esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Performances and Comparisons
#### 3.3.1 The Case of Larger Inventory Polygons
5 model, the results produced by the RF algorithm with a cost of 2000 displayed high accuracy, as illustrated in Table 5. For SVM-based modeling, Figure 12a indicated that the RBF method had unbalanced predictions with a significant missing error for the landslide source and the highest PA for the runout. However, the results of the linear and polynomial functions were similar. The second-order polynomial SVM and LRs, illustrated in Figure 12b, displayed accuracies similar to the RF, 2000. Figure 11: Performance (\(Y\)-axis) of the No. 4 model, where OA, AUC, PA, UA, S, and R represent overall accuracy, area under the ROC curve, producer’s accuracy, user’s accuracy, landslide source, and runout, respectively. (**a**) Using the RF algorithm with the costs; (**b**) using SVM with linear, the first- to third-order polynomial, and radial basis (RBF) functions; (**c**) comparing the optimal results of (**a**) and (**b**) with logistic (Logis) regression. Figure 10: Performances (\(Y\)-axis) of the No. 3 model, where OA, AUC, PA, UA, S, and R represent the overall accuracy, area under the ROC curve, producer’s accuracy, user’s accuracy, landslide source, and runout, respectively. (**a**) Using SVM with linear, the first- to third-order polynomial, and radial basis (RBF) functions; (**b**) comparing the optimal results of Table 4 and (**a**) by using logistic (Logis) regression. Significant improvements were observed in the No. 1 and No. 3 models. Moreover, the RF algorithm and cost-sensitive analysis provided favorable results in most cases. To identify the optimal model in which the samples extracted from different size areas of the inventory polygons, Figure 13 compares the RF method for the No. 2, 3, and 4 models, with costs of 5000 and 2000 for No. 1 and 5 models, respectively. Figure 13 indicates that the accuracies produced by the No. 1, 3, and 4 models were similar, whereas the No. 2 and 5 models displayed less favorable results for the detection of a landslide source class. Figure 13b indicated that the AUC of the No. 1 and 3 models were the most favorable. These evaluations indicated that the No. 1 and 3 models provided more reliable prediction results.
## 2 Experimental and Analysis Method  
### Setup and Procedure  
Figure 3 presents a schematic illustration and actual photo of the experimental setup. Note that a PZT ultrasonic probe was used as the sensor, which is composed of an oscilloscope, an ultrasonic receiver (B10C5N W-335, GNES Corp., Kyoto, Japan), a nanosecond pulsed-laser source (Surelite: SL-2-10, Continuum, Milpitas, CA, USA), and a reinforced concrete specimen. The ultrasonic guided wave is excited by irradiating the pulsed-laser on the bottom surface of the rod, and the waveform is measured, at the opposite surface, by the ultrasonic probe. Parameter settings of the pulsed-laser source are listed in Table 1. Investigation of the ultrasonic signals obtained from the experiment was conducted through Fourier and wavelet analyses, in order to compare the signals with the group velocity dispersion curves.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 1 Introduction
Since the ECG signal is a small signal with long-term dynamic changes, a graphene ECG electrode is required to achieve long-term ECG monitoring with high quality, high precision, and high stability. In this review, we introduce the recent advances in graphene-based flexible ECG electrodes. The first part introduces currently-proposed graphene ECG electrodes, which are generally divided into seven categories: bionic, fabric-based, biodegradable, laser-induced/scribed, modified-graphene, sponge-like, and invasive based on their design philosophy, structural characteristics, preparation methods, material properties, etc. Besides, some categories are further divided into dry and wet electrodes. Then, their performance, including electrode-skin impedance, signal-to-noise ratio, skin compatibility, and stability are further analyzed and discussed. The second part discusses the possible development directions of graphene ECG electrodes, with the help of some typical research progress on wearable flexible graphene-based ECG electrodes and systems. Finally, we give the future development prospects of graphene ECG electrodes from our perspective and conclude.
## 3 Results and Discussion
### Molecular Structural Analysis  
The amino acid sequences of the six isolated APs (P1 to P6) were analyzed by a protein sequencer and identified as Val-Glu-Glu (VEE, P1), Trp-Met-Phe-Asp-Trp (WMFDW, P2), Asp-Ala-Gly-Pro-Tyr-Gly-Pro-Ile (DAGPYGPI, P3), Trp-Met-Gly-Pro-Tyr (WMGPY, P4), Glu-Arg-Gly-Pro-Leu-Gly-Pro-His  
Figure 4: Reversed-phase high-performance liquid chromatography (RP-HPLC) chromatogram of GC-3 using a Zorbax, SB C-18 column (4.6 mm \(\times\) 250 mm).  
