esa-sceva/satcom-chunk-collection huggingface dataset:
# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 2 Related Work
### Geographic Knowledge Graph  
Current research on GeoKG focuses primarily on their construction. With the growth of the Internet, platforms such as OpenStreetMap (OSM) and Wikidata contain large amounts of data that can be used to extract and construct geographic knowledge. Based on this, several studies CrowdGeoKG [37] extracts different types of entities from OSM and enriches them with geographic knowledge from Wikidata. WORLDKG [38] refines the class hierarchy of OSM elements by analyzing large amounts of heterogeneous OSM data tags, combined with manual filtering, to create a top-down hierarchical classification of geographic entities covering a wide range of geographic categories. It also links geographic entities to specific classes in the Wikidata and DBpedia [30] ontologies. UUKG [39] models the hierarchical relationships between geographic entities, constructing a multi-level urban knowledge graph that provides strong support for spatiotemporal urban forecasting.  
Current research on geographic knowledge graph representation primarily focuses on incorporating spatial location information from geographic knowledge into traditional knowledge graph representation methods. In earlier studies [15], researchers used spatial distance as a constraint, employing translation-based models [18; 19; 20] to encode this distance constraint along with entities and relations in a low-dimensional embedding space. However, constraints based on spatial distance only capture the separation between entities and do not reflect their relative spatial positions. To address this limitation, researchers have begun to encode spatial information, such as point coordinates and entity boundaries, directly into the GeoKG embedding space [16].  
However, current geographic knowledge graph representation methods focus primarily on modeling the structure of GeoKG, while failing to take into account the multimodal data. Therefore, the MGKGR method proposed in this paper not only models the structural features of geographic knowledge graphs but also captures the features from multimodal data associated with geographic entities. By employing a two-stage multimodal feature fusion method, it integrates heterogeneous features from different modalities, extracting both common and complementary characteristics, thereby enhancing the model's ability to represent geographic knowledge graphs.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 2 Health Monitoring Techniques in Pipeline Industry
### In Pipeline Industry
#### 2.1.2 Electromagnetic Inspection  
Inspecting through electromagnetic means would entail the utilization of fields after generating them. Electromagnetic health monitoring allows for the detection of burning, impact lamination, fiber breaking, and liquid ingress. The electromagnetic method essentially sends an electromagnetic image to the receiver to identify possible damage in the structure [30]. Their measurements and interactions yield the conductivity of the material and the test component's permeability. The different types of eddy currents generated, namely, Conventional Field, Remote Field, and Pulsed Field, provide the information as to the time or extent or decaying through measurements of the variation of these currents. Impedance is decreased with the permeability and conductance, and the phase lag is related to thickness of the wall.  
\[\Phi_{p}\propto N_{p}I_{p}\sin(\omega t) \tag{3}\]  
\[\Phi_{e}=\Phi_{p}-\Phi_{\rm s} \tag{4}\]  
\[d=50\sqrt{\rho/f\mu_{r}} \tag{5}\]  
\[\theta=x\sqrt{\pi f\mu\sigma} \tag{6}\]  
where \(\Phi_{p}=\) primary magnetic flux, \(N_{p}=\) number of primary coils, \(\omega=\) frequency, \(\Phi_{e}=\) equilibrium flux, \(\Phi_{\rm s}=\) secondary magnetic flux, \(d=\) depth, \(\rho=\) electrical resistivity, \(f=\) excitation frequency, \(\mu_{r}=\) dimensionless relativity, \(\theta=\) phase lag, \(x=\) distance between coils, \(\mu=\) average permeability, and \(\sigma=\) electrical conductivity.  
The above two relations define the measurement of the eddy currents.
