esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A ground-based multichannel microwave radiometer (GMR) is commonly used to observe the atmospheric radiation brightness temperature (T\({}_{\rm B}\)) in order to retrieve atmospheric temperature and humidity profiles. At present, GMRs are used only in meteorology and climate monitoring. However, theoretical analysis showed that GMRs can be also used to observe the solar radiation. Therefore, we tried to improve the antenna servo control system of a GMR so that it could track and observe the sun, and the results showed that the GMR could respond to the variation of solar radiation. A further question was: can a GMR observe the variation of the sun during a solar eclipse? Fortunately, two solar eclipse events were captured by the GMR on 26 December 2019 and 21 June 2020 in Xi'an, China. We used the upgraded GMR to observe the variation of solar radiation during the two solar eclipses. The observation and analysis results showed that (1) the GMR could accurately track the sun and respond to the variation of solar radiation during the solar eclipse. We analyzed the variation features of the solar radiation by combining the solar phase during the two solar eclipses. (2) We found that the GMR could respond to the variation of the solar radiation arising from the Earth-Sun distance, and we further propose a novel method to measure the eccentricity of earth orbit with the GMR by using the passive solar observation. The results show that the eccentricity measured was 0.0169, which agreed quite well with the value of 0.0167 in the literature. (3) The average variation percentages of both the Earth-Sun distance and the intensity of the incident solar radiation throughout the year were estimated to be 3.44% and 6.6%, respectively. According to these results, the solar observation techniques can broaden the field usage of GMR.  
microwave radiometer; solar eclipse; solar radiation; eccentricity
# Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
## 6 Experiment and Evaluation
### Experiment Results  
#### 6.4.1 NASA Dataset  
The Bi-LSTM model shown in Table 4 achieves the smaller prediction error values on MSE, RMSE, MAE metrics compared to the ARIMA model in both single-step (one-step) and multi-step (five-step) predictions. This means the Bi-LSTM has better prediction accuracy than the ARIMA model. Furthermore, the \(R^{2}\) values of the Bi-LSTM model in both one-step and five-step predictions are higher than those of the ARIMA model, which shows that the Bi-LSTM can fit the data better than ARIMA. Finally, Bi-LSTM not only improves in terms of all metrics but also has a faster prediction speed by 530 and 55 times following one-step and five-step predictions, respectively, compared to the statistical method ARIMA.  
Figures 7 and 8 present the result of the predicted value versus the actual value. The performance of ARIMA model appears to be similar to the Bi-LSTM model. Then, we take a closer look by zooming in to the results. Figure 9 shows the absolute error of models for the first 100 minutes of the testing set. We can observe that the ARIMA model usually has higher absolute error values than the Bi-LSTM model. Therefore, the Bi-LSTM model performs better not only in terms of prediction accuracy but also prediction speed compare to the ARIMA model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ARIMA** & **BI-LSTM** & **ARIMA** & **BI-LSTM** \\
**Type** & **1 Step** & **1 Step** & **5 Steps** & **5 Steps** \\ \hline MSE & 196.288 & **183.642** & 237.604 & **207.313** \\ RMSE & 14.010 & **13.551** & 15.414 & **14.39** \\ MAE & 10.572 & **10.280** & 11.628 & **10.592** \\ \(R^{2}\) & 0.692 & **0.712** & 0.628 & **0.675** \\ Prediction speed (ms) & 2300 & **4.3** & 2488 & **45.1** \\ \hline \hline \end{tabular} Bold values indicate the best results.  
\end{table}
Table 4: Experiment result on NASA dataset.  
Figure 8: Bi-LSTM with one-step prediction on the testing set.  
Figure 7: ARIMA with one-step prediction on the testing set.  
Figure 9: Comparison of absolute error for the first 100 minutes of NASA testing set.
