esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research on Winding Looseness Tests of Power Transformers
### Transformer Winding Loose Fault Setting  
The internal windings of the 10 kV power transformer were tightened at both the top and bottom ends by the pull screw. As shown in Figure 2, the digital torque wrench was used to adjust the torque of the pull bolt to change the looseness of the winding.  
According to the national standard, the calculation formula for the rated torque of bolts is presented below:  
\[T=kF_{N}d \tag{1}\]  
where \(T\) is the bolt torque, \(k\) is the bolt tightening coefficient, and the value range is 0.1-0.3; according to the general machining surface, the value of this test is 0.13. \(F_{N}\) represents the rated preload of the bolt; in general, the rated preload is 80% of the yield strength of the bolt material. \(d\) stands for the nominal diameter of the bolt (unit: mm). The following shows the formula used to compute the rated preload of bolts:  
\[F_{N}=(0.5\sim 0.7)\sigma_{s}A_{s} \tag{2}\]  
where \(\sigma_{s}\) is the yield strength of the bolt material (unit: N/mm\({}^{2}\)) and \(A_{s}\) is the stress cross-sectional area (unit: mm\({}^{2}\)).  
The test transformer's winding upper and lower pressure plate pull bolt model was M12, with a strength grade of 4.6; nominal diameter: 12 mm; stress cross-sectional area: 84.3 mm\({}^{2}\); yield strength: 240 N/mm\({}^{2}\). From Formula (1) and Formula (2), the formula of bolt torque can be obtained as follows [26]:  
\[T=\frac{k(0.5\sim 0.7)\sigma_{s}A_{s}d}{1000} \tag{3}\]  
The rated torque range of the tension bolt at the end of the winding was calculated to be 16-22 N\(\cdot\)m by Formula (3). In the process of the winding loosening test, the digital torque wrench was used to measure the maximum rated torque of the winding to be 18 N\(\cdot\)m, so as to determine the rated torque of the bolt at the end of the winding to be 18 N\(\cdot\)m; that is, the transformer winding was not loose, expressed as 100% \(F_{N}\).  
Through the digital torque wrench, the torque of the bolts on the pull screw at the end of the three-phase winding of the transformer was adjusted in turn, to 13.5 N\(\cdot\)m, 9 N\(\cdot\)m, and 4.5 N\(\cdot\)m, respectively. The three degrees of looseness of the transformer winding were defined as 75% \(F_{N}\), 50% \(F_{N}\), and 25% \(F_{N}\), respectively. Therefore, when the value measured by the torque wrench is 13.5 N\(\cdot\)m, it can be considered that the transformer winding has a loose fault, which is an abnormal state.  
Figure 2: Transformer winding loose adjustment diagram.
## 5 Results and Discussion
Figure 15 demonstrated the BER performance comparison of ATC-SM over BPSK, two-source ATCC-SM over BPSK under \(\chi_{\text{S}_{1}\text{-}\text{S}_{2}}=\chi_{\text{S}_{2}\text{-}\text{S}_{1}}=3 \,\text{dB}\), ATC-VBLAST over 8 QAM, and two-source ATCC-VBLAST over 8 QAM under \(\chi_{\text{S}_{1}\text{-}\text{S}_{2}}=\chi_{\text{S}_{2}\text{-}\text{S}_{1}}= 3\,\text{dB}\) through the slow Rayleigh fading channel for 6b/s/Hz transmission. Furthermore, a BER performance comparison among ATC-SM in 16 QAM, two-source ATCC-SM in 16 QAM under \(\chi_{\text{S}_{1}\text{-}\text{S}_{2}}=\chi_{\text{S}_{2}\text{-}\text{S}_{1}}= 3\,\text{dB}\), ATC-VBLAST in 8 QAM, and two-source ATCC-VBLAST in 8 QAM under \(\chi_{\text{S}_{1}\text{-}\text{S}_{2}}\)= \(\chi_{\text{S}_{2}\text{-}\text{S}_{1}}=3\,\text{dB}\) over the slow Rayleigh fading channel for the same case of 6b/s/Hz transmission is presented in Figure 16. Both ATC-SM and ATCC-SM over BPSK had \(\text{N}_{\text{t}}=\ 32\) and \(\text{N}_{\text{r}}=\ 8\) antenna configurations. However, both ATC-SM and ATCC-SM over 16 QAM had different antenna configurations, i.e., \(\text{N}_{\text{t}}=\ 4\) and \(\text{N}_{\text{r}}=\ 8\). In ATC-VBLAST and ATCC-VBLAST over 8 QAM, \(\text{N}_{\text{t}}=\ 2\) and \(\text{N}_{\text{r}}=\ 8\) were adopted. In addition, the information length K = 2048 and the CMI were utilized in the simulation. As