esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 2 System Overview
#### Hardware Design  
Since the voltage received by hydrophones is ultra-low, the weak signals should be enlarged by a pre-amplifier, and then processed by MCU after ADC as shown in Figure 2. In the following subsections, each component is introduced.
## 3 Adaptive Ensemble Square Root Kalman Filter
### Adaptive Ensemble Square Root Kalman Filter  
The proposed AEnSRF combines ensemble root mean square Kalman filter with Sage-Husa algorithm. Comparing with ensemble Kalman filter, the proposed AEnSRF not only has a higher filtering-precision but also can be effectively corrected while measurement noise deviates, so as to suppress filter divergence and improve estimation accuracy.  
The specific steps are as follows:  
1. **Initialization**: The initial value \(\omega_{0}\) of generator rotor angular velocity is 1. The initial value \(\delta\) of generator power angle, electromagnetic power \(P_{e}\) and mechanical power \(P_{m}\) is its steady-state operation value.
2. The initial value of the state set with the number of set elements is generated by Monte Carlo method, and the initial estimation error covariance matrix is taken as the unit matrix. \[X^{0}=\left[\begin{array}{cccc}x_{11}&x_{12}&x_{13}&\cdots&x_{1N}\\ x_{21}&x_{22}&x_{23}&\cdots&x_{2N}\\ \vdots&\vdots&\vdots&&\vdots&\vdots\\ x_{31}&x_{32}&x_{33}&\cdots&x_{3N}\\ x_{n1}&x_{n2}&x_{n3}&\cdots&x_{nN}\end{array}\right]\] (49)
3. **State prediction:** \[X_{k+1}^{f_{i}}=f(X_{k}^{d_{i}},U_{k})+W_{k}^{i}\] (50) \[\overline{X}_{k+1}^{f}=\frac{1}{N}\sum_{i=1}^{N}X_{k+1}^{f_{i}}\] (51) \[E_{X\times k+1}^{f}=(\begin{array}{cccc}X_{k+1}^{f_{1}}-\overline{X}_{k+1}^ {f}&\cdots&X_{k+1}^{f_{N}}-\overline{X}_{k+1}^{f}\end{array})\] (52) \[E_{Zk+1}^{f}=(\begin{array}{cccc}Z_{k+1}^{f_{1}}-\overline{Z}_{k+1}^{f}& \cdots&Z_{k+1}^{f_{N}}-\overline{Z}_{k+1}^{f}\end{array})\] (53)\[P^{f}_{X\!Z,k+1}=P^{f}_{k+1}H^{T}_{k+1}=\frac{1}{N-1}E^{f}_{X\!Z,k+1}{(E^{f}_{Z \!Z,k+1})}^{T} \tag{54}\]  
\[P^{f}_{ZZ,k+1}=H_{k+1}P^{f}_{k+1}H^{T}_{k+1}=\frac{1}{N-1}E^{f}_{Z\!Z,k+1}{(E^{f }_{Z\!,k+1})}^{T} \tag{55}\]
4. **Calculating Kalman gain:** \[K_{k+1}=P^{f}_{X\!Z,k+1}{(P^{f}_{ZZ\!Z,k+1})}^{-1}\] (56)
5. **Calculate the measurement noise covariance:** \[e_{k+1}=Z_{k+1}-\overline{Z}^{f}_{k+1}\] (57)  
\[d_{k+1}=(1-b)/(1-b^{k+2}) \tag{58}\]  
\[\hat{R}_{k+1}=(1-d_{k+1})\hat{R}_{k}+d_{k+1}(e_{k+1}e^{T}_{k+1}) \tag{59}\]
6. **Calculate the mean deviation gain of the updated set:** \[\alpha=[1+\sqrt{\hat{R}_{k+1}{(P^{f}_{ZZ\!Z,k+1})}^{-1}}]^{-1}\] (60)  
\[\overline{K}_{k+1}=\alpha K_{k+1} \tag{61}\]
7. **State update:** \[\overline{X}^{a}_{k+1}=\overline{X}^{f}_{k+1}+K_{k+1}e_{k+1}\] (62)  
\[X^{\prime a_{i}}_{k+1}=X^{\prime f}_{k+1}-\overline{K}_{k+1}h(X^{\prime f}_{ k+1}) \tag{63}\]  
\[\overline{X}^{\prime a}_{k+1}=\frac{1}{N}{\sum_{i=1}^{N}X^{\prime a_{i}}_{k+1}} \tag{64}\]  
\[X^{a}_{k+1}=\overline{X}^{a}_{k+1}+\overline{X}^{\prime a}_{k+1} \tag{65}\]  
For convenience, the proposed AEnSRF method's implementation flow chart is presented in Figure 1.  
