esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## 3 Methodology  
### Flume  
The experimental study is carried out using the flume shown in Figure 1. This flume is 15.85 m long, 0.30 m wide and 0.45 m deep. The flume assembly comprises a centrifugal pump with three water tanks having a single inflow control valve, main flume, weir and outflow control valve. This pump moves the water from the recirculation tank and also supplies water to the flume. Subsequently, this flow discharges into a sump tank after passing through the test section. A valve installed at the downstream end of the flume controls the flow depth, while a valve at the upstream end regulates the flow rate.  
The quantities measured are the scour depth and flow velocity. Scour depth is measured by a point gauge and the flow velocity is measured by a velocimeter. The point gauge is placed at the aluminum railing and installed at the top of the flume. The point gauge can slide smoothly along as well as across the flume. Moreover, the velocimeter measures the  
Figure 1: (**a**) The schematic side view drawing of the flume used in the present study. (**b**) The schematic plan view drawing of the flume used in the present study.  
velocity with a precision propeller calibrated before the commencement of the experiment. The velocimeter is placed in the flume at a distance shown in Figure 1b, in such a way that it will not disturb the approaching velocity of the water. A rectangular weir is also installed at the end of the test section for two reasons. First, to measure the flow rate in the flume and second, to trap the sediments in the flowing water. The sediments are regularly removed to avoid choking the pump responsible for controlling the water discharge.  
Some necessary modifications are made to the flume to improve its performance. A 1.52 m long test section is prepared at a distance of 1.52 m from the upstream starting point, as shown in Figure 1. The test section had glass sheets installed at the upstream and the downstream side, thereby avoiding any turbulence at the section entrance. This glass bed is used as a datum for measuring velocity and water depth.  
The test section is overlain by a leveled and compacted fine sand before the test pile is installed, Figure 2. This section tended to erode during the water flow over the glass bed, consequently affecting the local scour depth around the test pile. Therefore, to avoid local erosion and ensure a smooth flow, a few gravel bags are stacked at the entrance of the glass bed. Similarly, 0.61 m long wooden plank is positioned near the downstream end of this section, preventing sediment transport and stabilizing the test section. In order to avoid any dislocation of the piles from their mean position, the piles are attached to the pile cap with the help of screws, as shown in Figure 2d. Moreover, a moderate load is placed over the pile cap to ensure an undisturbed position of piles during water flow, Figure 2e. Figure 2b,c,e,f show the model, its fitting in the testing flume and its subjection to the steady water currents, respectively.  
Figure 2: _Cont._
## 3 Fundamentals of GLI-Split
### Name Resolution
#### 3.3.1 Use of the DNS  
When a DNS name denotes a GLI-node in a multihomed domain, it returns a global GLI-address with a set GAP-bit (see Section 4.2.2). If the DNS name belongs to a GLI-node in a single-homed domain, the GAP-bit is not set. In both cases, the returned GLI-address is globally routable and hosts outside of GLI-domains can use this address without modifications. This requires no changes to the currently used DNS servers in the Internet. More details about multihomed domains are provided in Section 4.
## 3 Numerical Validation Examples
### Three-Dimensional Three-Story Shear Building
#### 3.2.3 Discussion on Practical Aspects  
This section presents the practical aspects, such as the accuracy, computation costs and practical applications, of the proposed BMUA in the real world. The two numerical examples in this paper illustrate that the proposed BMUA with stiffness modification has an advantage against the traditional BMUA in identifying mass and stiffness. In the traditional BMUA, at least one of mass and stiffness is accurately known to avoid the coupling effect, but this assumption is quite questionable; the traditional Bayesian ignores variations in mass or stiffness induced by possible damage. In contrast, the new BMUA with stiffness modification does not involve any assumption on mass and stiffness properties. In short, the proposed BMUA enables to deal with the coupling effect of mass and stiffness in order to successfully identify mass and stiffness. However, the traditional BMUA poorly updates both mass and stiffness because of the coupling effect caused by the classical eigenequation in Equation (1).  
Possible implementations in practice of the proposed BMUA with stiffness modification are described in Section 1 (see Figure 1). The magnitude of stiffness modification could be conveniently determined once the sectional properties of added structural components or the technical specification of the devices are known [65; 66]. Regarding the computational cost, the proposed Bayesian updating approach exhibites high efficiency, because the analytical formulations of the model parameters and uncertainty quantification are explicitly derived. The model updating work by the proposed Bayesian approach was carried out on the laptop flatform of MacBook Pro 2017 with 3.1 GHz Dual-Core Intel Core i5 for the two numerical examples. The six- and twelve-dimensional problem in the first and second example only took around 4 s and 8 s, respectively, suggesting that the proposed approach is fast-running and practically valuable even for real-time model updating.  
