esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 4 Results
### Computational Requirements Comparison
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Encoder**} & \multirow{2}{*}{**Resolution**} & \multicolumn{3}{c}{**Latency (ms)**} & \multicolumn{3}{c}{**GPU**} \\  & & & & **Std** & **Min** & **Max** & **Mem** & **Util (\%)** \\ \hline \multirow{6}{*}{DeepLabv3+ [9]} & EfficientNet-B2 & 960 \(\times\) 608 & 11.823 & 0.103 & 11.687 & 13.599 & 72 & 55 \\  & MobileNetV4 & 960 \(\times\) 608 & **5.646** & 0.269 & **5.355** & 13.505 & **24** & 53 \\  & ResNet-34 & 960 \(\times\) 608 & 10.999 & 0.405 & 10.817 & 19.826 & 138 & 60 \\  & EfficientNet-B2 & 480 \(\times\) 304 & 4.381 & 0.114 & 0.443 & 9.419 & 72 & 39 \\  & EfficientNet-B2 & 960 \(\times\) 608 & 11.114 & 0.327 & 10.934 & 20.954 & 72 & 55 \\  & MobileNetV4 & 960 \(\times\) 608 & 6.974 & 0.265 & 6.671 & 14.702 & **24** & 56 \\  & ResNet-34 & 960 \(\times\) 608 & 10.922 & 0.116 & 10.751 & 13.620 & 138 & 55 \\  & EfficientNet-B2 & 960 \(\times\) 608 & 12.937 & 0.319 & 12.770 & 22.477 & 72 & 58 \\ SegFormer [8] & MobileNetV4 & 960 \(\times\) 608 & 8.589 & 0.288 & 8.407 & 17.343 & **24** & **47** \\  & ResNet-34 & 960 \(\times\) 608 & 12.881 & 0.089 & 12.717 & 13.559 & 138 & 59 \\  & EfficientNet-B2 & 960 \(\times\) 608 & 12.220 & 0.438 & 11.976 & 20.344 & 72 & 59 \\ U-Net [10] & MobileNetV4 & 960 \(\times\) 608 & 8.562 & 0.273 & 8.362 & **10.776** & 42 & 54 \\  & ResNet-34 & 960 \(\times\) 608 & 12.637 & 0.416 & 12.424 & 2.613 & 138 & 61 \\  & ResNet34 & 480 \(\times\) 304 & 4.343 & 0.226 & 4.040 & 5.776 & 138 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Inference performance across semantic segmentation models (over 1000 iterations). Latency values (Mean, Std, Min, Max) are reported in milliseconds (ms), memory required for parameters loading in megabytes (MB), and peak GPU utilization in percentage (%). Underlines represent best results for low-resolution models. alternative. Despite a higher average latency of 8.59 ms, it requires only 24 MB of memory and reaches peak utilization of approximately 47%. Reducing input image resolution by a factor of four led to efficiency gains, with average latency times decreasing by over 2.5 times and maximum GPU usage dropping by 16 percentage points during tests of both architectures. The U-Net with ResNet-34 configuration achieved both the shortest single inference time of 4.04 ms, and minimum mean latency of 4.34 ms.
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 4 ESPEA for Solving the PVSP-MSS  
The PVSP-MSS is a complex problem in the pickup vehicle scheduling process. In order to improve the scheduling efficiency, each vehicle can flexibly select a yard according to its needs, which greatly increases the scheduling complexity and makes it difficult for the existing algorithms to obtain high-quality solutions within a reasonable time. Therefore, this paper proposes an enhanced algorithm based on SPEA2 [19] to solve the PVSP-MSS. Firstly, a cooperative initialization strategy is proposed, which utilizes the pickup vehicle sequence and pickup time in different yards to provide an initial solution for the optimal schedule. Secondly, to optimize the quality of the solution, an insertion decoding method is proposed to effectively utilize the idle time in the yard. Finally, local search technology is proposed in the ESPEA to improve the solution quality by swapping pickup operations on critical paths in the steel logistics park.
