esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Preconditioning and Tunnel Support on Strain Burst Potential
## 3 Methodology  
To evaluate the influence of the implementation of shotcrete and rockbolts support and destressing on strain burst potential, the following steps have been defined:  
1. Numerical modeling of the strain burst considering the DISL approach;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**[PERSON] et al., 1998 [47]** & **[PERSON], 2000 [48]** & **[PERSON], 2010 [49]** \\ \hline \(\sigma_{max}\)/_UCS_ & **Rockburst Potential** & \(\sigma_{max}\)/_UCS_ & **Rockburst Potential** & \(\sigma_{max}\)/_UCS_ & **Rockburst Potential** \\ \hline \(<\)0.34 & Few & \(<\)0.3 & No rockburst & \(<\)0.4 & Stable (no stress \\  & spalling & \(<\)0.3 & No rockburst & \(<\)0.4 & damage) \\ \hline
0.34–0.42 & Severe spalling & 0.3–0.5 & Weak rockburst & 0.4–0.6 & Minor spalling \\
0.42–0.56 & Moderate damages & 0.5–0.7 & Strong rockburst & 0.6–0.8 & Moderate overbreak \\
0.56–0.7 & Intense rockburst & \(>\)0.7 & Violent rockburst & \(>\)0.8 & Serious overbreak \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of the \(\sigma_{max}\)/UCS ratio and associated rockburst potential.  
Figure 3: Schematic failure envelope for brittle failure, showing the damage initiation and spalling limits. Failure envelope developed by [PERSON] [45] and presented on this form in [3]. \(\sigma_{c}\) is the unconfined compressive strength (UCS) of intact rock. The damage zones around an excavation are compared with the limits.  
Comparison of the results with the rockburst graphs of [PERSON] et al. [21] and [PERSON] [3] to validate the models. Excavation damage zone depth of failure is also considered;
3. Definition of the strain burst potential for each model;
4. Integration in the numerical models of mitigation measures: (1) shotcrete and rock-bolts support, (2) destressing, and (3) a combination of shotcrete support and defstress blasting;
5. Evaluation of the influence of the mitigation measures on the strain burst potential assessed previously.  
Fifty preliminary models were run, considering five different GSI values, five different UCS values and two different tunnel depths. These preliminary models evaluate the influence of the fracturing of the rock mass, of the strength of the rock mass and of the stress's environment. They do not consider any mitigation measures. When integrating these mitigation measures, only the deeper tunnels are considered as they are the ones presenting a higher strain burst occurrence probability.
## 1 Introduction
* The task-driven transfer approach and TT-VFDL-SIM designed based on it can be used as a transfer learning-based analysis technique for not only VFDL but also multivariate IoT time-series data from various domains. Meanwhile, the limitations of our study are as follows:
* Several options designed to apply similar feature spaces introduced in TT-VFDL-SIM for perturbation injection may still be affected by data or task characteristics. Although we were able to experimentally derive the option that could achieve good performance in most cases, the options of TT-VFDL-SIM are not organized into a single mechanism. Therefore, future work to address this problem is required. * Like representative existing VFDL mechanisms, TT-VFDL-SIM also ensures a basic level of security because it does not expose sensitive raw IoT time-series data. However, encoded label data are exposed for classification target tasks. Although encoded label data are less sensitive than raw IoT time-series data, future work is required on more advanced security techniques to solve the label leakage problem caused by the exposure of encoded label data. Section 2 summarizes existing studies and introduces the target system architecture and model structure of the target task for applying TT-VFDL-SIM. In addition, we summarize representative existing VFDL methods and introduce comparison methods used in the experiment. Section 3 introduces the task-driven transfer approach to design TT-VFDL-SIM. Section 4 introduces the TT-VFDL-SIM algorithms designed based on the task-driven transfer approach. We have designed two TT-VFDL-SIM algorithms, one for regression target tasks and the other for classification target tasks. In Section 5, we analyze the experimental results of applying TT-VFDL-SIM, centralized training, and two representative existing VFDL methods to three types of target task models and five multivariate IoT time-series datasets. Also, acronyms and terms used in this paper are summarized in Table 1.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods
### Data Acquisition  
Data was acquired between 28 February and 3 March 2017 (harvest), where data collected in situ was implemented with the two canopy treatments. The purpose behind the canopy treatments was to gain a direct line of sight to the bunches, which were generally hidden by the vine's canopy. No manipulation of the canopy, essentially the normal canopy condition, was classified as the FC treatment. The alternative LR treatment occurs after manual manipulation of the canopy, resulting in complete leaf removal in the bunch zone, effectively displaying the bunches.  
