esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 5 AI and Different Applications in Ophthalmology and Other Fields
### Precedents
#### 5.1.5 Supplementary AI Applications in Eye Healthcare  
The retina provides significant data on pathological conditions of the eye, and also serves as an interface to the rest of the body. Retinal imaging provides an abundance of data to assess cardiovascular (CV) health, anemia, and systemic disorders of the central nervous system, such as, for example, multiple sclerosis, Alzheimer's disease, and Parkinson's disease. These methods will act as the foundation of non-invasive and inexpensive techniques for screening, diagnosis, and monitoring of certain systemic conditions.
## 4 Experiments and Evaluation
### Hypothetical Scenario
#### 4.2.2 Analysis of Miswiring Identification Procedure  
In the case of the hypothetical scenario in the example of the meter H being a submeter of meter A, this procedure was applied to discover if any of A's submeters belonged to another subsystem. By querying the Brick model for all of A's submeters, it was discovered that the meter H contained information indicating its belonging to the ventilation system. Thus, the source of the underperformance was detected.
# Stability Control of Multilayer Roof Strata in the Large Mining Height Gob-Side Entry: A Case Study
## 3 Roof Separation Analysis of Bolted Joint Rock Mass
### Numerical Analysis of Roof Separation in the Gob-Side Entry  
#### 3.2.1 Numerical Model and Boundary Conditions  
To further investigate the failure mechanisms of the compound roof in the large mining height gob-side entry, a numerical model for stability analysis was established using Universal Distinct Element Code (UDEC) by Itasca Consulting Group Inc. (Minneapolis, MN, USA) [29]. Compared with the finite difference code, e.g., FLAC, UDEC allows to effectively represent the macroscopic mechanical responses of roof strata movement [18]. It is appropriate and efficient to address the non-continuous processes of roof strata separation and strata fracturing during coal mining, especially for compound multilayer strata [10,30].  
The simulation process is briefly described as follows:  
1. The initial stress calculated from the UDEC function was used to generate the overlying stress, which reproduces the in-situ stress state of rock strata before roadway excavation.
2. After the balance of the initial stress field is reached, the excavation process is simulated and the support scheme is applied in the roadway.
3. The movement and deformation characteristics of overlying strata in the mining process are simulated. The length of excavation is 10 m in each time step, and the total length is 300 m.
4. At different advancing distances away from the working face, the stress evolution, strata movement and caving characteristics of the overlying strata are analyzed.  
It should be noted that the focus of this paper is not the solution of numerical simulation, the simulation details with respect to developing mathematical relations; solving partial differential equations are neglected in the paper.  
The numerical model is presented in Figure 7. The dimensions of the model are 106 m in length and 42.1 m in height, simulating a 120-m-long 22301 working face. The model is divided into seven rock layers by the stratificational information with specific strata thickness. The width of the gob-side backfill is 2.2 m, with a 4.5-m-wide reserved head entry and a 20-m-wide coal pillar. The boundary conditions for the model are set as follows: the top boundary is a stress boundary, the bottom is fixed, the left and right boundaries are fixed horizontally.  
Figure 6: Movement of compound roof structure in the large mining height gob-side entry.  
The Mohr-Coulomb failure criterion is adopted to represent the elasto-plastic behavior of rock blocks, while a yield weakening model is used to describe the joint behavior considering the mining-induced effects. The properties of rock strata in the UDEC model include seven strata of rock matrices. The tensile strength values for different layers are approximately 10% of uniaxial compressive strength (UCS) values [30,31]. The rock parameters used in the simulation are shown in Table 2. The input parameters have been calibrated against an empirical database under static and dynamic loading condition [32].
