esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Comparison to Other Methods  
As with our previous work, our method compares to other methods in terms of accuracy and computational overhead [11]. A comparison is shown on Table 3. These comparisons are not intended to be exhaustive but rather serve as representative examples of the choices available. As there are differences in the parameters and experiments outlined for these methods, ranges or averages are provided for the purpose of comparison. Consequently, although we present a wide range of accuracy for our method, in practice, most implementations will fine-tune the parameters to align more closely with the lower end of the range.  
Moreover, the computational burden is influenced by the choice of algorithm, with certain techniques, like clustering approaches, being notably more resource-intensive. Models that necessitate training before making predictions, including our own method, often incur significant costs during the training phase but exhibit faster performance when identifying anomalies in a testing dataset. Table 3 further illustrates the variation in computational overhead, which is attributed to the number of nodes considered in each approach. Graph-based methods face feasibility challenges when dealing with networks that have numerous nodes, whereas other supervised models can handle such scenarios due to the inherent structure of data points in those methods. Therefore, when assessing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode** & **Thresh.** & **Hours** & **Precis.** & **Recall** & **Accur.** & **F2** \\ \hline Rand. 5\% & 0.25 & First 3 & **0.525** & **0.636** & **0.483** & **0.610** \\ \cline{3-6}  & & Last 3 & 0.543 & 1.000 & 0.543 & 0.856 \\ \cline{3-6}  & 0.5 & First 3 & **0.618** & **0.382** & **0.530** & **0.413** \\ \cline{3-6}  & & Last 3 & 0.543 & 1.000 & 0.543 & 0.856 \\ \cline{3-6}  & 1 & First 3 & **1.000** & **0.055** & **0.480** & **0.067** \\ \cline{3-6}  & & Last 3 & 0.560 & 0.687 & 0.537 & 0.657 \\ \hline Rand. 10\% & 0.25 & First 3 & **0.595** & **0.713** & **0.570** & **0.685** \\ \cline{3-6}  & & Last 3 & 0.513 & 1.000 & 0.513 & 0.841 \\ \cline{3-6}  & 0.5 & First 3 & **0.619** & **0.389** & **0.527** & **0.420** \\ \cline{3-6}  & & Last 3 & 0.513 & 1.000 & 0.513 & 0.841 \\ \cline{3-6}  & 1 & First 3 & **1.000** & **0.060** & **0.477** & **0.074** \\ \cline{3-6}  & & Last 3 & 0.530 & 0.688 & 0.527 & 0.650 \\ \hline Rand. 25\% & 0.25 & First 3 & **0.505** & **0.623** & **0.477** & **0.595** \\ \cline{3-6}  & & Last 3 & 0.557 & 1.000 & 0.557 & 0.863 \\ \cline{3-6}  & 0.5 & First 3 & **0.530** & **0.333** & **0.490** & **0.360** \\ \cline{3-6}  & & Last 3 & 0.557 & 1.000 & 0.557 & 0.863 \\ \cline{3-6}  & 1 & First 3 & **1.000** & **0.031** & **0.487** & **0.039** \\ \cline{3-6}  & & Last 3 & 0.567 & 0.689 & 0.533 & 0.660 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._the practicality of implementing an anomaly detection technique, the size of the network becomes a crucial factor that affects the computational demands.
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
###### Abstract  
Rebar corrosion monitoring techniques have been used in studies involving embedded sensors that can detect changes in the corrosion currents and the polarization resistance of rebars in large structures. Defect detection methods that employ infrared cameras are useful non-destructive testing methods to detect defects in concrete structures. However, the measurement results from these methods would vary depending upon several factors that affect thermography. Because of these reasons, they have not been able to provide sufficient reliability. The goal of this study is to develop a technique that uses infrared cameras to quantitatively measure rebar corrosion rates. To examine the impact of the cover thickness, the experiment variables were set at cover thicknesses of 10, 20, and 30 mm and rebar corrosion ratios of 0%, 1%, 3%, 5%, 7%, and 10%. Each variable was tested and a total of 60 specimens were created (i.e., 54 specimens and 6 preliminary specimens). In this study, corrosion was applied using an electrochemical corrosion method that employs [PERSON]'s law, i.e., the law of conservation of electric charge. The test results of height, width, and area of temperature distribution curve were analyzed, the height of temperature distribution curve was increased as the heating time was grown. In addition, the area of temperature distribution was varied dependent upon the corrosion rate and cover thickness.  
