esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### Description of Technology  
Open Data Kit (ODK) Scan is an Android-based mobile application and a part of the ODK 2.0 suite of tools [9; 19]. It uses the device's built-in camera to capture an image of a scan-compatible paper form, which it feeds into a series of image processing algorithms (SURF, fast approximate nearest neighbor, and RANSAC) to establish a point-by-point mapping between the template and the captured image [20; 21; 22; 23]. Using this mapping, Scan segments the form into small image snippets that each contains a single data field from the original form [24]. The image snippets fall into three possible prompt types: hand-filled bubbles, structured number boxes, and QR codes. Hand-filled bubbles are multiple-choice questions that are completed by filling in the bubble that corresponds with the desired response. Scan classifies hand-filled bubbles using a Support Vector Machine (SVM) and principal component analysis (PCA) [25; 26; 20]. Structured number boxes are a 2 \(\times\) 3 grid of dots that can be connected by straight lines to draw numerals. Scan classifies numbers using a Multi-layer Perceptron (MLP) with back propagation of error [27]. Finally, QR codes are two-dimensional barcodes that can be processed as alphanumeric strings. Scan uses the Zebra Crossing (Zxing) library to classify QR Codes [28]. After Scan has classified each prompt, it inserts the data into an editable record in the ODK 2.0 database. Each digitized field includes a copy of its associated image snippet from the original form, which can be used to verify digitized fields. Once the record is in the ODK 2.0 database, users can use the other tools, such as ODK Survey and ODK Tables to view, edit, verify, aggregate, and sync the data or create customized services, interactions and reports with the data [19].  
ODK Scan does not process freehand written text. Prompts that require written text will include boxes on the paper forms that users can fill with their hand written text, but Scan will leave the corresponding field in the database record blank. Hand written text fields can be manually transcribed on the device by viewing the prompt in ODK Survey, which will display the corresponding image snippet, and typing in the text via the phone's keyboard. Alternatively, they can be manually transcribed on a PC after the record has been synced, again by viewing the image snippet.  
Scan and the rest of the ODK 2.0 tools do not require a consistent Internet connection to function [19]. All of the image processing is performed by the device itself, and the data is stored in a local database. An Internet connection is only required for syncing records with the cloud, and this can be done at the leisure of the user.
## 6 Conclusions  
In this paper, we proposed an RIS-equipped UAV deployed in a non-LOS FSO communication scenario. We considered a practical scenario with atmospheric and pointing error loss. Our method combined the use of PSO for RIS phase shift optimization with PPO for UAV trajectory. Specifically, with PSO method, the pointing error loss was efficiently addressed by optimizing the phase shifts of RIS. This assures the FSO communication quality for all UAV flight times. Then, the UAV trajectory with optimized average capacity under determinate flight times was found. The PPO optimization was proposed because of its stable training compared to the other optimization learning methods as it limits the policy update at each step, reducing the possibility of policy divergence. Our results showed that our approach was effective and performed better than other optimization learning methods we looked into. However, there are more aspects and future work that can be explored. The PSO and PPO optimization methods have the potential to look into scenarios with multiple UAVs or multiple links, while other communication impacts could be considered as well. There is also potential to explore other algorithms or combine methods for even better results.  
Methodology C.H.; Writing--original draft, H.J.; Writing--review & editing, S.D. and J.A.C.; Supervision, G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LOS & Line of sight \\ UAV & Unmanned Aerial Vehicle \\ RIS & Reconfigurable Intelligent Surface \\ FSO & Free Space Optical \\ PSO & Particle Swarm Optimization \\ PPO & Proximal Policy Optimization \\ LS & Laser source \\ PD & Photodetector \\ \end{tabular}
# Multi-Modal Sonar Mapping of Offshore Cable Lines with an Autonomous Surface Vehicle
## 5 Conclusions  
In this study, an autonomous mapping system using an ASV is proposed to inspect offshore cable lines. The proposed multimodal sonar mapping system can inspect the seafloor, including power cables, using MBES and SSS simultaneously. For MBES mapping, dedicated point-cloud filtering methods along with sub-mapping can remove the outliers in real time. For SSS mapping, the proper projection of the sonar pixel intensity into the baseline was proposed to overcome the distortion of sonar images. Field tests conducted in a challenging ocean environment validate the performance of the developed online ASV inspection system. In future work, a heterogeneous autonomous system with surface and underwater vehicles can be used to survey larger offshore areas. In this regard, more advanced technologies such as mission planning, task allocation, and robust control schemes should be developed and validated. Providing satellite communication is also desirable for long-range monitoring capabilities.  
