esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Chronic diseases are characterized by long-lasting conditions that significantly impact patients' quality of life due to limitations in daily functioning and the necessity for long-term medical care [1, 2]. The global prevalence of these diseases continues to rise, contributing substantially to overall mortality and disability worldwide [3]. Researchindicates that, alongside pharmacological treatment, PA serves as an equally effective intervention, reducing mortality risk and improving the overall quality of life in patients with NCDs [4]. The latest WHO guidelines from 2020 recommend that all adults, including those with chronic diseases, engage in at least 150-300 min of moderate-intensity aerobic PA or at least 75-150 min of vigorous-intensity aerobic activity per week, aligning with the recommendations for the general population [5].  
Despite these clear recommendations, numerous studies suggest that patients with NCDs--including those with cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases--often fail to meet the recommended levels of PA [6; 7; 8; 9]. One of the key barriers to PA in this patient population is kinesiophobia--an irrational fear of movement stemming from concerns about pain, worsening health status, or the recurrence of a disease-related episode. Patients with NCDs frequently experience chronic pain, fatigue, and reduced physical capacity, which may contribute to the development of kinesiophobia and further physical inactivity, leading to a self-perpetuating cycle of health deterioration [10; 11].  
Despite the growing interest in kinesiophobia within the scientific literature, there is a lack of comprehensive reviews examining its impact across different chronic diseases, particularly in relation to PA, quality of life, and rehabilitation outcomes. Previous studies have primarily focused on individual conditions or specific aspects of kinesiophobia without providing a holistic understanding of this phenomenon. Additionally, there is no systematic synthesis of risk factors, diagnostic tools, and therapeutic strategies aimed at reducing movement-related fear in patients with NCDs.  
The present systematic review aims to address this gap by synthesizing current evidence on kinesiophobia in the context of cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases. This review provides a cohesive and comprehensive analysis of the impact of kinesiophobia on physical activity and quality of life. Furthermore, it identifies risk factors, discusses available diagnostic tools and therapeutic approaches, and consolidates scattered information on this topic into a single, structured framework.
# A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
## 6 Practical Considerations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & \(\times\) & \(+\) & \(/\) \\ \hline
**KF-BF**[13] & \(3(L^{2}+M^{2})+2 ML+3L+4M\) & \(2(L^{2}+M^{2})+2 ML+L+2M\) & 2 \\
**KF**[26] & \(3(ML)^{2}+4 ML\) & \(2(ML)^{2}+3 ML\) & 1 \\
**SKF-BF** (Table 2) & \(2 ML+2L+3M+6\) & \(2 ML+L+2M+4\) & 2 \\
**SKF**[26] & \(3 ML+6\) & \(3 ML+4\) & 1 \\
**OLMS-BF** (Table 3) & \(2 ML+2L+3M+12\) & \(2 ML+L+2M+8\) & 2 \\
**JO-NLMS**[29] & \(3 ML+6\) & \(3 ML+5\) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Computational complexity of the algorithms. identify, we can take it into consideration when setting the values of these parameters. For example, if we assume the spatial impulse response to be time-invariant, we could fix \(\sigma^{2}_{\overline{w}_{\mathbf{g}}}=0\) and tune only the parameter related to the temporal impulse response. Thus, based on the state equation related to \(\mathbf{h}(n)\), together with the approximation \(\|\overline{\mathbf{w}}_{\mathbf{h}}(n)\|^{2}\approx L\sigma^{2}_{\overline{w} _{\mathbf{h}}}\) (which is valid when \(L\gg 1\)), and replacing \(\mathbf{h}(n)\) and \(\mathbf{h}(n-1)\) by their estimates, we can evaluate  
\[\widehat{\sigma}^{2}_{\overline{w}_{\mathbf{h}}}(n)=\frac{1}{L}\left\| \widehat{\mathbf{h}}(n)-\widehat{\mathbf{h}}(n-1)\right\|^{2}. \tag{76}\]  
It can be noticed that the estimation from (76) is designed to achieve a proper compromise between good tracking and low misalignment. When the algorithm starts to converge or when there is an abrupt change of the system, the difference between \(\widehat{\mathbf{h}}(n)\) and \(\widehat{\mathbf{h}}(n-1)\) is significant, leading to large values of the parameter \(\widehat{\sigma}^{2}_{\overline{w}_{\mathbf{h}}}(n)\), therefore providing fast convergence and tracking. On the contrary, when the algorithm is converging to its steady-state, the difference between \(\widehat{\mathbf{h}}(n)\) and \(\widehat{\mathbf{h}}(n-1)\) reduces, thus leading to the parameter \(\widehat{\sigma}^{2}_{\overline{w}_{\mathbf{h}}}(n)\) taking small values and, consequently, to a low misalignment.