rebar corrosion; electrical resistance; infrared thermography; non-destructive corrosion detection; infrared thermography (IRT) database
## 6 Conclusions  
Developing a methodology that combined existing satellite prediction software, 2D vector mapping and basic elevation data in a GIS, a working GNSS prediction method was developed, thus negating the need for time-consuming and costly high-density topographic surveys. This work can facilitate satellite prediction surveys when city models are not already available, e.g., in towns or cities with basic digital mapping or limited elevation datasets. Using tools available to the majority of companies/government agencies with a standard digital mapping licence from the national mapping agency and ArcMap or similar GIS tools like MapInfo or QGIS/SAGA GIS, this has been shown to be a practical solution. Real-world tests using a GNSS receiver designed to eliminate erroneous signals verified the accuracy of this method, returning zero errors in 60% of the tests including sites adjacent to tall buildings. Although a number of potential error sources were apparent, the lack of vegetation in the obstruction model was potentially the largest source of errors. The next iteration of this methodology will incorporate vegetation, thereby significantly improving the accuracy of the predictions in vegetated areas. It will also be used to plan MMS routes through urban areas, with updated GNSS visibility calculations applied at regular intervals along the route. Additional identifiers for each satellite will also be added, helping to assess the influence of multi-path in the tests. The quality of the post-processed navigation solution will be assessed against existing satellite prediction methods and high density urban models. The prediction method presented in this paper is easily reproducible and will enable planning a survey using a mobile  
Figure 11: Possible error sources in GNSS predictions and validation (**a**) the goal is LOS to five satellites (**b**) multipath resulting in errors in validation results (**c**) a discrepancy between LOD2 models which better approximate real world objects and LOD1 models which ignore roof structure created with 2D vector mapping.  
GNSS receiver over a wide area. This will lead to improved GNSS visibility and a resulting increase in accuracy in areas where GNSS signals might be obstructed.  
Research presented in this paper was funded by the Irish Research Council (IRC) and the Enterprise Partner, Pavement Management Services Ltd. (Galway, Ireland), by the NRA research fellowship program, ERA-NET SR01 projects and by a Strategic Research Cluster grant (07/SRC/I1168) from Science Foundation Ireland under the National Development Plan.  
Vector mapping used in accordance with Ordnance Survey Ireland Licence No. EN 0063514 (c) Ordnance Survey Ireland/Goverment of Ireland.  
The author acknowledges the constructive input provided during review of this manuscript by each of the anonymous reviewers and the editorial staff.
## 4 Conclusions
These findings are also in good agreement with the recent studies [12; 13; 14], regarding the negligible aerodynamic effect of SGS on voice production. Using patient-specific geometries, we found several aspects that are consistent with previous simplified model studies, including the threshold of 75% stenosis for initiating significant changes, the role of flow resistance and pressure drops, and the overall progression pattern where flow changes precede vibration changes. However, at severe stenosis levels (>90%), the patient-specific model revealed crucial differences, including anterior-posterior opening patterns and focused jet effects. These differences led to changes in the glottal area waveform, open quotient, and fundamental frequency. These findings indicate that while simplified models work well for mild to moderate stenosis (up to 75%), patient-specific details become essential for severe cases. We would like to note the potential of the hydrodynamics/splitting method for investigating acoustic source fields across SGS severities. [PERSON] et al. [48] explored acoustic source fields in voice production using a hybrid aeroacoustic method based on a perturbed convective wave equation formulation. Their analysis quantitatively showed that the time derivative of incompressible pressure dominates acoustic generation in voice production. Building on this, investigating changes in acoustic source fields could further improve our understanding of acoustic characteristics of SGS. Conceptualization, X.Z. ; formal analysis, D.B., Q.X. and X.Z. ; investigation, D.B. ; writing--original draft, D.B. ; writing--review and editing, D.B., Q.X., S.T. and X.Z. ; supervision, X.Z. ; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Institute on Deafness and Other Communication Disorders (NIDCD), Grant No. 5R01 DC009616. Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to data volume and complexity. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDCD or the NIH. The numerical simulations were supported by the Extreme Science and Engineering Discovery Environment (XSEDE) (allocation award number TG-CTS180004) and The University of Maine System Advanced Computing Group. The authors declare no conflicts of interest.
