esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contact-Angle Measurements  
It was demonstrated that the wettability is an important property of the coating because it can have an important impact on the coating durability during its use [36; 37]. Furthermore, it was proven that a hydrophobic coating can be achieved by using a low-surface-energy material to modify a rough surface [38].  
The wettability of the uncoated PVC substrate and of the PVC substrates coated with the sol-gel silica materials was characterized by the measurement of static contact angles against water. The values of water contact angles and profiles of water droplets on the uncoated PVC substrate and coated PVC substrates are presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Film Thickness (nm)** & **Roughness (nm)** & **MSE** \\ \hline A & 426.96 & 2.90 & 0.89 \\ \hline B & 513.91 & 6.63 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The values of thickness, roughness, and MSE of the PVC substrates coated with the sol–gel silica materials, obtained by SE.  
Analyzing Figure 6, it can be observed that the surface modification with silica materials led to an increase in the contact angle from 83\({}^{\circ}\)\(\pm\) 1.4\({}^{\circ}\) for uncoated PVC substrate to 92\({}^{\circ}\)\(\pm\) 1.2\({}^{\circ}\) and 94\({}^{\circ}\)\(\pm\) 1.7\({}^{\circ}\) for PVC substrates coated with the sol-gel silica materials. It appears that the silica materials effectively raised the contact angle of the prepared coatings. Specifically, the transition of surface property was changed from hydrophilic (uncoated PVC) to hydrophobic (PVC coated with the sol-gel silica materials). It's obvious that the silica became less hydrophilic after functionalizing the silane precursors because the partial hydrophilic sites were replaced by hydrophobic organic groups (octyl or hexadecyl). This phenomenon is consistent with previous data reported in literature showing that long alkyl groups facilitate the formation of a more ordered structure and of lower-surface-energy material [39].  
In previous papers [40; 41; 42; 43], it was demonstrated that the incorporation of nonpolar groups (e.g., methyl, trimethylsilyl (-Si(CH\({}_{3}\))\({}_{3}\))) into the coating materials can prevent the collapsing of pores throughout solvent evaporation and lower the surface energy of the coatings. In this way, extra functionalities are provided, such as high surface area, good antireflective property, and hydrophobic character.
###### Abstract  
A 25.78-Gbit/s \(\times\) 4-ch active optical cable (AOC) with an ultra-compact form factor is proposed. The size of the proposed AOC is 5.2 cm\({}^{3}\), which is 55% smaller than the standard form factor of Quad Small Form-factor Pluggable (QSFP28), and 45% smaller than that of Micro Quad Small Form-factor Pluggable (uQSFP). As a result of utilizing a high-efficiency heat-dissipation structure and optimizing signal transmission lines and ground vias, the proposed AOC achieves high-heat dissipation and low-crosstalk characteristics. Furthermore, the proposed AOC demonstrated 25.78-Gbit/s error-free optical transmission over a 100-m Optical Multimode 3 (OM3) multimode fiber under all-channels (4-ch) operation and case temperature (\(T_{\rm c}\)) of 70 \({}^{\circ}\)C.  
Keywords:optical interconnect; active optical cable; compact form factor; high-heat dissipation structure; low crosstalk characteristics +
Footnote †: journal: Applied Physics Letters
## 4 Results
### Qualitative Data Analysis
#### 4.3.6. Ease of Use of Implemented Interactions  
The data in Figure 11a show that participants were generally satisfied with the ease of retrieving 3D objects at all sublevels of the first level, with a focus on the sublevels with shrunken objects and reduced contrast, where 81.14% and 69.48% of participants rated their subjective feeling with scores of 4 and 5, respectively.  
At the second level (Figure 11b), most participants at the level without distractors (50.95%) gave ratings of 4 and 5, indicating that most participants felt they could enlarge the 3D object with ease. The lower grades, 1 and 2, received a smaller number of votes, 33.96%, indicating that a smaller number of participants encountered difficulties when enlarging the 3D object. At the level with distractors, grades 4 and 5 were also prevalent at 49.05%, while the prevalence of grade 3 was slightly higher, suggesting that participants encountered greater challenges due to the presence of distracting 3D elements.  
