esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
For comparison we also show the results for pair Berryman-SC \(-\) Berryman-_f_ for gas-saturated model but for another measure of closeness in the elastic moduli obtained by the two methods, namely, 1% (Figure 4d). As seen, in this case, the results are more scatter compared to the same model but for measure of closeness 0.1 GPa. Regarding the relationship between the _f_-parameter and crack density, Figure 4 show that for fixed spherical porosity, the connectivity factor equals certain constant value until the crack density reaches its critical value (mark with dashed black line). After this point, the connectivity begins to increase steadily. We represent this behavior as the piecewise continuous function (Tables 1 and 2). For the most curves the increasing part is approximated with the following function \(f=a\log_{10}\left(\varepsilon\right)^{2}+b\log_{10}\left(\varepsilon\right)+c\). The obtained coefficients \(a\), \(b\), and \(c\) (Tables 1 and 2) in the equations for the pairs GSA-SC \(-\) GSA-_f_ and Berryman-SC \(-\) Berryman-_f_ are quite close. Interesting, that the constant _f_-value for the Berryman-SC \(-\) Berryman-_f_ and GSA methods are increasing together with the spherical porosity. Thus, higher porosities demonstrate higher connectivity even with the small crack densities. The DEM- Berryman-_f_ results show noticeably lower connectivity values (Figure 4c) for a wider range of the crack's aspect ratios and porosities than the pairs GSA-SC \(-\) GSA-_f_ and Berryman-SC \(-\) Berryman-_f_.
## 3 System Model
### Network Model
Therefore, the modified transmission range will be:  
\[r_{tx}=\sqrt{\frac{-\ln\left(1-0.99\dfrac{1}{100}\right)}{0.01*\pi}}=17.12\ \mathrm{m}\]  
After increasing the transmission range to 17.12 m, as shown in Figure 2b, the whole network is connected. Therefore, the node degree and the node density are considered in designing an adjustment model of the transmission range to establish an energy-efficient network with full reachability. Figure 2: (**a**) Isolated nodes are found. (**b**) By increasing transmission range \(r_{tx}\), connections are established in the whole network, and no isolated nodes are observed.
## 2 Materials and Methods
### Wavelets
The number of levels of the coefficients used in a wavelet decomposition can have a significant impact on the quality of denoising. A larger number of levels can lead to more aggressive denoising and removal of noise, but may also result in the loss of important signal information and distortion of the signal. In this study, 9 was picked because the features would be extracted from the SAWP. Therefore, some distortion in the time domain may occur and be accepted. The wavelet function used in the wavelet analysis should be carefully selected to reflect the features of the signal in the time domain. With similar reasoning, when the primary interest of the study is the SAWP instead of the time-domain signal then different types of wavelet functions will give similar results [48]. Symlet 4 (sym4), as the most widely used wavelet function, was chosen. To determine the denoising thresholds, the method of empirical Bayesian and median thresholding was selected.
## 3 Results and Discussion
### Spot Assay Analysis  
In spot assays, yeast colonies are grown as spots on agar plates in the absence or in the presence of the tested antimicrobial compound at different concentrations. The sensitivity of the yeast towards the antimicrobial compound is analyzed based on the density of the cells grown in each spot. Comparison of growth inhibition in a specific spot with and without the antimicrobial compound allows assessment of the sensitivity of a strain to that particular agent [38]. For the spot analysis, the mean gray values of the yeast colonies visualized as spots were measured [23], and results are shown in Figure 2. Spots with high yeast growth yield a higher mean gray level value than spots in which the yeast has not grown due to the presence of an antimicrobial agent. Therefore, this assay can quantitatively assess the growth capacity and survival of yeasts based on the density of the cells in spots of the same size [39].  
