esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 4 TB-CSPN: A Hybrid Architecture for Semantic and Structural Coordination  
TB-CSPN represents a fundamental departure from both traditional multi-agent systems and classical Petri Net approaches. While it incorporates a Colored Petri Net (CPN) substrate to manage coordination logic, the architecture enforces a conceptual and functional separation between this structural layer and the semantic layer. System intelligence is not encoded within the net itself but emerges from the interplay between token-level topic vectors--derived via LLM-mediated topic modeling--and the Petri Net's transition thresholds.  
This hybrid design enables TB-CSPN to integrate symbolic coordination with continuous, updatable semantic reasoning, thereby allowing for scalable, adaptive multi-agent interaction without the state-space explosion typically associated with monolithic formal systems. The following section introduces the formal components of the architecture with this distinction in mind.  
_The Hybrid Architecture Principle:_ TB-CSPN's core innovation lies in architectural separation: semantically rich topic-based tokens carry the system's intelligence and drive agent communication, while Colored Petri Nets provide formal coordination guarantees and workflow management. This separation means that increasing system complexity affects only the shared semantic spaces--coordination patterns remain constant regardless of agent population or task complexity.  
The framework coordinates multi-agent systems composed of human supervisors, LLM-based consultants, and specialized AI workers through topic-tagged tokens that traverse three communication layers (Surface, Observation, and Computation) corresponding to strategic, semantic, and operational responsibilities. Agent activation and group formation are governed by topic relevance thresholds rather than rigid coordination rules, enabling emergent, context-sensitive collaboration while maintaining formal verification properties.  
_Distinguishing TB-CSPN from Traditional Formal Systems:_ Unlike classical Petri Net applications, where the entire application state and logic are encoded within the net structure itself, TB-CSPN employs Colored Petri Nets solely as a coordination substrate.  
Figure 1 shows that the semantic intelligence, including topic distributions, agent reasoning, and adaptive behavior, resides in the token content and agent processing capabilities rather than in the net topology. Supervisor overrides in Human-in-the-Loop mode are isolated from transition logic and do not alter net topology, preserving formal guarantees such as deadlock freedom. This architectural choice allows TB-CSPN to achieve sub-linear scaling properties, contradicting traditional assumptions about the limitations of formal systems.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 2 Materials and Methods
### Kinetic Studies  
Kinetic experiments are designed to establish the influence between time, and the course of the sorption process. Kinetic tests were conducted as follows: 0.1 g of YL-M-L-AC was weighed into 9 propyl containers, and 20 cm\({}^{3}\) of methylene blue solution with a concentration of 250 mg/dm\({}^{3}\) was added. The samples were mixed using a shaker for: 0.167, 0.5, 1, 2, 3, 8, 12, 18, and 24 h at 25 \({}^{\circ}\)C. After the specified time, the solution was separated from YL-M-L-AC using an external magnetic field. The obtained solutions were analyzed spectrophotometrically to determine the MB concentration. All tests were repeated three times and the results obtained were averaged.  
Four models were used to model the MB sorption rate data on YL-M-L-AC: pseudo-first-order, pseudo-second-order, Elovich, and Weber-Morris (W-M).
