esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
###### Abstract  
Underwater wireless sensor networks (UWSNs) have attracted increasing attention in recent years because of their numerous applications in ocean monitoring, resource discovery and tactical surveillance. However, the design of reliable and efficient transmission and routing protocols is a challenge due to the low acoustic propagation speed and complex channel environment in UWSNs. In this paper, we propose a novel cross-layer routing protocol based on network coding (NCRP) for UWSNs, which utilizes network coding and cross-layer design to greedily forward data packets to sink nodes efficiently. The proposed NCRP takes full advantages of multicast transmission and decode packets jointly with encoded packets received from multiple potential nodes in the entire network. The transmission power is optimized in our design to extend the life cycle of the network. Moreover, we design a real-time routing maintenance protocol to update the route when detecting inefficient relay nodes. Substantial simulations in underwater environment by Network Simulator 3 (NS-3) show that NCRP significantly improves the network performance in terms of energy consumption, end-to-end delay and packet delivery ratio compared with other routing protocols for UWSNs.  
underwater sensor networks; network coding; routing protocol; cross-layer design +
Footnote †: journal: Sensors
## 3 Results
### Feature Importance Analysis  
When analyzing feature importance through the attention mechanism, the results indicated a relatively uniform distribution of importance across the 18 input features for both \(\delta_{IBT}\) and \(\delta_{LT}\). This uniformity can be attributed to the inherent behavior of attention layers, which tend to distribute importance evenly across input features, particularly in models with diverse and complex inputs. The attention mechanism within the bidirectional LSTM learns to weigh each feature, but in this case, it reflects a balanced consideration of the features rather than focusing heavily on any single predictor.  
While the attention mechanism results provided limited differentiation between features (see Figure 7), the perturbation-based analysis offered a more direct understanding of which features had the most significant impact on the model's predictions. For \(\delta_{IBT}\), the perturbation analysis highlighted \(dSIBT\_TO\) as the most important feature (Figure 7C). Other operational timing variables, such as \(dATOT\_TO\) and \(dAObject\_TO\), also contributed to the model's predictions. These results indicate that, despite higher prediction errors for in-block times, the model remained highly sensitive to changes in these key operational metrics. Additionally, other features such as geographical data and historical delays contributed to the model's predictions but were less impactful in comparison to operational timing.  
For \(\delta_{LT}\), key operational timing features such as \(dATOT\_TO\) and \(dAObject\_TO\) emerged as highly influential, as seen in Figure 7D. Additionally, features like the distance to a flight  
Figure 6: The dependency of predicted values on ground truth for predictions of \(\delta_{IBT}\) (**A**) and \(\delta_{LT}\) (**B**) along with performance characteristics of the models.  
point \(POINT\_DIST\) also contributed substantially to prediction accuracy, reflecting the critical role of real-time operational data in forecasting landing times. Other features, including wind conditions and historical delays, also contributed, although with less impact.
## 6 Conclusions and Future Research Directions  
This manuscript has introduced an innovative methodology, named BERT + DNN, which addresses the detection and evaluation of infringing content in the IoE for key categories such as movies and series, music, software, and books. This methodology has greatly benefited from a structured taxonomy, answering the first research question. The taxonomy not only effectively categorizes the content but also provides authors with a crucial tool for making informed and relevant decisions within their specific contextual frameworks. By accurately identifying and classifying different forms of infringing content, authors and stakeholders can adapt their strategies more efficiently and effectively based on the particular challenges they face.  
Moreover, in response to the second research question, the feasibility of developing an advanced methodology in ML, using tracking techniques in the IoE and sophisticated search algorithms, has been demonstrated. The BERT + DNN methodology not only analyzes, processes, and categorizes websites containing potentially infringing content but also does so with a precision of 98.71% and a recall and F\({}_{1}\)-score of 98.67%. This achievement underscores the viability and effectiveness of applying advanced ML approaches in the fight against copyright infringement in the IoE.  
