esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Influencing Factors on Carbon Emission Intensity
#### 3.2.2 Operating Load Rate
Emission Minimization Mechanisms During Overload Operation (>100% Design Capacity)  
1. Peak Equipment Efficiency: Aeration systems and pumps operate within optimal design windows, maximizing energy efficiency (e.g., oxygen transfer rate, motor load factor). 2. Accelerated Microbial Metabolism: Overload conditions induce logarithmic microbial growth, rapidly depleting organic substrates and shortening HRT. 3. Optimized Carbon Utilization: Elevated influent carbon-to-nitrogen ratios may reduce external carbon supplementation demands for denitrification. 4. Indirect Emission Dominance: Electricity consumption constitutes the primary emission source in WWTPs. Overload operation reduces specific energy consumption (kWh/m\({}^{3}\)), offsetting potential minor increases in direct emissions (e.g., chemical use) and yielding net carbon intensity reductions. In addition, there is also a clear negative correlation between the operating load rate and the power consumption per ton of water. When the operating load rate is below 40%, the average power consumption per ton of water is as high as 0.696 kW.h, and the corresponding carbon emission intensity also reaches the highest level. As the load rate increases, the operating efficiency of the equipment and facilities improves significantly, and the power consumption per ton of water gradually decreases, along with a reduction in carbon emission intensity. When the load rate is in the range of 60% to 80% and 100% to 120%, the power consumption per ton of water decreases to 0.282 kW.h/m\({}^{3}\) and 0.237 kW.h/m\({}^{3}\), respectively.
## 7 Simulations and Results
### Scenario 2: HEMS with an ESS  
In scenario 2, the electricity consumers had their own ESS in conjunction with the grid supply to fulfill their load requirements without any RERs. In off-peak hours, the consumers stored the energy from grid in their installed ESS. In each time slot, when the dischargeable energy of the ESS was less than the consumer demand, the consumers imported energy from the grid to fulfill their load requirements. On the other hand, when the dischargeable energy of the ESS was greater than the consumer load demand, the consumers neither purchased energy from the grid nor sold their excess energy to the grid. The hourly loads of this scenario are presented in Figure 12.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 4 Estimation of GHI from Other Meteorological Data  
### Method  
In this study, the data used were measured in Bouzareah (see Section 2.1). Figure 2 shows the ANN structure with all the available inputs.  
The sunshine duration is the time expressed in minutes during which the solar irradiance exceeds 120 W/m\({}^{2}\). It is strongly linked with the solar radiation (Angstrom relation for example).  
The number of inputs, 10 (7 measured, 3 calculated), makes the optimization of the MLP structure long and arduous: With 10 inputs, 2\({}^{10}\) \(-\) 1 = 1023 combinations of input data are possible.  
The choice of the best inputs combination is a prerequisite stage because the parsimony is a basic principle in ANN elaboration, essential for its generalization. Some of the variables bring little information, sometimes no information at all, some of them are redundant, even worse they reduce the model performance. Moreover, an increase of the input number is accompanied by an increase of the hidden neurons and of the calculation time.  
The Pearson's correlation coefficient between each input parameter and the output is determined before using an exhaustive selection (testing 1023 architectures).
# Dynamic Ring Exploration with \((H,s)\) View +
### Our Contributions  
In this paper, we consider the exploration of \(1\)-interval connected rings by a single agent with the \((H,S)\) view (formalizing the proofs and the pseudo codes and extending the results given in [19]). Remind that the agent with the \((H,S)\) view can see the link scheduling of the links within \(H\) hops (\(1\leq H\leq\lceil n/2\rceil\)) from its location for \(S\) time steps from the current time. To the best of our knowledge, this is the first work to generalize the agent capacity to see a link scheduling.  
