esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Short-Reach Systems Classification and Enabling Technologies
### Data Center Network Requirements and Challenges
#### 3.2.4 Scalability  
One of the major benefits of the existing DCNs is that they can efficiently scale to a fat-tree topology that is capable of interconnecting racks that host thousands of servers. This is facilitated by leveraging the resources and economies of scale of the Ethernet switches. In contrast, the majority of optical interconnects are based on centralized architectures, which causes significant scalability limitations. For instance, apart from the concerns about the associated cost of the devices, there are also related technical challenges that may hinder performance enhancement. Consequently, the means of achieving scalable optical interconnect schemes that are capable of hosting a huge number of servers in the DCN is of utmost importance to the operators [74].
## 5 Performance Analysis
### Ablation Study  
We created five distinct versions of ADDGCN and ran ablation experiments on the PEMS04 and PEMS08 datasets, respectively, to learn more about how different components affect the ADDGCN prediction performance. The following are the variations:  
(1) Without dynamic graph convolution network (DGCN): Based on ADDGCN, we replaced the DGCN module with an ordinary GCN module, and the input adjacency matrix of the GCN is the original matrix of adjacency.  
(2) Without down-sampling convolution network: Based on ADDGCN, we removed the down-sampling convolution network.  
(3) Without adapt adjacency (without \(A_{opt}\)): Based on ADDGCN, we removed the adaptive adjacency matrix \(A_{opt}\) in the dynamic graph convolution network, leaving only \(A_{opt}\) as the adjacency matrix input to the diffusion graph convolution network.  
(4) Without graph generator (without \(A_{L}\)): Based on ADDGCN, we removed the graph generator in the dynamic graph convolution network, leaving only \(A_{opt}\) as the adjacency matrix input to the diffusion graph convolution network.  
(5) Without feature augmentation module: Based on ADDCN, we removed the feature augmentation module.  
Table 3 displays the ablation experiment's outcomes. The PEMS04 and PEMS08 datasets show essentially comparable distributions for the impacts of the above-discussed model components. Firstly, DGCN and down-sampling convolutional networks are very important to ADDGCN, which shows that our proposed DS-DGC module is crucial to improving the model prediction performance. Furthermore, ablation experiments were carried out on the two adjacency matrices that are described in the DGCN module. The experimental results demonstrate the critical roles played by the learnable adjacency matrix and the adaptive adjacency matrix in the model. The dynamic adjacency matrix is created by combining the adaptive adjacency matrix with the learnable adjacency matrix. This greatly enhances the prediction performance of ADDGCN. Finally, we experimented with the feature augmentation module using ablation. We can observe from the experimental data that after removing the feature augmentation module, the values of each indicator are also increased to varying degrees, and the prediction performance of ADDGCN is affected. It is evident that the feature augmentation module has a positive effect on our model. Therefore, our proposed DS-DGC module, combined with the feature augmentation module, is effective.
## 1 Introduction
### Related Works
We will provide a supervisory hierarchical structure for MG corresponding to the SG conceptual model, as well as clarifying the situation of this structure in the distribution management system (DMS). Another idea behind this hypothesis is using MG to help power system stability by splitting the power grid into islands [28]. Table 1 shows how this paper, in comparison with related works, involves all examined MG control structures and wireless technology applications, in order to detect a roadmap for exploiting these technologies. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Contribution**} \\ \cline{3-8}
**Reference** & **Year** & \multicolumn{3}{c}{**MG Communication Requirements**} & \multicolumn{3}{c}{**MG Communication Technologies**} & \\ \cline{3-8}  & **Standard** & **Control** & **Application** & **Wired** & **Wireless** & **Future Trends** & **Roadmap** \\ \hline [PERSON] et al. [21] & 2013 & ✗ & ✗ & ✓ & ✗ & ✓ & ✗ & ✗ \\ [PERSON] et al. [22] & 2013 & ✓ & ✗ & ✗ & ✓ & ✓ & ✓ & ✗ \\ [PERSON] et al. [23] & 2014 & ✓ & ✗ & ✗ & ✓ & ✓ & ✓ & ✗ \\ [PERSON] et al. [16] & 2016 & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ \\ [PERSON] et al. [24] & 2017 & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✗ \\ [PERSON] et al. [20] & 2018 & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ \\ [PERSON] et al. [5] & 2018 & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ \\ [PERSON] et al. [26] & 2019 & ✗ & ✗ & ✓ & ✓ & ✓ & ✓ & ✗ \\ Present Paper & 2020 & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Present and related works—a comparison.
# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 4 Design and Experimentation of the Ultrasonic Transmission System  
### Design of Defect Detection System  
The functional requirements of the detection system include aligning the probe to the fixed sample position, transmitting and acquiring ultrasonic signals, and processing the ultrasonic signals for defect assessment. As shown in Figure 5, to meet these system functions, the ultrasonic transmission inspection system consists of three parts: an automated ultrasonic wave emission and reception system, a fixture assembly, and a data post-processing system.  
The ultrasonic wave emission and reception system consist of a 100 MHz data acquisition card, a DPR300 ultrasonic pulse generator and receiver, and two 10 MHz focused probes. The two probes operate in a send-receive mode, where one probe is used to transmit ultrasonic signals through the tested sample and the other to receive and convert the transmitted ultrasonic signals into electrical signals. The ultrasonic receiver sends the data to the acquisition card and converts it into digital signals. The probes and fixture assembly are immersed in coupling agent to reduce the acoustic impedance between the probes and the sample.  
As shown in Figure 6, the fixture assembly includes three parts: the base, the probe holder, and the sample holder. The base provides support and has two through-slides in different directions, restricting the movement of the holders to specific directions. The probe holder consists of rails of different heights and a probe fixture, which elevates the probes to a certain height and aligns them with each other, with the ability to adjust the distance between the probes. The sample holder also consists of rails of different heights and a probe fixture, with the rails oriented perpendicular to those of the probe holder, allowing the adjustment of the sample height to align the test area with the centerline of the probes. Each fixture can slide horizontally to adjust the horizontal position of the test sample.  
The system achieves precise alignment of the transmitting probe, defect, and receiving probe, as well as the ability to quickly adjust the scanning position in the ultrasonic transmission defect detection system, by organically connecting the components of the fixture system through the slides.  
The data post-processing system consists of a personal computer that utilizes a differential peak-seeking algorithm to determine the positions of the wave peaks, locate the ultrasonic transmission signal peaks, and calculate the energy of the sample signals.
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Detection Technology** & **Advantages** & **Drawbacks** \\ \hline Radar detection & Well-Ssuited for long-distance. No need cooperation from the target. & Expensive. Low/no waves reflect from non-reflective materials. Hardly distinguishable from birds or bats. Flying at an altitude of fewer than 100 feet would be difficult to detect. \\ \hline Acoustic detection & Low cost.Passive. Easy to be combined with other technology. & Easy to be deceived.Cannot detect fixed wing UAV. Detection distance less than 500 m. \\ \hline Visual detection & Flexibility. Inexpensive.Detect either large aircraft or small objects. & Limited by light and weather. Need to create recognition database. \\ \hline MAC address & Relatively inexpensive. Effective detection and accurate tracking. & Suffer from interference. Must have some knowledge of emitter parameter and protocol. \\ \hline Ray Tracing Simulations & Convenient to simulate different scenarios. & Unable to deal with micro-Doppler effect. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing UAV detection technologies. * We propose a novel method of using AC-WGANs model to classify the wireless signal to identify illegal UAVs. Specifically, we improve the generative model \(G\) to enhance the training datasets and use the loss function of the model to classify the wireless signals. * We establish an illegal UAVs classification system based on the wireless signal. The system is divided into three parts: collecting the wireless signal, pre-processing, and classification. Combined with AC-WGANs model and USRP, a real-time classification system is implemented. * We compare and analyze the UAVs signals in the indoor environment and the outdoor environment. The recognition rate can reach more than 95% in the indoor environment, and this system is also suitable for outdoor environments. The rest of the paper is organized as follows. In Section 2, we introduce the related work of the wireless signal classification system and the basis of the AC-WGANs model. In Section 3, we provide a network overview of the AC-WGANs model. In Section 4, we analyze the difference of the indoor environment and outdoor environment and propose a real-time classification system. Finally, conclusions and future work are drawn in Section 5.
