esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Bridge Design Parameter Extraction for Scan-to-BIM
## 2 System Development
### 3D Transformation
#### 2.2.1 Plane Prediction Using the m-Estimator Sample Consensus (MSAC)  
In this study, the planar geometric shapes of the bridge were used to set the reference vector before applying the 3D rotational transformation. Bridge components, such as the slab and girders, contain flat elements. Therefore, by locating a planar feature and setting the reference vector on this plane, a consistent reference can be obtained.  
The m-estimator sample consensus (MSAC) algorithm was applied to locate the planar element in the PCD through the steps shown in Figure 4[28]. The MSAC is an improved method of random sample consensus (RANSAC), in which sample data is randomly extracted, and a consensus is determined [29]. In this study, the consensus was based on a user-determined model, and the plane generated by sampling three random points of the PCD was taken as this model. The points within the selected threshold value were recognized as inliers and counted. These steps were repeated until the plane was determined by maximizing the number of points in the inlier; Figure 5 shows the results of this process.  
Figure 4: Procedure for plane prediction using the m-estimator sample consensus (MSAC).  
Figure 5: Example of the plane prediction for point cloud data (PCD).  
Figure 3: Measurement coordinate system (x-y-z) and bridge coordinate system (x’-y’-z’).  
A \(3\times 3\) matrix is required to apply the rotational transformation to the 3D elements in the PCD. The rotational matrix is as follows [31]:  
\[\mathrm{R}=\left[\begin{array}{ccc}\cos\theta+u_{x}^{2}(1-\cos\theta)&u_{x}u_{ y}(1-\cos\theta)-u_{z}\sin\theta&u_{x}u_{z}(1-\cos\theta)+u_{y}\sin\theta\\ u_{y}u_{x}(1-\cos\theta)+u_{z}\sin\theta&\cos\theta+u_{y}^{2}(1-\cos\theta)&u_ {y}u_{z}(1-\cos\theta)-u_{x}\sin\theta\\ u_{z}u_{x}(1-\cos\theta)-u_{y}\sin\theta&u_{z}u_{y}(1-\cos\theta)+u_{x}\sin \theta&\cos\theta+u_{z}^{2}(1-\cos\theta)\end{array}\right]\]  
The rotational matrix \(\mathrm{R}\) can be acquired by substituting the rotational axis \(\overrightarrow{V}\) and the angle of rotation \(\theta\) previously obtained. The rotational transformation is then applied by multiplying the rotational matrix \(\mathrm{R}\) by the PCD.  
When the \(z\)-axis of the PCD obtained from the LiDAR is the same as the model, it is possible to obtain PCD aligned to the model axis by applying the transformation once. However, for PCD wherein the x, y, and z axes are all not aligned with the LiDAR data, the aligned PCD can be obtained by applying the rotational transformation twice. In the 3D rotationally transformed bridge PCD, the width of the bridge is parallel to the \(x\)-axis, the length of the bridge is parallel to the \(y\)-axis, and the height of the bridge is parallel to the \(z\)-axis. Therefore, the design parameters can be extracted easily. Figure 8 shows the rotational transformation of the bridge PCD.
