esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Hyper Spectral Imaging System  
In this study, a hyperspectral camera (FX-10, Specim, Spectral Imaging Ltd., Oulu, Finland) was used. The camera was pre-equipped with a special lens from Scheiner (Cinegon 1.4/8 mm). As the camera works on the principle of line scan (one thin line of the object with full spectra at a time), it was mounted on a lab scanner to scan the complete sample. The scanner has a moving platform of \(40\times 20\) cm, three halogen lamps for illumination, and a camera mounting plate. The height of the camera mounting plate is adjustable thus it is kept 6 cm above from the sample. The height adjustment was made after several iterations to ensure the field of view almost equal to the sample. This adjustment enhanced the spatial resolution of the camera. The scanner was connected to a laptop directly via serial communication port, while for the camera the GigE-Vision interface was used to transfer data on a laptop. The complete experimental set-up is shown in Figure 3.  
In our previous work [32], we described the limitation of halogen bulb in blue wavelength. However, for this study, we minimized this limitation by installing two further light sources: blue and ultraviolet. The blue source covers the range of wavelength from 410 to 440 nm, while the ultraviolet source covers a range from 395 to 410 nm. These additional light sources increase the signal to noise ratio (as shown in Figures 4 and 5) in the starting bands of a camera and decreased noise variations in the spectrum over the entire range of the camera, i.e., 395-1000 nm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample Details** & **Total Sample** \\ \hline Pure Red Chili (Kunri) & 4 \\ Pure Red Chili (Hybrid) & 4 \\ Chili Adulterated with mustard oil & 20 \\ Chili Adulterated with olive oil & 20 \\ Chili Adulterated with Sudan dye (1g) & 20 \\ Chili Adulterated with Sudan dye (2g) & 20 \\
**Total Sample** & **88** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total samples prepared for each adulterant.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample Details** & **Total Samples** \\ \hline Pure Red Chilli (Kunri) & 4 \\ Pure Red Chilli (Hybrid) & 4 \\ Chili (Kunri + Hybrid) Adulterated with mustard oil & 5 + 5 \\ Chili (Kunri + Hybrid) Adulterated with olive oil & 5 + 5 \\ Chili (Kunri + Hybrid) Adulterated with Sudan dye (0.5 g) & 5 + 5 \\ Chili (Kunri + Hybrid) Adulterated with Sudan dye (1.5 g) & 5 + 5 \\ Total Samples & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test data set samples details.  
Figure 4: Red chili spectral response at 440 nm.  
Figure 5: Spectral signature difference with the addition of blue light source.  
Figure 3: Hyperspectral imaging system.
# New Generation Compact Linear Accelerator for Low-Current, Low-Energy Multiple Applications
## 2 LINAC 7 Accelerator Component Descriptions
### Control Systems  
Each of the elements of the accelerator requires a set of specific auxiliary systems to function, for example, as vacuum, refrigeration, high voltage power supply, magnet power supply, or RF power. In order to maintain the modularity of the accelerator, each accelerator element has its own control cabinets that house the controllers for the auxiliary systems. All these cabinets are connected to a general control system.  
The architecture of the control system is designed following a modular scheme. Each main element in the linac has a dedicated controller implemented in PXI or CompactRIO hardware and these controllers are connected to each other over a TCP/IP network. The auxiliary systems and diagnostics connect to their assigned controller directly over a TCP/IP connection. The PXIs or cRIOs have local controllers running in real-time LabVIEW and accept commands from the supervision and monitoring system. The local controllers acquire the relevant data from the machine and manage the local auxiliary elements and diagnostics, allowing, for example, for the insertion or retraction of the Faraday cup or pepperpot in the LEBT. The high power RF to the cavities, such as the RFQ,will be controlled by a low-level RF control system, which will use a direct digital architecture following the work presented in [12]. The control cabinets also include the interlocks and safety control system, which is implemented using PILZ safety PLC hardware. The controllers for each element connect to the Pilz hardware that insures the safety of the installation, especially the parts that could pose a danger to people, for example, unlatching the high-voltage enclosure for the ion source. All data is automatically sent to a monitoring system and selected parameters and measurements are saved in an InFluxDB database. Because this control architecture is modular, the scalability of the system is assured, allowing the integration of other technologies following a structure similar to an EPICS network that, in the future, could be integrated into an actual EPICS network.
