esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Synthesized Graphene and Its Applications
## 3 Applications of Laser Synthesized Graphene
### Energy Storage
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Author and Ref.** & **Configuration/Material** & **Electrolyte** & **Specific Capacitance** & **Power Density (Max)** & **Discharge Time (Max)** & **Stability (Cycles)** \\ \hline [PERSON] [77] & In-plane/CO & H\({}_{5}\)SO\({}_{4}\)/PVA & 3 mFcm\({}^{-2}\) & - & 60 s (0.05 m/cm\({}^{-2}\)) & 1000 \\ \hline El-Kady [19] & Sandwich/CO & H\({}_{5}\)PO\({}_{4}\)/PVA & 5 mFcm\({}^{-2}\) & 3 Wcm\({}^{-3}\) & 10 s & 10,000 \\ \hline El-Kady [56] & In-plane/CO & H\({}_{5}\)SO\({}_{4}\)/PVA & 2.35 mFcm\({}^{-2}\) & 200 Wcm\({}^{-3}\) & 30 s & 10,000 \\ \hline  & In-plane + & & & & & \\ [PERSON] [78] & Sandwich/CO & Na\({}_{2}\)SO\({}_{4}\) or TEARP\({}_{4}\) & 3 mFcm\({}^{-2}\) & 2.4 Wcm\({}^{-3}\) & - & 10,000 \\ \hline [PERSON] [75] & In-plane/CO + PANI & H\({}_{5}\)SO\({}_{4}\)/PVA & 72 mFcm\({}^{-2}\) & 81.4 mWcm\({}^{-3}\) & 3000 s (53 m/cm\({}^{-2}\)) & 1000 \\ \hline [PERSON] [43] & In-plane/LIG & Na\({}_{2}\)SO\({}_{4}\) & 0.34 mFcm\({}^{-2}\) & 0.01 mWcm\({}^{-2}\) & 200 s (0.625 m/cm\({}^{-2}\)) & - \\ \hline [PERSON] [21] & In-plane/LIG & LiC/PVA & 361 mFcm\({}^{-2}\) & 11.8 mWcm\({}^{-2}\)/2.8997 & 150 s (0.25 m/cm\({}^{-2}\)) & 2000 \\ \hline [PERSON] [54] & In-plane/LIG & H\({}_{5}\)SO\({}_{4}\)/PVA & 37 mWcm\({}^{-2}\) & 2 mWcm\({}^{-2}\) & 15 s (0.2 m/cm\({}^{-2}\)) & - \\ \hline [PERSON] [21] & In-plane/LIG & H\({}_{5}\)SO\({}_{4}\)/ionic liquid & 4 mFcm\({}^{-2}\) & 9 mWcm\({}^{-2}\)/3 Wcm\({}^{-2}\) & 25 s (0.2 m/cm\({}^{-2}\)) & 9000 \\ \hline [PERSON] [79] & In-plane/LIG + Fe\({}_{5}\)O\({}_{4}\) & H\({}_{5}\)SO\({}_{4}\)/PVA & 719 mFcm\({}^{-2}\) & 0.3 mWcm\({}^{-2}\) & - & 900 \\ \hline [PERSON] [99] & In-plane/LIG & H\({}_{5}\)SO\({}_{4}\)/PVA & 0.88 mFcm\({}^{-2}\) & 25\(\mu\)Wcm\({}^{-2}\) & 60 s (0.01 m/cm\({}^{-2}\)) & 10,000 \\ \[PERSON] [PERSON] [80] & Sandwich/LIG & H\({}_{5}\)SO\({}_{4}\)/PVA & 9 mFcm\({}^{-2}\) & 9 mWcm\({}^{-2}\)/4 Wcm\({}^{-3}\) & 30 s (0.5 m/cm\({}^{-2}\)) & 8000 \\ \[PERSON] [PERSON] [81] & In-plane/LIG & H\({}_{5}\)SO\({}_{4}\)/PVA & 16.5 mFcm\({}^{-2}\) & 4 Wcm\({}^{-3}\) & 9 s (1 m/cm\({}^{-2}\)) & 12,000 \\ \hline [PERSON] [82] & In-plane/LIG & potassium polyacrylate/KOH & 0.79 mFcm\({}^{-2}\) & - & 7 s (75 m/cm\({}^{-2}\)) & 10,000 \\ \hline [PERSON] [22] & In-plane/LIG & KOH & 320 mFcm\({}^{-2}\) & - & 600 s (1 m/cm\({}^{-2}\)) & 400 \\ \hline [PERSON] [76] & In-plane/LIG + Pry & H\({}_{5}\)SO\({}_{4}\)/PVA & 2412 mFcm\({}^{-2}\) & 0.325 mWcm\({}^{-2}\) & 4000 s (0.5 m/cm\({}^{-2}\)) & 10,000 \\ \hline Yuan [44] & In-plane/LIG & H\({}_{5}\)SO\({}_{4}\)/PVA & 60.6 mFcm\({}^{-2}\) & - & 200 s (0.5 m/cm\({}^{-2}\)) & 20,000 \\ \hline \end{tabular}
\end{table} 
Table 2: Key information from reports on LSG application for energy storage. PVA- polyvinyl alcohol. TEARP4- tetraethylammonium tetrafluoroborate. PANI: polyaniline. PPY-polypyrrole.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 3 Results
### Observations  
1. Pitch error alone increases the magnitude of the mean TE by \(\sim\)80%--from \(-5\times 10^{-5}\) deg in the baseline to \(-9\times 10^{-5}\) deg--and generates the shaft order tone, confirming that pitch error is a principal source of low-frequency torque ripple [17].
2. Measured microgeometry increases the peak-to-peak TE by nearly twenty times compared to the pitch error case--rising from \(1.6\times 10^{-5}\) deg to \(3.1\times 10^{-4}\) deg--and retains the shaft order tone. This confirms that local flank shape interacts with spacing deviation in a reinforcing, non-linear way rather than canceling it.
