esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Validation of the Numerical Model  
The model has been validated based on an experimental study conducted by [PERSON] et al. [22]. A large-scale direct shear box with the dimensions of (305 mm length \(\times\) 305 mm width \(\times\) 140 mm depth) was modeled using FLAC\({}^{\text{3D}}\). The upper part of the box is fixed whereas the lower part is movable to simulate the behavior of the shear box as in the lab experiment. The shear plane is located at 76 mm from the bottom and the used soil was dense sand. A 50 mm ordinary and geosynthetic encased granular column has been constructed using rad-cylinder code at the center of the box, the geosynthetic encasement was defined as a cylinder with 1 mm thickness. Strain softening Mohr\(-\)Coulomb (SS model) was used to describe the granular column and the soil. After running the program, the model was cycled to reach the equilibrium by the self-weight. Moreover, 75 kPa of the vertical stress was applied on the upper part of the box and the model was cycled to reach the equilibrium again. The applied horizontal velocity on the lower movable part is 2 \(\times\) 10\({}^{-4}\) mm/step. 2 \(\times\) 10\({}^{5}\) steps were applied to achieve a 40 mm horizontal movement. The numerical model was described in further details by [PERSON] et al. [19]. Table 1 presents the material properties adopted in the analysis. The results of the experimental study were consistent with those obtained through numerical analysis, as depicted in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Granular Column** & **Sand** \\ \hline Model & - & Strain softening & Strain softening \\  & - & Mohr-Coulomb & Mohr-Coulomb \\ Unit weight & kN/m\({}^{3}\) & 16.2 & 16.3 \\ Modulus of elasticity & MPa & 100 & 10 \\ Cohesion & kPa & 0 & 0 \\ Poisson’s ratio & - & 0.3 & 0.3 \\ Angle of friction & Degrees & 61 & 41 \\ Dilation angle & Degrees & 20 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristics of the materials used [19].  
Figure 1: Shear stress- horizontal displacement curve to validate the numerical model [22].
## 3 Results and Discussion
### Statistical Analysis  
Table 1 represents the analysis of variance for the significance of the study parameters in relation to the performance of the sunflower threshing machine. It is clear that the damaged seed percentage was affected significantly at the probability level of 1% by increasing both the roller's rotational speed and the feed rate. A higher effect was recorded for the feed rate than the roller's rotational speed, while the effect of the interaction between the feed rate and the rotational speed was non-significant. The unthreshed seed was affected significantly at a probability level of 1% by both the roller's rotational speed and the feed rate, with a greater effect for the roller's rotational speed than the feed rate and without a significant effect for the interaction between the roller's revolving speed and the feed rate. The threshing efficiency was affected significantly at a probability level of 1% by both of the study variables, with a greater impact from the roller's rotational speed than the feed rate, while the interaction between the study variables had a non-significant effect on the threshing efficiency. The machine's productivity was influenced remarkably at a probability of 1% by the roller's rotating speed and the feeding rate, with a high effect from the feed rate. Otherwise, the effect of the interaction between the feed rate and the roller's revolving speed had no significant effect. The power requirement was affected significantly at a probability of 1% by the feed rate and the roller's rotational speed, with a significance level for the feed rate greater than the roller's rotational speed, while the effect of the interaction between the feed rate and the roller's rotational speed was non-significant. The obtained data show that the specific energy was affected significantly at a probability level of 1% by the roller's rotational speed values, while the effect of the feed rate and the interaction between the roller's rotational speed and the feed rate was non-significant. The obtained data represented that the operational conditions of 300 rpm with 800 kg/h recorded the highest threshing efficiency, a close value to the lowest consumed specific energy and the closest value to the highest production efficiency. Therefore, this operation conditions was considered the optimal operation condition.  
Figure 9: Effect of the roller’s rotational speed (rpm) on the specific energy (kg.h/ton) at different feed rates (kg/h).
