esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Analysis  
The three-case simulations verification shows the amount of voltage drop along the distributed line, protection, and isolation of the fault branch which occurred in different areas at different times. The normal voltage drop along the feeder is 12 V, which is 3% of the rated voltage, as depicted in Figure 10. Also, when \(F_{2}\) occurs, the DC bus voltage is dropped to 235 V and returns to the normal situation after the fault interruption. In Figure 7, when \(F_{1}\) is happening in area A, due to the isolation of the load branch, the rate of charge in this area increases more rapidly than the other parts. Meanwhile, the fault currents (\(I_{F1}\), \(I_{F2}\)) reach its maximum values, 60 A and 72 A, respectively. After the disconnection of short circuits, the accumulated energy is attracted by MOV, and the fault current decays to 0 A within 15 \(\upmu\)s until the fault is removed from the system and is reconnected within 8 \(\upmu\)s, as shown in Figures 8 and 11. During this time, the current rise is limited to a certain value, and after the fault interruption the current flow increases to reach the neighboring areas, as shown in Figures 6 and 9. Furthermore, during \(F_{3}\) at 2 s, the current flow jumped to a high value from B and C to the faulted point, but the breakers which were connecting B and C acted abruptly at both sides and disconnected the fault. However, the high conduction loss and cost of the semiconductor breakers technology was considered as the main obstacle to wide application. However, there is a comparison among the cheap MCB with a low operation speed and fast semiconductor breakers with high conduction losses for protection application. Additionally, there are some current fluctuations at the end of the simulation in Figures 6, 9, and 11. At this time, the solar radiation is decreased rapidly in three areas and the PV outputs are too low to support the load, which causes high current fluctuation among the areas to supply the connected loads and share the power. The simulation results have shown that the SSCB safely turned off, and the rest of the system continued to operate normally. Also, the freewheeling diodes started conducting, while the currents reached values that were not affordable by converters.  
Figure 11: Current flow from B to C during \(F_{3}\).  
Figure 10: DC bus voltage at the source and load side during \(F_{2}\).
## 3 Results and Discussion
### Installation Angle  
Figure 7 demonstrates the dependence of the HTC of the system on the installation angle (IA) of the HP for pure n-pentane and nanofluids. As can be seen, there is another trade-off identified for the HP such that with increasing the installation angle, the HTC of the system initially intensifies reaching to a point in which the HTC starts to diminish with increasing the installation angle. For example, at IA = 5, the HTC for n-pentane and nanofluid at wt.% = 0.3 was 4500 W/(m\({}^{2}\)K) and 4900 W/(m\({}^{2}\)K), respectively.  
Figure 5: Variation of \(h_{nanofluid}\)\(h_{n-pentane}\) with the applied heat flux for various mass fractions of graphene nanoplatelets in n-pentane.  
Figure 6: Variation of the HTC of the system with the HTC value for pure n-pentane and nanofluids.  
However, by increasing the IA to 65\({}^{\circ}\), the HTC of the system improved to 4800 and 5300 W/(m\({}^{2}\)K), respectively and decreased to 4600 W/(m\({}^{2}\)K) and 5085 W/(m\({}^{2}\)K) for IA = 75\({}^{\circ}\). This can be attributed to the effect of gravity forces on the liquid working fluid. For small angles, the effect of gravity is insignificant; hence return of the working fluid to the evaporator is not strongly affected by the gravity. However, for larger angles, such return is facilitated due to the gravity. However, this adds another trade-off to the system. By facilitating the return of the working fluid to the evaporator, the residence time inside the condenser considerably decreases and working fluid does not have sufficient time to transfer the heat to the surrounding. Hence, at installation angle of 65\({}^{\circ}\), the largest HTC was recorded for the system. This angle was the same for nanofluids and for the n-pentane as well.  
