the total gas drag force is 0.8995 times the particles' gravity in the initial stage of fluidization. In this study, first, a series of experiments are carried out, and \(U_{0\text{mf}}\) and \(\Delta P_{\text{mf}}\) are obtained as experimental empirical data; the effects of material mass and particle size on \(U_{0\text{mf}}\) and \(\Delta P_{\text{mf}}\) are discussed. Second, we separately use the above two methods to fit the empirical data and derive models of \(U_{0\text{mf}}\) and \(\Delta P_{\text{mf}}\); by using these modeling correlations, workers can quickly determine \(U_{0\text{mf}}\) and \(\Delta P_{\text{mf}}\) based on some variables, and then determine the production parameters. Finally, the prediction \begin{table}
\begin{tabular}{c c} \hline \hline
**Scholars** & **Correlations** \\ \hline [PERSON] et al. [20] & \(\text{Fr}=0.2714(\text{Ar})^{0.3197}\left(\sin\alpha\right)^{0.6092}\left( \frac{\epsilon_{0}}{\phi}\right)^{-0.6108}\) \\  & \(\Delta P_{\text{mf}}=7.457\left(\frac{D_{1}}{D_{0}}\right)^{0.038}\left( \frac{d_{1}}{D_{0}}\right)^{0.222}\left(\frac{H_{4}}{D_{0}}\right)^{0.642}\left( \frac{\rho}{\rho_{6}}\right)^{0.723}\) \\ \hline [PERSON], [PERSON] [23] & \(\text{Re}_{\text{mf}}=10.396(\text{Ar})^{0.396}\left(\frac{d_{2}}{D_{0}}\right) ^{0.897}\left(\frac{\epsilon_{0}}{\phi}\right)^{-0.731}\left(\cos\alpha \right)^{-10.437},\alpha>4.5^{\circ}\) \\  & \(\frac{\Delta P_{\text{mf}}}{\rho_{6}\beta H_{1}}=106.729(\frac{\rho_{6}}{ \rho_{6}})^{-0.522}\left(\frac{d_{2}}{D_{0}}\right)^{0.309}\left(\frac{H_{0}} {D_{0}}\right)^{-0.379}\left(\cos\alpha\right)^{-10.858},0\leq\alpha\leq 4.5 ^{\circ}\) \\  & \(\frac{\Delta P_{\text{mf}}}{\rho_{6}\beta H_{1}}=163.419(\frac{\rho_{6}}{ \rho_{6}})^{-0.524}\left(\frac{d_{2}}{D_{0}}\right)^{0.269}\left(\frac{H_{0}} {D_{0}}\right)^{-0.976}\left(\cos\alpha\right)^{-3.277},\alpha>4.5^{\circ}\) \\ \hline [PERSON] et al. [24] & \(\text{Re}_{\text{mf}}=0.254(\text{Ar})^{0.585}\left(\frac{\epsilon_{0}}{\phi} \right)^{3.12}\left(\frac{H_{0}}{D_{0}}\right)^{0.264}\left(\frac{d_{2}}{D_{0}} \right)^{0.0016}\left(\cos\alpha\right)^{-13.2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlations proposed by other scholars for predicting \(U_{0\text{mf}}\) and \(\Delta P_{\text{mf}}\). ability of the proposed correlations and the characteristics of the two modeling methods are discussed, which can be used as a reference for selecting an appropriate method in practical application.
