esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data-Driven Segmentation of Space
For each cell \(c_{n}\) in C, remove all member points while keeping its centroid
4. For each point \(\vec{x_{p}}(t)\) in \(X\), find the closest cell \(c_{n}\) in C and set \(s_{p}(t)=c_{n}\). Where \(c_{n}\) is a cell in set \(C\) with index \(n\), \(\vec{x_{p}}(t)\) a seed point of the set \(\vec{X}\) that represents the coordinates of pedestrian \(p\) at time \(t\). ```  
**Algorithm 1** Description of the data-driven space segmentation algorithm presented by [13], which segments space into a set of cells and derives the sequence of cells visited by each of the pedestrians at the event. In essence, this algorithm derives a Voronoi diagram with cells which have a limited radius. The cell centroids of each Voronoi cell are accordingly used to partition the network in distinct zones, determine the boundaries between these zones and allocate all point of a GPS trace to each zone, hereafter the zones are named cells. The partitioned network is shown in Figure 2. The desired maximum cell radius is set to \(\gamma\)=100 m. The authors have tried several settings of the maximum cell radius and found that this setting provides the best forecasting results. The current setting of the maximum cell radius is a balancing act between largest radius that can practically be adopted for crowd management purposes in urban networks, the smallest radius for which the GPS traces will provide viable results and the steeply increasing burden of model estimation whenever a lower maximum cell radius is adopted. Given that a cell of at maximum 31,415 m\({}^{2}\) is quite a large area, the current setting limits the use of this forecasting approach to pedestrian infrastructures that consist of a network of large corridors and open areas. Important to note is that these settings cannot be used to indicate local crowd management issues, such as an increase in density or flow in a small path way. This setting relies on crowd managers to make the translation of global trends in the movement behavior to very specific local issues such as overcrowding. At free flow speed pedestrians can traverse 390 m per 5 min, which is more than the maximum cell radius. Moreover, not all cell boundaries will be limited by the maximum cell radius. That is, cells with a smaller cell radius will exist. These cells are generally located in-between cells with a high density of seed points. In both cases, pedestrians can cross multiple cells within the prediction horizon. Consequently, pedestrians might not be registered at intermediate cells. This limits the realism of the forecasted paths of each pedestrian. Yet, given that crowd management officials require fairly detailed forecasts in order to determine their intervention strategy, the authors deem the detail of the actual aggregate forecast more important than forecasting a realistic viable path for each pedestrian.
# Study on Spray Characteristics and Breakup Mechanism of an SCR Injector
###### Abstract  
Selective catalytic reduction (SCR) is currently one of the most efficient denitration technologies to reduce nitrogen oxide (NO\({}_{3}\)) emissions of diesel engines. AdBlue (urea water solution, UWS) is the carrier of the reducing agent of SCR, and the spray process of UWS is one of the critical factors affecting denitration efficiency. In this paper, a non-air-assisted pressure-driven full process spray (NPFPS) model is proposed to illustrate the breakup mechanism and the spray distribution properties of UWS through computational fluid dynamics (CFD). In the NPFPS model, the mechanism of the primary breakup is described by the volume of fluid (VOF) approach, which realizes the quantitative study of the critical parameters determining spray characteristics such as the breakup length, inclination angle, droplet size of the primary breakup, and primary velocity. The distribution of the spray after the primary breakup is depicted by the discrete phase model (DPM) coupled with the Taylor analogy breakup (TAB) model, through which the degree of secondary breakup can be obtained including quantitative studies of the droplet size distribution and velocity distribution in the different cross-sections. To verify the accuracy and feasibility of the NPFPS model, the experimental data are employed to compare with the simulation data. The results are in good agreement, which indicate the practical value of the model.  
liquid jet; spray; primary breakup; secondary breakup; breakup mechanism; selective catalytic reduction (SCR)
Sensors_**2015**, _15_, 24886-24902; doi:10.3390/s151024886  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Layered Location-Based Security Mechanism for Mobile Sensor Networks: Moving Security Areas**  
**[PERSON] *, [PERSON] \({}^{\dagger}\), [PERSON] \({}^{\dagger}\) and [PERSON]**  
School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China; E-Mails: [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (L.W.); [EMAIL_ADDRESS] (C.Z.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-22-5868-5318; Fax: +86-22-5868-5358.  
