esa-sceva/satcom-chunk-collection huggingface dataset:
# Single EX-CCCII-Based First-Order Versatile Active Filter
## 1 Introduction
For first-order universal filters, three filtering functions can be obtained, i.e., the LPF, HPF, and APF. The voltage input, current output of the TAM and current input, and voltage output of the TIM can be used to interconvert between voltage and current modes, which is an advantage of mixed-mode filters [49]. Therefore, various configurations of mixed-mode first-order universal filters have been investigated [39; 40; 41; 42; 43]. The circuit in [39] uses two OTAs, seven MOS transistors, and one capacitor; the circuit in [40] uses two OTAs and one capacitor; the circuit in [41] uses one CFOA, three resistors, and one capacitor; the circuit in [42] uses one VDGA and one resistor; and the circuit in [43] uses one EXCCTA, one resistor, and one capacitor. However, these mixed-mode filters cannot realize both non-inverting and inverting transfer functions of the LPF, HPF, and APF of the CM, VM, TIM, and TAM within the same topology--in other words, they are incapable of providing a total of 24 transfer functions. EX-CCIIs and EX-CCCIIs were used to realize the first-order universal filters in [44; 45; 46; 47; 48]. The filter in [44] provides a voltage-mode APF; the filter in [45] provides a current-mode LPF, six APF transfer functions (both non-inverting and inverting) of the current-mode LPF, HPF, and APF, and three transfer functions (LPF, HPF, and APF) of the TAM; the filter in [46] provides three transfer functions of the current-mode LPF, HPF, and APF; the appropriately programme the current gain of the EX-CCCII; and the filter in [48] provides three transfer functions of the LPF, HPF, and APF. In this paper, we propose a current-mode first-order versatile active filter with a single EX-CCCII and a grounded capacitor. The proposed first-order filter can realize non-inverting and inverting current transfer functions of the LPF, HPF, and APF (six filter responses) and offers the possibility of electronic tuning of the pole frequency of all filter responses. Favorable current-mode circuits and low-input and high-output impedances are available. Due to the low-input and high-output impedances of the proposed filter, it can be used for TAM, TIM, and VM (universal mixed-mode filter) operation. In the case of the TAM, a resistor is used to convert the input voltage to output current; in the case of the TIM, grounded resistors are used to convert the output currents to output voltages; and in the case of the VM, a resistor is used to convert the input voltage of the VM to the input current of the CM, and the output currents of the CM are converted to output voltages of the VM by grounded resistors. Thus, the filter offers 24 transfer functions of the LPF, HPF, and APF of the CM, TIM, TAM, and VM. The proposed active filters are simulated and designed using SPICE with 0.18 \(\upmu\)m CMOS technology. Experimental results using a commercially available AD844 IC are used to confirm the functionality of the new configurations.
# Biodegradable and Biocompatible Systems Based on Hydroxapatite Nanoparticles
## 2 Preparation of HAp Nanoparticles
An interesting approach to overcome these difficulties has been proposed [30] and specifically a simulated body fluid (SBF) with a similar salt composition to human blood plasma and an increased precursor concentration (i.e., Ca\({}^{2+}\) and PO\({}_{4}\)\({}^{3-}\) ions increased to 3.5 times) has been revealed effective. Preparation of this \"bone like\" carbonated HAp could be performed in only 3 h, having formed nHAp crystals with a uniform rod shape of dimensions close to 27 nm \(\times\) 7 nm. The short time required for preparation of nHAp makes this methodology highly promising for the development of HAp coatings on metallic biomaterials since productivity should be improved. In addition, short times would be helpful for using cells in combination with biomimetic nHAp in situ during coating processes. A recent but alternative approach to prepare nanoparticles is based on the use of synthetic templates which act as molecular cages that are able to block reactions in the nanoscale region [31; 32; 33]. This process has been successfully applied for preparing HAp nanoparticles using cages of saccharides that are stabilized by strong intermolecular interactions (Figure 2) [34]. Figure 1: Scheme showing the morphological changes of hydroxyapatite (HAp) induced by the increase of temperature and the change of R ratios. CTAB for cetyltrimethylammonium bromide. Adapted from [11], Elsevier, 2016. Figure 2: (**a**) Scheme showing the molecular cage system; (**b**) comparison between molecular-cage and ordinary method. Adapted from [34], American Chemical Society, 2016.
