esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A UPPAAL-Based Formal Methodology for the UEMS
### Analyses Techniques of the Formal Methodology  
Since the UEMS is a real-time system, the analyses of the UEMS need to take time evolution into account. Consequently, timed automaton is adopted in this paper to construct abstract models of UEMS behaviors.  
A timed automaton is essentially a finite automaton extended by time restrictions. As a formal analyses technique, the timed automaton has rigorous definition. In general, a timed automaton is defined as a tuple \((S,S_{0},Clk,Act,Var,E)\), where  
1. \(S\) is a finite set of states,
2. \(S_{0}\in S\) is the initial state,
3. \(Clk\) is a finite set of clocks,
4. \(Act\) is a finite set of actions,  
Figure 2: A general model for an intelligent terminal.  
* \(Var\) is a finite set of variables,
* \(E\) is a finite set of transitions. A transition is a tuple \((s,act,g,clk,var,s^{\prime})\) indicating that, starting by the state \(s\), the automaton executes the action \(act\), if the constraint \(g\) is satisfied; clocks of \(clk\) are reset, variables of \(var\) are updated and the new state is \(s^{\prime}\).  
The constraint \(g\) is defined as Equation (2). Incidentally, Equation (2) applies Backus-Naur Form (BNF).  
\[\begin{array}{l}g::=g\_clock|g\_data|g,g\\ g\_clock::=clk<t|clk<=t|clk>=t|clk>t\\ g\_data::=ex<const|ex<=const|ex==const|ex>=const|ex>const\\ ex::=const|-ex|ex+ex|ex-ex|ex\times ex|ex/ex\end{array} \tag{2}\]  
where '=' means 'defined as' and '|'means 'or'. Equation (2) implies that the constraint \(g\) is a logic expression of time (\(g\_clock\)) or a logic expression of data (\(g\_data\)) or a combination of the above two. The logical expression indicates a variable (\(clk\) or \(ex\)) is less than, no more than, equivalent to, no less than or more than a preset value. A variable of data (\(ex\)) can be further defined as a constant or the elementary arithmetic between those variables. Several examples of the constraint \(g\) are 'true', '\(clk<5\)' and '\(clk>10\) and \(ex<5\)'.  
To analyze communication requirements, timed computation tree logic (TCTL) is applied in this paper, which is an extension of computation tree logic (CTL) logic that can express real-time properties.  
In addition, another benefit of applying time automata is that timed automata are the input formalism of the UPPAAL model-checker [21], which is a dedicated software for the formal specification and verification of real-time systems.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 4 TPEG Framework
### Threshold Adjustment
#### 4.5.2. RSU Level Adjustment  
At the RSU level, \(\tau_{1}\) is adjusted according to the event detection result (Algorithm 2, Lines 14-16). If the event is verified to be false (false positive events) after an event-checking procedure, TPEG would adjust \(\tau_{1}\) to be a little larger:  
\[\tau_{1}=\tau_{1}\times(1+\Delta) \tag{14}\]  
where \(\Delta\) is a predefined increment factor, e.g., \(\Delta=0.05\) by default. Yet, if there are several events that are verified to be false, \(\tau_{1}\) might be adjusted to a larger value, and triggering the event-checking procedure would be hard. To avoid this drawback, \(\tau_{1}\) would also be set a little smaller with the time of epochs passed by:  
\[\tau_{1}=\tau_{1}\times(1-\Delta\times\frac{tu}{TU}) \tag{15}\]  
where \(tu\) is the time interval since the latest \(\tau_{1}\) adjustment and \(TU\) is the unit of time for the threshold adjustment, which is predefined.  
When the cloud receives the sensing data from the RSUs, it would store the data, check if the event happened and send the checking results to the RSU. If an event is verified to be _true_ after the event-checking procedure, it is registered at the ITS system, and actions would be taken to handle the event. For example, events such as \"bad road condition\" or \"road blocks\" would be handled by the municipal administration after a period of time. Yet, during this period of time, the RSU would decrease the chance of initiating event-checking procedures. It periodically broadcasts an event-occur message to alert vehicular nodes who newly enter its coverage area, and nodes that receive the event-occur message would keep working in LCS mode to save redundant sensing operations and message transmissions. When events are handled, they are removed from the ITS system, and the RSU would broadcast an event-removed message to notify ordinary nodes to transfer back to normal monitoring and event detection tasks.
