esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data Pre-Processing
#### 3.1.1 Data Collection  
For model construction and testing, we used one confidential dataset and one publicly available dataset. The former represents electricity consumption data collected from the electricity utility of Peninsular Malaysia from year 2019 to 2021. The dataset consists of the timestamp information and electrical energy consumption in MW with frequency of 10 min every day with total sample of 1728 points. The latter represents the local weather information dataset obtained online in \"Time and Date\" websites consisting of hourly temperature (\({}^{\circ}\)C), dew point (\({}^{\circ}\)C), and relative humidity (%) in the same period.  
Figure 2: Framework for electricity load forecasting.
## 3 Results
### RST Rule Integration for ACU Setpoint Control Using RST and FL
Table 13 provides a comparative evaluation of the performance of fuzzy inference systems (FISs) based on different datasets, rule sets, and FIS types. When using the complete range of possible input combinations with 550 rules, the general FIS achieved a low error rate of 1.01% for 1106 test results with a total execution time of 6.29 s. On the other hand, the RST-optimized FIS using the same data showed an improved error rate of 3.94%, as well as a significant reduction in execution time, taking only 3.06 s. For the scenarios validated using actual historical data with four rules (i.e., a 99.27% reduction in the number of rules), the general FIS showed a relatively higher error rate of 2.03% for 467 test results, and it took 2.03 s to execute. The RST-optimized FIS outperformed the general FIS with an error rate of 0.00%, demonstrating perfect accuracy and a relatively faster execution time of only 0.85 s.  
Overall, while the RST-optimized FIS exhibited a higher error rate for the comprehensive dataset, it showed superior performance with perfect accuracy and a significantly reduced execution time when applied to historical data. This further highlights the efficiency and precision of the optimized FIS in real-world scenarios. Conditions related to air quality, such as CO\({}_{2}\), CO, and TVOC, were not considered in the decision rules due to their poor correlation significance, as revealed by the data distribution and data analyses using PCA and K-means clustering methods. Their levels were distributed in a highly positive asymmetric way, indicating the infrequent occurrence of critical air quality.
## 3 Antenna Performance Enhancement Techniques
### Differential Feeding Network (Specific Feeding)  
In [39], a patch antenna fed differentially with wide-band and dual polarization capability with excellent filtering response is proposed. For excitation of square patch antenna, a feeding structure with novel E-shaped is proposed. One end of the feeding network is shortened to the ground for achieving the wider operation bandwidth, also an extra resonant mode is introduced for generation of a third resonance mode. A stepped.  
Resonator with symmetric short/open-circuit is proposed to further enhance the bandwidth. The same resonator not only makes feasible the enhancement in bandwidth but effectively enhances the gain suppression level outside the operation band. For an active VSWR value of fewer than 1.5, a wide operation bandwidth (3.12-3.9 GHz at 22.2%) is achieved with in-hand stable gain suppression level for out-band is better than 18.5 dB. The geometry of the differential feeding network is shown in Figure 7 as follows. The radiating patch and E-shaped feeding structure are printed on the top layer of the upper substrate. One end of each E-shaped feeding structure is shorted to the ground with metal posts. The lower layer is a F4B substrate with a dielectric permittivity of 2.65 and thickness of 0.8 mm. In addition, four pairs of O/SCSIRs are printed on the top layer of the lower substrate. The four ends that close to the center of the substrate are connected to the E-shaped feeding structures with metal posts. The other four ends of the O/SCSIRs are connected with 50 Ohm coaxial cables. A metal ground is printed at the bottom layer of the lower substrate with four U-shaped slots. When port1 and port2 are excited with differential signals, a 45\({}^{\circ}\) polarization is generated. Similarly, a 45\({}^{\circ}\) polarization can be obtained when port3 and port4 are excited with differential signals. When the shorting posts are introduced (the parallel inductance L2 is added), the overall inductance decreases, and the first resonant mode moves to lower frequencies. Thus, the two resonant modes are separated by the shorting posts, and a wide operation band is obtained. The proposed structure with metal post and upper substrate is shown in Figure 14.