## 2 Methods
### Decoder Architecture
#### 2.2.3 Spiking Decoders  
We constructed two different output types of spiking neural networks: one spike-weighted decoder and another based on membrane potential:  
Each output type had two different model makeups, using either a spiking LSTM model or a fully connected model. This created four different models: spike-weighted SNN (swSNN), spike-weighted SLSTM (swSNN-SLSTM), membrane potential SNN (memSNN), and membrane potential SLSTM (memSNN-SLSTM).  
The models were all constructed using the LIF neuron and reset via subtraction scheme instead of resetting to zero for membrane potential, as inspired by [38]. The 'Arctan' surrogate gradient (\(\overline{S}=\frac{1}{\pi}\text{arctan}(\pi U)\)) was used, as proposed by [39]. All layers had learnable threshold and beta parameters, as well as a dropout layer after the first three layers to prevent overfitting [40].  
An Adam optimizer [41] was used with \(\beta=(0.9,0.999)\), \(\epsilon=10^{-8}\). A Reduce LR On Plateau Scheduler was used with patience = 8, factor = 0.5, and mode ='min', and a mean square error loss function was used. The neuron parameters outlined below were initialized as tensors, where the value was just the initial tensor size.  
For all models, the input was the normalized spike data in discrete time steps, and the output represented the decoded joystick position variables in the corresponding time steps.  
The spike-weighted model's architecture is shown on the left of Figure 1, with the last fully connected layer having 600 nodes. This effectively acted as an output layer, as it had fixed weight connections into the joystick output. The output spikes were summed and scaled, either positively or negatively, by 0.01 to give the x and y joystick values. The value of 0.01 was chosen as this was the resolution of the original data, and 600 nodes were used to over-encompass the range of \(-1\)-1, as the joystick data varied within this range. The reason we chose 600 is because there was no significant difference in model accuracy between 400 and 800 nodes (see Section 3.2), so we decided to use the value that was in the middle of the feasible region we tested. Note there are only 300 nodes for each variable, as nodes are not shared, and half of these nodes are negative, so the max value is only 1.5. Over-encompassing also means that the model does not need to output a spike at every neuron if a max value is needed, which adds some redundancy to the model. The second and third rows of Table 1 show the hyperparameters for these models. For the membrane potential method, the architecture is shown on the right in Figure 1. The membrane potential \(\mathrm{U}[T]\) of the two neurons in the output layer was taken directly as a continuously valued output. In the last layer, the spikes were discarded as they were not used, and the membrane potential did not reset. In order to still train \(\beta_{4}\) (output layer \(\beta\)), the threshold was set at 0.1 to allow for spike output so \(\beta\) could be updated. The last two rows of Table 1 show the hyperparameters for these models.
# Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement
## 1 Introduction
We leverage the sequential modelling capability of self-attention to infuse the multiple generators of ISEGAN (two shared generators) and DESEGAN (two independent generators) with the power of capturing temporal dependency across an input signal sequence. The main contributions of this paper are summarized as follows:  
* To enhance the ISEGAN and DSEGAN networks, we incorporate the self-attention mechanism inspired by the implementation in [23; 37]. We refer to these enhanced versions as ISEGAN-Self-Attention and DSEGAN-Self-Attention, respectively. * Across the commonly used objective evaluation metrics, the proposed ISEGAN-Self-Attention and DSEGAN-Self-Attention demonstrated a better speech enhancement performance in all than the ISEGAN and the DSEGAN baselines, respectively. Figure 1: A representation of SEGAN with a single generator \(G\) (conventional GAN) and multi-stage GANs of multiple generators: ISEGAN with two shared generators \(G\) and DSEGAN with two independent generators \(G1\) and \(G2\)[22] (Figure adapted from [22]). * We also demonstrate that with the self-attention mechanism, the ISEGAN can achieve competitive enhancement performance with the DESGAN using only half of the model footprint of the DSEGAN. * Furthermore, we investigate the effect of the self-attention mechanism applied at different stages of the multiple generators in the ISEGAN and the DSEGAN networks with respect to their enhancement performance. The rest of the paper is organized as follows: Section 2 presents the background of the study. The proposed ISEGAN-Self-Attention and DSEGAN-Self-Attention are presented in Section 3. Section 4 describes the experimental setup of the study. The results are presented and discussed in Section 5. Finally, Section 6 concludes the paper with some future directions.
