esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 5 Influence of Design Parameters on EMC
### Winding Configuration  
It is found that the impedance of a series connected winding machine is four times higher than in a parallel connection one since a winding inductance varies as \(N^{2}\). Additionally, the first resonance frequency of a parallel machine connection is almost double the resonance frequency of a series winding connection machine [36; 37].  
Furthermore, the star and delta winding connections are also analyzed in the literature. In [32] it is shown that CM impedance of both connections is mainly capacitive, but the resonance frequency is 6 times higher in the delta connected machine. The impedance below and above the resonance points is equal as the CM paths in these frequency ranges are equal for both connections. Equally, even the shape of the DM impedances of both winding connections is equal, the amplitude of the star connection is higher than the amplitude of the delta connection in the whole frequency range. The authors mention that this difference is due to the DM path. For the delta connection, the DM path is composed of approximately two parallel windings, whereas for the star connection this path is made of one winding in series with two parallel-connected ones. In [36], the connections of the machine are also analyzed and a model valid for series, parallel, star and delta connections is proposed.  
In [37], where it measures a 5 hp induction motor, it mentions that a delta connection winding has twice the stator winding-to-ground effective capacitance compared to a star connection.  
Regarding the winding topology, in [14] circumferential (CW) and toroidal winding (TW) topology are analyzed. The first one is the typical concentrated winding in electrical machines, whereas in the second, the winding forms a kind of toroid, reducing the end windings. It is concluded that even TW may be better for thermal management and higher power density, they increase winding-to-ground capacitance, increasing the CM current by about 20 dB.
## 2 Materials and Methods  
The system is tested and developed using the rover SherpaTT that is shown in Figure 1. SherpaTT was built by DFKI for long-distance exploration applications [26], negotiation of highly challenging terrains or non-nominal conditions (sinkage in soft soil, getting entangled between rocks or alike), cooperation tasks between heterogeneous rovers in a collaborative sample return mission, search and rescue and/or security missions. SherpaTT is a four-wheeled mobile robot [27] outfitted with an actively articulated suspension system, independent drive and steer wheel motors and a six degrees of freedom (DOF) manipulation arm. The rover is also a hybrid wheeled-leg rover, so it can take advantage of both wheeled and legged locomotion according to the terrain difficulty. SherpaTT has a mass of about 166 kg and a payload capacity of at least 80 kg. Each of the four suspended legs has five DOF that include the rotation of the whole leg about the shoulder or pan axis with respect to the robot body, the two rotations of the inner and outer leg parallelograms, the steer and drive angle of the wheel. SherpaTT's footprint can vary between 1 \(\times\) 1m in a folded configuration to 2.4 \(\times\) 2.4 m in a maximum square footprint. Various predefined footprint shapes (stances) can be adopted as explained in Figure 2.  
Each of the 20 suspension and six arm joints delivers telemetry data at a rate of 100 Hz. The telemetry includes joint position (absolute and incremental), speed, current, PWM duty cycle and two temperatures (housing and motor windings). Additionally, a 6-DOF force-torque sensor (FTS) is placed at the mounting flange of each wheel-drive actuator enabling the direct measurement of the generalized forces that each wheel exchanges with the supporting surface. Active force control for the wheel-ground contact as well as the roll-pitch adaption are two processes of the so-called Ground Adaption Process (GAP) in SherpaTT [28]. Autonomy modules do not need to cope with the limb articulation in rough terrain [29].  
Figure 1: SherpaTT in soft sand dunes during Morocco field trials in 2018.
