esa-sceva/satcom-chunk-collection huggingface dataset:
# Punching Shear Stress in Post-Tensioned Transfer Plate of Multi-Story Buildings
## 5 Analysis Results according to the Modeling Method
### Punching Shear Stress under Gravity Load  
To analyze difference in punching shear behavior of the separated model and the complete model, a comparison of punching shear stress under gravity load was made. Figure 11 shows graphs of the ratio of maximum shear stress at the critical section for each column for the two modeling methods for each plan shape. Although shear stress is distributed along the critical section, shear stress was compared based on the maximum value, since the stress value that governs design is the maximum. The vertical axis of the graph represents the ratio of the maximum shear stress of the separated model to that of the complete model, and the horizontal axis represents each column number. The higher the value of the ratio, the greater is the overestimation of punching shear stress calculated by the separated model. The interior, exterior and corner columns are marked as square, circle and triangle, respectively.  
For the Flat-shape plan, the separated model showed greater maximum shear stress than the complete model for all columns. The similarity in the ratios depended on location (exterior, corner and interior). The ratio at exterior columns (black circle) was 3.2 to 4.9 and at corner columns (red triangle) 4.0 to 4.4. In comparison, interior columns (blue square) had relatively lower ratios, 1.4 to 1.9.  
In the L-shape plan, higher maximum shear stress in some columns than that noted in the separated model was illustrated. However, overall, the shear stress ratio was similar to the Flat-shape case. Exterior columns (black circle) had a ratio of 0.9 to 10.0, Whereas the ratio for the corner columns (red triangle) was 2.6 to 7.8. Additionally, similar to the Flat-shape case, the interior columns (blue square), in comparison to the other column locations, had relatively low ratios of 0.9 to 1.7.  
The results for the separated model and complete model showed differences in shear stress. The primary difference in the two modeling methods is the consideration of the upper shear wall. Figure 11 shows columns in which shear wall(s) exist or do not exist within the column critical section for the L-shape plan. Where no shear wall lies within the critical section, such as in Figure 12a,d, relatively similar shear stress values were calculated, regardless of the modeling method. Conversely, accuracy cannot be ensured where the shear wall(s) exists in the critical section to which interaction between the two structural systems should be considered, such as in Figure 12b,c. Thus, factors that  
Figure 11: Maximum punching shear ratio according to the modeling method (for gravity load only). (**a**) Flat-shape plan and (**b**) L-shape plan.  
cause a difference between the maximum shear stress values can be divided into column location and shear wall presence within the critical section. In the separated model, the upper shear wall is disregarded, and its impact on punching shear is not reflected, which can lead to over-design.
## 3 Numerical Analysis
### Stick-Slip Configuration  
In this section, the performance of the reduction techniques is assessed in the stick-slip configuration (\(N_{0}\) = 250 N and excitation frequency = 8.1 Hz). The beam deformation at different time instants during the steady state is shown in the Figure 12a, while the contact normal and tangential displacements are shown in Figure 12b,c.  
Figure 11: The transverse displacement (**left**) and the axial displacement (**right**) of the selected SMDs of fixed interface vibration modes and constraint modes.  
Figure 10: The transverse displacement (**left**) and the axial displacement (**right**) of the selected SMDs of free interface vibration modes and residual flexibility attachment modes.  
In this configuration, the beam amplitude of vibration decreased by about 90% compared to the reference system due to the higher normal load, leading to alternate stick and slip states. This is depicted in Figure 13, which shows the contact states and the contact forces with respect to the corresponding displacements. This contact configuration determines a higher amount of dissipated energy (see the area of the hysteresis cycle) and also an increase in the system stiffness. As a result, as illustrated in Figure 12a, not only are the resonance frequency and amplitude dependent on the contact state, and in particular on the contact stiffness, but the mode shapes of the contributing modes on structure behavior are also influenced.  
The beam tip vibration amplitudes obtained using the geometrical linear and nonlinear models are listed in Table 3. In this case, the linear model provides more accuracy compared to the results of the GL model in the slip-liftoff configuration, showing that the structure is in a geometrically linear regime.  
Although Table 3 shows that under this condition, geometric nonlinearity is not activated and the structure behavior is only influenced by the nonlinear contact forces, the accuracy of the reduction methods is compared in Figure 14.  
Figure 12: (**a**) Beam deformations, (**b**) contact normal displacement, and (**c**) the beam tip displacement in stick–slip configuration.  
