esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vision-Based Measurement System
### Measurement of Position and Orientation  
The relative position and orientation from different targets to cameras are unified by the well-known intrinsic and extrinsic parameters. The intrinsic parameters matrix \(K\) satisfies:  
\[Z_{c}\left[\begin{array}{c}\bar{u}\\ \tilde{v}\\ 1\end{array}\right]=\left[\begin{array}{ccc}a_{x}&0&u_{0}\\ 0&a_{y}&u_{0}\\ 0&0&1\end{array}\right]\left[\begin{array}{c}X_{c}\\ Y_{c}\\ Z_{c}\end{array}\right]=K\mathbf{P_{c}} \tag{1}\]  
which is \(Z_{c}P_{x}=K\mathbf{P_{c}}\), where \(a_{x}=f_{c}/dx,a_{y}=f_{c}/dy\), and \(X_{c},Y_{c},Z_{c}\) are the coordinates of three dimensional points in space; \(\bar{u},\bar{v}\) are the coordinates of the ideal imaging point on the image plane in the image coordinate system; \(f_{c}\) is the focal length; and \(a_{x},a_{y}\) is the equivalent focal length of the camera in the x,y direction.  
The position and orientation measured from the vision marker to the camera are expressed by transformation matrix \(\mathrm{cam}\mathrm{T}_{\mathrm{vm}}\). The cubic prism is fixed on the EVS and used for the calibration from camera to cubic prism, which is represented as \(\mathrm{c}\mathrm{PT}_{\mathrm{cam}}\). The EVS fixed on the EEF and matrices \(\mathrm{{}^{\mathrm{EEF}}T_{cp}}\) are calibrated from the cubic prism to the EEF tool coordinate. The pose information relative to the tool coordinate can be expressed as:  
\[\mathrm{{}^{\mathrm{tool}}T_{\mathrm{vm}}}=\left[\mathrm{{}^{\mathrm{EEF}}T_{ \mathrm{tool}}}\right]^{-1}\mathrm{{}^{\mathrm{.EEF}}T_{cp}\cdot^{cp}T_{cam} \cdot^{cam}T_{\mathrm{vm}}} \tag{2}\]  
where \(\mathrm{{}^{\mathrm{EEF}}T_{cam}}=\mathrm{{}^{\mathrm{EEF}}T_{cp}\cdot^{cp}T_{ cam}}\) is the extrinsic parameters, which are measured accurately \"before\" flight and do not change significantly \"during\" the flight. The control is proposed to move the payload to the desired position, \(\mathrm{{}^{\mathrm{tool}}T_{\mathrm{vm}}}\) is given as the desired berthing  
Figure 3: Composition block diagram of the EVS.  
Figure 2: The vision system of the EMM.  
position relative to the visual marker coordinate, which is listed in the central controller through external command. Then, the servo error can be estimated in Equation (3):  
\[\mathrm{{}^{tool_{d}}T_{Tool}=^{tool_{d}}T_{vm}\cdot\begin{bmatrix}\mathrm{{}^{ tool}T_{vm}}\\ \end{bmatrix}^{-1}=\begin{bmatrix}\mathbf{R_{e}}&\mathbf{e_{p}}\\ 0&1\\ \end{bmatrix} \tag{3}\]  
The servo controller that minimizes the tracking errors is defined as \(\mathbf{e}=\begin{bmatrix}\mathbf{e}_{p}^{\mathsf{T}},\mathbf{e}_{o}^{\mathsf{T}}\end{bmatrix} ^{\mathsf{T}}\in\Re^{6}\), where \(\mathbf{e}_{p}\) and \(\mathbf{e}_{o}\) are the position and orientation errors, respectively, where \(\mathbf{e}_{o}=\theta\mathbf{a}\) is computed by angle/axis parameterization for the rotation, which gives the minimum travel distance from the initial to the berth orientation and keep the marker at the center of the camera field of view.
