esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Analysis
### Stress at Failure
#### 3.4.3 ASTM Standard and BS Standard conversion factor  
Depending on the 80 tested data, the ratio of the \(\sigma_{\rm cc}\) of cement grout using a cylinder mold to the \(\sigma_{\rm cp}\) was 0.71 with R\({}^{2}\) of 0.95 and RMSE of 2.8 MPa (Figure 12).  
\[\sigma_{\rm cc}=0.71*\sigma_{\rm cp}, \tag{11}\]  
When samples are subjected to uniaxial compression, a recessed zone forms, establishing a triaxial compression zone [23,24].  
Figure 13 shows a typical flexural strength for a CG modified with water reducer polymer. Adding polymer to CG increases its flexural strength and its CS. Compared to the other sands, the cement grout made with finer sand grading (Sand #2) had the maximum \(\sigma_{\rm f}\) at all testing ages.  
The LR model parameters (Equation (12)) and NLR model parameters (Equation (13)) concluded that increasing the polymer content (P\(\%\)) significantly raises the \(\sigma_{\rm f}\) of cement grout (Figure 14a,b).  
\[\sigma_{\rm f}=-9.3*\frac{\rm W}{\rm c}+0.15*t-0.68*\rm d_{10}+19*P \tag{12}\]  
\[\sigma_{\rm f}=1.96(\frac{\rm t^{0.17}}{\rm w/c^{0.93}d_{10}^{0.1}})+2.73( \frac{\rm t^{0.25}P^{0.72}}{\rm w/c^{0.30}d_{10}^{0.16}}) \tag{13}\]  
Figure 12: Prismatic and cylindrical CS correlation of CG modified with water reducer polymer 3.5. Flexural Strength.  
Figure 13: Typical variation of \(\sigma_{\rm f}\) for CG modified with water reducer polymer at (**a**) 1 day of curing, and (**b**) 28 days of curing.  
Figure 14: Measured and predicted relationship for the flexural strength of CG (**a**) LR model, (**b**) NLR model, and (**c**) residual error.  
Because the NLR model has a lower residual error (3 MPa), an RMSE, and a higher R\({}^{2}\) than the LR model, it can be seen that the NLR model predicts a higher \(\sigma_{\text{f}}\) (Figure 14c). \(\sigma_{\text{f}}\) increased from 4 to 11 MPa while \(\sigma_{\text{PC}}\) improved from 20 to 60 MPa (Figure 15).
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 1 Introduction
Although there has been significant progress in the field of fluid structure interaction, the study of nonlinear hydroelastic responses of large ships in severe irregular sea conditions using 3D methodology is rather scarce in the present literature. Accordingly, the aim of the present paper is to systematically study the hydroelastic vibrational characteristics of a target large ship in both regular and irregular waves. A fully coupled 3D nonlinear hydroelastic method in the time domain is constructed to study whipping responses of a large ship with a pronounced bow flare in regular and irregular wave conditions. The structure of the vessel is idealized as a 1D Timoshenko beam, and the fluid flow is simulated by the 3D potential flow theory. Slamming loads are calculated based on the momentum method. An in-house hydroelastic analysis code is accordingly developed to predict ship responses in regular and irregular waves. To validate the numerical results, experimental tests of a target large ship in regular and irregular head waves were carried out in a towing tank. Quantitative time domain comparisons of numerical and experimental ship responses in regular head seas are discussed, with qualitative time domain comparisons of ship responses, statistical parameters, and probability of exceedance of response extrema being analyzed for ship short-term responses in irregular seas.
