esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Diurnal Variation  
#### 3.3.1 Evapotranspirationiration (ET)  
Figure 7 compares the multi-year average diurnal cycle of simulated and observed ET. The simulated and observed diurnal cycles are mostly consistent, both showing a \"single-peak\" structure peaking around noon (local time 12:00), characteristic of solar radiation-driven evapotranspiration. For several vegetation types (e.g., ENF, WSA, WET, SAV), the simulated values during daytime (especially 12:00-14:00) are significantly lower than the observed values. For ENF, the noon underestimation can reach 10-15%. Conversely, for CRO and MF, the simulated values exceed the observations during parts of the day; for CRO, around 14:00, the simulations are 5-8% higher. Nighttime ET is minimal in both the simulations and observations, with little discrepancy.  
Figure 8 shows the diurnal cycle of simulation bias (simulation minus observation) for ET across different seasons. Overall, the simulation biases are significantly larger during the summer under high temperature and radiation conditions. Winter simulations perform best, while spring and autumn are intermediate. Summer (JJA) exhibits the largest biases, with midday deviations amplified 2-3 times compared to winter (DJF), reaching up to 160 mm. This suggests challenges in simulating complex vegetation physiological and soil moisture processes under high-temperature, high-radiation conditions. In winter, the biases are generally small for all vegetation types, with bias curves close to zero, indicating good agreement. The simplified ET processes during low temperatures, dominated by basic physics, like soil heat conduction and vegetation dormancy, appear well-captured.  
Figure 7: Comparison between the observed and simulated ET flux on a diurnal course in different vegetation types (multi-year average): (**a**) ENF, (**b**) EBF; (**c**) DNF; (**d**) DBF; (**e**) MF; (**f**) CSH; (**g**) OSH; (**h**) WSA; (**i**) SAV; (**j**) GRA; (**k**) WET; (**l**) CRO.  
During the summer midday, the model tends to overestimate ET for EBF, DNF, MF, and CSH, while underestimating it for other vegetation types.
## Characteristics and Selection of Near-Fault Ground Motions
### Selection of Near-Fault Pulse and Non-Pulse Ground Motions  
A comparison with the non-pulse ground motions show that the pulse ground motions have the greater long-duration velocity and displacement pulse and peak ground acceleration (PGA). The ratio of PGV (peak ground velocity)/PGA is relatively large in the pulse ground motions, and 0.2 is generally used as the index to divide the pulse ground motions and non-pulse motions.  
The pulse seismic records considered in this study were selected in accordance with the site response spectrum based on actual engineering. Taking into consideration a gravity dam that is in southwest China as the research object, the region of seismic intensity for design is VIII degrees, the designed seismic acceleration is 0.2 g, the site category is II class, and the design seismic grouping is the first group. The design response spectrum was generated according to the above seismic requirements and bedrock conditions of the region. The three near-fault pulse records with the best fitting accuracy were obtained by taking the above design response spectrum as the initial screening condition and taking into account the earthquake magnitude (above magnitude six), the distance to fault (within 20 km), and the condition of pulse type (PGV/PGA > 0.2). All earthquake records were obtained from the database of PEER [35], they are at Parachute Test Site station in the Superstition Hills earthquake, Newhall-W Pico Canyon Rd station in Northridge earthquake, and Parachute Test Site station in El-Centro earthquake.  
The corresponding non-pulse ground motions were synthesized according to the acceleration response spectra of the pulse records by SeismoArtif software, and the fitting situation is shown in Figure 2. The red curves show the pulse ground motions, and blue curves show the non-pulse motions.  
Figure 1: The time-history curves of acceleration, velocity, and displacement of seismic records.  
In addition, the peak accelerations of six seismic records were uniformly adjusted to 0.2 g in order to avoid being affected by earthquake amplitude, and Table 1 shows the properties of selected seismic records. Figure 1 shows the time-history curves of acceleration, velocity, and displacement of seismic records. A comparison with the non-pulse seismic motions show that the pulse seismic motions have an obvious long period pulse at the beginning stage and have the larger PGV and PGD (peak ground displacement).