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 2 Research Methods  
### Krr  
The KRR regression model employs a kernel function to nonlinearly transform the original sample data and map it to a high-dimensional space [32; 33]. By weighting the regularization terms and the \(L_{2}\) norm with these parameters, the model effectively prevents overfitting and enhances its generalizability. For the training set, \(\{(x_{i},y_{i})\}_{i=1}^{n}\), \(x_{i}\in\mathbb{R}^{p}\), and \(y_{i}\in\mathbb{R}\) are the \(i\)-th observation and response variables, respectively. The count of observations is denoted as \(n\), whereas the count of independent variables is denoted as \(p\). The loss function is expressed as follows:  
\[f=\operatorname*{argmin}_{f\in\mathcal{H}}\left\{\frac{1}{n}\sum _{i=1}^{n}\|f(x_{i})-y_{i}\|_{2}^{2}+\lambda\|f\|_{\mathcal{H}}^{2}\right\},\lambda>0, \tag{1}\] \[f(x_{i})=\sum_{j=1}^{n}\hat{\alpha}_{j}\kappa(x_{j},x_{i}), \tag{2}\]  
where \(\left\|\cdot\right\|_{\mathcal{H}}^{2}\) is the \(L_{2}\) norm form in the function space, \(\lambda\) is the regularization parameter, and \(\kappa(\cdot,\cdot)\) is the kernel function. Given an \(n\times n\) kernel matrix, \(K_{N}\), the KRR estimate is given as follows:  
\[\hat{\alpha}=(K_{N}+\lambda nI)^{-1}y, \tag{3}\]  
\[\hat{g}=\sum_{i=1}^{n}\hat{\alpha}_{i}\kappa(x_{j},x_{i}), \tag{4}\]where \(x\) represents the provided sample, and \(\hat{y}\) stands for the predicted value. We use cross-validation to calculate the minimum value of the Bayesian information criterion (BIC) for the training set to select the best model's \(\lambda\) value.
## 3 Results  
### Simulation Results  
Figures 7-9 illustrate the simulation results. Requirements of the pulse width and bandwidth were 12 us and 500 MHz, respectively. Figure 7 illustrates the simulation results using conventional MDDS. Figure 7a is the output signal, and Figure 7b is the phase value. The chirp waveform center illustrates a non-central axis due to the phase error depicted in Figure 7b. The phase error is produced when the MDDS is used because the phase timing of conventional DDS has been changed. To compensate for the phase error, we added the CPE block, which can produce the ideal output.  
Figure 6: Developed MDDS waveform generator.  
Figure 7: Simulation results with phase error using conventional MDDS. (**a**) MDDS output signal; (**b**) phase value over time.  
The output of MDDS with the compensated phase error is depicted in Figure 8. We confirmed that the phase error is compensated for when comparing Figures 7a and 8a. In Figure 7b, the phase center is above the centerline, but the phase center in Figure 8b fits the centerline. This enables MDDS to produce the ideal output, and the center of the chirp waveform has the ideal value. Although the ideal chirp waveform is applied to the hardware, the final waveform is changed due to distortion, such as that caused by hardware characteristics and RF.  
Figure 9 is an example of pre-distortion. The pre-distorted waveform is not ideal, but it can minimize the phase error of the RF-modulated signal. We applied pre-distortion in nanosecond units to the FPGA.
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.8 Autoregressive Feature  
The autoregressive (AR) feature samples each activity signal as a linear combination of the previous sample plus the error sequence. These features map the particular pattern of an activity and return a true value if the pattern is detected. It is specifically used for feature extraction and it is defined as:  
\[Auto_{(reg)}=\sum_{n=0}^{N}a_{i}f_{n-i}+\omega_{n}, \tag{6}\]  
where \(f_{n-i}\) is the \(n\)-\(i\) feature of the sample data, \(a_{i}\) represents the AR coefficient and the current sample of the sensor signal \(n\) is the error sequence. The second- and fourth-order ARs are used in this paper since they give the best results for each activity pattern.
