esa-sceva/satcom-chunk-collection huggingface dataset:
# Brushless Field Excitation Scheme for Wound Field Synchronous Machines
## 2 Proposed Topology and Its Operating Principle
### Proposed Topology  
The proposed topology as presented in Figure 1a consists of three-phase and single-phase inverters. These inverters are connected to the armature winding and operate simultaneously; however, the currents they supply have different frequencies, i.e., 60-Hz and 180-Hz, respectively. The three-phase inverter is connected to the armature winding and supplies current at the frequency of 60-Hz, whereas the single-phase inverter is connected to the star-connected armature winding's common point and injects the current at 180-Hz frequency. The main reason behind operating two inverters at two different frequencies is to develop a unique shape for the armature currents. This shape of the armature currents comprises of the fundamental as well as the third harmonic current component that results in fundamental and third harmonic MMF in the airgap of the machine. The third harmonic MMF is employed to generate current in the harmonic winding of the rotor. The harmonic winding in this case has eight poles. However, the generated current in the harmonic winding has six times the frequency of the fundamental armature current and two times the frequency of the third harmonic current component. The generated harmonic current is rectified to deliver DC current to the rotor field winding. This current produces the main rotor field which interacts with the armature field generated by the equal number of armature poles, producing torque. To confirm the operation of the proposed topology a 4-pole, 36-slot machine was employed. This machine model is presented in Figure 1b. The employed 36-slot double-layered armature winding, and the rotor harmonic and field windings configuration, is explained in Figure 2[13; 14].  
Figure 1: (**a**) Proposed topology and (**b**) machine model.
# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 5 Simulation and Discussion
### Simulation Results and Discussions
#### 5.2.1 BubbleRap vs. Bubble-TLDA
From (16), when the social impact factor (\(\xi\)) decreases, the effect of neighbours' importance on the node's popularity weakens, and thus the node's popularity merely relies on its own mobility pattern or sociability level in the social network. In Figure 11, we depict the impact of varying social impact factor (\(\xi\)) on the GINI index and average delivery latency in Reality and Sassy. As illustrated in Figure 11 (left), when the social impact factor (\(\xi\)) increases, the GINI index in both scenarios decreases, with the reduction is more obvious in Reality. This demonstrated that considering neighbours' popularity influences on the node's global popularity computation indeed improves fairness in forwarding of BubbleRap. However, increasing the social impact factor (\(\xi\)) lengthens the delivery time in both scenarios. The greater the value of the social impact factor (\(\xi\)), the more traffic is redirected from optimal paths (via hub nodes) to sub-optimal paths (through non-hub nodes), which are often longer than the shortest routes (via hub nodes) to the destination. Finally, for the case of message overhead ratio, Bubble-TLDA marginally rises BubbleRap's delivery cost in both mobility scenarios (Figures 8 and 9 (lower-right)). This implies that reducing traffic in hub nodes, while increasing traffic in non-hub nodes shows a less impact on the delivery overhead, i.e., Bubble-TLDA is able to maintain the total message copies as high as BubbleRap.
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 4 Coupling Analysis of the Electromagnetic-Temperature Field
### Coupling Characteristic Analysis Based on Unidirectional Coupling Method  
#### 4.1.1 Establishment of the Finite Element Model  
By comparison, the finite element method is high precision and widely used [25; 26]. Using Comsol software, the finite element model was established according to the structure and parameters of the IWM in Section 2, and the dynamic mesh was set for the rotating part of the motor. In the process of mesh generation, the boundary meshes on both sides of the air gap were divided more strictly. On the two sides of the air gap, one side in the rotating region needed to be more refined than the one in the fixed region. Figure 8 shows the partial mesh partition of the two-dimensional IWM.  
Figure 7: Coupling methods.  
As can be observed from Figure 8, because the magnetic flux distribution was basically in the area surrounded by the stator and rotor cores, the mesh was small in the stator and rotor core, winding, and air gap, and large in the shell and motor shaft.
## 3 Results
### Compost Maturity  
The results regarding the maturity of the examined composts, the determined AT4 respiration activity values, are shown in Figure 1, on which the determined respiration activity (AT4) is recorded.  
