esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Smartphone Global Navigation Satellite System (GNSS) Data Collection and Analysis
### Data Quality Analysis
#### 2.2.2 Dual-Frequency Cycle Slip Analysis  
Cycle slip is an important indicator of carrier-phase quality. Figure 4 shows the time sequence diagram of satellite visibility and its cycle slip for the four constellations, and Figure 5 shows the statistical results of cycle slips. Each cycle slip is marked with a red plus sign, and it is mainly determined by the loss-of-lock indicator, which may also reflect carrier-phase interruption.  
Figure 4: Satellite visibility and cycle slips (red plus signs) of four constellations on two frequency bands: (**a**) global positioning system (GPS) L1/GLONASS G1/Galileo E1/BDS B1; (**b**) GPS L5/Galileo E5a. (PRN, pseudorandom noise).  
Figure 3: C/N0 of different types of observations. (**a**) C/N0 sequence. (**b**) Statistical results of mean C/N0.  
Figure 4 shows several satellites observed during the entire period, with reception from 34 satellites overall, including 11 GPS, 9 GLONASS, 5 BDS, and 9 Galileo satellites. In addition, seven GPS satellites deliver L5 signals, and five Galileo satellites deliver E5a signals. The cycle slips or interrupts are frequent, especially for L5/E5a signals. In terms of statistical results, for the first frequency band (L1/G1/B1/E1), the cycle slips of BDS are comparatively more frequent, while almost no slip occurs for the Galileo satellites. However, the cycle slips of GPS L5 and Galileo E5a signals are severe, accounting for more than 5%, which is much higher than the rate for L1/E1 signals. These results show that band L5/E5a is more prone to cycle slip. In addition, the number of satellites delivering signals in this frequency band is low, further reducing the availability of dual-frequency observations in smartphones.  
From the statistical results of cycle slips for different C/N0 ranges (Figure 5b), 90.6% of the cycle slips in band L1 are mainly concentrated in the range below 35 dB-Hz, and 99.0% of the cycle slips in band L5 are concentrated in the range below 30 dB-Hz. Overall, the cycle slips are highly severe when C/N0 is lower than 30 dB-Hz. Therefore, the C/N0 threshold should be set to prevent low-quality observations from participating in data processing or ambiguity resolution.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 5 Results and Discussion  
This section reports three sets of experiments derived from the scheme shown in Figure 3, which examine the impact of the introduced DL architectures and the post-processing phase on the overall performance of the proposed methodology to accurately classify pixels from satellite images in a sliding window fashion.
## 4 DRL for Resource Management
```
1:Input: double DQN structure, vehicular environment simulator and V2V pair delay requirements
2:Output: double DQN networks' weights
3:Initialize: experience replay buffer, the weights of train DQN \(\theta^{\text{train}}\) and target DQN \(\theta^{\text{target}}\)
4:for each episode \(j=1,2,\dots\)do
5: Start the V2X environment simulator for each episode
6: Reset \(L_{n,t}=L\) and \(T_{n,t}=T_{\text{max}}\), for all n \(\in N\)
7:for each iteration step \(t=1,2,\dots\)do
8: Each V2V observes the observation \(\mathbf{o}_{n,t}\) sends it to BS
9: BS based on the current state \(\mathbf{s}_{t}=\{\ \mathbf{o}_{1,t},\dots\ \mathbf{o}_{n,t},\dots\ \}\), select the action according to the \(\epsilon\)-greedy, then gets a reward \(r_{t+1}\), transforms to new state \(\mathbf{s}_{t+1}\)
10: Store transition (\(\mathbf{s}_{t}\), \(\mathbf{a}_{t}\), \(r_{t+1}\), \(\mathbf{s}_{t+1}\)) into experience replay buffer
11: Sample a mini-batch of \(D\) transition samples from experience replay buffer
12: Calculate the target value according to Equation (19)
13: Update \(\theta^{\text{train}}\) according to Equation (20)
14: Update \(\theta^{\text{target}}\) by setting \(\theta^{\text{target}}=\theta^{\text{train}}\) every \(K\) steps
15: end for
16:end for
```  