While limitations regarding scope, collection methods, characterization, and the algorithms used have been discussed, these findings open new avenues for future research and development in this field. Thus, the current study not only surpasses existing approaches in the detection of infringing content but also offers a robust framework for informed decision-making and the implementation of copyright protection strategies in an ever-evolving digital environment. Recognizing this as a step in an expansive, under-explored field, this study suggests future research directions:  
* Integrating Optical Character Recognition (OCR) for DOM exploration to identify non-explicitly depicted multimedia objects;
* Merging the approach with an API for direct reporting to authorities, enabling automated content takedown;
* Constructing the deployment phase of CRISP-DM by processing URLs for real-time monitoring and classification;
* Broadening the range of categories to encompass piracy, live-streaming, social media networks, and the DarkNet.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
The authors thank the National Council for Humanities, Sciences, and Technologies (CONHACYT) and the Instituto Politecnico Nacional for their support of this research. The authors declare no conflicts of interest.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 5 Performance Parameters
### Power Consumption  
The average power consumption of SMTrustOF and MRHOF shows a small difference, which is justified as nodes in SMTrustOF perform trust computations. Though SMTrustOF shows lesser power consumption for all three scenarios under Blackhole attack, and for Scenario II and III under Rank attack. The power consumption of SMTrust is slightly higher as compared to MRHOF (under Rank attack in Scenario I), SecTrust, DCTM, and MRTS, as shown in Figure 11. However, the difference is negligibly small, that is, up to 0.22 mW. This is due to difference in computations involved for trust index, attack detection and trusted parent selection.  
The power consumption in SMTrust is more because of the computations involved for trust metrics, and the DIO transmissions along with attack detection mechanism. Once the attacking nodes are detected and isolated by SMTrust, the overall power consumption of the network becomes stable. Furthermore, it is to be noted that the improvement of power consumption is beyond the scope for this research work; however, SMTrust shows a comparatively acceptable increase in power consumption.  
Overall SMTrust demonstrates better performance for both Rank and Blackhole attacks. The performance of SMTrust is better for Scenario II as compared to Scenario I and Scenario III, indicating the importance of mobility-based trust metrics computation for parent selection algorithm. Moreover, the effects of sink mobility degrade the network performance for SMTrust under Rank and Blackhole attacks as compared to the results  
Figure 10: Comparison of Throughput (kbps).  
of Scenario I and II. However, SMTrust still outperforms in overall network performance parameters, including topology stability, PLR, and throughput measurement of nodes, indicating significant improvement as compared to existing systems.
###### Abstract  
Brain magnetic resonance imaging (MRI) of infants with congenital heart disease (CHD) shows brain immaturity assessed via a cortical-based semi-quantitative score. Our primary aim was to develop an infant paralimbic-related subcortical-based semi-quantitative dysaturation score, termed brain dysplasia score (BDS), to detect abnormalities in CHD infants compared to healthy controls and secondarily to predict clinical outcomes. We also validated our BDS in a preclinical mouse model of hypoplastic left heart syndrome. **Methods:** A paralimbic-related subcortical BDS, derived from structural MRIs of infants with CHD, was compared to healthy controls and correlated with clinical risk factors, regional cerebral volumes, feeding, and 18-month neurodevelopmental outcomes. The BDS was validated in a known CHD mouse model named _Ohia_ with two disease-causing genes, _Sap130_ and _Pchda9_. To relate clinical findings, RNA-Seq was completed on _Ohia_ animals. **Findings:** BDS showed high incidence of paralimbic-related subcortical abnormalities (including olfactory; cerebellar, and hippocampal abnormalities) in CHD infants (\(n=215\)) compared to healthy controls (\(n=92\)). BDS correlated with reduced cortical maturation, developmental delay, poor language and feeding outcomes, and increased length of stay. _Ohia_ animals (\(n=63\)) showed similar BDS findings, and RNA-Seq analysis showed altered neurodevelopmental and feeding pathways. _Sap130_ mutants correlated with a more severe BDS, whereas _Pchda9_ correlated with a milder phenotype.  
**Conclusions:** Our BDS is sensitive to dysmaturational differences between CHD and healthy controls and predictive of poor outcomes. A similar spectrum of paralimbic and subcortical abnormalities exists between human and _Ohia_ mutants, suggesting a common genetic mechanistic etiology.  
