esa-sceva/satcom-chunk-collection huggingface dataset:
#### Review
###### Abstract
Different IoT attacks have also been detected by such proposed models or systems, such as denial-of-service (DoS), remote-to-local (R2L), user-to-root (U2R), and probing attacks. Different datasets are publicly accessible to researchers to use in intrusion detection systems in the IoT, such as KDDCUP99, NSLKDD, and UNSW-NB15. In order to verify the efficiency of these proposed models, various types of evaluation metrics are used for assessment, such as accuracy, recall, and precision. Few studies have analyzed device log traces from IoT devices to identify IoT attacks and monitor infrastructure using DevSecOps. Our study concentrates on different areas in the detection of IoT attacks. The aim of this study is to analyze, summarize, and evaluate the machine learning techniques used in the detection of IoT attacks. Moreover, we evaluate various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for the assessment of models, and monitoring infrastructures using DevSecOps pipelines. We recommend necessary methods and techniques for upcoming studies. [PERSON] et al. [16] introduced all studies that used machine learning methods and techniques to enhance IoT security. The authors identified challenges and ideas for future research for the enhancement of IoT security. [PERSON] et al. [17] performed a systematic literature review (SLR) of different authentication mechanisms for IoT system security. The authors reviewed various ways to implement authentication in IoT perimeters to identify  
Figure 1: Number of Internet of Things (IoT) connected devices estimated up until 2025 [7]. recommendations for future studies. [PERSON] et al. [18] surveyed the security risks in IoT systems and discussed counteractions. [PERSON] et al. [19] performed an SLR and analyzed the security issues related to IoT based upon various layers. [PERSON] et al. [20] performed an SLR based on the privacy of the IoT system. The authors identified challenges with regard to the privacy of the IoT system exposed, type of attacks occur in the IoT system and recommendations for future studies. [PERSON] et al. [20] performed an SLR based on IoT-based botnet attacks. The authors evaluated evaluation metrics for assessment of models, various datasets used for the models, and network forensic methods. Most of the proposed systematic literature reviews (SLRs) focused on authentication mechanisms, privacy, botnet attack avoidance or detection, security risks, and security aspects, while this study aims to (1) analyze, summarize, and evaluate the techniques of machine learning for analyzing device log trace from IoT devices to identify IoT attacks using DevSecOps pipelines and (2) monitor the infrastructure that is created and configured automatically. The rest of this paper is organized as follows: Section 2 discusses the research methodology. Section 3 describes and analyzes the selected primary studies. The last section concludes the paper and provides recommendations for upcoming work.
## 3 Results and Discussion
### X-ray Diffraction  
The crystallinity of the pristine and chemically modified chitosan (CH-ANP) was determined using X-ray diffraction analysis. Pristine chitosan showed poor crystallinity, as demonstrated in the diffraction pattern in Figure 6. At 20 = 9\({}^{\circ}\) and 20 = 18\({}^{\circ}\), both the characteristic broad peaks were observed for CH. The diffraction pattern of modified chitosan (CH-ANP) showed slightly different behavior as compared to pristine chitosan. The first peak shifted to a lower value and appeared at 20 = 8\({}^{\circ}\), while the second peak appeared at a higher value of 20 = 20\({}^{\circ}\). The chemically modified chitosan also displayed low crystallinity due to the insertion of a new molecule into the chitosan pendant chain. This result was also in accordance with those obtained from NMR spectroscopy that mark the precursor chitosan chemical alteration.  
Figure 5: Thermal behavior of chemically modified CH-based final product, i.e., CH-ANP in terms of DTG and mass loss profile.  
Figure 6: The typical X-ray diffractogram patterns of pristine CH and the chemically modified CH-based final product, i.e., CH-ANP.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 2 Background
### DetNet  
DetNet working group [8], intend to provide an architecture for networks requiring guaranteed bandwidth, extremely low loss, and an upper bound on maximum end-to-end latency at layer 3 paths across multiple layer 2 networks. The DetNet working group collaborates with the IEEE 802.1 Time-Sensitive Networking task group [9] which is improving on layer 2 operations to ensure deterministic capabilities. In essence, DetNet and TSN define a common architecture for both Layer 2 and Layer 3 to ensure applications requiring determinism can work across both layers.
