esa-sceva/satcom-chunk-collection huggingface dataset:
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## 5 Phase Jitter Performance Metric
#### Disadvantages of the Phase Jitter Metric  
Experimental results from prior work [4; 5; 6; 7] consistently show that in experiments, loss of lock occurs at \(C/N_{0}\) values that are higher than predicted by the jitter metric. Based on analysis using the phase jitter metric, [PERSON] et al. [6] claim that \"increasing coherent averaging time does not lower the \(C/N\) of the GPS signal which can be tracked if a tan-1 discriminator is used.\" Later in the paper, we explain why increasing coherent averaging time does not affect the phase jitter. However, we show that increasing coherent averaging time does affect the tracking error, and we argue that the tracking error may in fact be a more reliable lock indicator metric for cycle slips. This new metric, used in conjunction with the augmented linear model, will help explain why increasing coherent averaging time is essential for maintaining lock in interference environments. Our conclusions are supported by ([12], p. 159) as well as our own theoretical and experimental results.  
First, we will gain some insight into the augmented linear model, which will help us justify a new PLL design metric based on the PLL tracking error standard deviation. Then, we will compare this tracking error metric to the phase jitter metric and explain its advantages over the latter. We consider only the effect of AWGN for now. The effect of clock phase noise will be added later, in Section 9.
## 3 Methodology
### Model Validation
The calculations for the number of replications are shown below, where i is the number of replicas, and where the calculation performed must be less than or equal to the absolute error (\(\beta=15\) kg), where \(\mathrm{x^{-}}_{\mathrm{n}}=27\),894.73, \(\mathrm{S^{2}}_{\mathrm{n}}=624.38\), \(\beta=15\) and \(\alpha=0.05\). \[ \begin{split}\min\overset{\overset{\overset{\overset{\overset{ \overset{\overset{\overset{\overset{\overset{\overset{\overset{\overset{\overset{\overset{\overset{ \cdot}}}}{\)}}}}}}}}}{\mathrm{i}\geq 10}& \mathrm{i}\geq 10\cdot\mathrm{t}_{(i-1,0.975)}\ \mathrm{\surd}(624.38/\mathrm{i})\leq 15 \mathrm{]}\\ \mathrm{i}=10\cdot 2.26\ \mathrm{\surd}(624.38/10)& =17.87\geq 15\\ \mathrm{i}=11\cdot 2.22\ \mathrm{\surd}(624.38/11)& =16.78\geq 15\\ \mathrm{i}=12\cdot 2.20\ \mathrm{\surd}(624.38/12)& =15.87\geq 15\\ \mathrm{i}=13\cdot 2.17\ \mathrm{\surd}(624.38/13)& =15.09\geq 15\\ \mathrm{i}=14\cdot 2.16\ \mathrm{\surd}(624.38/14)& =14.42\leq 15\\ \end{split}  \tag{22}\]  
Therefore, the optimal number of replicas is 14.
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 4 Experimental Results
### Performance Evaluation  
The performance evaluation of the proposed method was carried out using the four following metrics:  
1. Accuracy, calculated as the ratio of the sum of the true positive (\(tp\)) and true negative (\(tn\)) predictions to the total number of predictions (\(tp\), \(tn\), false positive (\(fp\)), and false negative (\(fn\))) made by the model: \[\text{Accuracy}=\frac{tp+tn}{tp+tn+fp+fn}\] (6)
2. Sensitivity, determined as the ratio of true positive (\(tp\)) predictions to the sum of true positive (\(tp\)) and false negative (\(fn\)) predictionsL \[\text{Sensitivity}=\frac{tp}{tp+fn}\] (7)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**EEG Electrodes** & **Frequency Bands** & **Neurons** & **Accuracy** \\ \hline All (14) & All (5) & 2776 & 96.8\% \\ All (14) & Delta, Theta, Alpha & 2534 & 94.1\% \\ \(F7\), \(F3\), \(AF3\), \(F4\), and \(AF4\) & All (5) & 1444 & 94.9\% \\ \(F7\), \(F3\), \(AF3\), \(F4\), and \(AF4\) & Delta, Theta, Alpha & 1202 & 93.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of EEG classification accuracy with five electrodes and three sub-bands.  
Figure 8: The activity measured in the \(F3\) EEG electrode for the delta sub-band.  