Figure 3: Elution diagram of AC-4 in the Sephadex G-25 column of (**A**) and EC\({}_{50}\) value of AC-4 and its three active fractions (defined as GC-1 to GC-3) on the DPPH radical (**B**). The data are presented as the mean \(\pm\) SD (\(n=3\)). \({}^{\text{a-d}}\) The same superscripts indicate no significant difference (\(p>0.05\)).  
(ERGPLGPH, P5), and Glu-Met- Gly-Pro-Ala (EMGPA, P6), respectively (Table 1). In addition, the MWs of VEE (P1), WMFDW (P2), DAGPYGPI (P3), WMGPY (P4), ERGPLGPH (P5), and EMGPA (P6) were 375.39, 783.90, 788.85, 652.79, 861.95, and 503.58 Da, respectively (Figure 5). Those MW data of the six isolated APs (P1 to P6) agreed well with their theoretical masses (Table 1).
## 2 Materials and Methods
### FNAC Metabolomic Analyses Allowed Preoperative Diagnosis of Thyroid Nodules  
In the 78 SPME-purified FNAC samples, we identified 5081 and 2609 peaks in the positive and negative modes, respectively, using MZmine. Among these, 2282 and 815 peaks could be putatively identified by matching the \(m/z\) with the \([\mathrm{M}+\mathrm{H}]^{+}\) or \([\mathrm{M}-\mathrm{H}]^{-}\) of metabolites from the HMDB, in the positive- and negative-ion modes. As expected from the use of C18 and PDMS/DVB SPME, and as observed in the technical development experiments, sample preparation led to a loss of some non-polar metabolites with low retention times in both benign and malignant samples. Figure 2 shows a representative total ion chromatogram (TIC) of benign samples, malignant samples and blanks (acetonitrile solution only) for electrospray ionization in negative and positive modes. It can be observed that the intensities of specific signals were low and only a few peaks of the samples could be clearly distinguished in the TIC from those of the blanks. However, these low signal intensities were to be expected due to the small volume of the biological samples, i.e., small tissue section (FNAC), and the loss of ions due to the CytoRich(tm)/SPME purification method. Several specific data evaluation steps were performed to select accurate ions. In a first step, in order to reduce the background noise, a minimum value was set for the mean of the signal intensities of all samples. In a second step, only ions with a 50-fold higher peak intensity in the samples compared to the blanks were retained. After these two steps, the data set still comprised 2697 ions, which were further analyzed. A series of statistical analyses was then performed to identify the most relevant metabolites based on the VIP scores of the PLS-DA model. Of the top metabolites, those which did not show clear MS1 and MS2 spectra with Compound Discoverer were eliminated. Subsequently, a new analysis was performed using the same statistical procedure described above (see Section 2), but unfortunately it was not possible to discriminate benign from malignant samples with the unsupervised PCA method (Supplementary Figure S1A). The performance of the PLS-DA model showed satisfactory accuracy, but resulted in poor \"goodness of prediction\" (Q2 score) and high \"goodness of fit\" (R2 score), suggesting an overfitting (Supplementary Figure S1B-D). However, the same analysis performed with SAE allowed discrimination between benign and malignant tissue in the latent space of nearly all samples (Supplementary Figure S1E). The classification of the sample was uncertain in only a few cases, as indicated by the SAE kernel density results (Supplementary Figure S1F). Our results suggest that SPME-tip purification of CytoRich(tm)-based thyroid FNAC samples enables metabolomic analysis for preoperative diagnosis of thyroid nodules, even though observed signal intensities were low and some metabolites were probably subject to partial chemical alterations.
