esa-sceva/satcom-chunk-collection huggingface dataset:
# New Methods for Assessing External Sulfate Attack on Cement-Based Specimens
## 1 Introduction
On the other hand, artificial intelligence (AI) has the potential to revolutionize the measurement and assessment of expansion, damage, and crack appearance on concrete structures by leveraging advanced computational techniques for image analysis and pattern recognition [32]. In fact, AI algorithms, particularly deep learning models, can be trained to detect and analyze cracks in images of concrete structures with high accuracy and efficiency [33]. Based on the appearance, size, and orientation of cracks, these models can be trained to recognize and categorize different types of cracks. Additionally, it is possible to develop systems that can analyze image data in order to measure the expansion of concrete structures over time [34]. This type of system can provide quantitative measurements of structural changes by training AI models to identify specific markers of expansion, such as crack patterns and deformations in concrete. Despite AI's accelerated use in the construction field, it still requires improvement and its use is not always simple. This article aims to propose two new techniques to evaluate the performance of cement paste specimens against ESA. The first method consists in observing the penetration depth of sulfate ions after a drying procedure. A comparison is made between the results obtained by this method and those obtained by ICP. The second method is to measure the changes in length caused by ESA-induced expansion with an optical fiber (OF). The feasibility and effectiveness of this method are then challenged by comparing the OF measurements with those obtained with a simple digital extensometer.
## Data Presentation
### Calculation of the TEC and ROTI
Solve Equation (7) with the least-squares solution indicated by summation of Equation (6) \[STEC_{ijt}=\widetilde{L}_{I,arc,ijt}-b_{i,p}-b_{j,p}-\left\langle\varepsilon_{ p}\right\rangle_{arc}\] (7)
4. Calculating the residuals of the solution According to Reference [29], the residuals can vary from \(1\) TECU to \(\sim\)8 TECU, depending on the conditions. Vertical TEC denotes the \(STEC\) mapping at the zenith and is derived by  
\[VTEC=STEC\cdot\left[1-\left(\frac{R_{e}\cos(\theta)}{R_{e}+H_{ipp}}\right) \right]^{-\frac{1}{2}} \tag{8}\]  
where \(R_{e}\) represents the averaging radius of the earth; \(\theta\) is the elevation angle in radians; \(H_{ipp}\) denotes the average height of IPP and is considered as 450 km. To further describe the disturbance of the ionosphere TEC, the rate of the slant TEC index has been proposed, which takes the average standard deviation of the slant TEC rate, and is widely used by the researchers. The rate of the slant TEC is given as  
\[ROT=\frac{STEC_{k+1}-STEC_{k}}{\Delta t_{k}} \tag{9}\]  
where \(STEC_{k+1}\) and \(STEC_{k}\) are the slant TEC at the \(k+1\) and \(k\) time epochs; \(\Delta t_{k}\) is the time interval. Usually, the unit of ROT is TECU/min. The ROT is defined in Reference [19]:  
\[L_{I,arc}=L_{1}-L_{2}=STEC+b_{r,l}+b_{s,l}+c_{arc}+\varepsilon_{l} \tag{2}\]0  
where \(\left\langle ROT\right\rangle\) denotes the averaging ROT during \(N\) epochs. Experimentally, a threshold of 0.2 TECU/min is set to determine whether an irregularity exists [19]. The relationship between the ROTI and the scintillation indices has already been well studied to verify the belief of the ROTI as a reliable indicator of ionosphere irregularity [30; 31; 32]. The disturbance of TEC is characterized by the percentage of TEC variation over the average TEC under the geomagnetic quiet condition (five geomagnetic quiet days in the same month were chosen according to the ISGI quiet days data, in this work, the days of year 156, 155, 153, 154 and 171, 2015. The order of these days are according to the geomagnetic quiet levels revealed by the ISGI quiet days data, by selecting the first five quiet days), expressed as  
\[L_{I,arc}=L_{1}-L_{2}=STEC+b_{r,l}+b_{s,l}+c_{arc}+\varepsilon_{l} \tag{2}\]1
## 2 Results and Discussion
### Analysis of Aroma-Active Volatiles in Cooked Sorghum by Using GC-O/MS
#### 2.3.2 Green  
Green was another significant aroma note in the cooked sorghums. In this study, 11 green aroma compounds were detected, having total aroma intensities 20% higher in glutinous versus non-glutinous sorghum (Table 4). Seven of the eleven compounds were aldehydes. Saturated aldehydes and (\(E\))-2-enal unsaturated aldehydes often demonstrate grass and malty aromas [14; 17]. Nonanal and 3-methylbutanal (saturated aldehydes) and (\(E\))-2-octenal (an unsaturated aldehyde), were evaluated as having strong aroma intensities in both cooked sorghums. Lipids in sorghum are susceptible to oxidization, and the heat imposed during sorghum distillation may accelerate the oxidation process [21], possibly contributing to the presence of aldehyde compounds in the cooked sorghum samples. (\(E\))-2-Enal aldehydes have not been detected in uncooked sorghum [10; 11], while they have been detected in a variety of Chinese Baijiu [14], thus further substantiating that the distillation (cooking) process of sorghum may improve the release of bound aroma compounds and these compounds influence the final flavor quality of Baijiu. (-)-\(\alpha\)-Cedrene, a terpene, showed the strongest green aroma note (5.0) in glutinous sorghum (Table 3). As (-)-\(\alpha\)-Cedrene is also an aroma compound in the soy sauce aroma type Baijiu, this compound may strongly contribute to the overall aroma of Baijiu [22].