## 5 Experimental Results
### 25.78-Gbit/s Optical Signal Transmission  
Tx optical and Rx electrical waveforms when all channels (4-ch) are active and transmit in loop-back MMF link are shown in Figure 10. Data rate, amplitude, and pattern length of the input electrical signal were set to 25.78 Gbit/s, 500 mVppd, and 2\({}^{31}\)-1 pseudo-random binary sequence (PRBS), respectively. Bias and modulation currents of the VCSEL were set to 6.5 mA and 8.3 mA\({}_{\text{P-P}}\), respectively. The measured waveforms show clear eye openings for both the transmitter and receiver at room temperature (\(T_{\text{c}}\) = 25 \({}^{\circ}\)C) and high temperature (\(T_{\text{c}}\) = 70 \({}^{\circ}\)C).  
Measured bit-error-rate (BER) characteristics for crosstalk are shown in Figure 11. Results show that the measured eye aperture at BER = \(10^{-12}\) is more than 0.60 UI under all channels (4-ch) operation. The measured crosstalk jitter penalty is smaller than 0.03 UI (between 1-ch and 4-ch operations) in good agreement well with the simulation results shown in Figures 4 and 9.  
Figure 8: Layout of signal transmission lines and vias.  
Figure 10: Measured waveforms (**a**) \(T_{\text{c}}\) = 25 \({}^{\circ}\)C and (**b**) \(T_{\text{c}}\) = 70 \({}^{\circ}\)C.  
Figure 9: Measured insertion-loss-to-crosstalk ratio (ICR) characteristic.  
The BER characteristic of the AOC is shown in Figure 12. The result shows that the AOC demonstrated 25.78-Gbit/s error-free transmission under all channels (4-ch) operation and both at \(T_{\rm c}\) = 25 and 70 \({}^{\circ}\)C. According to Figure 12, minimum received optical sensitivities were about \(-\)8.3 dBm at \(T_{\rm c}\) = 25 \({}^{\circ}\)C and about \(-\)9.0 dBm at \(T_{\rm c}\) = 70 \({}^{\circ}\)C. The measured BER power penalty is smaller than 0.7 dB under high temperature (\(T_{\rm c}\) = 70 \({}^{\circ}\)C). Moreover, the measured BER power penalty less than 1.5 dB for 100-m MMF link was experimentally demonstrated. Minimum received optical sensitivity was about \(-\)6.9 dBm over 100-m MMF link under all channels operation and high temperature (\(T_{\rm c}\) = 70 \({}^{\circ}\)C). These results indicate that the proposed AOC, with highly efficient heat dissipation, achieved 25.78-Gbit/s operation.  
Figure 11: Measured bath-tub curve.  
Figure 12: Measured bit-error-rate (BER) characteristics. NRZ, non-return to zero.
# Mechanical Performance of Confined Autoclaved Fly-Ash-Brick Masonry Walls under Cyclic Loading
## 3 Numerical Simulation Analysis and Verification
### Holes and Seismic Performance of Assembly Autoclaved Fly-Aslt-Brick Walls
#### 4.3.2 Influence of Door Holes' Dimension on Seismic Performance of the Walls  
Compared to wall ZWZ-1 without an opening, walls ZWZ-7, ZWZ-8, and ZWZ-9, with different door hole dimensions were analyzed comparatively to study the influence of the different hole dimensions on the bearing capacity, stiffness degradation, and displacement ductility of the walls.  
The door holes of walls ZWZ-7, ZWZ-8 and ZWZ-9 were located at the center of the wall, as shown in Figure 25. Meanwhile, the dimensions and parameters of walls ZWZ-7, ZWZ-8 and ZWZ-9 are shown in Table 6. From Equation (9) and simulation results, the bearing capacity values of the walls with different door hole dimensions are shown in Table 9. The load-displacement and stiffness-displacement curves of the walls are shown in Figure 26. The stress nephogram of the walls with different door hole dimensions are shown in Figure 27.  
Figure 24: Stress nephogram of the walls with different window holes’ dimensions: (**a**) wall ZWZ-4; (**b**) wall ZWZ-5; (**c**) wall ZWZ-6.  