## 3 Application of CNCs
### Use of CNCs in Construction Applications  
The use of nanomaterials in existing construction materials can improve their durability-related properties such as the strength and salt resistance by improving the cement microstructure. Studies have shown that the powdered nanosilica (NS, \(\sim\) 40 nm) affects the production of calcium silicate hydrate (C-S-H) [42; 43]. Methods for optimizing the dispersion of CNCs were investigated for producing CNC/cement composites that are suitable for use in construction-related applications. In these composites, a nanoscale matrix is formed within the concrete owing to the well-dispersed CNCs. The flexural performances of the thus-produced concrete samples were evaluated based on their moduli of elasticity. It may also be possible to improve the tensile performance of the thus-formed concrete samples by combining them with existing fiber-reinforced concrete [18; 19; 20; 21].
# Metrics for Polyphonic Sound Event Detection
## 5 Choosing a Metric
### Measuring Performance of a System
#### 5.1.1 Segment-Based Metrics  
Segment-based metrics are presented in Table 1. Both instance-based and class-based averaging calculation methods are presented, to illustrate the effect of the averaging method on the metrics values. Detailed class-wise F-score is presented in Figure 6 for an evaluation segment of length 10 ms.  
The averaging method influences the values of most metrics. The difference in F-score between instance-based and class-based averaging comes from the significantly different performance on different classes, as seen in Figure 6--there are few highly performing classes (F-score \(>60\%\)) and few low performing classes (F-score \(<7\%\)) which have equal weight on the class-based averaging value of F-score. An inspection of precision and recall values reveals that the system output contains mostly correctly detected events (high precision), but there is a large number of events undetected (low recall). This results in a small number of substitutions--hence the difference in error rate values when counting substitutions (instance-based averaging) or counting insertions and deletions separately (class-based averaging) is not very large. ACC is not influenced by the averaging type, due to the total number of evaluated segments being the same for all classes.  
The evaluation segment length also influences the values. A longer segment allows the system to produce a temporally less accurate output while still being considered correct. This is reflected in the measures that represent the amount of true positives--such as the F-score (\(TP\) being involved in calculation of both precision and recall) and sensitivity (which is the true positive rate). At the same time, the larger segment produces more false positives by marking an event active for the entire one second segment even if it was only active for a fraction of its length--resulting in lower accuracy and specificity values.  
The choice between a short or a large segment in evaluation is dictated by the application--for applications that need precise detection of events' presence, a small evaluation segment should be used. However, this also depends on the quality of annotations, as sometimes annotations cannot be produced in the most objective way, and a 10 ms resolution is impossible to achieve. With a longer segment, the metric is more permissive on both the system output and subjectivity in onset/offset annotation.
# Secured Real-Time Machine Communication Protocol
## 4 Vulnerability Analysis
### Comparison with the OPC UA  
The OPC UA is renowned for its robust security features, such as built-in encryption, authentication, and user access controls. However, it exhibits several limitations:  
* **Performance Impact:** The OPC UA's extensive security measures, including certificate management and user authentication, introduce significant computational overhead, making it unsuitable for real-time applications. In contrast, SRMCP focuses exclusively on ensuring secure communication and delegating user authentication and authorization responsibilities to application-level software. Furthermore, SRMCP employs concurrent transmission mechanisms to offset computational overhead, maintaining high performance and ensuring low-latency communication.
* **Not Quantum-Safe:** The OPC UA relies on traditional cryptographic methods, such as the Diffie-Hellman key exchange, which are vulnerable to future quantum attacks. SRMCP overcomes this limitation by incorporating a post-quantum cryptographic technique, the Kyber Key Encapsulation Mechanism (Kyber-KEM), ensuring robust security even against adversaries equipped with quantum computing capabilities.
* **Complexity:** The OPC UA's extensive feature set increases its implementation and maintenance complexity, often resulting in a higher risk of misconfiguration and associated vulnerabilities. SRMCP simplifies deployment by minimizing configuration requirements, relying only on pre-established client and server certificates. Additionally, it offers a programmable interface for seamless integration with diverse data structures and IO functions, enhancing flexibility and usability.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Discussion and Analysis
#### 8.6.4 Scalability Considerations  
The throughput vs. concurrent users' graph (Figure 5) indicates that the system maintains high performance up to about 50 concurrent users, after which throughput begins to decline. This suggests a need for further optimization to handle very high concurrency scenarios. Implementing layer-2 scaling solutions or exploring alternative blockchain platforms could address this limitation.
## 7 Tests and Results
### Local Transmission Tests  
Other tests have been designed and realized to verify the overall communication ability between the coordinator, the secondary lampposts, and Internet. The first test verified how the system reacts when there is a fault in a lamp post or a presence sensor. Simulating the absence of one of them the hub software alerts of the malfunction on its graphical interface.  
