esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### NPP Trend in the TRH Region  
The Theil-Sen median, known as the Sen's slope estimation (senSlope), was used to compute the NPP's changing trends. This method is a robust nonparametric statistical trend analysis method, usually used in combination with the Mann-Kendall (MK) test. The senSlope tests the trend of a long-term series, and the MK indicates the significance of the trend [76; 77; 78]. The method has a strong ability to resist outliers and has a solid statistical theoretical basis for the test of the significance level, making the results scientific and credible. The equation of senSlope can be written as follows:  
\[\text{S}_{\text{NPP}}=\text{Median}\left(\frac{\text{NPP}_{\text{j}}-\text{ NPP}_{\text{i}}}{\text{j}-\text{i}}\right) \tag{28}\]  
where S\({}_{\text{NPP}}\) indicates the NPP's changing trends, i and j represent the time series (\(2001\leq\text{i}\leq\text{j}\leq 2020\)), NPP\({}_{\text{i}}\) and NPP\({}_{\text{j}}\) indicate the NPP values of years i and j, respectively. The NPP trends are divided into the following three cases by the threshold method: (1) S\({}_{\text{NPP}}>0\) means an increasing NPP trend, (2) S\({}_{\text{NPP}}=0\) represents a stable NPP trend, and (3) S\({}_{\text{NPP}}<0\) means a decreasing NPP trend.  
The Hurst (H) analysis was used to indicate the long-term sustainability of the NPP trends [79]. This method is an effective statistical approach for quantitatively describing the long-term dependence of a time series and can be used to explore the persistence of a time series. The Hurst (H) can be computed using the following equation:  
\[\frac{\text{R}(\tau)}{\text{S}(\tau)}=(c\tau)^{\text{H}} \tag{29}\]  
where R(\(\tau\)) is the range of the first \(\tau\) cumulative deviations from the mean, S(\(\tau\)) is the series of the first \(\tau\) standard deviation, \(\tau\) is the observation period, and c is a constant.  
The H value ranges from 0 to 1.0. When 0 \(<\) H \(<\) 0.5, it means that the time series has anti-persistence, illustrating a negative consistency of the time series in the future. When 0.5 \(<\) H \(<\) 1, it indicates that the series has persistence, meaning that the trend observed during the study period will continue. When H = 0.5, it indicates that the changing trend of the time series is unrelated to that of the study period [79].
###### Abstract  
Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 x 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.  
g 2019 19 2959 25 June 2019; Published: 4 July 2019
# In Vitro Antibacterialential Activity of Marine Microalgae Extract against _Vibrio harveyi_
## 2 Materials and Methods  
### Cultures and Preparation of Microalgae Extract  
The species of unicellular marine microalgae were tested. _N. oceanica_, _C. gracilis_, _Isochrysis_ sp. and _T. weissflogii_ microalgae strains were collected from the Culture Collection of the Live Feed Culture Laboratory, Borneo Marine Research Institute, University Malaysia Sabah. Each microalgae culture was produced and maintained in 250 mL Erlenmeyer flasks with 100 mL Guillard's F/2 media [15]. The microalgae culture was reactivated as an inoculum for four days and the starting culture concentration was 1 \(\times\) 10\({}^{5}\) cells mL\({}^{-1}\). The cultivation conditions were set at 25 \(\pm\) 1 \({}^{\circ}\)C with 24 h of continuous illumination at 1000 \(\upmu\)mol photon m\({}^{-2}\) s\({}^{-1}\) given by cool white fluorescent lights. The microalgae inoculum culture was put into 500 mL Erlenmeyer flasks with 200 mL of Walney's media [16]. After 10 days in culture, the cultivation reached the exponential phase of growth and cell density (1 \(\times\) 10\({}^{7}\) cells mL\({}^{-1}\)). The cell viability of cell microalgae was evaluated using a Malassez haemocytometer on a daily basis. By plating 50 \(\upmu\)L samples to tryptic soy agar (TSA) with 2% (\(w/v\)) NaCl and Thiosulfate Citrate Bile Salt Sucrose (TCBS) agar, the viability of cell microalgae culture was tested for contamination. The microalgae culture was afterwards recovered and harvested by centrifugation at low speed (2000\(\times\) g) for 10 min at 15 \({}^{\circ}\)C, and the algal pellet was filtered through Whatman no. 1 filter paper [17]. The extraction of microalgae biomass was carried out with four different solvents: methanol, ethanol, Dimethyl sulfoxide (DMSO), and saline water, with a ratio of 0.1 g of algal biomass for 1.0 mL of solvent. Every microalgae extract was fixed into 10\({}^{6}\) cells mL\({}^{-1}\). Before further usage, the crude extracts were promptly refrigerated at 4 \({}^{\circ}\)C.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 3 Descriptive Analysis  
