esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 5 Analyzing Influencing Parameters in Wind Energy with ML
### Detailed Literature Content Review of ML Applied in Wind Energy Optimization Process
[141] proposes an ML-based forecasting system to improve prediction accuracy in the wind energy sector, considering the wake effect on wind turbines. This paper uses SCADA data to predict wind speed and power generation, evaluating errors through various regression metrics; Three articles present applications in the field of this category. [PERSON] et al. [142] propose an ML model based on symbolic regression for forecasting wind turbine wakes under rotation conditions. This paper accurately predicted wind deflection and velocity deficit. [PERSON] and [PERSON] [143] study the use of metamodels for aerodynamic optimization of a ducted aperture in a tall building to maximize the wind speed when a wind turbine captures. This recorded remarkable results in increasing available power. The third paper analyzes the application of ML in the wind energy field from design to energy-water interaction, highlighting the importance of metamodels [144]. * Six papers address the optimization of hybrid systems and energy storage. These are divided as detailed next Three case study-type articles. Firstly, the research conducted by [PERSON] et al. [145] discusses advances in ML related to multi-energy communities by analyzing their mechanisms and applications. Secondly, [PERSON] et al. [146] also present a thermo-economic and environmental assessment of compressed air energy storage (CAES) integrated with a wind farm in Denmark. Thirdly, [PERSON] et al. [104] explore a DL model for short-term wind forecasting at the Lillgrund offshore wind farm;  
Three articles present new methods and advanced technologies for optimizing these systems. [PERSON] et al. [147] describe significant challenges in future wind turbines' design, manufacturing, and operation. Additionally, using ML techniques, [PERSON] et al. [148] present an efficient solution for hybridizing renewable energy with hydrogen-based storage to reduce peak demand. Moreover, [PERSON] et al. [149] propose an innovative methodology for optimizing a renewable energy system, power to gas, and solid oxide fuel cells. Figure 12: Categories and subcategories of the 60 papers. All these papers approach ML by optimizing specific processes in renewable wind energy production.
## 3 Results and Discussion
The highest wear value is observed at room temperature: the volumetric wear of the coating reaches 599.9 \(\times\) 10\({}^{-6}\) mm\({}^{3}\)/N \(\times\) m, the volumetric wear of the ball is 101 \(\times\) 10\({}^{-6}\) mm\({}^{3}\)/N \(\times\) m, and the mass wear of the coating is 6.371 \(\times\) 10\({}^{-6}\) g/N \(\times\) m. The probable cause of the increased wear is the high surface roughness, as well as the absence of oxide phases capable of protecting the material from intense abrasive interaction. At a temperature of 450 \({}^{\circ}\)C, wear is significantly reduced. The volumetric wear of the coating decreases almost eight times compared to room temperature. These observations indicate a positive effect of thermal heating, under which partial running-in and compaction of the contacting surfaces occur. In addition, it is possible to form an initial thin oxide layer, which helps to reduce the friction coefficient and wear intensity due to improved lubricating properties and stabilization of the contact zone. The maximum wear reduction is observed at a temperature of 550 \({}^{\circ}\)C, where the volumetric wear of the sample is 41.2 \(\times\) 10\({}^{-6}\) mm\({}^{3}\)/N \(\times\) m, the volumetric wear of the ball is 11.8 \(\times\) 10\({}^{-6}\) mm\({}^{3}\)/N \(\times\) m, and the mass wear is 0.839 \(\times\) 10\({}^{-6}\) g/N \(\times\) m. This behavior can be explained by the stable formation of a protective oxide film with high thermal and mechanical stability. In addition, under these conditions, optimal running-in and hardening of the surface layer occur, helping to minimize material losses. However, with a further increase in temperature to 650 \({}^{\circ}\)C, the wear increases again. Figure 8: Comparative analysis of the coatings surface topography (**a–d**) and corresponding wear tracks (**b–h**) at various testing temperatures: (**a,e**) at 21 \({}^{\circ}\)C; (**b,f**) at 450 \({}^{\circ}\)C; (**c–g**) at 550 \({}^{\circ}\)C; (**d,h**) at 650 \({}^{\circ}\)C.  
Increased wear at this stage may be associated with the thermal destabilization of the previously formed oxide film, its cracking or peeling as a result of thermomechanical stresses. In addition, at such a temperature, an increase in the brittleness of the surface layer of the coating is possible, contributing to the intensification of the processes of microcrack formation and destruction.
