esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 3 Experimental Results and Discussion  
### Load-Deflection Relationship and Crack Distribution  
The load-deflection relationship of each specimen is shown in Figure 3, and Figure 4 shows the crack pattern of each specimen after failure. The thick lines in the crack pattern show the dominant crack at failure. The shaded region in the PL-50 case indicates the concrete crushed region at failure. Dotted line and orange marks represent rebar arrangement and the strain measurement points of rebar, respectively.  
In both the PL series and the ST series, as the interlock area between the rebar and concrete decreased from 100% to 50% and 0%, the stiffness of the beam decreased after the formation of a flexural crack. Initial stiffness in elastic range of the PL-50 and ST-50 specimens was lower than that of the specimens with 100% and 0% interlock area, on account of the smaller cross section of the beam due to the absence of cover concrete.  
Figure 4: Crack pattern (thick line: dominant cracks at failure, shaded area: concrete spoiled area at failure).  
Figure 3: Load–deflection curve (**left**: PL series, **right**: ST series).  
Figure 2: Measurement positions for main rebar strain.  
The maximum loads for the PL series were 264 kN, 285 kN, and 280 kN for PL-100, PL-50, and PL-0, respectively. Although the maximum load of PL-50 was close to that of PL-100, load reduction of PL-50 was due to crushing at the top of the concrete at the center of the span instead of shear failure as in PL-100. This is because the tied arch formed in PL-50 and thus shear capacity increased. The failure of PL-0 was caused by crushing of the concrete around the anchoring nuts.  
The maximum loads of ST-100 and ST-50 were 466 kN and 368 kN, respectively, showing a different tendency from those of the PL series. Comparing ST-100 and ST-50, the two specimens showed similar crack propagation, and the both failed in shear, although the maximum load of ST-50 was approximately 20% lower than that of ST-100. In the ST-0 case, crack pattern, failure mode, and maximum load were similar to those of PL-0. In PL-0 and ST-0, the stiffness of beam declined markedly after cracking and the dispersibility of cracks was reduced. In these beams, because the bond was uniformly low, the stress on the anchoring parts was increased, and it ultimately caused anchorage failure.  
The difference in interlock area of the specimens also affected the dispersibility of flexural cracks. The number of flexural cracks was eight and seven for ST-100 and PL-100, respectively, but only three cracks were formed in the span center in ST-0 and PL-0. Effects of stirrup placement were evident in the behavior of PL-50 and ST-50. In PL series without stirrups, the number of flexural cracks was reduced from seven in PL-100 to four in PL-50, while the number of cracks in ST-50 with stirrup was eight, the same number as in ST-100. Placing of stirrups also affected crack location. Cracks were basically formed on or near the stirrup in ST series while cracks were located randomly in the PL series.
# Connectivity Alterations in Vascular Parkinsonism: A Structural Covariance Study
## 3 Results
### Structural Covariance Analysis
#### 3.1.1 Seed Region: Left Caudate
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{8}{c}{**ANOVA Comparisons among the Three Groups**} \\ \hline \multirow{2}{*}{_Brain Region_} & \multicolumn{4}{c}{_MNI Coordinates_} & \multirow{2}{*}{_F value_} & \multirow{2}{*}{_Z-value_} & \multirow{2}{*}{_p value_} & \multirow{2}{*}{_Cluster extent_} \\ \cline{2-2} \cline{5-7}  & x & y & z & & & \\ \cline{2-2} \cline{5-7}
