esa-sceva/satcom-chunk-collection huggingface dataset:
# Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges
###### Abstract  
Wideband microwave imaging is of interest wherever optical opaque scenarios need to be analyzed, as these waves can penetrate biological tissues, many building materials, or industrial materials. One of the challenges of microwave imaging is the computation of the image from the measurement data because of the need to solve extensive inverse scattering problems due to the sometimes complicated wave propagation. The inversion problem simplifies if only spatially limited objects--point objects, in the simplest case--with temporally variable scattering properties are of interest. Differential imaging uses this time variance by observing the scenario under test over a certain time interval. Such problems exist in medical diagnostics, in the search for surviving earthquake victims, monitoring of the vitality of persons, detection of wood pests, control of industrial processes, and much more. This paper gives an overview of imaging methods for point-like targets and discusses the impact of target variations onto the radar data. Because the target variations are very weak in many applications, a major issue of differential imaging concerns the suppression of random effects by appropriate data processing and concepts of radar hardware. The paper introduces related methods and approaches, and some applications illustrate their performance.  
microwave imaging; medical imaging; vital data capturing; ultra-wideband; target localization; M-sequence; pulse radar; through-wall radar; wooden pest detection Article
# Acquisition of Weak BDS Signal in the Lunar Environment
## 5 Conclusions  
This study explores the potential use of GNSS main and side lobe signals for receivers in a low lunar orbit. We have estimated the number of visible satellites, the carrier-to-noise ratio, the received power, the Doppler shift, and the GDOP values. Additionally, we have discussed the critical role of GNSS side lobe signals in lunar navigation, where the number of side lobe signals is approximately three times that of the main lobe at a reception threshold of 10 dB-Hz. The use of multiple GNSS constellations and side lobe signals can significantly increase the number of visible signals, providing greater opportunities for spacecraft navigation at lunar altitudes.  
To further examine the potential for BDS-only navigation in the lunar environment, we discuss the methodology for acquiring BDS B1I signals under these conditions. We analyzed the composition of the BDS B1I signal and simulated it using MATLAB. Several algorithms commonly used to acquire weak signals were then presented, and the results demonstrated that the semi-bit coherent differential integration algorithm is more effective. Specifically, this algorithm succeeds in acquiring a 15 dB-Hz BDS signal with a probability of approximately 35%.  
Figure 17: Histogram of the \(C/N_{0}\) distribution of the main (**left**) and side (**right**) lobe signals, when BDS-3, GPS, and quad-GNSS are applied.  
**Author Contributions:** Conceptualization, [PERSON] and L.C.; methodology, [PERSON], L.C. and J.Y.; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON], L.C. and J.Y.; investigation, [PERSON]; resources, L.C. and J.Y.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, L.C.; visualization, [PERSON]; supervision, L.C.; project administration, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under grant number 42171417, the Key Research and Development Program of Hubei Province under grant number 2022 BAA166, the Central Guided Local Science and Technology Development Special Project under grant number 20222 ZDH04090, the Medium- and Long-term Scientific and Technological Planning Projects for Radio, Television and Audio-Visual Networks, and the Special Fund of Hubei Luojia Laboratory under grant number 22010008.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Data Collection and Labeling
### Data Collection  
In this section, we begin by detailing the SAL dataset utilized in this study, collected from learners enrolled in the UWP course at Northeastern University. The data collection methodology has been described in our previous work [6]. Here, we briefly outline the types of SAL data captured for each learner:  
1. Search logs. We recorded learners' search activities with web-based search engines by developing a Firefox browser plug-in. Specifically, for a learner, we recorded searching activities such as their issued search queries, clicking on URLs, and reading duration times.
2. Search results. We recorded search results after each issued search query.
3. Learning outcomes. We recorded programming snapshots for each learner during compilation.  
In the initial five weeks of our study, we systematically introduced tasks with an incrementally increasing number of subtasks. The distribution of these subtasks within the assignments is illustrated in Figure 1. Research conducted in the domain of SAL has suggested that the act of searching within SAL can be perceived as a sequence of activitieswith the purpose of learning [28]. Although we cannot directly observe, we can predict their learning state by analyzing their search activities and learning outcomes.