## 3 Integrity Detection Method for Semi-Physical Simulation Data
### Statistical Envelope of the Confidence Region  
At present, statisticians have accumulated many methods to solve the confidence interval. According to whether prior information is used, these methods can be roughly divided into two categories. The first kind are traditional estimation methods represented by the Bayesian method, which integrates overall information, sample information, and prior information. The parameters are estimated in intervals using historical information as prior information. The other type are classical statistical estimation methods that only use the population information and sample information, which can be divided into the following two methods according to whether the type of the overall distribution is known: nonparametric interval estimation methods represented by Hodges-Lehmann, which estimates the parameter interval directly based on the sample information when the population distribution type is unknown, and interval estimation methods, when the population distribution is known, such as estimating the normal population parameter mean \(\mu\) or variance \(\sigma^{2}\) interval.  
Since the focus of this paper is estimating the confidence interval of the sample data after bootstrap resampling and expansion, there is no prior information available and Bayesian-class methods are not applicable. Conventional confidence interval solving methods, such as the interval estimation of the single-parameter mean \(\mu\) of the normal population, essentially cover the point estimate of the mean \(\mu\) with a confidence interval. This paper tests the integrity of the semi-physical simulation data. A polygon envelope function graph is drawn to judge whether the data are complete. To cover the original sample data, it is not suitable to use a conventional confidence interval solution method. As the coordinate data of the landing points are subject to a normal distribution after the above test, for a normal distribution, \(6\sigma\) intervals can cover \(99.7\%\) of the data. Assuming that the mean and standard deviation obtained by bootstrap resampling are \(\mu^{*}\) and \(\sigma^{*}\), respectively, then the confidence interval can be \(I_{X}=\big{[}\mu^{*}-3\sigma^{*},\mu^{*}+3\sigma^{*}\big{]}\).
_Article_  
Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation [PERSON] 1,*, [PERSON] 1 and [PERSON] 2(c)  
Received: 29 September 2019; Accepted: 24 October 2019; Published: 29 October 2019  
**Featured Application: The direct under-sampling compressive sensing method presented in the article can be applied to many types of acquisition systems for underwater echo signals.**  
**Abstract: Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling--compressive sensing (DUS-CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, \(-\)3 dB, and \(-\)5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.**  
**Keywords: compressive sensing; under-sampling; measurement matrix; echo signals**
# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Inception v3** & **VGG16** & **Xception** \\ \hline Accuracy\% & 82 & 79 & 78 \\ Sensitivity\% & 86 & 76 & 77 \\ Specificity\% & 79 & 82 & 79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of the performance of the different classification models tested. Figure 2: Detection of periapical areas (PRA) on panoramic radiograph by clinicians and by the AI. First image: OPG showing the PRA annotated by the expert clinician. Second image: OPG showing the proposed PRA generated by the detection model for the same image. All PRAs were detected by the proposed model (Faster RCNN). The detection and classification models were combined in order to produce our overall solution that was able to automatically identify the periapical lesions in the OPGs with red bounding boxes corresponding to Periapical Lesions. These boxes included a confidence score that represented a percentage calculated by the AI indicating how sure the AI was the output is correct. The overall solution was tested on a test set of 299 PL on 143 panoramic radiographs. The accuracy, sensitivity, and specify of the overall solution were 84.6%, 72.2%, and 85.6%, respectively. Figure 4 shows representative examples of the final output generated by the proposed method. This final tool was designed to detect periapical areas and ignore anything else in the radiograph, thus it was not affected by the presence of distractors and artifacts such as implants and crowns, also, the size of the detection box was able to adapt to the actual PRA, and it discriminated between different apices on the same tooth. On average, the proposed combined AI system took 2.3 s to detect and classify all PRAs as H or PL on a panoramic radiograph. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Predicted H** & **Predicted PL** \\ \hline Actual H & 312 & 48 \\ Actual PL & 65 & 282 \\ \hline Accuracy\% & & 84 \\ Sensitivity\% & & 81 \\ Specificity\% & & 86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix and classification metrics assessing the final performance of Inception v3 on the test set. Figure 4: Performance of the proposed AI system for periapical lesion detection. This figure shows OPGs of three different cases (**a–c**) labeled by the clinician (left) and the AI (right). For each row, the OPG on the left shows the boxes annotated by the expert including PL and healthy PRAs, while on the right, we show the same OPG labeled by the AI with red boxes indicating the PL detected by the AI. The confidence score calculated by the AI system can be found on top of each red boxes. Case “(**a**)” shows a periapical lesion detected in a multirooted tooth (#36). Case “(**b**)” shows two periapical lesions in teeth with root canal treatments and crowns in a patient with dental implants. Case “(**c**)” shows a case with a single PL in a multirooted tooth (#26).
