esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Consistent with the bivariate distributions shown in Figure 3, the NDVI (cyan) demonstrated the most severe sensitivity to substrate background, with a modal value for unvegetated spectra above 0.2, higher dispersion (s = 0.083), and an upper tail extending beyond 0.5. Figure 3: Bivariate distributions of spectral indices versus vegetation fraction. Six commonly used multispectral indices (y-axis) are compared to photosynthetic vegetation fraction (F\({}_{\rm v}\)) computed directly from hyperspectral AVIRIS-ng reflectances (x-axis). DVI and NIRv are highly correlated with each other (0.99) and with F\({}_{\rm v}\) (0.95), but not near the 1:1 line (red). Similarly, EVI and EVI2 are also highly correlated with each other (0.99) and with F\({}_{\rm v}\) (0.94 or 0.95). NDVI and SR exhibit substantially reduced correlation to F\({}_{\rm v}\) (0.84 and 0.81). Mutual information (MI) generally agrees with these correlations. MI values (relative to F\({}_{\rm v}\)) for DVI, NIRv, EVI, and EVI2 are all 1.35 +/ \(-\) 0.1. NDVI and SR MI values are lower, each at 0.69. SR\({}^{*}\) indicates that values are scaled by 0.1 for visualization. Spectra with values \(<-0.2\) or >1.2 are excluded. All Pearson correlation values are significantly different from the uncorrelated null hypothesis (\(p\) < 0.01). Bootstrapping via random selection of 30% of data values resulted in MI variability on the order of 0.01 or less. Both metrics of statistical similarity are found to yield similar overall patterns. Pairwise values (Tables S2 and S3 and Figure S1) are approximated via a monotonic, log-linear relationship. An important exception is found for the SR-NDVI relationship, which deviates markedly towards high MI values (MI \(>\) 10) without a coincident increase in \(\rho\) (further discussed below).
# Phytomicrobiones: A Potential Approach for Sustainable Pesticide Biodegradation
## 3 Phytomicrobiome and Pesticide Biodegradation
### 3.2. Leaf Microbiome  
Within the intricate realm of plant physiology, the leaf microbiome stands as a beacon of resilience against adverse weather conditions while simultaneously serving as a catalyst for plant growth and development. Dominating the tapestry of leaf microbiomes are the Actinobacteria, Firmicutes, Proteobacteria, Bacteroidetes, Leucosporidium, and Taphrina taxa, each playing a crucial role in the plant's survival. Recent studies have unearthed species within the leaf microbiome that exhibit remarkable potential in bolstering plant defenses against stress conditions. _Bacillus subtilis_, discrectively tucked away in mulberry leaves, emerges as a guardian against plant willing, providing a lifeline during challenging circumstances [97]. Meanwhile, _Burkholderia phytofirmans_, nurtured within the sanctuary of maize leaves, dons the mantle of drought tolerance enhancer, fortifying plants against the relentless grip of arid conditions [98]. On the other end of the spectrum, _Herbspirillum seropedicate_, a silent sentinel within both maize and rice leaves, takes on the responsibility of atmospheric nitrogen fixation, elevating the nitrogen content available to the plant [99; 100]. The leaf microbiome unveils another remarkable facet as a proficient degrader of toxic chemical compounds absorbed by plants from the soil. Within this enigmatic world, _Enterobacter_ spp., found nestled in the leaf buds of hybrid poplar trees, showcases an exceptional ability to degrade trichloroethylene, offering an invaluable detoxification service to the plant [101]. Furthermore, _Pseudomonas putida_, a guardian of pea leaves, emerges as a potential bioagent for phytoremediation, demonstrating its prowess in the degradation of 2,4-D and the removal of this herbicide from the environment. As pesticides are introduced into this delicate ecosystem, the composition of the leaf microbial community undergoes significant shifts. The application of chiral herbicide dichlorprop to _Arabidopsis thaliana_ plants, for instance, triggers a noteworthy increase in Proteobacteria alongside a decrease in Actinobacteria [102]. Similarly, the introduction of Penconazole fungicide induces alterations in the leaf microbiome, leading to a decline in the Enterobacteriaceae family's abundance and an elevation in the population of _Sphingobium_ spp. [103]. Despite these insights, the potential of the leaf microbiome in pesticide biodegradation remains largely uncharted territory, beckoning for further exploration and understanding. A journey into the intricate world of leaf microbiomes and their potential to mitigate pesticide-related challenges is a pressing frontier in the quest for sustainable and eco-friendly agricultural practices.
