esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation  
### Simulation Settings  
The behaviour of the conceived NDN-fog nodes in the campus network topology depicted in Figure 2 is evaluated in a realistic way by means of ndnSIMv2.5 [11], the official discrete-event ns-3-based simulator deployed by the NDN community.  
We have modified ndnSIM from its stock installation to implement the features of NDN-fog nodes and the abovementioned allocation strategies.  
We consider a catalog of 1000 services, each one requiring a randomly selected amount of CPU resources in the range [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75] Mycles. Similarly to Reference [49], since there are no datasets containing traces of the service workloads in real IoT scenarios, we assume that services are requested according to a Zipf distribution with skewness parameter \(\alpha\) set to 0.4 and 0.8. The request arrival rate follows the Poisson distribution.  
The following benchmarking schemes are considered:  
Figure 4: Main steps of the interest/data exchange in NDN-fog.  
* _Closest executor strategy_ (labeled as \(CE\) in the figures): Inspired by the work in Reference [10], it aims at executing the service as close as possible to the IoT providers.
* _Least loaded executor strategy_ (labeled as \(LLE\) in the figures): It targets at selecting as the service executor the node with the lowest CPU usage, similarly to the work in Reference [34].
* _Remote cloud_ (labeled as _Cloud_ in the figures): It resembles the case when all services are executed in remote cloud facilities. The following metrics are computed for the purpose of performance comparison:
* _Service provisioning time_ is computed as the time since the first interest is transmitted from a consumer to request the service until the output of the service execution is sent back (thus including the computation time at the executor).
* _Offloading-to-the-cloud percentage_ is derived as the percentage of times the service is executed in the remote cloud. It only applies to our proposal, labeled as _NDN-fog_.
* _Intra-edge exchanged traffic_ is computed as the overall number of NDN packets exchanged into the domain to request a given service, to select the executor, to retrieve the input data to be processed, and to return the result to the consumer.  
Simulation settings are reported in Table 2, and they hold unless differently stated in the text. Results are averaged over 100 runs and reported with 95% confidence intervals.
# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 2 Related Work
The proposed algorithm, also known as AI-enabled multi-hop and administrative-distance-based opportunistic routing (MHADBOR), processes the collected data from source to destination using the network's multi-hop count and administrative-distance-based communication infrastructure. In addition, the authors frequently used CHs, microbase, and macrobase stations in the network to efficiently manage the deployed network traffic in a communication environment without congestion. In [38], the authors proposed an optimization of resource utilization in smart agriculture systems using IoT (SMAIoT) that can monitor several low-cost IoT sensor types. This framework gathers information from the soil, air, water, and insects and uses them to produce suitable decisions. The innovative aspect of the proposed framework is the scientific automation of functions such as irrigation, fertigation, pest detection, and pesticide spraying with efficient productivity. In [39], the authors provide an inter planetary file system (IPFS) storage for protecting agricultural sampling data based on the double-blockchain method for IoT networks. It stores the content of the sampled data using the IPFS network, and the proposed system can obtain the entire data segment using an oracle technique. Then, the authors developed a consortium blockchain, Agricultural Sample Data Chain (ASDC) by using Ethereum technology, and enhanced the Merkle Patricia Trie (MPT)-based accounts for all categories of sampled data. To retain a public record in the case of malicious attacks, block hashes are generated and uploaded on Ethereum's main chain after storing the data in ASDC blocks.
## 7 Conclusions and Future Works  
In this research, we presented the Strike stream cipher. Strike is inspired by the stochastic behaviour of lightning strike phenomena. Each lightning strike has its own unique attributes, including the source and destination points, jump distances, peak intervals, power and pathways. These attributes represent the core security parameters of our lightning strike generator, which later generates keystreams for data encryption and decryption. In each iteration of keystream generation, the strike's attributes are updated and scrambled to ensure that the keystreams are correlation-free and resistant to statistical and cryptanalysis attacks.  