It is worth saying that there are several operating parameters which can potentially limit the heat-transfer rate and operation of the HP. These parameters include rate of the boiling heat transfer, the capillary effect, vapor velocity (sonic limitation) and entrainment of the liquid and vapor phases. Depending on the structure of the thermosyphon and heat pipe, any of above parameters can affect the thermal performance of the system. If the rate of the boiling heat transfer is relatively large, the dry-out phenomenon and critical heat flux occur, which can damage the evaporator section of the heat pipe. Also, if the rate of the evaporation is large, the gas velocity can reach to the sonic level (if diameter is very small, which causes a limitation in the heat transfer. Also, if the amount of vapor is high, it can causes a choked flow or even entrainment in the adiabatic or evaporator sections [34; 35].
###### Abstract  
Variation in plant species and extraction solvents play a crucial role in the recovery of their bioactive compounds and antifungal efficacy. Thus, in this study, a comparative investigation was carried out using extraction solvents: 70% acetone and 95% ethanol to obtain crude aqueous extracts from _Helichrysum odontissimum_ and _H. putulum_. Crude aqueous extracts were screened using gas chromatography-mass spectrometry (GC-MS), to gain insight into their chemical composition. Phytochemical properties (total polyphenols (TP) and radical scavenging capacity via 2,2-diphenyl-1-picrylyhydrazyl (DPPFH)), and antifungal activity against _Botrytis cinerea_ of the crude extracts were evaluated. Fungicide (Rovval(r) WP) and extraction solvents were used as controls. Variation in _Helichrysum_ spp. and extraction solvent had influence on the chemical composition, phytochemicals, and antifungal activities. Metabolites such as \(\gamma\)-terpinene (\(\approx\)0.1%), \(\alpha\)-amorphene (\(\approx\)0.6%) \(\alpha\)-gurjunene (\(\approx\)1.4%), \(\beta\)-selinene (2.2-3.2%), \(\gamma\)-gurjunene (\(\approx\)3.3%), and methyl cinnamate (\(\approx\)20%) were detected only in extracts of _H. putulum_. Crude extract of _H. odoratissimum_ using 70% acetone had the highest TP (19.3 \(\pm\) 0.76 g GA 100 g\({}^{-1}\)), and DPPH capacity (13,251.5 \(\pm\) 700.55 umol Torolox g\({}^{-1}\)) compared to _H. putulum_ (\(p\leq\) 0.05). Ethanolic extracts of _H. putulum_ showed highest antifungal efficacy (\(\approx\)65%) against _B. cinerea_ (\(p\leq\) 0.05) compared to other crude extracts. This study showed that _Helichrysum_ spp. differ in their potential as a source for bioactive compounds and antifungal treatments/formulations.  
Keywords:antioxidant capacity; crude extracts; extraction solvents; 'imphepho'; secondary metabolites; sesquiterpenoids +
Footnote †: journal: _plants_
## 2 Materials and Methods
### Histological Process  
The Excelsior AS (Thermo Fisher Scientific Inc., Waltham, MA, USA) was used to process the tissues. Demineralization was carried out in an aqueous solution, which was a mixture of 22.5% (vol./vol) formic acid and 10% (wt/vol) sodium citrate for 3-4 weeks [37; 38]. All specimens were washed with running tap water for 24-48 h, followed by ascending grades of ethanol. The root segments were marked at 1 mm, 4 mm, and 6 mm from the tip of the root and separated using a razor blade. Each portion of the root was individually embedded in paraffin for cross-sections. Twelve consecutive 5 \(\upmu\)m sections were obtained from the apical, middle, and coronal portion of the mesial and distal roots using a microtome (Leica RM 2235, Buffalo Grove, IL, USA). In the vital pulp tissue group (symptomatic irreversible pulpitis), all specimens were stained with hematoxylin-eosin (H&E) to screen for the presence of pulp tissue remnants. In the pulp necrosis group (necrotic pulp/symptomatic apical periodontitis), 6 out of the 12 consecutive 5 \(\upmu\)m sections in each radicular third were stained with hematoxylin and eosin and 6 consecutive sections were stained using the Gram technique to detect Gram-negative and Gram-positive bacteria. The same procedure was used for the histologic control groups. Slides were examined under the BX41 light microscope using CellSens imaging software (Olympus Corp., Center Valley, PA, USA) by the principal investigator. All 12 consecutive cross-sections obtained from each radicular third were microphotographed and randomly ordered in a PowerPoint presentation for observation and grading by two blinded graders. Evaluations were performed separately. In case of disagreement between graders, they reviewed the findings until an agreement was reached [39].  