Academic Editor: [PERSON]  
_Received: 23 June 2015 / Accepted: 21 September 2015 / Published: 25 September 2015_  
**Abstract:** Network security is one of the most important issues in mobile sensor networks (MSNs). Networks are particularly vulnerable in hostile environments because of many factors, such as uncertain mobility, limitations on computation, and the need for storage in mobile nodes. Though some location-based security mechanisms can resist some malicious attacks, they are only suitable for static networks and may sometimes require large amounts of storage. To solve these problems, using location information, which is one of the most important properties in outdoor wireless networks, a security mechanism called a moving security area (MSA) is proposed to resist malicious attacks by using mobile nodes' dynamic location-based keys. The security mechanism is layered by performing different detection schemes inside or outside the MSA. The location-based private keys will be updated only at the appropriate moments, considering the balance of cost and security performance. By transferring parts of the detection tasks from ordinary nodes to the sink node, the memory requirements are distributed to different entities to save limited energy.  
**Keywords:** mobile sensor networks; malicious attacks; location-based key; replication attack
## 5 Advanced Tools for High Average Power Laser Developments
### Single-Shot \(M^{2}\) Measurement  
The \(M^{2}\) parameter helps to assess the laser beam quality. The most common principle of the \(M^{2}\) measurement is to scan the beam profile discretely along the propagation direction. A measurement time of several minutes is often required, which is not suitable for the pulsed lasers. Several single-shot techniques have been proposed to measure the \(M^{2}\) using diffraction gratings [52] and a wavefront sensor [53], but were shown to be more complex and to yield inaccurate results for multimode beams. Another approach to measure the \(M^{2}\) observes Rayleigh scattering of the beam in a gas or liquid-filled cell, but it is quite difficult to adjust the scattering medium [54]. Moreover, the image of the laser light scattered in the cell contains many speckle patterns, degrading the accuracy of the \(M^{2}\) measurement.  
We have developed a single-shot \(M^{2}\) measurement based on a photosensitive glass (Figure 16a). The measurement system consists of the photosensitive glass plate and the imaging camera with a macro lens. When the laser beam is focused into the cross-sectional direction of the photosensitive glass plate, the visible fluorescence of the glass plate indicates the focusing property of the laser beam. Then, the visualized beam propagation in the glass is imaged precisely onto the camera to measure the beam diameters around the beam waist. Since the coherent laser beam is converted into incoherent fluorescence, the beam propagation image is free from speckle patterns. The \(M^{2}\) can be calculated from the single image within less than a second, though the beam widths at about thousand positions are evaluated. This simple technique allows the real-time monitoring of the beam quality. We obtained \(M^{2}=1.1\) from a fiber-coupled diode laser that is close to the actual value of \(M^{2}=1.18\) using the standard scanning method (Figure 16b) [55].
# A Method for Predicting Radiated Acoustic Field in Shallow Sea Based on Wave Superposition and Ray
## 2 Theoretical Model
### Ray Theory  
The ray solution exists in the form \(p(x,y,z,t)=A(x,y,z)e^{i[\omega t-k_{0}\varphi(x,y,z)]}\). Where \(k_{0}=\omega/c_{0}\) represents the wave number at the initial position, \(c_{0}\) is the sound speed of the initial position, \(n(x,y,z)=c_{0}/c(x,y,z)\) is the index of refraction, and \(k=k_{0}n\) (\(x,y,z\)) is the wave number at position \((x,y,z)\). The \(A(x,y,z)\) represents the pressure amplitude and \(k_{0}\varphi(x,y,z)\) shows the pressure phase. Substituting the ray solution into the wave equation, the following two equations can be obtained as [16]:  
\[\frac{\
abla^{2}A}{A}-{k_{0}}^{2}\
abla\varphi+k^{2}=0 \tag{12}\]  
\[\
abla^{2}\varphi+\frac{2}{A}\
abla A\cdot\
abla\varphi=0 \tag{13}\]  
When \(\
abla^{2}A/A\) is much smaller than \(k^{2}\), then  
\[\left(\
abla\varphi\right)^{2}=\left(\frac{c_{0}}{c}\right)^{2}=n^{2}(x,y,z) \tag{14}\]  
\[\
abla\cdot\left(A^{2}\
abla\varphi\right)=0 \tag{15}\]  
In ray theory, Equations (14) and (15) are called as eikonal equation and intensity equation respectively, and are the two basic equations of ray theory [17]. In ray theory, the propagation of waves are usually regarded as the propagation of an infinite number of acoustic rays. Each ray is perpendicular to the equiphase surface, and the propagation path in the sea is affected due to the medium and boundary. The sound ray carries energy, and the energy in each sound ray does not lose to the outside of the sound ray. These sound rays contribute to the acoustic field in ray theory.  