## 3 Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Results** & \(\boldsymbol{p}\)**Value** \\ \hline Gender, no. (\%) & & \\ Male & 12 (70.5) & 0.03 \\ Female & 5 (29.5) & \\ \hline Age, years & & \\ mean & 6.7 & \(\boldsymbol{-}\) \\ range & 3–16 & \\ \hline Side, no. (\%) & & \\ Right & 9 (53\%) & 0.2 \\ Left & 7 (41\%) & 0.2 \\ Bilateral & 1 (6\%) & \\ \hline No. of treatments, no. (\%) & & \\
1 & 14 (82.3) & \(\boldsymbol{-}\) \\
2 & 2 (11.8) & \\
3 & 1 (5.9) & \\ \hline Results, no.(\%) & & \\ Full resolution & 10 (59) & \\ Pain without swelling & 2 (12) & \(\boldsymbol{-}\) \\ Pain with swelling & 2 (12) & \\ Treatment failure & 2 (12) & \\ \hline Findings, no.(\%) & & \\ Decreased vascularization & 5 (16) & \\ A-vascularization & 19 (63) & 0.05 \\ Strictures & 14 (46) & \\ Mucus plugs & 19 (63) & \\ \hline Pathological finding in A-symptomatic gland & 11 of 12 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of patients’ (no. = 17) characteristics, treatment results, and sialoendoscopy findings. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Case Number** & **Gland Involved** & **Gland Treated** & **Endoscopy Findings (Left)** & **Endoscopy Findings (Right)** \\ \hline
1 & Right & Right & N/A & Few mucous plugs \\
2 & Right & Right & N/A & Vast mucous plugs \\ \hline
3 & Bilateral & Bilateral & \
\begin{tabular}{l} Restrictions \\ Vast mucous plugs \\ Decreased vascularity \\ \end{tabular} &
\begin{tabular}{l} Restrictions \\ Vast mucous plugs \\ Decreased vascularity \\ \end{tabular} \\ \hline
4 & Right & Right & N/A &
\begin{tabular}{l} Restrictions \\ Few mucous plugs \\ \end{tabular} \\ \hline
5 & Left & Bilateral & \
\begin{tabular}{l} Restrictions \\ Vast mucous plugs \\ Decreased vascularity \\ \end{tabular} &
\begin{tabular}{l} Restrictions \\ Vast mucous plugs \\ Decreased vascularity \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of sialoendoscopy findings. A structure in the duct negatively affects the salivary flow and may cause additional inflammation and the formation of mucous plugs. Mucous plugs (few or vast) were seen in 19 glands (64% of glands, 13 patients), (Figure 5). A mucous plug in the duct is evidential to improper salivary flow and may cause additional duct inflammation.
# Simplified Welch Algorithm for Spectrum Monitoring
## 4 Simulations  
### Simulation Setup  
This section explains how the modified algorithm can be implemented simply in MATLAB Simulink as a development environment. Figure 8 displays the block diagram of the design in Simulink. First, the signals are generated on the left, then they are combined and passed through the FFT block mentioned earlier to finally be processed by the updated Welch algorithm. A random data source generates a proper integer numbers for the Quadrature Amplitude Modulation (QAM) modulator. Then, a general telecommunication signal is generated by up sampling and pulse shaping based on raised-cosine filtering. The numerical oscillator generates a single tone at the desired center frequency. The idea here is to show how dependency on past data (\(a\) parameter) can obtain a smoother spectrum and help to clarify the Carrier Wave Interference (_CWI_) [25] as a frequency component. Numerical control oscillator generates _CWI_ based on the selected center frequency \(f_{0}\):  
\[CWI=exp(j\cdot f_{0}\cdot t) \tag{22}\]  
Figure 8: General block diagram of implementation of modified design in Simulink.  
Figure 7: Frequency response of the modified Welch with the correction factor.  
For example, in Figure 8, a single tone with a peak at the center frequency \(f_{0}=0.015\Big{(}\times\pi\frac{radian}{same}\Big{)}\) is generated.  