3. The appearance of mesh side-bands supports the analytical predictions that pitch-induced phase modulation spreads energy around the fundamental mesh frequency [17].  
These results set the stage for the discussion, where the implications for e-drive gear tolerance and future probabilistic modeling are developed.  
These mechanistic observations provide the basis for the comparative discussion in Section 4, where we benchmark our findings against classical thin-slice theory and recent experimental reports
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 4 System Experiment and Analysis Evaluation
### Advantage Theory Analysis  
The mine filling overlimit early warning system based on double chain and transfer learning that was proposed in this paper records the equipment operation data information on the blockchain, inherits the advantages of the blockchain such as having no center, transparency, distribution, and tamper-resistance, and ensures the security of the database storage and query process. At the same time, the transfer learning model is used to carry out the overlimit early warning of all the running data. According to the early warning results, overlimit equipment can be paid attention to in advance of any issues, and corresponding measures can be swiftly implemented to extend the service life of each piece of equipment:  
(1) Non-centering and transparency: the data storage and access process proposed in this paper evaluates the storage and access of a smart contract on the blockchain. Only when the conditions are met is the smart contract triggered to run the node to store data or query data to realize the centralization and transparency in the data storage query process;  
(2) Non-counterfeiting and security: taking advantage of the tamper-resistant characteristics of the blockchain, the CID value, user information, and query process returned  
Figure 6: Blockchain capacity comparison.  
by the IPFS after storing monitoring data are recorded on the blockchain. No person or organization can forge false monitoring data to query the CID value and user information, effectively eliminating security issues such as illegal access and ensuring the reliability of data access processes. All the data were generated during the operation of the IPFS storage device, and the early warning data were generated by the transfer learning model. Each monitoring data point stored on the IPFS has a unique corresponding CID value, which can ensure the security of the data storage query process and prevent the risk of data loss;  
(3) Overlimit warning: in the mine filling overlimit early warning system proposed in this paper, all data can be trained by the migration model to detect the overlimit data, and the position and timing of a possible overlimit situation in the future can be predicted according to the previous position and timing of the overlimit data. The results obtained by the prediction can be used to focus on the overlimit equipment in advance and implement timely corresponding measures to ensure the safety of the system during operation.
## 3 Sensor Data Security Estimator
### Overview of SDSE  
SDSE is a system that must be installed in the base station of the target WSN. Once started, SDSE is able to: (1) recognize the security mechanisms installed in the network; and (2) refer to those mechanisms in order to obtain the information necessary to perform security level calculation.  
