esa-sceva/satcom-chunk-collection huggingface dataset:
# Monotonic and Fatigue Properties of Steel Material Manufactured by Wire Arc Additive Manufacturing
## 3 Results
### Detailed Examination of Local Deformation Behaviour  
Although the results shown previously suggest a relatively homogeneous distribution of the fairly anisotropic properties, the following must be critically noted: the diameters of the specimens, especially the one used for fatigue testing, roughly corresponded to the layer thickness. In the lengthwise direction, the specimens contain both the microstructure from the middle of a single layer and the boundary between two layers. Thus, several layers were tested in crosswise direction at once. During the strain-controlled tests, the strain was measured using a strain transducer with a measuring length of 5 mm. In cases where the microstructural constituents in the layers and layer boundaries showed different behaviors despite the results shown, it could be expected that strain localizations occurred and that the strain measured in the tests represented the average value over the measuring length. In order to rate this effect, the local deformation behavior was observed with a digital image correlation (DIC) system.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Interlayer** & **Extraction** & **Fatigue Limit** & **Fatigue Limit** \\
**Temp/\({}^{o}\)C** & **Direction** & \(\sigma_{\text{a,E}}\)/MPa & **Converted to Strain \(\varepsilon_{\text{a,E}}\)** \\ \hline
150 & lengthwise & 256 & 0.137\% \\
150 & crosswise & 254 & 0.129\% \\
300 & lengthwise & 263 & 0.154\% \\
300 & crosswise & 267 & 0.148\% \\ \hline \end{tabular}
\end{table}
Table 5: Fatigue limits determined by staircase tests at \(5\times 10^{6}\) cycles, probability of failure P\({}_{t}\) = 50%.  
Therefore, strain-controlled experiments with flat specimens, Figure 2d, at a strain amplitude of \(\epsilon_{\text{a}}=0.4\%\) were carried out. Figure 13a shows the used specimen in relation to the WAAM material. The layers are perpendicular to the loading direction. The recorded strain distributions are shown in Figure 13b for both interlayer temperatures.  
It can be noted that an inhomogeneous distribution of local plastic strain is observable for both interlayer temperatures. The size of the regions with different plastic strain amplitudes indicate that the individual layers differ in their deformation properties. However, a clear allocability to regions in the center of the layers and layer boundaries, as expected, is not found.
# Study on Shear Mechanism of Bolted Jointed Rocks: Experiments and CZM-Based FEM Simulations
## 1 Introduction
[18] modeled the direct shear experiments of rock joints with PFC, and discussed the evolution characteristics and failure mechanism of joints during the direct shear experiment, from both macroscopic and microscopic perspectives. [PERSON] and [PERSON] [19] examined the influence of geometric characteristics and microscopic properties of joints on the shear performance by using PFC 3D to simulate the direct shear process. As evidenced by these studies, the discrete element method has become an important means and has been widely used to study the shear failure of jointed rock. However, when using the discrete element method to study bolted jointed rock, the bolt is often represented by discrete particles. For example, [PERSON] et al. [20] and others used the PFC software to simulate the interface mechanics of bolt. Although the results are good, the bolt is practically a homogeneous continuous medium, and the realistic mechanical properties of the bolt may be described more accurately by the finite element method. The finite element cohesive model has been widely used in the simulation of cracks in brittle materials, such as crystals and rocks. The CZM (cohesive zone model) was first proposed by [PERSON] [21] and [PERSON] [22] in the 1960s, which is different from the traditional finite element model. It focuses more on simulating the microstructure of materials and can describe the discontinuity of brittle materials such as rocks. CZM-FEM has been widely used in engineering. It can describe the bond-slip action between steel rebars and surrounding concrete and the crack propagation of composite material plates [23; 24; 25]. The crack tip singularity problem can be avoided by using this method to simulate the crack initiation. Recently, [PERSON] et al. [26] developed an algorithm of embedding cohesive elements in solid finite element grids, which allows for fracture simulation of complex structures. Considering the discontinuity of the jointed rock and the continuity of the bolt, here we propose a new numerical method to simulate the shear of the bolted joints based on the laboratory experiments on the bolted jointed rock. \"Discrete\" jointed rock with embedded zero-thickness cohesive element is used in this method, and the traditional finite element model is used to define the bolt. Hence, the advantages of the two methods are conveniently combined to simulate the shear of bolted joints. This numerical simulation method will provide another effective research tool for the study of the mechanical properties and reinforcement effect of bolted joints. In this paper, the characteristics of shear resistance of bolted jointed rock are studied through laboratory experiments and numerical simulation, and the bolt with the best performance is given.
