esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Scaffolds Tested  
Two osteochondral systems were used in this study--titanium matrix reinforced collagen tri-layered composite osteochondral scaffold (Ti-collagen) and collagen-hydroxyapatite tri-layered osteochondral scaffold (HA-collagen). The lower layer of the Ti-Collagen scaffold corresponding to bone tissue was designed as a porous Ti matrix to allow for bone ingrowth and vascularisation. Pore sizes of 300-800 \(\upmu\)m are considered beneficial for bone ingrowth [23]. As such, the Ti matrix was designed with a strut diameter of 0.5 mm and a pitch size of 0.5 mm. The Ti was manufactured from Ti6 Al4V alloy using a Laser Sintering (LS) system for metal (Lincotek, Trento, Italy) in compliance with ASTM F2924, as previously described [12]. The description of the osteochondral scaffolds has been reported elsewhere [24]. Four test groups were arranged within this research; Group 1: HA-collagen scaffold; Group 2: HA-collagen scaffold with BMC; Group 3: Ti-PLA-collagen scaffold; and Group 4: Ti-PLA-collagen scaffold with BMC. The test group arrangement is summarised in Table 1. The HA-collagen scaffolds contained a lower layer of 30% collagen and 70% hydroxyapatite, a middle layer of 60% collagen and 40% hydroxyapatite, and a top layer of 100% collagen, as schemed in Figure 1a. The preparation of these scaffolds was outlined in [25]. Alternatively, Ti-PLA-collagen/PLGA scaffolds contained a lower layer composed of a porous titanium lattice generated with direct metal laser sintering, a middle layer of polylactic acid generated with fuse deposition printing, and an uppermost layer of collagen/poly(lactic-co-glycolic acid) generated with casting and freeze-drying, as shown in Figure 1b. The biomaterials were selected because research has shown that metals are biocompatible, chemically stable, and easily manufactured to provide strong mechanical support to the overlying collagen layer [12; 26], while polylactic acid middle layer joins the collagen layer with the Ti-matrix together to form a tri-layered osteochondralscaffold [27; 28; 29]. Scaffolds which included BMC generated from the test subjects were submerged in 1.0 mL of solution for 20 min prior to implantation.
## 3 Case Study  
### Problem Description  
In this study, the reliability of a deep soft rock highway tunnel is analyzed using the proposed Adam-Bayesian-GPR-based MCS reliability calculation method. As shown in Figure 2, the horseshoe-shaped highway tunnel with a height of 10.23 m and span of 12.46 m is located in Gansu Province in China. The tunnel's surrounding rock is fully strongly weathered granite gneiss. The design parameters of the tunnel initial support are as follows: non-prestressed rock bolt length \(\phi\) 25-5 = 3.5 m; grille steel frame spacing 25 = 0.6 m; sprayed concrete thickness = 25 cm; steel \(\phi\) eight-mesh spacing = 20 \(\times\) 20 cm. The design parameter of the tunnel second lining is a reinforcement thickness = 33-45 cm.  
The two-dimensional numerical simulation is completed using ABAQUS. The top of the model is taken as a free surface, and both sides of the horizontal direction (X direction) boundaries and bottom boundary are constrained using displacements. In the Y direction (longitudinal direction of the tunnel), a single-layer element is adopted, and the displacement in the Y direction is strictly restricted. Quadrilateral elements are selected for the model. The FEM model is 100 m in the horizontal direction and 149.2 m in the vertical direction, with 5600 nodes and 2736 elements. The horizontal (X direction) lateral pressure of 1.2-times self-weight stress is applied on both sides. A self-weight stress field is applied in the model vertical direction. The lateral pressure coefficient is \(\lambda\) = 1.2; the surrounding-rock bulk density = 24.0 kN/m\({}^{3}\).  
The elastic-plastic model and the Mohr-Coulomb yield criterion are adopted to simulate the surrounding rock. The rock-bolt-combined composite concrete lining is installed as a support. The design parameters of the rock bolt, initial support, and second lining are as follows: i. rock bolt [PERSON]'s modulus \(E_{\text{bolt}}\) = 210.0 GPa; ii. rock bolt [PERSON]'s ratio \(\
u_{\text{bolt}}\) = 0.3; iii. [PERSON]'s modulus of grillage steel frame shotcrete \(E_{\text{IS}}\) = 26.311 GPa; iv. [PERSON]'s ratio of grillage steel frame shotcrete \(\
u_{\text{IS}}\) = 0.22; v. concrete lining [PERSON]'s modulus \(E_{\text{CL}}\) = 28.0 GPa; vi. concrete lining [PERSON]'s ratio \(\
u_{\text{CL}}\) = 0.27.  
Figure 2: Cross-section of highway tunnel (unit: cm).  