## 8 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Engine Name** & **Spyware (144)** & **Backdoor (122)** & **Trojan (500)** & **Ransomware (500)** & **Adware (500)** & **Worm (497)** \\ \hline Antiy-AVL & 0.500 & 0.730 & 0.868 & 0.978 & 0.980 & 0.742 \\ \hline APEX & 0.194 & 0.434 & 0.884 & 0.704 & 1.00 & 0.821 \\ \hline Arcabit & 0.681 & 0.803 & 0.880 & 0.996 & 0.994 & 0.797 \\ \hline Avast & 0.493 & 0.893 & 0.934 & 0.920 & 0.982 & 0.946 \\ \hline Avast-Mobile & 0.701 & 0.041 & 0.026 & 0.022 & \(\mathrm{N/A^{1}}\) & 0.010 \\ \hline AVG & 0.493 & 0.893 & 0.934 & 0.920 & 0.982 & 0.954 \\ \hline Avira & 0.944 & 0.893 & 0.842 & 0.750 & 0.980 & 0.867 \\ \hline Avware & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & 0.002 \\ \hline Baidu & 0.014 & 0.049 & 0.006 & 0.032 & 0 & 0.288 \\ \hline BitDefender & 0.854 & 0.959 & 0.882 & 0.994 & 0.992 & 0.980 \\ \hline BitDefenderFalx & 0.660 & 0.025 & 0.028 & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) \\ \hline BitDefenderTheta & 0.132 & 0.295 & 0.232 & 0.530 & 0.986 & 0.680 \\ \hline Bkav & 0.090 & 0.213 & 0.358 & 0.422 & 0.100 & 0.592 \\ \hline CAT-QuickHeal & 0.681 & 0.303 & 0.614 & 0.586 & 0.114 & 0.455 \\ \hline ClamAV & 0.083 & 0.279 & 0.432 & 0.550 & 0.114 & 0.565 \\ \hline CMC & 0 & 0 & 0 & 0 & 0 & 0.004 \\ \hline Comodo & 0.243 & 0.393 & \(\mathrm{N/A}\) & 0.100 & 0.086 & 0.425 \\ \hline CrowdStrike & 0.194 & 0.484 & 0.860 & 0.580 & 0.486 & 0.795 \\ \hline Cybereason & 0.118 & 0.262 & 0.352 & 0.514 & 0.006 & 0.638 \\ \hline Cylance & 0.194 & 0.508 & 0.950 & 0.764 & 0.838 & 0.825 \\ \hline Cynet & 0.910 & 0.811 & 0.944 & 0.952 & 0.980 & 0.907 \\ \hline Cyren & 0.646 & 0.631 & 0.912 & 0.922 & 0.980 & 0.895 \\ \hline DeepInstinct & 0.049 & 0.180 & 0.936 & 0.316 & 0.112 & 0.298 \\ \hline DrWeb & 0.861 & 0.754 & 0.832 & 0.918 & 0.748 & 0.851 \\ \hline eGambit & 0.014 & 0.025 & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & 0.042 \\ \hline Elastic & 0.222 & 0.590 & 0.910 & 0.824 & 0.986 & 0.873 \\ \hline Emsisoft & 0.826 & 0.943 & 0.872 & 0.976 & 0.972 & 0.952 \\ \hline Endgame & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & 0.002 \\ \hline ESET-NOD32 & 0.951 & 0.893 & 0.964 & 0.888 & 0.988 & 0.964 \\ \hline F-Prot & 0 & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & 0.002 \\ \hline F-Secure & 0.389 & 0.377 & 0.834 & 0.492 & 0.426 & 0.495 \\ \hline FireEye & 0.861 & 0.975 & 0.908 & 0.994 & 0.994 & 0.990 \\ \hline Fortinet & 0.924 & 0.820 & 0.966 & 0.914 & 1.00 & 0.897 \\ \hline GData & 0.861 & 0.967 & 0.964 & 0.990 & 0.990 & 0.980 \\ \hline Google & 0.535 & 0.615 & 0.926 & 0.930 & 0.980 & 0.626 \\ \hline Gridinsoft & 0.132 & 0.295 & 0.886 & 0.554 & 0.358 & 0.547 \\ \hline Ikarus & 0.931 & 0.779 & 0.962 & 0.932 & 0.964 & 0.873 \\ \hline Invincea & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) & 0.034 \\ \hline Jiangmin & 0.153 & 0.582 & 0.308 & 0.718 & 0.070 & 0.419 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection
### Sparse and Abnormal Trajectory Points Detection  
The sparseness and anomalies of the AIS trajectory data originate from a variety of factors, such as receiving equipment failures and data transmission interference, which all affect the authenticity of the AIS data. However, the subsequent research in trajectory analysis, mining, and modeling usually requires an accurate and reliable trajectory database. Therefore, to ensure the reliability and validity of the data, this paper develops a detection method to detect and mark the sparse and abnormal points in the trajectory data, which provides support for the subsequent trajectory restoration.
