esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Audio Features and Pre-Trained Neural Networks
### Audio Signal Features
#### 2.1.2 Spectral Features  
Spectral features are computed from the frequency components of the audio signal. The two-dimensional representation of the frequency components of an audio signal is called a spectrogram, which often results from the application of the short time discrete Fourier transform (STFT) to constantly compare the input signal with a sinusoidal analysis function [18]. Although this representation is known to work well with neural networks [19], the signal processing techniques used in order to display the representation can cause inconsistency within the structure of the spectrogram [18]. Further, the majority of the works concerning the spectrogram solely makes use of the magnitude component representation of the audio signal, omitting the phase information [20].  
Although spectral features have several advantages, the information yielded may not be sufficient for the characterization of multi-channel audio scene acoustics. Often, they are combined with other features in order to produce a considerable representation of the signal magnitude [8]. However, since different audio scenes have different requirements in terms of temporal and frequency resolutions [21], the combination of several spectral features does not necessarily improve the accuracy of the classifier. A study by [PERSON], [PERSON] et al. [22] had shown that combining several spectral features, including centroid, bandwidth, flatness,and asymmetry for sound classification, does not really improve the accuracy. Instead, an increase in the computational complexity is observed due to the individual computation of multiple features that had to be combined.  
Nonetheless, the log-Mel energy features are deemed beneficial for multi-channel acoustic scene classification and were utilized in notable related works mentioned in this research [23; 24]. Log-Mel energy features had also been a well-received choice of features for DCASE challenge entries, as per the review of [PERSON], [PERSON] et al. [25], due to the two-dimensional matrix output that it yields, which is a suitable input for the CNN classifier. Log-Mel features are extracted through the application of a STFT applied to Hamming windowed audio segments [9]. A Mel-scale filter bank is then implemented after taking the square of the absolute value per bin, which are then processed to fit the requirements of the system [9].
# Shear Mechanical Properties of Rock Joints Under Non-Uniform Load Based on DEM
###### Abstract  
Previous joint test studies have mainly been conducted under the condition of uniformly distributed loads, but in engineering, overlying loads are often non-uniformly distributed. It is necessary to investigate the shear mechanical properties of joints under non-uniform loads. This paper establishes three typical mechanical models: far-field concentration (FFC), near-field concentration (NFC), and focus on center (FOC). Direct shear test simulations using the DEM software PFC reveal that the location of load concentration affects main shear parameters. The closer the load concentration is to the far end along the shear direction, the greater the deformation of rock mass during failure is, the higher the proportion of shear cracks is, and the greater the strength and energy required for failure are. Compared to a uniformly distributed load (UD) condition, FOC shows similar mechanical properties; NFC yields inferior outcomes, while FFC results in superior mechanical performance compared to UD. By utilizing machine learning, five prediction models for non-uniform load shear strength are developed, with the Genetic-XGBoost algorithm demonstrating the highest accuracy. Weight calculation results indicate that load distribution form is the most critical factor influencing shear strength.  
Keywords:joint; non-uniform load; discrete-element method; crack evolution; energy evolution; machine learning _Article_
## 4 Performance Evaluation Metrics
### Traditional Performance Metrics  
Traditional classification performance indicators like accuracy, precision, recall, and F1-Score are helpful but have drawbacks when used for multi-label classification issues. Accuracy is an overall performance statistic that ignores class imbalance and false negatives.  