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 6 Conclusions  
This paper has proposed and demonstrated a GPS augmentation scheme, FAGPS, which uses geolocated fiducal images within an environment which can be observed and used to reduce GPS error (2 DRMS error of 5.5 m to 2.99 m) for a period of 20-30 min. The FAGPS scheme has DGPS-like characteristics (in that local corrections are applied to GPS data to improve accuracy), the difference is that the same physical receiver is used to make the corrections. Although the accuracy doesn't match that of DGPS, the cost of the system is orders of magnitude less than DGPS. DGPS operates on a much faster update rate than FAGPS (5 min compared to 20 min) which results in more up-to-date corrections being applied to GPS data.  
The FAGPS scheme was assessed both through time-dependent simulations and fieldwork which demonstrate the improved short-term (15-20 min) accuracy by correction of initial GPS captured offset  
Figure 12: Results from Experiment 3 with 26 labs of uncorrected path showing a larger error in the longitude.  
Figure 13: Results from Experiment 3 with 26 labs of corrected path showing a closer accuracy particularly for the longitude.  
points. Future work is required to quantify the improvements that FAGPS could render for altitude and to validate the technique for flying vehicles.  
Conceptualization, R.R.; Data curation, R.H.; Formal analysis, R.H.; Investigation, R.R.; Methodology, R.R.; Validation, R.H.; Writing--original draft, R.R. and R.H.; Writing--review & editing, R.R. All authors have read and agreed to the published version of the manuscript.  
This research recieved no external funding.  
The authors declare no conflict of interest.
# Multimedia Quality of Experience (QoE): Current Status and Future Direction
## 2 Contributions  
The first paper [6], written by [PERSON], [PERSON], [PERSON] and [PERSON], identifies the human-related factors which have significant influences on QoE. Furthermore, this study inspects the combined impact of perceptual factors, memory effect and the degree of interest. Based on this investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user's cumulative perception.  
[PERSON], [PERSON], [PERSON] and [PERSON] are the authors of the second paper [7], which explores the resource requirements for the real-time operation of High Efficiency video Coding (HEVC), which has become a contributing factor towards the QoE of the end users of emerging multimedia and future internet applications. In this context, this study proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The performance evaluation conducted in the paper demonstrates than the perceptual visual quality of the reconstructed videos of the proposed algorithm is minimally affected compared to state-of-the-art approaches.  
The third paper [8], written by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], identifies the importance of addressing the control of decoding complexity and energy use when decoding video to improve user QoE. As a solution, this study proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rate and decoding complexity levels using a decoding complexity rate distortion model.  
[PERSON], [PERSON], [PERSON] and [PERSON] are the authors of the fourth paper [9], which focuses on multi-view 3D content with dense depth map information. In this paper, the authors present a no-reference depth map quality evaluation model based on a novel depth map edge confidence measurement technique, in order to assist with the estimation of the quality of virtual views in multi-view video content.  
Finally, the last paper [10], written by [PERSON], [PERSON], [PERSON] and [PERSON], looks into the quality of experience (QoE) of static and animated floating action buttons (FABs) that are used in the user interfaces of web pages and mobile applications. The authors experimentally evaluate the perceived Quality of Experience of FAB functionality and compare it to the more traditional toolbar alternative.  
The guest editors wish to thank all the contributing authors, the professional reviewers for their precious help with the review assignments and the excellent editorial support from the _Future Internet_ journal at every stage of the publication process of this special issue.
## 2 Description of TALIS  
The THz Atmospheric Limb Sounder (TALIS) payload and observation characteristics are summarized in Table 1. The satellite for TALIS is designed at an orbit of 600 km. TALIS will use four heterodyne microwave radiometers that operate at four frequency regions about 118 GHz, 190 GHz, 240 GHz and 643 GHz to scan the atmospheric limb from about 0 to 100 km. The 1.6 m offset parabolic antenna provides the field of view (FOV) at the tangent point of about 5.5 km, 3.8 km, 3.3 km and 0.96 km, respectively. Two calibration targets (a hot target and an extra target) are set at the end of the arm. The hot target is used for the radiometer two-point calibration. The extra target is used to estimate the antenna effect and the nonlinearity. TALIS will view the hot target and the extra target at first for 3 s, and then scan the limb from 0 to 100 km in 10 s. Finally, it will view the cold space (used as a cold target) at 200 km for 5 s. The process of retrace (from 200 km to the surface) is the same (also record data). Four heterodyne radiometers operate in double-sideband mode (DSB). Eleven FFT spectrometers of 2 GHz bandwidth with 2 MHz resolution will be used to improve the performance of the observation. The spectral bands of TALIS are summarized in Table 2. The 118 GHz is used for temperature, 190 GHz for H\({}_{2}\)O, 240 GHz for O\({}_{3}\), 643 GHz for HCl and other trade gases retrievals. More details are provided in [24].
