esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 7 Responses to Research Questions
### Response to Research Question 3
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Model** & **QoS Metric** & **Sensors** \\ \hline
[46] & Matching based model & Latency, energy consumption & IoT \\
[47] & Deep Reinforcement & Energy consumption and latency & Mobile sensors \\
[48] & Learning (DRL) model & Energy consumption and latency & IoT \\
[49] & DL models & Energy consumption and latency & Medical and motion \\
[50] & Hybrid fog–cloud of offloading (HFCO) & delay & IoT \\
[51] & Confident information coverage (CIC) model & Energy consumption & Mobile sensors \\
[52] & Cluster-based hierarchical approach & Energy consumption & Smart Sensors \\
[53] & Cloud Based Models & Energy consumption & N/A \\
[54] & Agent-based modeling and Ontology & Overall QoS & Body sensors \\
[55] & Secure human-centric mobility-aware (SHM) model & Throughput and latency & CPS sensors \\
[56] & Grey Filter Bayesian Convolution Neural Network & Delay and latency & Smart IoT sensors \\
[57] & (GFB-CNN) & Latency & Smart IoT sensors \\
[58] & Clustered federated learning (CFL) model & Latency & Smart IoT sensors \\
[59] & Network model & Latency & IoT sensors \\ \hline \end{tabular}
\end{table} 
Table 4: Simulation models, QoS metrics, and sensor-based trends in filtered articles.
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 3 OA-UWSN MAC Protocol Design
### Analysis of the Upper Limit of the Optical Handshake Time  
In a frame of the superframe structure, the acoustic handshake time is fixed. In contrast, the optical handshaking needs to perform optical alignment through beam training. Therefore, the time required for the optical handshaking is uncertain. If it lasts too long, the throughput of the OA-UWSN is not necessarily higher than that of a UWSN with pure acoustic communication. Therefore, it is necessary to limit the optical handshake time to obtain throughput that is superior to pure acoustic communication. This section presents the relationship between the optical handshaking success rate and the upper limit of the optical handshake time when the optical communication rate and the acoustic communication rate are fixed.  
Network throughput refers to the ratio of all received packet bytes to the simulation time during the simulation time, as shown in Equation (1):  
\[\text{Throughput}=\frac{\text{received packet bytes}}{\text{simulation time}}. \tag{1}\]  
In the OA-UWSN, with the denotation that the frame length is \(n\), the acoustic handshake time is \(c\), the optical handshake time is \(d\), the acoustic bit rate is \(x\), the optical bit rate is \(y\), the success rate of the acoustic handshake is \(\beta\), the optical handshake success rate is \(\alpha\), the simulation time is \(t\), and then the throughput expression of the OA-UWSN is shown in Equation (2):  
\[T_{1}=\frac{(1-\alpha)\beta(n-c-d)x+\alpha\beta(n-c-d)y}{t}. \tag{2}\]  
The network throughput when purely using acoustic communication is  
\[T_{2}=\frac{\beta(n-c)x}{t}. \tag{3}\]  
If the OA-UWSN throughput is higher than the pure acoustic network throughput, namely \(T_{1}>T_{2}\), then  
\[d<\frac{\alpha(n-c)(y-x)}{x+\alpha(y-x)}. \tag{4}\]  
The upper limit of the optical handshaking time can be obtained by deriving Equation (4). Table 1 lists the parameter settings for the simulation in Equation (4). Figure 4 shows the relationship between the upper limit of the optical handshake time and the optical handshake success rate.  
Figure 3: Data transmission between nodes.  
Figure 4 shows that as the success rate of the optical handshaking increases, the upper limit of the optical handshake time gradually increases. If the optical handshaking success rate is constant, the throughput of the OA-UWSN is higher than that of a pure acoustic communication network only when the optical handshake time is lower than the upper limit. For example, if the optical handshake success rate is 20%, the throughput of the OA-UWSN is higher than that of a pure acoustic UWSN only when the optical handshake time is less than 1.4 s.
