esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Application Case
### Program Verification
#### 4.2.2 Second Step  
Next, deep Hough transform based on a line detector is used to detect and remove the irrelevant lines caused by changes in light and occlusion. The sea-sky background is segmented to reduce the scanning area and further improve the detection efficiency. Following the translation of the DHT features into the parametric space, the grid location \((\theta,r)\) will align to features in the feature space along a whole line. These tasks are performed using Equations (10)-(13). For this study's application case, \(S=0.8\), \(\Theta=0.688\), and \(R=0.33\). Entities close to the yellow line are translated to surrounding points near the target; the blue line separates the seawater from the sky. The results of this task are displayed in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Morphological** & **Number of** & & & \\
**Filtering Algorithm** & **Corrugated Bands** & **MSE** & **PSNR** & **EN** \\ \hline Before filtering & \(>\)20 & \(6.9786\times 10^{3}\) & \(9.7011\) & \(3.7717\) \\ After filtering & \(<\)10 & \(6.5377\times 10^{3}\) & \(9.9903\) & \(3.6399\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the suppression of noise at the sea surface.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adaptive Fast** & **Number of** & & & \\
**Median Filtering** & **Corrugated Bands** & **MSE** & **PSNR** & **EN** \\ \hline Before filtering & \(>\)10 & \(6.5377\times 10^{3}\) & \(9.9903\) & \(3.6399\) \\ After filtering & \(<\)5 & \(6.1832\times 10^{3}\) & \(10.0441\) & \(3.4001\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of impulse noise suppression.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 4 Key Recovery
### Assumptions  
Based on the model we described in Section 2.2, we suppose that an adversary procures a noisy version of a SIKE's private key encoding as it is stored by the reference implementation. In particular, the adversary procures an array containing \((\mathtt{s},\mathtt{sk}_{3},\mathtt{pk}_{3})\). Additionally, such adversary accesses the corresponding original public parameters (i.e., without noise), so such adversary may use them to properly locate the offset from which the secret key bits (\(\mathtt{sk}_{3}\)) are stored via running key-finding algorithms exploiting identifying features found in the memory formats for storing the SIKE's private key. These algorithms would be similar to those developed for the RSA setting [10].  
Additionally, by \(\alpha=P(0\to 1)\) we denote the probability for a 0 bit of the original secret key to change for a 1 bit, and by \(\beta=P(1\to 0)\) we denote the probability for a 1 bit of the original private key to change for a 0 bit. According to our model, the attacker knows both \(\alpha\) and \(\beta\), since these values are fixed across the memory region in which the secret key is located and can be easily estimated by comparing any noisy content with its corresponding original one (e.g., the public key). Moreover, memory regions are normally large and the size in bytes of \(\mathtt{sk}_{3}\) is at most 48 bytes according to Table 1.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 1 Introduction
### A First-Year Student's Approach to the Vertex Cover Problem
#### 1.1.1 Solving Vertex Cover Using Divide-and-Conquer?  
Unfortunately, our student quickly notices that the divide-and-conquer approach fails quite miserably when she tries to apply it to finding small vertex covers. The problem lies in the _dividing phase_: how does one divide, say, a clique into parts? Indeed, divide-and-conquer is only applicable to problems whose inputs are \"amenable\" to dividing them into parts. Thus, let us make the problem (much) easier for our student by allowing only trees as input graphs. Now, clearly,dividing the input is no longer a problem: For a tree \(T\) with root \(r\), we can recurse on the subtrees \(T_{1}\) to \(T_{m}\) rooted at the children \(c_{1}\) to \(c_{m}\) of the root.  
Our student must still tackle the _merging phase_ of the divide-and-conquer approach: How does one assemble optimal vertex covers \(C_{i}\) for the \(T_{i}\) into an optimal vertex cover \(C\) for the whole tree \(T\)? Clearly, this is not a trivial task since the \(C_{i}\) seem to lack some of the information needed for computing \(C\). The trick is to compute _two_ optimal vertex covers \(C_{i}^{\text{with root}}\) and \(C_{i}^{\text{without root}}\) for each subtree \(T_{i}\): one for the case that the tree's root \(c_{i}\) is part of the vertex cover and one for the case that it is not. Given these pairs of optimal solutions for each subtree, the best overall solution \(C^{\text{without root}}\) is of course given by the union of all \(C_{i}^{\text{with root}}\) (since we must cover the edges starting at \(r\)), while the best overall \(C^{\text{with root}}\) consists of \(r\) plus the smaller one of \(C_{i}^{\text{with root}}\) or \(C_{i}^{\text{without root}}\) for each \(i\). When our student modifies her divide-and-conquer algorithm so that these pairs are computed, she can solve for the vertex cover problem on trees in linear time.