## 4 Mathematical Formulation of the Optimization Problem
### Transmission Power Requirements for Terminal-HAP-Satellite Path
\begin{array}{c}\frac{dP_{r}}{dP_{s,2}}=1+\frac{dP_{s,2}}{dP_{s,2}}\\ \quad\quad=1+\frac{-R_{b,3}L_{m,3}(d_{3})N_{0}BO_{o}}{\alpha_{3}G_{s,3}G_{r,3}} \times\frac{dn(2(\epsilon-\epsilon_{2}))}{dP_{s,2}}\\ \quad\quad=1+\frac{-R_{b,3}L_{m,3}(d_{3})N_{0}BO_{o}}{\alpha_{3}G_{s,3}G_{r,3}} \times\frac{-2}{2(\epsilon-\epsilon_{2})}\times\left(\frac{-(1+\kappa_{2}) \alpha_{2}G_{s,2}G_{r,2}}{2R_{2}L_{m}(d_{2})N_{0}BO_{o}(1+\kappa_{2}+\gamma_{2} )^{2}}\exp\left(\frac{-\kappa_{2}\gamma_{2}}{1+\kappa_{2}+\gamma_{2}}\right)\right. \\ \quad\quad\quad\left.+\frac{(1+\kappa_{2})}{2(1+\kappa_{2}+\gamma_{2})}\exp \left(\frac{-\kappa_{2}\gamma_{2}}{1+\kappa_{2}+\gamma_{2}}\right)\times\left( \frac{-\kappa_{2}(1+\kappa_{2})}{(1+\kappa_{2}+\gamma_{2})^{2}}\times\frac{ \alpha_{2}G_{s,2}G_{r,2}}{R_{b,2}L_{m}(d_{2})N_{0}BO_{o}}\right))\\ \quad\quad=1-\frac{R_{b,3}L_{m,3}(d_{3})}{\alpha_{3}G_{s,3}G_{r,3}(\epsilon- \epsilon_{2})}\frac{\alpha_{2}G_{s,2}G_{r,2}}{R_{b,2}L_{m}(d_{2})}\times\frac{ (1+\kappa_{2})((\kappa_{2}+1)^{2}+\gamma_{2})}{2(1+\kappa_{2}+\gamma_{2})^{3} }\exp\left(\frac{-\kappa_{2}\gamma_{2}}{1+\kappa_{2}+\gamma_{2}}\right)\end{array}  \tag{17}\]  
Therefore, we get the first order derivative of the total transmission power \(P_{r}=P_{s,2}+P_{s,3}\), which can be found by:  
\[\frac{dP_{r}}{dP_{s,2}}=1-\frac{R_{b,3}L_{m,3}(d_{3})}{\alpha_{3}G_{s,3}G_{r,3 }(\epsilon-\epsilon_{2})}\frac{\alpha_{2}G_{s,2}G_{r,2}}{R_{b,2}L_{m}(d_{2})} \frac{(1+\kappa_{2})((\kappa_{2}+1)^{2}+\gamma_{2})}{2(1+\kappa_{2}+\gamma_{2} )^{3}}\times\exp\left(\frac{-\kappa_{2}\gamma_{2}}{1+\kappa_{2}+\gamma_{2}}\right) \tag{18}\]  
Since \(\epsilon_{2}\) is a convex function in the domain \(P_{s,2}\), \(P_{s,3}\) will also be a convex function in the domain \(P_{s,2}\). Then, the summation of \(P_{r}=P_{s,2}+P_{s,3}\) becomes convex. Therefore, when Equation (18) is equal to zero, an optimal \(\hat{P}_{s,2}\) will be calculated. By substituting \(\hat{P}_{s,2}\) into Equations (3) and (13), a corresponding BER \(\epsilon_{2}\) and the optimal \(\hat{P}_{s,3}\) can be obtained. Therefore, the optimal overall power \(P_{r}^{opt. }\) will be obtained by adding \(\hat{P}_{s,2}\) and \(\hat{P}_{s,3}\) together.
