esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System
### Software Architecture
#### 4.3.1 Control Messages  
CtrMs contain command and telemetry information obtained from the UAVs, allowing all components of the multi-UAV system to identify the aircrafts that compose the mission, their respective geographic positions and the eventual position of boats. Messages are sent periodically, that is, every three seconds to the BS. The CtrM format is described in Table 2 and a description of the fields that constitute the header is presented as follows.  
* Identification: unique identifier of the message within a sequence. The packet starts with zero value, while the following ones are incremented by one unit until the mission is finished;
* Message Type: it indicates the packet type, for example, type 1 in this case;
* Source Node Address: identifier of the node that generated the message;
* Destination Node Address: identifier of the node to which the message is addressed;
* Packet Number: unique identifier of a packet sequence. All packets that belong to a same sequence assume a constant value for this field. This field is used in the reassembly process. A node must never assign the same number to a different sequence;
* Time: it forms the date and time of the packet origin using Unix timestamp format;
* Current Location: it contains the geographic coordinates of the source node;
* Number of targets found: this parameter is initialized with zero, being incremented by one unit when a boat is detected;
* Location of found targets: it contains the geographic coordinates of possible targets.  
The CtrMs are intended to provide information to the operators in the BS on the area scanned by the UAV squadron, which includes the eventual location of a boat. The \"location of targets\" field is cumulative, being responsible for sending the coordinates of the detected target and the location of the previous target if it exists. Thus, when the imaging system detects a possible boat, the last two geographic coordinates remain in subsequent packets in such a way that if the packet is lost for some reason, the next ones continue to send information on the location of the target found. In order for the control messages to be of the same size, the fields are filled with zeros if any boats have not yet been found.
# Distance Estimation with a Stereo Camera and Accuracy Determination
## 3 Results
The average absolute error of 1.81 cm and the relative error of 4.07% are significantly higher, indicating that the system is less accurate for smaller objects. Such a result may suggest that the system has difficulty accurately detecting and measuring smaller objects, which may be related to the lower precision of segmentation or recognition of details in images. The limitations of the methods used are due to several factors. First, the quality of the stereo camera calibration is crucial to the precision of the measurements, and inaccuracies in calibration can lead to errors in depth estimation, which directly affects the accuracy of distance measurements. A practical solution to this problem could be the use of more advanced calibration techniques or the use of cameras with built-in calibration support. Second, the Mask R-CNN algorithm itself, while effective in segmentation and object recognition, does not always accurately reflect the shape and boundaries of objects, especially those that are small, poorly visible, or when the quality of the cameras is not high enough. More advanced segmentation algorithms or streamlined versions of Mask R-CNN, tailored to detect small objects, could be considered to help reduce these inaccuracies. An additional limitation is the selection of a low-cost camera module, such as the OV2640, which also limits precision. Switching to a higher-resolution camera module could significantly improve accuracy, especially for objects with more complex shapes and contours. Despite these limitations, the study confirms that stereo cameras can be used to measure distances and meet the work's objectives of determining the accuracy of the measurement system and identifying the range of distances where measurements are most precise. Future research should focus on improving the system calibration process and testing different segmentation algorithms tailored to objects of different sizes and shapes, potentially using the latest computer vision models. Testing different higher-quality cameras could provide valuable data on the impact of resolution on measurement accuracy. Additionally, exploring sensor fusion techniques that combine data from different types of sensors could improve the accuracy of object and depth detection under varying conditions. These directions could significantly expand the capabilities and precision of stereoscopy-based measurement systems in a variety of applications. It is also advisable to conduct further research on the effects of various factors, such as the lighting, shape, and texture of objects, on the accuracy of measurements. The consideration of different stereo camera models and alternative measurement methods could lead to the further optimization of the system and expand its usefulness in various fields.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Summary of Related Works  
Analysis of the above research reveals that both model-based and data-driven intent recognition methods have been widely applied, each offering distinct advantages and facing specific limitations, as summarized in Table 1. However, none of these methods can be directly applied to the multi-AUV domain. As previously analyzed, the above studies rely on fixed mapping relationships within their respective domains, and all of these methods require a large amount of data exchange to achieve intent recognition.