The scientific novelty of Strike cipher lies in the utilisation of the stochastic behaviour of lightning strike phenomena to reduce operations complexity and enhance the performance  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Cipher** & **Throughput (Mbit/s)** & **Ratio of Efficiency** \\ \hline Strike & 22,796.4 & - \\ [PERSON] et al., 2020) [28] & 22,222.2 & 2.5\% \\ Snow 2.0–128 & 22,026.4 & 3.4\% \\ Trivium & 10,026.7 & 56.0\% \\ Salsa-20 & 7987.2 & 65.0\% \\ AES-128 & 6581.8 & 71.1\% \\ HC-128 & 5980 & 73.8\% \\ Mickey-128 & 192.4 & 99.2\% \\ Grain & 13.8 & 99.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Stream ciphers performance results on M1.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **M1** & **M2** & **M3** & **M4** \\ \hline Throughput (Mbit/s) & 22,796.4 & 22,287.1 & 21,998.9 & 21,143.5 \\ Efficiency Ratio & - & \(-\)2.23\% & \(-\)3.5\% & \(-\)7.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Encryption throughout (Mbit/s) of Strike over different machines.  
Figure 9: Stream ciphers throughput analysis on M1.  
of data encryption. Such enhancements enabled Strike to work efficiently on a wide range of computing devices with limited resources. Strike outperforms other ciphers, including AES, Snow 2.0-128 and other well-known ciphers. The performance of Strike was tested on different environments with different computing capabilities. The results show that Strike managed to achieve a throughput of about 22,796 Mbit/s on a high-performance machine and about 21,143 Mbit/s on a low-performance machine. These results indicate that Strike is an efficient alternative for providing secure communication on various machines with different capabilities. In addition, further enhancement of Strike's performance is also possible through parallelism. The structure of Strike is designed to support multithreading, where multiple threads can be initiated to generate multiple keystreams simultaneously.  
All authors contributed equally to this research work. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created.  
The authors would like to thank Arab Open University, Saudi Arabia, for supporting this study.  
The authors declare no conflict of interest.
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
## 5 Experimental Simulation and Analysis
### Network Involurability Simulation Experiment and Analysis  
As we all know, the more important a node is, the greater its influence in the network. When the more important node is attacked and fails, the communication link will also fail and the efficiency of the entire network decreases faster. We will carry out the invulnerability simulation experiment on the network topology in Figure 4 and compare our method with two existing measurable invulnerability indicators (jump-surface node method and natural connectivity).  
In the previous section, we can see that node 2 is the most important in the network, node 1 is the second, node 4 is the third, and node 8 is the weakest in the network. When these four nodes fail in turn, we will simulate and calculate the invulnerability index of the spatial information network. The network invulnerability experiment results calculated by Equation (8) are shown in Table 6.  
It can be seen from Figure 8 that when no node fails, the network's invulnerability performance is the strongest. When any node fails, the network's invulnerability performance decreases by varying degrees. In Figure 4, node 2 is the most important. When node 2 fails, the network's survivability performance is the worst. It was found from the calculation results of natural connectivity that node 4 has the worst survivability when it fails, that is, the natural connectivity algorithm considers node 4 as the most important. Obviously, node 1 is more important than node 4. When node 1 fails, the survivability performance should be lower than when node 4 fails. The experimental results of the jump-surface node method are inconsistent with the actual results. In addition, node 8 has the lowest importance. When it fails, the network is the least affected and has the strongest invulnerability performance. Therefore, the entire graph should monotonically increase, which is consistent with the experimental results of this article. Based on the above analysis, we can observe that the methods in this article are more effective and accurate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Node 2** & **Node 1** & **Node 4** & **Node 8** & **No Failure Node** \\ \hline Our method & 1.5676 & 1.6711 & 2.1025 & 2.2469 & 2.2662 \\ Natural connectivity & 0.9917 & 1.0376 & 0.9683 & 1.0106 & 1.1119 \\ Jump-surface node method & 1.4800 & 1.5960 & 1.5100 & 1.6280 & 1.8270 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Involurability index when nodes fail in sequence.
