esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Adsorption Results  
In order to further investigate the SMX adsorption mechanism onto the BCs, rapid SMX adsorption onto 700 HSBC and 700 PSBC was observed within the first 10 h, showing a significant decrease in adsorption rates thereafter. This decrease was attributed to the reduction of available adsorption sites in the 700 HSBC and 700 PSBC, with equilibrium being reached after 160 h. The extended equilibrium time observed in this study compared to most biochars may be attributed to the intricate pore structures and rougher surfaces developed in the biochars at higher pyrolysis temperatures. These features potentially provide more adsorption sites, leading to a longer equilibrium time as the adsorption process takes place more gradually and extensively [27]. Additionally, the presence of specific functional groups, as influenced by the pyrolysis temperature, may also contribute to the extended equilibrium time due to their varied interactions with the adsorbate. More importantly, the extended equilibrium time was attributed to the complex urine matrix. However, both the 350 HSBC and 350 PSBC demonstrated limited SMX adsorption capacity due to their lower specific surface area. These findings underscore the substantial impact of biochar surface area on SMX adsorption. Additionally, the higher Zeta potential of 700 HSBC resulted in a greater adsorption capacity than 700 PSBC. Furthermore, the superior performance of 700 HSBC was attributed to its pore volume roughness, which enhances the interaction between SMX and the adsorbent [25]. Notably, the distinct surface roughness between 700 HSBC and 700 PSBC, despite their similar specific surface area and pore structure, was found to underlie the differences in adsorption capacity between 350 HSBC and 350 PSBC. Previous research has demonstrated that having similar pore size distributions eliminates the impact on adsorption results [15]. However, our current study suggests that the shape and distribution of pores require further investigation using FHH model to comprehensively explain their influence on the adsorption process. The kinetic results of SMX adsorption by the four adsorbents were analyzed using three different models, and the findings are presented in Table 3. The fitting parameters (R\({}^{2}\)) indicate that the Elovich model provides a better description of SMX adsorption onto the 700 HSBC, 700 PSBC, 350 HSBC, and 350 PSBC compared to the other models, suggesting that SMX adsorption is a chemisorption process [28; 29]. This phenomenon was influenced by the pore structure and surfaced function groups. The heterogeneity of the BCs' surface favours the superior fit of the Elovich model to the data. Due to the amorphous structure of 700 HSBC, SMX adsorbed onto it show a more rapid rate, attributing to the improvement of SMX diffusion.
## 5 Simulation Verification  
The efficacy and precision of the developed quantitative framework for fault current analysis is evaluated in this section. Under diverse FRT strategies, the trajectories of the fault current are quantified and verified against time domain simulation results. Post-FRT activation, the computed current waveforms across distinct fault phases are compared with simulation results. The DC current profiles under control saturation and non-saturation conditions are comparatively assessed. Furthermore, parametric sensitivity analyses are conducted to characterize the impact of current-limiting reactor and fault location variations on the estimation accuracy.  
Figure 11: Flowchart of the proposed fault current calculation model.
## 3 Results and Discussions
### Wavelet Spectrum Analysis
(2013) also saw similar behavior for low GLR values, mostly in the bubble flow or bubble-slug transition flow region. As bubble flow and motion are random and complicated, it is hard for low-resolution sensors or wavelet norm entropy to detect the change. Figures 27 and 28 show the wavelet norm entropy of bubble, bubble-elongated bubble, and slug flow regimes at different GLR combinations for the experimental setup used in this study. Figure 26: Change of wavelet entropy with gas to liquid ratio for gas/Newtonian flow. Figure 25: Change of wavelet entropy with gas void fraction for gas/Newtonian fluid. The wavelet norm entropy value may vary with higher resolution sensors. Wavelet norm entropy depends on the pressure fluctuation signal; therefore, the more sensitively and precisely the sensor can detect the flow condition, the more accurate the wavelet norm entropy will be. However, the wavelet entropy change pattern with a different type of flow regime should remain similar. [PERSON] et al. (2013) and [PERSON] et al. (2013) [32] both observed similar wavelet entropy change patterns. However, their sensors, experimental setups, and experimental conditions were different.
# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 3 Results
### Data Profiling  
The dataset includes thirty-one observations, and each observation has six variables. Categorical features were managed during preprocessing and modeling stages:  
1. The 'gender' variable was encoded as a binary categorical feature, with 0 representing male (M) and 1 representing female (F). This encoding was straightforward, given the binary nature of the aggregated data, and was directly utilized in machine learning models without further transformation.
2. The 'is_healthy' variable was also a binary categorical feature indicating the health status of the subjects (0 for patients with [PERSON]'s disease and 1 for healthy subjects). This binary encoding was chosen to enable clear distinction and modeling of health status as a response variable in predictive analyses.