## 3 Results
### Variations of Kivirfruit Orchard Carbon Fluxes  
#### 3.2.1 Diurnal Variations of GPP, NEE, and \(\mathrm{R_{e}}\)  
Consistent bell-shaped profiles were observed in the average diurnal variations in GPP, \(\mathrm{R_{e}}\), and NEE across different months in 2018 and 2019 (Figure 3a), with carbon fluxes typically remaining low in the early morning and night, nearing zero, and peaking around midday. The kiwifruit ecosystem transitioned from being a carbon source to a carbon sink during the time of 7:30-9:30 and returned to being a carbon source at 17:30-19:00. The diurnal GPP and NEE consistently peaked at approximately 12:30-14:30, while \(\mathrm{R_{e}}\) peaked at 13:30-15:30, signifying a distinct diurnal pattern. The mean maximum diurnal GPP was 1.36 and 1.20 mg C m\({}^{-2}\) s\({}^{-1}\) in the 2018 and 2019 growing seasons, respectively. The peak of mean diurnal NEE were \(-0.99\) and \(-0.80\) mg C m\({}^{-2}\) s\({}^{-1}\) in 2018 and 2019, respectively. The diurnal amplitude variability of \(\mathrm{R_{e}}\) was minimal among carbon fluxes, reaching its maximum in August, with values of 0.47 mg C m\({}^{-2}\) s\({}^{-1}\) in 2018 and 0.43 mg C m\({}^{-2}\) s\({}^{-1}\) in 2019, respectively.  
Figure 2: Monthly variations in environmental factors and leaf area index (LAI) during the kiwifruit growth period of 2018 and 2019. Environmental factors are (**a**) air temperature (\(\mathrm{T_{a}}\)), soil temperature (\(\mathrm{T_{s}}\)) at 5 cm depth and photosynthetically active radiation (PAR), (**b**) concentration of air carbon dioxide (\(\mathrm{CCO_{2}}\)) and concentration of air water vapor (\(\mathrm{CH_{2}O}\)), (**c**) soil water content (SWC) at 20 cm depth and air relative humidity (RH), and (**d**) vapor pressure deficit (VPD) and LAI.
# Unexpected Ferromagnetism--A Review
## 2 Examples of \(d^{0}\) Ferromagnetism
### Carbon Nanostructures
#### 2.3.2 Graphene  
Apart from the already known unique properties of graphene, several studies reported that ferromagnetic ordering could be found within the structural defects [63; 64; 65]. In a first-principle calculation study on nano-flakes graphene, it was shown that the zig-zag-edged structure of these nano-flakes could contribute to the exhibited ferromagnetic behavior [66]. Room temperature ferromagnetism was noticed in bulk graphene materials obtained from soluble functionalized graphene sheets, which was attributed to the structural defects of graphene [53]. Graphene-based materials with incorporated magnetism could be promising candidates for spintronic applications. Enhanced ferromagnetism was obtained by modifying edge state morphology in graphene nanoribbons [67].  
Twisted bilayer graphene has been recently intensely researched in regard to its potential ferromagnetic behavior. This system is basically composed of two sheets of graphene, which are superimposed with a slight twist angle between them. By misaligning the two graphene sheets relative to each other, a moire pattern is formed. Recently, it was demonstrated that three-quarters filling of the conduction band leads to the induction of a ferromagnetic state [68].  
Stabilizing the room temperature ferromagnetic ordering in materials based on graphene remains a significant challenge due to the wide area of applications in which they could be used, such as spintronics, electronics, biomedicine, and separation technologies [69; 70; 71; 72; 73].
## 2 Methods
### Topology Optimization
#### 2.1.2 Filter Function  
One issue that can arise in topology optimization is the presence of \"checkerboard effects\", which are patterns of alternating solid and void elements arranged in a checkerboard-like fashion. These checkerboard patterns can lead to problems such as buckling with zero performance [24].  
The basic density filter calculates a new relative density for elements based on the neighborhood. This is achieved by summing the relative densities \(x_{f}\) of the elements in the neighborhood \(N_{e}\) of a given element \(x_{e}\) and dividing this sum by the total volume \(V_{f}\) of these elements. The neighborhood \(N_{e}\) of an element \(x_{e}\) is defined as the set of elements \(f\) that are within a certain distance \(d(e,f)\) from the element \(x_{e}\). It can be visually represented, as seen in Figure 1.  