# Light-Weighting of Planetary Gearbox Based on Multi-Strategy Optimization Sparrow Search Algorithm
## 4 Application, Results, and Discussion  
### Application  
The transmission schematic of the yaw gearbox produced by a Chinese company is shown in Figure 8. The gearbox is a 4-stage planetary gear drive. It operates with an input power of 0.6 kW, an input speed of 700 r/min, a total transmission ratio of 1600, and is designed for a service life of 20 years. In this gearbox, each stage of the planetary transmission has three planetary gears. The material of the sun gear and the planetary gear are 17 CrNiMo6 treated by carburizing and quenching. The material of the inner gear is 42 CrMoA treated with gas nitriding technique. The light-weight objective is to reduce the volume of the gearbox while satisfying the strength condition, and the change in the total transmission ratio must not exceed 1.5%.
## 4 Results
### Validation of Modeled Daily ET with Ground Observations, PML_V2 ET, and SEBAL ET
TSEB-SM ET, PML-V2 ET, and SEBAL ET were all underestimated to varying degrees during the growing season, with TSEB-SM ET being closest to the site observations, followed by PML-V2 ET (R\({}^{2}\) of 0.53, RMSE of 0.75), while SEBAL ET (R\({}^{2}\) of 0.05, RMSE of 0.58) showed the largest deviation from the ground-based site observations. Overall, comparing the three ET products with the ground observations, TSEB-SM ET performed the best and had the closest fit to the ground observations (R\({}^{2}\) of 0.55, RMSE of 0.73).
## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.3.4 Motion Sickness in Experiment  
One of the main issues with MR is whether motion sickness occurs. In the case of the see-through device, there are studies showing that the effect on motion sickness is insignificant [30], but there are also studies showing that it has a similar degree of motion sickness to HMD devices [31]. Motion sickness, once developed, has a significant impact on the usability of MR applications, which can affect the study results. Therefore, in this study, the motion sickness condition of subjects was measured through pre- and post-questionnaires, including SSQ, to confirm whether motion sickness that could affect the study results occurred, and the measured results are shown in Figure 12.  
From Figure 12, it was confirmed that there is some difference in the average SSQ scores of the subjects measured through the pre- and post-questionnaires. To confirm whether the difference is significant, it is necessary to perform the paired-sample \(t\)-test for all SSQ scores. Tables 17 and 18 show the results of the normality test obtained by the paired-sample \(t\)-test, respectively.  
As a result of the normality test, as shown in Tables 17 and 18, in the case of nausea (pre-questionnaires), oculomotor discomfort (post-questionnaires), and total score (post-questionnaires), the significance level (red boxes in Tables 17 and 18) of both the Kolmogorov-Smirnov and the Shapiro-Wilk test was greater than 0.05. However, in the case of all other measured values, the significance level of both the Kolmogorov-Smirnov and the Shapiro-Wilk test was less than 0.05, so SSQ score data did not satisfy the normal distribution.  
Since the entire data could not satisfy normal distribution, a [PERSON] signed rank test, which is a non-parametric test, was performed instead of a paired-sample \(t\)-test, and the result is shown in Table 19.  
