esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work presents a novel method for detecting targets in the presence of an overwhelming source where a traditional event-based method was shown to fail. The developed method used frequency analysis, principal component background suppression, and statistical filtering on data from a simulated event-based sensor. By choosing smart temporal bin sizes and using only ON events, target detection exceeded that of a standard frequency-based Mahalanobis distance detector by over fourfold, with consistently low false-alarm rates. Current limitations include the detection of targets near the center of the overwhelming source and detection of low frequency targets, where target event generation was greatly limited. Overall, this method shows the ability to recover information from otherwise unusable recordings, preventing the loss of potentially critical data.  
**Author Contributions:** Conceptualization, W.J., S.Y. and M.D.; methodology, W.J., S.Y., B.M. and M.D.; software, W.J. and S.Y.; validation, W.J.; formal analysis, W.J.; investigation, W.J.; resources, M.D. and S.Y.; data curation, W.J. and D.H.; writing--original draft preparation, W.J.; writing--review and editing, S.Y., D.H., R.O., Z.T., B.M. and M.D.; visualization, W.J.; supervision, S.Y., R.O., Z.T., B.M. and M.D.; project administration, W.J., S.Y. and M.D.; funding acquisition, M.D., S.Y. and Z.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Space Vehicles Directorate, Air Force Research Laboratory, 3550 Aberdeen Ave SE, Albuquerque, NM 87117, USA. Contract number: 01705400005D.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Dual Use Research:** Current research is limited to signal processing, which is beneficial to emergency response, search and rescue, and autonomous vehicle safety algorithms; and does not pose a threat to public health or national security. Authors acknowledge the dual use potential of the research involving military applications and confirm that all necessary precautions have been taken to prevent potential misuse. As an ethical responsibility, authors strictly adhere to relevant national and international laws about DURC. Authors advocate for responsible deployment, ethical considerations, regulatory compliance, and transparent reporting to mitigate misuse risks and foster beneficial outcomes.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Disclaimer:** The views expressed are those of the authors and do not necessarily reflect the official policy or position of the U.S. Department of the Air Force, the U.S. Department of Defense, or the U.S. government.
## 2 Related Works
### Sdma-Pbfet  
SDMA-PBFT enhances the consensus performance of PBFT by dividing the participating nodes into smaller groups and establishing a hierarchical structure among these groups [17]. Initially, the client node broadcasts a consensus request message to the agent nodes. Each agent node acts as a leader for its corresponding lower-level group. Upon receiving the consensus request message, agent nodes broadcast it to any existing lower-level groups. If there are no additional lower-level groups to broadcast the consensus message, then those agent nodes initiate the PBFT consensus process within their group. Once consensus is achieved across the groups, the results are relayed back to the client node. Considering \(n\) consensus nodes overall and \(k\) nodes in each group, the communication complexity of SDMA-PBFT is calculated as \(O(n\times k\times\log_{k}n)\). SDMA-PBFT ensures determinism by involving all nodes in the consensus and partitioning the entire set of nodes into smaller groups for PBFT-based consensus. However, since SDMA-PBFT employs a fixed value for \(k\), which significantly impacts the consensus execution time, it may not consistently ensure optimal performance in terms of the total execution time required for each consensus operation, especially as the number of nodes in the consensus network varies.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 2 Related Works
### Utility-Based Routing  
The utility-based routing strategy selects the appropriate next hop node to avoid blind forwarding of messages. An energy-aware social-based routing scheme in opportunistic networks is proposed [15]. In this scheme, energy awareness as an important criterion in the routing decision is introduced. The result shows that the total life span of the OppNets is increased. However, delivery costs have been significantly improved. [PERSON]. [16] designs an energy-efficient routing protocol named EHBPR for infrastructure-less OppNets, which utilizes multi-factor constraints to reduce the number of packets transferred in the network and energy consumption of nodes. In [17], an energy-based routing protocol for OppNets is proposed, which performs a genetic algorithm on the personal information about a node to select an appropriate node as the next hop. However, the limitation is that the movement model used in this work is highly suited to human mobility scenarios. [PERSON] [18] introduces a probabilistic metric called delivery predictability, a decision is made on whether or not to forward the message to this node. [PERSON] [19] proposes a probabilistic routing scheme based on game theory (PRGT) to stimulate cooperation among selfish nodes. However, one major problem with OppNets is how to find and solve the problem of gangs defrauding the bounty.  
