esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Adaptive Modulation Scheme Performance
Figure 19: A comparison of the BER performance of the AM-CAZAC-TD-MIMO system and AM-CAZAC-TD-SISO system employing \(M\)-PSK for the SUI-5 channel.
# Switchable Broadband-to-Tunable Narrowband Magnetic Probe for Near-Field Measurements
## 4 Equivalent Circuit of Magnetic Probe  
The equivalent circuit of the proposed magnetic probe has been modeled and simulated using AWR Software [37]. The authors start by modeling the microstrip reference board with an input port of 50 \(\Omega\) connected to parallel inductors. On the other hand, the parasitic elements (series inductor, radiation resistance, and parallel capacitor) represent the shielded loop.  
In this work, the value of the first inductor is chosen to be \(L_{1}=0.5\) nH and this inductor is responsible for controlling the mutual coupling level at a specific frequency. In contrast, the values of the second inductor (self-inductor) and the self-capacitor are calculated using Equations (5) and (6), respectively. It is worth mentioning that the value of self-capacitance will be affected by the varactor diode's weight of capacitance \(C=(0.23\)-\(2.22)\) pF in controlling the resonance frequency.  
\[L =\mu_{r}\mu_{o}r\left(ln\left(\frac{8\times r}{w}\right)-2\right), \tag{5}\] \[C =\varepsilon_{r}\varepsilon_{0}r\ \left(ln\left(\frac{8\times r}{w} \right)-2\right), \tag{6}\]  
where \(\mu_{r}\) and \(\mu_{o}\) are the relative and absolute permeabilities, \(\varepsilon_{r}\) and \(\varepsilon_{0}\) are the relative and absolute permittivities, and \(r\) and \(w\) are the radius and width of the loop with values of \(r=1.7\times 10^{-3}\) mm and \(w=0.56\times 10^{-3}\) mm.  
The self-inductance and self-capacitance of the loop are equal to \(L=2.45\) nH and \(C=0.07\) pF by substituting the loop dimensions into Equations (5) and (6). A C = 100 pF blocking capacitor is inserted to isolate the high-frequency currents from the direct current of the varactor diode, which has one end connected to the blocking capacitor. In contrast, the second terminal is connected to the ground. An inductor with L = 0.5 nH and \(R=55\)\(\Omega\) represents the part of the via- 1 connecting the varactor diode to the stripline and the 5.35 mm transmission line. Similarly, an inductor with \(L=0.23\) nH and \(R=55\)\(\Omega\) represents the via-2 and the small piece of the transmission line connecting the PIN diode to the via-2. Another \(C=100\) pF blocking capacitor is utilized here to protect the probe  
Figure 21: Simulated and measured S-parameters with different biasing voltage results: (a) S11 parameters; (b) S21 parameters.  
from the DC current of the diode. The other path connects the arc to the input port through a 50 \(\Omega\) transmission line. The equivalent circuit diagram obtained using AWR Software is shown in Figure 22a, while Figure 22b presents the frequency response of this equivalent circuit with different biasing values.  
Figure 22: Modeling of the magnetic probe in AWR: (**a**) Equivalent circuit of the probe; (**b**) S-parameters with different switching modes.
## 4 Data Analysis in Maritime IoT Applications
### Benefits and Limitations
#### 4.3.3 Enhanced Decision-Making  
Data-driven decision-making has revolutionized how maritime operations are managed. By analyzing vast amounts of data generated by IoT devices, maritime organizations can make decisions that are more accurate, strategic, and effective. This could range from selecting the best shipping route based on real-time weather and sea traffic data, to deciding the optimal time for equipment maintenance based on predictive analysis. As a result, these data-driven decisions lead to improved operational outcomes and increased profitability [87].
###### Abstract
The main contributions of this work can be summarized as follows: (1) A proximity analysis method for large space debris and large satellites is proposed, and a space collision target filtering method for large-scale space debris and large-scale satellites is studied. (2) A collision evolution calculation method for large-scale space debris and large-scale satellites is proposed. By filtering and partitioning data, the computing efficiency is improved while satisfying high accuracy. (3) Aiming at the long-term collision risk problem after the deployment of low-orbit megaconstellations, a long-term collision risk calculation model for low-orbit megaconstellations is established. The paper is organized as follows: In Section 2, the calculation methods for short-term and long-term collision probabilities are presented. Based on the proposed method, the short-term and long-term collision probabilities of the Starlink constellation are calculated, as presented in Section 3. Furthermore, through comparison with the collision probability before constellation deployment, the increased collision risk percentage after constellation deployment is obtained. Finally, in Section 4, concluding remarks as well as future perspective are given.
