esa-sceva/satcom-chunk-collection huggingface dataset:
# A Process Decision-Making Method for Planar Machining of Box-Type Components
## 4 Ontology Modeling for Process Decision-Making
### Defining Classes and Their Hierarchical Relationships  
By ontologizing the acquired domain knowledge, an ontology representation model for the domain knowledge of planar machining process decision-making is developed, as illustrated in Figure 6.  
* In the ontology editing tool Protege, \"owl:Thing\" serves as an abstract class and the parent class of all entities. Its subclasses--\"Part\", \"FlatDF\", \"PartDF\", \"ProcessProgram\" and \"PSParameter\"--respectively represent box-type parts, design features of the machined surface, design features of the part, machining schemes, and process dimension parameters.
* The subclass \"MachineFlat\" under \"Part\" represents the machined surface.
* The subclass \"FlatDF\" includes \"PSize\", \"PDatum\", \"SRoughness\", \"AClass\", and \"FPTolerance\", which, respectively, denote nominal dimensions, positioning reference, surface roughness, accuracy class, and geometrical tolerance constraints. The subclass \"PSize\" further includes \"BasicSize\", \"Length\", and \"Width\", which define the basic size, length, and width of the machined surface. The subclass \"PDatum\" consists of \"FDatum\" and \"RDatum\", representing finishing datum and rough machining datum, respectively. The subclass \"FPTolerance\" represents various geometrical tolerances.
* The subclass \"PartDF\" includes \"HTreat\", \"PMaterial\", \"CMethod\", and \"PType\", which correspond to the heat treatment method, part material, casting method, and production type, respectively. These subclasses align with the design features of the part described in Section 3.2.2.
* The subclass \"ProcessProgram\" contains \"FlatMP\" and \"FlatMM\", which denote machining schemes and machining methods for planar surfaces. The subclass \"FlatMM\"  
Figure 6: Hierarchy of decision classes for planar machining process of box-type components.  
further categorizes various machining methods, such as rough milling, finish milling, and grinding.
* The subclass \"PSParameter\" consists of \"PAllowance\", \"PBDimension\", \"TMAllowance\", \"B_MAGrade\", and \"B_STGrade\", representing machining allowance, process dimensions, total machining allowance, blank machining allowance grade, and blank size tolerance grade, respectively. Additionally, the subclasses \"PAllowance\" and \"PBDimension\" specify the allowance and dimensions for each machining operation.
## 4 Experimental Section
### Frequency Resolution and LED-OFC Recognition Result Analysis
#### 4.3.1 Low Frequency Range  
As shown in Figure 9, the recognition accuracy rate rapid declined when the frequency dropped below 3 Hz. When the frequency was greater than 5 Hz, the recognition accuracy was acceptable. That is, to ensure high recognition accuracy, the frequency resolution should be greater than 5 Hz when in a low frequency range.
## 3 Results and Discussion
### Physicochemical and Microbiological Properties
#### 3.2.1 Physicochemical Characteristics of Meat  
pH is commonly used as an indicator of meat freshness. The pH values obtained in this study revealed a significant effect of the treatment (\(p<0.05\)) on the meat's acidity throughout the refrigeration period. As storage time increased, pH values slightly rose in both the control sample and those treated with magnetic field and pulsed light; however, these differences were not statistically significant. The increase in pH during refrigerated storage may be attributed to the breakdown of proteins into ammonia, amines, and alkaline substances, influenced by endogenous enzymes and microorganisms in pork [40; 41]. Furthermore, it was found that magnetic field and pulsed light had no effect on water activity, forced drip, redox potential, or the TBARS index. However, the combined treatment significantly reduced forced drip throughout the refrigeration period (\(p<0.05\)), compared to the control sample. Additionally, two-way analysis of variance revealed a statistically significant effect of both the applied treatment and refrigeration storage time on forced drip. Water absorption capacity of meat refers to its ability to retain its own water (juice) and absorb additional water during the processing stage. This property is one of the most important indicators of the technological suitability of meat and a key parameter for assessing its quality [42]. It influences the sensory characteristics of meat to some extent and is a critical factor in determining its technological usability. Analysis of the data presented in Table 2 shows a positive effect of the treatment on the meat's ability to retain water. Forced drip decreased with increasing refrigeration storage time. Moreover, refrigeration time significantly (\(p<0.05\)) influenced water loss in both the control sample and those treated with magnetic field and pulsed light. The magnetic field has the potential to modify the microstructure of meat, especially by affecting the molecular interactions between water molecules and proteins. It alters the binding mechanism of water to these proteins, thereby improving water retention within muscle fibers. As a result, this reduces the loss of water due to leakage, leading to meat with enhanced moisture retention, which ultimately contributes to improved juiciness and overall product quality.  
