esa-sceva/satcom-chunk-collection huggingface dataset:
# Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool
###### Abstract
[PERSON] et al. also adopted a genetic algorithm to optimize a BackPropagation neural network (BPNN) to reduce the instability of the thermal displacement estimation models. [PERSON] et al. [30] established a total of 222 TIPs from different structural positions of a machine tool; among the ITPs, KTPs were selected using density-based clustering. Subsequently, a steepest descent algorithm was replaced by the genetic algorithm in a BPNN to establish a thermal displacement model. [PERSON] et al. [31] analyzed 27 ITPs and displacement stability by using the ratio of temperature variations to displacement variations over time. Highly correlated ITPs were clustered using a fuzzy clustering algorithm, and the KTPs with the greatest stability for each cluster were defined. Finally, a BPNN was used to create a thermal displacement model. The results revealed that the screened KTPs had higher estimation accuracy than did the remaining points. By performing an analysis based on the grey prediction algorithm, [PERSON] et al. [32] analyzed the grey action values of 76 ITPs and their displacement levels, after which the fuzzy clustering algorithm was used to cluster these values for KTP selection. Finally, the adaptive neuro-fuzzy inference system was adopted to establish a spindle thermal displacement model. The results demonstrated that the root mean square errors (RMSEs) of the proposed model were within 4 \(\upmu\)m in thermal error experiments with different temperature increases and decreases. The aforementioned studies have demonstrated that appropriate KTP selection and the use of highly accurate and robust estimation models are necessary for the improvement of spindle thermal errors in machine tools. In the present study, several ITPs were defined according to an analysis of machine tool structures, experience, and internal and external heat sources. Subsequently, the spindle thermal displacement experiments were conducted to measure both the temperatures of all the ITPs and the spindle displacement in the z-direction for different spindle rotation speeds. After the experimental data were collected, a reference point for temperature increase was obtained by calculating the variance for each ITP. Highly correlated ITPs were then clustered, and the elbow method [33] was adopted to determine the optimal number of KTPs. Finally, according to the characteristics of the data, an artificial neural network (ANN) using long short-term memory (LSTM) architecture was designed to establish a spindle thermal displacement model in the z-direction. This model can be used as the basis for the spindle thermal displacement compensation in a machine tool to maintain machining accuracy.
## 1 Introduction
In particular, the cavity filter tuning problem was formulated as a continuous RL problem and an effective solution was proposed based on Deep Deterministic Policy Gradient (DDPG). Second, we appropriately designed a reward function inspired by the experienced tuning processes, which stabilizes and accelerates  
Figure 2: The process of manually tuning a cavity filter. The tuning technician (**a**) observes the S–parameters curve (\(S_{11}\)) (**b**) as shown in a Vector Network Analyzer (**c**) and adjusts the screws inserted into the cavity filter with a screwdriver (**d**) based on his own experience and tuning strategies (**e**). The whole process is very much similar to a reinforcement learning problem. the RL training. The proposed method was tested on a real cavity filter tuning issue and positive results were received. Third, we presented a framework to transfer the learned tuning policy to a new detuned product individual of the same type. With limited steps of exploration on the new product, a data-driven mapping model was built, and the relationship between the new and the old product could be learned. With this framework, the policy learned from RL can be generalized. The remainder of the article is structured as follows. Section 2 introduces the tuning method of cavity filter and the basic principles of RL. In Section 3, our approach to autonomic tuning using continuous reinforcement learning is developed and presented. We first illustrate the framework of the algorithm and then explain the reward shaping through human inspirations. We also consider the generalization issue to make the algorithm more adaptable. A set of simulation experiments are carried out and the results to verify the validity of the framework are given in Section 4. In Section 5, some relevant issues are discussed in depth. Finally, Section 6 concludes the paper and makes a plan for future work.
