esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The rankings indicate that Class S1 (5) has the highest mean LAI (4.65) and FAPAR (0.80) values, followed by Class S2 (4) and Class S3 (3). In contrast, Class N2 (1) has the lowest mean LAI (4.09) and FAPAR (0.76) values, indicating lower vegetation productivity. These results suggest a clear distinction between the classes, with Class S1 exhibiting the highest vegetation productivity and Class N2 the lowest. The final cropland suitability classes for soybean cultivation, which were based on reclassification of K-means classes to FAO standards and masked on agricultural areas from CORINE data, are presented in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Year** & **Variable** & **R\({}^{2}\)** & **RMSE** & **MAE** & **MAE** & **MAE** & **MAE** & **MAE** & **MAE** & **MAE** & **MAE** \\ \hline
2015 & LAI & 0.9425 & 0.3396 & 0.2755 \\ FAPAR & 0.9517 & 0.0212 & 0.0166 \\ \hline
2016 & LAI & 0.9301 & 0.3575 & 0.2819 \\ FAPAR & 0.9283 & 0.0215 & 0.0168 \\ \hline
2017 & LAI & 0.9383 & 0.3649 & 0.2903 \\ FAPAR & 0.9134 & 0.0366 & 0.0217 \\ \hline
2018 & LAI & 0.9541 & 0.2995 & 0.2403 \\ FAPAR & 0.9568 & 0.0186 & 0.0146 \\ \hline
2019 & LAI & 0.9279 & 0.3895 & 0.2982 \\ FAPAR & 0.9364 & 0.0340 & 0.0185 \\ \hline
2020 & LAI & 0.9569 & 0.2569 & 0.1725 \\ FAPAR & 0.9556 & 0.0167 & 0.0112 \\ \hline
2021 & LAI & 0.9385 & 0.3017 & 0.2229 \\ FAPAR & 0.9495 & 0.0183 & 0.0142 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction accuracy results of peak LAI and FAPAR data per the evaluated machine learning method according to final model fit. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Year** & **Variable** & **R\({}^{2}\)** & **RMSE** & **MAE** & **MAE** \\ \hline
2015 & LAI & 0.9425 & 0.3396 & 0.2755 \\ FAPAR & 0.9517 & 0.0212 & 0.0166 \\ \hline
2016 & LAI & 0.9301 & 0.3575 & 0.2819 \\ FAPAR & 0.9283 & 0.0215 & 0.0168 \\ \hline
2017 & LAI & 0.9383 & 0.3649 & 0.2903 \\ FAPAR & 0.9134 & 0.0366 & 0.0217 \\ \hline
2018 & LAI & 0.9541 & 0.2995 & 0.2403 \\ FAPAR & 0.9568 & 0.0186 & 0.0146 \\ \hline
2019 & LAI & 0.9279 & 0.3895 & 0.2982 \\ FAPAR & 0.9364 & 0.0340 & 0.0185 \\ \hline
2020 & LAI & 0.9569 & 0.2569 & 0.1725 \\ FAPAR & 0.9556 & 0.0167 & 0.0112 \\ \hline
2021 & LAI & 0.9385 & 0.3017 & 0.2229 \\ FAPAR & 0.9495 & 0.0183 & 0.0142 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Class centers of K-means unsupervised classification and final ranks according to FAO standards.
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 2 Methodology
### Generalized Settlement Prediction Model (GSPM)
#### 2.2.1 Physical Meaning of Parameters  
Accurate geotechnical settlement prediction is very difficult, or even impossible, because there are many influencing factors with a certain degree of randomness and variability. In a sense, the actual settlement-time curve is not always a single form [31], such as hyperbolic or S-type. The traditional settlement prediction model cannot predict both hyperbolic and S-type settlement simultaneously. Hence, a GSPM that follows Equation (1) is suggested. The idea of this model comes from the logistic model and the hyperbolic model. The denominator of this model is the imitation of the logistic model, and the numerator part of GSPM is the imitation of the hyperbolic model. The significance of Equation (1) is that it does not need to change the model when the concrete fitting operation is carried out because this model has already unified the S-type model and the hyperbolic model.  
\[y(t)=\frac{a(1-e^{\frac{-t}{b}})}{de^{\frac{-t}{b}}+1} \tag{1}\]  
Here, \(a\), \(b\), \(c\), and \(d\) are parameters; \(t\) is time; \(y(t)\) is settlement.  
