esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Quality of Service in a Robotized Comprehensive Geriatric Assessment Scenario
## 3 Modelling QoS Metrics for the CLARC Scenario
### QoS Metrics
#### 3.4.1 Our Model as a Probabilistic Network  
The specification defined in previous Sections for the CLARC scenario abstracts a probabilistic network, in which properties, such as Performance or Interaction, are represented by hidden Boolean variables. Therefore, a property has two possible states, i.e., the system may or may not be optimal in terms of the property, which cannot be observed directly, but rather what one has is the belief of being in a particular state based on observable evidence. The runtime quantification of this belief results in the corresponding QoS metric values. For example, a resulting value of 0.67 for Performance can be understood as the probability of the robot being optimal in terms of this property. On the other hand, observations are evidence Boolean variables given that a contextual situation may or may not occur but it can be perceived. These variables exhibit a direct probabilistic dependence with one or more hidden variables.  
Figure 2 (left) illustrates the Bayesian Network associated with Performance--other properties in our model will produce similar networks. The graph shows the causal interactions (arcs) among a set of variables (nodes). Any pair of unconnected nodes indicates (conditional) independence between variables. So that, an observation is a variable that depends on a property. In other words, the probability of the occurrence of an observation (i.e., observation = true) depends on whether the system is optimal in terms of the property (property = true) or not (property = false). In addition, although the concept of time is not an inherent characteristic of Bayesian Networks, we have considered a continuous-time Markov chain [15] to manage the internal dynamics of each observation (Figure 2 (Right) shows the transition graph). This allows properties to evolve over time according to the sequence of past observations. Further details will be provided in the following Section.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 5 Conclusions  
In this paper, the on-off power control of FBSs in order to reduce power consumption of MCSs was proposed when cooperative transmission is taken into account. Through the toy example, we showed that the conventional scheme where FBSs are turned off based on their traffic load is not appropriate when cooperative transmission is used. Then, the optimization problem to determine the FBSs to be turned off was formulated. In order to resolve the problem of huge computational complexity of the formulated problem, we proposed a new metric, i.e., LOC, which indicates the importance of specific FBS in cooperative transmission, and devised a heuristic scheme in which an FBS whose accumulated LOC is less than the threshold is turned off to reduce power consumption without excessively deteriorating the performance of cooperative transmission. Through simulations, we showed that the power consumption of MCSs can be greatly lowered without significant deterioration of the throughput of the system and with a lower number of computations. An interesting extension of this work might be the consideration of a more generalized cooperative transmission, in which FBSs in the same cooperative set can transmit different data with heterogeneous transmit power. Another  
Figure 8: Computation time vs. \(N\).  
Figure 7: Spectral efficiency vs. \(\sigma\) by varying \(N\).  
interesting extension will be the verification of performance in a more generalized environment using sophisticated links and system level simulators such as NS-3.  
This research was supported by \"Cooperative Research Program for Agriculture Science & Technology Development (Project title: Development of swine management model with animal-metric for livestock welfare, Project No. PJ0105412015)\" Rural Development Administration, Republic of Korea, and also supported by the Basic Science Research Program through the NRF funded by the Ministry of Science, ICT & Future Planning (MSIP) (NRF-2016R1A2B4014834). This work was also supported by Chungnam National University.  
[PERSON] and [PERSON] conceived and designed the scheme; [PERSON] and performed the simulations and analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
MCS Mobile communication system  
IoT Internet of Things  
EH Energy harvesting  
EE Energy efficiency  
RF Radio frequency  
BS Base station  
GCN Green cellular network  
QoS Quality of services  
MBS Macro base station  
FBS Femto base station  
CoMP Coordinated multi-point  
LOC Level of contribution  
MS Mobile station  
CMU Central management unit  
RSSI Received signal strength indication  
CSC circularly symmetric complex Gaussian  
CRN cognitive radio network
# Optimization of Buoy Shape for Wave Energy Converter Based on Particle Swarm Algorithm
## 2 Theory and Methodology
### Hydrodynamic Theory  
According to [PERSON]'s second law, the equation of motion of a swing arm wave energy buoy in the frequency domain can be obtained as follows:  
\[\left[-iw(M+M_{add})+\lambda-\frac{K}{iw}\right]\zeta=F_{exc} \tag{1}\]  
where \(M\) is the wave energy buoy mass, \(K\) is the restoring force coefficients, and \(\xi\) is the motion response amplitude.  
