esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimized Parameters
#### 3.3.3 Optimization of the Solar Cell Incorporating Graphene as TCO  
After the HIT solar cell optimization for the standard and the Atacama spectra, the effect of replacing ITO with graphene is computed, with the aim of optimizing the number of graphene layers for each spectrum. Thus, the modeling is performed using the cell's parameters of Table 5. In this work, graphene is considered to have a 3-dimensional nature, and each layer has a thickness of a 0.34 nm. Graphene's optical properties, such as refraction index and extinction coefficient, are shown in Figure 3. The series resistance (Rs) for graphene is calculated using Equation (7) and the sheet resistance values shown in Figure 4. The obtained series resistance as a function of graphene layers is presented in Figure 4 (see the Appendix A). In this simulation, we also consider the resistance in series and the resistance in parallel, and we make it tend to infinity.  
Figure 5 shows the electrical properties of the solar cell simulated with graphene instead of ITO. The open-circuit voltage curves for the AM1.5 standard spectrum and the Atacama Desert solar spectrum are similar, decreasing as the number of graphene layers increases. The short-circuit current density decreases as the number of graphene layers increases, reaching the maximum value when there is only one layer, as shown in Figure 5. Conversely, the fill factor increases as the number of graphene layers increases. These behaviours of \(I_{\rm SC}\) and FF are expected, since graphene's transmittance decreases with the number of layers while its resistance increases. In all, the energy conversion efficiency reaches the highest value for three graphene layers, and 18.8% and 20.1% for the standard and the Atacama spectrum, respectively. These obtained efficiencies with graphene are \(\sim\)10% lower than those computed with ITO. To understand this result, we calculated the optical properties and the external quantum efficiency of the HIT cell with both ITO and graphene.  
Figure 7: The blue line is the J-V curve of AM1.5/op. The red line is the Ata/op J-V curve values.
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 3 System Design
### Sequence Diagram  
In our system, two methods were used for collecting data between machines. First, when the instrument generated a new file and stored it in the PC, the data watcher service charged the PC for sending the latest data to the site server. A secure shell (SSH) port was used for the PC (having the installed Unix OS) to synchronize the data by Rsync, and FTP for the PC, which had Windows OS.  
Secondly, a data watcher was implemented to observe the root directory of data in the site server based on instrument type. Thus, this watcher would force the server to upload the latest file into the IPFS network each time it is created. Afterward, the server, like an IPFS node, would store the data as an object in the local repository and put the metadata of the object in the distributed hash table (DHT) (Figure 6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Observatories**} & \multirow{2}{*}{\begin{tabular}{c} **Total Amount of** \\ **Daily Data (MB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Bandwidth** \\ **(Mbps)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Internet** \\ **Connection** \\ **Link Type** \\ \end{tabular} } & \multicolumn{2}{c}{**Frequency of Server Down**} \\ \cline{5-7}  & & & & **Network Offline** & **Power** & **OS** \\ \cline{5-7}  & & & & **(Fiber OpticCut)** & **Outages** & **Failures** \\ \hline \hline \begin{tabular}{c} Garut \\ (Java Island) \\ \end{tabular} & 21 & 5 & Fiber Optic & 0 & 0 & 0 \\ \hline \begin{tabular}{c} Pasuruan \\ (Java Island) \\ \end{tabular} & 39 & 7 & Fiber Optic & 4 & 0 & 0 \\ \hline \begin{tabular}{c} Biak \\ (Papua Island) \\ \end{tabular} & 46 & 5 & Fiber Optic & 1 & 30 & 0 \\ \hline \begin{tabular}{c} Agam \\ (Sumatra Island) \\ \end{tabular} & 76 & 4 & Wireless & 2 & 0 & 0 \\ \hline \begin{tabular}{c} Kupang \\ (Nusa Tengsara Island) \\ \end{tabular} & 106 & 5 & Wireless & No data & No data & No data \\ \hline \begin{tabular}{c} Pontianak \\ (Borneo Island) \\ \end{tabular} & 110 & 7 & Fiber Optic & 3 & 4 & 0 \\ \hline \
\begin{tabular}{c} Manado \\ (Sulawesi Island) \\ \end{tabular} & 151 & 1 & Fiber Optic & No data & No data & No data \\ \hline
\begin{tabular}{c} Sumedang \\ (Java Island) \\ \end{tabular} & 175 & 4 & Fiber Optic & 4 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Observatories status in Q1 of 2019.
