esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Developments of Aerostatic Thrust Bearings: A Review
## 3 Advances in Static Performance
### The Discharge Coefficient  
The discharge coefficient is related to geometric parameters and gas flow parameters. Some researchers simply assumed the discharge coefficient as one constant number (ranging from 0.6 to 0.8) [54], while others used empirical formulas [55; 56]. The numbers and empirical formulas were derived in certain limited conditions. Some errors may be introduced in these formulas due to simplified assumptions. Many investigations have been carried out to improve the accuracy of discharge coefficient formulas. In the literature, the discharge coefficient empirical formula of aerostatic bearings was proposed [36]. This empirical formula was in good agreement with experiment data both in simple holes and circumferential grooves. [PERSON] [57] analyzed the properties of mass flow rate on aerostatic bearings with an annular orifice restrictor numerically and experimentally. The results indicated that the pressure distribution near the supply hole was seriously affected by a fillet or a chamfer between the supply hole and the pad surface, which had a large influence on the bearing characteristics. In addition, an experimental formula of discharge coefficients was proposed for both the annular restrictor and orifice restrictor in the literature [31; 58]. The coefficients were a function of the geometric parameters and Reynolds number. The detailed identification methods and results of the discharge coefficients are given in the literature [59]. The structures of the feed hole and the restrictor are various. There are some differences between them in the flow rate characteristics. [PERSON] carried out a series of CFD simulations and experiments on the mass flow rate characteristic of the orifice-type restrictor [60]. A new model was proposed to calculate the mass flow rate through an
## 2 Autonomous Underwater Vehicle (AUV) Architecture
### Hardware  
To develop underwater recognition and navigation technology, the UVRTS was integrated with imported visual sensors for environmental recognition. The image-processing module of Bigeye Barracuda has a ring-shaped light-emitting diode (LED) to provide underwater illumination in the fore part of the AUV, as illustrated in Figure 1. The image-processing module uses two vision devices: a wide-angle lens camera and a dual-lens camera. The wide-angle lens camera (9P006) has a resolution of 1980 (H) \(\times\) 1080 (V) and was installed in front of the fore part to perform wide-area surveying. The dual-lens camera (OV9712) is a combination of two complementary metal-oxide-semiconductor image sensors for bottom surveying, and the resolution for each lens is 1280 (H) \(\times\) 720 (V).  
The control room, as illustrated in Figure 1, is in the parallel midbody and is equipped with attitude sensing, communication, power management, motion control, and real-time integrated management systems. This design is advantageous in the initial stage because it enables the rapid installation and configuration of various components, sensors, and wiring as well as rapid weight adjustment. The control room uses a fanless microcomputer as its core controller (Intel ATOM processor E3845 microprocessor embedded with the Windows operating system) connected to 2.4-G WiFi to run software, GPS, and INS, measure temperature and humidity, and perform power management, rudder control, and propulsion control. Power is supplied by lithium-ion high-performance batteries and multi-DC voltage group conversion to produce DC control voltage of 24, 12, 7, and 3.3 V. The battery unit is located below the center line of the control room to enhance AUV stability.  
The aft part consists of a DC brushless motor and four servos that provide the main dynamics of the postdriver system, as depicted in Figure 1. The DC brushless motor uses a 24-V DC 7A power supply to provide 150-W propulsion, and the control driver provides forward and reverse motion. The four-blade propeller is right handed and has a diameter of 94 mm and a pitch ratio (pitch/diameter) of 0.8. The controls for roll, pitch, and yaw motion use four sets of independently controllable brushless servos. The servo provides a range of -30\({}^{\circ}\) to +30\({}^{\circ}\) and torque of 30 Kgf/cm. It has 6-degree-of-freedom maneuvering capabilities by directly driving the cruciform rudder.
