esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Analog RF Beamforming
### Relay Analog RF Beamforming
#### 3.3.1 Relay Analog Combiner  
To derive \(\mathbf{F}_{1k}\), a problem is formulated that attempts to maximize SNR at the receiving end of the \(k\)th relay node. Therefore,  
\[g_{k}=\max_{\mathbf{F}_{1k},k\in\{1,\ 2,\ldots,K\}}\frac{\lVert\mathbf{F}_{1k}^{H}\bm {H}_{k}\ \mathbf{F}_{opt}\rVert_{F}^{2}}{\sigma_{r}^{2}}\]  
\[s.t.\ |\mathbf{F}_{1k}(x,y)|=\frac{1}{\sqrt{N_{rel}}}\ \forall\ x,y. \tag{33}\]  
It is assumed that channel state information \(\mathbf{H}_{k}\) is available at the relay node and \(\mathbf{F}_{opt}\) has already been derived (20), and hence, it is possible to define the composite channel \(\mathbf{H}_{eff,k}=\mathbf{H}_{k}\ \mathbf{F}_{opt}\) from the source to the \(k\)th relay node. Therefore, the problem in (33) can be transformed into another useful form as  
\[g_{k}=\max_{\mathbf{F}_{1k},k\in\{1,\ 2,\ldots,K\}}\frac{\lVert\mathbf{F}_{1k}^ {H}\mathbf{H}_{eff,k}\rVert_{F}^{2}}{\sigma_{r}^{2}}\]  
\[s.t.\ |\mathbf{F}_{1k}(x,y)|=\frac{1}{\sqrt{N_{rel}}},\ \forall\ x,y. \tag{34}\]To tackle the problem in (34), it can be translated into another equivalent form such as  
\[g_{k}=\max_{F_{1k}}\left\{\frac{\|F_{1k}^{H}\mathbf{H}_{eff,1}\|_{F}^{2}}{\sigma_{r}^{ 2}},\ldots,\frac{\|F_{1k}^{H}\mathbf{H}_{eff,k}\|_{F}^{2}}{\sigma_{r}^{2}}\right\}. \tag{35}\]  
It is known that \(\mathbf{F}_{1k}\) can be derived by maximizing the composite channel \(\mathbf{H}_{eff,k}\) gain, \(\forall k\in\{1,\ldots,K\}\). Therefore, the optimization problem in (35) further boils down to the gain maximization of \(\mathbf{H}_{eff,k}\) over a set of different \(k\) values such as  
\[\max_{k\in\{1,\ 2,\ldots,K\}}\Big{|}\mathbf{H}_{eff,k}\Big{|}. \tag{36}\]  
To solve the problem in (36), all gain values need to be sorted and select the value of \(k\) over which \(\Big{|}\mathbf{H}_{eff,k}\Big{|}\) attains the maximum value. Phase-shift values are extracted from the corresponding \(\mathbf{H}_{eff,k}\) for designing \(\mathbf{F}_{1k}\). Finally, \(\mathbf{F}_{1k}\) is given by the following relation  
\[\mathbf{F}_{1k}=\bigg{(}\frac{1}{\sqrt{N_{rel}}}\bigg{)}e^{i\angle\mathbf{H}_{eff,k}}, \tag{37}\]  
where \(\Big{(}\frac{1}{\sqrt{N_{rel}}}\Big{)}\) is a power normalization constant.
