esa-sceva/satcom-chunk-collection huggingface dataset:
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 4 Simulation and Analysis
### Imaging Performance versus SNR  
In this simulation, we examine the impacts of SNR on the passive radar imaging performance. We add different complex noise with Gaussian distribution into the simulated passive radar echoes with a SNR of 5, 10 and 20 dB, respectively. Similarly, the MSE of the estimated scatterers' position is used to evaluate the reconstruction performance.  
Figure 5: Scattering model of an extended target in the 2D plane.  
Figure 6: Passive radar images obtained by using different methods: (**a**) reconstructed image with the polar format algorithm (PFA); and (**b**) reconstructed image with the proposed method.  
Figure 7 compares the performance of the ESPRIT, RELAX and proposed CS algorithms in the passive radar imaging under different SNRs. The first, second and third row represent passive radar image obtained by the ESPRIT, RELAX and proposed CS algorithm, respectively. Note that the RELAX and proposed CS algorithms can provide position estimation with better accuracy than the ESPRIT algorithm under different SNR conditions. It should be emphasized that, among these three super-resolution algorithms, the estimation precision of the proposed CS algorithm is the highest under the same SNR condition.  
It can be concluded from the MSE results given in Table 3 that, with decreasing SNR, the MSE of the estimated position obtained by the ESPRIT algorithm degrades rapidly. This implies that the noise level has a great effect on its estimation performance. However, the MSEs obtained by the RELAX and proposed CS algorithms are acceptable under different SNRs. Moreover, the proposed CS algorithm outperforms the RELAX algorithm in the sense of estimation accuracy. Even if the SNR is lower than 0 dB, for example, \(-10\) dB, the performance of the proposed CS-based passive radar imaging framework degrades slightly. Therefore, we can conclude that the proposed CS algorithm is suitable for super-resolution passive radar imaging, especially in low SNR situations.  
Figure 7: Passive inverse synthetic aperture radar (ISAR) imaging results with different reconstruction methods under different signal-to-noise ratios (SNRs).
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 2 Materials and Methods  
The DNNs used for the proposed speech enhancement and the results of previous studies on noise classification are described in Sections 2.1 and 2.2, respectively. Section 2.3 includes an explanation of the process and the application of the proposed algorithm to efficient speech enhancement for hearing aids. Section 2.4 introduces the conditions pertaining to self-recorded noises and determination of the DNNs architecture and hyperparameters. In order to evaluate the performance of speech quality, the objective measurements are described in Section 2.5. Overall, this section deals with the process of speech enhancement and the operation of the network presented in this paper based on the environmental noise for hearing aids.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Edge Complement  
In this subsection, we study the graph operation of creating a graph's edge complement graph. The _edge complement graph_ of a graph \(G\), denoted by \(\text{co-}G\), has the same vertex set as \(G\), and two vertices are adjacent in \(\text{co-}G\) if and only if they are not adjacent in \(G\), i.e.,  
\[V(\text{co-}G) =V(G)\quad\text{and}\] \[E(\text{co-}G) =\{\{u,v\}\mid u,v\in V(G),u\
eq v,\{u,v\}\
ot\in E(G)\}.\]  
Let \(K_{1,\ell}\) be a star graph with one dominating vertex \(v\) in the center and \(\ell\) vertices \(u_{i}\), \(1\leq i\leq\ell\), as satellites, all only adjacent to \(v\). Then, the edge complement graph of \(K_{1,\ell}\), \(\text{co-}K_{1,\ell}\), consists of an isolated vertex \(v\) and a clique of size \(\ell\) formed by all satellites \(u_{i}\), \(1\leq i\leq\ell\). Since neither \(K_{3}\) nor \(I(K_{1,3})\) is a minor of \(K_{1,\ell}\), we know from Section 3.7 that \(\text{pw}(K_{1,\ell})=\text{tw}(K_{1,\ell})=1\) holds. However, by Lemmas 3 and 7, it follows that \(\text{tw}(\text{co-}K_{1,\ell})=\text{pw}(\text{co-}K_{1,\ell})=\ell-1\) is true. Therefore, it is generally impossible to bound the tree-width (path-width) of an edge complement graph \(\text{co-}G\) in terms of the tree-width (path-width) of the original graph \(G\).  
Nevertheless, [PERSON] and [PERSON] [55] proved the following theorem, providing a lower bound for the tree-width of a graph's edge complement graph. Note that formulating bounds of the form \(f(G)+f(\text{co-}G)\) for a graph parameter \(f\) is known as the _Nordhaus-Gaddum problem_.  