Figure 1: The flowchart of adaptive ensemble square root Kalman filter (AEnSRF) algorithm.
## 2 Materials and Methods
### Sample Analysis  
#### 2.3.1 Agarose Gel Electrophoresis Analysis  
Electrophoretic studies were performed to assess the plasmid integrity in all the samples taken. Samples were analyzed by horizontal electrophoresis in 0.8% agarose gel in TAE buffer (40 mM Tris, 20 mM acetic acid, and 1 mM EDTA, pH 8.0) in the presence of 0.5 \(\upmu\)g/mL ethidium bromide. The molecular weight marker was a supercoiled DNA ladder from Sigma-Aldrich (Saint Louis, MO, USA) The gels were run at 75 V for 2 h and subsequently analyzed and photographed using the gel documentation software Multi-Doc, Digital Imaging System, Trans UV (BioRad, Hercules, CA, USA).  
To estimate the percentage of the released pDNA(sc) in each experiment, ImageJ (freeware [[http://imagej.nih.gov/](http://imagej.nih.gov/)]([http://imagej.nih.gov/](http://imagej.nih.gov/))) was used for image analysis. This freeware measures the intensity of pDNA bands in electrophoresis images. Using \"area\" and \"label peaks\" commands, the percentage of the area of each band can be calculated. This technique has been employed to conduct image analysis in several studies [10; 27; 28; 29].
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 4 DVars for Adversarial Attack Defense
### Feature Selection  
Feature selection plays a crucial role in identifying the most relevant input feature subset that positively influences the classification performance of ML models [55]. During our experiments, we focused on the analysis of four specific features of network flow: source bytes (sbytes), destination bytes (dbytes), source packets (spkts), and destination packets (dpkts), as explored by [PERSON] et al. [56]. These features represent the counts of bytes and packets transmitted between the source and destination of a network flow. We selected these four features because of their susceptibility to modification by attackers through small content changes. Moreover, changes in these features do not affect the underlying logic or maliciousness of the network flow. These four features are intrinsic to network flows and are often used to distinguish malicious from benign flows.
###### Abstract
Additionally, research has been conducted to explore methods for optimizing process parameters with the goal of attaining minimal surface roughness. The objective of this work was to create a method for optimizing injection molding process parameters to achieve both minimal surface roughness and volumetric shrinkage. This involved creating surrogate models that represent surface roughness and volumetric shrinkage as functions of process parameters. These models were then optimized using a multi-objective optimization approach, with the goal of simultaneously minimizing surface roughness and volumetric shrinkage. A solution to the optimization problem was found using the pattern search algorithm, which generated a Pareto front. This Pareto front highlighted the trade-off between the two objectives--minimizing surface roughness and volumetric shrinkage--providing plastic engineers with a valuable tool to make informed decisions on adjusting parameters to achieve the optimal balance between surface roughness and volumetric shrinkage within defined limits. What is unique about this methodology is that the surrogate models were built using data from actual injection molding experiments rather than simulations. This approach allowed for the inclusion of all machine-specific conditions relevant to the injection molding process, resulting in more accurate surrogate models and optimization results. This paper follows the following structure: Section 2 details the injection molding experiments, including the results and quantification of surface roughness and volumetric shrinkage. Section 3 covers the surrogate modeling of surface roughness and volumetric shrinkage, as well as a discussion on multi-objective optimization for these objectives. Finally, Section 4 offers concluding remarks.
###### Abstract
Employing existing machine learning approaches without considering these features means wind farm generation cannot be accurately forecast, especially for ramp events. The contributions of this paper can be summarized as follows:  
* We propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast, which is adaptive machine learning for wind farm generation forecasting. The proposed framework addresses the challenges of the non-stationarity and the ramp dynamics of wind farm generation and can greatly facilitate the integration of wind generation in the real world. * The proposed method first classifies the wind farm generation data into the ramp and non-ramp datasets, where time-varying dynamics are captured by utilizing an adaptive thresholding framework to separate the ramp and non-ramp events, based on which different neural networks are trained to learn the dynamics of wind farm generation. * As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of the DSPOT algorithm to enhance the quality of the training datasets and the corresponding performance of the neural networks, which enables the model parameters to be adjusted automatically. * The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The remaining parts of this paper are organized as follows. Section 2 elaborates the distinct features of wind farm generation. Section 3 introduces the proposed wind farm generation forecast approach. Section 4 validates the performance of the proposed approach by using the real wind farm generation data. Section 5 summarizes the paper. Figure 1: Illustration of the DSPOT-enhanced self-evolving neural networks.
