esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 1 Introduction  
Wind energy as a source of renewable energy is one of the essential pillars in the fight against climate change. In 2020, wind energy accounted for 16% of the overall electricity demand in the EU (EU27+UK) [1]. A further increase in capacity is urgently needed to reduce carbon emissions and requires: (i) improving the energy capture per turbine; (ii) reducing the levelised cost of energy; (iii) increasing the number of turbines and parks; and (iv) re-powering of inefficient turbines.  
This paper proposes a method for studying the vibrational characteristic of wind turbine blades. Detailed knowledge of the blades' dynamic response in operation of the turbine is essential: First, blades can optimally be controlled in operation to increase the energy capture. Second, costs can be reduced by reducing loads acting on the blades and thereby prolonging the lifetime of the blades. Blade costs amount to 20% of capital costs and blade damage is responsible for up to 18.2% of downtimes for onshore energy [2, 3]. By reducing those costs and downtimes, the competitiveness of wind energy can be increased. Third, monitoring is essential to detect structural changes and damage at an early stage and therewith guarantee safety. Finally, detailed knowledge of the dynamic behaviour of the blade in operation helps engineers in future blade design. With blades being designed ever longer in current trends, this knowledge gets increasingly important since aerodynamic forces as well as costs increase with blade length.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 2 Related Works
### Object Tracking  
Tracking in deep learning is the task of predicting the positions of objects throughout a video using their spatial and temporal features. Traditional object tracking methods can be broadly categorized into several types, including machine-learning-based methods [14; 15; 16], Markov random field-based methods [17], and optical flow and background subtraction-based methods [18; 19; 20; 21]. For ship object tracking, due to the complexity of the water surface environment, variability in ship scales, and the occlusion of ships, ship object tracking is more challenging. In addition to the above methods, [PERSON] used particle filtering to track ships based on color histograms within bounding rectangles [22]. [PERSON] et al. proposed an automatic ship object detection and tracking method based on a mean shift to improve the robustness of real-time detection and tracking [23].  
One of the challenges for object tracking is the Multi-Object Tracking (MOT) problem. The trackers must track multiple objects and even different classes simultaneously while maintaining high speed. Currently, mainstream MOT methods mainly include detection-based tracking methods (TBD), joint detection and tracking methods (JDT), and transformer-based tracking methods. Deep-learning-based models, such as RAN [24], DMAN [25], TRACK R-CNN [26], STAM [27], and MOT-RNN [28], have also been applied to multi-object tracking. Although MOT research has been widely applied, it has mainly focused on pedestrians and vehicles. For ship tracking, neural-network-based multi-object tracking methods (NN-MOT) have gained much attention from researchers due to their powerful feature extraction capabilities. For example, [PERSON] et al. [29] first introduced deep matching, which uses a deep learning framework to calculate optical flow features and achieved promising tracking results. [PERSON] et al. [30] proposed a fusion network that combines image and point cloud features captured from different modalities to improve the reliability and accuracy of the tracker. [PERSON] et al. proposed an enhanced SiamMask network for coastal ship tracking, which makes the tracking results more subjective. [PERSON] et al. proposed RoDAN (Robust Deep Affinity Network) [31]. Based on DAN (Deep Affinity Network), the ASPP multi-scale fusion module extracts semantic information at different feature scales.
## 4 Recommendation on Improving the Efficiency of Intensity Measures
### EDP-IM Relationship
#### 4.2.3 Site Class  
We now discuss the influence of soil types on the correlations, by grouping the ground motions according to site classes B, C, D and E, using the NEHRP soil classification. Site class A is not considered due to the small number of available records (only 0.2% in our database). To present these correlations, we again use the selected SP, IP, and LP frames (Figure 3). Figure 11 reveals that site class does not affect the correlations of \(PGA\) and \(ASI\) for SP structures and \(PGV\) and \(VSI\) for IP/LP structures. High correlation of \(PGV\) and \(VSI\) irrespective of site class was also reported by [PERSON] and [PERSON] [101]. For \(PGD\), the correlations decrease with site class. However, the correlations with \(PGV\) decrease with site class (B to E) or shear wave velocity \(Vs30\). \(ASI\) and \(VSI\) exhibit correlations similar to \(PGA\) and \(PGV\). Among the spectral measures, the correlation with \(Sa\)(\(T\)) shows the least variation with site class. Among the energy measures, \(A_{RMS}\) and \(I_{A}\) show good correlations and smallest dependence on site class (Figure 11). However, the correlations with \(CAV\) decrease with site \(Vs30\).  
