esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilization of an OLED-Based VLC System in Office, Corridor, and Semi-Open Corridor Environments
## 2 Simulation
### Scenarios  
Figure 2 shows the analyzed example of an office environment designed with the size of \(10\times 10\times 3\) m\({}^{3}\) and a number of objects within. Here, a curved OLED with the size \(1\times 0.5\) m\({}^{2}\) is mounted on the wall with a curvature radius of 32 cm. In the office environment, the scenario is to move the Rx over a semi-circular path, where the radius \(d\) is 2 m. An angle of radiation \(\theta\) with respect to the normal from the center point of OLED (i.e., \(-90^{\circ}<\theta<90^{\circ}\)) is given, see Figure 3. The Rx height is assumed to be 1 m above the floor to represent people holding mobile phones while sitting at their desks. In simulation, we have not considered the synchronization. However, in real time systems synchronization protocols defined by the standards will be adopted, which does not affect the transmission characteristics of the proposed system.  
Figure 3: The proposed scenario which shows the location of receiver (Rx) and transmitter (Tx) giving a half-circular lighting.  
Figure 2: The three-dimensional office environment.  
Figures 4a and 5a then show the 3D corridor and semi-open corridor environments inside a shopping mall, respectively, in which OLED acts as a light source. The semi-open corridor is typical especially for upper floors of the shopping mall, so we have chosen an example when the user walks on the first floor, having shop windows on one side and an open space on the other side. The user is moving on the straight path along the corridor (show by the red dashed-lines in Figures 4b,c and 5b,c). The Rx is positioned at the height of 1.3 m above the floor level (i.e., the holding position of mobile by people). The user is moving along the shop windows at a distance of 2 m on the path donated as \(d_{y}\) from \(-20\) to 20 m, where Tx is placed at 0 m position. For both empty and furnished corridor and semi-open environments, we have considered two scenarios of (case1), where the OLED panel is located on the inner shop wall behind the glass window and (case2) on the wall or shop window inside the corridor, see Figures 4b,c and 5b,c. All the key system parameters adopted in this work, including the reflectance values of surfaces and the transmission coefficient of the glass windows, are given in Table 1.  
Figure 4: (**a**) The three-dimensional corridor environment and proposed scenarios which show the location of Rx and Tx, (**b**) case1 where Tx is located on inner shop wall behind window, and (**c**) case2 on wall or shop window inside the corridor.  
Figure 5: **(a)** The three-dimensional semi-open corridor environment and proposed scenarios which show the location of Rx and Tx, **(b)** where Tx is located on inner shop wall behind window (case1), and **(c)** on wall or shop window inside the corridor (case2).
## 3 Numerical Simulation and Results  
The study in Section 2 shows that thermal wear is the primary tool wear mode in the test. The tool temperature during the impact process is so hard to measure directly. In contrast, numerical simulation techniques, especially the finite element method, can effectively predict the distribution of physical quantities such as cutting forces, chip morphology, and temperature fields of the workpiece, tool, and chip via thermal-structural coupling analysis [16]. Tool temperature is often used as one of the indirect indicators for tool wear detection. So thermal-structural coupling finite element analysis becomes a pivotal technique to study the relationship between tool temperature distribution and wear forms in the cutting energy-absorption process.  
Two contact algorithms, *ERODING_SURFACE_TO_SURFACE and *ERODING_SINGLE_SURFACE, are utilized to accurately simulate the complex tool-chip contact and chip self-contact in the cutting energy-absorption process. The time it takes for heat conduction between components is frequently longer than the brief duration of the collision [25]. Therefore, thermal-structural coupling simulations usually ignore the heat conduction between components.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 2 Fundamental Aspects of Microseismic Monitoring Technique  
### Microseismic Monitoring Principle  
Rock fracturing and the development of existing rock fractures generate microseismic events and radiates seismic waves from the fracturing source that can be detected by sensors [46]. Figure 1 shows the seismicity frequency spectrum at different scales of rock fractures in various fields. The frequency of mining-induced seismicities mainly ranges from several to a few thousand Hertz, and these events can be detected by a geophone or accelerometer array featuring an excellent frequency bandwidth.  
