esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 3 Results  
In this section, the model accuracy, prediction speed, overfitting level and variation in accuracy with noise level for five quantum phase estimation algorithms are presented in Table 2. Additionally, the performance of the trivial mean-predicting model is shown in the top row. Notable aspects of modeling using each of the algorithms are discussed below the table.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **2 Qubit** \\ **MSE** \\ \end{tabular} & \begin{tabular}{c} **3 Qubit** \\ **MSE** \\ \end{tabular} & \begin{tabular}{c} **4 Qubit** \\ **MSE** \\ \end{tabular} & \begin{tabular}{c} **5 Qubit** \\ **MSE** \\ \end{tabular} & \begin{tabular}{c} **Average Execution** \\ **Speed (Records/s)** \\ \end{tabular} & **Overfit** & **Notes** \\ \hline Trivial & 0.0839 & 0.0816 & 0.0843 & 0.0828 & N/A & – & Mean-predicting model \\ \hline Traditional & 0.1200 & 0.0160 & 0.0190 & 0.0180 & 10,000 & – & Mostly level noise profile \\ \hline Linear & 0.0500 & 0.0240 & 0.0330 & 0.0330 & 51,800,000 & \(<\)1\% & \
\begin{tabular}{c} No interaction terms used. \\ Highly varying noise \\ profile \\ \end{tabular} \\ \hline Random Forest & 0.0080 & 0.0070 & 0.0060 & 0.0035 & 228,000 & \(<\)15\% &
\begin{tabular}{c} Moderately varying noise \\ profile \\ \end{tabular} \\ \hline XGBoost & 0.0090 & 0.0065 & 0.0035 & 0.0015 & 798,000 & \(<\)15\% & Low varying noise profile \\ \hline Ax-tuned NN & 0.0069 & 0.0027 & 0.0011 & 0.0005 & 13,100 & \(<\)10\% & Low varying noise profile \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test/holdout MSE for each algorithm and dataset. The speed column contains the prediction speed as measured on a large batch of the dataset.  
Figure 8: Device topology for the IBM Perth quantum computer [21]. The numbers 0–6 refer to individual transmon qubits.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 5 Integrating Cognitive Radio in LPWAN for the Internet of Things  
Developing effective and efficient IoT devices/applications comes with its unique challenges, which may be solved by leveraging the advantages of CR technology. We motivate the need to integrate CR in LPWAN based on extensive views, opinions, and efforts in the literature. We then discuss CR-LPWAN at the PHY layer based on a simple front-end model, and then a typical network architecture is discussed to support CR-LPWAN systems.
# On the Use of PEDOT as a Catalytic Counter Electrode Material in Dye-Sensitized Solar Cells
## 4 Conclusions and Outlook  
Since 1991 DSSCs have emerged as efficient alternative devices for converting solar energy into electrical energy mainly thanks to their low cost, easy preparation, high efficiency, and environmental benignity. Over the last three decades, research has been oriented towards the optimization of photoanodes, electrolytes, and counter electrodes, leading to certified efficiencies of 13.0% in liquid electrolyte-based devices. The counter electrode is of fundamental importance since it significantly influences both the performance and cost of DSSCs. Among conductive polymers to fabricate Pt-free CEs, PEDOT exhibited the best performance.  
Figure 11: Use of PEDOT as hole transporting layer in ss-DSSCs: (**a**) energy level diagrams and (**b**) charge separation pathways relevant to ss-DSSC functioning. Reprinted with permission from [82]. Copyright 2013 American Chemical Society.  
This review outlines the use of PEDOT films as catalytic counter electrodes for DSSCs. While useful as an alternative substrate to noble metal electrodes when using iodide/iodine electrolytes, PEDOT electrodes really played a major role in the development of cells based on alternative redox mediators, notably copper- and cobalt-based electrolytes, which were instrumental in achieving record solar to electrical power conversion efficiencies. Given that the counter electrode plays a crucial role in building high performing as well as stable DSSCs, PEDOT is contributing to the practical implementation of transparent solar cells for building integration. In particular, energy conversion under indoor conditions is becoming one of the most appealing applications of DSSCs, where, under low intensity and diffuse illumination, they represent one of the most promising possibilities for clever energy conversion and reutilization. Future development should focus on the stability of the interface between PEDOT and the electrolyte, and on its role on the overall device stability, for which at the time of the writing of this manuscript, mostly preliminary but promising data are available.  
