esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Robot Trajectory Planning and Position/Force Coordination Control in Complex Welding Tasks
## 2 Problem Description
### Problem Description  
The multi-robot cooperative welding system is shown in Figure 1. To solve the constraint pose and wrench at the same time in welding process, it is difficulty to plan the cooperative motion trajectory and achieve the desired position/force cooperative effect during the transportation.  
In order to satisfy the welding process requirements in the continuous welding process, the transfer robots must continuously change the posture of the clamping workpiece to ensure that the welding spot is always in the preset welding position. The welding robot must constantly adjust the position of the welding torch to ensure the welding torch meets the welding requirements. It is required that both the transfer robots and welding robot should satisfy a certain position and posture constraints during the whole welding process.  
With the process of welding, the workpiece held by transfer robot is gradually welded, then the transfer robots and the workpiece will be formed as a closed-chain system. In the actual control system, the external disturbance and calibration errors are often existing. These disturbances and calibration errors will cause the time-varying trajectory deviations in the robot end-effector during cooperation, and the dynamic time-varying trajectory deviations can cause a huge internal forces between the robot and the workpiece. Improper control may lead a damage to the workpiece or robot.  
From the above analysis, the two key issues in the control of multi-robot cooperative welding system are:  
* How to achieve the coordinated motion of multi-robot, which is the problem of planing the multi-robot trajectory;
* How to effectively control the internal force between the robot and the workpiece, which is the problem of position/force cooperation.
# A Graph-Based Approach for Modelling Quantum Circuits
## 5 Supporting Tools and Model Transformations
### Transformation between the Modelling Strategies
As an example, the GitHub repository referred to in the \"Data availability statement\" at the end of the paper stores a model-to-model transformation, written in the Epsilon transformation language [31], that transforms a model that follows the strategy \"mixed swim-lane\" into one that conforms to \"swim-lane\". The transformation is straightforward for all elements except for Nodes and Edges, as outlined above, which must be handled carefully to properly transform nodes containing only one quantum gate and nodes containing more than one, while considering the edges entering and exiting them. Algorithm 1 provides a more complete yet succinct description of this transformation. ```
//\(\Longrightarrow\) means \"transformed into\" //\(\leftarrow\) means \"store the element/s generated for the RHS on the LHS\" // The values of the attributes/properties not specified in the transformation // are just copied from the input model to the output one f:In!Qubit \(\Longrightarrow\) tOut!Qubit f:In!H \(\Longrightarrow\) tOut!H f:In!X \(\Longrightarrow\) tOut!X  f:In!Control \(\Longrightarrow\) tn:Out!Node, tc:Out!Control  tn.gates = tc f:In!Node / f.gates.size()\(\leq\)1 \(\Longrightarrow\) tOut!Node  t.gates \(\leftarrow\) f.gates size()\(>\)1 \(\Longrightarrow\) tOut!Node  t.gates \(\leftarrow\) (f.gates-> selectOne (g | **not** g.isTypeOf(In!Control))) f:In!Edge(**not** (f.src.gates.size()\(>\)1 **and** f.dst.gates.size()\(>\)1)) \(\Longrightarrow\) tOut!Edge  set _t.src and t.dst so that the gate contained in each node operates on the same qubit_ f:In!Edge(f.src.gates.size()\(>\)1 **and** f.dst.gates.size()\(>\)1)) \(\Longrightarrow\) tOut!Edge*  create as many Out!Edge as needed to link Out!Nodes created from composite In!Nodes f:In!Circuit \(\Longrightarrow\) tOut!Circuit  t.qubits  t.qubits  t.nodes \(\leftarrow\) f.nodes  t.nodes \(\leftarrow\) In!Control.allInstances()  t.edges \(\leftarrow\) f.edges
