esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Smartphone Global Navigation Satellite System (GNSS) Data Collection and Analysis
### Data Quality Analysis
#### 2.2.1 Dual-Frequency C/N0 Analysis  
C/N0 is an important data quality indicator for GNSS, and it is related to the receiver hardware and signal path [5]. Figure 3 shows the C/N0 sequence and statistical results of the different types of observations. The maximum C/N0 is below 50 dB-Hz. The L1/G1/B1/E1 signals are mainly concentrated around 40 dB-Hz, but some satellites provide signals below 20 dB-Hz, while the Galileo satellites provide signals above 30 dB-Hz. The GPS L5, Galileo E1/E5a, and BDS GEO satellites were slightly stable during measurement, while other satellites fluctuated greatly. Overall, Galileo satellites provide the highest signal stability and strength, but their number is low.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**East (m)** & **North (m)** & **Up (m)** \\ \hline \(-\)0.027 & \(-\)0.015 & +0.191 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Offset of smartphone antenna phase centre with respect to FCP.  
Figure 1: Diagram of global navigation satellite system (GNSS) antenna phase centre in Xiaomi Mi 8 smartphone [21].  
Figure 2: Fixed control point (FCP) and equipment placement.  
For band L5/E5a, C/N0 is mainly concentrated at 30 dB-Hz, being lower than the mean C/N0 for band L1/G1/B1/E1. The L5/E5a multipath resistance is stronger in smartphones [8,22], and the signal quality is usually better than that in band L1/E1. However, in our results, the C/N0 of band L5/E5a is lower. Therefore, when establishing a stochastic model for multi-frequency observations, the difference in multi-frequency C/N0 should be considered.
## 4 Digital Twin Modelling Methodology
### Data Collection
A degree of multicollinearity among different parameters such as uniaxial compressive strength (UCS), rock quality designation (RQD), rock mass rating (RMR), and Q-value may affect TBM performance. Although each of these parameters provides key information regarding the rock characteristic but avoids the multicollinearity and computational complexity of the predictive model, the Q-values are selected for the input parameter. In previous studies, several machine parameters have been selected for the TBM performance prediction model, such as thrust, torque, boring energy, gripper pressure, and shield pressure. The parameters we selected for predicting TBM performance are boring energy, RPM, torque, TF, speed, gripper pressure, total revolutions, Q-value predicted by the site engineer, and TBM. The data collection of TBM input parameters from the Neelum Jhelum Hydroelectric Power (NJHEP) project site was carried out carefully, as shown in Figure 5. Figure 5a shows the inside of the TBM control cabin displaying and recording the real-time machine parameters, Figure 5b shows the real-timeTBM screen displaying machine operating data, and Figure 5c,d shows the TBM thrust cylinder, rotation motor, and boring data during operation, respectively. Figure 4: Data transmission from TBM to monitoring. Figure 5: Data collection and TBM input parameter of Neelum Jhelum project: (**a**) TBM control cabin showing and recording the real-time machine parameters; (**b**) snapshot of real-time TBM screen showing working machine data; (**c**) thrust cylinder and rotation motor data; (**d**) boring data of TBM during operation.
# Evidence-Based Investigation of Coronary Calcium Score in Cardiac Computed Tomography
## 3 Results  
### Patient Study  
Very high agreement was observed among the radiologists, with ICCs of 1 [0.999, 1] and 0.998 [0.996, 0.999] for the hypothetical overweight and obese patient images, respectively (\(p<0.001\)).  
The total calcium scores of the hypothetical overweight and obese groups were significantly higher than those of the normal weight group, with increases of 14.93% (\(p=0.014\)) and 22.19% (\(p=0.012\)), respectively (Figure 5a). In addition, diagnostic performance was lower in the hypothetical obese group (sensitivity, 100%; specificity, 72.72%; accuracy, 90.00%; PPV, 86.36%; NPV, 100%) than in the hypothetical overweight group (sensitivity, 100%; specificity, 81.81%; accuracy, 93.33%; PPV, 90.47%; NPV, 100%). The kappa value of the total calcium score was calculated to confirm the agreement of the values classified into the five levels of the Agatston calcium score risk category. It was 0.660 [0.597, 0.688] in the hypothetical overweight and obese patient images, showing high agreement among the three radiologists (\(p<0.001\)).  
When the average calcium score measured by the three radiologists was categorized into the five levels of the Agatston calcium score risk, five hypothetical overweight patients showed an increased risk level compared with normal weight patients: two from Normal to Minimal, two from Minimal to Mild, and one from Mild to Moderate (Figure 5b). Eight hypothetical obese patients showed an increased risk level compared with normal weight patients: three from Normal to Minimal, three from Minimal to Mild, and two from Mild to Moderate. Three cases increased in both the hypothetical overweight and obese groups, two in the hypothetical overweight group only, and five in the hypothetical obese group only.  
