esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Signal Quality Analysis
### SQA3: Signal Discontinuity Analysis  
SQA3 is used to determine the time of cycle slips, which are then compared with the C/N\({}_{0}\) values and elevation angles at a given moment. The above results (SQA1 and SQA2) are used to identify the reason for the signal discontinuity. The upper plot of Figure 11 shows the C/N\({}_{0}\) value for each satellite during a cycle slip and the lower plot shows the satellite elevation angle at that moment. A comparison shows that most GPS signal cycle slips occurred when the satellite elevation angle was low, confirming the results of SQA2. If data for satellite elevation angles of below 20\({}^{\circ}\) are removed, the cycle slip interruption time for a whole day is only four seconds. These four cycle slips occurred when the satellite C/N\({}_{0}\) value was irregularly low. Compared to other cycle slips results in this analysis, the four irregular satellites had a higher elevation angle. However, the signals from these satellites do not have the corresponding signal strength (all C/N\({}_{0}\) values were below 40 dB-Hz). One possible reason is that the GNSS receiver might have been influenced by an interference signal at that moment. When signal interference occurs, the C/N\({}_{0}\) value decreases due to the corresponding increase in signal noise. When there is no interference, cycle slips occur when the GPS satellites have a low elevation angle. As a result, the satellite elevation angle can be used as an indicator for GPS satellites to avoid cycle slips (in an open sky area).  
Figure 12 shows the SQA3 results for the BDS satellites. The BDS GEO PRN 5 satellite has the same result as those of the GPS satellites. For the PRN 5 satellite, the elevation angle is below 20\({}^{\circ}\) and most C/N\({}_{0}\) values are below 40 dB-Hz. This explains why the BDS PRN 5 has lower continuity. However, PRN 3 had a high elevation angle and a high C/N\({}_{0}\) value when a cycle slip occurred. This also occurred for BDS IGSO and MEO satellites. The high C/N\({}_{0}\) value proves that there is no interference signal. This result shows that the BDS signal loses lock when the signal strength is good. This problem might be caused by the BDS satellite. Table 8 summarizes the final results for SQA3.  
\begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline  & **GPS** & \multicolumn{4}{c}{**BDS**} \\ \hline
**Orbit** & MEO & GEO 1–4 & GEO 5 & IGSO & MEO \\
**Mean Continuity (\%)** & \textgreater{}99.99 & \textgreater{}99.99 & 99.86 & 99.98 & 99.97 \\
**Mean CSN \({}^{\star}\)** & 2.5 & 2.75 & 115 & 9.6 & 3.8 \\ \hline \end{tabular} \({}^{\star}\) CSN: Cycle slip number.  
\end{table}
Table 7: SQA2 statistical results.  
Figure 11: GPS cycle slips with respect to C/N\({}_{0}\) and satellite elevation angle.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 3 Elaboration of Sensor Materials  
The selection of material type is crucial and challenging in the preparation of flexible piezoresistive strain sensors because the material characters determine the working performance of the sensors. In general, a flexible piezoresistive strain sensor is composed of three parts: conductive layer, flexible substrate and electrode. When external strain is applied, the substrate begins to stretch, inducing resistivity change in the conductive layer and hence resistance change in the sensor. The resistance change is then transformed into an identifiable electrical signal through the electrode. Table 1 gives the symbols and abbreviations of the materials mentioned in this paper. This section presents specific introductions to the characteristics of the sensor materials as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviations** & **Material** & **Abbreviations** & **Material** \\ \hline AFM & Atomic Force Microscope & PDMS & Polydimethylsiloxane \\ AgNPs & Silver Nanoparticles & PEDOT: PSS & Polyethylenedixythiophene: Polystyrene Sulfonate \\ AgNWs & Silver Nanowires & PET & _Polyethylene terephthalate_ \\ AuNWs & Gold Nanowires & PI & Polyimide \\ CB & Carbon Black & PMVS & Polymethylvinylsiloxane \\ CNTs & Carbon Nanotubes & POE & Polydefin Elastomer \\ CPCs & Conductive Polymer Composites & PP & Polypropylene \\ CSF & Carbonized Silk Fabric & PPy & Polyyrrole \\ CuNWs & Copper Nanowires & PU & Polyurethane \\ DI & Deionized Water & PVA & Polyvinyl Alcohol \\ EG & Ethylene Glycol & PVC & Polyvinyl Chloride \\ FBGs & Fiber Bragg Gratings & PVDF & Polyvinylidene Fluoride \\ FDM & Fused Deposition Modelling & rGO & Reduced Graphene Oxide \\ FGS & Fragmented Graphene Sponges & SBCs & Styrenic Block Copolymers \\ FSG & Fish-Scale-Like Graphene & SBR & Styrene-Butadiene Rubber \\ GWF & Graphene Woven Fabric & SBS & Polystyrene-Block-Butadiene-Block-Styrene \\ IL & Ionic Liquid & SEBS & Styrene-Ethylene/Butylene-Styrene Triblock Copolymer \\ ILBW & Ionic-Liquid-Based Wavy & SEM & Scanning Electron Microscopy \\ MWCNTs & Multi-Walled Carbon Nanotubes & SPX & Spandex \\ LPG & Laser Patterned Graphene & SWCNTs & Single-Walled Carbon Nanotubes \\ NaCl & Sodium Chloride & TPU & Thermoplastic Polyurethane \\ NR & Natural Rubber & TPV & Thermoplastic Vulcanizate \\ OBC & Olefin Block Copolymer & WEP & Waterborne Epoxy \\ PANI & Polyaniline & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols and abbreviations of the materials.  
