esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description
### Application of NMF to AMT  
The NMF algorithm is used in AMT systems to decompose a music signal into multiple note events. The input music signal is transformed into magnitudes on a spectrogram, expressed as a nonnegative matrix \(\mathbf{V}\). The NMF algorithm decomposes the magnitude spectrogram \(\mathbf{V}\) into two nonnegative basis matrices, \(\mathbf{W}\) and \(\mathbf{H}\), representing the frequency and time bases, respectively.  
Figures 1 and 2 show examples of the application of the NMF algorithm for music signal processing. Figure 1 shows the spectrogram of a selection from Cello Suite No. 1 Prelude by [PERSON] (BWV 1007) comprising the notes F2, C3, A3, and G3. An example showing the application of the NMF algorithm to the music signal is shown in Figure 2. Figure 2a shows a set of column vectors of \(\mathbf{W}\), and Figure 2b shows a set of row vectors of \(\mathbf{H}\). As noted in previous studies [9; 23], good estimates of the matrices \(\mathbf{W}\) and \(\mathbf{H}\) indicate the frequency structures and time envelopes of the musical notes in the signal, respectively. Figure 2 shows that the column vectors of \(\mathbf{W}\) indicate the frequency structures of C3, F2, G3, and A3, and that the row vectors of \(\mathbf{H}\) indicate the time envelopes of the same notes, respectively.  
If \(R\) is not optimal, then the desired result cannot be obtained (Figure 2). Figure 3 shows the results obtained when \(R\) is greater than the number of notes (\(R=6\)); the target signal is the same as that in Figure 2. Compared with Figure 2, Figure 3 contains redundant information, namely, the third and fourth basis vectors in both \(\mathbf{W}\) and \(\mathbf{H}\). Similarly, Figure 4 shows the results obtained when \(R\) is excessively small (\(R=2\)). The decomposed basis cannot describe the characteristics of each musical note, with the consequence that the results do not yield any musically useful information. Based on these examples, an appropriately selected number of basis vectors is essential for obtaining useful results.  
Figure 1: Spectrogram of a selection from Cello Suite No. 1 Prelude by [PERSON] (BWV 1007) consisting of the notes F2, C3, A3, and G3. Vertical dashed lines indicate the starting points of every tenth frame.  
Figure 2: Example of nonnegative matrix factorization (NMF) analysis results in terms of (**a**) the column vectors of \(\mathbf{W}\) and (**b**) the row vectors of \(\mathbf{H}\).
## 2 Methods
### The Proposed Hybrid Early Warning Method
Conventional early warning is primarily based on cumulative displacement, velocity, and acceleration to determine the warning level, which is more empirical, requires a little prior information, and is consistent throughout the monitoring cycle. The critical sliding early warning is only activated after the cumulative displacement reaches \(S_{ka}\) and the normalized velocity is calculated, which gives the critical sliding judgment an advantage. The two algorithms are combined according to different strategies to balance the requirements of computational resources, the number of false warnings, and immediacy.
## 3 Evaluation
### Results
Thus, it is not possible to provide a fair comparison against the state of the art, and a new study following the same evaluation protocol and using the same databases, which should be different from those involved in the training process, must be carried out to this end. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**FaceNet**} & \multicolumn{2}{c}{**OpenFace**} & \multicolumn{2}{c}{**gb2s\_Model1**} & \multicolumn{2}{c}{**gb2s\_Model2**} \\ \cline{2-10}  & **FMR** & **FNMR** & **FMR** & **FNMR** & **FMR** & **FNMR** & & \\ \hline BioID & 1.34 & 0.07 & 6.99 & 7.73 & 16.17 & 17.72 & 13.70 & 14.30 \\ EUCFI & 1.21 & 1.85 & 2.44 & 2.16 & 2.55 & 3.71 & 2.79 & 3.30 \\ ORL & 0.35 & 1.25 & 2.62 & 5.60 & 3.29 & 7.25 & 6.35 & 8.25 \\ Ext. Yale B & 3.85 & 3.96 & 9.39 & 9.63 & 30.19 & 27.91 & 31.68 & 29.62 \\ Print Attack & 0.02 & 0.09 & 3.60 & 2.94 & 4.15 & 6.64 & 6.07 & 8.61 \\ gb2 sTablet & 0.05 & 0.07 & 2.20 & 2.19 & 0.72 & 1.21 & 0.68 & 1.07 \\ gb2s\_uMOD & 3.89 & 4.77 & 10.67 & 9.76 & 16.18 & 24.87 & 16.53 & 26.91 \\ gb2s\_Selfies & 1.66 & 0.00 & 13.91 & 17.90 & 10.60 & 11.83 & 8.67 & 11.45 \\ gb2s\_IDCards & 0.70 & 0.76 & 1.86 & 3.33 & 2.47 & 3.05 & 1.51 & 2.29 \\ Selfies - IDCards & 4.89 & 2.29 & 47.00 & 42.00 & 29.77 & 18.32 & 28.00 & 18.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Facial recognition using deep learning and DBC on single sample per person scenarios: test results (%). Figure 1: SSPP validation results. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Feature** & **BioID** & **EUCFI** & **ORL** & **Ext.** & **Print** & **gb2s** & **gb2s** & **gb2s** & **gb2s** & **Selfies-** \\
