esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 3 Results and Discussion
### Combustion Characteristics
The duration of combustion (CD) is the period from the 10% heat release angle to the 90% heat release angle. The duration of combustion is reflected in the efficiency of the engine: the longer the combustion takes, the greater heat losses can be expected across the system boundaries. On the other hand, if the combustion process is very fast, it is accompanied by large pressure increases, which are an undesirable phenomena in a combustion engine. Figure 4: Peak pressure rise (PPR) characteristics for dual-fuel engine fueled by diesel and propanol with an alcohol content from 0 to 65%. Figure 6 shows the influence of propanol content on the values of the characteristic combustion stages. The presented values are the obtained differences in relation to a conventional engine powered by diesel. The increased proportion of propanol in the process of co-combustion with diesel fuel increases the ignition delay time. The highest value of \(\Delta\)(ID), 6 deg, was obtained for the highest proportion of propanol (DP65), because in this case the share of fuel with the high heat of vaporization and low cetane number (CN) value was the highest, which significantly influences the ignition delay time. A dose of diesel fuel is injected into a propanol-air mixture that fills the cylinder at a temperature lower than in a conventional engine, where the air itself is compressed. In a dual-fuel engine, on the one hand, the high value of the heat of vaporization adversely affects the ignition initiation, because it lowers the initial temperature, on the other hand, the share of oxygen in the alcohol molecular structure promotes the combustion process. It turns out that in the initial stage of combustion, the dominant role is played by the influence of alcohol on the charge temperature before ignition, and in the second, the essential stage of combustion, the share of oxygen in the molecular structure accelerates this process. By analyzing the influence of propanol on combustion duration (CD), it was found that it significantly contributed to its shortening. For the highest proportion of propanol (DP65), the combustion duration was shortened by as much as 26 deg compared to the engine fueled with the reference fuel. The reduction of the combustion duration also influenced the value of the 50% heat release angle. The CA50 angle was close to TDC. The greatest effect of propanol on the CA50 angle was recorded for the DP30 and the difference was 5 deg. Figure 5: Normalized heat release (Q\({}_{\text{norm}}\)) characteristics for dual-fuel engine fueled by diesel and propanol with alcohol content from 0 to 65%. Figure 6: The stages of ID, CA50 and CD combustion.
## 3 Methods
### Statistical Modeling
#### 3.2.2 Generalized Additive Model (GAM)  
Because of the observed non-linear trends in glacier variations, the more advanced method of generalized additive models (GAMs) was implemented. GAM is a powerful nonparametric method that can potentially discover the complex nonlinear trends in the glacier variations. GAMs are more flexible than linear models and more interpretable than deep learning methods. GAM is essentially an additive model of nonparametric smooth functions [12; 13; 14]  
\[g(E[Y|X])=f_{0}+\sum_{j=1}^{K}f_{j}(x_{j}) \tag{4}\]  
where \(f(x)\) is a smooth function of predictor \(X\), and \(Y\) is the response variable with a distribution belongs to the exponential family (ex. Gaussian, Binomial, Gamma, etc.). The main advantage of GAM is its capability to model highly complex and nonlinear relationships. The consideration and the associated tradeoff between the simplicity of a model and its interpretability. GAMs relax the restriction that the relationship must be a simple weighted sum, like in the multiple regression model, and instead assume that the outcome can be modeled by a sum of arbitrary smooth functions of each predictor variable.  