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
###### Abstract  
Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the \(U^{2}-Net\) deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces.  
deep feature learning; crack detection; pixel-level segmentation; historical surfaces; U-Net; data augmentation +
Footnote †: journal:
## 2 Preliminaries
### Dempster-Shafer Theory  
DST is a generalization of probability theory that is useful for processing uncertain and ambiguous information. The following are the basic concepts of DST.  
**Definition 1** (Frame of discernment).: _The Frame of Discernment (FOD) is a set of mutually exclusive and exhaustive hypotheses or propositions about the state of the world. It is defined as [7]:_  
\[\Theta=\{\theta_{1},\theta_{2},\cdots,\theta_{n}\}, \tag{1}\]  
_where \(\theta_{i}\) is an element in the frame and \(n\) is the number of hypotheses in the frame._  
**Definition 2** (Power set).: _The power set of \(\Theta\) is denoted as follows:_  
\[2^{\Theta}=\{\emptyset,\{\theta_{1}\},\{\theta_{2}\},\cdots,\{\theta_{N}\}, \{\theta_{1}\cup\theta_{2}\},\{\theta_{1}\cup\theta_{3}\},\cdots,\Theta\}. \tag{2}\]  
**Definition 3** (Basic probability assignment).: _Based on the power set \(\Theta\), the BPA, which is also called mass function, is defined as_  
\[m:2^{\Theta}\rightarrow[0,1], \tag{3}\]  
_which satisfies the following conditions:_  
\[m(\emptyset)=0, \tag{4}\]  
\[\sum_{A\subseteq\mathsf{2^{\Theta}}}m(A)=1. \tag{5}\]  
_When \(m(A)>0\), \(A\) is called a focal element, and \(m(A)\) indicates the degree of evidence supporting the proposition \(A\)._  
**Definition 4** (Combination rule of Dempster).: _Let \(m_{1}(\cdot)\), \(m_{2}(\cdot)\) be two BPA on \(\Theta\), and the combination rule of Dempster is defined as_  
\[m(A)=\frac{\sum_{B\cap C=A}m_{1}(B)m_{2}(C)}{1-k}, \tag{6}\]  
_with \(k=\sum_{B\cap C=\emptyset}m_{1}(B)m_{2}(C)\)._  
**Definition 5**.: _Assume \(m(A)\) to be a BPA over \(\Theta\). Let \(\alpha\in[0,1]\) be the discounting factor, and the discounted BPA is defined as follows [7]:_  
\[\hat{m}_{j}(A)=\begin{cases}\alpha m(A)+1-\alpha,&A=\Theta\\ \alpha m(A),&A\subset\Theta,A\
eq\Theta\end{cases}. \tag{7}\]
## 3 Results and Discussion
### Fourier Transform Infrared Spectroscopy  
The FTIR spectra of the fiber samples analyzed in this work were compared to the cellulose, hemicellulose and lignin standards. The FTIR spectra showed similar behavior, with most of the peaks located on the same wave number range. The peaks of 3660-2800 cm\({}^{-1}\) were present in all the samples because they are characteristic of the stretching vibration of OH and CH bonds in polysaccharides. The shape of the spectra was like those seen for other types of plant fibers and published in previous papers [43; 44; 45]. The 3450 cm\({}^{-1}\) band observed in the spectra corresponded to the hydroxyl groups. A peak wave number of 2900 cm\({}^{-1}\) was also identified, which is attributed to the asymmetric stretching of CH and CH\({}_{2}\)[44; 46]. Both bands are characteristic of organic materials, exhibiting high peak intensities as seen in the spectra of Figure 2.  