Figure 13: (**a**) Contact node states, (**b**) contact tangential, and (**c**) contact normal forces in stick-slip configuration.  
\begin{table}
\begin{tabular}{l c c} \hline  & \(\mathbf{U_{y}}\) **(mm)** & \(\mathbf{U_{y}}\) **(mm)** \\ \hline Geometrically linear model & 19.73 & 0.00248 \\ Geometrically nonlinear model & 19.618 & 0.044 \\ \hline \end{tabular}
\end{table}
Table 3: Vibration amplitude obtained by geometrically linear and nonlinear models with \(N_{0}\) = 250 N at Freq = 8.1 Hz.  
Figure 14 shows that the performance of [PERSON] and Craig Bampton is acceptable due to being in a geometrically linear regime. However, the enhanced Craig Bampton model has the highest accuracy with its first normal mode, two constraint modes, and five modal derivatives, which shows the effectiveness of the modal derivatives when it comes to reducing structures with friction contacts.
## 4 Conclusions  
In summary, CQDs/TiO\({}_{2}\) microspheres were synthesized by the in-situ hydrothermal synthesis, which were further utilized with GO for the fabrication of PUF-supported 3D CQDs/TiO\({}_{2}\)/GO foams. The as-prepared CQDs/TiO\({}_{2}\) composite could expose more than 30% of high-energy crystal planes and exhibited an enhanced photocatalytic property. The addition of GO to this hybrid foam system had positive impacts on the transition of electrons and holes. These effects improved the photocatalytic activity of the fabricated CQDs/TiO\({}_{2}\)/GO foams. The adsorption and photocatalytic degradation experiments indicated that the fabricated CQDs/TiO\({}_{2}\)/GO foams exhibited a high efficiency, stability, and reusability for the decomposition of both MB and rhodamine dyes, revealing high potentials for the oil water separation, removal of oils, and the photothermal desalination of seawater.  
Figure 6: Photocatalytic degradation of (**a**) methylene blue (MB) (**b**) rhodamine with the loaded capacity of 10 mg (TiO\({}_{2}\)(1)), 20 mg (TiO\({}_{2}\)(2)), and 30 mg (TiO\({}_{2}\)(3)) CQDs/TiO\({}_{2}\) particles, respectively. (**c**) Comparison of the photocatalytic degradation ability of P25-TiO\({}_{2}\) and CQDs/TiO\({}_{2}\) nanoparticles in one hour to degrade MB. (**d**) Recycling capability test of CQDs/TiO\({}_{2}\)/GO foam of MB.  
**Supplementary Materials:** Experimental details and additional figures are provided that include the calculation of the percentage of the high energy lattice plane, XPS analysis, electronic band diagram of CQDs/TiO\({}_{2}\)/GO, up-conversion fluorescence test, and the UV\(-\)vis absorbance spectrum at dark.  
**Author Contributions:** [PERSON] and [PERSON] designed and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed experimental materials and analysis tools; [PERSON] and [PERSON] optimized the data analysis; [PERSON] and [PERSON] wrote the paper under the supervision of [PERSON], [PERSON], and [PERSON].  
**Acknowledgments:** [PERSON], [PERSON] and [PERSON] acknowledge the financial support of the National Natural Science Foundation of China (No. 51573013 and 51873016). [PERSON] thanks the financial support of the National Natural Science Foundation of China (No. 51873225) and the Deutsche Forschungsgemeinschaft (No. WE 5837/1-1).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Analysis  
#### 2.4.1 Identification of Significant Factors Influencing the Evaluation Parameters  
The time required to complete the tasks and number of positioning errors per task were averaged over the three trials of the test participants. By means of multifactorial ANOVA (\(\alpha\) = 0.05), it was analyzed for each factor (e.g., diameter of rotary control) whether the change in the factor (e.g., from 40 mm to 110 mm diameter) had a significant influence on an evaluation parameter (e.g., time on task). If a factor had a significant influence on an evaluation parameter, the mathematical relationship between the factor and evaluation parameter was calculated via linear regression. These equations were represented in a coded form. This means that the equations did not use numerical factor values, but \(-1\) for the lower level and \(+1\) for the upper level. In this representation, the relative effect of a factor can be determined by comparing factor coefficients. Statistical analyses and calculation of the mathematical models were conducted using Design Expert 13 software. The Cook's distance, which is a measure of how much the entire regression function changes when the values of a test run are not considered, was calculated to verify the accuracy of the mathematical models [70]. Large differences between [PERSON]'s distances indicate outliers. The Cook's distances above a value of one are considered critical and can falsify the model.
