esa-sceva/satcom-chunk-collection huggingface dataset:
# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
## 4 Conclusions  
This study conducted a triple validation of calibrated building energy models, using dry-bulb indoor temperature, total energy consumption, and CO\({}_{2}\) data as evaluation criteria. The validation framework adhered to the CIBSE TM63 standards for temperature, a zero energy use target during free oscillation periods, and the ASTM D5157 standards for air infiltration accuracy based on CO\({}_{2}\). Two calibration methods were employed: the classic method, which adjusted air capacitance, internal mass, and air infiltration, and a novel approach incorporating empirical air infiltration data, which resulted in the decay and REG models.  
In response to the first research question, both the decay and REG models met temperature reliability standards, with the REG model reducing mean absolute error (MAE) by 29% and the decay model lowering energy consumption by 31%, compared to the classic model, while maintaining similar temperature performance. This innovative approach provided more realistic model parameters, suggesting that empirical air infiltration data improve the model's physical fidelity.  
The second research question evaluated the REG model's capability to predict thermal conditions, leveraging its previous validation for indoor air quality. Findings indicated that REG models showed similar performance to the decay model, demonstrating its robustness over a period five times longer than the training phase, thereby confirming the reliability of the approach.  
Addressing the third research question, the classic model's infiltration did not meet the ASTM D5157 standards with R\({}^{2}\) values of 54% and 40%, while the required value is \(\geq\)90%. This indicates that its infiltration parameter acted as a tuning factor rather than a physically accurate representation of air leakage.  
In summary, this study highlights the importance of triple validation. Unlike single-variable temperature calibration, the multi-faceted approach demonstrated here reveals the sensitivity of building models to variations in air infiltration. Using arbitrary infiltration values can lead to significant errors, potentially compromising retrofit projects aimed at reducing air leakage. Limitations and recommendations for future research are discussed in the following section.
## 2 Related Work
However, this study did not consider the case where the speed of vehicles is changing during the time t = t\({}_{1}\)-t\({}_{0}\). It also delivers higher PDR values with limited network performance for the urban environment. [PERSON] et al. [26] suggested a novel Replication-Aware Data Dissemination (RADD) protocol for vehicular network. It is based on the idea where the vehicle position was initially estimated, and then the Bloom filter was utilized to find out a similar location. The results were examined for various evaluation metrics by applying different algorithms for route selection, position estimation, and data dissemination. However, this study did not consider the security aspects of data dissemination with the mobility of the vehicles. [PERSON] et al. [27] focused on the Directional-LAR (D-LAR) protocol utilizes in the VANETs for the ultra-dense network scenario. The idea is to calculate the hop count and the link life to estimate the path throughput. However, this study does not use a realistic mobility model to evaluate the protocol, and was not assessed with other quality of service metrics such as PDR, average E2E delay, and route costs. [PERSON] al. [28] suggested the Optimized Link State Routing (OLSR) protocol for the VANETs. This scheme could be easily integrated into the existing operating system without any change in the formats of the IP message headers. The results show that the utilization of OLSR is highly suitable to the higher density networks, especially in the applications with a shorter transmission delay. Moreover, this study evaluates some metaheuristic algorithms like the Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), and the Simulated Annealing (SA) for determining the automatically optimized routing protocol configurations. The observations noted for the simulation results are as follows:  
* GA was seen to be the highest-ranking algorithm since it outperformed the other algorithms and provided solutions to the defined optimization problems;
* PSO offered the best trade-off between the performance and the time required for the execution. It can also help to achieve optimum quality of service and the routing overhead;* Using the optimized configurations can decrease the routing load that was generated by the OLSR;
* The validation experiments that the optimized configurations are able to decrease the overall workload of the network;
* The automatically tuned OLSR was seen to be more scalable as compared to standard versions, since it has a low probability of being affected by the congestion problems and the medium access.
