esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Application of DNN Models for Haulage Operation Planning  
To design and plan the truck haulage work at a mining site, a production manager must first predict and analyze the short-term and long-term ore production and crusher utilization. The DNN model designed in this study can be used to predict the ore production and crusher utilization, as it changes according to the daily work time, number of trucks, and truck dispatch interval, among others, and can efficiently design haulage tasks.  
In this study, an optimal DNN model was used to analyze the changes in ore production and crusher utilization according to the number of trucks and truck dispatch interval, while considering the operational conditions of the ore haulage work performed by the trucks on 5 November 2018. A daily working time of 13 h and 11 min (791 min), a truck load capacity of 30 tons, and a daily ore production target of 3000 tons were assumed. Moreover, it was assumed that the number of times the truck visits the loading area had already been determined according to the quality of the delivered ore. The probability of Loading 1 occurring was set to 0.27, while the probability of Loading 2 occurring was set to 0.73.  
Figure 13a presents the results of using the optimal DNN model to analyze the ore production and crusher utilization, according to the number of trucks. As the number of trucks contributing to the ore haulage work increased, the ore production and crusher utilization exhibited an increasing trend. To satisfy the daily ore production targets, four or more trucks must be used in the haulage work, and the crusher utilization in this case is 9% or higher. Figure 13b illustrates the results of using the optimal DNN model to analyze the changes in the ore production and crusher utilization according to the truck dispatch interval, assuming that four trucks contributed to the haulage work. Even when the truck dispatch interval increased, the ore production was maintained at approximately 3500 tons and  
Figure 12: Results of correlation analysis between ore production and crusher utilization of training data.  
the crusher utilization was maintained at approximately 9%. To maximize the ore production, it is helpful to set the truck dispatch interval to approximately 1 to 2 min.
## 6 Numerical Example
### Step 1: ASCII Representation and Binary Conversion:  
The letter \"H\" in ASCII is 72. The binary representation of 72 is \(M=01001000\).
## 6 Analysis and Conclusions
### Performance Evaluation  
To evaluate the feasibility of extending a TSMP network to communicate with a mobile agent, a sample network was simulated using MATLAB. An area was populated with randomly distributed network nodes at varying densities (see e.g., Figure 7) through which moved the mobile node. Time varying connectivity between nodes was modeled using a stochastic noise penalty atop the Friis equation [28]. This allows for the empirically observable random packet loss despite high average signal strength.  
Under the proposed scheme, the MAV communicates into the mesh during a slot frame in which it receives an ADV packet from a nearby stationary node. If the stationary nodes send ADVs too infrequently, the MAV may not receive any packets; on the other hand, if the nodes transmit too often, the MAV may encounter a packet collision, again resulting in a failed ADV. The optimum is reached when each node transmits an ADV with probability inversely proportional to the number of neighbors it can hear.  
In that case, and accounting for stochastic packet loss, the MAV received a successful ADV \(\approx 35\%\) of the time, regardless of density (see Figure 8). In those slot frames, then, data can be offloaded from the MAV into the mesh. Once in the mesh, the data can be transmitted to the base station as governed by the TSMP network parameters [13]. Depending on the desired metric, dropped data packets can be retransmitted to ensure reliability, or ignored to preserve latency.  
Figure 7: A simulated environment has stationary network nodes through which a MAV may travel. Network connectivity is indicated by dotted lines. Note that due to the nature of RF communication, connectivity is not geographical. Similarly, as the MAV travels around the network, the node to which it can communicate may change.  
This is less frequent than can be achieved with a single hop always-on point-to-point link. However, this scheme is inherently robust to scaling. It allows for reliable multi-hop communication even as the position of the MAV and the network topology change. The underlying TSMP network also eliminates the problem of network saturation due to broadcast flooding: since each packet transmission is scheduled and routed, the number of packets does not increase exponentially as the MAV gets farther from the base station. In that regard then, the reduced throughput is necessary to ensure that the MAV data can arrive at the base station.
