esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ensemble Model for Multi-Level Speech Emotion Recognition
## 3 Proposed Method
### Expert 3: Multi-Level Model Based on HSFs and CRNN
#### 3.4.2 Design of Feature Extraction Model Based on CRNN  
When modeling speech signals, time-dependent models are often used, highlighting the effects of significant regions. Aiming at the requirements, a speech emotion recognition model based on CRNN is proposed. The model is mainly designed for the time domain features of the spectrogram. On the basis of segmentation of speech, the mode of spatial spectrum representing emotional information was effectively studied, and a multi-layer bi-directional LSTM structure was designed. The model is shown in Figure 3. The output of this model was used as the input to the second level of expert 3, which represents local features with timing relationships.  
The CRNN model consists of 2 parts, CNN and RNN. The former is based on the traditional CNN model, which is mainly used to extract the frequency domain features of the spectrogram. For pre-segmented speech, the features of CNN learning for each segment can be obtained. The input image is convoluted into 6 steps (3 consecutive sets of convolution and pooling operations) to generate a set of features. Then the features are sent to the RNN model, where a multi-layer bi-directional LSTM network is used, where each time step corresponds to a segment of the original speech input, thereby preserving long-term dependencies between regions.  
The goal of designing the CRNN model is not to directly judge the speech emotion, but to use the model obtained from the CRNN training for secondary verification. When verifying, the result is directly computed after the penultimate full connection. The result is used as the local feature for subsequent feature fusion. The model takes into account the characteristics of the time and frequency domains of the spectrogram. Using the spectrogram as the input of the network, the parameters of the CNN convolutional layer are trained, and the feature map output by the CNN is reconstructed. At this point, the advantages of CNN for image recognition and the characteristics of the RNN's ability to process serialized data are fully utilized.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 8 Conclusions  
A UOC system is primarily designed to fulfill the demand for large-data-rate connectivity, alleviate interference issues, and increase the security robustness of existing communication technologies' limitations. Due to the rapid growth of optical source, optoelectronic/electro-optics-integrated components, and optical detectors, UOC has reached a wide range of potential applications. Thus, a comprehensive review covering the complete technological evolution of UOC becomes significant, which has been reported in this paper. A comprehensive review on the complete evolution of UOC technology right from prehistoric fire-based communication was reported. The historical developments of today's communication technology and the working principles and deployment methodologies of UOC systems were reviewed. Major classifications: FSO communication, underwater communications, and wireless optical communications, were thoroughly reviewed, and their channel effects, mitigation techniques, communication quality evaluation methods,and potential applications were reported. The main emphasis was given to (i) FSO channel behaviors--channel characteristics, temporal/spatial distortions, aperture averaging, PAT systems, and wavefront corrections; (ii) hybrid FSO-RF systems--RO-FSO techniques, WDM and SDM, WiMAX and SOSC; (iii) underwater optical communication--UOWC and PAC; and (iv) wireless optical communications--Li-Fi, VLC, V2 LC, and OCC, in this review, in addition to a comprehensive analysis of both research and commercial UOC technology evolutions. The significances and contributions of AI in FSO communication and energy harvesting over the UOC channels were reviewed. Next-generation UOC network infrastructure and the research challenges, mainly in the field of optical turbulence sensing/modeling, underwater optical communications, optical networking for 5G/6G communications, high-speed UOC links for 5G/6G and IoT applications, THz wireless FSO links, UOC with multi-hop for medical services, and UOC between UAVs, were addressed. All the existing challenges listed in this review must be addressed at the research and commercial product level to attain the full potential utilization and advantage of UOC.  
