esa-sceva/satcom-chunk-collection huggingface dataset:
# Total Outer-Independent Domination Number: Bounds and Algorithms
## 2 Bounds and Exact Results for \(\gamma_{t}^{oi}(G)\)
### ILP Formulation  
In this subsection, we propose the following ILP formulation for the problem we are studying (TOIDS). Consider a graph \(G=(V,E)\) with order \(n\), and its adjacency matrix \(A=(a_{ij})_{n\times n}\) is given by: \(a_{ij}=a_{ji}=1\) if \((i_{j})\in E(G)\), and \(a_{ij}=a_{ji}=0\) in the other case. Given a TOIDS \(T\) of cardinality \(\gamma_{t}^{ai}(G)\) and \(i\in V(G)\), our decision variables are defined in the following way:  
\[x_{i}=\left\{\begin{array}{ll}1,&\text{if }i\in T\\ 0,&\text{otherwise,}\end{array}\right. \tag{5}\]  
The _TOIDS problem_ can now be expressed as follows:  
\[\min\sum_{i=1}^{n}x_{i}, \tag{6}\]  
\[\text{subject to :}\qquad\sum_{j=1}^{n}a_{ij}x_{j}\geq 1,\ \forall i\in V(G), \tag{7}\]  
\[\sum_{j=1}^{n}a_{ij}(1-x_{i})-\sum_{j=1}^{n}a_{ij}x_{j}\leq 0,\ \forall i\in V(G), \tag{8}\]  
\[\sum_{i=1}^{n}x_{i}\geq L, \tag{9}\]  
\[\sum_{i=1}^{n}x_{i}\leq U, \tag{10}\]  
\[x_{i}\in\{0,1\}. \tag{11}\]  
We have \(2n+2\) constraints: the \(n\) constraints of type (7) ensure that for each \(j\in V(G)\), at least one vertex in \(T\) is adjacent to \(j\), while the \(n\) constraints of type (8) ensure that no vertex in \(V(G)\setminus T\) has a neighbor in \(V(G)\setminus T\). Please note that in the adjacency matrix \(A\), we define \(a_{ii}=0\) for all \(i\). As a result, when \(j=i\), the term \(a_{ij}\cdot x_{j}\) evaluates to zero, ensuring that self-loops do not affect constraints of type (7), i.e., vertices do not dominate themselves. Additionally, constraints (9) and (10) limit the search space (feasible region), ensuring that the cardinality of the generated solutions is between the lower bound \(L\) and the upper bound \(U\). We can use the lower and upper bounds proposed in Section 2 to determine \(L\) and \(U\). It should be noted that, in practice, the heuristic proposed in Section 3 provided a better upper bound \(U\) than the theoretical bounds for the instances analyzed.
## 3 Critical Metrics for Industrial Monitoring and Control
### Power Consumption  
Unlike traditional wireless sensor networks, power consumption has a lower priority than other performance metrics, such as reliability and real-time capability in industrial sensor networks. However, the degree of importance of power consumption varies greatly depending on the class of application. Industrial control applications can be categorized into two main classes: (i) process control, and (ii) factory automation.  
Process control is typically used for monitoring fluids (e.g., oil level in a tank, pressure of a gas, _etc._). Such applications which typically involve non-critical applications requiring closed-loop control usually transmit process values at regular intervals. Furthermore, due to the non-critical nature of the process control applications, latency requirements are not usually stringent (>100 ms). This allows nodes to reduce power consumption by carrying out aggressive duty cycling of their radios and sensor sampling operations. Factory automation applications, however, involve machines (e.g., robots) that perform discrete actions and are highly sensitive to message delays. Thus, such applications generate 'bursty' data and may require latency in the region of 2-50 ms. In such instances, reducing power consumption has a lower priority than other performance metrics such as real-time capability and reliability.  
In terms of energy consumption, ZigBee Pro and WIA-PA (in the cluster/star level) do not perform as well as the other competing technologies as it carries out time synchronization using the beacon-enabled mode of IEEE 802.15.4. Using beacons introduces a large overhead in terms of higher energy consumption, as the radio needs to remain in listen mode for long periods. Conversely, TSMP solves the time synchronization problem in a more energy-efficient manner by only relying on ACKs to exchange timing offset information. WirelessHART, WIA-PA (in the mesh level) and ISA100.11a also benefit from this approach as they both utilize TSMP. Furthermore, the ISA100.11a specification allows the transmission power of individual nodes to be controlled. This can result in additional energy savings. However, the specifications do not describe any algorithms indicating the strategies to be followed to carry out adaptive power control.
