esa-sceva/satcom-chunk-collection huggingface dataset:
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## Materials and Methods
### Procedure for Using the Template  
The target of the template is to improve and optimize the standard vertebral arthrodesis procedure by guiding the screws in the optimal directions.  
This is obtained by positioning the template on the vertebral surface. The template must be positioned appropriately on the surface of the vertebra to be treated and, consequently, the template design is aimed at the appropriate choice of the contact surfaces in such a way as to make the positioning univocal and avoid errors.  
An accurate skeletonization has to be performed on the vertebral lamina before the positioning of the template. This way a stable match can be obtained making a few and small movements in finding the right position on the vertebra surface (thanks also to the innovative engage system).  
To simplify the positioning of the surgical template, a minor distraction will be required of the vertebral bodies via, for example, the supraspinatus ligament excision or the positioning of the patient in a hyperkypbiotic position. The anchorage to the vertebra is insured by the perfectly fitting shape of the template.  
Once verified the template/vertebra coupling, a first X-ray shot has to be done to evaluate the trajectories goodness and theoretical tracing via the Kirschner wires, which are introduced into the vertebral body via the guide tubes and adaptors. Positive X-ray feedback allows the surgeon to directly insert the screw using the direction tubes along the planned direction [17]. Finally, the surgeon can extract the template by breaking the tongues and disengaging the template.  
This procedure has been approved by the Ethical Committee of the Italian Health Ministry.
## 7 Conclusions  
This work has identified that the main distortion in the images reconstructed from the phase-only holograms calculated by IFTAs is the white Gaussian noise. The perceived quality of such images can be better evaluated by using SSIM instead of RMSE. As a result, the SSIM was proposed as the image quality metric in IFTAs in this work. To the authors' knowledge, this is the first time that perceived image quality has been used as the quality metric in IFTAs. Moreover, SSIM also proves to be less dependent on the image content as the natural images with similar perceived quality always have comparable SSIM values. All these properties enable the IFTAs using SSIM as the image quality metric to terminate when further iterations would not lead to significant improvement in the perceived image quality. As a result, the required number of iterations can be kept as the minimum and calculation speed is improved. In particular, up to 75% of unnecessary iterations were eliminated in the FiDOC algorithm. When necessary, SSIM can also be used in conjunction with other metrics, e.g., peak signal-to-noise ratio, diffraction efficiency and uniformity error, to determine the best results for the target application.  
Conceptualization, H.Y. and D.C.; investigation, H.Y.; formal analysis, H.Y. and D.C.; writing-original draft preparation, H.Y.; writing-review and editing, H.Y. and D.C. This research was funded by Fundamental Research Funds for the Central Universities (2242019k1G001). The authors declare no conflict of interest.
## 2 Methodology
### Strain Rate-Dependence and Cumulative Damage  
The [PERSON]'s model assumed that flaws and cracks exist within rock mass [7]. Under tension, these flaws and cracks may grow, and new flaws and cracks may be generated, leading to the reduction of the stiffness and strength of rock. It was assumed that the increase of crack density is controlled by the tensile strain of rock. Formula (3) was proposed to calculate the increase of the crack density [7,9,29]:  
\[\mathrm{d}C_{\mathrm{d}}/\mathrm{d}t=\alpha(\theta-\theta_{\mathrm{c}})^{ \beta} \tag{3}\]  
where \(C_{\mathrm{d}}\) is crack density; \(t\) is time; \(\alpha\) and \(\beta\) are material constants; \(\theta\) is equivalent tensile strain; \(\theta_{\mathrm{c}}\) is critical tensile strain, and can be determined by the uniaxial dynamic tensile test, \(\theta_{\mathrm{c}}=\ln(1+\epsilon_{t})\); \(\epsilon_{t}\) is ultimate tensile strain. When \(\theta<\theta_{\mathrm{c}}\), crack density does not increase.  
Damage variable \(D\) can be denoted by Formula (4):  
\[D=1-\mathrm{e}^{-C_{\mathrm{d}}^{2}} \tag{4}\]  
The relationship between the relative damage variable and the acoustic wave velocity for rock mass was denoted by Formula (5) when \(0\leq D\leq 1\) and \(c\leq c_{0}\)[30]:  
\[D=1-(c/c_{0})^{2} \tag{5}\]  
where \(c\) is the acoustic wave velocity of damaged rock after being loaded; \(c_{0}\) is the initial acoustic wave velocity of rock.  
