esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Time Reversal Experimental Results
However, as the position of Lens 3 moves farther away from the center point, the CMOS imaging becomes less clear, and the spot intensity at the focal point decreases at a certain extent. The figure shows that although the influence of the image definition on phase conjugate reduction is limited within a certain range, if we want to achieve high-quality phase conjugate reduction, the requirement of clear imaging of the DOPC system is indispensable. In order to further analyze the influence of phase noise to the phase conjugate reduction of light waves, random phase noise was artificially added to the calculated conjugate phase diagram. We controlled the intensity range of the random phase noise to test the resistance to phase noise of the DOPC system. Figure 7 shows the relationship between the spot intensity and phase noise. It can be seen from Figure 7 that the DOPC system still has a good conjugate phase reduction performance when the phase noise is in the range of (0, \(\pi\)/2). When the phase noise exceeds \(\pi\)/2, the phase conjugate reduction performance of the DOPC system decreases rapidly, so it is difficult to effectively realize the time reversal of the light wave. Figure 4: (**a**) Interference pattern when \(X\) = 300 \(\mu\)m; (**b**) the reflected focus and reconstructed focus size variation when OL2 was shifted by the \(X\)-axis. Figure 5: (**a**) Interference pattern when \(Y\) = 0.8 mm; (**b**) the reflected focus and reconstructed focus position variation when OL2 was shifted by the \(Y\)-axis.
## 3 Methodology and Implementation
### Methods of Compilation of the Neural Network Module
#### 3.2.2 Flask  
In order to avoid having to convert the model from the PyTorch environment to TensorFlow, another approach based on the use of the Flask environment, written in Python, was considered. It is a micro-framework that allows the creation of Application Programming Interfaces (APIs) for web or mobile applications. Flask was used to create a stand-alone server for querying the neural network model. Figure 9 shows a diagram of how the server works.  
The selected image from the PM or AM database is sent to the server using HTTP/HTTPS protocol and POST method in encoded (Base64) form. The image is decoded on the server, converted to byte and then tensor form adapted to the PyTorch framework (torch.float64). Conversion to a tensor also involves resizing to the standardized value expected by the neural network and normalizing the data, through standardization described by the following equation:  
\[z=\frac{x-\mu}{\sigma} \tag{6}\]  
\(x\)--a non-standardized variable,  
\(\mu\)--a mean value of the training dataset,  
\(\sigma\)--a standard deviation of the training dataset,  
\(z\)--a standardized variable with an expected value of 0 and a standard deviation of 1.  
Figure 9: The Flask server flow diagram.  
A one-element batch with dimensions--(1, number of channels, height, width) is artificially generated from the tensor. The tensor is passed to the input of the neural network model, which is directly imported and loaded when the Flask server is started. The result of the network operation in the form of an extracted face or vector representation (embedding) of the input image is re-encoded and stored in the database.  
The Flask server is also created using the Docker environment according to the following diagram--Figure 10. A Dockerfile is created importing the Python image and installing all the libraries and frameworks needed for the neural network, as well as running the API. Based on this, an image is built and then an instance of it is created--a container, which is an independent, stand-alone server that serves queries to the neural network models.  
In summary, this section describes a solution that bypasses the convertion of models from PyTorch to TensorFlow, based on Flask in which a standalone server is created to query neural network models. Images are sent to the server via HTTP/HTTPS using the POST method. Once received, the images are processed by the neural network model. The Flask server is deployed in a Docker environment, providing an independent and scalable server for efficient model querying.
