esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 4 Results and Discussion
### Performance Parameters  
Performance parameters used in this research are discussed below:  
Peak signal-to-noise ratio (PSNR): PSNR stands for the peak signal-to-noise ratio and is used to evaluate the image quality.  
\[PSNR=10\ log_{10}\frac{(L-1)^{2}}{MSE} \tag{25}\]Thus, \(L\) is an image's maximum number of intensity levels that can exist.  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{i=0}^{n-1}(O(i,j)-D(i,j))^{2} \tag{26}\]  
Here, \(O(i,j)\) refers to the original image's matrix data. \(D(i,j)\) refers to the filtered image's matrix data, \(m\), which is the number of pixel rows, and \(i\) denotes the row's index within the image. Meanwhile, \(n\) denotes the number of pixel columns, and \(j\) denotes the index of that column inside the images.  
Structural Similarity Index (SSIM): The Structural Similarity Index (SSIM) is a perceptual metric used to evaluate the degradation of image quality that may occur due to various image processing steps. Mathematically, SSIM is represented as  
\[SSIM(x,y)=l(x,y)^{\alpha}\cdot c(x,y)^{\beta}\cdot s(x,y)^{\gamma} \tag{27}\]  
Here, setting the weights \(\{\alpha,\beta,\gamma\}\) to 1, the formula can be reduced to the form shown above. For three comparison measurements between the samples of \(x\) and \(y\), luminance is given by \(l\), contrast by \(c\), and structure by \(s\).  
Feature Similarity Index (FSIM): The low-resolution features or low-level features of the image are measured by another variable known as the Feature Similarity Index (FSIM). These features are assessed and prove how effective the human visual system is. Phase congruency (\(Ph_{c}\)), a local feature or major feature for FSIM, is used to assess it. The measurement has no dimensions. Next, a different variable is assessed using gradient magnitude (\(Gr_{m}\)). \(Ph_{c}\) and \(Gr_{m}\) work together in a complimentary manner to enhance the local image quality.  
For \(Ph_{c}\) and \(Gr_{m}\), low-resolution (LR) features \(f_{1}(x)\) and high-resolution (HR) features \(f_{1}(x)\) are computed between them to determine how comparable the features are. The similarity is assessed as follows:  
\[S_{phc}(x)=\frac{2 Ph_{c1}(x)*Ph_{c2}(x)+E}{Ph_{c1}^{2}(x)+Ph_{c2}^{2}(x)+E} \tag{28}\]  
Here, \(Ph_{c1}\) and \(Ph_{c1}\) are the phase congruencies for the LR and HR images, respectively, and \(S_{phc}\) is the phase congruency similarity. \(E\) is a constant that improves \(S_{phc}\) effectiveness.  
Additionally, the evaluation of the similarity measures \(Gr_{m1}(x)\) and \(Gr_{m2}\) (\(x\)) between LR and HR, respectively, is as follows:  
\[S_{GM}(x)=\frac{2 Gr_{m1}(x)*Gr_{m2}(x)+E}{Gr_{m1}^{2}(x)+Gr_{m2}^{2}(x)+E} \tag{29}\]  
Here, \(Gr_{m1}\) and \(Gr_{m2}\) are the gradient magnitudes of the LR and HR images, respectively, and \(E\) is the variable used to increase the stability of the model.
# Optimal Design of Switchable Wearable Antenna Array for Wireless Sensor Networks
###### Abstract  
In the paper, we present a novel approach to the optimum design of wearable antenna arrays intended for off-body links of wireless body area networks. Specifically, we investigate a four-element array that has a switchable radiation pattern able to direct its higher gain towards a signal source and a lower gain towards an interference. The aim is to increase the signal to interference ratio. We apply a genetic algorithm to optimize both the spatial placement and the feed phasing of the elementary on-body antennas. We propose a simplified, computationally efficient model for the simulation of the array radiation pattern. The model is based on full-wave simulations obtained with a simplified cylindrical model of the human body. We also propose, implement, and evaluate four objective functions based on signal to interference ratio, i.e., min-max, nadir point distance maximization, utopia point distance minimization, and full Pareto-like. Our optimized design obtained with this approach exhibits a significant performance improvement in comparison to the initial heuristic design.  
