esa-sceva/satcom-chunk-collection huggingface dataset:
# Contrast Analysis of Polarization in Three-Beam Interference Lithography
###### Abstract  
This paper analyzes the effect of polarization and the incident angle on the contrasts of interference patterns in three-beam interference lithography. A non-coplanar laser interference system was set up to simulate the relationship between contrast, beam polarization, and the incident angle. Different pattern periods require different incident angles, which means different contrast losses in interference lithography. Two different polarization modes were presented to study the effects of polarization with different incident angles based on theoretical analysis simulations. In the case of the co-directional component TE polarization mode, it was demonstrated that the pattern contrast decreases with the increase in the incident angle and the contrast loss caused by the polarization angle error also grew rapidly. By changing the mode to azimuthal (TE-TE-TE) polarization, the contrast of the interference pattern can be ensured to remain above 0.97 even though the incident angle is large. In addition, TE-TE-TE mode can accept larger polarization angle errors. This conclusion provides a theoretical basis for the generation of high-contrast light fields at different incident angles, and the conclusion is also applicable to multi-beam interference lithography.  
pattern contrast; polarization; incident angle; interference lithography +
Footnote †: journal: Medical  
double-beam interference, multi-beam interference lithography (MIL) is the superposition of the intensity of multiple groups of non-coplanar interference with different periods, directions, and extremum [19; 20; 21]. The value of the incident angle controls the period of the interference pattern. For non-coplanar interference, the contrast of the interference light field is simultaneously affected by the azimuth angle, incident angle, and polarization state of each beam [22; 23; 24]. Therefore, it is necessary to select a suitable combination of beam polarization states for different incident angles to obtain the best contrast and consider the influence of the beam pointing stability on the contrast under that incident combination.  
In this paper, we discuss the two-beam non-coplanar interference, study the relationship between fringe visibility and the spatial incidence direction of the two beams, and analyze the source of the contrast loss in the non-coplanar interference. We calculate how to select the appropriate combination of beam polarization states to ensure sufficient contrast under different incident angles. Then, according to the non-coplanar interference model, we demonstrate the specific beam polarization combinations that should be used for different pattern periods in three-beam LIL. Although the focus of this paper is three-beam LIL, the same analysis of contrast loss and the beam polarization combination mode is applicable for N-beam (\(n>3\)) interference lithography.
###### Abstract  
There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks' challenges handled by SDN-based vehicular networks.  
software-defined vehicular network; vehicle-to-everything (V2X); modeling and implementation; software defined network +
Footnote †: journal: Sensors
# Pose Measurement Method Based on Machine Vision and Novel Directional Target
## 1 Introduction  
Vision measurement is the application of computer vision [1, 2] to measure and pose spatial geometries [3] accurately. Vision measurement technology has the characteristics of non-contact, high measurement accuracy, fast response speed, not being easy to disturb, etc., and it has begun to be applied to various fields of industry in recent years [4, 5]. According to the different selected features, machine vision pose measurement can be divided into cooperative target-based pose measurement and non-cooperative target-based pose measurement [6, 7]. Currently, the cooperative target-based measurement method is the most extensive and mature [8, 9]. However, this method often requires high-precision target mounting or a precise optical system to achieve high measurement accuracy in practical applications [10, 11], which greatly limits the application scope of vision measurement [12].  
A monocular vision measurement method based on a cooperative target is proposed to aim at the shortcomings of the existing machine vision pose measurement techniques. A new directional planar target is designed. The target only consists of circles and rings, has simple design and processing advantages, and is easy to adapt and use [13, 14]. The pixel coordinates of the feature points are also further optimised based on the rule of covariance of the circle centres [15, 16]. The self-calibration of the camera is achieved by combining it with the Zhang method [17]. The target is arbitrarily installed on the object to be measured when installing the camera. The relationship between the camera andthe target is re-established through the camera's self-calibration, and then high-precision measurement can be carried out [18; 19; 20]. By solving the PnP problem, the corresponding rotation matrices of the reference image and the image to be measured are obtained.The transformation matrix is calculated using the two rotation matrices, and finally, the pose angle of the target can be obtained by decomposing the transformation matrix.