Figure 5: Crack propagation under SEM view: microcrack propagation on the graphene–PDMS composite film under stretching [54] (Copyright 2012, the authors).
## 3 Results Analysis and Optimization
### Results and Analysis  
Quadratic regression analysis and multiple regression fitting were performed on the test results using Design-Expert software. A total of 30 treatments were conducted, as shown in Table 4.  
The regression and factor variance analyses were carried out on the test data using Design-Expert, and the factors with significant influence were screened out, as shown in Table 5.  
It can be seen from Table 5 that the regression model was significant (\(p<0.01\)) and that the lack of fit was extremely insignificant (\(p>0.05\)). The results indicated that the model terms and the factors on the maximum cutting force were extremely significant.  
Figure 6: Postprocessing of the simulation. (**a**) Effective stress at 0.007 ms; (**b**) effective stress at 0.250 ms; (**c**) effective stress at 0.830 ms; and (**d**) effective stress at 1.360 ms.
## 4 Experiment and Analysis
### Ablation Experiment of Root Cause Analysis Model for Cast Product Defects  
Table 4 shows the results of the ablation experiment of KGTBRN on the task of the root cause analysis of cast product defects. \"Model-attention\" is the model where the GAT layer of KGTBRN is removed, and only the two-branch reasoning network is used for the root cause analysis task. \"Model-concat\" is the model where the capsule layer in the two-branch reasoning network of KGTBRN is removed, and \"Model-capsnet\" is the model where the connection layer in the two-branch reasoning network of KGTBRN is removed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ablation** & **HITS@1** & **HITS@3** & **HITS@10** \\ \hline Model-attention & 31.5 & 43.7 & 69.8 \\ Model-concat & 32.6 & 46.9 & 71.0 \\ Model-capsnet & 35.2 & 48.2 & 73.4 \\ All & 37.2 & 51.6 & 76.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment results of KGTBRN on the task of the root cause analysis of cast product defects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MRR** & **HITS@1** & **HITS@3** & **HITS@10** \\ \hline TransE & 0.492 & 19.5 & 24.7 & 56.8 \\ DistMult & 0.614 & 21.9 & 30.2 & 65.4 \\ ComplEX & 0.597 & 22.8 & 31.5 & 62.1 \\ KGLM & 0.712 & 31.4 & 42.2 & 67.5 \\ KERMIT & 0.697 & 30.3 & 45.4 & 71.3 \\ Ours & 0.893 & 37.2 & 51.6 & 76.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of reasoning accuracy of each model on continuous casting data set.  
It can be seen from Table 4 that the GAT layer, the connection layer and the capsule layer improve the reasoning accuracy. Taking the HITS@1 index as an example, the addition of the GAT layer improves HITS@1 by 5.7%. Using the connection layer to process the 1-side reasoning task improves HITS@1 by 2%, and using the two capsule layers to process the N-side reasoning task improves HITS@1 by 4.6%.  
This shows that the two-branch reasoning network uses the idea of \"classifying relation types, and using different reasoning methods to deal with different types of relations separately\" is more suitable for the C2Q-KG. Meanwhile, the GAT layer can better identify the potential feature relations between relations and entities, so as to improve the reasoning accuracy of the overall model.