Specificity, calculated as the ratio of true negative (\(tn\)) predictions to the sum of true negative (\(tn\)) and false positive (\(fp\)) predictions:  
\[\text{Specificity}=\frac{tn}{tn+fp} \tag{8}\]
4. False positive rate (FPR), determined as the ratio of false positives (\(fp\)) to the sum of false positives (\(fp\)) and true negatives (\(tn\)):  
\[\text{FPR}=\frac{fp}{fp+tn} \tag{9}\]  
Table 5 shows the proposed method's performance in terms of the four metrics, including the accuracy, sensitivity, specificity, and false positive rate (FPR). An average accuracy of 96.8% was demonstrated for the subject-specific model, and precise identification of all three EEG-based mental states was achieved.  
The high average sensitivity (96.9%) highlights the model's robust ability to detect true positive cases, while an average specificity of 98.33% underscores its proficiency in correctly identifying true negative cases, thereby minimizing the risk of misclassifying normal brain activity as an indication of wrong situational awareness classification.  
Furthermore, the low FPR average of 1.63% indicates a low incidence of false positive predictions, ensuring reliable differentiation between different mental states based on the EEG recordings. Table 6 depicts a confusion matrix for evaluating the classification of the three mental states, demonstrating highly accurate classification results.
## 5 Experimental Evaluation
### The Effect of Noise and Network Distortion on Prediction Accuracy  
The noise-distorted speech data without any network distortion were used to examine the individual effect of noise distortions and combined noise and network distortion on speech data. Then, the general network distortion effect for each SNR was observed. The performances of MiniatureVQNet-Noise and MiniatureVQNet-Noise-Network were compared in both cases, as shown in Figure 3.  
The MSE of the MiniatureVQNet-Noise-Network model is low compared to Miniature VQNet-Noise. At a 0 dB signal-to-noise ratio, the MSE for MiniatureVQNet-Noise-Network and MiniatureVQNet-Noise models was 0.237 and 0.267, respectively. However, with the increase in SNR, both models' performances decreased. For example, at a SNR of 15 dB, the MSE of MiniatureVQNet-Noise was 0.452, whereas that of MiniatureVQNet-Noise-Network was 0.438. The performance decreases as the effect of network distortions is less at a high SNR than at a low SNR. Still, the noise-network-trained model outperforms the noisy dataset trained model at all SNRs.  
In the noise-only dataset, a dataset without network distortions (no packet loss, the delay is less than 3 ms, no jitter), both models MiniatureVQNet-Noise and Miniature VQNet-Noise-Network exhibited the same performance. There is no significant difference between the model trained on the noise-network dataset and the model trained on the noisy-only dataset, as shown in Figure 4. This is interesting as the model trained on the noise-network dataset improves the general performance, without a decrease in performance on the noise-only dataset. The model trained on the noise-network-distorted dataset can learn network distortion features without a loss of knowledge on noise features. Thus, the proposed model is suitable for deployment in noise and noise-network conditions.  
Figure 3: Model accuracy at different signal-to-noise ratios on noise-network-distorted speech data.  
Figure 2: MSE for different models.
## 1 Introduction
Our study is based on a spectrum sharing system consisting of two different waveforms. The first one is the Direct Sequence Code Division Multiple Access (DS-CDMA), which is defined as the underlay waveform used to convey control information. The second one is the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM), which is defined as the overlay waveform used to convey data information. The spectrum of NC-OFDM-based SUs is shared with the PUs which utilize DS-CDMA. Spreading Gain of DS-CDMA provides the required anti-jamming capability for the interference which may be caused by the SUs. In the meantime, based on the properties of the non-continuous power spectrum of NC-OFDM, it is more flexible for the SUs to access the SHs which are discontinuous in the frequency spectrum [8]. It is  
Figure 1: The dynamic spectrum access models. of great significance to discuss and study this issue, since the existing DS-CDMA is anticipated to be one of the spectrum sharing applications used in the future [9]. In order to set up the hybrid spectrum access model, several questions should be answered. The first one is the procedure for network setup between two SUs. The second one is the maximum access capacity of the SUTCH with different strategies. The third one is the reliability of the SUCCH. The fourth one is the power allocation strategies of the SUs between the SUTCH and SUCCH. In the rest of this paper, the above questions will be answered in detail. Specifically, Section 2 builds application scenarios and proposes a mechanism for establishing the CR sensor networks. In Section 3, a transmission channel model for analyzing the maximum access capacity for different polices with different objectives in the fading environment will be discussed. In Section 4, the reliability of the SUCCH is analyzed, and a hybrid spectrum access algorithm based on reinforcement learning model is proposed for the power allocation problem of the SUTCH and the SUCCH. Finally, Section 5 presents our simulation results and Section 6 concludes the paper.