Figure 2 illustrates the data-acquisition process that resulted in a total of ten datasets, as outlined by each step:  
1. RGB and RGB-D imagery acquired for the FC treatment taken at bunch-level (n = 21; randomly selected and labelled bunches from the 31 vines) and plant-level (n = 31; individual vines).  
The resulting four datasets included: (i) RGB: bunch; (ii) RGB-D: bunch; (iii) RGB: vine; and (iv) RGB-D: vine.
2. Manual manipulation of the canopy resulted in the environment for the four LR treatment datasets. These datasets were identical to the FC datasets and concluded the in-situ datasets (datasets = 8). At this point, the 31 vines were harvested.
3. RGB and RGB-D imagery of the harvested bunches (n = 21) captured under laboratory conditions resulted in the final two datasets. At this point, all datasets (datasets = 10) have been captured.
4. Reference measurements, captured under laboratory conditions, included mass (g) and displacement (mL) for the individual bunches (n = 21) and individual vines (n = 31).  
Figure 1: Location of the Shiraz vineyard in Stellenbosch, South Africa. Inset map (red rectangle) shows the three rows used for data collection.  
Figure 2: Data-acquisition protocol used in this study. Order of acquisition indicated by the grey arrow. [Key: FC = full canopy; LR = leaf removal; Lab = laboratory; Ref = reference measurements; Exp = experiment].  
RGB and RGB-D images were captured by two PRS sensors. A D3200 digital single-lens reflex camera (Nikon, Tokyo, Japan) was used for capturing 24.2-megapixel RGB images. The camera captured images in _auto_ mode, with the flash disabled. The second sensor, a Kinect(tm) V1 (Microsoft, Redmond, WA, USA), was used to capture RGB-D imagery as either a mesh (bunch-level) or a point cloud (plant-level). The following data-acquisition subsections provide experiment-specific details.
## 4 Simulation Assessment of the ARounD Communication Scheme  
In the previous sections, a set of communication mechanisms (ARounD) were proposed, enabling the setting-up of peer-to-peer communication paths upon a cluster-tree wireless sensor network.  
In this section, we present a simulation assessment of the proposed communication scheme. The rationale behind this assessment is to compare the behaviour of a source-to-destination message stream when its messages are transferred using the ARounD communication scheme vs. using the standard cluster-tree routing scheme (IEEE 802.15.4/ZigBee parent-child relationships). The cluster-tree network is shared with a set of background message streams, that are sending convergecast traffic towards the PAN coordinator.  
For this simulation assessment, it was used the Castalia Simulator [63]. Castalia (The Castalia Simulator for Wireless Sensor Network: [[https://castalia.forge.nicta.com.au](https://castalia.forge.nicta.com.au)]([https://castalia.forge.nicta.com.au](https://castalia.forge.nicta.com.au)).) is an open-source discrete event simulator for wireless sensor networks (WSN), Body Area Networks (BAN) and general low-power embedded networks. It was developed at National ICT Australia (NICTA) and is based on the OMNeT++ platform widely used by researchers and developers to test communication protocols using realistic wireless channel and radio models. According to [64], Castalia has become a very popular simulator as it was developed for research purposes. Castalia implements an advanced wireless channel model based on empirically measured data. Also, the simulator provides radio models based on real low-power communication radios. Moreover, important features to simulate WSNs are available, such as: realistic node behaviour, node _clock drift_, and energy consumption models.  
