esa-sceva/satcom-chunk-collection huggingface dataset:
# Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
## 4 Simulation Results  
In the simulation, we consider a fundamental scenario with two mega-constellations: \(\Psi_{A}\) and \(\Psi_{B}\). The altitude of \(\Psi_{A}\) is 500 km, which is jammed by \(\Psi_{B}\) at an altitude of 1000 km. The simulation ranges from 1 January 2024 04:00:00 to 10 January 2024 04:00:00. The simulation results are averaged over this period. The antenna models of \(\Psi_{A}\) and \(\Psi_{B}\) are both ITU-R S.1528. The parameter settings in the simulation are shown in Table 2.  
We analyze the impact of the jamming power \(P_{J}\) and constellation scale \(Z_{B}\) of \(\Psi_{B}\) on the network capacity of \(\Psi_{A}\), as shown in Figure 4. An increase in both the jamming constellation scale and jamming power can lead to an increased jamming intensity, which will finally decrease the overall network capacity.  
The constellation configurations of \(\Psi_{B}\) also have a significant impact on the jamming effects. We analyze the network capacity of \(\Psi_{A}\) when the inclination \(\mu_{B}\) of \(\Psi_{B}\) changes within \([50^{\circ},68^{\circ}]\), as shown in Figure 5. In our simulation, \(\mu_{A}=60^{\circ}\). The network capacity of \(\Psi_{A}\) is minimized when \(\mu_{A}\) and \(\mu_{B}\) are close. The probability of satellites in \(\Psi_{B}\) appearing in the MJR of \(\Psi_{A}\) will increase due to the proximity of \(\mu_{A}\) and \(\mu_{B}\), leading to an increased jamming intensity.  
Besides the inclination, we also analyze the impact of the altitude on the network capacity \(C_{A}\), as shown in Figure 6. It can be seen that there is no clear trend in \(C_{A}\) when \(h_{B}\) varies. As \(h_{B}\) varies, so does the jamming angle. Both of the above factors have an effect on the jamming intensity. We also investigate the influence of the phase factor on \(C_{A}\). Compared to \(\mu_{B}\) and \(P_{J}\), the effect of the phase factor is negligible, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & \(\Psi_{A}\) & \(\Psi_{B}\) \\ \hline Orbit prediction model & Two body & Two body \\ Altitude h/km & 500 & 1000 \\ Inclination \(\mu/(^{\circ})\) & 60 & 70 \\ Number of orbits \(N\) & 30 & 30 \\ Satellites per orbit \(M\) & 40 & 20 \\ Phase factor \(F\) & 1 & 1 \\ Satellite antenna model & ITU-R S.1528 & ITU-R S.1528 \\ Transmitting power/dBW & 7 & 7 \\ Communication frequency/GHz & 27.075 & 27.075 \\ Communication bandwidth/MHz & 50 & 50 \\ Transmitting antenna peak gain/dBi & 39.9 & 39.9 \\ Receiving antenna peak gain/dBi & 37 & 37 \\ Transmitting antenna HPBW/\((^{\circ})\) & 5 & 3.5 \\ Receiving antenna HPBW/\((^{\circ})\) & 5 & 3.5 \\ Temperature of terminal/K & 200 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters [16; 17; 18].  
Figure 4: Network capacity of \(\Psi_{A}\) under different \(P_{J}\) and \(Z_{B}\).  
Figure 5: Networkcapacity of \(\Psi_{A}\) under various \(\mu_{B}\).  
Figure 6: Network capacity of \(\Psi_{A}\) under various \(h_{B}\).  
Figure 7: Network capacity of \(\Psi_{A}\) under various \(F_{B}\).
