esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
### Dempster-Shafer Theory  
Information in the real world is affected by a great deal of uncertainty. Many existing theories (e.g., probability theory, fuzzy numbers [30; 31; 32; 33], Z-numbers [34; 35; 36], D-numbers [37; 38; 39], and Dempster-Shafer evidence theory) have been developed to represent various types of uncertainty. Dempster-Shafer evidence theory (D-S theory) can be regarded as a general extension of Bayesian theory. It was first proposed by [PERSON] in 1967 [40], and was developed to its present form by [PERSON] in 1976 [41]. D-S theory can present and handle uncertainty preferably than probability theory [42; 43; 44]. Moreover, it provides a combination rule to fuse different data from various information sources. D-S theory is now being studied for use in many fields, such as risk assessment [45; 46], decision making [47; 48; 49], fault diagnosis [50; 51], and others [52; 53; 54; 55; 56].  
Let \(\Theta=\{\{H_{1}\},\{H_{2}\},\cdots,\{H_{n}\}\}\) be a finite nonempty set of \(n\) elements that are mutually exclusive and exhaustive, \(2^{\Theta}\) is the power set composed of \(2^{n}\) elements of \(\Theta\), and \(\emptyset\) denotes the empty set. In D-S theory, mathematically a basic probability assignment (BPA) is a mapping: \(2^{\Theta}\rightarrow[0,1]\) that satisfies  
\[\sum_{A\subseteq\Theta}m(A)=1,m(\emptyset)=0. \tag{1}\]  
If m(A) \(>0\), A is called a focal element, and the set of all focal elements is named a body of evidence (BOE). The probability m(A) measures the belief exactly assigned to A and represents how strongly the evidence supports A. The mass of belief in an element of \(\Theta\) is quite similar to a probability distribution, but differs in the fact that the unit mass is distributed among the elements of \(2^{\Theta}\), not only on single subsets but on composite hypotheses as well. This is why BPA can better represent the uncertainty of information.  
Moreover, a combination tool is offered by D-S theory to fuse multiple belief assignments as follows:  
\[m=m_{1}\oplus m_{2}\oplus m_{3}\oplus\cdots\oplus m_{k}, \tag{2}\]  
where \(\oplus\) represents the operator of combination. For two BOEs, [PERSON]'s rule of combination is defined as:  
\[m_{1}\oplus m_{2}=m(A)=\frac{\sum_{B\cap C=A}m_{1}(B)m_{2}(C)}{1-K}, \tag{3}\]  
where  
\[K=\sum_{B\cap C=\emptyset}m_{1}(B)m_{2}(C). \tag{4}\]  
The denominator, \(1-K\), is a normalization factor. \(K\) is called the degree of conflict between BOEs [57; 58]. [PERSON]'s rule strongly implies the agreement between diverse information and ignores the conflict between them. When information sources are in support of a similar proposition, it is able to reduce uncertainty in the combination result.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement
### Data Preprocessing
#### 4.3.2 Data Normalization  
The preliminary blood pressure values obtained through arterial tonometry were combined with the extracted pulse wave feature parameters to form a new feature set. Considering the significant differences in feature values, the data were normalized using the following formula:  
\[\overline{X_{ij}}=\frac{X_{ij}-\mu(X_{i})}{\sigma(X_{i})} \tag{5}\]  
In the formula, \(\overline{X_{ij}}\) represents the normalized value; \(X_{ij}\) represents the jth value of the ith feature; \(\mu(X_{i})\) and \(\sigma(X_{i})\) denote the mean and standard deviation of the ith feature, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Parameters** & **Definition** \\ \hline \(St\) & Systolic time \\ \(Dt\) & Diastolic time \\ \(HR\) & Heart rate \\ \(S\)-\(PTT\) & Transit time between two peaks \\ \(St10+D10\) & Addition of systolic time and diastolic time @ 10\% of the pulse \\  & amplitude \\ \(St10/Dt10\) & Division of diastolic time and systolic time @ 10\% of the pulse \\  & amplitude \\ \(St25+Dt25\) & Addition of systolic time and diastolic time @ 25\% of the pulse \\  & amplitude \\ \(St25/Dt25\) & Division of diastolic time and systolic time @ 25\% of the pulse \\  & amplitude \\ \(St50+Dt50\) & Addition of systolic time and diastolic time @ 50\% of the pulse \\  & amplitude \\ \(St50/Dt50\) & Division of diastolic time and systolic time @ 50\% of the pulse \\  & amplitude \\ \(Pre\)-\(sbp\) & SBP obtained by arterial tonometry method \\ \(Pre\)-\(sdp\) & DBP obtained by arterial tonometry method \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extracted feature parameters.
