esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Compounds in the Battle against Microorganisms--Linalool
## 3 Antimicrobial Activity
### Mechanism of the Antibacterial Action of Linalool
#### 3.1.2 Change in Carbohydrate Metabolism  
Under various conditions of environmental stress, microorganisms generally reduce their energy metabolism, aiming to save energy for the longest possible period of time (Table 4). Glycolysis, the tricarboxylic acid (TCA) cycle and the electron transfer chain are keyways of providing the energy needed for bacterial cell activity. The influence of linalool on the necessary substances involved in the proper acquisition of energy by the bacterial cell, i.e., pyruvate, citrate, fumarate, lactate and 2-oxoglutarate, has been shown. Lowering their concentration affects the TCA cycle and glycolysis and contributes to the disruption of ATPase, ultimately leading to the disruption of energy production. Under such conditions, the death of the bacterial cell occurs [71].  
In addition, linalool has been shown to reduce the activity of pyruvate kinase (PK) and succinate dehydrogenase (SDH), enzymes that play a key role in the TCA cycle. When their activity is disturbed, the production of pyruvate and ATP (interference with the starting point of the TCA cycle) and the second mitochondrial complex of the electron transport chain are disrupted (the correct conversion of energy from nucleotides into ATP energy) [65].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Linalool** & **Effect** \\ \hline Alkaline phosphatase (a) & & Destruction of the integrity of the cell wall \\ Histidine and methionine & & Damage to the structure of the biofilm \\ L-glutamate, L-glutamine, L-arginine, L-aspartate & Disturbance of the pH homeostasis of the bacterial cell \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of linalool on amino acid metabolism.  
High levels of saturated fatty acids, i.e., linolenic, linoleic, oleic, arachidonic, myristic, octadecanediolic and palmitic acids, indicate low fluidity of the cell membrane. In the presence of linalool, the content of C16 and C18 fatty acids as well as saturated and unsaturated fatty acids increases. In addition, this monoterepne shows the ability to bind to phospholipids, thus damaging the structure of the membrane, because phosphadylcholine (PC) and phosphatidylethanolamine (PE), the main phospholipids present in the cell membrane, are responsible for the integrity and fluidity of the bacterial cell [72].
# Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images
## 2 Proposed Binocular Eye Gaze Tracking System
### Detection of Pupil and Corneal Reflection
Accordingly, depending on the minimum and maximum histogram values, we manually adjusted the pupil threshold value (\(T\)) at 120 after experimental results. Then, component labeling is applied to the segmented objects from background image. Small size parts are removed from the image to clear rough right and left eye pupils with erosion operation utilizing a morphological filter. We removed the objects smaller than 250 pixels in size, which was determined experimentally. The object size value is obtained after the trials of the users' small head movements which cause object size to change according to user to screen distance. Since human eye is not an exact circle, pupil size detection is not entirely possible with circular or elliptic methods. Hence, we used mass estimation of pupils. Thus, a weighted average technique (Equation (2)) was used to find the center coordinates of pupils. For the detection of glint mass and pupil mass center coordinates, we took advantage of LabVIEW Count Objects function. We presented our glint-pupil detection process in Figure 7.  
Figure 7: The glint and pupil center region detection process: **(a)** Eye region extracted from original image; **(b)** Binarization of gray level image for glint; **(c)** Application of morphological filter to the glint; **(d)** Weighted average technique applied to the glint; **(e)** Application of binarization to the pupil image; **(f)** Application of morphological filter to the pupil; **(g)** Application of weighted average technique to the pupil; **(h)** Pupil and glint detection of two eyes in the face image.
## 3 Results and Discussion
### Broken Grain  
Broken grain quality indicator in combine harvester performance was directly influenced by the mechanical impact, friction, and grain residence time within the threshing system. The broken grain percentage was evaluated across a range of guide vane angles (10-35\({}^{\circ}\)) for Combine A and Combine B. The objective was to assess how varying the axial crop flow conditions affected grain integrity under a constant engine load of approximately 90%.  
In Combine A, the broken grain percentage decreased with increasing guide vane angle at 10\({}^{\circ}\); broken grain was 1.81%, but this value dropped consistently to 0.25% at 30\({}^{\circ}\), and further to 0.18% at 35\({}^{\circ}\).  
This inverse trend suggests that higher guide vane inclination, which accelerates axial flow and reduces the number of impacts between rasp bars and grain, contributes to gentler material handling (Figures 12 and 13). As crop material spends less time being tumbled and compressed in the threshing zone, the risk of grain cracking or fracture diminishes. The design of Combine A--with its fully adjustable vanes--allows for uniform crop acceleration and improved flow continuity, thereby minimizing areas of excessive pressure that typically result in grain breakage.  
