esa-sceva/satcom-chunk-collection huggingface dataset:
# XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System
## 2 Materials and Methods
### Detailed Framework Workflow
#### 2.4.1 Stage I: Explainable Binary Classifier  
The first stage of the framework is the binary classifier phase. A diverse selection of eight ML models, each with unique capabilities, was trained on the datasets to ensure a comprehensive exploration of potential solutions for intrusion detection. The models included variations of Stochastic Gradient Descent (SGD), Linear SVM [48], Decision Trees (DTs) [48], RF [49], XGBoost, and LightGBM. Hyperparameter tuning was performed using Optuna, a framework for automated optimization, to maximize detection effectiveness while minimizing false alarms. Table 3 details the range of parameters optimized for each model.  
SHAP [50] is an explanation technique built upon the concept of Shapley values in game theory that calculates the contribution of each feature to the final output of the model. A significant advantage of SHAP lies in its versatility, as it can be applied across various models and classifiers. The framework combines Shapley values with local interpretations of the model to set an importance score for the dataset's features. SHAP's explanation for a particular instance is derived through Equation (6) as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameters** \\ \hline LinearSVC & Loss: hinge; Regularization C: 5 \\ \hline SGD (log, hinge, mod. huber) & Penalty: 11, 12; Alpha: powers of 10 [(\(-\)5)–2] \\ \hline Decision Tree & Max depth: [8; 9; 10; 11; 12], Min samples split: [4; 5; 6] Min samples leaf: [9; 10; 11; 12; 13] \\ \hline Random Forest & Estimators: [200; 21; 22; 23; 24]; Max depth: [20; 21; 22; 23; 24]; Min samples split: [2; 2; 2; 3; 4; 6]; Criterion: gini, entropy \\ \hline XGBoost & Learning rate: [0.001-0.1]; Max depth: [4; 5; 6; 7; 8; 9; 10; 11; 12]; \\  & Colsample bylevel: [0.1–1]; Subsample: [0.1–0.7]; \\  & Estimators: [200; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72\[g(s)=v_{0}+\sum_{i=1}^{N}v_{i}s_{i} \tag{6}\]  
where \(g\) represents the explanation model, \(s\) represents the simplified features, and \(v_{i}\) signifies the Shapley value corresponding to feature \(i\). \(N\) denotes the maximum size of the feature vector. Here, \(s\) belongs to the set \(\{0,1\}^{N}\), where the presence of '1' in \(s\) indicates that the features in the new dataset align with those in the original data, while '0' suggests a disparity between the features in the new and original datasets.  
The most important features can then be selected based on Equation (7) as follows:  
\[IF_{j}=\sum_{i=1}^{n}|v_{j}(x_{i})| \tag{7}\]  
where \(IF_{j}\) is the average absolute Shapley value for feature \(j\) and \(n\) represents the total count of records in the dataset.
## 4 Discussion of CPMA Application
### Data Prediction Result Visualization in CPMA  
As this system can process and display raw data from the machining process, it can be applied to monitor and predict the machine conditions or machining quality. A previous study [42] showed that the linear guide preload loss could be identified by feeding the vibration features, which were assessed by sensors attached to the working table of the machine tool and fed into the artificial neural network predictive model. The vibration features of the spindle tool can also be fused using machining parameters to predict the surface roughness of the machined parts [43]. Other potential applications of the monitoring system include failure prognosis systems, such as the incorporation of surface quality or  
Figure 16: CPMA applicated to two different machines: (**a**) first machine and (**b**) second machine.  
Figure 15: Monitoring arrangement of two machines.  
tool wear evolution during the continuous machining process of batch production. The system can issue an alert when a failure is detected in the machine component and even send an alert when the desired quality of a product is not achieved. This alert system was also developed using an API in another application in this study [44].  
Moreover, to support the visualization of the monitoring and prediction of the machine condition results, a new tab in the main UI was developed. This tab contains the parameter prediction result value bar, graph, and parameter properties, which depend on the parameters in the machine that are monitored and predicted. These tabs are then captured for another visualization in the CPMA, as shown in Figure 18.  
Figure 17: CPMA data displays: (**a**) MEMS accelerometer and piezoelectric sensor data for the first machine; (**b**) piezoelectric sensor data for the second machine.  
Figure 18: CPMA application in revisualized parameter prediction result: (**a**) first appearance and (**b**) second appearance.
