esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Challenges in Industrial Control Systems
Given the complexity of the SCADA systems infrastructure and how composite the cybersecurity assessment is, this may be an expensive outlay to deal with [51]. Another focal point is that Information Systems and SCADA operating infrastructures are directly linked and cyber-dependent. Thus, modern RAs methodologies must take into account this linkage when evaluating SCADA cyber-risks [51] and mind the cross-sector dependencies [7], which facilitate the entry points to access the SCADA network. Industrial Internet of things, because of its extending internet connectivity, is a new big challenge, requiring advanced security measures to be undertaken to protect it from cyber-threats, in which the latest communication protocols should be considered. Social media communication capability in the operation of SCADA technical systems, with phenomena such as alerts or spurious news on hazards, dangers, opportunities, such as false fire alarm, may distort the level of response in operational and emergency situations [5]. Consequently, a successful RA approach for SCADA CIs may have the characteristics listed below:  
* A structured body of cybersecurity knowledge using Knowledge Management practices to organize the knowledge [13; 55]. * Adoption of business modeling and simulation techniques to carry out different real-life cyber-attack scenarios and experiment with the results [7; 55]. * Taking into account rational decision-making techniques for probabilistic RAs of complex cyber-attack scenarios. * Identify common or cross-border scenarios throughout national and regional limits [7]. * Involvement of all CI operators, including entities of both public and private sector participating, in order to have a clear and detailed view of SCADA cyber-risks at the asset-individual level and to identify the overall cyberdependencies across SCADA Networks and hence detecting the impact at the system level [55]. * Be compliant with regulations and directives or international standards applying to the supply chain (e.g., IMO practices and ISO standards). * Introduce collaborative practices to facilitate the sharing and transfer of risk-related information across supply chain operators. Moreover, there is the need for new risk and resilience assessment approaches that may assess and demonstrate the ability to develop and implement effective RA strategies and ensure SCADA systems resilience against aftermath cyber-incidents. The MITIGATE security assurance services are capable of responding to these requirements. The current work aims to raise MLoSCs operators' awareness of ICS security and assist them in learning how to recognize and react to an ICS cybersecurity SCADA incident.
# Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects
## 2 Background
### Self-Admitted Technical Debt
The results can be used to plan TD management, study patterns, and provide recommendations to developers. Focusing on the prioritization aspect, an interesting work is the study conducted by [PERSON] et al. in ref. [12] where a prioritization scheme to minimize SATD in software development to aid in decision-making before software release to minimize high maintenance overheads is introduced. The scheme focuses on identifying, examining, and estimating network effort for prioritized tasks. The results showed that design debts are highly prone to software bugs, requiring a network effort of 10 to 25 commented LOC per SATD source file to address the few vital tasks. Developers often try to pay back design and test debt first, but system or infrastructure debt is often ignored. There are practices used to assist in SATD management, such as maintaining a list of prioritized SATD, grouping related technical debt items, and resolving development tasks jointly. However, determining the priorities of SATD and other works and convincing developers not to introduce SATD can be challenging [12]. During the same time frame, in ref. [21], [PERSON] et al. published a survey about SATD where they analyzed current approaches and techniques for detection, comprehension, and repayment. It identifies open challenges, areas missing investigation, and potential future research avenues. However, the majority of modern state-of-the-art methods use pattern matching to recognize SATD remarks, achieving great accuracy but relatively low recall. As a result, they could overlook several SATD remarks and lack sufficient practicality [22]. Due to this, in ref. [6], a method for automatically identifying SATD that combines source code comments, commit messages, pull requests, and issue tracking systems is suggested and assessed. [PERSON] et al. [6] discuss technical debt, which is defined as the compromise of maintainability and evolvability of software systems in the long term for the sake of short-term goals. Technical debt can accumulate if not proactively managed, resulting in a maintenance crisis. While there has been a fair amount of work studying SATD management in Open Source projects, SATD in industry is relatively unexplored. [PERSON] et al. in [23] discuss an exploratory case study with an industrial partner aimed at understanding core characteristics, developers' attitudes, triggers for SATD introduction, relations between sources, practices used, and challenges and tooling ideas for SATD management focusing on industrial context. The study, with the aim of understanding and improving the nature and management process of self-admitted technical debt in practice from the point of view of software engineers, analyzed self-admitted technical debt in source code comments, issue tracking systems, and commit messages in the context of the embedded systems industry.
