esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Operation of Proposed Scheme  
The proposed scheme runs through two phases, as shown in Figure 3. The scheme begins with a network-partitioning phase (NP_Phase), where an AUV computes network-partitioning information such as Voronoi generator points and CDMA codes, and then executes an NPT to broadcast the information. The NPT starts from a point **P1** at which a communication link to the sink is available.  
During the NPT, sensor nodes identify the cluster to which they belong, based on the network- partitioning information and accordingly elect a CH. Then, the CH further partitions the cluster into several subclusters and locates a PN in every subcluster based on the geographical distribution of traffic requirements by the MNs in a way that the overall energy consumption for data-gathering in MN\(\rightarrow\)PN is minimized. After the NPT, the proposed scheme enters a data-gathering phase (DG_Phase), where the DGT repeats rounds until a new network partitioning is required. During every round, data is gathered from all distributed MNs to one sink through three steps: MN\(\rightarrow\)PN, PN\(\rightarrow\)AUV and AUV\(\rightarrow\)sink. During DG_phase, the PN keeps gathering data from MNs except when the PN is working for PN\(\rightarrow\)AUV.
## 3 Results and Discussions
### XRD Analysis  
The crystal structure of NDG is studied using XRD analysis. The spectral pattern obtained for NDG is compared with the precursors. Figure 3 displays the XRD pattern of graphite, GO, and NDG. Pure graphite exhibits a highly crystalline nature as revealed from the XRD pattern of pure graphite and a sharp peak at 26.5\({}^{\circ}\) (002) with a \(d\)-spacing of 0.34 nm calculated using Bragg's equation [24]. After the oxidation and exfoliation of graphite into GO sheets, the peak shifted to 13.4\({}^{\circ}\) (001) with an increased interplanar distance of 0.66 nm. As observed in Figure 3, the diffraction peaks corresponding to graphite (20 = 26.5\({}^{\circ}\)) and GO (20 = 13.4\({}^{\circ}\)) are completely absent in the XRD pattern of NDG. NDG, being a turbostratic material, displays a broad peak at 23.4\({}^{\circ}\) corresponding to the (002) plane and a small shoulder at 42.8\({}^{\circ}\) corresponding to (004) or (100) planes [25]. The \(d\) spacing value NDG is calculated as 0.37 nm. The decrease in \(d\) spacing indicates the restoration of unsaturated conjugation through the removal of intercalated oxygenic functional groups.  
Figure 3: XRD pattern of graphite, GO, and NDG.  
Figure 2: FT-IR spectra of GO and NDG.
# Enhancement of Metabolite Production in High-Altitude Microalgal Strains by Optimized C/N/P Ratio
## 2 Materials and Methods
### Experimental Design  
The optimization of carbohydrates (analyzed in _Chlorella_ sp. UFPS019), proteins (analyzed in _Desmodesmus_ sp. UFPS021), lipids (analyzed in _Chlorella_ sp. UFPS019), and carotenoids (analyzed in _Desmodesmus_ sp U UFPS020) through the adjustment of the carbon/nitrogen/phosphate ratio was evaluated (in triplicate) using a central non-factorial response surface design with three central points and three blocks in the software STATISTICA 7.0 (Statsoft). The central points permitted a statistical check for the goodness-of-fit of the factorial model. Sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)), sodium bicarbonate (NaHCO\({}_{3}\)), and sodium acetate (NaC\({}_{2}\)H\({}_{3}\)O\({}_{2}\)) were tested to identify the carbon source that maximized the production of the metabolites. The variables and their levels can be found in Table 1. The concentrations of NaNO\({}_{3}\) (0.25 g/L), K\({}_{2}\)HPO\({}_{4}\) (0.075 g/L), and KH\({}_{2}\)PO\({}_{4}\) (0.175 g/L) were adjusted following the content of each nutrient in Bold's basal medium [102]. The final concentration of each carbon source was calculated according to its relative carbon content.  
For each experiment, the C/N/P ratio was adjusted in 300 mL of Bold's basal medium [103] at pH 7.0 according to the resolved design (Table 2). Each flask was attached to a gas line with an airflow of 0.18 L min\({}^{-1}\) (Resun, LP-100, Aarau, Switzerland) without the addition of extra CO\({}_{2}\), and a light:dark cycle of 12:12 h at 100 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) at 27 \({}^{\circ}\)C. To produce carbohydrates and proteins, the strains were grown for 20 days, while to produce lipids and carotenoids, the strains were cultured for up to 40 days. In the case of carotenoids, the light intensity was increased up to 250 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), with a light:dark cycle of 12:12 h at 27 \({}^{\circ}\)C. The pH was monitored twice a day and adjusted to pH 7.0 \(\pm\) 0.5 on a laminar flow cabinet using sterile solutions of either NaOH (1 M) or HCl (1 M).
