esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Clostridal Binary Toxins
### Clostrridioides Difficle  
Parallel to _C. perfringens_, _C. difficile_ is an anaerobic, Gram-positive, spore- and toxin-producing bacillus [45]. Its sporulating function makes _C. difficile_ persistent and oftentimes difficult to eradicate even when using harsh cleaning methods [8; 46]. _C. difficile_ infection (CDI) is prevalent in nosocomial settings, with 500,000 estimated cases and 29,000 deaths reported per year in the USA [45]. Clinical outcomes of CDI include severe diarrhea and damage to the colonic epithelium, resulting in pseudomembranous colitis. CDT is also associated with increased rates of mortality for immunocompromised and elderly individuals [8; 46]. Typically, a diverse and robust microbiome protects against the activation and colonization of _C. difficile_ in the large intestine of healthy individuals [47]. It is upon disruption of those protective commensal bacteria that _C. difficile_ is able to flourish. Factors that promote this disruption include different chemotherapies and antibiotic interventions.  
_C. difficile_ has an extensive AMR repertoire, including resistance to limomycin, clindamycin, aminoglycos, tetracyclines, macrolides, cephalosporins, penicillin, and fluoroquinolones [46]. As such, it is difficult to discuss _C. difficile_ without addressing its propensity for acquiring AMR. The two are interwoven to such a degree that initially CDI was referred to as \"clindamycin-associated colitis\" due to the high risk clindamycin posed in inducing CDI [46]. The current antibiotic regimen employed to treat CDI includes vancomycin and fidaxomicin, or metronidazolezele if the prior two are unavailable [46]. Once _C. difficile_ establishes its niche within the gut, disease manifests as a product of toxin secretion, resulting in cytotoxicity, cellular detachment from the intestinal epithelium, cell death, and inflammation of the infection site [45]. These toxins include two large fibroglycosylating enterotoxins referred to as toxin A (TcdA) and toxin B (TcdB), and a third \(\beta\)-pore-forming binary toxin known as CDT [11; 46]. CDT is associated with hypervirulent strains of _C. difficile_, such as the fluoroquinoline resistant ribotype 027 (RT027), resulting in higher frequencies of severe disease and recurrence in patients with CDT-positive strains of _C. difficile_[45; 46]. The binary toxin, CDT, is composed of the ADT ribosyltransferase (ART) toxic component, CDTa, and the oligomeric translocae/pore-forming component, CDTb [11; 15]. CDT shares high sequence and structural homology with the iota toxin, with CDTa and CDTb showing an 81% and 84% amino acid sequence identity to Ia and Ib, respectively [12]. This similarity between CDT and iota toxin is further highlighted by the interchangeability of their A and B subunits without abolishing or altering toxicity [12]. Therefore, the structure and function of CDT and the iota toxin, with focus placed upon their B subunits, will be discussed in conjunction in the following section.
## 3 Data Preparation
### Data Understanding  
The second stage involves acquiring the information or access to the data required to solve the problem. This initial collection includes procedures for data retrieval, description of data, and verification of the quality and accuracy of the data [27].  
Educational data extracted from VLE databases can be used for finding key attributes or dependencies among courses. Each VLE offers data suitable for knowledge discovery tasks. The knowledge obtained from logs and study results can enhance student's profiles [28]. The more is known about students' performance during the study course, the better and more useful prediction model can be created.  
The step of data understanding is essential to discover useful data patterns successfully [25]. The educational data used in this paper comes from 261 unique students enrolled in the introductory course of Database Systems at the Constantine the Philosopher University in Nitra between the years 2016 and 2020. Data has been anonymized during the extraction process.  
Subsequently, the proportion of dropout students was determined in each academic year. As a result, 21.92% of students in total failed to pass the course, as shown in Figure 1.  
The reasons for students' dropout were different. The insufficient number of obtained points, delayed or non-submitted assignment, as well as low activity in the course represent the most frequent reasons. However, a particular dropout reason has not been analyzed and has not played any role in the ongoing research.  
One of the already mentioned LA and EDM open questions is how to effectively predict student performance based on their activity and partial outcomes during the study. Student performance was examined according to available attributes in the course. The aim was to identify the extent to which the received final grades corresponding to the unsuccessful completion of the course could be explained or predicted by given attributes from the course.  
