esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
They also fail to take into account the impact of muscles during the chewing movements, and thus, the actual conditions of the dental implants are usually different from the experimental results. Therefore, if the mechanical effects of the different occlusion conditions on the dental implants are also considered during the experiment, we will be able to simulate the actual conditions of the dental implants more closely. At present, finite element analysis (FEA) is a commonly used analysis method in the field of dental biomechanics [19]. The FEA is a numerical method of analysis for deformations and stresses in any given structure [20]. Many studies have used FEA before. Based on previous literature, they explored how the mandible was affected under different occlusion conditions (simulations of incisal clench (INC), intercuspal position (ICP), right unilateral molar clench (RMOL), right group function (RGF), etc., the data for the loading and boundary conditions were obtained from a previous study by [PERSON] and [PERSON] [21]). They used the finite element analysis to conduct the biomechanical analysis of the impact of different occlusion conditions on the implantation of the total temporomandibular joint, the scaffold for the reconstruction of the mandibular defects, for bilateral sagittal split osteotomy, and the mandible and condylar fracture osteosynthesis [22; 23; 24; 25]. Therefore, the loading conditions and boundary conditions set in the FEA are also suitable for simulating and evaluating the forces exerted to the dental implant system under different occlusion conditions. As shown in the aforementioned studies, the dental implant treatment is currently the most commonly used treatment method for patients with lost teeth. However, due to the complex structure of the oral cavity, most of the previous biomechanics studies only focused on a single tooth and used a simple external force (vertical, horizontal, or oblique loading) in the biomechanical evaluation of the dental implant system [26]. Although the results of the previous studies greatly facilitate the design and improvement of dental implants, they are still different from the actual occlusions. The external force on dental implants should be closer to the actual loading conditions when performing biomechanics experiments. The dental implants in different positions should be subjected to different external forces under different occlusion situations. Therefore, the main objective of this study is to conduct a biomechanical analysis of the impact of four common occlusion conditions on dental implants with different positions using the finite element method. Furthermore, the investigation of the posterior region is subject to a larger external force when biting. The results of this study provide a biomechanical reference and can help clinicians, and dental implant researchers understand the impact of different chewing forces on the dental implant system at different positions after the implantation.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method
### Notations on 3D Line Representation and Triangulation in Plucker Coordinates
#### 2.1.1 Two-View Line Triangulation  
As illustrated in Figure 1, a 3D line \(\mathbf{L}=(\mathbf{m};\mathbf{d})\) is projected to two cameras. \(\mathbf{O}_{w}-XYZ\) denotes the world coordinate system. \(\mathbf{C}_{1}\) and \(\mathbf{C}_{2}\) are camera centers. Let \(\mathbf{R}_{wc}^{1}\) and \(\mathbf{t}_{wc}^{1}\) be the rotation and translation from camera 1 to the world coordinate system, \(\mathbf{R}_{wc}^{2}\) and \(\mathbf{t}_{wc}^{2}\) be the rotation and translation from camera 2 to the world coordinate system. \(\mathbf{p}_{s}^{1}\), \(\mathbf{p}_{e}^{1}\), \(\mathbf{p}_{s}^{2}\) and \(\mathbf{p}_{e}^{2}\) are the normalized homogeneous endpoints of the detected line segments in two images, respectively. \(\mathbf{n}_{c}^{1}\) and \(\mathbf{n}_{c}^{2}\) are the normal vectors of projection plane in respective camera coordinate system: \(\mathbf{n}_{c}^{1}=\mathbf{p}_{s}^{1}\times\mathbf{p}_{e}^{1}\), \(\mathbf{n}_{c}^{2}=\mathbf{p}_{s}^{2}\times\mathbf{p}_{e}^{2}\).  