## 2 Materials and Methods
### Research Methodology and Model Construction  
#### 2.3.1 Identification of Heavy Rain Events  
This study adopts the industry standard defined by the China Meteorological Administration, which classifies a precipitation event as a heavy rain if the 24-h rainfall exceeds 50 mm. Losses from heavy rain disasters are typically caused directly or indirectly by such events. Therefore, this paper focuses on precipitation events where daily rainfall is 50 mm. A day is recorded as a heavy rain day if the precipitation from 8 PM of the previous day to 8 PM of the current day exceeds 50 mm. The onset of a heavy rain event is marked when one or more meteorological stations within the study area detect heavy rain, and it is considered to end when no stations report further heavy rain. The duration from the start to the end of this event is recorded as the heavy rain process.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 8 Conclusions
For Severo-Kurilsk (Figure 3), Rio Caribe (Figure 4), and Ridgecrest (Figure 6) the applicable range is from 0.434 Hz to 0.041 Hz, for Ndoi Island (Figure 5) up to 0.082 Hz, frequency bands below have too weak fluctuations. After an increase in the ratios to positive values, Severo-Kurilsk and Rio Caribe show enhancements mainly in the X- and Y-components along the frequency band, Ndoi Island is higher in the Y-component with oscillating behaviour. Ridgecrest shows a sinusoidal modulation of the ratios from higher to lower frequencies. For all events usually at least two (varying) components have a parallel moving pattern. Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Path Planning for Laser Cladding Robot on Artificial Joint Surface Based on Topology Reconstruction
## 4 Generation of Laser Cladding Paths
### Generation of the Laser Cladding Interpolation Points
#### 4.2.1 The Slicing Algorithm Based on Topological Relation
If the _isSearched_ of that edge is FALSE, figure out the intersection between that unsearched edge and the slicing plane; if it is TRUE, skip it. **Step 3:** End the search when an unsearched edge cannot be found. Generate the path by joining all the intersections. This slicing algorithm had two advantages. Firstly, through this method, it only needed to traverse the list of edges once to find the first edge intersecting the first current tangent plane. In the actual  
Figure 7: (**a**) The topological slice process. (**b**) The cladding path generation in \(Z\) direction. slicing process, only the edges contained in the triangular facet intersecting with the current tangent plane were searched and calculated, which greatly reduced the number of intersection calculations. Secondly, the topological slicing algorithm was automatically executed in a definite search direction. The ordered intersection sequence was directly obtained, which avoids the reordering and reconnection of the intersection set and greatly improves efficiency. In this way, the cladding path, which included a set of parallel, equidistant and directed transversal lines, was obtained directly. To illustrate this procedure further, the pseudocode of this procedure is also given in Algorithm 2. ```
1/*Initialization*/
2 Set the slicing direction \(Z\) and the initial value for \(d\);
3 Calculate the value of \(Z_{\text{min}}\), \(Z_{\text{max}}\);
4/*Find the initial intersection*/
5 forS = Z\({}_{\text{min}}\) : \(Z_{\text{max}}\)
6 ifS < Z\({}_{\text{max}}\)
7\(S\) = \(S\) + \(d\);
8 Create an Eage variable \(E\);
9 fori = 1:number of ListEdge
10 if find an edge that intersects the slicing plane \(S\)
11 Calculate for the intersection \(P\); add \(P\) to \(\{P_{i}\}\);
12 Set \(E\) as the edge that contains the intersection \(P\);
13 endfor
14/*Find the remaining intersection*/
15 label:
16 forj = 1:6
17 if find an edge that intersects the slicing plane \(S\) from the surrounding edges of \(E\)
18 calculate for the intersection \(P\); add \(P\) to \(\{P_{i}\}\);
19 Set \(E\) as the edge that contains the intersection \(P\);
20 goto label;
