esa-sceva/satcom-chunk-collection huggingface dataset:
# Obtaining Human Experience for Intelligent Dredger Control: A Reinforcement Learning Approach
## 5 Evaluation and Results
### Intelligent Control of the Cutting Process
#### 5.2.2 Comparison of Production  
In Figure 6, we have obtained a general impression that all of the three intelligent control models can outperform the experienced human operator, but we cannot clearly distinguish the performance of the Q-learning, SARSA, and SARSA(\(\lambda\)). Thus, in order to quantitatively distinguish them, Figure 7 depicts the production curves. The production \(W\) at time \(t\) can be calculated  
\[W(t)=\pi(D/2)^{2}V_{f}(t)C_{p}(t) \tag{11}\]where \(D=800\) mm is the diameter of the pipeline, \(V_{f}\) is the flow velocity and \(C_{v}\) is the slurry density  
In accordance with Figure 6, we calculate the accumulative production in 500 s in Figure 7. We can see that when the human controls the swing process, its accumulative production is the lowest, i.e., 550 m\({}^{3}\). When Q-learning and SARSA take charge of the swing process, their respective production is not much different, and the Q-learning is slightly better than SARSA. Compared with others, the proposed SARSA(\(\lambda\)) can produce the highest production, i.e., 645 m\({}^{3}\), and its production has increased by nearly 17.27% in comparison with the human control.  
Therefore, we can conclude that the proposed SARSA(\(\lambda\)) is the most effective method to control the swing process for a CSD. It can outperform the human operator in two aspects. First, the intelligent control method can ensure that all the key variables, such as the motor current, suction vacuum, and the flow velocity, will be kept within a reasonable and safety range. Second, the intelligent control method can always try to maximize the slurry density while ensuring safe operation of equipment. As a result, its production also exceeds the human operation.
## 3 Methodology
### Dataset Description  
Certain aspects, such data structure and labeling, are critical for intrusion detection in network-based datasets. [PERSON] et al. [37] provide a comprehensive explanation of these aspects for both supervised and unsupervised intrusion detection approaches. The history of the CICIDS2017 dataset, which served as this study's intrusion detection dataset, is covered in this section. The Canadian institute for cybersecurity (CIC) provide a dataset that is freely accessible to all scholars [38]. One of the most recent datasets for network intrusion detection accessible in the literature includes 2,830,743 records with 79 network traffic variables and 15 classes [12]. The dataset consists of eight files with five-day benign and attack activities, each including a collection of real-world data [38]. Records with extra metadata are mostly in packet-based and bifacial flow-based formats [37]. The dataset is completely labelled. The focus of the dataset is on the classes listed in Table 3, because its generation is intended for use in network intrusion detection. All attack types are categorized as '1' in binary classification, whereas benign assaults are classified as '0'. All assault types were taken into consideration for multi-classification as presented.  
The seven common attack families making up the assaults included the botnet, DoS, DDoS, brute force, web, infiltration, and Heartbleed attacks. Passwords can be cracked, secret information can be found, and attacks may be attempted via brute force attacks. Botnet assaults use internet-connected gadgets to launch spam attacks. DoS attacks overwhelm the system networks and cause the system to become unavailable for a while. DDoS attacks happen when several systems become targets. Web assaults are software programs designed to search for weaknesses in the user system and attack it. A backdoor can be built in an infiltration assault in order to launch attacks on the system once the user system has  
Figure 1: Designed architecture using the CICIDS2017 dataset for binary and multi-class classification.  
been exploited. A Heartbleed attack operates by tricking servers into giving up their private encryption key, which then leaks their data. Figure 2 shows the CICIDS2017 dataset's sample distribution for each class. In the two-stage process, the first binary classification stage is followed by multi-class classification, where removing the normal traffic samples enabling the model to focus on distinguishing between different attack types.
## 3 System Model
### Resource Reallocation by Controller  
The controller plays a crucial role in implementing resource capacity planning by periodically sending signals to assess the need for resource reallocation. The main responsibility of the controller is to determine the most effective method for scheduling resources, guided by three principles: maximum matching for equal resource partitioning, maximum share of signals resulting in resource reallocation, and maximum resource utilization.  
Resource reallocation is triggered when the controller sends a signal and there are unused resources in one slice while there are pending requests from another slice. This type of signal is referred to as \"resulting\" and leads to resource reallocation. However, if all buffers are empty or all resources are currently in use when a signal is received, no reallocation occurs. Please refer to Figure 4 for a visual representation. Furthermore, during reallocation, the controller takes into account the average volume of elastic traffic conveyed by \(k\)-users \(\mu_{k}^{-1}\).
