esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Content of Glucans
[22] showed that after nitrogen starvation/limitation, _A. platensis_ also produces a water-soluble polysaccharide that is composed mainly of 1.3:1.4- or 1.3:1.2-\(\alpha\) glucans, which differs from glycogen. In the present study, it was demonstrated for the first time that besides \(\alpha\)-glucans (glycogen), _A. platensis_ also synthesize \(\beta\)-glucans to a high degree when phosphorus starvation/limitation is applied to the cultures. \(\beta\)-glucans are polysaccharides consisting of D-glucose monomers linked by \(\beta\)-D-glycosidic bonds, which commonly exist in the cell walls of plants (especially cereals, such as oat barley and wheat), mushrooms, yeasts and bacteria but are absent in human cells. \(\beta\)-glucans from different sources have different structures, different degrees of branching and branching patterns as well as different molecular weights [37]. Cereal \(\beta\)-D-glucan, which consists of 1,3 and 1,4 linkages is reported to reduce blood glucose and cholesterol for obesity and cardiovascular disease (CVD),whereas yeasts, mushroom and bacteria \(\beta\)-D-glucan, which is characterized by 1,3 and 1,6 linkages [12], are proposed to have antitumor, anti-inflammation, and antiviral activities against immune system disease [10; 11; 38]. Figure 3: Proportion of total glucans in the total carbohydrate content in cultures of _A. platensis_ grown with different phosphorus concentrations (mg-P/L). Results represents the average \(\pm\) SD of \(n=3\) replicates. Figure 2: Ratio of \(\beta\)- to \(\alpha\)-glucans in relationship to the phosphorus concentration (mg-P/L) in cultures of _A. platensis_. According to the U.S. Food and Drug Administration [39], regular consumption of 3 g/day of \(\beta\)-D-glucans reduces the risk of heart disease. _A. platensis_ is a photosynthetic cyanobacterium belonging to the domain of bacteria; hence, it is not surprising that it synthesizes 1.3:1.6-\(\beta\) glucans. This is of particular interest because 1.3:1.6-\(\beta\) glucans display antitumor and immune stimulating properties; therefore, _A. platensis_ enriched in \(\beta\)-glucans could potentially be used to develop functional foods [12].
## 3 Modified F/DR Algorithms
### DR-k Algorithm  
To introduce statistical diversity into the list generated by the DR method (i.e., \(L\)), the DR-k algorithm proposes to swap the cells in the list following the algorithm:  
1. Generate an ordering list following the DR criteria.
2. Exchange the cell \(L\{i\}\) at the end of the list with a probability of \(\frac{k}{100}\), where \(k\) is a number between 0 and 100.
## 4 Research Object and Flow of the Study  
Aircraft designed for flight training were used as the object of study. Consequently, the topics of reliability, efficiency, readiness, and aircraft availability are widely analyzed and considered. Availability has a direct impact on the level of training of pilots, while efficiency and reliability have a direct impact on the safety of flight training. Economic aspects recede into the background of priorities.  
The operational process of a helicopter used for flight training was analyzed, and its operational and utilization efficiency and utilization potential can be presented graphically.  
The research object is operated according to a mixed operation strategy, i.e., some of the built-in equipment, as well as major components, have a limited life span and are subject to replacement, irrespective of their technical condition, when a predetermined amount of work has been achieved or after a specific calendar time. These are components that have a direct impact on the safety of flight training in the air. Other equipment is operated according to its technical condition. Since the availability of individual helicopters was not at a satisfactory level and the time to restore airworthiness was significantly prolonged, an attempt was made to identify the reasons for this.
