esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 1 Introduction
In this study, we create stable feature databases using a variety of local invariant features and perform feature database matching experiments using a data set in the western Beijing region. Figure 1 shows the main flow of the proposed method. The main contributions of this paper are as follows:  
1. [leftmargin=*]
2. Imitating the control point database to create the feature database. Compared with a reference image or control point database, the feature database uses less storage space and takes a shorter time to match with remote sensing images. Figure 1: Main flow of the construction of the feature database. 2. A training feedback feature database iterative matching strategy is proposed. Unlike analyzing the robustness of features by methods such as information entropy or feature metric, this method analyzes the stability of features in different temporal training images longitudinally, constructs stable feature point sets, and improves the correct matching rate of optical image feature points and image matching success rate under multi-observation conditions. The remainder of this paper is organized as follows. Section 2 first briefly reviews the classical feature algorithms required to build feature databases and then describes the proposed feature database iterative matching strategy. In Section 3, the experimental results of the feature database matching approach are shown and the superiority of the feature database approach is demonstrated compared to direct matching using classical algorithms. In Section 4, our proposed feature database matching approach and suggestions for future work are discussed. Conclusions are drawn in Section 5.
# The Development of the Pipe Jacking Guidance Technology
## 3 Laser Target Method
### Measuring Principle
#### 3.2.3 Absolute Coordinate Calculation  
Absolute coordinate calculation means to convert the coordinates of the pipe jacking machine's center of the head and the pipe jacking machine's center of the tail in the laser target coordinate system to the absolute coordinates in the absolute coordinate system. This provides absolute coordinate information for subsequent calculations of horizontal and vertical deviation.  
The total station is utilized to illuminate the prism at the lower end of the laser target, thereby enabling indirect measurement of the absolute coordinates of the laser target within the absolute coordinate system. These coordinates are recorded as L(X\({}_{0}\),Y\({}_{0}\),Z\({}_{0}\)). The pipe jacking machine and the laser target are relatively fixed in space, therefore, the positional parameters of the laser target at the pipe jacking machine are constant. As shown in Figure 6, according to the spatial position relationship, the coordinate information under the coordinate system of the pipe jacking machine can be obtained: the head center of the pipe jacking machine coordinates H(0,0,0), the tail center of the pipe jacking machine coordinates T(\(-\)A\(-\)B,0,0) and the laser target coordinates L(\(-\)B,D,C); coordinate information in the laser target coordinate system: the head center of the pipe jacking machine coordinates H(B,\(-\)D,\(-\)C), the tail center of the pipe jacking machine coordinates T(\(-\)A,0,\(-\)C) and the laser target coordinates L(0,0,0).  
The absolute coordinates (X\({}_{1}\),Y\({}_{1}\),Z\({}_{1}\)) of the head center H of the pipe jacking machine are its coordinates (B,\(-\)D,\(-\)C) under the laser target coordinate system rotating \(\beta\) around the X axis, \(\lambda\) around the Y axis and \(\alpha\) around the Z axis, and the translations of the three axes  
Figure 6: Spatial position diagram of the laser target.  
are (X\({}_{0}\),Y\({}_{0}\),Z\({}_{0}\)), which can be calculated by the rotating matrix coordinate transformation. Equation (5).  
In Equation (8), \(\rm{L_{A}}\) is defined as the mileage at point A, and \(Z_{i}\) is the elevation coordinate value of point i (i = A, B, H).  
The horizontal angular deviation, designated as \(\rm{\Delta H}\), and the vertical angular deviation, denoted as \(\rm{\Delta P}\), at the center of the pipe jacking machine head are resolved through the application of Equation (9).  
\[\left\{\begin{array}{l}\Delta H=\alpha-\arctan[(Y_{B}-Y_{A})/(X_{B}-X_{A})] \\ \Delta P=\lambda-\arctan[(Z_{B}-Z_{A})/AB]\end{array}\right. \tag{9}\]  
In Equation (9), \(\rm{X_{i}}\) and \(\rm{Y_{i}}\) represent the coordinate values on the X and Y axes at point i (i = A, B).  