## 4 Virtual Inertia Control of the VSC-HVDC  
### Virtual Inertia Control of DC Capacitor  
Referred to the motion equation of the SG, based on the power balance of the DC capacitor (7), the virtual inertia control of the VSC-HVDC is given in (8).  
\[\frac{NC_{\text{DC}}U_{\text{DC}}}{S_{\text{VSC}}}\frac{dU_{\text{DC}}}{dt}=P _{\text{in}}-P_{\text{out}} \tag{7}\]  
\[\frac{NC_{\text{DC}}U_{\text{DC}}}{S_{\text{VSC}}}\frac{dU_{\text{DC}}}{dt}=2H _{\text{DC}}\frac{df}{dt} \tag{8}\]  
where \(U_{\text{DC}}\) is the voltage, \(C_{\text{DC}}\) is the capacitance, \(H_{\text{DC}}\) is the virtual inertia, \(P_{\text{in}}\) and \(P_{\text{out}}\) are the input and output powers, \(N\) is the number of the DC capacitors, and \(S_{\text{VSC}}\) is the rated capacity of the VSC. As for Equation (7), variables on the right side are per unit values. Meanwhile, variables on the left side are actual values. \(S_{\text{VSC}}\) is not only the rated capacity of the VSC, but also the base capacity of the system. In this case, the left side of Equation (7) also yields the result in per unit value. The number of capacitors \(N\) is set to 2 to indicate the two capacitors in the VSC-HVDC transmission belonging to the VSCs at the two ends of the transmission line. The capacitors can also be handled as an equivalent single capacitor, which can be referred to in [12].  
Integral to (8) yields the incremental relation of the DC voltage and the system frequency,  
\[\Delta U_{\text{DC}}=\left(\sqrt{\frac{4H_{\text{DC}}S_{\text{VSC}}}{C_{\text{ DC}}NU_{\text{DC0}}^{2}}\Delta f+1}-1\right)U_{\text{DC0}} \tag{9}\]
###### Abstract  
The purpose of this paper is to determine the level of polycyclic aromatic hydrocarbons (PAHs) from medicinal plants and infusions prepared using them, as well as assess the transfer of these contaminants from plants to infusions. The separation of compounds was achieved using microwave extraction for dried plants and liquid-liquid extraction in the case of infusions. The extracts were cleaned using solid-phase extraction, and the compounds were analysed using gas chromatography coupled with mass spectrometry (GC-MS). Exposure to PAHs through tea infusion consumption was evaluated by calculating the estimated daily intake (EDI, ng/kg.bw/day) and Margin of Exposure (MOE). The average total content of PAHs varied from 277.22 \(\pm\) 12.78 to 2466.46 \(\pm\) 203.45 \(\mu\)g/kg in dry plants, and the compounds benzo[b]fluoranthene (BbF) and benzo[a]pyrene (BaP) were present in all samples. In the herbal tea infusions, the average total PAH content varied between 612.55 \(\pm\) 46.12 ng/L and 2292.2 \(\pm\) 140.24 ng/L. The observation was statistically checked using a two-sample paired test. The analysis revealed that PAHs could be split into those for which the content in the medicinal plants is significantly larger than in the infusions and those for which the difference is not significant. The average transfer rates of \(\Sigma\)16 PAHs from plants to infusions varied from 7.25 to 32.86%. The MOE values confirmed that consumer exposure to PAHs via tea infusions is very low and safe for health.  
PAHs; medicinal plants; tea infusions; MOEs 2025 15 447
# Stability Analysis of Roadway Groups under Multi-Mining Disturbances
## 3 Theoretical Analysis
### Surface Subsidence Theory  
Mining subsidence theory refers to the size and spatial distribution of surface movement and deformation caused by underground mining and its relationship with geological mining conditions [35; 36; 37]. The damage types of surface movement and deformation can be summarized as surface movement basins, cracks, steps, and collapse pits. The previous studies mainly focused on the bending and subsidence of overlying strata caused by mining of the working face, forming the collapse zone, the fracture zone, and the bending subsidence zone.  