# Grape By-Products in Sustainable Cosmetics: Nanoencapsulation and Market Trends
## 5 Grapes and Their By-Products for Cosmetic and Skincare Applications  
### Identified Skincare Biological Activities  
As mentioned before, there are several types of grape by-products exhibiting potential health benefits. Since these ingredients are biodegradable and have therapeutic and functional qualities that are of value, they have an advantage over synthetic compounds [29]. In Table 1, a list of studies regarding these by-products and their skincare biological effects can be seen. In most of the presented cases, the grapevine variety used is _Vitis vinifera_ L., and the explored by-products comprise extracts from seed and oil, pomace and oil, grape skins, shoot, lees stalk and stem, and seed paste. Nonetheless, other studies tested the biological activities of other grapevine varieties, such as _Vitis labrusca_ L. [32]. Moreover, several other cultivars, such as _Mamaia_, _Carignano_, _Tempranillo_, _Touriga Nacional_, _Touriga Francesa_, and _Tinta-roriz_, have been explored [29; 30; 33; 34].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grapevine Variety** & **Grape By-Product** & **Type of Study** & **Biological Effect** & **Reference Source** \\ \hline \hline \multirow{2}{*}{_Vitis vinifera_ L.} & \multirow{2}{*}{_Seed extract_} & Human Dermal & Increased cell viability, & \multirow{2}{*}{[35]} \\  & & Fibroblasts & UVA protection & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & \multirow{2}{*}{_Seed extract_} & In vitro & UVA photoprotection, & \multirow{2}{*}{[36]} \\  & & antioxidant activity & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & \multirow{2}{*}{_Seed extract_} & In vitro & & [26] \\ \hline \multirow{2}{*}{_Vitis labrusca_ L.} & \multirow{2}{*}{_Pomace_} & In vitro & & [32] \\ \cline{2-2} \cline{5-5}  & & & & [30] \\ \hline \multirow{2}{*}{_Mamaia_} & \multirow{2}{*}{_Pomace extract_} & In vitro & & [34] \\ \cline{2-2} \cline{5-5}  & & & & [34] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grape by-products and their potential skincare biological effect.  
Figure 2: Grape by-products.  
Since many ingredients in the formulation of cosmetics are prone to oxidation, the presence of antioxidant agents, as the ones present in the grape by-products, is essential for their functionality and maintenance, extending their quality and shelf life.
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 3 Methods
### Seismic Multi-Attribute Method  
The seismic multi-attribute method for predicting coal thickness primarily involves two aspects: first, researching and selecting optimal seismic attributes related to coal seams; and second, characterizing the complex mapping relationship between coal thickness and various influencing seismic attributes to establish a coal thickness prediction model.
## 3 Illustrative Examples
### Example I: A Two-Layered Cohesive Slope
#### 3.1.2 Effect of SOF on Slope Reliability Analysis  
This part investigates the effect of SOF on the reliability of the slope. First, the conventional analysis where the same SOFs assumed for different soil layers were investigated, as shown in Figure 7. It is found that the SOF has a significant influence on the failure probability of the slope for different combinations of ACFs. The \(P_{f}\) decreases as the vertical scale of fluctuation (\(\delta_{v}\)) or horizontal scale of fluctuation (\(\delta_{h}\)) decreases. In addition, compared with \(\delta_{h}\), \(\delta_{v}\) is more sensitive to the \(P_{f}\). Such observations are expected and consistent with available studies in the literature [8].  
Then, the effect of multiple scale SOFs on the reliability of the slope was analyzed, where for different soil layers, the SOFs were different. Various parametric studies on the horizontal and vertical SOFs were investigated, and only one parameter was varied in each study while keeping others as the same as those for the baseline case. For simplicity purposes, only the cases where SEF and CEF were used for describing the soil spatial variability were considered here. Figure 8 shows the influence of the SOFs of clay 1 on the slope reliability evaluation. It shows that \(P_{f}\) increases as the horizontal or vertical SOF (\(\delta_{h1}\) or \(\delta_{v1}\)) of clay 1 increases for the two cases. Conventional analyses, which assume the  
Figure 5: Cumulative distribution functions (CDFs) for different cases: (**a**) Case 1–5; (**b**) case 1 and 6–9.  
