esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### S-Parameter  
Figure 9 shows the simulated and measured S\({}_{11}\). It can be observed that the antenna either operates in wideband mode, two dual-band modes, and tri-band mode, as depicted in Figure 9a-d. The biasing voltage of the PIN diode is 3V. When both diodes \(D_{1}\) and \(D_{2}\) were in ON state, which refers to case-11, the antenna act as a wideband antenna having S\({}_{11}<-10\) dB impedance predicted bandwidth of 2.29-4.47 GHz whereas, the measured bandwidth was observed to be 2.25-4.53 GHz, as depicted in Figure 9a. When either the diode \(D_{1}\) or \(D_{2}\) was kept in On-state while keeping the other diode in Off-state the antenna exhibits dual-band mode, as depicted in Figure 9b,c. The predicted bandwidth of the proposed antenna for case--01 was observed to be 2.3-2.65 GHz and 3-4.5 GHz, while for case-10 it was observed to be 2.1-2.4 GHz and 2.92-4.37 GHz, respectively.  
On the other hand, the measured S\({}_{11}<-10\) dB bandwidth for case-01 was observed to be 2.1-2.78 GHz and 3.09-4.57 GHz, respectively, as depicted in Figure 9b, while for case-10 it was observed to be 1.86-2.5 GHz and 3.03-4.47 GHz, as depicted in Figure 9c. At last, when both diodes were kept in Off-state, referring to case-00, the antenna exhibits triband operational mode having predicted passbands of 2.17-2.35 GHz, 2.64-2.85 GHz, and 3.09-4.34 GHz. The measured impedance bandwidths for case-00 were reported to be 1.93-2.4 GHz, 2.62-2.97 GHz, and 3.2-4.65 GHz, as depicted in Figure 9d. In general, a strong agreement was observed between simulated and measured results, while the small discrepancy between the results was owing to fabrication and measurement circuitry.  
Figure 8: Fabricated prototype of the proposed antenna: (**a**) top-view (**b**) bottom-view.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 4 Improved Detections
### The Decision Feedback-FNFT (DF-FNFT) Detection  
The decision-feedback FNFT (DF-FNFT) detection--sketched in Figure 1 in red--is an improvement of the I-FNFT. After applying the same procedure of the I-FNFT (iterative detection in the nonlinear frequency domain, removing part of the signal), the DF-FNFT cleans the signal from the noise before the \(k\)-th symbol, using the feedback from already detected symbols to digitally compute the noiseless (if the previous detections were correct) samples of waveform samples before \(t_{k-1}\). In this manner, in the nonlinear frequency domain the noise is located only in the symbol time of the \(k\)-th symbol, when detecting \(x_{k}\). The pseudocode for the DF-FNFT detection is shown in Algorithm 2.  
The computational cost of the DF-FNFT is that of one BNFT and two FNFTs. On the one hand, the FNFT evaluates the nonlinear spectrum independently on each nonlinear frequency, as for the I-FNFT, and, thus, two FNFT are used since, at the \(k\)-the step, the FNFT should obtain the nonlinear spectrum considering the received signal in the \(k\)-th time slot, and the digitally computed signal in the \(k-1\)-th time slot. On the other hand, the BNFT is implemented with the Nystrom conjugate gradient method [29; 30], which builds the signal samples starting from the left-most side (also considering the time inversion) and then grows on the right when considering additional symbol time--which in the DF-FNFT case are feedback symbols. As a consequence, at each step, the BNFT should be performed in a single time slot.
## 2 Dynamic Contact Model of SNBSP
### Dynamic Contact Stiffness Model of SNBSP  
In the dynamic contact stiffness model, the rolling element is usually regarded as a spring without mass, which is connected along the contact point with the screw and nut raceway (Figure 8).  
Figure 8: Simplification of rolling element in stiffness model.  
Figure 7: Formation of slip angle in contact area.  
Stiffness refers to the ability of the material to resist the deformation of external forces. When the ball is in contact with the internal and external raceway, the screw raceway and the nut raceway all have contact deformation. For the \(i\)th ball, its normal contact stiffness with the raceway is represented as [20].  
\[k_{\varsigma i}=Q_{\varsigma i}/\varepsilon_{\varsigma i}=K_{\varsigma}^{-1}Q_{ \varsigma i}^{1/3} \tag{41}\]  
The contact stiffness matrix of the SNBSP can be derived though the transformation relations of \(CSOxyz\), \(CSO^{\prime}\tau nb\) and \(CSO^{\prime\prime}_{\varsigma}x_{\varsigma}^{\prime}y_{\varsigma}^{\prime \prime}z_{\varsigma}^{\prime\prime}\). In \(CSO^{\prime}\tau nb\), the stiffness of the \(i\)th ball with the raceway can be represented as:  
\[K^{\prime\prime}_{\varsigma i}=\left[\begin{array}{ccc}0&0&0\\ 0&0&0\\ 0&0&k_{\varsigma i}\end{array}\right] \tag{42}\]  
where \(k_{\varsigma i}\) can be obtained through Equation (41).  