The results are summarized in Table 1. For the proposed model, we show that \(H+S\geq n\) and \(S\geq\lceil n/2\rceil\) (\(n\) is the size of networks) are the necessary and sufficient conditions to explore \(1\)-interval connected rings by a single agent. We also show that in the case where the above conditions holds, the exploration can be achieved within \(O(n^{2})\) time if \(2H^{\prime}-1>S\) or otherwise \(O(n^{2}/H+nH)\) time where \(H^{\prime}=\min(H,\lfloor n/2\rfloor)\). This is a new addition to the contributions of the previous work [19]. Moreover, we show that when \(S\geq n-1\), the exploration time can be reduced to \(O(n^{2}/H+n\log H)\). This leads to \(O(n\log n)\) time when \(H=\Theta(n/\log n)\). Finally, we show a lower bound of the exploration time, \(\Omega(n^{2}/H)\), for any \(S\). This implies that we have tight bound \(\Theta(n^{2}/H)\) when \(H+S\geq n\), \(\max(\lceil n/2\rceil,2H^{\prime}-1)\leq S\), and \(H\) is \(O(n^{0.5})\) and when \(S\geq n-1\) and \(H=O(n/\log n)\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(H\) **and**\(S\) & **Upper Bound** & **Lower Bound** \\ \hline \(H+S<n\) or & & \\ \(S<\lceil n/2\rceil\) & & The exploration is **impossible**. \\ \hline \(H+S\geq n\) and & & \\ \(\lceil n/2\rceil\leq S<2H^{\prime}-1\) & & \\ \hline \(H+S\geq n\) and & & \\ \(\max(\lceil n/2\rceil,2H^{\prime}-1)\leq S<n-1\) & & \(\Omega(n^{2}/H)\) \\ \hline \(n-1\leq S\) & & \(O(n^{2}/H+n\log H)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Upper and lower bounds of the exploration time in \(1\)-interval connected rings where \(H^{\prime}=\min(H,\lfloor n/2\rfloor)\).
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 2 Target Identification
### Charging Technologies
#### 2.3.1 Plug-in Charging
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Standard** & **Scope** \\ \hline SAE J1772 & Discusses all the equipment ratings for EV charging including circuit breaker current rating, charging voltage rating. \\ SAE J2293 & Establishes the requirement of on and offboard charging equipment. \\ SAE J2847 & Provides standard communication requirements between the utility grid and plug-in electric vehicles. \\ SAE J2954 & Defines a wireless power transfer of all types of plug-in electric vehicles and coil alignment methodology. \\ IEEE 1547 & Defines standards for interconnecting distributed resources with electric power systems. \\ IEC 61980-3 & Defines the magnetic field-based wireless power system and its specific requirements. \\ IEC TS 62840-1 & Gives a general overview for battery swap systems. \\ IEC 61851-21-1 & Defines requirements for conductive connection of an electric vehicle (EV) to an AC or DC supply. \\ IEC 61851-24 & Defines a digital communication between a DC charging station and an EV. \\ IEC 62196-1 & Gives a general overview of the interface between the EV and the charging station. \\ IEC 62196-2 & Extends the previous designs with detailed description of plugs and socket outlets. \\ IEC 62196-3 & Describes the DC vehicle coupler and defines the characteristics of the DC vehicle coupler. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relevant standards for charging stations [79,80]. optimize the number and size of chargers based on slow and fast chargers, including ADC type, as well as chargers that can shift between slow and fast charging modes. Study [46] allocates and sizes chargers between AC Level 2 (20 kW) or DCFC (80 kW), whereas [83] calculates the charging capacity of low voltage networks for both single-phase 3.7 slow and three-phase 11.7 kW chargers. A unique case is considered in [84], a recent study in which wireless solutions are implemented together with DC fast charging and AC Level 2 charging. However, no analysis is made to assess which technology brings the highest benefits.
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
###### Abstract  
In the literature, there are many different photovoltaic (PV) component sizing methodologies, including the PV/inverter power sizing ratio, recommendations, and third-party field tests. This study presents the state-of-the-art for gathering pertinent global data on the size ratio and provides a novel inverter sizing method. The size ratio has been noted in the literature as playing a significant role in both reducing power clipping and achieving system optimization. The majority of researchers observed that due to varying irradiance distributions and operating temperatures at particular sites, the sizing ratios were dependent on geographic latitude. This study will identify the issue that makes it challenging to acquire dependable and optimum performance for the use of grid-connected PV systems by summarizing the power sizing ratio, related derating factor, and sizing formulae approach. The present study recommends a Deep Learning technique that might, due to the dynamic behavior of the PV technologies, provide fully automatic computation for the DC/AC sizing ratio, and effectively lower the whole return on investment (ROI) over a variety of circumstances and climatic changes.  
optimization; inverter sizing; photovoltaic systems; grid-connected; DC/AC ratio; PV system cost
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 3 Methodology
### Multi-Destination \(k\) Simple Shortest Paths  
_Problem Definition_  
The KSSP problem is formally defined here, beginning with the typical case of a single origin to a single destination, followed by the single origin to multiple destinations case and its variants, which are of interest in this work.