## 4 Simulation Experiment
### Experimental Results
#### 4.2.2 Yak-42 Imaging Results  
The selected Yak-42 data includes 256 pulses, with each containing a 256 range cell. The other parameters are: the radar transmits a chirp signal, the carrier frequency is 5520 MHz, the bandwidth is 400 MHz, the pulse repetition frequency is 400 Hz, and the pulse width is 25.6 \(\upmu\)s. Range compression and motion compensation for the data have been completed. The range profile and the imaging results  
Figure 16: Imaging results of Boeing-727 data using the (**a**) MCFT, (**b**) STFT, (**c**) SPWVD, and (**d**) RWT algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Imaging Algorithm** & **RD** & **STFT** & **SPWVD** & **RWT** & **MCFT** \\ \hline Image entropy & 6.5821 & 6.1303 & 4.9231 & 5.5312 & 4.5851 \\ Contrast ratio & 2.4918 & 2.7934 & 6.5815 & 7.7614 & 9.1270 \\ Running time (s) & 0.0015 & 0.6861 & 7.3798 & 32.1524 & 1.5768 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of the Boeing-727 images.  
using different imaging algorithms are shown in Figure 17, and Table 6 provides the corresponding imaging evaluation indicators. Figure 17a is the range profile and Figure 17b is the RD imaging results, which shows that the RD algorithm could not eliminate the azimuth defocus caused by maneuvering because the RD algorithm only uses FFT calculation. The corresponding image entropy and contrast were also poor, but the running time was the shortest. Figure 17c depicts the result of the MCFT imaging proposed in this paper. The result shows that azimuth defocusing was effectively improved, the scattering point of the target was clear, and the corresponding image entropy and contrast were optimal. Figure 17d is the result of RWT imaging, and the result shows that the azimuth defocusing was partially improved, but the degree of improvement was general. The corresponding image entropy and contrast also prove this point, and the algorithm ran for a long time. Figure 17e,f are the RID imaging results based on STFT and SPWVD, respectively. From the results, it can be seen that the effect of STFT and SPWVD on the measured data was poor. In the MCFT algorithm, the estimated coarse rotation ratio was \(\widetilde{K}_{\Omega\omega}=0.58\), the cut-off condition was reached after 150 iterations, and the corresponding precise rotation ratio was \(\widetilde{K}_{\Omega\omega}=0.62\). From the running time in Table 6, the proposed algorithm was highly efficient for the measured data.  
Figure 17: Imaging results of Yak-42 data: (**a**) Range profile, (**b**) RD algorithm, (**c**) MCFT algorithm, (**d**) RWT algorithm, (**e**) STFT-RID algorithm, and (**f**) SPWVD-RID algorithm.
## 3 Results and Discussion
### Effect of Initial Concentration  
Figure 13 shows that when the dye concentration is increased, the adsorption efficiency of both dyes by both materials decreases [29; 30]. The lowest dye removal was measured at an initial dye concentration from 10 to 100 mg L\({}^{-1}\) and the dye removal efficiency is higher at low concentrations between 10 and 60 mg L\({}^{-1}\), R\(\%\geq 86\) for MGC and R\(\%\geq 56\) for X-PR on BS-HVL and R\(\%\geq 20\) for MGC and R\(\%\geq 18.5\) for X-PR on BC-HVL. Therefore, the amounts adsorbed by BS-HVL are 9 to 94 mg g\({}^{-1}\) and 7 to 74 mg g\({}^{-1}\) for X-PR and MGC, respectively. Moreover, BC-HVL adsorbs 9.8 to 54 mg g\({}^{-1}\) and 8.6 to 91 mg g\({}^{-1}\) with X-PR and MGC, respectively.
## 6 Results and Discussion
### Model Validation
#### 6.2.2 Onsite Applicability  
The item that experts considered would make the study's model superior in terms of the onsite applicability compared with the current scheduling methods received a score of 4.70. Many experts indicated that scheduling methods at the planning stage often do not align well with the construction phase. Consequently, using the proposed model could bridge this gap. They believed that this would facilitate scheduling tailored to actual onsite conditions.
## 2 Experimental Program
### Test Methods  
The mechanical equipment used for producing 3 DPC was a gantry-type 3D printer that included a 3D-printing material cylinder, an extrusion screw, a circular nozzle, and a movable printing platform. The maximum printing dimensions were 600 \(\times\) 600 \(\times\) 700 mm, with a repeatability of \(\pm\)0.05 mm, and it supported G-code file formats.  