## 4 Enhanced Series Resistance Model
### Model Verification and Predictions  
#### 4.2.1 Impact of the Probability of Hole Reflection  
The point here is just to confirm that the proposed model operates systematically and consistently. By inspecting Table 3 and the inserts in Figure 6a and in Figure 7a, it appears that under illumination and thermionic emission transport \(\mathrm{p_{peak}}\) in SHJ\({}_{1}\) decreases from 3.74 \(\times\) 10\({}^{19}\) to 1.86 \(\times\) 10\({}^{19}\) cm\({}^{-3}\) when A\({}^{*}\) increases from 9.56 to 20 A K\({}^{-2}\) cm\({}^{-2}\) due to reduced reflection. The weaker dipole and reduced electric field enhancement confirmed in Figure 7b and in Table 3 lead to less depletion of the double a-Si:H layer, as depicted in Figure 7a compared to Figure 6a, and hence to a smaller enhancement in the resistance of the double a-Si:H layer, as listed in Table 3, which leads to an improvement in the I-V characteristics depicted in Figure 8. As expected, only at very large values of A\({}^{*}\), i.e., when the reflection probability is very small, the cell series resistance is determined only by its value in the dark and the I-V characteristics tend to converge with those obtained assuming drift-diffusion transport.  
Figure 6: Simulated (**a**) hole concentration profile and (**b**) electric field distribution, in cell SHJ\({}_{1}\) under illumination-SC with thermionic emission transport (_Dash_) and drift-diffusion transport (also \(\sim\)equilibrium) (_Cont. line_).  
Figure 7: Results of AFORS-HET simulations of cell SHJ\({}_{1}\) under illumination-SC: (**a**) hole concentration profile assuming thermionic emission with A\({}^{*}\) = 20 A K\({}^{-2}\) cm\({}^{-2}\) (_Dash_) and with drift-diffusion (_Cont._); (**b**) electric field distribution with A\({}^{*}\) = 9.56 (default), 20 and 50 A/K\({}^{-2}\) cm\({}^{-2}\) and at equilibrium (_Cont._).
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 3 Recent SDN Topology Discovery Performance Studies
### Link Discovery Improvement Algorithms
#### 3.1.1 Periodic
The authors proposed a new method called enhanced Topology Discovery Protocol (eTDP) which has been classified the SDN-Switches into three types (core, leaf, and v-leaf). Leafs called for SDN-Switches that have only one adjacent switch while SDN-Switches that have more adjacent Switches called v-leaf. The remaining SDN-Switches are called a core. In addition, SDN-Switch ports also have different states according to their positions in the control tree formed by the topology discovery module (Standby, Parent, Child, and Pruned). These performed functions of ports and SDN-Switches are rotated between managed components and independent managers. Each performed function has four tasks: monitor, analyze, plan, and execute. Finally, the SDN-Controller will draw the network topology from the shortest control paths transmit to it by the SDN-Switch core. Moreover, other works related to topology discovery processes in low power and CPU devices have been discussed in [67; 86]. The authors of [67] analyzed the effect of OpenFlow performance over wireless networks in terms of QoS metrics. Meanwhile, the authors in [86] proposed a new topology discovery method called efficient Topology Discovery Protocol (TDP) to achieve simultaneous adaptive SDN-Node ID mapping and topology discovery for Underwater Acoustic Networks (UAN). To save power, the proposal eliminated outdated information about the metering network and node identification from the network topology procedure, leaving only the node identifier. Instead, the SDN-Controller separates the network into layers and employs a local timer in each SDN-Node to ensure that topology discovery packets are sent on time. Each node could determine the network topology and create its SDN-Node ID separately by utilizing the received topology discovery packets.