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 1 Introduction
On the one hand, a certain change in the environmental parameters can cause their explosive growth, leading to activated sludge bulking and the clogging of membranes, which significantly reduces the efficiency of treatment [26; 29; 41]. Bulking can be related to the ability of _Thiothrix_-like organisms to form flocks with poor settling characteristics [42], which raises operational problems in biological wastewater treatment systems worldwide [18; 25]. At the same time, a number of studies show that, under properly selected conditions, representatives of the _Thiothrix_ morphotype, on the contrary, can help to improve the quality of granular sludge and increase the efficiency of wastewater treatment [43; 44]. In addition to affecting the characteristics of activated sludge, bacteria of the _Thiothrix_ morphotype are capable of performing a number of metabolic processes leading to the removal of pollutants from wastewater. These include sulfide oxidation [45], denitrification coupled with the oxidation of reduced sulfur compounds [23; 46], the enhanced biological removal of phosphorus [47] and possibly the denitfying removal of phosphorus [48; 49]. Although the role of _Thiothrix_, _Thioth_ and _Thioline_ in the operation of wastewater treatment plants is well known and undisputed, no review has yet been published that discusses the role of these particular bacteria in wastewater treatment systems, rather than filamentous bacteria in general. The aim of this review is to summarize the current information on the types of wastewater treatment plants in which representatives of these three genera occur, the methods for their detection in wastewater and the metabolic processes that they carry out in wastewater treatment, as well as the conditions affecting the development of this group of bacteria. This review is the first to attempt to delineate the available published data on members of the _Thiothrix_ morphotype into three genera, _Thiothrix_, _Thiofilm_ and _Thioline_, according to recent advances in molecular phylogeny. It is also the first review to discuss information on the metabolic processes occurring in members of the genus _Thiothrix_ in wastewater treatment plants in the context of its pangenomic data.
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 2 TiCl\({}_{4}\) Hydrolysis Process and Mathematical Model
### Mathematical Model
#### 2.2.2 Nucleation Dynamics
For this scenario, the reaction rate is set to match the diffusion rate under the critical condition, i.e., \(r_{Ti^{+}_{i}}=\eta Q^{\prime}\) (here \(Q^{\prime}\) represents the rate when the diffusion is ignored). The diffusion rate is expressed as \(k_{D}(c_{b}-c_{s})\), where \(k_{D}=\frac{D! }{A}\), and \(A\) signifies the characteristic length (\(m\)), a variable contingent upon the agitator properties, liquid volume, solution density, viscosity, and particle diameter. The equation \(\eta Q^{\prime}=k_{D}(c-c_{sat})\left(1-\eta^{\frac{1}{\alpha}}\right)\) can be extracted in the reactor under consideration of agitation. Given the stirrer parameters (speed 10 rpm, impeller diameter 40 mm, power 60 W, DJ1-60 electric stirrer, Jintan Environmental Protection Instrument Factory), \(\eta\) and \(B\) can be obtained at distinct points in time through the Newton-Raphson iteration. On substituting these parameters into Formula (3) and organizing the experimental parameters, we arrive at the nucleation rate equation, which is computed as follows. \[C_{Ti^{4+}}=\exp(-0.098t+2.708) \tag{4}\]1  
\[C_{Ti^{4+}}=\exp(-0.098t+2.708) \tag{4}\]2  
The nucleation rate equation, as depicted in Formulas (14) and (15), is influenced by factors such as reaction temperature, reaction time, and crystal structure. When the temperature and crystal structure are fixed, the nucleation rate varies with time. The particle count can be expressed as a differential within a specific time period, as shown in Formula (16). \[C_{Ti^{4+}}=\exp(-0.098t+2.708) \tag{4}\]3  
At \(t=0\), \(B(0)\) equals zero. For \(t>0\), the particle count \(Y(t)\) (within a PSD mesh range of 10\({}^{-6}\)-1 \(\upmu\)m, where 1 \(\upmu\)m equals 12,000 mesh) is converted based on the correlation between the mesh number and particle size. During the hydrolysis process, the volume of water and the pH value can alter the concentration of titanium ions, which in turn affects the nucleation rate. Additionally, changes in \(K_{D}\) caused by ethanol can modify the final nucleation rate by altering the crystal structure.
# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 2 State of the Art  
### Smith-Magenis Syndrome  
Smith-Magenis syndrome (SMS) is a genetically determined neurodevelopmental disorder characterized by a constellation of congenital malformations and significant behavioral and cognitive impairments [34]. It arises from a missing piece of genetic material (microdeletion) on the short arm of chromosome 17 (17p.2) in approximately 90% of cases [35; 36]. In the remaining 10% [35; 36; 37], the cause stems from a mutation within a specific gene (RAI1) located in the same chromosomal region [38; 39; 40]. This gene, RAI1, is believed to play a critical role in the syndrome's development, as evidenced by its association with most of the clinical features observed in SMS patients [41; 42]. The two primary diagnostic techniques employed are fluorescence in situ hybridization (FISH) and microsatellite polymerase chain reaction (PCR) within the critical SMS region [35].  