## 4 Results and Discussion
### Effect of Laser Spot Overlap Rate
At this time, the Ra parameter of the roughness is only 2.491 \(\upmu\)m. When the longitudinal spot overlap rate \(\gamma_{2}\) is lower than 40%, as shown in Figure 12b, the adjacent grooves achieve a smaller degree of overlap, the number of grooves per unit area increases, and the Ra parameter increases to 6.915 \(\upmu\)m. With the further increase in the longitudinal spot overlap rate, the coating on the substrate surface is further removed, and the surface roughness is subsequently reduced, as shown in Figure 12c. When the longitudinal spot overlap rate \(\gamma_{2}\) reaches 80%, the coating removal effect is good, the substrate surface is relatively flat, there are no obvious traces of ablation craters, and the surface roughness is reduced to the minimum (Ra = 1.057 \(\upmu\)m). When the longitudinal spot overlap rate \(\gamma_{2}\) exceeds 90%, as shown in Figure 12d, the crater morphology with regular arrangement can be clearly observed, the degree of undulation increases, and the Ra parameter increases to 2.826 \(\upmu\)m at this time. Therefore, it can be obtained that the longitudinal spot overlap rate is 80%; that is, when the cleaning line spacing is 0.1 mm, the water jet-guided laser cleaning effect is the best. After water jet-guided laser cleaning with different spot overlap rates, the variation pattern of cleanliness \(\eta\) obtained by the image processing method with the number of cleanings is shown in Figure 13. When the spot overlap rate of the water jet-guided laser is 70%, the cleanliness \(\eta\) reaches 91.07% after cleaning twice. When the laser spot overlap rate is 60%, the cleanliness \(\eta\) reaches 92.26% after four times cleaning. When the laser spot overlap rate is 50%, it is necessary to clean six times to reach 91.84%. When the cleaning times continue to increase, the \(\eta\) value first increases and then decreases. The minimum number of cleaning times obtained are substituted into Equation (5), and the corresponding water jet-guided laser cleaning rate can be calculated as 10.13 mm\({}^{2}\)/s, 9 mm\({}^{2}\)/s and 9.38 mm\({}^{2}\)/s, respectively. Therefore, the 70% laser spot overlap rate with the highest cleaning rate is selected as the best spot overlap rate, and the cleaning speed can be obtained as 135 mm/s. Figure 11: Effect of longitudinal spot overlap rate of the water jet-guided laser on surface roughness of samples. Figure 12: Variation of sample surface roughness with the spot overlap rate of the water jet-guided laser. (**a**) Stage 1; (**b**) Stage 2; (**c**) Stage 3; (**d**) Stage 4.
# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 2 Related Works
### Category-Level 6D Pose Estimation  
Many methods do not utilize prior point clouds. Refs. [24,25] combine 2D detection and 3D segmentation for feature extraction, and then use a translation and rotation regression network to obtain the 6D pose of the object. Ref. [26] introduces a transformer structure where prior information is used as the query, and instance features as the key and value to obtain object embeddings. These embeddings are then constrained based on the relationships between neighboring points in the point cloud, ultimately regressing the object pose estimation. Ref. [7] proposes the NOCS and provided the correspondence between NOCS and the real 6D pose. A CNN network is used to simultaneously predict the target object's mask and NOCS map, with the pose calculated through the established correspondences. Similarly, [27] also proposes a new unified shape space for addressing the intra-class variation. Other studies like [8,28] incorporate priors as input and learn to deform the priors to obtain the object's coordinates in the normalized space. Refs. [24,29] propose various data augmentation techniques to train networks, addressing overfitting caused by limited dataset sizes, enhancing the model's generalization ability, and improving its robustness. CR-Net [30] proposes a cascaded network for estimating the 6D pose of objects. The first stage of the network is designed to extract instance features, while the second stage incorporates prior information. SAR-Net [31] focuses on solving the pose estimation of symmetric objects by aligning prior information with the observed instance to predict rotation. Subsequently, it employs symmetry modeling based on the observed parts of the instance and compares this with the observed instance information to predict translation and size.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 1 Introduction
This paper proposes a satellite network task deployment method based on SDNs /ICNs to improve the user experience of satellite network access equipment. The ICN cache characteristics enable the cached communication data in the satellite network, which can be used for computing services. The SDN technology is also used to control the LEO satellite network and obtain the status of the available resources to coordinate the resource status of the network for the deployment of computing tasks. Taking the task delay and satellite traffic as the optimization objectives, a genetic algorithm is used to coordinate the status of network resources to deploy computing tasks.
