esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method  
### Data Acquisition  
#### 2.1.1 Observation Stations  
Eddy covariance (EC) and automatic meteorological station (AMS) data in 2013 was collected at Daman superstation and Zhangye wetland station. These two observation stations were established during HiWATER experiment in May of 2012 [15]. Figure 1 shows the location and photos of Daman superstation and Zhangye wetland station.  
The observing items and information of Daman superstation and Zhangye wetland station are listed in Table 1. Meteorological data was sampled every minute and was converted to a 30-min average value for comparison with the flux data. Values were deleted if they were beyond the physical range or instrument range [16]. The EC systems at the two stations consist of an open-path infrared gas analyzer and a 3-D sonic anemometer [17,18]. The 3-D wind speed, H\({}_{2}\)O concentration and CO\({}_{2}\) concentration data were sampled at a frequency of 10 Hz by a data logger. Half hour fluxes were calculated by post-processing raw 10 Hz data using the EdiRe software. The processing steps include spike detection, lag correction of H\({}_{2}\)O/CO\({}_{2}\) relative to the vertical wind component, sonic virtual temperature conversion, planar fit coordinate rotation, the WPL (Webb-Pearman-Leuning) density fluctuation correction and frequency response correction [16].  
Half-hourly flux data were excluded if the instrument range was excessive, rainfall occurred or instrument malfunctions occurred. At night (downward short wave radiation \(<\) 1 W/m\({}^{2}\)), half-hourly flux data were excluded if friction velocity (u*) was lower than a specific threshold (0.15 m/s), because of weak turbulent mixing at a low friction velocity. Then, missing values in the fluxes were filled using non-linear functions, which use PAR and air temperature as input. Missing values in the fluxes were not filled when the meteorological measurements were not simultaneously available. The gap-filling data were used only to analyze the seasonal and annual variations in the carbon flux. GPP was the sum of the daytime net ecosystem production (NEP) and ecosystem respiration (ER). The daytime ER was estimated with the Van't Hoff function which was calibrated using the night-time air  
Figure 1: Location of the Daman superstation and Zhangye wetland station. (The background image is ASTER image, red: 556 nm, green: 807 nm, red: 661 nm).  
temperature and nocturnal NEE. The specific method and formulas in the NEE partitioning can be found in [PERSON]'s paper [19,20].
## 5 Simulation Verification  
In order to verify the operation performance of the two-stage WPT system controlled by the proposed double-side CC&OV-PT control strategy, the numerical models show the simulation diagram is built in MATLAB Simulink using the following simulation parameter specifications shown in Table 2. Simulation results are shown in Figures 7-10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline input voltage \(V_{1}\) & 20 V \\ reference voltage \(V_{\text{ref}}\) & 10 V \\ switching frequency of Buck converter \(f_{1}\) & 20 kHz \\ switching frequency of Inverter \(f_{2}\) & 200 kHz \\ Inductance \(L_{\text{P}}\) & 200 uH \\ Inductance \(L_{\text{S}}\) & 200 uH \\ capacitance \(C_{\text{P}}\) & 3.2 nF \\ capacitance \(C_{\text{S}}\) & 3.2 nF \\ load resistance \(R\) & 10 \(\Omega\)/7 \(\Omega\) \\ duty cycles of pulses \(P_{\text{H}}\) and \(P_{\text{L}}\) & 0.7/0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The system parameters of numerical models in Simulink environment.  
Figure 8: Waveforms of \(v_{1}\) and \(v_{\text{o}}\) under PWM control when the output load and input voltage changes.  
Figure 7: Waveforms of output voltage \(v_{\text{o}}\) and inverter input voltage \(v_{1}\).  
The waveforms of output voltage and inverter input voltage are shown in Figures 8-10 corresponding to the PWM control, traditional PT control and the proposed double-side CC&OV-PT control, when load and power supply voltage are changed abruptly. With regards to Figures 8,b-10,b, the output load of the system is altered from 10 \(\Omega\) to 7 \(\Omega\), and the input voltage is changed from 20 V to 15 V at t = 0.5 s, respectively. The transient time and the peak-to-peak values of the system output voltage under different control methods are shown in Table 3. As shown in Figures 8-10 and Table 3, as the operation condition is changed abruptly, the transient time of PT control is much shorter than that of the PWM control. Hence, the traditional PT control has a significant advantage in the recovery speed of the transient response. Regarding output voltage stabilization, the proposed CC&OV-PT control method can be effectively regulate the output voltage of this two-stage WPT system with a smaller oscillation amplitude than that of the PWM control and traditional PT control. Although the traditional PT control can achieve faster recovery, the low-frequency oscillations with high amplitudes greatly degrade the output voltage quality. Therefore, the traditional PT control method is not recommended to be used in this two-stage WPT system. In comparison, the proposed CC&OV-PT method has the best control performance from the simulation study.  