Figure 6: Probability of failure for different cases.  
same SOFs for different soil layers, can therefore be overestimated or underestimated. When only the SOFs for clay 2 are changed, similar results to those shown in Figure 9 can be observed. However, the \(P_{f}\) is not sensitive to the variation of the horizontal SOF \(\delta_{l\Omega}\), indicating that the SOF in clay 1 has a more significant effect on the \(P_{f}\) than the SOF in clay 2 does.  
Figure 8: Influence of SOFs in the lower stratum on slope failure probability: (**a**) Slope failure probability versus \(\delta_{v1}\); (**b**) Slope failure probability versus \(\delta_{v1}\).  
Figure 7: Influence of scale of fluctuation (SOF)on slope failure probability.  
Figure 9: Influence of SOFs in the upper stratum on slope failure probability: (**a**) Slope failure probability versus \(\delta_{v2}\); (**b**) Slope failure probability versus \(\delta_{h2}\).
# Trajectory Tracking with Adaptive Robust Control for Quadrotor
###### Abstract  
This work proposes three robust mechanisms based on the MIT rule and the sliding-mode techniques. These robust mechanisms have to tune the gains of an adaptive Proportional-Derivative controller to steer a quadrotor in a predefined trajectory. The adaptive structure is a model reference adaptive control (MRAC). The robust mechanisms proposed to achieve the control objective (trajectory tracking) are MIT rule, MIT rule with sliding mode (MIT-SM), MIT rule with twisting (MIT-Twisting), and MIT rule with high order sliding mode (MIT-HOSM).  
adaptive control; MIT rule; sliding mode; trajectory following +
Footnote †: journal: Applied sciences
## 4 Proposed Anomaly Detector  
Our proposed anomaly detector follows the hybrid design approach [5]; a normal model is trained first, and its output is fed to the AD component. In addition, a technique for generating synthetic examples is designed for this system. The proposed architecture is presented in Figure 4.  
In Figure 4, a time series (consisting only of \"normal\" observations) first undergoes delay embedding and is passed to a forecasting algorithm. The resulting forecasts, as well as the actual next-step observation, form the AD training dataset. This dataset is then expanded by adding synthetic examples to the AD dataset, in effect oversampling the \"normal\" predictions. This resampled dataset is then used to train the AD algorithm. Outputs from the AD are then passed to some alarm interface, be it an automated log, a warning signal for a human operator, etc. We discuss the three components of this architecture below, starting with the normal model and AD to provide context for the synthetic-data-generator algorithm.
# Functional and Healthy Features of Conventional and Non-Conventional Soundoughs
## 3 Nutritional and Functional Features of Sourdough
### Effects of Organic Acids  
Most of the valuable properties attributed to sourdough are due to the acidification activity, due to LAB and AAB. In fact, during fermentation, several organic acids, e.g., lactic, acetic, citric, pyruvate and succinic acid are produced. Among them, lactic and acetic acid are the most important as they can greatly affect the aroma profile and rheological properties of sourdoughs [58]. Their production depends upon several factor including flour type, starter used, metabolic activity, technological performance, and acidification properties of the wheat sourdoughs [63]. Organic acids produced during sourdough fermentation can have preservative and antimicrobial effects, improving storability and safety [64], as well as have positive health effects. For instance, acetic acid, propionic and lactic acid have the capacity of lowering the insulin response [65]. The mechanisms of these acids seem to vary; lactic acid acts to lower the rate of starch digestion in bread while propionic and acetic acids seem to extend the gastric emptying rate [66].  
As suggested by the work of [PERSON] et al. [67], lactic acid, in particular, is designed to lower blood sugar and promote an interaction between starch and gluten favoring the reduction of starch bioavailability.
## 3 Results and Discussion
### Minimum Transverse Reinforcement  
According to ACI 318-19 [19], in order to successfully distribute the internal forces, the amount of confinement should be greater than that of Equations (6) and (7). Here, the confinement provision does not consider axial compression. However, it does consider the effects of the compressive strength of concrete \((f_{c}^{\prime})\) and the yield strength of the transverse reinforcement (\(f_{yt}\)). In this research, NEWRC1 and NEWRC5 had the same configuration except for the yield strength of the transverse reinforcements (\(f_{yt}\)). In NEWRC1, it was 813 MPa, whereas, for NEWRC5, it was 341 MPa. Therefore, according to Equations (6) and (7), the minimum transverse reinforcement required would be different for NEWRC1 and NEWRC5 when considering the reinforcements' yield strength. Despite this, the same amount of transverse reinforcement was provided.  