The analysis of participants' subjective impressions of the ease of rotating the 3D object in Figure 11c shows that most participants found the implemented interaction easy to perform. Most participants (41.51%) gave the highest score of 5 for direct interaction, which means that the tasks were easy to perform with a combination of interaction. In the indirect interaction sublevel, where a slider was used to rotate the 3D object, 77.36% of participants gave a score of 5, indicating that using the slider as a graphical interface element was even more intuitive and made it easier for participants to interact with the 3D object.  
Figure 10. Participants’ satisfaction with the degree of precision achieved during: (**a**) 3D object retrieval; (**b**) resizing a 3D object; (**c**) rotating a 3D object; (**d**) pressing 3D button with a virtual finger.  
When analyzing the participants' perception of the button press in three different sublevels (Figure 11d), a decrease in satisfaction can be observed with a decrease in button size. The sublevel with horizontal orientation and the largest buttons has the highest number of positive ratings. This is followed by a moderate decrease in the sublevel with a vertical orientation. The sublevel with a distracting background has similar results than the sublevel with a vertical orientation, although it has the smallest buttons, which is probably due to the buttons being much closer to the virtual hand. Nevertheless, it is clear that the size of the button plays a role in the efficiency of the interaction.
## 4 Discussion
#### Limitations  
There are some limitations that should be recognized in this study. First, our sample size estimation was based on a previous study which did not reported the sample size calculation. Therefore, this sample and our results could not be representative. Second, although we considered a large number of internal and external variables that could bias the measurements, further studies should consider features which were not considered for this study (e.g., homogeneous weight, BMI, and foot size) and other important covariates in the maintenance of balance and postural control (e.g., lower limb strength and fat mass/lean mass percentage).
###### Abstract  
The increase in emissions of toxic gasses such as hydrogen sulfide (H\({}_{2}\)S) and carbon dioxide (CO\({}_{2}\)), resulting from growing urbanization and industrialization, has caused environmental and public health problems, making the implementation of air purification techniques through adsorption important. Thus, modeling the gas adsorption process is fundamental for good agreement with experimental data, employing mathematical models that enable the prediction of adsorption capacity. In this way, the present work aimed to compare different analytical breakthrough curve models ([PERSON], Yoon-Nelson, Adams-Bohart, and [PERSON]) for the adsorption of H\({}_{2}\)S and CO\({}_{2}\) in fixed-bed columns, using experimental data from the literature, estimating the curve parameters through the Markov Chain Monte Carlo (MCMC) method with the Metropolis-Hastings algorithm, and ranking using the determination coefficients (R\({}^{2}\) and R\({}^{2}_{\text{`Adjusted$}}\)) and the Bayesian Information Criterion (BIC). The models showed better agreement using the estimation of maximum adsorption capacity (q\({}_{\text{v}}\), N\({}_{0}\)) and the constants of each model (k\({}_{\text{th}}\), k\({}_{\text{yn}}\), and k\({}_{\text{ba}}\)). In the adsorption of H\({}_{2}\)S, the Yan model stood out for its precision in estimating q\({}_{\text{q}}\). For the adsorption of CO\({}_{2}\) the Adams-Bohart model achieved better results with the estimation of N\({}_{0}\), along with the Yoon-Nelson model. Furthermore, the use of this method allows for a reduction in computational effort compared to models based on complex differential equations.  
analytical models; fixed bed; gas purification; Bayesian statistics +
Footnote †: journal: Applied sciences
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 2 Related Work
### Propagation-Based Recommendation Methods
#### 2.3.2 Item Representation-Refinement Approaches  
The item representation-refinement approaches refine item representations by aggregating the embeddings of items' multi-hop neighbors, and usually adopt graph neural network architectures suitable for the embedding propagation process [25; 26]. For example, [PERSON] et al. [5] proposed the knowledge graph convolutional networks (KGCN) framework, which uses a graph convolutional network [26; 27] to iteratively aggregate the embeddings of neighboring entities to refine item representations. The authors [7] further proposed the knowledge-aware graph neural networks with label smoothness regularization (KGNN-LS) framework, in which a label-smoothness mechanism is added to the KGCN framework to propagate user-interaction labels, in order to simultaneously capture the semantic relations between entities as well as the users' personalized preferences for items, so as to provide effective recommendations. The gated knowledge graph neural networks for top-N recommendation system (GKGNN) method, proposed by [PERSON] et al. [28], refines item representations by selecting more relevant neighboring entities in the process of aggregating neighboring item embeddings. The knowledge-aware conditional attention networks (KCAN) method, proposed by [PERSON] et al. [29], condenses and refines the knowledge graph according to a given user, thus retaining useful information and refining item representations. The differentiable sampling on knowledge graph for recommendation with relational GNN (DSKReG) method proposed by [PERSON] et al. [30] adopts a differentiable sampling strategy, and uses the item categories and the relations between item entities to obtain the items related to the recommendations, thereby improving the item representations. However, this type of method only exploits the knowledge in knowledge graphs to learn item embeddings without leveraging the user-item interaction information.
