esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Impact of the Iron Oxide Nanoparticles on CMR Imaging  
By altering the relaxation properties of the surrounding hydrogen protons and changing the apparent proton density of the surrounding tissue, iron oxide nanoparticles influence the local magnetic resonance properties. Their use is associated with shorter T2 values and hypointense regions on T2- and T2-weighted images [23,28]. Several factors can affect the signal intensities on T2* sequences, such as the presence of oedema or haemorrhage. For this reason, the impact of USPIO accumulation can only be evaluated with the careful examination of pre- and post- contrast images of the area of interest [13]. Typically, patients are therefore subjected to two consecutive CMR scans, with the second one being 24 h after USPIO administration [35]. More recently, it was shown that multi-time-point (at 50 and 75 h post-contrast administration) multiparametric CMR helped differentiate active USPIO uptake from macrophages from passive tissue distribution [1].  
Their impact of iron oxide nanoparticles and, more specifically, USPIO can be quantified by measuring the changes in the T2* and R2* values (R2* = 1/T2*) [1,7]. Uniformly dissolved USPIO alter the relaxation rates, including the R1 (longitudinal magnetic resonance relaxation rate) and R2* (transverse magnetic resonance relaxation rate) [1,36]. Nevertheless, R2* particularly is sensitive to actively phagocytised USPIO, whereas R1 is not [1]. Therefore, the R2*/R1 ratio is extremely useful for differentiating the active USPIO uptake from the passively distributed USPIO [1,7]. In a study by [PERSON] et al., the ratio of R2*/R1 at 75 h post-USPIO administration was demonstrated to have a 90% sensitivity and 85% specificity for detecting active USPIO uptake in infarcted and remote myocardium in acute myocardial infarction and chronic ischaemic cardiomyopathy [1].  
Iron oxide nanoparticles also cause T1 shortening [37,38]. However, the T1-relaxivity of iron oxides can vary substantially depending on the strength of the magnetic field, the size of the core of the nanoparticle, and the degree of the particle aggregation [7,20,39]. More specifically, the T1 shortening caused by the nanoparticles is diminished in areas with significant particle aggregation, with the opposite phenomenon observed in areas with diffusely located nanoparticles [40]. This, however, can be avoided through the use of lower-flip-angle radiofrequency pulses and longer repetition times [7].
## 3 System Architecture and Design
### T-Mon Host  
The T-Mon Host manages the overall evaluation procedure and all nodes in the TSN. Before starting the evaluation, it initiates the whole TSN by sending a START_EVALUATION packet with the reference time information to every T-Sink node via the T-BS-com node, which is connected to the T-Mon Host via USB interface. Once each T-Sink node receives the Start_Gathering packet from the T-Mon Host, it updates its local time to the received reference time and initiates a local time synchronization procedure in each lane by broadcasting a Sync_Req packet, as explained in Section 3.2. After initiating TSN, the T-Mon Host starts the evaluation. Then it not only gathers every packet from TSN, but also records a video of passing vehicles. Whenever the T-Mon Host receives an event packet, it calculates real-time traffic information such as speed, occupancy, and estimated length of the vehicle. In addition, it updates management information including the battery level of the sensor node that sent the event packet and the communication link state. Moreover, it relates the event information to the frame number in the video file. By mapping each event packet to a frame number, an operator can jump to the position in the video related to the event packet after finishing gathering traffic information for the evaluation. With this traceability, an operator is able to verify any abnormal traffic information.  
Once finished gathering traffic information for the evaluation, the T-Mon Host provides post-processing to compare the traffic information measured by PES with that by VDS under evaluation. The T-Mon Host generates traffic information from the gathered event file in a Microsoft Excel file format. An operator can trace the position in the recorded video file for any event, since the T-Mon Host maps each event packet to the relevant frame number in the video file.
## Example Analysis
### Model Analysis
#### 6.2.1 Multicollinearity Test  
First, the Spearman rank correlation coefficient analysis was performed on the characteristic variables of sample data 1 to test whether there is multicollinearity [52]. Spearman rank correlation analysis is a linear correlation analysis using the rank order size of two variables. It is a type of statistical quantity obtained by replacing the actual data with the rank of each factor sample value. The calculation formula is presented in Equation (20). By calculating the characteristic variables, the correlation coefficient \(\rho\) between each of the two characteristic variables was obtained, as shown in Figure 4.  
\[\rho=1-\frac{6\sum d^{2}}{n^{3}-n}, \tag{20}\]  
where \(d\) is the difference between the ranks of the two columns and \(n\) is the length of each column.  
