esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Challenges
These wind tunnel tests are normally of large scales and require extra equipment to simulate rainfall, and so the cost is often high. Additionally, it is difficult to have the realistic surface tension in the wind tunnel test since the size of water droplets cannot be scaled down. In theory, this can be handled in a multiphase CFD simulation. However, CFD simulations in the study of RWIVs are significantly nascent. In the limited number of simulations conducted in this area, the water rivulet has been treated as either a solid attachment on the cable or a layer of water film that has a negligible thickness, both of which lose the fidelity. Although there have been attempts to consider the shape effect of the water rivulet on the cable, the author has not seen any study that considers the realistic evolution of both water and wind within the simulation. Performing 3D multiphase FSI simulations is recommended as they will be beneficial to the understanding and suppression of RWIVs. It can be concluded from the above findings that there is room for improvement in the application of both methods to achieve more validated results. In fact, state-of-the-art CFD techniques are ahead of what has been used in bridge aerodynamics. Existing research based on CFD modelling has already shown the competence of this method. It can be foreseen that CFD simulations will make more significant contributions to the study of bridge aerodynamics in the near future. **Author Contributions:** Investigation, Y.Z. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z., P.C. and J.K.; visualization, J.K.; supervision, J.K. and P.C. ; project administration, J.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by China Scholarship Council, grant number 201808300003. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article. **Acknowledgments:** [PERSON] acknowledges the scholarship received jointly from University College Dublin and the China Scholarship Council. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 5 Experiment and Analysis
### Rotation Invariance  
In the experiment, in order to test the effect of camera roll angle on skyline geolocation, the image skyline was rotated by 1-4 deg in turn along the Z axis, and the matching skyline in the DEM database with the rotated curve was found. This experiment determined the robustness of the proposed method when a skyline was subjected to rotation (see Figure 32).  
\begin{table}
\begin{tabular}{c} \hline \hline
**The Method of Increasing the Noise of Skyline** \\ \hline for i in range(len(skyline)): \\ noise = random(0,maxNoise 1) \\ skyline[i] = skyline[i] + noise \\ \hline \hline \end{tabular}
\end{table}
Table 6: The method of increasing the noise of a skyline (using Python).  
Figure 31: The retrieval performance of two methods with different levels of noise.  
Figure 30: The effect of added noise on a skyline.  
Figure 33 shows that the proposal method has a strong adaptability to rotation, and a rotation of 4\({}^{\circ}\) had few effects on its accuracy. But with the increase in the degree of rotation, the accuracy of contour word decreased rapidly.
## 4 Computing _Rmq_, _Psv_, and _Nsv_
### A Novel Practical Solution  
We propose now a different solution, inspired in [PERSON] and [PERSON]'s _range min-max (RMM) tree_ data structure [22]. We divide _LCP_ into blocks of length \(L\). Now we form a hierarchy of blocks, where we store the minimum _LCP_ value of each block \(i\) in an array \(m[i]\). The array uses \(\frac{n}{L}\log n\) bits. On top of array \(m\), we construct a perfect \(L\)-\(ary\) tree \(T_{m}\) where the leaves are the elements of \(m\) and each internal node stores the minimum of the values stored in its children. The total space for \(T_{m}\) is \(\frac{n}{L}\log n(1+O(1/L))\) bits, so if \(L=\omega(\log n)\), the space used is \(o(n)\) bits.  
To answer _NSV\((i)\)_, we look for the first \(j>i\) such that \(\textit{LCP}[j]<p=\textit{LCP}[i]\), using \(T_{m}\) to find it in time \(O(L\log(n/L))\). We first search sequentially for the answer in the same block of \(i\). If it is not there, we go up to the leaf that represents the block and search the right siblings of this leaf. If some of these sibling leaves contain a minimum value smaller than \(p\), then the answer to _NSV\((i)\)_ is within their block, so we go down to their block and find sequentially the leftmost position \(j\) where \(\textit{LCP}[j]<p\). If, however, no sibling of the leaf contains a minimum smaller than \(p\), we continue going up the tree and consider the right siblings of the parent of the current node. At some node we find a minimum smaller than \(p\) and start traversing down the tree as before, finding at each level the first child of the current node with a minimum smaller than \(p\). _PSV_ is symmetric. As the minima in \(T_{m}\) are explicitly stored, the heaviest part of the cost in practice is the \(O(L)\) accesses to _LCP_ cells at the lowest levels.  