###### Abstract  
For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand's six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales--diurnal, seasonal, and multiyear. The diurnal variation is represented by the four MODIS passages (10:00, 14:00, 22:00, and 02:00 local time) and the seasonal variation by summer and winter maps, with images covering a 14-year interval (2003-2016). Also, 126 Landsat scenes were processed to classify and map land cover changes for each city. To analyze and compare the SUHI patterns, a least-square Gaussian fitting method has been applied and the corresponding empirical metrics quantified. Such an approach represents, when applicable, an efficient quantitative tool to perform comparisons that a visual inspection of a great number of maps would not allow. Results point out that SUHI does not show significant seasonality differences, while SUHI in the daytime is a more evident phenomenon with respect to nighttime, mainly due to solar forcing and intense human activities and traffic. Across the 14 years, the biggest city, Bangkok, shows the highest SUHI maximum intensities during daytime, with values ranging between 4 \({}^{\circ}\)C and 6 \({}^{\circ}\)C; during nighttime, the intensities are rather similar for all the six cities, between 1 \({}^{\circ}\)C and 2 \({}^{\circ}\)C. However, these maximum intensities are not correlated with the urban growth over the years. For each city, the SUHI spatial extension represented by the Gaussian footprint is generally not affected by the urban area sprawl across the years, except for Bangkok and Chiang Mai, whose daytime SUHI footprints show a slight increase over the years. Orientation angle and central location of the fitted surface also provide information on the SUHI layout in relation to the land use of the urban texture.  
Keywords:land surface temperature; surface urban heat island; Thailand; MODIS; 2D-Gaussian surface +
Footnote †: journal: Remote Sensing, 10, 665; doi:10.3390/rs10050665
# Maintenance Time Prediction for Predictive Maintenance of Ship Engines
## 2 Materials and Methods
### Experimental Methods
The RMSE function uses the root of the MSE to reduce the error magnitude and convert it into values similar to the collected values, thereby facilitating analysis. It addresses the drawbacks of the MSE but remains dependent on the data scale and follows the standard deviation principle, as defined in Equation (7) [17, 18, 19]. The MAE function averages the absolute value of the error between the predicted and collected values; it is less affected by outliers than the other loss functions. This metric indicates the degree of the average error that depends on the data scale and is used to evaluate regression performance, as defined in Equation (8) [17, 19, 20, 21, 22]. \[MSE=\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2}, \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2}}, \tag{7}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}(|y_{i}-\hat{y}_{i}|), \tag{8}\]  
* \(n\): sampling number
* \(y_{i}\): collected value
* \(\overline{\hat{y}}_{i}\): predicted value  
Algorithm development and machine learning were implemented using Python TM 3. Python easily uses various libraries needed for data visualization, image processing, statistics, and other purposes; with open-source packages, various machine learning libraries can be used directly in Python [23, 24, 25]. Python was used in this study due to its high development efficiency. Its simple syntax construction reduces error rates in complex code, its concise code construction accelerates work, and it easily links with other programming languages and libraries [23, 24, 25].
## 3 Proposed Approach
### Speech Features Extraction  
In the domain of SER, the process of feature extraction holds a crucial role in capturing pertinent information from speech signals. This section presents our approach to feature engineering from audio recordings for emotion recognition tasks.  
The selection of specific features (Algorithm 1) such as Mel frequency cepstral coefficients (MFCCs), Mel spectrogram, Chroma, and Tonnetz is substantiated by their capability to capture distinct aspects of the audio signal, thereby enhancing the efficacy of subsequent emotion recognition tasks. The relevance of using these features is underscored by their unique contributions. MFCCs are widely employed in speech processing, since they encapsulate the short-term power spectrum of a sound, offering insights into its spectral characteristics. Mel Spectrogram representation offers valuable insights into the distribution of energy across different frequency bands in the Mel frequency scale, enriching our understanding of spectral dynamics. Chroma features captures the distribution of energy across the 12 pitch classes of the musical octave, providing crucial information about the harmonic content and tonal structure of the audio signal. Tonnetz features extracted from the harmonic component of the audio signal (tonal space), offer valuable insights into the tonal centroid, aiding in the characterization of harmonic relationships.  
After extracting time-frequency spectrogram-based features, the computation of statistical features derived from these outputs serves to reduce dimensionality while preserving significant information from the time-frequency spectrograms. These statistical features, represented by mean, standard deviation, and moments effectively summarize the distribution and characteristics of the audio signal.  