The schematic of the implanted FFT and modified algorithm can be seen in Figure 9a.b. The simulation design tries to have the similar behavior of the Xilinx DSP Generator blocks. Samples are first stored into a buffer of FFT size (\(N_{F}\)). After buffering \(N_{F}\) samples, the FFT block transfers the samples to the frequency domain and generates the FFT vector. The FFT vector is then converted to a series of data with the help of the un-buffer block. A free running counter generates the index of FFT samples. Because this counter is limited to count to \(N_{F}\), after reaching \(N_{F}\), it will reset to one and start to count up again. In this way, for each packet of FFT samples, the counter generates the correspondence index from one to \(N_{F}\). The relational block simply compares the counter output with \(N_{F}\) and generates a pulse if the output is equal to \(N_{F}\) as the data ready pulse. This pulse determines the boundaries of each calculated packet in the data stream. It should be mentioned that in the Xilinx DSP Generator, all sub-blocks of the buffer, FFT calculator, un-buffer, counter and data-ready are integrated as one component. The general implementation idea is explained in Figure 9b, where the output of the RAM is multiplied with \(a\) in feedback and then added to the new FFT samples to generate the exponential weighted averaging.  
Figure 9: (**a**) Schematic of the updated Welch algorithm, (**b**) Fast Fourier Transform (FFT) implementation of modified Welch.
# Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
## 3 Transformer and C3D-Based Two-Stream Network Modeling
### Positional Encoding  
Since the Transformer operates in parallel with the input data, resulting in the loss of information about the position of the image blocks, some information about the position of the sequence needs to be injected, so positional encoding is added to the bottom layer of the encoder. The original Transformer model used absolute conditional encoding, where only the absolute position of the sequence was input. Relative position coding, in addition to the absolute position of the input sequences, encodes the relative distance between the input sequences and learns pairwise relationships between the sequences. In this paper, context-based relative position coding is used in the attention mechanism to improve the expressiveness of the model [13]. Suppose that the input sequence is \(X=(X_{1},\cdots,X_{n})\), and the output sequence is \(Z=(Z_{1},\cdots,Z_{n})\). Each output element \(Z_{i}\) represents the relationship of the current position \(i\) to all positions in the sequence, which is the result of linear weighting. The calculation is published as follows:  
\[Z_{i}=\sum_{j=1}^{n}\beta_{ij}(X_{j}W^{V}) \tag{9}\]  
where \(\beta_{ij}\) is the weight. This is obtained by the softmax function  
\[\beta_{ij}=softmax(e_{ij})=\frac{exp(e_{ij})}{\sum_{k=1}^{n}exp(e_{ik})} \tag{10}\]  
where \(e_{ij}\) is obtained by scaling the dot product of the two elements:  
\[e_{ij}=\frac{(X_{i}W^{Q})(X_{j}W^{K})^{T}}{\sqrt{d_{k}}} \tag{11}\]  
Context-based relative positional encoding takes into account the interactions with queries, keys, and values; the core idea is to add learnable relative positional encoding to the \(e_{ij}\), incorporating learnable relative position encoding in the following:  
\[e_{ij}=\frac{(X_{i}W^{Q})(X_{j}W^{K})^{T}+b_{ij}}{\sqrt{d_{k}}} \tag{12}\]  
\[b_{ij}=(X_{i}W^{Q})r_{ij}^{T} \tag{13}\]  
where \(r_{ij}^{T}\) is a trainable vector. To calculate the relative position on the 2D image and define the relative weight \(r_{ij}\), it is mapped directionally using the crossover method, where the position encoding in the horizontal and vertical directions are computed separately and combined as follows:  
\[r_{ij}=p_{T(i,j)}^{x}+p_{I^{y}(i,j)}^{y} \tag{14}\]  
\[I^{x}(i,j)=g(x_{i}-x_{j}) \tag{15}\]  
\[I^{y}(i,j)=g(y_{i}-y_{j}) \tag{16}\]  
where \(p_{T(i,j)}^{x}\) and \(p_{I^{y}(i,j)}^{y}\) are learnable vectors that store the weights of relative positions, and \(g(\cdot)\) is a many-to-one segmented indexing function that maps relative positions to weights, mapping multiple relative positions to the same position encoding, reducing the number of parameters introduced by the relative positions.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 3 Methods for Path Loss Prediction
### Svr  
Support vector machine (SVM) is a kind of machine learning method based on statistical learning theory. The basic idea of SVM is to nonlinearly map a set of data in the finite-dimensional space to a high-dimensional space such that the dataset is linearly separable. As an extension of SVM, SVR is designed to solve regression problems, so it can be used for path loss prediction [20].  
