esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Understanding Biofilms
### Biofilm Formation
#### 3.3.2 Microcolony Formation  
Following the initial adhesion of individual cells to a surface in this stage, there is a significant increase in cell multiplication and clustering. This leads to the formation of small groups of cells known as microcolonies. Microcolonies are characterized by the proximity of cells, which is crucial for subsequent phases of biofilm formation [61]. This proximity enables important intercellular communication and collaboration, creating conditions for a more structured community. This stage is marked by certain activities and structures that establish the groundwork for a fully developed biofilm, a sophisticated and durable collection of bacteria [62]. During the microcolony stage, bacteria transition from individual cell lifestyles to communal ones, marked by many cells and physical contact between them. This lifestyle shift can potentially influence their behavior [63].  
During cell division, there is a notable uptick in the production EPS. These substances fortify microcolonies' structural integrity, protect the cells against environmental stressors, and assist in retaining moisture and nutrients. The EPS matrix is a vital element of biofilms, contributing significantly to their remarkable resilience and stability [64; 65].  
During the microcolony stage, bacteria utilize quorum sensing (QS) as a chemical communication. By perceiving their density, cells can effectively coordinate group actions like gene expression changes [66]. Creating microcolonies involves activating specific genes that promote these clusters' growth and pathogenic properties. In _C. albicans_, the development of microcolonies is significantly affected by the expression of genes such as _HWP1_ (hyphal wall protein 1) and _HYR1_ (hyphal regulator 1). _HWP1_ is essential for adhesion and the formation of hyphal structures, which are vital for biofilm stability. Likewise, _HYR1_ governs hyphal development and aids in the shift from yeast to hyphal growth, promoting microcolony aggregation and structural maturity. These mechanisms are crucial for establishing the thick, three-dimensional structure typical of _C. albicans_ biofilms [67; 68]. The regulatory network Sfl1/Sfl2 also governs the transcription of essential microcolony genes, impacting their development [61]. In _Vibrio cholerae_, gene expression responsible for the toxin-coregulated pilus (TCP) development is crucial for microcolony production [69].  
As microcolonies expand, they give rise to distinct microenvironments due to differing concentrations of nutrients, oxygen, and waste products. Studies have proven that microcolonies exhibit regions with a neutral pH at the outer edge of the biofilm, acidic pockets within the microcolony, and low-pH zones at the junction between the microcolony and the surface it adheres to [70]. The different biomolecules play a role in the life cycle stages of biofilm [Figure 2].
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Tin Oxide Films  
The SnO\({}_{2}\) films were deposited on glass slides and were annealed at 450 \({}^{\circ}\)C for 120 min [151]. These films indicated maximum conductivity, a film thickness of about 263 nm, and the least sheet resistance of 141 k\(\Omega\). The deposition of films was conducted by using ethylene diamine (C\({}_{2}\)H\({}_{8}\)N\({}_{2}\)) (complexing agent) on glass substrates [152]. Theannealing treatment can improve the crystallinity and reduce strain in the crystal lattice. Further, these films indicated excellent optical transparency in the 200-1000 nm region. [PERSON] and co-workers [153] reported the synthesis of thin films on glass slides at room temperature. The XRD and SEM studies revealed tetragonal structurals, and grains were covered well on the substrate surfaces. The band gap reduced (3.9 to 3.54 eV), the electrical conductivity changed (0.015-0.815 \(\Omega^{-1}\cdot\)cm\({}^{-1}\)), while surface morphology and crystallinity improved when the film thickness increased (215 to 490 nm). [PERSON] and co-workers [154] proposed the growth of SnO\({}_{2}\) films on glass substrates by using tin (II) chloride and H\({}_{2}\)O\({}_{2}\) solutions under various deposition cycles. Experimental results showed that film thickness (12 to 50 nm), crystallite size, and mobility of the films increased; however, transmittance reduced (92% to 85%) with the increasing deposition cycle (from 20 to 60 cycles).
## 3 Value Assessment Framework for Waste Household Appliances
### Value Assessment Framework for TELEVISION Scrap
#### 3.2.1 Data Preprocessing  
The data preprocessing module includes abnormal data processing, one-hot encoding, data diversity, and normalization submodules, as described below.  