21 else
22 endfor
23 endfor
24/*Output*/
25{\(P_{i}\)} is output as the set of the intersection. ```  
**Algorithm 2**Slicing algorithm based on topological relation.
## 1 Introduction
As the main benefits achieved by this technique, the following can be highlighted: increasing the speed of data mining algorithms, increasing the accuracy of prediction, reducing the complexity of the model [7,8]. The assumption is that better planning and design of networks and allocation of network resources can be achieved in the future if the value of end-to-end delay is known. Thus, this paper examines the performance of three predictive delay models for a VoIP service in an LTE network based on Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), whose input set of variables is optimized [9]. As a case study, the geographic area in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), in the vicinity of a three-segment road between the cities of Banja Luka (BL) and Doboj (Db), which is covered by the cellular network of the M:tel BL operator, is chosen. The main goal is to select an ML model with an optimal number of input variables, which provides the most accurate prediction results. The most important aims and objectives of this research are the following:  
* Reducing the dimensionality of the space of model input variables by optimization with Feature Selection techniques (RReliefF and Backward selection via the recursive feature elimination algorithms) and the Pareto 80/20 rule;Training and testing of ML models (MLP, SVM and k-NN) including the selection of the best delay prediction model in the LTE network using accuracy and complexity/interpretability criteria;
* Presentation of the aforementioned approaches to optimizing the number of predictors for LTE KPI predictive modeling, which is, according to the authors' knowledge and the review of former research papers, a particularly innovative solution;
* Implementation of a unique methodology of indirect assessment and calculation of delay values based on the average number of active users in the network;
* Creation of universally applicable predictive modeling of delays in the LTE network based on real research. For the case study, a data space related to one of the most important roads in the geo-road network of RS, BiH, was chosen. The structure of the paper consists of five sections. Section 1 provides an introduction. Section 2 presents a review of relevant published research papers, and Section 3 contains the materials and methods used in the paper. The main research focus is in Section 4, where the results and discussion are provided, after which the conclusions are drawn in Section 5. The references used are listed in the last section of the paper, after the conclusion.
# Video Super Resolution Using a Selective Edge Aggregation Network
## 2 Related Work
### Post-Processing  
First, post-processing used in the existing SISR or VSR operates based on CNN. [PERSON] et al. proposed the In-loop Filter CNN (IFCNN) to replace SAO [13]. In addition, [PERSON] et al. proposed a Variable-Sized Filter Residual Learning Convolutional Neural Net work (VRCNN) to replace both DB and SAO in HEVC intra-coding [14]. In their experiments, VRCNN was reported to achieve a promising result of an average reduction in BD speed of 4.6%. Both VRCNN and IFCNN are shallow networks with less than five layers. [PERSON] et al. proposed a Multi-Modal/Multi-Scale CNN (MMS-net) to replace the existing DB and SAO in HEVC using a deeper network of 30 layers [15]. [PERSON] et al. proposed a Deep CNN-based Auto-Decoder (DCAD) for post-processing [16]. In this paper, DCAD claims that there are average BD-rate reductions of 5.0%, 6.4%, 5.3%, and 5.5%, respectively, for AI, LDP, LDB, and RA configurations. [PERSON] et al. also proposed a residual-based video restoration network (residual VRN) for video post-processing [17].
## 4 Discussion
Our findings demonstrate that both physical adsorption and chemical bonding of RGD to HA surfaces significantly impact cell adhesion, with chemically immobilized RGD showing enhanced effects, particularly in the presence of serum proteins. This aligns with previous studies, such as those by [PERSON] et al. [21] and [PERSON] et al. [22], which underscore the importance of surface functionalization in promoting osteoblast adhesion and subsequent bone formation. However, our work also reveals that the method of RGD immobilization, whether physical or chemical, introduces variations in surface morphology and cell behavior, which are critical for the design of biomaterials. Mechanistically, the differences observed in cell adhesion between physically adsorbed and chemically bonded RGD can be attributed to the altered surface chemistry and topography resulting from each immobilization technique. Chemical bonding, as facilitated by cross-linking agents like EDC, creates a more stable and bioactive surface, promoting stronger integrin-mediated cell attachment, as supported by the work of [PERSON] et al. [16] and [PERSON] et al. [18]. This enhanced bioactivity is particularly evident in our AFM results, which show increased surface roughness and more uniform peptide distribution in chemically treated samples. These findings suggest that the specific surface interactions facilitated by chemical immobilization are crucial for optimizing cell adhesion and, ultimately, bone regeneration. The broader implications of these findings suggest that the method of RGD immobilization should be carefully considered when designing future osteoconductive scaffolds. While chemical immobilization offers greater stability and potentially enhanced cell adhesion in serum-free environments, it also alters the surface properties of HA in ways that could reduce its interaction with key serum proteins, potentially limiting its efficacy in vivo. On the other hand, physical adsorption maintains more of the native HA surface characteristics but may result in weaker peptide attachment, particularly in the presence of serum. These insights could influence the choice of immobilization method based on the specific clinical application, where stability, bioactivity, and the interaction with serum proteins must be balanced to optimize the scaffold's performance in promoting bone regeneration.
