esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID
## 3 Control Algorithm
### Fuzzy PID Controller
#### 3.2.1 Input and Output of Fuzzy Controller  
The vertical acceleration \"\(e\)\" and the rate of change of vertical acceleration \"\(ec\)\" serve as input signals to this controller. Through processes such as fuzzy inference and parameter adjustment, the controller obtains the final output values, namely \"\(\Delta K_{p}\)\", \"\(\Delta K_{i}\)\", and \"\(\Delta K_{d}\)\". These values primarily influence the adjustment of the switching time of the electromagnetic valves and the regulation of airbag pressure, thereby adjusting the stiffness of the air suspension. The final PID parameter values can be obtained accordingly.  
\[\left\{\begin{aligned} & K_{p}=K_{p}^{\prime}+\Delta K_{p}\\ & K_{i}=K_{i}^{\prime}+\Delta K_{i}\\ & K_{d}=K_{d}^{\prime}+\Delta K_{d}\\ \end{aligned}\right. \tag{3}\]  
Figure 4: PID Simulation System of Air Suspension.  
Figure 5: Schematic Diagram of Fuzzy PID Controller.  
In the Equation, the initial settings of the fuzzy PID are sequentially designated as \"\(K_{p}\)\", \"\(K_{i}\)\", and \"\(K_{d}\)\"; The correction amounts \"\(\Delta K_{p}\)\", \"\(\Delta K_{i}\)\", and \"\(\Delta K_{d}\)\" are real-time self-adjustments of PID parameters for the suspension system based on actual driving conditions, determined through fuzzy inference; The PID parameter real-time self-adjustment of the suspension system based on actual driving conditions is derived through fuzzy inference. The final parameter settings of the fuzzy PID are denoted as \"\(K_{p}\)\", \"\(K_{i}\)\", and \"\(K_{d}\)\".  
Based on the passive suspension test results, the fuzzy domains of the controller input variables \"\(e\)\", \"\(cc\)\", and output variables \"\(\Delta K_{p}\)\", \"\(\Delta K_{i}\)\", and \"\(\Delta K_{d}\)\" were all selected as \([-6,6]\). The quantization factor was \(K_{e}=7.8\), \(K_{cc}=1.2\), and the proportional factor was \(K_{u}=8\). The initial values of the fuzzy PID controller parameters were \(K_{p}\)\({}^{\prime}=1\), \(K_{i}\)\({}^{\prime}=53\), and \(K_{d}\)\({}^{\prime}=0.1\).
# Design of a Compact 1-MV-Class Radial Closing Switch for Tesla Type Generator
## 2 General Consideration  
### General Considerations Related to the Tesla-Based System  
Small-size switches have been previously implemented in Tesla-driven generators for producing very fast impulses of high power [24; 25; 26], but the present work is to develop a powerful pulsed electric field (PEF) system for proof-of-concept testing a novel and non-invasive food processing technique according to ref. [27]. Figure 1a shows the 3-D model of the Tesla-based generator, which comprises three parts: a Tesla transformer, a main switch, and a load. The Tesla transformer consists of a single-turned primary winding surrounding a multi-turned secondary one without using magnetic cores. The load consists of a parallel plate capacitor and a solenoid inductor. The global envelope of the generator is 1270 mm \(\times\) 600 mm, which is compact and light via mechanical estimations. The Tesla transformer charges the capacitor to about 1 MV in \(\sim\)2 us. Then the switch breaks down, resulting in a sinusoidal oscillation of the high voltage across the two plates of the capacitor. High electric field with high frequency between the two plates is then produced, which is going to be used for investigations of non-invasive food processing technique.  
Figure 1b shows the equivalent circuit of the 1-MV Tesla-type generator. The main switch (Ss) is used to crowbar the Tesla transformer circuit separating it from the load, while the load is used to process food using the PEF generated by the secondary capacitor (Cs). An operating cycle of this generator can be described as follows: first, the primary capacitor (Cp) is charged followed by the primary switch (Sp) closure. During the Cp discharge, the output voltage from the transformer charges the secondary capacitor (Cs) to values close to 1 MV. When the voltage reaches a pre-established value, the main switch (crowbar) closes, generating fast oscillations through the LC load circuit while the PEF processes the food.  
