esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synthesis of Transition-Metal-Sulfide-Based Nanomaterials
### Electrochemical Synthesis  
Recently, the electrochemical production of different catalyses, such as the TMS have become increasingly popular as the modification of the TMS surfaces to generate sulfur deficiencies in the surfaces was applied to generate catalytically active materials [96; 97; 98; 136; 137; 138]. Typically, an electrochemical synthesis is performed in a solution using cyclic voltammetry between specific voltages. [PERSON] et al. successfully synthesized Cu\({}_{2}\)S nanoparticles from aqueous solutions of Na\({}_{2}\)S, PVP (added as a stabilizer), and a KNO\({}_{3}\) electrolyte solution; a sacrificial Cu anode was converted to Cu\({}_{2}\)S [96]. The results showed that the median distribution of the nanoparticles was between 12 and 17 nm and the material was in the Cu\({}_{2}\)S phase. [PERSON] et al. reported results on the electrochemical synthesis of CuS nanoparticles [97]. The authors used a two-electrode system with platinum and sacrificial copper electrodes. The results showed that as the voltage in the solution was increased, the production of CuS was decreased. In addition, the size and shape of the nanoparticles were dependent on the concentration of sulfide in solution. [PERSON] et al. synthesized NiS nanoparticles using electrochemical synthesis techniques [98]. The study showed that NiS nanoparticles with spherical shapes with sizes ranging from 17 to 27 nm could be synthesized. The authors used voltages from 5 to 15 V with a sacrificial Ni electrode in a solution containing sodium sulfide and as the electrolyte. [PERSON] et al. synthesized a NiCoS nanomaterial for supercapacitors using electrochemical processes [136]. The authors prepared a Ni-graphite foam from which the Ni was dissolved to give a graphite foam. The graphite foam had the Ni-CoS deposited electrochemically from solution using cyclic voltammetry cycling from \(-\)1.2 to 0.2 V. The preparation was formed using a solution of thiourea and both NiCl\({}_{2}\) and CoCl\({}_{2}\); the pH was adjusted using ammonium solution. [PERSON] et al. showed a one-step electrochemical synthesis process to generate various TMS, including Fe, Ni, and Cu supported on a bare Ni foam [137]. The authors performed five cycles of applied voltages from 1.63 V to \(-\)0.16 V to an electrolyte solution with thiourea as the sulfur source. The results showed average grain sizes of \(\sim\)326 nm for FeS, \(\sim\)254 nm for CuS (with a spherical morphology), and \(\sim\)326 nm for the NiS and CoS nanoparticles. The FeS showed the highest current densities even higher than RuO\({}_{2}\). [PERSON] et al. synthesized MoS\({}_{2}\) quantum dots using electrochemical techniques [138]. The authors performed an electrochemical etching of bulk MoS\({}_{2}\) to generate nanoparticles ranging in size from 2.5 to 6 nm, which had photoluminescence properties. The results showed that in an aqueous ionic liquid solution of 1-butyl-3-methylimidazol chloride and lithium bis-trifluoromethylsulphonylimide with 5 V applied across the MoS\({}_{2}\) quantum dots could be grown.
## 2 Materials and Methods
### Machine Learning Analysis  
The machine learning analysis was performed using a Jupyter notebook written in Python 3.11.4 programming language.  
The block diagram illustrating the machine learning algorithm pipeline is presented in Figure 2. The input dataset comprises significant feature sets (N\({}_{\text{s}}\) features; see Section 2.4), extracted from all valid \"letter\" segments across all subjects. Subject-wise leave-one-out cross-validation (LOOCV) was employed as the validation strategy, dividing the dataset into training and test sets.  
The optimal feature subset was identified using the recursive feature elimination (RFE) algorithm [50], applied to the training set. Logistic regression (LR) served as the estimator for RFE (LR-RFE). To prevent overfitting, the regularization technique ElasticNet, which combines L1 and L2 penalty terms, was used. All hyperparameters configured for the LR model are detailed in Appendix B, Table A1.  
All sizes N (N \(\in[1,\text{N}_{\text{s}}]\)) for the optimal feature subset were considered, resulting in N\({}_{\text{s}}\) different lengths of the optimal feature subset.  