### Publication Details  
The number of studies conducted in the field of remote sensing of forest BA, BS, and post-fire recovery has increased substantially, as is evident from the number of articles published between 2000 and 2020 (Figure 2). This is well-supported by the general trend of increased publication activity in the field of remote sensing, availability, and open access policy for the Earth Observations data ([[https://www.earthobservations.org/open_eo_data.php](https://www.earthobservations.org/open_eo_data.php)]([https://www.earthobservations.org/open_eo_data.php](https://www.earthobservations.org/open_eo_data.php)), accessed on 10 May 2022), and the importance of the prevention of wildfires for sustainable forest management actions [1;3]. Within the decade 2000-2010, we selected 122 (38%) publications for further review, while during 2011-2020, the number was almost doubled, with 204 (62%) publications.  
Data analysis revealed that 48 peer-reviewed journals published the selected papers. The main source journals of the 329 papers were: _Remote Sensing of Environment_ (74 papers), _Remote Sensing_ (56 papers), _International Journal of Remote Sensing_ (39), _International Journal of Wildland Fire_ (26), _International Journal of Applied Earth Observation and Geoinformation_ (15), and _Forest Ecology and Management_ (14) ranked as the top seven journals that published no less than 10 research articles during the past two decades.  
The top 20 journals are presented in Figure 3, which together contained 285 publications or approximately 88% of the selected papers. The other 28 journals (12%) that published only two relevant articles were eliminated from the descriptive statistics in Figure 3. Most of them, which do not directly deal with remote sensing, were distributed across a wide range of journals (e.g., _Journal of Biogeography, Fire Ecology, Ecological Indicators, Climatic Change, Landscape and Urban Planning, Biogeosciences, Flowsta, Ecological Modelling, Science of Total Environment, Global and Planetary Change, Contemporary Problems of Ecology, Izvestiya, Atmospheric and Oceanic Physics, Biological Conservation, Catena, Journal of Tropical Ecology, Environmental Monitoring and Assessment, Geophysical Research Letters,_ etc.).  
Figure 2: Temporal distribution of available and selected publications from 2000 to 2020 for the review in the field of remote sensing of forest BA, BS, and post-fire recovery, obtained from Scopus and Web of Science databases.
# Assessment of AC Corrosion Probability in Buried Pipelines with a FEM-Assisted Stochastic Approach
## 3 Probabilistic Evaluation of the Leakage Current Density through a Holiday in the Coating
### Relationships among Induced Voltage, Current Density and Holiday Area  
In the previous paragraph devoted to the FEM simulations, it has been shown that the simple model of the holiday inside the pipeline insulating coating given by a cylindrical pore having area A, height d (equal to the thickness of the coating) and constant resistivity \(\rho_{\text{p}}\) is a fair approximation. (See Figure 8 for a schematic drawing).  
According to it, one has that the formula for the spread resistance \(R_{s}\) presented in many papers and textbooks, see for example [5; 10], is given by:  
\[R_{s}=\frac{\rho}{4}\sqrt{\frac{\pi}{A}}+\frac{\rho_{p}d}{A} \tag{4}\]  
the first addendum represents the earth (or leakage) contribution, while the second represents the pore contribution. If \(V\) is the AC-induced pipe potential in the point where the holiday coating is located, one has that \(V/R_{s}\) represents the AC leakage current to  
Figure 7: Flowchart of the calculation procedure.  
