esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Single-Cell Analysis  
#### 4.3.1 Study of Size versus Complexity  
Figure 8 reports on the size versus complexity for each of the selected phytoplankton species, measured with the flow cytometer. The forward scatter (FS in y-axis of Figure 8) represents the size of the cell, and the side scatter (SS in x-axis) is the granularity and complexity of the cell.  
As expected, each species presents different results in terms of size and complexity (Figure 8). _Nannochloropsis gaditana_ (Figure 8a) is the smallest (FS) and least complex (SS)  
Figure 8: Size as a function of complexity for each of the phytoplankton species, obtained by flow cytometry: (**a**) _Nannochloropsis gaditana_; (**b**) _Isochrysis galhana_; (**c**) _Tetraselmis suecia_.  
species studied, followed by the _Isochrysis galbana_ (Figure 8b), which is a little larger and more complex.  
_Tetraselmis suecia_ (Figure 8c) shows large and complex cells. When all the species are mixed, the results from Figure 9 can be expected, where differentiation and similarities between species can be better visualized. Moreover, the discrimination of toxic phytoplankton species by the flow cytometry method was clearly validated in [25]. The cytometry results have revealed excellent capabilities for rapid and automatic detection of several cells of phytoplankton within a flux, simultaneously analysing several optical parameters. Thus, a solution based on lab-on-a-chip flow cytometry could revolutionize the paradigm of portable and autonomous strategies for in situ phytoplankton quantification and species determination, as designed in Section 5.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
###### Abstract  
In this paper, a mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks (UWSNs) is proposed, which aims to improve location accuracy and shorten location time. First, to improve location accuracy, we design a support vector regression (SVR) based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. The proposed method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. Second, to shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. The location of the sensor node can be calculated based on the projection and the perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one-time trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulation results prove that the proposed scheme can obtain more accurate sensor node location in less time compared with the existing schemes.  
underwater wireless sensor networks (UWSNs); localization; mobile anchor node; RSSI 2019 19 4369; doi:10.3390/s19204369
## 5 Simulations and Discussion
### Simulation Results
The sum and user rates of the proposed system improved due to the fact that the LSTM CE gives a more accurate estimation of each user's channel condition. Therefore, this allows each user to adapt the data rate according to the SNR conditions, attaining the maximum possible transmission quality and, accordingly, the maximum rate. Figure 11: User rates for the proposed NOMA system versus the conventional NOMA system for two items of user equipment (UE). Figure 10: Outage probability against the SNR.
## 5 Discussion  
The experimental results demonstrate that clients operating under communication-constrained scenarios (Scenario I and Scenario II) can effectively utilize idle computational resources and limited communication bandwidth to contribute to the system's training process, thereby accelerating the model convergence. Specifically, under varying FRS environments and complex communication conditions (simulated through parameters \(p\) and \(r\)), FedRecI2C exhibits a robust convergence acceleration performance without compromising the model recommendation accuracy. Furthermore, its plug-and-play capability highlights significant practical value in accelerating the convergence for FRS implementations. The subsequent discussion addresses practical application considerations of FedRecI2C through three key aspects: client selection criteria, scalability analysis for large-scale deployment and efficiency analysis.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 3 Results
### Evaluation Metrics and Performance Analysis in MANETs
#### 3.2.2 Routing Overhead  
Routing overhead is calculated based on the proportion of signaling and useful traffic. Signaling traffic includes a routing protocol (OLSR) and quality of service (RSVP) traffic. To assess efficiency, the ratio of signaling traffic to the maximum amount of useful traffic per minute is considered, depending on the number of network nodes:  
\[E=(q*X_{H}+m*X_{TC}+k*X_{R})/(60*R-(q*X_{H}+m*X_{TC}+k*X_{R})) \tag{5}\]  
The calculations involve the following values for the transmitted information in the network:  
Standard WPA2 (CCMP) header: 50 bytes;  
OLSR header: 16 bytes;  
Hello packet length: \((8+n*4\), where \(n\) is the number of neighbors) bytes;  
TC message packet length: \((4+m*4\), where \(m\) is the number of MPR neighbor addresses) bytes;  
IP header: 20 bytes and UDP header: 4 bytes;  
Payload size (maximum and minimum, depending on packet length): 576 bytes;  
OLSR signaling information is periodically sent by nodes, with default broadcast intervals: the Hello packet interval is 2 s, and the TC packet interval is 5 s.  
