esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Although extreme SWHs from PoT analysis are evaluated reliably in the Northwest Pacific, where typhoons are frequently generated, this method may also have some limitations. Figure 15 shows the 11 \(\upmu\)m channel image of COMS/MI from 26 to 27 August 2012, when the typhoon Bolaven transected the Korean Peninsula. In Figure 15, all the altimeter data along the tracks of SWH observations were within 3 h of the COMS/MI observation time. Typhoon Bolaven passed near the IORS at 15 UTC on 27 August 2012, and an enormous SWH with a peak value of 11.1 m was measured by the instrument from the IORS (Figure 3). At nearly the same time, the altimeter observed an SWH of about 12 m while passing through the center of the typhoon (Figure 15g). As may be observed from the time series of the altimeter observations in Figure 15, the altimeters did not obtain data for the entirety of the storm, although all satellite altimeter data from Jason-1/2 and Cryostat-2 were collected. Therefore, it is highly plausible that extreme SWHs may be underestimated in regions in which the altimeter is unable to observe the high SWHs generated by storms. The observation limit of the altimeter is reported to be sensitive to PoT analysis [28]. In order to calculate the extreme wave heights with high confidence, the duration of the wave height data should be sufficiently long. Especially in the Northwest Pacific, where typhoons occur irregularly in space and time, the amount of satellite data should be accumulated over a longer period in order to include the effects of typhoons.  
Both buoys and altimeters have limitations on the measurements of extreme waves because the buoys measure instantaneous extreme waves in a single point and satellite altimeters measure a mean value in a footprint. Thus, a difference in the resulting measurements will inevitably appear. Despite the differences in these observations, field observations of wave heights are very important and provide us with invaluable verification data. In this regard, observational data from the IORS in the center of the East China Sea can provide very important clues regarding wave height changes as well as other oceanic and atmospheric values when typhoons or extreme events occur. In light of this, if sufficient data are accumulated, the IORS can be one of the most important sites for the validation of extreme SWHs using satellite data and for diverse kinds of oceanic research. Thus, more buoy stations for in situ measurements should be installed and operated in near-real time for the high-performance estimation of 100-year period extreme SWHs.  
Figure 14: Histograms of the PoT analysis with the generated Pareto distribution in (**a**) the typhoon region (128\({}^{\circ}\)E, 30\({}^{\circ}\)N) with a frequency of more than 10 and (**b**) non-typhoon region (174\({}^{\circ}\)E, 52\({}^{\circ}\)N) with a frequency of less than 10.
## 2 Materials and Methods
### Test Equipment and Scheme
The schematic diagram of this test device is shown in Figure 2.  
The main body of the test device is a hollow cylinder made out of acrylic material, with a diameter of 5 cm and a length of 15 cm. On the side wall of the cylinder, there are two holes, 5 cm apart, for voltage measurement at both ends of the soil sample. There are 5 cm diameter conductive copper sheets on both sides to measure the current passing through the soil sample. The device used in this paper has relatively stable and accurate current and voltage readings, and it is easy to convert these data into the true resistivity of the soil samples. The cylindrical acrylic outer wall allows for the observation of the effect of the soil sample during compaction. Figure 2: Electrical resistivity test device circuit diagram. The specific steps for resistivity measurements are as follows:  
Firstly, place a pre-prepared sample of concentration-contaminated soil into the test device and compact it with hydrostatic pressure. Next, connect the test device to the power supply and an ammeter/voltmeter, ensuring that the current and voltage are stable and constant at a frequency of 50 Hz. During this process, the corresponding resistivity values are measured. The test scheme is shown in Table 3.  