# Natural Frequencies and Modes of Electrostatically Actuated Curved Bell-Shaped Microplates
## 2 Materials and Methods
### Reduced-Order Model  
To analyze the plate modal behavior, the RO model of the plate is built using the Galerkin decomposition. The non-dimensional static and dynamic components of the response are approximated by the series  
\[u_{s}(r) \approx\sum_{n=1}^{N}p_{n}^{s}H_{n}(r), w_{s}(r) \approx\sum_{n=1}^{N}q_{n}^{s}\Phi_{n}(r) \tag{8}\] \[u_{d}(r,t) \approx\sum_{n=1}^{N}p_{n}H_{n}(r)e^{i\omega t}, v(r,t) \approx\sum_{n=1}^{N}q_{n}\Phi_{n}(r)e^{i\omega t} \tag{9}\]  
where \(i=\sqrt{-1}\), \(n=1\dots N\) is the mode/DOF number, \(\omega\) is the non-dimensional frequency, \(p_{n}^{s}\), \(q_{n}^{s}\) are the generalized coordinates of the static radial deflection and elevation, respectively, and \(p_{n}\), \(q_{n}\) are the corresponding dynamic counterparts. Note that the harmonic time-dependence in Equation (9) is directly introduced since the dynamic Equations (6) and (7) are linear and separation between the spatial and time variables is possible.  
The base functions \(\Phi_{n}(r)\) and \(H_{n}(r)\) are the eigenmodes of a flat circular clamped plate [46; 47], namely  
\[\Phi_{n}(r)=C_{n}\bigg{(}J_{m}(\lambda_{n}r)-\frac{J_{m}(\lambda_{n})}{I_{m}( \lambda_{n})}I_{m}(\lambda_{n}r)\bigg{)},\qquad H_{n}(r)=P_{n}J_{1}(\Theta_{n}r) \tag{10}\]  
Here, \(J_{m}\) and \(I_{m}\) are, respectively, the Bessel and the modified Bessel functions of order \(m\), \(C_{n}\) and \(P_{n}\) are constants, which are chosen such that \(\max\limits_{r\in[0,1]}(\Phi_{n}(r))\!=\!1\), \(\max\limits_{r\in[0,1]}(H_{n}(r))\!=\!1\). The eigenvalues for transverse vibrations, \(\lambda_{n}\), and the in-plane eigenvalues, \(\Theta_{n}\), are the solutions of the following characteristic equations  
\[J_{m}(\lambda_{n})I_{m+1}(\lambda_{n})+J_{m+1}(\lambda_{n})I_{m}(\lambda_{n}) =0,\qquad J_{1}(\Theta_{n})=0 \tag{11}\]  
In the present work, we consider only the axisymmetric vibrations of the plate and set \(m=0\). Moreover, the initial shape of the plate is assumed to be in the form of the fundamental symmetric mode of a flat plate, namely  
\[w_{0}(r)\approx h_{0}\Phi_{1}(r)=h_{0}\bigg{(}J_{0}(\lambda_{1}r)-\frac{J_{0 }(\lambda_{1})}{I_{0}(\lambda_{1})}I_{0}(\lambda_{1}r)\bigg{)} \tag{12}\]  
where \(\lambda_{1}=3.1962\).
# Development of Lift Control System Algorithm and P-M-E Analysis in the Workplace
## 2 Control Network Development
### Development of Lift Control Algorithm  
As stated previously, the algorithm employed is used to control the lift system through the division zoning and a fuzzy system. This takes into account the fact that the division zones represent lifts in the building, which are dependent on the hall-call requests. The present algorithm is comprised of three phases: (i) the identification in phase 1; (ii) the response in phase 2; and (iii) the execution in phase 3.  
In phase 1, the algorithm assigns zones with the aid of a fuzzy traffic controller, which was similarly described in the work of [PERSON] et al. [31], that identifies if a lift is free or not by analyzing information, such as the occupation and capacity of the lift on the floor from which the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**IF** & **THEN (F\({}_{\text{P}}\))** \\ \hline WT = Short & High \\ WT = Medium & Medium \\ WT = Long & Low \\ \hline SA = Large & High \\ SA = Medium & Medium \\ SA = Small & Low \\ \hline Dist = Less & High \\ Dist = Medium & Medium \\ Dist = High & Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy knowledge rules for waiting time (WT), space availability (SA) and distance (Dist).  
hall-call was made. Furthermore, it also utilizes the information related to whether this call was intended to go upwards or downwards.  
Phase 2 is only initiated in a case of a free lift. In this case, the fuzzy system fitting value calculations for quick hall responses are initiated by the algorithm. Once the lift is considered to be occupied, phase 3 commences, in which the algorithm executes the inputted information from the hall-call until the lift is free again. The lift control algorithm employs an open platform communication (OPC) between the fuzzy control system and the PLC, which is suitable for the reception of data from devices, such as HMI devices. The flowchart for the algorithm design is shown in Figure 1.  