Precision measures the model's ability to prevent false positives or classify positive cases as true positives. The model's capacity to recognise all relevant examples is shown by the recall (or sensitivity) of true positives. The harmonic mean of accuracy and recall, the F1-Score, balances false positives and negatives. These measures are helpful but do not handle multi-label classification, where many attack types may be present, or the balance between false positives and false negatives is more complicated.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 1 Introduction
This problem can be alleviated by using multiple channels in the same network. Using multiple channels also helps in reducing interference as well as the contention in the network that improves the communication performance. Current WSN hardwares such as MICAz [10] and Telos [11] that use CC2420 radio, provide multiple channels (16 channels with 5 MHz spacing in between the center frequencies) that can greatly reduce the overhearing problem to conserve energy. The problems imposed by variations of energy availability can be addressed by using mechanisms to _dynamically_ adapt their energy consumption based on estimated energy resources [8,9,12, 13, 14]. We demonstrate that dynamic channel selection and routing can solve this problem. However, designing effective mechanisms to jointly select channels and routes for energy management is a complex problem due to the fact that it has to be addressed by _network wide_ adaptations as opposed to independent adaptations at the nodes. We consider large-scale WSNs where implementation of network-wide time synchronization is a significant challenge. Hence, these networks must rely on asynchronous duty-cycling for energy conservation where it is critical to avoid energy wastage from overhearing. In this regard, our main contributions of this paper are as follows. First, we motivate the use of multiple channels to alleviate the overhearing problem and thereby improve the network lifetime. We show that the multi-channel allocation problem of sensor nodes is similar to a coalition game formation problem, which is proven to be NP-hard. Second, we develop a route quality and battery-health aware _Distributed Routing and Channel Selection (DRCS)_ scheme that dynamically chooses channels and routes to optimize network lifetime and performance. In addition to adapting to the variable energy consumptions at different nodes that occur due to location issues (such as node density), DRCS also has the capability of adapting to the variations of energy availability at the nodes that arise in energy harvesting WSNs. The objective is to dynamically equalize the remaining lifetimes of nodes as estimated from their current battery capacity and usage. Finally, the performance of DRCS is obtained from experiments using a MICAz testbed as well as from simulations. Performance comparison with an existing multi-channel routing protocol for WSNs is also presented from simulations. The rest of the paper is organized as follows. In Section 2, we summarize prior works related to this paper. Section 3 describes our motivations behind this work. Section 4 describes our multi-channel routing problem along with its computation complexity. In Section 5, we discuss our detailed multi-channel routing scheme. Simulation and experimental results of our proposed routing scheme are discussed in Section 6. We conclude our paper in Section 7.
# MIMO Over-the-Air Computation for Distributed Estimation
###### Abstract  
MIMO over-the-air computation (MIMO-AirComp) is a recently proposed technique that leverages the superposition property of the multiple access channel to compute the target multifunction of various applications. This article presents how the MIMO-AirComp principle can be applied to the state estimation problem using distributed sensing data. The representative target function is explicitly formulated as a nomographic function matched to the structure of the multiple access channel with the proper processing function. The proposed framework efficiently computes the target multifunction by coordinating local preprocessing at each node, aggregation through the wireless channel, and postprocessing at the fusion center. We analyze and demonstrate that the proposed approach significantly improves the computation throughput for the distributed estimation application. Specifically, the proposed MIMO-AirComp framework outperforms the conventional separated communication and computation approach when the network system relies on noisy measurements obtained by the densely deployed sensors.  
over-the-air computation; MIMO; distributed estimation +
Footnote †: journal: Applied  
1
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 4 Proposed DEOR Algorithm
### Analysis and Flowchart of DEOR  
According to the pseudocode of the three sub-algorithms, e.g., Algorithms 1-3, the DEOR algorithm mainly consists of a cycle in the calculation process, so the computational complexity of the proposed DEOR in this paper is \(O(N)\), where \(N\) is the number of nodes in the network. The energy consumption in DEOR entirely depends on how many nodes are in the sender's forwarding zone. For the post-disaster MIoT network, this complexity is usually within the computing capacity of nodes, and the proposed DEOR is an energy-saving routing strategy. Therefore, the surviving nodes have the ability to execute the DEOR algorithm, which restricts the number of duplicate packets generated in the network and avoids the routing void problem during data transmission.  
