esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment
## 1 Introduction  
The vehicular ad-hoc network (VANET) is also called network on wheels, which is used to provide communication between vehicular nodes. It is an offshoot of mobile ad-hoc networks. In VANETs, vehicular nodes are self-organized and communicate with each other in an infrastructureless environment [1; 2; 3; 4; 5; 6; 7]. Knowing the importance of vehicular ad-hoc network for providing safety-related applications in Intelligent Transportation System (ITS), the IEEE committee has developed the IEEE 802.11p standard for VANETs [1]. The US Federal Communication Commission (FFC) department has assigned 75 MHz of bandwidth at 5.9 GHz for dedicated short-range communication (DSRC), which is used to provide communications between vehicle to vehicle and vehicle to infrastructure [1]. The main aim of VANETs is to build an intelligent transportation system. DSRC can play an important role in building communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). The range of DSRC is about one thousand meters [8].  
From the last few years, inter-networking over VANETs has been achieving massive momentum. Realizing its intensifying significance, academia, major car manufacturers, and governmental institutes are making efforts to develop VANETs. Various significant projects are initiated by different countries and famous industrial firms such as Daimler-Chrysler, Toyota, and BMW for inter-vehicular communications. Some of these prominent projects include CarTALK2000 [9], Car-to-Car Communication Consortium (C2 CCC) [10], Advanced Driver Assistance Systems (ADASE2), California Partners for Advanced Transit and Highways (California PATH) [11], FleetNet [12], DEMO 2000 by Japan Automobile Research Institute (JSK) [12], Chauffeur in EU [13], and Crash Avoidance Metrics Partnership (CAMP) [14]. These developments are a key step toward the recognition of intelligent transportation services.  
VANETs can provide a wide range of services like accident avoidance, a mechanism for regulating traffic flow, provision of internet access to the on-road public, information about the location of parking lots, restaurants, gas stations and infotainment applications such as playing games and listening to music [8; 15; 16].  
The rest of the paper is structured as follows. The vehicular ad-hoc network architecture is explained in Section 2. Section 3 gives details about the position based routing protocols for an urban scenario. This section also contains the comparative study of the existing position based protocols. Simulation and analysis is presented in Section 4. Finally, we present conclusions and future research directions in Section 5.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 3 Results
### Orbit Determination Review  
Observables of all the baseline are used to evaluate the orbit accuracy during the critical operation, which aims to review the orbit accuracy and enhance the confidence to maneuver operation parameters. Figure 17 shows the residuals of DeltaDOR measurements before Mars orbit insertion, since Mars orbit insertion is one of the most important operations during the whole mission, and there would not be another chance if anything unexpected happened which was caused by inferior orbit accuracy in this case. The residuals are calculated with respect to an orbit determined by CLTC and are then predicted forward. The residuals of all baselines have an RMS of 0.135 m, which corresponds to 0.45 ns, which is much better than the expected accuracy of 1 ns.
## 3 Results and Discussion
### Parametric Analysis
#### 3.2.2 Scenario B  
Figure 12 displays the deviations between the comfort thresholds and the simulation results. At first glance, worse conditions of discomfort should be expected due to the large number of results below the lower comfort limit. Indeed, in the winter, the underheating discomfort worsened considerably in the three thermal zones (Table 7). In turn, TZ01 was the only thermal zone in risk of overheating in the summer and, even in this zone, the risk is low.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Thermal Zone**} & \multicolumn{2}{c}{**Thermal Discomfort (\%) *} \\  & **Winter** & **Summer** \\ \hline TZ01 & 34.18 & 14.26 \\ TZ02 & 30.01 & 0.06 \\ TZ03 & 34.69 & 5.27 \\ \hline \hline \end{tabular}  
* These values represent the total number of hours of discomfort, expressed in an annual percentage.  
\end{table}
Table 6: Thermal discomfort—Reference scenario.  
Figure 11: Thermal comfort analysis using EN16798-1 for the reference scenario.  
Figure 12: Thermal comfort analysis using EN16798-1 for Scenario B.  