Figure 5 in a graph presents the results of calculations for a single active network node. The upper curve corresponds to the practical speed value when the network operates in the 802.11 ac standard, while the lower curve corresponds to the network operating in the 802.11n standard.  
From the graph, it can be observed that for up to 500 nodes in the 802.11n standard and up to 1000 nodes in the 802.11 ac standard, the proportion of signaling information does not exceed 5% of the useful traffic.  
It is worth noting that when calculating the routing overhead for the entire network, it is necessary to consider that almost every node also passes through itself the signaling traffic of other nodes. For a specific network topology, it is possible to perform calculations taking into account the characteristics of routing protocols, communication channels between receiver and transmitter nodes, and other factors that influence the amount of transmitted signaling traffic.  
\begin{table}
\begin{tabular}{c c} \hline
**Number of Nodes** & **Route Acquisition Time \(T_{m}\) s** \\ \hline
50 & 0.01–0.02 \\ \hline
75 & 0.02–0.05 \\ \hline
100 & 0.04–0.1 \\ \hline
200 & 0.15–0.39 \\ \hline
350 & 0.5–1.24 \\ \hline \end{tabular}
\end{table}
Table 4: Dependence of the Route Acquisition Time on the number of network nodes of a fixed number of nodes.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 5 Conclusions  
In this paper, we developed an analytical model to characterize stochastic queuing behaviors and the effects of fading characteristics on performance in the proposed CF mMIMO system under the \(\kappa\)-\(\mu\) shadowed fading model. We derived approximate closed-form expressions for the PDF, CDF and MGF of the sum of i.n.i.d. \(\kappa\)-\(\mu\) shadowed RVs and improved their mathematical tractability. Then, based on the \(\kappa\)-\(\mu\) shadowed fading model, we derived approximate closed-form expressions for the PDF, CDF and MGF of the PPSNR after the zero-forcing detector in the proposed CF mMIMO system. The \(\kappa\)-\(\mu\) shadowed fading model can be extended to 6G wireless communication systems, and we verified the reasonableness and feasibility of its superior theoretical analysis performance with simulations and theoretical comparisons. In particular, we applied FBL information theory and SNC to model and characterize the arrival, service and departure processes, and we derived a closed-form expression for the UB-QDVP to analyze the effectiveness and reliability of the CF mMIMO system under the \(\kappa\)-\(\mu\) shadowed fading model. Furthermore, by applying the theoretical analysis above, we also conducted a set of simulations to validate and evaluate the UB-QDVP gaps under different systems (the CF mMIMO, PD-NOMA and OMA systems) and fading environments and the relationships between the UB-QDVPs and EE under different fading environments. Making full use of the characteristics of the \(\kappa\)-\(\mu\) shadowed fading model can improve the system EE, reduce the error probability and better satisfy the QoS requirements of 6G URLLC. Applying the \(\kappa\)-\(\mu\) shadowed fading model to accurately describe small-scale fading and to model and analyze unmanned aerial vehicles, underwater acoustics and other wireless communication systems in the future is a promising direction.  
Conceptualization, J.Z.; methodology, J.Z.; software, Y.S. and T.W.; validation, J.Z., Y.S. and T.W.; data curation, T.W.; writing--original draft preparation, Y.S.; writing--review and editing, J.Z. and Y.S.; supervision, J.Z., T.L. and S.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript  
This work was supported by the National Natural Science Foundation of China (no. 62001264), the Natural Science Foundation of Beijing (no. L192025), and the National Key R&D Program (no. 2018 YFB1801102).  
Not applicable.
