esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 1 Introduction
A core idea of the presented work is that GP can be used to leverage the application of a set of state-of-the-art IE operations to create automatically IE pipelines. We resort to ELAINE (EvoLutionAry Image eNhancEment) to handle the generation of the image pipelines [9]. Based on state of the art, Pix2 Pix GANs, such as Enlighten-GAN [8], showed promising results on solving several generalized contrast and illumination problems in benchmark datasets. Thus, the framework combines these two approaches into a conditional framework, wherein a first step the GAN attempts to perform generic IE and the GP pipeline can be optimized to subsets of data. These approaches combined offer a more dynamic and adaptable solution, while providing suitable solutions to solve different types of problems. Through this work, an IE system capable of enhancing the visual aesthetic quality of real estate images is developed. To accomplish this, we relied on different techniques, arranged in an end-to-end layout, to formulate a system that aims to process images with different characteristics properly. More precisely, we aim to combine image processing techniques by using Genetic Programming to create complete image pipelines combined with Machine Learning via GANs into a single framework for IE. This work is being performed while in contact with _PlotoLike_, a Spanish company dedicated to the use of Artificial Intelligence techniques for the aesthetic valuation of real estate images. They contribute by providing valuable tools and data acquired during the experimentation phase. In summary, this work contributes to the field of study by: (i) performing different IE experiments with Classical image processing filters, Pix2 Pix GANs, and a set of filters created with GP; (ii) performing different tests using state-of-the-art _no-reference_ Image Quality Assessment (IQA) metrics of image technical quality and aesthetics; (iii) using ELAINE, a Genetic Programming approach, for the generation of image enhancement pipelines; and (iv) developing a system capable of improving the visual aesthetics of real estate images. The remainder of the document is structured as follows. Section 2 reviews works related to image enhancement. Next, Section 3 describes the proposed approach to development of an adaptive IE system. Section 4 explains the experiments, as well as the setups, and shows and analyzes the results. Afterwards, a discussion on the results is presented in Section 5. Finally, Section 6 presents overall conclusions.
# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 2 Methods
### Local Feature Point Selection  
Given an input point cloud dataset, a minimum bounding box (MBB) [18] is constructed based on the maximum and minimum coordinates in the X, Y, and Z directions. Specifically, the MBB is defined by \(x_{max}\), \(y_{max}\), \(z_{max}\), \(x_{min}\), \(y_{min}\), and \(z_{min}\), representing the extrema of the point cloud in each dimension. The resulting MBB tightly encloses the entire point cloud, with edge lengths denoted as \(L_{x}\), \(L_{y}\), and \(L_{z}\). This is represented by the following equation:  
\[\begin{cases}L_{x}=x_{max}-x_{min}\\ L_{y}=y_{max}-y_{min}\\ L_{z}=z_{max}-z_{min}\end{cases} \tag{5}\]  
The edges of the minimum bounding box (MBB) are divided into \(n_{1}\), \(n_{2}\), and \(n_{3}\) equal segments along the X, Y, and Z axes, respectively. Consequently, the MBB is partitioned into \(n_{1}\cdot n_{2}\cdot n_{3}\) smaller voxels. The edge lengths of each voxel are denoted by \(m_{1}\), \(m_{2}\), and \(m_{3}\), as shown by the following equation:  
\[\begin{cases}m_{1}=\frac{L_{x}}{n_{1}}\\ m_{2}=\frac{L_{y}}{n_{2}}\\ m_{3}=\frac{L_{z}}{n_{3}}\end{cases} \tag{6}\]  
For each voxel, the centroid \(\overline{p}\) of the enclosed point cloud data is computed. The point within each voxel that is nearest to the centroid \(\overline{p}\) is then selected as the characteristic point \(P_{c}\). This is shown in the following equation:  
\[\overline{p}=\frac{1}{N_{j}}{\sum\limits_{i=1}^{N_{j}}p_{i}} \tag{7}\]  
where \(N_{j}\) represents the number of points within each voxel, with \(j\) denoting the index of the \(j\)-th voxel.