The surface sinks from its original elevation after being affected the mining. Therefore, a subsidence area much larger than the goaf is formed above the goaf. This kind of surface subsidence area is called a surface movement basin [38; 39]. As shown in Figure 6, H (m) is the buried depth of the coal seam, and \(\beta\) (\({}^{\circ}\)) is the mining subsidence angle. I is the caving zone, II is the crack zone, and III is the curve subsidence zone. a is a non-affected zone, b is a slightly affected zone, and c is a severely affected zone. If the roadway is located in a severely affected zone during coal seam mining, severe damage would occur. If the roadway is located in an unaffected zone, the roadway would not be affected by mining.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Buried Depth/m** & **Thickness/m** & **Lithology** & **Key Strata Type** \\ \hline
04 & 360.40 & 122.80 & gabbro & Main key strata \\
14 & 539.00 & 36.00 & siltstone & Inferior key strata \\
22 & 643.25 & 20.95 & sandstone & Inferior key strata \\
31 & 708.75 & 14.20 & siltstone & Inferior key strata \\
38 & 741.85 & 4.95 & siltstone & Basic roof \\
45 & 748.80 & 4.30 & coal 3 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock column diagram and the key strata types.  
Figure 6: The profile of surface subsidence curve and mining subsidence angle.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 3 Interference Management  
The interference from cellular users is one of the major weaknesses affecting D2D communication. The coexistence of CUs and D2D pairs that use the same cellular resources leads to an interference issue. Depending on the D2D network's operation mode such as uplink and downlink situations, the D2D users will suffer from intracellular and intercellular interference. The transmission success can be damaged by interference that compromises the signal-to-interference-plus-noise ratio (SINR) [30]. There are various proposed interference management techniques discussed in this section. The interference management scheme can be broken down into three categories, which are interference cancellation, interference avoidance and interference coordination. Table 3 shows the brief summarization of the management of interference in D2D communications.
## 2 Scanning Electron Microscopy and Mineral Characterization
### SEM Energy-Dispersive X-ray Spectroscopy (SEM-EDS)  
When the electron beam emitted from the gun penetrates and interacts with the volume beneath the sample surface, X-rays are generated. This is a well-established principle in physics: the deceleration of electrons due to their entrance into the Coulomb field of the specimen results in a loss of electron energy and emits photons. In SEM analysis, similar X-ray photons are emitted, which are characteristic of the sample under investigation [89], as shown in Figure 2.  
The quantification scheme is achieved by measuring the X-ray intensity. This was illustrated by [PERSON] and [PERSON] in 1968 in their publication, _Quantitative Electron Probe Microanalysis_[92], which later became the standard for developing X-ray fields. At that time, X-ray absorption, determination of correction factors at the instant of electron penetration and scattering, and conversion of X-ray intensity to the relative concentration were missing. Many problems pertaining to the electron probe field were solved with the development of energy-dispersive spectrometry (EDS). At present, various studies have incorporated SEM-EDS for qualitative and semiquantitative analysis in a variety of subject areas [93; 94; 95; 96; 97; 98; 99].  
A schematic diagram of an energy-dispersive spectrometer is shown in Figure 5. The X-ray detection system (which is a solid-state detector) separates the characteristic X-rays of various elements present in the sample. Then, the EDS system software analyzes the energy spectrum to determine the amplitudes of particular elements, and electrical signals are generated from the respective photon energies. This results in qualitative and quantitative determination of a chemical composition map of the elements present in the sample [89]. SEM-EDS has been used in a variety of fields for mineral characterization [100; 101; 102; 103; 104; 105; 106].
# DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors
###### Abstract  
Parallel hierarchical scheduling of multicore processors in avionics hypervisor is being studied. Parallel hierarchical scheduling utilizes modular reasoning about the temporal behavior of the upper Virtual Machine (VM) by partitioning CPU time. Directed Acyclic Graphs (DAGs) are used for modeling functional dependencies. However, the existing DAG scheduling algorithm wastes resources and is inaccurate. Decreasing the completion time (CT) of DAG and offering a tight and secure boundary makes use of joint-level parallelism and inter-joint dependency, which are two key factors of DAG topology. Firstly, Concurrent Parent and Child Model (CPCM) is researched, which accurately captures the above two factors and can be applied recursively when parsing DAG. Based on CPCM, the paper puts forward a hierarchical scheduling algorithm, which focuses on decreasing the maximum CT of joints. Secondly, the new Response Time Analysis (RTA) algorithm is proposed, which offers a general limit for other execution sequences of Noncritical joints (NC-joints) and a specific limit for a fixed execution sequence. Finally, research results show that the parallel hierarchical scheduling algorithm has higher performance than other algorithms.  
multicore; avionics hypervisor; DAG; hierarchical; parallel scheduling Article
# Building Cyber-Resilient Smart Grids with Digital Twins and Data Spaces
## 2 Digital Twins and Data Spaces
### Digital Twin  
A digital twin (DT) is a virtual replica of a physical system, created using sensors, data analytics, and machine learning algorithms. It captures and analyzes real-time data from the physical system and has the potential to transform the monitoring, analysis, and optimization of physical objects and systems in Industry 4.0 [10; 11; 12]. Digital twins allow engineers to gain insights from real-time data generated by IoT devices, enabling simulation, analysis, and optimization for increased efficiency, productivity, and cost savings.  
