esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 3 BDS DCB Positioning Correction Model  
### BDS Pseudo-Range Observation Equation  
At the present stage, the BDS has disclosed the single- and dual-frequency services of four signals, i.e., B1I, B3I, B1C, and B2a. The B1I or B1C signals are recommended for single-frequency services, while the signal combinations B1I/B3I and B1C/B2a are recommended for dual-frequency services [25]. With the satellite hardware delay considered, the observation equation of the BDS pseudo-range measurement can be expressed as  
\[P_{i}=\rho+c\cdot\delta t_{r}-c\cdot\delta t^{s}+T+k_{i}I_{1}+B_{i} \tag{1}\]  
where the subscript \(i\) (=1, 2, 3, 4) indicates the signals of B1I, B2a, B3I, and B1C and \(\rho\) represents the geometric distance between satellite and receiver. \(\delta t^{s}\) and \(\delta t_{r}\) stand for the satellite and receiver clock errors, respectively. \(T\) and \(I_{1}\) are the tropospheric delay and the corresponding slant ionospheric delay on the frequency \(f_{1}\), respectively. \(k_{1i}\) equals \(f_{1}^{2}/f_{i}^{2}\) is the ionospheric multiplier factor and \(f\) is the corresponding frequency, with \(c\) representing the velocity of light in a vacuum. The multipath errors and other unmolded errors for pseudo-range observations are ignored in the above equation for simplicity. The receiver hardware delay is the same for common-view satellites with the same frequency at each epoch, and it is usually absorbed into the receiver clock or ambiguity parameters. Therefore, the receiver hardware delay is no longer considered, and \(B\) only represents the satellite hardware delay in the following sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite Type** & **Signal** & \
\begin{tabular}{c} **Navigation** \\ **Message Type** \\ \end{tabular} &
\begin{tabular}{c} **Modulation** \\ **Method** \\ **(Data/Pilot)** \\ \end{tabular} & **Symbol Rate (sps)** \\ \hline BDS-2-MEO & B1I, B3I & D1 & BPSK & 200 \\ BDS-2-IGSO & B1I, B3I & D1 & BPSK & 200 \\ BDS-2-GEO & B1I, B3I & D2 & BPSK & 200 \\ \hline B1I, B3I & D1 & BPSK & 200 \\ B1C & B1C & B-CNAV1 & BOC/QMBOC & 100/0 \\ B2a & B-CNAV2 & BPSK/BPSK & 200/0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: BDS navigation message type and signal structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **OSB** & **Signal** & **The Number of Satellites** \\ \hline BDS-2 & C2I & B1I & C01–C14\(\backslash\)C16(15) \\  & C7I & B2b & C01–C14\(\backslash\)C16(15) \\  & C6I & B3I & C01–C14\(\backslash\)C16(15) \\ \hline BDS-3 & C5I & B2a & C19–C30\(\backslash\)C32–C46(27) \\  & C7Z & B2b & C19–C30\(\backslash\)C32–C46(27) \\  & C8X & B2a & C19–C30\(\backslash\)C32–C46(27) \\  & C9P & B2a & C19–C30\(\backslash\)C32–C46(27) \\  & C7Z & B2b & C19–C30\(\backslash\)C32–C46(27) \\  & C8X & B2 & C19–C30\(\backslash\)C32–C46(27) \\  & C2I & B1I & C19–C30\(\backslash\)C32–C46(27) \\  & C6I & B3I & C19–C30\(\backslash\)C32–C46(27) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of observed values and display satellites in OSB products of the CAS.
## 3 Results
### Descriptive and Inferential Analysis of the Likert-Scale Items  
Descriptive statistics, including mean scores and standard deviations, were calculated to summarise pre-service teachers' responses to the 18 Likert-scale items (see Table 1).  