## 2 Numerical Methods and Model Setup
### Evaporation Model  
In the case of UWS spray evaporation, the model has been adopted following the approach of [PERSON] [26; 27; 28]. The evaporation rate and temperature variation rate can be derived from the mass and energy conservation equations in the surrounding gas of the droplet surface, which are as follows:  
\[\frac{dm_{l}}{dt}=\pi d_{l}\rho_{g}\Gamma_{i,m}Sh\cdot\ln(1+B_{m}) \tag{5}\]\[\frac{dT_{l}}{dt}=\frac{dm_{l}}{dt}\frac{1}{m_{l}c_{p,l}}(\frac{c_{p,x}(T_{g}-T_{l}) }{B_{T}}-L) \tag{6}\]  
with droplet mass \(m_{l}\); droplet diameter \(d_{l}\); gas density \(\rho_{g}\); diffusion coefficient of vapor in the bulk \(\Gamma_{i,m}\); Sherwood number _SIi_; Spalding mass number \(B_{m}\); temperature of droplet \(T_{l}\); heat capacities of droplet \(c_{p,j}\); heat capacities of vapor \(c_{p,x}\); temperature in the bulk gas \(T_{g}\); droplet temperature \(T_{l}\); Spalding heat transfer number \(B_{T}\); latent heat of vaporization \(L\). \(B_{m}\) and \(B_{T}\) are calculated as:  
\[B_{m}=\frac{Y_{p,s}-Y_{p,g}}{1-Y_{p,s}} \tag{7}\]  
\[B_{T}=(1+B_{m})^{\frac{1}{L}\cdot\frac{S_{B}}{Nnp}}\cdot\frac{C_{p,x}}{C_{p,g} }-1 \tag{8}\]  
where \(Y_{p,s}\) and \(Y_{p,g}\) comprise the vapor mass fraction at the surface vapor and in the bulk gas; Le represents the Lewis number; \(Nu_{rp}\) corresponds to the Nusselt number calculated from the Reynolds number and the Prandtl number; \(c_{p,g}\) is the heat capacity of gas.  
The evaporation model considers the influence of Stefan flow on mass and heat transfer, where the mass transfer coefficient and the convective heat transfer coefficient are calculated with the modified Sherwood number and the Nusselt number [29; 30].
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
###### Abstract  
Open-pit mining offers significant benefits, such as enhanced safety conditions and high efficiency, making it a crucial method for use in the modern coal industry. Nevertheless, the comprehensive process of \"stripping-mining-discharge-redclamation\" inevitably leads to ecological disturbances in the mine and surrounding areas. Consequently, dynamic monitoring and supervision of open-pit mining activities are imperative. Unfortunately, current methods are inadequate for accurately identifying and continuously monitoring bare coal identification using medium spatial resolution satellite images (e.g., Landsat). This is due to the complex environmental conditions around mining areas and the need for specific image acquisition times, which pose significant challenges for large-scale bare coal area mapping. To address these issues, the paper proposes a novel bare coal index (BCI) based on Landsat OLI imagery. This index is derived from the spectral analysis, sensitivity assessment, and separability study of bare coal. The effectiveness and recognition capability of the proposed BCI are rigorously validated. Our findings demonstrate that the BCI can rapidly and accurately identify bare coal, overcoming limitations related to image acquisition timing, thus enabling year-round image availability. Compared to existing identification methods, the BCI exhibits superior resistance to interference in complex environments. The application of the BCI in the Chenqi Coalfield, Shengli Coalfield, and Dongsheng Coalfield in Inner Mongolia, China, yielded an average overall accuracy of 97% and a kappa coefficient of 0.87. Additionally, the BCI was also applied for bare coal area identification across the entire Inner Mongolia region, with a correct classification accuracy of 90.56%. These results confirm that the proposed index is highly effective for bare coal identification and can facilitate digital mapping of extensive bare coal (BC) coverage in open-pit mining areas.  