As can be observed in Figure 2, the \"yeast only\" samples for wine yeast (Figure 2a) and beer yeast (Figure 2b) in the absence of the natural extracts demonstrated higher growth capacity compared to all other samples in which the yeast was co-inoculated with a plant extract or carrier oils (\(p<0.05\)). A similar trend was observed for the baker's yeast, but only for _Hypericum perforatum_ and the carrier oils. This implies that the natural extracts negatively influence the growth of the yeast. However, in this method, the carrier oils (sunflower oil and caprylic triglyceride) also exhibited the same antifungal activity as the plant extracts, and therefore there is no evidence to suggest that the ingredients of the natural extracts are the causative agents of antifungal activity. The co-inoculation of the carrier oils with yeast cells onto the surface of the agar resulted in mild inhibition of yeast growth, in contrast to the agar disc diffusion method, in which the carrier oil exhibited a negligible antifungal activity (Figure 1). This difference may be due to the fact that the oils had not adequately diffused from the disc to the agar in order to inhibit the growth of the yeast, whereas in the spot assay method the carrier oils were directly influencing the growth of the yeast cells due to the co-inoculation. Fatty acids may exert antifungal activity mainly by interfering with the lipid bilayers of fungal membranes, and it has been shown that caprylic acid ester derivative has potent membrane disruptive actions against _C. albicans_ and _M. furfurfur_[40; 41].  
Figure 2: Spot assays for the three _S. cerevisiae_ strains studied. The third dilution (1:25) of spots at 24 h incubation was used for image analysis. Mean gray values are presented in the bar charts. The average values and SD were compared with ANOVA followed by Duncan post hoc test. * Denotes significantly higher (\(p<0.05\)) than all other states in yeast cell models (wine and beer yeasts). ** Denotes significantly higher (\(p<0.05\)) than ”yeast + Hypericum” (baker’s yeast) and carrier oils.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 3 Conventional Control of Grid-Following Inverters
### DC-Link Control Loop  
Considering the three dynamic models for the DC-Link presented in Equations (3), (8) and (14), three schemes can be used for DC-link control [23], which are:  
Using the simplified DC-Link model of (8) and designing a PI controller. This method is the prevalent control design scheme in the literature, known as \"conventional DC-link control scheme\";  
Using the operating point-dependent model of (14) and designing an adaptive DC-link controller;  
Using the nonlinear dynamic model of (3) and designing a nonlinear DC-link controller for a complicated system.  
Among the above options, using the simplified DC-link model of (8) results in a simplified control design. However, it imposes restrictions on the bandwidth of the DC-link control loop, especially in rectifying mode. The deficiency of the restricted bandwidth, and accordingly the poor disturbance rejection capability, can be solved by using a larger DC-link capacitor. As a result, this method is a commonly used approach in the literature for finding the \"d\" axis current reference. According to (8), the DC-link voltage dynamic depends on both \(i_{d}\) and \(P_{dc}\). To cancel the impact of \(P_{dc}\), it is considered as a measurable disturbance and compensated for by feedforward control. Finally, \(i_{d}^{ref}\) is determined as given by (21):  
\[i_{d}^{ref}=\frac{P_{dc}}{1.5v_{gd}}+\frac{K_{p-wc}s+K_{i-wc}}{s}\left(W_{c}^ {ref}-W_{c}\right) \tag{23}\]It is worth mentioning that the bandwidth of the DC-link control loop is selected at least four times lower that of \(i_{d}\) control loop. In addition, reducing the bandwidth improves the system stability, considering the uncertainty in the model of (8).
