esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Displacement Estimation Method  
External factors (e.g., earth pressure, water pressure, vehicle loads) create strain throughout sections of a tunnel during service. The strain measured at any point within  
Figure 1: Long-gauge FBG sensing principle: (a) long-gauge FBG sensor; (b) area sensing.  
the tunnel provides crucial information regarding the vertical, transverse, and axial displacement of any given section. A two-step approach was used in this study to accurately assess vertical displacement. First, the monitored coupled strain was separated to obtain the vertical bending strain. Second, the vertical bending strain separated from the coupled strain was used to estimate the vertical displacement of the structure.
## 3 System Model
### Network Model  
A non-uniform distributed WSN is shown in Figure 2, where a static sink locates at the center of the network. In this paper, we consider a network with N sensors randomly dispersed in a rectangular network.  
Figure 2: A non-uniform distributed WSN.  
The network is clustered into a group of clusters. Sensors are selected as cluster heads based on \(T(n)\), which will be discussed in Section 4. The sensors will transmit their sensed data to the sink through cluster heads via a single-hop or multi-hop transmission.  
The network model can be described as an undirected connectivity graph \(G(S,E)\), where \(S\) is the set of all sensor nodes and \(E(i,j)\) is the set of wireless link between node \(i\) and node \(j\). To indicate a sensor node condition, a function with position, residual energy, initial energy, and communication range is considered in Equation (1) where \((x(i,n),y(i,n))\) represents the position, \(e(i,n)\) is the residual energy, \(E(i,n)\) is the initial energy, and \(R_{i}\) is the transmission range:  
\[\Psi_{i}(n)=f((x(i,n),y(i,n)),e(i,n),E(i,n),R_{i}) \tag{1}\]  
In terms of energy consumption and network lifetime, a mobile sink based strategy is proposed in Figure 3 to achieve better network performance, where the gray thick line is the predetermined movement path for mobile sink. As is shown in Figure 3, every circle indicates a sensor with position of \((x(i,n),y(i,n))\) and residual energy of \(e(i,n)\). Mobile sink node will move back and forth along the path in order to gather data packets from cluster heads.
## 5 Conclusions  
According to the obtained results, the use of contact rings in interference fits allows one to significantly reduce the stress concentration at the hub edge. The contact ring thickness is revealed as a high influencing factor on stress reductions. Although the stress reduction is increased for high contact ring thickness, a redistribution of the radial stress (linked directly with the contact pressure at the interface) appears, causing a reduction in the contact pressure at the interface far from the stress concentrator, leading to a loss of the maximum transmitted torque. This way, the use of contact rings with \(0.125>\gamma>0.250\) allows one to notably reduce the stress concentration at the edges of the hub (around 40% versus a conventional shrink fit) with a slight decrease in the contact pressure always lower than the 10%. The quadratic dependence of stress reductions on contact ring thickness is expressed in terms of design equations that allow designers to quantify the stress reduction obtained for a given contact ring thickness ratio. In addition, the stress reduction obtained using contact rings with a thickness ratio within the range \(0.125>\gamma>0.250\) is also useful for moderate and severe interference fits. Thus, high contact pressure can be obtained without generating plastic strains at the interface, improving the performance of the interference fit during its service life. Finally, the results revealed a linear dependence of the stress reduction with the contact ring stiffness. Thus, the lower the Young modulus of the contact ring, the lower stress reduction at the hub edge vicinity without significant changes of the contact pressure, or in other words, without reducing the maximum transmitted torque. This linear dependence is expressed in terms of design equations that allow the designer to quantify the stress reduction for a given contact ring stiffness.  
Conceptualization, M.L.; methodology, M.L., E.I., M.R.-M. and R.G.; software, M.L., E.I., M.R.-M. and R.G.; validation, M.L., E.I., M.R.-M. and R.G.; formal analysis, M.L., E.I., M.R.-M. and R.G.; writing--original draft preparation, M.L., E.I., M.R.-M. and R.G.; writing--review and editing, M.L., E.I., M.R.-M. and R.G.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundacion Memoria D. Samuel Solorzano Barruso, grant numbers FS/32-2017 and FS/12-2021.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 4 The Implementation of MQC-MB
### Parameter Setting  
The essential setting for the FSO system is based on the existing products available in the market as listed and identified in Table 1[42].  