The main idea of SVR is to fine a hyperplane in the high-dimensional feature space to make the sample points fall on it. The hyperplane in the feature space can be described by the following linear function.  
\[f(\mathbf{x})=\mathbf{w}^{T}\phi\left(\mathbf{x}\right)+b \tag{4}\]  
where \(\mathbf{w}\) is the normal vector which determines the direction of the hyperplane, \(\mathbf{x}\) is an input feature vector, \(\phi\left(\cdot\right)\) is the nonlinear mapping function, and \(b\) is the displacement item.  
The solution to the optimal hyperplane is a constrained optimization problem, which can be written as [27]  
\[\begin{split}\min_{\mathbf{w},b,\xi_{i}^{*}}\frac{1}{2}\mathbf{w}^{T }\mathbf{w}+C\sum_{i=1}^{N}\left(\xi_{i}+\xi_{i}{}^{*}\right)\\ \text{s.t.}& f(\mathbf{x_{i}})-y_{i}\leq\epsilon+ \xi_{i}\\ & y_{i}-f(\mathbf{x_{i}})\leq\epsilon+\xi_{i}{}^{*}\\ &\xi_{i},\xi_{i}{}^{*}\geq 0,i=1,\ldots,N\end{split} \tag{5}\]  
where \(C\) is regularization coefficient, \(\epsilon\) is insensitive loss which means the predicted value can be considered accurate if the deviation between the predicted value and the actual value is less than \(\epsilon\), \(\xi_{i}\) and \(\xi_{i}{}^{*}\) are the slack variables which allow the insensitivity range on both sides of the hyperplane to be slightly different.  
Then, by introducing Lagrange multipliers and solving its dual problem, the approximate function can be expressed as  
\[f\left(\mathbf{x}\right)=\sum_{i=1}^{N}\left(-\alpha_{i}+\alpha_{i}{}^{*} \right)K\left(\mathbf{x}_{i},\mathbf{x}\right)+b \tag{6}\]  
where \(\alpha_{i}\) and \(\alpha_{i}{}^{*}\) are Lagrange multipliers, and \(K(\cdot,\cdot)\) is a kernel function, which is used to perform the nonlinear mapping from the low-dimensional space to the high-dimensional space.  
The choice of the kernel function is the key to the performance of the SVR-based predictor. At present, the most common kernel functions include the linear kernel, polynomial kernel, Gaussian radial basis function, sigmoid kernel, and their combinations. In this paper, Gaussian kernel with a tunable parameter \(\gamma\) is chosen as the kernel function and it is defined by  
\[K\left(\mathbf{x}_{i},\mathbf{x}_{j}\right)=\exp\left(-\gamma\|\mathbf{x}_{i} -\mathbf{x}_{j}\|^{2}\right). \tag{7}\]  
The Gaussian kernel is a commonly used kernel function [17; 18; 19; 20], which is suitable for tasks with small feature dimensions and lack of prior knowledge. The parameters including regularization coefficient, insensitive loss, and the kernel function parameter in this study were searched as the same method in [20].
## 3 Results and Discussion
### Evaluation of the Nemerow Composite Pollution Index  
#### 3.4.1 Results of the Evaluation of the Nemerow Composite Pollution Index  
The Nemerow pollution index was calculated for 24 sampling points in the Wanquan River basin to obtain the pollution level of each sampling point in the Wanquan River basin (Table 5). Of the 24 sampling sites, 6 (25%) were in the clean class, 4 (17%) in the cleaner class, 10 (42%) in the light pollution class, and 4 (17%) in the moderate pollution class (Table 4). Compared to the single factor pollution index method, the proportion of the first two classes of the Nemerow pollution index method decreased while the proportion of the third and fourth classes increased, resulting in a more stringent evaluation than the single factor index method.  
3.4.2 Spatial Distribution of the Results of the Evaluation of the Nemerow Composite Pollution Index  
The spatial distribution of the results of the Nemerow integrated pollution index evaluation is shown in Figure 7. Spatially, the entire Wanquan River is predominantly lightly polluted, reaching the clean (WQH05, WQH06, and WQH09) and cleaner grades (WQH02, WQH03, WQH04, and WQH07) in the northern section of the river. The loci near the Tayang River reach moderate pollution levels (WQH17, WQH18, and WQH19).  