* Abnormal data processing: For the dataset of 6000 waste television recycling data entries, outlier and noise data are handled using methods such as the 3-sigma rule, Boxplot, \(Z\)-score, and Grubbs. Outlier data can be deleted, while missing data can be supplemented using the mean.
* One-hot encoding: Since the dataset involves characteristics such as television brand and screen quality that cannot be recognized directly by neural networks, they need to be encoded as numerical data. One-hot encoding is used as an effective encoding method. Assuming there are M states to be encoded, the number of bits in one-hot encoding is M, and only one bit is 1, while the rest are 0. For example, there are two states in the case of screen quality in waste home appliances: good or bad. The code for good screen quality can be represented by \"01\", while the code for bad screen quality can be represented by \"10\". After all text information is one-hot-encoded, it is concatenated with the original data to form a new dataset.
* Data diversity and normalization: The 6000 encoded data entries are randomly divided into a training set and a test set in a ratio of 11:1. Both the training and test sets are standardized to eliminate numerical problems caused by different dimensions and scales of data [30]. In this paper, we adopt the MinMax normalization method, which scales the original data to the interval [0, 1]. Let \(x_{1}\), \(x_{2}\),...\(x_{j}\),...\(x_{n}\) be the data, where \(x_{max}\) represents the maximum datum, and \(x_{min}\) represents the minimum datum. The processed new data (\(x_{j}^{\prime}\)) of the original data (\(x_{j}\)) are represented by: \[\Delta_{j}=x_{max}-x_{min}\] (1) \[x_{j}^{\prime}=(x_{j}-x_{min})/\Delta_{j}\] (2)
## 3 Demand-Side Resources in Active Distribution Systems  
Demand-side resources are any technology hardware or software utilized by consumers that can deliver grid services, such as energy dispatch from rooftop solar to the grid, capacity to provide stored power from energy storage systems based on load fluctuations, and provide ancillary services for grid stability [25]. This is attained through the deployment of distribution generators (DG) on a medium- to low-voltage level. DG system is a small electrical generation technology that is located closer to the load. There are several such technologies, which include renewable energy sources, such as wind and solar, natural gas-fired fuel gas, biomass, tidal power, geothermal power, and non-renewable generators [26].  
These DG can be directly connected to the grid via power electronic devices or power a local load while dispatching access to the grid. DG can also affect the distribution network negatively due to the unidirectional power flow. The challenges with unidirectional power from the rise in DG lead to voltage quality, protection device settings, increments in maintenance, and fault currents that affect a significant portion of the distribution network. DG, however, possesses several benefits, such as a reduction in transmission and distribution loss due to proximity to the load, which could further assist in alternative expansion. In peak hours, they cater for a portion of the load, therefore, reducing the peak demand and improving power quality and network stability because of their quick start time and ability to dispatch almost instantaneously. In addition, that can also mitigate possible blackouts of the entire network [27]. Demand-side resources can be used in several ways in the energy markets. DR is an active source of active distribution networks (ADN) and also comprises flexible loads, electric vehicles (EVs), DR inflexible loads, and energy storage on the consumer side [28]. Load balancing still remains a challenge in DG dominated by renewable energy sources in which there is a frequent need to match between supply. There are several demand-side management (DSM) resources to mitigate load-balancing challenges and avoid the need to use the supply-side resources to balance renewable power increase. The demand-side resources can be classified into energy efficiency and demand responses. Electrical power networks have embarked on programs that encourage the use of high energy efficient appliances, such as lighting or equipment that employs variable speed drives. This, however, does not form an active resource, which means it cannot balance renewable fluctuations in real time, but energy efficiency, therefore, balances renewable fluctuations over a longer period through resource planning [29].
## Appendix A Baseline FE Model Updated Using Static Structural Responses of the Built Structure
### SVR Method  
The SVR method is a novel machine learning method based on the statistical learning theory. Assuming that \(x\) is the input variable, the regression function \(F(x)\) can be expressed as:  
\[F(x)=\phi(x)^{T}W+B \tag{1}\]  
where \(\phi(.)\) is the nonlinear mapping vector in the high-dimensional space; \(W=[w^{1},w^{2},\dots,w^{N}]\) and \(B=[b^{1},b^{2},\dots,b^{N}]\) are determined as N-dimensional linear regressors in the high-dimensional feature space.  