The displacement values of each measurement point or line under varied surrounding-rock parameters are used as the dataset for the proposed method in this study. The layout of monitoring points or lines is shown in Figure 3. The measured data of a section in Table 1 are selected as a representative.
## 3 Proposed Methodology
### Data Acquisition
#### 3.1.1 Opportunity Dataset  
In their investigation, [PERSON] et al. [34] introduced the Opportunity dataset for activity recognition, comprising 72 sensors capturing naturalistic activities in a sensor-rich setting. This dataset incorporates diverse sensors, including those embedded in objects, worn on the body, and placed in the environment. HAR research categorizes data from body-worn sensors, specifically targeting four types of movements and seventeen microactivities. For upper-body monitoring, five IMUs were strategically placed: on the back of the torso (Back), the left upper arm (LUA), the left lower arm (LLA), the right upper arm (RUA), and the right lower arm (RLA). Additionally, two sensors were attached to the user's footwear. The study involved 12 subjects, utilizing 15 interconnected sensor systems with 72 sensors across ten modalities. These sensors seamlessly integrate into the subject's surroundings, objects, and attire, making the dataset valuable for assessing various activity recognition approaches. Due to its unique attributes, the Opportunity dataset is exceptionally suitable for assessing various action identification methodologies.  
Initially framed as an 18-category multi-classification challenge, the Opportunity dataset was streamlined to 17 categories by excluding the null class, an extra label. Table 1 shows a comprehensive list of these categories. From the Opportunity dataset, we collected 90 samples for each window, employing 3-second intervals with a sampling rate of 30 Hz.  
Figure 1: Positions of wearable sensors in the Opportunity, PAMAP2, and REALWORLD16 datasets.
## 2 Related Work  
### Weather Multivariate Time Series Forecasting  
Multivariate time series data collected from weather stations serve as a fundamental cornerstone in modern weather forecasting systems. These data are characterized by their high dimensionality and complex temporal dependencies [13]. Traditional weather forecasting methods based on multivariate time series data primarily rely on various statistical approaches, including Autoregressive Integrated Moving Average (ARIMA) models, which assume linear relationships between variables, along with extensions such as SARIMA [14] for seasonal data. Other techniques have also been widely used, such as exponential smoothing [15] and Kalman filtering. Although these methods are interpretable and easy to implement, they often struggle to capture the complex and nonlinear relationships inherent in meteorological data, which may lead to unreliable predictions.  
Driven by improvements in deep learning algorithms along with increases in the scale of available weather data and parallel computational hardware resources, recent advances in deep learning have revolutionized the field of weather forecasting and enabled the training of larger-scale models [16]. These new models are capable of effectively capturing complex and nonlinear patterns and dependencies within meteorological data in an end-to-end manner while eliminating the need for time-consuming manual feature engineering [17]. Thus, interest in deep learning models for NWP tasks there has been developing rapidly. For example, multiple deep learning methods such as Pangu-Weather [18] and FengWu [19] based on global-scale meteorological data in mesh space have shown the ability to outperform traditional physical models in terms of both prediction performance and efficiency.  
For multivariate time series point data collected from weather stations, Recurrent Neural Networks (RNNs) and variants such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks have demonstrated remarkable success in prediction and analyzing tasks [20]. The stacked LSTM (stackLSTM) architecture stands out for its exceptional capacity to capture complex temporal dynamics at various time scales in weather time series by leveraging multiple LSTM layers [21; 22; 23]. Recently, several studies have explored the potential of transformers in the context of weather multivariate time series [24], leveraging the strength of transformers in capturing complex or long-term dependencies in sequential data to offer a promising alternative to traditional RNN-based approaches.
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 3 Finite Element Model
### Imperfections and Residual Stresses
#### 3.4.1 Equivalent Geometric Imperfections  
This approach combines the effect of the geometric imperfections and residual stresses into a pure geometric imperfection with increased amplitude. The shape of the equivalent imperfection is usually chosen to be the first eigenmode shape, obtained by an initial linear buckling analysis (LBA). The magnitude of imperfection is taken from the Annex C of EN1993-1-5 [4] which gives, for the case of local buckling, an equivalent imperfection of b/200, with b being the internal length of the plate. An extensive study of the equivalent imperfection features such as the effect of the chosen eigenmode, number of half-sine waves along whole model length, the direction of the initial buckling shape (inward or outward) was presented by [PERSON] and [PERSON] [42] in a detailed manner.