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 6 GitHub Actions CI/CD Pipelines
### Security Considerations  
A critical requirement for any automated infrastructure deployment pipeline is to ensure that security is not compromised in pursuit of speed and automation. In cloud environments, mismanaged credentials, overly broad permissions, and poor secrets management are frequent root causes of breaches. Therefore, a secure CI/CD design must explicitly address these risks by enforcing least privilege and auditable access controls throughout the deployment pipeline. In Azure, automation pipelines authenticate using service principal application identities registered in Azure Active Directory, which can be granted permissions through Role-Based Access Control (RBAC). It is a fundamental best practice to grant only the permissions required for the automation tasks. For this project, the service principal is assigned the Owner role, which enables the creation and management of resources within a defined scope. Another pillar of credential security is regular rotation. Storing long-lived secrets in GitHub without rotation increases the window of opportunity for exploitation in case of a leak. Secret rotation is not just a recommended practice; it is a requirement for compliance in many regulated industries, ensuring that even if a secret is exposed, its usefulness is time-restricted. GitHub provides encrypted secrets that are only decrypted at runtime within the context of a workflow execution. This design ensures secrets are not exposed in the repository's history or pull requests. These measures align with Microsoft's recommended guidelines for secure automation in Azure, supporting both operational efficiency and robust protection against modern threat scenarios. By building security into the CI/CD pipeline itself, organizations can achieve true DevSecOps, where security is an integrated, automated, and auditable part of every deployment [30; 31].
## 2 Theoretical Modeling
### Decentralized Feedback Controllers with Inertial Actuators
\begin{array}{cc}\frac{1}{m_{c}}\mathbf{u}+\frac{k_{c }}{m_{c}}\mathbf{w}+j\omega\frac{c_{c}}{m_{c}}\mathbf{w}\\ -\frac{1}{m_{p}}\mathbf{u}\end{array} \right], \tag{10}\]  
where the coefficients \(A\),\(B\),\(C\), and \(D\) can be expressed as,  
\[A=j\omega\frac{c_{c}}{m_{c}}+\frac{k_{c}}{m_{c}}+\frac{k_{p}}{m_{c}}-j\omega \frac{c_{p}}{m_{c}}-\omega^{2},\,B=j\omega\frac{c_{p}}{m_{c}}-\frac{k_{p}}{m_{ c}},\,C=-\frac{k_{p}}{m_{p}}-j\omega\frac{c_{p}}{m_{p}},\,D=\frac{k_{p}}{m_{p}}+j \omega\frac{c_{p}}{m_{p}}-\omega^{2}. \tag{11}\]  
If we define \(\left[ \begin{array}{cc}A&B\\ C&D\end{array} \right]^{-1}=\left[ \begin{array}{cc}X_{1}&X_{2}\\ X_{3}&X_{4}\end{array} \right]\), then the displacement of the casing \(\mathbf{w}_{c}(t)\) can be expressed as \(\mathbf{w}_{c}=X_{1}\Big{(}\frac{1}{m_{c}}\mathbf{u}+\frac{k_{c}}{m_{c}}\mathbf{w}+j\omega \frac{c_{c}}{m_{c}}\mathbf{w}\Big{)}-\frac{X_{2}}{m_{p}}\mathbf{u}\). Substituting the above expression into Equation (7), the vector of point force produced by the S identical inertial actuators can be expressed as:  
\[\mathbf{f}_{S}=\left(\frac{X_{2}}{m_{p}}-\frac{X_{1}}{m_{c}}\right)\!(k_{c}+j \omega\,c_{c})\mathbf{u}+\left[1-X_{1}\!\left(\frac{k_{c}}{m_{c}}+j\omega\frac{c _{c}}{m_{c}}\right)\right]\!(k_{c}+j\omega\,c_{c})\mathbf{w}. \tag{12}\]  
If we further define \(E=\left(\frac{X_{2}}{m_{p}}-\frac{X_{1}}{m_{c}}\right)\! (k_{c}+j\omega c_{c})\) and \(F=\left[1-X_{1}\!\left(\frac{k_{c}}{m_{c}}+j\omega\frac{c_{c}}{m_{c}}\right) \right]\! (k_{c}+j\omega c_{c})\), then Equation (12) can be expressed as the following matrix form:  
\[\mathbf{f}_{S}=E\,\mathbf{I}_{S}\mathbf{u}+F\,\mathbf{I}_{S}\mathbf{w}. \tag{13}\]  
In Equation (13), \(EI_{S}\) can be viewed as the transfer function matrix of the actuators, which indicates the active control effects of the actuators. \(FI_{S}\) can be viewed as the actuator input impedance matrix, which includes the dynamics of the actuator, i.e., passive effects of the actuators. For the whole decentralized velocity feedback control system, one can assume that the square matrices of the plant and controller responses (the mobility matrix of the rib stiffened plate and the control gain matrix) are \(\mathbf{G}(j\omega)\) and \(\mathbf{H}(j\omega)\), the closed-loop system can be illustrated by the block diagram in Figure 5.  
