esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Variable Pole Pitch (VPP) Electromagnetic (EM) Propulsion
### Numerical Analysis of VPP EM Propulsion
After comparison with the practical standard of an aluminum secondary, with tensile strength \(\sigma_{\mathrm{b}}=124\) MPa, shear strength \(\tau_{\mathrm{b}}=165\) MPa, and permitted deflection \(k_{\mathrm{b}}=0.05b_{2}=5\) mm, the LS-secondary DLIM seizing for the VPP EM propulsion system was shown to have met the structural requirements. In summary, the performance of the VPP EM propulsion system changes depending on the operating conditions, and a numerical analysis is therefore essential. By referring to the numerical calculations of VPP EM propulsion, it can be determined whether the type of VPP matches the desired propulsion system and how best to optimize the system while taking into account the practical factors.
# Coordination in Emergency Response System Design:
## 5 Illustrative Example  
Figure 2 depicts a network, where the locations of an ambulance station and a fire station are also shown. Figure 3 shows the response times for each node in the illustrative example, assuming 60 km/h velocity, as well as their averages, which we use as the targets. Based on these values, the average waiting time is 4 min.  
As shown in Figure 4, Nodes 12, 13, and 14 are in the preferred response time area. Nodes 4 and 11, that host one of the existing stations, are in the adequate response time area, since the wait time among the two resources is 8 eight minutes. The rest of the nodes are in the poor response time area.  
Figure 3: The response time of ambulance stations (_RTAS_) and fire stations (_RTFS_): (**a**) _RTAS_; (**b**) _RTFS_.  
Figure 2: An illustrative example.  
We solve the proposed model on the active standard platform of CPLEX 12.5 on a computer equipped with 2.40 GHz Inter processor i7-5500U and 8 GB RAM. Table 3 shows the optimal solution for different [NA, NF] values. (In the remainder of the paper, [..,] denotes [NA,NF] values referring to the numbers of ambulances and fire stations to be established (existing + new). This is not to be confused by a citation to the list of references, [..])  
It is interesting to note that, when either NA \(\geq\) 3 and NF \(\geq\) 3, the number of the new ambulance and fire stations do not change. Also, although the optimal solution constructs the ambulance and fire stations at different sites, the resulting emergency response is the same as that of NA = 3 and NF = 3, which implies a smaller budget.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Approaches to Overcome Coffee Mapping Challenges
However, studies using Sentinel-1 radar data [75; 79; 81; 82] still use multiple images to gather additional data and measure seasonal variability. Sentinel-1's twelve-day revisit time per satellite, or six days for the constellation, helps effectively create imagery time series. While we found only one study using SAR to map coffee, researchers have used SAR data to map various croplands and forest types and thus, could inform coffee mapping methodologies. SAR data could also allow researchers to study the structure of the observed vegetation, which would be useful in differentiating shaded coffee production systems from surrounding forests. However, the displacement effects of the backscatter signal together with relief shadows can limit the efficacy of SAR data in complex topographies. Although we aimed to review literature that can successfully map different coffee-growing systems, we only found one publication that mapped five different production systems [92], and we did not find any literature focused on mapping the five production systems that we had defined to frame our review (unshaded monoculture, shaded monoculture, commercial polyculture, traditional polyculture, and rustic). One reason for the limited number of published studies on coffee mapping could be a positive publication bias, where unsuccessful studies or inaccurate maps are not published. It is also possible that more research on mapping the five coffee systems has been published in non-English journals as coffee is mostly grown in the global tropics where English is not the dominant language. Such publications would not be captured as effectively in our literature review. Despite the limited number and variety of published studies, it is nevertheless still possible to make recommendations based on our findings.