## 3 The CART-Based Methods
### CART-Based Integrated Algorithms
#### 3.1.3 CART-AdaBoost Algorithm  
In contrast to the bagging algorithm, the boosting method dynamically adjusts the distribution of the original training dataset, allowing base regression models to focus on challenging instances [21; 22]. AdaBoost's core concept involves training various classifiers (weak classifiers) on the same training set and subsequently combining them to create a more robust final classifier [23]. Currently, the most prevalent approach is the AdaBoost integrated algorithm, which employs CARTs as base learners. As depicted in Figure 4b, unlike the CART-RF algorithm, which selects certain variables as split variables, the CART-AdaBoost algorithm increases the likelihood of selecting samples prone to misclassification during variable selection. It then assigns weights to the output of each CART to produce the final model prediction [24]. By adjusting the distribution of sample weights, the accuracy of weak learners is enhanced.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 3 Experiments
### Datasets  
The method is evaluated with the processing team dataset from the WorldView-4 satellite, including 6736 training images with a spatial resolution of 0.31 m and 1012 similar test images. After data augmentation including color jittering, horizontal flip and vertical flip, the image is processed as 1024 \(\times\) 1024. Finally, this research obtains 47,152 training images.  
Further, we train and test the D-DenseNet on the Massachusetts Road Dataset [39] with 1108 training images, 14 validation images and 49 test images. Since the image size is \(1500\times 1500\), we cut the images into 1024 \(\times\) 1024 because of memory limitations. The area in an image is labeled as the foreground for roads and the background for other. Then, the data are processed for the experiment.
## 3 Modeling of Heterogeneous Cluster Deployment Tasks
### Equations of Relative Motion  
When executing close-range collaborative detection tasks in satellite constellations, the distance between satellites of both sides is relatively close, and the motion between satellites can be described as relative motion. Under the premise of satisfying the following assumptions, the well-known Clohessy-Wiltshire (CW) equations can be employed to depict the relative motion between satellites. The fundamental assumptions are as follows:  
1. For satellite cluster missions in medium-high altitude low Earth circular orbits (LEO), when the mission duration does not exceed several orbital periods, the effects of atmospheric drag perturbation and J2 perturbation on spacecraft can be neglected [21].
2. A virtual satellite moving along a circular orbit is selected near the participating satellites as the origin of the reference orbit.
3. When the distance between the participating spacecraft and the origin of the reference orbit is much smaller than the orbital radius of the reference orbit, the second-order and higher-order terms in the relative orbital dynamic equations can be ignored, as the influence of these higher-order terms on the relative motion is minimal at this time.  
In a region of near-Earth orbit where distances are close, a virtual reference satellite (primary satellite) is selected, and its orbit serves as the reference orbit. The center of mass of the reference satellite is taken as the origin \(O_{b}\) of the coordinate system to establish a satellite orbital coordinate system \(O_{b}-XYZ\), as illustrated in Figure 2. In the coordinate system of the reference satellite, the equations of motion of a satellite relative to the reference satellite can be expressed as:  
\[\ddot{x}-3n^{2}x-2n\dot{y}=a_{x} \tag{1}\]  
\[\ddot{y}+2n\dot{x}=a_{y} \tag{2}\]  
\[\ddot{z}+n^{2}z=a_{z} \tag{3}\]  
Here, \(n\) represents the angular velocity of the reference orbit, and its expression is given by \(n=\sqrt{\frac{H}{a^{2}}}\).  
Figure 2: Establishment of satellite relative motion scenarios and relevant reference frames.
