esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation-Based Performance Evaluation
### Performance Under Representative Driving Scenarios  
To complement the WLTC-based evaluation, four typical driving scenarios are designed to further validate the control effectiveness of DWMPC in diverse and challenging conditions. These scenarios include a sudden cut-in event, stop-and-go traffic, lead vehicle  
Figure 8: Radar plot comparison of normalized performance indicators: energy efficiency, ride comfort (jerk), distance tracking accuracy, and emergency response time. Higher values indicate worse performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Controller** & \(\frac{E}{E_{\text{ref}}}\) & \(\frac{J}{T_{\text{ref}}}\) & \(\frac{FDE}{TDE_{\text{ref}}}\) & \(\frac{RT}{RT_{\text{ref}}}\) & **PEI** \\ \hline DWMPC & 0.985 & 0.571 & 0.660 & 0.582 & 0.700 \\ Fixed-MPC & 1.000 & 0.634 & 1.000 & 1.000 & 0.909 \\ PID & 1.030 & 1.000 & 1.260 & 0.873 & 1.041 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Normalized performance metrics and overall performance evaluation index (\(PEI\)).  
departure, and steady-state following. Each case isolates a key dynamic pattern to test specific aspects of controller behavior.  
As shown in Figure 9, the DWMPC controller responds effectively in all scenarios. In the cut-in case (Figure 9a), the controller initiates timely deceleration to maintain a safe distance without abrupt braking, achieving a smooth transition. During stop-and-go conditions (Figure 9b), it handles frequent acceleration and deceleration smoothly, minimizing overshoot and undershoot.  
In the lead vehicle departure case (Figure 9c), DWMPC smoothly increases speed and stabilizes at the predefined cruise speed of 100 km/h, indicating effective recognition of reduced urgency and efficient mode switching. In the cruise-following scenario (Figure 9d), the controller maintains consistent spacing with low tracking error, demonstrating precise velocity and distance control under low dynamic variation.  
These scenario-based results further highlight the adaptive intelligence of DWMPC. By dynamically adjusting control weights in response to varying traffic contexts, the controller demonstrates an inherent ability to balance responsiveness, comfort, and safety, without requiring manual mode switching. This context-aware adaptability not only ensures robust performance in transient and steady-state conditions but also paves the way for scalable integration into more complex, real-world autonomous driving systems.
# A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs +
## 5 Experimental Results
### Floorplan Visualization  
#### 5.3.1 Visualization of MCNC and GSRC Circuit Floorplan  
Comparison of MCNC circuit floorplan results are shown in Figure 4. The DS of the floorplan generated by the algorithm in this paper is only 5.0%, while the simulated annealing algorithm produces a floorplan with a DS of 8.8%. Comparison of GSRC circuit  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MCNC**} & \multicolumn{2}{c}{**Area (\(\times\)10\({}^{6}\))**} & \multicolumn{2}{c}{**Wirelength (\(\times\)10\({}^{5}\))**} & \multicolumn{2}{c}{**DS (\%)**} \\ \cline{2-7}  & **Ours** & **SA** & **Ours** & **SA** & **Ours** & **SA** \\ \hline apte & 47.82 & 48.73 & 0.52 & 0.61 & 2.63 & 4.45 \\ xerox & 20.75 & 21.59 & 0.57 & 0.68 & 6.75 & 10.38 \\ hp & 9.26 & 9.52 & 0.41 & 0.55 & 4.66 & 7.25 \\ ami33 & 1.27 & 1.32 & 0.68 & 0.81 & 8.66 & 12.12 \\ ami49 & 38.95 & 41.52 & 6.47 & 6.79 & 8.99 & 14.62 \\ Average & 23.46 & 24.51 & 1.73 & 1.89 & 6.34 & 9.76 \\ Normalization & 1.000 & 1.045 & 1.000 & 1.092 & 1.000 & 1.034 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of experimental results of MCNC test circuits with obstacles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**GSRC**} & \multicolumn{2}{c}{**Area (\(\times\)10\({}^{5}\))**} & \multicolumn{2}{c}{**Wirelength (\(\times\)10\({}^{5}\))**} & \multicolumn{2}{c}{**DS (\%)**} \\ \cline{2-7}  & **Ours** & **SA** & **Ours** & **SA** & **Ours** & **SA** \\ \hline n100 & 1.98 & 2.23 & 1.97 & 2.21 & 9.09 & 19.28 \\ n200 & 2.15 & 2.37 & 3.46 & 3.87 & 18.14 & 25.74 \\ n300 & 3.42 & 3.78 & 5.03 & 5.66 & 20.18 & 27.78 \\ Average & 2.52 & 2.79 & 3.49 & 3.91 & 15.80 & 24.27 \\ Normalization & 1.000 & 1.107 & 1.000 & 1.112 & 1.000 & 1.085 \\ normalization & 1.000 & & 1.000 & & 1.000 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of experimental results of GSRC test circuits with obstacles.  
