esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 5 Results
### Case Study 3
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case Study 1**} & \multicolumn{2}{c}{**Case Study 2**} & \multicolumn{2}{c}{**Case Study 3**} \\ \hline  & Base Route & Alternative & Base Route & Alternative & Base Route & Alternative \\  & Route & Route & Route & Route & Route \\ Travel time (minutes) & 16 & 26 & 31 & 49 & 82 & 97 \\ Route length (miles) & 9.7 & 11 & 21 & 25 & 59 & 52 \\ Incident & & & & & & \\ duration/additional time & 52.75 & 10 & 92.4 & 18 & 65.7 & 15 \\ (minutes) & & & & & & \\ AADT & 151,500 & 38,000 & 89,000 & 15,400 & 41,500 & 3100 \\ I-75 diverted one-way & & & & & & \\ AADT Trucks & & 4924 & & 4673 & & 2531 \\ Speed limit (mph) & 70 & 45 & 70 & 55 & 70 & 60 \\ Delay reduction (minute) & 42.75 & & 74.4 & & 50.7 & \\ Delay cost USD & USD 17,315 & USD 3283 & USD 28,783 & USD 5607 & USD 11,088 & USD 2531 \\ Delay saving USD & USD 14,033 & & USD 23,176 & & USD 8556 \\ Total fuel consumption & USD 1147 & USD 485 & USD 4229 & USD 1973 & USD 7663 & USD 4401 \\ Fuel saving USD & USD 662 & & USD 2256 & & USD 3263 \\ Total & USD 14,695 & & USD 25,432 & & USD 11,819 \\ Median & & & & USD 14,695 & & \\ Total for a year per corridor & & & & USD 52,165,570 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A summary of benefit estimation of three case studies. Figure 13: (**a**) Case study 3, base scenario route on I-75; (**b**) truck alternative route to bypass the congested segment for case study 3. * For stop-and-go, a value of 3.78 miles per gallon;
* For the alternative route, a value of 5.13 miles per gallon (The World Harmonized Vehicle Cycle) (WHVC);
* Conversion of the savings from reduced delays and fuel consumption to monetary value using the following values:
* Truck VOT = USD 96/h;
* Price of diesel = USD 2.55/gallon.
# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
###### Abstract  
The safety of power transmission lines is crucial to public well-being, with insulators being prone to failures such as self-detonation. However, images captured by unmanned aerial vehicles (UAVs) carrying optical sensors often face challenges, including uneven object scales, complex backgrounds, and difficulties in feature extraction due to distance, angles, and terrain. Additionally, conventional models are too large for UAV deployment. To address these issues, this paper proposes RML-YOLO, an improved insulator defect detection method based on YOLOv8. The approach introduces a tiered scale fusion feature (TSFF) module to enhance multi-scale detection accuracy by fusing features across network layers. Additionally, the multi-scale feature extraction network (MSFENet) is designed to prioritize large-scale features while adding an extra detection layer for small objects, improving multi-scale object detection precision. A lightweight multi-scale shared detection head (LMSHead) reduces model size and parameters by sharing features across layers, addressing scale distribution imbalances. Lastly, the receptive field attention channel attention convolution (RFCAConv) module aggregates features from various receptive fields to overcome the limitations of standard convolution. Experiments on the UID, SFID, and VISDrone 2019 datasets show that RML-YOLO outperforms YOLOv8n, reducing model size by 0.8 MB and parameters by 500,000, while improving AP by 7.8%, 2.74%, and 3.9%, respectively. These results demonstrate the method's lightweight design, high detection performance, and strong generalization capability, making it suitable for deployment on UAVs with limited resources.  
insudator defect detection; multi-scale object detection; unmanned aerial vehicle aerial images; YOLO; power inspection Article
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 3 Challenges of RRM in Cognitive HTS Systems
### Tradeoff Between Performance and System Overhead  
When conducting RRM in the considered cognitive HTS systems, system information overhead mainly consists of two aspects. On the one hand, for the frequency division duplexing (FDD)-based multi-beam satellite system, downlink training and feedback overhead scales linearly with the large number of satellite antennas and will overwhelm the precious downlink resources. Thus, it is imperative to design an interference-aware RRM framework based on limited feedback CSI. On the other hand, considering the fact that the capability of on-board processing is limited for satellites, signal processing should be partially conducted at on-ground gateways. However, in the considered multi-gateway multi-beam satellite network, each gateway can only access a certain set of feeds and, consequently, the channel information of the users within its own beams. Although this kind of per-group processing is practically favorable for requiring less cost in downlink training and CSI feedback than joint group processing, it may suffer significant performance degradation due to intergroup interference. In the existing literature, there are various kinds of multi-gateway cooperation in multi-beam satellite systems, such as joint multi-gateway processing with partial CSI and partial data sharing. The more information multiple gateways exchange, the more resources are consumed, and the higher complexity of the system will be, while achieving a superior RRM solution in return.  
