esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reduced-Order Model for the Vibration Analysis of Mistuned Blade-Disc-Shaft Assembly
## 3 Reduced-Order Modeling
### Parametric Projection of Disc Mistuning
By substituting Equation (22) into the expression of \(\overline{\mathbf{K}}_{b}^{d}\) and neglecting the non-diagonal terms, the disc part's reduced-order mistuning matrices \(\overline{\mathbf{K}}_{b}^{d}\) can be expressed as  
\[\overline{\mathbf{K}}_{b}^{d}\approx\left(\overline{\mathbf{p}}^{d}\right)^{\text{T}} \underset{h=1,2,\cdots,N}{\text{Bdiag}}\left[\underset{w=1,2,\cdots,W}{\text{ diag}}\left(\delta_{h,w}^{d}\lambda_{w}^{d}\right)\right]\overline{\mathbf{p}}^{d}, \tag{24}\]  
where \(\overline{\mathbf{p}}^{d}=\left(\mathbf{E}\otimes\mathbf{I}_{N_{d}}\right)\underset{h=1,2,\cdots,N}{\text{Bdiag}}\left(\overline{\mathbf{p}}_{h}^{d}\right)\), \(W\) is the number of retained modes of the disc component, \(\delta_{h,w}^{d}\) is the deviation ratio, and \(\lambda_{w}^{d}\) denotes the \(w\)th eigenvalue of the tuned disc component. On this basis, the free and forced vibration of tuned and mistuned blade-disc-shaft assemblies can be analyzed by using the governing equation of motion in Equation (8) and the coordinate transformation in Equation (3). As the mistuning in blade and disc is taken into account, the effects of blade and disc mistuning on the vibration characteristics of blade-disc-shaft assemblies can be investigated.
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 3 Results
### Single Drone for Aerial Surveillance  
As part of the survey exploration, a DJI Mini 3 Pro drone was utilized to execute a targeted mission at geographical coordinates (29.452177, 47.761308). Precision in navigation was achieved by directing the drone to a specific location (29.452263, 47.761576), where it captured an image of the designated area, depicted in Figure 4.  
Figure 3: (a). The screens and interface of Recognize application. (b). Recognize application architecture.  
Importantly, the drone maintained an approximate distance of 27.65 m from the survey target, as visually represented in Figure 5. The distance between them has been calculated using the Haversine method [28]. It is a mathematical formula used in navigation and geography to calculate the distance between two points on the surface of a sphere, such as the Earth. Details of the formula are explained in Appendix C.  
The navigation of the single drone to the designated point was orchestrated seamlessly through the Dronelink application, employing an automatic mission with predefined properties, as illustrated in Figure 6.  
The properties for the automatic mission for the single drone are as follows:  
* Latitude: 29.452263;
* Longitude: 47.761576;
* Altitude: 11 m;
* Speed: 30 km/h;
* Radius: 1;
* Direction: Clockwise;
* Rotations: 1;
* Circumference: 360\({}^{\circ}\);
* Automatic Capture: photos.  
This strategic integration of drone technology and automated mission planning enhances the efficiency and accuracy of geographical surveys, contributing to a robust methodology for data collection and analysis. The captured image underwent detailed analysis using the \"Recognize\" application, revealing identified objects within the surveyed picture.  
Figure 4: Picture taken for the target using single DJI mini 3 pro drone.  
Figure 5: Distance between the target and the single drone.  
Results from processing the above image with the \"Recognize\" application are summarized in Table 1 below.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 2 Background
### Hidden Markov Models  
The Hidden Markov Model (HMM) is a statistical model for Markovian systems where the future state depends only on the current state, irrespective of the historical states. In HMM, the states are hidden, in contrast to the Markov Model. The HMM behaves like a state machine where every state has a probability distribution associating the hidden state with the observation sequence. HMMs can be trained using the observation sequences as input and calculate the score for a test sequence. Thus, the probability of the occurrence of such a sequence can be calculated. Table 1 consists of the standard notation for HMM, while Figure 4 depicts its model. Additional details can be found in [6].  
HMMs are used to solve the following three standard problems [7]:  
**Problem 1**. Given a model \(\lambda=(A,B,\pi)\) and an observation sequence \(O\), we can determine the probability \(P(O|\lambda)\). This is to calculate the score of the test observation sequence.  
**Problem 2**. Given a model \(\lambda=(A,B,\pi)\) and an observation sequence \(O\), we can determine an optimal state sequence for the Markov model by maximizing the expected number of correct states.  
**Problem 3**. Given an observation sequence \(O\) and the parameter \(N\), we can determine a model \(\lambda\) that maximizes the probability of \(O\). Thus, we train a model to best fit an observation sequence.  
For this work, we use Problem 3 to train the HMM and Problem 1 to classify our test data.
