esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Performance Analysis
#### 5.1.3 Performance Analysis for State Switching and Energy Consumption  
Figures 10 and 11 show the change trends in the state switching rate \(\epsilon\) and energy consumption \(E\) under the proposed mixed mechanism.  
Figure 9: The changing trends in non\(-\)emergency packets’ average latency.  
In Figures 10 and 11, we can find that as the wakeup threshold \(H\) increases, the state switching rate \(\epsilon\) and energy consumption \(E\) of the CH show decreasing trends under the parameter settings of this experiment. This is because the wakeup threshold constrains the conditions for the CH to switch from sleep state to working state. The larger the wakeup threshold, the more time the CH will remain in the sleep state, resulting in a lower state switching rate and energy consumption.  
Moreover, in Figures 10 and 11, under the parameter settings of this experiment, increasing the adjustment factor \(\alpha\) will reduce the state switching rate while increasing energy consumption instead. According to the definition of access probability, the larger the adjustment factor, the greater the possibility of allowing non--emergency packets to access the CH. At this time, the CH remains in the working state for a longer period of time, reducing the possibility of switching to the sleep state, thereby reducing the state switching rate and increasing energy consumption. Similarly, increasing emergency packets' arrival rate \(p_{1}\) or decreasing service rate \(s_{1}\) shows the same trends on state switching rate and energy consumption.  
Figure 11: The changing trends in energy consumption of the CH.  
Figure 10: The changing trends in state switching rate of the CH.
# Simulation of Two-Phase Flow of Shotcrete in a Bent Pipe Based on a CFD-DEM Coupling Model
## 4 Calculation Results and Analysis
### Gas Phase Flow Field  
Figure 7 presents the global distribution of the gas phase static pressure during the pneumatic conveying process of the shotcrete material. The evident pressure loss within the rubber hose can be noted by comparing Figure 7, Figure 8a, and Figure 9. The pressure distribution in the horizontal, straight pipe section is relatively uniform compared to that near the bent pipe section. The pressures in the inside and outside areas of the bent pipe section are significantly different. The pressure in the outside area is high, and the pressure in the inside area is low, which is due to the centrifugal inertial force; in addition, the viscosity of the fluid on the pipe wall increases the fluid velocity on the wall surface. Moreover, both fluid velocities in the inside and outside areas are lower than that in the bent-pipe center, and the centrifugal inertial force of the fluid in the pipe center is greater than that in the inside and outside areas. The fluid in the pipe center flowing from inside to outside increases the external pressure, i.e., the pressure on the outside wall is much higher than that on the inside wall of the bent pipe. In addition, the gas velocity near the wall surface is relatively low, and the slow-flowing particles near the left and right boundaries move from the outside to the inside along the pressure decrease direction; the backflow at the central axis of the bent pipe results in a secondary flow. Subsequently, the secondary and main flows merge and move along the flow direction of the pipeline. The secondary flow limits the hitting velocity of the solid phase toward the outside wall of the bent pipe, and the central part of the outer surface of the bent pipe experiences more wear.  
Figure 6: Variation in pressure drop with air velocity.  
According to Figure 8b, during the flow of the shotcrete material through the pipeline, the pressure in the straight pipe section in front of the bent pipe tends to decrease at a constant speed; behind the bent pipe section, the pressure decreases significantly for various bend angles. The pressure decrease in the 90\({}^{\circ}\) bent pipe is the lowest, whereas the other bent pipes show no evident pressure difference. During pneumatic conveying, the particle velocity within the pipeline varies significantly (Figure 9). Furthermore, the pressure difference affects the gas flow velocity distribution near the bent pipe, and the higher pressure pushes the gas phase to the side with the lower pressure. There are a higher pressure and a lower flow velocity in the outside area of the bent pipe part, and lower pressure and a higher flow velocity in the inside area of the bent pipe part.
## 4 Dataset, Experimental Settings, and Evaluation  
### Dataset Description  
To verify the effectiveness of the method proposed in this study, the entry and exit metro flows with fifteen-minute granularity from Xi'an subway stations between October 2018 and March 2019 were collected. Table 1 shows the station names and numbers used in this study.  
