esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 3 The Proposed Framework for Li-Ion Battery State Prediction
### Process for Battery State Prediction  
The \"Process for Battery State Estimation\" component is composed of a set of seven procedures with different functionalities, meticulously designed to be executed in alignment with the predefined dimensions of the \"Configuration for Battery State Prediction\" component.  
The procedures that constitute this component can be categorized into two broader groups: Data Management and Model Management. Data Management consists of the data ingestion, data preprocessing, and data harmonization procedures. Model Management consists of the model building, model training, model testing, and model evaluation procedures.  
In Data Management, the data ingestion procedure refers to the data acquired from different sources, ensuring the integration of heterogeneous data to the pipelines. The data harmonization procedure provides the organization and unification of the data in a suitable way for subsequent analysis. The data preprocessing procedure refers to data cleaning, handling, structuring, and validating, needs that prepare the data and facilitate the upcoming analysis. It also includes feature engineering techniques for further selecting, manipulating, and transforming the raw data.  
In Model Management, the model building procedure forms the basis for instrumenting and fine-tuning the algorithms that are developed. According to the former dimensions' definition, the appropriate algorithms along with their configuration parameters are retrieved and prepared for further processing. After selecting and building the analytics model, the model training procedure performs parameter learning on the selected algorithm using the training dataset. The model testing procedure generates predictions about the battery state by employing the testing dataset. Finally, the data evaluation procedure enables the comparison of the predicted battery state with the reference value that has been defined in the configuration step. Additionally, this process enables the extraction of various evaluation metrics that reflect the performance of both the model and the overall pipeline.
## 3 Results
### IR Spectra of Formulated Aggregates  
Characteristic amide bands were defined in regions at wavenumbers of 1700-1600 cm\({}^{-1}\), 1600-1500 cm\({}^{-1}\), 1380-1200 cm\({}^{-1}\) and 3300-3500 cm\({}^{-1}\), and these were associated with amide I (C-O stretching), amide II (N-H bending and C-H stretching), amide III (N-H in plane bending coupled with C-N stretching) and amide A structures of proteins, respectively [34; 35; 36; 37]. The IR spectra of protein matrix and formulated aggregate are presented in Figure 3. Protein powder and formulated aggregates had these characteristic amide regions; however, the intensity of bands on the formulated aggregates was lower. Changes in the amide I region, which reflect the secondary structure of the protein, were detected. The band at the protein matrix on 1640 cm\({}^{-1}\) due to the adsorption of compounds from tart cherry juice shifted to 1632 cm\({}^{-1}\), and, additionally, a slight shoulder formed at 1647 cm\({}^{-1}\). Different secondary structures can be detected on proteins such as \(\alpha\)-helix (1658-1650 cm\({}^{-1}\)), \(\beta\)-sheet (1640-1610 cm\({}^{-1}\)), \(\beta\)-turn (1700-1660 cm\({}^{-1}\)) and random coil structure (1650-1640 cm\({}^{-1}\)) [36]. From our IR spectra, it was apparent that the change in \(\beta\)-sheet occurred with a slight change in the random coil structure. Changes in amide II structure were not detected but there were changes in the amide III region. The band at 1237 cm\({}^{-1}\) shifted to 1230 cm\({}^{-1}\) and broadened with a slight shoulder at 1200 cm\({}^{-1}\). These changes indicated changes in \(\beta\)-sheet [34]. Bands in the region 1060-890 cm\({}^{-1}\) are mostly assigned to C-C stretching with 1080 cm\({}^{-1}\), 1038 cm\({}^{-1}\) and 933 cm\({}^{-1}\), and related to \(\alpha\)-helices and C-O stretching vibration [38]. We also observed a change in this region that could be associated with \(\alpha\)-helices: the band at 1073 cm\({}^{-1}\) shifted to 1051 cm\({}^{-1}\) upon adsorption of tart cherry compounds. Additionally, upon adsorption of tart cherry compounds on the protein matrix, the band at 991 cm\({}^{-1}\) completely disappeared and two bands, at 916 cm\({}^{-1}\) and 879 cm\({}^{-1}\), combined into one at 900 cm\({}^{-1}\). Also, on the protein matrix, the band at 1730 cm\({}^{-1}\), which was assigned to C = O, disappeared.  
Figure 2: Principal component analysis of individual flavor notes in the overall flavor profile of tart cherry juice (TC), protein matrix (Cas) and formulated aggregates (Cas2_TC and Cas6_TC— aggregates with 2% and 6% of protein matrix).