**Extraction** & & & & **Yale B** & **Attack** & **Tablelet** & **\(\mu\)MOD** & **Selfies** & **IDCards** & **IDCards** \\ \hline Face Net & 1.47 & 1.16 & 0.25 & 3.60 & 0.05 & 0.05 & 4.28 & 1.91 & 0.55 & 4.66 \\ Open Face & 6.77 & 2.39 & 3.54 & 9.35 & 3.46 & 2.21 & 10.44 & 16.04 & 1.43 & 46.05 \\ gb2s\_Model1 & 16.85 & 2.65 & 6.65 & 27.76 & 4.09 & 0.76 & 18.94 & 11.98 & 2.56 & 28.86 \\ gb2s\_Model2 & 13.22 & 2.63 & 6.25 & 29.02 & 6.30 & 0.74 & 19.72 & 9.56 & 1.79 & 26.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Facial recognition using deep learning and DBC on single sample per person scenarios: validation results (EER (%)).
###### Abstract  
Measuring tree heights is a critical step for assessing ecological and agricultural parameters, including biomass, carbon stock, and canopy volume. In extensive areas exceeding a few hectares, traditional terrestrial measurement methods are often prohibitively expensive in terms of time and cost. This study introduces a GIS-based methodology for estimating olive tree (_Olea europaea_ L.) heights using very-high-resolution (VHR) satellite imagery. The approach integrates a mathematical model that incorporates slope and aspect information derived in a GIS environment from a large-scale Digital Elevation Model. By leveraging sun position data embedded in satellite image metadata, a dedicated geometric model was developed to calculate tree heights. Comparative analyses with a drone-based 3D model demonstrated the statistical reliability of the proposed methodology. While this study focuses on olive trees due to their unique canopy structure, the method could also be applied to other tree species or even to buildings and other vertically developed structures on the ground. Future developments aim to enhance efficiency and usability through the creation of a specialized GIS tool, making it a valuable resource for environmental monitoring, sustainable agricultural management, and broader spatial analysis applications.  
WorldView-3; monoscopic; orthophoto; UAV; point cloud; _Olea europaea_ L.
## 1 Introduction
We examine a simplified wireless telecommunication system, which is used as the medium to transmit biomedical data, where randomly distributed networking nodes use a specific modulation scheme with a specific Bit-Error-Rate (BER) and a constant bandwidth assuming the absence of any error control coding or any multiuser decoding scheme or any cooperation between transmitters and/or receivers. This model reflects the situation of multiple low power/low complexity communication networks such as sensor networks, suitable to work in a networking biotelemetry system, and it enables us to make observations, with practical significance, for the operation of these networks. We study how interference, separation distance, power and rate adaptation, for biomedical telemetry wireless communications, can allow concurrent transmissions, enhance network capacity by maximizing, when possible, individual link data rates, minimize transfer time of a data packet and allow the best possible usage of network's resources for all competing transmissions. Higher transmission rates can be achieved with higher levels of modulation (M-ary modulation), but higher transmission power is required to maintain acceptable bit error rate performance. If the total amount of interference at a receiver, during a reception of a packet, is high the network layer should decrease the transmission rate and/or increase the power to cope with it. This paper is organized as follows: first we present related work and the problem of wireless interference environments where many biomedical nodes share a common communication medium. In the following section we present our system model and the application scenario for our study. More specifically, we define an interference limited communication region around a receiver, exhibiting a large number of surrounding interfering transmission, within which a successful communication link can be formed. In Section 3, we present our results obtained from simulations that show the accuracy of our proposed model for the estimation of the interference and of the interference limited communication range, even when we relax our assumption on the receiver's position at the center of the networking area. In addition we present how interference levels and the interference limited communication range values are affected by the number of the surrounding interfering transmitters, the path loss exponent and most importantly on the selected mode and rate of operation. Section 4 concludes this paper.