Based on the determined respiration activity values (AT4), it can be stated that the biological stability of the examined backyard composts that can be determined based on the AT4 values differed considerably (1.6-25.2 mg \(O_{2}/g\) dry matter). In one case (compost \"M\"), the determined respiration activity (AT4) was extremely high (25.2 mg \(O_{2}/g\) d.m.) There were smaller differences and lower typical ranges experienced in respiration activity in the case of the composts which originated from the ecological farms (3.9-9.5 mg \(O_{2}/g\) d.m.) This difference in the determined biological stability values between the backyard and the ecological composts can be attributed to the fact that the producers in the ecological farms are highly aware of the environmental effects of composting and have more scientific background in the topic. There is no unified regulation in Europe regarding the stability and maturity of the treated biowaste products. In Austria and Germany, in order to determine stability, the extent of respiration activity (AT4) is taken into consideration.  
For accomplishing the proper stability, the respiration activity value (AT4) has to decrease below 5 mg \(O_{2}/g\) d.m. The suggested limit value in the EU for the same parameter is 10 mg \(O_{2}/g\) d.m. [23]. If we consider the Austrian and the German qualification aspects among all the examined composts, three backyard composts (E, F, and K composts) and only one ecological compost (P) met the requirements for the compost maturity. However, based on the suggested, more permissive unified EU limit value, most of the examined backyard composts (8 out of the examined 13 composts) and all of the examined ecological composts show appropriate maturity and stability values. Some backyard composts (A, B, C, J, and M), however, still did not meet the requirements even in the case of the less strict limit and showed inappropriate biological stability. In the case of these composts, the beneficial effects of composts on the soils and the soil-plant system notwithstanding, unfavorable effects can also occur due to their inappropriate biological stability. The results obtained regarding the low stability of backyard composts might reinforce the concern about its quality [17; 23; 59; 60]. It has to be emphasized that several methods are used worldwide to determine the maturity, biological stability, and the respiration activity of composts. These are the following: the Solvita test, the self-heating test, static respirometry index (SRI), dynamic respiration index (DRI), and the specific oxygen uptake rate (SOUR). The comparability of the results obtained by these different methods has been examined by several authors; as a result, correlations are available to help in the comparison of the available results [23; 38; 61].  
Figure 1: The determined respiration activity (AT4) and the number of weeds that emerged within 14 days.
## 2 Related Work
### On-Body Communication
#### 2.1.3 Waveguide Type: Radio Waves  
This type uses the human body as a waveguide, a structure that conveys signals. Compared to the electrostatic coupling type, the transmission quality is not affected by the surroundings. A previous study utilized the human body as a waveguide for electromagnetic waves for the purpose of intra-body communication [6]. The transmitter and receiver systems communicate through frequency-modulated electric currents of approximately 10.7 MHz. As an application example, they proposed a health care/medical application in which measured health data at the wristwatch, such as peripheral capillary oxygen saturation and heart rate, are transmitted to external touch devices.  
Thus far, we have discussed on-body communication mainly focusing on electrical/electromagnetic signals. In the present study, we propose a mechanical waveguide-based communication system, which is similar to [6], except that our approach uses mechanical signals. To the best of our knowledge, this is the first proposal of a communication between a smartwatch and a touched devices based on mechanical signals through human body parts (_i.e._, ultrasonic waves through skin/bone.) To better understand the characteristics of mechanical signals through the body, we briefly review the related work in the following section.
