esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 4 System Model  
The proposed system consists of two sensor node modules connected with a computer for the database by wireless connectivity such asa Bluetooth protocol. Each sensor node senses the data independently and communicates this to the computer for further processing and the database as shown in Figure 2.  
The proposed system functionally includes three categories.  
* Data sensing phase;
* Fault injection phase;
* Fault estimation and classification.  
Step1  
Data sense is involved in this Step of operation. The output of this sensed data is used for the data-generation block that creates an observation vector _Vt_. This vector incorporates two functions each from humidity functions of measurements, i.e., _H1_, _H2_, and two temperature functions of measurements, i.e., _T1_ and _T2_. _Vt_, _Vt-1_ and _Vt-2_ are the measured datasets aggregated successively. These are contributed to the new observation vector.  
Step2  
In this Step, faults are infused on the actual sensed datasets. Gain fault, Offset fault, Out-of-Bounds fault and Stuck-at fault are infused as referred from [1]. The data loss and spike faults are infused in the dataset at the second Step of this proposed system model.  
Step3  
This is the Step where the collected data are checked and classified based on the results. As pointed out in the introduction, the WSNs are formed with many clusters of sensor nodes connected wireless with other sensor nodes. A cluster head is selected from every cluster of sensor node, and it collects data from the own cluster nodes and passes the collected data to the next cluster head node for successful communication.  
In this Step, the classifiers which have been discussed before are used for the classification of faults based on the estimated result. The techniques used for classification such as SVM, CNN, SGD, MLP, RF and PNN, are implemented in the cluster head node of every cluster. A vector function is formed by each of these techniques to generate a decision function. Due to the limitations of WSN, the vector functions are not complex in the aspect of computational complexity. After the evaluation of the decision function of the sensor nodes, the collected data are classified into two classes. If the decision function is positive, the data are categorized as a normal class (fault-free case); otherwise, the data are classified as a faulty case. Data are sensed by two sensors nodes at the generation level. These sensed data are injected with predefined faults in the generation phase itself. In this phase, all the classifiers are run in the data with faults to detect the injected faults. Then, the faults are identified at different levels with different detection accuracy levels. From these results, the graph is plotted for better understanding.  
Figure 2: The system model of fault detection.
# In Situ Transformation of Electrospun Nanofibers into Nanofiber-Reinforced Hydrogels
## 2 Materials and Methods
### Antimicrobial Study  
Antimicrobial studies were conducted as described previously [52]. In brief, Gram-negative _Escherichia coli_ (ATCC 25922) and Gram-positive _Staphylococcus aureus_ (ATCC 29213) were spread evenly over the surface of LB agar plates, and dry or pre-hydrated sample punches (6 mm in diameter) or 30 \(\upmu\)g T discs as a control were placed on top of the plates. After 24 h incubation at 37 \({}^{\circ}\)C, images of the inhibition zones were recorded with ChemiDoc imaging system (BioRad Laboratories, Copenhagen, Denmark) and analyzed with ImageJ software (1.52a version). All experiments were performed in triplicate (biological replicates).
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 2 Theoretical Background
### Potential Use of CYGNSS Data  
CYGNSS receivers process delay-Doppler maps (DDM) as the main observatory product ([PERSON] et al., 2017). CYGNSS Level 1 v2.1 Science Data Products, definitions of which can be found in Appendix A, include a number of geometry- and instrument-related DDM-derived variables.  
With respect to the consideration of the dominant coherent reflections described previously, the surface reflectivity can be approximated by using either (4) or (5) with the CYGNSS data products. For instance, the CYGNSS data can be substituted into the calibration parameters in either equation as follows: \(gps\_tx\_power\_db\_w\) for \(P_{t}\), \(gps\_ant\_gain\_db\_i\) for \(G_{t}\), \(sp\_rx\_gain\) for \(G_{r}\), \(tx\_to\_sp\_range\) for \(r_{st}\), and \(rx\_to\_sp\_range\) for \(r_{sr}\). In order to perform the calculation by using (4), the bistatic received power, \(P_{RL}^{coh}\), needs to be substituted by a CYGNSS observation. Using either \(ddm\_snr\) or the peak of the \(power\_analog\) DDM was investigated by previous studies ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2018). The \(ddm\_snr\) product accounts for the peak DDM signal-to-noise ratio (SNR) and is computed as \(10 log_{10}(S_{max}/N_{avg})\), where \(S_{max}\) is the maximum value (in raw counts) in a single DDM bin and \(N_{avg}\) is the average raw noise counts per bin ([PERSON] et al., 2017).  