In general, there is a linear correlation between variables when \(\rho>0.8\). Linear correlation is a sufficient condition for multicollinearity. From Figure 4, it can be seen that the selected characteristic variables have obvious multicollinearity, which will make the coefficient estimation unstable.  
Removing secondary or alternative explanatory variables is a common method for solving multicollinearity. However, it is a rigorous process to determine the alternative explanatory variables; improper deletion can cause serious deviation in parameter estimation. Furthermore, the alternative explanatory variables that are deleted still contain useful information for load forecasting and deleting them will increase the error of the prediction.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 3 Construction of the Digital Twin Monitoring System
### Construction of the Exchange System Twin Data
#### 3.2.1 Data Storage Module  
Data storage is an important process for recording the data flow within the system, enabling the reuse and secondary mining of data to fully utilize its inherent value. The MySQL (v8.0) software is used as the system database of the roadheader digital twin monitoring system. When the program starts, it will connect to a specified database in MySQL on the corresponding computer. After receiving the data from any client, it first completes the data verification and sends the verification result to the user panel, then calls the relevant functions of the database interface to store the current data in the specified database according to the format.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Protocol Operation-Based Routing
#### 4.2.3 Shortest-Path Routing  
Energy efficient UAV routing for WSN (EFUR-WSN): In EFUR-WSN [55], the authors addressed the problem of energy consumption for data transmission in UAV-enabled WSNs, where a UAV is dispatched to collect data from sensors. In EFUR-WSN, a Voronoi diagram-based algorithm is introduced for efficient UAV routes in order to conserve the residual energy of sensors. The optimization problem of data collection and UAV traveling distance is solved on the basis of two different methods. Firstly, a feasible UAV routing path is provided based on the Voronoi diagram, which provides UAV hovering locations with low computational complexity. The Voronoi diagram is extensively exploited by focusing on sensor energy information and UAV hovering location in order to prolong network lifetime. The shortest UAV route is considered for data collection at all sensors. Secondly, to minimize the UAV overall trajectory distance, the number of UAV hovering locations supporting multiple nodes needs to be maximized. By sequentially adjusting each UAV hovering location based on the status of sensor energy, the finally optimized route for UAV is obtained. Simulation results showed that EFUR-WSN outperforms the existing schemes in terms of energy-efficient data gathering and energy consumption. EFUR-WSN significantly reduces the energy consumption of sensors, resulting in prolonged network lifetime.  
Figure 8: General architecture of a UAWSN system.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 4 Anatomical Perspective of WT
The carpus, brachium, and antebrachium regions, on the other hand, are areas that can provide information about arm strength, posture angle, or the correct execution of movements in sports that heavily involve hand and arm usage. Additionally, since the veins are close to the skin in these regions, information about heart rate and oxygen levels in the blood can also be obtained. Figure 7: Anatomical parts of the human body. \begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Commercial WT** & **Anatomical Region** & **Sensors** & **Physiological** & **Biomechanics** & **Location** & **Environmental** \\ \hline Apple Watch Ultra & Carpus & GNSS, HR, SpOZ, (ECG-indirect), IMU, Magnetometer, & Yes & Yes & Yes & Yes \\  & & Temperature, Light, Bavometer & & & & & \\ Fittic Charge5 & Carpus & GNSS, HR, SpOZ, (ECG-indirect), IMU & Yes & Yes & Yes & No \\ Gamma Fenix 7 & Carpus & GNSS, HR, SpOZ, (ECG-indirect), IMU, Magnetometer, & Yes & Yes & Yes & Yes \\  & & Thermometer & & & & & \\ Samsung Galaxy Watch Active2 & Carpus & GNSS, HR, SpOZ, (ECG-indirect), IMU, Bavrometer & Yes & Yes & Yes & Yes \\ WHOOPStrapt4.0 & Carpus & GNSS, HR, SpOZ, (ECG-indirect), IMU, Magnetometer, & Yes & Yes & Yes & Yes \\  & & Thermometer, CSR & & & & \\ Garmin Forevenner 945 & Carpus & GNSS, HR, IMU, Magnetometer & Yes & Yes & Yes & No \\ Polar Gritx & Carpus & GNSS, HR, Multi Magnetometer & Yes & Yes & Yes & No \\ Sunupup & Carpus & GNSS, HR, IMU, Magnetometer & Yes & Yes & Yes & No \\ COROS ApePro & Carpus & GNSS, HR, Multi Magnetometer & Yes & Yes & Yes & No \\ Swimmer & Carpus & HR, Multi, Magnetometer & Yes & Yes & No & No \\ ZepPitySocer & Carpus & GNSS, HR, SpOZ, IMU, Thermometer, CSR & Yes & Yes & Yes & Yes \\  & & & & & & \\ Sestro & Carpus & GNSS, HR, SpOZ, IMU, Magnetometer, Thermometer, & Yes & Yes & Yes & Yes \\ CSHG-SHCGRX MOVE & Carpus & GNSS, HR, SpOZ, IMU, Magnetometer, Thermometer, & Yes & Yes & Yes & Yes \\ GBD-H2000 SEREIES & & & & & & \\ Carupol & Dorsum & GNSS, IMU, Magnetometer & Yes & Yes & Yes & No \\  & & & & & & \\ Polar Tem Pro & Dorsum & GNSS, HR, Multi, Magnetometer & Yes & Yes & Yes & No \\ Myo Armband & Antherabium & EMG & & & & & \\ MotusQB & Brachium & IMU & No & Yes & No & No \\ Zepply-Bio Hamers & Thorax & ECG, HR, Respiration Rate & Yes & No & No & No \\ Ereoskin & Thorax & ECG, HR, Respiration Rate & Yes & No & No & No \\ ZepepGolf2 & Marus & GNSS, IMU, Magnetometer & No & Yes & Yes & No \\ Acdtoff Smart Ring & Marus & IMU, HR, Thermometer & Yes & No & No & Yes \\ KINEXON & Diverse body regions & GNSS, ECG, HR, SpOZ, IMU, Magnetometer & Yes & No & Yes & No \\ Dorsavi Mvement Suite & Diverse body regions & IMU, EMG & & Yes & Yes & Yes & No \\ Noraxon & Diverse body regions & IMU, EMG & & Yes & Yes & No & No \\ Xsens & Diverse body regions & GNSS, IMU, Magnetometer, Bavrometer & No & Yes & Yes & Yes \\ LumoRun & Diverse body regions & IMU & No & Yes & No & No \\ \hline \end{tabular}
\end{table} 
Table 1: Commercial Wearables and Features.
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 4 Study on the Deformation and Instability Modes of the Slope Under Seismic Loading  
### Analysis Conditions  
Dynamic calculations are based on the excavation post-condition model, where the velocity displacements of the model are first set to zero, and the existing static boundary conditions at the model's base are removed. Since the bedrock in the study area has a relatively high modulus in its natural state, no static boundary conditions are applied at the base. Instead, free-field boundary conditions are applied around the model to reduce the seismic wave reflection effect at the model's boundaries. Subsequently, dynamic loads are applied to conduct the dynamic analysis.  
High-frequency seismic waves with short periods can effectively excite the high-frequency response of soil in simulations, thereby facilitating the study of dynamic behaviors of slopes under the influence of high-frequency seismic waves [36]. The 7.3 magnitude Hanshin earthquake in Japan in 1995 is one of the most comprehensively recorded seismic events in recent decades. Its seismic waves have high frequencies and short periods, and the data are detailed and extensive, reflecting the dynamic characteristics under actual  
Figure 8: Plastic zone contour plot: (**a**) without contact surface; (**b**) with contact surface.  
seismic actions. For the dynamic load in this calculation, the seismic wave used is the 1995 Japan 7.3 magnitude Kobe earthquake wave [37]. The portion with the most significant vibration, from 4 to 28 s, is extracted. The seismic wave is applied as east-west and vertical acceleration time histories at the model's base in the form of a table, simulating the effects of shear and longitudinal seismic waves on the slope. To improve computational efficiency and accuracy, the original seismic wave is filtered and baseline-corrected. In this study, the Dynamic Input Wizard plugin in FLAC3D is used to filter out high-frequency components above 10 Hz, which contain minimal seismic energy. A low-frequency polynomial of the fourth degree is added to the original acceleration time history, ensuring that the acceleration time history curve starts and ends at zero, completing the filtering and baseline correction process. The processed seismic wave is shown in Figure 9.  
