esa-sceva/satcom-chunk-collection huggingface dataset:
# Size-Segregated Particulate Mass and Carbonaceous Components in Roadside and Riverside Environments
## 2 Methodology
### Thermal-Optical Carbon Analysis  
Carbonaceous components as organic carbon (OC) and elemental carbon (EC) of samples on all impactor stages were evaluated by using a thermal/optical carbon analyzer (Carbon Aerosol Analyzer, Sunset Laboratory, Tigard, OR, USA), following the IMPROVE-TOR protocol [38; 39; 40]. Samples collected on the IF stage were not analyzed since they are not applicable to the thermal/optical analysis. A QFF sample was punched to produce a 10 \(\times\) 15 mm area and the resulting sample was placed in the carbon analyzer for analysis. The sample was then heated inside the analyzer at temperatures of 120, 250, 450 and 550 \({}^{\circ}\)C to produce four different OC fractions, i.e., OC1, OC2, OC3 and OC4, respectively, in a 100% helium atmosphere and three different EC fractions, i.e., EC1, EC2 and EC3 at 550, 700 and 800 \({}^{\circ}\)C, respectively, in a mixture of 2% oxygen and 98% helium. Then followed the addition of O\({}_{2}\) to the experimental atmosphere, thus, pyrolysis organic carbon (pyOC) can be determined after the reflected laser light reaches its original intensity. The EC fraction was divided into char-EC and soot-EC [38; 41]. Char-EC is EC1-pyOC, while soot-EC is EC2+EC3. The total OC is defined as OC1+OC2+OC3+OC4+pyOC, whereas the total EC is EC1+EC2+EC3-pyOC. Concerning quality assurance and quality control (QA/QC), a reference standard, i.e., sucrose (C\({}_{12}\)H\({}_{22}\)O\({}_{11}\)) (196-00015, Sucrose, Wako Pure Chemical Industries, Ltd., Osaka, Japan) and blank filters (n = 4) were used for the calibration. The repeatability of the analyses of the particle spots deposited on the filters was acceptable, with a CV less than 3.2% for OC and 7.9% for EC. The minimum detection limits (MDL) for OC and EC based on the evaluation of the travel blanks were 0.57 and 0.00 \(\upmu\)g/cm\({}^{2}\)respectively. These values were somewhat smaller than the minimum value of samples in the present study, or, 5.41 and 1.38 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{cm}^{2}\), respectively, for OC and EC.
## 2 Evaluation Index of Segmentation Accuracy  
### Related Definitions  
First, the definitions of trajectory, trajectory segment, approximate compression of line segments and trajectory compression ratio are given.  
**Definition 1**.: _A trajectory is a set of ordered points in a multidimensional space, which can be expressed as \(TR=\left\{P_{1},P_{2},\ldots,P_{len}\right\},1\leq i\leq len\), where \(P_{i}\) is a multi-dimensional trajectory point and \(len\) is the trajectory length. For different trajectories in a data set, the values of \(len\) may be different. The points can be expressed as \(P_{i}=\left(x_{i},y_{i},t_{i}\right)\), in which \(x_{i}\) and \(y_{i}\) are the coordinates of the trajectory point and \(t_{i}\) is the recording time._  
**Definition 2**.: _A trajectory segment is a continuous subset of trajectory points, which can be expressed as \(TRSub=\left(P_{u},P_{u+1},\ldots,P_{u+n}\right)\), where \(P_{u}\) and \(P_{u+n}(1\leq u\leq len-n)\) are the two ends of the segment called segment feature points. The length of the trajectory segment is \(n+1\)._  
**Definition 3**.: _The approximate compression of line segments is that the segments are approximated as straight-line segments connected by end points. Thus, the compressed trajectory only stores the first and last two points of the original trajectory and segment feature points, represented as \(TR^{\prime}=\left\{P_{1},P_{u_{1}},P_{u_{2}},\ldots,P_{u_{k}},P_{len}\right\}\) or \(\left\{P_{u_{0}},P_{u_{1}},P_{u_{2}},\ldots,P_{u_{k}},P_{u_{k+1}}\right\}\), where \(P_{u_{1}},P_{u_{2}},\ldots,P_{u_{k}}\) are segment feature points. The trajectory is divided into \(k+1\) segments, and the compression ratio of trajectory data is \(\delta_{C}=\left(k+2\right)/len\)._
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 4 Computing \(F\) in Metric Space Based on Geometric Distance  
As we know, \(F\) includes the information in the projective coordination but not the Euclidean coordination. If metric reconstruction is necessary, it can only be estimated based on \(F\) via camera's intrinsic matrix \(K\). Auto- (or self-) calibration, which can result in \(K\) automatically, is the computation of metric properties of the cameras and/or the scene from a set of uncalibrated images. Our method does not need any intrinsic parameter measured previously to reconstruct under the metric frame of Euclidean coordinate. In other words, we use a simplified auto-calibration method to estimate \(K\).  
