esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis  
### Analysis of Peak Attenuation Law of Vibration Acceleration  
The peak acceleration in the vibration signal reflects the vibration response between the vibratory roller and filling materials. Under the action of the vibratory roller, the vibration response time of the filling materials is about 10s and the peak acceleration of the wheel is about eight times that of the first layer, as shown in Figure 5. Figure 6 shows the variation curve of the peak acceleration with the number of rolling compaction times for each measuring point. It can be found the peak acceleration of the vibration signal was gradually decreased during the propagation of the vibration signal from the vibratory roller to the deep filling layer, which is approximately inversely proportional to the depth. However, the peak acceleration does not decrease linearly, nor does it increase at the interface of different filling layers. The reason may be that the difference in the physical and mechanical parameters of filling materials on both sides of the interface results in the reflection of the vibration signal at the interface [41]. In addition, the peak acceleration of each measuring point increases with the increase of the number of rolling compaction times and reaches the maximum value in the fifth compaction. At this time, the peak acceleration of the wheel keeps around 6.06 g, and the peak acceleration of the first layer keeps around 2.06 g. The LWD detection value of the filling materials also tends to be stable  
Figure 4: The distribution of the measurement points.  
Figure 3: Accelerometers used in the test.  
after the fifth compaction, indicating that the peak attenuation law of the vibration signal is related to the soil compactness.  
In order to further quantify and analyze the attenuation law of the vibration signal from the vibration wheel to the deep filler, this paper divides the peak acceleration difference between the filling materials and the wheel by the peak acceleration of the wheel as the attenuation rate, as shown in Figure 7.  
According to Figure 7, it can be found that the peak attenuation rate of acceleration is the largest when the vibration signal is transmitted from the wheel to the surface of filling materials. With the increase of compaction times, the attenuation rate increases from 51% to 66%. As the stiffness of the filling materials increases, the vibration signal has to consume more energy for transmission to the filling layer. The transmission mechanism of the vibration signal from the shallow filling layer to the deep filling layer is the opposite. As the filler enters the dense state from the loose state, the stiffness between different layers gradually decreases so that the peak attenuation rate of the vibration signal also gradually decreases. The attenuation rate of the vibration signal transmitted to the fourth layer (1.2 m) is more than 90%. It can be seen that the critical depth of the vibration acceleration in the filling layer is about 1.2 m.
# Robust Anti-Jamming Algorithm Based on Transmit/Receive Time-Sharing Technology
## 2 Mathematical Model  
### Transmit/Receive Time-Sharing Model  
The satellite-ground link receiver of the BeiDou Navigation Satellite System (BDS) realizes bidirectional measurement and communication through T/R time-sharing technology [25; 26]. The T/R time-sharing module is illustrated in Figure 1. The time-sharing period of the satellite-ground link is 3 s [27]. Both the signal receiving and transmitting time slots are 1.5 s. When the navigation receiver communicates with the linked satellite, it first receives the downlink signal from the navigation satellite and then transmits new data to the satellite. The signal reception interval of the navigation receiver on the satellite-ground link can be expressed as:  
\[T_{r}=\text{min}\big{|}t_{i,j}-t_{j,i}\big{|}=1.5\text{ s} \tag{1}\]  
where, \(t_{i,j}\) is the signal receiving time from transmitter \(i\) to receiver \(j\).  
In the non-receiving interval, the navigation receiver stops performing the signal reception function, but the interference environment still exists [28]. Figure 2 illustrates the time-domain diagram of the received signal in the anti-jamming module. As shown in the figure, the navigation signals in different time-sharing periods are different satellite downlink signals, but the interference environment remains the same, showing the same characteristics.