The main parameter in the multiple-beam FSO link design is the distance between the transmitter and receiver. The design of the proposed multiple-beam should be able to cover farther distance compared to the single beam. To achieve the optimum secret key rate and the lowest value of QBER, the parameters for the FSO design that affect the value of the geometrical loss need to be controlled. The geometrical losses depend on the fixed parameters, such as transmitter diameter, receiver apertures, and beam divergences. Theoretically, to reduce channel losses, the receiver aperture needs to be large while the receiver aperture and divergence angles need to be small. To capture sufficient power at the receiver, a larger receiver aperture size is required [43]. This means that the value of \(D\) needs to be larger than \(dr^{2}\) in order to produce a valid output. In order to carry out the performance analysis of the proposed work, the FSO model described in the section III is verified using the set-up parameters from Table 1. The model is verified using Python programming language. Python is a free software that is widely used for scientific calculation and it is served with many scientific computation libraries [44]. The channel  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline Transmitted power \((P_{TX})\) & 12 mW (12 dBm) \\ Wavelength (\(\lambda\)) & 1550 nm \\ Divergence angle (D) & 1 mrad \\ Transmit \& receive aperture diameter (ds \& dr) \\ Visibility under clear weather (V) & 10 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: FSO’s transmitter and receiver setting.  
Figure 4: The optimum average number of photons \(\mu_{opt}\) as the function of the maximum number of photons \(N_{max}\) that [PERSON] can use to encode her bits.  
loss is also influenced by the wavelength since less photon loss occurs at a low attenuation channel. In this paper, \(\lambda=1550\) nm is chosen due to its lowest attenuation compared to other standard attenuations, 1300 nm and 800 nm. The proposed model is tested in clear weather conditions with a visibility of 10 km. The performance of the single beam and multiple beam in FSO is compared based on total losses and total received power.
# Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model
## 2 Methods
### Data Preparation
#### 2.2.1 Magnetic Anomaly Signal  
According to (1), when the magnetometer is fixed, the simulation of the target magnetic anomaly is determined by several parameters, including magnetic dipole moment, distance vector and sampling rate.  
We limited the magnetic dipole moment and speed to a specific range for simplicity without losing generality. The settings for simulation parameters are listed in Table 1. Due to the three-axis magnetometer, the signal of each axis differs in different directions. The magnetic anomaly signals are generated following Equation (1) and Table 1. We simulated a series of magnetic dipoles passing through the magnetometer straight from different directions. The data length for each simulated signal is 300 s. Figure 2 shows a typical signal waveform plot.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Value** \\ \hline  & Speed (Km/h) & [40–60] \\ \hline \multirow{3}{*}{Magnetic moment \(\widehat{m}\)} & X component (Am\({}^{2}\)) & [4500] \\  & Y component (Am\({}^{2}\)) & [0] \\  & Z component (Am\({}^{2}\)) & [\(-2500\)] \\ \hline \multirow{3}{*}{Magnetic moment \(\widehat{m}\)} & Sampling rate (Hz) & 128 \\  & Data length (s) & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for signal simulation.  
Figure 1: Our network architecture. The different types of network layers are shown in the illustration. The input is the magnetic field signal measured by the magnetometer and the output is the probability of magnetic anomaly detection at each sampling point. The details of each block are provided in the methods section. The convolutional layers read as: number of kernels, kr, kernel size.
