esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Based on the theoretical research and arithmetic simulation by the ideal permutation method, we observed that moderation of EMI with the ideal permutation of a three-phase, four-cable framework for a high-technology nano fab without any shielding system depressed the magnetic field from 9 mG to 0.4 mG at 3 m range. The simulated magnetic field at 3 m demonstrated an improvement of more than 90% when compared to the nano fab data. The simulated results were clearly in agreement with the data provided by the nano fab. The improvement in the magnetic field boosts the confidence of building new nano fab systems. Based on literature reviews, a reduction in EMI can improve the human body's communication and noise cancellation capabilities. Furthermore, this progressive technique will help to improve the efficiency and cost-effectiveness of nano fabs.  
Through the theoretical study and numerical simulation, we predicted the best permutation for reducing EMI noise from a three-phase electric power line system without any shielding system down to 1.2 mG at a 3.0 m distance, applying I = 150 A at 60 Hz with a 12-series cable tray system. Following the above discussions, this study indicated new perspectives that build on the understanding obtained from previous research. The measured magnetic field values of the nano fab are in good agreement with these simulation results. These good results increase confidence in the building of new nano fab systems. Furthermore, this innovation will reduce the cost of EMI shielding in high-technology nano fabs, especially for 40 nm (and below) processes.  
Conceptualization, Y.-L.S. and L.-M.C.; investigation, Y.-L.S. and H.-Y.L.; methodology, Y.-L.S. and L.-M.C.; validation, H.-Y.L.; writing--review and editing, Y.-L.S. and L.-M.C.; software, M.K.R.; data curation, M.K.R. and H.-Y.L.; writing--original draft, M.K.R.; funding acquisition, Y.-L.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Ministry of Science and Technology, Taiwan, through grants MOST 109-2221-E-002-023, MOST 109-2811-E-002-556, MOST 110-2221-E-002-165, MOST 110-2811-E-002-561 and MOST 110-2622-E-468-002.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 2 Motivation and Literature Review
Peak detection offers a cost-effective diagnostic feature-extraction alternative to spectral frequency-domain extraction and was employed in our study. Its diagnostic efficiency has been recorded in [PERSON] et al. (2018); [PERSON] et al. (2018). According to [PERSON] and [PERSON] (2018), peak detection was one of the techniques employed for fault localization in an automatic gear and bearing using vibration and acoustic signals. In their study, peak detection was one of their proposed filtering techniques, which aimed to unify the approach for both acoustic and vibration signals for enabling fault detection. Further, in [PERSON] et al. (2018), using peak detection as one of their techniques, the authors proposed a methodology for distinguishing bearing fault signals from masking signals emitted by drive-train elements. Peaks in the frequency spectrum were used as a discriminating technique for fault classification and separation in their proposed model. In our quest to develop an FT-MCSA-based diagnostic framework for SCIMs, this study makes the following contributions:  
* A proposal for a three-phase MCSA-based peak detection approach for diagnostic feature extraction. The proposed feature extraction method extracts the coordinates of the highest peaks from the FFT, PSD, and autocorrelation function (ACF) spectra as features. * An extensive comparison of ML-based diagnostic models to provide a generalization paradigm for SCIM diagnosis. * A computational cost assessment of ML-based diagnostic models is presented, and empirical assessments is conducted for improved diagnostic assessment of the models. The rest of the paper is structured thus: Section 3 presents the theoretical background of the key modules of our proposed study, while Section 4 presents an overview of the proposed MCSA-based diagnostic method. Section 5 presents the experimental study on a physical case study, while Sections 6 and 7 conclude the study.