## 5 Application
However, we can guarantee the convergence of [PERSON]'s method from Corollary 4, since Condition (20) is satisfied: \(\mathpzc{h}=0.3429\ldots<\xi(p)\frac{(1+p)p(1-\varepsilon\delta)^{1+p}}{ \varepsilon^{p}\mathpzc{h}^{p}}=0.3517\ldots\) with \(\varepsilon=0.7656\ldots\). Therefore, we can then apply [PERSON]'s method for approximating a solution of the system \(F(\mathbf{x})=0\) with \(F\) defined in (23) and obtain the approximation given by the vector \(\mathbf{x}^{*}=(x_{1}^{*},x_{2}^{*},\ldots,x_{16}^{*})^{t}\) that is shown in Table 1, reached after four iterations with a tolerance \(10^{-16}\). In Table 2, we show the errors \(\|\mathbf{x_{n}}-\mathbf{x}^{*}\|\) using the stopping criterion \(\|\mathbf{x_{n}}-\mathbf{x_{n-1}}\|<10^{-16}\). Notice that the vector shown in Table 1 is a good approximation of the solution of the system, since \(\|F(\mathbf{x}^{*})\|\leq C\times 10^{-16}\). See the sequence \(\{\|F(\mathbf{x_{n}})\|\}\) in Table 2. \begin{table}
\begin{tabular}{|c|c||c|c||c|c||c|} \hline \hline \(i\) & \(x_{i}^{*}\) & \(i\) & \(x_{i}^{*}\) & \(i\) & \(x_{i}^{*}\) & \(i\) & \(x_{i}^{*}\) \\ \hline
1 & \(0.979069\ldots\) & \(5\) & \(1.097445\ldots\) & \(9\) & \(1.291651\ldots\) & \(13\) & \(1.587503\ldots\) \\
2 & \(1.097445\ldots\) & \(6\) & \(1.245767\ldots\) & \(10\) & \(1.422935\ldots\) & \(14\) & \(1.664776\ldots\) \\
3 & \(1.291651\ldots\) & \(7\) & \(1.422935\ldots\) & \(11\) & \(1.561551\ldots\) & \(15\) & \(1.742203\ldots\) \\
4 & \(1.587503\ldots\) & \(8\) & \(1.664776\ldots\) & \(12\) & \(1.742203\ldots\) & \(16\) & \(1.843388\ldots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Approximation of the solution \(\mathbf{x}^{*}\) of \(F(\mathbf{x})=0\) with \(F\) given in (23). \begin{table}
\begin{tabular}{|c|c|c|} \hline \hline \(n\) & \(\|\mathbf{x_{n}}-\mathbf{x}^{*}\|\) & \(\|F(\mathbf{x_{n}})\|\) \\ \hline
0 & \(5.2093\ldots\times 10^{-1}\) & \(1.318622\ldots\) \\
1 & \(2.4028\ldots\times 10^{-3}\) & \(5.4477\ldots\times 10^{-3}\) \\
2 & \(8.4485\ldots\times 10^{-8}\) & \(1.2913\ldots\times 10^{-7}\) \\
3 & \(1.2409\ldots\times 10^{-16}\) & \(1.4741\ldots\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Absolute errors obtained by [PERSON]’s method and \(\{\|F(\mathbf{x_{n}})\|\}\). Finally, we note that if we interpolate the points of Table 1 and take into account that the solution satisfies the boundary conditions given in (22), we obtain the approximation of the numerical solution shown in Figure 3.
Sensors_**2015**, _15_, 21581-21612; doi:10.3390/s150921581  
\begin{tabular}{c}  \\ _Seriesors_ \\
**ISSN 1424-8220** \\ www.mdpi.com/journal/sensors \\ _Article_ \\ \end{tabular}  
Adaptive Correlation Space Adjusted Open-Loop Tracking Approach for Vehicle Positioning with Global Navigation Satellite System in Urban Areas  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology), Ministry of Education, School of Information and Electronics, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing 100081, China;  
E-Mails: [EMAIL_ADDRESS] (H.R.); [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (T.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-138-1002-2033.  
Academic Editor: [PERSON]  
_Received: 23 April 2015 / Accepted: 24 August 2015 / Published: 28 August 2015_  
**Abstract:** For vehicle positioning with Global Navigation Satellite System (GNSS) in urban areas, open-loop tracking shows better performance because of its high sensitivity and superior robustness against multipath. However, no previous study has focused on the effects of the code search grid size on the code phase measurement accuracy of open-loop tracking. Traditional open-loop tracking methods are performed by the batch correlators with fixed correlation space. The code search grid size, which is the correlation space, is a constant empirical value and the code phase measuring accuracy will be largely degraded due to the improper grid size, especially when the signal carrier-to-noise density ratio (\(C/N_{0}\)) varies. In this study, the Adaptive Correlation Space Adjusted Open-Loop Tracking Approach (ACSA-OLTA) is proposed to improve the code phase measurement dependent pseudo range accuracy. In ACSA-OLTA, the correlation space is adjusted according to the signal \(C/N_{0}\). The novel Equivalent Weighted Pseudo Range Error (EWPRE) is raised to obtain the optimal code search grid sizes for different \(C/N_{0}\). The code phase measuring errors of different measurement calculation methods are analyzed for the first time. The measurement calculation strategy of ACSA-OLTA is derived from the analysis to further improve the accuracy but reduce the correlator consumption. Performance simulation and real tests confirm that the pseudo range and positioning accuracy of ASCA-OLTA are better than the traditional open-loop tracking methods in the usual scenarios of urban area.  
