esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
Figures 9 and 10 show the results of the RMS errors in translation and rotation after hand-eye calibration by the two methods within the distance range of 1 m. Due to the measurement, position, and orientation errors of the end-effectors and other unmolded nonlinear factors, the experimental data are interfered and have more errors than that of the single-factor methods. Otherwise, it can be seen from Figures 9 and 10 that the actual effects are different from the results of the simulation above, but the results are still reasonable. As the scale and unit of the translation are different from the rotation, the threshold ranges of them are also different. It can be seen from the resulting curves in Figures 9 and 10 that the RMS relative errors in translation using MDQ are within 3.7 mm. This means MDQ can obtain better results for translation during the hand-eye calibration in the real experiment. The RMS relative errors in rotation using MDQ are within 0.13 rad. This means MDQ can obtain better results for rotation during the hand-eye calibration in the real experiment. After the steps of the hand-eye calibration for each camera and each arm, the relative pose data of the arbitrary target captured by one camera can be transformed to the other camera by using Equation (15) or Equation (16). The calibration procedure was repeated several times, and the last results of the linear offsets of camera 1 along the x, y, and z axes were 24.51, 9.67, and 45.32, respectively, in millimeters; the rotational offsets of camera 1 along the x, y, and z axes are 0.005, 0.062 and 0.003, respectively, in rad. The last results of the linear offsets of camera 2 along the x, y, and z axes are 34.92, 11.86, and 463.75, respectively, in millimeters; the rotational offsets of camera 1 along the x, y, and z axes are 0.006, 0.102, and 0.007, respectively, in rad. Moreover, Figure 11 shows the test of the movement after the hand-eye calibration of the dual-arm robot, which can be used in real applications and in further studies using a robot to hit a Ping-Pong ball. The full procedure of the dual-arm hand-eye calibration was completed and was necessary for further applications of the dual-arm robot. The comparison between the two methods shows that the MDQ algorithm has a better performance than the reference one, which means the effectiveness of the MDQ algorithm proposed in this paper can be illustrated.
# DTI Histogram and Texture Features as Early Predictors of Post-Radiotherapy Cognitive Decline
## 2 Materials and Methods
### Histogram and Texture Feature Extraction  
A customized radiomic feature extraction workflow [29], implemented in Python (version 3.9.7, Python Software Foundation, Wilmington, DE, USA, [[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)) (accessed on 23 November 2024)), extracted quantitative features from NAWM regions of interest (ROIs). For each DTI index (FA, MD, AD, or RD), ten features were analyzed, which included eight histogram features (10 th percentile, 90 th percentile, image energy (\(\Sigma\)I\({}^{2}\), where I represents voxel intensity), entropy, kurtosis, mean, skewness, and uniformity) and two GLCM-based texture features (contrast and correlation). Thus, in total, 40 DTI features were analyzed (10 features \(\times\) 4 DTI indices). The feature selection was based on several methodological considerations, including sample size constraints (extracting many features will inevitably lead to severe multiple comparison issues and risk overfitting) and interpretability of results (we focused on well-established, clinically interpretable features that have demonstrated utility in medical imaging). To evaluate treatment-induced changes specifically, we calculated the change (\(\Delta\)) in each feature by subtracting the baseline (before radiotherapy) value from the immediate post-radiotherapy value.
## 3 SDR Technology
### In-Phase and Quadrature Data  
The use of in-phase and quadrature (I/Q) data is a key concept when working with SDRs. I/Q data are a mathematical complex representation of the transmitted (or received) baseband signal. In this way, I/Q data describe the variations in the amplitude and phase of a signal. An I/Q signal can be represented as two orthogonal signals which form a new waveform. The creation of this new waveform is achieved by using a quadrature modulator; see Figure 2. The two real data signals at the transmitter end can be written as follows:  
\[i(t)=I_{t}(t)\cos(2\pi f_{o}t) \tag{1}\]  
\[q(t)=Q_{t}(t)\sin(2\pi f_{o}t) \tag{2}\]  
Combining the two components leads to the following:  
\[y(t)=I_{t}(t)\cos(2\pi f_{o}t)+Q_{t}(t)\sin(2\pi f_{o}t). \tag{3}\]  
At the receiver end, the in-phase and quadrature components can be recovered as follows by applying the product-to-sum identities:  
\[I_{r}(t)= [I_{t}(t)\cos(2\pi f_{o}t)+Q_{t}(t)\sin(2\pi f_{o}t)]*2\cos(2\pi f _{o}t)\] \[=\underbrace{I_{t}(t)}_{\text{term}1}+\underbrace{I_{t}(t)\cos(4 \pi f_{o}t)}_{\text{term}2}+\underbrace{Q_{t}(t)\sin(4\pi f_{o}t)}_{\text{term}3} \tag{4}\]  
Figure 1: Functional block diagram of a direct conversion or zero-IF-based SDR architecture. Note that the parallel lines indicate that this process is repeated; i.e., one flow is dedicated to the in-phase data stream and one flow to the quadrature data stream.  
