esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Setup  
As shown in Figure 2, the experimental setup mainly consists of a main flume, a cascade permeable dam, and a water reservoir. The main dimension of the reservoir is \(660\times 420\times 385\) mm, to which quartz sand with a particle size of 0.05 mm was added to simulate the upstream water before the dam. The mixture in the reservoir was transported to the front of the main flume by a pump through a water pipe with an internal diameter of 15 mm, with a valve installed on the water pipe to stabilize the required discharge. The main flume made of Plexiglass is 900 \(\times\) 150 \(\times\) 250 mm, with a horizontal bottom. Four permeable dam bodies were placed in it, in the order of 1\(\#\), 2\(\#\), 3\(\#\), and 4\(\#\) along the flow direction. The permeable dam is made of perforated steel plates filled with gravels of 1-2 and 3-5 mm, with dimensions of 100 \(\times\) 150 \(\times\) 200 mm. In this study, the zone in front of 1\(\#\) dam is called the Stagnant zone, the zone between two adjacent dams is called the Deposition zone, and the interior of the four dam bodies is collectively called the Dam zone (see Figure 2c). During the filling process of dams, the gravel was compacted and the dry mass of the gravel was recorded to ensure that the porosity of the dam was close for each group and the dams were stable at all flow discharges. After the beginning of the experiments, water levels were recorded every 5 min and water samples of 50 mL were taken every 10 min from the main flume and reservoir. The turbidity was measured by HACH 2100Q turbidity meter. The triangle in Figure 2a marks the water sampling position in the experiments. In addition, samples were taken from the reservoir frequently and the turbidity was measured immediately, then quartz sand was added to the reservoir in time to maintain the required inflow sediment concentration.  
Figure 2: Schematic diagram of the experimental setup: (**a**) Side view; (**b**) Top view; (**c**) Layout of the cascade permeable dam; (**d**) Sediment interception in pre-dam.The triangle in (**a**) marks the water sampling position in the experiments.
## 2 Theoretical Considerations  
### Ammonia-Water System  
In aqueous solution, total ammonia exists as both free ammonia (\(NH_{3}\)) and ammonium ions (\(NH_{4}^{+}\)), as shown in Equation (1), and the water is dissociated into ions, as shown in Equation (2) [10].  
\[NH_{3}+H_{2}O\leftrightarrow NH_{4}^{+}+OH^{-}, \tag{1}\]  
\[H_{2}O\leftrightarrow H^{+}+OH^{-}, \tag{2}\]  
The total ammonia concentration in the solution is expressed by Equation (3).  
\[\left[NH_{3,\text{L}}\right]^{T}=\left[NH_{3,\text{L}}\right]+\left[NH_{4}^{+} \right], \tag{3}\]  
where \(\left[NH_{3,\text{L}}\right]^{T}\), \(\left[NH_{3,\text{L}}\right]\), and \(\left[NH_{4}^{+}\right]\) mean the molar concentration (mol/L) of total ammonia, free ammonia, and ammonium ion, respectively.  
The ionization constants for water (\(K_{H_{2}O}\)) and ammonia (\(K_{NH_{3}}\)) are known in Equations (4) and (5), respectively.  
\[K_{H_{2}O}=\left[H^{+}\right]\left[OH^{-}\right], \tag{4}\]  
\[K_{NH_{3}}=\frac{\left[NH_{4}^{+}\right]\left[OH^{-}\right]}{\left[NH_{3,\text {L}}\right]}, \tag{5}\]  
where \(\left[H^{+}\right]\) and \(\left[OH^{-}\right]\) are the molar concentration (mol/L) of hydronium ion and hydroxide ion, respectively.  
In addition, the ionization constants are expressed as a function of absolute temperature (\(K\)), as shown in Equations (6) and (7) [6; 10; 11; 20].  
