esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### CSSR Multi-Task Learning with LID  
In CSSR, modeling units belonging to different languages but with similar pronunciation are easy to confuse. Meanwhile, the language information was not used explicitly during training. LID is a process by which a computer analyzes and processes speech to determine which language it belongs to. So, we believe that adopting LID prediction as an auxiliary task can improve the CSSR performance. We sent the feature output from the encoder to the decoder, and the decoder output to the LID sequence corresponding to the feature sequence. The LID task and the ASR task share the same encoder, therefore we call it multitask learning. The objective loss \(L_{LID}(Z|X)\) was added to extend the multi-task learning (MTL) objective loss:  
\[L_{MTL}=(1-\beta)L_{ASR}(Y|X)+\beta L_{LID}(Z|X). \tag{8}\]  
The \(L_{LID}(Z|X)\) loss is the cross entropy of the predicted LID label sequence \(\hat{Z}\) and the ground truth LID label sequence \(Z\). In this work, each LID label \(z_{l}\) corresponds to an  
Figure 2: CTC-Transformer baseline architecture.  
ASR label \(y_{l}\). So the length of the LID label sequence \(Z\) is the same as that of the ASR label sequence \(Y\). An example is shown in Figure 3.  
We used label '\(E\)' for English, and label '\(M\)' for Mandarin. We used label '\(N\)' for nonverbal, such as noise, laugh and so on, but we do not mention '\(N\)' below, as it is not important in this study. Because the length of the \(Z\) sequence is the same as that of the \(Y\) sequence, and is inspired by the decoding method of the \(Y\) sequence, we used a similar structure to predict the \(Z\) sequence. In the training stage, the Decoder of the ASR task used \(H_{e}\) and the information of the label sequence \((y_{0},\dots,y_{l-1})\) to predict \((y_{1},\dots,y_{l})\). We studied what label sequence should participate to predict the next LID label. Specifically, we propose two strategies to implement the \(Z\) label prediction task.  
* LID label sequence (LLS): as indicated in Figure 3(a), the LID predictor receives the encoded features \(H_{e}\) and the LID label sequence \((z_{0},\dots,z_{l-1})\) to predict \((z_{1},\dots,z_{l})\). The LID predictor does not share the embedding layer with ASR predictor, and it has its own embedding layer: \[E_{Z}=Embed_{LID}(Z[0:l-1]).\] (9) \[Embed_{LID}(\cdot)\text{ transforms a sequence of labels }Z[0:l-1]\text{ into a sequence of learnable vectors }E_{Z}\in\mathbb{R}^{l\times d^{\text{int}}}.\]
* ASR label sequence (ALS): just like LLS, except that the LID predictor does not receive the LID label sequence, but the ASR label sequence \((y_{0},\dots,y_{l-1})\), and in fact, the LID predictor shares the embedding layer with the ASR predictor. ALS can be implemented in two structures, one is the LID task sharing the decoder with the ASR task (ALS-share), the other is not sharing (ALS-indep), as indicated in Figure 3(b), respectively.  
Figure 4: _Cont_.  
Figure 3: An example: Y label sequence corresponding to \(Z\) label sequence.  
On this basis, inspired by [20], we also operate another set of experiments adding joint CTC training for the LID task.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 5 Stability Analysis
### Throughput Optimization of DHBP
Hence, DHBP obtains the optimal throughput by considering the destination hops' delay in the LEO satellite network.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Current Amplitude Optimization
#### 4.2.2 Design 7: Close-In Side Lobe Level Minimization of a 10-Element LAA  
In this design, the close-in side lobe levels are minimized using the MAOA optimization algorithm. The fitness function for this design is showcased in Equation (26), where \(\theta_{\text{AS}}\) is the total region of the side lobes, \(\theta_{\text{NS}}\) is the region of close-in side lobes, and \(\text{w}_{1},\text{w}_{2}\) are the weight factors. In this design, \(\theta_{\text{AS}}=[0^{\circ},76^{\circ}]\) and \([104^{\circ},180^{\circ}]\) and \(\theta_{\text{NS}}=[69^{\circ},76^{\circ}]\) and \([104^{\circ},111^{\circ}]\) are the two regions decided for the fitness function, and both the weight factors are taken to be unity.  
The results of the simulation are mentioned in Table 9, highlighting the optimized current amplitude along with the CSLL in decibels. The highest reduction of \(-29.79\) dB in the near side lobe is attained with MAOA. The result is benchmarked with other nature-inspired optimization algorithms, and MAOA provides a PSLL reduction of 16.82 dB from conventional ULA and also less than AOA, DA, and BBO optimized antenna arrays. The array pattern is illustrated in Figure 14.