\[Q_{r}(t)= [I_{t}(t)\cos(2\pi f_{o}t)+Q_{t}(t)\sin(2\pi f_{o}t)]*2\sin(2\pi f_{o}t)\] \[= \underbrace{Q_{t}(t)}_{\text{term 1}}-\underbrace{Q_{t}(t)\cos(4\pi f _{o}t)}_{\text{term 2}}+\underbrace{I_{t}(t)\sin(4\pi f_{o}t)}_{\text{term 3}} \tag{5}\]  
By using a low-pass filter (LPF), the high-frequency components, i.e., term two and term three in Equations (4) and (5), are removed, and the in-phase component _i(t)_ and quadrature component _q(t)_ can be recovered at the receiver.
## 3 Results
### Comparison of Results between Model-A and No Frozen Case  
The actual deformation areas of subgrade and rail, which were caused by frost heave, are marked at the top of Figure 10. It can be seen that the deformation of subgrade frost heave results in a wider range of track irregularities. To determine the influence of track irregularity caused by frost heave, the results from Model-A and the no frozen case are compared first.  
As shown in Figure 10, compared with the results of the no frozen case, the vertical wheel-rail force of Model-A starts to fluctuate at 0.30 s. Until 0.74 s, the vertical wheel-rail force under the two cases returns to the same. After the vehicle enters the affected area at 0.3 s, the sudden change in irregularity makes the wheel-rail force increase to some extent. The wheelset receives the crest of the track irregularity at 0.57 s, and the vehicle has to balance the centrifugal force to adapt to the decrease in the height of the rail surface, which leads to a significant reduction in vertical wheel-rail force. Compared to the no frozen case, the vertical force of Model-A decreases by 42.90% at the corresponding moment.  
Figure 10: History of vertical wheel-rail force with different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dynamic Response** & **Measured Results** & **Model-A** & **Model-B** & **Model-C** \\ \hline Vertical acceleration of car body(m\(\cdot\)s\({}^{-2}\)) & \(-\)0.6–0.8 & \(-\)0.36–0.40 & \(-\)0.35–0.42 & \(-\)0.40–0.44 \\ Lateral wheel-rail force (kN) & \(-\)22–31 & \(-\)24.5–32.5 & \(-\)25.5–32.1 & \(-\)27.50–31.7 \\ Peak derailment coefficient & 0.26 & 0.42 & 0.42 & 0.31 \\ Peak rate of wheel load reduction & 0.62 & 0.63 & 0.55 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between measured data and simulation.  
After this, the downward centripetal force of the vehicle results in the increase in wheel-rail force at 0.6 s. There is a curvature change in the same position which could also increase the impact effect. The maximum wheel-rail force of Model-A also appears at this moment. The maximum wheel-rail force of Model-A is around 140.95 kN, which is 26.44% higher than that of the no frozen case. This result is consistent with data obtained in [22].  
When the wavelength and amplitude of frost heave are 5 m and 10 mm, respectively, the length of the area, where the wheel-rail force is affected, is more than three times the wavelength of frost heave. Besides this, though the results of other models are different, the lengths of the affected area are basically the same (from 0.45 to 0.57 s in Figure 10). Moreover, the lengths of the affected area are also the same as the range of rail deformation, as marked by the chain lines in Figure 10. This illustrates that the affected distance of vehicle dynamic response in the longitudinal direction depends on the track irregularity caused by frost heave.
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 4 Machine Learning Models
### Logistic Regression Model  
Equation (6) illustrates how the concept of maximum likelihood is used to create logistic regression models. These models develop a linear functional form utility function, as seen in Equation (7). The coefficients of the utility function are estimated while maximizingthe log-likelihood function. The probability of an outcome is calculated by subjecting the utility function to a logistic function (see Equation (8)).  
\[Modellikelihood=Max[\sqcap P_{i}(n)] \tag{6}\]  
\[U_{n}=b+\sum_{j=1}^{I}Y_{j}X_{j} \tag{7}\]  
\[P_{i}(n)=1/(1+e^{-Un}) \tag{8}\]  
where \(P_{i}(n)\) is the probability of \(i\)th sample in the dataset (of '\(I\)' values) to have the outcome '\(n\)', which is the actual outcome, \(b\) and \(Y\) are intercepts and coefficients for the utility function, respectively, and \(X\) is an array of independent variables having significant impact on the model [77]. Logistic regression models are very commonly used in the prediction of categorical responses, which includes predicting crash severity [78].