## 2 Antenna Configuration and Performance Analysis
### Simulated Results and Analysis
To qualitatively analyze the reason why HPBW was broadened, the E-field amplitude and phase results of the basic ALTSA and the proposed wide\(-\)beam antenna are given, as shown in Table 2. At the same time, considering that the performances in each cut-plane were relatively close, only the corresponding phase diagrams in the phi = 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) cut-planes at 15 GHz are presented here. As can be seen from Table 2, before loading subwavelength units, the E-field energy distribution between the two tapers of basic ALTSAs was relatively uniform, and the beam energy was mainly shot toward the boresight. However, after adding subwavelength units, the E\(-\)field energy distribution in this area significantly changed, and the energy was mainly concentrated near the subwavelength units, deviating from the boresight and transmitting to both sides. Similarly, the subwavelength elements change the equivalent dielectric constant of the relevant region, thus affecting the transmission phase of the electromagnetic wave. Figure 4: HPBW values of two antennas and their ratios in different cut-planes. Additionally, the shape of the wavefront then changed, making the beam radiate to both sides. These are beneficial to broadening the beamwidth. The current distribution of the proposed antenna at 15 GHz is also given in Figure 6. From Figure 6, we can observe that the subwavelength units had a strong current response, which indicates that the subwavelength units play an important role in widening beamwidth.
## 3 Results and Discussion
### Analysis of Volatile Compounds by HS-SPME/GC-Ms  
When agricultural products are infected by pathogens, they usually release volatile compounds that are obviously different from those that occur in a healthy state [44; 45]. Using HS-SPME/GC-MS technology and an online search of NIST, a total of 65 kinds of volatile compounds were identified and quantified, as shown in Table S3, including 11 types of aldehydes, 11 types of alcohols, 7 types of alkanes, 19 types of alkenes, 7 types of ketones, 7 types of esters, and 3 types of other compounds. The changes in the contents of different categories of volatile compounds at 10 time points are shown in Table 1 and Figure 4. Results showed that there was no statistical difference in volatile compounds at 0 h between the _C. fimbriata_-infected group and the control group (\(p>0.05\)). At the time of inoculation (0 h), the volatile compounds were mainly aldehydes, alcohols, and alkenes. The contents of these three categories of volatile compounds were significantly higher than those of alkanes, ketones, esters, and others (\(p<0.05\)). From 48 to 72 h, the content of all categories of volatile compounds increased significantly, especially ketones. At 0 h, the content of ketones was extremely low--23.39 ng g\({}^{-1}\) FW, accounting for only 2%. At 48 h, the content of ketones was 2932.65 ng g\({}^{-1}\) FW, and at 72 h of infection, the content of ketones was as high as 17,346.42 ng g\({}^{-1}\) FW, accounting for 74% of the total volatile compounds. This was followed by alkenes, with a content of 133.39 ng g\({}^{-1}\) FW at 0 h, accounting for 13%. At 72 h, the content of alkenes was 2304.64 ng g\({}^{-1}\) FW, accounting for 10%. The content showed a significant increase overall (\(p<0.05\)) but fluctuated. At the time of inoculation (0 h), the content of alcohols was 296.54 ng g\({}^{-1}\) FW, accounting for 29%. The content was relatively stable from 0 to 40 h, with no significant change (\(p>0.05\)). At 48 h, the content began to increase significantly (\(p<0.05\)), and at 72 h it was 1473.76 ng g\({}^{-1}\) FW but the proportion had decreased to 6%. The content of aldehydes was 499.00 ng g\({}^{-1}\) FW at 0 h and the ratio reached 49%. After infection with _C. fimbriata_, the aldehydes content showed a fluctuating upward trend. At the late period of infection (72 h), the content increased significantly (\(p<0.05\)), reaching 963.91 ng g\({}^{-1}\) FW, but the proportion had decreased to 4%. The alkanes and esters showed a significant upward trend from 0 to 72 h in general, but the proportion was always low. The content at 0 h was 12.59 ng g\({}^{-1}\) FW and 50.95 ng g\({}^{-1}\) FW, respectively, accounting for 1% and 5%. The content at 72 h after infection was 376.55 ng g\({}^{-1}\) FW and 270.25 ng g\({}^{-1}\) FW, accounting for 2% and 1%, respectively. In general, _C. fimbriata_ infection led to the decrease or disappearance and increase or appearance of different types of volatile compounds, which was beneficial for the early discrimination of _C. fimbriata_-infected sweetpotatoes during the asymptomatic period.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 3 Numerical Simulations
### Validation of the Encryption Scheme  
As mentioned above, the encryption data of this encryption has 50 single bands. Therefore, it is difficult to estimate the difference between two different size hyperspectral images. To show the results convincingly, a random band extracted from the original hyperspectral image is used to calculate the PSNR with each band of the ciphertext. In this experiment, the 27 th band of the original image is chosen, and the PSNR values are listed in Table 1.  