## 3 Results  
### Candidate Descriptive Variables in Each Global State  
Figure 1 shows the box plots of V\({}_{\text{t}ander}\), V\({}_{\text{t}main}\), \(\Delta\)V, and \(\Delta\)T in each state to compare each descriptive candidate variable in the four global states. The mean and standard deviation  
of V\({}_{\mathit{leader}}\) (m/s) in each state were DEN: 13.67 \(\pm\) 0.73, STR: 14.51 \(\pm\) 1.28, DIV: 14.43 \(\pm\) 1.12, and ESC: 13.86 \(\pm\) 0.71 (Figure 1a). The result of ANOVA show that the effect of the state was statistically significant (F(3, 236) = 9.239, \(p\) = 7.84 \(\times\) 10\({}^{-6}\), \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.106). [PERSON]'s multiple comparison test shows that V\({}_{\mathit{lender}}\) in STR and DIV was significantly higher than V\({}_{\mathit{lender}}\) in DEN and ESC (STR vs. DEN: t = 4.200, \(p\) = 2.199 \(\times\) 10\({}^{-4}\), r\({}^{2}\) = 0.137, STR vs. ESC: t = 3.567, \(p\) = 0.002, r\({}^{2}\) = 0.091, DIV vs. DEN: t = 3.861, \(p\) = 8.350 \(\times\) 10\({}^{-4}\), r\({}^{2}\) = 0.120, DIV vs. ESC: t = 3.196, \(p\) = 0.009, r\({}^{2}\) = 0.076).  
For V\({}_{\mathit{main}}\), the mean and standard deviation in each state were DEN: 13.67 \(\pm\) 0.73 (m/s), STR: 14.26 \(\pm\) 1.39 (m/s), DIV: 14.42 \(\pm\) 1.11 (m/s), and ESC: 14.18 \(\pm\) 0.78 (m/s) (Figure 1b). The result of ANOVA shows that the effect of the state was significant (F(3, 236) = 4.478, \(p\) = 0.004, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.054). The multiple comparisons showed a significant difference between the DEN and STR or DIV, and no other significant differences were observed (DEN vs. STR: t = 2.828, \(p\) = 0.025, r\({}^{2}\) = 0.067, DEN vs. DIV: t = 3.588, \(p\) = 0.002, r\({}^{2}\) = 0.106).  
For \(\Delta\)V, the mean and standard deviation in each state were DEN: 0 \(\pm\) 0 (m/s), STR: 0.25 \(\pm\) 1.00 (m/s), DIV: 0.02 \(\pm\) 1.00 (m/s), and ESC: -0.32 \(\pm\) 1.25 (m/s) (Figure 1c). In DEN, because the group was not separated, the value of \(\Delta\)V was zero (Figure S1). The result of ANOVA showed a significant difference in \(\Delta\)V among the states (F(3, 236) = 3.667, \(p\) = 0.013, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.045); however, no significant differences were observed in the multiple comparison results.  
For \(\Delta\)T, the time difference between the leader and the front of the main group, the mean, and standard deviation in each state were DEN: 0 \(\pm\) 0 (s), STR: 0.69 \(\pm\) 0.80 (s), DIV: 3.00 \(\pm\) 1.15 (s), and ESC: 5.60 \(\pm\) 1.43 (s) (Figure 1d). The ANOVA result showed a significant difference among the states (F(3, 236) = 332.1, \(p\) = 2.00 \(\times\) 10\({}^{-16}\), \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.809). Multiple comparison results showed a significant difference in all combinations (DEN vs. STR: \(p\) = 0.004, others: \(p\) < 0.001).
## 2 Materials and Methods
### Xanthophyll Quantification  
The content of Ax and Cx in the livers and male and female gonads of rainbow trout _O. mykiss_ was measured using the HPLC-PDA method, described by [PERSON] et al. [25]. Analytical HPLC measurements were performed with a C\(-\)18 column Hypersil Gold (5 \(\upmu\)m; 150 mm \(\times\) 4.6 mm) on a Thermo system combined with a Surveyor LC Pump Plus, Surveyor Plus, and Surveyor photodiode array detector PDA Plus. The mobile phase was methanol: water (97:3 _v/t_) which was previously filtered through a 0.45 \(\upmu\)m membrane filter and degassed. Under isocratic conditions, the analysis was carried out at a flow rate of 1.0 mL/min at room temperature for 6 min for a single run. The chromatograms were recorded at a wavelength of 474 nm.  