## 1 Introduction  
For over seven decades, research has focused on manganese oxides exhibiting a perovskite-type structure, characterized by the chemical formula AMnO\({}_{3}\) (where A = Ca, La, Sr or Ba) [1]. [PERSON] and [PERSON] were pioneers in this field in 1950, being the first to synthesize mixed-valence manganite (La\({}_{1.5}\)Ca\({}_{x}\)MnO\({}_{3}\)) [2].  
Manganese-rate earth hybrid oxides have been investigated regarding the interplay between their orbital, lattice, magnetism and transport properties in various R\({}_{1.8}\)-A\({}_{x}\)Mn \({}_{1.7}\)-ByO\({}_{3}\) compounds (where A is a divalent cation, B is a transition metal and R is a rare earth metal). Mn ions in the LaMnO\({}_{3}\) system are trivalent, and super exchange interaction exists through Mn\({}^{+3}\)-O-Mn\({}^{+3}\) pathways [3]. Lanthanum manganese's materials present remarkable structural and electromagnetic properties [4; 5; 6]. Furthermore, doped LaMnO\({}_{3}\) compounds have attracted considerable attention due to their improved properties required for different application domains. These compounds have also gained recognition as appropriate cathode materials for fuel cells due to their excellent electrical conductivity and chemical stability, especially at high temperatures [7].  
Many techniques can be applied to synthesize undoped and doped LaMnO\({}_{3}\) materials, such as sol-gel, hydrothermal, solid-state reaction and combustion methods, or the ultrasonic method, which employs a sonotrode immersed in a reaction medium [8; 9; 10; 11; 12; 13; 14].  
By doping, the semiconductor's properties are enhanced by adding heteroatoms into a target lattice without changing the host crystal's basic characteristics and structure. For example, in the case of Ga-doped LaMnO\({}_{3}\) perovskite materials synthesized by a conventional ceramic procedure, the increase in the Ga content determines a decrease in the transition pressure to the higher-symmetry phases [13]. Previous reports on perovskite materials doped with various rare earth and transition metals indicate the influence of dopants on a material's properties. For example, electrical conductivity is influenced by both the ionic radius of the dopant and its concentration, as shown in [14], where, for the studied perovskite materials, the electrical conductivity increased as the ionic radius of the used dopants decreased [14]. In this context, the detailed study carried out in the work herein is based on the structural, thermal and electrical properties of undoped and Ga- or Y-doped lanthanum manganite synthesized by the sol-gel technique. The dopants' influence on the structural properties of LaMnO\({}_{3}\) perovskite materials is investigated, and for the first time, their effect on the electrical properties is shown through DC electrical conductivity measurements, where Ga or Y ions in the LMO perovskite sample lead to a decrease in the \(E_{A,cond}\). Moreover, the VRH (variable-range hopping) model of [PERSON] was applied to explain the mechanism of conduction in these samples, and comparisons of the Mott parameters were computed.