Figure 5: (a) Total calcium scores measured in the images of normal weight, hypothetical overweight, and hypothetical obese patients. (b) Frequency of total calcium scores for normal weight, hypothetical overweight, and hypothetical obese patients, categorized into the five levels of the Agatston calcium score risk.
# Whole-Body Motion Generation for Balancing of Biped Robot
## 2 Walking Pattern Generation
### Linear Inverted Pendulum Model  
Since the dynamics of a biped robot are very complex, the LIPM is widely used due to its simplicity. In the LIPM, a robot is represented by a single mass point, as shown in Figure 1. It is assumed that the position of the supporting foot is fixed at the ZMP on the ground.  
The angular momentum of a point mass \(m\) located at the COM, \(\mathbf{X}\triangleq\left[x\ y\ z\right]^{\mathrm{T}}\), about the ZMP at \(\mathbf{X}_{ZMP}=\left[p_{x}\ p_{y}\ 0\right]^{\mathrm{T}}\) under the influence of gravity is expressed as:  
\[\left[\begin{array}{c}x-p_{x}\\ y-p_{y}\\ z\end{array}\right]\times m\left[\begin{array}{c}\ddot{x}\\ \ddot{y}\\ \ddot{z}\end{array}\right]-\left[\begin{array}{c}x-p_{x}\\ y-p_{y}\\ z\end{array}\right]\times m\left[\begin{array}{c}0\\ 0\\ -g\end{array}\right]=0 \tag{1}\]  
Under the assumption that \(z\) remains constant at \(z_{0}\), and with \(\omega=\sqrt{g/z_{0}}\), Equation (1) can be simplified:  
\[\ddot{x} =\omega^{2}(x-p_{x}) \tag{2a}\] \[\ddot{y} =\omega^{2}\big{(}y-p_{y}\big{)} \tag{2b}\]  
With an initial condition of \(\mathbf{X}(t)\triangleq\left[x_{0}\ y_{0}\ z_{0}\right]^{\mathrm{T}}\) and \(\dot{\mathbf{X}}(t)\triangleq\left[\dot{x}_{0}\ \dot{y}_{0}\ 0\right]^{\mathrm{T}}\) at \(t=0\), the solutions for Equations (2a) and (2b) are:  
\[x(t) =\frac{1}{2}\bigg{[}\bigg{(}x_{0}-p_{x}+\frac{\dot{x}_{0}}{\omega }\bigg{)}e^{\omega t}+\bigg{(}x_{0}-p_{x}-\frac{\dot{x}_{0}}{\omega}\bigg{)}e ^{-\omega t}\bigg{]}+p_{x} \tag{3a}\] \[y(t) =\frac{1}{2}\bigg{[}\bigg{(}y_{0}-p_{y}+\frac{\dot{y}_{0}}{\omega }\bigg{)}e^{\omega t}+\bigg{(}y_{0}-p_{y}-\frac{\dot{y}_{0}}{\omega}\bigg{)}e ^{-\omega t}\bigg{]}+p_{y} \tag{3b}\]  
In each of Equations (3a) and (3b), there is a term that converges to zero as \(t\to\infty\) and a term that diverges to infinity as \(t\to\infty\). The latter is called the divergent component.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 4 Results and Discussion
### Discussion
#### 4.2.2 Advantages of Semi-Transfer Structure  
To demonstrate the advantages of the semi-transfer structure, we design four models as shown in Table 5. The classifier used in all models is softmax. M1 represents a model whose input is RGB images and the network structure is the pre-trained VGG16. M2 indicates that the input is RGB + NIR + nDSM images and the network structure is the self-built CNN. M3 indicates that the input is RGB images, the network structure is ST-CNN, and the input of both pre-trained VGG16 and self-built CNN is RGB images. M4 represents a model whose input is RGB + NIR + nDSM images and the network structure is ST-CNN, the input of pre-trained VGG16 is RGB images and the input of self-built CNN is RGB + NIR + nDSM images. From Table 4, M3 and M4 with ST-CNN network structure have higher OA and kappa coefficients than M1 and M2. M4 obtained the highest OA and kappa coefficient. Figure 10 shows the training curves of different models. From Figure 11, M4 can effectively combine the self-built CNN with the pre-trained VGG16 network. Speed up model training while avoiding overfitting caused by small amounts of data. Compared with M1, the OA and kappa coefficient of M3 increased by 0.3% and 0.38%, respectively. This is due to ST-CNN using parallel network designs of different depths, which can extract different depths of image features and improve recognition accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Network** & **Input** & **OA (\%)** & **Kappa Coefficient** \\ \hline M1 & Pre-trained VGG16 & RGB & 81.71 & 75.56 \\ M2 & Self-built CNN & RGB + NIR + nDSM & 75.84 & 67.92 \\ M3 & ST-CNN & RGB & 82.01 & 75.94 \\ M4 & ST-CNN & RGB + NIR + nDSM & 84.00 & 78.41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of different models for identifying UFZs.  