Eikonal equation combined with Snell's law, to obtain the trajectory of the ray, satisfies the following relationship [18] as shown below:  
\[\frac{\mathrm{d}^{2}z}{\mathrm{d}x^{2}}=-\frac{R}{\cos^{3}\alpha}\cdot\frac{ \mathrm{d}c(z)}{\mathrm{d}z}=-\frac{1}{R^{2}c^{3}(z)}\cdot\frac{\mathrm{d}c(z) }{\mathrm{d}z} \tag{16}\]  
After giving the initial glancing angle \(\alpha_{0}\) and the sound speed function \(c(z)\), the solution of the above equation can achieve ray tracking and acquire ray trajectories. The eigenray are determined in the distribution trend of the rays, which are special rays that directly supply the power at a certain point in the acoustic field. The energy of each sound ray can be derived from the intensity equation as:  
\[I(x,z)=\frac{W\cos\alpha_{0}}{x\left(\frac{\partial x}{\partial\alpha}\right) _{\alpha_{0}}\sin\alpha} \tag{17}\]  
where \(W\) represents the radiated power in unit solid angle. The energy of these rays reflects due to the boundary and distributes in the acoustic field in a steady state. After determining the position and strength of virtual sources by wave superposition method, the acoustic field is calculated by ray theory. Then the prediction of the structured radiation acoustic field is realized.
## 3 System Models
### Low-Carbon Energy Sharing Model of Multiple Gas-Electric IESs
#### 3.2.1 Objective Function  
The low-carbon economic operation of the gas-electric IES needs to be evaluated from the perspectives of economic performance and low-carbon. The two objective functions of minimum operation cost and minimum CO\({}_{2}\) emission of the system are analyzed in this chapter, and the multi-objective problem is converted through weighting into single-objective ones for research and analysis.  
The operation cost of the system includes the cost of purchasing electricity and gas from external girds, the cost of generating electricity by coal-fired units, the operation and maintenance cost, and the CO\({}_{2}\) emission cost.  
\[F_{O}=C_{B}+C_{G}+C_{Y}+C_{C} \tag{12}\]  
\[C_{B}=\sum_{t=1}^{T}\left(C_{e,t}P_{e,t}^{in}+C_{g,t}P_{g,t}^{in}\right) \tag{13}\]  
\[C_{G}=\sum_{t=1}^{T}\sum_{i=1}^{I}\frac{C_{g,t}}{LHV_{gas}}\times\frac{P_{i,t}} {\eta_{i,t}} \tag{14}\]  
\[C_{G}=\sum_{t=1}^{T}\sum_{i=1}^{I}\frac{C_{g,t}}{LHV_{gas}}\times\frac{P_{i,t}} {\eta_{i,t}} \tag{15}\]  
\[C_{C}=C_{g}\sum_{t=1}^{T}\max(0,\omega_{e}P_{e,t}^{in}+\omega_{g}P_{g,t}^{in}+ \mu_{i}P_{i,t}-M_{i,t}^{CC}) \tag{16}\]  
where \(C_{B}\), \(C_{G}\), \(C_{Y}\), and \(C_{C}\) are, respectively, the energy transaction cost, the electricity generation cost, the maintenance cost, and the CO\({}_{2}\) emission cost; \(T\) represents the total operating cycle; \(C_{e,t}\) and \(C_{g,t}\) are the prices of electricity and gas purchased during the period \(t\); \(P_{e,t}^{in}\) and \(P_{g,t}^{in}\) represent the amount of electricity and gas purchased during the period \(t\); \(P_{i,t}\) is the output power of the coal-fired unit at the time \(t\); \(C_{G}\) and \(Y\Psi_{G}\), respectively, represent the coal cost and the operation and maintenance cost of the \(i\)th coal-fired unit at the time \(t\); \(I\) represents the total number of coal-fired units; \(LHV_{gas}\) is the low heating value of natural gas; \(\eta_{i,t}\) is the conversion efficiency of the \(i\)th coal-fired unit at the time \(t\); \(K_{Y\Psi_{i,t}}\) is the maintenance factor of the coal-fired unit; \(\omega_{e}\) and \(\omega_{g}\) are, respectively, the amount of CO\({}_{2}\) consumed by purchasing electricity from the electricity grid and gas from the natural gas grid; \(\mu_{i}\) is the CO\({}_{2}\) emission coefficient of the coal-fired unit; \(C_{g}\) is the cost of CO\({}_{2}\) emission in each unit; and \(M_{i,t}^{CC}\) is the mass of the captured CO\({}_{2}\).