## 4 Results
### Performance Analysis
In the case of the energy bill, the VESS operation by **P0** saves more in comparison to the proposed VESS operation. This means that the proposed VESS operation uses more resources to achieve savings on the demand bill, so the savings on the energy bill is limited. Figure 5 shows the peak demand reduction ratio obtained with varying implemented VESS capacity. The peak demand not only relates to the demand bill from the participants' point of view but also to the system operation from the aspect of the utility grid. The peak demand reduction reduces the necessity of the regulation capacity and new power plant construction in the utility grid [37]. The higher peak demand reduction provides the utility grid with more opportunities to reduce the system cost. As shown in Figure 5, the proposed VESS operation can provide more system cost reduction than the VESS operation by **P0**, owing to the reduction in the peak demand. When the usage factor is limited, as shown by the result of **P1** with one cycle, the peak demand reduction converges, even if the implemented VESS capacity increases. However, the peak demand reduction is higher than that of **P0** in all the cases. This result shows that the proposed VESS operation exhibits  
Figure 3: Performance comparison of the VESS operation according to **P0** and **P1** with varying VESS capacity: (**a**) average net benefit of participants; (**b**) return of investment of participants. higher performance than the VESS operation by **P0**, not only from the participants' point of view but also from the utility point of view.
## 3 The Improved 3D Reconstruction Method of Satellite Images
### Stereo Matching  
The satellite stereo pair will be matched after rectification for obtaining dense disparity map. The traditional SGM algorithm adopts mutual information to calculate the matching cost, but the mutual information needs to obtain the information entropy of the satellite image, which leads to the low computational efficiency of the algorithm. The information entropy of image \(I\) can be calculated by Equation (14).  
\[H_{I}=-\int_{0}^{1}p_{I}(i)\log p_{I}(i)di, \tag{14}\]  
where \(p_{I}(i)\) denotes the probability distribution of the luminance of pixel \(i\) on image \(I\). The proposed method uses Census cost [27] to calculate the matching cost, and Census cost utilizes the luminance difference in the local pixel neighborhood to convert the luminance into bits, which can significantly improve the efficiency of the matching algorithm.  
Figure 4: The principle of the epipolar constraint error.  
After matching cost calculation, the SGM algorithm optimizes the initial cost through the matching cost aggregation, so that the optimized matching cost will be robust to the local noise. This process is essentially a dynamic programming problem. Different from the traditional SGM algorithm, when aggregating the matching costs, we replace the disparity information in one direction with the mean of it in two vertical directions. Actually, we add a direction that rotates the original direction by 90\({}^{\circ}\) counterclockwise for aggregation. The final step in stereo matching is the disparity map generation. The proposed method selects the disparity value corresponding to the minimum aggregating matching costs for each pixel as the final disparity; then, we use a left-right check [28] to refine the final disparity.
## 4 Simulation Results
### Case II  
In case II, a new operating condition and new load change pattern are investigated. From the time instant 0.5 min to 2 min of the simulation, all the loads in the IEEE 30-bus system are increased by 20% based on the initial load levels.  