The function \(g(\ )\) in the GAM model (Equation (4)) is known as the link function. The GAM links the sum of the smoothed predictors with the mean value of the assumed distribution using the link function \(g(\ )\), which can be chosen flexibly depending on the problem under consideration. In addition to the Exponential family of distributions and the link functions, the third essential component to the GAM is the smoothing functions \(f_{j}(x_{j})\) for the predictor variables in the model. This implies that the predictor variables become smooth curves by some function \(f\). This is achieved for each of the functions \(f_{j}(x_{j})\) in the GAM by using a basis of splines to create the nonparametric smooths using the Restricted Maximum likelihood method [14; 15; 16]. The GAM method employed in the current research regresses the terminus change by:  
\[d=f(CO2)+f(TMIN)+f(TMAX)+f(PRCP)+f(Global\_Temp)+\varepsilon \tag{5}\]  
and the area change in the glaciers against the climate factors:  
\[a=f(CO2)+f(TMIN)+f(TMAX)+f(PRCP)+f(Global\_Temp)+\varepsilon \tag{6}\]  
In both GAM models in Equations (5) and (6), the distribution selected from the exponential family is the Gaussian. This distributional assumption leads to the link function being the identity function. The basis functions for the nonparametric smoothing are Thin-Plate Splines where the dimension of the basis is varied from two to four basis functions over selected permutations of predictors in the model. The smoothing selection method REML is applied for smoothness selection. This leads to the functional form of the applied GAM for the glacier recession analysis. Subsets and permutations of the smoothed predictors will be evaluated against the full model using the Akaike Information Criterion (AIC) for model selection. The top performing GAM for each independent variable, terminal point distance, and area will be the model with lowest AIC value.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 2 System Architecture and Mathematical Modeling
### Throughput and Energy Efficiency Model  
The performance of the wireless network is evaluated in terms of throughput and EE metrics. According to [PERSON]'s information capacity theorem, total achievable throughput in a network at time \(t\) can be expressed by [25]:  
\[R_{total}(t)=\sum_{k=1}^{U}\sum_{i=1}^{N}BWlog_{2}(1+SINR_{i,k}) \tag{11}\]  
Figure 5: Flow diagram of the proposed energy sharing model.  
where, \(BW\) is the system bandwidth and \(SINR_{i,k}\) is the Signal-to-interference plus-noise-ratio at \(k\)th user equipment from \(i\)th BS.  
EE is defined as the ratio of total network throughput and total power consumed by the network. Thus, the EE metric denoted as \(\eta_{EE}\) for time \(t\) is defined as [25]:  
\[\eta_{EE}=\frac{R_{total}(t)}{P_{BS}(t)} \tag{12}\]  
Here, the received signal to interference plus noise ratio (SINR) at \(k\)th user equipment from \(i\)th BS can be given by  
\[SINR_{i,k}=\frac{P_{rx}^{j,k}}{P_{k,inter}+P_{k,intra}+P_{N}} \tag{13}\]  
where \(P_{k,intra}\) is the intra-cell interference, \(P_{k,inter}\) is the inter-cell interference is the transmitted power in dBm, \(P_{N}\) is the additive white Gaussian noise (AWGN) power given by \(P_{N}=-174+10 log_{10}(BW)\) in dBm, and with BW is the bandwidth in Hz.
## 2 Materials and Methods
### Model Building and Validation Strategy  
#### 2.3.1 Predictors and Mapping Units  
The selection of a set of geo-environmental variables potentially expressing the landslide preparatory causes (Table 1) was based on widely adopted geomorphological criteria [5; 6; 7; 34; 35; 36; 37]. In particular, outcropping lithology (GEO) and soil use (USE) were derived from an available thematic map [38] and a remote survey, respectively. By processing a 10 m pixel digital terrain model (DTM), the following continuous variables were derived: elevation (ELE), steepness (STP), plan (PLN), and profile (PRF) curvatures, topographic wetness index (TWI), and aspect, the latter expressed in terms of easternness (EASTNS) and northernness (NORTHNS). In addition, the landform classification (LCL) categorical variable was obtained. In this way, a set of three categorial and seven continuous variables was prepared.  
With regard to lithology, based on the geomechanical expected response, the outcropping lithologies were grouped as soft, medium, and hard rocks and very soft, soft, medium, and hard soils. On the basis of the landslide distribution in the study areas, very soft and hard soils account for more than 80% of the mapped cases. The very low number of landslides recognized in soft soils has to be ascribed to the very limited extension of the outcropping areas.  
All of the controlling factors were arranged in 10 \(\times\) 10 m raster layers. The same grid cell structure was then adopted as the susceptibility mapping unit, assigning a stable/unstable status depending on the intersection of LIPs. In fact, according to a number of debris flow susceptibility assessment studies (e.g., [7; 22; 29; 31; 32; 36; 37; 44; 45; 46; 47; 48; 49; 50]), we considered the instability conditions of each inventoried landslides to be effectively captured in the highest crown 10 \(\times\) 10 m pixel. In order to optimize the final selected predictors that were included in the MARS modelling procedure, the variance inflation factor (VIF) [51] test was performed for multicollinearity analysis through the continuous variables.