Comparing both scenarios (A and B) the results obtained reveal several differences in the overall building thermal behavior with an increase of about 20% in the discomfort during the winter season. However, both scenarios present high levels of underheating discomfort and an almost negligible overheating discomfort during the summer season. Despite the differences in the constructive solutions, the low levels of insulation in both scenarios give rise to these results in the winter. In turn, during the summer season, the high inertia of both solutions allows the building to absorb and release solar energy, maintain it within the comfort limits.
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 3 Multiple Sound Source Separation Framework  
The multiple-sound-source separation framework is proposed based on the sparsity of the speech signal. Previous studies have proved the existence of sparse and non-sparse component points of a speech signal in the TF domain [29; 30]. Specifically, the sparse component points refer to the TF points which are only dominated by the direct component from one sound source. In contrast, the non-sparse component points are the remaining TF points which are composed of the components of multiple sound sources and/or the component of room reflection. Figure 5 is the schematic diagram of the sparse and non-sparse component points, where each rectangular box represents a TF point, and a different color represents a different sound source. As shown in Figure 5, the TF points \((n,k_{1})\), \((n,k_{2})\) and \((n,k_{3})\) are dominated by the direct component of sound sources \(s_{2}\), \(s_{3}\) and \(s_{4}\), respectively. In contrast, the TF points \((n,k_{4})\), \((n,k_{5})\) and \((n,k_{6})\) are the non-sparse component points where there is no dominant component.  
Figure 4: The TF spectrum of the dereverberated signal.  
Subsequently, different separation methods for sparse and non-sparse component points are designed, and a multi-source separation method combining sparse and non-sparse component point recovery is proposed in this paper. In view of the relationship between the DOA estimates of the sparse component points and the real DOAs of sound sources, a separation method based on the distribution of DOA estimates is also proposed in this paper. Considering that more than one sound source is active simultaneously in a non-sparse component point, the deep neural network is adopted to estimate the IRM which is used for non-sparse component point recovery.
## 1 Introduction
The objective of this paper is to tackle these challenges by proposing an advanced model for IDSs based on dimensionality reduction and stacking ensemble learning. Initially, we apply dimensionality reduction techniques to reduce high dimensions and eliminate irrelevant and redundant features. Subsequently, we introduce a novel stacking ensemble technique called multi-stacking ensemble (MSE) to improve the identification of various types of attacks compared to singular learning approaches. Finally, we evaluate the performance of our model on the UNSW-NB15 and CICIDS2017 datasets across several evaluation metrics. Our study contributes the following insights:  
1. It develops a dimensionality reduction approach by combining feature selection and extraction techniques to address the curse of dimensionality. 2. The implementation of LogitBoost feature selection, based on XGBRegressor, identifies crucial features from datasets of varying sizes. 3. A ResNet deep learning algorithm is designed for feature extraction, leveraging features selected by the LogitBoost algorithm. 4. A novel multi-stacking ensemble (MSE) technique is devised to enhance the classification performance. The organization of this paper is as follows: Section 2 reviews previous studies on feature selection and extraction techniques for IDSs, along with classification types. Section 3 elaborates on the proposed methodology. Section 4 outlines the experimental analysis and assesses the outcomes. Finally, Section 5 provides conclusions and discusses future directions.
## 4 Full-Duplex Single-Antenna Energy Harvesting  
### Optimal Time Allocation Scheme  
The energy efficiency optimization problem is converted to  
\[\underset{\alpha}{max}\xi(\alpha)=\frac{(1-\alpha)log_{2}\bigg{(}1+min\bigg{\{} \frac{(1-\alpha)}{\eta\alpha\big{|}^{2}},\frac{\eta P_{s}|h_{1}|^{2}|g|^{2} \alpha}{N_{0}(1-\alpha)}\bigg{\}}\bigg{)}}{\beta P_{s}+P_{c}},\text{s.t.}0< \alpha<1 \tag{34}\]Like the full-duplex dual-antenna energy harvest, the time-switch threshold can be found by calculation, i.e.,  
\[\alpha_{2}=\frac{1}{1+\sqrt{\frac{\eta^{2}\left|\mathsf{f}\right|^{2}}{\left| \mathsf{g}\right|^{2}}\left|\mathsf{g}\right|^{2}{h_{1}}^{2}P_{s}}}. \tag{35}\]  
Similarly, the optimal time allocation scheme for maximizing energy efficiency in a single-antenna energy harvesting system can also be translated into two processes: \((1)0<\alpha<\alpha_{2}\)_and_\((2)\alpha_{2}<\alpha<1\).  