When rock is the undamaged material before being loaded, \(D\) can be denoted by Formula (6):  
\[D=1-(c/\hat{c})^{2} \tag{6}\]  
where \(\hat{c}\) is the acoustic wave velocity of undamaged rock. In this paper, rock mass with an integrity index of more than 0.8 is assumed as the undamaged material.  
The initial damage value of rock \(D_{0}\) can be calculated by Formula (7):  
\[D_{0}=1-(c_{0}/\hat{c})^{2} \tag{7}\]  
The initial integrity index of rock mass \(\eta_{0}\) is denoted by Formula (8):  
\[\eta_{0}=(c_{0}/\hat{c})^{2} \tag{8}\]Then, the approximate relationship between initial damage value and initial integrity coefficient of rock mass is expressed by Formula (9):  
\[D_{0}=1-\eta_{0} \tag{9}\]  
Considering the effect of initial crack density, \(D_{0}\) can be denoted by Formula (10) [9; 29].  
\[D_{0}=1-\mathrm{e}^{-C_{\mathrm{d0}}^{2}} \tag{10}\]  
where \(C_{\mathrm{d0}}\) is the initial crack density of the rock mass.  
Considering the initial crack density of the rock, Formula (3) can be expressed by Formula (11) [9; 29]:  
\[C_{\mathrm{d}}=\int_{\theta_{\mathrm{c}}}^{\theta}\frac{\alpha}{\dot{\theta}}( \theta-\theta_{\mathrm{c}})^{\beta}\mathrm{d}\theta+C_{\mathrm{d0}} \tag{11}\]  
where \(\dot{\theta}\) is the equivalent tensile strain rate; \(C_{\mathrm{d0}}=\sqrt{-\ln\eta_{0}}\).
# Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
## 2 Materials and Methods
### Methodology
#### 2.2.1 Data Period  
One year's worth of data, from May 2013 to April 2014, was used in this comparison study. Because the amount of data is large, we collected data every seven days during that period and created a matchup dataset including a total of 329 CPR overpasses. Because the CPR instrument has been in daylight-only data collection mode since October 2011 after undergoing a battery anomaly problem [21], only the daytime observations are used in this comparison study.
## 4 Analysis of Results
### Overall Model Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Construct**} & \multirow{2}{*}{**Variable**} & \multicolumn{2}{l}{**Standardized**} & \multicolumn{2}{l}{**Unstandardized**} & \multirow{2}{*}{**SE**} & \multicolumn{2}{l}{**CR**} \\  & & & **Factor Loading** & & & **T-Value** & _p_-Value \\ \hline PEOU \(\rightarrow\) PU & 0.789 & 0.751 & 0.049 & 15.354 & *** \\ \hline PEOU \(\rightarrow\) ATT & 0.326 & 0.368 & 0.066 & 5.581 & *** \\ \hline PU \(\rightarrow\) ATT & 0.541 & 0.642 & 0.073 & 8.817 & *** \\ \hline ATT \(\rightarrow\) TS & 0.650 & 0.732 & 0.051 & 14.393 & *** \\ \hline TS \(\rightarrow\) BI & 0.249 & 0.214 & 0.029 & 7.304 & *** \\ ATT \(\rightarrow\) BI & 0.732 & 0.709 & 0.041 & 17.112 & *** \\ \hline PEOU & PEOU1 & 0.908 & 1.034 & 0.040 & 25.940 & *** \\ PEOU2 & PEOU2 & 0.858 & 1.049 & 0.046 & 23.017 & *** \\ PEOU3 & 0.921 & 1.102 & 0.041 & 26.765 & *** \\ PEOU4 & 0.805 & 0.949 & 0.046 & 20.715 & *** \\ PEOU5 & 0.867 & 1.000 & & & & \\ \hline PU & PU1 & 0.864 & 0.976 & 0.049 & 19.827 & *** \\ PU2 & 0.920 & 1.052 & 0.049 & 21.497 & *** \\ PU3 & 0.902 & 1.014 & 0.048 & 20.982 & *** \\ PU4 & 0.899 & 0.999 & 0.048 & 20.911 & *** \\ PU5 & 0.787 & 1.000 & & & & \\ \hline ATT & ATT1 & 0.886 & 0.907 & 0.034 & 26.523 & \\ ATT2 & 0.892 & 0.937 & 0.035 & 26.998 & *** \\ ATT3 & 0.904 & 0.932 & 0.033 & 27.876 & *** \\ ATT4 & 0.748 & 0.689 & 0.036 & 19.021 & *** \\ ATT5 & 0.899 & 0.933 & 0.074 & 27.493 & *** \\ ATT6 & 0.890 & 1.000 & & & & \\ \hline BI & BI1 & 0.857 & 1.000 & & & \\ BI2 & 0.922 & 1.054 & 0.039 & 26.752 & *** \\ BI3 & 0.918 & 1.041 & 0.039 & 26.379 & *** \\ BI4 & 0.921 & 1.052 & 0.040 & 26.316 & *** \\ BI5 & 0.927 & 1.045 & 0.039 & 26.727 & *** \\ \hline TS & TS1 & 0.918 & 1.000 & & & \\ TS2 & 0.931 & 1.004 & 0.030 & 33.960 & *** \\ TS3 & 0.951 & 1.013 & 0.028 & 36.435 & *** \\ TS4 & 0.945 & 1.019 & 0.029 & 35.260 & *** \\ TS5 & 0.956 & 1.001 & 0.027 & 36.847 & *** \\ TS6 & 0.917 & 0.980 & 0.031 & 32.010 & *** \\ \hline \hline \end{tabular}  
* \(p<\) 0.001. Source: Calculated by this research.  
