esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Quality Assessment of COSMIC-2 GPS Observations  
In this study, the GPS observation data of the COSMIC-2 satellite rear antenna (\(-\)X (rear) POD antenna) provided by the Central Weather Bureau, Taiwan ([[https://tacc.cwb.gov.tw/](https://tacc.cwb.gov.tw/)]([https://tacc.cwb.gov.tw/](https://tacc.cwb.gov.tw/))) (accessed on 12 March 2021) are used. The time length of observation data is five days from 18 to 22 May 2021 (day of the year (DOY) 138-142), and the GPS precise ephemeris and GPS satellite clock error files applied are downloaded from CODE ([[http://ftp.aiub.unibe.ch/CODE/](http://ftp.aiub.unibe.ch/CODE/)]([http://ftp.aiub.unibe.ch/CODE/](http://ftp.aiub.unibe.ch/CODE/))) (accessed on 12 March 2021) with 15 min and 30 s sampling interval, respectively. The structure of COSMIC-2 is shown in Figure 1[1].  
LEO satellites move at high speed in space and are susceptible to the influence of the complex surrounding environment, and the quality of observation data is the key to the accuracy of satellite orbit determination. Therefore, it is essential to analyze the quality of observation data. The main factors affecting the quality of observation data include the visibility of GNSS satellites, the utilization of GPS observations, cycle slip ratio, SNR (SNR1, SNR2), multipath effect error (_MP1_, _MP2_) and _IOD_[20; 21; 22]. SNR1 and SNR2, and _MP1_ and _MP2_ are SNR and multipath errors on L1 and L2 frequencies respectively. The ratio of the total number of observations to the number of observations with cycle slip is called the cycle slip ratio, which reflects the situation of data cycle slips. The larger the ratio is, the fewer slips occur, and vice versa. The integrity of data reflects the performance of the space-borne GPS receiver and the observation environment [23]. In order to assess the quality of COSMIC-2 space-borne GPS data, the above indexes are studied in detail by the public software TEQC [24]. There are a large number of missing data for the two days of DOY140 and DOY141 for Sat-1, so the quality assessment of the data of Sat-1 on these two days will not be carried out.
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 5 Experiments  
We empirically evaluate our methods in both simulations and on three real-world datasets: E2006-tfidf, RCV1 and MNIST (Table 2, which are downloaded from the LibSVM website ([[https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/)]([https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/)), accessed on 1 July 2022)), and compare them against a baseline method. The baseline method is a standard Distributed IHT and communicates every local update to the central server, which then aggregates and broadcasts back to clients (see Appendix A.1 for more details). Specifically, experiments for simulation I and on the E2006-tfidf dataset are conducted for sparse linear regression. We solve the sparse logistic regression problem in simulation II and for the RCV1 data set. The last experiment uses MNIST data in a multi-class softmax regression problem. The exact loss functions for the various problems are available in the Appendix A.2.  
Following the convention in the federated learning literature, we use the number of communication rounds to measure the communication cost. For a comprehensive comparison, we also include the number of iterations. For both synthetic and real-world datasets, algorithm parameters are determined by the following criteria. The number of local iterations \(K\) is searched from \(\{3,5,8,10\}\). We have tested the performance of our proposed algorithms under different \(K\) conditions (see Figure 1). The stepsize \(\gamma\) for each algorithm is set by a grid search from \(\{10,1,0.6,0.3,0.1,0.06,0.03,0.01,0.001\}\). All the algorithms are initialized with \(x^{(0)}=0\). The sparsity \(\tau\) is 500 for the MNIST dataset and 200 for the other two datasets.