When \(\mathbf{L}\) is projected to camera \(i\), we can derive the constraint between \(\mathbf{m}\) and \(\mathbf{d}\):  
\[\mathbf{S}_{i}\mathbf{m}=(\mathbf{n}_{c}^{i})^{\mathrm{T}}\mathbf{t}_{wc}^{i} \mathbf{d}, \tag{4}\]  
where \(\mathbf{S}_{i}=(\mathbf{R}_{wc}^{i})^{\mathrm{T}}\left[\mathbf{n}_{c}^{i} \right]_{\times}\mathbf{R}_{wc}^{i}\) is a skew-symmetry matrix defined by the pose of camera \(i\). \(\mathbf{m}^{\mathrm{T}}\mathbf{d}=\mathbf{m}^{\mathrm{T}}\mathbf{S}_{i}\mathbf{ m}/((\mathbf{n}_{c}^{i})^{\mathrm{T}}\mathbf{t}_{wc}^{i})=0\), which satisfies the Plucker constraint. Given two different observations from\(\mathbf{C}_{1}\) and \(\mathbf{C}_{2}\)(the line passing \(\mathbf{C}_{1}\) and \(\mathbf{C}_{1}\) should not be parallel to the direction of motion), we can solve the Plucker line by:  
\[\mathbf{d} =\frac{(\mathbf{R}_{\text{\tiny{acc}}}^{1}\mathbf{n}_{\text{\tiny{ cc}}}^{1})\times(\mathbf{R}_{\text{\tiny{acc}}}^{2}\mathbf{n}_{\text{\tiny{ cc}}}^{2})}{|(\mathbf{R}_{\text{\tiny{acc}}}^{1}\mathbf{n}_{\text{\tiny{cc}}}^{1}) \times(\mathbf{R}_{\text{\tiny{acc}}}^{2}\mathbf{n}_{\text{\tiny{cc}}}^{2})|}, \tag{5}\] \[\mathbf{m} =(\mathbf{A}^{\text{T}}\mathbf{A})^{-1}\mathbf{A}^{\text{T}} \mathbf{b}\]  
where:  
\[\mathbf{A}=\begin{pmatrix}\mathbf{S}_{1}\\ \mathbf{S}_{2}\end{pmatrix}_{6\times 3},\mathbf{b}=\begin{pmatrix}(\mathbf{n}_{ \text{\tiny{cc}}}^{1})^{\text{T}}\mathbf{t}_{\text{\tiny{acc}}}^{1}\mathbf{ d}\\ (\mathbf{n}_{\text{\tiny{cc}}}^{2})^{\text{T}}\mathbf{t}_{\text{\tiny{acc}}}^{2} \mathbf{d}\end{pmatrix}_{6\times 1}. \tag{6}\]
## 2 ACS-SSI Algorithm and Its Verification
### Numerical Simulation in Multi-DOF System
#### 2.2.3 Effect of Damping on Identification Results  
Due to the negative influence of high damping ratio on the identification results, the stochastic subspace identification is rarely applied in the case of high damping ratios [30]. The damping ratio of a vehicle's suspension system is higher than that of other mechanical systems, and the highest value is even up to 20-30%. The higher system damping ratio tends to deteriorate the effect of modal identification. Therefore, it is necessary to verify the reliability of the improved average correlated random subspace algorithm under high damping ratios before its formal application.  
First, nine damping ratios varying between 2.5% and 40% are set, as shown in Table 2, to compare the changes in identification error of SSI and ACS-SSI with the changes of damping ratio.  
The sampling frequency was set to 200 Hz, and the modal assurance criterion (MAC) was set to 30% to avoid false modes being identified. The input data is set for a period of 100 s and is averaged 50 times. Monte Carlo tests were performed 10 times for each damping ratio, and corresponding mean value of the identification error is shown in Figure 3.  
At the highest damping ratio (40%), the frequency error of ACS-SSI (3.9%) is less than half of the SSI (8.7%). Although both of them have large errors in damping ratio, ACS-SSI (29.3%) is still lower than SSI (42.4%). The trend of modal vector error for ACS-SSI (2.1%) and SSI (6.3%) is basically consistent with the frequency error.  
As can be seen from Figure 3, the identification result of ACS-SSI is obviously superior to that of SSI. It can be seen from the identification stabilization diagram (Figure 4) that the ACS-SSI algorithm has better identification stability and better convergence of recognition results than SSI.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(\xi\) & 2.5\% & 5\% & 10\% & 15\% & 20\% & 25\% & 30\% & 35\% & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Damping ratios (\(\xi\) ) of test.  
Figure 3: Identification error varying the damping ratio.  
In the case of high damping ratio, the ACS-SSI algorithm is still applicable and maintains considerable accuracy. Since modal vectors are not easily disturbed by speed and load, it was selected for the development of condition monitoring algorithms.  
Simulations based on the three-DOFs model show that the ACS-SSI algorithm can effectively identify the modal parameters of the system containing random white noise and non-stationary impact components. ACS-SSI can effectively suppress the noise in the raw signal and maintain a good identification accuracy in the case of high damping ratio.