# Performance Evaluation of an Imaging Radiation Portal Monitor System
## 3 RPM Detection Performance Results  
### Background Evaluation  
According to the ANSI N42.35-2016 reference and standard test conditions, the gamma radiation background should be between 5-10 uR/h and less or equal to 200 background neutrons/s/m\({}^{2}\). Typical RPM systems with \({}^{3}\)He counters or similar thermal neutron detectors are sensitive to the full cosmic-ray neutron spectrum, including the down-scattered neutrons. Organic-scintillator based detection systems are, on the other hand, mainly sensitive to fast neutrons and, as a result, the neutron background rates are significantly reduced. The main contribution to the neutron background count rate for organic-scintillator detectors operated with neutron/gamma PSD is caused by particle misidentification where the detector signals are affected by electronic noise or pile-up. This effect is proportional to the average singles gamma rate in the scintillators with a factor typically around one to a few per thousand [15] depending on the PSD cuts applied. The background count rates measured by the KTH RPM prototype system during tests in the Detector laboratory of the KTH Nuclear Physics Division are given in Table 1. The average radiation background dose rate in this environment is around 0.2 uSv/h. The energy thresholds for gamma rays and neutrons were set to 20 keV and around 500 keV, respectively. It is noteworthy that the system was not shielded against background radiation in the presently reported configuration. Standard commercial RPM systems usually have several mm of lead shielding installed on the outer sides, reducing background count rates.
## 2 Numerical Analysis Setup
### Damping of Materials  
The stress-strain relationship of the soil during the dynamic process exhibits a strong nonlinear characteristic. In this paper, the shear modulus of liquefiable sandy soil decays with increasing shear strain and has some hysteresis properties. The hysteresis-type model is used as a supplement to one of the built-in nonlinear models in flac3d to represent the plastic yielding and hysteresis response of the material [24]. Four hysteresis-type models are available in Flac3D, of which Sig4 is a model with four parameters and more flexible control of the shear modulus and damping of sandy soils than the other models [7], the normalized secant modulus (\(M_{s}\)) can be calculated by fitting the formula:  
\[M_{s}=\frac{G}{G_{max}}=y_{0}+\frac{a}{1+exp\left(\frac{x_{0}-L}{b}\right)}. \tag{2}\]  
\(L\) is the logarithm of the shear strain, and \(a\), \(b\), \(x_{0}\), \(y_{0}\) are the parameters fitted to the Sig4 model to control the shear modulus decay curve. A comparison of the shear modulus degradation curves using hysteresis damping in this paper and the experimental data [26] is shown in Figure 5. At very low cyclic strains, the use of hysteresis damping can induce noise in the calculation results, and the stability of the numerical simulation at low strains can be ensured by adding a small amount of Rayleigh damping [24]. \(\alpha\) is the mass-proportional damping constant for Rayleigh damping, and \(\beta\) is the stiffness-proportional damping constant, both of which can be determined by the critical damping ratio (\(\xi_{min}\)) and the natural frequency of the oscillation (\(f_{min}\)), as follows:  
\[\alpha=\xi_{min}\times 2\pi f_{min}, \tag{3}\]  
\[\beta=\frac{\xi_{min}}{2\pi f_{min}}. \tag{4}\]  
In this paper, liquefiable sand is added to Rayleigh damping with a critical damping ratio of 0.002 and a natural frequency of 2.22 Hz. Non-liquefiable soil uses local damping with a critical damping ratio of 5%.  
Figure 4: (**a**) Wall with plastic hinges at members’ joint, and (**b**) bending moment at the plastic hinge under horizontal displacement load.
## 4 Component Modelling and Interaction Analysis  
### Component Design and Simulation  
Axial crushing simulations were run in Abaqus/Explicit 2017 [43]. Two cross-section geometries are considered, circular and square, both studied in their empty configuration or filled with either foam or cork. To further understand the relation between geometry and interaction, the cross-section dimensions and thickness were varied within the bounds in Table 1. All components were 350 millimetres long.  
The aluminium extrusions were meshed with linear shell elements with reduced integration and hourglass control. The foam and cork fillings were modelled with linear solid elements with an element size of five millimetres, also with reduced integration and hourglass control. The tangential contact between the materials was modelled with a static friction coefficient of \(\mu_{\mathrm{a}}=0.3\) for contact between aluminium and aluminium, \(\mu_{\mathrm{a-f}}=0.4834\) for aluminium and foam, and \(\mu_{\mathrm{a-c}}=0.3284\) for interactions between aluminium and cork. The coefficients \(\mu_{\mathrm{a-f}}\) and \(\mu_{\mathrm{a-c}}\) were obtained experimentally from tilt tests. Foam and cork samples with an additional mass on top were placed on an aluminium surface, which was tilted until the samples slid.  