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 1 Introduction
The excellence of a grayscale camera is that it can extract the details of the target with less computational effort. Active means, such as point cloud, require a large amount of computation when extracting relevant key features. Based on the advantage of two sensors, a pose estimation method based on stereo vision and point cloud tracking is proposed in this paper. The proposed method can settle the problem of initial pose acquisition and does not depend on a CAD model during spacecraft tracking. A new joint calibration method is proposed to ensure the minimum error in the conversion between different coordinate systems. A new feature point association criterion is also proposed in Section 3.2.1. The information generated in the initial pose acquisition is mapped to refine the frame-to-frame point cloud registration. Two kinds of motion experiments were performed, namely, high speed and low speed, in order to verify the feasibility of the proposed method. This method can be applied to the mission of the chaser satellite navigation systems in OOS. The rest of this paper is organized as follows. Section 2 defines the problem of non-cooperative spacecraft pose measurement. Section 3 describes in detail the pipeline of the proposed pose estimation method. Section 4 comprises the analysis of the calibration results and the low- and high-speed experiments' results. Section 5 summarizes the full text.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 7 Conclusions  
This study explored the security challenges of the AMQP protocol in IoT networks and introduced a statistical model for detecting and classifying malicious activities. By utilizing a purpose-built testbed, realistic scenarios of Denial-of-Service (DoS), Man-in-the-Middle (MiTM), and brute force attacks were simulated to generate extensive datasets of normal, malicious, and mixed traffic. Unique attack signatures were identified and rigorously validated, forming the basis for a practical and accurate detection mechanism tailored to the AMQP protocol.  
The statistical model demonstrated robust performance in distinguishing normal and malicious traffic patterns with minimal false positives. For DoS attacks, the model reliably identified RST-specific packets, while for MiTM and brute force attacks, it achieved precise classification of attack-related traffic. These findings emphasize the effectiveness of signature-based approaches in bolstering the security and reliability of AMQP-driven IoT systems.  
However, this research also highlights areas requiring further attention. While the model excelled in detecting known attack patterns, addressing more complex or subtle variations remains a challenge. To overcome these limitations, future work will focus on integrating advanced machine learning (ML) techniques capable of adapting to evolving threats. The statistical model developed in this study will serve as a benchmark for evaluating the performance of these ML-driven approaches, paving the way for a dynamic and comprehensive intrusion detection system (IDS).  
By combining the precision of signature-based detection with the adaptability of ML techniques, future efforts aim to create a resilient security framework for AMQP protocols. This approach will enhance the protection of IoT ecosystems, ensuring their reliability and robustness against an increasingly sophisticated cyber threat landscape.  
Conceptualization, M.A. and A.K.; methodology, M.E.H., M.A., A.K. and G.N.; software, M.E.H.; validation, M.A., A.K. and G.N.; formal analysis, M.A.; investigation, M.E.H.; resources, M.E.H.; data curation, M.E.H. and M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A.; visualization, M.E.H.; supervision, M.A., A.K. and G.N.; project administration, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 2 Definition, Data Characteristics and Causes of Multi-Mode Processes  
### Definition, Data Characteristics of the Multi-Mode Process  
1. Multi-mode: multiple operation states and stages in industrial production processes are referred to as multi-mode. Modes and physical phases do not necessarily closely correspond to each other.
2. Multi-mode process: a process with multiple stable mode states in the same production process affected by changes in the external environment and other conditions, or by changes in the production scheme or inherent characteristics of the process itself, is referred to as a multi-mode process. Depending on the different production modes adopted, multi-mode processes can be divided into multi-mode continuous processes and batch processes. A multi-mode system will continuously switch between the following modes: steady-state mode 1--transition mode--steady-state mode 2. The data characteristics corresponding to each mode will be different, such as shifts in the mean value of variables and changes in the covariance structure.
3. Continuous process: a continuous process is a mode that works stably according to the state. Its characteristic variables change slowly.
4. Batch process: batch processes involve a cyclic mode of work. There are multiple steady-state modes in batch processes and their time characteristics change with progress of the process; there are also short-term transition modes in adjacent batch processes.