## 5 Improved Performance of Harmonic Injection by the Phase Perturbation Method
### The Perturbation Injection (Section A)  
As has been demonstrated in Section 4.3, the proper frequency for a 50-Hz rated grid is close to 100 Hz. Based on the method explained in [29], the phase reference of the voltage on the AC capacitor \({U_{C}}^{*}\) to inject a 100-Hz disturbance can be described by:  
\[{\theta_{mod}}^{*}=\theta^{*}+k\cdot\cos\theta^{*} \tag{11}\]  
where \(k\) is an amplifying gain of the disturbance and \(\theta^{*}\) is the phase of the referenced voltage given by the droop control. In [29], it is demonstrated that the consequence of adding this perturbation corresponds to a second harmonic injection, but without changing the zero crossing position and keeping the maximum and minimum values as the original sinusoidal waveform, as has been magnified in Figure 13. It should be highlighted that this perturbation method requires a sum, a product, and a cosine. However, the cosine argument is directly \(\theta^{*}\) and avoids the inner product (\(2\cdot\pi\cdot\omega_{r}\)), where \(\omega_{r}\) is the rated angular frequency of the grid.
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 1 Introduction
### Vulnerabilities and Cyber-Attacks in Space Infrastructure
Additionally, there is a significant gap in the engineering methodologies tailored explicitly for cyber resilience in space systems. The absence of comprehensive and standardised engineering practices hinders the development of resilient space infrastructure [24], creating new opportunities for researchers.
###### Abstract  
Aim: This study aims to assess the impact of various reparimealizing agents, specifically topical fluoride (TF) and a combination of a carbon dioxide laser (CO2 laser) with TF, as well as regenerative agents such as Curodont(tm) Repair and NR-5(tm), on two key factors--the micro-Vickers hardness (VH) of eroded enamel and the micro-shear bond strength (uSBS) of composite restoration. Materials and Methods: A total of 50 single-rooted premolars with intact enamel were sectioned mesiodistally into two halves, making a sample size of 100 specimens. All of the samples were then exposed to Coca-ColaTM for 2 min each day over 1 month to induce erosion on the enamel surface. The specimens were then embedded in acrylic cold-cure resin facing a flat surface upward. The samples were then arbitrarily divided into five groups based on the remineralizing and regenerative agents used, as follows (n = 20): Group 1: No remineralizing agent, Group 2: Curodont(tm) Repair, Group 3: NR-5(tm), Group 4: TF, and Group 5: CO2 laser + TF. The VH of the pretreated enamel surfaces was analyzed and uSBS testing and failure mode of composite restoration were performed using a universal testing machine (UTM) and stereomicroscope. ANOVA and Tukey's post hoc were performed for data analysis. Results: In Group 3, the (NR-5(tm)-treated teeth exhibited the highest VH values and uSBS. In Group 1, the (No remineralizing agent)-treated specimens displayed the lowest VH and the lowest uSBS. An intergroup comparison analysis unveiled that Group 3, Group 4 (TF), and Group 5 (CO2 laser + TF) presented comparable outcomes of microhardness and bond strength. The Group 2 (Curodont(tm) Repair) samples exhibited no significant difference in VH and uSBS, as compared to Group 1. Conclusions: The use of a combination of NR-5(tm) technology and a CO2 laser in conjunction with TF has been shown to significantly augment the natural mineralization process. This enhancement results in increased microhardness and an improved bond strength in the treated enamel.  
micro-shear bond strength; Vickers hardness; CO2 laser; topical fluoride Article
## 3 Methods
### Algorithm Description of the DMP Model
#### 3.1.2 Co\({}_{2}\) Fertilisation Effect  
The CO\({}_{2}\) fertilisation effect is the increase in carbon assimilation due to CO\({}_{2}\) concentrations above the atmospheric reference level [37]. The CO\({}_{2}\) concentration level in CGLS-DMP was fixed to a constant value, although the globally averaged records show a tendency to increase from 1980-2012 and beyond (Figure 4). In Figure 5a, the dots represent the CO\({}_{2}\) fertilisation effect of CGDL-DMP at a fixed CO\({}_{2}\) level with variable temperature (from \(-20\)\({}^{\circ}\)C-40\({}^{\circ}\)C). In order to simulate the measured CO\({}_{2}\) increase, the yearly variable CO\({}_{2}\) values were used in the modified DMP.  