# Attention-Guided Wireless Channel Modeling and Generating
## 4 Experimental Simulation Analysis
### Similarity and Diversity Comparison  
We use the similarity index and diversity index to verify the consistency and discreteness of real channel data and generated channel data, and the similarity is expressed as follows:  
\[sim=\frac{1}{N_{g}}\  
where \(\frac{\left\|\mathbf{h}_{j}^{H_{i}}\mathbf{h}_{j}\right\|^{2}}{\left\|\mathbf{h}_{ j}\right\|^{2}\left\|\mathbf{h}_{j}\right\|^{2}}\) represents the normalized squared inner product of the generated and real channel vectors, serving as a similarity measure between the two vectors.  
Table 2 provides a detailed comparison of the similarity and diversity results for the channel data generated by both the SE-GAN and ChannelGAN models. The results clearly show that when the generated channel data have high similarity and low diversity compared to the actual channel data, the Multi/Sim ratio is low. A lower Multi/Sim ratio indicates that the distribution of the generated synthetic channel data closely matches the distribution of real channel data, which is a critical factor in assessing the quality of the generated data, Table 1 in Appendix A provides some of the generated data.  
From the numerical results, it is evident that SE-GAN outperforms ChannelGAN in terms of similarity. The data generated by SE-GAN shows significantly higher similarity to the real channel data, demonstrating SE-GAN's ability to more accurately capture the key characteristics and statistical properties of the real channel. Additionally, the diversity of the data generated by SE-GAN is notably lower than that of ChannelGAN, further reinforcing SE-GAN's superior performance. While diversity can be important in some contexts, the focus here is on the accuracy and realism of the generated data, where lower diversity aligns with a more faithful representation of real channel data.  
Moreover, the overall evaluation metric, Multi/Sim, is considerably lower for SE-GAN compared to ChannelGAN, signifying that the synthetic data produced by SE-GAN is much closer to the true data distribution. This lower Multi/Sim ratio directly reflects SE-GAN's better alignment with the real channel data, making it more suitable for applications that require high fidelity in channel modeling.  
In summary, the experimental findings clearly demonstrate that the SE-GAN model proposed in this study produces higher similarity and lower diversity in the generated channel data compared to ChannelGAN. As a result, SE-GAN generates a synthetic data distribution that is much closer to the real channel data distribution. These superior outcomes confirm that SE-GAN is the more effective model for channel modeling tasks, making it the optimal choice for generating high-quality, realistic channel data.
## 2 Materials and Methods
### Experimental Setup for Continuous LBI Measurement  
In addition, an LBI system was directly mounted on a sedimentation tank to perform continuous LBI measurements during the sedimentation process (Figure 2). The sedimentation process lasted for 30 days; during the experiment, the oil temperature was 21 \(\pm\) 3 \({}^{\circ}\)C.  
About 120 L of pressed crude oil was homogenized and poured into a transparent acrylic glass cylindrical tank (500 mm diameter, 1000 mm height), with a transparent acrylic glass lid put on the top. The crude oil height in the tank was 592 mm. The LBI system mounted on top of the tank consisted of a red laser (DC650-1-3-HT-CON-JST, Pictoronic, Koblenz, Germany) and camera 1 (ELP-USBFHD01M-BFV, Ailipu, Shenzhen, China). To minimize the direct laser light reflection and at the same time optimize the laser light penetration into the sesame oil, the incident angles of the lasers were set at 15\({}^{\circ}\) to vertical direction [21]. The image taken by camera 1 had a size of 1920 \(\times\) 1080 pixels with a resolution of 0.3 mm/pixel. A one-meter rule was pasted onto the oil tank, and camera 2 and camera 3 (same type as camera 1) were mounted at side positions to observe the interface between sediment and supernatant. All three cameras and lights were connected to a computer. As cameras 2 and 3 could not capture the complete view of the tank, manual images were taken from a larger distance every 24 h. Images were automatically taken every hour by camera 2 and camera 3 to measure the sediment's height. Afterwards, the light was automatically turned off for camera 1 to take an LBI image. When the oil interface height changed by 1 mm or more, the corresponding time was recorded. Both the time and interface height were used for further data analysis.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method
### Proposed Model
#### 3.4.4 Decoding Spatial Details  
The decoder intricately reconstructs spatial details, facilitating the faithful reconstruction of the input image. Transposed convolutions, coupled with concatenation techniques, enable the model to upsample efficiently, capturing details at multiple scales. This process is replicated across multiple decoder blocks, ensuring a comprehensive understanding of spatial relationships within the satellite imagery.  