# Influence of the Analytical Segment Length on the Tram Track Quality Assessment
## 1 Introduction
The tram networks are usually upgraded over long periods, so the homogeneity of the geometry degradation influential parameters along the tram tracks is very low. In addition, they are deeply rooted in the urban fabric, which complicates track geometry measurement, maintenance, and reconstruction, especially in large tram-road intersection zones. This concludes that due to the differences in the track geometry data collection process, track design, and operational requirements of conventional and tram tracks and vehicles, the track quality analysis of narrow-gauge tram networks should be performed on shorter analytical segments [14], by using a holistic TQI calculation approach with adjusted limit values. The research presented in this paper had two main objectives: (1) to assess the narrow-gauge tram-track geometry quality through the application of established synthesized TQI by using geometry measurements collected in unloaded track conditions; and (2) to analyze how a change in the analytical segment length affects the track geometry assessment. The research was conducted using tram network data (alignment, structure, and exploitation conditions) and five geometry parameter values measured by a trolley, manually operated along the 27.5 km of tram tracks in the City of Osijek. To avoid possible misconceptions, two different synthesized TQIs established for conventional rail systems--one based on a weighted value and the other based on standard deviation--were calculated on consecutive 200-, 100-, 50- and 25m long analytical tram-track segments. The conducted comparative analysis of the TQIs calculation results demonstrated and quantified the effectiveness of the analyzed segmentation concepts in the tram-track quality assessment and provided further insights for establishing TQIs implementation at a broader scale by the owners of tram infrastructure.
## 4 Molecular Effects of _Cannabis sativa_ L.
### Breast Cancer  
In preclinical studies, CB1 and CB2 agonists (CBD, THC, and synthetic) have been shown to inhibit the proliferation of estrogen receptors positive breast cancer cell lines [14]. Accordingly, a cytotoxic effect of CBD was observed in several cell lines including estrogen receptor (ER)-positive cells (MCF-7, ZR-75-1, T47D), ER-negative cells (MDA-MB-231, MDA-MB-468, and SK-BR3), and triple-negative breast cancer cells (SUM159, 4T1 up, MVT-1, and SCP2) [115; 116; 117; 118; 119].  
In particular, CBD induces apoptosis and reticulum stress in MDA-MB-231 and MCF7 cancer cells inhibiting their growth [119]. Other studies confirmed these data in several breast cancer cell lines, showing inhibition of cell growth, DNA fragmentation, and apoptosis [120; 121]. In addition, CBD treatment induces an enhancement in the level of ROS in breast cancer cells [70]. Furthermore, CBD induces apoptosis and blocks cell viability through the inhibition of the AKT/mTOR axis and cyclin D together with the enhancement of ROS generation [115; 116]. Similarly, THC showed anti-proliferative effects on several breast cancer cell lines [116; 122]. [PERSON] et al. observed that CBD specifically inhibits the epidermal growth factor-induced proliferation, suggesting it as a novel potential therapeutic option for breast cancer [115].  
Interestingly, CBD has been associated to sensitivity to chemotherapy; in MDA-MB-231 cells, it significantly reduces exosome release and inhibits microvesicle release [123] at a concentration of 1 and 5 uM. These regulatory processes may be associated with changes in mitochondrial function, including modulation of STAT3 and prohibitin expression, candidating CBD as a molecule useful to sensitize breast cancer cells to chemotherapy.  
Besides natural compounds, synthetic agonists for cannabinoid receptors (WIN55, 212-2, and JWH-133) have also been tested in breast cancer showing dose-dependent anti-proliferative and anti-migration effects [123; 124; 125; 126]. Comparably to natural ones, the effects induced by the agonists might be linked to the induction of autophagy and inhibition of cell-cycle progression through the enhancement of ROS production [119; 126].  
Regarding the minor phytocannabinoids, in MDA-MB-231 breast carcinoma cells, both CBG and CBC were shown to inhibit cell growth, inhibiting the uptake of [14\({}^{\circ}\)]anandamide and activating the vanilloid receptor TRPV1 [70]. Similarly, CBN has some antiproliferative effects in aggressive breast cancer cells [118], while CBC powerfully inhibits cell viability in both MDA-MB-231 and MCF-7 breast cancer cell lines, [70] and CBN has been revealed to have antiproliferative effects in aggressive breast cancer cells [127]. Among other terpen,pinene shows anti-proliferative effects against MCF-7 breast cancer cells [128; 34]. Several studies showed that limonene has anticancer effects on mammary carcinoma models causing regression and inhibiting subsequent tumor formation [129; 130; 131].
# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 2 Framework
### Feature Extraction
#### 2.3.2 Structural Features and Locational Features  
Structural features are derived from the house information, and three categories are extracted: area, elevator, and the age of the house. For locational features, to better characterize the living convenience of neighborhoods thoroughly, we construct features from the distance from the house to the nearest facilities. In this paper, ArcGIS is used to construct the traffic network, and the network analysis method is used to measure the road length between the residential area and the urban facilities to obtain the real road distance between the residential area and the urban facilities [42; 43]. Based on previous studies, eight location features are calculated in this paper [44]. The variable descriptive information is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Description**} & \multicolumn{2}{c}{**Beijing**} & \multicolumn{2}{c}{**Chongqing**} \\ \cline{3-5}  & & **Mean** & **S.D.** & **Mean** & **S.D.** \\ \hline \multicolumn{5}{c}{Locational features} \\ D\_Bus & Distance to the nearest bus station (km) & 0.424 & 0.931 & 0.28 & 0.25 \\ D\_Subway & Distance to the nearest subway station (km) & 1.457 & 1.565 & 0.953 & 0.672 \\ D\_Hospital & Distance to the nearest hospital (km) & 0.913 & 1.255 & 0.919 & 0.756 \\ D\_Kindergarten & Distance to the nearest kindergarten (km) & 0.778 & 0.698 & 0.313 & 0.325 \\ D\_PrimarySchool & Distance to the nearest primary school (km) & 1.018 & 0.897 & 0.831 & 0.584 \\ D\_MiddleSchool & Distance to the nearest middle school (km) & 1.287 & 1.167 & 1.157 & 0.735 \\ D\_Government & Distance to the nearest government (km) & 0.646 & 1.062 & 0.271 & 0.3 \\ D\_Park & Distance to the nearest park (km) & 1.564 & 1.515 & 0.965 & 0.653 \\ \hline \multicolumn{5}{c}{Structural features} \\ Elevator & Dummy variables (0 or 1) & 0.485 & 0.499 & 0.504 & 0.499 \\ Year & House age (year) & 24.773 & 7.843 & 13.617 & 5.293 \\ Area & Area (m\({}^{2}\)) & 86.849 & 25.485 & 95.487 & 29.804 \\ \hline \multicolumn{5}{c}{Visual features} \\ GVI & Green View Index (\%) & 14.743 & 4.194 & 15.298 & 7.872 \\ SVI & Sky View Index (\%) & 22.284 & 4.489 & 18.454 & 6.739 \\ MI & Motorization Index (\%) & 23.075 & 2.56 & 27.332 & 3.561 \\ WI & Walkability Index (\%) & 189.383 & 652.562 & 10.374 & 19.758 \\ NAI & Nature Artificial Index (\%) & 2661.079 & 9976.847 & 2853.694 & 43,102.086 \\ BVI & Building View Index (\%) & 3.309 & 1.192 & 5.021 & 2.895 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variables descriptive statistics.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 4 Theoretical Analysis  
### Data Rate  
A LoRa chirp can carry \(SF\) bits of information through frequency modulation, and its duration is \(T\)=\(\frac{2^{SF}}{BW}\), then the bit rate of LoRa is  
\[R_{b}^{L}=\frac{SF}{T}=\frac{SF}{2^{SF}}BW \tag{15}\]  
The basic unit of the cyclic right-shift operation is one sampling point. Therefore, the amount of information carried by amplitude modulation is dependent on the sampling rate \(f_{s}\). The duration of a QR\(-\)LoRa chirp is \(T\)=\(\frac{2^{SF}}{BW}\), then the sampling points \(\frac{2^{SF}}{BW}\cdot f_{s}\) can be obtained. That means the amplitude modulation can carry \(\log_{2}\left(\frac{2^{SF}}{BW}\cdot f_{s}\right)\) bit information. Thus, the bit rate of QR\(-\)LoRa is obtained as  
\[R_{b}^{QR}=\frac{BW}{2^{SF}}\left[SF+\log_{2}\left(\frac{2^{SF}}{BW}\cdot f_{s }\right)\right] \tag{16}\]  
Figure 10: Comparison of BER on the amplitude modulation before and after MADA.  
Figure 9: An example of the noise occurs at the position of the maximum descending gradient.  