Combine B followed a similar but less pronounced trend. Broken grain decreased from 1.34% at 10\({}^{\circ}\) to 0.11% at 35\({}^{\circ}\). Although the overall reduction is comparable to Combine A, the broken grain values at intermediate angles (e.g., 15-25\({}^{\circ}\)) were consistently higher in Combine B. This may be attributed to the fixed front vanes in Combine B, which can cause inconsistent material acceleration and localized buildup in the rotor. Such conditions may  
Figure 12: Influence of guide vane inclination of broken grain (Engine Load –90%). Combine A.  
lead to over-threshing or uneven impact forces on the grain, especially when rear vane angles are increased but the front zone flow remains constrained.  
At the optimal guide vane range of 25-30\({}^{\circ}\), Combine A achieved both high productivity and low grain damage (0.71% broken grain at 25\({}^{\circ}\) and 0.25% at 30\({}^{\circ}\)). In contrast, Combine B exhibited higher broken grain at similar productivity levels--1.12% at 25\({}^{\circ}\) and 0.31% at 30\({}^{\circ}\). These findings suggest that full-length vane control not only improves separation performance, but also helps to preserve grain quality by maintaining a more stable and controlled threshing environment.  
The results demonstrate that increasing guide vane inclination reduces broken grain in both combines; however, the degree of reduction and consistency across angles is significantly better in Combine A. The uniform crop acceleration enabled by its fully adjustable vanes reduces frictional and impact forces that cause kernel damage. Conversely, Combine B's segmented vane configuration results in greater variation in grain quality, particularly under higher feed rate conditions.
## Materials and Methods  
### Materials  
Thirteen pyrazine standards (2-methyl-pyrazine, 2,5-dimethyl-pyrazine, 2,6-dimethyl-pyrazine, 2-ethyl-pyrazine, 2-ethyl-5-methyl-pyrazine, 2-ethyl-6-methyl-pyrazine, trimethyl-pyrazine, 2,6-diethyl-pyrazine, 3-ethyl-2,5-dimethyl-pyrazine, 2-ethyl-3,5-dimethyl-pyrazine, 2-methyl-3,5-diethyl-pyrazine, iso-propenyl-pyrazine, 5H-5-methyl-6,7-dihydroxycoplecton-pyrazine) and internal standard of 3-methyl-pyridine were purchased from Sigma-Aldrich Co. Ltd. (Shanghai, China). Isotopically labeled internal standard: [\({}^{2}\)H\({}_{6}\)]-2-methyl-pyrazine was purchased from CDN Isotopes Inc. (CDN, Canada). Caprylic capric triglyceride (ODO) was from Maitian Chemical Co. Ltd. (Shanghai, China).  
Four SPME-arrow fibers, including polydimethylsiloxane (PDMS) (100 \(\upmu\)m \(\times\) 20 mm), polyacrylate (PA) (100 \(\upmu\)m \(\times\) 20 mm), polydimethylsiloxane/divinylbenzene (PDMS/DVB) (120 \(\upmu\)m \(\times\) 20 mm), polydimethylsiloxane/divinyl-benzene/carboxsen (PDMS/DVB/CAR) (120 \(\upmu\)m \(\times\) 20 mm), and traditional SPME DVB/CAR/PDMS (50/30 \(\upmu\)m \(\times\) 10 mm) fibers were purchased from CTC Analytics AG (Zwingen, Switzerland). The holder of fibers was purchased from PAL System (Zwingen, Switzerland).  
The refined pyrazine-free and flavor-enhanced rapeseed oils were obtained from the local supermarket, thus as the real samples of 3 flavor-enhanced oils, including peanut oil, sesame oil, and rapeseed oil. All oil samples were stored at 4 \({}^{\circ}\)C. Ethyl acetate was chromatographic grades.
# Self-Training with Entropy-Based Mixup for Low-Resource Chest X-ray Classification
## 2 Related Work  
### Data Augmentation  
Data augmentation is a technique that increases the number of training data by synthesizing new data from existing data without additionally collecting new data samples. Geometric transformations [16] for image data include horizontal axis flipping, vertical axis flipping, translation, rotation, and cropping of some images. Modern data augmentation technologies such as Cutout [17], Cutmix [18], and PuzzleMix [19] have been developed,starting with Mixup, which uses linear interpolation in the training data space to generate new data. In particular, Co-Mixup [20] ensures diversity while having as many salient regions as possible for all input data. Oversampling techniques such as SMOTE [21], SWIM [22], and its variants generate synthetic minority class instances to solve the class imbalance problem. In this paper, an algorithm based on Mixup was applied to solve the data imbalance problem in image classification.  