The second test verifies the case of break-downs of the coordinator lamp post. If the fault is either on the electrical section or in the local communication section, the hub software again shows the problem. Instead, if the fault is either on the RaspBerry-Pi card or on the WiMAX section, the impossibility to connect to the website means either breakage or theft. A secondary coordinator, activated on another lamp, could solve this problem.
# Doherty Power Amplifier Design via Differential Combining
###### Abstract  
This paper introduces a novel differential combiner designed to effectively address parasitic capacitances of transistors used in power amplifier (PA) designs with precise compensation at a specified frequency. The combiner consists of a \(\lambda/4\) transmission line with an integrated capacitor of value \(2C_{\text{{OUT}}}\) at its midpoint, which ensures accurate cancellation of parasitic effects. This design connects the drain pins of two transistors, which are considered identical in this configuration. By eliminating the need for complex parasitic compensation techniques, this method significantly simplifies the design process of Doherty Power Amplifiers (DPAs). Extensive simulations validate the effectiveness of this approach, highlighting its potential as a versatile and straightforward solution for next-generation communication systems.  
power combining; Doherty; power amplifiers; GaN; GaAs; millimeter-wave +
Footnote †: journal: Electronics Letters
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
###### Abstract  
With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC-WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.  
mobile edge computing; wireless power transmission; convex optimization; augmented Lagrangian method +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 233; doi:10.3390/612120233
## 4 Discussion
### Analysis of Trade-Off According to Patch Coverage Change
#### 4.2.3 Robustness Under Extremely Reduced Patch Coverage  
To further examine how each method performs under extremely reduced patch coverage, we conducted additional experiments at a higher scaling factor (3\(\times\) scaling), where patch coverage is approximately 1/9 of the original 1\(\times\) condition. Table 9 summarizes the model performance results under these conditions, where the context information per patch is drastically reduced but the number of extracted patches significantly increases, enhancing the aggregation effect.  
As shown in Table 9, all methods failed to achieve their peak performance under the 3\(\times\) scaling condition. This indicates that when patch coverage becomes excessively small, the context information within each patch is severely limited, causing a sharp decrease in individual patch-level prediction accuracy. Consequently, even the increased aggregation effect due to a higher patch count is insufficient to fully compensate for this decline. However, even under these extreme conditions of reduced patch coverage, the proposed CBS+RPE method exhibited the smallest performance degradation compared to the other methods, maintaining relatively high accuracy and stability despite the limited information available. This result suggests that CBS + RPE can provide comparatively robust performance, even in situations where patch coverage is extremely reduced.  
In conclusion, these supplementary experiments highlight the inherent trade-off between patch-level contextual information and the aggregation effect across patches. While reduced patch coverage can enhance aggregation, it simultaneously limits contextual cues crucial for accurate prediction.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Validation**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & **Accuracy** & **Macro F1** & **Kappa** & **Accuracy** & **Macro F1** & **Kappa** \\ \hline NCB + FPE (baseline) & 0.5817 & 0.4054 & 0.5165 & 0.2968 & 0.2783 & 0.2392 \\ NCB + RPE & 0.7721 & 0.6531 & 0.7401 & 0.3249 & 0.3216 & 0.2692 \\ CBS + RPE & 0.8894 & 0.8119 & 0.8744 & 0.7129 & 0.5946 & 0.6605 \\ CBS + RPE (Ours) & 0.9823 & 0.9584 & 0.9799 & 0.7861 & 0.6852 & 0.7481 \\ \hline \hline \end{tabular}
\end{table}
Table 9: 3\(\times\) Overall ablation performance metrics: accuracy and macro F1 score.  
Despite this trade-off, our results demonstrate that the proposed CBS + RPE method maintains high performance, even under severely reduced patch coverage, suggesting strong robustness in resource-constrained environments. This indicates that the method is well-suited for real-world tiling-based classification systems, particularly in scenarios involving limited hardware capacity or varying spatial resolutions.
## 2 Related Work
### IoU  
IoU is a metric for assessing object detection algorithms. It quantifies the overlap between predicted and ground truth bounding boxes by dividing their intersection area by their union area. IoU values range from 0 to 1, where 0 indicates no overlap and 1 signifies perfect overlap. Commonly used in tasks such as image segmentation, IoU measures localization accuracy, with higher values indicating more precise object localization. Higher IoU values typically signify superior precision in object localization.  