## 4 Discussion  
A new DH-BPSO was developed as a modification of the original AIS algorithm by introducing the DHW. DH-BPSO exhibited an affinity in optimizing complex, varying functions such as the Eggholder and Holder Table 2 functions. Its performance exceeded that of its AIS Hybrid, Gbest, and Lbest counterparts in terms of minimum cost achieved and iterations required to reach a minimum cost. Thus DH-BPSO has advantages in both speed and efficiency when solving problems with many satisfactory solutions and where iterations are computationally demanding. Future work may explore the effectiveness of non-linear, higher-order weightings on certain types of problems. For example, an exponential or quadratic weighting strongly favoring a Lbest approach may make DH-BPSO better suited for large domain problems with a variety of solutions. A drawback of DH-BPSO is its dependence on the maximum iterations shown in Equation (1). For small values of the maximum iteration, DH-BPSO would quickly transition to a Gbest approach and would lose the benefit of the explorative Lbest approach. Thus, a possible avenue of research is a dynamic weighting scheme that does not depend on the maximum iteration but possibly adapts to the current speed of convergence, which could be determined from the slope of its convergence trace. Experiments with IFA optimization show the applicability of DH-BPSO. Both measurements and simulations show an improvement in the \(-10\) dB bandwidth, the peak gain, and the efficiency when optimized via DH-BPSO. Optimizations with this algorithm improved simulated bandwidths by as much as 74.3% and measured bandwidths by as much as 58.6%. Bandwidth improvements could be improved by further pixelating the ground clearance area, simultaneously decreasing the patch size and increasing the number of parasitic patches. This would facilitate finer control of the antenna configuration by the algorithm, possibly allowing for better solutions with wider bandwidths. Although this paper only considered the IFA, the process can be easily applied to other antenna geometries such as patch, slot, and loop antennas. The parasitic patches can be configured around each antenna as previously done, and the number of dimensions of the optimization can be increased appropriately. This will lead to a longer convergence time, but in theory the number of parasitic patches can be increased indefinitely with appropriate time and computing resources. Another potential future area of work is extending DH-BPSO to find solutions to multi-objective problems. For example, the radiation pattern could be analyzed in addition to bandwidth. Additionally, desired resonant frequencies may be incorporated into the cost function to ensure increased bandwidth in a frequency band of interest. The principle of a dynamic weighting factor can also be applied beyond just the AIS BPSO to any PSO that incorporates local and global best approaches.
## 3 Results and Discussion
### Flow Control Effect by AC-DBD Plasma Actuation
#### 3.2.5 Frequency Analysis  
Frequency analysis was conducted to visualise the frequency characteristics of the wake flow at different AC-DBD plasma actuation frequencies. Proper Orthogonal Decomposition (POD) was used to decompose the wake region into the spatial and temporal eigenmodes. Figure 18 shows the mode energy of different spatial eigenmodes obtained by POD. From Figure 18, it can be seen that the mode energy is highest in Mode 1 which contributed 10.57% of the overall mode energy, followed by Mode 2 (8.1%) and Mode 3 (4.3%). After Mode 3, the energy contribution by each of the remaining individual  
Figure 17: Normalised z-vorticity profiles at various normalised x-locations along the wake region. x/H = (a) 0.1, (b) 0.3, (c) 0.5, (d) 0.7, (e) 0.9, and (f) 1.1.  
modes is less than 3%, and hence, the frequency responses from Mode 4 to 600 are not considered in the present study.  
Fast Fourier Transform (FFT) was conducted to obtain the frequency domain of the wake flow of the first three modes, and the results are present in Figure 19. It can be seen that due to the highly unsteady nature of the flow in the wake region and the use of the stationary ground in the present study, multiple dominant frequencies were shown in all the three modes resolved by FFT. This agrees with the findings of [PERSON] and [PERSON] [33]. In Mode 1, from Figure 19, it can be seen that the AC-DBD plasma actuation exerts no significant effects on the frequency response to the wake flow as the dominant frequencies in the flow field of all the controlled cases are situated in the frequency band between 80 and 100 Hz, which is same as that shown in the baseline trailer. This further confirmed the conclusion which was made earlier that no observable effect to the flow characteristics in the wake region could be obtained by implementing the AC-DBD plasma actuation at the rear end of the trailer at all the actuation frequencies studied.  
The situation changed slightly when the Mode 2 frequency response was considered. From Figure 19, it could be seen that compared to the baseline case, when the plasma actuation frequency was at 7, 8, or 9 kHz, the dominant frequency was shifted to the frequency band between 60 and 80 Hz, while 80 and 100 Hz was shown in the baseline trailer and the cases when the plasma actuation frequency was at 10 or 11 kHz. This suggests that the AC-DBD plasma actuation seems to be able to modify the Mode 2 frequency response of the wake flow. Finally, no general conclusion can be drawn from the Mode 3 frequency response. From Figure 19, it can be seen that the frequency response is considerably scattered and dominant frequencies appeared in both low, medium, and high-frequency bands in both the baseline trailer and all the control cases being studied.  
