esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Low-Speed Impact Test Process  
The test process of W1 under an impact height of one meter is shown in Figure 6. Before the start of the test, a tape measure was used to focus the center point of collision between the suspended sandbag and the composite wall, as shown in Figure 8a. In addition, the falling process of the sandbag showed that the decreasing rate of the sandbags in unit time increased, as shown in Figure 8b,c. Moreover, a small deformation occurred on the impact area and the compression deformation of the sandbag under the cushioning extrusion of the impact surface could be observed by the naked eye, as shown in Figure 8d.  
In order to reduce the influence of the compressional deformation and make the impact force generated by the falling object more evenly and effectively act on the collision surface, the sandbag was tied up before the impact test of W2, W3 and W4, and then the progress of the W1 test was repeated, as shown in Figure 9. In addition, the failure from of W2, W3, W4 at different impact heights was similar. Since the concrete slurry was poured into these wall specimens, the impact test results showed that the impact resistance of the W2, W3 and W4 wall specimens was significantly improved. In addition, after the impact test, there was no obvious deformation or visible cracks or depressions on the collision surfaces of these test pieces, and these specimens showed better overall stability.  
Figure 8: Test process diagram of W1. (**a**) Cross-center correction; (**b**) Falling process; (**c**) Moments before fall contact; (**d**) Collision contact.  
Furthermore, the deformation trend of the specimens under low-speed impact was similar. As the result, the W1 specimen was taken as a typical failure morphology for analysis. The failure form of W1 without grouting concrete inside was more serious than that of W2, W3, W4. Under the impact of the sandbag, the panel sank and cracked, and the panel was obviously sinking at the most unfavorable collision point within the contact range. In addition, the cracks in the impact area of the W1 specimen were densely and widely distributed, spreading out from the center to the surrounding area, as shown in Figure 10.  
Then, the damage patterns of the W2, W3 and W4 specimens were found to be basically similar, and the damage degree of the wall under the two sets of different impact height tests was not significant. After the collision, the panel had no obvious depression or deformation, and no cracks appeared on the collision surface. Moreover, after these test pieces were subjected to impact, the strain gauges on each measuring point could work normally, as shown in Figure 11. Figure 11 shows that the concrete in the composite wall can well absorb most of the energy generated by the impact, and these specimens only produce small deformations under the action of low-speed impact.
## 5 Positioning and Orbit Determination of Space Targets
### A Model for Determining Orbital Parameters of Space Debris Based on Space-Based Observations
On each small orbital arc, the motion trajectory can be considered as a uniform linear motion. Since the positioning information has a certain error, if the position between two points is directly divided by the elapsed time, the introduced speed error will be larger. About this point, we calculate the average speed of the arc through the speed fitting of each small arc, and the error will be minimized.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 4 Results  
This section describes the results of the single RF link MIMO system. The main performance of the system that needs to be considered include the system error rate, channel capacity, the dispersion degree of the demodulated signal normalized constellation and the maximum communication rate that the system can reach.
# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 1 Introduction
This approach aims to explore the uniqueness and density of information contained in a single seismic shot, thereby enhancing the efficiency and efficacy of neural network training in the recognition and interpretation of complex seismic patterns. We also modified the format and arrangement of the masks. Masks with distinct formats were adopted to match the characteristics of the noise better, as Gaussian noise is random and real seismic noise is generally directionally coherent. The formats and arrangements of the masks were adjusted to optimize the seismic results, reducing Gaussian noise across various signal-to-noise ratio (SNR) levels and taking into account the real seismic noise with selected metrics to better capture its particularities. To accurately compare and verify the efficacy of the proposed model, we implemented the N2N method [37] as well as a traditional supervised method. However, the application of the N2N method is limited to Gaussian noise because it requires two identical images with different noise realizations, which renders it unsuitable for the analysis of actual seismic noise. This paper is organized as follows. Section 2 provides a comprehensive overview of the theoretical framework underlying this research. First, an in-depth examination of the N2N method is presented, followed by an analysis of N2S, with a particular focus on its masking process. This section also delves into the theory behind deep learning architectures and discusses the metrics employed in this study. Section 3 outlines the dataset used for evaluation. The results of the proposed model are detailed in Section 4, where they are compared against those obtained via the N2N and supervised methods. Finally, Section 5 offers some concluding remarks.
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
## 4 Impact of Phytobiotics on Microbiota of Food and the Intestine
### Impact on Food-Associated Microbiota  
Maintaining a balance between beneficial microorganisms (e.g., _Lactobacillus_ and _Bifidobacterium_) and pathogens (e.g., _Salmonella_, _Listeria_, _E. coli_) is critical for gut and overall health. A stable gut microbiota protects the host from pathogens by competing for nutrients and attachment sites on the intestinal epithelium, producing antimicrobial compounds, and modulating the immune system. Diet is one of the most important modulators of the composition and function of the microbiota [169].  
