esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 4 Discussion
### Importance of Land Subsidence Monitoring in Java  
LS is caused by several factors, including groundwater extraction [76; 77], urban development [78], natural compaction [48; 79], and tectonic activity [80]. In Java, as has been extensively discussed in this paper, areas that suffer from LS are urban, industrial, and relatively highly developed areas. As is well established, LS contributes to substantial losses economically, socially, and environmentally. Therefore, continuous LS monitoring is crucial for (1) early detection and prevention [81], (2) infrastructure safety and risk management [82], (3) natural resource management [83], (4) effective urban planning [81], and (5) understanding its long-term mechanisms and trends [84].  
However, continuous LS monitoring faces some challenges that must be addressed. Firstly, continuous LS monitoring using GNSS measurement, despite the fact that it can provide a high-accuracy LS rate, requires a large number of GNSS stations, thus demanding huge financial support. Secondly, continuous LS monitoring using the InSAR-based method has its own challenges: the large amount of data and linear data growth. Therefore, advancement in computational infrastructure and methodological breakthroughs are required to overcome these issues.  
The approach used to retrieve the LS rate in this study is the least-square method, which requires high-specification processing tools. Computational costs will continue to increase linearly in proportion to the growth of the data. To overcome this problem, real-time parameter estimation methods, such as recursive least-squares, Kalman filtering, least-mean-squares, and moving-horizon estimation can be applied. In our case, recursive least-squares (RLS) is more suitable for LS monitoring using InSAR in Java. Compared to other methods, especially Kalman filtering, RLS is simpler to implement and performs very well for cases with stable parameters over time, such as in LS monitoring.  
RLS can estimate the adjustment solution when new data enter the calculation process without having to store all previously collected data [85]. It is efficient in reducing computational system resources and accelerating the time series inversion estimation process, particularly when processing SAR data for deformation monitoring, as the data continuously increase over time; thus, the parameter re-estimation process (deformation magnitude) does not have to be repeated entirely when new data are available.  
However, this method has some limitations, such as numerical instability and being slow in estimating time-varying parameters [86]. However, these limitations can be addressed through specific measures. Numerical instability, mainly caused by round-off error in recursive operations, can be mitigated using a decent decomposition method, such as singular value decomposition (SVD) [87]. The advancements in computation have significantly reduced these issues. Furthermore, computational efficiency can be improved by decomposing the systems into smaller subsystems [88].
## 4 Joint User Scheduling and Hybrid Beamforming Design for Maximizing EE
### Penalty Iteration Algorithm
Using CVX toolbox, calculate the variables \(\left(\boldsymbol{\eta},\boldsymbol{W},\text{SINR}_{l}^{\min},\zeta\right)_{ opt}^{(m)}\) at the \(m\)th iteration according to (77). 10. If \(\left\{\boldsymbol{W}_{l}^{(m+1)}\right\}_{l}^{L}\approx\left\{\boldsymbol{W }_{l}^{(m)}\right\}_{l}^{L}\), then
11. Update \(\lambda_{3}=2\lambda_{3}\). 12. else
13. Update \(m=m+1\). 14. end
15. end
16. Update \(\left(\boldsymbol{\eta},\boldsymbol{W},\text{SINR}_{l}^{\min},\zeta\right)^{( k+1)}=\left(\boldsymbol{\eta},\boldsymbol{W},\text{SINR}_{l}^{\min},\zeta\right)^{(m)}\), \(k=k+1\), \(\lambda_{1}=\lambda_{1}+1\), \(\lambda_{2}=\lambda_{2}+1\). 17. end Output(\(\boldsymbol{\eta},\boldsymbol{W},\text{SINR}_{l}^{\min},\zeta\))\({}_{opt}\). ```  