3. Although the 'UPDRS Group' variable was included in our dataset for a general overview, it was not used during the training of our models. The decision to exclude this variable from training was made because the UPDRS Group is related to the diagnosis and severity of [PERSON]'s disease, which could introduce bias into the model when predicting the health status based on broader, non-diagnostic features. Its primary role was to provide context and depth to the clinical profile of the PD patients for the readers and was not intended as a feature for prediction.  
To suggest potential connections between variables, we used a matrix of Pearson correlation coefficients, showing that age is strongly negatively correlated with the healthy status and positively with IRT and TTS (Figure 1).  
There was a notable age gap among participants, with a strong correlation between age and health status. Preliminary analyses indicated that using age as a predictor would lead to a model that essentially segregates the data into two clusters based solely on age.  
Finally, we conducted three experiments on the dataset. The goal was to calculate how distinguishable the web test results of PD patients and healthy subjects are. Surprisingly, unsupervised PD participants performed significantly better than we expected. Their  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **PD Patients (_n = 15_)** & **Health Subjects (_n = 16_)** & _p-Value_ \\ \hline Age & 70.80 \(\pm\) 5.931 & 23.26 \(\pm\) 0.964 & \textless{}0.001 \\ \hline Gender (0 = M, 1 = F) & 0.53 \(\pm\) 0.516 & 0.25 \(\pm\) 0.447 & 0.115 \\ \hline UPDRS Group (0 = HS, 1 = MILD, 2 = ADV) & 1.47 \(\pm\) 0.516 & 0 \(\pm\) 0 & \textless{}0.001 \\ \hline Web MoCA Score (total) & 24.13 \(\pm\) 3.543 & 26.69 \(\pm\) 1.302 & 0.017 \\ \hline Web MoCA IRT (avg, ms) & 5896.27 \(\pm\) 1514.26 & 2894.25 \(\pm\) 646.974 & \textless{}0.001 \\ \hline Web MoCA TTS (avg, ms) & 13,667.00 \(\pm\) 3445.42 & 6881.37 \(\pm\) 1589.145 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the characteristics of patients and healthy subjects.  
average MoCA score was higher than noted in the literature, oscillating around 24 points, creating a challenge for classification methods.
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
## 4 Measurement Results  
The performance of both setups is experimentally evaluated where 8B10B encoding is applied [2]. This encoding has the advantage that the probability of transmitting a '1' -bit is equal to the probability of transmitting a '0' -bit, which implies the matched filter output \(r\) has its optimal decision threshold at  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Setup A** & **Setup B** \\ \hline
**Photodetector** & Thorlabs PDA36A2 & Hamamatsu S5981 \\ Active Area & 2 \(\times\) 13 mm\({}^{2}\) & 4 \(\times\) 25 mm\({}^{2}\) \\ Peak Responsitivity & 0.65 A/W & 0.72 A/W \\ Transimpedance Gain & 7.5 k\(\Omega\) & 27 k\(\Omega\) \\ DC Bias & 0 V & 2.5 V \\
**DAQ** & National Instruments USB-6215 & National Instruments USB-6212 \\ Data rate & 4 kbps & 4 kbps \\ Sample rate & 80 kHz & 80 kHz \\ ADC input range & \([-0.2\) V, \(+0.2\) V] & \([-10\) V, \(+10\) V] \\ ADC resolution & 16 bit & 16 bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between both used setups.  
Figure 7: Block diagram illustrating the signal processing chain of both setups. Blocks indicated in striped lines only apply to setup B.  
the averaged expectation values of the matched filter outputs [22]. The transmitted data consists of an arbitrary text file of 150 kbit. This amount of data is transmitted at a specific angle over a wide range, resulting in an angle dependent measurement set.  
First of all, the light intensity is evaluated without any receiver polarizers equipped, while the LEDs are transmitting data to verify the stability of the DC light level. The results can be seen in Figure 8 for transmitted data with bit period \(T_{b}=250\,\upmu\)s. The remaining noticeable modulated signal is attributable to the deviation of the LED drive currents, but this could be eliminated or reduced by more accurately matching the drive currents.
## 3 The Proposed Method
### Grouping of Finder Patterns
#### 3.3.2 Grouping Pairs of Finder Patterns  
If any QR Code has one of the 3 Finder Patterns significantly damaged, then this Finder Pattern might not be identified and there remains two Finder Patterns in the Finder Patterns list, that were not selected (as the vertices of a right-angled triangle) in the previous step (Figure 10a). The goal of this step is to identify these pairs and determine the position of the third missing Finder Pattern. A square shape of the QR Code is assumed.  
