esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 2 Underwater Integrated Navigation System
### System Model
The measurement models of these two filters can be constructed respectively as  
\[Z_{k}^{(2)}=(H_{SINS}-H_{MCP})=H_{k}^{(2)}X_{k}^{(2)}+V_{k}^{(2)} \tag{8}\]  
\[Z_{k}^{(3)}=(h_{SINS}-h_{PS})=H_{k}^{(3)}X_{k}^{(3)}+V_{k}^{(3)} \tag{9}\]  
where \(H_{SINS}\) and \(H_{MCP}\) denote the heading provided by the SINS and the MCP, respectively. \(h_{SINS}\) and \(h_{PS}\) denote the height offered by the SINS and the PS, respectively. The measurement matrixes can be described as  
\[H_{k}^{(2)}=\left( \begin{array}{ccc}0_{1\times 2}&1&0_{1\times 12} \end{array} \right) \tag{10}\]  
\[H_{k}^{(3)}=\left( \begin{array}{ccc}0_{1\times 8}&1&0_{1\times 6} \end{array} \right) \tag{11}\]The measurement noise variances of the MCP and the PS are defined respectively by  
\[R_{k}^{(2)}=\sigma_{H}^{2} \tag{12}\]  
\[R_{k}^{(3)}=\sigma_{h}^{2} \tag{13}\]  
where \(\sigma_{H}^{2}\) and \(\sigma_{h}^{2}\) are the variance of the MCP measurement and the PS measurement, respectively. The master filter is in charge of fusing the local state estimates. If all subsystems are independent, the fusion algorithm can be presented as follows. \[P_{k}^{S}=\left[\left(P_{k}^{(1)}\right)^{-1}+\left(P_{k}^{(2)}\right)^{-1}+ \left(P_{k}^{(3)}\right)^{-1}\right]^{-1} \tag{14}\]  
\[\hat{X}_{k}^{S}=P_{k}^{S}\cdot\left[\left(P_{k}^{(1)}\right)^{-1}\cdot\hat{X}_ {k}^{(1)}+\left(P_{k}^{(2)}\right)^{-1}\cdot\hat{X}_{k}^{(2)}+\left(P_{k}^{(3) }\right)^{-1}\cdot\hat{X}_{k}^{(3)}\right] \tag{15}\]  
where \(\hat{X}_{k}^{S}\) denotes the global state estimation. \(\hat{X}_{k}^{(1)}\), \(\hat{X}_{k}^{(2)}\) and \(\hat{X}_{k}^{(3)}\) are the estimates of the error states obtained from the local filters. \(P_{k}^{(1)}\), \(P_{k}^{(2)}\) and \(P_{k}^{(3)}\) are the covariance matrixes of \(\hat{X}_{k}^{(1)}\), \(\hat{X}_{k}^{(2)}\) and \(\hat{X}_{k}^{(3)}\), respectively. \(P_{k}^{S}\) is the covariance matrix of \(\hat{X}_{k}^{S}\). The DVL, MCP and PS are run independent, which satisfies the requirement of the formulas.
###### Abstract  
Due to the low signal power, the Global Navigation Satellite System (GNSS) signal is vulnerable to interference and even cannot be captured or tracked in harsh environments. As an alternative, the Low Earth Orbit (LEO) satellite has been widely used in the navigation field due to the advantages of low cost and strong signals. It is becoming a significant component of the new combined navigation system with GNSS. The combination of an LEO Doppler signal and GNSS observables can improve the positioning accuracy and high-precision positioning convergence time of the GNSS receiver. However, the GNSS signal receiving capability cannot be improved from this data fusion level. We propose a novel assisted structure where GNSS signal acquisition and Doppler tracking are assisted by LEO Doppler positioning. The receiver uses the LEO signal to achieve Doppler positioning firstly. Then, the coarse position with the GNSS navigation messages received from LEO, as well as the estimated clock information, is used to assist in the acquisition and tracking of GNSS. In this way, the GNSS receiver's sensitivity can get the benefit from this integrated system. The paper presents the structure of the assisted receiver and analyzes the assisted GNSS signal acquisition and carrier tracking performance in detail. Simulation experiments of this assisted structure are carried out to verify its superiority of acquisition and tracking sensitivity in comparison with standalone GNSS receivers. Theoretical analysis and experimental results show that the proposed acquisition method can achieve 90% detection probability at a carrier-to-noise ratio (C/N\({}_{0}\)) of 15 dB-Hz, which is about 8 dB higher than the conventional acquisition method without assistance; the proposed tracking method can track weak signals of 5 dB-Hz, which is about 4 dB higher than the conventional method. Therefore, this novel LEO-assisted receiver has significantly improved weak signal acquisition and tracking sensitivity.  