As a result, it is interesting to investigate how to get a balanced tradeoff between the performance and the system overhead.
# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
## 2 Methods
### The ESLM-AE PAPR Reduction Scheme
#### 2.2.1 Autoencoder Network
\[X=f(x)=\rho_{Lf}\bigl{(}\left|W_{Lf}^{f}\rho_{Lf}{}^{-1}\bigl{(}\ldots\rho_{1} \Bigl{(}\left|W_{1}{}^{f}x+b_{1}{}^{f}\right|_{norm}\Bigr{)}\ldots\bigr{)}+b_{ Lf}^{f}\right|_{norm}\bigr{)}, \tag{9}\]  
where \(W_{Lf}^{f}\) and \(b_{Lf}^{f}\) are the weights and bias for the \(Lf\)th dense layer of the encoder respectively. Similarly, the output of the decoder can be expressed as Equation (10). \[\hat{x}=g(Y)=\rho_{Lg}\bigl{(}\left|W_{Lg}^{g}\rho_{Lg}{}^{L}{}^{-1}(\ldots\rho _{1}(\left|W_{1}{}^{g}Y+b_{1}{}^{g}\right|_{norm})\ldots)+b_{Lg}^{g}\right|_{ norm}\bigr{)}, \tag{10}\]  
where \(W_{Lg}^{g}\) and \(b_{Lg}^{g}\) are the weights and bias for the \(Lg\)th dense layer of the decoder respectively. As mentioned, the noise channel could distort the signal during the transmission. The AE aims to find an adequate encoding and decoding strategy to eliminate the complex optical channel and noise interference. To achieve the objective, the first network loss function can be set as the reconstruction error given in Equation (11). \[Loss_{1}(x,\hat{x})=\left\|x-\hat{x}\right\|_{2}, \tag{11}\]  
For the training of the AE, the stochastic gradient descent (SGD) [33] optimization method is popular used which starts with some random initial values of \(\theta=\theta_{0}\) and update \(\theta\) iteratively as  
\[\theta^{+}=\theta-\lambda\
abla_{\theta}Loss_{1}(x,\hat{x}), \tag{12}\]  
where \(\lambda>0\) is the learning rate, \(\theta\) denotes parameters of the AE and \(\
abla_{\theta}\) denotes the Gradient operation \(\frac{\partial Loss_{1}(x,\hat{x})}{\partial\theta}\).
## 3 Results and Discussion
### Evaluation of Poly (MMA-BA-ST) for Oil-Based Drilling Fluid Plugging Properties  
#### 3.5.1 Mud Cake Experiment  
Figure 8 shows the evaluation of the plugging effect of poly (MMA-BA-ST) on the mud cake at 150 \({}^{\circ}\)C and a differential pressure of 3.5 MPa. It can be seen from Figure 8 that the permeability of the basic drilling mud cake is 2.26 \(\times\) 10\({}^{-4}\) mD. After adding poly (MMA-BA-ST), the permeability of drilling mud cake decreases, and the lower the permeability, the better the leakage plugging effect. When the amount of poly (MMA-BA-ST) reaches 0.5%, the permeability drops to 0.63 \(\times\) 10\({}^{-4}\) mD, the plugging rate is 72.12%, and the plugging effect on mud cake is the best. When the amount of poly (MMA-BA-ST) continued to increase, the plugging rate did not improve. This shows that 0.5% poly (MMA-BA-ST) is the best addition amount in drilling fluids and has a good plugging effect.