To solve (5) for the surface reflectivity, \(brcs\) data product (Appendix A) of the CYGNSS mission can be used. In principle, this should produce an output equal to the use of peak power in (4); however, the resulting reflectivity approximations have differences from each other, which is most likely due to the internal calibration process when generating \(brcs\). Although the CYGNSS data products are originally calibrated for ocean surface sensing, using \(brcs\) over land is valid since it is only calibrated for the instrumental and geometric parameters ([PERSON] et al., 2018). \(brcs\) is published as a DDM within the CYGNSS Level 1 data; however, the peak value can be exploited under the coherency assumption. The rest of the derivation through (5) is based on the calibrations with respect to the range terms.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm
### Algorithm Flow  
Step 1: Establish a grid environment model. Create a grid model of the grid-based model for the logistics robot environment. Set up free grids, obstacle grids, and define the grid parameters. Set the coordinates for the initial point S and the destination point E.  
Step 2: Initialize the fundamental parameters. Initialize the algorithm's maximum number of iterations K, the count of ants M in the ant colony, the importance factor of pheromone \(\alpha\), the importance factor of heuristic information \(\beta\), the pheromone evaporation coefficient \(p\), and other relevant parameters.  
Step 3: Select path nodes. Ant k selects the next feasible node on the route following the formula for transition probability (1), logs the visited nodes in the tabu list \(TABU_{k}\), and continues this process until ant k reaches the target point. This step is repeated until all M ants in the current iteration have completed their search.  
Step 4: Record the distance of each route and the count of ants. Record the route traversed by each one of the ants and the extent of the path traversed in the current iteration. Additionally, record the total ant count on each trajectory from the starting point to the target point, and store this information in the \(CELL\) database.  
Step 5: Update pheromone. Using the tabu list \(TABU_{k}\) and the data in \(CELL\), find the highest quality route and the lowest quality route in the current cycle and size of the ant colony on each route. Update pheromone density according to Equations (11)-(13).  
Step 6: Complete the iteration by verifying if the algorithm has met the highest allowed count of iterations. If so, output the most effective path. In other cases, clear the tabu list \(TABU_{k}\) and repeat from step 3 for the next iteration.  
Step 7: Optimize the route. For the obtained optimal path, perform optimization using the triangle pruning method to obtain a simplified route.
## 3 Results
### Effect of Apigenin and Hesperidin on Arachidic Acid Metabolism Pathways  
Apigenin and hesperidin both targeted arachidonic acid's metabolism. The effect of apigenin and hesperidin individually is shown in panels A and B, respectively, and results from their combination are shown in Figure 4C. PGE2 levels were compared over a period of 2 days in the presence and absence of apigenin and hesperidin.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**In Silico Experiment** & **COX-2 (nM)** \\ \hline Apigenin First, Hesperidin Second & 1.93 \\ Hesperidin First, Apigenin Second & 1.54 \\ Apigenin and Hesperidin Simultaneously & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: COX-2 estimations for additive effects of apigenin and hesperidin.  
Figure 3: Individual (**A**,**B**) and combination effect (**C**) of apigenin and hesperidin on COX-2 production in chondrocytes over simulations periods of 2 days. Apigenin at 30 mg dose (**A**), hesperidin at 1000 mg dose, (**B**) and their combination at 1030 mg dose (**C**) reduced the levels of COX-2 over a period of 2 days. COX-2—cyclooxygenase 2.  
