esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Physical Audio Features Extraction Techniques
### Time Domain Physical Features
#### 4.1.2 Amplitude-Based Features  
Amplitude-based features are based on a very simple analysis of the temporal envelope of the signal. The following paragraphs describe the most commonly used amplitude-based temporal features, including the one from the Moving Picture Experts Group (MPEG) [45], (previously reviewed by [PERSON] _et al._[17]), and a feature extraction approach typically used to characterize voice pathologies which has recently found application in music analysis.  
* **Amplitude descriptor (AD):** it allows for distinguishing sounds with different signal envelopes, being applied, for instance, for the discrimination of animal sounds ([PERSON] _et al._[46]). It is based on collecting the energy, duration, and variation of duration of signal segments based on their high and low amplitude by means of an adaptive threshold (a level-crossing computation).
* **MPEG-7 audio waveform (AW):** this feature is computed from a downsampled waveform envelope, and it is defined as the maximum and minimum values of a function of a non-overlapping analysis time window [45]. AW has been used as a feature in environmental sound recognition, like in the works of [PERSON] and [PERSON] [47], or by [PERSON] and [PERSON] [48].
* **Shimmer:** it computes the cycle-to-cycle variations of the waveform amplitude. This feature has been generally applied to study pathological voices ([PERSON] [49], [PERSON] and [PERSON] [50], [PERSON] _et al._[51]). However, it has also been applied to discriminate vocal and non-vocal regions from audio in songs (as in [PERSON] and [PERSON] [52]), characterize growl and screaming singing styles ([PERSON] and [PERSON] [53]), prototype, classify and create musical sounds ([PERSON] [54]) or to improve speaker recognition and verification ([PERSON] _et al._[51]) to name a few.
# Research on Seismic Wave Quality Factor of Marble Jointed Rock Mass under SHPB Impact
## 1 Introduction
[PERSON] [27] researched that the increase in compressive strength in SHPB test was largely caused by the change in stress state in the sample; that is, when the strain rate was greater than the transition strain rate, the transverse limit was introduced, from uniaxial stress state to multiaxial stress state. [PERSON] et al. [28; 29] combined this with the deformation characteristics of granite, and acoustic emission (AE) technology was well applied in revealing the evolution law of micro-cracks in the process of rockburst. [PERSON] et al. [30; 31] established the elastic-plastic-brittle mutation rockburst model of coal rock with structural surface, introduced the concept of the coal rock volume energy potential function, and researched the relationship between energy accumulation and dissipation during the coal rock dynamic deformation and rupture process to obtain the rockburst energy condition. In this paper, to control blasting vibration and prevent its threat to adjacent structures, it is necessary to fully understand the propagation law of stress wave in rock mass. The marble from the roof and floor of Jinchanghe Lead-zinc mine in Baoshan was used as test material. The SHPB test system was used to conduct impact compression tests on several specimens from the same rock mass. The quality factor \(Qs\) was introduced to measure the dissipative degree of stress wave in marble jointed rock mass. Combined with the calculation principle of deformation energy and dissipation energy of quality factor, six groups of \(Qs\) experimental values were obtained. AUTODYN-Code was used to simulate the damage and fracture characteristics of rock mass with different quality factors under explosive dynamic loading.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 4 Results
### Simulated Datasets  
#### 4.1.1 Registration Accuracy Metrics  
As visually illustrated in Figure 4, the registration results of HiGoReg and the batch solution were in agreement for the Stanford datasets. Table 2 presents a detailed comparison of accuracy metrics between the two methods across all simulated datasets. For the Bunny dataset, both methods yielded identical results, with an RMSE of \(9.5738\times 10^{-13}\) m, a posterior unit-weight standard deviation (\(\hat{\sigma}_{0}\)) of \(3.9633\times 10^{-8}\) m, a rotation error (\(e_{R}\)) of 0 rad, and a translation error (\(e_{T}\)) of \(7.7008\times 10^{-8}\) m. Similarly, for the Monkey dataset, both approaches reported an RMSE of \(4.3130\times 10^{-7}\) m, \(\hat{\sigma}_{0}\) of \(2.0391\times 10^{-8}\) m, \(e_{R}\) of 0 rad, and \(e_{T}\) of \(1.0277\times 10^{-7}\) m. For the Dragon dataset, both methods also produced consistent results, with an RMSE of \(3.8049\times 10^{-8}\) m, \(\hat{\sigma}_{0}\) of \(8.5133\times 10^{-6}\) m, \(e_{R}\) of 0 rad, and \(e_{T}\) of \(6.3609\times 10^{-8}\) m. The large-scale synthetic dataset further confirmed this consistency: both methods achieved an RMSE of \(4.0863\times 10^{-6}\) m, \(\hat{\sigma}_{0}\) of \(7.4580\times 10^{-4}\) m, \(e_{R}\) of \(3.2452\times 10^{-5}\) rad, and \(e_{T}\) of 0 m. These results demonstrate that HiGoReg maintained accuracy comparable to the batch solution, even as the number of points in the point cloud increased.