# Optimization Study on a Novel High-Speed Oil-Free Centrifugal Water Pump with Hydrodynamic Bearings
## 4 Results and Discussion
### Effect of Hydrodynamic Bearings  
The pressure distribution at the thrust surface of the dynamic pressure bearing is displayed in Figure 7a. The area marked by black lines is the position of a single spiral groove. When the bearing rotated at a high speed, a low-pressure area formed at the inlet and drew the working fluid into spiral grooves. The retention and squeeze of the working fluid by the dam region promoted a high-pressure zone to form. The load capacity was achieved depending on the pressure difference. Figure 7b indicates that the bearing capacity decreased rapidly with the increase of the liquid film thickness. When the liquid film thickness was greater than 30 \(\upmu\)m, a further increase of the thickness slightly affected the bearing capacity. The thrust bearing supplied up to a 650 N load capacity with a 10 \(\upmu\)m fluid film thickness at 7200 rpm. Since the axial force for the pump structure was no more than 200 N, a 15 \(\upmu\)m fluid film at 7200 rpm satisfied the requirement. The flow rate through the dynamic pressure bearing was between \(10^{-5}\) and \(10^{-4}\) kg\(\cdot\)h\({}^{-3}\). Four orders of the magnitude difference between it and the mainstream slightly influenced the head loss.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### AI-Based Early Fault Diagnosis
#### 2.2.1 Knn  
KNN is one of the simplest data mining classification methods. In KNN classification methods, each training sample is described as an S-dimensional space according to the value of each of its S features [53]. The testing sample is then represented in the same space, and its K nearest neighbors can be obtained. The class of each of K nearest neighbors is counted, and the class with the largest number of \"votes\" is chosen as the classification of the testing sample. The K nearest neighbors are usually determined by computing the Euclidean distance between the testing sample and each of the training samples [54]. The Euclidean distance between the testing sample \(\textit{Test}_{s}\) and the \(m\)-th training sample \(\textit{Train}_{m,s}\) is defined in Equation (11):  
\[D=\left[\sum_{s=1}^{S}\left(\textit{Test}_{s}-\textit{Train}_{m,s}\right)^{2} \right]^{1/2}\quad s=1,2,\cdots,S,\ m=1,2,\cdots,M \tag{11}\]  
where \(S\) and \(M\) represent the number of features and training samples, respectively  
In order to show the KNN algorithm schematic clearly, a diagram of the KNN method is illustrated in Figure 5. The simplicity of the KNN method makes it easy and widely applicable in fault classification of rotating machinery.  
Figure 4: Early fault diagnosis framework using AI techniques for rotating machinery systems.
# Space Charge Accumulation and Decay in Dielectric Materials with Dual Discrete Traps
## 3 Bipolar Charge Transport Model
### Charge Reaction Dynamics  
The following four equations represent the first order charge dynamics in dielectric materials having two discrete trap levels, including charge trapping/de-trapping and recombination processes [22; 30].  
\[S_{\alpha\mu} = -R_{\alpha\mu\mu\mu}q_{\text{free}(i)}q_{\text{free}(i)}-\sum\limits_ {i=1}^{n}R_{\alpha\mu\mu}q_{\text{free}(i)}q_{\text{imp}(i)i}-q_{\text{free}( i)}\sum\limits_{i=1}^{n}P_{T(i)}(1-q_{\text{trap}(i)}/q_{\text{e}}N_{T(i)})+\sum\limits_{i=1}^{n}P_{D (i)}q_{\text{trap}(i)i} \tag{32}\] \[S_{eti} = q_{\text{free}(e)}P_{T(e)i}\Big{(}1-q_{\text{trap}(e)i}/q_{\text{ e}}N_{T(e)i}\Big{)}-P_{D(e)i}q_{\text{trap}(e)i}-R_{e\mu\mu}q_{\text{trap}(e)i}q_{ \text{free}(h)},i = 1,2\] (33) \[S_{e\mu} = -R_{\alpha\mu\mu}q_{\text{free}(i)}q_{\text{free}(h)}-\sum\limits_ {i=1}^{n}R_{e\mu\mu}q_{\text{trap}(e)i}q_{\text{free}(h)}-q_{\text{free}(h)}\sum \limits_{i=1}^{n}P_{T(h)i}(1-q_{\text{trap}(h)i}/q_{\text{e}}N_{T(h)i})+\sum \limits_{i=1}^{n}P_{D(h)i}q_{\text{trap}(h)i}\] (34) \[S_{eti} = q_{\text{free}(h)}P_{T(h)i}\Big{(}1-q_{\text{trap}(h)i}/q_{\text {e}}N_{T(h)i}\Big{)}-P_{D(h)i}q_{\text{trap}(h)i}-R_{e\mu\mu,\mu\mu}q_{\text{ free}(e)i}q_{\text{trap}(h)},i = 1,2 \tag{35}\]  
where \(S_{e\mu}\), \(S_{e\mu}\), \(S_{\mu\mu}\), and \(S_{\mu\mu}\) are the charge sources of mobile and trapped electrons and holes, respectively; and \(n=2\). \(R_{e\mu,\,\mu\mu}\) is the recombination rate between free electrons and free holes in cm\({}^{-3}\) s\({}^{-1}\). \(R_{e\mu,\mu\mu}\) and \(R_{e\mu,\mu\mu}\) are the recombination rate between free electrons and trapped holes and the recombination rate between trapped electrons and free holes in Cm\({}^{-3}\)s\({}^{-1}\), respectively.  