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 4 Phenology of Grasslands
The use of information from multiple sensors was also advocated by [PERSON] et al. (2019) [PERSON] et al. (2019), who used Sentinel-1 and -2 and Landsat-8 data to predict the biomass of tall grazing grasses with high accuracy (correlation of 0.76 with in situ data). Their study reported an improvement of over 50% in the prediction accuracy of LAI and above-ground biomass (AGB) from a multiple-sensor approach as compared to Sentinel-1 alone. The phenology (i.e., timing of SOS, peak and EOS) estimated from the combined Landsat-8 and Sentinel-2 data could also explain around 92% of the variability observed in gross primary productivity (GPP) estimates from flux towers. [PERSON] et al. (2018) [PERSON] et al. (2018) used Landsat-8 and Sentinel-2-based vegetation indices (VIs) and bands to assess the combination that could best describe grassland phenology and GPP from annual peak to senescence. This study revealed that, although many of the bands in the two sensors are similar, Sentinel-2 performed better (with an improvement of over 10% for predicting GPP) than Landsat-8 in VI-based models due to the presence of additional red-edge bands. Additionally, information from the red-edge and SWIR was found to be critical for improved GPP estimates.
## 3 Early-Warning Systems
### Weather Radar (WR)  
Radar technology is well-established and can nowadays provide fast 3D volume scanning together with Doppler and dual-polarization capabilities [35; 36]. The detection sensitivity of the WR depends on several factors, such as the distance between the radar antenna and the target, the transmitter central wavelength, the receiver minimum detectable power, and the resolution volume [20; 23].  
In this work, we used the observables of the X-band WR, working at 9.6 GHz, permanently positioned at the Catania airport (Fontanaroossa), approximately 32 km from Etna. This active sensor allows to probe the surrounding scene as a function of range (~160 km), azimuth (360\({}^{\circ}\)), and antenna-pointing elevation, with respect to the horizon covering a height of 20 km. The whole volume acquisition time was 10 min, with a radial resolution of 200 m [37].  
Applying the VARR algorithm to radar data, we estimated several ESPs. In particular, we determined the mass eruption rate (\(Q_{M}\)) based on an evaluation of the space-time variation of the pyroclastic concentration in the whole volume above the eruptive crater scanned by the WR [22; 23; 24; 25; 18]. It is worth noting that the WR extension to the automatic detection of the volcanic plume within an EW system for volcanic tephra hazard forecasting remains an open question [14].  
The eruption source parameters associated with the weather radar are named as WR-ESPsR (Figure 4). We used a similar scheme as for the two previous systems, but with thresholds consistently adapted to operate on the \(Q_{M}\) retrievals. The alert \(Q_{M}\)-_low_ was delivered when \(Q_{M}\) was between \(10^{5}\) and \(10^{6}\) kg/s for more than 10 min (orange in Figure 4), whereas \(Q_{M}\)-_high_ was delivered when \(Q_{M}\) > \(10^{6}\) kg/s for more than 10 min.  
Figure 3: TC-EW flowchart showing the relation between the exit velocity, \(v_{ex}\) (m/s), of pyroclastic material, as derived by TC, and the corresponding alert levels (EW0 in yellow, EW1 in orange, and EW2 in red) (modified from [6]).
