esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 4 Experimental Results
### Experimental Tests Under Weather Conditions, Lighting Conditions, and Road Types
Overall, the system demonstrated higher accuracy under clear weather conditions, where visibility is better. While object detection is still possible under adverse weather conditions like snow, the accuracy and confidence levels may decrease slightly due to occlusions and reflections caused by the snow. These findings highlight the need for further optimization to improve detection performance under challenging weather conditions. As a result, the following conclusions can be drawn: First, the detection performance is better on clear days compared to rainy or snowy conditions, as the system demonstrates higher accuracy and confidence under favorable weather conditions with improved visibility. Second, there is no significant difference in obstacle detection performance based on road type. However, the system shows slightly better results on roadways than on sidewalks. Lastly, the detection system performs more effectively during the daytime compared to nighttime, where improved lighting conditions naturally enhance the system's accuracy. The experimental results demonstrate that the model effectively recognizes obstacles encountered in electric wheelchair-driving environments. The proposed model detected key objects, including pedestrians, vehicles, and stationary items (e.g., benches and bicycles), on both roads and sidewalks, thereby underscoring its ability to provide swift and accurate obstacle detection.
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 2 Related Works
### MapReduce Based Skyline Computation  
In recent year, distributed skyline computations for big data has received more attention. [PERSON] et al. (2015) provided a good survey of skyline processing in highly distributed environments. MapReduce is a programming model and an associated implementation for processing a massive volume of data. It is widely used for computing skyline in recent years ([PERSON] et al., 2015; [PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019).  
Three MapReduce based algorithms for skyline query processing, MR-BNL, MR-SFS, and MR-Bitmap, are proposed by [PERSON] et al. (2018). Experiments illustrated the effectiveness and efficiency of these algorithms compared to conventional skyline algorithms. [PERSON] et al. (2017) proposed a new MapReduce skyline method to compute skyline queries. The new angular partitioning of the data space significantly reduced the processing time. [PERSON] et al. (2018) developed a MapReduce-based computation algorithm to implement k-dominant skyline query processing. In [PERSON] et al. (2017), [PERSON] et al. proposed a novel distributed skyline query processing algorithm (DSL) that discovers skyline points progressively based on the grid structure. [PERSON] et al. (2017) adapted skyline computation to the MapReduce framework and they recursively divided the dimensions of data into some parts on a grid-based partitioning scheme. Unfortunately, few works implemented the spatial skyline queries in such distributed way. In this paper, We develop MapReduce-based area skyline query computation, which is a distributed algorithm for spatial skyline queries.
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 1 Introduction
In recent years, more attention has been paid to the Earth observation satellites for the purpose of ERP estimation and even the realization of a reference frame. [PERSON] et al. [20] found that the GRACE-A satellite, as the co-location of SLR and GNSS techniques, allowed the precise estimation of SLR station coordinates, with a consistency at the level of 2-3 cm with respect to Satellite Laser Ranging Frame 2014 (SLRF2014). Using two years of SLR data from Sentinel-3 satellites, [PERSON] et al. [9] investigated the possibility of estimating geodetic parameters, only based on LEO observations. The result showed that it is feasible to solely use the SLR observations of LEO satellites for the determination of ERP and geocenter motion, and the combination of LAGEOS and Sentinel satellites can provide high-accuracy geodetic parameters. [PERSON] et al. [21] used SLR observations to Jason satellites to derive SLR station coordinates using the preprocessed observation-based attitude of Jason satellites. A posteriori variance factor of the estimation of SLR station coordinates improved by about 10% for Jason-1 and Jason-3, and almost 20% for Jason-2, when compared to that using a nominal yaw-steering model. The previous studies demonstrate the feasibility of ERP estimation based on the SLR observations to some Earth observation satellites. However, they only focus on a single LEO satellite or single LEO satellite mission, which leads to sparse SLR observations for ERP estimation. Considering that, currently, a number of LEO satellites are equipped with an LRR, the great potential of multiple LEO satellites from different missions for ERP estimation has not been fully explored. The goal of this study is to investigate the contribution of multiple LEO satellites to the ERP estimation with seven LEO satellites flying at different orbits. The combination of multiple LEO satellites and LAGEOS satellites is highlighted and investigated in detail. In addition, we also discuss the possibility of the joint processing of SLR and GPS observations to determine ERP and the orbits parameters simultaneously in this study. The paper is organized as follows: Section 2 addresses the data set and describes the detailed processing strategies. In Section 3, we validate the GPS-derived LEO orbit and evaluate the performance of ERP based on the single-LEO solutions. Subsequently, in Section 4, the ERP estimation, based on multiple LEO satellites, and the combination of LEO satellites and LAGEOS satellites is analyzed. Additionally, the two-step method and one-step method are compared. In Section 5, a discussion about the results obtained in this study is given. Finally, in Section 6, a summary of the results and corresponding discussions are presented.