As mentioned earlier, the copy-based routing scheme improves the delivery ratio; however it consumes a lot of resources and degrades network performance due to multiple message copies. In active movement-based routing, special nodes with strong mobility and capability to store messages are introduced to assist the data transmission. However, the assumption is made with fixed destination node location and the special nodes significantly differ from ordinary nodes. The utility-based routing strategy mainly relies on some metrics, such as the energy, delivery probability, etc., to perform the routing strategies. However, the problems are not considered that the case of no further forwarding nodes may occur and how node attributes affect forwarding opportunities in oceans.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 3 Trust Establishment and Trust-Based Monitor Selection
### Link Duration Estimation (LD)  
Although the use of GPS should become commonplace in mobile nodes, we introduce a scheme to estimate the LD without the need of GPS (in case the GPS is not able to effectively estimate the velocity of nodes or is simply not available). We use the Doppler shift subjected to packets to calculate the relative velocity of nodes. The distance between nodes is calculated using the scheme used in [30], which uses signal strength to calculate the distance between the nodes by using the simplified free space propagation model given in [31]. For the mobility model it is assumed that mobile nodes are pseudo-linear, and highly mobile in nature which is the case for FANETs.  
\[\begin{split} LD(i,j)=\begin{cases}\frac{1}{2v}(\sqrt{2d^{2}-4(d^ {2}-R^{2})}+d\sqrt{2}&(\frac{f}{f_{0}}<1)\\ \frac{1}{2v}(\sqrt{2d^{2}-4(d^{2}-R^{2})}-d\sqrt{2}&(\frac{f}{f_{0}}>1) \end{cases}\end{split} \tag{8}\]  
where \(f\) is the actual frequency of the signal, \(f_{0}\) is the observed frequency, \(R\) is the maximum communication range of the UAVs and \(d\) is the initial distance between two nodes given by:  
\[d=\frac{\lambda}{4\pi}\sqrt{\frac{P_{t}}{P_{r}}} \tag{9}\]where \(P_{r}\) is the initial received signal power, \(P_{t}\) is the known transmission signal power and \(\lambda\) is the carrier's wavelength. The link duration estimation for MANET can be also applied for FANET because of the continuous existence of Line of Sight (LoS) among communicating nodes [32].
## 3 Results
### CPR Performance with No-Feedback and with Real-Time Feedback System
#### 3.1.1 Chest Compression Depth
There was a significant difference in the compression depth parameter with feedback-assisted performance compared to the unassisted performance (\(p<\) 0.05).
## Appendix
### _The Strong Tracking Algorithm_  
Based on the idea as in the AKF, the synthesis of UKF and strong tracking filter leads to the strong tracking unscented Kalman filter (STUKF). The suboptimal fading factors are introduced into the nonlinear smoother algorithm:  
\[s_{i,k}=\frac{tr[\eta\mathbf{V}_{k}-\varepsilon\mathbf{R}_{k}]}{tr[\mathbf{P}_{ zz}]}=\begin{cases}s_{i,k},&s_{i,k}>1\\ 1,&s_{i,k}\leq 1\end{cases} \tag{46}\]  
\[\mathbf{V}_{k}=\begin{cases}\mathbf{v}_{0}\mathbf{v}_{0}^{T}\\ \frac{[\rho\mathbf{V}_{k-1}+\mathbf{v}_{k}\mathbf{v}_{k}^{T}]}{1+\rho},&k\geq 2 \end{cases} \tag{47}\]  
The covariance matrix needs to be updated the following way. The new \(\mathbf{P}_{k}^{-}\) needs to be modified and can be obtained by multiplying Equation (42) by the factor \(\mathbf{S}_{k}\) :  
\[\mathbf{P}_{k|k-1}=\mathbf{S}_{k}\left\{\sum\limits_{i=0}^{2n}W_{i}^{(c)}[ \zeta_{i,k|k-1}-\hat{\mathbf{x}}_{k|k-1}][\zeta_{i,k|k-1}-\hat{\mathbf{x}}_{k| k-1}]^{T}+\mathbf{Q}_{k}\right\} \tag{48}\]  
Similarly, the covariance matrix \(\mathbf{P}_{zz}\) and \(\mathbf{P}_{xz}\), as represented by Equations (43) and (44), also respectively, can be modified and rewritten as:  
\[\mathbf{P}_{zz}=\mathbf{S}_{k}\left\{\sum\limits_{i=0}^{2n}W_{i}^{(c)}[ \mathbf{Z}_{i,k|k-1}-\hat{\mathbf{z}}_{k|k-1}][\mathbf{Z}_{i,k|k-1}-\hat{ \mathbf{z}}_{k|k-1}]^{T}+\mathbf{R}_{k}\right\} \tag{49}\]  
\[\mathbf{P}_{xz}=\mathbf{S}_{k}\left\{\sum\limits_{i=0}^{2n}W_{i}^{(c)}[ \zeta_{i,k|k-1}-\hat{\mathbf{x}}_{k|k-1}][\mathbf{Z}_{i,k|k-1}-\hat{\mathbf{ z}}_{k|k-1}]^{T}\right\} \tag{50}\]  
where \(\mathbf{S}_{k}=diag\left(s_{1},s_{2},...,s_{m}\right)\). One approach is to assign the scale factors as constants. When \(s_{i}\leq 1\) ( \(i=1,2,...,m\) ), the filtering is in a steady state processing while \(s_{i}>1\), the filtering may tend to be unstable. For the case \(s_{i}=1\), it deteriorates to the standard Kalman filter. Further detailed information for the strong tracking unscented Kalman filter can be referred to Reference [9].  