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 3 Proposed Algorithm  
Figure 2 shows the block diagram of the proposed Improved Color Attenuation Prior (ICAP). The abbreviations used in this figure include those for Local White Balance (LWB), Low-Pass Filter (LPF), modified Hybrid Median Filter (mHMF), Minimum Filter (MF), Quad-Decomposition (QD), and Atmospheric Light Estimation (ALE). The five red dotted rectangles represent the functional blocks that distinguish the proposed algorithm from the original CAP. LPF is applied to the image's saturation channel to remove the background noise. Atmospheric light estimation based upon a quad-decomposition algorithm is effective in lowering the computational complexity and, also, leads to the use of a simple mHMF in place of a very large guided image filter to refine the depth map. An Adaptive Constraints Calculation block strictly constrains the transmission map, preventing the situation where image pixels become negative after dehazing and turn into black pixels. Post-processing blocks include Adaptive Tone Remapping and color converters. They are used to expand the dynamic range of the image after removing haze. We will delve deeply into these differences in following sub-sections.  
Figure 1: Block diagram of the haze removal algorithm based on Color Attenuation Prior (CAP).
## 4 Conclusions and Outlook  
In conclusion, we have reported on the design and performance of a velocity map imaging (VMI) spectrometer optimized for experiments using high-intensity extreme ultraviolet (XUV) sources such as laser-driven high-order harmonic generation (HHG) sources and free-electron lasers (FELs). The instrument is versatile and allows for combining photo-electron and -ion detection modes, such as ion time-of-flight (TOF), ion VMI and electron VMI, in different ways, depending on the required information and the process under study. The performance for the different detection modes was estimated using simulations, and first experimental results from an intense HHG source were presented  
Figure 8: Results of ion-electron covariance analysis. Covariance between the photoelectron transversal momentum spectrum and the ion-TOF channels of N\({}^{+}\) (blue) and N\({}_{2}^{+}\) (red). Qualitatively, the N\({}^{+}\) channel shows stronger correlation with low momentum photoelectrons, whereas the N\({}_{2}^{+}\) channel exhibits a stronger correlation with photoelectrons at higher transversal momenta.  
Figure 7: Results of ion-ion covariance analysis. (**a,b**) show covariance maps of the N\({}_{2}^{+}\) and the N\({}^{+}\) ion TOF fragments correlated with the ion VMI detector image, respectively. Furthermore, these maps were treated like raw VMI images and inverted. The results are displayed in the right halves of (**c,d**). For comparison the inverted image of the accumulated ion detector image from Figure 6b is reproduced in the left halves.  
together with proof-of-principle application of covariance mapping to mass selection of photoions and extraction of channel-specific photoelectron spectra.  
The analysis of the experimental results confirms that the acquired data contain information about the correlation between the products of the photoionization, and the first covariance mapping analysis demonstrates the feasibility of extracting such information, indicating that the suggested approach is viable for extracting feature-rich correlated information from future experiments using high-intensity XUV sources.  
Conceptualization: [PERSON] and P.J. Data curation: [PERSON], J.L. and S.M. Formal analysis: [PERSON], J.L., S.M. and P.J. Funding acquisition: P.J. Investigation: [PERSON], J.L., S.M., F.C., H.C.-A., B.O., J.P., H.W., P.R. and M.G. Project administration: P.J. Supervision: [PERSON]; Writing, original draft: [PERSON] and J.L. Writing, review and editing: [PERSON], [PERSON], S.M., F.C., H.C.-A., B.O., J.P., H.W., P.R., M.G. and P.J.  