Conceptualization, A.A.B.R., Z.G., P.K., U.D. and G.K.; methodology, Z.G. and A.K.M.; validation, A.A.B.R., P.K., M.I. and U.D.; formal analysis, A.A.B.R. and G.K.; investigation, Z.G. and G.K.; resources, A.A.B.R., Z.G., P.K., M.M.A., U.D., G.K. and A.K.M.; writing--original draft preparation, A.A.B.R., Z.G., P.K., U.D., A.K.M. and M.I.; A.A.B.R., P.K. and U.D. writing, A.A.B.R., Z.G., P.K. and U.D. review and editing, and visualization, A.A.B.R., Z.G. and P.K.; project administration, A.A.B.R. and M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Circuit Implementation of the Proposed Operational Amplifier
### Ripple Reduction Loop Design  
The offset of A\({}_{2}\) is shown in V\({}_{x}\) as the ripple due to the demodulation chopper and integrator A\({}_{3}\). When the transconductance of A\({}_{2}\) is G\({}_{m2}\) and the frequency of the chopper is f\({}_{ch}\), the ripple at the output of A\({}_{3}\) can be expressed as  
\[\mathrm{V_{ripple1}}\approx\frac{\mathrm{V_{os2}}\mathrm{G_{m2}}}{2\mathrm{C_{ ii}}\mathrm{f_{ch}}}. \tag{1}\]  
When the transconductance of A\({}_{4}\) is G\({}_{m4}\), this ripple is integrated again by integrator A\({}_{5}\) so the ripple of the operational amplifier output can be expressed as  
\[\mathrm{V_{ripple2}}\approx\frac{\mathrm{V_{ripple1}}\mathrm{G_{m4}}}{8\mathrm{ C_{m1}}\mathrm{f_{ch}}}. \tag{2}\]  
To reduce this ripple, the ripple reduction loop is used. The AC component V\({}_{ripple1}\) is converted to the current I\({}_{r}\) by the AC coupling capacitor C\({}_{r}\) and is input into the current buffer (CB).  
\[\mathrm{|I_{r}|}\approx 2\mathrm{C_{r}}\mathrm{f_{ch}}\mathrm{|V_{ripple1}|} \tag{3}\]  
The CB is shown in Figure 4. If the output impedance of current source M\({}_{1}\), M\({}_{2}\), M\({}_{7}\), and M\({}_{8}\) is large enough, the output voltage and DC output impedance of the CB are given in Equations (4) and (5). The A\({}_{CB}\) is the DC voltage gain of the NMOS cascode in the CB. The output of the CB through A\({}_{6}\) is combined to estimate the amount of ripple suppressed by the current adder, as given by Equation (6).  
\[\mathrm{V_{CB}}=\mathrm{|I_{r}|}\cdot\mathrm{R_{CB}} \tag{4}\]  
\[\mathrm{where,\,\,R_{CB}=\frac{A_{CB}}{2\mathrm{C_{r}}\mathrm{f_{ch}}}} \tag{5}\]  
\[\mathrm{I_{ripple\_can}}\approx\frac{A_{CB}\mathrm{G_{m2}}\mathrm{G_{m6}}V_{ os2}}{2\mathrm{C_{ii}}\mathrm{f_{ch}}} \tag{6}\]  
The simulation results of the ripple for V\({}_{x}\) when a 10-mV offset exists are shown in Figure 5. The red line is the output waveform of V\({}_{x}\) in Figure 2 when RRL is not working, and the blue line is the  
Figure 4: Schematic of the current buffer (CB).  
output waveform of V\({}_{\rm x}\) when RRL is operating. The ripple voltage was reduced by approximately 88%, from 68 mV to 8 mV by the RRL.  
Finally, the ripple suppression factor F can be expressed as  
\[\rm{F}=\frac{I_{\rm ripple\_can}}{I_{\rm os2}}=\frac{A_{\rm CB}G_{\rm m6}}{2C_{\rm ii 1}f_{\rm ch}},\ I_{\rm os2}=\ V_{\rm os2}G_{\rm m2}. \tag{7}\]
## 3 Conclusions  
The proposed QEPTAS is a useful tool for the investigation of photoacoustic and photothermal effects simultaneously. The commercially available QTF acted as a highly sensitive detector to collect the acoustic and thermal waves upon the absorption of laser radiation by target molecules. The remote sensing ability of the QEPTAS sensor was demonstrated by detecting H\({}_{2}\)O in ambient air. The feature of non-contact detection and the lack of laser beam shaping components of the QEPTAS sensor introduced a better error tolerance than that of QEPAS and QEPTS in sensor assembling. The QEPTAS sensor shows great potential for application in the recently developed room-temperature continuous terahertz quantum cascade lasers [39; 40], which are not easily used in QEPAS or QEPTS, since in QEPAS and QEPTS, the focused laser spot diameter \(d\) is limited by the diffraction limit \(d=\lambda/2\times\) NA, where NA represents the numerical aperture. The wavelength \(\lambda\) can be up to centimeters. The advantage of QTF is that the overall size of a typical sensor platform can be significantly reduced to a size that is suitable for applications requiring lightweight and compact structures, such as balloons or unmanned aerial vehicles (UAV) [41]. Further improvement can be made by utilizing custom tuning forks made of materials with higher piezoelectric and thermoelectric coefficients than a commercial quartz tuning fork.  
