esa-sceva/satcom-chunk-collection huggingface dataset:
# A Binocular Vision-Based 3D Sampling Moire Method for Complex Shape Measurement
## 2 Principles
### Three-Dimensional Shape Measurement Method with Binocular Vision  
Figure 2 shows the classical binocular vision model. The projector projects the fringe onto the object surface, and the surface images are captured by the left and right cameras. Taking the left camera as an example, the relationship between a certain point (Point \(A\)) on the object surface and the corresponding point in the digital image in the camera (Point \(A_{L}\)\({}^{\prime}\)) can be expressed as [25]:  
\[\lambda\left[\begin{array}{c}u_{L}\\ v_{L}\\ 1\end{array}\right]=K_{L}\left[\begin{array}{cc}R_{L}&T_{L}\\ 0&1\end{array}\right]^{-1}\left[\begin{array}{c}X_{W}\\ Y_{W}\\ Z_{W}\\ 1\end{array}\right] \tag{5}\]  
where (\(u_{L}\), \(v_{L}\)) are the pixel coordinates of the digital image, and \(K_{L}\) is the internal parameter matrix. The external parameter matrix, composed of the rotation matrix \(R_{L}\) and the translation vector \(T_{L}\), represents the spatial relationship between the camera coordinate system and the world coordinate system. By calculating the internal and external parameter matrices of the left and right cameras simultaneously, we obtain [26]:  
\[\left\{\begin{array}{c}\left(u_{L}m_{31}^{L}-m_{11}^{L}\right)X_{W}+\left(u_ {L}m_{32}^{L}-m_{12}^{L}\right)Y_{W}+\left(u_{L}m_{33}^{L}-m_{12}^{L}\right)Z_ {W}=m_{14}^{L}-u_{L}m_{34}^{L}\\ \left(v_{L}m_{31}^{L}-m_{11}^{L}\right)X_{W}+\left(v_{L}m_{32}^{L}-m_{12}^{L} \right)Y_{W}+\left(v_{L}m_{33}^{L}-m_{12}^{L}\right)Z_{W}=m_{14}^{L}-v_{L}m_{34 }^{L}\\ \left(u_{R}m_{31}^{R}-m_{11}^{R}\right)X_{W}+\left(u_{R}m_{32}^{R}-m_{12}^{R} \right)Y_{W}+\left(u_{R}m_{33}^{R}-m_{12}^{R}\right)Z_{W}=m_{14}^{R}-u_{R}m_{34 }^{R}\\ \left(v_{R}m_{31}^{R}-m_{11}^{R}\right)X_{W}+\left(v_{R}m_{32}^{R}-m_{12}^{R} \right)Y_{W}+\left(v_{R}m_{33}^{R}-m_{12}^{R}\right)Z_{W}=m_{14}^{R}-v_{R}m_{34 }^{R}\end{array}\right.\right. \tag{6}\]  
where \(m_{ij}^{L}\) and \(m_{ij}^{R}\) (\(i=1,2,3,4;j=1,2,3\)) are constants, which can be obtained by calibration. Thanks to the sampling moire method, the pixel coordinates of a certain point in two cameras can be calculated. Then, combining the calibrated internal and external parameters, the 3D coordinates of the object can be reconstructed to realize the 3D measurement based on Equation (6).  
Figure 1: The flow chart of the sample moiré method. (**a**) Fringe image, (**b**) Sampled points in the fringe image, (**c**) Multiple phase-shifted sampling moiré patterns obtained by sampled point interpolation, (**d**) The phase distribution of the initial sampling moire pattern.
## 1 Introduction
### Passive Methods (Passive Coatings)
#### 1.2.1 Malyuzhinets' Method  
The possibility of controlling both radiation and scattering fields created by an arbitrary physical body (Figure 1c) was shown [25] by [PERSON]. This becomes possible due to the use of Huygens' surfaces, characterized by unidirectional radiation (receipt). Practical implementation of this method requires the synthesis of Huygens' radiating (and receiving) surfaces (with discrete elements) and development of the algorithm which connects them. The Malyuzhinets solution has been formulated for monochromatic fields. Practical solutions require causal algorithmic realization of this system in real time, wideband frequency range, and a spatially discrete form, which lead to great difficulties in a literal realization of [PERSON] theory. To realize Huygens' surfaces, we need to use spatially discrete antenna arrays. The latter (and any devices required to attach them) must be acoustically transparent, _i.e._ the sizes of their elements and the spatial period of their spacing must be much smaller than the length of the waves to be suppressed. This is the reason why the magnitudes of the array's elements must be much greater than the magnitude of the waves to be cancelled. On the other hand, to retain the unidirectional characteristics of the Huygens surface, the field of the discrete version of the Huygens surface (with addition of the reactive field) must be very small (this permits exclusion of their interaction with the protected body). This interaction could break the unidirectional characteristics of Huygens' surfaces. This fact requires a permissible, but not very small distance between the array and the surface of a protected body. This creates a large technical difficulty: there is no way of placing a Huygens' surface immediately on the surface of the protected body. [PERSON] theory has been tested experimentally and numerically for acoustical and electromagnetic waves [24-29].