## 1 Introduction
Greedy Stateless protocols (e.g., CBF, GRAMT, GPCR, and AGF) rely on local geographic forwarding without maintaining route state, offering low overhead but limited adaptability in sparse or complex topologies [20]. Infrastructure-Assisted protocols such as SADV and IGRP incorporate fixed Roadside Units to enhance routing stability, particularly in urban settings. Street-Aware protocols (SAR, GSR, TO-GO, and GPDRJ+) utilize road map and city topology information to guide forwarding decisions. Connectivity-Aware protocols are further divided into statistical (A-STAR, GYTAR, and VADD) and real-time (MURU, RBVT, and CAR) subtypes, aiming to improve route reliability based on predicted or real-time link quality [21]. Foundational protocols like GPSR and AODV serve as baselines, from which many specialized protocols are derived. The interconnecting arrows in the diagram represent how newer protocols build upon or enhance existing ones, reflecting the evolution of routing strategies in response to VANET-specific challenges [21]. The manuscript consists of methods where all the techniques, tools, and procedures are presented. The following sections, Methodology, Results and Discussion, Conclusions, and References, are presented, along with Supplementary Materials (Figures S1-S30 and Tables S1-S6) that describe the detailed and comprehensive methodology.
## 5 Results and Discussion
### Comparison of Walking and Driving Jamming Experiments  
While initially the walking experiment was meant to validate the method for the jammer position calculation, it was lately observed that the jammer's movement was too slow to effectively identify the CNR minimum. However, the results obtained from the measurements of the CNR and the position error with respect to the jammer's position do not differ significantly from the measurements when the jammer was being driven in a car.  
Figure 14: Horizontal accuracy for smartphones. Note the horizontal accuracy deterioration and the smaller number of satellites used in the vicinity of the jammer (**a**). Some phones become completely confused in the presence of the jammer (**b**).  
Figure 15: Comparison between results given using different software: RTKLIB (**left**) and Leica Infinity (**right**).
# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 1 Introduction
However, when applied to dynamic clinical scenarios, these models often fall short, as speckle variability and real tissue movement introduce errors that accumulate, leading to drift and a compromise in accuracy [8; 9; 10]. Recent advancements in artificial intelligence (AI) and especially deep learning (DL) techniques have expanded the horizons for extracting detailed information from image data [11]. This has enabled significant progress in overcoming the intrinsic difficulties of image-based 3D US reconstruction. While traditional tracking technologies that require external reference are less suited to point-of-care and intraoperative settings, inertial measurement units (IMUs) have emerged as a viable and less obtrusive alternative for position tracking. These devices integrate a tri-directional magnetometer, a gyroscope, and an accelerometer into compact units. This offers a favorable balance between hardware independence and the need for positional information in trajectory reconstruction. With the growing adoption of IMU technology in clinical devices and developments in DL