esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of the Proposed Measurement Technique  
The test parameter is \(d\), while \(\sigma\) is the stray parameter. The value \(\sigma\) is not displayed on the indicator of the thickness gauge, as it is not relevant for the user.  
The graduation characteristic is represented as a fan-shaped grid on the plane. The grid thickens in \(d\) direction and thinning out in \(\sigma\) direction. In these conditions, the algorithm for processing the measurement information has only one task--to determine the section of this grid that corresponds to the point of the signal obtained during the measurement. Obviously, for the effective operation of the proposed technique in conditions of a large set of graduation points (approximately several thousands) and relatively low-performance of single-core ARM architecture of microcontrollers, it is necessary to optimize the computational algorithm.  
The proposed two-dimensional algorithm to calculate \(\sigma\) and \(d\) is based on the checking whether a point lies in a polygon [17] (pp. 142-146).  
2.5.1 General Description of Two-Dimensional Algorithm for the Calculation \(\sigma\) and \(d\)  
A generalized graphical interpretation of the two-dimensional algorithm for calculation of \(\sigma\) and \(d\) is shown in Figure 8. In Figure 8, for brevity, we denote graduation table as GT; graphical interpretation of the graduation characteristic in the form of hodograph diagrams of the signals of AP ECP finite element model is shown in Figure 6b.  
Figure 7: Non-conductive coating thickness simulating system. 1—precision linear actuator, 2—servo drive, 3—basic supporting structure, 4—reference base metal, 5—ECP.  
The lines of the graduation characteristic obtained on different reference base metals are presented as lines \(d(\sigma)\). These lines converge fanwise at a single point--an air point with coordinates (\(\mathrm{Re_{Air};Im_{Air}}\)). For clarity, the points with the same \(d\) value on different lines of the graduation characteristic are connected by lines which are denoted as \(\sigma(d)\). During a measurement process, the signal which has coordinates (Re; Im) is calculated. In the figure, it is denoted as the point \"(\(\mathrm{Re};\mathrm{Im}\))\".  
The task of the algorithm for calculation \(\sigma\) and \(d\) is to determine whether the signal belongs to one of the sections bounded by four lines of the grid and to calculate its value relative to these boundaries.  
Due to the large number of sections of the graduation characteristic, the calculation is carried out by using the \"half section\" method [18].  
Figure 8: Two-dimensional algorithm of the calculation \(\sigma\) and \(d\).  
An example of the graduation characteristic of AP ECP in tabular form is presented in Table 1.
# A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
## 4 Experiments
### Algorithm Initialization  
The algorithm was initialized (to obtain the set of auxiliary markers required to assist the reconstruction of the gaps) by using samples with artificially generated gaps as calibration trajectories. We have also considered the original MoCap sequences (fully labeled and without gaps) as calibration trajectories in order to evaluate the influence of gaps on the estimation of auxiliary markers.  
All subsequent experiments were reported using the original MoCap sequences as calibration trajectories to guarantee that the auxiliary markers' sets were constant throughout the experiments using that sequence, despite the generated gaps. In this manner, we could guarantee that the selection of the markers to reconstruct a missing one was only dependent on conditions one and two presented in Section 3.2.1. Thus, the analysis of the performance of the algorithm in our experiments only depends of the variables that we aim to analyse, i.e., influence of the length of the gaps and number of missing markers.  
In the experiments involving the reconstruction of files with gaps located in the middle of the sample, we considered two simultaneous initializations of the algorithm: (i) in the first frame of the trajectories, with forward filling direction; (ii) in the last frame of the trajectories, with backwards filling direction. The final reconstruction of each gap consisted of a fusion of the reconstructions in both directions by applying a sigmoid weighted average. This strategy ensured that more weight was provided for the reconstructions with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Filename** & **Activity** & **\# Frames** & **Duration (s)** \\ \hline \multirow{3}{*}{CMU} & 14\_01 & Boxing & 5593 & 46.6 \\  & 14\_09 & Reaching & 3286 & 27.4 \\ \cline{1-1}  & bd\_01-01\_01\_120 & Walking & 9841 & 82.0 \\ \cline{1-1}  & bk\_03-01\_01\_120 & Dancing & 9700 & 80.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MoCap sequences used in the experiments.  
the highest confidence, i.e., located in the first frames of the gap in forward filling direction and in the last frames of the gap in the backward filling direction.  