## 3 Results and Discussion
For instance, comparing the different prediction results for tube #1 in Table 2, the RF's RUS predictions for the AE signals within half-way through the degradation were very poor while the CNN models ranked the AE events linearly in a much better fashion. As for the RMSE results, the models' performance heavily depended on the individual acoustic signals belonging to a given tube. As previously discussed, a model's ability to predict an AE's RUS within a close range of the experimental RUS relies upon the unique features of the AE signal in the feature space. Notably, the following observations could be made:  
* The RF model provided a better prediction of RUS for the earliest acoustic emission signals. * The CNN models generally outperformed the RF model at predicting the RUS for signals, which occurred in the second half of the degradation process. Therefore, it is clear that the performance of our models depends on the corresponding task for S\({}_{\text{i}}\)C\({}_{\text{f}}\)-S\({}_{\text{i}}\)C\({}_{\text{m}}\) materials. With the goal of predicting the RUS for a single AE signal that occurs at the early stages of the materials' degradation, one should use the RF model; otherwise one should apply the CNN models. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Stage \#** & **Tube \#1** & **Tube \#2** & **Tube \#3** & **Tube \#4** & **Tube \#5** & **Tube \#6** & **Tube \#7** & **Tube \#8** & **Tube \#9** \\ \hline Stage-1 & 0.105 & 0.443 & 0.224 & 0.326 & 0.028 & 0.108 & 0.171 & 0.087 & 0.047 \\ Stage-2 & 0.74 & 1.206 & 1.14 & 0.717 & 0.826 & 0.652 & 0.744 & 1.295 & 0.587 \\ Stage-3 & 0.861 & 1.16 & 1.094 & 0.668 & 0.832 & 0.512 & 0.905 & 0.885 & 0.932 \\ Stage-4 & 1.163 & 0.739 & 0.706 & 0.431 & 0.725 & 0.807 & 0.578 & 0.357 & 0.638 \\ Stage-5 & 1.201 & 0.36 & 0.411 & 0.373 & 0.674 & 0.775 & 0.37 & 0.203 & 0.532 \\ Stage-6 & 0.994 & 0.201 & 0.338 & nan & 0.685 & 0.601 & nan & nan & 0.317 \\ Stage-7 & 0.85 & 0.186 & nan & nan & nan & 0.471 & nan & nan & 0.258 \\ Stage-8 & 0.657 & nan & nan & nan & nan & nan & nan & nan & 0.165 \\ Stage-9 & nan & nan & nan & nan & nan & nan & nan & nan & 0.136 \\ Stage-10 & nan & nan & nan & nan & nan & nan & nan & nan & 0.172 \\ \hline \end{tabular}
\end{table} 
Table 5: Average RMS of AE in each region by each tube (nan means no values). Figure 13: Stress versus RUS plots for each given tube. (**a**) Random-forest (RF), (**b**) Basic CNN and (**c**) SqueezeNet CNN. Blue represents the predicted values while red represents the true values.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Thresholding Methods  
Thresholding methods, also called Out-Of-Limits (OOL) alarms or statistical models, are the most commonly used techniques to detect anomalies in spacecraft [20; 21; 22; 23]. These approaches aim to check whether the spacecraft telemetry data measurements are within a predefined threshold. Any telemetry parameter that exceeds the said threshold is considered anomalous, and an alarm is activated. Statistical measurements such as the mean, standard deviation, maximum, and minimum are used to compute thresholds.  
Thresholding methods have exhibited certain benefits such as their simplicity and being easy to understand and interpret. However, they suffer from some shortcomings. In fact, setting the threshold value depends heavily on domain knowledge and expert experience, which make these methods less precise and very time-consuming. In addition, the presence of complex noise in spacecraft telemetry data could lead to high false alarm rates. In general, thresholding methods ignore complex interactions between telemetry parameters.  
To overcome the limitations of thresholding techniques, some improvements have been introduced. For example, [PERSON] and [PERSON] [9] have applied thresholding models in many ESA (European Space Agency) missions by integrating a new automatic telemetry monitoring prototype. The novel detection monitoring approach has been managed to analyze the behavior of 2000 parameters during the XMM (X-ray Multi-Mirror Mission) Newton orbit mission. In [24], another anomaly detection method, based on parametric causality and Double-Criteria Drift Streaming Peaks Over Threshold (DCDSPOT), was proposed to solve the problem of high rates of false negatives. The performance of the DCDSPOT method was assessed using four anonymous telemetry parameters generated by a military communications satellite. Compared to other baseline methods, the proposed method obtained the highest recall (91%) and precision (85%).
## 2 Understanding the Key Extracellular Matrix Proteins
### Fibrin  
The precursor of fibrin is the glycoprotein fibrinogen. Each unit of fibrinogen comprises of two copies of three polypeptide chains; A\(\alpha\), B\(\beta\) and \(\gamma\). These polypeptide chains are connected by 29 disulphide bonds and each fibrinogen molecule has outer D domains that are connected via a coiled central E domain [46; 47]. The N-termini of all six chains are contained within the E domains, while the carboxyl termini of both B\(\beta\) and \(\gamma\) are located within the D domain [48]. The overview of the synthesis of fibrin is illustrated in (Figure 5). If the vessel wall sustains an injury, a series of local events ultimately lead to production of thrombin, a protease, that convers fibrinogen to fibrin [48]. It does so by cleaving peptides at the N-terminus of fibrinogen, causing the release of fibrinopeptides generating fibrin monomers [48]. Fibrin monomers then undergo polymerisation to effectively produce fibres to stabilise the clot.  
