esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Mode of CMSR System  
The CMSR system consists of two first-order MSR systems whose specific expressions are described as follows [42; 43]  
\[\left\{\begin{array}{l}\frac{dx}{dt}=-a_{0}b_{0}\sin(b_{0}x)+r(y-x)+s(t)+n(t )\\ \frac{dy}{dt}=-ab\sin(by)+r(y-x)\end{array}\right. \tag{5}\]  
Figure 1: Periodic potential function.  
where the \(-a_{0}b_{0}\sin(b_{0}x)\) is the controlled system, the parameters \(a_{0}\) and \(b_{0}\) are fixed values, which are set \(a_{0}=b_{0}=1\). The system where the \(-ab\sin(by)\) is located is the control system and \(r(y-x)\) indicates that the y-channel input signal is used to adjust the x-channel input signal. The parameter variables are \(a\) and \(b\), and the final output of the CMSR system is \(x\) due to the signal and noise acting on the controlled system. Among them there is given, as follows:  
\[\left\{\begin{array}{ll}U_{1}(x)=-a_{0}\cos(b_{0}x)\\ U_{2}(y)=-a\cos(by)\end{array}\right. \tag{6}\]  
When the signal noise and system parameters match each other to produce a SR phenomenon the potential function \(U(x,y)\) is obtained as follows:  
\[U(x,y)=-a_{0}\cos(b_{0}x)-a\cos(by)+\frac{r}{2}(x^{2}-y^{2}) \tag{7}\]  
When there are \(a_{0}=b_{0}=1\), \(a=b=1\), \(r=0.1\), the potential function diagram is shown in Figure 2. It is found that the active range of the particles is increased, the noise utilization rate is increased, and the dynamic performance is more abundant when compared Figures 1 and 2.  
For the solution of Equation (5), the Runge-Kutta method is still used and the specific solution is obtained as follows  
\[\left\{\begin{array}{ll}k_{1}=h[-\sin(x_{n})+r(y_{n}-x_{n})+s_{n}]\\ l_{1}=h[-ab\sin(by_{n})+r(y_{n}-x_{n})]\\ k_{2}=h[-\sin((x_{n}+0.5k_{1})+r(y_{n}-(x_{n}+0.5k_{1}))+s_{n}]\\ l_{2}=h[-ab\sin(b(y_{n}+0.5l_{1}))+r((y_{n}+0.5l_{1})-x_{n})]\\ k_{3}=h[-\sin((x_{n}+0.5k_{2})+r(y_{n}-(x_{n}+0.5k_{2}))+s_{n+1}]\\ l_{3}=h[-ab\sin(b(y_{n}+0.5l_{2}))+r((y_{n}+0.5l_{2})-x_{n})]\\ k_{4}=h[-\sin((x_{n}+k_{3})+r(y_{n}-(x_{n}+k_{3}))+s_{n+1}]\\ l_{4}=h[-ab\sin(b(y_{n}+l_{3}))+r((y_{n}+l_{3})-x_{n})]\\ x_{n+1}=x_{n}+\frac{1}{6}(k_{1}+2k_{2}+2k_{3}+k_{4})\\ y_{n+1}=y_{n}+\frac{1}{6}(l_{1}+2l_{2}+2l_{3}+l_{4})\end{array}\right. \tag{8}\]  
where \(s_{n}\) and \(n_{n}\) represent the input signal and noise, respectively. \(x_{n}\) is the output signal and \(h\) is the iterative step of the numerical calculation.
## 4 Results
### Validation  
Habitat data can be used to validate whether the habitat potential maps were effectively predicted. Therefore, the halves of marten and leopard cat habitat data not used for training were used as the validation data. ROC curves were used to validate the habitat potential maps of marten and leopard cats generated using the ANN model. The ROC curves were generated for the marten and leopard cat habitat potential maps to compare the habitat locations of martens and leopard cats, respectively.  
