esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of a Resonance-Tunable BWT
### Hardware Design Parameters  
#### 3.2.1 Mast Design Parameter  
The mast is cylindrical in shape, as illustrated in Figure 1. It is made up of a polycarbonate tube with a thickness of 3 mm, which is sufficiently rigid and lightweight. The mast is supported by a rigid stainless steel rod (SUS304) with a diameter of 5 mm and a length of 0.4 m. The rod is pivoted by the pin joint to restrict the BWT's oscillation to a one-axis direction to simplify the vibration analysis. To reduce the energy loss from friction when the mast vibrated, ball bearings were used in the pin joint, which was located near the center of the rod.  
The diameter of the mast \(\phi\) and the wind velocity \(v\) were established as reference parameters to design the stiffness and resonance of the structure of the system. The mast was a cylinder with a diameter \(\phi=0.15\) m and a height of 0.4 m, and it was considered for testing using wind tunnel equipment. The total mass of the cylinder mast with the rod and the connecting part was measured to be 1.64 kg. With the lengths \(l_{1}\) and \(l_{2}\) of 0.2 m, the rotational moment of inertia \(J\) was calculated to be 0.0738 kg\(\cdot\)m\({}^{2}\).
# Uplink NOMA-MIMO Systems with Rayleigh Power Distribution
## 3 Capacity of Discrete-Continuous MU-MIMO Uplink Channel
\begin{split}&\overline{C}_{(bps/Hz)}=UN_{\rm{kr}}k_{b}-\frac{N_{ br}}{\log 2}-\\ &\frac{1}{2^{UN_{\rm{kr}}k_{b}}}E_{\rm{H}}\Bigg{\{}\sum\limits_{i= 1}^{2^{UN_{\rm{kr}}k_{b}}}\int\limits_{Z_{i}}\frac{1}{\left(2\sigma_{\eta}^{2} \right)^{N_{\rm{rx}}}}\exp\left(-\frac{\|Z_{i}\|^{2}}{2\sigma_{\eta}^{2}} \right)\log_{2}\left(\sum\limits_{j=1}^{2^{UN_{\rm{kr}}k_{b}}}\exp\left(- \frac{\|Z_{i}-\widetilde{\mathbf{H}}\left(X_{j}-X_{i}\right)\|^{2}}{2\sigma_{ \eta}^{2}}\right)\right)dZ_{i}\Bigg{\}}\approx\\ & UN_{\rm{kr}}k_{b}-\frac{N_{br}}{\log 2}-\frac{1}{N_{MK}}\sum \limits_{n=1}^{N_{MK}}\log_{2}\left(\sum\limits_{j=1}^{2^{UN_{\rm{kr}}k_{b}}} \exp\left(-\frac{\|Z^{(n)}-\widetilde{\mathbf{H}}^{(n)}\left(X_{j}-X^{(n)} \right)\|^{2}}{2\sigma_{\eta}^{2}}\right)\right)<UN_{\rm{kr}}k_{b}\end{split}  \tag{20}\]  
Figure 2 shows the dependencies of capacity of discrete-continuous MU-MIMO uplink channel (solid lines) for the following conditions: \(N_{tx}=2,\;\;N_{rx}=4\), QPSK modulation, number of users \(U=[1\;2\;3\;4]\), which corresponds to the channel load level of \(50\%\), \(100\%\), \(150\%\) and \(200\%\). Dotted lines are used to plot the dependencies of the continuous channel bandwidth. Solid and punctured curves of the same color correspond to the same values of number of users. Figure 2: The dependencies of the MU-MIMO uplink channel capacity for discrete–continuous (solid lines) and continuous (dotted lines) channels on the SNR ratio for different numbers of users. Figure 3 shows the dependencies of the MU-MIMO uplink channel capacity of a discrete-continuous channel on the number of users (system load) at different SNR ratios. The upper border is also shown there (dash dotted black line). The results on Figures 2 and 3 show that for a discrete-continuous channel with high SNR ratios the total channel rate increases linearly with the number of users. This allows us to conclude that such systems can work in overload mode.
