esa-sceva/satcom-chunk-collection huggingface dataset:
# The Complex Relationship between Virulence and Antibiotic Resistance
## 4 Regulation of Virulence and Antibiotic Resistance
### Complex Multi-Regulation Networks  
Regulation via genetic transcriptional factors represents a complex link between virulence and antibiotic resistance. A mutation in the _typA_ gene in _P. aeruginosa_ lowered virulence, thereby evading phagocytosis, increasing attachment, and enhancing biofilm formation. Additionally, this mutation, which initially may be viewed as a fitness cost led to increased antibiotic resistance, and down regulation of the type III secretion system [145]. Conversely, [PERSON] et al reported that _Pseudomonas viridiflava_, a phytopathogenic bacterium, has an inverse correlation between antibiotic resistance and pathogenicity, indicating that fitness may be species specific [170]. _TypA_ has also been shown to play a major role in regulation of virulence factors in enteropathogenic _E. coli_, including flagella, type III secretion system, LEE pathogenicity island, and _espC_ pathogenicity island [165]. Recently, a transposon based genetic screen performed in _Acinetobacter baumannii_ identified a subset of 30 transcription factors that were not only necessary for proliferation in the host but also were present in conditions of antibiotic resistance and environmental stress, highlighting the existence of a link between virulence and antibiotic resistance [171]. _A. baumannii_ is not the only bacteria to exhibit such a connection, [PERSON] et al reported that mutation in _P. aeruginosa_'s OprD entry channel, which provides resistance to carbapenem antibiotics, also led to a dramatic increase in the ability of _P. aeruginosa_ to colonize the mucosa in a mouse model, disseminate to the spleen, and prove cytotoxic to murine macrophages [172].
## 2 Materials and Methods  
### Materials  
Poly(butylene succinate-co-butylene adipate) (PBSA, PBE 001, density 1.24 g.cm\({}^{-3}\)) and poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV, PHI 002, density 1.23 g.cm\({}^{-3}\)) pellets were commercially procured from Natureplast, France.  
Cloisite 30B (C-30B), an organo-modified montmorillonite clay containing a methyl bis-2-hydroxyethyl ammonium quaternary salt with a cation exchange capacity (CEC) of 90 meq/100 g, were supplied by BYK additives and instruments, Wesel, Germany.  
An array of eight low molar mass molecules with a variety of chemical and physical properties as described in FDA and EFSA regulations [16; 17] was selected for representative plastic additives. The additives chosen included: chlorobenzene, methyl salicate, biphenyl, phenyl cyclohexane, benzophenone, methyl stearate and DEHA, all provided from Sigma-Aldrich, Saint-Quentin-Fallavier, France.  
Acetic acid, ethanol and 2,2,4 trimethyl pentane (isooctane) were commercially provided by Sigma-Aldrich with a purity > 99% and were used to prepare the food simulants for migration experiments: 3% acetic acid, 95% ethanol and isooctane.
## 1 Introduction
Some researchers have attempted to define suitable SZA thresholds that produce accurate pixel data from MODIS. Gridding artifacts have been found to affect the spatial properties of MODIS images because of mismatches between observed and predefined pixels when viewing zenith angles are high. In the along-track direction for 500-m data, the overlap between consecutive scan lines is larger than the observation's dimensions when the viewing zenith angle is \(\sim\)27\({}^{\circ}\)[18]. The angular threshold for the MODIS Snow Cover Area (SCA) and Grain Size (MODSCAG) snow cover model was determined to be 25\({}^{\circ}\), with a deviation of up to 10%, calculated from the area at nadir for the MODIS pixels considered [19; 20]. These studies have demonstrated that the variation in the SZA is intimately linked with uncertainty in the radiance or reflectance of MODIS data, which could ultimately result in the incorrect identification of targets such as wheat, cloud, vegetation, and/or snow cover. Pixel expansion and a \"gridding\" effect have been proposed as causes for this inaccuracy, and a preliminary linear relationship has been found between SZA and reflectance values. However, there is no firm conclusion or consensus about the value of the optimal SZA threshold, or how the SZA influences the final MODIS product. Determination of an angular threshold for MODSCAG requires investigation of the influence of SZA on MOD10A1 data. This work focused on investigating the relationship between the SCA of MOD10A1 data and its corresponding SZA in the Tarim River Basin, in order to produce daily snow cover data with greater reliability. A paired-date difference method was used for comparative analysis, which allowed the mechanism of overestimation to be determined based on statistical data obtained from five subwatersheds over a 10-year period. After determining an optimal threshold for a reliable SZA, a multiday refilling method was used to replace pixels related to large SZA values and high cloud cover with new snow cover interpretations. This modified dataset was evaluated in terms of its monthly and annual averages of snow cover percentage (SCP) and snow cover frequency (SCF), and it was validated using 30-m-resolution HJ-1A/B satellite data. The results indicated that the modified snow cover dataset presented herein is significantly more accurate than the unmodified version currently available.
# Analysis of Shell Egg Pasteurization Using Computational Fluid Dynamics
###### Abstract
The slowest heating point within an intact egg was reported to be around the geometrical center of the egg samples [11; 15]. Early studies examined the effects of the heat transfer coefficient between the heating medium and the egg surface on egg pasteurization [15]. Recently, a CFD study examined the heat and mass transfer of egg pasteurization and the efficiency of thermal treatment on _Salmonella enterica_, using the actual representation of intact egg components, such as eggshell, white, yolk, and air cells [11]. [PERSON] et al. [11], using a three-dimensional computational model, established the effects of eggshell thickness and the % of air cells in achieving target lethality. All of the above-mentioned studies [11; 15; 16] restricted their analyses to the heating part of the pasteurization process. The cooling of the egg, which inevitably follows the heating cycle of the pasteurization process, is of equal importance in terms of microbial destruction, as at the initial stages of cooling the product is still at elevated temperatures. Microbial destruction occurring during the cooling cycle does not only affect the required heating time; it can also alter the position of the \"cold spot\", the point inside of the egg that receives the least heat treatment and represents the critical point the pasteurization process should be based on. The cold spot does not necessarily coincide with the slowest heating point, and it can be assessed through lethality distribution calculations. Quality degradation during egg pasteurization is expected to be minimal, although quantitative calculations were not found. Considering the above, this study aims to investigate the whole egg pasteurization process using CFD analysis. It follows a holistic approach, with the goals of the current work being the following: (1) to develop a three-dimensional heat transfer model for whole egg pasteurization, (2) to predict the time-temperature profile and the critical point location based on experimentally determined heat transfer coefficients, (3) to estimate pasteurization efficiency (\(F\) values) considering microbial destruction at both the heating and the cooling cycle of the process, (4) to estimate quality degradation during pasteurization, and (5) to validate the theoretical model against experimental temperature data.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Monitoring
#### 2.5.4 Ultrasound Imaging  
The ultrasound imaging is based on the interaction of acoustic waves with human tissue. As ultrasound imaging is non-invasive, it is widely used in medical settings. It provides real-time imaging at a low cost, and it has minimal adverse health effects. Ultrasound imaging can be used for the manipulation of nanorobots. Some challenges and disadvantages associated with ultrasound imaging are that it cannot travel in the air and scatters very easily from bones. The scanning area of ultrasound imaging systems is minimal, and it can only penetrate the human body for a few centimetres. The resolution of ultrasound images is low, and can barely be used in the control or visual servoing of nanorobots. This method was used by some researchers for localization [124].
## 2 Materials and Methods  
### Animal Models  
Adult male Wistar rats (Crl:WI BR; Toxi-Coop Zrt., Balatonfured, Hungary) kept on a 12/12 h day/night cycle, fed standard laboratory chow (Farmer-Mix Kft., Zsambek, Hungary), and with free access to drinking water, were used throughout the 1-week acclimatization period and the experiments. The protocol was designed to minimize pain or discomfort to the animals. In all procedures involving experimental animals, the principles of the National Institutes of Health (Bethesda, MD, USA) guidelines and the EU directive 2010/63/EU ([[https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=O](https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=O)]([https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=O](https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=O))):L: 2010:276:0033:0079:en:PDF, accessed on 6 July 2023) for the protection of animals used for scientific purposes were strictly followed, and all the experiments were approved by the National Scientific Ethical Committee on Animal Experimentation (National Competent Authority), with the license number XX./1636/2019.  