# Integration of BIM Tools for the Facility Management of Railway Bridges
## 6 Discussion
### Practical Implications  
The process designed in this research on the automation between the BIM model and the automated spreadsheet has practical applications in various areas related to the facility management of railway infrastructures:  
* Inspection and maintenance of railway bridges: The main application of the automation focuses on improving the processes of inspection, monitoring, and maintenance of railway bridges, particularly in inspection. By clearly visualizing the condition of each element and consolidating the information into a single source, decision-making regarding future maintenance actions is facilitated, following a preventive approach.
* Facility management: This can be particularly useful for companies and organizations responsible for the FM of railway bridges, as well as being part of an integrated management system for such assets. By having precise and updated information about the condition of the bridges, resource allocation and planning of inspection and maintenance activities can be optimized, ensuring safety and continuity of service.
* Training and education: The parametric BIM model and the visualization of the elements' condition can be used as training and education tools for the personnel responsible for monitoring and maintaining railway bridges. This is of great value to professionals in charge of bridge inspections, as it provides an integrated platform to efficiently and accurately record and analyze data.
* Documentation and historical record: It allows maintaining a detailed historical record of all inspections, detected damages, and maintenance actions carried out on a railway bridge. This information can be valuable for audits, research, long-term analysis, etc.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## Appendix A Hyperparameters Settings  
Below, the reader can find the hyperparameters' search space that was used for the optimization of the multivariate model's performance, along with the hyperparameters that were always used for the model training.  
* **Linear SVM(\(random\_state=0\))*
* \(C\): (\(2\times 10^{-2}\), \(2\times 10^{-1}\), \(2\times 10^{0}\), \(2\times 10^{1}\), \(2\times 10^{2}\))
* **Logistic(\(solver=\)**'saga', \(11\_ratio=0.5\), \(random\_state=0\))*
* _penalty:_ ('11', '12', 'elasticnet', 'none')
* \(C\): (0.0001, 0.001, 0.1, 1)
* _max_iter_: (100, 120, 150)
* **RBF SVM(\(kernel=\)**'rbf', \(gamma=\)**'scale', \(random\_state=0\))*
* \(C\): (\(2\times 10^{-2}\), \(0.5\times 10^{-1}\), \(1\times 10^{-1}\), \(1.5\times 10^{-1}\), \(2\times 10^{-1}\), \(2.5\times 10^{-1}\), \(2\times 10^{0}\))
* **Random Forest(\(n\_estimators=500\), \(oob\_score=\)**True, \(random\_state=0\))*
* _min_samples_leaf_: [2; 36]
* _max_features_: ('sqrt', 0.4, 0.5)
* _criterion_: ('gini', 'entropy')
* **XGBoost(\(objective=\)**'binary:logistic', \(eval\_metric=\)'aucpr', \(n\_estimators=500\), \(boster=\)**'gbtree', \(random\_state=0\))
* _gamma_: [0.05; 0.5]
* _max_depth_: (\(2\), \(3\), \(5\), \(6\))
* _min_child_weight_: (\(2\), \(4\), \(6\), \(8\), \(10\), \(12\))
* _subsample_: [0.6; 1.0]
* _colsample_bytree_: [0.6; 1.0]
* _learning_rate_: (0.001, 0.01, 0.1, 0.2, 0.3, 0.4)
* _reg_alpha_: (\(0\), \(1\times 10^{-5}\), \(5\times 10^{-4}\), \(1\times 10^{-3}\), \(1\times 10^{-2}\), \(1\times 10^{-1}\), \(1\times 10^{0}\))
# Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry
## Principles
### Principle of a Kolsky Bar Based on Temporal Speckle Interferometry  
As showing in Figure 3, a Kolsky bar based on in-plane displacement measurement combines two sets of in-plane displacement measuring systems and a Kolsky bar. The beam emitted from the laser is expanded and filtered by the spatial filter, and then the incident light is divided into two beams by the beam splitter. Both of them illuminate the surface of the incident bar or transmitter bar at the same angle. The back-scattered beams from the surface of the incident bar or transmitter bar form a speckle. The intensity of the speckle will change if the surface moves in the \(y\)-direction, which is captured by the detectors. The collected information is converted into a series of voltage values by the data acquisition device. By processing these voltage data with the wavelet transform and the phase unwrapping algorithm, the displacement of the incident bar or transmitter bar can be obtained according to the following formula,  
\[\Delta x(t)=\frac{\lambda\Delta\Phi(t)}{4\pi sin\ \alpha} \tag{19}\]  
where \(\Delta\Phi(t)\) is a continuous phase based on time variation.  
The velocity record is derived from the derivative of the displacement after Gaussian fitting. Therefore, the stress, strain, and strain rate of the specimen can be calculated according to the Formulae (12)-(14).  