Figure 4: Elastin biosynthesis pathway: A. Tropoelastin (TE), precursor of elastin, is translated from mRNA in the rough endoplasmic reticulum. B. TE and elastin binding protein (EBP) form a complex in the Golgi apparatus. C. The TE–EBP complex is secreted to the extracellular space. D. The EBP detaches from TE, allowing TE to interact and align along the microfibrillar scaffold. E. Coaervation of TE together with the enzymatic reaction, catalysed by lysyl oxidase (LOX), result in the crosslinking of the TE molecules into the polymeric elastin fibre.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 5 Enhancing Safety Through Large Language Models
### Incident Investigation and Reporting  
When maritime incidents occur, thorough investigation and accurate reporting are essential for understanding root causes and preventing future occurrences. LLMs can significantly aid in the analysis of incidents by processing large volumes of data, including witness statements, logs, sensor readings, and regulatory reports.  
Through advanced natural language processing, LLMs can extract critical details from unstructured textual data, identify patterns, and reconstruct events leading up to the incident. The models can assist investigators in recognizing contributing factors such as equipment malfunctions, environmental conditions, or human errors. By providing a comprehensive analysis, LLMs enhance the depth and accuracy of incident investigations.  
Additionally, LLMs can generate comprehensive reports for stakeholders, including regulatory authorities, insurance companies, and internal management. These reports can be tailored to meet specific requirements, ensuring that all relevant information is presented clearly and concisely. By standardizing reporting formats and content, LLMs facilitate transparent communication and support the dissemination of lessons learned throughout the industry.  
Incorporating LLMs into incident investigation processes not only improves the efficiency and effectiveness of these activities but also contributes to a broader understanding of safety issues within the maritime sector. The insights gained can inform policy development, training programs, and operational changes aimed at preventing future incidents and enhancing overall maritime safety.  
By leveraging the advanced capabilities of Large Language Models across risk analysis, compliance support, human factors mitigation, and incident investigation, the maritime industry can make significant strides in enhancing safety. The integration of LLMs into these critical areas supports a proactive and informed approach to safety management, ultimately contributing to the reduction of accidents and the promotion of a safer maritime environment.  
The application of Large Language Models (LLMs) in maritime safety presents a transformative opportunity to enhance risk assessment, decision support, regulatory compliance, and emergency response. By leveraging AI-driven insights, maritime operators can mitigate human errors, improve communication, and streamline operational procedures.  
However, while these advancements highlight the significant benefits of LLM adoption, they also introduce technical, ethical, and operational challenges that must be carefully managed. The following section critically examines these limitations, addressing key concerns such as data security, bias, regulatory uncertainties, and integration difficulties that could hinder widespread implementation in maritime environments.
###### Abstract  
This paper presents an experimentally observed transition from the validity of the radial theories to the validity of the orbital theories that model the ion current collected by a cylindrical Langmuir probe immersed in low-pressure, low-temperature helium plasma when it is negatively biased with respect to the plasma potential, as a function of the positive ion-neutral collision mean free path to the Debye length ratio \(\Lambda=\lambda_{+}/\lambda_{D}\). The study has been also conducted on argon and neon plasmas, which allows a comparison based on the mass of the ions, although no transition has been observed for these gases. As the radial or orbital behavior of the ions is essential to establish the validity of the different sheath theories, a theoretical analysis of such a transition not only as a function of the parameters \(\Lambda\) and \(\beta=T_{+}/T_{e}\), \(T_{+}\) and \(T_{e}\) being the positive ion and electron temperature, respectively, but also as a function of the ion mass is provided. This study allows us to recognize the importance of the mass of the ion as the parameter that explains the transition in helium plasmas. Motivated by these theoretical arguments, a novel set of measurements has been performed to study the relationship between the \(\Lambda\) and \(\beta\) parameters in the transition that demonstrate that the effect of the ion mean free path cannot be completely ignored and also that its influence on the ion current collected by the probe is less important than the effect of the ion temperature.  
plasma surface technology; cold plasma; ion temperature; ion-neutral charge-exchange collisions; ion mean free path; plasma diagnosis; Langmuir probe; sheath theories +
Footnote †: journal: Applied Physics Letters
## 3 Proposed Real-Time 3D Mapping-Aided (3 DMA) GNSS-Positioning System
### Open-Sourced 3D City Models  
3D models of New York City (NYC) were obtained from open-sourced repositories released by the Department of Information Technology & Telecommunications' (DoITT) 2014 aerial survey [44] with a level of detail (LoD) of 2. The city model was registered to cartesian New York Long Island State Plane FIPS 3104 coordinates. The city model is shown in Figure 2.