Figure 10: Results of CNNs with different training models.
# Spatial and Momentum Mapping Modes for Velocity Map Imaging Spectrometer
## 4 Experiment
### High-Resolution Momentum Mapping Mode
However, the comparison was made between the ion image measured with the fine mesh grid with homogenous fields and that with imaging focusing lens. The destruction power of the mesh on the resolution may be overestimated. Another factor that influence the resolution is the magnetic field, which is very difficult to estimate. The residual magnetic field strength in the main chamber was measured using a magnetometer. It varies from 6 mGs at the interaction region where the electron kinetic energy is several tens of eV to 50 mGs near the detector where the electron kinetic energy is about 800 eV. The distribution of the magnetic field is irregular due to the openings on the u-metal shield. So, we empirically simplify the current situation as a 300 eV electron flying for 140 mm in an 8 mGs magnetic field. As a result, the distortion is about 0.13 mm, corresponding to a relative energy resolution of 0.87%. If the electron kinetic energy is increased by a factor of 10, the magnetic distortion will be decreased by a factor of \(\sqrt{10}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **KE [eV]** & \(\Delta E/E\) & **Mesh** & **Year** \\ \hline [PERSON] [9] & 0.87 & 0.38\% & NO & 2007 \\ [PERSON] [33] & 3 & 0.53\% & NO & 2014 \\ [PERSON] [10] & 60 & 1.5\% & NO & 2014 \\ [PERSON] [34] & 65 & 3.0\% & NO & 2017 \\ [PERSON] [35] & 100 & 1.8\% & NO & 2009 \\ [PERSON] [36] & 100 & 3.8\% & NO & 2019 \\ [PERSON] [37] & 300 & 4.0\% & NO & 2014 \\ [PERSON] [11] & 500 & 1.5\% & YES & 2021 \\ This work & 44.6 & 1.3\% & YES & 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of the performance of the measured relative energy resolution in VMI spectrometers. With simulated relative resolution and the distortions by the mesh and the residual magnetic field into account, the total relative energy resolution is estimated as 0.94%, which reasonably agrees with the measurement. It is always striking to observe the shadow of the mesh on the measured images, while the effect from the residual magnetic field is not readily visible. Here, we believe that, in order to further improve the energy resolution of our spectrometer, the first priority is to reduce the residual magnetic field.
## 2 Wireless Multimedia Sensor Networks
### Battery-Powered  
In WMSNs, camera sensors are prevalently powered by irreplaceable and non-rechargeable batteries. As a result, the operation time of a camera sensor is strictly restricted by its energy consumptions from basic system operation, multimedia data computation, and data communication [105]. To ensue a long lifetime of WMSNs, many actions are required. First, designing low-power camera sensor platforms is needed. Second, it is necessary to develop energy efficient computation algorithms and communication protocols to cut down energy drainage caused by the two main culprits, i.e., computation and communication. Lastly, to achieve an optimal energy usage pattern dynamic power management policies can be applied.