The ROC curve is a graphical plot showing the diagnostic capabilities of classification models with various thresholds. It is a widely used method for validation [37; 38; 39; 40]. ROC curves can show sensitivity and specificity on the \(x\)-axis and \(y\)-axis, respectively. In this study, specificity represented the percentage of area that martens and leopard cats could inhabit, while sensitivity represented the predicted potential marten and leopard cat habitat locations. For the ROC graph, the predicted habitat potential values after applying the ANN model were sorted in descending order. The ranks of the habitat potential values were identified and the values were equally classified into 100 classes of study area.  
Figure 6 shows the ROC curves of the marten and leopard cat habitat potential maps; martens showed 85.01% (0.8501) accuracy, versus 87.03% (0.8703) accuracy for leopard cats. These results are depicted as a graph of the validation rate (Figure 6), where 65% of the study area included 100% of all habitat locations for both species.  
Figure 5: Habitat potential maps generated using ANN model of (**a**) martens; and (**b**) leopard cats.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 6 Experimental Results
### Energy Consumption of the Wake-Up Receiver  
One of the requirements of a ULP WuRx is very low power consumption as it is always active, even when all the other components are in the sleep state. The power consumption of the ULP WuRx was measured to be 1.83 \(\upmu\)W when the radio front-end was active and the PIC was in sleep state and 284 \(\upmu\)W when the PIC was active at 3.3 V and was parsing the received data at 2 MHz. Therefore, the ULP WuRx power consumption becomes significant when the PIC is active. At each wake-up, the PIC is active for 19 ms to perform address matching. Hence, the energy consumed by the ULP WuRx at  
Figure 8: UPMA-X-MAC and PW-MAC protocols. BCN, Beacon. (**a**) Packet transmission using UPMA-X-MAC. (**b**) Packet transmission using PW-MAC.  
each wake-up of the PIC is 5.40 uJ. If we consider a typical node, not using a ULP WuRx, but using the duty-cycling approach with a duty-cycle set to a typical value of 0.05% and consuming 100 mW when the transceiver is active, then the total energy consumed by this node over a period of 24 h is 4.32 J. This amount of energy corresponds to more than \(8\times 10^{5}\) wake-ups of the PIC. The number of \"false\" PIC wake-ups, i.e., wake-ups not caused by WuBs, but by the wireless channel noise, was measured over a period of 24 h in an indoor environment. Figure 9 shows the total cumulative number of false wake-ups according to time. It is not surprising to observe that most of the false wake-ups happen during the daytime. In total, 3110 false wake-ups were counted over a 24-h period, which is two orders of magnitude below the previously-considered scenario assuming a typical 0.05% duty-cycle. Moreover, no false wake-ups of the main MCU happened. These results show the importance of the microcontroller embedded in the ULP WuRx. Indeed, performing address matching by a ULP microcontroller avoids numerous false wake-ups of the node MCU, whose power consumption is significantly higher.
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 3 Simulation of the Proposed Structure and Parameter Optimization
### Theoretical Method Descriptions  
One of the most suitable methods applicable to modeling such periodic structures is RCWA (rigorous coupled-wave analysis) [27], sometimes called FMM (Fourier modal method). We implemented an improved formulation of the RCWA that uses an inverse factorization rule that improves the convergence and speed of calculation for p-polarization [32]. Although this method was developed primarily for simulating rectangular gratings, it can be applicable to a variety of periodic profiles. This can be achieved by reducing an arbitrary periodic profile to a set of thin slices, each of which is approximated by a rectangular profile. This method has advantages compared to methods that use discretization in real space, such as finite element methods or finite difference methods, such as its relative simplicity of implementation and low computational resource requirements. This method allows for the calculation of reflection and transmission coefficients for each diffraction order, as well as the full electric and magnetic near-field distribution.  