The proposed DEOR is an opportunistic routing algorithm that considers both global and local information of the network. Nodes make routing decisions based on network density and multiple attributes of neighboring nodes. The flowchart of DEOR is shown in Figure 7. Firstly, during the network initialization phase, each surviving node defines a forwarding zone (FZ) according to Formulas (3)-(5). It is assumed that the source node \(n_{i}\) needs to send the perceived data information to the sink; that is, node \(n_{i}\) is the current sender. Then, the sender \(n_{i}\) constructs a candidate forwarding set (CFS) \(C_{\mathrm{up}}(i)\) according to the forwarding zone constraint, energy constraint, and neighboring node degree constraint, i.e., Formulas (7)-(9). Next, the nodes within the \(C_{\mathrm{up}}(i)\) calculate their routing quality \(\overline{Q}_{i,j}\) values and are sorted in descending order to \(C(\overline{Q}_{i})\). In addition, if the set \(C_{\mathrm{up}}(i)\) is empty, the recovery mechanism is activated. The sender \(n_{i}\) reconstructs a new forwarder set \(C_{\mathrm{down}}(i)\) according to Formulas (20)-(22). Then, the nodes within the \(C_{\mathrm{down}}(i)\) calculate their routing quality \(\overline{Q}_{i,j}\) values and are sorted in descending order to \(C(\overline{Q}_{i})\). The nodes in \(C(\overline{Q}_{i})\) are selected in sequence as relay nodes to forward the packets before the timer expires; otherwise, the data transmission fails. Finally, if any node in \(C(\overline{Q}_{i})\) successfully forwards the packet, the other nodes will discard the packet copies and loop the above process until the packet is routed to the sink.
## 4 Development of Proposed Method
### Proposed Method
#### 4.2.3 Adaptive Threshold Determination  
To separate the signal-dominant modes from the noise-dominant modes, the differences between consecutive scaling exponents are computed as  
\[\Delta\alpha_{k}=|\alpha_{k+1}-\alpha_{k}|,\quad k=1,2,\ldots,K-1, \tag{10}\]  
and a threshold index \(\theta\) corresponding to the maximum change in scaling exponent is then determined as  
\[\theta=\arg\max_{k}(\Delta\alpha_{k}). \tag{11}\]  
The classification consists of two types of modes: signal-dominant modes, which are modes with indices \(k\leq\theta\), and noise-dominant modes, which are modes with indices \(k>\theta\).
To date, regardless of the region, clinical inspections in apiaries are conducted according to the protocol recommended by the Italian Ministry of Health (MoH). This protocol for SHB detection includes examining the outer and inner covers, inspecting both sides of each comb, and placing the inspected combs in an empty hive. The bottom and corners of the hive are also checked. Finally, the combs are returned to their original positions. In addition, supers and the bottom board are visually inspected if present. During this process, both SHB larvae and adults can be visually recognized, collected, and killed. However, this protocol is time-consuming, as it requires careful inspection of both sides of each frame under adequate lighting [20]. Furthermore, finding SHBs is particularly challenging when many adult bees cover the combs, especially in Italy since the SHB population is low and SHB adults are more common than larvae. SHB adults are smaller and quicker than larvae, so they can easily hide themselves behind the adult bees or quickly fly away [5]. All these considerations highlight the need to develop new strategies to enhance both the effectiveness of detecting SHB adults within hives and the efficiency of the method in terms of speed and ease of execution. The Mobile Divider (MD) is a divider placed between the last frame and the hive wall (Figure 1). The MD method is a protocol designed to detect SHBs within colonies during hive inspections. This method involves inspecting the outer and inner covers of the hive, examining and removing each comb starting from the side opposite the MD, and transferring the inspected combs to an empty hive. Slow movements are used to encourage SHBs to concentrate on the remaining frames. When three combs and the divider remain, these are slowly moved to the empty side of the hive, and the divider, the space behind it, the corners, walls, and the bottom of the hive are inspected. Finally, the method includes observing the contents of the bottom board if present. The MD method was previous described as a time-saving method, saving 3 min and 44 s for every inspection compared to the classical inspection method by the MoH [20]. The aim of this study was to evaluate the efficacy of the MDmethod in concentrating SHBs behind the MD and improving SHB surveillance and early detection. In detail, early detection is crucial to hinder the spread of SHB to new areas where it is not yet present and to control the SHB infestation level within apiaries before significant damage occurs.