Crucially, the relationship between phytobiotics and the gut microbiota is bidirectional. On one hand, the presence of specific bacterial strains influences the degradation, absorption, and bioactivity of phytobiotics. On the other hand, phytobiotics themselves modulate the composition of the gut microbiota. This reciprocal interaction can yield significant health benefits, as the resulting metabolites often display strong bioactivity and promote the restoration or maintenance of microbial balance in the gut. Flavonoid-associated metabolic processes have been shown to exhibit both prebiotic (supporting beneficial bacteria) and antimicrobial (suppressing undesirable microbes) characteristics. For example, in vitro cultures supplemented with flavonoids demonstrated enhanced growth of _Bifidobacterium_, _Lactobacillus_, and _Enterococcus_ species, alongside inhibition of _Clostidium_ and _Bacteroides_[170]. Moreover, certain specific bacterial species, including _Eubacterium ramulus_ and _Clostidium orbischens_, participate in flavonoid metabolism, breaking them down into smaller metabolites [171; 172]. Additionally, the aglycone form of quercetin (i.e., lacking sugar residues) has been shown to dose-dependently inhibit the growth of various gut bacteria--including _Bacteroides galacturonicis_, _Lactobacillus_, _Escherichia coli_, _Enterococcus acacae_, and _Ruminococcus gauvreauii_[173]. Other flavonoids also exhibit antimicrobial activity against foodborne pathogens and are therefore employed as natural preservatives in the food industry [174]. For instance, proanthocyanidins from cranberries inhibit biofilm formation by _Pseudomonas aeruginosa_[175].
# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 2 Related Work
### Generation-Based Method  
The main idea of the generation-based method [6; 22; 24; 25; 27; 28] is to use generation adversarial network (GAN) for modal conversion, i.e., generating related modality or unified modal information. For example, [PERSON] et al. [24] generated cross-modal paired-images for effective feature alignment. [PERSON] et al. [27] translated the visible and infrared images to their infrared and visible counterpart, respectively, and then combined the original images and generated images to form multi-spectral images for feature learning. [PERSON] et al. [28] directly obtained fake infrared images from visible images by GAN, which alleviated the modality discrepancy. The GAN-based method achieves style transfer for reducing modal variations. However, methods with GAN usually converge slowly and make re-identification module complex.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 3 Method
### Tri-Axial Strip Pooling  
Currently, the commonly used pooling methods in neural networks include max pooling, average pooling, and spatial pyramid pooling. However, max pooling focuses only on prominent information, which may lead to the loss of detailed information. Average pooling emphasizes global information but may lose important image features. Spatial pyramid pooling can handle inputs of different sizes, but it increases the computational complexity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Advantages** & **Limitations** \\ \hline CNN & Simple, & Limited extraction \\ Architectures & effective & of global information \\ \hline Hybrid & Combines & \\ CNN-Transformer & CNN’s local features & High computational \\ Architectures & with & and memory requirements. \\  & Transformer’s global modeling. & \\ \hline Optimized & Efficient 3D attention & Targeting specific data, \\
3D & for better segmentation & targeting 3D data \\ Attention Mechanisms & tends to cause memory burden \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of deep learning models for meningioma segmentation.  
Figure 1: The overall segmentation architecture. ECA is embedded into the downsampling and skip connections to fully enhance tumor information.  