acquisition; Doppler shift; GNSS receiver; LEO satellite; weak signal tracking +
Footnote †: journal: Sensors
## 3 Wireless Body Area Network (WBAN)
### Temperature sensors  
A temperature sensor is a common medical sensor used to measure body temperature accurately and safely. It is designed specifically for medical applications to monitor patients' temperature in hospitals, clinics, and other healthcare settings. These sensors utilize various technologies such as infrared, thermocouples, or thermistors to detect and convert body heat into an electrical signal that healthcare professionals can measure and interpret [49].  
Medical temperature sensors come in different forms, including oral, rectal, ear, and forehead, and wearable patches or bands. Each type has its own advantages and limitations, and its usage depends on the patient's age, condition, and the healthcare provider's preference.  
These sensors are crucial in detecting fever, a common sign of many illnesses. Monitoring body temperature, accurately, helps healthcare professionals diagnose and monitor the progress of diseases or infections. It enables timely intervention and appropriate treatment, ensuring the well-being of patients [50].
## 4 The Contained Material Properties, Behavior, and the Imposed Loads  
After talking about the silos and their interactions with the external environment and the internal materials, the effective behavior of the material inside the silo is considered. As just mentioned in the Introduction, the main difference between silos and tanks is the contained material, which makes the difference in the loading conditions, grain-wall interaction condition, and the response to different excitations. While tanks are used to store liquids that exert only normal and symmetric pressure on the wall in the circumferential direction, silos are used for solid bulk materials exerting a normal pressure and interaction traction (symmetric or asymmetric) on the wall [1]. Different problems make the silos usage and design more problematic than tanks, which can be identified in the stored material anisotropy, material behavior asymmetry, and filling/flowing eccentricity. The stored material covers a large scope of free-flowing granular bulk with particle size ranging from micron size powders to lumps of 150 mm or larger. However, the contained material could be classified in multiple ways according to the relevant properties, as for example, largely treated in [98]. Thus, each silo is designed for a limited range of solids, where using the silo to store bulks out of the anticipated range could imply damages. On the level of practical design, the different international standards advise to determine the relevant material properties using either the provided tables or the experimental tests, as provided by the Australian standards (AS 3774-1996) [99]. Differently, the European (EN 1991-4) and the American standards (ACI 313-16) opt for the determination by test results [71; 100]. Still, the commentary part of ACI 313-16 [100] using tables as guide for the only initial estimation.  