open-pit mining; bare coal; landsat; bare coal index; monthly adaptation 2024  
1  
[PERSON], [PERSON]; [PERSON]; [PERSON] A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery. _Remote Sens._**2024**, _16_, 4648. [[https://doi.org/10.3390/rs16244648](https://doi.org/10.3390/rs16244648)]([https://doi.org/10.3390/rs16244648](https://doi.org/10.3390/rs16244648))
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 3 Test Results
### Performances of the (D + 2) to (D + 7) Day Forecasting Accuracies of the RNN Models  
Figure 10 presents the monthly averages of the (D + 2)-day to (D + 7)-day forecasting MAPEs of the RNN models with forecasting temperatures from 2018-2019. Note that the seasonal patterns appear in both years, and, as they are the (D + 2)-day to (D + 7)-day forecasts, the higher MAPEs due to longer-range forecasts are to be expected.
## 3 Modified F/DR Algorithms  
Two novel ordering strategies were proposed to modify the traditional F/DR algorithm. These strategies were designed to introduce statistical diversity to the list, \(L\), created under DR sorting. Therefore, the modified F/DR algorithms have a pseudo-code that is very similar to the conventional one, as summarized in Algorithm 1. However, they generate the ordered list, \(L\), using a modified DR criteria, as follow:
## 2 Energy Efficiency Landscape
### Battery Life Calculation  
Lithium-ion batteries are widely used in satellite energy storage due to their high energy density. This paper establishes a battery life model specifically for lithium-ion batteries, taking into account factors such as the discharge rate, operating environment, and depth of discharge (DoD). The DoD, being easily manageable, is the primary focus of this study. Equation (6) defines the DoD at time \(t\), \(D(t)\), where \(C_{\max}\) is the maximum battery capacity, and \(C(t)\) is the remaining battery capacity at time \(t\), with a higher DoD indicating greater energy consumption.  
\[D(t)=\frac{C_{max}-C(t)}{C_{max}} \tag{6}\]  
The cycle life of lithium-ion batteries is limited, and their depth of discharge (DoD), denoted by \(D\), directly affects lifespan consumption, as shown in Equation (7), where \(f(D)\) describes the consumption rate at DoD \(D\), and \(A\) is a constant characteristic of the battery [28].  
\[f(D)=10^{A(D-1)}(1+Aln(10)\cdot D)) \tag{7}\]  
When discharging between times \([t_{1},t_{2}]\), the life consumed can be calculated by integrating \(f(D)\), represented by the integral of \(g(D)\), as in Equations (8) and (9) [21].  
\[g(D)=D\cdot 10^{A(D-1)} \tag{8}\]  
\[L_{t_{1}t_{2}}=g(D_{t1})-g(D_{t2}),D_{t1}\geq D_{t2} \tag{9}\]  
The decay in battery life reduces its reliability, defined as the probability of failure-free operation over a specified period, related to the battery's life probability density function \(f(t)\), with \(R(t)\) indicating the reliability of the battery at time \(t\), as depicted in Equation (10).  
