esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
ZnO nanostructures were grown on a (100)-oriented \(n\)-type silicon substrate (dimensions: \(10\times 20\) mm) using a water assisted zinc source method. The fabricating process was carried out by using the system with a horizontal quartz tube placed in the furnace (see Figure 1).  
Firstly, 1 g Zn powders (purity 99.99%) used as the source was grinded and placed at the end of a ceramic boat. In the same boat, a piece of silicon substrate was placed at the other end for growing ZnO nanomaterials. Before loading the substrate into the ceramic boat, the silicon was firstly etched using hydrofluoric acid for about 10 min, then ultrasonically cleaned successively in acetone, ethanol, and deionized water for 15 min for each step, and finally was deposited with a 10 nm Au film on the surface by sputtering. The boat was then placed in a quartz vial inside a horizontal-tube furnace (see Figure 1). Another boat with 4 mL water was placed at the gas direction of the port of the horizontal-tube furnace. The system was vacuumed by a mechanical pump to KPa level, and then the furnace was heated to reaction temperature 730 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min, and kept for 40 min  
Figure 1: Schematic illustration of ZnO nanomaterial fabricating process using the system with a horizontal quartz tube placed in the furnace.  
under argon (Ar) flow of 50 sccm. The Ar flow was used as gas carrier, which carried water vapor into the quartz vial inside the horizontal-tube furnace. The water vapor can provide oxygen for the oxidation of Zn. The Zn powder would gradually vaporize to fill the quartz vial and then diffuse to the substrate when the temperature went above the melting point of zinc metal (420 \({}^{\circ}\)C). The Au particles on the substrate formed colloidal droplets, which became super-saturated with zinc vapor. ZnO nanostructures were synthesized on the silicon substrate via a vapor-liquid-solid (VLS) growth mechanism. The colloidal Au nanoparticles on the Si surface served as catalysts in the VLS synthesis mechanism. It has been reported that native defects, such as zinc interstitials and oxygen vacancies, may lead to the n-type semiconducting behavior of ZnO, and the defects serve as donors with a binding energy of 30-60 meV [16; 17].  
After the furnace cooled down, a bluish-white layer was observed on the substrate surface. Scanning electron microscopy (SEM) was employed to investigate the morphology of the as-grown ZnO nanomaterials. Energy dispersive spectroscopy (EDS) was used to analyze the composition of ZnO nanomaterials.
###### Abstract  
The ongoing Coronavirus Disease 2019 (COVID-19) pandemic has triggered the paralysis of dental services ascribed to the potential spread of severe acute respiratory syndrome (SARS)-CoV-2. Aerosol-generating procedures (AGPs) are common in dentistry, which in turn increase the risk of infection of the dental personnel due to the salivary presence of SARS-CoV-2 in COVID-19 patients. The use of rubber dam isolation (RDI) and high-volume evacuators (HVE) during AGPs is recommended to control dental aerosols, but the evidence about their effectiveness is scarce. This first study aimed to compare, in a simulated patient, the effectiveness of the following strategies: standard suction (SS), RDI and RDI + HVE. Using the laser diffraction technique, the effect of each condition on the volume distribution, average size and concentration of coarse (PM\({}_{10}\)), fine (PM\({}_{2.5}\)) and ultrafine (PM\({}_{0.1}\)) particles were evaluated. During the teeth drilling, the highest volume fraction of dental aerosol particles with SS was below 1 \(\upmu\)m of aerodynamic diameter. Additionally, the RDI + HVE significantly reduced both the ultrafine dental aerosol particles and the concentration of total particulate matter. AGPs represent a potential risk for airborne infections in dentistry. Taken together, these preliminary results suggest that isolation and high-volume suction are effective to reduce ultrafine dental aerosol particles.  
aerosol-generating procedures; airborne particulate matter; dental simulator; high-speed handpiece; ultrafine 2020; 10.3390/app10186345
## 4 Results
### GNSS Data Processing: Quality and Reliability  
#### 4.2.1 Alert Assessment with Short-Term Displacement  
As described in the previous Section 3.3.2, it was decided to analyze only the solutions with the ratio test [32] higher than or equal to four. We do not also analyze the solutions to unfixed ambiguity or the results that show the absence of daily data in these stations. These filters allow us to have more statistically robust results.  
