esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Circuit Topology and Operation Analysis
### Self-Excited Driving Voltages  
The center-tapped transformer has two additional windings--the turn ratio of the primary winding to the additional windings is m: 1:1. The self-excited driving voltages were obtained by stepping down the high-frequency square wave via the transformer. A pair of symmetrical square wave voltages (\(v_{gs1}\), \(v_{gs2}\)) were generated by the self-excited windings, as depicted in Figure 2. Their frequency \(f_{s}\) was in synchronism with the resonant frequency \(f_{r}\) which was created by the combination of resonant inductor \(L_{r}\) and resonant capacitor \(C_{r}\), as can be expressed by Equations (1) and (2).  
\[f_{r}=\frac{1}{2\pi\sqrt{L_{r}C_{r}}} \tag{1}\]  
Figure 1: The developed, synchronized, self-excited, single-stage, high power-factor light-emitting diode (LED) driver.  
\[f_{s}=f_{r} \tag{2}\]  
The voltage on the primary winding \(v_{p}\) of the transformer can be achieved by Equation (3),  
\[v_{p}=DV_{cbus}M_{vr} \tag{3}\]  
where \(M_{vr}\) is the voltage gain of the resonant circuit, and \(D\) is the duty ratio of the lower-arm power switch \(S_{2}\). The control voltages \(v_{gs1}\) and \(v_{gs2}\) may be approximated as square waves, equal to:  
\[v_{gs1}=-v_{gs2}=\frac{-v_{p}}{m} \tag{4}\]  
where the negative sign indicates the phase relationship between \(v_{gs1}\) and \(v_{gs2}\).
## 2 Related Work
### Feature Fusion  
Efficiently fusing multi-scale features proves indispensable for improving the detection accuracy of small targets. YOLO architectures initially adopted Feature Pyramid Networks (FPN) [25] to propagate deep semantic information in a top-down manner, enhancing multi-scale representation but losing shallow details due to unidirectional flow. Later, Path Aggregation Networks (PANets) [26] added bottom-up paths to improve localization but still suffered from feature blurring from repeated sampling. Solutions like Bidirectional Feature Pyramid Network (BiFPN) [27] introduced weighted cross-scale fusion to balance feature contributions, but they incurred an increase in parameters through complex connections. To reduce the number of parameters while preserving more detailed features, we designed the EFAN feature fusion network, innovating with three dedicated fusion pathways. These pathways integrate high-resolution details from P2, P3, and P4 layers with deep semantics from P3, P4, and P5 layers, while lightweight SCDown modules control parameter growth. This achieves high-fidelity fusion of shallow and deep features, advancing PCB defect feature preservation.
## 1 Introduction
### Relevant Works
Singular value decomposition (SVD) has a good effect on removing narrowband interference in noisy PD signals [15,16]. After determining the singular value number of narrowband interference by Fourier transform power spectrum, paper [17] effectively eliminates periodic narrowband interference by SVD. Paper [18] proposed to construct a Hankel matrix for SVD of noisy PD signals to realize the separation of narrowband interference subspace and PD signal subspace, so as to complete narrowband interference suppression. However, the reliability of using artificial selection of large singular values to distinguish narrowband interference subspace and PD signal subspace is poor, and it may cause some subspaces of the PD signal to be eliminated. Paper [19] used a K-means clustering algorithm to adaptively distinguish narrowband interference subspace and PD signal subspace. However, the suppression effect on small-amplitude narrowband interference is not further considered. In addition, paper [20] uses the adjustable resolution of the generalized S transform to invert the main frequency part of the PD signal to filter out periodic narrowband noise. However, the selection of adjustment factors \(\alpha\) and \(\beta\) is based on experience, and the algorithm has poor adaptability.
