esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Analysis of the Performance of the Reverse Combination (RC) Configuration
#### 4.1.2 Mismatch Loss Power  
Mismatch loss is defined as the difference between the sum of the theoretical maximum output powers of each module under the current irradiation conditions and the sum of the maximum possible output power of the PV array, which can be given by  
\[P_{\text{mis}}=\sum_{j}^{n}\sum_{i}^{m}P_{ij,MPP}-P_{array,GMPP} \tag{34}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum power & 6477.6 W \\ Open circuit voltage \(V_{oc}\) & 133.5 V \\ Short circuit current \(I_{sc}\) & 67.36 A \\ Maximum power point voltage \(V_{mp}\) & 105.3 V \\ Maximum power point current \(I_{mp}\) & 61.52 A \\ Temperature coefficient of \(V_{oc}\) & \(-0.363\%/^{\circ}\text{C}\) \\ Temperature coefficient of \(I_{sc}\) & \(-0.0843\%/^{\circ}\text{C}\) \\ Shunt resistance \(R_{sh}\) & 968.6 \(\Omega\) \\ Series resistance \(R_{s}\) & 0.58 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for PV module.  
Figure 7: Diagram of SP, VTCT, and HTCT.  
Figure 8: Characteristic curves for SP, VTCT, RC and HTCT: (**a**) I-V curve for condition 1; (**b**) P-V curve for condition 1; (**c**) I-V curve for condition 2; (**d**) P-V curve for condition 2; (**e**) I-V curve for condition 2; (**f**) P-V curve for condition 2; (**g**) I-V curve for condition 2; (**h**) P-V curve for condition 2.  
The maximum theoretical output power and mismatch losses are calculated for four conditions using Equation (34). Figures 9 and 10 present the statistical results for the mismatch loss power and power loss rate, respectively. The RC configuration demonstrates the lowest mismatch loss powers of 3.4 kW, 4.3 kW, 4.7 kW, and 3.1 kW, which are only 3% to 5% lower than the theoretical maximum output. This is attributed to the RC configuration's ability to achieve uniform irradiance along both the axial and radial directions of the airship array. As a result, the RC configuration exhibits a distinct advantage in terms of the array power retention rate.
## 5 Conclusions  
(1) Numerical simulation of the production effect of self-injection in the middle and late stages of production of horizontal wells in shale gas reservoirs under different tubing depth conditions was carried out, and the well structure model was established by OLGA software to determine the production capacity equations for this downward-dipping well.  
(2) By using a fully dynamic multiphase flow simulation program and taking a certain well as an example, a wellbore structure model was established. By changing the depth of the tubing, the limit values of formation pressure required to support self-injection production of gas wells under different tubing depths were simulated. The suitable tubing depth for self-injection exploitation of gas wells and the cumulative gas production at different tubing depths were studied. The simulation results can provide effective support for the actual production process for different shale gas wells and help on-site workers take effective measures in a timely manner to solve the problem of gas well injection stoppages in the middle and later stages.  
(3) The impacts of formation pressure on the gas well's spontaneous injection production dynamics under different tubing depth conditions were simulated, the critical formation pressure that can maintain the gas well's spontaneous injection production at different tubing depths was obtained, and it was concluded that when the tubing was lowered to 2200 m and 2400 m, the critical formation pressure of the gas well was 8 MPa; when the tubing was lowered to 2600 m, the critical formation pressure was lowered to 7.8 MPa; the critical formation pressure was around 8 MPa when the tubing was lowered to 2800 m and 3200 m; and the critical formation pressure was about 7.8 MPa when the tubing continued to be lowered to 3000 m and 3200 m.  
(4) According to the material balance equation and original geological reserves, the cumulative gas production of the gas well is \(56.19\times 10^{6}\) m\({}^{3}\) when the tubing is lowered to 2600 m, which is \(0.93\times 10^{6}\) m\({}^{3}\) more than that when the tubing is lowered to the sloping point (2200 m) and can extend the self-injection production time of the gas well by about 135 days.  
Methodology, L.Z., G.J. and J.L.; Software, J.L.; Writing--original draft, L.Z.; Writing--review & editing, L.Z. and G.J.; Supervision, G.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on the Prediction Model of Leakage between Supercritical CO\({}_{2}\) Pressure Fractures grant number [YQZC202406].  