## 2 Results
### Bactericidal Activity of LRM-CFS in ASM against CCCP-Generated P. aeruginosa Persisters
aeruginosa_ strains again displayed high levels of tolerance to tobramycin tested at 128 ug/mL (Figure 2c,d). Figure 2: Kinetics of PaCF1 (**a**) and PaCF4 (**b**) persister selection in ASM. Bacteria were exposed to tobramycin at concentrations of 64 ug/mL, 128 ug/mL, and 256 ug/mL, and their viability was assessed at 8 h and 24 h time points by CFU counts. Killing activity of LRM-CFS diluted at ratios of 1:4, 1:6, and 1:8 was evaluated against persisters of PaCF1 (**c**) and PaCF4 (**d**) in ASM after 24 h of incubation. In (**c,d)—CTRL: persisters selected by 24 h tobramycin exposure (as shown in (**a,b**)) and incubated in ASM in the presence of MRSB pH 4; tobramycin represents persister bacteria further incubated in ASM with tobramycin 128 ug/mL. Results are shown as mean \(\pm\) standard error of the mean values (\(n=4\)). Statistical significance was evaluated by two-way ANOVA for (**a,b**) and one-way ANOVA for (**c,d**), followed by the Tukey–Kramer post hoc test. \"_p_ < 0.05, \"\" \(p\) < 0.01, \"\" \(p\) < 0.001. Similar results were obtained with LRM-CFS diluted at 1:8. In contrast, LRM-CFS diluted at 1:6 caused a decrease in the viable bacteria number of approximately 5-log and 6-log against PaCF1 and PaCF4, respectively, compared to that of bacteria exposed to tobramycin (Figure 2c,d). LRM-CFS diluted at 1:4 killed almost all bacteria, with the number of survival cells under (PaCF4) or close (PaCF1) to the limit of detection (5 CFU/mL) (Figure 2c,d). To verify the antibacterial activity of tobramycin in ASM, PaCF1 and PaCF4 strains in the exponential growth phase were incubated in ASM with tobramycin 128 \(\upmu\)g/mL for 24 h. We observed a marked bactericidal effect of tobramycin against both bacterial strains, confirming that the lack of bactericidal activity of tobramycin against persisters was due to their tolerant state (Supplementary Figure S1).
## 4 Discussion
### Limitations  
This study has a number of limitations that should be acknowledged. Firstly, there is the factor of patient compliance when utilizing CAT. The study included subjects that completed initial series of aligners without major signs of fitment issues in efforts to minimize the effects of non-compliance in the study. Additionally, the treating orthodontist only provided subjects who demonstrated attendance to all scheduled appointments. This study also was reliant on two-dimensional radiography. More accurate three-dimensional imaging is now available in clinical practice and should be considered in future assessments. Finally, this study did not evaluate the inclusion or type of attachments used. CAT is typically accompanied by a number of resin composite attachments to aid in tooth movement, where location and shape are optimized through software algorithms. The inclusion of attachment type may influence the efficacy of some related tooth movements and results achieved.
## 3 Results and Discussion
50 mA g\({}^{-1}\) again, the specific capacity of the CNFs-2N anode was higher than 921.2 mAh g\({}^{-1}\), indicating a good rate performance. This is because N-doping and the hierarchical and heterogeneous porosity structure can improve the conductivity, increase the reactive sites, and improve the Li-ion transport rate. To the best of our knowledge, the outstanding electrochemical performance of the CNFs-2N anode is much better than that of other reported carbon fiber anodes for lithium-ion batteries (Table 1). Figure 7c show the constant-current charge-discharge curves of the CNFs-2N anode during the 1 st, 10 th, and 50 th cycles in the voltage range of 0.01-3 V. In the first cycle, the discharge and charge capacities were observed to be 2395 and 1108.9 mAh g\({}^{-1}\), respectively, corresponding to 46.3% of the initial Coulombic efficiency. For the 10 th and 50 th cycles, the charge capacity was 879 and 906 mAh g\({}^{-1}\), respectively, and the discharge capacity was 929 and 963 mAh g\({}^{-1}\), respectively. Compared with the first cycle, the decrease in capacity was due to the formation of SEI film and the irreversible lithiation of carbon in the initial discharge process [14]. To further estimate the lithium storage performance of the CNFs-2N anode, CV curves of the first five cycles were obtained from 0.01 to 1.5 V at a scan rate of 0.1 mV s\({}^{-1}\), as shown in Figure 7d. During the first lithiation, the generation of the SEI film and the decomposition of the electrolyte led to a broad irreversible peak at about 0.55 V, which disappeared in the following cycles. For the anodic peak at 0.51 V and the reduction peak at 0.21 V, the current intensity gradually increased with the increase in the number of cycles, which corresponds to the activation process of more active materials reacting with lithium ions [34]. To further explore the effect of N-doping on the performance of lithium-ion batteries, the EIS of the CNFs-0N and CNFs-2N anodes was tested. Figure 7e show the Nyquist plots of the CNFs-0N and CNFs-2N anodes in lithium-ion batteries. The curve consists of a semicircle in the high-frequency region and a slash in the low-frequency region. The semicircle in the high-frequency region represents the charge transfer impedance. The oblique line in the low-frequency region is related to lithium-ion diffusion [35]. The semicircle in the high-frequency region of the CNFs-2N anode is larger than that of the CNFs-0N anode--that is, the charge transfer impedance increases after N-doping. The
## 3 Simulation Results and Analysis
### Analysis of the Differences in Acceleration Effects between the Two Configurations
The maximum axial force density exerted on the armature by the second- to fourth-stage coils in both the PP and PN configurations, for inter-stage distances of \(D=5\) mm and \(D=15\) mm, are shown in Figures 10 and 11 respectively, with units of \(\mathrm{kN/m^{3}}\). Figures 10 and 11 show the axial force density cloud maps on the armature at different time points. Despite the different methods of energizing the driving coils, the armature experiences electromagnetic force accelerating it forward, primarily concentrated at the bottom of the armature. It can be observed that the armature has a greater axial force density in the PP configuration at D = 15 mm compared to the device at D = 5 mm, whereas the axial force density on the armature in the PN configuration shows little difference across different time points, consistent with the simulation results of the force curves obtained.