## 5 Discussion and Comparison
### Comparison  
Most of the protocols proposed in literature can reach high avoidance rates in their experiments. However, they all need some impractical assumptions or special hardware as listed in Table 3. The MHA routing protocol does not need impractical assumptions, such as precise synchronized time, zero delay time, or the awareness of all node locations, and so on, which are usually assumed in the schemes adopting the viewpoint of administrator. In addition, MHA does not need any special hardware either. Due to its simple concept, the hop-count analysis scheme can improve most routing protocols in MANET as well. The nodes only need to sort the received hop-counts, and select the legal ones according to the preset boundaries. Thus the computation overhead is very low for this process.  
We compare MHA with previous works in Table 3. In [9], a large amount of hash tree computation is required. In their discussion, the optimized condition performs 10 million hash function evaluations in 7.544 seconds on a Pentium III running at 1 GHz; and 45 seconds on a Compaq iPAQ 3870 PocketPC running Linux. A long waiting time for system setup shows their high overhead. In [11], all transmissions between node pairs have to be encrypted by the local broadcast key of the sending end and decrypted at the receiving end. As a result, the time delay accumulates per node traveled. The MDS-VOW mechanism in [10] uses a control center to accomplish the _O(n\({}^{3}\))_ operations if there are n nodes. This is a fairly heavy load for the control center.
## 2 Materials and Methods
### Model Evaluation  
Evaluating and identifying the most effective model requires assessing the performances of the various classifiers tested. There are several alternative or complementary methods for this, and here several evaluation metrics are employed, including prediction accuracy, recall, precision, and the F1 score. Classification accuracy typically refers to an easily accepted measure of accuracy, namely the fraction of correct calls among the total number of input cases Equation (1).  
\[\text{Accuracy}=\frac{\text{TrueNegative}+\text{TruePositive}}{\text{TruePositive}+\text{FalsePositive}+\text{TrueNegative}+\text{FalseNegative}} \tag{1}\]  
However, this measure is considered effective only when there is a relatively balanced distribution of samples across each class (and we give equal cost to each misclassification, or actually specify no cost for those but evaluate only by right or wrong).  
The F1 score is defined as the harmonic mean of precision and recall, so its values range from 0 to 1 because both precision and recall are in this range. This metric indicates the accuracy of your classifier, reflecting the proportion of correctly classified instances, as well as its reliability, demonstrating that it does not fail (misclassify) in a substantial number of instances [23].  
Achieving a high precision with a low recall results in exceptional accuracy, albeit at the cost of overlooking numerous instances that are challenging to classify. An increase in the F1 score indicates an improvement in the model's performance. This relationship is shown in Equation (2):  
\[\text{F}_{1}=\left(\frac{\text{recall}^{-1}+\text{precision}^{-1}}{2}\right)^{ -1}=2\cdot\frac{\text{precision}\cdot\text{recall}}{\text{precision}+\text{ recall}} \tag{2}\]  
The F1 score aims to establish a balance between precision and recall.  
Precision is defined as the ratio of correctly identified positive results to the total number of positive results predicted by the classifier Equation (3).  
\[\text{Precision}=\frac{\text{TruePositive}}{\text{TruePositive}+\text{ FalsePositive}} \tag{3}\]  
The recall is defined as the ratio of correctly identified positive results to the total number of relevant samples, which includes all samples that ought to have been recognized as positive Equation (4).  
\[\text{Recall}=\frac{\text{TruePositive}}{\text{TruePositive}+\text{ FalseNegative}} \tag{4}\]All the models were developed using Python (Version 3.9), a high-level interpreted programming language that is designed for general-purpose use, and for which public domain machine learning libraries are available free of charge.