Once the frequency domain equation of motion of the wave energy buoy is obtained, the time domain equation of motion of the wave energy buoy can be found by inversion of the Fourier transform:  
\[(M+M_{\infty})\ddot{x}(t)+\int_{0}^{t}\dot{x}(\tau)h(t-\tau)d\tau+Kx(t)=F_{exc }(t) \tag{2}\]  
Figure 1: Three-dimensional model of wind and wave combined device: (a) modelling diagram; (b) Partial enlargement diagram.  
Figure 2: Schematic representation of WEC in pitch motion.  
where \(M_{\infty}\) is the additional mass of the buoy at infinity and \(h(t)\) is the time delay function, which can be expressed as  
\[h(t)=\frac{2}{\pi}\int_{0}^{\infty}\lambda(w)\cos wtdt \tag{3}\]  
where \(\lambda(\omega)\) is the radiation damping function.  
For a particular swing-arm WEC with a PTO system, the equation of motion in the time domain can be expressed as  
\[(J+J_{\infty})\ddot{\theta}(t)+Kl\theta(t)+l\int_{0}^{t}h(t-\tau)\dot{\theta}( \tau)d\tau=M_{exc}(t)+M_{PTO}(t) \tag{4}\]  
where \(l\) is the equivalent length of the arm, \(M_{PTO}(t)\) is the linear PTO damping moment, \(M_{exc}(t)\) is the excitation moment, \(J\) and \(J_{\infty}\) are the rotational inertia of the buoy and the additional rotational inertia at infinity, and \(\theta(t),\dot{\theta}(t),\ddot{\theta}(t)\) are the oscillation angle, angular velocity, and angular acceleration of the system, respectively.  
The linear PTO damping moment is expressed as  
\[M_{PTO}(t)=-C_{PTO}\dot{\theta}(t) \tag{5}\]  
where \(C_{PTO}\) is the damping of the system.  
The instantaneous energy capture power of the device is  
\[P_{in}(t)=M_{PTO}(t)\dot{\theta}(t) \tag{6}\]  
The average energy capture power of the device from time \(t_{1}\) to \(t_{2}\) is  
\[P_{in}=\frac{1}{t_{2}-t_{1}}\int_{t_{1}}^{t_{2}}P_{in}(t)dt \tag{7}\]  
Therefore, the designed WEC system can be analyzed by the force analysis revealed in Figure 3.
# Kinematics Model Optimization Algorithm for Six Degrees of Freedom Parallel Platform
## 2 Materials and Methods
### Kinematics forward Solution Algorithm Based on Multivariate Polynomial Regression
Therefore, for all six degrees of freedom in the parallel platform, there are a total of \(259\times 6=1554\) polynomial coefficients to be solved. In order to realize the complete solution of all polynomial coefficients, at least 1554 sets of linearly independent input and output data need to be given. If and only if 1554 sets of linearly independent input and output data are given, the polynomial regression equations are positive definite, and the unique solution of polynomial coefficients can be obtained. More than 1554 sets of linearly independent input and output data will make the equation overdetermined. At this time, the polynomial regression equation has no solution, but there is a minimum norm least-squares solution. Due to the serious overfitting phenomenon of the positive definite equation, the error for the training sample is 0, but the error for the random test sample is often large. In order to make the polynomial function have a better generalization performance, the MPR parameters are solved by using the method of overdetermined equations to find the least norm least-squares solution. There are n groups of training sample points that are uniformly distributed in the selected parameter space. The equation for polynomial parameter solving using least squares is Equation (17):  
\[p=\left(X^{T}X\right)^{+}X^{T}Y \tag{17}\]  
In Equation (17), \(p\) is a polynomial regression parameter with one degree of freedom, and the dimension is 259. \(X\) is the vectorized robotic arm length data in \(n\) groups of training samples, and its dimension is \(n\times 259\). Due to the high probability of \(X^{T}X\) being singular in the case of large samples, the matrix is irreversible; thus, use the + operator to find its  
Figure 3: The polynomial regression function (red) approximates the sine function (Blue): (a) cubic polynomial approximation and (b) quintic polynomial approximation. pseudo-inverse. \(Y\) is the actual value of the degree of freedom to be solved in \(n\) groups of training samples, and the dimension is \(n\times 1\). When using MPR to solve it, the underfitting phenomenon will occur if the polynomial degree is selected to be too low. This makes the polynomial function unable to approach the kinematics positive solution operator very well, and the solution error is relatively large. If the degree of polynomial is too high, the time complexity of the algorithm will increase exponentially. Therefore, the balance between calculation amount and calculation accuracy should be considered in the selection of the polynomial degree.
