esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Adaptive Kalman Consensus Filters (a-KCF)
## 2 Problem Statement
### Graph Theory Preliminaries  
We consider \(n\) distributed agents working cooperatively through a communication network/topology characterized by a weighted graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\) with inconsistent information, where \(\mathcal{V}=\{1,2,...,n\}\) represents the set of agents, \(\mathcal{E}=[\epsilon_{ij}]\) denotes the set of edges in \(\mathcal{G}\), and \(\epsilon_{ij}\) represents the connected bridge of an ordered pair \((j,i)\). For each \(\epsilon\in\mathcal{E}\), we mean that the \(i\)th agent can only receive information from the \(j\)th agent, not vice versa. In such cases, we call \(j\) the neighbor of \(i\) and denote \(\mathcal{N}_{i}=\{j\in\mathcal{V}|(j,i)\in\mathcal{E}\}\) as the set of neighbors of the \(i\)th agent. If \((j,i)\
otin\mathcal{E}\), this means that there is no communication link between the \(j\)th and \(i\)th agents.  
Let \(\mathcal{A}=[a_{ij}]\in\mathbb{R}^{n\times n}\) represent the adjacency matrix of \(\mathcal{G}\), defined as \(a_{ij}=1,\forall i\
eq j\) and \(j\in N_{i}\); otherwise, \(a_{ij}=0\).  
We define the in-degree of the \(i\)th agent as \(deg_{in}(i)=\sum_{j=1}^{N_{i}}a_{ij}\) and the out-degree of the \(i\)th agent as \(deg_{out}(i)=\sum_{j=1}^{N_{i}}a_{ji}\); then, the graph Laplacian of \(\mathcal{G}\) can be defined as \(L=\Delta-\mathcal{A}\), where \(\Delta=diag(deg_{in}(1),...,deg_{in}(n))\in\mathbb{R}^{n\times n}\).  
One important property of \(L\) (see [19] for more details) is that all eigenvalues of \(L\) are non-negative, and at least one of them is zero. If we denote \(\Lambda_{i}(L)\) as the \(i\)th eigenvalue of \(L\), then we have the following valid relation for any graph \(\mathcal{G}\): \(0=\lambda_{1}(L)\leq\lambda_{2}(L)\leq...\leq\lambda_{n}(L)\)[32].  
In a special case, if the in-degree of the \(i\)th agent or \(deg_{in}(i)=n-1;\forall i=1,...,n\), then we call the graph \(\mathcal{G}\), as an all-to-all connected topology. In such cases, the graph Laplacian \(L\) is a symmetric and positive-semidefinite matrix with only one zero eigenvalue.
## 4 Implementation in Seismic Monitoring of a Large-Scale Building Using Two Vision-Based Systems
### Output of Image Enhancement  
Given the promising results obtained from the simple 1-inch block test, it was desired to extend the study to more realistic cases, including full-scale building vibration monitoring, which is the focus of the next section. The validation test described previously highlights several enhancement algorithms that result in less error compared to other methods. An example of the enhanced image histogram using the CLAHE method and the quality index metric for both sensor systems is shown in Figure 8. The input images initially captured by each system were initially underexposed for the HS system and low in contrast for the SD system, as shown by their histogram in Figure 8c,d. As previously highlighted in Figure 2, the identification algorithms are unable to locate any features from the original HS image, whereas the low-contrast SD image identifies a small number, but their total is inadequate for bundle adjustment convergence. After processing using the CLAHE method, the histogram of the HS vision system clearly shows the stretching of pixel distribution within the gray level intensity as the effect of image enhancement. The reduction in the pixel counts is also observed, especially in darker areas. Regarding the SD system, the CLAHE algorithm relaxes the pixel counts so the separation between dark and bright areas is more evident in the output image. Similar to the static test image, the entropy of the seismic test output image also measures higher values due to the applied image enhancement. It is more noticeable in the HS output, whereas less change is computed for the low-contrast image as recorded by the SD system. From the metrics shown in Figure 8, the enhancement procedure is observed to affect underexposed images more than low-contrast images, especially when the quality is estimated by the PQ metric.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 5 Technological Infrastructure
### Storage Technology  
Storage technology plays a critical role in the real-time metaverse by ensuring fast, secure, and efficient data handling for continuous immersive experiences [145]. As the metaverse involves interactions between physical and virtual environments, data from various sources such as sensors, edge devices, and AI systems must be cached and retrieved in real-time [146]. Technologies like macro/small base station caching [147] and device-to-device caching [148] help reduce latency by storing frequently accessed data closer to the user, ensuring faster retrieval times. This is essential in high-demand environments where real-time responsiveness directly impacts the user experience.  
