esa-sceva/satcom-chunk-collection huggingface dataset:
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Non-Metals  
Carbon-based materials (CNTs, graphene, carbon fibers, carbon aerogels) play a fundamental role in this type of conductive textiles. These materials are characterized by high electroconductivity (\(10^{4}\) Scm\({}^{-1}\)), superior strength, environmental stability, and a cost-effective manufacturing process that exposes these as promising materials for wearable electronics [59]. Many research studies have demonstrated the remarkable features of these non-metallic conductive textiles [60; 61]. In recent years, nanocomposites have been intensified for their mechanical, electrical, and thermal performances, where carbon, such as carbon nanofiber, CNTs, graphene, and graphite, are used as a filler [62]. CNTs and graphene have been given special attention due to their unique features, such as their high mechanical stiffness, tensile strength, and exceptional thermal and electrical conductivity [63; 64]. Researchers are continuing to discover new dimensions of carbon nanomaterials. After that, a more versatile material, graphene, was discovered and focused on flexibility, higher electron conductivity, and increasing the scope of applications [65].
## 3 Measurement of the Mirror Facet Alignment  
### Measurement Method  
In order to rapidly and cleanly measure the facet alignment of a mirror unit after installation, a method to reverse the mirror facet using the coordinates of three feature points was proposed. The steps for this method are as follows:  
(1) Three circular marks are pasted on the reflective mirror of the mirror unit forming a triangular distribution with known positions. The center of each circular mark is used for the feature points denoted T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\) with position vectors \(\mathbf{T}_{1}\) (\(\mathbf{T}_{1}\) = \(\mathbf{OT}_{1}\)), \(\mathbf{T}_{2}\), and \(\mathbf{T}_{3}\), respectively (Figure 2). Under ideal condition, the coordinates of the feature points are known.  
(2) After the initial installation of the mirror unit (i.e., facet 1), the points T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\)have moved to positions T\({}_{1t}\), T\({}_{2t}\), and T\({}_{3t}\), respectively. Using the photogrammetry method [29], the coordinates of point T\({}_{1t}\), T\({}_{2t}\), and T\({}_{3t}\) and their corresponding position vectors \(\mathbf{T}_{1t}\), \(\mathbf{T}_{2t}\), and \(\mathbf{T}_{3t}\) (Equation (1)) are determined.  
(3) To calculate the facet parameters (\(\mathbf{Q}_{t}\) and \(\mathbf{N}_{t}\)) and position vectors \(\mathbf{A}_{t}\), \(\mathbf{B}_{t}\), and \(\mathbf{C}_{t}\) of the ball-hinge center, a one translational two rotational rigid body moving method (OneT-TwoR method) was developed (Figure 3). Where the measure coordinate system of photogrammetry overlaps with the design coordinate system O-\(xyz\) of the concentrator, the cruciform target or checkerboard target are used for calibration, which will be described in subsequent sections.  
\[\left\{\begin{array}{l}\mathbf{T}_{1t}=\mathbf{T}_{1}+\mathbf{t}_{1}\\ \mathbf{T}_{2t}=\mathbf{T}_{2}+\mathbf{t}_{2}\\ \mathbf{T}_{3t}=\mathbf{T}_{3}+\mathbf{t}_{3}\end{array}\right. \tag{1}\]  
Figure 2: Diagram of the facet parameters and measurements of the parabolic mirror unit.  
