esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this work, an ECS was presented for home energy management in SPG. The formulated objective function (EM optimization problem) was solved using single and hybrid heuristic algorithms (GA, PSO, BFA, ACO, WDO, GPSO, GACO, and GBFA) along with a developed GWDO algorithm in aspects of chosen performance metrics. The simulations were conducted for two case studies: home is not equipped with DGs, and home is equipped with DGs, considering home appliances, pricing signals, and consumers and EUC constraints. The developed GWDO-based ECS optimally schedules consumers' appliances, and the returning results show that the electricity cost, PADR, and carbon emission are reduced by 21.78%, 40.1%, and 15.25% in case study 1; and by 26.43%, 40.1%, and 33.8% in case study 2, respectively. The results illustrate that the developed algorithm outperforms both single and hybrid algorithms in aspects of utility bill, PADR, pollution emission, and user comfort in both case studies.  
In the future, real-time multi-criterion optimization problems will be solved using the Lyapunov technique by utilizing fog- and cloud-based environments to service the on-site events and requests of both EUC and consumers.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization, supervision, project administration, funding acquisition. Author has read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & **Convergence** & \multirow{2}{*}{**Complexity**} & **Cost** & \multirow{2}{*}{**PADR**} & **Carbon** \\  & **Speed** & & & **Reduction** & & **Emission** \\ \hline GA & Slow & High & 11.9\% & 11.8\% & 18.8\% \\ \hline PSO & Moderate & Moderate & 7.1\% & 16.3\% & 8.2\% \\ \hline ACO & Moderate & Low & 11\% & 23.7\% & 13.3\% \\ \hline WDO & Fast & Low & 15.2\% & 23.7\% & 28.7\% \\ \hline GPSO & Slow & Moderate & 14.1\% & 19.26\% & 26.4\% \\ \hline GACO & Moderate & High & 16.8\% & 36.8\% & 28.1\% \\ \hline GWDO & Fast & Moderate & 26.43\% & 40.1\% & 33.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed model with existing models from different perspectives.  
Figure 12: Case study 2: User comfort evaluation.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Materials and Methods
### Dataset Statistics  
The final dataset consists of multiple time-series records, with each instance representing a 10 s window of collected data from 15 sensor nodes. A summary of the dataset characteristics is provided in Table 1.  
The dataset is the foundation for training and evaluating the hybrid anomaly detection model. By leveraging both unsupervised learning (Autoencoder and Isolation Forest) and supervised learning (XGBoost with SMOTE), our approach ensures robustness in detecting both sensor faults and network intrusions while minimizing false positives.
## 4 Experiments
### CEEMD-based signal processing  
As the first step of our proposed fault diagnosis method, CEEMD is utilized to adaptively decompose the raw signal into several sub-signals called Imfs. The related hyper-parameters are set as follows: the noise standard deviation (Nstd) is set as 0.2, the number of ensemble (Ne) is set as 60 and the shifting maximum number of iterations (MaxIter) is set as 500. Taking the modes of normal and outer-race fault as the examples, the corresponding decomposition results are illustrated in Figures 6 and 7.  
Figure 5: Data acquisition system of the centrifugal pump.  
As shown in Figures 6 and 7, using the CEEMD, each vibration signal is adaptively decomposed into 10 Imfs, which provide the multiple-scale representations for the status of centrifugal pumps. It is worth noting that some decomposed components still exhibit quite obvious non-stationarity characteristics, even when processed by CEEMD, which means that both their time domain and frequency domain need to be comprehensively considered during the subsequent feature extraction.
## 4 Noble Metal-Based Electrocatalysts for ORR and HER
### Pd-Based Catalysts in ORR and HER
As synthesized Co-Pd alloys achieved a low Tafel slope of 25.4 mVdec\({}^{-1}\)[60]. Furthermore, [PERSON] et al. prepared the Co-Pd alloys by using the electrochemical deposition of Co/Pd and investigated the HER activity of 0.1 M NaOH electrolytes. As-prepared Co-Pd alloys demonstrated outstanding HER activity [61]. Various studies also reported the effect of support materials on ORR and HER performances of Pd nanoparticles. [PERSON] et al. deposited Pd nanoparticles on graphene via the dip-coating method and investigated their activity towards HER in an acidic medium. The Pd graphene nanocomposite achieved improved catalytic activity as compared to that of Pd and graphene [62]. [PERSON] et al. reported that Pd deposited on activated carbon fibers exhibits outstanding HER performance in an acidic medium [63]. In addition, [PERSON] et al. prepared carbon paper-coated Pd nanoparticles via an electrochemical deposition method. These catalysts with a Pd loading of 0.0106 mg cm\({}^{-2}\) achieved improved HER performance compared to commercial Pt black electrodes [64]. [PERSON] et al. demonstrated the particle size effect of Pd nanoparticles towards HER. They prepared Pd nanoparticles on a carbon support within the size range of 3 to 42 nm and tested them in both acidic and alkaline mediums. They observed the similar size effect in both acidic and alkaline mediums, where the exchange current density initially increased from 3 to 19 nm and then reached a similar level as that of bulk Pd [65].
