esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 5 Conclusions  
This paper conducts an in-depth study of high-dimensional multi-objective optimization problems (LaMaOPs) characterized by large-scale decision variables. It addresses the challenges faced by traditional multi-objective optimization algorithms, such as insufficient search capabilities and difficulties in balancing convergence and diversity when solving LaMaOPs. To tackle these issues, a dual-population co-evolution algorithm based on decision variable analysis (DVA-TPCEA) is proposed. This algorithm utilizes decision variable analysis methods to categorize variables into convergence or diversity variables while establishing a directed search mechanism tailored for high-dimensional decision spaces. Moreover, the dual-population co-evolution mechanism comprises two independent populations: a convergence population and a diversity population. This setup enables independent optimization within each population while facilitating information exchange and co-evolution between them, further enhancing the algorithm's performance. The experimental results demonstrate that the DVA-TPCEA algorithm offers significant advantages in solving large-scale multi-objective optimization problems, effectively balancing conflicts between different objectives and finding global optimal solutions, particularly in complex optimization scenarios within high-dimensional decision spaces.  
Despite the achievements of the DVA-TPCEA algorithm, there remains room for improvement in the accuracy of decision variable analysis methods. Additionally, as the problem size increases, the computational complexity and runtime of the algorithm may pose new challenges. Future research could focus on several aspects, such as the following: first, refining decision variable analysis methods; second, exploring more efficient high-dimensional multi-objective optimization algorithms to accommodate larger and more complex problems.  
Conceptualization, W.D.; Resources, G.J. and W.D.; Software, J.L., S.X. and J.Z.; Validation, J.L., S.X. and J.Z.; Writing--original draft, J.L., S.X. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was sponsored by the National Natural Science Foundation of China (No. 62403201); the Shanghai Pilot Program for Basic Research, China, 22 TQ140010016; the Nature Science Foundation of Shanghai, China, 23 ZR1414900, 22 ZR1416500; and Key Lab of Information Network Security, Ministry of Public Security, China, C23600-03.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
## 3 Experimental Setup  
In order to verify the effectiveness of the proposed method, tension tests were carried out on steel strands with a specifically designed frame. The test involved two steel strands, a tension frame, a hydraulic jack, a laptop, and an NI multifunction data acquisition (DAQ) device (NI USB-6366). The overall test setup is illustrated in Figure 6. The circular two-hole anchor (YJM15-2) is used here, and its performance and technical indicators fully meet the requirements of the Chinese National Standard GB/T 14370-2007 [53]. The smart anchor was installed between the anchor and the tension frame. Two 1 mm thick PZT patches were utilized: one was attached to the upper annular cylinder as an actuator, and the other was attached to the lower annular cylinder as a receiver. They were used to emit and receive signals labeled PZT1 and PZT2, respectively.  
The purpose of this paper is to address the issue of saturation in the energy transfer process. Therefore, the maximum tensile force is controlled at 18 tons, which is the designated value for the prestressed steel strand. This value effectively ensures early monitoring of the prestress levels in the strand. During the stretching process, a hollow hydraulic jack  
Figure 5: Flow diagram of the calculation.  
Figure 6: The overall layout of the test.  
with a measuring range of 20 tons was used. The steel strands were stretched and tested at one-ton intervals, up to the controlled limit of 18 tons.  
In order to verify the reproducibility of the experiment, a total of five tests were conducted. The signal transmission process of the PZT-based prestress monitoring system is shown in Figure 7. Signal conversion is realized between the LabView program on the laptop and the data acquisition device. Additionally, signal input and output are achieved between the data acquisition device and the PZT patches on the tension device. The specific operational process is as follows:  
A laptop is used to control the transmission and reception of signals. First, the LabVIEW program on the laptop generated a linear sweep sine wave with a frequency range of 50 kHz to 1 MHz. According to the Shannon sampling theorem, the sampling rate should be greater than or equal to twice the highest frequency in the analog signal spectrum. Therefore, the sampling rate is set to 2 MHz. The NI USB-6366 converted it into an analog signal and then sent it to the PZT1. The signal was then transmitted via the contact interface to the lower annular cylinder and received by PZT2. The NI USB-6366 converted the received signal into a digital format, which was then saved and analyzed by the LabVIEW program. To ensure the independence of the test, the contact was fully disengaged at the end of each test by unloading the stress level to zero and turning off the power until the next test commenced.