## 2 Optimization Model of Massive MIMO System  
Firstly, the priority of different services is analyzed, and on this basis, channel state information and the number of unsatisfied user request slots in the previous scheduling cycle are introduced to calculate the dynamic priority of users, and user scheduling is performed based on the dynamic priority. Then, under the requirement of guaranteeing the minimum time slot of users, the bandwidth redistribution is carried out to the selected users, and the objective function of adaptive bandwidth distribution is established to optimize the system utility value.
###### Abstract  
We have developed a novel surgical/computational model for the investigation of unilateral vocal fold paralysis (UVFP) which will be used to inform future in silico approaches to improve surgical outcomes in type I hypoplasty. Healthy phonation (HP) was achieved using ciroathyroid suture approximation on both sides of the larynx to generate symmetrical vocal fold closure. Following high-speed videoendoscopy (HSV) capture, sutures on the right side of the larynx were removed, partially releasing tension unilaterally and generating asymmetric vocal fold closure characteristic of UVFP (sUVFP condition). HSV revealed symmetric vibration in HP, while in sUVFP the sutured side demonstrated a higher frequency (10-11%). For the computational model, ex vivo magnetic resonance imaging (MRI) scans were captured at three configurations: non-approximated (NA), HP, and sUVFP. A finite-element method (FEM) model was built, in which cartilage displacements from the MRI images were used to prescribe the adduction, and the vocal fold deformation was simulated before the eigenmode calculation. The results showed that the frequency comparison between the two sides was consistent with observations from HSV. This alignment between the surgical and computational models supports the future application of these methods for the investigation of treatment for UVFP.  
voice production; unilateral vocal fold paralysis; ciroathyroid suture approximation; finite element modeling; vocal fold eigenfrequency  
2021  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Numerical and Experimental Investigations on Vocal Fold  
2021  
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# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 4 Simulation Results and Analysis  
### Controller Design  
The parameters of the grid-connected H6 inverter with the LC filter are given in Table 1. Substituting the parameters in Table 1 into Equation (10), the open-loop transfer function of the inverter in the s-domain is:  
\[P\left(s\right)=\frac{4.8\times 10^{7}}{s^{2}+1.3333\times 10^{4}s+213.3333} \tag{29}\]  
Discretization of Equation (29) using the zero-order hold method at a sampling frequency of 20 kHz yields:  
\[P\left(z\right)=\frac{0.04862z+0.03896}{z^{2}-1.51342z+0.51342} \tag{30}\]  
Hence, based on the above analysis, the proposed RC parameter design mainly includes the following four parts.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
###### Abstract  
Since attention mechanism was introduced in neural machine translation, attention has been combined with the long short-term memory (LSTM) or replaced the LSTM in a transformer model to overcome the sequence-to-sequence (seq2 seq) problems with the LSTM. In contrast to the neural machine translation, audio-visual speech recognition (AVSR) may provide improved performance by learning the correlation between audio and visual modalities. As a result that the audio has richer information than the video related to lips, AVSR is hard to train attentions with balanced modalities. In order to increase the role of visual modality to a level of audio modality by fully exploiting input information in learning attentions, we propose a dual cross-modality (DCM) attention scheme that utilizes both an audio context vector using video query and a video context vector using audio query. Furthermore, we introduce a connectionist-temporal-classification (CTC) loss in combination with our attention-based model to force monotonic alignments required in AVSR. Recognition experiments on LRS2-BBC and LRS3-TED datasets showed that the proposed model with the DCM attention scheme and the hybrid CTC/attention architecture achieved at least a relative improvement of 7.3% on average in the word error rate (WER) compared to competing methods based on the transformer model.  
