esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aims to evaluate the accuracy of five different intraoral scanners and two different laboratory scanners for a complete arch. A computer-aided design (CAD) reference model (CRM) was obtained using industrial scanners. A CAD test model (CTM) was obtained using five types of intraoral scanners (CS3500, CS3600, Trios2, Trios3, and i500) and two types of laboratory scanners (3 shape E1 and DOF) (N = 20). In addition, the CRM and CTM were superimposed using a 3D inspection software (Geomagic control X; 3D Systems) and 3D analysis was performed. In the 3D analysis, the accuracy was measured by the type of tooth, the anterior and posterior region, and the overall region. As for the statistical analysis of the accuracy, the differences were confirmed using the Kruskal-Wallis H test (\(\alpha\) = 0.05). Also, the differences between the groups were analyzed by post-hoc tests including Mann-Whitney U-test and Bonferroni correction method (\(\alpha\) = 0.0017). There was a significant difference in the scanning accuracy of the complete arch according to the type of scanner (\(P\) < 0.001). The i500 Group showed the lowest accuracy (143 \(\pm\) 69.6 \(\upmu\)m), while the 3 Shape E1 Group was the most accurate (14.3 \(\pm\) 0.3 \(\upmu\)m). Also, the accuracy was lower in the posterior region than in the anterior region in all types of scanners (\(P\) < 0.001). Scanning accuracy of the complete arch differed depending on the type of scanner. While three types of intraoral scanners (CS3500, CS3600, Trios3) can be recommended for scanning of a complete arch, the two remaining types of intraoral scanners (Trios2 and i500) cannot be recommended.  
intraoral scanner; accuracy; digital workflow; complete arch; dentistry
## 7 Conclusions  
The inverse kinematic model of the UR10 robot based on the SQP algorithm and BP-SQP algorithm is studied. For the SQP algorithm is too dependent on the initial value, which is easy to produce deviation and affects the solution speed and accuracy of the algorithm, we use BP neural network to optimize the initial value, propose the BP-SQP algorithm, and add the closed solution method to analyze and compare these three algorithms. The results show that the algorithm accuracy of the closed-form solution of inverse kinematics was 100% but the closed-form solution took too much time to resolve the UR10 robot's inverse kinematics. The pose of the target subject needed to be determined and the solution to inverse kinematics by the closed-form method was not the optimal solution. Although the accuracy of the SQP algorithm was not 100%, the time consumed by the SQP algorithm was only half the time taken by the closed-form method, and the optimal solution was obtained. Although The SQP algorithm optimized by the BP neural network needed stringent preconditions for its operation, it took the least time to obtain the solution, which was the optimal solution, under the condition of 100% accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Success Rate** & **Time/s** & **Algorithmic Premise** & **Optimal Solution** \\ \hline The closed-form solution & 100\% & 78.8 & Target pose determination & no \\ The SQP algorithm & 99.5\% & 37 & Target position and axis determination & yes \\ The BP-SQP algorithm & 100\% & 4.2 & Target location and axis determination, & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of results.  
Figure 11: BP-SQP solution of inverse solution.  
**Author Contributions:** Conceptualization, Y.H. and J.W.; methodology, Y.H. and J.W.; software, J.L.; validation, J.L., X.Z. and X.L.; formal analysis, X.Z.; investigation, X.L.; resources, Q.H.; data curation, X.T.; writing--original draft preparation, Y.H. and X.T.; writing--review and editing, J.W. and S.C.; visualization, C.W.; supervision, S.C.; project administration, C.W. and Q.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52005169.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 4 Android System Architecture and Attacks
### Hardware-Based Attacks  
Any device is made of a range of hardware, such as a printed circuit board, processor (central processing unit, baseband processor, application processor, digital signal processor, graphical processing unit), memory, touch screen, radio frequency interface, Wi-Fi/Bluetooth device, SIM card, USB device, camera, and different sensors: GPS, motion detection, environmental sensor, heat detector, network sensor, etc. In a hardware attack, the attacker tries to exploit the preexisting vulnerabilities present in the hardware design component. These kinds of vulnerabilities are hard to patch because the patch is only available from distributors, and user-end updating is not possible. If these hardware components are repaired by a third party, then it causes a question mark and threat regarding the integrity of the device. This issue may be solved by cross-checking the communication integrity between the repaired device and the central processing unit.  