The classification of valid \"letters\" in two classes (DD and control) using the obtained optimal feature subsets was performed on test sets by three classifiers (logistic regression--LR; support vector machine--SVM; random forest--RF) for each size of the optimal feature subset. All hyperparameters that were set for each classifier are shown in Appendix B.  
Standard evaluation metrics--accuracy, recall, and precision--for individual \"letter\" classification were calculated across all test sets obtained through subject-wise leave-one-out cross-validation (LOOCV), resulting in Letter Accuracy, Letter Recall, and Letter Precision, respectively. These metrics were then used to estimate Subject Accuracy, defined as the percentage of subjects correctly classified as either belonging to the depressive disorder (DD) group or the control group. A subject was considered correctly classified if at least 50% of their letters were correctly labeled by the model.  
Figure 2: The machine learning algorithm pipeline. Logistic regression—LR; support vector machine—SVM; random forest—RF; recursive feature elimination—RFE.
# Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems +
## 1 Introduction
### Related Work
The concept of adding priorities to HAGs was introduced by [35]; if multiple exploit preconditions were met in a given state, only those with the highest priority value would be expanded into new states of the attack graph. Lower priority exploits would then only be considered if all exploits with higher priorities failed to meet their preconditions. By applying exploit priorities as a heuristic measure, attack graph states can be explored in a more strategic manner. A novel Hybrid Attack Model (HAM) was introduced by [36] that combines Probabilistic Learning Attacker, Dynamic Defender (PLADD) game model and a Markov Chain model to simulate the planning and execution stages of a bad data injection attack in power grid. The hybrid model is shown to be capable of modeling long time-to-completion actions in the preparation stage and short time-to-completion actions in the execution stage. Table 1 summarizes the main characteristics and limitations of the existed studies in HAG generation as compared to our Automatic Hybrid Attack Graph (AHAG) tool. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Main Characteristics** & **Limitations** \\ \hline \multirow{6}{*}{HAG [32]} & \(\bullet\) & Stateful graph. & \\  & \(\bullet\) & Captures blended attack vectors & \\  & & (comprising discrete and continuous & \\  & exploit events). & & \\ \cline{1-1}  & \(\bullet\) & Time is implemented using a single & \\ \cline{1-1}  & & group of global exploits, each of which & \\ \cline{1-1}  & & increments a class of assets’ position in & \\ \cline{1-1}  & & time depending upon its particular state. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics and limitations of the existed studies in Hybrid Attack Graph (HAG) generation.
## 4 Experimental Results
In Table 1, the traditional DNN converter we optimize, which points to DNN (96, 96, 96) with taps of 19, has a maximum signal Vpp dynamic range of 511 mV and maximum trainable parameters of 26,210 at the same time in 64 APSK systems. In order to make a better tradeoff between communication performance and computation complexity, a cascaded receiver consisting of a DNN converter and NN-based DD-LMS is utilized. The complexity of a DNN converter varies greatly with the number of nodes, but that of NN-based DD-LMS is fixed at 84. As the nodes of each hidden layer reduce from 96 to 32, the dynamic range of the cascaded receiver slightly reduces from 497 mV to 458 mV (from 97.3% to 89.6% of maximum), but the complexity greatly reduces from 23,222 to 3702 (from 88.6% to 14.1% of maximum). The ratio of signal Vpp dynamic range and complexity increases from 2.14 \(\times\) 10\({}^{-2}\) mV to 1.24 \(\times\) 10\({}^{-1}\) mV, while that of traditional DNN converter is 1.95 \(\times\) 10\({}^{-2}\) mV. Notably, DNN (32, 32, 32) with NN-based DD-LMS  
Figure 3:3 BER of different decoders versus different signal Vpp for (**a**) 64 APSK; (**b**) 64 APSK; (**c**) 64 QAM; (**d**) 64 QAM. and taps of 11 could achieve 89.6% of signal Vpp dynamic range with 14.1% of complexity, which experimentally verifies the effectiveness of the proposed algorithm. It can be seen that the conclusions are similar in the 64 QAM system in Figure 3:4 However, the tradeoff between communication performance and computation complexity is worse than 64 APSK. To be specific, as the nodes reduce from 96 to 32, the signal Vpp dynamic range reduces from 451 mV to 382 mV (from 89.1% to 75.5%), and the complexity greatly reduces from 23,222 to 3702 (from 88.6% to 14.1%). The ratio of signal Vpp dynamic range and complexity increases from 1.94 \(\times\) 10\({}^{-2}\) mV to 1.03 \(\times\) 10\({}^{-1}\) mV, while that of the traditional converter is 1.93 \(\times\) 10\({}^{-2}\) mV. Therefore, the proposed algorithm is experimentally validated to be an effective receiver to make a better tradeoff between communication performance and computation complexity than traditional DNN converter, especially in UVLC systems using 64 APSK modulation format.
