esa-sceva/satcom-chunk-collection huggingface dataset:
# Glycerol Steam Reforming in a Pilot Plant: Effect of Temperature and Catalyst Load
## 3 Results and Discussion
### Catalytic Glycerol Steam Reforming at Different Temperatures and Catalyst Concentrations
Figure 8: Comparison of outlet gas flow (**a**) and hydrogen composition (**b**) during glycerol steam reforming at different catalyst concentrations and temperatures (reaction conditions: water to glycerol ratio = 0.7, inlet flow = 40 mL/min). Figure 7: Gas composition over time with different catalyst (NiO) additions: (**a**) 12% (\(w/w\)); (**b**) 16% (\(w/w\)); (**c**) 20% (\(w/w\)). The rest of the operating conditions were common: water to glycerol ratio = 0.7, inlet flow = 40 mL/min, temperature = 750 \({}^{\circ}\)C.  
Comparing the power generation and LHV, according to Figure 9, the test with the highest catalyst content and temperature had the highest LHV (15.97 kJ/m\({}^{3}\)N), due to the lower H\({}_{2}\) content but a higher percentage of hydrocarbons (C\({}_{2}\)H\({}_{4}\) and C\({}_{2}\)H\({}_{6}\)) and CO, which have a strong influence on the LHV. Nevertheless, tests carried out with 12% NiO and 850 \({}^{\circ}\)C offered acceptable results regarding the LHV (14.84 kJ/m\({}^{3}\)N), and the gas composition was kept constant and at higher percentages compared to NiO 16%, whose composition is richer in CO\({}_{2}\), a compound that does not have an influence on the LHV. Consequently, NiO 12% had the highest power generation, observing a strong influence of the outlet gas flow on this parameter. Thus, this catalyst proportion and temperature were selected for further experiments and analysis (considering the optimum reaction conditions as the following: W/G = 0.7; inlet flow = 40 mL/min; oven temperature = 850 \({}^{\circ}\)C; NiO concentration = 12%). It should be noted that similar studies with Ni/CaO-Al\({}_{2}\)O\({}_{3}\) at different Ni loadings (from 2.5 to 20% wt) offered an optimum Ni loading of 15%, with a high glycerol conversion (99.4% at 700 \({}^{\circ}\)C) [46]. In addition, other works offered a high glycerol conversion and yields (83 and 85%, respectively) through the catalytic steam reforming of glycerol over an LDH-derived Ni-Al nanosheet [42].
## 3 Results and Discussion
### Demo User Feedback  
The feedback from the two demo users, Nalcor/NL Hydro and the Water Resources Management Division in the Department of Municipal Affairs and Environment (WRMD) of the Governmentof Newfoundland and Labrador, confirmed the significant importance of snow and run-off monitoring, which could be improved by using such a combined approach like the SnowSense service. They underlined that this is especially important for remote locations, as many regions in Newfoundland are only accessible by helicopter or snowmobiles and face limited access possibilities due to poor weather conditions. From their experience, standard in situ measurements sometimes fail, as the snowpack is often icy with a varying snow density and snow hardness, which makes it difficult to measure SWE with manual snow core equipment. Normally, manual snow surveys are performed three times per winter, which is expensive and is difficult to be completed under bad weather conditions, and causes labour safety risks. Existing automatic instrumentation (GMON sensors) is not providing enough information. The demo users state a need for accurate, reliable SWE information for each hydrological watershed and runoff forecasting that affects the real-time scheduling of hydrological assets and minimizes the use of thermal heat generation. From their point of view, a network of stations across the island of Newfoundland and Labrador would be the ideal scenario to give a province wide estimate of SWE for all users. In addition, they confirmed that SnowSense has the potential to fill a gap regarding the provision of spatial and temporal data at a high resolution by applying EO and modelling for spatial SWE maps of the entire region and run-off information at specific points of interest for real-time and forecasts. The received products matched with other sources of information, which they had for comparison and could even provide insights in hydrological processes. Both users stated that the in situ stations are competitive in their operation compared to other SWE monitoring technologies and therefore they have the potential to replace existing SWE monitoring stations like snow pillows or manual field observations.  
In general, the feedback provided by the demo users was very positive, which encourages us in our further developments and improvements.