Since grating is usually deposited on the top of a multilayered structure, it can give rise to numerical instabilities related to the substrate modes. To improve numerical stability, we employed an enhanced transmittance matrix approach [28]. Despite all these improvements, the RCWA method has some drawbacks. For instance, it can converge very poorly and even diverge for metallic gratings with low losses. On the other hand, the presence of a layer with some losses close to the grating can improve convergence because of significant damping.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### On-Chain Data Storage Methods
#### 2.1.4 Logs and Transaction Data  
Blockchain logs record the history of all transactions occurring on the network, as well as events triggered by smart contracts. These logs not only reinforce the transparency and traceability of blockchain but also serve as a critical source of information for network participants.  
* _Blockchain Logs_: Used as event logs in networks like Ethereum, they record smart contract events, providing transparency and traceability for decentralized applications (DApps). They enhance usability in DApp development and facilitate debugging for developers. However, scalability challenges may arise when dealing with large datasets [71; 72; 73].
## 2 UAV Network Communication Model and Architecture  
Communication architecture is a key factor in the cooperation and control of UAVs in swarms. In the early stages, UAV swarm control is performed by a single central station (usually located on the ground). The central station has enough communication resources to communicate with all UAVs in the swarm. This is the basic concept for centralized communication architecture. When the number of UAVs increases or the exchanged data arises due to complex and diverse tasks, a decentralized structure is needed to meet the requirements--namely, reducing the dependence on UAVs on central stations [25; 26]. In this section, we  
Figure 2: Structure and organization of this paper.  
systematically review and introduce studies on communication architectures for UAV networks. At the same time, we also analyze the advantages and disadvantages of these architectures, as well as related issues.
## 3 Global Positioning System (GPS) L1 Receiver: 2D Pattern vs. 3D Pattern
### Navigation Solution  
In this section the impact on the navigation solution when assuming a 2D L1 antenna pattern and a 3D L1 antenna pattern are compared. Initially the impact on the standalone GPS navigation solution is evaluated. In this case, we computed the single-epoch least squares position and velocity estimation errors. The accuracy given by unfiltered single-epoch least squares estimations would be very coarse at Moon altitude, not fulfilling the mission requirements. Indeed, at Moon altitude, the relative geometry between the GPS satellites and the receiver (very high GDOP values) is very poor and the ranging errors (user equivalent range error (UERE)) can be significant (due to the low signal powers).  
As presented in our previous works [7; 9], we used an adaptive orbital filter to process the GNSS measurements using a model of the orbital forces acting on the hosting spacecraft. The GNSS measurements processed by the filter, can be position and velocities or directly pseudoranges and pseudorange rates. The measurements and the predicted measurements have different error distributions, leading to an improved navigation solution compared to the ones obtained individually. A detailed implementation of the orbital filter is presented in [7]. Therefore, in addition to evaluating the effect of assuming a 2D L1 pattern or a 3D L1 pattern on the single-epoch least squares solution, it is useful to estimate the same effect on the filtered navigation solution as well.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 4 Experimental Results
### Simulated Data  
First, the performance is validated using simulated data, the latter consists of MTS of 5 variables (\(n=5\)) with a domain of 3 symbols (\(r_{i}=3\) for all \(i\)) along 10 time frames (\(T=10\)). More specifically, the present experiments subsist on training two separate stationary first-order DBNs, one for generating normal data and another to produce outliers. All data is mixed together in a single dataset and fed to the system. A DBN is trained using the combined dataset, with the aim of locating the anomalous subjects.  
To evaluate the performance of each experiment, the number of true positives (TP), false positives (FP) and false negatives (FN) is measured. Such are used to determine the Positive Predictive Value (PPV), representing precision, and True Positive Rate (TPR), representing recall. To conjointly consider both metrics, the F\({}_{1}\) score is computed along with the accuracy (ACC) of each test.  
Experiments are identified by their outlier ratio \(P_{O}\), indicating the percentage of anomalous subjects in the dataset, the anomalous model, DBN \(B\) or \(C\), used to generate anomalous subjects and the total number of subjects in the dataset \(D\) of size \(N\). The transition networks of the anomalous models are displayed in Figure 3 together with their dissimilarities with respect to the normal model \(A\).  
