esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Probabilistic Neural Network Model Based on Symmetric Alpha Stable Distribution
### Probabilistic Neural Network  
A probabilistic neural network is a type of feed forward neural network developed from a radial basis function network with hierarchical structures for the input layer, model layer, summation layer, and decision layer. The main computation steps are as follows:  
Figure 1: Symmetrical steady state distribution \(S_{\alpha}\)(1,0,0) with different \(\alpha\) values.  
Figure 2: Asymmetrical steady state distribution \(S_{\alpha}\)(1,0.5,0) with different \(\alpha\) values.  
1. Input layer There are n neurons in the input layer, representing the dimensions of the input samples: \[X=(x_{1},x_{2},\cdots,x_{n})^{T}\] (4)
2. Model layer The model layer contains \(h\) hidden neurons, meaning that \(h\) is the sum of training samples in each category. For input samples, the layer will compute the Euclidean distance between it and each training sample, and utilize a Gaussian probability-density function to learn the similarity, as shown in Formula (5). \[z_{k}=\exp\Bigl{(}-\|X-Y_{k}\|/2\sigma^{2}\Bigr{)}\] (5)  
where \(X\) stands for unclassified input samples; \(Y_{k}\) is the training sample; \(\|X-Y_{k}\|\) is used to find the Euclidean distance between unclassified input sample \(X\) and training sample \(Y_{k}\), and \(\sigma\) is the smoothing factor.  
(3) Summation layer The summation layer is used to cumulate the probability of the output of training samples belonging to the same category in the model layer, where the estimated probability density function is derived from Formula (5), where \(h_{j}\) is the sample number that belongs to category \(j\) in the training samples. The summation layer units sum all the outputs of the model layer in the same category, without considering those of other categories.  
\[out_{j}=\sum_{k=1}^{h_{j}}z_{k} \tag{6}\]
4. Decision layer For the output of the summation layer, the competitive layer takes the largest posterior probability density as the output of the whole system. The output represents the neuron with the largest probability density function of one category as 1, signifying its corresponding category as the one to be identified. The output of the other output neurons is 0; i.e., \[y_{j}=\left\{\begin{array}{c}1,out_{j}=\max\bigl{\{}out_{j},j=1,2,\cdots,m \bigr{\}},\\ 0,others.\end{array}\right.\] (7)
## 2 Materials and Methods
### Data Processing and Analysis  
The sessions were fully transcribed by the researchers. Transcriptions were anonymized, and each participant was assigned a number code. Field notes were also reviewed to improve accuracy. A thematic analysis approach was used to code and classify the comments in the different themes, subthemes, and categories and then compiled into two core areas of content: attributes to improve workflow in clinical care and administrative tasks. An iterative process of reading, analyzing, and coding the data was carried out individually by two of the researchers, using word-processing software to code the categories. These were grouped into similar concepts to form subthemes and themes. The researchers reviewed and compared their findings to reach a consensus throughout the process. The categorization and classification of each coded statement were systematically reviewed, discussed, and consensually validated by the research team. Three rounds of coding and discussion took place to enhance the credibility of the analysis and develop clear categories and themes [31].  
To check consistency, a third researcher cross-checked the results via a blind review for the same transcripts [31]. Any disagreements between the researchers were resolved by discussion. Confirmability was enhanced when the same categories emerged from the data of subsequent groups' transcripts.
# Multiband Frequency Tuneable Antennas for Selection Combining Strategy in White Space Applications
###### Abstract  
This article presents a reconfigurable antenna using digitally tuneable capacitors (DTCs). Mounted on a 120 \(\times\) 200 mm\({}^{2}\) ground plane, the radiating element has very compact dimensions of \(0.06\lambda_{g}\times 0.016\lambda_{g}\times 0.0016\lambda_{g}\) (with \(\lambda_{g}\) being the wavelength at 585 MHz). The proposed structure could be operated in the white-space frequency band from 470 to 700 MHz. The antenna system is composed of two radiated elements that are placed at the corner of the board. The active components were soldered onto the antennas and controlled by an embedded microcontroller via the I2C interface. Antenna impedances were simulated and evaluated by measurement with an impedance-matching bandwidth of 39.32% (470-700 MHz) for an S11 less than \(-6\) dB. Furthermore, the numerical results show a realized maximal gain ranging from \(-2.2\) dBi at 470 MHz to 1.87 dBi at 700 MHz. Lastly, the diversity gains based on the radiation pattern of two resonators were calculated. The results show envelope correlation coefficient (ECC) values lower than 0.5 for the different configurations.  