# Analysis of Planar Double-Layer Timber Spatial Frames by Using Parametric Tools
## 1 Introduction
### Construction Systems
#### 1.2.3 Topology  
The topology refers to the geometrical order of the bars and nodes. For greater stiffness of the flat spatial frames (indispensable in large spans), two layers are arranged connected to each other through the set of diagonal bars. Although the variety of typology is practically infinite, the present work focuses on those patented and commercialized for use in construction. The use of a custom-designed topology would also involve the design of the construction system of joints and bars, an area that is not the focus of this work. Based on the bibliography [12; 17; 18; 19; 20; 21], the seven most mentioned and used typologies have been chosen. A classification has been proposed (Figure 5). To order them, each one has been named with a letter and a number to be able to identify them easily throughout the paper (Figure 6).  
Group A: frames whose upper and lower layers are in parallel direction to the edges.  
A1--Square on square: cubic module with vertical and diagonal bars. Absence of oblique diagonals.  
A2--Square on orthogonally offset square: based on the pyramidal module with a square base. It is the most commonly used topology.  
A3--Square on orthogonally offset triple square: based on the previous frame (A2), bars are suppressed in the lower layer in order to lighten it.  
Figure 5: Frame topology classification. (**A**) group A; (**B**) group B; (**C**) group C; (**D**) group D.  
Figure 6: Analyzed topologies.  
Group B: frames whose upper and lower layers are diagonal (45\({}^{\circ}\)) with respect to the edges.  
B1--Square on diagonally offset square: pyramidal module frame with a square base arranged diagonally, at 45\({}^{\circ}\) to the edge.  
Group C: frames in which one layer is parallel to the edges and the other diagonal (45\({}^{\circ}\)).  
C1--Diagonal on square: created from the A1 frame, the top layer is given a 45\({}^{\circ}\) twist. It also has no oblique diagonals.  
Group D: frames based on the triangular module.  
D1--Triangle on offset triangle: created from the grouping of tetrahedral modules. It is the most rigid of the frames, as all the faces of the polyhedron are triangular, the undeformable geometry par excellence (Figure 7).  
D2--Triangle on hexagon: based on the D1 frame, the lower layer is lightened by removing bars to create equilateral hexagons.  
The different topologies behave differently depending on the applied loads, the position of the supports and the size of the module. Further variations can be introduced by changing the size of the top chord grid relative to the bottom chord grid. Wider geometries are usually possible in the bottom layer of a double-layer frame because the members are normally in tension. That is, the lower tension chords may be longer than the upper compression members (no buckling effect).
# Effect of Diagonal Pattern Training on Trunk Function, Balance, and Gait in Stroke Patients
###### Abstract  
Introduction: Trunk control disability commonly occurs after stroke. This study investigated the effect of diagonal pattern training in the sitting position to improve trunk control ability and gait performance. Method: 46 stroke patients were enrolled in this study. We used single plane training and diagonal pattern training. Additionally, the subjects were randomly assigned to the experiment group (diagonal pattern training) and the control group (single plane training). Diagonal pattern training was modified with proprioceptive neuromuscular facilitation technique's chopping and lifting pattern to create 10 movements. Results: the trunk impairment scale score, Berg balance scale score, 10 m walking test result, and gait significantly increased in the experiment group compared to the control group. Conclusions: diagonal pattern training can be regarded as a promising method to improve postural control and increase balance and gait in stroke patients  
trunk control; gait; diagonal pattern training; PNF
# Using Statistical Test Method to Establish a Decision Model of Performance Evaluation Matrix
## 2 Unbiased Estimators and Statistical Hypothesis Test  
As mentioned earlier, since the indices have unknown parameters, they must be estimated by sample data of the interviewed customers. Due to the application of the Central Limit Theorem in this paper to carry out the statistical test, the sample size must be sufficiently large. The sample size of the cases applied to the method in this study are usually larger than 100. They are much larger than the minimum sample size required in the Central Limit Theorem (the minimum sample size for requirements is 30). In this section, we propose the unbiased estimators and the statistical hypothesis test method for the satisfaction index and the importance index, respectively.