In recent research on data augmentation, the consistency regularization paradigm, which starts from the assumption that the prediction results of an unlabeled image should match the prediction results after the image is augmented, has been actively studied. Augmented learning models use dropouts to improve the performance of the model. The noisy student methodology combines semi-supervised learning and supervised data augmentation to achieve very good results. This methodology makes the model robust to small changes in the data--noise that would not significantly affect the label when viewed by a human.  
There are also hybrid methods that combine previous studies on self-training and consistency regularization that have shown good results in performance evaluation. In particular, the FixMatch [23] method combines pseudo-labels [24] and consistency regularization, which simplifies the overall process compared to previous studies while showing high performance. For labeled images, it trains the supervised model with a loss function using the cross-entropy error, and, for unlabeled images, it applies weak augmentation and strong augmentation for self-training and optimization. The image generated by weak augmentation serves to generate pseudo-labels, and it optimizes the model by calculating the cross-entropy loss with the pseudo-label values and the output of strong augmentation. FixMatch's weak augmentation method uses random horizontal flipping and random vertical and horizontal shifting, while the strong augmentation method uses cutout enhancement after applying augmentation methods such as RandAugment [25] and CTAugment [26].
## 4 Possible Methods for Computing Probability of Failure for Inspection Planning
### Subset Simulation  
The subset simulation developed by Au and Beck (2001) is an adapted MSC to estimate a low probability efficiently, wherein a low probability of failure is considered a productof a sequence of conditional probabilities of intermediate failure events. Using subset simulation, a low probability of failure (\(p_{f,s}\)) is computed as:  
\[p_{f,S}=P(F)=P(F_{1})\prod_{j=2}^{N_{m}}P\big{(}F_{j}\big{|}F_{j-1}\big{)} \tag{3}\]  
where \(F_{j}=j^{\text{th}}\) intermediate failure event, \(F_{Nm}=\) original failure event \(F\), \(N_{m}=\) number of intermediate failure events, and \(P(F_{j})=\) probability of occurrence of \(F_{j}\). \(F_{1}\) is the probability of failure estimated using Equation (2) when the random variables (\(\mathbf{X}_{i}\)) are independent and identically distributed according to PDF\(f_{\mathbf{X}}(\mathbf{x})\). The conditional probability (\(P\big{(}F_{j}\big{|}F_{j-1}\big{)}\)) is expressed as:  
\[P\big{(}F_{j}\big{|}F_{j-1}\big{)}=\int\limits_{\mathbf{X}}I_{f,j}(\mathbf{x} )f_{\mathbf{X}}\big{(}\mathbf{x}\big{|}F_{j-1}\big{)}d\mathbf{x} \tag{4}\]  
where \(I_{fj}\) (\(\mathbf{x}\)) is the indicator function of random variables (\(\mathbf{X}\)) for the \(j^{\text{th}}\) intermediate failure event. The conditional PDF \(f_{\mathbf{X}}\big{(}\mathbf{x}\big{|}F_{j-1}\big{)}\) in Equation (4) is defined as:  
\[f_{\mathbf{X}}\big{(}\mathbf{x}\big{|}F_{j-1}\big{)}=f_{\mathbf{X}}(\mathbf{x })I_{f,j-1}(\mathbf{x})/P\big{(}F_{j-1}\big{)} \tag{5}\]  
where \(f_{\mathbf{X}}(\mathbf{x})\) is the joint PDF of random variables (\(\mathbf{X}\)). Generating samples using Equation (5) and the acceptance-rejection method [42] is difficult because the acceptance probability is proportional to \(1/P(F_{j-1})\), and the generated samples are likely to be rejected before obtaining appropriate samples [42]. Therefore, based on Markov Chain Monte Carlo (MCMC) algorithms, the conditional probability (\(P\big{(}F_{j}\big{|}F_{j-1}\big{)}\)) given in Equation (4) can be computed as:  
\[P\big{(}F_{j}\big{|}F_{j-1}\big{)}=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}I_{f,j}( \mathbf{x}_{i}) \tag{6}\]  
where \(\mathbf{X}_{i}\) is represented by the conditional PDF \(f_{\mathbf{X}}\big{(}\mathbf{x}\big{|}F_{j-1}\big{)}\) for \(i=1,2,\ldots,N_{s}\) and is generated using MCMC algorithms. To improve the efficiency and accuracy of subset simulation, several MCMC algorithms, such as the component-wise Metropolis-Hasting algorithm [43] and the conditional sampling method [44], can be adopted. Detailed theoretical background on subset simulation method and its modifications for efficiency improvement and general applications can be found in [41; 43; 45; 46; 47; 48], among others.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 5 Simulations and Results
### Decentralized SDN Controller Simulation  
The first simulations of the decentralized SDN architecture are performed with the following objectives.  