# A Regionalization Approach Based on the Comparison of Different Clustering Techniques
## 4 Discussion
### Analytical Regionalization Versus Reference Regionalization  
The algorithm-based regionalization approach offers several scientific advantages, including reproducibility, adaptability to changes in data, and the capacity to address ecological questions in a more direct and comparable manner. In our case, when comparing our results with the reference regionalization, we found that the statistical or quantitative construction of regions by certain algorithms, such as Infomap, K-means++, or PDC, aligns with the biome level according to the kappa index obtained, indicating a certain degree of similarity. However, these algorithms do not accurately match regions at a finer level, such as ecoregions.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture
### Principles of Microalgae-Assisted Aquaculture
#### 3.1.2 Advantages  
According to the previous study, in a real-world application, the integration of microalgae with fish rearing not only brings technical advantages, but also creates economic benefits: (1) Oxygen production by microalgae alleviates the risks of oxygen depletion and reduces the energy consumption of traditional aeration devices. (2) Survival of microalgae in a fish rearing tank or pond may limit the growth of unfavorable or toxic microorganisms, creating a good environment for aquatic animals. Consequently, the water replacement frequency of a fish tank or pond would be minimized and the related cost would be reduced significantly. (3) As the immunity of aquatic animal is enhanced by the microalgae feed, overuse of antibiotics or medicines in aquaculture could be avoided, increasing the safety of aquaculture products and maximizing the market acceptance. Generally, pollution-free aquaculture products have much higher prices and larger market demands than traditional products. (4) The aquaculture effluent could be treated by advanced microalgae biotechnology at low cost.  
Thus, the wastewater treatment cost that was paid by the aquaculture industry to sewage treatment plant in the past could be avoided, reducing the financial burden of industry to some extent. (5) As the harvested biomass is used to partly replace the traditional aquaculture feed, the cost of fish rearing could be controlled.  
Therefore, considering the aforementioned advantages, it is expected that the aquaculture industry could be upgraded with the assistance of microalgae, bringing great benefits to factory, consumer and society. In a real-world application, to truly use microalgae for aquaculture wastewater remediation, some important issues should be considered. First, cost-saving cultivation systems for efficient biomass production are needed to grow microalgae for large-scale treatment of aquaculture wastewater. Second, microalgal strains with value-added components should be selected for both wastewater remediation and aquaculture feed production. Third, advanced harvesting technologies are needed to simplify the harvesting process and reduce the biomass cost. Fourth, effects of microalgae on the growth of aquatic animals and relevant mechanisms should be fully understood. Recently, researchers developed a lot of efforts in aforementioned issues and had a lot of important findings that merit attention from the fields of environmental protection and aquaculture.
## 4 Discussion
### Plants vs. Growth Media  
Formaldehyde and CO\({}_{2}\) were used as indicators of the effect of growth media and plants in reducing gaseous pollutants in a controlled environment. Table 1 shows that, in general, growth media were more effective in the depletion of formaldehyde inside of the chamber than the plants. Regarding CO\({}_{2}\) reduction inside of the chamber, as expected, Table 2 shows that plants were more effective than growth media: in most of the cases with only a growth medium present, CO\({}_{2}\) was released instead of reduced inside of the glass chamber. Figure 9 presents the different behaviors of the potted plants regarding these two elements. Even though the leaf area of the Boston fern (approx. 0.11 m\({}^{2}\)) was smaller than the peace lily (approx. 0.14 m\({}^{2}\)), the Boston ferns reduced the concentration of CO\({}_{2}\) inside of the chamber faster than the peace lilies, which indicates that the stomatal conductance of the Boston fern was higher than the peace lily, opening the hypothesis about the uptake of more gaseous pollutants by the stomata. Regarding the depletion of formaldehyde, in general, wet soil, dry soil, and expanded clay perform similarly and they are more effective than the other variables tested.  
As mentioned before, formaldehyde is soluble in water [1]. However, this study shows that high levels of humidity seemed to have no effect on the formaldehyde depletion inside of the chamber, because in most of the test conditions, the relative humidity level was above 90%. Nonetheless, it is important to mention that in the case of the plants, high humidity levels may affect the depletion of the CO\({}_{2}\) and the formaldehyde inside of the chamber due to the fact that plants close their stomata at high humidity levels [46; 47]. The temperature was quite stable during the experiments (Tables 1 and 2); therefore, it seemed to have no effect on the formaldehyde and CO\({}_{2}\) depletion, but, in general, in the presence of wet growth media, the depletion of formaldehyde was faster. Regarding the effect of the growth media on the depletion of formaldehyde and CO\({}_{2}\), it is important to mention that when the substrate (wet or dry) was tested without the plant, the whole surface of the substrate was exposed directly to formaldehyde and CO\({}_{2}\). However, when the plants were included, the exposed surface of the selected substrate was reduced and the results show that the depletion was also lower, which indicates that the efficacy of the growth media, in some cases, was higher. This effect is produced by the microbial activity in the root zone, where bacteria absorb the gaseous pollutants and metabolize them [22; 23; 24; 25].
# Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders
## 2 Materials and Methods
### Eigenvectors and Eigenvalues
#### 2.6.1 Example 1  
The first eight eigenvectors identified from the FEM model of the footbridge above S8 expressway together with the corresponding eigenfrequencies are listed in Table 2. At the same time, Figure 13 shows the identified mode shapes of the footbridge. It can be seen that five modes are mainly connected with the deck, and three with the arch. For the modes S8_Vd1, S8_Vd2, S8_Vd3, and S8_Vd4, the component Va is relatively large. It means that the defection of the deck is strictly connected with the deflection of the arch. The same situation occurs in the case of mode S8_Va1. In the modes S8_La1, S8_La2, and S8_Ld1, the other components are much smaller. The numerical model allowed to obtain a higher number of frequencies than was identified during the field test (Figure 9).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Mode Type**} & \multirow{2}{*}{**Frequency**} & \multicolumn{4}{c}{**Modal Component Ratio**} \\ \cline{3-8}  & & & **Deck** & & **Arch** & \\ \cline{3-8}  & & **Hz** & **Vd** & **Ld** & **Td** & **Va** & **La** \\ \hline
1 & S8\_Vd1 & 1.70 & 1.00 & 0.03 & 0.00 & 0.98 & 0.08 \\
2 & S8\_La1 & 2.00 & 0.14 & 0.10 & 0.28 & 0.14 & 1.00 \\
3 & S8\_Va1 & 2.29 & 0.95 & 0.10 & 0.00 & 1.00 & 0.70 \\
4 & S8\_Vd2 & 3.27 & 1.00 & 0.02 & 0.00 & 0.95 & 0.05 \\
5 & S8\_La2 & 3.74 & 0.13 & 0.01 & 0.26 & 0.08 & 1.00 \\
6 & S8\_Ld1 & 3.90 & 0.06 & 1.00 & 0.00 & 0.32 & 0.57 \\
7 & S8\_Vd3 & 4.94 & 1.00 & 0.03 & 0.00 & 0.88 & 0.16 \\
8 & S8\_Vd4 & 6.81 & 1.00 & 0.01 & 0.00 & 0.95 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modes and eigenvalues identified from the FEM model of the arch bridge above expressway S8.  
Figure 12: FEM model of the footbridge above University Route in Bydgoszcz.
## 3 Methods
### Constructions of the Digital Twin of an Underground Utility Tunnel Based on BIM-GIS  
Building information modeling (BIM) and geographic information systems (GIS) are two powerful technologies that have been applied separately in many engineering and construction projects. BIM creates detailed 3D models of a facility, including all its components and their characteristics, while GIS is designed to capture, manipulate, analyze, and present geographical data. Combining these two systems for underground facilities can provide numerous advantages, including better planning and design, more efficient construction and maintenance, improved safety, and cost savings. In this process, we first needed to collect relevant geographical and facility-specific data. These data were then integrated into the BIM and GIS systems, creating a 3D model of the facility and mapping it in its geographical context. This combined system can be used in the design and planning stages, offering insight into spatial relationships and potential issues. During construction, the system guides the process and is later used for maintenance tracking, predicting repairs and replacements. Finally, BIM and GIS can be used for simulating various scenarios to improve the facility's design and safety features, aiding in risk management decisions.  
The Ochang utility tunnel spans three segments with a cumulative length of 5586 m, of which the demonstration segment measures 1210 m. Some portions of the tunnel were selected as demonstration service segments owing to their challenging conditions. These segments include a 750 m stretch of the power section prone to fire incidents, a 60 m intense condensation area, and a 400 m part of the communications/water supply section. Figure 2 denotes these segments. To aid the research, 3D building information modeling (BIM) was implemented for the entire section, using the as-built drawings available at the time of its completion. However, for the demonstration segment, only level of development (LOD) 4-based 3D modeling was carried out through laser scanning.