## 4 Frozen Wall Elastic-Plastic Problem
### Modified Solution for the Plastic Zone Radius
The tunnel is constructed using artificial freezing method, and the designed average temperature of the freezing wall is \(-10\)\({}^{\circ}\)C. The buried depth of the tunnel is 187.0-248 m, with a longitudinal slope of \(i=1/1650\), and the initial pressure of the surrounding soil is between 0.3-0.9 MPa. The rock surrounding the tunnel is composed of N\({}_{2}\)L\({}^{2}\) argillaceous siltstone, sandy mudstone, sandy gravel layer, and N\({}_{2}\)L\({}^{28}\) loose sandstone. The elastic modulus and Poisson's ratio of frozen soil and unfrozen soil are respectively: \(E_{1}=150\) MPa, \(\mu_{1}=0.35\), \(E_{2}=20\) MPa, \(\mu_{2}=0.35\), and the excavation radius of frozen wall is 3.3 m. The horizontal freezing curtain and freezing hole layout of the tunnel are shown in Figures 7 and 8, respectively, and the relevant geological parameters are listed in Table 1.  
Figure 7: Schematic layout of the freezing pipes. Artificial ground freezing is an unstable thermal conductivity problem with complex boundary conditions, and the solution to its freezing temperature field requires consideration of factors such as phase transition, moving boundaries, and internal heat sources. Based on the basic theories of soil moisture migration, heat change, thermodynamics, and poromechanics during freezing, as well as [PERSON]'s coupled model of water and heat, COMSOL multi-physics finite element software and MATLAB were used to build the numerical calculation models of the freezing temperature field of A-A and B-B sections of the horizontal freezing curtain of the tunnel [2, 39, 40], as shown in Figure 9. Via finite element calculation and analysis, it was found that the thickness of the frozen wall of the tunnel was 2.
## 4 Results and Discussion
### The Results of the ADI Mitigation Models
#### 4.2.5 The Results of the SGD Classifier-Based ADI Mitigation System
The system's performance improves further with model combinations, particularly in extended configurations, which demonstrate the best error reduction (BER = 0.002) and highest SNR (up to \(-13.20\) dB), suggesting the effectiveness of higher learning rates and extended datasets for mitigating ADI and improving communication quality. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Scenario One**} & \multicolumn{4}{c}{**Scenarios Two**} \\ \hline
**Classifier** & & & & & & & & & & & & \\
**SGD** & & & & & & & & & & & \\ \hline Model 1 & 0.008 & 0.62 & 0.67 & 0.69 & 0.21 & 0.24 & 0.012 & 0.65 & 0.71 & 0.62 & 0.22 & 0.23 \\ \hline Model 2 & 0.012 & 0.61 & 0.60 & 0.66 & 0.22 & 0.29 & 0.024 & 0.61 & 0.72 & 0.69 & 0.21 & 0.20 \\ \hline Model 3 & 0.008 & 0.61 & 0.61 & 0.63 & 0.20 & 0.28 & 0.016 & 0.65 & 0.68 & 0.64 & 0.30 & 0.25 \\ \hline Model 1 and 2 & 0.024 & 0.69 & 0.68 & 0.68 & 0.34 & 0.29 & 0.028 & 0.68 & 0.66 & 0.69 & 0.32 & 0.25 \\ \hline Model 2 and 3 & 0.028 & 0.70 & 0.65 & 0.69 & 0.25 & 0.23 & 0.032 & 0.69 & 0.71 & 0.62 & 0.21 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 27: The evaluation parameters of the SGD classifier-based ADI mitigation models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**BER**} & \multicolumn{4}{c}{**SNR**} \\ \hline
**Classifier** & & & & & & & & & & \\
**SGD** & & & & & & & & & \\ \hline Model 1 & 0.008 & 0.012 & 0.004 & 0.002 & 0.004 & 0.003 & 0.008 & 0.012 & \(-11.30\) & \(-09.30\) & \(-10.30\) \\ \hline Model 2 & 0.012 & 0.024 & 0.003 & 0.003 & 0.003 & 0.003 & 0.012 & 0.024 & \(-10.30\) & \(-12.40\) & \(-10.10\) & \(-12.50\) \\ \hline Model 3 & 0.008 & 0.016 & 0.005 & 0.002 & 0.004 & 0.002 & 0.008 & 0.016 & \(-11.30\) & \(-11.40\) & \(-11.20\) & \(-11.30\) \\ \hline Model 1 and 2 & 0.024 & 0.028 & 0.004 & 0.002 & 0.004 & 0.002 & 0.024 & 0.028 & \(-13.20\) & \(-10.30\) & \(-12.70\) & \(-10.40\) \\ \hline Model 2 and 3 & 0.028 & 0.032 & 0.002 & 0.003 & 0.003 & 0.003 & 0.028 & 0.032 & \(-10.30\) & \(-11.20\) & \(-11.10\) & \(-10.70\) \\ \hline \hline \end{tabular}
\end{table} 
Table 28: The Bit Error Rates of the SGD classifier-based mitigation systems with different learning rates.