In this paper, some constraints [28] are enforced on the intrinsic parameters of camera to estimate \(K\). These constraints are: (1) Typically the aspect ratio is around 1. (2) The skew ratio can be assumed to be 0. (3) The principal point of the camera is projected to the center of image, \([w/2,\ h/2]\) where \(w,\ h\) is the width and height of image in unit pixel, respectively. After moving the origin of image coordinate to \([w/2,\ h/2]\), the intrinsic parameter matrix is the \(K\) defined in Equation (10), where \(f\) is the focal length of camera. This is a basic assumption for our method because this condition can be satisfied easily now and easier in the future with improvements in the manufacturing. Our method tries to fully make use of this assumption. Theoretically, it can achieve a perfect result if the real images satisfy this constraint of \(K\) ideally.  
\[K=\left(\begin{array}{ccc}f&0&0\\ 0&f&0\\ 0&0&1\end{array}\right) \tag{10}\]
# Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
## 4 Experimentation  
### Performance Matrices  
The performance of the model employed in the work was evaluated using various metrics, including the accuracy, precision, recall, and confusion matrix [40; 41; 42].  
* Accuracy measures the proportion of correct predictions made by the model.
* Sensitivity or recall is the measure to predict true positives out of all positive
* Precision is a measure of the ability to predict true positives from actual positive instances.
* The confusion matrix is a table that summarizes the presentation of a classifier by comparing the actual and predicted classifications.  
The aim of the research was to classify various types of cardiac disorders, and the outcomes of this classification are reported in Table 4. These evaluations were carried out in order to determine how well the models were able to correctly classify the different disorders.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Outcome** & **Definition** \\ \hline \(T^{-}\) & Correct identification of negative data \\ \(T^{+}\) & Correct identification of positive data \\ \(F^{-}\) & Incorrect identification of negative data \\ \(F^{+}\) & Incorrect identification of positive data \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction outcomes.  
To classify ECG images, the following terms are used to evaluate the parameters: \(T^{+}\) signifies true positive, \(F^{+}\) is false positive, \(T^{-}\) is true negative, and \(F^{-}\) signifies false negative. The formulas of accuracy, recall, and precision are obtained based on Table 4, and are given as Equations (1)-(3).  
\[\text{Accuracy}=\frac{(T^{+}+T^{-})}{(T^{+}+F^{-})+(F^{+}+T^{-})} \tag{1}\]  
\[\text{Sensitivity or Recall}=\frac{(T^{+})}{(T^{+}+F^{-})} \tag{2}\]  
\[\text{Precision}=\frac{(T^{+})}{(T^{+}+F^{+})} \tag{3}\]
## 3 Human Activity Recognition Techniques
### AI-Driven HAR Algorithms
#### 3.2.1 Traditional Machine Learning Models for HAR  
Traditional machine learning models were among the first AI-driven approaches to be applied to HAR. These models typically require hand-crafted features extracted from raw sensor data. Sensors like accelerometers and gyroscopes are typically used to capture motion and orientation data, which are then preprocessed and transformed into features such as mean, variance, and frequency domain components. Decision trees, support vector machines, and random forests have been widely used for HAR.  