H.Z. and L.D. ([PERSON]), methodology; L.D. ([PERSON]), software, H.L., validation; Z.H., formal analysis; H.Z., investigation and data curation; X.G., Writing--Original draft preparation; H.Z., Writing--Review and editing; L.D. ([PERSON]), supervision; J.Y., project administration; J.T., L.D. ([PERSON]) and W.Z., funding acquisition; H.Z., J.Y. and Z.C.  
This work is supported by the National Natural Science Foundation of China (11004086, 61675092, 61275046, 61475066, 61405075, 61601404), Natural Science Foundation of Guangdong Province (2016A030313079, 2016A030310098, 2016A030311019, 2014A030313377, 2015A030306046, 2015A030313320), Special Funds for Major Science and Technology Projects of Guangdong Province (2019B010138004, 2014B010120002, 2014B010117002, 2015B010125007), Project of Guangzhou Industry Leading Talents (CXLJTD-201607), and Planned Science & Technology Project of Guangzhou (2017A010102006, 2016A010101017, 2016B010111003, 201506010046), Joint fund of pre-research for equipment, Ministry of Education of China (6141A02022124), Aeronautical Science Foundation of China (201708W4001); Foundation for Distinguished Young Talents in Higher Education of Guangdong (2018 KQNCX009, 2018 KQNCX279), the Fundamental Research Funds for the Central Universities (21619402), State Key Laboratory of Applied Optics (SKLAO-201914).  
The authors declare no conflict of interest.
## 3 Analysis of Alignment Tolerance on the Double-Diffracted Grating Interferometer
### Analysis of the Roll, Yaw, and Pitch Angles  
Compared to the translational errors discussed above, these three rotational errors are more complex with greater impact. Different from translational movement, rotation involves a rotating center. However, in the configuration of a spatially separated grating interferometer, more than one beam exists. Thus, a rotation center at the origin shown in Figure 2 is selected for the following analysis.  
As Figure 4 shows, when a small yaw movement occurs, the incident angle will no longer be normal. Although the difference is slight, according to the grating equation, the absolute value of diffraction angles between \(+1^{\text{st}}\) and \(-1^{\text{st}}\) order diffracted beams are unequal. Similarly, the diffracted beams entered the retro-reflectors along their new angles respectively, creating offsets of spots on the surfaces of the RRs. After the second diffraction, deviations appear at the vertically transmitted beams. Through a beam-combined BS, the output measurement beams has a certain work-off. Although the deviations of the beams are in the same direction, the spots on the surface of PD move away from each other, resulting in a decreasing interference zone.  
Similarly, the geometrical analyses and influences of roll and pitch angles are displayed in Figure 5. In these circumstances, the diffraction angle remains while the propagating directions are no longer parallel to the x-O-z plane of the coordinate. Caused by roll motion, the beams deviate in a similar direction in Figure 5a. However, the pitch motion leads to opposite deviations of the two beams in Figure 5b, resulting in a work-off of these two spots.  
Figure 4: Geometrical analysis of the yaw movement and its influence on the measurement beam caused by spots deviation. (To be clear, the angles in the figure and their influence are exaggerated. Generally, these angles are no larger than several mrads or hundreds of arcseconds in actuality).