Figure 11: Probabilistic curves. Damage case No. 1: (**a**) mass; (**b**) stiffness. No. 2: (**c**) mass; (**d**) stiffness.  
Let us remark that the number of data sets does not affect the performance of the proposed BMUA against the traditional BMUA. The proposed BMUA adopts new eigenequations in Section 2.4. Only two groups of measured data are used to remove the coupling effect. Although BMUA is significantly promising, the proposed approach's performance under lab or field test conditions needs to be examined in future work.
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 2 Method
### Object Template Update Mechanism  
During the tracking process, the object's appearance and posture may change, and it can also be influenced by external factors. If the template is not updated in a timely manner, the tracking model may fail because it cannot adapt to the object's new appearance. Common template updating methods include updating templates periodically according to preset time intervals or frame numbers and large-scale time sequence information modeling methods that predict the future state by learning the dynamic change in the object with rich context information. However, the former may have the problem of low template image quality, while the latter is high in computing cost. In practical application, both the real-time functionality and accuracy of tracking should be considered. Therefore, this paper uses the object template update mechanism guided by the judgment conditions [22] proposed before to start the template update mechanism under specific conditions to achieve the timely and accurate updating of the object template. Specifically, Tranftpn-Mask is taken as the baseline, and the trained IoU prediction module is combined to judge the tracking results, and the combined model is named Transtfpn-T. The IoU prediction module leverages the outcomes of classification and regression to estimate the IoU value in the current frame. By gauging the correlation between the current frame and the prior two frames, it decides whether an update to the object template is warranted. The detailed template update model is illustrated in Figure 6.
To a certain extent, the complex structural characteristics of this type of geotechnical material are neglected; there is a lack of systematic and sufficient understanding of the effects of hidden cracks in basalt on its mechanical properties, making it difficult to accurately grasp its true mechanical behaviors and fracture mechanisms. In addition, the conventional size effect analyses are often only aimed at jointed rock mass with certain structural characteristics to obtain the corresponding REV and mechanical parameters, without applying the research results to engineering practice. Therefore, the multiscale mechanical properties and failure mechanisms of basalt should be further investigated. Taking the underground caverns in the Baihetan Hydropower Station as the engineering background in this work, the excavation responses and size-dependent mechanical behaviors of basalt with hidden cracks were systematically explored based on an SRM model combining FDEM and DFN models, thoroughly revealing the failure mechanism of the basalt in this area. The rest of this paper is organized as follows: Section 2 presents the distribution characteristics of the geometric parameters of hidden cracks inside basalt, obtained through a series of comprehensive techniques. Subsequently, numerical models of basalt with hidden cracks are established based on the above data, including an SRM model of basalt blocks and an FDEM model of the basalt rock mass from an underground powerhouse (Section 3). The research results are then analyzed, including the size-dependent mechanical properties of the basalt blocks and the fracture behaviors of the surrounding rock mass during underground cavern excavation (Section 4). Finally, a brief discussion is made, and conclusions are drawn (Sections 5 and 6).
# A Low-Profile Wideband Linear-to-Circular Polarization Conversion Slot Antenna Using Metasurface
## 2 Design and Analysis of the Proposed Antenna  
### Design of the Metasurface Antenna  
The final low-profile wideband CP metasurface antenna is presented in Figure 1. It is formed of two parts and three layers. Both of the parts were designed on an inexpensive FR4 substrate with a dielectric constant of \(\varepsilon_{\mathrm{r}}=4.4\) and loss tangent of \(\delta=0.02\). The bottom part is the slot microstrip LP antenna, i.e., the source antenna, and the top part is the loaded metasurface. The thicknesses of the bottom substrate h\({}_{0}\) and top substrate h\({}_{1}\) are 0.5 mm and 3 mm, respectively. They were designed to have the same area of Wm \(\times\) Wm = \(36\times 36\) mm\({}^{2}\). The top layer is the metasurface, which consists of four unit cells arranged in a \(2\times 2\) layout on a substrate with seven parasitic square cross gaps. The middle layer is the metallic ground plane with an air slot of ls \(\times\) ws = \(28\times 2.2\) mm\({}^{2}\) and two slots of p \(\times\) q = \(1.1\times 1\) mm\({}^{2}\). A step slot was used for better impedance matching [17]. To integrate the metasurface with the slot coupling antenna and excite the slot antenna radiating properly, the bottom layer adopts a coplanar waveguide (CPW) feeding line. All parameter values of the proposed CP metasurface antenna are tabulated in Table 1.  
