esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Materials  
#### 2.5.1 Eye Movement Equipment and Functions  
In this experiment, we used Tobii Pro Glasses 2 (Tobii Electronic Technology Co., Ltd., Suzhou, China) [48], as shown in Figure 3, an advanced wearable eye-tracking device that is lightweight, similar to normal glasses, and weighs only 45 g, ensuring participant comfort. The device connects via an HDMI cable to a recording device with an inbuilt SD memory card and is designed to provide a high degree of flexibility and portability, facilitating the recording of images and sounds as seen by the experimenter. The Tobii Pro Glasses 2 are characterized by their non-invasive measurement method, which greatly expands the range of applications of the experiment and provides great operability for eye movement studies in real-world environments. The Tobii Pro Glasses 2 are equipped with an advanced infrared (IR) sensor and a high-resolution camera; the device is capable of accurately capturing a participant's pupil position and corneal reflexes at a sampling rate of 250 Hz to determine the focal point of vision. In addition, Tobii Pro Glasses 2 supports wireless connectivity and features an efficient power management system to ensure stable operation over long periods of time.  
Before the experiment started, we calibrated the device using the Tobii Pro Glasses Controller software (version 1.114/Windows 64-bit) to ensure the accuracy of eye tracking. After calibration, the wearer could move freely, and the device was connected wirelessly to achieve the real-time observation function to simulate the eye movement state in the real experimental environment. During the experiment, the eye-tracker tracked eye movements, captured gaze, eye hopping, and other metrics in the temporal and spatial dimensions at a specified sampling rate, and recorded these data points in a video stimulus. Data analyses were performed on a Lenovo Legion Y9000P IAH7 TH PC (Lenovo Co., Ltd., Beijing, China), which is equipped with powerful processing power to efficiently run the Tobii Pro Lab software and process large amounts of eye-tracking data. For eye movement data analysis,we used the visualization tools in the software to process the wearer's focus into hotspots and trajectory maps on a static base map. The research team can choose the format and type of data to be exported as needed, including key metrics such as gaze time, gaze count, average gaze time, gaze duration, eye hopping distance, number of visits, pupil diameter, etc., which are important parameters reflecting the wearer's eye movement behavior.
# Production of Sustainable Postbiotics from Sugarcane Straw for Potential Food Applications
## 3 Results and Discussion  
### Saccharification and Fermentation Process  
The sugarcane straw went through a saccharification process with a commercial cellulase to breakdown cellulose and hemicellulose to release monosaccharides that will be the sugar source for the yeast. The saccharification occurred for 72 h, and the total sugar content increased from 1.72 \(\pm\) 0.10 to 3.46 \(\pm\) 1.06 mg/mL. Saccharification is the conversion of cellulose biomass to useful substances such as fermentable sugars through the enzymatic hydrolysis of the polysaccharide. Cellulose application during saccharification operates mainly in cellulose and hemicellulose degradation, releasing more water soluble carbohydrates [14].  
This final sugar rich mixture was applied for fermentation with _S. cerevisiae_, and the yeast growth and total sugars content along the 72 h are described in Figure 2. It was possible to observe an increase in yeast growth of ca. 5 log cycles accompanied by a consumption of ca. 2 mg/mL of the total sugars.
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 4 Examination
### Evaluation of Image Processing Models  
We evaluate the bolt extraction model and bolt tightening judgment model by calculating precision and recall. Precision means the ratio of bolts correctly extracted or correctly determined to be tightened out of the model's judgment results. Recall means the ratio of correct bolt extraction or correct tightening decisions among the correct data. High precision means the model makes fewer judgment errors. High recall means the model is less prone to oversight errors. Figure 6 shows an example of bolt and marker extraction results for a correctly tightened bolt.  
Figure 6: Example of bolt and marker extraction results for a correctly tightened bolt: (**a**) bolt extraction image and (**b**) marker extraction image.  
Set a threshold of 0.5 for the score of the bolt extraction result. This score means that the detection result has been determined to be a bolt with a probability higher than 50%. The results with a score of 0.5 or less are bolt extraction failures, and the results are not saved. The number of bolts extracted by the model was 396 from the input video, and all the target bolts were extracted. The bolt extraction precision value was approximately 91.4%. The bolt extraction recall value was 100.0%. It is thought that it is possible to deal with the extraction error by adjusting the score threshold.  