reconfigurable antenna; digitally tuneable capacitor (DTC); white space; IoT +
Footnote †: journal: Applied  
The components used in previous studies provide a discrete sweep in frequency. To obtain continuous variation, the varactor diode can be used in a reconfigurable antenna [15; 16; 17; 18; 19]. By controlling the DC bias voltage, the capacitance of the varactor could be changed. However, the high control voltage (up to 20 V) and low input power handling limit varactor diode application to the receiving mode. To go beyond these limitations, the digitally tuneable capacitor (DTC) is a promising solution. In [20], a reconfigurable frequency antenna was reported on the basis of a folded IFA concept. A DTC was used to connect the end of the radiating element to the ground plane. With different DTC capacitances, the antenna could cover the 600-900 MHz band. The authors in [21] used only four states of DTC. Due to this proposal, this antenna could be operated for the four frequency bands of 868 and 915 MHz, and 1.8 and 2.1 GHz.  
In this paper, multiple reconfigurable antennas using a DTC component are studied and presented in five sections. First, Section 1 is the introduction and the literature review. Section 2 presents the antenna design, with the structure proposed in the first subsection, while the description of the DTC is presented in the second subsection. Then, Section 3 reports the simulation results. In Section 4, antenna measurements are discussed. Lastly, a brief conclusion is given in Section 5.
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 3 Results
### Setting up the Experimental Platform  
As shown in Figure 15, the experimental platform of the fine tracking control system was set up. The laser acted as an optical source. The embedded digital signal processor was used as the main control unit, and the high-speed CCD camera served as a measuring unit for target spot's position. The high-speed movement of the target in the azimuth and elevation directions was simulated by controlling the disturbance FSM. The optical path of the experimental platform is depicted in Figure 16.
## 3 Simulation Results  
In this section, the performance evaluation and the analysis of the GFB-OFDM and the CP-OFDM of 5G NR are provided. In Table 1, it shows the simulation parameter setup. It is assumed that the CP-OFDM waveform can support an IFFT of 16,384-point (double bandwidth oversampled IFFT).
## 5 Role of sRNA in Quorum Sensing
### Qrr 1-5
Findings from this study suggest that QrrX and QrrT play essential roles in facilitating the transition from individual to community behaviors in _V. cholerae._Figure 4: **The role of Qrr sRNAs in quorum sensing regulation in _Vibrio_ spp. ((a) Quorum sensing at low cell density): At low cell density, the concentration of autoinducers is low, leading to the activation of the sensor kinase, which subsequently phosphorylates downstream regulators. This phosphorylation cascade induces the expression of quorum regulatory RNAs (Qrr sRNAs). Qrr sRNAs function to repress the master quorum sensing regulator LuxR, thereby inhibiting collective behaviors associated with high-density populations. Concurrently, Qrr activates AphA, a key regulator of virulence gene expression, enhancing pathogenicity. Additionally, Qrr sRNAs suppress the type VI secretion system (T6 SS), a mechanism involved in interbacterial competition. ((b) Quorum sensing at high cell density): At high cell density, the accumulation of autoinducers triggers a shift in sensor kinase activity from phosphorylation to phosphatase activity, leading to the degradation of Qrr sRNAs. In the absence of Qrr, LuxR expression is de-repressed, promoting collective behaviors characteristic of quorum sensing, including biofilm formation and luminescence. Additionally, the de-repression of LuxR enhances T6 SS activity while simultaneously repressing virulence gene expression, shifting the bacterial population toward cooperative rather than pathogenic behaviors. **
# Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation
###### Abstract  
The ever-increasing penetration of renewable energy sources and distributed generation needs developing and deploying more sophisticated and precise control techniques. The paradigm shift from high-rotational inertia towards inverter-connected facilities has made modern power systems subject to strongly non-stationary operating conditions. In this context, phasor measurement units (PMUs) represent a promising solution due to their accuracy and time synchronization. The current paper further develops the concepts recently published proposing performance assessment of PMUs relying on metrics specifically defined to quantify the estimation accuracy, reporting latency, and response time. Our focus is on determining the confidence interval associated with these metrics and thus derive a robust approach for their application in measurement-based network controlling efforts. The configuration of the used simulation model is detailed, and the state estimation results as a function of the selected measurement weighing criteria are presented, concluding as a possible application of the reliability metrics in Weighted Least Squares and Discrete Kalman Filter state estimators, highlighting the performance enhancement as well as the theoretical limits of the proposed approach.  
