esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Kinetics  
In reactions as studied in kinetics, the concentration of a reactant may decrease with time as the product forms. If \(c_{t}\) represents the concentration of a reactant at time \(t\), then the curve in the diagram \(c_{t}\) vs. \(t\) is a decreasing function. The data in the diagram \(c_{t}\) vs. \(t\) can be studied with equations of first- or second-order. For settings in which the concentration of the reactant tends eventually to 0, an equation of the first order is [31]:  
\[c_{t}=c_{0}e^{-kt} \tag{2}\]  
and of the second order:  
\[c_{t}=\frac{c_{0}}{1+c_{0}kt} \tag{3}\]  
where \(c_{t}\) is the concentration of a reactant at time \(t\), with initial concentration \(c_{0}\) at \(t=0\), and the parameter \(k\) is the reaction rate constant [31].  
The half-life (\(t_{1/2}\)) of the reactant is the time needed for the initial concentration of the reactant (\(c_{0}\)) to drop 50%, in other words, to reach \(c_{0}/2\). Equation (2) can be used to obtain the expression for \(t_{1/2}\) by putting \(c_{0}/2\) in place of the \(c_{t}\), and the expression for \(t\) obtained from that equation will be \(t_{1/2}\). The half-life for the first-order reaction (\(t_{1/2}\)) becomes:  
\[t_{1/2}=\frac{0.693}{k} \tag{4}\]  
Likewise, putting \(c_{0}/2\) in place of \(c_{t}\) in Equation (3), the half-life \(t_{1/2}\) for the second-order reaction becomes [31]:  
\[t_{1/2}=\frac{1}{kc_{0}} \tag{5}\]
## 3 Inertial Navigation and IMU Error Model
### Feature Extraction and Matching  
Feature extraction is the essential process for subsequent motion estimation. As an outdoor environment usually has a high-contrast texture in the image scene, image points with distinct structural information are selected as features. In computer vision community, the scale-invariant feature transform (SIFT) algorithm [24] is a well-accepted approach to describe and match the feature points which are usually invariant to distortion, scaling, orientation, affine transformation, and illumination changes. Although several faster algorithms, such as PCA [25] and SURF [26], facilitate reducing the computational load, the point location accuracy can be in turn degraded [27]. We, consequently, use the SIFT algorithm to locate the feature points and create the corresponding scale-invariant descriptor vectors by measuring the gradient magnitude and orientation in the pre-defined pixel neighborhood. To provide sufficient dimensionalities in the feature space for favorable matching result, a 128-dimensional descriptor:  
\[\vec{d}=(d_{1},d_{2},\cdots,d_{128})^{T} \tag{10}\]  
is used for each pre-selected feature point. In adjacent images, the similarity of two candidate feature points, \(A\) and \(B\), is given by the Euclidean distance:  
\[l_{AB}=\left\|\vec{d}_{A}-\vec{d}_{B}\right\|=\sqrt{\sum_{i=1}^{128}\delta d_{ i}^{2}} \tag{11}\]  
By setting the empirically examined accept-reject threshold, the feature matching process is evaluated using the ratio of the minimum distance to the next minimum [24]. Ideally, if an image scene contains a non-uniform texture, there should be a large amount of SIFT features which guarantee desirable redundancy for optimized motion estimation. However, there inevitably exist outliers due to the limited descriptor dimensionality and the imperfect matching determination criteria. Therefore, an outlier removal scheme is undertaken prior to the motion determination process. In order to remove the outlier correspondences which potentially give rise to undesirable estimation, the random sample consensus (RANSAC) algorithm [28] is taken into consideration in this paper.
