esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 2 Materials and Methods  
The code for the models was prepared and executed within a Python 3.10.12 environment, including sklearn 1.2.2 and keras 2.13.1. Each of the 2/3/4/5-qubit datasets were divided into an 80% training set and a 20% test/holdout set. For neural network algorithms, the training set was further subdivided into 80% training and 20% validation sets. The methods described in this section include the quantum circuit modeling, generation of the dataset, noise generation, details on each of the algorithms and a speed-of-prediction analysis. The methods are summarized in the pseudocode below:  
* Define the quantum circuit \(\bigcirc\) Select phase values uniformly between \([0,\,2\pi)\) for generating circuits. \(\bigcirc\) Create qiskit QPE circuits with qubits = [2,3,4,5] in the first register and \(1\) qubit in the second register. \(\bigcirc\) Specify the unitary operator and eigenvector.
* Define noise and create datasets \(\bigcirc\) Introduce depolarizing noise with probability \(p\) using qiskit NoiseModel. \(\bigcirc\) Simulate circuits with 21 noise levels \(p\) = [0.00, 0.01, 0.02, \(\ldots\), 0.20].
* For dataset in qubits = [2,3,4,5]: \(\bigcirc\) Create and split datasets with phase, noise level and features for each quantum circuit simulated. \(\bigcirc\) For algorithm = [linear regression, random forest, XGBoost ensemble, neural network]:
* Evaluate overall performance of traditional phase estimation
* For noise level \(p\) = [0.00, 0.01, 0.02, \(\ldots\), 0.20] \(\bigcirc\) Evaluate each algorithm to determine performance variation with noise
* Training parameters are summarized in Tables 1 and 3
* Analyze and compare model performance \(\bigcirc\) Compare metrics = [MSE, prediction speed, overfitting, variation of accuracy with \(p\)] for each algorithm. \(\bigcirc\) Select the optimal algorithm as the best tradeoff of these metrics.
* Validation \(\bigcirc\) Initialize IBMQ Perth quantum computer using qiskit_ibm_runtime. \(\bigcirc\) Send circuit output to trained models. \(\bigcirc\) Compare predictions from models to the analytic results.
# Pretreatments Applied to Wheat Straw to Obtain Bioethanol
## 3 Pretreatments Applied to Wheat Straw
### Biological Pretreatment of Wheat Straw  
The biological pretreatment of wheat straw to obtain bioethanol is carried out with the help of bacteria, microorganisms (_Aspergillus niger_, _Streptomyces_ sp., _Phanerochaete chrysosporium Clostridium_ sp., _Trichoderma viride_, _Cellulomonas_ sp., _Thermonospora_ sp., _Bacillus_ sp., _Trichoderma reseei_), and mushrooms (_Trametes_ sp., _Pleutrotus_ sp., _Pleutrotus floride_), which also factor into ecological pretreatment [62; 63; 64; 65; 66; 67; 68].  
Bacteria and fungi degrade lignin with the help of ligninolytic and hydrolytic enzymes, lignin peroxidase, lactose, and manganese peroxidase [76; 77].  
A greater degradation of lignin was observed using the bacteria _Sphingobium_ sp. _SYK-6, Rhodococcus_ sp., _Ceriporiopsis_ sp., _Pandoraa_ sp., _galactomyces_ sp., and _Mycobacterium_ sp. in the biological pretreatment of wheat straw. They release lignolytic and xylanalytic enzymes for the production of bioethanol [79].  
Hemicellulose and cellulose are hydrolyzed into monomeric sugars by the use of hemicellulose and cellulolytic microorganisms [87].  
Biological pretreatment has some benefits, such as low processing costs, chemical recycling after pretreatment, and low energy consumption. The main disadvantage is the low rate of hydrolysis [36]. Fungi and bacteria can generate hydrolytic and oxidative enzymes that can destroy the rigid structure of lignocellulosic biomass. Degradation of cellulose and hemicellulose can be achieved by anaerbes (_Clostridia_) [88].  
