esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Damage-Sensitive Features
#### 2.1.5 Features Originating from Operational Modal Analysis  
OMA methods comprise a super-set of well-known output-only system identification methods (such as Frequency Domain Decomposition [78] and Stochastic Subspace Identification [79] schemes) that enable the extraction of modal properties, i.e., natural frequencies, mode shapes, and--to a lesser extent--damping, from ambient data. Given the non-stationary nature of earthquake excitation and the strong deviation from the WN assumption that defines output-only methods, OMA-based methods are not particularly fit for identifying instances of nonlinearity but can be used for identifying residual (permanent) damage on the basis of treatment of ambient data sets prior to and after an earthquake event.  
In addition to two classical DSFs, namely the change in natural frequency \(\Delta f_{m}\) of the \(m\)-th mode and the corresponding mode shape \(MAC_{m}\), evaluated using the MAC criterion, two additional OMA-based DSFs are typically exploited: the change in mode-shape curvature, \(\Delta MSC\), and the change in the phase angle, \(PAAC\).  
The mode-shape curvature (MSC), which is the second spatial derivative of the modal shapes, is estimated using the central difference approximation formula:  
\[MSC_{k,m}=\frac{\phi_{k-1,m}-2\phi_{k,m}+\phi_{k+1,m}}{h^{2}}\,, \tag{13}\]  
where \(\phi_{k,m}\) is the the \(k\)-th component of the mode-shape vector \(\phi\) of the \(m\)-th mode. The absolute change in the MSC, which provides a feature that is sensitive to residual damage and its location, is calculated with respect to the healthy (reference) state:  
\[\Delta MSC_{k,m}=\frac{MSC_{k,m}^{h}-MSC_{k,m}^{d}}{MSC_{k,m}^{h}}\,, \tag{14}\]  
where \(h\) and \(d\) refer to the healthy reference and the potentially damaged post-earthquake building state.  
Finally, the \(PAAC\) is a DSF based on variations in the phase angle of an input-output sensor pair. The phase angle is estimated through Equation (15) on the basis of the the cross-power spectral density (CPSD) matrix, which can be obtained using Fourier analysis:  
\[PA_{io}(\mathbf{\omega_{m}})=\arctan\left(\frac{imag(CPSD_{io}(\mathbf{\omega_{m}}))}{ real(CPSD_{io}(\mathbf{\omega_{m}}))}\right), \tag{15}\]where \(CPSD_{io}\) is the CPSD evaluated for an input (\(i\))-output (\(o\)) sensor pair; \(\omega_{m}\) is the frequency bandwidth defining the \(m\)-th mode of the frequency-response function between \(i\) and \(o\); and \(imag/real\) refer to the imaginary/real part of the complex CPSD vector, respectively. The \(PAAC_{io,m}\) reports the change in the \(PA_{io}\) between a healthy reference state and a potentially damage post-earthquake state and is computed in a similar manner to the TAC, as defined in Equation (4).
## 4 Discussion
### Involvement of External or Peer Facilitators  
By using peers of an interventions' target population, some studies were able to successfully increase engagement and participation levels among students [82; 97; 98]. Through fostering relatability and more approachable social support, students develop deeper understanding and are more motivated to adopt healthier eating behaviours. In order to improve their general relatability and approachability, two studies emphasised the use of peer facilitators who are chosen based on their strong leadership qualities or their comparable socioeconomic position to the students participating in the intervention [82]. However, peer relatability is not guaranteed; therefore, it is crucial to take cultural and social variables into account when choosing peer leaders to make sure they can, to the best of their ability, inspire and connect with the learners involved. However, youth peers do invite the possibility of spreading misinformation if improperly informed, as well as the risk of peer pressure or a power dynamic in some settings.  