# A Fading Suppression Method for \(\Phi\)-OTDR Systems Based on Multi-Domain Multiplexing
## 2 Multi-Domain Multiplexing Principle
After IQ demodulation of the amplitude and phase demodulation of the IF signal, the phase of the IF signal with noise can be obtained as follows [14]:  
\[\varphi(t,z)=\tan^{-1}\frac{A_{SNR}\sin(\varphi_{s}(t,z))+\sin(\varphi_{n}(t,z ))}{A_{SNR}\cos(\varphi_{s}(t,z))+\cos(\varphi_{n}(t,z))} \tag{8}\]  
where \(A_{SNR}\) is the SNR of the IF signal, \(A_{SNR}=A_{IF}/A_{n}\), where \(A_{n}\) is the amplitude noise, and \(\varphi_{n}(t,z)\) is the phase noise. \(A_{SNR}\) can be used as a criterion for phase accuracy. When \(A_{SNR}\) is too low, the demodulated phase \(\varphi(t,z)\) has a relatively low accuracy. The higher the \(A_{SNR}\), the more accurate the phase. According to Equations (1), (4), and (7), the output light intensity is simplified as follows:  
\[I(t,z)\propto\sqrt{2a_{s}(t,z)SWe^{-2 az}}E_{s}E_{l}V\exp\left[\Delta\omega \cdot\frac{2z}{v_{g}}+\varphi_{s}(t,z)+\arctan\left(\frac{b_{\theta}}{a_{ \theta}}\right)\right] \tag{9}\]  
The factors affecting the \(I(t,z)\) signal in Equation (9) are not only limited to the scattering coefficient and position, but also include \(W\) corresponding to the width of the probe pulse, the optical frequency \(\Delta\omega\), and the visibility of the detection signal \(V\). Firstly, at different positions \(z\), \(I(t,z)\) has different distributions, which implies the feasibility of spatial multiplexing in fading suppression. However, it is not the case that the larger the spatial intervals for multiplexing, the better. When \(W\) is relatively small, the overall amplitude of the signal is relatively small, and fading is more likely to occur, which requires multiplexing more spatial intervals to make up for it. As \(W\) increases, the overall value of \(I(t,z)\) increases, and the probability of fading occurrence decreases, so the demand for the number of multiplexed spatial intervals also decreases. The amount of multiplexed spatial intervals and the spatial resolution should find a balance between fading suppression and other performances. Secondly, for different \(\Delta\omega\) and \(V\), \(I(t,z)\) has different distributions as well. To obtain polarization state-independent measurement channels, we can use PDM through receiving orthogonal polarization states in the polarization state domain. To obtain frequency-independent measurement channels, FDM can be employed by transmitting multifrequency probe light. Through \(A_{SNR}\) in Equation (8), the phase accuracy can be evaluated. Setting the magnitude of \(A_{SNR}\) as the criterion based on previous research [14], weights are assigned to multi-domain independent measurement channels. According to the assigned weights, the final phase is synthesized through weighting. Then, fading noise is eliminated.
## 3 Results
### On Site Effectiveness of UV-SAN System  
The effectiveness of the UV-SAN sanitation procedure in the operating environment was tested against the multi drug resistant (MDR) bacteria Methicillin resistant _Staphylococcus aureus_ (MRSA) and Extended-Spectrum \(\beta\)-Lactamase (ESBL) _Klebsiella pneumonia_, common pathogens wide-spread in the hospital environment. Contamination was focused especially in the part that are presumably closer or in direct contact with the patient during transport, as the headrest area, the arm-rest area, the chair and the area of the ambulance located near the urinary catheter tube passage or where the drainage bag lies in case the patient is carrying a urinary catheter, as shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**UVC Dose for Log3 Reduction [J/m\({}^{2}\)]**} & \multirow{2}{*}{**\(p\) Value**} & \multirow{2}{*}{**R\({}^{2}\)**} \\ \cline{2-2} \cline{5-6}  & & **95\% Confidence Level** & & & \\ \cline{2-2} \cline{5-6}  & **Lower** & **Mean** & **Upper** & & \\ \hline Polystyrene & 135 & 184 & 226 & \(5.3\times 10^{-3}\) & 0.944 \\ Metal & 92 & 129 & 164 & \(5.7\times 10^{-3}\) & 0.947 \\ Rubber & 29.9 & 54.4 & 98.3 & \(9.3\times 10^{-2}\) & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Linear Regression Model. Figure 5: Test Execution on Ambulance during the Pilot phase. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Log Reduction**} & \multirow{2}{*}{**\% Reduction**} & \multicolumn{4}{c}{**UVC Dose [J/m\({}^{2}\)]**} \\ \cline{3-5}  & & **Polystyrene** & **Metal** & **Rubber** \\ \hline Log1 & 90\% & 57.3 & 46.2 & 7.8 \\ Log2 & 99\% & 121 & 87.5 & 23.4 \\ Log3 & 99.9\% & 184 & 129 & 54.4 \\ Log4 & 99.99\% & 248 & 170 & 55.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: UVC dose for different materials and % of virus infectivity reduction. The UV-SAN treatment for 10 to 30 min of the ambulance resulted in a significant reduction of bacteria growth, as indicated in Table 3.  