# Energy Efficiency Improvement of Diesel-Electric Trains Using Solar Energy: A Feasibility Study
## 4 Methodology
The investment and O&M costs: The initial cost should include all capitalized preoperative and setup costs, as well as the cost of equipment, land, installation expenses, and EPC charges. At the same time, the O&M costs encompass routine maintenance of the equipment and minor part replacement to ensure maximum electricity generation [35]. 5. The financial parameters: Investing in a project requires understanding different economic variables to make an informed decision. Here are a few such variables: * Inflation rate (%): The projected annual average inflation rate over the project's life. * Project life (year): The duration over which the project's financial viability is evaluated. * Debt ratio (%): The debt ratio reflects the financial leverage created for a project; the higher the debt ratio, the more significant the financial leverage. * Debt interest rate (%): The annual rate of interest paid to the debt holder at the end of each year of the debt term. * Debt term (years): The number of years over which the debt is repaid. The debt term is equal to or shorter than the project life [35]. 6. The solar electricity price: In general applications, RetScreen uses market electricity price (approximately 0.1 $/kWh) to determine the revenues associated with solar energy production. In this study, the energy produced by the PV solar panels replaces the energy produced by the auxiliary generator. The electricity price is thus considered to be the one produced by a diesel generator at 0.3 $/kWh [36]. 7. The GHG emission parameters: The RetScreen software requires the GHG emission factor (excluding transmission and distribution (T&D) losses) for the alternative electricity source. The units are tons of CO\({}_{2}\) per megawatt-hour of end-use electricity delivered (t CO\({}_{2}\)/MWh). If any, the user enters an optional GHG reduction credit per equivalent ton of CO\({}_{2}\) (t CO\({}_{2}\)). It is used in conjunction with the net GHG reduction to calculate the annual GHG reduction revenue. However, GHG reduction credits' prices per equivalent ton of CO\({}_{2}\) (t CO\({}_{2}\)) vary widely depending on how the credit is generated and how it will be delivered [37]. The main results from RetScreen used for analysis are: (1) the ROI of the project (equity payback), which represents the length of time that it takes for the owner to recoup its initial investment (equity) out of the project cash flows generated, and (2) GHG emission reduction revenues per year ($). Then, by using a different software tool (Excel), we determine the profitability of using this method for each wagon in terms of annual energy consumption and the percentage of each wagon's consumption provided by PV solar panels for each studied case. The method and the steps of profitability calculation will be presented in Section 5.1.
## 6 Reliability Prediction Model
### AdaBoost-MKRVM  
Adaptive Boosting (AdaBoost) is a classical boosting algorithm used to improve the performance of a model. Its core idea is to improve model accuracy by combining multiple weak learners to form a strong learner. Although MKRVM has powerful feature extraction capabilities, a single MKRVM may still be affected by data noise or local extreme values. In order to further improve the generalization ability of the model, the AdaBoost mechanism was used in this study to integrate multiple MKRVMs to build a stronger learner for reliability prediction of rolling bearings. The following is a detailed description of the AdaBoost-MKRVM algorithm:  
1. Initialize weights: Suppose the training dataset is \(\{(x_{1},r_{1}),(x_{2},r_{2}),\ldots,(x_{n},r_{n})\}\), where \(x_{n}\) is the input sample and \(r_{n}\) is the corresponding label. Initialize the weight \(h_{i}\) of the \(i\)-th sample, set \(h_{i}=\frac{1}{n}\), meaning all samples have equal weight.
2. Iteratively train the MKRVM weak learners: Input the initial feature parameters into the MKRVM to obtain the \(j\)-th initial weak learners: \[Z_{j}=R\big{(}x_{j},r_{j},h_{j}\big{)}\] (28) Calculate the error \(\epsilon_{j}\) of the weak learners \(Z_{j}\): \[\epsilon_{j}=\sum_{j=1}^{n}h_{j}\cdot I(r_{j}\
eq h_{j}(x_{j}))\] (29) where \(I(y_{j}\
eq h_{j}(x_{j}))\) is the indicator function, which takes the value of \(1\) when \(y_{j}\
eq h_{j}(x_{j})\), and \(0\) otherwise. Adjust the weight coefficient \(\alpha_{j}\) of the \(j\)-th sample based on its error: \[\alpha_{j}=\tfrac{1}{2}\ln\left(\tfrac{1-\epsilon_{j}}{\epsilon_{j}}\right)\] (30) Next, according to the magnitude of the weight coefficients, redistribute the weights of each sample and adjust the sample distribution. The weight of the \(j\)-th sample is adjusted to \[h_{j}\gets h_{j}\cdot\exp(-\alpha_{j}\cdot r_{j}\cdot h_{j}(x_{j}))\] (31)
3. Construct the strong learners: The final strong learners \(M(x)\) is composed of the weighted sum of \(P\) weak learners, with the formula \[M(x)=\text{sign}\!\left(\sum_{j=1}^{P}\alpha_{j}\cdot Z_{j}(x)\right)\] (32)In each iteration, AdaBoost focuses on samples that have been misclassified by the previous round of learners, and by gradually adjusting the sample weights, subsequent learners can better handle samples that are difficult to classify. The algorithm is simple and easy to operate, and has strong generalization ability.  