Despite an estimated prevalence of 1 in 15,000-25,000 births [35; 40; 43], SMS remains an underdiagnosed condition. Fortunately, this delay appears to be decreasing over time. Before receiving a correct diagnosis, people with SMS often face several misdiagnoses, such as Down syndrome or autism. This long process, usually lasting years, happens because SMS is rare and not well known among physicians since it was only discovered in [44]. Delayed diagnosis of SMS has serious negative effects. The syndrome causes significant neurodevelopmental delays, leading to a severe slowdown in motor and cognitive skill development during early childhood.  
Several characteristics of SMS (such as low muscle tone, delayed standing, hyporeflexia, high pain threshold, hearing impairment, intellectual disability [45; 46], delayed language development [47], circadian rhythm disturbance, and behavioral disturbances [48], among others [35; 45; 49; 50; 51; 52], manifest slightly in the first few months of life, making early identification a challenge [44; 53]. Consequently, these early signs are often not interpreted as indicative of the underlying disorder. However, delayed diagnosis creates a substantial lag in implementing these vital clinical decisions, hindering efforts to address the marked developmental delays. This delay in diagnosis and intervention has a cascading effect, leading to a more pronounced and long-lasting impact of the syndrome's maturational impairments [30; 31].
# Interactive Part Segmentation Using Edge Images
## 2 Related Work
### Edge Detector  
Edge has been regarded as one of the most basic problems of computer vision because of the aforementioned characteristics. One of the typical classic edge detectors is the canny edge method. Most of the classic edge detectors show some performance, but the accuracy is not high enough for real-world applications.  
With the advent of deep learning, especially CNNs, many edge detectors based on CNNs have appeared. Holistically-nested edge detection (HED) [16] shows higher edge detection accuracy than the classical edge detector based on the VGG [17] model, which is one of CNN's models. Many studies for edge detection have been conducted compared to HED. The richer convolutional features for edge detection (RCF) [18] proposed a method of performing edge detection with high accuracy in real-time based on the VGG model. The dense extreme inception network for edge detection (DexiNed) [19], one of the most recently proposed edge detectors, accurately detects even small-sized edges with less noise than the results of edge detectors using CNNs.  
With the development of deep learning, many edge detectors have been studied, but there are not many applications or studies using edge images estimated from edge detector models.  
The existing interactive segmentation models are trained on how to interactively segment an object with a color image. Just like the method by adding or changing input images to solve the problem that a CNN is trained by focusing on texture features, an interactive segmentation model can be trained based on shape features instead of texture features when input images are changed. We propose a method to segment the target object or part with the model trained by edge images instead of color images.
# Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction
## 2 Background and Related Works
### Evolutionary Algorithm for Neural Network  
Evolutionary architecture search is also called NeuroEvolution, which uses an EA. Evolutionary architecture search is a combination of ANN and the evolutionary strategy [50]. The purpose of an EA is to optimize the neural network architecture topology.  
The PSO is a standard optimization algorithm that consists of a particle population [32; 51]. It has been used to optimize hyper-parameters of neural networks. Recently, improved PSO algorithms have been used to enhance the ANNs by HPO or stacking layers [31; 32; 33; 34]. It is useful; however, only few researchers have investigated the use of PSO for evolutionary design of ANNs because PSO is more successful on smaller networks than GA [33; 34].  
GWO is one of the latest bio-inspired optimization algorithms, which mimics the hunting activity of gray wolves [39; 40]. GWO determines the most important decision through social dominant hierarchy. It has been applied to the HPO of several types of neural networks [38; 39]. GWO is used to find the optimal number of hidden layers, nodes, initial weight and biases. In this research, the best result is obtained by GA; however, GWO shows a better average result.  
GSA shows a good performance in exploration search but a weak one in exploitation search. In order to overcome this, some hybrid methods have been proposed [35; 37]. Those hybrid algorithms show advanced results for optimizing the initial weights or parameters. GAs has been the most commonly adopted method for designing ANN architectures in recent neuro-evolutionary approaches [12; 15; 52; 53]. An EA evolves the population of the ANN architectures; automatically designed ANN architectures are trained and tested in each generation. Offspring inherit information of layers and links from their parents with mutations.  
In addition, some methods for tuning the granular neural network using EA have been proposed [38; 41; 42; 54]. Granular neural networks are defined by sub-granules as sub-hidden layers. An architecture of DNN is presented as stacked sub-granules. EA algorithms such as GWA, FA, or GA are applied to this granule neural networks to tune the hyper-parameters.  
Most of these methods are aimed to optimize hyper-parameters or to stack layers. However, we aim for the structuring of neural architecture. In order to structure an existing neural network, GA is used in this study. In addition, a chromosome non-disjunction operator is added to the dynamic linkage.