techniques, image-based reconstruction methods without external tracking are becoming increasingly viable. This approach additionally aligns with the ongoing shift towards more accessible and efficient POCUS applications. With the growing adoption of 3D US in clinical applications, there is an urgent need to systematically evaluate emerging trackerless reconstruction methods. These methods, particularly those leveraging DL, promise to overcome the limitations of traditional approaches, but their efficacy and applicability across various clinical scenarios remain underexplored. Given the rapid advancements in trackerless 3D US reconstruction, this review aims to systematically evaluate the current state-of-the-art methodologies. Specifically, our objectives are to identify existing gaps in the literature, such as the challenges associated with different data acquisition strategies, reconstruction accuracy, and clinical applicability. Additionally, we provide a comparative analysis of various reconstruction techniques, particularly focus ing on DL-based approaches like convolutional neural networks (CNNs). By highlighting the strengths and limitations of these methods, this review seeks to guide future research directions and enhance the implementation of trackerless 3D US in clinical practice. Overall, our primary contributions are summarized as follows: (1) it identifies existing gaps in the literature, particularly concerning the clinical applicability and accuracy of trackerless 3D reconstruction methods; (2) it offers a comparative analysis of current AI-based approaches, highlighting their respective strengths and limitations; and (3) it provides direction for future research, emphasizing the need for improved accuracy, robustness, and adaptability of these methods in diverse clinical settings.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 3 Conditional Volatility Models  
### Autoregressive Moving Average Models  
One of the traditional models used in modeling the dynamics of a time series \(Y_{t}=\{y_{1},y_{2},\dots,y_{T}\}\) is the Autoregressive Moving Average (ARMA) process. ARMA(\(p\),\(q\)) processes are stationary. For stationary processes, the characteristics of the distribution for a sequence of \(n\) observations are not dependent on the time origin [56]. A less restrictive requirement is that of a weakly stationary process, where, for a time series \(Y_{t}=\{y_{1},y_{2},\dots,y_{T}\}\), \(E(Y_{t})=\mu\) for all \(t\), \(Var(Y_{t})=\sigma_{Y}^{2}\) for all \(t\), and \(Corr(Y_{t},Y_{t-s})=\rho(s)\) for \(t\geq s\), where \(\rho(\cdot)\) is the autocorrelation function of a time series; thus, for a weakly stationary time series, the mean and variance are constant and the correlation between observations is only dependent on the time between the observations [56]. The property of weak stationarity is often employed in the analysis of time series [56]. Processes of the form under commonly used conditions  
\[Y_{t}=\sum\limits_{i=1}^{p}\alpha_{i}Y_{t-i}+\sum\limits_{j=1}^{q}\beta_{j} \varepsilon_{j-t}+\varepsilon_{t}, \tag{1}\]  
where \(\varepsilon_{t}\) is a white noise sequence with variance \(\sigma_{\varepsilon}^{2}\) for all \(t\) (for time series with non-zero mean, a mean term can be added to the model) [56; 57].  
