esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calibration Algorithm for Pixel and Steel Tape Display Error
### Display Error Calibration  
As the steel tape is verified by a measurement method that is compared with the standard steel tape, its indication error calibration is shown in Figure 8. The specific measurement principle is as follows:  
Figure 6: Tape retracting device: 1. Servo motor for tape retracting device; 2. fixed part for motor; 3. work driving arm; 4. steel tape box; 5. adjustable fixture base; 6. head clamp; 7. fixed base; 8. tail clamp.  
Figure 7: Pixel calibration: 1. Industrial camera (CCD camera); 2. high-precision glass line ruler.  
1. Firstly, the image recognition and measurement of the zero-scribed line distance between the steel tape and the standard steel tape is carried out under the starting camera, denoted as _L_pos_;
2. The distance between the other scribed line to be measured and the standard steel tape line under the other cameras is identified, measured, and denoted as _R_pos_;
3. The comparison reading of the scribed line can be calculated, denoted as _L_Read_. _L_Read_ = _L_real_ + _L_pos_ - _R_pos_ + _L_cail, where _L_real_ is the actual spacing of the standard steel tape, and _L_cail is the modified value of the standard steel tape at the scribed line.  
Then, the steel-tape indication error, denoted as _L_Err_, is calculated such that _L_Err_ = _L_Work_ - _L_Read, where _L_Work_ is the nominal value of the scribed line to be measured.
## 4 Results  
To ensure consistency, distance estimation units were converted into SI units (meters). The ratio of the difference between the actual distance and the estimated distance was used as relative error. The same formula was used to calculate the relative error for size. A relative error of 0 indicated a perfect estimation. A negative value represented underestimation in distance, whereas a positive value represented overestimation of distance.  
For each analysis, relative errors more than two standard deviations from the respective mean were considered outliers and discarded from the analysis [37]. We felt it was important to screen for and remove outliers to improve the reliability of the dataset; the importance of doing so was underscored by [PERSON] et al. [38], who observed that less than 10% of the studies they reviewed even checked for the presence of outliers. Strategies for dealing with outliers have generated debate (e.g., see [39] vs. [40]). For the present study, we followed the widely accepted criterion of 2 or 3 standard deviations (SD) for identification of outlier data points ([37; 41]). We found that there was no meaningful difference between using the criterion of 2 SD versus 3 SD in our dataset and so we used the more conservative cutoff of 2 SD. In dealing with the outlier points, it has been shown that elimination of extreme values from the data set results in more accuracy and less errors of statistical inferences [37]. In our preliminary study, the strategy of outlier elimination was adopted to aim for as accurate results as possible. Due to the within-subject design of the study for all tasks' conditions, the sample size remained acceptable even after removing the outliers (see the power analysis reported above).  
Figure 3: Size estimation task for a near (**A**) and a far (**B**) object. Participants first identified the shortest side of the cuboid and then were asked to define the aspect ratio of the other side using the proportions of the shortest side.
## 1 Introduction
The question that is raised for the direct sampling is whether a pre-diluter at ambient temperature should be added upstream of the SPN system in order to mimic the PD, or direct hot sampling of the exhaust gas with hot dilution is equivalent. Direct hot sampling does not allow any nucleation or condensation to take place, thus minimises the risk of volatile particles being counted [12]. This could lead to differences when the crankcase ventilation emissions or emissions during regenerations are measured. In the first case lubricant oil particles will be present [13], in the second high molecular hydrocarbons for non-catalytic coated particulate filters [14]. In Europe there is the intention to decrease the 50% detection efficiency of the CPC from 23 nm to 10 nm [9]. The reason is that high concentrations of particles below 23 nm have been found for light-duty vehicles [15; 16; 17], but also for heavy-duty ones [18]. Measuring from lower sizes poses the risk of falsely measuring volatile particles that go through the evaporation tube or are re-nucleated downstream of the evaporation tube (i.e., volatile artefact) [19]. For this reason, a hot catalytic stripper in place of (or in addition to) the evaporation tube has been suggested [20]. Thus, it is also necessary to confirm that the direct tailpipe sampling would be applicable also for the sub-23 nm measurements. The objective of this paper is to assess whether SPN measurements directly form the tailpipe are applicable for regulatory purposes. For this reason, the regulated procedure (PD with PMP system) will be compared with the direct tailpipe sampling methodology in many European laboratories. In order to cover the extreme case, the \"Golden\" system had catalytic stripper and sampled directly from the tailpipe with \"hot\" dilution (i.e., no cold dilution upstream). In some laboratories a system with a cold pre-diluter and an evaporation tube was used. The measurements were complemented with sub-23 nm particles (starting from 10 nm) in order to confirm the applicability for this lower cut-off size.
