esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Realization  
The developed direct georeferencing system is shown in Figure 2. Its dimensions are 11.0 cm \(\times\) 10.2 cm \(\times\) 4.5 cm, and its weight is 240 g, without the GPS antennas.  
The system consists of a dual-frequency geodetic-grade GPS receiver (Novatel OEM 615), which acts as the main positioning device. Together with the GPS raw data of a master station, which is transmitted via a radio module (XBee Pro 868), it allows for an RTK GPS (real-time kinematic) positioning with a rate of 10 Hz, leading to centimeter position accuracies.  
The dual-frequency GPS receiver also serves as one receiver of the onboard GPS baseline for the GPS attitude determination (1 Hz). However, for the attitude determination, only the L1 GPS frequency is used, since the second onboard GPS device is a low-cost single-frequency GPS receiver (Ublox LEA6T).  
The tactical grade IMU (Analog Devices Adis16488), including three-axis gyroscopes and accelerometers, provides high rate inertial data (100 Hz), which is used for the position and attitude determination. A magnetometer (Honeywell HMC5883L) is placed at the outer end of one of the rotor-free UAV arms, where it is not affected by electric currents.  
A real-time processing unit (National Instruments sbRIO 9606), which is a reconfigurable IO board, including an FPGA (field programmable gate array) and a 400-MHz processor, enables the onboard position and attitude determination in real time.  
Figure 3 shows the current version of the UAV platform, which has been developed within a project called Mapping on Demand [46]. In this project, the UAV is intended to fly fully autonomously. Therefore, additional sensors, such as two stereo camera pairs and a 5 MPixel camera, are also fixed on the platform.  
The GPS antennas of the attitude baseline can be seen on the left and the right side of Figure 3. The baseline length is 92 cm. Both antennas are geodetic-grade antennas (3G + C, navXperience), which are originally too heavy for UAV applications. In order to reduce weight, we omitted all unnecessary components of the antennas, such as the antenna housings and the 5/8\" screw threads. Due to the changes in the antenna design, we had to recalibrate the antennas in our anechoic chamber [9,47]. By comparison to the original antenna, the antenna modifications led to significant changes in the phase center offsets (approximately 4 cm in the up, \(<\)1 mm in the north and east component) and in the phase center variations (\(<\)5 mm) of the antennas.  
Figure 3: The UAV platform, equipped with different sensors and systems.  
Figure 2: The direct georeferencing system, with its components.
# Experimental Study on Engineering Properties of Cemented Soil with High Water Content
## 5 Conclusions  
Based on the UCS test and the unconsolidated-undrained triaxial test results of the cemented soil, the following conclusions can be obtained:  
1. The brittle failure feature of cemented soil decreased with the confining pressure. Due to the extremely poor permeability of the cemented soil, the sample was probably not fully saturated, and the peak deviatoric stress of the cemented soil increased with confining pressure.
2. The evenly mixed cemented soil was in a liquid state when the water content exceeded 60% for Hangzhou soft clay, and its strength was greatly improved with the increase in the cement content. The UCS of the cemented soil increased from 889 kPa to 2353 kPa when the cement content increased from 6% to 18% after being cured for 28 days.
3. There was a good linear correlation between the cohesion and UCS of the cemented soil. The UCS of the cemented soil was about 2.38-2.63 times that of the cohesion in the unconsolidated-undrained triaxial tests.  
Conceptualization, J.Y. and J.Z.; Data curation, Z.M.; Formal analysis, J.Y. and Z.M.; Funding acquisition, J.Y. and J.Z.; Investigation, Z.M. and Z.Y.; Methodology, J.Y., J.Z. and X.G.; Project administration, J.Y.; J.Z. and X.G.; Resources, J.Y. and J.Z.; Software, Z.M.; Supervision, X.G.; Validation, Z.M. and X.L.; Visualization, Z.M.; Writing--original draft, Z.M.; Writing--review & editing, J.Y. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51978610 and 52108350), and the Scientific and Technological Innovation 2025 Major Special Projects of Ningbo (2022Z030).  
The authors declare no conflict of interest.
# Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints
## 5 Case Study  
### Data Description  
In this case study, data from two inland wind turbines (WT1 and WT2) were used. Some observations were approximated to protect the confidentiality of the turbine manufacturers and wind farms [55]. The data span the first four years of turbine operation, specifically from 2008 to 2011, and all measurements were recorded as 10 min averages, which is standard practice in the wind industry.  
For the inland wind farm, the available data include power output (y), wind speed (\(V\)), wind direction (\(D\)), air density (\(\rho\)), turbulence intensity (\(I\)), and wind shear (\(S\)). Power output is directly measured from the wind turbine, while most environmental variables are collected from the nearest meteorological mast, except for wind speed and turbulence intensity. Wind speed and turbulence intensity are measured by nacelle-mounted anemometers on the turbines. Mast measurements are preferred for certain variables, such as wind direction, due to their greater reliability, and they are necessary for calculating air density through air pressure and ambient temperature. Figure 6 presents a schematic view of the essential parts of a wind turbine and an example of a real turbine [55; 56].  