**Keywords:** olfactory bulb; hippocampus; cerebellum; feeding outcome; neurodevelopmental outcome; _Ohia_ HLHS mouse mutants
## 3 Results
### Results on the Case Study with Real Data
This happens with very high consistency across the 100 iterations, as the average rank is very close to 1 and the SD of the rank is close to 0. However, when BMI and waist circumference are both included in the set of candidate predictors, as in approach 1, and the ranking is performed ignoring the correlation between these features, we can see that the obesity variables are assigned lower importance values. In particular, they are ranked in the third position, after HDL cholesterol and systolic blood pressure, and their rank is much more unstable across the 100 iterations; the SD is around 4, the second-highest value among all the candidate predictors. This means that the estimated rank obtained for these features with the standard RFE-Borda count method is very uncertain. This example confirms what we discussed in the introduction of this paper: the presence of highly correlated features, if not properly managed, can affect the results of variable ranking. The proposed algorithm is effective in solving this issue. Indeed, looking at the results for approach 2 in Table 5, we can see that the proposed algorithm, which takes into account the correlation between BMI and waist circumference, is able to identify the high predictive ability of the obesity features, which are now ranked again in first position with high confidence (the rank is 1.01 \(\pm\) 0.1). This result confirms what was already observed in the in-silico study: in presence of highly correlated features, the proposed algorithm allows to perform a more robust variable ranking compared to the standard RFE-Borda count method, without the need of pre-filtering the highly correlated features based on a priori knowledge or assumptions. This aspect is important because an effective pre-filtering of the features, as carried out in approach 3 and 4, could be difficult to perform using just the a priori knowledge, especially when the dataset includes multiple groups of highly correlated features. A bad a priori choice on important predictors could negatively affect the model performance and generalization ability. After performing the variable ranking, the optimal number of features was selected by analyzing the curves of C-index vs. number of features in the out-of-bag samples. In Figure 6, the C-index curve returned by the proposed algorithm (approach 2) is shown. As expected, the average C-index value increases with the number of features, until a certain plateau is reached. The resulting optimal number of features which maximizes the average C-index value is 8. The same optimal number of features was obtained with approaches 3 and 4, whereas, with approach 1, in which the two obesity variables are treated independently, the optimal number of features is 9.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 2 Mathematical Preliminaries: SVS Indices
### Trajectory Violation Integral (Tvi)  
By definition of upper and lower boundaries for post-fault voltage trajectory, the \(TVI\) index for each bus in the system is calculated as follows [16]:  
\[TVI(t)=\int_{t_{cl}}^{t}v_{v}(\tau)d\tau \tag{12}\]  
where \(v_{v}(t)\) is:  
\[v_{v}(t)=\left\{\begin{array}{ccc}T_{low}(t)-v(t)&&v(t)<T_{low}(t)\\ v(t)-T_{upp}(t)&&v(t)>T_{upp}(t)\\ 0&&o.w\end{array}\right. \tag{13}\]  
\(T_{low}\) and \(T_{upp}\) are lower and upper boundaries, respectively, and \(T_{low}\) is described by an expression in (14).  
\[T_{low}(t)=\frac{\left(\frac{t}{t_{f}}e^{\frac{1}{f_{f}}}\right)^{B}}{e^{B}}.v _{st}\hskip 56.905512 pt\forall t\in[t_{cl},t_{f}] \tag{14}\]  
In Equation (14), \(v_{st}\) is steady state voltage and \(B\) is used for calibrating damping of boundaries. In addition \(T_{upp}\) is calculated using (15).  
\[T_{upp}(t)=2-T_{low}(t) \tag{15}\]  
Figure 1 illustrates the \(TVI\)s concept in which \(t_{cl}\) refers to fault clearing time, and \(t_{f}\) is the time that is considered for a short-time voltage stability study. Index \(TVI_{tot}\) in (16) is used for a system based on the bus indices. In this equation, \(v_{v,i}\) is \(v_{v}\) at bus \(i\).  
\[TVI_{tot}(t_{f})=\frac{1}{n}\sum\limits_{i=1}^{n}\int_{\tau=t_{cl}}^{t_{f}}v_{ v,i}d\tau \tag{16}\]
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### System Management and Planning  
One of the largest gaps in the defense and public safety data ecosystem is digital analytics (data collection, transformation, evaluation and sharing). Much of the massive information collected by sensors is never used, and, as for the information that is used, it often depends on manual entry and processing, which incur in significant delays when getting important information in mission-critical scenarios. Those delays can cause missions fail or stall, or force decision-making without relevant facts. For example, USTRANSMCOM bulk supplies are tracked between major hubs using RFID tags, but when supplies are broken down and distributed beyond the central hubs, they are replaced manually. Officers in the field sign for orders on paper and enter serial numbers into computers by hand. This approach is burdensome and poses risks due to human errors.  