genetic algorithms; FDTD; finite-difference time-domain; wireless body area network; WBAN; signal-to-interference ratio; SIR; interference rejection; human body model; evolutionary computing; optimization; radiation pattern synthesis; switchable antenna +
Footnote †: journal: Sensors
# An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking
## 1 Introduction
This paper proposes an effective directional residual interpolation algorithm for color image demosaicking based on MLRI. We use the two weights, horizontal and vertical weights, while there are four directional weights in MLRI. In this way, our method reduces the computational complexity. Afterwards, we use a novel method to calculate the directional color difference estimations, and to combine and smooth the color difference estimations. The proposed algorithm can effectively reduce the influence of false edge information in irregular edges. In the following experiments, our proposed algorithm outperforms previous algorithms and provides better color fidelity. The rest of this paper is organized as follows. Section 2 starts with a brief of MLRI and related work. The proposed method is described in Section 3. Experimental results of the proposed method are presented in Section 4. Conclusions and remarks on possible future work are given in Section 5.
###### Abstract  
Objectives: This study aimed to evaluate the effects of different titanium (Ti) anodized surfaces on soft tissue healing around dental implant abtments. Methods: Discs of machined (MC), pink anodized (PA) and yellow anodized (YA) surfaces were morphologically characterized and evaluated in vitro. Cell adhesion and collagen synthesis by human gingival fibroblasts (hGFs) were assessed to evaluate the regenerative potential of the surfaces under study. Their inflammatory potential was evaluated in THP-1 cell cultures by measuring cytokine secretion, and their proteomic adsorption patterns were characterized using nano-liquid chromatography mass spectrometry (nLC-MS/MS). Statistical significance was considered at 5%. In relation to proteomics, statistical differences were evaluated using the Student \(t\)-test with the Perseus application. Results: The anodization process resulted in a reduction in the surface roughness parameter (Ra) relative to the machined titanium (\(p<0.05\)). No differences in nGF adhesion were found between the surfaces after one day. PA induced increased nGF collagen synthesis after 7 days (\(p<0.05\)). The secretion of TNF-\(\alpha\) was lower for anodized surfaces than for MC, and its concentration was lower for PA than for YA (\(p<0.05\)). In turn, TGF-\(\beta\) was higher for PA and YA versus MC after one and three days of culture. A total of 176 distinct proteins were identified and 26 showed differences in adhesion between the anodized surfaces and MC. These differential proteins were related to coagulation, lipid metabolism, transport activity, plasminogen activation and a reduction in the immune response. Conclusions: Anodized Ti surfaces showed promising anti-inflammatory and regenerative potential for use in dental implant abtments. Anodization reduced surface roughness, increased collagen synthesis and lowered TNF-\(\alpha\) secretion while increasing TGF-\(\beta\) levels compared to machined surfaces. Identified proteins related to coagulation and lipid metabolism supported these findings. Clinical relevance: Anodized surfaces could offer improved short-term peri-implant soft tissue healing over machined surfaces. The analysis of abtment surface, instead of implant surface, is a new approach that can provide valuable information.  
**Keywords:** biomaterial; fibroblast; immunomododulation; proteomics; protein adsorption; dental implant; dental implant; dental implant abutment
# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 5 Conclusions  
The proposed feedforward algorithms have been found to be effective for dynamic hand gesture recognition. In our experiments, smart phones equipped with accelerometers and gyroscopes were adopted for the data acquisition of hand gestures. The resulting implementations can be deployed for the remote control of devices such as home appliances. It is observed from the experiments that the PairNet algorithm attains average hit rate of 92.36% for 11 gesture classes over 3404 test gestures. The hit rate of the PairNet is 1.94%, 5.49%, and 3.70% higher than those of the basic CNN, LSTM, and Bi-LSTM, respectively. Furthermore, the proposed Residual PairNet, PairNet with inception, and Residual PairNet with inception have superior performance over PairNet. In fact, the improvement in average hit rate over the PairNet by Residual Pairnet, PairNet with inception, and Residual PairNet with inception are then 2.94%, 1.64%, and 1.74%, respectively. The proposed feedforward algorithms have superior hit rate because they have a large receptive field for accurate gesture spotting. Furthermore, the algorithms also have lower weight sizes as compared with its basic CNN counterpart. In particular, the average computation time on the Raspberry Pi 3 platform for the inference of a single gesture is only 162 ms for PairNet, while the basic CNN needs 410 ms. The proposed work is therefore beneficial for human-machine interface applications where reliable continuous hand gesture recognition with real-time computation on the embedded platform is desired.  