# Characterization of the Aroma Profile of Food Smoke at Controllable Pyrolysis Temperatures
## 2 Materials and Methods
### Production and Sampling of Food Smoke  
This research was conducted as part of the pilot plant production of Frankfurter sausages. Smoking chamber atmosphere samples were taken from the smoking chamber while sausages were smoked to picture the full smoking process. Frankfurters (around 1750 g in median per batch, standard recipe (Table S1 in the Supplementary Materials)) were produced in a 1800 L pilot plant one trolley smokehouse type \"Airmaster UK 1800\" connected with a smoldering smoke generator \"Maxxsmoker GH 505 H\" (REICH Thermoprozesstechnik GmbH, Schechingen, Germany) located in the Meat Technology Pilot Plant of the Department of Food Material Science, University of Hohenheim. Sausages were prepared on the day before smoking and stored at 4 \({}^{\circ}\)C to guarantee the same core temperature before smoking. Sausages were heated in the smoking chamber to a surface temperature of 48 \({}^{\circ}\)C before smoking (Table S2). During smoking, the chamber temperature was between 45 and 55 \({}^{\circ}\)C. As the smoking agent, beech wood chips (Rauchergold KL2-16, Rettenmeier & Sohne, Rosenberg, Germany) were used. The smoke generator can be divided into 3 zones. The upper one is the storage container for wood chips; the smoldering zone at the bottom creates the ember bed temperature by smoldering airflow. The transport air is fed into the middle zone of the smoke generator and transports the resulting smoke through the pipes left and right into the product chamber [24]. The pyrolysis temperature was changed by adjusting the air volume flow as the oxygen provided allows the pyrolysis temperature to be adjusted over a wide range [6]. The air volume flow was adapted by varying the parameters of the total air supply from outside through the electric fan, as well as the ratio of transport air to smoldering air and adjusting the airflow of both streams using dampers in the feed pipes. By adjusting these parameters, the pyrolysis temperature could be varied in the range of 150-900 \({}^{\circ}\)C. Representative temperature curves of the wood pyrolysis temperature in the smoke generator are shown in Figure S1. For a detailed description of all parameters, see Table S3. Representative samples from the smoking chamber atmosphere were taken directly from the product chamber via a pipe, 10 min after pyrolysis started in this study. Samples were filled from the pipe directly into gas-tight vials (20 mL) while purging the vials for 10 sec with smoke from the pipe, and then subsequently analyzed by HS-SPME-GC-MS. Samples of six different wood pyrolysis temperatures, 150 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 450 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 750 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, were analyzed. Each time, three independent samples were taken from the smoking process (\(n=3\)). Samples were stored at 4 \({}^{\circ}\)C for maximum 3 days prior to analysis.