# A Finite State Machine-Based Improved Cryptographic Technique
### Significance  
During the last several decades, the efficient design of Turbo Codes (TCs) for cutting-edge communication systems has been a hotbed of innovative study. Scientists have proposed numerous helpful methods, which can be roughly divided into five categories: TC structural analysis, TC ensemble analysis, TC energy allocation strategies, TC design with optimization approaches, and TC application with multiple structures in contemporary communication systems. It has been shown that either increasing the number of effective encoding and decoding modules or developing a competent interleaving unit may boost TC performance in this regard. In this context, the optimal puncturing pattern has also been thought about. Optimal power sharing between systematic and parity bits has also been studied. The top limit of BER for both 2D and 3D Turbo Codes has also been evaluated using a variety of ensemble methodologies including the state diagram methodology. As a bonus, by using several computationally clever methods, optimal TCs have been designed with remarkable efficiency. Several goal functions have been developed for this purpose, considering critical coding scheme characteristics. Considering these considerations, the primary goals of this thesis are as follows:  
First, creating energy-saving designs, a revolutionary Modified Symbiotic Organism Search (MSOS) algorithm is combined with several Superposition Modulation (SM) power allocation strategies to create a 3D Turbo Code (3D-TC).  
Symmetric key cryptosystems are used for both data encryption and decryption in the proposed system. The input was a 3 \(\times\) 3 matrix, which is a shorter matrix and, hence, simpler to solve. To add insult to injury, the key was produced using a range of bits from 16 to 255, which is too small to use a Finite State Machine and so makes the system vulnerable to cracking. We may extrapolate the following from this observation:  
(a) The security of a cryptographic system cannot be ensured by using a single key of insufficient length. To prevent hackers from having any hope of cracking the encryption, it is advised that long-length keys are utilized.  
(b) An intermediate cypher must be established to keep data secure from hackers, and it is preferred if dynamic keys are employed.  
(c) Key transfer through a secure channel should be unnecessary if keys can be generated from publicly available information.  
The article is divided into introduction, methods, experiments, results analysis, summary, and recommendations for further study.
# Optical Angle Sensor Technology Based on the Optical Frequency Comb Laser
## 1 Introduction
### A Method Employing the Dispersive Characteristics of a Diffraction Grating
#### 2.1.2 Light Intensity Detecting-Type Mode-Locked Femtosecond Laser Autocollimator
The above mentioned zero-angle position adjustment can be applied to the optical setup where the propagation directions of the incident beam and the reflected beam are not parallel to each other. Once the angle \(\Phi\) is confirmed during the fabrication process of the optical head of the mode-locked femtosecond laser autocollimator based on the principle of the optical frequency-domain angle measurement, absolute angle position of the grating reflector with respect to the incident femtosecond laser beam can be obtained with the following measurements. Figure 10 shows one of the examples of the absolute angle measurement based on the proposed method. As can be seen in the figure, the absolute angular position of the grating reflector in a step of 180 arc-seconds was well distinguished over the absolute angle range from \(-37.805\) degrees to \(-37.354\) degrees in the frequency domain; this means that the optical frequency comb can be directly converted into the angle scale comb for absolute angular position measurement. Figure 8: Reading output of the mode-locked femtosecond laser autocollimator with a visibility of 100% observed in the optical frequency domain [56].
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
## 2 Experimental Preparations
### Measurement for X-ray Diffraction  
The X-ray diffraction (XRD) technique is a nondestructive method for measuring the stress at the surface of crystalline materials [27]. [PERSON]'s diffraction law in the XRD technique determines the interplanar spacing at a special point, as shown in Equation (1):  
\[n\lambda=2 dsin\theta, \tag{1}\]  
where \(n\) is an integer, \(\lambda\) is the X-ray wavelength, \(d\) is the interplanar spacing, and \(\theta\) is the angle of reflection. The elastic strain is related to the difference in the interplanar spacing. Hence, based on the lattice spacing \(d\), and reference space \(d_{0}\), the strain value is calculated by the following equation:  
\[\varepsilon=\frac{d-d_{0}}{d_{0}}. \tag{2}\]  
The equation above supports strain calculations in different directions, and the in-plane stress components can be calculated using the generalized Hooke's law. To investigate the effect of the cryogenic temperature on the welded structure's residual stress, measurements captured residual stresses both after the welding process and after the cryogenic liquid leakage test. In this work, residual stress at three points (A, B, and C in Figure 5) on the middle cross-section's top surface was measured. Point A is located in the weld center line, Point B in the heat-affected zone, and Point C in the base metal. The distances of Points B and C from the weld center line are 20 and 30 mm, respectively. Figure 5 shows the XRD apparatus and position of residual stress test points.  