Printing parameters such as printing speed and layer height significantly influence the printability and final performance of 3 DPC. Higher printing speeds can reduce interlayer bonding, affecting mechanical strength, while lower speeds may cause deformation due to prolonged material flow. Similarly, layer height impacts buildability and structural integrity, where excessive height can lead to instability and insufficient height may affect extrusion consistency. In this study, a 15 mm diameter nozzle was used, with an extrusion flow rate of 7-10 cm\({}^{3}\)/s and an extrusion ratio of 1900-2000. The printing path speed was set to 35-40 mm/s using Prusa Slicer ([[https://prusaslicer.net/](https://prusaslicer.net/)]([https://prusaslicer.net/](https://prusaslicer.net/)), accessed on 26 March 2025), with a single-layer height of 10 mm.  
Figure 2: Picture of the printing process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix No.**} & **Water** & **Cement** & **GGBFS** & \multirow{2}{*}{**FA**} & \multirow{2}{*}{**Fine Agg.**} & \multicolumn{2}{c}{**Additive (kg/m\({}^{3}\))**} \\  & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **Anhydrous** & **PCE** \\ \hline S5F0 & 400 & 500 & 500 & 0 & 1500 & 50 & 5 \\ SF25 & 400 & 500 & 250 & 250 & 1500 & 50 & 5 \\ SOF5 & 400 & 500 & 0 & 500 & 1500 & 50 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mix proportions of PC/GGBFS/FA mortars.  
In this study, we conducted tests on the printable and hardened properties of 3 DPC. Each experiment on the printable properties, including flowability, slump, extrudability, and buildability, was repeated twice, and the values were averaged. If there was a significant difference between the two results, the experiment was repeated to ensure the consistency of the experimental results. For the experiments on the hardened properties, including unit weight, compressive strength, flexural strength, water absorption, and drying shrinkage, three specimens of each mixture were tested to determine the average results.
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
###### Abstract  
Communication transmission in the near-Moon space is a critical enabler of scientific exploration in this region. However, the communication network in near-Moon space shows trends of diversification, heterogeneity, and collaboration, posing significant challenges to the management of an integrated communication network. This paper proposes a networking routing method for near-Moon-space cross-domain network transmission. Considering the constraints of heterogeneous networks including Moon-Earth, Moon-surface, and relay transmission, the method enhances transmission routing efficiency at the network layer of near-Moon-space systems, thereby improving the overall efficiency of heterogeneous network interactions. This research focuses on the networking routing of cross-domain networks. To simplify the research problem, a mixed link resource and scheduling model of heterogeneous networks is proposed. Based on this model, a time-varying and fixed topology network sub-network clustering method was designed to reduce the complexity of the routing algorithm. A routing scheduling algorithm is provided in combination with hierarchical routing search, and related experiments and comparisons were carried out. Finally, considering the practical issues of communication relay channels and rate limitations in relay satellites, time windows and communication rate constraints were used to enhance the reliability of the simulation validation. Simulation results show that this method effectively addresses the issue of low transmission interaction efficiency in heterogeneous networks within cislunar space. Compared with previous designs, it improves link load rate by 31%, reduces average service delay by 8%, and significantly enhances link stability and load rate.  
