esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Types of Adsorbents Made from Polyacrylonitrile
### Polyacrylonitrile in Hydrogels
In semi-IPNs, one polymer is crosslinked and the other is present in linear form, as shown in Figure 7. These were prepared by polymerization of acrylonitrile with chitosan and were used for the adsorption of Rhodamine B dye. A 3D hydrogel was synthesized by immobilizing sodium alginate into graphene oxide (GO) by using ultrasonic radiation. This solution was then modified with acrylonitrile by stirring and washed with dimethylformamide to obtain the precipitate. Afterwards, calcium chloride solution was added into the resultant precipitate of the graphene oxide/alginate-polyacrylamideonitrile (GO/Ca-Alga-PAN) adsorbent. Generally, GO is a good adsorbent due to its high stability and large surface area, but sometimes it becomes difficult to regenerate GO due to its hydrophilic nature. Therefore, sodium alginate was encapsulated into GO to convert the single 2D structure into three dimensional networks. This adsorbent was used for the extraction of copper ions from aqueous media. [PERSON] et al. (2015) synthesized poly(acrylonitrile-co-acrylamidopropyl-trimethyl ammonium chloride) hydrogels to remove toxic ions such as As5+, Cr3+, and Cr6+ from the aqueous system [117]. The adsorbent was further treated with hydroxylamine hydrochloride (NH2 OH.HCI) to introduce a hydrophilic amidoxime group in the hydrogels. Freundlich and Langmuir adsorption isotherms were used to study the adsorption data. As5+ ion adsorption followed the Freundlich model, whereas Cr3+ and Cr6+ ion data fit well into the Langmuir adsorption isotherm. Moreover, the As5+ ion adsorption process followed the pseudo first-order kinetic model, while Cr3+ and Cr6+ ion adsorption followed the pseudo second-order models. [PERSON], [PERSON], and [PERSON] (2012) reported the copolymer of cassava starch (mainly found in Nigeria) and acrylonitrile by grafting technique and utilized it to remove lead, nickel, and copper ions from the aqueous system [118]. Hydrolysis of the resultant hydrogel with sodium hydroxide helped to enhance the removal efficiency of toxic metal ions. Regeneration of metal and hydrogel was carried out with 2% hydrochloric acid. [PERSON] et al. (2020) prepared a hydrogel by using amidoxime-modified polyacrylamide, chitosan, and graphene oxide for the extraction of U6+ ions [119]. The hydrogel was obtained in the form of small beads of a diameter of 3 mm. The Langmuir adsorption isotherm best explained that adsorption took place through the formation of a monolayer of adsorbate on the adsorbent through chemical bonding, and the rate of adsorption was described via a pseudo-second-order model. The desorption process was carried out by using 0.1 M sodium bicarbonate to obtain the adsorbent and metal ions. Table 1 gives an overview of the performance of various polyacrylamide-based adsorbents. Figure 7: **(I)** Polymer hydrogels: **(a)** polymer network, **(b)** polymer blend, **(c)** sIPN, **(d)** full IPN, and **(II)** schematic representation of preparation of semi-IPN. Adapted with permission from reference [22].
## 5 Sensitivity Analysis of Parameters
### Permeability Coefficient (\(k_{\mathrm{t}}\))  
Five distinct permeability coefficients (\(k_{\mathrm{t}}\)) were examined, which were 0.5 m/d, 1 m/d, 1.5 m/d, 2 m/d, and 2.5 m/d, respectively. By utilizing the method presented in this paper, the tunnel settlement and bending moment curves resulting from foundation pit dewatering for each of these five cases were calculated, as depicted in Figures 8 and 9. Figure 8 illustrates a notable trend where the vertical settlement of the tunnel and the extent of settlement increase with higher permeability coefficients. Specifically, as \(k_{\mathrm{t}}\) increases from 0.5 m/d to 2.5 m/d, the maximum vertical settlement of the tunnel escalates from 5.79 mm to 7.07 mm. This behavior can be attributed to the augmented permeability coefficient intensifying the scope and impact of foundation pit dewatering on the surrounding water level, consequently affecting the tunnel to a greater extent. In Figure 9, it is shown that the tunnel's bending moment experiences a slight reduction with increasing permeability coefficients. This suggests that the permeability coefficient exerts a limited influence on the bending moment. Additionally, the region of the tunnel generating the bending moment is primarily confined within the range of x = \(\pm\)100 m, which is smaller than the area responsible for the settlement.
