esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Vibrations caused by vehicles passing from the upper part of the lava tube are affected by a large number of factors. The faster the vehicle speed, the heavier the weight of the  
Figure 8: Decision tree using train data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Criteria** & **Levels** & **Number of Data** \\ \hline Vibration \textgreater{} 0.0139 & High & 16 \\
0.0094 \textless{} Vibration \textless{} 0.0139 & Middle & 16 \\ Vibration \textless{}0.0094 & low & 16 \\ \hline Total & & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Vibration dataset classification criteria.  
vehicle, and the lower the vibration resistance, the greater the vibration. However, the lava tube shape is not constant. Therefore, this paper analyzed the various geometric characteristics of lava tubes and the interaction effect on vibration. The design of experiments was used to analyze the mechanism by which vibration occurs according to the geometrical characteristics of lava tubes. When analyzing natural phenomena, both the main effect and the interaction effects should be analyzed. Accordingly, the multi-way factorial design was used to detect the main effect and all two-factor interaction effects. The lava tube is a natural heritage site, and the natural heritage is managed based on vibration. Therefore, vibration was selected as the characteristic value of the lava tube. Joint orientation, aspect ratio, and cover depth were selected as factors representing the geometrical characteristics of the lava tube, and vehicle speed was selected as an external influence factor. The conclusions of this study can be summarized as follows.  
1. Although the size of the main effect of the aspect ratio was the smallest among the internal feature factors of the lava tube, all the interaction effects with the other two factors (joint orientation, cover depth) showed significant effects. In particular, when the cover depth was 2 m, the vibration according to the level change in the aspect ratio increased significantly. It is believed that this result was obtained because the arcing effect is not easily expressed if the lava tube with a low cover depth has an elliptical shape with a long axis in the vertical axis.
2. Vibration reduced with an increasing aspect ratio and cover level. Through this, it is evident that the aspect ratio and the cover height are factors that suppress vibration.
3. Lava tubes with poor geometries, that is, lava tubes with a low cover depth and well-developed joints, in the shape of a semi-ellipse with a long vertical axis, are the most vulnerable to vibration, so the vehicle speed should be limited even if this inconveniences the traffic.
4. The relative effect plot and decision tree analysis on the coefficient of determination of the multiple regression model were able to explain and classify the main effect alone. It is not easy to explore interaction effects with these analysis methods.
## 3 Numerical Case Study
### Estimation Accuracy  
The estimation accuracy of SSI is demonstrated with similarity measure of VAF. VAF value is an indicator to quantify the relationship between the estimated and actual values of an obtained signal. Higher VAF values means that the estimates present a better fit into an existing signal, thus, better performance [43]. VAF criterion is calculated from the variance of the estimation residual \((y_{1}-y)\) on variance of the response signal \((y)\).  
\[\text{VAF}=\left(1-\frac{\text{variance }(y-y_{1})}{\text{variance }(y)}\right)\times 100 \tag{26}\]  
Figure 4: Principle angles in function of the block rows number (j) for subspace algorithms using CVA.  
Figure 5: The mode shapes and natural frequencies of the simulation mass–spring–damper system without considering damping effects.  
The extracted simulation response of the finite element model is used for the evaluation process. 100 different sets of noise patterns with the ratio of 30% were induced to the response signal in the fit analysis experiment. The VAF values of the noise-induced response signals are extracted and plotted in a Figure 6.  
The oscillation pattern of the weight matrices demonstrates that the UPC algorithm has the highest VAF value with less scattered data. It is an indicator of the outstanding prediction capability of the UPC method. The result of the PC and CVA method is only slightly worse. The lower ratios of the VAF values could be due to the short length of the response signal under influence of the damping effect.
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 5 Performance Evaluation in Practical Tests
### The Average Number of DIO Control Packets while Topology Changes  
The main objective of this test is to investigate the extent to which the proposed method is effective to preserve trickle timer; such that the transmission of unnecessary DIO messages are avoided. In other words, to what extent the creation of steady network graph can reduce the control overhead, and increase the available time of nodes as well. In the proposed method and BD-RPL, through providing a balance in the network graph, the degree restriction of parent nodes prevents the occurrence of congestion; therefore, the rate of DODAG Information Solicitation messages (DIS) and network instability is reduced in comparison with the base RPL, M-RPL, and A-RPL approach. In Figure 22, the average number of DIO messages was published in the network in the indoor environment over five times and averaged over time.  