audio-visual recognition; attention; cross-modality alignment; dual cross-modality attention; hybrid CTC/attention; transformer +
Footnote †: journal: Applied Sciences
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 1 Introduction
### Related Works  
To overcome the challenge of FDD operation mode in MaMi systems, several research works have investigated the DTS design by exploiting various channel models and design criteria. For example, approaches utilizing compressed sensing (CS) techniques have been proposed in [18; 19; 20]. In these research works, the channel estimation in the FDD MaMi systems has been reformulated as a CS problem to reduce the DTS overhead for channel estimation. Research investigations, which exploit time and spatial correlations with Kalman-filter (KF) are considered in [21; 22; 23]. In these works, both the spatial and temporal channel correlations are explored, to reduce the training overhead. In addition, the KF is utilized to track the variation in the DL channel and to improve the mean square error (MSE) performance. Besides the research directions that exploit the spatial and temporal correlations with KF and CS techniques, another line of research studies considers a two-stage precoding approach, termed as joint spatial division and multiplexing (JSDM) [24; 25]. The role of JSDM is to partition the users into different groups and to utilize a two-stage precoding process to reduce the DTS length for DL CSI estimation. To come up with the challenge of DL CSI estimation in the desired single-stage precoding process, different studies have considered the design of DTS by exploiting the low-rank structure of the channel covariance matrices [26; 27; 28; 29; 30]. In these works, sophisticated iterative algorithms were proposed to optimize the DTS for CSI estimation. However, the aforementioned research works on DTS design and CSI estimation rely on the assumption of weight noise, which limits their applications in more realistic environments. In particular, in practice, the wireless communication systems could exhibit colored noise; therefore, the framework analysis of the DTS developed in [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30] does not hold for the general scenario of the correlated noise environments. To the best of our knowledge, finding a feasible DTS design for CSI estimation to enable FDD operation with MaMi systems in the presence of colored noise has not been addressed in the literature. Therefore, this paper is the first research that proposes a novel DTS design for DL CSI estimation in the FDD MaMi systems in the presence of spatially correlated colored noise and with limited coherence time.
## 2 Principal component aggregation in wireless sensor networks
### Data aggregation  
#### 2.1.1 Aggregation service  
An aggregation service allows to aggregate data within a network in a time- and energy-efficient manner. A well-known example of aggregation service is the TAG system, developed at the University of Berkeley, California [10, 11]. TAG stands for Tiny AGregation and is an aggregation service for sensor networks which has been implemented in TinyOS, an operating system with a low memory footprint specifically designed for wireless sensors [24]. In TAG, an epoch is divided into time slots so that sensors' activities are synchronized according to their depth in the routing tree. Any algorithm can be relied on to create the routing tree, as long as it allows data to flow in both directions of the tree and does not send duplicates [10].  
The TAG service focuses on low-rate data collection tasks which permits loose synchronization of the sensor nodes. The overhead implied by the synchronization is therefore assumed to be low. The goal of synchronization is to minimize the amount of time spent by sensors in powering their different components and to maximize the time spent in the idle mode, in which all electronic components are off except the clock. Since the energy consumption is several orders of magnitude lower in the idle mode than when the CPU or the radio is active, synchronization significantly extends the wireless sensors' lifetime. An illustration of the sensors' activities during an epoch is given in Figure 2 for a network of four nodes with a routing tree of depth three. Note that the synchronization is maintained at the transport layer of the network stack, and does not require precise synchronization constraints as in TDMA. Rather, nodes are synchronized by timing information included in data packets, and a CSMA-like MAC protocol with a random backoff scheme is used at the link layer to resolve multiple access collisions.
