esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evolution of Magnetic Nanoparticle Resistance in _Escherichia coli_
FeNPs are involved in orientation, navigation, and iron metabolism in prokaryotic and eukaryotic cells [22; 25]. FeNPs are extensively used for applications in drug delivery [26], treatment of cancer cells [27], antimicrobials [28; 29; 30], and in general biomedicine and bioanalytics [31]. FeNPs are also now use for antimicrobial applications due to their very small in size and high surface area to volume [32; 33]. FeNPs disseminate ionic iron II and iron III species with low toxicity to eukaryotic host cells minimizing undesirable side effects [29; 34; 35; 36]. The antibacterial properties of the FeNPs have been tested against _Bacillus cereus_, _Escherichia coli_, _Staphylococcus aureus_, _Pseudomonas aeruginosa_, and _Serratia marcescens_. They have also been shown to effective against the yeast species _Candida albicans_[34; 37]. However, none of these studies considered the possibility that bacteria could evolve resistance to FeNPs. Therefore, it is of paramount importance to describe mechanisms by which bacteria can become resistant to magnetite NPs and determine if resistance to magnetite nanoparticles confers resistance to ionic metals and antibiotics in order to fill gaps in our knowledge. In this study, we investigated how rapidly bacteria can evolve resistance to FeNPs and evaluated the nature of the genomic changes responsible for that resistance. In addition, we wanted to determine if magnetite resistance conferred correlated resistances to ionic metals and antibiotics. Finally, we wished to determine the similarity of phenotypic and genomic changed due to magnetite to those produced by ionic iron (II) and (III) resistance.
## 3 Methodology and Mathematical Formulations
### Airflow Physics and Turbulence Modeling
#### 3.3.1 Transport Equations for Shear Stress Transport Turbulence Model  
The shear stress transport (SST) model has been widely used in many applications and building airflow simulations [27; 30; 31; 32; 33; 34]. The transport equation is also different based on our inclusion of the species transport to represent the moisture distribution in the stupa. The Reynolds-averaged Navier-Stokes equation was used to describe the fluid flow, with turbulence kinetic energy (TKE, \(k\)) and the turbulence-specific dissipation rate (SDR, \(\omega\)) used to represent the flow behavior. The set of steady-state equations is shown below:  
\[\
abla\cdot\left(\rho\overset{\rightarrow}{\mathbf{U}}\right)=0 \tag{1}\]  
\[\
abla\cdot\left(\rho\overset{\rightarrow}{\mathbf{U}}\overset{\rightarrow} {\mathbf{U}}\right)=-\
abla p+\
abla\cdot\left[\left(\
abla\overset{ \rightarrow}{\mathbf{U}}+\
abla\overset{\rightarrow}{\mathbf{U}}^{T}\right)- \frac{2}{3}\
abla\cdot\overset{\rightarrow}{\mathbf{U}}I\right] \tag{2}\]  
\[\frac{\partial}{\partial x_{i}}(\rho kU_{i})=\frac{\partial}{\partial x_{j}} \left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}}\right)\frac{\partial k}{\partial x _{j}}\right]+\Phi_{k}-\Psi_{k} \tag{3}\]  
\[\frac{\partial}{\partial x_{i}}(\rho\omega U_{i})=\frac{\partial}{\partial x _{j}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{\omega}}\right)\frac{\partial \omega}{\partial x_{j}}\right]+\Phi_{\omega}-\Psi_{\omega}+D_{\omega} \tag{4}\]  
\[\
abla\cdot\left(\overset{\rightarrow}{\mathbf{U}}(\rho E+p)\right)=-\
abla \cdot\left(\sum_{j}h_{i}\overset{\rightarrow}{j}_{j}\right) \tag{5}\]  
All the equation constants in Equations (1)-(5) can be found the ANSYS Fluent Theory guide [35].  
Figure 6: Boundary conditions for flow analysis.