Figure 4: Proposed SA-SC-U-Net: U-Net with self-attention and separable convolutions.
# Research on a PSO-H-SVM-Based Intrusion Detection Method for Industrial Robotic Arms
## 2 Related Work
In [34], a multi-model-based anomaly intrusion detection method was also proposed to detect anomalies in the system from the multiple perspectives of communication models, task models, resource models and control data flow models, and to classify alarms after anomalies are detected. Hidden Markov classifiers are also constructed to distinguish between anomalies and faults, due to the different handling of system anomalies and faults. Similarly, with the widespread use of machine learning and deep learning, researchers have introduced intelligent algorithms into intrusion detection methods with good results, making learning-based intrusion detection methods mainstream. [PERSON] et al. [35] proposed a learning-based intrusion detection method for the EtherCAT protocol, where an attack vector is created based on real-time data and a support vector machine training model is used to perform anomaly detection, which is used to detect anomalies in the communication protocol. In [36], the authors further classified the protocols into four categories by classifying the various features of the protocols. They produced 16 events in the 4 categories for anomaly detection, and the genetic algorithm-based support vector (GA-SVM) machine demonstrated good detection results. [PERSON] et al. [37] investigated an anomaly detection method based on Bayesian networks that can detect an attack on a robotic arm and distinguish whether it is an attack from the physical or network domain. [PERSON] et al. [38] used deep belief networks to detect certain known attacks in robotic arm systems, especially denial of service attacks and spoofing attacks. [PERSON] et al. [39] applied the one-class support vector machine (One-Class SVM) to the joint angle anomaly detection of the robotic arm, and introduced the concept of the tolerance envelope to train supervised learning models, both with good results. In summary, although there are many intrusion detection methods that can be used for robotic arm security, most of them analyze the communication model and data flow but lack a security analysis of the physical model itself. The physical process logic (PPL) attack occurs during robotic arm operation, that is, by overwriting the normal control data with the wrong state control data, which may cause the robotic arm operation system to be disrupted. This attack occurs if the joint state data at each moment of detection do not detect an abnormality, and although it is often the case that there is no abnormality observed in the data, the state disorder can cause serious damage to the physical domain. So, for this situation, this study aims to detect whether there is any abnormality in the logic of the physical process of the robotic arm, so as to ensure the safety of the robotic arm operation process.
# A Hybrid Learning Framework for Enhancing Bridge Damage Prediction
## 3 Results and Discussion
### Datasets  
To test our model, we used four bridge damage datasets and compare our work with other studies that used these datasets. The experiment datasets will be described in this section.  
* DIMEC-Crack Database: [PERSON] et al. (2018) developed a new dataset for crack semantic segmentation. The dataset contains images extracted from video captured by an unmanned aerial vehicle (UAV) equipped with high-resolution cameras for several concrete bridges. Each image has a resolution of 1920 \(\times\) 1080; in their study, they extracted non-overlapping patches of 96 \(\times\) 96 pixels from each raw image, but we will use the original raw images. The dataset contains 10,092 high-resolution images separated into two classes: 7872 crack and 2220 non-crack images.
* Bridge concrete damage (BCD): [PERSON] et al. (2019) introduced a dataset that includes 2068 images of bridge cracks and non-cracks. They captured the images using the Phantom 4 Pro's CMOS surface array camera, boasting a resolution of 1024 \(\times\) 1024. The images underwent two reductions, first to dimensions of 512 \(\times\) 512 and then to a size of 224 \(\times\) 224 to produce the dataset. This dataset consists of 4057 photos with cracks and 2013 images without cracks.
* Bridge datasets: The dataset created by [PERSON] et al. (2017) yielded a total of 1304 cracked and 1806 non-cracked RGB bridge images at a resolution of 200 \(\times\) 200. These images depict various concrete surfaces and cracks from the actual bridge examination. In order to minimize crack-like noise, the images were pre-processed using a 3 \(\times\) 3 median filter after being converted to grayscale.