The levels of PGE2 were estimated to be 823 AUC for the control condition. At the end of a 2-day period, PGE2 concentrations reduced to 526 AUC in the presence of 30 mg of apigenin, as shown in Figure 4A. In the presence of 1000 mg of hesperidin, PGE2 concentrations reduced to 447 AUC over a period of 2 days, as shown in Figure 4B. When combined, apigenin and hesperidin lowered PGE2 levels even further to 286 AUC (Figure 4C).  
To determine whether the apigenin and hesperidin combination value of 286 AUC reflects a synergistic effect, the additive effect range is calculated for the biomarker PGE2 by the sequential (forward and reverse) administration of apigenin and hesperidin. As shown in Table 2, the additive effect range for the combination of 30 mg apigenin and 1000 mg hesperidin is 217-289 AUC when apigenin and hesperidin were added sequentially.  
When combined simultaneously, apigenin and hesperidin lowered PGE2 levels to 286 AUC (Figure 4C). This value falls within the additive effect range of 217-289 nM for  
\begin{table}
\begin{tabular}{c c} \hline \hline
**In Silico Experiment** & ** PGE2 (AUC)** \\ \hline Apigenin First, Hesperidin Second & 289 \\ Hesperidin First, Apigenin Second & 217 \\ Apigenin and Hesperidin Simultaneously & 286 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PGE-2 estimations for additive effects of apigenin and hesperidin.  
Figure 4: Individual (**A**,**B**) and combination effect (**C**) of apigenin and hesperidin on PGE2 production in chondrocytes over simulation periods of 2 days. Apigenin at 30 mg dose (**A**), hesperidin at 1000 mg dose (**B**), and their combination at 1030 mg dose (**C**) reduced the levels of PGE2 over a period of 2 days. PGE2—prostaglandine E2.  
PGE2, indicating that this specific combination has an additive effect in reducing PGE2, which is associated with joint pain.
## 2 Materials and Methods
### Cerebral hNIRS and LDF Setup and Measurements  
We used a custom-built hyperspectral near-infrared spectroscopy (hNIRS) system [15; 16] to invasively measure the absolute concentrations of [_HbO2_] and [_HHb_] and the differential signal between oxidized and reduced cytochrome c oxidase (\(\Delta\)_rCCO_) in the brain tissue. The hNIRS optodes were placed on the dura mater at a 20 mm interoptede distance/through holes drilled in the skull. With a differential path length factor (DPF) of 5 and a 20 mm optode spacing, the average optical path length was calculated to be approximately 100 mm, with a penetration depth of about 16 mm [16; 17; 18], chosen to minimize interference from the dura mater, which is approximately 1 mm thick. The hNIRS system included a highly sensitive spectrometer (AvaSpec, Avantes, Lafayette, CO, USA) with a custom 0.5 mm slit that captured spectral data in the 700-1000 nm wavelength range  
Figure 1: (**a**) Experiment setup and (**b**) hNIRS system.  
(Figure 1). The hNIRS diffusion model was applied to analyze the data, allowing for the determination of changes in the cerebral [_HbO2_], [_HHb_], and \(\Delta rCCO\)[13]. The dynamic changes in chromophore concentrations were extracted using a multi-step data-fitting algorithm based on an analytical solution to the diffusion equation for a semi-infinite medium, incorporating an extrapolated boundary condition. This algorithm provided the tissue concentrations of [_HbO2_], [_HHb_], and \(\Delta rCCO\). The total tissue hemoglobin concentration ([_tHB_]) in \(\mu M\) per liter and the tissue oxygen saturation (\(tSO_{2}\)) were computed using the following equations:  
\[[tHB]=[HbO_{2}]+[HHb], \tag{1}\]  
\[tSO_{2}=[HbO_{2}]/([tHB])\times 100\%. \tag{2}\]  
The cerebral blood flow velocity index (\(CBFi\)) was measured using a laser Doppler flowmeter (LDF) (Periflux; Perimed Inc., Ardmore, PA, USA). The LDF probe was placed on the dura mater through a hole drilled in the pig's skull. Throughout the experiments, the mean aortic pressure and right atrial pressure were continuously monitored using catheters equipped with micro-manometer tips.  