Castalia provides an IEEE 802.15.4 model. However, this model is quite limited. Basically, it only implements the CSMA-CA functionality and a beacon-enabled star topology, including an association procedure, direct data transfer mode, and GTS communication. Therefore, we developed the CT-SIM simulation model [65], which includes a series of functionalities, such as: cluster-tree formation procedure, network scheduling, hierarchical addressing scheme, direct and indirect data communication, collision domain definition, data communication to the sink node (PAN coordinator), and source-to-destination message streams. The ARounD communication mechanisms were implemented upon the CT-SIM simulation model.  
Figure 16: Format of the _ARounD hello command_ frame.  
Figure 15: Format of the _ARounD closing command_ frame.
# Comparison of Radio Frequency Path Loss Models in Soil for Wireless Underground Sensor Networks
## 2 Experimental Methodology
The transmitter output power was fixed at 10 dBm for all tests. A signal spectrum analyser (PSA 2702 RF) was used to measure the signal intensity at specific positions. The frequency range of the spectrum analyser was approximately from 1 MHz to 2700 MHz and has a noise floor limitation of \(-\)96 dBm at \(-\)20 dBm reference level. A 434 MHz isotropic Omnidirectional quarter wavelength antenna was used in this experiment as a full wavelength antenna could not be placed in the container due to size limitations. The frequency used in these trials was 434 MHz and the distance between the holes was calculated based on the near-field and far-field radiation to avoid the near-field radiation effect of the signal transmission [19]. After some initial signal transmission tests, it was discovered that the difference between the permittivity of the materials (soil and container) caused the signals to be reflected (at the container  
Figure 2: Aluminium container with the six holes with 10 cm apart from each other. The holes are placed in order to measure signal intensity in different distances from the signal transmitter. and soil boundary) back into the container resulting in a wave guide action causing inconsistent measurements. In order to limit the effect of the container acting as a wave guide, it was earthed to the ground to cancel eddy current effects that were generated on the wall of the container. Results from the metallic container with the earth connection appeared to be consistent and not affected by the reflection and waveguide effect. Figure 3 shows the final setup for the container. To ensure that the comparison of the models was justified, identical soil permittivities were used as an input for both models. The Peplinski mixing model and the CRIM model were not used to determine the complex permittivity. Rather, the real and imaginary parts of the complex permittivity of the soil, used in the simulations, were determined from Time Domain Reflectometry measurements using the Topp model [11]. Topp [11, 20] have shown that the difference between the dielectric permittivity by these different methods is within 3 to 40 for the change of the volumetric water content (VWC) ranged 0 to 0.55. This complex permittivity was then used as an input into both the modified-Friis and the CRIM-Fresnel models to estimate signal attenuation for comparison with the measured values of the signal attenuation along the 10 to 50 cm path. The comparison between the models and the measured results is described in the next section.
## 5 Discussion  
As evidenced in the literature review, traffic flow refers to the number of vehicles passing through a particular road segment during a specified period. Traffic flow is an essential factor influencing the travel time of vehicles. It has been seen that in general, a high traffic flow leads to more congestion and slower travel times, while a low traffic flow results in faster travel times. For vehicle routing, it is crucial to consider traffic flow because it directly affects the travel time of vehicles. If a routing algorithm only considers the shortest distance between two points without taking traffic flow into account, it can lead to inefficient routing decisions. For example, a route that appears to be the shortest distance on the map may have a higher traffic flow, resulting in longer travel times.  
Predicting traffic flow may not be the most effective approach to determine the fastest route for vehicle routing but it does help to identify the prediction of the time it would take a vehicle to go from one place to another. For example, in practical cases, we have discussed how traffic speed or travel time prediction can be achieved by using machine learning algorithms that take into account various factors such as historical traffic patterns, time of day, weather conditions, and the characteristics of the road, the road network, and others. These algorithms can provide real-time information on traffic conditions, allowing vehicles to adjust their routes to avoid congestion and reduce travel times. In short, understanding traffic flow is essential for vehicle routing as it directly affects travel times. However, predicting traffic speed or travel time may be a more effective approach than predicting traffic flow when determining the fastest route for vehicle routing.