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Dielectric Constant
#### 2.3.1 Transient Electromagnetic Method  
The transient electromagnetic method [50] (TEM) is a time-domain transient electromagnetic method, which is based on electrical differences. An ungrounded return or grounded wire source is used to send a pulsed electromagnetic field to the ground. Under the excitation of the original magnetic field, eddy currents are generated in the ground, the magnitude of which depends on the electrical conductivity of the stratum material. After the disappearance of the original magnetic field, the eddy current does not disappear immediately. It undergoes an attenuation process, resulting in the propagation of an attenuated secondary magnetic field to the surface. By receiving and analyzing the induced secondary electromagnetic field, the water content in the medium can be inferred. Figure 8 shows the basic working principle of the geophysical detection method based on electromagnetic induction. The advantages of the TEM are ease of processing, adaptability to surface sites, and high efficiency [51]. In terms of its application, [PERSON] et al. [52] applied the TEM to the detection of underground metal objects and proved its feasibility through experiments. [PERSON] et al. [53] proposed the trend in the transient electromagnetic method in the ocean direction. [PERSON] [54] used model simulation to calculate the response signal of mine transient electromagnetic waves under different conditions and applied the transient electromagnetic interference correction method to analyze and correct the information with interference. The reliability and effectiveness of the method were proven by comparing the data processing and field verification results. Due to its own limitations, the TEM has a blind detection zone in shallow layers, because of which shallow leakage paths cannot be detected, and the detection resolution of the method is low. Therefore, it can only be used for approximate detection in a large area, mainly for finding low-resistance targets and studying geoelectric structures at shallow-to-medium depths. Figure 8 shows the principle of the TEM.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 1 Introduction
### Previous Work on Fully Connected Architectures  
Some fully connected hybrid beamforming architectures for narrowband single-user systems were discussed in [14; 15; 16]. The work presented in [14] considered a transmit precoding and a receiver combining scheme for mmW mMIMO. In this work the spatial structure of the mmW channels was exploited to design the precoding/combining schemes as a sparse reconstruction problem. In [15] an iterative turbo-like algorithm was proposed that found the near-optimal pair of analog precoder/combiner. A matrix decomposition method that could convert any existing precoder/combining design for the full digital scheme into an analog-digital precoder/combining for the hybrid architecture was addressed in [16]. Approaches for narrowband multi-user systems were also considered in [17; 18; 19; 20; 21; 22]. A limited feedback hybrid analog-digital precoding/combining scheme for multi-user systems was addressed in [17]. A heuristic hybrid BF was addressed in [18], where the proposed design could achieve a performance close to the fully digital BF with low-resolution phase shifters. A hybrid analog-digital precoding/combining multi-user system based on the mean-squared error (MSE) was proposed in [19]. The authors of [20] designed an iterative hybrid analog-digital equalizer that efficiently removed the multi-user interferences. In [21], an iterative precoder and combiner design was proposed by exploiting the duality of the uplink and downlink multi-user MIMO channels. In [22], a hybrid beamforming system based on a dual polarized array antenna was proposed for single user systems. The hybrid architecture for either single or multi-user the mmW mMIMO wideband systems was considered in [23; 24; 25]. Precoding solutions with codebook design for limited feedback spatial multiplexing in single user wideband mmW were developed in [23]. For the multi-user case, statistical MIMO orthogonal frequency division multiplexing (OFDM) beamformers without instantaneous channel information were designed in [24], where the beams were formed using the dominant eigenvectors to select the main directions. In [25], a downlink MIMO-OFDM hybrid multi-user precoder based on the vector quantization concept was proposed, where the total transmit power was minimized.
## 2 Experiment and Methods  
### Experiment Setup  
The schematic diagram of the experimental setup is presented in Figure 2. It consists of a gas feeding system, an NTP oxidation system, a discharge power measurement system, a wet scrubbing system, and a fuel gas analyzer.  
In this setup, the marine diesel exhaust was simulated using a gas mixture of 10% O\({}_{2}\), 7% CO\({}_{2}\), 6.6% H\({}_{2}\)O, 200 ppm C\({}_{3}\)H\({}_{6}\), 820 ppm NO, 320 ppm SO\({}_{2}\), and N\({}_{2}\) (balance). The simulated flue gas temperature at the DBD reactor inlet was 115 \({}^{\circ}\)C.  
The NTP oxidation system consisted of a coaxial cylindrical DBD reactor and an AC high-voltage power supply. A stainless steel rod with a diameter of 14 mm was used as the inner electrode of DBD reactor. A quartz tube with an outer diameter of 18 mm, an inner diameter of 15 mm, and a length of 450 mm was used as the dielectric layer. The quartz tube was wrapped with copper foil as the outer electrode with a length of 100 mm. The CTP-2000K system (Nanjing is plasma Co., Ltd., Nanjing, China) was used as a power supply for the NTP. The discharge frequency was fixed at 10 kHz and the discharge power in DBD reactor was controlled by adjusting discharge voltage.  
