esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 1 Introduction
To the best of our knowledge, for coding-based cooperative communication WSNs, RICC is the first data collection strategy that enhances the reliability without harming the lifetime. 2. The effectiveness of RICC is evaluated in this paper, and compared with Network Coding-based Cooperative Communications (NCCC), RICC has following features: (a) the network energy utilization ratio is enhanced by as much as 50% on average; and (b) it significantly improves the reliability of data collection, and specifically, under the same lifetime, RICC can reduce the weighted end-to-end MFR by approximately 59.4% to 62.8%.
## 5 Implementation Results
### Comparison  
Table 8 compares throughput performance of our implementation and the other work. For comparison in the same environment, we implemented the method presented in [12] on RTX 2070, and the throughput of 278 Gbps was measured. In [13], even though the performance was given in cycles per byte, we convert it as the throughput using the proposed CPU clock count.
# Machine Failure Prediction Using Survival Analysis
## 5 Results
### RadViz and Rank2D Depiction of Feature Relations  
For a better understanding of the features, feature analysis techniques such as the RadViz and Rank2D technique were used. Through them, the relationships of the characteristics between them can be analyzed and better understood, which provides better results in their sorting.  
In case of machine failure, the RadViz analysis, depicted in Figure 3, showed that a high rotational speed combined with a high process temperature is a possible factor contributing to the failure. This is evident as only failed machines are pulled towards the rpm feature, with some being close to the middle of rpm and the process temperature. The same results can be observed in the PWF case, where most of the failed machines in the machine failure category that were pulled towards the rpm feature seem to belong in PWF. So, we can assume that increased rpm is correlated with a PWF type of machine failure. Apart from that, we can see that most of the machines, working or otherwise, are closer to the torque feature and the area between the torque and the type of machine. The strong pull towards the torque feature indicates that torque is critical for the proper functioning of the machines. The fact that many of the machines are also located in the area between torque and type suggests that there may be a relationship between the type of machine and its torque output. Finally, the light pull towards the process temperature for both working and failed machines indicates the importance of this feature, especially in the failure categories of PWF, TWF, and RNF, where some of the failed machines can be seen moving towards that area of the diagram.  
Through the analysis of 2D ranking, a strong correlation between the process and air temperature features with the rpm of the machine is observed, which is logical in the mechanical context. Additionally, increasing the rpm of the machine results in a corresponding decrease in its torque, as shown in Figure 4. Moreover, using the Pearson ranking 2D model, as shown in Figure 5, we observe an almost perfect positive correlation between the air temperature and process temperature features. This leads us to the conclusion that an increase in one of these two variables may lead to the destruction of the machine. Similarly, the almost complete negative correlation between the rotational speed and torque variables is also evident from the same diagram. This negative correlation may indicate that an increase in one of the two variables results in a decrease in the probability of machine failure. These observations highlight the importance of considering these variables in machine failure prediction models.  
Figure 3: RadViz graphical representation of feature relations for different causes of machine failure.  
Figure 4: Results of linear correlation of features using covariance 2D ranking.  
Figure 5: Results of linear correlation of traits using Pearson 2D ranking.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Solving Phase II (Rescue): Signal Routing and Victim Extraction
#### 3.4.1 The Approach  
A graph is formed after exploration with agents as nodes, and the edges are links between agents. The links between agents are present if two agents are within each other's Line-of-Sight and Transmission Range. Using the graph formed, an adjacency matrix is then produced that has the weights of the edges in the produced graph, which is called the _Cost Matrix_. The weights of the edges are evaluated based on the Euclidean distance between two agents (\(u\) and \(v\)), defined in the following equation:  
\[EuclideanDistance(u,v)=\sqrt{(x_{u}-x_{v})^{2}+(y_{u}-y_{v})^{2}} \tag{4}\]  
In an ideal scenario, with a successful conclusion of Phase I, the agents would be already interspersed in the maze (or search space), with no big voids or obstacles between them. In this circumstance, a path back to the origin can be easily found using the classic Dijkstra algorithm. However, the presence of obstacles or non-uniformity in the arrangement of the agents, in the form of large voids that disrupt communication links, a position-based algorithm is proposed to find a feasible complete path. The algorithm focuses on stopping the agents in the right locations to achieve a better spread of the agents in the environment.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 3 Supercritical Water Gasification (SCWG)  
### Properties of Supercritical Water  
Supercritical water is water above its critical temperature, 374.1 \({}^{\circ}\)C, and critical pressure, 22.1 MPa [94]. Subcritical water (SbCW) occurs at temperatures below or near the critical temperature and at a pressure above the saturation pressure [95]. These concepts are clarified in Figure 3.  