Action (1) is performed through the communication of SDSE with the operating system of the sensor network (e.g., TinyOS [11] and Contiki [12]). Action (2) is performed through communication of SDSE with the modules and communication interfaces provided by the applications (security mechanisms and routing) running on the operating system.  
After being installed, SDSE is ready to calculate the security level of sensed data and report it to the requesting users. In order for SDSE to accomplish this task, the five steps below must be performed (as illustrated in Figure 1):  
1. WSN users execute data queries at the base station, in order to obtain certain sensor readings;
2. These user queries are forwarded from the base station to the sensor nodes. This task is generally accomplished by specific query manager modules installed in the base station (omitted from the figure for simplicity), such as TinyDB [13] and Cougar [14], or even data dissemination routing, such as directed diffusion [15];
3. The corresponding sensors send the requested data back to the query manager at the base station;
4. As soon as SDSE receives the sensor readings from the query manager, it communicates with the security mechanisms in order to obtain, at that moment, the parameters needed to calculate the metrics and then communicates with the routing algorithm to discover the route traveled by the received data. With this information, SDSE calculates the security metrics, and from those metrics, it calculates the security level of the received data;
5. Finally, SDSE delivers the sensor data, along with its respective security level, to the requesting user. In possession of the security level received, the user can properly decide on the use of the requested data.  
These steps are performed for each query made by any given user. This is necessary, because the data security level constantly changes, since it is calculated based on dynamic parameters, such as network topology, the path traveled by sensor readings and various security parameters, which will be presented in the next section.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
###### Abstract  
Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time-frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.  
remaining useful life; fault prognosis; ridge regression; optimization Article
## 5 Integrating 5G and Tactile Internet to Industrial Case Studies
### Architecture of Smart Manufacturing Oriented 5G-Based IIoT  
This section describes a 5G-based IIoT architecture based on the various Smart Manufacturing scenarios and the three communication modes of 5G wireless communication networks. The implementation methods of seven related Smart Manufacturing application scenarios are investigated in the context of 5G wireless communication technology, including real-time data collection of heterogeneous factors on the manufacturing shop-floor, identification and location of production factors, networked collaborative manufacturing, man-machine interaction, automated guided vehicles (AGV) collaboration, digital twin driven shop-floor convergence between physical and cyber space, product design, manufacturing and maintenance based on virtual and augmented reality. Big data, cloud computing, edge computing, digital twin, virtual reality/augmented reality, and service-oriented manufacturing are all discussed in detail as ways to apply advanced technologies based on 5G wireless communication technology in the architecture of 5G-based industrial Internet of Things (IIoT) [69].  
Figure 17: Integrating 5G and TI applications for integrated and adaptive AR maintenance and shop-floor rescheduling (adapted from [59]).  
The IIoT is used in the implementation of smart factories to integrate the underlying equipment resources. As a result, the manufacturing system has perception, interconnection, and data integration capabilities. In a smart factory, data analysis and scientific decision-making are used to achieve production scheduling, equipment service, and product quality control. In addition, the internet of services is used to virtualize manufacturing resources by moving them from a local database to a cloud server. The global collaborative process of intelligent manufacturing oriented to the order-driven market is built through human-machine interaction. As a result, the smart factory is an engineering system with three main components: interconnection, collaboration, and execution. The architecture of a smart factory [67,70] includes four layers, including a physical resource layer, a network layer, a data application layer, and a terminal layer. In order to transform a modern factory into a smart factory, key technologies in all layers must be thoroughly investigated, as shown in Figure 19.
## 3 Identifier-Related Challenges
### Global Uniqueness of Identifier  
The problem of duplicated IDs, representing the same IDs for different objects, can occur in merging two or more CIM XML message files, although rdfIDs in a single XML message are distinguishable. The concept of MAS can be applied to avoid the duplication of IDs [16; 20]. As indicated in Figure 3, two types of regionally divided MASs are defined; (1) regional sets containing no objects that reference objects in another regional set, and (2) boundary sets containing all objects that have relationships with objects in different regional sets. Each MAS can issue mRIDs for power system resources in its territory, meaning that most power system resources except for boundary ones have the dedicated authority for managing their mRIDs. The utilization of a universally unique identifier (UUID) algorithm is strongly recommended for the creation of the mRID of an object since it can ensure the global uniqueness of the identifier [8; 19]. With the help of MAS, mRIDs will never be duplicated by another MAS as long as the mRIDs for all power system resources are globally registered and managed by one and only one authority. That is, a single institution or system assigns or manages object IDs of all power systems using the UUID algorithm and MAS.  