This is mainly since, despite the existence of upper-threshold recognized instability in the network, a reset trickle timer that culminates in the transmissions of DIO messages, as well as some changes in the network graph are essential. An important note in this test is that the location of five nodes changed after the passage of 60 min from the initial start time. This is done to investigate and compare both approaches in terms of both the speed and the cost of restoring network graph in indoor and outdoor environments. According to the results of the test, the number of published DIO messages in the outdoor environment network is lower than the indoor environment in the proposed method of LA-RPL and BD-RPL, which indicates the stability of the network graph in these two protocols. Also, in the M-RPL and A-RPL methods, due to the lack of focus on the grid structure, there was no systematic and accountable management of the DIO message. The results of the five repetitions of the outdoor test are shown in Figure 23.  
Figure 22: The diagram of trickle timer control of DIO message in indoor environments.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
###### Abstract  
The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.  
Markov chain-based model; optimal number; multi-hop routing; non-associated nodes; energy efficiency +
Footnote †: journal: Sensors
# Design and Analysis of Dual-Band High-Gain THz Antenna Array for THz Space Applications
## 5 Conclusions  
In the present work, the dual-band THz antenna array is analyzed and compared with respect to design characteristic performance. The radiation properties of the proposed novel array antenna have been analyzed with multiple radiating elements. Furthermore, the effects of the separation distance among the radiating elements have also been analyzed for Type C antenna. The directivity and gain of the proposed novel THz array antenna are 12.5 and 11.23 dB, and 12.532 and 11.625 dBi, at 0.714 and 0.7412 THz with 99.76 and 96.6% radiation efficiency, respectively. The minimal return loss of the simulated type C antenna is examined in the parameter sweep, less than \(-37\) dB at a corresponding separation distance \(d\) of 533 \(\upmu\)m at a resonant frequency of 0.7123 THz. The effects of the variation in the number of radiating elements on the return loss and on the gain have also been observed, and it has also been concluded that with the increase in the number of radiation elements, the bandwidth of the antenna decreases significantly. Hence, the proposed array antenna is a potential candidate to be utilized for earth exploration satellite and space research.  
Conceptualization, W.S. and S.U.; data curation, W.S., S.U. and N.A.A.; funding acquisition, D.-y.C. and A.A.; methodology, W.S. and S.U.; project administration, W.S., S.U. and N.A.A.; resources, D.-y.C., S.U. and N.A.A.; software, W.S. and S.U.; supervision, S.U. and D.-y.C.; validation, W.S., N.A.A. and A.A.; writing--original draft, W.S.; and writing--review and editing, N.A.A. and W.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022R1I1A3064544).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Modeling and Analysis of Carrier-Phase Differential Positioning
### Modeling of Carrier-Phase Differential Positioning
\[\left(\varphi_{j}^{q}-\varphi_{i}^{q}\right)\lambda=-l_{j}^{q}dX-m_{j}^{q}dY- n_{j}^{q}dZ+c\left(V_{tr}^{i}-V_{tr}^{j}\right)+\left(N_{i}^{q}-N_{j}^{q} \right)\lambda+\left(\rho_{j}^{q}-\rho_{i}^{q}\right) \tag{7}\]  
By a double-differential between stations i.e., Equation (6) minus Equation (7), we can obtain the observation equation of carrier-phase as shown in Equation (8). \[\varphi_{ij}^{pq}=-\left(l_{j}^{q}-l_{j}^{p}\right)dX-\left(m_{j}^{q}-m_{j}^{ p}\right)dY-\left(n_{j}^{q}-n_{j}^{p}\right)dZ-\Delta N_{ij}^{pq}\lambda+L_{ij}^{pq} \tag{8}\]  
where \(\varphi_{ij}^{pq}=\varphi_{j}^{q}-\varphi_{i}^{q}-\varphi_{j}^{p}+\varphi_{i}^ {p}\), \(L_{ij}^{pq}=\rho_{j}^{q}-\rho_{i}^{q}-\rho_{j}^{p}+\rho_{i}^{p}\), \(\Delta N_{ij}^{pq}\), is the double-difference integer ambiguity. According to the double-difference observation equation of the carrier phase, the satellite clock error, the receiver clock error, and the error in the signal propagation path can be greatly reduced or even eliminated. If two dual-frequency GNSS receivers in a certain epoch have an observation of the same \(k+1\) satellites, a carrier phase double-differenceobservation equation can be established, and the linearized double-difference observation equation can be abbreviated as:  
\[L=\left[ \begin{array}{cc}A&B\end{array} \right]\left[ \begin{array}{c}X\\ N\end{array} \right]-\varepsilon \tag{9}\]  
where \(A\) is the \(k\times 3\) dimensional coordinate coefficient matrix; \(B\) is the \(k\times k\) dimensional integer ambiguity coefficient matrix; \(X\) is the three-dimensional baseline correction vector; \(N\) is an \(k\)-dimensional integer ambiguity vector; \(L\) is the \(k\)-dimensional difference vector between the double-difference observation and the calculated value; \(\varepsilon\) is the observed noise vector. Let \(A=\left[ \begin{array}{cc}A&B\end{array} \right]\),\(Y=\left[ \begin{array}{cc}X&N\end{array} \right]^{T}\), then the least squares solution of Equation (9) can be written as:  
\[\hat{Y}=\left(A^{\mathrm{T}}PA\right)^{-1}\!A^{T}PL \tag{10}\]  
where \(N_{0}=A^{T}PA\) is the parameter identification matrix; \(p\) is the weight matrix; \(Q_{Y}=N_{0}^{-1}\) is the covariance matrix.