## 1 Introduction
The current state-of-the-art algorithm [22] uses thresholding on the product of original and co-registered radargram for clutter detection; however, deep learning has not been attempted previously in studies involving discontinuity detection on the Martian subsurface. The DL/ML algorithm for detection of cavities and other objects in the subsurface environment of Earth has been discussed in [23; 24]. In terms of paleoclimatic conditions that, crucially, control the existence of a habitable environment, the subsurface strata of Martian topography have been of interest to the scientific community to indicate valuable clues representative of the geological history of Mars. The idea that the footprints of paleoclimatic factors such as precipitation, glaciation, and permafrost can be found in the underground layers of Mars promoted the development of GPR in Martian orbit and led to the deployment of Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) [19] onboard Mars Express and SHARAD onboard MRO [20], employing a very long electromagnetic wave, which enables the subsurface observation of Mars with significant penetration depth. However, as stated, the interpretation of orbital GPR data, especially over Mars, brought several difficulties. The noise components, such as ionospheric interferences and signal delay, are the primary issues, and must be considered a priority [21; 25]. The conversion of time-domain reflectance from subsurface strata into high-dimensional (2D/3D) mapping products requires some assumptions regarding the dielectric permittivity of the underlying materials [26; 27]. However, the primary barrier in Martian subsurface research, employing MARSIS and SHARAD, is the methodology to effectively trace the discontinuities from subsurface signals. Thus, in this study, we focused on the development of an automated algorithm for subsurface discontinuity detection using DL. The detected discontinuities were later utilized for subsurface reconstruction. Considering the high signal-to-noise ratio of SHARAD, we actively employed a deep-learning approach: specifically, a convolutional neural network along with add-on processors for noise reduction, registration between clutter simulation and radargram, and feature extraction. The constructed algorithms were tested on Martian geomorphic features such as Lobate Debris Apron (LDA), Polar Layered Deposits, and mid-latitude potential ice layers beneath the Phoenix landing site, as described in Section 2. The technical details of the algorithms are stated in Section 3. The result and validation outcomes employing error metrics are presented in Sections 4 and 5, along with relevant discussion.
## 4 Validation Experiments and Results
### Validation Experiment of Passive Rehabilitation Training Mode of the Controller  
Passive training is mainly used in the early rehabilitation stage for patients with motor dysfunction. The patient does not have motor ability and the robot dominates the whole rehabilitation training process. The robot drives the patient's arm to accurately track the rehabilitation trajectory. The subject's arm was fixed by a bandage to the handle, and the subject was required to be relaxed during the passive training testing: then, the subject's arm was guided by the robot to perform rehabilitation training on the circle trajectory. The testing tasks were performed, and the moving direction was counterclockwise with three turns. The Unity scene provides the subject with visual guidance and records the number of turns of movement. The parameters of the controller were \(R_{W}\) = 0 mm, \(K_{0}\) = [5000, 5000, 5000] N/m, \(K_{1}\) = [5000, 5000, 5000] N/m, \(K_{t}\) = [5000, 5000, 5000] N/m, and \(K_{2}\) = [5000, 5000, 5000] N/m.  