soil; therefore, it is possible to define an average current density \(J\) through the holiday (see Appendix A) by means of:  
\[J=\frac{V}{\rho_{p}d+\frac{\rho}{4}\sqrt{\pi A}} \tag{5}\]  
This quantity has to be compared with the limit value \(J_{lim}\) suggested by the standards to assess the AC corrosion risk for the pipeline, i.e., it has to be \(J<J_{lim}\). Thus, from Formula (5), one must have:  
\[\sqrt{\pi A}\leq\frac{4}{\rho}\bigg{(}\frac{V}{J_{lim}}-\rho_{p}d\bigg{)} \tag{6}\]  
that yields:  
\[A\leq A_{max}(V)=\frac{16}{\pi\rho^{2}}\bigg{(}\frac{V}{J_{lim}}-\rho_{p}d \bigg{)} \tag{7}\]  
\(A_{max}\) represents, for a given value of the induced potential V, the maximum value of the holiday area such that one can have a current density exceeding \(J_{lim}\).  
At the same time, being the left-hand-side of Formula (6) a positive quantity, it must be \(V\geq J_{lim}\,\rho_{p}d\). Thus, one obtains a threshold value \(V_{min}\) for the induced potential; such a quantity represents the minimum value for the induced potential V able to generate a current density greater than \(J_{lim}\) leaking to the soil through the holiday. Therefore, one has:  
\[V\geq V_{min}=J_{lim}\rho_{p}d \tag{8}\]  
Notice that \(V_{min}\) does not depend on soil resistivity but only on the resistivity of the material inside the pore.  
Finally, it is useful to emphasize that the above formulas involve only the area size of the holiday and are not based on a particular shape. See Appendix A for further details.
## 2 Experimental Methodology and Apparatus
### Unsupervised Learning Approach
#### 2.3.1 Logistic Regression  
Logistic Regression is a statistical model for binary classification tasks. It employs a sigmoid function to map input features to probabilities. Parameters are learned through gradient descent, minimizing a cost function. Regularization can be applied to prevent overfitting. It is widely used due to its simplicity and interpretability.
## 5 Results and Discussions
### LDS Velocity Measurement  
Figure 6 displays the results of the acquired displacement and reference velocity. The measurements were conducted five times under the same conditions, and the reference velocity was taken as the average of the measurements. Notably, the shape of the displacement does not exhibit ideal sinusoidal linear motion due to the inherent characteristics of the LS. When an amplified signal was directed to the LS, the displacement of the voice-coil inside the LS increased and became nonlinear. This nonlinearity led to distortions in the displacement, as evident in Figure 6. Furthermore, distortions in the displacement were caused by arm bending. When the arm was cantilevered, the oscillation exhibited a folding back point near the maximum displacement. At the maximum displacement \(A=3.4\) mm, the velocity of the object appeared to be \(-1.2\) mm/s, as depicted in Figure 6.  
The velocities were calculated as a second-order central-difference of the displacement data obtained by the LDS. The maximum reference velocity \(v=194\) mm/s was determined at a time of 8.3 ms, which was considered as reference position of the LS. The negative maximum reference velocity \(v=-191\) mm/s was acquired at a time of 0.11 s. The maximum reference velocity was estimated as being 39% lower than that acquired from the PA result.  
Figure 5: Result of PA velocity measurement with \(5^{\circ}\) interval. The object underwent sinusoidal oscillation at 5 Hz, completing one period every 0.20 s. The black dots represent the velocities obtained from the PA velocity measurement, while the green line illustrates the averaged velocities of PA measurement. The positive maximum velocity \(v_{\text{max}}=\)316 mm/s was recorded at 5.6 ms, while the negative maximum velocity \(v_{\text{max}}=-292\) mm/s was obtained at 0.11 s.  
Figure 6: Result of LDS measurement. The blue line indicates the displacement of the object, while the gray line depicts the velocities calculated using a central-difference scheme of the displacement data. The positive maximum velocity reached \(v=194\) mm/s at 8.3 ms, and the negative maximum velocity was \(v=-191\) mm/s at 0.11 s.  
As discussed in the next section, this deviation is due to the averaging included in the LDS. The SD of velocity measurements ranged from 0.32 to 7.8 mm/s. A relatively large SD was obtained at around 200 mm/s.
