esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 3 Design and Optimization of Double-Telecentric Optical System
### Double-Telecentric Optical Path Design and Optimization  
The initial structure of a double-telecentric system is obtained. After analyzing parameters such as telecentricity, MTF, and Airy disk radius, it is determined that the system fails to meet the requirements for high-precision detection of large-sized stepped shafts. Therefore, optimization of the system is necessary.  
After repeated optimization design of the system, the planning of various indicators is carried out. The system structural parameters are shown in Table 4, the system layout diagram is shown in Figure 5a, the system MTF is shown in Figure 5b, the system spot diagram is shown in Figure 5c, and the system distortion/field curve is shown in Figure 5d.  
According to Figure 5b, it can be concluded that at a spatial cutoff frequency of 71.4 lp/mm, the MTF of each field of view is higher than 0.6, meeting the design requirements. According to Figure 5c, the Airy disk radius is 3.204 \(\upmu\)m and the RMS radius is 2.660 \(\upmu\)m. The RMS radius is smaller than the Airy disk radius, indicating that the imaging quality of the dual-telecentric system meets the requirements. The field curvature reflects the degree of bending of the image plane, and the distortion affects the accuracy of imaging [23]. According to Figure 5d, the field curvature correction is within a range of 0.1 mm, meeting the design requirements. The system distortion is less than 0.1%, also meeting the design requirements.  
Figure 4: Image-space telecentric system (a) layout and (b) spot diagram.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Surface** & **Radius** & **Thickness** & **Material** &
\begin{tabular}{c} **Clear** \\ **Semi-Dia** \\ \end{tabular} \\ \hline
0 & OBJECT & Infinity & 90.000 & & 55.000 \\
1 & & Infinity & 0.000 & & 57.732 \\
2 & & 490.438 & 10.000 & H-ZLAF52A & 57.836 \\
3 & & \(-\)7741.174 & 0.242 & & 57.836 \\
4 & & 202.545 & 10.000 & H-ZLAF52A & 56.978 \\
5 & & 361.121 & 57.115 & & 55.927 \\
6 & & 96.900 & 19.729 & H-ZF88 & 44.434 \\
7 & & 117.148 & 67.678 & & 39.124 \\
8 & & 35.580 & 15.425 & H-ZLAF75A & 17.818 \\
9 & & 21.641 & 25.318 & & 6.785 \\
10 & STOP & Infinity & 23.171 & & 12.750 \\
11 & & \(-\)17.389 & 4.625 & H-ZLAF50D & 15.218 \\
12 & & \(-\)21.193 & 0.769 & & 15.218 \\
13 & & \(-\)45.474 & 5.719 & H-ZLAF68B & 18.485 \\
14 & & \(-\)36.297 & 0.206 & & 18.485 \\
15 & & \(-\)334.697 & 2.616 & H-ZLAF92 & 19.836 \\
16 & & \(-\)81.277 & 46.368 & & 19.836 \\
17 & & 108.794 & 10.000 & H-ZLAF76A & 23.180 \\
18 & & 963.628 & 51.019 & & 23.180 \\
19 & IMAGE & Infinity & & & 17.464 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structural parameters of double-telecentric system.  
Figure 5: Dual-telecentric system. (**a**) Layout diagram of dual-telecentric system; (**b**) MTF diagram of dual-telecentric system; (**c**) spot diagram of dual-telecentric system; (**d**) field curve and distortion curve of dual-telecentric system.