Mathematically,  
\[x_{e}=\frac{\sum\limits_{f=1}^{N_{e}}H_{ef}V_{f}x_{f}}{\sum\limits_{f=1}^{N_{e }}H_{ef}V_{f}} \tag{6}\]  
Figure 1: Neighborhood for the element and the filter size.  
where \(N_{e}=\left\{\ f:d(e,f)\ \leq r_{\min}\ \right\}.\)  
In Equation (6), the height factor \(H_{\text{ef}}\) is a function of the filter size \(r_{\min}\) and the distance between elements \(d(e,f)\). \(H_{\text{ef}}\) is calculated as the difference between \(r_{\min}\) and \(d(e,f)\) for the elements \(f\) in the neighborhood \(N_{e}\).  
\[H_{ef}=r_{\min}-d(e,f)\ \ \ \text{where}\ \ \ f\in N_{e} \tag{7}\]
## 5 Experiments with Frame Aggregations
### Stationary Scenario  
In this subsection, we evaluate the performance of the proposed MAB algorithm in a stationary scenario in which wireless nodes use the IEEE 802.11 ax standard. We used two wireless nodes located in fixed positions during a simulation time of 2.5 s. The two nodes are are quipped with one antenna each. The distance between the two nodes was set to 70 m. During the simulation time, one node (sender) sends CBR traffic of 150 Mbps to the other node (receiver). We disabled the sliding window in the proposed MAB algorithms by setting \(\tau=2.5\) because the two nodes were not moving, and no interfering nodes existed. Because of the large distance between the two nodes, the experimental results of the packet success rates shown in Figure 5b indicated that only the lowest MCS level (i.e., MCS 0) had packet success rates higher than zero. All other MCS levels exhibited zero packet success rates with the given distance. Thus, MCS 0 is the only MCS level that can result in successful reception of packets at the receiver in this scenario.  
Figure 6a shows the changes in the application throughputs (0.1 s average) achieved by the receiver when each of the four different MAB algorithms was adopted by the sender. The experimental results in Figure 6a show that the receiver achieved approximately 5.8 Mbps in its maximum. The experimental results clearly show that TS-logR and KL-UCB-logR reached the maximum throughput faster than TS-R and KL-UCB-R, respectively. Figure 6b shows the changes in the average MCS levels (0.1 s average) used by the four MAB algorithms. The MCS level used by TS-logR and KL-UCB-logR converged to MCS 0 faster than TS-R and KL-UCB-R, respectively.  
Figure 5: (a) Changes in packet success rate versus SINR with different MCS levels, (b) changes in packet success rate versus distance with different MCS levels in 1 \(\times\) 1 IEEE 802.11 ax networks.  
However, by comparing the results in Figure 6b with Figure 2b in Section 4.2, we observe that it took a longer time for the MAB algorithms to converge with the IEEE 802.11 ax standard than with the IEEE 802.11g standard, regardless of the MAB algorithms. This was primarily because of the increased size of the search space, i.e., \(K\) was 12 instead 8.
## Materials and Methods
### IAS Estimation  
Once the 13 parameters of interest are available over time, several different IAS estimations can be obtained. In this work, two methodologies are compared.  
The first and simplest consists in differencing the angle over time and rescaling over the time interval determined by the sampling frequency (i.e., 30 \(fps\to f_{\mathrm{s}}=30\,Hz\)). Being \(\alpha(n)\) is the angle of the vertex identified by the 8.8, and \(t=n\,\Delta t=n/f_{\mathrm{s}}\), it is easy to write:  
\[\omega(t)=\frac{d\alpha}{dt}\cong\frac{\Delta\alpha}{\Delta t}\ \ [rad/s] \tag{14}\]  
For this to be accurate, the angle signal should first be \"unwrapped\" (i.e., corrected by adding multiples of \(\pm 2\pi\) when absolute jumps between consecutive elements are greater than \(\pi\) radians). Furthermore, a perfect recognition of all the three templates is required. Nevertheless, even if the match for two hexagons is good for all the frames, it is not the same for the 8.8 logo, which, in some cases (often at a determined angle because of illumination issues), is confounded with the manufacturer logo \"JD\" (e.g., see Figure 6). In this case, an error of about \(\pm\pi\) radians should be compensated in a pre-processing stage.  
The second method is based on phase demodulation via the Hilbert analytic signal of a fan speed-related harmonic [7; 56]. In this particular case, it was noticed that the signal corresponding to the coordinates of the center of the outer hexagon (i.e., \(X_{c},Y_{c}\) in Figure 7a) features a speed-related harmonic because of the not perfect alignment of the optical axis of the camera with the fan axis, leading to a circular movement of the tracked center point.  