# A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs
## 3 Ground Motions  
In the present research, 12 earthquake ground motions in far-field have been used to analyze the frames. The magnitudes of far-field earthquakes as high as 6.4-7.5 on the Richter scale were recorded at a distance of at least ten km from the fault in type 3 soil with a shear wave velocity of 175-375 m/s as per [23]. Hence, in this study, the real ground motion records were selected from the PEER-NGA database. The selected ground motions are generally characterized in Table 4.  
The average value of the square root of the sum of the squares of ground motions (SRSS) was set to at least equal 1.4 times the standard design threshold for periods of 0.2-1.5 T s, in which T represents the fundamental vibrational period [23]. The scaling process for the 10-story R-BRBF and the elastic response spectrum for the selected far-field ground motions with a damping of 5% are illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Record** & **Earthquake** & **Station** & **PGA** & **M** & **Distance (km)** \\ \hline \#EQ1 & Big Bear-1992 & Desert Hot Spr & 0.22 & 6.4 & 39.5 \\ \#EQ2 & Imperial valley-06-1979 & Delta & 0.35 & 6.5 & 33.7 \\ \#EQ3 & Imperial Valley-1979 & Calexico Fire Station & 0.277 & 6.5 & 10.45 \\ \#EQ4 & Kobe\_Japan-1995 & Shin Osaka & 0.233 & 6.9 & 46 \\ \#EQ5 & Kocaeli\_Turkey-1999 & Duzce & 0.36 & 7.5 & 98.2 \\ \#EQ6 & Landers-1992 & North Palm Springs & 0.075 & 7.2 & 36.15 \\ \#EQ7 & Landers-1992 & Yermo Fire Station & 0.2445 & 7.3 & 86 \\ \#EQ8 & Loma Prieta-1989 & Gilroy Array \#3 & 0.559 & 6.9 & 31.4 \\ \#EQ9 & Loma Prieta-1989 & Coyote Lake Dam (Downst) & 0.18 & 7.1 & 20.8 \\ \#EQ10 & Imperial valley-06-1979 & El Centro Array \#11 & 0.379 & 6.5 & 29.4 \\ \#EQ11 & Superstition & El Centro Imp. Co & 0.357 & 6.5 & 35.8 \\ \#EQ12 & Superstition Hills-1987 & Poe Road & 0.45 & 6.5 & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ground Motion Records.
# Distributed Diffusion Multi-Distribution Filter with IMM for Heavy-Tailed Noise
## 5 Conclusions  
This paper presents a novel IMM distributed state estimation algorithm designed to handle heavy-tailed measurement noise in maneuvering target tracking. The proposed algorithm employs a multi-model, multi-distribution approach based on parallel Gaussian and Student-\(t\) filters. We successfully obtained a closed-loop solution for the hybrid noise distribution using the matrix matching method. The estimation accuracy is significantly better than that of a single noise distribution, both in the presence of obvious outlier noise and under normal conditions. We also used IMM to solve the problem of tracking maneuvering targets. It turned out that combining IMM with a hybrid noise distribution filter performed very well and was able to capture maneuvering targets effectively. The proposed diffusion algorithm exhibits good robustness when faced with different outliers in a single node. The method proposed in this paper provides inspiration for tracking and monitoring large radar networks, but specific deployment must still be considered in conjunction with various aspects (such as communications and hardware). However, the method proposed in this paper integrates algorithms from various fields, which results in a slightly cumbersome calculation process and a decrease in overall computing speed. This indicates that this method is only suitable for scenarios with sufficient computing resources and a focus on estimation accuracy and robustness. In the future, while further verifying this method, we plan to adopt an event-triggered approach to reduce the overall computing burden.  