## 4 Results
### Performance Evaluation
In all figures, the average delay for over 1000 sequential requests/file made to the storage cloud is presented. Figure 10: Average download times (in ms) of health record resources with various sizes (5 MB, 50 MB, 500 MB, and 1 GB) when the storage cloud and the prefetch engine were deployed in the same infrastructure but in different nodes (i.e., VMs). Figure 9: Average download times (in ms) of health record resources with various sizes (5 MB, 50 MB, 500 MB, and 1 GB) when the storage cloud and the prefetch engine were deployed in infrastructures with different physical locations. The abovementioned experiments, as mentioned, concerned sequential requests. For this reason, another set of experiments was also conducted in order to evaluate the performance of the prefetch-enabled storage cloud when parallel requests were made. What is important to note is that the following experiments were conducted in the third deployment configuration (i.e., when the cloud was deployed in different data centers) since, to the best of our knowledge, this is the most common situation when deploying services in a cloud infrastructure. Thus, additional scenarios were deployed, which are presented below. Let us start with the results depicted in Figure 12, where the transmission times for downloading several encrypted health records are shown when performing simultaneous requests. What can be observed is that the delay was significantly increased when the number of requests was bigger, but there was an important reduction in the transmission time when the data were prefetched. The final experiment with respect to the performance of the prefetch engine had to do with its behavior when performing individual requests asking for different files each time,  
Figure 11: Average download times (in ms) of health record resources with various sizes (5 MB, 50 MB, 500 MB, and 1 GB) when the storage cloud and the prefetch engine were deployed in the same node (i.e., VM). Figure 12: Average download times (in ms) of different encrypted health records while performing simultaneous requests to the deployed storage cloud and deployed in different data centers. and the results are shown in Figure 13 below. Similarly, there was a considerable reduction in the overall transmission time when the prefetching scheme was enabled, leading to the conclusion that the scheme optimized the performance of the storage cloud even in cases where multiple requests were performed.
# Mid-Infrared Multispectral Gaseous Stimulated Raman Scattering Laser
## 4 Conclusions  
In conclusion, we have first demonstrated multispectral mid-IR gaseous Raman lasers in free space in the range of 2-6 \(\upmu\)m by using a mixture of H\({}_{2}\) and D\({}_{2}\) as Raman gain medium. A high-power 1064 nm laser was used to pump the vibrational transitions of H\({}_{2}\) and D\({}_{2}\) to generate the first Stokes Raman components at wavelengths of 1560 nm and 1907 nm. The FWM process of 1064 nm, 1560 nm, and 1907 nm lasers was carried out and reduced the threshold of mid-IR Raman laser generation. With further SRS amplification, a 4.4 \(\upmu\)m Raman laser was obtained and a maximum peak power of 121 kW was achieved. Furthermore, the smaller Raman shift of the rotational transition of D\({}_{2}\) contributed to generating more mid-IR Raman components. Theoretically, by using a multipass Raman cell with a longer gain length and selecting an active gas mixture, mid-IR Raman lasers with more spectra and a wider wavelength range can be obtained, and even far-infrared Raman lasers can be generated by this method.  
Conceptualization, C.S., X.C. and T.Z.; methodology, C.S. and T.Z.; formal analysis, D.L.; investigation, Y.J.; data curation, D.L.; writing--original draft preparation, C.S.; writing--review and editing, J.G. and J.L.; funding acquisition, C.S. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (22073101) and the Innovation Foundation of Key Lab of Chemical Laser of Dalian Institute of Chemical Physics (KLCL-20509).  
Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Wavelength** & **SRS Process** \\ \hline
2071 nm & \(\omega_{1907\text{ nm}}-\Omega_{R}^{\text{ref}}(\text{D}_{2})\) \\
2266 nm & \(\omega_{1907\text{ nm}}-2\Omega_{R}^{\text{ref}}(\text{D}_{2})\) \\
2604 nm & \(\omega_{2920\text{ nm}}+\Omega_{R}^{\text{ref}}(\text{D}_{2})\) \\
2920 nm & \(\omega_{1064\text{ nm}}-2\Omega_{R}^{\text{ref}}(\text{D}_{2})\) \\
3322 nm & \(\omega_{2920\text{ nm}}-\Omega_{R}^{\text{ref}}(\text{D}_{2})\) \\
3743 nm & \(\omega_{4432\text{ nm}}+\Omega_{R}^{\text{ref}}(\text{D}_{2})\) \\
4432 nm & \(\omega_{1064\text{ nm}}-\Omega_{R}^{\text{ref}}(\text{D}_{2})-\Omega_{R}^{ \text{ref}}(\text{H}_{2})\) \\
5431 nm & \(\omega_{4432\text{ nm}}-\Omega_{R}^{\text{ref}}(\text{D}_{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mid-IR Raman components generated by each SRS process.
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 3 Overview
### Problem Description  
In an obstacle-present environment, assume that an obstacle exists between \(s_{1}\) and \(c_{1}\) as well as \(s_{1}\) and \(c_{2}\); then, \(s_{1}\) will receive only the position packets broadcast by the MAN at beacon points \(c_{3}\) and \(c_{4}\), as shown in Figure 3.  
Without loss of generality, in this work, an arbitrary convex polygonal obstacle is converted into a rectangle, as shown in Figure 4. \(L_{v}\) is the edge of the obstacle and is parallel to the \(Y\)-axis; \(W_{v}\) is the  
Figure 2: Communication radius, \(CR\), must be larger than or equal to \(\sqrt{(5/2)}d\) to ensure that all the unknown nodes in a basic square receive at least three position packets from the MAN.  
edge of the obstacle and is parallel to the \(X\)-axis. According to the relationship among \(L_{v}\), \(W_{v}\), and \(d\), obstacles may be classified into the following three types:  
\[\left\{\begin{array}{l}\text{Type I}:\ L_{v}<d\text{ and }W_{v}<d.\\ \text{Type II}:\ L_{v}<d\ \leq\ W_{v}.\\ \text{Type III}:\ W_{v}<d\leq\ L_{v}.\end{array}\right.\]  
\(k\)-covered areas are defined to demonstrate the effects of an obstacle on unknown nodes in different areas.  
Definition 2: \(k\)-covered area.: An area is \(k\)-covered if all the unknown nodes within this area can receive \(k\) non-collinear position packets from the MAN.  
The following two scenarios are feasible in a basic square, in which the unknown nodes that are present in areas where \(k<3\) cannot be localized:  
1. A two-covered area exists inside a basic square; the unknown nodes within this area can receive only two position packets from the MAN, as illustrated in Figure 5a.
2. One-covered and zero-covered areas are present within a basic square; the unknown nodes within these areas can receive only one and zero packets from the MAN, as shown in Figure 5b,c.  
Figure 4: For simplicity, rectangles are used to bind obstacles in an obstacle-filled scenario.  
Figure 3: The unknown node \(s_{1}\) is blocked by an obstacle and thus cannot receive a minimum of three position packets from the MAN.  
Evidently, additional beacon points are required to ensure that the entire basic square is a three-covered area.