## 4 Concept
### Planning and Installation
#### 4.1.1 Development Objectives for the ML Applications  
In the task \"Development objectives for the ML applications\", it is first determined which system components of the CPPS are to be monitored, analyzed, or controlled. In other words, what the data-driven methods are to be used for. For example, data-driven methods can be used to reduce downtime through predictive maintenance or improve process quality, productivity, and ecology. Use cases are used to define technical metrics that are to be investigated in a data-driven manner. In addition, usability requirements, deployment requirements, time and cost constraints, and other project constraints are also agreed upon.  
How do these planning requirements affect data quality? First, the basic development task of the ML application is defined. This also specifies the form in which the results are to be presented or made available. Furthermore, with the task, the technical target values are available that can evaluate the development success. The technical objective also describes the required analysis accuracy, which is significantly influenced by the quality of the  
Figure 3: Concept—V-Model for Data Quality Assurance in ML Applications for CPPS.  
underlying data. The achievable data quality is in turn influenced by the project conditions. The budget, for example, determines the performance of the sensors and IT components to be procured. The available time frame influences the scope of experimentation and testing and the learning of models. The scope of possible tests even determines the selection of suitable ones--KNN requires a great deal of data.
# Remediation of Heavy Metal Contaminated Farmland Soil by Biodegradable Chelating Agent GLDA
###### Abstract  
Chemical leaching is one of the effective methods to remove heavy metals from soil. The effects of biodegradable chelating GLDA (\(N\), \(N\)-bis(carboxymethyl)-)-L-glutamic acid tetrasodium salt) on contaminated farmland soil in four different places (collected from Shangba Village, Shaoguan city (SB); Huaqiao Village, Zhuzhou (HQ); Shaoguan Tielong (TL); and Liantang Village, Lechang (LT), respectively) were studied by the method of leaching. To explore the synergistic effect between GLDA and citric acid, the leaching conditions were also explored. The results showed that the leaching efficiency of heavy metal Pb in soil was improved by adjusting the GLDA solution to reach acidity by adding citric acid. The leaching efficiency of Pb after mixing GLDA and citric acid was higher than the sum of their respective leaching abilities. After leaching with 10 mmol/L of the GLDA solution with a pH of 10.86 for 120 min, the total Pb and the Pb available in the soil decreased significantly. It is known that citric acid has a significant effect on improving the leaching rate of GLDA on soil, and this method can also be applied to other heavy metals, such as Cd. This study provides a low-ecological-risk method for the remediation of heavy metal-contaminated soils.  
Pb; soil leaching; GLDA; citric acid; heavy metals Article
# Dynamic Reduction-Based Virtual Models for Digital Twins--A Comparative Study
## 2 Component Modal Synthesis  
The superposition of principal modes, also referred to as the normal mode method [65], is a fundamental and extensively used analytical approach for solving vibration problems. In this method, linear transformations in terms of modal vectors, in conjunction with the orthogonality of modal vectors, enable the transformation of a set of simultaneous equations of motion in physical coordinates into a set of independent modal equations. For large and complex industrial structures, an eigensolution, which is the most fundamental part of a dynamic analysis using the normal mode method, becomes uneconomical due to the sheer number of equations of motion. Dynamic reduction methods alleviate the computational constraints of dealing with large matrices by obtaining the principal modes of the overall system from parts rather than as a whole. In these methods, multiple eigenvalue problems involving individual constituent components represented by smaller matrices are used rather than a single eigenvalue problem representing the entire structure. The coordinate system obtained by synthesizing the components is assembled for system synthesis, yielding one final eigenvalue problem, typically of smaller size.  