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 3 Trajectory Planning of the Par4 Parallel Robot
### Space-Path Coordinate Transform  
For the convenience of our study, it is necessary to transform the spatial gate-shaped path in the OXYZ coordinate system into the one seen in the **UO'W** plane local coordinate system. Assuming the pick-up endpoints have the same spatial \(Z\)-axis coordinate value, the transformation from the OXYZ coordinate system to the **UO'W** plane local coordinate system only needs to take the translation of the origin and the rotation transformation along the X axis into consideration. The coordinates \((x,y,z)\) of any spatial point in the OXYZ coordinate system will be marked as \((u,w)\) in the **UO'W** plane local coordinate system after the transition, which can be described as Equation (7).  
\[\left[\begin{array}{c}x\\ y\\ z\\ 1\end{array}\right]=\textbf{Q}\left[\begin{array}{c}u\\ 0\\ w\\ 1\end{array}\right]=\left[\begin{array}{cccc}\begin{array}{cccc}\begin{array} []{cccc}\begin{array}{cccc}\begin{array}{cccc}\begin{array}{cccc}\begin{array} []{\[a_{\max}=5.625S_{a}/T^{2} \tag{9}\]  
where \(\tau=t/T\), \(t\) is the current time; \(S_{a}\) is the total pick-up displacement, and here \(S_{a}=2(H_{a}-f-e+I_{BC})+B_{a}\).  
* Sextic Polynomial Displacement Planning  
To reduce residual vibration at the falling point of the robot end, the proportion of the acceleration time and deceleration time is set as 4:6 in the sextic polynomial motion law, meaning that the maximum velocity is at 2T/5 time when the acceleration is zero, \(a(2T/5)=0\). Then, the expression of displacement can be described as in Equation (10).  
\[s(t)=S_{a}(-10\tau^{6}+36\tau^{5}-45\tau^{4}+20\tau^{3}) \tag{10}\]  
It should be remembered that both the quintic and the sextic polynomial displacement planning expressions are asymmetric.
## 2 Methodology
### Overview of TongueNet  
As mentioned above, the pipeline of TongueNet is displayed in Figure 2. The input of the system is an individual's tongue image captured (using a uniquely designed imaging apparatus [8]) using a special device, while the output of the system is a well-segmented tongue image.  
After a raw image showing the lower half of an individual's face with their tongue protruding is captured in the first step. The next step takes the captured image and converts it to a grey-scale image after selecting one channel of the raw image to enhance the tongue domain [13]. The dominant color analysis of one sample in the dataset is shown in Figure 3. In Figure 3, (a) is a sample selected from the tongue image dataset, (b), (c) and (d) are the bivariate histograms to describe the different RGB values coming from the channels of each pixel. The RGB model is a color model which takes red, blue and green as the primary colors. In the histograms of Figure 3b-d, the color ranges from deep blue to yellow representing the frequency of pixels in the image. The color bar beside each histogram displays the relationship between the colors and the pixel frequencies. As can be seen from the yellow regions (high-frequency regions) of Figure 3b,c, most pixels have a higher red channel value with a lower blue and green channel value. Meanwhile, (d) shows that the majority of pixels (in the yellow region) have almost the same number of blue and green values. In the end, this indicates that the red color is the dominant color of tongue images. Therefore, for all samples, the red channel is selected since it is the dominant color. Next, the third step is mask prediction. In this step, a grey-scale image from the previous step is fed into the segmentation model. Afterwards, the model will output a well-refined mask image, only containing binary information to indicate which pixels belong to the tongue. Followed by this, the fourth step is tongue image extraction. In this step, the completed tongue image is extracted from the original raw image according to the mask image. The extracted result is regarded as the final output of the system.
## 4 Experimental Results and Analysis
### Computation Time  
We implemented the proposed method using the C# language in Visual Studio 2013. We then conducted our experiments on a PC with Intel Core i7 Quad 3.5 GHz, 8 GB of RAM, and Windows 7 64-bit. Section 3 indicates that the computation time of the proposed method is dependent on the number of objects in the vector maps. With the GIS vector maps of Scotland, the computation time of our method ranged from 77 ms to 1896 ms, with the size of maps ranging from 1951 Kbs to 78,136 Kbs. We also implemented the methods of [PERSON] and [PERSON] using the C# language in Visual Studio 2013, in a similar environment, to measure and compare the computation time between methods. [PERSON]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scotland Maps**} & \multirow{2}{*}{**\# Objects**} & \multicolumn{3}{c}{**Entropy (dB)**} \\ \cline{3-5}  & & **Our Method** & **[PERSON]’s Method** & **[PERSON]’s Method** \\ \hline Railway & 9724 & 128,817 & 9216 & 4608 \\ Land-use & 41,242 & 632,315 & 55,296 & 4608 \\ Waterway & 53,389 & 838,434 & 124,416 & 4608 \\ Nature & 99,835 & 1,657,985 & 235,008 & 4608 \\ Road & 372,138 & 6,886,591 & 299,520 & 4608 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The entropy of the proposed method compared with previous methods.  