Decision trees are interpretable models that partition feature space into regions based on a series of hierarchical splits. Studies such as [66] demonstrated their effectiveness in recognizing basic activities such as sitting, standing, and walking. Support vector machines (SVMs) have been widely used for HAR due to their ability to handle high-dimensional data and nonlinear relationships. Kernel functions play a critical role in enhancing their performance in complex activity datasets. For example, [PERSON] et al. proposed a fuzzy proximal kernel SVM as a robust classifier, which transforms samples into a higher dimensional space and assigns their membership degree to reduce the effect of noise and outliers [67]. The study focuses on recognizing walking, sitting, standing, lying down, and transitioning between these states. Random forest is a popular ensemble learning method used for HAR due to its robustness and ability to handle large datasets with high dimensionality. It works by constructing multiple decision trees during training and outputting the mode of the classes or mean prediction of the individual trees. In [68], [PERSON] et al. investigated the effectiveness of a random forest machine learning algorithm to identify complex human activities using data collected from wearable sensors, demonstrating its potential for applications such as health care monitoring and activity tracking.
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 4 Conclusions  
Our experiments demonstrated that the introduced four normalized weighting schemes for linear image interpolation algorithms positively affected the image interpolation quality differently depending on the scaling ratio as well as the input image pixels' spatial distribution. The way the normalized weighting schemes affected image interpolation quality was extensively demonstrated objectively (using FR-IQA metrics) and subjectively (by the human observer). To ease the interpretation of the FR-IQA metrics-based results, a simplified interpretation scheme of FR-IQ-based results was also introduced and discussed. It is important to note that, in the first experimental part aimed at comparing all algorithms (i.e., traditional and new scheme methods), when the scaling ratio = 2 and 4, objective image interpolation quality assessment showed that the TC algorithm scored the highest at 100% among all other image interpolation algorithms mentioned. In the second part of our experimental demonstrations, which aimed at comparing new weighting schemes, when the scaling ratio = 2, objective image interpolation quality assessment showed that the MD scored the highest at 0%, HR scored the highest at 66.6%, AT scored the highest at 33.3%, AC scored the highest at 33.3%. In the same part, when the scaling ratio = 4, the objective image interpolation quality assessment showed that the MD scored the highest at 33.3%, HR scored the highest at 0%, AT scored the highest at 0%, and AC scored the highest at 66.6%. Subjective image quality evaluations showed that the overall performance of the AC algorithm was superior or comparable to TB, TC, MD, HR, and AT. Note that, in digital image interpolation, to date, there exists no image interpolation algorithm that best works for all types of digital images. It is also important to note that many medical/biomedical image processing and analysis software still rely on TN, TB, and TC. In terms of computational efficiency, the TN remained the fastest image interpolation algorithm among all algorithms mentioned. Future efforts may focus on developing intelligent weighting schemes capable of producing or generating realistic estimates for image interpolation operations.  
This research work was supported by Lund University between July and December 2020.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 15: (**a**) Clock test image inside a red square: (**b**) traditional nearest neighbor (TN), (**c**) traditional bicubic (TC), (**d**) traditional bilinear (TB), (**e**) normalized minimum side-based diameter-based (MD), (**f**) normalized hypotenuse radius-based (HR), (**g**) normalized virtual pixel length-based height for the area of the triangle-based (AT), and (**h**) normalized virtual pixel length for hypotenuse-based radius for the area of the circle (AC) interpolation algorithms.
# Research on Flexible Traceability System of _Agaricus bisporus_ Supply Chain
## 4 Design of the System
### Scalability and Security Analysis of the Model  
Scalability  
Traditional blockchain-based agricultural product traceability models may face challenges such as network congestion and slow transaction speeds during large-scale data processing. In contrast, the multi-chain traceability model for _Agaricus bisporus_ supply chains addresses the diverse traceability information needs of stakeholders. It is divided into three application scenarios: the public chain, enterprise chain, and supervision chain. Each main chain maintains its independent ledger, and each sub-chain has a relatively smaller capacity. This design helps to alleviate the storage pressure on the blockchain system, thereby enhancing the overall system's scalability.  
Security  
The design of the multi-chain traceability model for _Agaricus bisporus_, tailored to traceability stakeholders, can enhance the security of the blockchain system. Each main chain has its independent ledger, meaning that even if one main chain is attacked, it will not affect the other main chains or the entire system. Additionally, each main chain can have its consensus mechanism and security policies. This allows for the selection of different consensus mechanisms and security policies based on specific circumstances, thereby reducing the possibility of being attacked across the entire network and enhancing the overall security of the blockchain system.