\[\ln K_{H_{2}O}=140.932-13445.9/T-22.4773\ln T, \tag{6}\]  
\[\ln K_{NH_{3}}=97.976-5930.7/T-15.063\ln T-0.01127T, \tag{7}\]
# Voxel-Based Digital Twin Framework for Earthwork Construction
###### Abstract  
Earthwork construction presents significant challenges due to its unique characteristics, including irregular topography, inhomogeneous geotechnical properties, dynamic operations involving heavy equipment, and continuous terrain updates over time. Existing methods often fail to accurately capture these complexities, support semantic attributes, simulate realistic equipment-environment interactions, and update the model dynamically during construction. Moreover, most current digital solutions lack an integrated framework capable of linking geotechnical semantics with construction progress in a continuously evolving terrain. This study introduces a novel, voxel-based digital twin framework tailored for earthwork construction. Unlike previous studies that relied on surface, mesh, or layer-based representations, our approach leverages semantically enriched voxelization to encode spatial, material, and behavioral attributes at a high resolution. The proposed framework connects the physical and digital representations of the earthwork environment and is structured into five modules. The data acquisition module gathers terrain, geotechnical, design, and construction data. Virtual models are created for the earthwork in as-planned and as-built models. The digital twin core module utilizes voxels to create a realistic earthwork environment that integrates the as-planned and as-built models, facilitating model-equipment interaction and updating models for progress monitoring. The visualization and simulation module enables model-equipment interaction based on evolving as-built conditions. Finally, the monitoring and analysis module provides volumetric progress insights, semantic material information, and excavation tracking. The key innovation of this framework lies in multi-resolution voxel modeling, semantic mapping of geotechnical properties, and supporting dynamic updates during ongoing construction, enabling model-equipment interaction and material-specific construction progress monitoring. The framework is validated through real-world case studies, demonstrating its effectiveness in providing realistic representations, model-equipment interactions, and supporting progress information and operational insights.  
digital twin; progress monitoring; operational-level simulation; earthwork construction; voxelization; game engines; as-built BIM modeling; visualization; unity 3D ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Computational Simulation Models
### Smart Transformer
#### Isolated DC/DC Converter  
The isolated DC/DC converter was modeled following the principles of an ideal Dual-Active Bridge (DAB) DC/DC converter, according to the approach presented in [37]. The power output in the LV side of the DAB Converter (\(PDC_{LV}\)) can be obtained using Equations (8) and (9), defined as a function of the MV DC voltage in the primary stage \(VDC_{MV}\), the LV DC voltage in the secondary stage \(VDC_{LV}\), the transformation ratio \(N\), the primary-referred leakage reactance \(X_{L}\) of the High-Frequency Transformer (HFT) and the phase-shift \(\phi\) between the square waveforms applied to the HFT.  
\[PDC_{LV}=\frac{VDC_{MV}^{2}}{X_{L}}d\phi\left(1-\frac{|\phi|}{\pi}\right) \tag{8}\]  
\[d=\frac{VDC_{LV}}{NVDC_{MV}} \tag{9}\]  
Considering the results presented in [37], the dynamics of a DAB converter is much faster than the dynamics related to voltage variations in the MV DC bus. Therefore, two assumptions can be made in the modeling of the isolated DC/DC converter. First, the DC/DC power electronic converters can be modeled using two controllable sources according to the DAM approach [34], which are electrically independent in order to consider galvanic isolation. Second, the mathematical relations presented in Equations (10) and (11) are valid for an ideal DAB converter, where \(PDC_{MV}\) is the converter input power, \(IDAB_{MV}\) is the DC current in the primary stage, \(IDAB_{LV}\) is the DC current in the secondary stage and the remaining variables are as defined for Equation (8). Given this, the HFT and the control loops needed to regulate the \(d\) and \(\phi\) variables in Equation (8) can be replaced by simplified feedback control loops depending only on measured voltages and currents and on the transformation ratio, as shown in Figure 4. The transformation ratio \(N\) is assumed to be fixed, in order to regulate LV DC bus as a function of the DC voltage in the MV DC bus.  
\[PDC_{LV}=PDC_{MV}=VDC_{MV}IDAB_{MV}\Rightarrow IDAB_{MV}=\frac{VDC_{MV}}{X_{L }}d\phi\left(1-\frac{|\phi|}{\pi}\right) \tag{10}\]  
\[VDC_{MV}IDAB_{MV}=VDC_{LV}IDAB_{LV}\iffVDC_{MV}\frac{IDAB_{LV}}{N}=\frac{VDC_{MV}}{N}IDAB _{LV} \tag{11}\]
## 3 Case Study Analysis of Online PD Monitoring in Power Transformers
### Transformer B
#### 3.2.2 Results of Partial Discharge Source Localization  
Measurements conducted to locate the defect, which lasted from 10:45 to 14:00, were accompanied by parallel UHF monitoring that revealed very high PD activity. During this period, the average relative amplitude of UHF pulses was 83.9%, and the average number of pulses per 5 min was 998. Since the monitoring system recorded acoustic pulses exclusively near the L3 phase on the high-voltage side, the search for the PD source was confined to this area of the transformer tank. To precisely locate the defect, acoustic emission pulses were recorded at 18 measurement points, designated as P1-P18, whose coordinates are provided in Table 7.  