###### Abstract  
Presenting visual information, called phosphenes, is a critical method for providing information on the position of obstacles for users of walking support tools for the visually impaired. A previous study has established a method for presenting phosphenes to the right, center, and left of the visual field. However, a method for presenting information on the position of obstacles around the feet using phosphenes, which is essential for the visually impaired, has not been clarified. Therefore, in this study, a method for presenting phosphenes in the lower visual field is presented, towards the aim of realizing a safe walking support tool. Electrode placement is proposed in this paper for the presentation of phosphenes to the right, center, and left of the lower visual field based on the electrode placement method used in the previous study, which presents the phosphene in three locations of the visual field. In addition, electric field simulation is performed, focusing on the electric field value on the eyeball surface, in order to observe whether the proposed electrode placement is able to stimulate the intended region. As a result, it is shown that the intended region on the eyeball surface can be stimulated locally with each of the proposed electrode placements.  
Keywords:visually impaired people; phosphene; walking support tool; eyeball surface; FEM +
Footnote †: journal: Applied
## 4 Related Works and Comparison to the State of the Art
The FSO link enables last-mile applications, thereby increasing system flexibility and providing optical fiber-like throughput. The system performance was investigated by assessing the hybrid architecture in two distinct scenarios in terms of EVM\({}_{\text{RMS}}\) and in accordance with 3 GPP, namely the RoF/FSO fronthaul at the photodetector output and the dual-band 5G NR wireless system employing RoF/FSO as fronthaul. Overall, the proposed hybrid RoF/FSO fronthaul provided 3 Gbit/s total throughput with plenty of margin in terms of EVM\({}_{\text{RMS}}\), thereby enabling wireless access. The dual-band 5G NR wireless system assisted by a hybrid RoF/FSO fronthaul demonstrated the feasibility of the system by providing 1.4 Gbit/s total throughput, thus fulfilling the 3 GPP Release 15 specifications. Finally, the RGB-based VLC system for indoor applications was examined, in which immunity to electromagnetic interference is crucial. We transmitted a standard 5G NR signal and 3 LTE-A bands in order to attend to multiple users employing a single red LED, which offers the best trade-off between output power and quantum efficiency. The system performance was evaluated by assessing each transmitted signal in terms of SNR, constellation symbols dispersion, and consequently the EVM\({}_{\text{RMS}}\). The 5G NR VLC system provided 218 Mbit/s throughput and EVM\({}_{\text{RMS}}\) as low as 6.45% considering all transmitted signals, thereby demonstrating the viability of VLC for 5G networks.
## 4 Numerical Examples
### Reconstruction of the EPSD  
The estimation of EPSD information from real ground motion records is a significant task, but by no means simple work. In this paper, to obtain the estimated EPSD, the method shown in Equation (11) is used to process the time history data generated by the SGHW method. Figure 10 shows the reproduced EPSD from the generated samples (500 realizations).  
From a holistic perspective, the reproduced EPSD is consistent with the target EPSD shown in Figure 6. Further, an intuitive comparison between the target and reproduced EPSDs at typical different instances of time is plotted in Figure 11. Shown in Figure 12 is the comparison between these two EPSDs at different certain frequency bands.  
From the comparisons in Figures 11 and 12, it can be concluded that the reproduced EPSD and the target EPSD have a good agreement, and the difference lies in the beginning and end of these two EPSDs, which is due to the end effect of the wavelet method. The end effect can be eliminated by introducing the theory proposed by K  
Figure 10: Reproduced EPSD.  
Figure 11: Comparisons between the target and reproduced EPSDs at different frequency bands.  
Figure 12: Comparisons between the target and reproduced EPSDs at different time instants.  
In addition, to more intuitively see the advantages of the SGHW method compared with the classical SRM, we listed the relative errors and corresponding computational time under different components. From Table 1, it can be concluded that, regardless of the method being used, the increase in the number of components can increase the efficiency of the calculation as well as reduce the relative errors. However, for the SGHW method, although the number of retained components is much less than that of the SRM, the calculation time consumed by the SGHW method is only about one-third and even one-tenth of that using the SRM, and the relative error is less than one-fiftieth. The advantages of the GHW method over the SRM method have been demonstrated in [30]. The common disadvantage of these two methods is that the fact that they retained too many components may result in a large computational effort and low computational accuracy, while the SGHW method can overcome these shortcomings. These results prove that the SGHW method has higher computational efficiency as well as higher accuracy.