The introduction of Generalized Intersection over Union (GIoU) [38] has transformed the evaluation of object detection, as it takes into account both the overlap and alignment between predicted and ground truth bounding boxes. GIoU offers a more nuanced assessment of object localization accuracy, ensuring a comprehensive evaluation while avoiding unnecessary repetition.  
Distance Intersection over Union (DIoU) [39] extends its scope beyond measuring the overlap between bounding boxes. It also considers the distance between the centers of these boxes. This innovative approach provides a more precise and refined assessment of object localization, reducing redundancy in the evaluation process. CIoU introduces a significant innovation in the field of object detection evaluation by incorporating additional geometric insights. In addition to measuring intersection and union, CIoU considers the bounding box aspect ratio and the difference in diagonal lengths. This novel approach ensures a more holistic and precise evaluation of object localization accuracy, further minimizing redundancy in the assessment process. CIoU builds upon the foundation laid by DIoU by extending the IoU metric.  
Efficient Intersection over Union (EIoU) [40] directly takes into account the differences in length and width between the predicted and ground truth bounding boxes, in contrast to CIoU. On the other hand, Scylla Intersection over Union (SIoU) [41] places increased emphasis on the regression angle of the bounding box, enhancing the evaluation process.  
[PERSON] et al. [28] have noted that the IoU metric can be sensitive to the exact position of bounding boxes, particularly when dealing with small objects or objects requiring precise localization. In response to this challenge, an evaluation metric tailored for small objects was introduced. This metric is known as WIoU, and it was developed to address the balance issue between good- and poor-quality samples in bounding box regression (BBR). Through the use of the Wise gradient gain allocation strategy within a dynamic nonmonotone focusing mechanism, WIoU v3 achieves superior performance.
## 4 OWS Q-Learning Algorithm
### OWS Q-Learning Algorithm
#### 4.3.1 Q Function Updating  
In a random environment, the performance of the reinforcement learning algorithm Q-learning is poor [58]. The use of the maximum action value as an approximation of the maximum expected action value in the Q-learning algorithm introduces a positive bias, resulting in overestimation and affecting the overall performance of the algorithm [58]. The speedy Q-learning algorithm essentially takes the estimation function of the current Q value as an estimate of the historical Q value. Although the convergence speed of the algorithm is improved, it has the same overestimation problem as the Q-learning algorithm, resulting in a poor strategy for the agent to update the Q value in the early stage of training. Using the estimation function of the current Q value as the estimation function of the historical Q value will produce a large error, which makes the convergence speed of the algorithm slower at the beginning of the iteration [59].  
Aiming at the problem of the slow convergence speed of the Q-learning algorithm, and in order to further improve the convergence speed of the speedy Q-learning algorithm, this paper proposes the OWS Q-learning algorithm. This algorithm changes the way the Q function is updated and obtains a new estimation strategy by assigning different weights to the maximum estimate of the next state Q value and the maximum estimate of the current state Q value in the historical Q table, so as to obtain a more accurate estimate. Where the value of weight \(\beta\) is defined as:  
\[\beta=\frac{\left|\underset{a^{\prime}}{max}Q(s^{\prime},a^{\prime})-\underset {a^{\prime}}{min}Q(s^{\prime},a^{\prime})\right|}{c+\left|\underset{a^{\prime}} {max}Q(s^{\prime},a^{\prime})-\underset{a^{\prime}}{min}Q(s^{\prime},a^{ \prime})\right|} \tag{16}\]  
where \(\beta\in(0,1)\) and \(c\) is a free parameter that can be adjusted according to the environment of the experiment.  
Suppose that \(M(s^{\prime},a^{\prime})=\underset{a^{\prime}}{max}Q(s^{\prime},a^{\prime})\), \(M_{-}(s^{\prime},a^{\prime})\) represent the value function Q value of the previous iteration step update state action Q value when the next state is valued, and the Q function of the algorithm is updated as follows:  
\[\begin{split} Q(s,a)\leftarrow\alpha\{r+\gamma&[ \beta M(s^{\prime},a^{\prime})+(1-\beta)M_{-}(s^{\prime},a^{\prime})]-Q(s,a) \}\\ +&(1-\alpha)\gamma[M(s^{\prime},a^{\prime})-M_{-}(s^ {\prime},a^{\prime})]\end{split} \tag{17}\]  
The above is an improvement in the update method of the Q function, and an optimization and improvement in the learning rate \(\alpha\) and \(\varepsilon-\textit{greedy}\) strategies.