## 3 Data and Method
### InSAR Calculation in Cloud Platform
#### 3.2.2 Automated Full-Resolution Fast InSAR Time-Series Analysis Method  
The traditional SBAS approach may reduce the coverage of coherent points in some cases due to the multi-look procedure [7; 38]. Moreover, it is susceptible to losing information when the images have rich details or the spatial deformation signal becomes complex [39]. To address these limitations, the full-resolution InSAR method utilizes single-look data to circumvent the multi-look procedure. It performs time-series analysis directly in the SAR coordinate system, ensuring both high processing efficiency and precise monitoring results. Additionally, the statistically homogeneous pixel selection (SHPS) algorithm is used to filter the differential interferograms. The workflow of the full-resolution fast InSAR time-series analysis method for processing Sentinel-1 data is illustrated in Figure 3.  
* Employ the small baseline principle to select interferometric pairs and generate the optimal interferometry network [40].
* Calculate burst offsets between each image and the reference image, generating a burst offset file and determining the burst offsets of each slave image based on the AOI of the reference image.
* Automatically download the corresponding orbit auxiliary files and external DEM files. SRTM DEM with a resolution of 30 m was utilized to subsequently mitigate terrain phase effects.
* Utilize GPU to accelerate the generation of differential interferograms; details of GPU-accelerated InSAR processing are available in Section 3.2.1. Subsequently, all generated differential interferograms are resampled based on the registration parameters to ensure consistency with the SAR coordinate system of the reference image.
* Image cutting. Interferograms are cropped according to the specified range of the AOI.
* SHPS phase filtering and phase unwrapping. Utilize the SHPS algorithm to reduce noise in the interferograms while preserving the spatial resolution of SAR images. Coherent points surrounding each reference pixel are selected, aiming to retain interferogram details while eliminating phase noise from incoherent and low-coherence areas. Then, phase unwrapping of interferograms was achieved using minimum cost flow (MCF) networks [41].
* Corrections for orbital error and terrain-related atmospheric delay errors.
* Time-series analysis in SAR coordinate system. With high-pass and low-pass filters, the average deformation rate is calculated using the linear least squares (LS) method. Subsequently, a time-series analysis is performed. The InSAR time-series analysis module follows the traditional method, employing the Small Baseline Subset method to derive deformation time series through the singular value decomposition (SVD) algorithm [6].
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 3 Reverse Shortest Path Tree Based MJN Selection Method
### Construction of RSPT
#### 3.1.1 The K-Hop Sub-Graph Construction  
DR utilizes RSPT to the detect network load, but it may become overwhelmed when dealing with larger networks. To mitigate this, DR will only construct an RSPT that covers its own K-hop neighbourhood subgraph. During the network initialization phase, DR will broadcast requests with a maximum forwarding distance of K. This ensures that all K-hop neighbours of DR receive the requests. According to the \"Six Degrees of Separation\" principle, it is possible to connect with anyone within six hops in a social network. Therefore, we limit the value of K to be less than six.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 4 Decoding Performance Analysis and Degree Profile Design
### Degree Profile Optimization
The maximal achievable rate for both users should be the intersection point of the line \(R_{1}=R_{2}\) and the capacity region boundary, which is given by  
\[C_{1}\bigl{(}\mathbf{H}_{q},P\bigr{)}=C_{2}\bigl{(}\mathbf{H}_{q},P\bigr{)}= C\bigl{(}\mathbf{H}_{q},P\bigr{)}\triangleq\max\biggl{(}\min\biggl{(}B_{1},B_{2}, \frac{1}{2}B_{12}\biggr{)}\biggr{)}, \tag{28}\]  
