esa-sceva/satcom-chunk-collection huggingface dataset:
# The Static Frictional Behaviors of Rubber for Pipe-Laying Operation
## 2 Experimental  
### Samples  
The shapes and dimensions of rubber and pipe samples are shown in Figure 2. In order to simulate conditions that rubber blocks are pressed against the pipe wall, the rubber samples are manufactured in a shape of half oblique frustum cone. Two types of rubber samples are selected to test in this research, they behave different in elastic modulus and hardness distinctly due to their different components [23]. Meanwhile, two types of pipe samples are used in tests, where steel pipe is made of X70 steel which is a common metal material for pipeline manufacture. The other pipe sample is made of polymethyl methacrylate (PMMA), which is widely used as transparent substrate in soft contact research to uncover the contact area. It is necessary to note that the PMMA pipe is not used in real pipe-laying operations; the rubber-PMMA tests aim to observe the contact area in situ to identify its evolution with increasing tangential load.  
The surface roughness of the rubber and pipe samples are unified, respectively, so that the effect of roughness to the level of static friction can be neglected between different contact pairs. In addition, the values of roughness in average (Ra) and Root Mean Square (RMS) of rubber and pipe samples are measured by the roughness measuring machine, respectively (model LINKSTM 2025, Harbin Haliang Instrument Technology Co., Ltd., Harbin, China). The testing length is 0.25 mm, and the testing velocity is 0.1 mm/s. Tables 1 and 2 list the mechanical properties of rubber and pipe samples, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Mechanical Properties**} \\ \cline{2-5}
**Rubber Type** & **Elastic Modulus** & **Tensile Strength** & \multicolumn{2}{c}{**Roughness**} \\ \cline{2-5}  & **[MPa]** & **[MPa]** & **Shape Hardness** & **Ra [um]** & **RMS [nm]** \\ \hline A & 2.26 & 13.2 & 37 & 3.1 & 125 \\ B & 3.7 & 12.79 & 51 & 3.2 & 126 \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of rubber A and B; Ra: roughness in average; RMS: Root Mean Square roughness.  
Figure 1: Schematic view of the rubber contact during pipe-laying operation. (**a**) Schematic view of the beginning of the pipe-laying operation; (**b**) Functional mode of the tensioner; (**c**) Schematic view of the rubber- pipe contact system. (\(F_{N}\), the normal load; \(F_{T}\), the tangential load; \(V\), the velocity of the pipeline.)
# Methicillin Resistance of Food-Borne Biofilm-Forming Staphylococcus-Crocei
## 1 Introduction  
Biofilms are complex and highly structured aggregations of sessile bacteria, formed on biotic or abiotic surfaces, embedded in a self-produced extracellular matrix of exophysics-charides, proteins, and DNA [1, 2, 3]. The quorum sensing of microorganisms in the biofilm is oriented to mobilize resources for the survival of microorganisms, such as reducing their metabolism, activating protectors against the host's immune defenses, in addition to protecting against antimicrobial agents, often resulting in persistent and difficult-to-treat infections [1, 2]. Biofilm formation involves several steps such as adhesion, accumulation,maturation, and biofilm dispersion, determined by several genes, mainly from the _ica_ABCD operon encoding a Polysaccharide Intercellular Adhesion (PIA), the main component of biofilms [4; 5]. The ability of the bacterial strain to form a biofilm can be assessed by indirect methods, for instance based on the application of crystal violet, and by direct methods based on the viable cell count (CFUs) [6]. The crystal violet method involves growing biofilms in microtiter plates, staining them with crystal violet, and then solubilizing the bound dye to measure its absorbance [5; 6]. This method has been widely used for simple high-throughput screening of cultures [7]. However, it is based on the crystal violet binding to viable and non-viable cells and extracellular polymers of the biofilm matrix, not allowing the exact quantification of viable cells in the biofilm [7]. On the other hand, direct methods for measuring biofilm formation rely on removing cells from the surface and plating the resulting cell suspension to determine the number of CFUs [8]. Stainless steel is often used as a surface for the growth of biofilms but, in general, bacteria can adhere and form biofilms on different types of materials, such as polystyrene, Teflon, marble, granite, glass, and polypropylene [6; 8; 9]. _Staphylococcus_ spp. include important human and animal biofilm-forming pathogens that can cause severe infections [1; 10]. These bacteria can adhere to implanted medical devices or damaged tissues, causing persistent infections through the formation of biofilms [2; 5]. Multidrug-resistant strains such as methicillin-resistant _S. aureus_ (MRSA), which are isolated from various environments, are highly prone to forming biofilms and causing clinically complicated infections, increasing the likelihood of developing persistent, chronic, and recurrent infections [10].  