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 4 Experimental Setup
These cases were selected in order to demonstrate the advantages of fusing technologies and the performance of the MLE and its different searching methods for localisation. Two experimental cases were carried out as shown in the high-density deployed scenario illustrated in Figure 3a. In Case 1, seven regular UWB anchors (in green) and three Google WiFi devices are used to show the performance of fusion with high-accuracy ranges. The numbers of UWB and WiFi devices were chosen in order to fully cover the scenario with at least three anchors or AP ranging data in the whole area. In Case 2, two UWB anchors in bad locations (in red wine colour) were added to the Case 1. In this scenario, UWB and WiFi will complement their information and improve the geometry of the problem by having the reference points more evenly distributed. Thus, with multi-technology fusion in trilateration the problem becomes over-determined for each point, and it also has more reference points distributed over a wider area (which means that the coverage of the overall system increases). As opposed to the scenario shown in Figure 3a, Figure 3b shows a realistic scenario with one regular UWB device, one WiFi AP and three LTE base stations distributed on the laboratories. With this deployment, the high-precision information availability is reduced to a small area. LTE is used in order to augment the availability of the positioning service. Nonetheless, with this deployment, a high precision can only be achieved when there are three high-precision reference points in range. When LTE is used, a low precision location is provided, which is still better than a full outage in location provision.
# Adaptive Beamformer Combined with Phase Coherence Weighting Applied to Ultrafast Ultrasound
###### Abstract  
Ultrafast ultrasound imaging is a promising technique for measurement of fast moving objects. In ultrafast ultrasound imaging, the high temporal resolution is realized at the expense of the lateral spatial resolution and image contrast. The lateral resolution and image contrast are important factors determining the quality of a B-mode image, and methods for improvements of the lateral resolution and contrast have been developed. In the present study, we focused on two signal processing techniques; one is an adaptive beamformer, and the other is the phase coherence factor (PCF). By weighting the output of the modified amplitude and phase estimation (mAPES) beamformer by the phase coherence factor, image quality was expected to be improved. In the present study, we investigated how to implement the PCF into the mAPES beamformer. In one of the two examined strategies, the PCF is estimated using element echo signals before application of the weight vector determined by the adaptive beamformer. In the other strategy, the PCF was evaluated from the element signals subjected to the mAPES beamformer weights. The performance of the proposed method was evaluated by the experiments using an ultrasonic imaging phantom. Using the proposed strategies, the lateral full widths at half maximum (FWHM) were both 0.288 mm, which was better than that of 0.348 mm obtained by the mAPES beamformer only. Also, the image contrasts realized by the mAPES beamformer with the PCFs estimated before and after application of the mAPES beamformer weights to the element signals were 5.61 dB and 5.32 dB, respectively, which were better than that of 5.14 dB obtained by the mAPES beamformer only.  
adaptive beamformer; coherence factor; lateral spatial resolution; image contrast +
Footnote †: journal: Applied Soft 2018
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 4 User Study
### Apparatus, Experiment, and Experimental Design  
#### 4.1.1 Apparatus  
Our hardware equipment for the implementation of GazeGuide included the following:  
1. A laptop running on Windows 10 operating system with Intel (R) Core (TM) i9-8950 HK, 32-GB random access memory, and an NVIDIA Geforce GTX 1080 GPU (remote computer at User-End).
2. A pair of Pupil Labs eye trackers [7] with a binocular mount for the HTC vive HMD (virtual reality setup) running at 120 Hz connected to the laptop.
3. A DJI Zenmuse-X3 gimbal (Table 2) camera mounted on the DJI Matrice-100 UAV with v.1.3.1.0 firmware and the TB47D battery.
4. DJI Manifold [57] based on the ARM architecture with Quad-Core running on Ubuntu 14.04 LTS with 2-GB random access memory mounted (as the onboard computer at UAV-End) and connected to UAV through a UART cable.  
The software setup included the following:  
1. User-End: Pupil Lab's APIs were used to roll our GazeMapper (Algorithm 1) in Python. OpenCV was used to stream real-time video from the UAV's camera to the HMD.