Based on the proportion of heavy metal ion mass to the total mass of dry soil, the content of heavy metal pollutants designed in this paper was 0.5 mg/kg, 5 mg/kg, 50 mg/kg, 500 mg/kg, and 5000 mg/kg. Meanwhile, in order to study the influence of state parameters such as water content and porosity on the characteristics of heavy metal-contaminated soil, the water content of the prepared soil samples was 20%, 25%, 30%, 35%, and 40%. The porosity was 0.4, 0.425, 0.45, 0.475, and 0.50. The shear rate was set to 0.8 mm/min. The vertical stress of the test was 100 kPa, 200 kPa, 300 kPa, and 400 kPa. During data processing, the shear strength was the peak value of shear stress within 6 mm of the displacement on the shear stress and shear displacement curve. When there was no peak value, the shear stress corresponding to the shear displacement of 4 mm was taken as the shear strength. The test flow is shown in Figure 3.  
Figure 3: Test flow chart. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Content** & \multicolumn{3}{c}{**Sample Parameter**} \\ \hline Electrical test & Ion concentration & Moisture content & Porosity \\  & 0–5000 mg/kg & 20–40\% & 0.4–0.5 \\ Direct shear test & Ion concentration & Moisture content & Porosity \\  & 0–5000 mg/kg & 20–40\% & 0.4–0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Heavy metal-contaminated soil resistivity test scheme.
# A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis
## 3 Results
### Prognostic Model Construction and Validation  
Using P-WGCNA-Cox and EP-WGCNA-Cox, nine P-FRGs and eight P-FRGs were identified, respectively. Then, risk score models were established on the training set TCGA-STAD using the following formulas:  
P-WGCNA-Cox:  
\[\begin{array}{ll}\text{RiskScore}&=\text{g}(ALB)\times 0.07+\text{g}(AR) \times 0.03+\text{g}(CBS)\times 0.07\\ &\qquad+\text{g}(CDC25A)\times 0.03+\text{g}(BN1P3)\times 0.06\\ &\qquad+\text{g}(FABP4)\times 0.05+\text{g}(MEG3)\times 0.11+\text{g}(MYB) \times(-0.07)\\ &\qquad+\text{g}(PDK4)\times 0.02\end{array} \tag{2}\]  
EP-WGCNA-Cox:  
\[\begin{array}{ll}\text{RiskScore}&=\text{g}(ALB)\times 0.05+\text{g}(BN1P3) \times 0.05+\text{g}(DPEP1)\times 0.01\\ &\qquad+\text{g}(GLS2)\times(-0.18)+\text{g}(MEG3)\times 0.08\\ &\qquad+\text{g}(PDK4)\times 0.04+\text{g}(TF)\times 0.08+\text{g}( TSC22D3)\times 0.01\end{array} \tag{3}\]  
Based on the median risk score of P-WGCNA-Cox and EP-WGCNA-Cox models, 375 GC cases were classified into a high-risk group (\(n=187\)) and a low-risk group (\(n=188\)), as depicted in Figure 3A,B. The KM curves showed that the overall survival rate of the low-risk group was significantly higher compared to that of the high-risk group (\(p<0.05\)) for both methods. The timeROC curve showed that for the 5-year survival rate of GC patients, the prognostic model constructed by P-WGCNA-Cox predicted 0.69 (Figure 3C), while the prognostic model constructed by EP-WGCNA-Cox predicted 0.77 (Figure 3D), indicating that the EP-WGCNA-Cox model had a better performance in predicting the survival rate of GC patients.  
Figure 3: P-WGCNA-Cox and EP-WGCNA-Cox prognostic models were constructed on TCGA-STAD. (**A**,**B**): KM curves of overall survival of GC patients in the P-WGCNA-Cox and EP-WGCNA-Cox prognosticmodels in the high and low risk groups; (**C**,**D**): timeROC curve of 5-year survival in GC patients predicted by P-WGCNA-Cox and EP-WGCNA-Cox prognostic models.  