Similar results were observed by [PERSON] et al. [43], who investigated the effect of static magnetic fields on pork quality. The authors demonstrated a positive effect of the treatment on water absorption capacity, with values higher than those of the control sample.
# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 3 Results and Analysis
### Analysis of the Characteristics of Urban Heat Island Changes  
#### 3.2.1 Daily Time Scale  
The daily intensities of the SUHI and CLHI, as well as the distribution of the heat island center's latitude and longitude at the four transit times from 2003 to 2020, are depicted in Figure 3. It can be observed from Figure 3 that the nighttime intensity of the SUHI and CLHI is strongest in winter and the daytime in summer. The intensity of the SUHI and CLHI at night is mainly distributed in the range of 1.0-3.0 \({}^{\circ}\)C; the daytime intensity of the SUHI is distributed in the range of 1.0-3.3 \({}^{\circ}\)C, and that of the CLHI is distributed in the range of 0.5-2.1 \({}^{\circ}\)C, indicating that the daytime intensity of the SUHI is stronger than that of the CLHI. Moreover, the longitude and latitude of the footprint ellipse centers of the daytime SUHI and CLHI from 2003 to 2020 are relatively scattered. The longitude is mainly distributed in 116.2-116.4\({}^{\circ}\) E, and the latitude is mainly distributed in 39.7-40.0\({}^{\circ}\) N. At night, the longitude and latitude of the center of the heat island footprint ellipse are more concentrated, and the range of change is smaller than in the day. The longitude is mainly distributed in 116.3-116.4\({}^{\circ}\) E, and the latitude is mainly distributed in 39.90-39.95\({}^{\circ}\) N. At the same transit times, the standard deviations of the latitudes of the ellipse centers of SUHI and CLHI are both smaller than those of the longitude, indicating that the changes in the centers of the ellipses of SUHI and CLHI in the east-west direction were greater than those in the north-south direction between 2003 and 2020. This may be attributed to the influence of the sun rising in the east and setting in the west.  
The distribution of the daily SUHI and CLHI footprint ellipse area and direction from 2003 to 2020 are shown in Figure 4. The results show that the elliptical area and direction of the SUHI and CLHI footprints vary greatly during the day. The area of the heat island footprint ellipse is distributed in the range 1000-5000 km\({}^{2}\), and the direction of the heat island is between \(-20^{\circ}\) and \(40^{\circ}\). At night, the ellipse area of the heat island footprint and the direction of the heat island are more concentrated. The ellipse area is mainly 1000-2000 km\({}^{2}\), and the direction of the heat island is mainly between \(-20^{\circ}\) and \(20^{\circ}\).  
Figure 2: Distribution map of CLHI (**a**) in the day and (**b**) at night in Beijing on 5 September 2020.  
Figure 3: The daily distribution map of the intensity of heat islands and the longitude and latitude of the center of the SUHI and CLHI from 2003 to 2020 at the times of three border crossings (**a**) 2:00, (**b**) 13:00 and (**c**) 22:00.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 4 Error Recovery in FTT-CAN  
Unlike the methods reviewed in Section 2, in FTT-CAN message retransmissions are scheduled dynamically, in a controlled way, in response to actual errors [12]. This section describes how the error detection and rescheduling mechanisms are implemented on FTT-CAN, and presents an analytic formulation for computing the number of replicas required to attain a desired reliability level, applicable to messages that have short deadlines.
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 3 Numerical Results  
In this section, the results of our proposed beamforming networks are organized into three corresponding subsections. Excellent performance is attained for each case network, thereby proving that they are suitable for our envisioned switched-beam phased arrays.  
Figure 15: Schematic of the tree type network with \(N\) outputs.