Going into the available scientific literature, an experimental program was conducted by [PERSON] [101], aiming to examine the influence of particle properties and bedding structure (the filling method) on transmission of stresses in the layer of seeds (on the vertical/horizontal load exerted by the grain on the walls) considering rough and smooth wall surface. This experimental program indicated that the pressure ratio (identified as the ratio between lateral and vertical pressures) is strongly affected by the filling method and material type. Specifically, the results of this study reported that the circumferential filling provides advantages compared to the central filling.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 4 Dynamic Models
### Nearly Constant Velocity (NCV) Model  
The dynamics of the NCV movement are captured through a linear model, which is described by [5; 8; 20]:  
\[\mathbf{x}_{k}=\mathbf{F}_{k-1}^{(1)}\mathbf{x}_{k-1}+\mathbf{w}_{k-1}^{(1)}, \tag{26}\]  
where \(\mathbf{F}_{k-1}^{(1)}\) is the state transition matrix and \(\mathbf{w}_{k-1}^{(1)}\) represents the process noise during the interval from \(t_{k-1}\) to \(t_{k}\). Assuming \(\mathbf{w}_{k-1}^{(1)}\) to be zero-mean Gaussian white noise with the covariance matrix \(\mathbf{Q}_{k-1}^{(1)}\), the corresponding definitions for \(\mathbf{F}_{k-1}^{(1)}\) and \(\mathbf{w}_{k-1}^{(1)}\) are as follows [5; 8; 20]:  
\[\mathbf{F}_{k-1}^{(1)}=\begin{bmatrix}1&0&T&0\\ 0&1&0&T\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}, \tag{27}\]  
\[E\Big{\{}\mathbf{w}_{k-1}^{(1)}\Big{\}}=\mathbf{0}_{4\times 1} \tag{28}\]  
\[E\Big{\{}\mathbf{w}_{i-1}^{(1)}\mathbf{w}_{k-1}^{(1)}\Big{\}}=\delta_{jk} \mathbf{Q}_{i-1}^{(1)} \tag{29}\]  
\[\mathbf{Q}_{k-1}^{(1)}=q_{1}\begin{bmatrix}T^{3}/3&0&T^{2}/2&0\\ 0&T^{3}/3&0&T^{2}/2\\ T^{2}/2&0&T&0\\ 0&T^{2}/2&0&T\end{bmatrix}, \tag{30}\]  
where \(\delta\) is the Kronecker delta, \(T\) is the constant measurement time interval, and \(q_{1}\) denotes the power spectral density (PSD) of the acceleration noise process in continuous time for model 1, which is applicable to both the X and Y components [8; 20; 51].
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 6 Experiments on Rosario Data Set  
The first stage of the work focused on understanding the basic problems related to determining the position of objects. For this purpose, several experiments were conducted on publicly available data (Rosario dataset). The conclusions and experience gained allowed us to prepare both a simulation environment and to construct a predictive model for determining the robot's position based on data from the IMU module, also taking into account possible slippages while driving.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 5 Conclusions  
The development of fast computational strategies has transformed FSMPC for multi-phase drives, achieving significant reductions in sampling periods without requiring advanced hardware. Key innovations, such as reducing the ACS and simplifying cost functions, have streamlined complex computations while maintaining high control performance. Techniques like VVVs and lookup tables preselect optimal voltage candidates, minimizing the need for exhaustive searches.  
This paper shows that the main battlefields are those of multi-vector and single-vector, each offering unique strengths and trade-offs. Multi-vector strategies enhance precision by combining vectors to improve control accuracy, making them suitable for high-performance systems. Conversely, single-vector methods prioritize simplicity and computational efficiency, making them ideal for applications with limited resources or real-time constraints.  
Furthermore, paradigms such as region-based methods and DB control have further enhanced FSMPC's efficiency. Region-based techniques use Voronoi diagrams to bypass exhaustive searches, while DB control predicts system states to minimize errors within a single sampling period. These methods, alongside hybrid strategies, ensure FSMPC's adaptability to increasingly complex and demanding applications, such as electric vehicles and renewable energy.  
These advancements promise not only to refine predictive control methodologies but also to extend their application to more complex and demanding multi-phase drive systems. This evolution underscores the ongoing innovation and dynamic problem-solving required to meet the challenges of modern power electronics.  
[PERSON] provided conceptualization and wrote the first draft; [PERSON] reviewed, wrote the first draft, and improved the structure of the paper; [PERSON] and [PERSON] contributed to reviewing and editing the paper. All authors have read and agreed to the published version of the manuscript.  
This research is part of Proyecto I+D+i/PID2021-125189 OB-I00 funded by MICIU/AEI/10.13039/501100011033 and FEDER, UE.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Strategies**} & **Load (us)** & **Reference** \\ \hline \multirow{2}{*}{Single vector} & Regions & 9 & [50] \\ \cline{2-4}  & Others & 24.16 & [29] \\ \hline \multirow{4}{*}{Multi-vector} & 2 VVVs & 45.8 & [26] \\ \cline{2-4}  & 3 VVVs & 65 & [70] \\ \cline{2-4}  & 4 VVVs & 27.6 & [47] \\ \cline{2-4}  & Dead-beat & 50.36 & [83] \\ \cline{2-4}  & VVV+DB & 40.23 & [84] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reported computation times for some FSMPC techniques.  
