esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### HRV Analysis of Stroke and Control Group  
There was no significant difference in the RMSSD values between the strokes and controls. However, the analysis of calculating the variability of the adjacent RR-I values revealed that the RMSSD index was reliably lower in the stroke group, whereas the SDSD did not differ significantly. A similar trend was observed in the pNN50 index, which gives the proportion of NN50 divided by the total number of RR-I summarized in Table 2. From the frequency domain analysis, the RR-I variability in the VLF bandwidth shows no significant difference between the groups. In the HF and LF bandwidths, a downward trend was observed in the stroke group. A significant difference was evident in the LF/HF ratio, also called the sympathovagal balance (F = 8.21, \(p\) = 0.014). The post hoc comparisons using [PERSON]'s honestly significant difference (HSD) test indicated that the mean score for the stroke group (1.18 \(\pm\) 0.21) was significantly different than the control group (0.60 \(\pm\) 0.079), and the boxplot shown in Figure 4 represents the distribution of the LF/HF values. This analysis of the time and frequency domain variables indicated that subjects with a stroke showed a decrease in the heart rate variability.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variables** & **Stroke Group (_n = 52_)** & **Control Group (_n = 80_)** \\ \hline Age (years) * & 72.7 \(\pm\) 6.6 & 75.5 \(\pm\) 3.4 \\ Gender (male), \(n\) (\%) * & 33 (63) & 31 (39) \\ Smoking, \(n\) (\%) & 16 (31) & 17 (21) \\ Drinking, \(n\) (\%) & 12 (23) & 14 (18) \\ Family history of stroke, \(n\) (\%) & 5 (10) & 5 (6) \\ Hypertension, \(n\) (\%) & 18 (35) & 17 (21) \\ Dyslipidemia, \(n\) (\%) * & 3 (6) & 14 (18) \\ Diabetes, \(n\) (\%) & 8 (51) & 14 (18) \\ Heart disease, \(n\) (\%) & 3 (6) & 5 (6) \\ BMI (kg/m\({}^{2}\)) & 24.3 \(\pm\) 2.7 & 23.9 \(\pm\) 2.5 \\ SBP (mmHg) & 129.3 \(\pm\) 15.5 & 136.91 \(\pm\) 16.36 \\ DBP (mmHg) & 78.3 \(\pm\) 9.2 & 79.3 \(\pm\) 11.5 \\ Hemoglobin (g/dL) & 13.5 \(\pm\) 1.8 & 13.5 \(\pm\) 1.1 \\ TC (mg/dL) & 179.5 \(\pm\) 37.2 & 183.1 \(\pm\) 39.9 \\ LDL (mg/dL) & 97.1 \(\pm\) 34.3 & 104.7 \(\pm\) 35.9 \\ HDL (mg/dL) & 54.8 \(\pm\) 16.4 & 49.7 \(\pm\) 12.2 \\ Baseline NIHSS score & 25 (6) & - \\ \hline \end{tabular}  
* Baseline data are presented as mean \(\pm\) standard deviation or number (percentage),
* _p_-Value \(<\) 0.05 was considered as significant. BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, LDL: low-density lipoproteins, HDL: high-density lipoproteins, TC: total cholesterol and NIHSS: National Institute of Health Stroke Scale.  
\end{table}
Table 1: Demographics and clinical data of the stroke and control groups.
# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
## 4 Conclusions  
We have presented a new algorithm for simultaneous retrieval of two aerosol parameters (volume fraction of fine-mode particles and aerosol volume fraction) and three marine parameters (CDOM absorption, chlorophyll concentration, and mineral concentration). We tested the performance of the new algorithm by creating a set of synthetic data and simulated the TOA radiances in 9 MODIS channels for randomly generated aerosol and marine parameter combinations. The results show that with sufficiently low noise levels our algorithm is accurate in predicting all the five parameters. For high sun (SZA = 45\({}^{\circ}\), 53\({}^{\circ}\), and 63\({}^{\circ}\)) without white noise added to the TOA radiance the algorithm predicts the CDOM absorption, chlorophyll concentration, mineral concentration, volume fraction of fine-mode aerosol particles, and aerosol volume fraction with correlation coefficients greater than 0.77, 0.70, 0.93, 0.78, and 0.96, respectively. We also demonstrated that the algorithm is robust by varying the SZA between 45\({}^{\circ}\) and 75\({}^{\circ}\) while introducing white noise to the TOA radiance. For SNR\(\geq\) 100 the algorithm predicted the five parameters accurately. For SZA \(>\) 75\({}^{\circ}\) the plane-parallel geometry used in the forward model will give inaccurate results. For such large SZA values curvature effects must be considered. This parameter retrieval technique applies to any ocean satellite sensor, though it is limited to cloud-free sky conditions. In conclusion, the algorithm is faster than RTM by a factor of 200, accurate, and robust, and can be applied to coastal waters as long as the ranges of the five retrieval parameters are known, and the noise level is low.  
Figure 9: Effects of white Gaussian noise on the retrieved parameters.
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 3 Method for the Integration of Organizational Models
### Metamodel Merging  
Unlike the alignment, the merging of metamodels is a more deterministic process that can be fully automated by using several algorithms reported in the literature [44; 45; 46]. In general, these proposals for (meta)model merging can be described as merging based on _graphs_ and _morphisms_. In this case, the metamodels are abstractly conceived as graphs and the correspondences between two metamodels assume the form of an articulation between graphs. If \(MM_{1}\) and \(MM_{2}\) are two metamodels viewed as graphs, then an articulation \(art(MM_{1},MM_{2})\) between them is a triple composed of a graph \(G^{art}\) and two morphisms \(m_{1}:G^{art}\to MM_{1}\) and \(m_{2}:G^{art}\to MM_{2}\):  
\[art(MM_{1},MM_{2})=<G^{art},m_{1},m_{2}>\]  
Intuitively, the idea is that \(G^{art}\) is a representation of the common concepts and relations found in \(M_{1}\) and \(M_{2}\), and the morphisms \(m_{1}\) and \(m_{2}\) are the links mapping this common concepts and relations to their counterpart in both \(MM_{1}\) and \(MM_{2}\).  
Once characterized as graphs, the merging of two metamodels \(MM_{1}\) and \(MM_{2}\) is in essence an _amalgamated sum_ (or _pushout_) of \(MM_{1}\) and \(MM_{2}\), modulo \(art(MM_{1},MM_{2})\):  
\[merge(MM_{1},MM_{2})=MM_{1}\oplus_{art(MM_{1},MM_{2})}MM_{2}=<MM^{int},m_{1}^{ \prime},m_{2}^{\prime}>\]where \(MM^{int}\) is the resulting integrated metamodel at the end of the iteration and \(m^{\prime}_{1}\) and \(m^{\prime}_{2}\) are morphisms \(m^{\prime}_{1}:MM_{1}\to G^{int}\), \(m^{\prime}_{2}:MM_{2}\to G^{int}\). The integrated metamodel \(MM^{int}\) consists of the union of the nodes (concepts) and edges (concept relationships) of \(MM_{1}\) and \(MM_{2}\), where correspondent elements as described via \(art(MM_{1},MM_{2})\) are treated as only one element [44; 46]. In this way \(MM^{int}\) retains all non-duplicate information in \(MM_{1}\) and \(MM_{2}\) collapsing the elements that \(art(MM_{1},MM_{2})\) declares redundant. The morphisms \(m^{\prime}_{1}:MM_{1}\to G^{int}\) and \(m^{\prime}_{2}:MM_{2}\to G^{int}\) describe how translating from the particular to the integrated metamodel. The inverse, translating from the integrated to the particular metamodels, is performed via \(G^{art}\), \(m_{1}\) and \(m_{2}\).