In summary, for a given natural period of interest, \(ASI\) and \(VSI\) represent sufficient IMs for SP and LP structures, respectively. Figure 12 displays the variation of ISD with \(Vs30\), documenting that the mean response does not change much with soft sediment shear velocity. However, our assumptions related to the building models simplify the interaction with the sedimentary layers by using a fixed support at the column bases. Furthermore,  
Figure 9: Distance-dependent correlations of energy measures (\(A_{RMS}\), \(I_{A}\), \(CAV\)) for different magnitude classes (\(M_{w}\) 5–6, \(M_{w}\) 6–7 and \(M_{w}\) 7–8) (**a–i**). Rows correspond to variations in correlations of \(A_{RMS}\), \(I_{A}\) and \(CAV\) for three different types of structures (SP, IP, LP). Energy measures, especially \(A_{RMS}\) (**a–c**) and \(I_{A}\) (**d–f**), show very similar variation in correlations as that of \(PGA\) in Figure 7.  
lateral deformation of structures in loose soils will be affected by rocking and rotations of foundations, which are not considered in our approach.
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
## 3 Results
### Measurements during Rolling Contact on Rusted Rail  
In this section, we present preliminary measurements on a wheel-rail rolling contact under a normal load of 400 N and an AC supply current with a frequency signal of 2.3 kHz and 0.1 A peak to peak. These parameters are based on an actual track circuit configuration used on the FRN, as explained in Section 2.1. The rail is the one rusted according to the protocol described above. The rolling contact was displaced at a constant speed of 1 cm/s.  
Figure 16 illustrates the voltage drop across the wheel-rail contact and a known shunt resistance (the latter giving the image of the current). The voltage signal at the rusted  
contact exhibits a series of transient state with elevated contact resistance, characterized by frequent peaks. The phases of high-resistance transients could in real life give rise to deshunting occurrences.  
Figure 16: Voltage drop evolution across (\(-\)) the wheel–rail rolling contact and (\(-\)) a known shunt resistance (6.8 \(\Omega\)) during rolling contact under 400 N normal load on rusted rail.  
Figure 15: (**Top**) Evolution of the contact resistance of an oxidized wheel–rail contact during 4 phases: (Phase 1) injecting a current of 1 A for 10 min, (Phase 2) injecting a current of 1 A for 40 min, (Phase 3) injecting a current of 0.01 A for 10 min, (Phase 4) injecting a current of 1 A for 10 min. Between phase 1 and phase 2, current and measurement are interrupted during a few minutes (grey zone). (**Bottom**) zoom on phase 1.  
Figure 17 shows a closer examination of the voltage signals across the wheel-rail contact and the shunt resistance, revealing distinct behaviors. The left side of the figure presents a case of high contact resistance, resulting in significant signal deformation and non-linearity. Conversely, the right side of the figure corresponds to a healthier contact with lower resistance, where signals present more linear behavior.  
The high contact resistance and nonlinear behaviors of Figures 16 and 17 match the results previously observed in [10] with a 1/4 down-scaled test bench and a train on a real line. Consequently, the designed bench can be considered representative of the phenomenon at full scale.  