Then the contact stiffness between the \(i\)th ball and raceway in \(CSOxyz\) can be represented as  
\[K_{i}=T\!D_{0-2 SN}^{T}\!\left[\begin{array}{cc}K^{\prime\prime}_{Si}&\\ &K^{\prime\prime}_{Ni}\end{array}\right]T\!D_{0-2 SN} \tag{43}\]  
\(K_{i}\) is the symmetrical matrix, the form of which is expressed as,  
\[K_{i}=\left[\begin{array}{cccc}k_{i-xx}&&&\\ k_{i-xy}&k_{i-yy}&&sym\\ k_{i-xz}&k_{i-yz}&k_{i-zz}&&\\ k_{i-x\phi_{x}}&k_{i-y\phi_{x}}&k_{i-z\phi_{x}}&k_{i-\phi_{x}\phi_{x}}&\\ k_{i-x\phi_{y}}&k_{i-y\phi_{y}}&k_{i-z\phi_{y}}&k_{i-\phi_{x}\phi_{y}}&k_{i-\phi _{y}\phi_{y}}\end{array}\right] \tag{44}\]  
The leading diagonal elements reflect the local variation of contact pairs. Therefore, the concept of local contact stiffness is proposed in this paper. The expression of local contact stiffness in all directions is shown in Equation (45). \(K_{i-x},K_{i-y}\) are the local lateral stiffness. \(K_{i-z}\) is the local axial stiffness. \(K_{i-\phi_{x}}\), \(K_{i-\phi_{y}}\) are the local oscillating stiffness.  
\[K_{i-x}=k_{i-xx}\;K_{i-y}=k_{i-yy}\;K_{i-z}=k_{i-zz}\;K_{i-\phi_{x}}=k_{i-\phi _{x}\phi_{x}}\;K_{i-\phi_{y}}=k_{i-\phi_{y}\phi_{y}} \tag{45}\]  
In the ball screw pair, the relationship between each ball and the raceway contact pair is parallel. Therefore, the contact stiffness in all directions in \(CSOxyz\) is represented as:  
\[K_{x}=\sum_{i=1}^{n}K_{i-x}\;K_{y}=\sum_{i=1}^{n}K_{i-y}\;K_{z}=\sum_{i=1}^{n }K_{i-z}\;K_{\phi_{x}}=\sum_{i=1}^{n}K_{i-\phi_{x}}\;K_{\phi_{y}}=\sum_{i=1}^{ n}K_{i-\phi_{y}} \tag{46}\]
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 2 Materials and Methods
### Cross-Section Segmentation and Segment Extraction  
After removing the non-tunnel structural point cloud, the boundary features of the tunnel point cloud data become more distinct, prompting the development of the TMatch algorithm. This algorithm first identifies and removes tunnel lining joint features and then performs a preliminary classification of the tunnel segments. Subsequently, the Gaussian Mixture Model (GMM) is used to fit the coarsely classified scalar field, completing the inter-ring segmentation. Based on the Shield Tunnel Design Standard (GB/T51438-2021, in China) [26], fine classification is carried out, ultimately achieving precise segmentation of the tunnel segments.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 1 Introduction
### Related Work  
The procedure of channel estimation is not standardized, thus it is up to a UE designer to implement an algorithm with an appropriate balance between SNR performance and complexity [13]. Many solutions proposed in the literature are based on Least Squares (LS) or Minimum Mean Square Error (MMSE) algorithms which were originally intended for wireless OFDM links in general [14; 15]. MMSE is not preferable for NB-IoT, as it requires matrix inversion which results in higher complexity, although some modifications were proposed to reduce it [16]. The Maximum Likelihood (ML) approach is known to be optimal in terms of SNR performance, yet it is the most computationally intensive [17]. LS and Linear MMSE (LMMSE) algorithms were further adapted for the LTE radio interface [18; 19; 20; 21; 22]. However, the power consumption requirements on wideband LTE UEs are not so stringent as for NB-IoT devices. In recent years, several channel estimation methods for NB-IoT were  
Figure 1: Example of resource allocation for NRS in single NB-IoT downlink subframe.  
presented. In [23], the authors propose a sequential method based on an MMSE estimator. A modified LS approach with random sorting of channel impulse responses is described in [24]. A method using LMMSE with singular value decomposition (SVD) is described in [25; 26], while yet another approach, based on ML and a time-domain Wiener filter, is proposed in [27].