In the experiments involving the analysis of samples with gaps located in the initial or final frames, we initialized the algorithm at the midpoint of the sample and filled gaps using the forward filling direction--in case the gap was located in the final frames of the sample--or the backward filling direction--in case the gap was located in the initial frames of the sample.  
All experiments used \(d_{thr}=3\) cm and \(a_{thr}=6\). These values were empirically chosen by using as a basis some exemplary samples from the dataset.
## 8 Discussion
### Thermal Analysis  
The payload was powered on two hours before the balloon's launch, leading to an initial temperature higher than the surrounding environment which can be seen in Figure 10. The most significant temperature changes occurred between 5:00 a.m. and 6:00 a.m. UTC, possibly during the balloon's passage through different atmospheric layers, followed by stabilization post-ascent. The external high-altitude temperatures were substantially colder than the payload components. The Raspberry Pi's internal temperature remained stable, indicating effective thermal regulation. Temperature readings recorded lows of \(-13\)\({}^{\circ}\)C, \(-18\)\({}^{\circ}\)C, \(-16.7\)\({}^{\circ}\)C, and \(-7\)\({}^{\circ}\)C for the PCO components, \(-82.78\)\({}^{\circ}\)C for the environment, and \(-31.6\)\({}^{\circ}\)C for the Raspberry Pi. The subpayloads' components were both warmer than their environment, most likely due to the passive heating and thermal blanket insulation. The PCO camera generated more heat compared to the Raspberry Pi in subpayload 2. The internal temperature of the Raspberry Pi is noticeably lower than what would be expected during standard operating conditions. Therefore, it appears feasible to consider overclocking the Raspberry Pi in these circumstances, as it could potentially produce added heat and enhance its performance. However, overclocking could still lead to system instability if implemented improperly. As such, overclocking should be approached cautiously, with thorough testing prior to deployment in future missions. The standard microSD card, rated at UHD speed class 3 and utilized in the Raspberry Pi of subpayload 2, which was not designed for stratospheric temperature ranges, functioned below its expected temperatures, indicating the potential for using faster microSD cards within these temperature ranges in future missions.
## 2 Signal Model  
Let us consider an OFDM signal using \(N_{b}\) subcarriers. By using \(N\)-point inverse fast Fourier transform (IFFT) on the information symbols, the frequency-domain signal is transformed into the time domain. To remove ISI, a cyclic prefix (CP) with length \(N_{g}\) samples is appended to the beginning of each OFDM symbol to form an OFDM symbol. Since HomePlug GP is based on baseband OFDM transmission, there is no RF modulator and demodulator. For this reason, we can completely ignore the CFO. In a baseband PLC system using OFDM, synchronization process can be divided into a initial acquisition stage and a fine tracking stage. The symbol timing synchronization for pre-FFT removal of CP is performed during the acquisition step. The primary goal of fine tracking step is to handle fine symbol timing estimation and SFO tracking (post-FFT synchronization). To restrict our attention here to the estimation of SFO, it is assumed that the symbol timing offset is perfectly compensated at the receiver. In the HomePlug GP system, it is reasonable to assume a zero CFO scenario because the signal is transmitted directly without carrier modulation [25; 26]. Generally, SFO \(\Delta_{s}\) will be relatively small during the tracking stage. Let \(\Delta_{s}\) be the normalized SFO by the sampling rate \(f_{s}=1/T_{s}\).  