###### Abstract  
It has been proven that the bioenosis of microalgae and bacteria improves the chemical properties of biomass for its use in anaerobic digestion. However, this anaerobic digestion can be limited by the strong, compact, and complex structure of granulated biomass. Therefore, there is a need to search for an effective method for microalgal-bacterial granular sludge pretreatment, which has not been undertaken in previous scientific works. In this study, ultrasonic pretreatment was used to determine the effects of sonication on anaerobic digestion efficiency. Anaerobic digestion was performed in batch respirometric reactors. It was found that the ultrasonic pretreatment enhanced the biomass solubility; thus, the organic matter concentration increased more than six times compared to the variant without pretreatment. The study showed a positive effect of sonication on the kinetics of the anaerobic process and methane production. The highest methane yield was found in the variants in which the ultrasonication lasted from 150 s to 200 s, and this yield was from 534 \(\pm\) 16 mL CH\({}_{4}\)/g VS to 561 \(\pm\) 17 mL CH\({}_{4}\)/g VS. The data analysis confirmed strong correlations between the pretreatment time, the amount of biogas and methane production, and the gross energy gain. The highest net energy output and net energy gain were obtained for 150 s of sonication, and, respectively, were 4.21 \(\pm\) 0.17 Wh/g VS and 1.19 \(\pm\) 0.18 Wh/g VS.  
Keywords:algal-bacterial granular sludge; ultrasonic disintegration; ultrasonic pretreatment; biogas; methane; energy gain assessment +
Footnote †: journal: _Sciences_
## 6 Conclusions
### Limitations and Future Research Directions  
Although this study applied elevation-based 3D object generation and WIT texture mapping, several limitations remain:  
* This research was conducted using static spatial data, and the integration of real-time captured imagery was not considered.
* While CityGML 3.0 is well suited for representing building exteriors, it has limitations in detailed indoor modeling.
* This study did not address the challenges associated with complex textures on non-rectangular surfaces or dynamic lighting conditions, which are critical factors in realistic urban modeling.  
To address these challenges, future research should focus on the following:  
Integration of real-time data to enhance the dynamic adaptability of 3D city models.  
Optimization of large-scale models to improve computational efficiency for extensive urban environments.  
Fusion with BIM (building information modeling) data to enable detailed indoor representation and improve semantic data integration.  
Domain-specific optimized modeling approaches, tailored for applications such as smart cities, disaster management, and digital twins.  
Development of advanced texture mapping techniques capable of handling non-rectangular surfaces and adapting to dynamic lighting conditions to improve realism and applicability in real-world scenarios.  
By overcoming these limitations, future research can broaden the applicability of 3D urban modeling technologies, offering more effective solutions across various industries and scientific disciplines.  
Conceptualization, J.K. ([PERSON]), D.K. and J.B.; methodology, J.K. ([PERSON]), D.K. and J.B.; software, J.K. ([PERSON]) and D.K.; validation, J.B. and J.K. ([PERSON]); formal analysis, J.K. ([PERSON]) and D.K.; investigation, J.L. and H.L.; data curation, J.K. ([PERSON]) and D.K.; writing--original draft preparation, J.K. ([PERSON]) and D.K.; writing--review and editing, J.K. ([PERSON]) and D.K.; visualization, D.K.; supervision, J.B.; project administration, J.K. ([PERSON]); funding acquisition, J.K. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Land, Infrastructure and Transport of Korea and the Korea Agency for Infrastructure Technology Advancement (KAIA) through the Research Center for Digital Land Information under Grant No. RS-2022-00143804.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Land Use and Land Cover Assessment and Change Detection
