esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Background:_ The permanent maxillary canine is the most impacted tooth after third molars. There are many possible surgical techniques to approach impacted canines. The literature reports that high-intensity laser therapies (HILTs) can be used for opercolectomy of an impacted tooth. The aim of this study is to propose a new orthodontic-surgical approach using a laser for the disinclusion of palatally impacted canines. _Methods:_ Nine patients presented maxillary primary canine persistence in the dental arch. Orthopanoramic X-ray and cone beam computed tomography (CBCT) showed the impaction of permanent maxillary canines. Surgical exposure was performed using a diode laser ([PERSON], DMT, Lissoe, Italy, 980 nm + 645 nm). No orthodontic devices were applied for impacted tooth traction. Canine movement was monitored at 1, 8, 16 weeks post-surgery with photos and a CS3500 intraoral scanner (Carestream Dental, Atlanta USA) to evaluate their autonomous eruption. _Results:_ No complications were observed. In all the cases, complete disimpaction of the treated canines was obtained in only four months. _Conclusions:_ Impacted canine exposure with a diode laser has many advantages if compared with traditional surgery. The pre-orthodontic exposure and autonomous eruption of impacted canines provided simplified and predictable outcomes. The absence of traction and the reduced time for orthodontic treatment increased compliance during the orthodontic alignment.  
diode laser; ortho-surgical treatment; palatally impacted canines; disinclusion; no orthodontic traction; photobiomodulation Article  
D 2021 114869. [[https://doi.org/10.3390/app11114869](https://doi.org/10.3390/app11114869)]([https://doi.org/10.3390/app11114869](https://doi.org/10.3390/app11114869))
# Effect of Methylammonium Iodide (MACI) on MAPbI\({}_{3}\)-Based Perovskite UV-C Photodetectors
## 3 Results and Discussion
### Optical and Electrical Properties of Samples  
The UV-vis absorption spectrum in Figure 4a shows the absorbance of the fabricated film. The absorption spectrum was relatively improved as the Br\({}^{-}\) increased. Various results were obtained depending on the mixture of FABr in the 2-step process to the previously prepared MAPbI3 precursor solution containing Cl anions, with FABr 20 showing the best absorbance. These results appeared as the composition ratio of the X site in MAPbX3 changed. The addition of an optimal amount of Br\({}^{-}\) is expected to improve the binding density of particles and membrane properties, as shown in the absorbance measurement results [34]. The dependence of (F(R\(\infty\))hv)\({}^{2}\) on photon energy using the Kubelka-Munk function is shown in Figure 4b. Sharp band edges were observed for MAPbI3, indicating a  
Figure 3: SEM images of MAPbI3-based perovskite films: (**a**) Bare, (**b**) Anti-solvent, (**c**) 0.10 M MACI.  
Figure 2: XRD patterns of (**a**) MAPbI3 and (**b**) films with different amounts of FABr added.  
direct bandgap of MAPbI\({}_{3}\). This indicates that the bandgap can be adjusted by controlling the amount of FABr added.  
Resistivity, mobility, and carrier concentration shown in Figure 5 are important indicators in evaluating the optical properties of the produced film and were analyzed by the Hall measurement meter [35]. By varying the composition ratio of anions during the post-processing process, changes in the properties of the film can be observed. The resistivity of FABr 0 was 0.7604 \(\Omega\)-cm, whereas the FABr-doped perovskite films, FABr 5 to FABr 25, and exhibited resistivities of 0.2415, 0.2847, 0.2198, 0.1977, and 0.8211 \(\Omega\)-cm, respectively. The pure film (FABr 0) exhibited a mobility of 5.23 cm\({}^{2}\)/V-s, whereas FABr 20 exhibited a mobility of 26.57 cm\({}^{2}\)/V-s, the optimum value among the tested samples. Mobility is correlated with structural changes in the film. Electronegativity is Cl (3.16) and Br (2.96), which are higher than I (2.66), and the lattice constant and interatomic distance decrease due to changes in relative bond length. At the point where the mobility decreases, it can be seen that excessive anion has been added [36]. Therefore, it can be seen that the addition of appropriate anions can generate more photocurrent and improve the quality of PD [37]. The characteristic values covered in Figure 5 are shown in Table 1.  