According to the SVR method, in multi-output cases, the structural risk minimization is equal to the following constrained optimization problem:  
\[\text{min}L(W,B)=\frac{1}{2}\underset{j=1}{\overset{N}{\sum}}\|w^{j}\|\ +C \underset{i=1}{\overset{L}{\sum}}L(u_{i}) \tag{2}\]  
where \(L(u)\) represents the loss function defined in the hyper-spherical zone expression as:  
\[L(u)=\left\{\begin{aligned} & 0,u<\epsilon\\ & u^{2}-2 ue+\epsilon^{2},u>\epsilon\end{aligned}\right. \tag{3}\]  
where \(u_{i}=\|e_{i}\|=\sqrt{e_{i}^{T}e_{i}}\), \(e_{i}^{T}={y_{i}}^{T}-\phi^{T}(x_{i})W-B\), and \(\epsilon\) are the hyper-spherical insensitive zone. When \(\epsilon=0\), this problem is reduced to an independent regularized kernel least squares regression for each component. However, for a nonzero \(\epsilon\), in the solution, all of the outputs are considered to construct an individual regressor, in order to obtain the best global predictions.  
According to [PERSON] et al. (2019), the main steps of model updating based on the SVR method are: (1) Constructing the samples. More precisely, the samples of the design parameters can be determined based on any experimental design technique, while the responses of the structure can be obtained by FE analysis for each sample. (2) Training the SVR model. The SVR model is trained with the responses and the corresponding design parameters, considered as the inputs and the outputs, respectively. (3) Updating the designparameters. The measured responses are the input to the trained SVR model, while the outputs are the target design parameters.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 4 Experiments
### Experimental Results
#### 4.3.2 Testing Results  
Experiments for testing are conducted on a total of 30 different drivers from videos consisting of drowsy and non-drowsy states. Figure 16 shows the results of the average accuracy and run-time in the testing phase for drowsiness detection. The average accuracy of scenario 1 is 83%, scenario 2 is 96.1%, and scenario 3 is 97.3%. As can be seen, scenarios 2 and 3 outperform scenario 1 in terms of accuracy. On the other hand, the average prediction time of the three scenarios is 2.7, 4.3, and 5.1 s for three scenarios 1, 2, and 3, respectively. Scenario 1 gives results faster than the remaining two scenarios. However, giving quick results will lead to incorrect drowsiness, detection since it can be confused between blinking or eyes closing but not sleeping.
## 3 Theoretical Baseline
### Continuous Galerkin Finite Element Method  
The most straightforward step to conduct is to derive the continuous Galerkin (CG) from the DG method. The former is oftentimes also referred to as the classic finite element method, being the original formulation used to solve problems in structural mechanics [30].  
In this case, all degrees of freedom (DoFs) in the domain are global, in contrast to being local to each cell. However, each basis function associated with a given degree of freedom has compact support and is, thus, only nonzero within the direct vicinity. The resulting linear system hence remains sparse but has considerably fewer DoFs than an equivalent discretization produced by a DG method.  
One may obtain a CG method starting from the DGM by strongly coupling the degrees of freedom at cell interfaces. In other words, the previously discontinuous approximation must be made continuous. In terms of the weak form of a given problem, the numerical flux that has been introduced by partial integration has to vanish. This step is exactly taken in deriving weak forms for the CG scheme. The equivalent weak form of the advection equation given by Equation (2) is, then, the following: Find \(u\in V\) such that  
\[\int_{\Omega}v\cdot\partial_{t}\alpha\,dx-\int_{\Omega}\
abla v\cdot \underline{u}\alpha\,dx=0,\qquad\forall v\in V. \tag{7}\]  
By coupling coinciding DoFs, one may equivalently introduce shared DoFs between cells. This results in comparison to the DGM in a smaller global system that is in turn more coupled, yielding more nonzero entries per row and column in the system matrices.  
The condensation of such a system by coupling DoFs is illustrated in Figure 1. From the numbering of DoFs in both figures, it becomes apparent that the number of additional allocations grows drastically with increasing dimensionality of the problem.  