In the Langevin recombination model, the recombination rate between free electrons and free holes is determined by the carrier motilities of electrons and holes [30; 32], \(R_{e\mu,\mu\mu}=(\mu_{e0}+\mu_{h0})/\varepsilon_{0}\varepsilon_{r}\). The recombination rate between free electrons and trapped holes is determined by the carrier motility of free electrons; and the recombination rate between trapped electrons and free holes is determined by the carrier motility of free holes [30; 32], namely, \(R_{e\mu,\mu\mu}=\mu_{e0}/\varepsilon_{0}\varepsilon_{r}\) and \(R_{e\mu,\mu\mu}=\mu_{h0}/\varepsilon_{0}\varepsilon_{r}\).
## 4 Experimental Results and Discussion
### Experimental Results
Figure 11 shows a graphical representation of the corridors obtained by the SA algorithm for problems one (Figure 11a) and two (Figure 11b). For verifying that the solutions found by the SA algorithm are feasible, it is necessary to visualize them using a GIS. The 10% neighborhood structure allows finding better solutions with alternative routes (red routes) than the initial solution (pink routes). The neighborhood structure using 30% and 50% of the initial solution finds good solutions (green and yellow routes). Finally, the neighborhood structure using 70% of the initial solution allows refining the initial trajectory very finely (purple routes). Figure 11 shows the routes found inside the corridor with different neighborhood structures. These routes do not follow the same path indicating that the configuration of alternate routes obtained by SA presents a good diversity of independent paths since most of the routes found coincide only in some sections, and additionally, SA allows a good quality of the local optimal solutions. \begin{table}
\begin{tabular}{c c c c c} \hline
**Problem** & **Greedy-BFS** & **SA** & **Average** & **SD** \\ \hline \(586\times 732\) & 715 & 484 & 496.13 & 4.71 \\ \(585\times 1467\) & 1528 & 1113 & 1136.8 & 16.34 \\ \(5341\times 6727\) & 8335 & 6032 & 7146.9 & 416.75 \\ \hline \end{tabular}
\end{table} 
Table 6: Cost of the best solution with a neighborhood size of 30% of the solution \(s\). \begin{table}
\begin{tabular}{c c c c c} \hline
**Problem** & **Greedy-BFS** & **SA** & **Average** & **SD** \\ \hline \(586\times 732\) & 715 & 545 & 554.23 & 3.4 \\ \(585\times 1467\) & 1528 & 1221 & 1235.13 & 6.34 \\ \(5341\times 6727\) & 8335 & 6004 & 7179.9 & 611.6 \\ \hline \end{tabular}
\end{table} 
Table 7: Cost of the best solution with a neighborhood size of 50% of the solution \(s\). The average time required for the SA algorithm to find its best solutions are 5.38, 13.3, and 520.76 s for problems one, two, and three, respectively.