If \(p=0\) or \(q=0\), then the processes are described as a moving average process of order \(q\) (MA(q)) or an autoregressive process of order \(p\) (AR(\(p\))) respectively. If a time series is non-stationary, Autoregressive Integrated Moving Average (ARIMA(\(p\),\(d\),\(q\))) models may be used, where the \(d\)th difference of the time series is an ARMA(\(p\),\(q\)) process [56]. The parameters of an ARMA model are useful in summarizing a time series, while the modeling may not reveal much information concerning the data generating process; however, ARMA processes are useful in forecasting [57].
# Design and Stiffness Optimization of Bionic Docking Mechanism for Space Target Acquisition
## 3 Model of Bionic Docking Mechanism
### Partial Velocity Matrix  
Angular velocity of segment \(k\) in inertial coordinate system \(OXYZ\) is expressed as:  
\[\mathbf{\omega}_{k}^{0}=\mathbf{\omega}_{1}^{0}+\sum_{i=1}^{k-1}A_{i}^{0}\mathbf{\omega}_ {i+1}^{i} \tag{6}\]  
According to the definition of partial angular velocity  
\[\mathbf{\omega}_{kl}=\begin{array}{c}\frac{\partial\mathbf{\omega}_{k}^{0}}{ \partial\mathbf{y}_{l}}\qquad l=1,2,3,\ldots,\ 10\end{array} \tag{7}\]  
Take (6) into (7) to derive partial angular velocity, storing the partial angular velocity of the segment \(k\) to the generalized velocity \(y_{l}\) with a matrix \(\mathbf{W}_{15\times 10}\), then:  
\[\mathbf{W}_{15\times 10}=\left[\begin{array}{cccc}\mathbf{I}&\mathbf{O}_{3\times 1}&\mathbf{O }_{3\times 1}&\mathbf{O}_{3\times 1}&\mathbf{O}_{3\times 4}\\ \mathbf{I}&\mathbf{A}_{1}^{0}&\mathbf{O}_{3\times 1}&\mathbf{O}_{3\times 4}\\ \mathbf{I}&\mathbf{A}_{1}^{0}&\mathbf{A}_{2}^{0}&\mathbf{O}_{3\times 1}&\mathbf{O}_{3\times 4}\\ \mathbf{I}&\mathbf{A}_{1}^{0}&\mathbf{A}_{2}^{0}&\mathbf{A}_{3}^{0}&\mathbf{O}_{3\times 4}\\ \mathbf{I}&\mathbf{A}_{11}^{0}&\mathbf{A}_{2}^{0}&\mathbf{A}_{3}^{0}&\mathbf{O}_{3\times 4}\\ \end{array}\right] \tag{8}\]  
where  
\(\mathbf{I}\) is the \(3\times 3\) identity matrix;  
\(\mathbf{A}_{11}^{0}\) is the first column of the matrix \(\mathbf{A}_{1}^{0}\);  
\(\mathbf{A}_{22}^{0}\) is the second column of the matrix \(\mathbf{A}_{2}^{0}\);  
\(\mathbf{A}_{33}^{0}\) is the third column of the matrix \(\mathbf{A}_{3}^{0}\);  
\(\mathbf{O}_{3\times 1}\), \(\mathbf{O}_{3\times 4}\) are the \(3\times 1\) and \(3\times 4\) zero matrices.  
Position vector \(\mathbf{p}_{k}\) of the mass center of the segment \(k\) in inertial system:  
\[\mathbf{p}_{k}^{0}=\mathbf{s}_{1}^{0}+\sum_{i=1}^{k-1}\mathbf{A}_{i}^{0}\cdot(\mathbf{d}_{i}+ \mathbf{s}_{i+1}^{i})+\mathbf{A}_{k}^{0}\cdot\mathbf{r}_{k} \tag{9}\]  
where  
\(\mathbf{d}_{i}\) is the position vector of the segment \(i\) in the coordinate system \(O_{i}X_{i}Y_{i}Z_{i}\);  
\(\mathbf{r}_{k}\) is the centroid position vector of the segment \(k\) in the coordinate system \(O_{k}X_{k}Y_{k}Z_{k}\).  
Take derivative of (9) with respect to time, velocity of the mass center of the segment \(k\) in inertial system is derived as follows:  
\[\mathbf{v}_{k}^{0}=\dot{\mathbf{s}}_{1}^{0}+\sum_{i=1}^{k-1}\left(\dot{\mathbf{A}}_{i}^{0} \cdot(\mathbf{d}_{i}+\mathbf{s}_{i+1}^{i})+\mathbf{A}_{i}^{0}\cdot\dot{\mathbf{s}}_{i+1}^{i} \right)+\dot{\mathbf{A}}_{k}^{0}\cdot\mathbf{r}_{k} \tag{10}\]  
\(m_{k}\) is the mass of segment \(k\);  
\(\mathbf{I}_{k}\) is the inertia tensor of segment \(k\);  
\(\dot{\mathbf{\omega}}_{k}^{0}\) is the angular acceleration of segment \(k\);  
\(\mathbf{\omega}_{k}^{0}\) is the centroid acceleration of segment \(k\);  
\(\mathbf{\omega}_{k}^{0}\) is the angular velocity of segment \(k\).
