esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Diffusion Characteristics and Proportional Coefficients of Space States
### Proportional Coefficients of Different Space States  
(a) Proportional coefficients based on numerical simulation  
According to the variation law of electromagnetic response of three space states under the condition of a three-dimensional uniform medium, it can be seen that there was a certain relationship between the full space state, the 3/4 space state, and the half space state under the condition of the uniform medium. Referring to the investigation of the multiple relations of transient electromagnetism in mines, by [PERSON] et al. [16], the proportional coefficient of full space to half space is defined as \(k_{1}\), and the proportional coefficient of 3/4 space to half space is defined as \(k_{2}\), as shown in Formula (8). The results of the two proportional coefficients are shown in Figure 6.  
\[k_{1}=\frac{U_{Full}}{U_{Half}}\text{ and }k_{2}=\frac{U_{3/4}}{U_{Half}} \tag{8}\]  
where \(k_{1}\) and \(k_{2}\) are the proportional coefficients of full space and 3/4 space to half space, respectively, which are dimensionless; \(U_{Full}\), \(U_{3/4}\), and \(U_{Half}\) are the induced voltage in the receiving coil under the conditions of full space, 3/4 space and half space, respectively (unit: V). It can be seen from Figure 6 that the proportional coefficient curve of full space to half space presented a horizontal straight line on the whole with the increase of time delay, and the average value of the horizontal straight line was 2.5. Therefore, the full space electromagnetic response in uniform media was 2.5 times that of the half space electromagnetic response. Similarly, with the increase in time delay, the proportional coefficient curve of 3/4 space to half space also changed as a horizontal line on the whole, and the average value of the horizontal line was 1.42, that is, the 3/4 space electromagnetic response under the uniform medium was 1.42 times that of the half space electromagnetic response. Affected by the quality and size of the model grid, the proportional coefficient of full space and 3/4 space to half space fluctuated to a certain extent in the middle and late periods, but the relative error was less than 2%. Figure 5: Variation diagram of magnetic field and induced voltage in the Z direction of the center point of three space state models under the condition of uniform medium. 2. Proportional coefficients based on theoretical analysis  
Based on the analytical solution of Formula (7), when the direction of the magnetic field is perpendicular to the plane where the transmitting coil is located, the component of the full space magnetic field in the normal direction of the transmitting coil under the condition of the uniform medium can be obtained, as shown in Formula (9):  
\[H_{z}=\frac{2M}{4\pi R^{3}}\Bigg{[}\varphi(u)-\sqrt{\frac{2}{\pi}}u\mathrm{e}^{ -(u^{2}/2)}\Bigg{]} \tag{9}\]  
where \(H_{z}\) is the magnetic field component in the \(Z\) direction; \(M\) is the dipole moment; \(\varphi(u)\) is the probability integral, and its expression is as follows: \(\varphi(u)=\sqrt{\frac{2}{\pi}}\int_{0}^{u}e^{-(t^{2}/2)}\mathrm{d}t\).
## 3 Federated Kalman Filter Design
### Error Model of Strapdown Inertial Navigation System  
The error model of the strapdown inertial navigation system [30] was based on the ellipsoid model of Earth. The attitude error equation of the strapdown inertial navigation system is shown in Equation (1):  
Figure 3: Strapdown solution flow of the inertial navigation system. Figure 2: The geographic coordinate system (n system). \[\left\{ \begin{array}{rl}&\dot{\varphi}_{\rm E}=-\frac{\delta V_{\rm N}}{R_{\rm M}+ \rm H}+(\omega_{\rm ie}\rm sin\,L+\frac{V_{\rm Fe}\rm tan\,L}{R_{\rm N}+\rm H}) \phi_{\rm N}-(\omega_{\rm ie}\rm cos\,L+\frac{V_{\rm E}}{R_{\rm N}+\rm H})\varphi_ {\rm U}+\frac{V_{\rm N}}{(R_{\rm M}+\rm H)^{2}}\delta\rm H+\varepsilon_{\rm E} \\ &\dot{\varphi}_{\rm N}=\frac{\delta V_{\rm E}}{R_{\rm N}+\rm H}-\omega_{\rm ie} \rm sin\,L\delta L-(\omega_{\rm ie}\rm sin\,L+\frac{V_{\rm Fe}\rm tan\,L}{R_{ \rm N}+\rm H})\varphi_{\rm E}-\frac{V_{\rm N}}{R_{\rm M}+\rm H}\varphi_{\rm U} -\frac{V_{\rm E}}{(R_{\rm N}+\rm H)^{2}}\delta\rm H+\varepsilon_{\rm N}\\ &\dot{\varphi}_{\rm U}=\frac{\rm tanLSV_{\rm E}}{R_{\rm N}+\rm H}+(\omega_{\rm ie }\rm cos\,L+\frac{V_{\rm E}\rm sec^{2}\,L}{R_{\rm N}+\rm H})\delta L+(\omega_{ \rm ie}\rm cos\,L+\frac{V_{\rm E}}{R_{\rm N}+\rm H})\varphi_{\rm E}+\frac{V_{ \rm N}\rm\varphi_{\rm N}}{R_{\rm M}+\rm H}-\frac{V_{\rm Fe}\rm tanL\delta H}{ (R_{\rm N}+\rm H)^{2}}+\varepsilon_{\rm U}\end{array} \right. \tag{1}\]  