* Availability simulation to analyze the data path availability improvement of the decentralized controller distribution and the redundant link configuration.
* Redundant link simulation to analyze the data path availability improvement of the TESMS SDN architecture.  
To simulate the TE process, the simulations use the ICMP protocol (ICMP request and ICMP reply) that represents the proposed transaction, which includes the proposed price in ZAR/kWh and volume in kWh, between the hosts (prosumer, consumer, or P2P energy market). Packet loss represents the failed transactions in the TE process. Although a failed transaction will automatically be restarted, the possibility of a price change in the next attempt exists.  
Figure 9 is the simulation diagram representing the typical centralized SDN architecture through various interconnected networks on the Internet. The Mininet simulator has a limitation regarding the packet loss definition of each link. It must be a base 10 integer, i.e., the smallest packet loss % that can be configured for each link is 1%. In one direction of the data path between the hosts, there are 8 links, each with 1% packet loss. For the ICMP ping test (ICMP request and ICMP reply), the return will traverse through the same eight links. This data path is represented in the bidirectional blue and yellow line in Figure 9. The expected packet loss for the ping test through the 16 links is,  
* Ping success rate = \(1-\mathrm{e}^{-16\times(1-0.99)}=85.21\%\)
* Packet loss = \(1-\mathrm{Ping}\) success rate = \(14.79\%\)  
An extended simulation was initiated using a continuous ICMP test between the hosts, Figure 10 shows that the packet loss during the typical centralized SDN architecture simulation is \(14.9\%\). This means the data path availability of the sim  
Figure 9: Simulation diagram of a typical centralized SDN architecture.  
Figure 11 is the simulation diagram that represents the optimized decentralized SDN architecture. The same 1% packet loss parameter is configured on each link. The expected packet loss for the ping test through the 16 links is:  
* Ping success rate = \(1-\mathrm{e}^{-8\times(1-0.99)}\) = 92.31%
* Packet loss = \(1-\mathrm{Ping}\) success rate = 7.69%  
Figure 12 shows that the packet loss during this simulation is 7.9%, which means the data path availability of the simulated network is 92.1%.  
Figure 12: Simulation results of a decentralized SDN architecture.  
Figure 10: Simulation results of the typical centralized SDN architecture.  
The decentralized controller placement during the simulation increased the total data path availability by 7.0%. By using Equation (8), the calculated improvement is 7.1%. For the TE process, this improvement represents an improvement of 7% in successful transactions between the various hosts.
## 3 Strategic Development of Ultrafiltration Membrane
### Ionic Strength  
In UF, pH is primarily attributed to the alteration of fouling rate and ionic strength is on protein-surface or protein-protein interactions [157]. When many approaches target small peptides with higher transmission rate at operation in its IEP, adding NaCl can improve both the permeate flux and transmission. This value in one instance corresponded to 27.49 L m\({}^{-2}\) h\({}^{-1}\) and 48.81% transmission (no salt) to 33.29 L m\({}^{-2}\) h\({}^{-1}\) and 52.95% transmission (0.15 M NaCl) of CFPH fractionation. Higher ionic strength directed the anion binding to peptide, creating bigger size peptide and facilitating the transport of small peptides to reentate [197]. Nevertheless, other mechanism may take in charge of influencing peptide transmission. To illustrate, in fractioning myoglobin from hemoglobin, RF reached its peak while at higher concentration of NaCl because of self-dissociation of myoglobin doubled its apparent molecular weight [196].  
Ionic strength gives a positive impact in the separation of protein in EDUF. It was found that the increment in ionic strength accentuates the negative charge density of UF membrane, directing a more effective pore size by virtue of electrostatic repulsion between ions and thinner hydration layer caused by salting out effect at pore walls [210]. Moreover, 1, 3, and 5 g L\({}^{-1}\) KCl in recovery compartment amplified the relative abundance of arginine and lysine of snow crab by-product hydrolysate and overall peptide migration rate (13.76 \(\pm\) 3.64 g m\({}^{-2}\) h\({}^{-1}\)) [210].  