## 2 Research Methodology
### Modeling Methods Improvements
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Issue** & **Features** & **Causes** & **Detection Methods** & **References** \\ \hline Unbalance & Excessive vibration and noise; increased bearing loads. & Uneven mass distribution. & Vibration analysis; balancing tests. & [65; 66] \\ \hline Misalignment & High vibration; increased bearing temperature; coupling wear. & Improper installation; thermal expansion. & Laser alignment tools; vibration spectrum analysis. & [66; 73] \\ \hline Resonance & High-amplitude vibrations at critical speeds. & Structural design flaws; improper damping. & Modal analysis; frequency- response testing. & [76; 77] \\ \hline Rotor cracks & Subharmonic vibrations; unexpected frequency changes. & Fatigue stress; corrosion. & Non-destructive testing (NDT); modal analysis. & [69; 70] \\ \hline Bearing failures & Noise, irregular vibrations, increased temperature. & Lubrication failure; contamination. & Shock pulse monitoring; envelope analysis. & [81; 82; 83; 84] \\ \hline Oil whirl/whip & Self-excited vibrations at sub-synchronous frequencies. & Insufficient bearing stiffness; excessive lubrication. & Vibration analysis; bearing design assessment. & [71; 72] \\ \hline Rotor–stator rub & Sudden vibration spikes, high noise levels, and heat spots. & Thermal expansion; misalignment. & Acoustic emission; temperature monitoring. & [78; 79; 80] \\ \hline Looseness & Impact-like vibration; irregular waveform signals. & Loose fasteners; foundation problems. & Time waveform analysis; visual inspection. & [74; 75] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A summary of rotor system problems, their causes, and detection methods. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** & **Domain*** \\ \hline Particle swarm optimization (PSO) sub-interval similarity (SIS) metric [94] & It requires only upper/lower bounds for measurements and delivers guaranteed parameter intervals. & Correlations between parameters are not represented. & \\ \hline \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & High-speed efficiency. & Provides no probabilistic ranking. \\ \hline \multicolumn{4}{p{113.8 pt}}{* Signal processing domain (if applicable): TD—time domain; FD—frequency domain; TFD—time-frequency domain.} \\ \end{tabular}
\end{table} 
Table 9: _Cont._
# Concatenated Coding for GNSS Signals in Urban Environments
## 3 Concatenated Coding Scheme for GNSS Messages
### Proposed RS-LDPC Code
The outer RS code is decoded by the Berlekamp-Massey (B-M) algorithm. The final 889-bit hard decisions obtained in the output of the iterative LDPC decoder are first converted into 127 RS symbols, and then the symbols are fed into the RS decoder, which tries to correct the failed codewords in the earlier LDPC decoding. Finally, the recovered 117 RS symbols from the RS decoder are transformed into the 819-bit estimated message data.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison
#### 6.3.1 Energy Tax  
Figure 7 shows the energy tax of the baseline and proposed schemes. It clearly shows that as the density of nodes increases, it also increase the energy tax. Moreover, the high density of nodes also leads to more probability of data packet collision. This collision causes an increase in the packet drop ratio, which results in high energy consumption. The results in Figure 7 show that our schemes outperforms WDFAD-DBR in terms of energy tax. The proposed scheme A-DBR adjusts its transmission range adaptively when it finds no node in its range and continues to forward the data packet without any loss. In the proposed scheme C-DBR, the approach of clustering minimizes the transmission distance, directly affecting the energy consumption. In CA-DBR, fall back along with nomination of the forwarder node that has minimum number of neighbor nodes are selected. This also reduces the probability of packet loss and energy consumption.  
The existing scheme WDFAD-DBR has high energy consumption due to high packet loss. Moreover, RE-PBR shows higher battery dissipation due to the longer routing path from the source to the destination in search of a high quality link. Initially, it has a greater dissipation rate compared to  
Figure 10: Comparison of Accumulative Propagation Distance (APD).  
Figure 9: Comparison of Packet Delivery Ratio (PDR).  
WDFAD-DBR when the node density is 100-150 because it is difficult to find a high link quality node. Furthermore, the battery utilization is almost the same at a 200-node density with B-DBR, but the more nodes get deployed in the network, the better the proposed scheme performs. This is only because when the node density increases, the number of backward transmissions becomes very less, thus the consumption rate decreases, as well.  
The proposed scheme B-DBR uses one-hop backward transmission whenever it finds a void node. This scheme forwards the data packet towards the node that is located at a higher depth, and it has forwarders to forward the data packets towards the sink node. In a dense region, all protocols show almost similar behavior in terms of energy tax.