esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Theoretical Model
#### 3.3.2 Establishment Time  
The stability of the ZnAl\({}_{2}\)O\({}_{4}\) sensor could be determined by knowing the position of the poles belonging to the transfer function. The establishment time could be known with the sensor's \"response speed\" or its characteristic time.  
Since our interest was to detect propane gas atmospheres, we focused on the propane gas transfer function \(G(s)\):  
\[G(s)=\frac{R_{\text{PG}}(s)}{V(s)}=\frac{K_{\text{PG}}}{\tau_{\text{PG}}s+1}e^{ -(t_{0}+\frac{T}{2}(2n+1))s}=\frac{\frac{K_{\text{PG}}}{\tau_{\text{PG}}}}{s+ \frac{1}{\tau_{\text{PG}}}}e^{-(t_{0}+\frac{T}{2}(2n+1))s}. \tag{24}\]  
Substituting values from the first cycle, we calculated:  
\[G(s)=\frac{867.672\times 10^{3}}{s+0.0209}e^{-1129.346}. \tag{25}\]  
According to the denominator of Equation (25), the sensor's transfer function had one pole at position \(s=-\frac{1}{\tau_{\text{PG}}}=-0.0209\), located in the negative half-plane of the complex plane \(s\)[36; 37; 38] (see Figure 4a). It indicated that the sensor was stable since taking the inverse Laplace transform of the transfer function, a decaying process was obtained (as Equation (23) shows). If the establishment time is defined as \(4\tau_{\text{PG}}\)[36], the sensor reached its stability at \(4\tau_{\text{PG}}=191.2\)\(s\), with the exit signal at 1.83%. The stability time is shown in Figure 4b.  
Based on Figure 4, the sensor was stable, which allowed us to state that the ZnAl\({}_{2}\)O\({}_{4}\) could be safely applied to detect propane gas (for example, to localize explosion-safe zones).
## 2 Materials and Experimental Procedure  
The rock samples used in this research were drilled from the No. 5 coal seam floor of a mine in Lvliang, Shanxi Province, China, and they are sandstone. The sampled seam is in the lower to middle part of the Shanxi Formation. The Shanxi Formation is an interlockingsea-land coal-bearing deposit with an average thickness of 57.38 m, consisting mainly of sandy mudstone, carbonaceous mudstone, fine sandstone, and coal seams, where the sandstone is of medium-grain structure, laminated, and mud-cemented.  
In addition, the rock samples were selected for the absence of visible joints, ensuring that complex multi-crack failure would hardly occur after the uniaxial compression testing. Seven specimens were polished into cylinders with a diameter of 70 mm and a height of 140 mm, as shown in Figure 1a. The average density of the specimens was 2.39 g/cm\({}^{3}\) with a uniaxial compressive strength of 51.21 \(\pm\) 4.01 MPa. Two specimens were used for pre-experiment testing and scanning electron microscopy (SEM) testing, and five specimens were adopted for single-cracked testing.  
The SEM analysis result shows that at the micrometer scale, the cross-section is irregular, there are debris and a few original voids, the sedimentary distribution is disorganized, and the microstructure is not sufficiently cemented, as shown in Figure 1b. The results indicate that the sandstone particles are loosely cemented, and the microstructure is complex, reflecting the inhomogeneity of the sandstone and providing an interpretation for the generation of sub-cracks from sandstone damage.  
The single-cracked test was performed in a rock mechanics test system. It provides a loading speed in the range of 0.01 kN/s to 20 kN/s. The spatial distribution of microcracks was obtained by the acoustic emission monitoring system. The AE monitoring system consists of the AE processing system PCI-Express 8 model from Physical Acoustic Corporation, 5 AE sensors in the Nano 30 model, preamplifiers in the 1220A-AST model, and connection cables. The Nano 30 model has a resonant frequency of 140 kHz and an operating frequency from 150 to 400 kHz, which is in line with the expected frequency range for rock failure monitoring. A schematic of the mechanical loading and AE monitoring is shown in Figure 2a. The layout of the AE sensors is shown in Figure 2b.  
The loading test was implemented by uniaxial loading controlled by displacement, and the loading speed was set at 0.1 mm/min. Based on previous experimental experience and lead break tests [9; 24; 25], in this AE monitoring test, the acquisition threshold was set  
Figure 1: Sandstone specimens and fracture surface SEM. (**a**) Raw rock samples; (**b**) SEM of specimen at 200x magnification.  