In the CGLS-DMP model, the CO\({}_{2}\) fertilisation effect was parametrised for C\({}_{3}\) plants. For more details on the equation, we refer to [37]. Above-ground dry matter increases with elevated CO\({}_{2}\) for C\({}_{3}\) plants [88; 89; 90]. The blue to red lines in Figure 5b show the increase of the CO\({}_{2}\) fertilisation effect for C\({}_{3}\) plants from 1999-2012.  
For C\({}_{4}\) plants little evidence of biomass accumulation in response to CO\({}_{2}\) enrichment was observed over a wide range of temperatures [88; 91]. Based on the CO\({}_{2}\) assimilation rate of C\({}_{4}\) at lower CO\({}_{2}\) partial pressures [92], the evolution of \(\varepsilon_{\text{CO}_{2}}\) for C\({}_{4}\) plants can be rewritten (Equation (6)).  
\[\varepsilon_{\text{CO}_{2}}=\frac{\text{F}_{\text{CO}_{2}}}{\text{F}_{\text{ CO}_{2}}^{\text{ref}}}=\frac{\text{C}_{\text{CO}_{2}}*\ V_{pmax}}{\text{C}_{\text{CO}_{2}}+\text{K}_{ \text{P}}}*\frac{\text{C}_{\text{CO}_{2}}^{\text{ref}}+\text{K}_{\text{P}}} {\text{C}_{\text{CO}_{2}}^{\text{ref}}*\ V_{pmax}} \tag{6}\]  
where F\({}_{\text{CO}_{2}}\) is the CO\({}_{2}\) assimilation rate in year x, F\({}_{\text{CO}_{2}}^{\text{ref}}\) is the CO\({}_{2}\) assimilation rate in the reference year 1833, C\({}_{\text{CO}_{2}}\) is the actual CO\({}_{2}\) concentration, \(V_{pmax}\) is the maximum PEP (phosphoenolpyruvate) carboxylase activity, C\({}_{\text{CO}_{2}}^{\text{ref}}\)is the CO\({}_{2}\) concentration in the reference year 1833 (281 ppmv (or \(\upmu\)bar)) and Kp is the Michaelis-Menten constant for CO\({}_{2}\) (80 \(\upmu\)bar according to [92]). Since the variability in CO\({}_{2}\) assimilation rates of C\({}_{4}\) plants is dependent on CO\({}_{2}\) concentration, the changes from one year to another are very small (dotted green line in Figure 5c).  
Figure 4: Yearly (blue line) and monthly (red line) globally averaged CO\({}_{2}\) records (source: [87]).
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band ON and K/Ka-Band OFF
#### 7.2.1 Gain Performance  
Figure 21 shows the simulated gain for the receiver front-end with the X-band arm ON over 8-12 GHz. The simulated gain ranges from 23 dB to 27 dB at room temperature, demonstrating robust performance in the X-band.
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 2 Micro-Doppler Signal Model  
Micro-Doppler signals can be obtained over a variety of sensors, such as linear frequency-modulated (LFM) wave radar, ultra-wide band radar, and pulse Doppler radar [23; 24]. To date, researchers have developed micro-Doppler signal models for specific targets. Take the human target as an example. We adopt the Boulic model to represent a man with the detailed geometry, as shown in Figure 1.  
For simplicity, some typical point scattering centers of human body parts are selected to build the radar signal model: head, shoulder, torso, arm, hip, leg, and foot [20].  
The returned radar signals are composed of multiple sinusoidal frequency modulated (SFM) signal components expressed by  
\[s(t)=\sum_{i=1}^{N}\sigma_{i}\exp(j(2\pi f_{0}t+\beta_{i}\sin(2\pi f_{mi}t+\phi_{ i}))) \tag{1}\]  
Here, \(\sigma_{i}\) represents the reflectivity of the \(i\) micro-motion scatterer and is usually assumed to be constant. \(f_{0}\) and \(\beta_{i}\) are the carrier frequency and the modulation index of the scattering. \(\beta_{i}\sin(2\pi f_{mi}t+\phi_{i})\) denotes the Doppler frequency of the point scatterer. \(f_{mi}\) and \(\phi_{i}\) are the oscillating frequency and initial phase angle, respectively.  
According to (1), the instantaneous frequency (m-D) of the \(i\) scatterer can be written as  
\[f_{i}(t)=f_{o}+\beta_{i}f_{mi}\cos(2\pi f_{mi}t+\phi_{i}) \tag{2}\]  
Note that the echoes of the target with micro-motions are multicomponent SFM signals and the instantaneous frequencies are sinusoids viewed in the T-F domain [25]. Therefore, m-D signals of target are the combination of sinusoids with distinct frequencies, initial phases, and amplitudes. It should be emphasized that the m-D signals of human body parts may not be purely sinusoids, which will be indicated in the experiment section.