## 6 Conclusions  
In this paper a space robot with a RSEA device to protect the joint of the robot under impact torque during the satellite capture process is designed. The timely opening and closing of joint actuators was proposed to achieve buffer compliance control. The dynamic model of the post-capture hybrid system was derived from the Lagrange equations, law of conservation of momentum and the constraints of kinematics and velocity. Then, based on singular perturbation theory, the hybrid system was decomposed into a slow subsystem and a fast subsystem. A buffer compliance control based on a reinforcement learning algorithm was applied to control the slow subsystem with unknown unknown nonlinear disturbances term. The fast control was designed with speed difference feedback controller. The simulation results show that the proposed strategy can reduce the impact torque by 76.6% at the maximum and 58.7% at the minimum during the capture phase, which reflects a good anti-impact performance. In the stable control phase, the impact torque acting on the joint is guaranteed to be limited within the safety threshold, so as to avoid the overload and damage of the joint actuators. In addition, the proposed reinforcement learning strategy has strong online adaptability and autonomous learning ability under complex conditions and can be continuously optimized through real-time interaction with the complex space environment, so as to ensure the accuracy and stability of the system stabilization motion.  
Note that this paper only considers that space manipulators mounted on their spacecraft are rigid. For future research, the buffer compliance control of space robot with a flexible-link capturing a non-cooperative spacecraft control problem will be studied, and the control scheme extended to practical applications.  
Conceptualization, H.A.; methodology, H.A. and X.Y.; software A.Z. and J.W.; investigation, H.A. and A.Z.; writing original-draft preparation, H.A.; writing--review and editing, H.A. and X.Y.; supervision, H.A. and L.C.; funding acquisition, H.A. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 51741502, 11372073), Science and Technology Project of the Education Department of Jiangxi Province (Grant No. GJJ200864), Jiangxi University of Science and Technology PhD Research Initiation Fund (Grant No. 205200100514).  
Informed Consent StatementInformed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.  
Figure 13: Trajectory of the joint angle 2 without a fast controller.
## 3 Sliding Surface and Control Design
### Optimal Control-Based Sliding Surface Design  
In what follows, the \(x_{2}\) is regarded as the control input of subsystem (3) and assumed that it is admissible. For the continuously differentiable cost function \(J(x_{1})\), we can obtain the nonlinear Lyapunov equation is  
\[0 = \mathcal{Q}(x_{1})+U(x_{1},x_{2})\] \[+(\
abla J(x_{1}))^{T}(f_{1}(x_{1})+g_{1}(x_{1})x_{2}),\]  
where \(J(0)=0\). The Hamiltonian function of this optimal control problem is  
\[H(x_{1},x_{2},\
abla J(x_{1})) = \mathcal{Q}(x_{1})+U(x_{1},x_{2}) \tag{8}\] \[+(\
abla J(x_{1}))^{T}(f_{1}(x_{1})\] \[+g_{1}(x_{1})x_{2}).\]  
Define the optimal cost function (OCF) as  
\[J^{*}(x_{1}(t))=\min_{x_{2}\in\mathcal{A}(\Omega)}\int_{t}^{\infty}\mathcal{Q }(x_{1}(\tau))+U(x_{1}(\tau),x_{2}(\tau))d\tau, \tag{9}\]  
where \(\mathcal{A}(\Omega)\) is the admissible control set on the \(\Omega\), and the HJB equation of this system is given as  
\[0=\min_{x_{2}\in\mathcal{A}(\Omega)}H(x_{1},x_{2},\
abla J^{*}(x_{1})). \tag{10}\]  
Therefore, by differentiating (10) with respect to \(x_{2}^{*}\), the optimal control policy is  
\[x_{2}^{*}=-\frac{1}{2}R^{-1}g_{1}^{T}(x_{1})\
abla J^{*}(x_{1}). \tag{11}\]  
The control policy (11) is unbounded, and if the problem of input constraints is to be studied, an alternative approach is to use a generalized non-quadratic utility function [28].  
