esa-sceva/satcom-chunk-collection huggingface dataset:
# A High Full Quality CMOS Image Sensor for Space Applications
## 3 Experimental Results  
### Overall Performance with Enhanced Full Well Capacity  
The prototype CIS was fabricated using a 0.11 \(\upmu\)m 1P4M CIS process. The chip microphotograph and the captured image of the ISO 12233 chart are shown in Figure 7a,b, respectively.  
Figure 8a shows the measured pixel output voltages versus light intensity with different amounts of charge transfer. It is clearly shown that the saturation voltage of the pixel output linearly increases as the amount of charge transfer increases. The FWC of 103,448 e\({}^{-}\) with the pixel readout voltage of 1 V was achieved by transferring the charges three times and the measured read noise--which was mainly generated by the off-chip ADCs and measurement board--was 24.7 e\({}^{-}\). For the read noise measurement, the integration time of the CIS was set to 52 \(\upmu\)s and the VGA gain to 4 for minimizing the dark and shot noise. Then, two dark images were captured, and the standard deviation was calculated from the difference of the two images for removing the fixed pattern noise. The read noise  
Figure 7: Chip microphotograph and captured image: (**a**) Chip microphotograph including a pixel array, pixel control block, and switched capacitor amplifiers of 10 channels; (**b**) Captured image (ISO 12233 chart).  
was calculated from the standard deviation considering ADC resolution, VGA gain, and conversion gain. The DR was achieved 72.4 dB resulting in the read noise. The DR could be further improved by using on-chip ADCs, which achieve much lower read noise. For example, a 106.7 dB DR can be achieved with 0.48 e\({}^{-}\) read noise, which is reported in [43].  
With a 6.5 um pixel pitch, the PPD size is 28.31 um\({}^{2}\) and the fill factor is 67%. As shown in Figure 8b, the measured QE varies from 13.6% to 68.4% for the spectrum ranging from 375 nm to 875 nm, and the peak QE is 68.4% at 550 nm. Performance of the CIS is summarized in Table 1.
# An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder
## 2 Proposed Algorithm
### Iemo-Kc
#### 2.2.2 Optimizer  
NSGA-II [24] is employed as the optimizer due to its simpleness. It is slightly adjusted in IEMO-KC, and its pseudo-code is shown in Algorithm 1.  
```
Input: Maximum generation maxgen, population size \(N\), a range of \(k\) Output: A set of optimal one-dominated solutions with different \(k\), Pareto front
1: Initialize a set of \(N\) random parent solutions, \(PS\)
2: Assign to each solution with a random different \(k\)
3: While gen \(\leq\) maxgen
4: Generate \(N\) offspring solutions \(OS\) by crossover and mutation operators
5: Combine \(PS\) and \(OS\) together to form joint\(S\)
6: Evaluate joint\(S\) by the fast non-dominated sorting approach and the crowding distance [24]
7: Select the best \(N\) solutions from joint\(S\) to form the new parent PS
8: gen \(\leftarrow\) gen + 1
9: End while
10: Select a set of optimal non-dominated solutions with different \(k\) from \(PS\) to form the Pareto front
```  
**Algorithm 1**NSGA-II for IEMO-KC.  
The algorithm generates a set of \(N\) random initialized parent solutions (\(PS\)) and assigns each solution with a random different \(k\) before the iterations. During each iteration, the same number of offspring solutions (\(OS\)) is generated from \(PS\) through simulated binary crossover (SBX) and polynomial mutation (PM) [24]. Specifically, two offspring solutions are generated from two randomly selected parent solutions by using the SBX operator, with appropriate values of the probability of applying recombination (\(p_{c}\)) and the magnitude of the expected variation from the parent values (\(\eta_{c}\)). Note that the \(k\) values of the two offspring solutions are inherited from the two parent solutions. Subsequently, a new solution generated by using the SBX operator is further mutated by using the PM operator, with appropriate values of the probability of applying mutation (\(p_{\text{m}}\)) and a mutation distribution parameter (\(\eta_{m}\)). Note that the \(k\) of this solution remains unchanged in the mutation process. Then, \(PS\) and \(OS\) are combined as joint\(S\), which is then evaluated by the fast non-dominated sorting approach and the crowding distance. The best \(N\) solutions from joint\(S\) are then selected to form the new \(PS\). After the iterations, a set of optimal non-dominated solutions with different \(k\) can be selected from \(PS\) to form the Pareto front.