## 4 Experimental Setup
### The Models--Verifiers
SET:\(AUC_{max}\ \leftarrow-\infty\) BEGIN
1: FOR: \(t=1:T_{L}\) /* Add a new \(t\)- leaf /*
2: /* Gentle AdaBoost algorithm */
3: USE: \(\omega^{+}_{\mathbb{T}_{R}}\), \(\omega^{-}_{\mathbb{T}_{R}}\) by means of their vectored forms: \(v^{\pm}_{\mathbb{T}_{R}}\) (or \(dv^{\pm}_{\mathbb{T}_{R}}\))
4: TRAIN: The current model \(\Delta_{\text{DSC-BFS}}(t)\) with \(v^{\pm}_{\mathbb{T}_{R}}\) or \(dv^{\pm}_{\mathbb{T}_{R}}\)
5: USE: \(\omega^{+}_{V}\), \(\omega^{-}_{V}\) by means of their vectored forms: \(v^{\pm}_{V}\) (or \(dv^{+}_{V}\))
6: EVAL: Scores s_\(v^{\pm}_{V}\) (or \(s\_dv^{+}_{V}\)) with current model \(\Delta_{\text{DSC-BFS}}\big{(}t,v^{\pm}_{V}\big{)}\)
7: PERFORM: ROC analysis with \(s\_v^{\pm}_{V}\) or s_\(dv^{\pm}_{V}\). 8: EVAL: \(AUC_{t}\) from ROC analysis
9: IF\(AUC_{t}>AUC_{max}\)then
10: SET: \(AUC_{max}=AUC_{t}\)
11: SET: T\({}_{opt}\gets t\)
12: SET: counter = 0. 13: ELSE
14: \(counter\leftarrow counter\)+1
15: IF counter ==\(T_{H}\)then
16: EXIT by early stopping
17: end_IF
18: end_IF
19:end_FOR
20: RETURN: \(\Delta_{\text{DSC-BFS}}\big{(}T_{opt}\big{)}\) which corresponds to the \(AUC_{max}\) END
```  
**Algorithm 2**Learning a WI-SV verifier with the DSC-BFS. The dimensionality of the input \(v^{\mu\pm}_{\{g,S,I\}}\) and \(dv^{\mu\pm}_{\{g,S,I\}}\)\(a=1:14\) vectors over all the \(a-\) indexed equinass segments is conditioned on the two learning procedures, \(LFW1\) and \(LFW2\) (depicted in Figures 3 and 4 earlier introduced in Section 2.2). In the case of \(LFW1\) visually depicted in Figure 3, the dimensionality of the inputs \(v^{\pm}_{\{g,S,I\}}\) and \(dv^{\mu\pm}_{\{g,S,I\}}\) equals fifty-five, as Equation (13) implies for \(d=10\). In the case of \(LFW2\), visually depicted in Figure 4, the fused input vector is formed by concatenating all vectors \(v^{\pm}_{\{g,S,I\}}\) or \(dv^{\pm}_{\{g,S,I\}}\), resulting in an input dimensionality equal to \(14\times 55=770\).
# Multi-Attribute Collaborative Optimization for Multimodal Transportation Based on User Preferences
## 3 Problem Description and Model Construction
### Problem Description  
The multimodal transportation network is defined as shown in Figure 1, where \(V\) represents the set of nodes, \(E\) represents the set of arcs, and \(D\) represents the set of transportation modes. The multimodal transportation operator will plan a reasonable route to transport goods from the origin to the destination, taking into account factors such as cost, time, carbon emissions, and risk. The transportation route will pass through multiple intermediate nodes in the network and may involve mode transfers at some nodes.
## 2 Methods of Encapsulation
### Spray-Drying  
One commonly used technique for microencapsulation is the SD method. This approach involves the creation of a protective shell around the core by evaporating the solvent from the solution containing the wall-forming substance. Figure 2 shows the diagram of the SD encapsulation method. SD is particularly useful for encapsulating fatty or fat-soluble compounds, including fragrance oils, as well as aromatic substances composed of highly volatile aldehydes, ketones, alcohols, esters, and ethers [23; 24]. This method is also used to microencapsulate bioactive and health-promoting compounds such as polyphenols, flavonoids, etc. [25; 26]. Typically, polysaccharides such as modified starch or gum arabic  
Figure 1: Emulsion-based microencapsulation (created with BioRender.com (accessed on 26 November 2023)).  
are used as coating materials in the SD process. An essential factor affecting the quality of the capsules is the air temperature. It is recommended that the inlet temperature of the dryer should range from 160 to 210 \({}^{\circ}\)C, while the outlet temperature should be maintained at 80 to 90 \({}^{\circ}\)C. Due to the use of high temperatures, this method cannot be used for the microencapsulation of highly thermolabile materials. Substances that need to be encapsulated using other methods include lactic acid bacteria, proteins, or unsaturated fatty acids [27]. Spray solidification (spray chilling) is more widely used in the microencapsulation of thermally labile substances [28]. This method involves suspending core particles in a molten wall material with a low melting point (32-42 \({}^{\circ}\)C) and then spraying this mixture in cold air, water, solvent, or liquid nitrogen, which quickly hardens the wall [4]. Thanks to the complexation with polyethylene glycol, the SD of hydrophobic biomaterials is possible, and chitosan has been proven to be useful in creating systems for the controlled release of active substances in the body [29]. Microcapsules obtained by SD are spherical, their size range varies from 10 \(\upmu\)m to 2 mm and depends on the process parameters and encapsulated materials [30].