The packed scrubber was placed after the DBD reactor with an inner diameter of 25 mm and a height of 150 cm. The packing equipment used in the experiment included Cannon rings, Pall rings, and Dixon rings, as shown in Figure 3. The concentrations of NO, NO\({}_{2}\), O\({}_{2}\), CO\({}_{2}\), and SO\({}_{2}\) were measured using a flue gas analyzer (TESTO Pro.350), with the information from TESTO Pro.350 shown in Table 1.  
Figure 2: The schematic diagram of the experimental setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** & **Range** & **Accuracy** & **Resolution** & **Response Time** \\ \hline \multirow{3}{*}{NO} & \multirow{3}{*}{0–2000 ppm} & 5 ppm (0–99 ppm) & \multirow{3}{*}{1 ppm} & 30 s \\  & & \(\pm\)5\% of the measured value (100–2000 ppm) & & \\  & & 5 ppm (0–99 ppm) & & \\ \cline{1-1} \cline{3-4} NO\({}_{2}\) & 0–500 ppm & \(\pm\)5\% of the measured value (100–2000 ppm) & & \\ \cline{1-1} \cline{3-4} SO\({}_{2}\) & 0–2000 ppm & \(\pm\)5\% of the measured value & 1 ppm & 30 s \\ \cline{1-1} \cline{3-4} O\({}_{2}\) & 0–25 Vol\% & \(\pm\)0.2 Vol\% & 0.01\% & 20 s \\ \cline{1-1} \cline{3-4} CO\({}_{2}\) & 0–25 Vol\% & \(\pm\)0.3 Vol\% + 1\% of the measured value & 0.01\% & 20 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information obtained from TESTO Pro.350.  
Figure 3: The packing equipment used in the experiment. (**a**) Cannon rings; (**b**) Pall rings; (**c**) Dixon rings.
## 4 Experiments
### Experimental Results  
The experimental results of each model in the dataset DA are shown in Table 1. In this dataset, the four indicators of hit, F0.5-score, and NDCG predicted using the model proposed in this paper are 0.020, 0.083, and 0.076 higher than those of the baselines. This demonstrates the effectiveness of PreferSAGE-SCF in recommending BI's visualization analysis charts.  
The experimental results of each model in the dataset ml-1m are shown in Table 2. In this dataset, the four indicators of hit, F0.5-score, and NDCG predicted using the model proposed in this paper were higher than those of the baseline by 0.045, 0.019, and 0.023, which further proves the effectiveness of the PreferSAGE-SCF model in recommending high-scoring items, especially when the recommended result set is small.  
By observing the results of the above two sets of experiments, we can draw the following conclusion. The performance of UserCF and itemCF is not very stable, and their results are greatly influenced by the amount and distribution of data. Meanwhile, the content of work is often not determined by personal preferences, which affects the performance of ItemCF. The performance of GraphSAGE is far superior to non-neural-network-based collaborative filtering algorithms, but due to the indiscriminate feature propagation and aggregation of nodes, the final results are slightly worse. GAT has added an attention mechanism to GraphSAGE, thereby improving various indicators of the final result. ImprovedSAGEGraph not only utilizes node information but also uses user clustering information, resulting in better performance on two datasets. However, it is greatly affected by data volume and distribution and involves a large number of hyperparameters. This has resulted in the difficulty of making appropriate adjustments to these parameters. PinSAGE utilizes the spatial importance of nodes for sampling, which is essentially a special attention mechanism for graph space. The PreferSAGE-SCF model proposed in this paper reinforces this point of PinSAGE, significantly bringing the inner product between nodes of these high-rated items closer, while taking into account the knowledge of neighboring users, thus achieving better performance compared to other models.  
However, there are still areas for improvement in the model proposed in this paper. The model directly divides titles based on words, lacking understanding of the overall meaning of titles. In addition, the critical point position of SCF still need to be manually adjusted, so automatic determination of this position is a problem that needs to be considered. These issues can be further studied and improved in the future.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 1 Introduction
### Applications  
1. Secure Autonomous Cars: Recently, many car manufacturing companies are competing in the market to provide self-driving/autonomous cars and vehicle platooning. The connectivity within cars for exchanging information is authenticated for secure communication.