## 2 Design of the State Vector
### Definition of Variables in the ICEKF  
The descriptions of the main coordinate frames and variables used in this study are listed in Table 1. The relationships between the coordinate frames of the IMU-aided camera integration system are established, as shown in Figure 1, where \(\mathbf{p}_{c}^{i}\) and \(\mathbf{\overline{q}}_{c}^{j}\) represent the relative linear translation and rotation between the IMU and the camera, which are considered constants once calibrated. The IMU outputs linear acceleration \(\mathbf{a}_{mi}\) and angular velocity \(\mathbf{\omega}_{mi}\) in its rigid body coordinate frame. This paper considers the visual black-box measurement outputting rotation \(\mathbf{\overline{q}}_{w}^{c}\) and unscaled linear translation \(\mathbf{p}_{w}^{c}\) in the world frame. The fusion result is attached to the IMU coordinate frame, with the coupled linear translation and rotation denoted as \(\mathbf{p}_{w}^{ic}\) and \(\mathbf{\overline{q}}_{w}^{ic}\), respectively. The derivatives of the translation and rotation are \(\mathbf{v}_{w}^{ic}\) and \(\mathbf{\underline{\dot{q}}}_{w}^{ic}\). The units used in this paper are described in the SI, such as acceleration in m\({}^{2}\)/s and angular velocity in rad/s.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(w\) & fixed world coordinate frame \\ \(i\) & coordinate frame attached to the IMU \\ \(c\) & coordinate frame attached to the camera \\ \(ic\) & coordinate frame attached to the IMU-aided camera system \\  & \(\mathbf{x}\) represents a general viable vector, A is the coordinate frame \\ \(\mathbf{x}_{B}^{A}\) & attached to the vector, and B is the reference frame; for example, \(\mathbf{p}_{w}^{c}\) \\  & denotes the linear translation of the camera with the frame \(c\), \\  & measured with respect to the world frame \(w\) \\  & translation vector of rigid bodies along 3 axes, of which the \\ \(\mathbf{p}\) & quasi-quaternion description is \(\mathbf{\overline{p}}=[0,\mathbf{p}^{T}]^{T}\) \\  & unit quaternion according to the Hamilton notation [16], written as \\  & \(\mathbf{\overline{q}}=[q_{0},q_{1},q_{2},q_{3}]^{T}=[q_{0},\mathbf{q}^{T}]^{T}\) \\ \(\mathbf{\overline{q}}^{*}\) & conjugate quaternion of \(\mathbf{\overline{q}}_{*}\) and \(\mathbf{\overline{q}}^{*}\otimes\mathbf{\overline{q}}=1\) \\ \(\mathbf{R}\) & rotation matrix converted from \(\mathbf{\overline{q}}_{*}\) such as \(\mathbf{R}_{w}^{i}=\mathbf{R}(\mathbf{\overline{q}}_{w}^{i})\) \\ \(\lfloor x\times\rfloor\) & skew-symmetric matrix of \(\mathbf{x}_{*}\) and \(\lfloor x\times\rfloor\mathbf{y}=-\mathbf{x}\lfloor y\times\rfloor\)[23] \\ \(\mathbf{n}\) & white Gaussian noise vector with zero mean and covariance \(\mathbf{\sigma}^{2}\) \\ \(\mathbf{g}\) & gravity vector in the world frame \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coordinate frames and notations in the ICEKF.  
Figure 1: Relationships of the coordinate frames in the ICEKF.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 1 Introduction  
Along with other dynamic technological developments, new electrochemical materials are being developed, which have many properties desired in advanced automation and robotics systems [1; 2; 3; 4]. One of the very interesting groups of such materials is electroactive polymers, which show a change in selected properties when stimulated with an electric field. We can distinguish three types of this kind of electrically stimulated material, like dielectric electroactive polymers (DEAP), ionic polymer-metal composites (IMPCs), and magnetorheological elastomers (MRE). Due to their properties, these materials are classified in the literature as \"smart materials\", the definition of which determines the ability to control changes of properties in response to environmental stimuli [2; 5]. The possibility of strong deformation simultaneously with low weight, high resistance to cracking, and quiet operation constitute a huge potential for possible applications of electroactive polymers. The potential application of soft actuators covers such areas as soft robotics, artificial muscles, wearable sensors, and biomimetics. The main advantage of soft actuators, compared to conventional mechanical actuators, is their much greater flexibility, ability to withstand higher loads, and lightness [1; 2; 3; 4]. The application of soft actuators in soft robotics allows the obtaining of delicate tools. In addition to the mentioned advantages of soft actuators, there are several drawbacks that require further research and development. For instance, DEAPs require high voltage, in the range of kV, for their activation. Nowadays, it has been shown that it is possible to reduce the operating voltage to several hundred volts and even below 100 V by reducing the thickness of DEAPs and using stacked DEAPs [6]. However, reducing the thickness of the actuator to a few micrometers requires the development of a procedure for the DEAPs' precise manufacture. In the case of IMPCs, a change in the gap size between electrodes (electrode separation) is applied to optimize the torsion performance. The IPMCs with patterned electrodes fabricated using a masking technique are designed to accomplish a twisting motion [7]. The new trend in MRE preparation isan approach for improving magnetic particle dispersion during the fabrication of MRE to provide better magnetorheological properties [8].  