## 3 Results and Discussion  
### PMC for Algae and Organic Matters Removal, Lab-Scale  
Figure 2 shows the removal efficiency of algae and turbidity as a function of varied PMC-based coagulant dose from 0 to 70 mg/L at 22 to 25 \({}^{\circ}\)C. Overall, the degradation tendencies of algae and turbidity were similar. The removal efficiencies of both algae and turbidity tended to increase with increasing PMC dose up to 20 mg/L, with further increase in the PMC dose decreasing the algae and turbidity removal efficiency. The highest removal of algae was found to be 98% at 20 mg/L PMC. For the case of turbidity, the removal efficiency was around 94% at 20 mg/L PMC, but it was decreased to 83.4% at 70 mg/L PMC. It was implied that particles repelled each other due to the strong electrostatic repulsion forces caused by adsorbed polycations, as reported earlier in [22]. The algae removal efficiencies achieved with the PMC were approximately 10 to 19% higher than those reported in previous work using Al-based [2].  
Figure 3 shows the removal efficiency of UV\({}_{254}\) and DOC as a function of initial dose of the PMC. UV\({}_{254}\) and DOC are parameters for monitoring the dissolved organic matter (DOM) removal and controlling coagulant doses in water supply systems for the removal of TOC [23]. The initial UV\({}_{254}\) and initial concentration of DOC were 0.034 cm\({}^{-1}\) and 3.4 mg/L, respectively.  
In the coagulation process, the removal efficiency of both parameters, i.e., UV\({}_{254}\) and DOC, increased with increasing PMC dose (Figure 3). At 20 mg/L PMC, the UV\({}_{254}\) and DOC removals were found to be 51% and 14%, respectively. The higher UV\({}_{254}\) removal efficiencies than the DOC removal indicates that hydrophobic and large aromatic compounds were preferentially removed via the coagulation process [24]. [PERSON] et al. [25] also reported that small aliphatic compounds do not absorb UV light because they lack conjugated double bonds, and therefore are not detected through UV\({}_{254}\) measurements. This finding is in agreement with those of previous studies [25; 26; 27].  
Figure 2: Concentration of chlorophyll-a and residual turbidity after removal with varying doses of the PMC-based coagulant (0, 5, 10, 20, 40, and 70 mg/L).
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 4 Methodology
### On-Manifold Adversarial Examples  
An on-manifold adversarial example (denoted by adv-on) \(x^{\prime}\) is a perturbed version of a natural sample \(x\) that causes misclassification of the classifier, as discussed in Section 3.2. Unlike a regular adversarial example, \(x^{\prime}\) resides on the data manifold--in this sense, we can consider \(x^{\prime}\) as a natural example with noise large enough to induce incorrect classification. Adv-on's can be formally defined as follows [21]:  
**Definition 1** (On-Manifold Adversarial Example): _Suppose that \(P\) is the probability distribution of data points \((x,y)\) where \(x\in\mathbb{R}^{d}\) is an input and \(y\in\{1,2,\ldots,K\}\) is the corresponding true label. For a trained target classifier \(f:\mathbb{R}^{d}\rightarrow\{1,2,\ldots,K\}\), an on-manifold adversarial example \(x^{\prime}\) of \(x\) satisfies \(f(x^{\prime})\
eq y\) and \(P(Y=y|X=x^{\prime})>P(Y=y^{\prime}|X=x^{\prime})\) for all \(y^{\prime}\in\{1,2,\ldots,K\}\setminus\{y\}\)._  
The definition requires that the adv-on \(x^{\prime}\) induces misclassification of the classifier \(f\), but it still belongs to the original class \(y\) with respect to the data distribution \(P\). Despite the clearness of the definition, it is not straightforward to apply it for the creation of adv-on's since the data distribution \(P(X,Y)\) is typically unknown.  
To circumvent the issue, we make use of the variational auto-encoder (VAE) [50] trained on natural samples. In short, the idea is to add perturbations in the latent space and then to use the decoder to generate realistic on-manifold adversarial examples. We describe our method in two cases. In the first case, we know \(P(Y|Z)\) where \(Y\in\{1,2,\ldots,K\}\) is the class label and \(Z\) is the random variable describing the latent space of the VAE. The second case is for real datasets where we have no access to \(P(Y|Z)\), and therefore we estimate it by an auxiliary classifier. The overall process is depicted in Figure 1.
# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 3 Text Similarity Methods  
### Exact and Approximate Text Matching Scoring Algorithm [3]  
The initial approach for evaluating short-answer questions relied on matching the student's response with one to four teacher-provided reference answers. The scoring was performed on a continuous scale from 0 to 5, based on exact and approximate matching of the responses. Each answer was treated as a set of words, and numbers and acronyms were handled, especially, using a Polish dictionary with declensions [50]. The algorithm followed three main phases:  
1. Preprocessing: Removing punctuation, converting text to lowercase, splitting sentences into words, and lemmatizing these words.
2. Special handling: Treating acronyms and numbers using dictionary lookups or Levenshtein distance for nearest matches.
3. Scoring: Matching words between the student's and reference answers and calculating the similarity score.  
The pseudocode for this method (Split algorithm) involved iterating through reference answers and computing a proportional score based on matched words. While this method performed well in basic scenarios, it struggled with longer student answers compared to shorter reference answers.  
The whole algorithm with exact matching, named the Split algorithm, is displayed in Listing 1. Pseudocode of scoring algorithm with an exact match (Split algorithm).
## 3 Applications
### Light Modulators
#### 3.2.2 Meta-Surfaces  
Metasurfaces or two-dimensional metamaterials where light does not typically penetrate through the surfaces have been demonstrated to have usefulness in lenses, hologram and polarization control [92; 93]. However, optical loss is a common theme for all those devices, due to the metal elements in conventional metasurface devices. Another limitation is that conventional metasurfaces-based devices are not cost-effective since once a device is made, it only works for a certain functionality and wavelength. Therefore, there has been considerable interest recently in developing tunable all-dielectric metasurfaces for light modulation applications by using the phase change materials [94; 95; 96; 97] instead of metallic elements used in earlier works.
## 3 Calculation and Results
### Analysis of Stray Light
After setting the material properties and surface properties of the optomechanical structure, perform ray tracing on the optomechanical model to check whether it conforms to the design scheme of the optical system. Set the incident angle of light parallel to the optical axis. After passing through the optomechanical model, the main purpose is to determine whether the parallel light converges on the center of the imaging surface. Therefore, there is no need to set an excessive number of rays, and the light threshold does not need to be too small. The ray tracing of the \(0\,^{\circ}\) field of view is shown in Figure 9, which verifies the correctness of optomechanical modeling. Figure 8: Ray tracing model. Figure 9: \(0^{\circ}\) field of view ray tracing.
## 7 Evaluation
### Limitations  
The main purpose of the proposed method is to facilitate business process modeling by automating tasks that need to be executed between creating a specification of the process and obtaining the final model. However, modeling a business process will still require manual actions of different stakeholders of the process, i.e., process owners or process analysts, such as identifying tasks and participants, as well as defining the desired final outcome.  
In order to be commonly used in practice, such a method needs to be versatile enough not only by managing cases of high complexity, as described in Section 7.2, but also by assuring the support of commonly-used elements of the selected notation.  
Since our approach consists of multiple steps based on different algorithms, the support of modeling elements is limited in multiple phases, which include:  
* creating the spreadsheet specification (see Section 4),
* generating a synthetic workflow log (see Section 5),
* composing the final model (see Section 6).  
Therefore, if there is a need to include aBPMN element in the method, it should be supported by all three main stages of the process composition. In 2008, [PERSON] and [PERSON] [80] analyzed a set of business process models and ranked 50 differentBPMN 1.0 elements in terms of frequency distribution. Although the survey did not cover the most recent business cases, it is still considered valid in terms of element usage inBPMN process models [81]. Their results show that over 75% of the constructs were present in less than 30% of diagrams used in consulting engagements. Table 10 shows the current support of our approach for the 12 most frequently-occurringBPMN elements.  
In the presented approach, we do not explicitly support the multi-instance or loops markers for tasks. Our approach supports loops modeled in an explicit way using the sequence flow. A short loop containing a single task can be later refined into a task with a loop marker. However, such process refactoring is out of the scope of this paper.  
Figure 11: Comparison of applying different methods forBPMN diagram generation.
# Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks
## 3 Mechanical Design
### Actuation Unit  
The cable actuation unit is a servo-actuated winch where both the motor and the drum translate along a linear guide during the motor rotation (Figure 9). As described in Section 2.1, this design choice limits the overall machine cost, at the price of poorer actuator dynamic performances: given the quasi-static requirement for the robot operation, this design choice was deemed the best. In addition, to further limit the robot cost, most components were produced by Fused Deposition Modeling (FDM) technology using PETG as base material. The choice of a rotary actuation, instead of a linear one, is supported by the project requirements reported in Section 3. In particular a rotary actuator allows for easy scalability in case of wide-workspace applications and field deployability.  