Conceptualization, S.C. ([PERSON]), E.M., S.C. ([PERSON]); writing--original draft preparation, S.C. ([PERSON]); writing--review and editing, S.C. ([PERSON]) and C.A.B. All authors have read and agreed to the published version of the manuscript.  
E.M. and S.C. ([PERSON]) are grateful to Emilia Romagna Region (Fondo Sociale Europeo, Programa Operativo 2014-2020) for a research grant.  
Not applicable.  
**Data Availability Statement:** No datasets were generated or analyzed during the current study.  
The authors declare no conflict of interest.
## 2 Edge and Dew Computing  
Mobile edge computing is a paradigm that seeks to solve latency and network traffic problems found in mobile cloud computing environments [14]. Ref. [15] defines edge computing as \"_a model that allows a cloud-based computing capacity providing services making use of the infrastructure that is on the edge of the network_\". In this way, mobile edge computing allows servers or workstations within a computer network, thus ensuring low latency while enabling the efficient processing of information, which permits the deployment of more robust applications. Furthermore, this paradigm lets edge servers work with other nodes in proximity or collaborate with cloud services, thus deploying much larger and more efficient applications [16; 17]. However, while edge computing helps to reduce the network's problems, it still depends on the network backbone that may not be available or reachable in certain situations, like working with IoT devices in mines and on ships, in deserts, or on moving vehicles.  
Dew computing is a new paradigm where connected devices offload jobs to nearby devices in the same network. This paradigm proposes an architecture that reduces network latency, the energy cost of remote data communication, and the costs inherent to cloud infrastructure usage [18]. Through this, dew computing optimizes the usage of mobile and IoT devices in two manners. First, it treats mobile devices as clients in the network infrastructure to offload their work to other devices in the same network [19]. Second, dew computing considers mobile and IoT devices as resources to increase the available computational power from an existing system. In this approach, one device can offload its work onto another available device in the network (including other mobile and IoT devices) [20; 21].  
We note that a network topology is needed to use mobile and IoT devices as resources in a local network [3]. The _Smart Cluster at the Edge (SCE)_ is a network topology commonly used for that purpose in dew computing. Figure 1 shows how the devices are organized in this type of network. This topology can be established wherever an access point and a group of mobile and IoT devices coexist. The topology's main feature is a central scheduler coordinating the task assignment among the network's available resources. This central scheduler can be any capable device in the network [22]. In this work, we address the problem of distributing jobs in a dew environment, assuming that the network topology is an SCE.  
Figure 1: Example of SCE architecture: In this example, six devices are connected to the local network. The devices include two smartphones, two Raspberry Pis, two laptops, and one scheduler inside the network. Outside of the network are two smartphones.
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 2 Materials and Methods
### Angle Determination Method
#### 2.2.1 Polynomial Approximation Method  
Since the sensitivity profile of the receiving diode is well described by a 6 th-order polynomial, it can be assumed that the approximating function will also be a polynomial of this order. Thus, the polynomial approximation method consists of finding a 6 th-degree polynomial that approximates the intensity spectrum curve for the selected beacon \(B_{i}\). This method requires us to first remove some data from the measured spectrum (about half of the 32 photodiodes will register zero IR intensity) and leave only those corresponding to the approximating function. Numerical approximations, such as the least squares method, can be performed for the selected data. The polynomial describing the IR intensity takes the form:  
\[I_{1}(\theta)=a_{0}+a_{1}\theta+a_{2}\theta^{2}+a_{3}\theta^{3}+a_{4}\theta^{4 }+a_{5}\theta^{5}+a_{6}\theta^{6} \tag{2}\]  
The purpose of the polynomial approximation method is to find such a coefficient \(a_{i}\) that minimizes the cost function :  
\[\operatorname*{arg\,min}_{(a_{0},a_{1},a_{2},a_{3},a_{4},a_{5},a_{6})\in R} \;\sum_{j=1}^{n}(I_{D_{j}}-I_{1}(D_{j}))^{2} \tag{3}\]  
where \(I_{D_{j}}\) is the intensity of an infrared light recorded by the j-th receiver photodiode, and \(I_{1}(D_{j})\) denotes the polynomial (2) value for \(x=D_{j}\). Finally, the maximum of the approximated function \(I_{1}(\theta)\) will correspond to the relative beacon-receiver angle \(\varphi_{i}\).  