As the numerical flux is zero by definition for a CG scheme, we may also omit it from computation. Thus, the CGM is noticeably less arithmetically intensive in this regard. However, this computational saving is offset by the strong coupling of DoFs, resulting in a more dense linear system and possibly a more complex assembly process in terms of memory management.  
As equivalence can be shown here based on the weak form and, thus, early on in the model assembly process, the choice of finite element is unaffected. This, in consequence, also applies to the chosen type of triangulation or the order of approximation.
## 2 Motivation and Contributions  
This article makes significant contributions by presenting the current state of the art in resource management for various scenarios of HetNets beyond 5G wireless networks, identifying and discussing potential solutions to resource management challenges, and introducing new research areas. This survey discusses many challenges in developing resource management methods caused by the extensive features of future wireless networks, the unpredictability of user deployment, the impractical deployment, and unique service requirements. The following are the most important contributions made by this article:  
1. This article comprehensively analyzes the current work in resource management aspects such as power allocation, user association, mode selection, and spectrum allocation.
2. It highlights the most significant issues with the current resource management approaches and presents the best solution to overcome the existing problems.
3. It presents future directions and research challenges that have yet to be adequately addressed.  
The remaining sections of the article are as follows: Section 3 briefly summarizes research on resource management in HetNets. In Section 4, a taxonomy to classify several resource management strategies is discussed in detail. In Section 5, various research challenges and future directions are explained. In Section 6, the conclusion is presented. The organization of this study is shown in Figure 3.  
Figure 2: The system structure of future HetNets.
# Assessment of Seasonal and Diurnal Variations of Alkaline Phosphatase Activity in Pasteurized Milk
## 2 Materials and Methods
### Categorization of Milk Study Samples  
The milk samples were categorized into the following groups:  
* One hundred (100) samples of raw milk (subjected to the pasteurization process) collected from 100 farms of clinically healthy animals (sheep) in mountainous areas of the Prefecture of Aetolia-Akarmania, i.e., one (1) sample from each farm taken once per farm. One (1) sample of raw sheep milk was taken randomly from one (1) of the  
Figure 1: The workflow of the present study schematically.  
farms to serve as a positive control and to be used as part of the individual mixtures of pasteurized raw milk. These samples were used for: (i) the detailed recording of the qualitative color result of the newly developed test called \"AP test\", (ii) the detection of the sensitivity limit to alkaline phosphatase in a mixture of spiked pasteurized milk with raw milk in different proportions.
* One hundred (100) samples of fresh sheep and goat milk collected from 100 sheep farms in mountainous areas of the Prefecture of Aerolia-Akramania, i.e., one (1) sample from each farm was taken once per farm. The milk samples of group B were subjected to a pasteurization procedure and were then used to verify the results of the \"AP test\" by another certified and commercially accepted method for the detection of phosphatase in pasteurized milk. This procedure lasted 30 days divided into five (5) day calendar groups, where the repeatability of the results was certified.
* One hundred (100) samples of fresh sheep's and goat's milk from the same animals of group B processed by the pasteurization process in the cheese dairy unit N. NAKAS & SONS, Kampos Amphilochia-30500/Greece. One (1) sample of sheep's raw milk was taken randomly from one (1) of the farms to play the role of positive control and so that researchers could use part of it in the individual mixtures of pasteurized raw milk. These samples allowed a comparative analysis of the \"AP test\" test with the HEYL LACTOGNOST III kit and SENSObiz. Both Lactognost III and SENSObiz are compliant with the golden standard (ISO 11816-1:2013); therefore, comparison of the \"AP test\" with these commercial kits renders the \"AP test\" compliant with the golden standard as well. The limit of detection is 350 mU/L. The samples were taken from tanks T201 (50 samples) and T202 (50 samples) and pasteurization was carried out on the NT50X pasteurizer.
