esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we proposed TAPT, a novel MAML-based task-adaptive parameter transformation scheme that can alleviate the data shortage problem in ASC. TAPT generates transformation variables while generalizing common knowledge and uses them to adjust the parameters of each specific classification task. To evaluate the effectiveness of the proposed scheme, we conducted extensive experiments for five different learning schemes using two public datasets and two backbone networks. In the experimental results, TAPT showed better performance than other few-shot ASC schemes, achieving up to 20.32% improvement in ASC accuracy compared to the SOTA scheme. In addition, a sensitivity analysis confirmed that TAPT is robust to the number of inner-loop updates, and an ablation study proved that the ASC accuracy improvement in TAPT results from the proposed parameter transformation. Finally, training accuracy comparison demonstrated that TAPT learns to classify unseen tasks more efficiently than MAML.  
In the future, based on our few-shot learning scheme, we plan to make software that can be mounted on the audio sensor equipment for animal species classification. In addition, we will consider a hybrid MAML scheme that can incorporate features from the spectrogram as well as the waveform.  
Conceptualization, J.M.; methodology, J.M.; validation, J.M.; formal analysis, J.M.; investigation, E.K. and J.H.; data curation, E.K. and J.H.; visualization, J.M.; writing--original draft preparation, J.M.; writing--review and editing, E.H.; supervision, E.H.; project administration, E.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean Government (MSIT) (No. RS-2023-00262750, Development of Automated Surveillance System for Vector Mosquitoes (Japanese encephalitis, Malaria) using AI-Based Sound Recognition Technology).  
Not applicable.  
The data that support the findings of this study are openly available from Zenodo at \"[[https://doi.org/10.5281/zenodo.3666782](https://doi.org/10.5281/zenodo.3666782)]([https://doi.org/10.5281/zenodo.3666782](https://doi.org/10.5281/zenodo.3666782))\" (accessed on 10 November 2023) and \"[[https://doi.org/10.5281/zenodo.3667094](https://doi.org/10.5281/zenodo.3667094)]([https://doi.org/10.5281/zenodo.3667094](https://doi.org/10.5281/zenodo.3667094))\" (accessed on 10 November 2023).  
Figure 6: Comparison of training processes between TAPT and MAML according to training steps. (**a**) Training accuracy; (**b**) training loss.
## 3 Results and Discussion  
The designation, composition and mechanical properties of samples are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **Composites** & \begin{tabular}{c} **Relative Density** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Hardness (HRC)** \\ \end{tabular} &
\begin{tabular}{c} **Bending Strength** \\ **(MPa)** \\ \end{tabular} \\ \hline S0 & Matrix & 98.4 & 42.2 \(\pm\) 2.9 & 705.9 \(\pm\) 35.4 \\ S1 & Matrix + 0.5 wt\% nano-WC & 97.0 & 36.9 \(\pm\) 0.9 & 700.9 \(\pm\) 30.3 \\ S2 & Matrix + 1.0 wt\% nano-WC & 97.0 & 36.7 \(\pm\) 2.0 & 732.1 \(\pm\) 16.6 \\ S3 & Matrix + 1.5 wt\% nano-WC & 97.8 & 39.2 \(\pm\) 4.1 & 753.5 \(\pm\) 31.0 \\ S4 & Matrix + 2.0 wt\% nano-WC & 97.6 & 36.2 \(\pm\) 3.1 & 769.6 \(\pm\) 29.9 \\ S5 & Matrix + 2.5 wt\% nano-WC & 98.5 & 40.7 \(\pm\) 1.3 & 824.5 \(\pm\) 28.4 \\ S6 & Matrix + 3.0 wt\% nano-WC & 98.3 & 38.8 \(\pm\) 2.6 & 736.0 \(\pm\) 24.5 \\ SD0 & Matrix + Diamond & 95.8 & - & 391.0 \(\pm\) 25.5 \\ SD1 & Matrix + 2.2 wt\% nano-WC + Diamond & 93.1 & - & 461.6 \(\pm\) 32.0 \\ SD2 & Matrix + 2.5 wt\% nano-WC + Diamond & 94.8 & - & 398.8 \(\pm\) 19.2 \\ SD3 & Matrix + 2.8 wt\% nano-WC + Diamond & 96.1 & - & 384.3 \(\pm\) 7.4 \\ SD4 & Matrix + 3.1 wt\% nano-WC + Diamond & 95.3 & - & 398.0 \(\pm\) 32.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The designation, composition and mechanical properties of samples.  