## 2 Thrust, Torque, and Power Estimation Model for Modified EPB TBM in Mixed-Face Ground
### Thrust Model  
When the EPB TBM tunnels in the mixed-face stratum, the front side of the cutterhead not only has friction with the excavation surface, but also suffers from the reverse resistance from the excavation surface. The shield shell encounters friction with the surrounding strata. The high-speed rotation of the soil in the chamber generates enormous earth pressure. The equipment at the end of the shield will not only have a frictional effect with the lining pipe sheet but will also generate drag resistance. The equipment is subjected to turning resistance as it turns. For some types of TBMs, the cutter ring on the cutterhead is subjected to reverse resistance from the excavation face. Therefore, its total trust has around seven components, as shown in Equation (2):  
\[F=F_{1}+F_{2}+F_{3}+F_{4}+F_{5}+F_{6}+F_{7} \tag{2}\]  
where \(F\) is the total thrust of the TBM; \(F_{1}\) is the frictional resistance between the TBM body and the surrounding strata; \(F_{2}\) is the resistance generated by the cutters cutting the ground;\(F_{3}\) is the thrust force required to overcome the chamber pressure acting on the bulkhead; \(F_{4}\) is the friction between the shield tail and the segment, considering that the cutting ring of some TBMs protrudes from the cutterhead; \(F_{5}\) is the penetration resistance of the cutting ring; \(F_{6}\) is the resistance to change direction when the machine turns; \(F_{7}\) is the hauling force of the trailing equipment. In this paper, the total thrust is simplified to Equation (3):  
\[F=\lambda_{EPB}(F_{1}+F_{2}+F_{3}) \tag{3}\]  
Here, a simplified formulation is proposed based on an amplification factor \(\lambda_{EPB}\) removing some of the elements from the formula, which is based on two reasons: Firstly, as previous research has shown [9], \(F_{4}\), \(F_{5}\), \(F_{6}\), and \(F_{7}\) are difficult to measure in practical engineering. Secondly, the engineering verification in previous works show that \(F_{1}\), \(F_{2}\), and \(F_{3}\) account for much more than 90% of total thrust [21; 22; 23], and the other parts are too small to be ignored. Therefore, the value of \(\lambda_{EPB}\) is approximately 1.1. Previous studies have tended to neglect the differences in geological parameters between different layers and lack theoretical basis when calculating each section of the thrust, which can lead to mistakes and poor confidence in the estimation model. In the following, we introduce the calculation methods of \(F_{1}\), \(F_{2}\), and \(F_{3}\) in detail.
## 2 Materials and Methods
### Bioelectrical Impedance Analysis  
The use of a tetrapolar whole-body bioelectrical impedance analysis (BIA) device (101 anniversary sport edition, Akern, Florence, Italy) was utilized to perform bioelectrical impedance analysis (BIA) in order to evaluate the subject's body composition at both time points T0 and T1.  
This apparatus is a bioelectrical impedance analyzer that operates at a single frequency and generates a sinusoidal current operating at 50 kHz. Before beginning measurements, operators were given instructions to ensure that data acquisition was carried out appropriately, to minimize estimation errors as much as possible.  
Specifically, patients should lie supine for at least 2 min before testing to allow for even distribution of body fluids. Following that, certified electrodes were placed on the foot (metatarsus region) and hand (metacarpus region) of the hemisoma without hemiparesis, with a distance of 5 cm between the two electrodes. To prevent alterations in measurements, the skin at the contact point must be clean and not greasy, dry, or wet. In the final step, we ensured that the four limbs did not adhered to the body and were evenly spaced. Because of the possible alteration of bioelectric resistance due to body temperature, patients with fever or hypothermia were not analyzed. In addition, the absence of pacemakers was ascertained.  
The bioimpedance analysis measures the bioelectrical impedance (\(Z\)) of biological tissues. \(Z\) is a complex quantity composed of resistance (R\({}_{\mathrm{z}}\)), which represents the resistance of electrolytes to the flow of an electric current, and reactance (X\({}_{\mathrm{c}}\)) which is affected by the capacitance of the cell membrane. Body composition parameters can then be estimated by means of a predictive equation, using regression analysis including several variables such as R\({}_{\mathrm{z}}\), X\({}_{\mathrm{c}}\), sex, age, stature, and weight [33; 38].  
In order to analyze body composition, lean mass, fat mass, and muscle quality parameters are measured in this study.  
Total fat-free mass (FFM), appendicular skeletal muscle mass (ASMM), and body cell mass (BCM) were measured for body lean mass assessment. The fat-free mass index (FFMI), skeletal muscle mass index (SMI), and body cell mass index (BCMI) were then calculated by dividing the FFM, ASMM, and BCM measures for the height squared of each patient.  