## 3 Results and Discussion
### Comparison of Models Performance
The void area refers to the black area in the images (Figure 2), while the remaining area is considered as valid area. If comparison is done over the different test sets, the lowest results are obtained in the PICCOLO dataset, which might be because it contains a wider range of different polyps which makes it more difficult to learn with any of the used datasets. This result might be due to the presence of bias within each dataset, which would lead to the recommendation of multi-centre or cross-dataset evaluation for DL methods in order to properly understand the performance of such models in a real-world settings [40]. Similar to the results found in previous works [34], it can be concluded that in general terms, Densenet121 works better than VGG-16 as backbone and the LinkNet encoder-decoder architecture obtains better results than U-Net. Sets independence is assured in our dataset, as all lesions obtained from the same patient are assigned to the same dataset. This is also considered when determining train, validation and test sets in CVC-EndoSceneStill [23]. On the other hand, Kvasir-SEG does not indicate the origin of each image nor provide a predefined division into sets, it is not possible to determine if the random allocation of images might have violated this independency. Should this happen, then the higher values for models trained and reported on Kvasir-SEG might be explained. Regarding the clinical characteristics of the lesions in the test sets, it is not possible to establish a comparison as CVC-EndoSceneStill and Kvasir-SEG datasets do not provide clinical information. Lastly, the inclusion of WL together with NBI images in the PICCOLO dataset might increase independence against changes in colour when training the models, showing better generalization when results are reported over the test sets of the other two datasets. In this regard, metrics have been calculated considering only the WL frames of the test set in the PICCOLO dataset for a fairer comparison for the other datasets. Results in the PICCOLO test sets remains similar regardless of the type of images considered (Table S4). When the three datasets are analysed together, including only the WL frames from PICCOLO (Table S5), the performances of Kvasir-SEG and PICCOLO are more similar.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Dynamic Source Routing (DSR) Protocol  
The dynamic source routing protocol (DSR) is one kind of on-demand routing system. DSR is a simple yet powerful routing algorithm designed specifically for use in multi-hop wireless ad hoc networks with mobile nodes. Using DSR, the network is able to completely self-organize and self-configure, eliminating the requirement for a centralized network administration structure. Two major techniques work together to make the DSR protocol possible for ad hoc networks to discover and keep track of their source routes. The DSR protocol incorporates these elements.  
The Path's Uncovering: In order to transfer a packet from node S to node D, node S must first locate a source route to node D. The method used is called \"route discovery.\" Path discovery is used only when [PERSON] wants to send a packet to D but does not know a route to D.  
The phrase \"route maintenance\" refers to the technique that allows a node S, while utilizing a source route to D, to ascertain whether or not the topology of the network has changed in such a way that it is no longer possible for it to use its route to D due to a link along the route being inoperable. The mechanism for this is the upkeep of the routes. Because one of the source routes was discovered to be broken during routine maintenance. S can either try sending the packet over a different path it knows leads to destination D, or it can re-initiate route discovery to find a new path. If S is not actively sending packets to D, then this route will not be maintained.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
###### Abstract  
The accurate calculation of mining-induced surface deformation has important guiding significance for efficient and safe production in mining areas. The probability integral method (PIM) is a main prediction method in China, and the selection of its parameters is directly related to the prediction accuracy of surface deformation in mining areas. To overcome shortcomings of PIM and other methods, this paper proposed a prediction model of the parameters of PIM combining a multiple regression model and an extreme learning machine. In this paper, the Huainan mining area was selected as the research object, the influence factors of PIM parameters were analyzed and the accuracy of the model was verified. The influence of the number of hidden layer nodes, the selection of activation function and the proportion of training set and test set in the model were analyzed. The conclusions suggest that the PIM parameters calculated in this paper could be used to predict mining subsidence and obtain surface movement and deformation data. The research results provide an effective method for the selection of surface deformation prediction parameters of new working faces or faces lacking measured data.  
mining subsidence; PIM; surface deformation prediction; multiple regression model; ELM +
Footnote †: journal: Applied Sciences
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 2 TerraSAR-X Proposals, Scientific Orders and Literature Review
### Scientific Publications  
The publication of research results represents an important indicator for the assessment of key applications of a high-resolution SAR system and the scientific utilization of the TerraSAR-X mission. For this purpose, an analysis of renowned journals was performed based on Elsevier's Scopus data base, a comprehensive abstract and citation data base of peer-reviewed literature. For this study, we only considered papers written in English and published in scientific journals and books. Conference proceedings were not taken into account. In order to avoid confusion with the TanDEM-X mission, papers addressing a bistatic use of SAR data were excluded from this analysis. In total, 2850 publications refer to the TerraSAR-X mission.  