To calculate _RMQ\((x,y)\)_ we use the same \(T_{m}\) and separate the search in three parts: (a) We calculate sequentially the minimum value in the interval \([x,L\lceil\frac{x}{L}\rceil-1]\) and its leftmost position in the interval; (b) We do the same for the interval \([L\lfloor\frac{y}{L}\rfloor,y]\); (c) We calculate _RMQ\((L\lceil\frac{x}{L}\rceil,L\lfloor\frac{y}{L}\rfloor-1)\)_ using \(T_{m}\). Finally we compare the results obtained in (a), (b) and (c) and the answer will be the one holding the minimum value, choosing the leftmost to break ties. For each node in \(T_{m}\) we also store the local position in the children where the minimum occurs, so we do not need to scan the child blocks when we go down the tree. The extra space incurred is just \(\frac{n}{L}\log L(1+O(1/L))\) bits. The final data structure, if \(L=\omega(\log n)\), requires \(o(n)\) bits and can compute _NSV_, _PSV_ and _RMQ_ all using the same auxiliary structure. We call it _RMM-NPR_.
## 4 Discussion
### The SIF-GPP Relationship
In this perspective, the photochemical reflectance index (PRI) can bring complementary information to derive the photosynthetic efficiency component of GPP [62; 63]. In another perspective, the ratio between GPP and FRSIF (SIF760 in our case) can be used to assess the primary productivity efficiency. Data from Figure 7 shows that the slope of the regression line between GPP and SIF760 does not change with integration time (0.5 h or 6 h). Here, it is found to be 17.6 \(\pm\) 0.9 \(\upmu\)molC \(\cdot\) J\({}^{-1}\cdot\) sr \(\cdot\) um. After correction for the emission wavelength and adequately scaled for the GPP integration time, this value is within the range of the slopes of the linear models derived from space measurements with the TANSO instrument on board the GOSAT satellite [16; 17; 19],and comparable to the coefficient of the SIF-based GPP model for crops and grasslands derived from GOME-2 data (14.9 umolC \(\cdot\) J\({}^{-1}\cdot\) sr \(\cdot\) um) [20]. It should be noted that these results were obtained despite the differences in: (i) the measuring technique (atmospheric oxygen band vs. Fraunhofer lines in the case of GOSAT); (ii) the footprint (4 m\({}^{2}\) vs. 4\({}^{\circ}\times\) 4\({}^{\circ}\), or vs. 40 \(\times\) 80 km\({}^{2}\)); (iii) the time scale (daily measurement vs. monthly averages); and (iv) the measuring distance (21 m vs. around 800 km). Hence, the GPP-FRSIF relationship appears to be robust across methods, or time and spatial scales. This study confirms the use of ground-based studies to provide data for the development of SIF-based GPP models that could be used in the future at larger scales. However, further work is needed to investigate the variability of the GPP-FRSIF relationship among species and biomes, as well as its response to stress conditions.
## 1 Introduction
### Research Contributions  
The key contributions of this study can be summarized as follows:  
* We discuss remote elder caring service, emphasizing challenges associated with high packet rate and packet size, and discuss requirements from QoS and QoE perspectives.
* A haptic-driven elderly ECI-TeleCaring system is proposed that incorporates an SDN mechanism, edge intelligence, and, particularly, Machine Learning (ML) to predict the slave-side activity at the master domain.
* In the proposed system, dual LSTM models are employed at the edge to predict the remote-side caring robot's activity and localization in real time and display to the human operator at the master domain.
* We conducted various experiments to demonstrate that the proposed system achieves a balance between system stability and transparency while maintaining QoS and QoE requirements under unpredictable time delays and high packet rates.