In Equation (1), where \(\mathbf{t}_{1}\), \(\mathbf{t}_{2}\), and \(\mathbf{t}_{3}\) are the position error vectors of the feature points \(\mathrm{T}_{1}\), \(\mathrm{T}_{2}\), and \(\mathrm{T}_{3}\) determined by photogrammetry respectively.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 4 Numerical Results and Discussion
path loss exponents of 2 and 3 had values of around 750 Mbps/J and 280 Mbps/J, which were better than the AF scheme with values of around 35 Mbps/J and 25 Mbps/J. Finally, in Figure 7, we plot a graph showing the comparison of the proposed scheme with the stochastic scheme [3]. The graph depicts the values of the EE while varying the D2D transmit power. The D2D transmit power was taken from 0 mW to 25 mW for varying path loss exponent values, namely 2, 2.5, and 3. From the graph, it can be noted that with the increase in the D2D transmit power, the EE decreased gradually. The EE ranged from around 400 Mbps/J to 80 Mbps/J while varying the D2D transmit power from 0 mW to 25 mW at a path loss exponent of 3. This validated the efficacy of the proposed work, whereby the EE increased with a better coverage probability and higher SINR values. Figure 6: Comparison of the EE for the DF and AF relay schemes. Figure 7: Comparison of different schemes with the proposed scheme.
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 4 Experimental Results and Analysis
### Analysis of OIIR  
This section evaluates and discusses the results for OIIR using an RF classifier. The second approach for HOIR was based on OIIR, which directly classified the HOIs without any prior object information. We trained an RF classifier in this regard to classify a set of 54 HOIs based on the features extracted from the hand joint data. Table 5 presents the experimental results obtained for the HOI classification using an RF. The maximum performance was observed for interactions such as \"Bowl: Pick and Place\" and \"Kettle Pour Water into a Mug\", where they achieved F1-scores of 0.87 and 0.81, respectively. The overall average F1-score obtained for the HOI classification in this case was 60%. These results suggest that the OIIR approach was capable of handling tasks that involved direct and simple object interactions. However, there is room for improvement in the classification of complex HOIs, as depicted in Table 5. For example, such interactions that involved a drawer (like putting in or taking some object out of the drawer) were mostly confused with each other, and thus, need improvements. These interactions may have very similar movements and hand positions when interacting with the drawer, resulting in their lower recognition performance.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Object** & **Interaction** & **TP** & **FN** & **FP** & **TN** & **F1** \\ \hline
**Toy car** & Pick and place & 79 & 21 & 19 & 116 & 0.80 \\  & Push toy car & 44 & 21 & 30 & 140 & 0.63 \\  & Put it in the drawer & 19 & 24 & 19 & 173 & 0.47 \\  & Take it out of the drawer & 18 & 7 & 4 & 206 & 0.74 \\ \hline
**Trash can** & Open and close & 69 & 14 & 15 & 68 & 0.83 \\  & Throw something in it & 68 & 15 & 14 & 69 & 0.83 \\ \hline
**Average** & & & & & & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 6: Object-wise F1-score for interaction recognition.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 3 Nature of Input Stylometric Space
### Stylometric Characteristic Features  
The uniqueness of writing styles is the fundamental and underlying notion of stylometry. Thoughts and ideas presented in a written form capture the author's individuality in preferences with respect to applied language features. The elements, which are observable in many text samples irrespective of the subject matter or specific genre, constitute writer prints [27]. Most often, they refer to the lexical aspects of sentence construction, and syntactic annotation also plays a role.  
Unusual linguistic choices are more easily detected, so they are also more prone to falsification. Safer and more reliable descriptors of writing styles could be found among common parts of speech, in particular popular function words and regular punctuation marks. Stylometric markers typically refer to frequencies, averages, and distributions, which makes them continuous-valued. The nature of the input domain imposes some limitations on techniques and methods that can be involved in text analysis or requires discretisation to become part of the data preparation procedure [6].
