esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### HEV Simulations with GCI Engine Test Results
#### 3.2.3. CN15 Engine with a Plug-In Serial Hybrid Configuration  
The CN15 fuel engine experiments were performed on an SCE engine test cell, and the engine data used for the simulation was the full engine map created by GT-Power engine simulation results. Due to high resistance to the autoignition of the fuel, the CN15 engine is only able to run in the limited operating zone (medium and high loads as mentioned in Figure 5) without any ignition improvers, e.g., spark assist system and dual fuel system. Considering the operating range, a serial hybrid configuration was selected for the engine in this study. is the chosen engine was a 1.8 L, 4-valve CI engine with a 15:1 compression ratio. Emissions were also not considered in this engine approach because the engine operating area affected by hybridization might produce very low engine out emissions.  
Figure 10 shows the results for the speed, torque, power, and fuel consumption of the CN15 engine on the serial hybrid. The assessment of the electric components are an 8.8 kWh of battery (concern of PHEV with 50 km of e-driving range) and 100 kW of EM and 50 kW of generator. In case of non-PHEV, the electric driving mode is mostly utilized at low vehicle speeds to minimize the fuel consumption, and the ICE status was determined by the SOC values. In this approach, a PHEV makes sense for vehicles with a large battery package and electric device size. From the PHEV simulation, the CO\({}_{2}\) benefits are up to 75% for the configuration.  
Figure 11 shows the energy-weighted engine operating points of the CN15 engine on WLTC after the hybridization (serial hybrid configuration). Consequently, the CN15 engine is the most suitable for serial hybridization. In the serial configuration, the entire mechanical transmission between the engine and wheel was replaced by the electric transmission that consisted of EM as the electrical generator. With a generator, the EM and ICE operate independently of each other, with each operating at its most efficient range. The limited operating range of the CN15 engine with low reactivity fuel is matched into the hybrid configuration without any ignition improver. Thanks to hybridization, the engine is able to operate at the optimum efficiency range in WLTC. Therefore, the mean engine efficiency during the cycle reached a value of 39%. This result is based upon the warmth-measured engine maps. However, the disadvantage of this configuration is the large battery package and the large EM required for the vehicle (high cost of vehicle) because it has to provide the maximum possible power to the wheels.  
Figure 10: Speed, torque, power, and fuel consumption of CN15 engine with serial hybrid configuration (WLTC).
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion
### Fuzzy-PI Controller Driven Drive
#### 4.4.2 Step or Gradual Application and Removal of Load  
The simulation performance depicts that when a step load is applied from 1 s to 1.2 s, the motor settles to the steady state speed at 1.195 s with a \(-\)5.5% (\(-\)3.4% for PI, \(-\)0.14% for fuzzy controller) overshoot. During unloading at 3.5 s, the overshoot is 5.1%, with a settling time of 4.9 s and a steady-state error of just 2 rpm.  
When the load is linearly increasing from 1 nm to 2.5 nm between 1 s to 2.5 s, the settling time is 2.49 s, with a 59 rpm steady state error and a \(-\)4.5% (2.1 s to 2.5 s) overshoot. After 2.5 s and up to 3.5 s, the loading is kept constant with 2 nm; in this case, the settling time is 3.49 s. Between 3.5 to 4.5 s, unloading proportionately occurs and the speed settles down with an overshoot of 3.03% (14.28% for PI, \(-\)0.06% for fuzzy controller) with a 1 rpm error and a settling time of 4.49 s. After 4.5 s and until 5 s, the load is completely removed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Load** & **Characteristics** & **Time** & **Overshoot \%** & **Peak Time (s)** & **Rise Time (s)** & **Settling Time (s)** & **Steady** \\
**Variation** & **Characteristics** & **Interval (s)** & **Overshoot \%** & **Peak Time (s)** & **Time (s)** & **Time (s)** & **State Error** \\ \hline  & Starting & 0–1 & \(-\)0.0722 & 0.7664 & 0.0697 & 0.0881 & 1 rpm \\ Step load & Loading & 1–1.2 & \(-\)0.1424 & 1.0003 & 0.00024 & 1.2 & 2 rpm \\ variation & Unloading & 3.3–3.5 & \(-\)0.1213 & 3.3932 & 0.00025 & 3.5 & 3 rpm \\  & Load removal & 3.5–5 & \(-\)0.1177 & 3.8204 & 0.00019 & 5.0000 & 2 rpm \\  & Starting & 0–1 & \(-\)0.0722 & 0.7664 & 0.0697 & 0.0881 & 1 rpm \\ Linear & Gradual increases of load & 1–2.5 & \(-\)0.2328 & 1.0131 & 0.0026 & 2.499 & 3 rpm \\ load & Constant Max Load & 2.5–3.5 & \(-\)0.1517 & 2.6278 & 0.000207 & 3.499 & 4 rpm \\ variation & Gradual decrease in load & 3.5–4.5 & \(-\)0.0679 & 4.4920 & 0.1789 & 4.500 & 1 rpm \\  & No Load & 4.5–5 & \(-\)0.0679 & 4.4920 & 0.1789 & 4.500 & 1 rpm \\ \hline \end{tabular}
\end{table}
Table 3: Fuzzy Controller Characteristics.
# Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree
## 3 The Proposed Method  
### Problem Definition and Overview of the Proposed Method  
Consider a graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathbf{A})\), where \(\mathcal{V}=\{v_{1},v_{2},\ldots,v_{N}\}\) represents a set of \(N\) nodes, \(\mathcal{E}=\{e_{ij}\}\subseteq\mathcal{V}\times\mathcal{V}\) represents a set of edges, and \(\mathbf{A}\in\{0,1\}^{N\times N}\) is an adjacency matrix which can describe the topology of the graph \(\mathcal{G}\). \(\mathbf{A}[i,j]\) is 1 if there is an edge between \(v_{i}\) and \(v_{j}\), or 0 otherwise. In this paper, we focus on the semi-supervised node classification task on graphs. For each graph, there is a feature matrix \(\mathbf{X}\). Let \(\mathbf{x}_{i}\) be the \(i\)-th column of \(\mathbf{X}\), which represents the feature vector associated with node \(v_{i}\). Each node belongs to one of \(C\) classes. There is a set of nodes \(V_{L}\) (\(|V_{L}|<<N\)), and each node of \(V_{L}\) is assigned a label \(y_{i}\in\{1,2,\ldots,C\}\). The labels of all nodes in set \(V_{t}\) form a set \(\mathcal{Y}^{L}\). The task is to learn a function \(f:(\mathcal{G},\mathbf{X},\mathcal{Y}^{L})\rightarrow\mathcal{Y}^{U}\) and predict the labels \(\mathcal{Y}^{U}\) of unlabeled nodes.  
There are two major components in GCN-EHHD. One is the MLP module for pre-training estimators. Here we choose the multi-layer perceptron (MLP) as the estimator to explicitly estimate the homophily and heterophily degree between neighbouring nodes. The other is the GCN module that leverages the estimated homophily and heterophily degree to adaptively guide signed-message passing. In the improved message-passing mechanism, when updating the representation of each node in the graph, we use the homophily degree to guide the passing of a positive message and the heterophily degree to guide the passing of a negative message, respectively, and these signed messages are aggregated with the node self-representation. We also adopt a two-stage training process to train GCN-EHHD, where the MLP module is pre-trained first for obtaining estimators and then is jointly trained with the GCN module in the second step. An overview of GCN-EHHD is shown in Figure 2.
## 2 Materials and Methods
### Tube Tests
#### 2.2.2 Effect of Cementation Solution Concentration on the Reaction  
It is very important to control the reaction time between the urease and cementation solution when EICP is used to strengthen soil during practical engineering projects. To study the influence of cementation solution concentrations on the amount of carbonate precipitation and reaction rate, 4 precipitation tests with a cementation solution (CaCl\({}_{2}\):urea = 1:1.5) concentration of 0.8 M, 1.6 M, 2.4 M, and 3.2 M were performed. A total of 12 samples was prepared in 500 mL beakers including 400 mL of the treatment solution (urease:cementation solution = 1:1). The reaction was carried out for 7 days. EDTA titration was used to measure the calcium ion concentration at different times in the reaction, which is convenient for real-time measurement in the solution and is widely used in water-quality detection. For the first day, the calcium ion concentration was measured every 2 h, 4 h, 8 h and 12 h, and for the next day, it was measured every 12 h, for a total of 2 measurements. Then it was measured every 24 h. The precipitation ratio, which was defined as the ratio of the precipitation mass to the theoretical maximum amount of calcium precipitation, was obtained.