Shapiro-Wilk tests indicated significant departures from normality for all Likert-scale items (\(p<0.05\)) across all groups (A, B, and C), supporting the use of non-parametric tests for group comparisons. Accordingly, Kruskal-Wallis tests were employed (item Q4 was excluded from the internal consistency evaluation). Hence, this test identified statistically significant differences between groups for item Q7--related to the availability of technical resources (\(H=13.88\), \(p=0.001\)). Post hoc Mann-Whitney U tests with Bonferroni correction (\(\alpha=0.0167\)) revealed that Group B reported significantly lower satisfaction than both Group A (\(p=0.004\), \(Z=-2.877\)) and Group C (\(p=0.001\), \(Z=-3.241\)), while no significant difference was found between Groups A and C (\(p=0.218\), \(Z=-1.231\)). To further examine the statistically significant differences identified in the Kruskal-Wallis test for item Q7, post hoc Mann-Whitney U tests were conducted with Bonferroni correction (adjusted \(\alpha=0.0167\)). Effect sizes were calculated using the formula \(r=Z/(N)^{-1/2}\), where \(N\) is the combined sample size of the compared groups. The results revealed a small effect for the comparison between Groups C and A (\(r=0.176\)), a medium effect for Groups A and B (\(r=0.381\)), and a medium to large effect for Groups C and B (\(r=0.458\)). These findings suggest that the lower satisfaction with technical resources reported by Group B, particularly concerning Wi-Fi availability, was not only statistically significant but also practically meaningful.
###### Abstract  
Energy supply and information backhaul are critical problems for wireless sensor networks deployed in remote places with poor infrastructure. To deal with these problems, this paper proposes an airborne massive multiple-input multiple-output (MIMO) system for wireless energy transfer (WET) and information transmission. An air platform (AP) equipped with a two-dimensional rectangular antenna array is employed to broadcast energy and provide wireless access for ground sensors. By exploiting the statistical property of air-terrestrial MIMO channels, the energy and information beamformers are jointly designed to maximize the average received signal-to-interference-plus-noise ratio (SINR), which gives rise to a statistical max-SINR beamforming scheme. The scheme does not rely on the instantaneous channel state information, but still requires large numbers of RF chains at AP. To deal with this problem, a heuristic strongest-path energy and information beamforming scheme is proposed, which can be implemented in the analog-domain with low computational and hardware complexity. The analysis of the relation between the two schemes reveals that, with proper sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP antennas tends to infinity. Using the asymptotic approximation of average received SINR at AP, the system parameters, including transmit power, number of active antennas of AP and duration of WET phase, are optimized jointly to maximize the system energy efficiency. The simulation results demonstrate that the proposed schemes achieve a good tradeoff between system performance and complexity.  
Keywords:airborne massive MIMO; wireless energy transfer; energy and information beamforming; spectral and energy efficiencies +
Footnote †: journal: Sensors
# Design of a SIW Variable Phase Shifter for Beam Steering Antenna Systems
## 5 Conclusions  
A substrate integrated waveguide beam steering antenna system is presented for Ku-band applications. The system uses two reflection-type variable phase shifters, a 3-dB coupler with additional phase transition for creating sum or difference patterns, and two Vivaldi antennas. The entire structure is designed on a single substrate layer which makes it compact, low loss, and low profile. Four varactor diodes are used as electrical steering control elements. Experimental results are in good agreement with simulations, thus verifying the design approach.  
[PERSON] and [PERSON] conceived the initial designs of the phase shifters, Vivaldi antennas, coupler and 90\({}^{\circ}\) phase transition. [PERSON] designed the circuits and experiments, performed the experiments and wrote the first draft of the paper, which both authors finalized.  
This research received no external funding.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Antenna System** & **Gain [dBi]** & **Scan Angle (degree)** & **Polarization** \\ \hline
[2] & 15 & \(-\)30 to \(+\)30 & Linear \\  & & Port 1/\(-\)5.3 to \(-\)15.3 & \\
[3] & Not measured & Port 2/28.7 to 38.3 & Linear \\  & & Port 3/6.9 to 16.2 & \\  & & Port 4/\(-\)27.7 to \(-\)40.7 & \\
[4] & 11 & \(-\)35 to \(+\)35 & Linear \\
[5] & Not measured & \(-\)51 to \(+\)48 & Linear \\
[12] & 5.47 & Various in different frequencies. & Linear \\
[15] & 8.7–11.7 & \(-\)25 to \(+\)25 & Linear \\
[16] & 13 & \(-\)30 to \(+\)30 & Linear \\ Our work & 10.7 & \(-\)25 to \(+\)25 & Linear \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of seven recent beam steering antenna systems with our design in terms of gain, scan range, and polarization.  