The Rowhammer is a type of hardware attack in which malicious JavaScript is used to attack the Android device. After successful installation of the script, the attacker gains access to GPU (general processing unit) and DRAM (dynamic random-access memory) and causes the leakage of electrical current, i.e., Glitch, using the system library WebGL. The triggered Glitch can harm DDR3 and DDR4 memory chips and exploit the GPU. RAMpage classified as CVE-2018-9442 is an attack similar to Rowhammer where attackers gain access to the restricted memory location and the complete system. GuardION is an open-source technique that protects the system from authorized memory-access leaks and Glitches. QuadRooter is a set of four vulnerabilities identified as CVE-2016-2059 (Common Vulnerabilities and Exposures), CVE-2016-5340, CVE-2016-2503, and CVE-2016-2504, which are present in the Qualcomm chipset. The attacker can gain access to the root of the application and may exploit the vulnerabilities present. The patch is available from the distributor or Qualcomm.
## 4 Discussion
In addition to aiding in diagnosis, such measurements can be used to monitor the progress of treatment during the implementation of a targeted rehabilitation protocol. In addition, surgical procedures aimed at increasing muscle moments [14], e.g., in weakened muscles or impaired muscle balance, will have to be revised. It should be taken into account that with maximum forward body tilt, \(CoG_{2}\) can be moved forward in front of the frontal support point of the foot while maintaining balance, while \(CoG_{2}\) cannot exceed it. This means that the gravitational arm for \(CoG_{2}\), and therefore the moment of gravity at maximum tilt, is greater than previously thought, which is why the counteracting moment of force of the triceps muscle also has a greater value [18]. The main limitation in the study of the ground load mediated by the support platform, which is the foot, is the difficulty in making precise, repeatable measurements using analogue models, and it is even more difficult to transfer such measurement methodology to the human body. The prepared analogue model (flat-bar model) simulating the tilt of the object on the axis of rotation located on the support platform, although it captures the principle of operation, gives only an approximation of the actual result. It focuses on static research. In the future, this type of research should be extended to dynamic research using sensors with a high level of precision, and then transferred to computer simulation conditions. In addition, a series of measurements should be performed on people to estimate the average values of \(CoG_{1}\) and \(CoG_{2}\) positions and gravitational and muscle torques (mainly the triceps calf muscle) for the human population in specific age, gender, or e.g., in different sports disciplines (developed upper body--higher centre of gravity, developed lower body--lower). Such an approach will allow for more accurate calculations and transfer these observations to the human body with higher precision (the human general centre of gravity tilts on the axis of rotation of the upper ankle joint).
## 5 Performance Evaluation of the TPCT
### Numerical Simulations  
Numerical simulations have a lot of advantages over the two methods above. These can set different conditions and predict the future. For the complex systems, such as the thermal-mechanical stability of the embankment, it is necessary to use simulations.  
For theoretical analyses, several methods have been proposed for TPCT embankment. Including the linear heat flux method [74], the equivalent thermal resistance method [75], and the air-TPCT-soil-coupled heat transfer model [12,76]. Currently, the air-TPCT-soil coupled heat transfer model has been the most widely used model in permafrost engineering. Thus, we pay attention to the air-TPCT-soil coupled heat transfer model.  
[PERSON] et al. [76], on the basis of the thermal resistance method (in part 2), considered the coupled heat transfer process of the heat pipe with the atmospheric environment and the surrounding soil, established the coupled heat transfer mechanism between the heat pipe and the surrounding perennial permafrost environment and air, and established the equations for the calculation of the thermal resistances of each part of the TPCT. [PERSON] et al. established the theoretical model of coupled air-TPCT-soil heat transfer considering the effect of inclination angle, which are shown as follows (Figure 10).  
Then, the numerical simulation of heat transfer based on the actual temperature difference to regulate the start/stop of heat pipes was written. The new written procedure overcome the previous time regulation method which cannot accurately simulate the working process of the heat pipe defects, to achieve the efficient simulation of the TPCT.  