## 3 Proposed Model Using ANFIS
As shown in Figure 1, the ANFIS derives the results by means of rules, and the number of fuzzy rules is determined by the number of variables and fuzzy sets. In the algorithm constituted in this study, since there are three variables and two fuzzy sets for each variable, a total of 8 (=2\({}^{3}\)) rules are generated, and the effective compressive strength of the \(i\)-th rule (\(f^{\prime}_{ce,i}\)) can be calculated as follows:  
\[f^{\prime}_{ce,i}=a_{i}f^{\prime}_{ce}+b_{i}f^{\prime}_{cj}+c_{i}h/c+d_{i} \tag{3}\]  
where \(a_{i}\), \(b_{i}\), \(c_{i}\) and \(d_{i}\) are the consequent parameters determined according to the input variable and fuzzy rule, and the final result \(f^{\prime}_{ce}\) can be derived by defuzzification, as shown below:  
\[f^{\prime}_{ce}=\sum_{i}^{n}\overline{w_{i}}f^{\prime}_{ce,i} \tag{4}\]  
where the defuzzification is the process of converting a fuzzy value into a crisp value. In the fuzzy theory including ANFIS, an operation is carried out using a fuzzy value obtained from the fuzzification of a crisp value. Therefore, the result value of the rule is also a fuzzy value, which should be converted into a crisp value for quantitative representation. In this study, defuzzification was performed using a centroid method. Figure 3 and Table 2 compare the results of effective compressive strengths calculated using the ANFIS algorithm presented in this study, existing codes [3,4] and empirical equations [8,10] proposed by existing researchers. The equation for effective compressive strength specified in ACI 318-19 [3] provided very conservative results with the average (AVG) of the ratio of the analysis value to the test value (\(f^{\prime}_{ce,analysis}/f^{\prime}_{ce,test}\)) being 0.79 and the COV being 0.32. It also showed a larger scatter than did the other models. The equation in CSA A23.3-14 [4] was about as accurate as the ACI 318-19 code equation, with the average of the ratio of the analysis value to the test value and the COV calculated at 0.81 and 0.28, respectively. The equation proposed by [PERSON] [8], and [PERSON] and [PERSON] [10] provided predictive values better than those provided by the current design codes, but still showed a large deviation between the test and analysis values. On the other hand, the ANFIS model proposed in this study provided more accuracy than did the other models, with the average of the ratio of the analysis value to the test value and the COV calculated at 1.05 and 0.13, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{5}{c}{\(f^{\prime}_{ce,analysis}/f^{\prime}_{ce,test}\)} \\ \cline{2-6}  & **MEAN** & **STDEV** & **COV** & **MIN** & **MAX** \\ \hline ACI 318-19 [3] & 0.79 & 0.25 & 0.32 & 0.19 & 1.27 \\ CSA.A23-14 [4] & 0.81 & 0.23 & 0.28 & 0.19 & 1.27 \\ [PERSON] [8] & 0.91 & 0.22 & 0.24 & 0.29 & 1.56 \\ [PERSON] and [PERSON] [10] & 0.9 & 0.25 & 0.27 & 0.23 & 1.53 \\ Proposed ANFIS model & 1.05 & 0.13 & 0.13 & 0.87 & 1.45 \\ Simplified model using ANFIS & 0.99 & 0.18 & 0.18 & 0.63 & 1.40 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparisons of proposed ANFIS model and other existing models.
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 4 Policy-Dividing Algorithm in SUDC
### Algorithm Procedure
#### 4.2.1 Priority Queue for Policy to Be Divided  
The priority queues are utilized to control the order in which SRv6 policies undergo prefix matching. After analysis, it has been determined that the order in which SRv6 policies perform prefix matching significantly impacts the matching success rate. Therefore, the policies need to perform prefix matching sequentially in descending order of SID list length so as to ensure optimal matching success rate. The priority queue is characterized by elements being dequeued in descending order of priority. So, it serves as an essential tool for efficient prefix matching. This algorithm states that the longer the SID list length of the policy is, the higher the priority is.
# Mask Mixup Model: Enhanced Contrastive Learning for Few-Shot Learning
## 4 Methods  
### Method Overview  
We introduce a novel model called the Mask Mixup Model (as illustrated in Figure 2a), which comprises three key modules. The first module is M-Mix, which optimizes the infoNCE loss to suit few-shot learning, leveraging augmentation techniques to unearth challenging samples. The second component is a module named CCS, utilizing a confidence-based method to select desired query samples. The third module, SF, eliminates the masking process from the M-Mix method, primarily aimed at introducing noise for better embedding in the model.  
Data augmentation techniques play a crucial role in improving model generalization. In this paper, we propose the Mask Mixup and Saliency Fuse methods, which selectively retain and blend important features from images to generate more diverse and representative training samples. Additionally, the Confident Clip Selector enhances the diversity of training data by randomly cropping and selecting high-quality query samples. Our approach outperforms traditional methods such as random occlusion and mixup interpolation by effectively preserving important image information and clarifying class boundaries, thereby significantly boosting model performance in few-shot image classification tasks.  
Figure 2: (**a**) represents the overall scheme. (**b**) represents our M-Mix process. After the support image undergoes feature extraction, its saliency map is generated. Random blocking is applied, and the resulting positive and negative samples are fused separately. These samples are then used for contrastive learning against the query image. (**c**) showcases our CCS method. After random cropping of the query image, confidence scoring occurs post-feature extraction. The highest and lowest scores are filtered out. (**d**) displays our SF method, distinguishing itself from M-Mix by not applying blocking to the images. The resulting labels are mixed labels.  