In the vital group (symptomatic irreversible pulpitis), the specific characteristics analyzed were:  
1. Presence of pulp tissue and debris in the root canal lumen and fins.
2. Presence of pulp tissue and debris inside isthmuses (if isthmus present).
3. Presence of pulp tissue and debris inside lateral canals (if lateral canal present).  
In the necrotic pulp group (symptomatic apical periodontitis), the specific characteristics analyzed in gram stain slides were the presence of bacteria:  
1. Adhered to necrotic pulp tissue located in the root canal lumen and fins.
2. Adhered to necrotic pulp tissue located in the isthmuses (if isthmus present).
3. Adhered to necrotic pulp tissue in lateral canals (if lateral canal present).
4. Within dental tubules.  
Figure 1: The corresponding sealing cap attached to the procedure instrument (PI) and positioned on the platform.
## 4 Concerns about and Encounters with Underwater Wireless Sensor Network
### Underwater Acoustic Communications Characteristics
#### 4.3.4 Propagation Delay Models
The following are the calculations for the scale factor:  
\[\mathrm{h(l_{w},\;d_{b\mathrm{t}},\;h_{w},T_{w})}=\frac{\Big{(}\mathrm{h_{w}} \;\Big{(}1-(\frac{2\mathrm{d}_{\mathrm{b}}}{T_{w}})\;\Big{)}\Big{)}}{0.5}* \bigg{|}\sin\!\left(\frac{2\pi(\mathrm{mode\;T_{w}}}{T_{w}}\;\right)\bigg{|} \tag{14}\]  
The e() function characterizes a seemingly arbitrary word to describe circumstantial noise and the equation shown below:  
\[\mathrm{e(0)=20\bigg{(}\frac{s}{s_{max}}\bigg{)}R_{n}} \tag{15}\]  
For a cylinder-shaped symmetry:  
\[\frac{\mathrm{d}r}{\mathrm{d}s}=\mathrm{c}\varepsilon(s),\;\frac{\mathrm{d} \varepsilon}{\mathrm{d}s}=\frac{1}{c^{2}}\frac{1\partial\mathrm{c}}{\partial r} \tag{16}\]  
\[\frac{\mathrm{d}r}{\mathrm{d}s}=\mathrm{c}\varepsilon(s),\;\frac{\mathrm{d} \varepsilon}{\mathrm{d}s}=-\frac{1}{c^{2}}\frac{1\partial\mathrm{c}}{\partial z} \tag{17}\]  
Anywhere the r(s) then z(s) characterize the glimmer, there is a synchronization in the tubular organizes, then s is the length of the glimmer; the couple \(\mathrm{c}\varepsilon(s)\;[\xi(s),\;\zeta(s)]\) signifies the lengthwise curvature of the ray. Initial conditions for and r(s), and z(s), \(\xi(s)\) and \(\zeta(s)\) are:  
\[\mathrm{r(0)=r_{s}\;,z(0)=z_{s},\;\varepsilon(0)=\frac{\cos\theta_{s}}{c_{s}}, \;\varepsilon(0)=\frac{\sin\theta_{s}}{c_{s}} \tag{18}\]where \(\theta_{\rm s}\) represents the launching angle, (rs,zs) is the source position, and cs is the sound speed at the source position. The coordinates are sufficient to obtain the ray travel time:  
\[\rm r=\int\frac{ds}{c(s)} \tag{19}\]  
One of the most popular simulation tools is the Bellhop Ray, and numerous studies confirmed its accuracy in deploying real data scenarios in different places. If the environment is defined correctly, the outcomes delivered by the Bellhop are reasonably comparable to the experimental data gathered during experimentations. East of Vilamoura on the Portuguese south coast, an experiment incorporating elements of these studies--such as the Bellhop and modeling actual data collection circumstances--was conducted in June 2010. Actual measurements were gathered during underwater simulations on Italy's Pi-anosa Island to verify the Bellhop model. In order to evaluate the MAC practices, The bellhop and real measurements were compared in the Italian region of Calabria. The results of the experimental testing conducted in the Portuguese town of Setubal, some 50 km south of Lisbon, and the theoretical analysis of the underwater signal transmission were in perfect agreement.