## 2 Materials and Methods
### Eddy Covariance Data  
An eddy covariance (EC) tower was setup in the centre of the study area in 2017. The CO\({}_{2}\) and H\({}_{2}\)O measurements were performed by a closed-path EC system with adjacent weather station providing supporting climate and meteorological data. The EC system was a Li-7000 infrared gas analyser (IRGA, for CO\({}_{2}\) and H\({}_{2}\)O mixing ratios, Li-COR inc., Lincoln, NE, USA), a sonic anemometer (wind velocity, sensible heat flux and sonic temperature components R3-50, Gill Instruments Ltd., Lymington, UK) mounted 2.5 m above the soil surface on an instrument tower. Air samples pass through a heated intake tube at a flow rate of 10 L min\({}^{-1}\), (a PTFE tube, internal diameter 6 mm, length 8 m) with two 1.0 \(\upmu\)m pore size filters (Gelman(r)). The IRGA was housed in a climate-controlled cabin and calibrated monthly during the growing season. Supporting climate data were net radiation, relative humidity, photosynthetically active radiation, soil temperature, volumetric soil water content (at 5 and 20 cm depths) and air pressure. A CR3000 (Campbell Scientific Inc., Logan, UT, USA) 10 Hz data logger collected raw EC data. Missing supporting meteorological data were gap filled using data from the Maaninka weather station (Finnish Meteorological Institute), located 6 km southeast of the field site. Eddy covariance data processing was carried out using EddyUH [37]. Annual EC data for the period of the experiment (20 May 2017 to 31 May 2020) were employed for calibration and evaluation purposes.  
\begin{table}
\begin{tabular}{l c} \hline
**Unit** & **Value** \\ \hline Soil pH & 5.8 \(\pm\) 0.19 \\ EC mS m\({}^{-1}\) & 14 \(\pm\) 2.4 \\ SOM\% & 5.2 \(\pm\) 0.9 \\ SOC\% & 3.0 \(\pm\) 0.52 \\ C/N ratio & 15 \(\pm\) 0.4 \\ Total N\% & 0.2 \(\pm\) 0.03 \\ P mg L\({}^{-1}\) & 5.4 \(\pm\) 1.28 \\ K mg L\({}^{-1}\) & 104 \(\pm\) 12.9 \\ \hline \end{tabular}
\end{table}
Table 1: Topsoil (0–5 cm) measurements in the Antilla site.  
Figure 1: Measured air temperature (\({}^{\circ}\)C) and precipitation (mm) per day over the modelled period. (Data obtained from Finnish Meteorological Institute (FMI).
## 4 Engineering Application
### Model Performance Analysis
#### 4.3.3 Comparison of Fusion Effects of Anomaly Detection and Identification  
The proposed AD_SI model uses the scenario identification model to determine the scenarios of the detected abnormal sections after the anomaly detection model detects continuous anomalies. It integrates the results of the anomaly detection model and scenario  
Figure 8: Results of anomaly detection models based on different monitoring parameters.  
identification model to make the final judgment. A single-anomaly detection model may have certain false positives or false negatives, but incorporating scenario identification as supplementary information can help refine the anomaly detection results. Therefore, this fusion method combines results from two perspectives, further enhancing the accuracy and robustness of anomaly detection. Figures 9-11 demonstrate the performance of the AD_SI model and all other comparative models on three abnormal events. It can be observed that the anomaly detection performance of the AD_SI model has been further improved, with its \(F1\) score being consistently higher than that of any other model.  
Figure 10: The performance of each anomaly detection model on abnormal event 2.  
Figure 9: The performance of each anomaly detection model on abnormal event 1.  
[PERSON] et al. [7] designed an anomaly detection model KMCED driven by data and knowledge. They used the same shield tunneling project as the research object to detect abnormal event 3. We compared the detection performance of the KMCED model and the AD_SI model (Figure 11). Both KMCED and AD_SI demonstrated good capabilities in capturing abnormal event 3, with recall values exceeding 0.9. However, KMCED had a higher false alarm rate (precision = 0.6), leading to the imprecise localization of the actual range of anomalies and impacting the reliability of the anomaly detection results. Therefore, the AD_SI model performed significantly better in detecting abnormal event 3 (\(F1\) = 0.81) compared to the KMCED model proposed in reference [7] (\(F1\) = 0.56).