There are three types of digital twins:  
* Digital Twin Prototype. This type underpins the conceptualization and development phase of a physical asset, serving as a blueprint for its design.
* Digital Twin Instance. This variant maintains a continuous digital synchronicity with its physical counterpart, evolving over the asset's lifecycle.
* Digital Twin Aggregate. This type amalgamates data from multiple DT instances to conduct comprehensive simulations and forecasts pertinent to the overarching physical entity.  
The construction of a digital twin involves several key phases:  
1. Data Acquisition (\(DA\)): This phase involves the collection of real-time data from the physical system (\(PS\)) using a network of sensors (\(S\)). The function for data acquisition can be defined as: \[DA:S\to D_{rt}(PS)\] (1) where \(D_{rt}(PS)\) denotes the real-time data stream from the physical system.
2. Modeling (\(M\)): In this phase, real-time data \(D_{rt}\) are utilized to construct a virtual model (\(VM\)) that represents the physical system. The modelling function can be expressed as: \[M:D_{rt}\to VM\] (2) which maps the real-time data to a corresponding virtual model.
3. Simulation (\(Sim\)): Using the virtual model, this phase performs simulations to emulate the behaviour (\(B\)) of the physical system under various conditions (\(C\)). The simulation function is: \[Sim:VM\times C\to B\] (3) where \(B\) represents the behaviours or outcomes under simulated conditions.
4. Deployment (\(Dep\)): The final phase involves integrating the digital twin (\(DT\)) with the physical system to enable ongoing optimization (\(Opt\)). The deployment function can be formalized as: \[Dep:(DT,PS)\to Opt\] (4) which indicates that the DT and the physical system are integrated to achieve an optimized state.  
Each function represents a mapping from inputs to outputs, reflecting the transformation of data and models throughout the DT building process.
# The _Ampd1_ Gene's rs17602729 Polymorphism and Athletic Performance in Track and Field Athletes
## 3 Results  
The current study was conducted to explore the potential link between the _AMPD1_ rs17602729 polymorphism and the personal best (PB) or competitive performance of elite athletes in the presence of a control group.  
Initially, we assessed the genotype and allele frequencies of the polymorphism. The findings indicated a significant difference between the sprinters/power athletes and endurance athletes in their genotype frequencies (\(p=0.023\); Table 2). In terms of the allele frequencies, the G allele was more prevalent than the A allele (allele nomenclature differs from the database (C/T) owing to the selection of a complementary strand (G/A)); however, this difference was not statistically significant either within or between the groups (\(p>0.05\); Table 2).  
Additionally, when we compared the genotype distribution of sex and sports experience, there were, again, no statistically significant results (\(p>0.05\)).  
Due to the complete absence of the rs17602729 AA genotype in the sprinters/power athletes, only the GG and GA genotypes were considered for further analysis. The PB average of individuals with the GG genotype was higher than those with the GA genotype (GG: 1003.18; GA: 958.44). Nevertheless, this difference was not statistically significant in the sprinters/power athletes. Due to the complete absence of the rs17602729 GA genotype in the endurance athletes, only the GG and AA genotypes were considered for further analysis. The PB average of the individuals with the AA genotype was higher than those with the GG genotype (AA: 1067.50; GG: 1018.77). However, this difference was also not statistically significant in the endurance athletes. Therefore, to enhance the robustness of the statistical results, we opted to aggregate the sample. After pooling the data, the association was analyzed using various genetic models, including codominant, dominant, recessive, and over-dominant models. The analysis revealed no significant associations between the rs17602729 polymorphism and the PBs of the athletes in any of the models tested (\(p>0.05\); Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Genotype** & & \(p\)**-Value** & **Allele** & \(p\)**-Value** \\ \hline  & GG & GA & AA & & G & A & \\ \hline Sprinters/power athletes & 22 (33.8\%) & 9 (75.0\%) & — & & 53 (85.7\%) & 9 (14.3\%) & \\ \hline Endurance athletes & 27 (41.5\%) & — & 2 (66.7\%) & & 54 (93.0\%) & 4 (7.0\%) & \\ \hline Controls & 16 (24.6\%) & 3 (25.0\%) & 1 (33.3\%) & & 35 (87.5\%) & 5 (12.5\%) & \\ \hline \end{tabular}  
* *Sprinters/power athletes-endurance athletes  
\end{table}
Table 2: Genotype and allele frequencies of _AMPD1_ rs17602729 polymorphism in elite athletes and controls.