## 4 Simulation, Results, and Discussion
### PSO Algorithm Performance Analysis  
The initial phase of this study involves understanding how the division of space for particle initialization in the PSO algorithm affects its accuracy. This is explored by testing the algorithm with various swarm sizes, the results of which are depicted in Figure 3. The image showcases four distinct curves, each corresponding to the error with a different number of particles in the swarm: 9, 25, 100, and 400. The outcomes from these tests reveal a direct relation between the swarm size and the algorithm's accuracy: larger swarms yield more precise results. However, this increased precision comes at the cost of higher computational demand. For instance, the processing time for positioning with a small swarm of 9 particles is relatively quick at 19 ms, but this time jumps significantly to 54 ms with 100 particles and further escalates to 96 ms with a 400-particle swarm. Given these findings, a balance is struck by opting for a 100-particle swarm. This choice offers a compromise between achieving a desirable level of precision and maintaining manageable computational requirements during the testing phase.
## 2 Background and Related Work
### Security for SOAs in Automotive Domain
In our own work [15], we give a comprehensive overview about automotive SOAs. In detail, we describe and compare existing protocols and supported security features for on-board vehicle communication based on a hybrid (signal-based and service-oriented [47]) E/E architecture. In addition, we address specific security countermeasures such as Identity and Access Management (IAM), firewalls as well as IDS and discuss implications due to the upcoming SOA paradigm. Our previous work focused on describing the security challenges posed by the introduction of SOA but not on concrete technical solutions. In doing so, we also analyzed that new approaches are required for the implementation of network-based IDS (e.g., flow-based features, situation awareness) and, in particular, a generic approach based on the SOA-inherent communication properties is desirable. In comparison, in the present work, we start at this point with a first proposal.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 3 Angle Conversion Algorithms
### Geometric Approaches
#### 3.2.1 Simplified Geometric Approach  
The simplified variant of the geometric approach makes additional assumptions about the CCMs geometry in order to simplify the calculation. These assumptions are:  
1. All rolls have the same diameter.
2. The curvature of the circular section of the caster is negligibly small.  
The geometry that emerges for two consecutive rolls under these additional assumptions is illustrated in Figure 4.  
The geometry leads to the the equations  
\[\tau=\epsilon-\rho \tag{3}\]  
and  
\[d_{2}=d_{1}+b\cdot\tan(\tau). \tag{4}\]  
Here, \(\rho\) represents the measured angle. The points C and E mark the contact points of the rolls with the strand, if the roll's positions were ideal. The line b is part of the passline, which is not curved due to the additional assumptions. Angle \(\epsilon\) is the angle between line \(\overline{CE}\) and a vertical line. This angle is independent from the roll's positions and extracted from the roll plan. The angle \(\tau\) is used to calculate the tangent inside the triangle BGF, which is combined with b to yield \(s_{2}=\overline{GF}=b\cdot\tan(\tau)\). This is added to the known radial error component \(d_{1}\) of the first roll to finally yield the position error \(d_{2}\) of the second roll.  
The additional assumptions assure that the line \(\overline{BG}\) is parallel to the tangent line \(\overline{HD}\), which represents the ruler's edge. They also cause the triangle BGF to be right-angled. If these assumptions are not made and the curvature as well as the actual radii of the rolls are considered, the equations become much more complex, which is described in the following subsection.
