esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result  
### Data Setting  
We choose AMPds dataset [38] to test our ILDACP method. AMPds contains power consumption readings in a single house in Canada and provides the metered data at the end-use level at 1 min resolution. The data of AMPds dataset was obtained over a long period of time (two years) and ispresented with minor amounts of missing data. All of the mentioned above make the dataset meet the need of our algorithm. We considered several typical indoor appliances, i.e., security/network equipment, heat pump, clothes dryer, and fridge. In order to focus only on the most important indoor activities of residents, we did not take into account the outdoor appliance and undefined reading, e.g., room aggregate consumption.  
Due to the possible seasonality effects on power consumption, we extract four sub-periods from the dataset to test our algorithm. (1) Two weeks data from 1 April 2012 to 14 April 2012 with a week data for appliances signatures extraction and the following week data for validation. (2) Two weeks data from 14 October 2012 to 27 October 2012 for validation. (3) Two weeks data from 14 July 2013 to 27 July 2013 for validation. (4) 2 weeks data from 1 April 2013 to 14 April 2013 for validation.  
Since the minimum power of chosen appliances is more than 30 Watt, the preset threshold is set to 30 Watt.
# Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition
## 4 Evaluation and Results
### Summary  
All selected models exceed the defined minimum accuracy of 92%. Therefore, the overall result is satisfactory in a sense that both devices and activation mechanisms are capable of sensing gestures. Figure 19 shows the results for the best model representatives of each session. It is evident that the RF classifier is the best classifier for all three data sets with an accuracy ranging from 98.8% to 93.2%. The second-best classifier is the SVM classifier, but both are relatively complex model types when compared to KNN and NB.  
The results suggest that all data sets contain non-linear related features, since the stronger models are known to handle non-linear relationships. The RF classifier is based on decision trees and the SVM classifier uses a radial kernel for non-linearity. Whereas, the KNN classifier is based on a linear distance measure and the NB classifier assumes independent features.  
It can be observed that the data set originating from the smart ring has more variance, leading to weaker models with lower accuracy. The different activation mechanism of the ring additionally introduces ambiguities due to the varying ending points of selected  
Figure 19: Best model representatives for every model type and all three data sets. The RF classifier performs best in all cases and achieves higher accuracy scores than the defined minimum of 92%.  
gestures, resulting in even lower results. A common problem across all data sets is the clap gestures, which are confused frequently with other gestures due to the applied primitive preprocessing filter or an unintended deactivation of the sensors.
## 5 Interpretation and Discussion
### Coal Rank Determination  
As indicated earlier, coal is a complex mixture of organic matter and inorganic mineral matter formed over eons from successive layers of vegetation. Based on the degree of temperature and pressure, coal is classified into four main types, or ranks: anthracite, bituminous, subituminous, and lignite. Coal rank displays variation in the volatile matter, fixed carbon, inherent moisture, and oxygen. The percentage of carbon in coal is used to identify the rank of the coal and its position in the coal series, although no single parameter defines the rank. Typically, coal rank increases as the amount of fixed carbon increases and the amount of volatile matter and moisture decreases. Rank is the stage reached by coal in the course of its coalification. The rank depends on the maximum temperature to which the organic matter has been exposed during burial and the time it has been subjected to this temperature. Even though the heat flow from nearby intrusions may have contributed to the coalification, it is in general related to the depth of burial and the geothermal gradient [32]. The rank of the studied samples was determined using their vitrinite reflectance as shown in Table 4. The mean random vitrinite reflectance values obtained for the analyzed samples range between 0.75% and 0.80% RoVmr (mean random vitrinite reflectance), placing the samples in the medium rank C category, as per the UNEC in seam classification scheme (Figure 10). The medium rank C category is also referred to as the high volatile bituminous coal category. The standard deviation values are low, indicating a single population, with no blending or heat effect determined. In general, the high amount of inertinite, especially fusinite with empty cells and semi-fusinite in the coals, will pose a threat to coal mining because it aids the formation of dust during mining.  
Figure 9: Photograph showing: (**A**) carbonate minerals with pyrites shattering vitrinite, with more clay minerals surrounding the shattering; (**B**) carbonate minerals with pyrites shattering vitrinite with specific example to coltolelinite in darker grey shades; (**C**) carbonate mineral shattering vitrinite; and (**D**) pyrite mineral in darker yellowish-orange.
# Influence of Approaching Excavation on Adjacent Segments for Twin Tunnels
## 4 Numerical Analysis Results and Comparisons  
### Influence of Approaching Excavation on Ground Displacements  
#### 4.1.1 Ground Surface Settlement  
In order to avoid the influence of the model boundary, the calculation results from step 67 to step 76 and from step 119 to step 128 are not included in the following analysis. In other words, the range from step 77 to step 118 (corresponding to the excavation of the second tunnel from Cross-section A to Cross-section C in Figure 3b) is taken as the effective influence range of the approaching excavation.  