## 2 Problem Description and Methodology
### Grey Relational Analysis  
[PERSON] (1986) [27] proposed grey relational analysis as a method for studying the interrelationships among elements within uncertain systems. This analytical method holds a significant place in grey system theory, with its core being the assessment and description of the degree of correlation between different variables. By calculating the degree of correlation among variables, grey relational analysis can reveal their intrinsic connections, which is very helpful for understanding and predicting the dynamic development of systems [28].  
The basic steps of grey relational analysis applied in tunnel engineering are typically explained as followed [29]:  
The first step is data collection and serialization, which involves gathering pertinent raw data from the numerical results and converting them into an analyzable sequence. Typically, we need to establish the reference sequence matrix \(X\) and the subsequence matrix \(Y\):  
\[X=\left[\begin{array}{c}X_{1}\\ X_{2}\\ \ldots\\ X_{n}\end{array}\right]=\left[\begin{array}{cccc}x_{11}&x_{12}&\ldots&x_{1i }\\ x_{21}&x_{22}&\ldots&x_{2i}\\ \ldots&\ldots&\ldots&\ldots\\ x_{n1}&x_{n2}&\ldots&x_{ni}\end{array}\right] \tag{20}\]  
\[Y=\left[\begin{array}{c}Y_{1}\\ Y_{2}\\ \ldots\\ Y_{n}\end{array}\right]=\left[\begin{array}{cccc}y_{11}&y_{12}&\ldots&y_{1i }\\ y_{21}&y_{22}&\ldots&y_{2i}\\ \ldots&\ldots&\ldots&\ldots\\ y_{n1}&y_{n2}&\ldots&y_{ni}\end{array}\right] \tag{21}\]  
where the reference sequence matrix \(X\) contains the parameters of various influencing factors on the stability of inclined soil layers under unsaturated seepage conditions and the subsequence matrix \(Y\) includes the critical support force for which we use the dimensionless coefficient, normalized support force \(\sigma_{T}/\gamma D\), as a substitute.  
Secondly, the influence of different data dimensions and magnitudes should be eliminated to ensure that the data are standardized:  
\[{x_{ni}}^{\prime}=(x_{ni}-\min\,x_{ni})/(\max\,x_{ni}-\min\,x_{ni}) \tag{22}\]  
The difference sequence matrix \(\Delta\) will be obtained as follows:  
\[\Delta_{nj}=\left|{x_{nj}}^{\prime}-{y_{nj}}^{\prime}\right| \tag{23}\]Thirdly, the correlation coefficient matrix \(L\) and the degree of correlation \(\mathrm{I}_{nj}\) can be calculated:  
\[\mathrm{I}_{nj}=\left(\Delta_{\min}+\eta\Delta_{\max}\right)/\left(\Delta_{nj}+ \eta\Delta_{\max}\right) \tag{24}\]  
where the \(\Delta_{\max}\) and \(\Delta_{\min}\) are the maximum and minimum values in the difference sequence \(\Delta\). The \(\eta\) denotes the value of the distinguishing coefficient and its value is set to 0.5.  