Figure 1: Schematic diagram of the grinding ratio test.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 3 Crucial Techniques
### Lower-Power Techniques
#### 3.2.1 Random-Data-Selection-Based LMS Implementation  
The LMS adaptive FFE coefficient could be calculated by the following equation [4], Equation (1) is in the Appendix A:  
\[\mathbf{W}(\mathbf{n})=\mathbf{W}(\mathbf{n}-1)+2\mu ek(n)\mathbf{Y}(\mathbf{n}) \tag{1}\]  
where \(\mathbf{Y}(\mathbf{n})\) is the judgment result of the 64-way DFE, _ek(n)_ is the error of the actual value of the 64-way data relative to its ideal value, \(\mathbf{W}(\mathbf{n})\) is the current cycle tap coefficient vector, and \(\mathbf{W}(\mathbf{n}-1)\) is a vector of tap coefficients for the previous cycle. Figures 7 and 8 shows traditional LMS implementation in FFE and DFE, which utilizes all parallel data to perform the tap coefficient calculation. This full parallel data processing can quickly and accurately collect residual ISI for each tap, hence supporting a fast convergence speed. In practice,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Width** & **8** & **9** & **10** \\ \hline VEC & 5.01 & 4.84 & 4.69 \\ VEOR & 6.93 & 7.38 & 7.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The VEC and VEOR for different FFE coefficients width.  
Figure 6: The effect of truncating in FFE and ideal results of MATLAB. (**a**) 9 bits. (**b**) MATLAB results. (**c**) 8 bits. (**d**) 10 bits.  
the convergence speed is not a critical parameter as the channel loss usually varies very slowly for wireline systems. Based on this observation, we propose an LMS algorithm based on random data selection (see Figures 7 and 8), where the data applied to the LMS engine is selected from parallel data by a pseudo-random algorithm. Using the above random data selection idea, in the LMS algorithm of FFE, 4 data are randomly selected from 49 data and substituted into Equation (1) for calculation. Similarly, in the LMS algorithm of DFE, 1 datum is selected from 63 data and substituted into Equation (1) for calculation. This greatly reduces the hardware resources required by traditional LMS algorithms, thereby saving a lot of power consumption. And this data selection strategy helps to make the bandwidth of the FFE much higher than that of the DFE, thereby contributing to loop convergence. Taking DFE's LMS as an example, Table 2 shows the resource comparison between full data processing and randomly selected data computing. It is obvious that the proposed LMS based on random data selection can reduce hardware resources and power consumption to one tenth of the traditional full-data processing method.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 4 Experiments, Results, and Ablation Study
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**S**} & \multirow{2}{*}{**PE**} & \multirow{2}{*}{**SC**} & \multicolumn{4}{c}{**Rotation (reg)**} & \multicolumn{4}{c}{**Translation (cm)**} \\  & & & & \multicolumn{2}{c}{**Accuracy \(\uparrow\)**} & \multicolumn{2}{c}{**Error \(\downarrow\)**} & \multicolumn{2}{c}{**Accuracy \(\uparrow\)**} & \multicolumn{2}{c}{**Error \(\downarrow\)**} \\ \cline{3-12}  & & & **5** & **10** & **45** & **Mean** & **Med.** & **5** & **10** & **25** & **Mean** & **Med.** \\ \hline \multirow{3}{*}{\(\checkmark\)} & & & 94.6 & 96.9 & 99.2 & 2.6 & **0.8** & 79.0 & 91.7 & 97.0 & 5.8 & 2.3 \\  & & & 90.3 & 94.6 & 98.8 & 3.5 & 0.9 & 73.8 & 87.7 & 94.8 & 8.0 & 2.6 \\ \cline{1-1}  & \(\checkmark\) & & 45.6 & 75.0 & 99.4 & 7.9 & 5.6 & 9.8 & 31.3 & 74.1 & 20.7 & 14.5 \\ \cline{1-1}  & \(\checkmark\) & \(\checkmark\) & 94.5 & 97.1 & 99.2 & 2.5 & 0.8 & 80.4 & 91.9 & 97.0 & 5.7 & 2.3 \\ \cline{1-1}  & \(\checkmark\) & & 43.3 & 75.3 & 99.6 & 7.8 & 5.9 & 10.7 & 30.9 & 74.5 & 20.2 & 15.0 \\ \cline{1-1}  & \(\checkmark\) & & 94.6 & 97.4 & 99.3 & 2.4 & **0.8** & 79.4 & 91.8 & 97.2 & 5.6 & 2.4 \\ \cline{1-1}  & \(\checkmark\) & \(\checkmark\) & 95.1 & 97.6 & 99.3 & 2.3 & **0.8** & 80.0 & 92.4 & 97.3 & 5.5 & 2.3 \\ \cline{1-1}  & \(\checkmark\) & \(\checkmark\) & **95.5** & **97.9** & **99.6** & **1.9** & **0.8** & **81.5** & **92.6** & **97.7** & **4.8** & **2.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison between our complete method and the incomplete method is presented. “S” indicates whether self-attention layers were used in the fusion module, “PE” indicates whether position encoding was utilized, and “SC” indicates whether alternating self-attention and cross-attention modules were employed to learn the matching. The bold indicates the optimal metric under equivalent conditions. This is because a standalone self-attention layer, while capable of capturing more global information, weakens the distinctiveness of features, necessitating subsequent alternations between self-attention and cross-attention for optimization. Additionally, introducing positional encoding alone significantly degrades performance, as using positional encoding without further training disrupts the consistency of the original features, making effective matching difficult. The final results also demonstrate that the complete method achieved the best performance.