The reference materials, Ax (purity of min 97%, for HPLC analysis) from _Haematococcus pluvailis_ (Flotow 1844) and Cx OEKANAL(r) (purity of min 97%, for HPLC analysis), were provided by Sigma-Aldrich (St. Louis, MO, USA, and Seelze, Germany, respectively). All solvents--methanol and chloroform CHROMASOLV(r) HPLC grade, ethanol p.a., n-hexane p.a., and i-propanol p.a. were also purchased from Sigma-Aldrich. Deionized water (\(\sigma\leq\) 0.4 \(\upmu\)S/cm) was used thoroughly.  
The standard solutions used for the calibration were at five concentration levels in the range from 0.10 mg/L to 10.00 mg/L of both Ax and Cx. Each of the calibration standards was run in triplicate. The correlation coefficients (r\({}^{2}\)) of 0.9998 for Ax and of 0.9997 for Cx were obtained. They demonstrated an excellent relationship between the peak area and concentration according to the International Conference on Harmonization guidelines [26].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{**Monitoring Period**} & **Optimal [21]** \\ \hline
**August** & **September** & **October** & **\\ \hline Temperature, \({}^{\circ}\)C & 18.9 \(\pm\) 1.6 & 17.9 \(\pm\) 1.8 & 16.1 \(\pm\) 2.4 & not more than 20 \\ \hline Dissolved oxygen, mg/L & 9.4 \(\pm\) 0.2 & 9.6 \(\pm\) 0.3 & 9.8 \(\pm\) 0.3 & not less than 9.0 \\ \hline Nitrates, mg/L & 0.13 \(\pm\) 0.04 & 0.15 \(\pm\) 0.03 & 0.15 \(\pm\) 0.05 & not more than 2.00 \\ \hline Nitrites, mg/L & 0.05 \(\pm\) 0.01 & 0.06 \(\pm\) 0.01 & 0.06 \(\pm\) 0.01 & not more than 0.01 \\ \hline Amonium, mg N/L & none & none & none & none \\ \hline pH & 7.83 \(\pm\) 0.12 & 7.80 \(\pm\) 0.15 & 7.67 \(\pm\) 0.21 & 7–8 \\ \hline Harndess, mmo1/L & 6.24 \(\pm\) 0.08 & 6.12 \(\pm\) 0.05 & 6.18 \(\pm\) 0.04 & 6–14 \\ \hline \end{tabular}
\end{table}
Table 1: Water quality self-monitoring (mean \(\pm\) SD).  
The results in mg/kg were calculated using the following formula:  
\[\text{Xanthophylls }=\frac{\text{Xanthophylls in mg/L}*\text{V}}{\text{M}}\]  
where V is the volume of the methanol solution in mL and M is the mass of the sample in g.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 3 The Proposed DSAH Framework
### Hash Function Learning  
#### 3.2.1 Kernelization  
Kernelization is a widely used technique to handle the nonlinear relations in different modalities. Therefore, in this paper, we use Radical Basis Function (RBF) kernel to express the nonlinear correlations among original high dimensional features [49; 50; 51]. Specifically, we define the RBF function \(\phi(\cdot)\) as follows:  
\[\phi(\mathbf{x}_{i})=\left[\begin{array}{c}\exp\left(\frac{-||\mathbf{x}_{i }-\mathbf{a}_{i}||}{2\varpi^{2}}\right),\\ \cdots,\\ \exp\left(\frac{-||\mathbf{x}_{i}-\mathbf{a}_{i}||}{2\varpi^{2}}\right). \end{array}\right], \tag{1}\]  
where \(\mathbf{A}=[\mathbf{a}_{1},\mathbf{a}_{2},...,\mathbf{a}_{q}]\) denotes the randomly chosen \(q\) anchors from the database and \(\sigma\) is the free parameter. Therefore, the complex original feature \(\mathbf{X}^{(m)}\in\mathbb{R}^{d_{m}\times n}\) can be converted into a nonlinear relation representation \(\phi(\mathbf{X}^{(m)})\in\mathbb{R}^{q\times n}\).