On this subset of data, an estimation of the median absolute deviation (MAD) [33] type was performed for the calculation of the regression line. This analysis occurs if the data of the day just passed are present, but the parameters are calculated without using the latter data. The same treatment takes place with hourly data. With the estimate of the regression line, for each component, the measure of the coordinate of the last epoch, that is of the day or the hour just passed, is foreseen. The estimate also calculates the value of the standard deviation of the data. The current position is considered acceptable and without alarms, if the coordinates are in the range of twice the standard deviation (\(2\sigma\)) around the value foreseen by the regression line, without the use of the last measurement themselves (Figure 7).  
In the case of Madonna del Sasso, as mentioned before, was not possible to assess a GT, so we analyzed the results only from a statistical point of view, assuming that GT is close to ST (see Section 3.4). The results of five months of monitoring are resumed in Table 5.  
When the raw data showed low quality (R < 4), it was not possible to calculate the displacement rate and no alarm could be issued (code 0). In our case study, this happened in the 22% of the period for GNSS \"SAS a\", mainly for logistics or maintenance problems (e.g., lightning strike) to the experimental system.  
When there are data to be examined, we have followed the procedure described in Section 3.4 (Figures 7 and 8). We obtained for GNSS \"SAS a\" the following results:  
1. \"green-light\" or normal conditions (code 1), happened for about 98.5% of the valid measure,
2. \"yellow-light\" or attention level (code 2), happened only for 1.5% (two days over the entire periods) of valid data both for East (E), North (N) and Vertical (h) directions.
3. \"red-light\" or warning level (code 3). In the case of Madonna del Sasso, the level \"red light\" was never reached in E and N directions. The anomalous data (4%) for the vertical component (h) after a control were not validated for a processing problem.
## Appendix A  
We adopted the Stanford Method for Persistent Scatterers (StaMPS version 4.1b) to extract the postseismic time series over the 450 days following the 2021 Maduo mainshock. Detailed StaMPS software (version 4.1b) data-processing steps are provided to ensure that this experiment can be replicated by others. We selected a multilook factor of five looks in range and one look in azimuth for the interferograms to ensure a sufficient number of candidate PS points. The slave images were registered with a single master image, and a series of interferograms were generated using the GAMMA software (version 1.0-October 2014) (Figure S1). For initial PS selection, the amplitude dispersion threshold was set to 0.4, increasing this value if few points were selected. The number of patches in range and azimuth were set to 15 and 10, respectively. The number of overlapping pixels between patches in range and azimuth was set to 100. For pixel selection, the maximum acceptable percentage of selected pixels with random phase was set to 20%. The weeded PS points were merged with a resample size of 200 m and a standard deviation threshold of 2.0 after phase correction for spatially uncorrelated look angle (SULA) error. The unwrapping method was set to '3D', with the phase sampling grid set to 100 m. The phase rampfor each interferogram was estimated, and its corresponding parameter was set to 'y'. Based on the spatio-temporal characteristics of turbulence, a combination of low- and high-pass filters was adopted to effectively reduce turbulence noise. The time window and spatially correlated filtering minimum wavelength were set to 30 days and 50 m, respectively. Although StaMPS is mature software, modifying parameters can improve the accuracy of results. Therefore, the most critical factor is obtaining high-quality and abundant SAR data.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 5 Discussions
### The Merits and Limitations of the Current Work  
The land cover classification system used in the CLC-SW2010 was more suitable for various ecological applications than other classification systems, because it gave adequate consideration to life form, vegetation height, leaf type, vegetation phenology, vegetation cover, and so on [35]. In addition, this classification system was easy to interoperate with multiple land cover classification systems, such as FAO LCCS, which will help to widen the application scope of the CLC-SW2010 product. A full set of automatic mapping processes, including satellite image preprocessing, classification and quality control, were proposed in this study. They were conducted to generate the CLC-SW2010 product, which obviously shortened the mapping cycle. The main users, including the local environmental authorities and the research teams of the Carbon Special Program, have been involved in the process of generating the CLC-SW2010 product. They not only provided the field land cover samples, but also discovered potential classification errors existing in the CLC-SW2010 according to their experiences.  
Using the proposed HC-MMK approach, the overall accuracy of the CLC-SW2010 reached 87.14%. However, there are still some limitations in the current mapping work. Due to the lack of floodplainwetlands in land cover classification systems, the floodplain wetlands were classified as bare soils in the CLC-SW2010 product. They will limit its applications because distinct differences of ecological structure and functions existed between the floodplain wetlands and the bare soils. Meanwhile, some land cover classes have a lower producer's accuracy and user's accuracy in the CLC-SW2010 product, especially for mixed forests, steppes and meadows. The vague and non-quantitative definition and various spectral signatures are the main causes for the low accuracy of the mixed forests. The spectral signatures of the steppes and the meadows are easy to vary with the species, coverage, biomass and moisture content, which impacted distinguishing between steppes and meadows in the CLC-SW2010 product. Therefore, it is worth exploring how to further improve the accuracy of the mixed forests, steppes and meadows and optimize the land cover classification system according to application demands in the future.
