esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A wideband three-dimensional (3D) absorber based on a notch-slot resonator is proposed in this paper. A lossy coupled microstrip line was applied to eliminate the reflection band of the original lossy notch-slot resonator, resulting in a wide absorption band. A single-polarized design was first proposed, and the operating principle was demonstrated. Then, a dual-polarized design was presented, fabricated and measured. To eliminate the undesirable resonances caused by the securing slits cut between the two adjacent unit cells, some shorting pins were introduced. The measured results show that the dual-polarized absorber exhibits an absorption band from 2.74 GHz to 11.08 GHz (120.69% fractional bandwidth), and it is also stable under the incident angles up to 40\({}^{\circ}\) for both TE and TM polarizations.  
microwave absorber; wide bandwidth; notch slot; slot coupling +
Footnote †: journal: Applied Sciences  
two absorption bands corresponding to its fundamental and first harmonic resonance. However, the absorption bands are relatively narrow (47.15% and 17.39% FBW).  
In this paper, a 3D absorber utilizing a new lossy notch-slot resonator is proposed. Inspired by the coupled-feed wideband slot antennas [17; 18], the lossy notch-slot resonator was implemented using a coupled microstrip line loaded with a resistor, instead of directly loading the resistor at the open end of the notch slot [16]. The incident EM waves were first coupled into the notch slot, then coupled to the microstrip line and finally, absorbed by the resistor. This method eliminates the reflection band of the original lossy notch-slot resonator and merges the fundamental and third-order resonances, resulting in an ultrawide absorption band. Both the single- and the dual-polarized designs are presented, and the dual-polarized one has been fabricated and measured and exhibits a wide absorption band from 2.74 GHz to 11.08 GHz (120.7% FBW). In addition, the proposed 3D absorber has a robust structure which is easy to fabricate compared to conventional 3D absorbers.
## 3 Entropy Feature-Based LNMS LDPC Decoding Optimization
### Serial Entropy Feature-Based LNMS (S-EFB-LNMS) LDPC Decoding Optimization Scheme
The detailed implementation details and performance evaluation of the parallel processing strategy have been fully described in Algorithm 2. ```
1:Input: Initial channel soft information of each bit of one frame \(LLR_{\text{ch}}\), the entropy feature vector \(\mathbf{f}_{\text{eff}}\), the cosine similarity matrix \(CS(\mathbf{f}_{\text{eff}},\mathbf{c}_{\text{cmp}})\), and the maximum iteration number \(T_{\text{max}}\). 2:Output: Decoded bits \(\mathbf{c}\). 3: Obtain RLPUs and URLPUs by using the \(CS(\mathbf{f}_{\text{eff}},\mathbf{c}_{\text{cmp}})\) calculated by Equation (4). ```
1: Initialize the posterior information of \(j\)-th bit to the LLR from flash-memory channel, i.e., \(p_{j}^{(0)}=LLR_{\text{ch},j}\). Clear the check-to-variable information, i.e., \(CI_{ij}^{(0)}=0\). 2:for\(l\) from 1 to \(T_{\text{max}}\)do
3: Process RLPUs and URLPUs in parallel. 4: Update VN information, CN information, and posterior information calculated by Equations (5)-(7), respectively. 5:if\(v_{j}^{(l)}<0\)then
6:\(c_{j}^{(l)}=1\)
7:else
8:\(c_{j}^{(l)}=0\)