Table 1 lists the different structure of TPCTs including the vertical, the L-shaped, and the horizontal TPCT, and its evaluation method published in the literature. In the'method' column, the numbers 1, 2, and 3 mean observation, laboratory test, and numerical simulation, respectively.  
Figure 9: Schematic of the experimental equipment [15].  
Figure 10: The effective thermal resistance of the TPCT [12]: (**a**) The distributions of the thermal resistance; (**b**) The formula of the thermal resistance.
###### Abstract  
Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1--To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without falls. 2--To identify variables that stratify participants according to frailty status and falls. 3--To verify the sensitivity, specificity and accuracy of the model that stratifies participants according to frailty status and falls. Methods: Observational, multicenter case-control study. Participants, adults over 70 years with and without falls were recruited from two outpatient clinics and three nursing homes from September 2021 to March 2022. Clinical variables and gait parameters were gathered using the G-STRIDE inertial sensor. Random Forest regression was applied to stratify participants. Results: 163 participants with a mean age of 82.6 \(\pm\) 6.2 years, of which 118 (72%) were women, were included. Significant differences were found in all gait parameters (both conventional assessment and G-STRIDE evaluation). A hierarchy of factors contributed to the risk of frailty and falls. The confusion matrix and the performance metrics demonstrated high accuracy in classifying participants. Conclusions: Gait parameters, particularly those assessed by G-STRIDE, are effective in stratifying individuals by frailty status and falls. These findings underscore the importance of gait analysis in early intervention strategies.  
Keywords: gait analysis; inertial sensors; early detection; falls; frailty +
Footnote †: journal: _Sciences_
# The Impact of Distributed Energy Storage on Distribution and Transmission Networks' Power Quality
## 2 Methodology
### Modelled Benchmark Network  
The transmission and distribution network used as a benchmark for this project's empirical study follows a standard design approach for interconnected transmission and distribution that employs N-1 redundancy to lower the probability of any supply disruptions while increasing supply security. The designed benchmark network (see Figure 1) represents a 50 Hz AC power system network wherein electrical energy provided by a 132 kV network feeder is delivered to three categories of consumers, namely, residential, commercial, and industrial users, connected to distribution nodes (66 kV, 33 kV, and 11 kV) via several network equipment and power lines (132 kV, 66 kV, 33 kV, and 11 kV).
## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Testing and Validation of Control Mechanisms in Simulation Environments
#### 6.1.1 Simulation Research on Traffic Flow Stability Control  
Traffic flow instability (such as driving oscillations and stop-and-go phenomena) is one of the main causes of road congestion and energy waste. Multiple studies have shown that even at lower penetration rates, appropriately controlled CAVs can improve traffic flow stability.  
Model Predictive Control (MPC)-based methods have performed excellently in stabilizing heterogeneous traffic flows. [PERSON] et al. (2012) and [PERSON] et al. (2013) improved MPC applications in heterogeneous traffic environments from different perspectives. The Oscillation Mitigation-based MPC model (OM-MPC) proposed by [PERSON] et al. effectively mitigates traffic oscillations even under shorter CAV distances (100 m) by innovatively estimating total equilibrium spacing. [PERSON] et al. expanded the perspective to bidirectional vehicle dynamic influences, developing a Robust Fuzzy MPC (RF-MPC) strategy that considers uncertainties in both preceding and following vehicles, using Takagi-Sugeno fuzzy models to capture human driving behavior, thus improving the overall stability and comfort of mixed platoons. These two studies collectively demonstrate that considering more comprehensive traffic environmental factors (such as oscillation characteristics and impacts from both preceding and following vehicles) can significantly enhance the effectiveness of MPC in heterogeneous traffic.  
From a theoretical foundation perspective, [PERSON] et al. systematically constructed a heterogeneous traffic stability analysis framework, proposing the Ring String Stability (RSS) concept, which allows string instability between predecessor-follower pairs provided they can be compensated by autonomous vehicles (2013). Simulation results validated this theoretical framework and quantified the upper limit of human-driven vehicles that a single AV can stabilize, providing a theoretical basis for optimizing AV deployment in heterogeneous traffic.  