**Algorithm 1**Joint user scheduling and hybrid beamforming design algorithm.
## 5 Results
### Performance Evaluation as Compared to Baseline Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Inflow (RMSE)** & **Outflow (RMSE)** & **All (RMSE)** \\ \hline HA & 14.02 & 14.91 & 14.46 \\ ARIMA & 9.97 & 10.49 & 10.23 \\ LinUOTD & 9.56 & 10.11 & 9.83 \\ XGBoost & 6.89 & 7.06 & 6.97 \\ MLP & 7.25 & 8.07 & 7.66 \\ ConvLSTM & 7.74 & 8.32 & 8.03 \\ ST-ResNet & 6.08 & 6.63 & 6.35 \\ STDN & 5.98 & 6.51 & 6.24 \\ MGSTC & 5.57 & 5.91 & 5.74 \\ (Ours) & & & \\
3 DCNN(MBSTRU) & 4.01 & 4.26 & 4.13 \\
3 DCNN(MBSTAL) & 3.85 & 4.16 & 4.00 \\
3 DCNN(MBSTALRU) & 3.65 & 3.86 & 3.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset NCYBike inflow/outflow comparison. Figure 6: RMSE of inflow and outflow comparison with baseline methods on BJTaxi dataset.
## 4 Discussion
### Associated Performance of the VPM GPP Products  
Accurate estimation of GPP of terrestrial vegetation is vital for understanding the global carbon cycles in the face of a changing climate. Multiple GPP products are currently available based on different algorithms, but their performances vary substantially when validated against GPP estimates from eddy covariance data. Recently, many studies indicated that FPAR_chl_ can capture well the seasonal variation of vegetation photosynthetic capacity, which greatly improved the seasonal representation of GPP variability [69; 70]. Based on the tower-based flux measurements, these VPM products showed superior performance with regard to site-level validations across a wide range of biome types [23; 29; 71; 72]. The developed VPM GPP dataset has been validated against 113 eddy covariance-based flux towers across the globe, with the relatively high accuracy of the VPM GPP V20 [25]. For most biome types, the VPM GPP did not show a systematic bias across different terrestrial ecosystems in a large area. Particularly in the temperate steppe, VPM is an optimal model among eight satellite-based GPP models due to their representation of the effect of water stress on vegetation productivity [73]. Except for the evergreen forests, the VPM GPP dataset underestimated GPP to a certain extent [25]. Overall, VPM GPP offers a reliable GPP estimation in nonforested natural vegetation and cropland by distinguishing the C3/C4 plants [74]. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP proxy for regional to global carbon cycle studies.
## 2 Experimental Section
### Batch Adsorption  
Batch experiments were carried out in a 250 mL flask containing 50 mL of aqueous solution. The flasks were shaken at 150 rpm at 20 \({}^{\circ}\)C in a constant temperature vibrator (DQHZ-2001A, Taicang Huamei Instrument Co., Ltd., Taicang, China). For the adsorption equilibrium study, 0.01 g of Mag-PVP and Fe\({}_{3}\)O\({}_{4}\) were introduced into a series of 250 mL glass conical flasks containing 50 mL of BPA solutions at different initial concentrations (2.5-50 mg/L). For the kinetic study, 0.02 g of Mag-PVP was added to 100 mL of BPA solution at a concentration of 10 mg/L. Samples were taken at 3, 5, 10, 30, 60, 90 and 120 min for the analysis of residual BPA, respectively. The effect of pH was determined by adding HCl and NaOH into 100 mL of BPA solution at a concentration of 20 mg/L. The influences of ionic strength and coexisting cations were tested by adding NaCl and CaCl\({}_{2}\), respectively. The effect of HA was investigated by changing the concentration of HA from 5 to 60 mg/L, with the initial concentration of BPA fixed at 10 mg/L. The adsorption of BPA on Mag-PVP in several environmental water samples was also tested by fixing the initial concentration of BPA at 10 mg/L. All batch experiments were conducted within 4 h to reach adsorption equilibrium, except for the kinetic study. The regeneration of Mag-PVP was carried out by desorbing BPA from Mag-PVP with methanol, ethanol, toluene and cyclohexane. After reaching the adsorption equilibrium, 20 mL of methanol was added and shaken for 4 h. The regenerated resin continued to absorb BPA again, and the adsorption capacity was calculated after reaching equilibrium. All experiments were duplicated and repeated three times, and the average standard deviation of the duplicated experiments was \(\pm\)10.0% for the individual composition of the reaction system.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 5 Design
### Network Coding
#### 5.3.2 Transmission Strategy  
Multi-reception enables each relay to receive a duplicate packet from the end-devices. In order to reduce network traffic, we utilize an overhearing strategy.  