# Dynamic Impedance Estimation: Challenges and Considerations
## 2 Transducers Online Equalization
### Adaptive Transducers Equalization  
Online equalization of transducers in an adaptive way is a viable strategy but not yet explored in the literature. A possible solution suggested in this work is illustrated in Figure 2.  
In this methodology, a reference transducer \(H_{p}(z)\) is installed in substation \(p\). This transducer has a frequency response close to the ideal for the frequency range of interest. For example, a resistive divider can be used as a reference transducer for the entire distribution network. A constant k can approximate the transfer function (TF) of this transducer.  
The system transfer function between the \(p\) and \(q\) buses is considered known at this stage and is described in the \(z\) domain by \(H_{p,q}(z)\). However, the frequency response of the transducer \(H_{q}(z)\) is unknown. The objective is to introduce an adaptive equalizer, \(H_{e}(z)\), to equalize the measurement transformer connected to the bus \(q\), as shown in Figure 2. This adaptive equalizer has as inputs the signal \(u[n]\), the signal \(d[n]\) (a scaled replica of the measured signal on bus \(p\)), and error \(e[n]\). Note that as the bus \(p\) and \(q\) are geographically  
Figure 2: Methodology for online and adaptive equalization of transducers.  
distant, the signal _d[n]_ and _u[n]_ must be synchronized by a Global Positioning System (GPS) system for the proper functioning of the adaptive algorithm. Additionally, _d[n]_ must be sent to the transducer equalizer installed on the \(q\) bus, so the adaptive algorithms, such as the least mean squares (LMS) or recursive least squares (RLS) [21], can be used to identify the system equalizer. This procedure can be performed online throughout the day, ensuring adaptive equalization.
# Analysis of Different Stop-Jumping Strategies on the Biomechanical Changes in the Lower Limbs
###### Abstract  
The stop-jumping task is one of the most important technical actions in basketball. A previous study showed 70% probability of non-contact ACL injuries during stop-jumping tasks. Therefore, the present study aimed to investigate the differences in lower extremity biomechanical changes between the rear foot as the initial contact area to terminate the jump (SJR) and the fore foot as the initial contact area to also terminate the jump (SJF) during the horizontal landing during a stop-jumping phase. In total, 25 male amateur Ningbo University basketball athletes from China were recruited for this study. The participants were asked to jump vertically by using two different stop-jumping strategies. Kinematic and kinetics data were amassed during a stop-jumping task. Statistical parametric mapping (SPM) analysis was used to find the differences between SJR and SJF. Our results indicated that the change of different ankle range of motion caused significantly different values for knee angle (\(p<0.001\)), velocity (\(p=0.003\)) (\(p=0.023\)) (\(p<0.001\)), moment (\(p=0.04\)) (\(p<0.001\)), (\(p=0.036\)) and power (\(p=0.015\)) (\(p<0.001\)) during the stop-jumping phase and the horizontal landing phase. The same biomechanical parameters of the hip joint were also significantly different for hip angle (\(p<0.001\)), moment (\(p=0.012\)) (\(p<0.001\)) (\(p<0.001\)), and power (\(p=0.01\)) (\(p<0.001\)) (\(p<0.001\)). These findings indicate that altering the primary contact at the ankle angle might effectively reduce the risk of a knee injury.  