In this paper, we are focused on the recent progress in the preparation, modification, and application of electric-stimulated soft actuators, such as IPMCs, DEAPs, and MREs, especially in the last few years. Moreover, for a better presentation of the latest trends in the modification of soft actuators, we have prepared a diagram, shown in Figure 1.
## 3 Experiments and Discussion
### Simulated Experiments
#### 3.1.2 F-Theta Lens Surface  
Another simulation experiment was conducted on an f-theta lens freeform surface to evaluate the proposed method. Compared with the sinusoidal surface, the f-theta lens surface is relatively smoother without periodical features. The measurement of the f-theta lens surface is determined by:  
\[z_{1,2}=ax^{2}+bx^{4}+cy^{2}+\varepsilon_{1,2}, \tag{21}\]  
where \(a=-1/250\), \(b=1/92,000\) and \(c=-1/25\) are the design parameters of the surface and \(\varepsilon_{1}=40\)\(\upmu\)m and \(\varepsilon_{2}=30\)\(\upmu\)m are the added normal distributed noises which represent the different measurement errors. Figure 10 shows the simulated measurement surfaces.  
Figure 11 shows the results of Gaussian process modelling with the mean surface and the covariance surface determined. The RMS values of the covariance surface are 38.6 \(\upmu\)m and 31.0 \(\upmu\)m and the modelling error are 1.4 \(\upmu\)m and 1.0 \(\upmu\)m, respectively. The results show that the measurement uncertainty is estimated well using the Gaussian process. Figure 12 shows the result after maximum likelihood data fusion. The RMS value of covariance surface is 24.2 \(\upmu\)m which is smaller than that from the original covariance surface, and this is expected from the use of the data fusion principle.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Evaluation Items** & **With Sensor 1** & **With Sensor 2** & **Fused Data** \\ \hline RMS of uncertainty & 29.8 \(\upmu\)m & 19.8 \(\upmu\)m & 16.5 \(\upmu\)m \\ RMS of deviation & 3.5 \(\upmu\)m & 2.4 \(\upmu\)m & 2.2 \(\upmu\)m \\ \hline \end{tabular}
\end{table}
Table 1: RMS (root mean squared) value of associated uncertainties of the original measurement datasets and fused dataset.  
Figure 10: Simulated measurement results of the designed f-theta lens surface: (**a**) with 40 \(\upmu\)m measurement noise; and (**b**) with 30 \(\upmu\)m measurement noise.  
Figure 9: Deviations from the underlying surface from the results of: (**a**) Sensor 1; (**b**) Sensor 2; and (**c**) fused data.  
The deviations from the original surfaces and the surface after data fusion to the underlying surface are shown in Figure 13. Figure 13a,b presents the evenly distributed patterns along the whole surface which realizes the effectiveness of the modelling method and choice of zero mean surface. The RMS value of the uncertainties and deviations are shown in Table 2. Both the uncertainties and the deviations of the fusion result indicate improvement as compared with the original datasets. Similar to the one for the sinusoidal surface, the error in the edge and corner area are larger since the data in those areas are insufficient.  
Figure 11: Gaussian process modelling results of the two simulated measured datasets: (**a**) with 40 \(\upmu\)m measurement noise; and (**b**) with 30 \(\upmu\)m measurement noise.  
Figure 12: Fused result with the maximum likelihood data fusion method.  
Figure 13: Deviation from the underlying surface from the results of: (**a**) Sensor 1; (**b**) Sensor 2; and (**c**) fused data.