* Identical packets: Here, all packets in two relays are duplicated. Therefore, only relay with higher priority forward the combined packets.
* Intersection packets: Here, not all packets in the relays are duplicated. Hence, the relay with the lower priority should only transmit the linear combinations that constitute a missing packet from another relay.
* Without intersection: Here, all the packets in relays are different. Therefore, both relays may transmit all of the generated combinations.
## 5 Simulation and Evaluation Results  
In this section, we first evaluate the performance of the proposed DRTO algorithm, and then verify the effectiveness of our privacy protection schemes via simulations. All the simulation parameter settings are listed in Table 1. We assume that the average channel gain \(h_{tt}\) or \(h_{TC}\) follows the free-space loss model  
\[h=A_{d}\left(\frac{c}{4\pi f_{c}d}\right)^{d_{e}}. \tag{16}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline
**Parameters** & **Value** \\ \hline Transmission power of ST \(p_{tt}\) and satellite \(p_{SAT}\) (W) & \(1,3\) \\ Antenna gain \(A_{d}\) and path loss exponent \(d_{e}\) & \(4.11,2.8\) \\ Carrier frequency \(f_{c}\) (GHz) & \(30\) \\ Total bandwidth \(B\) (MHz) & \(800\) \\ Receiver noise power \(N_{0}\) (W) & \(10^{-9}\) \\ Task size \(L\) (MB) & \(100\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters setup.  
Figure 4: The index of optimal offloading location with \(K=N=5\).
# CFD Research for Air Bearing with Gradient-Depth Recesses
## 3 CFD Simulation and Structure Optimization  
### Simulation for Unified-Depth Recesses' Air Bearing  
When the guide sleeve moves along the y-direction, the asymmetry of the restrictors' position will lead to the asymmetry of pressure distribution, as shown in Table 1. Due to the pressure homogenization effect [21; 22], the bearing capacity \(W\) hardly changes unless it exceeds the range of \(L_{\text{cmax}}\). But the rotational torque \(M_{r}\) continues to increase as the eccentricity value increases. The air film itself has a certain degree of stiffness and rotational stiffness, which can offset the rotational torque. But the cost is that the air film must produce spontaneous tilting, which is not desirable because it will reduce the straight motion accuracy [23]. We calculate the rotational stiffness of the air film at different moving distances \(l\), which shows an inverse effect on the theoretical straightness error caused by rotational torque \(M_{r}\), shown in Figure 4.  
The rotational stiffness \(K_{\theta}\) when the air film is tilted by 6 \(\upmu\)m can be obtained by Formula (9). The \(K_{\theta}\) values at different \(l\) are considered as the average ones, which are used to draw an approximate straight line of the rotational torque vs. straightness error. According to the rotational torque result in Table 1, the curve of the maximum straightness error \(\Delta_{\text{max}}\) vs. the moving distance \(l\) is drawn up in Figure 4. It can be seen that when \(l=4\)-\(10\) mm, the straightness error is about 6 \(\upmu\)m, which is unacceptable for high-precision air bearings. Therefore, an improved structure needs to be invented to reduce the straightness error caused by an insufficiency of rotational stiffness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(l\) & \(\begin{array}{c}\text{Bearing}\\ \text{Capacity}\\ \text{W (N)}\end{array}\) & \(\begin{array}{c}\text{Eccentricity}\\ \text{Value (mm)}\end{array}\) & \(\begin{array}{c}\text{Rotational}\\ \text{Torque}\\ \text{(N-m)}\end{array}\) \\ \hline \hline \(0\) & \(242\) & \(0\) & \(0\) \\ \hline \(249\) & \(2-1.02=0.98\) & \(0.244\) \\ \hline \(258\) & \(4-1.34=2.66\) & \(0.686\) \\ \hline \(270\) & \(6-2.28=3.72\) & \(1.004\) \\ \hline \(268\) & \(8-2.73=5.27\) & \(1.412\) \\ \hline \(264\) & \(10-3.43=6.57\) & \(1.734\) \\ \hline \end{tabular}
\end{table}
Table 1: Guideway performance at different moving distances \(l\) (from 0 to \(L_{\text{cmax}}\)).