# Fast Optimization of the Installation Position of 5G-R Antenna on the Train Roof
## 3 A Fast Prediction Model for the Coupling Coefficient
### Structure of the PSO-ELM  
As mentioned above, improvements to the BPNN algorithm can also be made using alternative algorithms. Therefore, this paper applies the ELM algorithm, which can obtain the unique optimal solution only by setting the number of neuron nodes of the HL. It has the advantages of a simple structure, fast training speed, and strong generalization performance. But, during operation, the parameters (weights and thresholds) of the input layer (IL) and the HL will also be randomly generated. For more detailed algorithms and  
Figure 5: The specific flow block diagram of the MEA-BPNN.  
steps, see reference [21]. However, since these parameters are generated randomly, it may lead to the situation where the value is 0 when the parameters are given, which makes the output matrix unsatisfactory and there are many invalid neuron nodes, resulting in a poor accuracy and stability of prediction in the face of complex and irregular data. Therefore, the PSO algorithm can be used to optimize the initial parameters in the model in order to improve the accuracy and stability. When the PSO algorithm is used to train the ELM network, the weights and thresholds of neuron nodes in the structure are encoded into a particle swarm. The fitness value is the mean squared error (MSE) of the network output. First, the weight and threshold vector were initialized, and then the PSO algorithm is used to search for the optimal result; that is, the weights of the neuron nodes are searched within a defined number of iterations to minimize the output mean squared error of the network structure. The specific algorithm of the PSO algorithm can be referred to in reference [24]. The specific flow block diagram of the PSO-ELM algorithm is shown in Figure 6.
## 2 The Peak Shrinking and Interpolation (PSI) Technique
### Peak Shrinking Process  
Once the peak is detected and extracted, the shrinking process can be performed. Equation (5) shows that the shrinking process can be implemented by a multiplication. It should be noted that this multiplication is performed only on five data sample points when \(SUR=2\), as number of data points is equal to \((2\times SUR+1)\). Therefore, it does not need a large amount of memory. The shrinking process can be mathematically presented as:  
\[\mathit{Peak}_{Sh}=\mathit{Peak}_{0}\times SR, \tag{5}\]  
where \(\mathit{Peak}_{0}\) is the signal before process, \(SR\) is the shrinking ratio, and \(\mathit{Peak}_{Sh}\) is the resulting shrunk peak. In simulations we may modify the \(SR\), to achieve different results. The shrinking process illustrated in examples in Figure 7a-c is performed with \(SR=0.5\). As it is observed from Table 1, when \(SR=0.5\), the performance is the best compared to when \(SR=0.4,\ 0.6\). It should be noted that when \(SR=1\), it means that the peak is not shrunk; therefore, the PAPR remains the same. On the other hand, when \(SR\) is selected to be very small, this is the equivalent of removing the signal point. In the latter case, the \(EVM\) is degraded since there will be a sharp signal discontinuity. This has a similar effect to performing sharp clipping, and requires many filter stages to compensate. As the main aim here is to reduce the complexity of the system, \(SR\) is optimised.  
Figure 7: (a) An example of a peak border does not need matching/smoothing; (b) an example of a peak border that needs matching/smoothing; (c) an example of a peak border after the matching/smoothing process [24].
## 1 Introduction
### Active Distribution Network Management
#### 1.1.2 Centralized Management Schemes
In Reference [20], a voltage control method based on the active power variation of the DG unit's output to stabilize the bus voltage is proposed. The simulation results show that the proposed method can effectively improve the voltage control capability of the network and enable it to operate normally under severe conditions. Through the control method proposed, the communication requirements are greatly reduced and the calculation time is effectively reduced. In Reference [21], an algorithm that reduces voltage and frequency deviations by coordinating multiple energy storage devices is proposed. The algorithm reduces the total number of charging and discharging times by calculating the sensitivity coefficient of the storage devices at different nodes and then selecting the appropriate devices to operate. The algorithm was validated on a typical distribution testing system and the results show that the voltage and frequency are controlled within the permissible range, the state of charge of the storage devices is controlled within the normal range, and the total number of charging and discharging cycles are reduced to prolong life.