## 2 Materials and Methods
### Operational Sampling
#### 2.2.3 Simulation of GNSS Error  
A simulation script was written in the R language in order to assess the effect of horizontal positioning error on the safety status of individuals. For each one second time stamp in the operationaldata, a one second observation was selected at random from one of the four mature or clearcut plots in the controlled experiment described previously. We applied an error adjustment to the operational data based upon the UTM casting and UTM northing differences, as well as azimuth (in degrees) from the actual (Arrow 100) and observed (Atlas PT) coordinates. Thus, we assumed for the purposes of analysis that each worker location in the operational data was uncorrected, and then shifted each coordinate individually by a distance and direction corresponding to either mature canopy or clearcut error accuracy from the controlled experiment. To simplify analysis, we assumed that hazard locations were true coordinates; thus, they were not adjusted during simulation. 500 iterations of the simulation script were processed. After resampling and application of error adjustments to worker positions, inter-point distances from each of the three jobsite hazards were again summarized in zones of 5 m increments, and the proportions of safe and unsafe status were determined. Since adjustments to the operational data were sampled from individual GNSS errors recorded on multiple different sites and dates, simulated data do not represent the true location of each worker at a given time. Rather, we utilized simulation to provide an indication of the degree of impact to be expected from positioning error in relation to a fixed point (the geofence) in each GNSS environment (observed, mature, or clearcut).  
We determined whether GNSS positioning error would impact definitions of workers as safe or unsafe based on proportions of time spent inside geofenced hazard zones. Using the Marascuillo Procedure, we tested the null hypothesis that unsafe proportions were equal for observed, mature, and clearcut data for each hazard at each site, where observed data represented worker positions as recorded by the Atlas PTs, mature data represented simulated worker positions accounting for GNSS error associated with canopy, and clearcut data represented simulated worker positions accounting for GNSS error under un-obstructed conditions. The Marascuillo Procedure was performed for each iteration, and the mean value was compared to the mean critical range to determine if proportions differed significantly.
## 3 Predicting Capacities of Shear-Critical Columns Using the AASHTO Procedures
It is noted that many of the columns tested have small shear span-to-depth ratios, \(a/D\) or \(a/h\) (see Figures 1 and 2). These members that are subjected to an axial compressive load can develop an inclined compressive strut which helps to resist the shear along with the sectional shear resistance [26; 27]. The sectional model predictions shown in Figure 3 do not include the beneficial influence of a compressive strut for columns with low \(a/D\) or \(a/h\) ratios and hence in some cases these predictions can be overly conservative. The predictions that are most conservative shown in Figure 3 are columns with very low shear span-to-depth ratios. These columns acted as wall-type piers and displayed strut action as indicated by the cracking pattern observed in the tests. For columns having \(a/D\) or \(a/h\) of 2.5 or greater, the sectional design approach using the General Procedure gives accurate predictions with an average \(V_{exp}/V_{gen}\) of 1.11 and a standard deviation of 0.07. Figure 1: Shear-critical circular and octagonal columns investigated with reversed-cyclic shear applied in the direction as shown. Adapted from [6; 7; 8; 9; 10; 19; 20; 21; 22; 23; 24]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \(f_{\epsilon}^{\prime}\) & \(\frac{P}{A_{\epsilon}^{\prime}\prime}\) & \(\hat{\beta}\) & \(D\) & \(d_{\
u}\) & \(\rho_{\
u}\) & \(f_{\
u\mu}\) & \(\rho_{l}\) & \(V_{cap}\) & \(V_{sim}\) & \(V_{sim}\) & \(V_{sim}\) & \(V_{sim}\) \\  & \(\mathbf{MPa}\) & \(\mathbf{MPa}\) & \(\mathbf{mm}\) & \(\mathbf{mm}\) & \% & \(\mathbf{MPa}\) & \% & \(\mathbf{MPa}\) & \% & \(\mathbf{k}N\) & \(kN\) & \(\frac{V_{cap}}{V_{sim}}\) & \(kN\) & \(\frac{V_{cap}}{V_{sim}}\) \\ \hline \multicolumn{11}{c}{  
Figure 2: Shear-critical rectangular columns investigated with reversed-cyclic shear applied in the direction as shown. Adapted from [11; 12; 13; 14; 15; 16; 17; 25]. The 2020 AASHTO Specifications have different requirements for columns and wall-type pairs. If the ratio of the shear span to the dimension of the column in the direction of shear (\(a/D\) or \(a/h\)) is equal to or greater than 2.5, the member is considered to be a column and the sectional design procedure is appropriate. For shear span-to-depth ratios less than 2.5, a rectangular member is considered to be a wall-type pier. The AASHTO Specifications give empirical equations for determining the seismic shear resistance of wall-type piers. For rectangular columns with \(a/h\) less than 2.5, the use of these equations gives conservative predictions that are very similar to the predictions using the Simplified Procedure, while the General Procedure gives better predictions. More detailed predictions using nonlinear finite element analysis are given in Section 4.