## 2 Geometric Preliminaries  
In any \(\mathcal{E}\)-configuration \(Z=(z_{1},z_{2},\ldots,z_{n})\) and for any positive integer \(x\), we call the separation between \(e_{i}\) and its \(x\)th closest neighbour (not including \(e_{i}\)) its \(x\)_-separation_, and denote it by \(\sigma_{i}^{Z}(x)\). We call the closed ball with radius (called the _\(x\)-radius_ of \(e_{i}\)) \(r_{i}^{Z}(x)=\sigma_{i}^{Z}(x)+1\) and center \(z_{i}\), the \(x\)-ball_ of \(e_{i}\), and denote it by \(B_{i}^{Z}(x)\) (cf. Figure 2). We will omit \(Z\) when the configuration is understood. Note that, for all entities \(e_{i}\) and \(e_{j}\),  
\[\sigma_{j}^{Z}(x)\leq\|z_{j}-z_{i}\|+\sigma_{i}^{Z}(x), \tag{1}\]  
since the ball with radius \(\|z_{j}-z_{i}\|+\sigma_{i}^{Z}(x)\) centered at \(z_{j}\) contains the ball with a radius \(\sigma_{i}^{Z}(x)\) centered at \(z_{i}\) (by the triangle inequality).  
We have assumed that entities do not properly intersect. Define \(c_{d,x}\) to be the smallest constant such that a unit-radius \(d\)-dimensional ball \(B\) can have \(x\) disjoint unit-radius \(d\)-dimensional balls (not including itself) with separation from \(B\) at most \(c_{d,x}\). Thus, \(\sigma_{i}^{Z}(x)~{}\geq~{}c_{d,x}\).  
Let \(X_{d}\) be the largest value of \(x\) for which \(c_{d,x}=0\) (e.g., \(X_{2}=6\)). Clearly, if \(x\leq X_{d}\), there are entity configurations \(Z\) with \(\sigma_{i}^{Z}(x)=0\). Thus, for such \(x\), obtaining uncertainty degree at most \(x\), even at one specified target time, might be impossible, in general, for any query scheme. On the other hand, if \(x>X_{d}\) then \(c_{d,x}>0\), and therefore, it suffices to query all \(n\) entities in the time interval \([\tau-c_{d,x}/2,~{}\tau)\), using granularity \(c_{d,x}/(2n)\), in order to obtain congestion less than \(x\) at time \(\tau\).  
Remark 1: _Hereafter we will assume that \(x\), our bound on congestion potential, is greater than \(X_{d}\)._
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 3 Results and Discussions  
### Characterization of Ground Granulated Blast Furnace Slag (GGBFS) and Alkali-Activated Slag (AAS)  
#### 3.1.1 Mechanical Strength  
The data for the mechanical strengths of AAS mortars with four various Na\({}_{2}\)O concentrations are depicted in Figure 1. It is worth mentioning that the mortar achieved the highest mechanical strength at a Na\({}_{2}\)O concentration of 8% by slag weight. In terms of compressive strength, the strength gains were recorded to be 20.7 and 42.6% when raising the Na\({}_{2}\)O content from 4 to 6% and from 6 to 8%, respectively. However, the strength decreased by 13.8% with the Na\({}_{2}\)O content beyond 8%. In comparison with the compressive strength, the flexural strength exhibited negligible change when increasing the Na\({}_{2}\)O content from 4 to 10% but also showed the highest value at a Na\({}_{2}\)O concentration of 8%. The strength gain could be a result of the higher activation of alkaline solution with a higher concentration of Na\({}_{2}\)O. Consequently, the AAS mortar mixture with a Na\({}_{2}\)O content of 8% was chosen for the subsequent analysis.