seen from Figures 15 and 16, two-source ATCC-SM and two-source ATCC-VBLAST outperformed ATC-SM and ATC-VBLAST, respectively. Moreover, we can discover from Figure 15 that ATC-SM was better than ATC-VBLAST at an SNR of greater than \(-4.5\,\text{dB}\), while two-source ATCC-SM outperformed two-source ATCC-VBLAST at an SNR of greater than \(-4.7\,\text{dB}\). Figure 16 reveals that at an SNR of greater than \(0\,\text{dB}\), the BER performance of ATC-SM exceeded ATC-VBLAST, whereas at an SNR of greater than \(-0.2\,\text{dB}\), the BER performance of two-source ATCC-SM was superior to the two-source ATCC-VBLAST. This was because both the ATC-SM and two-source ATCC-SM schemes took advantage of a low modulation order, i.e., BPSK, as compared to a high order modulation, such as 16 QAM. Figure 15: BER performance comparison of ATC-SM over BPSK, two-source ATCC-SM over BPSK under a non-ideal inter-user channel scenario, ATC-VBLAST over 8 QAM, and two-source ATCC-VBLAST over 8 QAM under a non-ideal inter-user channel scenario over the slow Rayleigh fading channel for the case of 6b/s/Hz transmission, K = 2048 and \(\text{N}_{\text{r}}=\ 8\).
## 3 Background Study  
### Spatial Correlation  
Various applications of WSNs need dense deployment of sensor devices scattered in the sensor field. To satisfy the optimal coverage, the sensor nodes communicate with each other in the space domain. It results in the spatial correlation among the sensed data which are later processed and communicated in a huge number. The large volume of correlated data transmission increases the overall system overhead resulting in maximum energy utilization [18]. As the sensor nodes are characterized by battery-operated devices utilizing a finite source of energy it is important to exploit the spatial correlation among these data. Reducing the data redundancy by taking the advantage of spatial correlation the energy consumption can be decreased. Dense network topology results in multiple recordings of a single event which constitute the high correlation among sensor data. Inter-node and intra-node separation fluctuate the density of correlation [19].
## 3 Materials and Methods
### Research Process
#### 3.1.6 Model Application Stage  
Finally, the research team deployed the trained YOLOv8 model to a portable smart terminal device (see sub-image 6 in Figure 1). The device integrates a portable high-definition camera, an embedded computing module, and a visual software interface, which can perform real-time detection and analysis of wall images directly on the construction site. The system supports image capture, damage identification, and result export functions, has the convenience of field operation, and is suitable for various practical application scenarios such as cultural relics inspection and maintenance assessment.
# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 3 Concept Drift in Process Mining
### Distinguishing Concept Drift from Other Concepts  
Dealing with concept drift in process mining has its roots in data mining and machine learning, respectively. Concept drift describes _\"the situation when the process is changing while it is being analysed\"_[1; 13]. However, this definition not only neglects the fact that changes may occur at the process type or process instance level, but also considers concept drift as changes at the process type level. However, the latter is not the only level at which changes may occur. Moreover, this view on concept drift ignores the context and reasons of a change, though studying them would ease the drift detection process. Studying the context and reasons of a process change allows identifying whether a drift occurs as part of a corrective decision to a previous problem or to cope with new factors external to the organisation the business process is executed at. As positive side of this definition, the phrase _\"while being analysed\"_, emphasizes that process miners claim the instability of a business process, that is, it highlights the need for online support and monitoring.