Additional features such as Pitch, Energy, Zero Crossing Rate, and RMS Energy are employed because they capture pitch-related, amplitude-related, and temporal characteristics, offering a comprehensive understanding of the speech signal's dynamics.  
By leveraging these diverse features, we construct a feature vector that encapsulates both spectral and temporal aspects of the audio signal. Subsequent normalization ensures that these features are uniformly scaled, facilitating the convergence and performance of machine learning algorithms in emotion recognition tasks. This approach of extracting statistical information from time-frequency spectrogram-based features yields an efficient and informative representation of the audio signal, conducive to accurate emotion classification.
## 6 Simulation and Analysis
### Analysis of Simulation Results
#### 6.2.1 Metallic Multi-phase Faults Test  
For the feeder containing IBDG, B4B5 is used as a protected feeder section, and different types (AB-phase-to-phase, ABg-phase-phase-to-ground, and ABC-3-phase) of metallic multi-phase faults are set at different locations (5%, 50%, and 95% from B4) of B4B5. The simulation results are shown in Table 3. Similarly, for the feeder containing SBDG, B7B8 is taken as the protected section, and the corresponding simulation results are shown in Table 4. It can be seen from Table 3 that when metallic multi-phase faults occur in the feeder section B4B5, the amplitude ratio of the PSCFC on both sides of feeder B4B5 is greater than 1.5, and the phase angle compensation coefficient compensates the phase of the PSCFC on the IBDG side so that the phase difference of the PSCFC on both sides of feeder B4B5 is 0. At this time, ACDP can trip sensitively, and the sensitivity is higher than TCDP. It can be seen from Table 4 that when metallic multi-phase faults occur in the feeder section B7B8, the amplitude ratio of the PSCFC on both sides of section B7B8 may be greater than 1.5 or less than 1.5. Due to the inertia of SBDG itself, the phase difference of PSCFC on both sides of feeder B7B8 is very small, and CDP can trip sensitively regardless of compensation.
## 4 Applications of Bulk SnS
A ZT value of 0.41 for bulk SnS was obtained by [PERSON] et al. at 848 K. A large Seebeck coefficient of 403 \(\upmu\)V K\({}^{-1}\) was observed at 848 K parallel to the pressing direction. The Seebeck coefficient was found to be double that prepared by mechanical alloying [75]. SnS for thermoelectric applications was also prepared via [PERSON] et al. via a mechanical alloying and spark plasma synthesis method. A Seebeck coefficient of 352 \(\upmu\)V K\({}^{-1}\) was obtained at 232 K along two directions [30]. A hydrothermal synthesis method developed by [PERSON] et al. has also been used to create bulk SnS for thermoelectric applications. The Seebeck coefficient was between 300 to 200 \(\upmu\)V K\({}^{-1}\) across 300 K to just over 700 K, with a distinct change around 350-500 K [76]. ZT values determined by a relaxation-time formalism-based theoretical model obtained by [PERSON] et al. was also found to match well to experimental values [77]. Figure 4: (**a**) Schematic of solar cell produced with a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) interlayer; (**b**) corresponding J-V curve [5]. Adapted with permission from _ACS Appl. Energy Mater._**2018**, 7, 3042–3047.
## 3 Research Methodology
### Data Collection and Analysis Method  
This study targeted manufacturing companies from different industrial sectors who were familiar with and had some experience in Industry 4.0 technologies to test the hypotheses. The study did not include the service sectors, since some questions were specifically developed for manufacturing sectors. In addition, the questionnaire aimed to collect data as a primary source to test the hypotheses of the model's constructs. Respondents, members of upper-level management, were targeted to fill out the questionnaire to increase the credibility of the gathered data. This study used partial least squares-structural equation modeling (PLS-SEM) as a statistical software tool for data analysis to test our hypotheses because of its powerful capability in analyzing small sample sizes and its efficiency in analyzing mediating variables and indirect relationships [72]. SmartPLS can be also directly used for the assessment of convergent validity, discriminant validity and reliability. It has a very user-friendly interface and it has very attractive graphical outputs. Its output is very flexible compared to other software packages.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 4 Case Application
### Calculation of Required Separation
#### 4.2.1 Conflict Frequency  
Assume there are four UAVs operating within a three-dimensional space of 40 km\(*\)10 km\(*\)2 km, with a set speed of 12 m/s each, as shown in Figure 10.  