The values of Cp, Cs, charging time, inductance of the primary and secondary inductors of the Tesla transformer are optimized by circuit simulation, which is beyond the aim of this paper and will be introduced in another paper.  
Since the Tesla transformer is coaxial and the main switch is used to crowbar the Tesla transformer secondary circuit, the switch must be designed having a radial, rather than axial geometry. In addition, this switch should be as compact and as light as possible, to allow the overall system to be compact and light.
# WhistleGAN for Biomimetic Underwater Acoustic Covert Communication
## 2 Proposed Method
### Structure of WhistleGAN  
The \(G\) in the WhistleGAN uses fractional-strided convolution instead of a fully connected layer to generate whistles. To solve the problem of gradient vanishing/exploding, batch normalization (BN) is applied in all layers except the output layer. The output layer uses the tanh activation function, and the other uses the ReLU activation function.  
The \(D\) of the WhistleGAN uses strided convolution instead of a fully connected layer to reduce the number of features in the input whistle image and determine true/ false. The \(D\) utilizes batch normalization in all layers except the input and output layers and uses the sigmoid activation function to determine true/false in the output layer. In the remaining layers, the LeakyReLU activation function is employed, which introduces a slope of alpha to allow negative values unlike ReLU and thus enables the learning of weights to be influenced.  
Since the WhistleGAN contains the Mel filter bank, each network learns the input data with different values and sizes than the conventional DCGAN. Thus, the kernel and stride sizes are optimized through iterative research. The specific structure of the \(G\) of the proposed WhistleGAN is shown in Table 1 and the specific structure of the \(D\) of the WhistleGAN is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Kernel** & **Stride** & **BN** & **Activation Function** \\ \hline Input & \(4\times 4\) & \(1\) & Y & ReLU \\ Conv1 & \(4\times 4\) & \(2\) & Y & ReLU \\ Conv2 & \(4\times 4\) & \(2\) & Y & ReLU \\ Conv3 & \(4\times 4\) & \(2\) & Y & ReLU \\ Conv4 & \(4\times 4\) & \(2\) & Y & ReLU \\ Output & \(4\times 4\) & \(2\) & N & Tanh \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specific structure of the WhistleGAN’s generator network.
## Abbreviations  
* IECPS integrated energy cyber-physical system
* CCPA coordinated cyber-physical attack
* APT advanced persistent threat
* GPS global positioning system
* RA replay attack
* MITM man-in-the-middle attack
* FDIA false data injection attack
* TSA time synchronization attack
* DoS denial of service attack
* CCA coordinated cyberattack
* AGC automatic generation control
* IED intelligent electronic device
* FTU feeder terminal unit
* RTU remote terminal unit
* DTU data transfer unit
* AC alternating current
* LR load redistribution attack
* DC direct current
* PMU phasor measurement unit
* DDoS distribution denial of service
* EV electric vehicle
* AI artificial intelligence
* BDD bad data detection
* KF Kalman filter
* MTD moving target defense
* GLR generalized likelihood ratio
* QCD quickest change detection
* CUSUM cumulative sum test
* G2V grid-to-vehicle
* V2G vehicle-to-grid
* V2B vehicle-to-building
* IoT internet of things
* IP internet protocol address
* HTTP hypertext transfer protocol
* IDS intrusion detection system
## 6 Discussion  
This section discusses the advantages, limitations, and future development directions of the model.  
Advantages:  
High Prediction Accuracy: This method integrates the cement slurry rheological equation into the Wide and Deep neural network framework, further improving prediction accuracy compared with traditional neural network methods and enhancing the model's capability to recognize cement bond quality.  
Good Generalization Ability: The model has demonstrated stable performance on the test dataset, indicating strong generalization ability and maintaining high accuracy across different data distributions.  
Limitations:  
Dependence on Data Quality: The model's performance heavily relies on the quality and diversity of the training data. If there is bias in data collection, the prediction capability may be affected, leading to reduced generalization in certain complex conditions.  