Figure 4: (a) UV–vis absorbance spectra and (b) bandgaps of films with different amounts of FABr added.  
Figure 5: Electrical properties (Resistivity, Mobility, and Carrier concentration) of samples as a function of FABr 0–25.
# A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring
## 3 Proposed WSN System Architecture
### Physical Components  
In this WSN system, there are two main physical components: a wireless node module and an application point module. The wireless node module consists of an EZ430-RF2500 board, which is developed by Texas Instruments Inc. (Dallas, TX, United States). As shown in Figure 1, this board integrates an MSP430 microcontroller from Texas Instruments Inc. (Dallas, TX, United States), a CC2500 antenna chip from Texas Instruments Inc. (Dallas, TX, United States), and several signal input/output (I/O) pins for connecting heterogeneous environmental sensors. The board is powered by a battery pack that provides a supply voltage of 3 V. The application point module differs from the wireless node module in that it is powered by a USB port that provides a direct physical link to a computer.  
Next, we introduce the details of this microcontroller and antenna chip. The MSP430 microcontroller enables low power operation between data acquisition and transmission events. It embeds an analog-to-digital converter (ADC) that collects analog data from any signal I/O pin on the board. The analog-to-digital conversion involves internal voltage references for voltage comparison. In addition, the microcontroller facilitates wireless data communication between nodes through the CC2500 antenna chip. The antenna chip offers very low-power communications and operates on radios below 1 GHz and 2.4 GHz. Moreover, it provides preloaded open-source code for Advanced Encryption Standard (AES)-128 security.
# Selected Applications of Terahertz Pulses in Medicine and Industry
## 8 Conclusions  
It is now clear that terahertz technology offers a unique method of investigating different aspects of industrial process control, such as paint layers on cars, connecting silicon chips on single devices as part of chip scale packaging and internal interfaces and porosity in controlled release pharmaceutical tablets. Although the adoption of the technology requires a lengthy assessment period, it has, nevertheless, occurred and is becoming an integral part of manufacturing processes. The impact in biology and medicine is slower and there are other technologies that, whilst not directly competing with terahertz, offer some additional features. It is entirely possible that a multimode approach in which terahertz is combined with another modality may find a use in clinical practice. In summary, there is no doubt that terahertz has emerged from the laboratory and is becoming an accepted part of different industries. New applications will continue to emerge, as confidence in the technology in current applications continues to grow, and as advances in core technology progress. Of course, there are challenges to be faced. There is still a long adoption time for the technology. There needs to be more training of terahertz engineers in the application of terahertz pulses so that companies can rapidly adopt new methods. There is progress in the establishment of an international measurement standard, which will give confidence to our industrial customers that the use of terahertz pulses is evolving.  