## 3 Materials and Proposed Method
### Hyperparameter Optimization (HPO)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyperparameter** & **Description** & **AlexNet Range** & **VGG16 Range** & **CNN Range** & **MLP Range** \\ \hline \multirow{2}{*}{Kernel Size} & Convolutional & \multirow{2}{*}{\begin{tabular}{c} 3, 5, 7, 11 \\ layer kernel size \\ \end{tabular} } & \multirow{2}{*}{\(3,5,7,11\)} & \multirow{2}{*}{\(3,5,7,11\)} & \multirow{2}{*}{\(3,5,7,11\)} & \multirow{2}{*}{-} \\ \cline{1-1}  & & & & & \\ \hline \multirow{2}{*}{Filter number} & Convolutional & \multirow{2}{*}{\begin{tabular}{c} Min\_value = 64, \\ layer neurons \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Min\_value = 64, \\ 128, 256 \\ Max\_value = 256, \\ 384, 512 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Min\_value = 264, \\ Max\_value = 256, \\ 384, 512 \\ \end{tabular} } & \multirow{2}{*}{-} \\ \hline \multirow{2}{*}{Layer Depth} & Number of layers & \multirow{2}{*}{\begin{tabular}{c} 1, 2, 3 \\ in the network \\ \end{tabular} } & \multirow{2}{*}{\(1,2,3\)} & \multirow{2}{*}{\(1,2,3\)} & \multirow{2}{*}{-} \\ \cline{1-1}  & & & & & \\ \hline \multirow{2}{*}{Neuron Count} & Final FC layers & \multirow{2}{*}{\begin{tabular}{c} Min\_value = 1028, \\ Max\_value = 4500 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Min\_value = 1028, \\ Max\_value = 4500 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Min\_value = 28 \\ Max\_value = 256 \\ 4500 \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Min\_value = 256 \\ Max\_value = 256 \\ 1028 \\ \end{tabular} } \\ \hline \multirow{2}{*}{Learning Rate} & Updating weight & \multirow{2}{*}{
\begin{tabular}{c} 10\({}^{-2}\), 10\({}^{-3}\), 10\({}^{-4}\) \\ while training \\ \end{tabular} } & \multirow{2}{*}{\(10^{-2}\), \(10^{-3}\), \(10^{-4}\)} & \multirow{2}{*}{\(10^{-2}\), \(10^{-3}\), \(10^{-4}\)} & \multirow{2}{*}{\(10^{-2}\), \(10^{-3}\), \(10^{-4}\)} \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters and their range for the four models.
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 3 Results and Discussion
### Internal Imaging of the Mountain Using Simulated Data
#### 3.2.2 Scenario 2: The Detector Is Placed outside the Mountain
The southern part of each image is not drawn because it is within the detection range of the anti-coincidence detector. The densities of (**a**–**h**) are 0, 0.7, 1.4, 2.1, 3.5, 4.2, 4.9, and 5.6 g/cm\({}^{3}\), respectively. The color shows the ratio \(r\) of the number of muons passing through the mountain within the density variation regions to the number of muons passing through the reference mountain. The division is weighted according to the data acquisition time. We set the \(r\) of the bins with the statistical significance \(<3\sigma\) to 1, which means there is no difference from the reference one, when plotting. The ratio uncertainty \(r_{\text{unc}}\) of the colored bins satisfies \(r_{\text{unc}}\leq|r-1|/3\). For a six-month data taking time and at least a 25% (\(\leq\)2.1 or \(\geq\)3.5 g/cm\({}^{3}\)) density variation from the reference one, we find that the colored areas in the radar image successfully show the locations of the density variation region. The southern part of each image is not drawn because it is within the detection range of the anti-coincidence detector. The size of the region is 0.30 km, as defined in Section 3.2.1. The greater the density deviation, the greater the number of colored bins, the darker the color, and the better the imaging performance. The total event numbers are 81.9 k, 60.5 k, and 57.1 k for the region densities set to 0, 2.8 (reference), and 5.6 g/cm\({}^{3}\), respectively. The difference between the event number and the reference one can be used to measure the ability of the MT to distinguish the density variation regions.
## 4 Experiments  
### Dataset Construction and Implementation Details  
In the present study, 740 crack images with different background characteristics were selected from a public bridge crack dataset [56], in which 500 and 240 images were chosen for training and testing of the proposed DO-YOLOv4 for crack detection, respectively. Both training and testing crack images were labeled with a series of oriented bounding boxes tightly enclosing the cracks by professional annotation software roLabelImg [57].  