# Optimal Gliding Trajectories for Descent on Mars
## 1 Introduction
Geographic constraints were applied in the entry guidance of autonomous gliders at high lift/drag, showing that the vehicle reached the target point by adapting to different kinds of missions [16]. Fast analytical solutions for a hypersonic glider with lift/drag greater than 3.0 were analyzed to update the calculated trajectory and to land in a specific cross-range [17]. A short-range guidance with a predictor-corrector algorithm was explored for fast re-entry [18]. Optimal lift control subject to initial and final values of the trajectory were derived from parametric studies in [19]. In addition to the large system (spacecraft) and according to the mission, sometimes the implementation of aerodynamic subsystems or components, such as aerodynamic decelerators, is required. Both inflatable aerodynamic decelerators and mechanically deployable systems are promising technologies enabling several benefits, including large payload delivery to the Martian surface or orbit and aerocapture missions to Venus or other planets. By expanding to a larger diameter prior to entry, such concepts allow higher entry performance than rigid aeroshells suitable for a variety of planetary or Earth high-speed entries requiring high-temperature materials [20]. Furthermore, being the ballistic coefficient at entry unconstrained by the launcher configuration, these deployable technologies provide operational flexibility over rigid aeroshells along with reduced aero-heating and surface pressure experienced during entry. A deployable Mars aeroshell concept was studied in [21] based on thermal protection system (TPS) panels fitted between retractable ribs. However, such systems typically suffer from uncontrolled re-entry and reduced trajectory accuracy due to the uncertainties during re-entry operations. Morphing technology has shown the potential to provide more accurate guidance trajectories and precise landing of space vehicles by using aerodynamic drag [22]. Other variations in optimal trajectories for this kind of vehicle, including ascent, powered descent, and additional phases of flight, are available in the scientific literature but are far from the focus of the present paper. Different from the previous solutions explored in the literature, in this paper, the optimal control is selected (open loop) to find feasible trajectories from the spacecraft's atmospheric entry point to a preselected final zone. However, the final approach or landing is not detailed in this research. The novelty of this paper is the sensitivity analysis of optimal trajectories during the atmospheric descent of a spaceplane on Mars, subject to uncertainties on lift-to-drag ratios (\(l/d\)), satisfying a multi-objective cost function at the same time. Following that goal, this paper is organized as follows: the dynamical model and the formulation of the cost problem are presented in Section 2. Section 3 shows the results of the simulations, including the discussion. Finally, the conclusions are listed in Section 4.
## 5 Computational Experiments and Results
### Performance Evaluation
Figure 6: Experiment results of routing algorithms (average end-to-end delay). routing decisions for new routing constructions. As far as delay is concerned, NODQC performs better than OLSR, AODV, and DSDV with large numbers of nodes. Hence for real time traffic, NODQC is preferred over OLSR, AODV, and DSDV. This is significant for the fact that, as the variations of packet delay becomes more predictable, the routing mechanisms can factor in that delay to determine whether a packet is lost or not. Furthermore, the NODQC also takes QoS provisioning into account for the coming sessions, thereby causing system throughput with QoS satisfaction to be greater than others in larger networks. By taking into consideration both the perspective of the system as we as well as users, the NODQC has proven itself to have lower average end-to-end delay and delay jitter and higher system throughput with QoS satisfaction than other routing algorithms in large-scale networks. By leveraging the Lagrangian Relaxation method, the arc weight on each link can be applied by our routing metrics. It can employ the link-state routing protocol to construct the shortest or shorter paths within an acceptable delay. Within larger networks, routing strategies can be played to improve system throughput. Figure 8: Experiment results of routing algorithms (system throughput with QoS). Figure 7: Experiment results of routing algorithms (delay jitter). performance with lower average end-to-end delay and delay jitter than alternative algorithms (OLSR, AODV, DSDV). NODQC outperforms them in terms of system throughput with QoS satisfaction.
## 1 Introduction
Recently, contextual word embeddings such as Embeddings from Language Models (ELMo) [20] and Bidirectional Encoder Representations from Transformers (BERT) [21] brought new improvements to the named entity recognition (NER) task. Embedding algorithms like Word2 vec [22] and GloVe [23] are based on the meanings of words (the meaning is speculated by word co-occurrences), and they map each word to a vector using a language model. However, in different contexts, the same word may have different meanings. For instance, the meaning of the word \"bank\" in \"go to the riverbank\" and \"go to the bank to deposit\" is different. Contextual word embedding algorithms solve this problem by giving various embedding vectors of the same word in various contexts [20; 21]. In CNER studies, models with pretrained contextual word embeddings in medical corpora outperformed those with Word2 vec and GloVe [24]. With the proposal of contextual word embedding, methods like character combined embeddings and attention mechanism are yet to be tested, and prior studies based on deep learning did not pay enough attention to the system's perception of unknown entities, as the recall of those systems is relatively low. Our study hypothesizes that, through combining contextual word embedding, multitask, and attention mechanisms, the system can achieve better performance than previous works and recognize more unknown entities. Thus, we propose a multitask biLSTM-CRF model with pretrained ELMo contextual word embeddings to extract clinical named entities. The multitask mechanism separates the NER task into two parts: named entity discovery and named entity classification, in which the classification task is the primary task, and the discovery task is the secondary task. Usually, the secondary task in a multitask model can be seen as a regularizer [25]; this mechanism was implemented to reduce noise in the social media named entity recognition task [26]. We constructed the multitask mechanism to enhance the model's perception of unknown entities to improve recall. In addition, we drew a self-attention mechanism into the model. Experiments were done on the I2B2 2010/VA [8] dataset. The results show that our model outperforms the typical LSTM-CRF models with ELMo contextual word embeddings. Our work provides an approach to improve the system's performance and perception of unknown entities based on multitask mechanism. The paper is organized as follows: Section 2 summarizes previous studies on methods of clinical named entity recognition and describes the multitask recurrent neural network model and the ELMo pretrained contextual word embedding. Section 3 presents the experimental setting and results. Section 4 discusses the experimental results. At last, Section 5 concludes the findings of this study and describes some possible future directions based on this work.