One of the challenges in contrastive learning lies in the selection of informative positive and negative samples. Contrastive pairs were built using support features. Each query instance had its label. Thus, for each query instance \(x_{i^{\prime}}^{i}\), we considered support instances with the same label as its positives and instances with different labels as negatives. The same embedding network \(\Phi\) was used for both query and support instances. Our loss function was based on  
\[\mathcal{L}_{i}=-\log\frac{\sum_{y_{i}^{s}=y_{i}^{d}}e^{f_{i}^{s}f_{j}^{s}}}{ \sum_{y_{i}^{s}=y_{i}^{d}}e^{f_{i}^{d}f_{j}^{s}}+\sum_{y_{i}^{s}\
eq y_{i}^{d}}e ^{f_{i}^{d}f_{k}^{s}}}, \tag{1}\]  
where \(\mathcal{L}_{i}\) denotes the loss calculated for the \(i\)-th query sample. Here, \(f_{i}^{q}\) refers to the feature representation of the \(i\)-th query sample, while \(f_{j}^{s}\) and \(f_{k}^{s}\) represent the feature representations of support samples \(j\) and \(k\), respectively. Sample \(j\) shares the same label \(y_{i}^{q}\) as the query sample \(i\), while \(k\) has a different label \(y_{k}^{s}\
eq y_{i}^{q}\).
## 3 Results and Discussion
### Effects of Surfactant Concentration  
Figure 3 shows the effects of surfactant concentration on the mean particle size and zeta potential of St/AA submicrocapsules prepared at 0.33 and 2.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer for [AA]/[St] and AIBN concentrations, respectively. The mean particle size of the submicrocapsules decreased with the increasing surfactant concentration (Figure 3a). This result is in good agreement with those of other researchers, suggesting that more SDS molecules are available for stabilizing the oil-water interfacial area generated during homogenization at a higher level of SDS [29; 31; 32; 33]. The zeta potential of submicrocapsules, however, showed the optimum value at 1.0 \(\times\) 10\({}^{-3}\) mole of surfactant concentration (Figure 3b).  
Figure 1: Effects of the monomer feed ratio on (**a**) mean particle size and (**b**) zeta potential of the St/AA submicroparticles prepared at 1.0 \(\times\) 10\({}^{-3}\) mol and 2.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer for SDS and AIBN concentrations, respectively.  
Figure 2: SEM photographs of St/AA submicrocapsules prepared at 1.0 \(\times\) 10\({}^{-3}\) mol and 2.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer for SDS and AIBN concentrations, respectively, with a varying monomer feed ratio.  
[PERSON] and [PERSON] [34] reported that the collision efficiency of colloidal particles is independent of particle size. They also suggested that coupling of electrodynamics and hydrodynamics, and the possible effects of surface roughness have a significant effect on the kinetics of particle-particle interaction. With the increase in the concentration of SDS, a large number of smaller micelles can be obtained. Therefore, St/AA submicrocapsules prepared from these micelles are supposed to have a relatively thin and low molecular weight electrostatic polyacrylic shell. This meager swollen layer will become vulnerable to aggregation and, as a result, show lower zeta potential. This reduction in the absolute value of zeta potential at higher SDS content can be confirmed by Figure 4 which presents SEM photographs of St/AA submicroparticles with varying surfactant concentrations.
###### Abstract  
Light is a vital regulator of photosynthesis, energy production, plant growth, and morphogenesis. Although these key physiological processes are well understood, the effects of light quality on the pigment content, oxidative stress, reactive oxygen species (ROS) production, antioxidant defense systems, and biomass yield of plants remain largely unexplored. In this study, we applied different light-emitting diode (LED) treatments, including white light, red light, blue light, and a red+blue (1:1) light combination, to evaluate the traits mentioned above in alfalfa (_Medicago sativa_ L). Fluorescence staining showed that red light significantly triggered the oxidative stress indicators compared to blue and white light, while the combined red and blue light treatment significantly reduced the ROS (\(\text{O}_{2}\text{\textquoter}^{-}\), \(\text{H}_{2}\text{O}_{2}\)) intensity in alfalfa seedlings. Interestingly, the combined light treatment significantly boosted the seed germination rate (%), maximum photochemical quantum yield of PSII (Fv/Fm), leaf greenness (SPAD score), photosynthetic pigment levels (chlorophyll a, chlorophyll b, and carotenoids), and plant biomass yield in alfalfa seedlings. The red and/or combined (red+blue) light treatments significantly regulated antioxidant enzymes (SOD, CAT, APX, and GR) and the expression of genes related to the ascorbate-glutathione (AsA-GSH) pathway, including monodehydroacorbate reductase (_MsMDHAR_), dehydroacorbate reductase (_MsDHAR_), ascorbate peroxidase (_MsAPX_), and glutathione reductase (_MsGR_). These results indicate that light quality is crucial for regulating the morphological, physiological, and molecular traits linked to alfalfa improvement. These findings suggest a new approach to enhancing the adaptation, as well as the morphological and agronomic yield, of alfalfa and forage legumes through light-quality-mediated improvement.  