## 3 File Access Patterns
### Input Traces  
We perform our analysis over a set of 17 different production traces provided by Huawei Technologies and collected from hybrid (i.e., multi-tiered) data storage systems of the Huawei OceanStor Dorado and Pacific Series. Each input trace consists of a list of file 1/O requests to be processed by the storage system. Each request consists of a _process id_ of the application submitting the request, an epoch _timestamp_ of submission, a file _operation_ (e.g., open, read, write, close), a full _file path name_, a request _offset_ (in bytes), a request _length_ (in bytes), the _file size_ (in bytes), and the _duration_ of the operation (in microseconds). The traces exhibit a very high degree of variability in their characteristics: the number of file requests ranges from 3 K up to 6.7 M, originating from 1 up to 44 K applications running in parallel, accessing from 17 files up to 860 K files, with individual file sizes ranging between 0 bytes and 32 GB. In addition, 7 out of 17 traces are read-heavy (containing >90% read requests), while 4 traces are write-heavy (<50% read requests). After investigating the read requests, we observe that only 5 traces perform predominately sequential reads (>90%), while 7 traces perform a majority (>50%) of random reads. Table 1 summarizes the information about the traces, including the number of files accessed, the total number of requests, and a breakdown per request type (i.e., open, close, read, write). Overall, the traces exhibit very diverse and complex access patterns. Given that the proposed features and models work well across all 17 traces (as shown in Section 6), we believe they are generalizable to other workloads and systems.
# Study on the Optimization of Pile Length of Micropiles in Soil Landslides
## 2 Methods
### Optimization of Micropiles Length Based on Numerical Simulation
#### 2.4.3. Programmatic Implementation of Optimization Algorithms
As shown in Figure 8, the model length and height are consistent with the profile, and the width is 10 m. The meshing process employs hexahedral and tetrahedral cells, resulting in a total of 49,371 nodes and 44,310 cells. The calculation assumes a fixed pile bottom, and the determined length of the pile is based on empirical evidence to ensure that the minipile can penetrate the clay rock layer by at least 3 m, which is temporarily set as 18 m in this study. The soil parameters of the optimized calculation model were taken with reference to its surveyed stratigraphic parameters table, see Table 4. The structural unit parameters were taken according to Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Structure Type** & \begin{tabular}{c} **Cross-Sectional** \\ **Dimensions/m** \\ \end{tabular} & **Density g/cm3** & \begin{tabular}{c} **Modulus of** \\ **Elasticity/ECPs** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s Ratio** \\ **\#** \\ \end{tabular} & \begin{tabular}{c} **Polar Moment** \\ **of Inertia/m4** \\ \end{tabular} & \begin{tabular}{c} **Y-Axis Moment** \\ **of Inertia/m4** \\ \end{tabular} & \
\begin{tabular}{c} **Z-Axis Moment** \\ **of Inertia/m4** \\ \end{tabular} \\ \hline
\begin{tabular}{c} Piles \\ Connected \\ beams \\ \end{tabular} & \(\upmu\)0.15 & 2600 & 30 & 0.20 & \(4.97\times 10^{-5}\) & \(2.485\times 10^{-5}\) & \(2.485\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Table of values of structural unit parameters. Figure 8: Optimization calculation model. Figure 7: Design calculation results. The optimization procedure is calculated according to the preset relevant parameters, and its optimization process is shown in Table 8.  
The pile length obtained from the optimization calculation is PL = 15 m. The internal forces of the minipile after the optimization calculation is completed are extracted, and the results of its bending moment and shear force calculation are presented in the following figures (Figures 9 and 10). The numerical simulation results reveal that the rear row pile exhibits the highest bending moment, followed by the middle row pile, and finally, the front row pile displays the lowest bending moment. The maximum value of bending moment for all minipiles is 7.539 kN\(\cdot\)m, and the maximum value of shear force is 26.47 kN, which is small compared with the results of theoretical calculations. As depicted in the figure, the numerical simulation results indicate that the rear row pile exhibits the highest bending moment, followed by the middle row pile and the front row pile with the lowest magnitude. Overall, the optimized design has successfully achieved its intended objective, and through engineering practice, the calculation method and optimization approach outlined in the previous section have been demonstrated to be more dependable.