## 4 Experiments
### Experiment and Analysis
#### 4.2.3 Real-World Scenario Experiment  
To further assess the adaptability and robustness of the proposed model in complex real-world environments, we captured a video sequence at 30 fps, where debris splashes were generated by a handheld portable impact drill striking a wall. A portion of the speed measurement process is presented in Figure 8.  
From Figure 8, it is clear that, despite the complex texture of the wall and the presence of background distractions, such as weeds, the model successfully detects, tracks, and computes the speed of the target. The entire processing pipeline takes an average of 15 ms per frame, including 13 ms for object detection, 1 ms for tracking and matching, and 1 ms for speed estimation and label rendering. The resulting average frame rate is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Motion Type** & **Frame Rate** & **Variance/(m/s)\({}^{2}\)** & **Average Variance/(m/s)\({}^{2}\)** & **Accuracy/(\%)** & **Average Accuracy/(\%)** \\ \hline Free-fall & 30 & 0.0128 & 92.05 & \\ With initial & 30 & **0.0115** & **0.0122** & **97.61** & **94.83** \\ \hline Free-fall & 60 & 0.0360 & 84.32 & \\ With initial & 60 & 0.0424 & 0.0392 & 94.47 & 89.40 \\ \hline \hline \end{tabular} Boldface denotes the best result for each evaluation metric.  
\end{table}
Table 4: Speed measurement results.  
Figure 7: Simulated speed detection of debris with initial velocity, demonstrating trajectory change and accuracy consistency. (**Top**: original image; **bottom**: enlarged view).  
67 fps, which fully supports real-time monitoring of video streams at 60 fps or lower. This demonstrates that the method proposed in this paper is robust and adaptable even in real-world conditions with high background noise and uncertainty. These results further validate the practical utility of the proposed algorithm, showcasing its potential application for debris motion analysis and speed monitoring in engineering environments.
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 7 Electro-Magnetic VVA (EMVVA) Systems
### Rotary Motor EMVVA  
Camcon Technology [33] is developing a camless engine for passenger vehicles based on their proprietary IVA system, which allows valve lift, timing, and duration to be independently and continuously controllable.  
Different from earlier EMVVA systems using opposed solenoids, IVA employs a four-phase rotary actuator, i.e., a rotary motor, using a rotor which is extended to provide a separate camshaft for each individual poppet valve [34]. A desmodromic linkage connects this camshaft to the entirely conventional valve. The actuator is electronically synchronized with the crankshaft and drives the rotor through the required angular trajectory in order to provide the selected valve event, which is enabled via a non-contact absolute rotary encoder to determine the rotor position for each actuator.  
Camcon collaborated with Jaguar Land Rover to fit the intake valve IVA onto an Ingenium 2.01 four-cylinder gasoline engine, with favorable test results in power consumption, lift repeatability, noise level, durability, and fuel economy [34; 74].  
Further development work is being carried out to achieve capability for higher engine speed and exhaust valve actuation [34]. Brunel University London is using a single-cylinder version of IVA technology called single-cylinder intelligent valve technology (SCI) to study future powertrain concepts and speed up OEM and tier 1 engine development [75]
## 3 Test Results and Theoretical Analysis
### Analysis of Pre-Peak Loading and Unloading Results
It can be seen that the relationship between peak intensity, peak axial strain and water content of coal samples obey quadratic polynomial, as shown in Equation (2), and the fitting curve is shown in Figure 5.  
\[y=a_{1}a^{2}+a_{2}a+a_{3}, \tag{2}\]  
where, \(a_{1}\), \(a_{2}\), \(a_{3}\) are the fitted constants; \(w\) is the water content; \(\varepsilon_{1}\)' is the peak axial strain at failure; and \(\sigma^{\prime}\) is the deviatoric stress at failure (MPa). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Water Content/\%**} & \multicolumn{3}{c}{\(\sigma_{1}\) = 18.6 MPa} & \multicolumn{3}{c}{\(\sigma_{1}\) = 25.85 MPa} & \multicolumn{3}{c}{**Peak Strength (\(\sigma_{3}\) = 3 MPa)**} \\ \cline{2-9}  & \(\varepsilon_{1}\)/\% & \(\varepsilon_{2}\)/\% & \(\varepsilon_{v}\)/\% & \(\varepsilon_{1}\)/\% & \(\varepsilon_{2}\)/\% & \(\varepsilon_{v}\)/\% & \(\varepsilon_{1}\)/\% & \(\varepsilon_{2}\)/\% & \(\varepsilon_{v}\)/\% \\ \hline
0 & 0.4918 & –0.045 & 0.4011 & 0.7848 & –0.082 & 0.6211 & 1.968 & –0.505 & 0.9587 \\
0.15 & 0.4975 & –0.048 & 0.4017 & 0.7919 & –0.086 & 0.6206 & 1.87 & –0.709 & 0.455 \\
0.32 & 0.4983 & –0.04 & 0.4192 & 0.7903 & –0.07 & 0.6506 & 1.74 & –0.432 & 0.8757 \\
0.65 & 0.4803 & –0.054 & 0.373 & 0.7581 & –0.097 & 0.5639 & 1.614 & –0.417 & 0.7799 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deformation parameters of each sample in pre-peak. Figure 4: Coal sample Mohr stress circle under different water content conditions.