# Sound Activity Monitor Circuit for Low Power Consumption of Always-On Microphone Applications
## 2 Proposed Approach and Detailed Circuit
### Approach 3: Removal of Envelope Detector  
Figure 5a is a conventional SAM configuration. The envelope detector (ENV) extracts the envelope of the amplified signal via CA and PGA and compares the signal magnitude in the comparator (CMP). A typical envelope detector shown in Figure 5b consists of a big resistor (\(\text{R}_{\text{env}}\)) that causes a fine leakage current, a big capacitor (\(\text{C}_{\text{env}}\)) to maintain an output voltage, a current supply switch (\(\text{SW}_{\text{env}}\)) to increase a voltage of an output signal (\(OUT_{\text{env}}\)), and a comparator (\(\text{C}\text{MP}_{\text{env}}\)) that compares the amplitude of an input signal with the current output signal (\(OUT_{\text{env}}\)) [14; 15]. If the magnitude of the signal (\(IN_{\text{env}}\)) is smaller than the magnitude of the output signal (\(OUT_{\text{env}}\)), the comparator output (\(CSW\)) becomes logically low (\(LOW\)) and the switch (\(\text{SW}_{\text{env}}\)) will be turned off. Then, as the charges stored in the capacitor (\(\text{C}_{\text{env}}\)) are discharged through the resistor (\(\text{R}_{\text{env}}\)), the voltage of the output signal (\(OUT_{\text{env}}\)) gradually decreases. As the output signal (\(OUT_{\text{env}}\)) gradually decreases and becomes smaller than the input signal (\(IN_{\text{env}}\)), the output of the comparator (\(CSW\)) becomes \(HIGH\) and the switch is turned on. While the switch (\(\text{SW}_{\text{env}}\)) is turned on, current is supplied through the switch (\(\text{SW}_{\text{env}}\)), so that a charge is supplied to the capacitor (\(\text{C}_{\text{env}}\)) and the output voltage (\(OUT_{\text{env}}\)) rises rapidly. Therefore, even if a DC signal is input to the envelope detector (ENV), the envelope detector output signal (\(OUT_{\text{env}}\)) takes a sawtooth  
Figure 4: Architecture approach 2: (**a**) Closed-loop amplifier(CA) vs. (**b**) Open-loop amplifier(OA).  
shape as charging and discharging are repeated in the capacitor (Cenv). For this reason, as shown in Figure 5c, a ripple of size \(A_{ripple}\) occurs in the output signal of the conventional envelope detector. Since such a ripple (\(A_{ripple}\)) is not distinguished from the small sound signal, it defines a limitation of measurable sound. In addition, detecting the presence of sound after extracting the envelope of sound hinders rapid detection. Therefore, the use of envelope detectors in terms of low-sound detection and fast detection is disadvantageous, and the envelope detector is removed from the conventional SAM configuration as shown in Figure 4b.
## 1 Introduction
### Discussion on Various Reduced Bases
By making use of results from numerical analysis on Householder transformation using floating point arithmetic and rearrangement of computations in Gram-Schmidt algorithm [PERSON] [17] has given an improved segment reduction algorithm that performs \(O(n^{5+\epsilon})\) bit operations for input bases of length \(2^{O(n)}\). \begin{table}
\begin{tabular}{|l||c|c|c|c|} \hline Algorithm & Lower Bounds on \(\frac{\|b_{i}\|^{2}}{\lambda_{i}^{2}}\) & Upper Bounds on \(\frac{\|b_{i}\|^{2}}{\lambda_{i}^{2}}\) & Arithmetic Steps & Precision \\ \hline \hline LLL reduced [7] & \(\alpha^{1-i}\delta^{n}\) & \(\alpha^{n-1}\delta^{-n}\) & \(O(n^{3}\log_{1/\delta}M_{sc})\) & \(O(\ln M_{sc})\) \\ \hline LLL reduced [13] & \(\alpha^{1-i}\delta^{n}\) & \(\alpha^{n-1}\delta^{-n}\) & \(O(n^{3}\log_{1/\delta}M_{sc})\) & \(O(n+\ln M_{0})\) \\ \hline Modular LLL [14] & \(\alpha^{1-i}\delta^{n}\) & \(\alpha^{n-1}\delta^{-n}\) & \(O(n^{2}\log_{1/\delta}M_{sc})\) & \(O(\ln M_{sc})\) \\ \hline Semi-reduced [8] & \(\alpha^{1-i-2n}\) & \(\alpha^{2n-1}\) & \(O(n^{2}\log_{1/\delta}M_{sc})\) & \(O(\ln M_{sc})\) \\ \hline Kannan [9] & \(\frac{1+3}{1+3}\) & \(\frac{1+3}{4}\) & \(n^{O(n)}\ln M_{0}\) & \(O(n^{2}\ln M_{0})\) \\ \hline Block KZ [10, 15]1 & \(\frac{4}{1+3}\gamma_{2k}^{2-i-1}\) & \(\gamma_{2k}^{2}\frac{n+3}{4}\) & \(O(n^{\sqrt{n/2+o(\sqrt{n})}}+n^{4}\ln M_{0})\) & \(O(n\ln M_{0})\) \\ \hline Segment LLL [11] & \(\alpha^{1-i}\delta^{3n}\) & \(\alpha^{n-1}\delta^{-3n}\) & \(O(n^{2}\log_{1/\delta}M_{sc})\) & \(O(\ln M_{sc})\) \\ \hline Mod-Seg LLL & \(\alpha^{1-i}\delta^{3n}\) & \(\alpha^{n-1}\delta^{-3n}\) & \(O(n^{1.5}\log_{1/\delta}M_{sc})\) & \(O(\ln M_{sc})\) \\ \hline Mod-Seg LLL FMM & \(\alpha^{1-i}\delta^{3n}\) & \(\alpha^{n-1}\delta^{-3n}\) & \(O(n^{1.382}\log_{1/\delta}M_{sc})\) & \(O(\ln M_{sc})\) \\ \hline [PERSON] and [PERSON] [16]\(L^{2}\) & \(\alpha^{1-i}\delta^{n}\) & \(\alpha^{1-i}\delta^{-n}\) & \(O(n^{2}(n+\ln(M_{0}))\ln_{1/\delta}(M_{sc}))\) & \(1.58\)\(\mathtt{fl}\) \\ \[PERSON] [17] SLL & \(\alpha^{1-i}\delta^{7n}\) & \(\alpha^{n-1}\delta^{-7n}\) & \(O(n^{2}\ln\ln\log_{1/\delta}M_{sc})\) & \(3n+d\)\(\mathtt{fl}\) \\ \hline \end{tabular}  
* \(\gamma_{n}\) is the Hermite constant which is defined as \(\gamma_{n}=\text{sup}\{\lambda_{1}(L)^{2}d_{n}^{-\frac{1}{n}}:\text{ for lattices }L\text{ of rank }n\}\).  