# Multi-LEO Satellite Stereo Winds
## 2 Materials and Methods  
Our formulation of the multi-satellite or \"stereo\" winds method designates a geo-registered scene from one satellite as the reference scene and remaps the scenes from the others into the frame of the reference scene. After remapping, each pixel shares the same apparent geographic coordinates across all scenes. Apparent shifts in the locations of patterns between scenes are known as disparities. These disparities are then interpreted by the retrieval algorithm as a combination of geometric parallax due to feature height and horizontal advection (wind) during the time difference between pixel acquisitions. Disparities relative to the reference scene are measured with subpixel precision by interpolation on a surface of normalized cross correlation used to measure the similarity of a pattern with its translated copy. The best correlations are achieved when time differences are small enough so that cloud patterns retain their shapes (typically under 20 min in the cases presented here) and patterns represent features in a dominant atmospheric layer. Geo-registration accuracy is important for this application, as relative registration errors can map to wind vector and height assignment errors.
# Improving the Sensory Quality of Black Tea by Blending Varieties During Processing
## 3 Results and Discussion  
### Sensory Evaluation of Black Tea  
The sensory evaluation results show that significant differences (\(p<0.05\)) were observed among three black tea samples with a single variety. Fud presented better characteristics in terms of appearance, liquor color, and infused leaves, while JGY and JX scored much higher in aroma (Figure 1A). _Fudingdabai_ is normally used as a control variety to select and compare the flavor differences between varieties, and its sensory quality is relatively balanced across the five sensory evaluation indices [18]. _Jinguanyin_ and _Jinxuan_ are oolong tea varieties normally used to make flowery-aroma black tea due to their outstanding floral characteristics [19; 20]. When 15% of _Jinguanyin_ or fresh _Jinxuan_ tea leaves were added to _Fudingdabai_, resulting in the cooked black teas (FJG and FJX), aroma and taste were significantly enhanced, with minimal influence on the appearance and infused leaves compared with Fud (Figure 1B). The addition of oolong tea varieties in fresh leaves to the cooked tea products (FJG and FJX) presented a much stronger sweet and floral aroma as well as prolonged durability (Supplementary Materials Table S1). In addition, the richness of the taste was also enhanced via added oolong tea varieties in fresh leaves; FJG and FJX not only presented a mellow taste but were also endowed with sweet or fresh taste characteristics. The samples of finished tea blending (AFJG and AFJX) also had an improved sensory quality of the aroma compared with Fud (Figure 1C), though there was no difference (\(p<0.05\)) in total scores between black tea samples made from fresh leaf blending and those made from finished tea blending (Figure 1D). Therefore, it could be considered that fresh leaf blending can enhance the sensory quality of black tea without any significant difference compared to the finished tea blending method.
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
###### Abstract
As discussed in [12; 13; 14], this fixed inner controller is not an impediment to employing them as platforms for testing control algorithms or even implementing them for real applications. These commercial platforms represent an affordable and economical option for actual implementation and experimental tests of controllers for quadrotors. The works in references[15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25] addressed the control of a quadrotor under the assumption that an inaccessible and unmodifiable inner controller is present. Schemes such as inverse dynamic control, adaptive neural PID, cascade control, and adaptive generalized regression neural networks were introduced. In spite of the robustness of these controllers, some of the system parameters are required. To the best of the authors' knowledge, regressor-based adaptive control has yet to be applied to a quadrotor equipped with an embedded controller. Thus, this is a novel regressor-based adaptive controller since an accurate knowledge of the system parameters is not needed and the control law is system-parameter-free. Additionally, in contrast with the neural network-based approaches, the proposed scheme is simpler and easier to implement.