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 2 Random Field Theory
### Discretization of Transversely Anisotropic Random Field  
The discretization method is crucial to the construction of a random field. At present, several approaches have been provided to approach this, such as the local average method, covariance matrix decomposition method, Fourier transform method and Karhunen-Loeve series expansion method. Due to its capability of dealing with space transformation problems, covariance matrix decomposition is adopted to discretize random fields.  
The autocorrelation matrix \(\mathbf{M}_{a}\) can be established by discretizing the random field at the centroid of random field elements [24]:  
\[\mathbf{M}_{a}=\begin{bmatrix}1&\rho(\Delta x_{12},\Delta y_{12})&\cdots&\rho (\Delta x_{1n_{e}},\Delta y_{1n_{e}})\\ \rho(\Delta x_{21},\Delta y_{21})&1&\cdots&\rho(\Delta x_{2n_{e}},\Delta y_{2 n_{e}})\\ \vdots&\vdots&\ddots&\vdots\\ \rho(\Delta x_{n_{e}1},\Delta y_{n_{e}1})&\rho(\Delta x_{n_{e}2},\Delta y_{n_ {e}2})&\cdots&1\end{bmatrix} \tag{9}\]  
wherein \(\rho\) represents the autocorrelation coefficient between any two locations; \(\Delta x_{ij}\) and \(\Delta y_{ij}\) represent the absolute horizontal and vertical distances between the centroids of the \(i\)-th and the \(j\)-th elements; \(n_{e}\) is the number of elements.  
Via the Cholesky decomposition method, \(\mathbf{M}_{a}\) can be decomposed into the product of lower triangular matrix \(\mathbf{L}\) and its transpose:  
\[\mathbf{L}\cdot\mathbf{L}^{T}=\mathbf{M}_{a} \tag{10}\]  
Via \(\mathbf{L}\), the standard normal random field \(\mathbf{H}^{G}\) can be obtained:  
\[\mathbf{H}^{G}=\mathbf{L}\cdot\mathbf{Z} \tag{11}\]  
wherein \(\mathbf{Z}\) is the independent standard normal random variables. With the aid of the mean, standard deviation and margins of random variables, \(\mathbf{H}^{G}\) can be transformed into the expected non-normal random field.  
In this paper, the transversely isotropic random field with an exponential correlation structure is studied. Substituting Equation (8) into Equation (9) yields the autocorrelation matrix of transversely anisotropic random fields. According to Equations (10) and (11), an independent standard Gaussian random field \(\mathbf{H}^{IAG}\) can be obtained by setting \(\mu=0\) and \(\sigma=1\). Figure 1 shows a standard normal random field in a 100 \(\times\) 100 grid, where \(\delta_{1}=20\) and \(\delta_{2}=2\), respectively. Given the mean, standard deviation and probability distribution, the independent transversely isotropic standard normal random field \(\mathbf{H}^{IAG}\) in Figure 1 can be transformed into the desired non-Gaussian field.
## 2 Numerical Modelling
### Crack Front Evolution  
The basic hypothesis of this modelling is the assumption that the crack front can be characterized as an ellipse (see characteristic dimensions in Figure 1) and that fatigue propagation takes place in a direction perpendicular to such a front, following the Paris law [14],  
\[\frac{\mathrm{d}a}{\mathrm{d}N}=C\Delta K^{\mathrm{m}} \tag{5}\]  
The fatigue process uncertainties eliminate any advantages of using a fatigue crack growth law that is more complex and has more material parameters than the Paris law, cf. [25].  