Figure 2: A schematic diagram of the uniaxial loading and AE monitoring system. (**a**) The schematic diagram of the uniaxial loading; (**b**) the schematic diagram of an AE sensor’s layout.  
to 45 dB for minimizing the environmental noise effect, the sampling frequency was set to 1 MHz, and the preamplifier gain was set to 40 dB.
## 3 Three Methods for Calculating Satellite Repeat Shift TTime  
Theoretically, there are mainly three methods to determine the satellite repeat shift time, which include the satellite-based broadcast ephemeris method [7], receiver-based correlation coefficient method [13], and satellite receiver-based aspect repeat time method [19]. In particular, for the correlation coefficient method, it can be further divided into several methods from the correlation type and correlation quantity, such as auto-correlation and cross-correlation. These three methods will be elaborated in the following.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### CPS Conceptual Overview
#### 3.1.1 The Physical Process  
The physical process refers to the application environments to be monitored. In the water management context, this may include a whole water supply system, such as the one illustrated in Figure 1, or a section/stage in the operation of the WSS, such as monitoring water quality at the water treatment station, monitoring water quality and leakage outflows along the distribution networks, monitoring the energy consumption due to pumping at the pump stations, or water level at the service reservoirs. Also, monitoring the pressure control along the distribution networks is possible. In all of these, monitoring the distribution networks for leaks, pressure control, and water quality is quite challenging because of the complexity and topological structure of water distribution networks, hence, this becomes an utmost priority for most water utilities.
# Investigation of Fluid-Structure Interaction Induced Bending for Elastic Flaps in a Cross Flow
## 3 Results and Discussions
### Drag Coefficient  
For flow around solid bodies, the drag coefficient is defined as [42]:  
\[C_{D}=\frac{F_{D}}{\frac{1}{2}\rho v^{2}S},\]  
where \(F_{D}\) is the drag force exerted by the fluid on the body and \(\rho\) is the fluid density, \(S\) is the body area perpendicular to the flow direction and \(v\) is the velocity of the flow. For the present simulations, the drag coefficient, \(C_{D}\), for three representative flexible flap cases is computed, that is, for a flap with a thickness of 10 mm and the clamping angles of \(0^{\circ}\), \(90^{\circ}\) and the inclined angle of \(45^{\circ}\) at a Reynolds number of 12. The frontal area of the flap facing the flow is defined for drag calculations meant for a current 3D case. Figure 11 represents the variation of drag coefficients versus time. The mean \(C_{D}\) during t = 1.5-2 s for clamping angle of \(90^{\circ}\) is 3.61 and 1.95 for clamping position of \(0^{\circ}\). Whereas for an inclined angle of \(45^{\circ}\) it is smaller than the other two cases and gives a mean of 1.45. It appears that the more bended flap is a smaller obstacle to the flow and therefore reduces the resistance from the mainstream. Moreover, the drag coefficient values are comparable with a benchmark case of a flexible flat plate in a cross flow [28].
## 3 Optimization-Based Strategies
### Instantaneous Optimization  
Instantaneous optimization strategies mainly consist of the equivalent consumption minimization strategy and the model predictive control strategy, which can achieve real-time optimization control. The focus of ECMS is the determination and adaptive adjustment of optimal equivalent factor, while the key points of the MPC strategy are short-term speed prediction and the short-term optimization of power distribution.
## 2 Derivation of the Coherence Relationship
\begin{split}\mu^{\text{T}}(\mathbf{H})=\frac{\sum_{i\
eq j} \left\|\hat{\mathbf{H}}\hat{\mathbf{H}}^{\ast}\right\|_{ij}}{M(M-1)}^{2}\\ \left[\hat{\mathbf{H}}\hat{\mathbf{H}}^{\ast}\right]_{ij}=\langle h _{i}\left(\overrightarrow{\mathbf{r}}\right),h_{j}\left(\overrightarrow{\mathbf{r}} \right)\rangle=\int_{S}\frac{h_{i}\left(\overrightarrow{\mathbf{r}}\right)}{ \left\|h_{i}\left(\overrightarrow{\mathbf{r}}\right)\right\|}\frac{h_{j}^{\ast} \left(\overrightarrow{\mathbf{r}}\right)}{\left\|h_{j}\left(\overrightarrow{\mathbf{r} }\right)\right\|}\text{d}^{2}\overrightarrow{\mathbf{r}}\end{split}  \tag{15}\]  
where the superscript \({}^{\ast}\) denotes conjugate transpose and \(<>\) denotes coherence calculation. The coherence \(\mu^{\text{T}}\) accumulates from every \([\hat{\mathbf{H}}\hat{\mathbf{H}}^{\ast}]_{ij}\) and thus provides an indication of the mutual coherence level of the entire matrix \(\mathbf{H}\).