# Exploring Benefits of Using Blending Splines as Transition Curves
###### Abstract  
Track geometry is a fundamental subject in railway construction. With the demand for increased capacity in terms of load and speed, the need for suitable transitions between consecutive track sections is highly relevant. Properly constructed transition curves lead to improved travel comfort, increased safety, and reduced wear. The well known clothoid curve is widely used as a transition curve; however, the linear curvature is not sufficiently smooth to meet the requirements for railways carrying high speed trains or heavy hauls. Blending spline curves are flexible spline constructions possessing favourable smoothness properties at the end points, which makes them considerable for use as transition curves. This paper demonstrates some selected blending splines applied as transition curves between two existing circular arc segments selected from the Ofotbanen railway. The main results in this paper are related to the smoothness at the end points and the behaviour of the curvature of the curves, where the new transition curves were shown to be smoother than the original clothoid. Another new result is the observation that the proposed method allows for the improvement of existing railways without forcing extensive changes to the original track. Some representative examples are included to highlight the flexibility of this first instance of blending splines as transition curves.  
blending spline; clothoid; curvature; railway; transition curve +
Footnote †: journal: Applied Sciences
## 4 Enabling Technologies for FSO Communication
### Aperture Averaging  
Aperture averaging is a crucial technique in FSO communication systems, employed to mitigate the adverse effects of AT. By averaging the optical signal over a larger aperture, this method effectively reduces signal fluctuations and enhances communication reliability. This section will explore the principles of aperture averaging, its advantages in mitigating the impacts of turbulence, and its implications for the future of FSO communication systems.
## 5 Classification of EV Charging Infrastructures
### Microgrid-Based EV Charging Infrastructure
#### 5.3.1 DC-Microgrid-Based EV Charging Infrastructure  
The utilization of a DC-microgrid-based EV charging infrastructure enables the rapid and cost-effective charging of multiple EVs. The EV charging systems mentioned are considered superior to traditional AC charging methods due to their utilization of direct current technology. Owners of EVs can exert more control over the charging process with the help of DC microgrids, and they can also obtain more detailed information about their energy usage. The ability to connect to renewable energy sources such as solar and wind power enables the enhanced efficiency in their utilization. The devices can be easily retrofitted into pre-existing setups by connecting them to the larger electrical grid. DC-microgrid-based EV charging stations are highly suitable for installation in both urban and rural areas owing to their compact dimensions and exceptional efficiency [83; 84]. Figure 20a,b depicts a standard DC microgrid configuration, showcasing both grid-connected and non-grid-connected structures, respectively. This setup utilizes various energy sources, such as solar power, wind power, diesel generators, and fuel cells, along with energy storage devices. The purpose of this arrangement is to establish an efficient charging infrastructure.  
Figure 19: Classification of microgrid-based EV charging infrastructure.
# Optimal Siting and Sizing of FACTS in Distribution Networks Using the Black Widow Algorithm
## 1 Introduction
### Literature Review
Numerical results in the IEEE 33- and IEEE 69-bus grids showed an efficient numerical performance regarding processing times in comparison with the vortex search algorithm. Additional works regarding the optimal placement of FACTS in electrical distribution networks include sensitivity factors based on stability and power losses indices [26], genetic algorithms [27], and convex approximations [16], among others. Note that the main feature of these works is the use of an economic objective function that seeks to minimize the expected energy loss costs, including the investments made in reactive power compensators.
# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 3 Establishment and Handling of Mathematical Model
### Mathematical Modeling  
First, introduce several variables: Let \(x_{ijk}\) represent whether rider \(k\) travels from point \(i\) to point \(j\). It takes the value 1 if true and 0 otherwise. Let \(L_{ik}\) represent the vehicle load of rider \(k\) after completing order \(i\). Therefore, the mathematical programming model for the takeout delivery problem discussed in this paper can be expressed as follows:  
\[\min Z_{1}=\sum_{i\in V}\sum_{j\in V}\sum_{k\in K}c_{ij}d_{ij}x_{ijk}+c_{0} \sum_{j\in V}\sum_{k\in K}x_{0 jk}+\sum_{i\in I}\max(ca_{i},c^{\prime}a_{i},c^{ \prime\prime}a_{i},0) \tag{1}\]  
\[\max Z_{2}=\sum_{k\in K}R_{k}/m \tag{2}\]  
\[\max Z_{3}=\sum_{i\in I}s_{i}/n \tag{3}\]  
\[\sum_{j\in V}\sum_{k\in K}x_{ijk}=1,\forall i\in I \tag{4}\]  
\[\sum_{j\in V}\sum_{k\in K}x_{ijk}=\sum_{j\in V}\sum_{k\in K}x_{j,n+i,k}, \forall i\in I,\forall k\in K \tag{5}\]  
\[\sum_{i\in V}x_{ijk}=\sum_{i\in V}x_{jik},\forall j\in V,\forall k\in K \tag{6}\]  
\[t_{jk}\geq t_{ik}+h_{i}+d_{ij}/v-M(1-x_{ijk}),\forall i,j\in V,\forall k\in K \tag{7}\]  
\[t_{ik}\leq t_{n+ijk},\forall i\in I,\forall k\in K \tag{8}\]  
\[L_{jk}\geq L_{ik}+q_{i}-M(1-x_{ijk}),\forall i,j\in V,\forall k\in K \tag{9}\]  
\[L_{ik}\leq Q,\forall i\in V,\forall k\in K \tag{10}\]  
\[x_{ijk}\in\{0,1\},t_{ik}\geq 0,L_{ik}\geq 0,\forall i,j\in V,\forall k\in K \tag{11}\]  
Figure 3: Priority order penalty cost function.  
In this model, the objective function (1) represents the minimization of the total costs for the takeout platform, which consists of the vehicle travel cost, vehicle start-up cost, and penalty cost for priority orders. Here, \(c_{0}\) is the fixed cost for starting up a single vehicle. Objective function (2) represents the maximization of the average rider earnings. Objective function (3) represents the maximization of the average customer satisfaction. Constraint (4) ensures that every order is serviced. Constraint (5) ensures that each order is served by the same rider and follows the pick-up-before-delivery rule. Constraint (6) guarantees the continuity of each rider's route. Constraints (7) and (8) ensure the correctness of the start service time for each order. Here, \(h_{i}\) represents the time spent by the rider for either picking-up or delivering at point \(i\), and \(M\) is a sufficiently large positive number. Constraints (9) and (10) ensure the correctness of vehicle capacity at each point. Constraint (11) defines the range of values for the variables.
## 4 Methodologies and Materials
### Dependency Parsing  
Given a sentence from the dataset, first the sentence is tokenized with the tokenization tool \"jieba\". Then, dependency parsing is launched for the tokenization list by the tool of Stanford CoreNLP [37]. After the dependency graph is output by dependency parsing, the dependency relations are first encoded into an adjacency matrix \(A\). In particular, if there is a dependency edge existing between node \(i\) and \(j\), then \(A_{ij}=1\) and \(A_{ji}=1\), otherwise \(A_{ij}=0\) and \(A_{ji}=0\).