## 2 Materials and Methods
### Nutritional Value
#### 2.3.3 Melanin Content Measurement  
Melanin content was indirectly quantified using 1\(H\)-pyrrole-2,3-dicarboxylic acid (PDCA) and 1\(H\)-pyrrole-2,3,5-tricarboxylic acid (PTCA), which are the hydrolyses of melanin under alkaline hydrogen peroxide oxidation condition [16]. We accurately weighed 0.1 g of breast muscle sample, and added 0.5 mL of 30% hydrogen peroxide and 0.5 mL of 2 M ammonium hydroxide. We kept the meat sample soaked under the liquid surface, and stored it at 30 \({}^{\circ}\)C for 8 h. We then added 400 \(\mu\)L of 11.3% ammonium sulfite solution to the centrifuge tube. Subsequently, to modify the acidity for solid-phase extraction (SPE) column clean-up (Bond Elut SAX, 500 mg, 3 mL), 400 \(\mu\)L of 4 M acetic acid was introduced. Conditioning of the columns involved the use of 1 mL methanol followed by 1 mL of ultrapure water. The entire sample was subsequently introduced into the SPE column, allowing it to flow through the column using gravity. We added 3 mL of water and methanol in sequence for the washing process. The SPE column was then thoroughly dried using a vacuum pump. Elution was carried out with two portions of 1 mL of a 10% (\(v/v\)) triethylamine in methanol solution. The samples were dried under nitrogen gas. We then redissolved the dried sample in 1 mL of methanol containing 0.5% formic acid.  
The reconstituted sample underwent analysis, with us utilizing an LC-ESI-MS/MS system (QTRAP 6500, Sciex, Framingham, MA, USA) equipped with an ESI Turbo ionspray interface. The liquid chromatography column: Agilent (Santa Clara, CA, USA) ZORBAX C18 (3 mm \(\times\) 150 mm). Mobile phase A: water containing 0.1% acetic acid. Mobile phase B: acetonitrile containing 0.1% acetic acid. Gradient program: 2% B, 0 min; 2% B, 1 min; 20% B, 5.6 min; 100% B, 7.0 min; 100% B, 9.4 min; 2% B, 9.6 min; 2% B, 12.0 min. Flow rate: 0.35 mL/min. Column temperature: 45 \({}^{\circ}\)C. The mass spectrometer (MS) parameters were set as follows. Source temperature: 500 \({}^{\circ}\)C. Negative ion spray voltage: (\(-\)) 4500 V. Gas I: 50 psi. Gas II: 50 psi. Curtain gas: 35 psi. Collision gas: medium. The multiple reaction monitoring transitions were PTCA 198 \(\rightarrow\) 154 (quantifier) and 198.13 \(\rightarrow\) 110.19 (qualifier), PDCA 154.13 \(\rightarrow\) 110.13 (quantifier) and 154.13 \(\rightarrow\) 66.15 (qualifier).
## 3 Proposed Work  
In this proposed work, the performance of the V2X network using a deep neural network (DNN) was estimated with new robust loss functions, a set of robust statistical techniques called M-estimators. The robustness of DNNs for different levels of outliers on V2X datasets was demonstrated.  
The training datasets were created from the V2X network. The V2X network was simulated using MATLAB software. The collected data was analyzed and processed before the training phase. After loading the dataset as input to the network, the dataset was divided into two subsets: Input (I) and Output (O) columns. Then, it was also divided into training, testing, and validation subsets. The normalization of the input data must be in the interval \([-1,\,1]\), compatible with the actual maximum or minimum values. The ANN was generally trained using a standard backpropagation (BP) learning algorithm. This technique is the most popular supervised learning algorithm, where I/O pairs are provided to the network, and weights are adapted to minimize the error between the network's actual and estimated outputs using a loss function based on delta rules. During training, the training model was adjusted, the gradient of the loss function was calculated, and then the network weights and biases were updated in response to the gradients. This process was repeated until the minimum output error was reached.  
In the next stage, the test network requires test groups to assess the estimated model performance. The initial training stage generally reduces the validation error as an error of the training set. However, when the network overfits the training, the validation error always begins to increase. In this case, the network parameters were stored with the minimum error of the validation set.  