# Numerical Simulation of Rock Mass Structure Effect on Tunnel Smooth Blasting Quality: A Case Study
## 2 Material and Methods
### Blasting Scheme and Monitoring  
The blasting scheme of the monitoring section adopted the step method, which was divided into two parts: upper step and lower step. As only the upper step was blasted during the test, the numerical model only studied the upper step. The section height of the upper step was 6.26 m, the width was 13.00 m, and the area was 63.48 m\({}^{2}\). The blast hole layout is shown in Figure 6.  
At the tunnel site, the ZTSD-3 tunnel section instrument was used to measure the overbreak and underbreak of the smooth blasting tunnel face. In accordance with the field test results, the average overbreak area, average underbreak area, and average maximum overbreak of the tunnel in each monitoring section are plotted in Figure 7.  
Figure 5: Monitoring section DK 152 + 875–DK 152 + 902 structural surface information: (a) visible light photo of the rock wall; (b) 3D scan of the rock wall; and (c) geometric model.  
Figure 6: Layout of the blast hole (unit: cm).  
Figure 7: Relationship between overbreak and underbreak and rock mass structure types.  
The integrity of massive integrity structure, massive structure, layered structure, and cataclastic structure decreased successively. Figure 7 shows that the average overbreak area and the average maximum overbreak increased with the decrease in rock mass integrity, especially for cataclastic structures, whose overbreak area and maximum overbreak were much larger than other structural types. Therefore, in the smooth blasting of a cataclastic rock tunnel, appropriately increasing the peripheral hole spacing or reducing the charge was necessary. The relationship between the average underbreak area and the type of rock mass structure was not obvious, and the underbreak area was only approximately one-tenth of the overbreak area, which was not significant.
## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Experiment 2: Marine Trajectory Forecasting
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline LSTM-BPSO & \(\mathbf{3.30\times 10^{-5}}\) & \(8.75\times 10^{-5}\) & \(\mathbf{5.10\times 10^{-5}}\) & \(\mathbf{3.83\times 10^{-5}}\) & \(1.90\times 10^{-5}\) & \(3.62\times 10^{-10}\) \\ LSTM-PSO & \(3.83\times 10^{-5}\) & \(1.04\times 10^{-4}\) & \(6.72\times 10^{-5}\) & \(6.11\times 10^{-5}\) & \(2.84\times 10^{-5}\) & \(8.06\times 10^{-10}\) \\ LSTM-GA & \(5.82\times 10^{-5}\) & \(1.25\times 10^{-4}\) & \(1.01\times 10^{-4}\) & \(1.14\times 10^{-4}\) & \(2.83\times 10^{-5}\) & \(8.03\times 10^{-10}\) \\ LSTM-ABC & \(5.13\times 10^{-5}\) & \(9.17\times 10^{-5}\) & \(6.85\times 10^{-5}\) & \(5.75\times 10^{-5}\) & \(1.79\times 10^{-5}\) & \(3.20\times 10^{-10}\) \\ LSTM-BA & \(4.86\times 10^{-5}\) & \(\mathbf{6.04\times 10^{-5}}\) & \(5.28\times 10^{-5}\) & \(5.22\times 10^{-5}\) & \(\mathbf{4.80\times 10^{-6}}\) & \(\mathbf{2.30\times 10^{-11}}\) \\ LSTM-WOA & \(6.45\times 10^{-5}\) & \(1.02\times 10^{-4}\) & \(8.36\times 10^{-5}\) & \(8.65\times 10^{-5}\) & \(1.48\times 10^{-5}\) & \(2.19\times 10^{-10}\) \\ LSTM-HHO & \(5.17\times 10^{-5}\) & \(8.29\times 10^{-5}\) & \(6.73\times 10^{-5}\) & \(6.04\times 10^{-5}\) & \(1.21\times 10^{-5}\) & \(1.47\times 10^{-10}\) \\ LSTM-ChoA & \(5.22\times 10^{-5}\) & \(1.01\times 10^{-4}\) & \(7.22\times 10^{-5}\) & \(6.83\times 10^{-5}\) & \(1.82\times 10^{-5}\) & \(3.32\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Overall objective function results—trajectory forecasting experiment. A comparison between the best predictions of the proposed LSTM-BPSO and the basic LSTM-PSO is shown in Figure 8. It can be highlighted that the boosted PSO algorithm's performance was superior in comparison to the elementary PSO. Figure 7: Vessel trajectory result visualization. (**a**) Objective box plot; (**b**) \(R^{2}\) box plot; (**c**) Objective; kde plot; (**d**) \(R^{2}\) kde plot; (**e**) Objective swarm diversity; (**f**) \(R^{2}\) data diversity.