Experiments are divided in two groups according to the strategies in the score-analysis phase. Such means that both strategies are validating the proposed approach. Experiments considering a different approach are carried out afterwards.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Embeddings
#### 3.1.3 Google BERT  
Bidirectional Encoder Representations from Transformers (BERT) can be used as a feature extractor (embeddings); it is developed by Google and may convert textual data into numerical form [32]. Unlike directional models that read text linearly from left to right or right to left, BERT processes words bidirectionally [21]. It considers the relationship between each word and all other words in the surrounding context (both left and right) within the sentence. This approach allows for a profound understanding of the context and meaning of words, resulting in more accurate embeddings [21]. Therefore, in this study, we have used the 'bert-base-uncased' BERT model.
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
## 4 Simulation Studies
However, in Equations (21) and (22), if \(Q_{k}\) is equal to zero, the computed phase error is (23):  
\[\phi_{k}=\tan^{-1}\left(\frac{\int_{jT}^{(j+1)T}\frac{A}{2T}c_{k}(t-\tau_{\rm inter }-\tau_{\rm LTE})c_{k}(t-\tau_{k})\sin(\Delta ft+\Delta\phi)dt}{\int_{jT}^{(j+ 1)T}\frac{A}{2T}c_{k}(t-\tau_{\rm LTE}-\tau_{\rm LTE})c_{k}(t-\tau_{k})\cos( \Delta ft+\Delta\phi)dt}\right). \tag{23}\]  
It should be noted that due to the interference signal of the LTE system, the phase error cannot be minimized to zero, and the carrier tracking fails, which makes it difficult to obtain an accurate carrier wave. The GNSS signal dataset used in the simulation presented was collected using a GNSS receiver in Deblin, Poland, where the GPS and Galileo satellites were visible. The processing of GNSS system signals was based on the following parameters, i.e., sampling frequency: 16.3676 MHz, intermediate frequency: 4.1304 MHz, and character sample format (8 bits). However, the software used in this case was developed in the Matlab/Simulink environment. The simulated LTE system signal is transmitted at a distance of 25 km from the receiver, with different power values, modulated in a carrier wave with frequencies L1 and E5. The transmitting power of the interference signal varies, and the propagation losses follow the Okumura model. The signal length of both the GPS and Galileo systems in this algorithm is at least 36 s to ensure the delivery of all message subframes in the navigation message. The LTE signal is 3 ms long in order not to interfere with the acquisition process. For example, a GPSsignal includes five satellite signals: SV3, SV15, SV16, SV18, and SV19, SV21, and SV22. The interference signal of the LTE system is a series of sequences with many periods of the PRN code corresponding to satellite 22. Then, the IF signal will be processed in the tracking step based on the result of the acquisition. The trace output is the discriminator error and phase shift values. The phase shift value can be truncated to \(-1\) and \(+1\). In this case, finding the preamble is most important for decoding the navigation data, where the preamble indicates the start of the subframe. The bits appearing in the PRN code with the exemplary waveform [\(-1\ 1\ -1\ -1\ 1\ -1\ 1\ 1\ -1\) 1 1 \(-1\)] cause the value of the correlation function of two perfectly synchronized bits of the navigation message preamble to be 8 or \(-8\) if the inverted preamble is ideally located. In the tracking process performed by the GNSS system receiver, the information rate is 1000 sps, and each bit of navigation data is 20 ms long, which results in 20 times sampling. Thus, the value of the correlation varies between \(\pm 8\) and \(\pm 160\).
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 2 Materials and Methods
### Case Study on Sentinel-2
#### 2.2.2 Processing Steps  
As described in Section 2.1.3, the maximum cut-off frequency of S2 is for B01 with a value of \(f_{c}=0.43\) m\({}^{-1}\). Hence, GSD\({}_{min}=\frac{1}{2\cdot 0.43}=1.16\) m. Given that 1 m (\(\leq\)1.16 m) divides evenly into all the GSDs of all the S2 bands, we chose this value for \(d\). Each pixel in the initial image represents a \(1\,\mathrm{m}\times 1\,\mathrm{m}\) square. The scene is made of an image of size \(540\times 540\) pixels with the patch at the center. The pixels of the patch are filled with the configured material's reflectance values, taken from its spectral signature, while the other pixels are filled with the reflectance values of seawater. Out of one scene, 13 simulated images are generated, one for each of the 13 bands of S2, taking into consideration the characteristics of each band.  