As we can see from Table 1, the PSNR values between the decrypted data and original date range from 1.4699 to 2.4062, which indicate that the images are very different. In other words, the proposed cryptosystem can protect the original information successfully. In addition, to examine the PSNR values between the original band and correct decrypted band, another experiment is done. In the decryption approach, all the keys are given and the 27 th band is decrypted completely. The PSNR value between the 27 th image and the retrieved 27 th image is 295.7293, which indicates that the proposed algorithm recovered the original image completely. The original 27 th band and decrypted 27 th are displaced in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Original Data** & **Encrypted Data** & **PSNR Value** & **Original Data** & **Encrypted Data** & **PSNR Value** \\ \hline
27 th band & 1 st band & 1.7448 & 27 th band & 26 th band & 1.6473 \\
27 th band & 2 nd band & 1.7585 & 27 th band & 27 th band & 1.6227 \\
27 th band & 3 rd band & 1.8941 & 27 th band & 28 th band & 1.6121
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 6 Conversion of Halophytes into Biogas
### Inhibiting Effects of Halophytes on the AD Process
#### 6.3.1 Optimization of AD Microorganism Composition
[105] and [PERSON] et al. [106], who disclosed that the potential for the methanogenic group to acclimatze in order to tolerate higher saline conditions is dependent on the bacterial community. [PERSON] et al. [107] and [PERSON] et al. [73] also validated these results; they were able to achieve higher gas production under saline conditions after operating the test one more time. The performance and kinetics of biogas/biomethane production in AD affected by salt content was studied by different researchers [73; 74; 94] by utilizing the modified Gompertz equation model to statistically describe the growth of the microbial community by fitting the cumulative biogas/biomethane production at a specified digestion time, the ultimate biogas/biomethane production, the maximum biogas/biomethane production rate per day, the lag phase period or minimum time needed to produce biogas/biomethane and the exponential phase. According to [PERSON] et al. [94], with increasing sodium concentration, the ultimate methane production and maximum methane production rate were slightly increased from 0.49-4.42 g L\({}^{-1}\) Na and then decreased progressively from 4.42-24.08 g L\({}^{-1}\) Na concentration, as well as gradual increase in lag phase period up to 16.22 g L\({}^{-1}\) Na content. Thus, this implies that the methane production kinetics is considerably affected by sodium content. This is dissimilar to the findings of [PERSON] et al. [73; 74], who reported no notable difference between the kinetic biogas yield rates of salt-optimized inoculum with added salt of 6.3 g L\({}^{-1}\) NaCl. This is similar to the results reported by [PERSON] et al. [102], whose findings suggested that optimized inoculum could effectively convert saline substate into biomethane at 4.6 g L\({}^{-1}\) Na content. This may be due to the increase in salt-acclimatized microbes that evolved adaptation mechanisms and tolerance to saline environments. Conclusively, the potential for AD microbial communities to acclimatze to environmental variations through progressive optimization in composition and metabolism to environmental changes could propose a favorable approach to improve bacterial metabolism and optimize the microbial community to operate at higher saline concentrations in the AD process. It is with great anticipation that this halotolerant AD bacterial community has the potential to be further advanced and be a significant factor in narrowing the gap in the nutrient cycle related to the production of halophytic biomass-based biofuel production technologies.