All two element combinations of remaining Finder Patterns, whose distance is in a predefined interval, are evaluated. A pair of Finder Patterns can represent Finder Patterns that are adjacent corners of a QR Code square (Figure 10a) or are in the opposite corners. If they are adjacent corners, then there are two possible positions of the QR Code (in Figure 10b depicted as a red square and green square). If they are opposite corners, then there are other two possible positions of the QR Code.  
All four possible positions of the potential QR Code are evaluated against the following criteria:  
* Is there a Quiet Zone around the bounding square at least 1 \(MW\) wide?
* Is there a density of white points inside bounding square in interval (0.4; 0.65)?
* Is there a density of edge points inside bounding square in interval (0.4; 0.6)?  
Density of edge points is computed as the ratio of the number of edge points to area*2/\(MW\).  
Region of a QR Code is expected to have relative balanced density of white and black points and relative balanced ratio of edges to area.  
A square region that meets all the above conditions is considered a candidate for a QR Code. There are two possible corners in the QR Code bounding square where 3 rd Finder Pattern can be located (Figure 10c). For both possible corners Finder Pattern match score is computed and one with better score is selected (in other words question, \"In which corner is the structure that more closely resembles the ideal Finder Pattern?\" must be answered). Match score is computed as  
\[MS=\text{argmin}(OS+\text{min}(BS,WS)) \tag{7}\]  
where \(MS\) is match score (lower is better), \(OS\) is overall pattern match score, \(BS\) is black module match score and \(WS\) is white module match score. \(BS\) stores matches only for expected black points and \(WS\) stores matches only for expected white points between the mask and the image. \(BS\) and \(WS\) was introduced to handle situations when over the area of Finder Pattern is placed black or white spot, which would cause a low match score if only a simple pattern matching technique were used.  
The match score is computed for several Finder Pattern mask positions by moving the mask in a spiral from its initial position up to a radius of \(MW\) with a step of \(MW\)/2 (for the case of small geometric deformations of the QR Code).  
Figure 10: QR Code with one damaged Finder Pattern: (**a**) two known Finder Patterns; (**b**) two possible regions of QR Code; (**c**) two possible positions of 3 rd Finder Pattern.
## 4 Numerical Examples and Discussion
### Calculation of Long-Term Deflection of Medium-Span PSC Box Girder (Case B)
Figure 15 shows the stress and strain at the bottom concrete and the mean prestress at the mid-span section of the girder. As shown in the figure, the girder section remains in compression at the mid-span section throughout the analysis period. The compressive strain in the staggered  
Figure 14: Distribution of crack strain of the medium-span PSC box girder at 6 th, 144 th, and 1000 th days. analysis is the largest because of the compressive shrinkage strain at the bottom, whereas the equivalent load induces a tensile stress and strain at the bottom unlike in the staggered analysis, as explained earlier in Section 4.2. Thus, the equivalent load analysis yields the smallest compressive stress and strain, as shown in Figure 14(a),b. As the prestress loss is a multiple of strain changes and the elastic modulus of the prestressing tendon, it follows the same curve as that of the compressive strain of the concrete (see Figure 14(c)). Due to creep, the compressive strain at the bottom increases with increasing deflection as shown in Figure 14(a), but, as can be found in Figure 14(b), the stress at the bottom concrete becomes almost constant after 346 days in the analyses without considering nonuniform shrinkage. Since creep does not develop stress, the stress change after all fixed loads are imposed is mainly due to non-uniform shrinkage. In the equivalent load analysis, the additional tensile stress at the bottom due to the nonuniform shrinkage is reducing slowly as the moisture distribution is converging to equilibrium state with the environment RH, and thus, the compressive stress is increasing slowly. Whereas, in the staggered analysis, the compressive stress at the bottom increases due to restraint.
## 3 Materials and Methods
### Research Design  
Within the scope of the current study, the functionality of R is focused on the K-means clustering classification algorithm of RStoolbox to process the satellite images, with the aim of visualising and comparing the extent of vegetation in the central Congo River Basin for different years using a general workflow approach, as seen in Figure 2. The Landsat satellite images taken in 2013, 2015 and 2022 were used to visualise change detection between land cover or forest types. Thus, the algorithms of R support the operational monitoring of the dynamics of land cover types derived from the images. However, the packages of R can also be applied to additional applications of geospatial data analysis where automated image classification is needed. Examples of such cases include monitoring the desertification using time series of images, analysis of urban growth using retrospective data overlaid with current maps and actual images, or forest fire monitoring.  
The functionality of R programming presents a set of modular functions for the automation of image classification applications from Earth observation, spatial and remote sensing data, as seen in Figure 3.  
The packages of 'terra' and 'raster' include the functionality of pre- and post-processing of both data types (vector and raster), as well as extensible features for spatial data analysis. Raster data analysis includes reading the information on the spectral reflectance of the image bands, automated processing of the stacked bands, feature extraction for unsupervised classification using clustering, creating multi-layer colour composites, performing map algebra using raster bands, visualising the thematic maps as overlays, classification and basic statistical analysis, such as histograms.  