Conceptualization, J.J., Y.L., J.P. and T.-K.Y.; methodology, J.J., J.P. and Y.L.; software, [PERSON], [PERSON] and [PERSON]; validation, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON] and [PERSON]; investigation, [PERSON], [PERSON], [PERSON] and T.-K.Y.; resources, [PERSON], [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON] and [PERSON]; supervision, T.-K.Y.; project administration, T.-K.Y.; funding acquisition, T.-K.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 13: SSS mapping results.  
**Funding:** This research was supported in part by a grant from the Endowment Project (Development of Core Technologies for Operation of Marine Robots based on Cyber-Physical System) funded by the Korea Research Institute of Ships and Ocean Engineering under Grant PES4420, and in part by a grant from the Endowment Project (Development of Modeling/Simulation and Estimation/Inference Technology for Digital Twin Ship) funded by the Korea Research Institute of Ships and Ocean Engineering under Grant PES4430.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** The authors thank [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] from KRISO for their support during field tests.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 2 Binaural Recording and Rendering
### Binaural Rendering
#### 2.2.2 Dynamic Cues  
The dynamic cue arising from the motion of the listener, which changes the relative position of the source, can reinforce localization. Studies on this cue show that the well-known front-back confusion problem in static binaural rendering disappears when the listener can turn their head to assist localization [69]. It also demonstrates that the perception using non-individualized HRTFs can be strengthened if dynamic cues are appropriately included [42]. Thus, it is necessary in binaural rendering to have the simulated scene change with the listener movement. This is normally achieved using a low-latency head tracking system, based on computer vision techniques (such as regular cameras) to estimate the orientation of the listener's head. In smartphones, the low-cost sensors, such as accelerometers and gyroscopes, can be used for head-tracking.
## 2 Experimental Section
### Quantum Chemical Calculations  
The structure of (2\(E\))-HIPC was first optimized using the AM1 method and then refined with optimization by DFT/B3 LYP and _ab initio_ HF methods at the 6-311++G(d,p) level as implemented in the Gaussian 09 software [15]. The X-ray diffraction structure was used as the origin for the initial geometric optimization of the title compound. The vibrational frequencies at the HF and B3 LYP levelsof theory were evaluated for the optimized structures by assuming \(C_{1}\) point group symmetry. The B3 LYP and HF methods provide good predictions of the structural parameters and binding energies of (\(2E\))-HIPC. No negative frequencies were obtained during the optimization. To improve the agreement of the computed values with the experimental data, a spectral uniform scaling factor was used to balance the systematic errors caused by basis set incompleteness, neglect of electron correlation and vibrational anharmonicity. Therefore, the vibrational wavenumbers computed at the HF level were scaled by 0.905 [16], and the wavenumbers computed using B3 LYP were scaled by 0.958 for wavenumbers greater than 1700 cm\({}^{-1}\) and by 0.983 for wavenumbers less than 1700 cm\({}^{-1}\)[17, 18]. With a few exceptions, the differences from the experimental values were less than 10 cm\({}^{-1}\) after rescaling using these scaling factors. The Gauss viewing program [19] was used for visualization and verification of the assigned normal modes.  
The variations in the thermodynamic functional parameters (_i.e._, heat capacity, entropy, and enthalpy) were examined from 100 to 700 K using the computed wavenumbers of the title molecule. The electronic absorption spectra for the optimized molecule were calculated with TD-DFT at the B3 LYP/6-311++G(d,p) level. To analyze the reactive sites of the title compound, the molecular electrostatic potential were computed using the HF and B3 LYP methods in the gas phase. DMSO and acetonitrile solvents were used in the DFT/B3 LYP/IEFPCM calculations to improve the prediction of the NMR values and achieve good accuracy. The chemical shifts were calculated and analyzed.
# Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells
## 3 Methods for Determining Causes of Abnormal Formation Pressure
### Mining Similar Abnormal Pressure Mechanism Samples Based on Cluster Analysis
#### 3.1.1 Classifying Different Pressure Mechanism Sample Groups Based on Normal Trend Lines  
Using the [PERSON] equation ([PERSON] et al., 1974), which describes the velocity-density relationship, a normal trend line of the velocity-density cross-plot is established by fitting the velocity-density curve of the normal compaction section. This normal trend line represents the typical relationship between velocity and density under normal compaction conditions. This line is then applied to the velocity-density cross-plot of the abnormal high-pressure section to supplement additional loading curves. By integrating traditional discrimination approaches, data groups are carefully divided to identify characteristics of different abnormal high-pressure causes. In this process, data are categorized into three main types: undercompaction, fluid expansion, and shale diapirism, forming sample data for establishing a model to determine abnormal pressure mechanisms. A machine learning model is then developed to build a determination model of abnormal pressure mechanisms based on well logging parameters. The aim of this model is to use logging parameters like velocity and density as inputs to accurately classify and identify the different mechanisms causing abnormal pressure. By combining the normal trend line with loading curves of the abnormal high-pressure section, the model is provided with sufficient data samples. These samples include typical relationships between velocity and density under normal geological conditions and disturbances to this relationship under abnormal conditions. These data help train the machine learning model to recognize different abnormal pressure mechanisms within the well logging parameter space. The final model will be a powerful tool for interpreting abnormal formation conditions. By inputting well logging data such as clay content, porosity, velocity, and density, the model can automatically analyze and identify different abnormal pressure mechanisms like undercompaction, fluid expansion, and shale diapirism.