## 2 Random Spatial FE Models of the Subsoil
### The Monte Carlo FE Material Model
#### 2.2.1 The Homogenous FE Model Described with One Random Variable  
The initial model assigns a single random variable \(n_{E}=1\) (with the truncated normal distribution of Young's modulus of the soil: \(\mu_{E}=28,000\) kPa, \(\sigma_{E}=7000\) kPa, \(t=4\)) to all \(n_{r}=1000\) elements. Thus, the entire block stiffness is represented by a single randomly generated value. Such an FE model is straightforward in its numerical operation; hence, frequently introduced in probabilistic analysis.  
The first stage of the analysis checks the convergence of the investigated displacement with 200 FE models computed for different \(E_{i}\) random variable generations. The mean value \(\mu_{\delta_{1}}^{200}=0.1087\) m and standard deviation \(\sigma_{\delta_{1}}^{200}=0.0091\) m are obtained here. In the second stage, the test was expanded to a total of 10,000 samples, bringing a sample mean \(\mu_{\delta_{1}}^{10000}=0.1097\) m and standard deviation \(\sigma_{\delta_{1}}^{10000}=0.0174\) m. The computed displacement of each FE model (sample) \(\delta_{1}^{i}\) and convergence of both mean value \(\mu_{\delta_{1}}\) and standard deviation \(\sigma_{\delta_{1}}\) of the settlement are shown in Figure 6. The 200-sample test is separated in Figure 6a, and the complete 10,000-sample test is presented in Figure 6b.  
Figure 5: Soil block settlement dispersion versus the correlation decay parameter \(d\); random field approach.  
The 10,000-sample mean does not substantially differ from the 200-sample test, but the standard deviation does. The latter stems from excessive soil settlements reaching \(\delta_{1}^{10000}=1.1505\) m (the 9851 st sample) which, in turn, is produced by extremely low Young's moduli generated for the FE models. The highest settlement in the initial domain of 200 samples is \(\delta_{1}^{200}=0.1525\) m (the 149 th sample). The computed peaks exceed the settlement allowable for the ZSoil(r) nonlinear material model. The excessive cases should be analysed separately in terms of the large displacement theory. It should be stated that the single random variable solution (Young's modulus) for all finite elements is wrong.  
Figure 6: Convergence of simulation-based settlement estimation: (**a**) the 200-sample test; (**b**) the 10,000-sample test; the homogeneous FE model, \(n_{E}=1\).  
If a homogeneous FE soil model is incorporated, the dispersion is unclear. The dispersion decreases with a lower truncation parameter of Young's modulus distribution, e.g., \(t=1.0\); however, such an assumption is not justified by in situ soil studies. Soils in their natural state show significant variability, and the coefficient of variation may reach 0.4 (see [43; 44; 45]). Thus, larger values of the truncation parameter are recommended (\(t\gg 1.0\)).  
Although the presented results (Figure 6a, 200 samples) are improper and only accidentally reliable (no possible peaks are observable), they serve as a reference point for further analysis.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 5 Pricing: Creating Incentive for Caching
### Pricing User Cooperation  
Though user cooperation plays a central role in caching, selfish users may be unwilling to cooperate. Pricing is an effective tool to motivate user cooperation in various layers.  
Caching-oriented pricing should reward users who contribute more memory for caching or private data for request prediction. A user's hit ratio is increased with more memory used for caching. However, more memory means higher device cost for a user. To reward users contributing more memory for caching, they should enjoy a discount on the telecommunication service fee. On the other hand, the accuracy of request prediction increases with more historical request data or more knowledge about social connections. Sharing these data means more risk in leaking a user's privacy, with which some users are seriously concerned. To gather more data for request prediction, a lower price should be charged for cooperative users. Each VNO may announce its reward policy so that users may choose their favorite VNO according to their own willingness to cooperation in different layers. One powerful tool for developing rewarding mechanisms is a Stackelberg game.  
Coded caching holds the potential of scaling the service capability through user cooperation. However, pricing coded caching is rather challenging. As demonstrated in Figure 5, two users \(U_{1}\) and \(U_{2}\) may be interested in content items A and B. A natural question is raised as to how the two users should share the bandwidth cost in the delivery phase. If they have equal demand probabilities for A and B, it is fair for each user to pay for half of the cost. In practice, however, popularity skewness exists [4; 26]. If \(U_{1}\) prefers A with a high probability, it will enjoy a high hit ratio by caching A only. Even though the entire B has to be transmitted in the case that \(U_{1}\) asks for B, the expected bandwidth cost is lower than that of coded caching in which the delivery cost is equally shared. In other words, \(U_{1}\) is unwilling to participate in coded caching, unless it pays only a small portion of the bandwidth cost. Intuitively, the more demand uncertainty a user has, the more of the cost it should pay. Moreover, a RAN tends to encourage coded caching because the overall bandwidth consumption in the delivery phase can be reduced. A quantitative study on how to price coded caching with popularity skewness can be enabled by game theory [39].