Figure 7: Detection of failure with EOTPR (green = good device, red = failed unit), including the ability of EOTPR to determine the exact position of faults around contacts, confirmed by physical fault analysis (photo).  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The local research ethics committee approved the study; appropriate informed consent was obtained from all patients, and all material was anonymized.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect of Nanofluids on Boiling Heat Transfer Performance
## 3 Effects of Nanofluid Physical Properties and Heated Surface on Boiling Heat Transfer  
The nanofluid will form a sedimentary layer on the heated surface during the boiling process. The deposited layer will change the number of vaporization core points and the surface wettability, resulting in changes in the growth and development and escape of the bubble. Therefore, the boiling heat transfer performance of nanofluids is mainly affected by the size of nanoparticles, base fluids and heating surfaces. In addition, it affected by the microscopic motion of nanoparticles and different types of base fluids, including the Brownian motion of nanoparticles [63], and the Marangoni effect between different base liquids [64]. The applied electric field [65], magnetic field [66; 67], photothermal boiling [68], and pressure [69] also have a certain influence on the boiling heat transfer of the nanofluid. The physical properties of nanofluids are influenced by the type of nanoparticles and base fluids. At present, the research parameters of nanoparticles include the type, particle size and concentration of nanoparticles. The base liquid includes water, alcohol, oil, and a refrigerant. The heated surface contains the effects of material, surface shape and surface roughness. The current research results of nanofluids are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Nanoparticles** & **Base Liquid** & **Concentration** & **Heated Surface** & **CHF/HTC** & **Resasons** \\ \hline  & & 0.5, 1, 2, 4 wt.\% & copper surface & CHF enhancement & CHF enhancement is due to surface characteristics & \\  & & & & HTC deterioration & of nanoparticle deposition & [70] \\ \cline{2-5}  & & 0.1, 0.2, 0.3 wt.\% & stainless steel rod & CHF enhancement & Nanoparticle deposition causes changes in surface & \\  & & & & HTC enhancement & roughness and vaporized core points. & [71] \\ \cline{2-5}  & & 0.0007, 0.007 Vol.\% & copper block & HTC enhanced 15-75 & HTC is affected by surface roughness. & [72] \\  & & & & (0.0007\%) & \\  & & & & (0.0005\%) & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ Al\({}_{2}\)O\({}_{3}\) & DW & 0.02, 0.1 vol.\% & copper surface & CHF enhanced 26-37\% & \\  & & & & HTC deterioration & wettability and thickness of the porous & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \cline{2-5}  & & 0.001, 0.002, 0
# Implementation Aspects in Regularized Structural Equation Models
## 6 Summary of Simulation Findings
### RQ3: Direct BIC Minimization Is a Competitive Estimation Method  
Third, even if differentiable approximation were used in regularized estimation, selecting the optimal regularization parameter \(\lambda\) based on the minimal BIC requires repeated estimation of the SEM which can be computationally demanding. A recently proposed smooth direct BIC minimization approach by [PERSON] and [PERSON] [17] avoids the specification of a regularization parameter and directly minimizes a smoothed version of the BIC. In our simulation studies that involved SEMs, the direct BIC minimization approach performed surprisingly well and had similar performance to the ordinarily employed indirect BIC minimization approach that requires repeated estimations (research question RQ3). This finding is remarkable because it could change the practice of the implementation of regularized estimation.
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
## 1 Introduction
The development is concentrated to design of new adaptive law for the gain of the optimal control with the simplest model that can applied for linear time-varying system in both of theory and practice. The final target of this work is to formulate a new optimal sliding mode controller on the basis of the Bolza-Meyer criterion. The technical contributions associated with the scientific significance of this work are given as follows: (i) The Meyer term in the Hamilton-Jacobi-Bellman was designed with a chosen constant to reduce control energy. (ii) The problem of definite symmetry matrices of the Hamilton-Jacobi-Bellman with values larger than 1 was solved to accomplish both high control performances and robustness. (iii) The Bolza-Meyer method was used with the modified version of the Meyer term to achieve simplicity of the optimal control structure. These technical contributions were theoretically analyzed followed by the presentation of simulation results of several illustrative examples. In the simulations, in order to demonstrate some advantages of the proposed controller, a conventional optimal controller was adopted and its control performance was firstly investigated.
# Mode Choice Effects on Bike Sharing Systems
## 3 Survey Design and Choice Experiment
### Experimental Design and Choice Situations  
Attributes of the mode choice experiment were taken from former studies on mode choices and studies on selected transport modes such as bikes, PMT, and PT (see Literature Review in Section 2). Furthermore, they were discussed with external academic partners and practitioners from transport planning offices.  
As described above, RP information (for BSS users) or aggregated empirical figures (for BSS-non-users) were employed and empirical values for the alternative modes were collected. Next, an individually tailored SP questionnaire was created by varying the mode-specific characteristics in accordance with a predefined experimental design. An overview of the attributes and variation of attribute levels is provided in Table 2.  