The descriptive ability of the model is given in Figure 3. From Figure 3a, parameter a means final settlement. From Figure 3b, parameter b means the settlement stabilization time. The smaller parameter b is, the shorter the settlement stabilization time will be. Parameters a and b have a conventional meaning. However, parameter c and d are different from the traditional logistic model and hyperbolic model based on Figure 3c,d. Hence, parameters c and d can be called morphological change parameters.  
When \(d=0\), the GSPM can be written as \(y(t)=\text{a}\Big{(}1-e^{\frac{-t}{b}}\Big{)}\). Hence, parameter c has no effect when \(d=0\). This means that the GSPM can describe a hyperbolic settlement-time curve when \(d=0\). When \(d\
eq 0\), GSPM can describe an S-shaped settlement-time curve. Hence, physically, parameter d indicates a morphological switch. Moreover, parameter c also describes settlement stabilization time when \(d\
eq 0\).  
Figure 3: Descriptive ability of this model.
## 1 Introduction
Similarly, [40] demonstrated how electric vehicles can share charger location data without revealing private details. FL has also been applied to video management in smart cities. Videos captured by smartphones, bus cameras, CCTV cameras, and other IoT devices are analyzed locally, with only the results shared for further processing. In [41], a federated learning model was proposed for spatial-temporal prediction to address data heterogeneity while preserving user privacy. The authors introduced a global spatial-temporal pattern graph that integrates historical data and a personalized learning mechanism to extract heterogeneity across cities. Moreover, [42] explored FL for smart city applications, emphasizing its potential to handle complex spatial-temporal datasets while maintaining data security. This paper presents a novel energy-efficient federated learning approach for IoT devices. It focuses on the deployment and implementation of FL in IoT environments. The proposed approach, termed TFNN, integrates the low-energy adaptive clustering hierarchy (LEACH) protocol with k-Nearest Neighbor (kNN) classification to effectively recognize event patterns. Additionally, this study introduces an intermediate level of data processing at the cluster head. This approach leverages in-network processing and storage capabilities to enhance performance and extend the longevity of the network. Instead of converging and reaching results over multiple communication-intensive rounds, this paper proposes a single-round memory recall approach. Despite the increasing popularity of FL applications, many existing methods do not fully take advantage of the in-network processing capabilities inherent in IoT networks. In this approach, instead of transmitting all data to a base-station or cloud for processing, global consensus is achieved through in-network hierarchical aggregation, where data are processed as they traverse from the device to the base-station via the cluster head.
## 4 Carrier Phase Accuracy Tests
### Tests and Analysis Based on a Shake Table  
The shake table is another tool to simulate strong earthquakes. The carrier phase measurement accuracy was further verified on the shake table, shown in Figure 14. The GNSS antenna, MEMS accelerometer STIM300, raw data recording device and reference system POS310 were fixed on the shake table. POS310, a high-precision GNSS/INS coupled system, was used to provide dynamic reference value. STIM300 raw data was used to aid GNSS signal tracking. The lever arms between the GNSS antenna and inertial devices were measured and compensated in a data fusion algorithm.  
The shake table was static for some time, and then shook for 30 s in the horizontal direction. Figure 15 showed the GPS satellites distribution during the shake table tests. G29, a low elevation angle satellite, was to analyze the impact of shake at horizontal direction. Additionally, G26, a high elevation angle satellite, was to analyze the impact of shake at vertical direction. The acceleration  
Figure 12: Phase error of stand-alone third-order PLL.  
Figure 13: Phase error of accelerometer-aided second-order PLL.  
information measured by POS310 was shown in Figure 16. The main frequency of acceleration was about 2 Hz, and the maximum acceleration value was larger than1g.  
Phase errors of stand-alone second-order PLL, stand-alone third-order PLL, and STIM300-aided second-order PLL were compared. To reduce the impact of the accelerometer data recording delay, their bandwidths were all set as 15 Hz, and integration times were set as 1 ms. The results of G29 were shown in Figure 17. When the shake occurs, the phase error of the stand-alone second-order PLL was  
Figure 16: Record accelerometer raw data in shake table tests.  
Figure 14: Tests set based on a shake table.  
Figure 15: Satellite sky distribution in shake table tests.  
more than 20\({}^{\circ}\), that of the third-order PLL was more than 40\({}^{\circ}\), and the STIM300-aided second-order PLL was only slightly larger than that of the static. The testing results were consistent with the model analysis and simulator testing results. Figure 18 showed the results of G26. Since the dynamics at vertical direction were smaller than that at horizontal direction, the phase error of G26 was smaller. With the STIM300 aiding, its phase error caused by dynamics also had a big reduction. Shake table tests verify once more that the MEMS accelerometer aiding can effectively improve the GNSS carrier phase measurement accuracy under strong earthquake.  
Figure 17: Phase errors of low elevation angle satellite G29 based on shake table tests; (**a**) Phase error of second-order PLL; (**b**) Phase error of third-order PLL; (**c**) Phase error of accelerometer-aided second-order PLL.  
Figure 18: _Cont._
# Optimized Multiport DC/DC Converter for Vehicle Drivetrains: Topology and Design Optimization
## 2 Analysis of Non-Isolated Interleaved DC-DC Converter
### Losses of Converter
#### 2.3.2. Inductor Losses  
The inductor losses consist of conduction loss \(P_{cond\_L}\), core loss \(P_{core\_L}\), and air-gap loss \(P_{gap\_L}\). As shown in Equation (17), the conduction loss \(P_{cond\_L}\) known as ohmic loss is dependent on the internal resistance of winding \(R_{L}\). The core loss \(P_{core\_L}\) as in Equation (18) are produced from the flux density ripple \(B_{ac}\), which is proportional to the inductor current ripple \(\Delta I_{L}\). The core losses are estimated based on the charts given by the manufacturer (METGLAS, Inc., CC core) [21]. In addition, high-frequency gap loss \(P_{gap\_L}\) in nanocrystalline cores [22] can be computed as in Equation (19).  
\[P_{loss\_L}=P_{cond\_L}+P_{core\_L}+P_{gap\_L} \tag{16}\]  
\[P_{cond\_L}=R_{L}I_{L,rms}^{2} \tag{17}\]  
\[P_{core\_L}=W_{t}(6.5f_{sw}^{1.51}B_{ac}^{1.74})\]  
\[B_{ac}=\frac{0.4\pi N_{t}\Delta I_{L}10^{-4}}{g} \tag{18}\]  
\[P_{gap\_L}=k_{g}g\mathrm{c}^{1.65}f_{sw}^{1.72}B_{ac}^{2} \tag{19}\]  
where \(k_{g}=1.68\times 10^{-3}\) a numerical constant, c (mm): the depth of the iron core.  
The total losses of interleaved converter for one port is calculated in Equation (20).  
\[P_{loss}=N_{ph}(P_{loss\_IGBT}+P_{loss\_D}+P_{loss\_L}) \tag{20}\]
# An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes
###### Abstract  
The mobility of nodes leads to dynamic changes in topology structure, which makes the traditional routing algorithms of a wireless network difficult to apply to the opportunistic network. In view of the problems existing in the process of information forwarding, this paper proposed a routing algorithm based on the cosine similarity of data packets between nodes (cosSim). The cosine distance, an algorithm for calculating the similarity between text data, is used to calculate the cosine similarity of data packets between nodes. The data packet set of nodes are expressed in the form of vectors, thereby facilitating the calculation of the similarity between the nodes. Through the definition of the upper and lower thresholds, the similarity between the nodes is filtered according to certain rules, and finally obtains a plurality of relatively reliable transmission paths. Simulation experiments show that compared with the traditional opportunistic network routing algorithm, such as the Spray and Wait (S&W) algorithm and Epidemic algorithm, the cosSim algorithm has a better transmission effect, which can not only improve the delivery ratio, but also reduce the network transmission delay and decline the routing overhead.  