The main task of the design is related to the logic that is essential for controlling the movement of the lift between the floors of the building. The main conditions are stated as follows. (1) There must be upward and/or downward button(s) to make a hall-call. If there is no call, the lift retains its current position. In the cases of multiple calls from different floors, the response is made based on the time order of when the call was made. (2) The door of the lifts will have a programmed door that opens and closes automatically on every floor of the building.  
PLC programming languages that are generally used in lift control system design include the follows: (i) Structure text (ST); (ii) Instruction list (IL); (iii) Function block diagram (FBD); (iv) Sequence function chart (SFC); and (v) Ladder (logic) diagrams (LL/LD). Ladder logic is a graphical programming language that is extracted from the circuitry evolution of the relay control wiring [13]. In this present work, the ladder logic, which is the most commonly used language to program the PLC, was employed.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 3 System Model  
### Utility Model of MGs  
Each MG equipped with a PV system receives a government subsidy for clean energy plus the revenue from selling surplus energy to MMGs. The MG prefers the PV generation to satisfy its own load demands and charge its own ESS. If the PV generation is insufficient, the MG will buy energy form the MMG. The energy consumption in the MG can create revenue, especially for industrial and commercial users [18; 19]. Therefore, the utility model of the MG mainly considers the government subsidy for PV generation, the benefits of energy consumption, the benefits from selling surplus energy to the EMO and the costs of purchasing energy from the EMO. As the MG may be either a buyer or a seller in different periods, the utility function in time slot \(h\) can be expressed as follows:  
\[U_{i}^{h}=\theta pv_{i}^{h}+k_{i}^{h}\ln(+1l_{i}^{h})-p_{cb}^{h}nl_{s,i}^{h}-p_ {cs}^{h}nl_{b,i}^{h}-c_{i}e_{i}^{h2} \tag{2}\]  
where \(\theta\) is the subsidy for each kWh generated by the PV system, \(pv_{i}^{h}\) is the PV generation in the time slot \(h\). \(k_{i}^{h}\ln(1+l_{i}^{h})\) is the benefit that the MG \(i\) consumes energy \(l_{i}^{h}\). \(p_{cb}^{h}\) and \(p_{cs}^{h}\) are the buying price and selling price from the MG to the MMG, respectively. \(nl_{s,i}^{h}\) and \(nl_{b,i}^{h}\) respectively represent the selling energy and buying energy of the MG \(i\) in time slot \(h\). \(c_{i}\) is the degradation cost coefficient of the ESS in the MG \(i\), and \(e_{i}^{h}\) is the charging energy to the ESS in time slot \(h\). The other maintenance and scheduling costs are neglected in this paper. Since each MG in the MMG can only act as a buyer or seller at a special time slot, and the net energy should satisfy the following constraints:  
\[nl_{b,i}^{h}-nl_{s,i}^{h}=l_{i}^{h}+e_{i}^{h}-pv_{i}^{h}, \tag{3}\]  
\[0\leq nl_{b,i}^{h}\leq D_{b,i}^{h}nl_{i}^{\max}, \tag{4}\]  
\[0\leq nl_{s,i}^{h}\leq D_{s,i}^{h}nl_{i}^{\max}, \tag{5}\]  
\[D_{b,i}^{h}+D_{s,i}^{h}\leq 1. \tag{6}\]  
Equation (3) is the constraint of energy balance in the MG \(i\). \(nl_{i}^{\max}\) is the maximum energy transaction of the MG with the power grid in the connection line, \(D_{b,i}^{h}\) and \(D_{s,i}^{h}\) are the binary variables indicating the state of buying and selling energy of the MG \(i\).