## 4 Verification of Correlation Functions
### Nonstationary Process  
Based on the target EPSD and coherence function, the target auto-/cross- correlation functions \(R_{jk}^{0}(t,t+\tau)\) of the time-invariant coherent nonstationary process can be calculated by  
\[R_{jk}^{0}(t,t+\tau)=\int_{-\infty}^{\infty}\sqrt{S_{jj}(\omega,t)S_{kk}( \omega,t+\tau)}\gamma_{jk}(\omega)e^{i\omega\tau}d\omega;j,k=1,2,\cdots,n \tag{29}\]In order to verify the effectiveness of the simulated nonstationary wind field, the ensemble auto-/cross-correlation functions \(R_{jk}(t,t+\tau)\) need to be calculated by the generated multiple numerical samples \(\hat{x}_{j}(t)\), \(j=1,2,\cdots,n\), i.e.,  
\[R_{jk}(t,t+\tau)=E[\hat{x}_{j}(t)\hat{x}_{k}(t+\tau)] \tag{30}\]  
Similar to the stationary simulation, the analytical correlation functions of the nonstationary simulation can be also obtained by its simulation formula, i.e., Equation (12). The ensemble auto-/cross-correlation function of the simulated nonstationary samples \(\hat{x}_{j}(t)\), \(j=1,2,\cdots,n\) can be represented by [9]  
\[\begin{array}{c}R_{jk}(t,t+\tau)=2\Delta\omega\sum\limits_{m=1}^{n}\sum \limits_{l=1}^{N}\sqrt{S_{jj}(\omega_{l},t)S_{kk}(\omega_{l},t+\tau)}\big{|} \beta_{jm}(\omega_{l})\big{|}\big{|}\beta_{km}(\omega_{l})\big{|}\\ \times\cos\big{[}\omega_{l}\tau+\theta_{jm}(\omega_{l})-\theta_{km}(\omega_{l })\big{]};j,k=1,2,\cdots,n\end{array} \tag{31}\]  
It can be seen that the ensemble autocorrelation function is independent of the phase, while the ensemble cross-correlation function will be affected by the phase.  
For the time-varying coherent nonstationary simulation, the target auto-/cross- correlation functions \(R_{jk}^{0}(t,t+\tau)\) can be computed by [27]  
\[\begin{array}{c}R_{jk}^{0}(t,t+\tau)=\int_{-\infty}^{\infty}\sqrt{S_{jj}( \omega,t)S_{kk}(\omega,t+\tau)}\sum\limits_{m=1}^{n}\beta_{jm}(\omega,t)\beta _{km}^{*}(\omega,t+\tau)e^{i\omega\tau}d\omega;\\ j,k=1,2,\cdots,n\end{array} \tag{32}\]  
Based on Equation (13), the ensemble auto-/cross-correlation function of the simulated time-varying coherent nonstationary samples \(\hat{x}_{j}(t)\), \(j=1,2,\cdots,n\) can be represented by  
\[\begin{array}{c}R_{jk}(t,t+\tau)=2\Delta\omega\sum\limits_{m=1}^{n}\sum \limits_{l=1}^{N}\sqrt{S_{jj}(\omega_{l},t)S_{kk}(\omega_{l},t+\tau)}\big{|} \beta_{jm}(\omega_{l},t)\big{|}\big{|}\beta_{km}(\omega_{l},t+\tau)\\ \times\cos\big{[}\omega_{l}\tau+\theta_{jm}(\omega_{l},t)-\theta_{km}(\omega _{l},t+\tau)\big{]};j,k=1,2,\cdots,n\end{array} \tag{33}\]  
Clearly, both the ensemble auto- and cross-correlation functions are dependent on the phase.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## Materials and Methods
### Methods
#### 2.2.3 Questionnaire Design  
Surveys were administered at the end of each day and again at the end of each week. To prevent response fatigue on the part of the forecasters, each product being tested was limited to five questions.  
While all NUCAPS products were derived from the baseline soundings, they were at different stages of their development cycle and the evaluation strategy differed. During the 2019 HWT, the baseline soundings were already operational in AWIPS and gridded NUCAPS release was planned for AWIPS version 19.2.1 (operational in September 2019); these were considered \"mature\" products. Modified NUCAPS and NUCAPS-Forecast were experimental products and not available outside of the HWT. For mature products, our objectives were to focus on how soundings can enhance forecasts of complex scenarios. Modified NUCAPS and NUCAPS-Forecast were both experimental products, so we focused on when and where these derived products were valuable to forecasters and listened for features that needed improvement. We were able to gather the most information during the questionnaires and the debrief sessions before the beginning of each day and at the end of each week.  
