esa-sceva/satcom-chunk-collection huggingface dataset:
# Matched-Filter Thermography
## 4 Matched Filtering in Thermography
### Thermal-Wave Radar (TWR)
#### 4.1.1 Theory and Modeling
It can be observed that, as the sample thickness increases, the contribution is more delayed due to the increase in thermal-wave conduction distance. Figure 3e illustrates the effect of chirp excitation parameters on the TWR output. Simulations suggest that, at a fixed starting frequency (\(f_{s}\)), as frequency modulation sweep rate decreases, the theoretical detection range increases; however, at the same time, the range resolution (slope of lines) between shallow defects decreases. Therefore, the TWR chirp parameters can be tailored to the specifics of an application to yield optimal results. The simulation of TWR responses of absorbers in transparent media, Figure 3f, follows a similar pattern to that of the subtractive mode in opaque materials in that the registered cross-correlation peak delay time increases with increase in depth of the absorber, as expected. Similar to the conventional lock-in thermography, the continuous wave nature of excitation in TWR allows for definition of phase channel. [PERSON] et al. [23] introduced the concept of cross correlation phase to the thermal-wave sciences as an emissivity-normalized contrast parameter with enhanced sensitivity. The cross-correlation phase can be found by cross correlating the response with a pre-known signal/filter and its quadrature as:  
\[-\kappa\frac{\partial\theta(z,\omega)}{\partial z}\bigg{|}_{z\ =\ 0}\ =\ \Im\{f(t)\}\ =\ F(\omega),\]4Note how emissivity, \(\varepsilon\), is cancelled out in the cross-correlation phase channel. Careful interrogation of Equations (3) and (9) indicates that cross-correlation using mono-frequency excitations (i.e., without pulse compression) leads to identical results as conventional lock-in thermography. The abovementioned theoretical derivations and simulations suggest that four contrast parameters can be defined based on the output of TWR:  
1. Cross-correlation peak delay time (\(\tau_{p}\)),
2. Cross-correlation peak amplitude,
3. Cross-correlation phase at \(\tau\ =\ 0\),
4. Cross-correlation phase at \(\tau\ =\ \tau_{p}\).
# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 4 GTCN Algorithm  
GTCN algorithm consists of three parts: network traffic data segmentation, gated units, and TCN algorithm. Because the Space-Integrated Ground Information Intelligent Network has a big traffic sample, robust burst of network flows, and sequences of the network traffic with self-similarity, we need to use the network traffic block for data preprocessing, to facilitate the subsequent training and forecasting, by using the TCN algorithm in each segment and extracting the feature sequence. Finally, each section's characteristic sequence is added to the gating control unit in memory and updated.
## 6 Conclusions  
Laser transmission through airborne turbulence channels will inevitably be affected by the multipath transmission process, producing inter-symbol interference at the receiving end and negatively affecting the communication performance. The formation and severity of inter-symbol interferences are influenced by a variety of factors, such as the airborne air-to-air channel turbulence intensity and meteorological conditions (visibility), as well as the transmission baud rate, transmission distance, transmit power, receive sensitivity, and other communication link parameters. Airborne air-to-air channel laser communication needs to undergo equalization, interleaving, anti-jamming coding, and other technical processes in order to mitigate or suppress the impact of inter-symbol interference on communication performance.  
Conceptualization, S.W. and X.L.; methodology, Y.L. and J.W.; software, C.T. and Y.G.; validation, H.Z.; investigation, X.L.; data curation, F.G.; writing--original draft preparation, F.G. and X.L.; writing--review and editing, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Authors [PERSON] and [PERSON] are employed by the China Electronics Technology Group Corporation. Other authors declare no conflict of interest.