phasor measurement unit (PMU); uncertainty; goodness of fit; state estimation Article
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
###### Abstract  
Luojia-1A is a scientific experimental satellite operated by Wuhan University, which is the first low earth orbiter (LEO) navigation signal augmentation experimental satellite. The precise orbit is the prerequisite of augmenting existing Global Navigation Satellite System (GNSS) performance and improves users' positioning accuracy. Meanwhile, LEO precise orbit determination (POD) with BeiDou-2 observations is particularly challenging since it only provides regional service. In this study, we investigated the method of precise orbit determination (POD) for Luojia-1A satellite with the onboard BeiDou observation to establish the high-precision spatial datum for the LEO navigation augmentation (LEO-NA) system. The multipath characteristic of the BeiDou System (BDS) observations from Luojia-1A satellite is analyzed, and the elevation-dependent BeiDou code bias is estimated with the LEO onboard observations. A weight reduction strategy is adopted to mitigate the negative effect of poor BeiDou-2 geostationary earth orbit (GEO) satellites orbit quality, and the Luojia-1A orbit precision can be improved from 6.3 cm to 2.3 cm with the GEO weighting strategy. The precision improvement of the radial direction, along-track, and out-of-plane directions are 53.47%, 47.29%, and 76.2%, respectively. Besides, tuning the pseudo-stochastic parameters is also beneficial for improving orbit precision. The experiment results indicate that about 2 cm overlapping orbit accuracy are achievable with BeiDou observations from Luojia-1A satellite if proper data processing strategies are applied.  
Luojia-1A satellite; BeiDou observations; precise orbit determination; reduce-dynamic; navigation augmentation +
Footnote †: journal: remote sensing
## 3 Experiments and Results
### Empirical Properties of the PINN
#### 3.3.1. The Effect of Depth and Width of the Network on the Training Time  
The selection of hyperparameters plays a vital role in the convergence time of PINN. In this section, we explore the influence of the network's size, i.e., the depth and the width of the network, on the training time. We fix the the number of samples in the domain to 1000, and choose different sizes of the neural network. The practical epoch and the training time of each network are shown in Tables 2 and 3, respectively. According to the results in Table 2, we observe that except for the first set, i.e., one hidden layer with 5 neurons reaches the maximum epoch before reaching the cut-off threshold, other combinations' loss functions converge below the cut-off threshold within the maximum epoch. Furthermore, it is worth mentioning that the prediction errors, i.e., RMSEs, of all selections of the size are below \(5\times 10^{-4}\), which satisfies our requirement. Moreover, based on the Table 3, we can conclude that as the size of the neural network increases, the convergence speed gradually increases at the beginning; however, when it reaches a certain degree, then by increasing the size, the improvement in computation time stagnates or even becomes worse. For the considered example, the best result is obtained for 2 layers and 60 neurons.