To assess the robustness of the prognostic model, the risk score for each patient in the validation set GSE84426 was calculated using the same formulas employed in the training set. Similar to the classification in the training set, the patients in the validation set were divided into a high-risk group (\(n=37\)) and a low-risk group (\(n=38\)), and the KM curve showed that the overall survival rate of the GC low-risk group was also significantly higher than that of the high-risk group (\(p<0.05\)) (Figure 4A,B). The timeROC analysis showed that the AUC of the P-WGCNA-Cox was 0.59 at 5 years (Figure 4C). On the other hand, the AUC of the EP-WGCNA-Cox was 0.64 at 5 years (Figure 4D). This results indicate that the prognostic model constructed by EP-WGCNA-Cox had a good reliability.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 2 Multi-Objective Optimization Applied to Underwater Glider Path-Planning
### Methodology  
The validation of the hypothesis was based on a series of experiments executing different variants of the optimization algorithm (through changes of the input parameters, as described on Section 3.3)over a set of selected scenarios. A significant number of independent runs were carried out to obtain statistically robust results and to give support to the conclusions.  
A glider simulator took the inputs from the problem definition and environmental conditions, to produce the required outputs to be connected to the NSGA-II optimizer variants. After pre-defined stopping criteria, each algorithm returned the best individuals and the objective functions values.  
The different outputs were evaluated and compared, generating different groups of results, to decide what algorithm parametrization yields better performance.
## 2 Design and Analysis of Proposed Hybrid DRA  
### Proposed HDRA  
The configuration of the proposed probe-fed hybrid DRA is illustrated in Figure 1. This antenna is designed to operate at a frequency of 5 GHz and has overall dimensions of 60 mm \(\times\) 45 mm \(\times\) 0.787 mm. Both the height and radius of the dielectric resonator (DR) element are 10 mm. The antenna incorporates two types of dielectric materials: Rogers-5800, which has a permittivity of 2.2, is used as the substrate, and Rogers-6010, with a permittivity of 10.2, serves as the DR material.
## 4 Proposed Clustering Algorithm
### CH Selection Approach (Classical)
Because long-distance communication consumes more energy, \(D_{\text{agg}}\) minimizes the intra-cluster communication energy consumption. In the next step, we calculate residual energy \(E_{\text{re}}(s)\) for the candidate cluster heads. Now, in the final step, the minimum weighted node will be selected as the CH. The weighting factors which are assumed in order to calculate total weights in Table 2 are \(w_{1}=0.7\), \(w_{2}=0.2\), and \(w_{3}=0.1\)[42]. We note that weighting factors are chosen randomly, such that \(w_{1}+w_{2}+w_{3}=1\). This is basically used to normalize the appropriate combination of weighting factors, such as the degree difference, the distance from neighboring nodes, and the energy usage. By adjusting the weighting factors, the combination of various eligibility requirements can be set in a suitable way. In our example, node degree has the highest priority as a result, so the weight \(w_{1}=0.7\) is chosen to represent the node degree. In this experiment, each node begins with only 1 J of energy. Therefore, the energy ratio is one, as depicted in Table 2 for all of the nodes. From Table 2, the lowest weighted nodes, which are node no. 10 and node no. 13, will be selected as the cluster heads. It is worth mentioning that no two cluster heads are adjacent neighbors. We found that all member nodes of each cluster are quite close to the desired node degree set earlier: \(\gamma=7\). Figure 4d clearly identifies the CHs and the member nodes. The member nodes that are outside of the selected cluster head's transmission range (nodes 11, 3, 12, and 7) will send their data to the nearest neighbor node within the transmission range.
## 3 The Proposed Method
### Main Process of Tamper Detection and Recovery  
To strike a great balance between using fewer watermark bits and retaining sufficient significant information, we use the 3 LSBs of each pixel to store the watermark. Compared to Reference [7], we apply SVD and edge detection during block classification. After SVD, the sub-images and their weights are generated. The authentication watermark contains two bits, one bit is the result of the exclusive-or operation on each bit of the trace or weight matrix and the other bit is the result of applying the same operation to each even bit of that trace. The first condition for smooth blocks is that the block values of the first sub-image must lie in a range from 0.48 to 0.52. The larger this range is, the greater the number of blocks classified as smooth. After edge detection, a binary image containing 0 s and 1 s is obtained, where 1 represents an edge pixel. The second condition for the smooth blocks is that no block should contain more than one pixel with a value of 1.  