## 4 Conclusions  
In this experiment, TiO\({}_{2}\) and Ag/TiO\({}_{2}\) films were prepared via DC magnetron sputtering, and the effect of silver modification on the photocatalytic performance was controlled using different sputtering power and sputtering times. The results show that the addition of Ag significantly enhances the visible light absorption of the catalyst, which is primarily because the addition of Ag prevents the recombination of photogenerated electrons and holes, effectively reduces the band gap of TiO\({}_{2}\), and forms the carrier trapping trap, which improves the photocatalytic redox ability of the film. The best photocatalytic degradation of Mo is through Ag/TiO\({}_{2}\)-5 W-50 s, and the degradation rate can reach 100% within 55 min of light irradiation. After four test cycles, the degradation rate can still reach 100%, but more time is needed. However, the photocatalytic degradation efficiency of MO in MO-Na\({}_{2}\)SO\({}_{4}\) solution can reach 99.55% within 65 min. The results indicate that the presence of Na\({}_{2}\)SO\({}_{4}\) in MO solution can inhibit the degradation of MO through Ag/TiO\({}_{2}\). The principal cause of the degradation efficiency decreasing (MO) in the MO-Na\({}_{2}\)SO\({}_{4}\) solution may be that the addition of Na\({}_{2}\)SO\({}_{4}\) leads to the increase in pH, which is not conducive to the adsorption of dyes. SO\({}_{4}\)\({}^{2-}\) is adsorbed on the surface of the photocatalyst, acting as a pore-trapping agent to increase the content of SO\({}_{4}\)\({}^{-}\)-generated, and the oxidation degree of Ag is intensified. In addition, the added anion (SO\({}_{4}\)\({}^{2-}\)) and anionic dye molecules competed for adsorption onto the Ag/TiO\({}_{2}\)-5 W-50 s surface, thereby deactivating part of the catalyst and reducing the degradation efficiency.  
Conceptualization, L.S. and Z.Y.; methodology, L.S. and Z.Q.; software, T.R. and Z.Q.; validation, L.S., Z.Y. and Z.Q.; formal analysis, L.S., Z.Y. and Z.Q.; investigation, Z.Y. and Y.L.; resources, L.S. and Z.Y.; data curation, L.S. and Z.Q.; writing--original draft preparation, L.S. and Z.Y.; writing--review and editing, L.S. and Z.Y.; visualization, T.R. and Z.C.; supervision, L.S. and W.G.; project administration, L.S. and Z.Y.; funding acquisition, L.S. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 11904268, and the Hubei Province Education Department of China, grant number Q20181702.  
Not applicable.
Moreover, all the MAs employed in a particular partition have to carry the aggregation code (which is identical for all the MAs) to the target source nodes for the aggregation task. Multiple MAs carrying the same aggregation code within a single partition would result in an increase in energy consumption. In this paper, a spawn multi-mobile agent itinerary planning (SMIP) is proposed to reduce the substantial increase in cost of energy as well as time used in the data gathering processes. The proposed approach is based on the agent spawning such that the main MA is able to spawn other MAs within a single partition. The spawning MA has different tasks assigned from the main MA, such that it only returns the accumulated data to the sink. The main goal of the proposed SMIP approach is to alleviate the problem of workload due to multiple MAs carrying the same aggregation code within a single partition. The rest of the paper is organized as follows: Section 2 reviews the related work in this research. Section 3 presents our proposed SMIP approach. Simulation setup is performed in Section 4. Section 5 evaluates the performance of the proposed approach and discusses the simulation results. Finally, Section 6 concludes this paper with future research directions.
## 3 Threat Model and Security Requirements
### CVSS v3.1 Scoring Methodology  
Tables 1 and 2 present the CVSS v3.1 scoring details for each attack scenario.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Attack Type** & **Key Metric Justifications** & **Score** \\ \hline Stealth Timing & AV:A/Ac:H/PR:N/UI:N/S:U/C:N/I:H/A:H & 6.8 (M) \\ Desynchronization & AV:A/AC:L/PR:N/UI:N/S:C/C:N/I:H/A:H & 9.0 (C) \\ Burst Interference & AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:L & 5.4 (M) \\ Replay Attack & AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:L & 7.1 (H) \\ Multi-Channel Coord. & AV:A/AC:H/PR:N/UI:N/S:C/C:N/I:H/A:H & 8.3 (H) \\ Firmware Compromise & AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H & 7.2 (H) \\ \hline \hline \end{tabular}
\end{table}
Table 1: CVSS v3.1 vector strings and scores.  