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 1 Introduction  
A mobile ad hoc network (MANET) is a decentralised, wireless, self-configured network that is formed dynamically by multiple mobile nodes without the use of any centralised administration or existing infrastructure [1; 2; 3]. MANETs do not rely on a pre-existing infrastructure, such as access points, routers, or servers. Instead of using routers to forward data through the network, each node participates in routing, forwarding the data from the source node to the destination node. A route in MANET is created when it is needed, in other words when a node wants to send data to another node in the network [4]. There are many routing protocols for MANETs, each of which has its own mechanism for creating the routes through the network [5].  
The absence of a central administration leads to vulnerabilities that can be exploited to harm the network performance [6]. For example, uncooperative (malicious) nodes can join the network at any time, as can cooperative nodes, without any authentication or validation and launch a flooding attack. In the flooding attack, A malicious node sends a huge number of fake routing requests asking for routes to non-existent destinations. As a result, the network is flooded by fake requests that can consume its resources and lower its performance level.  
A potential solution that could help assure the performance of MANETs in the face of a network attack is to use the idea of trust management. The principle of trust management in MANETs is that each node monitors the behaviour of its neighbouring nodes and tries to detect any malicious activities. Once a node identifies that a neighbouring node is malicious, it will categorise it as untrustworthy and avoid dealing with it in the future.  
The notion of a security tradeoff with other aspects of a system, e.g., performance or reliability, has been widely explored, e.g., [7]. MANETs can be used to support many kinds of application, for example Internet of Things (IoT), sensor and vehicular networks andrecently cyber-physical systems [8]. Each new application area brings different demands and potentially different vulnerabilities. In this work we consider only the underlying MANET routing and focus on one kind of attack, however the approach we use could be applied to other protocols and other forms of malicious behaviour. Specifically, the aim of this paper is to show the impact of flooding on the AODV routing protocol and to explore whether a simple direct trust mechanism can improve performance.
## 3 Methodology
### Kr  
The KF is a recursive algorithm that can be used to optimize system state estimation as well as effectively minimize the impact of noise on the results. This paper introduces the KF to smooth the output of the BiLSTM and improve the robustness and stability of the fusion model. The state equation and measurement equation are as follows:  
\[x_{t}=A_{t}x_{t-1}+B_{t}u_{t}+w_{t} \tag{8}\]  
\[y_{t}=H_{t}x_{t}+v_{t} \tag{9}\]  
where \(\mathbf{x_{t}}\) presents the state vector at moment \(\mathbf{t}\); \(\mathbf{u_{t}}\) is the control input of the system; \(\mathbf{y_{t}}\) is the vector of observations; and \(\mathbf{A_{t}}\), \(\mathbf{B_{t}}\), and \(\mathbf{H_{t}}\) stand for state transfer matrix, input matrix, and observation matrix, respectively. \(\mathbf{w_{t}}\) is the process noise and \(\mathbf{v_{t}}\) is the observation noise,  
Figure 6: BiLSTM network structure.  
both of which are Gaussian noise. The process noise covariance \(Q_{t}\) and the observation noise covariance \(R_{t}\) are defined as follows:  
\[Q_{t}=E[w_{t}{w_{t}}^{T}] \tag{10}\]  
\[R_{t}=E[v_{t}{v_{t}}^{T}] \tag{11}\]  
The recursive process of the KF is divided into three steps:  
1. Initialization process The initial value of the state vector \(\hat{x}_{0}\) and the covariance \(P_{0}\) are set as follows: \[\hat{x}_{0}=E(x_{0})\] (12)  
\[P_{0}=E[(x_{0}-\hat{x}_{0})(x_{0}-\hat{x}_{0})^{T}] \tag{13}\]
2. Prediction progress Calculate the priori state \(\hat{x}_{t}^{\prime}\) and the priori error covariance matrix \(P_{t}^{\prime}\): \[\hat{x}_{t}^{\prime}=A_{t}\hat{x}_{t-1}+B_{t}u_{t-1}\] (14) \[P_{t}^{\prime}=A_{t}P_{k-1}A^{T}+Q_{t}\] (15)
3. Correction process The correction process involves the calculation of the Kalman gain \(K_{t}\) and the update of posteriori state \(\hat{x}_{t}\) and posteriori error covariance matrix \(P_{t}\): \[K_{t}=P_{t}^{\prime}H_{t}^{T}(H_{t}P_{t}^{\prime}H_{t}^{T}+R_{t})^{-1}\] (16) \[\hat{x}_{t}=\hat{x}_{t}^{\prime}+K_{t}(z_{t}-H_{t}\hat{x}_{t}^{\prime})\] (17) \[P_{t}=(1-K_{t}H_{t})P_{t}^{\prime}\] (18)  
The values of SOE estimated by BiLSTM are treated as observation values and the discrete power integral equation is employed as the state equation. The specific equations are as follows:  
\[SOE_{A,t}=SOE_{A,t-1}+\frac{\Delta t\times U_{t}\times I_{t}}{3600\times E_{N }}+Q_{t} \tag{19}\]  
\[y_{t}=SOE_{Bi,t}+R_{t} \tag{20}\]  
where SOE\({}_{A,t-1}\) is computed through discrete power integral equation and SOE\({}_{Bi,t}\) is the output of BiLSTM at time t. E\({}_{N}\), the nominal energy, is obtained by multiplying the nominal voltage by the nominal capacity according to the specifications of the LFP battery. It is negative for discharging and positive for charging.
