esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Car-Following Dynamics Model
### Relative Dynamics in Continuous Time  
The car-following behavior is modeled in a relative coordinate frame, where the ego vehicle's motion is expressed with respect to the preceding vehicle. Let \(d_{k}\) denote the inter-vehicle distance, \(v_{f,k}\) the ego vehicle speed, and \(v_{p,k}\) the speed of the preceding vehicle. The relative states are defined as follows:  
\[\Delta d_{k}=d_{k}-d_{\text{safe}},\quad\Delta v_{k}=v_{f,k}-v_{p,k}. \tag{1}\]  
Here, \(d_{\text{safe}}\) is the desired safety distance, modeled as follows:  
\[d_{\text{safe}}=d_{0}+T_{h}v_{f,k}, \tag{2}\]  
where \(d_{0}\) is the standstill spacing, and \(T_{h}\) the time headway.  
The state variables and control input are defined as follows. The acceleration of the ego vehicle is denoted as \(a_{f}(t)\), and the acceleration of the lead vehicle is denoted as \(a_{p}(t)\)  
Figure 1: Hierarchical architecture of the proposed adaptive cruise control (ACC) system.  
which is treated as a measured external disturbance. The control input \(u(t)\) represents the jerk applied to the ego vehicle, i.e., the derivative of its acceleration.  
The complete continuous-time car-following model can be written as a third-order system with the following state vector:  
\[\mathbf{x}(t)=\begin{bmatrix}\Delta d(t)\\ \Delta v(t)\\ a_{f}(t)\end{bmatrix}. \tag{3}\]  
Taking time derivatives of each state yields the system dynamics:  
\[\dot{x}_{1}(t)=\Delta v(t)=x_{2}(t), \tag{4}\]  
\[\dot{x}_{2}(t)=a_{f}(t)-a_{p}(t)=x_{3}(t)-a_{p}(t), \tag{5}\]  
\[\dot{x}_{3}(t)=u(t). \tag{6}\]  
Thus, the continuous-time state-space model becomes the following:  
\[\dot{\mathbf{x}}(t)=\begin{bmatrix}0&1&0\\ 0&0&1\\ 0&0&0\end{bmatrix}\mathbf{x}(t)+\begin{bmatrix}0\\ 0\\ 1\end{bmatrix}u(t)+\begin{bmatrix}0\\ -1\\ 0\end{bmatrix}a_{p}(t). \tag{7}\]  
This third-order model explicitly incorporates jerk as the control input, enabling smooth transitions in acceleration profiles, which enhances ride comfort and reduces actuator wear. It also allows the MPC to generate anticipatory control strategies that avoid aggressive or abrupt changes.
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
###### Abstract
In addition, physiological confounding factors (PCFs) not related to neurovascular coupling are generated by vascularized tissues (scalp and meninges, mainly), which respond to systemic cardiovascular hemodynamics and regulation, including the pulse, respiratory-related modulations, and slower vasomotor mechanism such as Mayer waves, hence possibly misinterpreting results associated with brain activation [13]. The problem with MAs and PCFs can be addressed by instrumental solutions, namely, by employing fNIRS technologies able to estimate absolute concentrations of \(HbO_{2}\) and \(HbR\), e.g., Time-Domain fNIRS, which can resolve the penetration depth according to the time-of-flight (TOF) of photons reaching the cortex [14], or Frequency-Domain NIRS that employs intensity-modulated and phase-sensitive detection based on the mean TOF [15]. Other strategies provide adaptive cancellation of artifacts based on ancillary sensors to regress superficial confounding factors from the fNIRS signal. Among these approaches, and considering the clinical experimental setting, the best balance could be represented by the short-separation channel regression [16; 17], whose performances can be additionally enhanced by movement sensors and hemodynamic sensors [18; 19; 20]. However, most commercial fNIRS instruments are based on Continuous Wave devices (CW-fNIRS) and are often not coupled to a separate artifact sensing system. Therefore, the present study addresses this condition, which leaves all the artifact removal burden (and uncertainties) to preprocess the raw fNIRS signals. In this perspective, some of the significant artifact reduction strategies will be presented, and results relevant to several combinations of them will be compared in a group of healthy volunteers performing a hand grasping motor task. Among the prospective benefits of this study, the presented results can be taken as a justification for adopting specific preprocessing and analysis pipelines because of further clinical studies, which may not employ a separate artifact sensing system. It is worth remarking that a thorough review of published fNIRS cleaning algorithms is out of the study scope. In addition, experimenting with all possible combinations of options in the various preprocessing steps might lead to many comparisons. Conversely, we used only a small set of the main MA removal algorithms [21; 22; 23; 24; 25], adding some variants explained in the Methods section, with a total of eight algorithms. The subsequent bandpass filtering and principal component analysis (PCA) steps were uniformly repeated in the eight compared pipelines. Despite the necessarily limited examples, we provide quantitative comparisons relevant to the energy reduction of each step in each pipeline and the final HRF outcome, aiming at giving the reader both a theoretical and an applicative perspective.