Applicability of Azimuthal Factors: Correlation analysis in this study indicates that azimuth has little impact on the model. This assumption is reasonable when the influence of the formation's horizontal stress direction is insignificant. However, in tectonically active basins, azimuth may play a crucial role in wellbore stability, and its effect may need to be further considered in specific application scenarios.  
Influence of Formation Pressure: Formation pressure also affects cementing quality. The formations considered in this study are all normally compacted formations, without abnormally high or low pressure zones. As a result, pressure data were excluded from the correlation analysis, and only well depth was retained. However, in abnormally compacted formations, this factor must be considered. Additionally, the rheological properties of cement slurry are also affected by pressure. However, current field tests of cement slurry rheology in oilfields are typically conducted under atmospheric pressure, meaning that no high-pressure experimental data are available for modifying the power-law model. This limitation may impact prediction results.  
Future Development Directions:  
Future research will focus on integrating laboratory-based cement slurry testing with intelligent modeling to more accurately simulate downhole conditions. Although the current model incorporates several key influencing factors, there remains considerable room for enhancement. For instance, plastic contraction of the cement slurry, which can significantly impact the integrity of the cement sheath, should be considered in subsequent studies. Additionally, other cement slurry properties--such as thickening time--could also be incorporated. These properties can be quantified through laboratory tests conducted prior to field operations and subsequently used as model inputs, thereby enabling a more precise characterization of cement slurry behavior under downhole conditions.
## 4 Simulation and Experimental Results
### Experimental Results
Figure 20: State variables of the drive: (**a**) reference and measured rotational speed, (**b**) electromagnetic torque and stator current component \(i_{sy}\), (**c**) estimated rotor flux vector module and stator current component \(i_{ss}\), (**d**) stator current components \(i_{sa,\beta}\); (experimental results), (\(\omega_{m}{}^{n\!f}=0.5\)\(\omega mN\), \(t_{L}=\) var). During the last test, the influence of the values of adaptation mechanisms coefficients on the estimator operation was analyzed. The coefficients were changed it the same way as during the simulation test. A noticeable influence on the dynamics was visible, however, the estimator operated stably for each of the sets of parameters (Figure 23). Figure 14:0 Operation of the PQ-MRAS estimator for different values of parameters in adaptation mechanisms: (**a**) estimated rotor resistance, (**b**) reference and estimated reactive power, (**c**) estimated stator resistance, (**d**) reference and estimated active power; (experimental results), (\(\omega_{m}{}^{off}=0.5\,\omega mN\), \(t_{L}=0.75\,tN\)). Figure 14:1 State variables of the drive: (**a**) reference and measured rotational speed, (**b**) electromagnetic torque and stator current component \(i_{sy}\), (**c**) estimated rotor flux vector module and stator current component \(i_{ax}\), (**d**) stator current components \(i_{sa,g}\); (experimental results), (\(\omega_{m}{}^{off}=\pm 0.5\,\omega mN\), \(t_{L}=\pm 0.75\,tN\)).
# Lattice Design of an Intermediate-Energy Electron Storage Ring Dedicated to Materials Research
## 6 Discussions and Conclusions  
Most DLSRs, in operation, under construction, or in the planning phase, that adopt a hybrid MBA or HOA MBA structure normally require magnets with very high magnetic field strength. Although stronger focusing can reduce the emittance of the electron beam and thus increase the brilliance of X-rays, it inevitably reduces the aperture of the vacuum chamber, which is not favorable for achieving high current operation. Furthermore, the DA and MA of the MBA lattice are generally small, which poses great challenges to the beam injection and Touschek lifetime.  
Based on the emerging DDBA lattice structure, the design of very compact intermediate-energy storage ring light sources to fill the gap between the third-generation and fourth-generation light sources in China is given, to serve as a dedicated X-ray light source for the materials research in SZLab, a newly established research institute in Suzhou, China.  