Substitute the optimal control (11) into the Equation (10) and then the HJB equation can be expressed as  
\[0 = \mathcal{Q}(x_{1})+U(x_{1},x_{2}^{*})+(\
abla J^{*}(x_{1}))^{T}(f_{1 }(x_{1})+g_{1}(x_{1})x_{2}^{*}) \tag{12}\] \[= \mathcal{Q}(x_{1})+x_{1}^{T}Qx_{1}+(\
abla J^{*}(x_{1}))^{T}f_{1 }(x_{1})\] \[-\frac{1}{4}(\
abla J^{*}(x_{1}))^{T}g_{1}(x_{1})R^{-1}g_{1}^{T}( x_{1})\
abla J^{*}(x_{1}),\]  
which means that \(H(x_{1},x_{2}^{*},\
abla J^{*}(x_{1}))=0\).  
The design of \(\mathcal{Q}(x_{1})\) is a key point in the robust stabilization scheme. In what follows, the term \(\mathcal{Q}(x_{1})\) is specified as  
\[\mathcal{Q}(x_{1})=\frac{1}{8}(\
abla J^{*}(x_{1}))^{T}\
abla J^{*}(x_{1})+2 \lambda_{f}^{2}(x_{1}), \tag{13}\]  
and \(\mathcal{Q}(x_{1})\geq 0\) meets. Based on the help of this form, the following lemma showing stability of the sliding-mode dynamics of the systems (1) and (2) is derived as follows.  
**Lemma 1**.: _Consider the nominal subsystem (3) and the cost function (5) with the term (13). It is assumed the HJB Equation (12) is of a solution \(J^{*}(x_{1})\), \(x_{2}^{*}\) is given by (11) and the SS designed as \(s=x_{2}-x_{2}^{*}=0\) exists. According to these conditions, on this SS, the sliding-mode dynamics of this mismatched uncertain nonlinear system described by (1) and (2) is asymptotic stability._  
Proof of Lemma 1.: According to the Theorem 1 of [30], we know that the optimal control policy (11) can ensure asymptotic stability of the uncertain nonlinear subsystem (1). On the SS, there is \(x_{2}=x_{2}^{*}(x_{1})\) and explicitly the state \(x_{2}\) is also asymptotically convergent to the equilibrium point.
## 2 Thermal Stability of the Chromatic Confocal Measurement Setup
### Principle of the Femtosecond Laser Chromatic Confocal Probe
Therefore, by obtaining the thermal stability of the detection of focused wavelength (\(d\lambda/dT\)) in experiments, \(dZ/dT\) can be calculated from the following equation:  
Figure 2.3  
To obtain the thermal stability of the detection of focused wavelength (\(d\lambda/dT\)), experiments were carried out by using the setup shown in Figure 2.4 A flat mirror, which was employed as the target sample, was held stationary in the setup while a variation of the focused wavelength \(\lambda_{\text{focused}}\) was observed at each 100 s over a period of 5000 s. As can be seen in Figure 2b, a temperature sensor was placed in the optical setup to monitor the temperature deviation. Figure 6 shows the result of the thermal stability of the detection of focused wavelength \(\lambda_{\text{focused}}\) contributed by not only the thermal stability of the chromatic confocal probe itself but also the thermal stability of the sample unit and the mounting plate. It should be noted that experiments were carried out in a laboratory room whose temperature was controlled to be \(20\ ^{\circ}\text{C}\pm 0.5\ ^{\circ}\text{C}\), while the temperature periodically deviated in a few