Figure 10: Entropy according to the number of objects.  
performed full encryption for GIS vector maps, which means he encrypted notation, header, text, and all geometric objects. Thus, the computation time of [PERSON]'s method is dependent on the size of the GIS vector maps. Compared with [PERSON]'s method, the computation time of our method was less (40 ms vs. 2870 ms; Table 3). In the method of [PERSON], the computation time is dependent on the computation time of the selection process, the number of selected objects, and the computation time of the DWT and inverse DWT processes. The computation time of the DWT and inverse DWT processes is dependent on the number of vertices in each object. If an object has many vertices, the computation time of the DWT process is long. Conversely, if the number of selected objects is small, the computation time is short (Table 3). Figure 11 shows the computation times of the proposed method compared to the methods of [PERSON] and [PERSON] according to the size of the vector maps. The time of the proposed method is significantly shorter than that of previous methods.
## 4 Evaluation
### Accuracy Evaluation Using Prefetching Module  
Analyzing the results from Figures 11 and 12, we confirmed that the model attaches module B to module S, which aggressively activates prefetching operations, hence the coverage ratio of our model can be enhanced. However, the aggressive prefetching mode inevitably leads to a degradation of prefetching accuracy, highly related to the entire system's performance.  
Figure 11: Coverage of module-S.  
Figure 12: Coverage of propose model.  
Figure 13 depicts the comparison of prefetching accuracy on various workloads based on module S and our proposed model. In Figure 13, we observe that our model shows a 10% reduction in the prefetching accuracy on average, compared to module S. Additionally, the results of prefetching accuracy show our model achieved 17% reduction in _bf_s workload. Because our model manages all memory access information requested by the LLC with the history table, the components of the table are not changed owing to prefetching. Hence, every degradation tendency of the accuracy ratio is caused by module B. Module B activates the prefetching operation considering the correlation of the recently accessed requests which are stored in the history table. Thus, the degradation of prefetching accuracy indicates that analyzing the correlation with only data from the history table is not a simple solution. In addition, the accuracy ratio is degraded by an aggressive prefetching; nevertheless, Figure 14 shows that the buffer hit count increases in the case of aggressive prefetching. Increasing the buffer-hit count means a decrease in the miss ratio for the total memory access. This indicates an improvement in the coverage aspect and leads to an improved performance in terms of total execution time and energy consumption. When an appropriate accuracy is maintained, the coverage will have a more decisive effect on the overall performance. Therefore, our proposed model can achieve a better performance than module S, which performs prefetching conservatively by maintaining a valid accuracy.  
Figure 14: Buffer hit count of graph processing.  
Figure 13: Prefetch accuracy for graph processing.
# Seismic Analysis of Steel Solid Web Girder-RC Tubular Column Hybrid Structure
## 4 Numerical Results and Discussion
### Lateral Deformation Performance  
Table 4 summarizes the numerical results of maximum lateral displacements and drift ratios. According to the China technical code for the design of civil structure of fossil-fired power plant [27], the maximum drift ratio on the top of column for the air-condenser supporting structure should not be larger than 0.2% under normal service stages. The numerical results showed that the maximum drift ratios on the top of column under the frequent and basic earthquakes were respectively 0.06 and 0.15 less than the upper limit value, indicating that the solid web girder-RC tubular column hybrid structure could meet the design requirements of the seismic resistant structures; that is, the structure should keep elastic under the frequent earthquake and be in use after repairment under the basic earthquake.  
Figure 8: Time-history curves of base shear force and roof lateral displacement under different hazard levels: (**a**) Base shear force; (**b**) roof lateral displacement.  