## 2 Materials
### Landslide Influencing Factors
#### 2.3.2 Geological Factors  
Lithology is an important factor affecting slope stability, which is commonly used as a key factor in landslide studies [41]. Changes in lithology largely alter the strength and permeability of rocks, resulting in differences in landslide susceptibility [56]. In this study, lithology primarily refers to rock mass strength. Classification criteria are based on the \"Engineering Rock Mass Classification Standard\", which is one of the national standards (GB50218-2014) of China.  
Earthquakes are commonly regarded as a direct factor leading to landslides, and many scholars have defined them as a landslide-triggering factor [9; 33; 44; 47]. The Wenchuan earthquake in 2008 triggered a large number of landslides in the study area [57] and indirectly led to changes in its ecological environment [45]. In this study, the seismic intensity of the Wenchuan earthquake was used as an influencing factor and such data was obtained from the China Earthquake Administration (CEA 2008).  
Faults are another important factor affecting slope stability. Because tectonic faults reduce the strength of the surrounding rock mass, landslides are likely to occur near faults [53; 58]. Active faults usually increase the probability of landslide occurrence [41]. The fault zones in the study area are relatively well-developed, making them prone to geological disasters [45].  
Permeability and strength of rocks and soils can vary significantly with stratigraphy (divided by geological ages), which is closely related to the change of slope stability [53; 59]. According to research [60], stratigraphy has a specific influence on the distribution of landslides. In certain stratigraphy, the slope will be more susceptible to sliding. Therefore, some scholars have considered this factor in the study of landslide susceptibility [5; 61]. There are 10 types of stratigraphy with different geological ages in the study area.
## 3 Results and Discussion
### Effects of Waste Type
#### 3.1.4 Thermal Conductivity  
The thermal conductivity of particleboard with two waste types is shown in Figure 8c. The obtained results revealed that the board made from flax shives particles had higher thermal conductivity (0.099 W/m K) compared to the rice straw particles (0.086 W/m K). The lower thermal conductivity of the rice straw particle is mainly due to the presence of non-polar groups and the higher SiO\({}_{2}\) content of straw, which reduces the connection between particles [10]. New insulating particleboards are manufactured using rice straw with UF resin as a binder for its fibers.
## 3 Result and Analysis
### Stress at Failure  
#### 3.4.1 ASTM Standard  
The compressive strength is increased by 53 to 459 percent when a water reducer polymer is added to the CG (Figure 8). The cc of the CG treated with 0.16 percent polymer using Sand # 1 improved from 4.33 to 27.59 MPa after one day of testing (Figure 8a). After 28 days of testing, the CG's cc increased from 24.86 to 54.66 MPa after being treated with 0.16 percent of the water reducer polymer (Figure 8b).  
According to the model parameters (LR), adding polymer to cement grout increases compression strength (Equation (7)).  
\[\sigma_{\rm cc}=-61.9*\frac{\rm w}{\rm c}+0.67*\rm t-23.2*\rm d_{10}+73.7*\rm P +46.4 \tag{7}\]  
In accordance to the equation variables, the polymer dosage with the model parameter of (73.7) is impacted on increasing the CS more than other variables (Equation (7)). Figure 9a,c also shows the performance of the LR model with residual errors ranging from +12 to -16 MPa. The performance of the models was examined by comparing the linear relationships with nonlinear regression analysis, as illustrated in Figure 9a,b.  
\[\sigma_{\rm cc}=2.89(\frac{\rm t^{0.24}}{\rm w/c^{1.47}d_{10}^{0.26}})+13.3( \frac{\rm t^{0.30}P^{1.48}}{\rm w/c^{0.13}d_{10}^{0.31}}) \tag{8}\]  
The polymer has a greater influence on improving the cylindrical CS than the w/c, testing age, and d\({}_{10}\), according to the model parameters in Equations (7) and (8). The NLR model predicted the \(\sigma_{\rm cc}\) stronger than the LR model, as shown in Figure 9a,b. The residual error was ranged between +12 to -16 MPa (Figure 9a,b).  
Figure 8: Typical cylindrical CS of CG modified with water reducer polymer at (**a**) one day of curing and (**b**) 28 days of curing.