In a mathematical framework:  
1. The analytic signal is computed via Hilbert transform \[\alpha_{m}(t)=x(t)+iy(t)=A(n)e^{i\Phi(t)}\] (15)2. The instantaneous frequency is defined as \[f(t)=\frac{1}{2\pi}\frac{d\Phi(t)}{dt}\ \ \ [Hz]\] (16)
3. From which, a more suitable discrete-time (\(t=n\ \Delta t\)) implementation can be derived [56] \[f(n)=\frac{fs}{2\pi}\text{atan}\bigg{(}\frac{x(n)y(n+1)-x(n+1)y(n)}{x(n)x(n+1)+ y(n)y(n+1)}\bigg{)}\] (17)  
Despite the \"unwrap\" and the error compensation, the IAS estimated from the two methods could still be noisy, with large observable discontinuities which are unphysical and incompatible with the principle \"Natura non facit saltus\", so that a smoothing could be required.  
Both the methods were tested on the data to validate the two IAS estimates.
## 3 Phase Modulation MEMS
This added capability allows for higher diffraction efficiency and the selection of the diffraction angle, which was not possible with the binary light valve. We were able to fabricate a one-dimensional (1-D) phase modulator array with a pitch of only 2.4 \(\upmu\)m [26]. This array is capable of steering light over 38\({}^{\circ}\) at 1550 nm wavelength, which is 10 times larger than with the square pixels configuration presented in Figure 8. The mirrors of the array are tethered to the anchors through a pair of springs. Each mirror is 2.1 \(\upmu\)m wide and 30 \(\upmu\)m long, and the pitch is 2.4 \(\upmu\)m, resulting in a 87.5% fill-factor. The mirrors and the top combs are grounded while the bottom combs are individually addressed to produce the desired phase profiles such as a highly efficient blazed grating (see Figure 5). The measured resonant frequency of this MEMS is 310 kHz, which corresponds to a switching time of a few microseconds. Compared with LCoS and binary MEMS devices such as the DLP, this new MEMS benefits from a fast response time (microseconds), a large field of view (tens of degrees), and low loss (\(-1.4\) dB, including fill factor and reflection losses). It is also wavelength-, polarization- and modulation-agnostic. All these properties make the piston mode MEMS array an attractive choice for optical switch applications.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 4 Methods
### Filtering Algorithms Used for GNSS/IMU Gyrocompass Measurements
\begin{array}{l}V_{E}^{ext}=V_{L}\sin K,\quad V_{N}^{ext}=V_{L}\cos K,\\ \overline{z}_{V_{E}}=\Delta V_{E}-V_{N}\alpha+V_{TE}-\
u_{V_{E}},\\ \overline{z}_{V_{N}^{IN}}=\Delta V_{N}+V_{E}\alpha+V_{TN}-\
u_{V_{N}},\end{array}  \tag{21}\]where \(V_{TE},V_{TN}\) are the Eastern and Northern components of drift or current velocity, respectively; and \(v_{VI}(i=E,N)\) are the measurement errors including instrumental errors approximated by discrete white noises. If we use two-component sensors of ground velocity \(V_{X_{b}},V_{Y_{b}}\) (such as Doppler speed logs or acoustic logs), velocity measurements take the form  
\[ \begin{array}{l}(\mathbf{V_{n}^{c}})^{ext}=C_{n,b}\cdot\big{[}V_{X_{b}},V_{Y _{b}},0\big{]}^{T},\\ \overline{z}_{V_{E}}=\Delta V_{E}-V_{N}\alpha-\
u_{V_{E}},\\ \overline{z}_{V_{N}^{L}}=\Delta V_{N}+V_{E}\alpha-\
u_{V_{N}}.\end{array}  \tag{22}\]  
The measurements in altitude (depth) are generated as  
\[z_{h}=h^{INS}-h^{ext}\approx\Delta h+\
u_{h}, \tag{23}\]  
where \(\
u_{h}\) is the error of external altitude (depth) sensor. Estimates of state vector components \(\hat{x}_{INS}\), \(\hat{x}_{IMU}\) are applied to update INS output, and to update gyro and accelerometer data with no satellite signals (\"Closed loop correction\" in Figure 4).