floorplan results are shown in Figure 5. The DS of the floorplan generated by the algorithm in this paper for the n100 test circuit is 7.0%, which is lower than the 9.9% DS achieved by the simulated annealing algorithm. From the figures, it is evident that the floorplans generated by the algorithm in this paper have a lower DS and overall floorplan areas than the floorplans generated by the simulated annealing algorithm, both in the small-scale and large-scale test circuits in both benchmark sets. This confirms the superior performance of the algorithm proposed in this paper.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 3 Experimental Framework
### Performance Measures and Evaluation  
To evaluate the performance of the different approaches, the Intersection over Union (IoU) and F-score metrics are used (Equation (3)). Metrics are computed for each label (background, building and road) individually. Additionally, these metrics are aggregated over the different testing cities to assess the overall performance. This aggregation is done, using the weighted arithmetic mean, taking as weights the valid extension of each city. Recall that this information is presented in Table 1. Note that in Equation (3), both \(y\) and \(\hat{y}\) are one-hot encoded masks.  
\[IoU(y,\hat{y},c)=\frac{\sum_{i\in I}y_{i,c}\hat{y}_{i,c}}{\sum_{i\in I}\max(y_ {i,c},\hat{y}_{i,c})}\qquad\quad F-score(y,\hat{y},c)=\frac{2\sum_{i\in I}y_{ i,c}\hat{y}_{i,c}}{\sum_{i\in I}y_{i,c}+\hat{y}_{i,c}} \tag{3}\]  
The performance of the different strategies is also qualitatively evaluated through the visual inspection of the multi-class segmentation maps. Additionally, the visual inspection of each label's IoU in the form of true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN) is also included. This outlook yields a better description of how the models are behaving, clearly identifying their differences.