## 4 Experimental Evaluations
### Effect of Actuator Magnitude  
To be able to create a multi-level vibrotactile response, it was important to understand how the actuator magnitude affects the vibration in the beam. It was expected that lowering the magnitude of the actuators would proportionally lower the acceleration produced in the beam. To study the effect of magnitude, the acceleration was measured along the beam, keeping frequency constant and varying magnitude. Figure 5a shows the vibration along the beam, exciting it at 168 Hz with one actuator. The maximum peak to peak acceleration was 61.19 m/s\({}^{2}\), occurring at the center of the beam. Two node points are present around 10 and 20 cm. At these locations, the acceleration is below 9.81 m/s\({}^{2}\) regardless of the actuator's magnitude. As expected, decreasing the magnitude of the actuator lowers the magnitude of the response in the beam. Once the actuator reaches 50% magnitude at 168 Hz, it produces an insignificant response, with the acceleration below 9.81 m/s\({}^{2}\) throughout the beam. At this magnitude, the actuator's spring force is stronger than the electrostatic force, limiting output acceleration.  
Figure 5b shows the vibration along the beam, exciting it at 196 Hz with one actuator. The maximum peak-to-peak accelerations that occurred were 97.77 and 86.59 m/s\({}^{2}\), occurring at 11.4 and 19.1 cm, respectively. Three node points are present around 6.4, 15.2, and 24.3 cm. At 15.2 cm along the beam, the acceleration is below 9.81 m/s\({}^{2}\) even when the actuator's magnitude was at 100%. At 6.4 and 24.3 cm, the acceleration is below 39.2 m/s\({}^{2}\), with the actuator's magnitude at 100%. As expected, decreasing the magnitude of the actuator proportionally lowers the magnitude of the response in the beam. Once the actuator reaches 50% magnitude at 196 Hz, it produces an insignificant response, with the acceleration below 9.81 m/s\({}^{2}\) throughout the beam.
## 3 Description of the Firework Routing Protocol
### Conventional Potential Routing  
We now explain conventional potential routing as an example of link-diversity routing [4] for which a typical network model is shown in Figure 1.  
When the source attempts to establish a connection to the destination, it broadcasts a route request to its neighbors, who forward the requests in a flooding manner, until the destination node is found. Each node, \(x_{i}\), has its own temperature parameter, \(\phi_{t}\), at time step \(t\) that is iteratively calculated as the average of its neighbors' temperatures and is exchanged among neighbor nodes (Figure 1(a)).  
\[\phi_{t+1}\left(x_{i}\right)=\begin{cases}\frac{\sum_{k\in\textit{abr}\left(x_ {i}\right)}\phi_{t}\left(x_{k}\right)}{\left|\textit{nbr}\left(x_{i}\right) \right|},&\left|\textit{nbr}\left(x_{i}\right)\right|>0\\ 0,&\left|\textit{nbr}\left(x_{i}\right)\right|=0\end{cases}\]  
The network contains the set of nodes \(\left\{x_{1},\cdots,x_{n}\right\}\), and the set of neighbor nodes of a node, \(x_{i}\), is expressed as \(\left\{x_{k};k\in\textit{nbr}\left(x_{i}\right)\right\}\). In addition, the temperature of the source node, \(x_{s}\), and destination node, \(x_{d}\), are kept constant and have their own boundary conditions set as:  
\[\phi_{t}\left(x_{s}\right) =0 \forall t\geq 0\] \[\phi_{t}\left(x_{d}\right) =1\]  
After the convergence of this iterative temperature calculation, each intermediate node forwards packets toward its neighbor node, which has the highest temperature. As a consequence, packets sent from the source node will reach the destination node along the steepest temperature gradient path, as shown in Figure 1(b).  
Figure 1: Setting up the temperature in generalized potential-based routing (link-diversity routing). (**a**) After the initial route to the destination node is found through broadcasting, the temperature is iteratively updated at each node as the average of neighbor temperature values; (**b**) the potential is constructed, and each node forwards packet to its neighbor with the steepest temperature gradient.  
While this method is very effective for static node scenarios, it faces problems under dynamic conditions. Depending on the network size, many iteration steps may be required until the temperature potential, i.e., the correct route to the destination node, has converged. Thus, each time a potential is calculated, it is only valid for the period of time when the topology does not change. If the destination node leaves the transmission range of its last hop node, the correct route cannot be found, and a new flooding of the entire network is required to update the potential. In our MTFR method, which will be described in the following section, we improve this behavior by including a limited broadcast process in the potential-based routing.