The horizontal position deviation and angular deviation of the center point of the pipe jacking machine tail are solved by Equations (8) and (9).
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-MEC Testbeds
#### 3.1.2 Mosaic5G  
The Mosaic5G platform [34] provides flexible and scalable service deployment. The platform includes five software elements such as Open-Air Interface (OAI) [35] for the RAN and CN functionalities of the LTE network. FlexRAN [36] is an open source implementation of Software-Defined RAN (SD-RAN) and is deployed to control tasks within different base stations in a centralized manner or perform scattered control policies. Reconfiguring the control functionalities with a RAN helps to manage those tasks. Low-Latency MEC (LL-MEC) is the MEC platform that separates the data plane and controls the plane traffic at the network's edge. The MEC functionalities and services are enabled using the LL-MEC platform. Mosaic5G deploys the NFV MANO platform using JOX to provide E2E network slices. Store includes modules for monitoring and controlling applications.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 2 Related Work
### GENetic DIscovery of Shapelets
The former can be handled using the Learned Shapelets variation described in Reference [23] whereas scale variations can be handled using Dynamic Time Warping [24] approaches. The genetic approach of GENDIS seems to cover them both, by generating/learning shapelets and shapelet combinations as features, which are then fed to a classification algorithm. However, an in depth experimental study that compares GENDIS against shapelets algorithm or other time-series classifiers is needed in order to validate this.
## 3 Background  
### Modified Density Peak Clustering Algorithm  
The purpose of clustering is to organize data into groups based on attribute similarity so that data from the same cluster have similar properties and data from different clusters are different from each other. The density peak clustering algorithm (DPCA) was originally proposed by [PERSON] et al. in 2014 [24]. It is a fast clustering technique based on density. The core of DPCA is to find cluster centers, which must satisfy two important basic assumptions. One is that the cluster center is surrounded by lower density neighbors. The other is that the cluster center is away from any other cluster center point with a higher local density. DPCA uses two measures to calculate cluster centers: the local density and the distance. Both measures need to calculate the distance between two data points. The original DPCA adopts the default Euclidean distance to compute the distance between two data points. However, when the dataset is complex and linearly inseparable, the Euclidean distance can cause severe misclassifications [39]. We improve the DPCA and name it the modified density peak clustering algorithm (MDPCA). A Gaussian kernel function is introduced to measure the distance by implicitly mapping the raw data into a high-dimensional feature space. Furthermore, to improve the performance of the proposed method, we introduce the Gaussian kernel instead of the cut-off kernel to calculate the local density of the data points. The MDPCA can detect non-spherical clustering which can not be detected by traditional distance clustering methods such as K-Means and K-Medoids clustering.
## 5 Conclusions  
In this study, it can be concluded that reflectance spectra of U and I seedlings showed significant differences in the NIR spectrum; therefore, all significant bands were identified from the NIR wavebands. The significant bands were optimized based on the average of all accuracy obtained by the models. For each optimal band (35, 18, 14, 11 and 9 bands), 23 classification models were constructed, e.g., decision trees, discriminant analysis, logistic regression, naive Bayes, SVM, nearest neighbor, and ensemble modelling. In general, SVM and kNN-based models have been identified as effective classifier algorithms for differentiating between U and I seedlings. However, the coarse Gaussian SVM with 9 bands was determined to be the best model due to the higher F-score and the shorter performance time, demonstrating its potential to attain excellent accuracy even when a small number of bands was used. The main benefit of this study is to assess the capability of machine learning models to detect _G. boninense_ infection at an early stage (asymptomatic). In future studies, the monitoring period of oil palm seedlings could be shortened to less than 10 months to assess the efficacy of hyperspectral imaging to detect _G. boninense_ at the earliest stage of infection.  