Figure 2 illustrates a general microseismic monitoring configuration in underground coal mines. The microseismicity generated by coal/rock fracturing can be recorded by a geophone or an accelerometer as an analog signal series and is digitalized by the data acquisition system of underground substations. The digital seismograms recorded by the substations are sent to the surface server by an Ethernet ring network. Furthermore, the seismic signal series can be manually processed in the surface office, and the parameters of microseismicities, e.g., the energy, source radius, and stress drop, are calculated by the supported software. The microseismicities can be intuitively displayed on the screen at the monitoring center as balls of different diameters and colors, and the risk of mining-induced instabilities is able to be assessed via an analysis of an extensive microseismic parameters dataset.  
Figure 1: Seismicity frequency spectrum and application of microseismic monitoring technique in various fields, revised from [46].
## 4 Results and Discussions
### Model Calibration
The template size (\(TS\)) is defined by width and height (that are equal for a square template as used here, with side length \(TS\)) over the image \(A\) (older image) where FFT is pursued. The size of the template is an important parameter. A large template is more efficient, so it is less likely that false matches are attained, i.e., finding the same (similar) traits in a wrong region of the image \(B\). On the contrary, a small template results into low computational time. A trade-off between these two objectives provides fast and efficient calculation. The search region over the image \(B\) (more recent) can also be either square or rectangular, but here, we used a square shape with side length \(RS\) (search region size), which is more regular. A better knowledge of the glacier motion allows to define the smallest search region as possible. Here, after a preliminary analysis, we found that a square template with \(TS\) = 70 pixels in size provides the best performance, without very large computational time. The default size of \(RS\), also used here after a preliminary assessment, is \(RS\) = \(TS\) + 41. The last tuning parameters \(dx\) and \(dy\) define the initial hypothesis of the 2-D directional displacement of the template \(TS\) from the initial point. An accurate initial hypothesis can improve the performances, allowing to use a smaller Search Region, already centered on the likely final destination of the template. Here, the \(dx\) and \(dy\) values were adjusted iteratively based upon preliminary assessment of flow velocity for each pair of images, and depending upon flow velocity and time lag \(\Delta t\).
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion
### ANFIS-Controller-Driven Drive
#### 4.5.1 Starting Characteristics  
During the starting of the motor without load, the starting torque rises to 12 nm and settles down at 0.5 nm, with a percentage overshoot of \(-\)1.07%, a peak time of 1 s, a rise time of 0.2046 s, and a settling time of 0.5742 s. The steady state starting current is 8.6A (12A for PI, 11A for fuzzy, and 13A for fuzzy-PI) and it is comparatively lower than other controllers. A steady-state error of just 2 rpm is realized.  
When the load is linearly increasing from 1 nm to 2 nm between 1 s to 2.5 s, the settling time is 2.49 s, with a 59 rpm steady state error, and a \(-\)4.5% (2.1 s to 2.5 s) overshoot. After 2.5 s and up to 3.5 s, when the loading is kept constant at 2 nm, the speed response has a settling time of 3.49 s. Between the time interval of 3.5 s to 4.5 s, the load starts proportionately reducing from 2 nm to 1 nm with an overshoot of 3.03% (14.28% for PI, \(-\)0.06% for fuzzy controller), with a 1 rpm error and a settling time 4.49 s. After 4.5 s and until 5 s, the load is completely removed.