**Keywords:** GNSS; vehicle positioning; open-loop tracking; pseudo range accuracy +
Footnote †: journal: Journal of Scientific Computing
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 2 Model Development
### Wave Synthesis Technique  
Traditional microwave scattering models are suitable for studies involving typical linear polarizations; however, for GNSS-R, the transmitted signals of GNSS constellations are RHCP. To study GNSS-R polarization, we need to modify the model to make it suitable for circular polarizations and we need to obtain various polarization properties. Therefore, the wave synthesis technique is employed in model development. The polarization state of the wave may also be expressed in terms of the Stokes vector, in which the orientation \(\psi\) and ellipticity angles \(\chi\) are sufficient for complete specification of the polarization. The bistatic scattering cross section for any combination of transmit and receive polarizations can be written as follows:  
\[\sigma_{rt}(\psi_{r},\chi_{r},\psi_{t},\chi_{t})=4\pi\tilde{Y}_{m}^{r}M_{m}Y_{ m}^{t} \tag{7}\]  
where \(\tilde{Y}_{m}^{r}\) and \(Y_{m}^{t}\) are the normalized Stokes vectors characterizing the transmitter and receiver polarizations. \(M_{m}\) is the modified Mueller matrix.
## 2 Related Work  
### APT Analysis  
APTs were first described in 2006 by United States Air Force analysts [4], who distinguished between the characteristics of an APT and existing cyber threats. An APT has a clear goal for a specific target instead of an unspecified number of victims. The characteristics of an attack are as follows [5]:  
* Advanced: Attackers can evade detection and access the network, adapt well to specific network environments, and have many attack tools.
* Persistent: It is difficult for defenders to protect themselves against persistent threats. After an attacker gains access to the system network, it becomes very difficult for the user to interrupt or remove that access.
* Threat: An attacker has the ability to access stored sensitive information.  
The APT attack phase has been studied in the literature. Lockheed Martin, a USA munitions company, coined the security term \"kill chain,\" and their framework consists of a seven-phase model to identify and prevent cyber intrusion activities [6; 7]. The phases are \"reconnaissance,\" \"weaponization,\" \"delivery,\" \"exploitation,\" \"installation,\" \"command and control,\" and \"actions on objects,\" depending on what the attack group needs to accomplish for its goals. This cyber-kill-chain model can identify the purpose of the attacker or the characteristics of the attack step-by-step and enables the sequence of each phase to be tracked. However, it is difficult to prepare detailed countermeasures against internal attacks because this model does not contain specific concepts for such attacks. Attack techniques related to persistence and defense evasion may be included in internal attacks. Therefore, there is a limit to presenting these within each phase [8].  
Unlike the cyber-kill-chain model, the MITRE ATT&CK framework analyzes real attacks and defines 12 attack categories [8; 9]. The attack elements defined by the ATT&CK framework are not confined to a specific order of operations and have the advantage of being able to specifically represent behaviors. For example, techniques that can be implemented after intrusions into a network can be classified into categories such as discovery, persistence, privilege escalation, and defense evasion. APTs can be represented by several models depending on the perspective, and each model focuses on a different aspect. Furthermore, because the methods for representing APTs are diverse and complex, rather than adopting one model, a model based on an integrated knowledge system that considers several models is a better approach.
