esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### System Accuracy Test Experiment  
#### 4.6.1 Detection Accuracy Test Experiment  
After the above tests were performed, the real-time tunnel deformation monitoring system was ready to operate. A high-precision micro motion platform was used to simulate the tunnel deformation process, and it moved at an interval of 5 mm. The obtained detection displacement is shown in Table 3.  
According to Table 3, the monitoring system had a high detection accuracy better than 0.12 mm.
## 6 Conclusions  
In this study, a trend analysis of the KEIDP from 2007 to 2016 was conducted using EIA Big Data. The calculation of the true KEIDP requires large amounts of time and money, and it is not always possible to be calculated due to the discontinuation of some data sets. However, in this study, KEIDP values were calculated for current development projects using EIA Big Data and an MLP-ANN approach. In conclusion, this study can be signified in the following aspects: (1) the proposed MLP-ANN approach can continuously produce the KEIDP, which was no longer calculated, (2) in situ-based EIA is possible by producing KEIDP using EIA Big data, which have been measured since 2007, (3) the impact of the development project on the environment can be quantitatively analyzed by comparing it with the ideal KEIDP (0.308), and (4) it was possible to determine theproportion of environmentally friendly projects that reflected their EIA results through the KEIDP trend analysis.  
The EIA Big Data used in this study were measured in EIAs conducted before the development and in the post-project EIA conducted after the development was completed. The EIA Big Data were stored in the KEI-managed Environmental Impact Assessment Support System (EIASS), and data on new projects were continuously being produced. Through a trend analysis of 21 different indicators, it was proven that the KEIDP can quantitatively represent the effects of development projects on the environment. As the trend of KEIDP values showed, the proportion of environmentally friendly projects in Korea is continuously increasing. In future EIAs, the impact of development projects on the environment could be minimized by using calculated KEDIP values in the assessment of their impacts.  
Conceptualization, S.-H.P., H.-S.J. and M.-J.L.; formal analysis, M.-J.L.; funding acquisition, M.-J.L.; investigation, S.L., H.-S.Y. and N.-W.C.; methodology, S.-H.P. and H.-S.J.; resources, H.-S.Y.; software, S.-H.P.; supervision, M.-J.L.; validation, N.-W.C.; visualization, S.-H.P.; writing--original draft, S.-H.P.; writing--review and editing, S.L. and M.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was conducted at Korea Environment Institute (KEI) with support from project \"Development of Technology for Multiple Natural Hazards to Analyze the Casual Relationship between cause of occurrence and damage status and to Predict Spatio-Temporal Damage (2021-027(R)),\" in the Basic Science Research Program funded by the National Research Foundation of Korea (NRF-2018R1D1A1B07041203). This research was also supported by a grant \"Establishment of a pilot service to support intelligent environmental impact assessment\", funded by Ministry of Interior and Safety (MOIS, Korea) & National Information Society Agency (NIA, Korea).  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://eias.go.kr/](https://eias.go.kr/)]([https://eias.go.kr/](https://eias.go.kr/)), accessed on 22 July 2021.  
The authors declare no conflict of interest.