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 3 Materials and Methods  
Given the above problems detailed in Section 2, this paper proposes a prediction method that can extract features directly from sparse and heterogeneous time trajectory data avoiding the interpolation process.  
Since the original observation data only include time, longitude, and latitude information and lacks specific critical physical properties of moving objects, such as velocity and acceleration, multi-dimensional trajectory features are designed and extracted to exploit the navigation characteristics fully. Before that, the raw input trajectory should be cleaned up with regard to dirty data and segmented into fixed-length trajectory sequences. The extracted features are constructed into a multi-dimensional trajectory feature matrix to meet the input format requirements of the CNN. Then, the trajectory prediction model based on CNN is constructed and trained on training samples until a trained prediction model is established.  
The overall flowchart of the method is shown in Figure 1 as follows:  
Three essential components of the methodology are detailed below. Section 3.1 deals with the extraction of multi-dimensional trajectory features, Section 3.2 with the architecture of CNN models, and Section 3.3 with the training method for the network model.  
Figure 1: The overall flowchart of the method.
## 4 Performance Evaluation Through a Case Study
### Use Case Overview  
The case study used for performance evaluation in this chapter involved a national road project with a total length of 5.46 km. The cross-sectional drawings for this project were specifically designed to represent various earthwork quantities and conditions encountered during the construction. A total of 237 cross-sections were utilized for training the model, incorporating 4051 distinct entities that express the earthwork quantities present within these cross-sections. Figure 6 shows several CAD cross-section drawings of the project that this study used.
# The Inheritance and Variation of Floral Scent Compounds in Parent-Progeny Relationships of _[PERSON]_
## 3 Results
### Comparison of the Volatile Compounds Among Different Malus Taxa  
A total of 51 different floral volatiles were identified in the five _Malus_ taxa (Table 1), belonging to three groups: terpenoids (9, 6.81-40.46%), benzenoids/phenylpropanoids (11, 9.45-40.34%), and fatty acid derivatives (31, 28.98-56.54%). However, the compounds and their contents in the different taxa were significantly different (\(p\) < 0.05) (Table 1, Figure 3). The total floral content (8247.59 ng\(\cdot\)g\({}^{-1}\) FW\(\cdot\)h\({}^{-1}\)) of _M. iocensis_ was significantly higher than that of other cultivars, whereas the number of compounds (33 compounds) identified in _M._ 'Brandywine' was the largest (Figure 3A,B). Compared with the other four taxa, _M. \(\times\) purpurea_ 'Lemoine' processed the most specific components (7), especially phenylmethanol (37.00%). Only four compounds were in common with the five taxa: _cis_-3-hexenol (0.77-25.62%), dodecane (1.94-7.63%), 3-methyltridecane (0.35-1.53%), and \(\alpha\)-farnesene (2.52-11.00%), respectively. Terpenoids were predominant in the flowers of _M. iocensis_ (40.46%), including geranylacetone (18.25%) and \(\alpha\)-farnesene (11.00%) (Figure 3C). The other prominent compounds of _M. iocensis_ were 4-methyleneeisophorone (18.29%), 4-methoxyphenethyl alcohol (11.49%), 2-phenylethylanol (8.89%), and 6-methyl-5-hepten-2-one (6.19%). In contrast to _M. iocensis_, _M. iocensis_ 'Prairie Rose' was dominated by benzenoids/phenylpropanoids, especially 2-phenylethanol (16.29%), while fatty acid derivatives predominated in the other three cultivars. Figure 2: Floral scent intensity evaluation based on the human nose and E-nose among five _Malus_ taxa. (**A**) Split-half reliability of sensory evaluation groups. (**B**) Box plots of the scent intensity score of sensory evaluation and its coefficient of variation. The middle region of each box plot (box body) covers 50% of the individuals, and the region between the upper and the lower transverse lines covers 90% of individuals. The points outside (\(\bigstar\)) of the box mean outliers. The mean values are presented in small squares (\(\square\)) inside the box bodies. Datasets (\(\bullet\)) of _Malus_ taxa are shown on the right of each box plot, and their distributions are fitted with a line. Difference letters represent the difference among different taxa to a significant level (\(p\) < 0.05). (**C**) Radar plot of response value (G/G\({}_{0}\)) of E-nose for five _Malus_ taxa flower scents. * indicates that the difference in response of the sensor to different taxa to a significant level (\(p\) < 0.05). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 3 Materials and Methods
