esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparative Analysis of Different Models
#### 4.2.2 Comparison of Prediction Errors at Different Cooling Stages
Figures 16-18 illustrate the prediction results for blocks 17-079, 11-012, and 7-030, respectively, where I denotes the 0-8-day stage and II denotes the 8-21-day stage. Comparatively, the model developed in this study reduced the average \(MAE\) by 31%, 44%, and 31% and the average \(RMSE\) by 39%, 36%, and 30% in the 0-8-day stage, compared to the BP, LSTM, and ISVR models, respectively. During the 8-21-day stage, the average \(MAE\) was reduced by 17%, 45%, and 25% and the average \(RMSE\) by 37%, 39%, and 31%, compared to the BP, LSTM, and ISVR models, respectively. Furthermore, a paired t-test (\(p\) = 0.05) was conducted on the error indicators, and the results were all less than 0.05, suggesting that the differences between models are statistically significant. During the 0-8-day period, it was difficult to accurately predict the true internal temperature of the concrete because of the intense cement hydration, resulting in generally large prediction errors across all models. During the 8-21-day period, as the hydration weakened, its influence on concrete temperature decreased, reducing the complexity of  
Figure 17: Model evaluation in 11#-012: (**a**) RMSE and MAE of different models in two tests and (**b**) R\({}^{2}\) of different models in two tests. Figure 18: Model evaluation in 17#-079: (**a**) RMSE and MAE of different models in two tests and (**b**) R\({}^{2}\) of different models in two tests. model features from a machine-learning perspective. This made it easier for the models to learn the process, thereby improving prediction accuracy and modeling efficiency.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 3 Experiments and Discussions
### Experimental Setup  
A 360-deg panoramic camera setup consisting of three sets of binocular cameras was constructed, as shown in Figure 3. The six cameras (industrial camera 130 SM, with an MX 1.3-megapixel 5.270 \(\times\) 3.960 mm\({}^{2}\) CMOS sensor) were placed coaxially to form three stereo camera pairs with field of view greater than 120\({}^{\circ}\) to ensure that the field of view between adjacent camera pairs overlaps to achieve the panoramic three-dimensional reconstruction. At the same time, it could provide an acceptable distortion level between each image pair to ensure accurate displacement measurement. In order to effectively suppress the inhomogeneous fluctuation of ambient light and chromatic aberration, three monochromatic blue LED light sources of 450-455 nm were employed. The object to be measured was placed on a translational stage in the center of the panoramic camera setup, where it could be evenly illuminated by blue LED light sources. All cameras were connected via an Ethernet hub and were controlled synchronously using a PC. A log with a height of about 320 mm and a diameter of about 260 mm with a natural texture on the surface was selected for the test sample, and the three-dimensional point cloud data of the log was obtained by using a handheld three-dimensional laser scanner (SIMSCAN, scanning accuracy 0.02 mm). The test sample and the scanning results are shown in Figure 4.  