# Nematic Liquid Crystal Composite Materials for DC and RF Switching
## 3 Electrical Conductivity of Liquid Crystal and Carbon Nanotube Composites
### Dual Frequency Switching
This comes from the fact that the activation energy of the PCCBB/CNT is low even for low doping concentration (about 0.05%) compared to the MBBA/CNT system studied by [PERSON] et al. [39]. In their experiment ([PERSON] et al.) the composite system was in a planer orientation initially [84]. For an applied electric field (above the Freedericksz threshold) and a frequency below the crossover frequency (f << f\({}_{\rm c}\)), the system is in a positive dielectric state. The system experienced a torque of \(\Delta\varepsilon\)E\({}^{2}\) which oriented the LC molecules along the field direction  
Figure 15: **(a)** A dual frequency nematic liquid crystals (DFNLC) mixture at the planar orientation: This mixture is composed of two categories of nematic liquid crystals (NLC), one having transverse (orange arrow) and other having longitudinal (blue arrow) dipole moments; **(b)** the bistable switching of a DFNLC/CNT composite with two applied frequencies for the ON state and the OFF state. (i.e., perpendicular to the electrodes). As the \(\sigma_{\parallel}\) is high for an LC-CNT composite system, the system reached to a high conductivity state by loosely connected CNT networks. When the applied field frequency was above the crossover frequency (f \(>\) f\({}_{\text{c}}\)), the system was in a negative dielectric state and the LC molecules were oriented parallel to the electrodes. In this case, the system showed a low electrical conductivity. Here, a faster bistable switching was done with two applied frequencies: 500 Hz for the ON state and 600 kHz for the OFF state (Figure 15b). Although, the LC molecules can be switched back to a planar orientation (parallel to the electrodes) by merely removing the electric field, this switching off process is quite slow as mentioned earlier. Using an alternating high- and low-frequency field, a faster switching can be achieved. In the present case, switching took place in milliseconds with on-off current ratio at 170 for an applied voltage of 10 V at an ambient temperature of 42 \({}^{\circ}\)C [84]. Further study on the DFNLC-CNT system is needed. The system can be optimized with other suitable hosts and by varying the concentration of CNT for attaining a higher ON-OFF ratio.
## 3 3D Sliding Force Balance Analysis
### Calculation of Local Instability of Double-Angled Trench
#### 3.2.2 Destructive Force Analysis  
To simplify the analysis process, the space problem is transformed into a plane strain problem by assuming that \(L>>a\) in the sliding body [22]. This simplification is shown in Figure 9, where the stress on the sliding body is depicted. In order to determine the moment of \(T\), which is the tangential shear resistance on the sliding face, we have to solve for \(P_{s}\), \(P_{w}\), \(W\), \(N\), and \(T\). While it is easy to calculate the moments of \(P_{s}\), \(P_{w}\), and \(W\), the moment of \(T\) is more challenging to determine. To address this issue, we have utilized a section method that separates the upper soil strip and the lower part. The soil strips are subjected to micro-unit force balance, and the moment of \(T\) can be solved for by analyzing the stresses in the micro-strip element, shown in Figure 9. By ignoring the longitudinal interaction force between the soil strips, the stress of each soil strip can be obtained.  
Figure 8: Local sliding body of the wall trench.  
According to the mechanical balance of the horizontal direction of the soil strip, the following formula can be obtained:  
\[N_{j}\cos\theta_{j}+P_{wj}=P_{jj}+T_{j}\sin\theta_{j} \tag{26}\]  
Then, the calculation formula of shear resistance can be obtained as follows:  
\[T_{j}=\frac{\frac{\alpha\gamma_{\mathrm{s}}\left(H\ \ \mathrm{H_{s}+\alpha\cos \theta_{j}}\right)}{n}+\frac{\alpha\gamma_{\mathrm{w}}\left(H\ \ \mathrm{H_{w}+\alpha\cos\theta_{j}}\right)}{n}\ \ \ \ \ \frac{\alpha\cos\theta_{j}}{n\sin\theta_{j}\tan\varphi}}{\sin\theta_{j}\ \ \ \ \frac{\cos\theta_{j}}{\tan\varphi}} \tag{27}\]  
For the overall force, the bending moment of the whole sliding body is calculated at point \(\mathcal{O}\), as shown in the following equations.  
