esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Phase Noise on the Optical Millimeter-Wave Signal in the DWDM-RoF System
###### Abstract  
In this study, we examined the effect of phase noise on the optical millimeter-wave (mm-wave) signal in a dense wavelength division multiplexing radio-over-fiber (DWDM-RoF) system. A single modulator was used to generate the optical mm-wave signal in the DWDM-RoF system. This paper addresses the impact of phase noise, which results from phase imbalance, on the optical mm-wave signal. To lower the effect of phase noise on the optical mm-wave signal, the phase imbalance should be controlled. The phase imbalance can be controlled and decreased by adjusting the phase at the phase shift (PS). The system performance was analyzed using various parameters such as bit error rate (BER), signal-to-noise ratio (SNR), optical signal to noise ratio (OSNR), and error vector magnitude (EVM). From the results, we found the phase imbalance affected the optical mm-wave signal due to the imbalanced splitting of the signal intensity at the MZM. The phase imbalance impacts the phase noise, which impacts the optical mm-wave signal. The phase noise could be decreased by controlling the phase imbalance at the phase of \(5\pi/12\). The best results at the phase of \(5\pi/12\) were collected for phase noise at 0.02 degrees.  
dense wavelength division multiplexing; radio over fiber; optical mm-wave signal; phase imbalance; phase noise +
Footnote †: journal: _electronics_  
Article
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 3 Results
### Zero-shot Super Resolution  
The FNO was trained on simulations with a 128 \(\times\) 128 \(\times\) 302 computational grid and was then used for zero-shot super resolution to simulate wave propagation on larger-sized grids. This was completed on an animal image from the testing dataset and a vasculature image to evaluate generalizability. The FNO simulations for both images strongly resembled their respective higher-resolution simulations using k-Wave (Figure 11). Errors could be seen in the background. The FNO simulations also had a more blurred appearance, as seen in the zoomed regions. This was likely due to the truncation of Fourier modes leading to the loss of high-frequency information that is useful for retaining sharper details in an image. Truncation errors became more prevalent for larger grid sizes using the FNO's super resolution feature (Table 2).  
For a computational grid of 128 \(\times\) 128 \(\times\) 302, k-Wave required on average 1.66 s to complete a simulation on a GPU. The FNO network on average required 0.11 s on the same GPU. This was approximately a 15\(\times\) reduction in computation time.  
\begin{table}
\begin{tabular}{c c c} \hline
**Grid Size** & **Animal** & **Vasculature** \\ \hline
128 \(\times\) 128 \(\times\) 302 & 9.8 \(\times\) 10\({}^{-3}\) & 2.7 \(\times\) 10\({}^{-3}\) \\
152 \(\times\) 152 \(\times\) 302 & 5.2 \(\times\) 10\({}^{-2}\) & 3.4 \(\times\) 10\({}^{-2}\) \\
176 \(\times\) 176 \(\times\) 302 & 7.1 \(\times\) 10\({}^{-2}\) & 4.5 \(\times\) 10\({}^{-2}\) \\
200 \(\times\) 200 \(\times\) 302 & 8.6 \(\times\) 10\({}^{-2}\) & 5.5 \(\times\) 10\({}^{-2}\) \\
224 \(\times\) 224 \(\times\) 302 & 1.0 \(\times\) 10\({}^{-1}\) & 6.3 \(\times\) 10\({}^{-2}\) \\ \hline \end{tabular} RMSE errors between k-Wave simulations and FNO zero-shot super resolution simulations for varying computational grid sizes.  
\end{table}
Table 2: FNO super resolution RMSE.  
Figure 10: RMSE and standard deviation bands between the k-Wave and FNO adjoint simulations for the AWA2 test dataset. Distribution of pressures (25 th, 50 th and 75 th percentiles) is provided as a frame of reference.
# Two-Terminal Electronic Circuits with Controllable Linear NDR Region and Their Applications
## 6 Applications
### Negative Differential Resistance Voltage-Controlled Oscillators
The conclusions concerning the accuracy of the current \(I_{Y}\) calculation for Table 6 are the same as those for Table 3.  