## 4 Research on Stress and Deformation Characteristics of CFRDs under Different Factors
### The Influence of Bank Slope Gradient on the Stress and Deformation of CFRDs  
The stress and deformation characteristics of the dam and panel under varying bank slope gradient conditions during the water storage period are presented in Figure 8. It was observed that the stress and deformation characteristics of the dam and panel both increased as the bank slope gradient became less steep. Specifically, the compressive stress along the slope of the panel gradually diminished with a gentler bank slope gradient, whereas the tensile stress conversely increased. This phenomenon occurs because as the slope gradient eases, the lateral constraints on the dam provided by the banks decrease, resulting in greater deformation of the dam and an associated increase in tensile stress along the slope of the face slab. An increase in tensile stress may result in the development of cracks within the panel. This is particularly true when the applied stress surpasses the tensile capacity of the concrete, causing the existing cracks to propagate and subsequently compromise the dam's seepage control capabilities. Moreover, such tensile stress variations can induce alterations in the internal stress distribution within the structure, thereby impacting the stress conditions of other structural elements. Furthermore, the axial stress of the panel progressively decreased as the bank slope gradient became more gradual. This trend is attributable to the diminishing compressive effect exerted on the panel by the banks as the slope gradient lessens, leading to a reduction in axial stress.  
The distribution area of the maximum characteristic values of the dam and panel along the height direction was found to be generally consistent with the corresponding area under different dam heights, as previously described. Figure 9 illustrates the distribution of stress along the slope and axial direction when the bank slope gradients are 1:0.5 and 1:1.5. From this figure, it was concluded that with a steeper slope, the extent of the tensile stress zone along the slope direction at the bank slope is relatively small. As the bank slope gradient becomes gentler, the range of the tensile stress zone along the slope direction at the bottom of the panel and at the dam shoulders on both banks progressively enlarges. In terms of stress along the axial direction, when the bank slope gradient is steep, the tensile stress zone of the panel is predominantly localized at the dam shoulders on both sides. Conversely, as the bank slope gradient decreases, the panel tensile stress zone broadens, extending from the dam shoulders on both sides towards the center of the valley and advancing downward along the slope direction of the bank towards the lower part of the panel.  
Figure 8: Changes in deformation and stress characteristics of the dam body and panel under different bank slope gradient conditions during the water storage period: (**a**) deformation; (**b**) stress.
## 1 Introduction
Additional technical details of the networking parameters, architecture and related QoS measures in a CPSN are discussed in subsequent sections.
## 2 Energy Optimization Challenges in UIoT Networks
### Environmental Characteristics  
[PERSON] et al. pointed out that most of the characteristics of IoT systems are suitable for the UIoT environment since UIoT is the subclause of IoT [35]. However, most available energy-saving models are created for terrestrial IoT and acoustic-based UIoT networks. Additionally, due to the heterogeneous functionalities of UIoT devices, the existing energy-saving models cannot apply directly to UIoT networks. This statement highlights why the existing energy optimization techniques are not applicable to multi-medium-based communication technology in UIoT.  
* Channel behavior: unlike terrestrial area networks, in UIoT networks, the devices are naturally interconnected through acoustic, visible light, infrared, radiofrequency, and magnetic induction mediums [57]. In effect, this causes high battery consumption, improper medium selection, unaware routing, data loss, and increased data error rate, etc. Likewise, the performance of each medium differs in the UIoT network. For example, the optical medium is high in bandwidth but compactable for a short-range communication scheme and the acoustic medium has a narrow bandwidth but is comparable for a long-range communication scheme. Other challenges such as absorption, turbulence, and scattering also affect the communication medium in UIoT networks [58]. Furthermore, due to the lack of energy management techniques in UIoT networks, the developers find it difficult to transmit data via a different medium and to solve energy-related issues in the underwater channel [36].