### Loss Function
#### 3.3.2 Global Loss  
The global loss term in RIS-DMRN proposed in this paper consists of two components: global structural loss term and boundary loss term, expressed as shown in Equation (4):  
\[l_{global}=l_{ms}+l_{border} \tag{4}\]  
The computation of the global structural loss term is expressed as Equation (5), where \(I_{p}^{c}\) represents the irregular image cropped based on the mask matrix, \(\mu_{I_{p}^{c}}\) is the mean of \(I_{I^{c}}^{c}\), \(\mu_{T}\) is the mean of \(T\), \(\sigma_{I_{p}^{c}}^{2}\) is the variance of \(I_{p}^{c}\), \(\sigma_{T}^{2}\) is the variance of \(T\), and \(\sigma_{I_{p}^{c}T}\) is the covariance between \(I_{p}^{c}\) and \(T\). Constants \(c_{1}\) and \(c_{2}\) are constants used to stabilize the formula.  
\[l_{ms}(I_{p}^{c},T)=\frac{(2\mu_{I_{p}^{c}}\mu_{T}+c_{1})(2\sigma_{I_{p}^{c}T}+ c_{2})}{(\mu_{I_{p}^{c}}+\mu_{T}^{2}+c_{1})(\sigma_{I_{p}^{c}}^{2}+\sigma_{T}^{2}+c_ {2})} \tag{5}\]  
The expression for the boundary loss term is given by Equation (6), where \(I_{m}\) represents the mask matrix of the original irregular image, and E represents the target template of an all-ones matrix. The boundary loss is adjusted based on the 0/1 mask matrix of the irregular stitched image, with an all-ones matrix as the true target, gradually approaching the rectangularization.  
\[l_{border}=\left\|E-D(I_{m},m))\right\|_{2} \tag{6}\]
## 2 Related Work
### Search Strategy  
Search strategy is a reasonable solution for event report transmission since it has no overhead when there is no event appears. It can forward the packets to the target without its location [5; 6; 7; 19]. Expanding ring search (ERS) [5] is a predominant search strategy based on flooding. In ERS, the search packets are broadcast and propagated in the network with a preset time-to-live (TTL) value. The TTL value deceases at every time the packet is relayed. When TTL value expires, the propagation stops. This process is called a _search attempt_. If the target locates within this area, it will reply an acknowledgement (ACK) packet back to the source. Otherwise, the source will eventually time out and initiate another search attempt that covers a larger network area. The setting of TTL reduces the energy consumption compared with simply flooding the search packets all over the network.  
There are some other flooding-based search strategies. The authors in [20] consider the recontamination problem that may lead to packets loss in the duty-cycled WSAN, and then propose the ballooning algorithm which builds a blocking and expanding circle during the flooding process to prevent recontamination and hence ensures the deliver rate. Concentrating on the data congestion and overloading that affect the overall performance of network, the research in [21] proposes the methodology that discovers the nearest neighbor and route to reduce the flooding and energy consumption in the network. Striped-flooding [7] adopts the striped deployment pattern in the design of flooding process, and thus improves the energy efficiency and scalability with minor impact on the deliver rate and search delay.  
The major disadvantage of flooding-based search strategy is the considerable energy consumption in the flooding process. Therefore, there are some research works design search paths to reduce the energy consumption. IRS [6] routes the search packet along a set of trajectories called rays that maximizes the likelihood of discovering the target information by consuming least amount of energy. The rays are organized such that if the search packet travels along all these rays, then the entire terrain area will be covered by its transmissions while minimizing the overlap of these transmissions. The paper in [22] proposes a forwarding protocol based on biased random walks where nodes only use local information about neighbors and their next active period to make forwarding decisions. Although these works reduce the energy consumption in the search process, they generally increase the search delay which can not be tolerated in the event report transmission.