The date that support the findings of this study are available from the corresponding author upon reasonable request.  
This study was supported by [PERSON], PetroChina Zhejiang Oilfield Company, and [PERSON], School of Petroleum Engineering, Yangtze University.  
The authors declare that they have no competing interests.
# Fast Frequency Acquisition and Phase Locking of Nonplanar Ring Oscillators
## 2 Design of the OPLL  
A block diagram of the OPLL is shown in Figure 1. The signal and local lasers were both NPROs, with two signal input ports for temperature and piezoelectric-transducer (PZT) frequency tuning, respectively. The temperature of the laser crystals was precisely controlled via a proportional-integral-derivative (PID) controller to keep the laser frequency stable. The control precision was 0.01 K for 1 h and 0.03 K for 8 h. The thermal tuning coefficient of Nd:YAG NPRO was 3 GHz/K. The typical frequency drift was measured to be around 30 MHz for 1 h and 100 MHz for 8 h. A piezoelectric element was bonded to the upper nonoptical face of the Nd:YAG crystal. The applied voltage can slightly change the dimension and refraction index of the laser crystal. Thus, the laser frequency was tuned. The PZT tuning coefficient was 1.6 MHz/V, and the response bandwidth was about 100 kHz.  
The output lights of local and signal lasers were combined and mixed via a 50/50 fiber coupler. Two output beat signals with a 180\({}^{\circ}\) phase difference were sent to the balanced photodetector, which had a model number of PDB480C and was made by Thorlabs, Inc. (Newton, NJ, USA). The converted electrical beat signal, acting as a phase error signal, was sent to the loop filter and the frequency discrimination and control module (FDCM). \(V_{c1}\) and \(V_{c2}\) are the output signal amplitude of the loop filter and FDCM, respectively. The loop filter was an active proportional-plus-integral filter, where the first time constant \(\tau_{1}=3\) ms and the second time constant \(\tau_{2}=5\)\(\upmu\)s. The main function of the loop filter was locking the phase, as the beat frequency falls into the pull-in range of the loop, which was around 700 kHz. The FDCM was used for the acquisition of signal frequency to keep the beat frequency below the pull-in range. \(V_{c1}\) and \(V_{c2}\) were amplified and added in the PZT driver, where the gain values \(G_{1}\) and \(G_{2}\) were 9 and 18, respectively. The output voltage amplitude of the PZT driver was \(\pm 190\) V.
## 7 Performance Evaluation
### Simulation Parameters and Topology  
The simulation parameters follow the performance evaluation parameters specified in Mobile WiMAX System Evaluation documents and WiMAX profiles [2]. These parameters are briefly summarized in Table 3. With 10 MHz system bandwidth, 5 ms frame, 1/8 cyclic prefix, and a DL:UL ratio of 2:1, the number of downlink symbol-columns per frame is 29 [2] (18 for uplink). Note that 1.6 symbol-columns are used for TTG and RTG. Of these, 1 symbol-column is used for preamble. In the Partial Usage of Subchannels (PUSC) mode, there are 30 subchannels in the downlink, and each slot consists of one channel over two symbol durations. As a result, there are _30 \(\times\) (24/2)_ = 360 downlink slots per frame. Each MS has only one flow, and is mapped to a different MCS used in Mobile WiMAX.  
Of these, Frame Control Header (FCH), DL-MAP, UL-MAP, Downlink Channel Descriptor (DCD), and Uplink Channel Descriptor (UCD) overheads can approximately range from 51 slots to 195 slots (including 31 slots for DCD/UCD in case of five, and fifteen mobile stations, respectively [5, 8, 10, 13]). The repetition of 4 with QPSK1/2 is used for MAPs.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**MCS** & **Bit per symbol** & **Coding rate** & **Bytes per slot** & **Throughput (kbps)** \\ \hline BPSK1/2 & 1 & \({}^{\prime}\!\!  
Notice that the total overhead depends on the number of actual burst allocations in both uplink and downlink; and other management messages.  