The bands at 1730 and 1625 cm\({}^{-1}\) correspond to the acetyl groups and C-O bonds characteristic of the hemicellulose. These bands were affected by the presence of pectin, whose peaks are found at 1735, 1680-1600 and 1260 cm\({}^{-1}\)[47; 48]. They showed a different behavior which is associated with a chemical modification of their fractions of hemicellulose and pectin. In the spectrum of samples before organosolv (FB and LB), they showed peaks in the range of 1750-1250 cm\({}^{-1}\) also observed in the standards of hemicellulose and lignin. The presence of lignin was observed in the band of 1595 cm\({}^{-1}\) due to aromatic vibrations in the C=C in the plane aromatic vibrations [47]. The cellulose is characterized by the bands of 1170-1150, 1050 and 1030 cm\({}^{-1}\)[44].  
With the spectra, it was possible to observe the differences between the FB and LB, the presence of hemicellulose and pectin in the fruit bagasse and the larger number of characteristic vibrations of the lignin and cellulose crystalline and the amorphous material in the leaf bagasse. Otherwise, only minor differences were observed after organosolv, which were associated with a chemical modification of the fibers of the material associated with hemicellulose and lignin. In the spectra of the fibers before and after organosolv, the typical bands assigned to cellulose were observed in the region of 1630-900 cm\({}^{-1}\), where the peaks located at 1633 cm\({}^{-1}\) correspond to the vibration of water molecules absorbed in the cellulose [37]. The band around 1420-1430 cm\({}^{-1}\) is associated with the amount of the crystal structure of the cellulose, while the band at 897 cm\({}^{-1}\) is assigned to the amorphous region of the cellulose [14].
## 4 Experiment  
### Data Acquisition and Visualization  
To create an RTI image, we captured 80 images using an RTI-dome equipped with 80 white light LEDs, illuminating one after another [12]. Subsequently, we combined these 80 images using the RTI builder software version 2.0.2 to generate a Polynomial Texture Mapping (PTM) file [12]. The PTM file was then converted to a JPG image using two different modes provided by RTI viewer set version 1.1. Specifically, we utilized the default mode to visualize the painting's brushstroke under natural light, and the static multi-light mode to visualize the corresponding three-dimensional brushstroke shape. The default mode uses natural light, while static multi-light rendering mode automatically divides the image into tiles and selects a light direction that maximizes a chosen energy function for each tile. This approach enhances as many details as possible in a single view. For reference, Figure 1 demonstrates the RTI dome and RTI viewer.  
The dataset used in this research consisted of oil paintings showcasing the specific style of a contemporary Korean artist, as well as two forgeries created by another Korean painter to evaluate the proposed model. Figure 2 illustrates examples of these paintings and the extracted RTI patches used for training and validation of the model. The photos of both the RTI images and the paintings were captured using a Sony a7R III camera at a resolution of 7952 \(\times\) 5304 pixels. To ensure better visibility of the brushstrokes, specific spots on the paintings were selected where the brushstroke could be easily seen.  
Figure 1: **(a)** Dome setting of capturing RTI images; **(b)** different rendering mode provided by RTI viewer software version 1.1.  
Figure 2: **(a)** The paintings were randomly chosen from training and validation datasets, respectively. **(b)** Patch-based RTI brushstroke images from training and validation datasets.  
We used three paintings as our test set (i.e., paintings A-C), which were distinct from those used in the training and validation sets. Painting A represented the original work of the artist, while painting B served as a forged sample of painting A. Painting C was created based on the brushstroke style of the intended artist, but its content was derived from the forger's imagination. The purpose of painting C was to evaluate the authentication of an unseen contemporary painting, with no corresponding original sample ever seen before. Figure 3 showcases all three paintings that were utilized in the testing phase. It is important to note that all images were captured using the same aspect ratio.
## 3 Numerical Model Tests with FEA
### Contact Pairs  
The interactions between gasket-gasket contact pairs and gasket-gasket groove contact pairs are simulated by the frictional contact element in ANSYS, which can be either normal separation or tangential sliding. Gasket-gasket contact pairs can be treated as flexible-flexible contact pairs, in which either surface is defined as the target surface or the contact surface. Gasket-gasket groove contact pairs may be considered rigid-flexible contact pairs, where the gasket groove is defined as the target surface, whereas the gasket is defined as the contact surface.  