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Data Pre-Processing  
Data were recorded initially in WinEEG ('.eeg?) format (one file per session) and saved in '.edf' format, and then, each separate session file was cut into eight separate '.mat' files (one for each 5 min'slot').  
Data were first filtered between 0.5 and 45 Hz using Matlab 2 nd order Butterworth filters ('butter' and 'filtflt!' functions). Mains power in the UK is supplied at 50 Hz, so for higher-frequency signals, a second-order 50 Hz Butterworth notch filter was also used (49-51 Hz). Independent Component Analysis (ICA) was then conducted using the extended Infomax algorithm [28] to reject non-neural artifacts, together with the multiple artifact rejection algorithm (MARA) [29]. Components were labelled and artefactual components removed in conjunction with ICLabel [30], an EEGLab plug-in [31]. Subsequently, the Trimoutlier EEGLab plug-in [32] was used to remove epochs exceeding an individual amplitude threshold defined as +/- 3 SD (standard deviation) above the mean amplitude across all channels. Finally, data were re-montaed with the CSD Toolbox (ExtractMontage.m function) [33], using the Laplacian form of local average reference.  
At this stage, data from several participants were excluded, either because of inadvertent differences in sampling rate (for four participants, in four sessions), missing or poor-quality data or because recordings were cut short for one reason or another (e.g., discomfort from wearing the cap or having to take a trip to the bathroom). Files for 48 participants remained \(-\)192 for each'slot', in.mat format\(-\)resulting in 1536 files in total. Figure 4 shows the data collection and pre-processing pipeline.  
Figure 4: The data collection and pre-processing pipeline.
# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 5 Conclusions  
To conclude, this study presents a novel handover approach for 5G Vehicle-to-Everything networks by utilizing a multi-criteria decision-making methodology and considering both the application-specific quality of service needs and conventional performance measurements. The implications of the integration of AR in the context of V2X are discussed, and the proposed technique greatly improves the effectiveness and dependability of network handovers. Providing seamless connectivity, this approach minimizes network signaling overhead and maximizes resource efficiency by reducing the frequency of needless handovers by incorporating the concept of stay time. The simulation results suggest that the proposed handover mechanism performs better than the traditional method and ACAVHD, particularly in settings with dense network deployment and high mobility. The technique not only lowers unwanted handovers but also guarantees a better quality of connectivity that is specifically designed to meet the rigorous requirements of contemporary V2X applications. The findings provide noteworthy results, which indicate that this handover mechanism may be modified for use in a wider range of high-mobility scenarios outside V2X communications.  
Future work can concentrate on scalability and the use of cutting-edge technologies, such as deep learning and Recurrent Neural Networks (RNNs), for performing predictions based on short-term trends. This can be used to predict UE trajectories, ST, and signal degradation, among other factors, before any HO decision is made. RL can also be integrated to optimize HO policies based on rewards. RL can adapt to dynamic network conditions and UE requirements to enable CAP's self-optimization. In terms of further 5G development, such as 5G and Beyond and 6G, CAP can be extended to respond to the requirements of enhanced multimedia, such as holographic communications and the use of Terahertz (THz) bands.  