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Networking
#### 4.1.4 5G Networks  
5G networks are the fifth generation of mobile networks that can support a maximum speed up to 10 Gbps theoretically, which are translated to about a hundred times faster than 4G networks that their corresponding speed are up to 100 Mbps only. Therefore, 5G networks can demonstrate a great low latency of 1 ms, promising faster loading times and enhanced efficiency. Eight criteria have been outlined by ITU IMT-2020 standard for mobile networks, which should be fulfilled in order to be considered as 5G [39]: 10 Gbps maximum achievable data rate, 10 (Mbit/s)/m\({}^{2}\) traffic capacity across coverage area, 1 ms latency, 106/km\({}^{2}\) total number of connected devices, 3 times higher spectrum efficiency compared to 4G, 100% network coverage, Mobility up to 500 km/h with acceptable Quality of Services (QoS), and 100 times lower network energy usage compared to 4G.  
The technology of 5G is anticipated to become a strategic priority for telecommunication operators and this is the reason why there is a persistent cooperation between operators and governing bodies to uncover their intentions to develop technologies such as smart cities or smart hospitals, including smart healthcare. 5G can be divided in three network services: enhanced mobile broadband (eMBB) focuses on data rates of 10 Gbps, massive machine type communications (mMTC) for a plethora of interconnected devices, and ultra-reliable low latency communication (URLLC) for solving both latency and reliability issues. Thus, it is obvious that 5G networks can play a vital role in many scientific areas, such as in the medical industry.
# Improving Automated Machine-Learning Systems through Green AI
## 3 Bayesian Optimization for Hyperparameter Optimization
### Definitions  
The hyperparameter optimization problem in machine learning involves finding the optimal set of hyperparameter values for a model that maximizes its performance on a given evaluation dataset. The problem can be formalized as a mathematical optimization problem as follows:  
Given a hyperparameter space \(\Theta\), an objective function \(f(\Theta)\), and a set of constraints \(\mathcal{C}\), the hyperparameter optimization problem is to find the optimal hyperparameter configuration \(\Theta^{*}\) that either maximizes or minimizes the objective function \(f(\Theta)\) subject to the constraints \(\mathcal{C}\).  
Here is a breakdown of the formalization:  
* \(\Theta\) is a vector of hyperparameters that configure a machine-learning model.
* \(f(\Theta)\) is the objective function that measures the model's performance on an evaluation dataset using a specific metric, such as accuracy, mean squared error, or area under the ROC curve.
* \(\mathcal{C}\) represents the constraints, which can include limitations on allowable values for hyperparameters, constraints on computational resources (such as maximum runtime or available memory), or any other relevant constraints for the problem.  
The aim is to find \(\Theta^{*}\) such that:  
\[\Theta^{*}=\arg\max_{\Theta}(f(\Theta))\]  
subject to:  
\[\Theta\in\mathcal{C}\]  
In other words, we want to find the hyperparameter configuration \(\Theta^{*}\) that maximizes the objective function \(f(\Theta)\) while satisfying all the constraints \(\mathcal{C}\).
## 4 The Experiment and Discussion  
### The Selection of the Camera, Line Laser, and Lithium Battery  
The 3D reconstruction part of the lithium battery defect detection system is constructed by the laser triangulation method. The main hardware of this method requires an external camera in addition to the computer terminal. The following are mainly about the type selection of the camera and line laser, and the size introduction of the lithium battery.  
The main acquisition target of the image acquisition system of the square lithium battery coating defect detection system is the laser line irradiated above the defect. The laser line and the lithium battery of the measured object have obvious contrast without color assistance. A camera with a black and white spectrum should be selected. The image acquisition system is a real-time online detection system with higher performance requirements for real-time transmission speed than general visual detection; the field of view only needs to meet all the information that needs to be extracted to contain the measured object. Therefore, the G3-GM12-M2590 area scan camera (Teledyne Dalsa, Billerica, MA, USA) as shown in Figure 10 can meet the requirements for the camera of this subject. The CMOS image sensor model is On-SemiPython5000P1 (Onsemi, Scottsdale, AZ, USA), the resolution is 2592 \(\times\) 2048, and the image is realized by using the GigE Gigabit Ethernet interface (high-speed real-time transmission, pixel size 4.8 \(\upmu\)m, black and white spectrum). The detailed parameters of the camera are shown in Table 1.  
The line laser in the defect detection system of lithium battery is used as the key hardware equipment of the triangulation method. A high-power laser should be chosen as far as possible. The laser lines shining on the defects of lithium batteries should have high contrast, clarity, and a small line width for easy extraction of image acquisition equipment and processing of the computer terminal. Therefore, a one-word line laser YN-118c (Shenzhen Hongda Technology Laser Co., Ltd., Shenzhen, China) shown in Figure 11, is selected in this work; the laser power is 150 mw; the adjustable focal length wavelength is 650 nm; and the selected laser meets the basic experimental requirements. The specific laser parameters are shown in Table 2.  
Figure 9: The improved grayscale center of gravity method.  
Figure 11: Line laser YN\(-\)118 c.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Argument** \\ \hline Core power & 150 MW \\ Output wavelength & 60 mm \\ Dimension & \(\varphi\)22 \(\times\) 70 mm \\ Operating temperature & \(-\)10 \({}^{\circ}\)C\(\sim\)+55 \({}^{\circ}\)C \\ Operating voltage & DC2.8 V–5.2 V \\ Working life & 13,000 h \\ Operating temperature & \(-\)10 \({}^{\circ}\)C\(\sim\)+50 \({}^{\circ}\)C \\ Storage temperature & \(-\)45 \({}^{\circ}\)C\(\sim\)+80 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Line laser parameters table.  
Figure 10: Camera G3-GM12-M2590.  
Finally, the ternary single lithium battery is selected with a size of 10 \(\times\) 120 \(\times\) 85 mm length and width, as shown in Figure 12.
## 3 Results and Discussion
### Chemical Composition of EO  
LaDEO and CnEO chemical compositions (compounds higher than 1.0%) were analyzed (Table 1). The complete CnEO composition was previously reported by our group [22]. As expected, most of the compounds identified were monoterpenes. The major constituents of LaDEO were dihydrocarvone isomer 1 (29.6%), limonene (25.2%), and dihydrocarvone isomer 2 (23.8%). It has been reported that within the _L. alba_ species, limonene is frequently present as one of the major compounds of EO, accompanied by at least one of the five monoterpenic ketones, including dihydrocarvone [27].  
On the other hand, pulegone (37.2%), menthone (26.6%), and isomenthone (11.7%) were the three most abundant monoterpenes found in CnEO. [PERSON] et al. [28] reported that at least three types of CnEO can be recognized. The most abundant components consist of C-3 oxygenated p-menthanes such as pulegone, menthone, isomenthone, and piperitone. Among them, pulegone is the main variant and the major component associated preferentially with menthone and/or isomenthone [28].  
Our findings on EO composition are in line with those expected for LaDEO and CnEO according to literature data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Volatile Organic Compound (VOC)**} & \multirow{2}{*}{**KI a**} & \multicolumn{2}{c}{**Chemical Composition (\%)**} \\ \cline{3-4}  & & **LaDEO b** & **CnEO c** \\ \hline Myrcene & 993 & 3.38 & - \\
3-Octanol & 996 & - & 2.43 \\ Limonene & 1036 & 25.23 & - \\ cis-Sabinene hydrate & 1074 & - & 4.21 \\ Linalool & 1106 & 1.33 & 1.25 \\ Menthone & 1165 & - & 26.59 \\ Isomenthone & 1178 & - & 11.71 \\ Isomenthol & 1182 & - & 4.61 \\ trans-Isopulegone & 1188 & - & 3.16 \\ Terpinen-4-ol & 1188 & - & 2.41 \\ Dihydrocarvone isomer 1 & 1217 & 29.64 & - \\ Dihydrocarvone isomer 2 & 1225 & 23.81 & - \\ Pulegone & 1255 & - & 37.22 \\ Carvol & 1261 & 1.40 & - \\
1-Cyclohexanone, & 1299 & - & 1.15 \\
2-methyl-2-(3-methyl-2-oxobutyl) & & 1405 & 1.27 & - \\ \(\beta\)-Elemene, (-)- & 1439 & 2.13 & - \\ \hline \hline \end{tabular} (a) KI: Kovats index. (b) LaDEO: _Lippia albu_ (chemotype dihydrocarvone) essential oil. (c) CnEO: _Clinopodium npte_ essential oil.  