## 2 Materials and Methods
### Simulation of SIF\({}_{760}\), GPP, and ET
#### 2.3.2 Calibration for Leaf Chlorophyll Fluorescence and Photosynthesis Parameterization
Utilizing the curve fitting tool in MATLAB 2019b, we derived the values of the three parameters, \(K_{N}^{0}\), \(\alpha\), and \(\beta\), by fitting them to Equations (1)-(4). The default values of \(K_{N}^{0}\), \(\alpha\), and \(\beta\) are 1.997, 3.818, and 0.04989, respectively. One set of calibration values was obtained based on all the measured data, while two sets of calibration values were, respectively, derived according to the measured data in the rapid vegetative growth and peak growth periods. The former was used to simulate the time series of SIF, GPP, and ET during March-November 2019, while the latter two sets of calibration values were used for the simulations during June-August and August-November, and the default values were used for the simulations in the other periods. The calibrated values of \(\alpha\), \(\beta\), and \(K_{N}^{0}\) in the fluorescence parameterization based on all the measured leaf gas exchange and fluorescence data, as well as the measured data grouped by time period, are shown in Table 2. The parameters from each group are input into the SCOPE model, producing a set of default results and two sets of results subsequent to parameter calibration. A comparative analysis is then performed to assess the simulation effectiveness of the calibrated results against the default results, thereby evaluating the efficacy of the parameter calibration procedure.
###### Abstract  
The present work aims at the thermodynamic analysis of different working pairs in adsorption heat transformers (AdHT) for low-temperature waste heat upgrade in industrial processes. Two different AdHT configurations have been simulated, namely with and without heat recovery between the adsorbent beds. Ten working pairs, employing different adsorbent materials and four different refrigerants, have been compared at varying working boundary conditions. The effects of heat recovery and the presence of a temperature gradient for heat transfer between sinks/sources and the AdHT components have been analyzed. The achieved results demonstrate the possibility of increasing the overall performance when internal heat recovery is implemented. They also highlight the relevant role played by the existing temperature gradient between heat transfer fluids and components, that strongly affect the real operating cycle of the AdHT and thus its expected performance. Both extremely low, i.e., 40-50 \({}^{\circ}\)C, and low (i.e., 80 \({}^{\circ}\)C) waste heat source temperatures were investigated at variable ambient temperatures, evaluating the achievable COP and specific energy. The main results demonstrate that optimal performance can be achieved when 40-50 K of temperature difference between waste heat source and ambient temperature are guaranteed. Furthermore, composite sorbents demonstrated to be the most promising adsorbent materials for this application, given their high sorption capacity compared to pure adsorbents, which is reflected in much higher achievable specific energy.  
adsorption; heat transformer; heat upgrading; efficiency; waste heat; working pairs Article
# A 11 mW 2.4 GHz 0.18 \(\upmu\)m CMOS Transceivers for Wireless Sensor Networks
###### Abstract  
In this paper, a low power transceiver for wireless sensor networks (WSN) is proposed. The system is designed with fully functional blocks including a receiver, a fractional-N frequency synthesizer, and a class-E transmitter, and it is optimized with a good balance among output power, sensitivity, power consumption, and silicon area. A transmitter and receiver (TX-RX) shared input-output matching network is used so that only one off-chip inductor is needed in the system. The power and area efficiency-oriented, fully-integrated frequency synthesizer is able to provide programmable output frequencies in the 2.4 GHz range while occupying a small silicon area. Implemented in a standard 0.18 \(\upmu\)m RF Complementary Metal Oxide Semiconductor (CMOS) technology, the whole transceiver occupies a chip area of 0.5 mm\({}^{2}\) (1.2 mm\({}^{2}\) including bonding pads for a QFN package). Measurement results suggest that the design is able to work at amplitude shift keying (ASK)/on-off-keying (OOK) and FSK modes with up to 500 kbps data rate. With an input sensitivity of \(-\)60 dBm and an output power of 3 dBm, the receiver, transmitter and frequency synthesizer consumes 2.3 mW, 4.8 mW, and 3.9 mW from a 1.8 V supply voltage, respectively.  