Figure 3: Principle of a Kolsky bar based on in-plane measurement; 1 and 8 are lasers; 2 and 9 are spatial filters; 3 and 10 are beam splitters; 4, 5, 11, and 12 are reflectors; 6 and 13 are detectors; 7 and 14 are computers; 15 is an oscilloscope; 16 and 17 are optical fibers; 18 and 19 are fiber detectors; 20 is the striker bar; 21 is the guide; 22 is the incident bar; 23 is the specimen; 24 is the transmitter bar; 25 is the support frame.
# Ultra-Thin Metamaterial Beam Splitters
## 2 Fishnet Metamaterial Design Parameters  
We design different beam splitters based on the fishnet metamaterial in [29]. The unit cell as shown in Figure 1 consists of three layers made by Ag-MgF\({}_{2}\)-Ag. The thicknesses of Ag layer and MgF\({}_{2}\) layer are \(t_{Ag}\) and \(t_{MgF_{2}}\), respectively. The wire widths of the fishnet structure along the \(x\) and \(y\) directions are \(w_{x}\) and \(w_{y}\), respectively. The periods are \(\Lambda_{x}\) and \(\Lambda_{y}\) and the structure is embedded in air. The Drude model is used to describe the permittivity of Ag,  
\[\epsilon_{Ag}=1-\frac{{\omega_{p}}^{2}}{\omega(\omega+i\omega_{c})}, \tag{1}\]  
Figure 1: Schematic of the (**a**) unit cell of a fishnet structure with the geometric parameters indicated. (**b**) Side view of the structure showing material layers.  
where the plasma frequency is \(\omega_{p}=1.37\times 10^{16}\) rad/s and the collision frequency is \(\omega_{c}=8.5\times 10^{13}\) rad/s [21]. The refractive index of MgF\({}_{2}\) is assumed to be 1.38 around 1.55 \(\upmu\)m wavelength based on a fabricated and experimentally tested fishnet metamaterial structure [21]. In the following sections, we will show different beam splitter designs by changing the geometry of the fishnet structures.
## 2 Materials and Methods
### DBN-Based Path-Tracking Deviation Pattern Identification Model and Dataset Preparation
#### 2.3.2 Design of the Deviation Pattern Identification Model  
As analyzed by Section 2.2.2, the association characteristics of each deviation pattern with different state variables are inconsistent. In order to avoid the problem of poor learning effect of a single DBN model, this study trained three models for the application scenarios of multiple deviation patterns. The description of each model is shown in Table 6.  
The workflow of each model: In the first stage, the data are input to Model I; if the identification result is normal, then there is no need to call the subsequent models; if the identification result is oscillation or sideslip, start the second stage; the data are input to Model II or Model III to identify the degree of oscillation or sideslip. The workflow is shown in Figure 7.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 6 Simulation Experiments
### Single Track Case  
Optimal ordering of runs can change following the addition of closures, even for a single track case. Unlike for the more complex structure of a train run network, here, it is possible to obtain, in a reasonable amount of time, optimal solutions to the time shift resolution problem. The authors find it valuable to exploit this opportunity to relate the dedicated algorithm to an optimal one in a series of experiments, which were constructed as follows.  
The overall time interval was limited to a single day, i.e., \(T^{\text{H}}=1440\) min. The network consisted of three stations (one artificial) \(\mathbf{V}=\{0,1,2\}\) with a single track between the two non-artificial stations \(K_{1,2}=1\). The number of runs \(TR\) varied with the experiment. Standing times were set to \(T^{\text{DL}}=15\), \(T^{\text{D}}_{i,p}=1\) for all runs \(p\) and stations \(i\). No substitutive transportation was allowed. Each experiment introduced a single closure with parameters \(t^{\text{CES}}_{1}=t^{\text{CLS}}_{1}=\{T^{\text{HLS}}_{1}=\{T^{\text{C}}_{1} \}/2\}\), where closure duration \(T^{\text{C}}_{1}\) varied with experiment. Moreover, two additional parameters \(\xi\) and the movement time \(c_{p,1,2}\) were introduced for each experiment to increase the flexibility of runs. Values of the former and the latter parameter were fixed by hand and randomly generated from the interval \([\underline{c},\underline{c}]\), respectively. Then, both parameters were the basis for the generation of arrival time \(t^{\text{ARE}}_{1,p}\) from the interval \([\underline{c},T^{\text{H}}-\underline{c}-c_{p,1,2}-2T^{\text{D}}_{i,p}]\), as well as for the setting other variables, namely: \(t^{\text{DEF}}_{0,p}\bar{\tau}_{0,p}=t^{\text{ERR}}_{1,p}-\xi\), \(t^{\text{DEP}}_{1,p}=t^{\text{ARR}}_{1,p}+T^{\text{D}}_{i,p}\), \(t^{\text{ARR}}_{2,p}=t^{\text{DEP}}_{1,p}+c_{p,1,2}\), \(t^{\text{DEP}}_{2,p}=t^{\text{ARR}}_{2,p}+T^{\text{D}}_{i,p}\), \(t^{\text{ARR}}_{2,p}=t^{\text{DEP}}_{2,p}+\xi\), \(T^{\text{DEP}}_{2,p}=\xi\). Values of other parameters were set to \(t^{\text{DEP}}_{i,p}=T^{\text{ARR}}_{i,p}=T^{\text{E}}=T^{\text{DE}}=T^{\text {H}}\), \(\bar{c_{i}}=TR\), \(b_{i,j}=0\). Tested were combinations of values \(\xi\), \(T^{\text{C}}_{1}\), \(T^{\text{C}}_{1}\), \(TR,\underline{c},\underline{c}\). The following cases were chosen for further presentation.  