Similarly, areas of evergreen forest in 2014 were converted into perennial trees and orchards and scrub forests in 2019. Furthermore, according to the pattern of LULC change identified in a previous study by [PERSON] [21] and the current study, the urban and built-up areas will continuously increase in the near future. Several potential causes of the transformation of the land use pattern in Phuket Island have been identified, such as rapid economic development, registered and non-registered population growth, and tourist growth. As a result, hotels and recreational and commercial areas have been constructed to support tourism. Phuket Island's urban and built-up areas have been primarily expanded by converting perennial trees and orchards and idle land. This finding is consistent with previous studies [54; 55], \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & \\ \cline{3-14}  & \multirow{3}{*}{**LULC Types**} & \multicolumn{1}{c}{**Ur**} & \multicolumn{1}{c}{**Pa**} & \multicolumn{1}{c}{**Fch**} & \multicolumn{1}{c}{**Po**} & \multicolumn{1}{c}{**Aq**} & \multicolumn{1}{c}{**Id**} & \multicolumn{1}{c}{**Ef**} & \multicolumn{1}{c}{**Mf**} & \multicolumn{1}{c}{**Sf**} & \multicolumn{1}{c}{**Wa**} & \multicolumn{1}{c}{**Mi**} & \multicolumn{1}{c}{**Total**} \\ \hline  & Urban and built-up area (Ur) & 161 & \multirow{3}{*}{7} & & & & & & & & & & 161 \\  & \multicolumn{1}{c}{Paddy field (Pa)} & & & & & & & & & & & & 8 \\  & \multicolumn{1}{c}{Field crop and horticulture (Fch)} & & & & & & & & & & & 13 \\  & \multicolumn{1}{c}{Perennial trees and orchards (Po)} & & & & & & & & & & & & 206 \\  & \multicolumn{1}{c}{Aquaculture (Aq)} & & & & & 17 & 1 & & & 1 & & 19 \\  & \multicolumn{1}{c}{Idle land (Id)} & & & & 1 & & 49 & 1 & 1 & 1 & 53 \\  & \multicolumn{1}{c}{Evergreen forest (Ef)} & & & & & & & 88 & & & & 88 \\  & \multicolumn{1}{c}{Mangrove forest (Mf)} & & & & & & & & 36 & & & 36 \\  & \multicolumn{1}{c}{Scrub forest (Sf)} & & & & & 1 & 2 & & 36 & & & 39 \\  & \multicolumn{1}{c}{Waterbody (Wa)} & & & & & & & & & & & 26 \\  & \multicolumn{1}{c}{Miscellaneous land (Mi)} & & & & & & & & & & & 11 & 11 \\ \hline  & Total & 162 & 7 & 12 & 193 & 17 & 56 & 98 & 36 & 41 & 27 & 11 & 660 \\ \hline  & \multicolumn{1}{c}{Producer’s accuracy} & 99.38 & 100.00 & 100.00 & 98.96 & 100.00 & 87.50 & 89.80 & 100.00 & 87.80 & 96.30 & 100.00 \\ \hline  & \multicolumn{1}{c}{User’s accuracy} & 100.00 & 87.50 & 92.31 & 92.72 & 89.47 & 92.45 & 100.00 & 100.00 & 92.31 & 100.00 & 100.00 \\ \hline  & \multicolumn{1}{c}{Overall accuracy} & 96.06 & & & & & & & & & & & \\ \hline  & Kappa hat coefficient & 95.15 & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Error matrix and accuracy assessment of the LULC map in 2019.
# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 3 Power Swing Blocking Method Using PCA
### Basic Principle
Thus, the distance can be used to quantify the difference between the power swing and fault conditions.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### The Impact of Eclipsing Satellites on GNSS PPP
#### 3.3.3. The Relationship between the Sun Angle and the Accuracy Improvement  
From the above analysis, the impact of the eclipsing satellites on the positioning accuracy is related to the magnitude of the PCO values, the maximum yaw angle differences, the duration of noon maneuver or the shadow crossing and satellite geometry structure. However, the maximum yaw angle differences and the duration of noon maneuver or the shadow crossing are related to the sun angles. Due to the large positioning accuracy improvement of S3 relative to S2 for the GLONASS-only and BDS-only PPP, the improvement of 3D positioning accuracy for every day in July 2017 are shown in Figure 14. For GLONASS, according to the results of the eclipsing monitor, the improvement becomes smaller as the sun angle becomes bigger. Because the tracked satellites of every day are different (the nominal orbital repeat period for GLONASS satellites is eight days) and the improvements of every station are also different, the mean improvements for all stations of every day do not necessarily conform to the above rules. When the sun angles of GLONASS satellites approach to the sun angle limit of the earth shadow (14.2\({}^{\circ}\)), the accuracy improvement is vital. Because BDS adopts the normal orbit mode and yaw steering mode, as soon as the attitudes of BDS satellites are switching to the normal orbit mode, the improvements become large, even when their sun angles are near 4\({}^{\circ}\).  
Figure 14: The 3D positioning accuracy improvements of S3 relative to S2 for every day in July 2017: (**a**) GLONASS-only PPP; and (**b**) BDS-only PPP. The dark blue represents the PPP dynamic mode while the yellow represents the PPP static mode.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition
### Steps of the Proposed Compression Acquisition Algorithm  
Algorithm 1 gives the specific steps of the proposed compression acquisition algorithm.  