cross-domain; adaptive routing network; near-Moon space; sub-network cluster +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Summary of Results  
This study discussed the effectiveness and risk factors of an overtopping prevention structure through comprehensive results from analyses, model experiment observations, and real-time measurements. Regarding the infiltration suppression, the overtopping prevention structure completely obstructed water leakage through the dam crest core at the overtopping water level (S3), effectively preventing erosion on the downstream slope surface caused by leaked water. The vertical filter designed to guide the water seepage into the dam body achieved appropriate structural performance. With increasing water levels, the vertical and horizontal filters contributed to maintaining dam stability, preventing further escalation of the seepage line and PWP within the dam. In terms of displacement suppression, the pressure effect of the overtopping prevention structure effectively mitigated the horizontal and vertical displacements, thereby minimizing the risk of damage or overtopping owing to structural movement. A potential risk factor arising from installing the overtopping prevention structure was the occurrence of gaps at the boundary between the gabion and the core, which was attributed to the drying shrinkage of the dam ridge core. The findings suggest the need for certain improvements, such as addressing the drying shrinkage in the core. Nevertheless, the structural design, as demonstrated through the analysis results, observations from model experiments, and concurrent measurements, was considered effective in maintaining the stability of the overtopping prevention structure.  
In contrast, when anticipating an increase in water level due to heavy rainfall, it is crucial to consider the potential for water leakage at the boundary between the structure and the dam crest in terms of overtopping height and resistance. Previous research findings on the parquet model, specifically designed for preventing dam overtopping, indicate that the design is restricted to a range of 0.5-1 m owing to the risk of water leakage at the dam crest interface [10].  
This research model, however, can accommodate designs up to 2 m in height, with the expectation that water leakage can be effectively blocked by the dam crest core. Nevertheless, the increase in structure height introduces the possibility of deformation during seismic events, thereby necessitating additional consideration for earthquakes in the future.  
As instances of heavy rain and flooding become more prevalent owing to abnormal climate conditions, the findings of this study should be explored further. This study presents an alternative approach to reduce the failure likelihood resulting from overtopping in reservoir dams.
# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### Power Management and Electrical Control
In this study, it was found that the T2 FLC is able to guarantee robust stability and robust performance under a large range of uncertainties and loads. In [81], a recent study presents a new feed water controller under the automatic power regulating system for an advanced boiling water reactor. This controller was designed using a rule-based hierarchical FLC algorithm and was implemented using an FGPA. It was proved that transient response and steady state tracking capabilities showed satisfactory results upon testing of two different trajectories in power and flow maps. It was also proven that FPGA-based hierarchical FLC is a practical and useful method in power operations in advanced nuclear power plant applications. An IT2 FLC was also utilized to control an active magnetic levitation system [82]. In this levitation system, electromagnets are used to support any component attached within the magnetic field. Due to the non-linearity of levitation systems, it was proposed to use an IT2 FLC instead of a traditional PID controller. This idea was further implemented by reducing the IT2 FLC to a single input IT2 FLC. In this study, it was shown that a less computationally-intensive controller was developed with a better response and speed than both T2 and T1 FLCs. IT2 FLCs have also been designed for controlling photovoltaic (PV) systems [83]. During the operation of a PV system, the voltage produced by the solar cells cannot be modeled as a constant DC voltage. PV systems are more comparable to a fluctuating current source, which changes according to the temperature, irradiation level, load current and other factors. Due to the nature of PV modules, they are modeled with a current-voltage (I-V) curve, which produces the most power at the maximum power point (MPP) on the curve. To do this, the PV system must be constantly monitored and changed to exploit the MPP. The IT2 FLC changes the duty cycle based on the rate of change of output power and the rate of change on the terminal voltage. Since the IT2 FLC is used, the uncertainty of MPP tracking is accounted for, and the simulation results showed that the MPP tracking was able to rapidly respond to uncertain atmospheric conditions. Another IT2 FLC was designed for a single-phase grid interactive PV system [84]. In this paper, an MPP tracking boost converter and grid interactive voltage source inverter were used. For the system, the DC source is converted to AC and imported to the electrical grid. Simulations were done in MATLAB, and it was shown that the IT2 FLC had a fast transient response in tracking the MPP.
