esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
## 3 Results
### Model Accuracy Evaluation  
To evaluate the effectiveness of the CBR model, 300 independent sample points obtained through CFD simulations were used to train three surrogate models: BP, RBF, and CBR. The proportions of training and test sets in the dataset were set to 80% and 20%, respectively, and the test set was an independent sample. The mean absolute error (MAE) and root mean square error (RMSE) were designated as evaluation metrics for the models. Table 5 illustrates the mean and median values of several models based on the sample data, with all measures executed independently 200 times.  
To ascertain whether the model presented in this research has a distinct data distribution compared to the outcomes of other models, the sample data were subjected to significance analysis. Initially, data from 200 independent runs were utilized as samples for the normal distribution test, and the Kolmogorov-Smirnov test (K-S test) was selected  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Scheme** & **Sample Size** & **Mean Value** & **Variance** \\ \hline LHS & 300 & 0.0086 & 0.0000067 \\ Random Sampling & 300 & 0.0074 & 0.0000104 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the mean and variance between each point and its nearest point in two sampling schemes.  
Figure 7: Latin hypercube sampling points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inspection Indicator** & **Model** & **Inspection Statistic** & **Freedom Degree** & **Significance** \\ \hline \multirow{4}{*}{MAE} & CBR & 0.038 & 200 & 0.200 \\  & RBF & 0.000 & 200 & 0.000 \\  & BP & 0.073 & 200 & 0.011 \\ \hline \multirow{4}{*}{RMSE} & CBR & 0.053 & 200 & 0.200 \\  & RBF & 0.000 & 200 & 0.000 \\ \cline{1-1}  & BP & 0.053 & 200 & 0.200 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the K-S normal distribution tests for the MAE and RMSE samples for the three models.  
Figure 9: Normal Q\(-\)Q plots for the three model RMSE samples.  
Table 6 indicates that in the MAE sample, only the CBR model exhibited a significant \(p\)-value exceeding 0.05 and conformed to a normal distribution. Thus, the independent sample median test was employed in the nonparametric analysis. In the RMSE sample, only the RBF model did not meet the normal distribution criteria. Therefore, an independent sample \(t\)-test was used for the CBR and BP models, while a nonparametric independent sample median test was applied to the CBR and RBF models. The nonparametric test and \(t\)-test assume that the CBR model is equivalent to the median and mean of the other models, respectively, and the null hypothesis is rejected when the significant \(p\)-value is below 0.05. Figure 10 illustrates the box plots of the MAE and RMSE samples for the three models, along with the significance test comparing the CBR model to the base model, with detailed findings included in Table 7.
## 5 Ductility Evaluation  
To assess the ductility of members reinforced with FRP bars, various methods have been developed. These methods include the energy-based approach [56] and the deformation-based approach [57]. In the energy-based approach, ductility is defined as the ability to absorb energy, represented by the ratio of total energy to elastic energy. Conversely, the deformation-based approach evaluates ductility by examining the deformability margin between the ultimate and service stages, considering both strength and deflection. Figure 12 illustrates the ductility index definition.  
[PERSON] et al. [43] used the deformation approach to determine the ductility factor of FRC beams reinforced with GFRP bars. It is reported that all the beams have failed due to the rupture of the bar. For this reason, the effect of the fiber had no noticeable effect on the ductility factor. They also reported that specimens with a higher reinforcement ratio showed a higher deformability factor. In this regard, increasing the reinforcement ratio and using discrete fibers indicated a positive effect on the concrete-crushing mode at higher temperatures when steel fiber was used.  
[PERSON] et al. [58,59] employed a modified deformation-based method to determine the ductility indices of BFRP bars in SS-ECC beams. As illustrated in Figure 13, the study examined how the longitudinal reinforcement ratio, shear span ratio, and stirrup ratio influenced the ductility indices. The findings indicated that the ductility index decreased with an increase in the longitudinal reinforcement ratio, while it increased with higher shear span and stirrup ratios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **FRP Type** & \begin{tabular}{c} **Fiber Type** \\ **in FRCC** \\ \end{tabular} & **Study Type** & **Type of Test** & **Variables** \\ \hline [PERSON] et al. [42] & GFRP & \begin{tabular}{c} PP, GF, and \\ SF \\ \end{tabular} & Analytical & & \begin{tabular}{c} •Fiber-volume fraction \\ \(\bullet\) \\ \end{tabular} & Fiber type \\ \hline [PERSON] et al. [43] & GFRP & SF & \
\begin{tabular}{c} Experimental and \\ analytical \\ \end{tabular} & Bending test &
\begin{tabular}{c} •F Temperature \\ \(\bullet\) \\ \end{tabular} & Reinforcement ratio \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 12: Definition of ductility index [51].  