Comparing the evaluation results of the single factor pollution index method (Figure 6), it can be seen that the overall distribution trends of both are similar, with the low and high values of pollution occurring in the western part of Qionghai city and the Tayang River, respectively. The difference lies in the fact that the results of the single-factor pollution index method are dominated by minor pollution at the second level, while the Nemerow pollution index method is dominated by light pollution at the third level.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sampling Sites** & **Nemerow Pollution Index** & **Pollution Level** \\ \hline WQH01 & 1.27 & Light pollution \\ WQH02 & 0.56 & Cleaning \\ WQH03 & 1.10 & Light pollution \\ WQH04 & 0.75 & Cleaner \\ WQH05 & 0.69 & Cleaning \\ WQH06 & 0.64 & Cleaning \\ WQH07 & 0.70 & Cleaning \\ WQH08 & 1.04 & Light pollution \\ WQH09 & 0.55 & Cleaning \\ WQH10 & 0.74 & Cleaner \\ WQH11 & 1.17 & Light pollution \\ WQH12 & 1.52 & Light pollution \\ WQH13 & 1.10 & Light pollution \\ WQH14 & 0.76 & Cleaner \\ WQH15 & 1.87 & Light pollution \\ WQH16 & 1.97 & Light pollution \\ WQH17 & 2.11 & Moderate pollution \\ WQH18 & 2.06 & Moderate pollution \\ WQH19 & 2.50 & Moderate pollution \\ WQH20 & 0.70 & Cleaning \\ WQH21 & 1.32 & Light pollution \\ WQH22 & 2.29 & Moderate pollution \\ WQH23 & 0.73 & Cleaner \\ WQH24 & 1.74 & Light pollution \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation results of the Nemerow integrated pollution index method for each sampling point in the Wanquan River.
## 4 Discussion
### Enhancing Performance
#### 4.2.2 Task Accuracy in TMT-VR  
In terms of task accuracy, this study found that high-gamers performed more accurately using the headset in TMT-VR Part A, while low-gamers were more accurate with the eye tracking modality. This finding suggests that individuals with more gaming experience may be better at fine-tuned interactions, such as those required by head movements, while those with less experience might benefit more from the naturalistic and direct control offered by eye tracking [45]. For TMT-VR Part B, eye tracking was generally more accurate than the controller, with no significant differences between the eye tracking and headset modes. These findings align with the existing literature that highlights the precision of eye tracking in tasks requiring accurate target selection, particularly in scenarios involving depth and spatial awareness [79]. The effectiveness of eye tracking and head movement as interaction modes in VR-based cognitive assessments is further supported by their ability to provide natural and intuitive control, minimizing the cognitive load on participants [2].
###### Abstract  
Ultra-long span unrepeetered systems using distributed Raman amplification are cost-effective solutions for bridging moderate transmission distances. However, there are two major limiting factors: nonlinear Kerr effect-induced nonlinear signal distortion and optical signal-to-noise ratio degradation due to spontaneous Raman noise. In this report, we proposed a model of three-segment hybrid fiber effective core area structure and developed a model covering: (1) generalized mathematical formulations, (2) analysis of three-segment Raman amplified link, and (3) simulation model of data transmission. The proposed model showed an improvement of the Raman gain profile, a reduction of the negative impact of the nonlinear Kerr effect, and an enhancement of the optical signal-to-noise ratio. A numerical simulation of the transmission performance of the three-segment hybrid structure was compared to conventional single-segment single fiber core structure on 80 Gb/s differential quadrature phase-shift keying (DQPSK) modulated data signals over a propagation distance of 390 km. The required optical signal-to-noise ratio was reduced by 2.71 dB to achieve the target error rate without using forward error correction. The numerical model and simulation of various data rates up to 100 Gb/s consistently showed that an improvement in transmission performance could be achieved by using three-segment hybrid fiber effective core area structure.  
Keywords:distributed Raman amplification; fiber effective core area; nonlinear Kerr effect; optical fiber communication; ultra-long span system +
Footnote †: journal: _photonics_
## 4 Identification Method for SRRV Deviation Using Wheel-Rail Force Trend Component  
As established in Section 3, SRRV deviation has a significant impact on wheel-rail forces and the turnout structural irregularity wavelength. Therefore, extracting features from wheel-rail force data that effectively reflect the turnout structural irregularity wavelength can help identify the effects of SRRV deviation on wheel-rail interaction. In this section, based on DWT and the results from Section 3, the wheel-rail force trend component applicable for SRRV deviation identification is derived, and the mapping relationship between different SRRV deviations and the 7 th-order trend component of the wheel-rail force is analyzed.