**Algorithm 1****Training Process for the Proposed Scheme**
## 3 Results
### Independent Samples T test  
In Table 5, the independent samples T test for the U14 age group compares the performance of the experimental (GE) and control (GC) groups in both initial (It) and final (Ft) phases across various agility tests. For the T agility test at the initial phase, [PERSON]'s test shows no significant difference in variances between groups (F = 0.68; \(p\) = 0.42), allowing for a standard \(t\)-test. The \(t\)-test indicates a significant mean difference of \(-0.97\) s (t = \(-2.64\); \(p\) = 0.01) favoring the experimental group. The final phase of the T agility test shows a larger mean difference of \(-2.12\) s (t = \(-7.67\); \(p\) < 0.001), suggesting more pronounced improvements in the experimental group. In the T agility test with ball, both initial and final phases show significant mean differences between the groups. The initial mean difference is \(-1.48\) s (t = \(-2.15\); \(p\) = 0.04), while the final mean difference is \(-4.48\) s (t = \(-8.56\); \(p\) < 0.001), again indicating a larger impact of the training in the experimental group. For the T reactive agility test, the initial phase shows a mean difference of \(-0.89\) s (t = \(-1.99\); \(p\) = 0.06), and the final phase a mean difference of \(-2.61\) s (t = \(-8.39\); \(p\) < 0.001). The T reactive agility test with a ball has initial and final mean differences of \(-1.88\) s (t = \(-2.51\); \(p\) = 0.02) and \(-5.26\) s (t = \(-7.87\); \(p\) < 0.001), respectively. The Illinois agility test also shows significant differences. The initial mean difference is \(-1.17\) s (t = \(-2.46\); \(p\) = 0.02), and the final difference is \(-2.65\) s (t = \(-8.41\); \(p\) < 0.001). In the Illinois agility test with a ball, the final phase shows a substantial mean difference of \(-3.62\) s (t = \(-9.44\); \(p\) < 0.001), while the initial phase difference is not statistically significant. These results indicate that for most agility tests, the U14 experimental group significantly outperformed the control group, both in initial capabilities and improvements over time. The differences in final performances are particularly pronounced, highlighting the effectiveness of the training or interventions experienced by the experimental group.
# A Review: Using Multiple Templates for Molecular Imprinted Polymer: Is It Good?
## 2 Multi-Template Strategy
### Polymerization Method  
The polymerisation method for an MT-MIPs is the same as for an ST-MIPs. What distinguishes the two methods is the composition of each template, functional monomer, crosslinker, and solvent used. An MT-MIPs uses two or more templates, and this approach affects the selection of the other reagents [32; 33]. Table 2 presents a summary of the polymerisation methods for MT-MIPs with the advantages and disadvantages of each method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Polymerisation Method** & **Procedure** & **Advantages** & & **Disadvantages** & **Reference** \\ \hline \multirow{4}{*}{Bulk polymerisation} & Polymerisation is & & & - & Shape, particle size, & \\  & carried out using an & - & Universal & & and active sites tend & \\  & organic solvent, & - & Easy and simple & & to be heterogeneous & \\  & followed by sieving & & method & - & The binding cavity & \\  & and grinding & & & & can be damaged in & \\  & Polymerisation & - & One step & - & & the grinding process & \\  & occurs in solution; & - & One step & - & Requires large & \\  & precipitation occurs & precipitation occurs & - & Same shape and & & amounts of templates & \\  & after the polymer is & - & Same shape and & - & Requires more & \\  & formed and makes it & & particle size and & & solvent than bulk & \\  & insoluble in solution & & high yields & & polymerisation & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of polymerisation methods for multi-template molecularly imprinted polymers. Figure 2: Illustration of the multi-template molecularly imprinted polymer production. The most common and widely used polymerisation method in the manufacture of ST-MIPs and MT-MIPs is bulk polymerisation (see Tables 1, 3-6). This is because bulk polymerisation is universal, simple, and easy [35]. This method requires sieving and grinding after polymer formation because the polymer is formed in bulk [58]. This sieving and grinding tend to produce various shapes and sizes of particles. In addition, this process damages the binding cavity, thereby reducing the effectiveness of polymerisation [37]. In MT-MIP synthesis (see Tables 3-6), bulk polymerisation is compatible with most monomers, namely MAA, AM, 3-aminopropyltriethoxysilane (APTES), 2-vinylpyridine (2-VP), and 4-VP. Bulk polymerisation can be used for templates such as drugs, organic compounds, and proteins. [PERSON] et al. [59] reported very good absorption capacity and imprinting factor (IF) values. This shows that MT-MIP has good quality even though it has gone through sieving and grinding [59]. Precipitation polymerisation is also widely used for MT-MIP synthesis because it is a one-step preparation, produces uniform shapes and sizes, and has a high yield [60].