# Effects of Bogies on the Wake Flow of a High-Speed Train
## 5 Results and Discussions
### Instantaneous Wake Flow
Figure 16: Iso-surfaces of \(Q\) (\(Q=200\), \(t=5\) s) of the (**a**) real model, (**b**) semi-simplified model, and (**c**) simplified model. Numerous studies [1; 5; 6; 7; 8; 9] have demonstrated that a pair of large counter-rotating streamwise vortices exists in the wake and this is also clearly observed in Figure 20 in the real model. These vortices move towards the ground and away from each other, accompanied by alternating vortex shedding, which is a result of their mutual inductions and interactions with the ground. The spatial structure of these wake vortices is quite complex and looks like a \"branch\". At \(x=37\) m, a clockwise vortex A detaches itself from the left side. At \(x=43\) m, the intensity of the previously detached vortex A' on the right side reaches its maximum and becomes dominant. At \(x=47.5\) m, the intensity of vortex A on the left side reaches its maximum and becomes dominant, while vortex A' nearly disappears at this location. At \(x=56\) m, the anticlockwise vortex A' on the right side is dominant again. The size, intensity, and spacing of the wake vortices are larger for the real model than the simplified model. Figure 19: Geometry of the nose of the trailing car in the simplified model. Figure 17: Contours of the instantaneous streamwise vorticity of the simplified model at different cross-sections. Clearly, the existence of the bogies has a large interference effect on the wake flow and results in large differences in the intensity between the two vortices in the same pair. In order to better demonstrate this point, the vorticity contours and velocity vectors of the semi-simplified model at different cross-sections are shown in Figure 21. It is observed that in the wake of the semi-simplified model, only one pair of vortices (A and A') exists and the vortices alternately dominate downstream. These results indicate the similarity in the wake flows of the real model and semi-simplified model, which are characterized by a pair of counter-rotating streamwise vortices that alternately dominate. However, the wake flow of the simplified model is quite different and is characterized by  
Figure 21: Contours of the instantaneous streamwise vorticity of the semi-simplified model at different cross-sections. Figure 20: Contours of the instantaneous streamwise vorticity of the real model at different cross-sections. two layers of vortex structures. The upper pairs of vortices decay rapidly, whereas the lower pair of vortices develops over a longer distance. The size, influence range, and other characteristics of these two layers of wake vortices are also quite different. The similarity of the wake flow between the real model and the semi-simplified model is due to the similar configurations of the bogies. The simplified model has no bogies; therefore, its wake flow is quite different from those of the other two models. Overall, the results lead to the conclusion that the bogies play a significant role in the generation and evolution of the wake vortices of high-speed trains.
## 2 Determining Vehicle Position
### Position from ART flags
#### 2.2.2 Contour Detection and Polygonal Approximation  
Next, the contours of the object are needed, whose edges were obtained in the previous step. For this purpose, the algorithm of the polygon contour description is proposed. Its main goal is to specify the vertices of the polygon according to the Ramer-Douglas-Peucker algorithm. Let's define vector \(D=[p_{0}\ p_{1}\ldots p_{n}]\) describing the points lying on the contour of the object. Define the segment \(L_{k}\) with the beginning at point \(p_{0}\) and ending at point \(p_{n}\). Additionally, determine all lines perpendicular to this segment \(L_{k}\) passing through the points from vector \(D\) and determine the longest norm \(L^{2}\ d_{k}\) for this straight line  
\[\{\bigwedge p\in D:max\|p_{i}\perp L_{k}\|_{2}=d_{k}\}. \tag{4}\]  
If \(d_{k}>T\), set a new segment \(L_{k+1}\) at the beginning at current \(L_{k}\) segment beginning and end at point \(p_{k}\), for whose \(d_{k}\) has been determined. In Equation (4) \(T\) is arbitrarily selected threshold. Save point \(p_{k}\) as the vertex of the polygon in vector \(\hat{D}\). If \(d_{k}<T\) calculate \(L_{k+1}\) such that the beginning is at point \(p_{k}\), and the end at point \(p_{n}\). Reassign \(d_{k+1}\) according to Equation (4). Finish the algorithm if the \(L\) segment cannot be created.
# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 4 Discussion
### Limitations and Future Research Directions  
While the simulation results validate the system's core functionality, several areas warrant future investigation:  
* The current competence update model assumes fixed responsiveness coefficients \(\lambda_{i}\) across learners. Incorporating adaptive learning rate estimation based on real-world learner behavior could further improve personalization precision.
* The simulation presently focuses on a single ATA domain (ATA 36 Pneumatic Systems); expansion to multi-domain, cross-ATA orchestration remains an important next step.
* Integration of real-world learner data into the federated learning modules will enable predictive modeling for skill gap evolution and facilitate early intervention strategies.  
* Extending orchestration algorithms to incorporate real-time instructor feedback may enable hybrid human-in-the-loop personalization models that balance algorithmic optimization with expert pedagogical judgment.
* While federated learning was implemented in a horizontal architecture for privacy-preserving updates, no adversarial scenarios (e.g., model poisoning or node dropout) were simulated. Future work should explore robust federated learning techniques, including differential privacy, secure aggregation, and federated unlearning, to improve resilience and trust in decentralized training environments.  
Future research should focus on validating the proposed framework through pilot deployments in actual training centers. This includes collecting empirical learner data, evaluating long-term learning retention, and assessing instructor feedback. Furthermore, integrating multimodal inputs--such as eye-tracking, biometric feedback, or voice-based interactions--could improve personalization and real-time adaptation of training pathways.