The common findings of these studies are as follows: (1) even at low penetration rates, rationally distributed CAVs can effectively stabilize traffic flow; (2) control effectiveness is closely related to CAV distribution strategies and control algorithms; (3) as external disturbances increase, the difficulty of maintaining stability also increases accordingly. These conclusions provide important references for traffic management strategies during the CAV transition period.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 2 Models and Methods  
### Phase Noise Model in Direct Intermediate Frequency Sampling Coherent Lidar  
Figure 1 illustrates the coherent lidar system featuring a direct intermediate frequency sampling structure. A continuous wave laser emits a laser signal, which is modulated into a specific waveform by an electro-optic modulator (EOM) to serve as the emission signal. In contrast to conventional FMCW lidar systems, the LO signal that beats with the echo signal is directly split from the laser output rather than from a modulated signal. Although this lidar system necessitates higher sampling rate equipment, it effectively maintains the continuous phase of the echo signal, facilitating compensation. The laser output signal (i.e., LO signal) is mathematically expressed as  
\[E_{\text{L}}(t)=A_{L}\exp[j(2\pi f_{c}t+\phi_{L}(t))], \tag{1}\]  
where \(A_{L}\) represents the amplitude of the LO signal, \(f_{c}\) is the carrier central frequency, and \(\phi_{L}(t)\) denotes the laser phase noise in the time domain.  
The probe beam generated by EOM exhibits a broad bandwidth. For illustration, we consider a pulse with linear frequency modulation (LFM):  
\[E_{p}(t)=A_{p}\exp[j(2\pi f_{c}t+\phi_{L}(t))]\cdot\exp(j\pi Kl^{2}), \tag{2}\]  
where \(A_{p}\) represents the amplitude of the probe beam, and \(K\) is the frequency modulation ratio.  
The probe beam reflects at the point target, beats with the LO signal, and is subsequently collected by the balanced photodetector (BPD) at a distance \(R\), producing the target channel signal \(I_{1}(t)\) (neglecting the fixed phase term):  
\[\begin{split} I_{1}(t)&=\eta\text{Re}\Big{[}E_{L}( t)E_{p}^{*}(t-\tau_{0})\Big{]}\\ &=\eta A_{p}A_{L}\cos\Big{[}\pi K(t-\tau_{0})^{2}+\phi_{L}(t)-\phi _{L}(t-\tau_{0})\Big{]},\end{split} \tag{3}\]  
where \(\eta\) represents the photoelectric conversion coefficient of the BPD, and \(\tau_{0}=2R/c\) denotes the delay time between the echo and probe beam. We generate an LFM signal without delay and phase noise as  
\[I_{d}(t)=\exp\big{(}j\pi Kl^{2}\big{)}, \tag{4}\]  
which can be utilized to complete the dechirp process in the digital domain [32]:  
\[\begin{split} I_{tar}(t)&=H\{I_{1}(t)\}I_{d}^{*}(t) \\ &=\eta A_{p}A_{L}\exp[j(-2\pi K\tau_{0}t+\pi K\tau_{0}^{2}+\phi_{L }(t)-\phi_{L}(t-\tau_{0}))],\end{split} \tag{5}\]  
where \(H\{*\}\) represents the Hilbert transform.  
Following the spectral analysis of the signal, from a ranging perspective, a target located at a distance R corresponds to a peak frequency given by \(f_{i}=K\tau_{0}\). The differential phase noise (DPN) defined as \(\phi_{\tau_{0}}(t)=\phi_{L}(t)-\phi_{L}(t-\tau_{0})\), is induced by the laser phase noise \(\phi_{L}(t)\). This DPN broadens the spectral envelope, impacting the accuracy and resolution of the ranging measurements.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 3 Methodology
### Data Processing  
The radius R (m) of the OS turning circle and the ROT (seconds/degrees) were obtained by processing OS trajectory data.  