Figure 5: A comparison of the selection rates of all split covariates between IRT and UIRT in the real dataset.  
Wind speed, the primary source of wind power generation, generally follows a monotonically increasing relationship with power output. However, other environmental factors--including wind direction, air density, turbulence intensity, and humidity--also impact wind power production. To compare turbine performance accurately across different turbines or time periods, it is crucial to control these environmental influences so that performance differences reflect the turbine's inherent characteristics. A covariate matching procedure was, thus, employed to ensure a fair comparison [57]. This observation implies that environmental factors and turbine-specific characteristics can introduce variability in the wind power and wind speed relationship, leading to heterogeneous constraints. Thus, identifying distinct power curve patterns is essential for optimizing wind turbine operation and establishing efficient management policies.
# Shape and Weighting Optimization of a Subarray for an mm-Wave Phased Array Antenna
## 2 Weighting Optimization for Individual Subarrays by Quadratic Programming
### Cavity-Backed Type Single Radiating Element Design and mm-Wave Analysis  
Figure 8 shows a cavity-backed patch antenna designed to operate at 30 GHz. This antenna has a radiator on the front, and a cavity filled with air is located on the rear. Power is supplied through a metal post connected to a feeder. The substrate of the radiator is the TLE-95 type (\(\epsilon=2.95\)) with a thickness of 0.127 mm and loss tangent of 0.003. The cavity-backed patch antenna is fed by coupling feeding technique by using a metal post with a diameter of 0.31 mm and positioned at a distance of 0.9 mm in the \(-y\) axis direction in the middle of the air-filled cavity. Table 2 presents the parameters used to design the cavity-backed patch antenna. Figure 9 shows the characteristics of \(S_{11}\) on the 30 GHz band, and the bandwidth that meets the requirement of \(-10\) dB or less is 27-36 GHz. The maximum realized gain of the antenna is 7.34 dBi at 30 GHz, and it has a unidirectional pattern.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline dx & 7.2 & cavh & 2.12 \\ dy & 7.2 & cavdx & 4.11 \\ patdx & 2.9 & cavdy & 5.31 \\ patdy & 2.8 & airh & 1.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters for a cavity-backed patch antenna (unit: mm).  
Figure 8: Configuration of the cavity-backed patch antenna: (**a**) overall view, (**b**) side-cut view.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)
### Mahalanobis-Taguchi System (MTS) Procedures
#### 2.2.2 The Role of the SNR in MTS  
The signal-to-noise ratio (SNR) concept which can be considered as the core essence of [PERSON] philosophy, is developed by [PERSON] who get inspired when he was practicing an engineering profession in a Japanese telecommunication company in the 1950s. In the telecommunication context, the SNR captures the magnitude of true information (i.e., signals) after making some adjustments for uncontrollable variation (i.e., noise) [26]. In [PERSON]'s robust engineering concept, the SNR is defined as the measure of the functionality of the system, which exploits the interaction between the control factors and the noise factors. A \"gain\" in the SNR value denotes a reduction in the variability, hence a reduction in the number of factors associated with the \"noise\" (factors that are considered insignificant for the classification effort) resulting in a reduction of the classification process in terms of time and cost. Refs. [27; 28] provide a detailed description of SNR concepts and their origin of the formulation.  
In the context of MTS, the SNR is defined as the measure of the accuracy of the measurement scale for predicting abnormal conditions [26]. In MTS, a higher value of SNR which is expressed in decibels (dB), means a lower prediction error. SNR is used as a metric to assess how significant each variable in the system contributes to the ability to discriminate between normal and abnormal observations. It could also be used to assess the overall performance of a given MTS model and the degree of improvement that it has made after underwent the optimization process.  
The two most commonly used SNRs in MTS are larger-the-better (LTB) and dynamic [23; 26; 29]. In this study, the larger-the-better SNR will be utilized due to less computational complexity.