## 4 Results from the Literature Review
### Smart Lighting Systems
#### 4.2.3 Innovations in Logistics and Supplies
Studies indicate that, in comparison to traditional vehicles, electric UAVs can significantly cut greenhouse gas emissions, contributing positively to environmental sustainability [155]. Drones have also proven valuable during health crises, as evidenced during the COVID-19 pandemic, where they enabled efficient, contactless delivery of medications, SARS-CoV-2 tests, personal protective equipment, and even vaccines [156; 157; 158]. The literature further highlights UAV-based logistics as a vital resource in humanitarian aid during natural disasters, social conflicts, and even armed conflicts. In such scenarios, drones effectively support emergency medical systems [159] and the transportation of essential resources, such as medications, food, and water [160; 161].
## 3 Optimal Decision-Level Fusion Criterion (O-DFC) and Its Simplified Form  
Since O-DFC has been discussed in the distributed detection system [48, 49] (in Figure 2), here we simply review it and extend it to our decision-level fusion based MUD model (in Figure 1) in Section 3.1. Then the majority voting decision-level fusion criterion (MV-DFC), which is the simplified form of O-DFC, is given in Section 3.2.
# Long-Range Multispectral Thermal Imager for Simultaneous Measurement of Distance and Temperature
## 1 Introduction
[PERSON] [11] analyzed the effects of matching the accuracy of feature points, pixel size, focal length, and baseline length on measurement results in binocular distance measurement. [PERSON] [12] proposed a non-parallel binocular model ranging system combining linear and nonlinear, in which a linear stereoscopic pinhole camera model was established and the internal and external parameters of the two cameras were determined by classical calibration methods. [PERSON] [13] proposed a constraint-matching three-eye vision imaging ranging algorithm, which significantly improved the accuracy compared with binocular model. [PERSON] [14] proposed a monocular and binocular visual ranging fusion method based on the Adaptive Untrace Kalman filter (AUKF), which introduced an adaptive noise adjustment mechanism to suppress outlier interference in the ranging process. In addition to improving accuracy through optimization algorithms, some researchers also improved the accuracy of binocular systems by introducing other information. [PERSON] [15] designed a binocular stereoscopic imaging perception system with the fusion of a wide field of view and infrared visible light. [PERSON] [16] proposed a multi-stage optical positioning method combining a binocular camera and single-point laser rangefinder, which was successfully applied to the positioning of large underwater targets. [PERSON] [17] found that the accuracy of binocular vision system could be effectively improved by increasing the baseline distance between two cameras. However, the traditional binocular camera or monocular camera ranging model only targets the normal temperature target [18; 19]. When the temperature of the measured target rises, the radiation energy received by the camera increases significantly [20]. The saturated signal limits the traditional camera's ability to measure the distance of the high-temperature target. In a high-temperature environment, the traditional contact or laser-ranging methods are often limited by the interference of high-temperature radiation or the physical limitation of equipment [21], but the multispectral thermal imager can use its own radiation measurement principle to obtain temperature and distance information at the same time. This capability not only provides a new solution for equipment condition monitoring and engineering parameter optimization, but also further expands the application prospect of multispectral thermal imager. The purpose of this study is to develop a remote high-temperature target measurement method based on a multispectral thermal imager, capable of accurately measuring flame temperature fields over long distances in complex environments. We hypothesize that by analyzing the image offset relationships across different wavelengths of the multispectral thermal imager, a mathematical model can be established between the target distance and the offset of the multi-aperture temperature field, thereby enabling simultaneous remote distance and temperature field measurements.