## 3 Virtual Measurement
### Calibration  
To calibrate this model, a high-fidelity GT-Power model is constructed with a configuration as Figure 1, and its combustion model is predictive combustion model which is calibrated by real in-cylinder pressure data and injection profile. It has been demonstrated in [23] that such a model can capture the combustion process for transient conditions.  
The injection timing and engine speed is set as -10 CA TDC and 2000 rpm, respectively. To cover an operating range as wide as possible, other operating conditions, such as the operating of EGR valve is set from 20% to 70%, the rack position for the VGT is adjusted from 0.2 to 0.8, and the fuel is injected from 35 mg to 65 mg. In total, 168 cases with different combinations of operating conditions are simulated, and the intake manifold temperature, pressure, ICOC and ignition delay are sampled for each case when the engine reached steady state.  
The intake manifold temperatures and pressures of the 168 cases are plotted in Figure 3.  
Figure 3: Intake manifold pressures and temperatures in steady state.  
Taking logarithm of both sides of Equation (13) and applying least square method, the model is calibrated as,  
\[\tau_{id}=e^{3.594}p_{int}^{-1.734}e^{\frac{613.25}{T_{int}}}\left[O_{2}\right]^{ -0.116}m_{f}^{-0.08} \tag{17}\]  
The modeled and simulated ignition delay are compared in Figure 4.  
Figure 4 tells that the modeled ignition delay matches well with the simulation, and the model error is roughly within 0.2 CA degrees (see lower part of Figure 4).
# Adjustable Capacity Evaluation Method Based on Step-by-Step Power Mapping of Offshore Wind Farms
## 3 Step-by-Step Mapping of Offshore Wind Farm Power
### Mapping of Collector System Power to Submarine Cable End Power  
After getting the power of the collector system, we can use it as the input to calculate the loss of the submarine cable by the BP network.  
\[P_{C}=f_{2}(P_{G}), \tag{12}\]  
where \(P_{C}\) is total loss of high voltage submarine cable, \(f_{2}(\cdot)\) is the trained network model.  
The power of the collector system connects the first and second sections of the offshore wind farm power step-by-step mapping model. It plays an important role in the calculation of the cable loss. It is the key variable of the model. The power of the collector system minus the line loss of the wind farm offshore station and the submarine cable is the power at the end of the submarine cable. The process is shown in Figure 5.  
Figure 4: The process of K-means clustering. (**A**) The initial cluster centers are randomly selected. (**B**) Calculate the distance between the sample and the cluster center and classify samples. (**C**) Reselect cluster centers. (**D**) The clustering result is confirmed.  
The Mapping from the Power at the End of the Submarine Cable to the Power at the Grid Connection Point  
Figure 6 shows the structure of the onshore part of the offshore wind farm.  
The power at the end of the submarine cable is first collected into the bus, and a part of the power is reduced for electric field consumption, including reactive power compensation equipment, reactors, and station electricity. By analyzing and calculating the characteristics of various losses respectively, the power at the end of the submarine cable minus all losses is the power at the grid connection point  
\[P_{K}=sum(P_{G})-P_{C}-P_{t}-P_{s}, \tag{13}\]  
Figure 5: Mapping process from wind turbine power to the power at end of submarine cable.  
Figure 6: The structure of the onshore part of the offshore wind farm.  
where \(P_{K}\) is the grid connection point power, \(sum(\cdot)\) is vector summation, \(P_{t}\) is station power, \(P_{s}\) is the loss of compensation equipment. If the maximum theoretical generated power of the wind turbine is input when calculating the power of the collector system, the final calculation result is the adjustable capacity of the offshore wind farm.