The course was divided into 13 sections according to the university standards. Each section contained several educational resources, as well as more than three assignments in the form of different LMS Moodle activities and quizzes. The assignments were used predominantly in the first two thirds of the term, followed by the midterm test and project assignment.
## 4 Measurement Setup and Dataset
### Data Acquisition  
The basic configuration [13] comprises two major components--a perception sensor unit (PSU) equipped with a reference RGB camera and the sensors under test (SUT), along with a weather sensor unit (WSU) that captures detailed weather information every 60 s (see Figure 3). Both units are remotely linked via a cloud-based platform which allows them to function independently from each other. Moreover, this platform also enables the data to be stored and displayed online.
## 1 Introduction
For this reason, we present a simplified design of MIMO-OCC grouped LED array-based Tx, which uses 64-neopixel LEDs distributed in an \(8\times 8\) array and a commercial, low-cost Raspberry Pi camera (RaspiCam) as the Rx. The Tx unit is divided into eight different groups, with eight LEDs per group in order to increase the data rate and achieve flicker-free transmission. At the Rx, RaspiCam, with a resolution of \(1920\times 1080\) pixels and a capture speed of 30 fps, captures the LED array in RS-mode at different shutter speeds (SS) and transmission links \(L\). The novelty of this work is on the design of a simplified Tx for multiple channels transmission-based LED grouping with perfect synchronization, and the use of an RS camera for flicker-free transmission in short-range and low data rates IoT applications. The paper gives a detailed analysis on the quality matrix of the captured image in terms of the peak signal-to-noise ratio, and the success rate of received bit sequences with respect to the transmission span and the camera's SS. The remainder of the paper is organized as follows: Section 2 describes the proposed RS-based MIMO-OCC scheme using the grouped LED array, while Section 3 shows the experiment setup, followed by the discussion of results. Conclusions are drawn in Section 4.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Taxifolin
#### 4.3.3 Resveratrol  
Resveratrol (Figure 4) is an organic chemical compound, a polyphenic derivative of stilbene. It can occur in two forms: geometric isomers cis and trans. The naturally occurring isomer is trans and only this form shows biological activity. Upon UV exposure, it can transform into its cis isomer [103]. Resveratrol acts as a powerful antioxidant, neutralizing free radicals that cause cellular damage. It is regarded as one of the most potent polyphenols, offering the strongest protection against skin aging. It is classified as a phytoestrogen, due to its ability to interact with estrogen receptors. It has a positive effect not only on the skin but also on the body, as it helps protect against circulatory system diseases, neurological diseases, e.g., Alzheimer's disease, and has even been proven to have anti-cancer effects [104; 105].  
Its main advantage is its ability to reduce the effects of skin aging. It also possesses skin-repairing properties, as it acts as an activator of sirutin--often referred to as the \"enzyme of youth\" (increasing sirutin activity in the human body by 13 times). Additionally, it protects against UV-induced damage and helps reduce skin hyperpigmentation caused by sun exposure [105].  
Moreover, resveratrol reduces the inflammation and redness of the skin, thanks to its soothing properties, and when used externally together with appropriate supplementation, it helps in the treatment of psoriasis and eczema; counteracts the negative effects of external factors, including free radicals; has a smoothing and elasticizing effect on the skin; has anti-aging properties; moisturizes dry skin; and evens out the skin tone and structure [106].  
The production of resveratrol for cosmetic use encompasses various methods, including plant extraction, chemical synthesis, and biotechnological approaches. Resveratrol is commonly extracted from plants such as grapes, berries, and Japanese knotweed (_Polygonum cuspidatum_). The extraction process typically involves solvent extraction techniques to isolate resveratrol from plant materials. However, the yield can be low, and the process may be influenced by environmental factors affecting plant growth. Innovations in biotechnological production and formulation techniques continue to enhance resveratrol's applicability and effectiveness in cystemology. Genetically engineered microorganisms, such as _Saccharomyces cerevisiae_ and _Pichia pastoris_, are utilized to biosynthesize resveratrol. To enhance resveratrol's solubility and stability, enzymatic glycosylation can be employed. For instance, using amylosucrase from _Deinococcus geothermalis_, resveratrol can be converted into resveratrol-O-glucosidase. These derivatives have demonstrated promising cosmetic activities, including antioxidant, anti-inflammatory, anti-aging, and skin-whitening effects [107; 108].