To further reasonably analyze the degree of correlation between the subsequence and the main sequence, the degree of correlation \(g_{n}\) should be calculated:  
\[g_{n}=\sum_{i=1}^{k}\mathrm{I}_{ni}/k \tag{25}\]  
Eventually, the calculated correlation coefficients should be ranked and analyzed to estimate which factor has a high degree of correlation with tunnel stability, providing a basis for engineering decisions.
# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 5 Numerical Evaluation  
In this section, the results are given of a detailed simulation-based numerical evaluation that was performed in order to demonstrate the operational characteristics and the priority of the TRUST-ME model. Specifically, its pure operation and performance are presented in Section 5.1 and a real-world application scenario of implementing the TRUST-ME model is discussed in Section 5.2. A detailed scalability analysis is performed in Section 5.3 in order to demonstrate the robustness and superiority of the TRUST-ME model. A thorough comparative evaluation is presented in Section 5.4 by considering different approaches in terms of enabling the users to select an MEC server and in terms of allocating the MEC servers computing resources.  
The following simulation parameters were used in the rest of the analysis: \(N=12\), \(S=3\), \(c=1.5\), \(\lambda=2\), \(\gamma=0.8\), \(w_{1}=0.05\), \(w_{2}=0.85\), \(w_{3}=0.05\), \(w_{4}=0.05\), \(\phi_{h}=1\)\([\frac{CPU-cycles}{bits}]\), \(e_{n}=10^{-9}\) J, \(f_{c}=73.5\) GHz, \(I_{0}=10\cdot 10^{-22}\), \(W=5\) GHz, \(P_{n}=0.2\) W, \(F_{s}=\) [3, 2, 1] \([\frac{CPU-cycles}{sec}]\), \(T_{n}=\) [20, 24, 27, 31, 34, 38, 42, 45, 49, 53, 56, 60] msec, \(B_{n}=\) [2.00, 1.92, 1.84, 1.75, 1.67, 1.59, 1.51, 1.43, 1.34, 1.26, 1.18, 1.10] Mbits, \(U_{n}=\) [90.0, 82.7, 75.5, 68.2, 60.9, 53.6, 46.4, 39.1, 31.8, 24.5, 17.3, 10.0], \(C_{s}=\) 0.007 \([\frac{5}{CPU-cycles}]\), \(\beta_{n}=10^{-1}\), \(d_{1,n}=\) [14.14, 20.75, 28.20, 35.96, 43.88, 51.88, 59.93, 68.01, 76.11, 84.23, 92.36, 100.50] m, \(d_{2,n}=\) [31.62, 35.08, 39.94, 45.75, 52.21, 59.09, 66.27, 73.66, 81.20, 88.85, 96.59, 104.40] m, and \(d_{3,n}=\) [50.99, 53.20, 56.52, 60.77, 65.77, 71.36, 77.41, 83.82, 90.52, 97.44, 104.55, 111.80] m, unless otherwise explicitly stated.
## 2 Materials and Methods
### Measurement Set-Up  
For practical implementation, a measuring set-up has been developed to induce material transport and erosion processes. The core of the arrangement is a cylindrical proctor mould (Figure 3). The material transport takes place within the cavity of the proctor mould. The test rig also includes a load frame in which the proctor mould is integrated. Water can be pumped through the cavity using the connections at the top and bottom. Using a constant pressure system and hydraulic accumulators, a hydraulic gradient is set before the test. This gradient can be set to constant, gradually increasing or decreasing for the test duration.  
To run an experiment, the cavity is filled up with two layers of different soil materials. In the case of simulating material transport or erosion processes in dikes, sand and gravel are used as two different soil types with different grain sizes. The finer material (sand) is placed in the lower half of the cell and the coarser material (gravel) above. Before a gradient is applied, the cell is completely saturated with water and sealed. The water flows up from the bottom to the top through the cavity, causing an erosion process since the finer material is washed into the pores of the coarser material (Figure 1). Because of the flow direction, the process of material transport is not amplified by gravity, and thus it is not distorted. However, gravity must be taken into account when determining the gradient in advance of the experiment.  
Figure 3: (**a**) Cross-section view of the prototype of the proctor mould without a load frame; (**b**) full assembly view.  