Any bacterial growth was detected at the headrest, armrest and chair surfaces, after contamination by increasing concentrations of bacterial suspension (0.5, 1.0 and 2.0 McF) at different exposure times (10, 20 and 30 min). In the area where the urinary catheter tube could presumably be located, in the event that the patient has it, area located in a recess on the side of the stretcher, a significant growth of MRSA has been detected after UV-SAN application. Exactly, 2 CFU/mL was recorded at the concentration of 0.5 McF after exposure for 10, 20 and 30 min, while at increasing concentrations (1.0 and 2.0 McF) the growth was higher and >100 CFU/mL at the exposure time of 10 min. To obtain a
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 5 Experiment
### Results
#### 5.3.4 Runtime and Convergence  
At last, we discuss the runtime of SoLE and the convergence of the training process in the embedding learning stage. Figure 4(a),b shows the convergence of SoLE on FB15K and DB100K, respectively, compared to ComplEx. We can see that the convergence times of ComplEx and SoLE were very close, both around 50 epochs on FB15K and 40 epochs on DB100K, respectively, which indicated that integrating additional rules would not affect the convergence of the training process too much. Table 8 lists the runtime of SoLE required for its two stages and ComplEx required for model training on FB15K and DB100K. The training time per epoch of SoLE was approximately two times longer than ComplEx, since SoLE computed the additional gradient of the loss function for soft rules. Besides, we can see that the grounding generation stage of SoLE was quite efficient and cost very little time considering the whole runtime. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **MRR** & **HITS@1** & **HITS@3** & **HITS@10** \\ \hline SoLE\_OTI & 0.809 & 0.792 & 0.802 & 0.854 \\ SoLE & 0.814 & 0.802 & 0.812 & 0.854 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Link prediction results achieved by SoLE_OTI and SoLE on the test sets of FB15K, which only contain the 14 relations given in Table 5. \begin{table}
\begin{tabular}{l} \hline \hline  /basketball/basketball\_position/player\_roster\_position./sports/sports\_team\_roster/team \\  /sports/sports\_position/players./ice\_hockey/hockey\_roster\_position/team \\  /sports/sports\_team/roster./ice\_hockey/hockey\_roster\_position/position \\  /sports/sports\_position/players./baseball/baseball\_roster\_position/team \\  /basketball/basketball\_team/roster./sports/sports\_team\_roster/position \\  /basketball/basketball\_position/player\_roster\_position./basketball/basketball\_roster\_position/team \\ Relations /sports/sports\_team/roster./basketball/basketball\_roster\_position/position \\  /basketball/basketball\_team/roster./basketball/basketball\_roster\_position/position \\  /sports/sports\_position/players./basketball/basketball\_roster\_position/team \\  /basketball/baseball\_team/current\_roster./sports/sports\_team\_roster/position \\  /sports/sports\_team/roster./baseball\_roster\_position/position \\  /base/aareas/schema/administrative\_area\_/administrative\_area\_type \\  /location/statistical\_region/gni\_in\_PPP\_dollars./measurement\_unit/dated\_money\_value/currency \\  /location/statistical\_region/gni\_per\_capita\_in\_PPP\_dollars./measurement\_unit/dated\_money\_value/currency \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fourteen relations from FB15K, which account for the most differences shown in the right picture of Figure 4.
## 4 Experiments
### Discussion  
In MINITFormer, we conducted experiments on two different types of datasets: multi-scale organ information datasets (Synapse and ACDC) and single-scale organ information datasets (Kvasir-SEG, ISIC2017, ISIC2018, and PH\({}^{2}\)). The experimental results indicate that MINITFormer exhibits excellent performance across all types of datasets.  