The calculation of the bending moment \(M_{1}\) due to its own weight is  
\[M_{1}=\frac{1}{2}\pi a^{2}\ \ \ \ \gamma^{\vartheta}\ \ \ \ \frac{4}{3\pi}a=\frac{2}{3}\gamma^{ \vartheta}a^{3} \tag{28}\]  
The calculation of sliding bending moment \(M_{2}\) caused by water pressure is  
\[M_{2}=\frac{1}{2}\ \ \ \ 2\gamma_{w}\ \ \ \ \ a^{2}=\gamma_{w}a^{2} \tag{29}\]  
The calculation of anti-sliding bending moment \(M_{3}\) caused by slurry pressure is  
\[M_{3}=\frac{1}{2}\ \ \ \ 2\gamma_{s}\ \ \ \ \ a^{2}=\gamma_{s}a^{2} \tag{30}\]  
The calculation of anti-sliding bending moment \(M_{4}\) caused by shear resistance is  
\[M_{4}=\sum_{i=1}^{n}T_{i}a+\sum_{i=1}^{n}T_{j}a \tag{31}\]  
Thus, the sliding body maintains mechanical equilibrium conditions as follows:  
\[M_{1}+M_{2}=M_{3}+M_{4} \tag{32}\]  
The safety factor \(K\) is defined as follows:  
\[K=\frac{M_{R}}{M_{T}}=\frac{M_{3}+M_{4}}{M_{1}+M_{2}} \tag{33}\]  
When, \(K=1\) and \(n\ \ \ \ \ \ \infty\) the critical slurry weight \(\gamma_{s}\) can be obtained.
# _Lactobacillus rhamns GG_ as Biosensor for Oral and Systemic Health Conditions: A Pilot Study
## 1 Introduction
_Lactobacillus rhamns GG_ (LGG) is one of the most well-researched probiotic bacterial strains, known for its health benefits driven by its superior ability to adhere and colonize the gastrointestinal tract. LGG can actively prevent bacterial colonization in the gut by producing antimicrobials [19]. Several molecular studies have elucidated the mechanisms of LGG [20]. LGG secretes proteins such as Msp1/p75 and Msp2/p40, which activate the Akt signaling pathway, thereby preventing apoptosis of intestinal epithelial cells and reducing inflammation [21]. Omics research has transformed the understanding of LGG by integrating molecular mechanisms. Proteomics is arguably the most suitable method for studying LGG as a biosensor due to its ability to capture real-time protein expression changes under various environmental conditions. However, there is a lack of scientific research and evidence that evaluates LGG as a biosensor for health detection. Based on this premise, this work aimed to explore the biosensing capacity of LGG through its proteomic expression in diverse oral environments. Specifically, the study focused on how LGG adapts and responds to key factors, including metabolism, oxidative stress, and general health markers. By analyzing proteomic profiles of LGG, its potential as a biosensor for maintaining oral health was evaluated. We hypothesized that the ability of LGG to detect shifts in protein expression under various environmental and systemic conditions might confirm its possible future applications in detecting some health-related conditions.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation  
### Experimental Dataset  
To evaluate the classification performance of botnet domain names using machine learning algorithms, we use the extracted and labeled domain name datasets, which include a set of benign domain names and a set of malicious domain names used by botnets. The set of benign domains includes 30,000 top domain names ranked by Alexa. Benign domain names are checked at the _virustotal.com_ website to make sure they are really benign. The set of malicious domain names collected at [17,18] includes 30,000 domain names created by the Conficker botnet and the DGA botnet. From the original datasets, the domain names are processed to remove the top-level domain. For example, with the domain name \"example.com\", after processing, the resulting data is \"example\".
# An All-Batch Loss for Constructing Prediction Intervals
## 3 Experiments
### Experiment Methodology  
Experiments were conducted on the comparison of PI generation performance among the MVE method, the LUBE method, the QD loss and the AB loss. In order to ensure the fairness of the experiment, the PI generation framework proposed in Section 2 was used as the basic structure in the implementation of the QD loss and the AB loss. In order to get the best possible results, the PSO method was applied for training in the LUBE method while the GD with the Adam [43] optimizer was adopted in the MVE method, the QD loss and the AB loss. Case study 1 compares the performance of the QD loss and the AB loss in the case of small batches, while case study 2 shows the PI generation performance of the MVE method and the AB loss under different noises. Other benchmark datasets verify the reliability and effectiveness of the AB loss on general regression tasks.  
