esa-sceva/satcom-chunk-collection huggingface dataset:
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 4 Results  
This section presents simulation and measurement-based evaluations of the flexible antenna. Firstly, the antenna matching is studied in Section 4.1 by evaluating S11 parameter as the antenna is fed with discrete port and with coaxial cables having different thicknesses. Then, in Section 4.2, the measured antenna matching is evaluated in different body parts, located directly on the skin or above the cloth of the volunteer. Additionally, impact of the bending is studied with measurements. Section 4.3 compares the simulated and measured S11 parameters as well as antenna efficiencies. In Sections 4.4 and 4.5, the antenna's usability for in-body and on-body communications at different frequencies is evaluated by studying power flow representations with the layer model as well as the voxel model. Channel characteristics between the flexible antenna and a UWB capsule endoscope model is evaluated in Section 4.6. Finally, the antenna's safety is studied by SAR evaluations in Section 4.7.
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 2 Materials and Methods
### Reduction of Physiological Interference by PCA  
PCA and correction by the removal of the largest components is one of the earliest fNIRS signal conditioning methods [39]. Several studies have compared PCA to other MA reduction algorithms; however, its limits in treating such unsmooth and heterogeneous artifacts have been pinpointed (see the discussion on tPCA in Appendix A.3). Conversely, PCA is fully valid in addressing the reduction of common-mode PCFs due to hemodynamic activities other than the HRF [40]. The working principle consists of enhancing the contrast of localized task-related activations by removal of the largest components of PCA, which are considered global effects common to all measurement channels. Indeed, considering the more extended framework presented in Section 2.2, fNIRS measurements are highly affected by extracerebral and non-evoked cerebral activity, making only the BPF operation insufficient to estimate the corresponding HRF [13], especially for Mayer waves and respiratory oscillation due to partial overlap with the frequency content of HRF [41].  
Alternative methods, not applied in this study, that do not involve auxiliary measurements of extracerebral and systemic activity are based on the extraction of a common-mode component as a median of all signals (or all signals in one hemisphere), next regressing it out from each channel (or each channel of the opposite hemisphere) [12]. Additionally, the impact of slow-varying physiological oscillations and scalp-related superficial confounds can be reduced by employing techniques such as Discrete Cosine Transform, pre-coloring, and pre-whitening methods directly in subject-level analysis [42; 43]. The critical parameter in PCA cleaning is the amount of power to be deleted, which in the present study was set to remove PCA components by up to 80% of the variance in the data, similar to other studies [44]. The PCA method is particularly suited to remove physiological interference over the considered experimental dataset, since we considered an fNIRS probe that almost covers the whole subject scalp surface (details are presented in Section 2.8). Hence, the largest PCA components do identify global variations of physiological oscillations, leaving only 20% in the variance of the data as localized HRF-related activity.
# Latent Dimensions of Auto-Encoder as Robust Features for Inter-Conditional Bearing Fault Diagnosis
## 5 Experimental Validation
### Architecture of the Auto-Encoder
```
1:Training Input: Labeled source domain data \(D_{s}=(X_{i}^{s},Y_{i}^{s})_{i=0}^{n_{s}-1}\)
2:Training Output: Min-Max Scaler (\(MMS_{i}\)), Auto-Encoder Network (encoder \(E_{i}\) and decoder \(DE_{i}\)) and K-Nearest Neighbours classifier (\(KNN_{i}\))
3:Testing Input: Labeled target domain data \(D_{t}=(X_{i}^{t},Y_{i}^{t})_{i=0}^{n_{t}-1}\) and \(MMS_{i}\), \(E_{i}\) and \(KNN_{i}\)
4:Testing Output: Predicted labels of target domain data \(Y_{i}^{t}\)
5:Begin:
6:for each value of \(i\) in range of \(n\):
7:Train and transform \(X_{i}\) with \(MMS_{i}\) as shown by Equation (3)
8:for (\(m\) epochs or stopping criteria met):
9:Optimize \((E_{i},DE_{i})\) with MSE as shown in (1)
10:endfor
11: Compute latent features \(F_{i}\) of \(X_{i}\) using Equation (2)