## 2 Methods
### Ordinary Kriging  
Ordinary kriging [23; 24] is a procedure for local estimation which calculates the best linear unbiased estimator of the modeled attribute. The restriction to the category of linear estimators means that only second-order moments of the random function (i.e., the covariance function or the variogram) are considered. This is because it is easier to derive these moments from the available data in practice.  
In practice, to estimate the value of an attribute \(Z_{0}\) in a point \(x_{0}\) of the space using a set of measurements on \(n\) nearby locations \(x_{\alpha}\) (\(\alpha=1\), \(n\)), it is necessary to form a linear combination of the type:  
\[Z_{0}^{*}(x_{0})=\sum_{\alpha=1}^{n}\lambda_{\alpha}^{0}Z_{0}(x_{\alpha}), \tag{1}\]  
where the matrix of coefficients \(\left\{\lambda_{\alpha}^{0}\right\}\) is calculated so as to ensure the unbiasedness of the estimator and the minimization of the difference between the unknown and the estimated (kriged) values on each estimation point. Minimization accounts also for the spatial variability of the attribute, as expressed by the variogram function \(\gamma_{0}(h)\), which equals the semivariance of the variable increment by the distance \(h\):  
\[2\gamma_{0}(h)=var[Z_{0}(x+h)-Z_{0}(x)]. \tag{2}\]  
A theoretical variogram model is then adapted to the calculated experimental variograms in order to be used in the kriging algorithm. Another important characteristic of kriging is that it provides an estimation error variance, which can be used as a measure of its accuracy. Furthermore, estimation error variance can also be used to identify areas requiring supplementary sampling.
# Effects of Biochar Production Methods and Biomass Types on Lead Removal from Aqueous Solution
## 2 Materials and Methods
### Lead Adsorption  
A single-point batch adsorption experiment was performed in duplicates to evaluate the biochar performance in removing Pb\({}^{2+}\). An activated carbon for aquaculture use (AquaTech, Spectrum Brands, Inc., Blacksburg, VA, USA) was included to compare biochar performance against the commercial activated carbon product. For the adsorption tests, a 1:40 ratio of biochar mass to Pb\({}^{2+}\) solution was employed based on [PERSON] et al.'s work (2019) that used the same solid-to-solution ratio [18]. While adsorbent dosage, solid-to-solution ratio, co-existed ions, and equilibration time are important experimental parameters in determining the adsorption efficiency of target contaminant [20], the scope of the current study was to cross-compare Pb\({}^{2+}\) removal across 9 different adsorbents at a fixed, initial Pb\({}^{2+}\) concentration (10 mg L\({}^{-1}\)) during a relatively short equilibration time (1 h). Individual biochar material (0.8 g) was equilibrated with 32 mL of 10 mg L\({}^{-1}\) Pb\({}^{2+}\) solution in a centrifuge tube and then shaken for 1 h in an end-to-end shaker. After shaking, the biochar suspension was filtered through 0.2 \(\upmu\)m membrane filter paper, and the resulting solution was measured for its final Pb\({}^{2+}\) concentration by HACH DR3900 spectrophotometer (HACH, Loveland, CO, USA) via the HACH method 10216 (TNT 850, HACH, Loveland, CO, USA). The Pb\({}^{2+}\) removal by the biochar material (%) was calculated by:  
\[\text{Pb}^{2+}\text{ removal (\%)}=100\ \times\ (1-C_{f}/C_{i}) \tag{3}\]where \(C_{i}\) is the initial concentration of Pb\({}^{2+}\) in the solution (mg L\({}^{-1}\)) and \(C_{f}\) is the final concentration of Pb\({}^{2+}\) in the solution after equilibration.
# Bearing Fault Diagnosis Based on Small Sample Learning of Maml-Triplet
## 3 Construction of the Dataset and Model
### Data Preprocessing  
The dataset selected in this paper is the vibration signals measured at different parts of the motor. These signals cannot be trained directly and need to be processed by preprocessing methods.  
Experimental data were collected at 12,000 samples/second and motor speeds of 1797 to 1720 RPM. It can be calculated that 400-418 samples are collected when the shaft rotates for one circle. As shown in Table 3, the fault frequencies BPFI, BPFO, and BSF are greater than the rotation frequency of the shaft, so a cycle can be covered by collecting fewer points. The more points selected, the higher the accuracy of the model prediction, but the higher the training cost. In this paper, 2048 points are sampled as one input data, which includes all bearing fault cycles and has good performance.  