## 3 Results and Discussion
### Wine Color Attributes by Instrumental and Sensory Analysis
[35], human perception of red wine color is comprehensive, considering the full spectrum of color and lightness. Instruments provide objective and repeatable results based on specific absorbance wavelengths, while panelists evaluate wine color intensity by considering complex visual information, including lightness with the assistance of a color swatch to reduce bias. This highlights the importance of sensory evaluation in capturing the subtleties of wine color attributes that instruments may overlook. [PERSON] et al. [34] suggested that the change in color attributes could also be due to the decrease in pH induced by macroating enzymes (with endopolygalacturonase, pectin methyl esterase, endo-pectinylase, and protease), therefore affecting the anthocyanin chemistry by shifting to the flavylium ion forms of anthocyanins. However, this effect could be ruled out in our study as there was no treatment effect on the pH value among all samples (average 3.3, as presented in Supplementary Table S2). Depending on the cultivars and enzyme types, the addition of macroating enzyme (containing predominantly polygalacturonase activity with arabinose side activity) may also have no effect on the color density and formation of the stable pigments in Shiraz wine, and no difference in color can be perceived by trained panelists [19]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & **Instrumental Measurement (Published in [22])** & \multicolumn{2}{c}{**Sensory Analysis**} \\  & **Hue** & **Color Intensity** & **Hue** & **Color Intensity** \\ \hline CTL & 0.6 \(\pm\) 0.0 & 11.3 \(\pm\) 0.1 & 1.9 \(\pm\) 2.9 & 2.8 \(\pm\) 2.0 B \\ ACE & 0.6 \(\pm\) 0.0 & 11.4 \(\pm\) 0.4 & 2.4 \(\pm\) 3.7 & 3.0 \(\pm\) 2.8 B \\ ACE-PEC & 0.6 \(\pm\) 0.0 & 12.4 \(\pm\) 0.6 & 3.0 \(\pm\) 4.7 & 4.8 \(\pm\) 3.5 A \\ ACE-RCE & 0.6 \(\pm\) 0.0 & 11.3 \(\pm\) 0.6 & 2.5 \(\pm\) 3.8 & 4.8 \(\pm\) 3.1 A \\  & & \(p\)-value & & \\ Treatment & 0.7613 & 0.0612 & 0.1338 & 0.0009 \\ Panelist & - & - & \(<\)0.0001 & \(<\)0.0001 \\ \hline \hline \end{tabular}  
* Hue (instrumental measurement): calculated as the ratio of the absorbance value at 420 nm to that at 520 nm. Different letters within the same parameter indicated statistically significant differences (\(p<\) 0.05) by one-way ANOVA following [PERSON]’s test.  
\end{table} 
Table 3: Color properties measured by instrumental (published in [22]) and sensory analysis.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 4 Numerical Analysis Using STERA 3D
### Results and Discussion  
Figure 26 presents the maximum displacement of the structure equipped with VE dampers under five intensity levels (100%, 80%, 60%, 40%, and 20%) of Kokuji waves. From the comparison of the shear wall with VE damper (Shear Wall + VE) and the STERA 3D simulation (STERA 3D), the analytical and experimental results are in good agreement for all intensity levels. Since only a single damper is installed at the top of the coupled shear wall system, the seismic performance of the structure is governed by the mechanical characteristics of this individual damper. This may be the reason for the high consistency between analysis and experiment.  
Figure 27 shows the hysteresis of a VE damper under Kokuji wave excitations obtained by STERA 3D under five intensity levels (100%, 80%, 60%, 40%, and 20%). The viscoelastic (VE) damper's performance varies with earthquake intensity. Under weak shaking (20-40%), it shows nearly linear behavior with small movements (0.5-1 mm) and modest energy absorption. As shaking intensifies (60-80%), the damper's movements increase (up to 2 mm) and it absorbs significantly more energy, shown by wider hysteresis loops. During extreme shaking (100%), the damper maintains stable performance while handling large displacements, proving its ability to protect structures during severe earthquakes.  
Figure 26: Displacement of the structure with VE damper under Kokuji waves: **(a)** 20%, **(b)** 40%, **(c)** 60%, **(d)** 80%, **(e)** 100%.  
Figure 28 presents the maximum displacement of the structure with RF dampers under five intensity levels (100%, 80%, 60%, 40%, and 20%) of Kokuji waves. From the comparison of the shear wall with RF damper (Shear Wall + RF) and the STERA 3D simulation (STERA 3D), the larger the input, the closer the alignment between the analytical and test results.  
Figure 27: Hysteresis of VE damper under Kokuji waves.  
Figure 29 depicts the hysteresis of a RF damper under Kokuji wave excitations obtained by STERA 3D under five intensity levels (100%, 80%, 60%, 40%, and 20%). The RF damper shows consistent frictional resistance across all shaking levels, which is its key advantage.