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 3 Regions of Study and Precipitation Climatology
### Southern Africa
At the same time, the Madagascar mountains represent a key factor for the spatial distribution of precipitation over the island, leading to orographically enhanced precipitation along the east coast. The easterly trade winds prevail throughout the year such that the eastern part of the island is rainy in all months with the exception of only August and September [51]. The precipitation gradient previously described, a positive gradient from south-west to north-east, is also apparent in Figure 4, where the monthly precipitation climatology is presented using the ensemble mean of the satellite products. Rainy months are characterized by precipitation minima of less than 30 mm month\({}^{-1}\) over the Namib desert and peaks up to 300 mm month\({}^{-1}\) in the north of Mozambique. Equally evident are the precipitation peaks in January and February in northern Madagascar; this region is often impacted by tropical cyclones responsible for much of the summer precipitation over the island, whereas the south-western part is substantially dryer [51]. Unlike EA, the precipitation seasonality in SA is much more spatially homogeneous, with most of the precipitation concentrated during the austral summer from October to March (Figure 4). A long dry season with rainfall lower than 30 mm month\({}^{-1}\) extending from May to September characterizes the whole territory, with the exception of the Cape Town area and the east coast of Madagascar. The Cape Town region receives on average about 100 mm month\({}^{-1}\) from May to August, when the track of the temperate weather systems (i.e., extratropical cyclones, cold fronts and cutoff lows) shifts northward. Moreover, the mountains of the Cape Fold Belt surrounding the area affect the precipitation spatial distribution, favoring more abundant rainfall in the coastal zones than inland [52]. Regarding Madagascar, the island's topography has a role intercepting the easterly moist air masses caused by the anticyclonic tropical belt [53]. However, the precipitation amount on the eastern coast is scarce when compared with that of the other months and does not exceed 100 mm month\({}^{-1}\). April and October can be considered transitional months from the rainy to the dry season and vice versa, respectively. In April, most of SA receives rainfall below 30 mm month\({}^{-1}\), while in the northern region of Angola and Mozambique and central mountainous region of Madagascar precipitation intensity is still higher than 100 mm month\({}^{-1}\). A similar precipitation spatial distribution is also found in October.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 2 Materials and Methods
### Meteorological Data and Environmental Data  
Temperature and precipitation data are utilized in this study to characterize the severity of compound drought and heatwave. Monthly temperature and precipitation data are sourced from ERA5 ([[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)), accessed on 21 March 2024), with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). ERA5, generated by the ECMWF, is the fifth generation of the atmospheric reanalysis dataset covering global climate from January 1950 to the present day. These meteorological data have undergone quality control and are widely used in various studies of past climate events.  
The soil moisture data used in this study also originate from the ERA5 reanalysis dataset. Due to variations in soil moisture infiltration rates at different depths, as well as differences in the impacts of evaporation, transpiration, and plant root distribution, soil moisture needs to be categorized into different layers to better understand the changes in soil moisture profiles. Therefore, we analyzed soil moisture data SM1-SM4 at four different depths (Layer 1: 0-7 cm, Layer 2: 7-28 cm, Layer 3: 28-100 cm, Layer 4: 100-289 cm). We utilized 2 m temperature data and 2 m dew point temperature data to calculate VPD. The resolution of both datasets is also 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\).
## 3 Dual-Band Doherty Power Amplifier
### Tri-Band and Quad-Band DPA  
In addition to the dual-band DPA, there are researches which focus on tri- and quad-band DPA [56; 57; 58; 59; 60]. A design approach was used in [58] to cover three frequency bands. In the design, it was taking into considerations that all sections of the DPA should be able to give good performance at each band. However, the implemented design showed a relative shift in the band center frequencies.  