To reduce the number of possible combinations of the presented variation levels, an efficient design [37] was generated with the software Ngene [38]; it resulted in 60 combinations to design the choice tasks, which were split into six blocks (the experimental design is available upon request). Each participant was assigned to one block and the empirical values were varied accordingly. Finally, each participant was asked to complete ten choice  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Attribute** & **Variation of Reference Values/Levels** \\ \hline BSS & access and egress time & \(-50\%/-10\%/+40\%\) \\  & travel time (TT) & \(-30\%/-10\%/+30\%\) \\  & travel costs & \(-100\%/-35\%/+20\%\) \\  & street type & cycleway/side street/arterial road \\  & surface type & asphalt/cobblestones/macadam \\ \hline PMT & travel time (TT) incl. parking search & \(-20\%/-10\%/+30\%\) \\  & fuel costs & \(-50\%/+150\%/+200\%\) \\  & parking costs & \(-50\%/+100\%/+200\%\) \\ \hline PT & access and egress time & \(-50\%/-10\%/+40\%\) \\  & travel time (TT) & \(-30\%/-10\%/+30\%\) \\  & travel costs & \(-100\%/-35\%/+20\%\) \\  & utilized capacity & middle/high/overloaded \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stated preference experiment: transport modes, attributes and variation.  
Figure 3: Schema of survey protocol.  
tasks in the paper-and-pencil questionnaire. Participation in the study was restricted to adults (18 years and older) owning a driver's license to make the alternative PMT realistic.  
The individually tailored paper-and-pencil questionnaires were created, printed and sent within one week after recruitment to avoid fatigue effects. To increase reliability in terms of PT utilized capacity, an illustration accompanied the questionnaire (see Figure 4; for more details on reliability see Ref. [19]).  
Both the survey protocol and instrument followed the suggestions by [PERSON] [39]. After four weeks of non-response, reminders were sent with a new copy of the questionnaire. An exemplary choice situation is shown in Figure 5.
## 4 Controller Design
### Backstepping Controller  
After acquiring the total disturbance information, a backstepping recursive controller is designed for the reference model to achieve attitude tracking of a given attitude command, where the disturbance rejection is incorporated as an independent part.  
As the first step of the backstepping controller design, let Lyapunov function candidate be  
\[L_{1}=\Theta_{e}^{T}\Theta_{e}/2 \tag{15}\]  
The subscript \(e\) represents the error between the real state and the given command, namely, \(x_{e}=x-x_{c}\).  
Then the derivative is \(\dot{L}_{1}=\Theta_{e}^{T}\dot{\Theta}_{e}=\Theta_{e}^{T}(\dot{\Theta}-\dot{ \Theta}_{c})=\Theta_{e}^{T}(S^{-1}(\Theta)\omega-\dot{\Theta}_{c})\).  
If \(\omega=\omega_{c}=\mathrm{S}(\Theta)\big{(}\dot{\Theta}_{c}-K_{1P}\Theta_{e} \big{)}\), then  
\[\dot{L}_{1}=-\Theta_{e}^{T}K_{1P}\Theta_{e}\leq 0 \tag{16}\]  
The next step is to select another [PERSON] function candidate  
\[L_{2}=L_{1}+\omega_{e}^{T}\omega_{e}/2 \tag{17}\]  
Thus, \(\dot{L}_{2}=-\Theta_{e}^{T}K_{1P}\Theta_{e}+\Theta_{e}^{T}S^{-1}(\Theta) \omega_{e}+\omega_{e}^{T}(\dot{\omega}-\dot{\omega}_{c})\) and \(\dot{\omega}=-J^{-1}(\omega\times J\omega)+\overline{Q}\).  
Choose \(\overline{Q}=\overline{Q}_{c}=J^{-1}(\omega\times J\omega)+\dot{\omega}_{c}- (S^{-1}(\Theta))^{T}\Theta_{e}-K_{2P}\omega_{e}\), then  
\[\dot{L}_{2}=-\Theta_{e}^{T}K_{1P}\Theta_{e}-\omega_{e}^{T}K_{2P}\omega_{e}\leq 0 \tag{18}\]  
The last step is to calculate the \(\theta^{*}\) from \(\overline{Q}=\overline{Q}_{c}\) and \(\overline{Q}=f+R\theta\), these equations can be satisfied by choosing \(\theta^{*}=R^{-1}(\overline{Q}_{c}-f)\).  