This research was supported by the Swedish Research Council, the Swedish Foundation for Strategic Research and the Crafoord Foundation. This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 641789 MEDEA.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 3 Results and Discussion
### Thermal Conductivity  
Figure 5 presents the average values of the thermal conductivity \(\lambda\) of the tested geopolymers. The determined \(\lambda\) values were close to each other. A slight increase in thermal conductivity with increasing WG content was observed, but only for 10% and 20% WG content. For 30% WG content, the \(\lambda\) value was slightly lower than that for the geopolymer without WG. While bulk density measurements show minimal variation, the microstructural changes induced by waste glass addition may affect the pore size distribution and connectivity, influencing thermal properties independently of bulk density. The thermal conductivity of the reference cement mortar RCM was \(\lambda\) = 1.77 \(\pm\) 0.02 W/m\(\cdot\)K (Table 2). All tested geopolymers have demonstrated a \(\lambda\) value that is lower by more than 80%; they show good insulation properties. However, the optimal WG content for thermal insulation appears to be 0% or 30%, rather than the intermediate concentrations. Table 2 presents the test results of the thermal properties and density of the cement mortar RMC after 28 days of curing.  
Figure 4: SEM micrographs of geopolymer paste specimens containing various amounts of WG: (**a**) 0% WG; (**b**) 10% WG; (**c**) 20% WG; (**d**) 30% WG.  
Figure 6 presents the average values of volumetric specific heat \(\rm c_{v}\). The differences in \(\rm c_{v}\) values for the particular specimens were only slight. However, compared to the reference mortar RCM, for which the cv value is 1.75 \(\pm\) 0.04 MJ/m\({}^{3}\)\(\cdot\)K (Table 2), the good insulating ability of all tested geopolymers has been confirmed. The good insulating ability was also confirmed by testing the thermal diffusivity \(\rm a\), presented in Figure 7. The character of changes with varying quantities of WG is similar to that of \(\rm c_{v}\). The RMC mortar's average value was 1.02 \(\pm\) 0.02 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(\rho\) & SD & \(\lambda\) & SD & \(\epsilon_{v}\) & SD & \(a\) & SD \\ [kg/dm\({}^{3}\)] & [kg/dm\({}^{3}\)] & [W/mK] & [W/mK] & [MJ/m\({}^{3}\)K] & [MJ/m\({}^{3}\)K] & [10\({}^{-6}\) m\({}^{2}\)/s] & [10\({}^{-6}\) m\({}^{2}\)/s] \\ \hline
2.07 & 0.04 & 1.77 & 0.02 & 1.75 & 0.04 & 1.02 & 0.02 \\ \hline \end{tabular}
\end{table}
Table 2: Density and thermal parameters of cement mortar RMC.  
Figure 5: The average values of thermal conductivity of the tested geopolymers.  
Figure 6: The average values of volumetric specific heat of the tested geopolymers.
## 4 Results
### Formation Control with Obstacle Avoidance  
As mentioned above, the anti-collision strategy of the proposed method includes keep away from each other and obstacle avoidance. Anti-collision is considered for all simulations. The experiment in this section aims to test the strategy in an environment with obstacles. Initially, we put seven swarm members in the bottom part of a 100 m \(\times\) 200 m environment, with random position and headings. The target direction of the swarm leader is set to the north. Some obstacles are placed on the way to the north, as shown in Figure 8a. The sector around the agent indicates the visual field of each member. Figure 8b shows the swarm leader starts to move to the north, and the other members are adjusting their positions and headings according to the proposed strategy. Each member chooses the corresponding target leader to achieve the cascaded leader-follower structure, and calculates control inputs according to predefined \(l_{d}\) and \(\varphi_{d}\), as shown in Figure 8c. Figure 8d shows that the formation is formed at around 57.4 s.  
When the moving formation encounters obstacles, as shown in Figure 9a,b, the members adjust their forward and turning speeds to obey the defined anti-collision rules to avoid obstacles. Meanwhile, they also keep away from each other during the adjustments. When they pass the barrier region, they start to reform the shape, as shown in Figure 9c. The formation is reshaped at 2 min 6.6 s, as shown in Figure 9d. The trajectories of this simulation are shown in Figure 10. In the figure, we can see the adjustments of the anti-collision movements of each members.  
Figure 8: Formation before encounter obstacles.  
Figure 10: The trajectories of member movements.  
Figure 9: Formation after encounter obstacles.