## 3 Results and Discussion
### Effect of the Magnetic Nanoparticles Amount  
The effect of the MNPs amount was investigated with 2-10 mg Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) being added into the AMTPS, no addition of MNPs was used as the control. It can be found in Figure 4 that the extraction efficiencies had minor increase with further increase of MNPs when the MNPs amount was larger than 4 mg. The results demonstrated that the extraction mainly depend on the TX-114, however, the MNPs can also increase the extraction efficiency to some extent and accelerate the phase forming. Thus, to comprehensively consider the extraction efficiency and the phase forming, 4 mg MNPs were added to the system for further studies.
## 2 Requirements Specifications and Measurements Gaps
### Sea Ice Extent  
Sea ice extent is an important parameter to understand the global climate. Previous works have shown that Ku-band scatterometer data are able to measure sea ice extent [40; 41]. The required revisit time is 3 h with 10 km as minimal spatial resolution and 5% accuracy. Scatterometers have a wide swath and a reduced revisit time, which makes them a valuable technology for this application. However, the main limitation is the coarse resolution of the data generated by this sensor. In this sense, many studies have been conducted to develop algorithms to improve the spatial resolution of the data, combining data from multiple passes of the satellite [42; 43] or from different sensors [44] (e.g., microwave imager, SAR and scatterometer).
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 4 Throughput Maximization and Resource Allocation Algorithms  
Higher throughput requirements of novel services defined under the enhanced mobile broadband (eMBB) category are one of the main drivers of creating resource allocation solutions with learning algorithms for throughput maximization. The learning methods for throughput optimization in mmWave radio and optical networks are presented Section 4.1. The existing solutions to throughput maximization problems are given in Table 3. In Section 4.2, we discuss the potential improvements of applying existing AI-ML solutions that are not part of the existing literature for converged optical and radio networks.
## 6 Recent Studies on CRSN Routing
### Joint Route and Spectrum Brokering  
In this method, the choices of spectrum and path are made jointly by the individual routing nodes or by the sink node after a path is chosen. Thus, the chosen route remains connected during the routing process. Energy- and cognitive-radio-aware routing (ECR) [32], which is analogous to the AODV protocol, is based on this concept. In ECR, the RREQ packet is broadcasted to the sink through a common control channel. Intermediate nodes forward the RREQ based on channel correlation with the sending node, energy threshold and channel availability. When multiple routes are found, the sink chooses the route with the least number of hops and further assigns the operating channel to individual nodes to reduce channel switching during the data transfer phase. Route maintenance is only performed locally if the affected node is in close proximity to the sink. Otherwise, a message must be sent back to the source to initiate a new route request, which can be costly. Another problem with this approach is that the channel availability metric is not properly accounted for.  
Another protocol under this classification is spectrum and energy-aware routing (SER) [33]. Although SER is not specific to CRSNs, the protocol takes energy into consideration and thus can be classified under viable solutions for CRSNs. The protocol also presents another modification of the AODV protocol and differs from the others based on its distributed joint routing and channel-timeslot allocation strategy for each link. However, CSMA/CA is still used at each link for channel access. The protocol excels in network balancing of energy consumption, reduction of contention in the MAC between nodes and the ability to decompose traffic over different channels or timeslots. However, a detailed implementation method of the MAC component is lacking, which leaves the assumptions open for verification. Furthermore, the ability of the constrained nodes to maintain periodic spectrum sensing to obtain an informed channel occupancy characteristic on which they base their decision is a critical matter of concern.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 6 Simulation Studies
### Fault Detection Performance  
Three canonical faults--step, ramp, and sinusoidal--are chosen to analyze fault detection performance. It is explained in [3] that potential failure modes for GNSS precise positioning as complied from the existing literature can be largely modeled as one of the three fault types. With full-state EKF, the test statistic \(\alpha_{EKF,\,k}\) at time step \(k\) is formed as  