The supercritical water (SCW) is a single phase that possesses the characteristics of both gas and liquid without surface tension and liquid/gas phase boundary. Supercritical water has sufficient density to give appreciable dissolving power, diffusivity higher than that in liquids, and lower viscosity to enhance mass transport [64]. The dielectric constant of water changes near the critical point, for example it decreases from 78.5 F-m\({}^{-1}\) at 25 \({}^{\circ}\)C and 0.1 MPa, to 5.9 F-m\({}^{-1}\) at 400 \({}^{\circ}\)C and 25 MPa [96]. Therefore, water is a highly polar solvent at room temperature and a non-polar solvent in supercritical conditions. SCW is, therefore, an excellent solvent for non-polar organic compounds [96; 97] such as lignin. Thanks to these properties SCW provides a homogeneous and rapid reaction environment for gasification.
## 2 Materials and Methods
### Machine Learning Models  
For the machine learning models, the following methods were used: linear regression, which is a statistical method used to determine relationships between a dependent variable (value to be predicted) and one or more independent variables (predictors), assuming a linear relationship between the variables [51]; neural networks (ANNs), which consist of a simplified process inspired by biological neural networks, enabling pattern recognition in complex datasets through learning [52; 53]; support vector machines (SVMs), with learning that involves pattern recognition by defining a hyperplane that partitions the data into homogeneous areas, to assist in the prediction processes [25; 54]; cubist (Cub), a model tree method characterized by the use of trees for further regression analysis, where regression is applied on the subset of data that are formed after the data partitioning performed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Abbr.** & **Equation** & **Authors** \\ \hline Brightness Index & BI & \(((\text{R}^{2}+\text{G}^{2}+\text{B}^{2})/3)^{0.5}\) & [39] \\ \hline Blue Green Pigment Index & BGI & B/G & [40] \\ \hline Green Leaf Index & GLI & \((2\times\text{G}-\text{R}-\text{B})/(2\times\text{G}+\text{R}+\text{B})\) & [41] \\ \hline Primary Colors Hue Index & HI & \((2\times\text{G}-\text{R}-\text{B})/(\text{G}-\text{B})\) & [42] \\ \hline Overall Hue Index & \({}^{\star}\) HUE & \(\text{cotg}(2\times(\text{B}-\text{G}-\text{R})/30.5\times(\text{G}-\text{R}))\) & [42] \\ \hline Normalized Green Red Difference Index & NGRDI & \((\text{G}-\text{R})/(\text{G}+\text{R})\) & [43] \\ \hline Modified Green Red Vegetation Index & MGRVI & \((\text{G}^{2}-\text{R}^{2})/(\text{G}^{2}+\text{R}^{2})\) & [44] \\ \hline Red Green Blue Vegetation Index & RGBVI & \((\text{G}^{2}-\text{B}\times\text{R}^{2})/(\text{G}^{2}+\text{B}\times\text{R}^ {2})\) & [45] \\ \hline Soil Color Index & SCI & \((\text{R}-\text{G})/(\text{R}+\text{G})\) & [46] \\ \hline Spectral Slope Saturation Index & SI & \((\text{R}-\text{B})/(\text{R}+\text{B})\) & [42] \\ \hline Visible Atmospherically Resistant Index & VARI & \((\text{G}-\text{R})/(\text{G}+\text{R}-\text{B})\) & [47] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive analysis of biomass and leaf area index of buffel grass and bushveld signal grass for each treatment.  
by the trees [55]; boosted regression trees (BRTs), notable for their high predictive power when considering the concept of recursive binary splitting in conjunction with a learning (boosting) technique [56]; and classification and regression trees (CARTs), based on rules that allow trees to be created from recursive partitioning, dividing data into subsets based on independent factors [57].
