esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Temperature on the Modal Variability in Short-Span Concrete Bridges
## 4 Discussion
### Analysis of Modes Removed of Temperature Effect  
Our analysis now examines the modal variability caused by other environmental factors influencing the modes of a bridge apart from temperature. The modes removed of the effect of temperature can be obtained by excluding the temperature-dependent modes  
Figure 12: Parameters of updated models: (**a**) elastic modulus of concrete; (**b**) rotational rigidity of bearing.  
Figure 13: Variation patterns of natural frequencies according to temperature in updated models: (**a**) Seolmun Bridge; (**b**) Gajwa Bridge.  
from the identified ones. However, these so-obtained modes correspond to residuals with an average value of 0. Therefore, the temperature-independent modes are obtained by summing up the averages for each mode.  
\[\widetilde{f}_{i}=f_{i}-\overline{f}_{i}+\mu_{i} \tag{3}\]  
where \(f_{i}\) and \(\widetilde{f}_{i}\) are the \(i\)th natural frequency and the natural frequency of the temperature-independent mode; and, \(\mu_{i}\) is the average of the \(i\)th natural frequencies.  
Table 1 arranges the average and standard deviation of the modes other than temperature effect. Figure 14 shows these values in boxplot. The average of the standard deviation by regression analysis is 0.134 Hz for Seolmun Bridge and 0.233 Hz for Gajwa Bridge. These values represent the extent of the variation of the modes even in absence of change such as damage in the bridge and reveal that the extent of the variation is not negligible. As compared with the Seolmun Bridge, the Gajwa Bridge has a larger width and stiffness, so that the mode variability is greater for vehicle load variation. When the temperature-dependent modes were removed by PCA, the amplitude of the standard deviation decreased compared to the regression analysis but the pattern remained almost unchanged. Accordingly, the evaluation of the health of the bridge relying on the variability of its dynamic properties shall consider the possibility that the identified modes may vary even in absence of damage in the structure.
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
###### Abstract  
Modern power systems are subject to waveform distortions that include spectral components (supraharmonics) in the range of 2-150 kHz. Due to the lack of regulation in this range and since supraharmonics may follow time-varying patterns, the operators can take advantage of the statistical characterization of supraharmonics, e.g., for determining convenient power quality limits or to analyze the residual capacity of networks toward further installations of power electronic converters. This paper studies the statistical characterization of supraharmonics in low-voltage distribution networks, considering both the overall supraharmonic distortion (through the characterization of the total supraharmonic distortion index) and individual supraharmonic components. Several probability distributions are proposed and compared, also considering multimodal distributions that can fit more general scenarios in which the supraharmonic emissions follow regime patterns. The outcome of numerical experiments based on publicly available data collected at actual low-voltage distribution networks suggests that multimodal distributions are useful in characterizing supraharmonics in most cases, with acceptable goodness of fitting even in the presence of stair-shaped empirical distributions. This paper can serve as a starting point for the development of probabilistic power system analysis tools accounting for supraharmonic emissions and for the convergence toward standardization in the 2-150 kHz range.  
multimodal distributions; probability distribution functions; statistical characterization; supraharmonics 2021113574. [[https://doi.org/10.3390/app11083574](https://doi.org/10.3390/app11083574)]([https://doi.org/10.3390/app11083574](https://doi.org/10.3390/app11083574))
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
###### Abstract  
In general, it is difficult to obtain a huge, labeled dataset for deep learning-based phoneme recognition in singing voices. Studying singing voices also offers inherent challenges, compared to speech, because of the distinct variations in pitch, duration, and intensity. This paper proposes a detouring method to overcome this insufficient dataset, and applies it to the recognition of Korean phonemes in singing voices. The method started with pre-training the HuBERT, a self-supervised speech representation model, on a large-scale English corpus. The model was then adapted to the Korean speech domain with a relatively small-scale Korean corpus, in which the Korean phonemes were interpreted as similar English ones. Finally, the speech-adapted model was again trained with a tiny-scale Korean singing voice corpus for speech-singing adaptation. In the final adaptation, melodic supervision was chosen, which utilizes pitch information to improve the performance. For evaluation, the performance on multi-level error rates based on Word Error Rate (WER) was taken. Using the HuBERT-based transfer learning for adaptation improved the phoneme-level error rate of Korean speech by as much as 31.19%. Again, on singing voices by melodic supervision, it improved the rate by 0.55%. The significant improvement in speech recognition underscores the considerable potential of a model equipped with general human voice representations captured from the English corpus that can improve phoneme recognition on less target speech data. Moreover, the musical variation in singing voices is beneficial for phoneme recognition in singing voices. The proposed method could be applied to the phoneme recognition of other languages that have less speech and singing voice corpora.  