5. Steady-state mode: a steady-state mode is a mode in which the operating state of the system is relatively stable during production. The variables characterizing the steady-state mode, such as the mean, variance, and correlation, do not change with operation time. In the production process, a system will usually operate in a steady-state mode, which is the crucial mode that determines product quality.
6. Transition mode: when a production process goes from one steady-state mode to another, it cannot do so suddenly. A gradual and dynamic transition mode is required to connect two steady-state modes. In the transition mode, the process variables and operation modes can change greatly over a short period, and the running track is generally different each time. The characteristic variables of the transition mode show strong time-varying, dynamic and non-linear characteristics and have a considerable impact on the production process.
## 2 System Model
### The Baseline Model
The GLU dynamically adjusts its activation level and output in response to the input signal's strength and characteristics, providing enhanced adaptability and flexibility for processing complex input data. The commonly used mean squared error (MSE) loss function is limited to capturing discrepancies among predicted signals solely in the time domain. Therefore, in the RIMformer, a hybrid time-frequency loss function is designed to encompass the errors of the reconstructed signal in both the temporal and frequency domains. The hybrid loss function is calculated as follows:  
\[\mathcal{L}_{\text{TF}}=\frac{1}{\sqrt{L}}\,||Y-\widetilde{Y}||_{2}+\frac{ \lambda}{\sqrt{L}}\,||\mathcal{F}(Y)-\mathcal{F}\Big{(}\widetilde{Y}\Big{)} \,||_{2}, \tag{2}\]  
where \(Y\) and \(\widetilde{Y}\) are the ground truth and predicted values, respectively; \(L\) is the length of the signal sequence; \(\mathcal{F}\) represents the spectrum of the signal after Fourier transformation; and \(\lambda\) is a hyperparameter used to adjust the weight between the two components. This dual-domain loss function enables the network to focus not only on the signal values at specific time points but also on optimizing the signal's frequency domain characteristics. Our analysis of RIMformer's inference latency shows that the self-attention mechanism and convolutional operations take up most of the computational time. Therefore, these two parts are the focus of the lightweight design in this paper. Figure 3: The overall framework of the baseline model RIMformer.
## 2 Materials and Methods
### Inverse Acoustic Characterization Using TMM  
For the purpose of predicting the sound absorption coefficient (\(\alpha\)) using the adopted TMM model (Figure 4a), four additional parameters were required in addition to the measured airflow resistivity to characterize the porous layer based on the JCA model: porosity (\(\phi\)), tortuosity (\(\alpha_{\infty}\)), thermal characteristic length (\(\Lambda^{\prime}\)), and viscous characteristic length (\(\Lambda\)). Porosity was estimated from Equation (5), where the solid density of the polyurethane matrix was 1160 kg/m\({}^{3}\)[27].  
\[\phi=1-\frac{\rho_{\text{bulk}}}{\rho_{\text{solid}}} \tag{5}\]  
The other parameters were obtained by inverse characterization using the least squares method: the TMM model was fitted to experimentally obtained sound absorption coefficients by minimizing the error between the experimental data and the theoretically predicted data:  
\[\text{min}F=\text{min}\sum_{i=1}^{N}\left(\alpha_{i}^{exp}-\alpha_{i}^{TMM} \right)^{2} \tag{6}\]  
where \(N\) is the total number of computed frequencies. The process of optimization was performed on the samples with a thickness of 7 cm and the same values per density were adopted. The inverse characterization was conducted for frequencies above 650 Hz. This limit was set in accordance with ref. [28], because the five-parameter JCA model used in the study is not very precise at low frequencies and with this limit some imprecise data at lower frequencies associated with the experimental set-up is also avoided.  