## 4 Common Driving Methods for Rotating Motion Mechanisms
### Electric Drive
#### 4.1.2 Piezoelectric Drive
The non-contact ultrasonic suspension drive generates tangential driving force through the pressure gradient of the gas film, which avoids contact \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Advantages** & **Disadvantages** & **Applications** \\ \hline Longitudinal vibration mode rotating ultrasonic motor [173] & Simple control, adjustable output characteristics, stable torque, and no magnetic interference & High voltage power dependence, high power consumption, low output torque and speed & Optical instrument focusing system, micro robot joint drive, low temperature/vacuum environment \\  & No contact friction, long & & \\ Non-contact ultrasonic motor [174] & lifespan, no wear and heat accumulation, fast operating speed & Air film stiffness limits load-bearing capacity & Medical devices that require silent operation in high precision cleaning scenarios, such as semiconductor manufacturing Scanning probe microscope for nano-positioning, joint drive for surgical robots, high torque velocimeter in precision manufacturing \\  & High efficiency, high power & & \\  & output, flexible speed & & \\  & regulation, and resonance & & \\ piezoelectric motor [175] & design improve energy & & \\  & efficiency & & \\  & High precision and fast & & \\  & response, high motion & & \\  & stability, low cost, & piezoelectric stack & \\  & noaxis offset & & \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Comparison of piezoelectric rotary motors. wear but faces the physical upper limit of gas film stiffness and load-bearing capacity. It requires a micro groove flow field control to strengthen the circumferential shear stress. The future development trend will present four major technological fusion features: firstly, superconducting motors will explore multi physics field collaborative design, and solve the problems of rotational quench and dynamic stability through topology optimization and adaptive cooling systems; Secondly, precision drive mechanisms will develop towards heterogeneous integration, combining the multi degree of freedom characteristics of spherical motors with the low inertia advantages of hollow cup motors to construct a composite motion platform; Thirdly, piezoelectric driving will deepen resonance mode control and intelligent material applications, utilizing the high strain coefficient of relaxor ferroelectric single crystal (PMN-PT) to enhance power density, and developing a self sensing self correcting integrated driver; Fourthly, digital twins and real-time impedance matching algorithms will drive the evolution of the driving system towards dynamic reconstruction, achieving nonlinear compensation under load disturbances through online parameter identification, and ultimately forming a cross scale, multimodal precision driving technology system.
## 2 Materials and Methods
### Characterization  
Scanning electron microscopy (SEM) was made via JEOL (Akishima, Tokyo, Japan) JSM-7600F high-resolution Field, emission electron microscope. Transmission electron microscopy (TEM) was made using a high resolution, JEOL, Akishima, Tokyo, Japan, JEM-1011 Transmission Electron Microscope. FeNP was dispersed in ethanol and sonicated for 30 min. A single drop of nanoparticle solution was placed onto the carbon-coated copper grid (400 mesh) then dried at room temperature. TEM imaging was then performed at an acceleration voltage of 90 KV. FT-IR spectra were collected for plant extract (ANE) and FeNP using Cary, South San Francisco, United States of America, 630 FT-IR spectrophotometer. Dynamic light scattering (DLS) was performed using Cilas, Orleans, France, dual scattering particle size analyzer Nano DS. The sample was prepared by sonication of FeNP in  
\begin{table}
\begin{tabular}{c c c c c c} \hline Experiment & C\({}_{i}\) (ppm) & V\({}_{\rm H2O2}\) (mL) & Time (h) & C\({}_{e}\) (ppm) & Removal Efficiency (R \%) \\ \hline
1 & 40 & 5 & 0.5 & 12.900 & 67.6 \\
2 & 40 & 5 & 1 & 6.049 & 84.9 \\
3 & 40 & 5 & 2 & 3.399 & 91.5 \\
4 & 40 & 5 & 3 & 0.755 & 98.1 \\
5 & 40 & 5 & 4 & 0.239 & 99.4 \\
6 & 40 & 1 & 3 & 0.216 & 99.5 \\
7 & 40 & 2 & 3 & 0.341 & 99.1 \\
8 & 40 & 3 & 3 & 0.368 & 99.1 \\
9 & 40 & 4 & 3 & 0.471 & 98.8 \\
10 & 30 & 5 & 3 & 0.376 & 98.7 \\
11 & 20 & 5 & 3 & 0.431 & 97.8 \\
12 & 10 & 5 & 3 & 0.465 & 95.4 \\
13 & 5 & 5 & 3 & 0.317 & 93.7 \\ \hline \end{tabular}
\end{table}
Table 1: Oxidation degradation of methyl orange conditions used and results.  