**Left Thalamus** & \(-8\) & \(-9\) & 15 & 16.40 & 4.28 & 0.001 ** & 1460 \\ \hline Right Insula & 41 & \(-11\) & 14 & 13.82 & 3.96 & 0.027 ** & 711 \\ \hline \multicolumn{8}{c}{**Post hoc comparisons**} \\ \multicolumn{8}{c}{**PD > VP**} \\ \hline \multirow{2}{*}{_Brain Region_} & \multicolumn{4}{c}{_MNI Coordinates_} & \multirow{2}{*}{_T value_} & \multirow{2}{*}{_Z-value_} & \multirow{2}{*}{_p value_} & \multirow{2}{*}{_Cluster extent_} \\ \cline{2-2} \cline{5-7}  & x & y & z & & \\ \hline
**Left Thalamus** & \(-8\) & \(-9\) & 15 & 5.48 & 4.62 & 0.034 * & 2993 \\ \hline Left Hippocampus & \(-15\) & \(-36\) & 3 & 5.07 & 4.36 & 0.02 ** & 1049 \\ \hline \multicolumn{8}{c}{**HC > VP**} \\ \hline \multirow{2}{*}{_Brain Region_} & \multicolumn{4}{c}{_MNI Coordinates_} & \multirow{2}{*}{_T value_} & \multirow{2}{*}{_Z-value_} & \multirow{2}{*}{_p value_} & \multirow{2}{*}{_Cluster extent_} \\ \cline{2-2} \cline{5-7}  & x & y & z & & \\ \hline Right Insula & 41 & \(-11\) & 14 & 5.13 & 4.40 & \textless{}0.001 ** & 2918 \\ \hline Right Thalamus & 23 & \(-30\) & \(-3\) & 4.73 & 4.13 & 0.026 ** & 975 \\ \hline \multicolumn{8}{c}{* \(p\) \(<\) 0.05 five, whole-brain corrected. **_p_ \(<\) 0.05 five cluster level correction using an exploratory threshold of \(p\) \(<\) 0.001 uncorrected. Nothing was found for the other post hoc comparisons.} \\ \end{tabular}
\end{table} 
Table 2: Structural covariance analysis. Seed region: Left Caudate. Figure 3: Group differences in the structural covariance of the Left Caudate. Brain maps are displayed using the neurological view. Panel **(A)**: Voxels of the Right Insula that expressed differences in thestructural association across the three groups. Panel (**B**): Correlations between the mean gray matter volume of the Left Caudate and the regional gray matter volume extracted from a 4 mm-radius sphere centered on the peak voxels of the significant cluster (x: 41, y = \(-\)11, z: 14). Panel (**C**): Yoxels of the Left Thalamus that expressed differences in the structural association across the three groups. Panel (**D**): Correlations between the mean gray matter volume of the Left Caudate and the regional gray matter volume extracted from a 4 mm-radius sphere centered on the peak voxels of the significant cluster (x: \(-\)8, y = \(-\)9, z: 15). Blue diamonds represent HCs, red circles represent PD patients and black crosses represent VP patients. GMV: gray matter volume; HCs: healthy controls; PD: Parkinson's disease; VP: vascular parkinsonism.
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 3 Results
### Area of Rice Crops Damaged
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Philippines** & **Vietnam** \\ \hline \hline
**Area1 losses (ha)** & & \\ \hline No landfall & 10,576 (5) & 18,774 (6) \\ Tropical storm & 64,839 (7) & 52,687 (46) \\ Category 1 & 91,999 (7) & 18,758 (14) \\ Category 2 & 13,032 (3) & NA \\ Category 3 & 185,953 (3) & NA \\ Category 4 & 425,134 (1) & NA \\ Overall & 83,571 (26) & 42,407 (66) \\ \hline
**Production lost (tonnes)** & & \\ \hline No landfall & 6402 (1) & NA \\ Tropical storm & 178,638 (6) & 718,600 (3) \\ Category 1 & 53,203 (4) & 800 (1) \\ Category 2 & 346,080 (4) & NA \\ Category 3 & 269,219 (4) & NA \\ Category 4 & 52,303 (1) & NA \\ Overall & 190,227 (20) & 539,150 (4) \\ \hline
**Value lost (USS)** & & \\ \hline No landfall & 8,689,695 (6) & NA \\ Tropical storm & 21,629,199 (14) & NA \\ Category 1 & 38,852,791 (13) & NA \\ Category 2 & 75,951,909 (6) & NA \\ Category 3 & 60,474,973 (10) & NA \\ Category 4 & 198,221,629 (1) & NA \\ Overall & 42,374,321 (50) & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average rice area, rice production, and rice value lost over the years from 1970 to 2018 according to landfall intensity. Cyclones that came within 500 km of each country but did not make landfall were considered cyclones with “no landfall”. Cyclone intensity was classified according to the Saffir–Simpson Hurricane Wind Scale. Numbers in parentheses refer to the number of data points. Figure 5: Relationship between landfall wind speed and the area of rice damaged in (**a**) Vietnam and (**b**) the Philippines. (**c**) Relationship between landfall wind speed and the volume of rice production that was lost in the Philippines. (**d**) Relationship between landfall wind speed and the value of rice crop lost in the Philippines.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 4 State Estimation
### Joint Model Estimation
#### 4.2.2 Accelerometer Model  
The accelerometer test results present even stronger evidence of the flaw in the joint model.  
The graph in Figure 17 was obtained from the first two seconds of the execution of a static test. Despite the absence of movements, the results imply that the manipulator is spinning its first joint at high speed.  