The finite element analyses simulated the axial crushing of the thin-walled tubes between two rigid plates. The lower plate was perfectly fixed and the upper plate was accelerated smoothly in the initial 10% of the simulation to ensure quasi-static conditions. Simulations were run to a crushing length of 70% of the component's length to prevent densification.  
In order to validate the numerical model, the axial crushing of a hollow circular extrusion with a diameter of 130 mm and a thickness of 1.9 mm was modelled and compared to the experimental results in [29]. The agreement between experiments and simulations is seen in the force-displacement and average force-displacement curves, Figure 7a,b.  
Figure 6: True stress-strain curves from a uniaxial compression test and simulation for Armaform PET/W AC 135 foam in the extrusion direction. Adapted from [31].
## 6 Discussion
### Discussion for Evaluation Results  
As shown in Table 15, the ML classifiers used in this study for performance evaluation of the detection algorithm are 1 Naive Bayesian, 2 support vector machine, and 3 random forest. Simply put, the Bayesian classifier is relatively faster in training speed than the SVM and insensitive in parameter values when compared to other classifiers. On the other hand, the SVM classifier works well on the small number of data features, irrespective of dimensionality of data. In the case where there are many datasets (at least 10,000 sets), however, the SVM is severely dependent on data preprocessing and parameter values. Lastly, the random forest classifier does not work well on high-dimensional data but has high performance in view of accuracy [30]. So, in this study, the evaluation results manifest that the random forest achieves the best detection performance, with an accuracy of 99.8%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison Features** & **This Work** & **[PERSON] et al. [15]** & **[PERSON] et al. [17]** & **[PERSON] et al. [11]** \\ \hline Target Mobile Network & 5G & 3G/4G & 5G & 5G \\ \hline Traffic Protocol & HTTP/2, PFCP & TCP, UDP, ICMP & RRC & RRC, SIP (Voice, SMS) \\ \hline Detection Algorithm & Naive Bayesian, & & & \\ or Methodology & SVM, RF & SVM & Randomization & CNN \\ \hline Average Detection & & & & \\ Accuracy & 98.4\% & 53.8\% & 59.8\% & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Signaling DDoS detection studies comparison.
## 3 Results and Discussion  
### Nutritional Analysis and Extraction  
The nutritional composition of the dried fruit body of FFL is detailed in Table 1. The moisture content, crude carbohydrate, crude protein, crude fat, and crude ash were 11.04 \(\pm\) 0.26%, 75.83 \(\pm\) 1.44%, 5.82 \(\pm\) 0.11%, 5.72 \(\pm\) 0.17%, and 1.58 \(\pm\) 0.18%, respectively, which aligns with a previous report [23]. Typically, edible dried mushrooms contain varying amounts of protein (ranging from 13.8 to 38.5 g/100 g), fat (0.4 to 5.9 g/100 g), carbohydrates (32 to 61.4 g/100 g), and ash (1.3 to 14.4 g/100 g) [24; 25]. Despite FFL being a lignified, non-edible mushroom, its biological activities make its composition noteworthy. Therefore, this experiment confirmed that FFL shares constituents like those found in other common mushrooms, suggesting a potential practical application in food material development.  
After nutritional analysis, FFL was extracted using various ethanol concentrations and their yields were assessed (Table 2). The hot water extraction (0%) yielded the highest at 10.4 \(\pm\) 3.4%, followed by 70% ethanol (6.8 \(\pm\) 1.0%) and 30% ethanol (5.1 \(\pm\) 0.6%), with lower yields observed for 50% and 100% ethanol extracts. Pressurized hot water extraction typically involves pretreating biomass with hot water via liquid or steam, garnering significant  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Content (\%)** \\ \hline Moisture & 11.04 \(\pm\) 0.26 \\ Crude carbohydrate & 75.83 \(\pm\) 1.44 \\ Crude protein & 5.82 \(\pm\) 0.11 \\ Crude fat & 5.72 \(\pm\) 0.17 \\ Crude ash & 1.58 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nutritional composition of FFL fruit body.  
Figure 1: Procedure and yields for solvent fractionations.  
attention due to water's unique solubility properties, the potential for increased hydrolysate compared to organic solvents, and the ability to extract diverse compounds [26]. Elevating the pressure and temperature leads to a reduction in the dielectric constant, bringing it closer to that of ethanol. This suggests that water becomes capable of extracting a broader spectrum of both polar and non-polar constituents [27; 28]. In contrast, organic solvent extraction using hexane, ethanol, and methanol is widely employed for extracting essential oil components, polyphenols, and steroids [29]. These differences are considered the main reason for a higher yield in the hot water extraction compared to ethanol extracts.