## 3 Source Localization by Means of Acoustic Emission
### Extended Kalman Filter (EKF)  
In this work, an adapted EKF-based algorithm is used to locate AE source. The Extended Kalman Filter (EKF) is a recursive data processing algorithm that estimates the state of a noisy dynamic system. The EKF has been widely used in a lot of applications due to its simplicity, optimality, robustness, sensor fusion ability, and its capability of filtering out the uncertainties due to the mathematic model and measurements [31,58]. In general, the EKF is a two steps process: (1) state prediction according to a mathematical model; and (2) the correction of the state according to the measurements collected by sensors. For this reason, the EKF has been used to handle the set of nonlinear Equation (3). The state of the system consists of three parameters, the AE source location \((x_{k},y_{k})\) and the wave velocity \(V_{g}\) of the involved elastic wave. The state of the system \(X_{k}\) is related to the measurement data vector through the nonlinear measurement function \(Y_{k}=\left[\left(\Delta t_{mi}\right)_{k}\right]_{(n-1)\times 1}\), \(n\geq 3\) defined in Equation (3). If one assumes that the AE source location can be considered as an unmovable (static) or slowly fluctuating point, and the state has no superimposed noise [31], under this assumption the prediction stage can be neglected and the equations take the following forms:  
\[\left\{\begin{array}{l}H_{k}=\
abla h_{k}\big{(}\hat{X}_{k-1}\big{)}\\ \hat{Y}_{k}=h\big{(}\hat{X}_{k-1}\big{)}\\ K_{k}=\hat{P}_{k-1}H_{k}^{T}\big{(}H_{k}\hat{P}_{k-1}H_{k}^{T}+R\big{)}^{-1}\\ \hat{X}_{k}=\hat{X}_{k-1}+K_{k}\big{(}Y-h\big{(}\hat{X}_{k-1}\big{)}\big{)}\\ \hat{P}_{k}=(I-K_{k}H_{k})\hat{P}_{k-1}\end{array}\right. \tag{14}\]  
The uncertainty related to the measurement vector \(Y\) is modeled as a zero mean white Gaussian noise with covariance matrix \(R\) with diagonal terms defined in Equation (13):  
\[R=\begin{bmatrix}\sigma_{\Delta t_{mi}}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&\sigma_{\Delta t_{m(n-1)}}\end{bmatrix} \tag{15}\]The iteration starts with the initialization of the estimated state vector \(\hat{X}_{0}\) and covariance matrix \(\hat{P}_{0}\), which are defined below:  
\[\hat{X}_{0}=\begin{bmatrix}\hat{x}_{s0}&\hat{y}_{s0}&\hat{V}_{g_{0}}\end{bmatrix} \text{; }\hat{P}_{0}=\begin{bmatrix}\sigma_{x_{s_{0}}}^{2}&0&0\\ 0&\sigma_{y_{s_{0}}}^{2}&0\\ 0&0&\sigma_{y_{s0}}^{2}\end{bmatrix} \tag{16}\]  
In Appendix A.2 is presented the approach used to determine the AE initiation location and the starting group velocity.  
Overall, and based on the abovementioned considerations, the proposed approach to simultaneously track and optimize the AE source location consists of two stages. The first stage provides information about TOA, frequency, and their uncertainties. The second stage iteratively estimates the state vector (AE source location and wave), according to the information obtained from the first stage.
## 2 System Model  
A schematic of the DORC is illustrated in Figure 1. The DORC consists of an HTL and an LTL. The HTL is used to recover the waste heat of engine exhaust gas, and the LTL absorbs the heat of the jacket cooling water and released by HTL.  
For HTL: An HTL working fluid pump pressurizes saturated liquid from a condenser/evaporator to an HTL evaporation pressure (1-2). The high-pressure liquid absorbs heat from an engine exhaust gas in the HTL evaporator and is heated to a saturated or superheat vapor (2-5). The saturated or superheated vapor from the HTL evaporator enters the HTL turbine and expands to perform work (5-6). The exhaust vapor exits from the HTL turbine and then enters the condenser/evaporator to release heat to the LTL (6-1).  
For LTL: An LTL working fluid pump pressurizes saturated liquid from an LTL condenser to an LTL evaporation pressure (1'-2'). The liquid from the LTL working fluid pump enters an LTL preheater and absorbs heat to preheating (2'-7'), and then working fluid enters the condenser/evaporator and absorbs heat for evaporating (7'-4'). Working fluid with a saturated state enters the LTL turbine and generates power (4'-5'). The exhaust vapor enters the LTL condenser and condenses to a liquid (5'-1').  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Molecular** & **Critical** & **Critical** & **Normal Boiling** & **ODP** & **GWP** \\  & **Mass/(g-mol\({}^{-1}\))** & **Temperature/K** & **Pressure/kPa** & **Point/K** & **ODP** & **GWP** \\ \hline cyclopentane & 70.13 & 511.7 & 4515 & 322.4 & 0 & low \\ cyclohexane & 84.16 & 553.6 & 4075 & 353.9 & 0 & low \\ benzene & 78.11 & 562.1 & 4894 & 353.2 & 0 & low \\ boluene & 92.14 & 591.8 & 4126 & 383.8 & 0 & low \\ R1234 ze(E) & 114.04 & 382.5 & 3636 & 254.2 & 0 & 6 \\ R600a & 58.12 & 407.8 & 3629 & 261.4 & 0 & low \\ R245 fa & 134.05 & 427.2 & 3651 & 288.3 & 0 & 950 \\ R601a & 72.15 & 460.4 & 3378 & 301.0 & 0 & low \\ \hline \end{tabular}
\end{table}
Table 1: Working fluids properties. ODP: ozone depletion potential; GWP: global warming potential.