Figure 2: Dielectric constant of a soil-water mixture.  
Due to the material transport, the total volume of the material in the cell is reduced as the fine material settles in the gaps. This can create cavities in the soil, which lead to a destabilisation of the soil region. These cavities are able to cause a hydraulic ground failure, which is not in the scope of the present research. To prevent this, the motor mold is integrated into a load frame. The load frame presses a stamp onto the cell by applying a controlled force of 4 kN. When a drop in volume occurs, the force on the stamp decreases and must be readjusted accordingly in a closed loop. Additionally, this prevents the fine material from settling back into the lower region of the mold again after the test.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 3 Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11  
The WuR system proposal presented in this paper is based on the transmission of legacy frames by an IEEE 802.11 standard-compliant transmitter during a TXOP period. According to the standard specification, after the transmitter gains access to the medium following the IEEE 802.11 MAC contention mechanism, frames are transmitted consecutively within a TXOP, being those frames separated from each other by a Short Inter-Frame Space (SIFS) interval (Figure 2). Note that the number of frames involved in the TXOP of Figure 2 are only for the sake of example; in practice, any number of frames fitting within the TXOP limit is allowed. Moreover, the proposal also exploits the PHY characteristics of specifications from IEEE 802.11n on, where different signal bandwidths can be employed for frame transmission, making use of the so-called channel bonding mechanism. In this way, IEEE 802.11n allows operation for 20 and 40 MHz of signal bandwidth, while IEEE 802.11 ac and 11 ax can use 20, 40, 80, and 160 MHz. If transmitted frames inside a TXOP employ MCSs using different signal bandwidths, and these signals of different bandwidths can be distinguished at the receiver, the bandwidth of such transmitted frames can be used to encode different symbols composing a WuC. That is, the information transmitted to the WuRx is not in the payload contained in those IEEE 802.11 frames but in the bandwidth used to transmit those frames. Such a solution will allow an IEEE 802.11 standard-compliant transmitter to be used as WuTx for WuC generation, while the corresponding reception and decoding at WuRx do not need to implement the complexity of an IEEE 802.11 receiver; a simple receiver capable of distinguishing transmissions of different bandwidth can be used as WuRx. Moreover, corresponding coded symbols in a WuC will be intrinsically protected in front of other WuCs and of IEEE 802.11 legacy transmissions through the usage of the TXOP.
## 4 Data Collection and Preparation  
### Study Dataset  
The study location included two interstate highways in New Jersey: the 68.5 miles long interstate I-80, which has a west-to-east alignment; and 67.5 miles long interstate I-287, which has a south-to-north alignment. The locations of I-80 and I-287 on the map of New Jersey along with the location of weather stations used in this study to collect the weather data are shown in Figure 1.  
As stated before, the proactive data can be collected before the crash occurrence, ideally in near-real-time. The proactive data sources and datasets used in this study are readily available in real-time, at a roadway segment level, and for all sections of the major roadways in the State of New Jersey. The analysis dataset was compiled from the following sources: (a) New Jersey Department of Transportation (NJDOT) crash records database, which contains records of all reported highway crashes in the State of New Jersey; (b) NJDOT Congestion Management System (NJCMS), which provides estimated, synthesized hourly volume and volume-to-capacity ratio at a roadway segment level for all highways in NJDOT jurisdiction; (c) Prevailing vehicle speeds and travel times aggregated from the probe vehicles in 1 min increments at the roadway segment level, obtained from the Regional Integrated Transportation Information System (RITIS), (d) Historical weather observation data obtained from the Local Climatological Data (LCD) database for the weather stations managed by the National Weather Service (NWS).  
Figure 1: The study area with the location of I-80, I-287, and weather stations.  
The reactive data is collected after the crash occurrence and it provides additional details about the crash, such as vehicle and driver characteristics. The reactive data used in this study is contained in the statewide NJDOT crash records database, and it was used in the crash injury severity model only.