## 4 Methodology
### Generation of Landslide Inventory (GLI)  
Generation of Landslide Inventory (GLI) is the set of previous and present locations of landslides and is the prerequisite for modeling the landslide susceptibility (LSS). LSS modeling can be assessed through understanding the connections between GLI and landslide causative factors [36]. GLI data set can be prepared through compiling various data sources considering field investigation, past landslide records and evaluation of the Google Earth imageries [37]. In this research, the GLI was prepared by compiling a total of 223 landslide locations recorded in between April 2018 and September 2019 through several field campaigns (Figure 3) using GPS and secondary historical record (record of National Disaster Management Authority, Uttarakhand). Among these inventory, over 40% landslides are translational slides. According to the Uttarakhand National Disaster Management Authority, huge landslides have occurred because of cloudburst rainfall that occurred in July 2019. For training the models, we have selected same amount of non-landslide points randomly over the study area. These historical landslides were then spatially mapped in a \(12.5\times 12.5\) m resolution, dividing randomly into two sub-set viz. training sub-set, which is comprised of 70% (156 nos.) of GLI and validation or testing sub-set, which includes remaining 30% (67 nos.). The separation of the whole GLI was performed using the extension titled \"Geostatistical Analyst\" in ArcGIS version of 10.3.1. Training sub-set was used for simulation of the models to generate LSMs and validation sub-set was applied to assess the models' performances (Figure 1). Landslides mainly occurred in this study area due to rainfall and earthquakes. Rate of landslide has increased with the anthropogenic activities as well. Among the recorded landslides, smallest and largest was 21.73 sq. m and 633.78 sq. m, respectively, while the mean extent was 272.41 sq. m.  
Figure 2: Methodological flow diagram of the present research. SPI (stream power index); TWI (topographical wetness index); NDVI (normalized difference vegetation index); LU/LC (land use /land cover).
## 4 Data Acquisition and Processing
### Zonation of the Study Area Using Experimental HVSR Curves  
Once the HVSR curves are obtained and the soil frequency peaks identified for each measurement site, a zonation map of the HVSR curves has been performed basing on the degree of similarity of the HVSR curves (Figure 4).  
Four distinct zones have been identified as follows:  
* This zone includes HVSR curves with two peaks.The fundamental one, f0, ranges from 1.8 to 3 Hz, meanwhile the second peak, f1, ranges from 6.5 to 15 Hz. The corresponding amplitudes range from 2 to 4 for both peaks (e.g., Pt R21 and Pt R10 in Figure 5).
* This zone includes 10 recordings with clear f0 between 2.5 and 4 Hz, with amplitudes around 3.5 (e.g., Pt 03 and Pt 33 in Figure 5).  
Figure 4: Zonation map of the HVSR curves. Black dots represent the measurement points, and red dots represents the HVSR flat curves.  
Figure 3: HVSR measurement points (yellow squares) and boreholes (red dots). The two black lines (AB and CD) indicate two cross-sections of Vs profiles.  
* A total set of 15 recordings with clear f0 between 5 and 7 Hz. The corresponding amplitudes vary from 2.5 to 6 (e.g., Pt 15 and Pt 27 in Figure 5).
* This zone covers the rock knoll in the central part of the city, with eight clear HVSR peaks between 12.5 and 15 Hz. The amplitudes range from 2.5 to 3.5 (e.g., Pt V2 and Pt 39 in Figure 5).  
In some areas of the city, no peaks have been identified (flat curves) according to the criteria established in the SESAME report [36]. In these cases, it means that the obtained H/V curve lies between around 0.5 and 2.0, without any clear peak.