9:endif
10:if\(\mathbf{c}^{(l)}\cdot\mathbf{H}^{\text{T}}==\mathbf{0}\)then
12: break
13:endif
14:endfor
```  
**Algorithm 2**_Cont_.
## 2 Background
### Optimization of the Proposed Objective Function
\begin{split}&\mathbf{U}_{k+1}^{*}=\min_{\mathbf{U}^{*}, \mathbf{w}}\,-\lambda\;tr\Bigg{(}\mathbf{U}^{*}{}^{\top}\bigg{(}\sum_{m=1}^{M }w_{m}\mathbf{U}^{(m)}{}^{\top}\bigg{)}\mathbf{U}^{*}\Bigg{)}\\ &\text{s.t.}\quad\|\mathbf{w}\|_{2}=1,\;\mathbf{w}\geq 0,\mathbf{U} ^{*}{}^{\top}\mathbf{U}^{*}=\mathbf{I}.\end{split} \] (25) Equation (25) can be written as a trace maximization problem: \[ \begin{split}&\mathbf{U}_{k+1}^{*}=\max_{\mathbf{U}^{*}, \mathbf{w}}\,\lambda\;tr\Bigg{(}\mathbf{U}^{*}{}^{\top}\bigg{(}\sum_{m=1}^{M }w_{m}\mathbf{U}^{(m)}{}^{\top}\bigg{)}\mathbf{U}^{*}\Bigg{)}\\ &\text{s.t.}\quad\|\mathbf{w}\|_{2}=1,\;\mathbf{w}\geq 0,\mathbf{U} ^{*}{}^{\top}\mathbf{U}^{*}=\mathbf{I}.\end{split} \] (26)Equation (26) is rewritten in terms of Tucker decomposition as [4; 29]:  
\[ \begin{split}\mathbf{U}_{k+1}^{*}=&\max_{\mathbf{U}^{ *},\mathbf{w}}\|\mathbf{\Gamma}\times_{1}\mathbf{U}^{*^{\top}}\times_{2} \mathbf{U}^{*^{\top}}\times_{3}\mathbf{w}^{\top}\|_{F}^{2}\\ \text{s.t.}&\|\mathbf{w}\|_{2}=1,\ \mathbf{w}\geq 0, \mathbf{U}^{*^{\top}}\mathbf{U}^{*}=\mathbf{I},\end{split}  \tag{27}\]  
where \(\mathbf{\Gamma}^{(m)}=\mathbf{U}^{(m)}\mathbf{U}^{(m)^{\top}}\), \(m=\{1,\ldots,M\}\). The common subspace between all layers of the multiplex network, \(\mathbf{U}^{*}\), and the weighting vector across layers, \(\mathbf{w}_{m}\), can be found efficiently using HOOI. * Finally, the Lagrange multipliers, \(\mathbf{X}_{1_{k+1}}^{(m)}\), \(\mathcal{X}_{2_{k+1}}\), and the penalty parameters, \(\beta_{1_{k+1}}\), \(\beta_{2_{k+1}}\), are computed as : \[\mathbf{X}_{1_{k+1}}^{(m)}=\mathbf{X}_{1_{k}}^{(m)}+\beta_{1_{k}}\bigg{(} \mathbf{A}^{(m)}-\mathbf{A}^{(m)}\mathbf{Z}_{k+1}^{(m)}-\mathbf{E}_{k+1}^{(m)} \bigg{)},\] (28) \[\mathcal{X}_{2_{k+1}}=\mathcal{X}_{2_{k}}+\beta_{2_{k+1}}\bigg{(} \mathcal{Z}_{k+1}-\mathcal{H}_{k+1}\bigg{)},\] (29) \[\beta_{1_{k+1}}=\min(\rho\beta_{1_{k}},\beta_{max}),\] (30) \[\beta_{2_{k+1}}=\min(\rho\beta_{2_{k}},\beta_{max}),\] (31) where \(\rho\) is set to be 2. The proposed algorithm keeps iterating until it meets the following stopping criterion: \[ \begin{split}\|\mathbf{A}^{(m)}-\mathbf{A}^{(m)}\mathbf{Z}^{(m)}- \mathbf{E}^{(m)}\|_{\infty}\leq\epsilon,\\ \|\mathcal{Z}-\mathcal{H}\|_{\infty}\leq\epsilon.\end{split} \] (32) A summary of the proposed algorithm is described in Algorithm 1. ```  
**Algorithm 1****Mx-CRSA**
## 2 Related Works
A fuzzy-logical-based clustering approach called LEACH-ERE [26] uses an extension to the energy prediction to prolong the lifetime of WSN by evenly distributing the workload. In addition to the residual energy, the expected residual energy has been introduced to act as a fuzzy descriptor during the cluster head selection process. There are also some new algorithms and new protocols applied in WSNs. The harmony search algorithm (HSA) [27] is music-based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain a better harmony. [PERSON] [28] applied it for minimizing the intra-cluster distance and optimizing the energy consumption of the network. IHSCR [29] was an energy-efficient clustering and routing based on the improved harmony search algorithm. It combines a discrete encoding scheme of HAS and the roulette wheel selection method. A three-level hybrid clustering routing protocol algorithm (MLHP) [30] based on the grey wolf optimizer [31] is proposed which use the bionics algorithm for probabilistic selection of CHs. In addition, PSO-ECHS [32] is an energy efficient cluster head selection algorithm based on particle swarm optimization for wireless sensor networks. It considers intra-cluster distance, sink distance and residual energy of sensor nodes, which demonstrated the superiority for prolonging the lifetime of WSN. In order to solve energy bottleneck problem, [PERSON] [33] proposed a pair-wise directional geographical routing (PWDGR) strategy. The source node sends the data to the pair-wise node around the sink node, which can effectively relieve the serious