###### Abstract  
The tailwater tunnel of the Wuyue pumped storage power station is located in bedrock and extends to depths between tens and hundreds of meters. It is impossible to analyze and evaluate the whole engineering area from geological exploration data, and the hydrogeological conditions are complicated. In the early stages of the tailwater tunnel's construction, the drinking water wells in four villages dried up. This paper reports the results from a field investigation, in situ tests, laboratory tests, and numerical simulation carried out to determine how the groundwater was affected when the tunnel was excavated. A hydrogeological model of the region was established from the inverted regional natural flow field parameters. The model was validated, and an analysis of the errors showed that there was an average error of 1.98% between the natural flow field and the hydrogeological survey flow field. The model was then used to simulate the three-dimensional transient seepage fields under normal seepage conditions and limited seepage conditions, as far as was practical. The results showed that, as the excavation of the tailswater tunnel advanced, the water inflow to the tunnel also increased. When the water inflow increased from 1000 to 5000 m\({}^{3}\)/d, the water level at a distance of 100 m from the axis of the tunnel dropped from \(-0.956\) to \(-1.604\) m. We then analyzed how the water level changed as the water inflow varied and proposed a formula for calculating the extent of the influence on the groundwater. We studied how the water level changed at different well points to ascertain how a groundwater well became depleted and determined the factors that influenced seepage in the regional flow field. The water level in different areas of the project area was simulated and analyzed, and the extent of the groundwater area affected by the tunnel construction was clarified. We then studied how the groundwater in different areas of, and distances from, the project area was influenced by normal seepage conditions and limited seepage conditions and proposed a formula for calculating the extent of the influence on groundwater for different water inflows. We constructed a'smart site' for visualizing data, sharing information, and managing the project. Time-frequency domain analysis was applied to explore the extent of the impacts and range of the vibration effects on residential housing at different distances from the project area caused by the different methods for excavating the tailswater tunnel. The results from this analysis will provide useful insights into how the excavation of this tailwater tunnel will impact the local residents and living areas.  
pumped storage; Wuyue Reservoir; transient numerical simulation; parameter inversion; seepage field 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Vehicle Subsystems
#### 3.2.8 Propulsion  
The nine engines aboard Charon were arranged in a symmetrical octa-web configuration, and each engine's performance is such that five engines could supply enough thrust for nominal operations. This allows for a two-engines-out capability. This configuration can be seen in Figure 14.  
For a young colony positioned to be established within the next two decades, the infrastructure to extract, refine, and handle the consumable resources required for a launch vehicle is expected to be quite underdeveloped. Liquid oxygen and methane are producible relatively simply, and a LOX/LCH4 combination provides the best trade-off between performance, handling, size and structural weight of the propellant tanks, and experience within the field of rocketry.  
To maintain reliability over a ten year period, pre-burners were excluded from the design, and instead it was decided to drive the turbines using the methane as in an expander cycle.  
With a 200 bar chamber pressure and a mixture ratio of 3.6, the chamber temperature reaches 3400 K. To cool down the chamber walls and the nozzle, the liquid methane is pumped through channels within the skin from the injector down to the nozzle exit, meanwhile a layer of liquid methane is injected along the combustion chamber walls. This combination of regenerative and film cooling will need to be validated during hot-fire tests of the engine.  
All told, each engine can produce a thrust of 310 kN, at a specific impulse of 371 s, with a total exit area of 0.877 m\({}^{2}\) and an area ratio of 125. The high specific impulse is notable, and is thanks to the high chamber pressure; overall the lsp is justified given recent high lsp methalox expander designs by JAXA [38] and ESA [39].
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 2 System and Channel Model  
### Notation  
A number of notations are used throughout the paper, here we highlight the following notations and their corresponding meanings: \(f_{x}(\cdot)\) denotes the probability density function (PDF) of the random variable (RV) \(x\); \(F_{x}(\cdot)\) represents the cumulative distribution function (CDF) of the RV \(x\). The symbols \(P_{Tx,\ell}\) and \(P_{Rx,\ell}\) are the transmitted power and the received signal power by node \(R_{\ell}\), respectively. The RV \(z_{\ell}\) represents the noise of the \(\ell\)-th link modelled by MCA model. The symbol \(\mathcal{P}_{\ell}\) represents the average BER of the \(\ell\)-th hop transmission; and \(\mathcal{P}_{\ell}^{E}\) denotes the average BER after the \(\ell\)-th transmission compared with the bits transmitted by source node \(R_{1}\).
# A Parametric Study of Wind Pressure Distribution on Facades Using Computational Fluid Dynamics
## 6 Conclusions  
This paper presents a parametric study that examines changes in facade bracket length, facade fin length and wind orientation using CFD. The goal is to seek the optimum design configuration that minimizes wind pressures on the facade elements. The critical fin for design is determined to be the leading fin in all cases. In the case where wind acts normal to the critical fin, the major findings are:  
* The Venturi Effect tends to reduce the magnitude of pressures at the end of the fin nearest the building. The narrower the gap for airflow, the smaller the net pressure at this location.
* The more the flow is constricted towards the building, the greater the suction on the leeward side of the critical fin.