Data Availability Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization
## 2 Data and Methodology
### Methods
#### 2.2.3 Hierarchical Clustering  
Hierarchical clustering is a widely used unsupervised learning method that organizes data into a nested sequence of clusters, forming a tree-like structure known as a dendrogram. This method is particularly useful for identifying relationships in data across multiple levels of granularity without requiring the number of clusters to be specified in advance [35; 36]. Hierarchical clustering methods are divided into two main categories: agglomerative (bottom-up) and divisive (top-down). Agglomerative clustering begins with each data point as its own cluster and iteratively merges the most similar clusters until a single cluster remains. In contrast, divisive clustering starts with all data points in one cluster and recursively splits clusters into smaller sub-clusters [36; 37].  
Hierarchical clustering relies on similarity or dissimilarity measures to guide cluster formation. A common dissimilarity measure is the Euclidean distance, defined as  
\[d(x,y)=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}} \tag{7}\]  
where \(x\) and \(y\) are two data points in \(n\)-dimensional space. Alternatively, Manhattan distance, cosine similarity, or other metrics may be used, depending on the data and application [35; 36].  
For agglomerative clustering, the process starts by computing a distance matrix that quantifies pairwise dissimilarities between all data points. At each iteration, the two clusters with the smallest dissimilarity are merged. The dissimilarity between clusters is determined using linkage criteria, such as the following: Single linkage (minimum distance), Complete linkage (maximum distance), and Average linkage (mean distance).  
For divisive clustering, the process begins with all data points in one cluster. Clusters are then recursively split based on criteria such as maximizing between-cluster dissimilarity or using techniques like bisecting k-means. For example, bisecting k-means splits a cluster into two sub-clusters by minimizing the within-cluster sum of squared errors [36].  
\[SSE=\sum_{i=1}^{n}\big{|}|x_{i}-\mu_{k}|\big{|}^{2}, \tag{8}\]  
where \(x_{i}\) is a data point, \(\mu_{k}\) is the centroid of cluster \(k\), and \(n\) is the number of data points in the cluster [38; 39]. Figure 2 shows flowcharts detailing the workflow for agglomerative and divisive hierarchical clustering.  
Hierarchical clustering can also be viewed as optimizing an objective function to minimize within-cluster dissimilarity or maximize between-cluster separation. The dendrogram encodes the hierarchical structure, with each node representing a cluster and branches showing how clusters are split or merged. The height of each node reflects the dissimilarity at which clusters are joined or split, providing a visual interpretation of clustering decisions [35; 36].
## 3 Materials and Methods
### Methods
#### 3.2.1 Construction and Implementation of the M.I.N.I-Based Diagnostic Tool  
With advancements in medical technology and the growth of digitalization, an increasing number of diagnostic tools are utilizing computer technology to boost their efficiency and accuracy. In this study, M.I.N.I version 6.0.0 serves as the basis for the auxiliary assessment of mental and psychological disorders. It is supported by technologies like finite state machines and web interaction for automated and intelligent information support.  
1. Overview of M.I.N.I  
M.I.N.I is an extensive diagnostic assessment tool, comprising 16 independent modules from A to P. Each module covers a broad range of content and employs intricate logical  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Category** & **Category Count** & **Total Count** \\ \hline Entity & 5 & 2430 \\ Attribute & 8 & N/A \\ Public Online & 9 & 5140 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Knowledge graph data.  
Figure 1: Overall architecture.  
calculations and judgment mechanisms. Considering the substantial differences in the assessment methods of diagnostic results among these modules, simplifying M.I.N.I into a standard questionnaire is not recommended.  
To realize the objectives of automation and intelligence, this paper adopts a specialized modeling approach for each module. By applying the finite-state-machine model, precise logic calculations and logical jumps based on user responses are possible. This approach not only facilitates the flexible collection of multiple indicator information but also enables comprehensive result assessment based on this data. Once information collection is completed in all modules, the system can generate an integrated preliminary assessment report. The use of this method is expected to significantly enhance the precision and efficiency of diagnoses. The implementation effect is illustrated in Figure 2. The picture presents three parts from left to right: an overview of the overall auxiliary diagnosis module, the information collection process, and the evaluation results.  