energy burden around sink node and also make the energy consumption balanced. Besides, reference [34] proposed an energy-efficient multi-hop routing algorithm based on grid clustering to tackle this problem. This algorithm optimizes the electoral process of functional nodes by combining various factors such as nodes' energy, nodes' location and levels of the network area. Communication nodes are introduced to select cluster heads and transfer data between clusters via multi-hop routing, easing the burden of cluster heads. To balance the energy among clusters, River Formation Dynamic based multi-hop routing protocol [35], which was integrated with clustering and a new hybrid technique, improves energy conservation by reducing the overall packet transmission distance of intra- and inter-cluster communication, which results in increased overall network lifetime.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 6 Discussion  
Before coming to our conclusions, we discuss some relevant issues in our proposition. While \(OFQS\) proved its efficiency in the experiments, a few things still need to be further investigated. In our instances classification (Section 3.4), the parameters \(\alpha\) and \(\beta\) were fixed for the three instances. This selection could be optimized and made dynamic using machine learning or fuzzy logic techniques in order to compute the most suitable classification for every traffic class. These techniques should respect the constraints of the Wireless Sensor Network in terms of energy and computational limitations. Furthermore, the multiple instances in RPL aim to differentiate the traffic in the network. Further analysis should be made in order to study the impact of one instance on another while running together on the same network, and how many instances can we maximum run by still ensuring a proper traffic differentiation between the instances.
## 6 Numerical Examples
### On Hyper-Reduction  
Projection-based model reduction for nonlinear systems (such as in Section 6.3) leads to a computational issue, the so-called lifting bottleneck, which results from the composition of the reconstructing projection \(U_{1}\) with the original, (nonlinear) vector field \(f\) and the reducing projection \(V_{1}\):  
\[f_{r}:=V_{1}^{\mathsf{T}}\circ f\circ U_{1},\]  
in the reduced vector field (2). This means for nonlinear systems, the high-dimensional state has to be used in the reduced order model, as opposed to linear systems (3), where the truncated projections can be applied directly to the linear vector field components (The composition operators become matrix-matrix multiplications in the linear case) beforehand.  
To reduce this computational complexity, hyper-reduction methods are utilized. Examples of hyper-reduction methods are: Gappy-POD [91], Missing Point Estimation (MPE) [92], Discrete Empirical Interpolation Method (DEIM) [93], Dynamic Mode Decomposition (DMD) [94] or numerical linearization [95]; for a comparison see [96]. These methods construct (linear) reduced representations of the nonlinearity in a data-driven manner.  
emgr does not address hyper-reduction and thus it has to be applied in a separate post-processing step [97]. Yet, the trajectory data used to assemble the empirical Gramians can be recycled by interposing the hyper-reduction computation via the solver configuration (Section 5.5).
## 3 Results and Discussion  
The general features of the microstructure of Sn-Pb alloy solders can be understood from the Sn-Pb binary alloy phase diagram (Figure 1). With decreasing melt temperature, the first precipitated solids are lead primaries, which contained approximately 19 wt% of dissolved Sn. Moreover, undercooling is generally required to nucleate Sn, and the solidification of the primary eutectic matrix may occur at temperatures less than the equilibrium temperature of the eutectic Sn-Pb alloy (183 \({}^{\circ}\)C) [19]. The solid solubility between Sn and Pb is limited, and Sn-rich and Pb-rich phases are separated. In general, a lamellar eutectic structure, which comprises alternating plates of Sn-rich and Pb-rich phases, appears through the slow solidification of binary alloys.  