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 1 Introduction
### Contribution of the Paper  
In this paper, we move forward toward overcoming the above mentioned limitations, by presenting a new algorithm, named Dynamic Public Transit Labeling (d-ptl, for short), that is able to update the information precomputed by ptl whenever a delay occurs in the transit network, without recomputing it from scratch. It is worthy to mention that, although decreases in departure times are typically not allowed in transit networks [1], hence updating the information in such case would not be necessary, our solution can be easily extended to manage such scenario. It is also worthy to mention that part of the work we present here already appeared in [23].  
The algorithm we present here is based on suited combinations of graph update routines inspired to those in Reference [1] and labeling dynamic algorithms, extensions of those in References [15; 21; 24]. In particular, we present different versions of the algorithm, that are compatible with the different flavors of PTL, namely single criterion and multi-criteria. Furthermore, we discuss on the correctness of d-PTL and analyse its computational complexity in the worst case.  
Asymptotically speaking, the proposed solution is not better than the recomputation from scratch. However, we present an extensive algorithm-engineering based experimental study, conducted on real-world networks of large size, that shows that d-PTL always outperforms the from scratch computation in practice. In particular, our results show that (i) d-PTL is able to update both the graph and the labeling structure orders of magnitude faster than the recomputation from scratch via PTL; (ii) this behavior is amplified when networks are massive in size, thus suggesting that d-PTL scales well with the networks' size. Our data also highlight that the updated graph and labeling structure induce both query time performance and space overhead that are equivalent to those that are obtained by the recomputation from scratch, thus suggesting the use of d-PTL as an effective approach to handle the journey planning problem in delay-prone transit networks.
# Railway Overhead Wiring Structures in Australia: Review and Structural Assessment
## 3 OWS in Australia
### Design and Construction of OWS  
In Australia, the modern design of OWS involves the loading specifications in AS1170 [48], design of steel structures AS4100 [49], design of concrete structures AS3600 [50], design of platforms and walkway AS1657 [51], design of piled foundation AS2159 [52] and hot-dip galvanized coatings in AS/NZS4680 [53]. In addition to these national standards, each state has its own governing body in rail and state-specific guidelines or manuals are followed. Generally, design loads on OWS include seven load cases and seven load combinations, as listed in Tables 1 and 2. Load combinations LC1 to LC5 are strength limit states, LC6 and 7 are serviceability limit states and LC8 is a stability limit state [54]. In a serviceability limit state, in-plane and out-of-plane deflection limits are imposed but the magnitudes may differ slightly across different jurisdictions. The stability limit state considers overturning of structure using a factor if 1.2 for forces causing overturn effect and 0.9 for forces causing stabilizing effect.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Load Combinations** & **Comments** \\ \hline LC1 & 1.35 \(\times\) (Dead load + radial load) \\ LC2 & 1.2 \(\times\) (Dead load + radial load) + 1.5 \(\times\) Live load \\ LC3 & 1.2 \(\times\) (Dead load + radial load) + Wind load on wire and structure (x-direction) \\ LC4 & 1.2 \(\times\) (Dead load + radial load) + 0.5 \(\times\) Wind load on wire + Wind load on structure (45\({}^{\circ}\)) \\ LC5 & 1.2 \(\times\) (Dead load + radial load) + Wind load on structure (y-direction) \\ LC6 & Dead load + radial load \\ LC7 & 0.65 (wind load on wire + wind load on structure x-direction) \\ LC8 & Out of plane restoring moment/overturning moment \(>\) 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Load combinations of OWS design.  
Figure 6: A signal gantry.  
Construction of OWS is carried out by specialist contractors who are experienced in steel work fabrications and installations. Segments of OWS are fabricated off-site in shop environment and delivered to site for installation. Construction is facilitated by specialized trucks which have modified wheels to travel on tracks and are equipped with elevated platforms and cranes. Structural joints are fabricated to facilitate rapid bolting. In-situ bolting is preferred over welding for better construction efficiency and workmanship control. Structural members are galvanized in order to provide good durability under exposed condition. In case of replacement of old OWS, temporary suspension of train services is required. It is common to install new OWS in close proximity to a decommissioned one to facilitate relocation of existing wiring, as shown in Figure 7.
# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## 3 The Proposed Approach
### Technical Information  
This subsection contains information about the software and hardware specifications that were utilized in the work described in this paper. Regarding our hardware specifications, we utilized a PC with an Intel Core i9 CPU (20 threads), and a base and max clock speed of 3.70 Ghz and 5.00 Ghz, respectively. In terms of RAM, we used 2 dual in-line memory modules, each having a size of 32 GB.  