## 3 Experimental Technology Validation for Bearing Race Defect  
The experimental rig (Figure 2) has a coupled variable speed drive (VSD) motor with 20-60 Hz shaft rotation frequency, adjusting eccentricity of the coupling, and angular or parallel misalignment. The shaft is supported by three pillow block rolling element bearings of the same specification (FK UCP203), where two bearings are fixed (left and middle) and one is a replaceable testing bearing (right).  
The specifications of the testing bearing are shown in Table 1, which lists characteristics of bearing geometry and frequencies related to defect of an inner race (BPFI), an outer race (BPFO), a cage (FTF) and a rolling element (BSF) with respect to the frequency of rotation f.  
Two sets of experiments were conducted at speed of 3600 rpm, and the resultant radial load of 192 N, which is adjusted by inserting pre-machined shims under the test bearing housing. The first experiment is performed with undamaged bearing, and the second experiment is performed using a bearing with damaged inner race. The inner race defect extended through 0.8 mm of circumferential length and is 0.2 mm deep, with the relative damage size equal to 1.2% of circumference, which indicates an early stage of damage development (Figure 3). The damage level is not critical, it is selected at an early stage of a damage, that is important for multiple industrial applications.  
Vibration signals are captured from an accelerometer model KCF AG107M, placed along vertical direction on a test bearing external housing with a specification in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline Number of Balls n\({}_{\text{re}}\) & 8 \\ \hline Bore diameter & 17 mm \\ \hline Outer race diameter & 40 mm \\ \hline Ball diameter & 6.75 mm \\ \hline Ball pass frequency inner race, _BPFI_ & 4.95\({}^{\prime}\!\!f_{r}\) \\ \hline Ball pass frequency outer race, _BPFO_ & 3.05\({}^{\prime}\!\!f_{r}\) \\ \hline Ball spin frequency, BSF & 1.99\({}^{\prime}\!\!f_{r}\) \\ \hline Fundamental train frequency FTF & 0.382\({}^{\prime}\!\!f_{r}\) \\ \hline \end{tabular}
\end{table}
Table 1: Test bearing specifications.  
Figure 3: Bearing inner race defect characteristics.  
Figure 2: Experimental rig.  
The output connector featured waterproof sealing. Active antialiasing filter (cut-off frequency = 13.5 kHz) is used before capturing the digitized data. A speed reference signal (1 pulse per shaft revolution) is also captured synchronously. The data sampling is done at 40 kHz to provide a necessary bandwidth for channel recording.
# Parasitic Motions of 3-PRS Parallel Mechanisms with Two Different Branch Chain Arrangements
###### Abstract  
The kinematics of 3-PRS parallel mechanisms (PMs), an important category of 2R1T PMs, is very important for the motion control and error compensation of PMs. In order to produce a spindle head that could meet the requirements of various machining accuracy levels, a definite kinematics analysis of the PMs par should be taken with caution firstly. Here, the mathematical models of two 3-PRS PMs with different branch chain arrangements were established using the Euler angle. The kinematic equations of two 3-PRS PMs were obtained by establishing the closed-vector loop equation according to structural conditions. Parasitic motions that would cause a great effect were obtained, and their variations with structural and kinematic parameters were also obtained through MATLAB. Since the relationship between parasitic motions, and the given structural and kinematic parameters, is inextricable, an analysis is performed for the purpose of reducing or eliminating parasitic motion to ensure the accuracy of end-effector movement. The caused effect of parasitic motion can be improved with scale optimization and precision compensation. Lastly, two improved 3-PRS PMs without the parasitic motion of different branch chain arrangements used as the spindle head were obtained. Parasitic motion was related to the mechanism configuration and could be avoided in the configuration design stage with the comprehensive optimization of the structural and kinematic parameters, which provides a reference to obtain a sufficient workspace and improve the precision of traditional five-axis CNC machine tools.  