## 3 Results
### Weather Anomalies
The analysis of ice has been conducted based on three emission scenarios, B1, A1B, and A2, which represent various paths of social and economic development of the world and related greenhouse gas emissions (Figure 7) [135]* Scenario B1 assumes moderate economic growth and simultaneous implementation of low-emission technologies;
* Scenario A1B assumes fast economic growth and sustainable utilization of energy sources;
* Scenario A2 assumes growth based on the dominance of regions, a low level of global cooperation, and high emissions. The forecast increase in global temperatures at the end of the 21 st century (compared to the base period of 1980-1990) is as follows:  
Figure 7: Forecast changes in ice severity rate in the Baltic Sea in 2011-2030, taking into consideration changes in regional atmospheric circulation (**a**); changes in mean air temperature from a level of 2 m above the ground (**b**); changes in mean air temperature from a level of 700 hPa (**c**) [132]. * For scenario B1--1.8 \({}^{\circ}\)C (within a range of 1.1-2.9 \({}^{\circ}\)C);
* For scenario A1B--2.8 \({}^{\circ}\)C (within a range of 1.7-4.4 \({}^{\circ}\)C);
* For scenario A2--3.4 \({}^{\circ}\)C (within a range of 2.0-5.4 \({}^{\circ}\)C) [132]. Based on the output data obtained from the ECHAM5 model adjusted to the conditions of the Baltic Sea, changes in ice range and intensity have been estimated on a timeline until the end of the 21 st century. The analyses aim to identify areas extremely vulnerable to climate changes and assess possible consequences of these changes for navigational conditions, the functioning of marine ecosystems, and the operation of the coastal infrastructure. The results of the forecast have been compared to the corresponding historical data (Table 9) for correct interpretation. The data unequivocally show strong spatial differentiation of ice intensity. The simulation results show slight changes in the ice severity rate on a regional scale. Even though in the selected areas of the Baltic Sea (especially in the marginal regions, taking into consideration the present range of ice) the ice severity rate may show an 80% deviation in extreme emission scenarios, its impact on the actual range and intensity of ice is negligible. This results from very low initial levels of these values. This means that in areas of minimal or occasional occurrence of ice, even significant relative changes translate to negligible absolute changes. As a result, the changes in the ice conditions forecast for the forthcoming years will have no critical impact on navigational conditions or the functioning of the Baltic's coastal environment. Their meaning is mainly statistical, and they have no effect on the environment.
# Assessing Cyber Challenges of Maritime Navigation
## 3 Findings
### Cyber Trends and Challenges
#### 3.3.5 Jamming and Spoofing
The first investigation provided by the Italian Coast Guard indicated 870 different vessels were created at two different moments (13:13 and 13:28) with a duration of 3 min in the first transmission and 2 min in the second. All the tracks appeared in an area of \(28\times 21\) nautical miles between Elba Island and Corsica with different routes and speeds, rendering the monitoring of the maritime traffic in the area impossible and impacting real vessel transmissions. At the Faculty of Maritime Studies and Transport, University of Ljubljana, we have thoroughly investigated the situation to support EMSA's analysis and found 3742 fake ships (861 false tracks with MMSI 24480 XXXX) that generated together 5133 reports, as illustrated in Figure 1. By using the European Marine Observation and Data Network (EMODnet) method [112], a traffic density map (TDM) is created by using ship positioning data collected from Terrestrial and Satellite AIS data sources, the maritime infrastructure, and the SafeSeaNet Ecosystem Graphical Interface (SEG) application. It showed, according to [PERSON] [44], shipping density of up to 45 ships/km\({}^{2}\). The analyses pointed out that the AIS spoofing generator was located in the Elba Island area and that spoofing was conducted by a transmitter tuned onto AIS channel A, occupying around 54% of the available slots. Data were further processed to see whether it affected traffic safety. Only one vessel can be seen to have deviated slightly, although it cannot be proven that the change in course was due to the spoofing. As evident through such examples, deliberate disruptions have affected shipping in international waters and engaged in innocent passage through territorial waters. Disorders to GNSS-enabled positioning and navigation have therefore become a global phenomenon, and to tackle a global problem, the GNSS community requires a global solution [35]. Another concern of the satnav community is related to the risks of being covertly tracked and of being exposed to malware through a satellite communication channel. The two-way communication that some GNSS systems now offer opens up that possibility. These risks have in particular been discussed in connection with the now officially operational BeiDou system [90; 91].
