esa-sceva/satcom-chunk-collection huggingface dataset:
# A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series
## 4 Results and Discussion  
### Station-Based Reconstruction of Land Surface Temperature  
When validating the accuracy of the remote sensing inversion of the land surface temperature and considering the effect of the elevation on the temperature, we found that the precipitation, dryness, and topography of this region were generally consistent. This study divides the existing land surface temperature observation stations in the Beijing-Tianjin-Hebei region into five sub-regions based on the DEM elevation data. We selected one meteorological station from each sub-region to conduct a correlation analysis between the in situ remote sensing inversion of the land surface temperature and the observed daily average temperature.  
This study uses data from 24 ground meteorological stations in the Beijing-Tianjin-Hebei region and their corresponding daily average surface temperatures provided by the China Meteorological Data Sharing Service Network. We selected observation stations with a near-average elevation for each sub-region to retrieve the remote sensing-inversed data for the daily land surface temperature in 2002 from these five stations. In addition, we introduced the in situ data for the daily average land surface temperature from the observation station into the Kalman filter as the \"true value\" to optimize the remote sensing-inversed data. The statistics before and after the reconstruction are presented in Table 1.  
The statistical results presented in Table 2 indicate that after Kalman filter reconstruction, the average root-mean-square error (RMSE), Pearson's coefficient, and mean absolute error (MAE) of the land surface temperature at each station improved significantly. Moreover, the change in the time series is more consistent with the change in the ground meteorological observations. Therefore, this confirms that the Kalman filter is able to optimize the time series of the short-period land surface temperature data well.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 3 White Box Calibration: A Generic Methodology  
The white box calibration can essentially be divided into seven steps, as represented in Figure 3.  
Knowing accurately where the wind is probed is crucial for the estimation of wind characteristics. Hence, the first three steps relate to the beam's position (see Section 4). Step (1) verifies the geometry of the beam(s)' trajectory. The values of the cone or opening angles are measured and compared to the manufacturer's specification. Step (2) is the inclinometers calibration, needed to correct for the lidar-indicated tilt \(\varphi_{ind}\) and roll \(\psi_{ind}\) angles, that are involved in the vertical projection of the reference HWS (Equation (2)). The inclinometers calibration allows uncertainty estimations on \(\varphi_{ind}\) and \(\psi_{ind}\). Steps (1) and (2) should be performed prior to the \(V_{LOS}\) measurements as they provide information necessary to the range configuration and data analysis. In step (3) the sensing range--obtained either by ranging or variable focus--is verified. As nacelle lidars measure in a flow having vertical and/or longitudinal gradients, a range error will cause biases.  
Steps (4) and (5) are the LOS velocity field measurements and uncertainty assessment, where data from the lidar and reference instrument(s) are collected, the calibration relation obtained, and uncertainties propagated to the lidar-measured \(V_{LOS}\) (see Section 5). They constitute the most central part of the white box calibration and can generically be applied to any Doppler wind lidar system.  
In steps (6) and (7), wind characteristics are estimated according to the reconstruction algorithms and uncertainties on their outputs assessed (see Section 6). For simple wind models assuming horizontal flow homogeneity, wind characteristics may be derived from an analytical expression, in which case the GUM methodology can be applied (see Section 6.2.2).  