After block classification, different types of information are used for watermarks. Because smooth blocks contain less information, we adopt the average pixel value directly, which forms a watermark with 5 bits of information. However, for texture blocks, the pixel values in the spatial domain are converted to the DCT coefficients in the frequency domain. To satisfy the requirements for watermark bits, we first need to scale the blocks. At the end of this process, the watermark information consists of four parts: the sign of the DC coefficient \(d_{11}\), the sign of the first AC coefficient \(d_{12}\), a 4-bit value for \(|d_{11}|\) and a 3-bit value for \(|d_{12}|\). The authentication watermark is inserted into its original pixels but the embedding position of the information watermark is scrambled through a mapping process, as shown in Equation (2). A schematic map of the embedding bits of the watermark is shown in Figure 5, where the authentication watermark is embedded in the orange bits. \(w_{i}\) (\(i=1,2,3,\dots,10\)) represents each bit of the information watermark and the embedding positions for these values are marked in green. \(flag\_ts\) denotes the block classification, where a 0 represents a smooth block.  
During the tamper detection process, a three-level detection mechanism similar to Reference [7] is used to locate tampered regions accurately and reduce the false alarm rate [7]. The image recovery process initially creates an image containing the inverse process of the watermark embedding methods, which is followed by neighborhood adjustment and the filtering operation to further improve the quality of the restored image, as discussed in Section 3.2.  
Figure 4: A flowchart of the filtering operation.
## 4 Building Blocks
### Secure Comparison Protocol  
In our scheme, the comparison operation is converted to binary sharing from arithmetic sharing to improve efficiency. As is introduced in [34], for the \(l\)-bit real number represented with binary stored in the computer, the sign of the number can be judged by getting the highest bit through computing the most significant bit MSB\((\cdot)\). When the highest bit is 1, which means that the number is negative, the lower \(l-1\) bit represents the complement of the number. The highest bit is 0, which indicates that the number is positive. At this time, the lower \(l-1\) bit represents the value of the number.  
The most significant bit (MSB) denotes the sign of a ring element, which implies that the comparison operation can be converted into the MSB extraction of the difference between the two operands. Let \(a=(x-y)\), then we can be computed comparisons \(b\) by extracting the most significant bit of \(a\) with \(b=\) MSB\((a)\). Because of \(\langle a\rangle:=(a_{1},a_{2},a_{3})\) and \(a\equiv a_{1}+a_{2}+a_{3}\) mod \(2^{l}\), \(b\) can be obtained by computing \(b=\) MSB \((a)=\) MSB \((a_{1})\)\(\oplus\) MSB \((a_{2})\)\(\oplus\) MSB \((a_{3})\oplus c\), where \(c\in\{0,1\}\) is a carry bit from \((l-2)\)-th index. If \(2^{l-1}\)\(\leq a_{1}+a_{2}+a_{3}\leq 2^{l}\), the value of \(c\) is 1. Otherwise, \(c=0\).  
To convert the arithmetic share into a binary sharing, \(2l\) AND gates are needed for \(\mathcal{O}(logl)\) rounds. For more details on the conversions of arithmetic and binary secret sharing, please refer to [35].  
The secure comparison protocol is described in Algorithm 1.  
```
0: The parties \(P_{0},P_{1},P_{2}\) hold the arithmetic shares of \(x\) and \(y\), \(\langle x\rangle\) and \(\langle y\rangle\) over \(\mathbb{Z}_{2l}\).
0: The parties get the boolean share of bit \(b=(x\stackrel{{?}}{{<}}y)\).
1: The parties compute the shares of \(a\) at local side, where \(a=x-y\).
2: The parties compute the boolean share of bit \(c\), where \(c=a_{1}+a_{2}+a_{3}\geq 2^{l-1}\).