If the sensitivity profile \(I_{s}(\theta)\) of the receiving photodiodes is known, it seems reasonable to assume that the recorded beacon spectrum will simply be scaled and shifted relative to the \(I_{s}(\theta)\) curve. Therefore, a different form of approximating function can be adopted, based on the known curve described by Equation (1):  
\[I_{2}(\theta)=AI_{s}(\theta-\theta_{0})=A(0.0706(\theta-\theta_{0})^{6}-0.2216 (\theta-\theta_{0})^{4}-0.2899(\theta-\theta_{0})^{2}+1) \tag{4}\]  
This function depends only on two unknown coefficients, \(A\) and \(\theta_{0}\), of which \(\theta_{0}\) is directly the sought beacon-receiver angle \(\varphi_{i}\) (so there is no longer a need to search for the maximum of the function, as in the case of \(I_{1}(\theta)\)). In order to verify the Approximation method, both \(I_{1}(\theta)\) and \(I_{2}(\theta)\) approximation functions were used, as will be shown further in the paper.  
Figure 3: Receiver photodiode sensitivity vs. Angular displacement.
## 3 Theoretical Analysis  
This part presents an approximate analysis of the performance of the proposed method DC-XFAST. The formulation considers signal in the presence of thermal noise without multi-access interference. To simplify the analysis, only delays at whole chips are considered in the correlation process. Also, the factor of residual Doppler is neglected. The simplification can be removed without affecting the theoretical analysis below.  
Firstly, the correlation method and the distribution of the correlation results are presented. Under simplifying assumptions, detection probabilities of different threshold numbers are derived through joint distribution of the items in correlation results. False alarm probabilities of each threshold numbers are then calculated as a function of block length N and basic false alarm probability based on max-value detection. A common formula of mean acquisition time is also given to analyze the performance of the method.
## 2 Materials and Methods
### Human-Assisted Feature Extraction Methods
#### 2.2.2 Wavelet Transform (WT)  
The wavelet transform method used a Daubechies wavelet function with three decomposition levels. The process of wavelet decomposition generates 23 dimensional features. This process provides a low-resolution representation of the original ECG signal (24 samples vs. 180 samples) that highlights the R-peak characteristics of the ECG segments. The results of the WT feature applied to the sample segments extracted are shown in Figure 3.
# Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding
## 2 Experiment Setup  
### Equipment and Sensors  
To analyze sound signals obtained from laser microlap welding of joints with various qualities, a number of experiments were conducted on a laser-microwelding research platform integrated with a microelectromechanical system microphone (SPM0408 LE5H-TB, Knowles, Itasca, IL, USA) that had a frequency range of 100-10 kHz, and an AE sensor (8152B121, Kistler, Winterthur, Switzerland) that had a frequency range of 50-400 kHz. For data acquisition, a PCI NI 6132 was used to collect AE and sound signals at a sampling rate of 2 MHz.
## 4 Case Study
### Data Presentation and Analysis
#### 4.1.2 Analysis of Cold Wave Weather Characteristics  
Through the analysis of the relevant data over the two-year period, it was determined that during the 730 days of the observation period, cold waves occurred for a total of 681 h, accounting for approximately 3.89% of the total duration. The distribution of the duration and frequency of cold waves is shown in Figure 9.  
The duration of most cold waves is concentrated between 1 and 10 h, and cold wave events lasting more than 30 h are rare. However, cold waves with longer durations can have  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Training Set** & **Test Set** \\ \hline Cold wave occurs & January–February, & November–December 2021 \\ No Cold wave occurs & March–October 2021 & September–October 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Forecasting data split training set and test set.  
a significant impact on the operation of wind turbines, particularly under extreme weather conditions, where turbine equipment may suffer from icing and unstable power output. These effects not only reduce the operational efficiency of wind turbines but also lead to a considerable decrease in the accuracy of wind power forecasts, which, in turn, affects grid scheduling and the stability of the energy supply. Therefore, accurately identifying cold waves and predicting their impact on wind farms is crucial for improving the accuracy of wind power forecasting and ensuring the safety of power systems.  