## 2 System Structure and Channel Model
The EGC algorithm is used to combine them for its high diversity gain and easy combining process [23; 24]. The weight coefficient of each branch is selected as 1, and the phase alignment is realized by calculating the relative phase between each channel and the reference channel with the largest SNR [25]. Finally, the constellation diagram and BER curves are obtained.
# Recognition of Vehicle License Plates Based on Image Processing
## 2 License Plate Recognition System
### License Plate Detection  
As there is much unnecessary information, such as noise and background area, in the entire vehicle image, it is not easy to recognize the license plate. To solve this problem, the algorithm must detect the area of the vehicle license plate, which is the area of interest. In this study, we used a pre-trained YOLO v2 network model for vehicle license plate detection. Table 1 shows the YOLO v2 architecture. Figure 2 shows the result of detecting the license plates using pre-trained YOLO v2 [9,10].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Filters** & **Size/Stride** & **Output** \\ \hline Convolutional & 32 & \(3\times 3\) & \(224\times 224\) \\ Maxpool & & \(2\times 2/2\) & \(112\times 112\) \\ Convolutional & 64 & \(3\times 3\) & \(112\times 112\) \\ Maxpool & & \(2\times 2/2\) & \(56\times 56\) \\ Convolutional & 128 & \(3\times 3\) & \(56\times 56\) \\ Convolutional & 64 & \(1\times 1\) & \(56\times 56\) \\ Convolutional & 128 & \(3\times 3\) & \(56\times 56\) \\ Maxpool & & \(2\times 2/2\) & \(28\times 28\) \\ Convolutional & 256 & \(3\times 3\) & \(28\times 28\) \\ Convolutional & 128 & \(1\times 1\) & \(28\times 28\) \\ Convolutional & 256 & \(3\times 3\) & \(28\times 28\) \\ Maxpool & & \(2\times 2/2\) & \(14\times 14\) \\ Convolutional & 512 & \(3\times 3\) & \(14\times 14\) \\ Convolutional & 256 & \(1\times 1\) & \(14\times 14\) \\ Convolutional & 512 & \(3\times 3\) & \(14\times 14\) \\ Convolutional & 256 & \(1\times 1\) & \(14\times 14\) \\ Convolutional & 512 & \(3\times 3\) & \(14\times 14\) \\ Maxpool & & \(2\times 2/2\) & \(7\times 7\) \\ Convolutional & 1024 & \(3\times 3\) & \(7\times 7\) \\ Convolutional & 512 & \(1\times 1\) & \(7\times 7\) \\ Convolutional & 1024 & \(3\times 3\) & \(7\times 7\) \\ Convolutional & 512 & \(1\times 1\) & \(7\times 7\) \\ Convolutional & 1024 & \(3\times 3\) & \(7\times 7\) \\ \hline Convolutional & & \(1\times 1\) & \(7\times 7\) \\ Avgpool & 1000 & Global & 1000 \\ Softmax & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: YOLO v2 architecture.  
Figure 1: Flowchart for the license plate recognition algorithm.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 2 Literature Review
### Field Programmable Gate Array (FPGA)  
A Field Programmable Gate Array (FPGA) [14] is a logic circuit written in a hardware description language and is an actual parallel architecture. Generally speaking, FPGA is slower than ASIC and is not suitable for designing very complex circuit structures. At the same time, the power consumed by FPGA is larger than that of ASIC. However, the biggest advantage of FPGAs is that they can be manufactured quickly, and the internal logic can be modified repeatedly, making debugging costs lower. In addition, FPGAs can perform a task at exactly the same speed over and over again, making them suitable for developing low-latency specialized chips.