Figure 2: CIM three-layered architecture.
## 5 Conclusions  
Maritime traffic pattern recognition is employed to obtain the conventional behavior of ships. The marine traffic environment is complex and changeable. The method of maritime traffic pattern recognition should be adaptable to changes in the traffic environment. Based on AIS data, a dynamic recognition method for maritime traffic patterns was proposed. This method is composed of two parts: trajectory online preprocessing and incremental clustering. The trajectory online preprocessing cleans, simplifies, and partitions the trajectory. Track cleaning can separate MMSI number shared trajectories and delete abnormal points. A DOTS algorithm is used to simplify the trajectory. The redundant points can be deleted on the premise of minimizing Synchronized Euclidean Distance error. The trajectory partition algorithm can divide a ship's trajectory into several segments. The divided trajectory can reflect the local behavior characteristics of the ship. Regarding trajectory clustering, the improved LCSS is used as the trajectory similarity measure. The measure can solve the impact of sparse trajectory points and debris trajectory. Incremental DBSCAN is used to cluster trajectories. It can reduce the impact of noise trajectory on clustering results and improve the speed of large data trajectory clustering. To check the effectiveness of the proposed algorithm, the AIS data near Yushan, Zhoushan Port, China was used for the experiment. The experimental contents included the dynamic recognition of maritime traffic patterns and the comparison of pattern recognition quality and speed between DBSCAN and incremental DBSCAN. The experimental results showed that the proposed algorithm could meet the needs of maritime traffic pattern dynamic recognition with changes in the navigation environment, and clustering speed was more stable with the increase of data volume.  
Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Software, Writing--original draft, Y.Z.; Writing--review & editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the general public welfare project of Zhejiang science and technology department (LGG22E090004).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Methodology
### Clustering  
#### 2.2.1 K-Harmonic Means  
To overcome the problem that the k-means algorithm easily falls into the local optimal solution, for each data object in the dataset, the KHM uses the sum of the harmonic means of the data to all the cluster centers, and the affiliation function and particle weights are used to update the cluster centers. The harmonized mean sum of all data is used as the evaluation index. The specific process of the KHM clustering algorithm is shown below [46].  
1. Select \(k\) initial centroids \(c_{1},c_{2},\cdot\cdot\cdot,c_{k}\) randomly, where \(c_{k}\) is the centroid of the \(k_{th}\) cluster, set \(t\) to the number of iterations.  
2. Calculate the fitness function:  
\[F(X,c_{1},c_{2},\cdot\cdot\cdot,c_{k})=\sum\limits_{i=1}^{\text{n}}\frac{j}{ \sum\limits_{k=1}^{K}\frac{1}{\left\|x_{i}-c_{k}\right\|^{p}}} \tag{9}\]  
In the formula: \(X=\{x_{1},x_{2},\cdot\cdot\cdot,x_{n}\}\), \(n\) is the number of objects to be clustered, \(p\) is an input parameter Generally \(p\geq 2\).  
3. For \(x_{i}\) calculate its affiliation at each center \(c_{k}\):  
\[m(c_{k}|x_{i})=\frac{\left\|x_{i}-c_{k}\right\|^{-p-2}}{\sum\limits_{k=1}^{K} \left\|x_{i}-c_{k}\right\|^{-p-2}} \tag{10}\]  
4. For \(x_{i}\), calculate its weight:  
\[w(x_{i})=\frac{\sum\limits_{k=1}^{K}\left\|x_{i}-c_{k}\right\|^{-p-2}}{\left( \sum\limits_{k=1}^{K}\left\|x_{i}-c_{k}\right\|^{-p}\right)^{2}} \tag{11}\]5. For \(c_{k}\), recalculate the clustering centers based on their affiliation, weights, and all data points \(x_{i}\):  
\[c_{k}=\frac{\sum\limits_{i=1}^{n}m(c_{k}|x_{i})w(x_{i})x_{i}}{\sum\limits_{i=1}^{ n}m(c_{k}|x_{i})w(x_{i})} \tag{12}\]  
6. Repeat 2-5 until the set number of iterations is exceeded:  
7. Classify sample \(x_{i}\) into the largest group \(k\) of \(m(c_{k}|x_{i})\).
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Traffic Status Dataset
#### 5.2.1 Logistic Regression  
The algorithm was run on a range of values for the parameters \"solver\" and \"C\" (regularization parameter), similarly to the \"lane detection\" dataset. These values were run in a loop to find the most ideal logistic regression model and its corresponding parameter values for the dataset at hand.  
