esa-sceva/satcom-chunk-collection huggingface dataset:
# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 4 Presentation-Attack-Detection Experiment
### Texture Method Evaluation  
To highlight the utility of infrared reflectance pattern-based PAD, a series of texture features are evaluated for performance evaluation. The deterministic features are generated using texture descriptors and then classified using a random forest classifier [68] via the science kit learn toolbox [61]. The deep-learning features are classified using a fully connected layer. All methods are evaluated using binary classification, a live face or a spoofed face. Given some of the deterministic features do over-fit with the COVID mask spoof (which is hybrid of both classes), an ensemble is also evaluated for the 3D classes (deterministic features only). This ensemble is a majority vote for three binary classifiers trained to each spoofing attack.  
Note that these methods are generally known for other texture classification applications. The novelty here is the application of illuminated near-infrared imaging for PAD via material-spectroscopy. Recall from the related works prior texture methods employ sophisticated ensembles [25]. These algorithms are simple to compute.  
All selected texture classifiers are trained using stratified cross-validation. Test values are the average of 10 randomized training and testing evaluations (80:20 participant ratio to maintain independence). Classification evaluation metrics are nominal presentation classification error rate (NPCER), attack-presentation classification error rate (APCER) and the average classification error rate (ACER, average of NPCER and APCER). NPCER is synonymous with the false-rejection-rate of live people; APCER is synonymous with the false-acceptance rate of spoofing attacks.
## 4 Results
### System-Level Performance Metrics  
Extensive benchmark testing demonstrates our method's performance characteristics under various operational conditions. The proposed system achieves recognition latency of 48.3 ms under optimal laboratory conditions, with typical field performance ranging  
Figure 3: Multi-modal feature space analysis and validation framework. (**a**) Spatiotemporal gesture dynamics with reflectivity discrimination; (**b**) t-SNE cluster analysis for intent recognition; (**c**) cross-modal feature correlation heatmap; (**d**) Riemannian manifold verification of nonlinear separability.  
from 55 to 85 ms depending on environmental factors. This compares favorably to baseline approaches: LSTM (72.1 ms), CNN (82.7 ms), and HMM (120.5 ms).  
The reported 99.1% accuracy with 0.08% false positives represents best-case performance achieved through rigorous optimization and controlled testing conditions (Appendix A Table A1). Mean performance across multiple experimental trials yielded 97.8% t.4% accuracy with 0.23% 0.15% false positives--still significantly outperforming LSTM (95.3% accuracy, 0.34% false positives) and other baselines. Real-world deployment typically experiences performance variations due to user familiarity, environmental conditions, and hardware thermal states(Table 2).  
The dual-modality validation framework maintains robust emergency detection performance, achieving 94-99% recall rates under high-vibration conditions (SAE J2380 standard), with metrics meeting SIL-2 safety certification requirements (IEC 61508). However, continuous performance validation is recommended for specific deployment scenarios.  
This comprehensive validation strategy--combining controlled testing, statistical analysis, and stress testing--provides evidence (\(p<0.01\)) supporting deployment in safety-critical applications, while acknowledging inherent performance variability in real-world conditions.
## 7 Practical Applications of NCS  
Apart from review of theoretical developments, many practical applications of NCS have been implemented to date. Some of the important areas of application of NCSs will be discussed briefly.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 3 Mesh in IEEE 802.11 Networks
### Standard: IEEE 802.11s
#### 3.1.3 IEEE 802.11s Routing Algorithm  
The default routing protocol for IEEE 802.11s is the Hybrid Wireless Mesh Protocol (HWMP) [11]. However, the IEEE 802.11s standard does not force to use this protocol, which can then be replaced by other algorithms. HWMP provides both proactive and reactive path selection--for this reason it is called hybrid--and is based on the AODV protocol, properly adapted to support MAC layer routing, together with a tree-based, proactive mechanism, where a mesh station--that generally works as as MPP--forwards control messages to other mesh stations. The discovery phase depends on the operating mode of the algorithm: if a mesh node is working in an on-demand mode, the network is flooded with RREQ messages to discover the route towards a specific destination; if, instead, the mesh node is working in proactive mode, there can be two strategies: (i) RREQ messages are periodically sent in order to keep all routes updated; (ii) RREQ messages, denoted in this case as Root Announcement (RANN), flood the network in order to provide information on how to reach a root node. However, this algorithm has been in the years substituted by other solutions, based on AODV and OLSR, which are discussed in the following; the most relevant are Better Approach To Mobile Ad-hoc Networks (B.A.T.M.A.N.) and Babel.