\[\mathrm{A_{v,minACI}}=0.062\sqrt{\ell_{\mathrm{c}}^{\prime}}\frac{\mathrm{b_{ w}s}}{\mathrm{f_{yt}}}\ \geq\frac{0.35\mathrm{b_{w}s}}{\mathrm{f_{yt}}}\ (\mathrm{MPa}) \tag{6}\]  
\[\mathrm{\rho_{t,minACI}}=\frac{\mathrm{A_{v,minACI}}}{\mathrm{b_{w}s}}=0.062 \frac{\sqrt{\ell_{\mathrm{c}}^{\prime}}}{\mathrm{f_{yt}}}\ \geq\frac{0.35}{\mathrm{f_{yt}}}\ ( \mathrm{MPa}) \tag{7}\]  
In Figure 15, the backbone curve, cumulative energy dissipation, and equivalent viscous damping ratio between NEWRC1 and NEWRC5 are shown. In Figure 15, the backbone curve for both the specimens is compared. It was seen that they had similar initial stiffness, elastic-plastic stage, and failure process. The specimens recorded a similar peak load, as only a minor difference of 1.5% was observed between them. Further, in Figure 15, the cumulative energy dissipated by both the specimens is shown. It can be observed that at every drift ratio, both dissipated similar energy. The ductility ratio for NEWRC1 was 5.49, whereas for NEWRC5, it was 5.81, i.e., almost a 6% increase. Both specimens also had similar residual drift ratios, as they recorded a residual drift ratio of less than 1% at even a larger drift ratio of 4%. Therefore, the comparison shows that even though a less amount of transverse reinforcement was provided in NEWRC5 than required by ACI 318-19 [19], it did not impact its seismic performance, as the seismic performance between NEWRC1 and NEWRC5 was similar.
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 2 Related Works
[19] proposed a rapid and effective scene depth estimation model based on underwater light attenuation prior (ULAP). In this method, the coarse estimated transmission map was calculated over local patches of the underwater image, resulting in blocking artifacts in the transmission \(t_{c}(x)\). In order to solve such a problem, guided filter [11] was used to refine the coarse \(t(x)\), which was computationally expensive. [PERSON] et al. [20] proposed a depth estimation method based on image blurriness and light absorption, which can be used in the image formation model to restore and enhance underwater images. Physical model unrelated methods, such as the well-known Fusion [7], directly modify image pixel values to improve visual quality. [PERSON] et al. [21] presented a method dubbed as dual-image Rayleigh-stretched contrast-limited adaptive histogram specification, which integrated global and local contrast correction. [PERSON] et al. [22] proposed a Relative Global Histogram Stretching (RGHS) method, which obtained dynamic parameters based on the original image. It stretched and adjusted each channel, meanwhile used bilateral filtering to eliminate noise, enhanced detail information, and finally corrected colors. RGHS has achieved an excellent visual effect on many underwater blurred images, but it does not solve the color deviation problem. Underwater image datasets are crucial to the development and evaluation of underwater image enhancement methods. [PERSON] et al. [10] presented the UIEB dataset, which contains 890 underwater images with corresponding reference images for training, and another 90 pairs of images for testing. Based on UIEB, they proposed a deep learning based underwater image enhancement network, named Water-Net, to demonstrate the potential of the UIEB dataset. Despite the effectiveness of Water-Net, it is a convolutional neural network and sometimes not suitable for power-limited or memory-limited devices. Recently, neural networks are also used to synthesize diverse underwater scenes and to build the neural rendering underwater dataset [2].