Conceptualization, A.N.N.A. and S.K.-B.; methodology, A.N.N.A. and S.K.-B.; software, S.K.-B. and A.N.N.A.; validation, A.N.N.A. and S.K.-B.; formal analysis, A.N.N.A. and S.K.-B.; investigation, A.N.N.A. and S.K.-B.; resources, S.K.-B. and M.J.; writing--original draft preparation, A.N.N.A.; writing--review and editing, S.K.-B.; supervision, M.J., F.M.M. and I.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Malaysia Ministry of Higher Education (MOHE) and Universiti Putra Malaysia (UPM) under Fundamental Research Grants Scheme (FRGS) (Project number: FRGS/1/2018/TK04/UPM/02/4) and PRGS/2/2020/ICT09/UPM/02/1.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions.  
The authors declare no conflict of interest.
# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
## 1 Introduction
Unlike research in unscented Kalman filter (UKF) filtering theory, [PERSON] et al. [38] proposed central difference filtering (CDF) based on the Stirling interpolation formula. [PERSON] et al. [39] first proposed divided difference filtering (DDF). [PERSON] et al. [40] unified these two essentially identical filters and collectively referred to it as the central differential Kalman filter (CDKF). The CDKF algorithm considers the statistical characteristics of Gaussian random variables and uses the prior distribution to construct a set of deterministic Sigma points. The Sigma points obtained after the linear regression transformation was used to represent the posterior distribution of the system state. The CDKF uses a simple interpolation formula to obtain the approximation form of the function. It does not require the calculation of the derivative of the function or the Jacobian matrix of the non-linear function, which can significantly reduce the number of calculations required. The UKF replaces the local linearization in the EKF with an unscented transform. UKF also does not need to calculate the Jacobian matrix, and in theory, the unscented transformation can at least approximate the posterior mean and covariance of any non-linear Gaussian system state with third-order Taylor accuracy. Unlike previous research, this study involves an effective combination of unscented transformation and Stirling interpolation center difference algorithm for online SoC estimation of lithium-ion batteries. The new algorithm constructs different Sigma points sets through proportional correction sampling and symmetric sampling and performs battery SoC estimation twice. The new algorithm overcomes the shortcomings of the original CDKF algorithm, such as weak initial error correction ability and large end error when the initial SoC value is inaccurate, ensuring efficient calculation. The proposed algorithm was compared with EKF, CDKF and UKF. Through different initial SoC guess values and different dynamic conditions, the initial error correction capability and error range of each algorithm was analyzed. Five error analysis indicators and initial error correction time were used to verify the error range and effectiveness of the new algorithm. Specifically, the SoC-OCV curve was accurately obtained by the interval static method in Section 2.1, and the parameter identification of the equivalent circuit model was performed using the DST operating conditions in Section 2.2. Section 3 describes the design process of the proposed algorithm. In Section 4, a semi-physical simulation platform based on dSPACE Rapid Prototyping Systems is constructed, and the proposed algorithm is verified under two working conditions, i.e., US06 Highway Driving Schedule (US06) and Federal Urban Driving Schedule (FUDS).
## 4 Results and Discussion
### Data Collection and ANN Results
#### 4.2.1 Best Validation  
The structures of the single and double hidden layers in the ANN model are presented as (4-10-1) and (4-10-10-1), respectively. The components of the structure indicate the number of input parameters (4), hidden layers (1 or 2), and output parameter (1). It can be seen that 10 neurons were recorded for the single and double hidden layers in the two architectures. Thus, final ANN structures can be expressed as (4-10-1) and (4-10-10-1) for single and double hidden layers, respectively (this is based on the number of input parameters, hidden layers, and output parameters). Meanwhile, as can be seen in Figure 8, the best validation was achieved at MSE values of 0.038333 at epoch 8 in the double hidden layer and 0.012346 at epoch 20 in the single layer. This suggests that the data were properly trained.
