esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
## 4 Experimental Verification and Result Analysis
### Dataset Preprocessing  
(1) Unique Thermal Encoding of Character-based Features:  
This is used to convert non-numeric character-based features into a numeric form that can be processed by a computer. The discrete feature values are extended into Euclidean space for data feature encoding, such that each possible feature value corresponds to a new binary variable. The UNSW_NB15 dataset is uniquely hot coded, where the features in columns 3, 4, 5 are character-based (corresponding to \"proto\" \"service\" \"state\"), \"proto\" \"service\" \"state\" and \"state\". The features in columns 3, 4, 5 are character-based (corresponding to \"proto\", \"service\", \"state\"), and \"proto\" is mapped to 131-dimensional features, \"service\" is mapped to 12-dimensional features, \"state\" is mapped to 7-dimensional features, and the feature data are mapped to 7-dimensional features. It is mapped to 7 dimensions; \"script\" and \"dstrip\" columns in the feature dataset represent the IP address, and since determining whether the data is abnormal has nothing to do with the IP address, the first column ID is only an identifier. In order to simplify the removal of these three columns, the dimension is increased from 43 dimensions to 185 dimensions after the preprocessing. The dimension is increased from 43 to 187 dimensions.  
(2) Feature data normalization:  
This is a technique that converts the entire range of values of a set of features into a predetermined range. Usually, there is a huge difference in the range of data values between different features, which can cause the training process of machine learning algorithms to be affected, and features with a larger range of values will be given more weight in the training of the algorithm. Therefore, min-max normalization is introduced to map all the data to the interval [0, 1], thus speeding up the convergence of the model and improving the accuracy of the classification results, which is given by the following formula:  
\[X^{\prime}=\frac{X-Min}{Max-Min}\]  
where \(Min\) is the minimum value of the feature, \(Max\) is the maximum value of the feature, and \(X\prime\) is the feature value after normalization.
## 3 Results and Discussion
### Strength Degradation  
In this study, the strength degradation of the specimen is represented by the ratio of the peak load of each cycle to its maximum load value in the same loading level, called the strength degradation coefficient. It is defined as follows:  
\[\lambda_{\text{i}}=\frac{F_{\text{ij}}}{F_{\text{max}}} \tag{3}\]  
where \(F_{\text{ij}}\) is the peak load of the specimen under cycle j in loading level i, and \(F_{\text{max}}\) is the maximum load value of the specimen in loading level i.  
The strength degradation curves of the three specimens are shown in Figure 14.  
Figure 14 shows that the bearing capacity of the three specimens degraded during the loading process. Before reaching the yield state, the strength degradation of the specimens is not serious, all above 0.98. After reaching the peak state, the strength degradation of the specimens gradually becomes obvious, and with increasing horizontal displacement, the strength degradation ratio increases.  
In addition, Figure 14 shows that the strength degradation of specimens CGSW-2 and CGSW-3 is more obvious than that of specimen RFSW-1, indicating that the increase in the coal gangue replacement rate accelerates the speed of strength degradation and has an adverse impact on the specimens. This is because of the low strength of coal gangue. It is more likely to be damaged or even crushed when subjected to lateral cyclic loading. As the ductility of the gangue concrete shear wall is poor compared with that of ordinary concrete, it is easier to fail suddenly. Moreover, the horizontal bearing capacity will also drop suddenly, so with the increase of the replacement rate of coal gangue, the degradation of the strength of the shear wall is gradually significant, but the impact is not obvious.
## 4 Time Domain Dynamic Equations for the Anti-Swaying System
### Equation for the Load Swaying Angle  
For the convenience of analysis, it is assumed that the hoisting rope length, \(l\), is constant, leading to \(\dot{l}=\ddot{l}=0\). When the trolley brakes, the following expression can be obtained from Equation (6):  
\[\begin{array}{l}m_{2}l\big{(}m_{2}\sin^{2}\varphi(t)+m_{1}\big{)}\ddot{ \varphi}(t)+\frac{1}{2}m_{2}^{2}l\sin(2\varphi(t))\dot{\varphi}^{2}(t)+\\ k(m_{1}+m_{2})v_{2}\cos\varphi(t)+2m_{2}g(m_{1}+m_{2})\sin\varphi(t)=0\end{array} \tag{8}\]  
If \(\varphi\leq 5^{\circ}\), then \(\sin\varphi=\varphi\), \(\cos\varphi=1\), and the higher-order trace containing \(\dot{\varphi}^{2}\) can be ignored. At the same time, when the trolley is stationary after braking, \(\ddot{x}_{1}=\ddot{x}_{1}=0\) applies. According to Equation (2), \(v_{2}=l\dot{\varphi}\) holds. Thus, Equation (9) can be obtained from Equation (8):  
\[\ddot{\varphi}(t)+Mk\dot{\varphi}(t)+2 Mm_{2}\frac{g}{l}\varphi(t)=0, \tag{9}\]  
where \(M=\frac{1}{m_{1}}+\frac{1}{m_{2}}\), \(M\), \(m_{1}\), \(m_{2}\), and \(l\) are dynamic parameters of the trolley-load system.  