3-PRS PM; branch chain arrangement; parasitic motion; spindle head Article
### List of Main Contributions  
The following is a list of the main contributions that this work makes:  
1. We propose a camera-LiDAR fusion pipeline that generates transparent Sparse Depth Images and normalizes depth values for accurate spatial mapping.
2. We introduce three novel object depth extraction methods. Complete Region Depth Extraction (CoRDE), Central Region Depth Extraction (CeRDE), and Grid Central Region Depth Extraction (GCRDE).
3. We fuse YOLOv8 object detection with LiDAR clustering to improve depth estimates for object regions.
4. We evaluate our methodologies on objects in the KITTI dataset with different occlusion levels, evaluating mean time and point, accuracy, and root mean square error (RMSE), through a truth table.
5. Our findings show the advantages of segmentation masks compared to bounding boxes in object distance estimation and establish CeRDE as a method that effectively balances precision and speed.
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 4 Numerical Results
### Simulation Results
maximum accuracy of 91.83% after 141 epochs (loss = 0.22), CNN + BN achieved 93.21% after 107 epochs (loss = 0.19), and CNN reached 90.54% after 132 epochs (loss = 0.262). In Layer 2, the CNN + LBN model once again achieved the highest test accuracy and the lowest loss. The initial loss values for the CNN + LBN, CNN + LN, CNN + BN, and CNN models were 3.61, 3.45, 3.21, and 2.24, respectively. After 200 epochs of training, these values decreased significantly to 0.225, 0.257, 0.253, and 0.258. The CNN + LBN model achieved a peak accuracy of 91.8% after 192 epochs (loss = 0.225), CNN + LN reached a peak accuracy of 90.55% after 137 epochs (loss = 0.26), and the CNN + BN model achieved 90.6% after 76 epochs (loss = 0.259), and CNN reached 90.54% after 132 epochs (loss = 0.262). In Layer 3, all models showed a consistent reduction in loss. In Layer 3, the models consistently exhibited a reduction in loss. The initial loss values for the CNN + LBN, CNN + LN, CNN + BN, and CNN models were 2.94, 2.87, 2.94, and 2.24, respectively. After 200 epochs, these values dropped to 0.17, 0.21, 0.19, and 0.25. CNN + LBN achieved a maximum accuracy of 93.65% after 124 epochs (loss = 0.17), CNN + LN reached 91.04% after 153 epochs (loss = 0.23), CNN + BN reached 92.91% after 129 epochs (loss = 0.19), and CNN achieved 90.54% after 132 epochs (loss = 0.262). The results of the ablation study clearly demonstrate that LBN significantly enhances the simulation accuracy of CNN-based indoor positioning systems. To better understand these results, we present a qualitative analysis below. Although LN has been widely used in RNNs and scenarios characterized by extremely small batch sizes [20], its effectiveness in CNNs might be constrained. Distinct from batch normalization, layer normalization estimates normalization statistics independently for each training case, without using batch-level statistics [20]. As a result, while LN stabilizes training dynamics, it does not explicitly mitigate internal covariate shift, which is one of the key advantages of BN in CNNs. Notably, the CNN + LBN model consistently outperformed other models across all layers. BN normalizes across the batch dimension, while LN normalizes feature channels independently within each sample. Different from them, LBN leverages localized normalization mechanisms, making it more effective for stabilizing feature distributions in WiFi fingerprint-based positioning.