## 3 Clustering via Adaptive Graph Convolution Using Heat Kernel
### Adaptive Graph Convolution Using Heat Kernel
#### 3.2.3 Cluster Evaluation Index  
According to Equation (11), the representation of connected nodes will be more similar as \(k\) increases, and we perform spectral clustering on node features \(\overline{X}\) filtered by graph convolution in each iteration. In detail, the pairwise similarity between nodes is measured as \(S=\frac{1}{2}\big{(}|K|+|K|^{T}\big{)}\)[15], where \(K=\overline{XX}^{T}\) denotes a linear kernel. Since \(K\) is symmetric and nonnegative, learning the eigenvectors of \(K\) is equivalent to computing the left singular vectors of \(X\) via singular value decomposition (SVD). Thus, we perform \(k\)-means on the left singular vectors associated with the \(m\) largest eigenvalues of \(X\) directly to obtain cluster partitions. Moreover, we do multiple spectral clustering in each iteration to preserve stable cluster partitions.  
As the iteration goes on, the features of connected nodes will become more and more similar; this is, the intra-cluster distance is getting smaller. Figure 3 illustrates that nodes with different labels are mixed together when iteration number \(k\) is smaller, while node features will be over-smoothing when iteration number \(k\) is larger. To determine an appropriate iteration number \(k\) comprehensively, we adopt Davies-Bouldin index (DBI) [16] as the criterion to evaluate cluster quality, which is defined as  
\[DBI\big{(}C^{k}\big{)}=\frac{1}{m}\sum_{i=1}^{m}\max_{i\
eq j}\frac{d_{i}+d_{j }}{r_{ij}}. \tag{12}\]  
Denote by \(m\) the number of clustering partitions, \(d_{i}\) the average distance between each point of a cluster and the cluster centroid \(i\), and \(r_{ij}\) the distance between cluster centroids \(i\) and \(j\). The score is defined as the average similarity measure of each cluster with its most similar cluster, and clusters that  
Figure 2: The linear low-pass filters in adaptive graph convolution (AGC) [7] and heat kernel comparison.  
are farther apart and less dispersed will result in a better (smaller) score. To stop iterating in time, we choose \(k\) corresponding to the first local minimum of \(DBI\!\left(C^{k}\right)\) as the most appropriate iteration number. More intuitively, considering \(d\_DBI\!\left(k\right)=DBI\!\left(C^{k+1}\right)-DBI\!\left(C^{k}\right)\), we stop iterating immediately once \(d\_DBI\!\left(k\right)>0\) as the iteration number \(k\) increases and obtain the final cluster partition \(C^{k}\). By leveraging the above strategy, AGCHK is able to capture the representation of graphs with different structures adaptively and avoid over-smoothi
# An Efficient Routing Protocol for Quantum Key Distribution Networks
The graphical representation and the architecture of the proposed QOLSR protocol are shown in Figure 1. First, we modify the link-state awareness mechanism in OLSR to reduce packet loss during path switching in QKD networks. A more efficient link-state awareness mechanism is proposed in which decisions on sending data and routing packets are determined by the state of the key pool. Second, a new routing metric based on secure key recovery capability is designed. The suggested route selection approach based on quantum secure key recovery capability can reduce the frequency of path switching in QKD networks and increase quantum secure key utilization. Finally, simulation tests are used to evaluate the proposed QOLSR protocol. The simulation data demonstrate the validity and efficiency of QOLSR. Moreover, the benefit of our protocol becomes more apparent with the growth of communication traffic. The remainder of this paper is organized as follows. In the next section, Section 2, the limitations of AODV, DSDV, and OLSR in QKD networks are first analyzed, and then the proposed efficient routing protocol, QOLSR, is introduced in detail. Simulation results and detailed analysis is provided in Section 3. Finally, the summary of this work is drawn in Section 4.
###### Abstract  
Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO\({}_{2}\)) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO\({}_{2}\) and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO\({}_{2}\) and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO\({}_{2}\) and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO\({}_{2}\) and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO\({}_{2}\) and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO\({}_{2}\) and HHb departs from random (\(t\)-test: \(t\)(509) = 26.39, \(p<0.0001\)). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO\({}_{2}\) and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable guideline for deciding whether to proceed with single or both Hb networks in FC analysis.  
fNIRS; functional connectivity; symmetry; prefrontal cortex +
Footnote †: journal: