esa-sceva/satcom-chunk-collection huggingface dataset:
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results
### North Jakarta  
North Jakarta boasts the most diverse urban green spaces compared to other administrative regions. It encompasses all six categories of urban green space, ranging from agriculture to wetland areas, with a total urban green space area of 51.01 km\({}^{2}\). Out of  
\begin{table}
\begin{tabular}{c c} \hline \hline  & RF \\ \hline PlanetScope only & Multiple images \\ \hline PlanetScope only & Multiple images \\ \hline PlanetScope only & Multiple images \\ \hline PlanetScope only & Multiple images \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results.  
a total area of 147.46 km\({}^{2}\), 34.59% is classified as urban green spaces. The largest portion of urban green space consists of mixed areas (17.83 km\({}^{2}\)), followed by shrubland (12.41 km\({}^{2}\)), grassland (11.7 km\({}^{2}\)), forested areas (3.89 km\({}^{2}\)), wetlands (3.23 km\({}^{2}\)), and agricultural zones (1.95 km\({}^{2}\)) (Figure 4). Located in the northern part of Jakarta, the wetland areas are predominantly covered by mangroves (Figure 5), while the agricultural zones are mainly dominated by paddy fields.  
This area, formerly utilized by numerous illegal fish farmers, commenced its restoration efforts in 1998. Transforming the region from a forested area into a fishpond not only led to the removal of trees but also inflicted harm on the environment and the mangrove ecosystem. Presently, it thrives as a wetland ecosystem primarily characterized by mangrove trees. This conservation site is known as the Angke Kapok Nature Tourism Park, and it is accessible to the public.  
Unfortunately, certain sections of North Jakarta's mixed urban green space are situated within the exclusive Pantai Indah Kapuk district, which is restricted to residents only. In North Jakarta, there are approximately 145 small parks accessible to the public, including a city park of Taman Hutan Kota Kebon Pisang Penjaringan, interactive parks, environmental parks, and a public building park of Taman Jakarta Islamic Center. The rest of the urban green space is green lane roads and water banks, and eleven locations provide open spaces for cemeteries.
# Fiber-Optic System for Monitoring Pit Collapse Prevention
## 4 Discussion  
According to the results of the experiments, an automatic approximation was carried out; the best mathematical model was a linear approximation. As a result, the laboratory sample of the fiber-optic sensor showed a fairly high linearity. The absolute error of 2.31, the relative error of 6.21% and the Student's coefficient of 2.120 were calculated with the confidence interval of 0.94 based on the measurement results.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 4 Results and Discussion
### Radiation Pattern  
The Ant4 antenna achieves a quad-beam in the broadside direction due to the non-uniform spacing, with two in the xz-plane and the other two in the yz-plane at an angle of \(\theta=\pm 54^{\circ}\). To provide a better perspective of the radiation pattern in the band of interest, it is shown at 30 GHz and 30.2 GHz in Figure 19. The simulated HPBW in the xz-plane is \(33^{\circ}\) and the measured HPBW is \(30^{\circ}\), with an X-polarization of \(-21/-23\) dB and \(-20/-20.2\) dB, respectively. Likewise, in the yz-plane, the simulated HPBW is \(31^{\circ}\) and the measured HPBW is \(35^{\circ}\), with an X-polarization of \(-28/-18\) dB and \(-32/-26\) dB, respectively. The  
Figure 17: (**a**) Prototype fabrication of the Ant4 antenna. (**b**) Measurement setup of the \(|\)S-parameter \(|\) and radiation pattern in the anechoic chamber.  
Figure 18: The simulated and measured reflection coefficient \(|\)S111 of the proposed Ant4 antenna.  
simulated and measured gains of Ant4 in the band of interest are 10.3 dBi and 10.6 dBi, respectively, with the simulated average total efficiency of 77%, as depicted in Figure 20.
## 5 Implementation of the Proposed Control Algorithm
### Implementation Procedure  
The implementation procedure of the control algorithm are as follows:  
1. Initial state setting for the control algorithm: The initial state of the proposed controller includes the pole \(p\) and gain \(K\) of \(G_{\rm uv}\), the gain coefficient matrix \(L\), the initial state of (6), namely \(\hat{\theta}(0),\hat{v}(0),\hat{d}(0)\), and the initial output of the PI module, i.e., \(u_{\rm C}(0)\). Among them, \(p\) and \(K\) can be easily obtained through experiment identification. \(L\) can be calculated according to (6) to (10), and \(\hat{\theta}(0),\hat{\theta}(0),\hat{d}(0)\) and \(u_{\rm C}(0)\) should be set to 0.