## 2 Materials and Methods  
### Preparation of Raw Meat Samples and Experimental Design  
Fresh chicken breast meat was obtained from a local abattoir 24 h after slaughtering and transported to the laboratory at a temperature of 4 \(\pm\) 0.5 \({}^{\circ}\)C. Before dividing the meat into treatment groups (including BACs) and the control group, it was minced, with the skin removed and homogenized using a meat grinder (BOSCH-Slovenia). Following this, the samples were vacuum-sealed in polyethylene bags using a packaging machine (Multivac C200, Multivac Sepp Haggenmuller GmbH and Co., Wolfertschwerden, Germany) and subsequently stored at 4 \(\pm\) 0.5 \({}^{\circ}\)C for a maximum of 14 days. The ambient temperature during experimentation ranged from 22 to 25 \({}^{\circ}\)C (Appendix A Figure A1).  
To investigate the antimicrobial effect of BACs in vitro, two assays, disc diffusion and minimum inhibitory concentration (MIC), were applied. The experiment incorporated various BACs, including \(\beta\)-citronello1 (\(\geq\)95%), geraniol (\(>\)96%), (\(-\))-\(\alpha\)-Bisabolol, allyl isothiocyanate (95%), thymol (5-Methyl-2-isopropylhenol) \(\geq\) 98.5%, eugenol (99%), cumilaldehyde (98%), limonene (97%), and butylated hydroxytoluene (BHT) (\(\geq\)99%) (SIGMA, Darmstadt, Germany). All the natural bioactive compounds assessed have been designated as generally recognized as safe (GRAS) by the Food and Drug Administration (FDA) of the United States. The selected BAC (AITC) was applied to minced chicken breast meat at different concentrations designated as MIC-1, MIC-2, and MIC-4, based on its in vitro antimicrobial activity. The ratio of 5% of a mixture containing 0.00088 g of AITC, 4.99384 g of distillated water (DW), and 0.0044 g ethanol, respectively, in 100 g of meat was used in MIC-1 (Appendix A Table A1). The proportion of AITC was doubled in samples labelled as MIC-2 and quadrupled in meat samples designated as MIC-4. Various measurements were taken on days 0, 3, 7, 10, and 14. Additionally, the quality properties of chicken meat that were assessed include physicochemical characteristics (pH, color, and WHC), meat pigments, lipid oxidation (thiobarbituric acid reactive substances--TBARS), odor detection (using an electronic nose), and microbiological characteristics (aerobic mesophilic counts (AMCs), _L. monocytogenes_, _S. Typhimurium, and _P. lundensis_).
## 5 Conclusions  
The ADRS described in this paper was designed to automatically perform emergency tasks when a strong earthquake is expected to strike in seconds in order to prevent the loss of lives, to reduce the chance of injuries and to minimize economic losses. We realized the idea by using Tibbo EM1000 TEV embedded boards to receive CAP earthquake warning messages published by the Central Weather Bureau, Taiwan, and to parse the messages to extract all of the information about the earthquake. Based on the extracted information, ADRS then controls microcontrollers to perform the corresponding emergency tasks. After the earthquake strikes, ADRS constructs a temporary network to collect emergency messages posted by trapped victims, to save the messages and to deliver the messages to command centers when connections to the outside world are reestablished. Compared to the manual operation of emergency tasks, ADRS can reduce the operation time by up to 15 s, which is long enough for people to get under sturdy furniture, or to evacuate from the third floor to the first floor, or to run more than 100 m, which indicates that ADRS can be applied to real scenarios for  
Figure 17: Estimate transmission time.  
practical use. In the future, we plan to further enhance the functionality of ADRS to handle false alarms and the conditions when the system fails to respond.
# Analytical Approach in the Development of RF MEMS Switches
## 4 Conclusions  
RF MEMS switches are of classic MEMS devices for RF and microwave applications. In these devices, mechanical motion is used to close or open the RF transmission line (for a given frequency band). In other words, the total input impedance of the RF transmission line is controlled by the mechanical motion of the moving element of the RF MEMS switch design.  
The field of application of RF MEMS switches is rigidly connected with their parameters. Solid-state analogs of RF MEMS switches have a good combination of low-power consumption, high switching power, low insertion losses and high isolation value in the closed state. However, electromechanical parameters such as control voltage and switching time are characterized by extremely high values, which complicates their integration into RF control circuits and reduces the scope of application. Electromechanical parameters are interrelated and the improvement of some can lead to the deterioration of others. It is possible to distinguish several main directions of improvement of electromechanical parameters: replacement of traditionally used materials on new perspective, and development of new original designs and technological decisions.  