## 3 Principals of Routing Protocols and Their Breakdowns
### Acar [57]
#### 3.3.2. Rdbf [59]  
In [59], the authors introduce relative distance-based forwarding (RDBF) that uses a fitness factor to calculate to limit the scope of candidate forwarders, this leads to a very limited choice of forwarding candidates. The neighbor nodes closest to the sink have a higher chance of being chosen as the next hop forwarder. A restricted number of forwarders minimizes packet collisions and reduces energy costs. If a node hears identical packets, it will simply reject the reserved packet. RDBF also takes into account the issue of energy balance. The authors use a residual energy threshold, and if a sensor node's residual energy level falls below it, it will cease forwarding packets. Only a few sensor nodes engage in packet transmission, which substantially decreases energy usage. Furthermore, transmitting data through the selected and more suitable sensor nodes enhances transmission efficiency and reduces end-to-end latency. REBF, on the other hand, the protocol demands that each node obtain its exact position information, which is difficult to ensure because accurate location information in a dynamic undersea environment is challenging to obtain.
## Abstract
### Comparison to Human Reported Results  
In the final experiment, we invited three senior neuroradiologists who have over ten years of experiences. We scanned 20 subjects (5 healthy and 15 pathological), and we pick five slices from each subject. For the healthy subjects, the five slices were selected randomly. For the pathological subjects, the five slices should contain the lesions by confirmation of all the three senior neuroradiologists.  
Afterwards, a double blind test was performed. Four junior neuroradiologists with less than 1 year of experiences were required to predict the status of the brain (either pathological or healthy). Each image was assigned with three minutes. Their diagnosis accuracies were listed in Table 8.  
From Table 8, we see our computer-aided diagnosis method achieves an accuracy of 96%. Compared to Table 4, the performance of our proposed method is affected in real-world scenario.  
The reasons are complicated. First, the source dataset is downloaded from Harvard medical school, which is for teaching. Hence, the source dataset itself highlighted the difference between pathological and healthy brains intentionally, and the slice positions were selected with care. Second, images from real hospitals are of poorer quality and of poorer localization of lesions. All these contribute to the worsen performance of our method. After all, an accuracy of 96% in rea-world scenario is good and promising.  
Another finding is Table 8 is that the four junior neuroradiologists obtained accuracies of 74%, 78%, 77%, and 79%, respectively. All are below 80%. This validates the power of computer vision and machine learning, since computers have proven to deliver better performance in face recognition, video security, _etc._ Nevertheless, there are other more visible symptoms suggesting that something may be wrong in the brain for neuroradiologists. Therefore, this simple test does not reflect the realistic diagnosis accuracy in real hospitals.
## 3 Proposed Deep-Learning-Based Defective Bean Inspection Scheme (DL-DBIS)
### GA-based Optimizer for the Proposed GAN Framework
This figure describes the corresponding module design in the top-left corner of Figure 7.  
Figure 9: The gene-wise single-point crossover over matrix of the selected parent chromosomes. **Mutation**: slightly modifies selected chromosomes for avoiding local minimum. Genes \(g_{j}^{i}\) of the selected chromosome \(\mathcal{C}^{i}\) are mutated with the bit string mutation method. The mutation of \(\mathcal{C}^{i}\) is denoted as \(\mathcal{C}^{i}=\odot\mathcal{C}^{i}\) with the mutation operator \(\odot\) as shown in Figure 8.  