# Routing Algorithm for Sensor Network Monitoring the Condition of Mining Equipment
## 6 Subsequent Modifications to Implement the Developed Algorithm
The decision to use at least 20 nodes was related to the results of simulation analyses, which showed statistically significant changes in the values of the individual criteria by which the algorithms were evaluated, only at this number of nodes. The limitation of the number of physical nodes to 30 is due to the project budget that made the testing possible. The network was connected according to the diagram shown in Figure 11. Up to four network nodes could be connected to each Raspberry Pi computer. Diagnostic data from each node were sent via serial protocol to the Raspberry Pi computer, and then wirelessly (MQTT protocol) the data were sent to the GMESH application installed on the PC. Figure 11: Connection diagram of a sensor network for 30 nodes. Figure 10: Proposed test bench for more nodes. node do not correspond to their physical locations. They are only used to give a quick interpretation of the path taken by the data packet. It was observed that, despite the proximity of a node, the data in some cases were being sent to further-away nodes (in the case of close distances between nodes, about a few centimetres, --due to the determination of the distance between nodes based on the transmit power of the neighbour's RSSI radio signal--interpreting the distance based on this signal for close distances can result in greater inaccuracy). However, in the case of close node distances, this has a negligible impact on the delivery time of the packet to the destination. Furthermore, if it is necessary to determine the distance with an accuracy of 1 cm, it is possible to implement additional modules using the UWB (ultra-wideband) radio signal in the nodes of the network. Figure 13 shows the dependence of the recorded parameters on the number of nodes in the network. The average values of the parameters and their recorded maximum and minimum are shown. It has been observed that as the number of nodes increases, the time it takes for a packet to reach its destination increases. This reduces the network throughput and also decreases the value of the PDR parameter (reliability of data delivery to the destination). However, the implemented algorithm makes it possible to limit the variation of the recorded parameters. For a network of 20 to 30 nodes, small differences in the recorded parameters are already visible. Figure 12: Packet path in a sensor network consisting of 30 nodes. Figure 13: Recorded parameters depending on the number of nodes in the network ((**a**)—packet arrival time; (**b**)—bandwidth; (**c**)—PDR).
## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.1 Autoregression-Based Frequency Features  
Autoregression-based features are derived from linear prediction analysis of signals, which usually captures typical spectral predominances (e.g., formants) of speech signals.  
The most commonly employed physical frequency features based on signal autoregression are described below.  
* **Linear prediction coefficients:** or LPC for short, this feature represents an all-pole filter that captures the spectral envelope (SE) of a speech signal (formants or spectral resonances that appear in the vocal tract), and have been extensively used for speech coding and recognition applications. LPC have been applied also in audio segmentation and general purpose audio retrieval, like in the works by [PERSON] _et al._[68; 69].
* **Line spectral frequencies:** also referred to as Line Spectral Pairs (LSP) in the literature, Line Spectral Frequencies (LSF) are a robust representation of LPC parameters for quantization and interpolation purposes. They can be computed as the roots phases of the palindromic and the antipalindromic polynomials that constitute the LPC polynomial representation, which in turns represent the vocal tract when the glottis is closed and open, respectively (see [PERSON] [70]). Due to its intrinsic robustness they have been widely applied in a diverse set of classification problems like speaker segmentation ([PERSON] and [PERSON] [71]), instrument recognition and in speech/music discrimination ([PERSON] [13]).
* **Code excited linear prediction features:** or CELP for short, this feature was introduced by [PERSON] and [PERSON] [72] and has become one of the most important influences in nowadays speech coding standards. This feature comprises spectral features like LSP but also two codebook coefficients related to signal's pitch and prediction residual signal. CELP features have been also applied in the environmental sound recognition framework, like in the work by [PERSON] _et al._[73].
# Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System
## 2 Materials and Methods
Higher-level IM, on the contrary, uses a single clock cycle to transmit more than one bit depending on the number of levels that power would be divided into. The power ratio then encodes the bit symbol into a certain voltage level that controls the illumination of the laser beam. Here, this is done by controlling the pulse width of the output transmitting power. A mapping algorithm is completed using MATLAB to compare the frequencies of the incoming data. As shown in Figure 3, if the frequency happens to be less than or equal to 115 kHz, OOK is used. Otherwise, IM is the chosen technique. Thus, the most suitable modulation is configured for reliable communication with less power consumption. Figure 4 shows how bandpass filters (BPF) can filter out random data ranges to determine the desired modulation used. These BPFs can be adjusted according to any further desired data frequency ranges. The FPGA was the kit of target due to being a compatible environment in which hardware building and reconfiguration is a fast process with easy modification availability. It also enables designers to create a bottom-up implementation of any hardware system prototype, ensuring that the final product is bug-free. Figure 2: System Block Diagram.