hours. As can be seen in the figure, a clear correlation can be found between the variation of \(\lambda_{\text{focused}}\) and the change in temperature. Three repetitive experiments were carried out, and a mean value of \(d\lambda/dT\) was evaluated to be \(-20.4\ \text{nm}/^{\circ}\text{C}\). Therefore, from Equation (7), the thermal stability of the overall measurement setup for axial displacement measurement (\(dZ/dT\)) was evaluated as \(dZ/dT\) = (\(-20.4\ \text{nm}/^{\circ}\text{C}\))-(\(-255\ \text{nm}/\text{nm}\)) = \(5.20\ \text{nm}/^{\circ}\text{C}\). Figure 2.5 The thermal stability of the focused wavelength \(\lambda_{\text{focused}}\) of the overall measurement setup evaluated in the experiment by the setup shown in Figure 2.6  
This result means that a temperature variation in the laboratory room ranging from 19.5 \({}^{\circ}\)C to 20.5 \({}^{\circ}\)C could result in a deviation of the detected axial displacement of \(\pm\)2.60 \(\upmu\)m in the overall chromatic confocal measurement setup. Regarding long-term displacement measurement or surface profile measurement where long-time scanning is required, the thermal stability of the developed chromatic confocal probe needs to be improved. A detailed investigation is therefore carried out in the following section to separate and characterize the thermal stability of the femtosecond laser chromatic confocal probe based on the measurement result of the overall measurement setup in Figure 2.7
## 3 Results and Discussion
### Leaching Tests
#### 3.2.2 pH dependence Leaching Behavior
\begin{table}
\begin{tabular}{c} \hline \hline \multicolumn{1}{c}{**Main Species**} \\ \hline MoO\({}_{4}\)\({}^{2-}\); Sb(OH)\({}_{3}\); Sb(OH)\({}^{2+}\); Pb\({}^{2+}\); Cd\({}^{2+}\); AsO\({}_{4}\)\({}^{3-}\);HSeO\({}_{3}\)\({}^{1-}\); SeO\({}_{3}\)\({}^{2-}\); \(\text{SeO}_{4}\)\({}^{2-}\); HSeO\({}_{4}\)\({}^{1-}\); Zn\({}^{2+}\) \\ \hline \multicolumn{1}{c}{**Elements**} \\ \hline Mo, Sb, Pb, Cd, As, Se, Zn \\ \hline \multicolumn{1}{c}{**Mineral Phases \({}^{*}\)**} \\ \hline Powellite (CaMoO\({}_{4}\)); Senarmonite (Sb\({}_{2}\)O\({}_{3}\)); Valentinite (Sb\({}_{2}\)O\({}_{3}\)); Antimony (VI) Oxide (Sb\({}_{2}\)O\({}_{4}\)); \\ Lead(II) hydroxide (Pb\({}_{2}\)(OH)\({}_{2}\)); Glavite (CdCO\({}_{3}\)); Arsenic(V) oxide (As\({}_{2}\)O\({}_{5}\)); Arsenates \\ CO\({}_{3}\)(As\({}_{2}\)O\({}_{4}\))\({}_{2}\); Metal Arsenates (Ca\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\);4H\({}_{2}\)O, Pb\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\); Zn\({}_{3}\)AsO\({}_{4}\);5H\({}_{2}\)O); Zincite (ZnO); \\ Selenium(IV) oxide (SeO\({}_{2}\)); Selenium(VI) oxide (SeO\({}_{3}\)). \\ \hline \multicolumn{1}{c}{**Mineral Adsorption**} \\ \hline Diffuse layer Surface complexation model of Fe- and Al-/hydro) oxides Surface (HFO) mineral adsorption \\ \hline \end{tabular} \({}^{*}\) Mineral phase names or formulas as they are entered into Visual Minteq Software are underlined.  