## 3 Results and Discussion
### Comparison with Other Single Label Multi-Class Classification Methods
Detailed experimental results are presented in Table 7.  
In predicting key subcellular locations such as cytoplasm, cytosol, exosome, membrane, nucleus, and ribosome, CSpredR demonstrated significant performance advantages, with its prediction accuracy notably surpassing that of other methods. This further confirms the outstanding effectiveness and reliability of CSpredR in mRNA subcellular localization prediction. Notably, in prediction tasks involving nine subcellular locations, CSpredR achieved accuracy rates exceeding 80%. These comprehensive comparative results once again highlight the exceptional performance of CSpredR in single-site prediction tasks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictors** & **ACC** & **Precision** & **Recall** & **F1 Score** \\ \hline IncLocator & 0.421 & 0.374 & 0.325 & 0.289 \\ iLoc-IncRNA & 0.509 & 0.524 & 0.470 & 0.474 \\ Locate-R & 0.368 & 0.362 & 0.321 & 0.321 \\ GraphLncLoc & 0.579 & 0.736 & 0.557 & 0.584 \\ CSpredR & 0.671 & 0.755 & 0.592 & 0.643 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of the CSpredR predictor based on the independent test set with other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **iLoc-mRNA** & **mRNALoc** & **mRNALocator** & **DM3 Loc** & **Clarion** & **CSpredR** \\ \hline chromatin & – & – & – & 81.47\% & 81.50\% \\ cytoplasm & – & 54.88\% & 38.90\% & – & 91.29\% & 94.62\% \\ Cytosol & – & – & – & 57.37\% & 79.77\% & 83.55\% \\ Exosome & – & – & – & 70.00\% & 92.10\% & 95.46\% \\ membrane & – & – & – & 70.92\% & 89.15\% & 91.12\% \\ nucleolus & – & – & – & – & 83.74\% & 83.88\% \\ Nucleoplasm & – & – & – & – & 80.74\% & 81.20\% \\ nucleus & – & 55.18\% & 57.42\% & 69.52\% & 79.23\% & 80.06\% \\ ribosome & 73.41\% & – & – & 69.03\% & 84.74\% & 86.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of the CSpredR predictor on single subcellular localization based on an independent test set compared to other methods.
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
## 3 Mathematical Model  
### Theoretical Derivation  
The variations of environmental temperature have an important influence on the mechanical properties of structural materials, especially elastic modulus. The elastic modulus of concrete decreases with the increase of environmental temperature, which leads to a decrease in eigenfrequency. Here, a simply supported beam is chosen for the investigation of the influence mechanism of temperature on structural modal characteristics. Span, elastic modulus, area of cross section, moment of inertia of cross section and uniform mass of the simply supported beam are expressed by L, E, A, I and \(\overline{m}\) respectively.  
The \(n\)-th eigenfrequency of the simply supported beam \(f_{n}\) is [37]:  
\[f_{n}=\frac{n^{2}\pi}{2L^{2}}\sqrt{\frac{EI}{\overline{m}}} \tag{2}\]  
The rate of change of eigenfrequency can be determined by:  
\[\frac{\delta f_{n}}{f_{n}}=\frac{1}{2}\frac{\delta E}{E}-2\frac{\delta L}{L}+ \frac{1}{2}\frac{\delta l}{I} \tag{3}\]  
Figure 10: MAC vs. mean temperature. (**a**) 1 st mode. (**b**) 2 nd mode. (**c**) 3 rd mode. (**d**) 4 th mode.  
where \(\delta\) represents an increment in corresponding variables. When temperature changes by \(\delta t\), the changes of the relative parameters are as follows, respectively:  
\[\frac{\delta E}{E}=\theta_{E}\delta t,\ \ \ \ \ \ \frac{\delta L}{L}=\theta_{L} \delta t,\ \ \ \ \ \frac{\delta l}{T}=\theta_{I}\delta t \tag{4}\]  
where \(\theta_{E}\) is the temperature coefficient of elastic modulus, which is an undetermined parameter; \(\theta_{L}=\alpha\) is the linear thermal expansion coefficient of materials. \(\theta_{I}\) is the same grade of \(\alpha^{4}\) and it can be neglected. The three coefficients are put in the Equation (3), and it can be rewritten as:  
\[\frac{\delta f_{n}}{f_{n}}=\frac{1}{2}\theta_{E}-2\alpha\delta t \tag{5}\]  
Equation (5) can be used to estimate the eigenfrequency variation of a simply supported beam caused by temperature change. The temperature coefficient \(\theta_{E}\) of concrete is much larger than the linear thermal expansion coefficient \(\alpha\)[38]. It can be seen that the influence of temperature dependence of elastic modulus of concrete is much greater than that of geometric size with temperature, which is the main reason for the change of natural frequencies.