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 4 Nephele Project as Enabler for SAR Operations
### Nephele's Added Value
To summarize, the NEPHELE framework will enable and support the integration of different technologies and solutions over the cloud-to-edge-to-IoT continuum. Indeed, combining all these elements into a single framework represents a breakthrough advance in the cloud-to-edge-to-IoT continuum-based applications. Better performance and enhanced situational awareness in SAR operations are nicely paired with advanced technological solutions offering smart decision-making and optimization techniques for mission control and robotic applications in mobile environments.
## 3 Simulation Results and Discussion
### Effect of Combustion Chamber Geometry on the Formation of the In-Cylinder Mixture  
The in-cylinder mixture approaches the target equivalence ratio at a hydrogen injection timing of \(-140\)\({}^{\circ}\)CA. However, the existence of a rich mixture region near the exhaust valve may heighten the risk of surface ignition, which could detrimentally impact combustion. Consequently, further exploration is undertaken to understand the effect of combustion chamber geometry on mixture formation using scheme P2. For this section, the hydrogen injection timing is established at \(-140\)\({}^{\circ}\)CA and the compression ratio is maintained at 10.  
Figure 17 depicts the impact of combustion chamber geometry on the distribution of the mixture in radial and circumferential sections at TDC. It can be seen from the figure that the mixture uniformity of the sloping combustion chamber is better than that of a flat-bottom combustor. The mixture concentration of the sloping combustion chamber is lower in the exhaust valve area, and the mixture near the spark plug is richer, which is very beneficial to reduce abnormal combustion and ignition.  
Figure 18 compares the distribution of turbulent kinetic energy in two combustion chambers to illustrate its impact on mixture distribution in the cylinder. The sloping combustion chamber can form stronger turbulent kinetic energy near the exhaust valve than that of the flat-bottom combustion chamber, which is not only conducive to the uniform mixing of the fuel and air, but also reduces the temperature of the exhaust valve  
Figure 16: NSD and mass fraction of mixtures with different equivalent ratios at TDC for different hydrogen injection timings.  
Figure 17: In-cylinder mixture distribution with different combustion chamber geometries at TDC.  
to a certain extent, so as to avoid the occurrence of abnormal combustion. In addition, stronger turbulence can increase the flame propagation speed, shorten the combustion duration, increase the combustion constant volume, and improve the indicated thermal efficiency. It can also be seen from the figure that the turbulence near the cylinder wall is weak, which is caused by the velocity attenuation caused by the viscosity of the mixture.  
Figure 19 shows the mass fraction of the different-equivalence-ratio in-cylinder mixtures and NSD at TDC for a flat-bottom combustion chamber and a sloping combustion chamber. The sloping combustion chamber exhibits a narrower range of equivalence ratio distribution with two peaks, one around an equivalence ratio of 0.4 and another around 0.7. From the circumferential section in Figure 17, it can be observed that the mixture with an equivalence ratio of approximately 0.7 is located near the spark plug in the sloping combustion chamber. The peak value of the curve for the sloping combustion chamber is lower than that for the flat-bottom combustion chamber, which means better uniformity. As a result, the former combustion chamber forms a more uniform mixture. Additionally, the concentration of the mixture near the spark plug is higher, which is advantageous for ignition.