\end{table}
Table 1: LaDEO and CnEO chemical composition.
## 3 Results and Discussion
### Fluorescence Results
#### 3.2.3 DOM Characterisation  
As shown in Figure 5, when analysing the score values from each modelled component, the HA-L (X1) and Try-L (X2) have significant fluorescence signals along with the whole dataset, whereas Tyr-L (X3) score values were negligible in all cases, only detected at the outlet of both treatment lines (sites I and M). It is important to highlight that score values cannot be related directly to the abundance of each chromophore, as the fluorescence emission is not only dependent on the concentration but also on its quantum yield, which is characteristic of each chromophore.  
In the lagoon inlet (site A), HA-L had a lower fluorescence signal than at the outlet (site B), and the same was observed for the Try-L ones. The influents had score values of 2.1 \(\pm\) 0.2 of HA-L and 0.42 \(\pm\) 0.02 of Try-L, respectively, and after the lagooning process, they increased to 3.2 \(\pm\) 0.6 of HA-L and 0.83 \(\pm\) 0.09 of Try-L. These observations might be attributed to organic matter derived from algae growth or the biodegradation of protein-like substances, whose by-products emit as HA-L and Try-L, resulting in an overall fluorescence increment for these two [39]. Comparable levels of these types of DOM were detected in raw river water (site C), being 2.6 \(\pm\) 0.3 and 0.60 \(\pm\) 0.06 for HA-L and Try-L, respectively.  
\begin{table}
\begin{tabular}{|c|c|c|} \hline
**Site** & **Measured EEM** & **Modelled EEM** & **Residual** \\ \hline L & & \\ \hline M & & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 4: Fingerprints from the three-component PARAFAC model: X1 (HA-L), X2 (Try-L), and X3 (Tyr-L).  
The characterisation of DOM is in line with that observed from the FI and BIX values (Figure 3). As expected, in the spot where the lagoon outlet and Po River are mixed (site D), the HA-L and Try-L scores were the average between the ones from sites B and C, being 2.9 \(\pm\) 0.3 for HA-L and 0.63 \(\pm\) 0.04 for Try-L, in line with NPOC results (Figure 2B).
## 4 WSN Platforms and Energy Harvesting Techniques Investigations
### WSN Platforms
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\begin{tabular}{c} **Prototype** \\ **Name** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Data Acquisition** \\ **Specifications** \\ \end{tabular} } & \multicolumn{2}{c}{**Embedded Computing Specifications**} & \multicolumn{3}{c}{**Wireless Channel Specifications**} \\ \cline{3-10}  & & **ADC** & **ADC** & **Embedded** & **Data Memory** & **Radio** & **Frequency Band** & **Protocol** &
\begin{tabular}{c} **Data Rate O/R** \\ **Outdoor Range** \\ \end{tabular} \\ \hline
[33] & Xnode & 8 & 24-bit & NXP LPCA537 & 32 MB SDRAM & AT86 RF23 & 6 LoWPAN & Zigbee & 250 Kbps O/R = 1 km \\ \hline
[34] & ODCWSN & 6 & 12-bit & TI MSF430 & 4 + 32 KB Flash & CC1101 & 920 MHz & - & 250 Kbps \\ \hline