Conceptualization, Y.-C.C., Y.-J.J., and T.-M.T.; methodology, Y.-C.C. and Y.-J.J.; software, Y.-C.C. and Y.-J.J.; visualization, Y.-C.C., Y.-J.J., and T.-M.T.; validation, Y.-C.C., Y.-J.J., and T.-M.T.; supervision, T.-M.T. and W.-J.H.; project administration, W.-J.H.; writing--original draft, W.-J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, under Grant MOST 107-2221-E-003-001-MY2.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Systematic Control Design Method with Active Damping Control in Voltage Source Converters
## 3 Design of the Control Gain Matrix
### Weighting Matrixes' Selection
#### Selection of \(r\).  
This parameter affects the norm of the control gain matrix. Lower values mean permission for higher control gains and vice versa. As expected, and as seen in Table 2, for the lower value of \(r\), the stability and performance indicators are superior, but the norm of the control gain matrix is high. High control gains cause many difficulties for practical implementation and increase the sensitivity to noise and time delay. Figure 6 shows the system output for two different norm values of the control gain matrix. For the higher one, chattering and oscillations in outputs are seen. By increasing \(r\) and reducing the norm of the control gain matrix, these oscillations disappear.  
Figure 6: Impacts of tuning parameter \(r\) and norm of control gain matrix (\(K\)) on the system outputs (\(q_{1}=1\times 10^{4}\), \(q_{2}=1\), and \(q_{3}=5\)): (a) (\(r=0.5\), norm (\(K\)) = \(1.7\times 10^{4}\), \(\sigma_{max}=-41\), and \(\xi_{min}=0.4\)) and (b) (\(r=1.5\), norm (\(K\)) = \(9.1\times 10^{3}\), \(\sigma_{max}=-41\), and \(\xi_{min}=0.28\)).  
A summary of the weighting factors design procedure:  
As shown so far, in the proposed design algorithm, an almost direct relation between tunning parameters and desired stability and performance requirements is identified that considerably simplifies the control system's design.  
In such a way, by adjusting \(q_{1}\), \(q_{2}\), \(r\), and \(q_{3}\), the desired maximum damping factor, minimum damping ratio, control gain matrix norm, and transient response are obtained.  
Hence, the design process can be summarized as follows:  
Step 1: Small values for \(q_{1}\), \(q_{2}\), \(q_{3}\), and \(r\) are selected.  
Step 2: Increasing \(q_{1}\) adjusts the maximum damping factor.  
Step 3: Increasing \(q_{2}\) adjusts the minimum damping ratio.  
Step 4: Increasing \(r\) reduces control gain matrix norm.  
Step 5: Increasing \(q_{3}\) changes dc-link voltage and inverter current transient response.  
As it can be seen, the design procedure is very straightforward, and repetitive actions are reduced considerably.
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 2 Related Work
### Template-Based Chip Design Methods  
[PERSON] et al. develop a toolset, called FabScalar, for automatically composing synthesizable RTL designs of arbitrary cores within a canonical superscalar template. The template defines canonical pipeline stages and interfaces among them. A canonical pipeline stage library (CPSL) provides many implementations of each canonical pipeline stage that differ in their superscalar width and depth of subpipelining. Rocket Chip, proposed by [PERSON] et al., is an open-source System-on-Chip design generator that emits synthesizable RTL. It leverages the Chisel hardware construction language to compose a library of sophisticated generators for cores, caches, and interconnects into an integrated SoC. Rocket Chip generates general-purpose processor cores that use the open RISC-V ISA and provides both an in-order core generator (Rocket) and an out-of-order core generator (BOOM). For SoC designers interested in utilizing heterogeneous specialization for added efficiency gains, Rocket Chip supports the integration of custom accelerators in the form of instruction set extensions, coprocessors, or fully independent novel cores. [PERSON] et al. present a framework called composable modular design (CMD) to facilitate the design of out-of-order processors. CMD designs can also be compiled into RTL, which can be run on FPGAs or synthesized using standard ASIC design flows. The atomicity properties of interfaces in CMD ensure composability when selected modules are selectively refined. Template-based chip design methods focus on the rapid construction of chips; therefore, modular and parameterized template design is the key point.