In addition, temperature and precipitation data of Xi'an city with the same time granularity were also collected for experiments. Moreover, POI data were collected according to the latitude and longitude range of the subway station and utilized to explain the experimental results in this study. A POI refers to a distinct geographic location that provides a particular service to individuals, such as a shopping center, industrial facility, or residential area. In total, there were 241,869 POIs collected near each subway station within an 800 m radius of line 1 to line 4. In this study, Zhonglou station and Hingtiancheng station were selected as the research objects.
# Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
## 2 Materials and Methods
### Phantom and In Vivo PA Imaging Using the Side-Fire Fiber  
PA imaging of a phantom using the side-fire fiber was demonstrated by imaging absorbing targets embedded in chicken breast tissue. The targets were polyurethane tubes (0.015\({}^{\prime\prime}\) ID and 0.033\({}^{\prime\prime}\) OD) filled with India ink (DeSerres, Vancouver, BC, Canada). The fiber with the 20 mm long window was coupled to the tunable laser (LAZR, FUJIFILM VisualSonics, Inc., Toronto, ON, Canada). The energy output at the 750 nm wavelength used for imaging was 3-5 mJ, as measured from the window region. B-mode and PA-mode images were acquired with a Vevo 3100 ultrasound imaging system (FUJIFILM VisualSonics, Inc., Toronto, ON, Canada) and a 20 MHz linear array (MX250; max. image width 23 mm) positioned to image the tube cross-sections. To evaluate the illumination along its length, the optical fiber was positioned parallel to the image plane, i.e., perpendicular to the tubes. The fiber was then aligned perpendicular to the image plane (i.e., parallel to the target tubes) and positioned centrally approximately 10 mm from the tubes to evaluate the tissue illumination by the fiber in the azimuth direction. A motor stage was used to translate the transducer along its elevation direction with a step size of 150 um and acquire a volumetric image of the tube targets.  
For in vivo PA imaging, a rat ear was placed on an anechoic and optically transparent support that held the optical fiber such that the ear was within the large illumination area of a 20 mm window side-fire fiber. Anesthesia of the animal was initiated by 5% isoflurane in oxygen and maintained at 2% throughout the imaging. B-mode and PA-mode imaging was performed with the same laser and ultrasound acquisition setup as for the phantoms described above. The optical fiber was inserted into a channel 5 mm below the top surface of a block of plastisol (50 \(\times\) 50 \(\times\) 50 mm\({}^{3}\)) [28; 43] to separate visually the strongly reflecting fiber from the thin ear in the images. The transducer was held by a motor stage above the ear, with ultrasound gel coupling between the ear and the plastisol block, and up to 8 mm thick above the ear. A 3D scan of the ear was set up to acquire an image volume of 25 mm (image width) \(\times\) 16 mm (image depth) \(\times\) 10 mm (scan length) with a step size of 150 um. Animal experiments were performed under the approved animal use protocol of Sunnybrook Research Institute. Image reconstruction and analysis for both phantomand in vivo imaging were performed using the Vevo LAB software (v5.6.1, FUJIFILM VisualSonics, Inc., Toronto, ON, Canada).
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 3 Methodology
The Bayesian approach that Optuna uses differs from traditional methods, such as grid search and random search, once it constructs iterative probabilistic models that guide the search toward more promising regions of the hyperparameter space. This capability is enhanced by the Tree-structured Parzen Estimator (TPE) algorithm, which employs nonparametric distributions to estimate the probability of superior performance across different configurations [43]. Minimizing the difference in \(T^{2}\) values between the training and validation sets ensured that the model could effectively discriminate between different structural states, promoting adequate generalization. Optuna performed 50 iterations to minimize the reconstruction error. Figure 4 provides an overview of the proposed methodology. This graphical representation facilitates the understanding of the process of analyzing and quantifying structural modifications in railway wheels. All the simulations performed in this study were conducted in a local environment set up in Jupyter Notebook, implemented in version 1.93 of Visual Studio Code [44]. The code used was developed in Python, version 3.10.11, ensuring compatibility with the necessary libraries and tools for performing the analyses. The machine used to run the simulations is equipped with an AMD Ryzen 5 5500 processor, 6 cores, and 12 threads, operating at 3.6 GHz (with a boost of up to 4.2 GHz), with a 19 MB cache and compatibility with the AM4 socket. The installed RAM consisted of a 16 GB DDR4 Mancer Dantalion Z module, with a frequency of 3200 MHz and C19 latency. In addition, the simulations were accelerated by a Mancer Radeon RX 5500 XT Streaky graphics card, equipped with 8 GB of GDDR6-128-bit memory (AMD, Santa Clara, CA, USA). The choice of a local infrastructure, as opposed to cloud-based platforms, was motivated by several reasons. Firstly, cloud environments often require specific subscriptions for access to more powerful Graphical Processing Units (GPUs) and Central Processing Units (CPUs), which can result in significant recurring costs for long-term projects. Additionally, such platforms may experience disconnections after prolonged continuous use, compromising the execution of lengthy simulations and requiring manual restarts. With the local setup, it was possible to ensure greater control over the execution environment, continuity in data processing, and resource optimization while maintaining the reproducibility and computational efficiency required for this study.