Specifically, CSWin's cross-window attention mechanism organizes attention in horizontal and vertical stripe patterns, enabling efficient capture of connective tissue information between organs. Benefiting from CSWin's capability to model long-range dependencies, it effectively addresses the issue of patch boundary discontinuities in traditional Transformer-based segmentation models. Meanwhile, MaxViT's multi-axis attention mechanism demonstrates outstanding performance in balancing local details with global semantics through the synergistic interaction of grid attention and block attention. Block attention divides feature maps into non-overlapping windows to extract local information, while grid attention aggregates global context through dynamic windows. The collaborative effect of Heterogeneous Encoders is particularly prominent in multi-scale organ segmentation tasks. On the Synapse dataset, MINITFormer achieves an average Dice coefficient of 84.74%, representing a 2.06% improvement over TransCASCADE, while the HD95 metric decreases by 3.31. This indicates that the heterogeneous encoders not only enhance semantic integrity for large organs (e.g., liver, Dice 95.38%) but also significantly improve detail capture for small organs (e.g., pancreas, Dice 69.72%). The core mechanism lies in CSWin complements structural relationships between organs through global context, while MaxViT refines organ boundary features via local attention. Through feature fusion in the Demodulate Bridge, they form a complementary segmentation paradigm of \"global semantic guidance--local detail enhancement.\"  
However, MINITFormer still has some shortcomings. Compared to other models, MINITFormer consumes more computational resources and relies on pre-trained models of MaxViT and CSWin. Additionally, there is room for improvement in MINITFormer's segmentation performance in certain extreme cases. As shown in Figure 11e, although MINITFormer can roughly mark the lesion location in complex backgrounds, it fails to fully capture the boundary details of the lesion. Therefore, making MINITFormer more  
Figure 12: Efficiency curve of MINITFormer.  
lightweight and further enhancing its ability to capture detailed information are among our future research directions.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.3 Land Use Data  
Land use data spanning 1991 to 2018 were obtained primarily through Landsat imagery, supplemented with visually interpreted samples from satellite time-series data, Google Earth, and Google Maps. Temporal metrics from all accessible Landsat images served as inputs for a random forest classifier to produce classification outputs. To improve the spatial-temporal coherence of the CLCD, a post-processing approach incorporating spatial-temporal filtering and logical analysis was applied ([[https://zenodo.org/records/81/76941](https://zenodo.org/records/81/76941)]([https://zenodo.org/records/81/76941](https://zenodo.org/records/81/76941)); accessed on 22 September 2024). The data have a spatial resolution of 1 km \(\times\) 1 km, using the Albers Conic Equal Area projection parameters. The spatial resolution of all data used in the study is 5 km \(\times\) 5 km using resampling.
## 5 Graphical User Interface (GUI)
The operator must first specify the initial parameters for the distance between the metasurface and the receiving antenna by adjusting the value of the DISTANCE slider. This value determines the starting point of the measurements. If the operator selects either the DISTANCE or BOTH characterization option, it is mandatory to provide information regarding the object's movement in the \(Z\)-axis during each measurement cycle, which must always be away from the receiving antenna. This is achieved by adjusting the DISTANCE STEP slider. The subsequent step involves setting the angle parameters between the metasurface and the receiving antenna. The operator first specifies the initial angle by adjusting the value of the INITIAL ANGLE slider, within a range of \(-45^{\circ}\) to \(45^{\circ}\), with \(0^{\circ}\) being the point at which the metasurface is perpendicular to the receiving antenna. The operator then sets the final angle for the measurements by adjusting the FINAL ANGLE slider. It is important to note that the difference between the absolute value of the initial and final angles must always be greater than the value set in the ANGLE STEP slider. If the FINAL ANGLE and ANGLE STEP sliders are configured to values that are not permissible, the GUI will display an error message and block the SEND buttons for these two settings. Figure 12 depicts the system operation. Functions performed by the microcontroller of the main board (Mega) are represented in green, functions performed by the secondary board (Uno) are represented in blue, and functions performed in the GUI (Raspberry Pi 4), in which the operator performs the equipment configurations, are represented in red. Figure 12b is the loop part (in green) that appears in Figure 12a within the three modes of equipment operation. They are separated to provide a clearer demonstration of how the instrument operates. Figure 11: Graphs representing the time elapsed to finish a measurement cycle as a function of accuracy, that is, the number of measurements that the microcontroller performs at each point, for (**a**) 2-step resolution, (**b**) 5-step resolution, (**c**) 10-step resolution, (**d**) 15-step resolution, and (**e**) 20-step resolution.