###### Abstract
The design has an AR BW in lower and upper bands of 19.98% and 3.07%, respectively. The design has overall dimensions of 50 \(\times\) 50 \(\times\) 5 mm\({}^{3}\). In [40] a novel complex dual facet spiral loop coupled equilateral triangular shaped dual band CDPRA is discussed. The design has overall dimensions of 50 \(\times\) 50 \(\times\) 10 mm\({}^{3}\) with an AR BW of 1.6% in the first band and 1.8% in the second band, respectively. The design has peak gain of 2.2 dBic and 3.7 dBic for both the operating bands, respectively. The proposed design is used for sensing applications. In [41], probe fed periodically deformed cylindrical CP DRA is proposed. The design has a large size of 50 \(\times\) 50 \(\times\) 16 mm\({}^{3}\) with IBW of 46.15%, an AR BW of 7.7% and an average peak gain of 5 dBic. In [42] a complex dual vertical microstrip fed CP CDRA is proposed. The design has large dimensions of 55 \(\times\) 55 \(\times\) 23 mm\({}^{3}\) with reasonable peak gain of 5.5 dBic, simulated AR BW of 23.07% and IBW of 29.78% for airport surveillance radar (2.7-2.9 GHz) and Wi-MAX (3.3-3.6 GHz) applications. In [43], triangular ring-shaped aperture coupled microstrip line fed dual band CP RDRA is discussed. The design has a size of 40 \(\times\) 40 \(\times\) 8 mm\({}^{3}\). The design is proposed for Wi-MAX and WLAN applications. The design has an average gain of 5.3 and 5.7 dBic, low AR BW of 1.03% and 3.4%, IBW of 2.58% and 15.78% in the upper and lower band, respectively. From the above literature review, it is clear that a compact DRA with a wideband response and a good CP performance is highly preferred. All of the above designs are either bulky, complex or have poor performances. Therefore, an antenna with simple design, compact size and good performance is highly demanded. To achieve all these packages in a single design is very challenging. In this work, a very compact wideband CP DRA for X-band wireless application is discussed in detail. The design is very compact with only 5.175 mm height of the DR. The corner feeding excites two orthogonal modes in the DR, thus making the design CP. The top copper strip further enhances the axial ratio performance. For improving the operating band and impedance matching, an air gap is added at the center of the DR. A ground slot is etched which further improves these parameters. To the best of the authors' knowledge, this is the most compact CP DRA with a very wide band response for the target X-band wireless application. The rest of the paper is organized as: Section 2 discusses the antenna geometry and design; Section 3 presents detail of the antenna design and analysis; Section 4 consists of the parametric study of the design; Section 5 gives detail of the final simulated and measured results and Section 6 concludes the proposed work.