3: The parties locally compute the boolean share of bit \(b\), where \(b=\) MSB \((a_{1})\)\(\oplus\) MSB \((a_{2})\)\(\oplus\) MSB \((a_{3})\oplus c\).
4: The parties output \([b]\).
```  
**Algorithm 1** SecureComparison \(\Pi_{SC}\{P_{0},P_{1},P_{2}\}\)
## 4 Results and Comparison  
### Design Implementation  
The evaluated signal is used to evaluate the proposed kernel accuracy. After fixed-point simulation using MATLAB, the signal-to-quantization-noise ratio (SQNR) was approximately 40 dB by using the proposed FFT kernel. This was implemented in a 1.0-V UMC 90-nm 1P9M complementary metal-oxide semiconductor process, the proposed FFT kernel used the Synopsys Design Compiler to synthesize the RTL code and employed Cadence SOC Encounter for placement and routing. The proposed FFT kernel consumed 10.72 mW of power and was operated at 80 MHz, and the core area of the proposed kernel was 739 \(\times\) 734 \(\mu\)m\({}^{2}\). Figure 15 shows the core layout of the proposed kernel and its characteristics. The kernel test chip could perform 128/64-point FFT in 4-6 streams depending on the GI\({}^{\prime}\) duration. A test module was included in this kernel chip to enable the serial storage of test patterns from the I/O and to send them to the kernel circuit along multiple paths. Moreover, the FFT results were sent to the test module and then sequentially read out through I/O for data evaluation. The post-layout simulation was performed based on the idea that the FFT kernel continuously processes the pattern stored in the test module.
## 2 The Theory of the Smooth Local Subspace Projection Noise Reduction and PE
### Smooth Local Subspace Projection Method  
In the process of that, original strands are projected to a low dimensional smooth subspace, weighting matrix **W**[34] is used to consider the bias during the projection. The \(n\) in the **W** means the number of points in each strand. There are three types of weighting functions which respectively are, the comb weighting function, the tent weighting function and the exponential weighting function, as in Equations (8)-(10).  
\[\textbf{W}=\text{diag}([\text{zero}(1,n)\ 1\ \text{zero}(1,n)]) \tag{8}\]  
\[\textbf{W}=[0:n,\ n-1:-1:0],\ \textbf{W}=\text{diag}(\textbf{W}/\text{sum}(\textbf{W})) \tag{9}\]  
\[\textbf{W}=\text{exp}(-([n:-1:0,1:n])),\ \textbf{W}=\text{diag}(\textbf{W}/\text{sum}( \textbf{W})) \tag{10}\]  
There are many denoising evaluation indicators. Visual identification is used at early stages until contemporary peak ratios, margin, and kurtosis can be regarded as evaluation indicators of denoising. The most basic indicator is the signal to noise ratio (SNR), which is adopted here. In addition to that, mean square error (MSE) and Cm, which represents similarity, are also used to evaluate the effectiveness of the proposed method, as shown in Equations (11)-(13) respectively.  
\[\text{SNR}=10\times\log 10(\frac{\text{P}_{s}}{\text{P}_{n}}) \tag{11}\]  
\[\text{C}_{\text{m}}=\frac{\sum x\cdot x(i)}{\sqrt{\sum x^{2}}\sqrt{\sum x(i)^{ 2}}} \tag{12}\]  
\[\text{MSE}=\frac{1}{N}\sum_{i=0}^{N-1}{[x-x(i)]^{2}} \tag{13}\]  
where \(P_{s}\) is mean power of the original signal, and \(P_{n}\) is mean power of the Lorenz signal with noises, \(x\) is the original signal without noises, \(x(i)\) is the denoised signal after iteration denoising of SOD. To verify the effectiveness of the proposed method, the following simulated signal is adopted for simulation research.  
\[x(t)=\sin(2\pi f_{1}t)[1+\cos(2\pi f_{2}t)]+s \tag{14}\]  
where \(f_{1}=75\) Hz, \(f_{2}=30\) Hz, and \(s\) denotes the added Gaussian white noise, with variance of 1, and mean of 0. The above three evaluation indicators can be utilized to signify the validity of the smooth local subspace projection denoising method intuitively. The above three factors have different effectiveness towards the smooth local subspace projection method, as shown in Figures 1-3.  