# Cyber5 Gym: An Integrated Framework for 5G Cybersecurity Training
## 5 System Implementation and Demonstration
### DoS Attack
#### 5.5.5 Countermeasures  
**IP Blocking:** Implementing IP blocking is a crucial strategy in mitigating DDoS attacks on 5G networks. This method focuses on identifying and subsequently blocking access from the malicious IP address of attacker UE using iptables. By preventing these identified sources from accessing the network, IP blocking not only stops ongoing attacks but also acts as a preventive measure against future attacks from the same origins. This approach is vital for safeguarding the network against unauthorized access and ensuring continuous, secure network operations. Figure 15 depicts logs where a gNB connection is forcibly terminated from the AMF, a key countermeasure in the context of a DDoS attack. This termination, indicated by messages such as Association terminated for AMF [2], signifies the disruption of an attacker UE connection, showcasing an effective defensive strategy in the network security protocol.  
**Regulating Concurrent Users:** One effective countermeasure against DDoS attacks in 5G networks involves controlling the number of concurrent users, or UEs. By managing the number of active UEs at any given time, the network can more effectively allocate and manage its resources. This regulation helps reduce the likelihood of the network becoming overwhelmed during a DDoS attack, thereby maintaining stability and performance.
###### Abstract  
Remote sensing (RS) and geographic information system (GIS) data is increasingly used in urban stormwater modeling. The undirected use of such data may waste economic and human resources. In order to provide guidance for practitioners to efficiently use different data collection resources, as well as give a reference for future works, this paper aims to assess the effects of using free access GIS data and ad hoc RS data on urban 2D-1D stormwater modeling. The 2D-surface Two-dimensional Runoff, Erosion, and Export model (TREX) model was published in Science of the Total Environment in 2008. The 1D-sewer CANOE (Logiciel integre de conception et de diagnostic des reseaux d'assainissement) model was published in Journal of Hydrology in 2004. The two models are integrated in the TRENOE (TREX-CANOE) platform. The modeling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km\({}^{2}\)). Simulation results reveal that the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations. Nevertheless, using the very high resolution LiDAR (light detection and ranging) data is not equally significant for the water flow simulations at sewage outlets. Finally, we suggest that using the free access GIS data accompanying the urban sewer network design might be an acceptable low-cost solution for accurate urban 2D-1D stormwater modeling during moderate rainfall events. Further studies of urban stormwater modeling could focus on the development of \"suitable\" models with \"enough\" input data, depending on the management/research objectives.  
2D-1D coupled modeling; multiple data sources; urban landuse; high-resolution LiDAR data; urban stormwater modeling; TRENOE platform
## 2 Composition of Resource Scheduling Module
### Task Scheduling Module  
Based on the task information in the above task request list, actual execution times for each task are scheduled under the constraints of radar resources such as time and energy. This scheduling is accomplished by selecting appropriate task scheduling strategies, such as prioritizing high-priority tasks, maximizing task scheduling success rates, and minimizing task execution time shifts. The tasks in the request list are then categorized into an execution task queue, a delayed task queue, and a deletion task queue. The tasks in the execution task queue are sent to the antenna front-end for execution, tasks in the delayed task queue are temporarily retained, and tasks in the deletion task queue are dropped, thereby completing the resource scheduling process.  
Considering the overall structure of the resource scheduling module described above, we observe that existing task scheduling strategies often face challenges when handling high-load and multi-type task scenarios under constrained radar resources. In particular, conventional scheduling approaches fail to make full use of the radar idle periods between transmission and reception phases and often lack effective mechanisms to control the deviation between actual and expected task execution times. These limitations motivate the design of a more refined scheduling method. In the following section, we introduce a novel scheduling method that integrates pulse interleaving with time-pointer-based scheduling, further enhanced via time shifting constraints, to improve scheduling efficiency and controllability under resource-constrained conditions.
## 3 SAM-Based Approach to Improve Panoramic Image Segmentation in Indoor Construction Environments  
This section details the proposed method for panoramic image segmentation. As summarized in Figure 1, the method consists of two major stages. The first stage is object extraction, where SAM is iteratively executed to extract all objects from panoramic images of indoor construction sites. However, a simple iterative execution of SAM results in duplicated detections and overlapping objects. To address these issues, filtering algorithms that incorporate three additional techniques were developed. The second stage is label extraction and labeling, in which a labeling algorithm was developed to assign the vertical and horizontal plane label masks extracted from the Panoplane360 to the object masks obtained from SAM. The labeling algorithm overlays the label masks from Panoplane360 onto the object masks extracted by SAM, assigning the most dominant label to each object mask. Additionally, objects with the same assigned label are merged into a single mask to ensure a one-to-one correspondence between objects and labels.
