esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 2 Analytical Model
### Mathematical Model  
#### 2.2.1 Reynolds Equation without Slip Flow Effects  
In order to simplify the description of the pressure solution model within the microscale flow field of the compliant end face gas film seal, this paper makes the following assumptions:  
1. The fluid inside the gas film conforms to the isothermal ideal gas model;
2. The flow state is laminar;
3. The pressure of the gas film does not change in the direction of the gas film thickness;
4. The surface of the sealing vice is smooth, and the dynamic ring has a rigid end face;
5. Neglect the influence of volume force and inertia force;
6. There is no misalignment in the assembly.  
The resulting dimensionless Reynolds equation, which does not account for boundary velocity slip effects, is obtained as:  
\[\frac{1}{R^{2}}\frac{\partial}{\partial\theta}\bigg{(}PH^{3}\frac{\partial P }{\partial\theta}\bigg{)}+\frac{1}{R}\frac{\partial}{\partial R}\bigg{(}RPH^{ 3}\frac{\partial P}{\partial R}\bigg{)}=\Lambda\frac{\partial}{\partial\theta }(PH) \tag{1}\]  
The dimensionless parameter is defined as:  
\[R=\frac{r}{r_{\rm o}}\quad\ P=\frac{p}{p_{\rm o}}\quad\ H=\frac{h}{h_{0}} \quad\ \Lambda=\frac{6\mu\omega r_{\rm o}^{2}}{p_{\rm o}h_{0}^{2}}\]  
In the above formula, \(R\) is the dimensionless radius value; \(r\) is the radius position of any point, mm; \(r_{\rm o}\) is the outer radius of the sealing ring, mm; \(P\) is the dimensionless gas film pressure; \(p\) is the gas film pressure of any position, Pa; \(p_{\rm o}\) is the outlet pressure, Pa; \(H\) is the dimensionless gas film thickness; \(h\) is the thickness of the gas film of any point position, mm; \(h_{0}\) is the minimum gas film thickness, mm; It is the thickness of the gas film when no deformation of the end face occurs, which is determined by controlling the distance between the sealing rings mainly through assembly; \(\theta\) is the expansion angle; \(\Lambda\) is the compressibility coefficient of the gas medium; \(\mu\) is the medium gas viscosity, Pa\(\cdot\)s; \(\omega\) is the rotational speed, \(r\cdot\min^{-1}\).
## 2 Materials and Methods
### Field Coral Fate-Tracking and Sampling  
To assess the efficacy of the models to predict colony bleaching susceptibility in situ, tagged and genotyped _O. faveolata_ colonies located at two inshore (Cheeca Rocks [n = 10 tagged & genotyped colonies] and The Rocks [n = 6]) and two offshore (Little Conch [n = 10] and Crocker Reef [n = 5-6]) sites were photographed alongside a Coral Reef Watch (CRW) reference card [14] and sampled on July 10 th (pre-bleaching), August 21 st (peak bleaching period), October 9 th (post-bleaching recovery period), and December 4 th of 2019. Note that biopsies from Crocker Reef represent true \"test\" samples for the CHD-lab-CHD-field models since no data from corals of this site were incorporated in initial model-building. When image quality was poor and the CRW color score data could not be interpreted with confidence, dinoflagellate endosymbiont densities were instead estimated from either host/symbiont DNA (qPCR-derived [15]) or protein ratios. Images of all sampled colonies at each of the four sampling times have been archived at NOAA's National Centers for Environmental Information (accession: 0243645). Colonies were sampled with a hammer and chisel (~50 mg biopsies, or 10-20 polyps) and immersed in a liquid nitrogen dry shipper (~150 \({}^{\circ}\)C) prior to transport to the lab. Proteins were extracted from a subset of 120 of the 128 biopsies, and a subset of 36 samples from July, August, and December was analyzed using iTRAQ/nano-LC/MS (both described below).
## 5 Half-Duplex  
In this part, we study the energy efficiency maximization time allocation scheme and power allocation scheme of the half-duplex relay system. The process is divided into three-timeslots. In the first time slot, \(\alpha T\), the source node transmits energy to the relay node. In the second time slot, \(\frac{(1-\alpha)T}{2}\), the source node transmits the information signal to the relay node. In the third time slot, the relay node uses the energy received in the first time slot to decode the signal received in the second time slot and transmit it to the destination node. In the system, the source node, the relay node, and the target node are each equipped with an antenna.  