As for metrics, a modified CWC was used for model validation. In the original CWC, it was difficult to achieve an appropriate trade-off between confidence and sharpness; this was resolved by introducing additional hyperparameters in the modified CWC [44].  
\[\begin{array}{l}PIARW=\frac{1}{n}\sum_{i=1}^{n}\frac{U_{i}-L_{i}}{y_{i}},\\ CWC=(1+\eta_{1}PIARW)(1\\ +\gamma(PICP)\exp(-\eta_{2}(PICP-\mu))),\end{array} \tag{29}\]  
where \(\eta_{1}\) and \(\eta_{2}\) are two hyperparameters that represent the importance between sharpness and confidence. By adjusting these two hyperparameters, the quality of PIs can be evaluated from different aspects.  
In case study 1, due to the specific data distribution, the CIs at the 95% confidence level can be easily obtained. The relationship between the PI Width (PIW), which can be estimated by CIs under ideal conditions, and the input variable in case study 1 is described as the formula below:  
\[\text{PIW }=\ 2\times 0.2\times 1.96\times x^{2}, \tag{30}\]  
where \(x\) is the input value.  
Therefore, for case study 1, a more reasonable metric is to calculate the Mean Square Error (MSE) between the predicted PIW and the expected PIW, which is described as follows:  
\[MSE_{PIW}=\frac{1}{n}\sum_{1}^{n}{(PIW_{pred.i}-PIW_{\exp c.i})}^{2}, \tag{31}\]  
where \(PIW_{pred.i}\) and \(PIW_{\exp c.i}\) are the predicted PIW and the expected PIW.  
The performance of the AB loss and the QD loss were verified by changing the batch size during training. In order to prove that the AB loss has better performance in small batches, the minibatch method is applied for training, and the batch size is set to 16, 32, 64 and 128 for experiments. In other benchmark datasets, the models were trained using the MVE method, the LUBE method, the QD loss and the AB loss separately. The dataset is divided into training set and test set at a ratio of 7:3, and a batch of 128 sample points is used for training according to the minibatch method.
## 2 Smart Grid Architecture
### System Description and Assumptions  
Figure 1 shows the architecture of the smart grid network. The architecture consists of the following system entities: smart meters, a concentrator, and central management facilities. Smart meters deployed in houses frequently transmit their meter readings to an electric utility for power transmission and distribution network control purposes through the communication network. Smart meters in the neighborhood communicate with each other and a concentrator, which is the data collection unit, through a wireless mesh network. The concentrator further communicates with the central management facilities such as the electric utility and grid operator through wired communication, and reliably reports the aggregated result of the meter readings in the neighborhood to the facilities.  
We assume that the central management facilities do not need to know which smart meter generates specific data. It is only interested in the aggregated measurements (that is, the sum of the current electricity consumption of individual smart meters) in some specified neighborhood over a given time period (e.g., every few minutes or hours) and the smart meters these metering data are associated with [3; 4; 5; 8]; it is not interested in the current meter readings of any individual customer (Our main target environment is one where the metering data need to be reported frequently over relatively short time periods (e.g., every few minutes or hours) for grid network management or power distribution control rather than real-time pricing for billing purposes. For billing purposes, central management facilities may require the aggregate data from individual meters at comparably longer intervals (e.g., daily or monthly). User privacy would not be violated in this case because outside entities would not have access to any useful information from the measurement on the time axis. Therefore, real-time pricing is not a concern in this study.). As in [PERSON] _et al._'s scheme [5; 6], data from individual meters in the neighborhood are delivered to the concentrator following aggregation paths, as shown in Figure 2, and it is assumed that the smart meters do not collude with each other in our data aggregation model. We also assume that the concentrator is honest-but-curious. That is, it will honestly execute the assigned  
Figure 1: Smart grid architecture.  
tasks and will not collude with other smart meters in the system; however, it would like to learn as much information about individual metering data as possible.