For the implementation of the proposed approach, we used the Python programming language and the Neo4j database to store our knowledge graph. We also used the following keyphrase extraction libraries: _YAKE!_ ([[https://github.com/LIAAD/yake](https://github.com/LIAAD/yake)]([https://github.com/LIAAD/yake](https://github.com/LIAAD/yake)) (accessed on 27 November 2021)), _TextRank_ ([[https://github.com/DerwenAI/pytextrank](https://github.com/DerwenAI/pytextrank)]([https://github.com/DerwenAI/pytextrank](https://github.com/DerwenAI/pytextrank)) (accessed on 27 November 2021)) and _SingleRank_ ([[https://github.com/boudinfl/pke](https://github.com/boudinfl/pke)]([https://github.com/boudinfl/pke](https://github.com/boudinfl/pke)) (accessed on 27 November 2021)). To create sublists of overlapping keyphrases obtained from the above approaches, we used the _get_close_matches_ () method of the _difflib_ built-in python library ([[https://docs.python.org/3/library/difflib.html#difflib.get_close_matches](https://docs.python.org/3/library/difflib.html#difflib.get_close_matches)]([https://docs.python.org/3/library/difflib.html#difflib.get_close_matches](https://docs.python.org/3/library/difflib.html#difflib.get_close_matches)) (accessed on 27 November 2021). For the implementation of the suffix tree, which is used to extract the longest common substring from each list of overlapping keyphrases, we used the _suffix-trees_ library ([[https://pypi.org/project/suffix-trees/](https://pypi.org/project/suffix-trees/)]([https://pypi.org/project/suffix-trees/](https://pypi.org/project/suffix-trees/)) (accessed on 27 November 2021)). For the NER task of extracting authors from text, we used spaCy v3 ([[https://spacy.io/](https://spacy.io/)]([https://spacy.io/](https://spacy.io/)) (accessed on 27 November 2021)) with a large pre-trained English model titled _en_core_web_lg ([[https://spacy.io/models/en#en_core_web_lg](https://spacy.io/models/en#en_core_web_lg)]([https://spacy.io/models/en#en_core_web_lg](https://spacy.io/models/en#en_core_web_lg)) (accessed on 27 November 2021)).
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 2 Related Work  
There have been lots of works conducted on how to deploy the services in the cloud environments without various performance metrics (e.g., deployment cost, reliability, response time, operational cost, etc.) [7; 8; 9; 10; 11; 12; 13].  
[PERSON] [7] formulated a multi-objective optimization problem to maximize the reliability of the services and minimize the deployment cost of the services. In addition, they proposed a heuristic algorithm that minimizes the selected objective while bounding the other objective. [PERSON] [8] formulated a mixed integer linear programming method to minimize the deployment cost while keeping the average response time of the services below the requirement. Then, they divided the problem into linearized sub-problems to solve the problem with the existing solutions. [PERSON] et al. [9] developed a genetic algorithm-based approach to balance the tradeoff between the response time and the deployment cost of the services. [PERSON] et al. [10] formulated an optimization problem to minimize the overall operational cost during a certain time interval. [PERSON] et al. [11] introduced a model for deploying sub-components-based services to minimize the deployment cost while satisfying response time requirements. [PERSON] et al. [12] developed a heuristic approach that deploys sub-components in the heterogeneous cloud environment to reduce transmission latency. [PERSON] et al. [13] formulated an edge service deployment optimization problem and proved that the formulated problem is NP-hard. Then, they proposed an approximation algorithm to find the near-optimal solution with low complexity. [PERSON] et al. [14] introduced a cooperative edge caching strategy that retrieves content from several servers in collaboration while taking into account the popularity of the contents. [PERSON] et al. [15] developed a heuristic algorithm aimed at reducing the average response time of services by taking into account the content popularity. [PERSON] et al. [16] formulated a joint optimization problem for service provisioning, server activation, and scheduling, taking into account the fluctuations in service popularity over time.  
However, there is no work that has considered the dynamics of service popularity and the pricing policy of pay-as-you-go to minimize the deployment cost (i.e., OPEX) of the service provider.
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 3 Results and Discussion
### Simulation Results and Analysis
#### 3.1.3 Chamber Volume  
The volume of the deposition chamber affects the deposition efficiency, while the relative positions of the inlet and outlet significantly alter the flow field characteristics. As illustrated in Figure 6, this study employs numerical simulations to analyze the deposition behavior in three different chamber volumes--V1, V2, and V3--corresponding to 81 cm\({}^{3}\)  
Figure 5: Deposition distribution contours for different nozzle-to-wafer distances.  
657 cm\({}^{3}\), and 3650 cm\({}^{3}\), respectively. Additionally, the effect of the relative positioning of the inlet and outlet on deposition behavior is examined.  
In general, in small-volume chambers, the incident particle flow is primarily concentrated near the wafer surface, suggesting that smaller chambers may exhibit higher particle collection efficiency under the same deposition conditions. However, simulation results indicate that the smallest chamber volume (V1) results in the smallest deposition spot size and the lowest deposition efficiency. As the chamber volume increases, the deposition spot size gradually enlarges and stabilizes when the chamber volume reaches V2, beyond which further increases in volume have a negligible effect on both deposition spot size and efficiency.  