The analytical approach to the development of RF MEMS switches allows us to develop designs with the best ratio of parameters for specific applications, which is demonstrated by the example of the developed design of an integrated RF MEMS switch with a capacitive switching principle with low control voltages, high switching speed and suitable for using in the X frequency range (8-12 GHz) in terrestrial and satellite radio communication devices.  
All authors contributed to the present paper with the same effort in finding available literature resources, as well as writing the paper.  
This research received no external funding.  
The results were obtained using Equipment of the student design bureau \"Elements and devices of inertial navigation systems of robotics\" of the Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University (Taganrog, Russia).  
The authors declare no conflict of interest.
## 2 Experimental Program
### Specimens Preparation  
In order to prepare CA mortar specimens, a specific mechanical CA mortar mixer with rotating speeds ranging from 140 rpm to 285 rpm was employed. The volume capacity of the stirring pot is 60 L. The following mixing procedures were taken into consideration: Initially, P.II 52.5R Portland cement, pelletized sand, aluminum powder, and thickening agent were blended in a specific CA mortar stirring pot (50 L volume capacity) and stirred at a speed of 140 rpm for 2 min. Second, after the well-mixed dry powder was removed for later use, the emulsified asphalt and tap water were poured into a new stirring pot and mixed at the same speed of 140 rpm for 1 min. During this stirring process, after a certain amount of antifoaming agent (3.75 g/L) was added, the pre-mixed dry powder was gradually introduced at a proper rate within 30 s to avoid lumps of dry powder. Finally, after continually stirring for 1 min at the same speed, the stirring speed was turned into 285 rpm for 2 min and then back to 140 rpm for another 30 s. It is worth mentioning that during the final stirring stage (30 s), another amount of antifoaming agent (1.25 g/L) was added in order to eliminate the small bubbles produced during the mixing process. The fluidity and gas content of fresh mortar were tested immediately after the stirring procedure was finished according to the standard of DSTMRPRC 74-2008 [56]. Test results showed the fluidity was 97.20 s and the gas content was 4.36%.  
The fresh mortar was placed into polyvinyl chloride (PVC) holds with inner dimensions of \(\varnothing 101.6\) mm \(\times 100\) mm for the monotonic compressive test in this study, considering cost limitation and simplicity. In addition, three standard cubic molds with dimensions of \(100\) mm \(\times 100\) mm \(\times 300\) mm were used for curing the fresh mortar in order to determine the 1 day and 28 days compressive strength according to the Chinese National Standard GB/T 17671-1999 [57]. Polyethylene film was applied for covering both surfaces of each PVC mold to prevent moisture from evaporating of the specimens. Once the specimen was demolded (typical after 24 h of placing), the mortar specimens were kept in a condition with a temperature of \(23\pm 2\)\({}^{\circ}\)C and relative humidity of \(65\%\pm 5\%\) for 28 days. Test results showed the average compressive strength at 1 day and 28 days were 2.5 MPa and 15.6 MPa, tested in accordance with Chinese National Standard GB/T 17671-1999 [57], which fulfill the standard of DSTMRC 74-2008 [56].  
The demolded CA mortar specimens were polished by a double-end face automatic polishing machine before the experiment to ensure the upper and lower surfaces were parallel; the horizontal error of double-end faces was controlled to be less than 0.05 mm. The polished specimens are shown in Figure 1.  
Figure 1: Polished CA mortar specimens.
## 4 Case Study on the Multiple-Update-Infill Sampling Methods  
This section evaluates the sampling efficiency and performance of the SS methods based on six infill criteria combinations. Since the purpose of this study is to evaluate the feasibility of the proposed method for a minimal number of the training samples, three case studies with stationary responses surfaces are considered as follows: (1) one mathematical test function with 5 inputs, (2) one toy test function with 8 inputs, and (3) an engineering application with 13 inputs. Firstly, this section presents the preliminary description of the case study and four performance measures. Then, the three case studies are described with the landscapes of their response surface (that is, contour plots). Lastly, the results of the three case studies are discussed, and we draw conclusions in terms of convergence rate, accuracy, sampling efficiency, and computational cost to provide a guideline for less experienced users.
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 1 Introduction
The proposed protection method, using the exclusion area as well as restricted areas, with Tx power control scheme are simulated. The main differences in methods, proposed schemes and considerations between our work and previous works are summarized in Table 1.  
The remainder of this paper is organized as follows. The basic scenarios and simulation parameters are described in Section 2. The interference derivations are presented in Section 3. The simulated and experimental results for the proposed system are presented, and discussed, in Section 4. Finally, we present our conclusions and an outline of our future research in Section 5.