## 5 Applications of 3D-SNOM
Table 1 below provides details of 3D SNOM microscope configurations that have been reported in the literature in terms of scanning range, type of tip, and illumination source used. From the table SNOM images can be collected over volumes that are hundreds of microns in size, with a variety of illumination sources ranging from infrared and visible light lasers to direct thermal heating of the tip and sample. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Ref.** & **Microscope Configuration** & \begin{tabular}{c} **z-Axis** \\ **Scanned** \\ \end{tabular} & \
\begin{tabular}{c} **\(x\)-\(y\) Dimension** \\ **(\(\mu\)m\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Illumination** \\ **Wavelength** \\ \end{tabular} \\ \hline Umeda & 1992 & [46] & Bent metal, 100 nm aperture & 30 \(\mu\)m & 400 & 669 nm \\ \hline Bacsa & 2006 & [47] & Optical fiber, 0.3 \(\mu\)m aperture & 2 \(\mu\)m & 9 & 780 nm \\ \hline Bek & 2006 & [13] & Various apertureless & 9 \(\mu\)m & 120 & 670 nm \\ \hline Lalouat & 2007 & [37] & 50 nm Cr-Au coated & 50 nm & 3 & 1.5 \(\mu\)m \\ \hline Burresi & 2009 & [32] & Al coated, 100 nm aperture & fixed \({}^{\dagger}\) & 8 & 1.53 \(\mu\)m \\ \hline Schnell & 2010 & [35] & Si apertureless & 30 nm & 1.3 & 9.3 \(\mu\)m \\ \hline Olmon & 2010 & [35] & Pt coated 400 nm apertureless & 200 nm & 5 \(\mu\)m line scan & 10.6 \(\mu\)m \\ \hline Guo & 2010 & [51] & Al coated 150 nm aperture & microns & 1 & 457 nm \\ \hline [PERSON] & 2011 & [43] & FTIR, Au coated apertureless & fixed \({}^{\dagger}\) & 5 \(\mu\)m line scan & 10.7 \(\mu\)m \\ \hline [PERSON] & 2012 & [44] & FTIR, apertureless & 10 \(\mu\)m & 5 & thermal heating \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configuration of SNOM instruments reported in the literature. Figure 5: Schematic of collection mode operation 3D SNOM system. (**a**) The piezo-scanner is capable of moving the tip in \(x,y\) and \(z\) directions to position the tip in any location in 3D space; (**b**) In the 3D SNOM configuration, SNOM and topography images can be simultaneously obtained from AFM/SNOM scans of the \(x\)-\(y\) plane along the sample surface. Scanning along \(x\)-\(z\) planes at a constant \(y\) can be used to obtain a cross-section of light scattered by nanostructures at different heights from the sample surface. Reproduced with permission from [48], Copyright Nanoscale, 2015.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 5 Physical Layer Performance Evaluation
### Interference Impact on LoRa
#### 5.2.2 LoRa Self-Interference  
In addition to interference from other technologies, LoRa communication will experience interference also from other LoRa based co-located networks. Inter-SF interference will impact LoRa communication even for end nodes that belong to the same network. Thus, LoRa self-interference was studied in several research papers [45; 47; 51; 63; 66; 67; 68].  
In [63], self-interference of LoRa-like network was studied. The simulated network topology consisted of two gateways that belongs to two different networks and are located at the opposite corners of a square with dimensions 1000 m \(\times\) 1000 m. Each network has 60 nodes distributed inside the square area. Results show that end nodes at distance 1000 m away from the gateway reach a PLR of up to 50% as a result of interference [63]. Even though SFs are thought to be orthogonal in theory, due to orthogonality imperfections they can interfere between each other.  
In [51; 66], SFs imperfection orthogonality impact on self-interference has been studied. It is shown that inter-SF interference deteriorate performance of the network even under low traffic load, especially for higher SFs (SF10-SF12). The impact of log-normal channel fading will have negative impact only in single gateway deployment scenarios, having up to 15% lower Date Extraction Rate (DER) [51]. On the other hand, for multi-gateway deployment, fading impact will be negligible. Due to gateway diversity, it is possible for a packet to get received by different gateways decreasing the impact of fading [51]. Another empirical study regarding the LoRa self-interference was presented in [67], where similar results as in [51] were observed.  
In [45; 47], the collision behavior at link level between two different LoRa packets was studied. Collision behavior depends on carrier frequency, spreading factor, received power and time difference between interfering transmissions. In [45] authors proved that LoRa experiences capture effect, meaning that the stronger signal can suppress the lower one, and can be decoded correctly at the receiver. Based on this outcome, in [45] authors proposed two mechanism to decrease the LoRa self-interference by using multi-gateway deployments and directional antennas for end nodes. The first solution is seen to be beneficial even for removing negative effects of fading channels [51]. By using directional antennas for end nodes, the received signal becomes stronger as well as the interference from other interferer network lowers. It is shown that, when directional antennas with 8 dBi gain are used, DER is improved by 0.06 compared to cases when omni-directional antennas are used [45].