2. Observation calculation of speed and torque disturbances: By substituting (6) and (7) into (8), the recursive value of \(\hat{v}(k)\) and \(\hat{d}(k)\) can be obtained as: \[\hat{d}(k+1)=\hat{d}(k)+L_{3}(v(k)-\hat{v}(k))\] (13)\[\vartheta(k+1)=\Big{(}\mathrm{e}^{-pT_{\mathrm{s}}}-L_{2}\Big{)}\vartheta(k)+L_{2} \cdot v(k)-\frac{K\Big{(}1-\mathrm{e}^{-pT_{\mathrm{s}}}\Big{)}}{p}(u(k)-d(k)). \tag{14}\]
3. Output calculation of the feedback controller: The recursive output value of the PI, i.e., \(u_{\mathrm{C}}(k)\), can be obtained by calculating (12).
4. Sum the output value of the SAKF and PI: The output of the SAKF and PI are summed to obtain a complete controller output as: \[u(k)=\hat{d}(k)+u_{\mathrm{C}}(k).\] (15)
5. Update the state variable of the system: After step 1 to step 4 are finished, then the output value \(u(1)\) can be calculated, and the state observations \(\hat{\theta}(k),\vartheta(k),\hat{d}(k)\) and the controller output value \(u_{\mathrm{C}}(k)\) need to be updated according to: \[\left\{\begin{array}{rl}\hat{\theta}(k+1)&=\hat{\theta}(k)\\ \hat{\upsilon}(k+1)&=\hat{\upsilon}(k)\\ \hat{d}(k+1)&=\hat{d}(k)\\ u_{\mathrm{C}}(k+1)&=u_{\mathrm{C}}(k)\end{array}\right..\] (16)  
Then, each subsequent iteration calculation should be performed in the order of step 2 to step 5.
## 3 Method  
### Framework  
As shown in Figure 2, the network framework consists of four parts: a backbone network, a channel correction module, a spatial attention module, and an ordinal loss function. A backbone network is used to extract features from the doctor-labeled slices, turning the data of size \(N_{i}\times 3\times 320\times 320\) into a feature map of size \(N_{i}\times C\times H\times W\), where \(N_{i}\) denotes the number of images of the ith patient annotated and \(C,H,W\) denote the dimensionality and size of the feature map. The feature maps are channel-corrected and then weighted by a spatial attention mechanism. The weighted feature maps are fed into the pooling and fully connected layers to obtain the vectors. In order to combine the information of multiple slices of the same person, the top feature at each position is taken as the representative feature for all slices, i.e., a vector of size \(1\times 1000\) is obtained by taking the maximum value, according to the conclusion of [PERSON] et al. (2018). Finally, the ordinal loss is used to train the fully connected layer's classification results.
###### Abstract  
In an active magnetic bearing (AMB) rotor system, the mass imbalance of the rotor is inevitable due to uneven materials, machining errors, assembly errors and other factors. When the rotor rotates, the unbalanced mass generates centrifugal force at the same frequency as the rotational speed, which causes vibration and affects the smooth operation of the rotor. Aiming at the mass imbalance of AMB rotor, a new method based on an adaptive notch filter (ANF) and the influence coefficient method (ICM) is proposed. Firstly, the improved ANF is used to track the rotor displacement signal, and the amplitude and phase information of the displacement signal are calculated. Then, according to the amplitude and phase information calculated by ANF, the ICM is used to calculate the counterweight information of the rotor dynamic balance, which includes the counterweight mass and counterweight position. Finally, the dynamic balance correction of the AMB rotor is realized by adding the calculated counterweight mass to both sides of the rotor. This paper validates the feasibility of the proposed method for the dynamic balance correction of the AMB rotor through simulation and experiment. The four radial displacement unbalances of the rotor were reduced by 56.6%, 62.8%, 49.2% and 63.7%, respectively.  
active magnetic bearing; ANF; influence coefficient method; rotor; dynamic balance correction 2020
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 6 Conclusions and Future Works  
In this paper, we address the service caching problem in a multi-access edge computing system. To increase the cache hit rate, we comprehensively exploit the spatio-temporal correlation structure in the service popularity distribution among the MECSs by using the ConvLSTM model. To achieve the goal, we construct a heatmap to collectively represent the service popularity distribution in a MEC system during a time slot. Using a sequence of heatmaps as an input to the ConvLSTM model, we simultaneously predict the popularity of each service in each MECS. We evaluate the performance of the proposed method through trace-driven simulations. The results verify that compared with a conventional method based on the LSTM model, the proposed method increases the cache hit rate by more than 6.97%. In addition, our method reduces the amount of storage required for predicting the popularity of each service in each MECS by 32.15%.  
