esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 3 The Progress of Energy Storage Technologies
### Capacitors and Supercapacitors  
Capacitors and supercapacitors store electric energy by accumulating positive and negative charges. A capacitor consists of at least two electrical conductors (usually made of metal foil) with a thin layer of insulator (usually made of ceramic, glass, or plastic film) in between [78]. The charge storage capacity in supercapacitors theoretically derives from two types of capacitive behavior: one related to the double-layer structure of the electrode/electrolyte interface and the other to the pseudo capacitance. Capacitors are suitable for storing small amounts of electrical energy and conducting different voltages; they have a higher power density and shorter charging times than conventional batteries. However, they have limited capacity, relatively low energy density, and high energy dissipation due to high self-discharge losses. Due to their characteristics, supercapacitors have applications in rail transportation, renewable energy, consumer electronics, energy buffers for inverter drive systems, and military equipment. An EPSRC-funded project to develop high-performance supercapacitors with enhanced energy densities has been implemented in the UK. Prototypes have been tested for the design of an efficient and sustainable power system. Some results of this project were published in 2013 [79]. [PERSON] et al. [59] investigated supercapacitors seeking efficient storage mechanisms and potential higher-performance electrolyte materials and classified their mathematical models into three categories: integer order, conventional type, and fractional model. A static and dynamic model capable of predicting supercapacitors across the spectrum was presented. [PERSON] et al. [80] proposed a new integrated supercapacitor that achieved a large increase in performance and a high energy density of 27.2 Wh/kg.  
There are some summaries of the different energy storage technologies in Table 6. Table 7 contains some of the relevant technical parameters. From the existing research on energy storage methods, it can be seen that various energy storage technologies have their the advantages and disadvantages, and it is difficult to meet the storage needs of new energy power systems with only one energy storage technology. It is necessary to increase the research and application of energy storage technology to realize a new energy storage technology with large capacity, high efficiency, fast speed, and low cost so that the new energy power system can operate efficiently and stably.  
\begin{table}
\begin{tabular}{c c c c} \hline
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 4 Simulation Verification
### Experimental Results and Analysis
#### 4.2.2 Sinusoidal Steering Scenario  
The sinusoidal steering scenario was set for further validation to verify the algorithm's effectiveness under extreme high-speed conditions. The system's initial state parameters are specified as \(x_{0}=[80/3.6,0,0]\), \(P_{0}=0.01\times diag([1,1,1])\). Currently, the road adhesion coefficient is kept at 0.85, and the vehicle is set to maintain a constant speed of 80 km/h. Figure 6 presents the driving trajectory of the estimator, along with the input and observation signals. Figure 7 compares simulation results between the IMM-MCCKF and the MCCKF and CKF algorithms.  
Figure 7 presents the simulation outcomes for the vehicle's sinusoidal steering scenario, with Figure 7a-c displaying longitudinal speed, lateral speed, and the centroid sideslip angle, respectively. As illustrated in Figure 7a, both IMM-MCCKF and MCCKF accurately track the actual value in longitudinal speed estimation, while CKF exhibits significant fluctuations and deviations in its estimates. All three algorithms exhibit estimation errors when the vehicle enters a nonlinear zone while cornering at high speeds, as illustrated in Figure 7b,c. This difference is due to the lateral force being calculated slightly differently from the actual value.  
Due to the vehicle's pronounced nonlinear characteristics during high-speed continuous cornering and the susceptibility of sensor data to outliers, the non-Gaussian nature of the noise is enhanced, resulting in more significant estimation errors at peak points. Consequently, CKF produces inaccurate estimates when non-Gaussian noise is present, and its performance drops dramatically. Although MCCKF has good noise suppression capabilities, its ability to resist noise variations is weak, resulting in decreased estimation performance. Conversely, IMM-MCCKF can quickly adjust model probabilities and maintain model outputs with more minor errors, thereby ensuring high estimation accuracy and superior tracking performance. In summary, the IMM-MCCKF algorithm continuously monitors the model with a minor mistake through a Markov probability  
Figure 6: Input signals for the sinusoidal steering scenario: (**a**) the vehicle trajectory, (**b**) the angle of the front wheels, (**c**) the longitudinal acceleration, and (**d**) the vertical acceleration.  