wireless sensor networks; RF transceiver; receiver; transmitter; frequency synthesizer; CMOS RF IC +
Footnote †: journal: Sensors
## 3 Results
### Comparison of Different Path Planning Algorithms  
In the simulation experiments focused on different picking manipulator path-planning algorithms, namely RRT, RRT-connect, RRT-star, and T-RRT, the ROS2 node was programmed to subscribe the status topic of the manipulator and record the required data. Python NumPy, Pandas, and other libraries were used for the subsequent data analysis. The detailed data are provided in Table 2. The experimental results show that the success rate of the four algorithms was 100% in 50 experiments, which proves their effectiveness in the path-planning task. T-RRT outperformed the other three algorithms in RMSE and \(R^{2}\) by 0.002% and 99.661%, respectively. In terms of planning time, the four planning algorithms did not show significant differences in performance; however, while the T-RRT algorithm was slightly slower than RRT-connect, in fine-detail operations such as tea-picking, accuracy is often more important than speed.  
In summary, the T-RRT algorithm--with its excellent precision and acceptable speed performance--achieved the best balance between precision and efficiency for the selection of a path-planning algorithm for a premium tea-picking manipulator. Compared with the other three algorithms, this algorithm should be the first choice for fine-detail operations such as tea picking.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Value**} \\ \cline{2-5}  & **RRT** & **RRT-Connect** & **RRT-Star** & **T-RRT** \\ \hline RMSE (\%) & 0.030 & 0.071 & 0.003 & 0.002 \\ \(R^{2}\) (\%) & 99.014 & 98.983 & 99.450 & 99.661 \\ Average & \multirow{2}{*}{0.001} & \multirow{2}{*}{0.002} & \multirow{2}{*}{0.001} & \multirow{2}{*}{0.001} \\ planning time (s) & & & & \\ Execution & & & & \\ success rate & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation test data for the manipulator under different path-planning algorithms.
## 3 Materials and Methods
### Anomaly Detection Techniques
#### 3.4.3 Local Outlier Factor (LOF)  
Introduced at the beginning of the 21 st Century, this anomaly-detection technique measures the difference of an observation based on its neighbors [41]. The central idea is that, to identify anomalous observations, we examine how isolated they are compared to their nearby neighborhood. The calculation of the LOF is given by Equation (6).  
\[\text{LOF}_{k}(A)=\frac{1}{k}\sum_{B\in N_{k}(A)}\frac{\text{LRD}_{k}(B)}{ \text{LRD}_{k}(A)} \tag{6}\]  
where  
\[\text{LOF}_{k}(A)\text{ LOF value for observation }A;\] \[N_{k}(A)\text{ set of }k\text{-nearest neighbors of observation }A;\] \[\text{LRD}_{k}(B)\text{ local range density of neighbor }B;\] \[\text{LRD}_{k}(A)\text{ local range density of neighbor }A.\]  
The technique makes use of Local Reachability Density (LRD), which is given by Equation (7), and it is a measure used to evaluate the abnormality of a specific point considering its neighbors.  
\[\text{LRD}(p)=\frac{1}{\frac{\sum_{c\in N}(\text{RD}(o,p))}{|N|}} \tag{7}\]  
where  
\[\begin{split}\text{LRD}(p)&:\text{ Local Reachability Density of point }p;\\ N&:\text{ set of local neighbors }p;\\ \text{RD}(o,p)&:\text{ relative range density between points }o\text{ and }p.\end{split}\]  
The relative range density is defined as the maximum of the k-distance of the neighbor point and the distance between two points. In simpler words, it is the distance needed to travel from a specific point to its neighbor point [42].