The present DO-YOLOv4 was implemented based on the open-source deep learning library PyTorch [58], in which the optimization algorithm Adam [59], widely used in object detection, was selected to train DO-YOLOv4. In this study, the training was performed for 100 epochs with a batch size of 2 for 256 \(\times\) 256 input images. The initial learning rate of the Adam algorithm was set as 0.001, and the decay rate for the learning rate was taken as 0.9 for each epoch. The Complete Intersection over Union (CIoU) loss function [60] and smooth L1 loss function [19] were adopted to optimize the parameters \((c_{x},c_{y},w,h)\) and \(\theta\) of the oriented bounding box, respectively, and the Focal loss function [61] was utilized for the classification of crack or background contained in the oriented bounding boxes.  
On the basis of crack detection by DO-YOLOv4, the IPTs were further employed in the present hybrid approach for crack segmentation, which was implemented based on the computer vision library OpenCV [62]. The region size for determining the mean pixel intensity, as illustrated in Figure 7, was taken to be \(5\times 5\) to \(21\times 21\) pixels, depending on  
Figure 8: The process of connected component analysis.  
Figure 7: The adaptive thresholding method for crack segmentation.  
the width of the crack, and the constant \(C\) is taken as 5 for determination of the adaptive threshold. In addition, if the size of the connected component was found to be smaller than 30 pixels by the connected component analysis, the pixels contained in this component were classified as false crack pixels and should be removed.  
All experiments were implemented on a computer with the Intel\({}^{\circledR}\) Core\({}^{\circledR}\) i5-10400 CPU @ 64-bit 2.90 GHz, 16 GB RAM (Intel, Santa Clara, CA, USA), and NVIDIA GeForce RTX 3080 GPU (NVIDIA, Santa Clara, CA, USA).
## 3 Results  
### Chemical Analysis of Feeds  
Results for chemical analyses of the varying rabbit feeds used in this study are presented in Table 1 below. The results are expressed as wet basis.  
Results revealed that chemical components differed, considering the composition of feed mixtures. The CP% of the diet fed to GC, G1, and G2 were 15.31, 22.25, and 17.42%, respectively. The S1-fed to G1--consisting of a linseed meal, ground corn, and fodder yeast--had CP% per component of 25.62%, 7.04%, and 46.69%, respectively. The S2-fed G2--containing PM and ground corn--had CP% per component of 19.9% and 7.04%, respectively (Table 1).  
Peak EE% was observed in the PM (21.87%), owing to a higher concentration of linseed meal, which had 15.76% fat, followed by the S2-fed G2 (10.86%). EE% for S1-fed G1 and basic diet were 7.08 and 5.88%, respectively (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Feed/Analysis** & **DM\%** & **M\%** & **CP\%** & **EE\%** & **CP\%** & **NFE\%** & **ME kcal/kg** \\ \hline Basic feed / alfalfa & 89.76 & 10.24 & 15.31 & 5.88 & 18.38 & 64.42 & 3703.6 \\ S1 for G1 & 85.99 & 14.01 & 22.25 & 7.08 & 24.40 & 31.74 & 3473.2 \\ S2 for G2 & 84.54 & 15.46 & 17.42 & 10.86 & 28.38 & 24.20 & 3756.6 \\ Ground corn & 85.68 & 14.32 & 7.04 & 2.3 & 3.50 & 73.06 & 3405.4 \\ Linseed meal & 91.49 & 8.51 & 25.62 & 15.76 & 9.29 & 46.99 & 4318.8 \\ Fodder yeast & 91.18 & 8.82 & 46.69 & 1.22 & 5.60 & 39.32 & 3541.2 \\ PM (linseed meal & 91.25 & 8.75 & 19.9 & 21.87 & 30.55 & 15.85 & 4613.1 \\ + selenium yeast) & & & & & & & \\ \hline \hline \end{tabular}  
* S1—supplement 1 for group 1; S2—supplement 2 for group 2; PM—prepared-mixture; DM—dry matter, M—moisture; CP—crude protein; EE—ether extract CF—crude fiber; NFE—nitrogen-free extract ME—metabolizable energy.  
