esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Network Model
Power density has been employed as the second measure to characterize the impact of NIR from mmWave wireless networks. Power density serves as a crucial metric for assessing the level of electromagnetic energy exposure in the surrounding environment and is utilized to evaluate the potential NIR effects of mmWave communication systems. By using power density as a key parameter, this study aims to gain valuable insights into radiation levels and their potential implications on human health and safety, contributing to a comprehensive understanding of the mmWave network's impact on the electromagnetic environment. The general calculation of MPE is derived from the Friis equation [33]:  
\[S_{r}=\frac{P_{t}G_{t}}{4\pi r^{2}} \tag{11}\]  
where \(S_{r}\) represents the PD, \(P_{t}\) is the transmit power, \(G\) is the gain of the antenna, and \(r\) represents the Tx-Rx separation. In this context, knowing the power gain of the transmitter, the MPE can be easily calculated at any distance.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Signal Pre-Processing
#### 7.3.5 Signal Reordering to Simulate the Monotonic Growth of a Defect  
The reflection of a glued mass deteriorates quickly over time because of the stresses caused by trains and of the progressive corrosion of the rail under the glue. A monotonically increasing defect, similar to a growing crack, can be simulated if the reflected signals recorded during the time window starting at the installation of the defect emulator and ending at its detachment are time reversed [PERSON] et al. (2018). A steady decrease in the amplitude of the reflection due to the artificial defect amplitude is expected, with some fluctuation due to temperature changes. Test results illustrated in Refs. [PERSON] et al. (2018); [PERSON] et al. (2018) have proven that the reflection peak due to an artificial defect does not have a simple correlation with temperature; no satisfactory explanation for this behavior has been provided, although it is known that measurements may have been affected by the complicated resonant conditions producing the large reflections [PERSON] et al. (2018).
* To validate the proposed framework's performance through comparisons with existing control techniques in simulated scenarios. Expected contributions include the following:
* A DRL-based control framework for executing robotic tasks through a limited number of examples presented by human-robot interaction, based on the synthesis of motion shaping methods using primitives and inverse reinforcement learning.
* An algorithm for determining and evaluating specific task-describing features from the assumed problem domain, based on statistical analysis and multi-criteria optimization in the time-dependent problem space, aimed at generalizing learned knowledge.  
In robotics, the field of motion planning encompasses problems such as path planning, trajectory planning, and task planning. Path planning deals with the problem of finding a path from some initial position to a goal (\"from point A to point B\"), while avoiding obstacles. The novelty of this work lies in the combination of dynamic trajectory modeling with time-dependent state analysis, which collectively enhance the robot's adaptability and performance. Experimental results demonstrate the efficacy of the proposed algorithm in robotic manipulation tasks and path planning within dynamic environments. Furthermore, the integration of these techniques allows the robot to efficiently navigate complex environments and execute tasks with precision.  
This research makes significant contributions to the field of robot learning methodologies by presenting a comprehensive framework that improves robot learning through human-robot interaction. The proposed model holds promise for practical applications in manufacturing, healthcare, and logistics, where robots are increasingly utilized for automation and assistance.
## 3 Materials and Methods
### Experimental Setup  
#### 3.4.1 Hardware and Software Platforms  
For the hardware configuration, a high-performance deep learning server equipped with state-of-the-art hardware was employed in this study. Specifically, the server was configured with two NVIDIA A100 GPUs, each offering 40 GB of memory, enabling large-scale parallel data processing and efficient training of deep learning models. The server was further equipped with an AMD EPYC 7742 processor with 64 physical cores, facilitating multi-threaded parallel computations and significantly accelerating data preprocessing and model training tasks. The system included 512 GB of DDR4 RAM to ensure seamless handling of large datasets during preprocessing and training. Additionally, NVMe SSD storage was utilized to provide high-speed data read/write capabilities, thereby optimizing data loading and model training efficiency throughout the experimental process.  