## 7 Discussion
### The First Modal Inertial Pushover Analysis Methods
Despite this, the method stands out for its ability to merge dynamic aspects into static analysis, providing a more nuanced understanding of seismic impact on structures. * The N1 method stands out for not requiring a well-defined equivalent single-degree-of-freedom (SDOF) system, facilitating its use in practical structures. It evaluates displacement demand using response spectrum analysis and adjusts for nonlinear behavior, offering accuracy comparable to the N2 method for certain load distributions. While effective for irregular or high-rise buildings by considering higher modes, the N1 method is primarily suitable for low-rise buildings and less effective for high-rise or torsionally flexible plan-asymmetric structures. A significant limitation is its inability to accurately predict seismic demands in structures heavily influenced by higher modes, owing to its limited capture of these effects. * The mass proportional pushover (MPP) procedure, introduced by [PERSON] and [PERSON], simplifies pushover analysis for estimating peak seismic lateral displacement demands. This method stands out by removing the need for complex modal analysis or combination, even when higher-mode effects are significant. Its main advantage lies in conducting a single pushover analysis with lateral forces distributed in proportion to the seismic masses at different levels of the structure, effectively consolidating higher-mode effects into one invariant distribution. This unique approach not only streamlines the estimation of displacement demands but also enhances practicality and efficiency in seismic analysis, particularly for structures where higher-mode influences are crucial.
## 2 Method  
### General Idea  
The overall research approach in this study comprises three main phases: data collection and preprocessing, machine learning model selection and determination, and machine learning model improvement and validation. In the data collection and preprocessing phase, charge-discharge cycle experimental data from multiple 18,650 LiFePO4 batteries were collected. After data preprocessing, multiple datasets suitable for LIB SOH prediction were obtained. In the machine learning model selection and determination phase, various machine learning models, including the DT model, CNN model, LSTM model, and RF model, were considered by referencing the relevant literature. Based on multiple sets of preprocessed LIB charge-discharge cycle experimental data, Python programming was employed to conduct SOH prediction analyses using these models. The model with relatively optimal SOH prediction results, determined from this analysis, was chosen as the machine learning model for this research, and is the RF model. In the machine learning model improvement and validation phase, to compare and analyze against the RF improved model proposed by [PERSON] et al. (2019), two models were constructed: RF improved model 1, which adaptively optimized three parameters (max_depth, n_estimators, and max_features), and RF improved model 2, which adaptively optimized five parameters (max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf). The RF model, RF improved model 1, and RF improved model 2 were compared based on their SOH prediction results to validate the effectiveness of the proposed approach in this study.  
The basic process of the RF improved model proposed in this study is shown in Figure 1, and the specific steps are as follows.  
Figure 1: Basic flow chart of RF improved model.  
Collect multiple sets of LIB charge-discharge cycle data, use the 3 Sigma method to identify and eliminate outlier data for noise reduction, and then apply the Min-Max standardization method for data processing.
2. Utilize a Python program to read the processed data and partition the dataset into training sets and testing sets (out-of-bag data), according to a specified ratio. Train the model using the training sets and calculate the importance of each feature and sort them based on the testing set data during prediction, in order to extract relevant information that aids in SOH prediction.
3. Using max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf as the major parameters, employ a Python program to invoke the PSO library for calculating the particles' positions, velocities, and fitness values within the search space. Conduct multiple rounds of iteration updates until the predetermined iteration time is reached, and output the best parameter combination for max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf.
4. Utilizing the best parameter combination, build an RF improved model using a Python program. Evaluate the constructed RF improved model using the testing datasets and output the model's SOH prediction and evaluation index.
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 3 Platform Design and Simulation
### DDoS Attack Model Parameter Configuration  
Since there is no Exata interface in STK that can be used directly, according to the use documents of the two simulation software programs, their joint simulation can realize interoperability by using the QualNet interface in STK and the AGI Satellite Toolkit Interface in Exata. The attack method chosen in this paper is a basic attack in Exata, and the data rate is set to 100 pkts/s. Layer transmission protocol (CBR) application between satellite No.207 and satellite No.66 is established. The application transmission start time is 1 s, the end time is 3600 s, the data of each packet is 204 bits, the transmission interval is 1 s, and the routing strategy is the shortest-path strategy. According to the constructed space-time diagram key-node identification model, the key nodes of the entire satellite network are generated through the NetworkX and MATLAB tools, using node degree centrality and average betweenness centrality as indicators, and the importance of key nodes in the AB regional network is sorted. Finally, satellite No.67 and satellite No.119 are selected as the key nodes of the link, which are the targets of this simulation. On Exata, No.208, No.209, and No.210 node devices are set up to simulate a DDoS attack launched by the botnet on the No.67 satellite, while No.211, No.212, and No.213 are bots that launch attacks on the No.119 satellite.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Orbit type & Circular \\ Altitude & 335 km \\ Inclination & 42.0 deg \\ RAAN & 0 deg \\ Number of tracks & 10 \\ Number of satellites & 207 \\ Regional area & \(8544\times 4589\) km\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbit parameter information.  
Figure 3: Simulated satellite 2D scene.