\end{table}
Table 1: Chemical composition analysis of the studied feeds.  
In terms of crude fiber, the PM had peak value of 30.55%, followed by the S2-fed G2, with a fiber percentage of 28.38%, and the S1-fed G1 having 24.40% CF. Furthermore, the basic diet (consisting of alfalfa pellets) had a fiber value of 18.38%, while linseed meal had 9.29% CF. Ground corn had the lowest CF% (3.50%) (Table 1).  
The NFE for rabbit feed components included in the study ranged from 39.32 to 73.06%, with PM value at 15.85%, whereas basic diet PM was 64.42%. Furthermore, NFE for the S1-fed G1 and S2-fed G2 were 31.74 and 24.20%, respectively (Table 1).  
Regarding the energy value of the investigated diets, by comparing the results obtained across all dietary datasets, it can be observed that peak ME value was identified for the S2-fed G2 group, followed by the basic diet group (3703.6 kcal/Kg). The lowest ME was observed in the S1-fed G1 group (3473.2 kcal/Kg). The energy content of PM was determined as 4613.1 kcal/Kg (Table 1).
## 3 Results and Discussion
### Effects of Alloy Type and Temperature on Hydrolysis Hydrogen Generation
Figure 8: The hydrogen generation yield curves of (**a\({}_{1}\)**) Mg-10 Zn, (**b\({}_{1}\)**) Mg-20 Zn, and (**c\({}_{1}\)**) Mg-30 Zn; hydrogen generation rate curves of (**a\({}_{2}\)**) Mg-10 Zn, (**b\({}_{2}\)**) Mg-20 Zn, and (**c\({}_{2}\)**) Mg-30 Zn; and the temperature variation curves of (**a\({}_{3}\)**) Mg-10 Zn, (**b\({}_{3}\)**) Mg-20 Zn, and (**c\({}_{3}\)**) Mg-30 Al. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Reaction** & **Maximum Hydrogen** & \multirow{2}{*}{**Conversion**} & **Reaction** & **Induction** \\  & **Temperature** & **Generation** & & **Yield (\%)** & **n Time** & **Time (s)** \\ \cline{3-6}  & (\({}^{\circ}\)**C**) & **Rate (L/min)** & & **(s)** & **Time (s)** \\ \hline \multirow{4}{*}{Mg-10 Al} & 420 & 0 & 0 & — & — \\  & 430 & 10.115 & 89 & 134 & 321 \\  & 460 & 12.365 & 89 & 120 & 302 \\  & 490 & 13.441 & 89 & 106 & 273 \\  & 520 & 17.151 & 89 & 98 & 254 \\  & 550 & 21.022 & 89 & 85 & 225 \\ \hline \multirow{4}{*}{Mg-20 Al} & 420 & 12.028 & 92 & 125 & 332 \\  & 430 & 13.377 & 92 & 113 & 294 \\  & 460 & 15.052 & 92 & 101 & 268 \\  & 490 & 17.413 & 92 & 90 & 244 \\  & 520 & 20.392 & 92 & 74 & 216 \\  & 550 & 23.543 & 92 & 63 & 199 \\ \hline \multirow{4}{*}{Mg-30 Al} & 420 & 13.789 & 97 & 117 & 270 \\  & 430 & 15.877 & 97 & 109 & 247 \\ \cline{1-1}  & 460 & 17.159 & 97 & 97 & 236 \\ \cline{1-1}  & 490 & 20.638 & 97 & 82 & 212 \\ \cline{1-1}  & 520 & 23.013 & 97 & 68 & 198 \\ \cline{1-1}  & 550 & 24.141 & 97 & 57 & 174 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hydrogen generation performance of the Mg-Al alloy samples in water vapor.