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 4 Calculation of Jacobian Matrix Elements Using Exact Zeoppritz and Biot-Gassmann Equations  
### The Calculation of \(\frac{\partial A}{\partial m_{j}}\)  
According to the [PERSON]'s law \(\frac{v_{p1}}{\sin\alpha}=\frac{v_{A1}}{\sin\beta}=\frac{v_{p2}}{\sin\alpha^{ \prime}}=\frac{v_{A2}}{\sin\beta^{\prime}}\), we can get \(\sin\beta=\frac{v_{A1}}{v_{p1}}\sin\alpha\), \(\sin\alpha^{\prime}=\frac{v_{p2}}{v_{p1}}\sin\alpha\), \(\sin\beta^{\prime}=\frac{v_{p2}}{v_{p1}}\sin\alpha\), \(\cos\beta=\sqrt{1-\left(\frac{v_{A1}}{v_{p1}}\sin\alpha\right)^{2}}\), \(\cos\alpha^{\prime}=\sqrt{1-\left(\frac{v_{p2}}{v_{p1}}\sin\alpha\right)^{2}}\) and \(\cos\beta^{\prime}=\sqrt{1-\left(\frac{v_{p2}}{v_{p1}}\sin\alpha\right)^{2}}\). \(\cos 2\beta\) and \(\sin 2\beta\) can be calculated using trigonometric functions \(\cos 2\beta=1-2\ \sin^{2}\beta\) and \(\sin 2\beta=2\sin\beta\cos\beta\). Similarly, we can obtain \(\cos 2\alpha^{\prime}\), \(\sin 2\alpha^{\prime}\), \(\cos 2\beta^{\prime}\) and \(\sin 2\beta^{\prime}\), respectively. The partial derivatives of matrix A with respect to each parameter will be derived in the following section.
###### Abstract  
Remotely piloted aircraft systems (RPAS) are increasingly becoming relevant actors that are flying through the airspace and will gain much more importance in the future. In order to allow for their safe integration with manned conventional traffic in non-segregated airspaces, in accordance with the overall air traffic management (ATM) paradigm, specific enabling technologies are needed. As is well known, the detect and avoid (DAA) technology is fundamental among the enabling technologies identified as crucial for RPAS integration into the overall ATM system. In the meantime, to support extended surveillance, the universal introduction of cooperative automatic dependent surveillance-broadcast (ADS-B) on-board aircraft is being increasingly implemented because it has the potential to allow for the coverage of the entire airspaces in remote areas not usually covered by conventional radar surveillance. In this paper, experimental results that were obtained through the real-time validation, with hardware and human in the loop (RTS-HIL) simulations, of an automatic ADS-B based separation assurance and collision avoidance system aimed to support RPAS automatic operations (as well as remote pilot decision making) are presented and discussed. In the paper, after an introductory outline of the concept of operations (ConOps) of the system and its architectural organization, in addition to basic information about the main system functionalities, a description of the tests that were carried out is reported, and the obtained results are described and discussed in order to emphasize the performance and limitations of the proposed system. In particular, the obtained quantitative performances are reported and commented on, and the feedback presented by pilots in order to improve the system, e.g., in terms of preferred typology of conflict resolution maneuver elaborated by the system, is described.  
remotely piloted aircraft systems; RPAS; unmanned aerial vehicles; UAV; unmanned aerial systems; UAS; detect and avoid; DAA; separation assurance; self-separation; collision avoidance; situational awareness; drones; aircraft; ADS-B; real-time simulations +
Footnote †: journal: Journal of the Acoustical Society of America
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 3 Proposed Interleavers
### FRF Interleaver Design  
Utilization of the proposed 2-D deterministic interleaver of size \(8\times 8\) with the helical interleaver produces highly strong randomization. In addition, based on our study, it has been found that the utilization of two-stage interleavers is not practical. Thus, this paper presents the novel FRF interleaver that can perform the task of two interleavers, but in a single stage.  
The main concept of the proposed interleaver is stated as follows:  
1. First, we arrange the data in a matrix with \(N_{r}\) rows and \(N_{c}\) columns, such that \(N_{r}\times N_{c}=L\), where \(L\) is the number of decoded bits, and each of them should be an integer and multiple of \(8\).  
Figure 4: Arrangement of bits for an \(8\times 8\) channel (**a**) before interleaving and (**b**) after interleaving.  