Figure 11 shows the trajectory of the subject's arm, in which the blue curve is the actual trajectory of the patient's hand and the black curve is the predetermined trajectory. Figure 12 shows the filtered interaction force and wrench between the robot and the subject's hand, and Figure 13 shows the filtered deviation between the subject's trajectory and the predetermined trajectory, as well as the total interactive force between the subject's hand and the handle. As can be seen from the experimental results, the robot guided the subject's hand to move along the predetermined trajectory accurately, and the root mean square (RMS) of the deviation between the actual trajectory of the subject's hand and the predetermined trajectory is 2.44 mm. The RMS of the interaction force between the subject's hand and the robot is 11.14 N, as shown in Figure 12a. In addition, the RMS
## 2 Materials and Methods
### Population Level Comparison  
The segmented objects are used in the project for defining the region contributing to a nucleus. Features are extracted within this region to describe each data point contributing to a population-based evaluation. The most important is the integrated density (ID) for modeling DNA content. A schematic representation of how it is measured is visible in Figure 7. The FCM DNA histogram results are also available as a reference for each examined sample. This is the basis of the measurement of accuracy on the population level.  
This measurement was conducted on a grayscale digital image, and in discrete format, as follows:  
\[ID=\sum_{i=j=1}^{i=m,j=n}((I_{m,n}\times J_{m,n})-\overline{BkgInt}) \tag{4}\]  
where \(ID\) is the integrated density, and \(I\) denotes the image intensities in the \(m,n\) neighborhood.  
If \(O_{i}\) is the currently analyzed object:  
\[O_{i}=\cup J_{m,n}I_{m,n} \tag{5}\]  
then \(J\) is an indicator function that has the value of 1 when it is a pixel corresponding to the measured object \(O_{i}\), and 0 otherwise.  
The neighborhood \(m\), \(n\) was defined to ensure at least \(D\) distance from \(O_{i}\). \(D\) was selected to be 35 pixels (the average object size in pixels at this magnification) so as to exclude other entire objects from the neighborhood; \(d\) was selected to be 5 pixels based on visual inspection (this is the distance where the bulk of the effect of object proximity is eliminated from intensity values).  
We have shown that ID models the FCM method's DNA content measurements well [9]. The nucleus populations identified in both datasets with the same process enables us to evaluate the difference that the segmentation algorithm change causes in the system. The mean values of the two populations of the DNA histogram of a healthy patient represent the 2n and 4n peaks. The peak ratio is the measured ratio of the DNA content at the 4n peak to the DNA content at the 2n peak. This theoretically equals 2.0 because, during mitosis, a normal body cell first replicates/doubles its DNA content before dividing into two new daughter cells. The location of these peaks showed great variance (even within the FCM result set), but the peak ratios show that the model parameters and the population classification are well defined.  
Greater resemblance of these ratios to the ones measured on the FCM data means better overall performance.  
For data manipulation and statistical evaluation, MATLAB (version R2019b) was used.
###### Abstract
The results showed that all metal-oxide based nanoparticles increased the cetane number, oxygen content, viscosity, and heating value of biodiesel. [PERSON] et al. [12] investigated the performance, combustion and emission characteristics of a single cylinder diesel engine fueled with diesel-juliflora biodiesel blends (B20, B30, B40, B100). They reported that the CO, HC and smoke for biodiesel and its blends are smaller or equal compared to diesel; however, the brake thermal efficiency (BTE) for B100 is 31.11% and it is closer to diesel (32.05%) at full load. In summary, the biodiesel is oxygenated fuel, which has an oxygen self-supported effect in the combustion process, therefore, adding biodiesel to diesel can help to improve the mixture, especially to reduce soot and PM emissions from diesel engines. At present, the preparation of biodiesel mainly included direct use and blending of vegetable oils, micro-emulsions, thermal cracking (pyrolysis) and transesterification. Among them, transesterification reaction is the most common method for the preparation of biodiesel [13]. Transesterification takes place between vegetable oils, animal fats, edible waste fats or oils and alcohols (methanol, ethanol, butanol) in the presence of homogeneous base catalysts (e.g., NaOH, KOH) [14]. However, many researchers have shown that when using acid or alkali as catalyst to produce biodiesel, a wastewater containing catalyst is mainly generated by the washing stage, resulting in secondary pollution to the environment. Moreover, the purification process of biodiesel has to go through a cumbersome water washing process and oil-water separation process, resulting in high production cost [15]. With the continuous updating of engine technology, the fuel injection system has been greatly improved in recent decades. The present diesel engines such as fuel direct injection pressures can be increased more than 200 MPa in a fuel pump injection system. Therefore, directly using the mixture of vegetable oil and diesel seems to be another innovation in the diesel engine. It will directly reduce the generation cost of biofuel and reduce the environmental pollution during in the biofuel generation process. Moreover, replacement of petroleum-derived fuels by biogenic fuels from renewable resources is of great significance to reduce dependence on petroleum oil, reduce greenhouse gas emissions and achieve the goal of global carbon neutrality. Filling a gap of the relevant literature, the present work presents the application characteristics of direct mixing of crude palm oil (CPO) and diesel in a four-cylinder, four-stroke, common rail direct injection (CRDI) diesel engine. The main input parameters are engine load and fuel blend ratio. The output parameters analyzed are in-cylinder pressure, peak in-cylinder pressure (Pmax), heat release rate (HRR), peak heat release rate (HRRmax), coefficient of variation of the indicated mean effective pressure (COVimep), brake specific fuel consumption (BSFC), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx) and smoke opacity.
## 3 Experiment and Application
### Engineering Application: Multi-UAVs Cooperative Path Planning
#### Modeling the UAV Cooperative Path Planning  
In path planning, an appropriate planning space must be established in accordance with the flight environment and mission requirements. In the present work, taking a mountain background as a task environment, a digital elevation model is established using a random function to simulate peaks and other threat obstacles. The mountain model function is proposed in Reference [72]. This model consists of the original digital and threat equivalent terrain models. The former is expressed as:  
\[z_{1}(x,y)=\sin(y+a)+b\sin x+c\cos(d\sqrt{x^{2}+y^{2}})+e\cos y+f\sin(f\sqrt{x ^{2}+y^{2}})+g\cos y \tag{13}\]  
where \(x\) and \(y\) refer to the point coordinates on a horizontal projection plane; \(z_{1}\) refers to the height coordinate that corresponds to the coordinate points on a horizontal plane; \(a\), \(b\), \(c\), \(d\), \(e\), \(f\), and \(g\) are the coefficients. The topography of a different landform can be obtained by changing parameters.  
The threat equivalent terrain model is:  
\[z_{2}(x,y)=\sum\
olimits_{i=1}^{k}h(i)exp(-\left(\frac{x-x_{0i}}{x_{si}} \right)^{2}-\left(\frac{y-y_{oi}}{y_{si}}\right)^{2}), \tag{14}\]  
where \(z_{2}\) refers to the height of the peak; \(h(i)\) refers to the height of the highest point of peak \(i\) on a base terrain; \(x_{0i}\) and \(y_{0i}\) refer to the coordinates of the highest point of peak \(i\); \(x_{si}\) and \(y_{si}\) refer to the variables related to the slope of peak \(i\) along the \(x,y\) axis. If \(x_{si}\) and \(y_{si}\) are large, then the slope of the peak is flat and abrupt.  
The final mountain threat model is obtained by integrating the original digital terrain model into the threat equivalent terrain model:  
\[z(x,y)=max[z_{1}(x,y),z_{2}(x,y)], \tag{15}\]  
We set the starting point of a certain UAV as \(\mathrm{S}(x_{0},y_{0},z_{0})\) and the target point as \(\mathrm{E}(x_{e},y_{e},z_{e})\). The number of waypoints is \(n\), and the waypoints searched can be represented by \(\{S,P_{1},P_{2},\ldots,P_{n},E\}\); among these variables, the coordinate of a track node is \(\mathrm{P}=\left(x_{p},y_{p},z_{p}\right)\).