For the software configuration, a widely used deep learning software stack was adopted to ensure flexibility and scalability of the framework. The experimental environment was built on Ubuntu 22.04, offering a stable platform for development and execution. PyTorch 2.0 was utilized as the deep learning framework, renowned for its dynamic computation graph and highly optimized GPU acceleration, which are ideal for the rapid development and validation of complex models. CUDA 12.1 and cuDNN 8.9 were employed to maximize GPU parallel computing capabilities. For data processing and analysis, Python 3.10 was utilized, along with several scientific computing and visualization libraries, including NumPy, Pandas, Scikit-learn, and Matplotlib 3.9. To ensure the reproducibility of experimental results, a Conda virtual environment was used for dependency management, allowing strict control over software versions. These combined hardware and software configurations established a high-performance and stable experimental environment, providing robust support for the proposed methodology.
# Modelling of Evenness of Runways as an Element of Sustainable Airport Maintenance
## 1 Introduction
The method of obtaining data on the condition of runways, the possibilities of their use, analysis, and interpretation, as well as the use of the data for the purposes of sustainable maintenance of airport runways, is the subject of this work. Air traffic system operators use computerized decision support systems known as traffic management systems (TMS). The air transportation system (ATS) was developed in parallel due to the specificity of airports. Decision makers use various technical and economic analyzes implemented in the pavement management system (PMS) for appropriate and timely maintenance of the road surface. The airport pavement management system (APMS) is also used to develop adequate runway maintenance strategies due to the special nature of airports. This system includes procedures to support the evaluation and search for adequate maintenance strategies [26; 27; 28]. Numerous airports worldwide use APMS [29]. The development of PMS and APMS has been studied many times [30; 31; 32]. The disadvantage of APMS is that it is intended for larger and more frequently visited airports. In contrast, it is not intended for airports with relatively low traffic, such as the Maribor Edward Rusjan Airport, which is intended for emergency flights, exhibitions, training, and school flights. In general, the integrity of runway surfaces is ensured through regular inspections. Maintenance of the pavement requires periodic renewal of the surface or wearing course. The interval between resurfacing depends on the type of surface. The most commonly used hard surfaces are concrete and asphalt. To help drain surface water, the former surfaces are often grooved on the sides to allow surface water to drain down the grooves, and the latter have a porous surface layer that allows surface water to drain under the surface instead of carrying it over the top. Certain types of asphalt can also be grooved. Minor repairs (such as resealing joints, plugging cracks, and removing rubber deposits in the touchdown zone) can take relatively little time, but major works require either a full or partial runway closure over a continuous period of several weeks, or a carefully managed program of night closures during which a complex surfacing program can be carried out in stages. In such cases, the friction characteristics of the various parts of the available surface may vary daily, requiring very careful attention to NOTAM (NOTices to AirMen) information prior to flight, particularly where adverse weather conditions may occur.
# Manufacturing Processes of Integral Blade Rotors for Turbomachinery, Processes and New Approaches
## 2 Monolithic Components: Manufacturing Processes
### Electro Chemical Machining (ECM)/Precise Electro Chemical Machining (PECM)  
The Electro Chemical Machining (ECM) is a non-conventional material removal machining process by electrolysis where a voltage is applied between a form electrode (cathode) and the part to be manufactured (anode) through an electrolytic liquid [26,27]. The removed material precipitates in the electrolytic solution as metallic hydroxide. The machining process is performed without thermal and mechanical stresses, thus material properties are not disturbed, so there is no presence of white layer. It is presented as a good alternative for big batches and very low machinable materials because the electrode presents a reduced wear, but the machine investment is high. In the classical process, a direct current voltage between 5 V and 10 V is applied, achieving feeds up to 10 mm/min. Figure 6 shows a  
Figure 4: Material removal rates for WEDM (wire electro discharge machining) and SEDM (sink electro discharge machining) manufacturing process on thermo resistant superalloys [21].  
Figure 5: Generated optimal tool-pathstoolpaths and a shrouded blisk manufacturing by EDM [24].  
diagram of ECM methodology and the application of this technique under different process parameters with the aim of determining optimal ECM parameters for Ti60 related to the surface roughness [28].  
