esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. The MoveONParkinson project aims to enhance exercise engagement among people with Parkinson's Disease (PwPD) in the Portuguese context through the ONParkinson digital platform, which provides mobile and web interfaces. While the broader MoveONParkinson project has been previously described from a health-focused perspective, this study specifically focuses on the development and integration of an AI-driven conversational agent (CA) for the Portuguese language, called PANDORA, within the mobile interface of the solution to assist and motivate PwPD in their exercise routines. PANDORA (Parkinson Assistant in Natural Dialogue and Oriented by Rules and Assessments), designed based on Self-Determination Theory (SDT), addresses the psychological needs of autonomy, competence, and relatedness. A preliminary study involving 20 PwPD, 10 caregivers, and 5 healthcare professionals informed the design requirements for PANDORA. The development process involved four main phases: (1) Design of the Chatbot's Motivation Model, (2) Design and implementation of the conversational agent, (3) Technical Performance Evaluation, and (4) User Experience Evaluation. Technical Performance Evaluation, conducted with three physiotherapists, assessed domain coverage, coherence response capacity, and dialog management capacity, achieving 100% accuracy in domain coverage and coherence response capacity and 89% in dialog management capacity. The User Experience Study involved eight PwPD users recruited from Portuguese healthcare units performing predefined tasks, with user satisfaction scores ranging from 4.2 to 4.9 on a five-point Likert scale. The findings indicate that integrating a conversational agent with motivational cues tends to increase patient engagement. However, further studies are required to determine PANDORA's impact on exercise engagement in PwPD.  
**Keywords:** conversational agent; chatbot; human-computer interaction; Parkinson's Disease; therapy; mobile health; physical exercise; motivation model
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 2 Materials and Methods
### Energy Model  
The optimization of the weights in the three KBs requires performing WSN simulations. In this process, it is required to model the energy spent in the communication of the nodes. In our case, this was based on the first-order radio model detailed in [41]. This well-known model defines a set of equations that take into account the energy spent in the data exchange between radio devices, like those installed in the nodes. In addition, it is a reference widely used in the related literature. The main equations of the model are shown in Equations (2)-(4). This energy model calculates the energy spent in the transmitter and in the receiver based on the length in bits of the data message and the distance between them, assuming free space and multi-path fading channels. Thus, the energy consumed by the transmitter is detailed in Equation (2), while the energy spent by the receiver is shown in Equation (4).  
\[E_{Tx}(l,d)=f(x)=\begin{cases}l\cdot(E_{elec}+E_{fs}\cdot d^{2}),&d\leq d_{0} \\ l\cdot(E_{elec}+E_{mp}\cdot d^{4}),&d>d_{0}\end{cases} \tag{2}\]  
\[d_{0}=\sqrt{\frac{E_{fs}}{E_{mp}}} \tag{3}\]  
\[E_{Rx}(l)=E_{elec}\cdot l \tag{4}\]  
where  
* \(l\) defines the number of bits of the message.
* \(E_{elec}\) measures the energy in Joules that the circuitry of the transmitter and the receiver consumes for each bit sent or received, respectively.
* \(d\) is the distance in meters between the source and the destination of the message.
* \(E_{fs}\) stands for the energy in Joules that the amplifier consumes in order to obtain a acceptable bit error rate, according to the free space model \((d\leq d_{0})\).
* \(E_{mp}\) is the energy in Joules that the amplifier consumes to obtain an suitable bit error rate in the multi-path (mp) model \((d\leq d_{0})\).  
This model also takes into account the reception of data and the aggregation process in every CH [42]. In a WSN aggregation, it is necessary to save energy by avoiding sending the value of each quantity measured by a node independently. Thus, it is a common practice that, through a statistical operation, only a summary of these values is sent from the CH to the BS, so that the energy spent in each bit of a received and aggregated message is defined as in Equation (5):  
\[E_{Rx-DA}=(E_{elec}+E_{DA})\cdot l \tag{5}\]  
where  
* \(E_{DA}\) stands for the energy in Joules spent by the CH when it receives and aggregates the data from a contributing node.