## 8 Navigation
### Mapping  
The rovers are equipped with a variety of sensors, including LiDAR, radar, and cameras, to create detailed maps of the agricultural environment [86]. These maps not only assist in navigation [87; 88] but are also crucial for monitoring the health of crops, assessing growth, and identifying areas that require special attention. The mapping process can greatly benefit from the use of advanced imaging technologies and the analysis of collected data to improve agricultural practices. Navigation is a complex task that involves the construction of a set of knowledge by the rover, most of it acquired in real-time and constantly updated. A common technique is the Simultaneous Localization And Mapping (SLAM) algorithm, which enables an autonomous robot to incrementally construct a comprehensive map of an unknown environment, continuously gathering reliable observations, which are used to estimate its own motion (egomotion) and current location. This process allows the robot to automatically explore the environment, and to build and refine the map, while simultaneously detecting obstacles and estimate its position in the environment. As reported by [89], the most common SLAM techniques are Visual SLAM, LiDAR SLAM [90], and multi-sensor Fusion SLAM [91], with each one depending on a different type of input sensor used for rover navigation.
# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 5 Conclusions  
In order to enhance the robustness of vision-based robots in natural environment, a dynamic recognition method was used to improve the recognition performance. For the common linkage robots in industry, the end-camera was driven by a manipulator to complete the target observation with high accuracy efficiently in this paper. The perspective optimization path planning of the manipulator was studied, and the path planning method for finding optimal view angles based on the A* algorithm was proposed. An observation geometry model was established and the viewing angle intervals were divided. Based on the active viewpoint selection mechanism, a multi-view classifier based on BM2 DKPCANet and Softmax was constructed. The output class probability was set as the viewpoint measure. The best identification performance and the shortest distances were taken as the objective functions of path planning. The directed graph programming model was constructed, and the directed graph adjacency matrix based on the planning distance was determined. The multi-objective optimization problem was transformed into the single-objective optimization problem by fusing the two adjacent matrices. The shortest path was planned based on the A* algorithm. The experiments were performed with different view count and different starting viewpoints. The results demonstrated that the method could plan the optimal path with higher recognition accuracy and efficiency.  
Figure 15: Recognition performance with different start points and view angles.  
**Author Contributions:** Conceptualization, X.Y. and X.L.; methodology, X.Y., X.L. and H.W.; software, X.L.; validation, Q.H., Q.Y. and N.W.; formal analysis, H.W.; investigation, Q.Y.; resources, X.L. and Q.H.; data curation, X.L. and H.W.; writing--original draft preparation, X.L.; writing--review and editing, Q.H. and N.W.; visualization, H.W.; supervision, X.Y.; project administration, Q.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52075306.  
Not applicable.  
The data that support the findings of this study are available within the paper.  
The authors declare no conflict of interest.
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 3 Results
### Experiment Results on New College Dataset  
We report the experimental results of our proposed Siamese convolutional neural network-based method and its comparisons with both traditional methods and other deep learning-based methods. A precision-recall curve of comparisons on the New College dataset can be seen in Figure 8.  
The yellow curve shows the experiment results of the global image descriptors GIST, which is proposed by [PERSON] et al. [24]. In their implementations, they utilized the GIST features to generate global descriptors, and then the Manhattan distance is adopted as a similarity metric for matching. Here, we repeated their feature extraction approach and matching strategy. However, it obviously can be observed that the global descriptor-based method is barely satisfactory in the loop closure detection tasks.  
[PERSON] et al. [11] proposed a baseline method, in which the Bag-of-Visual-Words (BoVW) model was employed to generate codebook representations for matching. Here, we use the green curve to represent the BoVW-based method and we can discover that the BoVW-based method slightly outperforms the method of [24].  
So far, the latest algorithm of loop closure detection is deep learning based. [PERSON] et al. [26] utilized AlexNet to automatically learn feature descriptors and then the similarity between descriptors is measured by the L2-norm distance. We use the purple curve to represent the performance and we observe that the standard CNN achieves comparable experiment results to that of the FV and the VLAD-based method.  