# Cyclostationary Crosstalk Cancelation in High-Speed Transmission Lines
## 4 Experimental Results
### Correlation Analysis of the Measured Signals
(**c**) The eigenvalues of the correlation matrices \(\mathbf{R}_{dd}\) and \(\mathbf{R}_{cc}\). (**d**) The scaled eigenvectors of the correlation matrices. Bit rate is 0.4 Gbit/s.
# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 2 Materials and Methods
### Improved YOLO8 Model  
This section unveils an upgraded model for detecting and categorizing forest fires, utilizing the YOLOv8 framework, as depicted in Figure 3. During the input phase, we initially enhance detection accuracy and discriminative capability through adaptive pre-processing methods, such as image scaling, concatenation, and anchor-box computation. Subsequently, adjustments are made to the C2f network structure, merging it with Deformable Convolution and SCConv to form \(DCN\_C2f\)[30] and \(SCConv\_C2f\). The middle part of the backbone network's C2f is replaced with \(DCN\_C2f\), while the remaining parts are substituted with \(SCConv\_C2f\). Introducing the Deformable Convolution C2f module enables adaptive adjustments to the network's receptive field. Additionally, including the SCConv module not only effectively reduces model parameters and FLOPs but also enhances feature representation. This optimization enables better adaptation to the challenges of forest fire detection and addresses feature redundancy issues in detecting forest fires from unmanned aerial vehicles.  
Moreover, the Detect module undergoes improvements to address the directional and position-sensitive characteristics of fires by introducing Coordinate Attention. This attention mechanism concurrently considers inter-channel relationships and positional information, capturing and leveraging these aspects more effectively for precise fire detection. Lastly, in the prediction phase, loss functions are utilized for position calculation, adapting the bounding box loss function to WIoU loss. This adjustment enhances the loss function with directional attributes, fortifying the capabilities of the detection algorithm in training and inference.
## 2 Main Blocks for Wirelessly-Powered Cage System
### Coil Design and Optimization
#### 2.1.2 Coil Optimization for Inductive Links Implemented in Wirelessly-Powered Cages
In this case, optimizing the four-coil inductive link means increasing the minimum PTE within the homecage to ensure PDL is enough to keep the headstage on when the CLPC adjusts the Tx power, as opposed to maximizing PTE in the perfectly aligned regions in traditional coil optimization. The coupling between loosely coupled Tx and Rx coils is the dominant factor in determining the PTE of the four-coil inductive link. Since the size of the headstage is considerably smaller than the homecage, the effective area of the Rx resonators should be maximized so that more Tx magnetic flux can pass through the Rx resonator, thereby improving the coupling between the Tx and Rx resonators. In [15], the Rx resonator wrap around the headstage, maximizing the area encompassed by the Rx resonator without enlarging the size of the headstage, as shown in Figure 8a. In [14], the largest possible area of the headstage is the diagonal planes of the headstage cube, therefore, the Rx resonators are tilting an angle of 25\({}^{\circ}\) compared to the horizontal plane in each four directions of the cubical headstage, as shown in Figure 8b. Due to the large separation and size difference between the Tx and Rx structures, the Tx and Rx resonators are loosely coupled. Hence, a single target resonance frequency for this system can be set, regardless of the Rx location in the homecage. Additionally, because of the strong coupling among the Tx resonators, only one Tx resonator needs to be finely tuned to match the resonance frequency of the entire Tx structure with the target carrier frequency. Such practical and convenient characteristics are also applicable on the Rx resonators, which are also strongly coupled with each other. Figure 8c shows the PTE distribution within the 3D volume of the cage. As we can see that while the center area has a weaker magnetic flux density, mutual coupling, and thereby lower PTE, the PTE measured at each height is more uniform, with smaller variations of less than 7%. Although the PTE reduces as the height increases, the deduction of the PTE is slowed, which is mainly credited to the enhancement of EM field by the Tx resonator at the top of the cage. Although the optimization procedures in [13; 14] are only dedicated to the specific geometry of the cage, which is difficult to be extended for a large arena, these techniques show high and homogeneous PTE inside the standard geometry of cage. Besides, only one PA is needed, which can significantly simplify the design of the power Tx. Therefore, the designer needs to choose the coil design and optimization procedure whether to adopt a modular coil design or a specific coil design dedicated to a designated area.
