esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 8 Conclusions  
In this paper, we propose an end-to-end anchor-free detection framework for TT&C signal detection and parameter estimation under constrained conditions. This framework addresses the issue of performance degradation in traditional methods under phase-restricted conditions. By combining the global attention mechanism of triplet attention with the local contextual correlation ability of the Transformer, TATR can effectively detect satellite signals with an SNR not lower than \(-10\) dB in the presence of strong background interference. Compared to representative object detection networks, TATR not only achieves superior results in mAP metrics and parameter estimation errors but also eliminates the need for the post-processing step of NMS that heavily relies on prior knowledge, making it more suitable for TT&C signal detection and parameter estimation.  
In future work, we plan to integrate and deploy TATR into existing satellite spectrum monitoring systems, which will impose further requirements on the real-time performance of the model. Expanding TATR to handle dynamic frames in real-time, such as waterfall plots, may represent a potential research direction in this field. Additionally, given the diversity of TT&C signals, enhancing parameter estimation accuracy and integrating temporal information or modulation features for further demodulation and interpretation of TT&C signals hold significant research significance.  
Conceptualization, [PERSON] ([PERSON]) and P.C.; methodology, [PERSON] ([PERSON]) and X.S.; data curation, [PERSON] ([PERSON]), X.W. and Y.L. ([PERSON]); validation, Y.L. ([PERSON]); formal analysis, F.Z. and Y.L. ([PERSON]); investigation, X.W.; resources, F.Z.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), X.S. and P.C.; visualization, [PERSON] ([PERSON]); supervision, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 62001350); China Postdoctoral Science Foundation (No. 2016M602775); Postdoctoral Science Research Projects of Shaanxi Province (No. 2018 BSHEDZZ39); Joint Fund of Ministry of Education (No. 6141A02022367); Fundamental Research Funds for the Central Universities (No. XJS210210).  
Data Availability Statement: Data are available on request due to privacy.  
We are grateful to the editor and anonymous reviewers for their assistance in evaluating this paper.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Instrumentation  
The samples were analyzed using a J200-EC LIBS system (Applied Spectra Inc., West Sacramento, CA, USA). A pulsed neodymium-doped yttrium aluminum garnet (Nd-YAG; Nd-Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\)) laser (1064 nm, 4 th harmonic generation) was used to ablate the sample with a pulse energy between 9.9 and 87.3 mJ at ambient temperature. The laser, operating at a repetition frequency of 10 Hz, emitted a pulse with an energy of 40 mJ. The beam was focused vertically onto the sample surface using a lens with a 25 mm focal length. The laser spot size was approximately 100 \(\upmu\)m. The emission signals of the laser-induced plasma were collected using an optical fiber bundle with a five-channel charge-coupled device spectrometer covering wavelengths ranging from 190 to 890 nm. This instrument was equipped with a high-efficiency particulate air filter, which could spurge the particles produced from the laser. To obtain the optimal signal/background ratio, the gate delay time and repetition rate were optimized to be 0.8 \(\upmu\)s and 10 Hz, respectively. The gate delay time is the time between the end point of the laser pulse and the point at which the emission lines are collected from the spectrometer. Initially, only low intensities were visible because the emitted spectrum was mainly continuous. After a few microseconds, the peak emission lines became apparent. However, if the time delay was set too long, the plasma could cool down, and the peak emission lines could not be distinguished. Therefore, it was important to set an appropriate time delay to obtain clear signals [15]. Although the sediment samples were ground, they exhibited some degree of heterogeneity. To reduce the heterogeneity and the shot-to-shot signal fluctuation, each sediment sample pellet was ablated at 64 different locations using an 8 \(\times\) 8 grid pattern on the sample surface with a 40 mJ laser pulse energy. The averaged intensities obtained from the 64 points were used to develop a PLRR model and to obtain two analytical spectra (32 points each) from the samples collected at each weir.