The aim of this study was to evaluate the biofilm-forming ability of _Staphylococcus_ spp. recovered from ready-to-eat (RTE) street food. Isolates included coagulase-positive _Staphylococcus_ and coagulase-negative _Staphylococcus_. Three different surfaces were used for biofilm formation (glass, stainless steel, and polystyrene). Since in _Staphylococcus_ methicillin resistance is closely associated with multidrug resistance, methicillin resistance of planktonic cells and biofilms were also investigated.
## 3 Theoretical Analysis of Ore Pass Wall Damage
### Theoretical Analysis of the Normal Impact
#### 3.2.1 Elastic Compression Stage
The velocity of the ore block before colliding with the shaft wall is:  
\[v_{1}=\sqrt{{v_{\rm{x}}}^{2}+{v_{\rm{y}}}^{2}+{v_{\rm{z}}}^{2}} \tag{13}\]  
By combining Equations (2) and (4), the following can be obtained:  
\[v_{1}=\sqrt{{v_{\rm{0}}}^{2}+\frac{{{\rm{g}}^{2}D^{2}\cos^{2}\beta}}{{{v_{\rm{0 }}}^{2}\cos^{2}\alpha}}}+2\cdot{\rm{g}}\cdot D\cdot\cos\beta\cdot\tan\alpha \tag{14}\]  
Since the angle between the normal velocity \(v_{\rm{n}}\) and \(v_{1}\) of the ore-rock block moving is \(\alpha_{1}\), then:  
\[v_{\rm{n}}=v_{1}\cdot\cos\alpha_{1} \tag{15}\]  
Assuming that \(v_{\rm{c}}\) is the velocity at which the ore-rock block reaches the yield limit when it collides with the shaft wall, then, according to the functional relationship:  
\[\frac{1}{2}{m{v_{\rm{n}}}^{2}}-\frac{1}{2}{m{v_{\rm{c}}}^{2}}=\frac{4}{3}{E^{ *}R}\frac{1}{2}\cdot\int_{0}^{\delta_{\rm{y}}}\frac{3}{2}\cdot d\delta \tag{16}\]  
By integrating Equation (16), we can obtain:  
\[\frac{1}{2}{m{v_{\rm{n}}}^{2}}-\frac{1}{2}{m{v_{\rm{c}}}^{2}}=\frac{8}{15}{E^ {*}R}^{\frac{1}{2}}\cdot\delta_{\rm{y}}\frac{5}{2} \tag{17}\]  
It can be concluded that, at the end of the elastic compression stage, the velocity of the ore-rock block \(v_{\rm{c}}\) is:  
\[v_{\rm{c}}=\left({v_{\rm{n}}}^{2}-\frac{16{E^{*}R}^{\frac{1}{2}}\cdot\delta_{ \rm{y}}\frac{5}{2}}{15m}\right)^{\frac{1}{2}} \tag{18}\]  
where \(m\) is the quality of the ore-rock block, kg, \(m\) equals \(\frac{4}{3}\pi\rho R^{3}\), and \(\rho\) is the density of the ore-rock block, kg\(\cdot\)m\({}^{-3}\). When the average velocity of the ore-rock block colliding with the shaft wall is larger than \(v_{\rm{c}}\), plastic deformation occurs in the shaft wall material, resulting in deformation damage. Otherwise, only elastic deformation occurs, which has little influence on the ore pass wall. Therefore, it is imperative to calculate the critical velocity during the compression phase. [PERSON] [36] deduced the maximum strain energy accumulated by the collider before the plastic deformation of the sphere:  
\[U_{\rm{c}}=\frac{\left(\pi\cdot{\rm{C}}\cdot{\sigma_{\rm{y}}}\right)^{5}R^{3 }}{60{{\left({E^{*}}\right)}^{4}}} \tag{19}\]  
Suppose \(v_{\rm{c}^{\prime}}\) is the critical velocity between the elastic deformation and the plastic deformation of the wall which can be obtained from the perspective of the energy conversion:  
\[\frac{1}{2}m{{\left({v_{\rm{c  
Specifically, \(v_{c^{\prime}}\) is the initial velocity that causes the wellbore to yield. When \(v_{c^{\prime}}\leq v_{\rm n}\), only elastic deformation occurs in the wellbore which will not cause damage to the wellbore. When \(v_{c^{\prime}}>v_{\rm n}\), plastic deformation occurs on the ore pass wall, resulting in impact damage.