## 4 Exact Algorithm
### Pruning  
In this subsection we introduce a pruning strategy, which is based on the following observation:  
**Observation 2**: _Assume that \(n>2\). For a given \(v\in V\), all nodes \(w\in N(v)\) with \(deg(w)=1\) have \(\varepsilon(w)=\varepsilon(v)+1\)._  
Node \(w\) is only connected to node \(v\), and will thus need node \(v\) to reach every other node in the graph. If node \(v\) can do this in \(\varepsilon(v)\) steps, then node \(w\) can do this is in exactly \(\varepsilon(v)+1\) steps. The restriction \(n>2\) on the graph size excludes the case in which the graph consists of \(v\) and \(w\) only.  
All interesting real-world graphs have more than two nodes, making Observation 2 applicable in our algorithm, as suggested in [6]. For every node \(v\in V\) we can determine if \(v\) has neighbors \(w\) with \(deg(w)=1\). If so, we can prune all but one of these neighbors, as their eccentricity will be equal, because they all employ \(v\) to get to any other node. In Figure 2, node G has two neighbors with degree one, namely J and K. According to Observation 2, the eccentricity values of these two nodes are equal to \(\varepsilon(\text{J})=\varepsilon(\text{K})=\varepsilon(\text{G})+1=4\). The same argument holds for nodes D and L with respect to node H. We expect that Observation 2 can be applied quite often, as many of the graphs that are nowadays studied  
Figure 4: Eccentricity values (horizontal axis) _vs._ number of iterations to compute the eccentricity of all nodes with this eccentricity value (vertical axis).  
have a power law degree distribution [18], meaning that there are many nodes with a very low degree (such as a degree of \(1\)).  
Observation 2 can be beneficial to our algorithm in two ways. First, when computing the eccentricity of a single node, the pruned nodes can be ignored in the shortest path algorithm. Second, when the eccentricity of a node \(v\) has been computed (line 11 of Algorithm 2), and this node has adjacent nodes \(w\) with \(deg(w)=1\), then the eccentricity of these nodes \(w\) can be set to \(\varepsilon(w)=\varepsilon(v)+1\).
## Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 1**  
\({}^{1}\) School of Software, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) School of Mathematics and Computer Science, Longyan University, Longyan 364000, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Computer Science, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) College of Computer Science and Technology, Huaqiao University, Xiamen 360000, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel. +86-592-2580566; Fax: +86-592-2580500.  
Academic Editor: [PERSON]  
_Received: 6 August 2015 / Accepted: 7 September 2015 / Published: 15 September 2015_  
Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period.  
keywords:proxy node selection; time slot allocation; data gathering; mobile sensor network +
Footnote †: journal: Journal of Information Theory
## 3 Optimization Method
### Optimization of Stations Locations  
The genotype for the first sub-problem is a simple table containing the following coordinates of stations (\(x_{i}\) and \(y_{i}\) denote coordinates of \(i\)-th station):  
\[\{(x_{1},y_{1}),\ldots,(x_{i},y_{i}),\ldots(x_{n},y_{n})\}. \tag{10}\]  
A mutation is a shift of a randomly selected station. There are random numbers added to its coordinates. The distribution of these shifts is normal with the mean equal to zero, and the standard deviation is a program parameter.  
\[\{(x_{1},y_{1}),\ldots,(x_{i},y_{i}),\ldots,(x_{n},y_{n})\}\stackrel{{ m}}{{\rightarrow}}\{(x_{1},y_{1}),\ldots,(x_{i}+\Delta x,y_{t}+\Delta y ),\ldots,(x_{n},y_{n})\}. \tag{11}\]  
A cross-over between a pair of individuals is an exchange of random subset of stations  
\[\begin{array}{l}\left\{(x_{1},y_{1}),\ldots,(x_{i}^{\mathbf{1}},y_{i}^{ \mathbf{1}}),\ldots,(x_{n},y_{n})\right\}\stackrel{{ X}}{{\rightarrow}}\left\{(x_{1},y_{1}),\ldots,(x_{i}^{ \mathbf{2}},y_{i}^{\mathbf{2}}),\ldots,(x_{n},y_{n})\right\}\\ \left\{(x_{1},y_{1}),\ldots,(x_{i}^{\mathbf{2}},y_{i}^{\mathbf{2}}),\ldots,(x _{n},y_{n})\right\}\end{array}\stackrel{{ X}}{{\rightarrow}} \left\{(x_{1},y_{1}),\ldots,(x_{i}^{\mathbf{1}},y_{i}^{\mathbf{2}}),\ldots,(x _{n},y_{n})\right\}. \tag{12}\]  
The probability of mutation and the probability of cross-over are program parameters.