**Author Contributions:** Conceptualization, F.R.A.A.H., A.T., N.A. and F.A.S.; Methodology, F.R.A.A.H. and A.T.; Software, F.R.A.A.H.; Validation, F.R.A.A.H.; Formal analysis, F.R.A.A.H., A.T. and F.A.S.; Investigation, F.R.A.A.H., A.T., N.A. and F.A.S.; Data curation, F.R.A.A.H.; Writing--original draft, F.R.A.A.H.; Writing--review & editing, F.R.A.A.H., A.T. and F.A.S.; Supervision, A.T., N.A. and F.A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 2 Fundamental Concepts and Background
### EEG Signal
The frequency and amplitude of physiological waveforms typically have an inverse relationship; waves with lower frequencies (delta, theta) have larger amplitudes, while waves with higher frequencies (alpha, beta) have lower amplitudes [75]. Amplitude is an essential descriptor for detecting interictal abnormalities in the EEG signals [76; 77; 78]. Morphology describes the overall shape and pattern of EEG transient events (spikes, sharp waves, and spike-and-wave complexes), which are distinct from the background activity and are associated with neurological disorders [79].
# Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention
## 4 Main Concept  
We present a novel MARL system implemented with MADDPG algorithm for traffic burst, elephant flow, and DDoS detection and prevention in an SDN-IoT environment. With compromised IoT devices, it has become extremely difficult to differentiate genuine transient or crowd event packets from the compromised malicious packets emanating from zombie IoT devices. The various attack methods such as SYN, TCP, ICMP, and UDP flooding [42] mimic genuine sudden spike traffic, and, if not blocked intelligently, will result in genuine packet loss to destination nodes. The primary concept of the framework, as shown in Figure 5, involves the implementation of two agents in the controller of the SDN architecture, agent 1 and agent 2. Agent 1 is responsible for genuine traffic burst and elephant flow multipath routing in the network while agent 2 is responsible for DDoS detection and blocking. During a packet miss from the OpenFlow switches, a packet-in message request is sent to the controller for new flow addresses to be installed in the flow table. The two agents instruct the SDN controller in installing new flow rules during packet-out messages to the OpenFlow switches.  
The SDN controller executes the instructions of the multi-agents through actions in the flow table of the OpenFlow switches, as depicted in Figure 6.  
With the proposed concept, each agent has a policy in updating the controller on the actions to execute on the forwarding devices based on the reward received from the environment. The reward gives an indication of the next action each agent should take. As the episode progresses, agents learn to take the best action that will result in higher rewards in an exploitation-exploration tradeoff.
## 2 Materials and Methods
### Laboratory Workflow Overview and Equipment
The TLA solution implemented in the laboratory consisted of a single line connecting a new pre-analytical COBAS p612 (F. Hoffmann-La Roche AG, Basel, Switzerland) system, one Liaison XL (Diasorin S.p.A, Saluggia, Italy) and two modules of a COBAS e801 (F. Hoffmann-La Roche AG, Basel, Switzerland) analytical unit, including an intelligent tracking system that auto-routes test orders and samples. The changes were implemented sequentially in six months from April to September 2021. Firstly, the pre-analytical equipment AQUA-7000 (Siemens Healthcare GmbH, Erlangen, Germany) was replaced with COBAS p612 (F. Hoffmann-La Roche AG, Basel, Switzerland), which integrates additional functions such as tube identification and registration, automated sample quality check, uncapping, aliquoting, archive tube generation and routing to specific immunoassay testing equipment. One of the main advantages of the new pre-analytical area is the avoidance of repetitive and manual workup existing in the pre-TLA solution for sample registration, identification, separation, re-racking and uncapping, reducing the risks of splashing and tendonitis. The elimination of manual transport and workup of specimens, biological materials or waste that are known or expected to contain biological agents between rooms in the laboratory, avoids biological hazards and reduce the risks of loss or damage of samples. Furthermore, the implementation of automation, eliminating repetitive tasks like uncapping, has led to a significant reduction in occupational health, especially tendonitis and physical fatigue, and safety risks for laboratory personnel.