red-blue light; ROS; antioxidant defense; agronomic traits; _Medicago sativa_
# Special Issue: Deep Learning in Remote Sensing Applications (Editorial)
###### Abstract
Superpixel segmentation can substantially speed up the processing of remotely sensed images, reducing the computational cost of the processing [10]. The variety of possible situations present in the images render standard machine learning tools ineffective in such scenarios. To overcome this, the authors develop a dynamic method that adapts to the processed images. The proposed solution is implemented and tested in numerical experiments on real data, and state-of-the-art results are obtained. [PERSON] et al. present \"_MFFNet: A Building Extraction Network for Multi Source High Resolution Remote Sensing Data_\". The extraction of buildings from remote sensing images is a well-known problem [11]. Its solution is relevant in many contexts, such as the monitoring of unauthorized constructions in urban areas. The presence of multiscale spatial information in the images to be processed degrades the performance of standard CNN-based methods. This paper presents MFFNet, a solution that implements a \"pyramid mechanism\" to efficiently handle the multiscale characteristics of the buildings present in the images. It is tested on several publicly available datasets, with very good results. [PERSON] et al. wrote \"_Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation_\". Sea ice segmentation of remote sensing images has been studied since the early days of remote sensing engineering [12]. In recent years, interest in this problem has grown due to ongoing attempts to monitor the effects of global warning. This paper presents U\({}^{2}\)-Net, an innovative solution that combines multilayer convolutional networks with data augmentation and data expansion techniques. The proposed solution is tested on real images in different circumstances. Its robustness and accuracy are shown. Finally, [PERSON] et al. present \"_GLFFNet: A Global and Local Features Fusion Network with Biencoder for Remote Sensing Image Segmentation_\". The problem of resolving remote sensing images detecting the presence of small objects is considered [13]. The authors present GLFFNet, a tool based on deep learning and image fusion. Several experiments on publicly available datasets are discussed. High-quality results are obtained. The results justify the conceptual and computational complexity of the processing implemented in GLFFNet. The Editor is grateful to and wishes to thank all the authors that have contributed to this Special Issue. The great innovations in the field of artificial intelligence and the dramatic climate change-related challenges associated with remote sensing will ensure that 'artificial intelligence and remote sensing' remains a highly active research field for the foreseeable future. It is possible that a new Special Issue on this subject will be published in the next few years. To the authors and readers of this Special Issue, the Editor says, 'See you then'. This research received no external funding. **Conflicts of Interest:** The author declares no conflict of interest.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 2 Wireless Sensor Deployment Optimization
### IHACA-COpSPIEL Deployment Method
#### 2.2.1 Chaos Optimized pSPIEL Algorithm  
The standard pSPIEL algorithm applies non-decreasability, submodularity, and locality to solving the problem of sensor node deployment. Compared with the greedy algorithm, pSPIEL can optimize the sensor layout and reduce the communication cost, but with a large number of sensors and slightly higher communication costs. The ergodicity of chaotic motion can effectively traverse each state within a specified range. Therefore, this paper introduces a chaotic operator to traverse all cluster numbers to determine the optimal cluster number. We propose a chaotic optimized pSPIEL algorithm (COpSPIEL). The basic idea of the chaotic locality parameter \(r\) adjustment strategy is to use a chaos generator to generate a set of chaotic variables and then use the carrier transform method to map to the locality parameters, and map it to the value range of the locality parameters. Logistic mapping is a typical chaotic system.  
\[z_{i+1}=\mu z_{i}(1-z_{i}),i=0,1,2,\ldots,z_{i}\in(0,1). \tag{5}\]  
In Equation (5), \(\mu\) is the control parameter. When \(\mu=4\), the system is completely chaotic.  