## 2 Experiment Details
### Corrosion Protection Performance of GE-EP@OIMs
#### 2.5.2 Weight Loss Experiment  
The weight of the sample (m\({}_{1}\)) was measured using a Sartorius Quintix 124-1S electronic analytical balance, with an accuracy of 0.1 mg. The sample was soaked in NaCl solution with and without the solid corrosion inhibitor, and the NaCl solution was replaced every 24 h. The L80 samples were taken out regularly. According to GB/T 16545-2015 [22], \"Corrosion of Metals and Alloys--Removal of Corrosion Products from Corrosion Samples\", the prepared pickling solution was used to quickly remove the corrosion products from the sample surface. The surface was then cleaned with deionized water and ethanol, and the mass of the sample (m\({}^{2}\)) was recorded after drying with cold air. The corrosion rate (CR, mm/a) and corrosion inhibition efficiency (IE, %) were calculated using Formulas (4) and (5), respectively:  
\[CR=\frac{\Delta w\times 87600}{Stp} \tag{4}\]  
\[n_{w}(\%)=\left(1-\frac{CR}{CR_{0}}\right)\times 100 \tag{5}\]  
where \(\Delta w\) is the mass difference of the sample before and after the corrosion test, g; S is the surface area of the sample, cm\({}^{2}\); t is the corrosion time, h; \(\rho\) is the density of the sample, g\(\cdot\)cm\({}^{-1}\); CR\({}_{0}\) is the corrosion rate of the sample without adding the corrosion inhibitor, mm/a; CR is the corrosion rate of the sample after adding corrosion inhibitor, mm/a, as shown if Table 2 below
## 3 Analysis of Results  
### Elements and Content of SiC Surface after Polishing  
The percentage of surface oxygen element content on the surface of SiC before and after dry polishing with five solid-phase oxidants was detected by SEM, and the results showed that the initial surface of SiC didnot contain oxygen before polishing. The atomic percentages of surface C and Si are shown in Table 3. After testing, oxygen appeared on the surface of SiC after dry polishing with five solid-phase oxidants.The percentage of oxygen atoms is shown in Figure 3.  
The occurrence of the tribochemical reaction of SiC can be reflected by the change of the atomic percentage on its surface, as shown in Figure 3. After polishing with five solid-phase oxidants, although oxygen was produced on the surface, the oxygen atomic percentage content varied, indicating that different degrees and mechanisms of tribochemical reactions occur between the five solid-phase oxidants and SiC. The highest percentage of oxygen atomson the surface reaction layer was observed after the dry polishing of SiC with the solid-phase oxidant Na\({}_{2}\)CO\({}_{3}\)--1.5 H\({}_{2}\)O\({}_{2}\), and the lowest percentage of oxygen atoms in the surface of SiC was observed after dry polishing with the solid-phase oxidant KClO\({}_{3}\).  