The locations of the AE transducers and the coordinate system are illustrated in Figure 12a, while sample time waveforms of PD pulses recorded at points P1, P2, P3, and P4, with the signal onsets marked (red lines), are shown in Figure 12b. Figure 12c presents a graphical representation of the PD source localization results (all solutions to the TDoA equation systems). Table 8 provides the ranges and modal values of the PD source coordinates.  
An analysis of the results indicates that the scatter of values in the \(X\) and \(Y\) axes is small compared to that in the \(Z\) axis, which reached as much as 90 cm. This may suggest a widespread defect developing, for example, along the windings, as well as along the pressboard barrier or strip. Given that the \(Y\)-axis (depth) values are relatively close to the tank (with the most frequently occurring value being \(Y=22\) cm), another probable cause of the PDs could be damaged lead insulation (e.g., micro-cracks, material aging, or localized overheating).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**The Range of Determined Coordinates of the PD Source**} \\ \hline \(X\) (m) & \(Y\) (m) & \(Z\) (m) \\ \hline
0.31–0.61 & 0.08–0.34 & 0.42–1.32 \\ \hline \multicolumn{3}{c}{**The Modal Value of the PD Source Coordinates**} \\ \hline \(X\) (m) & \(Y\) (m) & \(Y\) (m) \\ \hline
0.44 & 0.22 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ranges and modal values of the \(X\), \(Y\), and \(Z\) coordinates for the estimated location of the PD source in the tested ‘Transformer B’.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Measurement**} & \multicolumn{2}{c}{**AE Sensor Coordinates**} & \multicolumn{2}{c}{**Measurement**} & \multicolumn{2}{c}{**AE Sensor Coordinates**} \\ \cline{2-7}
**Point Designation** & \(X\) (m) & \(Y\) (m) & \(X\) (m) & **Point Designation** & \(X\) (m) & \(Y\) (m) & \(Z\) (m) \\ \hline P1 & 0.09 & 0 & 0.73 & P10 & 0.75 & 0.05 & 1.00 \\ P2 & 0.38 & 0 & 0.70 & P11 & 0.40 & 0 & 0.85 \\ P3 & 0.41 & 0 & 0.91 & P12 & 0.09 & 0 & 1.50 \\ P4 & 0.13 & 0 & 0.99 & P13 & 0.11
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 4 Case Study
### Fault Scenario I  
Baseline eigenvalues are estimated as seen in Figure 13:  
Clusters are generated using \(k\)-means clustering and the EDC, as seen in Figure 14:  
The solution successfully detects a C-Phase to ground fault with a fault resistance of 10 \(\Omega\), as illustrated in Figure 15:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bus** & **Generation** & \multicolumn{2}{c}{**Demand**} & \multicolumn{2}{c}{**Bus Voltage**} \\ \hline  & MW & MVAR & MW & MVAR & Voltage (PU) & Angle \\
1 & 130 & \(-7.4\) & 0 & 0 & 1.06 & 0 \\
2 & 40 & 30 & 20 & 10 & 1.047 & \(-2.806\) \\
3 & 0 & 0 & 45 & 15 & 1.024 & \(-4.997\) \\
4 & 0 & 0 & 40 & 5 & 1.023 & \(-5.329\) \\
5 & 0 & 0 & 60 & 10 & 1.017 & \(-6.1503\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power flow results of the 5-Bus system.  
Figure 14: Clusters.  
Figure 13: Baseline eigenvalues.  
Statistics for this case study are presented in Table 3:  
\begin{tabular}{c c c} \hline \hline
**Euclidean Distance Statistics** & **Normal Conditions** & **Fault Conditions** \\ \hline Mean & 27.7125 & 0.3400 \\ Standard Deviation & 18.9314 & 0.2334 \\ \hline \hline \end{tabular}  
The solution successfully detects a C-phase to ground fault with a fault resistance of 100 \(\Omega\), as illustrated in Figure 16:  
\begin{tabular}{c c c} \hline \hline
**Euclidean Distance Statistics** & **Normal Conditions** & **Fault Conditions** \\ \hline Mean & 27.7125 & 1.3481 \\ Standard Deviation & 18.9314 & 0.8519 \\ \hline \hline \end{tabular}  
The solution successfully detects a C-phase to ground fault with a fault resistance of 1000 \(\Omega\), as illustrated in Figure 17:  
Figure 16: 100 \(\Omega\) fault identified.  
Figure 15: 10 \(\Omega\) fault identified.  
\begin{table} \end{table}
Table 3: 10 \(\Omega\) fault statistics.  