## 2 Materials and Methods
### Remote Sensing of Disease Infestation  
Remote sensing was used for detecting appearance and possible infestation of cercospora on sugar beet leaves. Multispectral images should help for faster monitoring of growth, development, and attacks of the cercospora during the season. Cercospora infestation on sugar beet leaves can be detected using vegetation indices. Any damage to the leaves or the appearance, physical presence, is manifested in the way that the sugar beet leaf reflects sunlight differently. Images were taken (multispectral reflectance) from mid-June until the end of July. The plot was recorded with a multispectral camera, the MicaSense RedEdge 5-band sensor. These images were used to calibrate Planet Explorer 3 \(\times\) 3 m/pixel satellite images with a correction of 0.25 \(\times\) 0.25 m/pixels from the same source [25]. Any change on the leaf is manifested with a different reflection of light at different wavelengths of the light spectrum. The change in the spectrum is most often visible in the near-infrared, red, and green spectra of light. For that reason, different vegetation indices were selected, calculated, and tested, which should show the difference between a healthy and a damaged leaf. It was used triangular greenness index (TGI, 1). This index approximates the area of a  
Figure 3: Field treatment (tractor field crop sprayer and sugar beet crop in moment of fungicide application).  
triangle bounding a leaf reflectance spectrum, where the vertices are in the red, green, and blue wavelengths.  
\[\text{TGI }=\pm 0.5\left[(\lambda_{\text{RED}}-\lambda_{\text{BLUE}})(\rho_{ \text{RED}}-\rho_{\text{GREEN}})-(\lambda_{\text{RED}}-\lambda_{\text{CREEN}})( \rho_{\text{RED}}-\rho_{\text{BLUE}})\right] \tag{1}\]  
The lambda (\(\lambda\)) terms represent the center wavelengths of the respective bands. The Rho (\(\rho\)) terms represent the pixel values of those bands. The original TGI equation used 670 nm, 550 nm, and 480 nm for the red, green, and blue wavelength centers, with a 10 nm band width [26]. The TGI is highly correlated with leaf chlorophyll content. TGI values are positive when the green reflectance is greater than a line between the red and blue vertices. This corresponds to green vegetation. TGI is negative when the green reflectance is less than the line between the red and blue vertices. This corresponds to features such as red soils. Because this index measures red, green, and blue wavelengths, it is suitable for use with unmanned aerial vehicle (UAV) sensors with RGB cameras.
## 3 Results and Discussion  
Results presented in this section are reported according to the stages previously described. Four experiments were performed in order to identify the best classification algorithm for the aimed application. Two more experiments were carried out to find out the feature set that best describes the chromosome in the context of automatic classification.
# Effect of Sowing Dates on Fatty Acids and Phytosterols Patterns of _Carthamus tinctorius_ L.
## 1 Introduction  
Lipids, which are important components in the human diet, can be provided by plant oilseeds cultivated worldwide. They contain a high concentration of unsaturated fatty acids known to prevent cardiovascular diseases [1]. Oils contain also minor lipids like phytosterols that prevent the free radicals' damages by lowering the cholesterol level in human blood. Two to three grams of phytosterols are sufficient to reduce the LDL cholesterol fraction [2]. Their cellular antioxidant activity is related to their capacity to transform allylic free radical to more stable free radicals, particularly under frying conditions [3]. Considered as active food ingredients, phytosterols are used in diverse commercial food products, such as milk, cheese, spreads or chocolate products. Due to their high added value, chemically modified phytosterols are also used for non-food purposes as raw materials in the production of pharmaceuticals and cosmetics [4]. Mainly cultivated in Asia, North and South America, safflower (_Carthamus tinctorius_ L.) has several known industrial applications such as food, cosmetics, pharmaceuticals and feed industries. Safflower represents a very interesting crop to give an alternative option for countries that cannot afford to consume olive or other functional oils [5] and todevelop non-food industrial applications, due to the presence of functional components (saturated and unsaturated fatty acids and phytosterols). However, the seed yield and oil remains low due to the poor management techniques [6]. Moreover, it is well known that environmental conditions affect oil content and fatty acid composition of safflower and other oilseed crops [7,8]. A better understanding of the effects of management techniques on fatty acid and phytosterol contents and compositions content is important to further improve oil quality under the restricting environmental conditions. Only few studies aimed at studying the effects of environmental conditions on the unsaponifiable oil composition in oil crops [9,10].  
Here, the effect of the delay of sowing (conventional and late) in fatty acids and phytosterols composition was evaluated. It could help breeders and producers to understand the influence of environmental conditions on the composition of safflower oil. In addition, the phytosterol contents in the hull and in the embryo were measured to understand their distribution in the seed that also could give information to the breeders to investigate seeds with modified proportions for specific applications.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 4 Results and Discussions
### Experiment Comparison Analysis  
To further evaluate the performance of the parameters affecting the Hybrid Genetic Algorithm (HGA), we conducted experiments by varying the number of tasks, servers, and clients. The outcomes were compared against those obtained using the Longest Task First (LTF) algorithm, the Shortest Task First (STF) algorithm, and the Dynamic Synchronous Scheduling (DSS) algorithm, all of which are designed for synchronous scheduling scenarios. Each experiment was averaged over multiple runs to ensure reliability.  