## 2 Experimental Method  
### Materials and Preparation  
#### 2.1.1 Capsule Preparation Process  
As shown in Figure 1, the rejuvenator (healing agent) within the capsule structure was created by calcium alginate crosslinks inside the calcium alginate capsules [16]. Firstly, 6 wt.% sodium alginate solution was prepared in demineralized water. Then 2.5 wt.% solution of poly ethylene-alt-maleic-anhydride (PEMA) was mixed with the rejuvenator with a ratio of 40% PEAM and 60% healing agent, forming a healing agent solution. The PEMA was used to coat the rejuvenator and prevent the diffusion of rejuvenator into alginate shell. Subsequently, the sodium alginate solution and healing agent solution (rejuvenator and PEMA) were mixed. The sodium alginate and healing agent solution was prepared in varying A/R ratios: 100/0, 60/40, 50/50, 40/60, 30/70, 20/80 and 10/90. To remove any trapped air in the blend, the blend was vacuumed in a vacuum chamber for 60 min. After that, the blend was pumped through a needle dropwise into the CaCl\({}_{2}\) solution to allow capsule formation. The CaCl\({}_{2}\) solution was stirred at a low speed for the full duration of the capsule production process. This process prevented capsules from amalgamating. After a full capsule batch was produced, capsules were drained from the CaCl\({}_{2}\) bath and gently washed using demineralized water. Then the calcium alginate capsules were left to dry in a drying oven for 48 h at 30 \({}^{\circ}\)C. Around 113.2 g of capsules can be produced based on 100 g of rejuvenator.  
The healing agent used in this research is an industrially produced asphalt rejuvenator 'R20' supplied from Latefalt, Koudekerk aan den Rijn, Netherlands. All other chemicals used in the capsule preparation process were supplied from Sigma Aldrich, St. Louis, MO, USA.
Sensors_**2010**, _10_, 9349-9358; doi:10.3390/s101009349  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Reliable Data Delivery Mechanism for Grid Power Quality Using Neural Networks in Wireless Sensor Networks**  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 3**  
Footnote 11:  
+++ ==WARNING: Truncated because of repetitions==
footnotemark:  
++  
substations to end users such as houses, office buildings, and factories. Power quality is any power problem manifested as voltage, current, or frequency deviations, that results in failure or malfunctioning of the customer's equipment [1].  
In general, current systems monitor basic electrical quantities such as voltage and current from major pole transformers using their temperature. For evaluating the current status of power quality, finding places where power quality problems are occurring, and planning measures, we need additional information about power quality. We improve the current systems in order to gather and deliver power quality information parameters such as harmonics, voltage sags, and voltage swells.  
To expand the power quality monitoring system, there are various issues such as measurements, controls, databases, and communications. In order to design the communication network, wireless multi-hop communication paradigm is often employed to construct an electrical distribution system (EDS) to reduce the deployment and management costs. Many studies have paid attention to building EDS using wireless sensor networks (WSNs) [2,3]. The reason for using a WSN [4-8] is its efficiency in monitoring numerous computing and sensing devices distributed within a large-scale environment.  
A WSN for a power quality monitoring system delivers power quality information generated by pole transformers to a remote monitoring center in the residential division. Usually, the power quality information is periodically measured, gathered, and transmitted to the monitoring center. Once power quality measured at a pole transformer is out of a normal range, an alarm message with detailed contents is promptly sent in the event based manner. In the system, data delivery is not guaranteed in the case that a node is lost or the network is congested, because the system has in-line and multi-hop architecture.  
To solve the problem, we propose a reliable data delivery mechanism by modeling an optimal data delivery function. The performance of the function lies in determining the optimal coefficients in the function considering the wireless propagation environment or the topological environment around the node. To do that, we employ the neural network (NN) concept [9].  
The remainder of this paper is structured as follows. Section 2 describes our system architecture. Section 3 explains our data delivery mechanism. Following this, we verify the designed system by NS-2 simulations in Section 4. Finally, Section 5 summarizes our results, discusses our future plans, and offers conclusions.
# _Penicillin_ spp. XK10, Fungi with Potential to Repair Cadmium and Antimony Pollution
## 3 Results
### Effect of Initial pH on the Adsorption of Cd and Sb by XK10  
The effect of the initial pH on the adsorption of Cd by XK10 (Figure 5b) showed that the biomass of XK10 first decreased and then increased significantly with the increase in the initial pH (\(p<0.05\)). At pH\({}_{0}=4\), the biomass of XK10 was the smallest at 0.19 g. At pH\({}_{0}=6\), the biomass of XK10 was the largest, reaching 0.26 g. The pH of the medium increased gradually with the increase in the initial pH. When pH\({}_{0}=6\), the pH was the largest, reaching 3.96 (Figure 6b). With the increase in the initial pH, the removal rate showed a trend of increasing first and then decreasing significantly (\(p<0.05\)). When pH\({}_{0}=4\), the removal rate was the largest, which was 6.7% (Figure 7b).  