## 5 Bi-Objective Genetic Algorithm
### Crossover Operators  
Crossover operators allow the obtaining of new solutions (children) by combining individuals (parents) of the population. The crossover operator is applied under a certain probability. In this paper, we consider two operators: the partially mapped crossover and the order crossover.  
Partially mapped crossover (PMX) [32; 33]: This operator uses two cut points (Figure 5). The part of the first parent between the two cut points is copied to the children. Then, the following parts of the children are filled by a mapping between the two parents, so that their absolute positions are inherited where possible from the second parent.  
Figure 6 shows an example of the PMX crossover. The cross points in both parents serve to form the child chromosome. The 16 listed lots of the 4 jobs are randomly distributed on each parent chromosome. When crossed, they form the child chromosome with 16 lots.  
The cross parents are composed of the batches of the jobs listed in Table 8.  
Figures 7 and 8 show representations of tasks for two and four machines per stage. The original sequence is ordered considering the enumeration of the jobs and their batches. Each machine is assigned batches to process, which form the tasks of each job. Each machine must process a certain set  
Figure 5: Partially mapped crossover (PMX) operator.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Job** & **Batches** & **Batch Index** \\ \hline
1 & 4 & 1–4 \\
2 & 4 & 5–8 \\
3 & 4 & 9–12 \\
4 & 4 & 13–16 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Enumeration of the batches for jobs.  
Figure 6: PMX crossover operator example with batch index.  
of tasks; each set is assigned a processing order (Proc. Order). These sets are formed according to the processing compatibility.  
The tasks of Jobs 1, 3, and 4 can be processed in the same set at the same time. The tasks of Job 2 are put into a separate set because they are not compatible with each other. For example, in Figure 7, the first two tasks (1-1, 4-1) are processed by Machine 1, then the second set with one task (2-1). In Machine 2, two tasks of the first set (1-2 and 3-1) are processed, assuming that the machine has only the capacity to process up to 6 batches (60 kg). The second set will then be followed by one task (4-1).  
Figure 8 shows four machines per stage. Four batches are assigned to each machine. Machines 1 and 2 process two sets of tasks. Machines 3 and 4 process only one set. Upon completion of the tasks processed in sets in Step 1, in the later stages, they will be processed individually.  
Order crossover (OX) [32; 34; 35; 36; 37]: Two cut points are selected randomly; the part of the first parent located between these two cut points is copied to the child. The child's other spaces are left blank. The values copied to the child are deleted in the second parent. The following positions in the child are filled starting in the blank spaces and considering the order found in the second parent (Figure 9).
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 3 Game Theoretic Framework and Utility Function Design
### Potential Games and Their Characteristics  
To guarantee that the subchannel allocation game converges to a pure strategy NE, we focus on a class of strategic form games called PG [17], which ensures convergence to a pure strategy NE through BR dynamics. Specifically, a class of PG called EPG has at least one pure strategy NE [17], and is characterized by the existence of a potential function, \(\mathbf{P}:\mathcal{S}\rightarrow\mathbb{R}\), with property,  
\[u_{i}(s_{i},\mathbf{s}_{-i})-u_{i}(s_{i^{\prime}},\mathbf{s}_{-i})=\mathbf{P}(s_{i},\mathbf{s}_ {-i})-\mathbf{P}(s_{i^{\prime}},\mathbf{s}_{-i}), \tag{6}\]  
for all \(i\in\mathcal{M},s_{i},s_{i^{\prime}}\in\mathcal{S}_{i}\), and \(\mathbf{s}_{-i}\in\mathcal{S}_{-i}\).  
Here, the potential function acts as a global performance indicator, such that any increments/decrements in player's utility from the change in strategy are equal to the same increments/decrements in the overall networks' utility. In other words, at any instance of play, a individual player's best strategy is also the best strategy for the network. As such, the maxima of the potential function corresponds to a pure strategy NE [17]. Hence, with sequential updates of strategy by players following BR dynamics, the process will converge to a pure strategy NE regardless of the order of play and the initial strategy profiles of the game.  