# An Image Segmentation Method Based on Improved Regularized Level Set Model
## 3 Proposed Method
### Regularized Level Set Model-Based Image Segmentation Algorithm  
The flowchart of the proposed IRLS-IS method is outlined in Figure 1.  
After the abovementioned procedures of image segmentation, the detailed steps of the proposed IRLS-IS method can be summarized as follows.  
```
Input: An original image Output: The result of image segmentation Step 1: Initialize parameters \(\delta\), \(h\), \(\lambda\) and \(\
u\) Step 2: Set the level set function \(\phi_{0}(\mathbf{x},\mathbf{y})=1\) Step 3: ComCompute the Dirac delta functionStn Step 4: For n = 1: iterNum Step 5: Calculate \(\Delta G_{\sigma}\)*\(I\), that is, the Gaussian kernel function in [66] is convolved with image \(I\), and then the Laplacian operator is applied Step 6: Compute \(div\frac{\
abla\varphi}{|\
abla\varphi|}\) to determine the curvature of the level set function Step 7: Compute \(d_{p}(\mathbf{s})\) using Equation (8) Step 8: Update the level set function Step 9: If \(\min\frac{\partial\varphi}{\partial t}\) is found by using Equation (16), then output the result Step 10: Else return to Step 4 Step 11: End for
```  
**Algorithm 1**IRLS-IS  
For the IRLS-IS algorithm, the computational complexity mainly focuses on Step 5. In Algorithm 1, Step 5 is the most time-consuming to calculate the Gaussian convolution, and its time complexity is approximately \(O(K^{2}\times N)\), where typically \(K<7\) when a \(K\times K\) Gaussian convolution kernel is implemented in the spatial domain on an image with \(N\) pixels. Hence, the time complexity of IRLS-IS is close to \(O(N)\). Note that the computational complexity of reinitialization is \(O(N^{2})\)[50]. Since the IRLS-IS has eliminated the reinitialization step, its time costs and iteration operations are drastically reduced. Therefore, the computational complexity of our IRLS-IS method is lower than that of the other related level set models [33; 48; 67; 68; 69].
# Geometric Nonlinear Analysis of the Catenary Cable Element Based on UPFs of ANSYS
## 4 Verification and Numerical Examples
### Engineering Example Validation and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Buckle Cable Number** & **Experiment /kN** & \
\begin{tabular}{c} **Numerical Values (Development** \\ **Element User101)/mm** \\ \end{tabular} &
\begin{tabular}{c} **Numerical Values (ANSYS** \\ **Element Link10)/mm** \\ \end{tabular} \\ \hline X1 & 1501 & 1499(0.1) * & 1479(1.5) \\ X2 & 1434 & 1412(1.5) & 1398(2.5) \\ X3 & 1541 & 1499(2.7) & 1487(3.5) \\ X4 & 1550 & 1495(3.5) & 1506(2.8) \\ X5 & 1689 & 1621(4.0) & 1619(4.1) \\ X6 & 1843 & 1895(2.8) & 1786(3.1) \\ X7 & 1707 & 1657(2.9) & 1632(4.4) \\ X8 & 1834 & 1871(2.0) & 1772(3.4) \\ X9 & 1779 & 1757(1.2) & 1706(4.1) \\ X10 & 2129 & 2150(1.0) & 2018(5.2) \\ X11 & 2135 & 2048(4.1) & 2039(4.5) \\ X12 & 2338 & 2270(2.9) & 2235(4.4) \\ X13 & 2683 & 2576(4.0) & 2555(4.8) \\ X14 & 2772 & 2680(3.3) & 2613(5.7) \\ X15 & 2812 & 2659(5.4) & 2658(5.5) \\ X16 & 2765 & 26105(5.6) & 2646(4.3) \\ X17 & 2517 & 2426(3.6) & 2439(3.1) \\ X18 & 2180 & 2074(4.9) & 2092(4.0) \\ \hline \hline \end{tabular}  
* Numbers in parentheses indicate the absolute error percentage with respect to experimental results.  