In the first time slot, the energy signal received by the relay node can be expressed as  
\[y_{r1}=h_{1}\sqrt{P_{s}}x_{1}+n_{r}. \tag{46}\]  
Therefore, the energy that the relay node obtains from the source node can be expressed as  
\[E=\eta\alpha TP_{s}|h_{1}|^{2}, \tag{47}\]where \(\eta(0<\eta<1)\) is the energy conversion efficiency.  
Therefore, the transmission power of the relay node can be expressed as  
\[P_{r}=\frac{\eta\alpha TP_{s}|h_{1}|^{2}}{\frac{(1-\alpha)T}{2}}= \frac{2\eta\alpha P_{s}|h_{1}|^{2}}{(1-\alpha)}. \tag{48}\]  
In the second time slot, the signal received by the relay node is represented by  
\[y_{\tau 2}=h_{1}\sqrt{P_{s}}x_{2}+n_{r}. \tag{49}\]  
The relay node uses a decoding and forwarding protocol (DF). In the third time slot, the signal received by the destination node can be represented as  
\[y_{d}=gx_{r}(n)+n_{d}, \tag{50}\]  
where \(x_{r}(n)=\sqrt{\frac{P_{s}}{P_{s}}}x_{2}(n-\tau)\).  
The system's signal to interference and noise ratio can be expressed as  
\[\text{SINR}=min\Bigg{\{}\frac{|h_{1}|^{2}P_{s}}{N_{0}},\frac{2\eta P _{s}|h_{1}|^{2}|g|^{2}\alpha}{N_{0}(1-\alpha)}\Bigg{\}}. \tag{51}\]  
The objective function is to maximize the system energy efficiency, which is expressed as  
\[max\xi=\frac{(1-\alpha)log_{2}\Bigg{(}1+min\Bigg{\{}\frac{|h_{1}|^{2}P_{s}}{N _{0}},\frac{2\eta P_{s}|h_{1}|^{2}|g|^{2}\alpha}{N_{0}(1-\alpha)}\Bigg{\}} \Bigg{)}}{2(\beta P_{s}+P_{c})}. \tag{52}\]
# Removal of Crotamiton from Reverse Osmosis Concentrate by a TiO\({}_{2}\)/Zeolite Composite Sheet
## 3 Results and Discussion
### Cyclic Use of the HSZ-385/P25 Composite Sheet  
To effectively apply the HSZ-385/P25 composite sheet to remove croatmiton from RO concentrate in practical treatment processes, it is essential that the composite sheet can remove pollutants even after several cycles of reuse. The efficiency of removing croatmiton from RO concentrate achieved by the HSZ-385/P25 composite sheet after 24 hr ultraviolet irradiation in three circles of reuse were all over 95% (Figure 10). The croatmiton amount in the composite sheet after three cycles of reuse was 0.40 mg, which was ca. 27% of the total amount of three cycles of treated croatmiton (\(C_{0}\) = 10 mg/L, \(V\) = 50 mL), demonstrating continuous croatmiton photocatalytic degradation. The TOC removal efficiency slightly decreased with an increase in the cycles of reuse. It can be concluded that the HSZ-385/P25 composite sheet is feasible for the cyclic removal of croatmiton in RO concentrate.  
Some other composite materials used as adsorbents and photocatalysts for treating organic pollutants are summarized in Table 2. No previous publications applied composite materials for the treatment of pollutants in RO concentrate. A few cycles of pollutant removal were carried out in studies using TiO\({}_{2}\)-coconut shell powder composite [47], polyacrylic acid-grafted-carboxylic graphene/titanium nanotube composite [48], multi-walled carbon nanotubes/Fe\({}_{3}\)O\({}_{4}\) composites [49], multi-walled carbon nanotube/TiO\({}_{2}\) composites [50] and nitrogen-doped-TiO\({}_{2}\)/activated carbon composite [51] similar to that in this study. The stability of the photocatalyst and reusability of these materials were confirmed, thus making them promising cost-effective water purification materials.  
Except for reusability, some of the composites were designed to promote degradation capability through improving visible light utilization, photon yield, and so on. The presence of MoS\({}_{2}\) in the TiO\({}_{2}\)-MoS\({}_{2}\)-reduced graphene oxide composite worked as a co-catalyst to reduce electron-hole pairs, and improved the photocatalytic performance of TiO\({}_{2}\) for BPA removal [52]. The nitrogen-doped-TiO\({}_{2}\)/activated carbon composite was synthesized to regenerate spent powdered activated carbon using solar photocatalysis for cost-effective application in wastewater treatment [51]. The graphene/TiO\({}_{2}\)/ZSM-5 composite material showed higher stability, stronger absorption of visible light, and lower band gap value [53].  