# Online Monitoring of Power Converter Degradation Using Deep Neural Network
## 4 Conclusions  
This paper proposes a deep learning scheme for detecting the snubber resistors' abnormal conditions of the snubber resistors in the IGBTs in the power converter for a three-phase BLDC drive. Since the degradation of the power devices sheds light on the potential failure of the power converter, it is of vital importance to monitor the health states of the power devices. The proposed scheme is based on a deep CNN architecture; it uses either greyscale images or spectrogram-based RGB images converted from the shunt voltage waveforms in three phases to determine if the power converter is under normal conditions or if the snubber resistor in one of the IGBTs has degraded. Experimental results reveal that the proposed scheme can classify one normal condition and six abnormal  
Figure 7: (**a**) Confusion matrix on dataset A using greyscale images; (**b**) confusion matrix on dataset B1 using spectrogram images; (**c**) confusion matrix on dataset B2; (**d**) confusion matrix on dataset B3.  
conditions with an accuracy above 95%. Moreover, using greyscale images and using spectrogram-based RGB images yield similar accuracy. Moreover, to classify the conditions of the power converter successfully, the value of the snubber resistance must be within a specific range.  
Conceptualization, Y.L.; methodology, J.F. and Y.L.; investigation, J.F.; writing--original draft preparation, J.F.; writing--review and editing, J.L.; supervision, I.J. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Seoul National University of Science and Technology and supported by a grant from the Korea Institute of Radiological and Medical Sciences (KIRAMS), funded by Ministry of Science and ICT (MSIT), Republic of Korea. (No. 50538-2021).  
Data Availability Statement: Data sharing not applicable.  
This study was supported by the Seoul National University of Science and Technology and supported by a grant from the Korea Institute of Radiological and Medical Sciences (KIRAMS).  
The authors declare no conflict of interest.
## 6 Simulations and Results
### Optimality Guarantee  
We now prove how our CEVTI has the local optimum guarantee.  
**Theorem 1** (Optimality Guarantee).: _Our CEVTI has optimality guarantee._  
Proof of Theorem 1.: Recall that the optimum of a constrained optimization problem, also called stationary point, or sometimes fixed point, has a set of gradients that is orthogonal to the constraint set [34]. Therefore, we need to prove that the result of CEVTI is a stationary point.  
We first redefine our factor graph as a clique tree, with a set of vertex \(\mathrm{v}=\{\text{M users, N antennas}\}\), and a set of edges \(e=\{\text{useri}-\text{antennaj}\}\). Please note that any graph that has a maximum clique size of 1 can also be treated with a clique tree. In this case, our factor graph is a clique tree with clique size 1. According to [PERSON] et al. [35], the configuration \(\mathbf{q}\) of a stationary point of a clique tree only exists when its energy potential has the following form:  
\[f_{e_{i-j}} \propto\sum_{v_{i}-e_{i-j}}f_{v_{i}}\big{(}\prod_{k\in Nb_{i}-j}f _{e_{k-i}}\big{)} \tag{61}\] \[b_{i} \propto f_{v_{i}}(\prod_{j\in Nb_{i}-j}f_{e_{j-i}}) \tag{62}\]  
Figure 4: Mutual information comparison.  
in which \(f\) is the energy of the vertex and the edges, and \(b\) is the belief of the vertex. Recall that Equation (33) can be rewritten as:  
\[\begin{split} ln(q_{\mathbf{a}}^{t+1}(\mathbf{a}))\propto\sum_{d_{ i}}q_{d_{i}}^{t}\text{log}p(\mathbf{r}|\mathbf{a},\mathbf{d})\\ \propto\sum_{d_{i}}q_{d_{i}}^{t}\text{log}p(\mathbf{a},\mathbf{d},\mathbf{r})\\ \propto\sum_{d_{i}}q_{d_{i}}^{t}\text{log}p(\mathbf{a})\text{ log}p(\mathbf{d})\text{log}p(\mathbf{r}),\end{split} \tag{63}\]  
which is exactly the form of Equation (61) if we treat \(\mathbf{a},\mathbf{d},\mathbf{r}\) as the vertex, and the link between them as the edges. Therefore, the solution of our CEVTI is the stationary point of the variational optimization.