Figure 25: Wall models with different door holes’ dimensions: (**a**) wall ZWZ-7; (**b**) wall ZWZ-8; (**c**) wall ZWZ-9.  
According to Table 9, compared to wall ZWZ-1 without an opening, the cracking loads of walls ZWZ-7, ZWZ-8, and ZWZ-9 were decreased by 69%, 76%, and 89%, respectively; the ultimate loads were decreased by 50%, 67%, and 74%, respectively; the failure loads were decreased by 31%, 48%, and 68%, respectively; and the displacement ductility ratios were decreased by 40%, 46%, and 57%, respectively. The ultimate loads of the walls were reduced owing to the large door holes, and strengthening measures should be implemented to improve the displacement ductility and earthquake resistance capacity of these walls.  
As can be observed from Figures 26 and 27, with the increase in the door hole dimensions, the bearing capacity of the walls with the holes was decreased. The stress concentration near the hole was distinct. The color of the grid cell in the graph was deepened, indicating that the stress of the unit increased. Regarding the wall damage, the wall near the holes had a weak area, particularly in the corner, in which stress concentration could easily form. Therefore, the wall cracking of the subregion was premature, and the bearing capacity and stiffness of the walls were reduced
## 1 Introduction
The core principle steering the development of this system is centered around preserving the inherent data structure that delineates properties and captures their synergies, thus forming a comprehensive representation of the entities under scrutiny. The key aspects of the investigation include:  
1. Evaluating the performance of the HELIOS system in accurately defining homogeneous property groups;
2. Addressing challenges in data analysis and procedural approaches to determining homogeneity;
3. Offering recommendations for improving the definition of homogeneity in real estate markets, particularly for mass valuation processes. The distinctive hallmark of the proposed HELIOS system lies in its endeavor to assess the applicability of the LLM in precisely defining homogeneous objects. More specifically, it aims to address pivotal challenges encountered during the analysis phase and procedural approaches to determining homogeneity--an aspect poised for modification based on ensuing results. This investigation critically examines the efficacy of harnessing automated machine learning (ML) solutions to objectively articulate outcomes arising from the synergistic processing of data. Furthermore, the article prefers an extensive array of recommendations that specifically target the most challenging aspects inherent in defining homogeneity within the domain of human-machine analyses, particularly in the context of mass valuation, especially considering the magnitude of data processing involved. It must be underlined that the HELIOS concept, focusing on the intricate challenge of homogeneity in real estate markets, stands at the intersection of cutting-edge technological solutions and the imperative of sustainability. In a world increasingly mindful of environmental, social, and economic concerns, HELIOS is a novel concept grounded in linguistic intelligence and machine learning to reshape how we perceive and analyze real estate data. By exploring the synergies between human expertise and technological capabilities, HELIOS aims not only to enhance the efficiency of real estate analyses but also to contribute to the broader goal of sustainable and responsible data practices in the dynamic landscape of property markets. This article delves into the nuances of decision-making support systems, shedding light on their potential to drive positive change in the realms of real estate analytics and sustainability.
## 4 Results and Discussion
### Flexural Performance of Fiber-Reinforced Geopolymer Concrete  
#### 4.3.1 Flexural Strength  
The flexural test of fiber-reinforced geopolymer composites was carried out with 100 \(\times\) 100 \(\times\) 400 mm of specimens and a span length of 300 mm. The flexural strength of both specimens using polypropylene fiber and steel fiber are improved significantly, as shown in Table 9 and Figure 18.  
Figure 17: Compressive strength and splitting tensile strength relationship.  
When the volume fraction of fiber increased from 0.5% to 1.5%, the flexural behavior of PFRGC specimens were increased from 6.06% to 26.19%. At 0.5%, the flexural strengths of P1 and P2 specimens were 6.45 MPa and 6.30 MPa, compared with the 5.89 MPa of non-fiber specimen GC. At 1.0% and 1.5%, the improvement of flexural strength was higher, from 10.08% to 26.19%, and the greatest value was measured at series P2-15 with 7.98 MPa.  