In order to fundamentally reduce memory consumption during the segmentation of 3D medical images, TSP is proposed, as shown in Figure 2. To preserve the spatial information across all three dimensions, the data from the sagittal, coronal, and transverse planes are compressed separately. TSP applies weights via independently learnable vectors to emphasize the important features in each plane, enabling the ability to ignore unimportant regions and highlight tumor regions. For example, the pooling operation in the transverse plane is represented by the following formula:  
\[Y_{DW}=\sum_{i=0}^{H}\left(\frac{1}{H}\cdot W_{H}\cdot A_{i}\right) \tag{1}\]  
where \(Y_{DW}\) represents the pooling result along the transverse plane, \(H\) represents the height of this plane, \(W_{H}\) is a learnable weight matrix on the \(H\) axis, \(A\) is the input feature map. Using the previously mentioned formula, pooling is performed along these three planes, resulting in three 2D feature maps that capture tri-axial information. These feature maps effectively retain global information from different viewpoints. After processing through EGA and CFA, these three feature maps are merged to complement each other's spatial information.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Test case (4): \(R=0.7\)  
In this case, the cosine wave half period is chosen to be greater than the array length, \(H>L_{h}\) and equals \(H=7.05\;\lambda\), and the array length equal \(L_{h}=4.935\;\lambda\), hence \(R=0.7\). Accordingly, after applying the EPPs of the synthesized array elements for \(A_{c}\) changing from positive and the cosine wave amplitude is changing from positive to negative and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=1.5\lambda\), the array main beam tilted from the broadside direction of \(\theta^{\circ}=90^{\circ}\) to \(\theta^{\circ}=60.81^{\circ}\) with minimal changes in the HPBW that is increased by \(0.89^{\circ}\) at \(\theta^{\circ}=60.81\) compared to the broadside direction, while SLL is increased from \(-29.79\) dB to \(-12.68\) dB as the main beam is titled away from the broadside direction to \(\theta^{\circ}=60.81^{\circ}\). The achieved maximum scanning range is \(-29.19^{\circ}\leq\theta_{r}\leq 29.19^{\circ}\) around the broadside direction. The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range for the eight-element ULAA at a compression ratio \(R=0.7\) are listed in Table 12. The polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=0.7\) and \(A_{c}=1.5\;\lambda\) is shown in Figure 33.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cosine Wave Amplitude \(A_{c}\)** & **Main Beam Direction \(\theta_{\sigma}\)** & **SLL (dB)** & **HPBW in \({}^{\circ}\)** & **Maximum Scanning Range** \\ \hline
0 & 90\({}^{\circ}\) & \(-29.79\) dB & \(12.8^{\circ}\) & \\ \hline
0.25 \(\lambda\) & 85.05\({}^{\circ}\) & \(-29.28\) dB & \(12.82^{\circ}\) & \\ \hline
0.5 \(\lambda\) & 79.89\({}^{\circ}\) & \(-28.21\) dB & \(12.95^{\circ}\) & \\ \hline
0.75 \(\lambda\) & 74.74\({}^{\circ}\) & \(-26.2\) dB & \(13.17^{\circ}\) & \(-29.19^{\circ}\)\(\leq\theta_{r}\leq 29.19^{\circ}\) \\ \hline \(\lambda\) & 70.1\({}^{\circ}\) & \(-23.44\) dB & \(13.42^{\circ}\) & \\ \hline
1.25 \(\lambda\) & 65.46\({}^{\circ}\) & \(-18.94\) dB & \(13.64^{\circ}\) & \\ \hline
1.5 \(\lambda\) & 60.81\({}^{\circ}\) & \(-12.68\) dB & \(13.69^{\circ}\) & \\ \hline
1.75 \(\lambda\) & & & & \\ \hline
2 \(\lambda\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range \(\theta_{r}\) for 8-element ULAA at compression ratio \(R=0.7\).  
Figure 32: Polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=0.7778\) and \(A_{c}=1.25\;\lambda\).
## 3 Data Preprocessing  
### Data Description  
We use the daily power generation data and precipitation data of small hydropower stations in Hechi city (HC) and Guilin city (GL) in southern China for approximately 3 years to verify the effectiveness of the proposed CM-MDLN method based on a CNN and an MLP. HC and GL have a large rain collection area, many large and small rivers, and the average annual rainfall is between 1200 and 1600 millimeters. It is a typical region with abundant hydropower resources.
## 3 Signal-to-Noise Ratio (SNR) and Noise-Equivalent Sigma Zero (NESZ)
### SAR System Design  
In the context of the NESZ equation, the NESZ value is indicative of the system's noise floor. Typically, NESZ is expressed in decibels (dB), with a lower value signifying a better system performance. An NESZ value that is lower, such as \(-10\) dB, \(-15\) dB, or \(-20\) dB, translates to heightened system sensitivity, enabling the detection of weaker radar echoes or signals. A lower NESZ value indicates both enhanced sensitivity and a reduced noise floor. However, improving the system's sensitivity, such as by increasing the transmitting power, comes with trade-offs. Enhancing the transmitting power output necessitates a more robust electric power subsystem, potentially leading to additional solar panels and, consequently, increased satellite mass. Moreover, the altitude of the satellite is another important factor. Lowering the satellite's orbit can decrease access time, impacting the duration available for observation. The antenna area is also a key consideration; a larger antenna area yieldshigher antenna gain but adds to the satellite's mass. To minimize system losses, it is essential to refine the antenna feeding system. Yet, this improvement often requires more transceiver (T/R) modules, which in turn consume more power. Figure 1 shows the system design flow where the blue arrows represent inputs and constants, and the black arrows represent conditions and relationships.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
###### Abstract  
The traditional particle swarm optimization (PSO) algorithm often exhibits defects such as of slow convergence and easily falling into a local optimum. To overcome these problems, this paper proposes an enhanced variant featuring adaptive selection. Initially, a composite chaotic mapping model integrating Logistic and Sine mappings is employed to initialize the population for diversity and exploration capability. Subsequently, the global and local search capabilities of the algorithm are balanced through the introduction of adaptive inertia weights. The population is then divided into three subpopulations--elite, ordinary, and inferior particles--based on their fitness values, with each group employing a distinct position update strategy. Finally, a particle mutation strategy is incorporated to avoid convergence to local optima. Experimental results demonstrate that our algorithm outperforms existing algorithms on the standard benchmark functions. In practical engineering applications, our algorithm also has demonstrated better performance than other meta heuristic algorithms.  