\[R(t)=1-\int_{0}^{t}f(t)dt\approx\left(1-x^{2}\right)^{5} \tag{10}\]
## 4 Experiments
### Penetration Testing Execution
#### 4.4.3 Remote Vehicle Control Test
As depicted in Figure 10a, under normal circumstances the vehicle accurately identifies the traffic signal displayed on the signboard. Conversely, in Figure 10b, during non-targeted attacks, the recognized traffic signal deviates from its actual representation according to the traffic sign recognition system. Furthermore, Figure 10c illustrates an instance of a hidden-target attack scenario where the system fails to identify any traffic signals within its environment successfully. Lastly, Figure 10d demonstrates how target attacks alter vehicle's traffic sign recognition results as they vary with respect to distance from the signage. Figure 9: GPS spoofing attack scenario: (**a**) no attack; (**b**) jamming attack; (**c**) spoofing attack. Ultrasonic radar is extensively employed in the automotive industry due to its cost-effectiveness for measuring the distance between vehicles and obstacles. Adversaries can exploit an ultrasonic signal generator to produce ultrasonic waves of varying frequencies and phases, thereby disrupting the target ultrasonic radar's ranging capabilities. In the ultrasonic test, the tester employs an ultrasonic signal generator to produce ultrasonic signals of identical frequency as the in-vehicle ultrasonic sensor in order to verify the accurate measurement of obstacle distance by the target vehicle under interference conditions. The ultrasonic sensor test equipment is developed based on STM32 platform and CMSIS firmware library, ensuring excellent code portability. Four types of ultrasonic sensors (HC-SR04, US-015, US-100, and SR04T) are employed for cross-validation of experimental results. During the experiment, a 3.3-5 V level conversion module is utilized for voltage conversion between the STM32 platform and the ultrasonic sensor. As depicted in Figure 11b, when there are no pedestrians behind the vehicle, no warning sign is displayed in the reversing image. However, if a pedestrian approaches from the rear, as illustrated in Figure 11c, a warning sign appears on the reversing image along with simultaneous emission of a high-frequency alarm sound to alert the driver. Furthermore, as shown in Figure 11d, even without any pedestrians present behind the vehicle, an obstacle warning sign accompanied by a high-frequency alarm sound is displayed on the reversing image. The ultrasonic attack device successfully simulates obstacles at the rear of the vehicle, leading to the emission of high-frequency alarm sounds and the display of warning signs. Figure 10: Camera attack based on adversarial examples: (**a**) no attack; (**b**) no target attack; (**c**) target hide attack; (**d**) specific target attack.
## 3 Model Implementation
### Evaluation Criteria  
To evaluate the performance of each model, the mean absolute error (MAE), root mean square error (RMSE), and maximum absolute error (\(\text{AE}_{\text{max}}\)) are selected as the evaluation indicators of models. The relevant calculation formulas are shown in Equations (25)-(27). For these three indicators, the smaller the value, the better the performance of the prediction model.  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left|{y_{i}-\hat{y}_{i}}\right|} \tag{25}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}\left({y_{i}-\hat{y}_{i}}\right)} \tag{26}\]  
\[AE_{\text{max}}=\text{max}|y_{i}-\hat{y}_{i}|,i=1,2,\ldots,n \tag{27}\]  
where \(n\) is the number of sample data, \(y_{i}\) is the original dam deformation data, and \(\hat{y}_{i}\) is the predicted dam deformation data.
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 4 Experimental Setup and Evaluation Measures
\[\text{Jaccard Index}=\frac{|\hat{y}\cap y|}{|\hat{y}\cup y|}=\frac{|\hat{y}\cap y |}{|\hat{y}|+|y|-|\hat{y}\cap y|}\]  
In addition, confusion matrices were plotted to assess the Type I and Type II errors. Type I (False positive (FP)) and Type II (False negative (FN)) errors [30] are commonly used terms in machine learning and the main goal of the model is to minimize one of these two errors, depending on the context that which error is more critical in the given task. By definition, a Type I error is concluding the existence of a relationship, while in fact it does not exist (e.g., classifying an image as \"blocked\" while there is no blockage). Similarly, a Type II error is the rejection of the existence of relationship while, in fact, it exists (e.g., classifying an image as \"clear\" while there is a blockage). For the given culvert blockage context, Type II error is more critical to be minimized in comparison to Type I error because having notified as blocked while there is no blockage is tolerable in comparison to having notified as clear while there is a blockage. Type II error will result in damages because it may be very late for the response team to clear the blockage before the diversion of flow. Finally, the performance of implemented deep learning models was benchmarked against the conventional machine learning models (i.e., SVM, RF, xgboost) to demonstrate the effectiveness of CNN's for images (i.e., matrix) type dataset.