### Organization  
The current paper is structured into several key sections, each serving a distinct purpose. The paper was opened with an Introduction and overview, providing the context, objectives, and significance of the research. Section 2 delves into the conceptual model, offering a comprehensive discussion of the theoretical framework and foundational concepts that guide the study. Section 3 presents the mathematical formulation of the proposed method, detailing the equations, algorithms, and computational techniques employed. The proposed method is provided in detail in Section 4. Simulation results and discussions are presented in Section 5. Finally, Section 6 concludes the paper with a summary of the key conclusions, highlighting the main findings, contributions, and potential avenues for future research.
## 5 Case Study: Oral Health
### Designing the RL Algorithm Candidates
We consider two types of approximations, a Bayesian linear regression model (BLR) and a Bayesian zero-inflated Poisson regression model (ZIP), which are both relatively simple, well studied, and well understood. For BLR, we implement action centering in the linear model [1]. The linear model for the reward function is easily interpretable by domain experts and allows them to critique and inform the model. We consider the ZIP because of the zero-inflated nature of brushing durations in our existing dataset ROBAS 2; see Figure 2. We expect the ZIP to provide a better fit to the reward function by the contextual bandit and thus lead to increased average rewards. Formal specifications for BLR and ZIP as reward functions can be found in Appendix B.2.1 and Appendix B.2.2, respectively. To perform posterior sampling, both the BLR and ZIP models are Bayesian with uninformative priors. From the perspective of computability and stability, the posterior for the BLR has a closed form, which makes it easier to write software that performs efficient and stable updates. In contrast, for the ZIP, the posterior distribution must be approximated and the approach used to approximate the posterior is another aspect of the algorithm design that the scientific team needs to consider. See Appendix C for further discussion on how to update the RL algorithm candidates. 5. Choice of Cluster Size: We consider clustering users with cluster sizes \(K=1\) (no pooling), \(K=4\) (partial pooling), and \(K=N=72\) (full pooling) to determine whether clustering in our setting will lead to higher sums of rewards (personalization). Note that 72 is the approximate expected sample size for the Oralytics study. Clustering-based algorithms pool data from multiple users to learn an algorithm per cluster (i.e., at update times, the algorithm uses \(H_{i,t-1}\) for all users \(i\) in the same cluster, and at decision times, the same algorithm is used to select actions for all users in the cluster). Clustering-based algorithms have been empirically shown to perform well when users within a cluster are similar [39; 40]. In addition, we believe that clustering will facilitate learning within environments that have noisy within-user rewards [41; 42]. There is a trade-off between no pooling and full pooling. No pooling may learn a policy more specific to the user later on in the study but may not learn as well earlier in the study when there is not a lot of data for that user. Full pooling may learn well earlier in the study because it can take advantage of all users' data but may not personalize as well as a no-pooling algorithm, especially if users are heterogeneous. We consider \(K=4\) for the balance partial pooling offers between the advantages and disadvantages between no pooling and full pooling. Moreover, four is the study's expected weekly recruitment rate and the update cadence is also weekly. We consider the two extremes and partial pooling as a way to explore this trade-off. A further discussion on choices of cluster size can be found in Appendix B.3.
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 5 Experimental and Performance Evaluation
### Results and Discussion
#### 5.2.2 CRNN-Based Classifier  
This section presents the results obtained in the experiment using the CRNN-based classifier, which employs the same parameters as [2] except that instead of using log-mel energy it uses both monaural features, that is, MFCC, RASTA-PLP, and binaural features, that is, the combination of MFCC and RASTA-PLP. In this experiment, we split the urban dataset into two sub-tasks, namely, machine-generated sounds and natural sounds. For the first task we used a 0.7-s filter mask and the second we used a 0.3-s filter mask. Performance on car and large vehicle sound events was better as filter size is raised in post-processing, whereas for the other sound event classes the performance was worse, and vice versa. The results of the CRNN-based experiments are shown in Table 5.  