## Materials and Methods
### Data Processing Methods  
Three parallel samples were used for the abovementioned experiments; the results were averaged, and the standard deviations were calculated using Excel. The experimental data were processed and plotted using Origin 2019b. The adsorption amount (\(q_{e}\), mg/g) and removal rate (\(R\), %) were calculated as follows:  
\[q_{e}=\frac{(C_{0}-C_{e})V}{m} \tag{2}\]  
\[R=\frac{(C_{0}-C_{e})}{C_{0}}\times 100\% \tag{3}\]  
where \(q_{e}\) is the sorption amount, mg/g; \(C_{0}\) denotes the initial concentration of the pollutant, mg/L; \(C_{e}\) denotes the remaining concentration of the pollutant, mg/L; \(V\) is the volume of the pollutant solution, L; and \(m\) is the material dosing amount, g.  
The equations for the pseudo-first-order, pseudo-second-order, and Weber-Morris models are as follows [24; 25; 26; 27; 28]:  
\[\frac{dq_{t}}{d_{t}}=\mathrm{k}_{1}(q_{e}-q_{t}) \tag{4}\]  
\[\frac{dq_{t}}{dt}=\mathrm{k}_{2}(q_{e}-q_{t})^{2} \tag{5}\]  
\[q_{t}=\mathrm{k}_{d}t^{\frac{1}{2}}+\mathrm{E}_{i} \tag{6}\]  
where \(q_{t}\) denotes the amount of pollutants adsorbed by nZVI@ATP at moment \(t\), mg/g; k\({}_{1}\) is the quasi-primary kinetic equation rate constant, g/(mg\(\cdot\)h); k\({}_{2}\) is the quasi-secondary kinetic equation rate constant, g/(mg\(\cdot\)h); k\({}_{d}\) is the rate constant of the intraparticle diffusion equation, mg/(g\(\cdot\)h\({}^{1/2}\)); and E\({}_{i}\) denotes the intercept, mmol/g.
## 4 Testing and Result Discussion
### Preliminary Testing
The final flight test carried out was a manual flight, which was fully successful despite having several tall trees as obstacles. Again, this test taught the testing team a lesson about when to use guided or autonomous flights when they are required. It was observed that the movement of the UAV was agile and responded perfectly to the operator orders, with a stable flight and without any loss of control. In this occasion, both takeoff and landing were carried out smoothly.
Thus, edge computing is more suitable for IoT services; it can offer secure and efficient services for a large number of end users, and architectures based on edge computing can be considered for future IoT infrastructure [10]. There are some existing related studies in the field of edge computing that have provided a survey of papers and architectures in the area of edge computing, fog computing, or MEC. The authors in [31] presented a comprehensive survey on fog computing and related computing models and their differences and similarities. They also provide a summary and taxonomy of research into fog computing and its related computing models. Some works have focused on security and privacy issues and related countermeasures in edge computing paradigms [32, 33]. Other works have proposed architectures for EC-based IoT [34, 35]. Inspired by these works and using other materials from top journals/conferences, high-cited works, and references in the field, our study provides a detailed and up-to-date analysis of several subjects from a holistic perspective. The main contributions of this article are summarized as follows:  
1. We provide an overview and definition of edge computing and its relationship/difference with/from other similar computing models, such as fog computing, cloud computing, cloudlets, and MEC. 2. We present attacks and threats of EC-based IoT. Then, we discuss the possible solutions and countermeasures at different network layers and for different security and privacy issues. 3. We propose a secure edge computing-based architecture for IoT infrastructure through many research achievements concerning edge computing in IoT and define an application of edge computing in IoT for the architecture. The remainder of this paper is structured as follows: in Section 2, we discuss cloud computing and other similar computing paradigms and compare edge computing with other similar computing paradigms. Section 3 provides classifications of security and privacy attacks and threats for EC-based IoT. Section 4 describes possible security solutions and countermeasures. It also provides a comprehensive analysis of security and privacy issues for EC-based IoT. Section 5 presents an edge computing-based architecture for IoT infrastructure. Section 6 defines IoT scenarios based on cloud and edge computing as an application of the proposed architecture. Section 7 discusses the advantages/disadvantages of scenarios. Section 8 highlights the main challenges. Finally, Section 9 concludes this article.
# Modeling of RC Moment Frame Retrofit with Mortar Walls Reinforced with Steel Wire Mesh
## 1 Introduction
#### Research Significance  
This study proposes an improved method for modeling the connectivity and constraint conditions for nonlinear fiber elements of RC moment frames retrofitted with mortar walls reinforced with steel wire mesh. The modeling results are important as they show that the capacity curves of the retrofitted wall system underestimate the maximum strength, initial stiffness, and residual strength. The underestimation of the capacity curves from the model is caused by insufficient connectivity and constraint conditions between the border frame and the mortar wall elements.