According to the Nyquist-Shannon Sampling Theorem, Inter Symbol Interference (ISI) will not occur only when the sampling frequency \(f_{s}\geq 2f_{H}=BW\). Substitute \(\log_{2}\left(\frac{2^{SF}}{BW}\cdot f_{s}\right)\geq\log_{2}2^{SF}=SF\) into Equation (16), then we have  
\[R_{b}^{QR}\geq\frac{2 SF}{2^{SF}}BW=2R_{b}^{L} \tag{17}\]  
It can be seen that the bit rate of the QR\(-\)LoRa chirp can be **doubled** at least compared with the conventional LoRa.
## 5 Conclusions  
In this paper, a bio-inspired COA has been introduced for estimating an accurate model of PEM fuel cells. The algorithm identifies the optimum values of the model parameters accurately. Two practical cases including 250 W and 6 kW NedStack PS6 PEM system have been used to analyze the performance of the proposed COA method and its ability to generate a reliable model of PEM fuel cells stack. In COA the optimization process is based on the social structure and exchanging experiences among the coyotes. COA has only two parameters, the number of packs and the number of coyotes per pack that defines the population size, \(N_{P}\) and \(N_{c}\). No control parameters are required for COA. The features of COA are; rapidly, smoothly, and steadily convergence; no required efforts are needed to adjust the control variable of the algorithm as compared to other algorithms. The simulation results including the extracting parameters of the PEM stack and the minimum deviation between the generated model and the measured data point (I-V) are compared to the well-known optimizers. According to the numerical results obtained, COA has the ability and stability to estimate the global optimal parameters of the PEM fuel cell stack with minimal SEE compared to other algorithms. Well agreements between the estimated model using COA and the measured data ensure the effectiveness of COA and the accuracy of the PEM fuel cell stack model. The low value of the SSE emphasizes that COA is an effective and reliable technique in modeling PEM fuel. Thus, the COA is suggested to solve more complicated and large engineering optimization problems.  
All authors have contributed to the preparation of this manuscript. [PERSON] and [PERSON] designed the idea strategy, studied the data, and wrote the original manuscript. [PERSON] and [PERSON] revised and proofread the manuscript, and designed some figures. Finally, [PERSON] and [PERSON] reviewing, editing, and supporting different improvements for the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 7: The convergence of COA curve of 6 kW NedStack PS6 PEM fuel cell and polarization curve.
## 4 Conclusions  
The ZrO\({}_{2}\) whisker and nano coated CaF\({}_{2}\)@Al(OH)\({}_{3}\) powder were added to the Al\({}_{2}\)O\({}_{3}\)/Ti(C,N) self-lubricating ceramic material simultaneously. The modification of the Al\({}_{2}\)O\({}_{3}\)/Ti(C,N)/ZrO\({}_{2}\)/CaF\({}_{2}\)@Al(OH)\({}_{3}\) ceramic tool material was completed by using two different toughening mechanisms.  
1. The ZrO\({}_{2}\) whisker with 0, 3 vol%, 6 vol% and 9 vol% were added to the Al\({}_{2}\)O\({}_{3}\)/Ti(C,N) ceramic material. The result revealed that when the additive amount of the ZrO\({}_{2}\) whisker was 6 vol%, the hardness, the flexural strength and the fracture toughness values were 19.1 GPa, 584 MPa and 6.61 MPa-m\({}^{1/2}\), respectively. Whisker toughening and the phase transition toughening of the ZrO\({}_{2}\) enhanced the mechanical properties of the ceramic material.
2. The addition of nano coated CaF\({}_{2}\)@Al(OH)\({}_{3}\) powder also had a significant influence on the mechanical properties of the ceramic material. The intragranular structures played a good role in enhancing the mechanical properties of the ceramic material. When the nano coated CaF\({}_{2}\)@Al(OH)\({}_{3}\) powder addition was 10 vol%, the mechanical properties of the ceramic material was the best. The hardness, flexural strength and fracture toughness values of the prepared ceramic material were 18.58 GPa, 471 MPa and 6.50 MPa-m\({}^{1/2}\), respectively.
3. The ZrO\({}_{2}\) whisker of 6 vol% and nano coated CaF\({}_{2}\)@Al(OH)\({}_{3}\) powder of 10 vol% were simultaneously added to the Al\({}_{2}\)O\({}_{3}\)/Ti(C,N) ceramic material. The hardness of the Al\({}_{2}\)O\({}_{3}\)/Ti(C,N)/ZrO\({}_{2}\)/CaF\({}_{2}\)@Al(OH)\({}_{3}\) ceramic material was 16.72 GPa, the flexural strength was 520 MPa, and the fracture toughness reached 7.16 MPa-m\({}^{1/2}\). The fracture toughness of the Al\({}_{2}\)O\({}_{3}\)/Ti(C,N)/ZrO\({}_{2}\)/CaF\({}_{2}\)@Al(OH)\({}_{3}\) self-lubricating ceramic tool material was better than the added ZrO\({}_{2}\) whisker or added nano coated CaF\({}_{2}\)@Al(OH)\({}_{3}\) powder of the Al\({}_{2}\)O\({}_{3}\)/Ti(C,N) ceramic material, separately. The formation of intragranular structure, whisker toughening and phase transition of ZrO\({}_{2}\) were the main mechanisms.  