## 4 Application Examples
### Peaks in the Variation of the Earth's Length of Day due to Lunar Tidal Forcing  
Since the Earth's rotation rate is not a constant, each day has a different duration (length of day, LOD) when comparing the astronomically determined duration of a day on Earth with the duration of a day according to the SI units definition (1 day = 86,400 s) measured using atomic clocks [53]. The variations in LOD are highly non-stationary and comprise different oscillations. One of the strongest oscillations present in the LOD time series has the average period of 13.6 days. This oscillation is the result of a lunar tidal force that the Moon exerts on the Earth's rotation due to changes in the lunar declination during the lunar cycle [54].  
The proposed AMPD algorithm was tested to establish its ability to detect the peaks of this lunar-related oscillation in the unfiltered (raw) LOD data. To this end, (i) daily averaged LOD data were obtained from the International Earth Rotation and Reference Service (IERS) [55]; and (ii) the peaks of the LOD time series from 2008 to 2011 (1461 days) were detected. The results are given in Figure 9. In total 105 peaks were detected. One peak was not detected (one false negative error) but no false detection occurred (no false positive errors). As shown in Figure 9, the missed peak has low amplitude and is superimposed with a rapidly decreasing part of a slower oscillation (low frequency noise) which makes the detection difficult. The missed peak detection could be prevented by high-pass filtering the signal prior to analysis with the AMPD algorithm.  
Figure 8: Results of applying the AMPD algorithm to an ECG time series.  
The average duration of the intervals between the peaks was \(13.7885\pm 2.1974\) days, which corresponds well with the value reported in literature [54; 56].
# Weighted Constraint Iterative Algorithm for Phase Hologram Generation
## 4 Conclusions  
In conclusion, we propose a weighted constraint iterative algorithm for generating phase holograms with quality reconstruction. Different constraint strategies are addressed in the corresponding areas during the iteration process. The signal region is constrained directly based on an adaptive strategy, whereas the other part of the image plane is constrained indirectly by total energy control in the hologram plane. The proposed algorithm can improve reconstruction quality by broadening the search space for optimizing the phase distribution. Our method can provide quality reconstructions of phase holograms while maintaining overall and effective constraint in the image plane. Although this method is based on the Fourier transform, it can be extended to the Fresnel region and can also be used in holographic displays, beam shaping and optical trapping.  
Conceptualization, L.C. ([PERSON]) and H.Z.; methodology, L.C. ([PERSON]), H.Z., Z.H. and X.W.; software, L.C. ([PERSON]); validation, H.Z., L.C. ([PERSON]) and G.J.; writing--original draft preparation, L.C. ([PERSON]); writing--review and editing, H.Z., L.C. ([PERSON]) and G.J.; funding acquisition, H.Z. and L.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (NSFC) (61875105); National Key R&D Program of China (2017 YFF0106400).  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Cloning, Expression, and Purification of PelA\({}_{h}\) and PslG\({}_{h}\)  
PelA\({}_{\rm h}\) was purified as previously described [26]. To obtain the target gene sequence that encodes the protein construct fragment of _pslG_ (GenBank ID AAG05625.1) from the genomic DNA of _P. aeruginosa_, PAO-1 and pET28a were used as a vector template and amplified separately through polymerase chain reactions using Phusion(r) High-Fidelity DNA Polymerase (New England Biolabs). For _pslG_ gene amplification, primers were designed as follows: forward (5'-TGTTTAACTTTAAAGAGGAGATATACATGGAGATCCA GGTACTGAAGGC-3') and reverse (5'-AGCCGATTCAGGTGGGTGGGTGTGTGTGTGTGTGTGTGTGTGTGTCCCCAGACCAGATCTGCA-3'). For pET28a, primers were designed as follows: forward (5'-TGAGATCCGGCTGCTAACAAGC-3') and reverse (5'-CATATGGTCCGCCGGGGG-3'). Construct was prepared according to the FastCloning and SLiCE methods [26; 32]. The expression and purification of recombinant PslG\({}_{\rm h}\) was performed according to previous work [26; 32]. To check the purity of the obtained protein, SDS-PAGE analysis was performed. The protein concentration was measured via UV absorbance at 280 nm using a Biotek Synergy HT plate reader (BioTek, Santa Clara, CA, USA) using a molar extinction coefficient equal to 44,920 M\({}^{-1}\) cm\({}^{-1}\), as calculated according to the amino acid sequence of PelA\({}_{\rm h}\) (UniProt accession code: Q9 HZE4), and 111,840 M\({}^{-1}\) cm\({}^{-1}\), calculated according to the amino acid sequence of PslG\({}_{\rm h}\) (UniProt accession code: Q9H1N2).
# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 2 Materials and Methods
### Ugan  
The UGAN module simulates the process of disturbing a clear image, and directly generates an image containing the interference source on the clear image. The UGAN comprises a generator and a discriminator, and its structure is shown in Figure 2.  
UGAN Generator. The input of the UGAN generator comprises a clear image and an interference source from a common dataset. We use a U-net [19] model as a whole, which includes eight convolution layers and eight deconvolution layers. Each convolution layer has a LReLu layer in front of it and a batchnorm layer behind it except the first one. Similarly, except for the last deconvolution layer, each deconvolution layer has a ReLu layer in front of it and a batchnorm layer behind it. Specifically, to improve the efficiency of the network and better preserve the details of the image, we also concatenate the features at the convolution side to the deconvolution side at each layer. In this way, a mixed image the same size as the input can be generated.  
UGAN Discriminator. The purpose of the UGAN discriminator is to ensure that the mixed image generated by the UGAN generator follows the sample distribution of the real mixed image. The main architecture consists of five deconvolution layers. The loss between the ground truth and the generated image is calculated by the loss function. The discriminator determines whether the generated mixed image distribution conforms to the real image distribution, and outputs the possibility that it conforms to the real distribution.
## 4 Numerical Simulations
### Comparisons between Numerical and Experimental Result  
Figure 13 shows the experimental and numerical results of the acceleration response of the model tunnel with the isolation layer under normal fault creep-slip and subsequent z-direction seismic excitation (working cases #2-3 and #2-6) at the lining crown. It can be observed that there is a good agreement between the results of the numerical simulation and the model tests in terms of both acceleration response records and the magnitude of peak acceleration. Figure 14 plots the peak strain in the tunnel axis direction at the crown and invert of the typical section (1-1, 2-2, 3-3, 4-4) for the #2-2 and #2-6 working cases. The consistency of the strain responses in the longitudinal direction of the tunnel between the numerical simulation and the model test can also be seen in Figure 14. This indicates that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** & **Friction Angle (\({}^{\circ}\))** & **Cohesion (MPa)** \\ \hline Strongly weathered trachyte & 208 & 0.33 & 30 & 0.017 \\ Weakly weathered trachyte & 208 & 0.28 & 45 & 0.033 \\ Fault & 64 & 0.30 & 24 & 0.013 \\ Lining & 1000 & 0.2 & 55 & 0.1 \\ Isolation layer & 5–20 & 0.34 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mechanical parameters of the tunnel numerical model.  
numerical simulation can be used to reliably predict the dynamic responses of the model tunnel with an isolation layer.
# Design of XOR Photonic Gate Using Highly Nonlinear Fiber
###### Abstract  
In this paper, a comprehensive design and simulation of an all-photonic XOR logic gate is proposed. The design is based on the third-order Kerr nonlinear effect in highly nonlinear fiber, i.e., utilizing the self-phase and cross-phase modulations phenomena. This work presents the first photonic logic gate based on highly nonlinear fiber component only that achieves a data rate of 20 Gbps. Moreover, the design is based on two input binary bit sequences, narrow pulsed by a Gaussian distribution as 8-bit incoming data streams. Also, optical cross connectors with different coupling coefficients are used to generate pump and probe signals and tuneable optical band pass filters are leveraged to perform the logic gate functionalities. Remarkable performance outcomes are concluded from the eye pattern diagram and bit error rate analyzers. Simulation results show that the proposed XOR optical logic gate design is achieved at very low power penalties, low bit error rates, a significant Q-factor, and high extinction ratios as compared to existing methods.  
all-optical signal processing; photonic logic gates; highly nonlinear fiber; self-phase modulation; cross-phase modulation +
Footnote †: journal: Electronics Letters
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 4 Algorithm Performance Analysis and Comparison  
The performances of the three selected machine learning algorithms are evaluated and compared using the example filter circuit with connection faults, as described in Sections 2 and 3.1, as the common test vehicle for each algorithm. The performance of each is compared using the metrics described above in Section 3.2, and in the discussion below. The major items of concern are diagnostic accuracy, speed, and cost.