## 2 Related Work  
This section introduces the related research on the key technologies of MEC in order to highlight their differences from this paper. Table 1 summarizes all related works mentioned in this section.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Aspect** & **Ref** & **Main Contribution** \\ \hline MEC Architecture & [29] & Edge/Fog Computing Proposal Concept-Based Architecture. \\ \cline{2-3}  & [30; 31; 32] & MEC deployment scenario in front of Core networks; assumed that only player is Legacy Telecom Operators. \\ \cline{2-3}  & [33; 34; 35] & Viewpoint from function-level architecture, such as DNS, Information-Centric Networking, and ETSI-based. \\ \cline{2-3} MEC/Cloud Computing Cooperation & [36; 37; 38; 39; 40; 41] & Several works have already proposed offloading cooperation such as latency and power consumption with several architecture models. \\ \cline{2-3}  & [42; 43; 44] & Optimization of telecom operator’s revenue with the number of MEC as well as backhaul owner’s revenue with the backhaul capacity. \\ \hline MEC Implementation, Verification & [47; 48] & Describes the MEC orchestrator and signaling for service provision. \\ \cline{2-3}  & [47; 48] & Demonstration of edge computing: Distributed edge computing, Edge/cloud Cooperation Framework, etc. \\ \cline{2-3}  & [49; 50; 51; 52] & Platform controller implementation; discussion of implementation comparison of Fog Computing/cloudlet/MEC. \\ \cline{2-3}  & [53; 54; 55] & Application implementation (e.g., AR, Image Analysis) in edge computing. \\ \hline MEC Business & [56; 57; 58] & Several Consortiums and established Open Labs are discussed with respect to business model. \\ \cline{2-3}  & [25; 26; 27; 59; 28] & Legacy Telecom Operator scenarios in MEC are discussed, along with new private operators. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Related Technical Works from Several Perspectives.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 3 Task Definition  
The primary aim of this study is to enhance the interpretability of sentiment classification models applied to texts. Specifically, our focus is on identifying the key factors--words or phrases within a text--that sentiment classification models rely on to determine the sentiment polarity of that text. These influential words or phrases are collectively referred to as \"Sentiment Cues\". Therefore, we term the task we explore in this paper as Sentiment Cue Extraction (SCE). This endeavor seeks to uncover and articulate the rationale behind sentiment polarity judgments made by these models, making the decision-making process more transparent and understandable to both users and researchers.  
To clarify the task of SCE more distinctly, let us illustrate with the following two examples:  
* Instance 1: Very **friendly** customer service.
* Instance 2: If I could give a **zero star**, I would!  
Here, the word \"friendly\" in the first instance allows us to identify its sentiment as positive; similarly, the phrase \"zero star\" in the second instance indicates a negative sentiment. Hence, \"friendly\" and \"zero star\" serve as what we define as sentiment cues.
## 3 Model Construction and Solving Algorithm
### Model Construction
#### 3.1.2 Constraints  
(1) Water balance constraint:  
\[V(p,t+1)=V(p,t)+(Q_{in}(p,t)-Q_{out}(p,t))\Delta t \tag{4}\]  
where \(V(p,t+1)\) denotes the final reservoir storage for reservoir \(p\) during period \(t\); \(V(p,t)\) denotes the initial reservoir storage for reservoir \(m\) during period \(t\); \(Q_{in}(p,t)\) denotes the inflow for reservoir \(p\) during period \(t\); and \(Q_{out}(p,t)\) denotes the outflow for reservoir \(p\) during period \(t\).  
(2) Water level constraint:  
\[Z_{min}(p,t)\leq Z(p,t)\leq Z_{max}(p,t) \tag{5}\]  
where \(Z(p,t)\) denotes the water level for reservoir \(p\) during period \(t\); \(Z_{min}(p,t)\) and \(Z_{max}(p,t)\) denote the lower and upper limits.  
(3) Power output constraint:  
\[N_{min}(p,t)\leq N(p,t)\leq N_{max}(p,t) \tag{6}\]  
where \(N(p,t)\) denotes the power output for reservoir \(p\) during period \(t\); \(N_{min}(p,t)\) and \(N_{max}(p,t)\) denote the minimum and maximum power outputs.  
(4) Constraint of flow through the water turbine:  
\[Q^{f}_{min}(p,t)\leq Q^{f}(p,t)\leq Q^{f}_{max}(p,t) \tag{7}\]  
where \(Q^{f}(p,t)\) denotes the flow through the water turbine for reservoir \(p\) during period \(t\); \(Q^{f}_{min}(p,t)\) and \(Q^{f}_{max}(p,t)\) denotes the minimum and maximum flow through the water turbine.  
(5) All the variables mentioned above are non-negative.
## 3 Design of a Wireless Sensor Network for a Power Quality Monitoring System
### Design of Data Forwarding Protocol
If a node or the wireless link to the node fails, its parent node and child nodes do not receive any HELLO messages from the node for a given amount of time. Thus, the parent node and child nodes of the failed node release the soft state on the node. The node detecting the failure of its parent node tries to repair the broken tree branch by sending a REPAIR message to its neighboring nodes via the one-hop flooding. Once a neighboring node receives the REPAIR message, it responds with a REPAIR_ACK message having its path cost. Then, the node having sent the REPAIR message receives the REPAIR_ACK message(s) and it selects the node having the least path cost as its parent node. The detailed procedure of the tree construction is depicted in Figure 5.  