transition matrix by adjusting model probabilities in real time based on each submodel process and observation.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Dataset Description  
The experimental data used in this paper are acquired by the NASA/JPL SIR-C system in Qinghe Xinjiang, China, in 1994, including the 4-Look Complex L-Band and C-Band datasets with a resolution of 12.5 m \(\times\) 12.5 m. According to the ground truth acquired by the Chinese Academy of Sciences in 1994, there are eight classes of lithology in the experimental area, including Alluvium (Q\({}_{4}^{\rm a1}\)), Diluvium (Q\({}_{2-3}^{\rm p1}\)), Alluvium-Diluvium (Q\({}_{3-4}^{\rm a1}\)), Beitashan Formation (D\({}_{2\rm b}\)), Biotite granite, Leucogranite (\(\gamma_{4}^{2c}\)), Upper Aermente Formation (D\({}_{3\rm a}^{\rm 3}\)), Nanmingshui Formation (C\({}_{\rm In}\)), and Mayinebo Formation (D\({}_{\rm 1m}\)). The reference map of this area is provided in Figure 2. The L-Band and C-Band Pauli RGB images are shown in Figure 3. Figure 4 is the ground truth where black color means that the pixels have no class labels.
## 3 Methodology
### Image Preprocessing and Data Augmentation  
**Grayscale Normalization.** The problem of image brightness range variation is often encountered in the OCR task. To enhance the images, preprocessing of the brightness normalization techniques was applied. Based on the preliminary experiments using brightness normalization techniques such as min-max, averaging, and histogram equalization, we adopted the min-max normalization method (Equation (1)) and achieved the best recognition accuracy. Figure 7 shows an example of min-max normalization. Compared to the original image (Figure 7a), the normalized image appears to exhibit better contrast. This enhancement leverages feature extraction for deep learning:  
\[X_{minimax}=\frac{X-X_{min}}{X_{max}-X_{min}}. \tag{1}\]  
**Data Augmentation.** To ensure that the proposed model can adapt to a variety of different situations, considering that the ID cards may be aligned in different angles or the ID card images may be captured at slightly different distances from the cameras, the rotation angle and size of the text image are expected to change accordingly. Furthermore, despite the fact that our current YOLOv3-based [38] detection approach achieved 100% correct character cropping, the segmentation mAP has yet to reach 1.0, and the detection bounding box is occasionally slightly rotated or has different sizes. As a result, the rotation and rescaling of data augmentation are required during the training phase. Data augmentation was further applied to the synthesized data generated in Section 3.1, where each image was randomly rotated between \(10^{\circ}\) and \(-10^{\circ}\), and enlarged between the ratios of 1.1 to 1.3, so that the diversity of the data increased. Examples of data augmentation images are shown in Figure 8.  
Figure 8: Examples of data augmentation images.  
Figure 7: Example of normalization preprocessing: (**a**) Original image. (**b**) MiniMax normalization result.
## 3 Materials and Methods  
### The Least Squares Estimator  
The principle of the least squares channel estimation algorithm is to decrease the distance between the received symbol and the reference symbol [31].  
Let us denote by \(y_{i}\) the signal received. We can write the received sequence \(y_{i}\) based on the transmitted signal \(x_{i}\) as follows:  
\[y_{i}=h_{i}m_{i}+w_{i}, \tag{1}\]  
where \(h_{i}\) is the impulse response, and \(w_{i}\) is the Gaussian noise zero mean. Using matrix notation, Equation (1) can be formulated as shown below:  
\[y=Mh+w,\]where \(h=\begin{bmatrix}h_{0}&h_{1}&\ldots&h_{L}\end{bmatrix}^{T}\), \(w=\begin{bmatrix}w_{0}&w_{1}&\ldots&w_{L}\end{bmatrix}^{T}\), and \(M=\begin{bmatrix}m_{L}&\ldots&m_{1}&m_{0}\\ m_{L+1}&\ldots&m_{2}&m_{1}\\ \vdots&\vdots&\vdots&\vdots\\ m_{L+p-1}&\ldots&m_{p}&m_{p-1}\end{bmatrix}\) represents the Toeplitz matrix designating the sent training sequence \(m=\begin{bmatrix}m_{0}&m_{1}&\ldots&m_{p+L-1}\end{bmatrix}^{T}\).  
The LS algorithm minimizes the squared error amount given below to estimate the response of the channel:  
\[\overset{\wedge}{h}=\underset{h}{\text{argmin}}\|y-Mh\|^{2}, \tag{3}\]  
Then, the solution of Equation (3) can be determined using the following relation:  
\[\overset{\wedge}{h}_{\text{LS}}=\left(M^{T}M\right)^{-1}M^{T}y, \tag{4}\]
# The Future of Cybersecurity in the Age of Quantum Computers
## 2 Problem Statement  
In November 2017, 82 candidate algorithms were submitted to the NIST (National Institute of Standards and Technology) for consideration in a public competition for the process of selecting PQC (post-quantum cryptography) algorithms [15]. This initiative was launched to counter the impending security threats from quantum computers that may become real by 2030 when fully functional quantum computers become available [16]. These quantum systems will be able to run the algorithms that have the capability of decrypting most of today's asymmetric security protocols, such as the commonly used RSA or elliptical curve algorithms.  