# Interpretation of Frequency Response Analysis for Fault Detection in Power Transformers
## 2 Transformer Frequency Response Analysis
The fourth region is for frequencies above 1 MHz, in which the effects of measurement and grounding leads are considerable. It is crucial to analyze each frequency region because each frequency region is affected by different transformer faults. The core dominates the LF region, the MF region is  
Figure 2: FRA test setup for transformer winding at end-to-end open circuit test [10]. dominated by the parallel capacitance and mutual inductances, while the HF region is influenced by the winding capacitance [25; 31]. Essentially, the frequency response of a transformer can be simulated based on a complex circuit consisting of resistances, inductances, capacitances, and mutual inductances, as shown in Figure 5. Each of the circuit elements is related to the physical geometry of the transformer winding [32; 33]. In the transformer-equivalent high-frequency circuit shown in Figure 5, Rs, Cs, and Ls are the winding series resistance, capacitance, and self-inductance, respectively. Cg and Gg are the ground capacitance and conductance that represent the dielectric insulation system. Mij is the mutual inductance. CHL and GHL are the capacitance and conductance between high voltage and low voltage windings. Any changes in the winding will affect the RLC network, thereby altering the original or baseline frequency response [34]. The simulated frequency response of a simple RLC circuit is shown in Figure 6a. The winding resistance, 'R', contributes to the horizontal line, as shown in Figure 6b. The winding inductance, 'L', influences the negative slope illustrated in Figure 6c, at the low- and mid-frequency regions. The capacitive effect due to winding insulation affects the positive slope, shown in Figure 6d, at the mid- and high-frequency regions. Figure 4: The three typical frequency sub—bands for transformer FRA. Figure 3: End—to—end measurement result from 500 kVA transformer: (**a**) open circuit test response, and (**b**) short circuit test response.
# Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol
## 4 Conclusions  
In this research, a wireless sensor network for agricultural greenhouses based on the improved Zigbee protocol is designed. This research uses a variety of commonly used sensors and Zigbee to form an agricultural greenhouse data acquisition network, and improves the topology networking mode and routing algorithm of Zigbee nodes. After NS2 simulation experiments, the improved Zigbee sensor network shows lower network delay. The average end-to-end delay, high packet delivery rate, small routing control overhead and low routing discovery frequency prove that this research can effectively improve the network speed and reduce congestion, improving the overall performance of the agricultural information monitoring system. In the future, we envisage combining the wireless sensor network with 5G technology, connecting the 5G router through the Zigbee coordinator, and transmitting the collected sensor data to the remote terminal faster. In addition, we envisage combining the Zigbee network with the agricultural greenhouse control equipment so that the agricultural greenhouse control equipment can perform regular operations according to the environmental parameters of the Zigbee sensor network, so as to realize automatic production and remote terminal control.  
Conceptualization, R.T.; Methodology, R.T.; Software, R.T.; Validation, R.T.; Formal analysis, R.T.; Investigation, R.T.; Resources, N.K.A.; Data curation, R.T.; Writing--original draft, R.