## 2 Materials and Methods
### Data Analysis  
The spectral raw data recorded with the Raman spectrometers were uploaded onto a cloud and imported into Matlab R 2016b (MathWorks, Inc., Natick, MA, USA). Automated spectral processing and preprocessing were applied. Spectral preprocessing was carried out as weighted normalization using the standard normal variate (SNV) using Matlab together with PLS_Toolbox version 8.2.1 for Matlab (Eigenvector Research, Inc., Wenatchee, WA, USA). Spectra were also derivatized. Principal component analysis (PCA) was performed. A number of 3 latent variables were applied, yielding the best results. The singular value decomposition (SVD) algorithm was used for the PCA. Using a home-built Matlab script, the peak intensities of the 3 major Raman bands were identified and used for computing the four-level color-coded maturity index. The results were visualized, allowing instant evaluation after spectral investigation.
# Design and Application of New Aeration Device Based on Recirculating Aquaculture System
## 1 Introduction
[PERSON] [13] designed and tested a venturi aeration system for aquaculture, and its flow characteristics and actual performance parameters were experimentally confirmed, demonstrating high aeration efficiency. However, with the continuous increment in the density of fish cultured in RASs, these oxygenators are increasingly unable to meet the actual demand, showing poor performance. [PERSON] et al. [14], in their study, suggested that the oxygen consumption of four large fish is about 1.35 mg/(L-h); the \"oxygen capacity\" calculated from the data measured in the laboratory with some of the currently used oxygenators did not meet the oxygen demand of fish in high-density aquaculture. There is a need to design a device with better aeration performance for application in RASs. Thus, this study aimed to develop a new aeration device (NAD) for RASs. Its main components include two commonly used oxygenation devices, i.e., an impeller and a microporous aeration component. After we redesigned them, prototypes of these two oxygenation components were applied to a grass carp RAS in combination, so that we could test their actual culture oxygenation and flow generation effects. The rationality and feasibility of our design were verified by using experimental methods, and the optimal combination of operating parameters (i.e., the rotational speeds of the two components) was deduced to improve aeration performance and reduce costs in RAS aquaculture. In this study, ideas were provided for the improvement in the design of oxygenation devices and as a reference for their application in RASs.
## 8 System Design
### Open Spacecraft Design
#### 8.6.1 Subsystem/Payload Board Specification  
The OPEN framework defines a standard board configuration. This is shown in Figures 12 and 13. The board (shown in Figure 12) includes a PC/104-compliant pin set on the bottom and a receiver on the top. The board slides into the posts shown in Figure 15. The pins on the bottom of the board seat into the receiver base, shown in Figure 14. The board is 85 mm tall and 85 cm wide. This width allows the board to fit the PC/104 connector (81.79 mm wide) and also fit within the CubeSat volume limitations.  
To accommodate the boards fitting in the square pattern depicted in Figure 17, a reduced-height area has been designated on the sides of the boards. This is shown in Figure 13. For the front (interior) of each board, the first 5 mm on each side is required to have no raised components (PCB circuit printing can be utilized, as long as suitable contact protection is implemented between the board and the rails). The next 10 mm on each side has a maximum height of 5 mm. The center area on the front (55 mm in width) is allowed to reach 18 mm in height. The rear (exterior) of the board has a 5 mm no-raised-component area on each side. The rest of the board (75 mm in width) has a maximum height of 8 mm. This area protrudes beyond the side rails, as shown in Figure 17.  
Figure 11: CAD Diagram of OpenOrbiter Satellite Internal Structures.
# Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train
## 6 Experimental Verification and Analysis
### Observer and Performance Optimization
The parameter index comparison is shown in Table 1.  
Figure 8: Gap of performance degradation and normal operation. Figure 9: Residual of performance degradation and normal operation. After the performance of the suspension system is degraded, any small disturbance will cause the system to crash track, that is, the electromagnet first collides with the upper track and is adsorbed to the upper track. After a period, it falls and collides with the lower track, and finally the electromagnetic achieves normal suspension. Among them, the gap peak is the maximum value of the gap between the electromagnet and the upper track, and the smaller value means the lower the probability of collision with the lower track. The dead time is the duration of the electromagnet being attracted to the upper track. The adjustment time is the time from the minimum electromagnet gap to normal operation. As time goes by, \(\theta_{r}\) is continuously updated iteratively, the peak value of the gap is getting smaller. This means that the probability of the electromagnet colliding with the underlying track becomes smaller. Additionally, the dead time and adjustment time \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **Peak** & **Dead Time** & **Adjustment Time** & **Performance Index \(J\)** \\ \hline \(t_{1}\) & 0.00749 & 3.140 & 3.642 & 0.1576 \\ \(t_{2}\) & 0.00743 & 3.069 & 3.592 & 0.1444 \\ \(t_{3}\) & 0.00740 & 3.256 & 3.781 & 0.3617 \\ \(t_{4}\) & 0.00742 & 2.954 & 3.491 & 0.1320 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of indicators at four moments. Figure 11: Residual at four moments. Figure 10: Gap at four moments. are both smaller. Moreover, the performance index become smaller as the iteration time increases. In the iterative process of the dynamic compensator, the maximum value of the residual signal gradually becomes smaller. However, in normal suspension, the difference between the mean and variance of the residual signals at the four times is not big, mainly because the running time is not suffice and the control performance convergence is slow, which is the limitations of the proposed method in this article.