Figure 10: Waveforms of \(v_{1}\) and \(v_{0}\) under the CC&OV-PT control when the output load and input voltage changes.  
Figure 9: Waveforms of \(v_{1}\) and \(v_{0}\) under the traditional PT control when the output load and input voltage changes.
## 2 Literature Review
Their experimental results indicated that the proposed method performed best, with an accuracy of 87.36%, AUC of 0.8735, the sensitivity of 85.37%, and specificity of 89.33%, [PERSON] et al. [55] conducted a study to predict the appropriate study stream for students in higher secondary education. They found that the neural network (NN)outperformed the other approaches with a classification accuracy of 86.72%, the sensitivity of 0.92, specificity of 0.82, and MCC of 0.72. [PERSON] et al. [56] examined various types of ML predictions models on a dataset, which gave the best choice as a (NN) architecture that provides 6.26 an average root mean squared error, and a mean absolute error of 5.74 based on a scale of 0 to 100. The artificial neural network (ANN) method was adopted by [PERSON] et al. [57] to predict suitable student specialization in a dataset of 314 students based on student records from the iGracias Integrated Academic Information System at Teklom University in 2016, and they achieved an accuracy of 94.81%. The NB method and analytic hierarchy process (AHP) techniques were adopted by [PERSON] et al. [58] to build a decision support system to predict suitable specialization in technical faculty in Indonesia. A rule-based classification algorithm (PART) was adopted by [PERSON] et al. [59] to propose an intelligent model for selecting and predicting suitable university specialization. The model achieved an accuracy of 73.7%. [PERSON] and [PERSON] [60] built a system model to guide first-year undeclared/undecided engineering students to predict suitable engineering majors. They found that the RF approach outperformed the other classification algorithms, with the highest accuracy of 57%. [PERSON] [61] developed a rule-based expert system that suggested majors for students at the undergraduate level. [PERSON] et al. [62] has used RF classifiers to analyze students' personality and intelligence across various majors and academic programs and predict suitable college majors for students based on academic results, personality, and level of intelligence with an accuracy of level one at 96.1% and 94.72% at second level respectively, moreover, they have investigated that their framework has potential to recommend a student towards future higher degree options. Although the attractiveness of higher education institutions in many areas of student field of study selection has been extensively researched, there is a paucity of evidence available for modeling the relationship between these factors and intelligent recommendation of student fields based on the job market and student history and experience. To our knowledge, no studies have been conducted on the use of any ML algorithm specifically designed for the purpose of predicting student specialization and identifying the extent to which various parameters contribute to the determination of the specialization of students. As a result of this discovery, we were inspired to conduct our current study. As a result, the current research has implications for higher education, students, and the labor market.
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### Application of the Prediction Model  
In stage 3, we used ITU models to calculate channel damping due to climatic phenomena (clouds, rain, snow, etc.). The ITU-R method uses the concept of a sufficient path length using a reduction factor. The process calculates the long-term statistics of the slant path rain attenuation at a given location for frequencies up to 30 GHz and, provisionally, for higher frequencies. This method involves several steps.
## 5 Results and Discussion
### Parameter Sensitivity Analysis of the Trained MHMM  
To further evaluate the robustness and reliability of the trained MHMM, a sensitivity analysis was performed on its key emission parameters. This analysis involved systematically perturbing the learned mean and standard deviation values for HRV, TCT, and NASA-TLX distributions across the different proficiency states (0: Novice, 1: Intermediate, 2: Expert). The impact of these perturbations on the model's overall classification accuracy and F1-Macro score was then observed.  
As presented in Table 5, the results demonstrate the trained MHMM's resilience to variations in its emission parameters. Even with significant perturbations, such as a \(\pm\)10% change in the mean of HRV for Novice (state 0) or Intermediate (state 1), the model's accuracy and F1-Macro scores remained remarkably close to the baseline performance (0.925 accuracy, 0.912 F1-Macro). Similar minimal performance degradation was observed when the standard deviation of TCT for Novice (state 0) was varied by \(\pm\)15%, or when the mean and standard deviation of TLX for Intermediate (state 1) and Expert (state 2) were perturbed by \(\pm\)10%. Only the most impactful results are included in the table for brevity and clarity, as the remaining perturbations yielded changes in less than 0.002, statistically negligible and visually uninformative. This consistent performance across perturbed parameters underscores the inherent stability of the MHMM, suggesting that its ability to infer proficiency states is robust even when faced with minor inaccuracies or natural variability in the underlying data distributions. This finding is crucial for practical applications where precise parameter estimation might be challenging.