to model organisms, such as _E. coli_ and _S. cerevisiae_, _Aspergillus_ species possess an expansive repertoire of secondary metabolic gene clusters, including polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS), and terpenoid synthases, enabling direct biosynthesis of complex natural products (e.g., lovastatin, penicillin) [10; 11]. Moreover, _Aspergillus_ provides an abundant supply of key precursors and coenzymes required for the biosynthesis of bioactive natural products, complemented by compatible transcription, translation, and post-translational modification machineries, including glycosylation, phosphorylation, and acetylation [12; 13]. Thus, _Aspergillus_ has become increasingly indispensable in synthetic biology research, especially as a heterologous host for proteins and natural products. For example, industrial fungal strains for organic acid production, such as _Aspergillus terreus_ and _A. niger_, not only possess robust central carbon metabolism and highly efficient organic acid secretion capacity but also demonstrate excellent stress-resistant characteristics including acid tolerance and thermotolerance during industrial fermentation processes [14; 15]. The edible strain _A. oryzae_ has been successfully applied for the production of heterologous proteins, including bovine chymosin (CHY) and human lysozyme (HLY) [16].  
Although many heterologous proteins have been successfully expressed in _Aspergillus_ (e.g., human cytokine interleukin 6 (IL-6) in _A. niger_[17], adalimumab in _A. oryzae_[18]), there are still many challenges that need to be addressed, including improving production and optimizing expression systems. With the aid of _Aspergillus_ expression platforms, the biosynthetic pathways of numerous natural products, including terpenoids, polyketides, and non-ribosomal peptides, have been elucidated [19]. For example, _A. nidulans_ and _A. oryzae_ have emerged as model organisms for filamentous fungal biology, which are currently recognized as preferred fungal chassis for heterologous production of bioactive natural products [19; 20]. This has greatly facilitated the activation of silent gene clusters and the discovery of new compounds. Notably, among the _Aspergillus_ expression platforms, _A. oryzae_ exhibits exceptional advantages for terpenoid biosynthesis, making it an outstanding microbial host for the production of these compounds. For example, it has been efficiently engineered for the production of pleuromutilin (a diterpenoid antibiotic) and cephalospor for P1 (a triterpenoid antibiotic) [21; 22].  
In this review, we concentrate on the _A. niger_, _A. nidulans_ and _A. oryzae_ expression system and summarize recent advances in successfully expressing proteins and bioactive natural products through these species, emphasizing the biosynthesis of terpenoids through _A. oryzae_. Furthermore, we provide a comprehensive review of the latest advancements in the application of CRISPR-Cas9 for genetic engineering in _A. oryzae_, aiming to offer valuable insights for future exploration of _Aspergillus_ chassis and the construction of high-efficiency fungal platforms.
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 6 Applicable Future Trends
### Evolutionary Game for WSNs Security  
WSNs rational attacks [33; 34] can cause a harmful problem due to the intelligent manipulations based on some unfixed strategy. Consequently, this issue needs an intelligent defense behavior to deal with this situation. The evolutionary game can intelligently use the mixed strategy against the intelligent attack manipulations [19; 21]. In other words, the evolutionary game has the ability to change the defense strategy during the game progress against malicious attacks in WSNs [37; 38]. More concretely, the strategies of players are non-deterministic in which the player can select different pure strategies with a certain probability [21; 48] such as the Hawk and Dove problem [21; 39; 118]. In addition, we can use the dynamic evolutionary game that follows the Snowdrift game [39; 118; 119] which proposes a different point of view away from the other biologically based games. Dynamic evolutionary game is slightly different from the Hawk and Dove game which supports both the assigned cooperator and the other players [39; 118; 120]. Consequently, if the evolutionary game was generally used in the general trust model in Figure 3, it would introduce an adaptable solution for the intelligent attack dilemma.  
In biological terms, the biological organisms combat each other aiming at maximizing their benefit. In particular, bacteria strive to infect/control the benevolent cells [120; 121]. Similarly, the intelligent attacker in WSN aims at turning the benevolent nodes to malicious or selfish nodes based on unfixed strategy as a chameleon. Consequently, WSN nodes can be modeled as biological organisms using the evolutionary game that presents a promising solution for mitigating such situations [67; 68; 69; 70; 71; 72; 73; 74; 75]. The evolutionary game features can be summarized as follows.  