As shown in Figure 5, first, the multi-source signal fusion method is used to sample the original vibration signals measured at the fan end and motor end at the same time and the signal length of 2048 is intercepted to form an array with the shape of (2048, 2). For example, as shown in Formulas (15)-(17), the fan end signal \(X_{1}\) and motor end signal \(X_{2}\) are selected with a length of 2048 at the same time and \(X_{1}\) and \(X_{2}\) are combined into matrix \(X\). Secondly, after the normalization processing of Formula (18), signal data available for use are obtained. After that, on the basis of data preprocessing, the datasets of Maml neural network and Triplet neural network are constructed.  
\[X_{1}=\left[x_{11},\ x_{12},x_{13},\ldots,x_{1m}\ \right]^{T},m=2048 \tag{15}\]  
\[X_{2}=\left[x_{21},\ x_{22},x_{23},\ldots,x_{2m}\ \right]^{T},m=2048 \tag{16}\]  
\[X=\left[X_{1},\ X_{2}\ \right] \tag{17}\]  
\[s_{ij}=\frac{x_{ij}-\min}{\max-\min} \tag{18}\]
# Blockwise Frequency Domain Active Noise Controller Over Distributed Networks
## 1 Introduction
The experimental results to compare the performance of both the distributed solution proposed and the centralized algorithm are shown in Section 4, including a discussion considering communication constraints. Finally, Section 5 outlines the main conclusions of the present work. _Notation_: For the sake of clarity, the following notation has been used throughout this work: boldface lower-case denote vectors and matrices that contain information of signals in the time-domain (e.g., **e**) and boldface upper-case letters denote vectors and matrices that contain information of the Fast Fourier Transform (FFT) of the previous signals (e.g., **E**).
## 5 Experimental Results
### Biomedical Data Sets
* LO (Locate Endogenous data set [79]): this fairly balanced data set contains 502 images of endogenous cells divided into ten classes: Actin-cytoskeleton, Endosomes, ER, Golgi, Lysosomes, Microtubule, Mitochondria, Nucleus, Peroxisomes, and PM. This data set is archived at [[https://intebigo.jp/dbcatalog/en/record/nbdc00296](https://intebigo.jp/dbcatalog/en/record/nbdc00296)]([https://intebigo.jp/dbcatalog/en/record/nbdc00296](https://intebigo.jp/dbcatalog/en/record/nbdc00296)) (accessed on 9 August 2022). * TR (Locate Transfected data [79]): this is a companion data set to LO. TR contains 553 images divided into the set same ten classes as LO but with the additional class of Cytoplasm for a total of eleven classes. Data sets 1-8 can be downloaded at [[https://ome.grc.nia.nih.gov/icbu2008/](https://ome.grc.nia.nih.gov/icbu2008/)]([https://ome.grc.nia.nih.gov/icbu2008/](https://ome.grc.nia.nih.gov/icbu2008/)) (accessed on 9 August 2022), data sets 9-12 can be found on Zenodo at [[https://zenodo.org/record/](https://zenodo.org/record/)]([https://zenodo.org/record/](https://zenodo.org/record/)) (accessed on 9 August 2022) by concatenating the data set's Zenodo record number provided in the descriptions above to this URL. Data set 13 is available at [[http://murphylab.web.cmu.edu/data/#RandTagAL](http://murphylab.web.cmu.edu/data/#RandTagAL)]([http://murphylab.web.cmu.edu/data/#RandTagAL](http://murphylab.web.cmu.edu/data/#RandTagAL)) (accessed on 9 August 2022), and data sets 14 and 15 are available on request. Unless otherwise noted, the five-fold cross-validation protocol is applied (see Table 3 for details), and the Wilcoxon signed-rank test [80] is the measure used to validate experiments.
# Experimental Investigation of Free-Motion Task Implementation on a Serial Metamorphic Manipulator
## 4 Experimental Case Study
### Dynamic Performance Evaluation
#### 4.4.2 Optimizing End-Effector Translation Response  
In this section, the evolution of the ratio between the achieved TCP acceleration and the produced joint torques is investigated. Circular motion inherently involves variable acceleration and velocity profiles. As the TCP traverses the circular path, the centripetal and tangential accelerations continuously change, imposing varying dynamic loads on the manipulator's joints. This study aimed to determine which of the utilized performance measures provided the most accurate representation of the achieved ratio. The introduced EMI proved to be the most effective in capturing the evolution of the ratio with higher resolution, as it also considered the direction of motion.  