Figure 4: Arrangement and layout of the thermocouples at each measurement point.
## 5 Experimental Setup
### Experimental Process  
For the experiments, all of the instances consider a grid graph as an environment graph. An instance class is then defined by its grid dimension and the number of UAVs in the swarm (see Section 3.1.3). The set of parameters present is presented in Table 1. The maximal distance of communication is the distance in which two UAVs can communicate (see Section 3.1.2). The exploration rate is the percentage chance of choosing a random action instead of following the policy (see Section 4.3). The discount factor is the importance given to the final reward (see Section 4.2.3). The size of the movement frame is the number of movements considered for one reward (see Section 4.2.2). The next three parameters cannot be chosen empirically while they strongly affect the learning process. To have a relevant value for them, a factorial experiment is processed (detailed in Section 6.1). The learning rate defines how much of the gradient is used for a step of SGD (see Section 4.2.3).  
The embedding dimension is the length of the vectors representing both states and actions (see Section 4.2.1). The mini-batch size is the number of items selected from the memory to process the SGD (see Section 4.2.3). When the parameterisation is defined, a stability experiment is processed where the purpose is to train QLHH-II on instances from a certain class and to execute the generated heuristic on other classes (detailed in Section 6.3). The training then occurs on instances with 10 UAVs moving on a 20 \(\times\) 20 grid. 50 instances are randomly chosen from the latter instance class and the QLHH-II algorithm is executed 10 times on each (see Algorithm 2). The generated heuristic is finally executed on instances of other classes along with the heuristics presented in Section 5.2 to assess its performance.
# Molecular Regulatory Mechanisms Affecting Fruit Aroma
## 4 Regulation of Key Genes Involved in Aroma Formation
### Transcription Factors (TFs)  
Transcriptional regulation plays an important role in the regulation of plant genome expression, especially in the production of fruit aroma [4]. TFs play an important role in regulating the synthesis of esters, alcohols, aldehydes, eugenol, terpen, and benzene/phenylc aromas [45]. [PERSON] et al. [46] investigated the synthesis and regulatory mechanism of volatile linalool during peach fruit maturation. They concluded that the transcription factor PpERF61 can bind to the promoters of the _PPTPS1_ and _PPTPS3_ genes to activate their expression. In addition, another promoter, PpbHLH1, can interact with PpERF61 to activate _PPTPS3_ gene expression. In mature strawberry fruits, FaDOF2 is a key transcription factor that regulates the production of the phenylpropanol volatile compound eugenol. It also co-regulates the expression of the _FaEGS2_ gene with the _FaEOBII_ gene, thereby promoting the production of eugenol. In addition, the transcription factor FaMYB10 can further affect eugenol content by activating the _FaEOBII_ gene [47]. In peach, linalsol, an important aroma-contributing compound, is synthesized by the terpenoid synthases _PPTPS1_ and _PPTPS3_, and the TF _PpbHLH1_ can directly bind to the promoter of the _PPTPS3_ gene to activate its expression. The AP2/ERF family TF _PPERF61_ can bind the DRE/CRT motif in the _PPTPS1_ and _PPTPS3_ promoters to activate their expression, thereby promoting the production of linalool [25; 46].  
[PERSON] et al. [43] predicted the diploid strawberry _Fragaria nilgerrensis_ [PERSON] in the wild. There are 35 TFs associated with the aroma formation, including members from the _bHLH_, _MYB_, _bZIP_, _NAC_, _AP2_, _GATA_, and _TCP_ family. [PERSON] et al. [48] cultivated straw berry _Fragaria \(\times\) ananassa_ [PERSON]. A total of 498 members of the AP2/ERF superfamily were predicted in the genome, and the _FaAP2_ gene was found to be a key TF regulating aroma production. In the ripening process of apples, peaches, and tomatoes, _NAC_ TFs can directly target ester synthesis genes and promote the accumulation of ester substances [49]. In addition, the TFs _PuWRKY24_, _PuIAA29_, and _PuTINY_ are associated with aroma formation in 'Nanguo' pear (_Pyrus ussuriensis_ Maxim.) during the fruit ripening [36]. [PERSON] et al. [50] studied the impact of jasmonate application on the synthesis of volatile compounds in apples. They have found that, after the jasmonate treatment, the expression of the transcription factor _MdMYC_2 in apples increased, leading to a significant enhancement in the volatile compound synthesis.