## 2 Materials and Methods
### Case Studies Design
#### 2.3.2 Case Studies Parameters and Assumptions
* The intermittent operation (ON/OFF) of a desalination unit is considered throughout the day rather than the continuous operation for autonomous systems. This was allowed after the communication with desalination plant manufacturers who verified that the commercial units face no problems operating under that scheme. For the purposes of this study, the desalination units cannot be deactivated and activated again in a time period of less than 1 h.
* A simple controller was implemented essentially deactivating the plant either when the water tank was full or the battery bank had a state of charge below 5% (in line with the technical specifications of the batteries considered). * Land cost has not been taken into consideration since the prices vary extensively even in nearby islands, as is the case of islands in the Aegean Sea. Since most of the desalination plants operated either by the public sector or under a Public-Private Partnership agreement and in most of the cases of public land is used. * For the purposes of case study 15, it was assumed that the ORC unit is connected to an existing 95 \({}^{\circ}\)C geothermal well. This is in line with the geothermal potential found in the Cyclades islands complex (i.e., Milos) [44]. The cost of the well drilling is not included, since in many parts of the world, there are existing low-enthalpy geothermal wells being used for multiple applications such as for buildings' heating and agriculture-related applications [34] and it is assumed that such a well is utilized. * The ORC engine tested extensively in [47] was considered, along with the economic considerations presented in [44]. This unit produces 3.35 kW\({}_{\text{e}}\) when fed with a temperature of 95 \({}^{\circ}\)C. Since that unit cannot form a grid, a microgrid topology is considered for this system as well and a battery bank being able to meet the load for 1 h has been assumed.
## 3 FEA Model and Validation
### Tunnel Displacement and Convergence  
(1) Tunnel displacement  
The displacements of ER60 caused by the grouting pressure during the excavation of WR38 to WR50 (see Figure 8) are presented in Figure 10. The increasing C/D ratio in Figure 10 indicates the TBM advancing at the WB. A positive value of the vertical axis indicates lateral displacement towards the second tunnel. As illustrated in Figure 10, the lateral displacement towards the second tunnel decreases with increasing grouting pressure, indicating a notable ground supporting under sufficient grouting pressures. The inward displacement increases with the TBM advance in Case 2, Case 0, Case 3, and Case 4. However, the opposite phenomenon can be observed in Case 1, indicating that the effect of soil movement towards the second tunnel exceeds that of the second tunnel ovalization.  
As shown in Figure 11, settlement can be observed at the top of the first tunnel during the TBM-driven process of the second tunnel. The first tunnel settlement shows negative correlation with the face supporting pressure. With the TBM advance, the maximum settlement of each case occurs at a C/D ratio within a range of 0.56-0.59. As the TBM was driven away from the monitoring section (i.e., ER60), the vertical displacement increases gradually, ascribing to the influence of the second tunnel upheaval. It can be observed from Figure 11 that the cases with a \(K_{s}>1\) cannot prevent the first tunnel from settlement, although the settlement is small enough (less than 1 mm which is not easy to be measured by ordinary monitoring approaches) to be ignored.  
(2) Tunnel convergence  
Figure 12 illustrates the influence of grouting pressure and face supporting pressure on the convergence of ER60 in the first tunnel. As shown in Figure 12, a horizontally oval-shaped lining deformation can be observed. However, the lining ovalization is not symmetric, ascribing to the squeezing effect caused by the TBM-driven process of the second tunnel. As can be seen from Figure 12, with the increase of grouting pressure, the convergence of ER60 decreases at the top and increases at the side wall adjacent to the second tunnel, demonstrating that lower grouting pressure results in a smaller squeezing effect on the first tunnel. In Figure 12b, the development of lining convergence under different supporting pressure (i.e., Cases 5 to 8) shows the similar trend as that under different grouting  
Figure 10: Lateral displacement of the lining under different grouting pressures.  
pressures (i.e., Cases 1 to 4). However, different face supporting pressures have minor influence on the convergence of the first tunnel (ER60), ascribing to the reason that the acting direction of the face supporting pressure is perpendicular to the first tunnel convergence.  