Once the data forwarding tree is constructed, the power quality data is delivered to the monitoring center through the tree. Whenever a node has data to send, periodically or in the event-based manner, the node transmits the data to its parent node. The parent node checks whether the sender is one of its child nodes. If it is, the parent node forwards the data to its upstream node towards the monitoring center. This forwarding process continues until the monitoring center receives the power quality information.
# A Backwards-Tracking Lagrangian-Eulerian Method for Viscoelastic Two-Fluid Flows
###### Abstract  
A new Lagrangian-Eulerian method for the simulation of viscoelastic free surface flow is proposed. The approach is developed from a method in which the constitutive equation for viscoelastic stress is solved at Lagrangian nodes, which are convected by the flow, and interpolated to the Eulerian grid with radial basis functions. In the new method, a backwards-tracking methodology is employed, allowing for fixed locations for the Lagrangian nodes to be chosen a priori. The proposed method is also extended to the simulation of viscoelastic free surface flow with the volume of fluid method. No unstructured interpolation or node redistribution is required with the new approach. Furthermore, the total amount of Lagrangian nodes is significantly reduced when compared to the original Lagrangian-Eulerian method. Consequently, the method is more computationally efficient and robust. No additional stabilization technique, such as both-sides diffusion or reformulation of the constitutive equation, is necessary. A validation is performed with the analytic solution for transient and steady planar Poiseuille flow, with excellent results. Furthermore, the proposed method agrees well with numerical data from the literature for the viscoelastic die swell flow of an Oldroyd-B model. The capabilities to simulate viscoelastic free surface flow are also demonstrated through the simulation of a jet buckling case.  
viscoelastic flow; computational fluid dynamics; volume of fluid; immersed boundary methods
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 1 Introduction
The signal is self-similar if it can be decomposed into smaller parts, each of which is similar to the main signal. For example, when heart rate is recorded at different time intervals (minutes, hours, days), rapid signal fluctuations appear to be the same as slow fluctuations. The Hurst exponent accepts values that are in the range from 0 to 1. If \(\mathrm{H}>0.5\), then the investigated signal has the property of long-range dependence, ie. the autocorrelation function tends to zero slowly with the increase of the time parameter [11]. If \(\mathrm{H}<0.5\), then the signal has the property of short-range dependence, that is, the autocorrelation function decreases exponentially with increasing time. In the boundary case, when \(\mathrm{H}=1/2\), then \(\mathrm{D}=3/2\), and the signal studied is a Brownian motion. Among the methods for nonlinear analysis of PPG signals, methods of obtaining estimates of their large-scale invariance are of particular interest. The monofractal signal is homogeneous in the sense that it has the same scale properties that can be characterized both locally and globally by a single scale indicator, for example, the Hurst exponent, fractal dimension. In contrast to the monofractal signal, the multifractal signal consisted from a large number of homogeneous fractal subsets, whose features can be characterized by a spectrum of local Hurst exponents or fractal dimensions. The fractal and the multifractal approach allows for new estimates to be obtained that give an idea of the intrinsic, nonlinear, dynamic processes in the studied signals as compared to the classic Time- and Frequency-Domain analysis methods [12]. The decrease in the fractal dimension, respectively, the increase in the Hearst exponent, indicates an increase of the chaotic components of the cardio intervals. Non-linear methods for analyzing PPG signals can determine the presence or absence of a disease and they can be used both for rapid diagnosis and as an integral part of a more complex diagnostic system. The introduction of additional nonlinear methods, such as Poincare plot, Approximate entropy, and Sample entropy for characterizing PRVs, allow for an additional assessment of the qualitative and quantitative properties of PPG signals, which extend the scope for clinical interpretation [13].