(ii) Using Equation (7), we obtain the new bit locations after interleaving:  
\[\pi[k]=\pi[((ii+S\cdot jj)mod\ N_{r})\cdot N_{c}+jj],\ 0\leq k<L \tag{7}\]  
where \(S\) is a constant integer, and  
\[\begin{array}{l}ii=((i\cdot 3)mod\ 8)+(i\ mod\ 8)+1\,i=0,1,\cdots,N_{r}-1\\ jj=((j\cdot 3)mod\ 8)+(j\ mod\ 8)+1\,j=0,1,\cdots,N_{c}-1\end{array}\]  
Now, we take a look at how the helical and FRF interleaving mechanisms can correct error bursts. An example of the interleaving of an (\(8\times 8\)) square matrix for \(S=2\) is given in Figure 5. Assume a burst of errors affecting four consecutive samples (1-D error burst) as shown in Figure 5b,c with shades. After helical and FRF de-interleaving, the error burst is effectively spread among four different rows, resulting in a small effect for the 1-D error burst as shown in Figure 5d,e. With a single error correction capability, it is obvious that no decoding error will result from the presence of such a 1-D error burst. This simple example demonstrates the effectiveness of the helical and FRF interleaving mechanisms in combating 1-D bursts of errors. Let us examine the performance of the helical and FRF interleaving mechanisms when a 2-D (\(2\times 2\)) error burst occurs, as shown in Figure 5b,c with shades. Figure 5d,e show that although the two interleaving mechanisms effectively spread the \(2\times 2\) error burst, the FRF interleaver has a stronger randomization ability than the helical interleaver. As a result, a better BER performance can be achieved with the proposed FRF interleaving mechanism.  
Figure 5: _Cont._
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 3 Our Method
### Featured Challenges and Solutions
#### 3.1.4 Trust Deficit, HITL, and Swift Takeover  
The terrains navigated by wheelchairs are inherently more convoluted than those navigated by automated vehicles. Presently, machine sensors are not as adept at recognizing subtle details as humans, especially in discerning minor road impediments like rain-induced puddles, cracked sewer covers, and irregularities on mud-paved roads. While these nuances may not significantly alter route algorithms, they often lead to a trust deficit among users who might opt for detours due to safety and comfort concerns.  
Recognizing this complexity and the limitations of current machine sensors technology, The HITL solution, which embodies human-machine collaboration, capitalizes on human superiority in hazard detection, from unpredictable obstacles like tethered animals to structural hazards. Additionally, path quality assessment intertwines with personal predilections. Many might eschew inconsequential waterlogged patches. In instances of unappealing paths, users retain the prerogative to dictate immediate modifications or resume control, ensuring not only safety but also adherence to personal preferences and comfort levels.
# Bioinspired Algorithms for Multiple Sequence Alignment:
## 2 Multiple Sequence Alignment
### Progressive Method  
The solution to the Multiple Sequence Alignment (MSA) problem aims to reduce complexity in terms of time and space [8]. The progressive technique initiates alignment by aligning identical sequences, adjusting its increment against divergent sequences within the alignment. The widely accepted representation of the progressive approach is ClustalW [20]. Initially, weights are assigned to each pair of sequences in a restricted alignment, with modest weights for identical sequences and significant weights for different sequences. Following the calculation of scores for two protein residues, a substitution matrix based on similarity is considered. The third stage introduces two specific gaps: the residual gap and the locally reduced specific gap penalty [21]. In the final stage, locally reduced specific gaps  
Figure 1: Example of Multiple Sequence Alignment.  
Figure 2: Classification of the Multiple Sequence Alignment methods.  
are encouraged to receive opening gaps at that spot. The ClustalW representation allows for the seamless integration of these four stages [20]. While the progressive technique offers superior alignment for MSA in terms of accuracy and time, it has certain drawbacks, such as dependence on the scoring scheme and initial alignments (i.e., matching comparable sequences at the beginning) [22]. Failure to meet specific requirements may lead to the recording of local optima results.
## 5 Methodology
### LSTM Encoder-GRU Decoder Architecture with Teacher Forcing
#### 5.1.2 GRU Decoder with Teacher Forcing  
The GRU decoder with teacher forcing is responsible for generating an output sequence based on the hidden states obtained from the encoder. Given the hidden state sequence \(H^{\text{enc}}\) and an input sequence \(Y=\{y_{1},y_{2}\ldots y_{U}\}\) of length \(U\), the decoder computes an output sequence \(Y^{\prime}=\{y^{\prime}_{1},y^{\prime}_{2}\ldots y^{\prime}_{U}\}\).  