# Augmenting Environmental Interaction in Audio Feedback Systems+
## 2 Related Work  
### Audio Feedback in Computer Music  
Figure 1 shows the common structure of audio feedback. The incoming signal is connected to the output via transformation, and output re-enters the system again after a certain delay. In the digital domain, acoustic feedback is generally emulated using a delay line and filter. In some cases feedback sounds may occur suddenly due to slight increases of gain or changes of distance between a microphone and a loudspeaker, leading to the magnitude of the open-loop transfer function exceeding unity in a particular frequency region [6]. It is also defined as the violation of the Barkhausen stability criterion [7].  
The Karplus-Strong algorithm and digital waveguide synthesis [8; 9; 10] are also based on audio feedback mechanisms, in which the fundamental resonant frequency is determined by the internal delay. This is also evident in audio feedback occurring in physical environment: the fundamental frequency of the Larsen tone is mainly determined by the delay formed as a combination of the system-internal delay and the delay caused by the physical distance between microphone and loudspeaker. Acoustic feedback effects of the electric guitar have been emulated through feedback loop models [11; 12]. [PERSON] et, al. presented control of audio feedback simulation over harmonic content using a nonlinear digital oscillator consisting of a bandpass filter and a nonlinear component [7].  
Because of its unique sound-generating nature, performers have deliberately generated positive feedback tones to be included in music, and designers of new musical instruments have created interfaces incorporating feedback tones. Examples of acoustic feedback used for new musical instruments include the _hybrid Virtual/Physical Feedback Instruments_ (VPFI) [13], in which a physical instrument (e.g., a pipe) and virtual components (e.g., audio digital signal processing (DSP) processors such as a low-pass filter) constitute a feedback cycle. [PERSON] et al. documents the role  
Figure 1: Basic structure for audio feedback. Signals from a microphone are amplified, emitted by a loudspeaker, then received by the microphone again to be endlessly re-amplified.  
of feedback in actuated musical instruments, which uses the audio signal as a control signal for the instrument again and thereby is co-manipulated by performer and machine [14].  
[PERSON] presented a common structure for feedback-based audio/music systems (Figure 2) [2]. While only a single connection exists between a microphone and a loudspeaker (via signal processing components) in the basic audio feedback model, a subsystem is added for sonic control based on signal analysis of the real-time microphone input; this can be used to trigger the Larsen tone and to control internal signal-processing states.
## 3 Experimental Techniques to Check a Possible Degradation of PCM
### Reflected Light Microscopy (RLM)  
In the case of the \"ISE capsules\" test rig, reflected light microscopy (RLM) is used for repeated optical inspections of PCM macrocapsules. With RLM, possible damage of the capsules walls can be detected that would lead to leakage of the PCM out of the capsules.
# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
## 3 Node Management Methodology
On the other hand, \(QoE_{i}\) indicates the MOS estimated for the UE \(i\) by using network Key Quality Indicators (KQIs) as detailed in [32]. In addition, \(PRB_{i}\), \(RSRP_{i}\) and \(RSRQ_{i}\) represent the number of free PRBs, the signal level and quality metrics, respectively. It should be pointed out that these three measurements represents the mean of the values corresponding to both eNB and gNB, as the MOS value is the result of the service provided by both nodes. Therefore, the estimation model consists of \(C\), which is the constant value of the model, and the weights (\(W\)) of each introduced metric. In this regard, \(C\), \(W_{Load}\), \(W_{Power}\) and \(W_{Quality}\) are derived using the historical samples of \(QoE\), \(PRB\), \(RSRP\) and \(RSRQ\), and the use of an ordinary least squares estimator. This process is detailed in [33]. Afterwards, the QoE will be estimated for future situations as indicated in Equation (2):  
\[E_{t}=C+W_{Load}*PRB+W_{Power}*RSRP+W_{Quality}*RSRQ \tag{2}\]  
where \(E_{t}\) is the MOS value estimated by the proposed approach. Therefore, the eNB and the gNB that provide the highest \(E_{t}\) values will be selected to serve the UE. In addition, the Mean Absolute Percentage Error (_MAPE_) metric is computed to know how reliable is the created model. It indicates the difference between the MOS value \(R_{t}\) calculated using the QoE equation of the corresponding service introduced in [32] and the \(E_{t}\) value estimated by the proposed methodology (Equation (3)),  
\[MAPE(\%)=\frac{100}{n}\sum_{t=1}^{n}|\frac{R_{t}-E_{t}}{R_{t}}| \tag{3}\]  
The lower the _MAPE_, the higher the estimation accuracy. Therefore, the selection of nodes will be made with more reliability as the MAPE decreases, leading to further increase the overall QoE. The block diagram of this node management solution is represented in Figure 1. It should be noted that in this figure \(EstimationModel_{2,3,1}\) is selected because the UE uses the first service and is located in the orange square, which corresponds to the second column and third row in this example. Finally, the algorithm of the proposed methodology is detailed in Algorithm 1. ```
1:\([C,W_{Load},W_{Power},W_{Quality}]\leftarrow\) Stored estimation model for the corresponding square and service
2:for each eNB and gNB do
3:if\(RSRP_{reported}>Threshold\)then
4:\(E_{t}\leftarrow(2)\)
5:endif
6:endfor
7: Serving eNB and gNB \(\leftarrow\) eNB and gNB providing the highest \(E_{t}\)
```  
**Algorithm 1** Node management solution
## 5 Conclusions
the SS2 and SS4 wavelets appeared to be the better one, whereas the data inversion using the Ricker wavelet seemed to smooth the estimated parameters. Evaluation metrics, such as Mean Squared Error (MSE) values and Pearson correlation coefficient (C.C. ), were used to evaluate the model's performance over several iterations. The results indicate that, in most cases, the inversion using SS4 concerning the well location is essential for precise porosity prediction. These measures offered insightful information about the models' reliability and precision. It is important to emphasize that the methodology can be applied in areas other than the one used because values in the models and equations serve as adjustment parameters, allowing the effect of discrepancies between the values of the variables, in this case, \(V_{P}\) and \(V_{S}\), to be increased or decreased. In addition, values are used to change the scale of the variables, notably the velocities \(V_{P}\) and \(V_{S}\). These factors are influenced by geological conditions, allowing the methodology to be used in a range of places as long as the impact of the site's geological context is well-valued. J.A.T.M. : Conceptualization, Methodology, Writing--review and editing, Writing--original draft, Investigation, Validation, and editing; J.J.S.d.F. : Conceptualization, Methodology, Supervision, Writing--review and editing, Writing--original draft, Investigation, Validation, Writing--review and editing. C.E.S.A. : Editing, Validation and Software. All authors have read and agreed to the published version of the manuscript. The authors extend their sincere appreciation to CAPES, CNPq, and PROPESP-UFPA for their generous financial support, which facilitated the completion of this research. Not applicable. The modified codes used for Bayesian Linearized Inversion (BLI) and Amplitude Versus Offset (AVO) analyses are accessible at [[https://pylops.readthedocs.io/en/stable/](https://pylops.readthedocs.io/en/stable/)]([https://pylops.readthedocs.io/en/stable/](https://pylops.readthedocs.io/en/stable/)), accessed on 9 August 2021. For 3D data visualization, we utilized the Opendetect software v6.4, which can be accessed at [[https://www.dgbes.com/software/opendet](https://www.dgbes.com/software/opendet)]([https://www.dgbes.com/software/opendet](https://www.dgbes.com/software/opendet)), accessed on 9 August 2021. The Norne field dataset is available on request at [[http://www.ipt.ntnu.no/~norne/wiki1/doku.php?id=english:nornebenchmarkcase2](http://www.ipt.ntnu.no/~norne/wiki1/doku.php?id=english:nornebenchmarkcase2)]([http://www.ipt.ntnu.no/~norne/wiki1/doku.php?id=english:nornebenchmarkcase2](http://www.ipt.ntnu.no/~norne/wiki1/doku.php?id=english:nornebenchmarkcase2)), accessed on 9 August 2021. The supporting datasets and codes are available upon request from [PERSON] at UFPA (email: [EMAIL_ADDRESS]). The authors declare no conflicts of interest.