To study the change trend for the load swaying angle when the trolley brakes (\(t=0\)), the load swaying angle and its derivative were set to \(\varphi(0)=\varphi_{0}\) (\(\varphi_{0}\) is the initial swaying angle during braking) and \(\dot{\varphi}(0)=0\). When \(Mk^{2}l-4m_{2}^{2}g>0\), based on Equation (9), it was concluded that the change trend for the swaying angle of the load could be expressed by Equation (10):  
\[\varphi(t)=\xi_{1}e^{-at}-\xi_{2}e^{-\beta t}, \tag{10}\]  
where \(\xi_{1}=\frac{\varphi_{0}\left(Mk^{2}l+\gamma-8m_{2}g\right)}{2\omega}\), \(\xi_{2}=\frac{\varphi_{0}\left(Mk^{2}l-\gamma-8m_{2}g\right)}{2\omega}\), \(\alpha=\frac{Mkl-\frac{\gamma}{2}}{2l}\), \(\beta=\frac{Mkl+\frac{\gamma}{2}}{2l}\), \(\gamma=k\sqrt{\left(Mkl\right)^{2}-8 Mm_{2}g}\), and \(\omega=\sqrt{Mk^{2}l-8m_{2}g}\).  
Equation (10) is the expression for the load swaying angle after the trolley brakes. It is clear that the load swaying angle is attenuated in the form of an exponential function. Equations (9) and (10) indicate that the structure parameter, \(k\), the hoisting load, \(m_{2}\), and the hoisting rope length, \(l\), jointly affect the attenuation process of the initial swaying angle, \(\varphi_{0}\).
## 4 Results
### HMM Analysis Results

Table 3: Transmission matrix of all five states from HMM for total sum ozone with total sum aerosol as a covariate in Region 3.  
ozone levels then increase and the HMM system starts varying between States 4 and 5. After day 97 (6 December 19) to day 137 (15 January 2020), ozone levels then decrease again and the HMM system remains in State 2, with State 2 (mean of 6803.0 DU, aerosol level = 36.73). From day 137 (15 January 2020) to the end of the time series the mean ozone level then decreases again to the value in State 1 (mean of ozone level = 6563.19 DU, aerosol level = 15.29). The first observation over the study period of bushfire-induced aerosols occurs on the 68 th day (7 November 2019). The first point of change in ozone levels, as demonstrated by HMM, occurred approximately 1 day earlier at 67 days (6 November 2019). The last relatively small emission of bushfire-induced aerosols occurs on the 159 th day (6 February 2020), compared with the fifth state change-point, which occurs on day 137 (15 January 2020), 22 days earlier in Region 3 (see Figure 21).
# Research Status and Development Trend of Wastewater Treatment Technology and Its Low Carbonization
## 2 Conventional Wastewater Treatment Technology
### Delayed Aerated Activated Sludge Process  
The delayed aerated activated sludge process is also called the completely oxidized activated sludge process. The main characteristics of the process are low organic load, continuous internal metabolism of sludge, less residual sludge, and stable sludge with no need for further digestion treatment. This process can be called the comprehensive wastewater and sludge treatment process. The process has the advantages of high stability of water quality treatment, strong adaptability to the impact load of wastewater, and no need to set up a primary sedimentation tank. The main disadvantages are large pool capacity, long aeration time, high construction and operation costs, and large size. This process is suitable for the sewage and industrial wastewater needs of a small town, which requires high water quality, and therefore should not be treated by sludge alone. The aeration tanks used in the process are completely mixed or push-flow. The technological process is as shown in Figure 2. When the reference values of the various design parameters used in the treatment of urban sewage are the same as 2.2, the removal rate of BOD, COD and TSS can only reach 85-90%. The removal rate is also lower than that of the previous activated sludge treatment processes [26,27].  