## 1 Introduction
In this paper, the frequency-Bessel function transform is used to extract the multi-order dispersion curves from background noise data from stations in the karst area, and then the damped least squares algorithm with mature methods, fast operation speed, and high inversion accuracy is used to invert the multi-order dispersion curves of the Rayleigh surface wave. With the introduction of the new method, the theoretical and practical effect of higher-mode dispersion in the extraction of background noise data by the frequency-Bessel function method is discussed. The damped least squares algorithm for the inversion of the shear wave velocity structure improves the ability to depict karst in complex near-surface environments, and provides a new idea and method for detecting karst in near-surface engineering.
###### Abstract  
This study aimed to analyze anthropometric and whole-body/muscle-localized bioelectrical impedance vector analysis (BIVA) adaptations and their relation to creatine kinase (CK) as a biomarker of muscle damage in a group of seven male players in the maximum category of professional rink hockey. There were three checkpoint assessments in relation to a high-intensity training session: pre-session (PRE), post-session (POST), and 24 h-post-session (POST24H). The resistance, reactance, and impedance module were adjusted by height (R/h, Xc/h, and \(Z/h\), respectively). The Wilcoxon signed-rank test was used to compare the data at baseline and follow-up, while Spearman correlation was used to explore the relationship between CK and the rest of the parameters. The results registered a decrease in body mass at POST (\(p\) = 0.03) and a reestablishment at POST24H (\(p\) = 0.02). Whole-body BIVA registered a significant increase in R/h between PRE-to-POST (\(p\) = 0.02) and returned to baseline values at POST24H (\(p\) = 0.02), which was expected since this parameter is related to hydration processes. Muscle-localized BIVA in the rectus femoris muscle showed an increase in both Xc/h and phase angle in POST (\(p\) = 0.04 and \(p\) = 0.03, respectively) and a decrease in Xc/h at POST24H (\(p\) = 0.02). CK correlated with R/h in the rectus femoris at all the checkpoints (PRE-to-POST: r = 0.75, \(p\) = 0.05; PRE-to-POST24H: r = 0.81, \(p\) = 0.03; POST-to-POST24H: r = 0.82, \(p\) = 0.02). Our results indicate that BIVA is a sensitive methodology to assess general and muscle-localized hydration induced by a high-intensity training session in rink hockey players. A correlation between BIVA and CK was also reported.  
BIVA; BIVA; creatine phosphokinase; rectus femoris; hydration status; muscle damage; phase angle; reactance; resistance; hockey
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
## 3 BeiDou Timing Group Delay Bias from Network Solution  
Although the BDS TGD bias was calibrated in the previous section, it is, in fact, a difference relative to the WUM and DLR products. For the confirmation and independent calibration of the bias, a network solution based on BDS raw measurements is proposed in this section.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**PRN** & \(dtgd_{0}\) & \(dtgd_{1}\) & **PRN** & \(dtgd_{0}\) & \(dtgd_{1}\) & **PRN** & \(dtgd_{0}\) & \(dtgd_{1}\) \\ \hline
1 & \(-1.24\) & \(-0.97\) & \(12\) & \(2.74\) & \(-2.86\) & \(26\) & \(-0.72\) & \(0.44\) \\
2 & \(1.98\) & \(-1.29\) & \(13\) & \(1.28\) & \(-1.64\) & \(27\) & \(0.22\) & \(-0.29\) \\
3 & \(2.03\) & \(-1.55\) & \(14\) & \(1.67\) & \(-2.12\) & \(28\) & \(0.28\) & \(0.69\) \\
4 & \(-2.00\) & \(-1.37\) & \(16\) & \(1.65\) & \(-1.33\) & \(29\) & \(-0.59\) & \(0.71\) \\
5 & \(-2.01\) & \(-1.34\) & \(19\) & \(-1.30\) & \(2.84\) & \(30\) & \(0.17\) & \(1.22\) \\
6 & \(1.04\) & \(-1.53\) & \(20\) & \(-2.66\) & \(3.39\) & \(32\) & \(-1.15\) & \(1.94\) \\
7 & \(1.30\) & \(-1.90\) & \(21\) & \(-1.45\) & \(1.73\) & \(33\) & \(-2.03\) & \(2.53\) \\
8 & \(2.05\) & \(-2.64\) & \(22\) & \(-1.48\) & \(1.71\) & \(34\) & \(0.40\) & \(1.29\) \\
9 & \(2.00\) & \(-1.67\) & \(23\) & \(-3.29\) & \(2.32\) & \(35\) & \(-0.04\) & \(0.31\) \\
10 & \(1.85\) & \(-2.31\) & \(24\) & \(-1.13\) & \(1.07\) & \(36\) & \(-0.20\) & \(1.85\) \\
11 & \(1.13\) & \(-1.84\) & \(25\) & \(-0.96\) & \(1.85\) & \(37\) & \(0.43\) & \(0.79\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibrated BDS TGD bias from precise products (in ns). \(dtgd_{0}\) means the TGD bias at B3I frequency and \(dtgd_{1}\) is the residual TGD bias of TGD\({}_{1}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**BDS-2**} & \multicolumn{2}{c}{**BDS-3**} \\ \cline{2-5}  & \(dtgd_{0}\) & \(dtgd_{1}\) & \(dtgd_{0}\) & \(dtgd_{1}\) \\ \hline Average & \(1.03\) & \(-1.76\) & \(-0.86\) & \(1.47\) \\ STD & \(1.51\) & \(0.53\) & \(1.07\) & \(0.95\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: TGD bias of BDS-2 and BDS-3 (in ns).