## 5 Results and Discussion
### Throughput Analysis  
Table 4 depicts the analysis of the proposed throughput with the four existing methods. Figure 5 represents the throughput with respect to the different number of nodes. Additionally, by utilizing the CEM mechanism for information security, the proposed protocol lessens the possibility of rogue nodes impairing the operation of data transportation between the SNs and the BS. Furthermore, improving the delivery of packets and network access results from traffic monitoring between neighboring nodes and the BS, while the throughput is calculated by Equation (8).  
\[Throughput=\frac{Received\quad packets}{Total\quad time} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Area of simulation & \(300*300\) m \\ \hline SNs & 250 \\ \hline Infected nodes & 50 \\ \hline Size of packets & 64 bits \\ \hline Level of energy & 4 Joules \\ \hline Position of BS & 200, 600 \\ \hline Beamwidth & 4 \\ \hline Control messages & 40 bits \\ \hline Range of transmission & 40 m \\ \hline Type of traffic & CBR \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Sectrust-RPL** & **HBEER** & **SEHR** & **SEAMHR** & **Proposed** \\ \hline Throughput (\%) & 74.94 & 79.75 & 86.16 & 94.64 & 95.81 \\ \hline Packet drop & 25.66 & 19.71 & 13.32 & 7.34 & 5.12 \\ ratio (\%) & 0.178 & 0.162 & 0.136 & 0.114 & 0.10 \\ \hline Energy consumption (m) & 0.0326 & 0.0252 & 0.0190 & 0.0154 & 0.0102 \\ \hline Faulty routes (\%) & 17.07 & 13.31 & 10.46 & 7.83 & 6.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of proposed approach with state-of-art approaches.  
Figure 5 shows the 250 nodes with respect to the throughput. In the case of 50 nodes, the proposed approach acquires the maximum throughput, i.e., 95.8%, as compared to the other existing approaches; in the case of 100 nodes, the proposed approach acquires 96.2% throughput in comparison to the existing approaches; in the case of 250 nodes, the proposed approach acquires 93.25% as compared to the existing approaches.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**No. of Nodes** & **Sectrust-RPL** & **HBEER** & **SEHR** & **SEAMHR** & **Proposed** \\ \hline
50 & 78.8 & 85 & 89.8 & 95.2 & 95.8 \\ \hline
100 & 77.4 & 84.4 & 89.2 & 94.9 & 96.2 \\ \hline
150 & 76.2 & 82.3 & 86.3 & 94.76 & 95.9 \\ \hline
200 & 74.94 & 79.75 & 86.16 & 94.64 & 95.12 \\ \hline
250 & 70 & 76.2 & 85 & 92.8 & 93.25 \\ \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of proposed throughput (%) with state-of-art approaches.  
Figure 4: Comparative analysis of state-of-art approaches in terms of different parameters.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## 4 Conclusions  
In this study, GPPs were isolated from grape pomace and characterized using a series of technologies. We found that GPPs contain a high content of arabinose and mannose, which might possess antioxidant activity [43; 44]. To better determine the effect of GPPs, the antioxidant activity of GPPs in vitro was studied. The result show that GPPs exhibited potent antioxidant activity.  
Free radicals produced by CCl4 in hepatocytes can lead to oxidative stress, loss of enzyme function and lipid peroxidation of the cell membrane, ultimately leading to liver damage [45]. To explore its further antioxidant ability, an in vivo study was performed. The results show that GPP-pretreated mice improved the liver morphology caused by CCl4. Studies also showed that GPPs can reduce oxidative stress and thus achieve liver protection, which is related to free radical scavenging and antioxidant activity. This finding is consistent with other reports in which CCl4 inhibited oxidative stress [46; 47]. In addition, the expression of TNF-\(\alpha\) and IL-6 was significantly increased after treatment with CCl4.  
Figure 5: (**A**) Appearance of liver tissues. (**B**) Liver index of the different groups. (**C**) Histopathological evaluation of liver tissues, stained with H&E (200\(\times\)): (a) normal group; (b) positive group, pretreatment with silymarin; (c) model group; (d,e) GPP-treated groups at different concentrations of GPPs (100 mg/kg and 200 mg/kg). The black arrow indicates hepatic cell necrosis and vacuolization. Compared with model group, \({}^{*}\) in the same column indicates significant difference (\(p<0.05\)). Compared with the normal group, # means significant difference (\(p<0.05\)). Values are presented as means \(\pm\) SDs (\(n=3\)).  
However, GP pretreatment could significantly decrease the expression of inflammatory factors. The histopathological results further confirm that GPPs could reduce oxidative stress and inhibit inflammation. In sum, GPPs may be exploited as a new source of natural antioxidants with potential functional food value, and this study provides a theoretical foundation for the further investigation of GPPs as a functional food additive.  
Conceptualization, W.M. and Q.L.; methodology, Q.L.; software, R.H.; validation, R.H., X.L. and X.H.; formal analysis, F.G.; investigation, Q.L.; data curation, W.M.; writing--original draft preparation, W.M. and Q.L.; All authors have read and agreed to the published version of the manuscript.  