where \(B_{1}\), \(B_{2}B_{2}\) and \(B_{12}\) are the right-hand sides of (25), (26) and (27), respectively. Note that the average network throughput can be expressed as \(T=\frac{K}{\mathrm{E}\left[L\left(\mathbf{H}_{q}\right)\right]}\). Thus, it is inverse to the average Raptor code length of each user. Our optimization object is to find the optimal degree profile that minimizes the average code length while guaranteeing successful decoding at the BBU pool under all possible channel states. However, this problem is difficult to solve and the linear programming method in [39] cannot be applied here. We transform the original optimization problem. Instead, we minimize the required SNR for successfully decoding with a fixed average code length. Note that a similar technique has been used in the AWGN case for a multiple access relay channel [44]. In our problem, the fixed average code length is derived as a lower bound of the code length for each user to successfully deliver a message of length \(K\), as given below:  
\[L=\frac{K}{R_{p}}\sum\
olimits  
where \(F\in(0,1]\) is a scaling factor (we choose \(F=0.5\) in this paper), and \(r_{1}\), \(r_{2}\) and \(r_{3}\) are random integers between \(1\) and \(NP\). 3. Crossover: Cross \(\omega_{a}(G)\) and \(\mathrm{v}_{a}(G+1)\) to get (where \(a=1,2,\ldots,NP\), \(b=1,\ldots,d_{c}\)): \[u_{a,b}(G+1)=\left\{ \begin{array}{l}v_{a,b}(G+1),\ if\ rand(0,1)\leq CR\ or\ b=b_{rand}\\ \omega_{a,b}(G),\ otherwise\end{array} \right.,\] (32) where \(CR\) is the crossing probability (we choose \(CR=0.5\) in this paper) and \(b_{rand}\in[1,\ldots,d_{c}]\) is a random integer. 4. Selection: We select individuals for the next generation by (where \(\mathrm{a}=1,2,\ldots,\mathrm{NP}\)): \[\omega_{a}(G+1)=\left\{ \begin{array}{l}u_{a}(G+1),\ if\ (F(u_{a}(G+1))\leq F(\omega_{a}(G))\\ \omega_{a}(G),\ otherwise\end{array} \right.,\] (33) where the value of function \(F(\cdot)\) stands for the threshold SNR \(P_{th}/\sigma_{0}^{2}\) for successful decoding. If the minimal \(P_{th}/\sigma_{0}^{2}\) does not change within a certain number of iterations, the algorithm ends. The degree profile corresponding to the smallest \(P_{th}/\sigma_{0}^{2}\) is selected as the optimal edge degree profile \(\left\{\omega_{i,\mathrm{d}}\right\}\). Finally, according to (15), the optimal output node degree distributions \(\Omega_{i,\mathrm{opt}}(x)\) are derived.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### BOSS Algorithm Tests
#### 3.3.3 Real Image Test on BOSS Algorithm Output  
In this section, a real night sky image is considered to test the BOSS algorithm against it. The aim of the test was the demonstration of the BOSS capability of providing a suitable segmentation and stars localization with a real image and real noise sources. The real image is shown in Figure 10 together with the segmented image. The camera used is a Nikon D3100 whose image has been cropped and resized to the U-Net input size of 512 pixels. The image contains, besides real sensor noises due to the electronics, a strong stray light source coming from the bottom and due to nearby city light pollution (Acquisition site: Tusculum/Frascati, Rome, Italy). Parameters of the camera are listed in Table 2. Clustering algorithms extracted the centroids of the eight brightest stars in the FOV and correctly localized them. They are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Value** \\ \hline Size & 512 px \\ Aspect Ratio & 1:1 \\ focal length & 30 mm \\ FOV & 28.79\({}^{\circ}\) \\ pixel size & 30.07 \(\upmu\)m \\ Cut Off Magnitude & 4.0 \\ f-number & f/2.0 \\ \hline \end{tabular}
\end{table}
Table 2: Nikon D 3100 camera features. The image has been resized to 512 px and 1:1 aspect ratio.  
Figure 10: Ursa Major constellation segmentation provided by the BOSS algorithm. **Left**: real Ursa Major image; **right**: BOSS output of the Ursa Major real image.