Furthermore, under the smallest chamber volume (V1), two different inlet and outlet configurations--horizontal and vertical--are explored. The simulation results show that under the horizontal configuration, deposition spots are distributed along both sides of a fan-shaped region on the wafer surface, whereas under the vertical configuration, the deposition spots exhibit a circular distribution pattern.  
Based on these findings, to optimize deposition performance, prevent irregular deposition spot distributions, and ensure deposition efficiency, this study determines that the deposition chamber volume should be maintained above V2. Additionally, a horizontally arranged inlet and outlet design is adopted to enhance deposition stability and controllability.
## 4 Robust Geometries against Fabrication Errors
### General Observations  
Figure 4 shows the resonant wavelengths for two families of fundamental (solid line) and SH modes (dashed line). The first one corresponds to \(m_{f}=400\), \(Hz\) polarization, \(p=1\), and \(\lambda_{f}\) around \(2.3\)\(\upmu\)m. The second one corresponds to a SH mode with \(m=802\), \(Ez\) polarization, \(p=3\), and \(\lambda_{SH}\) around \(1.15\)\(\upmu\)m. For the sake of simplicity, the first modes are labeled \(F\), as in fundamental, and the second one is \(SH\), as in second harmonic, but these modes really correspond to fundamental and SH only when the energy conservation is fulfilled, i.e., only at the crossing points of the curves in the figure.  
Figure 4a shows the resonant wavelengths as a function of the thickness for three different radii. We can see that when the radius increases, the wavelength increases. This is easy to understand considering the resonant wavelength expression:  
\[\lambda=\frac{2\pi Rn_{eff}}{m} \tag{6}\]  
where \(n_{eff}\) is the effective index, and \(R\) the mean radius of the microring. The effective index does not change much with the radius, and thus, when the radius is larger, a longer wavelength is needed to keep the azimuthal number constant. In all cases, we can also observe that the wavelength increases with the thickness. This evolution is needed to keep the azimuthal number constant, since the effective index increases with the thickness. Finally, we can see that the slope decreases with the thickness. This is understandable since, when the thickness is large enough, the mode is well confined, and thus, the effective index becomes less sensitive to the thickness variation. For the small thicknesses, the SH slope is larger than the fundamental one, while it is smaller at large thicknesses.  
Figure 3: SHG conversion efficiency in Si microring cladded by Si\({}_{3}\)N\({}_{4}\). The pump power is \(P_{in}=1\) mW, \(\chi^{(2)}_{\mathrm{zxy}}=1\) pm/V, and the intrinsic and coupling Q-factors are \(10^{4}\). (**a**) Varying the internal radius \(R_{in}\), for a fixed thickness \(e=280\) nm and different widths \(w\). (**b**) Varying the thickness \(e\), for a fixed width \(w=800\) nm and a fixed internal radius (\(R_{in}\approx 60\)\(\upmu\)m) or a fixed fundamental azimuthal number (\(m_{f}=400\)) and different radii.  
Figure 4b shows the resonant wavelengths with respect to the radius for three different thicknesses. We observe the same general evolutions as described before: the wavelength increases with the radius or the thickness. Nevertheless, this time, the slope for the fundamental mode is always larger than the one for the SH. Since \(m_{f}\) is about half of \(m_{SH}\), it is normal to have a larger slope for the fundamental according to Equation (6).  
The general shapes of the curves in Figure 4 result from the chromatic and modal dispersions. Hence, the same kind of study performed in another material where \(\chi^{(2)}_{zxy}\
eq 0\) would lead to the same observations. Only the values for the wavelength range, thickness, and width would change.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 3 Latency-Inducing Receiver Digital Baseband
### Performance Figure Derivation  
The RX DBB components are in the present study based on best-in-class 65 nm IC designs found in literature, except for the exact- (28 nm) and approximative demapping (90 nm)--elaborated on in the corresponding subsection. The reported data propagation delays and finite throughput values are associated with the components making up the assessed RX DBB. However, other IC component implementations exist and might yield different latency results. The present work acknowledges IC design is a fast-evolving field and instead focuses on studying latency mitigation mechanisms, which are universally applicable.  
Given the separation of input data in Figure 3 into three types--short training field (STF), channel estimation sequence (CES), and payload--and the performance of the corresponding ingress components, the following sections assume their negligible contribution to latency. The remaining components contribute to MPDU latency through the data propagation time delay and finite throughput performance metrics. Except for the (de)scrambler, which does not significantly contribute to latency owing to the simplicity of the (de)scrambling function and state-of-the-art component throughput values surpassing 25 Gbps [33].