## 2 Materials and Methods
### Bacterial Growth and Cell Preparation  
_C. glutamicum_ ATCC 13032 (America Type Culture Collection, Manassas, VA, USA) was used for MFC operation. Prior to inoculation, _C. glutamicum_ was aerobically cultivated in Luria-Bertani (LB) medium at 30 \({}^{\circ}\)C and 160 rpm for 16 h and harvested by centrifugation (8000 rpm, 10 min) at 4 \({}^{\circ}\)C. The cells were washed three times with 50 mM sodium phosphate buffer (pH 7.0), and the cell amount was adjusted to a concentration of \(1\times 10^{9}\) cells mL\({}^{-1}\) (determined by viable cell counting). The cell suspension was used as an inoculum for MFC experiments. The anolyte of the MFCs consisted of 0.28 g MgSO\({}_{4}\cdot\)7H\({}_{2}\)O, 10 mg FeSO\({}_{4}\cdot\)7H\({}_{2}\)O, 8 mg MnSO\({}_{4}\cdot\)H\({}_{2}\)O, 30 mg biotin, 0.5 g KH\({}_{2}\)PO\({}_{4}\), 0.5 g K\({}_{2}\)HPO\({}_{4}\) and 1 g Yeast extract (per liter). One percent (1%) glucose was used as an electron donor.
## 2 Methods
### FECG Enhancement and Recovery of Nonstationarity and Nonlinear Characteristics  
The MECG residue is included in \(FECG_{n}\) in addition to other noise, which affects the morphology of the FECG significantly and the clarity of the QRS complex, as shown in Figure 2a. In order to eliminate these residues, we continue to apply the proposed method above to extract the FECG from \(FECG_{n}\) and denote it by \(FECG_{c}\). If \(FECG_{c}\) is taken as the final extraction result (clear FECG), we will lose the nonlinear and nonstationary characteristics of the FECG. For this reason, in this paper, the MECG residue is eliminated using the threshold method after the extraction of \(FECG_{c}\) and then superimposed with \(FECG_{c}\) so as to enhance the FECG and recover the nonlinear, nonstationary characteristics (see Figure 2b).  
1. Use \(FECG_{n}\) to estimate the reference heart rate of \(FECG\); the FECG reference heart beat \(h(k)\) is established using the dimensionless heart beat model.
2. Calculate the instantaneous correlation coefficient \(R_{F}(n)\).
3. Detect the R peak of the FECG using \(R_{F}(n)\).
4. Extract the FECG using the simplified coherent averaging method and denote it by \(FECG_{c}\).
5. Subtract \(FECG_{c}\) from \(FECG_{n}\) to obtain the remaining signal \(r_{F}(n)\). \[r_{F}(n)=FECG_{n}(n)-FECG_{c}(n)\] (15)
6. Calculate the standard deviation of \(FECG_{c}\) and define the threshold \(Th\): \[Th=C\cdot stdev(FECG_{c})\] (16) where \(C\) is the ratio, which is 1 in this paper.
7. Set the absolute value of \(r_{F}(n)\) greater than \(Th\) to 0. \[\Delta r_{F}(n)=\left\{\begin{array}{ll}0&\left|r_{F}(n)\right|>Th\\ r_{F}(n)&\leq Th\end{array}\right.\] (17)
8. Recover the nonlinearity and nonstationarity of the FECG, and obtain the final extraction of the FECG. \[FECG(n)=FECG_{c}(n)+\Delta r_{F}(n)\] (18)  
In the process of FECG extraction using the simplified coherent averaging method, the FECG's stationary information, nonstationary information, MECG residue, and noise are all left in the remaining signal. In order to recover the nonstationary information of the FECG, the residual signal should be processed. According to the analysis, the coherent averaging FECG represents the main component of the FECG, and the nonstationary value of amplitude is not larger than the variance of the coherent averaging FECG. Therefore, taking the variance of the coherent averaging FECG as the threshold value, useful signals can be retained by eliminating the values with absolute values larger than the threshold  
Figure 2: The extraction and enhancement of foetal electrocardiograms (FECGs) obtained using the proposed method for the DaSly database.  
value in the remaining signal, and the nonstationary characteristics of the FECG can be finally restored by adding them with the coherent averaging FECG.  
A flow chart of the method proposed in this paper is displayed in Figure 3.