Table 4 lists system throughput for each MCS. We show seven MCSs. The analysis can be easily extended for more flows or MCSs. In our analysis, interference is represented as a change of MCS. To keep it simple, the MCS level is constant over the simulation period.  
We consider only downlink resource allocation because we want to analyze the effect of overheads for various scheduling algorithms. For DL, the queue size in number of bytes is translated to the number of requested slots. The analysis for UL is very similar except that the bandwidth requests are used to derive the number of requested slots. However, BS has no information about individual packet sizes. Therefore, both DRR and DRRF cannot be used for uplink scheduling.  
We used a modified version of the WiMAX Forum's ns-2 simulator [15] in which a mobile WiMAX module has been added [16].  
Consider a topology configuration. Figure 4 shows the simulation topology, a standard point to multipoint configuration. BS is a central control for resource allocation for both uplink and downlink directions. The link between BS and MSs is the only bottleneck link.
###### Abstract  
Seasonal frost or permafrost soils may encounter frost heave or thaw settlement resulting from atmospheric temperature changes and/or heat emanating from the resource-carrying pipeline. Notably, these soil movements can damage the pipeline. Thus, various ground stabilization methods have been developed to prevent the onset of these phenomena in frost-susceptible soils, and the application of thermosiphons is a representative method. Recently, a numerical analysis method called the thermosiphon model for a pipeline and thermosiphons in frost-susceptible soil has been developed; however, the study only focused on the ability to reduce frost heave of the soil using the thermosiphon. Therefore, here, numerical analysis was conducted to determine the performance of a buried pipeline according to frost heave and thaw settlement by applying the thermosiphon model via ABAQUS. For the novel numerical analysis, two scenarios are established: frost heave and thaw settlement. For each scenario, the behaviors of the frost-susceptible soil and pipeline are compared in four cases, distinguished by the arrangement of thermosiphons applied. The results indicate that according to the arrangement of the thermosiphons, the frost-heave and thaw-settlement behaviors are verifiably reduced by up to 62% and 82%, respectively, compared to when no thermosiphons are applied.  
frost heave; thaw settlement; frost-susceptible soil; thermosiphon; finite element analysis Article
## 3 Results and Discussion
### Dataset for Conducting Experiments
#### 3.1.2 Amsterdam Dynamic Facial Expression Set  
The Amsterdam Dynamic Facial Expression Set (ADFES) [47] is a comprehensive reference dataset containing images and videos of faces showing different emotional states. The ADFES is available for academic purposes and can be accessed through a request to the Amsterdam Interdisciplinary Center for Emotion.  
The ADFES reference set stands out above other facial recognition datasets due to its focus on dynamic facial expressions. It contains sequences of images derived from video recordings of models that show a change in facial expression from a neutral state to a peak emotional state. This set includes 2764 videos of 22 models (12 male and 10 female) expressing emotions, with each model captured from five different angles to provide a comprehensive and detailed representation of facial expressions.  
In this study, to test the proposed technique, a 720 \(\times\) 576 high-resolution image was extracted from the original ADFES and compiled into a small set, ADFES_small, with five basic human emotional states: Anger, Fear, Joy, Neutral, and Sadness. The total number of images in ADFES-small is 110; all images included in ADFES-small are different from those used to determine the weights for the hyperplane classifier. Each emotion category in the selected dataset has the same number of samples--110.
## 3 HIBAS Implementation for Control of a Foraging System with Deviating Motor Speeds
### Energy Characteristics of Psi Swarm Robot Hardware  
In order to bring realism to the simulated experiments and reduce the reality gap data was taken from the PSI swarm robot platform [17] to obtain a power model similar to that produced in [18] for the MarXbot. This model would take an individual robots speed as an input and produce a realistic value for the power consumed at a given time step. As a result the total energy consumed by the swarm across an experiment could be recorded using reports of energy consumption from each swarm member, providing more meaningful data regarding changes of speed within the experiment.  