For the first case, the time allocation problem of maximizing energy efficiency can be translated into  
\[\underset{\alpha}{max}\xi(\alpha)=\frac{(1-\alpha)log_{2}\left(1+\frac{\alpha }{a_{3}(1-\alpha)}\right)}{\beta P_{s}+P_{c}},\frac{(1-\alpha)log_{2}\left(1+ \frac{\alpha}{a_{3}(1-\alpha)}\right)}{\beta P_{s}+P_{c}},\text{s.t.}0<\alpha< \alpha_{2} \tag{36}\]  
where \(a_{3}=\frac{\eta\left|\mathsf{g}\right|^{2}P_{s}\left|{h_{1}}\right|^{2}}{N_{ 0}}\).  
Similarly, according to [31], the optimal time allocation factor \(\alpha^{*}\) to maximize energy efficiency can be obtained in the range \(0<\alpha<\alpha_{2}\), which is expressed as  
\[\alpha^{*}=\frac{exp\left(W\left(\frac{a_{3}-1}{e}\right)+1\right)-1}{a_{3}-1+ exp\left(W\left(\frac{a_{3}-1}{e}\right)+1\right)}. \tag{37}\]  
For the second case, the energy efficiency optimization problem can be translated into  
\[\underset{\alpha}{max}\xi(\alpha)=\frac{(1-\alpha)log_{2}\left(1+\frac{(1- \alpha)}{\alpha\eta\left|\mathsf{f}\right|^{2}}\right)}{\beta P_{s}+P_{c}}. \tag{38}\]  
The energy efficiency derivation function can be obtained by  
\[\frac{\partial\xi(\alpha)}{\partial\alpha}=-\frac{1}{\beta P_{s}+P_{c}}{ \left(log_{2}\left(1+\frac{(1-\alpha)}{\eta\left|\mathsf{f}\right|^{2}\alpha }\right)+\frac{1-\alpha}{\left(1+\frac{(1-\alpha)}{\eta\left|\mathsf{f} \right|^{2}\alpha}\right)\alpha^{2}ln2}\right)} \tag{39}\]  
Since \(\frac{\partial\xi(\alpha)}{\partial\alpha}\) is always less than zero in the range of \(\alpha\in(\alpha_{2},1)\), the energy efficiency \(\xi(\alpha)\) monotonically decreases at \(\alpha\in(\alpha_{2},1)\), and when \(\alpha=\alpha_{2}\), the energy efficiency \(\xi(\alpha)\) reaches the maximum value in the range of \(\alpha\in(\alpha_{2},1)\). Therefore, the above two cases can be represented as  
\[\alpha^{*}=\begin{cases}\frac{exp\left(W\left(\frac{a_{3}-1}{e}\right)+1 \right)-1}{a_{3}-1+exp\left(W\left(\frac{a_{3}-1}{e}\right)+1\right)},&if \left(W\left(\frac{a_{3}-1}{e}\right)+1\right)<\frac{a_{3}}{\sqrt{a_{3}\left| \mathsf{f}\right|^{2}\eta}}\\ \frac{1}{1+\sqrt{\eta\left|\mathsf{f}\right|^{2}}},&otherwise\end{cases}, \tag{40}\]  
where \(a_{3}=\frac{\eta\left|\mathsf{g}\right|^{2}P_{s}\left|{h_{1}}\right|^{2}}{N_{ 0}}\).
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 7 Landslides
### IoT Architectures for Landslide EW Systems  
Table 8 reports the main technologies used and developed for each layer of the reference IoT EW architecture.  