## 5 Conclusions  
Simulation of water chemistry at 300 \({}^{\circ}\)C and under exposure to ionising radiation was performed using the hybrid method [14] and the measured rate constant of H\({}^{\bullet}\) + H\({}_{2}\)O \(\rightarrow\) \({}^{\bullet}\)OH + H\({}_{2}\) reaction [22]. The steady-state concentration of oxidants (O\({}_{2}\), H\({}_{2}\)O\({}_{2}\), HO\({}_{2}\)\({}^{\bullet}\), \({}^{\bullet}\)OH) were calculated for neutral water (pH573K = 5.8) and an alkaline solution (pH573K = 8.0), as a function of added hydrogen (0 \(\leq\) [H\({}_{2}\)] \(\leq\) 1.5\(\cdot\)10\({}^{-4}\) mol\(\cdot\)kg\({}^{-1}\)), dose rate (1 and 10 kGy\(\cdot\)s\({}^{-1}\)), and the fraction of \(\gamma\) rays (0 \(\leq\)\(f_{\rm g}\)\(\leq\) 1) in the mixed fast neutron-gamma radiation. The  
Figure 8: Contribution of key reactions responsible for the formation and decay of O\({}_{2}\) calculated for fast neutron (n) and gamma (g) radiolysis of neutral water, alkaline solution, and hydrogenated (3\(\cdot\)10\({}^{-5}\) mol\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)) neutral and alkaline systems. Based on the simulation for 1 kGy\(\cdot\)s\({}^{-1}\).  
Figure 7: Integrated contributions of key reactions responsible for the formation and decay of H\({}_{2}\)O\({}_{2}\) calculated for fast neutron (n) and gamma (g) radiolysis of neutral water, alkaline solution, and hydrogenated (3\(\cdot\)10\({}^{-5}\) mol\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)) neutral and alkaline systems. Integrated production rates are represented by P(g) and P(n). Based on the simulation for 1 kGy\(\cdot\)s\({}^{-1}\).  
concentration of main oxidants (O\({}_{2}\), H\({}_{2}\)O\({}_{2}\)) increases with the dose rate but decreases with the increasing fraction of \(\gamma\) rays. At higher pH and \(f_{\mathrm{g}}>0.3\) the total amount of O\({}_{2}\), H\({}_{2}\)O\({}_{2}\) is noticeably diminished. The simulation indicates that the addition of H\({}_{2}\) to the alkaline solution effectively suppresses the formation of oxidising environment. This synergic effect of H\({}_{2}\) and OH\({}^{-}\) may lower CHC.  
The importance of reactions contributing to the coolant radiolysis (Table 2) by fast neutrons and \(\gamma\) rays was discussed for neutral, deaerated water and the alkaline solution, and both of these systems contained 3-10\({}^{-5}\) mol\(\cdot\)kg\({}^{-1}\) H\({}_{2}\).  
The synergic effect of hydrogen gas and base added to the coolant, demonstrated in this study, provides an important guideline for the development of more efficient methods for controlling the oxidising environment and minimising corrosion problems in PWR engineering.  
This research received no external funding.  
Not applicable.  
The author thanks [PERSON] for collecting the literature data on reaction rate constants.  
The author declares no conflict of interest.
## 2 Theoretical Foundation
### Pulse Ignition Logic  
The ballistic performance of the pulse-controlled correction projectile will be simulated based on sample points obtained from the LHD experiment. These simulations will provide a sample library for establishing a surrogate model. The 6 DOF ballistic model used in this paper is consistent with the reference literature [27] and will not be elaborated on in here. To help readers better understand the simulation results, the following explanation is provided regarding the pulse control characteristics used in the 6 DOF ballistic simulation:  
1. The pulse thrusters are arranged uniformly in a circumferential ring along the projectile's surface. Under ideal conditions, the thrusters generate thrust in a plane located in the projectile's axis coordinate system, which is perpendicular to the projectile's axis;
2. Unlike traditional aerodynamic control, the pulse force only exists during the action time \(\tau\) and is of a discrete nature;
3. Each correction can only utilize a single pulse thruster;
4. Considering the effect of the rotation rate \(\dot{\gamma}_{0}\), the direction of the control force \(\gamma_{m}\) constantly changes due to the rotation of the projectile when the pulse thruster is active;
5. Consider the delay error of pulse ignition;
6. Due to the projectile's right-handed rotation, the pulse thruster number is searched for in the counterclockwise direction;
7. The pulse control force \(F_{\text{imp}}\) is processed based on the equivalent average control force, as depicted in Figure 2 (a transverse section of the projectile axis), and the final average effect \(\overline{F}_{\text{imp}}\) is considered to be along the bisector of the angle of rotation during the pulse's activation.