# Practical Application of Mesh Opportunistic Networks
## 2 Theoretical Background
### The Coverage and Scope of the Mesh OppNets  
As mentioned above, the coverage of these networks depends on the nodes that exist. Each device not only serves as a sender and receiver of its own communication but is also a node for the transmission of external communications. Considering that the range of these devices is usually a radius of around 4000 m, knowing how many users are around will give us information about the potential coverage we can obtain from our network. Thus, mesh OppNets represent a mode of communication with a scope that is dictated by the number and location of the other users (or nodes). For example, when the distribution of perhaps the most widely implanted device is studied, the goTenna Mesh(tm) device (see Figure 2), a disparity in implantation can be observed over the geographic area of interest with consequent differences in potential coverage. In areas such as California, Florida or New York, the coverage of this device is extensive due to its high density, whereas in areas such as northern and southern Europe, the absence of nodes (or repeating goTenna devices) makes widespread territorial coverage impossible. In the coming years, we are likely to witness an emergence of these small devices, as they are currently being developed bynumerous start-ups. At present, there are a few similar commercial devices registered, such as Garmin inReach, SPOT X Satellite Messenger, Beartooth (the most similar to goTenna Mesh), Fogo or Sonnet, but they still do not have the diffusion of goTenna Mesh. Sonnet is a little delayed in its development and was not commercialized in 2023, despite being announced for 2019. All these mesh OppNet devices are priced between $45 and $300, depending on their GPS characteristics, whether they have a screen or use of the mobile phone's screen, and their autonomy or coverage [13]. It should be remembered that at least two devices are required to establish a mesh network and indeed, they are commonly sold in pairs. In these cases, the mesh network is meaningless if there are no devices to create it.  
Figure 1: Typical OppNet communication protocol with common devices in a mesh network: (**A**) One-to-one and direct communication; and (**B**) one-to-one communication using a third-part node to increase the distance covered by the system. Note that the intermediate node is unaware that it is being used for the communication process.
# Lester: Rotoscope Animation through Video Object Segmentation and Tracking
## 1 Introduction  
In industries such as cinema or video games, rotoscoping is employed to leverage the information from live-action footage for the creation of 2D animations. This technique contributed to the emergence of a unique visual aesthetic in certain iconic video games from the 80s and 90s, such as \"Prince of Persia\" (1989), \"Another World\" (1991) or \"Flashback\" (1992), and it continues to be employed in modern titles like \"Lunark\" (2023). However, as in the case of Lunar, this technique still requires manual work at the frame level, making it very expensive.  
Recent advances in Artificial Intelligence (AI) have opened the possibility of automating this process to some extent. The latest progress in conditional generative models for video-to-video translation offers a promising approach to addressing this issue. However, these methods are not yet consistently able to deliver a polished, professional outcome with uniform style and spatial and temporal consistency. Their performance varies significantly depending on different aspects of the input video, such as scene and resolution. Additionally, they are slow and require enormous computational resources. Therefore, the need for an efficient and deterministic automated solution remains an open problem.  
This article describes a novel method, called Lester, to automate this process. A user enters a video and a color palette and the system automatically generates a 2D retro-style animation in a format directly usable in a video game or in an animated movie (see Figure 1 for an example).  
The method approaches the challenge mainly as an object segmentation problem. Given a frame depicting a person, a mask for each different visual trait (hair, skin, clothes, etc.) is generated with the Segment Anything Model (SAM) [1]. To univocally identify the masksin all the frames, a video object tracking algorithm is employed using the approach of [2]. The masks are purged to solve segmentation errors and their geometry is simplified with the Douglas-Peucker algorithm [3] to achieve the desired visual style. Finally, facial traits, pixelation and a basic rim light effect can be optionally added.