## 5 Simulations and Results
### Performance Evaluation and Discussion
By dynamically modulating control gains in response to real-time system states, the RLCA achieves an optimal balance between aggressive correction during disturbances and conservative control near equilibrium, which accounts for its reduced overshoot and faster convergence speed. The synergistic combination of the aforementioned three control actions enhances the controller's adaptability by enabling it to flexibly manipulate the control trajectory as per the state error variations. These theoretical insights are corroborated by the simulation results, where the RLCA consistently demonstrates lower \(RMSE_{\theta}\) values, reduced peak deviations, and shorter settling times across multiple test scenarios, including nominal tracking, impulsive disturbances, and model uncertainties. This highlights the RLCA's robustness and adaptability, making it a more effective solution for UAV longitudinal motion control compared to the fixed-gain LQI controller.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work
### Power Control  
One method of topology control is to save energy by reducing the transmission power of individual nodes. This method is referred to as \"power control\". Power control reduces the number of active links, but it should also preserve connectivity and coverage of the network [11].  
However, power control cannot always prevent the transmission of redundant packets in dense networks in regard to network flooding. Since in dense networks, nodes might be so close to each other that reducing the transmission power would not effectively reduce the number of active links. Hence, if nodes transmit packets simultaneously, they still might interfere the transmissions of each other, leading to packet loss. Therefore, power control cannot generally simplify a network's topology in order to reduce broadcast storm [11].
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 2 System Model and Problem Formation  
### Model of DC-DC Boost Converter with CPL  
The topology shown in Figure 2 corresponds to a DC-DC boost converter. It is assumed that this converter works in continuous conduction mode (CCM). Based on the state-space averaging method proposed in [42; 43], its averaged model is expressed as  
\[\left\{\begin{aligned} Li&=-(1-d)v+E,\\ C\dot{v}&=(1-d)i-\frac{v}{R},\end{aligned}\right. \tag{1}\]  
where \(i\in\mathbb{R}\) is the inductor current, \(v\in\mathbb{R}\) the output voltage, \(R\) resistance, \(E\) the input voltage, \(L\) inductor, \(C\) capacitance, \(d\in[0,1]\) duty ratio. The equilibrium set is described as  
\[\mathcal{E}:=\bigg{\{}i\in\mathbb{R},v\in\mathbb{R}\mid i-\frac{v^{2}}{RE}=0 \bigg{\}}. \tag{2}\]  
**Remark 1**.: _As stated in Section 2.1, it is assumed that the boost converter works in CCM. Hence, the present work does not consider discontinuous conduction mode (DCM). The model of the converter under DCM is different from Equation (1), which is shown in [44]._
## 3 System Model
The corresponding coordinates expressed as \(r_{\kappa}=\sqrt{x_{\kappa}^{2}+y_{\kappa}^{2}}\), and \(\psi_{\kappa}=\tan^{-1}(\frac{y_{\kappa}}{x_{\kappa}})\) possess Rayleigh and uniform distributions, respectively. Without any loss of generality, we set \(\phi_{0}=90\), therefore the Euclidean distance between the \(\kappa\)th sensor and a point located in \((x,y)\) plane at coordinates \((A,\phi_{0})\) is defined as \(d_{\kappa}\triangleq\sqrt{A^{2}+r_{\kappa}^{2}-2r_{\kappa}A\cos(\phi-\psi_{ \kappa})}\sim A-r_{\kappa}\cos(\phi-\psi_{\kappa})\), where \(A\gg r_{\kappa}\) in the far-field region. The array factor for \(N\) sensors is expressed as [18]  
\[AF^{\kappa}(\phi/r,\theta)\triangleq\sum_{\kappa=1}^{N}\exp^{\eta\varphi_{ \kappa}}\exp^{-\jmath\frac{2\pi}{\lambda}d_{\kappa}(\phi)} \tag{3}\]  
whereas \(\lambda\) denotes the wavelength and \(\varphi_{\kappa}\) is the initial phase of the \(\kappa\)th sensor carrier frequency. The phase delay is defined as \(\theta_{r}=\frac{2\pi}{\lambda}d_{\kappa}(\phi)\) at point \((A,\phi_{0})\). The gain of an antenna can be expressed in a spherical coordinate system as follows [15]  
\[G(\theta,\phi)=\eta\frac{u^{\kappa}(\phi/r,\theta)}{u_{(o)}^{\kappa}} \tag{4}\]  
