esa-sceva/satcom-chunk-collection huggingface dataset:
# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
## 1 Introduction
The existing background-pollution levels are assumed to remain consistent, and long-term climatic variations are excluded. Considering the short-term prediction, flat terrain, and proposed mining activities, an iron ore mine (IOM) was selected for the study. As mentioned, the construction and operation phases of suspended particulate matter (SPM) and coarse particulate matter (PM\({}_{10}\)) were considered for analysis in the two phases. The study focused on the impacts of mining on primary air quality. The objectives of the study were as follows:  
1. Survey of the various air quality prediction models (AQMs) and selection of the most suitable model for predicting air quality impacts for an iron ore mine project for short-term area sources and a flat terrain, while accounting for the actual field conditions. 2. To quantify SPM and PM\({}_{10}\) in the dry season during the construction phase and model the same with the established model and continue monitoring during operation phases. 3. To propose mitigation measures based on simulations to minimize air quality impacts based on AQM predictions and their implementation for compliance with environmental standards during operation phase. 4. To evaluate air quality trends over distance and the variability of SPM and PM\({}_{10}\) using exploratory analysis, while evaluating the effectiveness of implemented mitigation strategies.
## 3 Results and Discussion
### Marshall Stability Test Results
The stability value calculated according to the optimum levels that maximize the stability value was 1361.337 kg, an increase of 31.913% compared to the mixtures prepared with virgin bitumen. The stability increase is attributed to the additive creating a lubricating film at the interface between the asphalt and aggregate. This reduces the contact angle, making it easier for the bitumen to coat the aggregate and enhancing the mixing and compaction of the mixture, especially at lower temperatures [77; 78]. [PERSON] et al. [32] similarly stated that WMA Marshall samples prepared by adding Cecabase performed the same or even better than HMA control samples. As the additive ratio increased, the flow value decreased, and the Marshall quotient (MQ) increased. MQ is an important parameter that shows the stiffness of mixtures and their resistance to rutting and various deformations [79; 80]. Adding Cecabase RT BIO 10 to the mixtures in various rates increased the MQ and provided better resistance to permanent deformation. Va refers to the percentage of air voids between aggregate particles in compacted mixtures and is crucial for the durability of asphalt pavement [81]. A rise in the air void content of the asphalt mixture can make it more susceptible to damage from external elements, particularly moisture [82]. As the additive ratio increased, the Va value decreased while achieving acceptable Va values within the 4-6% standard range. A mixture with less than 3% air voids is unstable, while a mixture with more than 8% air voids is water permeable [83]. Furthermore, the use of Cecabase RT BIO10 allowed for improved workability and compaction due to densification (reduction in air void content) despite the decrease in temperature [76]. This encourages the application of Cecabase RT BIO 10 to improve the pavement's durability in WMA. The volume of voids in mineral aggregate (VMA) is an essential factor that ensures the durability of asphalt mixtures [84]. A decrease in VMA typically results in a reduction in rutting [85]. As the additive ratio increased, the VMA value decreased while providing better resistance to rutting. VFA represents the percentage of the VMA's volume that is filled with asphalt. It has an inverse relationship with the presence of air voids. As the additive ratio increased, the VFA value also increased. \begin{table}
\begin{tabular}{c c c} \hline
**Factors** & **Levels** & **Contributions to S/N** \\ \hline Cecabase & A2 & 0.637 \\ Temperature & B1 & 0.828 \\ Time & C2 & 0.355 \\ Speed & D1 & 0.367 \\ Total Contribution (S/N) & & 2.186 \\ Performance Statistics Average (S/N) & & 60.493 \\ Expected S/N and value at optimum levels (S/N)/(0.1 mm) & & 62.679/1361.337 \\ Result of verification experiment (S/N)/(0.1 mm) & & 62.190/1286.570 \\ Confidence interval (\(\alpha\) = 95\%) (S/N) & & 62.139/63.220 \\ \hline \end{tabular}
\end{table} 
Table 15: Performance estimations for the optimum conditions that maximize the stability.