As already mentioned, there are many different factors that can contribute to landslides, and as such different sensors can be used to detect them, and the right parameters to be monitored and sampling rates should be carefully chosen [91]. MEMS systems can be useful to reduce the cost of installations, and tilt sensors are particularly interesting because they do not need to be installed deep in the soil like inclinometers, and they require less expertise in installation than extensometers [92]. Since there could be the need to monitor wide areas, sensor placement should be evaluated carefully and based on previously performed risk analysis [93]. Rainfall is usually the main triggering factor for landslides [92], so rainfall historical data, if available, and its measurement through weather stations or radar systems should be integrated in such a system.  
Some of the reviewed papers did not specify the communication protocols used, nor did they all plan for redundancy in case of data transmission failures, even though more than one paper underlined the importance of taking into account possible network outages and planned accordingly. Using new technologies such as 5G could improve data reliability, coverage, and speed.  
Many Early Warning mechanisms for landslides are based on thresholds that can be obtained from both empirical or probabilistic models [18], and they need to be optimized to avoid raising false alarms. When it comes to Landslide EW systems, most solutions adopted a Cloud or central server based approach to data processing and did not process data directly at sensor or gateway level, but more recent articles such as [95; 96] started to integrate Edge solutions into their Landslide EW system to reduce latency and also to provide better reliability in case the connection to the Cloud fails.
The prediction of fracture and material failure is of major importance for the safety and reliability of engineering structures and the efficient design of novel materials. Experimental testing is often cumbersome, expensive and, in certain cases, unfeasible, for instance in civil engineering when it is not possible to test the structures in the laboratory. Therefore, computational modeling of fracture and failure of engineering systems and materials has been the focus of research for many years, and there has been tremendous advancements in the past two decades with methods such as the Extended Finite Element Method (XFEM) developed in 1999, peridynamics (2000), the cracking particles method (2004) and phase field models (2009). There has also been a great deal of effort made in developing multiscale methods for the design of new materials, such as the Extended Bridging Domain Method or the MAD method. The main focus of this book is computational methods for fracture. However, articles concerning issues related to validation, uncertainty quantification, large-scale engineering applications and constitutive modeling are also addressed.  
This book offers a collection of 17 scientific papers about computational modeling of fracture [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Some manuscripts propose new computational methods or the improvement of existing cutting-edge methods for fracture. Other manuscripts apply state-of-the-art methods to challenging problems in engineering and materials science.  
These contributions can be classified into two categories:  
1. Methods which treat the crack as strong discontinuity, such as peridynamics, scaled boundary elements or specific versions of the smoothed finite element methods applied to fracture;
2. Continuous approaches to fracture based on, for instance, phase field models or continuum damage mechanics. On the other hand, this book also offers a wide application range where state-of-the-art techniques are employed to solve challenging engineering problems including fractures in rock, glass, and concrete. Larger systems are also studied, including subway stations due to fire, arch dams and concrete decks.
## 4 Results
### Room Volume and Compression Coefficients  
The effect of room volume on reported distances was investigated by running a linear regression analysis applied to the individual \(a\) compression coefficients estimated in the CW group for the intensity-based model (which values are displayed in Figure 6). The logarithm of each reported volume was considered as factor for this regression analysis. According to the results of this analysis, a higher volume is significantly correlated with a lower parameter \(a\) (\(F=5.60\); \(p=0.021\)). This result confirms that a larger volume is linked to a stronger compression effect of reported distances of stimuli generated by the intensity-based model.  
Figure 5: Mean and standard deviation of individual fitting coefficients \(a\) (**left**) and \(k\) (**right**) classed by groups (CW group (black, \(n=55\)) and FW group (gray, \(n=53\))) and by rendering methods: measurements-based (red), envelope-based model (blue), and the intensity-based model (green). Coefficients for FW group are closer to 1 compared to those obtained for the CW group, demonstrating a lower compression effect of auditory distance perception. (*) \(t\)-test indicated a \(p\)-value \(p<0.05\); (**) \(t\)-test indicated a \(p\)-value \(p<0.01\).
# Latent Structure Matching for Knowledge Transfer in Reinforcement Learning
## 3 Method
### Value Function Transfer  
After finding the highly-matched source task, the learnt action value functions of the matched source task can be directly transferred to the target task. However, if there is not enough similarity between source tasks and target tasks, it will result in the risk of negative transfer due to direct transfer. So using the action value functions of the matched source task to guide the agent in action exploration may be a safe program.  
