esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 4 Robotic Maintenance Technology in Other Industries
### Overview of Mobile Manipulator Based on Mobility and Applicability  
Initially, the concept of a mobile manipulator started with attaching a manipulator with a wheeled robot. However, in many cases, the navigation capability of mobile ground manipulators is limited. Therefore, instead of using an unmanned ground vehicle (UGV), the use of an unmanned aerial vehicle (UAV) can extend the navigation capability of a mobile manipulator at any height and traverse terrain. Moreover, the mobile manipulator can operate underwater with proper design and fabrication. Apart from in the ground-, air-, or water-based applications, mobility features are required for mobile manipulators used in space or along overhead high-voltage power transmission lines. Based on the mobility features, the mobile manipulator can be divided into several categories: ground, aerial, marine, space and crawling. After analyzing all the selected mobile manipulator-related studies for this research, they have been divided into multiple categories based on their mobility features, as shown in Figure (a)a. More than half of the mobile manipulators, 54%, were based on a ground vehicle. In comparison, 20% and 12% had aerial navigation and crawling navigation capability, respectively.  
As sensor and instrumentation technology continues to improve, mobile manipulators are becoming more capable of performing many monotonous and repetitive tasks, which might pose a risk to human health and financial penalties. For example, a mobile manipulator can routinely survey and inspect nuclear reactors or perform inspection or repair at an overhead high-voltage power transmission line. Moreover, with the correct configuration, mobile manipulators can perform inspection and repair tasks inside and outside a pipe or clean a window, wind turbine, cellular tower, etc. The selected publications were divided into several categories, as shown in Figure (b)b, on the basis of their area of application.  
Among the selected studies, the mobile manipulator's highest percentage (20%) was used for inspection and repair tasks in high-voltage power transmission lines. A notable percentage, 12% of the mobile manipulators, were used to find defects in nuclear reactor vessels or other nuclear power plant infrastructure. Other relevant application fields included space vehicles, surveillance, and repair of civil infrastructure and construction sites, electric substation maintenance, teleoperation, agriculture, etc.
## 1 Introduction  
The first of the two main aspects in tunnel design that require focusing is a precise estimate of the probable ground condition and excavation behavior. The second aspect is the cost and safety assessment of the tunnel, which defines the support system. Rock mass characterization is used to address the first aspect, whereas the second one can be achieved by the proper classification of the rock mass excavation environment. A number of rock mass classification systems have been developed since [PERSON] pioneered his study on the rock load factor classification for tunnel support design [1]. Among these several rock mass classification systems, rock mass rating (_RMR_) and tunneling quality index (\(Q\)) are internationally accepted systems for support design that are widely used in the field of tunneling [2; 3; 4].  
The _RMR_ classification was proposed in 1973 as a jointed rock classification system [5]. It was refined several times, and major revisions in its characterization and structure were made in 1989 and 2014 [6; 7]. The \(Q\) system was developed in 1974 [8], and major changes in its characterization and classification were proposed in 1993 and 2002 [9; 10]. It was developed based on tunneling cases for hard and jointed rock mass [11]. The main objective of these classifications was preliminary support design. Over time, the two systems have been refined and updated so as to improve their reliability for tunnel support design. The benefits from the use of these empirical design tools in tunneling include simplicity, economy, reliability, and being a time-tested approach [12].  
In rock mass classification, rock mass characterization is critical. These classifications are the back-bone of the empirical design methods [13; 14]. The characterization of the rock mass deserves adequate attention and should be performed as per defined procedures of each system. The recent developments in tunnel support design in terms of shotcrete thickness and rock bolt spacing eliminate personal judgment in design [11; 15], and this is only possible as a result of proper rock mass characterization and classification. In engineering applications, particularly for tunnel support, \(RMR\) and \(Q\) systems should not be used independently, and at least two different systems should be used for evaluation; further, the correlation between them should be verified [2; 16].  
In this study, the reliability of \(RMR\) and \(Q\) classification system for tunnel support design is evaluated. A simple but precise characterization through the use of equations are proposed for the calculation of rock mass quality as per the suggestion of \(RMR\) and \(Q\) systems. The role of \(RMR_{89}\) in \(RMR_{14}\) is highlighted, and a correlation between them is proposed based on data from 462 sections of two tunnel projects. The back analysis approach is introduced for rock mass quality calculation. Moreover, the use and limitation of these two systems for support design is described and evaluated through published data. The correlation reliability is also assessed.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 5 Diagnosis Simulation
### Fault Detection Results  
We examined the effectiveness of the developed algorithm by detecting the track fault from the simulated car-body vibration data. For the training data, we used the feature space as shown in Figure 6. The detection results were divided into four classes: normal track, faulty track with longitudinal-level irregularity, faulty track with alignment irregularity, and faulty track with cross-level irregularity. The validity of the detection was evaluated for every 100 faults of longitudinal  
Figure 10: Calculated car-body vibration by using Figure 9. (**a**) Vertical acceleration. (**b**) Lateral acceleration. (**c**) Roll rate.  