## 1 Introduction
Here we report our experiments on multiheterodyne characterization of the excess phase noise in both fiber-optic and free-space distributions of an OFC. In fiber-optic distribution, we introduced an external, monotone acoustic perturbation and studied its impact on the phase noise spectrum and the total rms noise amplitude. In free-space distribution, we delivered a femtosecond pulse train from an OFC laser across a 60 m rooftop atmospheric link and measured the spectral broadening and phase noise spectra caused by turbulence. Our results demonstrate the effectiveness of multiheterodyne in characterizing the excess phase noise in various settings of multifrequency remote clock transfer.
## 3 Modeling the GNSS Observations with the Proposed HFM
### Constructing Tomography Observation Equations Using Inside Signals  
For inside signals, penetrating from the side of the tomography area, the SWD should first be estimated by HFM. The specific steps of computing the \(SWD_{inside}\) are as follows:  
1. The ZTD and horizontal gradients of effective side signals (e.g., OP in Figure 2) are estimated using GAMIT/BLOCK. The ZWD, north, and east wet gradients can be extracted from the estimated values [7];
2. The height \(h_{inter}\) of the intersection point between the effective side signals and the side face of the tomographic area is calculated. The \(\lambda_{iso}(h_{inter})\) and \(\lambda_{aniso}(h_{inter})\) are estimated by Equations (8) and (15), respectively. Then, the SWD of inside signals can be calculated using Equation (17);
3. The distance information of inside signals is obtained with the ray-tracing method [42], and the unknowns are related to the inside observations by the following equation: \[SWV_{inside}=A_{inside}\cdot X,\] (20) where \(SWV_{inside}\) is the observation vector of inside signals and \(A_{inside}\) represents the observation matrix derived from inside signals.
## 6 Conclusions  
This study mainly addresses the knowledge gap in three steps and conducts an in-depth analysis of how TGC and CET affect the sustainable development of the electricity market. Firstly, by reviewing the application of TGC and CET in China, the complex interactive relationship between TGC and CET was specifically analyzed, and the impact mechanism between TGC, CET, and the electricity market was proposed. Secondly, based on the literature analysis, identify the key factors that affect the sustainable development of the electricity market through TGC and CET, establish a causal feedback loop and system flow diagram of the impact of TGC and CET on the sustainable development of the electricity market, and analyze how TGC and CET jointly affect carbon reduction behavior in the electricity market through price signals and policy incentives. On this basis, the relationship equations between variables in the system flow diagram will be established, and historical data from Liaoning Province will be used as an example to test the effectiveness of the SD model. Ensure that the simulation results can be used to predict the sustainable development level of the electricity market in Liaoning Province. Finally, Versim PLE software 7.3.2 was used to explore the impact of different policy parameters on the sustainable development capacity of the electricity market and the decision-making of power enterprises in long-term dynamic processes.  
The research results indicate that TGC and CET play key roles in the sustainable development of the electricity market in Liaoning Province. The increase in the TGC quota ratio promotes the rise in demand in the TGC market, which can effectively mobilize the green investment enthusiasm of power enterprises, promote technological progress and large-scale application in the field of renewable energy, and contribute to the development of renewable energy generation in the electricity market. The tightening of the carbon emission cap target has directly driven up the price of CET, forcing power companies to take more proactive emission reduction measures, thereby reducing the total amount of carbon emissions. The carbon emission cap target, as a binding policy, has a profound impact on market structure, and its strictness is crucial for promoting the transition of the power industry to clean energy. The synergistic incentive effect of the TGC quota ratio and carbon emission cap target has played a key role in promoting the sustainable development of the electricity market. However, in the process of policy implementation, if the adjustment range of both is too extensive or too sharp, it may lead to intensified market price fluctuations and affect market stability. Therefore, policy design should pay more attention to flexibility and gradualness, and appropriate policy combinations should be implemented at different stages of the development of the electricity market to ensure the smooth operation of the market while promoting green transformation.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 4 Case Study
### Comparison with Existing NN-Based Methods  
This section compares the proposed method with other neural network-based approaches, evaluating their performance in terms of complexity, computational efficiency, and detection accuracy. The comparison is limited to NN-based methods, as they closely align with the framework of the proposed approach. This focused evaluation enables a meaningful assessment of the relative strengths and limitations. Furthermore, it highlights the advancements and contributions of the proposed method within this specific context.  