## 3 Results and Discussion
### Moisture Absorption Behavior
During water absorption, both free and bound water molecules existed in the epoxy network, based on the formation of either one or two hydrogen bonds with polymer chains [42]. Two types between water molecules and polymeric chains are shown in Figure 11. The black lines stand for the backbone chains of epoxy, and the water \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hygrothermal Aging Days** & **Average Weight \(W\) (\(g\))** & **Standard Deviation** & **Water Content (\%)** \\ \hline
0 & 15.6710 & 0.0320 & 0 \\
14 & 15.7069 & 0.0323 & 0.2288 \\
70 & 15.7267 & 0.0348 & 0.3550 \\
140 & 15.7564 & 0.0344 & 0.5448 \\
210 & 15.7799 & 0.0343 & 0.6944 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Moisture absorption parameters of 3D four-step braided composites after different cyclic hygothermal aging days. molecules are represented in red color. Figure 11a,b explain the case of one hydrogen bond for water molecules, and two hydrogen bonds, respectively. Continuing hygrothermal aging days, some fibers were exposed when the area and depth of epoxy hydrolysis increased, as shown in Figure 10c. Later on in the cyclic hygrothermal aging days, the cracks in both Figure 10d,e can be found, and the cracks were on the junction of the braiding yarns and epoxy; differential swelling stresses can be employed to explain the phenomenon. The combined effects of epoxy plasticization, epoxy hydrolysis and cracks caused by interfaces debonding, contribute to a change in dynamic mechanical performance after cyclic hygrothermal aging. Figure 10: Damage evolution on the cross section of the 3D four-step braided composites after different cyclic hygrothermal aging days, (**a**) 0 day, (**b**) 14 days, (**c**) 70 days, (**d**) 140 days, (**e**) 210 days. Figure 9: Contrast of moisture absorption between the 3D four-step braided composites and the T700/TDE85 laminated composites during 210 cyclic hygrothermal aging days.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Collision Ratio  
In OFSMA, collision happens when multiple sensors select the same subcarrier frequency channel. On the other hand, in our C-OFSMA, a collision occurs only when numerous sensors from one cluster try to transmit duplicate data and select the same  
Figure 10: Determine reliability for different clusters using 7-packet duplication. (**a**) Mathematical, (**b**) Simulation.  
subcarrier. Applying the C-OFSMA model, the overall collision of OFSMA is divided into two categories: inter-cluster collision and inner-cluster collision.  
\[\text{OFSMA collision}=\text{Inter-cluster collision}+\text{Inner-cluster collision}\]  
\[\text{C-OFSMA collision}=\text{Inner-cluster collision}\]  
Previously in the System Architecture section, we described our system fully minimizes the inter-cluster collision. The overall C-OFSMA collision compared with OFSMA was represented in Figure 11. A fixed 90 subcarrier channels and 6-clusters condition are considered for the proposed C-OFSMA system. In almost all cases of 3-, 5-, and 7-packet conditions, the collision of C-OFSMA is significantly lower than the OFSMA. For example, for 3-packet duplication and at an arrival rate of 5000 pkt/s C-OFSMA collision is 0.0728%, where OFSMA is 0.155%. For 5-packet duplication and at an arrival rate of 5000 pkt/s C-OFSMA collision is 0.0698% for the 6-cluster where OFSMA is 0.1351%. For 7-packet duplication and at an arrival rate of 5000 pkt/s C-OFSMA collision is 0.03%, OFSMA is 0.0584%.  
In all higher arrival conditions, without inter-cluster collision, the C-OFSMA shows better reliability than the OFSMA. On the other hand, in almost all lower arrival conditions, the C-OFSMA achieves lower collision than OFSMA except 7-packet in 100 kt/s to 500 pkt/s lower arrival rate condition. For 7-packet 100 pkt/s arrival condition C-OFSMA collision 0.000001% and OFSMA collision 0.000005% and for 7-packet 500 pkt/s arrival condition C-OFSMA collision 0.000001% and OFSMA collision 0.000009%. These indicate the difference between C-OFSMA collision and OFSMA is almost the same. Thus 7-packet duplication creates more duplicated data. When we apply C-OFSMA in 7-packet lower arrival conditions, the inner-cluster collision is increased massively, as the duplicated data are increased along with the lowest arrival condition. This happens because sensors can only select their subcarrier from their \(f_{set}\). Consequently, the same subcarrier selection happens frequently. On the other hand, OFSMA conditions have no cluster, so they have no inner-cluster collision as well as the sensors can choose any subcarrier channel within the whole frequency band. The 7-packet 1000 pkt/s arrival condition C-OFSMA scheme collision 0.0006% and OFSMA scheme collision 0.00436%. That means from 1000 pkt/s to the above arrival condition, the C-OFSMA system can minimize the collision and show better performance.  