* Finally, the proposed system is provided as an open-source tool along with the dataset used to facilitate further research and development in the field of remote elder telecaring services.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 7 Correlation Analysis
### Influence of Dominant Features on Fatigue Behavior
#### 7.3.6 Engineering Implications of ML-Driven Feature Importance  
Analysis  
The consistency between machine learning feature importance results and well-established material fatigue principles confirms the validity of the predictive models. These findings provide several engineering insights that can be leveraged for fatigue life optimization:  
* Reducing the bending radius significantly shortens fatigue life \(\rightarrow\) Designing tubing with larger radii, where feasible, can increase operational lifespan.
* Higher-grade materials exhibit greater fatigue resistance \(\rightarrow\) Material selection should prioritize grades with superior fatigue endurance properties.
* Wall thickness improves fatigue resistance but is not the primary driver \(\rightarrow\) Optimizing tubing thickness alone will not compensate for poor radius selection.  
* Internal pressure effects on fatigue life are secondary \(\rightarrow\) Operators should focus more on cyclic bending stress management instead of just pressure control.
* Welding effects can be minimized through quality control \(\rightarrow\) Proper welding techniques and post-weld treatments (e.g., stress relief and shot peening) can help reduce fatigue-related failures.
## 3 Experimental Setup  
For polarization analysis of the near-field signatures, a backscattering s-SNOM setup was adapted to include linear polarization control of both illumination and detection light paths, using a fully 360\({}^{\circ}\) rotatable phase retardation plate and a grid polarizer as analyzer, respectively. The setup is shown in Figure 2.  
The self-homodyne s-SNOM setup utilizes a p-polarized temperature-tunable CO\({}_{2}\) laser (Access Laser Co., Everett, WA, USA). Polarization control of the illuminating light is implemented by rotating the p-polarized incident state using a ZnSe 180\({}^{\circ}\) phase retardation plate (Thorlabs Inc., Bergkirchen, Germany), which is placed in front of the mylar beamsplitter (homebuilt). Note that reflection and transmission at the mylar beamsplitter itself are strongly polarization dependent, which is taken into account via the Jones matrix \(\hat{M}_{mylar}\) as discussed above and in [36]. The reflected part of the laser light gets directed towards a parabolic mirror (2\({}^{\prime\prime}\) diameter, 90\({}^{\circ}\)-off-axis, 3\({}^{\prime\prime}\) focal length) by a single gold mirror and is then focused onto the SNOM tip of a homebuilt SFM. Any light scattered off the tip-sample junction is then collected in backscattering geometry by the same parabola and directed toward the mylar beamsplitter via the gold mirror. The detection signal passes the mylar beamsplitter and eventually also the analyzer before then being focused onto the MCT detector (Teledyne Judson Technologies, Montgomeryville, PA, USA; Model J15D12) via a second parabolic mirror.  
All polarization measurements were performed with the s-SNOM tip kept at a fixed sample position (x,y), hence recording point-spectra. The higher-harmonic demodulation has been realized via a lock-in amplifier (integration time 50 ms), at multiples \(n=2\) of the tip oscillation frequency \(\Omega\approx 160\) kHz. In order to investigate the role of polarization in s-SNOM, three configurations/experiments were carried out with our setup, as will be detailed in the chapters to follow:  
* Section 4.1 and Figure 3: \(\theta\) controlled via the phase retardation plate, with no analyzer;
* Section 4.2 and Figure 4: \(\theta=45^{\circ}\) set via the phase retardation plate; \(\phi\) probed via the analyzer;
* Section 4.2 and Figure 5: \(\theta\) controlled the via phase retardation plate; \(\phi\) probed via the analyzer.  
Figure 2: s-SNOM setup for the polarization-sensitive measurement scheme. The light of a CO\({}_{2}\) laser is reflected by a mylar beamsplitter and focused via a parabolic mirror (PM) onto the SFM. The backscattered light is collected by the same PM, transmitted through the beamsplitter and detected by an MCT detector. Illumination polarization (\(\theta\)) control is implemented by adding a 180\({}^{\circ}\) phase retardation plate before the beamsplitter, whereas a grid polarizer is included in the detection path to analyze the scattered signal’s polarization (\(\phi\)).