where \(\omega_{\rm ie}\) is the Earth's rotation angular velocity; E, N, and U represent the geographical east, north, and upward directions, respectively; L is latitude in the WGS84 coordinate system; H is height in the WGS84 coordinate system; \(\rm V_{E}\), \(\rm V_{N}\), \(\rm V_{U}\) are the geographically eastward, northward, and astronomical components of motion velocity in the navigation coordinate system n, respectively; \(\rm R_{M}\) is the main curvature radius of the meridian; \(\rm R_{N}\) is the main curvature radius of the prime vertical; \(\varphi_{\rm E}\), \(\varphi_{\rm N}\), \(\varphi_{\rm U}\) are the attitude angle error of the heading, pitching, and roll angles in the navigation coordinate system, respectively; \(\delta V_{\rm E}\), \(\delta V_{\rm N}\), \(\delta V_{\rm U}\) are the east, north, and upwards velocity errors in the navigation system, respectively; \(\delta\rm L\) is the latitude position errors in the navigation coordinate system; and \(\varepsilon_{\rm E}\), \(\varepsilon_{\rm N}\), \(\varepsilon_{\rm U}\) are the gyro angular rate output errors in the east, north, and upward directions in the n system, respectively. The velocity error equation of the strapdown inertial navigation system is shown in Equation (2):  
\[\left\{
# A Communication Framework for Image Transmission through LPWAN Technology
## 1 Introduction
Because the SDR approach allows for more degrees of freedom than LoRa commercial radios, characteristics such as bandwidth, transmission power, spreading factor, preamble sequence definition, and sync sequence chirps definition can be easily adapted (even beyond the standard values) to meet the needs of the application. The implementation features of the framework are described in this paper, which include image processing, image classification, compressing sensing, postprocessing, and image reconstruction techniques. The structure of the papers is as follows: In Section 2, previous work is given. Section 3 describes the proposed framework. Section 4 presents the analysis results. In Section 5 the discussion is presented. Finally, Section 6 presents the conclusions.
## 4 Experiments
### Networks Training and Validation  
In the training process, we use a loss function (or cost function) to estimate the deviation between the predicted value and the actual label, and then train the network to minimize the loss value. The lower the loss value, the closer the predicted result is to the real label. Our output layer is a sigmoid function that handles the binary problems and generates an S-shaped curve whose values between 0-1, as shown in Equation (1). We choose a cost function called binary cross-entropy (BCE) as shown in Equation (2), where \(\hat{y}\) represents the predicted probability in which target is a genuine signature and \(y\) represents the correct one [29]:  
\[\text{Sigmoid}(x)=\frac{1}{1+e^{-x}} \tag{1}\]  
\[\text{BCE}=-y\ \log(\hat{y})-(1-y)\log(1-\hat{y}) \tag{2}\]  
Then we use a popular optimization technique called the stochastic gradient descent (SGD) algorithm [30] to optimize our neural networks by minimizing the BSE value in Equation (4). Since an extortionate learning rate may hinder the convergence, we set a relatively small value of \(e^{-4}\) and set the momentum parameter as 0.9, which is most often used in SGD. Our dataset includes 48 images with two genuine and eight forged authors. The sample allocation is according to the ascending order of signature ID number, as refer to Table 3. All these images are converted into 345,874 sub-image blocks for the VGG19 network and 213,449 sub-image blocks for the Inception-V3 network experiment. After per-train the networks for one epoch, our main training process is finished in two epochs for the author No. 14 dataset, and six epochs for the author No. 16 dataset. The training epochs are different due to author ID: 16 signatures have higher intra-class variability therefore the corresponding network needs more epochs to converge properly.  