```  
**Algorithm 1** Transformation from strategy \"mixed swim-lane\" to \"swim-lane\".
## 4 Two-Mode Encoding and Estimation Schemes  
### Identical Encoding  
First, consider a \"naive\" protocol, were [PERSON] encodes her classical real parameters \(a\) and \(b\) into two identical coherent states (\(\alpha_{1}=\alpha_{2}\)). This imposes the following constraints on the parameters of linear encoding:  
\[\begin{array}{ll}\epsilon_{x1}=\epsilon_{x2},&\epsilon_{p1}=\epsilon_{p2},\\ \eta_{x1}=\eta_{x2},&\eta_{p1}=\eta_{p2},\end{array} \tag{25}\]  
where at least \(\epsilon_{x1}\) or \(\epsilon_{p1}\) and \(\eta_{x1}\) or \(\eta_{p2}\) are not equal to zero. Let us show that this protocol cannot be optimal since it does not satisfy the two last conditions in Equation (23). In fact, by plugging Equation (25) into Equations (23), we have  
\[\begin{array}{ll}\epsilon_{p1}\eta_{p1}+\epsilon_{x1}\eta_{x1}&=\ 0,\\ \epsilon_{p1}\eta_{x1}-\epsilon_{x1}\eta_{p1}&=\ 0.\end{array} \tag{26}\]  
Note that the left-hand side of the second equation of Equation (26), which also corresponds to the attainability condition for a single-mode coherent state, is equal to the square root of the determinant of the single-mode QFIM (10); hence, the inversibility of the single-mode QFIM in Equation (10) and the attainability condition for a single-mode coherent state are incompatible conditions. Without loss of generality, we consider \(\epsilon_{x1}\) and \(\eta_{p1}\) to be non-zero, so that we have  
\[\epsilon_{p1}=-\frac{\epsilon_{x1}\eta_{x1}}{\eta_{p1}},\qquad\qquad-\eta_{x1 }^{2}=\eta_{p1}^{2}, \tag{27}\]  
Figure 1: Optimal joint estimation scheme for two modes using a beam splitter of transmittance \(T\) and two homodyne detectors. The phases are all set to zero in Equation (28).  
where the second equation shows a contradiction since \(\eta_{p1}\) is non-zero and all encoding constants (\(\epsilon_{i,j},\eta_{i,j}\)) are real numbers. Hence, there exists no protocol which uses identical encoding and saturates the QCRB for both parameters \(a\) and \(b\) simultaneously.
## 3 Methodology  
Understanding the current research regarding ML algorithms and pump health management is one of the objectives of this study. The study investigates previously published materials that provided knowledge of the potential applications and demonstrated academic curiosity about the main trends, important works, and future directions of research. As a result, the authors have made an effort to establish an organized reference point for the expanding body of literature on this new topic. The scope of this project spans the years 2010 to 2022 because there is a growing practical need for research in this area. This research is based on reviewing a range of journal articles and conference papers that are all closely relevant to pump health management principles and ML applications, in order to achieve the study's objectives. The articles were discovered to be dispersed throughout a variety of sources due to the breadth and diversity of these methodologies.  
This paper discusses the findings of the investigation in regard to the following questions: (1) What machine learning approaches and variables are available for predicting the reliability and integrity of oil and gas pumps? (2) Which datasets are significant for predicting the dependability and structural integrity of oil and gas pumps? The trajectory of the publications included in this study is depicted in Figure 7 illustrating how rapidly the use of machine learning methods for assessing the dependability of pumps has increased. Modeling based on machine learning is a developing and novel method. Prior to 2009, few publications on the topic existed. After that point, however, the number of publications began to increase, a trend that is expected to continue into the foreseeable future.  
However, as emphasized in this study, many review studies focused on general issues instead of specific context. Hence, the trend of ML applications for pumps can be found in Figure 7. A total of three hundred and fifty-three (353) articles were found from 2010 to 2022. It is evident that there has been a rapid increase in publication numbers over the years, with 2022 having the highest publication rate in ML, recording eighty-eight (88) publications.
# Deep Learning-Based Community Detection Approach on Multimedia Social Networks
## 3 Methodology
### Community Detection Approach Based on Deep Learning  
In this section, we describe the proposed convolutional network, whose details are shown in Figure 4, which uses as the input the user-user adjacency matrix with the goal of identifying communities by combining semantic and topological content.  
Figure 3: User-to-user sparse matrix related to the MSN shown in Figure 2.  
Figure 2: Example of a relevant path within the MSN defined in Figure 1.  
In MSNs, the number of users is increasing, usually in the order of tens of millions; thus, the proposed network involves the use of sparse matrices in order to reduce the expected computational burden. In particular, this CNN relies on the classical four layers, as shown in [32]: _ingestion_, _convolution_, _max-pooling_, _fully connected layer_. The ingestion layer deals with the building of the adjacency matrix from the MSN network, which is fed the convolutional layer as the input, performing a set of filters to extract the main features. This layer provides a set of matrices (_feature map_) as the output, on which a max-pooling operation is performed to reduce the problem resolution. The output of this layer is fed the fully connected layer as the input, whose aim is to define the probability distribution of each node over \(K\) classes.  
