esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 8 Discussion and Conclusions
This again highlights that CosPhase features complement spectrogram features, and the combination of both phase and magnitude spectrum information significantly enhances the detection performance for the AMR-WB codec. In conclusion, this research makes a significant contribution to DC AMR speech detection by introducing phase spectrum features with deep LSTM networks, focusing on the AMR-NB and AMR-WB speech codecs. Previous studies primarily focused on the narrowband AMR codec (AMR-NB), and while high detection rates were independently reported by other researchers, the performance of the AMR-WB speech codec in DC speech detection remained unknown. Our study reveals that detecting DC AMR-WB speech presents a greater challenge compared to detecting AMR-NB speech. However, the utilization of phase spectrum-based features notably enhanced the detection rates. Furthermore, integrating magnitude and phase spectrum features through the proposed parallel LSTM architecture further boosted the detection rates. These findings underscore the potential of phase spectrum features in DC audio or speech detection tasks, which hold significant relevance from an audio forensics standpoint. Future studies may explore the performance of phase spectrum features on perceptual audio codecs such as MP3 and AAC, expanding beyond speech codecs. Additionally, investigating the efficacy of phase spectrum features on DC AMR speech signals using various DNN architectures and angular margin softmax losses presents promising avenues for further research. Conceptualization, A.B. and C.H. ; methodology, A.B. and C.H. ; software, A.B. ; validation, A.B. and C.H. ; formal analysis, A.B. ; investigation, A.B. ; resources, C.H. ; data curation, A.B. ; writing--original draft preparation, A.B. ; writing--review and editing, C.H. ; visualization, A.B. ; supervision, C.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The authors declare no conflicts of interest.
## 3 Methodology
### System Model
#### 3.1.8. Neighbor Head Node Selection and Path Determination Phase  
During the neighbor head node selection and path determination phase, the sender node SI considers a total of n neighboring nodes within its transmission range. The priority order of these nodes plays a crucial role in determining which node will govern the most efficiently and dependably. Once the most optimal network node has been identified, it is selected as the NHN among the remaining network nodes. The NHN is responsible for collecting data from nodes within its neighbors and transmitting it to the sink or other NHN. The NHN selection process is based on the residual energy of the node, its distance from the sender node, and its capacity to communicate with other nodes. The node with the greatest weight value is chosen as the NHN. Using the neighbor node cooperation mechanism described earlier, the chosen NHN then transmits the data to the destination node. Distance between nodes and the energy levels of each node are utilized to determine the routing path. The proposed NBEER protocol employs NNS and collaboration to determine the optimal data transmission routing path.  
Sink nodes send hello messages to the network every 50 s during the neighbor head node selection and routing phase to detect any inactive nodes and calculate network parameters. When a destination is effectively reached, acknowledgements are broadcast to nearby sender nodes, eliminating the need for additional transmissions via nearby nodes. The sender node transmits messages to the neighbor node, which then identifies the payload and transmits it undetected to the sink node. When there are multiple NHNs along the route, the NHN is not responsible for initiating or generating a collaborative process when the sending node is a sink node with its subsequent steps, thereby reducing the residual energy after transmission. The eighth and ninth equations are used to exemplify this method.  
\[E_{re}(S_{i})>E_{re}(R_{i})Then\ direct\ transfer \tag{6}\]  
\[Else\ E_{re}(S_{i})\leq E_{re}(R_{i})\ then\ relay\ (CHs)\ path \tag{7}\]
## 4 Estimation Techniques
### Nonlinear Regression  
Regression is one of the most well-known statistical approaches for predictive data mining. The process of building a model using existing training data that allows you to predict the value of a continuous output variable based on fresh input variable values is known as regression [44]. In statistics, nonlinear regression is a type of regression analysis in which observational data are represented by a nonlinear combination of model parameters that are dependent on one or more independent variables. A system of successive approximations is used to match the results. A statistical model of the nonlinear regression can be given as:  
\[y\sim f(X,\beta) \tag{59}\]  
This model is used to relate a vector of independent variables, \(X\) with the associated dependent variables, vector \(y\). In contrast to linear regression, here the function \(f\) is nonlinear in the components of the vector \(\beta\).  