The Pulse/Precise Electro Chemical Machining (PECM) is a variant of ECM that combines a pulsed voltage with an oscillate movement of the cathode, obtaining a better finishing accuracy. The main inconvenience presented by this process is that the machine investment is higher and cutting feeds are lower, around 0.5 mm/min [5; 29].  
One of the main applications for these ECM and PECM techniques are the blisks located in the high-pressure turbines compressors. For aeronautical turbines, the material resistance and hardness are crucial to withstand the extreme working temperatures; thus, they directly impact on material machinability, leading to the necessity of difficult-to-cut thermoresistant superalloys, nickel forged parts, sintering materials, nickel-based alloys, among others.
## 3 Signal Performance
### Inter-Frequency Phase Bias Evaluation  
At present, four open service signals are available for the BD3 satellites and dual-frequency IF combinations are widely used in the precise orbit determination and clock bias estimation. In general, the IF combinations of signals can eliminate most of the ionosphere delay. Furthermore, the difference between the two IF combinations, namely, Geometry-Free Ionosphere-Free Phase (GFIFP) combination, is both geometry-free and ionosphere-free [30]. The GFIFP combination is expressed as  
\[\begin{array}{ll}\text{GFIFP}(i,j,k)&=IF(i,j)-IF(i,k)\\ &=\frac{f_{i}^{2}(f_{i}^{2}-f_{j}^{3})}{(f_{i}^{2}-f_{j}^{2})(f_{i}^{2}-f_{k}^ {2})}R_{i}-\frac{f_{i}^{2}}{f_{i}^{2}-f_{j}^{2}}R_{j}-\frac{f_{k}^{2}}{f_{i}^{ 2}-f_{k}^{2}}R_{k}\end{array} \tag{3}\]  
where \(f\) is the frequency of the signals, the subscripts \(i,j,k\) indicate the frequency index, and \(f_{i}>f_{j}>f_{k}\), \(R\) represents the phase observation (in meters).  
The GFIFP combinations for the BD3 satellites as well as the GPS Block IIF and Galileo FOC satellites are presented in Figure 7. It is noteworthy that the observation units are unified in meters and the statistical data comes from the station KUN1 in the iGMAS network. As shown in Figure 7,  
Figure 6: The root mean square (RMS) values of the two-day overlap in orbit determination using B1 CB2a, B1 CB3I, B1 IB2a and B1 IB3I in the along-track, cross-track, radial and 3D directions. The mean observation counts of four IF combinations in each arc are presented on the dual-Y axis.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Along-track** & **Cross-track** & **Radial** & **3D** & **Obs. Count** \\ \hline B1C-B2a & 157 & 94 & 40 & 200 & 1,643,662 \\ B1C-B3I & 226 & 148 & 60 & 295 & 1,436,591 \\ B1I-B2a & 192 & 110 & 47 & 241 & 1,496,300 \\ B1I-B3I & 144 & 87 & 39 & 184 & 1,731,146 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the overlap comparison of the BD3 two-day overlap in the along-track, cross-track, radial and 3D directions (units: mm). The mean observation counts used in each arc are listed in the last column.  
the frequencies in the legends are presented referring to the Receiver INdependent Exchange (RINEX) definition version 3.04 [31], where 1/2/5/6 in BD3 stand for B1C/B1I/B2a/B3I, 1/2/5 in GPS represent L1/L2/L5 and 1/5/7 in Galileo stand for E1/E5a/E5b. Seen from Figure 7, the GFIFP combination distribution of C20 is not white-noise and is not located around zero. Its maximum variation reaches about \(\pm\)2 cm and the magnitude is almost the same as both the B1C/B2a/B3I and B1I/B2a/B3I combinations. It should be noted that B2I is transmitted only by the BD2 satellites. The GFIFP combination results of B1I, B2I and B3I have been presented in previous research [32], which have shown a similar performance as BD3. In addition, similar to the BD3 satellites, the series variation of the GPS Block IIF can reach \(\pm\) 5 cm. It is noteworthy that the GFIFP combination of Galileo performs best and no obvious variation can be found.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 3 Multichannel SA-CNN-BiLSTM Network Model
### Data Preprocessing
#### 3.2.5 Sliding Time Window  
In the context of time series, the quantity of features is closely associated with time. To avert the slow convergence of the model resulting from long sequences, a sliding time window is frequently employed to partition the data while simultaneously achieving data expansion. A schematic diagram of the sliding time window is presented in Figure 10. The size of the time window impacts the prediction performance. If the time window is too small, it becomes difficult to capture the correlation between the feature quantity and time promptly. Conversely, if the time window is too large, although more useful information will be encompassed, it is prone to overlooking short-term changes, thereby leading to insufficient learning.  