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 2 Related Work
By using any casing, the sensors can be protected from weather conditions [19] but may result in the error in sensor readings. Computing techniques like machine learning (ML), helps in investigating and analyzing the available data from various fields and integrating them with the process of crop improvement. ML provides various analytical models and methods to analyze the crop disease, yield prediction and so on. The authors [PERSON] et al. [38] used the vision sensing approach to estimate nutrient contents in the wheat leaves. Nutrient estimation is necessary to avoid over fertilizing to the crops, which in turn harms the crop as well as environment. Because of the change in intensity of light, it becomes very challenging to estimate nutrition with inconsistent image. To tackle this problem, [PERSON] et al. [39], developed combination of neural networks by using color constancy method to normalize different color of images. Neural network-based prediction of soil water is used in the paper [40], for the management of water valves to achieve optimized irrigation. PA generates structured and unstructured datasets, which needs the extraction of knowledgeable information. Paper [41], uses the big data approach to get insights and explore the potential sources of big data in PA. These big data analytics helps in figuring out the trend and pattern which helps in decision making, future farming etc. Also, paper [42], opens the dataset to the community to stimulate research in this area. A review paper on research progress of ML approaches [43], demonstrates the rapid advances in ML techniques for better crop yield using cost-effective decision making. As future work, we will integrate the ML and MAC protocol [44] to secure data transmission and minimize the frequency of messages from sensor node to the gateway in order to achieve energy-efficient network. Article on IoT-based smart agriculture [45] by the author [PERSON] et al. identifies the current and future trends of IoT in agriculture. It states that LPWAN technology can be a game changer for smart agriculture and stands as better solution for connectivity because of long-range and affordable price for the farmers. In this paper, we use the LoRaWAN as connectivity enabler for precision agriculture and check the impact of using different box schemes by deployment in phases. Lastly, we realized the final end-to-end LoRaWAN-based deployment by using the air circulated box for the sensors on field. This will be helpful for both researchers and growers in future deployments for precision agriculture.
## 3 Methodologies
### Model Performance Evaluation  
Model accuracy can be evaluated using the ROC curve, which offers a comprehensive performance measure by plotting sensitivity (true positive rate) on the vertical axis against specificity (false positive rate) on the horizontal axis. A more convex ROC curve in the ROC space can present superior performance [38]. The AUC serves as a key metric for assessing predictive accuracy, with AUC values closer to 1 indicating higher model accuracy.  
In addition to the ROC curve, the confusion matrix is a widely used tool for evaluating the performance of machine learning models, providing key metrics, such as accuracy, precision, recall, and F1 score.  
True positive (_TP_): This indicates that the actual value is positive, and the model predicts it as positive.  
True negative (_TN_): This indicates that the actual value is negative, and the model predicts it as negative.  
False positive (_FP_): This indicates that the actual value is negative, whereas the model predicts it as positive.  
False negative (_FN_): This indicates that the actual value is positive, whereas the model predicts it as negative.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}, \tag{15}\]  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{16}\]  
\[\text{Recall}=\frac{TP}{TP+FN}, \tag{17}\]  
\[\text{F1 Score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{Recall}} \tag{18}\]  
The number and proportion of landslide points distributed across each susceptibility zone were recorded with the corresponding pixel quantities and proportions for each zone.
## 3 Formulation of Mathematical Models
### Model 2: Two Population Model  
Now, we present a two cell population model. In this model, we explicitly differentiate between CS and CR cells. \(x_{1}\) (mm\({}^{3}\)) and \(x_{2}\) (mm\({}^{3}\)) denote the CS and CR cell populations, respectively. The proliferation of each cancer cell population is denoted by  
\[G_{i}(Q)=\mu(1-\frac{q_{i}}{Q}),i=1,2,\]  
for \(x_{1}\) and \(x_{2}\) respectively. Since CR cell populations proliferate at lower levels of androgen, we assume that \(q_{2}<q_{1}\). Death rates are denoted by:  
\[D_{i}(Q)=d_{i}\frac{R_{i}}{Q+R_{i}},i=1,2,\]  
for their respective cell populations. We shall assume that \(d_{1}>d_{2}\), as CR cells are less susceptible to apoptosis by androgen deprivation than CS cells. Parameters \(\delta_{i},i=1,2\) denote the density dependent death rates, and we use these parameters to keep the maximum tumor volume in biological ranges.  