Table 3 presents several approaches based on different types of neural networks, all tested on the same dataset used to evaluate the proposed method. The proposed method achieves the highest detection accuracy (99.99) among all methods, demonstrating its performance while maintaining low complexity. Both the proposed method and the  
Figure 8: Maximum logit score values of FFNN fed with normal test samples (steady state and load change) and HIF samples indicate that the FFNN assigns HIF samples to both classes, making them indistinguishable from other normal samples.  
Figure 9: Maximum logit score values of FFNN with OSR improvements, when fed with normal test samples and HIF samples, reveal that the FFNN assigns HIF samples exclusively to class 1 (the class with transients). More importantly, it clusters HIFs separately from other normal samples.  
approach in [32] exhibit high computational efficiency; however, the proposed method significantly outperforms [32] in terms of the detection accuracy (99.99% vs. 70.09%). In terms of complexity, while the CNN-based method in [31] achieves high detection accuracy (96.79%), it suffers from high complexity, making it less suitable for resource-constrained or real-time applications. The back-propagation neural network (BPNN)-based method in [30] offers a balance between medium complexity and high efficiency but falls short in accuracy (95.31%) compared to the proposed method. Finally, unlike all other methods, the proposed method supports OSR, making it robust and adaptable to scenarios involving unknown or unexpected faults. This capability provides a significant advantage over other approaches.
# Power Control and Channel Allocation Algorithm for Energy Harvesting D2D Communications
###### Abstract  
This paper assumes that multiple device-to-device (D2D) users can reuse the same uplink channel and base station (BS) supplies power to D2D transmitters by means of wireless energy transmission; the optimization problem aims at maximizing the total capacity of D2D users, and proposes a power control and channel allocation algorithm for the energy harvesting D2D communications underlaying the cellular network. This algorithm firstly uses a heuristic dynamic clustering method to cluster D2D users and those in the same cluster can share the same channel. Then, D2D users in the same cluster are modeled as a non-cooperative game, the expressions of D2D users' transmission power and energy harvesting time are derived by using the Karush-Kuhn-Tucker (KKT) condition, and the optimal transmission power and energy harvesting time are allocated to D2D users by the joint iteration optimization method. Finally, we use the Kuhn-Munkres (KM) algorithm to achieve the optimal matching between D2D clusters and cellular channel to maximize the total capacity of D2D users. Simulation results show that the proposed algorithm can effectively improve the system performance.  
Keywords:device-to-device communication; energy harvest; power control; channel allocation; non-cooperative game +
Footnote †: journal:
## 5 Validation of Surrogated MIMO-NARX Model
### Accuracy Assessment of MIMO-NARX Outputs in Reduced Coordinates  
The MIMO-NARX model replicates four outputs of the order-reduced model in reduced coordinates as shown in Figure 11 for a typically selected ground motion with the four reduced coordinates presented from low to high. It can be observed in Figure 11 that the outputs of the MIMO-NARX model with the coefficients calibrated by Kriging, PCE, and APC meta-models are basically consistent with the MCS responses transformed to reduced coordinates. The matching accuracy of the four reduced coordinates, however, decreases with the increase of frequency due to the difficulty in capturing high-frequency nonlinearity as shown in Figure 12. For this specific selected ground motion, the APC meta-model has better accuracy for the fourth output prediction than that of Kriging and APC meta-models, while the three meta-models have nearly similar accuracy for the first three outputs.
# Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning
## 1 Introduction
In [24], the Kullback-Leibler (KL) divergence technique is applied to calculate the power transition probability matrices of the RL algorithm to find the optimal power distribution ratio between the battery and the super-capacitor. Simulation results show that this kind of control policy cannot only effectively decrease the battery charging frequency and control the maximum discharging current, but also maximize the energy efficiency to cut down the overall cost under diverse conditions. In [25], a novel RL based method is proposed combining with the remaining travel distance estimation, and the controller could continuously search for the optimal strategy and learn from the previous process. In [26], a RL method called TD (\(\lambda\))-learning is employed for the HEV, and simulation results manifest that the RL based policy can improve the fuel economy by 42%. In [27], a blended real-time control strategy is proposed based on the Q-learning (QL) method to balance the overall performance and optimality. A bi-level control strategy is proposed in [28], in which the fuzzy encoding predictor and the KL divergence rate are employed to predict the driver's power demand in the higher level, and the lower level is mainly focused on employing the RL algorithm to find the optimal solution. Based on the above discussion, it is imperative to further apply the RL technique for energy management of power-split PHEVs. Hence, the main motivation of the energy management strategy is to further refine the battery power based on the RL by selecting proper state and action variables. As a result, the objectives for both optimal fuel economy and battery power restriction can be met at the same time, thereby prolonging the battery life potentially. For the sake of achieving the target, the powertrain of a power-split PHEV is modeled and analyzed first. Subsequently, considering that the proposed method should be applicable in most driving conditions, the Markov chain is adopted to estimate the transition probability matrix regarding demanded power under different driving cycles. Finally, the QL algorithm is conducted to develop and finally form the EMS towards reaching the optimal target. Furthermore, the proposed EMS is compared with the CD/CS strategy to validate the optimality under different driving cycles by simulations. The rest of this article is structured as follows:Section 2 describes the simplified vehicle structure and the fuel consumption model. In Section 3, the RL based framework is proposed to realize the optimal EMS. In Section 4, corresponding simulations prove the proposed method is superior to the CD/CS algorithm. Section 5 concludes the article.
# Processing of Single-Walled Carbon Nanotubes with Femtosecond Laser Pulses
## Materials and Methods  
### Sample Preparation  
Single wall carbon nanotubes (SWCNTs) were procured from Emfutur (Vila-real, Spain) in the form of powder. Typically, SWCNTs are measuring a few nanometers in diameter and several microns in length. A small amount, approximately ranging from 0.1 to 0.5 g of agglomerated powder of SWCNTs, was dispersed in 50 mL ethanol. The solution was sonicated by the ultrasonicator (Q500, USA) to deagglomerate and disperse the SWCNTs in ethanol for around half an hour. After sonication, the uniformly dispersed SWCNTs in ethanol mixture were dropped out on glass substrates and made a thin film of SWCNTs. Several samples were prepared with different thicknesses in the range of one to few microns. The schematic for the steps involved in the preparation of SWCNTs film is shown in Figure 1. The thin films of SWCNTs were irradiated by femtosecond laser pulses by adopting different strategies to optimize the various machine parameters.
# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 4 Experiments and Results
### Results
#### 4.3.3 Comparison with State-of-the-Art Segmentation Methods  
Using AUPR as the evaluation metric, the comparison of the performance for four kinds of lesions between nmODE-Unet and state-of-the-art methods is shown in Table 6.  