* Two hundred (200) samples of pasteurized sheep, goat, and cow milk from four (4) different geographical areas of Greece with targeted calendar sampling to examine whether the qualitative color result of the \"AP test\", and its sensitivity limit, are affected by the type of milk, the measuring season, the atmospheric humidity, the ambient temperature or the reproductive animal's type.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Thermal Image Processing
#### 3.4.4 Abnormal PV Module Detection  
This step is responsible for classifying a PV module as defective or as normal. The decision is made based on the criterion in Equation (10). Firstly, the maximum temperature value \(T_{max}\), minimum temperature value \(T_{min}\), and average temperature value \(T_{avg}\) are calculated from the thermal image. This is followed by calculating the high temperature threshold value \(T_{high}\) or the low temperature threshold value \(T_{low}\) using Equations (7) and (8), respectively. The count value \(a_{count}\) is increased if the temperature value is lower than \(T_{max}\) and \(T_{min}\). If \(a_{count}\) exceeds 0.2% of the module area value \(area_{module}\), then the module is considered as abnormal.  
\[T_{high}=T_{avg}+T_{max}\times 0.2 \tag{7}\]  
\[T_{low}=T_{avg}+T_{min}\times 0.2 \tag{8}\]  
Figure 10: (**a**) Example of image rectification; and (**b**) example of extracted individual PV module.  
Figure 9: (**left**) Image before registration; and (**right**) image after registration.  
\[f(x)=\left\{\begin{array}{c}a_{count}++,\text{if }T_{val}<T_{max}\text{ or }T_{val}<T_{min}\\ a_{count},\text{otherwise}\end{array}\right. \tag{9}\]  
\[g(x)=\left\{\begin{array}{c}\text{abnormal }PV\text{ module},\quad\text{if }a_{count}> area_{module}\times 0.002\\ \text{normal }PV\text{ module},\quad\text{otherwise}\end{array}\right. \tag{10}\]
## 5 Conclusions  
The research introduced and experimentally validated a 3-phase 3-wire LVDC back-to-back converter architecture for use in multi-frequency power systems. The system allows the grid-side converter to receive fundamental, 5 th (negative sequence), and 7 th (positive sequence) harmonic voltages for power transfer enhancement, while the load receives only a clean 50 Hz sinusoidal voltage from the DC-link and inverter. The system operated in two modes, which included a configuration without energy storage and a configuration with a battery unit connected at the DC-link for bidirectional power flow. The laboratory tests showed that the system provided stable voltage together with clean waveforms at the load and maintained high efficiency across steady and dynamic operating conditions. The LVDC-link successfully isolated the multi-frequency grid side from the sensitive household load side while transferring the total multi-frequency power to the load side. The proposed device confirms the system's capability to be connected as an interface between multi-frequency networks and standard 50 Hz applications.  
The experimental and simulation results confirmed the core functionalities while demonstrating multiple essential outcomes, which demonstrate the practical value and technical novelty of the proposed architecture:The 5 th and 7 th harmonic components functioned as active power transfer channels on the grid side without reaching the load, which improved line utilization while preserving high power quality. - The LVDC link functioned as a strong frequency-selective barrier that maintained complete harmonic decoupling throughout all dynamic mode transitions. - The system works on increasing total power transfer through voltage harmonic injection while peak voltage stays within limits, thus maximizing existing infrastructure potential. - The load-side inverter produced pure 50 Hz sinusoidal voltage throughout all operating conditions to fulfill the strict power quality requirements.  
Conceptualization, D.R.; methodology, Z.A. and H.A.; software, Z.A., H.A. and D.R.; validation, H.A. and D.R.; formal analysis, Z.A., H.A. and D.R.; investigation, Z.A., H.A. and D.R.; resources, Z.A. and H.A.; data curation, Z.A.; writing--original draft preparation, Z.A.; writing--review and editing, H.A. and D.R.; visualization, Z.A. and H.A.; supervision, D.R.; project administration, D.R.; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research, Development, and Innovation Fund of Hungary grant number FIEK_16, project no. FIEK_16-1-2016-0007.  
Not applicable.  
All data that support the findings of this study are included within this article.  
The authors thank the BME Smart Power Laboratory (SPL) for supporting this research. The research was financed in part by the project no. FIEK_16-1-2016-0007, implemented with the support provided by the National Research, Development, and Innovation Fund of Hungary, financed under the FIEK_16 funding scheme.  
The authors declare no conflict of interest.