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
###### Abstract  
In coal masses, joints serve as crucial components influencing mechanical responses and failure mechanisms. The joint distribution complicates the acquisition of physical parameters for engineering-scale coal masses, and traditional laboratory or in situ tests often suffer from inaccuracies and high costs. Therefore, examination of the dynamic properties of engineering-scale coal mass is generally performed by numerical simulations. This paper proposes a model construction approach using two-dimension Particle Flow Code (PFC2D) software to study the mechanical properties of engineering-scale jointed coal mass, addressing the limitations of conventional models by integrating scale effects, accuracy, and computational efficiency. Firstly, the distribution characteristics and mechanical parameters of the joints in the coal mass were obtained based on field statistics and laboratory experiments. The parameters of the laboratory-scale model were calibrated by the numerical matching method. The discrete element model for the engineering-scale coal mass was constructed by the step-by-step matching method. The confining pressure effect on the coal mass under a biaxial loading condition was studied, while the strength change, fissure evolution, and failure mechanism under different confining pressures and fissure degrees were investigated. Based on the simulation results, a quantitative relationship was established between the mechanical parameters, fissure degree, and confining pressure under compression conditions. Ultimately, the failure zone ahead of the working face and the distribution of the abutment pressure were assessed using the mechanics parameters of coal masses with diverse joint distributions.  
Keywords:joint; coal mass; model construction; confining pressure; PFC2D +
Footnote †: journal: applied sciences
# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
## 2 Wireless Requirements and Applications
### Wireless Communication in New Applications  
Several emerging technologies, such as IoT and edge computing, greatly depend on wireless communication [40; 41]. Wireless communication systems are built with great consideration for aspects such as heterogeneity and mobility. For instance, consider the  
\begin{table}
\begin{tabular}{l l} \hline \((\cdot)^{T}\) & transpose operation \\ \hline \((\cdot)^{-1}\) & inverse operation \\ \hline \(\mathbf{I}_{N}\) & \(N\times N\) identity matrix \\ \hline \(\text{card}(\mathcal{S})\) & cardinality of the set \(\mathcal{S}\) \\ \hline \end{tabular}
\end{table}
Table 2: List of notations.  
great benefits of machine-to-machine (M2M) communication in IoT applications where multiple devices (e.g., sensors and data aggregation units) need to directly communicate with each other. This advance has been possible due to the improved capabilities of new wireless technologies, such as 5G networks. Wireless connectivity improves other aspects of communicating applications. For example, due to wireless connectivity, M2M communication contributes not only to data connectivity but also to other quality factors, such as data availability, redundancy, and reliability. In IoT applications, wireless connectivity allows for bidirectional communications between field devices and equipment to control and data centers [42], which improves domain knowledge and promotes pervasive data aggregation.  
Edge computing is a new computation paradigm, by means of which computation is brought closer to data sources to reduce latency and improve response time [19]. Wireless communication significantly contributes to the evolution of edge computing. This is because new wireless technologies provide multiple benefits, such as ultra-low latency and large throughput capabilities. Additionally, wireless communication supports mobility, which is a key factor in edge computing. Wireless communication also improves the management of applications that adopt the edge computing paradigm. For example, wireless network slicing allows an application to create different management layers based on exchanged data and communicating devices [43]. Figure 1 shows how wireless communication contributes to moving data among different domains in a smart city. The figure shows that different domains are managed through network slicing, where data is managed and controlled differently at each network tier. The slicing also enables edge computing to create sub-networks to manage the heterogeneity of devices and services.