## 4 Results and Discussion
### Case 2: Minimization of Power Loss
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **PSO** & **MVO** & **GOA** & **HHO** & **HO** & **ESDO** \\ \hline P\({}_{\text{G2}}\) (MW) & 41.51 & 68.955 & 80 & 53.01 & 67.351 & 43.308 \\ P\({}_{\text{G5}}\) (MW) & 34.8 & 19.039 & 44.67 & 50 & 22.946 & 42.242 \\ P\({}_{\text{G8}}\) (MW) & 16.87 & 21.288 & 14.539 & 25.694 & 33.611 & 31.505 \\ P\({}_{\text{G11}}\) (MW) & 30 & 17.556 & 24.313 & 27.655 & 29.986 & 28.36 \\ P\({}_{\text{G13}}\) (MW) & 22.26 & 38.306 & 26.47 & 17.609 & 40 & 37.615 \\ V\({}_{\text{G1}}\) (p.u.) & 1.0658 & 1.029 & 1.0175 & 1.0887 & 1.0306 & 1.0586 \\ V\({}_{\text{G2}}\) (p.u.) & 1.0409 & 1.0121 & 1.0116 & 1.0747 & 1.0174 & 1.0471 \\ V\({}_{\text{G5}}\) (p.u.) & 1.0253 & 0.9607 & 0.9913 & 1.0047 & 0.9854 & 1.0378 \\ V\({}_{\text{G8}}\) (p.u.) & 1.0175 & 0.9924 & 0.9844 & 1.013 & 1.0066 & 1.0375 \\ V\({}_{\text{G11}}\) (p.u.) & 0.9756 & 0.9795 & 0.9584 & 1.0681 & 1.1 & 1.0823 \\ V\({}_{\text{G13}}\) (p.u.) & 1.0696 & 1.0705 & 1.0675 & 1.0647 & 1.09 & 1.0594 \\ T\({}_{\text{11}}\) (p.u.) & 1.0103 & 0.9587 & 1.0051 & 0.9821 & 1.0739 & 1.0137 \\ T\({}_{\text{12}}\) (p.u.) & 1.0133 & 1.0967 & 0.9994 & 0.9542 & 1.0819 & 0.9866 \\ T\({}_{\text{15}}\) (p.u.) & 1.0387 & 1.0026 & 1.0334 & 1.02 & 0.9831 & 1.0394 \\ T\({}_{\text{36}}\) (p.u.) & 0.9992 & 0.9637 & 0.9823 & 0.9462 & 1.0258 & 1.0225 \\ Q\({}_{\text{c10}}\) (MVAR) & 1.87 & 4.0627 & 2.0029 & 4.1989 & 4.8259 & 6.5128 \\ Q\({}_{\text{c12}}\) (MVAR) & 1.11 & 3.222 & 1.143 & 2.2796 & 3.773 & 5.7736 \\ Q\({}_{\text{c15}}\) (MVAR) & 3.4 & 0.5512 & 4.9276 & 2.229 & 1.3436 & 4.0751 \\ Q\({}_{\text{c17}}\) (MVAR) & 3.06 & 2.7073 & 4.757 & 2.4167 & 2.7258 & 4.3258 \\ Q\({}_{\text{c20}}\) (MVAR) & 3.69 & 3.2085 & 2.8317 & 1.151 & 4.9092 & 4.4399 \\ Q\({}_{\text{c21}}\) (MVAR) & 2.73 & 0.8877 & 3.2476 & 0.281 & 4.956 & 5.357 \\ Q\({}_{\text{c23}}\) (MVAR) & 1.51 & 3.8854 & 3.0158 & 3.5741 & 5 & 5.0097 \\ Q\({}_{\text{c24}}\) (MVAR) & 2.93 & 2.7022 & 3.5583 & 5 & 3.9178 & 2.995 \\ Q\({}_{\text{c29}}\) (MVAR) & 2.55 & 0.115 & 4.9797 & 0.5129 & 3.2519 & 7.6933 \\ _P\({}_{\text{loss}}\) (MW)_ & **6.150** & **7.980** & **5.833** & **5.753** & **5.899** & **4.617** \\ Cost (USD/h) & 830.8549 & 842.7470 & 890.4579 & 879.9424 & 869.1381 & 877.7591 \\ V.D. (p.u) & 0.4189 & 0.5270 & 0.5001 & 0.6740 & 0.4235 & 0.7154 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Case 2 results.
# Modeling and Design Enhancement of Electothermal Actuators for Microgripping Applications
## 2 Materials and Methods
### Microgripper Design and Modeling  
In this section, we show how the design enhancement of the thermal actuators based on the added perforations can improve the operation of the microgrippers. The microgripper designed in this study consists of two hot/cold arm actuators where the hot arms are expected to extend and act as grippers. By applying a voltage, the Joule heating induces the thermal expansion of the arm with more generated heat compared to the other arm, causing the deflection and resulting in the opening of the microgripper. Disconnecting the voltage will lead the gripping arms to return to their original position.  