Figure 9 shows the maximum lateral displacement versus height curves under different hazard levels. Figure 10a,b shows the lateral deformation nephograms of the whole structure and steel solid web girder platform under the rare earthquake. It can be observed that the lateral deformation pattern of the steel solid web girder-RC tubular column hybrid structure was shear mode. Since the stiffness of the superstructure is much larger than that of the substructure, the in-plane deformation of the steel solid web girder was very small, as shown in Figure 10b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Hazard Level (PGA)**} & \multicolumn{2}{c}{**Maximum Lateral Displacement (mm)**} & \multicolumn{2}{c}{**Maximum Drift Ratio (\%)**} \\ \cline{2-5}  & **Top of A-Shaped Frame** & **Top of Column** & **Top of A-Shaped Frame** & **Top of Column** \\ \hline Frequent earthquake (0.07 g) & 36.04 & 31.16 & 0.06 & 0.06 \\ Basic earthquake (0.20 g) & 83.71 & 73.26 & 0.14 & 0.15 \\ Rare earthquake (0.40 g) & 149.25 & 143.65 & 0.24 & 0.29 \\ Very rare earthquake (0.62 g) & 263.45 & 259.02 & 0.43 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of maximum lateral displacements and drift ratios.  
Figure 10: Lateral deformation nephograms of the structure under the rare earthquake (PGA (Peak ground acceleration) = 0.40 g): (**a**) Whole structure; (**b**) steel solid web girder platform.  
Figure 9: Maximum lateral displacement versus height curves under different hazard levels.
## 6 Results and Discussion
### HPWH Aggregation Levels and Limits  
As discussed in Section 5, load aggregation is a requisite for the participation of residential segments in markets through DR and so define QHCG groups. In our case, the aggregation of 100 HPWH loads, which are represented by the elemental model described in Section 4.1, is developed by the summation of the response of elemental models, each one driven by the different characteristics of the proposed QHCG (tank size range, dwelling temperature, water uses, etc.; see Table 5).  
Usually, energy aggregators oversee this task, but due to the spread of loads and customer characteristics, some problems arise during the deployment of DR policies. For instance, aggregators can change ON/OFF periods (or Smart Thermostats settings) to adapt the total consumption to the DR targets, but these actions always impact the customers' comfort. For this reason, the software also surveys that the temperature in the mixed-water zone of each tank is always in the range 50-62 \({}^{\circ}\)C (Figure 12). As a rule, at the aggregation stage, if a control approach causes more than 10% of households to experience severe hot water run outs, the approach is assumed to be unacceptable for the entire QHCG.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter (Unit)** & **Reference Value** & **Range (and pdf) 1** & **Observations** \\ \hline Water flow q(t) (L/min) & Figure 9a & \(\pm\)25\% (normal) & Phase delay \(\pm\)45 min (uniform) \\ Inhabitants & 3 & 3–4 (uniform) & 80 L capacity for three inhabitants \\ Tank capacity (L) & 80 L & 80–100 L & Available models [32] \\ Ancillary resistor (W) & 1200 W & \(\pm\)10\% (normal) & Changes in voltage supply \\ HP Power (W) & 250 W & 250–300W & Depending on the temperature gradient. \\ COP (air at 7 \({}^{\circ}\)C) & 2.5 & 2.5–2.9 & Manufacturer data (80 L and 100 L) \\ Inlet temperature (winter) & 10 \({}^{\circ}\)C & \(\pm\)2 \({}^{\circ}\)C (uniform) & According to seasonal and weather data \\ External temp (winter) & 18 \({}^{\circ}\)C & \(\pm\)2 \({}^{\circ}\)C (uniform) & Indoor temperature of dwelling \\ Thermostat setpoint & 55 \({}^{\circ}\)C & 55–62 \({}^{\circ}\)C & HP max efficiency at 55 \({}^{\circ}\)C \\ \hline \end{tabular} \({}^{1}\) Probability density function.  
\end{table}
Table 5: Range of parameters for the aggregated HPWH load used to run PBLM probability density function (pdf).