In addition to reducing latency, efficient storage mechanisms like edge AI model caching contribute to the real-time metaverse's ability to manage large-scale, complex interactions without overwhelming the network [149]. Edge AI model caching allows real-time AI computations to occur closer to the user by caching AI models at edge nodes,  
Figure 7: A general 5G cellular network architecture.  
which improves performance and reduces the load on central servers [150]. This approach enhances scalability and ensures that the system can support a high number of simultaneous interactions without performance degradation, a necessity in the immersive and constantly evolving world of the real-time metaverse.  
Furthermore, optimal cache replacement is crucial in ensuring that the most relevant and frequently accessed data remains readily available, while outdated or less important data are efficiently replaced [151]. As the real-time metaverse generates and processes massive amounts of data, intelligent cache management prevents storage bottlenecks and ensures the availability of real-time data [152]. This ensures seamless transitions and interactions within the real-time metaverse, allowing users to experience smooth, uninterrupted virtual environments while maintaining the overall system's trustworthiness and performance.
# GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides
## 2 Data and Methods  
### Datasets  
The GPS/Galileo observations from 100 globally distributed stations (80 IGS stations and 20 EUREF Permanent GNSS Network (EPN) stations) were processed for 30 days (Figure 1) in 2022. The 20 EPN stations are close to the RDS stations [31; 32]. The reference frame for the GPS broadcast ephemerides is WGS 84 [33], and the Galileo reference frame is GTRF [34].  
The BE-PPP algorithm is implemented based on the open-source software GAMP [35], and the detailed processing strategies are shown in Table 1. In addition, the RT-PPP algorithm uses real-time orbit and clock products from the Wuhan University (WHU) analysis center in China, and the processing strategies are all the same as those used in BE-PPP, but a satellite antenna offset needs to be applied.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Description** \\ \hline Observations & Ionospheric-free combinations of GPS/Galileo carrier phase and pseudorange, 30 s sampling, 7-degree cutoff elevation \\ Satellite orbits and clocks & Only broadcast ephemerides \\ Pseudorange noise & G: 5 mm; E: 5 mm \\ Phase noise & G: 1 m; E: 0.5 m \\ Filter & Backward–forward KF \\  & Prior value from Saastamoinen and the global mapping function \\ Tropospheric delay & (GMF), and remaining errors estimated as random walk \\  & noise; \(\sigma_{0}\) of 0.6 m, \(\sigma_{p}\) of 6 mm, \(\tau\) of 3600 s \\ Ionospheric delay & First order was eliminated by ionospheric-free combination, and higher orders ignored \\ Receiver clock & Estimated as white noise with \(\sigma_{0}\) and \(\sigma_{p}\) of 60 m \\ Inter-system bias & Estimated as white noise with \(\sigma_{0}\) and \(\sigma_{p}\) of 60 m \\ Ambiguity & Float ambiguity is estimated as a time constant with a \(\sigma_{0}\) of 10 m \\  & Prior constraint of 0.5 m, estimated as random walk noise; \(\sigma_{0}\): \\ Signal-in-space error (SISE) & GPS of 0.5 m, Galileo of 0.2 m; \(\sigma_{p}\): GPS of 10 mm, Galileo of 3 mm, \(\tau\) of 30 s [30] \\  & Solid earth tide, polar tide model (IERS Conversion 2010), ocean \\ Tide models & tide model (i.e., FES2004) \\ Receiver antenna & IGS absolute phase center calibration (i.e., jigs20.atx) \\ phase center & None [29] \\ Satellite antenna & \\ phase center & \\ Other corrections & Relativistic effect, [PERSON] effect, satellite antenna phase \\  & wind-up corrections \\ \hline \hline \end{tabular} The initial standard deviation \(\sigma_{0}\), standard deviation \(\sigma_{p}\), and time constant \(\tau\) are listed for both processing strategies.  