From Exp. 1 to Exp. 12, we get nearly 100% training and validation accuracy. It indicates that our networks sufficiently capture the features from training set and are able to correctly distinguish between the genuine signatures and the forgeries. However, although the above training and verification stages are based on different signatures, they still come from the same authors. To further verify whether the networks can be used by completely unknown authors, we still need to run the testing stage (which will be explained in the next section).
## 4 Results and Discussion  
### Influence of Ultrasound Pre-Treatment on Product Quality  
In this work, the quality of the drying product is investigated by comparing the color of fresh and dried carrots. To ensure the accuracy of the appearance assessment, the drying processes were triply repeated for 100 g of carrot slices in each experiment, using a large stray. Exemplary images of fresh carrots and carrots dried under different drying conditions are presented in Figure 3. Furthermore, the color of the dried sample was measured using a Colorimeter CHN SPEC CS-10. The samples were explored under a standard light source. Reflected light was captured across the entire visible spectrum and electrically filtered into narrow bands of color. In CIELAB color space, color is characterized by three parameters, namely \(L\), \(a\), and \(b\)[29]. The \(L\) value, varying in range from 0 to 100, is used as an indicator of brightness. The \(a\) value indicates chromaticity on a green to red axis with the respective values of \(-60\) and of 60; \(b\) indicates chromaticity on a blue (i.e., \(b=-60\)) to yellow axis (i.e., \(b=60\)). Compared to the fresh carrot, the color changes (\(\Delta E\)) of dried product for the total color difference is computed as [34]  
\[\Delta E=\sqrt{\left(L-L_{\textit{fresh}}\right)^{2}+\left(a-a_{\textit{fresh} }\right)^{2}+\left(b-b_{\textit{fresh}}\right)^{2}}. \tag{29}\]  
The color parameters \(L\), \(a\), \(b\), and \(\Delta E\) are presented in Figure 4. As can be seen, a smaller \(\Delta E\) obtained from the dried sample pre-treated by US indicates that the US pre-treatment favors the color preservation of the carrot. However, with a high drying temperature \(T_{S}\) of 50 \({}^{\circ}\)C, the \(\Delta E\) deviation in cases with and without US pre-treatment becomes marginal. It implies that US pre-treatment significantly enhances the color preservation of carrots only at low drying temperatures (i.e., 30 \({}^{\circ}\)C and 40 \({}^{\circ}\)C). Additionally, for both US pre-treated and non-US pre-treated drying products, the color changes at a drying temperature \(T_{S}\) of 40 \({}^{\circ}\)C are the highest. It can be expected that the color changes were due to both drying time and drying temperature. For instance, at \(T_{S}=\) 40 \({}^{\circ}\)C, the incorporation of high temperature and long drying time leads to a significant color change. At \(T_{S}=\) 50 \({}^{\circ}\)C, although the drying temperature is the highest, the shorter drying time is the main reason for the marginal color change.  
Figure 4: The color change of carrots under different drying conditions: (**a**) The values of \(L\), \(a\), and \(b\) obtained from fresh and dried product; (**b**) Impact of drying temperature on the color change index.  
Figure 3: Fresh and dried carrot samples.
# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 1 Introduction
Solutions to the considered two problems are essential to enable reliable robotic grasping of plants in an application where plants need to be grasped and moved from one position to another, such as robotic plant propagation. The presented method for target plant detection involves using a high-performance convolutional neural network (CNN) for instance segmentation of objects even in the presence of object occlusion. For training of this CNN, firstly, a dataset from images of plant cuttings placed in random positions on a flat surface with varying degrees of occlusion was created. The dataset is a combination of real and synthetic images, as explained below. Using all available plant cuttings instances from real images, we have developed a framework to synthesize two-dimensional (2D) images as described in Section 3.1. The dataset of overlapping plant cuttings was created using Vaccinium cuttings, which means that the objects of interest are characterized by a thin stem with a number of overlapping leaves, as can be seen in Figure 1.  
To grasp the plant cuttings for vision-guided robotic grasping applications, each cutting should first be segmented. For this purpose, the problem of possible occlusions should be addressed. In the work presented in this paper, the occlusion problem is addressed using the method presented in [13], which details a robust network for segmenting objects with complex shapes and severe occlusion. The grasp should be detected for each segmented targeted plant cutting. In the presented system, a single RGB camera is assumed to remain in a fixed position and at a constant distance from the flat surface with the plant cuttings, thus an approach to detect the plant grasp in a 2D RGB image was followed. To cope with the specific shape characteristics of the considered objects of interest, plant cuttings with thin stems and a number of overlapped leaves, we developed a novel effective conventional image processing (CIP)-based method for finding the optimal plant cutting grasp. Figure 1 presents our proposed pipeline for grasp detection of plant cuttings on a flat surface to enable their further grasping by a robot. The rest of the paper is organized as follows. In Section 2, an overview of related work is given, with a focus on instance segmentation methods for occlusion handling and detection of target objects' grasps in 2D images based on object segmentation results. Section 3 presents our dataset named the \"occluded plants\" dataset, as well as the details of the developed methods for segmentation of plant cuttings and detection of grasps. The evaluation results of both methods are given in Section 4.