Polarization reconfigurability of the proposed metasurface antenna can be accomplished by rotating the metasurface relative to the source antenna. The rotation angle was studied with respect to the y-axis, as shown in Figure 2. It can be seen that, if the rotation angle is \(0^{\circ}\) or \(180^{\circ}\), the metasurface antenna gives a right-hand circular polarization (RHCP) wave, whereas, for \(90^{\circ}\) or \(270^{\circ}\), it gives left-hand circular polarization (LHCP). If the rotation angle is \(45^{\circ}\) or \(135^{\circ}\), the antenna is linearly polarized [13]. RHCP without a rotation angle is illustrated as an example in the following analysis for convenience of explanation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Wm** & **w** & **a** & **ls** & **ws** & **lf** & **wf** & **lg** & **wg** & **p** & **q** & **gf** & **h\({}_{1}\)** & **h\({}_{0}\)** \\ \hline
36 & 1 & 3.63 & 24 & 2.2 & 7 & 1.2 & 14 & 6 & 1.1 & 1 & 0.7 & 3 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the proposed circularly polarized metasurface antenna (in mm).  
Figure 1: Geometry of the proposed antenna: (**a**) top view; (**b**) rear view; (**c**) side view.
## 1 Introduction  
Polymer hydrogels are crosslinked materials capable of absorbing and retaining high amounts of water [1, 2]. A biopolymer commonly used to produce hydrogels is sodium alginate (SA) that is composed of different proportions and sequences of D-mannuronic (M) and L-guluronic acid (G) blocks [3, 4]. SA has been authorized by the US Food and Drug Administration for human biomedical applications due to its excellent properties such as biodegradability, renewability, cost-effectiveness, non-toxicity, and biocompatibility [5, 6]. Thus, this polysaccharide biopolymer is used in a broad range of industrial areas such as water treatment, plastic packaging, and many other potential bioengineering fields [7, 8, 9]. This biopolymer can be cross-linked with Ca\({}^{2+}\) cations to form hydrogels [10, 11] with many physical properties that can be enhanced by adding very small amounts of carbon nanofibers (CNFs) [12]. CNFs are interesting carbon nanomaterials (CNMs) because they possess a lower cost than other CNMs and provide high electrical conductivity that can be used to fabricate conductive composites [13; 14]. In addition, CNFs are filamentous 1D nanomaterials with excellent chemical and physical properties such as mechanical and electric performance [15; 16].  
These alginate/CNFs composites have similar biological properties to neat calcium alginate in terms of cell adhesion and non-cytotoxicity [12]. We have recently reported for the first time that calcium alginate/CNFs composites have antibacterial properties that can be exploited to combat multidrug-resistant pathogens such as the life-threatening multidrug-resistant _Staphylococcus epidermidis_[17]. In the field of virology, non-enveloped viruses are more resistant to inactivation than enveloped viruses [18].  
Calcium alginate-based materials have shown antiviral activity against the enveloped double-stranded DNA herpes simplex virus type 1 (HSV-1) [19]. However, calcium alginate's antiviral activity and the effect of incorporating a low amount (0.1% \(w/w\)) of carbon nanofibers have never been studied before using a non-enveloped double-stranded DNA viral model [20]. Based on these previously published antiviral results [19], we hypothesized that calcium alginate could exhibit antiviral activity against this non-enveloped viral model. Since other carbon nanomaterials such as graphene oxide have been shown to be antiviral against DNA and RNA viruses such as the pseudorabies virus and porcine epidemic diarrhea virus [21], we also considered that incorporating this other type of carbon-based nanomaterials would enhance the antiviral action of calcium alginate. As far as we know, the antiviral activity of pure CNFs or added to calcium alginate has never been studied before.