* Increasing the bracket length for a given fin length, achieves efficient distribution of load.
* Wind load is spread more evenly in cases where fin length is aerodynamically more efficient by utilizing the benefits of the Venturi Effect without the relative impact on continuity of the flow that occurs for less efficient fins.
* Reducing both fin length and bracket length tends to reduce moments.
* With these effects taken into account, the most efficient fin, as a combination of forces and moments has a length of 560 mm due to its ability to distribute stress through its aerodynamic efficiency, with a bracket of 80 mm recommended to minimize moments.
* The least efficient design is where the fins are directly attached to the building and with the longest fin length (800 mm).  
However, where wind direction exposes each of the fins equally to oncoming wind, average values for peak net wind pressures are reduced by a factor of up to 2.5. Therefore, an understanding of wind direction is significant for resultant loading.  
Figure 20: Peak net pressures for model numbers ‘1’, ‘11’, ‘14’ and ‘15’ versus distance from the top of the fin as a percentage of the total fin length.  
The findings represent useful information on the relative performance of outstand fin elements in extreme wind conditions. The conclusions also represent a set of design criteria recommendations relevant for this type of facade. For a given vertical height of the fin element, the data is sufficient to design the composition of the fin facade panel and robustness of the connections with the bracket and building.  
Conceptualization, J.K.; methodology, C.M. and J.K.; software, C.M.; validation, C.M.; formal analysis, C.M.; investigation, C.M.; writing--original draft preparation, C.M.; writing--review and editing, J.K.; visualization, C.M.; supervision, J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Operational Requirements
### Technical Specifications
#### 3.4.8 Interaction and Security  
The communication capability of a fruit-harvesting robot is critical to its efficient and safe operation. This feature allows operators to monitor and control the robot remotely, optimizing harvesting operations and ensuring a quick response to any necessary issues or adjustments [74]. The integration of Wi-Fi and Bluetooth modules allows a wireless connection between the robot and control devices, such as tablets or laptops. These technologies facilitate the transmission of data in real time and allow immediate adjustments in the robot's operation. For large-field operations, long-range communication technologies such as LoRa or 5G provide extended coverage and robust connection. These technologies allow the robot to be monitored and controlled from greater distances, ensuring that operations are not interrupted by range limitations [75].  
The ability to transmit data in real-time allows operators to see the robot's current status, including battery levels, location, and harvesting performance. This visibility helps to identify and fix problems quickly. Operators can send commands to the robot to adjust its route, speed, or harvesting method. This flexibility is crucial for adapting to changing conditions in the field or for addressing unforeseen situations [76]. An intuitive user interface is essential for effective setup and control of the fruit-harvesting robot. This interface should be user-friendly and provide access to all the features needed to operate the robot efficiently. Safety is a priority in the design of fruit-harvesting robots. Integrated safety sensors ensure that the robot operates safely, preventing damage to both the environment and the robot itself.  
An emergency stop system allows operators to stop the robot instantly in the event of imminent danger. This system can be activated manually or automatically through safety sensors. Continuous monitoring systems assess the condition of the robot and its surroundings, detecting any abnormal conditions that may pose a risk. Real-time alerts and diagnostics allow operators to take corrective actions quickly [77].
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 6 Intrusion Detection Overview
### Intrusion Detection and Prevention System Functionality  
As previously seen, there are two different main types of intrusion detection technology, including anomaly-based and signature-based. There are also other techniques such as statistic-based, knowledge-based, and machine-learning techniques. Hybrid systems have been observed to be the most effective. Moreover, in an application, the technology should provide a suitable notice to the security personnel (for example, a Security Analyst) to make them aware of the intrusive situation--typically by sending an alert on the interface of the machine. Next, reports should be generated which provide a summary of the events that have been highlighted during the detection process. Furthermore, many of these technologies can adapt their security characteristics when a new threat is posed. Using intrusion detection technology--they have the ability to simulate an attack automatically. They can also block access to the host, services, and other relevant network resources. Lastly, they can perform changes to the security background, disturbing an incoming cyber-attack. This can include a reconfiguration for network devices such as routers, switches, and firewalls which can defend against a cyber-attack.