In prior works with various UF membranes, high shear rate tangential UF [110], sequential UF/DF [113], and electrocardiification-UF [80] potentially managed phytic acid reduction, but then encountered the fouling problem. Pretreatment by KCl extraction alleviated the phosphorus to protein content (representing phytic acid content) during UF and diafiltration (DF) of soy protein isolate. Furthermore, although extraction procedure using water and KCl gave a similar fouling resistance, implementation of backwashing was not compulsory for KCl-mediated process on account of lower protein contents which may create compact fouling layer in UF step [207]. More importantly, the highest protein fraction they got was 91.8% with 0.06 M KCl, pH 9.0, 25 \({}^{\circ}\)C (extraction), pH 9.0 (UF), and pH 6.5 (DF). The result for that study was also confirmed in other works in soy protein isolate [199; 211] and in pea protein isolate [176; 208]. Despite all those advantages, a study that addressed the effect of pH on particle size distribution presented the condition of adding 0.12 M KCl to produce soy protein extract will result in the decrease of permeate flux with lower pH. For instance, in pH 6.0, the protein aggregates are possibly to grow due to minimum electrostatic repulsion, particle volume fraction, and protein transport to the concentrated layer was facilitated [205; 211; 212].
## 2 Materials and Methods
### Conversion Characteristics under Oxidizing Conditions
#### 2.1.2 Optical Single-Particle Burner (OSB)  
Another set of conversion experiments was performed with an optical single-particle burner (OSB). A schematic of the furnace is shown in Figure 3b. A detailed description of furnace and experiments has been provided elsewhere [17]. A stable flame at the bottom was provided by the combustion of CH\({}_{4}\) and the temperature of the flame was controlled by the ratio of CH\({}_{4}\) and O\({}_{2}\). The furnace contains a quartz windows, which stabilized the flow of gases and made it possible to observe the changes during the conversion. Gas temperatures were measured by a thin-wire thermocouple. During the experiments, first, the flame was ignited and stabilized before the sample was inserted into the center  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Ultimate Analyses**} \\ \hline  & H (wt \%) & O (wt \%) & N (wt \%) & S (wt \%) & C (wt \%) & Ash (wt \%) \\ \hline Coal & 4.8 & 5.2 & 1.3 & 0.30 & 84.0 & 4.4 \\ SRM & 6.1 & 12.9 & 1.4 & 0.20 & 57.3 & 22.1 \\ PE & 11.6 & 1.6 & - & 0.06 & 78.5 & 8.3 \\ PUR & 6.2 & 15.3 & 6.0 & 0.03 & 61.7 & 10.8 \\ \hline \hline \multicolumn{6}{c}{**Proximate analyses**} \\ \hline  & Moisture (wt \%) & Volatile (wt \%) & Fixed carbon (wt \%) & Density (g/cm\({}^{3}\)) \\ \hline Coal & 0.8 & 26.5 & 68.3 & 0.8 \\ SRM & 8.0 & 67.3 & 2.6 & 1.3 \\ PE & 0.3 & 89.1 & 2.3 & 0.9 \\ PUR & 1.6 & 80.8 & 7.1 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of materials, including ultimate and proximate analyses and densities.  
of the furnace on a platinum wire-mesh plate. The temperature during the tests was set to 1250 \({}^{\circ}\)C, and the chamber was at atmospheric pressure. A 3 CCD camera (CV-M9 GE JAI) at a frequency of 30 Hz recorded changes in the sample. The experiments were repeated to ensure the representability of the results, especially for SRM, which is a heterogeneous material.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 3 Results and Analysis  
### Random Forest Feature Importance  
Figure 4 presents the importance of different features which were output after the successful completion of classification. It is helpful to explore which features play an important role in the classification process. As shown in Figure 4a, the top four features in terms of importance are LAWI, NDPI, NDVI and SAVI. It can be seen that the vegetation index played more positive effects during winter wheat mapping. Figure 4b shows the influenceability of all features counted on a monthly basis, which clearly demonstrates that images from June, October, February and January are more effective for winter wheat identification. October and June are during the sowing and harvesting of wheat, and the wheat plots may be in a state of no vegetation cover while other vegetation is still growing densely. The winter wheat in January and February is around the period of the green-up and jointing stage [29]. Wheat will come into growth earlier relative to other vegetation. In summary, the wheat in these four months generally has unique characteristics that differ from other plots.