As a result of the [PERSON] signed rank test, as shown in Table 19, the significance level (red box in Table 19) of all the scores (nausea, oculomotor discomfort, disorientation, total score) was greater than 0.05. Based on this, it was confirmed that there is no significant difference between motion sickness measured by pre- and post-questionnaires and that the experimental results in this study were not affected by motion sickness.
# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 3 The Digital Twin Platform
### Physical Entity  
The key for realizing a DT is to enable a tight integration of physical and virtual space. To that end, a lot of information has to be exchanged with the physical entity. Data have to be recorded by sensors, and control signals have to be fed back to the actuators in the energy system. However, most DT applications do not mention the connection of the DT environment to the control system [16]. Hence, this critical implementation barrier needs to be dissolved.  
In a typical DT deployment scenario for IES, it can be assumed that there is already a pre-existing system for at least basic interfacing of the physical entity in place. Various measurement values are typically connected via a programmable logic controller (PLC) to a state-of-the-art SCADA system. Our DT platform builds upon on such SCADA systems; hence, we locate them inside the physical entity dimension (see Figure 2). Such SCADA systems (or merely PLCs) can then be interfaced to the DT via standard industrial IoT protocols. In case of our DT platform, this is MQTT.  
This tight integration would also allow realizing certain control tasks in the DT. The advantage is obvious: on the DT, the controller has access to more detailed models and more computation power. However, other factors that need to be taken into account are hard real-time and dependability requirements, which cannot be guaranteed for communication via the used network transmission protocols. For this reason, high-speed control tasks and plant safety measures should not be executed within the DT, but they always run on the PLC directly.  
For our DT implementation, XAMControl ([[https://en.evon-automation.com/](https://en.evon-automation.com/)]([https://en.evon-automation.com/](https://en.evon-automation.com/)), accessed on 10 June 2022 ) was used as the SCADA platform, which allows to define, visualize, program and configure PLCs. In order to integrate this system into the proposed architecture, a prototype version of the server software enables publishing and receiving messages to and from the MQTT broker. These messages can then be processed by any interested application by subscribing to the relevant MQTT topic.  
Figure 3: Exemplary request–response message pattern in the DT platform. Services subscribe to their respective request-topics and thereby receive messages published by the service orchestrator, e.g., to start operating. When a service instance has completed a run, it publishes to its response topic, which is monitored by the service orchestrator via subscription. Note: The function of the service orchestrator is explained in detail in Section 3.5. Character # denotes a multi-level wildcard, hence receives all messages of a topic that begins with the pattern before the wildcard character, and ” ” contains message payloads.
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 3 Junction-Aware Vehicle Selection Scheme
### Information Exchange Phase
Thus, a threshold has been assigned as \(VDR_{max}\), where the \(VDR_{max}\) is the estimated highest value of neighbor nodes that does not lead to congestion. Therefore, the \(VDR_{max}\) is set to 100 vehicles/km. We have considered a road environment with opposite lanes and assumed that 50 vehicles on both sides of opposite lanes in a 1 km road length will not result in congestion. This will result in a 20-meter road distance for each vehicle, which we consider a normal situation without congestion in urban road environments. The C-NFV case that has a greater number of vehicles than the \(VDR_{max}\) (\(VDR>VDR_{max}\)) is considered to be a congested network. Therefore, we obtain Equation (3):  
\[ VDR= \begin{cases}1& VDR\leq VDR_{max}\\ 0& VDR> VDR_{max}\end{cases}  \tag{3}\]  
The \(VDR\) is estimated for two multiple paths independently since the paths are dispersed. The road density of a C-NFV is compared with the other road densities of the neighbor vehicles of a Candidate Next Forwarding Vehicle, and the road with the highest density, but which is not greater than the \(VDR_{max}\), is considered for selection. Therefore, the vehicle density of the road is considered to be one of the important metrics that enables optimal NFV selection. This, in turn, improves the quality of the video transmission, since the faster selection of NFV has been considered based on the density of the vehicles on the road. The most suitable node depends on the two parameters of SINR and VDR, as represented in Equation (4):  
\[S_{n}=\alpha\left(M^{SINR}\right)+\beta(VDR_{value}) \tag{4}\]where \(S_{n}\) represents the suitable node, \(M^{SINR}\) is the maximum SINR value, and \(VDR_{value}\) is the value of the vehicle density of the road. Both \(\propto\) and \(\beta\) are the weighting factors assigned to each of the parameters. Considering that the SINR guarantees the quality of a link, the \(\propto\) is assigned a weight of 0.6, while the \(\beta\) is assigned 0.4 for the vehicle density of the road; thus, the total weight is 1. Therefore, the link of the suitable node selected is considered to be the most efficient link for video data transmission. Thus, an efficient link can be formulated as represented in Equation (5):  
\[Efficient\ link=\frac{TR^{Success}}{TR^{Total}} \tag{5}\]  
where \(TR^{Success}\) is the number of packets successfully delivered through a link, and \(TR^{Total}\) is the overall attempts performed during data packet transmission.