Statistics for this case study are presented in Table 5:  
\begin{tabular}{c c c} \hline \hline
**Euclidean Distance Statistics** & **Normal Conditions** & **Fault Conditions** \\ \hline Mean & 27.7125 & 171.1992 \\ Standard Deviation & 18.9314 & 228.4471 \\ \hline \hline \end{tabular}
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 2 Background
### Trust-Based Routing  
Once an inside attacker is detected, a node normally will find a different route to avoid the attacker. We use the popular greedy perimeter stateless routing (GPSR) [29] as an example to show how a trust mechanism can help to detect and avoid inside packet drop attackers.  
Consider a network with 20 nodes as shown in Figure 1. Node 3 relays its packets as well as those from nodes 4, 5, 9, 10, and 15 to node 2, which will then send them to the BS based on GPSR (depicted by solid lines with arrowhead). In a trust mechanism, node 3 will use its watchdog to monitor node 2.  
When node 2 drops packets from node 3, node 3 will re-evaluate the trust value of node 2. If the trust value falls below the trust threshold value, node 3 will treat node 2 as an inside attacker. A trust-based routing algorithm will then find a new routing path to avoid node 2. In this case, node 3 will forward the packets to node 7, hoping that node 7 will deliver the packets to the BS (as shown by the dotted lines with arrowheads in Figure 1).
Molecules20152005201520152001005220010052 molecules  
**ISSN 1420-3049**  
www.mdpi.com/journal/molecules  
Effect of Monomer Dosing Rate in the Preparation of Mesoporous Polystyrene Nanoparticles by Semiconinuous Heterophase Polymerization  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2 and [PERSON] 1,* 1 Departamento de Procesos de Polimerizacion, Centro de Investigacion en Quimica Aplicada, Blvd. [PERSON] No. 140, C. P. 25294 Saltillo, Coahuila, Mexico**  
Departamentos de Quimica e Ingenieria Quimica, Centro Universitario de Ciencias Exactas e Ingenierias, Universidad de Guadalajara, Blvd. [PERSON] No. 1451, C. P. 44430 Guadalajara, Jalisco, Mexico**  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +52-844-4389847; Fax: +52-844-4389839.  
Academic Editor: [PERSON]  
_Received: 15 October 2014 / Accepted: 15 December 2014 / Published: 23 December 2014_  
**Abstract:** The semicontinuous heterophase polymerization of styrene in the presence of cross-linking and porogen agents was carried out. Latexes with close to 20% solid content, which contained mesoporous nanoparticles with 28 nm in average diameters, up to 0.5 cm\({}^{3}\)/g in porosity and 6-8 nm in pore diameters were obtained. By varying the monomer dosing rate over the micellar solution, an unexpected direct dependence of instantaneous conversion on the monomer dosing rate was found. This was ascribed to the higher average number of radicals per particle attained in the polymerization at the higher dosing rate, which in turn would arise from the higher gel percentage in the polymer. It is believed that the cross-linked chains prevent encounters between radicals, delaying the bimolecular termination reactions and allowing the existence of more than one radical inside the particles, which in turn increases the propagation rate.  
**Keywords:** mesoporous polystyrene nanoparticles; semicontinuous heterophase polymerization; monomer dosing rate effect
# Autonomous Active Tag Using Energy Harvesting Strategies
## 3 Results
### Complete Tag
#### 3.2.2 Transmission Scenario  
The data are transmitted periodically at a rate of one package per second. Figure 8 displays the currents for transmitting one datum and a data package with 4.2 V in the supercapacitor. The lower consumption line pertains to the microcontroller due to its continuous active mode. The time needed to perform the transmission of a data package is higher than that needed to perform a transmission of one datum. In addition, the consumption for both graphics are similar for each element, as can be seen in the values of the figure.  
Figure 8: Current draw by the tag in the transmission scenario at 4.2 V in the supercapacitor. The upper graphic shows the transmission of one datum and the lower graphic the transmission of all data. The numbers represent which element is the one that has the largest contribution to the consumption of the tag in that interval in each case: 1 is for the transceiver, 2 is for the EEPROM and 3 is for the microcontroller. There is a value per number to represent the average consumption of each zone in the two graphics.  
The time needed to complete one of these transmissions is approximately 50 milliseconds for a package with a simple datum and 450 milliseconds for a data package of maximum length, as can be seen in Figure 8.  