[PERSON] et al. [51] employed both energy-based and deformation-based methods to assess the ductility indices of GFRP-reinforced FRC beams. They found that the energy-based approach inadequately accounts for the influence of discrete fibers in the concrete, particularly regarding the improvement in ultimate moment capacity and the corresponding increase in deflection. On the other hand, the ductility index derived from the deformation-based method effectively describes factors like load capacity and the impact of deformation on ductility.
# Scan to BIM Mapping Process Description for Building Representation in 3D GIS
## 4 Scan to BIM Mapping in 3D GIS
### Use Cases Design Considering SBMD  
A separate method to describe the process is needed for Scan to BIM mapping to select or reuse an algorithm model according to user requirements. Most of the studies and practical procedures investigated above repeat adjusting the PCD's LoD using various software to obtain the desired result and modifying the parameters to generate appropriate DEM, footprints, and building geometries. If the SBMD language describing the Scan to BIM mapping is provided to the user, they can reuse the pre-described process according to the requirements, and the modified model can be partially used.  
The use cases are designed in Figure 3 and Table 2 to derive the SBMD method considering them.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Stage** & **Description** \\ \hline P1 & Collect scan dataset using ALS & Acquire scan data and GPS sensor data using ALS. \\ \hline P2 & Post-processing for PCD & Each sensor data is synchronized using a timestamp and saved as a PCD file. \\ \hline P3 & Register PCD & Using Ground Control Points (GCP), align PCD files to a unified coordinate system. \\ \hline P4 & Divide PCD into Blocks & Divides PCD files into workable units called block files. In the case of urban building modeling, point clouds are usually divided based on road networks so that modeling objects are not divided. \\ \hline P5 & Convert PCD to DEM(Raster) & Convert PCD files to DEM raster files. In the conversion process, a user specifies parameters such as the resolution of raster pixels. \\ \hline P6 & Classify object types, such as building and PCT. \\ \hline P7 & Extract Building Footprint & Extract footprint from classified building data. \\ \hline P8 & Regulation (Simplification) & Normalize footprint polygons. \\ \hline P9 & Extract Building Roof Height & Extract the building roof height for each footprint using DEM. \\ \hline P10 & Extract Building Ground Level & Extract the building floor height for each footprint using the ground surface. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The building mapping process description from the ALS dataset in 3D GIS.  
Figure 2: The building mapping process from the ALS dataset in 3D GIS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Use Case** & **Function** \\ \hline U1 & Define SBMD project with input PCD file & Define the SBMD project. For example, PCD input file path is specified in the project file. \\ U1 & Define SBMD project with input PCD file & project = \{SBMD, input, output\} (1) \\  & SBMD = SBMD file name & input = input file path \\  & & output = output file path \\ \hline U2 & Define the process stage in SBMD. & Deffines the stage that composes Scan to \\  & & BIM mapping stage in the project. The \\  & & stage format is as follows. \\ U2 & Define the process stage in SBMD. & SBMD = \{stage*\} \\  & & stage = \{model, parameters\} \\  & & parameters = \{name, value\}* \\  & * = multiple \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Use cases diagram for SBMD.
## 3 Theoretical Background
### Free Space Loss  
It is obligatory to measure the losses due to distance and wavelength. The Free Space Path Loss (FSPL) depends on the frequency of the carrier signal and the distance between the transmitter and the receiver. The model loss can be calculated by  
\[\mathrm{L_{FS}}^{\mathrm{[dB]}}=92.45+20\mathrm{log(d)}+20\mathrm{log(f)}, \tag{1}\]  
where \(d\) is the distance in km and \(f\) is the frequency in GHz.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Comparison of BFRP Tendons and Steel Strands
#### 4.3.2 Prestress Loss during the In-Service Stage  
Figure 2 presents the prestress loss of both BFRP tendons and steel strands during the in-service stage at various slab widths and distribution angles. Similar to the prestress loss in the prestressing stage, the use of BFRP tendons can greatly reduce the prestress loss during the in-service stage given the prestress loss of BFRP tendons and steel strands of 5.2% and 10.4%, respectively. This was mainly because the application of the BFRP tendon led to a significant decrease in the prestress loss \(\sigma_{\text{15}}\), 4.9% less than the prestress loss of steel strands.  
Figure 1: Comparison of prestress loss of BFRP tendons and steel strands in the prestressing stage at various slab widths and distribution angles (%).