Set a threshold of 0.5 for the score of the bolt tightening judgment result. The results with a score of 0.5 or less are bolt tightening failures. The bolt tightening judgment model receives 396 images as an input dataset. The number of bolts that were determined to be successfully tightened by the model was 396, and all the target bolts were determined to be successfully tightened. The bolt tightening judgment precision value was approximately 91.4%. The bolt extraction recall value was 100.0%. However, the model also determined 34 extraction errors as successful tightening. For example, Figure 7 shows an example of a bolt extraction error. To improve the accuracy of judgments, it is necessary to add training data to the model, relearn the model, and revalidate the judgment results.
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.1 Configuration for Routing
In this manner, we can transmit a data packet from a given source node to a pseudo-random destination node within a simulation run and the results will not be different, even if the simulation is run again, as the same random numbers will be generated at each time step, because simulation time is given as the seed for the random number generator. In order to have different levels of contention delay (\(t_{cont}\)) for routing, we scheduled the routing of packets with a minimum time gap between packets sent by two different source nodes (\(T_{g}\)), given by Equation (45) (note that, for multiple packets sent by the same source node, the time gap is zero):  
\[T_{g}=\left\{ \begin{array}{l}40(M\ mod\ 125)\ \upmu\mbox{s};\ \ if\ (M/125)\ mod\ 2==0\\ -40(M\ mod\ 250)+10,000\ \upmu\mbox{s};\ \ if\ (M/125)\ mod\ 2==1\end{array} \right. \tag{45}\]  
As evident from Equation (45), the time gap between routing schedules for a given routing cycle has a minimum value of 0 \(\upmu\)s and a maximum value of 5000 \(\upmu\)s. Thus,the above packet scheduling approach generates both low-contention and high-contention network scenarios, which are required to test the effectiveness of the proposed routing framework. Furthermore, as there is only a 40 \(\upmu\)s increment or decrement in time gap and 0.1 packet per source increment or decrement in each routing cycle on average, the average contention delay per channel (\(\overline{t_{cont,WL},\overline{t_{cont,WI}}}\)) and average queue size of node (\(\mathcal{R}_{i}\)) computed for the previous data gathering cycle effectively approximate the average contention and queuing delays for the present cycle. However, the exact one-hop channel delay at a given hop in the present routing cycle was computed using machine learning with the aid of approximated average delay values computed, using the collected metadata.
## 3 Materials and Methods
### Convolutional Neural Network Performance Evaluation  
Measuring the performance effectiveness of a CNN model is an important step in evaluating the effectiveness of the model in classifying an object. In this research, several evaluation metrics are used to measure the performance of the model detector in the SCDS system. These metrics include accuracy (\(A\)), precision (\(P\)), recall (\(R\)), and \(F1\)-_score_.  
\[A=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
\[R=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\text{-}Score=\frac{2\times P\times R}{P+R} \tag{4}\]  
True Positive (\(TP\)) can occur when the model correctly identifies an object as positive. True Negative (\(TN\)) can occur when the model correctly identifies an object as negative. False Positive (\(FP\)) can occur when the model incorrectly identifies an object as positive, and False Negative (\(FN\)) can occur when the model fails to identify an object that exists.  
Accuracy (\(A\)) measures the extent to which the model can correctly and effectively classify samples using the formula in (1). Precision indicates the proportion of correct positive predictions compared to all positive predictions made by the model. This metric  
Figure 5: Architecture of the proposed convolutional neural network.  
can measure the accuracy of the model in identifying the correct object by calculating the formula in (2). Recall measures the extent to which the model can capture all positive samples that are present in the dataset. The higher the recall, the better the model is at recognizing all relevant objects, using the formula in (3). The F1-score is a metric between precision and recall. This metric becomes a reference of the balance between the two metrics Precision and Recall. F1-score is calculated with the formula in (4).
## 3 Technology Overview
### Semantic Web Services (SWS)
#### MicroWSMO  
MicroWSMO is an extension of hRESTS that adds semantic annotations [11]. As explained earlier, SAWSDL is an extension to WSDL that specifies how to annotate service descriptions with semantic information. Given that the hRESTS service model is so similar to the WSDL model, it is not surprising that MicroWSMO is very similar to SAWSDL. MicroWSMO defines three link relations _i.e._, anchors with the rel attribute set as follows:  
* _model_-the _href_ attribute points to an ontology concept or instance.