* Energy consumption and storage: in UIoT networks, the sensor nodes are designed with low battery capacity, less computational power, and limited memory [59]. Moreover, the nodes consume extra energy for sensing, gathering, processing, and transmitting information. The terrestrial area networks are designed with high battery capacity and huge memory size, and also, the batteries are replaceable and rechargeable. However, in the case of UIoT networks, it is hard to replace or recharge device batteries, and also memory management becomes complex due to the constrained behavior of natural behavior. This may cause power constraints in UIoT networks.  
Figure 3: Taxonomy of energy optimization challenges in UIoT applications.  
Environmental state: in UIoT environments, internal activities such as mammal behavior, fast waves, and external noises, etc., lead to the formation of frequent changes in UIoT network topology [60]. In effect, this may cause node damage, connectivity issues, data accuracy issues, and unaware rerouting, etc. [61]. In addition, compared with terrestrial area networks, the UIoT devices are sparsely installed in UIoT environments, and they consume high amounts of energy for data sensing and transmission.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ1. What Are the Identified Challenges in the MSA Domain?
#### 5.1.3 Testing  
Testing plays a critical role in a system being ready before going live, and for developers moving forward confidently. However, for MSA, it is a challenging task to satisfy testing activities due to the distributed nature of MSA. Each microservice lives in a distributed environment and can be developed using different technologies, languages, and infrastructures, which as such provides additional complexity for the testing process.  
Testing the resilience capabilities of MSA is identified as an important challenge. Resilience enables systems to handle failure cases properly, but in a distributed architecture it is not easy to achieve this because microservices architecture is composed of a set of services that operate together and thus are prone to frequent changes. They should be loosely coupled and autonomous for being resilient as well.  
Another challenging issue is performance tests. Non-functional requirements such as throughput and response time are important performance parameters for software systems. It is necessary to have performance tests to measure these kinds of performance parameters to trace the system properly and prevent any failure. In distributed environments, however, measuring these quality parameters is not as easy as in a monolithic architecture because of the diversity and number of microservices.  
Regression testing is needed to ensure that the system is still running after a newly added feature or after a bug that has been resolved. Regression testing for MSA is not trivial, since all the test activities need to be handled in an agile way, they must be automated and included in the continuous delivery process.  
Another issue is related to acceptance tests. They are a set of test activities that must be run to ensure customer satisfaction and create robust systems, but this requires high maintenance costs to be handled in the microservice world because of the agility of MSA.  
In addition, defining a comprehensive testing framework has emerged as a tough work in recent years because it consists of many sub-challenging points such as self-validation of interfaces, unit validation, and integration validation of services. Additionally, each validation should be automated to provide continuous and agile deployments.  
The last issue is automating tests. It is directly related to defining or reusing a framework to run tests automatically. The proper testing flow needs to be prepared and run from time to time depending on decisions on the test plan and at each testing cycle, it needs to ensure that it is not affecting the system's reliability, and it is not an easy work.
## 2 Methods
### Graph-Based Wall Topology Analysis
#### 2.2.1 Line Segment Topology Graph  
The graph structure is a widely used way to represent the target objects and their mutual adjacency relationships. In the field of LOD-2 building reconstruction, the roof topology graph (RTG) is often adopted to represent the plane-plane topology [32]. Analysis of the graph structure can help distinguish roof features and primitive types by searching for loops or typical sub-graphs inside [33,34]. In this work, similar to the RTG, a wall line segment topology graph (WLTG) is constructed, with the obtained wall line segments as graph vertices and the adjacency relationships between the line segments as graph edges. The existence of graph edges can be decided based on the point-point connection between the inliers of line segments. As shown in Figure 3b, the Triangulation Network (TIN) is built based on the CGAL library, and we search the TIN edges that connect points from different line segments. Since only the wall points are considered in the TIN structure, the TIN edges that are longer than a given threshold \(Th_{len}\) (0.5 m in the test) are omitted to  
Figure 3: The workflow of graph-based wall topology analysis. (**a**) the input line segments, (**b**) the extraction of wall-wall topology via TIN-based adjacency, (**c**) the initial wall line topology graph generated by local analysis among adjacent walls, (**d**) the connection of line segments based on the topology relationship, (**e**) the modified wall line topology graph after the analysis of wall topology and (**f**) the final results for edge boundaries.  