## 2 Experimental Setup
### Experimental Model  
The experimental models include a circular cylinder (baseline cylinder) and three wavy cylinders with different spanwise wavelengths and amplitudes, as shown in Figure 3. The profile parameters of the wave on all cylinders are shown in Table 1. All the cylinders are manufactured using a numerical-controlled machine and made of stainless steel to guarantee the strength of the experimental models. The wavy cylinders may have potential use in the pantograph of high-speed trains, submarine pipelines and so on to reduce flow-induced noise and vibration. The circular cylinder is regarded as the baseline cylinder, and the others are wavy cylinders used to study the noise reduction performance. The span length of the cylinder (\(L\)) is 400 mm, and the mean diameter of cylinder (\(D_{m}\)) is 22 mm. The experimental model was vertically mounted in the potential core of the jet flow with the ceiling and ground plates in the aeroacoustic wind tunnel, as shown in Figure 4. One baseline cylinder and three wavy cylinders with different aspect ratios (\(\lambda/a\)), i.e., 40, 30 and 24, are studied to evaluate the influence of the wavelength and amplitude on noise reduction. The three different aspect ratios of the wavy cylinders were selected based on the experience that the amplitude of the wavy shape (or aspect ratios of wavy cylinders) is an important factor that has to be investigated. The profile of the wavy cylinder is depicted to be a cosine function as follows:  
\[D(z)=D_{\rm m}-2a\text{cos}(2\tau z/\lambda) \tag{1}\]  
where \(a\) is the amplitude and \(\lambda\) is the wavelength of the wave profile.  
Figure 2: Sketch of the 0.55 m \(\times\) 0.4 m aeroacoustic wind tunnel.
## 3 Results
### Pressure Differences between COSMIC and Radiosonde Data  
As shown in Figure 4, the vertically averaged COSMIC pressure biases compared to radiosonde observations in China are mostly negative in northern China and some parts of central China, but positive in the remainder of China. All the pressure biases vary between \(-0.93\) hPa and \(3.43\) hPa. Among the 120 stations, 76 stations (63%) have biases with a magnitude of less than 1 hPa, and 32 stations (27%) with a magnitude of between 1 hPa and 2 hPa. The large pressure biases mostly appear in the QTP and in southern China, and the maximum pressure bias of 3.43 hPa occurs at a station in southern China. On average, the COSMIC pressure bias in China is 0.69 hPa. Notably, the COSMIC pressure bias varies from positive to negative with increasing station latitude, and a negative correlation is found between the absolute pressure bias and the station latitude, with a correlation coefficient of \(-0.4558\) at a significance level of greater than 99%. This indicates that station latitude is one of the impact factors on the pressure bias between COSMIC and radiosonde.  
Figure 5 presents the pressure RMSEs between COSMIC and radiosonde data in China. The pressure RMSE is small in eastern China and large in southern and north-western China. There are 63 stations (53%) with an RMSE of less than 10 hPa, and 49 stations (41%) with an RMSE of between 10 hPa and 20 hPa. Like the pressure bias, the large pressure RMSEs mostly appear in southern China. The minimum pressure RMSE is 2.31 hPa, the maximum is 29.77 hPa, and the average pressure RMSE is 10.66 hPa.  
Figure 4: The COSMIC pressure biases compared to radiosonde observations in China. The red dots are positive biases, and the blue dots are negative biases. The number of radiosonde stations for each bias class is listed in parentheses.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 3 Architecture of QKD Network Enabled by SDN
### Application Layer  
The application layer is at the top of the architecture of the QKD networks enabled by SDN. It directly faces demands of users and also abstracts network resources for users. Thus, it may meet some requirements such as topological visualization and quality of service from users. Also, the controller can allow the abstraction of network resources like light-path building for QKD and routing for secret-key generation occurring in QKD layer though northbound interfaces. Unlike classical optical networks, the application layer includes two major services, i.e., secret-key provisioning services and security management services. Secret-key provisioning services provide secret keys for the security demands of the networks such as authentication, encryption, and signature. Security management is mainly responsible for the functions such as intrusion detection, virus protection, and security posture sensing, etc.