Much of the value of IoT is generated by automation, allowing systems to react quicker and with more precision than humans. Few military systems include fully autonomous responses. For example, most unmanned systems deployed are not autonomous but remotely controlled by operators.  
This management effort needs the development of new lightweight management protocols. For example, monitoring the M2M communications of IoT objects is important to ensure constant connectivity. LightweightM2M [74] is a standard developed by the Open Mobile Alliance (OMA) to interface between M2M devices and servers to build an application-agnostic scheme for the remote management of a variety of devices. The NETCONF Light protocol [75] is an Internet Engineering Task Force (IETF) effort for the management of resource-constrained devices. In [76], the authors propose a framework for IoT management based on the concept of intercepting intermediary nodes in which they execute heavy device management tasks on the edge routers or gateways of constrained networks. The OMA Device Management working group specifies protocols for the management of mobile devices in resource constrained environments [77].
## 3 Theory
### Wavelength or Spatial Period of the Interference Pattern for the Star and Planet
\[\Lambda_{Pmin}(\lambda,\Delta\varphi,\theta_{P})=\Lambda_{Smin}(\lambda, \Delta\varphi,\theta_{S})\frac{sin\theta_{S}}{sin\theta_{P}} \tag{23}\]  
The ratio of the minimum wavelength of the fringe pattern to that of the star is equal to the ratio of the star elevation angle to the planet elevation angle. The error in the star alignment position is the star elevation angle. Returning to the example of Figure 5, this ratio is equal to the star radius, our order-of-magnitude pointing error. The star position error is assumed to be equal to its radius, \(10^{-4}\)\(\upmu\)rad, and the star-planet separation is 2\(\upmu\)rad. This means that for each star fringe, there are 20,000-planet fringes. One can safely state that  
Figure 5: A solar system, that includes a Sun-like star and a Jupiter-like planet at 10 parsecs, subtends an angle of 2 \(\upmu\)rad at the Earth. At that distance, the star and the planet subtend an angular radius of \(10^{-4}\) and \(10^{-5}\)\(\upmu\)rad at the Earth. Due to the resolution limitations of the available telescopes, both the planet and the star are unresolved, or point, sources. the planet fringes float on the negligibly inclined star exitance. The detection wavelength of 20 micron has been assumed, where the quantum signal-to-noise ratio is highest. The wavelength of the fringe pattern of the star and the planet may be rewritten as a function of the minimum fringe wavelength and its angular separation. Equations (19) and (20) are substituted into Equations (21) and (22):  
\[\Lambda_{S}(\lambda,\Delta\varphi,\ \varphi;\varphi_{S},\theta_{S})=\frac{ \Lambda_{Smith}(\lambda,\Delta\varphi,\theta_{S})}{\sin(\varphi-\varphi_{S})}, \tag{24}\]  
and  
\[\Lambda_{P}(\lambda,\Delta\varphi,\varphi;\varphi_{P},\theta_{P})=\frac{ \Lambda_{Pmin}(\lambda,\Delta\varphi,\theta_{P})}{\sin(\varphi-\varphi_{P})}. \tag{25}\]  
Using Equations (24) and (25), Equation (18) may be simplified:  
\[M_{SP}(\rho,\varphi;\varphi_{S},\theta_{S};\varphi_{P},\theta_{P})=\left\{ \begin{array}{c}M_{S}\Big{\{}\cos^{2}\Big{[}\frac{\pi\rho}{\Lambda_{Smith}} \sin(\varphi-\varphi_{S})\Big{]}\Big{\}}\\ +M_{P}\Big{\{}\cos^{2}\Big{[}\frac{\pi\rho}{\Lambda_{Pmin}}\sin(\varphi- \varphi_{P})\Big{]}\Big{\}}\end{array} \right.. \tag{26}\]  
In the inverse or the Fourier-transform space, this signal corresponds to two separate signals. Many techniques exist to eliminate the lower frequency signal, most notably filtering in the inverse space. Even for large values of the error in the star position, the star contributes to the incidence distribution simply as a constant background with a negligible gradient.