## 2 Material, Experiment, and Measurement
\begin{split} E_{evapn}&=m_{s_{n}}\{c_{s_{n}}\cdot(T _{melt_{n}}-T_{0})+L_{melt_{n}}\}+m_{ll_{n}}\{c_{l_{n}}\cdot(T_{evap_{n}}-T_{ melt_{n}})+L_{evap_{n}}\}\\ &=\rho_{s_{n}}V_{melt_{n}}\{c_{s_{n}}\cdot(T_{melt_{n}}-T_{0})+ L_{melt_{n}}\}\\ &\quad+\rho_{l_{n}}V_{evap_{n}}\{c_{l_{n}}\cdot(T_{evap_{n}}-T_{ melt_{n}})+L_{evap_{n}}\},(n=\text{CC and AE})\end{split}  \tag{3}\]  
where \(\mathbf{m}\) is the mass, \(\mathbf{c}\) is the specific heat, \(\mathbf{\rho}\) is the density, \(T_{melt}\) is the melting temperature, \(T_{evap}\) is the evaporation temperature, \(T_{0}\) is the ambient temperature, \(L_{melt}\) is the latent heat of melting, and \(L_{evap}\) is the latent heat of evaporation. In addition, subscription s and \(l\) indicates solid and liquid, respectively. Subscription \(n\) can be a current collector (CC) or active electrode materials (AE). The total energy (E) used to remove a cathode is obtained as follows:  
\[\mathbf{E}=\mathbf{E_{evapAE}}+\mathbf{E_{evapCC}} \tag{4}\] \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**Cathode Information**} \\ \hline Structure & One-side sandwich \\ Active electrode Material & LiFePO4 \\ Active electrode Thickness (Uncompressed) & 70 \(\upmu\)m \\ Active electrode Thickness (Compressed) & 70 \(\upmu\)m \(\rightarrow\) 56 \(\upmu\)m \\ Binder & Polyvinylidene fluoride \\ Conducting agent & SuperC65 \\ Solution & \(N\)-Menthy-2 pyrrolidone \\ Current Collector Material & Aluminum \\ Current Collector Thickness & 20 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material information of cathode. If we rewrite the equation with measurable kerfs, then it can be written as  
\[ \begin{split} E&=\rho_{sAx}I_{g}d_{AE}W_{top}\big{\{} \epsilon_{sAx}\cdot\big{(}T_{melt_{AE}}-T_{0}\big{)}+L_{melt_{AE}}\big{\}}\\ +&\rho_{Ltx}I_{g}d_{AE}W_{top}\big{\{}\epsilon_{tAx} \cdot\big{(}T_{evap_{AE}}-T_{melt_{AE}}\big{)}+L_{evap_{AE}}\big{\}}\\ +&\rho_{scc}I_{g}d_{AE}W_{kerf}\big{\{}\epsilon_{scc} \cdot\big{(}T_{melt_{CC}}-T_{0}\big{)}+L_{melt_{CC}}\big{\}}\\ +&\rho_{lcc}I_{g}d_{AE}W_{top}\big{\{}\epsilon_{tcc} \cdot\big{(}T_{evap_{CC}}-T_{melt_{CC}}\big{)}+L_{evap_{CC}}\big{\}}\end{split}  \tag{5}\]  
Since material properties of LiFePO\({}_{4}\) are not fully available, those were derived by the law of fraction and the material properties are shown in Table 3. The material properties of aluminum can be found from [PERSON]'s paper [12].
## 5 Conclusions and Recommendations
### Recommendations  
These results should be treated with caution as there are no full-scale laboratory results that can be used to validate the results of the numerical model. Therefore, it is recommended to verify the consistency of these numerical results using a much finer mesh and, thus, perhaps improve the accuracy and stability of the numerical simulations. When objects in movement are included in the simulation, it is important to consider the parameters of inner and outer distances. Although the torque values were included in the numerical results, the effect of the hydrodynamic forces needs to be considered, as it has a direct relation with the ratio of the arm and radii of the buoy.  
When modeling the buoy as a breakwater, an important parameter is the relative length of the buoy, which determines the amount of wave energy reflected. However, since the buoy has a degree of freedom, the change in the relative length could be misleading; a suitable alternative methodology has been described [56], in which the individual parameters are closely related to each other [57].  
Finally, related to onshore sediment transport, further analyses are needed to understand these effects under spectral wave fields and with larger buoy configurations, which are beyond the scope of this research.  