## 3 Medium Access Control (MAC) Layered UWAC Protocols  
When wireless signals propagate through water, while having a very small bandwidth, the established underwater acoustics protocols face various challenges, such as time synchronization, hidden and exposed terminal issue, and very large delays [10; 47; 67]. The aforementioned issues have drawn researchers' attention to FD-MAC layer-based underwater acoustics communication protocols, resulting in the development of various FD-MAC layered UWAC protocols in the literature. In the following sections, we will look at a variety of FD-MAC layer-based UWAC protocols, as well as the problems they face and the solutions suggested in previous works to solve each problem.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**System Type**} & \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Modulation**} & \multirow{2}{*}{**Data Rate**} & \multirow{2}{*}{**Implementation**} & \multicolumn{6}{c}{**Performance Matrices**} \\ \cline{6-9}  & & & & & **BER** & **Efficiency** & **Transmission Range** & **Power Consumption** & **SNR** \\ \hline \multirow{2}{*}{Physical layer-based FD-Modems} & [33] & OFDM & Low & Shallow water & NA & Medium & High & High & High \\ \cline{2-9}  & [45] & OOK & High & Swimming pool & Low & High & Low & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of various full-duplex underwater acoustic modems.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 1 Introduction
### Literature Review
#### 1.2.2 Related Research on Detecting Ship Encounter Scenarios
[22] proposed a vessel encounter risk identification method based on an improved DBSCAN algorithm, which combines the TCPA and the DCPA with a fuzzy comprehensive evaluation model. This method filters out vessels with actual collision risks in AIS data and outputs the risk evolution trends of encountering vessels, providing effective support for collision avoidance.
## 3 Characterization and Performance Testing
### Property Test of the Asphal
#### 3.2.3 Fourier Transform Infrared Spectroscopy Test (FTIR)  
Fourier Transform Infrared Spectroscopy test was used to analyze the functional groups of BA, CBMA and NHSSMA under F-T aging procedure from chemical characteristics. A Vertex 70 Fourier Transform Infrared Spectroscopic (Bruker Optics.co, Changchun, China) was employed with wavelength ranging from 40 cm\({}^{-1}\) to 4000 cm\({}^{-1}\)[27]. From the peak position and size, the chemical bonds and the functional groups of the materials in the asphalt can be determined. Based on the previous research, waves of representative chemical bonds are obtained. The results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Wave Number (cm\({}^{-1}\))** & **Chemical Bonds** \\ \hline
3676 & \
\begin{tabular}{c} Intermolecular hydrogen bond (O–H) vibration \\ The antisymmetric stretching vibration absorption band of the \\ alkyl (C–H) \\ 2852 \\ 1607 \\ 1456 \\ 1377 \\ 1250 \\ 1031 \\ 966 \\ 747 \\ \end{tabular} &
\begin{tabular}{c} The C–H asymmetric deformations in CH\({}_{2}\) and CH\({}_{3}\) vibrations \\ The C–H symmetric deformation in CH\({}_{3}\) vibrations \\ The C–O stretching vibration in saturated alcohols \\ The sulfoxide group (S=O) stretching vibration \\ The C–H out-plane bending vibrations in unsaturated \\ hydrocarbons \\ Bending vibration of aromatic branches \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 4: Featured chemical bonds of asphalt binder.