Figure 2: Point cloud merging example. (**a**) A reconstructed surface with partially overlapping. (**b**) The elevation view of the red part, with local overlap of the data. (**c**) Identifies the overlapping areas and finds points within the overlapping areas which are far from the laser point cloud. (**d**) The result after removing the overlapping points.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 3 Results and Discussion
### Experimental Results and Analysis of XJTU-SY Dataset
As shown in Figure 7, the five models can successfully distinguish and identify three different fault types with high probability, but the recognition rate of some models with fault types labeled as 0 and 2 is low, with a minimum of only about 76%. However, the proposed model has achieved 96% and 97% recognition rates for fault types with labels of 0 and 2. The recognition rate of the five models for the fault type with the label of 1 is higher. The reason may be that the fault type is easier to distinguish. In general, the proposed model achieves the highest recognition rate on the three fault types. Figure 6: The accuracy curves of XJTU-SY dataset. Figure 7: Normalized confusion matrix of XJTU-SY dataset: (**a**) WDCNN; (**b**) DRCNN; (**c**) Dialet-edNN; (**d**) Vision Transformer; (**e**) MultiDilatedFormer. The t-SNE visualization is introduced to show the aggregation effect of different model classifications. Figure 8 is the test cluster diagram. The three colors in the figure represent three types of faults, and each point represents a sample. The density and separation between data points of different colors are mainly observed to judge the effect of model classification. The red box in the figure roughly circles different types of mixed parts, and the more mixed parts indicate that the model classification is more unclear. It is obvious from the figure that WDCNN and Vision Transformer have more mixed parts, and the boundary of the proposed model MultiDilatedFormer clustering effect is clearer, which verifies the effectiveness and superiority of the proposed model in fault classification under shorter window samples. Figure 8: The t-SNE cluster diagram of XJTU-SY dataset: (**a**) WDCNN; (**b**) DRCNN; (**c**) DialetedNN; (**d**) Vision Transformer; (**e**) MultiDilatedFormer (the red circle in the figure represents the mixed parts). The three colors in the figure represent three types of faults, and each point represents a sample. The density and separation between data points of different colors are mainly observed to judge the effect of model classification. The red box in the figure roughly circles different types of mixed parts, and the more mixed parts indicate that the model classification is more unclear. It is obvious from the figure that WDCNN and Vision Transformer have more mixed parts, and the boundary of the proposed model MultiDilatedFormer clustering effect is clearer, which verifies the effectiveness and superiority of the proposed model in fault classification under shorter window samples.
## 6 Application of Plasma Treatment in Advanced and Emerging Packages
### 2.5D Interposer-Based Packages  
2.5D packaging employs an interposer, a substrate acting as an intermediary between the die and the printed circuit board. In this configuration, multiple dies are typically placed side-by-side on the interposer (which may contain TSVs), connecting laterally through it, rather than being directly stacked vertically via TSVs passing through the active dies. A key advantage of this approach is reduced heat buildup compared with dense 3D stacks. During assembly, plasma cleaning plays a crucial role. Specifically, achieving high surface wettability on the interposer is critical for uniform underfill flow between the die and interposer. The literature reports the common use of oxygen plasmas for this purpose,effectively treating the surface to enhance wettability, which consequently allows for the underfill to flow more easily and fill gaps completely [147].  
Another reviewed paper indicated that the primary goal of plasma surface treatment in such packages was to improve process performance (including underfill filling time, void reduction, and flow characteristics) and enhance the interfacial adhesion strength of the underfill, specifically on polyimide (PI)-coated RDL interposers. The aim of the plasma process was therefore to improve both processability and reliability. The gas used to produce the plasma was oxygen and the plasma treatment significantly increased surface wettability, making it more hydrophilic. After the plasma process was conducted, the water contact angle dropped drastically from 79\({}^{\circ}\) (untreated) to around 10\({}^{\circ}\) after 200 s of treatment. The paper demonstrated that O\({}_{2}\) vacuum plasma treatment effectively modified the PI-coated RDL interposer surface by increasing its hydrophilicity and roughness, also incorporating oxygen functional groups. This surface modification leads to significantly improved underfill processing (faster and fewer defects) and enhanced interfacial adhesion, peaking around 200 s of treatment time, thereby boosting the overall reliability potential of the 2.5D package [151].
## 2 Data and Method
### MODIS Surface BRDF Products  
Surface BRDF information has been available from the MODIS sensors onboard Terra and Aqua satellites since the year 2000. The basic principle of the MODIS algorithm is that by collecting the atmospherically corrected surface reflectance within a 16-day period the surface BRDF can be determined by the majority of either clear-sky snow-free or snow-covered observations [25,30]. The MODIS products include surface BRDF parameters and the corresponding data quality that can be used for calculating surface white-sky albedo (independent of solar zenith angle) and black-sky albedo for any given solar zenith angle for each of the seven MODIS land bands and three broadbands. The MODIS products are updated every eight days and available at a nominal 500-m spatial resolution globally. In this study, the good quality 500-m surface BRDF parameter products (Collection 5) of the spectral bands and shortwave broadband were used.
# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 7 Conclusions  
In this paper, we design a pricing-driven joint MEC server orchestration and network slice deployment scheme. In the proposed system, the IPL handles pricing, the NPL handles MEC server orchestration as well as slice deployment, and the RAL handles allocation of radio, computational and storage resources. A three-stage Stackelberg game model is used to describe the relationships among these three layers. In order to efficiently achieve the Stackelberg equilibrium, we propose a three-layer DRL algorithm. D3 QN is used to provide strategies for the IPL, while DMBDQ is used to make decisions for the NPL and the RAL to deal with large-scale action spaces. Simulations verify the convergence of the proposed D3 QN-DMBDQ algorithm. It is also demonstrated that MBDQ with an illegal action modification capability is advantageous in large-scale action space problems. Moreover, numerical results show that our scheme outperforms other benchmarks in terms of the utilities of the InP and slices.  
Conceptualization, Y.H. and K.Z.; methodology, Y.H.; software, Y.H.; validation, Y.H. and K.Z.; formal analysis, Y.H. and K.Z.; investigation, X.C.; resources, G.C.; data curation, W.G.; writing--original draft preparation, Y.H.; writing--review and editing, S.L.; visualization, G.C.; supervision, W.G.; project administration, X.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Beijing Natural Science Foundation under Grant L234025.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results
### Geometrical Deviations Evaluation
#### 3.1.3 Straightness Deviations  
Straightness deviation results for the constant Z strategy and circular pocket strategy are presented in Figure 20, and for the constant stepover-pocket Auto border strategy, the spiral strategy is shown in Figure 21 with 500\(\times\) magnification. The red lines indicate the size of the tolerance, i.e., 0.01 mm.  
Figure 19: Cylindricity deviations (**a**) constant stepover-pocket auto border; (**b**) spiral strategy.  
Figure 18: Cylindricity deviations (**a**) constant Z strategy; (**b**) circular pocket strategy.  
Figure 21: Straightness deviations (**a**) constant stepover-pocket Auto border; (**b**) spiral strategy.  
Figure 20: Straightness deviations (**a**) constant \(Z\) strategy; (**b**) circular pocket strategy.  
The smallest straightness deviation has the part that was produced by the constant \(Z\) strategy.
## 1 Introduction
The dielectric loss factor (\(\varepsilon^{\prime\prime}\)) decreased with frequency and increased with temperature over almost the entire frequency range. In a 2018 study, the fermentation process of yogurt was monitored by measuring changes in the dielectric properties of the raw material at frequencies between 10 and 3000 MHz. The results have revealed that the dielectric loss factor of yogurt showed a positive correlation with fermentation time, with irregular changes only at the endpoint of fermentation (7 h). The dielectric constant showed a correlation with the pH and tittatable acidity of the yogurt, but only at a low frequency of 10 MHz [17]. [PERSON] et al., in a recent study, investigated the dielectric properties of milk to develop a microwave continuous-flow milk sterilization apparatus and found a correlation between the dielectric constant of milk and the covariance of the heating field temperature [18]. Monitoring methods using dielectric measurements enable the development of low-cost, chemical-free, and non-destructive in-process monitoring methods to determine both the rapid detection of microbial degradation processes and the efficiency and optimal time requirements of the coagulation process. Low-power dielectric measurements do not interfere with microbial and physicochemical processes in biological materials. Further research may also allow dielectric monitoring of these processes through packaging, where the product does not become waste during testing. Some studies have investigated the correlation between dielectric and rheological properties in certain food materials [19; 20; 21]; however, despite the extensive research on milk coagulation, a comparative study of enzyme-induced and acid coagulation using dielectric and rheological measurements is still needed to gain a deeper understanding of the differences between these two coagulation mechanisms and their impact on the physical properties of milk. In this study, we aim to investigate the enzyme-induced and acid coagulation of milk by measuring the dielectric and rheological properties of milk samples during these two processes. By conducting a systematic comparison of the coagulation mechanisms, we seek to explain the differences in the evolution of the protein network structure, the impact on the electrical properties of milk, and the changes in rheological behavior.