The number of the considered frequencies in the optimization process was \(N=721\) and they were equidistantly distributed in the frequency region 650-2000 Hz. Several constrains were imposed in the process of optimization:  
\[\left\{\begin{aligned} & 1\leq\alpha_{\infty}\leq 4\\ & 1\leq\frac{\Lambda^{\prime}}{\Lambda}\leq 8\\ & 30\cdot 10^{-6}\leq\Lambda\leq 300\cdot 10^{-6}\end{aligned}\right. \tag{7}\]  
The technique of inverse acoustic characterization using phenomenological models is not new in the field of applied acoustics. Indeed, many papers have been written on this topic. It is used when it is not possible to obtain direct information on the acoustic characteristics of materials due to possible measurement difficulties; thus, phenomenological models consider the wave propagation inside pores at a microscopic level and the transfer matrix method was employed for calculation of the sound absorption coefficient [28; 29; 30; 31; 32; 33; 34; 35].
## 4 Conclusions  
A new formulation is presented to improve the accuracy of DPSM for the near field computation. The classical DPSM technique works very well for the far field computation but cannot accurately model the near field--the field on the transducer surface and very close to the transducer surface--because DPSM based on the Huygens-Fresnel principle assumes that the point of observation is not very close to the transducer face modeled by distributed point sources. The new formulation explores a new possibility of equivalent source density and makes the DPSM technique accurate in both the near field and the far field. This is achieved by replacing every point source in the classical  
Figure 11: Pressure variation along the central axis in front of the transducer: dashed line—theoretical value; dotted line—computed by superimposing individual point source contributions, as shown in Figure 1; solid lines—computed results using source clusters, as shown in Figure 3, for three different radiation patterns.  
Figure 10: The normal velocity variation on the transducer surface: dashed line—true value; dotted line—computed by superimposing individual point source contributions, as shown in Figure 1; solid lines—computed results using source clusters, as shown in Figure 3, for three different radiation patterns.  
DPSM formulation by families of quantum sources. An individual family of sources can generate any radiation pattern, unlike an individual point source for which the radiation wave front can only be spherical in an isotropic medium. The new formulation is developed in such a manner that it does not increase the dimension of the square matrix that needs to be inverted for the source strength calculation. Thus, the main advantage of DPSM, that it requires low computational time compared to FEM, is not compromised. In both FEM and classical DPSM formulations the standard approach for improving accuracy is to increase the mesh size for FEM or the number of point sources for DPSM. Both of these approaches significantly increase the computational time and is avoided here. Numerical examples are presented demonstrating the improvement in the computational accuracy with the new technique.  
The third author acknowledges the financial support from Ecole Normale Superieure de Cachan, France towards his visit as an Invited Professor to conduct this collaborative research.  
All three authors contributed to the paper in terms of conceiving the research idea, generating the numerical results and writing the paper. However, [PERSON] took the leading role in conceiving the idea, [PERSON] was leading in generating the numerical results and [PERSON] took the leading role in writing the paper.  
The authors declare no conflict of interest.
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
## 4 Discussion
The availability of real-time data at high temporal frequency can help water utilities identify issues, schedule timely maintenance or upgrades, and ultimately meet goals for sustainable water demand [48]. To estimate the water demand from smart water meter data, we can plan timeframes for better water distribution and supply management. These findings can be extended to other regions and countries, providing insights into local consumption patterns and infrastructure conditions. Implementing similar data-driven strategies in diverse contexts can lead to more efficient and sustainable water management practices globally. Overall, this study highlights the importance of understanding water consumption patterns to inform better management. By considering both daily and seasonal variations in water consumption, suppliers can develop more helpful approaches to maintaining water quality via smart water meter data.
## 2 Materials and Methods
### Tissue Models Simulated
#### 2.3.2 Three-Layer Models  
The second set of MC simulations modeled experimental measurements. Each measurement from a phantom (for given depth and flow speed) was modeled as a 3-layered slab (following the schematic in Figure 1b). Layer 1 was liquid covering the flow chamber, whose layer thickness represented depth of channel from surface. Layer 2 was the flow channel having fixed thickness of 0.15 cm (to match the inner diameter of the tubing used) and Layer 3 representing the solid phantom block (thickness of 5 cm).  