Essentially, the genericity of the white box approach lies in the \(V_{LOS}\) calibration. Indeed, the procedures to complete steps (1)-(3) are specific to the lidar technology (CW, pulsed, scanning or step-staring, etc.). Steps (6) and (7) depends on the reconstruction algorithm, although the framework for uncertainty assessment may be similar.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves
### Hydrodynamic Theory in Ship Hydroelasticity
#### 2.3.4 Radiation Wave Force  
For ships in regular waves, the linear radiation force acting on the mean wetted hull surface in calm water can be written as:  
\[\mathbf{F_{R}}(t)=-[\mathbf{A}(t)]\mathbf{\dot{p}}_{r}(t)-[\mathbf{B}(t)] \mathbf{\dot{p}}_{r}(t) \tag{13}\]  
where \([\mathbf{A}(t)]\) and \([\mathbf{B}(t)]\) are called hydrodynamic coefficients matrices of added mass and added damping, respectively. The components of \([\mathbf{A}(t)]\) and \([\mathbf{B}(t)]\) can be derived as follows [24]:  
\[\left\{\begin{array}{ll}A_{rk}(t)=-\frac{\rho}{\omega_{e}^{2}}\text{Re} \iint\limits_{S(t)}\mathbf{n}\cdot\mathbf{u}_{r}(\text{i}\omega_{e}-U\frac{ \partial}{\partial x})\phi_{k}ds\\ B_{rk}(t)=\frac{\rho}{\omega_{e}}\text{Im}\iint\limits_{S(t)}\mathbf{n} \cdot\mathbf{u}_{r}(\text{i}\omega_{e}-U\frac{\partial}{\partial x})\phi_{k} ds\\ \end{array}\right.(r,\ k=1,\ 2,\cdots,m) \tag{14}\]  
For ships in irregular waves, the wave memory effects which result from the persistence of radiated waves generated by the ship's motion and its scattering of incident irregular waves play an import role in the determination of radiation wave force. The radiation wave force in irregular waves can be expressed as [25]:  
\[F_{R}(t)=-[\mathbf{A}^{\infty}]\mathbf{\ddot{p}}_{r}(t)-\int_{0}^{t}K(\tau)p _{r}(t-\tau)d\tau \tag{15}\]  
where \([\mathbf{A}^{\infty}]\) is the infinite frequency added mass, \(\mathbf{K}(\tau)\) is the retardation function matrix. The component \(K_{rk}(\tau)\) of \(\mathbf{K}(\tau)\) can be calculated according to the Kramers-Kronig relation [26]:  
\[K_{rk}(\tau)=\frac{2}{\pi}\int_{0}^{\infty}B_{rk}(\omega_{e})\cos(\omega_{e} \tau)d\omega_{e} \tag{16}\]  
where \(B_{rk}(\omega_{e})\) is the hydrodynamic coefficient of added damping already given in Equation (14). Note that based on the assumption that the wave encounter frequency \(\omega_{e}\) is relatively high and the vessel velocity \(U\) is relatively low [27], the operator \(U^{\partial}/_{\partial x}\) in Equations (6), (8-10) and (14) can be dropped.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 2 MXenes as Supercapacitor Electrodes
### Advantages of MXenes in Supercapacitors  
Supercapacitors, also known as ultracapacitors or electric double-layer capacitors (EDLCs), are advanced energy storage devices that have gained increasing attention due to their high power density, rapid charge/discharge capabilities, and extended cycle life. Unlike traditional batteries, which store energy through chemical reactions, supercapacitors store energy electrostatically. This fundamental difference makes supercapacitors an appealing technology for applications requiring quick bursts of power, such as regenerative braking in electric vehicles, grid energy storage, and consumer electronics. However, despite their advantages, supercapacitors face challenges related to their energy density, which is typically lower than that of batteries.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 4 Experimental Section
### Evaluation Metrics  
To comprehensively evaluate the quality of point cloud generation, we used CD (Chamfer Distance) and EMD (Earth Mover's Distance) [45] as evaluation metrics. CD measures the average squared distance between the generated point cloud and the ground truth point cloud. A smaller CD distance indicates that the generated point cloud is closer to the vicinity of the building's surface, reflecting the completeness of the point cloud shape and the smoothness of the edge contours. A smaller EMD distance is preferable, indicating that each spatial point generated by the model can be paired with real sample point cloud positions with smaller distances, reflecting the similarity in point cloud resolution and density.  
Let \(G\) be the generated point cloud set and \(R\) be the real point cloud set. \"\(x\)\" and \"\(y\)\" are point clouds in point sets \(G\) and \(R\), respectively.  
\[d_{CD}(G,R)=d_{CD}(G\to R)=\frac{1}{|G|}\sum_{x\in G}\min_{y\in R}\|G-R\|_{2}^ {2}, \tag{15}\]  
In the equation, for each point in set \(G\), you should first find the closest point in set \(R\), calculate the Euclidean distance, and then sum these distances and take the average.  
\[d_{CD}(R,G)=d_{CD}(R\to G)=\frac{1}{|R|}\sum_{y\in R}\min_{x\in G}\|R-G\|_{2} ^{2}, \tag{16}\]  
The above equation follows the same calculation process as Equation (15), but with the point sets taken in reverse.  