By using advanced insertion device technologies, which include IVU, MPW, APPLE-II type EPUs, SC wigglers, and SC undulators, we are expecting high photon performances to fulfill the diverse requirements of most users from the material science communities, e.g., structural materials, energy materials, information and communication technology materials, and some frontier functional materials, to name a few.  
Figure 11: Brilliance of planned IDs.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ID Type** & **Period (mm)** & **Number Periods** & **Max B(T)** \\ \hline IVU & 22 & 190 & 1.15 \\ MPW & 100 & 13 & 1.6 \\ Apple-II (EUV) & 75 & 53 & 1.08 \\ Apple-II (SX *) & 56 & 71 & 0.44 \\ SC wiggler & 48 & 22 & 4.2 \\ SC undulator & 10 & 116 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Typical parameters of planed IDs.  
**Author Contributions:** Conceptualization, C.L. and J.C.; methodology, C.L.; software, C.L. and H.W.; validation, C.L., J.C. and Q.Z.; formal analysis, C.L.; investigation, C.L., H.W. and K.W.; resources, J.C.; data curation, C.L. and H.W.; writing--original draft preparation, C.L.; writing--review and editing, C.L., J.C., H.W., Q.Z. and K.W.; project administration, J.C.; funding acquisition, C.L. and J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China, grant number 12305161 and Suzhou Laboratory Youth Talent Strategic Subject, grant number QN202415.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Acknowledgments:** We acknowledge support from the Shanghai Advanced Research Institute and Suzhou Laboratory.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Agriculture Industry  
One of the primary uses of kaolin in agriculture is as a pesticide and growth enhancer. Kaolin-based sprays are applied to crops to form a protective film that helps to deter insect pests, reduce heat stress, and prevent sunburn on plants [120]. This physical barrier not only protects the plants but also reduces the need for chemical pesticides, promoting a more sustainable agricultural practice. Additionally, kaolin improves soil health and fertility [121]. When used as a soil amendment, kaolin enhances water retention and nutrient availability, contributing to better root development and overall plant growth. Its fine particle size and high surface area allow it to retain moisture and release it slowly to the plants, which is particularly beneficial in drought-prone areas. Recent studies have also highlighted the role of kaolin in improving the quality of harvested produce [122]. The reflective properties of kaolin sprays help to manage the light environment around the plants, leading to improved photosynthesis and potentially higher yields. Moreover, kaolin treatments can reduce the incidence of certain fungal diseases by creating a hostile environment for the growth of pathogens.  
In terms of environmental impact, kaolin is a naturally occurring mineral that is non-toxic and environmentally friendly, making it an ideal choice for sustainable agriculture. Its use can lead to reduced chemical inputs, lower production costs, and healthier ecosystems. The versatility and efficacy of kaolin in various agricultural applications underscore its importance as a tool for enhancing crop production and promoting sustainable farming practices.
# Laser Fabrication of Titanium Alloy-Based Photothermal Responsive Slippery Surface  
[PERSON]  
resistance, heat radiation, and anti-reflection [25; 26; 27; 28; 29]. Especially considering the camouflage and photothermal applications, great efforts have been devoted to the development of laser-induced metallic anti-reflection surfaces [30]. For example, [PERSON] et al. proposed and proved through experiments that a highly disordered, super hierarchical micro-nano structure formed on the surface of a metal (such as Cu) exhibits a high degree of anti-reflection ability in the ultra-broadband spectrum from ultraviolet to far infrared [31]. [PERSON] et al. proposed a hybrid method consisting of ultra-fast laser-assisted texturing and chemical fluorination treatment to significantly reduce the reflectivity of the titanium alloy surface by creating ultra-light trapping micro-nano structures [32]. In spite of these pioneering works, innovations on rational design and fabrication of anti-reflective micro-nano structures through a biomimetic manner are still highly desired. Open problems with respect to the reduction of reflectivity, the increase of light absorption, and especially the applications of anti-reflection metal surface in different fields are still challenging.  