## 2 Conventional Methods to Handle Variable Length Sequences
### Target Length
#### 2.1.2 Global Mean  
Global mean (denoted as \(mean\)) means that the target length is set to the average length of all the sequences over the given dataset. In the example (Figure 1c), the average length is 4 (the decimal point is rounded off). The sequences shorter than 4 (i.e., **X\({}_{4}\)**, **X\({}_{6}\)**, and **X\({}_{9}\)**) are padded, and those longer than 4 (i.e., **X\({}_{2}\)**, **X\({}_{3}\)**, **X\({}_{5}\)**, **X\({}_{7}\)**, and **X\({}_{8}\)**) are truncated to have a length of 4. The total truncated and padded lengths are 8 and 5, respectively, and therefore, the total amount of computation is decreased compared to global maximum.  
Figure 1: Examples using four types of target length based on an (a) original: (**b**) \(max\), (**c**) \(mean\), (**d**) \(bmax\), and (**e**) \(bmean\). The white frames represent original input frames. The blue frames represent padded frames. The red frames represent truncated frames.  
It has a relatively smaller computation burden. Also, like in max, all the sequences can be padded in advance. However, it is almost inevitable there is information distortion because there are both truncated and padded sequences.
## 4 Discussion
### Discussion of ARIMA, LSTM, Informer, and Linear Model Results  
The MSE and MAE values were typically low for all four models. It can be asserted that all the developed ARIAM, LSTM, Informer, and Linear models have demonstrated effective water quality prediction capabilities. The Linear model demonstrated notable enhancements over the baseline model across nearly all water quality measurements, exhibiting significant superiority compared to the other two models. The Linear model consistently demonstrated enhanced predictive capabilities for all water quality measurements in comparison to the baseline model. On average, the Linear model demonstrated a reduction in MAE of approximately 0.8% compared to the LSTM model and 0.4% compared to the ARIAM model. Additionally, the Informer model exhibited slightly superior performance when compared to the LSTM model. The Informer model demonstrates superior performance compared to the LSTM model of all time series. This finding suggests that the Informer model is more effective in capturing nonlinear information. In the context of pH prediction, it has been observed via analysis concerning MSE that the Informer model exhibits a slightly superior performance compared to the LSTM model. However, it is worth noting that both models demonstrate a very similar level of performance. In the context of predicting turbidity, the ARIMA model demonstrated the second highest level of performance. Overall, the Linear model exhibited superior prediction accuracy compared to the other models. This suggests that the model is effective in enhancing the accuracy of short-term water quality prediction in the Huangyang Reservoir. Furthermore, the Linear model is also deemed more appropriate for long-term water quality prediction in the Huangyang Reservoir, as well as in the entire country.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 4 IoT Device Hardware Design  
To utilize all the functions designed in Section 3, we need MCU, LoRa module, GPS module, Wi-Fi module, and Bluetooth module. They also needed an antenna for reliable connection with the LoRa network, Battery, and Charging module to supply power. Thus, we used Heltec MCU including Wi-Fi and Bluetooth, LOM102A LoRa module of SEONG JI INDUSTRIAL and NEO-6M GPS module. We also wanted to increase portability with a lithium polymer battery. The Schematic Diagram of our IoT device in order to implement IoT device functionalities aforementioned is shown in Figure 5.
## 3 Results and Discussion  
In the previous step of this study [1], the relationship between average engine quantities and combustion parameters were developed referring to test Sets 1 and 2. The main targets were the validation of available data through expected correlations and the prediction of further links, which may be applied for engine and sub-systems modelling, the design of experimental campaigns and the definition of control strategies.  
Considering these targets and following the approach presented in Reference [33] and applied in Reference [34] for control purposes, linear correlations were tested, limiting the range of involved parameters in order to simplify the description of engine behaviour for each selected operating condition. Therefore, even if internal combustion engines are not linear systems, the goal of the applied method was to verify if observed trends could be fitted with linear relationships. This approach led also to a reduction of computational effort and to a simplification of functions inversion, if necessary.  
The following step was to consider measured levels of engine quantities derived from test Sets 3 and 4, which corresponded to include fuel rail pressure among the control variables previously selected and tested. As a result, the number of parameters considered for the development of statistical links was further enlarged, thus giving additional justification to the linear approach.  
In this section, a sample of the most significant results of the processing of average engine quantities, in-cylinder pressure diagrams, heat release curves and combustion parameters is presented and discussed, referring to the whole experimental database. In particular, the effects of heat released in the premixed phase of the main combustion, maximum of the rate of heat release, maximum of heat release rate first derivative, 50% heat released crank angle will be deepened, taking also into account mutual interactions.  
The Figures present trends referring to the four sets. Where different curves were available (Set 2, Table 5), the best set for each operating condition was chosen according to the highest determination coefficient. In any case, a threshold of 0.7 for R\({}^{2}\) was set to guide the selection of relationship to be presented and discussed, with a few exceptions.  
The discussion starts from the analysis of the influence of rail pressure control on pressure diagrams and heat release curves, which also allows us to outline the general characteristics of these outcomes. The corresponding analysis presenting the effects of EGR systems and VNT control was included in Reference [1].