The initial pH on the adsorption of Sb by XK10 (Figure 5e) showed that the biomass of XK10 increased first and then decreased significantly with the increase in the initial pH (\(p<0.05\)). When pH\({}_{0}=4\), the biomass was the largest, reaching 0.29 g. At pH\({}_{0}=6\), the biomass was the smallest at 0.22. The pH of the medium increased trend with the increase in the initial pH. When pH\({}_{0}=6\), the pH of the medium was the largest, reaching 3.89 (Figure 6e). When the initial pH of the solution was 2-4, the removal rate of antimony by XK10 increased significantly (\(p<0.05\)), and when pH\({}_{0}=4\), the removal rate of antimony by XK10 was the highest, reaching 15.5%. When the initial pH\({}_{0}\) was 4-6, there was no significant difference in the removal rate of antimony by fungi (Figure 7e).  
Figure 7: Effects of different factors on the remove rate of XK10 in the adsorption process. (**a**) Initial concentration for Cd adsorption; (**b**) Initial pH\({}_{0}\) for Cd adsorption; (**c**) Adsorption time for Cd adsorption; (**d**) Initial concentration for Sb adsorption; (**e**) Initial pH\({}_{0}\) for Sb adsorption; (**f**) Adsorption time for Sb adsorption. Results are expressed as mean \(\pm\) SD of three replicates. Different letters indicate significant differences between the same indicators (\(p<0.05\)).
## 4 Proposed Framework
### Mathematical Formulation
#### 4.1.2 Relations  
**Entity-Credential Relation**: \(\mathcal{R}_{\text{ec}}\subseteq\mathcal{E}\times\mathcal{C}\) represents the relation between entities and their held credentials.  
**Credential-Signature Relation**: \(\mathcal{R}_{\text{cs}}\subseteq\mathcal{C}\times\mathcal{S}\) signifies the relation between credentials and their associated signatures.
## 4 Results
### Association Rule Mining Results
A lot of interesting information can be obtained from the association rules in Table 3. First, the lift of all these association rules is much greater than 1, indicating that the presence of rainfall former terms in these association rules has a significant positive effect on the high-speed deformation of landslides. Second, if the rainfall characteristics are classified into the current moment (3 h, 6 h), short-term (12 h, 24 h), and long-term (3 days, 7 days), then the recent rainfall characteristics are not significant in the association rules. For example, in Rules 3-8 and 11-16, these association rules with recent rainfall characteristics can be considered as subordinate rules of the four main rules: Rule 1, Rule 2, Rule 9, and Rule 10. Third, from the four main rules of Rule 1, Rule 2, Rule 9, and Rule 10, the high-speed deformation of landslides requires not only the occurrence of short-term rainfall characteristics, but also long-term rainfall characteristics, and the occurrence of only one of them does not induce the high-speed deformation process of landslides. Fourth, for the DB01 monitoring station, the long-term heavy rainfall characteristics are more important for high-speed deformation of the landslide, because the three-day or seven-day rainfall characteristics in Rule 1-8 are heavy rainfall, and the 12- and 24-h rainfall characteristics can be low-intensity rainfall. Fifth, for the DB02 monitoring station, not only the long-term heavy rainfall characteristics of 3-7 days but also the short-term heavy rainfall characteristics of 24 h are required. In conclusion, by analyzing the monitoring data of the Lisnanyuan landslide, it can be initially concluded that the landslide is caused by rainfall. Through association rule mining, the disaster factors can be more accurately identified as the combination of short-term rainfall and long-term heavy rainfall. When making early warning decisions, a rainfall within 24 h and a heavy rainfall with a cumulative rainfall greater than 130.60 mm within 7 days can be used as a precursor to identify the high-speed deformation of the landslide.