Figure 18: Mode energy at different spatial Eigenmodes.
# Anomalous Beam Transport through Gabor (Plasma) Lens Prototype
###### Abstract  
An electron plasma lens is a cost-effective, compact, strong-focusing element that can ensure efficient capture of low-energy proton and ion beams from laser-driven sources. A Gabor lens prototype was built for high electron density operation at Imperial College London. The parameters of the stable operation regime of the lens and its performance during a beam test with 1.4 MeV protons are reported here. Narrow pencil beams were imaged on a scintillator screen 67 cm downstream of the lens. The lens converted the pencil beams into rings that show position-dependent shape and intensity modulation that are dependent on the settings of the lens. Characterisation of the focusing effect suggests that the plasma column exhibited an off-axis rotation similar to the \(m=1\) diocotron instability. The association of the instability with the cause of the rings was investigated using particle tracking simulations.  
plasma trap; space-charge lens; beam transport; instability; proton therapy +
Footnote †: journal: Physics Letters A  
Article
# Routing Strategies for Isochronal-Evolution Random Matching Network
## 3 Path Planning and Routing Decision-Making in the IERMN
### Path Planning  
Path planning is done to design a prior path for a packet before its transmission. In previous studies, a packet was always supposed to keep moving among several vertices until reaching its destination and it was believed to be abnormal for the packet to stay at any vertices. However, in an IERMN, a packet is permitted to stay at some vertices, which is normal and helpful. That is, some vertices delay the sending of the packet in order to make the path shorter. For example, in the IERMN shown in Figure 1, if the vertices are forbidden to delay the sending of packets, we get  
\[P_{1,5,1}=\left\{(v_{1},e_{1,2}^{[1]},v_{2}),(v_{2},e_{2,A}^{[2]},v_{4}),(v_{ 4},e_{3,4}^{[3]},v_{3}),(v_{3},e_{3,5}^{[4]},v_{5})\right\} \tag{11}\]  
Both the temporal length and the spatial length of \(P_{1,5,1}\) in (11) are 4. However, if the vertices are allowed to delay the sending of packets, we obtain  
\[P_{1,5,1}=\left\{(v_{1},e_{1,5}^{[3]},v_{5})\right\} \tag{12}\]  
Both the temporal length and the spatial length of \(P_{1,5,1}\) in (12) are 1. Therefore, when we study path planning in the IERMN, we give permission for each vertex to delay the sending of packets if necessary.  
Although the range of slots for an IERMN is \(\mathbf{N}_{+}\), paths should be planned with finite slots to decrease unnecessary calculations and data redundancy and to lower the lengths of the paths. Given the planning slot \(k\), slot \(k+\Gamma\) is set to be the latest time to plan paths and \(\Gamma\) represents the planning time window (\(\Gamma\in\mathbf{N}_{+}\)).
## 4 Performance Evaluation
### Energy Detection Method  
The signal detection problem can be modeled as a binary hypothetical testing problem with hypotheses \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) defined as  
\[\left\{\begin{array}{l}\mathcal{H}_{0}:r=w\\ \mathcal{H}_{1}:r=x+w\end{array}\right. \tag{9}\]  
where \(\mathcal{H}_{0}\) represents the null hypothesis, and \(\mathcal{H}_{1}\) represents the alternative hypothesis that a useful signal exists. The energy of the received signal is calculated in a bandwidth of \(W\) Hz over a period of \(T_{\text{int}}\). Users are to detect whether \(\mathcal{H}_{0}\) or \(\mathcal{H}_{1}\) is true based on the test statistic \(V\).  