## 3 Results
### Machine Learning Models  
Figure 2 illustrates the R\({}^{2}\) values of the prediction models developed using linear regression, decision trees, SVM, and GPR. Models such as the interaction linear regression, coarse decision tree, and cubic SVM models are not presented in Figure 2 because their R\({}^{2}\) values are less than 0.50. Among the models considered, six demonstrated R\({}^{2}\) values exceeding 0.92: linear regression, robust linear regression, linear SVM, squared exponential GPR, Matern 5/2 GPR, and rational quadratic GPR.  
For a more comprehensive evaluation of model accuracy, Table 3 summarizes the mean absolute percentage errors (MAPEs) and R\({}^{2}\) values for models with an R\({}^{2}\) value of 0.90 or higher.  
According to Table 3, the GPR models share an R\({}^{2}\) value of 0.92 with linear regression, robust linear regression, and linear SVM but exhibit the lowest MAPEs, i.e., 2.37%, indicating their excellence among the ML models examined. Additionally, the GPR models displayed identical MAPEs of 2.37%, with the predicted yield strength values differing only by decimal places. Among these, the Matern 5/2 GPR can be considered a representative model based on previous research findings [29]. The robust linear regression and linear  
Figure 1: ANN structure as a diagram.  
SVM showed comparable MAPEs of 2.51%, differing insignificantly from the GPR models by only 0.14%. Moreover, the linear regression model deviated by only 0.02% compared to the robust linear regression and linear SVM.  
Consequently, among the ML models listed in Table 3, those excluding the squared exponential GPR and rational quadratic GPR, which share nearly identical prediction characteristics with Matern 5/2 GPR, were selected for comparison with the MRA and ANN models.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 5 Performance Evaluation
### Reliability  
The main factors affecting the packet delivery ratio are buffer overflows caused by congestion and bit errors according to the link conditions. As shown in Figure 5, all schemes cause packet loss owing to congestion as the traffic bit rate increases. The linear weighted sum-based scheme considers link quality in the decision-making process. However, since the weights among the multiple metrics are equal, the parent node is rarely changed even if a buffer overflow occurs. On the other hand, since the MCMD-based scheme has a higher weight of the buffer occupancy ratio, packet loss owing to congestion is less than that of the linear weighted sum-based scheme. In addition, it adaptively selects a node with good link quality as the bit error rate increases, thus showing a better packet forwarding rate.  
However, in the MCDM-based scheme, the weights between metrics cannot be changed at runtime; thus, it has a clear limitation in that it is difficult to respond to changes in network scenarios. In contrast, the proposed scheme shows the best performance because it learns how to cope with changes in network conditions at runtime through trial and error.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of sensor nodes & 100 \\ Number of sink nodes & 5 \\ Network size & 5000 m \(\times\) 5000 m \\ Simulation time & 3600 s \\ Transmission range & 300 m \\ Data processing rate & 15,000 bits/s \\ Buffer size & 100,000 bits \\ Traffic model & IP traffic flow \\ Traffic bit rate & 1000 to 5000 bits/s \\ Bit error rate & \(10^{-2}\) to \(10^{-6}\) \\ MAC & 802.11 \\ Build message interval & 20 s \\ Hello message interval & 5 s \\ Maximum number of episodes & 4000 \\ Maximum number of steps & 300 \\ Minimum epsilon/epsilon & 0.1/1.0 \\ Exploration ratio & 0.5 \\ Reward discount factor & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway
## 2 System Model
### Threat Model  
**Adversary goals and capabilities:**_\(M\)_ is an enemy who tries to go through the PoF verification without following the platoon. The adversary aims to access the platoon and insert bogus cooperation signals. Public and private keys (\(pk_{M}\), \(sk_{M}\)) issued by a trustworthy certificate \(cert_{M}\) authority are in the hands of the attacker. Directly or through C-V2X, the enemy may communicate with the platoon.  
Figure 2 depicts a distant opponent in cyberspace (a). Opposing forces may be aware of the platoon's path either before they arrive on the scene or as they go. An imposter car seems to be following them due to his use of the mobile phone network's architecture. The same situation happens when the enemy is at a distance or ahead of the unit. The adversary does not target the verifier's range sensors since he is presumed to be far away. The distance sensing technology may be safeguarded even in an assault using a secure ranging protocol [23; 24; 25].  
**Man-in-the-middle adversary.** An attacker can start a man-in-the-middle (MitM) assault while a legal candidate [PERSON] tries to join the platoon. A MiTM assault is shown in Figure 2b. The opponent prevents the platoon from entering by intercepting [PERSON]'s platoon entry request and replacing it with his own. \(M\) pretends to be the verifier to [PERSON] at the exact moment. Using a PoF, the genuine verifier asks \(M\) to demonstrate that it is following the instructions. In the PoF protocol, [PERSON] receives the identical challenge from an opponent.  
Figure 2: The threat model. (**a**) a remote adversary; (**b**) an MiTM adversary.