# S.O.V.O.R.A.: A Distributed Wireless Operating System
## 2 Wireless Distributed Systems
### Distributed Architectures
#### 2.2.1 MANET Mobile Ad Hoc Network  
A MANET [12] is a set of wireless mobile devices (nodes), which can form a network without the support of any infrastructure or centralized control. MANETs are multi-hop networks in which a packet is sent from a source to its destination and must cross a path formed by two or more hops (nodes).  
Therefore, every node has a dual behavior in a MANET as a router and host. MANETs are autonomous networks that can determine their configuration parameters and recover in the case of failure by themselves. Further, they are implemented to set up communications for specialized applications where there is no pre-existing infrastructure or the one available is not the most suitable for the operation's needs.  
There can be two types of MANETs: Wireless Mesh Networks (WMNs) [13] and Wireless Sensor Networks (WSNs) [14]. As is common in MANETs, a node can function as a router and as a host, while the WMN nodes are classified as mesh routers or mesh nodes. On the one hand, mesh routers have minimal mobility, provide access for normal nodes and mesh nodes, and can communicate with other mesh routers; besides, they are in charge of routing, bridging and network functions and do not have power limitations. On the other hand, mesh nodes can be stationary or mobile and require the efficient use of their energy supply like MANET nodes. WSNs are made up of a set of wireless sensor nodes that are usually deployed in hostile environments and are used for event detection (e.g., temperature and pressure measure, etc.). These sensors can perform some processing over the information obtained and transmit the data through the network, which provides the final user with a better understanding of the current state of the environment.  
Compared to common wireless mobile devices, WSN nodes are smaller, less expensive, and have fewer hardware characteristics and power consumption. However, due to their operating nature, once a node has depleted or damaged its battery, it may never be retrieved.
## 2 Materials and Methods
### Statistical Analysis
Because the Wilcoxon test does not rely on strict parametric assumptions, it provides a robust alternative for detecting model differences under distributional irregularities. To further account for the correlation among repeated measures, three corrected methods introduced by [PERSON] and [PERSON] [20] and by [PERSON] and [PERSON] [21] were integrated [35]. A corrected random resampling \(t\)-test was performed by randomly partitioning the dataset into training (67%) and test (33%) subsets across 100 iterations, while a corrected K-fold cross-validation \(t\)-test followed the traditional 10-fold design with the applied correction from [PERSON] and [PERSON]. The corrected repeated K-fold cross-validation \(t\)-test extended this approach through ten repetitions of 10-fold cross-validation, producing a total of 100 performance estimates. These correction algorithms adjust conventional variance estimators to better capture the dependencies arising from repeated usage of the same data folds, thereby enhancing the reliability of the resulting statistical inferences. [PERSON]'s test was applied to compare two classifiers based on matched pairs of outcomes. This test is well-suited for assessing performance differences when the same instances are evaluated by both models because it focuses on the consistency of misclassifications. By examining whether one classifier tends to misclassify instances that the other classifier labels correctly--and vice versa--[PERSON]'s test furnishes a robust way to detect statistically significant differences in model performance [18]. In addition, [PERSON]'s test is less influenced by distributional assumptions than certain parametric methods and therefore serves as a valuable tool when the goal is to compare the proportionsof errors directly. Unlike the conventional application of [PERSON]'s test to a single train/test split, this study implemented a repeated [PERSON] test across multiple data splits (10-fold-CV-splits). This approach ensures a more robust and generalizable assessment of performance differences by aggregating the results across several train/test configurations, thus mitigating the sensitivity and variability introduced by any single partition. Finally, the [PERSON] test was employed on the aggregated results from the 10 repeated 10-fold cross-validation experiments (100 estimates). As a non-parametric test, the Friedman procedure does not rely on assumptions of normality and is particularly appropriate for comparing more than two algorithms across multiple datasets or folds. This method ranks the performance of each algorithm on each fold and evaluates whether observed rank differences are statistically significant. Owing to its ability to handle multiple models simultaneously without requiring normally distributed performance metrics, the Friedman test is frequently recommended for comprehensive model comparisons in machine learning research. If statistically significant differences are detected, follow-up post hoc analyses (e.g., the Nemenyi or Bonferroni-Dunn test) can be performed to identify which pairs of models differ meaningfully.