## 2 H\({}_{2}\)O Collisional Broadening Measurements
### Setup for H\({}_{2}\)O Self-Broadening and Temperature Dependence at 1.37 \(\mu m\)
#### 2.1.1 H\({}_{2}\)O Self-Broadening at 1.37 um
The red boxes (at the end of the measurement step where the highest temporal stability is found) show the regions where the analysis of the FWHM was performed at the different gas pressures (repeatability of the measured H\({}_{2}\)O pressure was 0.007 hPa, i.e., 0.09% at the lowest gas pressure of 8 hPa) shown on the y-axis (right). A check of the Lorentzian width versus line area showed no significant systematic effects due to absorption/desorption. The absorption/desorption effects are folded into the repeatability of the FWHM measurements. Figure 3 shows the Lorentzian FWHM (derived from the data in Figure 2b, bottom panel) for pure self-broadening as a function of the H\({}_{2}\)O pressure \(p_{\rm H2O}\). The uncertainties (error bars) of the Lorentzian FWHM and \(p_{\rm H2O}\) are 1.5% relative (as presented, estimated taking into account the repeatability of the measurements, fitting and the uncertainty of the wavenumber axis estimated similar to [7]) and 1.2% relative at 28.6 hPa, respectively. The uncertainties of the measured gas pressure were estimated considering the repeatability of the measurements and the respective uncertainties (derived using the calibration certificate) at the different pressure points. A generalized linear regression (GLR) was applied to the data in Figure 3 in order to derive the self-broadening coefficient for the H\({}_{2}\)O absorption line at 7299.43 cm\({}^{-1}\). The GLR was performed using B_Least software promoted by ISO standard 6143 [44]. From the GLR in Figure 3, a self-broadening coefficient \(Y_{\rm H2O}\) (evaluated from the slope, according to Equation (2)) of (4.243 \(\pm\) 0.118)\(\cdot\)10\({}^{-4}\) cm\({}^{-1}\)/hPa was derived at (296 \(\pm\) 1) K, corresponding to (0.430 \(\pm\) 0.012) cm\({}^{-1}\)/atm, \(k\) = 1. The combined uncertainty of the self-broadening coefficient was 0.012 cm\({}^{-1}\)/atm (2.8% relative, \(k\) = 1). The quantity  
Figure 2: (**a**) Plot of measured—purely self-broadened—spectra of H\({}_{2}\)O at 296 K as a function of wavenumber. A Voigt profile is fitted to the data. Residuals of the Voigt fit are plotted in the bottom panel. (**b**) **Bottom panel**: Lorentzian width measured at different H\({}_{2}\)O pressures as a function of measurement number. The red dashed rectangular boxes represent the data region chosen for the determining of \(Y_{\rm H2O}\) (repeatability—50 measurements: 0.6%, 0.05%). **Top panel**: The gas temperature over the entire measurement time. is a coverage factor [28]. In this work, the units cm\({}^{-1}\)/atm were used in order to compare the results to those reported in the HITRAN database [26].
###### Abstract  
Ionic liquids (ILs) are new green solvents, which are widely used in lignocellulosic and microalgal biorefineries. However, high-temperature operating conditions limit their application in the extraction of heat-labile algal products, such as bioactive astaxanthin. In this study, we report the technical feasibility of room-temperature astaxanthin extraction from _Haematococcus Iacusstris_ cysts with a thick and complex cell wall structure, by combining ultrathin \(\alpha\)-quartz nanoplates (NPLs) with ethyl-3-methylimidazolium ([Emimi])-based ILs. When four different [Emimi]-based ILs with thiocyanate (SCN), diethylphosphate (DEP), HSO\({}_{4}\), and Cl anions were applied to 90-day-old \(H\). _Iacusstris_ cysts at room temperature (~28 \({}^{\circ}\)C), the astaxanthin extraction efficiency was as low as \(g\)-14.2%. Under sonication, \(\alpha\)-quartz NPLs disrupted the cyst cell wall for a short duration (5 min). The astaxanthin extraction efficiencies of a subsequent IL treatment improved significantly to 49.8% for [Emimi] SCN, 60.0% for [Emimi] DEP, 80.7% for [Emimi] HSO\({}_{4}\), and 74.3% for [Emimi] CI ions, which were 4.4, 6.1, 8.4, and 5.2 times higher than the extraction efficacy of only ILs, respectively. This finding suggests that \(\alpha\)-quartz NPLs can serve as powerful cell-wall-disrupting agents for the room-temperature IL-mediated extraction of astaxanthin from robust algal cyst cells.  
microalgae; _Haematococcus Iacustris_; astaxanthin; ionic liquid; nanoplate; cell disruption; room-temperature extraction
## 3 Modulation Techniques
### Gaussian Minimum Shift Keying  
GMSK is the modulation technique where the frequency of the carrier wave (Gaussian pulse shape) is varied in accordance with the modulating signal. BER of GMSK is given by:  
\[\mathrm{P_{GMSK}}=\mathrm{Q}\left(\sqrt{\frac{2\delta E_{b}}{N_{0}}}\right), \ \delta=0.68 \tag{11}\]