Figure 8: HGA optimization results across different scenarios. The results are displayed in panels for the following scenarios: small-scale (**a**), medium-scale (**b**), and large-scale (**c**).  
Figure 7: Results of the hybrid genetic algorithm  
The key performance metric used was the maximum completion time of the scheduling strategies derived by the algorithms. A shorter maximum completion time indicates a more efficient scheduling strategy and, consequently, better algorithm performance.
# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 4 Discussion
### Limitations of the Study  
Although this study offers a thorough bibliometric analysis of anomaly detection in blockchain systems, several limitations should be acknowledged.  
The data sources were limited to the Web of Science Core Collection (WoSCC). While WoSCC is a reputable and extensive database, restricting the analysis to this single platform may have excluded relevant studies indexed in other databases such as Scopus, IEEE Xplore, or Google Scholar.  
The study focused exclusively on peer-reviewed research articles and reviews within the Science Citation Index and Social Sciences Citation Index. This focus excludes other valuable sources like conference proceedings, books, and gray literature, which might provide additional perspectives on emerging techniques and practical applications in blockchain anomaly detection.  
The language restriction to English publications introduces a bias, potentially overlooking important contributions published in other languages such as Chinese, Spanish, or French. This may limit the inclusion of diverse regional insights and alternative approaches to blockchain security.  
The bibliometric analysis relied on the CiteSpace 6.4.R1 software. Although CiteSpace is a recognized and specialized tool, the results may vary depending on the parameters, algorithms, and visualization methods used. Employing different analytical tools or methodologies could produce alternative yet valid interpretations.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Active Frequency Drift (AFD):  
In this method, a distorted output of the inverter feedback to the utility at a slightly higher or lower frequency at a point of common coupling. AFD frequency (\(\prime\)) can be calculated as:  
\[f^{\prime}=f(1/(1-Cf)), \tag{7}\]  
where \(Cf\) is the chopping fraction. It is the ratio of zero conduction interval to half of the period of voltage waveform, which determines the difference between the frequency of inverter's output current and the frequency of grid's voltage.  
The added distorted frequency is used as a factor for detection of the islanding condition; however, under normal conditions, the added distorted frequency cannot change the frequency. The weakness of this method is that it requires a decrease in the output quality.
## 3 Results and Discussion
### Plasma Treatment  
The effect of plasma exposure was observed only on the third day of plant development in the mode 16 kHz/1.5 kV (discharge at RMS voltage of 1.5 kV and 16 kHz for 1 min). In other modes, either no effect was observed, or it was inhibitory in nature. The action of the discharge in all considered modes did not affect the relative frost resistance.  
When the action of the plasma and the constant electric field (mode 16 kHz/1.5 + 5 kV) was combined, the effects were combined. The obvious stimulating effect on the morphophysiological characteristics of seedlings was lost on the third day, but at the same time, the relative frost resistance increased at freezing temperatures of \(-18\) and \(-20\)\({}^{\circ}\)C, similar to the variant in which only a constant electric field was applied.  
The mode 16 kHz/1.5 kV was researched in combination with the disinfectant as this mode had the greatest influence on the development of seedlings. The effect was found to be similar to that on untreated seeds. On the third day, stimulation of seedling development was observed. On the third and seventh days, the indicators did not differ significantly from the control ones. Frost resistance of seedlings obtained from seeds treated with both the disinfectant and plasma at a freezing temperature of \(-14\)\({}^{\circ}\)C was higher than in the control plants and higher than that of seedlings treated only with the disinfectant. At all other temperatures, the stability was comparable to the control plants but lower than that of the variant treated only with the disinfectant.  
The constant electric field in combination with the action of a gas discharge in the 16 kHz/1.5 kV mode, applied to seeds pretreated with the disinfectant, did not change the values regarding treatment without the application of a constant field.  
Electrophysical treatment with plasma and a constant electric field were combined, at least for short exposure times, and applied to wheat seeds. This allows the use of these techniques in real industrial solutions, where it is necessary to minimize the time of seed treatment. However, the combination with preliminary chemical seed preparation with a disinfectant has obvious limitations. It could be assumed that the aggressive action of the discharge products destroys the active substance of the disinfectant, but, under the action of a constant electric field, the negative effect is more pronounced. Thus, the reasons for this negative interaction reside in physiological processes. Moreover, the electric field plays an important role in this effect. The solution to this problem may be to change the sequence of seed treatment, i.e., the seeds should be treated with a disinfectant after treatment with electrophysical methods.
###### Abstract  
The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9-4.2% for sunflower oil, 6.5-10.7% for pomace olive oil and 8.0-12.8% for refined olive oil; with the limit of detection (LOD) typically being 3-12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2-14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.  
food security; food adulteration; Raman spectroscopy; SORS; chemometrics