The received frequency-domain OFDM symbol during the \(l\)-th period can be described as former works [16; 17]  
\[R_{l}(k)=\alpha(\phi(k))H_{l}(k)X_{l}(k)e^{i2\pi\phi(k)(IN_{u}+N_{g})/N}e^{i\pi( N-1)\phi(k)/N}+I_{l}(k)+Z_{l}(k),\ 0\leq k<N_{b} \tag{1}\]  
where \(\phi(k)=k\Delta_{s}\) considering the baseband PLC scenarios, \(\alpha(\phi(k))=\sin(\pi\phi(k)/N\sin(\pi\phi(k)/N)\) stands for the magnitude attenuation introduced by \(\phi(k)\), \(N_{u}=N+N_{g}\), \(N_{b}\leq N/2\), \(H_{l}(k)\) means a zero-mean CFR with variance \(\sigma_{H}^{2}\) in the \(l\)-th symbol period, \(Z_{l}(k)\) is a zero-mean Gaussian noise with variance \(\sigma_{Z}^{2}\), and \(I_{l}(k)\) is the SFO-induced ICI with variance \(\sigma_{l}^{2}\) written by  
\[I_{l}(k)=\sum_{n=0,n\
eq k}^{N-1}H_{l}(n)X_{l}(n)e^{i2\pi\phi(n)(IN_{u}+N_{g})/ N}\alpha(\phi(n)+n-k)e^{j\pi(N-1)(\phi(n)+n-k)N}. \tag{2}\]  
During the tracking phase, SFO is small enough that \(\alpha(\phi(k))\approx 1\).  
HomePlug GP standard includes three ROBO modes, namely, standard ROBO mode, high-speed ROBO mode, and mini ROBO mode [11], which is summarized in Table 1. The HomePlug GP employs special interleaver for the ROBO modes, whose throughput depends on the degree of an outer repetition code. It supports a maximum PHY rate of 10 Mbps over severely impaired PLC channels. A diversity copier maps the 256 interleaved bits for QPSK mapping onto non-masked carriers so that each bit is duplicated at least 2 times, relying on the degree of repetition code. Besides, a HomePlug GP terminal shall be able to support tone masks of any combination of unmasked carriers within frequency range of 1.8\(\sim\)30 MHz. Thus, the synchronization receiver must be able to accommodate the spectral mask requirements of the region or application.
## 2 Materials and Methods
### Protein Groups, Peptide Sequences, and PSM  
The database search algorithms and de novo algorithms were employed for the analysis of output LC-MS/MS data. Protein identification results comparison from mgf files using Mascot [27, 28] and PEAKS [29, 30] tools are represented by red and blue lines in Figure 2 (identical search parameters for precursor and fragment error tolerance were applied for both tools). PEAKS Studio 11, a newer tool that incorporates deep learning algorithms, yielded more protein groups, peptide sequence identifications, and peptide-spectrum matching (PSM) than Mascot. Additionally, the GUI of PEAKS provides a comprehensive and user-friendly interface for reporting, result verification, and data visualization.  
The line graphs illustrate a gradual increase in protein identifications for methods A, B, C, and D, with a notable difference in the number of peptide sequences identified between Mascot and PEAKS (Supplementary Materials Tables S1-S3). Increasing the MS spectra acquisition rate showed a significant correlation with PSM increases for methods B and D compared to methods A and C. However, while increasing the MS acquisition frequency resulted in more protein identifications, an examination of the output CSV files revealed that many PSM contained duplicate peptide sequences. Section 4.3 and Supplementary Materials Figures S2 and S3 provide an analysis of identification efficiencies for methods A, B, C, and D. Supplementary Materials Figures S4-S6 present a comparison of sequence coverages for Mascot and PEAKS. The use of higher frequency MS and MS/MS acquisition resulted in a 1.5-2.0-fold increase in raw file size (Supplementary Materials Table S4). This increase in file size correlates with the higher number of PSMs at higher acquisition frequencies observed for methods B and D.  
Figure 1: Sample preparation and LC-MS/MS conditions. (**a**) The experimental workflow outlines the analysis of samples from CHO cells, _E. coli_, and albumin-depleted human plasma using methods A, B, C, and D. Filled green circles depict parameters changed during this workflow: 1—MS acquisition frequency, 2—LC gradient, 3—file formats, and 4—DL boost feature of PEAKS. (**b**) Total Ion Current chromatograms for the different methods (A, B, C, D) are shown from the CHO digest sample.