###### Abstract  
With the continuous development of the Internet of Things (IoT) and communication technologies, the demand for low latency in practical applications is becoming increasingly significant. Mobile edge computing, as a promising computational model, is receiving growing attention. However, most existing studies fail to consider two critical factors: task dependency and service caching. Additionally, the majority of proposed solutions are not related to the optimal solution. We investigate the task offloading problem in mobile edge computing. Considering the requirements of applications for service caching and task dependency, we define an optimization problem to minimize the delay under the constraint of maximum completion cost and present a \((1+\epsilon)\)-approximation algorithm and an exact algorithm. Specifically, the offloading scheme is determined based on the relationships between tasks as well as the cost and delay incurred by data transmission and task execution. Simulation results demonstrate that in all cases, the offloading schemes obtained by our algorithm consistently outperform other algorithms. Moreover, the approximation ratio to the optimal solution from the approximation algorithm is validated to be less than \((1+\epsilon)\), and the exact algorithm consistently produces the optimal solution.  
m pacs: mobile edge computing (MEC); task offloading; task dependency; service caching; approximation algorithm +
Footnote †: journal:  
_title= Article_
## 3 Design and Development of the Measuring System
### Hardware Design of the System
The maximum is acceptable (less than 0.01 mm), indicating that the designed frame, which is lower than 5 kg, could be used. Furthermore, in the latter formal measuring process, with the help of a standard calibration platform, the error resulting from the deformation could be rectified. As a consequence, the frame has advantages in mechanical performance, like a good rigidity and light weight, fulfilling the demands for application. The integrated controlling component stays in protective housing and is composed of the upper computer, the RS485/RS232 converter, the proximity for each eddy current probe, and the power supply and display module. The upper computer adopts the RS232 communication mode. Through the RS232 to 485/422 passive bidirectional converter UTEK UT-217E (China UTEK technology, UOTEK), data transmission is realized between the sensor and the upper computer. The sensing and controlling components, which are shown, respectively, in Figure 8a,b, are collected and communicated with armoring wires. Figure 7: The designed supporting frame. (**a**) Model of the sensor array component; (**b**) The meshed geometry being analyzed; (**c**) The deformation status under horizontal and vertical positions (**d**).
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 5 Experiment Results
### Analysis of Variance (ANOVA)  
The ANOVA Reference [37] is used to validate the simulation findings, which is a statistical test. It analyzes if a particular set of conclusions statistically differs from another data set in a meaningful manner. The null hypothesis (\(H_{0}\)) and the alternative hypothesis (\(H_{1}\)) are tested, and are defined as  
\[H_{0}:=U_{1}=U_{2}=U_{3}.\ldots.=U_{n} \tag{17}\]  
\[H_{1}:\
eq U_{1}\
eq U_{2}\
eq U_{3}.\ldots.\
eq U_{n}\ (\textit{Means are not equal}) \tag{18}\]  
As a result of the test, it fails to reject the null hypothesis since F statistical is more diminutive than F critical, \(p\)-value is less than 5% (0.05) and accepts the alternative hypothesis if, on the other hand, F statistical is greater than F critical. In this study, a three-factor ANOVA was undertaken to examine the system, response time, and throughput effects on resources (Pes). The difference in true mean responses for a given category of QoS  
Figure 7: QoS Satisfied Throughput.  
parameters and the system is the use of resources. The ANOVA test indicates the interaction between the different QoS factors of the cloud by the resources.  