2. Secure Drones: A drone network is usually required for smart farming; package delivery for food, products, medical vaccines at remote or higher altitude places; tracking lost people on mountains; synched drones for entertainment; etc.
3. Secure Satellite Communication: The satellite network for providing region/countrywide internet access needs to be synchronized and interconnected. New satellites can join and later can reconnect using an autonomous mutual authentication protocol. Thus, dependency on third parties is reduced.
4. Secure IoT and Device Communication: All the end devices within a network can connect to each other securely by authentication, i.e., IoT devices, laptop, computer, tablet, etc. The communication between these authenticated devices is secured by cryptography and has a dynamic session key instead of using a master secret key.  
The description of this work is organized herein as follows: presentation of literature survey in Section 2; protocol phases as methodology in Section 3; and theorems with proofs in Section 4. Afterward, the protocol verification logic is given in Section 5 and protocol defense in Section 6. Lastly, the experiments are given in Section 7 followed by the conclusions.
# Investigation of the Two-Way Injection Slurry-Supply Method for the Cu CMP Process
## 2 Materials and Methods
### CMP Evaluation  
The Cu coupon wafers (4 \(\times\) 4 cm\({}^{2}\)) were polished for 1 min using a polisher (Poli-400, GnP technology, Busan-si, Republic of Korea) with a polyurethane pad (HD-300, SKC, Seoul, Republic of Korea) and pellet-type conditioner (20 PPW60 EBC0, Saesoldiamond, Ansan-si, Republic of Korea). Three wafers were polished for each condition, and the thickness of Cu was measured using a four-point probe (CMT-SR5000, AIT Co., Ltd., Seoul, Republic of Korea), which is called the sheet resistance measurement. MRRs were calculated by comparing the thickness of the Cu wafers before and after polishing. The slurry-supply method had the same processing conditions except for the flow rate. Down pressure and rotational speed (platen/head) were 3 psi and 93/87 rpm, respectively. The flow rate was 150 mL/min, and in the two-way injection method, the flow rates injected from two nozzles (slurry/H\({}_{2}\)O\({}_{2}\)) were divided as follows: 100/50, 75/75, and 50/100 mL/min.
## 4 Development of Proposed Method
### Proposed Method
#### 4.2.5 Signal Reconstruction
Therefore, the reconstructed signal is expected to be recovered with a small error when \(\text{NMSE}\rightarrow-\infty\) and MI is as large as possible. The experiments have been performed for input SNR levels of 0 dB, 10 dB, and 20 dB, and 20 random realizations have been considered to compute the averaged results. Additionally, the mean, median, and standard deviation (SD) were calculated to assess the average performance, the distribution's central tendency, and consistency of the denoising methods across different conditions, with these statistics being directly linked to the overall denoising effect. For the clarity of presentation from the viewpoint of performance measure and a class of denoising methods, the results are tabulated in Tables 3-6, where bold is used to highlight the best result and underline signifies the runner-up. Figure 4:5 Denoised signal (in black) for various VMD-based methods along with the original signal (in red): (**a**) VMD-DFA [18], (**b**) VMD-DFA-DWT [19], and (**c**) the proposed method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SNR** & & **EMD-SC [10]** & **EMD-MI [14]** & \begin{tabular}{c} **EMD-** \\ **DFA [11]** \\ \end{tabular}  & \ \begin{tabular}{c} **EMD-** \\ **SWT3 [13]** \\ \end{tabular}  & \begin{tabular}{c} **Previous** \\ **Method [21]** \\ \end{tabular}
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### ETC Gantry Data Completion Method Based on Improved Tensor Decomposition  
This study introduces an Improved Dynamic Tensor Decomposition (IDTD) model, specifically designed to extract high-dimensional and dynamic traffic features with enhanced accuracy and efficiency. To counter the sparsity issue prevalent in high-dimensional data, a Laplacian matrix was employed to regulate parameter sparsity. Furthermore, approximate calculations were conducted to ascertain the tensor core and factor matrices, significantly reducing computational overhead.