However, the traditional BP algorithm is not robust when data contains outliers that can contaminate the training data. To overcome the potential of outliers, a robust BP algorithm was used in this work. A robust performance measurement function, i.e., the well-known M-estimator loss function, was proposed as a replacement for the conventional MSE loss function to improve the robustness of DNNs and obtain the optimal performance on contaminated data in V2X datasets. DNNs can estimate the optimal performance of a V2X network based on the collected V2X dataset; V2X throughput was used as input and packet loss rate as output (desired output). A comparative study between robust and traditional DNN performance using RMSE and MAPE shows that the proposed algorithm provides outstanding results for the desired application. Figure 1 illustrates the flowchart of the proposed DNN for estimating V2X performance.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 3 System Model and Problem Formulation
### Multi-Objective vs. Multi-Constrained Routing  
In the _multi-objective_ formulation, multiple objective functions are established to be optimized simultaneously. For example, one objective might minimize cost, while another aims to maximize bandwidth. These objectives can often contradict one another; the path that minimizes cost may not be the best for bandwidth and vice versa. A solution is deemed Pareto-optimal if no objective can be improved without compromising at least one other objective. Consequently, there may be several Pareto-optimal solutions for a given problem. Related routing analyses can be found in [50], with examples in [51]. Additionally, multiple metrics can be combined into a composite metric, allowing multi-objective research to be reformulated into a single-objective optimization problem.  
The goal of _multi-constrained routing_ is to identify a feasible (or, in some cases, optimal) path that satisfies a set of QoS constraints. In our study, the parameters are additive. Since this problem is NP-complete, the solutions proposed in the literature either take considerable time or do not reliably locate a solution, even if one exists. The most efficient algorithm for solving this routing problem is SAMCRA [52]. This routing is essential for supporting QoS in real-time multimedia applications within wireless mesh networks [51]. Further examples of routing protocols can be found in [17; 53]. Multiple objectives and constraints can be integrated to define the goals of the routing process further.
# Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
## 4 Experimental Verification
### Case 1: Bearing Dataset with One Missing Failure Sample
#### 4.1.1 Data Pre-Processing  
In this section of experiments, the signals of bearing outer ring fault (6:00 position) obtained from the dynamic simulation model, inner ring fault and rolling element fault from real measurement are investigated. Firstly, in order to verify that a good diagnosis can be achieved in the proposed method in this paper, the dataset in Tables 2 and 3 is used as the experimental data. 200 samples are randomly selected from each health state, then the envelope signal of the sample is obtained, each sample contains 200 envelope signal data points. Half of the samples are used as training data and the other half as test data. In dataset B, the training data are the signal obtained from the simulation model and the test data are the signal obtained from the real measurement in the outer ring fault type.  
indicating that the WGAN-GN has learned the distribution of the real data, and the generated samples are sampled from the real distribution instead of simply copying the original samples, which can enhance the robustness and generality of the proposed method. To put it another way, WGAN-GN has taken the major features from the original signal and removed some noisy components that are bad for pattern recognition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Fault Location** & \
\begin{tabular}{c} **Signal Source** \\ **Dataset B** \\ \end{tabular} &
\begin{tabular}{c} **Sample Size** \\ **(Train/Test)** \\ \end{tabular} \\ \hline NC & Normal & Measurement & 100/100 \\ RF & Ball & Measurement & 100/100 \\ IF & Inner Race & Measurement & 100/100 \\ OF & Outer Race & Measurement & 100/100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Situation of Dataset A.  
Figure 6: Fault diagnosis model design.  
Figure 7: Rotating machinery fault diagnosis experimental device.  
It becomes vital to look into the feature learning process in order to further demonstrate the feature extraction capacity of WGAN-GN. The extraction process for all the NC tomographic samples is displayed in the 3D space as shown in Figure 9. It is seen that the variation amplitude becomes larger and the characteristics become more and more obvious as the network layer deepens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Fault Location** & **Signal Source** & **Sample Size** \\  & & **Dataset B** & **(Train/Test)** \\ \hline NC & Normal & Measurement & 100/100 \\ RF & Ball & Measurement & 100/100 \\ IF & Inner Race & Measurement & 100/100 \\ OF & Outer Race & Simulation & 100/100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Situation of Dataset B.  
Figure 8: Comparison between raw and generated signals for bearings: (**a**) Raw signals, (**b**) Generated signals.
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 3 Algorithms
### Baseline Algorithm
#### 3.1.4 Summing Up Analysis  
The sorting step takes \(O(mnb_{max}+(mn+b_{max}+\log k)k\log k)\) time. Because CWC works with any consistent measure, we do not need to use \(\|\textbf{B}_{i}\|\) in full accuracy, so we assume that \(b_{max}\) is set to be constant. Under the assumption, we obtain the following theorem.  