# Hydration, Setting and Crack-Resistance Properties of Activated HVFACs at Early Stages
## 1 Introduction
Based on previous studies, the ions of Cl\({}^{-}\), CO\({}_{3}\)\({}^{2-}\) and NO\({}_{3}\)\({}^{-}\) could affect the alkaline solution of concrete to achieve the appropriate composition, which retards the setting of FAC subsequently [23]. In order to improve the early strength of concrete, sodium and potassium sulfate (SO\({}_{4}\)\({}^{2-}\)) have been reported to activate FA in concrete [24]. Considering the possible effect of SiO\({}_{2}\)\({}^{-}\) on pozzolanic materials (e.g. volcanic ash), silicate also can be a potential activator for the FAC system [25]. [PERSON] and [PERSON] [26] reported that Na\({}_{2}\)SO\({}_{4}\) is a more efficient activator compared to CaCl\({}_{2}\) in high calcium fly ash pastes. Via investigating the strength and microstructural properties, [PERSON] et al. [27] pointed out that Na\({}_{2}\)SO\({}_{4}\) is most effective in accelerating the consumption of Ca(OH)\({}_{2}\) and increasing the amount of Ettringite in fly ash-cement pastes. Similar results have been reported by [PERSON] et al. [28]. Comparing to the grinding of FA, using Na\({}_{2}\)SO\({}_{4}\) is more effective to promote the activity of FA [29]. However, until now, there has not been research which focused on the comprehensive early properties of activated FAC using Na\({}_{2}\)SO\({}_{4}\), including hydration, setting and cracking. Therefore, to promote HVFAC in concrete industries, the studies on the effects of silicate, sulfate and calcium chloride ions in these early properties of FAC will be helpful to understand the activation of FA in a high volume FA-PC system well. Meanwhile, considering the effect of lime on low calcium FAC, the combined use of lime and Na\({}_{2}\)SO\({}_{4}\) might be a potential way. The primary objectives of this study are to investigate the influences of the main variables on the hydration heat of the FA-PC system, the setting time and crack-resistance performance of FAC/HVFAC activated by several easily obtained chemical agents, Na\({}_{2}\)SO\({}_{4}\) orNaSiO\({}_{3}\) or CaCl\({}_{2}\), as well as to examine and modify the existingmodel for predicting the setting time of FACs/HVFACs and to analyze the crack-resistance mechanism of FACs/HVFACs.
## 3 Multi-Objective Optimization
### Multi-Objective Optimization Size  
So as to obtain higher torque performance for proposed model two, this research will further optimize the rotor structure size parameters of proposed model two through multi-objective optimization. Figure 11 is a schematic diagram of the rotor topology parameters of proposed model two, and Table 4 shows the value range regarding relevant size parameters. The multi-objective optimization flowchart is shown in Figure 12. The main goal of this multi-objective optimization is to maximize the average torque, followed by minimizing torque ripple as a secondary objective. Therefore, in order to obtain higher average torque and lower torque ripple, the optimized objective functions can be defined using Equations (4) and (5), as shown in Equation (6).  
\[\left\{\begin{array}{l}O_{1}=max(\textit{Tave})\\ O_{2}=min(\textit{Trip})\end{array}\right. \tag{6}\]  
where, \(O_{1}\) and \(O_{2}\) are the average torque optimization objective and torque ripple optimization objective, respectively.  
Figure 10: Torque characteristics of the three models. (**a**) Basic model. (**b**) Proposed model 1. (**c**) Proposed model 2.  
Figure 11: Rotor topology size parameters of proposed model 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Item** & **Unit** & **Optimization Scope** \\ \hline PM1 length & LM1 & mm & 4–12.5 \\ PM2 length & LM2 & mm & 2.5–3 \\ PM3 length & LM3 & mm & 5–5.9 \\ PM4 length & LM4 & mm & 6–7.9 \\ PMS length & LM5 & mm & 3–4.4 \\ PM1 width & DM1 & mm & 1.4–2 \\ PM2 width & DM2 & mm & 1.4–1.6 \\ PM3 width & DM3 & mm & 2–3 \\ PM4 width & DM4 & mm & 2–3 \\ Flux barrierl width & DB1 & mm & 1–1.5 \\ Flux barrier2 width & DB2 & mm & 1–1.5 \\ Flux barrier3 width & DB3 & mm & 1–1.2 \\ Flux barrier4 width & DB4 & mm & 0.5–2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Range of values of rotor structure parameter dimensions for proposed model 2.  
Figure 12: Multi-objective optimization flowchart.  
We set the step size of all relevant parameters to 0.1 mm, conducted sensitivity analysis on their key parameters, removed parameters with a relatively small impact, and then performed multi-objective optimization on the remaining parameters to ensure motor performance while saving as much optimization time as possible.