Based on the analysis of cold wave judgment criteria, cold wave events are primarily influenced by temperature and wind speed. Figure 10 presents the curves of power, wind speed, temperature, and dew point temperature during all cold wave periods.  
In order to further investigate the key factors influencing the occurrence of cold waves, this paper performs Pearson correlation coefficient analysis on the relevant meteorological data during cold waves, aiming to reveal the relationship between meteorological variables and wind power during these events. The Pearson correlation coefficient formula is shown in Equation (20).  
\[r=\frac{n\sum x_{i}y_{i}-\sum x_{i}\sum y_{i}}{\sqrt{\left(n\sum x_{i}^{2}- \left(\sum x_{i}\right)^{2}\right)\left(n\sum y_{i}^{2}-\left(\sum y_{i}\right) ^{2}\right)}} \tag{20}\]  
\(r\) is the Pearson correlation coefficient, \(n\) is the number of data points, \(x_{i}\) is the \(x\) value for the \(i\) th sample, and \(y_{i}\) is the \(y\) value for the \(i\) th sample.  
Temperature 1 represents the hourly temperature, while temperature 2 refers to the dew point temperature. Difference 1 denotes the hourly temperature difference, and difference 2 refers to the daily minimum temperature difference. According to the analysis results presented in Figure 11, the correlation between power and the 100 m wind speed, 10 m wind speed, daily minimum temperature difference, and hourly temperature difference is highest during cold wave events.  
Figure 11: Correlation between power and meteorological data during the cold wave period.
# Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
## 4 Methodology  
Real-world log parsing tasks require both batch and stream processing modes, where the former parses historical logs, and the latter parses continuously generated logs. Considering this, we design a deep learning-based framework comprising both offline and online parsers for batch and stream processing, respectively. To achieve the three aforementioned requirements of IICTS log parsing, we have the following specific designs:  
* **Log parsing consistency:** For _generalization_, the event labels teach our parsers the parsing standards directly without task-specific data preprocessing. Additionally, the online parser needs to follow the same parsing standards as the offline parser. Our framework, as shown in Figure 2, ensures this by periodically importing the offline parser models into the online parser.
* **Quality of log record representations:** To lower _the human labor cost_ of our method (mainly for labeling log records), we significantly reduce the number of labeled records required for model training by enhancing the quality of record representations. In particular, we use word2 vec [17] to vectorize the log records based on a neural network trained on the corpus of all log text.
* **Supervised learning by event labels:** Our method leverages event labels directly to train our neural network model, which enables it to adjust itself for different parsing criteria. In this way, our method achieves the _transferability_ that is needed for various log parsing tasks.  
Since log parsing influences all subsequent log analyses and mining tasks, log parsing accuracy should be considered the key metric. In particular, we have the following designs to improve the accuracy of offline and online log parsing:  
**Accuracy of offline parsing:** To attain high offline _parsing accuracy_, we design an LSTM-based offline parser that exploits the natural contextual relevance of log messages, which aids in recognizing the essential word representations. Our method leverages vectorized log records, which are word representations in a unified semantic space.  
**Adaptability of online parsing:** For stream processing, to achieve high _parsing accuracy_, online parsing needs to cope with the challenge of concept drift, i.e., the dynamic changes in patterns in the data stream over time. To address this challenge, we propose an online parser that incorporates BiLSTM [20] and attention mechanisms [21] to enhance its adaptability to new data patterns.
## 5 Discussion
### Merging Behavior in Work Zones with Different Layouts  
Work zone layout significantly influences merging behavior, as seen at Sites A3, B4, and A4. In restrictive layouts like Site A3, where solid white lines prevent early lane changes, drivers are forced to merge closer to the taper, leading to more concentrated merging and increased traffic oscillations. The constrained layout causes greater variability in follower vehicle speeds, which elevates the risk of traffic disruptions and accidents. This pattern suggests that while restrictive markings are intended to regulate traffic, they may unintentionally contribute to congestion by limiting merging options.  
In contrast, Sites B4 and A4, which feature fewer restrictions, show a more evenly distributed merging pattern, with vehicles merging earlier and traffic flowing more smoothly. The flexibility in these layouts allows drivers to choose merging points based on traffic conditions, reducing the impact on surrounding vehicles. Speed reductions are also less severe, and the variability in speed between merging vehicles and followers is reduced. These findings indicate that work zones with flexible designs, allowing for earlier merges and more choices for drivers, can improve overall traffic efficiency and safety by minimizing traffic disruptions.