The experimental workflow is summarized in Figure 2, outlining the sequential phases of animal preparation, experimental protocols, data acquisition, data interpretation, and analysis.
## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks
### Remote Infrastructure Security Concerns
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dimension** & **Parameters** \\ \hline Routing & Black Hole Attack, Hello Flood Attack, Worm Hole Attack, Denial of Service, Sybil Attack, Counterfeit \\ Routing Selective Forwarding, Sink Hole Attack, Flood Attack, Acknowledgement Spoofing \\ \hline Localization & Routing Misuse, Inaccurate Distance Measure \\ \hline Transmission Modes & Broadcast Attacks, Multicast Attacks \\ \hline Transport Layer & End-to-End Connection Disruption, Node Exhaustion, Network Link Exhaustion \\ \hline Node Position & End Node Software Attack, End Node Hardware Attack, False Data Injection in Aggregator Cluster \\ Attacks & Role Destruction, False Relay Data Route \\ \hline Cover Service & \\ Disruption Attack & Service Disruption, Service Misuse, Service QoS Loss \\ \hline In-Network & Technical Failure, Employee Issue, Network Negligence, Internet Connection Concerns Deployment \\ Security & Area Access, Uncontrollable Circumstances \\ \hline Consequences & Loss of Network Control, Loss of Node Control, Inaccurate Information Interoperability Failure, \\  & Loss of Engineering Effort \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cyber physical sensor network infrastructure-related security concerns.
## 2 Materials and Methods
### Sample Preparation  
With the aim of producing natural rubber sheets with varying degrees of crosslinking density, three distinct natural rubber compounds were considered. The only differing factor in the formulation of these compounds was the level of sulfur present. The composition of the other rubber chemicals was defined after preliminary analysis. The composition in parts per hundred rubber (phr) of the prepared NR sheets along with their codes is reported in Table 1. A conventional cure system, i.e., rubber formulations characterized by a sulfur-to-accelerator ratio ranging between 0.1 and 0.6, was selected for the NR formulations. This choice was dictated by the fact that physical properties such as tensile strength, fatigue resistance and elongation at break are superior in this type of system [21]. The code of the natural rubber samples is constituted by the term NR followed by the letter S, which stands for sulfur, and a number referring to the sulfur content (in phr) in the sample.  
The rubber compound's production process referred to ASTM 3182 standard [22], with the mixing stages summarized in Table 2. First, natural rubber underwent a mastication process lasting five minutes in an internal mixer (Thermo Haake Rheomix 600, Thermo Fisher Scientific, Waltham, MA, USA), equipped with counter-rotating rotors, at a temperature of 50 \({}^{\circ}\)C and a rotation speed of 60 rpm. Subsequently, the rubber chemicals were sequentially added to natural rubber. The order of addition was as follows: ZnO, stearic acid, ZDBC and, lastly, sulfur. Upon the introduction of these rubber compound chemicals, the mixture was blended and homogenized for two minutes.  
The resulting natural rubber compounds were then subjected to vulcanization using a Carver hydraulic hot press (Carver, Wabash, IN, USA). The vulcanization process occurred at a temperature of 120 \({}^{\circ}\)C and a pressure of 2 bar. The dimensions of the mold were 120 \(\times\) 120 \(\times\) 1 mm\({}^{3}\).