In past HWTs, many of the case studies in written descriptions performed side-by-side comparisons of NUCAPS to model and/or radiosondes, which demonstrate real-world strengths and limitations to supplement quantitative validation studies on NUCAPS [18]. We provided additional training materials in the quick guides and addressed the limitations during the training. This helped get forecasters up to speed on the strengths and weaknesses that are known from past HWTs.  
Survey questions were a mixture of open-ended verbal descriptions and multiple choice, where forecasters selected from a list of provided categories or a ranking of a product's utility. During past HWT demonstrations, questions were primarily open-ended to gather qualitative information; the advantage of this is to gather as much information as a forecaster is willing to give. However, multiple choice questions collect quantitative information, which is helpful for direct year-to-year comparisons of numerical rankings of product value. So, the mixture was useful for achieving both verbal descriptions and numerical rankings.
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 2 Related Works
### Graph Neural Networks  
Due to the enhanced capability of graphs in representing the structural information of text and accurately capturing word-level dependency information, researchers have begun to explore the utilization of graph neural networks (GNNs) for learning feature representations from dependency trees. [PERSON] and [PERSON] [11; 12] presented a graph convolutional network (GCN) method to learn from the node representation of the dependency tree. [PERSON] et al. [13] proposed a method to construct a syntactical dependency graph for a sentence based on a specific aspect. It introduces the interactive graph convolutional networks (InterGCNs) model to extract aspect-focused and inter-aspect sentiment features for a particular aspect. [PERSON] et al. [14] introduced a relation graph attention network (R-GAT) that constructs a node relation graph within the dependency tree. It employs graph attention mechanisms to capture complex dependencies between nodes and aggregates node information into a graph-level representation. [PERSON] et al. [15] proposed a DualGCN model to simultaneously consider the complementary interaction between syntactic structure and semantic relevance.  
With the increasing number of GNN-based models demonstrating superior performance, research has proven the effectiveness of enhancing the dependencies between contexts and aspects. However, these models treat each dependency item equally and they ignore the affective information within contexts and aspects.
# Passive Sampling and Analysis of Naphthalene
## 4 Results and Discussion  
### Variability Associated with the Same SPME Fiber Type and Fiber Coating Type  
The main goal was to test the effects of SPME fiber type on the mass of naphthalene extracted in retracted mode. Our working hypothesis was that the fiber type does not influence the amount of mass extracted (i.e., it follows [PERSON]'s law of diffusion and SPME coating acts as a zero sink sorbent). Thus, the goal of the experiment was to study differences in naphthalene extraction effectiveness within three randomly selected (\(n\) = 3) PDMS, PA, and DVB fiber coatings, respectively. An Analysis of variance (ANOVA) analysis of variance showed that the effect of the same type of PDMS, PA, and DVB fiber coating on extracted mass was significant, F(2,6) = 5.49 (Figure 1) while the critical value F(2,6) is 5.14 at \(p\) = 0.05. This result rejected the null hypothesis.  
One plausible explanation is that observed differences between extracted mass by the same type of PDMS, PA, and DVB fiber coating can be impacted by the variability of SPME fibers within one type of coating.  
ANOVA showed no effect on mass extracted by randomly selected PA and DVB fiber coating (Figure 2b,c), F(2,6) = 4.93 and F(2,6) = 4.07 for PA and DVB fibers, respectively, the critical value F(2,6) = 5.14 at \(p\) = 0.05. Randomly selected PDMS fibers extract significantly different masses of analytes, F(2,6) = 62.4 (Figure 2a), while the critical value F(2,6) = 5.14 at \(p\) = 0.05. However, reproducible results with lower RSD (4.6 \(\pm\) 1.3%) were obtained with PDMS coating (Figure 3). RSDs for PA and DVB fiber coating were 9.4 \(\pm\) 5.7% and 9.0 \(\pm\) 2.4%, respectively. The 100 \(\upmu\)m PDMS fiber coating was chosen as an optimal fiber for further experiments.  
For achieving the highest accuracy by preventing additional errors caused by random differences in SPME fiber efficiency, a separate calibration must be obtained for each particular PDMS fiber for TWA extraction of naphthalene.  