[35] & Prototype & 8 & 12-bit & TI MSF430F548 & 16 KB RAM + 256 KB Flash & CC1101 & 433 MHz & - & 500 Kbps O/R = 500-80 m \\ \hline
[36] & 5-Mote & - & 12-bit & TI MSF430F1611 & 10 KB SRAM + 48 KB Flash & CC2420 & 2.4 GHz IEEE802.15.4 & - & 250 Kbps \\ \hline
[37] & - & 4 & 24-bits & ARM90 & 40 KB SRAM + 512 KB Flash & - & IEEE802.11 & Wi-Fi & - \\ \hline
[38] & GENESI Node & 12 & 10-bit & TI MSF430F274 & 1 KB RAM + 32 KB Flash & CC2420 & 2.4 GHz IEEE802.15.4 & - & 250 Kbps O/R = 75 m \\ \hline
[39] & AEPod & 8 & 12-bit & Atmel ATmega1281 & 8 KB & Atmel AT86 RF23 & 2.4 GHz IEEE 802.15.4 & - & 250 Kbps \\ \hline
[40] & Thinner & 8 & 10-bit & ATmega128 L & 4 KB SRAM + 128 KB Flash & Xbee & 2.4 GHz IEEE802.15.4 & Zigbee & 250 Kbps O/R = 100 m \\ \hline
[41] & WISAN & 12 & 12-bit & TI MSF430F1611 & 10 SKRAM + 48 KB Flash & CC2420 & 2.4 GHz IEEE802.15.4 & - & 250 Kbps O/R = 75 m \\ \hline
[42] & Self-powered & - & 10-bit & TI MSF430F20 xx series & 128 B + 2 KB Flash & Nordic nRF24L01 & 2.4 GHz IEEE802.15.4 & - & 2 Mbps \\ \hline
[43] & PCB & 4 & 16-bit & Atmel ATmega128 & 4 KB SRAM + 128 KB & MaxStream 90 Xtte & 900 MHz & - & 38.4 Kbps O/R = 300 m \\ \hline
[44] & - & 8 & 10-bit & ATmega644 PA & 128 Kbytes Flash & Xbee & 2.4 GHz IEEE802.15.4 & Zigbee & 250 Kbps O/R = 100 m \\  & & & & & memory & & & \\ \hline
[45] & - & - & 12-bit & STM32F-405 RG & SRAM & CC2538 & - & - & - \\ \hline
[46] & Xnode & 8 & 24-bit & NXP LPCA537 & 32 MB SDRAM & Atmel AT86 RF23 & 2.4 GHz IEEE802.15.4 & Zigbee & 250 Kbps O/R = 1 km \\ \hline \hline \end{tabular}
\end{table} 
Table 8: WSN platforms for the SHM of bridges. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.
## 2 Materials and Methods
### Qualitative Property Tests (Destructive)
#### 2.3.4 Titratable Acidity (TA)  
Titratable acidity (TA) was determined by titration with 0.1 mol/L sodium hydroxide (NaOH) solution. Phenolphthalein was used as an indicator, and the titration was continued until a stable light pink color was reached, corresponding to a pH of approximately 8. For this analysis, 5 mL of fruit juice was mixed with 1 mL of phenolphthalein solution (10 g/m) and diluted to a total volume of 200 mL with distilled water. The titration endpoint was indicated by a persistent light pink color of the diluted extract. The calculated TA was expressed in terms of malic acid (the predominant fruit acid) according to Equation (1).  
\[\text{TA}=\frac{\text{mL(NaOH)}\times\text{N(NaOH)}\times\text{acidmeq.factor}}{ \text{mL (juice)}}\times 100 \tag{1}\]  
TA represents the percentage of titratable acidity. mL(NaOH) is the volume of sodium hydroxide (NaOH) consumed during titration. N(NaOH) denotes the normality of the consumed NaOH, which is 0.1 mol/L. The acidmeq factor is the acid equivalent factor of the dominant fruit acid, equaling 0.067. Finally, mL(juice) signifies the fruit juice volume, which is 5 mL [35].