```
0: Received signal, \(r\); PRN code vector, \(\mathbf{c}\); Measurement matrix, \(\mathbf{\Phi}\); Thresholds, \(\xi\); Maximum iteration times, \(t_{\text{max}}\); Step size, \(\alpha\); Initial gradient, \(\mathbf{d}_{0}\)
0: Code phase, \(\tau\); Doppler offset, \(f_{\text{d}}\)
1: Circularly shift code vector \(\mathbf{c}\) to get matrix \(\mathbf{C}\)
2: Obtain sensing matrix \(\mathbf{\Theta}\) from equation(5)
3:for\(t=1;t\leq t_{\text{max}}\); do
4: Update the target matrix by \[\mathbf{G}_{\text{t}}^{k}(i,j)=\left\{\begin{array}{cc}1,&i=j\\ \mathbf{G}_{\text{t}}^{k-1}(i,j),&i\
eq j,\xi\geq\left|\mathbf{G}_{\text{t}}^ {k-1}(i,j)\right|\\ \xi\cdot\text{sign}(\mathbf{G}_{\text{t}}^{k-1}(i,j)),&\left|\mathbf{G}_{\text {t}}^{k-1}\right|\geq\xi\end{array}\right.\]
5: Normalize the columns in the matrix \(\overline{\mathbf{\Theta}}^{k}=\mathbf{\Theta}^{k}\mathbf{\Theta}_{x}^{k}\)
6: Update the matrix \(\overline{\mathbf{\Theta}}^{k}=\overline{\mathbf{\Theta}}^{\text{T}}\overline{\mathbf{\Theta}}\)
7: Calculate gradient \(-\frac{\partial\rho}{\partial\mathbf{\Theta}}|\mathbf{\Theta}=\mathbf{\Theta}^{k}\) and \(\beta_{k}\)
8: Update gradient direction \(\mathbf{d}^{k+1}=-\frac{\partial\rho}{\partial\mathbf{\Theta}}|\mathbf{\Theta}=\mathbf{ \Theta}^{k+1}+\beta_{k}\mathbf{d}^{k}\)
9: Update the sensing matrix \(\mathbf{\Theta}^{k+1}=\mathbf{\Theta}^{k}+\alpha\mathbf{d}^{k}\)
10:endfor
11: Sparsely represent the received signal \(r(n)\) by Equation (4)
12: Using \(\mathbf{\Theta}_{\text{opt}}\) to reconstruct signal \(\mathbf{X}\)
```  
**Algorithm 1** Acquisition Algorithm Based on Sensing Matrix Optimization  
Algorithm 2
## 4 Measurement of the 8-Channel T/R Module with 7-Bit TTD
### Receive Gain  
The characteristics of receive were measured using the Agilent N5244A Network Analyzer.  
The standing wave ratio (SWR) of the receive input port is shown in Figure 9. The SWR within the bandwidth (from 16.1 GHz to 17.3 GHz) is less than 1.4.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Theoretical TTD/\(\Lambda\)** & **Measured TTD/\(\Lambda\)** & **Absolute Error/\(\Lambda\)** & **Relative Error/\%** \\ \hline
0.25 (of 4-bit TTD) & 0.2880 & 0.0380 & 15.20\% \\
0.5 (of 4-bit TTD) & 0.4957 & 0.0043 & 0.86\% \\
1 (of 4-bit TTD) & 0.9565 & 0.0435 & 4.35\% \\
2 (of 4-bit TTD) & 2.0057 & 0.0057 & 0.29\% \\
1 (of 3-bit TTD) & 0.8954 & 0.1046 & 10.46\% \\
2 (of 3-bit TTD) & 2.0998 & 0.0998 & 4.99\% \\
4 (of 3-bit TTD) & 4.1074 & 0.1074 & 2.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement results of 7-bit TTD and accuracy of each state.  
Figure 8: Measured results of NF.  
The minimum output power of the network analyzer is \(-30\) dBm and a 10 dB attenuator is added in front of the receive input port so that the whole module works in the linear region. The measurements of receive within the bandwidth of 1.2 GHz are shown in Figure 10. S21 is approximately 22 dB with a difference within the bandwidth of less than 0.5 dB. The net gain of receive is 22 + 10 (10 dB attenuator) + 9 (4-way and 2-way divider) = 41 dB.
## 4 Numerical Simulation of the Formation Effect of Grout Curtain in Ore-Rock Contact Zone
### Single-Hole Grouting Curtain Body Formation Effect
#### 4.2.2 Curtain Body Morphology at Different Grouting Rates  
The morphology simulation of the curtain body under the slurry injection rate of 0.1 m/s, 0.2 m/s, and 0.3 m/s were carried out, respectively, and the results are shown in Figure 8. The blue part of the figure indicates the slurry volume fraction, which is used to describe the diffusion pattern of the slurry in the ore-rock contact zone and the yellow part of the contact zone indicates the groundwater. From the figure, it can be seen that a better curtain body morphology can be formed at different grouting rates. As the grouting rate increases, the radius of the curtain body increases significantly, and the morphology of the curtain body surface becomes smoother. The curtain body in the contact zone before and after the two surfaces can be better fit to form a whole. In the ore-rock contact zone, the slurry diffusion is obviously affected by gravity, and the downward diffusion trend of the slurry occurs, and the trend increases with the increase in the grouting rate.