Figure 4: Detailed SMART DShot system architecture showing internal components, data flow paths, and the integration of ML algorithms within the real-time processing pipeline.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attack Type** & **Key Metric Justifications** \\ \hline Stealth Timing & AV:Adjacent (ESC line access required) \\ AC:High (precise timing manipulation) \\
1/A:High (can cause flight instability) \\ \hline Desynchronization & AC:Low (relatively easy execution) \\ SChanged (affects entire flight system) \\ A:High (immediate crash risk) \\ \hline Burst Interference & I/A:Low (only temporary disruption, no persistent damage) \\ \hline Replay Attack & I:High (arbitrary command injection possible) \\ A:Low (partial control loss) \\ \hline Multi-Channel & AC:High (complex synchronization required) \\ S:Changed (system-wide impact) \\ \hline Firmware & AV:Local (physical/bootloader access) \\ PR:High (admin rights required) \\ CIA:High (complete system compromise) \\ \hline \hline \end{tabular}
\end{table}
Table 2: CVSS metric justifications.  
The scoring follows the CVSS v3.1 specification [25], where Attack Vector (AV) ranges from Network to Physical, Attack Complexity (AC) indicates the conditions beyond the attacker's control, and Scope (S) captures whether a vulnerability impacts resources beyond its security scope.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 2 Related Work  
We investigated traffic-scheduling techniques for LEO satellite networks. Traffic-scheduling approaches can be categorized into two types: routing- and latency-based schemes.
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 3 Fault Diagnosis Methodology Using the Defect Signature Wavelet Image
### Deep Convolution Neural Network Structure Specification
The proposed DCNN model training process is run over 100 epochs to learn the robust features for one normal operating condition and each type of faulty condition.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 5 Experiments
### Ablation Study  
To analyze the effectiveness of each component in _MPGT-Align_, we conduct a series of ablation studies on the _DBP-15K_ dataset, with the results presented in Figure 4.  
**MPGT-Align's components.** To demonstrate the validity of each component design in _MPGT-Align_, we provide four _MPGT-Align_ variants for ablation studies:  
* _W/o entity-context_, where the entity-context embedding component is omitted.
* _With cosine_, where the consistent similarity-based function is replaced by cosine function.
* _With hop2 Token_, where the Pruning-hop2 Token component is substituted with hop2 Token.
* _W/o AMF_, where the attention-based multi-hop fusion layer is removed.  
As illustrated in Figure 4, the variants _w/o entity-context_ and _with cosine_ exhibit significant performance degradation, confirming that the components of _entity-context embedding_ and the _consistent similarity-based function_ introduced in _MPGT-Align_ are highly effective. This is attributed to the fact that entity-context embedding can extract more nuanced information about an entity than relying solely on a single entity name. In contrast to the cosine function, the consistent similarity-based function not only considers the intrinsic similarity between two entities, but also their similarities with other entities, thereby providing a more comprehensive assessment of the similarity between candidate entity pair.  
Secondly, to validate the effectiveness of our two new components, Pruning-hop2 Token and Attention-based multi-hop fusion layer, we, respectively, replace or remove them and conduct experiments. As shown in Figure 4, while _MPGT-Align_ demonstrates performance improvements in both _ZH-EN_ and _JA-EN_, it falls short in achieving similar results for _FR-EN_. Notably, there is a significant improvement in performance over the variant _w/o AMF_ on _ZH-EN_ and _JA-EN_, indicating that adaptive aggregation of information from diverse neighborhoods enhances entity representation learning and further elevates model performance in EA tasks. In summary, these experimental results comprehensively demonstrate the effectiveness of each component's design within _MPGT-Align_.
# Role of Restored Underwater Images in Underwater Imaging Applications
## 2 Proposed Restoration Method  
The image restoration process employs the Image Formation Model (IFM) given in Equation (1), to obtain the original scene from a captured underwater scene, with the process involving estimation of the different parameters of the underwater IFM.  
\[I^{c}(x)=J^{c}(x).t^{c}(x)+(1-t^{c}(x)).B^{c},\,c\in\{R,G,B\} \tag{1}\]  
As can be seen, there are two distinct parts of the captured underwater image \(I^{c}(x)\). \(J^{c}(x).t^{c}(x)\) describes radiance \(J^{c}(x)\) of the object as it travels in the underwater medium, whilst \((1-t^{c}(x)).B^{c}\) represents the scattering of background light \(B^{c}\) as it travels towards the camera. Transmission map \(t^{c}(x)\) describes the part of the object radiance that reaches the camera, after considering for absorption and scattering.  