The goal of the model is to obtain the total of the squares to be as small as feasible. The sum of the squares is a metric for measuring how close the \(Y\) measurements are to the nonlinear (curved) equation that was used to estimate \(Y\). The difference between the fitted nonlinear equation and each \(Y\) point in the data set is calculated to obtain it. Following that, each gap must be squared. All the squared figures are brought together at the end. The more the equation matches the data points in the sample, the smaller the sum of these squared figures. Logarithmic functions, trigonometric functions, exponential functions, power functions, Lorenz curves, Gaussian functions, or other similar nonlinear functions are used in nonlinear regression [44]. In the same way that linear regression modeling does, nonlinear regression modeling aims to graphically monitor a certain response from a set of factors. Nonlinear models are more complex to create than linear models since the equation is formed by a series of approximations (iterations) that may come through trial and error [44].
## 5 Recent Developments in RF Energy Harvesting
### AI and Machine Learning in RF Energy-Harvesting Optimization  
The integration of AI and machine learning has revolutionized RFEH by enabling adaptive and intelligent power management. AI-driven algorithms are now used to optimize energy-harvesting efficiency based on predictive RF energy availability. One of the major applications of AI in RF energy harvesting is spectrum prediction. A machine learning-based model was developed to analyze real-time RF spectrum data and predict the optimal frequency bands for energy harvesting [81]. The model improved energy-harvesting efficiency by 25% by dynamically switching between different frequency bands. Another key innovation is the use of reinforcement learning for adaptive impedance matching and power management. A deep reinforcement learning algorithm was designed to adjust rectifier circuit parameters based on real-time energy-harvesting conditions, achieving significant efficiency gains over conventional methods [82]. Such algorithms commonly use reward functions that maximize output voltage or power while minimizing response time. They are trained in simulated environments that mimic stochastic RF conditions, such as mobile or urban scenarios. AI has also been applied in power distribution and storage optimization. A neural network-based energy management system was introduced to predict energy demand and distribute harvested energy efficiently across multiple IoT devices [83]. In addition, federated learning frameworks have emerged to enhance distributed energy management by allowing local AI models on edge devices to collaboratively improve global energy-harvesting strategies without centralized data sharing. These AI-driven solutions contribute to the automation and efficiency enhancement of RF energy-harvesting networks.
## 6 Conclusions  
In this paper, a hybrid LSTM-RNN framework for forecasting the SI was proposed and compared with the hybrid approach with FFNN and SVM. K-means clustering was used to classify the days as either sunny or cloudy, then the LSTM-RNN model was used to forecast the SI, and exogenous features were used to train the model. The proposed model was shown to provide the best performance for all three study locations (Golden, Colorado (USA), Jena (Germany), and Basel (Switzerland)) followed by the FFNN and SVM models. In detail, the hybrid LSTM provided maximum and minimum average NRMSEs 4.15% and 5.93% over both types of the day at Golden and Basel, respectively. In terms of the NMAE, the average error percentage was around 2% for both types of day. Overall, the proposed model provided better performance on sunny days than on cloudy days. Furthermore, the hybrid LSTM model exhibited less error than the conventional LSTM model, as indicated by the maximum NRMSEs of 4.15% and 5.56%, respectively.  
In addition, the proposed approach is tested for classification considering two parameters (clearness index and cloud covers). Results have shown no improvement in the forecasting as compared to the former way of clustering. Therefore, using one dimension k-means clustering should be significant to use with the advantages of better performance and less computational burden. Finally, it is concluded that as compared to other methods, the proposed hybrid approach to day-ahead SI forecasting can be a more suitable option for power-system applications such as unit commitment, economic dispatch, and other day-ahead market operations.  