## 4 Dynamic Response Characteristics of the TDP-VSG
### Transient Oscillation Mechanism of TDP-VSG Control  
Equations (6) and (10) reveal that the introduction of transient damping adds a non-zero zero point and a pole to the APL of TVSG. The position of the zero point is related to the set decay time constant \(T_{\text{d}}\). To analyse the impact of TDP-VSG control parameters on the system's stability performance, Figure 6 displays the zero-pole distribution diagrams under various \(T_{\text{d}}\), while the transient damping \(D_{\text{s}}\) is increased from 0 to 100.  
Comparing Equations (6) and (10), it becomes apparent that for small values of \(T_{\text{d}}\), Equation (10) closely approximates Equation (6), suggesting that the power compensation characteristic of transient damping is minimal. Additionally, as illustrated in Figure 6, when the transient damping \(D_{\text{s}}\) is constant, the system's real pole progressively moves away from the imaginary axis as \(T_{\text{d}}\) increases, resulting in an increased damping ratio and a decreased response rate. Therefore, to balance the stability and rapidity of the TDP-VSG system, the decay time constant is set to \(T_{\text{d}}\) = 0.5.  
From Figure 6, it is evident that at \(T_{\text{d}}\) = 0.5, the introduction of transient damping \(D_{\text{s}}\) causes the real pole \(s_{3}\) move closer to the imaginary axis, while the conjugate poles \(s_{1}\) and \(s_{2}\) shift away from the imaginary axis, and the system continues to exhibit oscillatory characteristics. As \(D_{\text{s}}\) continues to increase, the conjugate poles \(s_{1}\) and \(s_{2}\) gradually transition into real poles, transforming the system from underdamped to overdamped, thereby eliminating oscillations. However, with further increases in \(D_{\text{s}}\), the real poles \(s_{1}\) and \(s_{2}\) reform a conjugate pair, resulting in a reduced damping ratio and diminished stability of the TDP-VSG system, indicating that the selection margin for the transient damping coefficient \(D_{\text{s}}\) is quite limited.  
Figure 6: The trend of pole distribution in APL small-signal model of TDP-VSG.
## 4 Results and Discussion
### Model Validation and Experimental Results  
In this section, the FEM and theoretical stress analyses will be validated. Furthermore, the ANN fitting model based on the FEM simulation results will be constructed and discussed. Finally, based on the experimental results and pressure model, the fluid pressure variations under variable experimental conditions will be analyzed and discussed.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### Clustering Graph  
For intensive large-scale observation, clustering operations can effectively reduce the scale of the solution search space, decrease task conflicts, and improve problem-solving efficiency. This paper focuses on leveraging clustering information to generate the clustering graph and enhance the search speed for the optimal solution. The clustering model for the satellite mission is built using graph theory in accordance with the provided constraints [12; 37]:  
\[w_{v}^{e}-w_{u}^{s}\geq\Delta T \tag{7}\]  
\[|\theta_{u}-\theta_{v}|\leq\Delta\theta \tag{8}\]  
Equations (7) and (8) are the detail constraints of the time window and slewing angle in the clustering process, where \(\Delta T\) and \(\Delta\theta\) are the specific observation time and angle limit thresholds, respectively. Whenever two adjacent tasks, \(u\) and \(v\), meet the constraint conditions, the points \(u\) and \(v\) are connected by the edge \(e_{uv}\).  