# Iterative Camera Calibration Method Based on Concentric Circle Grids
## 2 Materials and Methods
### Iterative Camera Calibration Algorithm Considering Lens Distortion
Step 5: Update the calibration values \(\mathbf{K},\mathbf{D}\) using the optimized feature points \(\hat{\mathbf{P}}_{u_{ij}}(\hat{u}_{ij},\hat{\
u}_{ij})\). Step 6: Continue performing Steps 2 to 5 repeatedly until the difference \(\Delta\) between the two consecutive iterations is smaller than the threshold or the number of iterations reaches the maximum limit. In the iterative camera calibration algorithm, the position deviation threshold is set to 0.02 pixels and the maximum number of iterations is set to 10. In Algorithm 1 (Pseudocode of the iterative camera calibration algorithm), we assume that there are \(n\) calibration images with \(m\) feature points on each calibration image. Camera_calibration function means the Zhang calibration method; Calculate_deviation function means the position deviation. Figure 5 shows the flow chart of the iterative camera calibration method in this paper. Firstly, we fit the projection conics of the ellipses and the line that traverses the ellipses' centers. By combining the line equation and the projection conic equations, four intersection points are calculated. Then, according to the cross-ratio invariance of collinear points, the location of the imaged centers is obtained. Furthermore, based on the Zhang calibration method, the initial camera parameters are determined. Ultimately, we calculate the position deviation and compensate for the imaged center to update the calibration values repeatedly. In this paper, we use the concentric circle grid as the calibration target, which provides more constraints for the center location. Compared to previous research, this paper makes the following contributions. Firstly, an imaged center compensation method for concentric circles is proposed which provides a detailed description of calculating position deviation under lens distortion. Secondly, a generalized compensation framework is proposed which can refine the calibration results. Figure 5: Flow chart of the iterative camera calibration method.
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
## 5 Test and Analysis
### Algorithm Simulation  
Figure 9 presents the simulation results obtained by the head-wave extraction algorithm. As shown in Figure 9a, when the energy ratio (Ratio) is greater than \(\alpha\), the flag of the head-wave arrival (ArrFlag) is set to logic-high. The current counter represents the arrival time of the head wave, which is stored in ArrNum. In this case, the head-wave arrival time is the 184 th sampling point. Figure 9b demonstrates the result of head-wave amplitude detection. The echo amplitude detection is completed once the amplitude output flag (Tx_Amplitude) is set, and in this case, the head-wave amplitude (AD value) is 31,753. The results of the arrival time and amplitude detection are consistent with the waveform of the acquired data, which is shown in Figure 9c, and the expected processing results, thereby indicating that the head-wave extraction algorithm based on FPGA is correct and can meet the downhole requirement.  
Figure 9: Simulation results of head-wave extraction: (**a**) Head wave arrival detection; (**b**) Head wave amplitude detection; (**c**) Verification of simulation.
## 4 Simulation Results
### Shading Pattern 2  
Figure 12b demonstrates the power waveforms of the PV configurations designed for two conventional techniques and the proposed one for shading pattern 2 (middle GMPP case). Power oscillations more than approximately 100 and 84 W were measured in the conventional INC and P&O MPPTs, respectively, than in the proposed technique.  
This shows that conventional MPPT algorithms are stuck at one of the local MPPs instead of tracking true GMPP on the PV curve, resulting in greater power losses in the algorithms. Therefore, the tracking accuracy of the proposed algorithm is higher than that of the traditional algorithms by 36.7% and 36.5%, respectively. Similarly, a substantial number of oscillations are drawn in the voltage and current waveforms in conventional techniques; (20.5 V and 1.5 A) associated with the INC, and (15.6 V and 1.54 A) with P&O (Figure 12c,d). Moreover, the tracking speed (0.22 s) of the proposed technique is much faster than those of the conventional ones (0.29 and 0.3 s), as the operating point converges the duty cycle rapidly toward the GMPP. The proposed algorithm requires only a few duty cycle perturbations to reach the GMPP, resulting in substantial improvements in tracking speed and convergence time.  