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 1 Introduction
* We designed a new 3D object detection method, PIFNet, by using the fusion of LiDAR and camera sensors. To compensate for the sparsity and the irregularity of point clouds for far objects, camera images can provide rich fine-grained information. The point cloud features and image features are fused to take advantage of the complementary characteristics of these two sensors. * We developed an Encoder-Decoder Fusion (EDF) network to effectively extract image features. To obtain the multi-scale feature maps, we used multiple-level feature maps. The low-level feature maps have high resolution but poor semantic information whereas the high-level feature maps have low resolution but dense semantic information. By using this EDF module, we can obtain accurate localization information from low-level feature maps and useful semantic classification information from high-level feature maps. * A fusion module is proposed to integrate the color and texture features from images and the depth information from point clouds. Each intermediate feature map is fed into the fusion module to integrate with its corresponding point-wise features. This module can cope with the irregularity and sparsity of the point cloud features by capitalizing the fine-grained information extracted from images by the EDF module. * The experiments using our method and several other state-of-the-art methods show that our proposed neural network architecture can produce the best results on average. In particular, on the KITTI dataset, PIFNet achieved 85.16% mAP (the best results) in 3D object detection and 90.96% mAP (second best results) in BEV detection. The rest of this paper is organized as follows. In Section 2, the related works of 3D object detection are briefly reviewed. Our proposed PIFNet is presented in Section 3. The experimental results are described in Section 4. Finally, the conclusions are given in Section 5.
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 4 Experiment
### Parameter Setting  
In the experiment, we set the following parameters. The values of \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), and \(\lambda_{4}\) in CSPFE-Net of view A are 0.3, 0.3, 0.2, and 0.2. FIMV-SVM parameters are determined by grid search method and five-fold cross validation during training. The determination of parameters is based on the highest accuracy. \(C\), \(C_{A}\), \(C_{B}\), and \(C_{P}\) select their values from the set \([10^{-3},10^{-2},10^{-1},1,10,10^{2},10^{3}]\). The value set of \(\gamma\) and \(\gamma_{P}\) is \([0.2,0.4,0.6,0.8,1]\). Gaussian radial basis function (RBF) kernel function \(K(x_{i},x_{j})=exp(-\frac{\|x_{i}-x_{j}\|^{2}}{2\sigma})\) is the kernel function of SVM method, \(\sigma\) for the Gaussian RBF kernel function is selected from \([10^{-3},10^{-2},10^{-1},1,10,10^{2},10^{3}]\). For the association between parameter tuning and training, see Algorithm 1 above.
# On the Role of Clustering and Visualization Techniques in Gene Microarray Data
## 1 Introduction
### A Biological Introduction: Microarray Gene Expression Technology  
Biologists can now acquire a a pattern of gene expression equivalent to the reaction of the organism to a specific experimental situation. The technique of microarray offers a way for simultaneous identification of the expression of several (or the whole set) of genes of an organism at any fixed time instant [8], generating models of gene activity that retain dynamic information about a cell function. This information is crucial if complicated cell relationships are to be investigated. They also have very significant applications in pharmaceutical and clinical studies, in addition to the huge science contents of microarrays in the basic study of expressions, regulations and interactions of genes. The _cDNA_ microarray [9; 10] oligonucleotide arrays are the two main kinds of microarray studies. Despite variations in the details of their experimental protocols, three prevalent fundamental processes are involved in both kinds of tests: [11]:  
**Chip manufacture:**: A microarray is a tiny chip (produced of chemically covered glass, nylon mesh, or silicon) on which tens of thousands of molecules (samples) of DNA are connected in set grids.
**Target preparation, labeling, and hybridization:**: Usually, two samples of mRNA (i.e., test and control samples) are backward transcribed into cDNA (targets), marked with either fluorescent or radioactive isotopics, and then hybridized with the samples on the chip surface.
**The scanning process:**: To read the signal intensity emitted from the marked and hybridized targets, chips are scanned.  
In general, both cDNA microarrays and oligonucleotide arrays experiments evaluate the amount of expressions for each DNA sequence by the signal intensity proportion between the test and control samples, so that the data sets generated from both techniques contain the same biological information content [12].