In future works, we are aiming to perform the analysis and comparison on the effect of errors from forecasted meteorological data. More analysis will be performed on k-means clustering with various combinations of the parameters to see the effect of the classification on the overall model performance. Furthermore, generalization of the proposed model will be performed to make the model more suitable for practical use.  
Conceptualization, R.Z., B.H.V., M.H. and I.-Y.C.; investigation, R.Z.; methodology, R.Z. and B.H.V.; formal analysis, R.Z. and I.-Y.C.; resources, R.Z., M.H. and I.-Y.C.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z. and I.-Y.C.; supervision, I.-Y.C.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1003880) and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (2019371010006B).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Zero-Baseline Double-Differenced Carrier Phase and Code Residuals  
It has already been proved that the zero-baseline double-differenced residuals could be applied to evaluate the noise characteristics of the GNSS observations [7; 35]. In this experiment, we also form a zero-baseline with two iGMAS receivers and calculate its double-differenced carrier phase and code residuals. The corresponding results for representative satellite pairs of C19-C22, G08-G01, and E09-E24 are shown in Figure 6. For the definition of zero-baseline double-differenced carrier phase and code observations together with their reduction, please refer to reference [7] for details. We observe that overall, the noise of phase and code observations from the B1C/B2a signals of BDS-3 operational satellites is comparable to GPS L1/L5 and Galileo E1/E5a signals. Most double-differenced carrier phase residuals for all available signals vary within a range of \(-0.005\) to \(0.005\) cycles (\(-1\) to \(1\) mm), while \(-0.1\) to \(0.1\) m for the code residuals. Similar results are also obtained for M300 Pro receivers, which are not shown here due to the limited space.  
Figure 4: Multipath (MP) combinations against elevation for BDS-3 C20 and BDS-2 C11 with the iGMAS (**top panels**) and M300 Pro (**bottom panels**) receiver.  
Figure 5: Root Mean Square (RMS) of multipath and noise error of the MP combination for BDS-3, GPS, and Galileo signals with the iGMAS (**a**) and M300 Pro (**b**) receiver.  
Differential ISB Estimation and Performance Evaluation of Tightly Combined RTK with BDS-3, GPS, and Galileo  
In this section, we present observation models, differential ISB estimation results, and performance evaluation of tightly combined RTK with GPS, Galileo, and BDS-3 operational satellites. All GPS (including block IIF, IIA, IIR, and IIR-M) and Galileo satellites available are involved in the data analysis. However, Galileo E14 and E18 are not used in tightly combined RTK since they are not tracked by the M300 Pro receiver.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 1 Introduction  
The digital attack surface is expanding. Every day, more devices are connected to the Internet. Malicious actors also have access to this global network and leverage it for nefarious purposes. Identifying and tracking packets or flows on the network that are (part of) a cyberattack is of obvious utility. Researchers have been working on this problem since at least 1985 [1, 2]. During that period, network connectivity was not ubiquitous so researchers started their analysis on the hosts under attack, not looking at network traffic. This type of intrusion detection is called host-based intrusion detection (HIDS). As more and more clients became part of networks, it became necessary to add a second branch to intrusion detection. Network-based intrusion detection (NIDS) tries to model the attacks from network traffic.  
These models can exist at different levels of abstraction. Deep packet inspection works based on the data encapsulated in the network packets. Packet-level IDS broadens its view by including features extracted from protocol headers and other metadata [3]. Flow-level IDS does not look at individual packets, but treats them as aggregated in flows.  