## 5 Sleep Stages Classification Using DL Models
### Sleep Databases  
Eight main sleep databases have been used for automated sleep stage classifications. Five of the databases are free to download from PhysioNet [53], namely the Sleep-EDF [54], the expanded Sleep-EDF [54], the St. Vincent's University Hospital/University College Dublin Sleep Apnea Database (UCD) [53], the Sleep Heart Health Study (SHHS) [55; 56], and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) [57] database. The ISRUC-Sleep datasets [58] can be downloaded from the official websites. Permission is required to obtain the sleep datasets from the Montreal Archive of Sleep Studies (MASS) [59].  
The PSG recordings, in most of the sleep databases, are scored according to R and K rules [51], wherein scoring is done based on wakefulness, NREM sleep and REM sleep. NREM sleep is then subdivided into four stages (S1 to S4). Exceptions are ISRUC and MASS which follow the AASM guideline and partition the recordings into five sleep stages instead of six [21].
## 5 Discussion
### Sediment Source  
In different sedimentary environments, the contents and ratios of major and trace elements in shale vary [20]. Therefore, certain specific content and ratios of major and trace elements can serve as important indicators for identifying sediment sources and discerning the tectonic background of source areas. For instance, the value of n(Al\({}_{2}\)O\({}_{3}\))/n(Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\)) can indicate the tectonic background of sediments, while the value of n(SiO\({}_{2}\))/n(Al\({}_{2}\)O\({}_{3}\)) can indicate the provenance of sediments.  
Previous studies suggest that when the n(SiO\({}_{2}\))/n(Al\({}_{2}\)O\({}_{3}\)) ratio in rock samples approaches 3.6, it indicates a predominantly terrestrial source for the rock sediment [21]. In the study area, the n(SiO\({}_{2}\))/n(Al\({}_{2}\)O\({}_{3}\)) ratio ranges from 3.52 to 6.33, with an average of 4.81, and shows minimal variation longitudinally (Figure 5), indicating a predominantly terrestrial source. The tectonic background of sediment is typically determined using the n(Al\({}_{2}\)O\({}_{3}\))/n(Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\)) ratio. Ratios between 0.1 and 0.4 suggest an oceanic ridge and seamount sedimentary environment, while ratios between 0.6 and 0.9 indicate a continental margin sedimentary environment. Ratios between 0.4 and 0.7 suggest a deep-sea oceanic sedimentary environment [22; 23]. In the study area, the n(Al\({}_{2}\)O\({}_{3}\))/n(Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\)) ratio ranges from 0.31 to 0.74, as shown in the Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\)/(Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\)) ternary diagram (Figure 6), indicating a transitional tectonic environment from continental margin sedimentation to deep-sea oceanic sedimentation. The intersection of the La/Yb and EXRE diagrams (Figure 7) indicates that the sediments primarily originate from deep-sea sediments, granite, and alkaline basalt. Considering the provenance tracing of Qiongzhusi formation sediment and the Paleoproterozoic sedimentary configuration of the ancient continent, it is likely that the Lumian ancient continent provided deep-sea sedimentary mudstone, submarine volcanic basalt, and mafic rocks for the Qiongzhusi formation in the rift zone to the east. Based on geochemical analysis, during the Qiongzhusi period, the Lumian ancient continent served as a source area, with the Qiongzhusi shale exhibiting particles of fine sand and mud grades. This is likely the result of long-distance transport and is closely related to the fine particle size and low quartz content of the source area sediment. The sedimentary materials of the Qiongzhusi formation are primarily derived from sedimentary rocks and granite.  
Figure 5: Variation characteristics of major and trace element indicators in the shale sedimentary environment of the Qiongzhusi formation in the southern Sichuan Basin.  
Figure 6: Al/Al + Fe vs. Fe/Ti binary diagram depicting the sedimentation environment of the Qiongzhusi formation in the southern Sichuan Basin (Based on the modification by [PERSON], 1978).
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 3 The Proposed Scheme
### Initialization Phase  
The initialization phase is executed offline by the gateway. Gateway \(GWN_{k}\) randomly chooses \(S_{K}\) as its master key, \(Gid_{k}\) as its identity, a large prime number \(q\), and a secure one-way hash function \(h(.)\). Meanwhile, the gateway chooses a challenge \(C_{G}\), and computes the corresponding response, \(R_{G}=PUF1(C_{G})\), \(GSK=S_{K}\bigoplus h(R_{G})\), or \(M_{0}=T_{S_{K}}(x)\ mod\ q\). Then, the gateway chooses \(x\in(-\infty,+\infty)\), and publishes the public parameters \(\{q\), \(x\), \(h(.)\}\). In the same way, the gateway chooses \(Sid_{j}\) as the identity of the sensor node \(Sn_{j}\), and computes \(S_{j}=h\big{(}Sid_{j}\parallel Gid_{k}\parallel S_{K}\big{)}\) according to the topological relationship for the sensor node \(Sn_{j}\). The gateway sends \(\{Gid_{k},Sid_{j},\ S_{j}\}\) to \(Sn_{j}\), and stores \(\{M_{0},Gid_{k},\ C_{G},\ Sid_{j},\ PUF1(),\ GSK\}\).  