Other studies benefited from the inclusion of external facilitators, such as college students, dietitians, or nutritionists, to help provide guidance and specialised knowledge to peer facilitators through lessons on nutrition, physical activity, and self-efficacy. The expertise that they provide can further contribute to the effectiveness of an intervention by improving the participants' knowledge in specific areas, such as the relationship between protein intake and exercise [68; 87; 102]. Although external facilitators offer experience, their recruitment is resource-intensive, which might restrict intervention scalability, particularly in underfunded education settings. In order to ensure that specialised knowledge is presented in an approachable and interesting way, the most successful interventions integrate the advantages of peer-led elements and external facilitators. However, some of the major limitations of these studies included their reliance on self-reported data in the form of post-study surveys, which may introduce bias. Another limitation would be the concept that the effectiveness of peer-led initiatives is directly linked to the learner's individual situation, which can vary across different contexts and demographics. Customisability seems necessary to address socio-cultural variation; however, this introduces further challenges, such as the standardisation of interventions. Additionally, the reliance on either peer or external facilitators can significantly affect intervention costs and feasibility. While peer-ledapproaches may offer a low-cost alternative, they require robust training frameworks. In contrast, external facilitators enhance quality but increase programme costs. Careful consideration of these trade-offs is necessary when designing scalable models.
# Response of Corroded Steel Pipeline Without and with CFRP Reinforcement to Reverse Fault Movement
###### Abstract
Consequently, pipelines can be retrofitted quickly and more economically by employing CFRP compared with conventional methods [16]. In addition, CFRP materials have excellent fatigue strength and superior corrosion resistance. Recent studies have also explored the application of CFRP materials inside pipelines. For instance, [PERSON] et al. [17] developed a bonded internal reinforcement system that boosts pipeline stiffness. [PERSON] et al. [18] tested a repair system for X52 pipelines with carbon fiber and glass fiber composites and found that the repaired pipeline had a 17% higher burst strength than the unrepaired ones. However, there are no studies on the effectiveness of CFRP treatment for corroded steel pipelines in enhancing its performance when subject to fault-induced PGD. To address this identified gap in the literature, this paper investigates the response of corroded steel pipelines without and with CFRP reinforcement to reverse fault movement. It evaluates the effects of corrosion location and depth on the failure modes of buried pipelines and, further, on adopted reinforcement measures. A three-dimensional finite element model of the pipeline with corrosion defects subjected to reverse fault is developed and employed to examine the effectiveness of CFRP treatment considering the influence of CFRP reinforcement thickness and length on the responses and failure modes of pipelines crossing faults.
## 4 Discussion  
This article presents a novel methodology to enable decision support for the scheduling of operations during the installation of offshore wind farms. This methodology combines a Model Predictive Control scheme with a Mixed-Integer Linear Program to optimize offshore operations using a receding horizon to contradict the high uncertainties implied by ever-changing weather conditions and limited forecast accuracies. Therefore, this article presents several extensions and augmentations to the original approach presented in [9] and presents a broader survey of the approach's properties and capabilities. On the one hand, these extensions allow the inclusion of several installation vessels and restrictions on port-side resources, i.e., on the availability of chartered loading bays. On the other hand, this article proposes a new approach to discretize uncertain weather conditions using Markov Chains, to avoid computationally intensive stochastic optimizations or expensive, robust optimizations.  
\begin{table}
\begin{tabular}{c r r r r r r} \hline \hline  & \multicolumn{2}{c}{**April**} & \multicolumn{2}{c}{**June**} & \multicolumn{2}{c}{**August**} \\  & **Duration [h]** & **O/T [h]** & **Duration [h]** & **O/T [h]** & **Duration [h]** & **O/T [h]** \\ \hline
2 & 731 & 21.76 & 769 & 22.52 & 729 & 22.28 \\
5 & 717 & 21.90 & 760 & 22.58 & 696 & 21.78 \\
15 & 735 & 21.10 & 766 & 21.48 & 689 & 21.54 \\
30 & 717 & 21.80 & 767 & 22.60 & 707 & 22.00 \\
60 & 697 & 21.50 & 736 & 22.02 & 689 & 21.16 \\ \hline Mean & 719.40 & 21.61 & 759.60 & 22.24 & 702.00 & 21.75 \\ Std.Dev & 13.35 & 0.29 & 12.18 & 0.44 & 15.02 & 0.38 \\ Mean (5+) & 716.50 & 21.58 & 757.25 & 22.17 & 695.25 & 21.62 \\ Std.Dev (5+) & 15.52 & 0.36 & 14.50 & 0.53 & 8.50 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of different optimization times for three vessels.