Theorem 3.1: _For a graph \(G\), it holds that_  
\[tw(G)+tw(\text{co-}G)\geq|V(G)|-2.\]  
In their work, [PERSON] and [PERSON] also showed that the specified bound is tight. As \(\text{pw}(G)\geq\text{tw}(G)\) is true for every graph \(G\), the corollary below follows immediately.  
Corollary 6: _For a graph \(G\), it holds that_  
\[pw(G)+pw(\text{co-}G)\geq|V(G)|-2.\]  
For the path with four vertices, \(P_{4}\), it holds that \(\text{co-}P_{4}\cong P_{4}\) as well as \(\text{pw}(P_{4})=1\). Consequently, we obtain  
\[\text{pw}(P_{4})+\text{pw}(\text{co-}P_{4})=\text{pw}(P_{4})+\text{pw}(P_{4} )=2=|V(P_{4})|-2,\]  
such that the bound specified in Corollary 6 is also tight.
# An Improved V-Net Model for Thyroid Nodule Segmentation
## 3 Materials and Methods
### Performance Assessment  
The assessment of model performance in medical segmentation involves measuring how closely their predicted outputs align with actual accuracy masks defined by experts. Commonly employed metrics for evaluating model accuracy in segmentation issues include precision, sensitivity, Dice, and IoU. This study utilized Dice and IoU metrics to evaluate the performance of the methods used for comparison with the improved V-Net approach.  
**Dice Similarity Coefficient (Dice):** The Dice metric quantifies the extent to which spatial overlap exists between actual (ground truth) masks and predicted masks [38]. Ranging from 0 to 1, this metric is commonly utilized in scenarios where pixel discrepancies arise within the segmentation classes [39; 40]. Figure 8 shows an area graph of the Dice similarity coefficient.  
To compute this coefficient--which assesses the similarity between two sets--refer to the equation below:  
\[\text{Dice}=\frac{2\times|G\cap T|}{|G|+|T|} \tag{1}\]  
where G denotes pixels from the actual segmentation mask, and T represents those from the predicted segmentation mask. Intersection G\(\cap\)T represents the overlapping pixels found in both masks [38].  
**Intersection Over Union (IoU):** The IoU metric refers to the similarity between the predicted and actual regions of an object in the image. This is defined as the ratio of the intersection of the predicted and actual regions to the union [41]. Equation (2) presents the correlation that illustrates the definition of IoU.  
\[\text{IoU}=\frac{|G\cap T|}{|G\cup T|} \tag{2}\]  
where G represents the actual segmentation mask pixels, and T defines the predicted segmentation mask pixels. The IoU metric, also called the Jaccard index, compares the similarity between these two regions to assess the predicted pixels [42].
# A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board
###### Abstract  
Identifying the printed circuit board (PCB) orders with high reorder frequency for batch production can facilitate production capacity balance and reduce cost. In this paper, the repeated orders identification problem is transformed to a reorder level prediction problem. A prediction model based on a modified Bayesian network (BN) with Monte Carlo simulations is presented to identify related variables and evaluate their effects on the reorder level. From the historically accumulated data, different characteristic variables are extracted and specified for the model. Normalization and principal component analysis (PCA) are employed to reduce differences and the redundancy of the datasets, respectively. Entropy minimization based binning is presented to discretize model variables and, therefore, reduce input type and capture better prediction performance. Subsequently, conditional mutual information and link strength percentage are combined for the establishment of BN structure to avoid the defect of tree augmented naive BN that easily misses strong links between nodes and generates redundant weak links. Monte Carlo simulation is conducted to weaken the influence of uncertainty factors. The model's performance is compared to three advanced approaches by using the data from a PCB manufacturer and results demonstrate that the proposed method has high prediction accuracy.  
printed circuit board; reorder level; principal component analysis; Bayesian network Article
###### Abstract  
The study of lag phase provides essential knowledge for food quality control. With respect to significance of _Geotrichum candidium_ in the food context, the aim of this study was to quantitatively characterize the relationship between temperature (6-25 \({}^{\circ}\)C) and initial decline period during _G. candidium_ lag phase. The decrease in _G. candidium_ cells in the lag phase was primary modelled by [PERSON]'s model to define the first-decimal reduction time (\(\delta\)). Subsequently, the lag death rate (\(LDR\)) values were recalculated from \(\delta\) and further modelled by using Arrhenius equations, as well as a square root model, and the models' suitability was proven by selected statistical indices. The square root model with the estimated parameters \(b\) = 0.016 \({}^{\circ}\)C\({}^{-1}\) h\({}^{-0.5}\) and \(T_{\mathrm{min}}\) = \(-0.72\)\({}^{\circ}\)C showed better indices relating to goodness of fit based on a low root mean sum of square error (\(RMSE\) = 0.028 log CFU mL\({}^{-1}\)), a higher coefficient of determination (\(R^{2}\) = 0.978), and the lowest value of _AIC_ (_AIC_ = \(-38.65\)). The present study provides a solution to the possible application of secondary predictive models to the death rate dependence on temperature during the microbial lag phase. Despite limited practical importance, under specific conditions, it is possible to consider its use, for example, in exposure assessment.  