###### Abstract  
Though microplastic (MP/MiP) pollution of the environment is a popular research topic, a relatively limited number of studies are investigating its geomorphological context. However, site-specific hydrological and morphological parameters fundamentally affect the MP transport, deposition and mobilization. Therefore, we aimed to evaluate the geomorphological influencing factors on MP deposition in the fluvial sediments of the Tisza River (Central Europe). Between the two surveys (in 2019 and 2020), small flood waves rearranged the MP pollution, as in the sediments of the Tisza it decreased by 30% and in the tributaries by 48%. The previously highly polluted upstream and downstream sections became moderately polluted, but the contamination increased in the Middle Tisza, and the hot-spots were rearranged. The increasing longitudinal trend in the MP content exists if the minimum values of the hydrologically uniform sections are considered. The tributaries are important MP sources, as 80% of them had a higher (by 20%) MP content in their sediments than the Tisza had near the confluence, and they increased the MP content of the Tisza by 52% on average. The point-bars were the most polluted in-channel forms, while the side-bars and sediment sheets had less MP content, by 18 and 23%, respectively. The spatial trend of the MP content of these forms was not the same. Therefore, during the planning of sampling campaigns, it is very important to consider the geomorphological setting of a sampling site: we suggest sampling side-bars. No clear connection between the particle size of the sediments and their MP content was found.  
geomorphology; in-channel forms; downstream changes; grain size; re-distribution Article
# Multi-Scenario Robust Distributed Permutation Flow Shop Scheduling Based on DDQN
## 3 Simulation Model
### MSRDPFSP Model Construction
#### 3.2.3. AGV Carts and Robotic Arms and Path Setting  
With the advancement of logistics automation, workshop logistics transportation tools forklifts and rickshaws are gradually replaced by AGVs. Since the price of AGV is generally in the hundreds of thousands of dollars, the reasonable number of AGVs and the design of logistics routes have become key considerations in the design of automated workshop logistics [39]. To address this problem, a distributed system with AGV carts and robotic arms is constructed to simulate the collaborative work of AGVs and robotic arms, and to ensure the efficient operation of the system by means of a scheduling system, path planning, etc. The paths of AGV carts constitute a critical factor in determining their movement patterns within a factory or production environment [40]. In this paper, preset paths, which are usually defined through node objects and connecting lines, are used. The AGV will move in the order between these path points. This paper does not consider the problem of path optimization but introduces this component to simulate machine logistics and replicate a multi-scenario shop floor environment. As illustrated in Figure 11 below, the path is configured as a circle track to facilitate the flow of the whole system. The sensors on the track are used to detect the position and status of AGV (Automated Guided Vehicle), other transportation equipment, and the items passing through the position. When an AGV traverses a node, these sensors are triggered. As an important part of the intelligent logistics system, the track-mounted sensors enable precise detection of transporters or AGVs' positions, facilitating dynamic path control, obstacle avoidance, task triggering, and data analysis. The workpieces are transported to the sensors via AGV carts and then transported to individual workshops via PickAndPlace.  
The simulation time of MSRDPFSP workshop is affected by many conditions, such as the number and speed of AGV carts, whether the transportation path is blocked or not, the loading time and unloading time of the robotic arm, etc. Each will affect the final simulation time. The parameters of the Transporter need to be set reasonably. Similar to the allocation of workers across workshops, the number of AGV carts directly affects the final simulation time. The speed of each Transporter and other information also matter considerably. If a cart runs too slowly, it will lead to congestion in the entire AGV system along its path. As  
Figure 11. AGV path and sensor settings.  
shown in Figure 12 below, when the speed of one of the carts is 1 m/s and the time of other carts is 4 m/s, such speed discrepancies lead to system-wide blockages.  
The difference between the first two parts is that when the workpieces enter each workshop, they need to be delivered by the AGV carriage to the buffer of the robot arm and wait for the robot arm to deliver the workpieces to each workshop.