# Brushless Field Excitation Scheme for Wound Field Synchronous Machines
## 3 Finite Element Analysis  
To validate the proposed brushless filed excitation technique for WFSM, the finite element method (FEM) was used. The parameters of the machine are listed in Table 1. The structure of the machine was implemented as discussed in [7]. The armature winding was supplied with a three-phase input current of 10 A (peak) at 60-Hz, and single-phase input current of 10 A (peak) at 180-Hz. As the input armature current comprised of the fundamental and third harmonic, the fundamental component generated the main armature field; however, the third harmonic current component generated 12-pole harmonic current in the harmonic winding of the rotor. This harmonic current was rectified to inject the excitation current to the rotor filed. The simulations were performed for 1.5 s using JMAG-Designer 18.1. The magnetic field density plot achieved during these simulations along with the three-phase flux linkages are presented in Figure 6a,b, respectively. The magnetic field density plot shows that the machine was operating under the saturation level. The induced harmonic current along with the rectified field current is demonstrated in Figure 7a. Figure 7b illustrates the output torque of the  
Figure 5: Working principle of the proposed brushless WRSM.  
machine. The average torque achieved during the complete simulation time was around 14.372 Nm with a torque ripple of 66% as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Value** \\ \hline Rated power & 2.5 kW \\ Speed & 1800 rpm \\ Stator outer/inner diameter & 177 mm/100 mm \\ Airgap & 0.5 mm \\ Rotor diameter & 99 mm \\ Shaft diameter & 23 mm \\ Machine poles/Stator slots & 4/36 \\ Rotor main teeth/sub-teeth & 4/8 \\ Harmonic and field winding number of turns & 9/150 turns \\ Stack length & 80 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Machine specifications.  
Figure 6: (**a**) Flux density distribution plot and (**b**) flux linkage.
## 1 Introduction  
The combination of skeletal Class III malocclusion, anterior open bite, and asymmetry has been considered one of the most difficult problems to treat in orthodontics. In adult patients with these problems, orthonghatintic surgery with orthodontic decompensation [1; 2; 3; 4; 5; 6] or non-surgical orthodontic camouflage treatment [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] would be the appropriate treatment options.  
Non-surgical correction of skeletal Class III malocclusion can be performed by total distalization of the mandibular dentition, protraction of the maxillary dentition, and clockwise rotation of the mandible [9; 12; 13; 14; 18; 19]. Non-surgical correction of an anterior open bite can be accomplished by the intrusion of the posterior teeth, extrusion of the anterior teeth, counterclockwise autorotation of the mandible, and their combination [7; 8; 9; 10; 11; 16; 17; 18; 16; 17; 20; 21; 25; 26]. Non-surgical correction of mandibular dental asymmetry can be achieved by unilateral retraction of the mandibular dentition with skeletal anchorage, but skeletal asymmetry can only be corrected to a limited extent [19; 20].  
The intrusion of the posterior teeth induces a counterclockwise autorotation of the mandible, which can improve the open bite but can also degrade a patient's profile to a more concave pattern in a Class III patient. Therefore, orthodontists should consider changes in the anteroposterior relationship as a result of vertical movement of dentition when treating cases where there is a combination of open bite and Class III malocclusion [9; 15; 20; 22; 23; 24].  
The dental midline, amount of exposure at rest, and inclination of the maxillary central incisors should be carefully examined to achieve an acceptable camouflage treatment result. Therefore, the three-dimensional position of the maxillary incisors should be considered as a starting point in determining the pattern of orthodontic tooth movement to achieve an acceptable facial appearance and dental occlusion [27].  
The present case report describes the non-surgical orthodontic camouflage treatment of a young adult patient with skeletal Class III malocclusion, anterior open bite, and asymmetry using skeletal anchorage and intermaximillary elastics.
## 6 Evaluated Algorithms
### Results over 2017 IEEE Congress on Evolutionary Computation (CEC2017)
The boxplot results for FVIDDE across the CEC2017 benchmarks (F25-F30), as shown in Figure A7, continue to highlight its exceptional performance relative to other algorithms. FVIDDE consistently displays minimal error measures and low variability, which is evident in the boxplots across all functions. For example, in functions F25, F26, and F28, FVIDDE's error measures are notably lower than those of other algorithms, such as MTDE and ZOA, which exhibit significantly larger error ranges and higher interquartile ranges. In particular, the wide dispersion of error measures in MTDE across these functions suggests that FVIDDE offers a more stable and reliable performance. Similarly, in F29 and F30, FVIDDE maintains minimal errors, contrasting with the high variability and outliers observed in other algorithms.