**Author Contributions:** Conceptualization, G.X. and Z.C.; methodology, Q.L.; formal analysis, R.G.; investigation, M.Y. and J.Z.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, Z.C and Q.L.; supervision, C.X.; project administration, Z.C.; funding acquisition, C.X and Z.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the Key Research and Development Project of Shandong Province (grant number 2019 GXGX104084); the Project for the Innovation Team of Universities and Institutes in Jinan (grant number 2018 GXRC005); the National Natural Science Foundation of China (grant number 51,575,285); and the Natural Science Foundation of Shandong Province (grant number 2R2017 LEE014).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 5 Results and Discussion
### Numerical Results  
In this section, the numerical results from all scenarios explained before will be presented. In addition, five metrics have been used to evaluate the performance of each scenario: latency, throughput, packet deliver ratio (PDR), power consumption, and model performance. Firstly, latency in a network refers to the time for data transmission across the network to its desired destination. One-way latency is measured in packet-based networks by sending a precisely timestamped packet across the network. The one-way delay is calculated by comparing the packet's timestamp with the time of the receiving unit's reference clock [57]. Secondly, the amount of data that may be transmitted from source to destination in a particular timeframe is referred to as network throughput [58]. Throughput capacity is often measured in bits per second, although it may also be measured in data (Kilobit or Megabit) per second. Throughput is calculated by dividing the data bits delivered to the destination by the delay time of communications data packets [59]. Moreover, (PDR) is defined as the ratio of packets transmitted to total packets sent from the source node to the destination node in a network. PDR describes the packet loss rate, which restricts the network's throughput. The routing protocol's performance improves as the delivery ratio rises. PDR is computed as:  
\[PDR=\frac{P_{tr}}{P_{s}}\times 100 \tag{1}\]  
where \(P_{tr}\) is the total packet received at the destination, \(P_{s}\) is the number of packets sent from the source. For power consumption, we utilised the Contiki power tracer model, which reads the number of Ticks on a regular basis and computes the power consumption using the equation below [60].  
\[Pour(mW)=\frac{Energyst\times current\times voltage}{RTMER\times Runtime} \tag{2}\]  
where \(Energyst_{value}\) is the values in TICKS read from the simulator, \(current\) and \(voltage\) are the sensor values of current and voltage from the datasheet of the used sensors. \(Runtime\) is the simulation time and \(RTMER_{second}\) is the constant \(value=32,768\) used to convert the values of the simulator from TICKS to seconds.
## 2 Cluster Head Selection
### Multi-Factor Evaluation Schemes
#### 2.3.1 Analytical Hierarchy Process  
AHP [63] is a structured technique for organizing and analyzing complex decisions. In the AHP technique, a complex decision is decomposed into a hierarchy of more easily understood sub-problems using numerical values. At the outset, two matrices should be generated using the fundamental scale for pairwise comparisons: the weight of decision factors towards the topmost goal matrix and the weights of alternative senor nodes towards each factor. A sample table of the evaluated number rating for verbal judgment of preferences is presented in Table 2. Then, the global weight of each sensor node can be obtained through summing the products of the weights of nodes by their corresponding decision factor weights. Thus, the nodes having the largest weight are the most suitable nodes to be selected as CHs. A simple AHP hierarchy consisting of three sample nodes is presented in Figure 5. According to the presented sample, Node 2 alternative's priority with respect to reaching the goal of choosing an appropriate CH is the highest among other nodes; and hence Node 2 is the optimum selection for CH position for current round.  
In [64], AHP is used to deal with the issue of CH selection by considering three factors of energy, mobility and distance to the cluster center that contribute to the network lifetime. To minimize the load of data transmission for the nodes which their demises leads to disconnection of the network, the authors of [65] takes into account the vulnerability index of the nodes besides the factors considered in [64]. Therefore, the nodes with higher level of vulnerability have less chance to be selected as CH. For networks with numerous nodes, CH selection using AHP entails solving large dimensions matrices, which should be performed by the BS due to its unlimited energy resources and high processing capabilities.