During the load increase, the voltage magnitudes at the monitored buses (bus #3 and #12 in this case) are within the pre-determined voltage limits, but static voltage stability margin of the power system is less than the threshold level of 0.3 p.u. at \(t=~{}2\) min. This means that the system has a great potential of voltage collapse without any remedial control. Under such situation, however, no control actions will be initiated by the traditional SVC methods which consider only bus voltages as trigger signal. Instead, the proposed scheme employs not only voltage magnitudes but also static voltage stability margin as criteria; therefore, inadequate VSM (\(VSM=0.23<0.3\) p.u.) is identified.  
After applying the proposed ASVC, all the voltage magnitudes are maintained within the range of predefined constraint and the static voltage stability margin is enlarged as well. The simulation result is illustrated in Figure 7, showing that the VSM is significantly increased from 0.23 to 0.57 p.u.- The enlargement of the static voltage stability margin is a result of the improvement in the system voltage profile.  
Figure 6: Simulation result of case I. (**a**) The trace of \(|V_{30}|\); (**b**) The trace of VSM.
## 4 Verification of the Theoretical Modeling Method  
In order to verify the correctness of the modeling approach developed in this paper, the finite element model of the rotor system was established according to the same parameters and constraints as the theoretical modeling based on the Ansys Workbench platform, as shown in Figure 35.  
The specific steps of the finite element analysis process are as follows: establishing the geometric model of the rotor system in Creo; importing the model into Ansys Workbench for finite element analysis; defining material property to keep it consistent with the data used in the theoretical calculation; meshing the model of the rotor system. The number of elements is 15,070, the number of nodes is 27,179, the type of the element is tet10, the mean jacobian ratio is 1.039, the mean element quality is 0.729, the mean aspect ratio is 2.27, the mean skewness is 0.407, the mean maximum turning angle is 1.815, these parameters are chosen as the quality check standards; imposing the constraints. The rotor is limited at the two ends by bearings; the bearings are equivalent to springs; the spring stiffness is 1.0 \(\times\) 10\({}^{7}\) N/m which is consistent with the stiffness of theoretical calculation; the axial translation degrees of free (\(z\) direction) are limited; the vibrations of \(x\) direction and \(y\) direction are focused on; applying loads. The displacement response of the rotor vibration is calculated after applying the unbalanced load to the rotor.  
After calculating, the time history curve and frequency domain diagram of the vibrational displacement at the center of mass for the disk under unbalanced load are drawn, and a comparison of the Finite Element Method (FEM) result and the Theoretical Modeling Method (TMM) result is carried out, as shown in Figure 36a,b. Because all the model parameters and boundary conditions are consistent with the theoretical calculation, the results of the finite element calculation and the theoretical calculation results are comparable. It can be shown from the figures that the theoretical and finite element calculations agree well, which validates the correctness of the theoretical modeling method.  
Figure 35: Finite element model and constraints of the rotor system.
## 3 Classification Algorithm Design
### WT-DBN Classification Steps  
In response to the measured partial discharge and corona discharge, a classification process based on WT-DBN is proposed, as shown in the flowchart in Figure 8, and the prescribed steps are outlined as follows:  
Figure 7: DBN model structure.  
(1) Signal Acquisition. Partial discharge signals and corona discharge signals at vehicle-mounted cable termination are measured using the HFCT method.  
(2) Signal Denoising. The detecting signals are denoised by employing a wavelet denoising technique grounded in the empirical Bayes method to eliminate white noise interference.  
(3) Dataset Formation. Individual signals are extracted with 500 sampling points selected, resulting in a signal time length of 5 us per individual signal, with 150 sets each for partial discharge and corona discharge signals.  
(4) Data standardization. To simplify the dataset, address any existing outliers, speed up gradient descent for the most effective solutions, and improve model precision and convergence rate, the data from step (3) are standardized by formula (13):  
\[Y_{i}=\frac{y_{i}-y_{min}}{y_{max}-y_{min}} \tag{13}\]  
where \(y_{i}\) is the original dataset, \(Y_{i}\) is the standardized dataset, \(y_{min}\) is the minimum of the dataset, and \(y_{max}\) is the maximum of the dataset.  
(5) DBN recognition and classification. The standardized results derived in step (4) serves as the input data for the DBN model, yielding classification outcomes following traversal through numerous visible and hidden layers.