A substructure, in general, contains a set of constraints at its interfaces coupled with neighboring substructures. General displacement within a substructure is defined in dynamic reduction methods by superimposing displacements relative to component boundaries, from which a set of generalized coordinates applicable to the entire structure is synthesized. One of the subcategories of dynamic reduction methods is fixed-interface component modal synthesis. The fixed-interface component modal synthesis by [PERSON] is one of the most widely used dynamic reduction techniques, and most FEA-based systems have built-in libraries, as outlined in the following section.
# The Effect of an Ag Nanofilm on Low-Temperature Cu/Ag-Ag/Cu Chip Bonding in Air
## 3 Results and Discussion  
### Ag Diffusion into Cu  
The Ag nanofilm deposited on the Cu(111) was about 15 nm thick, and was deposited as a rather porous film, not in the form of a uniform and dense crystalline thin film [14]. As shown in Figure 3, the Ag3d and Cu2p XPS profiles at the surface (i.e., the profile of XPS etch time at 0 s) show no oxidation of Cu, which indicates that the porous Ag nanofilm was sufficient in preventing oxidation of the Cu surface at room temperature.  
The Ag nanofilms on the Cu samples were maintained at RT (25 \({}^{\circ}\)C), 100 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 200 \({}^{\circ}\)C for one hour on a hot plate to analyze the diffusion behavior of Ag and Cu. Then, XPS depth profiling was performed, and the results are presented in Figure 3. As the temperature increased, the Ag concentration on the surface decreased and the Cu concentration increased. Even in the O1s profile, surface oxidation by the Ag nanofilm hardly occurred at room temperature, but as the temperature increased, Cu diffused to the surface and surface oxidation occurred rapidly. The activation energy of Ag to Cu was about 3.95 times higher than that of Cu to Ag [14]. According to our previous study [14], the activation energy and the pre-exponential factor were 17,876 J/mol and 0.064 m\({}^{2}\)/s, respectively. Therefore, the diffusion coefficient of Ag to Cu at 180 \({}^{\circ}\)C was determined to be 5.56 \(\times\) 10\({}^{-4}\) m\({}^{2}\)/s, which is much faster than the diffusion of 200 nm Ag into single crystal Cu or bulk Ag into Cu [15; 16]. This is explained by the rapid progress of Cu diffusion into the porous Ag nanofilm. This is in reasonable agreement with Ag diffusion into Cu at high temperature. But, unlike the lattice diffusion between bulk Ag and Cu at high temperature, Ag nanofilm diffusion into Cu occurs mostly through grain boundary  
Figure 1: Schematic diagram of the process flow.  
Figure 2: Equipment setup for the shear measurements.  
diffusion, indicating a high diffusion coefficient and low activation energy [16]. At high temperatures, atomic diffusion occurs generally by substitutional diffusion, but at low temperatures, atomic diffusion occurs mostly by grain boundary diffusion rather than bulk diffusion because the activation energy of grain boundary diffusion is generally half that for bulk diffusion [16; 17].
# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 3 Results
### Data Augmentation  
We maintained the dimension of the training dataset, added and trained multiple variations of the models by adopting data augmentation methods. The original data input format is inline-crossline slices converted to dense arrays. Each data slice is divided into smaller tiles for training, in order to fit within GPU memory limitations. Stride tiles were generated, overlapping within the chosen dimension. Next, we applied an adjustment of the degree of overlapping stride size, i.e., amount of pixel offset for the next sampled tile compared to the previous sampled tile. These tiles were sampled at size (128 \(\times\) 128) with a stride of (32 \(\times\) 32). This configuration allowed us to generate a total of 145 tiles from each inline-crossline slice of size (1024, 256). Without stride tiles generation, splitting the inline-crossline slice into tiles directly will result in only sixteen non-overlapping tiles. We applied random flipping, random rotations from 0\({}^{\circ}\) to 45\({}^{\circ}\) to all tiles, and corresponding labels to prevent possibilities of data overfit and to generalize potential aspects of features that DL algorithms have not seen before.