Figure 5: ER comparison using ANN classifier.  
Figure 6: F1-score comparison using ANN classifier.  
For example, the MFCC feature in the CRNN for squeaking brakes has the highest F1-score (i.e., 28) and the lowest ER (i.e., 0.81). In the case of car sounds, the RASTA-PLP in CRNN achieves the lowest ER (i.e., 0.38), whereas the baseline [1] has the highest F1-score (i.e., 74). In addition, for children, F1-score achieved is 0, and RASTA-PLP achieves the lowest ER (i.e., 1). In the large vehicle class, the lowest ER is 0.67 and the highest F1-score is 52, both achieved with RASTA-PLP in CRNN. For people speaking, the baseline yields the highest F1-score of 18 and the lowest ER (i.e., 0.95). Similarly, in the people walking class, MFCC in CRNN achieves an F1-score of 56 and an ER of 0.57.  
In short, the MFCC feature in the CRNN for squeaking brakes has the highest F1-score (i.e., 28) and the lowest ER (i.e., 0.81). In case of cars, RASTA-PLP in CRNN achieves the lowest ER (i.e., 0.38) whereas the baseline model has the highest F1-score (i.e., 74). In addition, for children, the F1-score achieved is 0, and RASTA-PLP achieves the lowest ER (i.e., 1). In the large vehicle class, the lowest ER is 0.67 and the highest F1-score is 52, both achieved with RASTA-PLP in CRNN. For people speaking, the baseline model yields the highest F1-score of 18 and lowest ER (i.e., 0.95). Similarly, in the people walking class, MFCC in CRNN achieves an F1-score of 56 and an ER of 0.57.
###### Abstract  
Earthquakes occur as a result of ruptures on faults along plate boundaries. It is possible to reveal the approximate location and magnitude of the earthquake rupture, but this requires that the seismic cycle and kinematics of the study area are well known. Different measurement methods and modeling techniques are used to determine fault kinematics. Near-surface annual slip can be determined using various methods, such as the Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR), or geological studies. As a result of modeling using these methods, the slip amounts of the fault at any depth can be revealed. Interseismic modeling with the 3D Finite Element Model (FEM) is one of them. Considering the studies conducted in the literature, the effects of the discrete method of fault kinematics in the modeling performed with FEM have not been revealed. In order to fill this gap, 3D FEM modeling has been performed using velocity data from GNSS stations located around the Main Marmara Fault. The accuracy of the models made using different mesh types in ANSYS (Analysis System) software has been examined. The fault slip deficit values of the faults of the models with the lowest and highest Root Mean Square Error (RMSE) values have been compared. Possible earthquake magnitudes have been obtained after calculating the total slip deficit through taking into account the seismic gap. The moment magnitude of possible rupture difference has been revealed to be between 0.01 and 0.014 through using the lowest RMSE and the highest RMSE model.  
finite element method; mesh type; GNSS; modeling; seismic potential +
Footnote †: journal: _Sciencedence_
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 6 Experiments and Results
### Evaluation Indicators
#### 6.3.1 Average Delay  
Figure 4 shows the comparison of average delay for different sending rates.  
As is shown in Figure 4, with the increase of individual data transmission rate, the average delay will increase. Under the small individual data transmission rate and light network load, the average delay of centralized DSP routing algorithm is larger than that of the other two algorithms. This is because interval type-2 fuzzy set routing algorithm and ELB algorithm can send the traffic via alternate paths, which greatly reduces the queuing delay. As individual data transmission rate increases, the load increases. Because the centralized DSP algorithm continues to try the same transmission path to reduce the average delay at the cost of the higher packet loss rate, the average delay of the centralized DSP algorithm is smaller than that of interval type-2 fuzzy set routing algorithm and ELB algorithm when the load is larger.