\[\alpha_{EKF,\,k}=\sum_{\ell=k}^{k-N+1}[\Delta\rho_{\ell}^{T}\ \Delta\rho_{\ell}^{T}]\mathbf{R}_{\ell}^{-1} \begin{bmatrix}\Delta\rho_{\ell}\\ \Delta\rho_{\ell}\end{bmatrix}  \tag{40}\]  
where \(\mathbf{R}_{\ell}\) is the innovation covariance matrix at time step \(\ell\). \(\Delta\rho_{\ell}\) and \(\Delta\rho_{\ell}\) are carrier-smoothed pseudorange and range rate innovation vectors of the full-state EKF. \(N\) is determined the same way as that in Equation (7), but for the EKF. Under no fault conditions, \(\alpha_{EKF,\,k}\) is chi-squared distributed. Its threshold is determined using \(P_{FA}\) and DOF. As for WLS and SKF, thresholds are calculated from the non-central chi-squared distribution with the non-centrality parameter 25 for natural biases, following [37]. Test statistics \(\alpha_{1}\) and \(\alpha_{2}\) each have the probability of false alarm \(P_{FA}/2\). From the PL calculation of WLS RAIM and KF RAIM with SKF, it is noted that simultaneous faults in PRN 28 of GPS (G28 in the skxplot) and PRN 2 of NavIC (N2 in the skyplot) result in maximum mean VPE (or FMS). Hence, faults are injected into these satellites (either N2 or (G28 and N2)) at 100 s into the simulation. In the case of dual fault modes, both satellites have the same fault. Tables 1 and 2 summarize the fault detection performance for a particular simulation run. Since PLs are not computed for full-state EKF, its change in position error under faulty conditions is only noted. For the -5 m step fault and cosine fault of 5 m amplitude, both \(\alpha_{1}\) with SKF and \(\alpha_{EKF}\) exhibit an initial peak, but the test statistic of WLS does not. However, the peak of the EKF test statistic crosses its threshold, whereas that with the SKF does not. As a result, no detection of faults occurs with the test statistics of SKF. A possible reason for non-detection is that the magnitude of the faults is close to that of the innovation sequences for SKF, as is evident from Figure 7. It is also important to note that the ramp fault is detected later by both WLS and SKF test statistics when two satellites are faulty as opposed to a single satellite. This is probably because faults in G28 and N2 maximize the FMS for mean VPE, and therefore they take longer times to become detected. The WLS test statistic detects the ramp fault before that of SKF. However, the position error of the latter crosses its PL later.
# Depletion-of-Battery Attack: Specificity, Modelling and Analysis
## 2 Related Works
Before the victim identifies that the data is spoofed, the victim consumes more energy to receive and decrypt data. The threats for traditional wireless sensor networks may also occur in networks with more powerful sensor modes. Even in wireless multimedia sensor networks, an intruder may rapidly drain the energy of nodes [30]. It has been shown that mobile devices supporting IEEE 802.11 and IEEE 802.15.1 standards are vulnerable to battery exhaustion attacks [31]. Authors remark that this is DoS attack. It violates the overall operation of the device. Generally, sensors are deployed in unattended and hostile environments. Therefore, an intruder can easily receive a physical access to a sensor and extract all the stored keys [32]. The recent encryption schemes use a source-destination path key to protect data transmitted over the route instead of usingmultiple pairwise shared keys to repeatedly perform encryption and decryption over every link [33]. Thus, a big part of the computational and energy expenses is delegated to the sink. It improves the energy efficiency of intermediate sensors. However, it makes a system vulnerable to flooding attack. In the paper [34] a malware based battery depletion attack is considered. The authors focus on the strategy of malware behavior and optimize the rate of network nodes infection. The infective state is modeled by one state of the four-state Markov chain. Moreover, the energy depletion time is assumed to be exponentially distributed at time. Therefore, it is assumed that the residual battery charge does not depend on time. It is not applicable for limited capacity of sensor battery. Similar simplified assumptions have been used in our previous paper [18], when the energy exhausting DoS attack has been considered. These simple models are convenient for a general consideration of defense strategies. However, it cannot be applied for comprehensive analysis of concrete attacks. In the present paper, we provide more detailed and complicated models. Our previous papers [35; 36] presented for the first time the idea that DoB is a novel type of attack that is not necessary DoS. In the present paper, we provide the next generation of research in this direction. We essentially improve the background information and totally revise the system model and survivability metrics.
# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 3 Trust Model Architecture Components  
### Trust Representation and Usage  
The fundamental trust model concepts have been identified and published in our previous articles [3; 25]. These fundamental trust model concepts are: (a) behavior trust, (b) reputation, (c) honesty and (d) accuracy. The trust model elements are organized to function and evolve trust in a fully distributed manner, and no entity is omniscient. Rather, each entity \(x\) has its opinion of how trustworthy other entities are and stores this knowledge in its _direct trust table_ (\(DTT_{x}\)) that contains trust levels for nodes with which \(x\) had prior direct experience. For example, \(x\) trusts node \(y\) to act as expected within context \(c\) at time \(t\).  
Similarly, each node cooperates with other nodes by sharing information in the form of recommendations. In the trust model, node \(x\) maintains a _recommender trust table_ (\(RTT_{x}\)) that associates trust levels for node \(y\) based on prior direct experience for node \(y\) with other nodes. For example, \(T_{ry}^{c}\) means that \(x\) associates a trust level for \(y\) based on recommendation received from recommender \(r\) based on prior direct experience for \(y\) with \(n\). This recommendation is for context \(c\).
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 2 TL-Based Damage Identification
### SVD for Damage Detection  
POD is a statistical method adopted for a wide variety of applications, including damage identification in structural systems. Orthogonal bases are ordered based on energy content [22] and, in the context of structural analysis, response data are stored in the form of a snapshot matrix. To obtain corresponding POMs, the SVD of the snapshot matrix is calculated as:  
\[\mathbf{U}=\mathbf{L}\ \mathbf{\Sigma}\mathbf{R}^{T} \tag{1}\]  
where \(\mathbf{U}\) presents the snapshot matrix; \(\mathbf{L}\) and \(\mathbf{R}\) denote matrices containing left and right singular vectors, respectively; and \(\mathbf{\Sigma}\) is a diagonal matrix containing singular values. Damage is identified using the left singular vectors, \(\mathbf{L}\), of the snapshot matrix, which are the POMs [14; 33]. The first POM is commonly considered for damage feature extraction.
# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline Apostolopoulos et al. (2022) [340] & COVID-19 detection & The authors mentioned that explainability in their specific application is important to provide insights into the decision-making process of the deep learning model. Also, they mentioned that transparency is also essential for building trust in the model's decisions, ensuring accountability, and enabling the actual users of the model to understand and interpret the image findings. \\ \hline Henzel et al. (2021) [61] & COVID-19 data classification & The authors mentioned that explainability in their specific application is important to explain the relationship between symptoms and the predicted outcomes. And also to enhance the interpretability of the models and provide a transparent understanding of how the classifiers make decisions. \\ \hline Deramgozin et al. (2023) [341] & Facial action unit detection & Explanations in systems that detect facial expressions give interpretability and deeper understanding about how the model works. \\ \hline Maouche et al. (2023) [43] & Breast cancer metastasis prediction & The authors mentioned that explainability in their specific application is important to address the issue of model interpretability. Further, they mentioned that the increased complexity of the models is associated with decreased interpretability, which causes clinicians to distrust the prognosis. \\ \hline Zaman et al. (2021) [263] & Control chart patterns recognition & Researchers and other users need explanations of ML-made decisions to evaluate the model and to make correct final decisions. It is important to find explainable and efficient ML systems that do not require too many resources. \\ \hline Dassanayake et al. (2022) [342] & Autonomous vehicles & The authors mentioned that explainability in their specific application is important to enhance the confidence of DNN-based solutions. They mentioned that for autonomous systems operating in unpredictable environmental conditions, the rationale behind the decisions made by DNNs is essential for accountability, reliability, and transparency, specifically in safety-critical edge systems like autonomous transportation. \\ \hline McFall et al. (2023) [112] & Early detection of dementia in Parkinson’s disease & The authors mentioned that explainability in their specific application is important to selectively identify and interpret early dementia risk factors in Parkinson's disease patients. \\ \hline \end{tabular}