esa-sceva/satcom-chunk-collection huggingface dataset:
# Response to Sulfur Dioxide Addition by Two Commercial _Saccharomyces cerevisiae_ Strains
## 3 Results
### Yeast-Derived Secondary Metabolite Composition
Plots depict (**A**) the individual factors map and (**B**) the variable factors map showing secondary metabolites as vectors. On the individual factors map, each point represents the entire volatile secondary metabolite composition of a single wine sample. Points that are closer together contain more similar volatile profiles than points that are further apart. On the variable factors map, the length of the variable vector reflects that variable’s relative contribution in producing this ordination. Variable vectors that point towards a sample are positively correlated with that sample, and those that point away from a sample are negatively correlated. Variable vectors that appear at a \(\sim\)90\({}^{\circ}\) angle are not correlated with each other, while those that appear at \(<\)0\({}^{\circ}\) and \(>\)90\({}^{\circ}\) angles are positively and negatively correlated with each other, respectively. Both QA23 and BRL97 increased their production of 2/3-methylbutyl acetate, hexyl acetate, 2-phenylethylly acetate, and 2-phenyl ethanol in the presence of SO\({}_{2}\) (Table 3). BRL97 also showed an increase in the production of 2-methyl propanol, 2/3-methyl butanol, and methanol, and a decrease in the production of hexanoic acid/ethyl dodecanoate when SO\({}_{2}\) was added. In general, the production of volatile compounds was higher in the SO\({}_{2}\)-supplemented wines, especially with regards to the wines fermented by BRL97. QA23 was less affected by SO\({}_{2}\) addition in terms of the production of volatile compounds. Two of the four replicate wines fermented by BRL97 in the SO\({}_{2}\) treatment were positively correlated with the production of 2/3-methylbutyl acetate, hexyl acetate, methanol, 2-phenylethyll acetate, 2-phenylethyll, and ethyl octanoate (Figure 6). The other two replicate wines fermented by BRL97 in the SO\({}_{2}\) treatment were found to be more similar to the wines fermented by QA23 in the presence of SO\({}_{2}\), which were positively correlated with the production of ethyl decanoate (grape) and decanoic acid (sour/rancid/fatty), and negatively correlated with the production of ethyl acetate (fruity/solvent), ethyl hexanoate (fruity/fermented pear), and ethyl 3-methylbutanol (apple/strawberry) (Figure 6). A single replicate wine fermented by BRL97 in the control treatment was positively correlated with the production of ethyl butanoate (apple/strawberry), and negatively correlated with the production of octanoic acid (sweat/cheese) (Figure 6). In general, the wines produced from the SO\({}_{2}\) control treatment (no SO\({}_{2}\) added) were negatively correlated with the production of compounds such as 2-methyl propanol, ethyl 2-methylpropanoate, ethyl 2-methylbutanoate, and 2/3-methylbutanoic acid.
# A Proposal for a Standardized Approach to Inducing Low Energy Availability in Athletes
## 3 Results
### Low-EI Protocol  
The second protocol requires only EI to be lowered (Figure 2). Macronutrient content probably influences LEA in more ways [7]. To eliminate the impact from nutritional standpoint we suggest equal reductions is protein, fat and carbohydrate energy daily content. This means EI should be changed by equally reducing all portion sizes by equally reducing macronutrients' energy value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Strenghts** & **Opportunities** \\ \hline \(\bullet\) & easy to measure. & to compare differences when EEE is increased with low/medium/high intensity or with the same intensities and modalities as in a real setting \\ \(\bullet\) & measurement is not dependent on the accuracy of the athlete & & \\ \(\bullet\) & less burdening on some athletes who & & \\  & prefer EI to remain the same & & \\ \hline \multicolumn{3}{c}{**Weaknesses**} & **Threats** \\ \hline \(\bullet\) & difficulties in settings where HR & HR lowers in cases of increasing training \\  & monitoring is not optimal (disciplines in water) & volume, this makes reaching EEE increasingly difficult. The question remains if EEE is still correlated in the same way to HR as in rested state \\ \(\bullet\) & reaching extremely low values of LEA in endurance disciplines could be hard to achieve where baseline training volumes are already very high (too many hours spent in training would reduce/eliminate recovery and sleep time and time for eating) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: SWOT analysis of the high-EEE protocol (SWOT = Strength, Weakness, Opportunity and Threat Analysis, EEE = exercise energy expenditure, EI = energy intake, LEA = low energy availability, EB = energy balance).  
Unchanged EEE is achievable by repeating identical training regime than in the baseline measurements.  
As in the high-EEE protocol, the same starting FFM should be ensured and in case of a longer protocol multiple corrections be made according to FFM changes to ensure the calculated EA value. As concluded by [PERSON] et al. [20] there seem to be powerful biological processes and environmental factors opposing food restriction in individuals trying to reduce body mass. This means that as the individuals reduce EI without any increases in physical activity, they require extraordinary willpower to fight these pressures that do not seem to dissipate over time [20]. We speculate that this protocol might be more burdening on the athlete from motivational standpoint. Other characteristics of low-EI protocol are represented in Table 2.
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Types of Sensors
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Sensor Type**} \\ \hline
**Reference** & **Camera** & **Ultrasonic** & **Cellular** & **Sensors** & **Infrared** & **Radar** & **Other** & **Magnetometer** \\ \hline
[15] & - & \(\bigcirc\) & - & - & - & - & - \\ \hline
[30] & - & - & \(\bigcirc\) & - & - & - & \(\bigcirc\) \\ \hline
[41] & - & - & - & - & - & - & \(\bigcirc\) \\ \hline
[42] & - & \(\bigcirc\) & - & - & - & - & - \\ \hline
[43] & - & \(\bigcirc\) & - & - & - & - & - \\ \hline
[44] & - & \(\bigcirc\) & - & - & - & - & - \\ \hline
[45] & \(\bigcirc\) & - & - & - & - & - & - \\ \hline
[46] & - & - & - & - & - & - & \(\bigcirc\) \\ \hline
[47] & \(\bigcirc\) & - & - & - & - & - & - \\ \hline
[48] & - & \(\bigcirc\) & - & - & \(\bigcirc\) & - & - \\ \hline
[49] & \(\bigcirc\) & \(\bigcirc\) & - & - & - & - & - \\ \hline
[50] & - & \(\bigcirc\) & - & - & - & - & - \\ \hline
[51] & \(\bigcirc\) & \(\bigcirc\) & - & - & \(\bigcirc\) & - & - \\ \hline
[52] & - & - & - & \(\bigcirc\) & - & - & - \\ \hline
[53] & - & - & - & \(\bigcirc\) & - & - & - \\ \hline
[54] & - & - & \(\bigcirc\) & - & - & - & - \\ \hline
[55] & - & \(\bigcirc\) & - & - & - & - & - \\ \hline
[56] & \(\bigcirc\) & - & - & - & - & - & - \\ \hline
[57] & - & - & - & - & \(\bigcirc\) & - & - \\ \hline
[58] & \(\bigcirc\) & - & - & - & - & - & - \\ \hline
[59] & - & - & \(\bigcirc\) & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensor classification. From the research performed, it can be seen that among the scientific community the most used sensors are ultrasonic, whereas cameras and smartphones (accelerometer, gyroscope and magnetometer) are in second and third place, respectively. This can be attributed to the fact that ultrasonic sensors can detect with greater precision the depth and thickness of surfaces in addition to working at high frequency, having high sensitivity and high power. The classification listed in the previous table shows that sensors are not always combined to produce smart parking solutions. Some proposals, like that in [51], do not specify a type of sensor, but the authors assume that a vehicle might contain cameras, ultrasonic, and radar sensors. Other approaches, like [52], specify that a TSOP 1738 infrared sensor is in charge of detecting the presence or absence of a vehicle. All in all, the combination of sensors will certainly produce quality solution since the input data will come from many sources that have different features. The sensor in each reviewed work aims to cover the need to gather information depending on the characteristics of the environment. A brief explanation of how sensors are used is described below.
## 3 Results
### Antibiotic Resistance of Enterococcus spp  
The _E. ficeium_ strains revealed 80.5% ampicillin resistance and a high level of aminoglycos resistance (HLAR), with 41.1% to gentamicin and 43.8% to streptomycin, respectively, 41.9% ciprofloxacin resistance, 54.7% erythromycin resistance, 24.2% tetracycline resistance, and 10% penicillin resistance. The antimicrobial resistance to _E. ficealis_ was lower than that to _E. ficeium_, especially for ampicillin (7.3%), but the higher resistance to tetracycline (31.8% vs. 24.2%) and penicillin (20% vs. 10%) was uncommon. Ciprofloxacin resistance was 41.9% in either _E. ficealis_ or _E. ficeium_. Both species of enterococci were 98.8% sensitive to linezolid. Testing for nitrofurantoin and norfloxacin was inconsistent and was achieved in 10 strains of _E. ficealis_ originating from urinary infections, with only one nitrofurantoin-resistant strain. Regarding the glycoptide resistance, we found the VanA phenotype in 12% of _E. ficealis_ and in 26% of _E. ficeium_ species, while the VanB phenotype was less frequent, in 5.8% of _E. ficealis_ and 5% of _E. ficeium_ (Figure 2). The rate of MDR was 75.6% in _E. ficeium_ and 21.9% in _E. ficealis_. There ware only three strains of _E. gallinarum_, all of them isolated from hemoculture, but they had the remarkable  
Figure 1: Distribution of _Enterococcus_ spp. isolates according to the type of biologic sample in Galati, Romania. Figure 2: The distribution of antibiotic resistance in the strains of _E. gallinarum_, _E. ficeium_, and _E. ficealis_ and from Galati, Romania. MDR of 100%. Additionally, the three species of _E. gallinarum_ were resistant to ampicillin and ciprofloxacin, whereas two strains expressing the phenotype VanA. _E. hirae_ were also isolated from hemoculture, but conversely, they were sensitive to all the antimicrobial tested agents (Table 1). The age of the patients hospitalized with enterococcal infections ranged between 0 and 98 years, with an average age of 52.11 \(\pm\) 30.46 years. There was no significant correlation of age with gender, living area, antibiotic multidrug resistance, or the vancomycin resistance pattern (Table 2). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline _Enterococcus_ & **N** & **1R (n)** & **1R (\%)** & **2R (n)** & **2R (\%)** & **MDR (n)** & **MDR (\%)** \\ \hline _E. faecalis_ & 41 & 3 & 7.3 & 7 & 17.1 & 9 & 21.9 \\ \hline _E. faccium_ & 41 & 2 & 4.9 & 4 & 9.8 & 31 & 75.6 \\ \hline _E. gallinarum_ & 3 & 0 & - & 0 & - & 3 & 100 \\ \hline _E. hirae_ & 1 & 0 & - & 0 & - & 0 & - \\ \hline Total & 86 & 5 & 5.8 & 11 & 12.8 & 43 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Antibiotic resistance profile of _Enterococcus spp_.
## 3 Proposed Research Methodology
### Word Embedding Techniques
#### 3.3.4 Combining Features  
This study employs a combination of three word embedding techniques, namely, fastText, fastText subword, and GloVe. These are individually trained word embedding techniques that help the deep learning models obtain more accurate predictions. First, a deep learning model is trained on three word embedding techniques individually, and then by blending these techniques, with a ratio of 33% for fastText and Glove, and of 34% for fastText subword.
## 2 Selective Breeding Strategies for Large Yellow Croaker Seedlings
### Genetic Diversity Selection  
Genetic diversity forms the foundation for the genetic improvement and sustainable breeding of the large yellow croaker (_Larinichthys crocea_), playing a key role in maintaining population vitality, enhancing environmental adaptability, and improving stress resistance.  
In recent years, with the rapid development of the large yellow croaker aquaculture industry, the population has increasingly relied on artificial breeding systems, leading to a significant decline in genetic diversity, an increase in inbreeding levels, and adverse effects on economically important traits such as growth performance and disease resistance [24]. Multiple studies have shown that wild populations of large yellow croaker generally exhibit high levels of genetic diversity, while farmed populations are prone to allele loss, a decrease in effective population size, and reduced genetic differentiation, indicating that their genetic foundation is becoming increasingly limited [25]. To mitigate these issues, researchers have introduced wild populations into breeding programs through hybridization or backcross strategies, which have effectively restored genetic diversity in farmed populations, thereby enhancing the stability and adaptability of the genetic foundation [26][PERSON] With the continuous advancement of molecular marker technologies, molecular-assisted monitoring and evaluation techniques have become important tools for managing genetic diversity. Among these, Amplified Fragment Length Polymorphism (AFLP) and microsatellite markers are widely used in the analysis of genetic diversity in the large yellow croaker. For example, [PERSON] et al. [27] used AFLP technology to evaluate the genetic diversity of the F\({}_{4}\) generation of the [PERSON]Donghai No. 1\" population, finding that the proportion of polymorphic loci was 39.67%, the Nei's gene diversity index was 0.1399, and the Shannon index was 0.2099. These results were similar to those of the original parent population, indicating that scientific breeding strategies can effectively maintain genetic diversity within the population. [PERSON] et al. [28] used microsatellite markers to analyze the genetic structure of small yellow croaker populations along China's coastline, revealing high genetic diversity and low genetic differentiation, providing theoretical support for the conservation and utilization of large yellow croaker germplasm resources[PERSON]  
In ongoing breeding practices, it is essential to scientifically avoid the genetic bottleneck effects caused by excessive inbreeding and strong selection pressure. Regular monitoring of population genetic structure and diversity is needed to ensure the genetic stability and potential for genetic improvement within the population[PERSON] Scientifically and rationally managing genetic diversity not only helps optimize productive traits and reduce the risk of inbreeding depression but also strongly supports the long-term sustainable development of the large yellow croaker industry.
## 2 Overview
### Clustering Strategies in WSN  
Because battery power is limited, proper clustering is essential for significantly extending the network's life span. To perform clustering, there are many methods to choose from [60]. The clustering strategies in WSN [61] can be classified as shown in Figure 6.  
1. Deterministic: Here, the CHs are set at fixed positions in the network [62]. The sensors broadcast a HELLO message to their neighbors, and the node that first receives the maximum number of these messages is elected as CHs and initiates the cluster formation phase. The important attributes of these clustering schemes are node identity numbers (IDs) and node degree (number of neighboring nodes).
2. Adaptive: Instead of random CH selection, adaptive clustering schemes are based on the selection of CH considering particular parameters, such as remnant energy, the distance between nodes, energy dissipated in the last round, and distance to BS [63]. Specific combinations of these parameters form the objective function for CH selection that can adapt to the rapid variations in the network. Adaptive schemes can be further categorized as BS-assisted or probabilistic (self-organized) based on who has the power to initiate the CH selection process. Again, considering the parameters for the role of a sensor node, the probabilistic scheme can be classified as resource adaptive and fixed parameters.
3. Hybrid: This clustering strategy considers combined clustering metrics with other architectures to increase energy efficiency.
# Industry 4.0 and World Class Manufacturing Integration: 100 Technologies for a WCM-I4.0 Matrix
## 1 Introduction
### Brief Introduction to Industry 4.0  
The Fourth Industrial Revolution is derived from the increasingly widespread and efficient use of digitization in production environments. The concept was first mentioned in 2011 by the German Government, inside some hi-tech strategic guidelines [5]. I4.0 is defined by a pervasive interconnection between objects that lead to fully automated production, as well as a smarter and networked world [6]. There are many definitions assigned so far, among which the following:  
* Flexible production for the entire plant life cycle based on the integration of data-based models and decisions [7];
* The profound transformation of business models enabling the fusion of virtual and real worlds in robotics, the application of digitization, automation and robotics [8];
* Knowledge-based and sensor-based self-regulating production systems [9].  
The design principles of I4.0 are agility, decentralization, digitalization/communication/ standardization, information transparency, integrated business processes, interoperability, modularity, predictive maintenance, real-time capability, service orientation, self-aware/optimization/configuration, technical assistance and virtualization [10,11].  
This leverages on the analysis of a huge amount of data (Big Data), on the interconnection between objects via the Internet (called the Internet of Things, henceforth IoT), using a scalable calculation resource open to all participants in value creation (Cloud). In addition, the relationship between man and machine has undoubtedly improved and, at the same time, the process of exchanging information between machines and their drivers is more collaborative. Predictive and condition-based maintenance will also benefit from the possibilities offered by I4.0 [12].  
The topic of the \"active\" role of the machine is significant: through the implementation of training and telecontrol software in real time, any device in the plant can make its status known, not only at the IT level, but also to all the other members of the shop floor. In such a scenario, elements of novelty regarding advanced robotics and other enabling technologies characteristic of this industrial revolution, such as additive manufacturing, i.e., the application of 3D printing in the production context, overlap.  
The technological areas that form the I4.0 universe [13,14] are: Cyber-Physical Systems (CPSs), Cognitive Computing, Cybersecurity, Cloud Computing, Mobile Technologies, Machine-to-Machine, 3D Printing or Additive Manufacturing, Advanced Robotics, Big Data/Analytics, Internet of Things and RFID (Radio-Frequency IDentification) Technologies. These are briefly summarized in Section 4.
## 2 The Centrifugal Compressor Stage Investigated  
The centrifugal compressor stage selected for the current study represents the existing industrial centrifugal compressor, which consists of a backswept shrouded impeller, a diffuser, and a return channel. The design mass flow coefficient (\(\varphi\)) of the compressor stage studied is 0.196, and the design machine Mach number \(M_{\text{u2}}\) is 0.9 corresponding to the rotational speed of 16,382 rpm. Due to the power limitation of the test rig used, the maximum machine Mach number \(M_{\text{u2}}\) of 0.8 corresponding to the rotational speed of 14,562 rpm (about 88.9% design rotation speed) was reached. To compare with the experimental data, numerical simulations in this paper applied the same operating conditions as the experiment. The impeller includes 17 blades. The diameter of the impeller exit is 360 mm. The return channel consists of 18 blades [6]. The primary geometric parameters of the centrifugal compressor stage are shown in Table 1. Four types of diffusers were investigated. Three of them were vaned diffusers, and each included 22 vanes. The fourth was a vaneless diffuser. The diffusers had an identical meridional profile.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & & **Value** \\ \hline Impeller exit diameter & \(D_{2}\) & 360 mm \\ Diffuser entrance diameter & \(D_{3}\) & 390 mm \\ Diffuser exit diameter & \(D_{4}\) & 465 mm \\ Impeller exit width & \(b_{2}\) & 17 mm \\ Number of impellers & \(Z_{\text{imp}}\) & 17 \\ Number of diffuser vanes & \(Z_{\text{diff}}\) & 22 \\ Number of return channels & \(Z_{\text{r}}\) & 18 \\ Rotational speed & \(\omega\) & 14,562 rpm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary geometric parameters of the centrifugal compressor stage.  
Three different vaned diffusers were selected for this study: a traditional full-height vaned diffuser (TVD), a shroud-side partial vaned diffuser (SVD), and a hub-side partial vaned diffuser (HVD). The vane height of SVD/HVD is about 21.4% of the TVD vane height. Our team carried out a numerical design and investigation for the vane height of a partial vaned diffuser [6]. The geometries of different types of diffusers are presented in Figure 1.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Pairwise Comparison** & **Mean (SD)** & **Mean Difference** & \(p\)**-Value a ** \\ \hline \multirow{4}{*}{HAPS} & ALG vs. PVS-2 & \(15.31\) (0.03) & \multirow{2}{*}{0.12} & \multirow{2}{*}{\textless{0.001 *}} \\  & & \(15.19\) (0.04) & & \\ \multirow{4}{*}{HAPS} & ALG vs. DIGITAL & \(15.31\) (0.03) & & \\  & \(15.57\) (0.05) & \(-0.26\) & \(<\)0.001 * \\ \multirow{4}{*}{PVS-2 vs. DIGITAL} & \(15.19\) (0.04) & & \\  & \(15.57\) (0.05) & \(-0.38\) & \(<\)0.001 * \\ \hline \multirow{4}{*}{HAPC} & ALG vs. PVS-2 & \(41.41\) (0.08) & \multirow{2}{*}{0.02} & \multirow{2}{*}{0.772} \\  & \(41.39\) (0.05) & & \\ \multirow{4}{*}{HAPC} & ALG vs. DIGITAL & \(41.41\) (0.08) & & \\  & \(41.75\) (0.05) & & \(-0.34\) & \(<\)0.001 * \\ \multirow{4}{*}{PVS-2 vs. DIGITAL} & \(41.39\) (0.05) & & \multirow{2}{*}{\(-0.36\)} & \multirow{2}{*}{\(<\)0.001 *} \\  & \(41.75\) (0.05) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Multiple pairwise comparisons of the accuracy of the two impression materials and digitally printed models using Tukey’s HSD test. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**HAPS**} & \multicolumn{2}{c}{**HAPC**} & \multicolumn{2}{c}{**HCA**} & \multicolumn{2}{c}{**V**} \\ \hline \multicolumn{2}{c}{**Type of**} & \multirow{2}{*}{\%} & \multirow{2}{*}{\(\upmu\)m} & \multirow{2}{*}{\%} & \multirow{2}{*}{\(\upmu\)m} & \multirow{2}{*}{\%} & \multirow{2}{*}{\(\upmu\)m} & \multirow{2}{*}{\%} & \multirow{2}{*}{\(\upmu\)m} \\ \multicolumn{2}{c}{**Impression**} & & & & & & \\ \hline ALG & \(1.41\) & \(213\) & \(-0.32\) & \(132\) & \(0.67\) & \(303\) & \(-5.29\) & \(228\) \\ PVS-2 & \(0.58\) & \(88\) & \(-0.37\) & \(151\) & \(0.54\) & \(245\) & \(-4.56\) & \(197\) \\ DIGITAL & \(3.09\) & \(467\) & \(0.50\) & \(210\) & \(0.58\) & \(260\) & \(-4.96\) & \(214\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The percentage of deviation (%) and absolute change (\(\upmu\)m) from the typodont master cast.