\end{table} 
Table 10: Discriminant validity between constructs. evaluation of the proximity payment, so they are more willing to use the proximity payment function. Table 10 shows a positive correlation coefficient between attitude and behavioral intention (\(p\)-value \(<\) 0.05), that is, there is a significant positive direct effect (standardized factor loading is 0.732), so H4 is valid.
# Modeling and Simulation of Sky Survey
## 3 Proposed Methods
### Analysis of Orbital Constraints  
Orbital constraints refer to the exclusion of time periods when observations can not be made prior to the official sky survey, including the South Atlantic Anomaly region, space station docking, etc. After these times are excluded, the remaining time is the optimized observation time period, and what needs to be optimized is which region of the sky to observe at each moment.  
South Atlantic Anomaly (SAA): As is well known, SAA is a zone that receives and transmits significant amounts of radiation from outer space [39]. Space telescopes may experience instrument malfunctions and communication errors, as well as a large amount of particles forming background noise that reduces the signal-to-noise ratio of the images. Therefore, when a survey passes through this region, the survey module should stop observing (standby or shutdown) unless there is a special observing need.  
Satellite Orbit Maintenance (SOM): The station performs orbital maintenance every 80 days for 12 orbital periods and is not observable during orbital maintenance.  
Satellite Docking with Space Station (SDSS): The telescope is currently designed to fly with the Chinese space station, so it needs to dock with the Chinese space station periodically to complete status checking and some necessary maintenance, etc. Each space station docking takes between 20 days and 3 months. It is completely invisible throughout this time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Detailed Description** \\ \hline \(N,M\) & Total number of time windows and sky areas \\ \(\alpha\) & Tangential angle between sunlight and satellite \\ \(\beta\) & Tangential angle between lunar light and satellite \\ \(T=\{t_{i},i=1,2,\ldots,N\}\) & Set of observed time windows \\ \(S=\{s_{j},j=1,2,\ldots,M\}\) & Set of Sky area division \\ \(R=\{r_{k},k=1,2,\ldots,K\}\) & Set of observed orbital period \\ \(C=\{e_{p},p=1,2,\ldots,8\}\) & Set of CMG rotation times \\ \(v_{j}\) & Weighting factor of the \(j\)-\(th\) sky area \\ \(x_{ijk}=\{0,1\}\) & \(s_{j}\) is observed during \(t_{i}\) in \(r_{k}\) \\ \(r\) & The unit radius \\ \(\omega_{ip}\) & The \(p\)-\(th\) CMG rotation angle at the \(i\)-\(th\) time window \\ \(Low\) & Inefficient observation time period \\ \(DifSky\) & Difficult to observe sky areas \\ \(A=\{Eph,Sun,Moon\}\) & Satellite, Sun, and Moon ephemeris data \\ \(\gamma_{i}(i=1,2)\) & Threshold parameters of efficiency and difficulty \\ \(e_{i}(i=1,2)\) & Threshold parameters of sunlight and moonlight \\ \(St,Et\) & Start time and end time of sky survey \\ \(Oc=\{SAA,SOM,SDSS\}\) & Set of orbital constraints \\ \(Tc=\{C_{1},C_{2},C_{3}\}\) & Set of observation constraints \\ \(\delta\) & Zenith angle \\ \hline \hline \end{tabular}
\end{table}
Table 3: Symbols used and specific instructions.