## 4 Results and Discussion
### Attenuation Behavior of Ferrous Alloys  
Pure iron and 46 types of iron-based alloys were tested. For five alloys, multiple samples were used with different heat treatments, while directional effects were also evaluated for plate samples when it was possible to identify three directions. TDM-2 was used in all but three cases, when a short sample was prepared from a long rod. Since the transmission coefficients for this group are small (\(\sim\)0.06 dB), TDM-2 was deemed adequate in this regard. In comparing the observed data with the literature values of attenuation coefficients, the varied material processing conditions make this difficult. However, the range of observed \(\alpha\) values overlaps with those of reported data. At 5 MHz, Table 2 shows the range to be 8 to 855 dB/m, while it was 10 to 340 dB/m in the collected list [7], which covered about 20 steel grades. Figure 10: Attenuation coefficients with TDM-1 vs. frequency of PVC (dark blue) and PC (dark red). The \(\alpha\) spectra with TDM-2 are plotted for PVC (blue) and PC (red), giving slightly lower \(\alpha\) values. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
For this group, \(\alpha\) values mostly exhibited two frequency dependencies of the linear type (Equation (2)) and the Mason-McSkimin relation (Equation (4)). Two examples for the two types are shown in Figure 11. Here, the \(\alpha\) values are given as dB/m after normalization of propagation distances. Figure 11a gives two widely used steel grades--4340 (UNS 43400) shown by the blue curve and 1020 (UNS G10200) in red. Note that UNS stands for unified numbering system. For 4340 steel, the linear regression fit is also given as a blue dotted line, which is given by Equation (2) with \(\rm C_{d}=9.14\) dB/m/MHz (\(\rm R^{2}=0.936\)). In this case, the linear frequency dependence was observed to 15 MHz. For 1020 steel, the linear region was limited to 6 MHz, above which additional attenuation from Rayleigh scattering became significant. A best fit curve according to the Mason-McSkimin relation is plotted by a dashed red curve and its linear part is shown by a red dotted line. This curve fitting was performed by visual estimate of good fit and its \(\rm R^{2}\) value was 0.995 using the definition of \(\rm R^{2}=1-(squared\) residual sum)/(squared total sum) [72]. The fitted curve in terms of frequency f (MHz) is  
\[\alpha\;(\rm dB/m)=7.63\;f+0.00861\;f^{4}. \tag{18a}\]
## 3 Results
### Characteristics of PEO-Coated Samples  
The surface morphology of the porous samples coated using the PEO process is shown in Figure 12.  
It can be seen that Ti particles are covered with a rough PEO coating, showing some traces of electrical discharges in the form of craters on the surface. These micro-craters are formed locally due to the dielectric breakdown of the growing oxide layer accompanied with gas evolution during the coating process, leaving interconnected microporous channels, with openings resembling the mouth of volcanic crater. The lifetime of these micro discharges during PEO treatments of Ti often ranges from 100 to 800 ms with sizes being in the range of 80-380 \(\upmu\)m [28]. The average pore diameter for these craters was found to be about 4.2 \(\pm\) 0.4 and 5.5 \(\pm\) 0.5 \(\upmu\)m for coatings formed during 4 and 8 min, respectively. It can also be noted that some micropores, which resulted from the partial sintering and necking of the Ti particles during the production process, were covered with the oxide layer, as shown in Figure 12. Four partially sintered Ti particles around a pore (at the ground surface for clarity) before PEO treatment and another group (not ground) after treatment are shown in Figure 13.  
Figure 12: Morphology of the coating developed by PEO treatment for: (a) 4 min; (b) 8 min.  
It can be argued that the micropores among the Ti particles before the PEO treatment were larger with more irregular shapes, whereas after the PEO treatment, pore sizes were significantly smaller and more round.  
The result of the phase compositional analysis for the sample treated for 4 min is illustrated in Figure 14.  
It was found that the surface was mainly composed of anatase with some rutile titanium dioxide regardless of the treatment time. The phase compositional results for these PEO-coated porous Ti samples are quite comparable to those reported for dense commercially-pure Ti PEO coated using trisodium orthophosphate electrolyte with a concentration in the range 5-15 g\(\cdot\)L\({}^{-1}\)[29].