To gain further insight into this complex phenomenon of rolling contact behavior, future studies are envisaged. These studies will continue to focus on understanding and characterizing rolling contact behavior as a function of various parameters such as current intensity, frequency, oxide thickness and displacement speed.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Challenges  
The main challenges faced by researchers focusing on chaotic image encryption include encrypted image processing, attack protection, and evaluation. These challenges are studied below.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Clustering Ensemble Approach  
Generally speaking, a clustering ensemble, also referred to as a clustering aggregation or consensus clustering makes a combination of several clustering results into a single consolidated partition [20]. Theoretically, for a given data set \(C=c_{1},c_{2},\ldots,c_{n}\) that has n data, X clustering algorithms are utilized to find X single partitions and cluster C (Figure 2). Then, the clustering ensemble member set can be formed with x partitions \(\Gamma=P_{1},P_{2},\ldots,P_{x}\) and consensus function F, and denoted by \(F=P_{1},P_{2},\ldots,P_{x}=F(\Gamma)\). Finally, the clustering ensemble process gets a partition \(P^{*}\) of data set C by combining these ensemble members \(P_{1},P_{2},\ldots,P_{x}\) with \(\Gamma\) without access to original features or algorithms [25; 26].  
Ensemble clustering approaches promise efficient methods that would significantly benefit from combining the strengths of many individual clustering algorithms [19]. Therefore, by combining single clustering models, ensemble clustering can go beyond what a single clustering algorithm is capable of achieving in several respects:  
* _Robustness: better average performance compared to individual clustering algorithms._
* _Novelty: finding a new consolidated solution unattainable by any single clustering algorithm._
* _Stability: Final solutions with lower sensitivity to noise and outliers._
* _Scalability and Parallelization: Ability to compute distributed data and to integrate solutions (parallel clustering) from multiple sources of data or features._ See [9; 19; 27; 28; 29].  
The clustering ensemble approach is generally conducted in two principal steps: generation mechanism to obtain the ensemble members and consensus function to combine the ensemble members.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 4 Simulation Results  
In this section, simulation results for our proposed algorithms are presented. At the beginning of the simulation, the WBANs are deployed, i.e., the gateway and the nodes are placed in an area of 10 \(\times\) 10 m\({}^{2}\). Each zone of 5 \(\times\) 5 m\({}^{2}\) starts with an equal number of nodes. In the cases when mobility is also considered, the nodes can move across all the zones freely and randomly. The key simulation parameters are listed in Table 4.  
We have performed the simulations for various scenarios modeling realistic nursing home activity patterns. These scenarios are modeled from the idlest time (when all nodes are stationary) to the busiest activity time, i.e., all nodes are mobile. These scenarios are:  
* Case A: All the nodes are stationary. This case depicts that all patients are sleeping at night. They are not moving so there is no traffic change during this time.
* Case B: This corresponds to 100% mobility. All the nodes are moving. This situation corresponds to the busy working hours when all patients have to perform routine tasks.
* Case C: In this case, eight nodes are moving and four nodes are stationary.
* Case D: In this case, four nodes are moving and eight nodes are stationary.  
Cases C and D correspond to the situations when some patients are resting and some of them are moving around, for example during the first hours of the working day, after lunch, or evening before bedtime. To evaluate the performance of our proposed RL-CAA, we compare its results with the classical static channel assignment scheme SCA. The RL-CAA is also analyzed based on various QoS requirements. The analysis of the simulation results are done on the basis of three performance parameters; (i) bit error rate (BER), which is the average bits in error received at the gateway; (ii) throughput, average throughput in bits per second (bps) available at the gateway; and (iii) dissatisfaction probability. The data involved in the WBANs can be quite critical depending upon the nature of the application, so the users (nodes) must have sufficient quality of service (QoS) to transmit their data properly. Dissatisfaction can give an indication of the QoS at any given time. Here, the dissatisfaction probability is defined as the percentage of time in which the users' QoS (i.e., user throughput) is below the target (user) throughput. This target throughput is often referred as satisfaction throughput. Here the satisfaction throughput is chosen as 971.4 kbps, i.e., the maximum allowed by the IEEE 802.15.6 standard.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 3 System Model and Analysis
### System Model and Assumptions  
The proposed methods enable the localization of one or more underwater nodes within a specified rectangular area. Underwater nodes can be stationary or mobile. Each node is assumed to be equipped with a VLC receiver; mobile nodes additionally are assumed to have an inertial navigation system. After the nodes are deployed, an airborne unit hovers over the area to aid in determining the global coordinates of the underwater nodes. This airborne unit is equipped with a VLC transmitter and a GPS-based locator.