Figure 11: Collision percentages for different packet duplication.
## 4 Analysis and Discussion
### Influence of Bol Radius \(R_{b}\) on Radial Displacement \(u_{r}\) of Joint  
To investigate the influence of the bolt radius \(R_{b}\) on the radial displacement \(u_{r}\) of the rock joint at the compression-shear side, the value of \(R_{b}\) was set as 4, 5, 6, 7, 8, 9, and 10 mm, respectively, ensuring that \(E_{b}\) and \(E_{r}\) did not change. Figure 11 shows the change of \(u_{r}\) when \(R_{b}\) increases continuously. Overall, the curve showed a trend of decline, but when \(R_{b}\) increased from 6 mm to 7 mm, \(u_{r}\) had a sudden drop, and then the decrease was roughly the same. This study argues that, in this example, the bolt radius \(R_{b}\) (7 mm) is the optimal bolt radius of this model. This test also indicates that in engineering practice, for a particular rock, the economic benefit does not always increase with the increase of bolt radius, so a bolt of appropriate size should be selected to reinforce the rock.
## 1 Introduction
The working mechanism of OTFT dosimeters is based on the trapping of photogenerated charges on the interface or within the organic semiconductor upon X-ray exposure. To increase the sensitivity of OTFT dosimeters, we explore the use of organic memory devices, such as polymer electret OTFTs, where additional charges are stored within the bulk of the dielectric film and at the interface between gate dielectrics and dielectric/semiconductor channels [20; 21; 22; 23]. In a polymer electret, the channel conductance is modulated by the electrostatically trapped charges inside the polymer electret upon applying a programming or erasing gate voltage. Programming of the polymer electret provides a built-in electric field in the dielectric of the device. Radiation-generated carriers within the dielectric recombine with trapped charges, reducing the built-in field. Although dosimeters based on the electret principle are not new, published literature in this area has focused on gas-filled, ion chamber-like devices [24; 25; 26; 27]. To the best of our knowledge, there are no studies dealing with dosimeters based on an OTFT electret design. An advantage of dosimeters based on an OTFT electret design is the ability to program it for use in either a wired or wireless configuration for in vivo dosimetry. Wired devices permit real time detection, whereas wireless devices are significantly more convenient for clinical use. Additionally, such devices can be reprogrammed for repeated use. In this work, we describe the design, fabrication, and testing of pentacene OTTFs with a polystyrene electret gate on a silicon substrate for preliminary evaluation as ionizing radiation dosimeters. Wepropose a mechanism for charging a polymer electret and demonstrate wireless and wired detection of 100 kVp and 6 MV energies X-rays, produced with an orthovoltage X-ray unit and a medical linear accelerator, respectively.
# Multilevel Regularization Method for Building Outlines
## 2 Materials and Methods
### Overall Regularization of Building Outlines Using Hausdorff Distance and MBR
#### 2.2.1 Calculation of Hausdorff Distance  
Given two point sets \(P=\{P_{0}\), \(P_{1}\), \(P_{2}\), \(P_{3}\), \(\ldots\), \(P_{m}\}\) and \(Q=\{Q_{0}\), \(Q_{1}\), \(Q_{2}\), \(Q_{3}\), \(\ldots\), \(Q_{m}\}\) in Euclidean space, the distance between these two point sets can be measured by the Hausdorff distance, which is defined as follows:  
\[H(P,Q)=max[h(P,Q),h(Q,P)], \tag{1}\]  
\[\left\{\begin{array}{l}h(P,Q)=\underset{p\in P}{\underset{q\in Q}{\max}} \underset{p\in P}{\underset{q\in Q}{\min}}\|p-q\|\\ h(Q,P)=\underset{q\in Q}{\underset{p\in P}{\max}}\underset{q\in P}{\|q-p\|} \end{array}\right., \tag{2}\]  
where \(\|p-q\|\) is the Euclidean distance between points \(p\) and \(q\), \(h(P,Q)\) is the one-way Hausdorff distance from point set \(P\) to point set \(Q\), \(h(Q,P)\) is the one-way Hausdorff distance from point set \(Q\) to point set \(P\), and \(H(P,Q)\) is the two-way Hausdorff distance.  