Figure 10: UAVs in free flight.  
Figure 9: Normal distribution fitting plot of MAE data.  
The number of aircrafts per unit volume, the area of the conflict region, and the relative speed are as shown in Equations (13)-(15):  
\[\rho_{0}=\frac{4}{10\cdot 40\cdot 2}=0.005 \tag{13}\]  
\[\sigma_{\text{cft}}=\pi\cdot\left(D/1000\right)^{2}=3.14D^{2}\times 10^{-6} \tag{14}\]  
\[v_{\text{rel}}=\sqrt{\left(12\cdot 3.6\right)^{2}+\left(12\cdot 3.6\right)^{2}}=61.1 \tag{15}\]  
The conflict frequency is:  
\[Freq=\frac{N}{2}\cdot\rho_{0}\cdot\sigma_{\text{cft}}\cdot V_{\text{rel}}= \frac{4}{2}\times 0.005\times 3.14D^{2}\times 10^{-6}\times 61.1=1.92D^{2} \times 10^{-6} \tag{16}\]
## 4 Enhanced Series Resistance Model
### Model Verification and Predictions
#### 4.2.2 Impact of the Thickness of the Intrinsic a-Si:H(i) Layer  
The results of the simulations of cell SHJ\({}_{1}\) show that reducing the thickness of the a-Si:H(i) layer (\(t_{i}\)) while fixing the probability of hole reflection at the hetero-interface results in larger values of P\({}_{\text{peak}}\) and of the electric field at equilibrium, as shown in Table 4. Under illumination-SC, however, the total value of P\({}_{\text{peak}}\) including the reflected holes as well as the enhanced electric field at the hetero-interface are practically independent of \(t_{i}\), as seen in Table 4. Therefore, the relative boost in the electric field decreases from 100% when \(t_{i}\) = 5 nm to 61% when \(t_{i}\) = 2 nm and to 37% when the a-Si:H(i) layer is totally omitted. The field enhancement, however, is still large enough to fully deplete the a-Si:H(i) layer and hence to enhance its resistance. As the layer thickness is reduced, however, its resistance decreases proportionally which forces V\({}_{\text{SC}}\) to decrease as confirmed in Table 4. The total a-Si:H enhanced resistance \(R_{a-Si}\) estimated using Equation (5a), its components \(R_{a-Si(i)}\) in a-Si:H(i) estimated using Equation (5b), and \(R_{a-Si(p+)}\) in a-Si:H(p\({}^{+}\)) estimated using Equation (5c) are listed, respectively, in Columns A, B, and C of Table 4. The value of \(R_{a-Si(p+)}\) is practically independent of \(t_{i}\) and falls in the range 2.7 to 2.85 \(\Omega\) cm\({}^{2}\), which is expected since the a-Si:H(i) layer is fully depleted and hence its thickness does not influence the electric field passing through. When the a-Si:H(i) layer is totally omitted, the value of \(R_{a-Si(p+)}\) is estimated to be 2.55 \(\Omega\) cm\({}^{2}\), which is slightly smaller since all free holes in a-Si:H are confined now inside the doped layer. Note that the voltage drop \(\delta\) attributed to dark cell series resistance at short circuit condition remains unaffected by the a-Si:H(i) thickness, which confirms that the dark resistance of the double a-Si:H layer is relatively small in the present case. The I-V characteristics simulated under illumination for different values of a-Si:H(i) thickness displayed in Figure 9 show a continuous increase in the \(FF\) from 40% when \(t_{i}\) = 5 nm to 64% when the intrinsic layer is totally omitted. The latter value, however, is still small, which indicates that the excess hole depletion in the doped a-Si:H (p\({}^{+}\)) under illumination and the associated enhanced resistance still play an important role in the \(FF\) degradation even if the intrinsic a-Si:H(i) layer is removed.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & \multicolumn{2}{c}{**V\({}_{\text{SC}}\) – \(\delta\)**} & \multicolumn{2}{c}{**Max.