The GRU decoder updates its hidden states and produces the output using the following equations, incorporating the concept of teacher forcing:  
\[h_{t}^{\text{d}} =\text{GRU}(y_{t},h_{t-1}^{\text{d}}) \tag{8}\] \[r_{t} =\sigma(W_{yr}y_{t}+U_{hr}h_{t-1}^{\text{d}}+b_{r})\] (9) \[z_{t} =\sigma(W_{yz}y_{t}+U_{hz}h_{t-1}^{\text{d}}+b_{z})\] (10) \[\tilde{h}_{t} =\tanh(W_{y_{t}}y_{t}+r_{t}\odot(U_{h\tilde{h}}h_{t-1}^{\text{d}} )+b_{\tilde{h}})\] (11) \[h_{t}^{\text{d}} =(1-z_{t})\odot h_{t-1}^{\text{d}}+z_{t}\odot\tilde{h}_{t}\] (12) \[y^{\prime}_{t} =\text{Regressor}(h_{t}^{\text{d}}) \tag{13}\]  
where GRU represents the operation of a Gated Recurrent Unit; \(\sigma\) denotes the logistic sigmoid function; \(\odot\) represents element-wise multiplication, \(y_{t}\) is the input at time step \(t\); \(h_{t}^{\text{d}}\) represents the hidden state at time step \(t\); \(r_{t}\) and \(z_{t}\) denote the reset and update gates, respectively; \(\tilde{h}_{t}\) is the candidate hidden state; \(W\) and \(U\) are weight matrices; and \(b\) is a bias vector. Regressor is a function that maps the hidden state to the output space.  
The GRU decoder with teacher forcing employs the use of teacher forcing, a technique where the decoder receives the true values of the previous time steps as inputs during training instead of its predictions. This technique facilitates learning by providing more accurate guidance to the model during training. The probability of using teacher forcing at each time step can be controlled by a parameter \(\alpha\in[0,1]\), where \(\alpha=1\) indicates full teacher forcing (always using true values), and \(\alpha=0\) indicates no teacher forcing (always using model predictions). The probability of using teacher forcing at time step \(t\) can be expressed as follows:  
\[P(\text{teacher forcing at }t)=\alpha \tag{14}\]  
Thus, the input \(y_{t}\) to the decoder is determined by whether teacher forcing is applied or not:  
\[y_{t}=\begin{cases}y_{t-1}&\text{with probability }\alpha\\ y^{\prime}_{t-1}&\text{with probability }1-\alpha\end{cases} \tag{15}\]  
where \(y^{\prime}_{t-1}\) represents the predicted output of the decoder at time step \(t-1\).
## 5 Conclusions  
Results revealed that the analysis of the complete FHR signal achieved a classification performance of 81.7%. Then, by including features extracted from resting periods, the classification performance increased to 83.2%. These results coincide with recent, similar research, which has shown that considering features extracted from the entire FHR segment and contraction-dependent features, the classification performance improved to 79% [19].  
From a physiological perspective, these results are highly interesting, because the optimal modal-spectral features, resulting from the automatic evaluation operation, are computed from indicators that involve spectral information inside the LF band (0.03-0.15 Hz), which is associated with fetal sympathetic ANS activity. Besides, considering that these features are extracted from the complete signal and resting periods independently, both epochs may involve significant information related to the sympathetic ANS activity, whose independent analysis can improve the CTG classification performance.  
Our approach, based on CEEMDAN and TV-AR modeling, applied separately to the complete signal and resting periods, has shown to be a promising approach for fetal distress estimation during labor. Note that in this feature extraction approach, the classification strategy was used only to evaluate and select our proposed features. Therefore, in order to prove the hypothesis and validate our classification results, further investigation is needed. In this context, together with the proposed analysis and evaluation, it would be necessary to study different class formation criteria, other techniques for automatic classification, and a higher number of CTG recordings extracted from different databases.  
This method was intended to be used for assisting clinicians in fetal condition assessments in future. Based on the proposed strategy, the next step requires a strategy to feed back the information to clinicians for a better understanding of the CTG signal. The idea is to provide real-time information concerning significant events such as FHR variability, baseline, and decelerations, and a classification of fetal health that can be in agreement with the known states defined in proposed CTG guidelines.  
Conceptualization, P.F., A.I., and F.O.; methodology, P.F., A.I., and F.O.; software, P.F. and A.I.; validation, P.F., A.I., and F.O.; investigation, P.F. and A.I.; writing--original draft preparation, P.F.; supervision, A.I. and F.O.; project administration, A.I. and F.O.; funding acquisition, P.F., A.I., and F.O.  
This research was funded in part by the National Commission for Scientific and Technological Research CONICYT, Chilean National Scholarship Program for Graduate Studies.  
The authors declare no conflict of interest.