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 4 The Fundamentals of Clustering  
The three modes of SNs are sensing, computing, and communication, and these are energy-intensive processes. Technically, the level of energy required by the processor to transfer one bit of data is equivalent to the energy needed to compute several arithmetic operations. Furthermore, the physical environment of a heavily deployed SN network can generate a similar data rate in almost all the SNs, and the transmission of such data is redundant. Hence, it is crucial to merge all the factors that encourage the clustering of SNs in an intelligent manner that allows for the transmission of compact data only (this is referred to as clustering).  
In WSNs, clustering effectively manages network problems related to service life and energy use [18; 19]. Clustering ensures energy conservation by adopting low-cost communication techniques [20] when it divides the network into different groups of nodes, called clusters. Each cluster has a CH that oversees the activities of the other nodes in the cluster [21]. The CHs can also reach the BS by creating a group and communicating with the BS in a multi-hop pattern. The CH first gathers the data collected by all the nodes before forwarding them to the BS [22]. The schematic of a clustered network in a WSN is shown in Figure 2. The designation of CHs in the clusters removes the problem of redundancy and reduces network energy consumption [23]. Different clustering techniques depend on various procedures and methods when executing clustering activities [24,25].
# A Star-Identification Algorithm Based on Global Multi-Triangle Voting
## 2 Algorithm Description
### Triangle Unit and Feature Extraction  
The GMTV algorithm utilizes triangle units built by several bright stars in FOV as the basic match element and then extracts their feature values using the PCA method, which is the foundation of searching for the matching unit and implements a voting scheme. Hence, the triangle unit building and feature values calculation are briefly described as follows.  
As shown in Figure 1, any three stars sharing the same FOV can be selected to build a triangle unit, such as unit k, which consists of Star-a, Star-b, and Star-c. By calculating the angular star distances between Star-a, Star-b, and Star-c, the triangle edge vector F of unit K can be obtained as Equation (1)  
\[F=\left[ad_{ab}\quad ad_{ac}\quad ad_{bc}\right] \tag{1}\]  
where \(ad_{ab}\), \(ad_{ac}\), and \(ad_{bc}\) are star angular distances between Star-a and Star-b, Star-a and Star-c, Star-b and Star-c, and \(ad_{ab}<ad_{ac}<ad_{bc}\). The PCA method is then utilized to calculate feature values of triangle edge vector F to reduce feature dimension while retaining the main distinguishing features, as described in Equation (2).  
Feature values extracted by PCA are invariant to rotation and robust to noise. In this paper, we select \(pv1\) and \(pv2\) with larger values to keep unit geometric feature information. By utilizing the PCA method, a 1\(\times\)3 triangle edge vector F is transformed into two feature values \(p\)\(pv1\) and \(pv2\). After obtaining \(pv1\) and \(pv2\), we then discretize them in  
Figure 1: Triangle unit built.  
Equation (3), and the discretized feature values \(p\psi_{1}\) and \(p\psi_{2}\) are employed as the index to obtain candidates stored in the lookup table in the initial match step of GMTV.  
\[\begin{cases}\overline{pv1}=fix(pv1/u1)\\ \overline{pv2}=fix(pv2/u2)\end{cases} \tag{3}\]  
where fix(\(x\)) returns the nearest integer value, which is no more than \(x\), and \(u1\) and \(u2\) are discretized units of \(pv1\) and \(pv2\), respectively.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology  
The proposed full-field mode shapes identification method based on the subpixel edge-detection and tracking techniques involves four main steps, as shown in Figure 1.  
Step 1: Apply the Canny operator [29] on each frame of the structure vibration video to extract the pixel-level edges that are expressed in integer-type coordinates.  
Step 2: Apply the improved ZOM-based subpixel edge-detection technique to relocate the subpixel-level edges that are expressed in real number-type coordinates.  
Step 3: Apply the edge tracking technique on the dense subpixel edges to obtain the preliminary full-field displacement time history and subsequently calibrate the displacement using the proposed adaptive scale conversion method.  
Step 4: Apply the SSI-COV and hierarchical cluster analysis on the full-field displacement time history to automatically identify the high-resolution mode shapes of structures. Each step is detailed in the following subsections.  
Figure 1: Flowchart of full-field mode shapes identification of structures.