Individual contributions. Conceptualization, I.M.-T., R.P., R.S. and C.S.; Methodology, I.M.-T. and C.S.; Software, C.S.; Formal Analysis, C.S., I.M.-T. and R.P.; Investigation, C.S.; Resources, C.S., R.S., I.M.-T. and R.P.; Data Curation, C.S. and I.M.-T.; Writing--Original Draft Preparation, C.S.; Writing--Review and Editing, C.S., I.M.-T., R.S. and R.P.; Visualization, I.M.-T., C.S. and R.P.; Supervision, I.M.-T. and R.P.; Project Administration, R.S. and I.M.-T.; Funding Acquisition, I.M.-T. and R.S. All authors have read and agreed to the published version of the manuscript.  
[PERSON] received a scholarship from CONACYT-Mexico for his Ph.D. studies; this work was funded through the CONACYT-SENER-Sustentabilidad Energetica project: \"Centro Mexicano de Innovacion en Energia del Oceano (CEMIIE Oceano),\" FSE-2014-06-249795.  
The data obtained in this study are openly available in https://cinvestav365 -mysharepoint.com/personal/carlos_sosa_cinvestav_mx/_ layouts/15/onedrive.aspx?id=%2 Fpersonal%2 Fcarlos%5 Fosa%5 Fcinvestav%5 Frmx%2 FDocuments%2 Fsimulation%5 FDData%2 DWEC&ga=1.  
The authors declare no conflict of interest.
# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 3 System Model  
In a 5G cellular network, the devices communicate with a base station of 5G core (5G), called the gNB, or a D2D link. Under the 3 GPP, the base station of 5G is called the gNB, and the core network is called 5 GC [31; 32]. In Figure 1, DUE1, DUE2, DUE3, and DUE4 are on the cellular network of gNB1, and DUE5 and DUE6 are on the cellular network of gNB2. In addition, DUE5 and DUE6 communicate through a direct link. Here, the cellular link communicating with the gNB on the uplink system is affected by traffic-flow interference and congestion from D2D users sharing resources, whereas the D2D link is subject to resource interference and congestion from cellular users sharing the same resource block and other D2D users. Figure 1 shows the procedure of traffic flow interference and congestion in the process of sharing resource blocks between the cellular link and the D2D link on gNB1 and gNB2. When DUE3 of the D2D link shares resources with DUE1, it is subject to interference from the cellular link. However, since cellular users DUE1 and DUE4, and D2D users DUE5 and DUE6, are under different gNB channels, they are not affected by interference due to resource sharing.  
If they sustain a sufficient distance on gNB1, they are relatively less affected by interference, but if DUE3 and DUE1 do not sustain a sufficient distance, DUE3 is subject to severe traffic flow congestion, owing to resource interference from DUE1.  
Therefore, the communication service of the 5G cellular network is optimized by preferentially allocating link resources to each device with high control weight under the gNB.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.7 Mathews Correlation Coefficient  
The Mathews MCC correlation coefficient also comes from the categories or classes of the confusion matrix. Given that it is a particular case of the Pearson correlation coefficient, it allows quantifying the existing correlation between the real classes (TP and TN) and the classes predicted (FP and FN).  
\[\text{MCC}=\frac{(\text{TP}\cdot\text{TN})-(\text{FP}\cdot\text{FN})}{\sqrt{( \text{TP}+\text{FP})\cdot(\text{TP}+\text{FN})\cdot(\text{TN}+\text{FP})\cdot( \text{TN}+\text{FN})}}. \tag{6}\]  
The value of the Mathews correlation coefficient, according to Equation (6), is within the range \([-1,1]\), where \(-1\) indicates a complete misclassification, while \(+1\) indicates a perfect classification. A null value refers to a random prediction. MCC is especially interesting because it takes a high value only if the prediction performed well in all categories of the confusion matrix (TP, TN, FP and FN) in proportion to both the size of the positive samples and the size of the negative samples of the _dataset_ in question [57]. It is also very helpful in scenarios with unbalanced classes. Contrary to other metrics, such as _accuracy_, _precision_, _recall_ or F1-score, MCC recognizes the inadequacy of the model with respect to predicting instances, reflecting its real predictive power correctly.