Figure 18: 50\({}^{\circ}\) scan range provided by changing the biasing voltage of the varactor diodes from 0 V to 20 V.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Antenna System** & **Number of Antenna Elements** & **Phase Shifter/Mode** & **Bandwidth (GHz)** \\ \hline
[2] & Four & PIN diode/variable & 9.88–10.2 \\
[3] & Four & Nolam matrix/fixed & 12.25–12.75 \\
[4] & Four & Butler matrix/fixed & 27–29 \\
[5] & One & CSRR/fixed & 1.98–2.05 \\
[12] & One & PIN diode/fixed & 2.8–4.8 \\
[15] & Four & 4 × 4 Butler matrix/fixed & 23–27 \\
[16] & Four & Metal-insulator-metal Varactor/variable & 7–8.4 \\ Our work & Two & Varactor diode/variable & 16–18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of seven recent beam steering antenna systems with our design in terms of phase shifter type, bandwidth, and number of elements.
## 5 Discussion
### Discussion of Example 1  
The proposed algorithm serves to determine the areas reachable by left-(L) and right-(R) hand configurations for a robot with two degrees of freedom. These areas in the workspace can be easily determined analytically, but in the configuration space, even for this simple case, this is not an easy task. The algorithm generates multiple points in the configuration space (Figure 13) and defines the three zones \(q_{L}\), \(q_{R},q\) in which solutions of type L, R and a combination of the two are present. The location of the points in the configuration space is not uniform due to the non-linear nature of the IK and the setting of the points in polar coordinates.  
Figure 19: Areas reachable with relative singularity configurations of type \(S_{0,2}\), \(S_{0,3}\) (**left** and **center**) and their location relative to the other areas (**right**).  
Even if a trajectory falls entirely within the workspace of the robot, it may not be executed by the robot (for example, trajectory \(t_{1}\)). The transition from zone \(f_{L}\) to \(f_{R}\) or vice versa is possible only if passing through a singularity configuration (trajectory \(t_{2}\)). Singular configurations lie on the outer boundary of \(f\) and therefore the robot must pass through this region. Only a trajectory (for example, \(q_{12p}\)) that falls entirely within the configuration space of the robot can be executed (Figure 13). The size and shape of the regions depends on the lengths of the links and is strongly influenced by the joint constraints. Graphical representation of trajectories in the configuration space helps to understand problems in motion planning or optimization.  
Although it is possible to realize joints without constraints, in practice, such cases are very rare in robotics. When rotating at an angle close to or greater than \(2\pi\)[rad], many problems arise: a cantilever bearing of the joint between the two links is required, which is less favorable for the distribution of forces; the transmission of signals and energy becomes difficult due to the twisting of the cables; finally, control is difficult because it is necessary to check which direction of rotation is closer to the target angle and to record the rotation history. As can be seen from the examples, especially in the presence of joint constraints, the questions related to the type of initial and final desired configuration are essential.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 4 DPCM (Density Power Control Mechanism)
### Power Control Mechanism Definition
#### 4.1.1 Addition Factor  
It indicates how much should be added or subtracted to the current power of a node according to Algorithm 1. This factor is calculated based on the transmission range (which depends on the channel propagation loss model), and receiver sensitivity. The actual value is obtained as a difference between the default transmission power and the transmission power needed to transmit without losses at distance X.  
By simulation, we can determine that the maximum distance to get a successful transmission using a propagation loss model based in normal log distribution is 113 m with the default transmission power of 16.026 dBm. Nonetheless, any more suitable propagation model can be used depending on the scenario. In this case, we calculate the loss propagation at 113 m as follows:  
\[L_{d=113}=Lo+10 nlog(d/do)=108.269\text{ dB} \tag{1}\]  
where:  
\(L_{o}\): Trajectory losses at reference distance (dB).  
\(d_{o}\): Reference distance (m).  
\(d\): Distance (m).  
\(n\): Exponent of path loss.  
Considering the definition of reception power we have  
\[P_{i}-L_{d}=P_{Rx} \tag{2}\]  
where:  
\(P_{i}\): Default transmission power (16.026 dBm).  
\(L_{d}\): Loss propagation at distance d (dB).  
\(P_{Rx}\): Reception power (dBm).  