## 2 Materials and Methods
### Post-Earthquake Indicators
#### 2.2.3 Existence of Soils with High Amplification Capacity of Seismic Waves (\(I_{SAC}\))  
The capacity of the soil of the city of Lorca to amplify or reduce the seismic waves according to their geotechnical or geological nature has also been the object of some study. Regarding this aspect, soil analysis has been addressed from different approaches. In this case, the conformation of zones of soil with homogeneous characteristics in influencing the effects of the earthquake has been approached, taking into account two parameters contained in the Spanish Earthquake Resistant Norm [17]. The first is the so-called terrain amplification coefficient, whose value is expressed as a function of parameter C (Figure 5a). This parameter varies at a very local scale depending on the geotechnical characteristics of the terrain, so it is discretized in the database with spatial-level point values. The second is the terrain classification coefficient that establishes four categories (I-IV) as a function of the propagation velocity of the shear elastic waves (Figure 5b). This second parameter of a more geological character varies on a larger scale, spatially generating larger homogeneous contours. Since both coefficients have a certain correlation when obtaining them, it has been possible to form a spatial Index of Soil Amplification Capacity (\(I_{SAC}\)) that assesses the effect of the soil on earthquake damage. This indicator has been made from the weighted average convolution geo-process of both parameters:  
\[I_{SAC}=\frac{\iint P(t)\bigotimes U(x,y)}{\oint H(x,y)} \tag{2}\]  
where \(P\) is the discrete average value of the parameter for a homogenous area, taking into account the point cloud of values existing in this area; \(U\) is the function that models the weighted surface in each homogenous sector of the soil, taking into account the four different categories from [17]; and \(H\)(\(x\),\(y\)) is the total area analyzed for a homogenous sector.  
Figure 5: (**a**) Discretized model of the values of the amplification coefficient C, and (**b**) spatial model of the classification coefficient of the land superimposed with different levels of damage to buildings (data taken from previous work [22]).
## 3 Results and Discussion
### Fatty Acid Analysis
In the current study, C14:0, C16:0, C16:1-n-7, C18:0, C18:1n-9, C18:3n-6, C18:2-n-6, C18:3n-3, C20:1n-9, and C20:4n-6, were analyzed in the meat of animal fed concentrated and animal fed formulated feed. These fatty acids profiles were not altered significantly between control and treatment group animals. However, we noted slight changes in C14:0 (myristic acid), C16:1n-7 (palmitic acid), and C20:1n-9 (eicosanoic acid) between experimental groups. The calculated saturated, unsaturated, monounsaturated, and polyunsaturated fatty acid profiles remained unchanged in control and treatment animals. At the same time, a slight increase in unsaturated fatty acid was noted in the animals fed formulated feed compared to concentrate fed animals. The \(\omega 6/\omega 3\) ratio was lower for animals fed formulated feed than the control group (Table 6). Overall data suggested that the formulated feed supplementation did not alter fatty acid profiles in the meat compared to concentrate-fed animals. Our study was slightly inconsistent with previous studies, which may be due to differences in the animal breed, plant-based silages, and production. There were few limitations to the present study, (I) sample size was relatively small; (II) blood parameters are essential to confirm the new feed impact on the clinical markers, particularly cytokines, but we did not analyze the clinical parameters change in the control and treatment animals; (III) gut microbiota plays a significant role in animal growth performances, so microbiome study is essential to confirm the microbial changes in the experimental animals. Advantages of the current study: we replaced commercial concentrate with fermented feed; it was prepared from grass, legume, and other natural substrates by microbial fermentation. Our formulated feed showed significant positive impacts on the animal's growth performance and meat quality. It is a very cost-effective strategy that reduces the economic burden for livestock cultivars. Future aspect study: in-depth study is required to determine the changes in mRNA expressions in response to formulated feed supplementation, intramuscular fat deposition status, cytokines, adipokines and myokines changes, and associated microbiome mechanisms.