A tri-band DPA targeting three types of mobile applications--LTE, universal mobile telecommunications service (UMTS), and WiMAX--was studied in [60], where two-stage DPA was designed for an average output power of 35 dBm. The results in [60] showed a good scattering parameters response. At the input side of the designed power amplifier, a broadband divider was used to cover the selected frequency bands, on the other hand, the impedance inverter network and the impedance transforming network were designed to fulfil the requirements of the tri-band. The designed PA was fabricated and tested using 10 MHz LTE for the lower frequency band, and two carriers with a WCDMA signal of 10 MHz for the upper bands. Each testing signal is used individually for the design evaluation.  
The work of [58] has been extended in [56] to cover quad-band instead of tri-band operation. The chosen bands were 0.9 GHz, 1.5 GHz, 2.1 GHz and 2.6 GHz. Two transistors were used to implement asymmetrical DPA: these transistors are 10 W and 25 W with different power supplies for the main and the carrier. Although asymmetrical DPA was used, the authors in [56] were looking for only 6 dB OBO. The most complicated part in multiband DPA is the multiband impedance inverter network which should give good impedance inversion over the selected bands so that, in [56], at the input side, a wideband Wilkinson power divider was used to cover the four bands.  
Proof of multi-band Doherty was presented by [61], where three boards were designed targeting three bands; the boards were identical in terms of the artwork, however for each band different capacitors and resistors were used. The work of [61] can be improved by using digitally controlled switches so that one reconfigurable board can be used for the three bands. In [62], a 3-way Doherty power amplifier targeting 1.8-2.2 GHz frequency bands suitable for mobile base-stations was achieved depending on LDMOS RFIC technology, a 47% efficiency accomplished at 12 dB back-off.
# _electronics_
## 6 Conclusions  
To achieve efficient content distribution in satellite networks, in this paper, we design an NDN-based code cache strategy for low-orbit satellites; we use coding operations to remove the transmission correlation of data, place satellite node-coded packages to minimize backhaul link loads and content acquisition delays, solve the optimization problem through a variety of ant colony algorithms, and realize the rapid retrieval of content. Through the cooperative delivery mechanism of the cluster, the management of the satellite network is simplified, and the user can reduce the load of the satellite link for content delivery when covered by multiple satellite nodes.  
Figure 8: Backhaul link load diagram under the number of satellite nodes accessed by different users.  
Figure 9: Comparison of the average cache hit rates of different cache strategies.  
In the simulation experiment section, we compare the coded caching based on the multi-objective (CCMO) method proposed in this paper with the most popular (MP) strategy and coded caching based on random (CCR). We conducted a comparative analysis of the indicators, such as the average cache hit rate, backhaul link load, and content acquisition delay. The simulation results show that CCMO performs better in various aspects.  
For the coded caching of satellite networks, since the coded caching needs to code the original data blocks, it is necessary to attach the coding coefficient to the header of the generated coded data blocks, so as to facilitate the subsequent linear decoding operation. For the coding overhead of coded caching, further research is needed.  
Conceptualization, Z.L. and X.J.; methodology, Z.L. and X.J.; software, X.J. and Y.L.; validation, X.J. and Y.L.; investigation, X.J., Y.L. and L.Z.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., X.J., Y.L. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest.
## 4 Experiments
### Results and Analysis
#### 4.2.8 Hyperparameter Sensitivity Analysis  
To validate the robustness of our model configuration and provide comprehensive guidance for reproduction, we conducted sensitivity analysis on key hyperparameters. Table 6 presents the model's performance under different hyperparameter settings, with all other parameters fixed at their default values as shown in the table.  
The results demonstrate that ARCS maintained a stable performance across a reasonable range of hyperparameter values. The learning rate shows the most significant impact on model performance, with \(1\times 10^{-3}\) providing the optimal balance between convergence speed and stability. Batch size primarily affects training efficiency and memory usage, with 128 offering the best trade-off between computational cost and model performance. The discount factor exhibits less sensitivity, though values close to 0.99 consistently yield better results, which is likely due to the temporal nature of security events requiring longer-term reward consideration. These findings suggest that, while ARCS benefits from proper hyperparameter tuning, it maintains robust performance across a reasonable range of configurations, making it practical for real-world deployment where exact parameter optimization might not always be feasible.