Once the preferred parameter values were obtained (C = 0.5, solver = \"lbfgs\"), all the evaluation metrics of interest in addition to accuracy were determined. The visual representation is shown in Figure 6.  
Similar to what is noted in the precision-recall curves of the \"lane detection\" dataset, the curves across all three curves are highly inconsistent and varied. However, in Figure 6a, precision-recall curves consistent with the amount of data in each class are observed. It is also observed that the aggressive class (class 1) shows a varied precision-recall curve compared to the other two classes.  
The low ROC curve suggests the likelihood of the normal class data having been overfit with a much larger sample ratio to the remaining two classes.  
The overall metric scores are consistent on an overall scale as well as across the different classes (Table 5). However, the scores are low. The model does not seem to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Logistic Regression** & **Gradient Boosting** & **Random Forests** & **Neural Networks** \\ \hline
**Accuracy** & 0.48 & 0.60 & 0.56 & 0.30 \\
**Micro Precision** & 0.48 & 0.60 & 0.56 & 0.30 \\
**Micro Recall** & 0.48 & 0.60 & 0.56 & 0.30 \\
**Micro F1-score** & 0.48 & 0.60 & 0.56 & 0.29 \\
**Macro Precision** & 0.51 & 0.59 & 0.57 & 0.30 \\
**Macro Recall** & 0.46 & 0.59 & 0.54 & 0.28 \\
**Macro F1-score** & 0.39 & 0.58 & 0.51 & 0.27 \\
**Weighted Precision** & 0.50 & 0.60 & 0.57 & 0.30 \\
**Weighted Recall** & 0.48 & 0.60 & 0.56 & 0.29 \\
**Weighted F1-score** & 0.41 & 0.59 & 0.52 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lane detection—results comparison.  
Figure 6: Logistic regression simulation results: (**a**) precision–recall curve; (**b**) ROC curve.  
provide a highly encouraging classification system. The normal class metrics as observed in the ROC curve show poor results relative to the drowsy and aggressive classes.
# An Integrated Cyber Security Risk Management Approach for a Cyber-Physical System
###### Abstract  
A cyber-physical system (CPS) is a combination of physical system components with cyber capabilities that have a very tight interconnectivity. CPS is a widely used technology in many applications, including electric power systems, communications, and transportation, and healthcare systems. These are critical national infrastructures. Cybersecurity attack is one of the major threats for a CPS because of many reasons, including complexity and interdependencies among various system components, integration of communication, computing, and control technology. Cybersecurity attacks may lead to various risks affecting the critical infrastructure business continuity, including degradation of production and performance, unavailability of critical services, and violation of the regulation. Managing cybersecurity risks is very important to protect CPS. However, risk management is challenging due to the inherent complex and evolving nature of the CPS system and recent attack trends. This paper presents an integrated cybersecurity risk management framework to assess and manage the risks in a proactive manner. Our work follows the existing risk management practice and standard and considers risks from the stakeholder model, cyber, and physical system components along with their dependencies. The approach enables identification of critical CPS assets and assesses the impact of vulnerabilities that affect the assets. It also presents a cybersecurity attack scenario that incorporates a cascading effect of threats and vulnerabilities to the assets. The attack model helps to determine the appropriate risk levels and their corresponding mitigation process. We present a power grid system to illustrate the applicability of our work. The result suggests that risk in a CPS of a critical infrastructure depends mainly on cyber-physical attack scenarios and the context of the organization. The involved risks in the studied context are both from the technical and nontechnical aspects of the CPS.  
Keywords:cybersecurity; risk management; cyber-physical systems; cybersecurity attack scenario; supervisory control and data acquisition (SCADA) systems; cascading effect +
Footnote †: journal: Applied Sciences