Figure 9: Degraded track geometry in some sections. (**a**) Longitudinal level geometry. (**b**) Alignment geometry. (**c**) Cross level geometry.  
level, alignment, and cross level irregularities in 5000 m track lengths. Figure 11 shows the results of detection by using the algorithm.  
As shown in Figure 11, the developed algorithm detected the track fault with an accuracy of 80 percent or more for normal and highly degraded tracks. Clearly, the longitudinal level irregularity can be detected with high accuracy for normal, intermediate, and highly degraded tracks. Meanwhile, the detection accuracy of the alignment and cross level irregularities are lower than that for the longitudinal level irregularity, especially for the intermediately degraded track. However, it is noted that most of the track faults in the regional railway are longitudinal level irregularities. Therefore, the developed diagnosis algorithm is considered effective for regional railway lines.  
The effect of vehicle faults such as wheel flat will appear on the car-body vibration. It is possible to identify the possibility of the faulty train by looking at the measurement data of different places.
## 2 Materials and Methods  
The implementation of local network-driven data-synchronization methodology was realized in the original ad hoc local network, a 3-member single-level network between equal microcontrollers with data flow control.  
This experimental setup covers the human-gait analysis system, which is suitable for rehabilitation and medical monitoring of human-gait issues. The setup collects data from the sensor on each foot, acquiring a three-point foot-pressure sensor array and a single three-axis accelerometer and gyroscope module for each foot. Data are transmitted to the microcontroller, which accumulates data and transmits data to the cloud storage or PC via USB cable for further analysis.  
For such monitoring systems, the monitored process and monitoring aim set the main requirements for performance and accuracy. Continuous monitoring of human gait, trying to define minor changes, requires a data-acquisition rate of about 30 samples per second and a data-synchronization accuracy in the range of a few milliseconds. Such a sampling rate is not an issue for popular microcontrollers like Atmega, but adding wireless data transfer from a few devices and strict data-synchronization requirements to such a task became relatively challenging.  
The developed prototype and the proposed method were tested by conducting practical experiments in the scientific laboratories of the Department of Mechatronics, Robotics, and Digital Manufacturing of Vilnius Gediminas Technical University.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 4 Autonomous Data Offloading and Price Setting  
### Problem Formulation  
Following the above described reinforcement learning technique of the stochastic learning automata, each end-user has concluded to the selection of a MEC server to offload its data. Then, the goal of each MEC server is to maximize its profit by processing the end-users' data, while the goal of each end-user is to maximize its perceived satisfaction, as expressed by its utility function, by offloading the optimal amount of data to the selected MEC server. Thus, a two-layer optimization problem is formulated, as follows.  
\[\mathbf{b^{(t)*}}=argmax_{b^{(t)}_{u,s}}U^{(t)}_{u}(b^{(t)}_{u,s}, \mathbf{b^{(t)}_{-u}},\mathbf{p^{(t)}}) \tag{10a}\] \[\mathbf{p^{(t)*}}=argmax_{\mathbf{p^{(t)}}}P^{(t)}_{s}(\mathbf{b^{ (t)}},\mathbf{p^{(t)}}) \tag{10b}\]  
As it is observed by Equations (10a) and (10b), the MEC servers optimal price \(\mathbf{p^{(t)*}}\) and the end-users optimal data offloading \(\mathbf{b^{(t)*}}\) are interdependent, thus the joint optimization problem is formulated as a two-layer optimization framework. Initially, the end-users determine their optimal data offloading \(\mathbf{b^{(t)*}}\) via confronting the optimization problem of their personal utility functions as a non-cooperative game among them. Then, at the second layer, the MEC servers determine their optimal announced prices \(\mathbf{p^{(t)*}}\) given the data offloading of the end-users, via solving an optimization problem. The formulation and solution of the optimization problem is performed at the SDN controller, where its advanced computing capabilities enable the fast decision-making. In the following two subsections, we analyze in detail each layer of the optimization problem.