Figure 6 shows the evolutions of ground surface settlement at the measured Cross-section B during the approaching excavation. When the second tunnel is far from the measured cross-section (at Cross-section A), the transversal settlement trough is approximately symmetric with respect to the curved tunnel axis. It indicates that the small overcutting of this project has little effect on the surface settlement. When the second tunnel approaches the measured Cross-section B, the settlement trough shifts toward the second tunnel due to the construction loadings caused by the approaching excavation. When the second tunnel approaches Cross-section C, the peak value and width of the settlement trough is increased, and the settlement 80% larger than that when approaches Cross-section A is not located over the middle position between twin tunnels. This asymmetric characteristic of the settlement trough was also predicted by experimental, analytical and numerical methods [16; 32; 43; 44].
# Native Plant Capacity for Gentle Remediation in Heavily Polluted Mines
## 1 Introduction
The technique should not have negative effects on the soil structure or function [20]. This option requires a series of decisions that take into account all the characteristics of the contaminated area [18] in order to achieve a stable, self-supporting biological community [21]. The study of native species in contaminated media has been a topic of interest for many researchers. [PERSON] et al. [22] assessed the criteria for plant species selection for the phytostabilization of mining wastes in semiarid areas using, among other plants, _Cistus nonspeliensis_ L. and _Dittrichia viscosa_. Some of these works have been carried out in mining areas of Sardinia with species common in the area such as _Inula viscosa_, _Euphoria dendroides_, and _Poa annuua_[23]. Recently, studies have been conducted in other industrial and mining areas. In this sense, [PERSON] et al. [24] used _Nocaea rotundifolia_ ssp. _cepaeifolia_ in Austria and [PERSON] et al. [19] worked with _Brassica napu_ in Spain. Plant species such as _Dittrichia viscosa_ (L.) Greuter subsp. _viscosa_ and _Cistus saluifdius_ L. grow spontaneously in a great variability of soils in the Mediterranean area, being well adapted to the climate, as in the case of Sardinia. _Euphoria pilthyusa_ L. subsp. _cupanti_ ([PERSON].) Radcl.-Sm. is endemic to Corsica, Sicily, and Sardinia. These three species are able to live in environments with highly variable concentrations of trace elements such as Pb, Zn, Cu, and Mn, which, together with Fe, frequently appear in soils of mine tailings such as those in Sardinia [3]. However, it is unknown how the nature and the amounts of trace elements in the soil can alter the final concentrations in leaves and roots. _Dittrichia viscosa_ is a pioneer shrub belonging to the _Asteraceae_ family with broad distribution in the Mediterranean region. It develops vigorous biomass and can be considered a metallophyte [10] since it grows in soils both contaminated and not contaminated by heavy metals [25; 26]. _Cistus saluifdius_ is a fast-growing chamaephyte adapted to a broad range of environmental stresses [27], which is included in the group of pseudometallophytes and is commonly found in contaminated soils. Moreover, this species has been used in numerous phytoextraction studies [3; 4; 23; 28]. Our hypothesis is that the above three plant species can help stabilize trace elements and therefore can be useful in gentle remediation. In previous works [3], we analyzed the concentration of trace elements in both soil and plant as well as the physical-chemical characteristics of the soils and the bioavailability of trace elements. However, the objective of this work was to test the bioaccumulation of trace elements by relating the concentrations in leaves and roots with the concentrations of trace elements in contaminated and uncontaminated soils. Furthermore, in the case of _D. viscosa_, to know the concentration of each element by biomass, the pool of trace elements was determined both in the aerial part and in the roots.