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.3 Dubinin-Radushkevick (D-R) Isotherm  
The D-R isotherm [50,51] was employed to model was used to study the mean adsorption energy, and is described by Equation (11):  
\[\ln q_{e}=\ln Q_{DR}-\beta_{DR}\varepsilon^{2} \tag{11}\]  
where \(Q_{DR}\) is the monolayer saturation capacity (mg/g), and \(\beta_{DR}\) is the D-R adsorption energy constant related to mean adsorption energy (mol\({}^{2}\)/J\({}^{2}\)). \(\varepsilon\) is the Polanyi potential expressed as Equation (12):  
\[\varepsilon=RT\ln[\frac{C_{0}}{C_{e}}] \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter  
where \(R\) is the gas constant (kJ/mol K), and \(T\) is the temperature (K). The mean adsorption energy \(E\) (kJ/mol) can be calculated by Equation (13):  
\[E=\frac{1}{\sqrt{2\beta}} \tag{13}\]  
A value of \(E\) under 8 kJ/mol is for physical absorption and ranges from 8.0 to 16.0 kJ/mol for chemical processes [37]. Fitting of the experimental results to the D-R isotherm is shown in Figure 8d.  
The value of the mean free energy (\(E\)) of adsorption is 4.36-6.28 kJ/mol, which reveals that the type of P adsorption onto Al-Ti oxide follows a physical adsorption mechanism.
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 4 Discussion
### Comparison with Results of Current Research
Hence, semi-automatic and automatic tools for the supplementary initial assessment of patient functioning and the subsequent cyclical re-evaluation of gait to assist in the rehabilitation process are constantly being developed. Clinical gait analysis in telemedicine and telerehabilitation primarily involves the application of established devices, such as the Kinect (Microsoft), the PlayStation Eye (Sony), and the Nintendo Wii. Adapting proven devices for biomedical purposes in this way issuitable for the development of a commercial off-the-shelf (COTS) strategy, which will involve lower research and customization costs and shorter deployment times [61; 62]. A summary of the development of rapid and low-cost gait diagnostics should also include wearables and virtual reality (VR) devices. Better testing and analytical tools and more extensive testing infrastructure can enable preventive interventions that do not require the use of complex testing procedures and specialized gait analysis laboratories [63]. Gait analysis in the natural environment, i.e., preventive testing of children at school, or of athletes or soldiers during normal activity, may become the norm. The additional capabilities of mobile applications are already enhancing the capabilities and safety of sportspeople (e.g., runners), and solutions such as smartbands, smartwatches, and breath sensors are easily integrated into telemedicine applications. The development of wearable sensors has been progressing for about 15 years, but developers have tended to focus on solutions that do not process data in real time, are not intended for people under 40 years of age; the accuracy of these sensors are typically similar to that of marker-based video image analysis, and they are used to monitor patients during the activities of daily living, e.g., stair climbing and cycling. The key issue here is the standardization of sensor mounting, and the systems described still need further research [64; 65]. An important element is full digitization and the purposeful and planned use of data already collected. New analytical capabilities, combined with the wealth of patient records, can contribute to significant advancements in knowledge, not on lyn the field of gait biomechanics, but in the planning and implementation of therapy and sports training in general [63]. The proposed AI-based methods and techniques can be effective clinical and home-based tools for monitoring and rehabilitating gaits resulting from neurological and neurodegenerative disorders.
## 1 Introduction  
In orthognathic surgery, surgical splints have been widely used to position the jaws. Therefore, their fitting plays an important role in the success of the treatments. The more accurate they are, the better the predicted outcomes. In fabrication methods, alginate impressions were mandatory during the last few decades. The conventional impression procedure has been challenging for patients with gag reflexes, causing feelings of discomfort and being time-consuming [1]. Nowadays, an optical record of dental arches can be acquired with the aid of an intraoral scanner (IOS). This allows a patient's oral cavity morphology to be captured comfortably [2]. Using digital model systems, the disadvantages of plaster models, such as storage and data query problems, can be overcome [3]. Moreover, IOS can be utilized in several fields of dentistry such as restorative dentistry, prosthodntics, orthodontics, implantology, and oral surgery.  