## 3 Experimental Results
### Comparison of Existing Methods and Our Method  
As seen from our experimental results, we achieved a total accuracy of 89.17%. Our experiment comprised five tempo ranges, three gestures, and seven filming angles. Our method can also be applied to a complete song with very few errors.  
Our experimentation is relatively more complete compared with similar methods of recognizing a musical gesture. Being a musical conductor means moving around and turning toward different musicians at different times, and our method allows a range of 180\({}^{\circ}\). It also allows the camera to be above or below the conductor, which is more useful in the real world. For example, if a camera is placed directly in front of the conductor, it may block some musicians and will not be aesthetically pleasing for the audience. However, if a camera is hidden behind the musicians or above the conductor, it will not block the musicians or the conductor and the audience will not be able to see it.  
We compared our method with the methods reported by three other studies that are closely related to ours. The first one is by [PERSON], [PERSON], and [PERSON], who used a depth camera to recognize some musical gestures through feature points [5]. The second one is by [PERSON], [PERSON], [PERSON], and [PERSON] [35], who used a depth camera and an inertia sensor to detect the conductor's movements and where the conductor was facing. The final method is by [PERSON], [PERSON], and [PERSON] [36], who used an inertia glove that could transmit signals to a robot to let the robot know what gesture the robot was making. Tables 8 and 9 show the comparisons of the related work with our method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Musical Piece** & \begin{tabular}{c} **Number of** \\ **Gestures Made** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Classified Data** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Misclassified Data** \\ \end{tabular} & **Accuracy** \\ \hline Canon in D & 94 & 94 & 0 & 100\% \\ Mozart Symphony No. 40 & 107 & 107 & 0 & 100\% \\ Hallelujah & 111 & 110 & 1 & 99.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of musical conducting gesture recognition in a whole musical piece.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiments** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **Our Method** \\ \hline BPM 50-60 & \(\mathbf{\check{\
u}}\) \\ BPM 121-150 & & & & \(\mathbf{\check{\
u}}\) \\ BPM 150-200 & & & & \(\mathbf{\check{\
u}}\) \\ \(45^{\circ}\) Upwards & & & & \(\mathbf{\check{\
u}}\) \\ \(90^{\circ}\) Downwards & & & & \(\mathbf{\check{\
u}}\) & & \(\mathbf{\check{\
u}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparisons of related work with our method.  
Although our method did not reach the highest accuracy when compared with the other methods, we experimented with more gestures, angles, and tempos than the method with the highest accuracy. In addition, we only used a depth camera for our experiments, which is cost-effective for the results we achieved.
# Survey for Soil Sensing with IOT and Traditional Systems
## 2 Tradicitonal Soil Sensing System
### Soil Sample Pretreatment
#### 2.2.1 Preparation of Soil Samples  
The first step in soil attribute sensing is collecting soil samples. The approach to collecting soil samples can vary. Broadly, soil samples can be categorized into two groups: those collected directly from the field and those where natural soils are combined with a target element solution. In the study by [33], soil samples (0-20 cm depth) were sourced from 10 distinct rice-paddy and cropland locations in the Aceh Besar district of Aceh Province. From each location, two samples were extracted from rice-paddy fields and two from the neighboring croplands. The research in [34] utilized soil gathered from the Horticulture Research Centre in Kamrup, Assam. They employed a grid sampling method, collecting soil from 20 cm below the surface. The samples in the study by [32] came from uncultivated and unfertilized farms. Typically, such natural soil samples are also procured from about 20 cm beneath the surface.  
In contrast to using only naturally collected soil, some studies aim for compatibility with higher concentration levels of the target attributes. For instance, in [36] mixed natural soils, sourced from Hui Zhuang Agricultural Development Company in Huainan City, Anhui Province, China, with Nitrogen solutions at concentrations ranging from 0% to 30% (increasing in 2% increments) for their experiments. The method of soil sample preparation is usually determined by the desired concentration levels for the target elements.