The search \(r_{i}\) is mapped to the domain (0,1) of the logistic equation by Equation (6).  
\[z_{i}=(r_{i}-r_{\min})/(r_{\max}-r_{\min}),r_{i}\in(r_{\min},r_{\max}). \tag{6}\]  
It iteratively generates a chaotic sequence by Logistic equation:  
\[z^{m}(m=1,2,3,\ldots). \tag{7}\]  
The generated chaotic sequence is inversely mapped by Equation (8):  
\[r_{i}^{m}=r_{\min}+(r_{\max}-r_{\min})z^{m}. \tag{8}\]  
This returns to the original solution space and produces a solvable chaotic sequence containing chaotic variables.  
\[r_{i}^{m}=(r_{1}^{m},r_{2}^{m},\ldots,r_{i}^{m}) \tag{9}\]  
The locality parameter \(r\) optimizes the search space in this sequence.
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 2 Non-Gaussian and Nonlinear Environment (NGNLE) with Finite Memory
### OFDM-Based Fiber Optic System  
OFDM has received significant attention from the researchers in optical communication systems [28; 29; 30] because high data rate fiber optic links are the backbone of modern communications such as the internet, TV, or medical data transmissions. Figure 3 shows the conventional OFDM-based fiber optic system. The binary data is mapped onto symbols using a symbol mapper that modulate the subcarriers using the inverse fast Fourier transform (IFFT). Usually, a cyclic prefix (CP) can be used in the guard intervals (GI) between OFDM frames to improve distortions due to chromatic dispersion (CD) [30].  
The baseband OFDM data in the digital domain is converted into the optical domain using an optical IQ modulator. After propagating through a long-haul fiber optic link, the coherent receiver [31; 32] converts the data into the digital domain. The fast Fourier transform (FFT) demultiplexes the data in orthogonal subcarriers, and then, the signal passes through a linear distortion and phase noise mitigation (LDPNM) block. The LDPNM can adequately compensate for CD and linear distortions. But nonlinear distortions in a fiber optic link cannot be efficiently removed by conventional nonlinear mitigation methods in practical applications. These nonlinear distortions can be deterministic such as self-phase modulation (SPM), cross-phase modulation (XPM), and four-wave mixing (FWM) between subcarriers or stochastic nonlinear impairments such as the interaction between amplified spontaneous emission (ASE) and Kerr effect leading to Gordon-Mollenauer phase noise [33].  
Figure 2: Decision making between \(S\) known situations using \(m\) focus levels.
## 4 Recurrence-Analysis-Based Detector  
The Recurrence Rate (RR) is an essential tool of RQA. Our previous works based on the RQA detection model [20] show that an RR-based detection model (RRD) suffers from major shortcomings, as follows:  
1. It cannot detect the presence of a communication signal when \(\text{SNR}\leq-5\,\text{dB}\).
2. It is very sensitive to the recurrence threshold \(\varepsilon\).
3. The computational cost is relatively high
4. The performance of an RRD is sensitive to the types of modulations of a communication signal.  
To overcome the above limitations, we develop hereinafter a new detection model, the Recurrence Analysis Detection model (RAD), which is able to operate in very low SNR conditions. In order to reduce drastically the computational cost and avoid the delicate issue of the recurrence threshold \(\varepsilon\), the RAD only uses the distances \(d_{ij}=\left\|\mathbf{x}_{i}-\mathbf{x}_{j}\right\|_{2}\) of the upper triangular part of the distance matrix.
## 4. Degradable Sensor Materials
### Degradable Metallic Materials
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metal** & **Degradation Rate** & **Degradation Product** \\ \hline Mg & In Hank’s solution, the degradation rate of magnesium is approximately 0.35 mm/year, as reported in reference [PERSON] (2016). \\ Mo & In the simulated physiological solution (c-sbf-ca, pH = 7.4), the corrosion rate of pure molybdenum is approximately 10 \(\upmu\)m/year, as presented in reference [PERSON] (2016). \\  & Pure iron was immersed in the simulated body fluid (SBF) solution, and the mass loss was measured. The response of human endothelial cells (EC) to various \\ Fe & concentrations of ferrous ions was investigated via the WST-8 assay. The results indicated that the average in vitro degradation rate of iron is approximately 20.4 \(\upmu\)g/(cm\({}^{2}\cdot\)h), as described in reference [PERSON] (2016). \\ Zn & The corrosion rate of pure zinc in the simulated body fluid is approximately 0.02 mm/year, as shown in reference [PERSON] (2016). \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The degradation rates of various metals within the human body environment.