More in detail, the convolutional network takes an adjacency matrix obtained by analyzing the content published or the actions carried out by other users within social networks, forums, etc., as the input. Then, this matrix is analyzed through a series of filters (also called the _kernel matrix_) in order to extract the _feature_ characteristics that allow discriminating how a user interacts with other nodes. The output of the convolutional layer is processed by the max-pooling layer, reducing the size of the problem. After the max-pooling operation, the convolutional layer aggregates the features of the various feature maps to provide the required prediction. Finally, a learning phase is carried out to optimize the weights of the network for the analyzed task. Regarding the complexity of the convolution operation, it depends on the size of the input, i.e., the matrices, and of the kernel. If \(M\), \(N\) represents the input matrix size and \(k\), \(j\) the kernel matrix, the computational cost is equal to \(O(MNkj)\) (worst case).
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 5 Experiments and Results
### Results Analysis and Discussion
The emphasis of the experimental work was to evaluate the performance of the proposed VL-OCC system for EEG. After DAC conversion, the EEG received was also compared with the actual EEG signal; however, with increase in distance, the BER increased significantly. The successful transmission and reception of the EEG signal shown in comparison between the transmitted and received signals illustrates that the proposed system could be deployed in healthcare for EEG.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Previous Study** & **Data/Technique** & **Subsidence Rate** \\ \hline [PERSON] et al. [16] & Sentinel-1A,B/PSI from 2015 to 2019 & Avg: \(-2\) mm/year \\ \hline [PERSON] et al. [17] & Sentinel-1/PSI, SBAS, leveling from 2014 to 2016 & Avg: \(-8\) mm/year \\ \hline [PERSON] et al. [18] & Sentinel-1, ALOS-PALSAR-2/SBAS from 2015 to 2020 & Avg: \(-12.5\) mm/year \\ \hline [PERSON] et al. [19] & Sentinel-1/PSI from 2017 to 2018 & Avg: \(-30\) mm/year \\ \hline [PERSON] et al. [20] & Sentinel-1/PSI from 2014 to 2018 & Avg: \(-35\) mm/year \\ \hline [PERSON] et al. [21] & Sentinel-1/SBAS from 2015 to 2019 & Avg: \(-16\) mm/year \\ \hline [PERSON] et al. [22] & Sentinel-1/PSI from 2014 to 2017 & Avg: \(-10\) mm/yr \\ \hline [PERSON] et al. [23] & TerraSAR-\(\chi\)/PSI from 2008 to 2013 & Avg: \(+1\)–\(4\) mm/year \\ \hline [PERSON] et al. [24] & Sentinel-1/PSI from 2017 to 2020 & Avg: \(-20.95\) mm/year \\ \hline [PERSON] et al. [25] & Sentinel-1/PSI from 2017 to 2020 & Avg: \(-35\) mm/year \\ \hline [PERSON] et al. [26] & Sentinel-1/PSI, leveling 2015 and May 2020 & Avg: \(-2\) to \(-6\) mm/year \\ \hline [PERSON] et al. [27] & Sentinel-1/PSI from 2015 to 2018 & Avg: \(-20\) to \(2\) mm/year \\ \hline [PERSON] et al. [28] & Sentinel-1/PSI from 2014 to 2019 & Avg: \(+2\)–\(24\) mm/year \\ \hline [PERSON] et al. [29] & Sentinel-1/PSI, SBAS and GNSS year 2016 & Avg: \(-20\) to \(10\) mm/year \\ \hline [PERSON] et al. [30] & ERS-1/-2, ALOS PALSAR/SBAS, leveling from 1993 to 2010 and 2014 to 2017 & Avg: \(-28\) mm/year and \\ \hline [PERSON] et al. [31] & Sentinel-1, Envisat/PSI, SBAS, GPS from 2003 to 2019 & Avg: \(+5\) mm/year \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous studies and estimated land subsidence rates in coastal areas worldwide, combining space- and ground-based techniques. Using space-based deformation monitoring techniques to investigate the land subsidence occurring along the Limassol coastal front, the purpose of this work is two-fold. As the first objective, this research focuses on quantifying the land subsidence of the area using remote sensing techniques and validating the detected deformation, if any, using conventional in-situ ground-based data. As a secondary objective, this research investigates the possibility of automatically monitoring land subsidence in urban coastal areas, consisting of skyscrapers and tall buildings, through space-based techniques. Concordantly, taking into account that there is a significant number of skyscrapers that are in the planning or developmental stages, this study attempts a preliminary assessment, characteristics, and discussion of the impact these structures will pose on the coastal front of the area of Limassol through the Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System (CyCLOPS) strategic research infrastructure unit [36].