## 2 Study Area, Data, and Methodology
### Methodology
#### 2.3.2 Precipitation Indices
However, instead of summing all wet days' precipitation, r95p accounts only for those precipitation values equal to or greater than the 95 th percentile threshold. Furthermore, for r99p, the threshold is the 99 th percentile. The percentage contribution of these extreme events (r95p and r99p) to the total annual precipitation (PRCPTOT) defines r95 pTOT and r99 pTOT. Frequency-Related Precipitation Extremes (R10, R20, R50, and R100)  
Similar to R1, these frequency-based precipitation indices (R10, R20, R50, and R100) were computed by counting the days when the daily precipitation value exceeds a certain threshold. However, instead of counting all wet days, R10 accounts only for those days when the precipitation value equals or exceeds 10 mm. Furthermore, the threshold for R20, R50, and R100 are 20, 50, and 100 mm, respectively. Consecutive Dry and Wet Spells (CDD and CWD)  
For the calculation of CDD, a time series of the length of consecutive dry days (dry spells) was computed. Then, the maximum dry spell length over a year is taken as the CDD of that year. Similar to the CDD, CWD was computed for consecutive wet days (wet spells). Maximum 1-Day, Consecutive 3-, 5-, and 7-Day Precipitation Extremes  
The maximum value of daily wet days precipitation over a year is taken as the RX1 day of that year. For RX3 day, a time series of 3 consecutive days of precipitation total was computed using a rolling window of three days. Then, the maximum value of 3 consecutive days precipitation total is taken as the RX3 day of that year. Similar to the RX3 day, RX5 day and RX7 day were computed for consecutive 5- and 7-days. The percentage contribution of these extreme events to that year's total annual precipitation (PRCPTOT) defines RX1 dayTOT, RX3 dayTOT, RX5 dayTOT, and RX7 dayTOT. Seasonal Precipitation and Monsoon Contribution (MonsoonTOT)  
Similar to the PRCPTOT, these seasonal precipitations (pre-monsoon, monsoon, post-monsoon, and winter) were computed by summing the wet day precipitation value during its period. We computed MonsoonTOT to provide the percentage contribution of monsoon precipitation to that year's total annual precipitation (PRCPTOT). Extra Precipitation Indices (CDDmonsoon and CWDwinter)  
We computed some extra seasonal indices to capture the maximum dry spell information during the monsoon season (CDDmonsoon) and the maximum wet spell information during the winter season (CDDwinter). The computation of these extra indices involved the same techniques as for the annual time series, except the computation was limited to only monsoon season for CDDmonsoon, and winter for CWDwinter. The above-mentioned twenty-eight indices were calculated for each IMERG grid point and GP station. For spatial mapping, values of all grid points are presented with the proper color legends where the same color for 25 GP stations was used.
## 3 Results and Discussions  
### Signal Processing  
Three sets of the force signal were selected. The time domain of the force signal shown in Figure 6a is the expulsion signal, while Figure 6b,c shows the other signals without expulsion. The length of the signal was 20,000 points, and the sampling frequency was 10,000 Hz. From the three figures below, the impact feature was very obvious and clear in the time domain during the welding time when expulsion occurred compared with the other ones. Multiple sets of experiments confirmed the same results. Therefore, the impact feature could be used as the main expulsion feature in distinguishing the expulsion defect.  