avoid the TIN edges that pass through the whole house. We connect the line segments that meet the requirements of TIN connections in the graph structure as an initial WLTG. Edge labels can also be added to the graph edges, similar to the work in [32], which indicates further information about the connection when trying to connect adjacent lines or conduct the topology analysis.
# Low-Complexity Online Calibration for Large-Scale Multi-Beam Antennas
## 5 Implementation Results
The power of the eighth calibration signal is 25 dB lower than the first and its calibration results are the worst, 0.0236 dB and 0.1497 deg. The power of the second calibration signal is equal to the first and its calibration results are 0.0236 dB and 0.1497 deg. This experiment verifies that the proposed calibration method can be implemented in communication traffic signal flow. The additional noise source is not introduced in this experiment. The parameter \(\gamma_{k}\) in this experiment seems infinitely large, and the calibration accuracy approaches the true value infinitely. However, the practical system is affected by thermal noise and quantization noise of ADC chip, resulting in lower experimental calibration accuracy than theoretical and simulation results. Therefore, the main reason for poor calibration accuracy of low-power signals such as the eighth calibration signal is quantization, not channel noise. Figure 11: Experimental results. (**a**) Relative amplitude. (**b**) Relative phase. Figure 10: Experimental system for calibration.
## 2 FM-CSK and QCPSK Communication Systems  
The current section describes the frequency-modulated chaos shift keying (FM-CSK) and quadrature chaos phase-shift keying (QCPSK) data transmission systems. Both systems presented in the section utilize chaotic waveform as an information carrier to increase the security level of information transfer, yet the systems use similar principles of frequency-shift keying (FSK) and quadrature amplitude modulation (QAM). The section further outlines the main nodes that form the FM-CSK and QCPSK communication systems and the hardware implementation of the two systems, outlining the hardware components used to implement the main nodes of the systems. The section also describes the source of chaotic signals--the Vilnius chaos oscillator and the use of chaotic signals in each system.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 2 Problem Formulation and Proposed Algorithm Description
### Extract the Staggered PRI Sequence and Estimate the Sub-PRIs  
The class-matched filter algorithm is used to determine whether the pulse sequence with the same PRI originates from the same radar after the sequence separation. The pulse sequences with the same PRI are recovered as a staggered sequence if they originate from the same radar. The difference histogram approach is used to estimate the sub-PRIs. Considering the effect of PRI jitter, a clustering histogram method is proposed. The clustering method is the same as Section 2.3.2, \"Improved Square Sine Wave Interpolation Function\".
# Effect of LNR-g-MMA on the Mechanical Properties and Lifetime Estimation of PLA/PP Blends
## 3 Results and Discussion
### Mechanical Properties  
#### 3.3.1 Tensile Properties  
The tensile properties of neat PLA, neat PP, and PLA / PP (70:30 wt%) blends with different contents of LNR-g-MMA are presented in Figure 5a-c. In general, the stress-strain curves (Figure 5a) were highly dependent on the blend compositions. The tensile strength of the PLA and PP and the PLA/PP blend with LNR\(-\)g\(-\)MMA compatibilizer are shown in Figure 5b. It was found that pure PLA and PP have a tensile strength of 55.31 and 31.61 MPa, respectively. The tensile strength of pure PLA was much higher than that of pure PP due to the toughness of PP. Compared to pure polymers, the lower tensile strength in the blends may be caused by an incompatibility between non-polar PP and polar PLA because the applied stress could not be effectively transferred through the interface of the blended component. For the PLA/PP (70:30 wt%) blends with LNR\(-\)g\(-\)MMA, the tensile strength of the blends increased as the LNR\(-\)g\(-\)MMA content was increased. The addition of LNR\(-\)g\(-\)MMA to the PLA/PP blends improved the miscibility of the PMMA component as the grafting shell in the PLA phase and the yield strength due to the elastomeric nature of the LNR component as a core in the LNR\(-\)g\(-\)MMA particles.  