Each of the 13 simulated images from the same scene is convolved with the PSF corresponding to the band. The resulting convolution is then re-sampled so it conforms to the GSD of the band (e.g., 60 m for B01 or 10 m for B02). Figure 3a shows the result of the convolution between a scene with a circular plastic patch and the PSF of band B02-whose GSD is 10 m. The plastic is in dark blue and the seawater in the background is white. The values of the pixels are reflectance values corresponding to plastic and seawater.  
The image is then sampled (see Section 2.1.3). Figure 3b shows the resulting image for a specific jitter. As can be seen in Figure 3c,d, a different jitter leads to dramatically different results.  
Optionally, once the 13 simulated images are convolved and sampled, noise can be added on each image based on the model presented in Section 2.1.2. We specialized the normal law as \(\mathcal{N}(0,\,\sigma^{2})\), with \(\sigma\) being one of the following values: 0.01, 0.02, 0.05, 0.1. The standard deviations have been chosen for sensitivity analysis and do not correspond to the SNR of S2.  
Figure 3: Illustration of aliasing effects due to differences in sampling. (**a**) Image after convolution with the PSF. Jitter \(5,5\) is represented by the red dots. (**b**) Image after sampling with jitter \(5,5\). (**c**) Image after convolution with the PSF. Jitter \(1,1\) is represented by the red dots. (**d**) Image after sampling with jitter \(1,1\).  
The output of the simulation is a package of 13 processed image and a binary mask that marks which pixels are part of the patch. Figure 4 illustrates the process, as described in the previous paragraphs.
###### Abstract  
The durability of reinforced concrete structures in aggressive environments is strongly influenced by the ingress of chloride and other harmful ions, which is further complicated under partially saturated conditions, due to the coexistence of liquid and gas phases within the pore network. This study aimed to develop a predictive moisture-chemical-temperature model and to elucidate the mechanisms governing ion transport in partially saturated concrete. A multi-species hygro-chemo-thermo transport model was formulated based on the Nernst-Planck equation, incorporating electroneutrality, zero current conditions, and the coupled effects of moisture and temperature gradients. The model was numerically implemented using a parallel FE method with the Crank-Nicolson scheme, supported by domain decomposition and SPMD techniques for high computational efficiency. As a result, experimental validation was performed through chloride ponding tests under varying temperature conditions (20 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, 50 \({}^{\circ}\)C), water-to-cement ratios (0.55, 0.65), and relative humidity differences (100%, 60%). The simulation results showed good agreement with the experimental data and confirmed that the proposed model can effectively predict chloride penetration under both isothermal and non-isothermal conditions. Additionally, the simulations revealed that moisture gradients accelerate ion transport, as the inward migration of the moisture front enhances the diffusion rates of chloride, sodium, and calcium ions until a steady-state moisture distribution is reached.  
Keywords:transport model; coupled hygro-chemical-thermal; Nernst-Planck equation; multi-species; parallel FEM; durability +
Footnote †: journal: Applied Sciences
# Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment
## 3 Communication Model for Microservices-Based IoT Applications  
This section describes the proposed Publish/Subscribe communication model for microservices-based IoT applications. In the model, IoT applications are constructed from microservices that provide small and autonomous services. These microservices use the publish/subscribe system as an event-based communication tool to collaborate and exchange data between each other efficiently.
## 4 Discussion  
The correct choice of the filter type and the frequency adopted is also important for better signal processing. The results in this review showed that the fourth-order Butterworth low-pass filter with a cutoff frequency of 4 Hz was the most used feature employed in the studies.