esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization Method
### Case Study Descriptions and Simulation Results
#### 5.1.2 Case B: Deterministic Multi-Objective RPP Considering the Loadability Factor
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{X}\) & \(\mathbf{f_{1}(\mathbf{S})}\) & \(\mathbf{f_{2}}\) & \(\mathbf{f_{3}}\) & \(\mathbf{\overset{\wedge}{F_{1}}}\) & \(\mathbf{\overset{\wedge}{F_{2}}}\) & \(\mathbf{\overset{\wedge}{F_{3}}}\) & \(\mathbf{\overset{\wedge}{min}}(\mathbf{F_{1}}\mathbf{\overset{\wedge}{F_{2}}}\mathbf{F_{3}})\) \\ \hline
1 & \(2.9015\times 10^{6}\) & 0.1272 & 0.0000 & 1.0000 & 1.0000 & 0.0000 \\
2 & \(3.1145\times 10^{6}\) & 0.1309 & 0.0236 & 0.9511 & 0.9286 & 0.0724 & 0.0724 \\
3 & \(3.3885\times 10^{6}\) & 0.1347 & 0.0472 & 0.8881 & 0.8571 & 0.1448 & 0.1448 \\
4 & \(3.7008\times 10^{6}\) & 0.1384 & 0.0708 & 0.8164 & 0.7857 & 0.2172 & 0.2172 \\
5 & \(4.0028\times 10^{6}\) & 0.1421 & 0.0944 & 0.7470 & 0.7143 & 0.2896 & 0.2896 \\
6 & \(4.3051\times 10^{6}\) & 0.1459 & 0.1181 & 0.6776 & 0.6429 & 0.3620 & 0.3620 \\
7 & \(4.6107\times 10^{6}\) & 0.1496 & 0.1417 & 0.6074 & 0.5714 & 0.4344 & 0.4344 \\ \hline
8 & \(4.9361\times 10^{6}\) & 0.1533 & 0.1653 & 0.5326 & 0.5000 & 0.5068 & 0.5000 \\
9 & \(5.2798\times 10^{6}\) & 0.1571 & 0.1889 & 0.4537 & 0.4286 & 0.5792 & 0.4286 \\
10 & \(5.6345\times 10^{6}\) & 0.1608 & 0.2125 & 0.3722 & 0.3571 & 0.6515 & 0.3571 \\
11 & \(5.9691\times 10^{6}\) & 0.1645 & 0.2361 & 0.2953 & 0.2857 & 0.7239 & 0.2857 \\
12 & \(6.2864\times 10^{6}\) & 0.1683 & 0.2597 & 0.2224 & 0.2143 & 0.7963 & 0.2143 \\
13 & \(6.6184\times 10^{6}\) & 0.1720 & 0.2833 & 0.1462 & 0.1429 & 0.8687 & 0.1429 \\
14 & \(6.9648\times 10^{6}\) & 0.1757 & 0.3070 & 0.0666 & 0.0714 & 0.9411 & 0.0666 \\
15 & \(7.2547\times 10^{6}\) & 0.1795 & 0.3262 & 0.0000 & 0.0000 & 1.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Obtained Pareto optimal solutions for the deterministic multi-objective RPP considering the loadability factor. \begin{table}
\begin{tabular}{c c} \hline \hline
**Control Variable** & **Optimal Value** \\ \hline \(V_{g1}\) (p.u.) & 1.06300 \\ \(V_{g2}\) (p.u.) & 1.05310 \\ \(V_{g5}\) (p.u.) & 1.07610 \\ \(V_{g8}\) (p.u.) & 1.05420 \\ \(V_{g11}\) (p.u.) & 1.10000 \\ \(V_{g13}\) (p.u.) & 1.08520 \\ \(t_{6-9}\) (p.u.) & 1.02690 \\ \(t_{6-10}\) (p.u.) & 0.90000 \\ \(t_{4-12}\) (p.u.) & 1.00600 \\ \(t_{28-27}\) (p.u.) & 0.97310 \\ \(Q_{c24}\) (MVAR) & 0.00000 \\ \(Q_{c25}\) (MVAR) & 0.00000 \\ \(Q_{c26}\) (MVAR) & 0.00000 \\ \(Q_{c29}\) (MVAR) & 3.03920 \\ \(Q_{c30}\) (MVAR) & 2.66360 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The optimal values of the control variables for the deterministic multi-objective RPP considering the loadability factor.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 3 Results and Discussion  
### Background Segmentation  
To obtain the outline information of corn seeds from the hyperspectral images, it is necessary to segment the targets from the background. Hyperspectral images have a large number of bands, and the data dimension is high. Therefore, accurate image segmentation is the premise and foundation of variety classification based on hyperspectral images. By comparing and analyzing the reflectance spectrum, there was a significant difference in the change rate between the corn seed edge spectrum and the background spectrum in the 979.6-1110.1 nm bands. Therefore, the variance map in the 979.6-1110.1 nm band was used for threshold segmentation to generate a binary image. The binary image was applied as a mask to the original hyperspectral image for image segmentation under each band [38]. The background segmentation process is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Kernel Function** & **Mathematical Formula** & **Characteristic** \\ \hline Linear & K(x,y) = xy & Few parameters and fast calculation speed \\ Polynomial & K(x,y) = (xy + 1)\({}^{\text{q}}\) & A large number of parameters and easy to overfit \\ RBF & K(x,y) = exp\(\left[-\frac{|\text{x}-\text{y}|^{2}}{\sigma^{2}}\right]\) & Wide application and strong locality \\ Sigmoid & K(x,y) = \(\tan\text{h}[\text{v}(\text{xy})+\text{c}]\) & Multilayer neural network \\ \hline \hline \end{tabular}