Figure 4: FT\(-\)IR spectra of (**a**) PLA, (**b**) PP and PLA/PP (70:30 wt%) blends with LNR\(-\)g\(-\)MMA of (**c**) 0 phr, (**d**) 10 phr, and (**e**) LNR\(-\)g\(-\)MMA.  
Figure 5c illustrates that pure PP had a much better elongation at break than pure PLA due to the toughness of PP. Adding PP to the PLA matrix increased the elongation at break in the blend. This result implied that blends transitioned from brittle to ductile failure with the addition of PP. Compared to the blend without LNR\(-\)g\(-\)MMA, the binary blend system with LNR\(-\)g\(-\)MMA added demonstrated an increase in the elongation at break. However, the elongation at break increased gradually as the amount of LNR\(-\)g\(-\)MMA increased. This research found that adding LNR\(-\)g\(-\)MMA to the blend system increased the blend system's tensile strength and elongation at break.
## 3 Test Results and Analysis
### Flexural Performance Analysis  
Four-point bending tests were performed on the specimens, and the flexural tensile strength at various thickness ratios is shown in Figure 5. The top right corner shows the error bar chart of parallel experiments under different working conditions.  
As can be seen in Figure 5, the flexural tensile strengths of the specimens with different thickness ratios show a general trend of first increasing and then decreasing. They increase from 6.255 MPa for the thickness ratio \(\lambda_{h}=0\) to 7.563 MPa for the thickness ratio \(\lambda_{h}=2\). The thickness ratio \(\lambda_{h}=0\) is the base group without the interface effect. When the interface is in the middle of the sample, different working conditions cause the interface (weak surface [21; 44]) to move down, reducing the height of the area that can stretch and increasing the height of the area that is being compressed, leading to more compressive stress; the friction at the interface increases, providing more resistance, and along with the adhesive force, it helps support the load. Stress optimization is maximized at the thickness ratio \(\lambda_{h}=2\), which is manifested in the maximum flexural tensile strength. The flexural tensile strength decreases from 7.563 MPa at the thickness ratio \(\lambda_{h}=2\) to 6.441 MPa at the thickness ratio \(\lambda_{h}=5\). The main reason is that due to the further downward movement of the interfacial interface (weak surface), the interfacial shear stress increases significantly, and the slippage effect appears at the beginning. This results in a reduction in the cooperative deformation capacity between different layers and even the occurrence of interfacial detachment, which leads to the gradual reduction in the flexural tensile strength of the fiber-sprayed concrete beams.  
The flexural tensile strength of the specimens with different thickness ratios shows a general trend of increasing and then decreasing, but the flexural tensile strength of the specimens with other thickness ratios is still increased compared to the benchmark group with the thickness ratio \(\lambda_{h}=0\). Among them, compared with the thickness ratio \(\lambda_{h}=0\)  
Figure 5: Flexural tensile strength versus thickness ratio.  
of 6.255 MPa, the flexural tensile strengths of the thickness ratio \(\lambda_{h}=1,2,3,4,5\) were 7.455 MPa, 7.563 MPa, 6.855 MPa, 6.567 MPa, and 6.441 MPa, which increased by 19.2%, 20.9%, 10.1%, 5%, and 3%, respectively, compared with that of the thickness ratio \(\lambda_{h}=0\). It can be seen that the layered construction has a greater effect on its structural flexural tensile strength, different layered interface locations have different effects on its flexural tensile strength, and the optimum thickness ratio is \(\lambda_{h}=2\), which is 20.9% higher than the baseline group.