## 3 Two-Loop Closed-Loop Control Design
### Voltage Control Loop Design  
The outer voltage control loop regulates the output voltage at the reference value by setting reference for the current through the input inductors as shown in Figure 10. The inner current control loop has faster dynamics compared to the outer voltage loop. Hence, the current loop dynamics are neglected during the design of the voltage controller [23]. Its transfer function is not included, and the perturbation in the duty cycle can be neglected. Therefore, the inductor current to output voltage transfer function \(T_{p2}\)(s) is obtained as:  
\[T_{p2}(s)=\frac{v_{o}(s)}{\hat{t}_{L1}(s)+\hat{t}_{L2}(s)}=\frac{(\mathbf{1} -\mathbf{D})}{nC_{o}\Big{(}s+\frac{\mathbf{1}}{R_{L}C_{o}}\Big{)}} \tag{39}\]  
\[T_{\mathbf{P2}}(s)=\frac{\mathbf{0.35}}{\mathbf{0.001848}s+\mathbf{0.025}} \tag{40}\]  
LEM sensor LV20-P is used to sense output voltage and also to provide the necessary isolation. Here, the voltage feedback gain is chosen as:  
\[H_{2}(s)=24 \tag{41}\]The PI controller transfer function \(T_{\text{C2}}\)(s) is given by Equation (33).  
Figure 11 shows the bode plot for the uncompensated voltage control loop. For the given specifications and selected component values, the crossover frequency is high with poor low-frequency gain.  
The overall open-loop transfer function of the voltage loop is given by:  
\[T_{\text{OL2}}(s)=T_{\text{C2}}(s)\times T_{p2}(s)\times H_{2}(s)\times\frac{1 }{H_{1}(s)}(47) \tag{42}\]  
\[T_{\text{OLI}}(s)=\frac{0.35K_{p}\Big{(}s+\frac{K_{s}}{K_{p}}\Big{)}}{0.001848 8^{2}+0.025\text{s}} \tag{43}\]  
The gain crossover frequency for the voltage controller is selected to be 10 times slower than that of the inner current loop. Application of angle and gain conditions similar to the current control loop for the desired PM of 60\({}^{\circ}\)\({}^{\text{a}}\) at gain crossover frequency of 3150 rad/s results in the gain \(K_{p}\) and \(K_{i}\) as 16.83 and 9767.8, respectively. Taking into account the dynamics of the current control loop, the overall transfer function of the system can be given as Equation (44).  
\[T_{\text{OL}}(s)=\left[\frac{T_{\text{CI}}(s)\times T_{p1}(s)\times T_{m}(s) \times H_{1}(s)}{1+T_{\text{C1}}(s)\times T_{p1}(s)\times T_{m}(s)\times H_{1} (s)}\right]\times T_{\text{C2}}(s)\times T_{p2}(s)\times H_{2}(s)\times\frac{ 1}{H_{1}(s)} \tag{44}\]  
The Bode plot of the compensated voltage control loop is shown in Figure 12. Low-frequency gain is improved, indicating reduced steady-state error. The desired positive PM of 60\({}^{\circ}\) indicates a stable system rejecting disturbances for wide operating input voltage and load power variations.  
Figure 11: Bode plot of uncompensated plant in voltage control loop: PM = 94.1\({}^{\circ}\) at 1990 rad/s.  
Figure 10: Outer voltage control loop schematic diagram.
## 3 Results and Discussion
### Case II: Experimental Work
#### 3.2.1 Experimental Setup
The 200 measuring points were aligned to one line in the middle of the blade width, located directly under the simulated damage. The electromechanical shaker with maximum excitation forces equal to 31 N was used to induced vibrations of the blade during the measurement. For this purpose, the sinusoidal signal of constant amplitude and a linearly varying instantaneous frequency range from 0 Hz to 550 Hz was generated. The first stage of the experiment procedure, which was repeated for each damage scenario, was measuring of the Frequency Response Function (FRF) with a resolution of 6400 FFT lines. The FRF function was used to determine the values of resonant frequencies. In the second step, the modes shapes corresponding to the resonant vibrations were determined separately using a sinusoidal excitation at a constant frequency and a narrow frequency bandwidth of 0.02 Hz. The test procedure also included multiple measurements at each point of the grid (from 3 to 5) and averaging the result. This type of measurement was used to minimize the noise level and obtain high accuracy data. However, such a procedure is associated with an extended measurement time. It should be noted that the data collected from LSDV were not subjected to additional noise removal or any other type of signal pre-processing, and it was not possible to eliminate the noise from the signal completely. The problem of high-level noise was mainly related to the form of low-frequency vibrations, which had a high amplitude. This can be particularly noticeable in Figure 11a,c.