In Figures 13(a) and 14(a), the ratio extracted from robot data and the performance measures extracted from task simulation are plotted for a CP executed by the optimized EMI CP (ID: 1) and the optimized LCI RP (ID: 3) anatomies, respectively. The utilized performance measures are plotted in order to discuss the achieved accuracy in tracking the efficiency of producing TCP acceleration from the joints' torques. As expected, the DCI did not provide adequate detail on the evolution of the ratio. Furthermore, the LCI presented the three main path segments where an optimal ratio could be found but adequate fidelity was still not achieved. The EMI demonstrated high sensitivity to dynamic changes, closely reflecting performance variations, thus providing detailed performance insights, but demanded a higher computational cost. In summary, the EMI presents a behavior that should become a useful tool for identifying maximum load scenarios during path execution, while metrics such as the DME and DCI can be useful for broader performance assessments where stability and less noise are advantageous. As previously discussed, the DCI should not be considered for a task-based dynamic performance analysis.  
To obtain a more detailed view of the dynamic performance of the robot during task execution, the DME index is plotted in Figures 13(b) and 14(b) for the best- and worst-case path waypoints. For the highest scoring waypoint, it is observed that the largest axis of the ellipsoid, which corresponds to the highest eigenvalue, is aligned with the direction of motion. Conversely, in the case of the lowest scoring waypoint, one of the smaller axes, corresponding to a lower eigenvalue, is aligned with the TCP velocity direction.  
Figure 13: Optimized LCI CP anatomy (ID: 2, task path: 19) vs. reference anatomy (ID: 0, task path: 25). (**a**) Joint torques’ comparison. (**b**) Joint velocities’ comparison.
## 6 Streaming Protocol for Service-Oriented architectures
### PUMA Transport Protocol (PTP)  
In the following the PUMA Transport Protocol and its technical specification is described. If a transmission of media files, respectively chunks, should be performed between the Web services in the workflow, all files are sent using this protocol. The PTP consists of three parts: Envelope, Header and Payload. Figure 12 depicts the format of PTP.  
The envelope covers the whole transmission and is only transmitted once. It contains the protocol version, which is an Int32. Int32 must always be 4 bytes big endian (high byte first) encoded. The second part of the envelope is another Int32 value, defining the number of files to be transmitted. This value is intended for a concluding verification if the amount of transmitted files matches the expectation.
## 4 Discussion on Regional Dust Events
### Dust Outbreaks in Central Asia
On 19 and 20 March, the satellite flies over the Himalayan ranges and the comparison shows a quite good agreement between the model and observational data, the model succeeds in simulating the main patterns with high concentrations in the free troposphere up to 8000 m. Even the small structure observed by CALIOP very far from emission source areas at 77\({}^{\circ}\) N on 19 March is captured by the model. For the last pass (CAS6) on 30 March, the satellite flies over Iran, the dust concentration structure is again very well reproduced by the model with high concentrations captured by the model between 27\({}^{\circ}\) N and 55\({}^{\circ}\) N.  
As shown in Figure 23, several meteorological SYNOP stations in central Asia recorded the presence of dust based on manned observations in the north of the Caspian Sea confirming the pattern predicted by the model in this region, unfortunately the extension of the plume toward the northeast to central and east Siberia cannot be evaluated by any measurements However, Figure 23 shows a good agreement of the manned observations with surface dust concentrations where available. The patternin China and Mongolia over the Gobi Desert on 16 March is particularly well captured; this dust outbreak is discussed in the next section of the paper. Figure 22: Cross section of CHIMRE dust concentrations (**right**) for various dates zoomed over the highest values area versus the corresponding CALIOP-CALIPSO qualitative data (**left**) showing in yellow the dust load and in orange the polluted dust, for the CALIPSO trajectory from top to bottom respectively, CAS1, CAS2, CAS3, CAS4, CAS5 and CAS6 as reported in Figure 2. The dotted line represents the height of the boundary layer, the dashed lines represent the fraction of fine dust in percent.