## 4 Experimental Results and Analysis
### Experiments on the Efficiency of the CSCL-WVE-UDI Methodology
In summary, it can be seen that the CSCL-WVE-UDI method proposed in this paper has good recognition ability in the environment where three unknown IoT devices coexist, and accomplishes the efficient recognition of unknown IoT devices. This experimental result proves that the CSCL-WVE-UDI method is suitable for network traffic-oriented unknown IoT device identification.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 1 Introduction
In this work, we investigate the impact of rainfall on key mobile signal metrics under realistic non-line-of-sight (NLoS) scenarios, which are more common in urban and suburban environments. At the current stage, although 5G is rapidly developing, the primary network coverage in Australia remains LTE [18]. In addition, 5G network coverage in some areas might still be patchy and inconsistent. To ensure the generalizability and reliability of our research, we have chosen to conduct this phase of the study under the LTE network coverage that is more uniformly established across the region. We conduct measurements of LTE signal indicators (RSRP, RSSI, RSRQ, and SNR) at three different locations under NLoS conditions. Both small-scale and large-scale variations are analyzed during dry and rainy days. Our findings reveal that daily patterns in LTE signal indicators are evident on dry days. By employing spectrum analysis techniques, we find that rainy days display increased power at lower frequencies for both RSSI and SNR measurements compared to dry days. The remainder of this paper is organized as follows: Section 2 introduces the physical-layer basics and performance metrics of LTE BSs. Section 3 details the experimental setup and data collection process, followed by data pre-processing in Section 4. The data-driven results are presented in Section 5, with a comparison to previous work and discussion on future directions in Section 6. Finally, Section 7 concludes the findings.
# Distributed Uniform Streaming Framework:
## 4 DUST Coordinator for Distributed Resource Optimization
### Cost-Function  
The coordinators will evaluate the current state of the device and the requirements of the application component with the cost function to create an offer for an application component (see Section 4.2). This offer is used in the CNET algorithm. [PERSON] et al. [19] describe an equation (see Equation (1)) which can be used to estimate the total resource consumption and map these Key Performance Indicators (KPIs) to a total system cost. Their equation uses KPIs such as CPU usage or network usage. These KPIs are multiplied with a weight \(w_{ij}\) which represent the importance of a certain KPI. When we add every weighted KPI of every application component and for every device, we achieve the total distributed resource consumption.  
\[C=\sum_{i=1}^{\#Components\ \#KPI}w_{ij}C_{ij} \tag{1}\]  
To calculate a weighted offer for a single application component, we adapt Equation (1) by using the inverse cost for every KPI. This calculation is carried out in Equation (2).  
\[Offer=\sum_{j=1}^{\#KPI}\frac{1}{w_{j}C_{j}} \tag{2}\]  
In this research, we focused on the following KPIs: CPU usage, and the amount of network communication between application components. For every KPI, the weight \(w\) is determined empirically. In future research, we could take more KPIs into account; examples are memory usage, energy consumption, and response time. Another way to create an offer for a device is to take the execution cost of a real-world application component into account, which can also be based on the aforementioned KPIs.
###### Abstract  
Knowledge of the 3-dimensional space use of large marine predators is central to our understanding of ecosystem dynamics and for the development of management recommendations. Horizontal movements of white sharks, _Carcharodon carcharias_, in eastern Australian and New Zealand waters have been relatively well studied, yet vertical habitat use is less well understood. We dual-tagged 27 immature white sharks with Pop-Up Satellite Archival Transmitting (PSAT) and acoustic tags in New South Wales coastal shelf waters. In addition, 19 of these individuals were also fitted with Smart Position or Temperature Transmitting (SPOT) tags. PSATS of 12 sharks provided useable data; four tags were recovered, providing highly detailed archival data recorded at 3-s intervals. Horizontal movements ranged from southern Queensland to southern Tasmania and New Zealand. Sharks made extensive use of the water column (0-632 m) and experienced a broad range of temperatures (7.8-28.9 \({}^{\circ}\)C). Archival records revealed pronounced diel-patterns in distinct fine-scale oscillatory behaviour, with sharks occupying relatively constant depths during the day and exhibiting pronounced yo-yo diving behaviour (vertical zig-zag swimming through the water column) during the night. Our findings provide valuable new insights into the 3-dimensional space use of Eastern Australasian (EA) white sharks and contribute to the growing body on the general ecology of immature white sharks.