Keywords:stop-jumping task; horizontal landing phase; landing injury; statistical parametric mapping +
Footnote †: journal: Applied Sciences
## 2 Numerical Methods
### Mooring System  
This study utilizes a quasi-static method to calculate deformation and tension of mooring lines [22]. This method counters distributed mass, stress, and elastic of mooring lines; however, it neglects inertia, viscosity, inner damping, bending, and twisting of mooring lines. When mooring lines partly lie on the seabed, governing equations for mooring lines can be expressed as  
\[x_{\text{F}}(H_{\text{F}},V_{\text{F}})=L_{B}+\frac{H_{\text{F}}}{w}\ln\left[ \frac{V_{\text{F}}}{H_{\text{F}}}+\sqrt{1+\left(\frac{V_{\text{F}}}{H_{\text{ F}}}\right)^{2}}\right]+\frac{H_{\text{F}}L}{EA}+\frac{C_{B}w}{EA}\left[-L_{B}^{2}+ \left(L_{B}-\frac{H_{\text{F}}}{C_{B}w}\right)\text{Max}\left(L_{B}-\frac{H_{ \text{F}}}{C_{B}w},0\right)\right], \tag{24}\]  
\[z_{\text{F}}(H_{\text{F}},V_{\text{F}})=\frac{H_{\text{F}}}{w}\left[\sqrt{1+ \left(\frac{V_{\text{F}}}{H_{\text{F}}}\right)^{2}}-\sqrt{1+\left(\frac{V_{ \text{F}}-wL}{H_{\text{F}}}\right)^{2}}\right]+\frac{1}{EA}\left(V_{\text{F}}L -\frac{wL^{2}}{2}\right), \tag{25}\]  
in which \(x_{\text{F}}\) and \(z_{\text{F}}\) are horizontal and vertical distances from fairlead to anchor (Figure 2); \(H_{\text{F}}\) and \(V_{\text{F}}\) are horizontal and vertical mooring tension components at fairlead; \(L_{B}=L-V_{\text{F}}/w\) means mooring line relaxation length; \(w\) represents wet weight per unit length of mooring lines; \(L\) indicates mooring line unstretched length; \(EA\) is mooring line axial stiffness; and \(C_{B}\) means static friction coefficient between mooring lines and seabed. By solving Equations (24) and (25), mooring tensions at fairlead can be calculated.  
Then, deformation and tension of the entire mooring line can be calculated further. For any point on the mooring line, its horizontal and vertical distances to anchor are labeled as \(x\) and \(z\), respectively; meanwhile, mooring tension at a specified point is labeled as \(T_{e}\). They can be computed, respectively, using:  
\[x(s)=\left\{\begin{array}{lr}s&0\leq s\leq L_{B}-\frac{H_{F}}{C_{Bw}}\\ s+\frac{C_{Bw}}{2 EA}[s^{2}-2s\Big{(}L_{B}-\frac{H_{F}}{C_{B}w}\Big{)}+\lambda \Big{(}L_{B}-\frac{H_{F}}{C_{B}w}\Big{)}],&L_{B}-\frac{H_{F}}{C_{B}w}<s\leq L _{B}\\ L_{B}+\frac{H_{F}}{w}\ln\!\left[\frac{w(s-L_{B})}{H_{F}}+\sqrt{1+\left(\frac{w (s-L_{B})}{H_{F}}\right)^{2}}\right]+\frac{H_{F}s}{EA}+\frac{C_{Bw}}{2 EA}[ \lambda\Big{(}L_{B}-\frac{H_{F}}{C_{B}w}\Big{)}-L_{B}^{2}],&L_{B}<s\leq L\\ \end{array}\right., \tag{26}\]  
\[z(s)=\left\{\begin{array}{lr}0&0\leq s\;\leq\;L_{B}\\ \frac{H_{F}}{w}\ln[\sqrt{\left(\frac{w(s-L_{B})}{H_{F}}\right)^{2}}-1]+\frac{w (s-L_{B})^{2}}{2 EA},&L_{B}<s\leq L\\ \end{array}\right., \tag{27}\]  
\[T_{e}(s)=\left\{\begin{array}{lr}\text{Max}[H_{F}+C_{B}\omega(s-L_{B}),0],& 0\leq s\;\leq\;L_{B}\\ \sqrt{H_{F}^{2}+\left[w(s-L_{B})\right]^{2}},&L_{B}<s\leq L\\ \end{array}\right., \tag{28}\]  
\[\lambda=\left\{\begin{array}{lr}L_{B}-\frac{H_{F}}{C_{B}\omega},&\Big{(}L_{ B}-\frac{H_{F}}{C_{B}\omega}\Big{)}>0\\ 0,&\text{otherwise}\\ \end{array}\right., \tag{29}\]  
where \(s\) means distance from target point to anchor.