A double-helicoidal-groove aluminum drum (3) is rigidly connected to the shaft of a Wantai (57 HBM20-1000, 2.1 Nm holding torque) Nema 23 servo-stepper motor (1) equipped with an incremental encoder (\(\approx\)0.1\({}^{\circ}\) resolution). Drum dimensions were chosen in order to coil 2.2 m of a 0.51 mm diameter dyneema cable (DAIWA j-braid X8, 550 N breaking tension), and limit (for the given motor) the realizable maximum tension to 160 N (cable tension safety factor of \(\approx\)3.5). Thus, drum diameter was selected as 27 mm, and groove pitch as 2 mm. A brass nut (4) is rigidly centered to the drum and coupled to a trapezoidal screw shaft (5), which is clamped at one end to the actuation unit frame, in a support element (6). While the motor rotates, the drum-motor system, fixed to a carriage (11) using two brackets (2), moves on a linear guide (9) made by two circular rods and linear ball  
Figure 8: Reconfigurable distal anchor points on the mobile platform: (1) swivel pulley, (2) slots for the translation of the swivel pulley, (3) platform support. (**a**) Standard layout. (**b**) Crossed layout.  
Figure 9: 3D model of the winch: (1) servo-stepper motor, (2) brackets, (3) aluminum drum, (4) brass nut, (5) screw shaft, (6) support element, (7) base plates, (8) end-stop switches, (9) circular rods, (10) cable guidance slot, (11) carriage.  
bearings. The drum and the shaft have the same pitch, so the cable portions exiting the drum always have a fixed direction and position with respect to the winch frame. Two end-stop switches (8) are mounted at the physical limits of the drum-motor system, to provide a safety-stop signal to the motor controller. The winch frame is ultimately fixed to the overall robot frame with bolted connections via two plates (7).  
This winch houses one of the four cables. One end of the cable is fixed to the drum employing a screw. Then, the cable is coiled onto the first start of the drum and conveyed to the guidance system through a thin slot (10) on the carriage (11). After being guided to the distal anchor point on the platform (as detailed in Section 3.2), the cable returns to the drum through the slot (10) and is then wound onto the other start of the drum and secured with another screw.
## 2 Experimental Section
### Spme  
SPME is less amenable to _in situ_ sampling due to the difficulties associated with sealing the fibre from external contamination during transport and storage. The SPME fibres are fragile and can be damaged in the field (_i.e._, for analyzing soil _in situ_), particularly when sampling in adverse conditions. Field portable SPME devices are available but were not cost efficient based on the number of daily replicates required for the study. Field or mobile laboratory access is not always immediately available meaning that VOC extraction must be performed back in the laboratory. Ex situ sampling was conducted on approximately 1 g of experimental and control soils collected from each site. Soils were collected in an airtight 20 mL headspace vial and sealed with a magnetic screw cap containing a polytetrafluoroethylene/silicone septum of 1.3 mm thickness.  
Ideally, SPME sorbent phase would be identical to sorbent tubes. However, commercially available sorbents between the two techniques are not the same and therefore SPME fibre selection needed to be performed. SPME fibre choice was assessed using reference decomposition soil in the lab prior to analysis of samples. Fibres tested were Polydimethylsiloxane (PDMS), PDMS/Divinylbenzene (PDMS/DVB), and PDMS/DVB/Carboxen (PDMS/DVB/CAR). A PDMS/DVB fibre (65 \(\upmu\)m, 24 ga, Supelco) was chosen based on literature examples [1,2,12] and lab confirmation which allowed for the widest possible range of compounds to be collected in test samples. While the sorbents used with each collection technique differed, this was thought to represent how these techniques have been used in previous literature.  