Figure 5: The block diagram of the closed-loop system.
## 3 Materials and Methods
### Measurements  
\({}^{1}\)H NMR and \({}^{29}\)Si NMR analysis was performed on Varian NMR System 500 MHz spectrometer (Palo Alto, CA, USA) using dry CDCl\({}_{3}\) as a solvent. GC analysis was carried out using the Agilent 7820A GC System equipped with an HP-5 column, used to study the conversion of EC. The molar mass and molar mass distribution were determined by GPC on a Viscotek system comprising GPCmax and TDA 305 (triple detection array (TDA): RI, IV, LS) equipped with DVB Jordi gel column(s) (linear, mixed bed) in DCM as an eluent at 30 \({}^{\circ}\)C at a flow rate of 1.0 mL/min. MALDI-ToF mass spectrometry was performed on Bruker Daltonics UltrafileXtreme(tm) (Billerica, MA, USA). Trans-2-[3-(4-tert-butylphenyl)-2-methyl-2-pro-penylidene] malonomitrile (DCTB) was used as the MALDI matrix. FTIR spectra were recorded on a Thermo Scientific Nicolet i55 spectrometer. Tensile strength tests were carried out using Instron 5566 testing machine equipped with a 100 N measuring head and pneumatic grips. The measurement was performed at room temperature with a running rate of 200 mm/min. Tensile strength test species in the shape of \"dog bone\". A total of 10 samples before aging and 3 samples after aging were tested. Data were processed with BlueHil2 software. DSC measurements were taken with a TA Instruments Q2000 calorimeter. Polymer samples (in the amount of about 10 mg) were subjected to time-tested temperature profiles: heating 1 from 25 \({}^{\circ}\)C to 200 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, cooling from 200 \({}^{\circ}\)C to \(-\)80 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min and heating \(\Pi\) from \(-\)80 \({}^{\circ}\)C to 200 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min. TGA measurements were recorded with a TA Instrument SDTG600. Polymer samples in the amount of approx. 10 mg were heated in the temperature range from 10 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C, with a heating rate of 10 \({}^{\circ}\)C/min. The process was carried out in two variants: with air and in an inert gas atmosphere (Ar).
_Appl. Sci._**2015**, \(5\), 988-997; doi:10.3390/app5040989  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Study on Sintering System of Calcium Barium Sulphoaluminate by XRD Quantitative Analysis**  
**[PERSON] *, [PERSON] and [PERSON]**  
School of Civil Engineering, Dalian University of Technology, Dalian 116024, China;  
E-Mails: [EMAIL_ADDRESS] (X.S.); [EMAIL_ADDRESS] (J.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-0411-8470-7171.  
Academic Editor: [PERSON]  
_Received: 18 August 2015 / Accepted: 20 October 2015 / Published: 2 November 2015_  
**Abstract:** Calcium barium sulphoaluminate (CBSA), derived from calcium sulphoaluminate (CSA), has excellent cementitious properties. In this study, the sintering system of CBSA with a theoretical stoichiometric Ca3 BaAl6 SO16 was investigated. Rietveld refinement was performed using TOPAS 4.2 software to quantitatively calculate the content of CBSA and the actual ionic site occupancy of Ba2+. The results indicate that the content of Ca4\(\rightarrow\)BaxAl6 SO16 increases with increasing sintering temperature in the 1200-1400 \({}^{\circ}\)C ranges. When sintered at 1400 \({}^{\circ}\)C for 180 min, the content of CBSA reaches 88.4%. However, CBSA begins to decompose at 1440 \({}^{\circ}\)C, after which the content decreases. The replacement rate of Ba2+ was also enlarged by increasing sintering temperature and prolonged sintering time. Sintering at 1400 \({}^{\circ}\)C for 180 min is considered as the optimum when replacement rate of Ba2+ and the content of CBSA were taken into account. Ca3.2 Ba0.8 Al6 SO16 with a content of 88.4% was synthesized.  