* _lifting_ and _lowering_-the _href_ attribute points to the transformations to and from the semantic layer respectively.
## 4 Antenna Array
### Array with Sequential 90\({}^{\circ}\) Rotation of the Antenna Elements
#### 4.3.2 Beamforming  
For the case of side-looking, the amplitude tapering and phase distribution values are given by Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Polarization H** & \multicolumn{2}{c}{**Polarization V**} \\ \hline  & **Simulation** & **Measurement** & **Simulation** & **Measurement** \\ \hline Normal Array & 28.6 dB & 22.87 dB & 30.46 dB & 28.1 dB \\ Rotation 90\({}^{\circ}\) & 40.17 dB & 32.15 dB & 40.28 dB & 35.72 dB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the cross-polarization suppression @ \(f_{0}\) = 1.325 GHz. Uniform amplitude and phase distribution.  
Figure 16: Measured cross-polar suppression @ \(f_{0}\) = 1.325 GHz. Uniform amplitude and phase distribution. Horizontal polarization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Port H** & **Amplitude** & **Phase** & **Port V** & **Amplitude** & **Phase** \\ \hline \(H_{1}\) & 0 dB & 0\({}^{\circ}\) & \(V_{1}\) & 0 dB & 0\({}^{\circ}\) \\ \(H_{2}\) & 0 dB & 70\({}^{\circ}\) & \(V_{2}\) & 0 dB & 110\({}^{\circ}\) \\ \(H_{3}\) & 0 dB & 180\({}^{\circ}\) & \(V_{3}\) & 0 dB & 180\({}^{\circ}\) \\ \(H_{4}\) & 0 dB & 250\({}^{\circ}\) & \(V_{4}\) & 0 dB & \(-70^{\circ}\) \\ \(H_{5}\) & 0 dB & 0\({}^{\circ}\) & \(V_{5}\) & 0 dB & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Amplitude and phase values. Uniform amplitude and phase distribution. Array with sequential 90\({}^{\circ}\) rotation.
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 3 Mathematical Model
### PDF of the Relative Velocity  
As illustrated in Figure 2, the relative velocity of a given node (A) with respect to another given node (B) is defined as the velocity of which that node (A) might appear to an observer located in node (B) and moving towards it. As illustrated in Figure 2, when the two nodes (A) and (B) are very close to each other, their initial starting coordinates can be considered the same. Therefore, the relative velocity of (A) with respect to node (B) is defined as the velocity of which node (A) might appear to an observer located in node (B) and moving towards it.  
The relative velocity is calculated using Equation (1) [35]  
\[v_{r}=\sqrt{v_{1}^{2}+v_{2}^{2}-v_{1}v_{2}\cos(\alpha)} \tag{1}\]  
where \(v_{r},v_{1},v_{2}\) are the relative velocity, node (A) velocity and node (B) velocity, respectively; and \(\alpha\) is the angle between \(v_{1}\) and \(v_{2}\).  
The angle between the two moving nodes is within the interval [0, \(\pi\)] and the Probability Distribution Function (PDF) of \(\alpha\) equals (1/\(\pi\)). To find the PDF of the relative velocity \(v_{r}\), it is assumed that the angle between the two nodes (A & B) is fixed at an angle of (\(\pi\)/2). This value represents the average value of all possible angles. In this case, the relative velocity \(v_{r}\) (Equation (1)) is relaxed to the following equation (Equation (2)):  
\[V_{r}=\sqrt{v_{1}^{2}+v_{2}^{2}} \tag{2}\]  
As demonstrated in Figure 3, the relative velocity written in this form represents a circle of radius \(v_{r}\). This means that the rules of the circle can be easily applied to find the Cumulative Density Function (CDF) to estimate the value of the relative velocity.  
Figure 3: Relative velocity represented using equation of a circle.  
Figure 2: Relative velocity for which nodes A and B diverge.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 2 System Model and Performance Metrics
### User Distribution  
Considering a user density of \(\lambda\) users/km\({}^{2}\), a set of users \(\mathcal{I}\) with 2D coordinates are randomly distributed over the HAP service area of \(A\) km\({}^{2}\). The users in \(\mathcal{I}\) are independently and identically distributed over the space according to a Poisson distributed random variable with mean \(\lambda A\). The number of users is \(|\mathcal{I}|\), where \(|.|\) denotes cardinality. This follows a bivariate Poisson point process (PPP) \(\Phi_{p}\in\mathbb{R}^{2}\).  