## 4 Results  
### Simulation Study  
Performance Metrics  
MakeSpan (Total completion Time): MakeSpan is defined as the total time required to complete all jobs in a single simulation episode. It is computed as follows:  
\[Makespan=max_{i}(C_{i}-A_{i}) \tag{7}\]  
where  
\(C_{i}\)= completion time of the job \(i\);  
\(A_{i}\) = arrival time of job \(i\).  
A lower makespan indicates faster overall system throughput.  
High-Priority Jobs Delay: This metric captures the cumulative delay experienced by all high-priority jobs (priority level = 2). Figure 8 schematically depicts the timeline for a representative high-priority job from its arrival at t1, through blocking, to the start of service before giving the formal definition.  
It is computed as follows:  
\[High-Priority\ Delay=\sum_{j\in H}(C_{j}-A_{j}) \tag{8}\]  
where  
\(H\) = set of all high-priority jobs;  
\(C_{j}\) = completion time of high-priority job \(j\);  
\(A_{j}\) = arrival time of job \(j\).  
Lower values reflect the system's ability to respond promptly to critical tasks.  
Figure 8: Schematic timeline for a high-priority job. After arriving at t1, the job is blocked until t4 (the High-Priority Delay), then receives service from t4 to t7.  
Priority Inversion Count: This metric counts the number of incidents where a high-priority job was blocked by a lower-priority job due to resource contention. A priority inversion is recorded when  
\[Inv\ count=\sum 1\{prio=2\ waits\ \&\ AMR\ held\ by\ prio<2\} \tag{9}\]  
* A job with priority level = 2;
* Is waiting for an AMR;
* And the AMR is occupied by a job with priority \(<\)2.  
The total count is tracked per episode. Fewer inversions reflect better concurrency control and prioritization enforcement.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 4 Proposed Protocol
### Algorithm Complexity  
For the W-PAGPSR in the greedy forwarding strategy, the neighbor nodes under the restricted range were first determined, and the relay nodes were then determined by calculating the weight of each node. Assuming that the number of neighbor nodes in the reliable communication range is m, it was obvious that m \(\leq\) n. By calculating and comparing the weights of m nodes, the neighbor node with the largest greedy weight was selected as the next hop node to forward the packet, and we then selected the neighbor node with the largest greedy weight as the next hop node to forward the packet. Therefore, for each node, the time complexity of the greedy forwarding strategy in W-PAGPSR was O(n). For the entire network, the time complexity of W-PAGPSR's greedy forwarding strategy was O(n\({}^{2}\)). W-PAGPSR needed to establish a cross-free network in the perimeter forwarding strategy. In a crossover-free network, the sending node selected the node with the largest perimeter weight as the next hop node. Therefore, for each node, the time complexity of the perimeter forwarding strategy in W-PAGPSR was O(n\({}^{2}\)). For the entire network, the time complexity perimeter forwarding strategy in W-PAGPSR was O(n\({}^{3}\)). Table 4 shows the time complexity comparison of W-PAGPSR, GPSR, MMGPSR, and PA-GPSR.
## 2 Description of the Forward Model
### The Surface Model  
In this study, we take the surface reflection over land into consideration. Because CO\({}_{2}\) can only be retrieved at low AOD, in which circumstance single scattering dominates the radiative transfer process, and to keep consistent with CO\({}_{2}\) retrieval algorithm, we assume a Lambertian surface whose albedo is parameterized as follows. Based on the principle of the Medium Resolution Imaging Sensor (MERIS) [48] and LANDSAT TM data [49], wavelength-dependent surface reflectance data are constructed by assuming that any land surface is covered by both green vegetation and bare soil with a fraction. Thus, the spectral surface albedo \(\alpha(\lambda)\) is taken to be a weighted linear mixture of the actual spectra of the vegetation albedo \(\alpha_{\text{veg}}(\lambda)\) and bare soil albedo \(\alpha_{\text{soil}}(\lambda)\):  
\[\alpha(\lambda)=c\alpha_{\text{veg}}(\lambda)+(1-c)\alpha_{\text{soil}}( \lambda). \tag{2}\]This is a simplified model that simulates most of the Earth's land surface, but ignores other less common land cover types. However, for MERIS, the fraction of soil and vegetation is derived from the normalized difference vegetation index (NDVI) in the satellite scene, while in our model, this fraction is treated as a state vector element.  
The spectra of vegetation and bare soil are taken from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) database [50]. In our retrieval scheme, the surface spectral albedo is then characterized by the single parameter c in Equation (2) above. The analytical Jacobian with respect to this parameter is easy to obtain from the Lambertian albedo Jacobian.