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
###### Abstract  
Rebar corrosion, which causes section loss of rebar, is one of the serious deterioration factors for RC structures. Section loss affects not only stiffness or load capacity but also interlock condition between lugs on deformed rebar and surrounding concrete. Interlock is a dominant factor of bond between rebar and concrete and interlock effects on structural behavior of RC member can be significant. This research focused on the influence of interlock loss on the structural behavior and bond performance of RC member from experimental and analytical investigations. The static loading test for the six beams and FE analysis were conducted for the investigation on the effects of residual interlock, with or without confinement effects from stirrups. In order to evaluate interlock effects precisely, a rebar shape including lugs was reproduced by fine hexahedron elements in the FE analysis. The authors also conducted FE analytical case studies for investigating the effects of non-uniform lug loss or partially interlocking condition due to section loss of rebar. Through these investigations, it was seen that interlock could work and keep sound bond as long as contact between a lug and concrete was maintained even when the rebar lug was flattened due to section loss. Furthermore, under the situation with non-uniform distribution of section loss, pull-out behavior of rebar was prevented by interlocking of parts in a member even when other regions completely lost their interlock due to serious section loss.  
bond deterioration; interlocking; section loss; rebar corrosion; deformed bar
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 3 Results
### Relationship between Impact Factors and Ecosystem WUE  
The multi-year averages of temperature, precipitation, and EVI in the study area are presented in Figure 4(a1)-(a3), respectively. Vegetation growth is improved in warm and humid areas, and areas with greater temperatures and precipitation have high distributions of EVI. The spatial distribution patterns of temperature, precipitation, and EVI are very similar to those of GPP and ET, demonstrating that temperature, precipitation, and EVI influence the spatial distribution pattern of ecosystem WUE, mainly in the form of overall decreases in temperature, precipitation, EVI, and ecosystem WUE with increasing latitude and altitude.  
The partial correlation coefficients of the mean annual temperature, total annual precipitation, EVI, and annual ecosystem WUE exhibited heterogeneous spatial distributions (Figure 4(b1-b3)). Temperature was generally positively correlated with ecosystem WUE, accounting for 68.90% of the total study area, with significant associations in 12.37% (\(p<0.05\)). The regions positively correlated with temperature were mainly distributed in Guizhou and Guangxi (Figure 4(b1)). The proportions of the area with positive and negative correlations between precipitation and ecosystem WUE in the study area were not significantly different, accounting for 57.65% and 42.35% of the total study area, respectively, with 9.58% and 7.95% demonstrating a significant correlation (\(p<0.05\)). The  
Figure 4: Spatial distribution patterns of multi-year mean of temperature (**a1**), precipitation (**a2**), and EVI (**a3**). Partial correlation coefficient between annual mean temperature (**b1**), total annual precipitation (**b2**), EVI (**b3**) and ecosystem WUE.  
regions with negative correlations were concentrated in western Yunnan and the border between Guizhou and Guangxi (Figure 4(b2)). The overall positive correlation between EVI and ecosystem WUE was significant, and the pixels with positive correlation coefficients were distributed throughout almost the entire study area, and the proportion of positive correlation was 77.97% of the total study area with 33.46% significantly correlated (\(p<0.05\)). Meanwhile, regions with negative correlations were sporadically distributed throughout the study area (Figure 4(b3)).
## 4 Experiments
### Third Experiment  
The third environment represents a real-world application in which the robot is mounted on a stand to reach the conveyor during a pick-and-place operation. The conveyor is located inside a robotic safety cage, as shown in Figure 9. Unlike the previous experiments, this setup utilizes the ABB IRB 2400 robot. The same fitness function weight configurations as in the second experiment were used, specifically, IG1_T0_R0, IG0_T1_R0, IG1_T05_R01, and IG1_T025_R01.  
The exploration progresses over time, as shown in Figure 10, and confirms the findings from the second experiment. The exploration phase began when approximately 60% of the space had already been explored in the initial phase, leaving the NBV algorithm responsible for exploring the remaining 40%. Although the configuration maximizing the T weight (with other weights set to zero) initially showed the fastest growth, its progress slowed after a few iterations and ultimately required the most iterations to complete the exploration until the stop condition was hit.  