## 3 System Model and Problem Formulation
### Correlation and Data Aggregation  
As mentioned previously, data from multiple child nodes along the routing tree can be aggregated in order to reduce the communication load of network. The aggregation process is an essential data compression process and the compression ratio is related to data correlation and redundancy. Due to the uncertainty of the ratio in different application scenarios [7], we use an abstract parameter \(\rho\) to denote the data reduction ratio due to aggregation. To be more specific, if node \(u\) is a child node of node \(v\) in the constructed routing tree and \(u\) transmits its data to \(v\), we can summarize the aggregation function at node \(v\) as:  
\[\omega\big{(}v\big{)}=\big{(}\omega\big{(}u\big{)}+\varpi\big{(}v\big{)}\big{)} \big{(}1-\rho_{uv}x_{uv}\big{)} \tag{1}\]  
where \(\varpi(v)\) and \(\omega(v)\) denote the data amount of node \(v\) before and after aggregation respectively, and \(x_{uv}\in\{0,1\}\) denotes whether aggregation process occurs between node \(u\) and node \(v\). That is to say, if node \(v\) is an aggregation point, the data amount of \(v\) after aggregating income data of node \(u\) is \(\omega(v)=\big{(}\omega\big{(}u\big{)}+\varpi(v)\big{)}\); otherwise, \(\omega(v)=\big{(}\omega\big{(}u\big{)}+\varpi(v)\big{)}\).
# Scaling of FAPAR from the Field to the Satellite
## 2 Study Area and Data
### Measurement and Data Preparation  
The data used in the study are summarized in Table 1, including _in-situ_ measurement, coarse-resolution FAPAR products, fine-resolution satellite imagery (ASTER-Advanced Spaceborne Thermal Emission and Reflection Radiometer) and other ancillary data. All data can be openly accessed from the HiWater website at [[http://westdc.westgis.ac.cn/data/](http://westdc.westgis.ac.cn/data/)]([http://westdc.westgis.ac.cn/data/](http://westdc.westgis.ac.cn/data/)).
## 1 Introduction  
In partnership with scientific laboratories and industry, and for several years, CNES has studied the feasibility of a high-resolution ocean topography mission based upon a new class of wide-band Ka-band altimeter in preparation of the post-ENVISAT mission and in order to complement the OSTM/Jason-2 mission. A preliminary definition study of a coupled altimeter-radiometer instrument has been performed in 2001-2003 with the support of Alcatel Space.  
Altimetry is a key technique to provide ocean users with the dynamic data to contribute to the development of operational ocean applications. To answer the user needs in terms of time-space sampling of the ocean for operational purposes, the AltiKa payload could be embarked on a constellation of three identical satellites flying in the same orbit plane with a 120 degrees lag in terms of position on the orbit, as a complement to Jason-like reference missions [1]. The central objective is the retrieval of the ocean mesoscale circulation and data assimilation in global or regional ocean models. Moreover, other applications of the mission have been identified: coastal altimetry, continental water studies, ice sheet monitoring, low-rain systems characterization are the main'secondary' objectives identified so far.  
The proposed architecture for the Ka-band altimeter is based on the classical deramp technique for pulse compression and takes benefits of Alcatel Space and CNES experience in the development of Poseidon-1, -2 and -3 and SIRAL (European Space Agency Cryosat mission). A bi-frequency radiometer is part of the compact Ka-band payload. Both altimeter and radiometer share the same antenna.  