G.C.: Conceptualization; validation; formal analysis; investigation; resources; data curation; writing--original draft preparation. C.J.: Writing--review and editing. W.F.: Project administration. T.C.: Supervision. P.D.: Methodology; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **DCKF** & **DCSTF** & **DCKFIMM** & **DDMDFIMM** \\ \hline RMSE & 27.0767 & 21.9831 & 21.2361 & 9.7129 \\ TIME & 0.3042 & 0.1466 & 0.5061 & 1.8484 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison without outliers.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **DCKF** & **DCSTF** & **DCKFIMM** & **DDMDFIMM** \\ \hline RMSE & 34.7188 & 17.7127 & 16.6515 & 12.1128 \\ TIME & 0.3031 & 0.1450 & 0.4971 & 1.8303 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison with outliers.  
**Funding:** This research is funding by The National Natural Science Foundation of China.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Mode Profile Shaping in Wire Media: Towards An Experimental Verification
## 2 Modulated Wire Media  
This work is based on the understanding of wire media, a class of metamaterials consisting of a regular (rectangular) array of parallel wires or rods, whose radius is small compared to their spacing [13; 14]. When the wire radii also vary, such three-dimensional inhomogeneous media are difficult to analyse in the absence of a simpler model for the system. Fortunately, the fixed-radius case is understood [13], which provides us with a starting point.  
Any (fixed-radius) wire medium has an electromagnetic dispersion which follows the hydrodynamic Lorentz model, with a dependence on a resonance frequency, a polariton velocity, and a plasma frequency. In [3] we showed that, in effect, we could assume this held for any thin transverse slice of a wire medium. Thus, by stacking different wire radius slices together, to form not a uniform but a varying wire, we would construct a medium where the plasma frequency changed with position. This varying plasma frequency then leads to a correlated change in the effective local refractive index, thus--according to the electromagnetic wave equation--also changing the local curvature of the electric field profile. Since this curvature control changes the shape of the resulting wave, we can calculate how to link any desired field profile to a variation in wire radius.  
This procedure, despite its efficiency, is still reliant on numerics. The analytic calculation of [PERSON] et al. [13] was for infinitely thin wires, and so lacked the radius dependence we rely on to control their spatially dispersive properties. However, once we complete the step of numerically characterising a set of finite radius wires covering a suitable range of sizes, we can easily extract the necessary radius dependence of the plasma frequency. Please note that although the other hydrodynamic Lorentz parameters fitted to the numerical results do change, they are much less sensitive, and so that variation can be reasonably ignored.
## 3 Proposed Framework
### Mechanism for Enhancing Privacy and Security
#### 3.5.1 Principle and Mechanism of Salsa20 Encryption  
Salsa20 is a stream cipher that operates through a series of highly optimized arithmetic and bitwise operations, collectively referred to as ARX (Addition, Rotation, XOR). Unlike traditional block ciphers such as AES, Salsa20 does not rely on substitution boxes (S-boxes) or complex key scheduling algorithms, making it more suitable for IoT devices with constrained resources.  
The core of Salsa20 involves generating a pseudorandom keystream from a secret key and a nonce, which is then XORed with the plaintext to produce the ciphertext. Given a plaintext model update \(m_{i}\) from IoT device \(i\) and a secret encryption key \(k_{i}\), the encryption process can be expressed as:  
\[c_{i}=m_{i}\oplus\text{Keystream}(k_{i},\text{nonce}), \tag{6}\]  
where \(c_{i}\) represents the ciphertext. The keystream is generated by repeatedly applying the Salsa20 hash function.  
The computational efficiency of Salsa20 stems from its reliance on ARX operations, which are highly efficient on modern hardware and can be parallelized effectively. This ensures that encryption and decryption incur minimal computational overhead, maintaining the timeliness required for federated-learning processes.