## 4 Node Importance Evaluation Model
### Indices for Evaluating the Importance of Urban Rail Transport Nodes
#### 4.1.2 Based on Network Operational Performance  
Considering the importance of passenger traveling services in the operation of rail transit networks, network efficiency accessibility loss and unsatisfied passenger demand are selected as dynamic evaluation indices to reflect the impact of station failure on network operational performance.  
(1) Network efficiency accessibility loss  
Network efficiency accessibility is based on the network efficiency indicators, adding the consideration of the passenger flow between the stations in the network, which can reflect the difficulty of different passengers in the urban rail transit network to reach thedestination station from the initial station [33], and network efficiency accessibility loss is shown in Formulas (4) and (5).  
\[A=\frac{1}{f}\sum_{i}^{n}\sum_{j}^{n-1}\frac{f_{ij}}{t_{ij}} \tag{4}\]  
\[\Delta A_{a}=A_{0}-A_{a} \tag{5}\]  
\(A\) is the network efficiency accessibility, \(f_{ij}\) is the flow of passengers from station \(i\) to \(j\), \(f\) is the total number of passengers in the network, \(t_{ij}\) is the time it takes a passenger to travel from station \(i\) to \(j\), \(A_{0}\) is the initial efficiency accessibility of the network, \(A_{a}\) is the network efficiency accessibility after the failure of station \(a\), and \(\Delta A_{a}\) is the loss of network efficiency accessibility before and after the failure of station \(a\).  
(2) Unsatisfied passenger demand  
The disruption of some stations in the rail transit network splits the network into several disconnected sub-networks, resulting in the absence of connected paths between the origin and destination points of some passengers' trips, and the passengers are unable to complete their trips by the rail transit, which is the unsatisfied passenger demand, and the expression is shown in Formula (6).  
\[U=\frac{f_{a}}{f} \tag{6}\]  
\(f_{a}\) indicates passengers who are unable to complete their journeys using urban rail transit after the failure of station \(a\), and \(U\) indicates unmet passenger demand after the failure of station \(a\).
# Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry
## 2 Materials and Methods
### Modelling \(dh\) Uncertainty
#### 2.3.2 Theoretical Uncertainty of \(d\Delta\varphi\)
\[\gamma_{vol}=\frac{2\ \sin\left(k_{z}\frac{h_{v}}{2}\right)}{k_{z}h_{v}} \tag{12}\]  
Equation (12) assumes a uniform radar backscatter cross section from canopy volume and no significant scattering from the background. By inverting Equation (12), it would be possible to estimate forest height if decorrelation was caused entirely by volumetric effects. Unfortunately, this is unrealistic due to canopy signal attenuation, scattered movements, and the proportion of area filled by trees. These issues determine significant observational errors [50] making this approach with S1 data a poor choice. Within this framework and according to the above-mentioned simplifications, a new synthetic variable \(\gamma_{baseline}=\gamma_{geom}\cdot\gamma_{vol}\) can be considered as directly related to the baseline value.
## 2 Port Cranes Overview
### Evolution Timeline of Port Cranes
#### 2.2.2 Industrial Revolution Era (Late 18 th to Mid-19 th Century)  
* Steam-powered crane introduction (Late 18 th Century)--During the Industrial Revolution, the late eighteenth century saw the emergence of steam-powered cranes, which triggered a paradigm shift in port crane technology. Steam engines were introduced to replace the manual and animal power. In 1785, [PERSON] developed the first practical steam engine, not long before this technology was adapted for crane use. The introduction of steam-powered cranes increased the lifting capacity to approximately 20 tons, thereby significantly improving the efficiency of port operations. However,these cranes require stationary boilers, which limits their mobility, and their complex maintenance requirements are a drawback [2].
* Advancements in Structural Materials (1830s-1850s)--In 1834, the first cast iron crane was constructed. Cast iron offers greater strength than wood, thereby enhancing the durability of cranes. Subsequently, in 1851, more advanced steam-powered designs emerged, and iron and steel began to be widely used in crane structures. This further increases the load capacity and stability of the cranes [3].