phoneme recognition; Korean singing voices; self-supervised learning +
Footnote †: 2024: 14, 8532. [[https://doi.org/10.3390/app14188532](https://doi.org/10.3390/app14188532)]([https://doi.org/10.3390/app14188532](https://doi.org/10.3390/app14188532))
## 4 Mathematical Model  
### Coordinate System  
The earth coordinate system \(\mathrm{O}_{0}\)-\(\mathrm{X}_{0}\)\(\mathrm{Y}_{0}\)\(\mathrm{Z}_{0}\), as indicated in Figure 7, is fixed at \(\mathrm{Z}_{0}\) = 0, i.e., free surface, with the \(\mathrm{Z}_{0}\)-axis directing upwards and the \(\mathrm{X}_{0}\)-axis denoting wave incidence. \(\omega\) is denoted as the wave angular frequency; wave incident angle \(\theta\) = 0 represents waves propagating in the positive \(\mathrm{X}_{0}\) direction; h is defined as the water depth. The body-fixed coordinate system \(\mathrm{O}\)-\(\mathrm{XYZ}\), which is able to define the free surface boundary condition, is fixed at COG along with transition or rotation of the floating body.  
Figure 6: Snapshots of the unidirectional irregular waves interacting with (**a**) OC3-Hywind spar; (**b**) UMaine-Hywind spar; (**c**) MIT/NREL TLP; and (**d**) UMaine TLP.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 4 Discussion
### Potential Interference  
In the process of measuring milk composition, there are various potential interference factors that may affect the accuracy, such as light scattering [40], environmental temperature changes [41], sample bacterial infections [42], environmental light, and the material and fixation of colorimetric plates.  
Scattering interference is a common problem in the measurement of milk composition, and the propagation of light in milk is very complex. When light shines on milk, scattering occurs on the surface of protein and fat particles. To reduce the impact of the aforementioned interference factors, a new multi-wavelength sensor with temperature compensation and strong penetration was adopted. It provides six channels of wavelength data for milk samples. Calibration models were established between wavelength and protein fat through machine learning algorithms, effectively eliminating the influence of scattering and improving model prediction accuracy.  
Temperature changes in the environment can cause instability of the light source, and the scattering coefficient decreases with increasing temperature. The experiments in this study were conducted at room temperature (25 \({}^{\circ}\)C), which had little impact on the experimental results. In the next step of research, we will consider adding different temperatures as new features to machine model training to resist the influence of temperature on the experiment.  
When excessive external bacteria are mixed with milk samples [43], they decompose the protein and fat in the milk, causing measurement errors, which cannot be completely avoided. The material of the cuvette can affect the refraction of light. In this study, a glass cuvette was used, and the measurement error in the visible light region can be ignored. The colorimetric dish was fixed vertically through a spring lock to ensure that light shined vertically onto the transparent surface.
# Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM
## 2 Materials and Methods
### Simulation Contact Model  
Considering the distinctive attributes of soda saline-alkali soil in Northeast China, characterized by the soil's propensity for bonding and elevated water content, we opted for the JKR model, which is particularly well-suited for small particles and materials exhibiting wet bonding properties, in our simulation analysis. Within the JKR model, the calculation of tangential contact force, normal damping force, and tangential damping force closely parallels that of the Hertz-Mindlin (no-slip) (HM) contact model. Within the JKR model, the tangential contact force, normal damping force, and tangential damping force are calculated similarly to the Hertz-Mindlin (no-slip) (HM) contact model, and the cohesive force explains the normal contact force in the JKR theory [30; 31]. In this paper, the macroscopic bonding characteristics between soil particles are explained by surface energy, and the contact force between particles can be expressed as follows:  
\[F_{JKR}=\frac{4E^{*}}{3R^{*}}\pi^{3}-4\sqrt{\pi\Delta\gamma E^{*}\pi^{3}} \tag{1}\]  
\[\delta_{n}=\frac{\alpha^{2}}{R^{*}}-\sqrt{\frac{4\pi\gamma\alpha}{E^{*}}} \tag{2}\]  
\[F_{t}^{c}=K_{t}\delta_{t} \tag{3}\]  
\[F_{n}^{d}=2\sqrt{\frac{5}{6}}\beta\sqrt{K_{n}m^{*}v_{n}} \tag{4}\]  
\[F_{t}^{d}=2\sqrt{\frac{5}{6}}\beta\sqrt{K_{t}m^{*}v_{t}} \tag{5}\]  
where \(F_{JKR}\) is the JKR normal contact force, N; \(F_{t}^{c}\), \(F_{n}^{d}\), and \(F_{t}^{d}\) are the tangential contact force, normal damping force, and tangential damping force, N; \(\delta_{n}\) is the normal overlap of particles, m; \(\delta_{t}\) is the tangential overlap of particles, m; \(\alpha\) is the contact radius of particles, m; \(\gamma\) is the surface energy, N/m; \(v_{n}\) and \(v_{t}\) are the normal and tangential components of the relative velocity of particles, m/s; \(E^{*}\) is the equivalent elastic modulus, Pa; and \(R^{*}\) isthe equivalent contact radius, m. The equivalent elastic modulus, \(E^{*}\), and the equivalent contact radius, \(R^{*}\), are defined as shown in Equations (6) and (7).  
\[\frac{1}{E}=\frac{1-v^{2}}{E_{1}}+\frac{1-v^{2}}{E_{2}} \tag{6}\]  
\[\frac{1}{R^{*}}=\frac{1}{R_{1}}+\frac{1}{R_{2}} \tag{7}\]  
In the equation, \(E_{1}\) and \(E_{2}\) are the elastic moduli of particles 1 and 2, Pa; \(v_{1}\) and \(v_{2}\) are [PERSON]'s ratios of particles 1 and 2; and \(R_{1}\) and \(R_{2}\) are the contact radii of particles 1 and 2, m.
## 4 The Proposed Methodology  
### The Fractal Signature Features  
Since we are working with images of different sizes, the fractal signatures are of different sizes. To avoid the size of the uneven signatures, we work with the energy \(E_{k}\), the variance \(\sigma_{k}^{2}\), and the entropy \(H_{k}\) of the signatures [53], computed like  
\[E_{k} = \sum_{j=1}^{n}F_{k}^{2}(j), \tag{4}\] \[\sigma_{k}^{2} = \frac{1}{n-1}\sum_{j=1}^{n}\left(F_{k}(j)-\overline{F}_{k}\right)^ {2},\] (5) \[H_{k} = -\sum_{j=1}^{n}\left[F_{k}(j)\cdot\log_{2}\left(F_{k}(j)\right) \right], \tag{6}\]  
where \(\overline{F}_{k}\) is the mean of \(F_{k}\), \(k=R,G,B\), and \(n\) is the length of signature \(F_{k}\), Equation (1).  
Because the color of the lesion is one of the features in the ABCDE rule, it is imperative to preserve the color information of the images. Hence, the energy, variance, and entropy of the three signatures of the image, one signature per RGB color-channel, compose the image's fractal signature, in the following manner.  
\[S_{F}=\big{[}E_{R},E_{G},E_{B},\sigma_{R}^{2},\sigma_{G}^{2},\sigma_{B}^{2},H _{R},H_{G},H_{B}\big{]}. \tag{7}\]  
As the fractal signatures have different lengths, to have feature vectors of the same length, we decided to use the following statistical features: energy, variance, and entropy. The energy measures the signature's strength. The variance measures the spread or dispersion of the signal around its mean value, and the entropy is the expected information content of the signal [53]. These three features obtained from each of the three 1D fractal signatures were added to the 100 deep-features extracted from the DenseNet-201. If the signal is viewing as a histogram, more statistical features could be explored like: skewness, kurtosis, mode, interquartile range, percentiles [21]. In addition, the statistical features obtained from grey level co-occurrence matrices (GLCMs, 2D signals) could be adapted to 1D signals [9; 11; 21]. That will be studied in the next step of this work.
# Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)
## 4 Discussion
In fact, the longer the flight duration, the higher the probability of a leap in the GNSS calculated position, due, for example, to an abrupt satellite constellation change. This allows us to conclude that the GNSS RTK sensor mostly influences the uncertainty of laser measurements. Consequently, a strong effort should be put into selection of the GNSS RTK, and a bigger economic investment should be devoted to this sensor. The new LasUAV is a low-cost laser scanner system; its hypothetical selling price (20,000 \(\upxi\)) would be lower than the price of the two most commonly used commercial all-integrated LiDAR systems for forestry application. From the hardware point of view, the relative incidence of each component in the total cost of the LasUAV is illustrated in Figure 13. The laser scanner is the sensor that most influences the cost (-10,000 \(\upxi\), 50%), followed by the INS (-6000 \(\upxi\), 30%), then the integration parts (2000 \(\upxi\), 10%), and finally the GNSS RTK receiver (-1000 \(\upxi\), 5%) and the logging and power (-1000 \(\upxi\), 5%). The levels of uncertainty in Z measurements in in-flight conditions of our LasUAV are comparable to those achieved by typical airborne laser scanners [2; 3] and are similar to those obtained by [13] (RMSE of the point cloud against ground control point equal to 0.34 m, standard deviation of tree height equal to 0.15 cm). LasUAV will provide efficiencies for projects and create new opportunities in areas such as linear corridors, dense vegetation, and steep mountains. In UAV laser scanning, the inherent direct georeferencing workflows do not require, for example, the redundancy required in many UAV photogrammetric workflows. Processing data to infer terrain is more straightforward if using an appropriate LiDAR sensor, and in mountainous areas, being able to reduce dependency on ground control points reduces on-site risk. The growing popularity of low-cost UAVs is resulting in the development of miniaturized laser scanner devices and GNSS RTK receivers increasingly at a low cost, which the forestry sector and  
Figure 13: The relative incidence of each component in the total cost of the LasUAV hardware. other disciplines such as plant physiology and phenotyping will take advantage of. As a matter of fact, laser scanning from a system such as LasUAV allows data acquisition for the accurate digital three-dimensional reconstruction of canopies. Variables originating from LiDAR data, such as leaf density, leaf angle, and curvature, can capture differences in canopy architecture and can be used to quantify actual photosynthetic productivity, which is usually difficult to estimate in complex canopies, partly because of a lack of high-resolution canopy structural data [21]. The integration of such data will establish links between vegetation structure, function, and physiology and consequently, will generate more accurate photosynthetic productivity estimation models.
## 5 Implementation  
The use case scenario described in Section 3.1 is implemented using material passports (MP), the IPFS for decentralised storage, backend servers, and Polkadot as the blockchain platform as shown in Figure 2. In the designed use case for our blockchain architecture, we define a system in which the manufacturer, logistics, and construction companies operate as separate entities, each with its own dedicated _parachain_. This specialised blockchain infrastructure enables tailored functionalities and governance models to meet the needs of each ecosystem participant. The manufacturer begins the product's life cycle by creating an MP that contains all relevant information about the product's origin, characteristics, and history. This MP is then pushed on the relay chain, which serves as the Polkadot network's central communication hub, ensuring interoperability across the entire blockchain. Ownership is a critical component of this architecture, and the smart contract deployed on the manufacturer's _parachain_ records the initial ownership status. It strictly enforces that only the recognised owner can transfer ownership rights, preventing unauthorised transactions.
# Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees
## 2 Description of Database
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**S. No.** & **Mine** & **Country** & **\(W\) (\(m\))** & **\(H\) (\(m\))** & \(W\)/\(H\)** & \(\sigma_{\text{acs}}\) (**MPa**) & \(\sigma_{p}\) (**MPa**) & **Pillar Stability** \\ \hline
1 & Nega Jamehari (Amritnagar) & India & 3.6 & 4.5 & 0.8 & 45 & 5.01 & Failed \\
2* & Nega Jamehari (Amritnagar) & India & 3.6 & 6 & 0.6 & 45 & 4.68 & Failed \\
3 & Begonia (Begonia) & India & 3.9 & 3 & 1.3 & 26 & 5.8 & Failed \\
4 & Bushar (Amali) & India & 4.5 & 5.4 & 0.83 & 25 & 3.61 & Failed \\
5 & Sendra Bangya (X) & India & 4.65 & 8.1 & 0.57 & 24 & 2.77 & Failed \\
6 & W. Chirmir (Main) & India & 5.4 & 3.75 & 1.44 & 45 & 8.12 & Failed \\
7 & Johilla top (Birsingpur) & India & 7.5 & 3.6 & 2.08 & 38 & 10.45 & Failed \\
8 & Lower Kajora (Pure Kajora) & India & 5.4 & 3.6 & 1.5 & 33 & 6.02 & Failed \\
9* & Lower Kajora (Pure Kajora) & India & 4.95 & 3.6 & 1.38 & 33 & 7.43 & Failed \\
10 & Jambed bottom (Sankarpur) & India & 4.5 & 4.8 & 0.94 & 47 & 4.2 & Failed \\
11 & Begunia (Ramnagar) & India & 2.85 & 1.8 & 1.58 & 26 & 7.76 & Failed \\
12 & Begunia (Ramnagar) & India & 3 & 1.8 & 1.67 & 26 & 6.17 & Failed \\
13 & Kankane (XIII) & India & 19.8 & 6.6 & 3 & 27 & 5.88 & Failed \\
14 & Kankanee (QIV) & India & 18.6 & 8.4 & 2.2 & 27 & 5.83 & Failed \\
15 & Ross (Bellampalli) & India & 5.4 & 3 & 1.8 & 48 & 4.01 & Stable \\
16 & Nega (Nimcha) & India & 9.9 & 6 & 1.7 & 50 & 3.09 & Stable \\
17 & Salarjung (Morgapiti) & India & 8.1 & 3 & 2.7 & 46 & 14.08 & Stable \\
18 & Rannagar (Ramnagar) & India & 9.9 & 2.7 & 3.7 & 28 & 5.2 & Stable \\
19* & Lower Kajora (Lachibrup) & India & 7.2 & 5.1 & 1.4 & 33 & 2.25 & Stable \\
20 & N. Salapur (Q), India & 9 & 5.1 & 1.8 & 21 & 2.08 & Stable \\
21* & Jambed top (Bankola) & India & 10.1 & 4.8 & 2.1 & 35 & 3.09 & Stable \\
22 & Jambad top (Bankola) & India & 6.3 & 3 & 2.1 & 35 & 5.2 & Stable \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of pillar stability data Reprinted with permission from Ref. [18]. Copyright © 2011 The Nonferrous Metals Society of China, Elsevier Ltd.  
For implementing the random tree and C4.5 DT in this research study, a total of 40 datasets were considered for the training dataset, and the other six datasets were considered as the testing datasets. The testing and training datasets are the same as used by [PERSON] et al. [18].
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 3 Double-Layer CS Framework-Based DOA Estimation  
In this paper, we focus on how to obtain high accuracy and efficient DOA estimation in a WSAN, which consists of an \(H\)-element ULA and one single FC. The array sensors are connected to the FC via low-power wireless links. Array sensors are battery powered and have limited computational capacities, while the FC is linked to the infrastructure and able to bear heavy processing tasks. Therefore, it is essential to shift as much of the processing burden to the FC as possible. Specifically,  
Figure 4: Histograms of the occurrence number for different sizes of block data loss under different packet sizes.  
Figure 3: Packet reception rate (\(PRR\)) and normalized transmission efficiency \(E\) versus payload length under different \(BER\).  
the array sensors just sample the impinging signals, perform computationally-simple operations when needed and transmit the data to the FC, while the complicated DOA estimation algorithm is implemented at the FC. Due to the lossy nature of low-power wireless links, the transmission process from the sensor array to the FC is subjected to a high packet loss rate. Data loss, especially block data loss resulting from a data packet with a large size being lost, damnifies the WSAN DOA estimation performance significantly. To eliminate the adverse effects of block data loss on DOA estimation, we propose the double-layer CS framework. The random packet loss during transmission is modeled as a passive CS process, and an active CS process is introduced at each array sensor to do a dimension-reduced projection on the acquired raw signal before transmission. Under the double-layer CS framework, DOA estimation can be implemented using two techniques, named DCS-DOA and DCS-DDOA, respectively. The entire process is described in Figure 5. For clarity, the notations are summarized in Table 1.