To construct a power model, power consumption was measured using a Keysight N6705B power analyser [19]. Results for power consumption as speed increases were recorded through 10 repetitions and a quartic trend line was fit to the mean of these results, this is illustrated in Figure 2. The resultant equation for power consumption with speed as the input was:  
\[P=1.05-7.76\times 10^{-3}s+2.2\times 10^{-3}s^{2}-8.89\times 10^{-5}s^{3}+1.14 \times 10^{-6}s^{4} \tag{1}\]  
where \(P\) is energy consumption per second (Watts) and s is the current speed of the robot (cm/s). When implementing this equation in the robot swarm simulation, the offset of 1.05 was reduced to 0.05, as it was assumed that most of the offset was due to the base consumption of energy used by robot peripherals. The offset of 0.05 was left to ensure a negative power was never experienced during experiments. The equation was also scaled for the appropriate time frame, ensuring that the correct amount of power per wheel was collected per experiment tick. This equation was then used at each time step to calculate the current energy consumption based on the speed of each individual robot. Energy consumption could then be used to feed into the value of energy efficiency that would be used to measure the fitness of the systems tested in the experiments presented in this paper.  
After implementing Equation (1) in the simulation, the analyses of energy efficiencies at different speeds were conducted. In these tests, 20 robots foraged in a simple environment for 500 simulated seconds or until 100 food items were gathered. The average final energy efficiency (food item per unit of energy consumed) from 50 trials at speeds ranging from 1 to 50 cm/s were then plotted (illustrated in Figure 3). Taking the peak value of energy efficiency for a given speed, a value was chosen to act as a baseline for the following experiments.
## 3 Results and Discussion  
### Characterization of the N-TiO\({}_{2}\) Powder  
#### 3.1.1 XRD Analysis  
An XRD analysis was conducted to investigate the crystallinity of the N-doped TiO\({}_{2}\) powder produced. Figure 1 presents the diffraction diagram recorded for the N-TiO\({}_{2}\) powder. The TiO\({}_{2}\) crystal phase of anatase is the dominant one, while the rutile phase is the second one existing. The highest intensity diffraction peak of anatase (I\({}_{\text{A}}\)) is at 20 = 25.35\({}^{\circ}\) corresponding to (1 0 1) crystal plain, with all the other peaks of anatase present being in accordance with the PDF No 03-065-5714. These results are supported by the relevant literature [34; 35]. Ruelle is observed on its highest intensity (I\({}_{\text{R}}\)) at 20 = 27.30\({}^{\circ}\), corresponding to (1 1 0) crystal plain [34; 35]. The percentage of the rutile face in the powder is calculated by the following equation [36]:  
\[\%\text{Ruelle}=\left(\frac{1}{1+0.884\frac{\text{I}_{\text{A}}(101)}{\text{I }_{\text{R}}(110)}}\right)\times 100 \tag{1}\]  
The percentage of rutile in the N-TiO\({}_{2}\) powder produced is 37.9%. This percentage is within accepted limits regarding the values found in the literature [37; 38]. In fact, the higher the percentage of rutile, the higher the photocatalytic performance in N-doped powder [39]. The presence of rutile in N-doped TiO\({}_{2}\) could be associated with the calcination temperature (450 \({}^{\circ}\)C) followed in the experimental procedure [37; 40].  
The average crystallite size of the as-produced powder was calculated by using [PERSON]'s equation.  
\[\text{d}=\frac{0.89\ \lambda}{\beta\cos\theta} \tag{2}\]  
where d is the average crystalline size, 0.89 is the Scherrer's constant, \(\lambda\) is the X-ray wavelength, \(\theta\) is the diffraction angle, and \(\beta\) is the FWHM (full-width-half-maximum). This is calculated for the main peak of anatase (1 0 1) at 2 = 25.35 [41]. The average crystallite size of the powder is on the nano-scale, estimated at \(\sim\)14 nm.  
Figure 1: XRD spectrum of N-doped TiO\({}_{2}\) powder.