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 3 Numerical Solution of Internal Beveloid Gear with Parallel Axes
### Influence of Excitation Parameters on Nonlinear Vibration Characteristics of Transmission System
#### 3.2.3 Influence of Input Torque on Vibration  
The influence of torque fluctuation has been analyzed above, torque fluctuation \(T_{f}=0\) N \(\cdot\) m, input speed \(n=1000\) r/min, bearing backlash \(b_{b}=20\) \(\upmu\)m, transmission error \(E=20\)\(\upmu\)m, tooth backlash \(2\)\(b=10\)\(\upmu\)m, and change the input torque \(T_{at}=10,\,15,\,20\) N \(\cdot\) m to obtain the time domain graph of external gear vibration, as shown in Figures 6 and 7 below.  
In accordance with Figures 6 and 7, when the input torque is \(T_{at}=10,\,15,\,20\) N \(\cdot\) m the radial vibration amplitude is 29.738 \(\upmu\)m, 30.938 \(\upmu\)m, and 34.828 \(\upmu\)m, while the axial vibration amplitude is 6.754 \(\upmu\)m, 6.875 \(\upmu\)m, and 7.019 \(\upmu\)m.  
Figure 6: Time domain diagram of external gear radial vibration under different input torques.  
Figure 7: Time domain diagram of external gear axial vibration under different input torques.  
When the torque increases from \(10\) N \(\cdot\) m to \(20\) N \(\cdot\) m, the external gear's radial vibration peaks and troughs increase simultaneously, as does the amplitude, yet the equilibrium point hardly shifts. The equilibrium point of the axial vibration curve of the external gear displays a clear rising tendency overall. Simultaneously, the amplitude grows but the changing trend of the two curves remains unchanged.
## 5 Simulation and Experimental Results  
In this section, we evaluate the performance of the proposed systems. First, we investigate the latency and improvement of the proposed data fusion schemes. Second, we test for accident scenarios using real-world data. The first scenario concerns predicting accidents caused by blind spots, which can be avoided using V2V communications. The second scenario shows how an erroneous detection can be corrected.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 4 Applications and Benefits of the IoV  
### Intelligent Navigation of Ships  
The IoV system makes it possible to realize the intelligent navigation of ships. The IoV system has a variety of functions including the completed navigation, collision avoidance, information display, monitor, alarm, satellite communication, ship management and so on, which are convenient for the crew and shore-based commander to observe, operate and optimize the information process collected from the equipment. The use of the IoV system will play a greater role in the guarantee of ship safety navigation than before [63].  
With the perception information including the ship navigating state, environmental factors and the ship operating data obtained from the sensors, the IoV system will take advantage of the data resource layer to distribute and share these data and choose the optimal path to operate the ship automatically.  
With the development of unmanned control theory, the driverless car and unmanned aerial vehicle have become reality. The IoV system is quite helpful for research on unmanned ships. In the inland river, due to the complicated channel environment and the traffic flow density, the unmanned ships have high requests for obstacle perception, route planning and tracking ability. As described in Sections 2 and 3, the IoV system has the ability to meet these requirements for the unmanned ships. Figure 11 is the schematic diagram for the ship automatic collision avoidance [64].