## 2 System Model and Problem Formulation  
In V2X communication, resource allocation and power control are crucial. As we study our system model, we focus on the dynamic interaction between vehicles and infrastructure. We aim to understand V2X resource allocation and power control optimization through a detailed problem formulation.
## 2 Materials and Methods
### Simulation Procedure
#### 2.2.2 Calibration and Contrasting  
Reducing the gap between simulated and real behavior of the surgical suite is the main goal to obtain accurate results. In this respect, real data from the first set of experimental tests were used to calibrate the model (see Section 2.1.2). The calibration method was executed using the software GenOpt 3.0.1 [49]. The GPS Hooke-Jeeves algorithm \"Hybrid Generalized Pattern Search Algorithm with Particle Swarm Optimization Algorithm\" was applied. The technique consists of performing successive simulations while varying parameters within predetermined limits and looking for the minimization of a function, in this case the coefficient of variation of the root mean square error (CV(RMSE)), defined in Equation (1). The CV(RMSE) measures the bias between measured and simulated values [50].  
\[CV(RMSE)=\frac{\sqrt{\sum_{i=1}^{n}\left(X_{sim}-X_{real}\right)^{2}/n}}{ \overline{X}_{real}} \tag{1}\]  
where:  
\(\overline{X_{real}}\) mean of measured values.  
\(n\) number of measured data points.  
\(X_{real}\) measured value.  
\(X_{sim}\) simulated value.
## 2 Emerging Applications of 6G
### Ar/vr/xr  
Augmented reality (AR), virtual reality (VR), and extended reality (XR) have become prominent technologies, specifically in areas such as education and gaming [13]. These technologies are capable of recreating virtual experiences of real-world scenarios, thereby delivering a fully immersive experience to users. AR and VR technologies can be very useful in education, as they have the capability to provide an interactive learning environment to students, which will enhance their learning outcomes [14]. This technology can be convenient in instances such as medical documents, where two-dimensional explanation is not enough; children's books, where more interaction and entertainment can be provided; and research articles and proceedings where concepts can be further illustrated. However, applications of this nature demand resources such as high bandwidth and stringent latency. The use of VR/AR/XR technologies, audio/video recordings, and holographic images are currently facing challenges, as existing wireless networks struggle to satisfy their requirements. As such, further investigations into novel technologies are required to support these applications.
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 5 Machine Learning Analysis for Construction Cost Prediction with Properties
### Configurations of Test Cases  
The prediction performance of the construction cost may vary depending on the activation function, the depth of the model, and the configuration of the node. In addition, the composition of the learning model may vary depending on the type of data, the amount of data, and the type of results to be predicted, so it is necessary to define and test various cases to find the optimal construction cost prediction model.  
This study attempted to derive an optimal learning model by defining the six types of 'Case A', 'Case B', 'Case C', 'Case D', 'Case E', and 'Case F', which consist of various nodes, learning rates, and epochs. All cases (Case A-Case F) used 'Adam' as a loss function, and the ratio of test data to training data was fixed at 50:50. Details of each case are as shown in Table 5. Configurations of test cases for machine learning.  
To ensure reliable results, the k-fold technique was employed in this study, given the limited number of samples available for artificial intelligence learning, which was only 78. Additionally, the 'DNN' algorithm and the 'fully connected layer' were utilized. To assess potential issues of underfitting or overfitting, the difference between the error rate of the test data and that of the learning data was quantified as the'relative error between error rates'.