Figure 1: Analysis of variance (ANOVA) between solid-phase microextraction (SPME) fiber coating groups (polydimethylsiloxane (PDMS), polyacrylate (PA) and divinylbenzene (DVB)).
## 2 Materials and Methods
### Second Phase--Implementation of Deep Learning Models and Algorithms
#### 2.3.3 MobileNet Model  
The third model was the MobileNet model, the V2 version, which is characterized as a lightweight CNN-based model, developed for mobile and embedded vision applications. It was developed by researchers at Google as part of their efforts to create efficient deep learning models optimized for mobile and edge devices [44; 45]. The MobileNet modelutilizes depth-wise separable convolutions, which it significantly reduces computational complexity and model size while maintaining strong performance on various image classification and object detection tasks. MobileNet's modular design makes it a popular choice for transfer learning and customization across diverse domains [29,30].  
In our case, the fine-tuned model based on the MobileNet architecture has been adapted for the 7-class mineral classification task, with the fully connected layers at the top removed and replaced with a custom classification head [30]. The head consists of a Flatten layer, followed by four dense layers. It also utilizes dropout layers with a rate of 0.4 that are included after each dense layer, with the final dense layer containing 7 units and softmax activation been enabled for the multi-class classification.
## 2 Materials and Methods
### Compositional Analysis
#### 2.2.3 Total Polyphenol Content (TPC)  
According to the method described by [PERSON] et al. (2003) [30], 1 g of sample was ground and mixed with 40 mL of methanol/water solution 1:1 (\(v/v\)) at pH = 2. The mixture was then stirred at room temperature for 1 h. Afterwards, the mixture was filtered using a vacuum pump (Rocker mod. 400). The extracted liquid was kept apart while the solid was extracted again at room temperature for 1 h with a methanol/acetone solution (70:30 (\(v/v\))) and it was then vacuum-filtered. Filtered liquids from the first and second extractions were mixed and used for the spectrophotometric determination.  
Total polyphenol analysis was carried out using the Folin-Ciocalteu method [31]. This method consisted of the addition of 0.5 mL of the extract to 2 mL of an aqueous solution of sodium carbonate (7.5%) and 2.5 mL of Folin-Ciocalteu reactant previously diluted 10-fold. The mixture was stirred in a vortex, and it was left to stand for 15 min at room temperature. Afterwards, the absorbance at 765 nm was measured. Since a previous calibration curve was generated using solutions of known concentrations of gallic acid as standard, results were expressed initially in terms of mg of gallic acid equivalents/mL (mg GAE/mL) and then converted into mg GAE/g sample.
## 3 System Architecture
### OFDM Signals  
Python-generated OFDM baseband signals are used for both the DL and UL transmission for the ease of implementation and the possibility to experiment with physical layer signal processing.  
The related parameters are listed in Table 1. The parameters are similar to those defined in the IEEE802.11 ac specifications [20]. The signal bandwidth is selected to fit the  
Figure 6: Block diagram of the synchronization circuit. (AAU: active antenna unit; ADC: analog-to-digital converter; PLL: phase lock loop.)  
available ADC sampling rates. It should be noted that the proposed network architecture is standard agnostic. Digitized signals, which can be single-carrier or OFDM, with a bandwidth up to 40.96 MHz can be transmitted.
## 5 Case-Study
### Pump-Orders Building  
Figure 13a shows JADE interaction scenario among the CHs (i.e., customer-1 agent and customer-2 agent) and the PH (i.e., pump agent). In this scenario, customer-1 agent sends the product order in form of an ACL-message with an AGREE communicative act to the pump agent. The AGREE-message contains a Pump-Customer-Order as has been shown in Figure 12a. When the AGREE-message is received by the pump agent, the pump agent confirms the receiving by sending back a CONFIRM-message to customer-1 agent. The same mechanism is used again between cusomter-2 agent and the pump agent. Two facts can be extracted by the pump agent from the received customer order AGREE-message, which are the casing color and the hydraulic power.  
Figure 13: (**a**) JADE interaction among the customer holons and the pump holon; (**b**) Drools reasoning to build a pump details from a customer order; (**c**) JADE interaction between the pump holon and the order holon.  