# Performance of the Dual-Chamber Fungal Fuel Cell in Treating Tannery Wastewater
## 2 Materials and Methods
### FFC Performance
The change in COD consumption for the studied FFCs during the anodic oxidation reduction reaction, organic consumption represented in COD and total nitrogen were consumed, and an increase in power generation and energy output, especially in the electrolytes containing FFCs, occurred [48]. The results showed that at 165 h, electrolytes contained in the FFCs had high COD, total nitrogen, and total chromium removal efficiencies which were 77.9%, 94.2%, and 73.0%, respectively, while non-electrolytes containing FFCs had COD, total nitrogen, and total chromium removal efficiencies of 75.0%, 93.1%, and 61.0%, respectively, due to generation of OH\({}^{-}\), CO\({}_{3}\)\({}^{-2}\), and HCO\({}_{3}\)\({}^{-}\) in the electrolyte FFCs resulting from the difference in anodic redox reaction mechanisms and microbial metabolism [49; 50]. This variation may be due to the electrolyte energy used in oxidation reduction reactions resulting from acetate and electrolyte materials that increase the electron production from the metabolic activity needed for organic and inorganic degradation to the external circuit of the cell, then to the cathode electron acceptor electrode. The increase in power output due to the increase in hydroxyl groups in the anode chamber, due to the decrease in organic consumption and the oxidation-reduction reaction rate, gradually decreased [51] due to oxidation of sodium acetate and the presence of the phosphate buffer, resulting in a decrease in cell voltage [52]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time/h**} & \multicolumn{2}{c}{**CD/mA\(\cdot\)m\({}^{-2}\)**} & \multicolumn{2}{c}{**PD/mW\(\cdot\)m\({}^{-2}\)**} & \multicolumn{2}{c}{**U/V**} \\  & **Non-Electrolytic** & **Electrolytic** & **Non-Electrolytic** & **Electrolytic** & **Non-Electrolytic** & **Electrolytic** \\  & **FFC** & **FFC** & **FFC** & **FFC** & **FFC** & **FFC** \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 \\
15 & 0.1061 & 0.1194 & 0.0800 & 0.0972 & 0.754 & 0.814 \\
30 & 0.1050 & 0.1165 & 0.0783 & 0.0925 & 0.746 & 0.794 \\
45 & 0.1042 & 0.1150 & 0.0771 & 0.0902 & 0.740 & 0.784 \\
60 & 0.1011 & 0.1127 & 0.0726 & 0.0865 & 0.718 & 0.768 \\
75 & 0.0957 & 0.1086 & 0.0651 & 0.0803 & 0.680 & 0.740 \\
90 & 0.0892 & 0.1046 & 0.0566 & 0.0746 & 0.634 & 0.713 \\
105 & 0.0678 & 0.0899 & 0.0327 & 0.0551 & 0.482 & 0.613 \\
120 & 0.0508 & 0.0704 & 0.0183 & 0.0338 & 0.361 & 0.480 \\
135 & 0.0338 & 0.0506 & 0.0081 & 0.0175 & 0.240 & 0.345 \\
150 & 0.0165 & 0.0276 & 0.0019 & 0.0052 & 0.117 & 0.188 \\
165 & 0.0089 & 0.0156 & 0.0006 & 0.0016 & 0.063 & 0.106 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variation of energy production of both electrolytic and non-electrolytic FFC.
# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
###### Abstract  
In order to solve the noise reduction problem of acoustic emission signals with cracks, a method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and wavelet packet (WPT) is proposed and named CEEMD-WPT. Firstly, the single Empirical Mode Decomposition (EMD) used in the traditional CEEMD is improved into the WPT-EMD with a more stable noise reduction effect. Secondly, after decomposition, the threshold value of the correlation coefficient is determined for the Intrinsic Mode Function (IMF), and the low correlation component is further processed by WPT. In addition, in order to solve the problem that it is difficult to quantify the real signal noise reduction effect, a new quantization index \"principal interval coefficient (PIC)\" is designed in this paper, and its reliability is verified through simulation experiments. Finally, noise reduction experiments are carried out on the real crack acoustic emission dataset consisting of tensile, shear, and mixed signals. The results show that CEEMD-WPT has the highest number of signals with a principal interval coefficient of 0-0.2, which has a better noise reduction effect compared with traditional CEEMD and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Moreover, the statistical variance of CEEMD-WPT is evidently one order of magnitude smaller than that of CEEMD, so it has stronger stability.  
c pacs: 1
## 2 Results and Discussion
### Volatile Composition of Investigated Fruits and Vegetables
#### 2.1.8 Tomato  
Tomato (_Lycopersicon esculent_ L) is rich in folate, vitamin C, and potassium. Carotenoids, namely lycopene, the most prominent carotenoid, followed by \(\beta\)-carotene, \(\gamma\)-carotene and phytoene, as well as several minor carotenoids, are the most abundant phytonutrients, in addition to a wide variety of volatile compounds. Of the 77 identified volatiles, aldehydes and furanic compounds constitute the most predominant classes, representing about 71% of the total volatile profile. The remaining identified groups contributing to the aroma and taste of this sample were alcohols (6.6%), esters (5.9%), terpenic compounds (5.5%), carboxylic acids (4.7%), organosulfur compounds (2.9%) and ketones (1.6%). The remaining chemical families identified contribute less than 1% to the total volatile profile of tomato.  
Figure 4: Formation of the 2-phenylethylly isothiyanate compound through the synthesis of 2-phenylethylly glucosinolate.  