In the ANSYS software, the contact element in the contact pairs only works when the contact surface and the target surface are in contact with each other, and therefore one requires to identify which surfaces may be in contact as the gasket deforms. As shown in Figure 5, trial calculations showed that the following 32 contact pairs can be observed in the gasket compression:  
1. Gasket-gasket contact pairs: C1-C1, C2-C2, C3-C3, C4-C4, C5-C5, C6-C6, D1-D1, D2-D2, D3-D3, D4-D4, E1-E2, F2-F3, F6-F7;
2. Gasket-gasket groove contact pairs: A1-B1, A1-B2, A1-B3, A1-B4, A1-C1, A1-C2, A1-C3, A1-F1, A1-F2, A1-F5, A1-F6, A2-B5, A2-B6, A2-B7, A2-B8, A2-F3, A2-F4, A2-F7, A2-F8.  
A2-B6 suggests that the face represented by A2 and the face represented by B6 may contact each other during the gasket deformation and similarly for the rest.  
Generally, experimental measurements are used to calculate the friction coefficients (FC) of the gasket-gasket groove contact pairs. According to the results of laboratory experiments, [PERSON] [27] suggested that the friction coefficient varies between 0.5 and 2.5 depending on different scenarios. In this study, the friction coefficient is uncertain since experimental data do not include it. To address it, numerous simulations were conducted with different friction coefficients to investigate the impact of the friction coefficient. Figure 6 shows that the simulation results with different friction coefficients were almost overlapped, especially in the first two stages, which indicates that the friction coefficient has a slight effect on the load-displacement behavior. Hence, the friction coefficient between the gasket and gasket groove in the finite element is adopted as 1.0 for the sake of simplicity since it will not impact the result significantly. As for the friction coefficient between the gaskets, it is adopted as 0.57 [19].
# Compressive Sensing-Based IoT Applications: A Review
## 3 Compressive Sensing Overview
### Mathematical Overview  
Let the orthogonal basis \(\{\mathbf{\Psi}_{i}\}_{i=1}^{N}\) span \(\mathbb{R}^{N}\). Then, any signal \(\mathbf{x}\in\mathbb{R}^{N}\) can be expressed as a linear combination of the elements of \(\mathbf{\Psi}\) with the elements of the vector \(\mathbf{s}=[s_{1},s_{2},\cdots,s_{N}]^{T}\in\mathbb{R}^{N}\) such that \(\mathbf{x}=\sum_{i=1}^{N}\Psi_{i}s_{i}\). The signal \(\mathbf{x}\) is said to be \(k\)-sparse if the vector \(\mathbf{s}\) has only \(k\ll N\) non-zero entries.  
The set of the indices corresponding to the positions of the non-zero entries of \(\mathbf{s}\) is called the support of \(\mathbf{s}\) and denoted as \(\Sigma_{k}\). In addition, the matrix \(\mathbf{\Psi}\) is called the sparsifying matrix in the context of CS.  
To perform a compressed acquisition on the data, the input signal \(\mathbf{x}\) is multiplied by a tall random sensing matrix; hence, the acquisition process in CS can be modeled by:  
\[\mathbf{y}=\mathbf{\Phi}\mathbf{x}=\mathbf{\Phi}\mathbf{\Psi}\mathbf{s} \tag{1}\]  
where \(\mathbf{\Phi}\in\mathbb{R}^{M\times N}\) represents the sensing matrix used to acquire and compress the data. Moreover, the ratio \(\frac{N}{M}\) is defined as the compression factor (CF).
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results  
Our main objective is to determine the behavior of the V2X system model in different channels and QoS scenarios and evaluate the effect that different coding schemes have on FER performance. Additionally, we examine important QoS parameters and their effectiveness for each QoS scenario in Table 2 according to [13,14], which will be also used for the overall QoS evaluation of the coding schemes. More specifically, we analyze the FER performance for all the coding schemes and system simulation scenarios and analyze the impact of vehicle speed, channel state and traffic model on the system simulation model. Additionally, we study the minimum required SNR, transmitted power, maximum range, and throughput that achieve the minimum reliability requirements for each QoS scenario. Furthermore, we examine the end-to-end latency, the maximum number of vehicles and the vehicle density for each QoS scenario, and, finally, we present an overall QoS evaluation of each coding scheme, based on the aforementioned research results.