\end{table}
Table 1: The expressions and characteristics of four kernel functions.  
Figure 2: Background segmentation of hyperspectral images.
## 5 Potential Use Cases and Applications
### Network Performance and QoE Improvement  
_Local content caching:_ By leveraging content caching technology--for example music, video streams, and web pages--at the edge, improvements to content presentation can be distributed massively [89]. Latency could be significantly reduced [17]. Content providers work to distribute CDNs more broadly to the edge, thus ensuring flexibility and customization on the network depending on user traffic requirements.  
_Telecom Industry in a 5G:_ different business models have been used in Telecom to improve operations and enable service providers to keep in business such as connectivity, partnership and digital service models [49]. With the emergence of OTT providers, telecom operators have been greatly affected. SMS is dominated by OTT Messaging Service, and VoIP is a major contributor to international calling [45]. The revenue lost by the telecom industry due to OTT services is only increasing. Now with the advent of 5G, the telecom industry will need to make some serious changes to its current business models [58].  
_5G Networks:_ IoT edge hardware and the Intelligence Edge Computing framework are expected to enhance already existing 5G networks [78]. When it comes to 4G technologies, speed, efficiency, and bandwidth limitations exist as a long-term solution, especially with the start of autonomous vehicles. On the other hand, 5G can offer companies the means to expand computing and networking capabilities [72]. The numerous 5G towers will definitely make the aforementioned future autonomous vehicles possible.  
_Smart Networks:_ IEC is a key technology in the widespread adoption of smart grids and helps allow enterprises to better manage their energy consumption [22]. Sensors and IoT devices connect to sophisticated platforms in factories and offices to monitor energy use and analyze energy consumption in real time [90]. With real-time visibility, enterprises and energy companies can strike new deals, for example, where high-power machines are turned on at off-peak times of electricity demand [84]. This can increase the amount of green energy (such as wind energy) the enterprise consumes.  
_Market Research:_ Many kinds of research have been conducted to improve 5G infrastructure due to the importance of IEC development [91]. (ETSI) Industry Specification Group (ISG) introduced IEC to active more open-standard environments. This work has allowed vendors and service providers to integrate applications efficiently and effortlessly. During the last few years, much previous research investigated standards and specifications of IEC [90]. These publications help to understand IEC infrastructure, challenges, benefits, security, operations and requirements of IEC. Additionally, the ETSI ISG has proposed different solutions to several major problems in IEC deployment in different environments.
## 2 Structural Principle, Detection Mechanism, and Performance Indicators
### Structural Principle of Optical Waveguide Refractive Index Sensing
#### 2.1.1. Refractive Index Sensing Principle of a Microring Structure
Other parameters such as the resonance radius, radius-wavelength dispersion equation, free spectral region (\(FSR\)), and other expressions can be calculated for each resonance based on Equation (2).