# Removal of Metals and Dyes in Water Using Low-Cost Agro-Industrial Waste Materials
## 2 Materials and Methods
### Adsorption Assays  
The MB adsorption assays were performed following the ASTM C1777-20 [22]. The effect of the pH from 7 to 11 was evaluated by employing a 40 mg/L MB solution in contact with 30 mg of adsorbent in 10 mL of solution, considering the cationic nature of the pollutant. The pH of the solutions was adjusted using NaOH and HCl 0.1 M [23]. In addition, the effect of the initial concentration (10, 20, 40, 60, 80, and 100 mg/L) by adsorption assays in the batch system during 24 h in an orbital shaker HS-120460, Heathrow Scientific (Vernon Hills, IL, USA) [24]. The final concentration was determined by UV-Vis at 664 nm [22].  
On the other hand, the adsorption tests of Cr (VI) were done by following the ASTM D1687-17. The effect of the pH from 2 to 7 was done using a 100 mg/L solution of Cr (VI), considering that at acid pH, the chromium remover was better [25]. The effect of the initial concentration was analyzed (5, 10, 15, 20, 30, 40, 50, 75, 100, 150, 200, 250, and 300 mg/L) by putting 30 mg of adsorbent in contact with 10 mL of solution during 24 h. The residual concentration of Cr (VI) was determined by the photo-colorimetric method of the difenilcarbazide, using an UV-Vis at 540 nm [26].  
All the experiments were done by duplicate at room conditions and a stirring rate of 200 rpm. The efficiency of the removal (RE) was determined by Equation (2), and the adsorption capacity by Equation (3).  
\[RE[\%]=\frac{C_{i}-C_{e}}{C_{i}}\times 100 \tag{2}\]  
\[q_{e}[mg/g]=\frac{(C_{i}-C_{e})\times V}{M} \tag{3}\]  
where \(RE\) is the pollutant removal percentage, \(q_{e}\) is the adsorbent adsorption capacity [mg/g], \(C_{i}\) y \(C_{e}\) is the initial and equilibrium concentration, respectively, [mg/L], \(V\) is the volume of solution [L], and \(M\) is the amount of adsorbent [g].
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.2.6 Resistance to Linear Speed Changes  
In order to prove the validity of DTW, 200 audio segments are randomly selected and artificially modified playback speeds at a random rate (Database 7). These segments are used as a query set and the fingerprint with the smallest size is applied. An experiment is carried out not using DTW for the purpose of comparison.  
The experiment obtains a higher recall and precision rate with using DTW (Table 8), so the DTW can indeed improve the resistance of the retrieval system to linear speed changes.
# Luminescence Materials for Dye-Sensitized Solar Cells: Advances and Directions
## 4 Luminescent Materials in DSSCs
### Quantum Dots  
Quantum dots (QDs) are semiconductor nanoparticles with size-tunable optical properties, allowing them to absorb light over a broad spectrum and emit it at specific wavelengths [51]. Figure 4c shows the research interest of the quantum dots luminescent materials in the past decade. The highest research interest was achieved in 2022, followed by 2021 and 2019. A slightly similar research interest was achieved in 2019 and 2020. Overall research interest yielded 107,950 publications in the past decade for quantum dots.  
Materials such as CdSe, CdTe and PbS quantum dots have been extensively studied for their potential to enhance DSSC performance due to their tunable emission properties, good quantum yield and broad photon absorption spectrum. Quantum dots are commonly synthesized using colloidal methods, vapour phase synthesis or molecular beam epitaxy and can be integrated into DSSCs by embedding in the photo-anode or mixing with the dye. The primary advantage of quantum dots lies in their high absorption coefficients and potential for multiple exciton generation, which can lead to higher photocurrents and enhanced efficiency.  
Generally, up-conversion materials are particularly beneficial for utilizing NIR light, while down-conversion materials enhance the use of UV light, and quantum dots offer broad-spectrum absorption. As elucidated above, up-conversion produced a total of 86,316 publications, down-conversion gave 14,098 publications and quantum dots produced 107,950 publications. These created a remarkable research interest of 208,364 publications  
Figure 4: Luminescent materials research interest in the past decade (**a**) up-conversion, (**b**) down-conversion and (**c**) Quantum dots.  
all combined in the past decade. Each material type also presents unique challenges, such as the stability of quantum dots and the need for precise synthesis and doping in up-conversion and down-conversion materials.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 2 Materials and Methods
### Teager-Kaiser  
The Teager-Kaiser energy methods are techniques used in signal and image analysis [15]. They can reveal important structures and characteristics within signals through specific energy measurements. These methods involve calculating the energy of a modified signal, derived from the original signal through nonlinear operations. This modified energy provides valuable information of the signal's amplitude, frequency, and other characteristics.  