## 4 Conclusions  
We have comparatively investigated the ZnO thin films grown on the quartz glass and sapphire (001) substrates by magnetron sputtering and high-temperature annealing of 600 \({}^{\circ}\)C for 1 h. XRD detections revealed that the ZnO thin film grown on the quartz glass substrate had a better crystalline quality than that grown on the sapphire (001) substrate. It was found by SEM and FAM measurements that both ZnO thin films were composed of ZnO nano-particles and have a very dense as well as very smooth surface with a surface RMS roughness value of 0.50-0.65 nm. Furthermore, it was demonstrated by XPS that both ZnO thin films were of very high purity. The ZnO thin film grown on the quartz glass substrate contained much fewer O vacancies or defects than the latter, and that grown on the sapphire (001) substrate may have had a large number of internal holes. It was confirmed by UV-Vis spectra and PL spectra that the optical properties of the ZnO thin film on the quartz glass substrate was comparable to that on the sapphire (001) substrate. It is verified that the average residual stress of the ZnO thin film on the quartz glass substrate was slightly larger than that on the sapphire (001) substrate, while the surface residual stress of the former was significantly smaller than that of the latter. As a result, based on the same growth process in this work, the ZnO thin film grown on the quartz glass substrate was comparable to that grown on the sapphire (001) substrate in the morphological properties. In particular, the former was superior to the latter in the crystalline quality and the absorption of the UV light; hence, ZnO thin films with high quality could be obtained on quartz glass substrates.  
Writing-original draft, Supervision, W.Y.; Investigation, F.W.; Investigation, Z.G.; Investigation, P.H.; Investigation, Z.L.; Investigation, L.H.; Formal analysis, M.C.; Resources, C.Z.; Resources, Supervision, X.H.; Investigation, Y.F.  
This work was funded by the Science Foundation for Young Teachers Projects of Wuyi University (2018 td(03); Innovation Project of Education of Guangdong Province (2017 KTSCX187; 2016 KTSCX142; 2018 KTSCX233); Innovative Leading Talents of Jiangmen (Jiangmen(2019)7); Cooperative Education Platform of Guangdong Province ([2016]31); Key Laboratory of Optoelectronic materials and Applications in Guangdong Higher Education (2017 KSYS011); Science and Technology Projects of Jiangmen City (Jiangke [2016]189, [2017]307, [2017]149, [2017]268, [2018]352, [2018]359); PhD Start-up of Wuyi University (2017/BS04); the College Students maker space' Innovation and Entrepreneurship Training Project of Wuyi University (18 KWL01, 18 KWL07); the College Students' Innovation and Entrepreneurship Training Project of Guangdong Province (201811349072); Special Fund for Scientific and Technological Innovation Cultivation of College Students in Guangdong (Special Fund for Climbing Program) (pdjh2019a0490).  
The authors declare no conflict of interest.
## Discussion
### Overexpression of tHMGR does not Circumvent Native Regulatory Mechanisms  
Additional strategies have been developed to overcome regulation by HMGR and increase MVA carbon flux in other eukaryotes. Truncation of HMGR to remove N-terminal membrane and SSD domain was first reported in plants and yeast with the aim to express only the catalytic domain of HMGR and avoid regulatory effects [50; 51]. Although HMGRs of plants do not contain an SSD sequence (Figure 2), expression of an N-terminal truncated HMGR has been reported to increase sterol levels. Expression of tHMGR from hamster in tobacco resulted in augmented sterol content in leaf tissue [52]. Constitutive expression of tHMGR from _Hrevea brasiliensis_ in tobacco resulted in an 11-fold increase of seed HMGR activities and 2.4-fold increase in total seed sterol content [53]. However, overexpression of a tHMGR has not always been effective at altering sterol content; the overexpression of tHMGR in yeast resulted in accumulation of squalene and no changes in ergosterol, the final sterol in yeast [50; 51]. Likewise, in this study we did not observe a statistically significant alteration in total sterols of _T. pseudonana_ and _P. tricornutum_ after constitutive expression of a tHMGR-mVenus (Figure 4 and Figure S12). Yet, accumulation of the intermediates squalene, cycloartenol, and obtisofiloli was observed in _P. tricornutum_. Interestingly, levels of those intermediates were higher when expressing HMGR-mVenus, suggesting that truncation may have affected enzyme activity, performance, or access to substrates. Although we observed changes in intermediates and minor sterol levels in _P. tricornutum_ expressing HMGR-mVenus and tHMGR-mVenus, total sterol levels remained unchanged. These results suggest that diatoms have a tight sterol regulation system that may not be related to the conventional regulation model that involves SSD but rather a complex system with several regulation points not only in the MVA pathway but further down the sterol metabolic pathway.