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## Appendix B Additional Details on the Cox1-HET Simulation  
Our biological model tree is based on the mitochondrial \(cox1\) barcode fragment gene, because mitochondrial genes have long been established to evolve under heterogeneous rates across the tree [31]. Additionally, the \(cox1\) gene has a very low insertion/deletion (indel) rate and thus the estimated multiple sequence alignment for this dataset is reliable. Finally, \(cox1\) is used as a barcode marker for many animal groups, which makes it abundant in online databases.  
To avoid the burden of having to detect contamination, we picked an existing, hand-curated, list of 16,959 GenBank identifiers of \(cox1\) barcode fragments for the Neogastropoda order provided to us by [PERSON]. For species delimitation, we used ASAP [36] through its web interface in default mode with a Kimura 2-parameter model (referred to as the K80 model) [37], and we kept only one representative individual per species. The number of sequences was too high to pass them through ASAP and so we divided the dataset into three subsets corresponding to the Conoidea clade, the Muricidae clade, and a group composed of the rest of the Neogastropoda. For each run, the partition with the best asap-score was selected (in the case of ties, we picked the partition with the most species). The total of the 3 runs resulted in a set of 2341 species. The final biological dataset is a set of 2341 mitochondrial cox1 fragment sequences, and the alignment (which was trivial to produce due to conservation in this marker) has 658 bp length. We then ran FastTree v2.1.11 with a GTR+G model to produce a tree, which we used as the topology for the model tree in this study. As described in Materials and Methods, we then performed additional analyses to select different GTR+G model parameters for different regions within this model tree topology, and used the final model tree (topology plus numeric parameters) to evolve sequences.  
See Data Availability statement for how to access the list of GenBank identifiers, alignments, selected individuals, ASAP results, and the FastTree tree.
## 4 The Application of Artificial Intelligence in the Design of Basic Materials for Sports
### Metallic Materials  
Metal materials are widely used in the field of sports, mainly due to their high strength, durability and processability. For example, aluminum alloys and high-strength steel are commonly used in the manufacture of golf clubs, bicycle frames, and fitness equipment, providing lightweight and high-strength properties. Because of its excellent corrosion resistance and biocompatibility, titanium alloys are used in the manufacture of high-performance sports equipment.  
In 2024, [PERSON] et al. (2024) leveraged Machine Learning (ML) to address the intricate relationship between metal processing, structure, and performance. The study focused on two primary challenges: material design, which involves identifying optimal microstructures for desired performance, and process design, aimed at determining the most effective processing path to achieve these microstructures. By targeting multiple microstructures with similar behaviors, the model effectively guided the manufacturing process to achieve optimal performance.  
In 2022, [PERSON] et al. (2024) introduced a chemically encoded Convolutional Neural Network (CNN) to predict the adsorption characteristics of metal-organic frameworks (MOFs) for CO\({}_{2}\) gas absorption. The model was trained on approximately 10,000 MOF structures simulated at the molecular level. The CNN demonstrated high prediction accuracy, showcasing its capability to accelerate the discovery and design of MOF materials with enhanced gas absorption properties.