The combination of AdaBoost and MKRVM can give full play to the advantages of both, thus significantly improving the generalization ability and accuracy of the model. AdaBoost improves adaptability and robustness to complex data by integrating multiple weak classifiers, while MKRVM takes advantage of kernel functions to efficiently handle nonlinear problems. After the combination of the two, the model can not only capture data features more comprehensively, effectively reducing the risk of overfitting, but also maintain a high computational efficiency, which is an effective strategy to improve performance. The specific flow chart of the algorithm in Figure 4:
# Obstacle Avoidance Drone by Deep Reinforcement Learning and Its Racing with Human Pilot
## 5 Experiment
### Evaluation of Networks
#### 5.3.2 Experiment on Robustness of Actor Network  
As previous studies suggested that the segmentation model could offer robustness against a certain environmental change, experiments were designed to see how an actor network and the corresponding U-Net segmentation model would perform when there was a change in the environment. Since our actor network could learn to follow the maximum reward zone suggested by U-Net, the actor network could perform well if the segmentation would be successful with given obstacles.  
Our segmentation network combined with the actor-critic network was trained using the arena track with Figure 12a. Three more tracks were reconfigured from the arena (a) by calculating how much each track was changed in ratio \(\sigma\). The arena track (b) consists of shuffled obstacles (\(\sigma=65\%\)), having the same 4 large obstacles, whereas the arena track (c) was made by shuffling large and small obstacles as well as by adding new obstacles with different colors (\(\sigma=75\%\)). The arena track (d) was made from (c) with more obstacles (\(\sigma=81\%\)). Results suggest that our network cooperating with U-Net could reach the goal in (b) and (c), where there were changes about 65% and 75%, respectively, from (a). However, when the environment was too different from what it had been, such as 81% in (d), it failed to reach the goal probably because of a severely reconfigured environment. Note that \(\sigma\) has been calculated as follows:  
\[\sigma=\frac{\text{Number of obstacles added or changed from track (a)}}{\text{Number of obstacles in a target track}}\times 100 \tag{29}\]  
Figure 11: Illustration of drone flight trajectories by two deep reinforced learning (RL) algorithms such as DD-DQN (**a,c**) and ACKTR (**b,d**). (**a,b**) were collected from the woodland and (**c,d**) from the block world, respectively. Here, training models at step 90 k (green), 95 k (yellow), and 100 k (red) were deployed for each algorithm. Note that DD-DQN made discrete trajectories, whereas ACKTR generated continuous trajectories. Note that, in each panel, only 3 trials among many are drawn for clarity.
## 3 Results and Discussion
### Editing the YOLOv2 Output Data into a Convenient Form
#### 3.4.2 Thresholding by Logical Indexing  
Thresholding is a simple and intuitive image segmentation method in which objects of interest are extracted from the background. During this step, an input grayscale image is converted to a binary image only containing values 0 (black) or 1 (white). If the pixel value in an input image is greater than a threshold value of \(T\), the output pixel is assigned a value of 1 and called an object point. When the pixel value is smaller than \(T\), the output pixel is assigned a value of 0 and called a background point. Thresholding can be described with the following equation:  
\[r(x,\,y)=\left\{\begin{array}{ll}1,\,o(x,\,y)>\,T\\ 0,\,o(x,\,y)\,\leq\,T\end{array}\right. \tag{14}\]  
where \(r(x,\,y)\) is the threshold image and \(o(x,\,y)\) is the grayscale image [35]. The threshold \(T\) was set using the histogram of the grayscale image containing a joint between two concrete  
Figure 8: Illustration of dividing an orthomosaic into a square grid with 224 \(\times\) 224 image blocks.  