## 5 Future Prospects
### The Development of AIoT (Artificial Intelligence of Things)
Table 3 presents various intriguing prospects based on the examination of the solar-hydrogen storage system and the future development of AIoT (Artificial Intelligence of Things). The development and implementation of solar-hydrogen storage systems could be completely transformed by the integration of AIoT technologies, opening the door to intelligent, self-sufficient, and sustainable energy solutions for a more environmentally friendly future. These systems can enhance energy efficiency, optimize resource use, and pave the road for a more robust and decentralized energy ecosystem by utilizing AI-driven analytics, predictive algorithms, and autonomous control.
# Late Reverberation Synthesis Using Filtered Velvet Noise +
## 6 Computation and Memory Costs
### Comparison Against Other Algorithms  
Next, we compare the computational and memory costs of the proposed methods to other convolution reverberation approaches. We enumerate the number of FLOPs and the number of signal memory samples required for a 88,200 samples-long impulse response, as in the previous section.  
The values listed in Table 3 for the direct convolution are based on the direct-form FIR implementation, which leads to the same number of multiplications as the number of RIR samples (88,200) and one less addition (88,199). In direct convolution, the required amount of fast memory is the same as the RIR length, since it defines the delay-line length (88,200 samples). The values for the partitioned fast convolution are taken from the recent improvement of the algorithm by [PERSON] and [PERSON] (see Table 1 in [8]).  
Table 3 shows that the proposed algorithms are over 100 times more efficient computationally than the direct convolution and approximately as efficient as the best partitioned fast convolution algorithm, which is only 12% more efficient than the advanced FVN. The memory consumption of the new method is the same as that of the direct convolution and 50% smaller than that of the partitioned convolution algorithm.  
Table 3 also shows that the FVN method is useful for compression of RIR data: whereas the direct and partitioned convolution algorithms must store all RIR samples, the FVN methods only store two arrays of pointers, which give the locations of the positive and negative impulses, an array of segment lengths (20 in this case), plus 12 filter parameters per segment (a gain factor and 11 feedback coefficients of the 10 th-order all-pole filter). The advanced FVN method is even more efficient in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Additions** & **Multiplications** & **Percentage** \\ \hline VNC & 160 & 0 & 26\% \\ H & **200** & **200** & **64\%** \\ G & 0 & 20 & 3\% \\ Sum & 19 & 0 & 3\% \\ SAP & 14 & 14 & 4\% \\ \hline Total & 393 & 234 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operations required to process one sample in each module of the basic FVN algorithm. The largest number in each column is in bold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Additions** & **Multiplications** & **Percentage** \\ \hline VNC & **139** & 0 & 31\% \\ \(\Delta\)H & 131 & **106** & **52\%** \\ \(\Delta\)G & 0 & 25 & 6\% \\ Sum & 24 & 0 & 5\% \\ SAP & 14 & 14 & 6\% \\ \hline Total & 308 & 145 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operations of the advanced FVN algorithm. Note that \(\Delta\)H also includes the first coloration filter \(H_{1}(z)\). The largest number in each column is in bold.  
this respect, as there are less impulses in the VCN block and the differential filters only require five parameters each. This yields a total of 294 parameters to be stored. The amount of data is only 0.33% compared to the original RIR samples. This implies that the FVN approach enables very efficient storage of multichannel RIR data.
# Thermographic Stall Detection by Model-Inspired Evaluation of the Dynamic Temperature Behaviour
## 3 Experimental Setup
### Image Processing  
The evaluation of the dynamic surface temperature response of the resulting image series is based on the temporal evaluation of single pixels with image series consisting of \(J\) images. The spatial dimension of each image is \(M\times N=151\times 386\) pixels, and for the region of interest, i.e., without background, \(151\times 312\) pixels. According to the different signal processing approaches, two different evaluation methods are used.  
The evaluation of the surface temperature response to transient input signals is based on time-averaged temporal temperature gradients \(\overline{dT}_{\text{s,}mm}\), which are calculated by  
\[\overline{dT}_{\text{s,}mm}=\frac{1}{J-1}\sum_{j=0}^{J-2}\left(T_{\text{s,}mm }(j+1)-T_{\text{s,}mm}(j)\right). \tag{6}\]  
where, \(j\) denotes the time step and \(m,n\) pixel row and pixel column, respectively.  