Our future works are as follows. Firstly, we will continue our research to further enhance the prediction accuracy. To verify our method, we used the MovieLens dataset and Divvy mobility dataset. We will use other datasets on the user mobility and the service request patterns and show the generality of the proposed method. We will also investigate the explainability of our cache decision algorithm.  
Conceptualization, J.P.; methodology, J.P.; software, H.B.; writing--original draft preparation, J.P.; validation, H.B.; visualization, H.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1F1A1065371). The present research was conducted using a Research Grant of Kwangwoon University in 2023.  
Not applicable.  
Figure 11: Distribution of the cache similarity degree when \(\eta=16\), \(K=128\), and \(M=16\).
## 5 Conclusions  
In this study, we proposed a sophisticated MPR selection algorithm that leverages a multicriteria-weighted approach, taking into account the mobility, energy levels, and trust metrics of mobile nodes. This approach addresses the key challenges of mobility, energy constraints, and security vulnerabilities in MANETs, leading to improvements in network reachability, stability, and longevity. By employing this innovative method, we ensure superior network performance, with enhanced link stability and security.  
The MCW_OLSR protocol was extensively compared against three SOTA baselines, including the original OLSR, DSDV, and SR_OLSR protocols. The results demonstrate that our multicriteria-weighted MPR selection method consistently outperforms these traditional protocols, particularly in dynamic and dense network environments. MCW_OLSR achieved significant improvements in terms of reduced packet loss, lower delay, decreased jitter, enhanced PLR, increased PDR, and improved overall throughput across both the Random Waypoint and ManhattanGrid mobility models.  
The superior performance of MCW_OLSR highlights the effectiveness of integrating mobility, energy, and trust metrics into the MPR selection process. This leads to more reliable and efficient communication, making it a robust solution for highly dynamic MANET environments.  
Future work will focus on refining the multicriteria function by incorporating additional parameters, such as node density and link quality. Additionally, we will explore the integration of machine learning techniques to predict and adapt to network dynamics proactively, further improving the adaptability and performance of the proposed algorithm in more complex scenarios.  
Conceptualization, A.A.; methodology, A.A. and Y.H.; software, A.A.; validation, A.A.; formal analysis, A.A., Y.H., O.A. and S.A.; investigation, A.A. and Y.H.; resources, Y.H., S.A., W.M., J.E. and H.A.-A.; writing--original draft, A.A., Y.H. and O.A.; writing--review and editing, Y.H., O.A., S.A., W.M. and H.A.-A.; visualization, A.A., Y.H. and S.A.; supervision, S.A.; project administration, Y.H., W.M. and H.A.-A.; funding acquisition, Y.H., W.M. and H.A.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data will be shared upon request of the readers.  
The authors declare no conflicts of interest.  
Figure 14: Throughput comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.
## 4 Discussion  
In order to figure out the optimal nonlinear scaling factor to enhance the compensation performance and reduce the computational complexity, multiple improved A-DBP methods have been proposed [16, 27]. However, their performances are sensitive to hyperparameter settings such as the momentum term and learning rate. Here, we propose a low-complexity and high-robustness nonlinear compensation method termed A-DBP-LTM. In the simulation results, our proposed method, when compared to conventional DBP and A-DBP based on the GDA, achieves significant complexity reductions of 31.35% and 58.47%, respectively. Furthermore, it shows high robustness with few iterations and a 0.33 dB improvement in OSNR tolerance through experimental demonstration.  
Although promising results can be seen in the simulation and experiment, the current approach still has several limitations and challenges. First, a relatively low symbol rate of 63 Gbaud was used in our experiment. It is necessary to further verify the feasibility of the proposed algorithm in a coherent system operating at a higher symbol rate such as 130 Gbaud and beyond. Second, in addition to the nonlinear scaling factor \(k\) and the learning rate \(\mu\), there are still multiple parameters which influence the compensation performance but rely on artificial selection, such as the step size \(h\) of SSFM. In the future, research on blind optimization for them should be considered.