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 3 Cluster Analysis
### Centroid-Based Clustering  
Centroid-based clustering divides data points into clusters based on the distance from the cluster center. Algorithms of this type start with a predetermined amount of clusters to create. Centroids, i.e., central points of clusters, are then chosen and data are assigned to them based on distance. The centroid positions are adjusted over several iterations to find the best cluster division. Perhaps the most widely used and well-known algorithm of this type is K-means, which follows these steps:  
1. Choose the K value determining the cluster count.
2. Assign data points into clusters based on distance.
3. Calculate new cluster centers.
4. Repeat steps 2 and 3 until the K clusters are chosen, i.e., no or a very small amount of data points are changing the assigned clusters.  
As mentioned, the algorithms of centroid-based clustering depend on the prior choice of the cluster amount. This is equally true for K-means, which requires the user to specify a positive integer K as the number of clusters. This is very simple and intuitive. Therefore, it is easy to predict the outcomes and choose the correct value of K.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 6 Effects of Noise and Interference
### Frequency Estimation Errors  
Frequency shift estimation errors are introduced by the receiver components and processing stages. In the case of analog filter components, multiple identical low-pass or high-pass filters may be installed in series to achieve more robust filtering of unwanted frequency content. As it pertains to signal processing, the FFT size should be sufficiently large to allow for frequency resolutions on the order of \(10^{-2}\) Hz to account for a continuous spectrum of speeds at which RSOs may travel. Too large of an FFT size may result in frequency ambiguation as the signal energy is spread across several FFT bins.
## 2 Developed 3-D Profilometry Using Dual Sensing
### Measuring Principle
#### 2.2.4 Segmentation of Specular and Diffuse Images  
An image averaging algorithm is developed to extract specular and diffuse measuring regions with high S/N ratio from the acquired deformed fringe images. Removal of the projected fringe from the acquired fringe is essential for segmentation because these deformed fringes contain light intensity modulation, thus making segmentation difficult. In phase shifting, several phase-shifted sinusoidal patterns are projected onto the tested object and their corresponding deformed fringe images are acquired sequentially. A background image is defined here as an illuminated object image without structured fringes. To obtain the background image of these deformed fringe images, the image averaging algorithm can be performed by simply summing all the phase-shifted images to remove the fringe modulation and generate the background image.  
It is important to note that the above algorithm can reliably work even when the reflectivity of the tested surface is extremely variant. The algorithm operates on every individual pixel for the summing; hence, the result is generally insensitive to the pixel intensity. In an experimental test on the algorithm, the sinusoidal projection grating was made by a precise lithography process and a semiconductor's wafer surface having laser-etched groves was tested. A precise linear stage with positioning repeatability less than 1 \(\upmu\)m was adopted to perform four-step phase shifting. Figure 9a-d illustrates the captured images from the four-step fringe projection with phase-shift \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\), respectively. As can be seen, all the projected fringes are effectively removed with the image averaging algorithm, and the illustrated object image (also called the background image) is extracted. The experiment result shown in Figure 9 was acquired by the tilted camera for specular light detection on a smooth wafer surface. An excellent image contrast between specular and diffuse measured regions can be achieved in the background image. The same can be performed on the diffuse images to achieve equal effectiveness.  
Figure 8: Flowchart of the proposed dual camera mapping procedure.
## 5 Results and Discussion
### The Residents' Satisfaction of Multi-Dimensional Environments  
Questionnaires on the residents' satisfaction with multi-dimensional environments were distributed in Tianquan Community and Doushan Village. A total of 360 question  
Figure 8: The weights in criterion layer.  
Figure 9: The weights in index layer.  
naires were distributed to the local residents in the two areas by random sampling. A total of 358 were collected, of which 326 were valid, with an efficiency rate of 90.56%. The amount of 142 questionnaires were from Tianquan Community, and 184 were from Doushan Village. The distribution of the samples, gender, age, education level, and monthly income in the two areas are shown in Figures 10 and 11, respectively. The Cronbach's alphas of the questionnaire from the two communities were 0.866 and 0.862, respectively, both higher than the standardized value of 0.858. Therefore, the consistency and reliability of the survey were proved. The five-point Likert Scale was applied in the questionnaire. Participants circled the score according to their level of satisfaction. The score ranged between 20 and 100, with a higher score indicating higher satisfaction [51]. The weighted satisfaction scores of the criterion and the objective layer were calculated successively by cumulating the scores of the corresponding index according to their weights. The comprehensive residents' satisfaction with multi-dimensional environments was thus obtained. The satisfaction of residents with each alternative was ranked according to scores. The results of the two rural communities are shown in Tables 3 and 4 respectively.