2. Finite state machine and its application in this study  
State machines [34; 35] are computational models that describe the behavior or state transitions of objects or systems in response to external events or conditions. In event-driven systems, their behavior or state changes in response to external events or conditions. Table 3 shows the basic components of a state machine. A simple diagram illustrating this concept is shown in Figure 3.  
Figure 3: Working principle diagram of finite state machine.  
Figure 2: Auxiliary assessment effect.  
In this study, we utilize the principles of the state-machine model to structure each module of M.I.N.I 6.0.0. Each user's response to a question triggers an event, determining the next question to be asked. This process continues until either the assessment criteria are met or all questions in the module are answered, followed by result evaluation. The flowchart for this process is depicted in Figure 4.
## 4 Evaluation
### Scenario and Parameter Configuration  
We assume an urban road traffic site with an intersection at the center as shown in Figure 3, where the road is shaded. The size of the concerned area is 300 m by 300 m, and there are five RSUs deployed in a planned manner with a 150 m spacing between the nearby RSUs. In the figure, each RSU is marked with a black dot, where the top-most starts with RSU 1, followed by RSU 2, 3, 4 and 5 are deployed horizontally from left to right. VUs, marked with red triangles, are randomly located on the road, and when the mobility is assumed, they move at a random speed in the direction toward the center of the intersection. The speed is randomly chosen from the following distribution Uniform [30; 60] km/h or Uniform [8.33; 16.67] m/s. The number of vehicles varies depending on the scenario. Each user generates a downlink transmission request per time slot, and the amount of requests is randomly drawn from the distribution of Uniform [0.6, 2.0] Mbps.  
There can be one or multiple orthogonal channels available to each RSU. The channel bandwidth is 180 kHz each [25], and the center frequency is 850 MHz which has been utilized for cellular communications in the Republic of Korea. Each RSU's transmit power budget is 46 dBm (39.81 watt) [29]. The channel model and its parameters are taken from the 3 GPP 5G standard as follows;  
* Scenario: Urban macro (UMa) with line of sight (LoS).
* Path loss (dB): \(28.0+22\times\log_{10}(d)+20\times\log_{10}(f_{c})\), where \(d\) is the distance (m) between the transceiver-receiver pair and \(f_{c}\) is the central frequency.
* Shadow fading standard deviation [dB]: \(\sigma_{SF}=4\).  
The fundamental experiment setup is summarized in Table 3.  
The setup strategy for evaluation is summarized as follows.  
* Radio propagation and channel models along with the related parameters are taken from the 3 GPP 5G standard.
* The center frequency, channel bandwidth, and mobility model and VUs' moving speed are taken from the related articles.
* The remaining parameters are randomly chosen.
## 5 Discussion  
Although there have been several published studies on the topic of GLONASS PPP ambiguity resolution, the problem caused by the IFCB in WL ambiguity fixing has not been well resolved. The shortcomings in the current studies are: they require homogeneous receivers for both UPD estimation and PPP-IAR [26; 31]; or require a very high-precise ionosphere model [15; 29]; or use an extra-narrow-lane combination with a short wavelength of only about 5 cm [27]. It's revealed by [PERSON] et al. [31] that, we should use the HMW method for GLONASS PPP-IAR, because it is independent on the precise ionosphere model and has a relatively long wavelength. In this study, we proposed a strategy for calibrating the GLONASS IFCB to achieve rapid GPS + GLONASS PPP-IAR with heterogeneous receivers over a large area. Experiment results show that the proposed IFCB calibration method is efficient for heterogeneous receivers and can even achieve better performance than the homogeneous receivers.  
The presented method is limited in its application to existing networks, in that it requires a reference network of homogeneous receivers. However, rapid PPP ambiguity fixing is only required in industrial applications provided by commercial companies, who tend to establish their own reference network rather than rely on existing global navigation satellite system networks with few homogeneous receivers. For example, both Trimble (Sunnyvale, California, USA) and Fugro (Leidschendam, The Netherlands) have their own reference network of homogeneous receivers [37; 38]. Furthermore, because the proposed IFCB calibration method does not rely on a dense reference network of homogeneous receivers, it is easy for commercial companies to deploy a sparse network of homogeneous receivers at intervals 500-1000 km to provide a GPS + GLONASS rapid PPP ambiguity fixing service in an area.