**} & \multicolumn{2}{c}{**\(R_{s}\)**} \\  & **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}}}\) [\mathbf{m}\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 3 Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pattern Type** & **Key Variables** & **Parameter Range/Values** & **Notes** \\ \hline \multirow{4}{*}{**Circular**} & Altitude (AGL) & 20–80 m (relative to rooftop) & \multirow{2}{*}{Tilt angle oriented towards building center} \\  & Radius (B-Rad) & 10–50 m (from center point) & \\  & Tilt Angle & adjusted to face center & \\ \hline \multirow{4}{*}{**Surface**} & Distance (A-Offset) & 10–30 m & \multirow{4}{*}{Designed for facade scanning} \\  & Forward Overlap & 1 m, 2 m, 4 m & \\  & Side Overlap & 1 m, 2 m, 4 m & \\  & Tilt Angle & 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\) & \\ \hline \multirow{2}{*}{**Aerial**} & Altitude (AGL) & 20 m, 40 m & \multirow{2}{*}{Nadir/Oblique grid pattern} \\  & Overlap (Forward/Side) & Fixed at 80\% & \\  & Tilt Angle & 60\({}^{\circ}\)–75\({}^{\circ}\) & \\ \hline \multirow{2}{*}{**All Patterns**} & Positioning Accuracy & \multirow{2}{*}{Horizontal and Vertical \textless{2}\textless{3} cm} & \multirow{2}{*}{Verified via pre-flight simulation} \\  & (GNSS-RTK) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Defined shooting patterns and parameters. Figure 2: Shooting patterns list. To ensure consistency and data quality in collecting building envelopes using UAVs, the experimental implementation environment was specifically defined and controlled (Table 3). In this study, a commercial UAV model, DJI Mavic 3 Enterprise (DJI, Shenzhen, China), was used, and the camera mounted on it had the following specifications: effective pixel count of 5280 \(\times\) 3956 (\(\sim\)20.9 MP), 35 mm equivalent focal length of 24 mm (original focal length of 12.3 mm), and a fixed aperture value of F/2.8. To minimize image blur during shooting, the shutter speed was set to 1/2000 s, the ISO sensitivity was fixed at 400 to manage noise levels, and the color space was set to sRGB to ensure consistent color reproduction. White balance was set to manual, exposure mode was set to manual, and exposure compensation was maintained at 0. In addition, to minimize data bias caused by changes in light conditions, all shots were taken on a cloudy day with no direct sunlight (measured illuminance of 20,000 lux or less). Buildings with architectural features similar to those of apartment complexes were selected as the experimental subjects, and a total of 13,416 images were collected through 126 different flight path designs with an average flight time of 10 min (number of images per path: circular 30-41, front 18-411, aerial 40-125) (Figure 3). All collected images were saved in JPEG format with an average size of 7.8 MB, and each image was accompanied by detailed metadata, including the shooting time, GPS coordinates, and camera and lens information.
## 4 Methods  
The proposed graph structure registration framework, as shown in Figure 1, consists of four core modules: the Feature Construction Module (FCM), the Feature Learning Network Module (FLM), the Node Feature Matching Module (NFM), and the Geometric Consistency Discrimination Module (GCDM). The specific workflow is shown as follows:  
For two geometric point cloud subgraphs with different poses and positions, the key point features and edge features are first extracted using the feature construction module. This module integrates geometric features and complex network indicators to generate 15-dimensional node feature vectors and 5-dimensional edge feature vectors. Subsequently, these features are fed into the designed EKNet graph neural network. It employs GraphKAN layers and EdgeConv layers to perform hierarchical feature aggregation, thereby enhancing the feature vectors of each point.  
Next, based on a cosine similarity threshold, matching point pairs are filtered according to their feature vectors. The rotation and translation matrices are then solved using Singular Value Decomposition (SVD). Finally, through the Geometric Consistency Discrimination Module, the registration result is obtained after another round of SVD, aligning the point clouds into a unified coordinate system.  
Figure 1: The proposed graph structure registration framework.