12:Train \(KNN_{i}\) with \(F_{i}\) and \(Y_{i}\)
13:for each value of \(k\) in range of \(n\) and \(k\
eq i\):
14:Transform \(X_{k}\) with \(MMS_{i}\)
15: Compute latent features \(F_{k}\) of \(X_{k}\) with \(E_{i}\)
16:Predict \(Y_{k}^{{}^{\prime}}\) with \(F_{k}\) and \(KNN_{i}\)
17: Compute accuracy of prediction with \((Y_{k})\) and \((Y_{k}^{{}^{\prime}})\)
18:endfor
19:endfor
```  
**Algorithm 1** Pseudo algorithm used for training and testing various transfer tasks of CWRU and PU datasets. \begin{table}
\begin{tabular}{l l} \hline \hline
**Layers** & **Parameters** \\ \hline Conv1 & Kernel size: (5,1), number of Kernels: 30, Stride: 1, activation: relu, Padding: Same \\ Pool1 & Average Pooling, Size: 4, Stride: 1 \\ Conv2 & Kernel size: (3,1), number of Kernels: 15, Stride: 1, activation: relu, Padding: Same \\ Pool2 & Average Pooling, Size: 2, Stride: 1 \\ Flat1 & converts 2d output from previous layer to 1d \\ Dense1 & Dense, Size: (50,1), activation: relu \\ Latent & Dense, Size: (20,1), activation: relu \\ Dense2 & Dense, Size: (CWRU: (100,1), PU: (256,1)), activation: relu \\ UpSamp1 & Upsampling1d, Size: 2 \\ ConvT1 & Conv1 dTranspose, Kernel size: (3,1), number of Kernels: 15, Stride: 1, activation: relu, Padding: Same \\ UpSamp2 & Upsampling1d, Size: 4 \\ ConvT2 & Conv1 dTranspose, Kernel size: (5,1), number of Kernels: 30 Stride: 1, activation: \\  & relu, Padding: Same \\ ConvT3 & Conv1 dTranspose, Kernel size: (3,1), number of Kernels: 1 \\  & Stride: 1, activation: sigmoid, Padding: Same \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Used Multi-Layer Convolutional Auto-Encoder architecture for the experiments. As an important note, the normalisation part of our pre-processing step has two separate methods for the train and test datasets. Considering the goal of using a source-only dataset for algorithm training, the test dataset (target domain data) will not be used during the training process. The same scaler that is used to fit against and transform the training data will be used to transform the test data. In this way, we make sure that no information of the target domain is used during training.
## 2 Related Work  
This section presents relevant studies that proposed detecting DDoS attacks in SDN based on the entropy method.  
[PERSON] et al. [16] suggested a fusion entropy method for calculating the randomness of network traffic, which was evaluated and verified using eight simulation scenarios [3]. The proposed approach detects DDoS attacks by comparing an entropy value with a threshold value. The suggested method sets a threshold, which assists in increasing DRs and lowering FPRs. However, the dataset and the number of features used to detect DDoS attacks were unclear, and this study only focused on high-rate attacks.  
[PERSON] et al. [17] proposed an entropy-based detection method that analyzes network traffic flows to ascertain the probability of the incoming packets' flow in the controller. This approach appears to be lightweight and effective in spotting early-stage threats. Nonetheless, while the suggested method seeks to detect attacks as early as possible, significant drawbacks limit its effectiveness since it has trouble detecting varying rates of DDoS attacks with a high DR and a low FPR.  
A DDoS attack can be seen as an outlier by the variation in the probability distribution of incoming network traffic flow. Therefore, [PERSON] et al. [18] proposed an attack detection method by combining information distance and generalized entropy to detect low-rate DDoS attacks on the controller. The proposed method experiment showed that it effectively detects low DDoS attack traffic rates by combining generalized entropy with the information distance. However, the proposed method was not very successful at detecting high traffic rates because of the fluctuations in attack traffic rates of the network traffic flow.  
[PERSON] et al. [19] presented a flexible method to detect and mitigate low-rate and high-rate DDoS attack traffic in SDN networks with similar behavior to normal traffic. The proposed strategy relies on intrusion detection and prevention systems (IDS and IPS). The proposed approach employs six machine learning models for DDoS classification in the IDS. One issue is that the proposed method has a fixed threshold that is ineffective in detecting low-rate DDoS attack traffic against multiple victims.  