We consider a microgripper made of two thermal actuators with similar properties to those of actuator 2 reported in Table 1. We apply the same boundary conditions as those defined in earlier sections. Three anchors are added: the bottom and top anchors are supplied with the terminal voltage, and the middle anchor is connected to the ground. The layout of the proposed microgripper along with the imposed boundary conditions are presented in Figure 12. We note that the length of the hot arm of the microstructure is increased to 400 \(\upmu\)m to facilitate the operation of the microgripper, as will be discussed next. We show in Figure 13 the detailed geometry of the microgripper, including its main elements. It consists of three main parts: three anchors, two gripping arms, and two electrothermal actuators. The two symmetrical gripping arms have a length of 150 \(\upmu\)m, width of 3 \(\upmu\)m, thickness of 25 \(\upmu\)m, and their initial opening distance is 10 \(\upmu\)m (before applying any voltage). The microgripper is designed to function via an integrated thermal element (hot/cold arm) which is controlled by applying a voltage. The application of a voltage to the thermal layers will produce the further opening of the microgripper to enable the gripping of micro objects with diameters larger than 10 \(\upmu\)m. Releasing the micro objects after manipulation requires the application of higher voltages. When the voltage is switched off, the gripping arms of the structure will return to their original position with a gap distance of 10 \(\upmu\)m.  
Mesh independence analysis is also carried out for the proposed microgripper design in order to obtain accurate results at reasonable simulation times. The results show that the same mesh settings applied to actuator 2 are also suitable for the microgripper analysis. We show, in Figure 14, the variations of the gripping arm tip displacement, temperature (with respect to the initial opening) and the corresponding simulation time with the number of mesh elements. The mesh setting is selected based on the convergence behavior of the microsystem while the simulation time is minimized in order to speed up the numerical study.  
Figure 11: Time response of single-row square-perforated and non-perforated electrothermal actuator: (**a**) actuator 1 and (**b**) actuator 2.
# Method for Measuring Cooling Efficiency of Water Droplets Imping onto Hot Metal Discs
## 5 Results for Stainless Steel and Aluminum with a Surface Roughness of Ra 0.4 and Ra 3
### Error Analysis  
It was not possible to get exactly the same water application rate and exactly the same droplet diameters for each measurement series with the current setup. However, the standard deviation was within 2% for the water application rate and within \(\pm\)1% for the droplet diameter. The 25.0-cm elevation for droplet acceleration was measured to be within 1 mm, i.e., representing less than 1% error in the droplet speed. Given the rather large standard deviations observed for the stainless steel discs, the droplet diameter, application rate, and droplet speed can be considered sufficiently constant for comparing the results from the different measurement series.  
The discs were aligned horizontally to within 0.5\({}^{\circ}\) before each measurement series. However, there may be some slight misalignment during the heating and cooling cycles. This may to some extent influence the results. However, misalignment larger than 1\({}^{\circ}\) after the completion of each measurement series was not discovered. It is therefore reasonable to assume that the differences recorded in the water droplet cooling efficiencies were due to the different metals used, and for some temperature regions, the surface roughness.  
The reason for the larger standard deviation in the recorded efficiency for stainless steel compared with aluminum may be due to the lower thermal conductivity/diffusivity of the stainless steel. When two consecutive droplets hit exactly the same spot, which ideally was the goal, the stainless steel surface did not recover thermally as much between each droplet impact as was the case for the aluminum discs. If a droplet then hit just off the impact point of the previous droplets, it would be  
Figure 17: Average cooling efficiency for water droplets impinging onto the stainless steel and aluminum disc (marked on the figure) with a surface roughness of Ra 3.  
partly exposed to a higher surface temperature. The one order of magnitude higher heat transport properties of aluminum better evened out the cooling impacts by the previous droplets. When the temperature gradients for aluminum were less steep, it did not make much difference whether the subsequent droplets hit the same location, or were slightly off from that location. Therefore, it was expected that the standard deviations experienced for the droplets impacting onto the aluminum discs would be lower than for the stainless steel discs.
## 5 Evaluation Protocol and Results
### Analysis of Features  
The convolutional neural network is a black box where the features are automatically extracted from the raw data and used later for classification. Our model has four extracted features, representing the activations of the first fully connected layer in the best-selected model. To understand the relations between the extracted features by the model and detecting schizophrenia in EEG signals, we present features analysis in the following subsections using box plots, relations between features and EEG signals, relations between features and trials, and relations between classes and sub-bands.