(c 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ([[http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/)]([http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/))).
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Experimental Results and Analyses
#### 4.4.2 Prediction Results for Different Window Steps  
To investigate the impact of step size on the prediction accuracy of the model, we conducted experiments using the proposed EL-MTSA model with step sizes of 1, 3, 5, 7, 10, and 15, respectively. Table 3 presents the comparative experimental results for different synchronization lengths, while Figure 6 illustrates the prediction results for the CSI 300, SSE 500, and SCI A50 datasets.  
Through experimental comparison, it is observed that a step size of five yields the lowest errors and the highest prediction accuracy. Adjusting the step size allows the model to better align with the prediction time horizon, enabling more accurate capture of market fluctuations and trends. A step size of five is chosen primarily because there are exactly five trading days in a week, and sentiment indicators within this interval can effectively reflect specific market trends and emotional states. Additionally, a step size of five strikes a balance between prediction  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Model** & **MAE** & **SMAPE** & **RMSE** \\ \hline \multirow{8}{*}{CSI 300} & LSTM & 103.2417 & 86.3657 & 6.9361 \\  & LSTMs & 92.97682 & 76.8875 & 6.1656 \\  & GRU & 110.4894 & 94.0744 & 7.4454 \\  & GRUs & 96.11654 & 81.9423 & 6.6180 \\  & Bi-LSTM & 112.3565 & 98.4684 & 7.9174 \\  & Bi-LSTMs & 103.2319 & 84.5469 & 6.7844 \\  & CNN-GRU & 134.956 & 121.7692 & 9.8259 \\  & CNN-GRUs & 129.1301 & 115.4156 & 9.3368 \\ \hline \multirow{8}{*}{SSE 500} & LSTM & 123.5744 & 87.2047 & 5.1516 \\  & LSTMs & 118.9043 & 89.0538 & 5.2994 \\  & GRU & 131.2696 & 104.1869 & 6.1949 \\  & GRUs & 123.5361 & 87.0197 & 5.1353 \\  & Bi-LSTM & 133.6997 & 107.8816 & 6.4218 \\  & Bi-LSTMs & 130.2673 & 105.2568 & 6.2729 \\  & CNN-GRU & 220.4278 & 177.5467 & 10.3707 \\  & CNN-GRUs & 196.7126 & 159.0642 & 9.3620 \\ \hline \multirow{8}{*}{CSI A50} & LSTM & 63.0576 & 51.7572 & 5.6813 \\  & LSTMs & 56.9408 & 41.5815 & 4.6440 \\  & GRU & 63.4638 & 53.4567 & 5.9116 \\  & GRUs & 59.6080 & 41.0670 & 4.5420 \\  & Bi-LSTM & 76.4491 & 66.4530 & 7.2827 \\  & Bi-LSTMs & 69.2172 & 59.2439 & 6.5821 \\  & CNN-GRU & 82.5765 & 68.9709 & 7.5493 \\  & CNN-GRUs & 77.5468 & 65.1126 & 7.1062 \\ \hline \end{tabular}
\end{table}
Table 2: Experiment results of sentiment indicators.  