Processing the customer-order to build a pump order is done via a forward chaining reasoning and can be seen in Figure 12(b). When a new customer order AGREE-message is received by the pump agent it triggers the reasoning session-1. The casing color fact derives new fact which is the casing position. While the hydraulic power fact derives other three facts which are the motor power, the impeller type, and the shaft material. Those new facts are being updated to the PH working memory. When the reasoning engine realizes that some of the new facts in the working memory are matching some of rules in the production memory, it triggers the reasoning session-2. In this session, the motor power fact derives the motor position fact, the impeller type fact derives the impeller position, and the shaft material derives the shaft position. The new facts at the PH working memory can be now used to formulate the pump manufacturing operation message which has been shown before in Figure 11(b). The communication act of the pump manufacturing operation message is PROPAGATE-message. The same interaction scenario which has been shown in Figure 12(a) is followed between the PH and the OH as can be seen in Figure 12(c).
## 4 Results and Discussion
### Shear Stress  
The shear stress distribution for the Mohr-Coulomb stress theory in each scenario is shown in Figure 7, and the numerical analysis results are presented in Table 7. Figure 7 shows the concentration trend of shear stress according to the installation of elastomeric bearings. When elastomeric bearings are not installed, stress concentration occurs around the shear keys; however, when elastomeric bearings are installed, the shear force is dispersed due to the strong lateral contact stiffness of the rubber plate, and the grout strut is very faint as well. Therefore, the installation of elastomeric bearings significantly reduced the concentration of shear stress.  
Figure 8 shows the maximum shear stress in the scenario in which the size of the shear key was increased from 20 mm (S1) to 100 mm (S5) at intervals of 20 mm without applying the elastomeric bearing. Through this, the maximum shear stress generated in S1-S5 was 19.60-63.91 MPa, and, as listed in Table 7, that in S3 was 25.95 MPa. When the elastomeric bearings were applied, the maximum shear stress values were 5.78 MPa in S-E1 and 4.90 MPa in S-E2, which were reduced by about 77-81% compared to S3.  
Figures 9-12 are graphs showing the maximum shear stress at the interface of the grout, where the GC is divided into a compression part and a tension part in terms of the load direction. Through this, the maximum shear stress of the scenario using CR material for each elastomeric bearing (S-E1) was relatively high.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Shear Stress of Grout** & **S3** & **S-E1** & **S-E2** \\ \hline \(f_{\text{fk, max}}\) (MPa) & 25.95 & 5.78 & 4.90 \\ \(f_{\text{fk, mean}}\) (MPa) & 0.420 & 0.366 & 0.365 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum and mean shear stress of grout for each scenario.  
Figure 8: Comparison of shear stress according to shear key size and installation of elastomeric bearings.  
Figure 7: Distributions of shear stress of (**a**) S3 with MP, (**b**) S3 with TP, (**c**) S-E1 with MP, (**d**) S-E1 with TP, (**e**) S-E2 with MP, and (**f**) S-E2 with TP.  
Figure 11: Maximum shear stress at grout contact on MP of tension side.  
Figure 10: Maximum shear stress at grout contact on MP of compression side.  
Figure 9: Maximum shear stress at grout contact on TP of compression side.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 4 A 2-Port MIMO System Using Two Opposite 1 \(\times\) 4 Antenna Arrays
### Design of 2-Port MIMO System Using Two Opposite 1 \(\times\) 4 Arrays Arrangement  
Boosting network capacity, bandwidth, and increasing gain maximization benefits, a MIMO of two 1 \(\times\) 4 arrays design is simulated and fabricated. The proposed design is arranged as two flipped 1 \(\times\) 4 array elements positioned at a half wavelength distance with a flipped opposite array direction, constructing a MIMO of two 1 \(\times\) 4 arrays. A 1 mm-thin copper grounding squiggly defective square line connects the two flipped opposite 1 \(\times\) 4 arrays. This grounded connection increases the electrical length and inductance, which influences the flow of surface currents on the ground plane. As a result, it reduces wave propagation in unwanted directions and helps block mutual coupling currents, particularly around resonance. This method can be effective in suppressing surface wave propagation between antenna elements. Figure 22 displays both the simulated design and the fabricated prototype of the MIMO of two 1 \(\times\) 4 arrays. Figure 22e illustrates that the maximum radiation efficiency reaches 97.46% at 5.8 GHz and 95.16% at 3.5 GHz. Table 7 provides the actual design dimensions, which have an overall size of 140 \(\times\) 132 \(\times\) 1.57 mm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Substrate Width** & **Substrate Length** & **L1** & **L2** & **L3** & **W1** \\ \hline (mm) & 140 & 132 & 18.4 & 76.66 & 27.67 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Dimensions of the two 1 \(\times\) 4 arrays MIMO.  
Figure 21: The E-plane radiation pattern: (**a**) measured and simulated at 3.5 GHz; and (**b**) at 5.8 GHz.