## 3 Results and Discussion  
### NDVI Trends  
#### 3.1.1 Trends at Country Scale  
To investigate the overall trend of NDVI in China, regionally averaged mean, standard deviation and minimum of NDVI for each year were calculated and their linear trends were analyzed (Figure 2).  
The mean NDVI of China increased significantly from 1982 to 2011 (\(5.8\times 10^{-4}\) NDVI\(\mathrm{yr}^{-1}\) or \(0.14\%\)\(\mathrm{yr}^{-1}\); \(p=0.002\)), which was close to the growing season NDVI trend (\(0.0007\) NDVI\(\mathrm{yr}^{-1}\)) in China from 1982 to 2010 [35] and slightly lower than the NDVI trend (\(6.22\times 10^{-4}\) NDVI\(\mathrm{yr}^{-1}\)) in Asia-Australia region [50]. The TP was in 1987. Before that TP, annual mean NDVI increased at a high rate (\(0.003\) NDVI\(\mathrm{yr}^{-1}\) or \(0.73\%\)\(\mathrm{yr}^{-1}\); \(p=0.247\)). Afterward, the rate of increase fell to \(0.00035\) NDVI\(\mathrm{yr}^{-1}\) or \(0.08\%\)\(\mathrm{yr}^{-1}\) (\(p=0.176\)), which can be attributed to urbanization in China during the 1990s [58]. The AIC values of the linear regression and piecewise regression models were 266.87 and 266.10, respectively. The difference (0.77) is less than 2, indicating that no significant TP existed at national scale and vegetation cover in the country has continued to increase since 1982, consistent with the findings of [PERSON] _et al._[35] and [PERSON] _et al._[58].  
The standard deviation of NDVI in China also increased significantly from 1982 to 2011 (\(2.8\times 10^{-4}\) NDVI\(\mathrm{yr}^{-1}\) or \(0.21\%\)\(\mathrm{yr}^{-1}\); \(\mathrm{p=0.003}\)). The AIC test shows that there is a significant presence of a TP in the year 2007. The standard deviation of NDVI increased slightly (\(1.0\times 10^{-4}\) NDVI\(\mathrm{yr}^{-1}\) or \(0.08\%\)\(\mathrm{yr}^{-1}\); \(p=0.48\)) before 2007 and increased rapidly after that (\(32.1\times 10^{-4}\) NDVI\(\mathrm{yr}^{-1}\) or  
Figure 2: Trends of (**a**) annual mean; (**b**) standard deviation and (**c**) minimum of NDVI and (**d**) air temperature/precipitation, from 1982 to 2011. Solid line is the linear regressive line. The dashed line is piecewise regressive line.  
2.37% yr\({}^{-1}\); \(p\) = 0.059), indicating that seasonal changes of vegetation have increased significantly, especially after 2007. In contrast, the annual minimum of NDVI decreased from 1982-2011 (0.7 \(\times\) 10\({}^{-4}\) NDVI\(\cdot\)yr\({}^{-1}\) or \(\sim\)0.04% yr\({}^{-1}\); \(p\) = 0.73). The AIC test for this value indicated that no significant TP existed. We also compared the trends using annual mean NDVI _vs._ growing season mean values and found that the identified trends and changes in trends are similar. The trend in annual or growing season NDVI is an average of trends in each month. To detect more detailed changing signals at finer temporal scales, we conducted the same analysis at monthly scales.