# Study on Single-Event Transient Hardness of Semi-Enclosed Gate NMOS
## 2 Simulation Setup
Subsequently, SET responses of semi-enclosed gate NMOS were analyzed under different Linear Energy Transfer (LET) values: 20 MeV\(\cdot\)cm\({}^{2}\)/mg, 30 MeV\(\cdot\)cm\({}^{2}\)/mg, 40 MeV\(\cdot\)cm\({}^{2}\)/mg, 50 MeV\(\cdot\)cm\({}^{2}\)/mg, 60 MeV\(\cdot\)cm\({}^{2}\)/mg, and 70 MeV\(\cdot\)cm\({}^{2}\)/mg. This range corresponds to ions commonly encountered in space environments. For instance, Fe ions with LET values around 20 MeV\(\cdot\)cm\({}^{2}\)/mg-30 MeV\(\cdot\)cm\({}^{2}\)/mg in silicon are typical components of Galactic Cosmic Rays (GCRs). Heavier ions, such as Xe, with LET values as high as 60 MeV\(\cdot\)cm\({}^{2}\)/mg-70 MeV\(\cdot\)cm\({}^{2}\)/mg, are also present in GCR and solar particle events. The incident positions of the particles are illustrated in Figure 3, and all undefined simulation conditions adhere to Table 2. After TCAD simulations and analysis of individual devices, the semi-enclosed gate NMOS and conventional NMOS were configured as inverter structures using Virtuoso simulation software (the version number is 6.1.7). A double-exponential current source was then employed to perform SET simulations on both types of inverters, analyzing pulse voltage variations at the output.
## 3 Dynamic CH Selection and Energy-Efficient Routing Design
### Energy Model  
A typical first-order radio paradigm is used to estimate the transmission & reception energy. Equations (1) and (2) represent the amount of energy used to broadcast and capture packets of bits at a given distance.  
\[En_{txcr}(\delta,d)=\left\{\delta\times E_{dis}+\delta\times e_{fs}\times d^{2 }\;if\;d\leq d_{0}\right. \tag{1}\]  
\[\left.\delta\times E_{dis}+\delta\times e_{mp}\times d^{4}\right\}\qquad if\;d> d_{0} \tag{2}\]  
\[En_{txcr}(\delta,d)=\delta\times E_{dis} \tag{3}\]  
where the energy consumed at the receiver and transmitter is denoted as \(En_{txcr}\)_and_\(En_{rxcr}\) and the threshold distance is defined by \(d_{0}\), \(\delta\) represents the information bits, and \(d\) is  
Figure 1: Structure of WSN network model.  
the distance between the sender and receiver. Equation (3) is used to compute the threshold distance.  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{3}\]  
where \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) indicate the amplification energy in the multipath and free space models, respectively. The transmitter amplifier model was used to determine these \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\).
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### U-Net Architecture  
Although the \"vanilla\" version of U-Net is usually trained from scratch, in this work we modify it to use a different backbone, ResNet-50, that can be easily pre-trained separately using a contrastive objective and then plugged into the U-Net architecture. Figure 1 describes graphically this architecture.  
The network takes as input a 64 \(\times\) 64 pixel patch with 6 channels corresponding to the B2 (B), B3 (G), B4 (R), B8 (NIR), and B11 and B12 (SWIR) spectral channels. The downsampling path reduces the width and height through strided convolution layers while increasing the numbers of channels. The last layer of the ResNet50 backbone has 2048 channels. A \"bridge\" is added between this layer and the first UConv block of the upsampling branch of the U-Net.  
The output layer uses pointwise convolution, equivalent to applying a fully-connected layer at each pixel, to produce a 2-dimensional vector \(\mathbf{p}=p^{i},i\in\{0,1\}\). This vector contains class probabilities of the pixel belonging to the ship class \(i=1\). The classification decision \(p\) is taken by \(argmax(\mathbf{p})\) of this output vector in each pixel, giving a binary mask at the resolution of the input image.  
The input patch size, \(64\times 64\) pixels, is chosen such that SSL training of the ResNet-50 backbone is technically possible on a desktop GPU, as detailed in the following section.