Finally, [PERSON] et al. [20] proposed an entropy-based DDoS defense mechanism (EDDM) that thwarts attacks on legitimate network users in flash crowd scenarios by tracking attack traffic flows. The EDDM consists of three phases drawing upon traffic statistics gathered inside a given window size employed to calculate an entropy value for detecting DDoS attacks. The entropy value in sFlow is compared with a static threshold to determine the traffic packet rate targeting a single victim. However, a lag exists in processing new packets, and the multiple-victim attack detection accuracy is low.  
Table 1 summarizes related work on attack detection methods and analyzes their weaknesses and strengths according to their approaches.
## 5 Numerical Simulations
### Noisy Inversions
In every case, complex visibilities \(V\) have been blurred by an additive Gaussian noise \(\Delta V\) with standard deviation set to 0.1 K and the singular vectors \(\mathbf{V}_{\ell}\) of \(\widetilde{\mathbf{G}}\) corresponding to the 16 smallest singular values \(\sigma_{\ell}\) have been discarded prior to the inversion. These maps have to be compared to those in Figures 10a and 11a where these singular vectors were taken into account in the computation of \(\widetilde{\mathbf{G}}^{+}\). Figure 7:8 Error maps \(\widetilde{\mathcal{T}}_{n}-\mathcal{T}\) obtained in \(X\) polarization with the cross-shaped array shown in Figure 4 with the shortest spacing \(d\) set to \(0.69\lambda\) when (**a**) complex visibilities \(V\) are free from any additional random noise \(\Delta V\) and when (**b**) complex visibilities \(V\) are blurred by an additive Gaussian noise \(\Delta V\) with standard deviation set to 0.1 K (similar results are obtained in \(Y\) polarization). In both cases, the singular vectors \(\mathbf{V}_{\ell}\) of \(\widetilde{\mathbf{G}}\) corresponding to the 16 smallest singular values \(\sigma_{\ell}\) have been discarded prior to the inversion. These maps have to be compared to those in Figures 8a and 9a where these singular vectors were taken into account in the computation of \(\widetilde{\mathbf{G}}^{+}\). \(d=0.71\lambda\) and to 4.19 K with \(d=0.69\lambda\). This additional truncation of \(\mathbf{G}\) therefore has no impact on the radiometric sensitivity, as the estimation is still following the theoretical value with the same uncertainty. Finally, the impact of this supplementary truncation of \(\mathbf{G}\) onto the angular resolution also has to be addressed. It turns out that the synthetic response of the cross-shaped array of Figure 4 with \(\widetilde{\mathbf{G}}_{n}^{+}\) is almost identical to that with \(\widetilde{\mathbf{G}}^{+}\) as a consequence of the small number of additional singular vectors discarded prior to the inversion: although these \(n=16\) singular vectors might be associated with high-resolution features, the spectral content in the remaining \(N_{f}-n=\) 13,845 singular vectors is almost identical to that in the \(N_{f}=\) 13,861 ones. In both cases, the angular resolution is about 0.43\({}^{\circ}\) without an apodization window and about 0.61\({}^{\circ}\) with a Blackman apodization window, as illustrated in Figure 7:9
## 1 Introduction
It can be generated by a chemical process (Fenton (H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\)) [39], photochemical processes UV/TiO\({}_{2}\)[40], UV/H\({}_{2}\)O\({}_{2}\)[41; 42], photo-Fenton (UV/H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\)) [43; 44], and electrochemical (anodic oxidation, electro-Fenton) [45; 46]. This process represents the decomposition of hydrogen peroxide into HO\({}^{\bullet}\), which occurs through two routes: (1) Catalytic decomposition by Fe\({}^{2+}\) (Equations (5)) and (2) photodecomposition by UV irradiation at 254 nm (Equation (6)). Additionally, there is the production of further HO\({}^{\bullet}\) by photo-reduction of ferric to ferrous iron (Equation (7)), combined with H\({}_{2}\)O\({}_{2}\) photolysis (Equation (6)), or by photolysis of complexes formed between the organic compounds or their intermediates with Fe\({}^{3+}\) (Equation (8)) [41; 47; 48]. \[\mathrm{Fe}^{2+}+\mathrm{H}_{2}\mathrm{O}_{2}\rightarrow\mathrm{Fe}^{3+}+ \mathrm{HO}^{\bullet}+\mathrm{HO}^{-} \tag{5}\]  
\[\mathrm{H}_{2}\mathrm{O}_{2}+\mathrm{h}\mathrm{v}\rightarrow\mathrm{2 HO}^{\bullet} \tag{6}\]  
\[\mathrm{Fe}(\mathrm{OH})^{2+}+\mathrm{h}\mathrm{v}\rightarrow\mathrm{Fe}^{2+ }+\mathrm{HO}^{\bullet} \tag{7}\]  
\[\mathrm{Fe}(\mathrm{RCO}_{2})^{2+}+\mathrm{h}\mathrm{v}\rightarrow\mathrm{Fe}^{2 +}+\mathrm{CO}_{2}+\mathrm{R}^{\bullet} \tag{8}\]  
As a result, the photo-Fenton process is reported to be effective in removing several varieties of pollutants such as pesticides, dyes, and pharmaceuticals [49; 50; 51; 52]. The effectiveness of these techniques is greatly improved by the addition of a catalyst or an oxidant. The aim of this study is to realize the degradation of TA aqueous solution by UV/persulfate and photo-Fenton processes. Besides, the effect of different factors such as initial pH, oxidant dosage, ferrous iron dose, initial TA content, and temperature on the performance of degradation and mineralization by UV/persulfate and photo-Fenton processes was also evaluated.