# An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming
###### Abstract
Moreover, these methods typically struggle to balance repair efficiency and patch quality when addressing different types of software defects. On one hand, the high computational complexity and multiple iterations inherent in search-based methods often lead to lengthy repair processes; on the other hand, code similarity methods are susceptible to differences in code style and semantics when searching for matching code snippets, resulting in slower search processes and lower repair success rates. Addressing the issues mentioned above, this paper proposes a defect automatic repair method called MGPRepair based on multi-objective genetic programming. Firstly, the method employs a lightweight context analysis strategy to filter out unpromising repair materials. Secondly, a lower-granularity patch representation is used to encode patches in the search space, and classification rules are applied to decouple replacement and insertion statements in repair materials. This method describes automatic defect repair as a multi-objective search problem and uses the multi-objective optimization algorithm NSGA-II [19] to find simpler repairs. Then, to reduce computational complexity and search space, a test filtering and patch overfitting detection process is introduced, which can accelerate the fitness evaluation of genetic programming and reduce patch verification time. Finally, tests were conducted on 395 defects in six projects from Defects4J [20]. The experimental results show that MGPRepair can generate patches that pass test cases for 51 defects, with 35 of them equivalent to manually generated patches. Compared to other similar methods, MGPRepair shows significant performance improvements. The contributions of this paper are as follows:  
1. To address the issue of low-quality repair material selection, a lightweight context analysis strategy is employed to identify suitable repair materials. The decoupling of replacement and insertion statements within the repair materials is implemented to enhance the accuracy of defect matching. 2. Targeting the problem of inefficient search in the search space caused by traditional patch encoding methods, a lower-granularity patch representation is proposed to encode patches in the search space. 3. To solve the issue of incomplete offspring patch selection resulting from single-objective search. An improved fitness function utilizing the multi-objective optimization algorithm NSGA-II [19] is proposed to select offspring populations. Thus, it addresses the offspring selection problem.
## 2 Materials and Methods
### Data Processing and Analysis  
Data processing and analysis used custom-written algorithms in MATLAB (Mathworks, 2021). All data were filtered using a 4 th-order low-pass Butterworth filter with a cut-off of 1 Hz. This cut-off frequency was chosen as it provides a balance between smoothing the data and preventing aliasing, with no influence on levels of agreement between OEM and accelerometer-derived angles [10]. Tangents from the OEM data were used to compute the resultant angle between the two 'limbs' of the jig and served as the gold standard reference. Limits of agreement (95%) were calculated between the accelerometer-derived angles and OEM angles. Only accelerometer data were used from the IMU sensors due to the known challenges of metallic environments and gyroscopic drift [15].  
Figure 2: The Movella Dot inertial measurement unit with sensor axes alignment.  
Data Analysis and Statistics: Step 1 The effect of initial sensor orientation on the error of accelerometer-derived angles.  
To achieve the aim outlined in step 1, angles were derived from the accelerometer using the 4-quadrant arc tangent function (ATAN2), with the absolute sagittal movement of each sensor (flexion extension (rotation about Y)) calculated from acceleration X and Z, and the absolute frontal movement from each sensor (lateral bending (rotation about Z)) calculated from acceleration X and Y. The resultant angle was calculated through angle subtraction between the two absolute sensor angles.  
The effect of the initial orientation on the error of the angle computation was explored through direct comparison to simultaneously captured OEM data. Peak range of motion (ROM) estimates (40\({}^{\circ}\), 80\({}^{\circ}\), and 120\({}^{\circ}\) forward bending and 40\({}^{\circ}\) and 80\({}^{\circ}\) lateral bending) were compared, with root mean square errors (RMSE) and 95% confidence intervals (CI95%) being calculated.  
Error estimates for different initial orientations were plotted and fitted with a linear regression function to provide estimates of error based on the initial orientation. Data were also explored for reliability though intraclass correlation coefficients (ICC 2,1) for peak ROM.  
Accelerometer-derived angles and OEM angles were analyzed through the whole ROM to explore the effect of movement velocity on the RMSE between the data. OEM data were differentiated to yield angular velocity, and data where movement was'static' (<1\({}^{\circ}\) s\({}^{-1}\)) were removed from the analysis. Therefore, RMSE for angles between the accelerometer and OEM were established across a range of velocities up to 148\({}^{\circ}\) s\({}^{-1}\), and velocities were sub-classified into slow (average 49\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 11\({}^{\circ}\) s\({}^{-1}\)) and fast (average 83\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 24\({}^{\circ}\) s\({}^{-1}\)) to facilitate predictive equation fitting.