## 3 Hardware Design
### Power Supply Module Design  
According to the requirements of the Technical Specification for Remote Monitoring Devices for Elevators (Trial), the remote monitoring device should be equipped with an emergency power supply, and the remote monitoring device should be capable of storing and sending the status of the device before power failure, and at the same time, it should ensure that an elevator's image acquisition device works for at least one hour. Therefore, the elevator monitoring device must be continuously powered by the main power supply during normal operation and automatically switched to its own battery for continuous power supply when an elevator power failure occurs. The power supply module consists of three parts: a 12 V to 5 V step-down part, as shown in Figure 7a, F1 and D2 are used to protect the circuit, R8 is used for voltage division to ensure the stability of the '5 VOUT' voltage in the figure, C10 and C11 are filter capacitors; a lithium battery boosting part, as shown in Figure 7b, VBAT-IN--battery input, VBAT-OUT--battery output, L1 and D3 guarantee voltage output, R9 and R10 are sampling resistors, C12 and C13 are filter capacitors; and a dual power supply automatic switching circuit, as shown in Figure 7c, three MOSFETs Q1, Q2 and Q3 are used to automatically switch power supply.  
Figure 6: NB-IoT module peripheral circuit design.  
Among them, the 12 V to 5 V step-down circuit uses a 78M05 linear regulator, which is able to reduce the 12 V power supply from the external DC power input to 5 V; due to the 3.7 V lithium battery having different amounts of power, with a voltage range of 2.5 V-4.25 V, in order to ensure that it can stably output the 5 V voltage, the MT3608 power supply chip is used to increase the voltage. The output voltage is between 5.14 V and 5.142 V, the test results are shown in Figure 8, the circuit can automatically switch between the main and secondary power supplies when the two voltages are the same, and the main power supply is used to power the main power supply when the main power supply V1 is input, and when the main power supply V1 is disconnected, it is automatically switched to the external power supply V2 for power supply.  
Figure 8: MT3608 operating characteristic curve.  
Figure 7: Circuit design of power supply section. (**a**) The 12 V to 5 V step-down circuits; (**b**) Li-ion battery boost circuitry; (**c**) dual power supply automatic switching circuits.
###### Abstract  
This manuscript describes a sensitive, selective, and online in-tube solid-phase microextraction coupled with an ultrahigh performance liquid chromatography-tandem mass spectrometry (in-tube SPME-UHPLC-MS/MS) method to determine chlorpromazine, clozapine, quetiapine, olanzapine, and their metabolites in plasma samples from schizophrenic patients. Organic poly(butyl methacrylate-co-ethylene glycol dimethacrylate) monolith was synthesized on the internal surface of a fused silica capillary (covalent bonds) for in-tube SPME. Analyte extraction and analysis was conducted by connecting the monolithic capillary to an UHPLC-MS/MS system. The monolith was characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectrometry (FTIR). The developed method presented adequate linearity for all the target antipsychotics: R\({}^{2}\) was higher than 0.9975, lack-of-fit ranged from 0.115 to 0.955, precision had variation coefficients lower than 14.2%, and accuracy had relative standard error values ranging from \(-\)13.5% to 14.6%, with the exception of the lower limit of quantification (LLOQ). The LLOQ values in plasma samples were 10 ng mL\({}^{-1}\) for all analytes. The developed method was successfully applied to determine antipsychotics and their metabolites in plasma samples from schizophrenic patients.  
in-tube SPME; UHPLC-MS/MS; organic-based monoliths; antipsychotics; plasma samples; schizophrenic' patients +
Footnote †: journal:  
Article
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 5 Artificial Intelligence Applied to Building Height Estimation
### Height Comparison Method  
In this section, building height is estimated using the 2D information from the image. On a given street, there are certain objects with known height, such as cars, people or the windows and doors of buildings. By comparing the size of the building with the size of a reference class such as those mentioned, we are able to obtain the building height. Windows and doors would be our best option as a reference class, but unfortunately, these are not labels contained in the training dataset. Hence, in order to use these objects as a reference class, we would need to create our own semantic segmentation dataset, which is a task beyond the capabilities of this research. We therefore decided to use cars and people as the reference classes, although cars only provide a good reference if they are parked near the building that is being measured.  