\end{table}
Table 1: Details of the implementation of the BE-PPP procedure.  
Figure 1: Distribution of selected IGS stations (red dots) around the world, EPN stations (blue triangles), and RDS stations (black pentagons) in Europe.
## 2 Results and Discussion  
### Results  
#### 2.1.1 Knockdown of \(DOTIL\)  
Recent genome-wide ChIP-seq analyses revealed that alternatively spliced exons are preferentially marked with H3K4 me1, H3K27 me3, and H3K79 me2 [20]. Furthermore, a genome-wide study across different species revealed that H3K36 me3 was depleted in skipped exons [13,16]. To investigate whether these epigenetic marks are associated with male-specific splicing of _Imp_ pre-mRNA, we performed RNAi knockdown of several histone methyl transferases (HMTases) such as ASH2, EZH2, SETD2, and DOT1L known to modify H3K4, H3K27, H3K36, and H3K79, respectively, in embryos. Microinjection of dsRNA into _B. mori_ embryos has been used successfully in many studies, although silencing levels vary [29]. siRNAs were injected into eggs during the early embryonic stage 6-8 h after oviposition, a developmental period known to be sensitive to RNAi-mediated gene knockdown [30]. Total RNA was extracted from each egg 4 days after injection. As shown in Figure 1A, qRT-PCR confirmed a significant reduction in _EZH2_, _SETD2_, and \(DOT1L\) transcript levels in embryos injected with siRNAs targeting these HMTase-coding genes. Injection of _ASH2_ siRNA failed to reduce the level of the target gene mRNA even though we used several siRNA sequences. Therefore, we focused on the knockdown effects of _SETD2_, \(DOT1L\), and _EZH2_ on the expression of the male-specific _Imp_ mRNA (\(Imp^{M}\)). RNAi knockdown of _SETD2_ and _EZH2_ had no influence on the expression of \(Imp^{M}\) (Figure 1C, lanes 3, 4, 8, and 9). Notably, the expression of \(Imp^{M}\) was completely abolished when the \(DOTIL\) expression level was repressed to \(<\)10% of that in control males (Figure 1C, lane 5). Five of six examined individuals whose \(DOT1L\) level was less than 10% also showed the disappearance of male-specific _Imp_ expression. Further study is required to determine whether a similar effect on the expression of \(Imp^{M}\) occurs when the expression levels of _SETD2_ and _EZH2_ are repressed \(<\)10% of that in control males.
# Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)
## 5 Experimental Study
### Discussions  
The experimental results presented in this section are merely a preliminary study on top of a proof-of-concept platform. Our MinT relies on a permissioned network to provide basic security primitives, such as identity registration, authentication and access control, etc. Given the assumption that the adversary cannot control microservice control the module to send false parameters, we verify that SSA-based detection can identify a single dishonest miner that uses either static or adaptive mining violation strategies. Regarding byzantine scenarios that multiple dishonest miners collude to disturb fair mining mechanism, the PoB consensus algorithm adopts Krum rule in behavior score calculation, which only chooses \(n-f-2\) closet behavior vectors and precludes those \(f-1\) malicious vectors that are far away from the center of distribution. Given the assumption that an adversary cannot control more than \(f\) nodes of a mining network \(\mathcal{N}\) that satisfies \(n\geq 2f+3\), all honest participants can still make agreements and output the unique benchmark behavior vector \(B^{*}\).  
Our MinT architecture envisions large-scale IoT networks based on a hierarchy of edge-fog-cloud paradigm. However, there are open questions that need to be addressed before bringing the proposed framework into real-world applications. We leave them for our future work.  