\end{table} 
Table 8: Comparison of calculated and measured values of the maximum cable force of the arch ring buckles. Figure 12: Comparison of maximum buckle force values: (**a**) Comparison of different elements with experimental values; (**b**) Comparison of the absolute error of different elements. In summary, by comparing the deflection values of the above arch casting sections, the maximum cable force of the buckled anchor cable, and the maximum tensile stress of the top and bottom slabs, it can be concluded that the use of the user101 element to simulate the buckled anchor cable is more matching with the experimental results, with higher accuracy and faster calculation efficiency than the link10 element.
## 3 Field Experiments
### Experimental Results
#### 3.2.1 Direct Experiment  
A schematic diagram of the experiment is depicted in Figure 8a. The relationship between UAV-UE vertical distance and throughput was investigated. Throughput measurements were taken by increasing the altitude of the UAV by each 5 m step. The measurement  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Purpose** \\ \hline Monitor & To be used as a screen for wireless devices \\ \hline Keyboard & To operate wireless devices \\ \hline PC & To check data \\ \hline Safety Corn & To accurately align the drone \\ \hline Thermal Box & To maintain battery temperature and efficiency \\ \hline \end{tabular}
\end{table}
Table 4: Experiment equipment.  
Figure 7: Extended millimeter-wave communication device mounted on a UAV.  
results are shown by the green circle in Figure 9. The maximum physical layer throughput of the antenna device is 4.62 Gbps. However, the measurement results in this study are not throughputs of the physical layer but those measured at the MAC-SAP (that corresponds to the MAC layer throughput). It can be observed from the Figure 9 that a saturated throughput of approximately 1.4 to 1.5 Gbps is achieved up to an altitude of about 40 m. The quality deteriorated after 45 m due to distance attenuation, resulting in lower throughput.
## 1 Introduction
### Investigation of the Generalized Nonlinear Kinetic Equation of Ion Relaxation  
In Section 2.3, we provide a generalized quasi-classical kinetic equation of ion relaxation in dielectrics with ion-molecular chemical bonds in the form of a balance equation for the number of particles in potential wells (see Expression (1)). Also, in Section 2.3, based on the available theoretical information on quasi-classical models of ion relaxation in dielectrics, a study of the properties and types of symmetry of kinetic coefficients is provided (regarding the velocities of probabilities of ions crossing the potential barrier), taking into account the classical and quantum mechanisms of ion transfer between neighboring potential wells (see Expression (2)) in the form of an infinite power series by degrees of a small parameter of perturbation theory \(\zeta_{\mathrm{q}\mathrm{j}}=\left|\frac{\Delta\mathrm{U}_{\mathrm{i}\mathrm{j }}}{k_{\mathrm{B}}T}\right|\ll 1\) (see Expression (6)). The coefficients from (5), (10), and (10a)-(10c) formally coincide with the results from [2, 3]. On the basis of (6), by taking into account \(\left|\Delta\mathrm{U}_{\mathrm{i}\mathrm{j}\pm 1}\right|\approx\frac{\mathrm{qE} _{\mathrm{i}\mathrm{j}}}{2}\), \(\left|\Delta\mathrm{U}_{\mathrm{i}\pm 1\mathrm{j}}\right|\approx\frac{\mathrm{qE} _{\mathrm{i}\mathrm{j}}\pm 2}{2}\)[1], we find that  
