esa-sceva/satcom-chunk-collection huggingface dataset:
# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 3 Proposal
### Quality Data  
The proposed Industrial IoT architecture represented in Figure 5, focuses on the use of discrete and continuous values, prevalent in industrial control. Digital sensors generate binary signals, while analog sensors measure within a numerical range. These sensors and actuators can be connected to PLC interfaces, industrial PCs, or operate independently via network cables or wireless connections. In addition to sensor and actuator data, manufacturing information, such as production volumes, line performance, and downtime calculations, is crucial and is provided by control systems, such as PLCs and SCADA. External data, to complete the product, may also be essential for certain processes.  
In selecting pertinent data for analysis, one must consider the following aspects:  
* The user experience of the workstations, processes, or machines of the manufacturing line will be important to consider when obtaining the dataset.
* The approach to philosophies, such as total productive maintenance (TPM) [44], is important to create multidisciplinary teams that acquire the knowledge about the manufacturing line or process to track and establish criteria to determine what data and signals from sensors and actuators are needed to define the information set related to the bottleneck.
* The support provided by a Big Data system or data mining of manufacturing variables in contrast to production results will also be strategic to determine which sensors have provided sensitive data in the face of yield decreases, shutdowns, delays, and other aspects related to the occurrence of bottlenecks.
# Improved Interpolation and Anomaly Detection for Personal PM\({}_{\mathbf{2.5}}\) Measurement
## 3 Experimental Tests
### Optimal Bandwidth Selection  
Before we carried out our interpolation on the data, we needed to decide on the value of the bandwidth used for our interpolation method. For this task, we randomly chose a complete part of the data that did not have missing data to estimate optimal bandwidth for the data set. Then, the complete data were split into two different sets for training and validation. The training dataset was used to set up a kernel regression model, and then the validation was carried out for the remaining data set. In this bandwidth-selection step, we increased the bandwidth from 1 to 100 to find out which bandwidth provided the smallest error that could be applied to our interpolation procedure. This bandwidth estimation step can in fact depend on the nature of the data. That is, since the results of the estimation may differ significantly depending on the shape of the data pattern, different bandwidths for each of the three data patterns were estimated and used for this study. However, because it was hard to detect the changing point of these different data patterns in our time-series data, we used the bandwidth estimated for the flat data pattern for the following two real-world data experiments. Further research on automatically calculating the appropriate bandwidth depending on the data patterns is necessary in the future.
# Stability of a Compacted Sand Slope Model Subject to
## 2 Experimental Study  
### Materials and Methods  
The experimental slope model comprises compacted sand resting on a stratum layer. The geometrical parameters of this scaled 3D test model, shown in Figure 1, are as follows: maximum height = 0.40 m at the crest level; minimum height at the toe level = 0.25 m; total length L = 0.69 m and thickness B = 0.25 m. This laboratory model simulates at full scale an excavation of a 6 m depth with a slope of length equal to 11.2 m and width equal to 10 m. The scale used is 1/40.  
The experimental study considered two values of the slope angle, \(\alpha\) = 30\({}^{\circ}\) and 35\({}^{\circ}\). Cohesion less sands have a friction angle that ranges from 28\({}^{\circ}\) (loose state) to 38\({}^{\circ}\) (dense state). When a slope angle is less than the friction angle of sand, the slope stability holds. Therefore, since in practice, it is much better to excavate at the highest slope angle, the minimum value of the tested slope angle is 30\({}^{\circ}\). In addition, excavating a dense sand layer of an in situ friction angle equal to 38\({}^{\circ}\) causes little disturbance to the soil, so that the friction angle is reduced. Hence, the logical maximum slope angle should be approximately 35\({}^{\circ}\), which is the highest value tested in the experiments.  