Through a detailed analysis of the data in Table 7, we can obtain the following results: (1) compared to [18], the AUPR score of nmODE-Unet is 8.66%, 2.68%, and 5.50% higher in the segmentation of EXs, SEs, and MAs, respectively, but 1.12% lower in HE segmentation; (2) compared to [20], the AUPR score of nmODE-Unet is 1.45%, 4.49%, and 2.97% higher in the segmentation of EXs, SEs, and MAs, respectively, but 6.07% lower in HE segmentation; (3) compared to [34], the AUPR score of nmODE-Unet is 4.51% and 4.42% lower in the segmentation of EXs and HEs, but 1.00% and 10.25% higher in the segmentation of EXs and HEs, respectively; (4) compared to [35], the AUPR score of nmODE-Unet is 3.14% and 1.27% lower in the segmentation of EXs and HEs, but 2.56% and 0.29% higher in the segmentation of SEs and MAs, respectively; (5) the method in [20] achieved the highest value in HE segmentation, the method in [34] achieved the highest value in EX segmentation, the method in [35] achieved the highest value in mAUPR, and nmODE-Unet achieved the highest value in SE and MA segmentation, respectively.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Team** & **EX** & **SE** & **HE** & **MA** & **mAUPR** \\ \hline VRT (lst) & 0.7127 & 0.6995 & 0.6804 & 0.4951 & 0.6469 \\ PATech (2 nd) & 0.8850 & - & 0.6490 & 0.4740 & - \\ iFLYTEK-MIG (3 rd) & 0.8471 & 0.6588 & 0.5588 & 0.5017 & 0.6484 \\ SOONER (4 th) & 0.7390 & 0.5396 & 0.5395 & 0.4003 & 0.5539 \\ SAIHST (5 th) & 0.8582 & - & - & - & - \\ Izvunc\_fusion (6 th) & 0.8202 & 0.6259 & - & - & - \\ SDNU (7 th) & 0.5018 & 0.5374 & 0.4572 & 0.4111 & 0.4769 \\ CIL (8 th) & 0.7554 & 0.5024 & 0.4886 & 0.3920 & 0.5346 \\ MedLabs (9 th) & 0.7863 & 0.2637 & 0.3705 & 0.3397 & 0.4401 \\ AIIM (10 th) & 0.7662 & 0.2733 & 0.3283 & 0.4627 & 0.4367 \\ nmODE-Unet & 0.8361 & 0.7381 & 0.6262 & 0.5177 & 0.6795 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison with top ten teams in IDRiD challenge.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Model** & **Dice** & **IoU** & **ROC-AUC** & **AUPR** \\ \hline FCN [31] & 0.7591 & 0.6066 & 0.8536 & 0.6977 \\ DeepLab 03+ [32] & 0.8228 & 0.7005 & 0.9534 & 0.8615 \\ U-net [26] & 0.8057 & 0.6748 & 0.8988 & 0.8024 \\ Unet++ [33] & 0.8352 & 0.7185 & 0.9188 & 0.8028 \\ nmODE-Unet & 0.8470 & 0.7441 & 0.9583 & 0.8825 \\ \hline \end{tabular}
\end{table}
Table 5: The segmentation results on the LGG segmentation dataset.
## 5 Experimental Results
### Comparative Analysis
#### 5.2.2 Impact of Number of Dead Nodes  
The dead nodes refer to the nodes which are exhausted out of energy due to the inefficient management of the energy by the sensor nodes. In Figure 7, the number of dead nodes of the proposed MiA-CODER mechanism is compared with various existing works with respect to simulation time. The figure makes it clear that the simulation time and number of dead nodes are directly proportional to each other which means that as the simulation time increases the number of dead nodes also increases. The proposed mechanism has a low number of dead nodes than existing works because of the implementation of the unequal cluster topology and dynamic duty cycling of nodes in the network. The unequal cluster topology improves robustness and reduces the hotspot problem for the nodes near the base station. The execution of duty cycling reduces the unwanted wastage of energy in the network. The existing works have ineffectual topology structures and inefficient management of energy in the network.  
In Table 7, the analysis of the number of dead nodes is presented for the proposed MiA-CODER mechanism with respect to other existing methods in terms of simulation time. The number of dead nodes is lower in the proposed method than in other approaches. This shows that the lifetime of the nodes is increased by our approach.
## 3 Results and Discussion
### Concentrations of Metal(loid)s in Corn and Wheat  
Among the crops, there are differences in the concentrations of metal(loid)s between corn and wheat. The average corn crop values for Cu, Pb, Zn, Cr, Ni, Cd, As, and Hg are 1.4, 0.05, 21.46, 0.03,  
Figure 3: The distribution of metal(loid)s and soil properties.  
0.22, 0.0043, 0.01, and 0.004 mg/kg, respectively. The average wheat crop values for Cu, Pb, Zn, Cr, Ni, Cd, As, and Hg are 4.63, 0.06, 32.07, 0.03, 0.10, 0.03, 0.03, and 0.0022 mg/kg, respectively. Cu and Zn are essential to plant micronutrients [56; 57; 58] and are the most abundant metals in wheat and corn [59]. Based on 230 wheat grain samples, Zn concentrations ranged from 17.4 to 63.4 mg/kg and Cu ranged from 2.38 to 8.82 mg/kg. Using 230 corn grain samples, the concentration of Zn ranged from 10.9 to 34.84 mg/kg and Cu ranged from 0.06 to 2.36 mg/kg.  
Our results showed that the mean content of Cu, Zn, and Cd in wheat was significantly higher than in corn. Within the wheat and corn crops, Pb, Cd, As, and Hg had variation coefficients that exceeded 50%. When compared with the food safety limits (GB2762-2012), the Cd, Hg, and Pb concentrations in wheat, and Hg and Pb in corn, exceeded the standard samples (Table 1). Industrial production (smeleters, chemical plants, paper mills) in the study area is the likely source of excessive metal(loid) levels [60; 61].