Figure 10: Schematic of the sliding time window.  
Figure 9: Segmented linear degradation model.
## 3 Optimization
### Optimization of YOLOv3  
Prior experiments showed that YOLO's detection results are considerably robust concerning changes in hyperparameters such as the learning rate, momentum or weight decay. According to these experiments, an optimization of these parameters would not significantly improve the detection results. Therefore, we use these already empirically collected values in our work and do not perform any fine tuning. The most important factor, however, is the size of the input image, since this influences both the detection speed and the precision of the detector. Thus, we conduct a grid search to find the optimal input image size for our purpose. For this purpose, we measure the detection speed in milliseconds (ms) for each image size and calculate the mean Average Precision (mAP) to determine the detection accuracy. Due to YOLO's downsampling architecture the input image size should be a multiple of 32 px in width and height. We begin the grid search at a factor of 13 for both image dimensions resulting in an image size of 416 \(\times\) 416 px and gradually increase the factor by 3 up to a final input image size of 800 \(\times\) 800 px. All these instances of YOLO are trained on the entire training dataset described in Section 2.1 and are evaluated on the validation dataset.  
We found that the detection accuracy generally rises with an increasing image size, although the detection speed drops simultaneously (see Table 1). At the maximum image size of 800 \(\times\) 800 px, YOLO yields the best detection accuracy. The processing time for a single image, however, is 97 ms which corresponds to about 10 fps when processing each image of a video. With a processing time of 46 ms per image (22 fps), using the minimal input image size is still too slow to run the detection on every single frame of our video sequences in real-time. Since our camera captures frames at 25 fps, we decided to use an input image size of 608\(\times\)608 px as tradeoff. This way, we are able to process at least every second frame with a desirable precision.  
Figure 7: Calculated accuracy of the classifier when varying the number of weak classifiers.
## 2 The Fundamentals of SRM and DITC  
### Mathematic Model  
The 12/14 bearingless SRM is used as the implementation example since it is the prototype in our recent project, and the torque control system of the motor is independent. The structure and prototype are shown in Figure 1. According to the basic voltage equation of conventional SRMs, the voltage of phase \(k\) for the studied motor is as follows:  
\[U_{k}=R_{k}i_{k}+\frac{\mathrm{d}\psi_{k}(i,\theta)}{\mathrm{d}t} \tag{1}\]where \(U_{k}\) is the voltage, V; \(R_{k}\) is resistance, \(\Omega\); \(i_{k}\) is current, A; and \(\psi_{k}\) is flux linkage of phase \(k\), wb, respectively.  
The mechanical motion equation of the SRM can be described as follows:  
\[J\frac{\mathrm{d}\omega}{\mathrm{d}t}=T_{e}{}^{*}-T_{L}-B\cdot\omega \tag{2}\]  
where \(J\) is the rotational inertia, kg\(\cdot\)m\({}^{2}\); \(\omega\) is the angular speed of the motor, rad/s; \(T_{e}{}^{*}\) represents the reference total torque, Nm; \(T_{L}\) is the load torque, Nm, and \(B\) is the coefficient of friction, N\(\cdot\)m\(\cdot\)s, respectively.  
Considering that the motor is disturbed by the external environment, (2) can be further expressed as follows:  
\[\dot{\omega}=\frac{1}{J}(T_{e}{}^{*}-B\cdot\omega_{L}-T_{L})+D \tag{3}\]  
where \(\dot{\omega}=\mathrm{d}\omega/\mathrm{d}t\) and \(D\) is the total disturbance, Nm.