## 2 Methods  
### Time-Frequency Representation via Continuous Wavelet Transform  
A signal that varies over time and consists of a mixture of signals at various frequencies can be decomposed into a time-frequency representation using the continuous wavelet transform. Unlike the Fourier transform, which uses fixed scales regardless of the signal resolution, this method improves efficiency by using scales that vary with the resolution. A continuous wavelet transform decomposes the signal into wavelets of various scales that can reconstruct the signal using a mother wavelet. Equation (1) presents the wavelet transform formula, where \(f(t)\) denotes the signal undergoing decomposition, and \(\psi_{\text{a,b}}(t)\) represents the mother wavelet. The parameter 'a' signifies the scale, while 'b' denotes translation. One notable example among established mother wavelets is the Morse wavelet, as defined in Equation (2) [35; 36; 37; 38], which serves as a prominent example.  
\[\text{T}(\text{a},\text{b})=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}f(t)\; \psi\left(\frac{t-\text{b}}{\text{a}}\right)dt \tag{1}\]  
\[\text{a}\text{e}\text{R}^{+}-\{0\},\text{be}\text{R}\]  
\[\Psi_{\text{P},\gamma}(\omega)=\text{U}(\omega)a_{\text{P},\gamma}\omega\, \overline{v^{2}}\,\text{e}^{-\omega\,\gamma} \tag{2}\]  
\(\text{U}(\omega)\) is the function of unit step, \(\text{P}^{2}\) represents the time-bandwidth product, \(a_{\text{P},\gamma}\) is a normalization factor, and \(\gamma\) is a parameter that controls the symmetry of the Morse wavelet.
## 3 System Design and Platform Subsystems
### Mechanical Structure  
The mechanical structure is a 3U modular structure based upon the CubeSat standard from Innovative Solutions In Space (ISIS) [17]. It consists of three 1U PCB stacks that are contained within a secondary structure that forms the chassis of the structure that holds the PCBs in the stack (Figure 6).  
Figure 5: MeznSat system architecture.
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 5 Conclusions
### Limitations  
While the multi-agent GraphRAG architecture advances explainable and transparent question answering for e-government, several key limitations must be acknowledged. One primary concern is the dependency on the quality and scope of the internal data sources. Both the Embeddings RAG and GraphRAG pipelines leverage curated content from the EC Press Corner, with 20 total press releases and statements, and their outputs are bound by what is explicitly available within these datasets. Web search, in contrast, is incorporated to extend the assistant's knowledge with up-to-date and external information from the broader internet. However, the integration of external data introduces challenges, including the invalidation of information or reconciling potential conflicts between information retrievedfrom official internal sources and less-controlled web content. The current system prioritizes internal, authoritative knowledge in the event of such conflicts.  
A second limitation concerns system evaluation and user engagement. The faithfulness assessment relies exclusively on automated scoring by a large language model (GPT-4o) using custom evaluator components. While this method provides consistency and scalability, it cannot always capture nuanced context or complex ambiguity and may occasionally misjudge borderline or multifaceted responses. Moreover, the evaluation framework does not yet incorporate direct user feedback regarding the relevance of retrieved documents or the sufficiency and correctness of generated answers. This gap limits the system's ability to adapt and improve dynamically based on end-user experience, underscoring the need for human-in-the-loop evaluation in real-world use case applications.  