## 4 Case Example: Australian Public-Private Partnership (PPP) Schools
### Satisfaction Analysis of End-User's Perceived Service Quality  
The questionnaire conducted in t = 9 is composed of three parts: the end-user's satisfaction for teaching space and non-teaching space, operation activity and maintenance activity; in total 23 items. A qualitative analysis is conducted to identify the most satisfaction and dissatisfaction factors for the three schools from the perspective of the end-user on a basis of interview scores and comments. The interview result is shown in Figure 6.  
The real body (filled stick) is the lowest and highest score observed from the interviews, and the upper shadow and lower shadow (solid line) is the scope of satisfaction score (covered 95.44% possibility zone) that such items can reach based on statistical theory. It is concluded that storage and multi-purpose hall are the most dissatisfied factors from the end-user's perspective. Insufficient space for archiving and using for another purpose are the potential reason behind the dissatisfied factors from the comments. Also, good design for IT/communications space and a larger car park would be  
Figure 5: Reliability analysis for PPP schools in Australia case example at t = 9.  
Figure 6: Satisfaction analysis for PPP schools.  
welcome. In [PERSON]'s motivation theory [50], there are hygiene factors that will not encourage employees to work harder but they will cause them to become unmotivated if they are not present. In this case, it seems that storage space, multi-purpose hall, IT/communication and larger car parks are the hygiene factors, which no matter how well they are done will not enhance the end-user's satisfaction, while the absence of these will lead to dissatisfaction.  
On the other hand, the end-user is happy with canteen space, teaching preparation space, and the resources center. It is noted that teachers spend a lot of time in the teaching space provided and it can be seen that this might be a motivating factor to give them a sense of achievement, but increased student number is a major concern for future satisfaction. The motivating factors are those that will really influence customer satisfaction. Putting more effort into solving problems relating to motivating factors might be more useful in enhancing end-user satisfaction. Furthermore, the good relationship with facility manager on operation and maintenance is also an important reason for the principal and teachers' satisfaction. From satisfaction analysis, it is obvious that not enough space is consistently raised as an issue for PPP schools, especially if student numbers increase as envisaged. Many of these areas of dissatisfaction relate to scoping rather than performance.
## 3 Discussion
### Metabolite Identification and Biological Significance
#### 3.2.4 Limitations  
The results from this study agree with the literature examining metabolomic differences between PsA patients of varying disease activity, or that comparing PsA, psoriasis, and healthy individuals. However, this study is limited by several factors. Although all samples were sourced from patients suffering from PsA, the observed metabolome differences could be confounded by comorbidities, diet, and pharmacotherapy (Table 1). Some tentatively identified metabolites included unrelated drugs, food biomarkers, and metabolites associated with other diseases that could influence the expression of other metabolites. Comorbidities like hypertension, diabetes, and hyperlipidemia share common inflammatory pathways involving inflammatory cytokines such as tumor necrosis factor (TNF) [44]. Given these diseases share common pathways, they may also share common metabolite expressions that could confound the results of our machine learning. According to Tables S4 and S6, our method detected metabolites such as Hydroxyibuprofen, which is associated with Ibuprofen, a non-steroidal anti-inflammatory drug (NSAID). The presence of NSAIDsIDs and other drugs is known to alter the metabolomic profile of mouse serum, especially for lipid and fatty acid compound classes [45]. In addition, there was a very wide range in PASI scores for high disease activity, from approximately 10 to 55, whereas low and moderate PASI scores approximately span a range of 5. Although this is the general rule applied in a clinical setting, it is uncertain whether a patient with a PASI score of 10 and a PASI score of 55 will exhibit similar disease-related metabolomic markers. Ideally, a control group of psoriasis patients classified into three groups based on PASI and who do not have PsA would have helped us identify PsA-specific markers. However, we have not included such a comparison, which is a significant limitation.  
Another point of improvement for this study would be to use a multiple chromatographic methodology and SPME coatings. Although this study used 1:1 HLB:PS-DVB WAX, a coating that was evaluated to extract a wide range of compounds [46], using multiple specialized coatings may greatly expand the metabolome coverage for analysis. Similarly, this study used a chromatographic method developed for fatty acids, but use of other methods such as an HILIC method for more polar metabolites could also expand the metabolome coverage for analysis. This expanded coverage could uncover other classes of compounds that allow for insights into the disease state.
# Trajectory Classification and Recognition of Planar Mechanisms Based on ResNet18 Network
## 3 Selection of the Network  
The ResNet18 network is a type of deep convolutional neural network model, commonly used in computer vision, particularly for image classification [24]. The core idea of this network is the introduction of residual blocks, as shown in Figure 6, to address the issues of gradient vanishing and degradation in deep networks.  