Local building height is obtained as the quotient on each column of pixels belonging to a building in the segmented image and pixels belonging to one of the reference classes multiplied by the height of the latter. The result of this operation is a vector with a length equal to the horizontal dimension of the image.  
This method is only accurate if the reference object is next to the building. If this is not the case, then the reference object will appear larger on the image, and the measurement will not be valid. This may occur if street geometry is not standard, such as at an intersection, or where there is a moving car or a person crossing the street. Reference objects with non-standard sizes can also lead to inadequate measurements. As the output is the average height prior to calculating the mean of the local building height vector, invalid data can be statistically discarded. This is done by calculating the mean and standard deviation of the local height vector and by cleaning the vector of data that deviate excessively from the mean value. Invalid measurements tend to be lower than the true height, as inappropriate reference objects appear larger in almost every case. Hence, positive deviation over the mean of the height vector should be less tolerated than negative deviation. Should multiple images and reference classes be used, it is possible to perform more statistical computations to further clean up the height vectors.
## 2 Materials and Methods
### Opportunistic Grouping Strategy for the Preventive Maintenance Plan
#### 2.2.3 An Optimization Model for the Maintenance Grouping Framework  
Based on the proposed framework, the optimization model considers the following decision variables and parameters (See Table 3).  
The following optimization model is formulated under the mixed-integer linear programming (MILP) paradigm. The model considers the following set of constraints for the use of time-window tolerances for the opportunistic grouping scheme:  
\[\min\ \sum\begin{array}{c}\begin{array}{c}j\epsilon J_{i}\\ i\ e\ I\end{array}\ \ d_{i,j}\end{array} \tag{8}\]  
s.t.  
\[t_{i,j}^{s}\leq t_{i,j-1}^{f}+T_{i}+e_{i}\cdot T_{i}\forall j\in J,\ \forall i\in I \tag{9}\]  
\[t_{i,j}^{s}\geq t_{i,j-1}^{f}+T_{i}-e_{i}\cdot T_{i}\forall j\in J_{i},\ \forall i\in I^{\prime} \tag{10}\]  
\[t_{i,j}^{f}=t_{i,j}^{s}+r_{i,j}\forall j\in J_{i},\ \forall i\in I \tag{11}\]  
Figure 6: Application example. Schematic representation of the optimal solution for the instance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Decision Variable** & **Description** & **Nature** \\ \hline \(t_{i,j}^{s}\) & Start time for the \(j\)-th & \(t_{i,j}^{s}\in\mathbb{R}_{0}^{+}\) \\ \(t_{i,j}^{f}\) & Resumption time for the \(j\)-th & \(t_{i,j}^{f}\in\mathbb{R}_{0}  
of the unavailability associated with each execution. In this sense, constraint set (23) ensures that for the set of variables \(\left\{u_{i,\ j},\ v_{i,j,k,l},v_{i,j,n,o}\right\}\), only one of them is activated for each pair \((i,\ j)\), establishing precisely the best adjusted upper bound, so that the expression (27) is translated into:  
\[r_{i,j}=\max_{\begin{array}{c}i,i^{\prime}\in I:\\ i\
eq i^{\prime},\\ z_{i,j,i^{\prime},i^{\prime}}=1\end{array}}\left\{p_{i^{\prime}}\right\} \tag{28}\]  
In this way, the set of constraints (17)-(23) allows a correct assignment of the downtime values through variable \(r_{i,j}\). Constraints (24) and (25) limit the growth of these variables in the absence of grouping schemes, so that \(v_{i,\ j,\ n,\ o}=0\), if \(z_{i,\ j,\ n,\ o}=0\). Therefore, constraints (16)-(25) generate the following behavior: if the tuple \((i,\ j)\) is grouped, then its associated downtime corresponds to the expression (28); otherwise, this time corresponds to \(r_{i,\ j}=p_{i}\). Additionally, constraint (26) is proposed to correctly count the unavailability associated with each work package only once. Finally, the objective function corresponds to the minimization of expression (8). In this way, the counting of \(d_{i,\ j}\) corresponds exactly to the unavailability of the system, so that expression (8) corresponds to the minimization of the system's downtime. In summary, the model conformed by the objective function (8) and constraints (9)-(26) corresponds to the optimization model for the opportunistic grouping framework proposed in Section 2.2.1.