The EMD requires a one-to-one point-to-point mapping between two points.  
\[d_{EMD}(G,R)=\min_{\phi:G\to R}\frac{1}{|G|}\sum_{x\in G}\left\|x-\phi(x) \right\|_{2}, \tag{17}\]  
\[d_{EMD}(R,G)=\min_{\phi:R\to G}\frac{1}{|R|}\sum_{y\in R}\left\|y-\phi(y) \right\|_{2}, \tag{18}\]  
In the equation, \(\phi(\cdot)\) is the bijection between them.
# An Efficient Parallel Algorithm for Polygons Overlay Analysis
## 3 Methods
### Parallel Spatial Union Algorithm
The specific process is as follows:  
(i) First, create a data structure featureInfo for each feature in each layer. The main member variables are fid, layer_num, hilbert_code, and mbr_pt. The fid is used to record the cursor of the feature in the layer. As a query condition for quickly extracting data in the future, layer_num records which layer is one of the filtering conditions of the data query. The hilbert_code defaults to a null value when the FeatureInfo is initialized, which is used to record the encoding of the Hilbert space sorting. Mbr_pt records the center point of the desired MBR as a feature point for Hilbert ordering. typedef struct  
long fid, hilbert_code;  
GTPoint* mbr_pt;  
} featureInfo;  
(ii) Put the featureInfo in the two layers into the vector array Vector\(<\)featureInfo*\(>\) and use the Hilbert spatial sorting function hilbert_sort() to sort and encode the elements in the array. (iii) According to the number k of available computing nodes, the total n elements are sent to one node every n/k according to the Hilbert coding order. The polling method only sends the Hilbert space sorting number interval instead of sending the specific geometry object. (iv) The child node receives the Hilbert space sorting coding interval, extracts the geometric objects of the feature from the local database according to fid and layer_num in featureInfo, and starts to merge the intersecting polygons in the interval. (v) Continue the merge in the form of a single layer binary tree merge until all intersecting polygons are merged. According to this layer, the overall layer is added to perform Hilbert sorting decomposition. The advantage is that the data is divided into non-redundant polygons of each node. The unique characteristics of Hilbert coded values can ensure the low coupling of joint calculation between processes. The binary tree parallel combination can reduce the pressure of the main node recovery. Therefore, the scheme is feasible, and theoretically, an acceleration effect can be obtained. The following is an experimental verification of the multi-layer Hilbert joint method using actual data. The computational complexity of this algorithm is O(N+K), which is derived from the count of polygons for the overlayed map layers. Given \(N\) polygons in each layer, if using the brute-force overlay test, the complexity of this algorithm should be O(N\({}^{2}\)). However, we performed an intersecting filter process that can exclude the unnecessary computing for polygons that do not overlay to each other in the space. Thus, there would be K polygons in the overlayed layer that participate in overlay computation. Ultimately, the complexity of this algorithm should be O(N+K).
### Limitations of Field Method
Differently from Sentinel, PRISMA satellite (hyperspectral precursor of the application mission) is a recently introduced earth observation system launched by ASI (Italian Space Agency), equipped with an innovative electro-optic hyperspectral sensor capable of chemical scene characterization [28]. The sensor high spectral resolution allows for environmental analysis, ranging from geological mapping, vegetation analysis, and pollutant diffusion [29; 30; 31].
## 5 Conclusions
### 5G Can Improve Information Security in IOTA and IoTeX  
Considering the findings of our study and our prior conclusions, we state that 5G is well positioned to contribute to the security of IOTA and IoTeX platforms. The three main 5G services, i.e., enhanced Mobile BroadBand (eMBB), massive Machine Type Communication (mMTC) and URLLC (Ultra Reliable and Low Latency Communications) can:  
1. Enable faster communications between IoT nodes so that high-value nodes can distribute their wealth more securely and quickly.