Figure 4: The structure of the Back Propagation (BP) neural network.  
Figure 5: The flow chart of the algorithm.  
The wavelet decomposition could refine the signal in multi-scale by calculating flex and translation, which fitted for the impact signal above better. Therefore, it was a suitable method to extract the impact signal using the wavelet decomposition. In this paper, the Daubechies5 wavelet was chosen and the decomposition level was set at 7. Twenty sets of the data were decomposed as introduced in Section 2.2. Figure 7a,c,e shows the time domain of three sets of processed signal in each layer, Figure 7a shows the expulsion signal, while Figure 7c,e shows other signals without expulsion. From Figure 7a,c,e, the impact components during the welding time were extracted from the original expulsion signal in each layer with different amplitudes. The impact components extracted from other signals were not mainly during the welding time but concentrated around the early period. Therefore, the wavelet decomposition was effective in separating impact components from the original signal.  
Figure 6: The time domain of the electrode force signal: (**a**) the expulsion signal; (**b**) other signal 1; (**c**) other signal 2.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 8 Conclusions and Future Scope  
The advancement of IoT technology, coupled with mobile and wireless systems for long-distance data transmission, has introduced increasingly complex security challenges. The LTE-based C-IoT represents a new approach that has been developed for long-range IoT applications. In this research, we present a novel ML-AKA security protocol aimed at providing three-layer mutual authentication in a C-IoT architecture. The ML-AKA protocol incorporates an improved approach to D2D authentication. We employ an enhanced symmetric key cryptography method that provides greater flexibility for D2D authentication and validation in a C-IoT environment. The simulation results, presented in Section 7, indicate that the proposed ML-AKA protocol achieves higher efficiency in terms of execution time.  
Figure 11: Communication overhead comparison for key agreement and session establishment using the ML-AKA protocol.  
Figure 12: Comparison of communication overhead for key agreement step.  
Figure 13: Comparison of communication overhead for session establishment step.  
cution time and authentication delay. Additionally, the ML-AKA protocol exhibits lower communication overhead compared to existing protocols.  
The proposed protocol is tested over a small homogeneous network environment and achieves a significant improvement in the reduction of spoofing and DDoS attacks. However, establishing a secure authentication mechanism for a large network with heterogeneous types of devices requires a complex key distribution mechanism instead of the use of uniform keys distribution. Future work on this topic will consist of the design and development of a complex key distribution and authentication mechanism that can be used for next-generation cellular and IoT networks.  
Conceptualization, B.M.; methodology, B.M.; software, B.M.; validation, B.M. and V.S.; formal analysis, B.M., R.B., C.R.S. and V.S.; Investigation, B.M., R.B., C.R.S. and V.S.; resources B.M., R.B., C.R.S. and V.S.; data curation, B.M.; writing--original, B.M.; writing--review and editing, B.M., R.B., C.R.S. and V.S.; visualization, B.M., R.B., C.R.S. and V.S.; supervision, V.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the articlel. Further inquiries can be directed to the corresponding author(s).  
The authors declare no conflicts of interest.
# Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency
## 1 Introduction
In contrast, we innovatively combined the traditional lithography exposure technology with chemical etching technology to propose a novel method to fabricate rMLA, which can be used to fabricate high-efficiency, low-cost and large-area rMLA. Meanwhile, in order to further reduce the surface roughness of microlenses, three kinds of chemical solutions were prepared in this paper: the fundamental chemical etching solution (deionized water, hydrofluoric acid solution (HF solution) (40%) and ammonium fluoride particles (NH4F)); the H2 SO4 chemical etching solution (deionized water, HF solution (40%) and the H2 SO4 (95-98%)); and the HNO3 chemical etching solution (deionized water, HF solution (40%) and the HNO3 (65-68%)), respectively. We compared the etching effects of the three kinds of solutions and obtained a best suitable solution ratio. Adopting the proposed method, we fabricated two types of rMLAs, which were the honeycomb rMLA and the rectangular rMLA, respectively. Moreover, a chromium film layer was plated on the surface of the glass substrate, which masterly avoided side etching and the curl-up of the edge. In addition, the high-quality surface of the microlenses can effectively guarantee the homogenization quality of the beam, and the randomness of the rMLA is able to successfully break the coherence of the laser beam so that the uniformity of the modulated beam can be improved. Section 2 describes the beam homogenization model of rMLA, while Section 3 demonstrates the fabrication process of the rMLA in detail, Section 4 shows experimental results and verifies the validity of our method and Section 5 is a summary.