Figure 16: Evolution of estimates of the joints’ angles when testing the joint model using gyroscope measurements.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Point** & **B** & **C** & **D** & **E** & **F** & **G** & **A** \\ \hline Error in \(\theta_{1}\) (\({}^{\circ}\)) & 1.85 & 5.49 & 13.74 & 18.06 & 19.28 & 23.27 & 23.74 \\ \hline Error in \(\theta_{2}\) (\({}^{\circ}\)) & 4.48 & 1.85 & 6.54 & 10.66 & 16.63 & 17.20 & 9.17 \\ \hline Error in \(\theta_{3}\) (\({}^{\circ}\)) & 1.35 & 2.80 & 4.61 & 4.54 & 3.40 & 26.81 & 3.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error of the angles of the joints during the accuracy test for the link model.  
Once again, the culprit behind this problem is the tilt, which causes a misalignment of the links from their expected frames. This tilt causes a part of the gravity acceleration vector to be projected into the \(yz\)-plane of the IMUs. However, if the manipulator was perfectly vertical, the same vector should only be noticed in the \(x\) axis of the IMUs. Since there is no acceleration expected in that plane from that vector, the acceleration in that plane is justified as centrifugal acceleration. Considering the distance of the IMUs to the axis of rotation, the angular velocity must be high in order to justify the small acceleration measured in the horizontal plane. This angular velocity translates to a quickly changing value of \(\theta_{1}\).  
The solution adopted for this problem was to deny the model the ability to justify the acceleration in the horizontal plane as centrifugal acceleration. In other words, the accelerometer model was simplified to assume the only acceleration measured was due to the acceleration of gravity:  
\[{}^{i}g(k)=R_{\theta_{i}}^{T}(k)\,{}_{i-1}^{i-1}R^{T\,i-1}g(k) \tag{62}\]  
\[f_{\mu_{i}}(k)=-\,^{i}g(k)+e_{a_{i}}(k) \tag{63}\]  
This is the same assumption as the one used in the link model. This new model is similar to the model of the magnetometer since it is equivalent to measuring a rotated vector that is static in inertial space. The results of this corrected model to the test with the usual movements are presented in Figure 18. Note that this model is still incapable of finding the value of \(\theta_{1}\).  
Figure 17: Evolution of the estimates of the joints’ angles during a static test of the joint model using accelerometer measurements.  
Figure 18: Evolution of estimates of the joints’ angles when testing the corrected joint model using accelerometer measurements.
## 2 Case Study  
_Traffic Survey Data_  
In view of the inside left-turn lane, traffic surveys were conducted at the intersection of Southwest Rd. and Nansha St. and the intersection of Southwest Rd. and Huanghe Rd. in Dalian during the peak hours of 7:00-8:00 and 17:00-18:00 on 12-13 July 2019. In view of the unconventional outside left-turn lane, traffic surveys were conducted at the intersections of Xinlong St. and Shiji Road and Xinggong St. and Shenliao Rd. in Shenyang during the peak hours of 7:00-8:00 and 17:00-18:00 on 5-6 July 2019. All the surveys were conducted manually. Four investigators were set up at each intersection and two at each of the entrances opposite the main road. During the investigation, the number of straight-going lanes on the entrance road, the length of the upstream weaving section, and the setting location of the left-turn lane were recorded firstly for both inside and outside left-turn lanes. One investigator observed and recorded the number of straight-going vehicles in the entrance lane, and the other investigator observed and recorded the number of left-turning vehicles in the outside lane. The survey distinguishes only small and large cars. Therefore, four investigators are required for each intersection, and two intersections are investigated at the same time every day. A total of 8 investigators are required. Another investigator is required for data summary and collation, as shown in Table 1. This survey was designed and made according to the research content and approved by Dalian Jiaotong University. The data used in the case study were obtained from the survey. Additionally, road conditions at the four intersections can be clearly seen in Figure 1.
# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 4 Experiments and Results
### Datasets
#### 4.1.2 FVUSM Dataset  
The Finger Vein Universiti Sains Malaysia [41] database was collected from 123 subjects (83 males and 40 females) with ages ranging from 20 to 52 years old. The images were acquired in two sessions with an interval of more than two weeks. In a single session, each subject provided four samples (left index, left middle, right index, and right middle fingers) which were captured six times each. Therefore, a total of 5904 images (2 sessions \(\times\) 123 subjects \(\times\) 4 fingers \(\times\) 6 images per finger) at 640 \(\times\) 480 pixels were collected. In our research, we utilized only one session containing 2952 images in total. In the FVUSM database samples, 146 of 492 subjects were categorized as good quality; the rest, poor.