## 3 Results and Discussion
Although no formal statistical tests were applied, the confidence intervals for the Control and FIV groups did not overlap, suggesting a potentially meaningful reduction in thermal resistance in the presence of the fractionated essential oil. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**PEO**} & \multicolumn{2}{c}{**FIV**} \\ \hline
**Temp (\({}^{\circ}\)C)** & \(\delta\)**-Value (min)** & \(p\) & \(\delta\)**-Value (min)** & \(p\) & \(\delta\)**-Value (min)** & \(p\) \\ \hline
52 & 8.470 \(\pm\) 1.510 & 0.51 \(\pm\) 0.05 & 1.750 \(\pm\) 1.050 & 0.52 \(\pm\) 0.10 & 0.640 \(\pm\) 0.240 & 0.40 \(\pm\) 0.04 \\
54 & 1.800 \(\pm\) 0.500 & 0.53 \(\pm\) 0.07 & 1.350 \(\pm\) 0.350 & 0.61 \(\pm\) 0.07 & 0.540 \(\pm\) 0.350 & 0.44 \(\pm\) 0.09 \\
57.5 & 0.330 \(\pm\) 0.170 & 0.17 \(\pm\) 0.03 & 0.170 \(\pm\) 0.090 & 0.47 \(\pm\) 0.07 & 0.020 \(\pm\) 0.010 & 0.35 \(\pm\) 0.02 \\
63 & 0.007 \(\pm\) 0.004 & 0.15 \(\pm\) 0.02 & 0.002 \(\pm\) 0.001 & 0.32 \(\pm\) 0.05 & 0.002 \(\pm\) 0.002 & 0.33 \(\pm\) 0.04 \\ \hline \end{tabular}
\end{table} 
Table 2: Weibull–Mafart model parameters estimated by fitting the inactivation data obtained at 52, 54, 57.5, and 63 \({}^{\circ}\)C for the PEO (Pure Oregono Essential Oil), FIV (Fractionated Oregono Essential Oil), and Control groups. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**One-Step**} & \multicolumn{2}{c}{**Two-Step**} \\ \hline
**Group** & **RMSE** & **RMSE\({}_{\text{std}}\)** & **z-Value (\({}^{\circ}\)C)** & **z-Value (\({}^{\circ}\)C)** & **RSE** & **t Value** & **Pr (\textless{}) \textless{}1 \\ \hline Control & 0.31 & 0.06 & 5.75 \(\pm\) 0.28 & 3.63 \(\pm\) 0.19 & 0.12 & 19.29 & 0.003 \\ PEO & 0.43 & 0.08 & 5.20 \(\pm\) 0.14 & 3.69 \(\pm\) 0.46 & 0.28 & 8.00 & 0.02 \\ FIV & 0.46 & 0.09 & 5.00 \(\pm\) 0.13 & 4.03 \(\pm\) 0.55 & 0.28 & 7.27 & 0.02 \\ \hline \end{tabular}
\end{table} 
Table 3: z-values (\({}^{\circ}\)C) estimated by the one-step and two-step procedures and the Root Mean Square Error (RMSE), Residual Standard Error (RSE) and Standardized RMSE (RMSEstd) indices, estimated from the thermal inactivation of \(L\). _monocytogenes_ using PEO (Pure Oregono Essential Oil), FIV (Fractionated Oregono Essential Oil), and the Control group. Table 4 summarizes the findings from previous studies on thermal inactivation of _L. monocytogenes_ at temperatures ranging from 52 to 65 \({}^{\circ}\)C, using EOs, phenolic compounds and plant extracts as natural antimicrobial agents. Notably, at 55 \({}^{\circ}\)C, the D-value varied depending on the food matrix, highlighting the influence of the matrix on the effectiveness of thermal treatments. The lowest D-values were observed with treatments containing isolated EO compounds, such as carvacrol, thymol, and cinnamaldehyde, indicating their strong antimicrobial properties. These results aligned with those obtained in our study using FIV, where the higher concentration of active compounds and reduced interference from OEO components with lower antimicrobial activity contributed to more effective bacterial inactivation.