The solidified microstructure can be altered by changing the solidification rate or undercooling degree. With increasing melt undercooling, the lamellar eutectic structure may transform into an anomalous eutectic microstructure [20]. During centrifugal atomization, the melt droplets are solidified at strong undercooling, and the formation of a lamellar structure is suppressed. The resulting microstructure is a mixture of fine Pb-rich and Sn-rich solid phases. Figure 2 presents an SEM micrograph of the centrifugally atomized eutectic Sn-Pb powders, which exhibit a fine structure immediately after solidification. Dark and light regions in the micrograph are Sn-rich and Pb-rich phases, respectively. The Pb-rich phase is finely dispersed in the Sn-rich matrix.
###### Abstract  
This is the first study on the effect of carbonic maceration on the quality (color, aroma profile and sensory properties) of Muscat Hamburg, contrasting two winemaking procedures used in Tianjin (classical white and red-winemaking techniques). The values of \(C^{\star}\) (psychometric chroma), \(a^{\star}\) (measure of redness) and \(b^{\star}\) (measure of yellownness) were significantly higher (\(p<0.01\)) in the carbonic macerated wine than in red wine. However, there were no visual differences in color, and classical red wine and carbonic macerated wine had similar \(h\) (hue angle) values and located in the red region. Thirty-two aromatic compounds were identified and quantified in Muscat Hamburg wines. The content of volatile compounds (6384.97 ug/L) was significantly higher (\(p<0.001\)) in the carbonic macerated Muscat Hamburg wine than in the other kinds of wine. This result led to the carbonic macerated wine having the highest odor activity values (OAVs) and sensory evaluation scores (86.8 points), which correlates with an \"excellent\" sensory perception. This study demonstrated that carbonic maceration significantly improved the quality of Muscat Hamburg wine based on volatile analysis and sensory evaluation compared with other conventional methods. Therefore, carbonic maceration could be well suited for making Muscat Hamburg wine.  
Muscat Hamburg wine; carbonic maceration; color; aroma; sensory evaluation +
Footnote †: journal: molecules
# Machine Failure Prediction Using Survival Analysis
## 5 Results
### Number of Selected Features  
The recursive feature elimination technique was used to select the number of characteristics to be used in the final prediction model. Based on this, as well as the results of the negative influence of features obtained from RSF and COX, we can formulate the characteristics that will give us the best possible prediction results.  
According to the results for the general prediction of machine failure, RFE used two features to achieve the best prediction, as shown in Figure 6. The addition of more features reduced the prediction accuracy, which is normal considering the previous feature ranking evaluation. However, if we also consider the results of the RSF and COX, we can be more generous with the number of features that will be used, by adjusting their quantity to a number quite close to the original number given by RFE. The same methodology can be used for predicting the type of machine failure.  
Furthermore, in Figure 6, it was observed that in most cases, the best prediction accuracy was achieved with only two features. The only exceptions were the TWF case, which required three features, and the OSF case, which required all five features to achieve the best possible prediction. The decrease in the precision with the addition of more features could be an indicator that the most relevant information for the prediction was contained within a small subset of the available features.  
Figure 6: _Cont_.
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### The Impact of Data Sparsity  
This experiment evaluates the impact of data sparsity on prediction performance by setting different matrix densities to represent the sparsity of the data. By observing Figure 4, it can be seen that the prediction performance of the four traditional collaborative filtering algorithms gradually decline when the data sparsity gradually becomes smaller. The three methods combined with deep neural networks are less affected by data sparsity than traditional algorithms. Regardless of the sparsity, the prediction performance is always higher than the traditional methods. However, the LDIF approach proposed in this paper has the best performance regardless of the data sparsity, and the prediction performance is relatively stable. This phenomenon shows that this approach is relatively less affected by the data sparsity and can effectively alleviate the data sparsity problem.  
In order to view the impact of data sparsity in more detail, we compare this approach under different data sparsity conditions separately. As can be seen from Figure 5, both in MAE and RMSE indicators, the prediction accuracy shows a trend of improvement as the data sparsity increases. But at the same time, we can also see that in the RMSE indicator on TP, the prediction accuracy fluctuates slightly when the data sparsity is 25%. After many experimental analyses, we can conclude that prediction performance may fluctuate within a controllable range when the data sparsity increases, but the overall performance is improving.  