This research was funded the High-level Scientific Research Foundation of Qingdao Agricultural University (665/1119007 and 665/1118011).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Qingdao Agricultural University (code: IACUC-20220722-2).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Impact of Satellite Attitude on Altimetry Calibration with Microwave Transponders
## 5 Conclusions
\begin{tabular}{l l} ADCS & Attitude Determination and Control System \\ APC & Altimeter Phase Center \\ Cal/Val & Calibration and Validation \\ CDDIS & Crustal Dynamics Data Information System \\ CoG & Center of Gravity \\ CRS & Celestial Reference System \\ DORIS & Doppler Orbitography and Radiopositioning Integrated by Satellite \\ ECV & Essential Climate Variables \\ \end{tabular}
###### Abstract  
Exploring the built environment factor's impact on metro ridership can help develop metro station area planning strategies. This is in order to compensate for the shortcomings of previous studies, which mostly used all uniform pedestrian catchment areas (PCA) around metro stations. Beijing was divided into two zones and 12 built environment explanatory variables were selected as independent variables based on the \"7D\" dimension of the built environment. The boarding ridership during the morning peak hours was used as the dependent variable. Nineteen PCA radii from 200 to 2000 m were assumed. The optimal PCA of metro stations for each zone was determined by using the eXtreme Gradient Boosting (XGBoost) model with the objective of minimizing the Mean Absolute Percentage Error (MAPE). The nonlinear impact of the built environment factor of each zone on metro ridership is analyzed under the optimal PCA of metro stations. The study results show that (1) the optimal PCAs of metro stations inside the 4 th Ring Road and outside the 4 th Ring Road are the circular buffer zones with a radius of 800 m and 1300 m, respectively. (2) There is a nonlinear influence of the built environment factor on metro ridership, with strong threshold effects and spatial heterogeneity. The PCA results can be used for the built environment's zoning of metro stations. The XGBoost model and the nonlinear impact results provide significant implications for the practice of station-level ridership forecasting and integrating TOD development and built environment renewal.  
metro station-level ridership; built environment; eXtreme Gradient Boosting (XGBoost); pedestrian catchment areas (PCA); nonlinear effect
## 2 SRT Structure and Dynamics Model
### Vehicle Dynamics Model for Path-Tracking Control  
For a four-module six-axle SRT, based on Equation (7), the matrix assembly method can be used to establish the full-state dynamics model of the vehicle:  
\[\dot{X}=AX+BF_{\text{CG}}+B_{h}F_{h} \tag{16}\]  
\[\dot{X}=\begin{bmatrix}\dot{X}^{1}\\ \dot{X}^{2}\\ \dot{X}^{3}\\ \dot{X}^{4}\end{bmatrix}_{24\times 1,}\ \ \ \ X=\begin{bmatrix}X^{1}\\ X^{2}\\ X^{3}\\ X^{4}\end{bmatrix}_{24\times 1,}\ A=\begin{bmatrix}A^{1}&0&0&0\\ 0&A^{2}&0&0\\ 0&0&A^{3}&0\\ 0&0&0&A^{4}\end{bmatrix}_{24\times 24,}\]  
\[B=\begin{bmatrix}B^{1}&0&0&0\\ 0&B^{2}&0&0\\ 0&0&B^{3}&0\\ 0&0&0&B^{4}\end{bmatrix}_{24\times 12,}\ F_{\text{CG}}=\begin{bmatrix}F_{ \text{CG}}^{1}\\ F_{\text{CG}}^{2}\\ F_{\text{CG}}^{3}\\ F_{\text{CG}}^{4}\end{bmatrix}_{12\times 1,}\]  
\[B_{h}=\begin{bmatrix}B^{1}_{h,r}&0&0&0&0\\ 0&B^{2}_{h,f}&B^{2}_{h,r}&0&0&0\\ 0&0&0&B^{3}_{h,f}&B^{3}_{h,r}&0\\ 0&0&0&0&B^{4}_{h,f}\end{bmatrix}_{24\times 18,}\ F_{h}=\begin{bmatrix}F_{ hx}^{1}\\ F_{hy}^{2}\\ F_{hy}^{2}\\ F_{hy}^{3}\\ F_{h,r}^{4}\end{bmatrix}_{18\times 1}\]  
By combining constraint Equations (8)-(15) with Equation (16), the hinge force can be expressed by the vehicle state and the CG generalized forces:  
\[F_{h}=JPX-JQF_{\text{CG}}, \tag{17}\]  
where \(X\) is the vehicle state, \(F_{\text{CG}}\) is the CG generalized forces of each module, and \(JP\) and \(JQ\) are the constant matrix.  