## Appendix B Task Specifications  
**## Document Title: Robotic Health Attendant (RHA) Task Specifications**  
**## General Notes: 1. RHA situational understanding: RHA situational understanding describes how the RHA knows which tasks to perform in given situations. This is a trigger to select certain task specifications and perform actions as described in the task specifications. 2. RHA role: RHA role provides a general description for the expected roles.**  
**## Task Specification 1: Fetch Resources - Description: Nurses or patients need items for patient care (e.g., water, medications, or medical supplies).**  
**- RHA Role: The RHA should promptly respond to requests for fetching items, navigate to the required locations, and retrieve the necessary resources efficiently.**  
**- Situational Understanding: Triggered by commands received from nurses or patients requesting specific items.**  
**- Actions to Complete Operation: Action 1. Navigate to item location: The RHA will navigate to the location where the requested item is located using the skill navigateToItemLocation, with the first input argument being the name of the object and second input argument being the coordinates of the item location. Since this is 2D navigation, provide X, Y, and orientation of the object. Z should be 0. Action 2. Pick item: The RHA will fetch the required item based on the request using the skill pickObject. The first input argument is the name of the object and second input argument is the coordinates of the item location. Action 3. Navigate to where the item is needed: The RHA will navigate to a designated location (e.g., patient monitoring, patient-robot interaction) where the item needs to be delivered using the skill navigateToLocation, with the input argument being the coordinates of the location needing the item. Don't navigate directly to a person. Action 4. Handover the item: The RHA will hand over the item to the intended recipient using the skill releaseObject. No input argument is needed for this action.**  
**Note: All these four actions must be carried out sequentially, with each step executed after the previous one. The robot has to submit a prompt saying the previous action is completed, before giving another action to the robot.**
# An Optimization Design of Bi-log Hybrid Antenna with Taguchi's Method for EMI Measurements
## 3 Antenna Optimization using Taguchi's Method  
The Taguchi method also has proven effective in the realm of antenna design, contributing to the creation and development of this study. It compares the results of the simulated experiments with the orthogonal table and those with the response table and then keeps the better result for the next iteration that is required to be performed. The multi-objective optimization leads to the optimum dipole lengths, spacings between the dipoles, and dipole wire diameters. During the optimal process, the orthogonal tables obtain better experimental data with fewer experiments and gradually in a systematic way converge the search range.
## 3 Wenchuan EQ Analysis and Discussion
### Results and Discussion  
#### 3.2.1 Monte Carlo Simulation of Ionization Processes  
Based on the variation of alpha and beta ion concentrations with height, the Monte Carlo simulations were used to simulate their ionization rates at different heights. For the Wenchuan EQ alpha and beta particle concentrations given in Section 3.1, the variation of the ionization rate with height was derived from the simulation method in the methodology, as shown in Tables 2 and 3 and Figure 5. Tables 2 and 3 show that the heights and simulation number are the simulation input conditions, and the average energy deposition is the output from the GEANT4 calculation. It was noteworthy that the total energy deposition is the case of one-hour accumulated radon concentration. Next, the ionization rate variation was obtained by Equation (3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Altitude/m**} & **Average** & **Simulation** & **Total Energy** & \multirow{2}{*}{**Ion Pairs**} & **Ionization** \\  & **Energy Deposition/keV** & **Number** & **tion/eV** & & **Rate/cm\({}^{3}\)-s** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Alpha particle simulation parameters and results.  
As shown in Figure 6, the black dots are the results obtained from the simulation, and the red curve is the best-fit result. An exponential fit was used as the fitting function to match the actual ionization rate variation more closely with height. As a result, the ionization rate fitted alpha and beta ionization rate curves were Equations (6) and (7).  
\[Q_{\alpha}=e^{21.263-4.25\times 10^{-4}z} \tag{6}\]  
\[Q_{\beta}=e^{18.459-4.245\times 10^{-4}z} \tag{7}\]  
here the ionization rate fit is close to perfect, where the \(Q_{\alpha}\) fitting accuracy \(R^{2}=1\) and the \(\beta\) fitting accuracy \(R^{2}=0.99995\). Therefore, the variation of the ionization rate with height was \(Q_{\alpha}+Q_{\beta}\) caused by radon.