## 3 Experimental Results and Analysis
### Experiments on Maximum Adhesion Coefficient Estimation
#### 3.4.2 Setting of the Affiliation Function  
The affiliation functions to be set include creep and slide speed affiliation function, adhesion coefficient affiliation function, similarity affiliation function, rail surface condition affiliation function, u\({}_{1}\) and \(u_{4}\) affiliation function, and rail surface condition correction coefficient affiliation function. Analyzing the four adhesion characteristic curves in Figure 11, it can be seen that when the creep and slide speed is below 1 km/h or above 2.5 km/h, the distribution of the four curves is denser, with less differentiation, according to which the affiliation function of creep and slide speed is set up; the affiliation function of the rail surface condition is set up according to the state quantity recognized by the identification module, and the other affiliation functions are dynamically adjusted according to the experimental specific conditions. The affiliation function diagrams are shown in Figures 12-17.  
1-4 in Figure 13 correspond to the four standard rail surfaces.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rail Surface Condition** & \(a\) & \(b\) & \(c\) & \(d\) & \(\mu_{max}\) \\ \hline Track 1 & 0.54 & 1.20 & 1.20 & 1.20 & 0.343 \\ Track 2 & 0.27 & 0.83 & 0.50 & 0.50 & 0.197 \\ Track 3 & 0.19 & 0.54 & 0.40 & 0.40 & 0.147 \\ Track 4 & 0.09 & 0.54 & 0.20 & 0.20 & 0.117 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Table of values for standard rail surface parameters.  
Figure 11: Adhesion characteristics graph.  
Figure 14: Plot of the similarity affiliation function.  
Figure 12: Plot of the creep and slide speed affiliation function.  
Figure 13: Plot of the adhesion coefficient affiliation function.
## 2 Relevant Techniques
### Multiple Output LS-SVR  
[PERSON] et al. [34] proposed the MLS-SVR network. Multiple output regression aims to learn the mapping from a multiple input feature space to a multiple output space. Although the standard formula of least squares support vector regression (LS-SVR) has potential practicality, it cannot handle multiple output situations. Multiple independent LS-SVRs are usually trained, thereby ignoring the potential (potentially nonlinear) cross-correlation between different outputs. To solve this problem, [PERSON] et al. [34] used the multi-task learning method to propose a new machine learning network. The multiple outputs function \(\Psi(\chi)\) is  
\[\Psi(\chi)=\Phi(\sum_{i=1}^{m}\sum_{j=1}^{l}\tau_{i,j}^{s}K(\chi,\chi_{j}),1,m )+\frac{m}{\lambda}\sum_{j=1}^{l}\tau^{j,s}K(\chi,\chi_{j})+b^{*T}. \tag{17}\]  
where \(\chi\) is the sample, \(\tau\) is Lagrange multiplier, \(K(\chi,\chi_{j})\) is the kernel function, \(b\) is parameter of the model, and \(b\in\mathbb{R}\), \(m\) is the number of output parameters, \(l\) is the number of \(b\), \(\lambda\) is positive real regularized parameter, \(\lambda\in\mathbb{R}_{+}\), \(\Phi(*)\) is the replicate matrix function (repmat), B = repmat (A,n) returns an array containing n copies of A in the row and column dimensions. The size of B is size(A)*n when A is a matrix.  
In our paper, the above-mentioned machine learning model is used for geometric correction. The inputs of this model are six low-order features of Zernike moments [31], and the outputs of this model are parameters of geometric distortion.  
Figure 2: Information expression of pseudo-Zernike moments for images. (**a**–**d**) are the original images. (**a**–**d**) are reconstructed images of (**a**–**d**) using pseudo-Zernike moments, respectively.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
###### Abstract  
Artificial intelligence chips (AICs) are the intersection of integrated circuits and artificial intelligence (AI), involving structure design, algorithm analysis, chip fabrication and application scenarios. Due to their excellent ability in data processing, AICs show a long-term industrial prospect in big data services, cloud centers, etc. However, with the conceivable exhaustion of Moore's Law, the size of traditional electronic AICs (EAICs) is gradually approaching the limit, and an architectural update is highly required. Photonic artificial intelligence chips (PAIC) utilize light beam propagation in the silicon waveguide, contributing to a high parallelism configuration, fast calculation speed and low latency. Due to light manipulation, PAICs perform well in anti-electromagnetic interference and energy conservation. This invited paper summarized the recent research on PAICs. The characteristics of different hardware structures are discussed. The current widely used training algorithm is given and the Photonic Design Automatic (PDA) simulation platform is introduced. In addition, the authors' related work on PAICs is presented and we believe that PAICs may play a critical role in the deployment of data processing technology.  
photonic artificial intelligence chip; neural network; training algorithm +
Footnote †: journal: Applied Sciences