Figure 10: The removal efficiency of croatmiton and TOC in RO concentrate using the HSZ-385/P25 composite sheet after cyclic use. Results are means \(\pm\) standard deviations (\(n\) = 2).
## 2 Materials and Methods
### Methods
#### 2.2.1 The Proposed Calibration Method for OHS
* The atmosphere correction is then conducted to obtain the retrieved surface reflectance, which is compared to the ground measured reflectance. The closer they are, the more accurate the comprehensive gains are. Figure 7: Laboratory test results regarding the relationship between the measured counts of OHS and integration stages. (**a**) The change in the measured counts of OHS when the integration stage varied; (**b**) the ratio between the measured counts at different integration stages (B3).
# Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic
## 4 Formalizing Cryptographic Protocol
### Analysis of a Specific Cryptographic Protocol  
In an insecure network, [PERSON] has a secret message \(m\) and wants to send it to [PERSON]. They only have their own key. We call [PERSON] as Sender (\(S\)) and [PERSON] as Receiver (\(R\)). The Sender has her own key \(k_{s}\). The Receiver has his own key \(k_{r}\). They only can decrypt data that is encrypted by himself. This protocol is as below.  
1. \(S\to R:m_{s}\)
2. \(R\to S:m_{sr}\)
3. \(S\to R:m_{r}\)  
At the first step, Sender encrypts \(m\) with \(k_{s}\) and gets \(\{m\}_{k_{s}}\). For simplicity, we write \(\{m\}_{k_{s}}\) as \(m_{s}\). Then, the Sender sends \(m_{s}\) to Receiver. After it is recieved, the Receiver encrypts \(m_{s}\) with \(k_{r}\) and gets \(m_{sr}\). \(m_{sr}\) is sent back to Sender. Sender decrypts \(m_{sr}\) and gets \(m_{r}\). At the last step, Sender sends \(m_{r}\) to Receiver. Receiver can decrypt \(m_{r}\) and gets \(m\). Finally, \(m\) is transmitted from \(S\) to \(R\). We apply the encryption that is commutative: \(m_{sr}=m_{rs}\). Here, the key can be a symmetric key or asymmetric key. We formalize this protocol. In this protocol, \(M=\{m,m_{s},m_{sr},m_{r}\}\), \(Ag=\{S,R,T\}\), where, \(S\) is the Sender, \(R\) is the Receiver, \(T\) is an intruder. At first, we give the initial model figure and then formalize the execution of the protocol according to the actions of the protocol itself.  
In the protocol, we have \(M=\{m,m_{s},m_{sr},m_{r}\}\). However, in the initial state, only \(S\) has \(m\), others do not have \(m\). We give a figure to display which agent has secret message \(m\). According to \(I_{w,S},I_{w,R},I_{w,T}\), we draw the picture. Each world \(w\) is expressed by the agent name that has \(m\). The initial model is shown in Figure 1.  
Figure 1: Initial model of the protocol.  
In Figure 1, the solid lines present the worlds at both ends are distinguishable for \(S\), and the dotted lines are for \(R\). The dashed lines are for \(T\). Accordingly, the dotted lines and the dashed lines are indistinguishably a relation for \(S\). The solid lines and the dashed lines are indistinguishably a relation for \(R\). The solid lines and the dashed lines are indistinguishably a relation for \(R\). The solid lines and the dotted lines are indistinguishability relation for \(T\).  
So, we have \(\mathfrak{M},S\models has_{S}m\land\
eg has_{R}m\land\
eg has_{T}m\) in the initial state.  
Table 2 describes actions that are stipulated in the protocol. Now, we add actions from intruder(T) in Table 3. We assume the intruder is a weak intruder. He can not do anything but eavesdropping. He only can get all messages from In-buffer and forward it to Out-buffer. He can not construct any other message because he does not have any key.
# Adaptive Emergency Call Service for Disaster Management
## 3 System Model  
An overview of the system model considered to design and implement an emergency call protocol named 5G-SOS is presented in this section. In this work, it is assumed that all UEs \(\in M\) are 4G/5G emergency service enabled. In such a network area, each UE can behave both as an emergency call initiator and as a relay. Since a disaster scenario is evaluated, it is assumed that the UEs are stationary and their location is fixed at the initial period just after the disaster.  