# Cooperative Reception of Multiple Satellite Downlinks
## 3 Results and Discussion  
The BER results of the combining trials are presented in Figure 7. It is clear to see that the BER improved as we combined more receiving sites. Interesting behavior in the combining trials was that the BER of the system slightly improved when observing two successive trials from an odd number of sites to an even number. On the other hand, there was significant BER improvement between two successive trials when the first trial was for an even number of sites and the following trial was odd. This behavior was due to the fact that the detection hypothesis was based on the bit disagreement rule which has more errors likely to occur when the number of entrees is even. To enhance the performance of the system even more and to overcome this drawback, couple of SP techniques were proposed and examined for optional further improvement. So far, the presented combining algorithm neither required quality metrics evaluations (e.g., SNR) nor involved any weighting. However, the following suggested further improvement techniques would require metrics evaluation, but no weighting would be needed.  
Figure 7: \(Cont.\)
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 3 Methods and Techniques  
A unique approach of DNN-NCA-MLP (Deep transfer learning with Neighborhood Component Analysis and Multi-Layer Perceptron) for speech emotion classification is introduced. At first, our proposed method carried out preprocessing on a raw speech signal to generate suitable input (Mel-spectrogram) for the model. The structural framework of the proposed model is shown in Figure 2 below. The input image generated from the audio signal is fed into the pretrained DCNN model for robust utterance-level [31] features extraction. Thereafter, a feature selection technique is applied to carefully determine relevant and pertinent features for the accurate classification of emotion with the chosen classifier at the top layer of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Techniques**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Accuracy/**} & \\  & & & **Unweighted** & **Number** \\  & & & **Average Recall** & **Emotion** \\  & & & **(UAR)** & **(UAR)** & **(UAR)** \\ \hline [PERSON] et al. [29] & DNN-BiLSTM & EEG & 58.44\% & - \\ Blumetals and Salimbajos [30] & DNN-LSTM & TESS, RAVDESS, IEMOCAP & 86.02\% & 7, 4 \\ [PERSON] et al. [28] & ResNet & IEMOCAP & 66.02\% & 4 \\ [PERSON] and [PERSON] [27] & SiameseNet & RAVDESS & 32.8\% (UAR) & 7 \\ [PERSON] et al. [26] & DCNN- & EMODB, & 87.86\%, 68.5\% & 7, 4 \\ [PERSON] et al. & BiLSTMwA & IEMOCAP & 76.9\%, 41.0\%, & \\
[25] & AFS-SVM & EMOVO, SAVEE & 42.4\% (UAR) & 6 \\  & EMODB, SAVEE, & 95.10\%, 82.10\%, & 7, 4 \\ [PERSON] et al. [24] & DCNN-CFS & IEMOCAP, & 83.30\%, 81.30\% & 7, 4 \\ [PERSON] et al. [23] & CNN & EMODB & 77.01\%, 92.02\% & 7, 4 \\ [PERSON] and [PERSON] [22] & DNN & EMODB & 96.97\% & 7 \\ [PERSON] and [PERSON] [21] & DNN-INCA & EMODB, SAVEE, & 95.0\%, 82.0\%, & 7 \\ [PERSON] et al. [19] & CNN-VGGNet & RAVDESS & 85.0\% & 69.9\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Related Works.
## 4 Experiments and Results
### Hand Position Classification Results  
To evaluate our ML models, we used three metrics:  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}, \tag{5}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{6}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{7}\]  
where:  
Accuracy indicates the amount of correct classifications out of the total number of instances in the dataset;  
Precision measures the accuracy of positive predictions made;  
Recall measures the number correctly classified true positives;  
TP denotes true positives;  
TN denotes true negatives;  
FP denotes false positives;  
FN denotes false negatives.  