Figure 12e shows the waveforms of the duty cycle perturbation in different algorithms. The duty cycle of the GMPP location is calculated as 0.647, which is satisfied by the proposed technique under SSC for shading pattern 2. However, a fluctuation of 0.02 in the duty cycle step size is found in the conventional HC MPPT algorithms, whereas the constant duty cycle is operated in the proposed scheme under SSC.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities
### Feature Analysis
#### 2.3.1 Data Unit  
IPv6 uses packets as its fundamental data unit, augmented by extension headers that add functionalities such as fragmentation or routing, while IPv7 employs bundles extended via Extension Blocks. Both designs embrace modularity, although IPv7's bundles are optimized for DTN environments. Given this similarity, IPv6's modular approach could be adapted by defining new extension headers that replicate IPv7's DTN-specific features.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### Tsr-Darda Scheme  
In the scheme, the key point is how to partition the surface of the sphere into circular tiers with careful designed widths and how each tier is further divided into cells except for the hotspots, as shown in Figure 1. In the following, more details will be given on the key points and their implementation.
# The Influence of Vertical Ground Motion on the Design of Common R/C Frames
## 4 Redesign of Frames  
The frames were redesigned, considering that the two seismic components act simultaneously ('H+V' excitation). The geometry (Figure 2), the material properties, the static loads and the modeling assumptions were the same as those described in Section 2. Specially for the simulation of mass, the model presented in Section 3 (Figure 5) was adopted. The seismic actions are represented by the design spectra in Figures 3 and 4 for \(\gamma_{1}\) = 1, S = 1 and a behavior factor equal to the values tabulated in Table 1 for the horizontal seismic component and equal to 1.5 for the vertical one. The structural analysis of the frames for both gravity and seismic load combinations was performed using the SAP2000v23 program. The modal response spectrum analysis method as prescribed in Eurocode 8 was implemented, considering the first fifty natural vibration modes. Thus, over 90 percent of the total mass was activated for both horizontal and vertical excitations. The modal superpositioning and the directional combination of seismic actions were conducted applying the CQC and the SRSS rules, respectively. The natural periods and the modal participating mass ratios of the predominant modes are given in Tables 9 and 10, respectively. The cross-sectional dimensions of columns and beams are summarized for all the redesigned frames in Tables 11 and 12.  
limitation of the resistance ratio of the members framing each joint. Obviously, the increase in the columns' dimensions was reflected in the natural periods of the frames, which were decreased due to the greater stiffness. Apart from affecting the cross-sections' dimensions, vertical ground motion also caused a considerable increase in the reinforcement of almost all columns. In contrast to the columns, the dimensions of the beams were not affected at all (see Tables 3 and 12). The deficiencies reported in Table 6 were overcome by increasing their reinforcement.
# Broadening Bandgap Width of Piezoelectric Metamaterial by Introducing Cavity
## 1 Introduction
demonstrated numerically and experimentally that the piezoelectric metamaterial has bandgap width of 500 Hz around 5000 Hz [32]. On the other hand, the mass-in-mass metamaterial with mechanical resonance, has bandgap width of up to 5.6 kHz around the frequency of 24.8 kHz [33]. Therefore, the goal of this research is two-fold. Firstly, the lumped-parameter model of the piezoelectric metamaterial is established, which is built upon the continuum mechanics characterization of the host substrate and piezoelectric transducer at the unit-cell level, to reveal the parametric influence on bandgap width. Transverse wave is considered throughout the modeling and analysis, and the lumped parameters are derived based on the wavenumber involved. Secondly, a new and effective design is introduced, based on incorporating geometric cavities in the host structure, to increase the electro-mechanical coupling of the piezoelectric metamaterial. The increased electro-mechanical coupling yields significantly broadened bandgap width. Systematic case investigations through finite element analysis are conducted to demonstrate the modeling and new design.