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 4 Performance Evaluation
### Simulation Settings  
The model of the network used in this work was created based on the illustration in Figure 3 and channel properties in [2; 4; 13; 36; 37]. The model resembles a basic communication network. It has all the basic blocks to fulfil all the requirements of an OFDM network. At the source, the QPSK signal constellation is 4. The number of data points varied from 64 to 1024. The OFDM block size is varied accordingly from 4 to 32. The CP length is a rounded value of 10% of the block size and the number of Fourier points is equal to the block size. The equations used at each stage considered '\(N\)' parallel streams of data points with X(\(k\)) being the transmitted symbol by carrier '\(k\)' for \(k=0,1,2,3,\ldots\) N - 1. The OFDM signal 'S' for the symbols '\(s\)' that are assigned to a chosen constellation at a given frequency is given by (11):  
\[S=\sum_{n=0}^{N-1}\left[\;s\times\sin(2\pi ft)\;\right] \tag{11}\]  
The Fourier transform breaks down the modulated signal into different frequency packets by variably multiplying the signal with well-defined sinusoids. This converts the signal from time to frequency domain. The IFFT highlighted in Figure 3 performs the conversion process on the QPSK modulated signal as expressed in Equation (12):  
\[x(n)=\{\sum_{n=0}^{N-1}\mathrm{X}(k)\sin(2\pi k_{n})-j\sum_{n=0}^{N-1}\mathrm{ X}(k)\cos(2\pi k_{n})\} \tag{12}\]  
To convert back to the original message signal, an FFT is performed. It differs from the IFFT on the coefficient and addition sign. It reverses the IFFT. The FFT which does the signal conversion on the channel output signal has the form given in Equation (13):  
\[X(k)=\{\sum_{n=0}^{N-1}\mathrm{x}(n)\sin(2\pi k_{n})+\;j\sum_{n=0}^{N-1} \mathrm{x}(n)\cos(2\pi k_{n})\} \tag{13}\]  
Since most signals are in the time domain, the IFFT is used at the transmission stage and FFT at the receiver. This is interchangeable depending on the domain of the received signal. The corresponding dual will have to be used at the receiving end. The Rayleigh fading channel used in this research is based on [PERSON]'s model whereby the fading of the symbol '\(k\)' over time '\(t\)' is given in Equation (14):  
\[Y(t,k)=2\sqrt{2}\left[\sum_{n=1}^{M}(\rho)(\delta)+\frac{1}{\sqrt{2}}\left( \tau\right)(\varphi)\;\right] \tag{14}\]  
where \(\rho\) is \((\cos\beta_{n}+\;\mathrm{j}\mathrm{i}\mathrm{n}\beta_{n})\), \(\delta\) is \((\cos 2\pi f_{n}t+\lambda_{n,k})\), \(\tau\) is \((\cos\alpha+\mathrm{j}\mathrm{i}\mathrm{n}\alpha)\), and \(\varphi\) is \((\cos 2\pi f_{d}t)\). In the simulations, \(\alpha\) was mostly set to 0 and \(\beta_{n}\) was carefully selected to avoid cross-correlation between the real and imaginary parts of \(Y(t)\). \(\lambda_{n}\) was also set at 0 because the simulations were executed in a single-path channel.
## 3 Test Network Model, Case Study and Optimization Settings Description
### Optimization Settings  
The co-simulation approach is realized by interfacing _OpenDSS_[33] as a power system analyzer and _PyGMO_[34] optimization package with embedded CI optimization methods. The interaction is established by the API interface. The code is implemented in _Python_ programming language. Although _PyGMO_ features a wide range of CI optimization methods and any of these methods can be used, PSO [35] is applied in this paper. The aim of this paper is not to improve the CI optimization method; it is only used as a tool for solving optimization the problem. The PSO parameters are fine-tuned by the trial method to achieve the best solution as soon as possible.
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 5 Discussion  
Figure 6 depicts open research challenges in connection with EdgeML-based smart LSs. They are discussed after a brief recap of the results presented in the previous section.  
The output of the research on Scopus, starting from the search string, was very promising. Indeed, the Scopus engine returned 89 studies. Unfortunately, 68 of them did not pass the filter criteria (i.e., IC/EC and quality assessment--Section 3). From this stage of the present SLR, it emerges that only two secondary studies have appeared (up to the end of October 2024) on the topics of this review. This finding is the consequence of the very low number of published primary studies that we have found (i.e., 19). This package of studies was increased with 7 more primary studies selected by carrying out the forward snowballing.  
In total, 7 primary studies propose the adoption of the EdgeML computing paradigm to implement a new generation of smart LSs. This scarcity of research on the adoption of EdgeML in the lighting domain confirms the conclusion in [99], a book chapter that focuses on the state-of-the-art of IoT and ML applications in the smart city transportation domain. In the work, city transportation is the umbrella that comprises street lighting. [PERSON] et al. state that from the extant body of research it can be seen that ML is inadequately represented on smart LSs, which means that nothing has changed since this deficiency was first reported in [89].  
The answer to RQ2 reveals that energy efficiency is the most investigated research topic. This finding confirms previous studies such as, for instance, [4,100]. As already remarked in the introduction, the motivation that pushes the research on smart LSs in such a direction is the huge energy consumption caused by them.