## 4 Experiments and Results
### Experimental Results
#### 4.3.2 Baseline Model Analysis  
This paper first trains each classifier in the initial classifier set using the original feature space, laying the experimental foundation for subsequent experiments to build model ablation experiments and explore feature space reconstruction and the advantages of ensemble classification techniques. Table 3 shows the performance of each dataset for classification on six initial classifiers (decision trees (DT), random forests (RF), Adaboost,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range** \\ \hline Attribute culling factors \(\beta\) in feature space & 0.1, 0.2, 0.3, 0.4, 0.5 \\ The number of subsets of feature subspace & \(2\alpha,\alpha\in[1,2,3]\) \\ The number of classifiers required for ensemble & \(2\alpha,\alpha\in[1,2,3]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The setting of the parameters.  
gradient boosting (GB), extreme trees (ET), and support vector machines (SVM)) using the original feature space without reconstruction.  
We observed that the 12 high-dimensional datasets performed differently under different classifiers, with differences in data dimensionality and size leading to varying advantages of a single classifier. For example, the experimental precision of the SCADI dataset (70 samples, 206 feature dimensions) under Adaboost is 0.2143. Compared to the other five classifiers in the initial set of classifiers, the precision decreases by 50% to 64.41%. The accuracy of the Gait dataset (number of samples 48, feature dimension 321) under the decision tree and Adaboost, compared with the accuracy of the other four classifiers, also differed by 40% to 80%.
## 4 Discussion
### Characteristics of Silicon Reduction  
The reduction process accomplished by the ferrosilicon alloy is a solid-solid reaction, and the rate-controlling step of this reaction potentially includes the following possible situations: an interfacial chemical reaction, the diffusion of reactants through the product layer, and an interfacial chemical reaction and diffusion. The ferrosilicon reduction is a strong exothermic process, which is different from the carbon-thermal reduction reaction. The heat released by this reaction locally promotes the fusion of the sample. According to Figure 9, the disappearance of the interface between the particles in the sample starts at 800 \({}^{\circ}\)C, and the sintering phenomenon occurs in the same neighborhood. As shown in Figure 5B4, the whole sample melted after further increases in temperature.  
\[\mathrm{ZnO(s)+Fe(s)=Zn(g)+FeO(s)\Delta G_{10}^{\theta}=201066.8-138.33T} \tag{12}\]  
In this way, ZnO should be successfully reduced to elemental zinc, and good conditions for the internal structure of the sample are required for the evaporation of zinc in EAFD. In the initial reaction, the contact is good between the EAFD and ferrosilicon alloy, and a large reaction interface exists. In this way, the interfacial reduction reaction can be carried out smoothly. When the temperature is lower than 800 \({}^{\circ}\)C, the reaction rate of ZnO in the EAFD reduced by silicon is slow, and the reaction is carried out relatively slowly. However, the instantaneous heat released by the reduction reaction of ferrosilicon changes the internal structure of the sample with an increase in temperature. The reduction reaction can occur smoothly, and a good condition for the escape of Zn (g) still remains. Therefore, the evaporation rate of zinc is 35.26% at 800 \({}^{\circ}\)C, far higher than the 0.50% of the reduction of EAFD by carbon powder. However, SiO\({}_{2}\), the oxidation product of silicon, gradually increased and a silicate-phase layer formed during the preceding reaction, as shown in Figure 5. The ferrosilicon and EAFD particles are separated by this product layer, so further reduction is limited. At the same time, the samples started to be sintered and melted, and the porosity gradually decreased, which was not conducive to the diffusion and the evaporation of the reduction product (zinc). The evaporation rate of zinc in sample B6 is 90.47%, which is less than that of sample A6, 99.80%. According to the test results, even though the reactants diffused very slowly through the solid product layer, the reduction and evaporation of zinc still occurred slowly and mainly depended on the reduction of the iron particles, as shown in Equation (12) [19; 20]. On the basis of Figure 4 and Table 3, the relationship between the metal iron and FeO content suggests that ZnO is reduced by part of the metallic iron that is reduced via silicon during the heating process.  
Figure 9: The microstructure of samples in B2 reduced at 800 \({}^{\circ}\)C.