Replacing (1) in (2) we have the minimum reception power necessary to a transmission without losses.  
\[P_{Rmin}=-92.243\text{ dBm} \tag{3}\]  
Now, it is necessary to calculate losses at a bigger distance; we consider 115 m using (1).  
\[L_{d=115}=Lo+10 nlog(d/do)=108.498\text{ dB} \tag{4}\]  
Replacing (3) and (4) in (2), we obtain a equation of the minimum power required to transmit at bigger distance without losses.  
\[P_{Tx}=16.255\text{ dBm}=42.218\text{ mw} \tag{5}\]  
With (5), we can establish a difference with the initial transmission power and with that we have the addition factor.  
\[\Delta_{power}=P_{Tx}-Pi=2.168\text{ mw} \tag{6}\]
## 4 Test Results and Discussion
Indeed, the current and torque responses are more stable and present fewer ripples with the DRL approach than with the MOGA-NN approach. In addition, the MOGA-NN approach tends to be unstable for important mismatches in electrical parameters, as can be seen in Figure 11(a). As in the previous case, Figure 13 gives more information regarding the operation of the proposed control strategies. The same overall behavior of the DRL and MOGA-NN approaches as the ones of Figure 11 can be observed. Indeed, the DRL approach gives fewer switch transitions and, as a consequence, a reduced switching frequency, except in the case of the DRL approach, the DRL approach is unstable. Figure 11: Model predictive current controller performance under different load variations at a shaft speed of 600 rpm. Figure 10: Performance evaluation under different load variations at a shaft speed of 600 rpm. of increased electrical parameter values. However, even in this last case, we observe a stable and consistent value for the weighting factors and reduced current errors \(\Delta i_{d}\) and \(\Delta i_{q}\) with the DRL approach, while the MOGA-NN approach exhibits unstable behaviors. These results therefore validate the proposed DRL approach as a superior solution for MPCC optimization, ensuring stable and efficient motor operation even in the presence of significant parameter variations. The proposed approach effectively handles the external disturbance and parametric variation due to its capability to explore and exploit under a wide range of operating conditions during training. By dynamically adapting the cost function weights, the controller maintains a balanced response between competing objectives such as current tracking accuracy and switching transition minimization, improving the overall performance of the control system. Figure 12: Performance analysis for mismatched electrical parameters. Figure 8.0 Model predictive current controller performance under mismatched electrical parameters. Finally, while maintaining a constant load and motor speed of 5 Nm and 300 rpm, respectively, we measured the instantaneous THD of the motor phase currents. Figure 14 presents the THD values at 0.05 s, 0.2 s, and 0.35 s. We can observe that the DRL-based controller achieves consistently lower THD compared to the MOGA-NN-based one as we obtain at the different time instants, respectively, 9.38 %, 11.43 %, and 15.77 %, while the MOGA-NN approach gives 9.64 %, 11.76 %, and 16.78 %. This reduction in THD showcases the DRL-based controller's enhanced capacity to mitigate harmonics dynamically, contributing to improved drive performance and reduced harmonic distortion in varying operational conditions. The results once again affirm the effectiveness of the DRL approach in maintaining a cleaner current waveform, which is critical for robust and efficient motor operation.
## 2 Proposed Feed-Forward NN Structure-Based Estimation Methods
### Proposed NN-Based Estimation Method 3: RBFN  
RBFN generally gives excellent, fast approximations for curve-fitting problems. This multilayer feed-forward NN has a single hidden layer and uses radial basis activation functions in the hidden neurons. The radial basis function (kernel) is a function whose value depends on the distance between the input value and center points, where the centers and width can be determined by methods such as random selection, \(k\)-means clustering, supervised selection, unsupervised learning, etc. [58]. Unlike in an FFNN, the output of the hidden neuron \(y_{hid}\) is given by the product of the bias and the Euclidean distance between the input and weight vectors. Figure 5 shows the RBFN structure used to estimate the process mean and standard deviation functions.  
In this essay, the design of an RBFN requires a two-step training procedure. First, the kernel positions, kernels widths, and weights that link the hidden layer inputs to the output layer nodes are estimated using an unsupervised LMS algorithm. After this initial solution has been obtained, a supervised gradient-based algorithm can then be used to refine the network parameters. The dispersion constant, or spread, exhibits a strong relationship with the output of the network. Specifically, the input region covers a large area when the spread is vast; if the spread is relatively narrow, the radial basis function curve is steeper, and the neuron output is much more significant relative to the weighted input vector approach. As  
Figure 5: Proposed RBFN-based RD modeling method. The operation “\({}^{ass}\)” denotes the element-by-element multiplication.  