Table 3 summarizes the performance of each configuration in the third experiment. Although IG1_T0_R0 searched space in the fewest iterations, it also produced the highest values across all cost-related criteria, indicating inefficient motion. In contrast, IG0_T1_R0 and IG1_T025_R01 achieved significantly lower values in joint and Cartesian distances, orientation change, and overall robot displacement, suggesting more efficient and smoother trajectories. Notably, IG1_T025_R01 maintained comparable exploration performance while minimizing motion costs, making it a well-balanced option. The best results are highlighted in bold, while row colors align with the data plots in Figure 10.  
Figure 8: The explored volume of the workspace in each iteration in the second experiment.  
Figure 9: (**a**) The third experiment setup; (**b**) the occupied and unknown space after the exploration phase.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Overview of Domain Adaptation Techniques
#### 2.1.2 Traditional Domain Adaptation Methods  
Traditional domain adaptation techniques match the source and target domains' feature spaces using statistical techniques. TCA, which intends to narrow the dispersion mismatch between domains by mapping the data onto a common latent space, is one extensively utilized approach. TCA has been used to effectively align feature distributions and enhance model performance in computer vision applications like object recognition [86].  
MMD, which quantifies the difference between the means of the source and target data in a replicating kernel Hilbert space, is another well-liked technique. In terms of domain adaptation for image classification tasks, MMD has yielded encouraging results [87]. MDD uses a multi-domain discriminator to train models to learn domain-invariant features across diverse data sources, enabling better generalization to new domains. MMD-DA (Maximum Mean Discrepancy--Domain Adaptation) extends MCD by adding domain adaptation techniques to enhance feature distribution alignment, which is beneficial when you have labeled source data and unlabeled target data.
## 5 Results and Discussion
### Comparison of GB, ANN and SVM  
From a statistical standpoint, the randomness in the data set needed to be carefully considered when comparing models. In this section, to compare the performance of the three optimized models, 300 samplings were performed taking into account the random division between training set and testing set. In these samplings, the training and test set sizes were kept the same, however the index number of the training and test data were randomly selected in the original data set. The models would be built on the training set and then validated on the testing set.  
Figures 8-10 showed a density curve of the performance results after 300 samplings on the training set and testing set. The summary of the performance indicators of each models was presented in Tables 4-6. It can be seen that the values of R of all three models showed a strong prediction UCS of cement-stabilized soil as the values of R were in the range of 0.9-1 on the training set and in the range of 0.8-1 on the testing set. The values of RMSE were in the range of 150-350 (kPa) on training set and in the range of 200-650 (kPa) on the testing set while the value of MAE varied from 50 to 250 (kPa) on the training set and from 100 to 400 (kPa) on the testing set.  
It also can be seen that out of the three models, the ANN model gave outstanding performance, reflected in the average of all performance indicators, namely R = 0.925, RMSE = 419.82 and MAE = 292.2 on testing set. The GB and SVM models showed equal performance when the GB model had better performance at R but worse at RMSE and MAE. To be more specific, GB had the average performance indicator of R = 0.912, RMSE = 446.79 and MAE =319.23 while SVM model had the average criteria R = 0.903, RMSE = 446.67 and MAE = 309.76 on the testing set. In addition, the minimum and maximum values of the performance indicators of the ANN model all allowed it to outperform the other models, proving that the model was more stable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Gradient Boosting**} & \multicolumn{2}{c}{**Artificial Neural Network**} & \multicolumn{2}{c}{**Support Vector Machine**} \\ \hline Learning rate & 0.2 & Number of neurons & 50 & Regularization parameter (C) & 1000 \\ Number of tree & \(117\) & Solver & Adam & Kernel coefficient Gamma (\(\gamma\)) & 0.1 \\ Min samples split & 9 & Activation function & Logistic & Kernel type & Radial bias function \\ Min samples leaf & 3 & Max iteration & 3000 & & \\ Max depth & 16 & Learning rate & 0.1 & & \\ Best criteria R & 0.929 & Best criteria R & 0.93 & Best criteria R & 0.871 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimum hyper-parameters of ML models.  
Figure 8: Density curve of the performance indicator R on 300 samplings with the: (**a**) Training set and (**b**) Testing set.  
Figure 10: Density curve of the performance indicator MAE on 300 samplings with the: (**a**) Training set and (**b**) Testing set.  
Figure 9: Density curve of the performance indicator RMSE on 300 samplings with the: (**a**) Training set and (**b**) Testing set.