Where \(K_{1P}\) and \(K_{2P}\) are parameter matrices which are positive definite.  
Finally, after all these steps, the control input \(\theta^{*}=\big{[}\theta_{1c}^{*},\theta_{1s}^{*},\theta_{t}^{*}\big{]}\) is derived.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 3 Results
### Correlation Analysis  
To confirm the linearity between the dependent and explanatory variables, correlation analysis was conducted using Pearson correlations based on the data measured in field experiments. Table 5 shows the results of correlation analysis for the variables used in this study. The gearbox output axle torque, which is a dependent variable, showed a high correlation coefficient in order of the tillage depth (r = 0.931), slip ratio (r = 0.897), travel speed (r = \(-\)0.688), engine torque (r = 0.505), and engine speed (r = \(-\)0.396). This indicates that these variables are the main factors directly affecting the axle torque. Overall, there was a very significant difference (\(p<0.01\)) for most variables, and there was no significant difference between the travel speed and engine speed (\(p>0.01\)).
## 2 Study Area and Data
### Land Cover Data  
We use the Global 30 m Fine Surface Coverage Product in 2020 [39] to classify land types for the study area in southeast Australia. This dataset is based on the 2015 Global Fine Surface Coverage Product, combined with the time-series surface reflectance data of the Landsat satellite from 2019 to 2020, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge datasets, etc., which produced the Global 30 m Fine Surface Coverage Product in 2020. Figure 1 shows the spatial distribution of the land cover in southeast Australia.
## 5 Conclusions  
In this paper, we present a novel approach to construct complex networks for EEG emotion recognition by synergizing the phase space reconstruction and visibility graph methods. The main innovation in our proposed method lies in the seamless integration of the phase space reconstruction and visibility graph methods. From the perspective of the visibility graph of ordinal patterns, we proposed a new construction method of complex networks from EEG signals, ComNet-PSR-VG. With the help of the phase space reconstruction method, EEG signals are mapped to a series of ordered partitions and symbolized to obtain a sequence of ordinal patterns. Subsequently, the generated symbolic sequence of ordinal patterns is transformed into a graph network using the visibility graph method. To validate the effectiveness and versatility, we constructed the experiment on random signals, Lorenz signals, and the SEED emotion dataset by the proposed method. Two types of network node measures, AND and NDE, are extracted from the resulting graph networks. These extracted network features are then utilized as the input features for emotion classification, employing SVM as the pattern classifier to discern positive and negative emotions. The experimental results demonstrated outstanding classification performance, reinforcing the effectiveness and universality of our method. Furthermore, we compared our experimental results with existing research methods, showcasing the superior performance of our proposed entropy measure in EEG emotion recognition. The outstanding generalization observed in our proposed method suggests its significant practical potential in the field of EEG emotion recognition. Overall, our method stands out as a promising and effective approach for EEG emotion recognition, paving the way for advancements in the broader domain of EEG pattern-learning research.  
Conceptualization, L.Y.; methodology, L.Y. and Y.L.; software, L.Y. and Y.L.; validation, L.Y.; formal analysis, L.Y., H.L. and Y.Q.; writing--original draft preparation, L.Y. and Y.L.; supervision, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Natural Science Foundation of China, under grants No. 62276076 and No. 62176102, as well as by the Guangdong Basic and Applied Basic Research Foundation (grants No. 2020A1515110498 and No. 2023A1515140109), the Professorial and Doctoral Scientific Research Foundation of Huizhou University under Grant 2020 JB058.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://bcmi.sjtu.edu.cn/~seed/seed.html](https://bcmi.sjtu.edu.cn/~seed/seed.html)]([https://bcmi.sjtu.edu.cn/~seed/seed.html](https://bcmi.sjtu.edu.cn/~seed/seed.html)).  
The authors gratefully acknowledge the use of the SJTU Emotion EEG Dataset (SEED) provided by [PERSON], Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, China.  
The authors declare no conflicts of interest.