For the acute hyperglycemic experiments, before STZ injection the rats (280-320 g, 12-week-old) were divided randomly into two groups: STZ-induced 5-day diabetics (acute diabetics, n = 7), and age-matched controls (n = 6). The controls were treated with vehicle,while diabetes was induced by a single intraperitoneal injection of STZ (Sigma-Aldrich, Budapest, Hungary) at 60 mg/kg, as described previously [15; 16]. The animals were considered diabetic if the non-fasting blood glucose concentration was higher than 18 mmol/L. The blood glucose level and weight of each animal were measured daily.  
For the chronic hyperglycemic experiments, before STZ injection the rats (200-260 g, 7-8-week-old) were divided randomly into four groups: STZ-induced 10-week diabetics (chronic diabetics; n = 14), insulin-treated STZ-induced diabetics (insulin-treated diabetics; n = 13), reverted STZ-induced diabetics (reverted diabetics; n = 4), and age-matched controls (n = 20). The controls were treated with vehicle, while diabetes was induced by a single intraperitoneal injection of STZ, as previously mentioned. The animals were considered diabetic if the non-fasting blood glucose concentration was higher than 18 mmol/L. From this time on, one group of hyperglycemic rats received a subcutaneous injection of insulin (Humulin M3, Eli Lilly Nederland, Utrecht, The Netherlands) each morning (3 IU) and afternoon (3 IU). The rats of the chronic diabetic group were considered to be reverted diabetic if the non-fasting blood glucose concentration decreased and stayed under 18 mmol/L during the ten weeks. Equivalent volumes of saline were given subcutaneously to the diabetic, the reverted diabetic, and the control rats. The blood glucose level and weight of each animal were measured weekly.
## 4 Methods
### Data Leveling Methods  
#### 4.2.1 CR Method  
The main principle of the CR method is to treat the mean value of the data in each block, which contains systematic errors, as the background of that block. The mean values of all data are adjusted to the same level (typically the mean level of the entire study area). At this level, data between different groups can be compared (Equation (1)). After this adjustment, it is reasonable to use the corrected data for drawing geochemical contour maps or geochemical maps [9].  
\[X^{\prime}_{i\ j}=\frac{\overline{X}_{j}}{\overline{X}}\times X_{i\ j} \tag{1}\]  
where \(X^{\prime}_{i\ j}\) is the corrected value for the \(i\)-th sample in the \(j\)-th block, \(X_{i\ j}\) is the measured data for the \(i\)-th sample in the \(j\)-th block (the raw data value), \(\overline{X}_{j}\) is the mean value of the specific element in the \(j\)-th bloc, and \(\overline{X}\) is the mean value of the specific element across all blocks in the entire area.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### N-Shot Learning  
N-shot learning is a broad concept, as it includes few-shot, zero-shot, and one-shot learning. Few-shot learning is a classification task where a very small number of training examples are given for each class that is used to prepare a model. In zero-shot learning, the model observes images from categories that were not monitored while training and predicting the class they belong to. Similarly, one-shot learning is a classification task where one example is given for each class. One of the networks used for N-shot learning is the Siamese neural network. A Siamese neural network consists of twin networks that accept two images but are joined by an energy function at the output. In Siamese networks, feature vectors are learned by using convolutional neural networks, which are obtained from labeled nonmatching and matching image pairs. The parameters between the twin networks are identical. Similar initial weights assure that two similar images could not possibly be mapped by their respective networks to very different locations in feature space, because each network computes the same function. Then, the similarity between these feature vectors is measured by Euclidean distance. The image is classified based on the nearest-neighbor approach's similarity score. N-shot learning is a data-efficient approach which can be used efficiently in the medical field. One-shot learning specifically is helpful for the classification of rare diseases.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 5 Performance Evaluation