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 5 Simulation Experiments  
In this section, the hazards of ship navigation in inland river bridge areas are studied as example. In the case study, a two-way inland waterway is selected and simulated using the safety potential field model proposed in Chapter III. The study is divided into two steps: firstly, the bridge area as a whole at a certain moment is selected as the research scenario, and the superposition field is used to calculate the overall danger of the bridge area environment to obtain the instantaneous potential field strength; secondly, the process of upward and downward vessels crossing the bridge area is taken as the object of the study, and changes in the potential field under the predicted state and the real AIS data are compared and analysed. Based on the calculation results of the safety potential field, the magnitude of the risk distribution in the bridge area under the static scenario and the warning level of ship collision prevention in the bridge area under the dynamic traffic situation are determined.
## 2 The Permanent Facility for Altimeter Calibration  
This section describes the evolution over time of the permanent Altimeter Calibration Facility in Crete, Greece, in terms of its infrastructure, instrumentation, and also presents field measurements carried out to develop local and regional reference models for essential geophysical and geodetic parameters.  
The Gavdos/Crete Permanent Facility for Altimetry Calibration has been continuously operating and providing absolute altimeter biases for more than 15 years. It includes a major set of permanent Cal/Val sites and prototype scientific equipment at various locations in Crete and Gavdos. At present, this infrastructure includes 17 permanent Global Navigation Satellite System stations, 8 tide gauges, 6 meteorological systems, several communication links, one microwave transponder and a central facility for data archiving and processing, and also to remotely control all field units. The PFAC allows calibration of satellite altimeters over ascending and descending passes, and it also permits multi-mission calibration at crossover locations and over land and water at the same time. At the same locale, connection and cross-comparison of various altimeters can be made using the same orbits, conditions, and settings by employing diverse methods and instrumentations on the ground (sea surface and transponder) for absolute assessment.  
All international altimetry missions (i.e., Sentinel-3 and CryoSat-2 (European), Jason series (American-French), HY-2 (Chinese), and SARAL/AltiKa (Indian-French) have been calibrated at this facility with sea-surface techniques as of 2004. In the sequel, the different components of the Cal/Val sites of the infrastructure are described.  
Figure 1: The sea-surface (Gavdos, CRS1, RDK1) and transponder (CDN1) Cal/Val sites of the Permanent Altimetry Calibration Facility along with the Operations Control Center at the Technical University of Crete in western Crete, Greece (**a**,**b**). The multi-mission, diverse Cal/Val service is illustrated by the ground-tracks of existing (Jason-3, Sentinel-3A, Sentinel-3B, and HY-2A), as well as future satellite altimetry missions (**a**). The CryoSat-2 satellite is also calibrated using either the CDN1 Cal/Val transponder site or the Gavdos Cal/Val site with sea-surface techniques.
# Sorting by Multi-Cut Rearrangements +
## 3 Sorting by Multi-Cut Rearrangements in Permutations
### Hardness for Constant Number of Cuts
#### 3.1.1 Breakpoints and Cycle Graph
From top to bottom: The cycle graph for \((S,T)\) contains three non-trivial cycles and a trivial one (breakpoint \(u\) is depicted to the right of the label for \(u\)). Blocks 1 to 4 of a 3-cut rearrangement (cutting after 5, 8, and 1) are given: it solves cycle \(5\to 8\to 1\to 5\) (denoted \((5,8,1)\) for short). Two other efficient rearrangements solve cycles \((0,6,4)\) and \((2,7,3)\), thus reaching \(T\) in three steps. Note that some cycles do not admit efficient 3-cut rearrangement, e.g., \((0,6,4)\) in the original permutation \(S\). **Proposition 3**. : _If \(r\) is efficient, it solves a cycle if, and only if, it solves any breakpoint in it. Furthermore, \(r\) solves all breakpoints in a union of cycles of total size \(k\)._  
Proof. : If \(r\) is efficient, then it solves all breakpoints that it cuts (since it may not solve a breakpoint without cutting them, and it solves and cuts \(k\) breakpoints). Due to Proposition 2, if \(r\) solves a breakpoint in a cycle, then it must solve all subsequent arcs in the same cycle. Hence, \(r\) either solves all breakpoints of a cycle or none at all. The size constraint follows from the fact that all cycles are disjoint. Cycle \(C_{1}\) is _tied_ to another cycle \(C_{2}\) through the pair of breakpoints \((x,y)\) if \(x\) is in \(C_{1}\), \(y\) is in \(C_{2}\), the permutation \(S\) has \(S_{i}=y\) and \(S_{i+1}=x\) for some \(i\), and \(T\) has \(T_{j}=x\) and \(T_{j+1}=y\) for some \(y\) (for an illustration, see Figure 5). A breakpoint is _without ties_ if no cycle is tied to the cycle containing it. **Proposition 4**. : _If \(C_{1}\) is tied to \(C_{2}\), then any efficient rearrangement solving \(C_{1}\) must also solve \(C_{2}\)._  
Proof. : Let \(r\) be an efficient rearrangement solving \(C_{1}\) and, in particular, \(x\). Then, \(r\) must place \(y\) after \(x\) in \(r(S)\), although \(y\) is before \(x\) in \(S\), so \(r\) must have a cut somewhere between \(y\) and \(x\), i.e., just after \(y\). So, \(r\) cuts breakpoint \(y\), and solves cycle \(C_{2}\).