The model consists of a graph, \(G=\langle V,E\rangle\), comprising a set of vertices, \(V\), and a set of edges, \(E\). The vertices, \(V(G)\), represent the observation tasks. The edges, \(E(G)\), represent the connecting lines between points that satisfy the clustering constraints. All edges that satisfy the clustering constraints are included in \(E(G)\). This way, the undirected graph model, \(G=\langle V,E\rangle\), can be utilized to represent the clustering scenario of the satellite among the observable original tasks. After the embedding operation described in Section 3.3.1, the original tasks \(r_{1}\), \(r_{2}\),..., \(r_{n}\) are transformed into a set of vertices \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) in the graph. Each vertex \(v_{i}\) represents one task in the original task set. If a pair of vertices \((V_{u},V_{v})\in E(G)\), then the matrix entry \(A_{uv}\) is assigned a value of 1; otherwise, \(A_{uv}\) is set to 0. Below are the steps to construct the task cluster graph:  
1. Iterate through all tasks in \(V(G)\) and construct an \(n\times n\) adjacency matrix \(A\) initialized with zeros. If the two original tasks \(u\) and \(v\) satisfy the time window constraint, set \(A_{uv}\) to one. This process generates the edge, \((V_{u},V_{v})\), and eventually constructs the graph \(G_{1}\).
2. Examine each edge in \(G_{1}\) and remove it if its two endpoints do not meet the roll angle constraint. This results in a task clustering graph \(G_{2}\).
3. The clustering algorithm concludes, yielding the final clustering graph \(G\) and its corresponding \(n\times n\) adjacency matrix \(A\).
## 2 Study Area and Data Sets
### Data Set Description  
#### 2.2.1 Rain Gauge Data  
The rain gauge data were obtained from the Xinfengjiang Reservoir Administration, which integrated the daily rainfall data from 2000 to 2019. The observed daily rainfall data from 15 surface rainfall observation stations that are evenly distributed over the XRRB (Figure 1) were already quality-controlled. The monthly rainfall data are calculated from the daily rainfall data, and both of them are used to evaluate the accuracy of the IMERG series products and runoff simulation over the XRRB.
## 3 Results and Discussion
### Optimization of Deff, Sp and dE  
#### 3.3.1 Optimal Parameters and Indicators  
The optimization is based on the three indicators Deff, Sp and dE, which are typical for technical and economic characteristics.  
The optimal indicator of the moisture diffusion coefficient function Deff is a characteristic quantity for the economic criteria of drying equipment. It is defined as the highest value of the moisture diffusion coefficient obtained from the experimental research.  
The optimal indicator of saponin content Sp in Bo Chinh ginseng is a characteristic quantity for the technical parameters of drying equipment. It is defined as the highest value of saponin content obtained from the experimental research.  
The optimal indicator of color change function dE is a characteristic quantity for the technical parameters of drying equipment. It is defined as the lowest value of color change obtained from the experimental research.  
Figure 23: Effect of T and A on color change dE.  
Figure 24: Effect of P and A on color change dE.  
The general optimal indicator, also known as multi-objective optimization, is defined as the balance between the economic and technical criteria of drying equipment so that the moisture diffusion coefficient and saponin content can reach the highest value while the color change reaches its lowest value. Optimal parameters are the input values that ensure the optimal indicators (output).  