2. Allow for the implementation of more comprehensive, more fine-grained and faster identity and access management systems that would serve IOTA and IoTeX nodes.
3. Apply these 5G improvements not only at the edge level to tackle communications with IoT nodes but also between edge and cloud servers participating in the IoT platform, also known as \"fog computing\" or \"fog networking\", as it is the case in IoTeX [26]. This would mean that IoTeX could have the potential to quickly reap benefits from 5G given its edge and cloud design.  
We use _app.diagrams.net_ in Figure 8 to summarise in an infographic the main points of this study.
## 2 Related Works
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Result**} & \multicolumn{1}{c}{**MAPE\%**} & **Reference** \\ \hline MLR with AR2() & Bayesian estimation periods consistent and future accuracy compared to OLS estimation & 1\% to 9\% & [25] \\ \hline PSO with ANN & Implementing PSO on ANN model outperformed shallow ANN model & 3.44\% & [43] \\ \hline OLS & \multicolumn{1}{c}{Inattention of variable improves the prediction accuracy} & \multicolumn{1}{c}{\textgreater{}4\%} & [44] \\ \hline GIS and Bayesian estimation & Including temperature variable in a model can improved the prediction accuracy up to 20\%. & 2\% to 3\% & [25] \\ \hline PSO to CA with ANN & TSPCA+ outperformed PSO with ANN & 53\% & [25] \\ \hline OLS, GSAR, RNN & OLS and GSAR models showed better forecasting accuracy than RNN & 1.74\% to 2.95\%. & [22] \\ \hline Ensemble for regression and ML & Lowers the test MAPE implementing Blocked Cross Validation scheme. & 2.6\% & [27] \\ \hline FINN, RNN based LSTM \& GRU & For weekdays and for aggregate data GRU shows better accuracy & 2.47\% to 3.44\%. In this study \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of published works based on 2009–2013 EGAT dataset [27].
# UAV-Borne Profiling Radar for Forest Research
## 2 FGI-Tomoradar Development
### Range Calibration  
For FMCW radar, the linearity of the frequency modulation is critical for the accuracy of the outputs, as shown conceptually in Figure 2. That is, if the frequency modulation is not linear, the beat frequency for a point target will not be constant, and the range resolution will deteriorate. This limitation is a fundamental problem with the previous FMCW radar system. If the sweep bandwidth is increased, the range resolution improves. However, simultaneously, the total nonlinearity increases, and the range resolution degrades. Thus, the time variant of the beat frequency introduces a smeared frequency spectrum. Using a DDS device in ramp generation and replacing the voltage-controlled oscillator (VCO), which have been reported as the primary sources of nonlinearity in the FMCW system [25; 26], is one method to improve Tomoradar's linearity. Theoretically, the nonlinear characteristics of the frequency modulation module and the combined non-flat in-band response of all the RF components of the system generate nonlinearities in signal frequency. Although the non-linearity of the frequency modulation module is mitigated, the measurement should be calibrated to assure its accuracy. The calibration of profiling radar is also discussed in [PERSON] et al. [27].  
Based on the linear relationship, the range calibration was designed to probe a target at different positions using Tomoradar and to record the target backscatter waveform intermediate frequencies. In addition, the real distance between the target and Tomoradar was measured and then compared with the target range derived from the frequency. Eventually, a calibrated linear model was calculated. A 30 cm Luneburg lens (RCS = 100.35 m\({}^{2}\)) was used as a target and positioned on a wooden cart. Wood produces less backscatter signal, which was treated as noise in the experiment. A tape ruler was fixed between the radar and the target to offer an approximate range measurement during the test. As the experiment was conducted, the lens was moved along the tape ruler, and the real distance between the lens and radar was measured using a laser distance meter.  