###### Abstract  
Monitoring drought precisely and evaluating drought effects quantitatively can establish a scientific foundation for understanding drought. Although solar-induced chlorophyll fluorescence (SIF) can detect the drought stress in advance, the performance of SIF in monitoring drought and assessing drought-induced gross primary productivity (GPP) losses from lush to senescence remains to be further studied. Taking the 2019 drought in the middle and lower reaches of the Yangtze River (MLRYR) as an example, this study aims to monitor and assess this drought by employing a new global, OCO-2-based SIF (GOSIF) and vegetation indexes (VIs). Results showed that the GPP, GOSIF, and VIs all exhibited significant increasing trends during 2000-2020. GOSIF was most consistent with GPP in spatial distribution and was most correlated with GPP in both annual (linear correlation, R\({}^{2}\) = 0.87) and monthly (polynomial correlation, R\({}^{2}\) = 0.976) time scales by comparing with VIs. During July-December 2019, the precipitation (PPT), soil moisture, and standardized precipitation evapotranspiration index (SPEI) were generally below the averages during 2011-2020 and reached their lowest point in November, while those of air temperature (Tem), land surface temperature (LST), and photosynthetically active radiation (PAR) were the contrary. For drought monitoring, the spatial distributions of standardized anomalies of GOSIF and VIs were consistent during August-October 2019. In November and December, however, considering vegetation has entered the senescence stage, SIF had an obvious early response in vegetation physiological state monitoring compared with VIs, while VIs can better indicate meteorological drought conditions than SIF. For drought assessment, the spatial distribution characteristics of GOSIF and its standardized anomaly were both most consistent with that of GPP, especially the standardized anomaly in November and December. All the above phenomena verified the good spatial consistency between SIF and GPP and the superior ability of SIF in capturing and quantifying drought-induced GPP losses. Results of this study will improve the understanding of the prevention and reduction in agrometeorological disasters and can provide an accurate and timely method for drought monitoring.  
**Keywords:** the middle and lower reaches of Yangtze River; drought; solar-induced chlorophyll fluorescence; vegetation indexes; gross primary productivity
## 3 Dual-PRF Waveform Design and Signal Processing
### Signal Processing  
In this subsection, we introduce the signal processing methods of the dual-PRF waveform. Without loss of generality, we apply two commonly used types of signal processing methods to verify that the designed waveform has good adaptability to different signal processing methods. And the two types of methods are based on the DFT method and compressed sensing (CS) theory; namely DFT-based method and CS-based method in the following, respectively.  
Figure 7: Ambiguity function of different waveforms. (**a**) Range ambiguity function. (**b**) Velocity ambiguity function of uniform pulse. (**c**) Velocity ambiguity function of \(2T_{r}\) under uplink-flexible mode. (**d**) Velocity ambiguity function of \(3T_{r}\) under uplink-flexible mode. (**e**) Velocity ambiguity function of \(2T_{r}\) under uplink-fixed mode. (**f**) Velocity ambiguity function of \(3T_{r}\) under uplink-fixed mode.
## 3 Results and Discussion
### Multi-Factor Optimization Experiment Based on Response Surface Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment No.** & **A** & **B** & **C** & **Compressive Strength/MPa** \\ \hline
1 & 12 & 12 & 100 & 18.91 \\
2 & 18 & 12 & 100 & 22.13 \\
3 & 12 & 16 & 100 & 18.85 \\
4 & 18 & 16 & 100 & 24.04 \\
5 & 12 & 14 & 50 & 18.90 \\
6 & 18 & 14 & 50 & 23.14 \\
7 & 12 & 14 & 150 & 17.44 \\
8 & 18 & 14 & 150 & 21.34 \\
9 & 15 & 12 & 50 & 18.96 \\
10 & 15 & 16 & 50 & 22.07 \\
11 & 15 & 12 & 150 & 18.10 \\
12 & 15 & 16 & 150 & 18.81 \\
13 & 15 & 14 & 100 & 22.42 \\
14 & 15 & 14 & 100 & 22.54 \\
15 & 15 & 14 & 100 & 22.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental design and results of response surface method. After optimization of the response surface method, the optimal process parameters for aluminum drops to prepare non-fired bricks can be obtained: 18 MPa forming pressure, 15.19% mixing-water amount and 82.61-132.61 mesh aluminum dress particle size. The compressive strength of the final non-fired bricks can reach 24.77 MPa. The plan is revised according to the actual situation. The mixing-water amount was corrected to 15%. The aluminum dress particle size was revised to 80-130 mesh. At this time, the model's predicted value was 24.75 MPa, and the compressive strength of the obtained non-fired bricks was verified to be 24.66 MPa. The error rate was 0.36%, which is consistent with the prediction results of the response surface model.
# Galileo: The Added Value for Integrity in
## 1 Introduction
The remainder of this paper is organized as follows: the multi-constellation approach along with the estimation techniques adopted are described in Section 2. The RAIM algorithm implemented is introduced in Section 3, and the set-up adopted for the experimental analysis is described in Section 4. Experimental results are analyzed in Section 5, and finally, conclusions are provided in Section 6.