## 4 Discussion  
This study examined the test-retest and the MDC of the 1 RM maximal strength test and muscle power test in upper and lower limbs. Knee extension is involved in most daily activities and in sports, moreover, quadriceps is the strongest and most voluminous extensor muscle of the knee. SBP involves powerful and important muscles for the motor activities of the upper extremities. In addition, the performance of both exercises is easy and safe. Our data showed that repeatability of 1 RM evaluation both in LE and SBP exercises analyzed by test-retest was excellent, ranging from 0.876 to 0.951 for the LE and from 0.918 to 0.985 for the SBP. Studies on the test-retest reliability of 1 RM in children under 16 years of age are few. In one such test, [PERSON] et al. [39] reported an ICC of 0.98 for the LE exercise and an ICC of 0.93 for the bench press exercise in a sample of 24 children of 10 years of age. In a recent systematic review analyzing 32 articles in populations aged 16 to 85 years, the ICC ranged from 0.74 to 0.99, and in the exercise of SBP from 0.96 to 0.99 [32]. The repeatability determined for the current evaluation of the power at 60% of 1 RM in the LE exercise ranged from \"fair to good reliability\" to \"excellent reliability\" (ICC: 0.651-0.853). This was lower than that shown by the SBP; although qualitatively in the same range, the SBP ICC in two of the groups was higher than 0.910 and was 0.663 only in the youngest group. Studies analyzing the repeatability of muscle power in children are limited and, on most occasions, measure the repeatability of jump ability as an indirect estimate of muscle power, either alone or in the context of other physical tests [40; 41]. The study of [PERSON] et al. [29], which measured power in a leg press exercise with loads at different percentages of the proper body weight in 36 males (11-15 years old), found an ICC between 0.97 and 0.99. In this field, few studies exist in which the MDC of 1 RM test-retest repeatability is made explicit. One studied 21 healthy children aged 5 to 12 years to determine the repeatability of strength and power of knee flexors and knee extensors in an isokinetic device. Although the authors did not present the value of MDC%, using the values provided for the knee extensors, we calculated that the MDC% was 28% for the peak torque and 36.4% for the power, i.e., higher values than those found by our study [27]. The 1 RM agreement limits of LE and SBP were uniform, regardless of the children's maximum strength, and were within a range of 25 kg in LE and 14.9 kg in SBP.
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 3 Materials and Methods  
In work presented within this paper, we rely solely on invoice-level data, which can reasonably be expected to exist in virtually any company regardless of its industry or geographical region of operations. The motivation for this is twofold: one, it is our desire to make this approach as widely applicable as possible by using the smallest common denominator in terms of data, without taking into account any information which might be domain or business-specific (which is in line with [9]) and two, relevant research has shown repeatedly that Length, Recency, Frequency, and Monetary (LRFM) features, which are easily derived from invoice-level data alone, often represent the most important features in predictive churn models [38; 39; 40]. In the remainder of this section, we describe the raw data we obtained for the empirical study, the approach we take to derive the training and test sets from it, the algorithms we leverage to create predictive churn models, and the metrics we use to evaluate them.