## 1 Introduction
Fog computing is a potential choice since it reduces query latency for IoT devices and makes the most efficient use of available resources. An attractive alternative for managing the growing number of IoT devices is fog computing due to the importance of efficient scheduling algorithms for optimizing system performance and resource utilization. Scheduling encompasses various policies, as outlined in ref. [8]:  
* Increasing the workload's efficiency. * Improving throughput. * Decreased latency. * Cost-cutting measures. * Protecting the system from harm. * Obtaining load balance. The swarm intelligence paradigm is utilized by numerous artificial and biological systems [9]. The combination of the simple processes performed by many small, independent entities, driven by local information, allows for the emergence of sophisticated, intelligent behavior. The foraging behavior of ants, for instance, was studied. Another example is how birds are able to quickly adjust their motions to the changing characteristics of their environment through flocking behavior. A set of basic principles that individual birds obey can be defined to achieve this behavior. The authors in [10] proposed the gray wolf optimizer algorithm, inspired by the behavior of gray wolves to obtain prey. In this paper, we propose the PC-GWO algorithm, which is a hybrid algorithm, to enhance scheduling in a fog-cloud environment. In this paper, we offer a unique scheduling method to assure the predictable execution of IoT activities and answer concerns about latency reduction, resource efficiency improvement, and decreased energy consumption. This method considers both task cost and power energy consumption constraints. This paper is structured as follows: Section 2 analyzes related work, and presents the system model, Our proposed algorithm is described and analyzed in Section 3, Section 4 explains the assessment, simulation model, and results, and Section 5 discusses the conclusion and future works.
## 3 Methods
### Driving Factor Analysis  
This study employed the SHAP model to calculate the SHAP values of each driving factor and created a summary plot of driver importance (Figure 6). In terms of importance ranking, the driving factors are ordered as follows: NDVI, TMAX, SRAD, PDSI, VPD, and PRE. Among them, the SHAP values of the NDVI, TMAX, and VPD increase from negative to positive as their feature values shift from low (blue) to high (red), indicating that these three factors generally promote an increase in WUE. On the other hand, the SHAP values of SRAD and the PDSI shift from negative to positive as their feature values decrease, suggesting that these two factors generally suppress WUE growth. For PRE, the changes in feature values are more complex, indicating that there may be a non-monotonic relationship between PRE and WUE or that its influence may not be statistically significant. This analysis reveals the different mechanisms through which various drivers influence WUE changes. Specifically, changes in the NDVI, TMAX, and VPD tend to promote an enhancement in WUE, while changes in SRAD and the PDSI may somewhat limit its growth.  
Based on the SHAP values from the model, this study further examined the driving relationships between each factor and WUE (Figure 7). The results show that the NDVI and SRAD have monotonic effects on WUE, exhibiting linear and quadratic polynomial relationships, respectively. Specifically, the NDVI has a linear, monotonically increasing effect on WUE, with a slope of 0.87, meaning that WUE consistently increases as the NDVI rises. In contrast, SRAD exhibits a monotonically decreasing quadratic relationship with WUE, where the rate of decline in WUE decreases as SRAD increases. This suggests that the negative impact of high solar radiation on WUE is mitigated at higher SRAD values, possibly due to the influence of other factors. The effects of TMAX, the PDSI, VPD, and PRE on WUE are more complex, showing an initial increase followed by a decrease. For instance, the relationship between TMAX and WUE follows a quadratic pattern, indicating that while higher temperatures may initially enhance WUE, their effect becomes inhibitory as temperatures continue to rise. Similarly, precipitation (PRE) also follows a quadratic curve, suggesting that moderate levels of precipitation boost WUE, while excessive amounts may reduce it. The relationships for the PDSI and VPD are more intricate, following cubic  
Figure 5: Significant changes in WUE from 2003 to 2020 (**a**) and the area proportions (**b**).  
polynomial trends, which indicates that the impacts of drought conditions and vapor pressure deficit on WUE vary non-linearly under different climatic circumstances.  
Figure 6: Summary of the importance of driving factors for WUE.  
Figure 7: Fitted relationship between WUE and driving factors.