## 3 Experimentation and Evaluation
### Validation of Textured Material Applicability of Detection Methods  
In this section, we present several common materials from daily life as examples to illustrate the effectiveness of our method, as shown in Figure 9. Following the methodology, we display the images that appear during the detection process. Specifically, these include, in order, the material sample; the measured detection area; the Curl Gradient image of the material surface optimized by photometric stereo vision; the background texture image and the contrast-enhanced material image based on fast Fourier transform (FFT) processing;  
Figure 8: The effect of image contrast enhancement after fast Fourier transform based on Gabor filter. Sample A represents fine textured leather, sample B represents coarse textured leather, and sample C represents light textured leather.  
and the image detected by the OTSU threshold segmentation method. The images after scratches are detected and marked; we then analyze the correct rate of the number of scratches detected for scratch defects in different textured materials, the correct percentage of the length of scratches detected versus the actual length, and the percentage of the area of scratches marked versus the actual area of scratches, as shown in Table 1.  
By observing the detection effect of all the samples and analyzing the experimental data, we find that the ratio of the number of detected scratches to the actual number of scratches is consistently at 100%; the ratio of correctly identified scratches is stable at 98.43% \(\pm\) 1.0%;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Object/Evaluation**} & **Number of** & **Correct Rate** & **Scratch Detection** & **Correct Rate** & **Detection** \\  & **Scratches Detected** & & **Length** & **Coverage Rate** \\ \hline A & 14 & 100\% & 148.54 mm & 99.87\% & 98.91\% \\ \hline B & 2 & 100\% & 87.88 mm & 98.60\% & 98.43\% \\ \hline C & 7 & 100\% & 186.14 mm & 97.24\% & 97.97\% \\ \hline D & 3 & 100\% & 71.23 mm & 101.18\% & 101.67\% \\ \hline E & 4 & 100\% & 74.91 mm & 102.51\% & 103.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective evaluation of textured material applicability in detection methods.  
Figure 9: Validation of textured material applicability of detection methods. Sample A represents coarse-textured kraft paper; Sample B represents fine-textured leather; Sample C represents coarse linen fabric; Sample D represents fine-textured kraft paper; and Sample E represents a textured wood panel.  
the accuracy of the detected scratch lengths is at 97.84% \(\pm\) 2.0%; and the coverage rate of the detected scratches over the real area is stable at 99.51% \(\pm\) 5.0%. We can conclude that the method proposed in this paper, \"Enhanced Scratch Detection for Textured Materials Based on Optimized Photometric Stereo Vision and FFT-Gabor Filtering\", is applicable to the detection of scratch defects on most textured materials encountered in everyday life.
## 4 Security Threats
### Distribution of Sharing Rules
#### 4.3.4 Fake Rules Injection  
One other vulnerability, which can be considered complementary to the previous one, threatens the normal operation of DSA networks. According to this one, an attacker may attempt to inject fake rules in such networks, motivated by various facts. To begin with, such an attack seems ideal for someone who aims at causing chaos in a target network, driven by pure malice. If new rules are infused, which are neither defined nor previously approved by the manager or the entities themselves through negotiation (in case of distributed spectrum sharing schemes), network operation will be affected. Additionally, such an attack seems also ideal for rational adversaries who aim at increasing their personal profit through improper use of a network's available resources. In other words, a node may create and inject fake rules to prevent, either carefully or randomly selected, legitimate nodes from exploiting special transmission opportunities. Since these opportunities will remain unused, a selfish node may attempt to occupy them at zero cost and without running the risk of disturbing any of his competitors (and thus to be detected) [28]. Obviously, to what extent the introduced sub-optimality and unfairness can affect the nodes composing a DSA network is absolutely dependent on each attacker's greed.  
The outcome of this attack is also highly dependent on the type of the injected fake rules. To elaborate, in case some fake positive rules are infused in a DSA network, the interference levels suffered by its nodes will be unavoidably increased, since more nodes than expected will be allowed to have simultaneous access to the available spectrum resources. On the other hand, in case a number of fake negative rules are infused in the network, spectrum access will become more restrictive than before, less transmission opportunities will be efficiently exploited, and consequently, the spectrum utilization as well as node satisfaction levels will be significantly decreased.  
To prevent potential attackers from exploiting such vulnerability, each single rule should be signed with its creator's unique signature [54]. Rules not signed by a trusted authority should be immediately filtered out, while their respective sender can be black-listed. This simple solution cannot be considered efficient since the rules enforced in a DSA network may constantly change. Most of the methods proposed in the literature for providing secure communication between nodes are not only resource-intensive in terms of computation power and memory needs, but also introduce significant delays in the overall communication procedure.