To discretize the crack front, modelled in an elliptical way, it was divided into \(z\) segments of equal length using the Simpson's rule. Subsequently, each of the points \(i\) was moved perpendicularly to the crack front, in accordance with the Paris law, so that the maximum crack increase, \(\Delta a(\mathrm{max})\), corresponding to the point of the maximum SIF (maximum parameter \(F\), \(F(\mathrm{max})\)) was kept constant during the whole computation. From this maximum increase \(\Delta a(\mathrm{max})\) and the SIF, the advance \(\Delta a_{\mathrm{i}}\) of each of the front points can be obtained as:  
\[\Delta a_{\mathrm{i}}=\Delta a(\mathrm{max})\bigg{[}\frac{F_{\mathrm{i}}}{F( \mathrm{max})}\bigg{]}^{\mathrm{m}} \tag{6}\]  
The new points, fitted by the method of least squares to minimize the error, form a new ellipse, as described elsewhere [26], so that the crack advance progress is repeated iteratively up to reaching the required relative crack depth. The ranges of the used variables were as follows: \(0.2\leq a/b\leq 2\), \(0.02<a/t<1\), and \(0\leq\phi\leq\pi/2\) (only a quarter of the ellipse was studied in the three cases due to the symmetry exhibited by both the embedded and the surface cracks). Parameters \(z\) and \(\Delta a(\mathrm{max})\) were determined by a study of convergence to find out the most adequate values to obtain a reliable result (\(z=12\) and \(\Delta a(\mathrm{max})=0.00001t\)).  
The number of cycles \(N\) required for fatigue crack propagation can be calculated, from the Paris law given by Equation (5), with the expression,  
\[N=\frac{1}{C\Delta\sigma^{\mathrm{m}}\pi^{\mathrm{m/2}}}\int\limits_{a_{0}}^{ a_{\mathrm{f}}}\frac{Q^{\mathrm{m/2}}}{F^{\mathrm{m}}a^{\mathrm{m/2}}}\; \mathrm{d}a \tag{7}\]  
where \(a_{0}\) and \(a_{\mathrm{f}}\) are, respectively, the initial and final crack depth.  
The dimensionless number of cycles \(n\) necessary for fatigue crack propagation is obtained through the following expression (on the basis of the trapezoidal rule),  
\[n=\int\limits_{a_{0}}^{a_{\mathrm{f}}}\frac{Q^{\mathrm{m/2}}}{F^{\mathrm{m}}a ^{\mathrm{m/2}}t^{(2-\mathrm{m})/2}}\;\mathrm{d}a \tag{8}\]
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 3 Physical Characterization and Results  
This section presents the numerical evaluation and physical validation of the proposed hybrid collision assessment framework. The objective is to assess the method's effectiveness in accurately estimating the time of closest approach and minimum separation distance between space objects using publicly available TLE data. The analysis also quantifies the framework's computational performance and suitability for operational deployment. The evaluation integrates three core components Monte Carlo uncertainty sampling, spline-based deterministic refinement, and multi-stage temporal narrowing into a single collision risk estimation pipeline. Performance is assessed through benchmark scenarios involving active satellites and cataloged debris in low Earth orbit. All simulations were conducted using Python 3, with orbit propagation performed via [PERSON]'s SGP4 model implemented through the ssp4 Python package [21]. Tests were executed on a standard Intel Core i5 machine running Windows10, with an average execution time of approximately 20 s per conjunction case. Subsequent subsections report on screening performance, TCA estimation accuracy, spatial distance error, and collision probability consistency. These results validate the hybrid method's capacity for reliable and scalable conjunction analysis in real-world applications.
## 3 Results and Discussion
### Selenium  
Se contents displayed a range of 15-407 ug/kg for red deer and 24-564 ug/kg for wild boar. However, despite the similar ranges for both species, Se medium contents were higher for wild boar (193 ug/kg) than red deer (53 ug/kg). Comparable Se content values in wild boar were reported by other authors, ranging from 130 to 150 ug/kg [25; 52], while recent studies have indicated Se values in deer ranging from 40 to 80 ug/kg [11; 53]. Moreover, the Se content in wild boar meat aligns with that reported for pork (123-176 ug/kg) [40].  