## 3 Algorithm and Platform
### Distributed Algorithm  
In actual practices, the training dataset is often distributed, e.g., measurements collected by multiple sensors in a WSN (wireless sensor networks). Especially in the Big Data era, centralized data storage becomes technologically unsuitable. Under this circumstance, some reforms for distributed processing are necessary. For a classic PSO, a particle \(i\) can be represented by a position vector \(\mathbf{x}_{i}\) and a velocity vector \(\mathbf{v}_{i}\), where \(\mathbf{x}_{i}=[x_{i1},x_{i2},\cdots,x_{iD}]\), \(\mathbf{v}_{i}=[v_{i1},v_{i2},\cdots,v_{iD}]\) and \(i=1,2,\cdots,M\). \(M\) is the number of particles in a population and \(D\) is the number of decision variables. Each particle in the population should update its own velocity and position according to Equation (7) during the evolution. \[\left\{ \begin{array}{l}\mathbf{v}_{i}(t+1)=\omega\mathbf{v}_{i}(t)+c_{1}r_{ 1}(\mathbf{P}_{i}-\mathbf{x}_{i}(t))+c_{2}r_{2}(\mathbf{P}_{g}-\mathbf{x}_{i} (t))\\ \mathbf{x}_{i}(t+1)=\mathbf{x}_{i}(t)+\mathbf{v}_{i}(t+1)\end{array} \right. \tag{7}\]  
where \(t\) is the iteration time, \(\omega\geq 0\) is the inertia weight coefficient, \(c_{1},c_{2}\geq 0\) is the acceleration coefficient, \(r_{1}\) and \(r_{2}\) are uniformly distributed random numbers in the closed interval [0, 1], \(\mathbf{P}_{i}\) is the local optimal solution of the \(i\)-th particle and \(\mathbf{P}_{g}\) is the global optimal solution. In this paper, a P-PSO algorithm is presented to solve the distributed training problem of the ESN. Since the training dataset \(S\) is dispersed among a cluster of \(L\) nodes, the idea of the P-PSO is to redevelop the classic PSO by introducing particles for every node, and forcing them to fly synchronously, and reaching a global optimal solution at convergence. Suppose that all nodes in the cluster have accepted a uniform choice of the fixed matrices \(\textbf{W}_{i}^{r}\), \(\textbf{W}_{r}^{r}\) and \(\textbf{W}_{o}^{r}\). Denoted by \(\textbf{H}_{k}\) and \(\textbf{d}_{k}\), the hidden matrices and output vectors can be precomputed at the \(k\)-th node according to Equations (4) and (5), with their own local dataset, and stored into memory, with respect to the training complexity, where \(k=1,2,\cdots,L\). The same works with a classic PSO, where \(\textbf{x}_{i}\), \(\textbf{v}_{i}\) are the position and velocity of the \(i\)-th particle, respectively. The fitness function \(f_{fit}(\cdot)\) of a particle \(\textbf{x}_{i}\) is defined as Equation (8):  
\[ \begin{array}{c}f(\textbf{x}_{i}^{k})=\frac{1}{2}\Big{(}\|\textbf{H}_{k} \textbf{x}_{i}-\textbf{d}_{k}\|_{2}^{2}\Big{)}\\ f_{fit}(\textbf{x}_{i})=\sum\limits_{k=1}^{L}f(\textbf{x}_{i}^{k})\end{array}  \tag{8}\]  
where \(f(\textbf{x}_{i}^{k})\) is the fitness value of particle \(\textbf{x}_{i}\) in the \(k\)-th node. Besides, define \(\textbf{P}_{i}\) as the optimal solution of the \(i\)-th particle and \(\textbf{P}_{g}\) as the optimal solution of the current moment. As the center of the population, \(\textbf{P}_{g}\) is relatively variable, which makes the whole particle group more brisk, and a larger area will be explored.