## 3 Results and Discussion
### bAuNPs Characterization
#### 3.1.2 Atr-Ft-Ir  
The ATR-FT-IR spectrum (Figure 3) exhibits a strong and broad vibration band at 3267 cm\({}^{-1}\), which can be assigned to the stretching of the hydroxyl groups. Additionally, a weak vibration at 3076 cm\({}^{-1}\), characteristic of N-H stretching, and an absorption peak at approximately 2965 cm\({}^{-1}\), attributed to the C-H stretching vibration of alkanes, are observed. All ATR-FT-IR spectra also show vibrations at 1632 and 1579 cm\({}^{-1}\), which can be assigned to amides containing carbonyl groups (C=O), as well as absorption bands at 1453, 1399, and 1077 cm\({}^{-1}\), attributed to the C-O stretching vibrations of aromatic and aliphatic amines, respectively [33]. The presence of various compounds suggests the involvement of organic molecules in the bAuNPs synthesis process. The presence of amines, likely due to amino acid residues, indicates the coexistence of proteins from _P. aeruginosa_ extract with bAuNPs. This finding leads to the hypothesis that these coexisting proteins could be one of the factors influencing the rate of internalization of bAuNPs in tumor and bacterial cells.  
Figure 2: UV-vis spectrum and TEM photographs of the obtained bAuNPs at 58 \({}^{\circ}\)C under alkaline pH (9.0) for 24 h: (**a**) UV-Vis spectra and the colloidal solution obtained for bAuNPs synthesized. (**b**) TEM photographs (200 nm). Note: NC refers to the normal condition, which represents the basal medium only, while bAuNPs represents biosynthetic gold nanoparticles.  
Consequently, there is a reduced probability of cells recognizing bAuNPs as foreign agents and instead internalizing them as a food supplement.
## 1 Introduction
### Overview of WBAN  
WBAN follows three-tier architecture. The first tier defines the types of sensors, and the sensor sends the data to a base station, which is known as inter tier. The base station sends data to the doctor through a sink node, which is known as an extra node. The general three-layer architecture of WBAN is shown in Figure 1.  
As shown in Figure 1, tiny sensor nodes are attached to the body parts, which are further connected to a sink node, which helps the nodes to transfer their data to the medical server. This makes the role of the sink node and routing algorithm very crucial to attain better assistance from layer 3, i.e., the medical server. This experimentation is based on identifying the best routing algorithm out of already existing algorithms for WBAN, which will help to provide better data dissemination in WBAN.
# Quadcopter Adaptive Trajectory Tracking Control:
## 1 Introduction  
Unmanned vehicles have currently been playing a crucial role in many aspects of our daily lives where many robots can be used to operate and assist with complex tasks. Especially, unmanned aerial vehicles (UAVs) have been widely researched to create many versatile applications for replacing human from impossible missions and dangerous tasks [1], e.g., in agriculture [2], industry [3; 4], and military [5; 6]. Quadcopters, which are one of the most popular class of UAVs, have only four actuators, while there are six degrees of freedom, in that it is definitely an under-actuated system. By being able to independently control the speed of each actuator, the vehicle can execute horizontal and vertical movements to carry out a range of different assignments that can range from irrigating large agricultural fields, over investigating and locating lost objects in remote or inaccessible areas (e.g., forests, oceans, deserts), to simulating geographical maps. These tasks are all, somehow, based on a reliable trajectory tracking controller. However, the quadcopter tracking control performance always suffers from several influencing sources such as the system parameterized uncertainties and external disturbances. Therefore, the design and stability analysis of a superior quadcopter trajectory controller becomes one of the most important efforts that scientists and researchers all around the world are undertaking.