# Decision Framework for Predictive Maintenance Method Selection
## 5 Evaluation of the Proposed Framework
### Case 1: Electrical Components of a Naval Vessel
\begin{table}
\begin{tabular}{c l} \hline \hline
**Step in Framework** & **Result and Explanation** \\ \hline Starting point & **Decision pull**—ambition to optimise maintenance intervals \\ \hline \multirow{8}{*}{Ambition level} & - & Assets used in highly variable situations \\  & - & Include environmental and operational variabilities (e.g., temperature, saline conditions) \\  & - & Average degradation level of components can be \\  & - & estimated based on usage and environmental conditions (thus: no individual monitoring) \\  & \(\rightarrow\)**Ambition level 3** \\ \hline \multirow{8}{*}{Types of data available} & - & Expert knowledge \\  & - & Low-quality historical data: \\  & \(\bigcirc\) & Amount of failure data limited (low failure \\  & & frequency, small number of systems) \\  & \(\bigcirc\) & Incomplete data, unreliable input and poorly \\  & & filled recording systems \\ \hline \multirow{2}{*}{Match ambition level—data?} & **No**—AL3 also requires data on the operational environment (stressors) \\ \hline \multirow{2}{*}{Can these data be collected?} & **Possible**—Install sensors or collect more environmental data \\  & by hand \\ \hline \multirow{2}{*}{Positive business case?} & **No**—Too costly and time-consuming due to variety of \\  & equipment types \\ \hline \multirow{2}{*}{Improve with a lower AL?} & **Yes**—Experience-based techniques will also improve the \\  & current maintenance plans \\ \hline \multirow{2}{*}{Other data available?} & **Yes**—Expert knowledge is available \\ \hline \multirow{2}{*}{Positive business case?} & **Yes**—No major investments required (knowledge is available, \\  & organising FMECA studies for critical equipment is expected \\  & to improve maintenance) \\ \hline \multirow{4}{*}{MM selection} & **Advice:** Apply an experience-based maintenance method. \\  & This method will not satisfy original ambition level, but with \\ \cline{1-1}  & available data and expertise, it is expected to contribute to the \\ \cline{1-1}  & maintenance program. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Application of the decision framework to case 1.
## 6 JCS Sidelink Performance Evaluation Using CCM-SPS
### Simulation Setup  
The main settings are reported in Table 1 and discussed hereafter.  
In this section, we simulate the performance of a JCS sidelink system using a system-level simulator. In particular, vehicles perform SINR evaluation for collision detection and CRLB calculation after each transmission.  
Scenario. We simulated a three-lane bidirectional highway scenario, with vehicle density varying between 50, 150, and 250, depending on the setting. The average speed of vehicles was 70 km/h, and the STD of vehicle speed was 7 km/h. The radar cross-section of the vehicle is 10 dBsm.  
Power settings and channel model. The channel model is WINNER + B1, with a fixed available bandwidth of 40 MHz and a center frequency of 5.9 GHz, to simulate a V2V channel scenario. The transmission power of the vehicle is 23 dBm, assuming that the gain of both the transmitting and receiving antennas is 3 dBi and the noise gap is 6 dB.  
Physical layer and data traffic. In terms of physical layer settings, we used fixed SCS and MCS sizes. In terms of data traffic settings, we simulated V2V periodic message transmission with a period of 100 ms and support packet sizes of 350 or 1000 bytes to represent CAM and CPM business types, respectively. The packet size, under fixed MCSand SCS configurations, will affect the bandwidth occupied by vehicle transmission packets, as shown in Table 2.