The optimization analysis is carried out using Statgraphics 19 software. The results of multi-objective optimization are presented as follows:  
* Multi-objective function: Deff\(\rightarrow\)max; Sp\(\rightarrow\)max; dE\(\rightarrow\)min; Conditional function: 40 \(\leq\) T \(\leq\) 50; 80 \(\leq\) P \(\leq\) 120; 0 \(\leq\) A \(\leq\) 0.4 Optimal parameters:
* Drying temperature: T = 45.2 \({}^{\circ}\)C;
* Ultrasonic power: P = 127.7 W;
* Intermittency ratio: A = 0.18; Optimal indicators:
* Moisture diffusion coefficient: Deff = 5.23 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s;
* Saponin content: Sp = 96.4%;
* Color change: dE = 5.66;
# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 4 Text-Based Methods
### Matrix-Factorization-Based Methods  
Matrix factorization is a group of algorithms that decompose a matrix into the product of two smaller matrices with reduced dimensions. In hashtag recommendation, matrices have been constructed following the assumption that _although hashtags and words are both made of terms, hashtags are typically used with a different intention and serve a different purpose_[59]. Thus, [PERSON] et al. [59] constructed their matrix based on the hashtag-terms of tweets. They proposed a hashtag recommendation model using non-negative matrix factorization (NMF) to generate tweets' latent features. To find hashtags relevant to a given tweet, tweets containing these two hashtags were retrieved, and their average cosine similarity score was calculated.
# Environment- and Genotype-Dependent Irrigation Effect on Soybean Grain Yield and Grain Quality
## 3 Results
### Grain Yield  
All of the interactions (AB, AC, BC and ABC) were highly significant (\(p<0.01\)) sources of variation for soybean grain yield (Table 2). The irrigation effects varied significantly across the growing seasons (AB). In the A1 growing season, all three treatments resulted in similar grain yields. In the A2 growing season, A2B2 resulted in a 4.5% mean grain yield increase, but A2B3 resulted in a 2.1% decrease compared to A2B1. In the A3 and A4 growing seasons, the irrigation effect was higher than in the other two growing seasons. In A3B2, a 27.9% mean grain yield increase compared to A3B1 was determined, while the increase in A3B3 was 44.4% compared to B1. In A4B2 and A4B3, the mean grain yields increased by 35% and 41.2%, respectively, compared to A4B1.  
The soybean genotype (C) was a significant source of variation for the grain yield. C2, C3 and C4 all had maximal grain yields in the A2 growing season and minimal grain yields in the A1 growing season. In contrast, C1 had a maximal grain yield in A4 and a minimal grain yield in the A2 growing season. The irrigation effect also varied among the genotypes (BC). The biggest mean grain yield increase due to irrigation was observed in C1. B2C1 resulted in a 25.15% mean grain yield increase and C1B3 resulted in a 35.59% increase compared to B1C1. C2 had a 21.27% mean grain yield increase in B2 and an 18.87% increase in B3 compared to B1. However, C3 and C4 had considerably smaller mean grain yield increases in irrigation than C1 and C2.  
Figure 3: Water balance and irrigation rates (mm/month) in the B2 and B3 irrigation treatments during the four growing seasons (A1–A4).
## 3 Dataset and Calculation of Objective Values
### Dataset  
The datasets used in our experiments come from the Jhuoshuei River irrigated region in the Chiayi-Tainan Plain of Taiwan. We chose this region because it is the largest agricultural area in Taiwan, with the highest agricultural yield and the largest number of irrigation canals. The target region has a total of 55 irrigation branches that belong to three major branch systems. The relationships between them are shown in Figure 1. The data formats for each branch and the relationships between them are presented in Appendix A (Tables A1-A3). The black text presents the numbers of the branches, and the thickness of the lines represents the width of the branches. According to the irrigation data of 2017 provided by the Yunlin Management Office of the Irrigation Agency, the main crops in this irrigation area are rice, sugar cane, and vegetables, with total cultivation areas of 2926.456 has, 5568.742 has, and 1893.242 has, respectively. The total yield per hectare of the three types of crops was 5776 kg, 47,836 kg, and 18,367 kg, respectively, and their daily standard water demand was 183.6, 44.2, and 52.41 m\({}^{3}\), respectively. Their farm prices per kilogram were NTD (new Taiwan dollar) 22.13, 0.96, and 28.78, and the government ultimately set the compensation amounts per hectare for the three types of crops at NTD 93,000, 82,000, and 75,440. Most papers use data from multiple years to ensure the accuracy of the objective values; however, as the focus of the current paper is the methodology of filtering solutions and not the accuracy of prediction, we use data from only a single year. We verify the proposed approach by measuring its performance in selecting a small set of solutions among many. Performance is, thus, related to the number of solutions and not the amount of input data.