## 2 Data and Methods
### Study Area and Data
#### 2.1.4 Drifting-Buoy Data  
In situ ocean buoy data can be used to compare and verify MDT, because the geostrophic ocean current velocities can be extracted from buoy data and derived from MDT. The buoy data were obtained from the Global Dritter Program (GDP) provided by the U.S. National Oceanic and Atmospheric Administration (NOAA) and the Atlantic Ocean and Meteorological Laboratory (AOML), and they have been quality-controlled and kriged in order to provide 6-hour velocity measurements. The GDP uses satellite-tracked drifting buoys on the ocean surface to obtain current velocity, temperature, and salinity data. This study used buoy data from 1993-2016, and the buoys' observed current parameters included geostrophic, tidal, Ekman, inertial, and high-frequency nongeostrophic currents. Since only the geostrophic ocean current could be deduced by the MDT, the buoy observations needed to be processed to remove the nongeostrophic component. The Ekman component can be modeled based on the data of the wind speed and wind stress. Thus, the Ekman current model developed by [PERSON] [16] was used to remove the Ekman current from the buoy data, and a 3-day low-pass filter was applied to the residual buoy current to remove tidal, inertial, and high-frequency nongeostrophic components [26,27]. The mean zonal and meridional geostrophic velocities were finally averaged into 1/4\({}^{\circ}\) grids. Moreover, to evaluate the accuracy of the geostrophic velocities, the errors of this data set was estimated, which can be given as the standard deviation of the observations divided by the square root of the number of observations [5]. The statistics of the errors are shown in Table 3. It shows that the RMS (root mean square) of the errors of the zonal (meridional) drifter geostrophic velocity were 14.3 mm/s (11.9 mm/s), respectively.
# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
## 4 Branching Acceleration for Unit Commitment
### Feature Design
#### 4.2.1 Branching Tree-Related Features  
These features exclusively include information from the B&B search tree, the solution to the relaxed problem, and its objective value. Being universal to all MILP problems, these features provide branching acceleration for UC with a solid foundation for generalization capability. Specifically, they can be categorized into the following three types:  
1. Node features: The action \(r_{n}\) taken by the algorithmic policy is closely related to the state of the current node \(N_{n}\). Therefore, key information about the node is essential for effective decision-making, such as its depth in the B&B search tree and the objective value of the relaxed problem, i.e., \(v_{n}^{*}\).
2. Branching features: The action \(r_{n}\) also depends on the branching variable that generated the current node \(N_{n}\), determined during the variable selection step of its father node. These features include the value of the branching variable at the current node, i.e., \(x_{n}^{*}[a]\), and the value at the root node, i.e., \(x_{1}^{*}[a]\).
3. Tree features: Information from the B&B tree search process is critical. This includes the solution to the relaxed problem at the root node, i.e., \(v_{1}^{*}\), the current local upper bound \(c^{*}\), and the number of solutions identified so far. Most of the above features exhibit significant variation across different UC problems. Firstly, there is considerable variation in the objective values; therefore, all objective values as input features in the B&B search tree should be normalized by \(v_{1}^{*}\). Secondly, the search depth of nodes and the number of solutions found vary greatly depending on the number of units, necessitating normalization to mitigate the impact of this scale factor.
## 3 Positioning System Experimental Platform  
### WinBus  
The WinBus is the experimental platform integrated by the Automotive Research and Testing Center (ARTC) and related manufacturers. The sensors deployed in the WinBus are shown in Figure 1.  
Two 3D lidars are deployed on the front-end and back-end roof. Lidar is a kind of optical device. The device is based on a laser beam that is used to detect the obstacle. Lidar measures the time gap between the laser beam transmission and reception to better understand the obstacle's distance. It predicts the shape of the target using the reflected signals of multiple beams. It can also use the reflectivity of the received beam to judge the material of the target surface. The front, back, and side effective ranges are each 60 m, and the side range is 20 m. The position, relative speed, and ID index constitute the obstacle data. The technique used here captures 3D lidar point cloud data. Two radars are mounted, one at the front and one at the back of the WinBus. Similar to the 3D lidar, the radar has the ability to identify obstacles. This information helps the vehicle locate the obstruction. Thissensor can deliver information regarding obstacles, including their location and relative speed. Both the 3D lidar and the radar can simultaneously find obstacles in the path of the vehicle. These two sensors can aid in the development of a control rule that will boost safety. The cameras were mounted all over the body. The front camera shows information about lanes and traffic signals. The GPS and IMU deliver data on the dynamics and location of the vehicle. For radio signal transmission, the GPS uses 24 to 32 Earth-orbiting satellites. The GPS receiver can obtain the position, time, and speed with the assistance of the signal. An appropriate received signal quality is necessary for GPS accuracy. There are many factors affecting the positioning signal, including the environment, the ionosphere, and electromagnetic interference. The GNSS network is the foundation of real-time dynamic technology [26], which assesses the positioning inaccuracy of the region serviced by the reference station. The IMU determines the attitude of the item by monitoring the gyroscope and G-sensor. The information provides the positioning system with an angular velocity and acceleration reference.