The ANOVA test was employed to examine any significant differences in the system with policy-based resource allocation. It quantified resource allocation attributed to the system, response time, throughput, and Processing Elements (PEs). Results of the T-test, shown in Table 6, indicated a significant difference in the Systems and throughputs (1, 3) where \(p\)-value is less than 5%. The interactions of the system with the throughput and interaction among the system, response time, and throughput (5, 7) are significant. The results show that the system behavior has been changed after assigning the Quantified resources, as shown in the table. The F statistical is more diminutive than the F critical (\(p\)-value) of System, Throughput, System: Throughput, and System: Res_Time: Throughput, which shows a significant difference and rejects the null hypothesis. It indicates that ReSQoV performs better to satisfy QoS than the policy-based resource allocation.
## 3 Results and Discussion
### RSSI Localization
#### 3.2.1 Channel Modeling  
The collected test data were statistically analyzed, and the averaged results from the five orientations are plotted in Figure 9 as a function of the distance of the UPD from the router. The blue dotted line in Figure 9  
For this study, the CCIR, HATA, and Walfisch-Ikegami path loss models were compared with the sampled result. However, we considered the free space loss model, described in Equation (3), to be the most suitable because it is possible to adjust the parameter K to best fit the measured values:  
\[\mathrm{P_{i}[dBm]=P_{Tx}+G_{t}+G_{t}-\left(K+20\mathrm{log}_{10}(d_{km})+20 \mathrm{log}_{10}(f_{MHz})\right) \tag{3}\]  
Equation (3) defines the received signal power \(P_{i}\) (in dBm) at a node \(i\) placed at a distance \(d_{km}\) from the transmitter as the sum of the transmitted power \(P_{Tx}\) (in dBM) and the transmitter and receiver antenna gains \(G_{t}\) and \(G_{r}\), respectively, minus the logarithm of the distance \(d_{km}\), the logarithm of the frequency \(f_{MHz}\), and a parameter K. We applied the Solver tool in Microsoft Excel to the mean square error to optimize the path loss model parameter K. Compared to the value of K = 32.45 obtained in [17], the optimization process yielded K = 45.35. However, as can be observed in Figure 9, the fit to the experimental results is reasonably good.  
Some discrepancies exist between the measured and modeled values at distances between the UPD and the router of less than 10 m. However, these errors should not hinder the performance of the hybrid positioning system due to safety measures that limit the distance between the user and the autonomous tractor to greater than 10 m. When the user needs to perform maintenance on an autonomous tractor, the system can acquire the coordinates of that tractor to determine the user's current position
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 2 Internal Calibration System
### Calibration Pulses
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Calibration Mode** & **Calibration Loops** & **Remarks** \\ \hline non-delay calibration & synchronization transmitting calibration loop & \\ synchronization receiving calibration loop & & full array (sub-array, single T/R module) \\  & chirp transmitting calibration loops & chirp transmitting calibration loop \\  & chirp receiving calibration loop & full array (sub-array, single T/R module) \\  & chirp reference calibration loop & chirp receiving calibration loop \\ \hline delay calibration & chirp transmitting calibration loops & full array (sub-array, single T/R module) \\  & chirp transmitting calibration loop & chirp transmitting calibration loop \\  & chirp receiving calibration loops & full array (sub-array, single T/R module) \\  & chirp receiving calibration loop & chirp receiving calibration loop \\  & chirp reflecting calibration loop & chirp transmitting and receiving calibration loop \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Design of the internal calibration loops in LT-1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Calibration Loops** & **The Signal Path** \\ \hline synchronization transmitting calibration loop & \(LFM^{i}\to STX^{i}\to CR_{A}^{i}\to RX^{i}\) \\ synchronization receiving calibration loop & \(LFM^{i}\to CR_{A}^{i}\to SRX^{i}\to RX^{i}\) \\ chirp transmitting calibration loops & \(LFM^{i}\to TX^{i}\to CN_{A}^{i}\to CR_{A}^{i}\to RX^{i}\) \\ chirp receiving calibration loops & \(LFM^{i}\to CR_{A}^{i}\to CN_{A}^{i}\to RX_{A}^{i}\) \\ chirp reference calibration loop & \(LFM^{i}\to CR_{1}^{i}\to RX^{i}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Signal path of the non-delay calibration loops.