Figure 4: Comparison of prediction performance. (**a-d**) represent the prediction performance of all methods on MAE and RMSE metrics in TP and RT, respectively.  
Figure 5: Show impact of data sparsity. (**a-d**) represent the prediction performance of LDIF methods on MAE and RMSE metrics in TP and RT, respectively.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 4 Conclusions  
This paper presents a panoramic full-field three-dimensional shape and displacement measurement method for measuring complex shaped objects, assisted by laser point cloud data. A measurement system containing three simultaneous Stereo-DIC configurations is established based on an encircling configuration that captures clear images of cylindrical specimens from multiple views at fixed angular positions. In order to obtain correct and reliable measurements, key algorithms are proposed, including separate calibrations of intrinsic and extrinsic parameters, laser point cloud-assisted scale factor calibrations, and panoramic three-dimensional surface merging, which make up for some shortcomings of the camera calibration and surface merging methods commonly used in multi-view three-dimensional DIC measurements. Through a series of verification tests, the feasibility of the panoramic DIC system based on the above algorithms for panoramic shape and displacement measurements is demonstrated, and it is shown that the system can accurately and reliably measure complex 360-deg shape and three-dimensional displacement fields. A major advantage of the proposed panoramic measurement method is that only a simple calibration process is required with the aid of laser point cloud data and the surface texture of the object being measured, even when a large number of cameras are used. The measurement errors associated with the entire 360-deg panoramic DIC system were evaluated by measuring the merging errors, three-dimensional reconstruction errors, and displacement errors of the log with the natural texture. The local shape measurement error was less than 1 mm, the combined error was about 0.5 mm, the whole three-dimensional reconstruction error was less than 2 mm, and the displacement error was less than 0.1 mm. In our case, this is accurate enough to be acceptable in most applications involving complex shape displacement measurements of large dimensions.  
Conceptualization, Y.L.; methodology, Y.L. and D.Z.; software, J.Z. ([PERSON]); validation, D.Z. and J.Z. ([PERSON]); formal analysis, Y.L.; investigation, Y.L.; resources, D.Z.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., D.Z., X.M. and J.Z. ([PERSON]); project administration, J.Z. ([PERSON]); funding acquisition, J.Z ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2021 ZY67.  
Not applicable.  
Figure 7: Displacement measurement results. (**a**) Mean displacement for translation tests versus the pre-applied displacements; (**b**) mean bias error and standard deviation error of the measured displacements.  
Data Availability Statement: The data that support the findings of this study are available on request from the corresponding author, upon reasonable request.
# Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study
## 2 Related Work
### Distributed Consensus Algorithms in \(d\)-Regular Bipartite Graphs  
As shown in the literature, only a few papers are concerned with distributed average consensus algorithms in regular bipartite graphs even though this topology may prevent some of these algorithms from functioning properly.  
In [51], the authors examine MD and GMH in this graph topology and identify that both MD and GMH with the optimal mixing parameter (LD) diverge in these graphs. In [52], a novel distributed mechanism for detecting MD in regular bipartite graphs (and ensuring its convergence after reconfiguration) is proposed and examined. In addition, a comprehensive spectral analysis justifying the divergence of the algorithm is provided in that paper. In [53], a comprehensive analysis of MD and GMH with the optimal mixing parameter (LD) is presented. In the paper, it is shown how the inner states oscillate in graphs of various connectivities, and it is further identified that the mean square error of the estimates cannot drop below a certain threshold value. Surprisingly, this threshold value is not conditioned by the graph connectivity, as identified in the paper. The paper [54] is focused on a heterogeneous diffusion-based communication protocol that optimizes the convergence rate of the average consensus algorithm. In that paper, the authors compare their approach with concurrent ones in regular bipartite graphs (not only in this topology) and identify that this approach outperforms other algorithms. As further seen in [55; 56; 57; 58; 59; 60; 61], the authors of these papers assume distributed consensus algorithms in either (only) bipartite, (only) regular, Ramanujan, triangular regular, strongly regular, distance regular, irregular, or disconnected graphs with regular bipartite components. Thus, these papers do not exactly address the same issue as we do in this paper. As shown above, no paper addressing a comparative study of distributed average consensus algorithms in \(d\)-regular bipartite graphs can be found in the literature. Therefore, our work represents a significantly novel contribution compared to related work.