## 4 Results and Discussion  
In this section, we are illustrating a set of simulation experiments conducted for evaluating the efficacy of our proposed novel framework. In the simulation experiments, we have used network simulator (NS) version 2.34. For illustrating the outstanding performance of our proposed novel framework, we present a detailed comparative analysis with existing schemes, namely, the ABC-based framework [44] and GA-based framework [35]. The detailed setup for the simulation experiments is presented in Table 2.  
Next, Figure 4 illustrates the central methodology adopted for conducting the set of experiments. Broadly, we have divided the set of experiments into four categories, namely, based on varying the number of SNs, varying the network size, variations in the supply of harvested energy, and also varying the number of RNs. We have used a total of five metrics for assessing the effectiveness of our proposed novel framework, namely, the lifetime of the network, throughput, average hop distance, end-to-end delay, and lastly the energy consumption. Further, in the first category of experiments, we conducted four simulation experiments, covering the number of RNs vs. variations in the number of SNs; the lifetime of the network vs. variations in the number of SNs; the throughput vs. variations in the number of SNs; and the average hop distance vs. variations in the number of SNs. Furthermore, in the second category of experiments, we again conducted four simulation experiments, covering the number of RNs vs. variations in the size of the network; the lifetime of the network vs. variations in the size of the network; the throughput vs. variations in the size of the network; and the average hop distance vs. variations in the size of the network. Next, in the third category of experiments, we conducted two simulation experiments, covering the lifetime of the network vs. variations in the supply of harvested energy (uniform); and the lifetime of the network vs. variations in the supply of harvested energy (random). Furthermore, in the fourth category of experiments, we conducted two simulation experiments, covering the end-to-end delay vs. variations in the number of RNs; and the energy consumption vs. variations in the number of RNs.
## 3 Discussion
aeruginosa_ (Figure 2) but enhanced activity _vs._ the Gram-positive organism _S. aureus_ (Figure 3), and generally conserved the broad-spectrum activity of the parent peptide 1018. Most intriguing was the effect of deletion of the hydrophobic residues valine and alanine in peptide HE10 which decreased anti-_Pseudomonas_ activity but increased anti-MRSA activity observed. Peptide HE12, which was the same length as HE10 and had the same balance of charged and hydrophobic amino acids, lacked anti-biofilm activity against both the tested Gram-negative and Gram-positive bacteria (Figures 2 and 3), demonstrating the importance of the amino acid sequence rather than composition. Interestingly, peptides HE4 and HE10 retained the ability of 1018 to modulate the immune response, as they enhanced the production of chemokine MCP-1 (Figure 4A) while strongly suppressing the production of TNF-\(\alpha\) (Figure 4B), an anti-inflammatory property. Conversely, peptide HE12 did not significantly stimulate MCP-1 production (Figure 4A), but did inhibit the release of TNF-\(\alpha\) from PBMCs (Figure 4B). This strongly suggests that chemokine induction and suppression of pro-inflammatory cytokines is independently determined, as proposed earlier [35]. Importantly, none of the amino acid modifications tested led to increased cytotoxicity of the peptides (Figure 4C). In conclusion, we report that anti-biofilm peptide 1018 inhibited biofilm formation by multiple clinical isolates from CF patients. Moreover, 1018 derivative HE4 showed equivalent activity _vs. Pseudomonas_ and enhanced anti-biofilm activity against MRSA while retaining the immunomodulatory properties of parent peptide 1018. On the other hand, peptide HE12 provided a potential negative control since it lacked anti-biofilm activity and did not substantially stimulate MCP-1 production. Since _P. aeruginosa_ and _Burkholderia_ strains are the most prevalent pathogens in the CF lung and are known to form biofilms, our findings suggest that peptide 1018 and its derivatives may be used in combination with conventional antibiotics [27] against CF-related inflammatory lung infections.