2. UAV-End: DJI provided a well-documented software development kit (SDK) and APIs to control the UAV and the mounted gimbal camera. Hence, the camera controller was implemented by DJI SDK using an open-source robot operating system [58] framework and its ecosystem in Python. All the data exchange between the different parts of GazeGuide was achieved over a wireless network through the TCP/IP sockets.
## 3 Neural-Network-Based Interference Cancellation in LIS Systems
### Neural Network Architecture
* ReLU Activation: Another reluLayer to enhance non-linearity and robustness. Figure 2: Block diagram of the MRC/EGC with neural receiver. * Fully Connected Output Layer: A fullyConnectedLayer with two neurons, reconstructing the in-phase and quadrature components of the transmitted signal. * Regression Output Layer: A regressionLayer to optimize the network for minimizing signal reconstruction errors. This configuration was selected after an extensive evaluation of various network depths and neuron configurations. More complex architectures with additional layers and neurons did not provide significant performance improvements, while reducing the network's depth led to a noticeable degradation in signal reconstruction accuracy.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 2 Characteristic Functions and Parameters of PMs versus GSs
### 2.1. GSs and PM Scanning Heads
For other applications where GSs of different apertures are utilized (higher, as in laser manufacturing, for example [60]), the values of such limits and dependences are different, but the phenomenon is similar [10]. In contrast, for PMs, the FOV is constant and irrespective of the rotational velocity, but dependent on the dimensions of the PMs. The latter are determined in the literature using a simple, common design algorithm that addresses the calculus of \(f_{\rm s}\)[1], as discussed in Section 6. In terms of geometrical parameters, GSs are characterized by the mirror aperture and the distance \(L\) from the mirror pivot O (which is commonly the incidence point of the incoming laser beam) to the scanned plane or lens. In the latter case, \(L\) is equal to the object focal distance \(f\) of the lens, as shown in Figure 1a. In contrast, PMs have three more main geometrical parameters: \(R\), the apothem of the polygon; \(e\), the eccentricity of the fixed incident laser beam from the pivot O; \(n\), the number of PM facets, which gives the angle that corresponds to half of a PM facet, \(\alpha=\pi/n\)--Figure 2a. Other constructive parameters of PMs in Figure 2a, such as facet width \(a\) and thickness \(b\), are necessary only for the FEA (and not for the optical analysis), therefore they are considered only in Sections 5 and 6. As, in general, an objective lens is utilized for both setups (with GS or PM), both scanners are also characterized by its diameter \(D\) (or aperture in the meridian plane O\(yz\)). In most of this study, to simplify the mathematical discussion, only the axis of the laser beam is considered, therefore it is as if the beam is reduced to its center ray, a situation that may roughly correspond to a well-collimated laser beam. We return to this simplifying hypothesis in Section 3, where a finite diameter \(2\rho\) of the beam is considered, as well. The advantage of the theory to be developed is that, as observed from Figure 2b, a variation of the eccentricity \(e\) can consider this beam radius \(\rho\) in the equations to be deduced, as demonstrated further on in Section 3.5.
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 2 Dataset and Preprocessing  
### Data Collection  
As for our ground truth dataset, a self-built Raman spectrometer was employed to collect the Raman spectra for this study. This spectrometer was specifically designed for the detection of organic chemicals, offering high sensitivity and accuracy in capturing Raman signals. The system includes a 785 nm excitation laser with a 600 mW capacity, a spectrum range of 792-954 nm, a resolution of 0.7 nm, and an S13256 CCD detector. A photo of the spectrometer is shown in Figure 1 to illustrate the experimental setup. The main parts include the ethernet interface, power source, control line, laser source light inlet of the spectrometer, and the probe.  
The dataset consists of 83 sets of Raman spectral data, which include 38 sets of Raman spectra for pure substances (containing five types of food additives) and 45 sets of Raman spectra for mixtures. The mixtures were created by dissolving food additives in different proportions, with some sets having no additive at all (proportion = 0). Each set contains 10 individual Raman spectra, leading to a total of 830 Raman spectral entries.  
The dataset encompasses a diverse range of organic chemicals used in food safety applications. The pure substances and mixtures were selected to provide a comprehensive representation of the chemicals of interest. For each mixture, spectral measurements were taken under controlled conditions to ensure consistency.  
Additionally, all Raman spectra were preprocessed using airPLS to remove baseline noise, and normalization was applied to ensure uniformity across different spectra. These preprocessing steps are critical for enhancing the quality of the data and preparing them for deep model training. It is worth mentioning that converting raw data from wavelength-based units to wavenumber-based units is a common practice in Raman data preprocessing. Since the data were collected using our self-built spectrometer, we skipped this step for simplicity.