Figure 2: Workflow of the study project summarising general steps of the study, including aim and objectives, methodology and results used in a framework of R application for remote sensing data processing and land cover change detection by unsupervised classification (k-means clustering) of Landsat satellite images for selected regions of Congo, D.R.C.  
R operates with major features of spatial data, such as dimensions, resolution, reading data extent (min/max analysis), plotting the data, manipulating with Coordinate Reference Systems (CRS), reprojecting the data, assigning the CRS and transforming the data. An important function of R 'terra' package consists of creating the SpatRaster objects, which are a class of raster data with a longitude/latitude CRS as spatial parameters and a specific geometry of the file (cells are arranged in the rows/columns of the internal grid). Moreover, the 'terra' package enables the cartographic visualization of continuous fields, including topographic elevation, vegetation based on the classified land cover types, and climate data in a spatial context (regional variations in temperature and precipitation). Furthermore, R enables us to perform data clipping using an overlay of spatial data and imagery over their area of interest.
## 4 Discussion
### Influences of V/A-C and PSV Ventilation Mode  
The first trial show the interaction with the simulated patient during the V/A-C mode, which is based on a continuous mandatory ventilation mode. In this case, simulated patient's breathing frequency is not in phase with the ventilator's frequency representing realistic conditions. Patient-ventilator asynchrony can be identified and manifests as a forced second inhalation every third breathing cycle (double triggering). This event represents a force working against the effort of a spontaneous breathing patient. The mechanical ventilator intervenes despite the patient being sufficiently ventilated. However, this is in concordance with the principle of the V/A-C ventilation mode where the patient always receives at least the set tidal volume. The forced second inhalation, in general, occurs when the ventilator inspiratory time is shorter than the patient's inspiratory time. The results are corresponding with clinical findings reported by [PERSON] et al. [22].  
The second trial demonstrate the influence of PSV ventilation mode during simulations of spontaneous breathing and apnea phases. Mechanical ventilation in PSV mode is supportive but leads to an excessively high peak pressure during spontaneous breathing phases caused by premature cycling. The volume supplied by the mechanical ventilator is compounded upon the tidal volume delivered by spontaneous breathing. Consequently, the total volume during SB phases doubles in comparison to the apnea phases. This leads to insufficient ventilation during the apnea phases.  
Furthermore, the pressure changes inside the thoracic chamber of the xPULM(tm), which are complementary to the data set recorded by the ventilator, have been presented. This provides additional information about how asynchronies influence the conditions in the thoracic chamber in comparison to synchronous mechanical ventilation. These pressure changes in the thoracic chamber partially reflect the interplay between changing compliance of the lung, the chest wall mechanics and the varying respiratory muscle effort (P\({}_{\text{MUS}}\)), which is at the heart of most clinically relevant PVA.
## 4 Discussion
### Challenges and Limitations
#### 4.4.5 Regulatory Compliance Across Jurisdictions  
Cross-border regulatory compliance presents additional complexity layers that significantly impact organizational adoption strategies for blockchain-enabled circular supply chains. Different jurisdictions maintain distinct regulatory frameworks for waste management, product stewardship, and circular economy reporting, creating compliance challenges that extend beyond technical implementation. For instance, the European Union's Waste Framework Directive [48] requires detailed tracking of waste streams and recycling rates, while China's Extended Producer Responsibility regulations [49] mandate specific take-back obligations that differ substantially from North American frameworks. Organizations operating across multiple jurisdictions must navigate these regulatory heterogeneities while maintaining consistent data structures within their blockchain systems, often requiring jurisdiction-specific smart contract logic to accommodate varying compliance requirements.  
The challenge intensifies when considering data sovereignty and cross-border data transfer regulations such as GDPR in Europe, China's Cybersecurity Law [50], and various national data localization requirements. Blockchain networks inherently distribute data across multiple nodes, potentially conflicting with regulations that restrict data movement across national boundaries. Organizations must implement sophisticated governance frameworks that can compartmentalize data access based on geographic and regulatory constraints while preserving the transparency benefits of blockchain technology. Furthermore, the immutable nature of blockchain records, while advantageous for audit purposes, may conflict with \"right to be forgotten\" provisions in privacy regulations, requiring careful consideration of what data is stored on-chain versus off-chain. These regulatory compliance requirements necessitate substantial legal expertise and ongoing monitoring of evolvinginternational frameworks, adding significant operational overhead that organizations must factor into their blockchain adoption strategies. Future implementations should incorporate regulatory compliance automation features and establish partnerships with international legal experts to navigate this complex landscape effectively.