Apart from the combined altimeter and radiometer, this payload also consists of a DORIS plus LRA (Laser Retroreflector Array) orbitography system that will ensure a high level of accuracy in terms of orbitography.  
Sections 2 to 4 are devoted to an extensive overview of the context in which mission requirements were defined. The altimeter / radiometer instrument is described in sections 5 and 6. Specific items are detailed in section 7 to explain how this payload shall meet the science and operational requirements. Expected performances are displayed in section 8. Last, section 9 gives the current status of the payload development and flight perspectives.
# Quantitative Modeling of Near-Field Interactions in Terahertz Near-Field Microscopy
## 4 Conclusions  
In this study, we propose an FEM model to investigate the near-field interactions between the tip and sample in THz s-SNOM. By modeling the tip vibration, the electric field of each point in the near field can be determined. Moreover, the result of our proposed 2D model is consistent with the result of [PERSON]'s model which proves the model's accuracy. After that, the edge effect in THz s-SNOM is discussed by our proposed 2D model. The third-order harmonic signal S\({}_{3}\) is proven to reduce the effect effectively. In addition, in the actual imaging process, extracting the S\({}_{3}\) signal for imaging can obtain a more ideal imaging effect; the reason can also be better explained by our 2D model. Finally, the proposed 2D model is used to examine the effects of different incident wave frequencies and tip lengths on the enhancement of the near-field electric field, by considering the antenna effect. Our results do not show a good consistence with [PERSON]'s study [14]. The strongest enhancement effect occurs when the length of the tip is equal to 0.3\(\lambda\) in our model, but 0.5\(\lambda\) in [PERSON]'s model. The inconsistencies can be explained by the fact that our simulation settings considered a lower incident wave frequency and longer tip length. Further study would verify the result in experiments. Moreover, our model can simulate the influence of any tip geometry on the electric field strength of each point in the near-field space range and can obtain images of signals of all orders of the harmonic signal. Our 2D model allows us to change the shape of the tip or the surface of the sample as needed to simulate the situation of a complex tip or samples in actual experiments. The simulation model and method can help us to better understand the phenomenon occurring in THz s-SNOM, such as the edge effect and antenna effect, and serve as a reference for the s-SNOM probe design.  
Conceptualization, Z.P.; Methodology, Z.P.; Validation, D.Z.; Formal analysis, Z.P.; Data curation, S.G.; Writing--original draft, Z.P.; Writing--review & editing, D.Z. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Innovation Promotion Association CAS, grant number E1213A041S.  
Not applicable.  
Figure 11: Simulation results of electric field enhancement of matching the tip length to the wavelength.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors would like to thank the anonymous reviewers for their valuable comments that improved the quality of this article.  
The authors declare no conflict of interest.
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.4 Path Loss Prediction Method Based on GNN  
Graph neural networks have recently been explored for path loss prediction in [23]. We implemented this network based on PyTorch version 2.3.0 using appropriate graph neural network-related libraries. The hyperparameters of this network are the number of graph convolution layers, the number of hidden units per layer, the activation function, the K-value of the K-nearest neighbor, the learning rate, the weight decay coefficient, and the type of loss function. Table 8 presents the optimal combination of hyperparameters for the graph neural network determined through the hyperparameter optimization process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Search Space** & **Selected Value** \\ \hline Number of convolutional layers & [1, 2, 3] & 2 \\ Filters in the first conv layer & [16; 32; 64] & 32 \\ Filters in the second conv layer & [16; 32; 64] & 64 \\ Kernel size of the first conv layer & [3, 5] & 3 \\ Kernel size of the second conv layer & [1, 3] & 1 \\ Pooling kernel size & & 2 \\ Activation function & [”relu”, “tanh”, “sigmoid”] & “relu” \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyperparameter search space and selected values for CNN.