# Optimization Scheme for Modulation of Data Transmission Module in Endoscopic Capsule
## 2 Principle of the MIP-MDCSK Scheme  
### Channel Model  
The traditional endoscopic capsule communication transmission model is shown in Figure 1. In order to better address the issues of low cost and low power consumption, we have optimized the signal design scheme for the transmission module. For the pathway dissipation of the body's surface and inner body, we can represent it using logarithmic functions. However, this is not applicable for intracellular signaling pathways. Nevertheless, the formula provided for pathway dissipation is essentially applicable to intracellular signaling pathways [35].  
\[P_{dB}(d)=P_{0_{dB}}+a(\frac{d}{d_{0}})^{n}, \tag{1}\]  
where \(d_{0}=5\) mm represents the reference distance, \(d\) denotes the distance from the skin surface to the chest cavity, with units in mm, and \(P_{0}\) represents the pathway dissipation, measured in dB. \(n\) is the exponent of pathway dissipation, while \(a\) represents the fitted constant. Equation (1) provides a good fit for the dissipation of pathways within the body with \(a=11.6\), \(n=0.5\), and \(p_{0}=6.3\) dB.  
Based on the body surface profile of UWB and free space, the logarithmic distribution of narrowband channels is quite similar to the probability density function (PDF) of shadowing effects [36]. Computational results have demonstrated that the logarithmic distribution also accurately represents shadowing data for different distances in intracellular signaling pathways. Therefore, Equation (1) can be rewritten as  
\[P_{dB}(d)=P_{0_{dB}}+a(\frac{d}{d_{0}})^{n}+N(\mu(d),\sigma(d)), \tag{2}\]  
where \(N\) represents a random variable that follows a normal distribution, with a mean value of \(\mu\) and a standard deviation of \(\sigma\). The parameter \(N\) varies depending on the value of \(d\).  
Figure 1: Endoscopic capsule implementation scheme.  
In the channel model for in-body to on-body communication, [PERSON] proposed in reference [37] that the multipath gain and delay depth (\(d\)) follow linear Gaussian distributions with means \(\mu_{l}(d)\) and \(\mu_{l}^{\prime}(d)\) for the \(l\)-th multipath, and variances \(\sigma_{l}(d)\) and \(\sigma_{l}^{\prime}(d)\), respectively. The Moreover, \(\mu_{l}(d)\) can be expressed as  
\[\mu_{l}(d)=\omega_{0}(d)e^{-(l-1)\lambda(d)}, \tag{3}\]  
where the \(\omega_{0}(d)\) is the peak average power, and \(\lambda(d)\) is the exponential decay factor of peak power. For a given depth d, \(\mu_{l}^{\prime}(d)\) is always a constant. Similarly, \(\sigma_{l}^{\prime}(d)\) can be expressed as  
\[\sigma_{l}^{\prime}(d)=\Sigma_{0}(d)e^{-(l-1)\Lambda(d)}, \tag{4}\]  
where the \(\Sigma_{0}(d)\) is the Peak average power, \(\Lambda(d)\) is the exponential decay factor of peak power.
## 3 Results
### The Lowest Cloud Base  
The data sets of the lowest cloud base are presented in the form of scatter plots in Figures 7 and 8. In order to allow for a better visual comparison, the y-axes of Figure 7 was cut at 8000 ft (1 ft-0.3 m), instead of displaying the outliers of the AWOS values exceeding 10, 15, and sometimes 24 thousands of feet (associated with LO data from the interval 800 to 3000 ft). Note that cloud base height is expressed in non-metric units (ft), due to the convention of their usage in aviation.  
Figure 8 noticeably indicates a significant improvement in the coefficient of determination of the data sets (\(R^{2}\) = 0.759) when compared to Figure 7 (\(R^{2}\) = 0.150). It is striking that using the RO approach, the outliers clearly diminished, and the data points are scattered relatively tightly around the hypothetical 1:1 straight line.  