\end{table} 
Table 3: Input parameters of the geochemical modeling. Figure 6: Measured and predicted leaching behavior of the selected trace elements as a function of pH (on the left); chemical speciation modeling of the selected trace elements.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 3 Classification of Phishing Detection Strategies
### RL-Based Systems
#### 3.4.1 Mathematical Model: Q-Learning for Phishing Detection  
The most widely used RL algorithm in phishing detection is Q-Learning, a value-based method that estimates the optimal action-selection policy using a Q-function.
## 2 Experimental Methods
### Fatigue S-N Curve
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**Loading Stress/MPa**} & \multicolumn{2}{c}{**Stress Amplitude/MPa**} & \multicolumn{2}{c}{**Fatigue Life/Time**} \\ \cline{2-7}  & \(\sigma_{max}\) & \(\sigma_{min}\) & \(\sigma_{m}\) & \(R\) & \(\Delta\sigma\) & \(N\) \\ \hline M24-22 & 575 & 345 & 460 & 0.6 & 230 & 282,422 \\ M24-23 & 575 & 345 & 460 & 0.6 & 230 & 282,422 \\ M24-24 & 600 & 360 & 480 & 0.6 & 240 & 195,186 \\ M24-25 & 600 & 360 & 480 & 0.6 & 240 & 214,782 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Constant amplitude fatigue test results of M24 torsional shear-type high-strength bolt with pretension (\(\sigma_{max}=575\)–600 MP). by calculating the standard deviation of fatigue test data, we can acquire the S-N curve equation under 97.72% survival probability, as shown in Equation (8). \[\text{lg}N=32.365-11.419\ \text{lg}\Delta\sigma-2\times 0.141 \tag{8}\]  
The Design Method for Constant Amplitude Fatigue Based on the Nominal Stress 4.1. The Establishment of the Formula  
The nominal stress method takes the nominal stress of the parts as the parameter. Through a lot of fatigue tests, the fatigue S-N curves of different structural details are established, so as to design the fatigue strength or fatigue life of the structure. The high cycle fatigue problem mainly involves this method. At present, the nominal stress amplitude method is still the representative fatigue design and checking method in the steel structure design specifications of various countries. Therefore, we used the nominal stress amplitude as the control parameter, establishing the allowable stress amplitude fatigue design method of M24 twisted-shear high-strength bolts under the condition of full pretension. The method can be applied in practical engineering and check the fatigue strength of M24 twisted-shear high-strength bolts. The calculating formula with the nominal stress amplitude as the design parameter is as follows:  
\[\Delta\sigma\leq[\Delta\sigma] \tag{9}\]  
\[[\Delta\sigma]=\left(\frac{C}{N}\right)^{1/\beta} \tag{10}\]  
Figure 8: The power regression S-N curve with the stress range as parameter. Figure 9: S-N curve of M24 high-strength bolts obtained from the tests.
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 3 Quantitative Evaluation  
The papers were evaluated based on inclusion, exclusion, and quality criteria, which led to the selection of 39 publications out of the 75 found during the search process. The systematic literature review process is illustrated in Figure 1. Initially, 75 articles were identified from Google Scholar, with selections made based on the relevance of their titles and abstracts. After the exclusion criteria were applied, 36 articles were removed, resulting in 39 articles being retained for in-depth analysis.  
Table 1 provides an overview of the works that were rejected based on specific exclusion criteria. Several papers were excluded outright due to restricted access (EC1). Additionally, works focusing on fire or chemical safety in production environments were deemed incompatible with the objectives of the research (EC2). Other works that were rejected on the same basis are those related to predicting human movement and body part trajectories within the context of stationary robots. Criterion EC3 addressed the issue of combining the keywords \"Trajectory Data\" and \"Warehouse\", which inadvertently referred to a type of database known as a Data Warehouse. Although trajectory data falls within  
Figure 1: A Systematic Literature Review work flow.  
the scope of the research, publications that solely focused on the predetermined storage method were consequently rejected. Furthermore, criterion EC4 pertained to predictions of human trajectory and body part movement within the context of stationary robots. This did not align with the research's purpose and thus resulted in rejection. Criterion EC5 was instrumental in the rejection process. It functioned to exclude papers that were explicitly unrelated to warehouse settings. Examples of such unrelated topics included predicting trajectories of ships or aircraft. Another example was the analysis of basketball player movement. Finally, criterion EC6 played a role in removing certain publications. These were publications where indoor localization systems primarily relied on visual modules. This approach conflicted with the research objectives, which were focused on using UWB technology for localization. Alongside this, maintaining a cost-effective solution was another key objective.