# Combination of Variable Loads in Structural Design
## 1 Introduction
### Current Theory
The current draft Eurocodes [2] include equations for variable load combination factors \(\psi_{0}\):\(Q_{1}\) is dominating:  
\[\psi_{0,2}=\frac{F_{Q_{2,max,T_{1}}}^{-1}\left\{\phi\left(\alpha_{Q_{2,max,T_{1}}} \beta_{t}\right)\right\}}{F_{Q_{2,max,T}}^{-1}\left\{\phi\left(\alpha_{Q_{2,max,T }}\beta_{t}\right)\right\}},\text{ with }F_{Q_{2,max,T_{1}}}\left(q\right)=F_{Q_{2,max,T}}^{ \left(\tau_{1}/T\right)}(q) \tag{5}\]  
\(Q_{2}\) is dominating:  
\[\psi_{0,1}=\frac{F_{Q_{1,max,T_{1}}}^{-1}\left\{\phi\left(\alpha_{Q_{2,max,T_{1 }}}\beta_{t}\right)\right\}}{F_{Q_{1,max,T}}^{-1}\left\{\phi\left(\alpha_{Q_{2, max,T}}\beta_{t}\right)\right\}},\text{ with }F_{Q_{1,max,T_{1}}}\left(q\right)=F_{Q_{1,max,T}}^{ \left(\tau_{1}/T\right)}(q) \tag{6}\]  
where \(F_{Q_{1,max,T}}\) is the extreme value distribution function for \(Q_{i}\), where \(i=1,2\) for reference period \(T\); \(\tau_{i}\) is the basic period, where the load intensity is assumed to be constant (\(i=1,2\)); \(F_{Q_{1,max,T_{1}}}(q)\) is the extreme value distribution function for \(Q_{2}\) for reference period \(\tau_{1}\); \(F_{Q_{1,T_{1}}}(q)\) is the distribution of an arbitrary realization \(Q_{1}\); \(\phi\) is the cumulative standard normal distribution function; \(\alpha_{Q_{i,max,T}}\) is the FORM sensitivity factor of \(Q_{i}\) dominating (\(i=1,2\)), \(\approx\)0.7; \(\alpha_{Q_{i,max,\tau_{1}}}\) is the FORM sensitivity factor of \(Q_{i}\) dominating (\(i=1,2\)), \(\approx\)0.24; \(\beta_{t}\) is the target reliability for the reference period \(T\), usually 1 year. Assuming \(V_{Q}=0.4\), i.e., \(\mu_{Q}=0.491\), \(\sigma_{Q}=0.196\), \(T=1\), \(\tau_{1}=1\), \(\beta_{t}=4.7\), \(\psi_{0}=0.45\); if \(\tau_{1}=1/6\), i.e., the annual load duration is two months, \(\psi_{0}=0.28\). Equations (1)-(6) apply to ideal material and the combination factor when the loads have the same magnitude, i.e., the load proportion is 0.5. These equations have limited significance as they provide the combination factor for a load proportion of 0.5 and for an ideal material with \(V_{M}=0\) only. Table 1 of the Eurocodes [1; 2] comprises combination factors for snow. The factors are different in the north, i.e., Finland, Iceland, Norway, and Sweden, vs. the south, and at different altitudes, \(\psi_{0}=0.7\), 0.5, which is due to snow load duration. This issue is addressed later in this article.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Plant Growth Factors and Phytolormones  
Studies on the interaction between _Arabidopsis thaliana_ and the endophytic fungus _Harpophora (= Falciphora) oryzae_ revealed an important role of fungus-derived phytohormones, indole-3-acetic acid (IAA) and indole-3-carboxaldehyde (ICA), in the stimulation of lateral root growth [76]. Comparative genomic analysis resulted in the recognition of orthologgenes involved in the biosynthetic pathway of IAA and ICA in _H. oryzae_ genome. Both pathways share _ASB1_, _IGS_, _TSB1_ genes, while _TAM1_, _YUC3_, and _IAD1_ were identified specifically for IAA biosynthesis and _CYP79B3_, _CYP71A13_, and _CYP71B6_ were specific for ICA production. Interestingly, the upregulation of these genes was only observed after treatment with _Arabidopsis_ exudate, suggesting dependence of IAA and ICA production by _H. oryzae_ on plant auxin signaling and transport, and that lateral root growth promotion by the fungus derives by an auxin-dependent mechanism.  
Although the biosynthesis of plant hormones is well known in many endophytic bacteria and fungi, there is still an information gap concerning genomics and transcriptomics. To address this need, a broad study was conducted on the genome and transcriptome of strain WPI of _Rlodotorula graminis_, an endophyte isolated from _Populus trichocarpa_[77], revealing the absence of genes for the biosynthesis of IAA via indole-3-pyruvate and indole-3-acetate. However, other enzymes were found which are putatively involved in the formation of IAA from l-tryptophan via tryptamine: a monoamine oxidase, an aromatic-l-amino-acid decarboxylase, and an indol-3-acetaldehyde dehydrogenase. Moreover, 15 proteins were annotated in strain WP1 as primarily involved in the biosynthesis of other plant hormones, i.e., gibberellic acid, jasmonic acid, and aminobutyric acid.