## 2 Design, Working Fluid, and Experimental Apparatus of the MF-AHPA
### Experimental Apparatus and Processes
#### 2.3.2 Evaluation of Heat Pipe Temperature Uniformity during Start-Up  
As shown in Figure 4, a setup was designed to evaluate heat pipe temperature uniformity during start-up using an infrared camera. The test system consists of a thermostatic water tank, an infrared camera, and a personal computer. The IR camera model was FLIR ThermoAM SC300 (FLIR Systems Inc, North Billerica, MA 01862, USA), with an accuracy of \(\pm\)2 \({}^{\circ}\)C and resolution of 320 \(\times\) 240 pixels. When the test started, the three heat pipes with different working fluids were immersed into the thermostatic water tank at 55 \({}^{\circ}\)C. The length of the section immersed in the water was 200 mm. The infrared camera recorded the temperature distribution for more than 2 min.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Working Fluid** & **Flow Viscous Friction** & **Inertial Vapour Force Factor** & **Bond Number (Bo** & **Galilei Number (Ga** & **Laplace Number** \\  & **F\({}_{\text{I}}\) (N\(\cdot\)s/m\({}^{3}\cdot\)J)** & **f\({}_{\text{inv}}\) (10\({}^{-12}\) m\({}^{3}\)kg/J\({}^{2}\))** & **Non-Dimensional)** & **Non-Dimensional)** & **(La Non-Dimensional)** \\ \hline Acetone & 230.0 & 1.57 & 0.0123 & 621.2 & 50,205 \\ Cyclopentane & 405.2 & 2.22 & 0.0113 & 257.9 & 22,512 \\ N-hexane & 696.0 & 2.34 & 0.0186 & 272.6 & 14,592 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The calculated parameters of selected working fluids using properties specified in Table 2.
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 1 Introduction
The key idea is to use the raw processing power to embed all the inner control loops within a single algorithm that takes into account the model of the converter and its associated filter. This structure provides flexibility and inherent fast response and it has been addressed in [22; 23] with a focus on balancing the power in between two parallel converters. Usually, CFs based on one  
Figure 1: (**a**) Schematic representation of an islanded microgrid; and (**b**) the electrical model of the VSC. step prediction/short-horizon become problematic when controlling higher-order plants (as in the case of a LC filter) due to the couplings between different state variables [24]. This problem has been solved in this paper by giving respect for all orders in order to decouple each variable and control it separately without a need for more computations compared to the computational burden in the long-horizon method [25; 26]. That was done by proposing a solution, which explicitly deals with the dynamic coupling between the inductor current and capacitor voltage, allowing the usage of and one step prediction/horizon for effective voltage control of the converter associated with the LC filter. Besides, the conventional FCS-MPC has a problem of variable switching frequency [27], and this has led to some difficulties in the LC filter design resulting in low converter performance due to the use of an inappropriate filter. Thereby, high power losses are introduced by including an oversized filter [28]. Therefore, many research studies have been devoted to finding a feasible solution to obtain a fixed switching operation and facilitate the filter selection process [29; 30]. The paper is structured as follows: the converter model and the proposed controller are described in Section 2. In Section 3, frequency control approaches are explained in detail. In Section 4, the obtained results and remarks to them are given. Finally, the summary of the work is described in Section 5. Novelty and contributions of this research are briefly summarized as follows:  
1. Propose a new term in the CF, resulting in less computation and higher power quality compared to the traditional schemes. 2. Address the suitability of different frequency control strategies such as Simple Penalization (SP), Notch filter (N), and Periodic control (P) combined with the new improved algorithm (IMPC). 3. Discusses the suitable size of LC filter that can be associated with the converter when the above-mentioned frequency control strategies are used with IMPC and CMPC algorithms. 4. Address the proper selection of sampling time for all conventional (CMPC) and improved (IMPC) algorithms. 5. Shed light on many aspects such as spectrum analysis, steady-state operation, transient operation, power quality and factors tuning for eight different predictive schemes such as CMPC, IMPC, SP-CMPC, SP-IMPC, N-CMPC, N-IMPC, P-CMPC, and P-IMPC. Figure 2: Power circuit and FCS-MPC control structure of a standalone inverter for microgrid applications.