An improvement of the methane yield by more than 100% was obtained through the enzymatic pretreatment [69; 89]. Bacterial and enzymatic pretreatments can be completed in a few hours compared to those with mushrooms, which take a longer incubation time (weeks or months). Through the fungal pretreatment of wheat straw, an improvement of cellulose degradation of up to 80% was obtained [90], the enzymatic digestibility was improved by increasing the yield of xylose and glucose [91], and the degradation of lignin increased [92]. After ten weeks of pretreatment with the _Ceriporiopsis subvernispora_ strain, an increase of up to 60% in biomass digestibility and an increase of up to 44% in sugar yield were obtained [40].  
After pre-treating wheat straw with white-rot fungus _Irgex lactaeus_ for 20 min at 120 \({}^{\circ}\)C, the bioethanol yield was 12.5 g/L, sugar reduction was 11.5%, and the delignification rate was 45.8% [93].
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 4 Simulation Results And Errors Analysis
### Experimental Results  
To verify the ranging performance, we use two USRPs as the transmitter and receiver, respectively, to conduct the actual test of the aforementioned scheme. The Co-Band PRS signal is generated in real time by the positioning server and then connected to a switch to ensure that the devices are on the same network segment. For the signal source, the USRP uses two antenna ports, each connected with an equal length of coaxial cable to extend the indoor antenna's reach. A rubidium atomic clock is installed as the clock source to ensure that two orthogonal Co-Band PRS signals are broadcasted simultaneously. The USRP at the receiving end uses one antenna port to receive the two signals, utilizing the local clock reference. The experimental environment and equipment connections are shown in Figure 12.  
To remove the ambiguity of whole cycles, we track two Co-Band PRS signals that are transmitted simultaneously and determine the receiver's one-dimensional position by calculating the code phase difference. We first correlate the received signal directly with the  
Figure 11: RMSE of DLL tracing results at different number of iterations.  
Figure 12: Test environment and equipment connection diagram.  
locally generated signal in the time domain (as shown in Figure 12(a)). At this time, the two signals differ by a distance of two code slices, which is equivalent to a distance difference of 4.88 m (since each code slice is approximately 2.44 m). However, the actual difference in distance measured using the laser rangefinder is 3.24 meters, indicating the presence of significant errors in the direct correlation method in the time domain. By changing the position of the receiver antenna and taking several measurements, we collected 100 sets of Co-Band signal data. The ranging performance of Co-Band signals was analyzed using the time-domain direct correlation method [7], the MUSIC ranging method [16], and the iterative DLL tracking method proposed in this paper, respectively. As can be seen from Figure 12(b), the ranging accuracy of the proposed method can reach 0.16 m@90%, and its performance is better than that of the MUSIC algorithm with ten times sampling frequency.
## 3 Results and Discussion
### Study of the Degree of Grinding and Screw Rotation Speed on the Power Consumption of Screw Press  
The power consumption of the press was measured to evaluate the energy requirements for pressing the cake under different grinding degrees and screw rotation speeds. The obtained data provided insights into the optimal parameters for efficient oil pressing.  
The results revealed that increasing the screw rotation speed during the pressing process led to a corresponding increase (\(p<0.05\)) in power consumption. For instance, at a screw rotation speed of 30 rpm, the power consumption varied depending on the grinding degree, ranging from 0.48 kW without pre-grinding to 2.88 kW when the seeds were ground to 2 mm. Similarly, at higher screw rotation speeds of 60 rpm, 120 rpm, and 250 rpm, the power consumption increased consistently (\(p<0.05\)) with finer grinding degrees (Figure 15).  
Moreover, the installation of grinding sieves with smaller hole diameters resulted in higher power consumption compared to pressing without the use of grinding mechanisms. This indicates that the presence of grinding components in the oil pressing process imposes an additional energy demand.  