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
### M-Mimo  
The concept of M-MIMO was first introduced in [12]. This technology plays a significant role in 5G and the future sixth-generation (6G) wireless mobile networks [13]. Indeed, M-MIMO has the potential to enhance the energy efficiency and spectral efficiency of communications systems [13]. The idea behind M-MIMO is to deploy a vast (massive) number of antennas at the base station and use multiuser MIMO transmission to serve a smaller number of terminals simultaneously (in the same time and frequency sample). In the downlink transmission (from the base station to the terminal), beamforming exploits the uplink-downlink reciprocity of the radio channel, since M-MIMO uses the time division duplex (TDD) mode. The base station array mainly exploits channel estimates acquired from uplink pilots transmitted by the terminals. The application of massive MIMO in precision agriculture has not been widely considered in the literature. The authors of [14] detailed the state-of-the-art deployment of massive MIMO technology in precision agriculture. [PERSON] et al. also discussed the M-MIMO channel characteristics that should be thoroughly considered when deploying an M-MIMO system in smart agriculture, mainly for beamforming design, network planning, and signal processing. Paper [15] considered a massive MIMO as a large-scale communications solution for IoT-based smart agriculture. In [16], [PERSON] et al. proposed an M-MIMO network model with multiple cells and users. The authors analyzed the M-MIMO system's spectral and energy efficiency with pilot contamination and hardware impairment. Moreover, [PERSON] et al. in [17] studied the energy efficiency of trajectory optimization schemes for UAV-assisted IoT networks. Notably, the authors optimized the UAV trajectory design by simultaneously considering the total energy consumption, the average data rate, and the fairness of coverage for the IoT devices. The authors also evaluated the effectiveness of the developed scheme by carrying out extensive numerical simulations.
## 3 Results
### Player Role  
There were significant differences between starter and non-starter players with small effect sizes in match RPE (\(p<0.05\), ES = 0.25) (Figure 2b), fatigue (\(p=0.03\), \(\eta p^{2}=0.046\)), muscle soreness (\(p=0.03\), \(\eta p^{2}=0.042\)), and the Hooper index (\(p=0.046\), \(\eta p^{2}=0.039\)) (Figure 4). Particularly, starters evidenced a small increase in match RPE (5.96 \(\pm\) 2.07 a.u.) compared to non-starters (4.85 \(\pm\) 2.55 a.u.) (\(p=0.036\), ES = 0.25). In relation to well-being responses, starters reported slightly more fatigue (3.23 \(\pm\) 1.36 a.u.), more muscle soreness (2.71 \(\pm\) 1.32 a.u.), and greater Hooper index scores (9.46 \(\pm\) 2.95 a.u.) in MD+1 compared to non-starters (2.66 \(\pm\) 1.28 a.u.; 2.12 \(\pm\) 0.95 a.u.; 7.94 \(\pm\) 2.91 a.u., respectively) (\(p=0.036\), ES = 0.24; \(p=0.031\), ES = 0.24; \(p=0.012\), ES = 0.29, respectively). However, no differences were found in MD+2.  
Additionally, there were significant differences with small to very large effect sizes between match day in fatigue (\(p<0.001\), \(\eta p^{2}=0.403\)), sleep (\(p=0.01\), \(\eta p^{2}=0.055\)), and the Hooper index (\(p<0.001\), \(\eta p^{2}=0.256\)). More specifically, starters and non-starters reported moderately less fatigue and lower Hooper index scores in MD+2 compared to MD+1 (\(p<0.001\), ES = 0.82-0.86; \(p<0.001\), ES \(>1.20\), respectively). Additionally, starters reported slightly less sleep in MD+2 compared to MD+1 (\(p<0.01\), ES = 0.39). Furthermore, no significant interaction effect (player role vs. match day) was found in any well-being responses (\(p>0.05\)).  
Figure 3: Differences according to playing positions on well-being status. Significance level is indicated by the number of symbols: one symbol for \(p<0.05\), two for \(p<0.01\), and three for \(p<0.001\). * = Significant differences between playing positions. \(\dagger=\) Significant differences vs. MD+1.
## 2 Preliminaries
### Controller Design  
The controller equations derived here incorporate the control law partitioning scheme from [43], which yields an affine nonlinear type of flight controller by cancelling the nonlinear terms. The cancellation of these terms renders a system with unit mass. This facilitates the tuning of the controller gains, as these become independent of system parameters. The whole control structure of the flight controller is divided into two blocks: inner and outer loop blocks. The inner loop block holds three attitude states (roll, pitch and yaw) and one Cartesian state (altitude state), while the outer loop block holds two remaining Cartesian states (\(x\) and \(y\)). The partitioning scheme is only applied to the inner block, while a linearization assumption is made in order to derive a relationship between the inner and outer control blocks.