Recovering the original object radiance \(J^{c}(x)\) from the acquired image \(I^{c}(x)\) at the camera requires knowledge of the background light \(B^{c}\) as well as the transmission map \(t^{c}(x)\), with this information commonly estimated. Taking \(\widetilde{t^{c}}(x)\) and \(\widetilde{B^{c}}\) as the estimated transmission map and background light, respectively, the recovered scene radiance \(\widetilde{J^{c}}(x)\) may be estimated as:  
\[\widetilde{I^{c}}(x)=\frac{I^{c}(x)-\widetilde{B^{c}}}{\max(\widetilde{t^{c}} (x),t_{0})}+\widetilde{B^{c}},\,c\in\{R,G,B\} \tag{2}\]  
\[\widetilde{t^{c}}(x)=e^{-\beta^{c}\,\,\widetilde{d}(x)},\,\,\,\,\,\,\,\,\,c \in\{R,G,B\} \tag{3}\]  
where, \(\beta^{c}\) is the spectral attenuation coefficient of the direct signal and \(\widetilde{d}(x)\) is the estimated depth map of the image.  
Figure 2 depicts the flowchart of the proposed underwater restoration method for estimating the recovered scene radiance \(\widetilde{J^{c}}(x)\) from the captured underwater image \(I^{c}(x)\). Blurriness estimated image \(p_{\text{blr}}\) and background light neutralized image \(I^{c}_{BL}(x)\), are calculated from the input image \(I^{c}(x)\), which are then used, together, with the red-light intensity \(I^{r}(x)\), to estimate depth \(\widetilde{d}(x)\) of the underwater image. Subsequently, transmission map \(\widetilde{t^{c}}(x)\) may then be estimated using the estimated depth map \(\widetilde{d}(x)\) by selecting the appropriate spectral attenuation coefficients. The input image \(I^{c}(x)\), estimated background light \(\widetilde{B^{c}}\), and transmission map \(\widetilde{t^{c}}(x)\), are then used to find the final scene radiance recovered image \(\widetilde{J^{c}}(x)\), as per Equation (2).
## 4 Experiments
### Ablation Study
#### 4.6.1 Effect of Incorporating LGA  
We considered the following encoder architectures: squeeze [34] and shuffle [15]. We evaluated the improvement in performance (in terms of mIoU %) of both models when the LGA module was incorporated. For the squeeze architecture, the mIoU values of the original model before and after incorporating the LGA module were 41.5% and 44.6%, respectively, respectively indicating an improvement of more than 3%. The shuffle architecture significantly benefited from including the LGA module, with an improved mIoU of 45.5% against the 36.9% of the original architecture. We can conclude from the Table 4 that the inclusion of LGA improved the performance of these networks. We also note that the problem of transparent object segmentation is quite challenging, with the best mIoU value of \(\sim\)41% and \(\sim\)45% for the squeeze architecture in the original form and after incorporating LGA, respectively.
## 5 Classification of EEG Records  
After finding the MSEn features from the EEG signals, they are classified into three different categories, namely normal, seizure-free and seizure.  
We have selected the optimal set of MSEn features using the wrapper-based feature selection method [48] and classified using the SVM [49] classifier with the radial basis function (RBF) kernel [50].  
The classification performance measurement metrics [51], namely accuracy (Acc), sensitivity (Sens) and specificity (Spec), are obtained using the ten-fold cross-validation method [51; 52]. The ten-fold cross-validation method has been used widely to get unbiased performance of the classifier in the area of bio-medical signal processing [50; 51]. All of the classification tasks along with wrapper-based feature selections have been performed using the WEKA machine learning toolbox (Weka 3.6.13, University of Waikato, Hamilton, New Zealand) [53]. For all of the classification tasks, the cost parameter of SVM is set to one and gamma parameter of the RBF kernel to \(\frac{1}{\text{number of selected features}}\) (default in WEKA).