## 2 System Modeling  
### System Description  
The focus of this work is a multi-energy microgrid for buildings, which is a small-scale version of an integrated energy system consisting of electricity, natural gas, solar PV panels, different energy converters and multi-energy storage units to supply many types of electrical, cooling and heating loads. The structure of the studied MEMG system is shown in Figure 1.  
Key features of the MEMG system can be summarized as follows:  
1. Electricity and natural gas are the two principal energy carriers for the energy inputs, which are converted and delivered to end users. Coupling electrical and gas infrastructures is an efficient approach to the optimal operation of the two different energy systems.  
Figure 1: Structure of the studied MEMG system. CHP: combined heat and power; MEMG: multi-energy microgrids; PV: photovoltaics.  
2. Solar PV panels are integrated into the MEMG system. Unlike other renewable resources, solar power can be more easily applied to the demand side in distributed patterns, e.g., solar PV-integrated buildings [37].
3. The MEMG consists of a combined heat and power (CHP) system, gas boiler (GB), electrical heat pump (EHP), electric chiller (EC), absorption chiller (AC) and heterogeneous energy storage (HES), which are integrated into the MEMG system for transferring, converting or storing heterogeneous energy to meet the multi-energy demands and to improve the efficiency.
4. The HES consists of electrical energy storage (ES), a cooling storage (CS) tank and a heat storage (HS) tank, which are deployed for the purposes of tackling the intermittent outputs from solar power, shaving peak energy demands and achieving higher energy utilization flexibility.
5. The use of variable solar power and HES adds to the flexibility and complexity of MEMG operation at the same time.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review
### ISODATA Algorithm  
* Select an initial threshold value, \(T_{o}\), for instance, half of the maximum dynamic range. Loop
* Divide the histogram into two such that one segment corresponds to the foreground and the other to the background.
* Calculate the sample mean of gray values of foreground and background pixels (\(h_{f}\) and \(h_{m}\)).
* Determine a new threshold value, \(T_{1}\); this is the average of these two samples' means.
* Re-segment the histogram again into two.
* Check if any mean value has changed. If so, go to loop or else terminate.
# Can Isotopes Be Used as Lead Tracers in Shooting-Range Soils?
## 2 Experimental Design, Materials, and Methods  
Sample Locations and Lead Content in Soils and Shoot Pellets, and Their Measurements of Lead Isotopes by MC-ICP-MS  
The studied soils in this work were previously characterized, and their results were published in different works [14; 23; 24]. For this work, we have used the same soil samples previously analyzed (0-15 cm depth), with different degrees of contamination and distance from the firing point, while the lead contents were analyzed using X-ray fluorescence (XRF) [23; 24]. The stable lead isotopes in the analyzed soils (\({}^{204}\)Pb, \({}^{206}\)Pb, \({}^{207}\)Pb and \({}^{208}\)Pb) were determined in all the samples according to the methodology indicated by [3; 4; 25]. Lead stable isotope ratio measurements were performed with MC-ICP-MS (Thermo-Finnigan Neptune) at CACTI-UVigo (Universidade de Vigo, Vigo, Spain), and the lead contents in blank samples were less than 0.1 ppb (detected signal was lower than 0.002 V). Detailed conditions about isotope analysis were previously reported [4], and all samples were analyzed in duplicate. The MC-ICP-MS and instrumental parameters for isotope ratio analysis and the Pb stable isotope ratios in reference material SRM 981 (NIST) are indicated in the Supplementary Material (Tables S1 and S2).  
Regarding the shoot pellets, they were randomly selected from all ammunition debris collected from the studied soils and during the soil sieving for physicochemical analysis. Due to the shooting range facility being abandoned in 1999, we cannot assess the manufacturer of these shoots' pellets. In this sense, shoot pellets showed different degrees of weathering, such as irregular surfaces, slightly brown crust, and lower weight than expected [24]. Samples were digested according to the methodologies indicated by different authors [5; 18; 26], and isotope ratios were carried out using the same methods as for soil samples. After that, soil samples and shot pellets were compared with isotopic data from probable sources [27; 28; 29; 30; 31; 32]: leaded petrol, burned coal, natural background, waste incinerators, industrial sources and other ammunition types (bullets, shot pellets, etc.).