In this work, we propose the laser fabrication of anti-reflection titanium alloy surface and its applications in developing light-responsive slippery surface. Laser processing has been employed to create anti-reflection structures, similar to the black region of Little Angel Swallowtail Butterfly wings, on titanium alloy through a laser ablation and nanoparticle deposition process. The laser-structured titanium alloy surface demonstrates good anti-reflection properties (300\(\sim\)2000-nm reflection smaller than 10%) and improved light absorption ability. In addition, the anti-reflection titanium alloy surface is employed as a photothermal substrate to prepare a light-responsive slippery surface, which can be used to control the sliding behavior of liquid droplets. This method enables the preparation of anti-reflection and high-absorption metal surfaces, revealing great potential for applications in bionic devices and microfluidics.
# Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
## 3 Results
### Comparison of EOF Density Fields of Different Orders
Table 2 shows that the error range of the characteristic root corresponding to the first-order atmospheric density eigenvector field is [1.84 \(\times\) 10\({}^{-21}\), 2.976 \(\times\) 10\({}^{-21}\)]. The second-order's error range of the characteristic root is [2.53 \(\times\) 10\({}^{-23}\), 4.09 \(\times\) 10\({}^{-23}\)]. The error ranges of the third, fourth, and fifth orders are [1.264 \(\times\) 10\({}^{-23}\), 7.817 \(\times\) 10\({}^{-24}\)], [4.170 \(\times\) 10\({}^{-24}\), 2.580 \(\times\) 10\({}^{-24}\)], and [1.008 \(\times\) 10\({}^{-24}\), 6.232 \(\times\) 10\({}^{-25}\)], respectively. The error upper limit of the characteristic root of the second-order atmospheric density eigenvector field is less than that of the first-order atmospheric density eigenvector field. There is no intersection between the measurement field's error ranges. Therefore, the first-order atmospheric density eigenvector field passes the modal significance test of EOF. Similarly, according to the above steps, the EOF modal significance tests for the corresponding eigenvalues of the second, third, and fourth-order atmospheric density eigenvector fields show no intersection between the upper and lower limits of the error. Therefore, atmospheric density's first, second, third, and fourth-order eigenvector fields are valuable signals and vector fields with independent characteristics. It should be noted that the eigenvector fields that do not pass the significance test are generally high-order eigenvector fields. Suppose a situation does not pass the significance test in the follow-up study. In that case, special instructions will be given when the elimination operation is needed.
## 4 Flood Decision Support System (FDSS): Operations Portal  
The FDSS Portal provides a centralized web-based information interface/dashboard that incorporates multiple information tools, enabling MNFE and other authorized operators to monitor and predict/forecast imminent flood events in real-time. This includes rapid access to decision-ready tools for determining the potential impacts in advance. The portal is built within the Climate Data for the Environment Services application Client (CiIDESc) framework [42] and provides a flexible platform for future MNFE data visualization needs.  
The portal is cloud-based and is password-protected. It can be configured to enable additional or new data/information sources required to support hydrometeorological or climate operational decision-making as these needs arise in future. Information ingested and displayed within the Vaisigano FDSS includes (Figure 6), but is not limited to:  
* Real time rainfall and river stage data from monitoring stations within and surrounding the Vaisigano catchment;
* Real time sea-level data from the Apia tide gauge;
* Nowcast (observations-based) flood prediction tool;
* Real time NWP (WRF 3 km) rainfall forecasts and flood forecast tools; and
* Pre-computed flood hazard and exposure/impacts maps and data tables for available flood scenarios.
## 6 PSK Modulation and Demodulation
### PSK Modulation for General Implants  
To reduce the size and power consumption, [PERSON] and [PERSON] [74] further developed their previous work keeping the same parameters in [70] but using BPSK modulation for general body implants. The BPSK demodulators in [70,71] were further developed by [PERSON] _et al_. [75] using QPSK modulation and modified COSTAS loop technology. The program was simulated using same carrier frequency of 13.56 MHz with a data rate of 4 Mbps, _i.e._, faster data transmission. Again, a BPSK demodulator is designed using a phase frequency detector based PLL for robust performance [76]. Two different circuit implementations for demodulator with a carrier frequency of 13.56 MHz are used in this system.