###### Abstract  
The expansion of Zigbee-enabled IoT networks has generated significant security issues, especially around threat detection and secure key management. Using RBF and blockchain technology, this study shows a smart hybrid framework to find threats early and distribute keys safely on IoT networks enabled by Zigbee. This methodology incorporates Radial Basis Function (RBF) networks for prompt threat detection and a blockchain-based trust framework for decentralized and tamper-proof key distribution. It guarantees safe network access, comprehensive authentication, and effective key updates, reducing risks associated with IoT-related DoS attacks and Man in the Middle Attacks. The Trust-Based Security Provider (TBSP) enhances security by administering critical credentials across diverse networks. Comprehensive simulations and performance assessments illustrate the effectiveness of the framework in increasing threat detection precision, minimizing key distribution delay, and bolstering overall network security. The findings confirm its efficacy in safeguarding IoT settings from new risks while ensuring scalability and resource efficiency. We proposed an RBF-based threat detection framework for network keys using the ZBDS2023 dataset and the J48 decision tree algorithm. In conclusion, we demonstrate the security and efficiency of our proposed work.  
Zigbee; IoT security; RBF network; blockchain; key distribution; threat detection; authentication +
Footnote †: journal:  
Article
## 7 Implementation and Evaluation
### Implementation  
For digital signature algorithms, we used RSA and ECDSA with SHA-256 and respectively used 3072 bits and 256 bits for the length of a public key \(pk_{u}\) which achieve the same level of security according to the recommendation of NIST [26]. To securely store the user's sign key \(sk_{u}\), \(sk_{u}\) was encrypted using the AES-256 algorithm in Galois/Counter Mode (GCM) without padding. We note that padding is not required because random keys are encrypted. To derive the AES encryption key, PBKDF2 with HMAC-SHA256 was used where the number of iterations was 10,000, and the salt was generated by a cryptographically secure pseudorandom number generator (CSPRNG). Similarly, CSPRNG was also used to generate the initial vector for the AES encryption in GCM. We used a SHA3-256 hash function to compute the hash value of the user password.  
As shown in Figure 2, the proposed user authentication system consists of three communication channels between the user device and salt server, '\(d-s\)', user device and mobile device, '\(d-m\)'; and user device and controller, '\(d-c\)'. For the salt server implementation, we used Spring Boot ([[https://spring.io/projects/spring-boot](https://spring.io/projects/spring-boot)]([https://spring.io/projects/spring-boot](https://spring.io/projects/spring-boot))) to build the Representational State Transfer (REST) architecture. Therefore, HTTPS was used for the channel '\(d-s\)'. For the channels '\(d-m\)' and '\(d-c\)', short-range communication based on Bluetooth with Serial Port Profile (SPP) was used.  
For further information, the source codes of our implementation are available in the public GitHub repository ([[https://github.com/rymuff/keyprotection](https://github.com/rymuff/keyprotection)]([https://github.com/rymuff/keyprotection](https://github.com/rymuff/keyprotection)), see Supplementary Materials).
## 4 Discussion
We measured a set of model compounds used to perform sample fitting with the purpose of obtaining a collection of model spectra of sulfur-containing chemicals with meaning in plants sulfur metabolism. From these fitting results, we gathered information on the chemical speciation and contribution of each species to the sample spectra. In the XANES approach, speciation means the determination of the oxidation states and functional groups in which the selected atom is present as well as the fraction of each of those groups [50]. From Table 2, which shows the results of the LCF of XANES measurements, it can be seen that sulfate is the predominant form of sulfur in _Bixa orellana_ seeds. This sulfate may serve as a reserve for protein synthesis and related metabolic processes during the early stages of seedling development [57]. Sulfur is delivered to the seed either in the form of sulfate or reduced sulfur compounds such as reduced glutathione or methyl methionine [58]. Sulfate that arrives in mature leaves through the xylem can be redistributed via the phloem to young leaves and developing fruits by specific membrane transporters [59]. Our results allow us to outline a profile of the sulfur forms present in _Bixa orellana_ fruits and seeds. From our results, it is shown that sulfur accumulates in the seed in the form of sulfate and is also present in reduced sulfur forms (thioether and disulfide) that could be used in the development of the seedling. It is observed that sulfate is the predominant form of sulfur in the pericarp, from where it is transported to the developing seed. In the seed, its distribution changes a little, and more sulfur is accumulated in a reduced form than in the pericarp. However, inorganic sulfate is still the major form of sulfur in the seeds. The low number of sulfur-containing proteins in the seeds is also verified in this study, where the amount of sulfur belonging to thioether/disulfide groups is in the order of 0.5 to 0.7 g/100 g of the total mass, calculated from the total sulfur concentrations (Table 1) and the corresponding percentage of each sulfur type (Table 2). This is in agreement with the content of sulfur-containing amino acids in seeds of _Bixa orellana_, reported to be 0.4 g/100 g [60]. Our results highlight the fact that the seeds of _Bixa orellana_ accumulate sulfur in an inorganic form that has an important energetic cost as a source of sulfur in the early stages of plant development. Future studies are needed to assess the accumulation and metabolism of sulfur in these stages.