Furthermore, two modes of emergency call transmission, D2D and classical, are utilized to forward emergency calls to the network infrastructure, as illustrated in Figure 3. In this example, three UEs (orange, blue, and red) are in an emergency and are out of coverage due to the failure of the covering gNB. The orange UE sends a broadcast D2D message that is only received by one UE and then relayed in the classic mode to the operational gNB at the top of the Figure. The emergency call of the blue UE is sent in D2D mode and is received by three UEs. The two relay devices in black transfer the emergency call to both working stations, while the red UE ignores the blue UE's emergency call because its RSSI is worse than the blue UE. The red UE diffuses its emergency call to the blue UE, which relays it to all neighboring UEs until the emergency call reaches the working station at the bottom of the figure. The transmission redundancy allows improving the reliability of the protocol. However, the number of copies of the same message has to be controlled to prevent radio network saturation.
## 3 Results and Analysis
### Simulation Experiment and Result Analysis
#### 3.3.4 Analysis of the Convergence of the Algorithm  
To observe the convergence of the improved BAS algorithm at high dimensions, the convergence curve of functions \(f_{1}\sim f_{11}\) when D = 200 is developed, as shown in Figure 6a-k. It can be seen from the figure that the convergence curves of the three algorithms are basically coincident in the early stage. Since the AFS algorithm is used for optimization at this stage, the degree of convergence is basically the same. In the later stage, except for functions \(f_{10}\) and \(f_{11}\), the convergence curve of the AFS-MMSBAS algorithm drops faster than that of the AFS-DMBAS and AFS algorithms. This shows that the improved BAS algorithm has better convergence speed than the MDBAS algorithm. In addition, the convergence curve of the AFS-MMSBAS algorithm is lower than those of the other algorithms, which also shows that the optimization ability of the algorithm is better than that of the AFS-MDBAS and AFS algorithms.
# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
## 4 Cooperative Sensing with Counting Rule  
The concept of cooperative spectrum sensing is to utilize multiple SVUs at different locations and fuse their independent sensing messages into one unified decision about the existence of the PU. In this section, we ponder this approach based on hard fusion, and also look at the impact of the reporting channel fading to the global detection performance.
## 3 Results and Discussion
### Morphology and XRD Analysis of P3 HB/C20 and P3 HB/C116 Nanocomposites  
The morphology and distribution of nanoclays within the P3 HB matrix were analyzed using both TEM (Figure 8a) and SEM (Figure 8b). TEM images of ultrathin sections at various magnifications revealed a consistent and effective bulk dispersion of nanoclays in all samples. Representative nanocomposites containing 5 wt% of both Cloisite C20 and Cloisite C116 demonstrated the presence of well-dispersed, sheet-like nanostructures aligned with the melt flow direction. This alignment indicated the partial orientation of the exfoliated clay particles during extrusion.  
Interestingly, the exfoliation and dispersion of both organically modified (C20) and unmodified (C116) clays were observed, with C116 exhibiting similar alignment despite the absence of surface modification. This suggested that the extrusion process effectively promotes the exfoliation and orientation of nanoclays within the bulk of the polymer. SEM imaging (Figure 8b) further corroborated these findings, showing a uniform distribution of exfoliated nanoclays across the matrix in both nanocomposite types.  
Additionally, observations from thin slices taken after the initial extrusion indicated a similar clay morphology and distribution, although with a lower degree of alignment compared to the USM-processed samples. These results suggested that while nanoclay exfoliation predominantly occurs during the extrusion process, the subsequent USM processing effectively preserves the dispersed and partially oriented structures.  
X-ray diffraction analyses of P3 HB nanocomposites processed through initial compounding, conventional microinjection, and ultrasonic micromolding confirmed the retention of the crystalline \(\alpha\)-form of P3 HB across all samples. No additional diffraction peaks indicative of clay stacking were observed for either P3 HB/C20 or P3 HB/C116 nanocomposites. Peaks typically associated with stacked clay structures, such as \(d\) = 3.3 nm (\(q\) = 1.9 nm\({}^{-1}\)) for C116 and \(d\) = 1.3 nm (\(q\) = 4.8 nm\({}^{-1}\)) for C20, were absent in all difraction profiles. Additionally, no evidence of intercalated structures, which would produce reflections at higher spacings, was detected (Figure S2).  
The absence of these peaks supports the predominance of exfoliated nanocomposite structures in both P3 HB/C20 and P3 HB/C116. These exfoliated configurations were initially formed during the compounding process and preserved through subsequent USM and conventional microinjection processing. This conclusion aligned with observations from TEM analysis, which also demonstrated well-dispersed and exfoliated nanocalays in the polymer matrix.