Figure 1: Signal to noise ratio (SNR) values along with iterations.  
Figure 4: SNR values along with iterations.  
Figure 5: Cm values along with iterations.  
Figure 6: MSE values along with iterations.
## 4 An Integer Programming Formulation
### Subloop Elimination  
To eliminate subloops appearing in a solution, we introduce new constraints. We add them to the original formulation, and re-solve the updated one. We iterate this process every time subloops appear in a solution until we finally obtain a Tantrix solution. Now we call a border that divides a board into two parts a _cut_. Then if the loop of the designated color is unique, it crosses over an arbitrary cut more than once. Therefore, we introduce the following new constraint.  
**Constraint 5** (C5). Lines of the designated color cross the cut over which they do not cross.  
To describe this constraint, we introduce for convenience a new \(0\)-\(1\) variable \(z_{j\ell}\).  
\[z_{j\ell}=\left\{\begin{array}{ll}0,&\text{for place $j$, the color of the line corresponding to edge $\ell$}\\ &\text{is not the designated color $(y_{j\ell}=1,2)$},\\ 1,&\text{for place $j$ the color of the line corresponding to edge $\ell$}\\ &\text{is the designated color $(y_{j\ell}=3)$}.\end{array}\right.\]  
More precisely, we define a cut \(K\) as a pair \((j,\ell)\) of place \(j\) and its edge \(\ell\), where one of the divided part by that cut is adjacent to. Then C5 can be formulated as follows:  
\[\text{C5:}\quad\sum_{(j,\ell)\in K}z_{j\ell}\geq 2\]  
and we give this constraint for all the cuts that lines of the designated color do not cross in a solution. For example, in a solution of challenge number 20 (Figure 10(b)), such a cut is \(K=\{(19,1)\), \((19,6)\), \((14,1)\), \((11,2)\), \((11,1)\), \((7,2)\), \((8,3)\}\), and thus C5 for this cut becomes \(z_{19,1}+z_{19,6}+z_{14,1}+z_{11,2}+z_{11,1}\)\(+z_{7,2}+z_{8,3}\geq 2\).  
The value of this variable \(z_{j\ell}\) is, in fact, determined by those of the variable \(y_{j\ell}\); that is, \(z_{j\ell}=0\) if \(y_{j\ell}=1\) or \(2\) (i.e., not the designated color), or \(z_{j\ell}=1\) if \(y_{j\ell}=3\) (i.e., the designated color). In other words, possible combinations of values for \(y_{j\ell}\) and \(z_{j\ell}\) are \((y_{j\ell},z_{j\ell})\in\{(1,0),(2,0),(3,1)\}\). Therefore, \(z_{j\ell}\) can be represented by the following two inequalities:  
\[\left\{\begin{array}{ll}z_{j\ell}\leq\frac{y_{j\ell}}{2}-\frac{1}{2}\\ z_{j\ell}\geq y_{j\ell}-2\end{array}\right.\]  
For challenge numbers 20, 25 and 35, whose solutions include subloops, we iterated adding C5 to each of their formulations and re-solving it until we obtain a Tantrix solution. Table 2 is the results for this experiment, and it shows the computational time required in each iteration (#iteration) and the sum of those for all iterations (total). (We define the first trial of solving the original formulation without C5 as the 0 th iteration.) Figure 10(c) depicts the final solution for challenge number 20, where we can confirm that a subloop in the original solution (Figure 10(b)) is eliminated and that there is a single loop. We are now able to obtain a Tantrix solution by formulations using C1, C2, C3, C4 with adding C5 if needed.