## 3 Results and Discussion
### Non-Covalent Interaction-Reduced Density Gradient (NCI-RDG)  
We characterized the types of interactions occurring in the system using the non-covalent interaction-reduced density gradient (NCI-RDG) approach. The NCI method utilizes an RDG to visualize spatial interactions, where the RDG is a dimensionless quantity derived from the electron density and its first derivative. The method was developed by [PERSON] et al. [70], and the RDG is defined as follows:  
\[RDG(r)=\frac{1}{2\sqrt[3]{3}\pi^{2}}\frac{|\
abla\rho(r)|}{\rho(r)^{\frac{4}{3}}} \tag{8}\]  
The RDG was plotted against (sign \(\lambda_{2}\))\(\rho\) to generate an NCI-RDG scattered diagram. The nature of the interaction can be predicted by the (sign \(\lambda_{2}\))\(\rho\) values (where \(\lambda_{2}\) is the second eigenvalue of the Hessian electronic density matrix) as follows:  
1. (sign \(\lambda_{2}\))\(\rho\) > 0: repulsive/steric effects;
2. (sign \(\lambda_{2}\))\(\rho\) < 0: attractive interactions (hydrogen bonding);
3. (sign \(\lambda_{2}\))\(\rho\)\(\simeq\) 0: van der Waals forces, which arise from overlapping electron clouds and occur over larger distances. The colored RDG scatter plots in Figure 16 were prepared using Multiwfn 3.7 software and plotted using Gunplot version 5.4.  
Figure 16: Cont.  
From Figure 16, the importance of two strong intra-molecular hydrogen bonds for both DIT-SAL and dithranol is clearly seen, which stabilize these molecules. What is interesting is that salicylic acid is not stabilized by hydrogen bonding (as can also be seen from the RDG plot), but when it is part of the DIT-SAL molecule, it participates in the formation of a form of hydrogen-bond-mediated stabilization of the proposed drug.
###### Abstract  
As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named SITormer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, SITormer demonstrates satisfactory performance in simulation experiments.  
photovoltaic microgrid; deep learning; cyberattack detection; attention mechanism +
Footnote †: journal: Applied
## Materials and Methods
### Description of the Experimental Setup  
A wide range of relevant air quality parameters were measured in indoor environments of two elementary schools located far apart 20 km, namely: an elementary School \"Aleksa Santic\" in Belgrade suburb Kaluderica, and in an elementary school \"20. oktobar\" in a residential background of the New Belgrade municipality, referred to as School 1 and School 2 in further text, respectively. The measurement campaigns in both schools, that have natural ventilation, were conducted during March 2017. Measuring spots in both schools were in classrooms on the ground floor, occupied on workdays. Instruments were arranged rather densely, and we can assume that the sampled air was well-mixed. Area surrounding location of School 1 and 2 is depicted in Figure 1. School 1 is in the near vicinity of a major local road, and also in the neighborhood where there is a significant amount of domestic heating sources. School 1 itself has a coal-based heating system. School 2 on the other hand is located further apart from the major roads, and is situated in a block of buildings connected to the district heating system.  
The measurement instrumentation setup included the following. SI concentration was measured using a Gerdien-type air ion detector [22]. PM concentrations for diameter of particles going from 10 nm to 420 nm in 13 size channels were detected using TSI NanoScan SMPS Model 3910, and PM concentrations for diameter of particles going from 0.3 \(\upmu\)m to 10 \(\upmu\)m in 16 channels were recorded using TSI Optical particle size 3330. In addition, the radon concentration level was measured hourly using a Radon Scout, along with the local temperature, pressure and humidity. The Radon Scout consists of a measurement chamber with high voltage collection and Si detector, and samples air by diffusion. Remaining instruments sample air actively, either via a pump (particle sizes) or a fan (air ion detector). Accuracy, traceability and manufacturer info of the used instruments if further described in Appendix B and Table A1.  
The collected data describes all relevant processes quantified by a balance equation: 2-minute SI concentration measurements describe steady state; radon concentration gives insight into the rate of volumetric ion pair generation and 1-minute PM measurements give insight into main loss mechanism for SI.