We should note that the accuracy of calculating the current \(I_{D}\) by Formula (17) is quite high. Indeed, for all two-terminal NDR circuits at \(V_{Y}=2.8\) V, the current \(I_{D}\)(simulated) = 2.79 mA, and the current \(I_{D}\)(calculated) = 2.67 mA. Therefore, \(\Delta I_{D}\) % = -4.5 %, where \(\Delta I_{D}\) % is the relative accuracy of calculating the current \(I_{D}\) by formula  
\[\delta I_{D}\%=\frac{I_{D}(\text{calculated})-I_{D}(\text{simulated})}{I_{D}( \text{calculated})}\times 100\%. \tag{27}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of NDR Circuit**\(\rightarrow\) & \multicolumn{3}{c}{**Two-Terminal NDR Circuit with Multiple-Output**} \\ \hline
**Parameter of I–V Characteristic** & **Cascode Current** & **Wilson Current** & **Improved Wilson Current** \\ \(\downarrow\) & **Mirror** & **Mirror** & **Mirror** \\ \hline \(V_{X}\) (V) & 1.2 & 1.2 & 1.2 \\ \hline \(I_{X}\) (mA) & 0.3 & 0.3 & 0.3 \\ \hline \(V_{Y}\) (V) & 2.8 & 2.8 & 2.8 \\ \hline \(I_{Y}\) (mA), \(m=0\) & 4.9 & 5.1 & 5.2 \\ \hline \(I_{Y}\) (mA), \(m=1\) & 6.7 & 7.2 & 7.3 \\ \hline \(I_{Y}\) (mA), \(m=2\) & 8.3 & 9.1 & 9.3 \\ \hline \(V_{Z}\) (V) & 13.6 & 13.6 & 13.6 \\ \hline \(I_{Z}\) (mA) & 3.2 & 3.2 & 3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulated parameters of current-voltage characteristics for different two-terminal NDR circuits (\(T_{0}\)—PHEMT). Figure 19: Current-voltage characteristics of the NDR circuit with multiple-output improved Wilson current mirror; \(m=0\)—purple curve, \(m=1\)—blue curve, \(m=2\)—red curve.
## 4 Discussion
### Dependence of DER on Proton Beam Energy  
Considering the simulation geometry of the gold nanoparticle with size of 25 nm (radius) and a distance of 80 nm from the DNA (Figure 2a), it is found that the DER value decreases from 1.74 to 1.21 with an increase of proton beam energy. The higher energy deposition at the DNA with lower proton beam energy is due to the energy of secondary electrons. These low-energy secondary electrons from the gold nanoparticle, irradiated by the low-energy proton beam, cause more electron scatter between the nanoparticle and DNA molecule. This leads to more secondary electrons to reach the DNA and produce energy deposition. When a high-energy proton beam irradiates the gold nanoparticle, the secondary electrons generated have higher energy and may pass through the DNA without energy deposition. It should be noted that as the DNA is positioned in the downstream of the proton source behind the gold nanoparticle, and only the secondary electrons moving towards the DNA direction (i.e., from left to right in Figure 1) would cause a dose enhancement and therefore an increase of DER value. In addition, it can be seen from Figure 2 that the decrease of DER value from the low-energy proton beam (e.g., from 0.5 to 5 MeV) is more significant than the high-energy beam (e.g., 5 to 25 MeV). This is because the secondary electron scatter is particularly more significant at a low protonenergy (0.5 MeV). When the proton energy is in the range of 5 to 25 MeV, the decrease of DER value is only slight, as shown in Figure 2.
## 4 Results and Discussion
### Comparison of SOE Estimation Under Different Preload Forces  
The generalization performance of the model is further verified using data from the US06 at 25 \({}^{\circ}\)C ambient temperature with preload forces of 500 N, 1000 N, and 1500 N. The results under 500 N have been shown in Section 4.1. To avoid repetition, only the test dataset is selected with preload forces of 1000 N and 1500 N.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Training Data** & **Test Data** & **RMSE\%** & **MAXE\%** & **MAE\%** \\ \hline LSTM & DST, BJDST & & 4.20 & 15.70 & 3.28 \\ BiLSTM & (without force) & FUDS & 3.20 & 15.18 & 2.51 \\ Proposed & & & 2.29 & 6.67 & 1.73 \\ \hline LSTM & & & 2.34 & 9.33 & 1.74 \\ BiLSTM & (with force, 500 N) & FUDS & 1.69 & 6.05 & 1.27 \\ Proposed & & & 0.62 & 2.66 & 0.48 \\ \hline \end{tabular}
\end{table}
Table 4: SOE estimation results of different model.  