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.11 Interpolation  
This block aims to upsample the IFFT output, add a certain number of samples at the starting of each symbol, known as cyclic prefix (CP), to remove the intercarrier interference (ICI) and the intersymbol interference (ISI), and convert the symbols from parallel to serial flow of samples [9]. The interpolation procedure varies with the frequency spacing, \(\Delta f\) as follows:  
* When \(\Delta f=15\) kHz, the upsampling becomes 8 times to convert the 16 samples to 128 samples with 10 samples for the CP of the first symbol in the time slot, and 9 samples for the CP of the other 6 symbols in the same slot.
* When \(\Delta f=3.75\) kHz, the upsampling becomes 32 times to convert the 16 samples to 512 samples with 40 samples for the CP of the first symbol in the time slot, and 36 samples for the CP of the other 6 symbols in the same slot.
# Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models
## 2 Materials and Methods
### LUE Models  
The LUE model is an effective tool to estimate the GPP using remote sensing data. LUE models can be generalized as the following formula:  
\[\text{GPP}\ =\ \text{FAPAR}\ \times\ \text{PAR}\ \times\ \text{LUE}_{max}\times\ \text{f}(\text{T},\ \text{S},\ \text{--}) \tag{1}\]  
where FAPAR is the fraction of absorbed photosynthetically active radiation (PAR). \(LUE_{max}\) is the maximum light using efficiency, and f(T, S, --) is an environmental scalar on the photosynthesis rate. According to different schemes to quantify the environmental stress on photosynthesis, many LUE models have been developed. In this study, five widely used LUE models were selected to simulate the GPP seasonal dynamic at dominant vegetation types of the study area. They were Global Production Efficiency Model (GLO-PEM) [17], Vegetation Photosynthesis Model (VPM) [18], Carbon fixation model (C-Fix) [5], Eddy Covariance-Light Use Efficiency (EC-LUE) [6], and MODIS GPP product algorithm (MODIS-PSN) [7]. All the models were forced using in-situ measured meteorological data and default parameters for each site. Information regarding these models is listed in Table 2.
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
## 4 Energy and Transmission Time Analysis  
### System Model  
To build the model, we consider a linear structure described in Section 3. For the analysis, ideal conditions are assumed: no collision, no overhead for turning on the transceiver, and prompt response in the MAC layer. Note that these assumptions are especially realistic for many LWSN networks that monitor some parameters to know the state of the infrastructure (pressure, vibrations, leaks, etc.) in vast areas: small amount of traffic, few nodes within the coverage area, environments where the presence of interference is minimal (e.g., in networks for pipe monitoring, road borders, etc.). Our proposal does not support sensors that require large bandwidths, such as cameras, radars, or lidars, that could be used to monitor especially relevant areas. Nevertheless, a separate network could be easily deployed for such traffic in the required locations.  
Although the processing of the frame for its forwarding is not negligible, we do not consider that time for the analysis because the delay caused is the same for scenarios that use iACK and eACK. Other considerations are presented below.  
* All the frames have the same length, so the transmission times are fixed.
* Distance between transmitter and receiver is 50 m.
* Probability of error when transmitting a frame in each link is the same. We include a model for comparing the latency added by a retransmission using eACK or iACK. Nevertheless, for the sake of simplicity, we did not consider retransmissions in the end-to-end analysis.
* ACK frames have a constant length.
* If the transmitted data collides at the receiver, the _macAckWait_ duration (an IEEE 802.15.4 attribute dependent on constants and attributes of the physical layer, which  
Figure 11: TXN node state diagram.  