## 3 Background  
In theory, an equal beam bending angle of all dipole magnets throughout the chicane results in zero beam momentum dispersion \(R_{36}\). However, the challenge arises regarding how to achieve zero dispersion in practice, due to imperfection in the chicane installation, the inexactly identical magnetic field of each chicane dipole magnet, magnetic fields from other beamline components, etc. Additionally, at PITZ, the chicane was installed with a mechanical constraint for positioning the four dipole magnets, resulting in offsets in the magnet positions. In terms of the mechanical constraint, the second and the third chicane dipole magnet are installed above the original beamline components--an energy measurement station including a horizontal dipole magnet, a vertical beam dump, etc.--with clearance of 3-4 mm. Such clearance is very small due to the attempt to minimize the bending angle for the CSR effects. A vertical offset of any chicane dipole magnets results in the fringe field difference depending on the vertical position at the dipole magnet entrance due to the finite width of the magnets. As a result, the offset could introduce energy dispersion. For example, according to ASTRA tracking simulation results, the dispersion is increased by 0.07 mm/MeV/\(c\), once both the first and fourth chicane dipole  
Figure 2: Commissioning concept without beam position measurement throughout chicane.  
magnet vertically are moved by \(-1\) mm from the designed positions. Table 1 represents the energy dispersion for different bending angles offset to 19 deg, where \(\Delta D_{1},\Delta D_{2},\Delta D_{3}\), and \(\Delta D_{4}\) denote bending angle offsets (errors) of the chicane dipole magnets CHICANE.D1, CHICANE.D2, CHICANE.D3, and CHICANE.D4, respectively. Therefore, in this study, the magnitude of the energy dispersion is ultimately minimized via direct measurements.  
According to Table 1, the magnitude of the energy dispersion in the cases of \(\Delta D_{1}=\Delta D_{3}\) and \(\Delta D_{2}=\Delta D_{4}\) is smaller than the case of the single bending angle offset. This suggests the possibility of performing the commissioning by tuning the chicane dipole magnet currents with some constraints.
# Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
## 3 Research Motivations
### Challenges  
**Transaction graph has complex inter-node relationships**  
Generally, in the Ethereum transaction network, there are multiple transactions with varying amounts occurring at different times between two nodes. In other words, there will be multiple adjacent edges between nodes. Figure 2 shows a simple transaction graph with only five nodes. The simple addition of weights leads to the unexpected fusion of the features, which limits the effective utilization of the discrete properties.  
**Significant imbalance between phishing and normal nodes**  
In the Ethereum transaction network example presented in [25], there were 2,973,489 nodes and 13,551,303 transactions, but only 1165 phishing nodes. In other words, there was a significant imbalance between phishing and normal nodes, which can impact the results of the classification.  
**Unlabeled nodes**  
The labeling of phishing nodes relies on reports from users of specific websites, such as etherscamdb.info and etherscan.io. In other words, these websites can track phishing incidents only if they are reported, and significant numbers of frauds and scams are not  
Figure 2: A simple multiedge graph example in the transaction network. It can be observed in this figure that there are many edges between nodes, and these edges have different amounts of transactions and time. Thus, it is challenging to merge them. When the number of transactions is greater than three, we use the symbol “...” to represent the remaining transactions.  
reported [26, 27]. Therefore, having these unknown/undetected phishing nodes in the \"normal node\" set can skew and impact the classifier's performance, a situation that is also referred to as a Positive Unlabeled (PU) learning challenge.
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 2 Mining Conditions and Methods
### Early Warning Analysis  
To identify the critical warning parameters, the number of microseismic events and the microseismic energy were selected as the warning indicators, and a fixed monitoring area was selected for analysis and warning, according to the station network, to eliminate the influence of lithology and production. However, if the monitoring range is large or the mining methods in the monitoring area differ greatly, lithology correction indicators need to be introduced.  
When selecting the early warning analysis steps, the statistics regarding the duration of microseismic events and their energy indicators for early warning, generally according to the mine downhole resting arrangement, must be considered, along with safety management that facilitates shift handover. Real-time analysis of the processed waveform data was performed by counting the cumulative number of microseismic events \(\sum E_{i}\) and the cumulative microseismic energy \(\sum N_{i}\) within a time step.  
In order to eliminate the abnormal increase or decrease in the number of microseismic events and their energy due to the system failure, the correction should be made according to the number of stations (measurement points) in the network, and the corrected cumulative microseismic event number \(\sum E_{i}=k\sum E_{i}\), the cumulative microseismic event energy \(\sum N_{i}=k\sum N_{i}\), and the correction criteria are as follows: (a) when the number of normal operating stations is greater than or equal to 4 and less than or equal to 1/2 of the total number of stations, the number of microseismic events and the energy correction factor k are taken as 1.2. The number of microseismic events and the energy correction factor k are 1.2; (b) when the number of normal operating stations is greater than two and less than four, the number of microseismic events and energy correction factor k are taken as 1.5. The discriminant Equation (5) is satisfied.  
\[\left\{\begin{array}{ll}\frac{\sum E_{i+1}}{\sum E_{i}}\leq 1\\ \frac{\sum N_{i+1}}{\sum N_{i}}\geq 2\end{array}\right. \tag{5}\]