Similar to the analysis of the TerraSAR-X proposals, a keyword scan was applied to all titles, authors and index keywords within the Scopus data base. Here, the 80 keywords of the proposal analysis were supplemented by another 36. Those 116 keywords were grouped in a specific way to represent the same fields of research as applied for the proposals. The publications were then assigned to the research sphere with the majority of hits revealed from the keywords search. Like the proposals, a publication can only belong to one sphere so that the sum of all proposals is 100 percent.
# Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video
## 2 Preliminaries  
### Grassmann Manifold  
Recently, some discriminant analyses on Grassmann manifold [7] have been achieved by researchers. The basic principle of the Grassmann manifold algorithm is compressing datasets that are loose distributed on Grassmann Manifold into a low-dimensional Grassmann manifold through non-linear transformation, so that the distribution of the dataset is more compact on the low-dimensional Grassmann manifold. [PERSON] proposed Grassmann manifold Discriminant Analysis (GDA) [8], in which the subspace is represented by points on Grassmann manifold, to achieve nonlinear discriminant analysis on Grassmann manifold by measuring the similarity of subspace using Grassmann kernel. Although GDA performs superior properties, it does not explicitly consider the intrinsic geometry of data [9; 10; 11], which affects the ability of discriminant for the dataset and the generalization. In view of this, [PERSON] [12] proposed a Graph Embedding Discriminant Analysis on Grassmannian Manifolds (GEDAGM). In the case where global solutions do not work, the local learning method provides an effective solution.  
Some fatal concepts on Grassmann mainfolds of Riemannian geometry are supposed to be illustrated before presenting our algorithm. Details on Grassmann manifolds and related topics can be found in [13].  
Grassmann manifold \(G(p,d)\)[13] consists of the set of all linear \(p\)-dimensional subspaces of \(R^{d}(0<p<d)\), which stands on a space of \(p\times d\) matrices with \(d\) orthogonal columns:  
\[G(p,d)=\{X\in R^{d\times p}:X^{T}X=I_{P}\} \tag{1}\]  
There are two methods to measure the distance in Grassmann manifold. One is to map the Grassmann points into tangent spaces where there exist measures [14; 15]. Another method is to embed the Grassmann manifold into the symmetric matrix spaces while the Euclidean distance is available. The latter one is easier and more effective in practice, and the mapping relation can be represented as [16]:  
\[\Pi:G(p,d)\to Sym(d),\Pi(X_{i})=XX^{T} \tag{2}\]  
The embedding \(\Pi(X)\)is diffeomorphism [17]. In this paper, we adopt the second strategy on Grassmann manifold to define the following distance inherited from the symmetric matrix space under the (2) mapping:  
\[d_{g}^{2}(X,Y)=1/2\|\Pi(X)-\Pi(Y)\|_{F}^{2} \tag{3}\]  
One point on Grassmann manifold is actually an equivalent class of all orthogonal high matrices in \(\mathbb{R}^{d\times p}\), any one of which can be converted to the other by a \(p\times p\) orthogonal matrix.
## 4 Optical Performance Display  
The modulation transfer function (MTF) is an important parameter to evaluate the imaging quality of an optical system. Figure 8 is the MTF curve of the MEPI. In Figure 8, the lines in different colors correspond to fields between \(-1.25^{\circ}\) and \(+1.25^{\circ}\). The black curve in the figure is the diffraction limit MTF curve, and the other curves are the MTF curves under different fields of view and wavelengths.  
\[N=\frac{1000}{2D_{d}} \tag{14}\]  
At 52 cycles/mm (calculated using Equation (14), \(D_{\text{d}}\) refers to the pixel size [20]), the minimum MTF of the system is greater than 0.5, and the image quality and spatial resolution are good.  
Figure 9 is the spot diagram on the ray tracing image plane under different fields of view of the system. The RMS radius values of the spot diagram are all less than 9.6 \(\upmu\)m,  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Front Surface** & **Back Surface** & **Thickness** & **Material** \\  & **Curvature** & **Curvature** & **Thickness** & **Material** \\ \hline
1 st lens & 39.171 & 501.552 & 8.248 & H-FK61B \\
2 nd lens & 56.774 & 31.182 & 1.458 & H-ZLAF92 \\
3 rd lens & 31.056 & 49.063 & 3.381 & H-ZF7 LAGT \\
4 th lens & \(-\)34.272 & \(-\)24.365 & 3.937 & H-LAK1 \\
5 th lens & \(-\)22.324 & \(-\)38.321 & 5.812 & H-ZLAF55D \\ \hline \end{tabular}
\end{table}
Table 6: MEPI sub-channel lens data.  