# Extensible Hook System for Rendezvous and Docking of a CubeSat Swarm +
###### Abstract  
Deployment of CubeSat swarms is proposed for various missions necessitating cooperative interactions among satellites. Commonly, the cube swarm requires formation flight and even rendezvous and docking, which are very challenging tasks since they require more energy and the use of advanced guidance, navigation, and control techniques. In this paper, we propose the use of an extensible hook system and its corresponding GNC architecture to mitigate these drawbacks, i.e., it allows for saving fuel and reduces system complexity by including techniques that have been previously demonstrated on Earth. This system is based on a scissor boom structure, which could reach up to five meters for a 4U CubeSat dimension, including three degrees of freedom to place the end effector at any pose within the system workspace. We simulated the dynamic behavior of a CubeSat with the proposed system, demonstrating that the required power for a 16U CubeSat equipped with one extensible hook system is considered acceptable according to the current state-of-the-art actuators.  
Keywords:space; robotics; CubeSat; swarm +
Footnote †: These authors are also supported by the National Science Foundation of China (Grant No. 1131100000).
# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 4 Deep Learning Techniques for Load/Generation Forecasting
### Simulation Results of Short-Term PV Power Forecasting Using LSTM-RNN  
Following the same prediction criteria as before, in this section, the LSTM-RNN framework is used for short-term PV-power forecasting. However, the selected training period and forecasting time-horizon are different from the previous case. This is due to the characteristics of solar power that follow a daily-base pattern and depend on geographical and seasonal parameters. This is in contradiction with household load trends that follow appliances scheduled consumption or arbitrary user decisions.  
A database of synthetically generated PV-power measures provided by the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy was used for this simulation [37]. From the database ten days (1 January 2006 to 10 January 2006) of solar generation data corresponding to the state of Alabama were selected with 5 min resolution, Figure 8. The LSTM-RNN was trained for 9 days and predicted PV-power for the following day. It should be noted that solar generation strongly depends on immediate weather conditions and phenomenon which was not considered in this simulation and more complicated algorithms are essential for very-accurate predictions.  
The results are presented in Figure 9, where 200 hidden units and 1000 epoch # were selected as the LSTM-RNN prediction settings. Two different trends are provided that correspond to the two cases of network update with observed values and forecasted values from previous time steps. Similar to the load forecasting test, when the networks is updated with observed values, Figure 9a, the prediction accuracy is much higher, however, this criteria is not suitable for the application of our interest as discussed in the previous section.  
It is obvious that by using different LSTM-RNN parameters settings (other than the ones of these tests) various levels of prediction accuracy can be achievable. However, performing further tests to produce better forecasting results is out of the scope of this paper since here we are presenting the practicality of performing sensible predictions on time dependent data (Load/PV-Generation) by using LSTM-RNN algorithms as a proof of concept.  
Figure 8: Ten days of PV-power dataset.