Figure 5: Reliability study of sentiment indicators.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 6: Troup Training  
Augmented reality seamlessly blends the virtual world with reality. In a military context, this technology greatly enhances soldiers' capacity to spot adversaries and acquire real-time battlefield information. Notably, augmented reality proves indispensable for military training. By donning virtual reality glasses, it becomes feasible to simulate a computer-generated scenario that closely mirrors reality. This enables soldiers to train with authentic equipment they would employ on actual missions.  
Virtual reality glasses have several uses within military training; one of them is to connect directly with the image recorded by the surveillance cameras on a supposed battlefield in such a way that they can intercept the enemy much faster. Additional technologies like trusted and edge computing, tactile interfaces, and tactical internet play a pivotal role in advancing virtual and augmented reality technology. These technologies primarily center around interactions between humans and machines, as well as between different machines. They incorporate tactile and haptic feedback to facilitate real-time interaction with the environment. This setup ensures high availability, security, and exceptionally swift response times, particularly when using virtual reality glasses. Furthermore, artificial intelligence aids in the recreation of environments, allowing troops to immerse themselves in simulated scenarios.
## 4 Traffic Simulation Modeling
### Validation  
The PTV Visum code was preliminarily validated through a comparison with the queue length measured by the motorway management agency after a real frontal collision between a heavy vehicle and a car occurred within the north tube.  
The traffic accident occurred at 7:30 a.m., leading to the complete closure of the north tube. After 30 min from the incident (i.e., 8:00 a.m.), the motorway management agency imposed all vehicles to exit the motorway at Junction B of Figure 2, which is located before the entrance portal of the tube blocked. The measured queue length upstream of the mentioned Junction B was found to be equal to 2 km and 4 km at 8:30 a.m. and 9:15 a.m., respectively. Finally, at 9:30 a.m., one lane of the tube was opened and at 11:00 a.m. the traffic queue ended.  
The aforementioned event was simulated by using the PTV Visum, and by assuming that a passenger car and HGV in the queue occupy a space of 7 m and 14 m (default values in PTV Visum), respectively. Under these hypotheses, the queue length upstream of the mentioned motorway junction was computed to be in simulation equal to 2.1 km at 8:30 a.m., and 4.2 km at 9:15 a.m.; finally, the simulated traffic queue ended at 11:10 a.m. Therefore, there was a good level of conformity between the measured and simulated values of the queue (i.e., an error of no more than 5%).
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
## 3 Finite Element Simulation of Corroded Reinforcement  
### Finite Element Model  
#### 3.1.1 Model Establishment  
The propagation of GUWs in a reinforcement (length \(L=1100\) mm, diameter \(D=18\) mm, modulus of elasticity MOE = 208 GPa, density \(\rho=7850\) kg/m\({}^{3}\), [PERSON]'s ratio \(\
u=0.3\), no material damping effects) is simulated by ABAQUS/Explicit. The axial unit size in the model is 1 mm, and the automatic integration time step is used in the analysis process. The simulation process is divided into two stages: ultrasonic excitation and ultrasonic propagation. The mesh division and ultrasonic excitation of the reinforcement model are shown in Figure 3. One end of the model is completely fixed, and the other end only releases the axial displacement.
## 3 Experimental Results
### Motion Scenarios  
The effectiveness of the aerial manipulator CoG compensation mechanism is tested by moving the end-effector to three different locations in the workspace and quantifying the tilt angle reduction by comparing the tilt angle with, and without, the CoG compensation system slider motion. Table 2 shows the motion scenario coordinates and the tilt angle reduction percentage, \(\Delta\theta_{r}=\left|\theta_{s}^{\prime}-\theta_{s}\right|\), where \(\theta_{s},\;\theta_{s}^{\prime}\) are the average steady-state tilt angle values with and without the CoG compensation mechanism, respectively.