# A Status Evaluation of Rock Instability in Metal Mines Based on the SPA-IAHP-PCN Model
## 4 Results and Discussion  
### Establishment of the Status Evaluation Index  
In this study, a lead-zinc metal mine in Shaanxi Province is used as a case example to demonstrate and validate the practicality and feasibility of the proposed method. Based on the data collected from microseismic monitoring, four parameters--cumulative number of events, cumulative microseismic energy, cumulative apparent stress, and cumulative Logarithmic seismic moment--were selected for the evaluation index system. The cumulative number of events (\(A_{1}\)) reflects the frequency of microseismic activity, which is a direct indicator of rock mass fracturing and deformation. The cumulative microseismic event energy (\(A_{2}\)) quantifies the energy release associated with these events, providing insights into the intensity of rock failure processes. The cumulative apparent stress (\(A_{3}\)) is a critical parameter that links the released energy to the stress conditions within the rock mass, offering a measure of the stress state and its changes over time. Finally, the cumulative logarithmic seismic moment (\(A_{4}\)) serves as a robust indicator of the overall deformation magnitude, integrating both the size and number of microseismic events. These parameters were chosen over others, such as rock mass properties or groundwater conditions, because they directly reflect the real-time dynamic response of the rock mass to mining-induced stress changes. While rock mass properties and groundwater conditions are important, they are often static or slow-changing factors that may not capture the immediate precursors to instability. By focusing on these four parameters, the study aims to provide a more dynamic and predictive assessment of rock instability, which is critical for early warning  
Figure 4: Three-dimensional views results of microseismic events.  
systems and risk mitigation in mining operations. The established evaluation system is shown in Table 3.
# Smart Parking: A Literature Review from the Technological Perspective
## 4 Discussion
### Types of Sensors  
Among research papers that were found relevant, only 58% presented detailed information about kinds of sensors used. These focused on proposing a solution without no real implementation. Distribution regarding sensors found on research papers is shown in Figure 5.  
The results of the most used types of sensors are displayed in Figure 5. This graphic shows that ultrasonic sensors are the most popular among research community, reaching nearly 35% from the reviewed papers. On the other hand, cameras with almost 26% are the second type of sensor used for designing or implementing smart parking solutions. Third, although technically smartphones can include some of the sensor capabilities mentioned above, they have a 23% of use within the research work revised.  
As appreciated, ultrasonic sensors are the most used category, and this tendency has reached a maximum that is still maintained, as shown in Figure 6. The highest peaks in relation to the use of ultrasonic sensors border 23% in 2014 and 2017. The graph shows an increasing trend in the use of these sensors over time. This might be because of their easy installation and management, meaning that a great variety of devices that can be connected either through wired or wireless media.  
Figure 5: Sensor type.  
Figure 6: Ultrasonic sensor usage.  
Using video cameras and image processing algorithms can become a complicated process for training and detection; although, in overcoming these difficulties, it is possible to cover a great area with only one device, resulting in a convenient solution. These do not need an existing infrastructure, resulting in a simpler implementation. Figure 7 shows the trend regarding camera usage. The trend in using cameras remained in the last two years after a substantial drop. However, because of the percentage of papers dealing with the subject of the sensors and taking into account the trends, it can be established that the importance of establishing a sensor has been decreasing to focus on the development of generic solutions that can make use of a wireless sensor either. The scientific community might focus on the approach of providing an infrastructure model that could be adapted to specific needs.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 4 Experimental Results
### Parameters Setting  
The SPCM proposed in this paper can effectively extract the spectral-spatial features. In the stage of spatial structure perception, parameter setting is particularly important. Figure 6 shows the effects of different \(L\), \(S\) and \(K\) on OA, AA, and Kappa on the KSC dataset. \(S\) and \(K\) were set to \(0.8L^{2}\), and then the size of \(L\) was changed. It can be found in the figure that the with increase of \(L\), OA, AA and Kappa increased first and then decreased slightly. This is because when the window size is very small, the same kind of pixels that can be provided will be very few, which is not enough with the feature representation; when the window is too large, there will be too many heterogeneous pixels, which will affect the accuracy of feature representation. After determining the value of \(L\), we fine-tuned \(S\) and \(K\) to achieve the best classification results. Finally, the three parameters that can produce the best classification result can be obtained.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 6 Discussion
### Addressing Research Questions  
As outlined in Section 1.1, our study directly addresses the research questions posed in this paper. Specifically, Adaptive-DecayRank successfully detects the sudden (dis)appearance of anomalous patterns in dynamic graphs, including node and structural anomalies **(Q1)**. The proposed method achieves high detection accuracy, precision, and AUC through its _adaptive decay factor_ and _Bayesian updating_, effectively prioritizing recent anomalous behaviors. Additionally, our scalability analysis (Figure 6) demonstrates that Adaptive-DecayRank efficiently processes large-scale dynamic graphs while maintaining sub-linear time complexity, confirming its suitability for real-time anomaly detection in streaming graph environments **(Q2)**.