**Fitness function**: calculates the fitness score for each chromosome as follows. Genes \((g_{1}^{i},\cdots,g_{14}^{i})\) of a chromosome \(\mathcal{C}^{i}\) are converted to BSTN structure parameters, where the corresponding BSTN can then generate a labeled data set \((IMG_{i}^{GA},L_{i}^{GA})\) with the bean image render module. Next, the current defect inspection deep network with the model \(M_{k}^{DS}\) are used to calculate the loss values of the data sets \(IMG_{i}^{GA}\) and \(L_{i}^{GA}\), where the loss values can be obtained with the DIDN. The mean of these loss values from the data sets \(IMG_{i}^{GA}\) and \(L_{i}^{GA}\) is the fitness value of the chromosome. **Selection method**: determines which chromosomes can survive in the population of the GA procedure. Once the fitness function is defined, all chromosomes can be sorted in ascending order according to their fitness values. The last half of chromosomes are discarded in each iteration of the GA procedure. These are the main designs of the GA used in this optimizer. Algorithm 1 summarizes the detailed steps for the GA-based GAN optimizer. The GA process terminates after pre-defined iterations \(\zeta\). After the GA process, the optimal chromosome found can be converted into the corresponding BSDN model \(M^{gen*}\) as the optimizer output. ```
Input: The models of Bean-Shifting Deep Network; Output: The optimal BSDN model \(M^{gen*}\); // Initialization  Initialize population (create random chromosomes): \(G(0)\) and set \(i=0\); /* \(i\): iteration  counter for GA optimizer. */ while(\(i\leq\zeta\))do /* termination condition is not met yet. */  Map each individual (BSDN model) in the population to its phenotype (matrix representation);  Evaluate fitness score for each individual, where the evaluation involves the DIDN to compute loss function for an individual;  Select parents from \(G(i)\) based on their fitness in the population;  Apply crossover/mutation to parents and generate offsprings; // ref. to Figure 9.  Select individuals from \(G(i)\) and offsprings based on their fitness, and add the selected ones into \(G(i+1)\); \(i=i+1\);  end while Set \(\mathcal{C}^{*}\) is the chromosome with maximum fitness in the current population;  Transform \(\mathcal{C}^{*}\) to the BSDN model \(M^{gen*}\);// ref. to Figure 9. return\(M^{gen*}\);
```  
**Algorithm 1**GA-based GAN Optimizer.
## 4 Experimental Validation
### Add Prefixes and Destroy  
Once the domain controller realized the network slices, and we have validated the service status. The next step was to verify the correct establishment of the data-plane sessions. Thus, the Spirent testers added 5k prefixes to each of the services(VRF-Blue and VRF-Red). To confirm the status of each service, we have captured the virtual routers BGP information, as depicted in Figure 7, where each VRF has 5002 prefixes received (PfxRed).  
After the end-to-end service creation and control and data plane validation, we have rebooted the devices to validate the service continuity after a simulated power failure. We validate that after the recovery was complete, all the traffic flows again between the network slices. To confirm the status of each device, we have captured the counters before and after the reboot process Figure 8. Each instance recovered sequentially, and it took up to 4.6 min for the latest service to completely restore the traffic flow.  
Figure 5: Capture of the workflow used to instantiate the network slices in the network (Orange). The capture shows the IP network configuration (Red) and the T-API Optical domains (Purple).  
Figure 6: A cumulative step histogram of the time consumed for the slice creation, and the time consumed in during the VPN instantiation in the vRoutes.  
Figure 8: Depicts comparing the counters in the network slice Blue, prior (Up) and after (Down) the device reboot. Once the devices are down, the counters go down to zero; after 4.6 min and once the device is ready, the counters move up again.  
Figure 7: Comparison of the Prefixes received and Sent in each of the network slices deployed in the network. Each Network slice (Blue and Red) has 5K prefixes announced.
# Inertial Propulsion of a Mobile Platform Driven by Two Eccentric Bodies
## 6 Experimental Proof of the Concept
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Speed of the Driving** & **Distance** & \multicolumn{3}{c}{**Measured Time [s]**} & **Average** & **Calculated Average** \\
**Masses [rot/min]** & **[mm]** & **t** & **t** & **t** & **Time \(\mathbf{t_{m}}\) [s]** & **Speed \(\mathbf{v_{m}}\) [mm/s]** \\ \hline
500 & & 10.7 & 10.7 & 10.6 & 10.67 & 93.72 \\
750 & 1000 & 4.7 & 4.9 & 4.8 & 4.80 & 208.33 \\
1000 & 2.7 & 2.7 & 2.6 & 2.67 & 374.53 \\