## 2 Materials and Methods
### Stage 1. Input Data Management  
The methodology is based on a data structure integrated into a real-time query procedure that uploads information from the SCADA master system and saves them in two databases (Topology Database and Profiles Database) accessed by the OPF module. The databases contain the topology recognized through the technical and area characteristics of the main components (buses, conductors, and power transformers) and the power profiles transmitted from the electric substations and those injected by the classical power plants and wind farms. This structure enables high-speed data processing.
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 2 Materials and Methods  
### Dataset  
Two datasets were used in this research. The first one is the Human Activity Recognition dataset, and the second one is the mHealth dataset from the UCI data repository [33; 34]. In the Human Activity Recognition dataset, light to moderate intensity activities are available (e.g., laying to walking) and vigorous activity like running is not available. Although the mHealth dataset has different data acquisition protocol, it contains vigorous activity (e.g., running). Thus, MHEALTH data were also used in this research.  
The experiments of the Human Activity Recognition (HAR) dataset collection were carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (walking, walking upstairs, walking downstairs, sitting, standing, and laying) while wearing a smartphone (Samsung Galaxy S II) on the waist. Three-axial linear acceleration and three-axial angular velocity were logged at a constant rate of 50 Hz using its embedded accelerometer and gyroscope.  
Laying, sitting, and standing data were considered as static activities, and walking, walking up, and walking down were considered as dynamic activities. In the case of the HAR dataset, the first 20 s of data were used in this experiment, because some unexplained jump that may be induced by hardware default has been explained in the limitation section.  
In the MHealth dataset, body motion and vital signs were recorded for ten volunteers of diverse profiles while performing several physical activities (e.g., walking, running, and so on). Sensors placed on the subject's chest, right wrist, and left ankle are used to measure the motion experienced by diverse body parts. The sensor positioned on the chest also provides two-lead ECG measurements, which can potentially be used for basic heart monitoring and examining the effects of exercise from the ECG.  
Although the MHealth dataset contain three accelerometers in different body positions, among them, the chest is the only location that is more stable with respect to the waist compared with the wrist and ankle. As the HAR dataset was acquired from a waist-mounted smartphone, to validate the different datasets with the same model, data coherence was an importance consideration. So, only the chest-mounted accelerometer named Shimmer 2 [BUR10] sensor with constant rate of 50 Hz was taken into consideration for the MHealth dataset.
## 3 Results and Discussion
### Effect of Biochar and Humic Acid on the Content, Characterization and Component of DOM in Soil
#### 3.2.2 Soil DOM UV-Vis Absorption and Fluorescence Index  
In this study, the properties of soil DOM were evaluated using UV parameters and 3D fluorescence spectroscopy parameters. The results of the parameters are shown in Table 1. Among them, SUVA254 denotes the ratio of absorbance to DOM at 254 nm, a larger value indicates a higher degree of aromatization in the organic matter [47]. The results demonstrated that the aromatization level of soil DOM in treatments YB3 and YB6 did not exhibit a significant increase compared to the Y treatment. Nevertheless, in treatments YB3H and YB6H, the aromatization level of soil DOM increased significantly by 760% and 188.25%, respectively (\(p<0.05\)). This indicates that HA, serving as the primary raw material, incorporates a substantial amount of functional groups including benzene rings into DOM, thereby markedly enhancing its stability [48]. Furthermore, the alignment in  
Figure 3: Soil DOM content of each treatment. Y: CK; YB3: 3%Biochar; YB6: 6%Biochar; YB3H: 3%Biochar + 1%HA; YB6H: 6%Biochar + 1%HA. Letters denote significance at \(p<0.05\), and different letters represent treatment differences.  
t trends between SUVA260 and SUVA254 further suggests that the incorporation of HA elevates the proportion of highly hydrophobic compounds within the DOM [47].  
On the other hand, the fluorescence index (FI) presented in Table 1 provides insights into the origin of soil DOM. Specifically, an FI value below 1.4 suggests that DOM primarily originates from terrestrial inputs, whereas an FI value above 1.9 indicates that DOM is predominantly of endogenous origin, resulting from the decomposition of soil organic matter by microorganisms [49]. The results showed that the FI values of all treatments exceeded 1.9, indicating that DOM in maize rhizosphere soil was mainly derived from the decomposition and transformation process of added biochar and HA by soil microorganisms, rather than directly from land input.  