User Association  
For evaluating the performance of the proposed model, the distributed users are associated to the cells formed using the coordinates obtained from the proposed algorithm. It is important to note that the user distribution does not affect the tessellation performance of the proposed pointing algorithm. The tessellation described in Section 4 is aimed at providing contiguous coverage of cells over the entire service area considering the footprints of the HAP antenna beams. Users are distributed over the service area to assess both the CNR and CINR distribution on the ground resulting from the algorithm's cell tessellation.  
The users associate with cells based on their perceived CNR. User \(i\) associates to cell \(c_{i}\) where it achieves the highest CNR, which must be at least equal to a given threshold. If a user's CNR is less than the threshold in any of the cells, the user is not connected in that cell. Furthermore, if user \(j\) is located in a region where two or more cells overlap, it is assigned to the cell that maximises its received power. Invariably, users in an overlap region can detect all the overlapping cells \(\{c_{l}\}\). The set of coordinates of the overlapping cells \(\mathcal{C}_{o}\) for all users an overlap region is obtained as follows,  
\[\mathcal{C}_{o}=\{c_{l}\mid l\in\{1,2,\ldots,N\}\},\forall j\mid l\
eq j, \tag{6}\]  
where \(N\) is the number of overlapping cells. Then, the set of received powers \(\mathcal{P}^{j}\) for user \(j\) is defined in (7). User \(j\) associates to cell \(c_{l^{*}(j)}\) such that \(l^{*}(j)\) realises the maximum in \(P_{r}^{l}\).  
\[\mathcal{P}^{j}=\{P_{r}^{l}\mid l\in\{1,2,\ldots,N\}\}. \tag{7}\]
## 3 Results and Discussion
### Mortars
#### 3.1.2 Thermo-Hygrometric Tests  
The results of the thermo-hygrometric tests are presented in Tables 7 and 8, showing their close correlation with the density of the specimens. As the density increases, according to the R-EPS fraction replacing the sand, conductivity (Figure 9) and thermal diffusivity (Figure 10) increase, while vapor permeability (Figure 11) decreases.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen Code** & **D 1** & **TC 1** & **TD 1** & **VHC 1** \\  & **[kg/m3]** & **[W/mK]** & **[10-6 m2/s]** & **[106 J/m3K]** \\ \hline M1 & 2160 & 2.76 & 1.54 & 1.80 \\ \hline M2 & 870 & 0.48 & 0.30 & 1.63 \\ \hline M3 & 950 & 0.46 & 0.28 & 1.61 \\ \hline M4 & 870 & 0.29 & 0.18 & 1.58 \\ \hline M5 & 1460 & 0.98 & 0.56 & 1.78 \\ \hline M6 & 2280 & 1.40 & 0.79 & 1.79 \\ \hline M7 & 1720 & 1.22 & 0.67 & 1.82 \\ \hline M8 & 1570 & 0.94 & 0.57 & 1.65 \\ \hline \end{tabular}
\end{table}
Table 7: Thermal properties of mortars.  
Figure 8: Mortar. Flow test results referring to the samples specified in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen Code** & **D 1** & **WVP 1** & **WVRF 1** \\  & **[kg/m3]** & **[10-12 kg/(msPa)]** & **[-]** \\ \hline M1 & 2160 & 1.17 & 164.3 \\ \hline M2 & 870 & 3.71 & 52.1 \\ \hline M3 & 950 & 4.23 & 45.6 \\ \hline M4 & 870 & 2.70 & 71.6 \\ \hline M5 & 1460 & 3.00 & 64.4 \\ \hline M6 & 2280 & 0.89 & 216.4 \\ \hline M7 & 1720 & 3.79 & 50.9 \\ \hline M8 & 1570 & 3.34 & 57.7 \\ \hline \end{tabular}
\end{table}
Table 8: Hydrothermal properties of mortars.  
The EPS granules contribute to creating air voids within the microstructure of the material, causing an increase in the insulating performance of the end product. Mixtures M1 and M6, without EPS, exhibit higher thermal conductivity values compared to those of the other blends. This is related to the absence of R-EPS within the mixture, leading to a subsequent increase in density.  