As shown in Figure 2a, if the nodes of two building outlines are represented as two ordered point sets \(P\) and \(Q\), the Hausdorff distance method can be used to calculate the Hausdorff distance between any line segment \(P_{i}P_{i+1}\) on building outline \(P\) and any line segment \(Q_{i}Q_{i+1}\) on building outline \(Q\).  
The calculation method is illustrated in Figure 2b. Firstly, line segment \(P_{i}P_{i+1}\) is equally divided into \(t\) segments, and line segment \(Q_{i}Q_{i+1}\) is equally divided into \(s\) segments. Then, the Euclidean distance between each division point \(P_{ik}\) (\(k\) = 0, 1, 2, \(\ldots\), \(t\)) on line segment \(P_{i}P_{i+1}\) and each division point on line segment \(Q_{i}Q_{i+1}\) is calculated to obtain the minimum distance. In Figure 2b, \(H_{i0}\) and \(H_{ik}\) represent the minimum distances corresponding to points \(P_{i0}\) and \(P_{ik}\), respectively. After obtaining the minimum distances for all divided points on building outline \(P\), the maximum value among all the minimum distances is taken as the one-way Hausdorff distance \(h(P,Q)\) from line segment \(P_{i}P_{i+1}\) to line segment \(Q_{i}Q_{i+1}\). Then, \(h(Q,P)\) and \(H(Q,P)\) can be similarly calculated.  
Figure 2: Process of calculating the Hausdorff distance. **(a)** Schematic of outline segmentation; **(b)** Schematic of Hausdorff distance calculation.
# A Compact Sequentially Rotated Circularly Polarized Dielectric Resonator Antenna Array
## 2 Resonating Element
### Rectangular Dielectric Resonator  
The RDR shape has the advantage over other shapes because its dimensions can be chosen independently, which enables dimensional freedom selection [17]. The rectangular geometry offers further elasticity with respect to aspect ratios and bandwidth management [18]. A dielectric waveguide model was employed to investigate the rectangular DRA [19]. Once the DRA is placed on the bottom plane, the TE modes are triggered. Equation (5) is used to calculate the resonant frequency of the fundamental mode TE\({}_{111}\)[20]. The proposed single resonating element is depicted in Figure 2.  
\[f_{0}=\frac{c}{2\pi\sqrt{\varepsilon_{r}}}\sqrt{k_{x}^{2}+k_{y}^{2}+k_{z}^{2}} \tag{5}\]  
where \(\varepsilon_{r}\) is the dielectric constant, \(c\) the speed of light, and \(k_{0}\) the wave number in free space, while \(k_{x},k_{y}\), and \(k_{z}\) represent the wave number in \(x,y\) and \(z\)-directions inside the DR, respectively.  
The dielectric wave guide mode (DWM) provides no mathematical formula for calculating the resonant frequency for higher-order modes. These modes can be determined by monitoring the electric field distribution within the DR. Higher resonant frequencies correspond to higher TE modes. For the proposed design of an ES coupling a RDR, the simulated E-field distribution plot is illustrated in Figure 3. It can be observed from the figure that totally two orthogonal modes are obtained, namely \(TE_{g21}^{x}\) and \(TE_{11}^{y}\) at 5.8 GHz.  
Figure 2: The 3D geometry of the proposed RDR coupled to an ES at the bottom plane.