Keywords:opportunistic network; routing algorithm; cosine distance; similarity; delivery ratio; routing overhead +
Footnote †: journal:
# A Collocation Method for the Numerical Solution of Nonlinear Fractional Dynamical Systems
## 3 Results
### Numerical Solution of Linear Dynamical Systems  
The collocation method described in Section 2.5 can be also used to solve linear problems of type  
\[\left\{\begin{array}{l}{{}_{C}D_{t}^{\beta}Y(t)=A\,Y(t)\,,\quad t>0\,,\quad 0< \beta<1\,,}\\ \\ Y(0)=Y_{0}\,,\end{array}\right. \tag{36}\]  
where \(A\in\mathbb{R}^{m\times m}\) is a real matrix. In this case, the Jacobian matrix is independent of \(\ell\) and reduces to  
\[\mathcal{J}_{G}=\mathsf{l}_{m}\otimes M_{\beta}-A\circ\left(\mathsf{l}_{m} \otimes M_{0}\right),\]  
so that the numerical solution is computed by solving a linear system (cf. [11]). As a test problem we consider the linear dynamical system  
\[\left\{\begin{array}{l}{{}_{C}D^{\frac{1}{2}}y_{1}(t)=y_{1}(t)+y_{2}(t)+y_{3} (t)\,,}\\ \\ {{}_{C}D^{\frac{1}{2}}y_{2}(t)=-y_{1}(t)+y_{2}(t)-y_{3}(t)\,,}\qquad t>0\,,\\ \\ {{}_{C}D^{\frac{1}{2}}y_{3}(t)=-\frac{\sqrt{\pi}}{2}\,y_{3}(t)\,,}\\ \\ y_{1}(0)=0.05,\quad y_{2}(0)=0.05,\quad y_{3}(0)=0.05.\end{array}\right. \tag{37}\]  
taken from [23]. We solved the problem on the discretization interval \([0,8]\) using a set of equidistant collocation nodes with time step \(\Delta t=\frac{1}{32}\) for a total of 254 collocation points. We used as function basis the cubic B-spline basis and the linear B-splines basis with \(h=\frac{1}{16}\) so that the basis has 131 functions and 129 functions, respectively. Thus, the final linear system to be solved has 765 equations and 390 unknowns in the first case and 765 equations and 384 unknowns in the second case. The numerical solutions obtained by the algorithm implemented in the Mathematica Notebooks (Supplementary Materials) are displayed in Figure 6.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 3 Proposed Architecture
### Design Architecture of Proposed Scheme  
The architecture of the proposed scheme consists of a physical/user layer, blockchain edge layer, and blockchain network layer as shown in Figure 1, with the function of each layer is described as follows.  
* **Vehicle Network Layer**: This layer is responsible for vehicle registration and authentication, message broadcasting by the sending vehicle, and rating generation by the receiving vehicle. Before access the network, each vehicle should register to TA (e.g., Department of Transportation for vehicle management) to be an authorized vehicle. After passing the authentication process, vehicles with many sensors can automatically collect data using its sensing devices related to the occurring events on the road. With the help of OBUs, the vehicle broadcasts the information message at a specific time and location using V2V and V2I communication. However, the sender vehicle may act as a malicious vehicle that provides the wrong information. To cope with that issue, the neighboring vehicles at a certain distance and time close to events occurred are allowed to evaluate the received information. Then, they generate the rating based on the message's credibility, and they upload the rating to the nearest RSUs in the edge layer.