At every level, a further distinction can be made between rule-based systems and anomaly detection systems. Rule-based systems have the advantage of being able to identify specific intrusion patterns. These methods are built on signature databases. Malicious activity that has been reported on is transformed into a unique signature. Further occurrences of that pattern will be picked up by the system. The biggest downside to this tailored approach is that it is thwarted by alterations to the attack patterns. This has created an arms race between the malicious actors who employ obfuscation and evolutionary strategies to create mismatches with the existing rules and the defense researchers who combat them with novel techniques to generalize the rules [(4)]. Anomaly detection systems take a different approach. These try to model behavior and report on deviations from normality. This branch is currently the most popular, because it promises models that have a solid general representation and are thus less likely to be fooled by attackers.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 4 Design Example: Super Class AB QFG Amplifier in a 130 nm CMOS Process
### Requirements and Figures of Merit for Energy Efficiency  
An ideal amplifier, in terms of energy efficiency, should achieve the highest small-signal and large-signal performance for a given (and small) quiescent current. A usual way to quantify these requirements is using two conventional figures of merit [68]. The first one is \(\text{FoM}_{\text{L}}=\text{SR}\cdot\mathcal{C}_{L}/I_{supply}=I_{maxL}/I_{supply}\), with SR as the Slew Rate, \(C_{L}\) the load capacitance, \(I_{maxL}=\text{SR}\cdot\mathcal{C}_{L}\) as the maximum load current and \(I_{supply}\) as the total quiescent current consumption. \(\text{FoM}_{\text{L}}\) quantifies the large-signal performance for a given \(I_{supply}\). The second one is \(\text{FoM}_{\text{S}}=\text{GBW}\cdot\mathcal{C}_{L}/I_{supply}\), which quantifies the small-signal performance for a given \(I_{supply}\). A power-efficient amplifier should also maximize current utilization [69], defined as the portion of supply current delivered to the load, which is optimal when the large dynamic currents are generated directly at the output branch without internal replication of them.  
An optimal choice in terms of energy efficiency is the so-named super class AB amplifiers [69]. They are single-stage topologies combining adaptive biasing at the input differential pair and nonlinear current amplifiers to convey and additionally boost the differential pair current to the output. Super class AB amplifiers can potentially achieve very large \(\text{FoM}_{\text{L}}\), due to this double current boosting process, and can also achieve very large \(\text{FoM}_{\text{S}}\) if the adaptive biasing and nonlinear current amplifiers employed provide enhanced  
Figure 7: Two-stage Miller amplifiers. (**a**) Class A/AB. (**b**) Class AB/AB.  
transconductance. Moreover, current utilization is very high, as the large dynamic currents achieved are generated directly at the output transistors of the nonlinear current amplifier, right at the output branch.
## 4 Results and Discussion
### Effect of Vibration on the Shear Stress
#### 4.2.2 0.3 g Vibration at Different Frequency  
Figure 9 shows the comparison of the shear stress at the internal wall of the extrusion liquefier between DMMFB and FEA. The amplitude of the applied vibration is 0.3 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz. From the comparison, it can be seen that the shear stress predicted by DMMFB agrees well with that simulated by FEA, no matter without or with vibration applied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (MPa)** & **Drop (\%)** & **FEA (MPa)** & **Drop (\%)** \\ \hline
0 (No vibration) & 0.89 & - & 0.88 & - \\
100 & 0.75 & 15.7 & 0.74 & 15.9 \\
200 & 0.63 & 29.2 & 0.62 & 29.5 \\
300 & 0.52 & 41.6 & 0.51 & 42.0 \\
400 & 0.44 & 50.6 & 0.43 & 51.1 \\
500 & 0.38 & 57.3 & 0.37 & 58.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effective value of the shear stress without and with 0.25 g vibration applied at different frequencies.  
Figure 8: The shear stress at the internal wall of the extrusion liquefier without and with 0.25 g vibration applied at different frequencies. (**a**) No vibration applied, (**b**) 100 Hz, (**c**) 200 Hz, (**d**) 300 Hz, (**e**) 400 Hz, (**f**) 500 Hz.  
The effective value of the shear stress for each condition is detailed in Table 7. The results show the same trend as above, i.e., the pressure drop is decreased with the application of vibration, and it is further decreased with the increase of applied vibration frequency. The reason for this phenomenon is the same as that discussed in Section 4.2.1.