particle swarm optimization algorithms; composite chaotic mapping; adaptive inertia weights; cross-learning; differential evolution (DE) 2020 IEEEexample:BSTcontrol
## 3 Results
### Libya-4 PICS--Hyperion Surface Reflectance Stability Using Three Atmopheric Correction Models  
Hyperion precession from 2004 through 2016 was studied with three atmospheric correction algorithms to characterize temporal stability of surface reflectance products and to understand uncertainties introduced by terrain shadow in the Libya-PICS. Hyperion data were stable in most bands over this time period, independent of the atmospheric correction model used. However, the imagery degraded at different rates throughout the spectrum for the visible (VIS), NIR, infrared (IR), and SWIR between 2004 to 2016, as indicated through the several atmospheric correction techniques. The combined model variation expressed as a quadrature was within 10% for the VNIR and in most SWIR bands the variability was within 20%, excluding bands near atmospheric absorption features (Figure 10). The ATREM corrected images expressed the lowest change over time for the VNIR and IR, as described by the trend values (close to zero) and lowest CV. Overall, the CV was \(<\)5% across the spectrum, except in bands adjacent to atmospheric absorption regions. No significant (\(p<\) 0.01) or rapid degradation was apparent for any spectral interval during the precession period for the three correction approaches. The quadrature values were similar for the entire dataset (Quadratures 2004-2016) as well as for the last five years of the mission (Quadratures 2011-2016). We found that locations having terrain slopes greater than 15\({}^{\circ}\) introduced a peak value in anomaly trends as compared to dune flats  
Figure 9: Change in reflectance anomaly (\(\Delta\)p) at select wavelengths. A mean was established by averaging values from 2001–2008. The dashed line above the bars shows the time of acquisitions and the key for wavelengths is presented in the lower right legend.  
of 0\({}^{\circ}\) to 10\({}^{\circ}\). Trends progressively increased for slopes up to 15\({}^{\circ}\), then oscillated 2% to 5% in the VIS and >20% in the SWIR, due to Hyperion imaging both the illuminated and shaded portions of dunes. The BRDF information about large dunes in the Libya-4 PICS is important for cross-calibrating Earth observing sensors.
## 3 Silicon Germanium Waveguides and Nonlinear Applications  
Silicon germanium has emerged as an attractive platform for implementing both micro- and opto-electronic devices thanks to the potential it offers for bandgap and lattice parameter engineering [49]. Multiple SiGe device demonstrations have already been reported such as modulators [50], light emitters [51] and photodetectors [52; 53]. Furthermore, SiGe also offers the potential for implementing efficient nonlinear applications in the midwave and longwave infrared due to the stronger nonlinearities it possessed as compared to pure Si [31; 54].  
Until recently, the absorptive properties of Ge at the near-infrared region have restricted the applicability of the SiGe platform at this wavelength regime to mostly detection applications. However, it has been recently demonstrated that through an appropriate design one can engineer SiGe devices that exhibit excellent optical properties for nonlinear applications at telecommunication wavelengths [55]. The percentage of Ge concentration is found to impact a host of optical characteristics of the SiGe device, such as linear and nonlinear loss, nonlinearity, dispersion and modal properties. As such, the SiGe platform offers a unique versatility and a strong potential for realizing a wide range of efficient all-optical signal processing applications at the near-infrared, particularly those that require strict phase matching conditions.  
In this section, we review the main optical properties of the Si\({}_{1-x}\)Ge\({}_{x}\) alloy as well as the results of wavelength conversion of 16-QAM and 64-QAM signals.
## 3 Experiments and Discussions  
The performance of the implemented ANFIS-PID controller is evaluated through experimental tests. First, FPGA-in-the-loop (FIL) simulation experiments are used to pre-validate the effectiveness of the algorithm. Then, based on the FIL simulation, the actual effect is tested using ex vivo experiments and compared with the PID controller in both simulation and ex vivo experiments. However, due to the differences between the model of the RFA temperature system in the simulation of this paper, as well as the power amplification module of the RF signals in the ex vivo experiments and the existing literature, it is not possible to directly use the parameters of the PID controllers of the existing literature to compare with the effect of the ANFIS-PID controller.  
Due to the low power of the self-designed power amplifier module, the target temperature was set to 40 \({}^{\circ}\)C and the temperature enabled by the temperature control algorithm was 30 \({}^{\circ}\)C for the ex vivo experimental test.