However, the dynamics model in Equation (16) cannot be directly used for model-based control algorithm design because it explicitly includes the system's internal force term, the hinge force. In addition, for a four-module VTT, due to the hinges and the decoupling of longitudinal motion, the DOFs involved in the vehicle's path-tracking problem are only the lateral motion of the first module and the yaw motion of each module. Therefore, the state vector of the vehicle is taken as  
\[X^{*}=\begin{bmatrix}v_{y}^{1}&\gamma^{1}&\gamma^{2}&\gamma^{3}&\gamma^{4}&y^ {1}&\psi^{1}&\psi^{2}&\psi^{3}&\psi^{4}\end{bmatrix}^{T}. \tag{18}\]Substituting the expression of hinge force in Equation (17) into Equation (16) and only retaining the vehicle state in Equation (18), a simplified dynamics model which can be directly used for path-tracking control is obtained as follows:  
\[\dot{X}^{*}=A^{*}X^{*}+B^{*}F_{\text{CG}}^{*}, \tag{19}\]  
\[Y^{*}=C^{*}X^{*}, \tag{20}\]  
where  
\[A^{*}=\begin{bmatrix}0&-v_{x}^{1}&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0\\ 1&0&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0&0\\ 0&0&0&1&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0\\ \end{bmatrix},C^{*}=\begin{bmatrix}0&0&0&0&1&0&0&0\\ 0&0&0&0&0&1&0&0&0\\ 0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&1\\ 0&0&0&1&0&0&0&0&0\\ \end{bmatrix},F_{\text{CG}}^{*}=\begin{bmatrix}F_{y}^{1}\\ M_{1}^{2}\\ F_{2}^{2}\\ M_{2}^{2}\\ F_{3}^{3}\\ M_{3}^{2}\\ F_{4}^{4}\\ M_{2}^{4}\end{bmatrix}_{8\times 1},\]
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 2 Materials and Methods  
### FH System Capacity Enhancement Methodology  
Previous FH systems selected carriers of different frequencies for modulation through frequency-hopping patterns; FH patterns carry no information [24] (see Figure 1). A_I denotes the I-way of information A and A_Q denotes the Q-way of information A.  
The FH system capacity enhancement method, which is used to enhance the information transmission capacity of the anti-jamming communication system by modulating the information at the frequency-hopping point dimension, is presented in Figure 2. A_I denotes the I-way of information A and A_Q denotes the Q-way of information A. In this method, the information transmitted in the original FH system is referred to as Information A, and the incrementally transmitted information is referred to as Information B.  
At the transmitter side, for Information A, BPSK modulation is generally used. After symbol mapping, carrier modulation is performed. The carrier is a frequency-hopping carrier, and the frequency-hopping rate is selected based on Information B. The frequency-hopping period is generally an integer multiple of the period of the code element of Information A. For Information B, at the transmitting end, sequence modulation is performed according to the mapping relationship to transform Information B into control information that does not use frequency carriers. In this case, one code element cycle of Information B corresponds to one or more frequency-hopping cycles. The output at the transmitter side is an FH capacity boost signal carrying both Information A and Information B.  
Figure 1: Schematic diagram of information rate enhancement for frequency-hopping systems.  
After passing through the AWGN channel, at the receiving end, carrier reconstruction is conducted to form a local carrier using Information B; carrier demodulation is then carried out to form a baseband signal. After the completion of clock synchronization and judgement, Information A is demodulated. At the receiving end, the carrier frequency point is measured using the power detector method, and then the baseband Information B is demodulated according to the modulation mapping relationship.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.13. Forest Structure, Biomass and Productivity in Amazonia at 5 km Spatial Resolution  
[PERSON] et al. [74] combined field data from 226 RAINFOR network plots and satellite observations of leaf area index, tree cover, soil type, climate and topography, and derived Amazonia biomass data at 5 km spatial resolution using the MaxEnt method. This dataset was not validated with independent plot data, and nor was it filtered, which could affect the estimated results. The uneven distribution of the measured data could also introduce some uncertainties.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Optical Sensors
#### 3.5.1 Optical Fiber-Based Sensors  
Optical fibers possessing a chemical reagent (e.g., chemical dye) or a sorbent phase (e.g., polymeric film) as a reactive layer, are commonly used as transduction elements in optical gas sensors [6; 143] specifically for VOC detection [6]. Upon vapor exposure optical or structural changes of the reactive layer alter the effective index and hence the light transmission properties of the fiber [6].