According to the SEM analysis, the appearance of oxygen on the surface of SiC indicated that the solid-phase oxidant could generate oxygen to oxidize the SiC surface under the action of frictional heat to produce an oxidation reaction film on the SiC surface.  
Figure 3: Percentage of oxygen atoms on SiC surface after dry polishing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **C (\%)** & **Si (\%)** \\ \hline Atomic percentage of SiC surface elements & 45.67 & 54.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initial surface elements and content of SiC.  
Therefore, it has been shown that SiC can generate a more shearable reaction film by tribochemical reaction at room temperature [20; 21].
# New Subclass Framework and Concrete Examples of Strongly Asymmetric Public Key Agreement
## 4 Breaking Complexity of SAPKA Algorithms
### [PERSON] Attempts an Attack against NDH  
We assume the following:  
* a prime number \(p\), where \(|p|=n\);
* a semi-group \(\mathcal{S}:=\mathbb{Z}_{p}\);
* a subset \(\underline{\mathcal{S}}:=\{y\in\mathcal{S}\;:\;|y|_{\mathcal{S}}\leq m\} \subset\mathcal{S}\);
* \(x_{A}\in\underline{\mathcal{S}}\);
* \(n_{B},x_{B}\in\mathcal{S}\);
* a map \(x_{g}:\mathcal{S}\to\mathcal{S}\), \(x_{g}(y):=g^{y}\), where \(g\) is a primitive element of \(\mathcal{S}\);
* a non-linear and monic polynomial \(s\), where \(n=s(m)>m\) for all \(m\in\mathbb{N}\). [PERSON]'s goal is to obtain \(x_{A}\) from the following public keys
* \(y_{B,1}(y)=N_{1}^{-1}\circ x_{4}(y)=N_{1}^{-1}(g^{y})=(g^{y})^{n_{B}}=g^{n_{ B}y}\)
* \(y_{B,2}(y):=x_{1}\circ x_{2}(y)=(g^{x_{B}})^{y}\), and public elements
* \(g^{x_{B}}\)
* \(g^{n_{B}}\)
* \(y_{A}=N_{1}^{-1}\circ x_{4}(x_{A})=g^{n_{B}x_{A}}\). She must calculate \[y_{B,1}^{-1}(y_{A})=x_{4}^{-1}\circ N_{1}(y_{A})=\log_{g}(y_{A})^{n_{B}^{-1}}= \log_{g}g^{n_{B}x_{A}n_{B}^{-1}}=\log_{g}g^{x_{A}}=x_{A}.\] (21)  
As she does not know \(n_{B}\), it appears that she must obtain the \(n\)-bit logarithm \(n_{B}\) by calculating \(\log_{g}g^{x_{B}}=n_{B}\) before obtaining the \(m\)-bit logarithm \(x_{A}\). However, (21) can be described as  
\[y_{B,1}^{-1}(y_{A})=x_{4}^{-1}\circ N_{1}(y_{A})=n_{B}^{-1}\log_{g}y_{A}=n_{B} ^{-1}\frac{\log_{g^{x_{B}}}g^{n_{B}x_{A}}}{\log_{g^{x_{B}}}g}=\log_{g^{n_{B}}} g^{n_{B}x_{A}}=x_{A}.\]  
Thus, when we define a map \(x_{g^{\prime}}:\mathcal{S}\to\mathcal{S}\) such that  
\[x_{g^{\prime}}(y):=x_{g^{n_{B}}}(y)=(g^{n_{B}})^{y},\]  
\(y_{B,1}^{-1}=x_{g^{\prime}}^{-1}\), we can construct \(x_{g^{\prime}}\) using only \(g^{n_{B}}\), which is public. For this attack, [PERSON] only needs to calculate \(x_{g^{\prime}}^{-1}(y_{A})=x_{g^{\prime}}^{-1}\circ x_{g^{\prime}}(x_{A})\) to obtain the \(m\)-bit logarithm \(x_{A}\). Therefore, it can be said that [PERSON] selecting a bit length set \(\underline{\mathcal{S}}\) reduces the breaking complexity for [PERSON]; that is, Requirement 1 of Section 2.1 is not satisfied in this case.