Meanwhile, the data in Table 1 show that the humification index (HIX) of DOM in soil increased significantly with YB3-YB6-YB3H-YB6H treatments. In particular, the HIX values of YB3H and YB6H treatments increased by 181.59% and 209.07%, respectively, compared with the Y treatment. In terms of biodegradation index (BIX) of soil DOM, the BIX values of treatments with only biochar (YB3 and YB6) were not significantly different from those of Y treatment, but the BIX values of treatments with biochar and HA (YB3H and YB6H) were significantly lower than those of Y treatment, with decreases of 17.28% and 16.05%, respectively. This suggests that the addition of HA further deepened the humification of DOM and reduced the ability of DOM to be decomposed and utilized by microorganisms, thus enhancing the potential of DOM chelating HM when biochar is used alone [50].
## 4 Experiments
### Quantitative Evaluation on the Model
#### 4.2.1 Quantitative Evaluation of AnyLogic Simulation Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **LSTM** & **Social-LSTM** & **Social-Attention** & **Social-GAN** & **STGAT** & **SR-LSTM** & **Social-STGCNN** & **Ours** \\ \hline ADE & 9.47 & 6.19 & 4.82 & 8.36 & 4.15 & 4.96 & 5.04 & 2.73 \\ FDE & 18.32 & 12.36 & 10.27 & 16.73 & 8.98 & 8.35 & 8.91 & 5.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative results for all models on our dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(t_{pred}\) & **8** & **12** & **16** & **20** \\ \hline ADE & 0.26 & 0.41 & 0.57 & 0.74 \\ FDE & 0.54 & 0.69 & 1.24 & 1.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Our model predicts results at different time steps on the univ dataset. generative adversarial networks may face challenges in generalizing to highly specific or dense environments. The SR-LSTM [51] model enhances its analysis by incorporating a state refinement module, enabling it to effectively process information from a pedestrian's immediate surroundings. This refinement significantly advances its performance over the Social-LSTM model, achieving a 19.87% reduction in ADE and a 32.44% decrease in FDE. Among the models evaluated on our dataset, STAGT stands out for its holistic approach, considering not just pedestrian interactions but also the temporal and spatial aspects of their trajectories. However, it falls short by not accounting for obstacles within the environment. Our model distinguishes itself by addressing this gap, factoring in both obstacle presence and pedestrian destination data. As a result, it achieves significant improvements, with ADE reduced by 34.22% and FDE by 38.04%.
## 3 Results and Discussion
### Kinetic Modeling of Cell Growth
Numerous models have been utilized to define the sigmoidal curves of microbial growth, containing the logistic model which fitted microbial growth over time. In a study (Table 3), the logistic model was used to define the kinetics of the growth of _Aspegillus terreus_ and _Kluyveromyces marxianus_ in a batch fermentation system using banana and pineapple wastes. For validation, \(R^{2}\) and root-mean-square-deviation (RMSD) were used. It was reported that logistic model was good adapted to the observed data with high \(R^{2}\) (0.98) and low RMSD (0.49 g/L) for simultaneous saccharification and co-fermentation using banana waste in comparison with pineapple waste (\(R^{2}\) = 0.93 and RMSD = 1.64 g/L) ([PERSON] and [PERSON], 2016). Similarly, the logistic model was employed to describe the growth of the recombinant _Escherichia coli_ on the pyrolysate sugars derived from waste cotton. It was found that initial and maximum biomass levels (0.95 and 3.75 g/L, respectively) were calculated to be 0.95 and 3.75 g/L, respectively. Since a high \(R^{2}\) value (0.976) was obtained, it was determined that the model was reliable [24] (Table 3). Additionally, two different strains of _S. cerevisiae_ were used for ethanol production from red beet juice and the results were kinetic modeled by researchers. Findings were almost the same as our results as seen in Table 3 [25]. Accordingly, these findings indicated that our results were highly compatible with the existing results in the literature. After modeling, the kinetic parameters including \(\Delta X\), \(Y_{X\!S}\), \(Q_{X}\), \(\lambda\), and \(\mu_{max}\) related to cell growth were also calculated and compared with their experimental results (Table 2). Based on the results in Table 2, the experimental values of \(\Delta X\) and \(Y_{X\!S}\) were slightly higher estimated by the model. Besides, during the batch culture with Media A-E, the values of \(Q_{X}\) and \(\mu_{max}\) of _S. cerevisiae_ were predicted to be 0.77 g/L/h and 0.41/h, 0.79 g/L/h and 0.40/h, 0.65 g/L/h and 0.30/h, 0.67 g/L/h and 0.36/h, and 0.65 g/L/h and 0.33/h, which are in a very good agreement with their experimental results,  
Figure 3: Cell growth, ethanol production and substrate consumption curves fitted by the logistic model, LP model and MLP model. respectively (Table 2). However, the lag times were all lower based on the model (Table 2), which might be due to the lower estimation of the initial biomass concentrations.