* **Blockchain Edge Node Layer**: This layer performs the critical process of calculating message credibility and the consensus mechanism. Here, RSUs are chosen to verify the data from the vehicle network layer. The sending vehicle periodically transmits the information message while the receiving vehicle uploads the evaluation result of the information message's trust value. Then, RSU is authorized to store the data to the blockchain network layer by performing the consensus mechanism. RSUs, as the blockchain edge nodes, contain LSSC and RPSC smart contracts. LSSC is responsible for collecting data uploaded by vehicles and receiving data shared by other RSUs in distributed frameworks. RPSC stores the data into a blockchain network layer using the PBFT consensus mechanism. The data collected by RPSC contains the result of the calculation trust value rating based on majority rule to become the candidate block in the consensus process.
* **Blockchain Network Layer**: After a consensus mechanism creates the new data block and is stored into the blockchain as a distributed ledger, the blockchain network provides a distributed incentive mechanism based on participants' contribution to maintain the system's sustainability.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 3 Experimental Results
### Stop-And-Go Correction  
The contour in Figure 8c is not tidy enough, and this might be caused by the stop-and-go approximation in the DCSA. After stop-and-go correction, the result can be found in Table 7 and Figure 9. Obviously, the compression result in both slant range and azimuth was better compared with results in the last subsection. The sidelobes can be found in the contour shown in Figure 9c. However, the performance is still not good enough and the _sinc_ pulse seems a little noisy. The reason can be summarized as follows.  
At first, in order to remove the deviation between the actual range history and the HREM, a novel formula shown in Equation (15) is compensated in azimuth-time domain. But Equation (15) can only remove the derivation of the reference target located in the middle of imaged scene. In reality, different targets have different range histories, and Equation (15) cannot remove the deviation of all targets located in the whole imaged scene. At the same time, we obtain the sensor's real-time coordinates via the GPS receiver mounted on the satellite and then estimate orbit parameters. But the accuracy of the coordinates and the orbit parameters will influence the model error correction. If we use the coordinates provided by a dual-frequency GPS, a more accurate result might be obtained.  
In addition, the boat is an integration of a set of scatters instead of an ideal point target, and other targets around the boat shown in Figure 6b will also influence the compression.  
Finally, as the pulse signal passes through atmosphere, refraction will occur. Then, the propagation path will be longer than the theoretical range history, and this might affect the parameters' calculations and phase error compensation.  
Figure 8: The compression result after model error correction. **(a)** The compression result of the corner reflector in slant range; **(b)** The compression result of the corner reflector in azimuth; **(c)** The two-dimension contour in azimuth and slant range.  
Figure 9: The compression result after stop-and-go correction. **(a)** The compression result of the corner reflector in slant range; **(b)** The compression result of the corner reflector in azimuth; **(c)** The two-dimension contour in azimuth and slant range.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 3 The Proposed Method
### Embedding and Extraction of the Watermark
#### 3.3.3. Watermark Extraction Steps  
When the watermarked images are illegally manipulated during transmission, an effective way to protect the owner's copyright is to examine the suspicious images with proper watermark extraction algorithms. At the watermark extraction phase, parameters \(\alpha_{1}\), \(\alpha_{2}\), and \(T\) should be known in advance. The detailed steps are as follows.  
Step 1: Calculate the histogram \(H^{\prime}\) from the received watermarked image \(I^{\prime}_{W}\).  
Step 2: Extract \(H^{\prime}_{T}\) in the same way as was done in the embedding rule. In each of the detection ranges, each of the three consecutive equally-sized bins is divided as a group.  
Step 3: Set the pixel numbers of Bin 1, Bin 2, and Bin 3 in the current group as \(a^{\prime\prime}\), \(b^{\prime\prime}\), and \(c^{\prime\prime}\), respectively; according to their proportional relationship, extract the watermark information \(W^{\prime}(i)\).  