## 2 Method and Materials
### Data Collection and Processing  
It is necessary to gather and integrate detailed information of the study area, and a database was first built, covering geophysical factors, climate factors, vegetation factors, ET and land use data from 2000-2008. Climate factors include the daily relative air humidity and daily wind velocity, all of which may have great impacts on the spatial and temporal patterns of wind erosion. All of those climate data between 2000 and 2008 were derived from daily records of observation stations maintained by the China Meteorological Administration. The original meteorological data, which was saved in form of text, was interpolated into 1 km \(\times\) 1 km grid data using gradient plus inverse distance squares method [43, 44]. Besides, geophysical factors include the slope and soil properties. The slope data was obtained with the 90 m resolution digital elevation model data (DEM) which was downloaded from [[http://strm.csi.cgiar.org/](http://strm.csi.cgiar.org/)]([http://strm.csi.cgiar.org/](http://strm.csi.cgiar.org/)) [44]. The soil properties, including the average soil particle radius and soil hardness, were extracted from the HWSD soil dataset covering the Heihe River Basin and literatures [45, 46]. In addition, the vegetation coverage rate was calculated with the cloud-free NDVI data from 2001-2011 in the Heihe River Basin [47]. What is more, the ET data for the study area were extracted from the monthly evapotranspiration datasets (2000-2012) with 1 km spatial resolution over the Heihe River Basin Version 1.0 [48]. This study also used the 1 km resolution land use data derived from Landsat TM/ETM images in 2000, 2005 and 2008, which were interpreted by Chinese Academy of Sciences (CAS) with the overall interpretation accuracy of 92.7% [49]. All these data were processed to be the same with the climate data in terms of their spatial reference and resolution.
# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 5 Discussion
### Limitations and Further Work  
Finally, this work presents some limitations that we plan to improve in the future. The current graph contains 127 articles, which is a small number in comparison to the number of articles published. This is due to the fact that the input to our method are files in JATS format, which is not currently provided by many journals. PubMed is one of the few resources providing content in JATS format, so we used it as content source. However, PubMed is more oriented to biomedicine, so the number of papers related to agronomy is low. Therefore, we will extend our tools to accept input data in other formats, which will enable the inclusion of data from more sources. Another aspect that could be improved is the annotation context. At the moment, our model relates annotations at the level of paragraphs; however, sometimes these paragraphs are large and they could contain annotations that are semantically far between them. Therefore, we could improve this by relating annotations at thelevel of sentence, which would provide a more accurate context. Finally, the current Neo4j knowledge graph can be difficult to query and exploit by non-experienced users. Thus, we plan to fill the gap between the knowledge graph and the final user by developing a web query interface.
## 1 Introduction  
_Antrodia camphorata_ (AC) is a medicinal fungus, a fungus of the genus _Antrodia_ of the Fomitopsidaceae family, which is only native to Taiwan and has been used for treatment of liver cancer for many years [1]. There are abundant pharmacologically active ingredients in AC, and more than 80 components have been isolated and identified, including triterpenoids, polysaccharides, adenosine, superoxide dismutase (SOD), benzenoids, benzoquinones and maleic/succinic acid derivatives. Triterpenoids are the main active components of AC fruiting bodies, and they are similar in structure [2]. Antcins are the main triterpenoids in AC [3], including Antcin A, B, C, H, K and N [4]. At present, increasing attention has been paid to the research on their pharmacological activities, such as antioxidant, anti-inflammatory, anti-cancer and immunomodulating properties, etc., [5, 6].  
The mechanochemical-assisted extraction method (MAEM) is an innovative extraction technology, which has been widely used in the extraction of bioactive substances in recent years [7, 8, 9]. At present, the extraction of the bioactive substances from herbal medicine is mainly based on traditional solvent extraction in industrial processes [10]. However, the traditional solvent extraction method is time-consuming, requiring a high extraction temperature and low extraction rate, which decreases the quality and pharmacological activity of the target extracts [11]. MAEM is a technology that can destroy lignocellulosic in order to obtain more target products [12]. Some studies have shown that the extraction of flavonoids from bamboo leaves by MAEM [13] improves the yield of flavonoids and that the obtained flavonoids have strong hydrophilicity, which is more conducive to subsequent experiments. Most of the active components in plants are found inside cells [14], which is the same for AC and its triterpenoids. MAEM can destroy plant tissues during grinding, and the addition of solid alkali can react with the components in the plant to increase the solubility of the original substances, which increases the yield of extracted components that are barely extracted by other extraction methods.  
In this study, we aim to prove mechanochemical-assisted extraction method can achieve a higher yield and maintain the initial activity of triterpenoids in AC, as compared with the traditional ethanol thermal reflux extraction method.