**Keywords:** calcium barium sulphoaluminate; sintering system; Ba2+ replacement rate; Rietveld refinement
# Impact of LGE-MRI in Arrhythmia Ablation
## 4 Use of the Processed LGE-MRI for Ablation Procedures  
LGE-MRI in VT or AF ablation procedures has proved to be an important tool to plan and guide ablation. Regarding planification, LGE-MRI allows us to perform a preprocedural assessment of cardiac anatomy and myocardial scar (location of conducting channels in the case of VT and PPVV gaps in AF), to decide the optimal access approach (especially in VT where both endocardial and epicardial approaches can be selected) and exclude intracardiac thrombus. In addition, 3D-LGE-MRI reconstruction can be visualized side by side or merged with electroanatomical maps during the procedure. In VT ablation, for example, MRI-aided ablation has demonstrated a lower need for RF delivery, higher noninducibility rates after substrate ablation, and a higher VT recurrence-free survival [58].
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 5 Conclusions  
In this paper, considering the demand for high control accuracy in the ne tracking system, we proposed a compound control method that combines P-I rate-dependent model feedforward control with PID feedback control. Firstly, we established a rate-dependent hysteresis model and designed the feedforward controller based on its inverse model. Then, we conducted a simulation analysis. Finally, we set up the experimental platform for the ne tracking system to verify the high accuracy of the P-I rate-dependent hysteresis model across a range of frequencies, from low to high, and con mred the optimization effect of compensating for hysteresis nonlinearity on the ne tracking system. The experimental results show that the nonlinearity of the PZT FSM is improved by 30% in the middle- and high-frequency ranges, and the error suppression bandwidth of the ne tracking system is improved by 41.7%.  
The experimental results show that the linearity of PZT FSM and the error suppression bandwidth of the ne tracking control system signi cantly improved. Therefore, this method is applicable not only to various laser communication systems but also to optical dynamic target tracking systems based on PZT FSM. In the future, we will further improve the system model and simultaneously investigate other disturbances within the ne tracking control system, adopting effective methods to suppress them.  
Methodology, F.L. and Y.L.; Software, F.L. and Y.L.; Formal analysis, F.L. and Y.L.; Writing--Original draft, F.L.; Writing--Review and editing, F.L., Y.L., S.G., H.W. and F.G.; Supervision, S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 11973041, No. 12122304) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2019218).  
Not applicable.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no con ict of interest.
## 5 Simulation Experiment
### the Experiment Process
#### 5.2.2 Output Noise Case  
Figures 12 and 13 show the relationship of the Euclidean distance and correlation coefficient to signals to noise ratio (SNR), respectively. It can be seen from Figure 12 that even if SNR is small, the Euclidean distance can converge to the optimal value of 0. This indicates that the fitness function based on the Euclidean distance is more robust to output noise. Figure 13 shows that the optimization method based on correlation coefficient is more affected by noise. Tables 1 and 2 respectively show the optimization results of the two optimization methods under different SNR. The values of the estimated parameters and state indicate that the higher the SNR, the better the estimation performance. It can also be seen from Tables 1 and 2 that under the same SNR conditions, the optimization results of the method based on Euclidean distance is better than that based on correlation coefficient. This is consistent with the results shown in Figures 12 and 13.  
Figure 11: Correlation coefficient based \(h_{3}\) estimation error (noise free).  
Figure 9: Correlation coefficient based parameters estimation error (noise free).  
Figure 12: the relationship between the output Euclide distance and signal to noise ratio (SNR).  
Figure 13: the relationship between the correlation coefficient and signal to noise ratio (SNR).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Time Windows** & **Parameters and State** & **Real Value** & **SNR = 40** & **SNR = 20** & **SNR = 0** & **SNR = \(-\)20** \\ \hline T1 & p1 & 1.0 & 0.999087 & 0.999364 & 0.999813 & 0.999967 \\  & p2 & 1.0 & 0.999991 & 0.999723 & 0.999940 & 0.999252 \\  & p3 & 1.0 & 0.999701 & 0.999870 & 0.997682 & 0.999543 \\  & \(\times\)30\_1 & 60 & 59.966276 & 59.932078 & 59.99610 & 60.544748 \\ \hline T2 & p1 & 0.7 & 0.700820 & 0.700481 & 0.703425 & 0.697855 \\  & p2 & 0.5 & 0.500058 & 0.500025 & 0.500036 & 0.497927 \\  & p3 & 1.0 & 0.995941 & 0.997649 & 0.984109 & 0.996104 \\  & \(\times\)30\_2 & 16.9627 & 16.960300 & 16.967100 & 16.984000 & 16.990400 \\ \hline T3 & p1 & 0.4 & 0.399617 & 0.400500 & 0.398200 & 0.466186 \\  & p2 & 0.5 & 0.500000 & 0.498595 & 0.500644 & 0.489645 \\  & p3 & 0.5 & 0.502152 & 0.501541 & 0.503773 & 0.371251 \\  & \(\times\)30\_3 & 42.5734 & 42.591400 & 42.586000 & 42.664200 & 42.799100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Euclide distance based State Estimation with different SNR in different time window.