Case 1. \(\underline{\xi}=60\), \(T^{\text{C}}_{1}=60\), \(TR=\{5,6,\,...,\,10\}\), \([\underline{c},\underline{c}]=[15,15]\).  
Case 2. \(\underline{\xi}=60\), \(T^{\text{C}}_{1}=120\), \(TR=\{5,6,\,...,\,10\}\), \([\underline{c},\underline{c}]=[15,15]\).  
Case 3. \(\underline{\xi}=240\), \(T^{\text{C}}_{1}=120\), \(TR=\{5,6,\,...,\,10\}\), \([\underline{c},\underline{c}]=[60,180]\).  
Case 4. \(\underline{\xi}=240\), \(T^{\text{C}}_{1}=60\), \(TR=\{5,6,\,...,
# Analysis of the Vibration Characteristics of Ballastless Track on Bridges Using an Energy Method
## 2 Methods
### Wheel/Rail Interaction Model
#### 2.3.1 Wheel/Rail Contact Geometry  
To solve the wheel/rail contact geometry, some assumptions need to be explained:  
(1) The wheels and rails are all considered rigid bodies.  
(2) The wheel/rail profiles are all discretized by means of cubic interpolation.  
Based on those assumptions, the function of the wheel/rail relative displacement in the lateral direction can be employed to obtain the contact patch [14].  
The contact geometry is illustrated in Figure 3. The coordinates of the wheel/rail contact point can be obtained by Equation (3):  
\[\left\{\begin{array}{l}\mathrm{x}=\eta_{r}l_{x}+l_{x}R_{w}\tan\delta_{w}\\ y=\eta_{r}l_{y}-\frac{R_{w}}{1-l_{x}^{2}}(l_{x}^{2}y\tan\delta_{w}+l_{z}m)+y_{ w}\\ z=\eta_{r}l_{z}-\frac{R_{w}}{1-l_{x}^{2}}(l_{x}^{2}l_{z}\tan\delta_{w}-l_{y}m) \end{array}\right., \tag{3}\]  
where \(\eta_{r}\) denotes the lateral distance between the rolling circle and the center of the wheelest axle; \(R_{w}\) is the rolling radius of the wheel; \(\delta_{w}\) represents the contact angle of the wheel tread; \(y_{w}\) represents the lateral displacement of the wheelest; and \(\mathrm{m}=\sqrt{1-l_{x}^{2}(1+\tan^{2}\delta_{w})}\). \(l_{x}\), \(l_{y}\), and \(l_{z}\) refer to the cosine in the \(x\), \(y\), and \(z\) directions, which can be derived by Equation (4):  
\[\left\{\begin{array}{l}l_{x}=-\cos\theta_{w}\sin\psi_{w}\\ l_{y}=-\cos\theta_{w}\cos\psi_{w}\\ l_{z}=\sin\theta_{w}\end{array}\right., \tag{4}\]  
where \(\theta_{w}\) and \(\psi_{w}\) denote the rolling and yawing angles of the wheelest, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Mass of rail & kg/m & 60.64 \\ Cant of rail & & 1:40 \\ Elastic modulus of rail & GPa & 210 \\ Vertical/lateral stiffness of fasteners & kN/mm & 40/50 \\ Spacing distance of fasteners & m & 0.65 \\ Density of track slab & kg/m\({}^{3}\) & 2500 \\ Elastic modulus of track slab & GPa & 35.5 \\ Density of CA mortar & kg/m\({}^{3}\) & 2400 \\ Elastic modulus of CA mortar & GPa & 8.5 \\ Density of base slab & kg/m\({}^{3}\) & 2500 \\ Elastic modulus of base slab & GPa & 30 \\ Surface stiffness of the slab mat layer & MPa/m & 40 \\ Length of track slab/CA mortar/bridge & m & 6.45/6.45/32 (\(\times\) 4 span) \\ Density of bridge body & kg/m\({}^{3}\) & 2600 \\ Elastic modulus of bridge body & GPa & 35.5 \\ Density of bridge pier & kg/m\({}^{3}\) & 2500 \\ Elastic modulus of bridge pier & GPa & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The track–bridge model parameters.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs
#### 3.2.3 Fourier Transform Infra-Red (FT-IR) Spectroscopy  
It provides information about the molecular composition, functional groups, and chemical bonds present on the surface of MNPs.  