## 2 Methodology
### Bayesian Network Improvement  
This method constructs a reliability Bayesian network based on FTA, and integrates reliability information from multi-source data fusion using the forward and backward propagation characteristics of dynamic Bayesian networks. Additionally, through cloud models and scale-free network coupling analysis, it calculates the accuracy of the system reliability analysis and clarifies the relationships between components. The aim is to better assess the reliability of the ship lubricating oil system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component Name** & **Applicable Proportional Hazards Model** & **Main Failure Modes** \\ \hline Turbine lubricating oil pump & Weibull proportional hazards model & Bearing wear, seal leakage, impeller \\  & (WPHM) & damage (aging) \\ \hline Power unit lubricating oil cooler & Weibull proportional hazards model & Scaling blockage, corrosion, reduced heat \\  & (WPHM) & exchange efficiency (aging) \\ \hline Electric lubricating oil pump & Exponential proportional hazards model & Motor burnout, electrical control failure \\  & (EPHM) & (random failure) \\ \hline Generator set lubricating oil cooler & Weibull proportional hazards model & Blockage, leakage, reduced heat exchange \\  & (WPHM) & efficiency (aging) \\ \hline Return Oil Valve & Exponential proportional hazards model & Sticking, spring fatigue, seal failure \\  & (EPHM) & (random failure) \\ \hline Overhead oil tank & Normal proportional hazards model & Sensor failure, sediment accumulation \\  & (NPHM) & (symmetrical lifetime distribution) \\ \hline Safety Pressure Relief Valve & Exponential proportional hazards model & Spring fatigue, seal leakage, valve failure \\  & (EPHM) & (random failure) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model Selection Summary.
## 4 Experiments and Results
After two random movements, the position with the minimum possible voltage value is selected and, from that position, the hill climbing algorithm is activated and it keeps finding the best  
Figure 14: Transmitting coil 1 voltage under wireless power transmission. (For case scenario in Figure 13). Figure 15: Voltage under no load (normal) and load (wireless power transmission) for transmitting coil 2.  
possible positions to align the transmitting and receiving coils until the minimum level is achieved. Once the minimum level is achieved, the transmitting and receiving coils are aligned properly and wireless power transmission starts with full efficiency. The results obtained from the test bench shows that the proposed system is quite efficient in resolving the misalignment issue. The power transmission efficiency of 85% is reasonable for resonant inductive-based wireless power transmission, while also reducing the need for the implementation of complex tracking and landing algorithms for the drone. The time to align the centroid is also minimized due to the use of the hill climbing algorithm. The drone is free to land anywhere on charging station and, within a time of just 2 s, the centroid of the coils are aligned properly and the power transmission loss is mitigated.