## CNN Model Solution Framework Foundations
### Test Data  
In this study, eye diagrams and their SNRs are used as the source of feeding the CNN with the required testing data. A Python code was written and ran on a Microsoft Azure VM to generate 576 received pulses, including some random noise; then, the eye diagram pattern images were drawn, and their related SNRs based on the received pulses were calculated. For eye pattern generation, we used four-channel models, including the following:  
* DGFs with distances of 5.47 m and 45.45 m for harbour and coastal waters, respectively, and (20\({}^{\circ}\), 180\({}^{\circ}\)) field of view (FOV).
* WDGFs and CEAPFs with distances of 10.93 m and 45.45 m for harbour and coastal waters, respectively, and 20\({}^{\circ}\) FOV.
* BP with 5 m and 10 m distances for harbour and coastal waters, respectively, and 180\({}^{\circ}\) FOV.  
We also utilised two transmitted pulse shapes, Gaussian and Rectangular, to implement a binary OOK modulation in our simulation. The range of pulse widths (FWHM) was 0.1 to 0.95. It is worth mentioning that pulse widths beyond 0.6 are unrealistic, but we added these scenarios as a \"burn test\" for our solution. The ranges of the FOV values across channel models were not similar because we had to use the published double gamma fitting parameters (shown in the last row of Table 1) and their corresponding FOV value ranges.  
After that, the names of the images and SNR values were stored in an SQL database, while the images were stored in one folder. Consequently, the data were ready to have CNNs applied to them. These test data have some properties, and they are as follows:  
The background of the eye diagram images is black, while the diagram itself is white (greyscale) to speed up and simplify the CNN calculations. All the eye diagram images' sizes (height \(\times\) width) are 2366 \(\times\) 3125; this size was taken from the shapes of the images' arrays (it is already an output from the code). The SNR has a normal distribution (which means there is no bias in our data before applying ML), as shown in Figure 6. The minimum SNR value is 0.5723, the maximum SNR value is 8.1478, the mean is 3.0004, and the standard deviation is 1.5061.  
The data preprocessing steps before applying the CNN:  
1. Loading the images' names and SNRs from the database.
2. Converting the data into a 'pandas' data frame.
3. Shuffling the data frame.
4. Using TensorFlow library on Python, we conducted the following: * Loaded the eye diagram images based on their names and normalised them using max normalisation (dividing each pixel by 255). * Split the dataset into training (70%) and validation (30%). * Converted the colour mode from RGB into grayscale. * Used the images' original size instead of resizing them to keep the resolution high.
# Why Do Tethered-Bilayer Lipid Membranes Suit for Functional Membrane Protein Reincorporation?
## 4 Different Types of Tethered-Bilayer Lipid Membranes
### Protein-tBLMs or ptBLMs
The ptBLMs methodology has been applied (i) to study the voltage-dependent structural changes of a seven-helical transmembrane protein sensory _rhodopsin II_ belonging to the family of microbial rhodopsins that act as light-driven pumps [244], (ii) to investigate photoexcitation of bacterial reaction centers (RCs) from _Rhodobacter sphaeroides_, a purple bacterium that can obtain energy through photosynthesis [245], or (iii) to functionally probe the energy-converting _NADH_ (reduced nicotinamide adenine dinucleotide)-_ubiquitone oxidoreductase_, the respiratory complex I of _Escherichia coli_, against various substrates and inhibitors [246] via SEIRAS, SERRS or CV. It has been also transposed to functionalize hydrogel deposited as cushions on indium-tin oxide (ITO) used as transparent electrode. In this new approach of polymer-supported lipid membrane, the hydrogel was functionalizedwith NTA group; then, the His-tagged enzyme (i.e., \(\mathit{C}\mathit{C}O\)) was first immobilized and the lipid bilayer was then formed around the immobilized protein. Since the mesh size of the hydrogel was smaller than the protein size, ptBLMs were formed only on the top of the hydrogel cushions. EIS showed good electrical sealing properties and in the presence of reduced cytochrome c, CV demonstrated that C\(\mathrm{C}\)O was incorporated into the gel-supported ptBLM in a functionally active form [247]. Recently, ptBLMs have been transposed from flat surfaces to spherical particles from \(\upmu\)m sized agarose gels beads [248; 249] to nm sized silica nanoparticles [250], to develop proteo-lipobeads (PLBs) applicable to fluorescence and UV/Vis spectroscopies, and laser-scanning confocal fluorescence microscopy (LSM) [133]. Multi-redox center proteins (MRPs), such as _cytochrome c oxidases_ (CoO) [249; 250] or photosynthetic reactions centers (RCs) [248; 251] were used as models to demonstrate that these enzymes keep their electron and proton transfer activities on PLBs, which opens a new way for investigation of reconstituted integral membrane proteins in bilayer lipid membranes.