# Solution Algorithms for the Capacitated Location Tree Problem with Interconnections
## 4 CLTPI Solution Algorithms  
Instances of the CLTPI can be solved using exact and metaheuristic methods. Exact methods are based on mathematical programming formulations. On the other hand, metaheuristic methods are inspired by natural phenomena, physics phenomena, and animal behavior, among other sources. In this section, three solution approaches are presented. The first is an exact method (EM) based on the formulation presented in Section 3. The EM uses a branch-and-bound algorithm over the CLTPI formulation to obtain solutions. A good description of the branch-and-bound algorithm can be found in [27]. Since the CLTPI is an NP-Hard problem, solution methods like EMs are non-polynomial and consume many computational resources. In other words, the complexity of the EM grows exponentially as the size of the instances increases, and so does the computational effort.  
The other two are metaheuristic evolutionary algorithms. It is known that large instances of NP-Hard problems are tough to solve using exact methods and require a significant computational effort to obtain solutions. Therefore, using metaheuristic algorithms that can provide good-quality solutions with a reasonable computational effort is relevant. Different kinds of heuristics and meta-heuristics have been used for solving combinatorial optimization problems like Genetic Algorithms [28], Simulated Annealing [29], Ant Colony Optimization [30], the Gravitational Search Algorithm [31], Tabu Search [32], and Particle Swarm Optimization [33], among many others. To solve instances of the CLTPI, two metaheuristic optimization algorithms were selected: (i) the Particle Swarm Optimization algorithm (PSO) and (ii) the Biased Random-Key Genetic Algorithm (BRKGA). The reason for selecting both algorithms was their proven performance in solving tree-related combinatorial optimization problems (see [21; 34]). In the following, a brief description of these metaheuristic optimization algorithms is provided.
## 3 Simulation System Construction
(3)  
Calculating the standard deviation  
The root-mean-square error (RMSE) \(\sigma_{x_{j}}\) and \(\sigma_{y_{j}}\) are considered to evaluate the average detection error of QD in the effective detection range, that is, the position detection accuracy. \[\left\{ \begin{array}{l}\sigma_{x_{j}}=\frac{\sum_{n=1}^{N_{s}}x_{j_{m}}-\overline {y}_{j_{m}}}{\sqrt{N_{s}}}\\ \sigma_{y_{j}}=\frac{\sum_{n=1}^{N_{s}}y_{j_{m}}-\overline{y}_{j_{m}}}{\sqrt{ N_{s}}}\end{array} \right. \tag{23}\]  
where \(N_{s}\) represents the number of simulations, \(x_{j_{m}}\) and \(y_{j_{m}}\), respectively, represent the actual values of the x-axis and y-axis obtained from the \(m\)-th simulation of the \(j\)-th beam, which are values estimated using the Monte Carlo method. \(\overline{x}_{j_{m}}\) and \(\overline{y}_{j_{m}}\), respectively, represent the average values of the \(j\)-th beam, which are expectant values. The comparison between the true spot position values and the actual spot position values of the x-axis is shown in Figure 5. The comparison between the real values and the actual values of signal 1, signal 2, and signal 3 is shown in Figure 5a when the three spots are located at the center of the QD. And the comparison between the real values and the actual values of signal 1, signal 2, and signal 3 is shown in Figure 5b when the three spots are located at the (0.1, 0), (0, 0), and (\(-\)0.05, 0) of QD. It can be seen that the positions of the different light spots can be calculated from Figure 5.  
Figure 5: The comparison between the real values and the actual values of the QD: (**a**) the three spots are located at the center; (**b**) the three spots are located at the (0.1, 0), (0, 0), and (\(-\)0.05, 0). The assessment of the multi-spot position detection accuracy of the QD based on OFDM is principally categorized into the average relative error (ARE) and final relative error (FRE). The ARE represents the mean Euclidean distance between the true spot position values and the actual spot position values, while the FRE corresponds to the Euclidean distance between the true spot position values and the actual spot position values at the final temporal instance. The FRE \(e_{x_{j}}\) and ARE \(e_{meanx_{j}}\) can be written as Equations (24) and (25), where \(p\) is the total number and \(e_{x_{j\!f}}\) is \(f\)-th of the FRE. \[e_{x_{j}}=\frac{\left|\sigma_{x_{j}}-\sigma_{j_{nn}}\right|}{\sigma_{x_{j}}} \times 100\% \tag{24}\]  
\[e_{meanx_{j}}=\frac{\sum_{t=1}^{p}e_{x_{j\!f}}}{p} \tag{25}\]