### Performance Metrics  
Performance metrics include data delivery ratio (\(DR\)), dissemination overhead (\(DO\)) and dissemination delay (\(DD\)). In the following, we describe each of these metrics:Data Delivery Ratio (\(DR\)); which measures the percentage of coverage. For full coverage, every vehicle should receive a copy of any sent message. At the end of the simulation, the number of messages that should be received is equal to the number of vehicles that exist whenever a message is sent, which we refer to as expected messages \(M_{exp}\). For every message \(i\), \(DR\) is obtained by dividing the number of successfully received copies of the message \(M_{rcv}\) by \(M_{exp}\). Assuming a total of sent messages \(M_{sent}\), \(DR\) is computed as: \[DR=\frac{1}{M_{sent}}\sum_{i=1}^{M_{sent}}\frac{M_{rcv}}{M_{exp}}\] (8)
* Broadcast Overhead (\(BO\)); which measures the average broadcast overhead per message reported by an arbitrary vehicle \(j\), which is obtained by dividing the total number of duplicate messages \(M_{dup}\), by the number of uniquely identified messages received \(M_{rcv}\). Assuming \(N\) vehicles contributing in data dissemination, the global \(BO\) is computed as: \[BO=\frac{1}{N}\sum_{j=1}^{N}\frac{M_{dup}}{M_{rcv}}\] (9)
* Dissemination Delay (\(DD\)); which considers all reported delays whenever a new message is received by a vehicle to estimate an average delay for any vehicle to receive a new message. Whenever a new message \(i\) is received, the local delay is reported by computing the difference between the message arrival time \(t_{m}\) and the message creation time \(t_{0}\) (which is stored in the message Timestamp field). Assuming the total of received messages \(M_{rcv}\) at the end of the simulation, reported local delays are considered for computing the global average delay as follows: \[DD=\frac{1}{M_{rcv}}\sum_{i=1}^{M_{rcv}}t_{m}-t_{0}\] (10)
# Union Models for Model Families: Efficient Reasoning over Space and Time
## 5 Propositional Encoding Language with Annotations (PELA)
### Example  
This section provides a simple yet complete example for two versions of state transition diagrams, \(M1\) and \(M2\). The example illustrates the formalization of both models as E-graphs. In addition, it illustrates their encoding into propositional variables as well as their union. In this example, \(M1\) and \(M2\) are assumed to be the first and the second version of a model that together represent configuration A. Hence, the elements of \(M1\) and \(M2\) are respectively annotated with <\(ver_{1}\), \(conf_{A}\)> and <\(ver_{2}\), \(conf_{A}\)>.  
Figure 14 represents \(M1\) in the conventional representation of state transition diagrams, Figure 15 illustrates the representation of \(M1\) as a canonical typed attributed E-graph, and Figure 16 represents \(M1\)'s propositional encoding.  
Figure 14: First version of a state transition diagram, M1.  
Figure 13: _GraphToPropositionWithAnnotation_ encoding of model M in Figure 12.  
In the same manner, Figure 17 shows the conventional representation of \(M2\) as a state transition diagram, Figure 18 represents it as an E-graph, and Figure 19 details its propositional encoding.  
After encoding \(M1\) and \(M2\), we can now construct their union model \(M_{U}\) as the union of their propositional encodings with annotations (Definition 11), as shown in Figure 20:  
Figure 16: Propositional encoding of M1.  
Figure 19: Propositional encoding of M2.  
Figure 17: Second version of a state transition diagram, M2.  
Figure 18: Representation of M2 as an E-graph.  
Figure 15: Representation of M1 as an E-graph.  
The representation of the union model as an E-graph is depicted (with annotations) in Figure 21, and its conventional representation as an ordinary state transition diagram (again with annotations) is shown Figure 22.
# E-Nose Quality Evaluation of Extra Virgin Olive Oil Stored in Different Containers
## 3 Results and Discussion
### Gas Chromatographic Analysis of Volatile Compounds  
The volatile organic compounds (VOCs) that contribute to taste and flavor belong to several chemical classes, namely aldehydes, alcohols, hydrocarbons, ethers, esters, carboxylic acids, and ketones (Kal  
in the Tinplate containers over the storage time. These aldehydes are produced by the oxidation of unsaturated fatty acids; this leads to a loss of quality and develops a rancid taste and smell [31]. The second family most prominently represented in fresh olive oil was alcohols (Table 2), with a similar percentage to the aldehydes group. (Z)-2-penten-1-ol, (E)-2-hexen-1-ol, and (E)-3-hexen-1-ol are related to fruit and the green odor in fresh oils. The concentration of the first two practically did not vary, but the concentration of (E)-3-hexen-1-ol decreased in all containers (Figure 3B).  