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
## 4 Advances and Challenges of ULSASs
### Exploiting Superhydrophobic Anti-Icing Surfaces in Real Applications  
Based on the excellent anti-icing properties of ULSASs, many outdoor tests have been conducted to exploit their feasibility for practical applications. [PERSON] et al. installed a laser-fabricated superhydrophobic Ti64 metal sheet on an A350 port-side slat for a real flight test [177]. The total flight duration was 171.2 h, traveling mostly across Europe and experiencing two long flights to New Zealand and the Philippines. By measuring the contact angle changes and roughness, they found that surface superhydrophobicity was mostly lost after the flight. The degrading of superhydrophobicity was mainly ascribed to the failure of the low-surface-energy coating under long-time UV exposure. [PERSON] et al. conducted outdoor tests with laser-fabricated SHSs [178]. The test went from September 2016 to the end of March 2019 in Moscow, where regular snowfalls, sheet, and freezing rain accompanied by high humidity and frequent air temperature changes occurred frequently. They found that the bare aluminum surfaces had significant pitting corrosion in outdoor conditions, which greatly increased the snow/ice adhesion. For the SHSs, their icphobicity could be well maintained after continuous long-time rain and could effectively keep snow from accumulating when the wind speed exceeded 3-5 m/s, as shown in Figure 18. Even at a lower wind speed, the buildup of snow cups could still induce the spontaneous shedding of snow by their gravity. Although the practical test results also revealed some problems and limitations of ULSASs, these attempts and advances provided tremendous reference values for guiding ULSASs designs and accelerating their applications.  
Figure 18: Long-term outdoor anti-icing tests of ULSASs and pristine surfaces (reproduced with permission from [178]. Copyright (2019) American Chemical Society).
# Evaluation of Hazelnut Cake Flour for Use as a Milk Powder Replacer in Ice Cream
###### Abstract  
Hazelnut oil cake, a by-product in the cold-pressing of hazelnut oil, is a rich in valuable nutrients, which makes it a promising option for supplementation or as a raw material in the development of functional products. The aim of this work was to study the influence of partial or complete replacing of skim milk powder (SMP) with hazelnut press cake flour (HPCF) in varying ratios (0%, 25%, 50%, 75%, and 100%) on the physiochemical properties and sensory attributes of milk ice cream. The replacement modified the chemical composition of the ice cream mixture, resulting in a reduction (_p_ < 0.05) of milk solids non-fat (MSNF), protein, and carbohydrates content, while simultaneously elevating the hazelnut content, and total fat content. This modification influenced the rheological characteristics of the ice cream mixtures, leading to an increase in the consistency coefficient from 1.32 to 7.66 Pa s\({}^{\text{n}}\). Furthermore, a decline in overrun values (from 26.99% to 15.85%), an increase in hardness (from 6881.71 to 23,829.30 g), retarded melting properties, and variations in colour attributes were observed with higher concentrations of HPCF. In the sensory evaluation test, it was found that consumer acceptance was enhanced for the samples with partial substitution of SMP when compared to standard milk ice cream. The findings suggest that a replacement of milk powder with hazelnut cake by up to 75% is achievable, in order to obtain functional ice cream with adequate physicochemical and sensorial qualities.  