# Energy-Saving Routing Protocols for Smart Cities
## 3 Results
### Simulation Results and Discussion
As this work is focused on applications for smart cities, our results take into account the existence of moving nodes (e.g., deployed in a vehicle) and node speeds from 0 to 180 km/h are considered. As expected, this scenario constitutes a very challenging environment for  
Figure 6: Average Jitter (JIT) as a function of the source node’s speed for the DVR, AODV, DSR, and Modified DSR protocols. routing protocols. Consequently, all results in Figures 4-7 use the source node's speed as the independent variable. The simulation results demonstrate that the speed influences the adopted metric (Packet Delivery Rate (PDR), Throughput (THR), End-to-End Delay (E2 ED), Average Jitter (JIT), and Energy Consumption). Therefore, these results prove the energy savings provided by our proposed DSR with RPA routing protocol has an average total power consumption that is 11.32% lower compared to the same protocol without the proposed RPA. Table 2 summarizes the achievements of this work compared with other routing protocols considering a mobile WSN with 24 sensor nodes and one concentrator node at two speed values (18 km/h and 90 km/h).
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 2 Theoretical Analysis and Methods
### Theoretical Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Antenna Gain & 12 & dB \\ Antenna beam width & 38 & deg \\ Satellite altitude & 20200 & km \\ Receiver altitude & 120 & m \\ Elevation & 31 & deg \\ Frequency & 1561.098 & MHz \\ Delay range & \(-1\)-2 & chip \\ Wind speed & 2, 6, 10, 20 & m/s \\ Wind direction & 20, 40, 60, 80 & deg \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters.
# Machine Unlearning by Reversing the Continual Learning
## 3 Preliminaries
### Continual Learning  
Continual learning is particularly challenging for neural networks because knowledge from previously learned tasks (e.g., task A) is often suddenly lost when it is merged with the current task (e.g., task B). This phenomenon is known as catastrophic forgetting, and multitask learning to overcome these problems is called continual learning.  
Learning a task requires adjusting the parameter weights \(w\) of the model to optimize performance. Different \(w\) can lead to the same performance [20]. This overparameterization allows for the existence of a solution for task B that is close to the previously found solution for task A. Continual learning ensures that task A is remembered when further training for task B. Training trajectories are shown in a schematic parameter space, with parameter regions leading to good performance on task A (gray) and on task B (cream color), as shown in Figure 1. In other words, continual learning is learning task B on the model trained on task A to obtain task C (including A and B). Intuitively, continual learning is about finding the intersection of the parameter space of multiple tasks.  
When learning task B, EWC protects the performance of task A by restricting the parameters to a low error region around task A. The EWC explicitly calculates the importance of the parameters for task A and finds a corresponding solution for task B, provided that it does not significantly compromise task A. Specifically, EWC approximates the posterior as a Gaussian distribution whose mean is determined by the parameter \(w_{A}^{*}\).The importance of each parameter is measured by the diagonal accuracy given by the diagonal of the Fisher information matrix \(F\). Given this approximation, the function \(\mathcal{L}\) that we minimize in EWC is  
\[\mathcal{L}(w)=\mathcal{L}_{B}(w)+\sum_{i}\frac{\lambda}{2}F_{i}(w_{i}-w_{A,i }^{*}) \tag{2}\]  
where \(F\) is the Fisher information matrix for the new task B and \(i\) labels each parameter.  
In addition, IMM has been proposed to solve the catastrophic forgetting. Unlike the EWC-based approach, the IMM first learns the new task B separately and then averages the parameters of the two networks for the old and new tasks. Specifically, IMM minimizes the average of the KL divergence between an approximated posterior distribution for the combined task and each Gaussian posterior for the single task. As shown in Figure 1, the IMM finds the intersection of the two task parameter spaces by averaging \(w_{A}^{*}\) and \(w_{B}^{*}\) as follows:  
\[\mu_{C}^{*},\Sigma_{C}^{*}=\operatorname*{argmin}_{\mu_{C}\succeq\Sigma_{C}} \frac{1}{2}[KL(q_{A}\|q_{C})+KL(q_{B}\|q_{C})] \tag{3}\]  
where \(q\) denotes an approximation of the true posterior distribution for the task, while \(\mu_{C}^{*}\) and \(\Sigma_{C}^{*}\) are the mean and covariance in the posterior distribution for task c.