As can be seen from Figure 15, the capacity of the oil press increases with increasing screw speed. This trend is due to the increase in the amount of seed mass that passes through the press in a certain period of time. This results in a heavier load on the screw and increases the power required to move it and press the seed. In addition, the pressure generated by the screw increases, resulting in an increase in the force that must be applied to compress the raw material and squeeze out the oil.  
When grinding and pressing processes are combined, the power required to overcome the cutting and squeezing forces through the grids also increases. The smaller the diameter of the grinding plate, the more power is required for the operation.  
Figure 14: Dependence of residual oil content of cake on squeeze pressure and degree of grinding (different letters above the lines indicate significant differences between samples, \(p<0.05\)).
###### Abstract  
In this study, the performance and safe operation of the fuel cell (FC) system and battery-based energy storage system (ESS) included in an FC/ESS/renewable hybrid power system (HPS) is fully analyzed under dynamic load and variable power from renewable sources. Power-following control (PFC) is used for either the air regulator or the fuel regulator of the FC system, or it is switched to the inputs of the air and hydrogen regulators based on a threshold of load demand; these strategies are referred to as air-PFC, fuel-PFC, and air/fuel-PFC, respectively. The performance and safe operation of the FC system and battery-based ESS under these strategies is compared to the static feed-forward (sFF) control used by most commercial strategies implemented in FC systems, FC/renewable HPSs, and FC vehicles. This study highlights the benefits of using a PFC-based strategy to establish FC-system fueling flows, in addition to an optimal control of the boost power converter to maximize fuel economy. For example, the fuel economy for a 6 kW FC system using the air/fuel-PFC strategy compared to the strategies air-PFC, fuel-PFC, and the sFF benchmark is 6.60%, 7.53%, and 12.60% of the total hydrogen consumed by these strategies under a load profile of up and down the stairs using 1 kW/2 s per step. For an FC/ESS/renewable system, the fuel economy of an air/fuel-PFC strategy compared to same strategies is 7.28%, 8.23%, and 13.43%, which is better by about 0.7% because an FC system operates at lower power due to the renewable energy available in this case study.  
fuel cell; hydrogen economy; fuel starvation; safe operation; electrical energy efficiency
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 1 Introduction
### Main Contribution  
Most existing literature assumed that the PCC voltage perturbation at \(f_{p}\) will only bring the same frequency error components in Park's transformation. Moreover, the output current harmonic component at coupled frequency \(2f_{1}-f_{p}\) was neglected. However, these assumptions are obviously incorrect according to time-domain simulation results.  
Based on the existing researches, this paper constructs the impedance model of grid-connected D-PMSG by using harmonic linearization small-signal modeling method. By reasonable simplification, the outer-loop control of converter is not considered [26], and the converter model is treated as voltage-controlled voltage source. It is worth mentioning that the frequency coupling of PLL and the relationship between sub/supsynchronous coupling currents are analyzed quantitatively. The simplified PWM converter and related theoretical analysis were all verified by simulation then. Furthermore, based on the correct impedance model and relative stability criterion, the impedance sensitivity of wind farm operation parameters and the influence on system stability are discussed. Finally, a time-domain simulation of SSO appearance when SCR decreases is carried out, which is theoretically explained.
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 1 Introduction  
The extensive consumption of fossil fuels, particularly over the past century, has significantly contributed to environmental pollution. As environmental and socio-economic consciousness continues to grow, governments and legislative bodies worldwide are expressing concern and examining the factors associated with pollution that affect the energy landscape. Consequently, there is an urgent requirement to replace fossil fuels with renewable, eco-friendly, and alternative energy sources [1; 2; 3; 4; 5].  