Experts suggest that industries should prepare for PQC based on data shelf life and system lifetime [17]. For example, according to a McKinsey Digital report, and as illustrated in Figure 2, some data with a long shelf life--such as classified government documents, personal health data, or corporate trade secrets--will still be valuable when the first quantum computers are expected to become available. Any such data that remain relevant for a long time, transferred today on public networks, will be at risk of interception and future decryption by quantum computers. For instance, a long-term life insurance plan or a 30-year home mortgage loan contract may already be vulnerable to future quantum threats because such data will still be active in the future when quantum computers become commercially available.  
Figure 2: Risk of quantum-powered attack by industry. Source: McKinsey Digital [17].  
After a rigorous evaluation process sieving through the initial submissions from the international crypto research community over a five-year period that included three \"NIST PQC Standardization Conferences\" [18] and countless deliberations on NIST's PQC forum, four finalists were selected by the NIST [19]. Early this year, _New Scientist_ reported that a post-quantum encryption algorithm (Rainbow) verified and approved by the NIST could be easily cracked using a standard laptop [20]. More recently, using a single core of a regular Intel Xeon CPU (2013 release), researchers from the Security and Industrial Cryptography group (CSIS) at KU Leuven [21] were able to crack the second (named SIKE, short for Supersingular Isogeny Key Encapsulation) of the four encryption algorithms that the NIST considered as likely candidates to resist decryption by quantum computers [22]. SIKE was built by a consortium comprising Amazon, Infosec Global, Microsoft Research, Texas Instruments, and a number of international universities. The KU Leuven team won a USD 50,000 bounty for their breakthrough that startled the quantum world. With two of the four candidate PQC algorithms that reached the fourth round of the NIST validation process failing, the future of the remaining two candidates is, at best, shaky. Actual quantum computers do not exist yet, but the cryptography to defeat them is already here. If PQC can be cracked so easily, the future of cybersecurity indeed looks dravery.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Image Security  
Deep-Neural-Network (DNN) and CNN-based image processing have many applications in security, one of the most common being computer vision [222]. Examples include facial and body detection [222], detecting threats (specifically explosives) in X-ray images of passenger baggage [223], and detecting man-made items in millimeter wave images.  
Neural networks also see much use in improving the security of image encryption algorithms. In a proposal by [PERSON] et al. [224], steganography techniques were improved by splitting the source image into n shares and using neural networks to determine where in the destination image to hide the shares to maximize its security. In another proposal by [PERSON] et al. [225], a privacy-preserving DNN was developed. This DNN takes specially encrypted images as the input so that it can still extract the required features without being able to see the plain image. Furthermore, the testing and training phases for the model can use different encryption keys for further privacy.  
In another proposal by [PERSON] et al. [226], a DNN was trained on plain images to transform them into visually secure images. Through analysis, the network was shown to be resistant to inverse transformation attacks where DNNs were trained to invert the transformation. In a different study by [PERSON] et al. [227], this same inverse transformation attack was performed against many existing DNN-based encryption algorithms. It was found that if the images were encrypted using use different keys, then the encryption was secure. If the same key was used for different images, then the data were able to be retrieved.  
CNNs can even be used to determine the security of already encrypted images. In a paper by [PERSON] et al. [228], a CNN was developed to rate an encrypted image based on visual security and visual quality. [PERSON] et al. [31] also examined existing algorithms to determine their security. In their study, 30 state-of-the-art image encryption algorithms using neural networks were examined to determine the optimal algorithm.
# A Jug-Shaped CPW-Fed Ultra-Wideband Printed Monopole Antenna for Wireless Communications Networks
## 2 Antenna Design Analysis
### Equivalent Circuit Model  
A circuit model for the UWB presented antenna for wireless communications is presented in Figure 6a. The main purpose of the circuit model is to validate the scattering parameters of the ultra-wideband antenna with the S\({}_{11}\) obtained from the circuit model. The circuit model is designed by using advanced design system (ADS) software. The circuit model consists of four inductors, four capacitors, three resistors, and three resistor-capacitor (RC) circuits connected in series with one resistor and an inductor for each, as given in Figure 6a. By varying the values of the resistors, the S\({}_{11}\) of the circuit model can be varied, while by fluctuating the values of the capacitors and inductors, the S\({}_{11}\) of the antenna can be tuned. The values of the lumped components are given in Table 3. The S\({}_{11}\) (dB) of the circuit model is illustrated in Figure 6b. It covers the bandwidth from 3.1 GHz to 11.5 GHz.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Capacitors** & **Values (pF)** & **Inductors** & **Values (nH)** & **Resistors** & **Values (f)** & **High** & **Values (\(\Omega\))** \\ \hline C1 & 1 & L1 & 7 & R1 & 2 & r1 & 1500 \\ C2 & 0.1 & L2 & 0.8 & R2 & 65 & r2 & 1000 \\ C3 & 0.5 & L3 & 0.5 & R3 & 65 & r3 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of the components used in the circuit model.  