## 3 The Proposed Protocol  
A route planning approach is proposed to forward MAs for sensory data collection in rural or hard-to-reach areas. The objectives are to establish optimal MA migration paths, minimise data collection delay and increase data robustness. The proposed approach, called MINDS, forms a context-aware tree-structured route by interconnecting the sink and local sensory data stations (LDSs). This tree infrastructure is reactively formed according to a data-centric pattern to meet the consumer's queries/requirements. LDSs locally capture and aggregate sensory data from (mobile) sensory devices (e.g., wearable sensors) when they are interconnected (e.g., Zigbee ties). They are clustered using a data-centric and lightweight clustering technique, named Hamming distance, to limit network transmission. Each cluster-head joins the tree infrastructure using NDN routing if it has a data sample matching consumer's query. MAs start a data aggregation journey from the sink to visit LDSs residing on the tree-structured route according to a hop-count-aware Depth-First Search (DFS) algorithm. They collect and aggregate sensory data records according to the query and return to the sink to deliver the final results.  
The key steps of MINDS are summarised as below:  
1. Query Dissemination * Technique: An NDN routing to disseminate user's queries in the WSN according to the application features, e.g., time, location and data type. * Objective: Reduce transmitted network traffic.
2. Network Clustering * Technique: Hamming distance; lightweight, de-centralised and data-centric clustering. * Objective: Reduce communication delay and transmission.
3. Tree Route Establishment * Technique: Hop-count-aware NDN routing, to establish a data-centric spanning tree for MA migration. * Objective: Reduce path hop-count and establish data-centric links.
4. MA Path Planning * Technique: hop-count-aware DFS. * Objective: Maximise data aggregation robustness, avoid loops and minimise path length.  
The network model is comprised of three components: sensory devices, a local data station (LDS) and a sink. Sensory devices are able to freely move and repeatedly capture body and/or ambient sensory data samples, such as body temperature, spo2, air pollution (e.g., PM2.5) and/or traffic congestion according to data collection application. The sensory devices report data samples to LDSs once there is a tie.  
LDSs are static stations which collect sensory data records from sensor devices and aggregate them to deliver the results to a network data collector (e.g., MA). They are reactively labelled according to their available datasets (e.g., environmental condition, weather or fire status). For example, an LDS is tagged by \"fire\" if it recently received fire status data from mobile sensory devices. However, it may remove the tag once the fire data record is expired.  
The sink is the end-user access-point and has sufficient resources (e.g., power and bandwidth) for communication and computation. This retains the duties of consumer query dissemination, data collection/analysis and decision making. Table 1 outlines the acronyms which are used in MINDS algorithm.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 4 Discussion  
From the experiments, the human gaze has been evidenced to be an encouraging modality for GGC. Our proposed method was expanded to explore motion control by straight-gaze input to perform hands-free control of the mobile robot. The gaze-based control system has good potential for future work. We think that this technology has a significant function in facilitating the life of a disabled person. The gaze-based control system is still in its infancy and its function and features need to be further developed in order to be implemented in more complex situations. The use of an eye-based interaction modality can reduce both physical and mental burden. The main aim will be to increase the usability of the system by increasing the learning and ease-of-use by considering the overall efficiency A low-cost eye-tracking device used in our experiments is crucial in the stability. The user's head must be stable and the eye tracker must snapshot the eye frame in certain positions. Although this situation is difficult to set-up, it is preferred because it is inexpensive and robust in the gaze position for motion control commands. The profoundly accurate eye trackers are able to present control of vehicles that are as good in term of speed and error as mouse control. It is clear that the differentiation between our model and controlling a wheelchair-based gaze guide drive is using overhead camera images. This type of motion control could be profitable in remote control situations like hazardous, poisonous places where the hands are needed for other tasks. A signification limitation of the current work is that the surveillance system is managed from a fixed location. The correct determination of gaze width and height positions (start and end) is important for the designed controller. When the eyes are closed, the engines are driven to zero and thus the robot is stopped. For this purpose, the sub-program has been audited without choosing the rule base. This is considered as the mobile robot's go-to-goal behavior. For this reason, the behavior of robot retraction has not been taken into account. This choice was made for the purpose of feasibility in testing a novel idea for socially assistive robots. It is our expectation that a new idea is implemented for target audiences who need socially assisted robots.  
The proposed method was implemented for an indoor experiment. As the work accomplished in this paper progressed, many ideas came to mind, which could be discussed in future research, such as outdoor experiments and investigations on how the gaze based control of devices and how it compares with traditional methods such as omnidirectional wheels. These wheels are troublesome individuals compared to the traditional ones. Lastly, our proposed method may sub serve as a simple, secure and affordable method for future gaze guided vehicle control prototypes.