Figure 8: Precision–recall curves for different methods on the New College dataset.  
We test our proposed Siamese CNN-based method on the New College dataset and show its performance with the red curve. Three learning strategies (softmax supervision, contrastive supervision, and multi-task supervision) are annotated with different line shapes. As we can see in Figure 8, the performance of single-task supervision is similar to the standard CNN method. However, Siamese CNN with the multi-task learning strategy yields a more than 90% precision rate with an 83% recall rate, significantly increasing the previous loop closure detection approaches and achieving the state of the art.
# A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions
## 1 Introduction  
This paper focuses on one aspect of the deep learning revolution that the field is going through, namely the application of deep learning algorithms to time-series analysis and forecasting. This field was spawned by the work mainly completed in the field of finance, where accurate predictions of stock prices can yield a large sum of money to traders [1]. However, recent advances in deep learning technologies have led to a significant increase in the accuracy and performance of these algorithms, which is why this paper investigates them and implements several in the context of efficiently managing and scaling a computing system with heavy usage.  
The main novelty which this paper brings is a comparative study which evaluates the performance of various time-series model architectures. There are several previous attempts at predicting resource usage using deep learning techniques, however they focus on a single method, such as Recurrent Neural Networks (RNNs) in the case of [PERSON] et al. [2] or using very specific architectures, such as restricted Boltzmann machines [3].  
The motivation of this paper is to compare several cutting-edge deep learning solutions for resource usage forecasting, in order to identify the most suitable one for the problem presented. The results are discussed and the potential improvements in terms of real load CPU usage are highlighted towards the end. The end-goal is the implementation of an predictor which can be used to adapt the number of resources a computing service needs in near-real time, adjusting itself to periods of high or low load. A secondary constraint of the implemented solution is to not under-allocate resources for an extended period of time, as it could cause service degradation.  
The concrete contributions provided by the paper are provided below.  
* The paper describes the process of gathering live metrics from distributed applications running on a Kubernetes [4] cluster, inspiring itself from a real production system deployed at the European Laboratory for Nuclear Physics (CERN). Kubernetes runs containers based on snapshots (called images). The most famous container orchestration platform that Kubernetes can run on is [5].
* A few different modern techniques of analyzing and forecasting time-series data are presented and compared with more traditional approaches.
* The performance of the different forecasting algorithms is presented, showcasing which ones are more applicable to the specific use-case presented in this paper, CPU usage forecasting.
* An experiment is conducted based on the algorithm found to be the best performer. The results from the experiment allow us to understand and quantify the potential energy-saving benefits of using this implementation. We compare the ideal scenario with the predictions provided on the validation data, with encouraging results.
## Abbreviations  
* [1] ANN-MLP Artificial Neural Network with Multilayer Perception ANOVA Analysis of Variance BOSS Bootstrapping soft shrinkage CA Cluster analysis CAR/PDMS Carboxen/Polydimethysiloxane CARS Competitive adaptive reweighted sampling CI Univariate conformity index DA Discriminant analysis DVB/CAR/PDMS Divinylbenzene/Carboxen/Polydimethysiloxane EEC European Community EVOO Extra virgin olive oil FA Fatty acid FDA Factorial discriminant analysis FT-IR Fourier transform infrared GCXGC-TOFMS Two-Dimensional Gas Chromatography and Time-of-Flight Mass Spectrometry GC-FID Gas chromatography with flame ionization detection GC-ITMS Gas chromatography-ion trap mass spectrometry GC-MS Gas chromatography combined with mass spectrometry HCA Hierarchical cluster analysis HS-SPME Headspace solid-phase microextraction IOC International olive oil council IRIV Iteratively retaining informative variables PLS IRMS Isotope ratio mass spectrometry
## 2 Nonlinear Model Predictive Control
### Principles of Model Predictive Control
#### 2.1.3 Feedback Correction  
The objective function will include lateral deviations and heading angle deviations, which will be used to correct the steering control sequence and improve the trajectory tracking performance of the controller. This feedback correction provides the MPC controller with a strong ability to resist interference and overcome system uncertainty. The design architecture of the MPC trajectory tracking controller is shown in Figure 1. The controller obtains the initial state firstly, uses the predictive model and the target trajectory to get the objective function secondly, and finally calculate a control sequence to minimize the objective function at each optimization period.