Although hydrocarbons have a negligible impact on the odor of olive oils, compounds such as octane and \(\alpha\)-farnesene, which contribute to a positive odor with a strawberry aroma [32], appeared in very low proportion in the fresh oil and decreased at the end of the storage time (Table S1). Other hydrocarbons such as 4-methylheptane and 2,4-dimethylhexane, which are related to negative attributes with an unpleasant odor [33], increased slightly during storage, except in the Tinplate containers. In the case of ethers, which contribute to a positive aroma [33], their values did not vary significantly in any of the containers. Similar behavior was observed with esters. As with the previous compounds, they contributed to positive aromas, a green grassy and mild sweet smell [34], and their values did not vary significantly in any of the containers either. Acetic acid, belonging to the group of carboxylic acids and contributing with vinegy and sour sensory attributes [35], significantly increased its concentration in the Dark crystal bottles and the PET containers  
Figure 3: Evolution of the most prominent VOCs during the storage time. (**A**) (E)-2-Hexenal; (**B**) (E)-3-Hexen-1-ol.  
and slightly in the Tinplate containers. In the case of the ketones group, both 6-methyl-5-hepten-2-one and geranylacetone, which are positive compounds with a green odor [35; 36], showed a similar concentration throughout the study (Table S1).  
In line with what has been commented on in the study on the evolution of volatiles, the container that best preserved the aroma of the olive oil was Tinplate. (E)-2-hexenal, (E)-3-hexen-1-ol, and 1-hexanol in t6 had a higher percentage representation in this container type and all had a positive aroma. However, hexanal, 2-methyl-2-propanol, 4-methyl-1-(1-methyl) cyclohexanol, 4-methylheptane, 3-decyne, 3,5-dimethyloctane, 3-ethyloctane and acetic acid had a negative odor. These VOCs represented the highest percentages in Dark crystal and Green PET containers.
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 6 Performance Evaluation
### Performance Evaluation
If the attacker knows the topology of the smart grid, the FDI attack vector can be constructed without changing the measurement residuals. The FDI attack characteristics closely resemble the original signal, making it invisible and difficult to detect using common defense mechanisms. In this case, the traditional bad data detection (BDD) method cannot detect the FDI attack, and the same blacklist/whitelist detection mechanism for detecting known attacks also faces a failure. Thus, the power system may get wrong state estimation results, which further affects various decisions of the power system and jeopardizes the safe operation. However, the MADDPG algorithm proposed in this paper applies to the situational space of continuously changing elements and is capable of aware small changes in situational elements. In the following, we injected false data attack vectors with different deviation amplitudes for the IEEE-14 bus system and the IEEE 118-bus system with the active power of a single node as the attack target, respectively. Tables 3 and 4 show the proposed model's detection results against the FDI attack at the edge agent side with Gaussian white noise \(N(0,0.1)\) added to the testing data. In the IEEE 14-bus system, the detection accuracy decreased as the deviation amplitude of the FDI attack vector decreased. However, when the deviation amplitude to 0.05, the detection accuracy was still more than 85%, the false acceptance rate (FAR) was less than 8%, and the false rejection rate (FRR) was below 6.8%. With the IEEE 118-bus system, the detection accuracy reached more than 92.3% when the deviation amplitude was above 0.1, the FAR was maintained  
Figure 8: The processing time of situational awareness under heterogeneous terminals. at about 7.7%, and there were no false rejection cases. However, influenced by increased data dimensionality, the detection accuracy dropped to 72% for deviation amplitude less than 0.1, and the FAR and FRR increased correspondingly. According to the above analysis, the detection accuracy of the MADDPG-based model proposed in this paper can achieve up to 92% over the small-amplitude FDI attack for small power systems. Still, up to 92% over the FDI attack for large power systems when the deviation amplitude of the false data injection attack is more than 0.1. This responds that the multiagent deep deterministic policy gradient algorithm under the edge computing paradigm works well in solving the security situational awareness problem of false data injection attacks in the smart grid.