Finally, the computational and operational costs associated with the multi-agent, agentic pipeline present practical challenges, especially in large-scale or real-time deployments. Because the agentic pipeline orchestrates several resource-intensive LLM pipelines (Embeddings RAG, GraphRAG, and web search) in parallel or sequence, it exhibits substantially higher latency, averaging over a minute per answer, and can produce cloud computing expenses, particularly when commercial LLM APIs are used. While flexible frameworks like Haystack offer a path toward more cost-effective solutions, careful resource allocation, continuous performance monitoring, and tailored scaling strategies will be essential to balance system capabilities with economic feasibility. Addressing privacy, security, and computational efficiency thus remains critical for trustworthy and sustainable e-government AI adoption.
## 3 Results
Similar to results shown in Figure 2B, there was a significant reduction in cell-occupied area at 24 h on ultrasound-fabricated versus sham-exposed gels (Figure 2C, 24 h). On day 3, there remained a small but significant difference still present between sham- versus ultrasound-fabricated gels (Figure 2B, 3 h; sham vs. 8.8 W/cm\({}^{2}\)). The fraction of the gel surface area occupied by cells serves as a simple and effective parameter for quantifying changes in cellular self-assembly. Thus, in subsequent studies, this parameter was used to  
Figure 1: Effects of ultrasound on fibril structure in collagen–fibronectin composite gels. Neutralized solutions of type I collagen (2 mg/mL) and fibronectin (25 ug/mL, 10% AF546-labeled) were co-exposed to either 8.8-MHz ultrasound at an intensity of 8.2 W/cm\({}^{2}\) or sham conditions (0 W/cm\({}^{2}\)) in a 25 \({}^{\circ}\)C water bath. Unbound fibronectin was removed by media exchange prior to fixation and multiphoton imaging of collagen (white) and fibronectin (red) fiber structures. Maximum intensity z-projection images through a depth of 15 μm (5-μm z-steps) at the gel surface were assembled using FJJI software. Shown are representative images from 1 of 3 samples per condition fabricated on independent days. Scale bar = 100 μm. quantify the effect of various fabrication conditions on the ability of collagen-fibronectin hydrogels to influence the self-assembly processes. Figure 2: Acoustically modified collagen–fibronectin composite gels accelerate microtissue formation. Neutralized solutions of type I collagen (2 mg/mL) and fibronectin (25 \(\upmu\)g/mL) were co-exposed to 8.8-MHz ultrasound or sham conditions during gel polymerization in a 25 \({}^{\circ}\)C water tank. Acoustic intensities were calibrated in the free field for 0 (sham), 3.8, or 8.2 W/cm\({}^{2}\). FN-null MEFs (4 \(\times\) 10\({}^{4}\) cells/cm\({}^{2}\)) were seeded on the surface of polymerized gels and cultured for up to 6 d. (**A**) Phase-contrast images were collected from the region of the gel corresponding to the center of the acoustic exposure at 24 h, 3 d, and 6 d post-seeding. Scale bar = 200 \(\upmu\)m. (**B**) The number of microtissues at each time point was quantified for each polymerization condition by counting the number of isolated cell clusters within a 3.7 mm\({}^{2}\) ROI corresponding to the center of the acoustic exposure. (**C**) The area occupied by cells in each ROI was quantified by manually tracing the outlines of cell groups and is presented as the percentage of the ROI surface area occupied by cells. Data are presented as mean \(\pm\) SEM for \(n\geq\) 4 gels fabricated on independent days. Sham, white; 3.8 W/cm\({}^{2}\), gray; 8.2 W/cm\({}^{2}\), black. Significantly different means, \({}^{*}\)\(p<\) 0.05 by one-way ANOVA with [PERSON]’s post-hoc test.