### Previous Research on the Blending of Material  
[PERSON] et al. [12] observed the flow behavior of cement pastes by adding aminosulfonic acid-based SPs (AS) within cement systems containing two retarders including sodium gluconate (SG) and citric acid (CN). They reported a reduction in adsorption of AS in the presence of these two retarders, mostly due to the unavailability of reaction sites within the cement systems. [PERSON] and [PERSON] [13] observed the effect of three PCE polymers on the fluidity of a cement paste, using a mini-slump test. They simultaneously added three PCE polymers to the same paste and reported increases in slump loss of cement pastes containing multiple SPs compared to those with only one PCE polymer. However, a higher SP adsorption was achieved in these mixtures, suggesting that the non-adsorbed polymers increased the fluidity of the system, thus causing slump loss, and the steric hindrance between polymers prevented further adsorption.  
[PERSON] et al. [14] investigated the effect of non-adsorbing SPs on the fluidity of cement pastes containing silica fume by comparing the degree of SP adsorptions and subsequent impact on their rheological behavior. They used eight PCE SPs with an average molecular weight of approximately 10,000 g/mol, differentiated only by the lengths of their side chains. The adsorption was determined by the total organic carbon content utilizing the depletion method, and the rheological results were determined using a rotational cylinder viscometer with cup and bob. It was concluded that SPs containing higher COO\({}^{-}\) units exhibited a higher degree of adsorption while the fluidity of the corresponding paste was more dependent on the length of the side chains of the non-adsorbed SP. Superplasticizers with longer side chains tended to increase the fluidity of the cement pastes following the larger hindrance effect within the medium.  
It is, therefore, understood that the dispersing ability of a PCE-based SP is highly dependent on the adsorption ability and effectiveness thereof, which determines the fluidity of a fresh concrete mixture. The difference in the adsorption ability is also dependent on the chemical composition of the cement particles because of the affinity the SP has towards the aluminate phases, which leaves room for further investigation [9].  
In practice, HRWRAs are produced by blending two agents based on the effect it has on the cement workability [13]. However, the effect of competitive adsorption between multiple SP polymers with different molecular structures when simultaneously added to the same mixture has not been fully explored [15]. This work investigated the effectiveness of a blended PCE polymer on the rheological behavior of different cement pastes with distinct chemical compositions.
## 4 Main Features in Wireless Sensor Networks Design  
The main features of WSNs, as could be deduced by the general description given in the previous sections, are: scalability with respect to the number of nodes in the network, self-organization, self-healing, energy efficiency, a sufficient degree of connectivity among nodes, low-complexity, low cost and size of nodes. Those protocol architectures and technical solutions providing such features can be considered as a potential framework for the creation of these networks, but, unfortunately, the definition of such a protocol architecture and technical solution is not simple, and the research still needs to work on it [5].  
The massive research on WSNs started after the year 2000. However, it took advantage of the outcome of the research on wireless networks performed since the second half of the previous century. In particular, the study of ad hoc networks attracted a lot of attention for several decades, and some researchers tried to report their skills acquired in the field of ad hoc networks, to the study of WSNs.  
According to some general definitions, wireless ad hoc networks are formed dynamically by an autonomous system of nodes connected via wireless links without using an existing network infrastructure or centralized administration. Nodes are connected through \"ad hoc\" topologies, set up and cleared according to user needs and temporary conditions [11]. Apparently, this definition can include WSNs. However, this is not true. This is the list of main features for wireless ad hoc networks: unplanned and highly dynamical; nodes are \"smart\" terminals (laptops, etc.); typical applications include realtime or non-realtime data, multimedia, voice; every node can be either source or destination of information; every node can be a router toward other nodes; energy is not the most relevant matter; capacity is the most relevant matter [11].  
Apart from the very first item, which is common to WSNs, in all other cases there is a clear distinction between WSNs and wireless ad hoc networks. In WSNs, nodes are simple and low-complexity devices; the typical applications require few bytes sent periodically or upon request or according to some external event; every node can be either source or destination of information, not both; some nodes do not play the role of routers; energy efficiency is a very relevant matter, while capacity is not for most applications. Therefore, WSNs are not a special case of wireless ad hoc networks. Thus, a lot of care must be used when considering protocols and algorithms which are good for ad hoc networks, and using them in the context of WSNs.