## 2 Methods  
### Study Site and Field Data Collection  
The study site is located within the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental field (39.030\({}^{\circ}\)N, 76.845\({}^{\circ}\)W) maintained by the USDA Beltsville Agricultural Research Center (BARC) in Maryland, USA. Field campaigns were conducted on corn (_Zea mays_ L.); different corn cultivars were planted during each of the four growing seasons in 2008, 2010, 2011, and 2012 (Table 1). Measurements were made on a total of 30 sunny days spanning different growth stages, ranging from early vegetative stages when the corn crop was actively growing at \(\sim\)1 m tall and had produced 7-9 leaves (e.g., V7, V9) to fully mature canopies in the reproductive stage at \(\sim\)2 m tall (e.g., VT, R1), through advanced reproductive development in the senescent stage (e.g., R4, R6). On each field day, measurements were taken multiple times between 9 am to 6 pm local time with approximately one-hour intervals, to sample the diurnal course. Sampling was done every one meter along a marked 100 m north-south direction transect in the middle of the field to minimize disturbance to the field. One complete set of sampling the transect was usually done within 30 min. Average of the samples was calculated to represent the field for the specific sampling time [84].  
Canopy level hyperspectral reflectance spectra (400-1,000 nm, \(\sim\)1.5 nm Full Width Half Maximum; FWHM) were obtained using a USB4000 Miniature Fiber Optic Spectrometer (Ocean Optics Inc., Dunedin, FL, USA) with a bare fiber. All spectral observations were acquired at nadir, above the canopy at a height of approximately 1 m. This was accomplished by placing the fiber optics on a height-adjustable pole-mount, where a custom-made fixture was designed to position the instrument at a desired view zenith angle and relative azimuth angle [85]. Incident solar irradiance was determined using a Spectralon reference panel (Labsphere, North Sutton, NH, USA). Incident solar irradiance was measured in between approximately every five samples. An additional spectrometer was set up to measure the reference panel continuously to monitor detailed changes of irradiance during the field days. Supplemental measurements included LAI and fPAR. LAI was measured with the LI-COR LAI-2000 plant canopy analyzer (LI-COR, Lincoln, NE, USA) and fPAR was determined using the LI-COR LI-191 line quantum sensor (LI-COR, Lincoln, NE, USA).
## 3 Results
### Performance of v-ddPCR on Routine and Retail Samples
#### 3.7.1 Routine Samples  
A total of 19 chicken neck skins (NS) from Bavarian slaughterhouses (1:10 and 1:2 diluted), as well as 13 chicken breast meat (BM) from Bavarian supermarkets (1:10 diluted) were investigated for routine analysis. All samples were analyzed by three methods, with the exception of three samples (NS_02, 03 & 04) which were not examined by qPCR analysis.  
Out of nineteen slaughterhouse chicken neck skin samples (Figure 4), solely one sample (NS-12) was analyzed negative with all methods, nine crossed the PHC limit with all methods, and three were detected below the PHC limit with all methods. An inconsistency was observed for the remaining six chicken NS. For three of them, at least one method detected _Campylobacter_ above the PHC limit: NS_19 and NS_35 were above PHC only in v-qPCR, NS_20 was above PHC only on agar plates, no positive droplet was detected in v-ddPCR for these three samples. Finally, at least one of method out of three detected _Campylobacter_ below PHC for the last three chicken neck skins NS_13, NS_22, and NS_29.  
Out of 13 breast meat samples (Figure 5), nine were negative with all three methods, and the other four did not reach the PHC limit. BM-30, BM-31, BM-32 were detected exclusively in the v-ddPCR method, whereas BM-11 was detected only on plates. No signal was detected in a v-qPCR analysis of all four samples.  
Figure 4: Analysis of LGL routine samples: chicken neck skin (NS) from slaughterhouse. Analysis in culture, v-ddPCR, and v-qPCR. The Process Hygiene Criterion (PHC: log\({}_{10}\) 3.0) is marked with a black line.  
Two routine samples (BM_27 and BM_34), as well as two retail samples (RS_21 and RS_24) were prone to matrix inhibition, as the IAC was not amplified in v-qPCR. The extracted DNA was further diluted to resolve the mentioned complication, and it was analyzed again in a v-qPCR. The absence of _Campylobacter_ was confirmed in three samples (BM_27, BM_34, and RS_24) but RS_21 was _Campylobacter_ positive in the v-qPCR. A signal in the IAC detection system showed that the ddPCR was not affected from this inhibition.