## 5 Simulation Results  
In this section, a number of experiments are designed to evaluate both the reliability and security of the proposed secure transmission system. The receiving gain and BER are chosen as indicators to assess the feasibility and security of the proposed physical layer security communication system. The receiving gain, which was defined in section II implies the phenomenon of SNR improvement caused by the under-sampling method on [PERSON]'s side. For secure wireless communication systems, it is desired that the BER at [PERSON]'s side is decreased rapidly with the increase in received SNR, while the BER at [PERSON]'s side is always unacceptably high. To illustrate the robustness of the proposed physical layer security communication system, simulations are conducted over both AWGN and fading channels. In simulations, the signal \(s(t)\) is assumed to be a BPSK-modulated signal with a bandwidth of 10 MHz, which means \(f_{B}=10\) MHz. Furthermore, the parameter \(L\) is set as \(L=1\). It is noticed that all simulations in this work are implemented using Matlab. The diagram of system model simulations is shown in Figure 6.
## 2 Compact Model Development  
### Model Description  
The UTC-PD compact model can be represented by an equivalent circuit with three terminals. Among these terminals, two are for the electrical part and one for the optical input signal. The model translates the analytical equations based on the physics of UTC-PDs into an equivalent electrical circuit representation. Figure 2a shows a SEM micrograph taken after the UTC-PD mesa formation. Figure 2b illustrates the schematic of an intrinsic UTC-PD mesa structure, showing different epitaxial layers. On this schematic, the small signal electrical model is superimposed depicting different electrical elements of the equivalent circuit representation. The active region consists principally of the epitaxial p-doped absorption layer and a slightly n-doped collection layer. The electrical representation of this region is composed of a junction capacitance, \(Cj\), which describes the absorber-collector junction. In parallel to this capacitance, three current sources model the forward current, the reverse current, and the photocurrent [14], respectively. Note that here the photocurrent implementation in Verilog-A is more complex than simple current source representations since it includes transfer functions (Figure 2c). In series with the active region, there are resistances representing the resistive contribution of the non-depleted part of the collector, the sheet resistances, and the resistances of the metal contacts. The device can be biased via the doped P+ and N+ contacts at the top and the bottom, respectively. On both sides of the mesa, there are the access lines, which are connected to the Ground-Signal-Ground (GSG) pads (Figure 2d).
## 3 Results
### Satellite-Derived Metrics  
SGS dates derived from the MODIS VIs are graphed in Figure 3. NDVI\({}_{\text{maiac}}\) and sPRI\({}_{1}\) exhibited a relatively high degree of consistency regardless of the methods used. Only in 2002 did the estimates diverge considerably. Since there are no data points in the MAIAC VI dataset before July 2002, SGS for this year was excluded in the following validation of the results. EGS in 2014 was likewise excluded due to lack of data after June 2014. In comparison the MAIAC VIs, NDVI\({}_{\text{prod}}\) produced less reliable results as indicated by the significant variation in the SGS estimates, which in some years diverged highly from the mean ground-estimated SGS. The Savitzky-Golay smoothing method produced unrealistic results when applied to the NDVI\({}_{\text{prod}}\) data (specifically in 2003, 2004, 2012 and 2014). For all three VI time series, EGS estimates varied considerably depending on the methods applied (Figure 4).
# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
###### Abstract  
The Rapidly Exploring Random Tree algorithm, renowned for its randomness, asymptotic properties, and local planning capabilities, is extensively employed in autonomous driving for path planning. Addressing issues such as pronounced randomness, low search efficiency, inefficient utilization of effective points, suboptimal path smoothness, and potential deviations from the optimal path in the RRT algorithm based on random sampling, we proposed an optimization algorithm that integrates Kalman filtering to eliminate redundant points along the path. Initially, this algorithm addresses the issue of inverse growth in the RRT algorithm's search tree by implementing a variable steering angle strategy, thereby minimizing oscillations and unnecessary pose adjustments. Secondly, by merging collision detection with Kalman filtering, and by comparing the step sizes between newly generated child nodes and random tree nodes towards the root node, we filtered redundant points from the path, thereby reducing the count of effective points and optimizing the path. Lastly, we utilized a second-order Bezier curve to smoothen the path, eliminating sharp corners and discontinuities, ultimately yielding the optimal path. Across diverse map environments and two distinct dimensional scenarios, we conducted multiple sets of simulation experiments to validate the algorithm's feasibility. The experimental outcomes demonstrate notable improvements in parameters like average path length, average planning time, average count of effective points, and average sampling points, highlighting the enhanced accuracy and efficiency of the improved algorithm in path planning.  
a Article  
15 March 2022  
19 March 2025  
**Keywords:** automatic driving; path planning; Rapidly Exploring Random Tree (RRT); Bessel curve; Kalman filter
## 1 Introduction
[11] also studied the performance of a smartphone using network RTK positioning with differential correction observations from the Continuously Operating Reference Stations, achieving centimetre-level precision but only decimetre-level accuracy. They found that to improve decimetre-level accuracy in smartphones, the integer ambiguity should be resolved. A correct and reliable ambiguity-fixed solution can lead to centimetre-level positioning accuracy [12], even in smartphones. [PERSON] et al. [13] conducted experiments on a Huawei P30 smartphone, and the results showed that only the GPS (global positioning system) L1 signal can resolve ambiguity. [PERSON] et al. [14] attached an external survey-grade GNSS antenna to a smartphone, achieving a fixed solution, while [PERSON] et al. [15] used a choke-ring to reduce multipath interference, obtaining an ambiguity fixing rate up to 89% and RTK positioning accuracy of 1-2 cm. [PERSON] et al. [16] also used a survey-grade antenna to resolve ambiguity in RTK positioning, achieving 100% fixing rate and millimetre-level positioning accuracy. These methods have demonstrated the ability of GNSS modules in smartphones to achieve high-precision positioning. However, applicability may be reduced by using an external GNSS antenna or a choke-ring on a smartphone because it increases the equipment burden for surveying. In addition, the signal reception of the built-in smartphone antenna should be shielded to prevent interference from the external antenna. Despite the difficulty in achieving high-precision positioning using only the internal smartphone antenna, further optimisation in the underlying algorithms may improve the positioning performance. Regarding practical applications, [PERSON] et al. [17] found that smartphones can achieve centimetre-level accuracy when ambiguity is resolved, enabling geodetic and monitoring applications. [PERSON] et al. [18] used smartphones in forestry surveys, finding that the high sensitivity of smartphones is advantageous for forest sheltered environments, but the positioning accuracy should be further improved. These applications illustrate the importance of high-precision positioning in smartphones. Although the current accuracy has certain bottlenecks, high-performance positioning would be of great significance to many users.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 5 Results  
The evaluation of the equalization techniques presented in this paper is done by computer simulations. We focus on the performance of the FD implementations of both algorithms considering two different setups: a single-mode setup for 2 \(\times\) 2 equalization, for those SDM scenarios with no or negligible modal impairments; and a multi-mode setup consisting of three modes requiring 6 \(\times\) 6 equalization. The two setups modelled by computer simulations have the following specifications:  
1. SSMF transmission, consisting of a 32 Gbaud non-return-to-zero (NRZ) dual-polarization QPSK signal, modelling the signal impairments of SoP rotation, PMD, polarization dependent loss (PDL) and CD.
2. Three mode transmission over FMF, consisting of three decorrelated replicas of the same 32 Gbaud QPSK signal. Besides those impairments in SSMF, mode mixing and DMGD are also considered.  
The implemented DSP stack is shown in Figure 4, beginning with de-skewing the received signal and compensating possible imbalances of the 90\({}^{\circ}\) hybrids by orthonormalization. Then, interpolation and time-recovery are applied to obtain a 2-sample per symbol signal, followed by the FD-LMS or FD-SSA, compensating for the mode mixing and signal impairments. Frequency-offset and phase noise are compensated by a 64-taps Viterbi-and-Viterbi [28] and the differential Viterbi-and-Viterbi algorithms [1]. Finally, the received signal is decoded and the bit-error rate (BER) is calculated.  