double distilled, deionized water using power sonic 405 at 20 \({}^{\circ}\)C for 1 h. Dispersion of nanomaterial suspension was performed in a 10 ml of demineralized water and the tube was immersed in a cold water bath during sonication with an ultrasonic system (power sonic 405). X-ray diffraction (XRD) was used to investigate the material structure of iron nanoparticles. The XRD analysis was conducted with a Philips XRD 3100 diffractometer (Amsterdam, Netherlands) at 45 kV and 30 mA. It used copper K\(\alpha\) radiation and a graphite monochromator to produce X-rays with a wavelength of 1.54060 A. Iron nanoparticles were placed in a glass holder and scanned from 10\({}^{\circ}\) to 60\({}^{\circ}\) with a scanning rate of 2.0 \({}^{\circ}\)/min.
# Design of HIFU Treatment Plans Using Thermodynamic Equilibrium Algorithm
## 2 Materials and Methods
### Working Sequence of TEA  
#### 2.3.1 Initialize Thermodynamic Systems  
The goal of optimization is to find the optimal solution according to the problem variables. In genetic algorithm (GA) terminology, an array of variables that need to be optimized is frequently called the chromosome, but here, the term is replaced with the thermodynamic system. In a \(N_{var}\)-dimension optimization problem, each system is a dimensional array defined by Equation (1).  
\[x=[T,V_{1},V_{2},\ldots,V_{N_{var}-1}] \tag{1}\]  
where \(T\) is the temperature and \(V\) is the volume, which may have more than one parameter. Therefore, for convenience, the problem can be turned into a two-dimensional problem by defining a new variable \(\forall\) called \"overall volume\":  
\[\forall=\frac{\sum_{i=1}^{N_{var}-1}V_{i}}{N_{var}-1} \tag{2}\]  
Now, the thermodynamic state of each system can be simply defined as:  
\[x=[T,\forall] \tag{3}\]  
The cost of each system is determined by the cost function \(f\), as shown in Equation (4).  
\[cost=f(x)=f(T,\forall) \tag{4}\]  
To start the algorithm, the initial optimization population is created by the number of \(N_{sys}\). Systems are randomly scattered in the domain of the function based on the upper and lower limits.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 1 Introduction
Then, the contributions are fivefold: (i) design of the system based on hardware that is commercially available, (ii) programming of the microcontroller to enable the communication with the GNSS receivers, (iii) synchronization of the three GNSS receivers in real-time, (iv) design of the algorithms for attitude and position computation, and (v) construction and validation of the prototype in real scenarios. The paper is organized as follows. In Section 2, the GNSS-RTK technology is introduced, and the position computation and error correction for this kind of system is explained. The communication protocols used and the prototype design are detailed in Section 3. In Section 4, the experimental tests of the prototype are shown for both static and dynamic scenarios, and for different configurations of the GNSS antennas. Finally, conclusions and future work are explained in Section 5.
## 2 Materials and Methods
### Comparisons and Statistical Analysis  
Received data were analyzed using Statistica 12 (StatSoft, Krakow, Polska) and the following tests: Shapiro-Wilk test to check the normality of data distribution; t-student test (parametric data); and Mann-Whitney U and Wilcoxon tests (nonparametric data) to check the differences between the data acquired on the right and the left side, and between the data acquired for females and males. The statistical significance was assumed as \(p\leq 0.05\).
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### COSMIC-2 Excess Phase Calculation
#### 2.4.2 COSMIC-2 Receiver and GNSS Transmitter Clock Bias Removal
However, the clock bias calculated from Bernese GNSS software shows that the COSMIC-2 clock does not perform better than that of COSMIC-1. Figure 3g-l shows a considerable drift in the clock with a maximum rate of about 1-2 milliseconds/day. The significant clock bias can be effectively modeled and removed using a least-square polynomial curve fitting. The residual clock bias is comparable to the COSMIC-1. The excess phase/bending angle results using the single and zero differences are compared in Section 5. The GNSS clocks bias (and drift) data can be downloaded from CODE products with a clock solution of 30 s. Considering the GNSS satellites' extremely stable clock drift, a 4 th order polynomial interpolation scheme has been used to estimate the GNSS clock bias in the excess phase calculation. These interpolations can still display a small discontinuity in the excess phase, which causes excess Doppler noise. This can bring more significant noise than UCAR operational products, where the double differencing with ground observations was used to remove the GNSS clock bias. However, this may only affect the bending angles at the upper altitudes, where very small bending angle values are located.