slabs filled with asphalt grout (Figure 9a). A histogram is a useful tool for identifying an appropriate threshold, where the x-axis is a gray level intensity (in the case of RGB image intensity of each color channel), and the y-axis is a count of pixels in the given intensity. The dark color of an asphalt grout can be seen in the histogram on the left-hand side of the gray level intensity scale (Figure 9b); thus, the threshold was experimentally set to value \(T=90\). Based on this threshold value, concrete slabs were formed as the black-colored background (0) and the asphalt grout sealing the joints were formed as the white-colored foreground object (1). The experimentally set threshold was based on the assumption that the runway inspection is carried out after the end of the winter season when the length of the daily sunshine period is extended. The UAV flights were conducted in clear weather when the sun was directly over the horizon outside the first and last hours of the solar day to avoid the time when the light was soft, the shadows were long, and the color of the light was significantly warmer. Another factor to consider is the presence of clouds, which results in more dark pixels in the gray level intensity scale in the histogram. Adjusting the threshold \(T\) toward lower values would be necessary in these cases.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### System Model  
Based on the defined variables, we present the system model that describes how the sensor data are collected by the receiver drone.
# Automatic 2D Floorplan CAD Generation from 3D Point Clouds
## 3 Proposed Method  
The input to our method consists of unstructured 3D point clouds, and the output is the 2D floorplan CAD having topological relationships between the walls. The proposed method is composed of the 3D reconstruction and 2D floorplan CAD generation parts. In the first part, walls are detected as follows:1. 3D point cloud data preprocessing
2. Floor segmentation based on horizontal planes
3. Wall proposals based on vertical planes for each floor
4. Wall detection using the horizontal projection of wall proposals
5. IFC file generation from the detected wall points  
In the second part, we create structural elements from the IFC file, after which, based on this information, we generate the 2D floorplan CAD. Our approach is fully automated and implemented using the PCL (point cloud library) the most popular library that works with 3D point clouds [27]. Figure 1 shows the overall flow of the proposed method. The code for the proposed method is available at [[https://github.com/joyjo/to-generate-2D-floorplan-CAD-from-3D-point-clouds](https://github.com/joyjo/to-generate-2D-floorplan-CAD-from-3D-point-clouds)]([https://github.com/joyjo/to-generate-2D-floorplan-CAD-from-3D-point-clouds](https://github.com/joyjo/to-generate-2D-floorplan-CAD-from-3D-point-clouds)).
# On the Redundancy in the Rank of Neural Network Parameters and Its Controllability
## 4 Proposed Method
### Making a Matrix Rank Deficient  
We need to find \(\mathbf{a}\) that satisfies (4), which in turn identifies vectors that can be represented with a linear combination of the other vectors. However, (4) has infinitely many solutions since for any \(\mathbf{a}\) that satisfies (4), \(k\mathbf{a}\) where \(-1\leq k\leq 1\) also satisfies it. Furthermore, without explicit control on the interdependence between vectors of \(\mathbf{W}\), there is no mean to impose any bounds in \(\boldsymbol{\xi}\). To alleviate this issue, we propose a novel objective function that encourages a matrix to be rank deficient.  
A matrix is rank deficient when there are linearly dependent vectors, and the most simple form of linear dependence is \(\mathbf{v}_{i}=k\mathbf{v}_{j},k\
eq 0\in\mathbb{R}\). This means that \(\mathbf{v}_{i}\) and \(\mathbf{v}_{j}\) are parallel or opposite to each other. Therefore, an objective function that encourages vectors to be this way can make the matrix rank deficient. Among the family of trigonometric functions, the value of sine function gets closer to zero when the angle between two vectors approach either \(0^{\circ}\) or \(180^{\circ}\). Based on this fact, we build our objective function upon the pairwise squared sine values of a matrix.  