## 5 Materials and Methods
### Experimental Setup  
In this study, we deployed a modular system architecture, as shown in Figure 6, in an industrial environment to evaluate the feasibility of real-time data acquisition and knowledge modeling. Below, we provide detailed information regarding the hardware components, software configuration, and data acquisition parameters used in our experiments.  
* Hardware:
* Programmable Logic Controllers (PLCs):
* S7-400 (CPU 6 ES7414-2 XG00-0 AB0, Siemens AG, Munich, Germany) connected via Profibus with a bitrate of 1.5 Mbit/s.
* S7-200 (CPU 6 ES7214-1 AD23-0 XB0, Siemens AG, Munich, Germany) coupled via an IBH Link S7++ (IBHsoftec GmbH, Friedrichsdorf, Germany) interface. This setup enables communication with older S7-200 systems that do not natively support modern fieldbus protocols.
* Coating Thickness Measurement Device: A dedicated sensor for measuring lacquer coating thickness (Manufacturer: NDC Technologies Ltd., Essex, UK), integrated using an OPC UA interface.  
Host PC: Intel(r) Core i5-9400T CPU, 16 GB DDR4 RAM, 3 Ethernet interfaces with 1 GB and a 1 TB SSD (Dell GmbH, Vienna, Austria). A Siemens CP 571 6 GK1571-1 AA00 as a communication interface to receive data from the S7-400 via Profibus. This machine served as the central data acquisition and processing node.
* Software Configuration:
* Data Acquisition: ibaPDA (Version 8.1), chosen for its real-time data handling capabilities and compatibility with multiple industrial protocols.
* Database Systems: InfluxDB for short-term time-series storage and an SQL-based solution (e.g., Microsoft SQL Server 2019) for long-term archiving and more complex queries.
* Visualization: ibaQPanel to monitor live signals and perform preliminary analyses (e.g., parameter tuning, status checks).
* Data Acquisition Parameters:
* Sampling Rate: A frequency of 50 Hz was selected based on preliminary trials, which indicated that critical process changes can occur in the sub-second range.
* Fieldbus Throughput: The Profibus connection operates at 1.5 Mbit/s. For the S7-200 via IBH Link S7++, the effective throughput depends on the PLC scan cycle and link configuration, but typically remained stable in our setup.
* Network Traffic: Typical data throughput on the host PC reached up to 10-15 MB/s during peak load, requiring Quality-of-Service (QoS) mechanisms on the local network to minimize packet collisions or loss.
_Appl. Sci._**2013**, \(3\), 325-337; doi:10.3390/app3020325  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**A Comparative Analysis of Energy Costs of Photovoltaic, Solar Thermal, and Wind Electricity Generation Technologies**  
**[PERSON]**  
Global Climate & Energy Project, Stanford University, Stanford, CA 94305, USA;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +1-650-725-8579; Fax: +1-650-723-9190  
_Received: 31 December 2012; in revised form: 13 February 2013 / Accepted: 5 March 2013 / Published: 25 March 2013_  
**Abstract:** Global installed capacity of renewable energy technologies is growing rapidly. The ability of renewable technologies to enable a rapid transition to a low carbon energy system is highly dependent on the energy that must be \"consumed\" during their life-cycle. This paper presents the results of meta-analyses of life-cycle assessments (LCA) of energy costs of three renewable technologies: solar photovoltaic (PV), concentrating solar power (CSP), and wind. The paper presents these findings as energetic analogies with financial cost parameters for assessing energy technologies: _overnight capital cost_, _operating costs_ and _levelized cost of electricity_ (LCOE). The findings suggest that wind energy has the lowest energy costs, followed by CSP and then PV.  
**Keywords:** renewable energy; solar photovoltaic; concentrating solar power; wind power; life-cycle assessment; LCA; meta-analysis; technology assessment; net energy analysis