Table 4 summarizes the scores for each algorithm in each of these metrics. It can be seen that naive Bayes had the worst performance in terms of accuracy, while random forest was the best-performing algorithm. Naive Bayes presented the poorest performance as it assumed probabilistic independence between each finger's position, which cannot be assumed in the considered case. Our simplest algorithm was \(k\)NN, as we used the Euclidean distance to classify whether each position was correct.
###### Abstract  
Geological surveys have vital importance at the planning stage of dammed reservoir construction projects. The results of these surveys determine the majority of the technical solutions adopted in the construction design to ensure the proper safety and stability parameters of the structure during water damming. Where the ground type is found to be different from what is expected, the construction project may be delayed or even cancelled. This study analyses issues and design modifications caused by the identification of different soil conditions during the construction of four new flood control reservoirs in the Nysa Klodzka River basin in south-western Poland. The key findings are as follows: (1) a higher density of exploratory boreholes in areas with potentially fractured rock mass is essential for selecting the appropriate anti-filtration protection; (2) when deciding to apply deep piles, it is reasonable to verify, at the planning stage, whether they can be installed using the given technology directly at the planned site; (3) inaccurate identification of foundation soils under the dam body can lead to significant design modifications--in contrast, a denser borehole grid helps to determine the precise elevation of the base layer, which is essential for reliably estimating the volume of material required for the embankment; (4) in order to correctly assess the soil deposits located, for instance, in the reservoir basin area, it is more effective to use test excavations rather than relying solely on borehole-based investigations--as a last resort, test excavations can be used to supplement the latter.  
geological survey; boreholes; excavations; earth dam; reservoir Article
## 4 Experimental Validation and Results Analysis
### Stray Light Simulation and Path Analysis  
#### 4.3.1 Energy Composition Analysis of Stray Light  
To evaluate the background interference level under the co-propagation structure, simulation modeling and energy ray-tracing analysis were performed separately for the solar illumination module and the signal light module. The total output energy at the system aperture can be categorized into four components: direct solar illumination, solar-induced stray light, communication signal light, and signal-induced stray light.  
For the solar illumination module, as shown in Figure 9a, the primary solar beam power is 130 mW. The main stray light caused by specular scattering paths accounts for approximately 2 \(\times\) 10\({}^{-4}\) W, while secondary stray light arising from multiple internal reflections within the enclosure contributes about 1 \(\times\) 10\({}^{-7}\) W. The signal light module, as shown in Figure 9b, delivers an effective communication signal power of approximately 9.6 \(\times\) 10\({}^{-11}\) W, with its own stray light component as low as 1.64 \(\times\) 10\({}^{-15}\) W, which is negligible relative to the signal power.  
Figure 8: Analysis of system spectral uniformity: (**a**) curve of the spectral filter after shaping (the plot uses color gradients to represent radiance intensity); (**b**) comparison of the spectral uniformity before and after applying the filter uniformity curve.  
Figure 9: Energy composition of stray light: (**a**) solar light simulation energy; (**b**) signal light simulation energy.  
Table 3 summarizes the primary sources of stray light at the system output aperture and their corresponding energy proportions. The results indicate that the dominant source of stray light originates from specular scattering on the reflective surface of the beam splitter, accounting for 99.95% of the total stray light. The secondary transmission path, formed by solar radiation transmitted through the beam splitter into the internal cavity and subsequently reflected multiple times, contributes only 0.05% of the total stray energy. However, despite its relatively small proportion, the absolute energy level of this secondary path is significantly higher than that of the signal power and therefore cannot be neglected.
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 1 Introduction  
The importance of the sustainability of outer space as the sum of measures ensuring that the outer space environment is preserved for current and future generations has gained international recognition from a technical, legal and policy perspective. Given a backdrop of the risks posed to some of the most used orbital regions in near-Earth space by the growing amount of space debris, adequate consideration of instruments aiming at the mitigation and remediation of space debris represents an important tool to ensure the viability of space activities in the future. However, studies modelling the orbital evolution of space debris show that the application only of mitigation measures will not suffice to ensure the future access and usability of outer space [1]. Thus, space debris remediation (SDR) will play a crucial role for the sustainability of outer space and must become an integral part of the legal framework regulating its use.  
While technical mitigation measures for future missions have acquired legal relevance by being included in instruments regulating space activities, space debris remediation models have not yet become part of the legal framework. The aim of this work is to provide an overview of the legal background relevant to SDR and to analyse the main legal challenges for the future regulation of SDR. By exploring the factual background to space debris, it will be shown that effective technical SDR mechanisms need to be defined also at the legal level and that, although the law is still far behind the technology, legal avenues exist to legal implement SDR.