## 6 Conclusions  
In contrast to the conventional H-bridge inverter, the differential boost inverter is a flexible DC-AC inverter topology providing voltage step-up capability and could be a potential candidate for many DC-AC electrical energy conversion applications. In this work, the modeling procedures and the multiple control design of a single-stage grid-connected DC-AC differential boost inverter has been addressed. By applying the same reasoning and procedures used for DC-DC converters and based on the frequency response of the inverter in the quasi-steady sense, average models of the inverter under a differential current mode control has been developed. This large-signal model has been used to derive the small-signal model in the frequency domain and based on the obtained frequency response, the controller design has been addressed. Steady-state analysis including the derivation of the AC ripple PV voltage has been performed and a design procedure of the system power stage parameters and controllers has been presented. It has been shown that the differential boost inverter can exhibit interesting features when used in single stage PV systems with the conventional hill climbing MPPT scheme based on P&O algorithm by which the operating voltage of the PV generator can be controlled by perturbing its desired reference and observing the resulting effect on the extracted power. This includes a reduced number of components, low THD and optimal power tracking with standard design procedures for the multiple loop controller. These features have been verified using computer simulations performed on the switched model of the converter under several weather conditions. The entire simulation was performed in the PSIM\({}^{\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\copyright\]  
Conceptualization, A.E.A.; Methodology, A.E.A. Software, A.E.A.; Validation, M.A.-N.; Formal Analysis, A.E.A. and M.A.-N.; Investigation, A.E.A.; Resources, M.A.-N.; Data Curation, R.H.; Writing--Original Draft Preparation, A.E.A.; Writing--Review & Editing, M.A.-N. and M.H.; Visualization, M.A.-N., R.H. and M.H.; Supervision, M.A.-N.; Funding Acquisition, M.A.-N. All authors have read and agreed to the published version of the manuscript.  
This work has been sponsored by the Spanish Agencia Estatal de Investigacion (AEI) and the Fondo Europe de Desarrollo Regional (FEDER) under grants DPI2017-84572-C2-1-R. [PERSON] and [PERSON] acknowledge financial support from the Researchers Supporting Project number (RSP-2020/150), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
Figure 12: Steady-state waveforms of the grid voltage and the grid current delivered by the differential boost inverter for \(S=500\) W/m\({}^{2}\) (\(P_{\text{max}}\approx 670\) W) and \(S=\)1000 W/m\({}^{2}\) (\(P_{\text{max}}\approx 1.4\) kW). \(\Theta=25\)\({}^{\circ}\)C.
## 4 Experiment
### Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Split** & **Method** & **F1-Score** & **Precision (P)** & **Recall (R)** & **Recall (MIOU)** \\ \hline \multirow{4}{*}{1/4} & UNet & 0.5430 & 0.5893 & 0.5120 & 0.4175 \\  & FixMatch & 0.5377 & 0.6714 & 0.4820 & 0.4150 \\  & UniMatch & 0.5553 & 0.6298 & 0.5128 & 0.4275 \\  & CSRA-Net & 0.5712 & 0.6445 & 0.5287 & 0.4392 \\ \hline \multirow{4}{*}{1/8} & UNet & 0.4563 & 0.5827 & 0.4317 & 0.3584 \\  & FixMatch & 0.4982 & 0.6541 & 0.4543 & 0.3895 \\  & UniMatch & 0.5207 & 0.6254 & 0.4799 & 0.4039 \\  & CSRA-Net & 0.5389 & 0.6397 & 0.4978 & 0.4166 \\ \hline \multirow{4}{*}{1/16} & UNet & 0.4377 & 0.5776 & 0.4220 & 0.3434 \\  & FixMatch & 0.5091 & 0.6646 & 0.4689 & 0.3966 \\ \cline{1-1}  & UniMatch & 0.5110 & 0.6578 & 0.4676 & 0.3972 \\ \cline{1-1}  & CSRA-Net & 0.5298 & 0.6495 & 0.4859 & 0.4039 \\ \hline \multirow{4}{*}{1/32} & UNet & 0.3731 & 0.4404 & 0.3634 & 0.2953 \\ \cline{1-1}  & FixMatch & 0.4210 & 0.6812 & 0.3877 & 0.3298 \\ \cline{1-1}  & UniMatch & 0.4400 & 0.6060 & 0.4036 & 0.3410 \\ \cline{1-1}  & CSRA-Net & 0.4562 & 0.6204 & 0.4195 & 0.3544 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison experiment results on the Conic dataset.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 7 Simulation and Protocol Performance Analysis
### Communication Overhead  
After the analysis of the ML-AKA protocol total communication overhead was calculated and compared with other existing protocols. We initially fixed the size of each security system variable to calculate the communication overhead. Table 6 listed all the security parameters used to calculate the communication overhead. The communication overhead of all the entities (Devices, APs, and eNodeBs) was calculated during the device key agreement and session establishment phase. In [4], the EPS-AKA communication overhead was calculated based on the security key length, which was \(708+608*a\) bits, where \(a\) is the authentication vector count. In the session key generation phase, the communication overhead of the device-to-device session request from devices (\(D_{i}\)) to access points (\(AP_{i}\)) is \((2*128+64=320)\) bits; D1 and D2 represent the first part of the data overhead and APID represents the second part of the data overhead.  