* The evolutionarily stable strategies (ESS) or evolutionary equilibrium assists in the NE perfection, specifically when multiple Nash equilibria exist.
* The evolutionary game modeling is suitable for human scenarios with agents that may not have hyper-rational or strong rational behavior.  
* The evolutionary game is based on an evolutionary process due to its dynamic nature, which organizes the dynamics of interactions among agents in the population. In other words, the strategy adaptation continues over time. On the contrary, most of the traditional non-cooperative games are evolved in a static setting [21].
###### Abstract  
In this work, the implementation of HfZrO layers for the tunneling, charge trapping, and blocking mechanisms within the device offer benefits in terms of programmability and data retention. This configuration has resulted in a memory device that can achieve a significant difference in threshold voltage of around 2 V per memory level. This difference is crucial for effectively distinguishing between multiple levels of memory in MLC applications. Additionally, the device operates at low programming voltages below 14 V. Furthermore, the device showcases impressive endurance and data retention capabilities, maintaining a large memory window over extended periods and under varying temperature conditions. The advancement in the a-IGZO-based memory device, characterized by its uniform oxide stacking, presents a viable solution to the industry's requirement for memory storage options that are efficient, dependable, and economical.  
Keywords:homogeneous oxide stacking; multilevel-cell (MLC) NAND memory; hafnium-zirconium oxide (HfZrO); charge trap memory (CTM) +
Footnote †: journal: Applied
## 2 Materials and Methods  
### Compound Parabolic Concentrator (CPC) Solar Collector  
The non-tracking CPC collector used in this study is based on the CPC design presented in detail in a previous work (Kal
# Comparing New Wireless Sensor Network Protocols through Simulation and Data Analysis +
## 4 Results and Analysis
### Fixed Nodes Scenarios with Network Size (Scalability) and Node Failure  
We worked on two primary scenarios while using a network with fixed nodes. In the first scenario, we raised the number of fixed nodes in order to test how the protocols behaved as the size of the network changed. We achieved this by analyzing the WLAN metrics and the routing overhead. In the second scenario, we tested a small network consisting of 25 nodes and a large network consisting of 50 nodes for the same metrics while there was a random failure. Both of these hypothetical situations were devised with the intention of illustrating the data-gathering applications. As a result, all of the participating nodes in both sets of circumstances were judged to be fixed and submitting nodes, which meant that they communicated with the sink node at predetermined intervals. FTP was the application that was utilized for each and every scenario. The packet size that was utilized was 512 bytes, and the packet rate that was utilized was four packets per second, as Table 1.
## 3 Existence of Synchronous Motion
### Clinging Ability Coefficients of Three CRs  
When the vibrating system operates synchronously at the steady-state, the three CRs should perform rotational motion along the surfaces of the cavities and cling to them tightly, for the purpose of successfully crushing the materials. Consequently, it should be clear that the reaction force on the three CRs exerted by the cavities' surfaces is more than zero, i.e.,  
\[N_{i}=m_{R}(\ddot{x}\cos\phi_{Ri}-\ddot{y}\sin\phi_{Ri}+r_{R}\dot{\phi}_{Ri}^ {2}+g\sin\phi_{Ri})>0,\,\,i=1,\,2,\,3 \tag{46}\]  
Simplifying Equation (46), we obtain  
\[r_{R}\omega_{\text{m}}^{2}>-\ddot{x}\cos\phi_{Ri}+\ddot{y}\sin\phi_{Ri}-g\sin \phi_{Ri},\,\,i=1,\,2,\,3 \tag{47}\]  
Since in the far-resonant vibrating system, the operating frequency is much greater than the natural frequency and the damping constants are small [19; 20], it is considered that \(\gamma_{x}\approx\gamma_{y}=\gamma_{0}\), \(\mu_{x}\approx\mu_{y}=\mu_{0}\). Introducing Equations (12) and (16) into Equation (47), we have  
\[C_{Ri}<1,\,\,i=1,\,2,\,3. \tag{48}\]  
Here, the symbol \(C_{Ri}\) is referred to as the clinging ability coefficient, and the corresponding detailed expressions are provided in Appendix D. Only if \(C_{Ri}\) is much less than one do the CRs possess the strong clinging ability, which is indispensable for crushing the materials.