## 2 Materials and Methods  
### Reagents and Standards  
Acetonitrile (ACN) HPLC grade, methanol (MeOH) GC-MS grade, absolute ethanol (EtOH) for analysis, formic acid 98-100% for analysis, butanol for analysis, and toluene for analysis were provided by Merck (Darmstadt, Germany). Acetonitrile LC-MS grade was purchased from Scharlau (Barcelona, Spain). Ultrapure water was prepared using a Direct-Q system (Millipore Iberica, S.A., Madrid, Spain).  
Analytical standards of polyvalent carboxylic acids and polyols were used for confirmation purposes: phthal acid \(\geq\)99.5% (PA, CAS 88-99-3), isophthal acid \(\geq\) 97% (IPA, CAS 121-91-5), terephthalatic acid \(\geq\) 98% (TPA, CAS 100-21-0), adipic acid \(\geq\) 99.5% (AA, CAS 124-04-9), trimellitic acid \(\geq\) 99% (TMA, CAS 528-44-9), 1,4-cyclohexaniedmethanol mixture of cis and trans 99% (CAS 105-08-8), neopentyl glycol 99% (CAS 126-30-7), ethylene glycol anhydrous 99.8% (CAS 107-21-1), 1,6-hexanediol \(\geq\)96.5% (CAS 629-11-8), diethylene glycol \(\geq\) 99.5% (CAS 111-46-6), and 2-methyl-1,3-propanediol 99% (CAS 2163-42-0) were purchased from Sigma-Aldrich (Schnelldorf, Germany). 1,3-propanediol \(\geq\) 98% (CAS 504-63-2) was obtained from Merck (Darmstadt, Germany).  
Other analytical standards used for identification, 2-butoxyethanol \(\geq\) 99%, hexanal, diisobutyl phthalate 99%, dibutyl phthalate 99%, methyl palmitate 97%, 2,4-di-tert-butylphenol 99%, 1-(benzyloxy)naphthalene, caprolactam \(\geq\) 99%, octocryelene 97%, squalene \(\geq\) 98%, and hexamethylenettermine 99%, were purchased from Sigma-Aldrich (Schnelldorf, Germany). Nonanal 98.7% was obtained from Supelco (Bellefonte, PA, USA). Phenol \(\geq\) 99.5% and dimethyl isophthalate 99% were provided by Merck (Darmstadt, Germany). Bisphenol A \(\geq\) 99% was obtained from Aldrich-Chemie. Bis(2-ethylhexyl) adipate \(\geq\) 99%, bis(2-ethylhexyl)phthalate 99%, and acetyltributyl citrate 99% were purchased from Fluka (Steinheim, Germany).  
Standard solutions of each carboxylic acid (50 mg/L) were individually prepared in acetonitrile, except for terephthalatic acid which was prepared in methanol. Regarding polyols, a stock solution at a concentration of 1000 mg/L was prepared in ethanol for each one. Working solutions of all compounds were prepared by subsequent dilutions and stored at 4 \({}^{\circ}\)C.
###### Abstract  
Visible light positioning (VLP) is a promising technology for indoor navigation. However, most studies of VLP systems nowadays only focus on positioning accuracy, whereas robustness and real-time ability are often overlooked, which are all indispensable in actual VLP situations. Thus, we propose a novel VLP method based on mean shift (MS) algorithm and unscented Kalman filter (UKF) using image sensors as the positioning terminal and a Light Emitting Diode (LED) as the transmitting terminal. The main part of our VLP method is the MS algorithm, realizing high positioning accuracy with good robustness. Besides, UKF equips the mean shift algorithm with the capacity to track high-speed targets and improves the positioning accuracy when the LED is shielded. Moreover, a LED-ID (the identification of the LED) recognition algorithm proposed in our previous work was utilized to locate the LED in the initial frame, which also initialized MS and UKF. Furthermore, experiments showed that the positioning accuracy of our VLP algorithm was 0.42 cm, and the average processing time per frame was 24.93 ms. Also, even when half of the LED was shielded, the accuracy was maintained at 1.41 cm. All these data demonstrate that our proposed algorithm has excellent accuracy, strong robustness, and good real-time ability.  
visible light positioning (VLP); real-time positioning and tracking; image sensor (IS); mean shift (MS); unscented Kalman filter (UKF); robustness +
Footnote †: journal: Accepted: 25 February 2019; Published: 4 March 2019
# Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation
## 3 Proposed Method
### Periodicity Analysis  
Tempo is estimated by computing an autocorrelogram with a frame length of 5 s and hop factor 5%, for a range of time lags between 60 ms and 2.5 s, corresponding to a tempo range between 24 and 1000 BPM. The autocorrelation curve is normalized so that the autocorrelation at zero lag is identically 1.  