Mutation between cell populations is assumed to take the form of a Hill equation of coefficient 1, given by:  
\[\underbrace{\lambda(Q)=c\frac{K}{Q+K}}_{\text{CS to CR}}.\]  
The CS to CR rate, \(\lambda(Q)\), is a decreasing function of the androgen levels. We assume that when cells are experiencing androgen depletion, they have higher selective pressure to develop resistance. Likewise, in an androgen rich environment, CS cells are more likely to stay sensitive. IAS started under this assumption, with the intention to delay resistance [10]. \(c\) is the maximum rate of mutation between cells and \(K\) is the cell concentration for achieving half of the maximum rate of mutation. In this model, \(d_{i}\)s are held constant and are not time dependent, as the mechanism of the development of resistance is due to mutations from \(x_{1}\) to \(x_{2}\) via \(\lambda(Q)\) and not by a decreasing androgen dependent death rate.  
The increase of intracellular androgen levels by diffusion from the serum level is modeled by \(\gamma(Q_{m}-Q)\). For simplicity, and in contrast to the PKN model [13] and the model in [PERSON] et al. [32], we assume the same PSA production rate \(\sigma\) for both cell populations:  
\[\frac{dx_{1}}{dt} = \underbrace{\mu(1-\frac{q_{1}}{Q})x_{1}}_{\text{growth}}- \underbrace{(D_{1}(Q)+\delta_{1}x_{1})x_{1}}_{\text{death}}-\underbrace{ \lambda(Q)x_{1}}_{\text{CS to CR}}, \tag{7}\] \[\frac{dx_{2}}{dt} = \underbrace{\mu(1-\frac{q_{2}}{Q})x_{2}}_{\text{growth}}- \underbrace{(D_{2}(Q)+\delta_{2}x_{2})x_{2}}_{\text{death}}+\underbrace{ \lambda(Q)x_{1}}_{\text{CS to CR}},\] (8) \[\frac{dQ}{dt} = \underbrace{\gamma}_{\text{production}}\underbrace{(Q_{m}-Q)}_{ \text{diffusion}}-\underbrace{\mu(Q-q_{1})x_{1}+\mu(Q-q_{2})x_{2}}_{\text{ uptake}}, \tag{9}\]\[\frac{dP}{dt} = \underbrace{bQ}_{\text{baseline}}+\underbrace{\sigma(Qx_{1}+Qx_{2})}_{ \text{tumor production}}-\underbrace{eP}_{\text{clearence}}. \tag{10}\]  
In a biologically realistic situation, one expects that \(Q_{m}>\max\{q_{1},q_{2}\}\).
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 3 Research Method
### The Coordinate Registration of the Point Cloud and BIM
#### 3.2.1 Coarse Registration Based on Grid  
After obtaining the point cloud data, a coarse registration of the point cloud is performed with the design BIM, as shown in Figure 2a, where the blue line frame represents the design BIM, and the pink point represents the point clouds. Figure 2b shows a schematic result of the coarse registration. Similar to [52], the original point cloud is sliced into multiple layers, and the line features are extracted using the double radius threshold line tracking method. After the line features are extracted, they are regularized using the constrained least squares method.  
In subway stations, typical underground structures, i.e., facades, slabs (top and bottom slabs), and structural columns, are composed of straight lines that usually exhibit parallel or orthogonal spatial geometric relationships [52]. As noise is inevitable in measurements, the extracted line features are subject to accuracy errors. Therefore, it is necessary to regularize the line features; the condition is defined in Equation (1), where \(\theta\) is the angle between the line to be measured and the reference line, and \(t_{\theta}\) is the empirical threshold (set to \(10^{\circ}\) in this study). If condition 1 is satisfied, the line to be measured will be marked as parallel; if condition 2 is satisfied, the line to be measured will be marked as perpendicular.  