Figure 11: Vertical displacement of the lining under different supporting pressures.  
Figure 12: Cont.
## 2 Materials and Methods  
A prospective, comparative clinical study took place in the Ophthalmology Clinic of St. Spiridon Hospital and Oftaprof Clinic, in Iasi, during October 2018 and December 2019. Patients were evaluated for a period of 6 months after enrollment. The study was approved by the Ethics Commission of University Hospital Sf. Spiridon Iasi, approval no.49/9.12.2018 in compliance with ethical and deontological rules for medical and research practice. The study was conducted in accordance with the Helsinki Declaration and with several published principles [7].  
The patients were divided into two groups based on the following inclusion criteria: patients with sleep apnea syndrome at different stages, with associated glaucoma (primitive open-angle or normal tension glaucoma) and ocular surface disease representing the study group and the control group (patients with glaucoma but without SAS). The exclusion criteria were patients with SAS in whom full evaluations were not performed and patients in whom the diagnosis of SAS was denied, i.e., those who refused further evaluations. The study included 65 subjects (65 eyes) OSA-diagnosed subjects (45 subjects/eyes with glaucoma and SAS as well as 20 subjects, 20 eyes with dry-eye syndrome and SAS) who did not follow the CPAP (continuous positive airway pressure) treatment and a control group made up of 45 subjects (45 eyes) with primary open-angle glaucoma (mild or moderate), treated, without OSA. All patients underwent the following determinations, according to a standardized protocol: the best corrected visual acuity (Snellen optotype), the Schirmer I test (without anesthetics); intraocular pressure (with the Goldmann explanation tonometry), OCT (optical coherence tomography Cirrus HD OCT Zeiss) with the evaluation of the optic nerve, macula, and measurement of the retinal nerve fiber layer, determination of the visual field (Humphrey perimeter--C24-2 program), measurement of the central corneal thickness (Tomey ultrasonic pachymeter) and the corneal hysteresis with ORA (ocular response analyzer, Reichert technology). Moreover, the respiratory functional parameters (apnea-hypopnea index--AHI) and body mass index were recorded. The age of the patients, sex, background data, and associated general conditions (hypertension, obesity, diabetes, etc.) were also recorded.  
Statistical analysis. The database was created in the EXCEL program and was statistically processed with the SPSS 18.0 program. The ANOVA (analysis of variance) test, student t test, \(\chi^{2}\) test, Kruskal-Wallis test, non-parametric test comparing 3 or more groups, the study of the correlation between different phenomena were performed using the \"r\" (Pearson) correlation coefficient, receiver operating characteristic (ROC) curve--highlighting the specificity/sensitivity balance as a prognostic factor and logistic regression (multivariate analysis) were used as analytical tests.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 4 Smart City De-Anonymization and Re-Identification Use Cases
### Smart Grids  
A key component of smart grids are smart meters, which are devices that measure the energy consumption of households with high precision and frequency. These data allow for the inference of certain information about the daily routines of households. In 2013, a study conducted by [121] demonstrated the potential privacy risks associated with smart meters. The study analyzed features of energy consumption data and was able to re-identify 68 households out of a sample of 180 households, by studying 60,480 energy-consumption records metered at a frequency of one hour over a period of two weeks. Specifically, the authors were able to re-identify households through features such as the consumption on weekdays and the first increase of energy consumption in the morning. This study highlights the importance of considering the privacy implications of smart grids, as sensitive information about household daily routines can be inferred from energy consumption data.  
Reference [130] proposes a privacy-preserving data aggregation scheme, named PPDB, for a fog-computing-based smart grid (FCSG) that supports dynamic billing and arbitration. The scheme uses fog nodes and the ElGamal cryptosystem to encrypt and aggregate electricity consumption data, and employs a trusted third party to arbitrate disputed bills. The scheme is designed with a four-layer data aggregation framework that guarantees confidentiality, authentication and integrity of data. However, as the trusted authority knows the key of each participant, it is easy to pose a threat to privacy.