The fibre was exposed in each headspace vial for 20 min at 40 \({}^{\circ}\)C. Increasing the length of exposure beyond 20 min provided no additional benefit. Heating the vials to 40 \({}^{\circ}\)C enhanced the release of volatiles that naturally adhere to soil yet maintained a temperature within range of what cadaver dog, handheld devices, or portable instrumentation could reasonably be exposed to in practice. Prior to exposure, each headspace vial was injected with 5 \(\upmu\)L of 20 ng/\(\upmu\)L bromobenzene (GC grade, Sigma Aldrich) in methanol (HPLC grade, Sigma Aldrich). Vials were placed in a dry bath heating block during exposure which allowed for controlled and uniform heating of the soil sample. Fibres were desorbed for 5 min in the inlet of the gas chromatograph at 150 \({}^{\circ}\)C using a 0.75 mm I.D. SPME injection sleeve in splitless mode.
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 3 Optimal Resonant Frequency Band is Established Based on the EWT
### Fault Feature Extraction Based on the AEWT  
When the \(i\)-th mode corresponding to the highest value in the AASN is selected as the center of fault information, there may be a case where the demodulation band is relatively narrow, so it is necessary to consider whether to merge adjacent modes. In this paper, the correlation coefficient is used as the index to judge the merger. According to practical experience, if the adjacent mode is greater than 0.25, it should be merged with the central mode [32]. The framework of the proposed method is illustrated in Figure 1.  
For the detection of periodic fault features, the following steps are given to apply an AASN to the EWT (AEWT) and construct an optimal resonance frequency band in a completely data-driven way:  
Step 1: Fourier spectrum \(X(f)\) is obtained by performing the DFT on the input signal \(x(t)\).  
Step 2: Apply a parameterless clustering scale-space histogram segmentation boundary to \(X(f)\).  
Step 3: Application EWT is used to decompose the input signal into a set of empirical modes.  
Step 4: Calculate the ASN and correlation coefficient values on each mode.  
Step 5: The AASN is calculated according to Equation (15). The mode corresponding to the highest value is selected as the center band.  
Step 6: Determine whether the correlation coefficient \(\rho_{X,Y}\) of the adjacent mode is greater than 0.25, and if the requirement is met, the reconstruction signal is composed.  
Step 7: Fault types are diagnosed by observing Hilbert envelope spectrum.  
Figure 1: Flow chart of the overall procedure of AEWT.
## 4 Results and Discussion
### Results of Dust Removal Efficiency  
#### 4.2.1 Relationship between Liquid-Gas Ratio and Dust Removal Efficiency  
Keeping the velocity of dust airflow at 14.0 m/s, the relationships between different liquid-gas ratios and dust removal efficiency and resistance of SCDC are studied. The results are shown in Figure 11.  
With the increase of the liquid-gas ratio from 0.05 L/m\({}^{3}\) to 0.15 L/m\({}^{3}\), the total dust removal efficiency and the respirable dust removal efficiency of SCDC are both improved, with the latter increasing from 85.28% to 99.35%. Therefore, the increase of liquid-gas ratio is very significant for the dust removal efficiency of respirable dust. As shown in Figure 11, the total dust removal efficiency is mainly not increased when the liquid-gas ratio is greater than 0.15 L/m\({}^{3}\). However, the respirable dust  
Figure 11: Relationship between liquid–gas ratio and dust removal efficiency.  
removal efficiency is decreased significantly. Thus, it can be concluded that the optimum liquid-gas ratio of a swirling curtain dust collector is 0.15 L/m\({}^{3}\).
## 3 Case Study
### Data Standardization and Preprocessing  
The Pearson correlation coefficient is used to analyze the correlation between the shield tunneling construction parameters, as shown in Figure 9. According to the classification provided by [PERSON] (2018), correlations are categorized as very weak (0 to 0.2), weak (0.2 to 0.4), moderate (0.4 to 0.6), and strong (0.6 to 0.8). Notably, there is a strong correlation between HDST and HDSH (0.75) and a very weak correlation between HDSH and VDSH (0.04), indicating that shield attitude correlations are stronger within the same category of horizontal shield attitude. The correlation between vertical shield attitude parameters (VDST and VDSH) is weak (0.35), which is lower than that of the horizontal shield attitude parameters (HDST and HDSH). The possible reason for this is that the shield machine traverses a soft upper and hard lower stratum, resulting in more complexity in the vertical attitude compared to the horizontal attitude. In addition, it is noted that the total thrust shows a moderate correlation with the oil pressure in all six divisions (A#, B#, C#, D#, E#, F#), with similar correlation values ranging from 0.48 to 0.53. This is because the total thrust is provided by the total oil pressure in each division.