The rigid box comprises 2 \(\times\) 2 vertical Plexiglas plates of respective areas 0.69 \(\times\) 0.70 m\({}^{2}\) and 0.70 \(\times\) 0.25 m\({}^{2}\) (Figure 1). These plates were assembled at the corners by four steel  
Figure 1: Studied slope model for slope angle equal to 30\({}^{\circ}\).  
stiffeners; this set was fixed at the bottom side of a metallic rigid plate of area 0.69 \(\times\) 0.25 m\({}^{2}\) (Figure 1). As adopted, the geometry, boundary conditions, and loading conditions of the built test model were set as a reference model.  
For running the experimental investigation aiming at the determination of the failure load of the slope model shown in Figure 1, a box made up of Plexiglas plates, thickness of 0.03 m, and stiffened by steel corners was used with the following dimensions: height = 0.70 m; length = 0.70 m; and width = 0.25 m (Figure 2). This box model served initially to perform experiments on the sand slope model designed by [PERSON] et al., 2018, [10].  
The built slope model resulted from the placement of a homogenized mixed sand, at water content equal to 8%, inside the Plexiglas box by sub-layers each of 0.10 m thickness. Then, the sand sub-layer was gently compacted by hand using a rigid plate of width of 0.06 m and length of 0.24 m. The friction angle of the tested sand equaled 27\({}^{\circ}\) and exhibited a weak cohesion of 2 kPa. It is worth noting that these strength parameters are quite different from those of the sand tested in [10]. Table 1 presents the geometry of the built slope models and the configurations of applied loading detailed below.  
Figure 2: Built Plexiglas box for the scaled test model.
## 4 Fluid Structure Interaction Analysis
### LNG CCS Model  
A Mark III CCS was selected for structural assessment. The insulation panel of a Mark-III CCS consists of a thin corrugated membrane, plywood, reinforced poly-urethan-form (R-PUF), triplex, and mastic, as shown in Figure 11. It also consists of two layers of R-PUF divided by triplex material, and it is attached to the hull by mastic. Since the main interest of this study is to investigate the dynamic response of the R-PUF, plywood, and the highly loaded region around the mastic supports, an insulation system without triplex and membrane is considered for the finite element meshes. Hence, the primary membrane and triplex is ignored for the structural finite element (FE) model.  
In this study, we will consider the local FSI analysis method proposed. The structural analysis should select a model that matches the CCS used in the sloshing strength evaluation. However, from the viewpoint of examining the methodology of the strength evaluation method presented in this study, a flat CCS model was selected as shown in Figure 12.  
The boundary conditions of the FE model for structural analysis are that the bottom side of the mastic support is fixed, and symmetry is applied to the side surfaces of the CCS. Moreover, to match the insulation box of Mark III containment, a unit section of the insulation box is selected from the side shell of LNG vessels. Then an LNG CCS assembled from plywood, R-PUF, and mastic is modeled as shown in Figure 12. For the FE model of the insulation box, the mastic and R-PUF were assumed to be isotropic materials and the plywood was assumed to be an orthotropic material.  
Figure 11: Schematic view of a Mark III type LNG insulation panel [29].  
Figure 12: FE model of a Mark III insulation panel.
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
## 3 Method
### Output Calculation  
Slip \(s\) means the actual velocity of the was wheel different from the command velocity for each of the wheels; it is defined as Equation (1).  
\[s=(v_{c}-v)/v_{c} \tag{1}\]  
where \(v\) and \(v_{c}\) are the actual velocity and the expected velocity of the wheel, respectively. Similar to the wheel slip, calculating the rover slip needed both the expected velocity of the rover and the actual velocity of the rover. Slip was measured in the rover body frame as shown in Figure 6a, and included longitudinal slip (slip_x), lateral slip (slip_y), and rotational slip (slip_z) [29]. This paper reports on slip_x and slip_y of the rover, which are defined as Equations (2) and (3), respectively [32].  
\[\text{slip\_}x=(v_{c}-v_{x})/v_{c} \tag{2}\]  
\[\text{slip\_}y=v_{y}/v_{c} \tag{3}\]  
where \(v_{c}\) is the command velocity of rover, \(v_{x}\) is the actual longitudinal velocity, and \(v_{y}\) is the lateral slip velocity of the rover (Figure 6b).