a result, the network output is closer to the expected output [59]. The output of RBFN is expressed as  
\[\hat{g}_{out}=\sum_{p=1}^{m}\left\{u_{ip}\left[exp-\left(\frac{\left|\left|\sum_{i =1}^{k}\left(v_{ip}-x_{i}\right)\right|\left|e_{p}\right.\right.\right.\right)^ {2}}{2\sigma^{2}}\right]\right\}+f \tag{15}\]  
In this case, the estimated mean and standard deviation functions are  
\[\hat{\rho}_{RBFN}(\mathbf{x})=\sum_{p=1}^{m\_mean}\left\{u_{p}^{mean}\left[ exp-\left(\frac{\left|\left|\sum_{i=1}^{k}\left(v_{pi}^{\text{mean}}-x_{i} \right)\right|\left|\left|e_{p}^{mean}\right.\right)^{2}}{2\sigma^{2}}\right] \right\}+f_{ment}^{RBFN} \tag{16}\]  
\[\hat{\sigma}_{RBFN}(\mathbf{x})=\sum_{p=1}^{m\_std}\left\{u_{p}^{std}\left[ exp-\left(\frac{\left|\left|\sum_{i=1}^{k}\left(v_{pi}^{\text{std}}-x_{i} \right)\right|\left|\left|e_{p}^{std}\right.\right)^{2}}{2\sigma^{2}}\right] \right\}+f_{std}^{RBFN} \tag{17}\]  
where \(m\_mean\), \(m\_std\), \(e_{p}^{mean}\), \(e_{p}^{std}\), \(f_{ment}^{RBFN}\), \(f_{std}^{RBFN}\), \(u_{p}^{mean}\), \(u_{p}^{std}\), \(v_{pi}^{mean}\), and \(v_{pi}^{std}\) denote the number of hidden neurons, bias at hidden node \(p\), bias at the output neuron, weight connecting hidden node \(p\) to the output, and weight connecting input factor \(x_{i}\) to hidden node \(p\) of RBFN for the mean and standard deviation, respectively.
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 3 Implementation of the Proposed Method  
### Contrast Stretching in Low-Frequency Sub-Band  
The low-frequency component contains much of the background information of the image, and the contrast of the image can be enhanced by adjusting the coefficients of the low-frequency sub-band. In this paper, contrast stretching is applied to deal with the low-frequency component, and the corresponding formula is defined as follows [28]:  
\[f(y)=255(y-y_{\min})/(y_{\max}-y_{\min}) \tag{7}\]  
where \(y_{\max}\) and \(y_{\min}\) are the maximum and minimum values of gray image, respectively.
# The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition
## 1 Introduction  
We live in a world surrounded by various acoustic signals. People react from their sense of hearing in situations like passing streets, finding someone in a building, or communicating with others. The development of computer vision has given machines the ability to support our lives in many ways. Hearing sense, as another important factor of our lives, is also an appropriate target to develop with artificial intelligence. A machine assistance acoustic detection system could be applied in several aspects, such as healthcare [1], monitoring [2], security [3] and multi-media applications [4].  
In the artificial intelligence domain, neural networks have been a popular research field in recent years. Many acoustic topics have been researched with this technique, such as speech recognition [5, 6] and music information retrieval (MIR) [7, 8]. However, this kind of acoustic research only work for a certain purpose. Unlike this kind of content, the general acoustic events in our lives might not have periodicity or clear rhythms that can be detected, and the non-stationary properties of environmentalsound make this problem difficult and complex. To achieve a system that can deal with general acoustic cases, the first step might be to recognize the current environmental scene. Scenes such as coffee shops, streets, and offices all have a unique event set; by adding the scene information into the detection system, the system complexity could be reduced. This is why environmental sound recognition techniques are important and essential.  
This study attempted to provide an end-to-end solution for an environmental sound recognition system. There were two major contributions from this research. First, we improved the performance of the network feed with a raw audio signal. Second, we proposed a more flexible parallel network that could combine several kinds of features together. The result showed that this kind of network could combine raw audio signals and the log-mel spectrum efficiently.  
The rest of this paper is organized as follows: In Section 2, we introduce the background of this research, including the current research on environmental sound recognition and the fundamental knowledgement of neural networks. In Section 3, a detailed description of our network and development methods is introduced. In Section 4, we perform experiments to examine our network architecture and the proposed development method, and we compare our results with those of other research, using a number of public datasets. In Section 5, we present a conclusion of our work and provide suggestions for further research.