# The Effects of Adhesive and Bonding Length on the Strain Transfer of Optical Fiber Sensors
## 2 Strain Analysis of Surface Bonded Optical Fiber  
When the optical fiber is used as a sensor to monitor the strain or stress in a structure, the capability of measurement depends on the bonding characteristics between the optical fiber and host structure. The measuring sensitivity of the optical fiber sensor is affected by the protective coating, adhesive layer, and bonding length. In this investigation, the optical fiber is surface bonded on the host structure while the external loads (stress or strain) are applied on the host structure only. In fact, the deformation of optical fiber induced by the host structure is transferred via the adhesive layer and protective coating. The deformation of the optical fiber induces the change of the optical signal transmission since the stress or strain is directly applied to the host structure other than the optical fiber, so that the strain or stress in the optical fiber is subjected to shearing at the interface between the optical fiber and the adhesive. The analytical model is shown in Figure 1 with cylindrical optical fiber and coating on the top and the host material under the bottom, with adhesive in between. Host material is subjected to far field strain. In this work, the transformation of the strains between the host structure and surface bonded optical fiber sensor was derived based on the following assumptions:(1) all interfaces are perfectly bonded _i.e.,_ displacement continuity along the interfaces; and (2) the adhesive and protective coating are subjected to shear deformation only. This assumption is reasonable since the Young's moduli of the adhesive and protective coating are significantly smaller than host structure and optical fiber. [PERSON] and [PERSON] [18] have derived the analytical solution basing on the shear lag model to predict the optical fiber strain induced by the host material as follows:  
\[\left(\varepsilon_{f}\right)_{the}=\frac{\int_{\Gamma_{s}} \varepsilon_{f}(x)dx}{s}=\frac{\varepsilon_{0}}{\frac{\pi r_{f}^{2}}{\pi r_{f} ^{2}}+\frac{1}{E_{f}}}[1-\frac{\sinh(\lambda_{1}L_{f})}{L_{f}\lambda_{1}{\rm cosh }(\lambda_{1}L_{f})}]\] \[\lambda_{1}=\sqrt{[\frac{2r_{p}}{\pi r_{f}^{2}}(\frac{\pi r_{f}^{ 2}}{2 hr_{p}E_{h}}+\frac{1}{E_{f}})\int_{0}^{\cos^{-1}(\frac{b}{r_{p}})}\frac{1 }{G_{a}}+\frac{r_{p}}{G_{p}}{\rm ln}(\frac{r_{p}}{r_{f}})d\theta]} \tag{1}\]  
where \(E_{h}\), and \(E_{f}\) are [PERSON]'s moduli of the host material and optical fiber, respectively; \(G_{a}\) and \(G_{p}\) are the shear moduli of the adhesive and coating, respectively; \(r_{f}\) and \(r_{p}\) are the radii of the optical fiber and coating, respectively; h is the thickness of the host material; \(L_{f}\) is half of the surface bonded length; \(\varepsilon_{0}\) is the strain applied to the host material; \(b\) is the gap between the adhesive and optical fiber as shown in Figure 1; and \(\Gamma_{s}\) and \(s\) denote the bonding length ranging from \(-L_{f}\) to \(L_{f}\).
# A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems
## 4 Proposed Method
### Example  
Consider the following scenario of transactions. We have six transactions T1-T6 that are reading and writing data. Each thread is responsible for an area that contains a group of transactions. The areas could be, for this example, the relationships between the tables or simply all pair-wise combinations of tables:  
* T1 reads A to write C;
* T2 reads C to write B;
* T3 reads C;
* T4 reads B and C to write D;
* T5 writes E blindly;
* T6 reads B and E to write F.  
Assume that T1 is the malicious transaction. T1 writes C. Thus, any transaction that reads C will be infected:  
_testing_list_: [T1]  
All threads start to test from T1, which is in the testing list, to add transactions from their areas that interacted with T1 (i.e., read data item C). They will find T2, T3, and T4:  
_testing_list_: [T1, T2, T3, T4]  
Now, each thread continues from its last checked index, so, they check for T2. They find the transactions that interacted with T2 (i.e., read data item B), which are T4 and T6:  
_testing_list_: [T1, T2, T3, T4, T6]  
T3 writes E blindly, which means that it does not read any data item, so it will not update the testing list.  
The next transaction is T4, and when the threads run, they return transactions that read D, which is an empty list since no transaction read D. Thus, the testing list remains unchanged.  
The threads then test T6, but do not add anything to the testing list. Hence, the final list of transactions that are affected is as follows:  
affected transactions: [T2, T3, T4, T6]  
All the threads are running in parallel. Each thread can read the index of the last tested transaction in the testing list so that it starts from the next transaction. A transaction may belong to some areas and not others, so the thread moves through the testing list at different speeds. The testing list is synchronized to ensure proper appending and no duplicates. Once all threads are at the end of the testing list, then there are no more affected transactions, and termination is initiated.