Another important factor to consider is the task-specific training objective of the network. When the neural network is optimized for a task, the process can be seen as finding the set of basis vectors of the affine frame that would lead to a desirable feature space transformation. However, minimizing the pairwise squared sine values of a matrix could push these basis vectors to other directions, which in turn can act adversarially against the model's primary training objective. Our preliminary experiments also supports this hypothesis, where naively minimizing the pairwise squared sine value led to unstable training and inferior performance.  
To avoid this side-effect, we design the objective function so that it first chooses a pair of vectors that is the closest to being parallel or opposite, and pushes it further towards this direction. More formally, for a matrix \(\mathbf{W}\in\mathbb{R}^{m\times n}\), we propose RaRe (Rank Reducing) loss which is defined as follows:  
\[RaRe(\mathbf{W})=\min_{i}\min_{j\
eq i}\Bigl{(}1-D_{cos}(i,j,\mathbf{W})^{2} \Bigr{)} \tag{5}\]  
\[D_{cos}(i,j,\mathbf{W})=\frac{\mathbf{w}_{i}\cdot\mathbf{w}_{j}}{\|\mathbf{w} _{i}\|\|\mathbf{w}_{j}\|}, \tag{6}\]  
where \(\mathbf{w}_{i}\) denotes the \(i\)th column of \(\mathbf{W}\). Since this loss function affects only a single pair of vectors, it can only reduce the rank of a matrix by 1. This, however, is not a problem since this pair will be merged into a single vector using the method described in the following subsection. An alternative formulation which reduces the squared sine value of \(k\) pairs instead of one is possible but performed worse than the proposed form in our preliminary experiments.
# Raman Spectroscopy Analysis of Free Fatty Acid in Olive Oil
## 2 Experiment  
Five extra virgin olive oil samples, one pomace olive oil sample, and their mixtures have been investigated. Sample A: Pago Baldios San Carlos Extra Virgin Olive Oil (originally from Spain) and sample B: Lorenzo No. 1 Extra Virgin Olive Oil (originally from Italy) were purchased from a Shinsegae Department Store, in Seoul, Korea. Sample C is Huilerie Loued Extra Virgin Olive Oil (originally from Tunisia), which was purchased from a duty-free store in Wuhan, China. Sample D: Luhua Extra Virgin Olive Oil (originally from Spain) and sample E: Olivoila Extra Virgin Olive Oil (originally from Italy) were purchased from a Carrefour store in Wuhan, China. Sample F is Costa d'Oro Pomace Olive Oil (originally from Italy), which was purchased from Taobao in China. The FFA contents of Sample A, B, C, D, E, and F were 0.1%, 0.15%, 0.4%, 0.6%, 0.8%, and 3%, respectively; which were declared by the producers of these samples. Also, the mixtures of C and F, C and D, D and E, and C and E were prepared by mixing two samples with a desired volume ratio under stirring, then waiting one day for equilibrium.  
Raman spectra of the samples were obtained in backscattering configuration with two different micro-Raman systems, XperRam200 system from NANOBASE company and DXR system from ThermoFisher company (Waltham, MA, USA). For the XperRam200 system, the excitation source was a 532 nm green laser. For the DXR system, two excitation sources were used: a 633 nm red laser and a 785 nm near infrared laser. The scattered signal was detected by air-cooled charge-coupled device (CCD) detector. The samples were placed on glass flasks. The laser power on sample was varied up to 5 mW to check the laser power influences on the Raman spectral features of the samples, which indicated that laser heating or damage of the samples was negligible in our experiment. All the spectra were recorded at room temperature about 25 \({}^{\circ}\)C with 15 s acquisition time and 2-scan average. All the spectra were baseline corrected with Origin software, and wavenumber calibrated using the 521 cm\({}^{-1}\) vibrational mode of a Si substrate. Also, the Si spectrum was measured after experiments to ensure the calibration was not changed during the measurements. For each sample, 5 random spectra were measured and the average spectrum was used for the analyses of relative intensity ratios.