Figure 12: RXN node state diagram.  
defines the maximum number of symbols to wait for an ACK frame) for the transmitting node is the sum of the turnaround time of 12 T\({}_{\text{s}}\) and the ACK duration of 22 T\({}_{\text{s}}\), a total of 34 T\({}_{\text{s}}\) considering T\({}_{\text{s}}\) = 16 \(\upmu\)s and 1 symbol is 4 bits.
* A CCA is successful when no transmission is detected from any node, at the time of initiation of its CCA. In the IEEE 802.15.4 standard, the channel state is averaged over 8 T\({}_{\text{s}}\).
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 3 Theoretical Backgrounds  
### Original DV-Hop Algorithm  
DV-Hop is one of the most widely used localization algorithms due to its simplicity and the fact that it does not require ranging. Its implementation consists of the following three steps:  
STEP 1: Calculation of the minimum hop count between sensor nodes  
Each anchor node broadcasts data packets to the network in a flooding manner, which include the anchor node's position and an initialized hop count of 0. When adjacent nodes receive the packet, they retain only the minimum hop count from the same anchor node and forward it, incremented by 1, to neighboring nodes. Once the flooding process is complete, all nodes will know the minimum hop count to each anchor node.  
STEP 2: Estimating the distance between nodes  
The anchor nodes calculate the average hop distance using an unbiased estimation method, as defined in Equation (1).  
\[AHS_{i}=\frac{\sum\limits_{j\
eq i}^{N_{a}}\sqrt{\left(x_{i}-x_{j}\right)^{2}+ \left(y_{i}-y_{j}\right)^{2}}}{\sum\limits_{j\
eq i}^{N_{a}}h_{ij}} \tag{1}\]  
where \(AHS_{i}\) denotes the average hop distance of anchor node \(i\), \(N_{a}\) is the number of anchor nodes, \((x_{i},y_{i})\) and \((x_{j},y_{j})\) represent the position of anchor node \(i\) and \(j\), respectively, and \(h_{ij}\) denotes the minimum hop count between anchor node \(i\) and \(j\).  
Once the average hop distance of anchor nodes is broadcast to the network, unknown nodes use the average hop distance of the first received anchor node as their own. The estimated distance between the unknown node and the anchor node is then calculated using Equation (2).  
\[d_{ui}=AHS_{u}\times h_{ui} \tag{2}\]  
where \(AHS_{u}\) denotes the average hop distance of the unknown node \(u\), while \(h_{ui}\) and \(d_{ui}\) represent the minimum hop count and the estimated distance from the anchor node \(i\) to the unknown node \(u\), respectively.  
STEP 3: Calculation of the unknown node coordinates  
Based on the distance estimates from the previous step and the known information of anchor nodes, the following distance equations are derived:  
\[\left\{\begin{aligned} \sqrt{\left(x_{u}-x_{1}\right)^{2}+ \left(y_{u}-y_{1}\right)^{2}}&=d_{u1}\\ \sqrt{\left(x_{u}-x_{2}\right)^{2}+\left(y_{u}-y_{2}\right)^{2}}&  
The unknown node's position can be determined by solving the above equation using the least squares method:  
\[\mathbf{X}=\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}\mathbf{A}^{T}\mathbf{B} \tag{5}\]  
where  
\[\mathbf{A}=2\begin{bmatrix}(x_{1}-x_{N_{u}})&(y_{1}-y_{N_{u}})\\ (x_{2}-x_{N_{u}})&(y_{2}-y_{N_{u}})\\ \vdots&\vdots\\ (x_{N_{u}-1}-x_{N_{u}})&(y_{N_{u}-1}-y_{N_{u}})\end{bmatrix} \tag{6}\]  
\[\mathbf{B}=\begin{bmatrix}x_{1}^{2}-x_{N_{u}}^{2}+y_{1}^{2}-y_{N_{u}}^{2}+d_{ uN_{u}}^{2}-d_{u1}^{2}\\ x_{2}^{2}-x_{N_{u}}^{2}+y_{2}^{2}-y_{N_{u}}^{2}+d_{uN_{u}}^{2}-d_{u2}^{2}\\ \vdots\\ x_{N_{u}-1}^{2}-x_{N_{u}}^{2}+y_{N_{u}-1}^{2}-y_{N_{u}}^{2}+d_{uN_{u}}^{2}-d_{ u(N_{u}-1)}^{2}\end{bmatrix} \tag{7}\]  
\[\mathbf{X}=\begin{bmatrix}x_{u}\\ y_{u}\end{bmatrix} \tag{8}\]