Figure 10: SOE estimation results under different model: (**a**) SOE curves without force; (**b**) SOE errors without force; (**c**) SOE curves with force; (**d**) SOE errors with force.  
The results under preload forces of 1000 N and 1500 N are presented in Table 5. The RMSE, MAXE, and MAE of the BiLSTM-PSO-KF model are as low as 0.59%, 1.09%, and 0.46% and 0.95%, 2.48%, and 0.70%, respectively, which demonstrates the model's robustness and stability under various preload conditions.  
Figure 11 presents the curves and absolute errors of the three models under different preload forces, further verifying the superior performance of the BiLSTM-PSO-KF model in SOE estimation compared to the LSTM and BiLSTM models.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 4 Realization of DRFC-TSP on an Embedded Linux Platform
### Realization Process  
#### 4.2.1 Synchronization Process of the Root Node  
The clock of the root node is considered to be the reference time; that is, the time offset is always 0 and the frequency skew is always 1. The root node will provide a time reference to the network and initiate the time synchronization process. The forwarding certification round will be carried out after \(N\) rounds of time synchronization.  
During a synchronization round, only the root node can update the round number. When a new round of time synchronization or forwarding certification is carried out, the synchronization round number is increased by 1.  
When a new round of synchronization is initiated, the current round number should be check. If the round number is the integral multiple of N, forwarding certification should be initiated in the current round; otherwise, the time synchronization process is carried out.
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 2 Materials and Methods  
Hybrid positioning is a technique used to determine a device's or user's geographic location by combining multiple positioning methods and technologies, leveraging the strengths of each to improve the overall accuracy and reliability. This is accomplished through sophisticated algorithms that assess the accuracy and availability of each positioning source in real time to provide the most reliable and precise location estimate. Since the measurements are affected by noise (e.g., receiver noise, multipath) and the applied models are not perfect, adjustment techniques are required for accurate position parameter estimation.  
The proposed hybridization methods are based on trilateration and include adjustments using the deterministic Gaussian method with coordinate increments, as described in [28; 29]. These methods integrate GNSS data and 4G/5G mobile network measurements into a mathematical model represented by a system of observation equations [30], which is then used to estimate the position of the UE.  
The two proposed hybridization strategies are designed to operate at different levels of abstraction, which has important implications for both their implementation and their capacity to model measurement errors. The position-level hybridization method combines geodetic coordinates (x, y, z), independently provided by the GNSS chipset and the cellular network. These coordinates are internally processed and corrected by the respective systems, and thus do not allow for the direct modeling of physical-layer error sources such as multipath, ionospheric delays, or signal strength variations. In contrast, the measurement-level hybridization method works with GNSS pseudoranges and cellular distances computed between the user equipment (UE) and the base stations (BSs). However, it is important to note that, in this case, the BS-UE pseudoranges are not directly measured, but simulated or inferred based on network-side position estimates, and therefore, they may not fully reflect raw radio signal characteristics. These differences impose distinct constraints on the error-modeling capabilities of each method and motivate the structure of the two independent estimation frameworks described in the following subsections.
## 3 Model Architecture
### Flowchart of the Model  
The prediction process is illustrated in Figure 2.  
First, we input the initial historical data for pre-processing, generate the initial hidden layer output matrix \(\mathbf{H}_{0}\), and estimate \(\mathbf{\beta}_{0}\) and \(\mathbf{P}_{0}\). Then, we input the weather forecast data of the next period of time and calculate the prediction value. When the historical data such as the weather and power data arrive, we calculate the prediction error and save the data. After that, we check the time. If it is equal to 1 h, we update the matrices \(\mathbf{H}\), \(\mathbf{\beta}\) and \(\mathbf{P}\); otherwise, the above process is repeated.
# Tailored Quantum Alternating Operator Ansatzes for Circuit Fault Diagnostics
## 3 Circuit Fault Diagnostics
### Valid and Invalid Configurations Around Gates  
Enforcing the logic of the circuit on our configurations is essentially enforcing the logic of each individual gate on the part of the configuration that corresponds to that gate's inputs and outputs.  
Valid configurations around gates follow the same logical delineation as valid configurations for the entire circuit. For any circuit or subcircuit, including a single gate, we can list the potential valid configurations by considering the different input/output pairs. If a configuration is valid for a particular input/output pair, it cannot be valid for any other, and so the valid configurations always make disjointed sets. For that circuit or subcircuit, as discussed before, the inputs can potentially be faulty, but the outputs must match their empirical values. However, the size of the different valid configuration sets is always the same.  