## 4 Simulations and Experiments
### Experiments
#### 4.2.2 Experiment Results
(**c**) Roll angular attitude (calculated by gyro, DFBGA, DFBGA and SLAIM, the proposed method, encoder) comparison. (**d**) Pitch angular attitude (calculated by gyro, DFBGA, DFBGA and SLAIM, the proposed method, encoder) comparison. Figure 12: Roll angular attitude comparison. The first subfigure is the attitude calculated by gyro, the second subfigure is the attitude processed by DFBGA, the third subfigure is the attitude processed by DFBGA and SLAIM, the fourth subfigure is the attitude processed by proposed method. Figure 13: STD of X and Y axes acceleration corresponding to pitch angle. Figure 14: Pitch angular attitude comparison. The first subfigure is the attitude calculated by gyro, the second subfigure is the attitude processed by the HFBGA, the third subfigure is the attitude processed by the HFBGA and SLAIM, the fourth subfigure is the attitude processed by proposed method. Figure 15: Yaw angular attitude comparison. The first subfigure is the attitude calculated by gyro, the second subfigure is the attitude processed by SLAIM, the third subfigure is the attitude processed by proposed method. Table 6 shows the comparison of time required for data processing once. The proposed method improves the accuracy of sensor data processing at the expense of little processing time. The method proposed in this paper is superior to the traditional processing method in measurement accuracy. Under the gyro drift and maneuver interference, the proposed method can accurately estimate 3-axis angular attitude without phase lag. This high-quality signal can effectively improve the performance of the control system.
# A Termination Criterion for Probabilistic Point Clouds Registration
## 3 Materials and Methods
### Probabilistic Point Clouds Registration
Equations (3) and (4) were derived from the same Expectation-Maximization model of Equation (2), but using a multivariate t-distribution instead of a Gaussian pdf. For the full demonstration of the derivation, please consult the original paper [4]. In Equations (1)-(4) we need an estimate of the rotation and translation; however, these are estimated by solving the optimization problem whose error terms are weighted with the weights we want to calculate. Hence, our problem cannot be formulated as a simple least-square error problem, but it has to be reformulated as an Expectation Maximization problem. During the Expectation phase, the latent variables, that is, the weights, are estimated using the previous iteration estimate of the target variables (the rotation and translation); during the Maximization phase, the problem becomes a least-square error optimization problem, with the latent variables assuming the values estimated during the Expectation phase. The proposed approach, in its multi-iteration version, is composed of two nested loops. The inner one finds the best rototranslation that minimizes the sum of weighted squared errors (as in Equation (1), very similarly to ICP. However, differently to ICP, our problem cannot be solved in closed form and, thus, we use an iterative algorithm such as Levenberg-Marquard. It has to be noted that, at each iteration of Levenberg-Marquard, the associations are not estimated again, but their weights are recalculated. Thus, we solve an iteratively reweighted mean squared error problem. In the outer loop, we move the source cloud with the result of the optimization, re-estimate the associations and build a new optimization problem. This structure has been already briefly described in our previous work. However, here we present a novel way to decide when the outer loop should stop, instead than using a predefined number of iterations.
# Stimulation Montage Achieves Balanced Focality and Intensity
## 4 Discussion
### The Choice of Lambda  
The selection of \(\mathbf{\lambda}\) strongly affects the outcome of intensity and focality. From the simulation study, we found that the optimal \(\mathbf{\lambda}\) varies from case to case, and thus there is no single fixed optimal value for every stimulation problem. Consequently, it is critical to decide the parameter \(\mathbf{\lambda}\) such that the balance between intensity and focusing ability can be obtained. The current selection is based on some sweep simulations and sophisticated methods, such as Bilevel Optimization [42; 43] and Cross Validation [44; 45], can be employed to choose \(\mathbf{\lambda}\) dynamically for closed-loop simulations in the case of evolving sources. Nevertheless, insights could be obtained by studying the relationship among \(\mathbf{\lambda}\), the number of target voxels, and the size of target regions. For example, \(\mathbf{\lambda}\) is higher when stimulating a single target with only one voxel, and is much lower when stimulating multiple targets with hundreds of voxels. Thus, it is reasonable to predict that optimal \(\mathbf{\lambda}\) is inversely proportional to the number of target voxels, and adjusts \(\mathbf{\lambda}\) accordingly.