# A Study on the Application of Laser Cleaning on the Weld of the Gas Fuel Supply Pipe for DF Engine
## 2 Materials and Methods  
### Experimental Materials and Equipment  
STS316L, austenitic stainless steel, is utilized as a gas fuel supply pipe material for a DF engine. In this regard, this study produced and utilized butt and fillet weld specimens based on a similar material used for a gas fuel supply pipe. Table 1 presents the chemical composition of the experimental material.  
The laser cleaning device used in this study is a low-power portable laser cleaning equipment composed of a Q-switching fiber laser with an average power of 100 W. Table 2 presents the specification of this equipment. Laser applied in this experiment has a wave length of 1064 nm, and oscillates based on a short pulse duration time and high peak power. As illustrated in Figure 1, the experimental laser cleaning equipment comprises a fiber laser source, laser and stage control panel, a portable laser optic head, an Y stage, and a suction duct.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **C** & **Si** & **Mn** & **P** & **S** & **Cr** & **Ni** & **Mo** & **Fe** \\ \hline Wt\% & 0.02 & 0.572 & 1.089 & 0.03 & 0.003 & 16.213 & 10.120 & 2.077 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of STS316L.
## 3 Design of Fixed-Time Consensus Protocol
### Design of Consistency Protocol
 \tag{16}\]  
From (15) and (16) can be obtained  
\[\dot{V}\leq-k_{1}[\lambda_{2}L(A)]^{\frac{q+p}{2q}}V^{\frac{q+p}{2q}}-k_{2}N^ {\frac{q-r}{2s}}[\lambda_{2}L(A)]^{\frac{q+p}{2q}}V^{\frac{q+r}{2q}}-2k_{3} \lambda_{2}L(A)V \tag{17}\]  
Defining \(\xi=(q+p)/2q\); \(\eta=(s+r)/2s\); \(a=-k_{1}[\lambda_{2}L(A)]^{(q+p)/2q}\); \(b=-k_{2}N^{(s-r)/2s}\)\([\lambda_{2}L(A)]^{(s+r)/2s}\); \(c=-2k_{3}\lambda_{2}L(A)\) the following inequalities can be obtained:  
\[\dot{V}\leq-aV^{\xi}-bV^{\eta}-cV \tag{18}\]  
According to Lemma 3, the setting time is  
\[T_{\max}=\frac{1}{c(1-\xi)}\ln(1+\frac{c}{a})+\frac{1}{c(\eta-1)}\ln(1+\frac{ c}{b}) \tag{19}\]
## 3 Results and Discussion
### Optimization of the Extraction Conditions  
The optimization of subcritical water extraction parameters us to determine values of the process temperature, raw material fraction size and time (process duration) at which polyphenols and flavonoids content together with the antioxidant activity measured using the DPPH and FRAP methods reached maximum value. The highest polyphenols and flavonoids contents together with the antioxidant activity were reached at a temperature of 131.6 \({}^{\circ}\)C, a raw material fraction size of 0.9 mm and a process duration of 10 min. Table 9 shows the values of the maximum content of polyphenols, flavonoids and the maximum antioxidant activity of the extracts obtained, together with the corresponding extraction parameters.  
The strength of the conducted research work is the determination of the optimal parameters of the walnut bark extraction process using water under subcritical conditions. Walnut bark has never been researched in this respect before. Subsequent research work should determine the variability of the chemical composition of the obtained extracts by examining selected bioactive compounds using chromatographic methods.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \
\begin{tabular}{c} **Sum of** \\ **Squares** \\ \end{tabular} & **df** &
\begin{tabular}{c} **Mean** \\ **Square** \\ \end{tabular} & **F-Value** & _p-Value_ & \\ \hline Model & 45.42 & 1 & 45.42 & 4.96 & 0.0443 & significant \\ A\({}^{2}\)—Temperature\({}^{2}\) & 45.42 & 1 & 45.42 & 4.96 & 0.0443 & \\ \hline Residual & 119.15 & 13 & 9.17 & & & \\ \hline Lack of Fit & 85.31 & 11 & 7.76 & 0.4584 & 0.8408 & not significant \\ \hline Pure Error & 33.84 & 2 & 16.92 & & & \\ \hline Cor Total & 164.58 & 14 & & & & \\ \hline Std. Dev. = 3.03; Mean = 21.47; C.V. \% = 14.10; R\({}^{2}\) = 0.2760; Adjusted R\({}^{2}\) = 0.2203; & & & \\ Predicted R\({}^{2}\) = 0.0244; Adeq Precision = 3.1552. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Analysis of variance for antioxidant activity measured using FRAP method.
# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 4 Use Case Scenarios and Simulations
### Use Case 4: Small-Size Network--Random Deployment  
A small-size network of 20 \(\times\) 20 m\({}^{2}\), with 15 nodes and 1 sink node, was fed to the NN solver, which consisted of two hidden layers (use case 1a). We used a random topology, which means that we placed 15 nodes inside the area of 20 \(\times\) 20 m\({}^{2}\), the coordinates of which derive from the Gaussian distribution.  
According to Table 5, which gives parameters and values for the simulation, the sink node was placed at (0, 0); for case 4a, we used two hidden layers, and for case 4b, we used three hidden layers. The solver relocated 8 nodes out of 15 nodes, which means that almost 53.34% of the topology nodes changed positions, lowering the objective function value. According to the simulation:  
1. The initial topology leads to a network lifetime of 11,896 rounds.
2. The final topology, as proposed by the solver with (2) hidden layers, leads to a network lifetime of 12,443 rounds, which is an increase of 4.6%.
3. The final topology, as proposed by the solver with (3) hidden layers, leads to a network lifetime of 12,505 rounds, which is an increase of 5.2%.
## 4 Establishment of Simulated HSI Dataset Based on Experimental Measurement
### Experimental Preparation  
The experimental measurement instrument is a multi-angle spectral reflectance measurement system designed and manufactured by the research group. As shown in Figure 3, the light source incident angle of the measuring instrument can be adjusted in the range of 0-90\({}^{\circ}\), and the detection angle can be adjusted in the range of 0-90\({}^{\circ}\). The halogen tungsten lamp with a uniform light cover is used as the light source. ASD spectrometer and its optical fiber lens are used as the reflected light-receiving end. The rotary displacement table, the adjustable-angle stage, and the adjustable-angle light source platform are used as the position and angle adjustment mechanism. The computer is used as the data signal processing terminal. The grating dispersive spectrometer ASD (FieldSpec 3, Analytical Spectral Devices) has a detection range of 350-2500 nm, a minimum sampling interval of 1.4 nm, and a minimum spectral resolution of 3 nm. The spectral response range of the polarization module is 430-860 nm and 860-1650 nm.  
The research samples selected in the experiment are divided into two categories. The first class is vegetation leaf samples: clove leaves, shrub leaves and grass leaves. The second class is artificial objects similar to vegetation: green coatings, artificial leaves and cloth with camouflage patterns. The samples used for data acquisition are shown in Figure 4: all samples are similar in color and are green. Among them, clove leaves are large, heart-shaped, relatively flat, and smooth, and the leaves are overlapped. Shrub leaves are narrow and oval, curly, with smooth surfaces and overlapping leaves. The grass leaves are thin, narrow, long strip, rough surface, and the leaves are staggered. The surface of the green coating is flat and has a fine texture. The fake leaves are human-made, simulated landscapes, and their surfaces are rough and imitate the leaves' grain. The camouflage cloth is green as a whole, composed of light green, dark green, brown and yellow.  
Figure 4: (a) Clove leaf; (b) shrub leaf; (c) grass; (d) green coating; (e) artificial leaf; (f) camouflage cloth.  
Figure 3: Multi-angle spectral reflectance measurement system.