## 4 Results
### Results Using Industrial Shaker  
Next, Figure 13 presents a scatter plot of all data collected from the industrial shaker test, with the green line representing the ideal target displacement. As discussed in Section 4.1 regarding the sensor data offset caused by the MMIC characteristics, this offset is also observed in the current experiment. In Figure 12, the radar sensor data consistently  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PRT Setting** & **Unit** & **Short PRT Setting** & **Long PRT Setting** \\ \hline Data count & - & 528,960 & 528,960 \\ (plotted in Figure 11) & - & 528,960 & 528,960 \\ \hline Mean & deg. & 0.116 & 0.137 \\ \hline Standard deviation/\(1\sigma\) & deg. & 0.064 & 0.074 \\ \hline \(\pm 3\sigma\) [deg.] & deg. & \(\pm 0.192\) & \(\pm 0.222\) \\ \hline Maximum phase offset (\(\pm 3\sigma\)) & deg. & +0.308 & +0.359 \\ \hline \multicolumn{4}{c}{Equivalent target displacement (\(\pm 3\sigma\))} \\ \hline Max. offset (accuracy) & \(\upmu\)m & +1.637 & +1.907 \\ \hline Max. precision & \(\upmu\)m & \(\pm 1.023\) & \(\pm 1.180\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of loopback waveguide test results.  
Figure 12: Histogram of phase data.  
show a positive offset across the entire measurement range, though linearity is maintained. To quantify this relationship, linear regression was applied to the entire dataset, evaluating how well the data fit the green line. The resulting R\({}^{2}\) (coefficient of determination) values were 0.99 for the 700 Hz vibration scenario (i.e., Short PRT setting) and 0.80 for the 1600 Hz vibration scenario (i.e., Long PRT setting). These high R\({}^{2}\) values indicate a very strong correlation across the entire tested range, from 15.198 \(\upmu\)m to 0.019 \(\upmu\)m, which was generated by the shaker.  
As seen in Figure 13, the data exhibit two error components: first, a positive offset error across the entire range, and second, a gradient error, which can be considered a gain error related to displacement. The first error was previously estimated in Section 4.1 and is attributed to the characteristics of the radar MMIC. The second error could be caused by either the MMIC characteristics or the shaker itself, necessitating further experimental measurements with various shaker machines and a wider reference displacement scenario, which should be addressed in future work.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.3 Channel Estimation/Allocation  
In [110], the authors propose a RL-based algorithm (based on auction theory model) for channel allocation. Each user try to converge to the optimal allocation while achieving an optimal regret order O (log T ), where T is the length of time horizon. The algorithm is based on a Carrier Sensing Multiple access (CSMA) implementation. Simulations show that the algorithm performs very well on realistic LTE and 5G channels and has great potential for B5G systems. In [111], a Markov decision process (MDP)-based algorithm for channel allocation is proposed. The model allocates channels in densely deployed WLANs, leading to enhancement of throughput. The proposed method can achieve more efficient channel allocation or realizes the optimal channel allocation and reducing the number of changes in the systems performance, when compared with state-of-the-art approaches.
## 1 Introduction  
The usage of the Internet and its applications has changed the world to the Internet of Everything (IoE). An extension of the Internet of things (IoT), the Internet of Nanothings (IoNT) has emerged as a new paradigm to incorporate the Internet into various domains, such as businesses, social networks, healthcare, teaching and learning techniques, and our daily activities [1; 2; 3]. RnR Market Research expects the IoT market to be worth about $1423.09 billion by 2020, and the IoNT will play a vital role in the future IoT market holdings by about $9.69 billion market value by 2020 [4].  
Nanoscale devices (also called nanomachines or nanothings) can be connected with a nano-network to communicate and share appropriate data with each other, and these nanodevices in IoNT environments are able to monitor and detect tiny environmental changes and activities [5; 6]. Body area networks, wireless sensor networks, and edge cloud networks can benefit from IoNT technology [7; 8].  
For example, regarding the edge cloud, mobile services using this can reduce latency and turnaround time [9; 10].  
Figure 1 shows an example of mobile services using IoNT with and without the edge cloud layer. Without this layer, when a user submits a task for the service, the sensing data from the nanodevices are forwarded to the central cloud server. Then, the central cloud server responds to the mobile device or application. However, in this scenario, the latency between the nanodevices and the central cloud server and the central cloud server and the mobile device is relatively long.  