## 3 Results  
We obtained 669 detections of the test transmitters from the combined drone flights. Signal strength decreased significantly with increasing distance to drone, with the furthest detection at 534 m (Figure 2, \(F_{1,646}\) = 181.7, \(p\) < 0.001). The orientation of the transmitter's antenna also had a significant effect on signal strength (\(F_{2,646}\) = 62.6, \(p\) < 0.001), with antennae pointing upward performing best.  
Given that we conducted seven flights, each with five test transmitters deployed, we obtained 35 detection sets. Five to 42 detections were obtained for each detection set (mean = 19.9). Based solely on drone locations when signal strength was greatest, mean location error was 132.3 m (range: 28.8-294.2 m, n = 35). The reliability of the maximum strength method is questionable when the strongest signal is on the periphery of the drone route, because in those cases the transmitter was likely outside of the range covered by the drone. To prevent this, we removed all detection sets where the strongest signal came from a drone location on the periphery of the convex hull enclosing the detection set. The resulting subset of data yielded a mean location error of 134.0 m (range: 43.9-278.0 m, n = 17).  
Nearest distances to transmitters yielded strongest signals in only two of the 35 detection sets. Furthermore, signal strength did not always increase nearer transmitters (Figure 3), leading to only seven cases where quadratic regression coefficients of signal strength against X or Y coordinates were negative, i.e., leading to a maximum estimated signal strength as required for position estimation.  
Of the seven cases with estimable positions, we dropped one case with an estimate error (3034 m) greater than the maximum known distance between the drone and the transmitter (534 m). Figure 4 illustrates the remaining six cases where quadratic curve-fitting yielded estimable positions.  
The mean location error from the quadratic method was 69.9 m (range 20.8-161.3 m).  
Figure 2: Signal strength in response to the distance between the drone and the transmitter.  
Figure 4: Transmitter location errors from quadratic regression estimation, based on signal strength. Test transmitter locations in yellow, estimated locations in red.  
Figure 3: Left: best case scenario, with signal strength, depicted by cross size, roughly increasing toward the location of the test transmitter (blue dot). Right, worst case scenario, with signal strength showing no obvious relationship with distance to transmitter.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### gMIMO
#### 3.1.4 Array Configuration  
The gMIMO uses more antenna elements in a panel and has a larger aperture than the previous versions used in older technologies. In contrast to those, the distance between antenna elements must be less than half a wavelength (\(\lambda/2\)), especially in THz communications, ref. [33]. Various configurations and advanced forms, such as IRS/ITS, can be used to cover specific operational needs. ULA and DULA configurations are particularly effective in scenarios requiring high spatial resolution and coverage, refs. [32; 33]. URPA configurations provide superior 3D beamforming capabilities, essential for applications like urban deployments and satellite communications, ref. [33]. In mmWave and THz systems, IRS and ITS emerge as critical enablers, integrating active and passive elements to achieve unprecedented expandability and energy efficiency. These surfaces dynamically controlphase shifts to steer beams and optimize coverage, addressing the significant path loss and molecular absorption in high-frequency bands (refs. [33; 34]). Additionally, modular MIMO configurations enhance adaptability and expandability in low-frequency bands, enabling integration in compact devices and dense urban networks (ref. [35]). Collectively, these configurations are fundamental in addressing diverse propagation challenges, enabling high-capacity, low-latency communication across various 6G scenarios.