## 2 Materials and Methods
### Mechanical Testing  
Strain-controlled thermomechanical fatigue (according to the European Code-of-Practice [35]) experiments were carried out, applying cylindrical specimens (gauge length/diameter ratio: 15/7 mm) and utilizing servo-hydraulic fatigue testing machines with inductive heating. Specim temperature was controlled by Type R sling thermocouples attached to the center of the gauge length. At a controlled heating/cooling rate of 10 Ks\({}^{-1}\) out-of-phase (oop) cycles were performed in the temperature range from 250 to 650 \({}^{\circ}\)C. Specim cooling by compressed air was initiated immediately after reaching the maximum temperature, i.e., holding times at \(T_{max.}\) were excluded, to minimize creep deformation as far as possible. The thermal expansion (\(\varepsilon_{th.}\)) of the materials was completely obstructed (i.e., so-called 100% oop cycles were performed, \(R_{c.~{}mech}=-\infty\)) by mechanical strain (\(\varepsilon_{mech.}\)), applied by the testing machine, during heating and cooling. The majority of experiments were interrupted at the given number of cycles to provide TMF-loaded material for in-depth microstructure investigation. Note that executing TMF experiments with 100% obstruction of thermal strain at different materials causes deviations of the effective strain ranges, because of differing thermal expansions of the materials. In our case, the higher Cr-content of Crofer(r)22 H (Table 1) results in a, compared to P91 (0.56%), slightly lower strain range of 0.49%. This was deliberately applied, because this difference occurs in real life application in exactly the same way.
# Recent Development of High-Energy Short-Pulse Lasers with Cryogenically Cooled Yb:YAG
## 2 Changes in Crystal Properties with Temperature
### Thermodynamic Properties
It can be seen from the formula that the thermally induced birefringence effect decreases with the quantum loss, the decrease in thermal expansion coefficient, and the increase in thermal conductivity. When the laser passes through the crystal, the beam propagation direction will change. Generally speaking, the temperature gradient of the laser crystal gradually decreases from the inside to the outside, so the refractive index of the laser crystal gradually decreases from the inside to the outside. Therefore, this thermo-optic distortion is similar to adding a convex lens to the optical path, so it is also called the thermal lensing effect. Similar to stress-optic effects, a figure of merit of distortion (\(FOM_{D}\)) that can measure the thermo-optic distortion can be defined by  
\[FOM_{D}=\kappa/\left(\chi_{QL}|dn/dT|\right), \tag{4}\]  
the smaller the \(FOM_{D}\), the greater the thermo-optic distortion, which means a greater thermal lensing effect [10]. In the formula above, \(dn/dT\) is the thermo-optic coefficient. Similarly, it can be seen from the formula that the thermal lensing effect decreases with the quantum loss, the decrease in the thermo-optic coefficient, and the increase in the thermal conductivity. As mentioned above, Yb:YAG crystal has a small thermal expansion coefficient, small thermo-optic coefficient, and large thermal conductivity at low temperatures. In addition, compared with Nd:YAG crystals, Yb:YAG crystals have a smaller quantum loss. Changes in properties greatly reduce the thermally induced birefringence effect and thermal lensingeffect [14], which is beneficial to the enhancement of the laser power and improvement of the beam quality.
###### Abstract
The recently developed distributed scatterer (DS)-InSAR technologies [15] expand research keys from high coherence point targets with relatively stable scattering properties to distributed targets with moderate scattering properties and related scholars around the world began to shift their research focus to distributed targets with relatively weak backscatter to maximize the spatial sampling of the InSAR-based displacement results [16]. Previous research have shown that the integration of the high coherence point targets and distributed targets can be used to not only increase the point target density in the monitoring area significantly but also overcome shortcomings of conventional time series technique that it cannot acquire detailed ground displacement monitoring results in areas where have low image coherence caused by high vegetation coverage [15,17,18,19,20,21]. In this paper, an approach for monitoring ground displacement in mining areas was proposed by integrating PS point, slowly decoherent filtering phase (SDF) point and DS point based on signal-to-noise ratio (SNR). This method first acquired the PS point, SDF point, and DS point by using PS-InSAR, SBAS-InSAR, and DS-InSAR technologies. Second, the phase-value of the CPs can be obtained by integrating the PS point, SDF point, and DS point based on their SNRs. Finally, the deformation model was solved by a three-dimensional unwinding and least-squares algorithm to acquire the results of ground displacement in coal mining areas. A case study based on a coal mine in Heze, Shandong Province was carried out. Based on the 25 Sentinel-1A satellite SAR images, the ground displacement monitoring results of the study area were acquired by the proposed method and a cross-verification was conducted with monitoring results of conventional PS-InSAR and SBAS-InSAR technologies. The proposed method proved effective to monitor ground displacement in the mining area.