G. candidium; lag death rate; secondary modelling +
Footnote †: journal: Applied Sciences  
**2021**  
11
## 4 Experiments and Results
### Results and Analysis of MSTAR Experiments
#### 4.3.3 Results from EOC Experiments  
For the EOC experiments, detailed information on three types of SAR vehicle articulated targets is shown in Table 4. There are 896 and 384 samples of SAR vehicle data at 17\({}^{\circ}\) and 30\({}^{\circ}\) pitch angles in the training and test datasets, respectively. With fewer SAR data samples, higher similarity among target categories, and a large difference in pitch angles, the recognition task was more challenging than that in SOC experiments, requiring a higher model performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **SOC** & **EOC** \\ \hline epoch & 180 & 180 \\ batch size & 16 & 8 \\ lr & 0.00390 & 0.00016 \\ wd & 0.0335 & 0.0356 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization results of Bayesian hyperparameters in SOC and EOC experiments using the MSTAR dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} \\ \hline
**Class** & **Depression** & **Number** & **Depression** & **Number** \\ \hline
2S1 & 17\({}^{\circ}\) & 299 & 15\({}^{\circ}\) & 274 \\ BMP2 & 17\({}^{\circ}\) & 233 & 15\({}^{\circ}\) & 195 \\ BRDM2 & 17\({}^{\circ}\) & 298 & 15\({}^{\circ}\) & 274 \\ BTR60 & 17\({}^{\circ}\) & 256 & 15\({}^{\circ}\) & 195 \\ BTR70 & 17\({}^{\circ}\) & 233 & 15\({}^{\circ}\) & 196 \\ D7 & 17\({}^{\circ}\) & 299 & 15\({}^{\circ}\) & 274 \\ T62 & 17\({}^{\circ}\) & 299 & 15\({}^{\circ}\) & 273 \\ T72 & 17\({}^{\circ}\) & 232 & 15\({}^{\circ}\) & 196 \\ ZIL131 & 17\({}^{\circ}\) & 299 & 15\({}^{\circ}\) & 274 \\ ZSU\_23\_4 & 17\({}^{\circ}\) & 299 & 15\({}^{\circ}\) & 274 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed information on vehicle targets under SOC in the MSTAR dataset.
## 3 Results and Discussion
### The Influence of Operating Parameters on Column Sorption  
#### 3.3.1 The Effect of Bed Depth on Cu(II) Removal  
The effect of varying bed depth (5, 10, 15, 20 cm) on breakthrough curves at a constant residence time of 10 min and inlet Cu(II) concentration was 0.5 mg/L and the results are shown in Figure 7. The results indicate that the breakthrough times increased with the increase of bed depth. The breakthrough times were 0.78 h, 2.69 h, 4.99 h and 7.35 h for 5 cm, 10 cm, 15 cm and 20 cm bed depths, respectively. The bed exhaustion time increased from 45.76 h to 154.92 h with the bed depth increase from 5 cm to 20 cm. This may be due to the adsorption of more binding sites at higher bed depths to obtain adsorption, resulting in a higher number of mass transfer zones [44]. Therefore, one can assume that more wastewater can be treated at a higher bed depth, while a higher percentage of metal can be removed. On the other hand, a smaller bed depth gets saturated in a shorter time which results in a shorter breakthrough time. Therefore, it can be concluded that as the bed depth increases, the increase in Cu(II) absorption in the column is due to an increase in contact time with the bricks. The adsorption capacity of the bricks increased from 75.57 mg/kg to 83.98 mg/kg with the bed depth increase from 5 cm to 20 cm. A length of 20 cm was chosen as the optimum bed depth for further experiments.