In order to capture benefits associated with an EPG, it is desirable to design the utility function for players according to some specified learning rules. This can provide predictive performance in the subchannel allocation game when players rationally play according to BR dynamics. Desirably, to optimize the total capacity of the SCN while players distributively chooses their strategies, a simple and straightforward option would be to assign the players' utility function and potential function equal to the total capacity (similar to the identical interest games [32; 33]), such that:  
\[u_{i}(s_{i},\mathbf{s}_{-i})=\mathbf{P}(\mathcal{S})=\mathcal{C} \tag{7}\]  
However, for the utility function in Equation (7), each link \(i\) would require information feedback from all other links, which would become extremely complicated in a dense SCN. Looking forward to reduce the computational complexities while keeping the objective to maximize total capacity of the SCN, we resort to utilizing design rule of utility function based on the concept of MC that is also proven to be an EPG [8]. The concept of MC considers designing utility function derived directly from the potential function, and is defined as [8],  
\[u_{i}^{\text{MC}}(\mathbf{s})=\mathbf{P}^{\text{MC}}(\mathbf{s})-\mathbf{P}^{\text{MC}}(\mathbf{s} _{-i}) \tag{8}\]  
where \(\mathbf{P}^{\text{MC}}(\mathbf{s})\) and \(\mathbf{P}^{\text{MC}}(\mathbf{s}_{-i})\) are the value of potential function with presence and absence of player \(i\) in the game, respectively.
## 1 Introduction
The ACCESS1-3 model of CMIP5 is selected for the study purpose, although data from 34 simulation models are available. Four bias correction methods are used for precipitation viz. linear scaling (LS), local intensity scaling (LIS), power transformation (PT), and distribution mapping (DM); and three bias correction methods are used for temperature viz. linear scaling (LS), variance scaling (VS) and distribution mapping (DM). The evaluation of bias correction methods is performed by using the Kolmogorov-Smirnov (K-S) non-parametric statistical test. This test assesses the level of discrepancy between the cumulative distributions of the parameters to determine the results. The novelty of the study is to evaluate the bias correction methods in the Upper Bhima sub-basin, which has a very unique geographical location that contains diverse agro-climatic zones such as the Western Ghat Zone, Transitions Zones, the Water Scarcity Zone, and the Assured Rainfall Zone. The major area is occupied by the Water Scarcity Zone, which is a rainfall shadow zone. Hence, it is very important to assess whether the CMIP5 model data are useful in analyzing the impacts of climate change at the regional level. The objectives of the present study are: to perform bias correction on the precipitation, maximum temperature, and minimum temperature data of the CMIP5-simulated data, of the ACCESS1-3 historical model output data, in the Upper Bhima sub-basin in Maharashtra, India; and to evaluate the bias correction methods applied on the CMIP5 model-simulated precipitation, maximum temperature, and minimum temperature data with the help of observed meteorological data using the K-S non-parametric test. The structure of the article is as follows: Section 2 provides a comprehensive overview of the study area, selected GCM simulated data, and the observed reanalysis dataset used for the study. Section 3 presents the overall methodology adopted for the study, including bias correction methods and a statistical analysis test, i.e., K-S test. The results and their discussion are explained in Section 4. Based on the observed results and the main findings, the paper concludes with the main outcomes and the concluding remarks are described in Section 5.
## 4 Results and Analysis
### Range Analysis  
Range analysis, also known as visual analysis, is one of the most common methods used to analyze the result of orthogonal tests. The problem is analyzed by the difference between the maximum and minimum in the mean effect. The specific formula is as follows:  
\[R_{i}=\max\{k_{i1},k_{i2},\cdots,k_{ij}\}-\min\{k_{i1},k_{i2},\cdots,k_{ij}\} \tag{6}\]  
where \(k_{ij}\) presents the mean value of the test results of the \(i\) influencing factor on the \(j\) level. \(R_{i}\) is refers to the extreme difference in the value of the influencing factor of \(i\), that is, the difference between the maximum and minimum values of the test results at each level of the \(i\) influencing factor. The value reflects the variation range in the test results when the influencing factor of \(i\) changes. The larger the value of \(R\), the greater the influence of the factor on the overall experiment results, which is a major influence. Conversely, it has a small effect.  