ice cream; hazelnut press cake; food by-products; physicochemical properties; sensory properties +
Footnote †: journal: applied sciences
## 3 Results
### Bmd
#### 3.2.2 BMD and Type of Trauma  
In the present study, 42 patients (82.4%) reported ankle fractures due to low-energy trauma, while 9 (17.6%) resulted from high-energy trauma. The correlation between the type of trauma and the DXA result was investigated. Among patients with a normal T-score, the fracture resulted from low-energy trauma in 66.7% of cases, in osteogenic patients in 82.8%, while in osteoporoptic patients, low-energy trauma was observed in 100% of the cases (Table 3).  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{3}{c}{Total Fractures} \\ \hline Type of Fracture & N & \% \\ \hline Unimalleolar & 10 & 19.6 \\ \hline Bimalleolar & 20 & 39.2 \\ \hline Trimalleolar & 21 & 41.2 \\ \hline Non-specificated & 0 & 0.0 \\ \hline Total & 51 & 100.0 \\ \hline \multicolumn{3}{c}{Normal BMD (T-Score \(>-1\) DS)} \\ \hline Type of Fracture & N & \% \\ \hline Unimalleolar & 2 & 16.7 \\ \hline Bimalleolar & 7 & 58.3 \\ \hline Trimalleolar & 3 & 25.0 \\ \hline Non-specificated & 0 & 0.0 \\ \hline Total & 12 & 100.0 \\ \hline \multicolumn{3}{c}{Osteopenia (\(-2.5\) DS \(<\) T-Score \(<-1\) DS)} \\ \hline Type of Fracture & N & \% \\ \hline Unimalleolar & 6 & 20.7 \\ \hline Bimalleolar & 12 & 41.4 \\ \hline Trimalleolar & 11 & 37.9 \\ \hline Non-specificated & 0 & 0.0 \\ \hline Total & 29 & 100.0 \\ \hline \multicolumn{3}{c}{Osteoporosis (T-Score \(<-2.5\) DS)} \\ \hline Type of Fracture & N & \% \\ \hline Unimalleolar & 2 & 20.0 \\ \hline Bimalleolar & 1 & 10.0 \\ \hline Trimalleolar & 7 & 70.0 \\ \hline Non-specificated & 0 & 0.0 \\ \hline Total & 10 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BMD correlation with type of fracture: the percentage of each type of fracture was calculated after dividing the patients into normal, osteogenic, and osteoporoptic.  
The association of fractures with low-energy trauma seems to be correlated with a reduction in BMD. The group of patients whose ankle fracture occurred due to low-energy trauma had a mean T-score (\(-\)1.87 SD) lower than the group reporting high-energy trauma (\(-\)1.09 SD), with a difference that proved to be statistically significant (\(p<0.05\)).
# Edge Computing Architecture for the Management of Underwater Cultural Heritage
## 3 Conceptual Framework  
This work proposes an innovative IoUT platform that leverages edge computing technologies for the real-time monitoring and management of UCH. By distributing computing resources and data storage closer to where data are generated through edge nodes,this architecture addresses the issues of network congestion and delays that plague traditional cloud-centric approaches. The platform seamlessly integrates real-time sensor measures with historical data stored in databases, painting a comprehensive picture. Its modular design allows the various components, such as sensors, edge nodes, and cloud services, to operate independently and collaboratively with other systems. This interoperability is crucial as UCH sites often span large areas requiring coordination across different types of infrastructure.  
The orchestration of the elements is conducted via the platform modules, utilising a layered architectural model. This design facilitates a phased approach to developing application and service control, encompassing three layers. The first of these, the sensor layer, integrates sensor and actuator hardware for data gathering and command execution. The second, known as the edge layer, consists of edge nodes that are accountable for data processing, storage, network, and additional infrastructure resources. The third, the application layer, incorporates a range of cloud services for data analysis and visualisation. Figure 1 illustrates the proposed edge computing platform.  
Data collected from the sensor layer are instantly processed by the edge layer nodes, which execute the initial filtering and diagnostics of critical information. This allows the system to detect and identify relevant events or anomalies so that critical data are the only data sent to the application layer. These data include unprocessed data points, which are filtered for further processing, analysis, storage, and visualisation in the cloud-based application layer. This interaction between the different layers makes it possible for the platform to provide a high level of system performance without delay and to automatically send critical information to the cloud, ensuring the platform provides real-time alerts on crucial data information. Figure 2 illustrates the proposed system's communication architecture.  
Below, the measuring systems and support elements are described. Some existing solutions have already been identified in the market. For example, we selected innovative and cutting-edge marine systems and technologies developed by H2O Robotics (H2O Robotics Ltd., Zagreb, Croatia) [44] and BlueRobotics (Blue Robotics Inc., Torrance, CA, USA) [45].  