Figure 11, shows that the maximum communication overhead is an exponential operation of key \(H_{i}\) and session key generation. Figure 12 compares the communication overhead for the key agreement phase for the EPS-AKA, UAKA-D2D and ML-AKA protocols. The outcome shows that the communication overhead is constant for EPS-AKS for all input bit lengths, whereas for UAKA-D2D and ML-AKA protocols, it increases linearly with reference to the increase in the bits length of each key. The outcome also shows that the ML-AKA is 5-10% and 20-50% more efficient than UAKA-D2D and EPS-AKA, respectively, under the same simulation conditions and different bit lengths. Similarly, Figure 13, compares the communication overhead required for a different protocol for the session establishment step. The figure illustrates a steady-state relationship between communication overhead and security parameter bit length for EPS-AKA, whereas for UAKA-D2D and the proposed ML-AKA, the communication overhead increases with the increase in bit length.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{3}{c}{**Length of the Security Parameters (Bits)**} \\ \hline
**Parameters** & **Size (Bits)** \\ \hline DID & 128 \\ eNodeB/APID & 64 \\ SID & 64 \\ hint & \(len_{p}\) \\ \(r_{i}\), \(R_{k}\) & \(len_{p}\) \\ \(MAC_{i}\) & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Length of the security parameters (bits).  
Figure 10: Time cost of Phase IV operations.
# Maintaining the Sense of Agency in Semi-Autonomous Robot Conferencing
## 5 Experiment
### Conditions  
To confirm the three hypotheses, we set the following three conditions.  
**Teleoperated (tele) condition:** The subject controlled the robot by using our interface. The robot reproduced the movement directed by the vision line of the subject at all times, so it did not move autonomously. This condition is hereafter referred to as the \"tele\" condition.  
**Autonomous (auto) condition:** This condition also used the same interface as the tele condition, but the subject did not control the robot. The robot autonomously paid attention to the speaker. The rules for the autonomous behavior of the robot are defined as follows.  
**Rule 1:**: When the subject begins to answer the question, the robot looks at the face of the questioner.
**Rule 2:**: When the questioner or the answerer begins to speak, the robot looks at their face.
**Rule 3:**: When the answerer provides a backchannel response to the voice of the subject, the robot looks at the answerer's face for 1.0 s.
**Rule 4:**: When the questioner provides a backchannel response to the voice of the answerer, the robot looks at the questioner's face for 1.0 s.  
It goes without saying that looking at the face of someone when they begin to speak is normal social behavior. In addition to Rule 1, we considered that taking a glance at a person who provided a backchannel response is also reflective of social behavior. After Rules 3 and 4, the robot turned its attention to the face of the questioner or answerer, based on Rules 1 and 2, respectively. The recognition of the speaker can be automated by a microphone array, but the experimenter (who is different from the questioner or the answerer) executed these rules to prevent erroneous recognition.  
In order to guide the subject's gaze toward the eye-gaze position of the robot, the green circle on the interface moved according to the autonomous vision line movement of the robot. This condition is hereafter referred to as the \"auto\" condition.  
**Semi-autonomous (semi) condition:** Not only did the robot move according to the teleoperation of the subject, but also autonomously moved based on Rules 1-4. As in the auto condition, the green circle on the interface moved to synchronize with the autonomous vision line movement of the robot in order to guide the subject's gaze. After the autonomous behavior was performed, the teleoperation was immediately enabled. In this condition, the duration of all of the autonomous movement was 1.5 s (the time it takes to complete the robot eye-movement (0.5 s) + its gaze time (1.0 s)), based on Rules 1-4. This condition is hereafter referred to as the \"semi\" condition.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model
### Data Preprocessing
#### 2.2.2 The Pure Randomness Stationarity Test  
Stationary time series are not necessarily predictable, and pure random stationary time series are unpredictable. Therefore, it is also necessary to detect the pure randomness of the TEC differential sequence. In this paper, the LB (Ljung-Box) method is used to test the pure randomness of time series. The LB method is used to test whether a series of observations in a given time period is purely random. LB test results show that TEC data after first-order difference processing is not purely random data and can be predicted.  
Figure 2: TEC data of eight regions selected in this paper.