After the seismic wave input, damping is applied to simulate energy loss due to friction between soil and rock particles, as well as possible sliding effects at the contact surfaces. Considering the characteristics of various damping forms and the properties of the selected seismic wave, this article employs the commonly used Rayleigh damping in the numerical calculations [25]. The minimum critical damping ratio (\(\xi_{\rm min}\)) is set to 0.05, and the minimum central frequency is set to 2.625 Hz.
## 4 Performance Evaluation and Discussion
### Performance Using Two RATs for Communication  
Figure 10 shows the characteristics of the mutual information with respect to the shortened information length when the sender and the receiver communicate using two RATs. The square marks indicate the mutual information between the sender and the receiver when one RAT cannot be used for communication due to an attack, and the cross marks indicate the mutual information between the sender and the cyberterrorist when one RAT is wiretapped. When the receiver cannot use one RAT for communication, communication is not possible without shortening. The mutual information of the receiver is slightly increased until the shortened information length is 112, but when the shortened information length is set to 113 bits, the mutual information suddenly becomes one and communication becomes possible. On the other hand, the mutual information of the cyberterrorist continues to decrease with no improvement even if the shortened information length is increased.  
Figure 11 shows the characteristics of the secrecy capacity. The square marks, the circle marks and the cross marks indicate the secrecy capacity of the cyberterrorist attacking on two RATs, attacking on one RAT and wiretapping on two RATs, respectively. When the cyberterrorist attacks or wiretaps on two RATs, the secrecy capacity does not improve and is always zero. However, if the cyberterrorist attacks and wiretaps on another RAT simultaneously, the secrecy capacity can be improved by increasing the shortened information length, such that if the shortened information length exceeds 113, the value is 0.7 or greater. As a result, it is confirmed that information-theoretic security is improved even when the sender and the receiver communicate using two RATs.  
Therefore, when the designed shortened RA code is used, by setting the shortened information length to 113 bits or more, it is possible to achieve both countermeasures against wiretapping and attacks. To increase the resistance of distance measurement error, the shortened information length may be set large. However, when the shortened information length is increased, the transmission efficiency decreases. Therefore, the shortened information length should be determined by the relationship between the ranging error and the transmission efficiency. The decision algorithm of the shortened information length should be the focus of further study.  
Figure 10: Characteristics of the mutual information versus the shortened information length when the sender and the receiver communicate using two RATs. The square marks indicate the mutual information between the sender and the receiver when one RAT cannot be used for communication due to an attack, and the cross marks indicate the mutual information between the sender and the cyberterrorist when one RAT is wiretapped.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 6 Simulation Results
### Modified LSTM-Based State Estimator  
The voltage estimation results of the proposed modified LSTM-based state estimator are shown in Figure 6. Since the measurement data used in the estimation process were time-series or sequential data, the LSTM network's special ability in predicting these types of data gave a more accurate estimation of all bus voltages when compared to the conventional WLS state estimator. The voltage estimations for bus 1 in Figure 6a, bus 2 in Figure 6b, bus 3 in Figure 6c, bus 4 in Figure 6d, bus 7 in Figure 6g, and bus 9 in Figure 6i gave better results than the WLS estimator (Figure 5a,b,c,d,g,i), with the estimation pattern in the period from hour 9 to hour 24 being arguably closer to the real simulated voltage values. Furthermore, owing to the modified WLS loss function applied in the training process, the modified LSTM state estimator gave more consistent accuracy for all bus voltage estimations, even though the voltage estimations for bus 5 in Figure 6e, bus 6 in Figure 6f, and bus 8 in Figure 6h were not as good as when using the WLS method (Figure 5e,f,h), as there were 0.1 V deviations from hour 9 to hour 12 and from hour 14 to hour 17. However, unlike the estimation in the conventional WLS state estimator, all voltage predictions with the modified LSTM-based state estimator showed the same pattern as the true simulated voltage value, with deviations of up to 0.2 V and with less noise. This resulted in smaller errors than all other previous state estimators that had been tested. Furthermore, the model is shown to quickly converge within 38 epochs with the use of the WLS loss function.  