## 5 Results
### WAI Reconstruction
#### 5.2.3 Comparative Analysis with [PERSON]'s Method  
[PERSON]'s method [23] has strict requirements for natural illumination, is not suitable for cloudy weather and requires sunlight. Moreover, the accuracy of WAI reconstruction varies with the position of the sun. Here, we adopt [PERSON]'s method and our algorithm to reconstruct the same WAI (Figure 10), respectively. The relationship between the absolute error distribution of the reconstructed WAI and the ground truth with the incident angle of the sunary \(\theta_{\text{sun}}\) using [PERSON]'s method is shown in Figure 13. Figure 11 shows the RMSE and the maximum absolute error (\(Ae_{\text{max}}\)) as a function of the zenith angle of the sunray using [PERSON]'s method. Combining Figures 13 and 14, it is obvious that the restoration accuracy is limited by the illumination conditions. As the zenith angle of the sunray increases, the maximum absolute error and the stability of the system decrease. Furthermore, as shown in Figure 14, the minimum mean square error of [PERSON]'s method is 3.4603 mm, while the RMSE of our algorithm is 1.7845 mm.  
The research results show that, compared with the model included in [PERSON]'s method, the proposed algorithm can overcome the influence of changes in natural illumination conditions for WAI reconstruction and improve the accuracy of WAI restoration.  
Figure 13: Absolute error distribution between reconstructed WAI and the ground truth for the different \(\theta_{\text{sun}}\) using [PERSON]’s method. (**a**) Absolute error distribution when \(\theta_{\text{sun}}=0^{\circ}\); (**b**) absolute error distribution when \(\theta_{\text{sun}}=15^{\circ}\); (**c**) absolute error distribution when \(\theta_{\text{sun}}=30^{\circ}\); (**d**) absolute error distribution when \(\theta_{\text{sun}}=45^{\circ}\); (**e**) absolute error distribution when \(\theta_{\text{sun}}=60^{\circ}\); (**f**) absolute error distribution when \(\theta_{\text{sun}}=75^{\circ}\).
###### Abstract  
The influences of rockfall on human engineering have been increasing in Tibet with the rapid development of the western region of China. This study proposed a multi-approach to carry out rockfall investigation and hazard assessment. As a case study, the rockfall hazard from Nang County to Jiacha County in Tibet was assessed. Firstly, we summarized the characteristics of spatial distributions of typical rockfall sources using Digital Elevation Model (DEM) and unmanned aerial vehicle (UAV) aerial images with resolution of 10 m. According to the thresholds of slope angle, slope aspect and elevation distribution of typical rockfall sources, we obtained all of the rockfall source areas in study area semi-automatically in ArcGIS platform. Secondly, we improved the efficiency and accuracy of detailed field investigation by using a three-dimensional (3D) point cloud model and rock mass structure extraction software. According to the analysis result, the dominant joint set was J1, whose orientation was basically consistent with the Yarlung Tsangpo Fault. The combination of J1, J2 and J4 cut the rock mass into blocks of wedge with J1 as potential sliding planes. It was indicated that the stability of the rock mass in study area was mainly controlled by the characters of joint sets. Finally, we applied the improved reclassification criteria of the Rockfall Hazard Vector (RHV) method in rockfall hazard assessment according to protection capabilities of the current protection facilities, making the result more valuable for geohazards prevention work. Based on this multi-approach, we obtained that 10.92% of the 306 provincial highway and 9.38% of the power line were threatened by potential rockfall hazards in study area. The hazard assessment results of study area were also of certain guiding value to the linear project planning and geohazards prevention work.  
rockfall; investigation; 3D numerical simulation; hazard assessment +
Footnote †: journal: Applied Soft. 2020, 10, 247; doi:10.3390/app10010247