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Motion Estimation Methods
#### 2.4.2 Channel Correlation  
While speckle tracking is effective, it is a tool designed for varying motion estimation throughout a field of view. Here a more direct estimation tool for the problem of transducer displacement is introduced. Channel correlation relies on the idea that, for small motions, there will be subapertures from the initial and displaced position that spatially overlap (possibly with some sub-pitch offset). The backscattered echoes observed by these subapertures should be highly correlated given a relatively constant transmit field. One  
Figure 4: (**a**) Assuming low decorrelation over small displacements, motion of the array (here by one element pitch) can be estimated using a cross-correlation search between plane wave transmissions. (**b**) Speckle tracking uses a 1-D search of an image kernel between a reference and target image over a field of view relative to each array position. (**c**) Channel correlation compares shifted pairs of raw channel signals with varying offsets, reducing the active subaperture with increasing shifts.  
can, therefore, search for transducer motion by correlating the recorded channel data from these subapertures without the need for focusing or image formation.  
Subsets of channels, as shown in Figure 4c, were studied by removing channels from opposing ends of the array for the initial and displaced data sets to increase the search offset. The same estimation depth and axial kernel size were used as in speckle tracking (using the raw echo signals rather than beamformed images) and searched up to offsets of 10 elements (3 mm).
## 3 High-Dimensional Mapping Entropy
### Simulated Signal Analysis  
To assess the performance of the suggested approach, a mixed signal is generated by superimposing a deterministic signal, a random signal, and a noise:  
\[mix(N,p)=(1-p)X(i)+pY(i) \tag{23}\]  
\[X_{1}=mix(3000,\ 0.3)+wgn(1,\ 3000,\ 0.2) \tag{24}\]  
\[X_{2}=mix(3000,\ 0.5)+wgn(1,\ 3000,\ 0.5) \tag{25}\]  
where \(X(i).=\sqrt{3}sin(\pi/4),Y(i)\) is the random signal \(Y(i)\in[-\sqrt{5},\sqrt{5}]\), \(N\) the length of the signal, and the ratio \(p\in[0,1]\). The sample entropy and high-dimensional mapping entropy of the signals \(X_{1}\) and \(X_{2}\) are denoted as SampEn1, SampEn2, HDME1, and HDME2, respectively, and the entropy of the mixed signals with different similarity tolerances r are shown in Figure 4.  
As depicted in Figure 4, both the SE and HDME entropy of the mixed signals exhibit better similarity in general. As the similarity tolerance r changes, SampEn1 and SampEn2 show larger fluctuations. SampEn1 and SampEn2 are also more sensitive to changes. The sensitivity to these variations could present issues during the analysis of complex signals. Notable fluctuations in sample entropy may result in misleading interpretations of the dynamic behavior of signals and affect the reliability of diagnostic conclusions. In contrast, both the HDME1 and HDME2 curves are more uniform, with better performance in parameter consistency and smoothness. This indicates that HDME captures the dynamic characteristics of signals with greater stability and less susceptibility to parameter selection, which is advantageous for practical applications. It is important to note that the signal ratio has a low signal-to-noise ratio, resulting in an irregular signal and a high level of uncertainty. Consequently, the entropy of the signal surpasses that of the majority of theoretical thresholds, signifying a higher degree of complexity in the signal, in line with the principles of entropy theory. A low signal-to-noise ratio increases the difficulty of signal processing as it necessitates differentiation between fault characteristics in the signal and background noise. Under these conditions, HDME retains its efficacy in revealing the intrinsic complexity of the signal, thereby validating its effectiveness as a metric for signal complexity assessment.  