## 1 Introduction
Furthermore, to enhance the identification efficiency, the adaptive query splitting protocol (AQS) and adaptive binary splitting protocol (ABS) have been proposed to reduce the identification time and collision cycles by recalling the success prefixes of the past identification frame (for AQS) and maintaining tags states of the past frame (in other words, by remembering the allocated slot counter (ASCs) values (for ABS) [10]). Lastly, hybrid protocols are anti-collision protocols that possess the advantages of both classes, i.e., aloha and tree-based protocols. Furthermore, the BA protocol has shown the best identification efficiency amongst the protocols that do not employ bit tracking technology [11]. In practical implementation, the deviation in tags responses increases as the number of tags increases. Therefore, the position of the collided bit cannot be detected accurately, and bit-tracking technology is difficult to employ [12]. GQT1 and GQT2 are enhanced query tree protocols for coping with the capture effect. However, they have the shortcomings of low identification efficiency (especially for non-uniform distribution of tag IDs) and sensitivity to the type of tags distribution and the length of the tag IDs. Moreover, GBT and TBT protocols have utilized the capture-effect treatment; however, they experienced many collision cycles, even when the tags motion was slow (they do not employ the blocking technique). Moreover, these protocols are sizably affected (in many collision and idle cycles) by interference signals, even when the tags motion is slow, since they cannot recognize between the staying and arriving tags and the staying tags alone (i.e., they cannot separate between collision and interference signals for the period of staying tags). Consequently, we improve the BA to deal with the capture effect (C.E) in order to avoid losing tags when the reader decodes the tag with the strongest signal during the simultaneous response of tags. Furthermore, the improvements include distinguishing between the interference response resulting from the signal distortion (due to noise) of one tag response and the collision response caused by the multiple tags responses at one time during the period of staying tags. In this paper, Section 2 shows the basic principle of the BA anti-collision protocol. Section 3 introduces the modified BA protocol and its analytical analysis. Section 4 introduces the performance and simulation results of the modified protocol and other protocols under the impact of various parameters including the capture effect probability, interference probability, total number of tags, length of tags, and distributions of tag IDs. At the end, Section 5 concludes this paper.
# Electroencephalogram Emotion Recognition via AUC Maximization
## 3 Dataset
### Feature Extraction
#### 3.2.2 Frequency-Domain Features  
One of the techniques commonly applied to extract frequency domain features from EEG signals is the Fast Fourier Transform (FFT). FFT is a computationally efficient algorithm that transforms a signal from its time domain into its frequency domain, allowing for the analysis of the signal's frequency components. Given a discrete time domain signal \(x[n]\) of length \(N\), the Discrete Fourier Transform (DFT) of the signal can also be expressed using [PERSON]'s formula as  
\[X[k]=\sum_{n=0}^{N-1}x[n]\cdot\left[\cos(\frac{2\pi}{N}kn)-j\sin(\frac{2\pi}{N }kn)\right],\quad k=0,1,\ldots,N-1 \tag{1}\]  
Here, the DFT is decomposed into its real part, represented by the cosine term, and its imaginary part, represented by the sine term. The FFT is an efficient implementation of the DFT, reducing the computational complexity from \(O(N^{2})\) to \(O(N\log N)\), making it particularly suitable for analyzing large datasets like EEG signals. By applying the FFT, the EEG signal can be decomposed into its constituent frequencies, including theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). Subsequently, the Power Spectral Density (PSD) was calculated for each frequency band within each sliding window segment. The PSD quantifies the power distribution of the signal over the different frequency components, providing insights into the underlying neural oscillations within each epoch. This feature is crucial for characterizing the energy present in specific frequency bands, which is often associated with various cognitive and emotional states [52; 53; 54].