## 2 Materials and Methods  
Routing protocols may be limited in scope or rely on a small set of parameters if they are expected to fulfill all of the critical requirements for information exchange. To solve all of these issues with ITS in one fell swoop, this study proposes an intelligent cluster optimization approach (p-WOA), including efficient cluster formation, packet delivery ratio, average throughput, and latency, and involves probabilistically seeding the road with a single vehicle, increasing the number of cars to 100, and accounting for the time and resources required to run such a simulation. To optimize the paths taken by data messages as they travel through the network, a clustering technique that takes its cues from the laws of probability was employed. Figure 3 depicts the suggested framework with an emphasis on inputs and outputs:The proposed framework works as follows:  
1. Representation: Individuals inside evolutionary algorithms are defined in representation.
2. Evaluation Function: The fundamental for enabling improvements is the identification of a fitness function or maximized function. To determine the legitimacy of a solution, this threshold value must be attained.
3. Population: This contains every potential resolution.  
Figure 3: Framework for the developed method.  
Parent Selection Mechanism: This identifies solutions that can serve as the foundation or parents for the following generation.
* Variation Operators: To separate the novel solutions from the old ones, two variation operators, mutations and rearrangements, were used.
* Selection Mechanism: This works exactly like parent selection, only it happens in the following cycle of evolution when the candidate solution is mature enough to be judged.
* Best Solution: Once all of the fitness functions are evaluated, the cluster head is selected based on the best packet Delivery Ratio, latency, and average throughput.
## 4 Materials and Methods
### Thermal Unstable Amino Acid Quantification Using HPLC-UV  
Two thermally unstable amino acids, arginine and citrulline, were quantified using high performance liquid chromatography (HPLC) with ultraviolet (UV) detection. Thawed watermelon samples were centrifuged (4000 rpm, 10 min) and 1 mL of the supernatant was combined with 9 mL of DI water (10\(\times\) dilution). The diluted samples were then passed through a 0.45 \(\upmu\)m syringe prior to HPLC injection.  
A Restek Ultra AQ C18 column (5 \(\upmu\)m, 250 \(\times\) 4.6 mm) was connected to a HPLC system (Shimadzu USA Manufacturing, Canby, OR, USA) with the following configuration: a binary pump (LC-20 AR), a degassing unit (DGU-20A 3R), an autosampler with a 100 \(\upmu\)L sample loop (SIL-20A), and an UV detector (SPD-20A). Gradient elution utilized the following 2 solutions: mobile phase A (0.1% orthophosphoric acid [stock-85% purity] in water, \(v/v\)) and mobile phase B (100% acetonitrile). Injection volume and flow rate were 20 \(\upmu\)L and 1.0 mL/min, respectively. The UV detection wavelength was 200 nm. Mobile phase A began at 100%, reaching 5% by 15 min retention time. By 20 min retention time, mobile phase A was back at 100%. The column was flushed with 95% acetonitrile at the end of each chromatographic run, followed by 10 min at the initial solvent composition. Total analysis time was 35 min.  
A 0.64 mg/mL DI water stock solution was prepared separately for citruline and arginine, and then serially diluted as follows: 0.64, 0.32, 0.16, 0.08, 0.04, 0.02, and 0.01 mg/mL. These 14 solutions were run in the same manner as the samples. Peak areas were used to construct linear calibration curves, which were not forced through the origin. A range of five to seven calibration levels were employed in order to obtain sufficient quantification ranges (citrulline: 0.01-0.64 mg/mL, arginine: 0.02-0.32 mg/mL) with r\({}^{2}\geq\) 0.99.
## 3 Results  
### Search Results and Study Selection  
A total of 729 records were obtained from searching various databases using specified keywords: 154 from MEDLINE, 276 from Web of Science, and 299 from Scopus. After automatically removing duplicates, the titles and abstracts of the remaining 334 articles were initially screened to determine their alignment with the inclusion criteria. Following excluding reviews, case reports, and articles not written in English, 77 articles were identified for full-text retrieval. Then, 44 of them were excluded for reasons including the full text being unavailable, the lack of artificial intelligence in the research methodology, or the focus being unrelated to tooth segmentation. Additionally, one article that met the criteria was obtained through manual search. A total of 34 studies was finally included in this systematic review. The PRISMA diagram illustrating the search results is depicted in Figure 1.