During the calibration, the distance from the radar to the lens increased from 10 m to 40 m in one-metre steps. The digitizer recorded the reflected IF spectrum for each step. By repeating the measurement at different distances, the effect of multipath propagation was decreased. Figure 3 presents the calibration arrangement and surrounding environment. Figure 4 shows the asymptotic  
Figure 2: Effect of the non-linearity of frequency modulation on beat frequency.  
fitting line between the measured frequency and distance. The relation between the true distance \(R\) and the measured intermediate frequency \(f_{if}\) is as follows:  
\[R=\frac{f_{if}-2.5893}{2.1707} \tag{7}\]
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 3 Background and Preliminaries  
### Macro-Femtocell System  
Since the invention of the femtocell was intended to compensate for blind spots and edges in the macrocell base station transmission coverage, it improved the service quality of the network [15]. Hence, we chose a combination model with one macrocell base station and several femtocell base stations. The macrocell base station serves as one of the transmission stations that vehicles can select. The base station plays the role of overall control [16]. All upstairs requests and resource allocations are processed through the macrocell base station. The setting of the femtocell base stations is within the coverage of the macrocell base station [17]. On the one hand, this approach could strengthen surrounding signal strength. On the other hand, it could share the transmission pressure of the macrocell base station. Thus, we set the macrocell base station to occupy the dominant position, which also has the function of data statistics and a unified deployment of communication resources [18].
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 2 Wireless Sensor Node: An Overview
### Power Supply Sub-System  
The power supply sub-system usually consists of a battery and/or a supercapacitor for storing and powering the sensor node. For the different application scenarios of wireless sensor networks, the intended lifetime of the sensor nodes may be several years, and battery replenishment or replacement can be costly, inconvenient or even impossible, especially in large-scale, remote and hazardous environments. Therefore, designers introduce ultra-low-power and energy-efficient sensor nodes including the associated communication protocols and the motes' sleep/awake mode schedules. Energy harvesting from potential sources such as RF signals, solar, vibrations and so on may also be incorporated to recharge the batteries [17].
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 6 Conclusions  
In this paper, we introduce a novel malicious traffic detection method based on an efficient FL framework of BERT, called MT-FBERT. MT-FBERT pre-trains the network traffic model to take advantage of the unlabeled traffic data to learn generic traffic patterns without relying on expert experience. Furthermore, we employ an FL framework optimized for efficient malicious traffic detection, which operates across distributed clients possessing labeled malicious traffic datasets. This framework employs an adaptive neuron selection mechanism that dynamically identifies and updates only the most significant neurons in the global model during client-side fine-tuning. MT-FBERT successfully addresses the dual challenges of privacy preservation and detection efficacy in multi-organization cybersecurity environments.  
Through extensive experimentation across multiple benchmark malicious traffic datasets, MT-FBERT demonstrates its powerful ability to generalize from limited data and various distribution shifts. The efficient FL framework of MT-FBERT maintains strong performance while demanding minimal computational resources. This makes it particularly advantageous in resource-constrained environments. In the future, we will deploy MT-FBERT in the real-world network environment to validate its practical efficacy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AC** & **PR** & **RC** & **F1** \\ \hline MT-FBERT\_FedAvg & 0.8688 & 0.8700 & 0.8655 & 0.8683 \\ \hline MT-FBERT\_FedOpt & 0.8750 & 0.8798 & 0.8796 & 0.8841 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Global model aggregation evaluation on the CICMalAnal2017 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AC** & **PR** & **RC** & **F1** \\ \hline MT-FBERT\_MBM & 0.7149 & 0.6693 & 0.6892 & 0.7570 \\ \hline MT-FBERT\_MBM+NBP & 0.8688 & 0.8700 & 0.8655 & 0.8683 \\ \hline \hline \end{tabular}
\end{table}
Table 12: NBP evaluation on CICMalAnal2017 dataset.  
**Funding:** This research was funded by the Basic Research Program of Yunnan Provincial Science and Technology Department, China, grant number 202501 AS070131.  
**Data Availability Statement:** The datasets and codes used during the current study are available from the corresponding author on reasonable request.  
**Acknowledgments:** We would like to express our sincere gratitude to the anonymous reviewers for their invaluable feedback and insightful comments. We also appreciate their time and effort in reviewing our work, which significantly contributed to the improvement of our paper.  
**Conflicts of Interest:** Author [PERSON] and [PERSON] were employed by the company China International Water & Electric Corp. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.