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 1 Introduction
### Literature Review  
In [1], an implantable coil was positioned in the left ventricular area at a depth of 3.5 cm for monitoring cardiac pressure continuously. This work focused primarily on inductive power transfer (IPT), but it also provided continuous monitoring of cardiovascular pressure and involved electronic circuitry within the human body due to its primary emphasis on IPT. A pressure sensor was incorporated into this design of an implantable medicaldevice to allow for constant monitoring of a broad blood pressure range of 5-300 mm of mercury [2]. This system is quite complex and is only suitable for measuring pressure within the heart. In [3], an implantable, wireless vascular electronic system, consisting of an inductive stent and printed soft sensors was designed for real-time monitoring of arterial pressure, pulse rate, and flow. Although this system works without circuits inside the human body, it is only capable of sensing blood vessels and is unable to monitor the ventricular chamber size. Wireless technologies were applied to wearable chemical sensors in the design of an antenna's RLC circuit in which an antenna's resonance frequency varies with analyte concentration. A wireless, compact, percutaneous near-infrared spectroscopic device for continuous monitoring of local-tissue oxygenation was created in [4], and the sole purpose of this work was to monitor the saturation level of oxygen in organ grafts and flaps. A self-tuned open interior micro-coil antenna was designed for blood vessel imaging, data telemetry, and efficient wireless power transmission in [5]. This work was geared towards implantable wireless magnetic resonance imaging (MRI), and it operates at a very high frequency of up to 920 MHz, which is quite harmful to patients as it focuses on the brain. In [6], a left ventricular assist device (LVAD) was autonomously operated for evaluation of the ventricular chamber size using sensors that have a relation between the transmission coefficient and spatial separation in a resonantly coupled regimen. Despite this work having a greater impact on determining the chamber size, it did not provide a method for determining heart rate and other hemodynamic parameters. These days, physiological functions including heartbeat, local stimulation, data recording, and even medication distribution can now be influenced by implantable medical devices. This includes high blood pressure telemetry systems, cardiac recorders, and defibrilators [7; 8; 9]. Although all of these works are deemed valuable, they involve complex procedures and active electrical components inside the human body that are uncomfortable and possibly harmful to the patient [10; 11; 12].
## 3 Methods
### Data Collection Experiment
#### 3.5.1 Signal Normalization  
The signal normalization procedure was conducted at the outset of each data collection phase. This session aimed to achieve a consistent value on a scale from 0 to 1 for both the resistance readings of the stretch sensor and the RMS of the EMG using the  
Figure 10: Experimental environment of data collection experiment.  
Figure 9: Attachment of the armband EMG sensor and the proposed stretch-sensor glove.  
maximum normalization approach. Each stretch sensor and EMG channel underwent this normalization process separately. The first step was to normalize the glove data for a 10-s period. In the first 5 s, the participant will close their hand tightly and open it in the next 5 s. Subsequently, the normalization for the RMS of the EMG was held individually for each cylinder.  
In cases where the patient is unable to produce a maximal voluntary contraction (MVC), an alternative normalization method for EMG was employed. A study used a sub-MVC as a reference value for EMG normalization, where an individual emitted a muscle power weaker than the maximal condition [44]. Another study utilized the isometric contraction based on manual muscle testing of grade 3 (isoMMT3), where an individual performs an isometric contraction against gravity [45]. Therefore, this study employed an alternative EMG normalization method that combines concepts from the aforementioned studies. The participants were instructed to lift each cylinder weighing 1500 g, which was 500 g heavier than the heaviest cylinder in the weight variations mentioned in Section 3.2.3. The participant was instructed to grasp and lift the cylinder with minimal power to prevent it from falling. The maximum value of stretch-sensor data and the RMS of EMG were used as dividers for normalizing the signal during the data collection process. While it is rare, we add an extra measure to treat any data that exceeds 1 or falls below 0, automatically adjusting it to 1 or 0, respectively.
## 3 Results and Performance Evaluation
### Comparative Analysis of Fault Diagnosis Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Proposed** & **CWT-CNN** & **FFT-CNN** & **STFT-CNN** \\ \hline Accuracy (\%) & 99.6 & 96.5 & 87.5 & 93.5 \\ \hline Precision (\%) & 100 & 97 & 95 & 91 \\ \hline F1 Score (\%) & 100 & 97 & 94 & 91 \\ \hline Recall (\%) & 100 & 96 & 94 & 91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of comparative methods with the proposed method. The STFT-CNN method uses a short-time Fourier transform (STFT) to convert AE signals into a time-frequency representation. The STFT-CNN method achieves an accuracy of 93.5%. STFT-CNN performs better than FFT-CNN. The windowing information loss inherent in STFT results in lower resolution in both domains compared to CWT, which likely contributes to the slightly lower accuracy. The results of our study demonstrate the effectiveness of the proposed method, which integrates AE signal processing with advanced deep learning and evolutionary algorithms. By addressing the limitations of traditional approaches, our method provides a reliable and accurate solution for fault diagnosis in milling machines, achieving higher accuracy than the CWT-CNN, FFT-CNN, and STFT-CNN methods. This research paves the way for improved predictive maintenance and operational efficiency in industrial settings.