Total fat mass (FM) was included along with its index (fat mass index, FMI), which was calculated as described previously for the body lean mass assessment.  
The BIA-derived PhA, calculated from Rz and Xc obtained from the BIA analysis and expressed in degrees, was also included for muscle quality assessment. The manufacturer recommends a normal range from 5\({}^{\circ}\) to 7\({}^{\circ}\), and the variation may be influenced by age, gender, ethnicity, body composition, amount of physical activity, and body fat [39].  
All the parameters were valued from proprietary manufacturer algorithms using Bodygram Plus software (Akern, Florence, Italy).
## 2 Materials and Methods
### Additional Information Regarding the Sample Preparation  
For the ferrite treatment experiments, we used a precisely controlled procedure: Each experiment was conducted in triplicate at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) in 15 mL glass reactors with magnetic stirring at 300 rpm. PVA wastewater samples were prepared by diluting high-concentration PVA wastewater (37.8 g/L) with deionized water to achieve the target concentrations (3-7 g/L). The pH was continuously monitored using a calibrated Orion pH meter (Thermo Scientific, accuracy \(\pm\)0.01). For H\({}_{2}\)O\({}_{2}\) pre-oxidation, 30% H\({}_{2}\)O\({}_{2}\) solution was added at a molar ratio of 1:10 (H\({}_{2}\)O\({}_{2}\):Fe) to the PVA solution and mixed for 10 min. Ferrite solution (containing Fe\({}^{2+}\)/Fe\({}^{3+}\) ions at a molar ratio of 1:2) was then added dropwise (1 mL/min) during continuous stirring. For sequential ferrite treatment, two separate additions of ferrite solution were made with a 30 min interval between additions. For magnetic precipitate recovery, the solution was allowed to settle for 10 h, followed by vacuum filtration using 0.45 \(\upmu\)m membrane filters. The precipitates were washed three times with deionized water and then divided into two portions for thermal treatment: one portion was dried at 100 \({}^{\circ}\)C for 24 h in a convection oven, while the other was calculated at 650 \({}^{\circ}\)C for 2 h in a muffle furnace with a heating rate of 5 \({}^{\circ}\)C/min. All experiments were conducted in triplicate, and results are reported as mean \(\pm\) standard deviation. The coefficient of variation for treatment efficiency measurements was maintained below 5% across all experimental conditions.
# Optimization of Laser Fixed Contact Process for the Formation of an Al-BSF Layer
## 2 Materials and Methods  
In our study, we used c-Si wafers with a thickness of 200 \(\upmu\)m and resistivity of 1-3 \(\Omega\)-cm. The saw-damage removal (SDR) process was performed first, to remove saw damage on the substrate, and then the texturing process--which included anisotropic etching with a mixture of KOH and isopropyl alcohol (IPA)--was performed, to lower wafer reflectivity. This process was performed using a process temperature of 80 \({}^{\circ}\)C, and a process time of 30 min. After texturing, wafers with random pyramids were doped with phosphorus, by thermal diffusion in a tube furnace, to form p-n junctions. After doping, the sheet resistance of the emitter layer was approximately 60 \(\Omega\)-cm.  
The front SiNx, as an anti-reflection coating (ARC) layer, was formed by plasma-enhanced chemical vapor deposition (PECVD), the front electrode was formed by screen printing, using Ag paste. Aluminum electrodes were formed on the back side, using an e-beam evaporator, and a firing process was performed, using a laser, to form a local back surface field (LBSF) layer.  
A nanosecond (ns) green laser, with a wavelength of 532 nm, was used for the laser process, and the \(z\)-axis setting was fixed to avoid differences in the process ability due to laser focus size. The parameters for the ns laser used in this study have been listed in Table 1. To optimize the LFC process, the characteristics of the layer to be processed were analyzed, and the wavelength region of the laser was selected. In this experiment, the LFC process was performed using varied laser parameters; the laser pitch for the LFC pattern varied between 250-1000 \(\upmu\)m.  
After laser processing, Si surface morphologies were analyzed, using a Hitachi S-4300 scanning electron microscope (SEM), with an operating voltage of 25 kV, and quantum efficiencies were measured, using a McScience K3100 ATX spectral incident photon-to-current conversion efficiency (IPCE) measurement system, under AM 1.5 G illumination, and 100 mW/cm\({}^{2}\) power density.