\[\left\{\begin{array}{l}\text{Condition1}:\text{if}\cos(\theta)>\cos(t_{ \theta})//\cos(\theta)<\cos(\pi-t_{\theta}),\;\text{L}_{i}\;\text{is parallel.}\\ \text{Condition2}:\text{if}\cos(\theta)<\sin(t_{\theta}),\;\text{L}_{i}\; \text{is orthogonal.}\\ \cos(\theta)=\frac{\sqrt[\text{ref}-\text{ref}-\text{ref}]{\cdot}\
abla_{i}} {\|\
abla^{\text{ref}}_{\|}\|\cdot\|\
abla_{i}\|}\end{array}\right. \tag{1}\]  
After regularization is completed, the remaining straight-line segments are extended to form a rectangle, and the geometric center and quadrant angle of the point cloud slices are calculated and used as a virtual grid to roughly match the BIM grid. The geometric center of the BIM grid is represented as \(p_{0}\) (\(\alpha_{\text{x}}\), \(\alpha_{\text{y}}\), 0) and the quadrant angle is \(\theta_{1}\); the point cloud center is represented as \(p_{1}\) (\(\beta_{\text{x}}\), \(\beta_{\text{y}}\), 0) and the quadrant angle is \(\theta_{2}\). Then, the point cloud is transformed into the predefined survey coordinate system in BIM by Equation (2). The coarse registration process is shown in Figure 3. After horizontal registration, the \(z\)-axis is adjusted according to the height of the top and bottom slabs.  
\[M=T(p_{0})\cdot R_{z}(\theta_{2}-\theta_{1})\cdot T(-p_{1}) \tag{2}\]
## 2 Materials and Methods
### Bioequivalence Data-Noncompartmental Analysis  
Donepezil is a widely used drug for symptomatic relief of Alzheimer's disease. The actual C-t data used in this study were obtained from a two-sequence, two-period, crossover BE study in 26 healthy volunteers who received a single dose of donepezil 10 mg tablets (Verisfield SA) and donepezil (Aricept(r)) from Pfizer, separated by a 21-day washout period. The pharmaceutical company (Verisfield SA) kindly provided the blind C-t data of the study participants in order to perform this computational study. According to the BE study protocol, the blood samples were obtained at 0.5, 1, 2, 3, 4, 6, 8, 12, 24, 48, 96, 144, and 192 h after the treatment. Overall, there were 14 observations per subject and 26 subjects per period of the study. Taking into consideration that the data come from a 2 \(\times\) 2 crossover  
Figure 1: Strategy of the analysis. The basic purposes, actions, and software utilized for each phase of the analysis. Actual (steps: A1 and A2) and simulated (steps: B1–B4) bioequivalence data are used.  
study and there was no discrimination between the two medicinal products, there are 52 subjects with 14 observations each. As a result, 52:14 = 728 measurement points.  
Following the appropriate clinical and analytical procedures, the blood samples were finally analyzed to quantify donepezil concentration using a validated high-performance liquid chromatography/tandem mass spectrometry (LC-MS/MS). The analytical approach demonstrated great sensitivity, specificity, accuracy, and speed. The linearity of the curve was reported at concentrations ranging from 0.1 to 100 ng/mL. The lower limit of quantitation for donepezil was equal to 0.1 ng/mL.  
In the BE study, participated male and female volunteers between the ages of 18 and 45 with a BMI of 18.5 to 24.9 kg/m\({}^{2}\). To confirm that they were healthy volunteers, a comprehensive medical history was collected, and a physical examination and laboratory tests were performed 21 days before enrollment in the study. After an overnight fast, the medication was taken orally with roughly 240 mL of water. Subjects fasted for 4 h, 8 h, 12 h, 24 h, 28 h, 32 h, 36 h, and 48 h following administration before eating regular meals. All the participants signed a written consent form, and the study was performed according to the ethical rules of the Helsinki Declaration.  
Non-compartmental approaches (PKanalixTM, MonolixSuiteTM 2021R2, and Simulation Plus) were used to calculate the PK parameters of donepezil. These parameters were AUC, Cmax, Tmax, lambda, and the area under the C-t curve extrapolated from time zero to infinity (AUCinf). The linear trapezoidal rule was used to determine AUC and AUCinf. The Cmax/AUC and Cmax/Tmax ratios were calculated for each subject based on these estimates. The term \"lambda\" refers to the apparent terminal elimination rate constant, which is found by applying a least squares regression analysis to the terminal log-linear phase of the C-t curve, in line with the regulatory guidelines [1; 2].