Figure 4: Comparison of entropy for similar tolerances r.  
Figure 3: Flowchart of feature extraction for HDME.
## 5 Conclusions  
Currently, Transformer has become a new research hotspot, which can parallelize training, can obtain global information, and has the momentum to challenge traditional neural networks. Among them, the performance of ViT in many image classification tasks is directly comparable to that of CNN-based SOTA, which opens up new possibilities in the field of computer vision.  
To improve the deficiencies of the existing models in classifying and extracting open-pit mining areas in complex backgrounds, such as the processing errors faced by small mining areas, the poor adaptive performance shown with multi-scale mining areas, and the sticking of extracted boundaries, a new semantic segmentation model based on Transformer--SegMine--is proposed. SegMine consists of a Vision Transformer-based encoder and a lightweight attention mask decoder. Compared to the Transformer family of models, which are currently outstanding performers in the visual field, this model performs better at classifying and extracting open-pit mining areas, is more friendly to small mining areas, and can extracts finer boundary details. In the comparison experiment with seven models, the proposed model had the highest overall performance. Of the four evaluation metrics, SegMine had the highest mIoU (86.91%), precision (89.90%), and dice (92.27%), and its recall (85.33%) was only slightly lower than SegFormer (85.56%). During the detail processing of seven different scenes, SegMine was able to effectively retain the detail information of fragmented and small-scale targets and achieve clearer contour boundaries and better resistance to complex background interference. Considering Transformer's good parallel computing and global feature acquisition capabilities, as well as SegMine's good performance, it would be a promising model for the classification and extraction of open-pit mining areas. In future research, we will pay more attention to determining how to utilize defective labeled sample data to better achieve high-precision intelligent extraction of open-pit mining area extraction.  
Conceptualization, Q.Q.; data curation, Y.L.; formal analysis, Y.L.; investigation, H.L.; methodology, Q.Q., Y.L. and H.L.; project administration, Q.Q.; resources, H.L. and Y.L.; software, Y.L.; supervision, Q.Q.; validation, Y.L. and H.L.; visualization, Y.L.; writing--original draft, Q.Q.; writing--review and editing, Q.Q., Y.L. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Scientific Research Operating Funds of the Chinese Academy of Surveying and Mapping (No. AR2212, No. AR2203).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank Google for providing remote sensing images.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 4 CFRP Conductivity Measurements and Techniques for Non-Destructive Testing Applications
### Delamination Measurements Based on Electrical Conductivity
#### 4.2.2 Electrical Resistance Change Method
The four-electrode technique can avoid the effect of potential contact resistance changes at the inner electrodes under the influence of mechanical strain on the contact. (2) Two electrodes (Figure 5): why the two-probe technique other than the four-probe technique was adopted in some experiments? Firstly, it can avoid the effect of mechanical strain on the inner probes. Secondly, it can make sure that the measured potential difference reflects the effects of all the carbon fibers in the entire sample cross section, other than that of the fibers adjacent to the laminate surface. [PERSON] and [PERSON] indicated that the two-probe technique might lead to the misunderstanding because the method included the electric resistance change of electrodes. The change in electric potential caused by delamination was calculated with the equivalent electric conductance and the method had been proven with the finite different method (FDM) [147]. ERCM is proven to be an effective method to detect the CFRP delamination. However, ERCM requires a huge amount of hardware circuits to supply an alternating current (AC) between all adjacent electrodes. The measurement precision is strongly affected by the contact state between copper electrodes and specimens. Most of these studies in this field were experimental studies. The mechanisms of damage to the bulk resistivity change were not theoretically explored [139].