## 2 Materials and Methods
### Experimental Set-Up  
#### 2.2.1 Optimization Assays  
The new generation of BA\(\mu\)E devices were prepared \"in house\" by using nylon supports having bar-shaped geometry (7.5 mm in length and 1.0 mm in diameter) and an adhesive film coated with the powdered sorbents, previously cleaned with ultra-pure water. The detailed description of the devices manufacturing can be consulted in previous reports [3; 5]. For the optimization assays, 25 mL of ultra-pure water spiked with the working standard solution (8.0 \(\mu\)g L\({}^{-1}\) for PBS and 40.0 \(\mu\)g L\({}^{-1}\) for BZ4), a BA\(\mu\)E device, and a conventional Teflon magnetic bar were introduced into glass sampling flasks. The microextraction stage was performed in a univariate optimization strategy using a multipoint agitation plate (Variomag H + P Labortechnik AG Multipoint 15, Germany) at room temperature (25 \({}^{\circ}\)C), under the floating sampling technology mode. Parameters such as stirring rate (750, 1000, and 1250 rpm), equilibrium time (1, 2, 4, 6, and 16 h), matrix pH (HCl/NaOH; 1.0, 2.0, 4.0, 6.0, and 8.0), organic modifier (MeOH; 0, 5, 10, and 15%, \(\
u\)/\(\
u\)), and ionic strength (NaCl; 0, 5, 10, 15, 20, and 30%, \(w\)/\(\
u\)) were systematically studied in triplicate. The back-extraction stage was performed using a single LD step, in which the devices were removed from the sampling flasks using clean tweezes and placed directly into inserts containing 100 \(\mu\)L of the stripping solvent (HPLC mobile phase) inside glass vials (2 mL), ensuring their total immersion. Then the vials were sealed using a handy crimper prior to ultrasonic treatment at room temperature. To evaluate the best LD conditions, different desorption times (10, 15, 30, 45, and 60 min) using ultrasonic treatment were systematically studied in triplicate. After the back-extraction assays, the vials became ready for analysis and were placed into the autosampler trays of the HPLC-DAD system. Blank assays were also performed in triplicate, using the procedure described above without spiking.
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 3 Example  
### Control Plant  
The control plant is a nonlinear, isothermal CSTR in which the van de Vusse reaction takes place. These CSTRs are a popular benchmark due to their nonlinearity and difficult dynamics, willingly used to test newly developed control algorithms; see, e.g., [48; 49; 50; 51; 52; 53]. The process model of the reactor is composed of two composition balance equations; see, e.g., [54]:  
\[\begin{array}{c}\frac{dC_{\rm A}}{dt}=-k_{1}\cdot C_{\rm A}-k_{3}\cdot C_{ \rm A}^{2}+\frac{F}{V}(C_{\rm Af}-C_{\rm A})\ \,\\ \frac{dC_{\rm B}}{dt}=k_{1}\cdot C_{\rm A}-k_{2}\cdot C_{\rm B}-\frac{F}{V}C_{ \rm B}\,\end{array} \tag{49}\]  
where \(C_{\rm A}\), \(C_{\rm B}\) are the concentrations of Components A and B, respectively, \(F\) is the inlet (and also outlet) flow rate, \(V\) is the volume in which the reaction takes place (assumed constant and \(V=1\) L), and \(C_{\rm Af}\) is the concentration of Component A in the inlet flow stream (if not declared otherwise, it is assumed that \(C_{\rm Af}=10\) mol/L). The values of the parameters are: \(k_{1}=50\) 1/h, \(k_{2}=100\) 1/h, \(k_{3}=10\) 1/(h\cdot\) mol). The output variable is the concentration \(C_{\rm B}\) of Substance B. The manipulated variable is the inlet flow rate \(F\). \(C_{\rm Af}\) is the disturbance variable.  
The described control plant was used during the tests also in [12]. Its nonlinear steady-state characteristic is reminded in Figure 1. The control plant has the inverse response; thus, it is natural to use an MPC algorithm in this case.  
The fuzzy model used to generate the dynamic matrix, in the researched algorithms, is the same as in [12]. It is composed of three step responses obtained near the following operating points:  
R1 \(C_{B0}=0.91\) mol/L, \(C_{A0}=2.18\) mol/L, \(F=20\) L/h;  
R2 \(C_{B0}=1.12\) mol/L, \(C_{A0}=3\) mol/L, \(F=34.3\) L/h;  
R3 \(C_{B0}=1.22\) mol/L, \(C_{A0}=3.66\) mol/L, \(F=50\) L/h.  
The assumed membership functions are reminded in Figure 2.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 2 Method
### BIM Exporting Formats  
At present, many BIM authoring tools support the gbXML and IFC data formats. In this paper, we chose Autodesk Revit 2020 as the BIM engine, in which only the geometry and envelopes of the buildings were modelled. As mentioned previously, HVAC was not considered in the BEM.  