In an x-y plane coordinate system, a mean R can be approximated as follows:  
\[\text{R}=\left(D_{x}+D_{y}\right)/4, \tag{10}\]  
Figure 5: Flowchart summarizing the study procedures.  
where \(D_{x}\) and \(D_{y}\) represent the \(x\)-axis diameter and \(y\)-axis diameter of the OS trajectory, respectively, and \(D_{x}\) and \(D_{y}\) can be written as:  
\[\left\{\begin{array}{l}D_{x}=\sqrt{\left(\max\!\left(x_{diff}\right)-\min\! \left(x_{diff}\right)\right)^{2}}\\ D_{y}=\sqrt{\left(\max\!\left(y_{diff}\right)-\min\!\left(y_{diff}\right) \right)^{2}}\end{array}\right., \tag{11}\]  
This difference can be expressed as follows:  
\[\left\{\begin{array}{l}x_{diff}=x_{tr}(t=0)-x_{tr}(t=t)\\ y_{diff}=y_{tr}(t=0)-y_{tr}(t=t)\end{array}\right., \tag{12}\]  
where \(x_{tr}\) and \(y_{tr}\) represent the position in the \(x\)-\(y\) coordinate system relative to OS trajectory position (latitude, longitude). These can be written as follows (distance given in arcmin from reference point to a position on circle):  
\[\left\{\begin{array}{l}x_{tr}=dLat\ \sin(\vartheta)\times 1852\\ y_{tr}=dLat\ \cos(\vartheta)\times 1852\end{array}\right., \tag{13}\]  
where \(\vartheta\) (\(0\leq\vartheta\leq 2\pi\)) represents the bearing between the turning start position of OS (\(Lat_{S},Lon_{S}\)) and its turning position at time \(t\) (\(Lat_{t},Lon_{t}\)). This and \(dLat\) is defined in Equation (16). The angle \(\vartheta\) can be presented as follows:  
\[\vartheta=\arctan\!\left(\frac{Med}{dlon}\right), \tag{14}\]  
where \(Med\) represents the meridian difference when the Mercator method [35] is applied, which can be written as follows:  
\[Med=7915.7\!\left[\log\tan\left(\frac{180^{\circ}}{4}+\frac{Lat_{S}}{2}\right) -\log\tan\!\left(\frac{180^{\circ}}{4}+\frac{Lat_{t}}{2}\right)\right]. \tag{15}\]  
In addition, \(dLat\) and \(dLon\) represent the latitude and longitude differences between OS trajectory positions, respectively, expressed in arcmin, which can be represented as follows:  
\[\begin{array}{l}dLat=Lat_{S}-Lat_{t}\\ dLon=Lon_{S}-Lon_{t}\end{array}\right\}. \tag{16}\]  
ROT can be represented as follows:  
\[\text{ROT}=\Delta\theta_{\text{TC}}/\Delta t_{turn} \tag{17}\]  
where \(t_{turn}\) represents the turning time of the turning circle observed, which is the time taken until OS finishes one turn of turning, and \(\theta_{\text{TC}}(0\leq\theta_{\text{TC}}\leq 360^{\circ})\) is the bearing angle given with a certain angle from the turning start position of OS.
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 3 Location Estimation Based on the Manhattan Distance
### Properties of the Minimizer
#### 3.3.2 Example 2  
There are four optimal facility locations (black hollow squares) in Example 2 (see Figure 1b) because of weight construction. Then, the weighted medians are given by  
\[\widetilde{u}=\text{ weighted median }(x_{1},\ x_{2},\ x_{3})=3,\ 5,\text{ or }4 \tag{27}\]  
\[\widetilde{v}=\text{ weighted median }(y_{1},\ y_{2},\ y_{3})=3,\ 6,\text{ or }4.5 \tag{28}\]  
It is easy to obtain the optimal facility locations through permutation and combination of the weighted medians on different axes, which are (3, 3), (3, 6), (3, 4.5), (5, 3), (5, 6), (5, 4.5), (4, 3), (4, 6), and (4, 4.5). It is obvious that two of them, i.e., (3, 3) and (5, 6), are located at the demand points, but the others are not. As they have the same objective function value, any one among them can be considered as the location for the facility.