## 5 Diversity Parameters of mm-Wave \(1\times 4\) Linear MIMO Array Antenna  
In this section, the key diversity parameters of the MIMO antenna are calculated and compared with the measured results. In Figure 11, we assess the ECC and DG across all operational bands. Here, it should be noted that the ECC value is calculated using 3-D far-field radiation patterns [19]. From Figure 11, we can see that the DG between different ports lies above 9.97 dB throughout the applicable band. Additionally, the ECC is also less than 0.02 across all the reasoning bands. The DG and ECC values are under the limit of acceptable values in MIMO systems.  
MEG is a crucial parameter for assessing the potential application of MIMO antennas in 5G and wearable communication systems. It is defined as the ratio of the mean received power to the average incident power for a linear MIMO array antenna, compared to that of an isotropic antenna. The MEG is determined using far-field radiation patterns, as shown in Figure 12. Both simulated and measured MEG values are \(\leq\)\(-\)6.05 dB across all relevant frequency bands, which is below the acceptable limit of \(-\)3 dB [23].  
Figure 10: Comparison of radiation patterns at (**a**) 28.0 GHz and (**b**) 38.0 GHz.  
The TARC serves as a crucial metric within MIMO antenna systems, signifying the balance between incident and reflected signals. Mathematically, TARC is determined by [24], as depicted in Figure 13. This figure reveals that with a consistent increment in the phase angle of the input signal, TARC is stable without deviation.  
The CCL is also another significant parameter of MIMO antenna, and it measures the signal alteration between the transmitter and receiver end. The CCL value is mathematically expressed in [25], and the simulated and measured graph is illustrated in Figure 14. The simulated and measured CCL values across four bands are <0.29 and 0.31, 0.32 and 0.34, 0.28 and 0.31, and 0.24 and 0.27 bits/sec/Hz, respectively. Notably, these values consistently remain below 0.4 bits/sec/Hz [26], aligning with the established threshold value for MIMO antenna performance.  
Figure 11: Simulated and measured ECC and DG.  
Figure 12: Simulated and measured curves of MEG.
## 5 Vehicle Tests
### Load Test and Shift Performance Test  
A load test is used to simulate the torque output of a vehicle under a specific road resistance coefficient. The accuracy of vehicle inertia modeling can be verified by a load test. Load tests were carried out on a tracked vehicle with a mass of 30 t. According to Formula (34), the equivalent inertia is J = 2723.6 and the road resistance coefficient is set to 0.05, so the road resistance moment is \(-\)4204 Nm.  
Figure 18(a) shows the gearshift process of the tracked vehicle on a flat road. Figure 18(b) is a local amplification of uniform acceleration in a fixed gear. Its acceleration is 0.65 km/h, i.e., a = 0.63 rad/s\({}^{2}\). The required torque under the above equivalent inertia and deceleration is as follows:  
\[\mathrm{T}=\mathrm{J}\alpha=2723.6\times 0.63=1724.03\ \mathrm{Nm}\]  
\begin{table}
\begin{tabular}{c c c} \hline  & **Before** & **After** \\ \hline Input inertia/kg:m\({}^{2}\) & 2703.05 & 2723.6 \\ \hline Angular acceleration/rad:s\({}^{-2}\) & 0.61 & 0.538 \\ \hline Theoretical torque/Nm & 5861.01 & 5669.29 \\ \hline Measured torque/Nm & 5804.51 & 5654.23 \\ \hline Deviation & 0.96\% & 0.27\% \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of results before and after correction.  
Figure 18: Load and shift test.  
Therefore, the required torque in the above period should be  
\[\mathrm{T}=4204+1724.03=5928.03\ \mathrm{Nm}\]  
While the actual average value is 5957.2 Nm and the relative error is 0.49%.  