# A Simple Foot Plantar Pressure Measurement Platform System Using Force-Sensing Resistors
## 3 Results and Discussion
Figure 4 presents the measurement of static load and the display of the results on a computer screen. The colors on the sensors (yellow and green) presented in Figure 4b indicate that the sensors are exposed to the external load from the subject being measured. In addition, the green spots indicate that the measurement points have higher load than the yellow spots. According to the measurement results, it is noted that the left foot in medial mid foot area, i.e., in sensors 5 and 7 are not exposed to the load. This result was revealed in almost all subjects except subject number 3, with a load of 0.196 kg on sensor 7. The highest average load occurs in the heel area i.e., at a sensor 1 the average is 0.713 kg and the smallest average load occurs in the sensor 5, 0 kg. In the soles of the right leg the smallest average load occurs in the medial mid foot area as well, i.e., each amounting to 0.147 kg in sensor 5 and 0.088 kg in sensor 7. Meanwhile, the largest average load occurs in the heel area indicated on sensor 1 of 0.767 kg. The measuring result also shows the burden in the heel area and the arch is larger than in the metatarsal area and the thumb is 86.9% and 70.5% respectively for the left and right foot [1]. In addition, when the load stands quite large occurs in the first and second metatarsal area (sensors 13 and 14) and thumb (sensor 15) [8] namely 0.650 kg, 0.526 kg, and 0.593 kg for the left leg and 0.698 kg, 0.599 kg, and 0.642 kg for the right leg. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**No.**} & \multicolumn{10}{c}{**Load Per Sensor (Gram)**} & \multicolumn{10}{c}{**Total Load**} \\ \cline{2-13} \multicolumn{1}{c}{**Subject**} & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **Sensor (kg)** \\ \hline
1 & 810 & 567 & 549 & 0 & 0 & 0 & 0 & 0 & 525 & 714 & 625 & 300 & 737 & 571 & 784 & 6.2 \\
2 & 715 & 51 & 692 & 621 & 0 & 535 & 0 & 229 & 676 & 792 & 718 & 681 & 678 & 446 & 631 & 7.5 \\
3 & 699 & 739 & 664 & 680 & 479 & 635 & 394 & 625 & 635 & 634 & 572 & 517 & 654 & 623 & 824 & 9.4 \\
4 & 785 & 725 & 665 & 614 & 118 & 578 & 0 & 434 & 562 & 574 & 531 & 573 & 666 & 580 & 571 & 8.0 \\
5 & 823 & 670 & 740 & 684 & 420 & 626 & 487 & 469 & 612 & 669 & 482 & 511 & 685 & 730 & 641 & 9.2 \\
6 & 745 & 649 & 770 & 461 & 381 & 401 & 1 & 513 & 364 & 581 & 694 & 691 & 685 & 551 & 411 & 7.9 \\
7 & 819 & 780 & 693 & 632 & 74 & 549 & 0 & 0 & 482 & 617 & 350 & 466 & 762 & 738 & 722 & 7.7 \\
8 & 860 & 480 & 639 & 0 & 0 & 0 & 0 & 0 & 91 & 529 & 550 & 678 & 565 & 530 & 820 & 5.7 \\
9 & 721 & 405 & 413 & 387 & 0 & 533 & 0 & 217 & 425 & 721 & 608 & 533 & 788 & 685 & 554 & 7.0 \\
10 & 691 & 690 & 429 & 550 & 0 & 418 & 0 & 0 & 0 & 509 & 300 & 900 & 763 & 533 & 464 & 6.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Load data on the soles of right leg 10 research subjects.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 4 The Design of a Multi-Line Prefetch Covert Channel with Huge Pages
### Multi-Line Encoding for Flexible Communication  
In this improved implementation, messages are encoded by selectively accessing \(n\) cache lines during each iteration, leading to \(\log n\)-bit transmissions. Each accessed cache line's contents do not matter. It is the count of accessed cache lines that encodes information and not the actual cache line contents. From a domain of 512 cache lines, if \(0\leq m\leq 511\) cache lines are accessed, the encoded value is \(m\), leading to \(\log(512)\) or 9-bit message transmission--in summary, the number of accessed lines is the message, rather than the contents of the cache lines. This coarser encoding leads to better noise tolerance. This multi-line encoding significantly increases the bandwidth compared to that under the binary encoding in traditional prefetch implementations. The count of accessed lines corresponds to a specific message, enhancing the flexibility of the encoding mechanism. For example,  
Accessing one cache line encodes Message 1.  
Accessing two cache lines encodes Message 2.  
Accessing \(n\) cache lines encodes Message \(n\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Config** & **Huge Pages** & **Prefetching** & **Avg Latency (Cycles)** \\ \hline Baseline & OFF & OFF & 3.71 \\ Prefetch Only & OFF & ON & 3.26 \\ Huge Pages Only & ON & OFF & 3.19 \\ Huge Pages + Prefetch & ON & ON & 3.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average latency comparison under different configurations.