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 5 A Producer Mobility Link Service in NDN Face
### Handover Operation of PMLS  
The handover operation of PMLS is illustrated in Figure 4.  
Figure 3: Transaction structure at the Producer Mobility Link Service.  
_Step 1._ The NMP exchanges Interests and Data packets with the previous NAR (pNAR).  
_Step 2._ The PMLSs at both the pNAR and the NMP detect the movement of the NMP. These nodes employ the link-layer indication sent from the link-layer interface to detect the movement of the NMP [29]. The link-layer triggers alert the network layer module to the status of the link (link up/down). After detecting the movement of the NMP using this link-layer indication, the PMLS at the pNAR starts buffering for the newly arrived Interests. If the pNAR does not receives Face Update message from NMP for a certain period of time as the NMP suddenly goes down, it drops the Interest and deregister the corresponding content using the routing protocol.  
_Step 3._ The NMP changes its point of attachment to a new NAR (nNAR).  
_Step 4._  
* The NMP and nNAR create new NDN faces to connect to each other. The APP at the NMP then registers the content name prefixes to the nNAR.
* The PMLS at the NMP re-establishes the connection between itself and pNAR by sending a \"Face update\" message to the peer PMLS at the pNAR. Upon receiving this, the pNAR could know the new transport end point information of the NMP through the address information field in the Face Update message. This allows the pNAR to know in which NAR the current NMP is located.  
_Step 5._  
* The PMLS at the pNAR re-synchronizes the transaction states with the peer PMLS at the NMP by sending a Status Report message per an instance of the transaction structure. The PMLS at the NMP checks whether it has missed any sent Interests before it moves. If any missed Interests exist, it demands re-transmission of the missed Interest to the pNAR. It also checks the last \"Data  
Figure 4: Handover operation of PMLS.  
Segment\" message received by the pNAR if any. The NMP immediately transmits the remaining data segments. If any buffered Interests exit from the PMLS of the pNAR, they are forwarded to the NMP.
* The NLSR at new NAR advertises newly registered content name prefixes.  
_Step 6._ When the convergence timer at the NMP's PMLS expires and it does not receive a Status Report message from the NAR for a certain period of time, it sends the \"Face Deletion\" message to the pNAR to request the deletion of the Face. If there are no more pending Interests in the pNAR's PMLS, the NLSR at the pNAR withdraws the context name prefixes registered by the NMP's APP operation. _Step 7._ After the convergence timer for the de-registration of the content name prefixes has expired, the old NDN faces connecting the NMP with the pNAR delete themselves.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 2 Materials and Methods
### Thermoacoustic Equation  
Once an initial pressure is found, via either optical or radio frequency electromagnetic radiation, the pressure must then propagate to ultrasound transducers located outside the tissue boundary. Extremely short (nanosecond) pulses of energy are assumed to be irradiating the medium. Therefore the initial pressures are assumed to be delta functions, and only present for the initial conditions when formulating the equations. The pressure wave in photoacoustics is usually modelled using a standard scalar wave equation, with initial conditions:  
\[\begin{split}\frac{\partial^{2}}{\partial t^{2}}p(x,t)& =c^{2}(x)\
abla^{2}p(x,t)\;\;x\in\Omega\\ p(x,0)&=f(x)\\ \frac{\partial}{\partial t}p(x,0)&=0,\end{split} \tag{7}\]  
where, \(f(x)\) corresponds to the initial pressure found from the initial pulsed excitation, and \(p(x,t)\) is the pressure wave that propagates to the transducers. Similar to the electromagnetic wave equation, the pressure propagation also requires a perfectly matched layer (PML) to prevent reflections from the boundary of the numerical domain [36; 37]. Therefore, the pressure is received at transducers along a curve (surface in 3D) \(\gamma\), which is not the boundary of the domain \(\partial\Omega\).