1250 & & 1.6 & 1.7 & 1.7 & 1.67 & 598.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average speed of the IPS at different speeds of the driving masses. provides a graphical representation of the correlation between the speed of the driving masses and the average speed of the platform. Finally, the experiments investigated the influence of the eccentric's mass on the speed of the IPS. For this purpose, each of the two driving masses was laser cut from steel sheets (S 235 JR, according to EN 10025-2: 2019 [21]) having higher thicknesses (t = 5, 6, 8, and 10 mm) than the parts used until this stage of the experiments (t = 4 mm). Each of the supplementary driving masses shown in Figure 7 was incorporated, pair by pair, in the construction of the Inertial Propulsion System and the times for covering a distance of 1000 mm by rotating the driving masses at 500 rot/min were measured. As before, three measurements of time were performed for each pair of driving masses, and the average traveling time was established. Figure 6: Influence of the eccentric’s driving speed on the platform’s displacement speed. Figure 7: Eccentric bodies with different masses (cut from steel sheets with different thicknesses). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mass of the Eccentric Bodies m [kg]** & **Distance** & \multicolumn{2}{c}{**Measured Time [s]**} & \multicolumn{2}{c}{**Average**} & **Calculated Average** \\ \cline{3-6}
**Nodes** & **[mm]** & **t** & **t** & **t** & **Time t\({}_{\text{m}}\) [s]** & **Speed v\({}_{\text{m}}\) [mm/s]** \\ \hline
0.180 & & 10.7 & 10.7 & 10.6 & 10.67 & 93.72 \\
0.225 & & 8.7 & 8.6 & 8.6 & 8.67 & 115.34 \\
0.270 & 1000 & 7.4 & 7.3 & 7.3 & 7.33 & 136.45 \\
0.360 & & 5.6 & 5.6 & 5.7 & 5.66 & 176.67 \\
0.450 & & 4.7 & 4.6 & 4.6 & 4.67 & 214.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average speed of the IPS at different masses of the eccentric bodies. The platform's displacement speed increases with the mass of the eccentric bodies. Thus, by multiplying 2.5 times the weight of each driving mass (from 0.18 to 0.45 kg), the speed of the platform increases 2.28 times (from 93.72 to 214.13 mm/s). For a clearer image of how the driving masses influence the traveling speed of the platform, Figure 8 provides a diagram of this interconnection. The movement speed of the platform does not vary directly proportionally to the mass of the eccentrics because, as Equations (18) and (19) show, in the speed calculation, the constant mass M of the platform is included at the denominator.
The Effect of Ultrasonication Pretreatment on the Production of Polyphenol-Enriched Extracts from _Moringa oleifera_ L. (Drumstick Tree) Using a Novel Bio-Based Deep Eutectic Solvent  
[PERSON]  
1 School of Agricultural Sciences, University of Thessaly, N. Temponera Street, 43100 Karditsa, Greece; [EMAIL_ADDRESS] (A.L.); [EMAIL_ADDRESS] (O.K.); [EMAIL_ADDRESS] (I.K.); [EMAIL_ADDRESS] (G.B.); [EMAIL_ADDRESS] (E.B.); [EMAIL_ADDRESS] (S.L.)  
2 Food Quality & Chemistry of Natural Products, Mediterranean Agronomic Institute of Chania (M.A.I.Ch.), International Centre for Advanced Mediterranean Agronomic Studies (CIIREAM), P.O. Box 85, 73100 Chania, Greece; [EMAIL_ADDRESS]  
3 November 6, 2021 November 6  
(kaempfer and quercetin glycosides) and phenolic acids and their derivatives (i.e., caffeoylquinic acids and their isomers) [4; 5]. In fact, the strong antioxidant effects exerted by _M. oleifera_ leaf extracts and decoctions have been attributed to their high flavonoid content [6; 7; 8]. By virtue of their flavonoid richness, _M. oleifera_ leaves have been a subject of recent research on the production of polyphenol-containing extracts, and several methodologies have been used for such a purpose, including pressurized hot water [9] and aqueous two-phase extraction [10], ultrasound-assisted extraction with aqueous ethanol [11], and subcritical ethanol extraction [12]. However, extraction with novel, green solvents, the so-called deep eutectic solvents (DES), have lately been proposed as eco-friendly and particularly effective means of polyphenol recovery from _M. oleifera_ leaves [13; 14]. DES are neoteric designer liquids, composed of food-grade, low-cost biomaterials, such as polyols, organic acids, amines, organic acid salts, etc. [15]. Their facile and straight-forward synthesis, as well as their unique properties, including low or no toxicity, absence of flammability, tunability, recyclability, and biodegradability, make DES ideal solvents for the development of green extraction processes, and over the last five years, DES have attracted particularly high interest for the polyphenol extraction from a wide number of plant tissues [16].  
Ultrasonication is a state-of-the-art technology implicated in sustainable \"green\" extraction procedures, since numerous studies have demonstrated the efficiency of ultrasounds in accelerating the rate of solid-liquid extractions. By implementing ultrasonication techniques, effective extractions can be accomplished in minutes with high reproducibility, requiring reduced solvent volume, lower energy input and simpler sample manipulation, providing at the same time in some cases higher purity of the final product [17]. On such a ground, this study was performed with the aim at examining the extraction of _M. oleifera_ leaf polyphenols and the impact of ultrasonication as a pretreatment step. Response surface was deployed to optimize extraction variables and kinetics was used to obtain a deeper insight into the effect of ultrasonication. A final assessment of the process was conducted through the determination of the ultrasonication-treated and non-treated samples.