## 3 Methods for Calculating the Aerodynamic Lift of the Pantograph
### Numerical Simulation Method  
The detached eddy simulation (DES) method combines the advantages of Reynolds-Averaged Navier-Stokes (RANS) and large eddy simulation (LES). In this study, the DES simulation method is employed, with the SST turbulence model selected. The SST model is based on the assumption of shear stress transport (SST) and combines the characteristics of the k-\(\epsilon\) and k-\(\omega\) turbulence models through a blending function [19]. The numerical simulation results are compared and analyzed against the experimental results of a 1:8 scaled model of high-speed train aerodynamic performance conducted in the 8 m \(\times\) 6 m wind tunnel at the China Aerodynamics Research and Development Center, as shows in Figure 10. The aim was to validate the accuracy of the numerical simulation method. The  
Figure 8: Definition of wind angle.  
Figure 7: Dimensions of the computational domain.  
Figure 9: Division of pantograph and computational domain grids.  
results show that the error between the numerical simulation and model experiments was within a reasonable range, demonstrating the feasibility of analyzing the aerodynamic loads on high-speed train pantographs using numerical simulation.
## 2 HSI Procedure  
As mentioned before, compared to the normal images, hyperspectral images are very different in both spectral and spatial features. While regular images record light in three broad bands, namely red, green, and blue (RGB), hyperspectral images record hundreds of narrow contiguous spectral bands ranging from 380 to 2500 nm. This provides the HSI with detailed spectral information on the identification of materials and their properties, which is very important in many applications like agriculture and environmental monitoring [16]. Taking different types of images to RGB requires different types of devices, accessories and processes. Moreover, the preprocessing and feature extraction techniques of hyperspectral images may different from regular images.
### Motivation, Limitations, and Applications  
We designed the CHP2 system to enable communications, synchronization, and positioning services from a single platform. Many existing technologies already offer these different services; notably, the GPS offers high-precision positioning and timing services, while modern cellular networks offer high-speed communications services. CHP2 does not outperform these solutions, nor do we present it as a competitor to these technologies in any capacity. Instead, we developed CHP2 as an accessible and efficient option for a subset of applications that only require moderate precision and throughput, but additionally benefit from the consolidation of different services into a smaller form factor.  
Compared to existing solutions, CHP2 has several advantages and limitations, which we explicitly acknowledge. GPS provides high-precision positioning services over the entire planet, and GPS receivers are small and efficient. Unfortunately, broadcast systems such as GPS are particularly susceptible to spoofing and jamming cyberattacks [20; 21; 22]. Cooperative ranging systems, such as CHP2, cannot achieve the same positioning precision and coverage area, but they are naturally more resilient to these forms of cyberattacks, so they may be suitable for applications that care more about security than precision. Furthermore, cellular network infrastructure enables significantly faster network communications throughput than small systems such as CHP2. Integrating various sub-tasks with modern 5G and LTE infrastructure is a rich research topic in its own right, but we are more focused on the subset of problems that either do not need massive communications throughput, do not want the additional layer of complexity incurred by integrating with cellular networks, or do not have access to this infrastructure at all (natural disasters, civil conflicts, ad hoc deployments, etc.).