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 6 Results
### Dataset Acquisition
#### 6.1.3 T-SNE Visualization of Open-Set Passori Rhythm Patterns  
In the encoder-decoder architecture, the input Mel spectrogram dimensions are formatted as \(1\times T\times W\), where \(T\) denotes the number of temporal frames, \(W\) represents the Mel frequency bins, and the number 1 signifies the channel size, akin to the three color channels in RGB images. Post-encoding, followed by a mapping block, the dimensions of the embedding feature space are reduced to \(\hat{N}\times T\times 1\) (refer to Figure 2, excluding the batch size B), achieved through pooling operations that compress the Mel bins to a single value. In the absence of center and contrastive losses, T-SNE visualizations (Figure 7) reveal a relatively dispersed distribution of features within the same class, despite some degree of clustering observed in classes such as \"zungzungmori\" and \"zungmori\". Notably, the boundaries between different classes remain indistinct, leading to a mingling of class features. This overlap complicates the distinction between known and unknown classes during network testing, exemplified by instances where the unknown class is confounded with \"zajinnori\". Conversely, the integration of proposed losses--center and contrastive--significantly refines the structure of the embedded space. Under this regime,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Backbone** & **Baseline** & **w. Center Loss** & **w. Center/Contrastive Loss** \\ \hline HRNet32 & 0.676 & 0.776 & 0.874 \\ HRNet48 & 0.662 & 0.756 & 0.844 \\ HRNet64 & 0.754 & 0.888 & 0.918 \\ ResNet101 & 0.803 & 0.815 & 0.884 \\ WideResNet101 & 0.793 & 0.843 & 0.881 \\ ResNet152 & 0.659 & 0.804 & 0.833 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental performance (AUROC) of the Passori rhythm-pattern dataset in various encoder networks.  
features corresponding to the same rhythm pattern class are tightly clustered, whereas features of different classes are distinctly separated, as evident with \"zungzungmori\", \"zungmori\", and others. Moreover, the unknown class, represented by a luminous yellow dot, predominantly occupies isolated regions of the feature space, denoted as \"white space\", characterized by clear demarcations and minimal overlap with known categories. This structured separation enhances the efficacy of the detection mechanism, enabling more accurate differentiation between known and unknown categories.
## 4 Experimental Setup
### Experiments Results
#### 4.1.2 Result on CF Test Suit  
The comparative results of SA-MPCMOEA and other algorithms on the CF test problems [32] are presented in Table 2.  
As shown in Table 2, the SA-MPCMOEA algorithm demonstrates a significant advantage across the CF benchmark problems. Among all compared algorithms, SA-MPCMOEA is only outperformed by ABSAEA on three problems (CF1, CF3, and CF10) in terms of IGD values because the feasible region of the problem is relatively large in CF1, CF3, and CF10. SA-MPCMOEA starts to focus on the diversity of the population after finding a sufficient number of feasible solutions, as shown in Figure 3, while maintaining superiority or equivalent performance on the remaining seven problems. Notably, SA-MPCMOEA exhibits exceptional performance on complex constrained problems (CF2, CF4, CF6, and CF7), particularly in the equality-constrained problem CF7, where it achieves the best results, highlighting its robust constraint-handling capability. In high-dimensional problems (CF8, CF9, and CF10), the infeasible regions disrupt the feasible domains, leading to the inferior performance of other algorithms compared to SA-MPCMOEA and ABSAEA. Benefiting from the angle-based convergence model for feasible domains, SA-MPCMOEA efficiently traverses complex and vast search spaces, rapidly crossing infeasible regions to locate feasible solutions. As observed in Figures 4 and 5, SA-MPCMOEA effectively prevents population convergence toward CPF gap regions through its constraint-dominated exploration mechanism when handling problems with a discontinuous constrained Pareto front. Compared to ABSAEA, SA-MPCMOEA achieves more uniform distribution along the CPF, demonstrating superior diversity preservation capabilities in maintaining solution spread.