A peak picking is applied to each frame of the autocorrelogram separately. The beginning and the end of the autocorrelation curves are not taken into consideration for peak picking as they do not correspond to actual local maxima. A given local maximum will be considered to be a peak if its distance with the previous and successive local minima (if any) is higher than 5% of the total amplitude range (i.e., the distance between the global maximum and minimum).  
As discussed in Section 2.2, one important problem with autocorrelation functions is that a lag can be selected as prominent because it is found often in the signal although the lag is not repeated successively. We propose a simple solution based on the following property: For a given lag to be repeated at least twice, the periodicity score associated with twice the lag should have a high probability score as well. These heuristics can be implemented as a single post-processing operation applied to the autocorrelation function, removing any periodicity candidate for which there is no periodicity candidate at around twice its lag.
## 2 GS between Two FHN Neurons  
### GS in Unidirectionally Coupled FHN Neurons  
A single FHN neuron is given in the following form  
\[\dot{v}=v-v^{3}/3-w+I_{ext},\] \[\dot{w}=\gamma(v+a-bw), \tag{1}\]where \(v\) and \(w\) represent the potential difference and a recovery current variable, respectively. \(a,b\), and \(\gamma\) are positive parameters, and \(0<b<1\), \(0<\gamma\ll 1\). \(I_{ext}\) is the external current input. Assume that \((v_{0},w_{0})\) is the equilibrium point of System (1), that is,  
\[v_{0}-v_{0}^{3}/3-w_{0}+I_{ext}=0,\] \[v_{0}+a-bw_{0}=0.\]  
For convenience, move equilibrium \((v_{0},w_{0})\) to the origin in System (1). Substituting \(u_{1}=v-v_{0}\), \(u_{2}=w-w_{0}\) into System (1), one has  
\[\dot{u}_{1} =-u_{1}^{3}/3+cu_{1}^{2}+du_{1}-u_{2},\] \[\dot{u}_{2} =\gamma(u_{1}-bu_{2}), \tag{2}\]  
where \(c=-v_{0}\), \(d=1-v_{0}^{2}\), \(v_{0}\) is the unique real root to equation \(-\frac{v_{0}^{3}}{3}+(1-\frac{1}{b})v_{0}-\frac{a}{b}+I_{ext}=0\).  
The behavior of two unidirectionally coupled FHN neurons is considered  
\[\dot{u}_{1} =-u_{1}^{3}/3+cu_{1}^{2}+du_{1}-u_{2},\] \[\dot{u}_{2} =\gamma(u_{1}-bu_{2}),\] \[\dot{u}_{3} =-u_{3}^{3}/3+cu_{3}^{2}+du_{3}-u_{4}+k(u_{3}-H(u_{1})),\] \[\dot{u}_{4} =\gamma(u_{3}-bu_{4}), \tag{3}\]  
in which \(k\) is the coupling strength, \(H(u_{1})\) is a continue function of \(u_{1}\). The first and second neurons in Equation (3) are referred to as a drive and response, respectively. In this paper, we consider that System (3) possesses the following property  
\[\lim_{t\to\infty}||u_{3}-H(u_{1})||=0. \tag{4}\]  
In fact, it can be said that there exists the property of GS in System (3) with a transformation \(H\) between \(u_{1}\) and \(u_{3}\).