Therefore, to date, the most pressing scientific and technological challenge is the development of a renewable and clean energy that does not rely on fossil fuels. From this perspective, one of the most appealing options for large-scale utilization is hydrogen as a recyclable energy carrier. Consequently, green approaches to hydrogen production, such as the photocatalytic process from water under solar radiation, has garnered considerable interest [6; 7]. For photocatalytic hydrogen production, titanium dioxide (TiO\({}_{2}\)) represents a promising candidate among various inorganic semiconductor photocatalysts [8; 9] due to its high efficiency, high stability, non-toxicity, and low cost. However, the practical application remains constrained, since TiO\({}_{2}\) responds solely to ultraviolet light, which constitutes approximately 4% of the solar spectrum. Therefore, it is mandatory to design and synthesize TiO\({}_{2}\)-based photocatalytic systems capable of reacting to the whole visible light range, accounting for \(\sim\)43% of the solar spectrum, thus improving the efficiency [10; 11].  
The storage of hydrogen to provide grid energy from intermittent energy sources is another major challenge for the further development of hydrogen energy [12]. On the other hand, magnesium hydride (MgH\({}_{2}\)) stands out as one of the most promising candidates in solid-state hydrogen storage thanks to its high capacity (7.6 wt%), excellent reversibility, and cost-effectiveness. Nonetheless, its practical application is constrained by its high operating temperature requirements and suboptimal kinetic performance [13]. An effective approach to enhance the hydrogen storage performance of MgH\({}_{2}\) involves the introduction of catalysts, which may encompass metals [14; 15; 16], metal oxides [17; 18; 19; 16], metal sulfides [20; 21], and metal halides [19; 22].  
Focusing on work published in the last decade, this review firstly delves into the utilization of TiO\({}_{2}\) for hydrogen production under visible and solar light, as well as its role in enhancing the hydrogen storage performance of MgH\({}_{2}\). Successively, in the domain of hydrogen production, various strategies aimed at enhancing TiO\({}_{2}\) performance without the use of noble metals are explored.
## 2 Materials and Methods
### Evaluating Metrics  
The following metrics were used to evaluate segmentation performance on the test set, with the manual segmentation done by a specialist used as ground truth: Dice coefficient, false negative error, false positive error, Jaccard coefficient, and average Hausdorff distance. However, we focused solely on the Dice coefficient and average Hausdorff distance, as they will be used for the purpose of an open benchmark.  
The requirement for different metrics to evaluate segmentation results arises from the complementarity of these metrics in comparing method output to the ground truth. For example, whereas the Dice coefficient evaluates the overall accordance between two objects, the average Hausdorff distance indicates the behavior of agreement in the shells of the volumes [60].  
To define some of the metrics used in this study, we consider the four basic cardinalities of the confusion matrix that indicate overlap between the two masks. _True positives (TP)_: number of positive voxels included in both the automatic segmentation and ground-truth mask; _false positives (FP)_: number of positive voxels included in the automatic segmentation but not in the ground-truth mask; _true negatives (TN)_: number of voxels taken as negative (background) in both the automatic segmentation and ground-truth mask; _false negatives (FN)_: number of positive voxels in the ground-truth mask that were not included in the automatic segmentation.  
The _Dice similarity coefficient_, or _Dice coefficient (DC)_, is a spatial overlap index that ranges from 0 to 1, denoting zero overlap and complete overlap, respectively, [61].  
\[DC=\frac{2 TP}{2 TP+FP+FN}\]  
Figure 7: Thalamus segmentation framework: standard U-Net trained with multi-channel patches, and aggregation of multiple models for final prediction.  
The _Hausdorff distance_ (\(HD\)) is a measure of dissimilarity comparing image segmentations [62]. Given the set of voxels \(V_{A}\) of mask \(A\) and the set of voxels \(V_{B}\) of mask \(B\) the _Hausdorff distance_ is such that  
\[HD(V_{A},V_{B})=max(h(V_{A},V_{B}),h(V_{B},V_{A}))\]  
where \(h(V_{A},V_{B})\) is the directed Hausdorff distance given by  
\[h(V_{A},V_{B})=max_{x\in V_{A}}\{min_{y\in V_{B}}\{||x-y||\}\}\]  
where \(||x-y||\) is an arbitrary norm (in our case, Euclidean distance). In this work, the dimension of the Hausdorff distance is expressed in voxel size.  