## 2 Materials and Methods
### Standardized Anomaly  
In order to capture drought stress signals, standardized meteorological variables such as P, PET, T\({}_{\text{max}}\), and VPD, along with vegetative photosynthesis variables such as SIF and GPP were employed. The standardized anomaly can be determined using the following calculation [37]:  
\[A_{i,j}=\frac{X_{i,j}-\overline{X_{j}}}{\sigma_{i}} \tag{10}\]  
where \(A_{i,j}\) represents the anomaly of \(X\) in pentad j of year \(i\). Here, \(\overline{X_{j}}\) signifies the average value of \(X\) in pentad \(j\) for the period 2001-2020. Furthermore, \(\sigma_{i}\) denotes the standard deviation of \(X\) in pentad \(j\) during 2001-2020.  
The response characteristics of vegetation photosynthesis capacity to flash droughts are usually characterized by response frequency and time [3; 8]. On a grid scale, the response frequency is expressed by dividing the number of flash droughts that exhibit negative SIF or GPP anomalies by the total count of flash droughts. A higher response frequency indicates a greater risk to the ecosystem. Response time is the time when the first negative SIF or GPP anomaly occurs during a flash drought. A shorter response time indicates a more rapid response by vegetation photosynthesis to flash droughts.
## 5 Discussion
### Sensor Data Collection  
Various IoT communication technologies like LoRaWAN, ZigBee, Wi-Fi and Bluetooth are used in the reviewed papers. The main conclusions are as follows: the short-range and large-data-rate communication technologies (ZigBee, Bluetooth, Wi-Fi), which were exploited in IoT communication, are now substituted by Low-Power Wireless Area Networks (LPWANs) (LTE-M, NB-IoT, SigFox, LoRaWAN). Indeed, LPWAN protocols allow for long-range communications with low data rates and low power consumption. It allows us to transmit signals using licensed or unlicensed spectra and satisfies IoT embedded system requirements. In particular, the LoRaWAN protocol was developed to minimize the network deployment complexity while increasing network coverage in orders of kilometresand represents the most exploited wireless protocol. Most of the reviewed IoT systems consist of a three-tier architecture: sensor/actuator-gateway-cloud. Data collected by the sensors is forwarded to the gateway, which is in charge of transferring it to the cloud through an internet connection. However, some prototypes do not use a gateway, and data is directly sent from the sensors to the cloud.  
As a final remark, it is worth noting that some authors did not use any wireless communication network but only relied on the use of sensors with manual data collection.
## 4 Results and Discussion
### Estimation of Best-Fit Distribution  
The best-fit probability distribution for each subsystem of the analyzed HVAC system was estimated using the Weibull++ software and the procedure described in Section 3.5. The results of the best-fit probability distributions for each subsystem are presented in Appendix A. It is observed that most of the subsystem follows an exponential distribution, indicating that the subsystems are experiencing failures at a consistent rate over time. This helps schedule preventive maintenance or replacement activities to minimize system downtime and associated costs. The HVAC subsystem does not experience wear-out or aging effects, and it suggests that failures are more likely to be caused by random events or external factors rather than internal wear and tear. Constant failure rates can reflect well-designed and high-quality HVAC systems. If failures occur consistently, it suggests  
Figure 11: Pareto analysis of A/C condenser front side.  
Figure 12: Pareto chart for all A/C louvers.  
that the system components are manufactured to a uniform standard and do not exhibit significant variations in reliability. Continuous monitoring of the HVAC system is still necessary to promptly identify any deviations from the expected failure rate and address them accordingly. HHC is the most critical component, with the lowest MTTF and median values of 1924 Km and 428 Km, respectively. AEFS, AERS, and ACC are the highly reliable components, with each subsystem having MTTF and median Time-To-Failure values greater than 30,000 Km. The subsystem's reliability and Cumulative Distribution Function (CDF) are shown in Figure 13 and Figure 14, respectively.  
are well-designed with minimum variation in reliability. It also shows that there is no indication of early failures during the warranty period. The HHC is the weakest failure mode with an expected life of 1924 km and a median life of 428 km, whereas the AEFS, AERS, and ACC are highly reliable components with a Mean Time-To-Failure (MTTF) and median life greater than 30,000 km.