## 3 Results
### Cyberattacks in Smart Grids: Challenges and Implications of Cybersecurity
#### 3.1.1 Overview of Power Generation, Transmission, and Distribution in Smart Grids
Smart grids encompass substations that receive high-voltage electricity, stepping it down for distribution through networks involving overhead lines, underground cables, and transformers [148]. Utilizing distribution automation (DA) systems, smart grids monitor and control distribution networks in real time, leveraging data such as voltage and current to optimize operations, detect faults, and ensure prompt responses [149]. Fault detection mechanisms integrated into smart grids swiftly identify issues, enabling self-healing capabilities to isolate faults and reconfigure the network for uninterrupted power supply, enhancing distribution reliability and reducing outages [150, 151]. The deployment of advanced metering infrastructure (AMI), featuring smart meters and communication infrastructure, is pivotal for power distribution. Smart meters facilitate two-way communication between utilities and consumers, offering real-time insights into consumption, load profiles, and power quality. These data empower utilities to optimize distribution, implement demand response programs, and enhance customer services [152, 153].
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 3 5G Antennas as a Candidate for Massive MIMO Technique at Sub-6 GHz  
An essential component of wireless communication systems is the antenna. Wireless systems that use a large number of antenna elements/arrays at both the transmitter and receiver are called massive MIMO. 5G antennas enable multiple-input multiple-output (MIMO). Various 5G antennas at sub-6 GHz designs are considered as candidates for developing massive MIMO antenna arrays. Figure 18 shows several structures of 5G antennas can be summarized in the block diagram below.
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
###### Abstract  
Analysis of traffic flow signals plays an important role in traffic prediction and management. As an intrinsic property, the singular point of a traffic flow signal labels a new nonsteady status. Therefore, detecting the singular point is an effective approach to determine the moment of traffic flow prediction. In this paper, an improved wavelet transform is proposed to detect singular points of real-time traffic flow signals. The number of detected singular points is output via the heuristic selection of multiple scales. Then, a weighted similarity measurement of historical traffic flow signals is utilized to predict the next singular point. The position of the next singular point decides the duration of prediction adaptively. The detected and predicted singular points are applied to dynamically update the unequal interval prediction of traffic flow. Furthermore, a Vasicek model is used to predict the traffic flow by minimizing the sum of the relative mean standard error (RMSE) between the traffic flow increment in the predicted interval and the sampled increments of previous intervals. A decomposition method is used to solve the unequal matrix problem. Based on the scenario and traffic flow imported from the real-world map, the simulation results show that the proposed algorithm outperforms existing approaches with high prediction accuracy and much lower computing cost.  
dynamic traffic flow prediction; unequal interval; singular point; wavelet transform; weighted similarity measurement; Vasicek model Article
###### Abstract  
Various numerical methods have been used to simulate the rock cutting process. Numerical simulation is a useful tool for estimating the performance of a cutting tool and for understanding the mechanism of rock cutting and interaction between a cutting tool and the rock. These methods supplement the rock cutting test, which is commonly referred to as the linear cutting machine (LCM) test. Mechanical excavators, such as roadheaders, longwall shearers, and trenchers, generally use pick cutters as the cutting tool. In this study, a rock cutting simulation with a pick cutter was developed using the smooth particle hydrodynamics (SPH) technique, which is a mesh-free Lagrangian method. The Drucker-Prager (DP) strength model was used to simulate the brittle behavior of rock. The cumulative damage (CD) model was used to simulate the degraded fragmentation process of rock and the distinctive behavior of rock in the compression and tensile stress regions. In this study, an attempt was made to simulate sequential cutting by multiple pick cutters. The results showed that the numerical simulation matched the experimental results closely in terms of cutter forces, specific energy, and the fragmentation phenomenon. These results confirmed the applicability of the SPH technique in simulating the rock cutting process.  
rock cutting; point attack pick; smooth particle hydrodynamics (SPH); cumulative damage model; cutter force; specific energy +
Footnote †: journal: Applied Sciences
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 7 Conclusions  
The paper develops a concept of information depositories for decentralized evacuation. The information depositories bridge the temporal gap of asynchronous communication for awareness sharing in decentralized evacuations. Depositories collect the environmental and risk information from passing by agents, maintain this information by fusion, and convey the fused information to passing-by evacuees. Simulations verify that information depositories enhance the spatial awareness of the environment and risk awareness by tracking states and updating information, thus raising the chance that people survive and or can reduce their evacuation time. This paper also suggests that the information depositories should be deployed at strategic locations such as staircases. A comparison of the alternative approaches of fading memory and information depositories shows that information depositories are more reliable than the fading memory when improving the evacuation outcome. However, fading memory also has advantages that can be combined by information depositories. Overall, simulation results support the hypothesis that even a small number of information depositories will enhance risk awareness of evacuees and improve the evacuation results.  