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 6 Performance Evaluation
### Simulation Setup  
In our simulations, 160 sensor nodes are distributed schematically in a cylindrical region with a radius of 250 km and an altitude of 20 km. The nodes are in a sparse setting, and are in clusters based on DMDG protocol. The initial topology is shown in Figure 8. The nodes are designated into three classes: low mobility nodes (\(Group_{1}\)1-2), with the velocity randomly varied between [0, 18] m/s; middle mobility nodes (\(Group_{2}\)1-3), with the velocity varied between [18, 100] m/s; and high mobility nodes (\(Group_{2}\)4-6), with the velocity varied between [100, 300] m/s. In each simulation, the percentage of each class of nodes varies from 10% to 30%, with an increment of 10%.  
All nodes are identical, and use hybrid antenna arrays in free space (\(\lambda=3,\theta=\pi/4\)); and all active nodes are saturated, i.e., their data buffers are always nonempty. The default parameters used in the simulations and the analyses are summarized in Table 3.  
Furthermore, three kinds of simulation scenarios are conducted: (i) all nodes communicate in PC mode; (ii) parts of nodes communicate in 1-hop transmission range, that is, OC mode in Scenarios 1 and 2; (iii) parts of nodes communicate exceeding 1-hop transmission range, that is, OC mode in Scenario 3.
## 4 Experimental Results and Discussion
### Performance of Different Sizes of Imputted Images  
The difference in the model performance for different image sizes is depicted in Figure 22; Figure 23. The depicted performance is not related to the ability to detect objects or the behavior of the models, but to the number of pixels occupied by objects. Because our models were trained with nighttime images with a size of \(500\times 375\) pixels, they were designed to make predictions about the usual size of objects in general \(500\times 375\)-pixel nighttime images. Thus, the number of pixels occupied by an object in a nighttime image with a size of \(500\times 375\) pixels is less than that occupied by the same object in a nighttime image with a size of \(1920\times 1080\) pixels. Consequently, the output distribution when a large image is sent to the convolutional layers is different from that when a small image is sent to the convolutional layers, because all images are manipulated by convolutional operations with the same capacity. Therefore, the models trained with our \(500\times 375\)-pixel nighttime images may not perform well for a large image size.  
Our systems focus on the balance between the detection performance and the efficiency. If large images are used during training, the convolutional parameters are trained to deal with objects occupying a relatively large number of pixels. For example, a motorbike in a large image of \(1920\times 1080\) pixels may consist of \(200\times 600\) pixels; however, it consists of only \(50\times 150\) pixels in an image of \(500\times 375\) pixels. Because of the behavior of convolutional operations (not adapted to every size), the parameters trained with large images can only recognize an object with a pixel configuration similar to that of the training images. Any inconsistency in the training size and inference size leads to unsatisfactory performance because the convolutional layers are trained using objects with relatively large amounts of pixels. A small image depicting comparable objects may suffer from loss of features. Our method is intended for use in automobiles. Thus, our study focuses on the efficiency of in-car detection. Therefore, we referred to PASCAL VOC [6] and selected \(500\times 375\) pixels as the configuration for our system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Size of Images**} & \multicolumn{2}{c}{**Processing Time**} \\ \cline{2-3}  & **VGG16** & **ResNet101** \\ \hline \(500\times 375\) & 0.032 s & 0.064 s \\ \(1000\times 750\) & 0.079 s & 0.107 s \\ \(1920\times 1080\) & 0.151 s & 0.168 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Processing time of a GeForce GTX 1080 Ti graphic card for different image sizes and feature extractors.  
Figure 22: Inference on a \(500\times 375\)-pixel nighttime image.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
###### Abstract  
Ontologies have been widely used to facilitate semantic interoperability and serve as a common information model in many applications or domains. The Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project, aiming to facilitate coordination and cooperation between heterogeneous underwater vehicles, also adopts ontologies to formalize information that is necessarily exchanged between vehicles. However, how to derive more useful contexts based on ontologies still remains a challenge. In particular, the extreme nature of the underwater environment introduces uncertainties in context data, thus imposing more difficulties in context reasoning. None of the existing context reasoning methods could individually deal with all intricacies in the underwater robot field. To this end, this paper presents the first proposal applying a hybrid context reasoning mechanism that includes ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning methods to reason about contexts and their uncertainties in the underwater robot field. The theoretical foundation of applying this reasoning mechanism in underwater robots is given by a case study on the oil spill monitoring. The simulated reasoning results are useful for further decision-making by operators or robots and they show that the consolidation of different reasoning methods is a promising approach for context reasoning in underwater robots.  