## 1 Introduction
### Research Motivation and Goal  
In wireless sensor networks, topology control is an important research area. Ideal topology control benefits reachability and energy efficiency. The primary objectives of topology control in wireless sensor networks are to maintain network reachability, rather than optimizing network lifetime. In the past, network connectivity was considered as an evaluation of network quality and reachability are used as performance indices to evaluate the connection quality of a network. The connectivity is defined as the percentage of direct connections between arbitrary node pairs in a network. Through direct links, nodes communicate with each other in a hop. However, full connectivity does not guarantee the quality of network service. For an example, power consumption and signal interference become serious when each node will transmit packets in a high power level to reach all nodes. Instead, a hop-by-hop approach is more efficient than that of full connectivity. The reachability is the percentage of either indirect or direct delivery between any nodes. Each node is able to communicate with all distant nodes through relay nodes. In other works, there is no any isolated node in the full reachability network. And the necessary transmission range of each node in a full connectivity network is greater than it in a full reachability network. Network reachability is directly affected by connectivity. Network connectivity is affected by node degree, which is the number of connected neighboring nodes. In an open space, node degree is controlled by the transmission power of nodes. The node degree increases when the transmission range is increased by a higher transmission power. However, the energy-saving performance is reduced. Network reachability is poor when the connectivity of the network is too low. High connectivity will consume more power and decrease the quality of service. How to determine the optimal transmission power of a node is a critical question investigated in [2,3].  
The goals of this research on optimal transmission power control of nodes include the realization of full reachable network and the improvement of energy efficiency. In this study, a transmission optimization with a minimum node-degree algorithm is proposed to construct an energy-efficient wireless sensor network with full reachability. The objective of constructing a fully reachable network topology is to guarantee the transmission routes of any node pair and avoid creating unreachable nodes. A wireless sensor network with a minimum node degree can be constructed according to the proposed algorithm. Additionally, an optimal wireless sensor network will be energy-efficient. Furthermore, the proposed algorithm adjusts the transmission range according to various node deployment densities. This study analyzes the relationships between the transmission power and reachability with different densities. Finally, simulations are performed to verify the energy efficiency and full reachability of the proposed approach.
## 1 Introduction  
Lower extremity strength is the most important determinant for walking and daily living [1]. Muscle weakness is impaired in diverse medical conditions, including Parkinson's disease [2], motor neuron disease [3], myopathy [4], neuropathy [4], stroke [5], major surgery [6, 7], infection [8], and sarcopenia [9]. Impairments in muscle strength can serve as a predictor of important outcomes, such as mortality, hospital length of stay, and hospital readmission [10]. In light of these facts, practical tests for measuring muscle strength are needed. Therefore, a test wherein the examiner objectively and quantitatively measures the loss of strength or muscle power is required. Moreover, for early detection and prevention of muscle weakness progression, it is essential to accurately measure muscle strength. The manual muscle test (MMT) is a grading scale used to measure muscle strength that was first applied in patients with poliomyelitis in 1915 [11]. While MMT measures muscle strength, the subjective nature of the test lacks reproducibility [10]. In addition to MMT, conventional methods of measuring muscle strength include a hand-held dynamometer (HHD) [12] and isokinetic dynamometry [13]. Evaluations using an HHD must be performed in a specific posture and cannot be performed in all muscles; further, each muscle's strength must be separately measured [14]. It is difficult to assess overall strength by measuring the hand grip strength using a dynamometer. While isokinetic dynamometry is the gold standard for limb strength measurement, it requires a considerable amount of space, an expensive apparatus, and technical expertise. In addition, it can only test specific muscles [15]. In clinical practice, sit-to-stand (STS) and heel-raise tests are widely used to assess muscle strength [10].  
These tests [16] typically document the time required to complete a given number of movements such as STS [17] or count the number of repetitions completed in a given time [10]. Despite the frequent use of the STS test, its performance can be affected by balance capability. Therefore, in this study, a new method of measuring functional lower limb muscle strength was developed and tested. The degree to which the weight shifts laterally at seat-off during STS was measured by the ground reaction force (GRF) using a low-cost force plate.  
We hypothesize that the GRF asymmetry index (AI) measurement is useful as a method for evaluating lower extremity strength. To verify the validity of the new test, the correlation with the conventional test was analyzed. Therefore, this study aimed to evaluate (1) the reliability of the new GRF AI at seat-off and (2) the validity of this new test by comparing it with other muscle strength measurement methods (HHD and hand grip strength) and walking speed.