Figure 5: Conventional weighted least squares (WLS) voltage estimation results for: (**a**) bus 1; (**b**) bus 2; (**c**) bus 3; (**d**) bus 4; (**e**) bus 5; (**f**) bus 6; (**g**) bus 7; (**h**) bus 8; (**i**) bus 9.  
Figure 6: _Cont._
# Theoretical and Numerical Research on High-Speed Small Refrigeration Twin-Screw Compressor
###### Abstract
The high-speed operation of screw rotors introduces additional challenges beyond clearance and airflow pulsation effects, including insufficient heat dissipation and reduced motor efficiency [15; 16]. [PERSON] et al. [15] discovered that the compressor's working chamber walls undergo irregular thermal deformation at high temperatures, with lateral outward expansion and inward deformation at the tip points. Increasing the cooling water flow in water-cooled casings effectively reduces the average clearances, enhances the discharge capacity, and minimizes tip deformation, thereby improving the operational conditions under extreme scenarios. [PERSON] et al. [16] developed a high-speed miniaturized airborne screw refrigeration compressor with a maximum cooling capacity of 47 kW using the R134a refrigerant, designed for 15,000-25,800 rpm operation. Experimental tests revealed stable compressor efficiency within the 15,000-19,000 rpm range, as the motor efficiency reduction in the field-weakening control region offset the efficiency gains from increased rotor speeds. The current research lacks comprehensive theoretical frameworks and experimental validation for the high-speed miniaturization of small screw compressors. To explore this potential in refrigeration screw compressors, this study designs rotors with varying rated speeds at a constant displacement, employing normalization methods for geometric analysis and exergy analysis for power loss characterization, while developing a CFD model to analyze the gas force variations across speeds. Furthermore, it investigates how the lobe number and wrap angle impact the thermodynamic performance.
## 3 Materials and Methods
### Improved PNN-SVM System  
This paper proposes improving the PNN-SVM system, demonstrating its effectiveness in solving Ti-based alloy's quality evaluation task for medical implant manufacturing. The basic version of the system is presented in [39].  
The existing system is based on the principle of preprocessing data using PNN and expanding the initial dataset with the PNN-based outputs to improve the accuracy of solving the classification task by the SVM method. The use of SVM here is explained by the highest accuracy of this method among the classifiers based on machine learning methods that are studied in [14]. The proposed system is based on additional modeling of relationships between all attributes of the extended due to the outputs of the PNN summation layer dataset based on the use of the second-degree Ito decomposition. Theoretically, the use of such an approach is fully justified by [PERSON]'s theorem on the separation of images [42].  
A flowchart of the improved smart PNN-SVM system is shown in Figure 6.  
Since PNN does not require a training procedure, but it is necessary to implement SVM, the proposed smart system based on a combination of these two machine learning methods provides procedures for both training and application.  
The training procedure for the improved smart system is as follows:  
* prepare and apply a modified PNN to obtain a set of probabilities of belonging of each observation from the training set to each of the defined classes of the stated task;
* form a new set of training data by combining the initial dataset with the corresponding probability vectors obtained in the previous step;
* perform nonlinear expansion of a new dataset using the second-degree Ito decomposition according to (1);
* perform SVM-based linear classifier training using the extended training dataset in the previous step.  
Figure 5: Probabilistic Neural Network: (**a**) classical version; (**b**) a modified version.  
Accordingly, the main steps of the testing procedure for a pre-trained improved smart system are as follows:  
* apply the prepared PNN to the current vector to obtain the vector of probabilities of its belonging to each of the defined classes of the stated task;
* form a new vector by combining the current vector with the probability vector obtained in the previous step;
* perform the second-degree Ito decomposition on the extended vector to model the relationships between the input attributes of a given vector and the probabilities of its belonging to each of the defined classes of the stated task;
* perform classification using pre-trained SVM with the linear kernel;
* get the result of the system work.