## 6 Performance Evaluation
### Simulation Results  
In this sub-section, we provide the results of the simulation conducted based on five different simulated scenarios. These comprise of three different simulated scenarios for sensor network density test, as well as transient and permanent node failure rate tests. Details of the simulated scenarios are provided in the subsequent subsections.
## 3 Results and Discussion
Accordingly, we conducted the DPPH radical scavenging assay to preliminarily investigate the antioxidant capability of AuNPs encapsulated in carbon nanosheets, which were fabricated through the hydrothermal process using the pectin-capped AuNPs as precursors. DPPH is a stable nitrogen-containing free radical molecule with a dark purple appearance. DPPH can capture other free radical molecules, and their characteristic absorption peak, which can be attributed to the resonance between one nitrogen atom and three benzene rings, decreasing with the occurrence of free radical reactions. In this study, we quantified the decrease in absorbance to determine the antioxidant activity of AuNPs encapsulated in carbon nanosheets at different concentrations. Figure 7 presents a comparison of the antioxidant activity of apple extracts with that of the AuNPs/carbon nanosheets. The DPPH free radical removal rate was higher for the AuNPs. We further used an EPR spectrometer to determine the DPPH free radical removal rate. In EPR, the presence of unpaired electrons, such as free radicals, indicates the occurrence of magnetic resonance. The resonance intensity can be used to determine the concentration of DPPH free radicals. Figure 8 presents the EPR spectra for the apple extract and AuNP/carbon nanosheet solutions. Similar to the preceding comparison results, the EPR results indicated that the DPPH removal rate was higher for the AuNPs/carbon nanosheets than it was for the apple extract. Moreover, we calculated the DPPH radical removal rate by integrating the EPR spectra; the results showed a 79% removal rate for the AuNPs/carbon nanosheets and a 30% removal rate for the apple extracts at a concentration of 3 mg/mL. Figure 6: (**a**) HRTEM images of pectin-capped AuNPs encapsulated in carbon nanosheets and (**b**) corresponded energy-loss spectroscopy (EELS) analysis indicating the presence of Au and carbon.
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 3 Methodology
### Assessment of the Accuracy of the Model  
For the metric evaluation of the model produced, the Root Mean Square Error (RMSE) metric was utilized. This is a common metric used for the evaluation of regression-based models and is estimated by the function given in Equation (1).  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Y_{i,m}-\ Y_{i,e})^{2}} \tag{1}\]  
where \(Y_{i,m}\) = measured value, \(Y_{i,e}\) = estimated value, \(n\) = number of observations.  
To further explain the model's results accuracy, the RMSE was normalized based on the mean of the dependent variable, using a metric called Normalized Root Mean Square Error (NMRSE) to allow for a comparison across different scales and one that is more interpretable to users. These metrics are typical and representative of metrics utilized when assessing the accuracy of machine learning forecasting models [62; 63]. This was generated using the function in Equation (2) below.  
\[NRMSE=\ \frac{\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Y_{i,m}-\ Y_{i,e})^{2}}}{\overline {Y_{i,m}}} \tag{2}\]  
The evaluation was done utilizing the validation set during the model to establish the model's accuracy. Once this was completed, the study then tried to establish a pipeline on how the model could be used in a practical scenario combining other low-cost surveys methods. This pipeline and the results are further discussed in Sections 4-6.