# A Novel Ultrasonic Cavitation Peening Approach Assisted by Water Jet
## 1 Introduction
They utilized the surface plastic deformation method to estimate the optimal process time. It was found that the optimal process time is before the end of the incubation period. [PERSON] et al. [19] explored the improvement of the properties of deep notches by ultrasonic cavitation peening both theoretically and experimentally. Most cavitation bubbles near the notch tip during the process were observed. The improvement of residual stress near the notch tip was obtained as well. Therefore, it is indicated that the converging geometry of the notch shape is advantageous for ultrasonic cavitation peening. In the above mentioned studies, traditional ultrasonic cavitation peening makes use of the advantage of the incubation period during the cavitation erosion process [20], which results in small mass loss and much plastic deformation. This surface enhancement process is considered as an environmentally friendly technology since no polluted water is produced except the recycled metal oxide powder [21]. Additionally, this method is inexpensive to perform and is completed in only one step. Therefore, it has not only the positive aspects of ultrasonic technology [22; 23] but also has the advantage of cavitation to metal surfaces. However, during the traditional ultrasonic cavitation peening process the workpieces have to be placed in a water container and immersed in water. Meanwhile, a temperature control is necessary since the dynamics of cavitation bubbles are greatly influenced by the fluid temperature. In order to overcome these disadvantages, a novel ultrasonic cavitation peening approach is developed and described in this paper. This approach has potential applications in highly stressed components, for example, axles of trains, gears, shafts of cars, aircrafts, and so forth. To evaluate treatment effectiveness of the novel process, a piezoelectric sensor is used to measure the impact loads introduced by the collapses of cavitation bubbles. The topographies of treated specimens are observed by a microscope. The roughness and hardness after treatment are measured as well.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 2 Materials and Models
### Models  
#### 2.2.1 Design  
Since the process and mechanism of how land subsidence affects GPS horizontal motion are not yet fully understood, there is no published result on physical models or numerical methods that can remove the impact of land subsidence on GPS horizontal motion.  
Deep learning has the ability to learn complex relationships between data and build nonlinear mappings to solve seismological problems where the physical processes are not yet fully understood [24; 25]. We used sequence-to-sequence modeling techniques in deep learning to develop a model to remove the impact of land subsidence on GPS horizontal motion.  
We used gated recurrent units (GRUs) to construct models to learn and predict implicit relationships between horizontal motion and vertical data from stations in the land subsidence area. The GRU is a special kind of recurrent neural network that is a powerful tool for processing sequential data, especially suited for tasks that require the capture of long-term dependencies, and have been widely used in the field of time-series forecasting.  
The corresponding GRU models were constructed for the north and east horizontal components of the 10 stations in the Tianjin subsidence area, with a total of 20 GRU models. A corresponding dataset was generated for each model to train the model to predict the motion of the target horizontal component given the vertical data from the same station. After training was completed, the vertical data without land subsidence were used as input to generate the prediction output, which can be regarded as the horizontal motion of the station without the impact of land subsidence.
# Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks +
## 2 Preliminaries
### Gaussian Mixture Reduction  
Gaussian mixture reduction (GMR) approximates an M-component GMM with a reduced number N < M of components. Several methods for GMR have been proposed, for example, in References [32; 33; 34; 35; 36; 37].  
A straightforward method for performing GMR is the following:1. Find the two closest components in a GMM according to a distance criterion.
2. Merge the two selected components into one component.
3. Update to a GMM with one fewer component.
4. Repeat steps 1-3 until a stopping criterion is reached (e.g., a predefined number of components and a predefined precision).  
As a distance criterion, [PERSON] [37] proposed the Kullback-Leibler (KL) divergence [38]. The distance criterion using the KL divergence is written as follows Equation (20) in Reference [37].  
\[d\big{(}(w_{i},\mu_{i},\sigma_{i}),(w_{j},\mu_{j},\sigma_{j})\big{)}=\frac{ \big{(}(w_{i}+w_{j})\log\det(\sigma_{ij})-(w_{i})\log\det(\sigma_{i})-(w_{j}) \log\det(\sigma_{j})\big{)}}{2}, \tag{16}\]  
where \(i\) and \(j\) denote the \(i\)-th and \(j\)-th component of a GMM, respectively and \(w_{k}\), \(\mu_{k}\) and \(\sigma_{k}\) are the weight, mean and covariance of the \(k\)-th component, respectively. The function \(\det(x)\) denotes the determinant of a square matrix \(x\).  
Recently, a more efficient algorithm using constraint optimization was proposed [39; 40].