The sensor node \(Sn_{j}\) chooses a challenge \(C_{S}\), and computes the corresponding response \(R_{S}=PUF2(C_{S})\) and \(SNJ=S_{j}\bigoplus h(R_{S})\), and stores \(\{Gid_{k},Sid_{j},SNJ,C_{S},\ PUF2()\}\).
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 3 Fault Diagnosis Methodology Using the Defect Signature Wavelet Image
### 2-D Data Representation with Defect Signature Wavelet Image Generation
The matrix of wavelet coefficients is established with the wavelet coefficients in a range which is defined as below:  
\[ \begin{gathered}\text{P}_{i,j}=f(\omega)\\ \text{with }\omega\ =0..f_{\text{max}}\text{ and }f_{\text{max}}=\max(FO, FI,SF)\times k+f_{side}\end{gathered}  \tag{5}\]  
where \(k\) is the number of considered harmonics and \(f_{side}\) is the sideband of the highest defect frequency. Moreover, the initial low cutoff frequency is set to zero hertz for the fine resolution analyses in frequency. Because the frequency range is the function of rotation speed, it is robust when the rotating speed changes. Therefore, the DWSI always contains the damage frequency harmonics of bearing faults. Using these settings, the 2-D coordinate matrices are constructed, and then the values of coefficients in the matrix are employed to define the vertex colors by scaling the values in the matrix to the full range of the colormap to convert the representation of a 1-D vibration signal as a 2-D spectrogram image. Then, the 2-D which is similar to the spectrogram image is fed to a DCNN model which is designed and trained for feature learning and classification.
# Output Feedback Tracking Control for Vessel with Collision-Avoidance and Performance Constraints
## 4 Collision-Avoidance Switch Control Strategy
### Simulation Experiment with a Moving Obstacle  
To further appraise the performance of the CASC, a moving obstacle is considered in the simulation experiment. The initial position of the moving obstacle is \([4\) m, \(-1\) m, \(0\) rad\(]^{T}\), and its velocity is \([0.1\) m/s, \(0\) m/s, \(0\) rad/s\(]^{T}\). Figures 11-13 depict the simulation results. Figure 11 shows the tracking results, where the dynamically positioned vessel smoothly avoids obstacles under the CASC. In Figure 11a \(t=0\)-\(10\) s, the dynamically positioned vessel successfully tracks the desired trajectory. The obstacle comes from the port side of the dynamically positioned vessel. In Figure 11b \(t=50\) s, the dynamically positioned vessel is taking action to avoid collision with the moving obstacle. In Figure 11c, \(t=60\) s, the dynamically positioned vessel is overtaking the obstacle from its port side. In Figure 11d, \(t=100\) s, the dynamically positioned vessel is moving away from the moving obstacle. Figure 12 shows the time-varying error \(\sqrt{\eta_{e}^{T}\eta_{e}}\). At \(t=0\)-\(30\) s, the vessel successfully tracks the desired trajectory with a small tracking error. At \(t=30\)-\(70\) s, to avoid obstacles, the tracking error increases gradually and then decreases gradually. At \(t=70\)-\(100\) s, there is no risk of collision, and the tracking error converges to a small compact set. Figure 13 shows that the control inputs are saturated at 0-\(10\) s and 30-\(70\) s. At \(0\)-\(10\) s, due to a large initial tracking error, the control inputs are saturated. At \(t=70\)-\(100\) s, to avoid obstacles, the control inputs suffer from sudden jumps.  
Figure 11: Control results with moving obstacle. (**a**) Tracking result at \(t=10\) s. (**b**) Tracking result at \(t=50\) s. (**c**) Tracking result at \(t=60\) s. (**d**) Tracking result at \(t=100\) s.  
Figure 10: Unknown function and estimation under the ESO.