## 3 Results and Discussion
### TPC and Antioxidant Activities  
Figure 4 presents the results of TPC (A, a), DPPH value (B, b), and H-ORAC value (C, c). S treatment for \(\geq\) 2 min resulted in significantly higher TPC, DPPH, and H-ORAC values compared with NS, which were 7040.2 \(\pm\) 66.4 mg CTN eq/100 g DW, 508.4 \(\pm\) 16.4 \(\upmu\)mol TE/g DW, and 1448.8 \(\pm\) 149.2 \(\upmu\)mol TE/g DW, respectively. S2 values were 8319.3 \(\pm\) 98.2 mg CTN eq/100 g DW, 598.2 \(\pm\) 16.5 \(\upmu\)mol TE/g DW, and 2466.6 \(\pm\) 161.0 \(\upmu\)mol TE/g DW, which were approximately 1.2, 1.2, and 1.7 times higher than NS, respectively. S1 values were significantly lower relative to NS for TPC and DPPH, and no significant difference was observed for H-ORAC. Above S2, the inactivation of polyphenol oxidase (PPO), which is a polyphenol-degrading enzyme, occurs, and a decrease in polyphenols during the lyophilization process is suppressed. It is assumed that S1 prolonged the rise in the leaf temperature, PPO inactivation was incomplete, and polyphenol content and antioxidant properties were not retained during the drying process. Among the different drying methods, FD showed the highest TPC and DPPH values (Figure 4a,b), whereas H-ORAC values were similar among FD, SD, and MD 40 \({}^{\circ}\)C (\(p\) > 0.05; Figure 4c). FD showed the best results in terms of quality preservation effects, such as composition and color [27]. However, the present study revealed that the H-ORAC activity of low-cost SD and MD 40 \({}^{\circ}\)C is comparable to that of FD. Although SD and MD 40 \({}^{\circ}\)C were minutely inferior to FD, they can maintain high values of TPC and DPPH (Figure 4a,b). During MD, all assays showed significantly lower values at 50 \({}^{\circ}\)C compared with that at 40 \({}^{\circ}\)C and a further significant decrease in activity at 60 \({}^{\circ}\)C compared with that at 50 \({}^{\circ}\)C (\(p\) < 0.05) (Figure 4a-c). Significant differences were not observed in any of the assays at drying temperatures \(>\) 60 \({}^{\circ}\)C (\(p\) < 0.05). PPO possibly remained active during drying because S treatment was not performed. Antioxidant properties were reduced due to oxidation reactions with PPO between 50-80 \({}^{\circ}\)C with respect to its active temperature range [28]. However, PPO activity is unlikely in drying treatments \(>\) 90 \({}^{\circ}\)C, and the decrease in its values may be primarily attributed to oxidation reactions. However, to determine this, it is necessary to measure the PPO activity value. These results indicate that S2 and drying at a temperature of \(\leq\)40 \({}^{\circ}\)C (FD, SD, or MD) is optimal for maintaining the antioxidant properties of Genova.
# EMF Assessment Utilizing Low-Cost Mobile Applications
## 2 Materials and Methods
### Measured Data Processing
#### 2.3.3 Routes Segmentation  
Since the exact locations of the measurements differed between route repetitions (due to variations in travel speed, traffic conditions, etc.), it was necessary to define fixed segments within each selected route and assign each measurement point to one of these segments. By doing so, the data could be analyzed for their behavior over different route repetitions within the same segments.  
The creation of these segments was achieved by using one of the route repetitions as a prototype and creating circles with a constant radius to cover the entirety of the route. This method resulted in 167 segments for the first route using 25-m radius circles, as presented in Figure 5, and 120 segments for the second route using 30-m radius circles, due to the higher sampling rate of the exposimeter, ensuring that all segments included at least one data point per repetition.  
It is worth noting that some of the route repetitions were not included in the calculation hereafter due to slight route changes that could be accommodated by the above segmentations, leading to a total of 17 repetitions for the first route and 5 repetitions for the second.  
The analysis of the variability of the received power for each selected route was performed with five separate calculations: one for the exposimeter and four for the app (one for each operator separately and one for the sum of all three operators). In every route repetition, these five calculations were made for each measurement point, and the result for each point was allocated to the corresponding route segment. Subsequently, the average value of the calculations was determined for each segment. This process resulted in 5 \(\times\) 17 \(\times\) 167 values for the first route and 5 \(\times\) 5 \(\times\) 120 values for the second. Based on the average value for each segment and route repetition, we identified the segments with high and low variability.  
Figure 5: Segments formed by circles with a 25-m radius for the first route in which measurement points are allocated to (circle color has no significance and it is only used to guide the eye).