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Stability Proof of Satellite Control
#### 2.3.3. Selection Criteria of Quadratic Potential Function Variables
\begin{array}{c}\mathrm{ d}b_{1}/\mathrm{d}t-\mathrm{d}b_{1\mathbf{t}}/\mathrm{d}t\\ \mathrm{d}b_{2}/\mathrm{d}t-\mathrm{d}b_{2\mathbf{t}}/\mathrm{d}t\\ \mathrm{d}b_{3}/\mathrm{d}t-\mathrm{d}b_{3\mathbf{t}}/\mathrm{d}t\\ \mathrm{d}c/\mathrm{d}t-\mathrm{d}c_{\mathbf{t}}/\mathrm{d}t\\ \mathrm{d}\theta_{1}/\mathrm{d}t-\mathrm{d}\theta_{1\mathbf{t}}/\mathrm{d}t\\ \mathrm{d}\beta_{1}/\mathrm{d}t-\mathrm{d}\beta_{1\mathbf{t}}/\mathrm{d}t\\ \mathrm{d}\psi/\mathrm{d}t-\mathrm{d}\psi_{\mathbf{t}}/\mathrm{d}t\end{array}  \right]=\left[ \begin{array}{c}0\\ 0\\ 0\\ 0\\ 0\\ n_{i}-n_{\mathbf{t}}\\ n_{i}-n_{\mathbf{t}}\end{array} \right] \tag{59}\]  
Therefore, relative orbital elements \(b_{1},b_{2},b_{3},c,\theta_{1}\) are ideal control variables that ensure control stability. When \(a_{i}=a_{\mathbf{t}}\), \(\beta_{1},\psi\) can be chosen as control variables. To sum up, the selection criteria of quadratic APF control variables are given as follows. If the time derivative of \(\mathbf{X}_{\mathbf{i}}\) is the same as that of \(\mathbf{X}_{\mathbf{t}}\) under the two-body condition, the corresponding APF control is stable.
## 4 Numerical Simulation  
To validate the functionality of the proposed damage detection method in the CS framework, numerical models were established in ABAQUS, a finite elements method (FEM) simulation software, to demonstrate the performance. The geometry of the basic parts of the model was created based on the full scale of the concrete-filled tube of the STS structure in Olympic Center Station including steel tubes, the concrete, and the connecting bolts, which are shown in Figure 3. The diameter of the steel tube is 900 mm with a thickness of 16-mm. The 900 mm diameter steel tube is modeled with an elastic modulus (\(E_{tube}\)) of 200 GPa, and Poission's ratio is 0.33. The concrete is modeled with an elastic modulus (\(E_{conc}\)) of 15.0 GPa, and Possion's ratio is 0.3. Each of these components are meshed with cubic elements with sizes of 10 mm. This meshing size resulted in a total of 63,480 elements in the concrete, 13,236 in the steel tube, and 48 elements each bolt. C3D8R (8-node, linear) solid element was used for the model. Ultrasonic waves are excited by a series of square waves with the central frequency of 50 kHz. There are two models inspected in this chapter: (1) a single-damage model and (2) a multi-damage model. The entire tube was divided into 180 microstructures to apply the ultrasonic CT for damage inspection of the flaws and holes in the concrete-filled tube. Thus, the conventional ultrasonic CT requires at least 20 measurement points and 190 (\(20\times 19/2\)) measurement paths for the mapping of damage information. The locations of measurement points are represented as small circles on the external shell, and the measurement paths are shown in blue lines in Figure 4a. However, according to the CS theory, the number of measurement paths is chosen as 80, which is larger than the minimum number of detections (\(m>\upmu\!\cdot\!K\!\cdot\log(N/K)=58.09\)), meeting the requirement of recovery with minimal reconstruction error. As we can see, the measurement net including 80 paths selected by the Bernoulli distributed matrix is shown in Figure 4b. That indicates that the ultrasonic CT improved by the CS can significantly reduce the measurement times as expected. All three damage cases are measured using the same selected measurement net to detect the damage in the models.