## 2 Simulation Methodology
### FEM-SPH Adaptive Algorithm
#### 2.2.2 Time Steps  
The time step of the FEM is  
\[\Delta t_{FEM}=\frac{d_{\min}}{\sqrt{\frac{E(1-v)}{\rho(1+v)(1-2v)}}} \tag{20}\]  
\(d_{\min}\) is the minimum element size. \(E\), \(\rho\), and \(v\) are the elastic modulus, density, and Poisson's ratio of the material, respectively.  
The time step of SPH is bounded by the following condition [40]:  
\[\Delta t_{SPH}\leq\min(\frac{0.3h}{\sqrt{K/\rho_{i}}+|\upsilon_{i}^{\max}|}) \tag{21}\]  
\(K\) is bulk modulus of the material, \(\upsilon_{i}^{\max}\) is the maximum velocity of all SPH particles.  
This paper takes the smaller value of \(\Delta t_{FEM}\) and \(\Delta t_{SPH}\) as the time step of FEM-SPH adaptive algorithm.  
\[\Delta t_{FEM-SPH}=\min(\Delta t_{FEM},\Delta t_{SPH}) \tag{22}\]
## 4 Substructure Pseudo-Dynamic Test of Soil-Pile-Structure Interaction
### Results  
#### 4.3.1 Acceleration Time History  
Table 4 shows the peak accelerations of each floor of the superstructures in the two models, with and without the effect of SPSI. The acceleration time history curves of the first, third, and fifth floors are shown in Figures 10 and 11. In general, when the effect of SPSI was included, the peak acceleration of the superstructure was reduced, and the maximum difference was up to 33%. This conclusion was consistent with that in the references of [PERSON] et al. [67], [PERSON] et al. [59], and [PERSON] et al. [60]. When the peak value of the earthquake input was 0.1 g, the differences in the peak accelerations of each floor of the superstructure between the two models including the effect of SPSI and not including the effect of SPSI were 9%, 15%, 27%, 30%, 32%, and 31%, respectively, and the corresponding differences were 22%, 30%, 33%, 27%, 19%, and 16%, respectively, when the peak value of the earthquake input was 0.2 g. As the peak value of the earthquake input increased, the effects of the lower floors (the 1 st to 3 rd floors) due to the soil and structure interaction were larger, while the effects of the higher floors (the 4 th to 6 th floors) were less. This reason is attributed to the fact that the dynamic nonlinearity characteristics of the soil and structure interaction are more significant with the increase in the earthquake input, and the lower structures are closer to the soil, so the effect is larger.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Floor**} & \multicolumn{2}{c}{**Acceleration (m/s2)**} & \multicolumn{4}{c}{**Acceleration (m/s2)**} \\  & \multicolumn{2}{c}{**(for Input 0.1 g)**} & \multirow{2}{*}{**Difference (\%)**} & \multicolumn{2}{c}{**(for Input 0.2 g)**} & \multirow{2}{*}{**Difference (\%)**} \\ \cline{2-2} \cline{5-6}  & **SPSI** & **No SPSI** & & & **SPSI** & **No SPSI** \\ \hline
6 & 1.823 & 2.657 & \(-\)31 & 4.453 & 5.319 & \(-\)16 \\
5 & 1.686 & 2.466 & \(-\)32 & 3.978 & 4.933 & \(-\)19 \\
4 & 1.551 & 2.214 & \(-\)30 & 3.214 & 4.427 & \(-\)27 \\
3 & 1.402 & 1.914 & \(-\)27 & 2.554 &With the increase in the time step, the acceleration responses of the model including the influence of SPSI lagged behind those of the model without the influence of SPSI, and the lag time was longer under the earthquake input of 0.2 g. Further, the lag phenomenon was more obvious for higher floors. The main reason is that the soil dynamic nonlinearity is significant in the soft soil foundation under the excitation of the earthquake, resulting in intensifying the nonlinear dynamic interaction between soil and structure, and the nonlinear dynamic interaction between soil and structure, and the nonlinear dynamic interaction between soil and structure, and the nonlinear dynamic interaction between soil and structure.  
Figure 10: Acceleration time history curves for the earthquake input 0.1 g.