* \(l\) is the number of bits of the final aggregated message.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 2 Requirements for Detection Indicators of Bridge Facility Operation Status
### Classification of Existing Factors Influencing Information Transportation  
The existing information types and various traffic-influencing factors that have been identified can be basically divided into two categories based on their impact on traffic operation status. One category is considered to have direct influencing factors, and the other category is considered to have indirect influencing factors. When determining the weight of the impact of infrastructure on traffic, the weight determination methods can be divided into two categories: the expert scoring method and experimental determination method.  
Determine the weights of the impacts of various traffic-influencing factors on existing information using expert scoring and experimental analysis methods. Among them, the expert scoring method mainly focuses on determining the weights of the impacts of traffic-influencing factors that are closely related to other factors, such as the long detection cycle of bridge pavement scales. The experimental analysis method mainly focuses on the traffic influencing factors that can be measured in the field and lead to direct changes in vehicle speed and driving. By measuring relevant parameters and designing corresponding experimental processes, the weights of the impacts of the above traffic-influencing factors can be measured.  
This article analyzes various traffic influencing factors of existing information, and combines experimental data from the analysis method and expert scoring results to obtain the traffic influencing factors and degree of bridge infrastructure, as listed in Table 1. The degree of impact increases from 0 to 1. The classification and definition of each scale standard in this Table are consistent with the current \"Technical Condition Evaluation Standards for Highway Bridges\".
### Extracting Research Gaps from Related Works
#### 1.2.3 Related-Work Analysis
No particular attention was paid to the effects of numerous distortions, which include outliers, noise, and other disturbances. The unique aspect of this experiment is that only data from three loads with speed fluctuations between 6000 and 24,000 rpm were chosen to confirm the viability of the suggested approach. As a result, this study had several limitations and did not examine the complete diagnosis dataset. Additionally, no work was performed on prognosis issues. Likewise, the problem of data drift and system's dynamics received no attention. Interesting work was performed in [14] to provide solutions to both data unavailability and data drift using domain adaptation, generative modeling, and adaptive learning. Complexity was seen as a problem of automatic feature extraction via deep learning, but no additional data complexity and outlier analysis were considered, with the exception of sliding-window overlap sampling, which increases the number of samples while normalization is performed, without further details regarding dataset splitting and the evaluation methodology.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Behaviour of IRP System in 152.4 Mm (6 inch) Discontinuous Host Pipe
#### 3.2.1 Deflection Performance  
Figure 28 illustrates the load versus deflection behavior of the IRP in a host pipe with 152.4 mm (6 inch) wide discontinuity, offering valuable insights into its mechanical response under different levels of adhesive shear strengths. Table 10 compares the performance of the 152.4 mm (6 inch) IRP system at a load of 56.9 kN (12.8 kip) for different levels of adhesive strength. The results reveal that the unclean surface, with an adhesive strength of 5 MPa (0.7 ksi), experiences 20% more deflection, whereas the ablated preparation, with an adhesive strength of 40 MPa (5.8 ksi), exhibits a deflection of less than 16% when compared with the benchmark adhesive strength of 20 MPa (2.9 ksi). It is evident that the 152.4 mm (6 inch) IRP system is much less affected by changes in adhesive strength compared with the 12.7 mm (0.5 inch) IRP system, as highlighted in Table 7. This behavior can be explained by the relatively wide discontinuity of 152.4 mm (6 inches), resulting in a lower overall bending stiffness and allowing for better absorption of deflection compared with the shorter circumferential discontinuity of 12.7 mm (0.5 inches); therefore, it could leverage the deflection change with respect to adhesive bonding change. The behavior can also be observed in Figure 29.  
Figure 27: Equivalent stress results of a 152.4 mm (6 inch) discontinuous IRP system employing the adhesive shear strength of 40 MPa (5.8 ksi) at a load of 56.9 kN (12.8 kip) (magnified 7.2 times).  