The performance of the ED method is always evaluated by two probabilities, \(P_{d}\) and \(P_{fa}\). \(P_{d}\) implies the probability of detection that \(\mathcal{H}_{1}\) is accepted when \(\mathcal{H}_{1}\) is true, while \(P_{fa}\) is the false alarm probability that \(\mathcal{H}_{1}\) is assumed when \(\mathcal{H}_{0}\) is true. The probability density function of normalized decision statistic \(Y=2V/N_{0}\) has a central chi-square distribution with \(v=2T_{\text{int}}W\) degrees of freedom when \(\mathcal{H}_{0}\) is true. It can be written by  
\[P_{\mathcal{H}_{0}}(Y)=\frac{1}{2^{v/2}\Gamma(v/2)}y^{(v-2)/2}e^{-Y/2} \tag{10}\]  
where \(\Gamma(u)\) is Gamma function defined by \(\Gamma(u)=\int_{0}^{\infty}t^{u-1}\exp(-t)dt\).  
Meanwhile, the decision statistic obeys a non-central chi-square distribution with \(v\) degrees of freedom and non-central parameter \(\lambda=2E/N_{0}\) when \(\mathcal{H}_{1}\) is true. The \(E\) implies the signal energy in the time period \(T_{\text{int}}\). The PDF can be written by  
Figure 5: Receiving gain \(\eta\) versus sampling phase offset \(\Delta\) over fading channel.  
\[P_{\mathcal{H}_{1}}(Y)=\frac{1}{2}\bigg{(}\frac{Y}{\lambda}\bigg{)}^{(y-2)/4}e^{-(Y +\lambda)/2}I_{(y-2)/2}\bigg{(}\sqrt{Y\lambda}\bigg{)} \tag{11}\]  
where \(I_{n}(u)\) is the Bessel function of the first kind of order \(n\). Therefore, the performance of ED can be described by  
\[P_{fa}=\int_{2V_{T}/N_{0}}^{\infty}P_{\mathcal{H}_{0}}(Y)dY \tag{12}\]  
and  
\[P_{d}=\int_{2V_{T}/N_{0}}^{\infty}P_{\mathcal{H}_{1}}(Y)dY \tag{13}\]  
where \(V_{T}\) denotes the decision threshold.  
According to the central limit theorem, \(P_{\mathcal{H}_{0}}(Y)\) and \(P_{\mathcal{H}_{1}}(Y)\) will converge to a Gaussian distribution as \(v\) goes to infinity. The approximated PDF can be written by  
\[P_{\mathcal{H}_{0}}(Y)\approx\frac{1}{\sqrt{2\pi\sigma_{w}}}e^{-\frac{(Y-\mu _{w})^{2}}{2\sigma_{w}^{2}}} \tag{14}\]  
\[P_{\mathcal{H}_{1}}(Y)\approx\frac{1}{\sqrt{2\pi\sigma_{sw}}}e^{-\frac{(Y-\mu _{w})^{2}}{2\sigma_{w}^{2}}} \tag{15}\]  
where \(\mu_{w}=2T_{\text{int}}W\), \(\sigma_{w}^{2}=4T_{\text{int}}W\), \(\mu_{sw}=2T_{\text{int}}W+2E/N_{0}\) and \(\sigma_{sw}^{2}=4T_{\text{int}}W+8E/N_{0}\).
###### Abstract  
Impedance aggregometry (Multiplate(r)) detects the effects of platelet aggregation inhibitors and can predict thrombotic complications after coronary and cerebrovascular stent interventions. The bedside method uses whole blood samples not corrected for platelet count. It is claimed but not proved that the findings are unrelated to platelet count in the physiological range. We therefore investigated in the experimental study: (1) whether impedance aggregometry findings and platelet count are correlated and (2) whether the aggregation/platelet count ratio expresses platelet function independent of platelet count. Following ethics committee approval, platelet-rich plasma from healthy probands was diluted with platelet-poor plasma to obtain different platelet counts. Thereafter, platelet count was measured and samples were subjected to impedance aggregometry using thrombin receptor activating peptide (TRAP) for platelet activation. In all probands, impedance aggregometry findings and platelet count were highly correlated (r = 0.88 to 0.94; \(p<0.05\)). The combination of all experiments revealed the proportionality between impedance aggregometry findings and platelet count (n = 31, r = 0.78, \(p=0.0001\)). In contrast, the ratio of impedance aggregometry findings and platelet count was not significantly correlated with platelet count (r = 0.017; \(p=0.3\)) and thus constitutes a specific measure for platelet function. In conclusion, impedance aggregometry findings subsequent to the activation with TRAP are dependent on both platelet function and platelet count. Normalization of impedance aggregometry findings for platelet count can be achieved by a ratio resulting in more specific results.  