The _average Hausdorff distance_ (_AHD_), or simply _average distance_, is more stable and less sensitive to outliers than the conventional \(HD\), representing the mean \(HD\) over all points  
\[AHD(V_{A},V_{B})=max(d(V_{A},V_{B}),d(V_{B},V_{A}))\]
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 5 Simulation Performance and Analysis
### Self-Similarity Analysis and EMD-Based Decomposition  
This section analyses the self-similarity of the satellite network traffic and decomposes the traffic into short-range dependent traffic using EMD. In this work, self-similarity is measured using the Hurst index (\(H\)), which is a general index to detect whether a stochastic process is self-similar [34]. \(H\in(0,0.5]\) indicates that network traffic is short-range dependent, whereas \(H\in(0.5,1)\) indicates that network traffic has self-similarity and long-range dependence. A larger \(H\)-index indicates a higher self-similarity degree, burstiness and dependency. Estimation methods for the Hurst index mainly include residual variance, periodogram, R/S graph and wavelet analysis, among which R/S graph has better robustness for analyzing the edge distribution of random processes. Meanwhile, R/S graph has better adaptability to different distributions. Therefore, R/S graph was adopted to estimate the Hurst index of the satellite network traffic. For the raw traffic demonstrated in Figure 10, \(H\) = 0.9414, indicating the strong self-similarity and long-range dependence of the raw satellite network traffic.  
The raw traffic is decomposed according to Equation (6) and the steps demonstrated in Table 2. The resulting 10 IMFs (IMF1-IMF10) are shown in Figure 10. It can be found that when the raw traffic is decomposed into 10 IMFs, the magnitude of the residuals is very low and negligible. As the order of IMFs increases, the residuals tend to decrease. Therefore, 10 IMFs are sufficient to meet the forecasting accuracy requirements while reducing the computational complexity of the EMD decomposition.  
The envelopes of the autocorrelation function of the raw traffic and IMFs are shown in Figure 11. The grey region is the envelope of the autocorrelation function of the raw traffic, whereas the blue region denotes the superposition of the envelopes of the IMFs autocorrelation functions. It can be seen that the decay of the raw traffic autocorrelation function is similar to a hyperbolic function, with a slow decay rate. After EMD decomposition, the decay rate of the autocorrelation function of the IMFs is significantly faster than that of raw traffic, which indicates that EMD achieves a reduction in self-similarity, making the decomposed components exhibit short-range dependence instead of long-range dependence. Thus, it is proved theoretically and experimentally that the satellite network traffic after EMD no longer has self-similarity. On this basis, IMFs can be forecasted using ARIMA to obtain a higher short-term forecasting accuracy with lower complexity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow Number** & **Source** & **Destination** \\ \hline
1 & Tokyo & Delhi \\
2 & Shanghai & São Paulo \\
3 & Mumbai & Mexico City \\
4 & Beijing & Osaka \\
5 & Cairo & New York \\
6 & Dhaka & Karachi \\
7 & Buenos Aires & Kolkata \\
8 & Istanbul & London \\ \hline \hline \end{tabular}
\end{table}
Table 4: Source and destination of flows.
## 3 VRIS Image Steganography Model  
To achieve dual deception of machine learning models and human visual systems, this study designs the VRIS image steganography model (as shown in Figure 1), aiming for efficient completion of image steganography tasks. The VRIS technology combines the data reconstruction capability of autoencoders with the generative-discriminative framework of Generative Adversarial Networks (GAN), falling under the category of unsupervised learning.
## 4 Simulation Results and Discussion  
In this section, we evaluate the performance of the VLC system implemented with an IRS applied to a typical indoor environment. To achieve this goal, we studied typical performance metrics of the communications systems, such as CIR and BER. The results are obtained through computational simulations through Matlab software, the ray-tracing methodology by varying the number of Non-LoS links (reflections) that reach the receiver, and the Monte Carlo simulation scheme. The parameters used for the simulations are summarized in Table 1.