As such, the valid configurations around a gate are split by what the inputs before considering the fault status are (If they differ from this value, there must be an associated fault on this input.) and what the output after considering the fault status is (i.e., the same situation as for the circuit at large). Table 1 gives a full description of all the valid subconfiguration spaces for an NAND gate, where the first element in each entry is the trivial valid configuration that exists for every subcircuit (or the whole circuit) by applying  
Figure 1: All possible valid fault configurations for a small circuit with one FAN gate and one NAND gate that has a faulty output. The diagrams on the top left and right show minimum fault explanations for this instance.  
the faults on the outputs only (i.e., all wires are healthy, except the outputs that disagree with the logical value they should have).  
If a configuration is valid and maintains a state status as described by an entry in Table 1, then swapping this subconfiguration with a state status in the same entry will also be valid, since we did not change the values that came from previous gates or were set as global inputs to the circuit, nor did we change the value of the output (which is then used as an input to a gate or is a global output for the circuit).  
For a circuit with a collection of gates \(G\), we consider the input and output wires \((I_{g}\) and \(O_{g})\) for that gate. The collection of valid configuration sets around this gate  
\[V_{g}=\{V_{g}^{(S_{I_{g}};S_{O_{g}})}|S_{I_{g}}\in\{0,1\}^{|I_{g}|},S_{O_{g}}\in \{0,1\}^{|O_{g}|}\},\]  
is indexed by what the potential values the wire inputs \(S_{I_{g}}\) are set to without faults and what the potential wire outputs \(S_{O_{g}}\) are set to. For example, \(S_{I_{g}}\) for an NAND gate in the fault model is a collection of the possible inputs \(\{(0,0),(0,1),(1,0),(1,1)\}\), and \(S_{O_{g}}\) is a collection of the possible outputs \(\{(0),(1)\}\).
## Materials and Methods
### Antimicrobial Compounds  
_Quillaja saponaria_ saponin extract (QE) was purchased from Sigma Aldrich (Saint Louis, MO, USA). The extract is a heterogenous mixture of molecules, with sapogenin, the main aglycon, comprising 20-35%. A working solution of QEs 15% (\(w/v\)) was sterilized by membrane filtration (0.45-um filters, Merck-Millipore, Darmstadt, Germany). \(N\)-ethyl-\(N\),\(N\)-dimethylhexadecylammonium bromide (quaternary ammonium compound, QAC) was synthesized by the Center of Molecular and Macromolecular Studies of the Polish Academy of Science (Lodz, Poland). Peracetic acid (PAA) was purchased from Envolab Fine Chemicals (Czestochowa, Poland). The solutions of disinfectants (4%) were prepared in sterile deionized water immediately before use.
## 3 Results and Discussion
### Data Recovery Model Training and Optimization  
The hyperparameters of the LSTM network were optimized by using the K-fold cross-validation and grid search optimization to make the trained model meet the requirements. The LSTM network training used 90% of the data as the training set and 10% as the validation set, then normalized the data. The hyperparameters of the LSTM network were optimized by a grid search combined with 5-fold cross-validation. The residual sum of squares (RSS) was used as the loss. Finally, the optimizer type was Adam, the activation function was sigmoid, the number of hidden neurons was 12, and the maximum number of cycles of the neural network was 1000. For the dataset in this experiment, a time step of 24 was found to be advantageous over longer time steps during training. This meant that the data of the past 24 moments could be used to determine the output of the current moment. The data were divided into a training set and validation set according to 4:1. The initial learning rate of the Adam optimizer was set to 0.01, and the loss on the validation set was monitored to use the learning rate decayed callback function. If the loss did not decrease after five consecutive iterations, the learning rate was reduced to be half and the lower limit of the learning rate reduction was set to 0.001. When the loss of the validation set did not decrease after five consecutive iterations, the callback function of the early stop was triggered to avoid overfitting.