# A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks
## 3 Experimental Results
### A Comparison Study
**In the presence of many samples, the determined total score is compared based on the coefficient of variation of the samples, and thus, the lower the coefficient of variation, the better the performance of the scan statistic. According to the results (Appendix B), both the Space Time Scan Statistic and the Space Time Permutation Scan Statistic perform better in identifying outbreaks in narrow areas than in wider areas. The Space Time Permutation Scan Statistic performs well in detecting the surrounding areas of exact outbreak zip codes, while the Space Time Scan Statistic performs well in identifying exact outbreak zip codes. The experimental results of Section 3.1 reveal that the novel Spatiotemporal MW Scan Statistic performs significantly well in detecting the area of disease outbreak in a larger geographical region. Moreover, it can effectively detect the exact disease outbreak zip code in the majority of cases. Even if the exact outbreak zip code was not detected in the most likely cluster, it was detected in one of the secondary clusters by this scan statistic.
## 4 Receiver-Triggered Handshake Protocol
### Receiver-Triggered Handshake Protocol
#### 4.2.3 Communication between a Carrier Terminal and a Shelter Terminal  
The algorithm for the communication between a carrier terminal and a shelter terminal is as follows:  
1. Shelter terminal, H, transmits an S\({}_{\text{M}}\) every T seconds.
2. Carrier terminal, C\({}_{\text{k}}\), receives the S\({}_{\text{M}}\), and sends back an R\({}_{\text{M}}\), which contains collected safety confirmation information.
3. H sends back an A\({}_{\text{M}}\).
4. C\({}_{\text{k}}\) receives the A\({}_{\text{M}}\) from H and deletes the R\({}_{\text{M}}\).  
The sequence of communication from a victim terminal to a shelter terminal is shown in Figure 7.
## 5 Future Prospects
### AIoT-Based Solar-Hydrogen System in the University  
Energy management and sustainability can be significantly improved by integrating artificial intelligence (AI) and Internet of Things (IoT) technology into a university's solar-hydrogen system (Table 6). Using IoT sensors and AI-driven analytics, this AIoT-based system improves predictive maintenance, real-time monitoring, and overall system efficiency. Critical parameters including solar irradiance, hydrogen generation, weather, and equipment performance are continuously monitored via IoT sensors. Artificial intelligence algorithms analyze the gathered data to forecast maintenance requirements, enhance operational effectiveness, and maximize energy generation and storage. For example, AI can automatically modify operations to maintain constant hydrogen production levels if the system senses a drop in solar irradiation.  
The university gains a number of advantages from this combination. Quick responses to changes in system performance are made possible by real-time monitoring and control, and AI-driven predictive analytics helps to detect equipment failures, which lowers maintenance costs and downtime. Furthermore, by maximizing the efficiency of renewable energy sources and lowering the carbon imprint, the system helps the university achieve its sustainability goals. The AIoT-based solar-hydrogen system also functions as a research and teaching tool, giving academics and students hands-on exposure with cutting-edge energy technologies. In addition to enhancing energy management, this arrangement supports the university's overarching goals of advancing sustainability and cutting-edge energy solutions.
## 4 Performance Analysis of an FSO RIS-ACs Link over F-S \(\mathcal{F}\) TI fading Channels
### Average Fade Duration (\(A_{\gamma}\))  
The average fade duration (\(A_{\gamma}\)) is the mean time of the TI-faded signal being below a specified threshold for the received SNR of the considered FSO RIS-ACs system over F-S \(\mathcal{F}\) TI fading propagation channels and was calculated as:  
\[A_{\gamma}(\gamma_{th})=\frac{F_{\gamma}(\gamma_{th})}{N_{\gamma}(\gamma_{th})} \tag{45}\]  
where \(F_{\gamma}(\gamma_{th})\) and \(N_{\gamma}(\gamma_{th})\) are the CDF and LCR for a \(\gamma_{th}\), given in (23) and (43), respectively.  
The numerical analysis and observations of S-O statistical results for the SISO FSO RIS-ACs system over F-S \(\mathcal{F}\) TI fading propagation channels are provided in the Numerical Results.