## 1 Introduction  
The internet [1] has attracted significant attention over the last few decades due to the diversity of network technologies, applications, and services. However, its rapid growth faces many problems and many issues which makes the adoption of a new architecture or the modification of an existing architecture a very challenging task. To overcome the ossification limitation, Network Virtualization (NV) has emerged as a solution to support the exponential growth of Networks [2]. It has been introduced to ensure several privileges such as providing flexibility, boosting diversity, promoting security, and enhancing network manageability [3]. The main idea of the NV is the abstracting of network resource by introducing two business entities: the Infrastructure Providers (InPs) and the Service Providers (SPs), instead of having the traditional Internet service providers. An InP manages and controls the physical infrastructure. The critical concerns of InP are service provisioning and QoS guarantee. An SP leases multiple resources (bandwidth, CPU, storage, memory, substrate links, etc.) from InPs to provide services to the end-users. One of the challenges of network virtualization is how to efficiently provide services to the users. Several approaches and techniques have been introduced for resource management to cover all the service provisioning goals such as maximizing resource allocation. Moreover, the Game Theory (GT) has drawn considerable attention by paving its way to next-generation network and gradually to modern technologies to achieve one/multiples resource management goals for the vitalization environment. In this paper, we provide a survey illustrating the game theory approaches for network virtualization. We define the game theory by presenting all the details and the concepts related to this mathematical tool. Then, we introduce a taxonomy of the game theory approaches classification for resource management in the virtualization environments. The new classification aspects are based on three axes: the game characteristics, the players during the game, and the goals for the resource management. The taxonomy should provide the reader with a better understanding of game theory solutions to the resource management problems in the NV environment. The remainder of this paper is organized as follows: We start, in Section 2, by introducing an overview of the network virtualization and explaining the main concepts and technologies. In Section 3, we will give an overview of the game theory by detailing the concept and the game characteristics, defining the components of the game analysis, and providing the different types/models of the games. In Section 4, we will present a novel sophisticated taxonomy for the classification of the game theory-based solutions in the NV that has been designed to enhance the service provisioning. We will investigate and discuss, in Section 5, the solutions by exploring the future key research. Finally, we will conclude the paper in Section 6.
## 3 Experimental Results
### Select Fire Detection Equipment  
N sensors were placed in the fire detection system to input data from the fire scene. It is important to select the fire parameters. In general, the content of CO is low in air. When a fire takes place,  
Figure 2: Space-time fusion based on D-S evidence theory.  
CO content will greatly increase. Therefore, the content of CO may be used as an important parameter for the combustion phenomenon in the built environment. Because the building space is essentially closed, fire is often accompanied by a rise in temperature, an increase in smoke concentration, the production of flame radiation and a reduction in humidity. Therefore, we can select five fire parameters: the content of CO, the change of air temperature, the change of smoke concentration, the wavelength of the flame radiation and humidity. The detailed descriptions of the sensors are as follows.  
1. CO gas sensor: A chemical gas detector was used as the CO sensor. The chemical gas detector utilizes a high sensitivity semiconductor, with the characteristics of resistance change when the combustible gas adsorption is discovered, to achieve detection purposes. A FIGARO semiconductor gas sensor TGS2600 was selected for this experiment. It can detect low concentrations of pollutant gases, even a few millionths of a mass percent of the content of polluting gases.
2. Temperature sensor: Thermal resistance temperature detectors sensitively respond to temperature parameters of a point or a segment within the warning area. The temperature sensor was a HUMIREL HM1500, which is a low cost, small, long life, high stability sensor.
3. Smoke sensor: In this study, a linear infrared beam smoke detector BEAM1224S produced by Honeywell was used. It has properties, including intelligent judgment, automatic and quick location calibration, strong anti-jamming capability and an automatic dust compensation function. Moreover, it is easy to install.
4. Flame sensor: A UV photosensitive fire detector is sensitive to ultraviolet radiation by flame radiation. The flame detector used in this experiment was Honeywell's C7027A, which is a small observational UV flame detector.
5. Humidity sensor: Capacitive-type humidity sensors are typically made of a polymer film capacitor. They commonly incorporate polymer materials, such as polystyrene, polyimide, cellulose acetate acid, and so on. In this work, the humidity measurement module AM2301 based on the capacitive-type humidity sensor was used. By using this industrial-grade, high-performance device, factors, such as measurement accuracy, wide temperature range, compactness, ease-of-use, fast response and low power consumption, reached high specifications.