Figure 29c is a local zoom-in of the upshifting. In a normal shifting process, the power is released before shifting, so the system has instantaneous deceleration; after a high gear, due to the increase of the clutch front-end speed, explained by the clutch disc engagement process due to the speed difference between the front and rear discs, instantaneous large torque mutation occurs. Figure 29d is the local enlargement of downshifting. When the clutch is released after shifting, the speed of the front end of the clutch is greater than that of the back end, so the output torque has a sudden change process. In the process of the rapid change of torque, the difference between the actual speed and the target speed becomes larger due to the lag of loading of the control system. However, after the shift is completed, the speed error converges rapidly, and there is no big fluctuation in the shift process of the speed tracking control bench test. The maximum speed error is controlled within 2 km/h. The load simulation test results show that the accuracy of the equipment simulation vehicle engine load is controlled within 1%, and thus has a high accuracy.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods  
Three different classification methods, the improved k-NN (ik-NN), multinomial logistic regression (MLR) and support vector machine classifier (SVM), were tailored for windstorm damage detection to reach a desired detection accuracy level. The observations units in the models were forest stand level averages of the Sentinel-1 intensities or other stand level quantities of the input variables. Forest patches identified and derived using a segmentation algorithm were tentatively tested as optional observation units. The reason was that the damages do not necessarily follow the stand boundaries given by the Forest Centre. The segmentation-based units were tested only with the SVM classifier. Figure 7 shows the logic and processing phases of the classification models training and windstorm-map production. The methods are described in detail in the following sections.  
The main software tools used for analyses were as follows: (1) SNAP software by European Space Agency [40] was used for Sentinel-1 image pre-processing; (2) GDAL [41] was used for other image raster data pre-processing; (3) statistical computing package R [42] with own codes was used for other data handling including the field data, as well as for MLR, SVM and statistical analyses (Sections 3.4-3.6); and (4) own algorithms for ik-NN (Section 3.3), segmentation and adaptive filtering, written mainly in GNU Fortran [43] (Section 3.2).  
Figure 6: Volume of the growing stock on 31 July 2017 on the sub-area of the study area based on multi-source national forest inventory (EPS:3067).  
Figure 7: The flowchart of the windstorm detection methodology using Sentinel-1 time series: model training and damage-map production phases.
###### Abstract  
A facile one-pot synthesis route for the preparation of water-soluble, biocompatible patchy Fe\({}_{3}\)O\({}_{4}\)-Au nanoparticles (Fe\({}_{3}\)O\({}_{4}\)-Au pNPs) was developed. Biocompatibility was attained through surface functionalization with 1-methyl-3-(dodecylphosphonic acid) imidazolium bromide. The morphology, composition, crystal structure and magnetic properties of the Fe\({}_{3}\)O\({}_{4}\)-Au pNPs were investigated by conducting experiments with transmission electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction and superconducting quantum interference device, respectively. Internalization of the Fe\({}_{3}\)O\({}_{4}\)-Au pNPs by MCF-7 cells occurred via endocytosis. The performance of the Fe\({}_{3}\)O\({}_{4}\)-Au pNPs as X-ray radiosentizer in tumor cells was compared with that of gold nanocluster and Fe\({}_{3}\)O\({}_{4}\) NPs. For this reason, MCF-7, A549 and MCF-10A cells were loaded with the respective kind of nanoparticles and treated with X-rays at doses of 1, 2 or 3 Gy. The nanoparticle-induced changes of the concentration of the reactive oxygen species (ROS) were detected using specific assays, and the cell survival under X-ray exposure was assessed employing the clonogenic assay. In comparison with the gold nanocluster and Fe\({}_{3}\)O\({}_{4}\) NPs, the Fe\({}_{3}\)O\({}_{4}\)-Au pNPs exhibited the highest catalytic capacity for ROS generation in MCF-7 and A549 cells, whereas in the X-ray-induced ROS formation in healthy MCF-10A cells was hardly enhanced by the Fe\({}_{3}\)O\({}_{4}\) NPs and Fe\({}_{3}\)O\({}_{4}\)-Au pNPs. Moreover, the excellent performance of Fe\({}_{3}\)O\({}_{4}\)-Au pNPs as X-ray radiosensitizers was verified by the quickly decaying radiation dose survival curve of the nanoparticle-loaded MCF-7 and A549 cells and corroborated by the small values of the associated dose-modifying factors.  
Keywords:X-ray radiosensitizer; radiotherapy; patchy Fe\({}_{3}\)O\({}_{4}\)-Au nanoparticles; MCF-7 cells; A549 cells; MCF-10A cells +
Footnote †: journal: Applied Sciences