## 4 Problem Decomposition and Solution
### Best Response-Based Centralized Multi-TaV Computation Resource Allocation Algorithm
#### 4.3.2 Analysis of the Convergence and Complexity  
The aforementioned game model is an EPG, so all TaVs can gradually improve their utility step by step based on the finite improvement property [28]. In addition, the change of value in the constructed utility function and its corresponding potential function is just the same when any TaV changes its decision, i.e., \(U_{n}(\widetilde{a}_{n},a_{-n})-U_{n}(a_{n},a_{-n})=\Theta(\widetilde{a}_{n},a_ {-n})-\Theta(a_{n},a_{-n})\) in Equation (24). It means the optimization objective will improve with the rise of utility. Since the finite strategy space bounds the utility, if no TaV wants to change its current decision, the proposed algorithm will converge and achieve the maximization of the optimization problem locally or globally. In other words, the BR-DMCTO algorithm converges to the NE point of the optimization problem.  
The complexity of the proposed algorithm is analyzed here. As shown in Algorithm 1, the number of iterations is \(\lambda\)N. In each iteration, the complexity mainly comes from the ES computation resources selection of TaVs. In the first iteration, the TaV with the maximum increment utility is selected for updating. The complexity is \(\log(\text{N})\). In the subsequent iterations, only the TaV that allocated computation resources will update its increment utility before selection. The complexity is \(\log(\text{N1})\) or \(\log(\text{N2})\) based on steps 3 and 4, where N1 and N2 represent the TaVs that satisfy \(\Delta\text{T}_{n}<0\) and \(\Delta\text{T}_{n}\geq 0\), respectively. Let us consider the worst-case scenario where the complexity of each iteration is \(\log(\text{N})\). Hence, the complexity of the proposed algorithm is \(\text{O}(\lambda\text{N}*\log(\text{N}))\).
## 3 Results and Discussion
### RMS Delay  
The RMS delay measures the temporal dispersion of the CIR. The RMS delay between the \(i\)th LED and the \(j\)th PD, for \(i,j=1,2,3\), can be calculated by [7]  
\[\tau[i,j]=\sqrt{\frac{\int_{0}^{\infty}(t-\mu_{\tau}[i,j])^{2}(h[i,j](t))^{2} \,dt}{\int_{0}^{\infty}(h[i,j](t))^{2}\,dt}}\,, \tag{101}\]  
where \(\mu_{\tau}[i,j]\) is the mean delay spread and is given by  
\[\mu_{\tau}[i,j]=\frac{\int_{0}^{\infty}t(h[i,j](t))^{2}\,dt}{\int_{0}^{\infty} (h[i,j](t))^{2}\,dt}\,. \tag{102}\]  
Figure 9 shows the RMS delay results for the proposed CSK-based UM-VLC channel model and compares it with the same reference channel model defined in Section 3.2. In Figure 9a-c, the RMS delay for the red channel \(\tau[1,1]\), the green channel \(\tau[2,2]\) and the blue channel \(\tau[3,3]\) is shown over the simulation space for the proposed model. Figure 9d shows the RMS delay for the reference model, calculated using the CIR from Equation (64) with PD responsivity \(R_{\text{ref}}=0.53\) A/W. Figure 9e presents the ECDF for the RMS of the three channels and for the reference model. It is clear that in the NLOS-dominant sectors of the simulation environment, the RMS spread is much larger. This is expected, since the NLOS link experiences more time delay as the light has to travel further. All channels, including the reference one, have almost the same RMS delay pattern. This is not surprising, since the proposed model does not modify the time components of the channel with respect to the reference model.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Values** & **References** \\ \hline  & **4-CSK**: 24 Mb/s & \\ Data rate & **8-CSK**: 72 Mb/s & [11] \\  & **16-CSK**: 96 Mb/s & \\ IEEE 802.15.7 CSK constellation & 100-010-001 & [11; 26] \\ RX samples per symbol & 2 samples/symbol & - \\  & \(\lambda_{1}=632.5\) nm (red) & \\ Center-of-band wavelengths & \(\lambda_{2}=530\) nm (green) & [42] \\  & \(\lambda_{3}=472.5\) nm (blue) & \\ Data bits per frame & 524,232 bits & [11] \\ Frames transmitted per BER data point & 20 frames & - \\ Color calibration sequence length & 24 symbols & [11] \\ \hline \hline \end{tabular}
\end{table}
Table 3: BER simulation parameters.