Keywords:multiplate-device; impedance aggregometry; platelet aggregation; platelet count +
Footnote †: journal: Applied Soft. 2020, 10, 7971; doi:10.3390/app10227971
# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 4 Results
### ML Models and Tasks  
Figure 7 represents the mostly used ML models in recent XAI papers (please note that more than one ML model can be used in the same paper). Various neural network models (predominantly deep NN) are mostly used ML models (used in 59% of papers), followed by the tree-based modes (e.g., decision tree, random forest, gradient boosting, used in 37% of papers), support vector machine (11%), linear or logistic regression (9%), K nearest neighbor (4%), Bayesian-based models (3%), and Gaussian models (e.g., Gaussian process regression and Gaussian mixture model, used in 2% of papers). The distribution of the ML models used in the reviewed articles is comparable to what is generally used.  
Besides the most common ML models, there are some others that are less used and could therefore provide interesting alternative views on XAI. These include methods based on fuzzy logic (e.g., fuzzy rule-based classification [262], rule-based fuzzy inference [226; 263], fuzzy decision tree [264], fuzzy nonlinear programming [95]), graph-based models (e.g., graph-deep NN [265; 266], knowledge graph [267]), or some sort of optimization with computational intelligence (e.g., particle swarm optimization [148; 160], clairovance optimization [268]).  
The ML models have been used mainly for classification purposes (70%), followed by regression (21%), clustering (4%) and reinforcement learning (1%), as can be seen in Figure 8. Other tasks, which occurred in only one or at most two articles, include segmentation [97; 269], optimization [270; 271], semi-supervised [272; 273] or self-supervised tasks [274], object detection [275], and novelty search [276].  
There is no substantial difference between the major ML models with regard to the ML task of their target application. The distributions of ML tasks for specific ML models (NN, DT, LR, kNN, etc.) are all very similar to the overall one represented in Figure 8. Among all major ML models, SVM stands out the most, which is used for classification somewhat more often than the others (in 80% of cases).  
With regard to the application domain, health, environment, industry, and security and defense are among the top five domains for all the major ML models, with the only exception being linear or logistic regression. In the case when linear or logistic regression was used as an ML model, finance is among the top three application domains, which is never the case for other major ML models. As finance is the second most used with the tree  
Figure 6: Most common explanation techniques used in the papers in our corpus (only XAI techniques used in at least five papers are shown).  
based ML models, which, similarly to linear and logistic regression, can be characterized as the most transparent and inherently interpretable models, it suggests that the users in the financial domain are especially keen on getting insights and explanations on how the ML models operate on their data.
## 3 Results
### Multiple Cells  
#### 3.2.1 Baseline Condition  
The time variation of the temperatures in multiple cells (the central cell and the adjacent cells) under baseline conditions have been evaluated through both theoretical analysis and numerical simulations with \(q_{heating}\) = 3.0 \(\times\) 10\({}^{5}\) (W/m\({}^{3}\)). Herein, the thermal conductivity between cells \(K_{cell}\) is assumed to be 0.2 (W/m\(\cdot\)K), and the temperature frequency \(f\) is assumed to be 1/400 (1/s), corresponding to the rough time until thermal decomposition starts, respectively.  
Figure 6a shows the temperature profiles, while Figure 6b presents the temperature change _dT/dt_. As seen in Figure 5a, in the numerical simulations, the central cell experiences a significant temperature rise around 350 s, and the adjacent cells show a marked temperature increase around 600 s, indicating a thermal runaway spread. The theoretical solution for the temperatures of the central and adjacent cells generally reproduces this trend. Note that, similar to the results for the single cell (refer to Section 3.1.1), the theoretical temperature of both cells' rise occurs slightly later than the numerical ones. Moreover, in the numerical simulation, the central cell shows a slight temperature increase after 600 s, following an initial rise due to thermal decomposition reactions around 350 s (indicated by the arrows in Figure 6a,b). This indicates the result of thermal influence from the thermal decomposition reactions in the adjacent cells, which is not accounted for in the theoretical solution.