## 1 Introduction
A learning strategy for distributed channel selection in cognitive radio networks was proposed in [30], by which the QoS of competing SUs converges to their rank-optimal channels to avoid the collision on their own orthogonal channels. The authors of [31; 32] performed channel ranking based on the channel state prediction, which is related to the duration of the channel availability. [PERSON] et al. [33] proposed the dynamic channel selection and parameter adaptation scheme based on the genetic algorithm to provide better QoS for the CR such that the best channel can be selected in terms of the quality, the power, and the PU activity. The CSPA deals with the problem of channel switchings, and it provides better QoS to the CR user. These techniques rank the channel using some parameters, perform well under specific settings, consider parameters separately in the ranking, and exclude critical parameters, which cannot lead to the selection of the best channel. To address these issues, [PERSON] et al. [34] proposed a multiple attributes utility-based model to rank the frequency channels, which associates a weight to each parameter involved in the ranking mechanism. The weights corresponding to these parameters are determined using a nonlinear regression algorithm. In short, even though the proposed schemes utilize the spectrum efficiently and perform channel selection well, they do not jointly consider threats such as eavesdroppers and jammers that can attack the channel. Consequently, the channel quality is degraded. Thus, the channel selection mechanism combined with the security on the physical layer remains a significant open issue in CR networks. In this paper, we investigate physical layer security in a multiuser and multiple eavesdropper cognitive radio system where multiple SUs are transmitting their private data to a common data center (DC), while multiple eavesdroppers execute independent eavesdropping on SU-DC transmissions. Each eavesdropper randomly chooses a channel of interest for an attack. Each SU shares its access strategy, but makes decisions independently. To optimize the PHY security for a CR network, in the paper, we propose an anti-eavesdropping scheme based on multiple games. In the proposed scheme, the interactions of SUs and EVs are formulated as the framework for multiple repeated games in order to choose the best action that provides the best channel selection for the SU. By accessing the best channel, the SU can achieve the maximum secrecy rate that mitigates the effects from eavesdropping and from other SUs that compete for the same channel. For performance evaluation of the proposed scheme, we utilize the secrecy rate of the SU in terms of the expected reward (i.e., throughput) of both SUs and EVs.
###### Abstract  
In the classic Precise Point Positioning (PPP) approach, observations of Global Navigation Satellite Systems (GNSS) are processed at the network level to generate satellite clocks and positions. This information can be used to enable accurate point positioning for a single GNSS receiver. In the PPP Real-Time Kinematic (PPP-RTK) approach, satellite phase biases are considered as well, enabling ambiguity resolution at the network and user levels. In this research, 30 s multi-frequency raw GPS, Galileo, and BDS-2/3 observations are processed at the network and user levels in a sequential Kalman filter. PPP-RTK enabling products are generated for up to five frequencies, and ambiguity resolution is performed at the network and user levels using a flexible ambiguity reparameterization approach, comparable to wide- and narrow-laning, which has shown to yield a significantly improved single epoch coordinate solution when multi-frequency observations are available. Different assumptions regarding the time stability of receiver and satellite phase biases have been made and compared. The availability of a precise user coordinate solution when multi-frequency and dual-frequency observations are processed is assessed and compared. A precise ambiguity-fixed solution is available in three epochs or fewer in 77% of all cases with an average of 24 visible satellites for static and kinematic receivers when multi-frequency observations are processed. When only dual-frequency observations are considered, a fixed solution is available in seven epochs or fewer in 71% of all cases. The fastest fixed solution was found in two epochs with multi-frequency observations and in six epochs with dual-frequency observations. Estimating a reference phase clock did not lead to an improvement in the coordinate solution. The findings indicate that a fixed solution can potentially be found faster than often suggested, with potential for further improvement when more satellites or regional atmospheric corrections are considered.  
G 2023, 15, 2792. [[https://doi.org/10.3390/rs15112792](https://doi.org/10.3390/rs15112792)]([https://doi.org/10.3390/rs15112792](https://doi.org/10.3390/rs15112792))  
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