When classifying the trajectories of the planar four-bar mechanism in regular and inverted configurations, multiple image classification networks are used for training. These networks include the AlexNet [25], VGG16 [26], and ResNet18 [27]. Finally, the model is selected based on its classification accuracy and performance. This approach not only ensures that the selected model has higher accuracy but also saves resources during the training process. The training set consists of 10,000 images for b  
Figure 6: Residual block diagram.  
configurations of the planar four-bar mechanism. The loss function is the mean-squared-error loss function, the learning rate is set to 0.001, the optimizer used is the Adam optimizer, the training epoch is 100, and the batch size is set to 8. Using these three different networks, three models are trained, and the training errors are shown in Figure 7. The obtained models are then tested on a test set containing 500 images for both regular and inverted configurations of the planar four-bar mechanism. The results are shown in Table 1.  
From Figure 7, it can be observed that with 100 training epochs, the learning training time is shorter and memory usage is lower. Therefore, for the classification of trajectories in other planar mechanisms, the ResNet18 network is chosen for model construction.
## 1 Introduction
As it was reported previously [12], the gratings with greater symmetry may increase the efficiency of the wavefront sampling. A higher degree of periodicity of the grating structures improves the quality of the final image, thus, it is necessary to remove, or at least reduce to the minimum, the number of irregularities in the grating pattern. Another point to draw attention to is the profile of the grating structures. Due to the limitations imposed by the fabrication technique in use, usually, it is not a rectangular function. A possible slope of the grating sidewalls can introduce different absorption levels resulting in a blurring of the image, additional to the blurring by the X-ray tube focal spot size and point spread function of the camera. Thus, the improvement of gratings periodicity and shape can enhance the quality of the final image and possibly diminish the requirements on the X-ray tube and camera. To evaluate the performance of the two-dimensional grating at the specific X-ray energy, one needs to study the wavefront sampling induced by it. The efficiency of wavefront modulation is a characteristic that directly influences the contrast of the image acquired by the detector. Here, by the efficiency of wavefront modulation (WFM), we mean the intensity variation between opaque and transparent grating areas as recorded by the camera. This can be expressed similarly to the visibility characteristic used in the interferometric methods [18]:  
\[\text{WFM}=\frac{I_{t}-I_{op}}{I_{t}+I_{op}} \tag{1}\]  
where \(I_{t}\) is the intensity, transmitted by the transparent structure, and \(I_{op}\) is the intensity, transmitted by the opaque structure. It is important to note that the values of \(I_{t}\) and \(I_{op}\) vary significantly with X-ray energy, and the same grating will perform differently at the different radiation energy. Moreover, as conventional sources, such as X-ray tubes, are not monochromatic and emit energy in a certain spectrum, the ideal case of \(\text{WFM}=1\) is not possible to attain in reality. Single-shot X-ray imaging has been demonstrated even with \(\text{WFM}<0.5\)[4; 15]; however, increasing the efficiency of wavefront sampling can enhance the quality of the final image. WFM, being of relative value, can serve as a benchmarking measure for a particular grating to be used in a setup with the defined source and detector. To explore the possibilities of the single-shot imaging approach, one should achieve the highest wavefront modulation, keeping a high degree of periodicity for the gratings in use. In this regard, the objective of this work is to develop high-quality gratings of various periods to be used for customized materials science and medical applications at energies up to 25 keV.
# Example usage calculate_urban_resilience('input_file_name')
## 7 Validation  
The RIACT process delineates a robust methodology to elevate urban resilience, harnessing risk-informed, asset-centric decision-making. This innovative framework extends across multiple scales, from continental to municipal, anchored by the principles of ISO 31000. The process proactively embeds a stratified risk and asset analysis within its fabric, fostering an adaptive urban milieu resilient to both natural and man-made perturbations. The process' iterative core mandates frequent reassessments, facilitating alignment with the evolving landscape of urban risks.  
The efficacy of the RIACT methodology is substantiated through the meticulous assessment of earthquake hazards in Portugal, serving as a testament to its robustness. While the entropy weighting method effectively harmonizes diverse datasets, including earthquake, demographic, and socioeconomic dimensions, the process' versatility allows for alternative techniques such as the random forest classifier to be employed when confronting multi-faceted variables of phenomena like flooding. The entropy weighting method, employed in this case study, systematically mitigates subjective biases by anchoring the analysis to a data-driven foundation. This approach ensures that the process' framework and the analytical procedures maintain a transparent, replicable nature, thus bolstering the RIACT process as a scientifically grounded framework that propels evidence-based urban planning and policymaking.  
In practical deployment, the RIACT process revealed stark regional disparities in earthquake risk across multiple Portuguese municipalities. The entropy weighting algorithm's precise application to standardized data facilitated the development of nuanced, locale-specific resilience strategies. Vital to this endeavour is stakeholder engagement, which infuses data analysis with localized insights, rendering the resulting urban resilience strategies both relevant and holistic. This iterative loop of ongoing scrutiny, refinement, and adaptation injects a dynamic aspect into the process, ensuring that urban resilience strategies mature congruently with unfolding data and revelations, enhancing their applicability and potency in tangible settings.