context reasoning; uncertainty; Multi-Entity Bayesian Network (MEBN); underwater robots; ontology; context awareness Article
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 3 Energy Efficiency Analyses of Radio-and-Fiber Networks
### Power-Saving Techniques in the Optical Domain of FiWi Networks
In the past, the fast/cyclic sleep mechanism of the ONU has been extensively researched to reduce energy consumption in XG-PON. However, due to the emergence of new types of network traffic with stringent demands, further improvements in sleep time interval selection are required. Hence, the paper [32] proposed the use of an artificial neural network (ANN) to enable the ONU to determine the optimal sleep time interval values by learning from past experiences. The M/G/1 queueing system was used for theoretical analysis prior to simulation, and the ANN was trained and tested for the XG-PON network to make optimal sleep time interval decisions. The results indicate that as the network load increases, sleep time interval decreases for both methods. The ANN network records a wider range of sleep time interval values than the theoretical values. As a result, these findings will enable network operators to determine the optimal sleep time interval values at the current network conditions with more flexibility.
## 1 Introduction
### Paper Contributions  
The main contributions of this paper are as follows:  
* We propose a novel Well-Being Index (_WBI_) that integrates multiple health and activity parameters, providing a measure of employee well-being;
* We introduce a model for accurately predicting well-being based on various employee metrics using a classification ML algorithm;
* A case study is introduced that comprises a comprehensive classification evaluation by modifying training parameters, resulting in 384 tests;
* The design of a novel dissatisfaction score component (_DSC_) to calculate the stress identified through messages between employees;
* The incorporation of an analysis study of advanced sentiments expressed in messages, achieving high accuracy in identifying positive and negative employee sentiments;
* A case study including 2000 text messages for sentiment identification using Azure Text Analytics is presented;
* We propose a new mathematical formalism named employee global indicator (_EGI_), calculated with the _WBI_ and _DSC_, to identify overall employee well-being;
* We explore the relationship between _EGI_ and traditional KPIs, proposing the Employee KPI Algorithm (EKA) as an intervention measure when employee stress is identified;
* We provide tools for organizations to address employee dissatisfaction preemptively based on EGI score.  
The paper is structured into five sections. Section 2 details the methods employed, including developing the _WBI_ using physiological data from wearable devices, implementing the LbfgsMaximumEntropy algorithm for the Well-Being Model (WBM), and sentiment analysis via Azure Text Analytics. The section includes the EKA for specialized intervention to understand employee feelings and stress. Section 3 discusses the results, showcasing the strong correlation between _WBI_ scores and employee performance, with the WBM achieving _Macroaccuracy_ and _Microaccuracy_ levels of 69.44% and 88.29%, respectively. The section demonstrates the contribution of Azure Text Analytics and _WBI_ to employees' _EGI_ scores. Section 4 addresses discussions and limitations, acknowledging privacy concerns related to continuous monitoring and the subjective nature of sentiment analysis while suggesting areas for future research. Finally, Section 5 provides conclusions, emphasizing the importance of AI and health metrics in improving workplace satisfaction and organizational performance.
# Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
###### Abstract  
(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3-15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems.  
complex environment; binocular stereo vision; object detection; YOLOv5; real-time and accuracy
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 3 Compliance Architecture
### General Approach  
Before we focus on our architecture, we clarify the meaning of compliant and accountable robotic application development in the context of our concept. For this, we suppose a client wants to execute a program composed of a sequence of workflows on our robotic system and present measures, which guarantee the fulfillment of the previously defined requirements:  
**Workflow sequence signatures:**: Workflow sequences intended to be executed on the robot system have to be signed by a trusted third party that supports automation of this process i.e., a dedicated signing service. Additionally, the system must be able to verify the signature before the execution is initiated. If the verification fails, the process is aborted in a graceful way without compromising the overall state of the system. This measure not only ensures the integrity of the workflow sequences per se, moreover it allows preventing a client from executing workflow queues, that do not fulfill a set of predefined criteria. In this section, we focus on the first aspect and deal with the second in a later part of the paper.
**Public-Key-Cryptography:**: Fulfilling the requirement of authenticity in our context demands to guarantee that the workflow sequence was not signed by the client itself. A common way to achieve this behaviour is the implementation of a public key infrastructure where each entity is initialized with a public key certificate and a corresponding private key. Using the latter for signing and providing the certificate to other entities allows checking whether the signature originates from the right source or not. In our context, this means that the trusted third party signs the workflow sequence with its private key and the robot system can verify the signature by using the corresponding public-key. For simplicity we reduce our implementation to using only a private key for the signing process leaving the verification of the signature to the trusted party. We refer to this aspect in our discussion section also targeting the related non-repudiation requirement. Matters of key-management including generation, initial distribution, storage, update, use and destroying keys are out of the scope of this work and are assumed subject to standardized organizational regulations designed for this purpose.