where \(u(\theta,\phi)\) is the radiation intensity in a given direction \((\theta,\phi)\), and \(\eta\) is the efficiency factor, which is set to be one since each antenna is assumed to be lossless and \(u_{(o)}^{\kappa}\) is the radiation intensity of an isotropic radiator. The isotropic antenna model has been frequently used in WSN to model several IoT applications [18; 37; 41]. Figure 2 illustrated the deployment of the elements along a line, rectangle (mesh), or random with the corresponding distance between two neighboring connecting elements that achieves both connectivity and full coverage. Applying isotropic antennas ensures the connectivity for example in Figure 3 if sensor (A) connects to sensor (B), then sensor (B) must connect to sensor (A). Nevertheless, it is not guaranteed for directional antennas. Sensor (A) might point to a sensor (B), but the (B) might connect elsewhere such as sensor (D). Sensor (C) has a packet for sensor (A), but the (C) can not transmit the data, since sensor (A) is connected to sensor (B). Therefore, it seems that the connectivity of the network requires global knowledge of all sensors' directions for which several challenges arise. The first challenge is interference caused by higher gain. Meanwhile, the second is the optimal deployment rules that apply to every sensor to ensure the connectivity with as few other sensors as possible. We consider three kinds of antenna arrays, Uniform Linear Array (ULA), Uniform Mesh Array (UMA), and Random Antenna Array (RAA) to address this problem in terms of typical antenna models. Antenna gain can be rewritten again in terms of array factor of \(N\) sensors as  
\[G(\theta,\phi)=\frac{AF^{\kappa}(\phi/r,\theta)^{2}}{\frac{1}{4\Pi}\int_{0}^{2 \Pi}\int_{0}^{\Pi}AF^{\kappa}(\phi/r,\theta)^{2}\sin(\theta)d\theta d\phi} \tag{5}\]
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### GAN Model Construction
#### 3.5.1 GAN Audio Preprocessing  
Prior to training the GAN, audio samples are preprocessed to ensure consistency and compatibility with the model. The preprocessing steps are as follows:  
1. Audio loading: the audio files are loaded using the torchaudio.load function, and the first channel is selected in case of multi-channel audio.
2. Standardized length: audio lengths are padded to match the longest sample in the dataset, ensuring uniformity.
3. Sample rate: a sample rate of 48,000 Hz is used to capture audio details effectively, as higher rates (e.g., 96,000 Hz) significantly increase computational costs with marginal gains in quality.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### ELR-W: Operational Steps
#### 3.3.3 Forwarding Phase  
Once the next-hop is elected, the BSN will send data packet to the selected node which will further transfer packet to BNC. BNC is a gateway for all BSNs, which receives data from BSNs and transmits to medical server though internet.  
```
Notations:\(N_{i}=\) Source node \(NH_{i}=\) Next-Hop node for \(N_{i}\)\(BNC=\) Body Node Coordinator \(NT=\) Neighbor Table \(PCF=\) Path Cost Function Input: records in \(NT\) Process:\(1\) start
2. if\(N_{i}\) is at one hop to BNC then
3. send packet directly to BNC
4. else
5. for each record in \(NT\)do
6. Calculate \(PCF=\sum\left[\hat{a}\times\frac{1}{E_{\textit{ks}}}+\beta\times\frac{1}{LE}+ \gamma\times HC+\delta\times d\right]\)
7. List \(RT\leftarrow\ PCF\) value of each neighbor node in \(NT\)
8. \(NH_{i}\leftarrow\) min(\(RT\))
9. endfor
10. endif
11. end
```  
**Algorithm 2** Next-hop selection procedure
# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 4 The Interacting Dimensions of this Complex Socio-Technical Ecosystem
### Quantum Physics, Quantum Computing, and Quantum Machine Learning  
The incorporation of quantum computing and quantum machine learning (QML) algorithms in 6G networks is aimed at enhancing the processing capabilities of network infrastructure compared to the traditional ML techniques [70]. The rapidly increasing wave of data services that is pushing the boundaries of processing power of the communication networks is not passive. The data are increasing pervasively and at an exponential rate and the data traffic presents imminent challenges to all the aspects of wireless systems design. This then inspired the advent of quantum computing in wireless communication. The quantum computing concept, which is aimed at fundamentally changing the telecommunication landscape using quantum physics properties, has since inspired a whole new generation of researchers. These properties are used by quantum computers to store data and perform complex computations [71]. As a result, the quantum computing community has since achieved major breakthroughs by building systems that are stretching the limits of classical simulations to enable cloud-based research. Apart from enabling communication technologies and bringing advantages to all the layers of the protocol stack, quantum computing has become a catalyst in the seamless communication ecosystem for the cyber-physical convergence. The ideas of quantum physics were applied in a spectrum occupancy reconstruction problem often encountered in CRNs [72].