* Although experimental results verify feasibility of SSA-based fair-mining violation detection, there still need investigation on SSA performance and accuracy given the impact of parameters, such as optimal/sub-optimal threshold selection and detection latency as scaling up miners.
* The PoB consensus is promising to guarantee byzantine fault tolerance in mining violation detection; however, the threat model based on attack scenarios in SSA detection needs more investigation, such as communication security between miner and twin and container's robustness given failed or compromised conditions. Therefore, the security mechanisms for communication between PO and LO, and container management are among the tasks of top priority.
* It is inevitable that extra overheads are incurred by security enforcement and data synchronization in fair-mining mechanism. Therefore, a comprehensive performance evaluation of the twinning process is necessary, such as computation and communication cost, network latency and storage requirement, etc.
* Furthermore, we also need to tackle scalability and heterogeneity issues such as as applying MinT into large-scale IoT networks. A hierarchical federated network frame  
Figure 6: False alarm rate with different threshold h.  
work is promising to handle the trilemma in blockchain solutions that decentralization, security and scalability cannot perfectly co-exist [4].
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 6 Discussion
### Revealing Features in Population  
Researchers perform gait, balance, and RoM analysis using wearable sensors for several purposes including: to reveal features that describe a population, to study changes in patient characteristics over time, and to analyze the effect of interventions. This is possible due to the capability of wearable sensors to provide quantitative and objective measurements of gait, balance, and RoM parameters. People with neurological disorders and the elderly [24; 27; 28; 48; 49; 72; 76] and the elderly [50; 63; 68; 75] are common target populations. Often, the goal of studies that focus on these populations is to assess the efficacy of rehabilitation or pharmacologic interventions or to evaluate falling behavior.  
A study of individuals with Alzheimer's Disease (AD) showed that the number of strides, stride length, stride speed, and stride time extracted from wearable devices served as strong indicators for early diagnosis of AD [28]. Another study showed that, for quiet stance with eyes closed, people with Multiple Sclerosis (MS) have a greater sway acceleration amplitude than healthy controls [24]. Moreover, multiple studies that investigated people with Parkinson's disease (PD) have shown that wearable sensor measurements serve as indicators to distinguish between people with PD and healthy subjects and also serve as an assessment of the efficacy of rehabilitation and drug interventions [27; 48; 49; 56; 59; 76].  
Research with elderly populations has shown that postural sway parameters, statistical features, such as mean and coefficient of variation, and step length extracted from wearable devices can be used to categorize falling behavior [50]. However, another study of the elderly demonstrated that smart devices are not recommended for regular stance in conditions such as eyes open, eyes closed, and dual task since they demonstrated weak to moderate correlations between the force plate center of pressure and smart device measures. Although, they found that the correlations between the force plate center of pressure and smart device measures were high for semi-tandem, tandem, and single leg stance conditions, showing the possibilities of the use of smart devices to evaluate such conditions [68].  
These studies show the ability to perform tele-health rehabilitation to monitor home exercise programs, especially for targeted populations who may have difficulty going to a research laboratory to perform the assessments. Additionally, wearable devices show a high possibility to assess the quality of natural locomotion out in the community. It is important to note that these systems should be validated against gold standard assessments and instruments to show more clinically relevant parameters. Comparing wearable devices to gold standards methodologies proof the feasibility, reliability, and validity of wearable devices for gait, balance, and ROM analysis.
## 4 Energy Efficiency
### Hybrid Precoding for the Partially Connected Architectures  
The study of hybrid precoding in MIMO-based systems has been mentioned in many works [13; 14; 15; 16; 17; 18; 19; 32]. In this section, the digital precoder \(\mathbf{F^{D}}\) and the analog precoder \(\mathbf{F^{A}}\) are designed by minimizing the Euclidean distance between \(\mathbf{F^{A}}\mathbf{F^{D}}\) and the equivalent fully digital precoder \(\mathbf{F}\), which is given by  
\[\begin{array}{l}\min\limits_{\mathbf{F}_{I}^{\mathbf{A}}\mathbf{F}_{I}^{ \mathbf{D}}}\sum\limits_{l=1}^{L}\left\|\mathbf{F}_{I}-\mathbf{F}_{I}^{ \mathbf{A}}\mathbf{F}_{I}^{\mathbf{D}}\right\|_{F}^{2}\\ \text{s.t.}\ \mathbb{E}\Big{\{}\|f\big{(}\mathbf{F}_{I}^{\mathbf{A}} \mathbf{F}_{I}^{\mathbf{D}}\mathbf{s}\big{)}\|^{2}\Big{\}}=\mathbb{E}\Big{\{} \|f(\mathbf{F}_{I}\mathbf{s})\|^{2}\Big{\}},\forall l\in\{1,2,\cdots,L\}.\end{array} \tag{32}\]  
We will solve the design of this hybrid precoding architecture by dividing it into two subproblems: analog precoder design and digital precoder design [33].
# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
###### Abstract  
The tracking accuracy of the traditional Strong Tracking Unscented Kalman Filter algorithm (ST-UKF) decreases when the motion state of the traffic target changes significantly. A multidimensional adaptive factor-based strong tracking UKF (MAST-UKF) algorithm is proposed. The method introduces multidimensional attenuation factors in the prediction and updating process of filtering, and realizes the strong tracking filtering of vehicle targets by adjusting the uncertainty of state noise covariance and observation noise covariance and dynamically updating the multidimensional attenuation factors by adaptively adjusting the threshold based on the observation residuals and the state estimation error. Target tracking simulations are performed under system model uncertainty, and the tracking errors of MAST-UKF are reduced by 32.67%, 28.54%, and 23.17% compared to UKF, ST-UKF, and AST-UKF, respectively. The real vehicle experiments show that MAST-UKF reduces the distance error by 18.29% and speed error by 15.25% compared to AST-UKF. The results demonstrate that the MAST-UKF algorithm is able to adaptively adjust the noise covariance and effectively cope with the inaccuracy of the state noise and observation noise, thus realizing the accurate tracking of the target under complex conditions.  
m +
Footnote †: journal: applied sciences  
article
## 4 Results and Discussion
### Prediction of Human Glucose Concentration
#### 4.2.2 Bland-Altman Analysis  
These predicted results were also characterized using Bland-Altman analysis in order to assess the practical use of medical instruments in more detail [43]. As can be seen from Figure 5a, when applied to our network using the SNE algorithm, this analysis shows that the mean difference (MD) between the predicted and measured glucose concentrations is 0.2445 mmol/L, as well as that the 100% limit of agreement region is located within [\(-\)1.2638 mmol/L, 1.7529 mmol/L], the 95% limit of agreement. Also, a Bland-Altman analysis of predicted results produced by our network using the diffusion map algorithm, as shown in Figure 5b, shows how the smaller MD of 0.1197 mmol/L and 95% of the predicted points are situated in [\(-\)1.4436 mmol/L, 1.6829 mmol/L], thus meeting the 95% limit of agreement. Furthermore, Figure 5c presents a Bland-Altman analysis of predicted results produced by our network using the factor analysis algorithm, showing that the smallest MD of \(-\)0.0092 mmol/L and the 100% limit of agreement region are located in [\(-\)1.5011 mmol/L, 1.4827 mmol/L], thus falling within the 95% limit of agreement acceptable for medical instruments.
# Thermal Optimization Design for a Small Flat-Panel Synthetic Aperture Radar Satellite
## 4 Thermal Analysis
Under extreme high-temperature conditions, the total thermal control power of the support plate is 31.8 W, while under extreme low-temperature conditions, this power increases to 60.2 W, which occupies little power of the satellite to maintain the support plate at 20 \({}^{\circ}\)C.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 4 Results  
This section presents the evaluation and results regarding the experimental methodology. The pre-processing techniques were applied to emphasize any distinct pattern from each class. Figure 12 shows the labelled dataset after the pre-processing stage.  
It provides a much more separable pattern between classes than the dataset plotted in Figure 11. The dataset forms a unique but linearly distinguishable pattern favoring the linear models over proximity-based models. Subsequently, the ML data were split and fed into the ML modelling phase. The rest of the section explains the evaluation of the ML models' performances.