###### Abstract  
_Introduction_ The use of scaffolds in tissue engineering is becoming increasingly important as solutions need to be found to preserve human tissues such as bone or cartilage. Various factors, including cells, biomaterials, cell and tissue culture conditions, play a crucial role in tissue engineering. The in vivo environment of the cells exerts complex stimuli on the cells, thereby directly influencing cell behavior, including proliferation and differentiation. Therefore, to create suitable replacement or regeneration procedures for human tissues, the conditions of the cells' natural environment should be well mimicked. Therefore, current research is trying to develop 3-dimensional scaffolds (scaffolds) that can elicit appropriate cellular responses and thus help the body regenerate or replace tissues. In this work, scaffolds were printed from the biomaterial polycaprolactone (PCL) on a 3D bioplotter. Biocompatibility testing was used to determine whether the printed scaffolds were suitable for use in tissue engineering. _Material and Methods_ An Envisionetre 3D bioplotter was used to fabricate the scaffolds. For better cell-scaffold interaction, the printed polycaprolactone scaffolds were coated with type-I collagen. Three different cell types were then cultured on the scaffolds and various tests were used to investigate the biocompatibility of the scaffolds. _Results_ Reproducible scaffolds could be printed from polycaprolactone. In addition, a coating process with collagen was developed, which significantly improved the cell-scaffold interaction. Biocompatibility tests showed that the PCL-collagen scaffolds are suitable for use with cells. The cells adhered to the surface of the scaffolds and as a result extensive cell growth was observed on the scaffolds. The inner part of the scaffolds, however, remained largely uninhabited. In the cytotoxicity studies, it was found that toxicity below 20% was present in some experimental runs. The determination of the compressive strength by means of the universal testing machine Z05 by ZWICK according to DIN EN ISO 604 of the scaffolds resulted in a value of 68.49 \(\pm\) 0.47 MPa.  
bone tissue engineering: 3D printing; PCL; Collagen I; coating; biocompatibility; MG-63; MLO-74; Human MSC +
Footnote †: journal: Applied Sci.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 5 Performance Results
### Urban Scenarios with mmWave Deployments  
In this study we consider a typical urban scenario mmWave radio access (up-to three equispaced BSs), deployed, for instance, at border of a circle with radius \(d=50\) m or \(d=100\) m. The UE location (in 2D) is selected randomly such that \(\|\mathbf{q}\|_{2}<d\). The rotation is also a random variable with uniform distribution \(\alpha\in\{-\pi,\pi\}\). Both at the UE and at each BS, a uniform linear array with \(N=8\) and \(M=16\) antenna elements are considered, respectively. The OFDM parameters for pilot transmissions are same as in Section 3.3. LoS is always assumed dominant.  
Positioning is performed with a single base station (1 BS with LoS link) and multi-connectivity (3 BSs with LoS links) approach. The method is described in Section 4.2. Furthermore, both a DFT-based codebook and non-orthogonal codebook are considered to evaluate the impact of the beam scanning strategy (see Section 3.3) on the localization accuracy.  
First, we consider the scenario with 50 m radius and in Figure 12 we show the spatial distribution of the localization error with 1 or 3 connected BSs. As expected, the multi-connectivity approach outperforms the single-BS case in terms of localization accuracy and distribution of the low-error. This advantage can be better evaluated from Figure 13, where we can notice that in 90% of cases UE is located within <0.02 m accuracy, while in case of single-BS, the available accuracy is within <0.10 m. Also the distribution of the localization error is generally more smooth with the 3 BS scenario. In fact, with single BS scenario higher errors occur as the distance between UE and BS is greater at the bottom of the circular area. In the 3-BSs scenario, nonetheless, slight errors occur at the bottom of the circular area. This is due to the orientation of antennas on BSs, which are aligned horizontally so that the main lobe is directed along the \(x\)-axis.  
In Figure 13 we also compare two different codebooks for beamsearch strategy-based. It is shown that non-orthogonal beams approximately provide 50% improvement over the DFT-based codebook, for instance, looking the CDF function of 90% accuracy.  
Finally, we consider the scenario with 100 m radius. As in the study of the 50 m case, Figure 14 show the localization error of the cumulative distribution for 100 m case. Looking at the performance on 90% CDF point we note that the localization error on the single-BS scenario grows, e.g., in DFT codebook case, the CDF of 90% is achieved at 0.09 m within <50 m scenario and at 0.17 m within <100 m scenario. Oppositely, in the 3-BSs scenario, the localization error is very close in both scenarios (difference approx. only 0.01 m), in both cases, with DFT and with non-orthogonal codebook.  
Figure 12: Urban scenarios (<50 m) mmWave NLS positioning using non-orthogonal codebook and LoS link available from one (**left side**) or three (**right side**) base stations.