# Joint User Pairing, Channel Assignment and Power Allocation in NOMA based CR Systems
## 2 System Model and Problem Formulation
Now, it remains to solve the dual problem. We employ sub-gradient method [32] to solve the problem for dual variables; the dual variable updates in the \(u\)th iteration are given by:  
\[\lambda_{i}^{u} =\lambda_{i}^{u-1}+\delta\left(\psi-\sum_{n=1}^{W/2}\sum_{i=1}^{W/ 2}x_{n,i,c}(Q_{n,i,c}|h_{  
Figure 5 shows the impact of \(I_{th}\) on the sum rate. Initially, an increase in \(I_{th}\) results in increasing the sum rate of each scheme. At these points, the transmit power of the users are upper bounded by \(I_{th}\). Thus, when \(I_{th}\) increase, the transmit power of each pair also increases. This results in enhancing the sum rate. After a certain point, an increase in \(I_{th}\) has no impact on the sum rate. At these points, each scheme is already transmitting with full available power. Hence, no more power is available for transmission. It can be seen that Opt out performs all the other schemes. For \(I_{th}<1.5\), the worst performance is provided by Fix-\(Q_{n,i,c}\) because the power allocation to user pairs is bounded by \(I_{th}\) and the scheme cannot reallocated the extra power to the pairs that generate less interference. However, when \(I_{th}>1.5\) the Fix-\(Q_{n,i,c}\) performs better than Random-\(x_{n,i,c}\). At larger values of \(I_{th}\), the power allocation of less user pairs is bounded by \(I_{th}\) so Fix-\(Q_{n,i,c}\) can take advantage of the available power and so the sum rate of Fix-
# Near-Nature Ecological Technique for Pier Scour Countermeasure in a Submerged Overfall
## 4 Results and Discussions
### Protective Effect Due to Flexibility of CKAW  
As mentioned in Section 2, the swinging part of CKAW can arrange and weaken the approaching flow, thus reducing the overall- and pier-induced scour depth. The material's flexibility is a response to the size and length of CKAW. Through this experimental study, we found that the flexibility parameter of CKAW is an important index for the protective effect of scour depth and the stabilizing capability against approaching flow. On the basis of the experimental results in Tables 4 and 5, Figure 18 depicts the relationship between  
Figure 16: Developing history of scour depth at 0\({}^{\circ}\) of pier due to different strip lengths.  
Figure 17: Response of water surface due to different strip lengths.  
flexibility parameter (\(F_{p}\)) and protective effect (\(P_{s}\)) on the scour depth. A linear relationship was found, with a correlation coefficient of 0.92, as shown in Equation (9).  
\[p_{s}\ =\ 2.72F_{p}, \tag{9}\]  
This equation presents that the flexibility parameter has a strong influence on the protective effect; a large flexibility parameter corresponds to increased protective effect. The flexibility parameter can be estimated by using Equation (5). This formula can be used to estimate whether any new synthetic fiber is suitable for CKAW.
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
###### Abstract  
In light of the difficulty of the inspection and maintenance of a transmission line condition monitoring system in remote mountainous areas, this paper proposes a long-term online monitoring scheme based on a low power wide area network (LPWAN). Considering different failure rates, three monitoring periods of transmission lines in mountainous areas are proposed. An online monitoring framework of transmission lines in mountainous areas was designed based on long range radio (LoRa) and a cellular mobile network, and a dynamic group network model of LoRa was established. The multi-objective particle swarm optimization algorithm can be used to optimize the energy and delay of the system, and then the suitable working mode for the three monitoring periods can be obtained. The simulation results showed that the minimum packet loss rate of the system could be less than 1%, the energy consumption of the system was 80% lower than the existing monitoring system, and the service life of the system can reach 15.13 years under the normal failure rate. Compared with the existing schemes, the proposed work shows the advantages of high reliability transmission, low cost and long-term monitoring, which is especially for transmission line monitoring in mountainous areas.  
transmission line monitoring; LPWAN; dynamic grouping; long term online monitoring; multi-objective optimization Article
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.8 Medium Gaussian SVM  
Medium Gaussian SVM [55] is a robust ML algorithm designed for high-accuracy classification of non-linear datasets. It leverages the Gaussian Radial Basis Function (RBF) kernel to enhance classification performance by mapping input data into a higher-dimensional space. Unlike the standard SVM, Medium Gaussian SVM employs a moderate gamma parameter, striking a balance between decision boundary flexibility and model complexity. This adjustment mitigates the risk of overfitting, thereby improving the model's generalization capability. By identifying a subset of the training data as support vectors, the algorithm constructs an optimal hyperplane for classification. The use of the Gaussian kernel enables effective handling of datasets that are not linearly separable, making it particularly suitable for complex pattern-recognition tasks.