# The Analysis and Verification of Unbiased Estimator for Multilateral Positioning
## 5 Experiments and Results
### Simulation Experiment  
Simulations were carried out in an area of 100 \(\times\) 100 m. Three anchor nodes were placed at (100, 100), (0, 100) and (100, 0) respectively, and the target node was assumed to be at (50, 50). The ranging error model used was \(\hat{d}_{i}=d_{i}(1+e)\), where \(e\) obeys a normal distribution with mean 0 and standard deviation \(\sigma\) meters. It can be seen from Figure 2 that as the variance in the error model increases, the positioning errors of the two algorithms will increase, but the positioning errors solved by the unbiased multilateral positioning method grow more slowly, that is, they are less susceptible to fluctuations in ranging errors. It can be concluded that, when compared with the multilateral positioning method, the unbiased multilateral positioning method can improve the positioning error, and the degree of improvement will increase with the increase of the variance in the ranging error model.  
For the next experiment, the positions of the three anchor nodes remained unchanged, namely at (100, 100), (0, 100) and (100, 0) respectively, and the target node was set at (50, 50). The ranging error model was again \(\hat{d}_{i}=d_{i}(1+e)\), where \(e\) obeys a normal distribution with mean 0 and a standard deviation of 1 m. The experiment was repeated 1000 times, enabling the creation of the scatter plot shown in Figure 3. The cumulative distribution function graph of the solutions can be shown in Figure 4.  
It can be seen intuitively from the Figure 3 that by using the unbiased multilateral positioning method to locate the target node, the estimated coordinates are closer to target node, while the estimated coordinates of the multilateral positioning method are relatively divergent.  
It can be seen from Figure 4 that 70% of the positioning errors of the unbiased multilateral positioning method are within 45 m and 80% are within 50 m of the target node, while 80% of the positioning errors of the multilateral positioning method are within 60 m.  
Figure 2: Comparison of root mean square error between multilateral positioning method and unbiased multilateral positioning method.  
Compared with the multilateral positioning method, the unbiased multilateral positioning method shows an obvious improvement on the error.
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 7 Conclusions  
In this paper, to extend the lifetime of low power and lossy network and minimize the energy consumption, a new resource-aware and reliable objective function, RAROF, has been presented. In RAROF, both node characteristics and resource availability are taken into consideration to select the routing path. Reliable data communication is essential to ensure the ideal emergency monitoring system in different industries, for which the multi-queuing based traffic differentiation approach has been adopted that prioritizes the packets according to their sensitivity. With this approach, each node forwards the data packets to the channel of fixed-sized slots or reserved slots according to the traffic priority. In a heterogeneous network, the RAROF constructs routing path with the aim of extending node's life and minimizing energy consumption, paving the way towards green IIoT. Besides, a simple conceptual cloud-assisted IEMS architecture provides the promising potential functionality that collects, monitor, store, and analyze the data to reinforce the real-time emergency monitoring system. In this study, we found that the occurrence of network partition due to energy-constrained nodes increase when the nodes have low transmission range and inter-packet interval and high traffic flow. The simulation results obtained indicate that the proposed RAROF makes significant improvements in network lifetime, energy consumption, and reliability, as compared to OF-ER and OF-EC. During the study, we noticed that congestion is more likely to occur in resource-constrained networks due to fixed-sized channel slots. An efficient message scheduling technique is necessary to resolve this issue; however, it is part of our future work.  
[PERSON] conceptualized the proposed approach, performed an experimental investigation, and wrote the paper. [PERSON] supervised the work and review the manuscript. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the research funds of Jeonbuk National University, in 2020. The authors declare no conflict of interest.