The individual compounds identified in this study, hexanal (38), (Z)-3-hexenal (49), 3-methyl-butanal (11), (E)-3-hexen-1-ol (128), \(\beta\)-ionone (289) and 2-isobutylthiarole (138), are considered the most important volatile components for defining the aroma of tomatoes, giving a fresh character to this matrix [28]. The main precursors of these volatile compounds in tomatoes are free amino acids, fatty acids and carotenoids [28; 65].
## 2 Materials and Methods
### Liquid Chromatography-Mass Spectrometry (LC-MS/MS)  
Liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) is an analytical chemistry technique that combines the mass analysis capabilities of liquid chromatography (HPLC) and the physical separation capabilities of mass spectrometry (MS). Coupled chromatography--MS systems are popular in chemical analysis because the individual capabilities of each technique are synergistically developed. Liquid chromatography separates mixtures with multiple components, while mass spectrometry provides the structural identity of individual components with high molecular specificity and detection sensitivity. Analytes separated from the LC unit are ionized and sent to the MS/MS unit. In the MS/MS unit, the ions are separated from the first quadrupole in mass/charge (\(m/z\)) ratios and directed to the interference cell known as the collision cell. Here, molecules collide with high-purity nitrogen gas, are broken down, and sent to the second quadrupole. These second ions in the second quadrupole unit are determined by mass by separating them in mass/charge (\(m/z\)) ratios. While there may be many molecules with the same \(m/z\) ratio, the rate of molecules having the same fragmentation ions is 1/10,000 in nature. This tandem technique is preferred for sensitive analysis of biochemical, organic, and inorganic compounds commonly found in complex samples of environmental and biological origin. Therefore, LC-MS/MS can be applied in a wide variety of industries such as biotechnology, environmental monitoring, food processing, and the pharmaceutical, agrochemical, and cosmetic industries. LC-MS/MS allows qualitative and quantitative analysis of analytes. It is possible to obtain results with high sensitivity and accuracy in the analysis of single or multiple analytes in complex environments, in the analysis of trace substances, impurities, metabolites, and drug active ingredients, in the analysis of additives or pesticides in foods, and in the analysis of biological samples such as blood, urine, and tissue. LC-MS/MS is one of the most sensitive methods preferred for the determination of trace analytes in samples as well as multi-analyte determination. It gives information about the amount, structure, and molecular weight of the components in a sample. It has a wide range of uses, from small pharmaceutical compounds to the determination of large proteins, as well as polar ionic, thermally labile, and non-volatile compounds.
## 1 Introduction
Sophisticated robotic devices, such as the da Vinci Surgical System (SIC) [21], can be used to increase the accuracy of surgical guide positioning [22]. However, they are expensive, bulky and require a special operating room (OR) configuration [23]. The main contribution of this paper is to present a novel Mixed Reality-based methodology to support the mandibular reconstruction procedure. Our methodology has been exploited to develop an application whose goal was to assess whether an improvement of the surgical guide positioning process was achieved, providing the correct reference position through holograms projected into the visual field of the user wearing a headset device. The development and testing of the application have been conducted jointly with the Maxillo-Facial Surgery Department located in Molinette hospital (Azienda Ospedaliero-Universitaria Citta della Salute e della Scienza di Torino, Italy). This paper is structured as follows. In Section 2, our Mixed Reality-based methodology is presented. In Section 3, we present the application that implements our methodology. This section addresses the reconstruction and tracking of 3D fibula and surgical guide models required by the application. A detailed description of the steps used to realize the application is also provided. Results obtained from the testing phase are presented in Section 4, while in Section 5, conclusions and future work are drawn.
# Photogrammetry-Based 3D Textured Point Cloud Models Building and Rock Structure Estimation
## 4 The Accuracy Verification of the 3D Point Cloud Model
distance were employed to verify the accuracy of the total station. Table 1 shows the results of the outdoor experiment. From the table, the following points can be drawn. First, the absolute values of the dip direction differences for the 2 measuring methods range from 0.20 to 1.90 degrees, and the mean value at the distance of 10 m is 0.69 degrees. The value generally rises following the increase of the measuring distance. Second, the absolute values of the dip differences range from 0.08 to 1.25 degrees, and the mean value of 10 m is 0.315 degrees. The value is smaller compared with the data of the dip direction difference at the same distance. The same trend of the value with distance can also be observed here. Third, the absolute values of the distance differences range from 0.32 to 1.33 mm, and the mean value at 10 m is 0.51 mm. The value basically increases following the rise of the measuring distance. Fourth, because the distance between the slope surface and the total station is not greater than 30 m, the absolute values of the differences of the dip direction, dip, and distance are not higher than 1.19, 0.83 degrees, and 0.82 mm, respectively (taking the largest error values at the distances of 10 and 30 m). The results mean that the accuracy of the total station is high enough as the reference for verifying the accuracy of the 3D point cloud model, and the method of calculating the orientations of joints according to the coordinates of points is applicable.