## 3 Results and Discussions
The actual wind power density is represented by the blue curve, while the ANN-GA, ANN-PSO, Rayleigh, and Weibull results are represented by green, red, purple, and yellow curves, respectively. As can be seen from Figure 10, the Weibull distribution, which is one of the statistical methods, shows a superior performance compared to the Rayleigh distribution. However, it is understood that ANN-based hybrid algorithms give much better results in WP estimation than these two statistical methods. Then, considering Table 5, when the proposed ANN-based hybrid techniques were evaluated internally, it was observed that the ANN-GA model performed superior to the ANN-PSO model. According to Table 5, an MSE of 0.3653, an RMSE of 0.6044, an MAE of 0.3994, and an MAPE of 16.66% were obtained, and a correlation of 94.839% was achieved for the ANN-GA model. For the ANN-PSO model, an MSE of 0.4721, an RMSE of 0.6871, an MAE of 0.4304, and an MAPE of 17.07% were acquired, and a correlation of 94.042% was accomplished. The results show that the proposed ANN-GA model improves the prediction accuracy better than ANN-PSO. As a result, it is understood that the ANN-GA method has the best prediction results among the four methods for wind speed and wind power density. Figure 9: Correlation between target and predicted results for the training and test data (ANN-PSO). Moreover, Table 5 is given to compare the performance of the presented ANN-based hybrid estimation techniques in this study. The performance of the proposed algorithms was evaluated using error metrics such as R\({}^{2}\), RMSE, MSE, MAE, and MAPE. The range of RMSE, MSE, and MAE is (0, +\(\infty\)); smaller values of these parameters mean that the estimation model has higher accuracy. In addition, 10% < MAPE < 20% indicates a good prediction result. As understood from Table 5, ANN-GA exhibited a more satisfactory performance compared to ANN-PSO.
# Study on Dynamic Loading Characteristics of Rock Containing Holes
## 3 Validation of Experimental Results and Reliability of the Numerical Model
### Verification of Dynamic Stress Equilibrium  
To ensure the validity of the data derived from Split Hopkinson Pressure Bar (SHPB) simulation experiments, verifying the dynamic equilibrium of the rock specimen under axial impact is essential. During the axial impact loading, the stress history documented at both ends of the specimen should demonstrate a high degree of uniformity. A direct measurement strategy is utilized to monitor the stress history at the interfaces between the bars and the specimen. The equilibrium of stress at the specimen's ends is determined using the force balance coefficient, computed by the following formula:  
\[\mu=2(\mathrm{F_{In}-F_{Tr}})/(\mathrm{F_{In}+F_{Tr}}) \tag{4}\]  
Previous sections contrasted stress-strain curves and failure mechanisms from laboratory and numerical simulation experiments, validating the accuracy of the numerical model's outcomes. To highlight different stages of the specimen's failure process, this experiment segmented the stress-damage time curve of the rock specimen and selected specimens of notable significance for illustration, as shown in Figure 8.  
The stress-damage time curve shown in Figure 8 highlights four crucial phases: degradation onset, yield point, peak stress, and complete failure. The degradation onset indicates the initial crack formation, whereas the yield point represents a marked acceleration in crack propagation. As their designations suggest, peak stress refers to the highest stress level experienced by the specimen, and complete failure marks the point at which the specimen fails entirely.  
Generally, the stress history data recorded at the incident end face of the specimen should align closely with those from the transmission end face. A discrepancy of less than 10% from a defined critical value falls within acceptable precision limits for numerical experiments. Figure 9 illustrates the time-stress curves at the specimen's end faces, and examination of these curves reveals a tight concordance between the stress profiles at both specimen ends. The calculation of the stress balance coefficient shows that the variation in stress at the specimen ends consistently stays below 10% between \(\mathrm{T_{d}}\) and \(\mathrm{T_{W}}\). This compliance with the dynamic equilibrium standards validates the dependability of the data from the dynamic numerical experiment.  
Figure 8: Stress–damage time curves (**a**) for an 8 mm hole specimen; (**b**) for a double 6 mm hole specimen.