Without explicitly tested, it is anticipated that there will be an upper bound of the density of information depositories. Beyond that bound, no significant improvements will occur by increasing further the number of information depositories. Deploying information depositories at strategic locations such as staircases are advantageous possibly because staircases are \"bottlenecks\", or, generally, have a high centrality in the evacuation graph, which can be investigated in the future. Future work may also explore whether information depositories work equally well in decentralized evacuation for events with substantially different behavior, such as earthquakes or a roaming sniper, and whether deploying depositories near staircases or in corridors are better options for these kinds of disasters. Since depositories improve situation awareness, and more situation awareness may contribute to better prediction, it is expected a hybrid model combining information depositories and fading memory will significantly improve the evacuation performance.  
This work has been supported by the ARC (DP170100153) and a CSC research scholarship.  
[PERSON] developed and implemented the concept of information depositories under the supervision of [PERSON] and [PERSON]. All authors discussed and approved the final version.  
The authors declare no conflict of interest.
## 5 Summary and Analysis of Some Simplified Estimation Schemes
### Simplified Estimation--Estimation B  
In order to obtain a low-complexity estimator that is not only as efficient as Equation (9) but valid for any cases, an equivalent description for Equation (9) was given in [10] by  
\[\widehat{\varphi}=\begin{cases}\tan^{-1}\left(\frac{Im(Y)}{Re(Y)}\right),& \text{if}\quad Re(Y)>0\text{ and }|Re(Y)|\geq|Im(Y)|,\\ \frac{\pi}{2}-\tan^{-1}\left(\frac{Re(Y)}{Im(Y)}\right),&\text{if}\quad Im(Y)>0 \text{ and }|Re(Y)|<|Im(Y)|,\\ -\pi+\tan^{-1}\left(\frac{Im(Y)}{Re(Y)}\right),&\text{if}\quad Re(Y)<0\text{ and }|Re(Y)|\geq|Im(Y)|,\\ -\frac{\pi}{2}-\tan^{-1}\left(\frac{Re(Y)}{Im(Y)}\right),&\text{if}\quad Im(Y)< 0\text{ and }|Re(Y)|<|Im(Y)|.\end{cases} \tag{11}\]  
Equations (9) and (11), originally obtained using different intuitive reasonings, are two approaches for computing the principal value of the argument of the measurement \(Y\).  
We observe immediately from Equation (11) that the complex observation space is subdivided into four equi-angular sectors illustrated in Figure 2, which are identical to the four regions presented in Table 1 of [8]. Both of them can be distinguished from one another by only simple comparison of the measurement magnitudes and signs of real and imaginary parts of \(Y\). From this viewpoint, Equation (11) differs from Equation (9) in two respects: (1) the observation space is further subdivided into four subspaces, not two; and (2) the subspace locating criteria is redesigned. It is such characteristics that make Equation (11) attractive for devising a simplified estimator as shown in the following.  
As indicated in Equation (11), \(\left|\frac{Im(Y)}{Re(Y)}\right|\) or \(\left|\frac{Re(Y)}{Im(Y)}\right|\) in an observation region is not only never more than 1, but irrelevant to CFOE being estimated or SNR conditions. This appropriately allows us to use the mathematical approximation that \(\tan^{-1}\left(x\right)\approx x\), for small \(x\), in Equation (11) without undesirable calculation errors. Then, another efficient simplified detection scheme was proposed in our recent work [10], where the estimator is valid for arbitrary CFO and SNR, given by  
Figure 2: Division of the complex observation space.  