## 4 Simulation Results and Performance Evaluation  
Table 1 presents the circuit parameters of the DAB, while Table 2 provides specific values and the ranges for the parameters related to the GA used. In practical operating con  
ditions, the DAB exhibits better performance in operating conditions involving reference tracking and load increase, while it shows relative difficulties in operating conditions with load decrease. Therefore, based on these parameters, a simulation circuit is constructed using MATLAB/SIMULINK. Against the background of a sudden decrease in load resistance from 20 \(\Omega\) to 12 \(\Omega\) (t = 0.1 s), the control parameters of PI control, SMC control, and MPC control are optimized using the error between the actual output voltage and the desired value as the objective function.
# Stride-TCN for Energy Consumption Forecasting and Its Optimization
###### Abstract  
Forecasting, commonly used in econometrics, meteorology, or energy consumption prediction, is the field of study that deals with time series data to predict future trends. Former studies have revealed that both traditional statistical models and recent deep learning-based approaches have achieved good performance in forecasting. In particular, temporal convolutional networks (TCNs) have proved their effectiveness in several time series benchmarks. However, presented TCN models are too heavy to deploy on resource-constrained systems, such as edge devices. As a resolution, this study proposes a stride-dilation mechanism for TCN that favors a lightweight model yet still achieves on-pair accuracy with the heavy counterparts. We also present the Chonnam National University (CNU) Electric Power Consumption dataset, the dataset of energy consumption measured at CNU by smart meters every hour. The experimental results indicate that our best model reduces the mean squared error by 32.7%, whereas the model size is only 1.6% compared to the baseline TCN.  
temporal convolutional networks; deep learning; time-series forecasting Article
# Multi-RAT Orchestration Method for Heterogeneous Wireless Networks
## 4 Results  
Figures 6 and 7, respectively, present the results for the two simulated scenarios A and B. In the scenario A, we observe that in the first period of time, LTE transmission has higher throughput and also higher energy efficiency. The network condition was then changed and as the Wi-Fi available data rate increased, the orchestrator toggled the active network from LTE to Wi-Fi. Such a scenario may represent coming with a terminal in an area with better Wi-Fi coverage (e.g., closer distance to the Wi-Fi access point) or less interference in the Wi-Fi unlicensed band.  
Scenario B reflects very congested Wi-Fi networks, which in standalone operation have better measured energy efficiency; however, is unable to deliver continuously adequate high data rate to the end user. In this case, to achieve the required minimum performance (i.e., throughput) the multi-RAT orchestrator more frequently switches transmission to the LTE network.  
The orchestrator analyzes the current network conditions and, based on EE rules (defined in Equation (2)), switches the transmission from one RAN network to the other. Results of the energy efficiency and throughput measurements are shown in Figures 6 and 7.  
Figure 5: Testbed hardware: 1—AP Wi-Fi, 2—EPC Server, 3—USRP B210 (eNB), 4 and 5—set of antennas, and 6—USRP B210 as spectrum analyzer for spectrum observation.  
Figure 6: Scenario A—(**Top**) Instantaneous throughput from UE perspective. (**Bottom**) Energy efficiency from UE perspective for different RAT with the indication of which network is selected by the orchestrator to maintain user data transfer.  
Figure 7: Scenario B—(**Top**) Instantaneous throughput from UE perspective. (**Bottom**) Energy efficiency from UE perspective for different RAT with the indication which network is selected by the orchestrator to maintain user data transfer.  
The aggregated outcomes for the presented charts are shown in Tables 4 and 5. In scenario A, the orchestrated approach allows for higher data volume transfer than in the case of a single RAT (27% better than LTE and 23% better than Wi-Fi). Total energy efficiency is 42% better compared to the single LTE case. Wi-Fi network in standalone mode of operation would get 7% better EE; however, then only lower data rates can be achieved, not fulfilling the requirements.  
In the second scenario (B), it may be observed that the total energy consumption by Wi-Fi standalone transmission would be 38% lower than the orchestrated approach. However, owing to the applied dynamic steering, the data path is switched toward LTE which has a higher data rate giving adequately defined throughput for the users. Finally, Energy Efficiency gain is higher (11% for LTE, 0% for Wi-Fi).