Figure 6: (a) Equivalent circuit model, and (b) reflection coefficient of the equivalent circuit model.
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 3 Energy-Efficient Game-Theory-Based Spectrum Decision
### Coordination Mode (C Mode)
#### 3.2.4 Data Transmission Module of C Mode  
The data transmission in this module is the environment-sensing data collection (not spectrum sensing results). The data transmissions from the CMs to the CHs follow the IA-DTS, whereas the data transmissions from the CHs to the sink follow the IE-DTS. However, after we conducted a number of simulations, we noticed that sometimes the operating channels selected by the CHs were mostly the same, particularly when the idle licensed channels were limited. In that case, the sink received only a few different sets of operating channels from a larger set of CHs. In other words, multiple CHs were selecting the same operating channels. If we allow this kind of configuration, the sink would end up creating IE-DTS where the intervals of data transmission of each CH were brief, not allowing the CMs to go to sleep state. Therefore, we included a threshold for a minimum active channel. If the set of operating channels reported by the CHs is less than the minimum active channel, then the sink inserts some gaps between data collections.
## 1 Introduction
LMD can decompose a multi-component signal into a series of mono-components that are defined by product functions (PFs). Each PF is obtained from the product of an envelope signal and a purely frequency-modulated signal. The LMD method avoids the overshooting and undershooting effect by using moving local means instead of cubic spline interpolation and improves upon the accuracy of the EMD method. However, the LMD method still has the problems of mode mixing [23] and time-consuming calculation [24]. The wavelet transform (WT) [25; 26; 27] has advantages of higher resolution and time-frequency information and is widely applied to the fault diagnosis of the rotating machinery. WT provides good results for analyzing noisy and non-stationary signals by selecting a suitable wavelet-based function. But the WT is non-adaptive decomposition method, which limit its applications, and the decomposition performance heavily degrades in a strongly noisy environment. Based on the theoretical framework of WT and the adaptivity of EMD, the empirical wavelet transform (EWT) was proposed by [PERSON] [28]. EWT can adaptively establish an optimum spectrum segmentation to decompose complex vibration modes according to the segmentations of the Fourier power spectrum. [PERSON] et al. [29] showed that the EWT technique can avoid the mode-mixing problem and has better performance than EEMD. In practical applications, researchers need to give more attention to manufacturing many types of the faulty gears in order to increase the knowledge of fault information in the time domain,frequency domain, or time-frequency domain by experimental measurements. In this case, Automatic Dynamic Analysis of Mechanical Systems (ADAMS) is used as a multibody dynamic simulation tool for a faulty gearbox system. The gearbox system model can be established using the Euler-Lagrange method in ADAMS software. The spectral subtraction method has been widely used in speech signal processing [30], but it can also be applied to gearbox fault diagnosis to remove partial additive noise. The EWT technique is adopted in this present work to determine the segment boundaries accurately and improve the fault-detection performance. This paper is organized as follows. The spectral subtraction method and EWT method are introduced in Section 2. The experiment analysis is carried out in Section 3. The dynamical model of the gearbox system was established with ADAMS software, and the simulation verification of the proposed method is presented in Section 4. Finally, conclusions are presented in Section 5.
## 10 Social Impact and Clinical Applications of Robotic Prostheses with 3D Printing
### Empowerment and Quality of Life Enhancement  
The functionality and aesthetic flexibility of 3D-printed robotic prosthetics have re-defined societal perceptions of disability by enabling users to perform everyday tasks independently and participate more fully in recreational and work-related activities. This empowerment not only boosts quality of life but also grants individuals greater autonomy. By allowing users to customize prosthetics not only for fit but also for appearance, 3D printing enables a unique personalization that enhances self-esteem and confidence.  
Example and Impact: Individuals with access to 3D-printed prosthetics, capable of integrating myoelectric or brain-computer interface control, report improvements in daily task performance, such as handling small objects or engaging in activities requiring dexterity. This functional customization is critical in restoring user independence and confidence (Figure 12).  
3D Printing Advantage: The versatility of 3D printing enables prosthetic designs that reflect personal identity, contributing to users' self-confidence and providing tangible improvements in lifestyle. Additionally, 3D-printed prosthetics are designed with easy replacement parts, which lower maintenance barriers for users.