## 5 Design of Greedy Grouping Algorithm
Finally, the destination nodes will judge if they have received \(k\) linearly independent packet combinations. If so, the \(k\) original packets can be solved with the GE method. It can be proven that the computational complexity of this greedy grouping algorithm is \((k^{3})\). The proof is omitted here because of space. If the wireless channel environment is too unfavourable, the destination nodes will not receive sufficient packets to satisfy the GE condition. In this harsh environment, the source nodes will start there transmission of lost packets. If the destination nodes do not receive enough required information, they will request their neighbour nodes to re-transmit the associated information. The neighbour nodes will also continue to forward lost packets until the GE solution condition is satisfied. For simplicity, it is assumed here that a one-hop retransmission will usually satisfy the GE solution requirement. In order to better explain the grouping process, Figure 5 gives an example. In Figure 5, there are four source nodes: S1, S2, S3, and S4, and the maximum number of link-disjoint paths between the sinks and the gateway is three. Therefore, the maximum number of source nodes in feasible grouping is 2. Using the greedy grouping algorithm in Table 3, we can derive an output result SET1 = {[S1,S2], [S3,S4]}. In contrast to the other two grouping results SET2 = {[S1,S2], [S1,S3], [S1,S4]} and SET3 = {[S1,S3], [S2,S4]}, we found that the optimal solution is SET1. Because this scheme obtains a fairness index more than SET2, and consumes less bandwidth resources than SET3. In this greedy strategy, each packet uses only three links to forward data to the minimum cutting edge, while four links in SET3.
## 5 Results
### Orbit Quality  
In order to assess the quality of the estimated clocks and orbits, the differences over the overlapping time of two adjacent three-day solutions are utilized as usual. As shown on Figure 3, the orbit of the last day in a three-day solution is compared with that of the middle day of the next three-day solution. The RMS of the differences in along-track, cross-track and radial directions are taken as orbit quality indicator. The statistical results for orbits and clocks are listed in Table 5.  
From the orbit RMS in Table 5, the along-track RMS is significantly larger than that of the other two directions, as expected. GEOs have the largest RMS in along-track direction of 114 cm compared to 24 cm and 45 cm for IGSOs and MEOs, respectively. RMS in cross-track and radial are very similar  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Satellites**} & \multicolumn{4}{c}{**Orbits (cm)**} & \multicolumn{2}{c}{**Clocks (ns)**} \\ \cline{3-8}  & & **Along** & **Cross** & **Radial** & **3D** & **STD** & **RMS** \\ \hline \multirow{8}{*}{GEO} & C01 & 163 & 10 & 6 & 163 & 0.25 & 0.45 \\  & C03 & 37 & 10 & 5 & 39 & 0.29 & 0.39 \\  & C04 & 171 & 11 & 8 & 171 & 0.29 & 0.74 \\  & C05 & 87 & 10 & 6 & 87 & 0.23 & 0.40 \\ \cline{2-8}  & Mean & 114 & 10 & 6 & 115 & 0.27 & 0.49 \\ \hline \multirow{8}{*}{IGSO} & C06 & 27 & 17 & 7 & 33 & 0.31 & 0.39 \\  & C07 & 26 & 16 & 7 & 31 & 0.18 & 0.25 \\  & C08 & 25 & 17 & 10 & 32 & 0.32 & 0.39 \\  & C09 & 21 & 14 & 7 & 26 & 0.29 & 0.35 \\  & C10 & 20 & 13 & 5 & 24 & 0.43 & 0.49 \\ \cline{2-8}  & Mean & 24 & 15 & 7 & 29 & 0.31 & 0.37 \\ \hline \multirow{8}{*}{MEO} & C11 & 48 & 13 & 12 & 51 & 0.43 & 0.48 \\  & C12 & 42 & 12 & 11 & 45 & 0.39 & 0.44 \\ \cline{1-1} \cline{2-8}  & Mean & 45 & 13 & 12 & 48 & 0.41 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Averaged overlapping RMS of the estimated orbits and clocks for each individual satellites and the mean of each satellite type.  
Figure 7: _Cont._for the three types of satellites, _i.e._, 10 cm and 6 cm for GEOs, 15 cm and 7 cm for IGSOs, and 13 cm and 12 cm for MEOs. In general, the orbit quality can still be further improved by optimizing the tracking geometry. For example, along-track RMS for GEOs can be reduced by extending the network. The larger RMS in across-track and radial for IGSOs and MEOs could be caused by inaccurate modeling of the satellite antenna phase center correction and the satellite attitude control.