###### Abstract  
Robust estimation aims to provide accurate and reliable parameter estimations, particularly when data are affected by noise or outliers. Traditional methods like random sample consensus (RANSAC) struggle with handling outliers because they treat all observations as equally important. A series of advanced deep learning methods have recently emerged, which use deep learning techniques to estimate the probability of each sample being selected, prioritizing higher confidence for observations that are closer to the ground truth model. However, optimizing solely based on proximity to the ground truth model does not guarantee higher-quality estimations. Meanwhile spatial relationships between the data points in the minimum sampled set also influence the accuracy of the final estimated model. To address these issues, we propose Spatial-Aware Net (SA-Net), a dual-branch neural network that integrates both confidence and spatial encodings. SA-Net employs a confidence distribution encoder to learn the confidence distribution and a spatial distribution encoder to capture spatial correlations between point features. By incorporating multi-perspective sampling, the minimum sample set can be selected based on different spatial distributions in the output of the neural network, and applying Chamfer Loss constraints, our approach improves model optimization and effectively mitigates suboptimal solutions. Extensive experiments demonstrate that SA-Net outperforms the state of the art across various real-world robust estimation tasks.  
Keywords:robust estimation; random sample consensus; camera pose estimation; image matching +
Footnote †: journal: _Algorithms_
## 5 Conclusions  
In this study, we confirmed the mouse flowmotion mean spectrum complete with the inclusion of the lowest frequency endothelial components.  
Our study also highlights the interest of this mechanistic insight offered by WA to better understand the LDF perfusion signal. WA consistently shows the progression of the perfusion adaptation process following an intervention on cardiovascular homeostasis that, in this case, results in hypotension, inotropic reduction, and tachycardia. These results seem to reveal a regulatory crosstalk between central and peripheral microcirculatory effectors, observed here in mice, as previously observed in humans. Although more data and broader research are needed, this enhanced non-invasive analysis appears to be a powerful tool for further developments in vascular medicine.  
[PERSON] and [PERSON] conceived and designed research; [PERSON] and [PERSON] performed experiments and acquired data; [PERSON] and [PERSON] analised data and interpreted results; [PERSON] and [PERSON] drafted, revised and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by funds from FCT--Fundacao para a Ciencia e Tecnologia, LP (Portugal) within the project UID/DTP/04567/2019 and by INSERM--U Bordeaux (France).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Frequency Ranges (Hz)**} \\ \hline  & \multicolumn{2}{c}{**Mouse**} & \multicolumn{2}{c}{**Rat**} & \multicolumn{2}{c}{**Human**} \\ \hline LDF & [PERSON] et al. & [PERSON] et al. & [PERSON] et al. & [PERSON] et al. & [PERSON] et al. & [PERSON] et al. \\ Flowmotion & [14] & [15] & [13] & [23] & [24] & [11] \\ Components & & & & & & \\ \hline Cardiac & 4.9–8.8 & 2.0–8.0 & \(\sim\)6 & 2.0–5.0 & 0.4–2.0 & 0.6–2.0 \\ Respiratory & 1.6–2.8 & 0.2–2.0 & \(\sim\)1 & 0.74–2.0 & 0.15–0.4 & 0.016–0.4 \\ Myogenic & 0.09–0.26 & 0.05–0.2 & \(\sim\)0.1 & 0.2–0.74 & 0.05–0.15 & 0.06–0.16 \\ Sympathetic & 0.02–0.09 & 0.016–0.05 & \(\sim\)0.05 & 0.076–0.2 & 0.02–0.05 & 0.02–0.06 \\ Endothelial & & 0.005–0.016 & \(\sim\)0.01 & 0.01–0.076 & 0.008–0.02 & \\ NO-dependent & 0.01–0.02 & - & - & - & - & 0.0095–0.02 \\ NO-independent & 0.006–0.010 & - & - & - & - & 0.005–0.0095 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reported frequency ranges (Hz) for the LDF perfusion flowmotion components in mice (see also Figure 2b) and rats, with human ranges as reference.