FT-IR spectroscopy measures the absorption of infrared light by the sample. Different functional groups absorb specific wavelengths of infrared light, resulting in characteristic peaks in the spectrum. The Fourier Transform technique is used to convert the raw data into a Fourier Transform Infrared spectrum.  
This technique is particularly useful for studying the surface functionalization of nanoparticles. Peaks in the spectrum can be assigned to specific functional groups, providing information about the ligands or surfactants attached to the NPs' surface. It can also identify chemical bonds present in the system, allowing researchers to characterize the composition of the material.  
Other applications are materialized through quantitative analysis by enabling the determination of the concentration of functional groups on the nanoparticle surface, particle size evaluation by monitoring the chemical reactions and, last but not least, the NPs' agglomeration state quantification [106].
## 3 Materials and Methods
### Conducting the Search
#### 3.2.2 Screening and Selection of Relevant Articles  
Next, we evaluated the articles based on the inclusion criteria to determine their relevance to our study. An article had to include the search terms as the core technology under analysis. This was typically evidenced by its emphasis on the title, abstract, and keywords. We selected only academic peer-reviewed journal papers and conference proceedings and excluded others, namely: (a) papers without full availability, (b) papers not available in English, (c) duplicate articles, and (d) papers that did not discuss the reputation system, or its models, from technical, engineering, or computing science perspectives. The identification and inclusion process of our systematic review is presented in Figure 1. Our initial search was carried out in May 2021 and yielded 622 articles. Once we eliminated duplicates, entries without full-text availability in English, and papers that were not e-commerce-reputation centered, we were left with a population of 581 papers. Next, our research team, including a professor, two doctoral students, and a master's student, who reviewed this collection of articles for relevancy. In the first round of our inclusion process, we assessed the articles for their relevance based on title, abstract, and keywords. This process led to the selection of 41 articles. Any articles we did not agree upon, were also excluded. In the next round of revisions, we assessed the articles based on the full paper. We eliminated 26 papers that only concerned the architecture or where the prototype of the blockchain-based user reputation model was not presented. Thus, we identified 15 relevant articles for analysis, which are listed in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Title** & **Contribution** \\ \hline \multirow{3}{*}{[19]} & A Blockchain-Enabled Quantitative & Proposal and evaluation of a trust and \\  & Approach to Trust and Reputation & reputation management (TRM) \\  & Management with Sparse Evidence & framework based on a \\  & & mathematical model. \\ \hline \multirow{3}{*}{[18]} & \multirow{3}{*}{DTrust: A Decentralized Reputation  System for E-commerce Marketplaces} & Design, implementation, and \\  & & evaluation of a decentralized and \\ \cline{1-1}  & & sharable reputation system for \\ \cline{1-1}  & & e-commerce, based on the Ethereum  blockchain and IPFS. \\ \hline \multirow{3}{*}{[16]} & EthReview: An Ethereum-based  Product Review System for Mitigating  Rating Frauds & Ethereum blockchain-based P2P  product review system for e-commerce. \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Papers included in the systematic review.
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 3 Results and Analysis
### Temperature-Feld Distribution  
#### 3.3.1 Initial Heating  
Figure 8a,b show the temperature-field distribution during heating for 1 h. At the start of the induction-heating process, the highest-temperature region for the original furnace is at the graphite heater. Heat is transferred from the graphite heater to the charge and thermal insulation. For the graphene-coil furnace, the highest-temperature region occurs at both the heater and the coil, with the temperature slightly higher at the heater. Combined with Figure 8c,d, it is evident that the heat flux at the heater of the graphene-coil furnace is smaller than that at the heater of the copper-coil furnace. This is due to the fact that the heat source of the graphene-coil furnace consists of both the heater and the coil, and the ohmic heat of the coil can be used to insulate the heater during the heating process, resulting in higher energy efficiency compared to the original furnace.