# Solar Radiation Pressure Models for BeiDou-3 I2-S Satellite: Comparison and Augmentation
## 2 Characteristics and SRP Models for BeiDou-3 I2-S Satellite
### Satellite Structure  
As described in [3], BeiDou-3 experimental satellites are based on two different satellite platforms developed by China Academy of Space Technology (CAST) and the China Academy of Science (CAS). The CAS platform weighs approximately 848 kg, and has an elongated shape. On the other hand, the CAST IGSO platform has a cuboid shape and weighs 2800 kg. BeiDou-3 I2-S is believed to be developed based on CAST's Dongfanghong-3A bus, which was also adopted by BeiDou-2 satellites. However, the structure of the satellite bus has changed to approximately 2 \(\times\) 2.5 \(\times\) 3.6 m. As there are no available optical properties for BeiDou-3 I2-S, the coarse values from BeiDou-2 IGSO and MEO satellites have been used. Table 1 listed these initial values for satellite bus and SPs. As can been seen, the body of BeiDou-3 I2-S is also an elongated shape along \(Z\) axis, similar to Galileo and QZSS Michibiki.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 5 Conclusions  
This study proposes a small sample target detection method of side-scan images using CycleGAN and the improved Yolov8 models to address the limitation in underwater target detection. To improve the detection accuracy of side-scan images, the original YOLOv8 model is revised by adding GAM to the backbone and neck modules, replacing the C2f module with the C2f-DCNv3 module, and updating the loss function by using EIoU. The results of the pseudo image generation experiment demonstrated the effectiveness and advantage of CycleGAN by comparing it with other style transfer models. In the target detection experiments, the effectiveness of our improvement was validated through an ablation study. The experimental results confirm the advantage of our detection model compared with modern models, with a [EMAIL_ADDRESS] increase of 13% compared with the original YOLOv8 model. Moreover, the combination of data augmentation using CycleGAN and the improved detection model further enhances the detection accuracy ([EMAIL_ADDRESS]) to 0.924, which proves the validity of our method.  
Conceptualization, J.Y.; methodology, J.Y. and Y.Z.; software, Y.Z.; validation, J.Y., J.M. and Y.Z.; formal analysis, J.Y.; investigation, Y.Z.; resources, J.Y.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, J.Y., J.M. and M.L.; visualization, Y.Z.; supervision, J.Y.; project administration, J.Y.; funding acquisition, J.Y., J.M. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant numbers 42104036 and 41906168), the Natural Science Foundation of Anhui Province (grant numbers 2308085 MD124 and 1908085 QD161), the University Natural Science Research Key Project of Anhui Province (grant numbers 2022 AH050256 and KJ2019A0024), the University Synergy Innovation Program of Anhui Province (GXXT-2022-020), the Key Research and Development Project of Anhui Province (grant number 2022107020027), and the Natural Science Residual Fund Project of Anhui Jianzhu University (grant number JZ02366).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Terminology  
In this work, the following definitions were adopted:  
* Action: A class understood to be a situation with a semantic meaning, happening in the scene typically related to Objects, which are either the subjects or the objects of the Action. They occur during a specific time interval (frames). It is necessary to distinguish between intransitive and transitive actions because they are semantically different.
* Intransitive action: Express status of Objects, and thus can be expressed as adjectives or verbs in the present continuous form: \"the car is parked\". In this example, the object is not specified, but the \"Car\" is known to be the subject and \"Parked\" is the predicate of the sentence.
* Transitive action: Can be naturally treated as triples, where there is a subject, a predicate, and an object. For example, \"a child is running in the park\", where \"Child\" is the subject, \"Park\" is the object, and \"Running\" is the predicate or the Action.
* Attribute: A quality or feature of a class element or axiom of the ontology.
* Axiom (Relationship): Statements that are asserted to be true in the domain being described [20]. They structure the ontology and provide semantic information. They are represented as relationships in the graph database.
* Class: Concept of the domain represented as a node in the graph database.
* Context: A class for elements that describe the general situation and circumstances of the scenario. Contextualizing can involve any aspect that helps the user or application define the surroundings and general conditions of the scenario.
* Event: A class to represent anything that happens in an instant of time (frame). Therefore, any instantaneous change of state caused by an Object can be defined as an Event. These changes usually cause a new occurrence and, depending on the duration, this can be defined as a new Event or an Action.
* Individual: Instance of an ontology class. In the case of automotive scenarios, a named class should be assigned to each individual of any scene. Hence, individuals of a class are defined by a unique identifier (UID) and a unique name that is specific to each analyzed case.
* Object: A class that represents anything tangible, i.e., a person or thing. They are the main elements of the ontology and can be related to attributes or actions.
* Ontology: Formal description of concepts (Class) and their relations (Axioms) according to a common understanding of experts in the domain. The definition of these elements can be completed with properties or restrictions.
* Scenario: A quantitative and qualitative description of the situation (e.g., traffic environment), as the sequence of Actions and Events performed by Objects.