The accuracy of IOS and alginate impression play an important role in the success of the treatment. According to ISO 5725, the definition of accuracy can be divided into two aspects, \"trueness\" and \"precision\". \"Trueness\" describes the closeness of agreement with the actual dimensions of the reference object, while \"precision\" refers to the closeness of agreement between individual measurements [4; 5; 6]. The accuracy of alginate impressions is affected by manipulation guidelines, water temperature [7], environment, and water-to-powder ratio. Therefore, clinicians must control these factors strictly to obtain accurate models. On the other hand, the accuracy of IOS is affected by the length of the scanning area, clinician's skills, IOS brands, and capturing area of IOS tips [8; 9; 10; 11; 12; 13]. Recently, [PERSON] et al. [14] reported that the existence of orthodontic brackets and their positions also affected the accuracy of IOS. For some IOS systems, the powder had to be poured onto the models or the patient's teeth to reduce the reflection of the metal before scanning. Nevertheless, inaccuracy was found in both IOS and alginate impression for the whole arch [15].  
Nowadays, the relative degree of accuracy between alginate impressions and the IOS method remains controversial. [PERSON] et al. [16] reported that intraoral scans were more detailed and less error-prone compared to alginate impressions. On the other hand, [PERSON] et al. [10] reported that the accuracy of IOS and alginate impressions in a full arch model without orthodontic brackets were not different. Moreover, the accuracy of each method has not been widely studied in patients with orthodontic brackets. The aims of this study were to evaluate the accuracy of IOS (TRIOS3(r)) and alginate impressions in models with and without orthodontic brackets and to compare the accuracies of IOS and alginate impressions in models without orthodontic brackets, IOS and alginate impressions in models with orthodontic brackets, IOS in models with and without orthodontic brackets, and alginate impressions in models with and without orthodontic brackets.
## 3 Finite Element (FE) Modeling of Side Joints with URSP Connectors
### Geometry, Element and Materials of the FE Model  
As shown in Figure 4a, the investigated side joint is composed of a rectangular concrete-filled steel tubular (CFST) column, a steel beam and a concrete slab. The length of the steel beam is \(L\) for load case 1 and 0.5\(L\) for load case 2 (\(L\) is 3 m in this example). The height of the column has the value of 3 m. The thickness and width of the concrete slab are 100 mm and 2 m, respectively.  
The reinforcement ratios of transverse and longitudinal rebars are 1.5% and 1%, respectively. All the dimensions are determined based on the common sizes of high-rise buildings in China. To provide a fully shear connection between the RC slab and steel beam, the shear studs are adopted with the diameter and total height of 16 mm and 70 mm, respectively. The spacings of shear studs along transverse and longitudinal directions are both 100 mm, so the actual degree of shear connection is up to 2.0. The detailed model parameters and boundary condition of the joint model are illustrated in Figure 4a. The top and bottom ends of the column are set as hinges, while the beam end is free.  
A shell-solid elaborate finite element model of the side joint is established using the software [PERSON]. [PERSON] (2015). As shown in Figure 4b, the steel beam, the steel tube of the column and the RC slab are modeled by a four-node shell element with six degrees of freedom per node and full integration schemes. The concrete of the CFST column is modeled by eight-node solid elements with three degrees of freedom per node and full integration schemes. The mesh size is selected to be 50 mm, and a mesh sensitivity study was performed, which ensures the precision of all FE models presented in this study. A multilayer material model composed of three rebar layers and four concrete layers is adopted  
Figure 3: Two load cases simplified from a typical frame structure. (**a**) Typical steel-concrete composite frame structure; (**b**) Load case 1; (**c**) Load case 2.  
to simulate the RC slab [38]. A Rusch curve is employed as the uniaxial compressive stress-strain relationship of the slab concrete. In order to consider the cracking of concrete, the fracture energy can be calculated based on the calculation recommendations stated in CEB-FIP MC90 [39]. The steel and the reinforced bar in the RC slab are assumed to be an idealized elasto-plastic material with the Von Mises yield criterion. The concrete material of the column is assumed to be elastic based on the design criterion of the weak beam-strong column. The nodes corresponding to the steel tube and the concrete core are connected by the node-sharing approach.  
The above model has already been adopted to investigate the behavior of steel-concrete composite joint and frame structures, and the feasibility and accuracy of the aforementioned finite element modeling strategy have been experimentally validated [40; 41].