Figure 1: The suggested edge computing platform architecture demonstrates how real-time data that are processed in the edge rather than via the cloud can help improve management and safeguard underwater cultural heritage sites by minimising reliance on cloud connectivity.
# Key Strata Identification of Overburden Based on Magnetotelluric Detection: A Case Study
## 3 Identification Mechanism of MT Detection
### Strata Identification Mechanism  
In order to identify strata, many researchers have studied the \"threshold value\" of AR [28,29]. Based on the basic principle of MT detection, the lithology and thickness of strata can be determined by studying the characteristics of AR curve of different strata, so as to identify each stratum. According to the principle of relative error of AR in one-dimensional medium model, a method of identifying strata is provided, as shown in Equation (6).  
\[\eta=\left|\frac{\sum_{i-1}^{n}\rho_{i}-n\rho}{n\rho}\right|\times 100\%,\ (i=1,2,3 \cdots n). \tag{6}\]  
Generally, \(\rho\) is uniform half-space resistivity without thin layer; \(i\) is each frequency point's AR in the presence of a thin layer. In this paper, the relative error of 5% AR is taken as the resolution standard; that is, when the relative error of overburden AR is greater than 5%, it will be regarded as a distinguishable anomaly.
# Plant Synthetic Promoters
## 2 Sections of Plant Promoters
### Core Promoter
Probably, plants' GA elements play a role similar to mammalian CpG islands; however, GA elements are not methylated [101]. Until now, the significance of the presented differences in the context of a proper transcription initiation has not been studied experimentally. Moreover, the distribution of TSS and the corresponding core promoters in the plant genome should not be seen as a stable process but rather as a dynamic one that could be altered by the introduction of foreign DNA into the genome [102]. The association of TATA-box promoters with conditional, regulated gene expression was recently tested [103]. The Coreless promoters indicated a bias towards uniform expression regulation in dicots but not in monocots. A comparison of different plant species found they tended to demonstrate similar expression patterns for a particular promoter. However, the orthologs of the uniformly expressed genes could be found more easily than those of conditional genes. Moreover, none of the screened core-promoter types was consistently associated with changes in gene expression patterns. Therefore, a correlation only occurs between the promoter architecture and the expression parameters [103]. The last cis-active element observed in plant core promoters is the CCAAT motif, which is localized on its borders, predominantly between \(-120\) and \(-40\) in dicots and \(-460\) to \(-140\) in monocots (Figure 1) [104]. The CCAAT motif is also observed in plant enhancers [105]. The CCAAT cis-element is bound by the CCAAT-binding factor (CBF), also known as nuclear transcription factor Y (NF-Y) [106]. The NF-Y transcription factor is composed of three subunits: NF-YA, NF-YB, and NF-YC [107]. Of these, NF-YB and NF-YC dimerize through their histone fold domain (HFD), which can bind DNA in a non-sequence-specific fashion. The obtained DNA-protein complexes are bound by NF-YA to build trimeric structures. Upon trimerization, NF-YA specifically recognizes the CCAAT-box [105]. In soybean (_Glycine max_), the NFYA interacts with FVE, which is complexed with histone deacetylase HDA13 to potentially repress transcription by reducing H3K9 acetylation at target loci [108]. An important role in the regulation of gene expression is played by 5'-UTR regions. Some of the trans-factor binding sites, such as those for ERF, SBP, G2-like, GATA, and ARR-B, are concentrated within the region localized 200 bp downstream from TSS in _A. thaliana_[109; 110].
## 5 Discussion
Hence, using different modulations for packet re-transmissions can be used as a mechanism to address scenarios where nodes are subject to multi-path propagation and where multiple wireless technologies coexist in the same band. Until today, modulation diversity has been limited in low-power wireless communications due to the lack of standards and the availability of commercial transceivers. However, the IEEE 802.15.4-2015 standard included the definitions of the IEEE 802.15.4g amendment, which defines a variety of physicallayers covering a wide variety of frequency bands, allowing to meet the requirements of different types of applications. In addition, off-the-shelf transceivers that implement all the physical layer definitions of the IEEE 802.15.4-2015 standard are now available, which allows the nodes to use different modulation schemes using only one radio transceiver. Thus, modulation diversity may now be used to deal with the different challenges of low-power wireless communications that need to be faced in industrial environments.