Figure 8: MTF curve of the four MEPI channels.  
and the image points in the maximum field of view can be relatively concentrated in the range of a single detector pixel.  
Both the MTF and spot diagrams have difficulties in fully describing the imaging performance of optical systems. The reason is that neither of them provides an intuitive visual representation of the distortion situation. Therefore, Figure 10 shows the distortion curves of each channel at the maximum FOV, with different colors representing different wavelengths. Statistics show that the slight change of image height is less than 1 pixel at the maximum distortion.  
Figure 10: The distortion curves of each channel of the MEPI.  
Figure 9: Spot diagrams of the four MEPI channels.
# Research on the Mechanism and Dynamic Characteristics of Intermitt Failure of Electrical Connectors
## 4 Simulation Case
### Dynamics of Intermittent Failure Under Different Degradation Stages  
According to the electrical contact model constructed in Section 2, the plowing radius \(L\) could be used to represent the degradation stage of electrical connectors.  
Let the plowing radius be initially \(L\) = 20 \(\upmu\)m. Next, take \(L\) = 20 \(\upmu\)m, 10 \(\upmu\)m, 5 \(\upmu\)m, and 0 \(\upmu\)m separately, to simulate the different degradation stages. The other parameters remain as Table 1, and the contact resistance curve under different degradation stages is shown in Figure 10.  
It is seen that, with the continuous growth of the oxidized film, the duration and numbers of intermittent failures both increase progressively. When \(L\) decreases to 0, that is, the contact surface is completely covered by oxidized film, the intermittent failures evolve to permanent failure, as shown in Figure 10d. The three phases of dynamic evolution for intermittent failures shown in Figure 10a-c are consistent with the dynamic behaviors of intermittent failures proposed in reference [21], as shown in Figure 11.
# Gate-Level Hardware Countermeasure Comparison against Power Analysis Attacks
## 5 Comparative Analysis
### Gate-Level Masking  
To better analyze the gate-level masking solutions, depicted in Table 2, Figures 16 and 17 present the respective area vs. delay metric and the ADP vs. security metrics.  
From these figures, the iMDPL implementation is clearly seen as the outlier with a significantly higher area cost in relation to the other gate-level masking solutions. Its predecessor, MDPL, has a much better area and delay trade-off; however, its security level is low, since it is vulnerable to DPA attacks [82]. By itself, the masked-AND shows a lower security level with higher costs than RSL. However, if a different mask is used in the masked-AND solution for each circuit signal, it may be more secure than RSL, which is shown in [77] to be potentially compromised if a first filtering is performed to obtain the mask value and then perform the DPA attack.  
The DRSL has a good trade-off between security and performance while improving the security level of the RSL by removing the early propagation effect; however, in [82] the authors point out that if a first analysis is made to retrieve the secret mask, the dual-railcharacteristic does not increase the security, given the imbalances in the differential wires when no special routing is performed.  
Finally, the masked_SABL solution shows that by combining gate-level masking with full-custom hiding techniques, higher security levels can be achieved with good performance trade-offs.  
Figure 16: Area vs. delay of gate-level masking countermeasures.  
Figure 17: ADP vs. security of gate-level masking countermeasures.  
To provide a visual overview of the top five best state-of-the-art countermeasures, Figure 18a,b depict the metrics for area overhead, frequency degradation, power consumption, and resulting security for both masking and hiding.  
Figure 18a provides a visual comparison of the top five countermeasures with the best security levels. Figure 18b depicts the top five countermeasures with the best trade-off between security values and ADP performance and area overhead.  
From these figures, it can be seen that, typically and as expected, the higher the security, the higher the cost. However, this is not always the case. For example, in Figure 18b it can be seen that the SABL approach has approximately the same power and area costs as BCDL but provides significantly less protection against SCA. Nevertheless, in addition to performance degradation and security levels, it is also important to consider the inherent design difficulties of each proposal, as well as the feasibility of including the countermeasure in the design.