###### Abstract  
Ordinary solar cells are very difficult to bend, squash by compression, or extend by tensile strength. However, if they were to possess elastic, flexible, and extensible properties, in addition to piezo-electricity and resistivity, they could be put to effective use as artificial skin installed over human-like robots or humanoids. Further, it could serve as a husk that generates electric power from solar energy and perceives any force or temperature changes. Therefore, we propose a new type of artificial skin, called hybrid skin (H-Skin), for a humanoid robot having hybrid functions. In this study, a novel elastic solar cell is developed from natural rubber that is electrolytically polymerized with a configuration of magnetic clusters of metal particles incorporated into the rubber, by applying a magnetic field. The material thus produced is named magnetic compound fluid rubber (MCF rubber) that is elastic, flexible, and extensible. The present report deals with a dry-type MCF rubber solar cell that uses photosensized dye molecules. First, the photovoltaic mechanism in the material is investigated. Next, the changes in the photovoltaic properties of its molecules due to irradiation by visible light are measured under compression. The effect of the compression on its piezoelectric properties is investigated.  
Keywords:hybrid skin (H-Skin); solar cell; sensing; piezoelectricity; photovoltaics; natural rubber; electrolytic polymerization; magnetic cluster; magnetic field; magnetic compound fluid (MCF); artificial skin; robot; humanoid +
Footnote †: journal: Sensors
## 3 Operating Characteristics Analysis and Parameter Design
### Operating Characteristics
#### 3.1.2 Voltage Gain  
The output voltage gain of the proposed inverter is defined as \(G_{vi}\), and expressed is  
\[G_{vi}=\frac{U_{i}}{U_{dc}}(i=1,2,3,4) \tag{15}\]  
According to Equation (11), \(U_{1}\) is only related to \(D_{1}\) when \(D\) is determined. The relationship between \(G_{v1}\) and \(D\) and \(D_{1}\)  
\(D=0.5\), as \(D_{1}\) and \(D_{2}\) increase, \(G_{v4}\) overall first increases and then decreases. When \(D_{1}=1-D_{2}\), the maximum value of \(G_{v4}\) is \(G_{v4-max}=0.91\). Before reaching the maximum voltage gain, \(G_{v4}\) is positively correlated with \(D_{1}\) and \(D_{2}\). After reaching the maximum voltage gain, \(G_{v4}\) shows a negative correlation with \(D_{1}\) and \(D_{2}\) until it reaches 0, at which point \(D_{1}=D_{2}=D=0.5\). When \(D=0.7\), as \(D_{1}\) and \(D_{2}\) increase, \(G_{v4}\) first increases and then decreases with the increase in \(D_{1}\). Before reaching the maximum voltage gain, \(G_{v4}\) is positively correlated with \(D_{2}\). After reaching the maximum voltage gain, \(G_{v4}\) is negatively correlated with \(D_{2}\). During the increase in \(D_{1}\) and \(D_{2}\), the overall trend of \(G_{v4}\) is to first increase and then decrease, with a maximum voltage gain value of \(G_{v4-max}=1.49\).  
Figure 5: Relationship between \(G_{v1}\) and \(D\) and \(D_{1}\): **(a)** 3D curve; **(b)** 2D curve.  
Figure 6: Relationship between \(G_{v2}\) and \(D\) and \(D_{1}\): **(a)** 3D curve; **(b)** 2D curve.  
Figure 7: Relationship between \(G_{v3}\) and \(D\) and \(D_{2}\): **(a)** 3D curve; **(b)** 2D curve.