Figure 28: Lateral loading versus deflection of the 152.4 mm (6 inch) discontinuous IRP system under different levels of adhesive strengths.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Load kN (kip)**} & \multirow{2}{*}{**Locations inch**} & \multicolumn{4}{c}{**Deflection mm (inch)**} & \multicolumn{2}{c}{**Difference with Benchmark**} \\ \cline{3-7}  & & **5 MPa** & **40 MPa** & **20 MPa (Benchmark)** & **5 MPa** & **40 MPa** \\ \hline \multirow{2}{*}{56.9 (12.8)} & 1.7065 & 31.671 (1.247) & 22.323 (0.879) & 26.441 (1.041) & 20\%\(\uparrow\) & 16\%\(\downarrow\) \\  & 23.3005 & 17.400 (0.685) & 11.926 (0.470) & 14.234 (0.560) & 22\%\(\uparrow\) & 16\%\(\downarrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison between various levels of adhesive strength for the 152.4 mm (6 inch) discontinuous IRP system at a load of 56.9 kN (12.8 kip).  
Figure 29: Average debonding length of the adhesive layer at the maximum load of 56.9 kN (12.8 kip) for 152.4 mm (6 inch) discontinuous IRP system versus different levels of adhesive shear strength.
## 2 Materials and Methods
### Data
#### 2.2.3 Reanalysis Products  
CLDAS  
CMA Land Data Assimilation System (CLDAS) Version 2.0 [35] is a reanalysis grid product developed by CMA that covers Asia (0-65\({}^{\circ}\) N, 60-160\({}^{\circ}\) E) and consists of model, ground, and satellite data ensembles; it is of better quality than similar international products for China. We used the more accurate near real-time type LST product, which contains 0.0625\({}^{\circ}\) equal latitude and longitude grid data with 1 h temporal resolution. The data agreed well with ground observations: for China, the average correlation coefficient was 0.98, root mean square error (RMSE) was 1.8 K, and Bias was 1.4 K. The product was available from [[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 13 March 2022). The data were saved in netCDF format.  
ERA5-Land  
The European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land is a fifth-generation reanalysis land surface dataset. ERA5-Land is produced by replaying the land component of the ECMWF ERA5 climate reanalysis; it combines model data with observations from across the world into a globally complete and consistent dataset. The product is available at 1 h temporal resolution and -9 km spatial resolution from ERA5-Land servers. The ERA5-Land LST product is a state-of-the-art global reanalysis dataset for land applications. When compared with the MODIS LST, it showed a good correlation (correlation coefficient 0.94, Bias 1.36 K, RMSE 3.78) [36]. The product was available at [[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)) (accessed on 2 April 2021). The data were saved in netCDF format.
## 3 Methods
### Fabry-Perot Interferometer  
Figure 1 shows the comparison of variations in I 557.7 nm synthesized from SABER data and variations in integral intensity observed with FPI. Figure 1 presents day-to-day variation of I 557.7 nm average from 2017 to 2021. It should be noted that at some time points, there are similar variations of I 557.7 nm from different sources, up to interdiurnal variations, for example, from day 40 to 130 or from day 330 to 365. The results of I 557.7 nm measurements obtained with Fabry-Perot interferometer are given in arbitrary units, because the equipment evaluating the light flux intensity was not calibrated. Arbitrary units of intensity observed with the interferometer can be converted to Rayleighs using SABER data for the periods when intensity variations show the best similarity.  
Figure 2 shows the scatter diagram for I 557.7 nm series by FPI and SABER for days 0-125 and 325-365 of year (best similarity time). Despite the existing dispersion, positive correlation of the two studied series is well-defined, and the Pearson correlation coefficient of the series equals 0.471. The method of determining the intensity from interferograms assumes background subtraction [17]; therefore, for the suggested interferometer calibration, correlation of FPI and SABER intensities can be considered to be linear but without the zero shift. The linear regression line of two datasets that runs through the origin is  
Figure 1: Annual average I 557.7 nm from 2017 to 2021 since January 1 from SABER (red) and FPI data (green).  
also given in the scatter diagram. Tangent of the line inclination angle 6.322 R/arb. unit (Rayleighs/arbitrary units) will further be used to convert the interferometer arbitrary units to Rayleighs.
# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 5 Future Directions and Opportunities
### Potential for Real-Time Decision-Making Using Quantum-Enhanced ML Models  
The combination of QC and ML offers the potential for real-time decision-making in critical medical scenarios [111]. Quantum-enhanced ML models can process and analyze vast datasets at unprecedented speeds, enabling rapid diagnosis and personalized treatment planning. These capabilities could redefine fields such as emergency medicine and precision oncology, where timely interventions are crucial.
# Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine
## 3 Materials and Methods
### Considered Deep Learning Algorithms  
#### 3.4.1 Long Short-Term Memory (LSTM)  
Before explaining LSTM, it is important to understand recurrent neural networks (RNN) given the close relationship between them. The structure of the RNN consists of an input layer, one or more hidden layers, and an output layer. RNNs have chain-like structures of repeating modules, which are used as memory for storing important information from previous processing steps [15].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Category** & **Mathematical Symbol** & **Physical Measuring Unit** \\ \hline Electrical power & Target & P\({}_{\text{elec}}\) & MW \\ at the output of the generator & & & \\ \hline Pressure losses & Feature & \(\Delta\)P\({}_{\text{FB}}\) & mmHg\({}_{\text{2}}\)O \\ between furnace and boiler & & & \\ \hline Steam temperature & Feature & T\({}_{\text{emp}}\) & \({}^{\circ}\)C \\ \hline High pressure of driving steam & Feature & P\({}_{\text{HP}}\) & bar \\ \hline Flow rate of steam & & & \\ _(at the boiler output)_ & & & \\ \hline Flow rate of steam & & & \\ _(at the collector output)_ & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main feature parameters that influenced the variation in power in the studied power plant.  
LSTM is an evolution of RNNs and was introduced to eliminate the drawbacks of RNNs related to vanishing / exploding gradients and rectify the problems of the short memory linked to RNNs by adding complementary interactions per cell.
## 2 Study Area and Data
### Agricultural Parcel Data  
This study involves the use of a vector dataset that contains high spatio-temporal resolution information on agricultural parcels of the three studied crops from different years (2019-2021) in the study area indicated in Figure 1. The data set is collected and updated yearly through the Integrated Administration Control Systems (IACS) among the German Federal States. Access is limited due to privacy concerns in certain states. It consists mainly of the geometry of the agricultural fields and the types of crops planted in each cropping season. The parcel data for the three crops explored in this research and their respective years are outlined on the study area map (Figure 1).
## 3 Results and Discussion
### Water Extraction Experiments and Response Surface Analysis
#### 3.2.2 Optimization for the Water Extraction  
The main goal of the experiments described in Section 3.2.1 was to determine the best process conditions for inulin extraction, although it was proven that polyphenols were also extracted using water as an extracting solvent. Then, it was possible to perform a multiple-response optimization, in order to find the experimental conditions that maximize the extraction of inulin and polyphenols with the highest antioxidant activity in the extracts.  