## 4 Experiment Results and Discussion
### Testbed  
The three-degree-of-freedom testbed of the NQTAUV is illustrated in Figure 4, and it is similar to the one in Reference [9]. This testbed was designed to verify the performance of the presented controller. The body of the vehicle was mounted on a ball joint, which allowed the vehicle \(\pm 30^{\circ}\) of roll and pitch, and free rotation of yaw. A desktop PC was used as the host computer, sending mode-switch commands and receiving data to and from the NQTAUV using serial communication. The controller law was implemented on a Nucleo STM32 F401 RE board with 512 KB memory and 84 MHz CPU frequency. The control frequency was 100 Hz. In each control cycle, the control board sends data to the host, including the attitude, control torque, and disturbance observer output \(\hat{d}\). An attitude sensor module reads three-axis-accelerometer, gyroscope, and magnetometer data from an MPU-9250 motion-tracking device, and then runs a Kalman filter algorithm to obtain the fusion attitude. The thruster is a brushless DC motor with a mounted propeller, and provides a maximum thrust of 15 N. The thruster curve was obtained by experiment. The thruster was mounted on the servo, which rotated at maximum of 6.9 rad/s.  
The mechanical parameters of the NQTAUV are listed in Table 1.  
The control law ran at 100 Hz on an STM32 board. The parameter of the LESO was selected as \(w_{o}=1.5\) rad/s, and the parameters of the controller were selected as \(k_{1}=3.2,k_{2}=0.6\). To verify the disturbance-estimation effect, roughly 0.08 N-m torque was applied to both the \(x\) and \(y\) axes as the disturbance. The disturbance was loaded by hanging a weight on the axes, which resulted in the applied torque. The advantage of this method is its simplicity and accuracy.
# Seismic Response of a Large LNG Storage Tank Based on a Shaking Table Test
## 3 Simplified Mechanical Model of the Storage Tank
### Equation of Motion of the Isolated Storage Tank  
Considering the large difference between the horizontal stiffness of the isolation bearing and the lateral stiffness of the pile foundation, the pile foundation is not considered when establishing the model of the isolation storage tank, and the isolation storage tank is simplified as a three-degree-of-freedom model. Among them, the mass and height of the dome are \(M^{*}\) and \(h^{*}\), respectively. The equivalent mass and equivalent height of the outer tank wall are \(M_{1}\) and \(h_{1}\), respectively. The mass and height of the platform are \(M_{3}\) and \(h_{3}\), respectively. The simplified model of the isolated storage tank is shown in Figure 8.  
The mass matrix and stiffness matrix of the seismic isolation storage tank are:  
\[M^{\prime}=\begin{bmatrix}M^{*}&0&0\\ 0&M_{1}&0\\ 0&0&M_{3}\end{bmatrix}K^{\prime}=\begin{bmatrix}K^{*}&-K^{*}&0\\ -K^{*}&K^{*}+K_{1}&-K_{1}\\ 0&-K_{1}&K_{1}+K_{3}\end{bmatrix} \tag{20}\]  
Let the displacement of the outer tank be \({x^{\
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 1 Introduction
This enables researchers to develop and benchmark novel algorithms for fault diagnosis, signal processing, and machine learning in industrial transport applications. However, there are potential difficulties in applying this data set to other industrial systems. First, the data set is specifically tailored to independent cart systems with linear motors, which differ fundamentally from conventional rotary systems in terms of mechanical configuration and operational dynamics. Systems with different designs or bearing materials may exhibit different vibration patterns and fault behaviors, which limits the direct applicability of the data set. Second, the data set assumes a specific set of parallel guide rails and steel bearings, which may not align with the configurationsof other industrial systems. Finally, the reliance of the data set on specific fault types and severities may require adaptation or extension to address the unique fault scenarios encountered in other systems. Key Contributions
* This is the first publicly available data set specifically designed for bearing fault diagnosis in independent cart systems. * A comprehensive system description is given, including experimental setup, experimental configurations, sensor configurations, and fault injection methodology. * It is a multisensor data set capturing vibration signals and system variables under varied operating conditions. * The data set is structured to support future research in condition monitoring, feature extraction, and predictive maintenance. The organization of this paper is as follows. Section 2 provides a comprehensive description of the experimental system, including its key components and associated parameters. Section 3 presents the experimental campaign, detailing the phases of data collection, fault injection strategies, and improvements made over time. Section 4 outlines the methodology for injecting controlled bearing defects, describing the fault injection procedures. Section 5 covers the experimental data acquisition process, explaining the configurations and protocols used to collect the data set. Section 6 introduces the data set repository, describing the structure of the collected data and how it is organized for further analysis. Section 7 presents feature extraction and preprocessing techniques, followed by Section 8, which discusses data analysis and visualization. Section 9 summarizes the findings and their implications, serving as the conclusion. Appendix A explains the file and variable naming conventions and provides details on exporting data files into the desired formats for further analysis. Finally, the references are included in a separate section.