# A Transformer-Less Buck-Boost Grid-Tied Inverter with Low Leakage-Current and High Voltage-Gain
## 3 Operation Principles
### Step-Up Mode
When the switch \(S_{Bu2}\) is switched on, the diode \(D_{4}\) is switched off, and the switch \(S_{2}\) remains in the on state to provide a path for energy transmission. At this time, the input voltage \(V_{in}\) stores energy on the inductor \(L_{2}\), and the capacitor \(C_{f}\) provides the energy required by the output. Figure 8a shows the corresponding circuit. When the switch \(S_{Bu2}\) is switched off, the diode \(D_{4}\) is switched on. At this moment, the input voltage \(V_{in}\) transfers energy to the capacitor \(C_{f}\) and the output simultaneously through the path of the coupled inductors \(L_{2}\), \(L_{4}\) and the switch \(S_{2}\). Figure 8b shows the corresponding circuit. The working principle of this state is similar to that of the positive half-cycle, and the voltage gain and boundary conditions are the same as those shown in Equations (7), (10), and (11). Since the continuous conduction of the switch \(S_{2}\) can provide a path for connecting the mains and the negative end of the input source, the leakage current of the PV array can be suppressed. As reported by the analyses mentioned above, Table 1 lists the voltage gain and the condition of each switching component within one AC cycle. As shown in this table, there are only one switch and one diode switching with high frequency (marked in green) in each state, resulting in lower switching losses. In addition, in each state, there is only one switch and one diode (or two switches) conducting continuously (marked in orange), which can reduce the conduction losses.
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 2 Previous Relevant Research
Our proposed approach overcomes these challenges by accumulating perceptual 3D features in an NR-based manner applicable to video streaming and streaming platforms along with a reduced computational complexity to meet the real-time capacity of the decoder and measure the optimal 3D video quality.
## 3 Results
### Impact of a High Proportion of Noise Variables on Prediction Performance of Approaches  
The proportion of noise variables can influence the effectiveness of variable selection methods. Therefore, we evaluated the robustness of approaches under scenarios with a high proportion of noise variables (23 noise and 7 signal variables), focusing on their predictive accuracy and the complexity of the selected models in both small and large sample sizes within low-correlation settings. All methods were tuned using cross-validation.  
Figure 5 shows the prediction accuracy (upper panel) and the average number of variables selected by each approach (lower panel). In scenarios with small sample sizes (top-left panel), classical methods (BE and FS) consistently exhibited inferior prediction accuracy across all SNR levels, with FS marginally better. In contrast, the lasso performed best overall, with its advantage most pronounced at low SNR levels. The predictive accuracy of NNG, RLASSO, and ALASSO was comparable, with ALASSO slightly less accurate. In large sample settings (top-right panel), the predictive performance varied with SNR. At low-to-moderate SNR levels (SNR \(<\) 1), classical methods were markedly outperformed by penalized methods, which exhibited similar accuracy. At high SNR  
Figure 4: Relative test error of classical and penalized methods tuned using CV, AIC, and BIC. Results are shown for small (n = 100, **upper panel**) and large (n = 400, **lower panel**) sample sizes under low correlation (C2) with beta-type A coefficients.  
levels, lasso performed worse, while classical methods achieved the best accuracy. Overall, classical methods selected simpler models, followed by RLASSO, ALASSO, and NNG. Conversely, lasso consistently selected a larger number of variables (lower panel).
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
###### Abstract  
On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%.  
cross-calibration method; BRDF model; Dunhuang and Golmud test sites; validation and evaluation 2019  
1 1 707; doi:10.3390/rs110607007
## 3 Taxonomy
The wavelet-artificial intelligence fusion approach (WAIFA) couples wavelet transform with artificial neural network to blend MODIS and Landsat land surface temperature data [15]. The main improvement of WAIFA is that the artificial neural network can cope with the non-linear nature of the land surface temperature data. The extreme learning machine (ELM) method uses a powerful learning technique, extreme learning machine, to build a feature mapping function between raw pixels of coarse and fine images. Compared with SPSTFM, ELM achieves a better performance and uses less computing time [58]. Hybrid methods integrate two or more technologies from the above four categories. The aim is to improve the performance of spatiotemporal data fusion through combining the advantages of different methods. One representative of this kind is the Flexible Spatiotemporal Data Fusion (FSDAF) developed recently. FSDAF combined ideas from unmixing-based methods and the weighted function-based methods, as well as spatial interpolation. As a result, FSDAF is capable of fusing images in challenging scenarios, i.e., heterogeneous landscapes and abrupt land cover changes occurring between the input images and prediction. For the highly mixed pixels, their reflectance change can be estimated through solving the linear mixing equations. For the abrupt land cover changes, the spatial interpolation can capture it as long as it is shown in the coarse image. The idea of FSDAF was recently adapted by the BLEed Spatiotemporal Temperature (BLEST) method for fusing land surface temperature with different spatial resolutions [28]. The weight function was further used to combine the unmixing results and spatial interpolation to reduce the uncertainties. Another example is the spatial and temporal reflectance unmixing model (STRUM), which directly unmixed the change of coarse pixels by Bayesian theory to estimate the change of fine-resolution endmembers and then use the idea of STARFM to create the fused image using moving windows [24]. A recently developed method, Spatial-Temporal remotely sensed Images and land cover Maps Fusion Model (STIMFM), integrates the spectral linear mixing model and Bayesian framework for fusing multi-temporal coarse images and a few land cover maps from fine images to directly produce a series of fine-resolution land cover maps [22].