## 6 Application of the Optimization Techniques: Routing Protocols
### Attribute-based or Data-centric Routing Protocols
#### 6.1.3 Rumor  
In this algorithm, the queries generated by the sink are propagated among the nodes that have observed an event related to the queries [11]. To do so, a node that observes an event inject a long-lived packet called agent. The agents are propagated in the network so distant nodes have knowledge about which nodes have perceived certain events. To optimize the behavior of agents, when an agent reaches a node which has detected another event, the agent is still forwarded but aggregating the new discovered event. Additionally, the agents maintain a list of the recent visited nodes so loops are partially avoided.  
On reception of agents, nodes can acquire updated information about the events in the network. This knowledge is reflected in the node's event caches. By using the event cache, a node can conveniently send a query message. However, some nodes may not be aware of the event's originator. Under these circumstances, the query is sequentially propagated to one of the neighbors selected randomly. Once the query arrives at a node with an entry related to the demanded event in its event cache, the query is then forwarded through the learnt path. Following this procedure, the cost of flooding the network with the query is clearly suppressed.
## 3 Model Development
### Dynamic Mechanistic Model
#### 3.2.3 Membrane Hydration  
The water content and mass flow rate of water through the membrane are crucial factors in the operation. Water transport within the membrane is affected by two conditions: electro-osmotic resistance due to proton transfer from the anode to the cathode and back diffusion due to the water concentration gradient [39]. To simplify the calculations, some assumptions are made:  
1. The water mass flow is distributed uniformly across the membrane.
2. The membrane water content is the average of the cathode water content \(\lambda_{\alpha}\) and anode water content \(\lambda_{\alpha}\), \(\lambda_{\alpha}\), and \(\lambda_{\alpha}\) can be expressed as \[\lambda_{i}=\left\{\begin{matrix}0.043+17.81\phi_{i}-39.85\phi_{i}^{2}+36\phi_ {i}^{3}&0<\phi_{i}\leq 1\\ 14+1.4(\phi_{i}-1)&1<\phi_{i}\leq 3\end{matrix}\right.\] (24) where \(i\) represents the cathode and anode.  
The electro-osmotic drag coefficient \(n_{d}\) and water diffusion coefficient \(D_{w}\) are determined by the water content of the membrane, and \(D_{w}\) is also affected by the temperature [40]:  
\[n_{d}=0.0029\lambda_{m}{}^{2}+0.05\lambda_{m}-3.4\times 10^{-19} \tag{25}\]  
\[D_{\lambda}=\left\{\begin{matrix}10^{-6}&\lambda_{m}<2\\ 10^{-6}(1+2(\lambda_{m}-2))&2\leq\lambda_{m}<3\\ 10^{-6}(3-1.67(\lambda_{m}-3))&3\leq\lambda_{m}<4.5\\ 1.25\times 10^{-6}&\lambda_{m}\geq 4.5\end{matrix}\right. \tag{26}\]  
\[D_{w}=D_{\lambda}\exp(2416\big{(}\frac{1}{303}-\frac{1}{T_{st}}\big{)}) \tag{27}\]  
The water flow rate \(N_{v,m}\) depends on the two conditions of water transport as mentioned above:  
\[N_{v,m}=n_{d}\frac{I}{F}-D_{w}\frac{(c_{v,\alpha}-c_{v,m})}{t_{m}} \tag{28}\]  
where the first term is related to the electro-osmotic resistance, and the latter is related to the water concentration on the two sides of the membrane and the membrane's diffusion coefficient. The water concentration on the two sides of the membrane \(c_{v,\alpha}\) and \(c_{v,m}\) are determined using the following equation:  
\[c_{v,i}=\frac{\rho_{m,dry}\cdot\lambda_{i}}{M_{m,dry}} \tag{29}\]  
where \(\rho_{m,dry}\) is the density of the dry membrane (kg/cm\({}^{3}\)) and \(M_{m,dry}\) is the molar mass of the dry membrane (kg/mol).