The general formula for calculating the Teager-Kaiser energy of a discrete signal x(n) is [16; 17]:  
\[E_{\text{TK}}(n)=x^{2}(n)-x(n-1)\cdot x(n+1) \tag{1}\]  
where x(n) represents the value of the signal at time \(n\), \(E_{\text{TK}}(n)\) is the Teager-Kaiser energy at time \(n\), and \(x(n-1)\cdot x(n+1)\) refers to the product of the signal values shifted by one unit in time with respect to \(x(n)\).  
In image analysis, these methods can be applied by treating an image's pixels as signal samples and using similar operations to calculate the modified energy. However, it should be noted that while these methods are useful for extracting relevant features in certain signals and images, they are not suitable for all situations and may require adjustments depending on the specific application context.  
The TKEO (Teager-Kaiser energy operator) was proposed by [PERSON] and Teager as an approach to quantify the energy required for signal generation. It is primarily used to analyze rapid variations of energy in a signal. Unlike other methods based on power or amplitude measurements, the TKEO focuses on abrupt changes and the instantaneous evolution of the signal.
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 3 Critical Influence Factors
### Windowing  
To measure ADDR as a function of frequency, the specified frequency band is fixed, for example 20 kHz for audio DUTs. It is hard to guarantee the process of ADDR measurement satisfies the optimal coherent sampling condition for all the frequencies, then windowing operation improves the measurement accuracy. Kaiser windows are the most recommended to be used in the proposed ADDR measurement in this situation.  
Kaiser window controls the proportion of the signal energy captured by the mainlobe mainly through an adjustable shape factor, \(\beta\)[31]. The shape factor ranges from \(\beta\) = 0 to \(\beta\) = 40. The minimum \(\beta\) is equivalent to a rectangular window and the maximum \(\beta\) offers a wide enough mainlobe capturing the vast majority of the spectral energy representable in double precision. Moreover, a typical intermediate value of \(\beta\approx\) 6 approximates a Hanning window closely. Thus, the value selection of \(\beta\) is of great significance, which depends on the actual spectrum of the DUT. If the test satisfies the coherent  
Figure 3: Magnitude error percentage for a shift in \(M\) from \(M-0.5\) to \(M+0.5\). \(M=51\), incremental size of \(M\): 0.0026, corresponding to 0.05 Hz; Testing signal: 1 V\({}_{\text{PP}}\); Sampling points \(N=2048\), sampling frequency = 40 kHz.  
sampling conditions for the fundamental signal, \(\beta\) should be set as 0; If the spectral resolution is very critical, which means there exists spurious very close to the fundamental signal, \(\beta\) should be small; If the spurious and harmonics are far from the fundamental from the spectral resolution point of view, \(\beta\) should be large. The appropriate value of \(\beta\) is obtained by adjustment, and value \"6\" is a good initial, which reduces frequency side lobes by at least 18 dB per octave away from the signal.  
However, windowing affects the power of frequencies within the DUT output spectrum, which has an impact on the accuracy of ADDR. Thus, the spectral bins need to be corrected [32]. According to [24; 32], the true magnitude of the narrow-band input signal, harmonics and spurious spectral components \(X(f_{i,h,s})\) can be corrected by  
\[X(f_{i,h,s})(\text{dB})=P_{disp}+\text{CPG}+\text{Scalo}pLoss \tag{4}\]  
where \(P_{disp}\) is the displayed amplitude, \(\text{CPG}\) is the coherent power gain, which represents the degradation in magnitude induced by the windowing operation. The parameter \(\text{Scalo}pLoss\) is caused by the signal power propagation on two closest bins. The largest scalloping loss occurs when the signal frequency falls exactly in the middle of the two bins. Due to scalloping, the gain of the Kaiser window is not constant at all noise frequencies, depending on parameter \(\beta\)[24]. On the contrary, if the power of the signal focuses in one FFT bin, \(\text{Scalo}pLoss\) in (4) should be set as 0. The correction factors and maximum scallop loss for the Kaiser window with different \(\beta\) parameters are illustrated in Table 1[32].