## 1 Introductions  
Two-degree-of-freedom motors, which produce rotary, linear and helical motions, are widely applied in the industrial fields of robots and automobiles. A two-degree-of-freedom direct drive induction motor (2 DoFDDIM) consists of a rotary armature, linear armature, and solid rotor coated with a thin copper layer [1; 2; 3; 4; 5] and has the advantages of a simple structure and high mechanical integration. However, this motor has the disadvantages of high rotor loss and low efficiency. Slotting on the solid rotor surface is an effective method for reducing the rotor loss.  
For the slotted solid rotor, the rotor core is part of the magnetic and current paths. The magnetic field distribution and equivalent circuit parameters of the solid rotor motor are difficult to calculate accurately. Numerous studies have been conducted on an induction motor with a slotted rotor. In Ref. [6], time-stepping finite element method (FEM) was used to design and analyze a solid rotor induction motor (SRIM) with skewed slits in radial and axial directions. FEM needs a long computing time. Furthermore, the physical interpretations of field quantities are indefinite. In Ref. [7], a 2D analytical method was proposed to achieve the equivalent circuit parameters of slit SRIMs. In Ref. [8], a bar-crossed rotor was presented and applied to a 2 DoF induction motor, but the motor characteristics were not analyzed. In Ref. [9], a novel slotted solid rotor (SSR), which is slotted along the axial and circumferential direction on a smooth solid mover surface first and copper is cast in the slots was applied to the 2 DoFDDIM, and the characteristics were analyzed through FEM. However, the equivalent circuit model (ECM) was not provided. Thus, this study aims to establish the ECM of the 2 DoFDDIM to calculate the motor characteristics easily.  
In this work, first, the structure and parameters are provided. Second, the SSR is equivalent to the cage rotor by analyzing the current distribution. Third, an ECM of 2 DoFDDIM with SSR is established, and the characteristics are calculated. Finally, the computed and FEM simulation results are compared to verify the accuracy of the ECM.
## 5 Influence of Floods on GPS MP Signals  
Considering that the ground track repeat period of the GPS satellite is a sidereal day, data sets collected on the day before the flood can be used to compare the characteristics of the GPS MP signal. Figure 8 demonstrates the MP of G32 satellite L1 frequency before and after the flood. The red line denotes the MP of the G32 satellite on DOY:201, 2021, which the flood occurred in this day. The blue line denotes the MP of the G32 satellite on DOY:200, 2021, which used to compare the influence of floods on the MP. The RMS of MP residuals of these two days is presented on the bottom of Figure 8.  
From Figure 8, it can be seen that the trend of the MP before and after the flood remains consistent as a whole. For example, both of these two days show a crest at epoch 160. This phenomenon also demonstrates the repeatability of GPS satellites for two adjacent days. Due to fluctuation, the MP of these two days is difficult to effectively distinguish in Figure 8. Therefore, the RMS of MP residual is presented to show that the MP during the flood is increased compared with the MP without the flood. Concretely, the RMS of MP increases from 29.65 cm to 32.72 cm. The RMS increase rate of the MP is approximately 10.35%, which indicates that the flood can largely affect the MP of GPS satellites.  
In addition, to further analyze the influence of floods on the MP of GPS satellites, the RMS increase rate of all other satellites with an entire arc both on DOY:200 and DOY:201 is presented in Figure 9. It can be seen that the MP of these satellites increased to a certain degree compared with the non-flood scenarios. Considering the maximum increase rate of the G29 satellite, the RMS of the MP on DOY:201 increases approximately 17.85% compared with DOY:200. It should be noted that the RMS increase rate of some satellites is relatively small, such as with the G05 satellite, which is only 0.23%. The reason for this phenomenon should be related to the surrounding environment of the satellite ground track. Moreover, for all satellites in Figure 9, the average RMS increase rate is approximately 6.55%. Thus, based on the above analysis, it can be concluded that the MP of the GPS satellite can be affected by floods. The experimental results remain consistent with the theory analysis.  
Figure 8: MP of GPS G32 satellite L1 frequency. The blue and red lines denote the MP on DOY:200 (no flood) and DOY:201 (with flood), 2021, respectively.