# An Integrated Testbed for Power System Cyber-Physical Operations Training
## 2 Cyber-Physical Systems  
A power system is a complex CPS consisting of power plants, substations, and transmission and distribution systems. The physical parcel of the power grid relies on the cyber system for monitoring, control, and operation. The cyber system comprises smart information and communication technology at the substations and the SCADA system at the control center [33]. Several power system applications are supported by the SCADA system, and the measurements at the substations are delivered to the control center through the ICT network [34]. The control commands, such as the opening and closing of a switch, can be sent from the control system to the remote terminal units or gateways in the power stations. The high penetration of the ICT system on the modern SCADA system makes it more vulnerable than before to cyber intrusions. Substations are also critical, as they have power system components such as intelligent electronic devices, transformers, breakers, and switches [35]. Usually, the information from the substation is analyzed for energy management systems in the smart power grid. Thus, it is crucial to enhance the cybersecurity of substations and analyze security as an integrated model to enhance the cyber-physical aspects of smart grids [36]. Table 1 summarizes the most recent research on cyber-physical aspects of power systems based on the simulation environment. It shows that the study of CPS is growing towards using real-time simulation environments and a co-simulation platform between the cyber layer and the physical layer. These simulation environments provide an ideal platform to perform system evaluation, considering the cyber layer and physical layer together under abnormal operational scenarios without disrupting the operation of the actual system. The study incorporates cyber and physical layers to help investigate cyber threat models [37] and power grid dynamics by integrating real-world physical components commonly found in practical settings. They enable decision-making based not only on theoretical analyses but also on practical studies. In addition, the use of these CPS testbeds can be expanded to facilitate the proactive assessment of cyberattack or fault mitigation and control strategies before deploying the corresponding hardware in the field, thus reducing the risks associated with the costly and unpredictable deployment process [38]. Thus, rather than focusing on the technical aspects, our research is more inclined towards practical implementation within the real-time environment using real-time co-simulation testbeds.
## 3 Results and Discussion
### Local Ionization Potential Map (LIPM)  
When assessing chemical reactivity and selectivity in terms of electrophilic reactions, the ionization potential--which reflects the overlap of the ionization energy on the electronic density--is a valuable metric [50]. In Figure 5, colors toward red represent negative values, while colors toward blue represent positive values. Electrostatic potentials on molecular surfaces can be complemented by local ionization potential maps when processes involve charge transfer and bond formation, as in the electrophilic attacks [51].  
The analysis of the local ionization potential map (LIPM) (Figure 5) provides significant insights into the electronic distribution and reactivity of the studied molecules. The three polymer molecules exhibit both hydrophilic regions (positive and negative electrostatic potential) and hydrophobic regions (neutral electrostatic potential). Similarly, the QPhNO\({}_{2}\) molecule also displays positive and negative electrostatic potential values, indicating the presence of hydrophilic and hydrophobic regions. The LIPM reveals specific areas with varying ionization potentials. The QPhNO\({}_{2}\) molecule demonstrates the most negative and  
Figure 5: Map of the local ionization potential (LIPM) of (**A**) PMMA, (**B**) PCL, (**C**) PLGA with six repeating units, and (**D**) QPhNO\({}_{2}\).  
positive ionization potentials, respectively, with values of 9.41 eV and 14.15 eV. These values indicate regions with varying tendencies for electrophilic and nucleophilic interactions, respectively. The negative EPM regions, indicated in red, correspond to electrophilic reactivity, whereas the positive regions, shown in blue, are responsible for nucleophilic reactivity. These findings are significant for elucidating the chemical behavior and potential applications of these molecules in drug delivery and polymer science.
## 2 Literature Review
[45] developed a new response spectrum method by incorporating the ductility factor and strain rate into the conventional response spectrum method. [PERSON] et al. [46] presented the results of studies on two important seismic parameters namely, ductility and response modification factor for moment resisting frames with concrete-filled steel tube columns. [PERSON] and [PERSON] [47] proposed simplified procedure to estimate the peak inter-story drift ratios of steel frames with hysteretic dampers for SDOF and MDOF systems where the energy dissipated by hysteretic behavior and the involved ductility were explicitly considered. The abovementioned studies represent a significant contribution regarding the evaluation of ductility or force reduction factors, however, in most of them SDOF systems, plane shear buildings, or a limited level of inelastic deformation were considered. Therefore, they did not explicitly consider the inelastic behavior and energy dissipation of the structural elements existing in actual systems. It has been shown [48; 49; 50] that ductility demands as well as the ductility reduction factors depend on the amount of dissipated energy, which in turn depends on the plastic mechanism formed in the frames as well as on the loading, unloading and reloading process at plastic hinges. In addition, a limited level of inelastic deformation is not associated to the ductility capacity. Moreover, local ductility taking into account the maximum inelastic curvature and tensional elongations as well as relationship between local and global ductility, estimated by dynamic and pushover analysis, have not been considered.