In this sense, it was found that T = 89 \({}^{\circ}\)C and t = 139 min give a desirability factor of 0.805 and result in expected values of inulin content = 34.47 mg glu eq/g (d.b.); TPC = 12.45 mg GAE/g (d.b.) and Aox = 52.89 mM trolox/g (d.b.). These values would  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **F-Ratio** & \(p\)**-Value * \\ \hline \multicolumn{6}{c}{Inulin content} \\ \hline A:T (\({}^{\circ}\)C) & 386.052 & 1 & 386.052 & 23.07 & 0.0020 \\ B:t (min) & 561.996 & 1 & 561.996 & 33.58 & 0.0007 \\ AA & 1542.62 & 1 & 1542.62 & 92.17 & 0.0000 \\ AB & 13.9128 & 1 & 13.9128 & 0.83 & 0.3922 \\ BB & 494.137 & 1 & 494.137 & 29.52 & 0.0010 \\ Residual & 117.162 & 7 & 16.7374 & & \\ Total & 2922.23 & 12 & & & \\ \hline \multicolumn{6}{c}{Total polyphenol content} \\ \hline A:T (\({}^{\circ}\)C) & 1.97400 & 1 & 1.97400 & 4.55 & 0.0704 \\ B:t (min) & 5.36587 & 1 & 5.36587 & 12.37 & 0.0098 \\ AA & 0.65622 & 1 & 0.65622 & 1.51 & 0.2585 \\ AB & 0.97023 & 1 & 0.97023 & 2.24 & 0.1785 \\ BB & 4.34977 & 1 & 4.34977 & 10.03 & 0.0158 \\ Residual & 3.03712 & 7 & 0.43387 & & \\ Total & 15.996 & 12 & & & \\ \hline \multicolumn{6}{c}{Antioxidant activity} \\ \hline A:T (\({}^{\circ}\)C) & 30.7509 & 1 & 30.7509 & 2.04 & 0.1961 \\ B:t (min) & 11.2008 & 1 & 11.2008 & 0.74 & 0.4170 \\ AA & 12.9178 & 1 & 12.9178 & 0.86 & 0.3852 \\ AB & 29.9209 & 1 & 29.9209 & 1.99 & 0.2015 \\ BB & 88.5585 & 1 & 88.5585 & 5.88 & 0.0458 \\ Residual & 105.414 & 7 & 15.0592 & & \\ Total & 271.639 & 12 & & & \\ \hline \hline \end{tabular}  
* Terms showing a \(p\)-value lower than 0.05 (in red) are significant. Terms with a \(p\)-value higher than 0.05 (in black) are non-significant.  
\end{table}
Table 5: ANOVA results for the water extraction experiments.  
represent 80% of the inulin content, 60% of the TPC and 56% of the Aox, when compared to those obtained in the compositional analysis.  
Figure 1: Pareto and surface plots for the water extraction experiments: (**a**,**b**) inulin content; (**c**,**d**) polyphenol content; (**e**,**f**) antioxidant activity.
## 6 Performance Evaluation in Experiment  
Finally, we show the performance difference between the six timing estimation algorithms (see Figure 8) for signals with different roll-off factors based on experimental results. To this end, a DP-16 QAM signal with 32 GBd and a varying roll-off factor was measured. To enable a roll-off factor between 0.01 and 1, an oversampling of two was used for all of the evaluated signals.  
Figure 12 shows the experimental setup. DP-16 QAM signals with the desired pulse-shape (roll-off factor 0.01-1.0) and random data with a length of \(2^{19}\) bits were generated offline. The in-phase and quadrature parts for both polarizations of the signal were converted to the analog domain by four digital-to-analog converters (DAC) with a sampling rate of 64 GSa/s and 20 GHz electrical bandwidth. The output of the DACs was used to modulate the signal laser with a dual-polarization in-phase and quadrature (IQ) modulator. Noise was added to reach the desired BER of \(10^{-3}\). A coherent receiver consisting of a 90\({}^{\circ}\) hybrid with four balanced detectors and four analog-to-digital converters (ADC) with 32 GHz electrical bandwidth received the optical signal. Finally, offline processing was used to evaluate the signal and determine the roll-off dependence of the compared algorithms.  
The frequency domain signal processing chain described in Figure 1 was used to evaluate the received signal. Since a transmission over a longer distance would not give further insight to the comparison of the timing estimation algorithms, the CD correction step could be skipped for our comparison. Figure 13 shows the jitter over roll-off factor for the two versions of Godard, modified Godard, and Gardner timing estimation algorithms. These findings match our simulations that were presented before (see Figure 4).  
Figure 12: Overview of measurement setup. The transmitter consists of offline signal generation, four digital-to-analog converters (DAC), and a laser being modulated with a dual-polarization in-phase and quadrature (IQ) modulator. Noise was added to reach the desired BER. A coherent receiver with a local oscillator (LO) and four channels with balanced detectors and analog-to-digital converters is used to receive the signal. The detected signal is evaluated in offline digital signal processing (DSP).