# Orthodontic Surgical Treatment of Impacted Mandibular Canines: Systematic Review and Case Report
## 1 Introduction  
Altered tooth eruption indicates a tooth that fails to achieve the correct position in the arch, and can result in two clinical conditions that can be combined: inclusion, and transmigration. The incidence of included mandibular canines (IMCs) and transmigrated mandibular canines (TMCs) is rare, and is less frequent than that of impacted maxillary canines (IMXCs) [1]. IMCs have a prevalence of 0.05% to 0.4%, while IMXCs have a frequency of inclusion ranging from 1 to 3% [2].  
The included tooth may occasionally remain in the same hemiarch or migrate laterally via a phenomenon called transmigration [3]. The mandibular and maxillary canines play a fundamental role in maintaining the arch form and function. Failure of included canines to recover may result in resorption or migration of near roots, loss of natural arch form, or the formation of dentigerous cysts [4].  
According to [PERSON]'s classification (Figure 1), TMCs are classified into five types [5]; type 1 is the most frequent. Early diagnosis makes it possible to assess the extent of canine transmigration and reduce aesthetic and functional complications [6; 7; 8; 9; 10].  
There are some hypotheses as to the causes of included mandibular amines, and these include premature loss of deciduous teeth, retention of the deciduous canine, odontomas, crowding, and supernumerary teeth [11; 12]. However, the etiology and exact mechanism are still unclear [12]. Consequently, it is difficult to find clinical guidelines derived from valid studies based on large patient samples. The current state of the art provides for various approaches to the problem of included amines. Depending on the age of the patient and the severity of the inclusion, interceptive therapy is provided where possible; guided forced eruption with repositioning in the arch is implemented in cases of medium severity; and in severe cases that cannot be recuperated, avulsion of the element is performed [13]. Early diagnosis and clinical and radiological evaluation can help the dentist to plan the surgical technique and avoid possible complications [14]. Surgical techniques provide for guided eruption with a closed or open technique in the case of buccal positioning of the included tooth [15; 16]. Few systematic reviews on this subject can be found in the literature; therefore, it is difficult for clinicians to identify clear guidelines on the topic [17]. The aim of this article is to describe the buccal surgical approach used to engage and return the mandibular canine to the arch, and to review the scientific literature regarding the recurrence of IMCs, the successes described, the periodontal health of the treated tooth, and the techniques adopted.  
Figure 1: Mupparapu classification.
# A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem
## 2 Problem Description  
As mentioned before, two types of objective functions have been taken from the literature for the problem: (i) the hierarchical one (minimize the number of routes and then minimize the total traveling cost) and (ii) the minimization of the total traveling cost.  
Consider a complete directed graph \(G=(V,A)\), with \(V=\{0,1,\ldots,n\}\) the set of vertices and \(A\) the set of arcs. The reason to consider a directed graph is that in real-life applications, cost (distance) matrices are not symmetric (See Section 5). Notice that vertex 0 (depot) is the departure point of all vehicles. Each vertex (except the depot) \(i\in V\setminus\{0\}\), has an associated demand \(d_{i}\). Also, each arc \(a=(i,j)\in A\), has an associated cost \(c_{ij}>0\). Feasible solutions to the OVRP are open routes originated at the depot, that satisfy capacity and route length constraints.  
Before formally defining the OVRP, some notation is introduced. Let \(R\subset A\) denote a given set of open routes of \(G\) originated at the depot (0):  
* \(V^{+}=\{1,\ldots,n\}\), the set of vertices without the depot.
* Cost of \(R\), \(c(R)=\sum\limits_{a=(i,j)\in R}c_{ij}\).
* \(st\), the time required to service any client.
* First vertex, a vertex directly connected to 0 in \(R\) (\(a=(0,i)\in R\)).
* Subroute (\(R_{i}\subseteq R\)), an open route in \(R\) originated at vertex \(i\in V\).
* \(s\)-route (\(R_{i}\subseteq R:a=(0,i)\in R\)), a subroute originated at \(i\), where \(i\) is the first vertex of \(R_{i}\).
* \(V(R_{i})\subset V\), the set of vertices that are part of subroute \(R_{i}\).
* \(d(R_{i})=\sum\limits_{j\in V(R_{i})}d_{j}\), the sum of the demands of the vertices in \(R_{i}\).