Meat and meat products are one of the main dietary sources of Se in the human diet [54]. A recent study [55] has shown higher serum Se concentrations in omnivores (62.85 ug/L) and flexitarians (61.37 ug/L) than vegetarians (55.12 ug/L) and vegans (52.84 ug/L), with statistically significant differences between both groups. According to this, wild boar meat could be a reliable dietary source of Se. The probabilistic model showed Se intakes of 5.5 ug (9% Se DRI) for the 50 th percentile and even 44.7 ug (75% Se DRI) for the 95 th percentile from wild boar meat consumption (one service size) in the hunter-consumers. Se intakes from red deer meat consumption were lower. Moreover, the EFSA Panel on Dietetic Products, Nutrition and Allergies [56] estimated a Se intake in adults from EU countries between 31 and 66 ug/day. In this study, Se intakes resulting from the consumption of wild boar are found to fall within this range, notably remaining well below the established tolerable upper intake level for Se in adults (255 ug/day) [57]. Furthermore, most of the Se present in meat is found in an organic form (selenocysteine and selenomethonine) and therefore has greater bioavailability.
## 3 Results and Analysis
### Reduction of Power Line Interference
#### 3.1.3 Analysis of EMG Recordings  
Figure 8 shows the results obtained after analyzing the EMG signals of a representative subject collected in Experiment 3. In the picture, the forearm muscles of the representative subject were initially in their rest state (without activation/contraction), and a second later the muscles were activated. The waveform of the non-activated muscles as obtained by Method 1 represents a series of sine waves that jitters up and down after the activation of the arm muscles; the waveform of the non-activated muscles as obtained by Method 2 denotes a noisy line, and when the muscle is contracted, the EMG signal patterns become obvious. The waveform obtained after applying Method 3 is similar to that of Method 2, but with relatively smaller amplitude; the waveform of non-activated muscles obtained by applying Method 4 appears to be cleaner compared to Method 3, and there is a significant EMG signal burst when the forearm muscles are activated. Therefore, it can be deduced that the conclusion  
Figure 7: The comparison of the time waveforms (**a**) and spectra (**b**) of the EOG signals (subject #7) under different conditions. The 50 Hz notch condition (2 nd row) was obtained by applying 50 Hz notch filter (software) to raw data of the No-Shield condition (1 st row).  
of Experiment 3 is consistent with those of Experiment 1 and Experiment 2, which further proves the potential of the proposed shielded drive technology.  
By analyzing the EMG signal spectrum at 50 Hz, Method 1 achieved a high amplitude of 19.7 dB; Method 2 recorded an amplitude of 4.9 dB, which is 14.8 dB lower than that of Method 1; Method 3 achieved an amplitude of 2.4 dB, which is 17.3 dB lower than that of Method 1. Meanwhile, Method 4 recorded an amplitude of \(-25.6\) dB, which is 45.3 dB lower than that of Method 1, which appeared to be very efficient in comparison to the other methods.  
From Figure 8, the graph on the second row denotes the time domain waveform and spectrum after the raw signal (the output of Method 1) was subjected to a 50 Hz notch filtering. Its time domain waveform is similar to that of Method 3, but the spectrum is depressed around 50 Hz. That is, the frequency range of the EMG signal is 0-500 Hz, and the useful components of the signal are lost. In contrast, the hardware-shielded drive processing has a better effect.  
Figure 9 shows the mean values of the spectral values at 50 Hz averaged across all the 20 subjects for the ECG, EOG, and EMG experiments under NLC. It could be observed that the spectral amplitude of the 50 Hz interference gradually decreased from Method 1 to Method 4 for all three different experiments, with Method 4 achieving the lowest power line interference that almost fell below the noise floor level. The averaged results in Figure 9 were in accordance with the observations of each individual subject from Figures 6-8.
## 3 Results
### Hardware Setup  
All the methods were evaluated on the same hardware platform with the same available computational and power resources using Ubuntu 18.04 and 16.04 operating systems, depending on each algorithm's software requirements. For this evaluation, we selected readily available and cheap hardware components to assemble a desktop based on the AMD Ryzen(tm 7 3800X processor and the GPU NVIDIA GEFORCE GTX 1080 Ti. The technical specifications are summarized in Table 1.  