## Results
### Robust Feature Selection Method
Moreover, the newly derived robust features with the same settings and classifier enhanced the model's performance by reducing the computation time. The proposed framework combines the efficiency of DNNs for extracting distinctive feature and the powerfulness of the \(l2\),\(1\)-norm for selecting robust features. The \(l2\),\(1\)-norm is well known for its ability to deal with the outliers in images. Since the unconstrained image database contains various outlier images, the \(l2\),\(1\)-norm based sigmoid cost function enables our model to focus on finding robust age-related features and reducing the negative effects of the outliers. The contribution of \(l2\),\(1\)-norm for selecting robust features can be controlled by tuning the \(\lambda\) parameter. Figure 4 shows the impact of choosing different values for \(\lambda\) on the classification accuracy.
## 4 Numerical Examples  
Wireless base stations provide the interface between mobile phone users and the conventional telephone network. It can take hundreds of wireless base stations to provide adequate coverage for users within a moderately sized metropolitan area. Controlling the cost of an individual base station is therefore an important objective. On the other hand, the availability of a base station is also an important consideration since wireless users expect the system availability to be comparable to the high availability they experience with the conventional telephone network. The software in this numerical example runs on an element within a wireless network switching center. Its main function includes routing voice channels and signaling messages to relevant radio resources and processing entities [35]. Dataset #1, field failure data for Release 1 listed in Table 2, was reported by [PERSON] and [PERSON] [35]. Release 1 included Year 2000 compatibility modifications, an operating system upgrade, and some new features pertaining to the signaling message processing. Release 1 had a life cycle of 13 months in the field. The cumulative field exposure time of the software was 167,900 system days, and a total of 115 failures were observed in the field. Table 2 shows the field failure data for Release 1 for each of the 13 months. Software failure data is available from the field for Release 1. Dataset #2, test data for Release 2 listed in Table 3, was also reported by [PERSON] and [PERSON] [35]. The test data is the set of failures that were observed during a combination of feature testing and load testing. The test interval that was used in this analysis was a 36-week period between. At times, as many as 11 different base station controller frame (BCF) frames were being used in parallel to test the software. Thus, to obtain an overall number of days spent testing the software we aggregated the number of days spent testing the software on each frame. The 36 weeks of Release 2 testing accumulates 1001 days of exposure time. Dataset #2 also show the cumulative software failures and the cumulative exposure time for the software on a weekly basis during the test interval. Tables 4 and 5 summarize the results of the estimated parameters of all 11 models in Table 1 using the least-squares estimation (LSE) technique and the values of the five common criteria (MSE, SAE, PRR, PP and AIC).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month Index** & **System Days (Days)** & **System Days (Cumulative)** & **Failures** & **Cumulative Failures** \\ \hline
1 & 961 & 961 & 7 & 7 \\
2 & 4170 & 5131 & 3 & 10 \\
3 & 8789 & 13,920 & 14 & 24 \\
4 & 11,858 & 25,778 & 8 & 32 \\
5 & 13,110 & 38,888 & 11 & 43 \\
6 & 14,198 & 53,086 & 8 & 51 \\
7 & 14,265 & 67,351 & 7 & 58 \\
8 & 15,175 & 82,526 & 19 & 77 \\
9 & 15,376 & 97,902 & 17 & 94 \\
10 & 15,704 & 113,606 & 6 & 100 \\
11 & 18,182 & 131,788 & 11 & 111 \\
12 & 17,760 & 149,548 & 4 & 115 \\
13 & 18,352 & 167,900 & 0 & 115 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Field failure data for Release 1—Dataset #1.