In terms of HFRGC using steel fiber, the flexural strength is also enhanced strongly from 13.64% to 42.37%. With dosage of fiber at 0.5%, flexural strengths were 6.82 MPa and 7.02 MPa, according to 13.64% and 16.10% of the difference. The flexural strength continued to increase with 1.0% and came to highest value at 1.5%. The specimen H2-15 using 0.15% steel fiber in concrete mixture had the highest strength of 10.22 MPa with a large difference of 42.37%.  
Four series specimens P1, P2, H1 and H2 showed the best flexural performance when embedding 1.5% of fiber to the geopolymer concrete mixture. There were 7.98, 7.32, 9.65 and 10.22 MPa for P1, P2, H1, H2, respectively. Between the highest strength of PFRGC and HFRGC, the difference was 21.9% between 7.98 MPa and 10.22 MPa. The properties as well as the workability of fiber in concrete mixture were the main reason for this difference. Based on the properties of steel fiber, including tensile strength, elastic modulus, the specimens reinforced with steel fiber performed the higher behavior than those of specimens using polypropylene fiber. HFRGC exhibited a good influence on geopolymer composites, compared with PFRGC. Besides, hooked end steel fiber had a special shape at two ends, and its rough surface led to the increment of adhesion between steel fiber and base material (as shown in SEM photo analysis) and the improvement of significant flexural strength.
## 4 Results and Discussion
### Topic Modeling Analysis  
Topic Modeling is a text-mining tool frequently used for discovering the semantic constructs of the given text. It is a statistical modeling technique with a potential application for NLP domains like sentiment analysis. This study applies topic modeling to reveal the potential benefits of online education, as well as uncover the problems associated with it. The required preprocessing and data cleaning procedures are carried out on the dataset for applying topic modeling. The data from tweets have been transformed into an appropriate structural format for topic modeling. TF-IDF features are used to facilitate identifying the most significant terms in the corpus and a total of 4000 features are utilized. Topic modeling is performed on the tweets from positive and negative classes to identify the pros and cons of online education. Table 20 shows the LSA (Latent Semantic Analysis) results for positive tweets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technique** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{Textblob} & LSTM & 0.94 & 0.92 & 0.91 & 0.91 \\ \cline{2-6}  & CNN & 0.91 & 0.90 & 0.87 & 0.88 \\ \cline{2-6}  & CNN-LSTM & 0.92 & 0.89 & 0.88 & 0.89 \\ \cline{2-6}  & Bi-LSTM & 0.94 & 0.93 & 0.94 & 0.93 \\ \hline \multirow{4}{*}{VADER} & LSTM & 0.91 & 0.89 & 0.89 & 0.89 \\ \cline{2-6}  & CNN & 0.88 & 0.87 & 0.85 & 0.86 \\ \cline{2-6}  & CNN-LSTM & 0.89 & 0.87 & 0.86 & 0.86 \\ \cline{2-6}  & Bi-LSTM & 0.92 & 0.91 & 0.89 & 0.90 \\ \hline \multirow{4}{*}{SentiWordNet} & LSTM & 0.85 & 0.82 & 0.80 & 0.81 \\ \cline{2-6}  & CNN & 0.82 & 0.79 & 0.77 & 0.78 \\ \cline{2-6}  & CNN-LSTM & 0.82 & 0.79 & 0.77 & 0.78 \\ \cline{2-6}  & Bi-LSTM & 0.85 & 0.82 & 0.80 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Performance of deep learning models with each lexicon technique. Figure 6: Architecture of deep learning models used for sentiment classification. LSA is the most commonly used topic modeling approach that makes use of the distributional hypothesis which infers that the semantic of words can be obtained by analyzing the contexts of words. It indicates that, if words appear in a similar context, their semantics would be the same [53]. LSA can be used with different features, where this study uses TF-IDF. LSA results show that students, while learning through online education during the COVID-19 pandemic, protect themselves from the disease. The most often appearing words in subjects in LSA are online education, online courses, and COVID-19. The positive opinions about online education are summarized in Table 20. Similarly, topic modeling with LSA for negative words is shown in Table 21. The issues that students have concerning online education are highlighted in this table by topic keywords. The major issue of discussion is the lack of technical skills and network challenges in rural regions. Similarly, children's disability to grasp online education is a serious threat to the efficacy of online education.