## 3 Nanoparticles in EOR
### Aluminum Oxides  
Aluminum oxide (Al\({}_{2}\)O\({}_{3}\)) NPs can be fabricated by several processes such as flame spray pyrolysis [119], modified plasma arc [120], and mechanochemical methods [121]. [PERSON] et al. [122] reported that Al\({}_{2}\)O\({}_{3}\) NPs with an average size of 40 nm dispersed in water and brine exhibited higher additional oil recovery (12.5%) than other metal oxide NPs. [PERSON] et al. [123] investigated the adsorption of Al\({}_{2}\)O\({}_{3}\)-based NPs that could alter wettability through their coating mechanism. Dispersing Al\({}_{2}\)O\({}_{3}\) NPs in an anionic surfactant with a concentration from 0.01 to 1.00 wt %, they observed that Al\({}_{2}\)O\({}_{3}\) NPs and a surfactant solution could alter the wettability of rock from a strong oil-wet to strong water-wet surface, as proved by the change of residual water saturation S\({}_{\rm wr}\) from 0.07 to 0.23 (Figure 2).  
Figure 2: Oil-air contact angle on rock surface before (**a**) and after (**b**) being treated with 100 ppm Al\({}_{2}\)O\({}_{3}\) NPs. Reproduced with permission from reference [124]. Copyright 2013 American Chemical Society.  
[PERSON] et al. [124] investigated the effect of Al\({}_{2}\)O\({}_{3}\) NPs dispersed in an aqueous surfactant (sodium dodecyl sulfate). Using two different kinds of Al\({}_{2}\)O\({}_{3}\) NPs (sol-gel and commercial product), they could achieve up to 53.5% of remaining oil. Interestingly, commercial Al\({}_{2}\)O\({}_{3}\)-RM (commercial) achieved 63% more oil than Al\({}_{2}\)O\({}_{3}\)-SG (sol-gel), even though the Al\({}_{2}\)O\({}_{3}\)-SG yields a lower interfacial tension (IFT) with a smaller size. This suggests that lower IFT does not always lead to higher recovery since other factors such as sweep efficiency and fluid rheology are also important.  
[PERSON] et al. [2; 87] studied the potential of aluminum, titania, and silica NPs in a two-phase coreflooding process on the Berea core plug. Polyvinylpyrrolidone (PVP) dispersant was added to stabilize the nanofluid in light of the natural tendency of NPs to aggregate into bigger ones. Coreflood experiment results showed that the additional oil recovery was higher for other metal oxide NPs than for silica NPs (especially in water-wet system), since other metal oxides are adsorbed into the pore surface more easily than silica [2]. In addition, even though titania presented a larger contact angle reduction (up to 54\({}^{\circ}\)) between oil-water and rock, alumina still proved to be a good candidate for EOR. Similar experiments using silica, alumina, and iron oxides diluted in propanol solution proved that alumina has better results in oil displacement than iron oxide, with a similar effect to silica NPs [86]. [PERSON] et al. [125] indicated that mixed alumina and silica NPs at a 0.05 wt % concentration resulted in the highest amount of recoverable oil. They suggested using a combination of alumina and silica NPs for handling harsh reservoir conditions (high temperature, salinity, and pressure) and for controlling asphaltene precipitation.
###### Abstract  
Many composite cantilever plate-like structures have found engineering applications in different industries. For attaining a meaningful assessment of the plate vibration characteristics, it is important to have efficient and effective methods for determining the natural frequencies/mode shapes of composite cantilever plates. In this paper, a method formulated on the basis of the Ritz method and a simple first-order shear deformation theory (SFSDT) is presented to analyze the free vibration of thin as well as thick rectangular composite cantilever plates for determining their natural frequencies. In the SFSDT, the total deflection is the sum of two deflection components, namely, bending and through-thickness shear-deformation-induced deflections. The successful application of the Ritz method together with the SFSDT for the free vibration analysis of thick composite plates relies on the selection of two independent sets of characteristic functions for the bending and through-thickness shear-deformation-induced deflections, respectively, to satisfy the requirements for the displacement and force conditions at the fixed edge of the plate. The novelty of the proposed method is that two independent sets of characteristic functions, namely, polynomials and trigonometric functions, which satisfy the displacement and force conditions at the fixed edge have been identified and used in the variational method to construct the eigenvalue problem for extracting the modal characteristics (natural frequencies and mode shapes) of the plate. It has been shown that the uses of the selected characteristic functions can produce excellent natural frequencies for both thin and thick composite cantilever plates. Some existing theoretical and experimental natural frequencies of thin as well as thick composite plates have been used to demonstrate the accuracy of the proposed method in predicting natural frequencies. The significant effects of through-thickness shear deformation on the natural frequencies of composite cantilever plates are studied to show the merit of the present method. Finally, for illustrating the application of the proposed method in free vibration analysis, a novel procedure established on the basis of the sensitivity analysis of natural frequencies is presented to assess the material degradation of composite cantilever plates. The numerical examples have shown that fewer than 10 iterations are required in the identification process to produce a good estimation of the current value for each material constant.  