## 3 Results and Discussion
### Liqueur Sample  
The complete composition of the HS of the hibiscus liqueur is reported in Table 5. Overall, 34 compounds were identified, representing 100.0% of the total composition.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Compounds** & **l.r.i. 1** & **Class.** &
\begin{tabular}{c} **Relative Abundance** \\ **(\%) \(\pm\) SD** \\ \end{tabular} \\ \hline ethyl acetate & 743 & nt & 1.7 \(\pm\) 0.02 \\ _iso_amyl alcohol & 736 & nt & 1.7 \(\pm\) 0.05 \\ 2-methylbutanol & 737 & nt & 1.0 \(\pm\) 0.02 \\ _is_opentyl acetate & 876 & nt & 1.5 \(\pm\) 0.07 \\ \(\alpha\)-thujene & 926 & mh & 1.4 \(\pm\) 0.11 \\ \(\alpha\)-pinene & 933 & mh & 13.0 \(\pm\) 0.57 \\ camphene & 948 & mh & 0.5 \(\pm\) 0.03 \\ sabinene & 973 & mh & 4.3 \(\pm\) 0.10 \\ \(\beta\)-pinene & 977 & mh & 7.5 \(\pm\) 0.23 \\ myrcene & 991 & mh & 9.4 \(\pm\) 0.08 \\ \(\alpha\)-terppinene & 1017 & mh & 0.8 \(\pm\) 0.07 \\ \(p\)-cymene & 1025 & mh & 10.2 \(\pm\) 0.16 \\ limonene & 1029 & mh & 34.4 \(\pm\) 0.04 \\ 1,8-cineole & 1031 & om & 0.3 \(\pm\) 0.04 \\ \(\gamma\)-terpinene & 1058 & mh & 4.1 \(\pm\) 0.01 \\ terpulence & 1089 & mh & 0.9 \(\pm\) 0.02 \\ ethyl ccanoate & 1199 & nt & 0.8 \(\pm\) 0.10 \\ 2-phenylethyl acetate & 1257 & nt & 0.2 \(\pm\) 0.03 \\ (_E,Z_)-2,4-decadienal & 1293 & nt & 0.2 \(\pm\) 0.04 \\ (_E,E_)-2,4-decadienal & 1316 & nt & 0.6 \(\pm\) 0.13 \\ \(\alpha\)-cubebene & 1350 & sh & 0.4 \(\pm\) 0.05 \\ \(\alpha\)-copaene & 1376 & sh & 0.1 \(\pm\) 0.02 \\ \(\beta\)-elemene & 1392 & sh & 0.4 \(\pm\) 0.07 \\ ethyl decanoate & 1396 & nt & 0.6 \(\pm\) 0.10 \\ \(\beta\)-caryophyllene & 1419 & sh & 1.2 \(\pm\) 0.17 \\ \(\gamma\)-elemene & 1433 & sh & 0.5 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Chemical composition of the hibiscus liqueur headspace.  
Monoterpene hydrocarbons were the main class of VOCs determining the aroma of the hibiscus liqueur, as they reached 86.3% of the whole chemical profile: among these, limonene (34.4%), \(\alpha\)-pinene (13.0%), p-cymene (10.2%), myrcene (9.4%), and \(\beta\)-pinene (7.5%) were the main components. On the contrary, oxygenated monoterpenes were detected in negligible relative amounts (0.3%). Sesquietrpene hydrocarbons were also well represented in the sample, accounting up for 4.8% of the volatilome. In total, 12 compounds belonging to this class were identified, but only \(\beta\)-caryophyllene was revealed in appreciable relative abundance (1.2%). Finally, non-terpene derivatives were the second relevant chemical class characterizing the liqueur aroma (8.5%). Among these, esters and alcohols were the most represented as they reached 4.9% and 2.7%, respectively.
## 3 Experimental Set-Up
### Assessment and Method Comparison  
We computed the ACC, AUC, and Kappa performance measures in Equations (13)-(15) as a quantitative assessment. As a method comparison, we tested the Power-based EEG Features with SVM classifier presented in [40], the Filterbank CSP (FBCSP) with LDA [45], and the following well-known end-to-end EEG classification networks: EEGNet [46], DeepConvNet [47], ShallowConvNet [48], and TCFusionnet [49]. It is worth nothing that, to maintain consistency with previous research on the relationship between neural activity and pain perception and to ensure the preservation of critical information necessary for accurate pain classification, a band-pass filter was applied between 4 and 60 Hz to preserve Theta (4-8 Hz), Alpha (8-13 Hz), Beta (13-32 Hz), and Gamma (30-60 Hz) rhythms, as these have been associated with various aspects of the pain experience.