Additionally, we provide some CPU performance metrics obtained by averaging the metrics of ten executions of each algorithm over the _sequence_01_ of the TUM-Mono dataset. All these CPU usage metrics were obtained using the hardware detailed in Table 1, using the official codes provided in their repositories and the same dependencies' versions listed by each author. The metrics selected to provide an idea of the computational expenses required by each algorithm were: the overall CPU usage (multicore), the current CPU usage, the amount of GPU memory required by the algorithm, the amount of RAM used while running the algorithm, the time that the algorithm required to process the _sequence_01_ of the TUM-Mono dataset, and the number of frames per second that the algorithm processed. The computational expenses associated with each approach to reconstruct the _sequence_01_ scene are presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Specifications** \\ \hline CPU & AMD Ryzen(tm 7 3800X, eight cores, 16 threads, 3.9–4.5 GHz. \\  & NVIDIA GEFORCE GTX 1080 Ti. Pascal architecture, 1582 MHz, \\  & 3584 CUDA cores, 11 GB GDDR5X. \\  & 16 GB, DDR 4, 3200 MHz \\  & SSD NVME M.2 Western Digital 7300 MB/s \\  & 750 W1 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Specifications of the hardware used during experimentation.  
Figure 3: Algorithm executions for the outdoor sequence _seq-02_ of the TUM-Mono dataset. The implemented methods were: (**a**) ORB-SLAM2, (**b**) DF-ORB-SLAM, (**c**) LSD-SLAM, (**d**) DSO, (**e**) CNN-DSO, (**f**) LDSO, (**g**) DSM, (**h**) DynaSLAM, (**i**) SVO and (**j**) CNN-SVO.  
As can be noticed in Table 1, the SVO was the fastest algorithm that required fewer computational resources to be implemented, closely followed by the DSO and ORB-SLAM2. However, as described in the following sections, the SVO presented strong trajectory loss issues and poor 3D reconstruction quality; so, it might not be considered the best alternative. In addition, it can be noticed that adding ML techniques to geometric-based approaches implied a considerable increase in the CPU, GPU, and memory use.
# Stability Criterion for Dynamic Gaits of Quadruped Robot
## 1 Introduction
The less likely the robot is to tip over, the smaller the difference among ground-reaction forces at each support foot are, meaning that the robot has higher capability to stay stable. Based on the above ideas, by defining and adjusting the virtual-support plane, the traditional _ZMP_ can be extended to three-dimensional (3D) space so that it can be used to analyze the motion state on irregular terrain. An extra term containing the velocity of the robot's body is added to the equation for the calculation of _ZMP_ to better reflect the robot's motion state. Here, we define the virtual-support quadrilateral and propose a stability criterion for quadruped robots with dynamic gaits. The robot can stay stable if the expanded _ZMP_ is located within this quadrilateral, because the current or next set of support feet can provide the moment necessary to prevent the robot from tipping over. The stability is quantified using the distance between _ZMP_ and the boundary of the virtual-support quadrilateral, the angle between the vector from CoM to _ZMP_ and the normal vector of the support plane, and the distance between _ZMP_ and the support line. The simulation results show that the trotting robot can still run stably even if _ZMP_ is not on the current support line, and the distance between _ZMP_ and the current support line cannot accurately assess the robot's stability. The results also indicate that the proposed stability criterion and measure can efficiently and accurately evaluate the stability of a quadruped robot using dynamic gaits. This paper is outlined as follows. The method for expanding _ZMP_ to 3D space is described in Section 2. The stability criterion for the motion of a quadruped robot with a dynamic gait over irregular terrain is introduced in Section 3. The method of calculating the dynamic-stability measurement is presented in Section 4. The simulation results and discussion are reported in Section 5. Finally, conclusions are presented in Section 6.