\[W^{(-)}_{\mathrm{i}-1\mathrm{j}}=\sum_{l=0}^{\infty}\frac{1}{l!}\cdot\left( \frac{\mathrm{q}a}{2\mathrm{k}_{\mathrm{B}}T}\right)^{l}\cdot W^{(l)}\cdot \mathrm{E}_{\mathrm{i}-1}^{l},\ W^{(+)}_{\mathrm{i}+1\mathrm{j}}=\sum_{l=0}^{ \infty}\frac{(-1)^{l}}{l!}\cdot\left(\frac{\mathrm{q}a}{2\mathrm{k}_{\mathrm{B }}T}\right)^{l}\cdot W^{(l)}\cdot\mathrm{E}_{\mathrm{i}+1}^{l}, \tag{13a}\] \[W^{(-)}_{\mathrm{i}\mathrm{j}+1}=\sum_{l=0}^{\infty}\frac{1}{l!}\cdot\left( \frac{\mathrm{q}a}{2\mathrm{k}_{\mathrm{B}}T}\right)^{l}\cdot W^{(l)}\cdot \mathrm{E}_{\mathrm{i}}^{l},\ W^{(+)}_{\mathrm{i}\mathrm{j}-1}=\sum_{l=0}^{ \infty}\frac{(-1)^{l}}{l!}\cdot\left(\frac{\mathrm{q}a}{2\mathrm{k}_{\mathrm{B }}T}\right)^{l}\cdot W^{(l)}\cdot\mathrm{E}_{\mathrm{i}}^{l}. \tag{13b}\]  
Substituting (13a) and (13b) into (1) yields  
\[ \begin{array}{c}\frac{\partial n_{\mathrm{i}}}{\partial t}=\sum_{l=0}^{\infty }\frac{1}{l!}\cdot\left(\frac{\mathrm{q}a}{2\mathrm{k}_{\mathrm{B}}T}\right)^ {l}W^{(l)}\cdot\left[n_{\mathrm{i}-1}\mathrm{E}_{\mathrm{i}-1}^{l}+n_{\mathrm{ i}+1}(-1)^{l}\mathrm{E}_{\mathrm{i}+1}^{l}\right]-2\sum_{l=0}^{\infty}\frac{1}{ (2l)!}\cdot\left(\frac{\mathrm{q}a}{2\mathrm{k}_{\mathrm{B}}T}\right)^{2l}W^{ (2l)}\cdot n_{\mathrm{i}}\mathrm{E}_{\mathrm{i}}^{2l}=\\ =\sum_{l=0}^{\infty}\frac{1}{(2l)!}\cdot\left(\frac{\mathrm{q}a}{2\mathrm{k}_{ \mathrm{B}}T}\right)^{2l}W^{(2l)}\cdot\left[n_{\mathrm{i}-1}\mathrm{E}_{ \mathrm{i}-1}^{2l}-2n_{\mathrm{i}}\mathrm{E}_{\mathrm{i}}^{2l}+n_{\mathrm{i}+1 }\mathrm{E}_{\mathrm{i}+1}^{2l}\right]+\\ +\sum_{l=0}^{\infty}\frac{1}{(2l+1)!}\cdot\left(\frac{\mathrm{q}a}{2\mathrm{k} _{\mathrm{B}}T}\right)^{2l}W^{(2l)}\cdot\left[n_{\mathrm{i}-1}\mathrm{E}_{ \mathrm{i}-1}^{2l+1}-n_{\mathrm{i}+1}\mathrm{E}_{\mathrm{i}+1}^{2l+1}\right]. \end{array}
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
###### Abstract
[PERSON] et al. proposed deformable DETR [21], introducing deformable convolutions and multi-scale features to better focus on target areas, improving convergence speed. Conditional DETR [22] guides the model to focus on specific areas through conditional queries, thereby enhancing DETR's performance on small targets and in complex scenes. DINO [23] incorporates denoising techniques to enhance DETR's robustness under real-world noise conditions. In 2023, [PERSON] et al. proposed the real-time detection transformer (RT-DETR) [24], further enhancing the speed and accuracy of DETR-based detectors. In this context, we propose a new model, remote sensing DETR (RS-DETR), based on RT-DETR, to enhance object detection performance in remote sensing imagery. The main contributions of our work are as follows:  
* To enhance the interaction between features at different levels and improve computational efficiency, we incorporate cascaded group attention into the attention-driven feature interaction module. * To improve multi-scale feature fusion and object detection accuracy, we propose an enhanced bidirectional feature pyramid network. * To improve detection performance for small and overlapping targets, we propose a novel bounding box regression loss function, Focaler-GloU.