# Solution to the Inverse Problem of the Angular Manipulator Kinematics with Six Degrees of Freedom
## 2 Methods and Algorithms
### Determination of Orientation Angles  
The resulting angles also determine, in addition to the distance of point \(K\) from the manipulator stand, the orientation in space of the elbow \(BB_{2}\), see Figures 1 and 5. Let us write the expression for the elbow rotation matrix in accordance with the sequence of rotations by angles (10) by multiplying the matrices of elementary rotations  
\[\mathbf{S}_{(B_{2}xyz)}^{(OXYZ)}=\mathbf{S}_{z}(\varphi_{1})\mathbf{S}_{y}( \varphi_{2})\mathbf{S}_{y}(\varphi_{3})=\mathbf{S}_{z}(\varphi_{1})\mathbf{S} _{y}(\varphi_{2}+\varphi_{3}),\]  
where \(\mathbf{S}_{\xi}(\varphi)\), \(\xi=x,y,z\) designates the matrix of elementary rotation around axis \(\xi\) on the value of angle \(\varphi\). Based on this, the corresponding rotation matrix takes the following form  
\[\mathbf{S}_{(B_{2}xyz)}^{(OXYZ)} =\begin{bmatrix}\cos\varphi_{1}\cos(\varphi_{2}+\varphi_{3})&- \sin\varphi_{1}&\cos\varphi_{1}\sin(\varphi_{2}+\varphi_{3})\\ \sin\varphi_{1}\cos(\varphi_{2}+\varphi_{3})&\cos\varphi_{1}&\sin\varphi_{1} \sin(\varphi_{2}+\varphi_{3})\\ -\sin(\varphi_{2}+\varphi_{3})&0&\cos(\varphi_{2}+\varphi_{3})\end{bmatrix} \tag{11}\]
## 4 Regulations and Laws  
Medical security and privacy is an imperative requirement in healthcare organizations all over the world, so there are many different regulations and acts that affect healthcare providers. In fact the regulations and acts vary greatly from country to country. Here we discuss the American Health Insurance Portability and Accountability Act of 1996 (HIPAA) [39] and the Health Information Technology for Economic and Clinical Health Act (HITECH) [77].  
HIPAA regulates many different rules to be followed by doctors, hospitals, healthcare organization and other health related professionals. The Act requires comprehensive data security measures for data administration policies, data safeguards and supporting systems. According to the Act, healthcare providers are clearly subjected to strict civil and criminal penalties (_i.e._, either fine of $250,000 or imprisonment for 10 years) for those who acquire or disclose the patient health information for money-making or malevolent harm. Furthermore medical providers must guarantee that their system, and those of their business associates, ensures [78] the following:  
* Security and confidentiality, _i.e._, patient health information is secure, and is in a proper format.
* Providing protection against any infringements of security, confidentiality and integrity, if they occur.
* Providing protection against unauthorized access to or usage of the patient health information.  
Further HIPAA regulates some other areas:  
* Secure patient health records, particularly from those who do not need them.
* Establish systems that need user identities (_i.e._, both internal staff and consumers).
* Limit the access to sensitive data and applications to authorized personnel (_i.e._, role-based access control).
* Ensure integrity of patient health information throughout its life-cycle within the system.
* The HITECH Act includes provisions to enlarge the use of information technology (IT) to store, capture, transmit, properly share and use health data. It introduces the new Act that states that those who manage patient health information (PHI) should notify the affected individuals if there is any breach that discloses their PHI [77].  
Although the involvement of wireless technology and Internet access are providing a low-cost communication infrastructure that is suitable for home care monitoring, ambulatory monitoring, hospital and clinical monitoring, and so on, it should also be considered that in some special cases such as medical emergencies, or disaster medical management that there may a need to disclosure the patient information to many people involved in the rescue activities, so regulations should have some flexibility and users also have to compromise with their privacy to some extent. The next section discusses the existing security mechanisms that can provide security to wireless healthcare applications, and ensure patient privacy.
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 3 Advanced LSFP  
### Lsfp  
In the LSFP (large scale fading precoding) network, several assumptions were made, and they are as follows.  
1. Any base station in a cell can accurately estimate and obtain a large-scale fading factor between itself and any user in the group.
2. Any base station in a cell can send a large-scale fading factor matrix to any user in the cell.
3. All base stations in a cellular network are connected to the same network hub controller. The large-scale fading factor of the entire cellular network is visible to this network hub controller.
4. Any downlink signal in a a cellular network is visible to this network hub controller.  
By adjusting the hardware of the base station equipment and using the reciprocity of uplink and downlink [12; 13], the network hub controller in a cluster generates a LSFP matrix denoted as \(W^{k}\) based on the large-scale fading factor \(\beta_{j}^{kl}\)[14]:  
\[W^{k}=\left(\begin{array}{c}W_{1}^{k}\\ \vdots\\ W_{L}^{k}\end{array}\right)k\in(1,K) \tag{3}\]  
The was is specifically divided into the precoding of downlink information from a base station to the user, and the decoding of uplink information from a user to the base station.