We note that this modeling architecture does not account for the plastic tube-wall (present experimentally). Modeling the tube wall as an additional layer with (with low values of absorption, scattering and Brownian correlation-decay coefficient relative to the liquid layer) did not change simulated outputs relative to an equivalent 3-layer model and thus we chose to represent flow phantoms as a 3-layer model. Since Layer 1 and Layer 2 contained the same liquid medium, and since the optical coefficients for Layer 3 (solid block) was similar to that of the liquid phantom, we modeled all layers as having identical transport coefficients and refractive indices, matching those of the liquid phantom used.  
Table 1 shows the required inputs (with values for experimentally known parameters) needed to simulate photon correlation from the three-layer phantom models. In Layers 1 and 3 the correlation flow coefficients were modeled as diffusive with a given Brownian diffusion coefficient; for Layer 2 it was modeled as diffusive only when the pump was inactive (phantom \(F_{0}\)). The flow in Layer 2 was modeled as random flow, when the fluid was actively pumped. The Brownian flow-coefficient inputs for layers 1 and 3 were kept fixed for all flow speeds (for fixed depth). Input values of all Brownian flow coefficients needed were obtained using theoretically analyses (described in Section 3.3.1).  
Increasing depth of the flow channel were modeled via MC simulations by increasing the thickness of Layer 1 (from \(z_{1}=D_{0}\)) in increments of 0.03 cm, to match experimentally expected values. The thickness of layer 1 at depth \(D_{0}\) needed to be empirically determined (described in Section 3.3.2). Changes in channel flow rates were modeled by varying flow coefficients used for Layer 2 such that the relative change in flow \(F_{2}\)/\(F_{1}\) and \(F_{3}\)/\(F_{1}\) in the MC models matched experiments. The flow speed in Layer 2 at flow rate \(F_{1}\) (1.44 mL/h) was needed and was also empirically determined (described in in Section 3.3.2). MC predictions of \(g_{2}(\tau)\) simulated for experimental phantoms were obtained for the experimental SDS of 0.15 cm.
# Low-Density Geopolymer Composites for the Construction Industry
## 2 Materials and Methods  
### Materials  
The raw materials used in the study, which were mixed to synthesize the light-weight geopolymers, are described as follows.  
The commercial geopolymer \"Baucis k\" (Ceske Lupkove Zavody, city, Czech Republic) is composed of metakaolin (Mefisto L05) and potassium alkaline activator [15].  
Liapor (DEK, Vintirov, Czech Republic) (Figure 1a) is the trade name of a light-weight ceramic aggregate made from plasticity clays. It has an almost spherical shape with an average particle size ranging between 1 and 4 mm. Liapor is chemically stable and is a great alternative aggregate because of its low density (0.5 g/cm\({}^{3}\)) and thermal conductivity (0.11 W/m\(\cdot\)K).  
Figure 1: Raw materials used as fillers: (**a**) Liapor, (**b**) glass microspheres, and (**c**) EPS polystyrene.  
Other low thermal conductivity aggregates (filler) suitable for constructions are hollow ceramic microsphere (Figure 1b) and polystyrene (Figure 1c). Polystyrene is a synthetic polymer made from the styrene monomer. The polystyrene used in this study is expanded polystyrene (Synthos, city, Poland); it has a mean range particle size of 1-1.8 mm, a density of 0.18 g/cm\({}^{3}\), and a thermal conductivity of 0.08 W/m\(\cdot\)K. Instead, the hollow ceramic microsphere (Elite Industrial, manufacturer, city, China) has a range particle size of 0.037 to 0.425 mm, a density of 0.35 to 0.45 g/cm\({}^{3}\), and a thermal conductivity of 0.11 W/m\(\cdot\)K.  
Silica fume is a by-product of the production of elemental silicon or alloys containing silicon in electric arc furnaces [16]. Silica fume significantly improves the overall compressive strength and bond strength of the geopolymer composites [4; 17; 18]. Basalt fiber help improve the mechanical properties and fire resistance of the building materials [19; 20]. These two materials were used to improve the overall properties of all samples studied.