This process has a very strong advantage in the operation and management of sludge, and can make full use of sludge resources, so as not to need much sludge disposal, and save part of the energy in the whole treatment system. However, its long aeration time, high operating cost and large area are great shortcomings of the process, and these shortcomings are difficult to make up objectively. Therefore, the process can only be applied if the site conditions are sufficient, and the water quality conditions are more suitable to better use the advantages of sludge treatment to better realize the purpose of energy saving, emissions reduction and low carbonization of sewage treatment.
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 2 Development Approach: Design Goals, Materials, and Methods
### Design Goals
#### 2.1.2 Design Goals and Target Specifications  
In the initial stage of the rectenna development process, we defined the design objectives and identified the associated electrical and mechanical requirements, as outlined in Figure 1. The primary goal of this work is to develop a compact, flexible, low-cost, eco-friendly rectenna for ambient RF energy harvesting in the 5G lower mid-band (n40 and n7) and ISM 2.4 GHz band, suitable for seamless integration in next-generation wireless IoT devices with ultra-low power consumption. Table 1 summarizes target specifications associated with this goal.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Specifications** \\ \hline Application & For powering devices with ultra-low power consumption \\ Operating Frequency & 5G lower mid-band—n40 and n7—and ISM 2.4 GHz \\ Bandwidth (GHz) & \(\geq\)0.5 \\ \(|\)S\({}_{11}\) | in operating frequency range (dB) & \(\leq\)\(-\)10 \\ Rectifier RF-to-DC Conversion Efficiency (\%) & \(\geq\)30 \\ Maximum size (cm\({}^{2}\)) & \(\leq\)30 \\ Weight (g) & \(\leq\)10 (antenna and rectifier integrated) \\ Total Thickness (Profile) & \(\leq\)2 mm (antenna and rectifier integrated) \\ Rectenna Materials & Flexible, eco-friendly, biodegradable, or recyclable materials \\ \hline \end{tabular}
\end{table}
Table 1: Target specifications associated with the design goal.  
Figure 1: Flowchart of the rectenna design, optimization, fabrication, and testing methodology.
## 5 Model Results
### Cross-Validation Results after Hyperparameter Tuning  
Post tuning, the performance of the final MARS models was assessed using a five-fold cross-validation procedure. The RMSE, R\({}^{2}\), and MAE were computed for each fold to estimate the prediction performance. The results for both models are presented below.  
For the \(w_{opt}\) model, the five-fold cross-validation results are presented in Table 2:  
Notably, the RMSE, R\({}^{2}\), and MAE values indicate the high predictive performance of the MARS model for the \(w_{opt}\) parameter, with high R\({}^{2}\) values over 0.87 for all folds in both cases. This highlights the models' ability to capture a significant portion of the variance in the target variables. The RMSE values were relatively low across all folds for both models, underscoring the models' robustness and their ability to minimize errors  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Folds**} & \multicolumn{3}{c}{**Performance Measures**} \\ \cline{2-4}  & **RMSE (\%)** & **R\({}^{2}\)** & **MAE (\%)** \\ \hline Fold 1 & 1.513 & 0.929 & 1.182 \\ Fold 2 & 2.368 & 0.870 & 1.867 \\ Fold 3 & 1.827 & 0.886 & 1.351 \\ Fold 4 & 1.710 & 0.889 & 1.361 \\ Fold 5 & 2.323 & 0.895 & 1.728 \\ Average & 1.948 & 0.893 & 1.498 \\ SD & 0.380 & 0.023 & 0.287 \\ CoV (\%) & 0.195 & 0.026 & 0.192 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results derived from a five-fold cross-validation for the \(w_{opt}\) model.  
Figure 5: Optimization results of hyperparameter tuning for the MARS models: (a) \(\rho_{\text{dmax}}\) and (b) \(w_{opt}\).  
in prediction. Moreover, the MAE values were reasonably low, revealing the models' capability to produce accurate predictions with small deviations from the actual values.  
The \(\rho_{\rm{dmax}}\) model's cross-validation results are presented in Table 3.  
Once again, the RMSE, R\({}^{2}\), and MAE values for each fold reflect the high predictive performance of the model. The average values highlight the overall accuracy of the model, while the standard deviations suggest that the model's performance is consistent across different folds.  