When the edge cloud is enabled in the architecture, the sensing data from the nanodevices are delivered to the nearby edge cloud server. This edge cloud server interacts with the central cloud server if needed and sends results back to the mobile device. Compared to the flow without the edge cloud layer, the latency is relatively short here. Although the latency is relatively short in this case, it is still necessary to determine whether to use the layer or not. To efficiently use the layer, we consider the cost of using the edge cloud server. That is, the central cloud server must send the appropriate data and offload the task to the edge cloud server for the service.  
In this paper, we develop an intelligent and cost-efficient resource consolidation algorithm in nanoscale computing environments. The core concept of the proposed algorithm is to determine whether we should use the edge cloud layer by employing one of the artificial intelligence techniques (the hidden Markov model). The proposed resource consolidation algorithm considers the cost of using the edge cloud layer and effectively predicts the future application usage pattern, which is a difficult problem in cloud computing environments [11]. Then, it performs the task migration between the central cloud server and the edge cloud server for the service when necessary, which achieves the load balancing, one of the important features in cloud computing environments [12].
## 3 Principle of the AE Source Location Technique
### Time Difference of Arrival (TDOA)
Finally, using the least-squares method (LSM), the optimal solution from the smallest sum, S, can be picked up as Equation (3) given below:  
\[S=\sum_{i=1}^{N}\Bigl{[}(x-x_{i})^{2}+(y-y_{i})^{2}+(z-z_{i})^{2}-(t-t_{i})^{2}v ^{2}\Bigr{]}^{2} \tag{3}\]  
Thus, the critical condition is that, when the sum of the squared difference of the distance between the calculated event and the candidate AE source is the minimum, the calculated AE source is regarded as the position where the AE event occurred.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 4 Experimental Design
### Baseline Methods  
CIB is compared with five state-of-the-art class-imbalance learning methods and the no class-imbalance learning method (None, for short). A brief introduction of these previous methods is presented, as follows.  
* _MAHAKIL_. It is a novel synthetic oversampling approach that is based on the chromosomal theory of inheritance for software defect prediction, which is proposed by [PERSON] et al. [44] in 2017. MAHAKIL utilizes features of two parent instances in order to generate a new synthetic instance which ensures that the artificial sample falls within the decision boundary of any classification algorithm.
* _AdaBoost_. It is one of the most well-known and commonly used ensemble learning algorithms, which is proposed by [PERSON] and [PERSON] [46] in 1995. AdaBoost iteratively generates a series of base classifiers. In each iteration, the classifier is trained on training dataset with specific distribution weights of instances and it is assigned a model weight according to the training error. The distribution weight of training instances is then updated. Specifically, the training instances that are misclassified  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **\# of Metrics** & **\# of Instances** & **\# of Defective Instances** & **Defect Ratio** \\ \hline CM1 & 37 & 327 & 42 & 0.1284 \\ KC1 & 21 & 1183 & 314 & 0.2654 \\ KC3 & 39 & 194 & 36 & 0.1856 \\ MC1 & 38 & 1988 & 46 & 0.0231 \\ MC2 & 39 & 125 & 44 & 0.352 \\ MW1 & 37 & 253 & 27 & 0.1067 \\ PC1 & 37 & 705 & 61 & 0.0865 \\ PC2 & 36 & 745 & 16 & 0.0215 \\ PC3 & 37 & 1077 & 134 & 0.1244 \\ PC4 & 37 & 1287 & 177 & 0.1375 \\ JM1 & 21 & 7782 & 1672 & 0.2149 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of 11 cleaned NASA datasets used in this study.  
get a higher weight, otherwise get a smaller weight, which ensures the decision boundary will be adjusted to the misclassified instances. AdaBoost was identified as one of the top ten most influential data mining algorithms [79].
* _AdaC2_. AdaC2, proposed by [PERSON] et al. [45], combines the advantages of cost-sensitive learning and AdaBoost. [PERSON] et al. [45] argued that AdaBoost treats samples of different types (classes) equally, which is inconsistent with the common situation that the minority class samples usually are more important that the majority class ones. AdaC2 introduces the cost items into the weight update formula of AdaBoost by outside the exponent.
* _SMOTE_. It is proposed by [PERSON] et al. [40], which is the most famous and widely used oversampling approach for addressing class-imbalance problem. SMOTE tries to alleviate the imbalance of the original imbalanced dataset by generating synthetic minority class samples in the region of original minority class samples.
* _RUS_: RUS decreases the number of majority class samples (i.e., non-defective modules) by randomly removing the existing majority class samples, such that both two classes of samples have the same number of samples. RUS has been used in previous studies [6; 35].