## 3 Results and Discussion
### Complementary Physical Characterization Tests
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Property** & **Source** & **Sum of Squares** & **Df** & **Mean Square** & **F-Ratio** & \(p\)**-Value** \\ \hline Shrinkage (mm/m) & A: Dosage & 0.0083003 & 1 & 0.0083003 & 78.49 & 0.0000 \\  & B: Fiber type & 0.0137499 & 1 & 0.0137499 & 130.02 & 0.0000 \\  & AB: Interactions & 0.0002219 & 1 & 0.0002219 & 2.10 & 0.1855 \\  & Residual & 0.0008460 & 8 & 0.0001058 & & \\  & Total (Corrected) & 0.0231180 & 11 & & & \\ \hline Freeze/Thaw Resistance: & A: Dosage & 0.0001333 & 1 & 0.0001333 & 0.03 & 0.8734 \\ Flexural Strength (MPa) & B: Fiber type & 0.0280333 & 1 & 0.0280333 & 5.69 & 0.0441 \\  & AB: Interactions & 0.0000333 & 1 & 0.0000333 & 0.01 & 0.9365 \\  & Residual & 0.0394000 & 8 & 0.0049250 & & \\  & Total (Corrected) & 0.0676000 & 11 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Analysis of variance (ANOVA) for shrinkage and freezing tests.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 3 TSCH Energy Model
### TSCH Energy Consumption Model  
Having identified all states per time slot, the model for the charge drawn during a time slot can be constructed. The resulting charge (in coulombs) drawn from the battery during a slot, \(Q_{Slot}\), is represented by:  
\[Q_{Slot}=\sum_{StateState\in Slot}\Delta t_{State}\times I_{State} \tag{1}\]  
with \(\Delta t_{State}\) and \(I_{State}\) the state duration and current drawn in each state, respectively. The unit of the duration is milliseconds (ms), while the unit of the current is milliamperes (mA), meaning that the unit of the resulting charge is microcoolumbs (\(\mu\)C). This can be used to calculate the total charge drawn for each of the slot types discussed in Section 3.1.  
Subsequently, the model previously proposed by [PERSON] et al. [2] can be employed to calculate the total charge drawn across a slot frame. This in turn can be used to compute the lifetime of a more. That model, however, has one major shortcoming. It does not consider the actual packet size when calculating the charge drawn by a slot. Instead, it takes the consumed charge values for the maximum packet size and scales those linearly based on the actual packet size:  
\[Q_{slot}^{N_{sent}}=\frac{N_{sent}}{max_{PRSize}}\times Q_{slot}^{max_{PRSize}} \tag{2}\]  
With \(N_{sent}\) the number of bytes being sent in the packet and \(max_{PRSize}\) the maximum packet size for which measurements were performed. However, this leads to highly inaccurate estimates, especially for small packet sizes, as the duration of most states with the slot is independent of the packet size. In contrast, we propose a more accurate estimation of the charge drawn in a slot \(Q_{slot}^{N_{sent}}\), based on actual measurements with different packet sizes. This is achieved by expressing the duration of each state that depends on the packet size, as a linear function of the packet size, rather than a fixed value for the maximum packet size. This is elaborated on in Section 4.
## 3 Results and Discussion
### GP and FNNs Behavior in Test 1  
The metrics for Test 1 with similar ranges to the training showed (Table 5) that the GP model achieved 96.1% precision (1-MAPE), 3.90% MAPE, an average error between a real output value and a predicted one of 1.4384 watts (SEE), and 92.67% effectiveness at explaining the variability of the output variable (R\({}^{2}\)). The MAE estimated for the GP model was 1.1239 and 3.0649 in the testing stage for the cross-validated MAE, i.e., a cross-validated error which was higher than the test error, which indicated a model without overfitting. In this context, the FNNs model obtained an accuracy or 1-MAPE, a MAPE, and the average errors between the real output value and a predicted one were 98.99%, 1.01%, and 0.4827 watts (SEE), respectively. The FNNs model effectively explains 99.34% of the variability of the output variable (R\({}^{2}\)). The MSE for the ANN model was 0.3007 and 0.8666 in the testing stage for the cross-validated MSE, i.e., a cross-validated error higher than the test error, which indicated a model without overfitting.  
Furthermore, FNNs showed slightly better performance than GP in the testing stage. The MAE and MSE levels were lower in FNNs, despite the training measures, as indicated in Table 5.  
The GP model response (red line) and the desired output (blue line) are shown in Figure 4a. The absolute error (AE) is plotted in Figure 4b to identify the highest error per sample, i.e., 7 watts compared to 1.1239 watts of MAE.  
The FNNs model response (Figure 5a) exhibited a higher accuracy than the GP model, with finding a lower MAE, i.e., 0.3007, but a bigger punctual error that reached almost 9 watts (Figure 5b).