Keywords:composite plate; shear deformation theory; Ritz method; free vibration; natural frequency; composite materials +
Footnote †: journal: Applied Physics Letters
## 7 Heuristic Security Analysis
### Replay Attack Resistance  
Our approach incorporates timestamps and random numbers to defend against replay attacks. During authentication, the freshness of the timestamp is checked first. The random values \(b_{i}\) and \(c_{k}\) are utilized in establishing the session key, providing a solid defense against replay attacks.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 3 Results
### Experiment 1
It showed that out of 520 recordings exhibiting the _FB_ scene, 389 were classified correctly, 129 items were misclassified as representing the _FF_ scene, while the remaining 2 excerpts were erroneously classified using the _BF_ category. As far as the _BF_ recordings were concerned, 471 ones were classified correctly, 45 recordings were incorrectly classified as _FF_, and the remaining 4 excerpts were misclassified as those exhibiting the _FB_ scene. In the case of the _FF_ recordings, 339 were classified correctly, 115 excerpts were misclassified as those representing the _FB_ scene, and 66 as the recordings signifying the _BF_ scene. Figure 3b shows that the model trained for the sbs BRIR set made only three mistakes. It correctly identified all the recordings exhibiting the _BF_ scene. A good classification performance could also be observed in the case of the models obtained for the pair and calypso BRIR sets (Figure 3c,d), as most of the correctly identified recordings are placed on the diagonal of the confusion matrices. However, the confusion matrices obtained for the models trained using the tuburo250 and the tuburo310 BRIR sets showed an increased number of misclassified recordings. Nevertheless, all the confusion matrices illustrated in Figure 3 indicated that the models exhibited a high sensitivity with regards to the identification of the _BF_ scene. Note that in the case of the models trained using the sbs and calypso BRIR sets, all the _BF_ recordings were classified correctly. Likewise, the remaining models made a relatively small percentage of mistakes in this respect. Figure 3: Confusion matrices obtained for the models trained and tested using recordings synthesized with selected sets of BRIRs: (**a**) all BRIR sets; (**b**) sbs; (**c**) mari; (**d**) calypso; (**e**) tuburo250; and (**f**) tuburo310.
# A Scalable Deep Network for Graph Clustering
## 4 Experiments
### Scalability Analysis
#### 4.4.2 Time and Memory Consumption Comparison  
On the one hand, to fully demonstrate the superiority of SDN in terms of memory consumption, we conduct experiments on Flickr and compare the SDN with baselines that have the state-of-the-art (SOTA) performance, such as AGCN and SDCN. The statistics of the Flickr are shown in Table 1. The results of the comparative experiments, the total number of parameters, and memory consumption of AGCN, SDCN, and SDN are shown in Table 4.  
On the other hand, to show the superiority of SDN in terms of time consumption, we record the time consumed by AGCN, SDCN, and SDN when processing the same dataset. The specific results are shown in Figure 2.  
First, according to the results in Figure 2, the time consumption of AGCN and SDCN is mostly higher than that of SDN. In summary, the two-part experimental results show that existing SOTA methods have larger memory consumption and longer processing time than SDN. Second, for the experimental results in Table 4, AGCN and SDCN cannot be applied to large-scale datasets, such as Flickr, mainly because they adopt GNN and its variant methods as the result of the backbone network, while the computational costs of GNN recursively increase with the deepening of the network layer, which makes it difficult  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of Parameters** & **Memory Consumption** & **Flickr** \\ \hline AGCN & 4,614,831 & Exceed 11G & OOM \\ SDCN & 4,566,650 & Exceed 11G & OOM \\ \hline SDN & 4,569,667 & 9.959G & 43.60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of graph clustering on Flickr. “OOM” means “out of memory”.  
for this type of model to handle large-scale graph data, and cannot effectively expand the neighborhood range to obtain better node embedding and clustering results.  
In contrast, SDN utilizes linear encoders as the backbone network and linear propagation layers for feature propagation. Our proposed model not only effectively reduces the computational costs when processing the large-scale graph-structured data but also solves the orthogonal issue of the neighborhood range and the model depth, which shows the high scalability of SDN.