Figure 6 shows the data analysis of the influencing factor level on the elastic modulus and UCS, respectively. The results showed that the influence degree of the four influencing factors on the elastic modulus of the fractured rock mass was in the order of track length, bulk density, dip angle, and inclination. However, for the UCS, the track length, inclination, bulk density, and dip angle were arranged in descending order. Therefore, the fracture trace length based on the extreme difference analysis was the main factor affecting the elastic modulus of the fractured rock mass and UCS.  
The range analysis is easier to calculate and can prioritize the effect of factors on the calculated results. However, it is impossible to determine whether the influencing factors of the experiment are significant. Furthermore, errors are inevitable during the experiment, and range analysis makes it difficult to distinguish whether the differences corresponding to each factor level are due to experimental error or the factor level itself.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & \multicolumn{4}{c}{**Influencing Factors**} & \multicolumn{4}{c}{**Numerical Results**} \\ \cline{2-7}  & **Inclination/\({}^{c}\)** & **Dip Angle/\({}^{c}\)** & **Trace** & **Bulk** & **Elastic** & **UCS/MPa** \\ \hline T-1 & 47.24 & 28.60 & 1.48 & 0.029 & 20.362 & 29.794 \\ T-2 & 94.49 & 28.60 & 2.96 & 0.058 & 20.063 & 31.378 \\ T-3 & 141.73 & 28.60 & 4.44 & 0.0871 & 18.114
## 1 Introduction
A different approach is to use the sub-area scheduling methodology, but the sub-area paths can be updated according to the coverage progress and the available vehicles can be updated by removing the broken UGVs [32]. From the above literature, several different approaches to solving the CSP have been discussed. However, very few papers have explored using the ACO algorithm to generate the optimal path for the CSP. Furthermore, no studies have used the ACO algorithm to generate paths in fleet UGV surveillance applications. In this study, the ACO algorithm will be used to generate optimal scanning paths within sub-areas for surveillance applications, while a scheduling algorithm will be used to distribute the vehicles amongst the several sub-areas. ACO was also selected due to its exceptional global optimization performance and seamless transition for parallel computation. Since the ACO algorithm has not been used for UGV surveillance applications, this is one of the contributions of the research resented. The UGV paths can then be post-processed such that the kinematic constraints of the vehicle are considered. Several different approaches have been discussed in the above literature; however, each has its pros and cons. In this study, a combination of two different approaches will be used to improve the path curvature. The first approach is the corner-cutting method, and the second approach is a Reeds-Shepp curve. As previously discussed, the Reeds-Shepp curve adds additional traveling with its forward and backward motions. Therefore, it is used alongside an additional algorithm (the corner-cutting method) to minimize the distance added by the Reeds-Shepp curve. Since this combination of path refinement strategies has not been explored in existing studies, this is another contribution of the following research. Additionally, several recovery strategies for breakdown scenarios have been proposed in the literature for vehicle fleet scheduling. In this study, the higher-level path generation approach from the authors' previous work was adapted to the UGV surveillance problem for real-time scheduling. This is the final contribution of our study since existing research has not considered the breakdown scenario in UGV surveillance applications. A study for the proposed approaches was conducted in Goheung, South Korea, and the results will be presented to highlight the effectiveness of the developed technology. The results presented in this article were generated from simulations. The authors took measures to ensure that the simulations could replicate the physical aspects of the project (such as including the sensor scanning range and vehicle turn radius). Additionally, GPS data were supplied by our research partners to define the testing environment that included the outer boundary, several pre-defined sub-areas, and the location of static obstacles (buildings, etc.). The remainder of this article is organized as follows: Section 2 covers the route generation methodology, Section 3 discusses the study parameters, Section 4 shows the results for several test scenarios in the study, and Section 5 concludes the article.
## 3 Networks and Protocols of Communication
### Network and Communications Protocols for IoT Applications  
We are in the era of connected objects; IoT is gaining importance in almost all fields, such as business, industry, electronics, automotive and more. All of the objects in today's world are connected in one way or another. We can control the lights and equipment in our homes or offices. In industries and other fields, equipment, such as public lighting [28], can be controlled from a remote location. Currently, several protocols have been developed for use in IoT and Smart Grid applications. Each of them within its peculiarities, meet aspects related to the reduction of energy consumption, speed of exchange of data, processing capacity, among other parameters.