All the simulations are performed with random blocks of 1 million symbols, where the number of iterations used for equalization is adjusted for each case. The accumulated CD employed was smaller than the equalizer size for each case for avoiding penalties.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 2 Current-Voltage Curves
### Aging Faults  
Aging faults in solar modules refer to the gradual decline in performance and efficiency over time, primarily due to prolonged exposure to sunlight, temperature variations, humidity, and environmental pollution. As the modules age, their energy conversion capacity weakens, leading to reduced output power. Aging faults may also result in increased internal resistance, higher leakage currents, and changes in transient and temperature characteristics [15].  
Aging faults can be classified into two types: one involving the change of the \(R_{s}\) parameter in the solar cell model to age all cells; the other involves connecting two solar cell modules in series, where one module has an additional resistor in series with some cells to simulate partial cell aging, this allows for the simulation of string's current-voltage curves with different aging faults. These two types of aging faults exhibit distinct characteristics on the current-voltage (I-V) curves.  
Each type of aging fault is simulated with 2500 instances. Total cell aging is simulated through a single solar module, as shown in Figure 4, where the parameter \(R_{s}\) is adjusted to achieve different levels of overall aging. The number of cells in series ranges from 330 to 450, with series resistance \(R_{s}\) adjusted from 60 m\(\Omega\) to 80 m\(\Omega\). Partial cell aging divides the cell string into two groups, as shown in Figure 5, one group being normal and the other experiencing aging faults with an additional resistor. The percentage of aged cells is between 18% and 33% of the total, and the resistance connected in series is adjusted from 40 \(\Omega\) to 60 \(\Omega\) to simulate varying degrees of partial cell aging. On the I-V curve, the characteristic of total cell aging shows the maximum power point moving slightly towards the origin, while the characteristic of partial cell aging shows a change in slope near the open-circuit voltage, as illustrated in Figure 6.  
Figure 4: Aging current–voltage curve simulation circuit diagram with \(R_{s}\) parameter change.  
Figure 3: Normal current–voltage curve.
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
## 2 Phase Filtering of Shearlet Domain Based on Interferometric Phase Statistics
### Phase Model  
An interferometric phase in the complex domain can be expressed as  
\[e^{i\phi}=\cos(\phi)+j\sin(\phi) \tag{3}\]  
where \(\phi\) is the interferometric phase.  
The interferometric phase can be characterized by an additive noise mode [6] as  
Figure 1: (a) Phase distribution for \(|r|=0.1\sim 0.9\) and phase \(\phi_{x}=0\) ; (b) Phase standard deviation versus coherence (\(L\) indicates the multi-look number).  
\[\phi=\phi_{\omega}+\phi_{n} \tag{4}\]  
where \(\phi\) is the noisy phase, \(\phi_{\omega}\) is the noise-free phase and \(\phi_{n}\) is the noise. In addition, \(\phi_{\omega}\) and \(\phi_{n}\) are assumed to be independent from each other.  
According to the analysis presented in [PERSON] and [PERSON] [20], the interferometric phase in the complex domain is continuous. The real and imaginary parts of the phase can be written as [20].  
\[\begin{array}{l}\cos(\phi)=N_{c}\cos(\phi_{\omega})+v_{c}\\ \sin(\phi)=N_{c}\sin(\phi_{\omega})+v_{s}\end{array} \tag{5}\]  
where \(\cos(\phi)\) and \(\sin(\phi)\) are real and imaginary parts of \(\phi\), respectively. \(v_{c}\) and \(v_{s}\) can be considered as zero-mean noise and are independent from \(\phi_{\omega}\). \(N_{c}\) is a factor related to the coherence.  
Since the real and imaginary parts of the phase are periodic characteristics, the phase is wrapped from \((-\pi,\pi)\). Therefore, the filtering should be performed on the real and imaginary parts separately to maintain the phase jumps [30].