\end{table} 
Table 1: Summary of various LLL-related Algorithms.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Application Case Studies in Industrial Environments
#### 3.2.2 Applications of Internet of Things (IoT)  
Reference [83] Surveyed the applications of IoT and mobile phone computing and categorized them based on areas of application. The authors' survey indicates a wide range of applications of IoT in the current state of technology. The applications of IoT also extend to the underground such as mining of mineral resources. In [84], the paper introduced an underground emergency hedging subsystem that aids the use of IoT technology to improve its underground application. In the agricultural field, IoT has found application in arable farming as well, which is detailed in [85]. The paper analytically reviewed the current state of IoT application in arable farming as well as the potential. The authors in [86] explained how the applications of IoT are making things smart, such as smart infrastructure, smart homes, smart traffics systems, smart healthcare, etc. The application of IoT can also be found in an electric power system where the stability of the grid can be improved using an interline power flow controller (IPFC) [87]. The authors explained how this was achieved logically. With the recent emergence and deployment of 5G technology around the world, Reference [88] provides an overview of how IoT could be integrated into the 5G wireless system.  
The opportunities provided by IIoT through remote management are one of the main advantages the industries (manufacturers, health services, agriculture, etc.) are leveraging to improve efficiency and productivity [21]. IIoT finds its application in smart manufacturing, for effective wireless communication among IIoT devices and gateways, for example, [PERSON] [89] proposed a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) to achieve this, while [PERSON] [90] added a suite consisting of macro-services-based. To provide an effective predictive maintenance culture for Industrial types of machinery, IIoT has been deployed as a solution through the NGS-PlantOne System as detailed in [91]. The presentation of a concept for the application of IIoT in shipyards has been provided in [92]. The connection between physical devices and communication between information systems was studied.
## 4 Results and Discussions  
### Experimental Observation  
The study of the growth dynamics of the fluidized zone revealed different grain dynamics depending on the initial density of the material (Figure 4):  
* The loose stack fluidized quickly in a turbulent and chaotic manner from the injection zone to the top of the granular layer, with regular expansion of the entire stack.
* The dense stack was destabilized in a different way. An upward movement of the assembly without deformation in the material was observed. Moreover, the lower surface was gradually destabilized, thus releasing a shower of grains that gradually supplied the fluidized zone. The size of the ascending grain layer in solid translation motion decreased regularly while the fluidized layer increased.  
The dilating character of the sand volume seems to be at the basis of the observed phenomenon. Indeed, an imbalance of the contact forces induced by the flow on the grains causes this destabilization phenomenon, when these induced forces are more significant than the apparent weight of the granular stack. With an initially loose medium, there can be fluidization with large relative movements of grains in the interior and a more or less regular expansion of the whole material (Figure 5). In contrast, if the medium is initially dense enough, it can maintain a solid body behavior and thus take off vertically. In order to fluidize the material in the dense case, the grains first should be disentangled in the granular stack. This involves increasing the volume of the material in the box. Moreover, the box, by its rigid side walls, imposes a geometric confinement to the material. Hence, the observation of the vertical takeoff of the material in the dense case.  
Figure 4: Transitional phases of destabilization: (**a**) starting sample of loose stack; (**b**) starting sample of dense stack; (**c**) turbulent and chaotic fluidization phase for the loose stack; (**d**) sand takeoff phase for the dense stack.