One potential reason some folds perform better than others is due to imbalanced data distribution. If the data are not evenly distributed and one fold ends up with a disproportionate number of certain classes or types of data, the model may perform poorly on that fold. For instance, if one fold contains many outliers or rare events, the model might struggle to generalize. Despite this, the consistently high R\({}^{2}\) values, low RMSE and MAE values, and the low standard deviations for both models across all folds underscore the effectiveness of the MARS models and their robustness in predicting the \(w_{opt}\) and \(\rho_{\rm{dmax}}\) parameters of compacted soil.
# Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
### Description of the Tests
#### 2.4.1 Influence of the Standard Deviation assigned to Projection Centers in the BBA  
As already noted, in the BBA of UAV blocks with GNSS-AT an important issue is the assessment of the camera coordinate precisions at each shooting time, to define the weights of the pseudo-observations equations with respect to that of the collinearity equations on tie points. Processing of the GNSS observations also delivers an estimate of such precision; however, it is well known that they are normally quite optimistic and that perhaps only the ratios of the standard deviations reflect to some extent the different positioning quality of two camera stations. Failure to assign the appropriate weight might lead to bias in the solution.  
Depending on the on-board software implementation, the nominal precision of the camera position can be encoded in the image EXIF header or saved in a separate file. For instance, the eBeeRTK only stores this info in the image EXIF; interestingly, out of the three software packages, only Pix4D is able to recover this information from the eBee images. As far as the actual estimated precision of the RTK data processing for the 4 flights is concerned, the values recorded are fairly stable within each flight (with just a few mm of variation in all coordinates) while the mean values range from 1 cm to 1.5 cm in X and Y and from 1.8 cm to 2.5 cm in Z.  
To gain some insight on how the standard deviations of camera stations are used in the BBA, in a first series of tests a systematic exploration of the block orientation accuracy with GNSS-AT has been carried out for each software package and each block. Using the default setting, the standard deviation of the camera coordinates in XY has been varied in the range from 1 cm to 20 cm, while the elevation standard deviation has been set twice as much as the horizontal one, to account for the gap in accuracy of GNSS positioning (i.e., from 2 to 40 cm). The standard deviation assigned to the tie points image coordinates has been kept fixed at 1 pixel, a value that turned out to be practically coincident with the average value of the mean reprojection error of the BBA with all packages and in all blocks.  
In a second set of tests, only executed on PhotoScan using a Python script, both the standard deviations of the camera positions and of the tie points have been varied respectively in the (admittedly large) range from 1 to 20 cm and from 0.01 to 4.0 pixels.  
For each adjustment of these two tests, the RMSE in the horizontal coordinates and in elevation have been computed: in these experiments, the coordinates of all the 26 known points have been used as CP.
# PAGE--Practical AES-GCM Encryption for Low-End Microcontrollers
## 1 Introduction
### Research Contributions  
* Compact Implementation of AES-CTR on Low-end Embedded Processors Previous AES-CTR implementation methods (i.e., FACE) mainly focused on high-end processors, such as Intel processors. Furthermore, the FACE implementation requires recalculation of the cache table after 256 encryption operations. The proposed method optimized the implementation of AES-CTR on low-end microcontrollers. The previous method is re-designed for the target low-end microcontroller by considering supported instruction sets and the number of general purpose registers. The proposed implementation requires the cache table update in the initialization step once. Proposed implementations achieved 138, 168, and 199 clock cycles per byte for 128-bit AES-CTR, 192-bit AES-CTR, and 256-bit AES-CTR on 8-bit AVR microcontrollers, respectively. Furthermore, the masked AES implementation is also accelerated with the proposed method for high performance.
* FACE Extension for Round 3 The original FACE method pre-computes the AES operation by ShiftRows of Round 2. In this paper, we further extended the FACE method by caching one Sub-Bytes and one Shift-Rows operations more (i.e., Round 3). Proposed implementations achieved 122, 153, and 183 clock cycles for 128-bit AES-CTR, 192-bit AES-CTR, and 256-bit AES-CTR, respectively.
* Optimized Implementation of GHASH Function for AES-GCM Encryption Binary field multiplication is a performance-critical building block for the GHASH function. The target 128-bit binary field multiplication is optimized with Karatsuba Block-Comb method and optimal register utilization. The countermeasure is also applied to the implementation, which ensures high security against SPA, TA, and CPA. The previous GHASH implementation requires 7172 clock cycles on 8-bit AVR microcontrollers, while the proposed method requires only 4230 clock cycles. With the optimized implementation of GHASH, AES-GCM for 128-bit, 192-bit, and 256-bit require 415, 466, and 477 clock cycles per byte, respectively.  