The most commonly used exploration strategy is the \(\epsilon\)-greedy exploration strategy [35], whose development purpose is to strike a balance between exploration and exploitation. This balance determines whether one should explore the invisible parts of the space or use the knowledge that has been acquired during the learning process. Specifically, the \(\epsilon\)-greedy exploration strategy performs exploitation with the probability \(\epsilon\) by selecting a known action that maximizes the value function of a target task, and performs exploration with the probability 1-\(\epsilon\) by randomly selecting an unknown action.  
By using the knowledge from the source tasks, the \(\epsilon\)-greedy exploration strategy is modified as shown in Algorithm 2. The modified strategy, which we call an LSM-based exploration strategy, performs exploitation as normal \(\epsilon\)-greedy exploration strategy, but performs exploration with a probability of 1-\(\epsilon\) by selecting the learnt action that maximizes the value function of the matched source task.  
```
\(targetQ\); \(sourceQ\); \(\epsilon\)
```  
**Output**: Action \(Action\leftarrow\left\{\begin{array}{l}Argmax_{a}[targetQ],\text{with probability}\quad\epsilon\\ Argmax_{a}[sourceQ],\text{with probability}\;(1-\epsilon)\end{array}\right.\) ```  
**Algorithm 2** LSM-based exploration strategy  
This exploratory action advice from LSM-based exploration strategy can reduce the instability from random exploration. Moreover, the modified exploration strategy can greatly speed up the learning of a target task, while achieving at least as good performance as \(\epsilon\)-greedy exploration strategy. The Equation (8) gives a definite proof for this assertion, and the specific proof details of (8) are given in Appendix B.:  
\[Q_{i}^{\pi}(s,a)-Q_{i}^{\pi^{\prime}}(s,a)\leq\varrho\,min_{j}\sum_{s^{\prime} }p\sum_{a^{\prime}}\omega\|Q_{i}^{\pi}(s^{\prime},a^{\prime})-Q_{j}^{\pi}(s^{ \prime},a^{\prime})\|, \tag{8}\]  
where \(p\) denotes the probability \(p(s^{\prime}|s,a)\) in brief form, \(w\) denotes the average probability \((\epsilon/\mathbf{\alpha})\) over the total number of actions \(\mathbf{\alpha}\). \(\varrho\) is defined as \(\gamma/[1-\gamma(1-\epsilon)]\) with the discount factor \(0<\gamma<1\). \(Q\) denotes the action value function with its superscript denoting the used exploration strategy, and its subscript denoting the task index. \(\pi\) and \(\pi^{\prime}\) denote respectively the \(\epsilon\)-greedy exploration strategy, and the LSM-based exploration strategy.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### Exploratory Data Analysis  
This section presents the exploratory data analysis conducted to identify suitable features. For this purpose, the relationships of each feature to different damage states were observed. A good feature demonstrates a certain pattern with increasing damage. Figure 5 shows the relationships of each of the four considered features with the displacement ductility for the entire dataset (Set-1 and Set-2). In these plots, the green, yellow, orange, and red circles represent undamaged, minor damage, moderate damage, and major damage cases, respectively.  
The \(CAV\) values showed an increasing trend with the increase of the displacement ductility. However, undamaged and minor damage \(CAV\) values were difficult to distinguish. On the other hand, the \(R_{CAV}\) distinguished undamaged from minor damage where \(R_{CAV}\) values other than 1.0 correspond to damaged cases. Although for significant damage, the acceleration of the structure typically decreases, for certain ground motions where the undamaged structural responses fall on the initial ascending branch of the response spectrum, small damage leads to an increase in acceleration and subsequently an increase in the \(R_{CAV}\) value. The overall decreasing pattern with increasing ductility of this feature made it well-suited for damage assessment. The \(\Delta_{CAV}\) values showed a nonlinear increasing trend with the increase of the displacement ductility. Finally, the \(S_{CAV}\) did not show any specific trend with displacement ductility, making it unsuitable as a damage feature.