* \(l(R_{i})=\sum\limits_{a=(i,j)\in R_{i}}(c_{ij}+st)\), the total length of \(R_{i}\).  
The capacity constraints for the OVRP impose that for a given open route originated at 0 the total demand of any subroute can not exceed a given value \(Q>0\). That is \(d(R_{i})\leq Q\) for any subroute \(R_{i}\) of \(R\). Open routes are also subjected to length constraints, which impose that route duration can not exceed a given value \(L>0\). That is \(l(R_{i})\leq L\) for any subroute \(R_{i}\) of \(R\).  
The OVRP can be defined as the problem of finding a set of open routes originated at the depot, visiting all clients and satisfying the capacity and route length constraints while minimizing one of the above mentioned objective functions.
## 3 Results and Discussion
### Adsorption Behavior of GO-MIPs for 4-MEC  
#### 3.2.1 Adsorption Kinetics  
In order to study the adsorption behavior of GO-MIPs towards the template (4-MEC), an adsorption kinetics analysis was first carried out. Figure 4a shows the dynamic adsorption curves of GO-MIPs, and the adsorption process proceeded in two steps. The existence of a large number of active binding sites on the external surface of GO-MIPs made the adsorption rate quite fast in the first step, and about 62% of the maximum adsorption amount of 4-MEC was taken up from the solution in 20 min. In the subsequent step, the adsorption was slow at reaching equilibrium, and the relatively long contact time could be associated with the fact that the 4-MEC in outer space had to diffuse to the inner cavities [35]. The maximum adsorption capacity of the experiment was gradually achieved within about 2 h, indicating the successful formation of the cavities with specific shapes in the GO-MIP nanocomposites during the imprinting process. For comparison, the adsorption capacity of the GO-NIPs was lower, which might only have been caused by non-specific electrostatic and hydrophobic interactions.  
Figure 3: TEM images of the prepared GO sheets (**a**) and GO-MIPs (**b**).  
Figure 2: SEM images of the prepared GO sheets (**a**), GO-RAFT (**b**), and GO-MIPs (**c**).  
In order to examine the mechanism of the adsorption process, a pseudo-first-order rate equation and a pseudo-second-order rate equation have been used to analyze the kinetic data [36; 37; 38]. The results can be calculated according to the following equations:  
\[\ln(Q_{e}-Q_{t})=\ln Q_{e}-k_{1}t \tag{2}\]  
\[\frac{t}{Q_{t}}=\frac{1}{Q_{e}^{2}k_{2}}+\frac{t}{Q_{e}} \tag{3}\]  
In Equations (2) and (3), \(t\) is the adsorption time (min); \(Q_{e}\) (mg g\({}^{-1}\)) is the capacity of the 4-MEC adsorbed at the equilibrium, and \(Q_{t}\) (mg g\({}^{-1}\)) is the capacity of the 4-MEC adsorbed at the time, \(t\). \(k_{1}\) and \(k_{2}\) mean the pseudo-first-order and pseudo-second-order rate constant, respectively. The plots of ln (\(Q_{e}-Q_{t}\)) vs. \(t\) (Figure 4b) gave a straight line, and the values of \(k_{1}\) and \(Q_{e}\) could be obtained from the slope and intercept. Similarly, the slope and intercept of the plots of \(t/Q_{t}\) vs. \(t\) (Figure 4c) were used to calculate the values of \(k_{2}\) and \(Q_{e}\). Table 1 shows the summarized adsorption rate constants and linear regression values. The linear correlation coefficient value (R\({}^{2}\)) of the pseudo-first order (0.9393) was obviously lower than the pseudo-second order (0.9927), indicating that the adsorption of 4-MEC onto GO-MIPs followed pseudo-second order kinetics. The results suggest that the adsorption process of GO-MIPs towards 4-MEC is a chemical process and that the adsorption rate is controlled by the movement of 4-MEC within the specific cavities of GO-MIPs [35]. Furthermore, it was noticed that the data of GO-NIPs also fitted well with the pseudo-second-order model, which may be attributed to the multi-interactions, such as the chemical and physical adsorption of 4-MEC and NIPs on the surface [36; 39].