# A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Turkiye
## 3 Ground-Motion Prediction Models (GMPMs)
### Aleatory Variability Model  
[PERSON] et al. [39] describe total residuals as the difference between observed ground motions and the intensity measures forecasted by GMPE. These residuals can be divided into two components: between-event (\(\delta B_{e}\)) and within-event (\(\delta W_{es}\)) terms.  
\[\Delta=\delta B_{e}+\delta W_{es} \tag{9}\]  
The subscript e denotes the event, while the subscript s refers to the site. \(\tau\) and \(\phi\) correspond to the standard deviations for the between-event and within-event residuals, respectively. The within-event standard deviation can be separated into two terms, where \(\delta W_{es}\) and \(\delta S2S\). \(\delta S2S\) denotes the systematic deviation of the observed ground motion at site s from the median-event-corrected ground-motion prediction. \(\delta W_{es}\) denotes the site- and event-corrected residual. The standard deviation of the residuals describing the variability from earthquake to earthquake and from record to record is denoted as \(\phi_{S2S}\) and \(\phi_{SS}\), respectively. The total standard deviation (\(\sigma\)-sigma) can be written as follows:  
\[\sigma=\sqrt{\tau^{2}{+}{\phi_{ss}}^{2}+{\phi_{S2S}}^{2}} \tag{10}\]  
The trilinear magnitude-dependent between-event standard deviation model used in this study is presented below:  
\[\tau=\left\{\begin{array}{cl}\tau_{1}&M_{W}\leq 5.5\\ \tau_{1}+(\tau_{2}-\tau_{1})\frac{(M_{W}-5.5)}{1.25}&5.5<M_{W}\ \leq 6.75\\ \tau_{2}&M_{W}>6.75\end{array}\right. \tag{11}\]  
In the equation above, \(\tau_{1}\) and \(\tau_{2}\) define the between-event variability for \(M_{W}\leq 5.5\) and \(M_{W}>6.75\), respectively.
## 3 Home Energy Management Architecture  
Figure 1 shows the energy management architecture for a smart home in which EMC directly receives the DR signals from the utility via a smart meter. The main purpose for the deployment of EMC is to reduce the electricity bill and PAR by rescheduling the home appliances to ensure power system security and stability. Generally, the smart home consists of advanced metering infrastructure (AMI), smart meters, EMC, an in-home display (IHD) and renewable energy devices. For communication purposes, wireless technologies, such as Wi-Fi, ZigBee, Bluetooth, infrared, _etc._, are used. AMI is treated as a central nervous system in the SG infrastructure for two-way communication between the utility company and the smart meter. Moreover, AMI is also responsible for relaying both the energy consumption data from the distributed smart meters to the utility company and DR signals from the power distribution company to customer premises, respectively. Generally, smart meters are installed outside the homes between EMC and AMI.  
Figure 1: Energy management architecture in the home.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 3 Applications of FFD in Early Fault Diagnosis of Rotating Machinery
### Adaptive Decomposition Methods
#### 3.1.4 Ewt  
The EWT algorithm adopts adaptive wavelets to extract the AM-FM components of a signal. [PERSON] et al. [80] used EWT to decompose the signal into a set of sub-components. Then, the sensitive component was further input into a bistable stochastic resonance system to enhance the fault features. Finally, the weak fault features were identified from the FFT spectrum of the bistable stochastic resonance output. [PERSON] et al. [81] utilized wavelet spatial neighboring coefficient denoising with a data-driven threshold to increase the SNR. Then EWT was used to extract inherent modulation information by decomposing signals into mono-components under an orthogonal basis. Finally, this method had been applied to identify weak faults and compound fault diagnosis. [PERSON] et al. [82] proposed a EWT methid which applied an appropriate wavelet filter bank with Hilbert transform in the early detection and condition monitoring of tooth crack fault signals. [PERSON] et al. [83] utilized the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal was filtered using the EWT. The l(q)-regularized sparse regression was implemented to obtain a sparse solution of the defect signal in the frequency domain. These method are summarized in Table 4.