The first case study is a residential building of 143.60 m\({}^{2}\), with two floors (Figure 2) and 13 thermal zones. It has a simple and straight-line geometry, with vertical circulation per staircase. Its Revit file had 4.20 megabytes of information.  
On the other hand, the second case study was a much bigger and more complex building (Figure 2). It is an industrial warehouse of 9.677 m\({}^{2}\), with three floors and a basement, in which 21 thermal zones were defined. Although it has a simple geometry, the roof is curved, as well as some of the walls. In comparison with the residential case, this one had 75.15 megabytes of information.  
Figure 1: Paper process diagram. Numbers to follow the diagram and import processes.
## 3 Material and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS): Compound Identification and Quantification  
An Agilent 7890 gas chromatograph (GC) system coupled with a 5973C mass spectrometer (MS) was used to analyze the aroma compounds of black tea. The operated mode was electron ionization (EI) mode (70 eV, ion source temperature 230 \({}^{\circ}\)C), and the quadrupole was in scanning mode (scanning range \(m/z\) 30-450 and scanning rate 1 scan/sec). With helium (purity = 99.999%) as the carrier gas (the flow velocity was 1.8 mL/min), two columns with different polarities were used to separate the compounds: Innowax-Wax (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness, Agilent Technologies) and DB-5 (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness, Agilent Technologies). The oven temperature was optimized: Initial oven temperature was 40 \({}^{\circ}\)C, holding for 6 min, then, ramped to 150 \({}^{\circ}\)C by 3 \({}^{\circ}\)C/min, and then, increased to 230 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min with a 15 min hold [32].  
The compounds were identified by comparing with the mass spectra in the NIST17 library, comparing the RIs with the previous reported in the literature, or comparing the mass spectra and RI with the reference chemical. The aroma active compounds were quantified by constructed external standard curves. The slope and intercept were fitted by the ratio of the peak area of the authentic standard to the internal standard and the ratio of the authentic standard's concentration to the concentration of the internal standard (refer to the previous research description [33; 34; 35]).
## 3 Methodology
### Catadioptric Camera Calibration  
To perform 3D reconstructions with metric dimensions, it is necessary to know the cameras' intrinsic parameters, such as the focal length, optical center, mirror curvature, and extrinsic parameters composed by the rotation and translation between them. These parameters are obtained with the camera calibration. This section briefly describes the process to calibrate the catadioptric stereo vision system based on the work presented in [41].  
To calibrate the catadioptric cameras, we used the geometric model proposed in [42]; this model uses a checkerboard pattern (shown in Figure 2a) to obtain the intrinsic parameters of each camera and the extrinsic parameters between the reference system of the cameras and that of the pattern. The pixel coordinates of a point in the image are represented by \((u,v)\). Equation (1) shows the relation between a 3D point \(\mathbf{X}_{W}\) in the mirror \(\mathbf{x}_{\text{PM}}=[x,y,z]^{T}\) and a 2D point in the image.  
\[\mathbf{x}_{\text{PM}}=\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=\left[\begin{array}{c}u\\ v\\ f(u,v)\end{array}\right] \tag{1}\]  
Since the mirror is rotationally symmetric, the function \(f(u,v)\) only depends on the distance \(\rho=\sqrt{u^{2}+v^{2}}\) leading to Equation (2).  
\[\mathbf{x}_{\text{PM}}=\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=\left[\begin{array}{c}u\\ v\\ f(\rho)\end{array}\right] \tag{2}\]  
where \(f(\rho)\) is a fourth degree polynomial that defines the curvature of the parabolic mirror:  
\[f(\rho)=a_{o}+a_{1}\rho+a_{2}\rho^{2}+a_{3}\rho^{3}+a_{4}\rho^{4} \tag{3}\]  
the coefficients \(a_{0},a_{1},a_{2},a_{3},a_{4}\) correspond to the intrinsic parameters of the mirror.  
To calibrate the catadioptric camera, we projected the checkerboard pattern shown in Figure 2a at different positions around the catadioptric stereo system. Figure 2b shows images from the pattern acquired with the upper camera and Figure 2c shows the images acquired with the lower camera.  
Figure 2: Calibration pattern. (**a**) Chessboard pattern (**b**) Images of the pattern using the upper camera. (**c**) Images of the pattern using the lower camera.