In order to analyse the surface temperature response around predefined operating points, firstly the temporal temperature gradients are deducted from the measured data using a linear regression function. As a result, evaluation around an operating point only takes place for the oscillating part of the input signal and is independent of the input signals' large-scale behaviour. Subsequently, the surface temperature response is evaluated by harmonic amplitudes. For this reason, a pixelwise discrete Fourier transformation is performed in order to calculate the Fourier coefficients \(P_{mm}(k)\) for each frequency component \(k\), according to  
\[P_{mm}(k)=\frac{1}{J}\sum_{j=0}^{J-1}\text{ exp}\left(-2\pi i\frac{jk}{J} \right)T_{\text{s,}mm}(j)\text{ \,}k=0,\cdots,J-1. \tag{7}\]  
With the aim to evaluate the surface temperature response to fluid temperature oscillations, the Fourier coefficients are filtered for the frequency range of the fluid temperature oscillation. Therefore, the frequency range is limited to 0-0.2 Hz in order to achieve the maximum signal-to-noise ratio. Finally, an averaging over the \(K\) filtered Fourier coefficients is conducted, in order to attain the mean amplitude  
\[\overline{P}_{mm}=\frac{1}{K}\sum_{k=1}^{K}\text{ }P_{mm}(k), \tag{8}\]  
as the evaluation quantity of the harmonic signal processing approach around an operating point.  
In addition, the classical approach of calculating time-averaged temporal surface temperatures \(\overline{T}_{\text{s,}mm}\) is used as reference method. The temporal mean surface temperatures are calculated by  
\[\overline{T}_{\text{s,}mm}=\frac{1}{J}\sum_{j=0}^{J-1}\text{ }T_{\text{s,}mm}(j). \tag{9}\]  
The aim is to evaluate improvements in the proposed dynamic signal processing approaches compared to the classical approach regarding identifiability and distinguishability of flow regions. Comparison criteria are the identifiability of flow regions using flow-dependent features and the calculated contrast-to-noise ratio between different flow regions according to Equation (5).
## 2 NOMA System Model  
The main idea of PD-NOMA is the efficient utilization of the power domain for multiple access to simultaneously serve several users in the same time slot, frequency band, and with the same code. NOMA assigns larger power to users with lower SNR values, with the purpose of providing a balanced distribution of resources. As shown in Figure 1a, OFDMA divides the available RBs (spectrum) among the available users while keeping the transmitted powers at a constant value. On the other hand, NOMA uses all the available RBs (spectrum) by assigning a different power value to each user, as shown in Figure 1b. Additionally, in OFDMA, the subcarrier separation must follow a certain frequency constraint in order to attain orthogonality. In NOMA, this separation is not applicable as all the bandwidth is utilized by all users [8; 33].  
The concept of NOMA is based on applying SC at the transmitter and SIC at the receiver, as shown in Figure 2. To transmit the data in NOMA, each user sends an independent pilot sequence at the same time slot for the sake of channel estimation. At the transmitter side, all the separate users' sequences are superimposed into a single signal.  
Figure 1: Spectrum sharing and power allocation in (**a**) orthogonal frequency division multiple access (OFDMA) and (**b**) non-orthogonal multiple access (NOMA).  
Figure 2: Block diagram of the conventional NOMA system.  
In NOMA, the transmitter applies superposition coding to superimpose the waveforms of all users and then allocates more power to the user with a lower SNR (worse channel conditions), which is far from the transmitter, and allocates less power to the user with higher SNR (better channel conditions), which is closer to the transmitter. At the receiver terminals, each user uses SIC to decode and receive their own signal, since all users receive the same superimposed signal holding data. This process is performed iteratively until the weakest user receives their own clean signal [2]. The user located near the transmitter cancels the signals of the far users as they act as interference due to their weak SNRs. On the other hand, the farthest user, which has the highest allocated power and, therefore, the maximum contribution to the received superimposed signal, will initially decode and receive their signal [21]. The main challenge for the transmitter is to correctly assign the power for each user according to the channel conditions, as this affects the performance of the SIC [21; 34].