Figure 7: Scatter plot of the values of the lowest cloud base determined by the local observers (LO) and the standard automated sensors (AWOS) at LZTT. The color scale indicates the number of occurrence of the given data point.  
In other words, Figure 7 points out to the fact that the deviations of the AWOS data set from the ground truth are predominantly positive. This means that the standard AWOS falsely overestimates the actual cloud base heights, and therefore, tend to mislead the pilots with conveying information on better flight conditions than actually are. On the other hand, the RO approach, through the proposed software brings significant improvement in the quality of determining the minimum height of the cloud base, keeping the chance of disinformation of the pilots on minimum.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 5 Evaluation
### Results
#### 5.4.2 2D Random Walk Test  
The 2D walk test is a statistical test used to assess the randomness of a sequence of two-dimensional positions or movements, which helps ensure the quality and reliability of random number generators. It involves simulating a random walk in a two-dimensional space based on the given sequence of numbers or data. The random walk typically starts at the origin (0, 0) and moves in random directions according to the values in the sequence (e.g., \(-1\), 0, or 1). The test examines the distribution and properties of the resulting positions or movements to evaluate the randomness of the sequence. The 2D walk test can help detect any patterns, correlations, or biases in the sequence that deviate from randomness.  
We have conducted 2D random work tests to evaluate and compare the randomness of Xorshift PRNG, Mersenne twister PRNG, and IDQ QRNG. Figure 6 presents the test results. Figure 6 visualizations use consistent axes (x: \(-60\) to \(20\); y: \(-50\) to \(30\)) for an equitable comparison. The clustering patterns observed in the results of Xorshift (Figure 5(a)) and Mersenne twister (Figure 5(b)) reveal the inherent spatial correlations characteristic of their deterministic algorithms. In contrast, the QRNG (Figure 5(c)) output maintains a uniform distribution. The results demonstrate the structural bias and dimensional limitations of the PRNGs while confirming that QRNG maintains a higher level of true randomness.  
Figure 6: Results of 2D random work tests to evaluate and compare the randomness of (**a**) Xorshift PRNG, (**b**) Mersenne twister PRNG, and (**c**) IDQ QRNG
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 3 Results  
### Performance of SAM in the Google Colab (SAM) + Matlab Application  
The unsupervised zero-shot segmentation using SAM enabled the generation of a set of binarized masks with well-defined contours, where at least one of the generated masks corresponded to the actual olive (Figure 7). For this operation, a GPU rental version was chosen over the demo option, considering the high computational cost associated with processing the large number of images in the series and the characteristics of the processing equipment (a conventional PC). This rental option, available on the Google Colaboratory (Google Colab), offers greater capacity and speed compared to the free version. As a result, the total execution time was approximately six hours for processing 9150 Hojiblanca images, as an example.  
By selecting the unsupervised mode (\"Everything\"), it was necessary to complement the segmentation process with post-segmentation filtering using the Selector_BN.m script. This step was implemented to reduce the multiplicity of images in the automatic output folder generated by SAM. After execution, 98 duplicated mask cases (1.1%) were identified, requiring manual correction (examples: Figure 8, mask_0_8.bmp and mask_9_8.bmp, the latter containing an external pixel). Some of the binarized images resulting from the segmentation process exhibited anomalies (Figure 9), including background noise or voids within the olive mask. These were filtered using the blob_and_filling.m script, ultimately ensuring optimal segmentation in 100% of the cases. At this stage, 1305 binarized images of oxidized black Gordal olives segmented from their metallic background (Figure 10a) with dimensions of 320 \(\times\) 240 pixels are available. Under the same conditions, there are 9150 binarized masks of the green Hojiblanca variety (Figure 10b) with dimensions of 144 \(\times\) 176 pixels. These are stored in two separate folders corresponding to those downloaded directly from the CV system.  
Figure 7: Set of masks generated by SAM for a single raw image.