## 4 Conclusions  
We used ac-susceptibility to measure the blocking temperature, T\({}_{\text{B}}\), and energy barrier to magnetization reversal, E\({}_{\text{B}}\), in highly diluted magnetic fluids synthesized by dispersing 8 nm-diameter Fe\({}_{3}\)O\({}_{4}\) nanoparticles in hexane. Data collected on five samples of volume concentrations (\(v/v\)) of 4 \(\times\) 10\({}^{-2}\)%, 1.3 \(\times\) 10\({}^{-2}\)%, 1.7 \(\times\) 10\({}^{-3}\)%, 8.3 \(\times\) 10\({}^{-4}\)%, and 4 \(\times\) 10\({}^{-4}\)%, show that, in each case, the system undergoes superparamagnetic transitions. The corresponding blocking temperatures increase with the increase in concentration, from T\({}_{\text{B}}\) = 39 K at c = 4 \(\times\) 10\({}^{-4}\)% to T\({}_{\text{B}}\) = 53 K at c = 4 \(\times\) 10\({}^{-2}\)%, according to a power law T\({}_{\text{B}}\) = A-c\({}^{\gamma}\), with A = 64 K and \(\gamma\) = 0.056. As a monotonic increase in T\({}_{\text{B}}\) with c was predicted by the Dormann-Bessais-Fiorani (DBS) model, we used the DBS equation, \(\tau(\text{T})=\tau_{\text{r}}\) exp \(\left(\frac{E_{\text{B}}+E_{\text{ad}}}{k_{\text{B}}T}\right)\) to analyze the observed superspin relaxation behaviors, \(\tau(\text{T})\), of the nanomagnetic fluids. Expectedly, we found E\({}_{\text{ad}}\) = 0 for the lowest concentration sample (c = 4 \(\times\) 10\({}^{-4}\)%), which allowed us to determine E\({}_{\text{B}}\)/k\({}_{\text{B}}\) = 366 K. We used this value  
Figure 6: Concentration dependence of the reduced additional energy barrier to magnetization reversal E\({}_{\text{ad}}\)/k\({}_{\text{B}}\) (solid symbols) obtained from the DBF analysis in Figure 5. E\({}_{\text{ad}}\)/k\({}_{\text{B}}\) increases linearly with the common logarithm of the concentration (solid line).  
to determine the additional energy barriers \(\rm E_{ad}/k_{B}\) in the denser samples. We found that \(\rm E_{ad}/k_{B}\) increases linearly with the common logarithm of the volume concentration, from 138 K at c = 8.3 \(\times\) 10\({}^{-4}\)% to 745 K at c = 4 \(\times\) 10\({}^{-2}\)%.  
C.E.B.: conceptualization, methodology, investigation, project administration, formal analysis, and writing--original draft. A.D.P.: methodology, formal analysis, investigation, and writing--review, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the U.S. Department of Defense Army Research Office under Award No. 64705 CHREP.  
Not applicable.  
The data presented in this study are available from the corresponding author upon reasonable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Test Design  
Two test series have been carried out. In each of the test series, the same artificial damage is inflicted on two pinions. The artificial damages are milled manually along the meshing region, resulting in severe damage with a large change in gear meshing (see Figure 3). The implementation of geometrically defined damage was rendered infeasible due to the presence of helical gearing. Consequently, manual damage was employed as an alternative. The objective of this test series is to conduct a comparative analysis between the gear wheel sensor with classical encoders and the acceleration sensors. To this end, a range of server damage conditions are introduced. The artificial damages are introduced by dismounting the gearbox adapter, milling the artificial damage, and mounting the gearbox adapter again. To account for the mounting procedures for every damage stage, the mounting procedure is carried out again after all operating conditions are tested; this is referred to as the \"repetitions\" in the following.  
For each damage stage and two repetitions, three different torque and speed conditions are tested. The operating conditions and the artificial damages are described in Table 1. Each test series has two pinions to which the same artificial damages are applied. Each pinion is tested in the new condition and with the same first two artificial damages throughout the test series. The first test series has one additional artificial damage, and the second test series has three additional artificial damages. Therefore, for the first test series, 288 test conditions were executed, and for second test series, 432 test conditions are executed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **First Test Series** & **Second Test Series** \\ \hline Torque in Nm & & 20, 55, 85 \\ Speed in rpm & & 300, 1300, 2000 \\ Quadrants & Q1 and Q4, where the damaged teeth flanks are in contact \\ Repetitions & & 2 \\ Pinions & & 2 \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{_New:_ healthy pinion; _1T_: One tooth damaged; _3T_: Three adjacent teeth damaged; _3T_: Three adjacent teeth damaged; _3T_: Three adjacent teeth _  
The test conditions with the same damage conditions for all four pinions lead to more data points for these damage conditions. Therefore, for the following data evaluation and classification, half of these data points are randomly discarded to acquire the same amount of test data for all test conditions.