Theorem 3.1: _For the two-party feature selection problem, we can securely simulate CWC in \(O(kmn\log k+k\log^{2}k)\) time and \(O(kmn)\) space without revealing the private data of the parties under the assumption that TFHE is secure._  
Proof.: According to the discussion above, computing \(\textbf{B}_{i}\) for all features takes \(O(kmn)\) time and space, sorting features takes \(O(mnb_{max}+(mn+b_{max}+\log k)k\log k)=O(kmn\log k+k\log^{2}k)\) time, and selecting features takes \(O(kmn)\) time.  
Finally, party A computes in \(O(k\log k)\) time an integer array \(P\) with \(P[h]=R[h]\cdot\pi(h)\), which stores the original indices of selected features. In the outsourcing scenario, party A simply sends \(P\) to party B as the result of CWC. In the joint computing scenario, party A randomly shuffles \(P\) to conceal \(\pi\) to B. As a result, we can securely simulate CWC in \(O(kmn\log k+k\log^{2}k)\) time and \(O(kmn)\) space.
## 3 Results and Discussions
### NMR Analyses  
After each oxidation cycle, the resulting products were subjected to the \({}^{1}\)H-NMR and \({}^{13}\)C-NMR analyses. Since \({}^{1}\)H-NMR spectra for polysaccharides are difficult to discriminate due to high overlapping of the proton signals, more useful information can be acquired from \({}^{13}\)C-NMR, seen in Figure 4 and Figures S1-S6 in Supplementary Materials attached to this paper. The spectra of pullulan and oxidized pullulan present characteristic peaks of the C6 atoms around 63-60 ppm. In addition, the signals of C1 atoms are around 100-104 ppm, C4 atom signals are around 8076 ppm, and a multitude of signals assigned to atoms C\({}_{2,3,5}\) are located between 69 and 76 ppm [24; 25; 26]. After oxidation in the pullulan sample, a new peak in the region 170-180 ppm corresponding to the carboxylic acid, which was missing in the original sample, is clearly visible.  
The intensity of the newer peak appearing after oxidation is linked with the number of carboxyl groups introduced during oxidation. The electrokinetic charge (EC, %) of each reaction product was determined using a particle charge detector (PCD 03). This technique measures the amount of electrokinetic surface charge directly, combining an electrokinetic probe with titration of a charge-compensating polyelectrolyte. This technique is quite simple, and no additional model assumptions are required. The results shown in Table 2 show differences in the EC of pullulan after using the three catalysts. It appears that the BMPS catalyst possesses the highest performance, followed by APS and AMPS. For each catalyst, it can be seen that its efficiency does not decrease with subsequent reuse; by contrast, a slight increase of the EC of the oxidized samples can be observed when the catalysts have been reused for the third or fourth time, especially in the cases of BMPS and APS catalysts. To test the catalysts' efficiency, key control reactions were performed in the absence of any catalyst. Not surprisingly, as revealed by \({}^{13}\)C-NMR spectra, no oxidation was observed in this case.  
Figure 3: Proposed reaction mechanism for the pullulan oxidation in the presence of NHPI-supported catalysts/NaClO/NaBr.
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Strategies for Enhancing ZnO-Based Nanomaterials Antibacterial Activity
#### 4.4.5 Organic Antimicrobial Agents Doping into ZnO  
Studies have shown that the composites obtained by co-doping and fusion of organic antibacterial agents and ZnO nanoparticles exhibited stronger antibacterial activity than ZnO nanoparticles alone [205,206]. The organic antimicrobial agents are usually immobilized or embedded on the ZnO surface. Taking chitosan (CS) as an example, it is an abundant natural biopolymer derived from the deacetylation of chitin in crustacean shells and can be made into films, fibers, beads, and powders. Cationic polymers are generally antimicrobial [207,208]. In general, antibacterial activity depends on molecular weight (Mw), degree of deacetylation, temperature, and solution pH [209,210].  