The associated energy to transmit one datum starts with the peak in the EEPROM. The value is calculated with the sum of the EEPROM energy, 16.7 \(\upmu\)J, the transceiver energy, 127.9 \(\upmu\)J, and the microcontroller energy between the last two, 313.6 \(\upmu\)J. The total is 458.2 \(\upmu\)J for one datum.  
In the case of all data it is calculated in the same way than for one datum. The values are: 272.4 \(\upmu\)J for the EEPROM, 1.17 mJ for the transceiver and 2.54 mJ for the microcontroller. The total is 3.98 mJ for all data.
## 6 Conclusions  
The work in this paper mainly proposes an end-to-end model called the TIN to solve the LTR problem in engineering. First, a text module was introduced as a supplementary part of the visual feature mechanism with CLIP, which provides the model with an inherent multimodule advantage. Then, visual attention further improved the feature extraction process by strengthening the correlations between pixels in different channels. Finally, a seesaw loss was used to balance the weights between different categories. TIN achieved a state-of-the-art performance without bells and whistles on both datasets, outperforming the advanced classifiers developed in recent years. This demonstrates that text-supervised signals are important in the field of vision, especially in LTR, and can be an effective means of complementing the existing information. This work focuses on long-tailed image classification tasks, but the proposed approach is generalizable and may benefit other applications. For example, TIN can be applied to an image segmentation task. We can design a set of keywords that correspond to different detection categories in the image segmentation task. Since the final output feature map of the segmentation task is the same size as the input and has a high level of fine-grained information, we can match the similarities between each layer of the segmented feature map with the keywords to achieve better classification results. Although the proposed TIN achieved a good performance on multiple long-tailed recognition benchmarks, it still has some flaws. First, due to the limited text corpus, our method heavily relies on the use of pre-trained weights to learn high-quality language representation. Second, these results are trained on two Nvidia 3090, which are demanding graphics cards that are not easy to deploy. To allow for a better use in industrial equipment, the latter could be used in an attempt to simplify the model in terms of model quantization and pruning.  
Figure 8: Results visualization: The results of the other models and our network are shown in each picture’s upper left and right corners, respectively. Our model is more robust to rare classes and can recognize pictures more accurately.  
**Author Contributions:** F.Y.: investigation, resources, writing original draft. H.Z.: Methodology, software, validation, datacuration. Y.L. ([PERSON]): formal analysis, visualization. Y.Y.: conceptualization, writing review and editing, project administration. Y.L.([PERSON]): supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Jiangsu Provincial Key Research and Development Program (BE2020006-2) and Jiangsu Province Postgraduate Practice Innovation Program Project (SJCX22_0356).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 3 Feature Extraction Based on TMIs
### Tchebichef Polynomials Expansion  
From Equations (4) and (5), the scaled discrete orthogonal Tchebichef polynomials \(\widetilde{t}_{p}(x)\) can be expanded into a power series as [35]:  
\[\widetilde{t}_{p}(x)=\sum_{k=0}^{p}C(p,k)(-x)_{k} \tag{8}\]  
where the coefficient \(C(p,k)\) is given by:  
\[C(p,k)=\frac{(-1)^{p}}{\sqrt{\rho(p,N)}}\frac{(p+k)!}{(p-k)!(k!)^{2}}\frac{(N -k-1)!}{(N-p-1)!} \tag{9}\]  
Furthermore, using the Stirling numbers of the first kind \(s_{1}(k,i)\), the Pochhammer symbol \(\left(x\right)_{k}\) can be expressed as:  
\[\left(x\right)_{k}=\sum_{i=0}^{k}\left(-1\right)^{k-i}s_{1}(k,i)x^{i} \tag{10}\]where \(s_{1}(k,i)\) satisfy the following recurrence relations:  
\[\begin{array}{l}s_{1}(0,0)=1\\ s_{1}(0,i)=s_{1}(k,0)=0\\ s_{1}(k,i)=s_{1}(k-1,i-1)-(k-1)s_{1}(k-1,i)\end{array} \tag{11}\]  
with \(k\geq 1\), \(i\geq 1\).  
Substituting Equation (10) into Equation (8), the scaled Tchebichef polynomials \(\widetilde{t}_{p}(x)\) can be rewritten as:  
\[\widetilde{t}_{p}(x)=\sum\limits_{k=0}^{p}\sum\limits_{i=0}^{k}(-1)^{k}C(p,k) s_{1}(k,i)x^{i}=\sum\limits_{i=0}^{p}B(i)x^{i} \tag{12}\]  
where \(B(i)=\sum\limits_{k=i}^{p}{(-1)^{k}C(p,k)s_{1}(k,i)}\).