Figure 11: Mortar. Trend of vapor permeability with respect to the density of the samples.  
Figure 9: Mortar. Relationship between conductivity and density in the samples.  
Comparing mixtures M2, M3, and M4, in which the fine fraction has been completely replaced with R-EPS, it can be observed that specimen M4 has the lowest thermal conductivity value. This data is likely related to the most mixed particle size distribution of R-EPS used in this specimen.
###### Abstract  
Due to their extensive use in various applications, helicopters need to be able to land in a variety of conditions. Typically, a helicopter landing gear system with skids or passive wheel-dampers is designed based on only one critical touchdown condition. Thus, this helicopter landing gear system may not perform well in different landing conditions. A landing gear system with magnetroheological (MR) dampers would be a promising candidate to solve this problem. However, a semi-active controller must be designed to determine the electrical current applied to the MR damper to directly manage the damping force. This paper presents a new skyhook controller, called the skyhook extended controller, for a helicopter with multiple landing gears equipped with MR dampers to reduce the helicopter's acceleration at the center of gravity in off-normal landing attitude conditions. A 9-DOF simulation model of a helicopter with multiple MR landing gears was built using RECURDYN. To verify the effectiveness of the proposed controller, co-simulations were executed with RECURDYN and MATLAB in different initial pitch and roll angles at touchdown. The main simulation results show that the proposed controller can greatly decrease the peak and rms acceleration of the helicopter's center of gravity compared to a traditional skyhook controller and passive damper.  
helicopter landing systems; magnetroheological damper; skyhook control; semi-active control; skyhook extended control
## Materials and Methods
### Characterization and Analysis  
Elemental analysis was performed with an elemental analyzer (EA, Flash 2000, Thermo Fisher, Basingstok, UK). The molecular weight of the synthesized polyol was measured using liquid chromatography-mass spectrometry (LC-MS, G6130, Agilent Technologies, New York, NY, USA), while the structure was analyzed using Fourier-transform nuclear magnetic resonance (FT-NMR, Avance 3300, Bruker, Karlsruhe, Germany). The LC-MS system ionized the sample using electron spray ionization (ESI), and the eluent had a 1:1 ratio of acetonitrile:dried water (formic acid 0.2%) under a 0.7 mL/min flow rate. The molecular weight of the synthesized polyurethane was measured using gel permeation chromatography (GPC, 1260 Series, Agilent Technologies, New York, NY, USA). The glass  
Figure 1: Preparation of polyol (EP-DOPO) reaction scheme.  
transition temperature (\(T_{g}\)) of polyurethane was measured using differential scanning calorimetry (DSC, Q2000, TA Instrument, New Castle, DE, USA) from \(-\)80 to \(-\)20 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min. The thermal stability of the polyurethane was examined using thermogravimetric analysis (TGA, Q500, TA Instrument, New Castle, DE, USA) in the range of 25 to 800 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min. Then, the tensile strengths of the cured epoxy compositions were measured with a universal testing machine (UTM, INSTRON 5982, Instron, USA) for test specimens processed to sizes of 150 mm \(\times\) 13 mm \(\times\) 3 mm based on the ASTM D 638 method. The flexural strengths of the cured compositions processed to sizes of 60 mm \(\times\) 25 mm \(\times\) 3 mm were tested by using the ASTM D 790M method. The tensile and flexural tests were repeated five times to obtain average values. The Izod impact strengths were measured using the ASTM D 256 method for cured compositions with sizes of 63.5 mm \(\times\) 12.7 mm \(\times\) 3 mm with a pendulum impact tester (HIT-2492, JJ-TEST, Chengde, China). The dynamic mechanical analysis (DMA, Q800, TA Instrument, New Castle, DE, USA) of the epoxy compositions was performed using a Q800 with test specimens measuring 60 mm \(\times\) 12 mm \(\times\) 3 mm. The specimens were installed on the dual cantilever probe while the operating temperature increased at a heating rate of 5 \({}^{\circ}\)C/min under a 1 Hz frequency to measure both the storage moduli and tan \(\delta\) values. Furthermore, the thermal stabilities of the cured epoxy compositions were measured using TGA (TGA, Q500, TA Instrument, New Castle DE. USA) in the range of 25 to 800 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min.