## 3 Results and Discussion
### Analysis of Orthogonal Experiment Results
#### 3.4.1 Range Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Group**} & \multirow{2}{*}{**Geotextile**} & \multirow{2}{*}{**Clay**} & \multirow{2}{*}{**Spray Thickness**} & \multirow{2}{*}{**Blank**} & \multirow{2}{*}{**Transmittance**} & **Insulation** \\  & & & & & & **Temperature** \\  & & & & & & **(\({}^{\circ}\)C)** \\ \hline
1 & 300 g/m\({}^{2}\) (2.2 mm) & 14.29 & 1 layer (2 mm) & 1 & 0 & 15.22 \\
2 & 300 g/m\({}^{2}\) (2.2 mm) & 16.28 & 2 layers (4 mm) & 2 & 0 & 16.73 \\
3 & 300 g/m\({}^{2}\) (2.2 mm) & 18.18 & 3 layers (6 mm) & 3 & 0 & 17.60 \\
4 & 400 g/m\({}^{2}\) (2.8 mm) & 14.29 & 2 layers (4 mm) & 3 & 0 & 17.10 \\
5 & 400 g/m\({}^{2}\) (2.8 mm) & 16.28 & 3 layers (6 mm) & 1 & 0 & 18.10 \\
6 & 400 g/m\({}^{2}\) (2.8 mm) & 18.18 & 1 layer (2 mm) & 2 & 0 & 16.68 \\
7 & 500 g/m\({}^{2}\) (3.4 mm) & 14.29 & 3 layers (6 mm) & 2 & 0 & 18.67 \\
8 & 500 g/m\({}^{2}\) (3.4 mm) & 16.28 & 1 layer (2 mm) & 3 & 0 & 16.36 \\
9 & 500 g/m\({}^{2}\) (3.4 mm) & 18.18 & 2 layers (4 mm) & 1 & 0 & 17.52 \\ \hline K\({}_{\text{ij}}\) & 49.55 & 50.99 & 48.26 & 50.84 & & \\ K\({}_{\text{2j}}\) & 51.88 & 51.19 & 51.35 & 52.08 & & \\ K\({}_{\text{3j}}\) & 52.55 & 51.8 & 54.34 & 51.06 & & \\ range R & 3 & 0.81 & 6.08 & & & \\ \hline factor & & & & & \\ Primary \(\rightarrow\) & & & CAB & & \\ Secondary & & & & & \\ Primary and & & & & & & \\ secondary & & & & & \\ order & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Range analysis results.
## 5 Testing and Validation  
Core assets are rigorously tested to ensure quality, robustness, and reusability. Datasets are validated for completeness, accuracy, and representativeness, while models are evaluated for baseline performance. Pipelines are tested for correctness and modularity, ensuring they can handle variability and scale effectively. Standardized benchmarks and metrics are used to guide testing.
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
## 2 Numerical Model  
### Computational Domain and Mesh  
The schematic diagram of the two-dimensional (2D) computational domain considered for a single wave energy converter is shown in Figure 1. The domain consists of a 5 m \(\times\) 0.7 m numerical wave tank, with the 0.3 m \(\times\) 0.2 m WEC shown in Figure 1a located 2.15 m from the inlet. The entire domain comprises five boundaries: the inlet, outlet, atmosphere, bottom, and WEC.  
A hexahedral-based structured mesh consisting of non-uniform cells was generated. Overall, 1.6 million cells were present in the multiregional segmented tank. The overset mesh method was applied to the surroundings of the solid WEC to capture its motion. A coarse mesh was first built in the tank and then refined around the WEC. To minimize mesh deformation, a dynamic mesh technique based on topological change was applied. For the problem simulated here, the cell size in the core of the domain should be larger than the cell surrounding the solid surface; see Figure 1b. The mesh resolution surrounding the WEC was significantly refined, and ten inflation layers were built near the buoy wall, with the thickness of the first layer falling within 0.08-0.085 mm.
## 1 Introduction
Specifically, in view of q-space, DW image volume along one diffusion direction can be taken as a node in q-space, with the DW images being the features of the node. Accordingly, we first express the DW image volumes along different directions as a graph; then, the GFT is implemented on the graph to map the DW signals into the coefficients at different spectral bands. By computing the coefficients' similarities at different bands, the influence of the noise on the similar DW image pair matching can be decreased. After that, using the matched image pairs as input and target, respectively, a ResNet-like network architecture is used to denoise. In addition, to avoid the over-smooth phenomenon, a novel edge-aware loss based on dilation and erosion (realized by pooling operations) is proposed. It can adaptively adjust the loss weights on edge and smooth regions to improve the further denoising performance.