The organization of the paper is as follows. Section 2 presents Fast implementation of AES-CTR Encryption (i.e., FACE), Galois Counter Mode (GCM) of operation, and secure implementation of binary field multiplication. Sections 3 and 4 introduce the fast implementation of AES-CTR (i.e., FACE-LIGHT)and AES-GCM (i.e., PAGE) on microcontrollers, respectively. In Section 5, we review the performance of the suggested implementation. Section 6 concludes the paper.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 3 Application of Deep Learning Methods to Pig Posture Detection  
Many studies have demonstrated that the incorporation of deep learning techniques with image systems has superior performance and feasibility relative to traditional methods, which is expected to be the optimal solution to pig posture detection. As a typical implementation of deep learning methods, CNNs have become one of the most common models for various visual detection tasks and have been introduced into pig posture detection. Based on CNNs but with a different design concept, deep learning methods prevalent in pig posture detection can be mainly divided into two types: two-stage detection methods and one-stage detection methods [72]. Two-stage detection methods usually have fairly high accuracy and robustness but a slow detection speed due to the need to generate a region proposal before the classification and localization of the object and refining the bounding boxes in the post-process stage [73]. A typical two-stage model generally consists of an RPN and a downstream detection network, as Figure 6 illustrates. Nevertheless, one-stage detection methods can directly estimate the classification accuracy and coordinate the position of objects in an image in only one evaluation without the need for region proposals, as Figure 7 demonstrates. A united network structure can confer one-stage detection methods with a significantly high detection speed when maintaining an acceptable accuracy [73].
## 2 Materials and Methods  
### Study Area  
HBO is located in the middle and upper reaches of the Yellow River in Inner Mongolia, spanning 37\({}^{\circ}\)37\({}^{\prime}\)-42\({}^{\circ}\)44\({}^{\prime}\) N and 106\({}^{\circ}\)31\({}^{\prime}\)-112\({}^{\circ}\)17\({}^{\prime}\) E, with a total area of 1.32 \(\times\) 10\({}^{5}\) km\({}^{2}\)[32] (Figure 1). The HBO region has a semi-arid climate, with an average annual precipitation of 300 mm and an annual mean temperature of 8 \({}^{\circ}\)C [33]. Since 2000, Inner Mongolia has positioned the HBO region as a core area to promote the development of a regional economic cluster with distinct characteristics. Capitalizing on their respective resource advantages, the three cities have rapidly developed into the most dynamic urban economic cluster in Inner Mongolia, often referred to as the \"Golden Triangle\" [34]. Hohhot, the capital of the Inner Mongolia Autonomous Region, serves as a strategic hub connecting the Yellow River Economic Belt, the Eurasian Land Bridge, and the Bohai Economic Rim. It is also an important gateway for trade with Mongolia and Russia [35]. Baotou is renowned for hosting the world's largest rare earth deposit, the Bayan Obo Mine, which contains 43.5 million tons of reserves, accounting for 83.7% of China's and 37.8% of the world's total [36]. Ordos is notable for its abundant coal resources, which account for one-sixth of China's proven reserves, making it a key region with an economy centered on coal mining [37]. The primary land-use and land-cover type in the HBO region is grassland, accounting for 60% of the total study area. Bare land ranks second, covering 25% of the total area.  
For ease of analysis, we use the abbreviations of the names of 18 banners, counties, and cities in the study area (hereafter, refer to Figure 1). Among them, Baotou, Hohhot, and Ordos refer to the three regions, including four, six, and eight counties or banners, respectively. HH (Hohhot) refers to the capital city, while BT (Baotou) and OD (Ordos) refer to the cities.  
Figure 1: Land use and land cover in the Hohhot–Barotou–Ordos (HBO) region (2020). Note: HH: Hohhot; WC: Wuchuan; TKT: Tuoketuo; TMZ: Tumd East; QSH: Qingshuihe; HL: Helinger; HJ: Hangin; DLT: Dalate; ZGE: Zhungeer; DS: Dongsheng; ETK: Etuoke; YJHL: Yijhinhuluo; WS: Wushen; ETKQ: Etuokeqian; TMY: Tumed Right; BT: Baotou; GY: Guyang; DMJ: Darhan–Muminggan Joint County.