## 3 Proposed System Architecture  
In this section, we provide an overview of the proposed home care monitoring system to detect abnormal events or falls occurring to ambulant people living independently. The term abnormal represents the falls and normal represents normal daily activities such as walking, standing, sitting and lying down. The proposed system uses a virtual grounding point concept, and its observable visual features are as shown in Figure 1. The proposed system includes four main components: object detection, feature extraction, analysis of abnormal and normal events, and the decision-making process for event detection. The technical details for each component are described in the following sub-sections.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 2 EGNOS Performance Prediction
### EGNOS Outputs  
Users of EGNOS use the additional information to improve their positioning accuracy by applying the corrections and excluding Not Monitored or Do Not Use satellite measurements. In addition, they can use the GIVEI and UDREI to compute horizontal and vertical protection levels (HPLs and VPLs) that can be seen as a measure of the integrity of their position [4]. Protection levels can be seen as upper bounds to the position error and are typically expressed as a distance, such as a radius, within which the actual position of the user is expected to be located with a high level of probability. The size of the protection levels thus characterizes the overall EGNOS performance.  
The protection levels are updated regularly based on the GIVE and UDRE values received from the EGNOS system. Users can determine in real-time whether their operational needs are met based on the protection levels, which should be sufficiently low to allow for specific safe operations.  
The prediction of the protection levels in the future (in the next one or two days) thus serve the purpose of allowing for users to anticipate whether EGNOS will providesufficient performance. This prediction can be particularly interesting in the case of planed operations (satellite decommissioning, satellite maneuvers, RIMS reparation, etc.) that may impact the performance of EGNOS over a specific area.  
To predict protection levels, it is required to predict what values of the UDREI and GIVEI will be broadcast by EGNOS. Based on these predicted values, as well as the predicted satellite positions, predicted protection levels can be obtained as a function of time and user location. The GPS satellite positions can simply be computed from the GPS almanacs published by the U.S. Coast Guard. With the most recent almanac, the satellite positions can be determined quite accurately and predicted for the next few days. Hence, the remainder of this paper only focuses on the prediction of the UDREI and GIVEI values, which can benefit from machine learning (ML) using a variety of inputs.
## 3 The Dataset  
### Data Description  
Crash data gathered over three years (2015 to 2017) covering all fatal crashes in Mersin City, Turkiye, were used in this study. The crash data were sourced from the General Directorate of Security (GDS) database, which contains highly detailed, disaggregate-level information for each crash. The variables include occupant characteristics (e.g., age, gender, injury severity), crash characteristics (e.g., vehicle and crash type), road characteristics (e.g., road type, horizontal and vertical geometry), driving behavior (e.g., alcohol influence, speeding), and temporal and environmental conditions.  
It should be noted that multi-vehicle crashes were excluded from the scope of the study. After the raw data were preprocessed, the dataset contained a total of 13,234 crashes. In Turkiye, fatal crashes are classified into the following three driver injury severity levels: uninjured (coded as class 1), injured (coded as class 2), and killed (coded as class 3). For crashes involving two vehicles, one driver was randomly selected, and their attributes were included in the model. Accordingly, the dataset includes 13,234 drivers, of which 68 (0.51%) were killed, 6202 (46.86%) were injured, and 6964 (52.63%) were uninjured. The descriptive statistics of the attributes in the crash data used in this study are presented in Table A1.
# Methacrylate Polymer Monoliths for Separation Applications
## 3 Applications of Methacrylate Polymer Monoliths in Separation Science
### Biological Separations and DNA Purification
#### 3.2.1 Amplification and Pre-Concentration of DNA, Plasmids, Viruses and Peptides
Scale up to eight liters monolith volume is expected to be possible however the authors noted that flow rate would need to be reduced in order to avoid prohibitively high back pressures due to the increased column length required. It was observed that the elution profiles did not change with varying flow rates over the range of 800-1600 cm/h. More recently, [PERSON] _et al._[143] used a CIM(tm) monolith treated with a CuCl\({}_{2}\) solution to purify plasmid DNA from other species in the matrix such as endotoxins, and then purified the DNA further using another CIM(tm) monolith phase. The monolith mediated separation and purification yielded a threefold increase in production of the plasmid compared with a packed column stationary phase. Other groups have also used similar monolithic stationary phases for plasmid separations [144; 149], and bacteriophage separation [145]. The removal and purification of viruses are important and complicated process steps in environmental samples and pharmaceutical processes. [PERSON] _et al._[152] reported the removal of viruses from a wastewater sample using a methacrylate monolith functionalized with quaternary amine moieties. Purification of the water samples from the 5 enteric viruses tested was achieved to a level where the viruses were not detected using Real Time-quantitative Polymerase Chain Reaction (RT-qPCR). Baculoviruses are an important source of recombinant drugs and biopharmaceuticals and an efficient purification process for this precursor was demonstrated using CIM(tm) monoliths modified for anion exchange [153]. Both strong and weak anion exchange monoliths showed good binding characteristics, with the quaternary amine (strong anion exchange) exhibiting up to three times more binding efficiency. Good recovery of 33%-70% was observed for the virus under gradient elution, increasing the HEPES (4-(2-Hydroxyethyl) piperazine-1-ethanesulfonic acid) buffer content led to increased recovery. These % recovery values increased further to almost 90% in some cases, with the addition of a CIM(tm) epoxy monolith to remove lipid content in the sample matrices. Viral enrichment factors of around 51 can be achieved in a single purification cycle.
# Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
## 2 Methodology
### Pre-Segmentation  
In the first stage, the CLUS-BPT framework applies the adapted Watershed segmentation algorithm [21] on HSI cube to obtain a segmented map. First, the algorithm assumes an \(N\)-band hyperspectral image as a set of \(N\) one-band images. The gradients of every spectral band separately results in \(N\) gradient images. The images are further combined into one-band gradient image using the supremum operator. A classical Watershed is used onto this map, resulting in each pixel (watershed pixel) containing the label of the region it belongs to. Finally, each pixel is assigned to the neighboring region with the closest median. As an output of this stage, an initial 2D-oversegmented map is obtained. The purpose of this initial spatial partition, rather than using the initial set of pixels, is to reduce the computational time for BPT building [26]. In addition, the Watershed algorithm is used in order to avoid the creation of an under-segmented map in which further region splitting can be challenging. Instead, a produced over-segmented map contains regions that are small and accurate enough to be reconstructed with high accuracy in the BPT building process [26].
# Models of HF Interference over Cyprus
###### Abstract  
Interference from other users, due to the long-distance propagation of HF signals, causes spectral congestion, which is considered a primary detrimental effect on HF (3-30 MHz) communication systems. More than a year of HF electric field measurements from a calibrated monopole HF antenna collected by a dedicated measurement system installed in Cyprus have been used to compare neural networks and non-linear regression for the development of single-allocation congestion models in the HF spectrum. These models can be used to advise operators on typical interference spectral congestion levels and assist in the planning and management of frequency usage by assessing the interference level to alleviate spectral congestion in the HF band over Cyprus. The performance of these alternative modeling approaches is discussed in the context of their ability to successfully capture the diurnal variability of HF interference at a certain incident field strength threshold.  
HF radio propagation; interference; ionospheric electromagnetic propagation; radio spectrum management; neural networks; non-linear regression +
Footnote †: journal: _Sci._**2022**, _12_, 11808. [[https://doi.org/10.3390/app122211808](https://doi.org/10.3390/app122211808)]([https://doi.org/10.3390/app122211808](https://doi.org/10.3390/app122211808))
## 3 Methodology
### Node Centrality and Vulnerability Assessment Methodology Based on Network Topology  
[PERSON], [PERSON]. (2002) defined centrality as a metric that quantifies the importance of specific nodes within a network based on graph theory [76]. Centrality indicates the position and role of a node within the network and serves as a fundamental concept in network structure analysis. In particular, degree centrality and betweenness centrality were proposed as key centrality measures. Degree centrality measures the number of direct connections a node has, with a higher degree centrality indicating that the node is highly connected and serves as a key hub within the network. It is defined as follows:  
\[Degree\;Centrality=\sum_{i=1}^{n}U(p_{i},\;p_{k}) \tag{10}\]  
\(P_{i}\), \(P_{k}\): Node \(i\), \(k\);  
\(U(p_{i},p_{k})\): 1 if nodes \(i\) and \(k\) are connected; otherwise, 0.  
Betweenness centrality measures how often a node appears on the shortest paths between other nodes. A node with high betweenness centrality plays a crucial role in information transmission and acts as a key controller within the network. It is defined as follows:  
\[Betweenness\;Centrality=\;\sum_{s\
eq v\
eq i}\sigma_{st}(v)/\sigma_{st} \tag{11}\]  
\(\sigma_{st}\): Number of shortest paths between node s and node t;  
\(\sigma_{st}(v)\): Number of shortest paths between node s and node t that pass through node v.  
[PERSON] et al. (2015) proposed a vulnerability metric that quantifies the vulnerability of a node by assessing the number of neighboring nodes it is connected to. A node is considered vulnerable if it has relatively fewer connections to the rest of the network compared to its immediate neighborhood. A positive vulnerability value indicates that the selected node is relatively isolated from its neighboring nodes [61]. The vulnerability metric is defined as follows:  
\[Vulnerability\;=\;|T|-|N(T)| \tag{12}\]  
\(|\)T\(|\): Selected set of nodes  
\(|\)N(T)\(|\): Neighboring nodes of \(T\)