[PERSON] et al. [204] used CS and ZnO as raw materials to prepare a new composite material chitosan/poly(vinyl alcohol)/zinc oxide (CS/PVA/ZnO), which was used as a novel antibacterial agent with wound healing properties. CS/PVA/ZnO was proved to be an effective antibacterial nanomaterial after being analyzed by various characterization methods. SEM images of various prepared CS/PVA/ZnO-related materials and precursors are shown in Figure 22A-C. The surface of sole chitosan was obtained to be smooth. The sole ZnO nanoparticles showed nanosheet-like morphology. The surface of sole CS/PVA/ZnO microbeads presents a certain pore structure, and the surface of the microbeads is rough, which is conducive to exerting the ability of photocatalytic antibacterial. The antibacterial activities of CS, CS/PVA, and CS/PVA/ZnO are shown in Figure 23. The diameter of the inhibition zone against _E. coli_ cultures (G-) was 10 mm in the CS group, 14 mm in the CS/PVA group, and 19 mm in the CS/PVA/ZnO group (Figure 23A). Likewise, _S. aureus_ cultures (G+) had a diameter of 12 mm in the CS group, 15 mm in the CS/PVA group, and 20 mm in the CS/PVA/ZnO group (Figure 23A).  
Figure 22: SEM images of various prepared CS/PVA/ZnO-related materials and precursors: (**A**) pure chitosan, (**B**) ZnO, and (**C**) CS/PVA/ZnO. Reprinted/adapted with permission from Ref. [204]. Copyright © 2022, Elsevier B.V.
# The Sun and Space Weather
## 8 Solar Cycle Variation of Space Weather Events  
Solar activity represents the appearance and dispersal of closed and open magnetic field regions on the Sun. While CMEs originate from closed-field regions, high-speed streams originate from open-field regions. Indices such as sunspot number (SSN) and the radio flux at 10.7 cm wavelength (F10.7) are typical measures of solar activity, although there are many other indicators. For example, the magnetic butterfly diagram [183] provides information on the magnetic nature of solar activity and how the low-latitude and high-latitude magnetic regions are related. Since CMEs and CIRs originate from enhanced closed- and open-field regions on the Sun, solar activity has clear relevance to space weather events. When the SSN is high (solar maximum phase), there are more closed-field regions on the Sun, and hence the probability is high for the occurrence of CMEs. Similarly, when there are many low-latitude coronal holes in the declining phase of the solar cycles, there are more HSS and the related CIRs and geomagnetic storms.
# Research on Residual Life Estimation Method for KMN Steel Based on Nonlinear Ultrasonic Testing
## 3 Results  
### \(\beta\)-N Curve  
The RitecSNAP ultrasonic system is used to perform nonlinear ultrasonic testing on KMN bending fatigue specimens with different fatigue cycles. The relationship curve between the normalized nonlinear ultrasonic coefficient \(\beta/\beta_{0}\) and the fatigue cycles is obtained as shown in Figure 4. It can be found that the nonlinear coefficient of the bending fatigue test increases first and then decreases with the increase in the fatigue cycle. At the very beginning of fatigue (about before 3 \(\times\) 10\({}^{5}\) cycles), cracks begin to initiate and the nonlinear coefficient \(\beta\) increase slightly. In the early stage of fatigue (before 10\({}^{6}\) cycles), the nonlinear coefficient \(\beta\) gradually increases with the increase of fatigue cycles, and the nonlinear coefficient reaches a peak as the cracks start to grow (about 10\({}^{6}\) cycles). In the later stages of fatigue (after 10\({}^{6}\) cycles), the non-linear coefficient \(\beta\) begins to decrease. This shows that there is a certain corresponding relationship between the nonlinear coefficient \(\beta\) of the material and the fatigue damage of the specimen. With the increase in the fatigue cycle, the fatigue damage inside the specimen also accumulates. When the ultrasonic Lamb wave in S\({}_{1}\)-S\({}_{2}\) mode propagates inside the specimen, the second harmonic wave excited by the sound wave at the damage sites will also increase, and finally the nonlinear coefficient \(\beta\) of the material will continue to increase. With the increase in fatigue damage inside the specimen, the growing cracks increase the attenuation coefficient of the material, resulting in a decrease in the nonlinear coefficient, which is also the reason why the nonlinear coefficient decreases in the late stages of fatigue.