Without loss of generality, if it meets the condition \(b^{\prime\prime}\geq T\times a^{\prime\prime}\quad\&\quad b^{\prime\prime} \geq T\times c^{\prime\prime}\quad\&\quad|a^{\prime\prime}-c^{\prime\prime}|< (a^{\prime\prime}+b^{\prime\prime}+c^{\prime\prime})/4\), we can extract watermark information as \(W^{\prime}(i)=2\); if it meets the relationship \(b^{\prime\prime}\geq T\times a^{\prime\prime}\quad\&\quad c^{\prime\prime} \geq T\times a^{\prime\prime}\quad\&\quad|b^{\prime\prime}-c^{\prime\prime}|< (a^{\prime\prime}+b^{\prime\prime}+c^{\prime\prime})/5\), the watermark information is extracted as \(W^{\prime}(i)=3\). In a similar way, the proposed method can extract all of the other watermark information.  
Step 4: Repeat Step 3 until all of the watermark is extracted.  
Similarly, we also give a partial version of the pseudo-code of the watermark extraction process, as shown in Algorithm 2.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods  
As it is challenging to detect abandoned salinized farmland directly from remote sensing imagery, a method that adopts a step-by-step strategy to exclude the other land types was used in this study. First, all pixels (a pixel is an n-dimensional vector where n is the number of investigated satellite imageries) in the stacked time-series NDVI imagery were classified into 20 unlabeled classes via the \(K\)-means clustering algorithm. Then, representative final clusters corresponding to the main land types were manually selected to build a plant shape model. Second, the pixels of non-vegetative areas and water bodies were detected by merging the final clusters based on the predefined threshold; for the vegetative area, the shape-model-fitting method was used to classify each cluster into a specific plant type. Next, sand dunes and residential areas were detected using pixel-based thermal information and the salinity index, and the area of abandoned salinized land was detected after stripping sand dunes and residential areas from non-vegetative areas. Finally, the number of pixels for each land type was counted, and the area was equal to the pixel number multiplied by 30 m \(\times\) 30 m, where 30 m is the spatial resolution of Landsat imagery. A brief workflow chart is shown in Figure 2, and details of the methodology are described in the following sections.
## Nomenclature  
\begin{tabular}{l l} m & mass flow rate (kg/s) \\ C & specific heat (J/kg \({}^{\circ}\)C) \\ T & temperature (\({}^{\circ}\)C) \\ A\({}_{\rm c}\) & surface area (m\({}^{2}\)) \\ P & power (W) \\ G & solar radiation (W/m\({}^{2}\)) \\ NPV & net present value \\ DCF & discounted cash flow \\ CF & cash flow \\ E\({}_{\rm s,y}\) & annual electricity saved by PV/T system \\ E\({}_{\rm el}\) & annual electricity saved by electrical load \\ E\({}_{\rm th}\) & annual electricity saved by thermal load \\ D\({}_{\rm el}\) & annual degradation of electrical performance \\ D\({}_{\rm th}\) & annual degradation of thermal performance \\ COE\({}_{\rm I}\) & domestic electricity cost \\ P\({}_{\rm ee}\) & embodied energy (J) \\ Greek & \\ E & exergy rate \\ \(\eta\) & efficiency \\ \(\eta_{\rm ex,th}\) & exergy thermal efficiency \\ \(\eta_{\rm ex,el}\) & exergy electrical efficiency \\ \(\eta_{\rm ex,PVT}\) & total exergy efficiency by PV/T system \\ i & inflation rate \\ i\({}_{\rm d}\) & discount rate \\ i\({}_{\rm el}\) & domestic electricity inflation rate \\ Subscripts & \\ w & circulating water \\ a & circulating air \\ in & Inlet \\ o & Outlet \\ \(\infty\) & ambient air \\ th & thermal \\ el & electrical \\ tot & Total \\ max & maximum \\ sc & short circuit \\ oc & open circuit \\ \end{tabular}