\[\widehat{\varphi}\approx\begin{cases}\frac{Im(Y)}{Re(Y)},&\text{ if }\quad Re(Y)>0\text{ and }|Re(Y)|\geq|Im(Y)|,\\ \frac{\pi}{2}-\frac{Re(Y)}{Im(Y)},&\text{ if }\quad Im(Y)>0\text{ and }|Re(Y)|<|Im(Y)|,\\ -\pi+\frac{Im(Y)}{Re(Y)},&\text{ if }\quad Re(Y)<0\text{ and }|Re(Y)|\geq|Im(Y)|,\\ -\frac{\pi}{2}-\frac{Re(Y)}{Im(Y)},&\text{ if }\quad Im(Y)<0\text{ and }|Re(Y)|<|Im(Y)|,\end{cases} \tag{12}\]
## 3 Results and Discussion
### Contrastive Analysis of Prediction Models Based on Different Techniques  
The prediction results of the MLR model based on the electronic nose data preprocessed by the mean-center method are described in Figure 8a, with an Rc\({}^{2}\) value of 0.8936, an RMSEC value of 1.9716, and an Rp\({}^{2}\) value of 0.9247. The prediction results of the PLSR model based on the NIRS data preprocessed by the Savitzky-Golay first derivative and mean-center method are described in Figure 8b, with an Rc\({}^{2}\) value of 0.9989, an RMSEC value of 0.2003, and an Rp\({}^{2}\) value of 0.999. These results demonstrate that the regression model based on the NIRS data is superior to that based on electronic nose data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{\
\begin{tabular}{c} **Regression** \\ **Model** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Preprocessing** \\ **Method** \\ \end{tabular} } & \multicolumn{3}{c}{**Calibration Set**} & \multicolumn{3}{c}{**Cross-Validation Set**} & \multicolumn{3}{c}{**Prediction Set**} \\ \cline{3-10}  & & \(\mathbf{R}_{\text{cv}}^{2}\) & **RMSEC** & \(\mathbf{R}_{\text{cv}}^{2}\) & **RMSEC** & \(\mathbf{R}_{\text{cv}}^{2}\) & **RMSECV** & \(\mathbf{R}_{\text{cv}}^{2}\) & **RMSEP** & **RPD** \\ \hline \multirow{5}{*}{PCR} & RAW & 0.9797 & 0.8585 & 0.9791 & 0.8702 & 0.986 & 0.7436 & 8.1599 \\  & MSC (mean) & 0.9843 & 0.7543 & 0.9842 & 0.7571 & 0.9857 & 0.7233 & 8.3889 \\  & Mean center & 0.9883 & 0.65 & 0.9873 & 0.6777 & 0.9892 & 0.6255 & 9.7005 \\  & S-G 1 st and & 0.9942 & 0.4594 & 0.9938 & 0.4757 & 0.9941 & 0.4643 & 13.0685 \\ \hline \multirow{5}{*}{PLSR} & RAW & 0.9979 & 0.2764 & 0.9977 & 0.289 & 0.9978 & 0.2836 & 21.3952 \\  & MSC (mean) & 0.9872 & 0.6813 & 0.9868 & 0.6924 & 0.9878 & 0.6666 & 9.1024 \\ \cline{1-1}  & Mean center & 0.998 & 0.2689 & 0.9978 & 0.2833 & 0.9979 & 0.2756 & 22.0163 \\ \cline{1-1}  & S-G 1 st and & 0.9989 & 0.2003 & 0.9987 & 0.2157 & 0.999 & 0.1905 & 31.8514 \\ \cline{1-1}  & Mean-center & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction performances of different regression models based on the NIRS data.  
Figure 8: (**a**) Performance of the multiple linear regression (MLR) model in spray-dried porcine plasma (SDPP) concentration prediction based on the electronic nose data; (**b**) performance of the partial least squares regression (PLSR) model in SDPP concentration prediction based on the NIRS data.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 2 The Water Cycle  
Taking a land surface water balance perspective, the cycle can be described as  
\[P-E=Q+dS/dt \tag{1}\]  
where \(P\) is precipitation, \(E\) evapotranspiration, \(Q\) runoff, and \(S\) total storage (e.g., over a river basin). In reality, \(Q\) is a composite term, with the most important components being snow water equivalent, soil moisture, surface water (lakes, wetlands, reservoirs, rivers), and groundwater, with a separate mention to glaciers and ice sheets. This simple model tells us that the atmospheric moisture divergence is mostly compensated for by evapotranspiration and precipitation, since changes in atmospheric moisture storage are small. At the surface, \(P-E\) is balanced by surface and sub-surface runoff and changes in soil moisture and groundwater.  
Estimates of the global water budget and of its annual cycle using observations and model data are available in the literature [1; 2; 3; 36; 37], and an evaluation based on the recent climatology of the Global Precipitation Climatology Center (GPCC) is shown in Figure 3. Note the dominant role played by precipitation over land, but especially over oceans.  
Figure 3: Average water transport/exchange in km\({}^{3}\) per year. Figure modified from [1].