## 4 Performance Evaluations
### Simulation Results
#### 4.2.2 Random Deployment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **M = 65/18.6\%** & **M = 75/21.4\%** & **M = 85/24.3\%** & **M = 95/27.1\%** & **M = 105/30.0\%** \\ \hline \multicolumn{6}{c}{**Communication Radius \(R\) = 150**} \\ \hline DV-hop & 192.2/79.7\% & 190.6/79.3\% & 186.4/79.5\% & 186.9/80.9\% & 188.2/81.5\% \\ Amorphous & 391.7/90.0\% & 382.4/89.7\% & 380.4/89.9\% & 382.0/90.6\% & 379.9/90.8\% \\ MDS-MAP & 225.4/82.7\% & 226.8/82.6\% & 230.2/83.4\% & 228.1/84.3\% & 230.6/84.9\% \\ PDM & 54.8/28.9\% & 52.1/24.4\% & 49.9/23.4\% & 47.8/25.2\% & 47.5/26.7\% \\ LSVR & 51.5/24.3\% & 49.0/19.7\% & 47.3/19.0\% & 46.8/23.7\% & 46.3/24.8\% \\ LSRR-LA & 38.9 & 39.4 & 38.3 & 35.7 & 34.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of ALE under different numbers of anchors in the random deployment network. Figure 6: The ALE results of C-shaped random networks. Figure 7: Comparison of ALE when \(R\) = 150 m and \(M\) = 70.  
performance regardless of whether it is in C-shaped, L-shaped, or D-shaped network; however, it is not the same in regular deployment networks, for it performs the best in the C-shaped network while it performs the worst in the D-shaped network. Similar to Figure 5, we also demonstrate the 3D localization errors in Figure 9 for a better comparison of positioning accuracy between LSRR-LA and DV-Hop. Our LSRR-LA still outperforms DV-Hop in the random deployment networks.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Remote Monitoring Applications in VANETs  
Remote monitoring applications in VANETs can be broadly categorized into emergency telemedicine, commercial driver safety, and mobile surveillance. The scope of these applications in vehicular networks is briefly explained next.
## 3 Experimental Setup
### Data-to-Image Conversion
#### 3.4.5 Id-DWT Image Construction  
The sequentially reshaped single-phase data were decomposed using the same method as the three-phase DWT approach. A Daubechies 4 (db4) wavelet was used, with decomposition performed up to Level 3.  
Due to the horizontal alignment of the time-series data from the reshape method, the horizontal detail coefficients (cH) at Level 3 were selected, as they best captured the fault characteristics. The extracted cH coefficients were used to construct a grayscale image.
###### Abstract  
Debonding detection of composite structures is a vital task as such damage weakens the structure leading to a failure. As adhesive bonding is a more preferable choice over the conventional mechanical fastening method, detecting debonding as early as possible could minimize the overall maintenance costs. For this reason, a vast amount of research in the nondestructive testing field is being conducted as we speak. However, most of the methods may require well-trained experts or heavy equipment. In this study, the piezoelectric (PZT) material-based method known as the electromechanical impedance technique is investigated to seek the possibility of making the technique very cheap and portable by temporarily attaching the sensor. Furthermore, ANSYS simulation studies using smaller PZT patches as small as 0.1 mm \(\times\) 0.1 mm are simulated to investigate the impedance signatures acquired from the simulations. The results show the possibility of using smaller PZT patches compared to the conventional PZT sizes of 10 mm \(\times\) 10 mm for a successful damage identification process.  
debonding; composite damage; electromechanical impedance; piezoelectric; nondestructive testing; FEM simulation +
Footnote †: journal: Applied Sciences