# Semantic Representation Using Sub-Symbolic Knowledge in Commonsense Reasoning +
## 4 Experiments
### Experimental Results
#### 4.4.4 Experiment on Official Test Set  
Because the ELECTRA-based model with ACP-ARG graphs performed best on the new test set, we evaluated our model using the official training set of ELECTRA-large (1140 examples). For the official test set, the accuracy of our model was 75.79%, as shown in Table 5. The average score was 79.42%, which is higher than that of [PERSON] et al. [34] by 1.37%.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Language Model** & **Ndev-Acc(\%)** & **Ntest-Acc(\%)** & **Avg** \\ \hline BERT-base & 51.81 & 51.59 & 51.70 \\ ELECTRA-base & 71.25 & 70.19 & 70.72 \\ \hline BERT-base with ACP-ARG-mini [16; 34] & 53.97 & 53.58 & 53.78 \\ ELECTRA-base with ACP-ARG-mini [16; 34] & 71.99 & 70.91 & 71.45 \\ \hline BERT-base with ACP-ARG & 54.38 & 53.51 & **53.95** \\ ELECTRA-base with ACP-ARG & 73.63 & 71.72 & **72.68** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Commonsense QA results on different language encoder types.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Language Model** & **Setting** & **Odev-Acc(\%)** \\ \hline BERT-base-cased & Original & 51.81 \\ BERT-base-cased with ACP-ARG & Original & **54.38** \\ \hline BERT-base-cased & SRL-C & 46.03 (\(-\)5.78%p) \\ BERT-base-cased with ACP-ARG & SRL-C & **53.32** (\(-\)1.06%p) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adversarial Attack test with Semantic Role Labeling. SRL-C denotes the setting where one distractor is replaced with the random argument in the AMR graph.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **Odev-Acc(\%)** & **Otest-Acc(\%)** & **Avg** \\ \hline ELECTRA-large with ACP-ARG-mini [16; 34] & 82.15 & 75.43 & 78.79 \\ ELECTRA-large with ACP-ARG & 83.04 & 75.79 & **79.42** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiment with ELECTRA-large with full training set on the official test set.
# Analysis of Cement Sheath-Rock Damage Mechanism--A Case Study on Water Injection Wells
## 4 Numerical Simulation Analysis of Water Channeling outside Casing in Water Injection Wells
### Investigating the Influence of Different Cement Sheath-Rock Modulus Ratios on Damage Behavior  
The quality of cementing directly affects the integrity of the cement sheath during the cementing process. Furthermore, different types of cement slurries can also influence the durability of the cement sheath after cementing. To investigate the influence of cement slurry on the damage to bonded rock surfaces during cementing, numerical simulations were carried out using various shear modulus values for the cement sheath: 6 GPa, 10 GPa, 15 GPa, 18 GPa, and 20 GPa. We conducted numerical simulations, setting the ratio (represented as \(\lambda\)) of Young's modulus between the cement sheath and the rock to different values of 0.12, 0.2, 0.3, 0.36, and 0.4.  
Based on Figures 15 and 16, it is observed that when the \(\lambda\) value ranges from 0.12 to 0.36, damage occurs on the bonded rock surfaces under the influence of water injection pressure. The damage propagates towards the vicinity of the interface, parallel to the cement sheath, while the fluid pressure is distributed along the bonded surface and cracks. When the value of \(\lambda\) is 0.4, the damage on the bonded rock surfaces initially expands along the interface between the cement sheath and the rock and subsequently propagates towards the wellbore. The fluid pressure is significantly controlled by the damaged zone, resulting in a shorter length of damage on the bonded rock surface and a smaller range of diffusion for the fluid pressure.  
Figure 14: The variation of peak stress during the damage of cemented interface rock under different widths.  
Figure 13: The numerical and analytical curves of shear stress for different widths.  
Based on Figures 17 and 18, it is observed that as the \(\lambda\) value increases, the peak fluid pressure during the damage of bonded rock surfaces also increases. The peak shear stress values obtained from the analytical solutions for different ratios (\(\lambda\)) of [PERSON]'s modulus between the cement sheath and the rock are 2.34 MPa, 3.861 MPa, 5.65 MPa, 6.618 MPa, and 7.642 MPa. In contrast, the numerical solutions yield peak shear stress values of 2.13 MPa, 3.616 MPa, 5.516 MPa, 6.632 MPa, and 7.9 MPa. The maximum error between the two methods is 8.97%, with an average error of 2.82%. This indicates some deviation or discrepancy between the analytical and numerical results in predicting the peak shear stress. After linear regression of the numerical solutions for the peak shear stress under different \(\lambda\) values,