esa-sceva/satcom-chunk-collection huggingface dataset:
Hybrid Pd-Nanoparticles within Polymeric Network in Selective Hydrogenation of Alkylnols: Influence of Support Porosity  
[PERSON]  
1 Department of Biotechnology, Chemistry and Standardization, Tver State Technical University,  
A. Nikitina Strz, 22, 170026 Tver, Russia; [EMAIL_ADDRESS] (A.V.B.); [EMAIL_ADDRESS] (A.L.S.)  
[PERSON]  
2A.N. Nesemyanov Institute of Organoenclement Compounds, Russian Academy of Sciences,  
119991 Moscow, Russia; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Biotechnology, Chemistry and Standardization, Tver State Technical University,  
A. Nikitina Strz, 22, 170026 Tver, Russia; [EMAIL_ADDRESS] (A.V.B.); [EMAIL_ADDRESS] (A.L.S.)  
[PERSON]  
1 Department of Biotechnology, Chemistry and Standardization, Tver State Technical University,
provide cross-links between propagating polymer chains, yielding three-dimensional network materials. Until today, the most popular class of these materials is based on the polystyrene-divinylbenzene (PS-DVB) system [14]. Hyper-cross-linked polystyrene (HPS) networks and sorbents, described for the first time in the scientific literature in the early 1970s, came into routine practice by the end of the 1990s [15]. HPS possesses high sorption potential due to a specific structure that, in turn, is determined by the synthesis conditions [16]. In spite of a number of commercially available HPS resins, there is still room for the improvement of the synthesis procedure aiming at better catalytic properties, for the reaction of interest. This work describes the synthesis of HPS-based catalysts with Pd content below 0.2 wt.%, for the selective hydrogenation of alkynols to the corresponding olefinic alcohols at ambient hydrogen pressure. The following alkynols were studied (see Figure 1): 2-methyl-3-butyn-3-ol (MBY), 3-methyl-1-nonyn-3-ol (MNY) and dehydrodisophyll (DHIP). MBY and DHIP are semiproducts in the synthesis of fat-soluble vitamins, such as A, E, and K [17; 18; 19], while MNY is a model compound, which closely relates to the very industrially important alkynol--3,7-dimethyl-6-octen-3-ol (dehydrolinalool, DHL) [20; 21].  
In our previous works [22; 23; 24; 25], we have studied the catalytic properties of nanoparticulate Pd/HPS systems. It was shown that in the reaction of MBY semi-hydrogenation, more than 95% selectivity was achieved at complete substrate conversion, while using commercial non-functionalized HPS as a support. MBY (C\({}_{\text{5}}\)alkynol) is a substrate with relatively low molecular weight and can be successfully hydrogenated in the presence of Pd nanoparticles (NPs) confined within a highly porous polymeric network. For terminal alkynols with higher chain length, such as DHL (C\({}_{\text{10}}\)) or DHIP (C\({}_{\text{20}}\)), the porous structure may lead to noticeable internal-diffusion limitations affecting catalytic efficiency (activity/selectivity). Thus, in this work we explore, for the first time, the influence of HPS porosity on the catalytic properties of Pd-containing polymers.
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 3 Materials and Methods
### Crowd Representation and Behaviour Descriptors
#### 3.1.4 Visual Descriptors
Conversely, if the connected point belongs to a different cluster, the distance contributes to the inter-cluster weight. This analysis is performed using a first-order clique \(C_{1}\left(V_{i}^{k}\right)\), which facilitates the assessment of the spatial relationships between vertices within and across clusters [37]. Hence, the terms stated in Equation (17) can be reformulated as in Equation (18): \[\left\{ \begin{array}{l}A(N_{i},N_{i})=\sum_{p\in N_{i}}\sum_{q\in C_{1}(p) \
otin N_{i}}\frac{1}{\|pq\|^{2}}\\ A(N_{i},N)=\sum_{p\in N_{i}}\sum_{q\in C_{1}(p)\
otin N_{i}}\frac{1}{\|pq\|^{2} }\\ A(N,N)=\sum_{i\in N}\sum_{p\in N_{i}}\sum_{q\in C_{1}(p)}\frac{1}{\|pq\|^{2} }\end{array} \right.\] (18) Shorter within-class distances and longer between-class distances are indicative of a high level of spatial uniformity within each grouping. The proposed visual and local descriptors capture both interactive and individual properties, offering valuable insights into crowds' spatial distributions and movements. These descriptors are particularly relevant for high-level analysis as they closely align with the crowd behaviour and semantic information. Each descriptor is encoded using a 1D histogram with 16 bins, enabling statistical computations at both local patch and global frame levels. The histogram effectively represents the distribution of the descriptor values within specific regions, making it a suitable choice for our analysis. After scaling, the histograms can be concatenated to form a feature vector. The current methodology serves as a robust foundation for the detection of crowd anomalies across diverse scenarios.
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
## 2 The Pathophysiology of Alzheimer's Disease  
The basic working unit of the brain is a neuron, a specialized cell designed to transmit and receive information from other nerve cells. The brain consists of approximately one hundred billion such neurons that can have more than 15,000 connections via synapses (contact points of neurons for communication). Neurons are known to communicate with each other using electrical and chemical signals that can be lost in some instances due to certain conditions, and neurodegenerative diseases are one of them. These diseases are characterized by having such nerve cells either die or stop working. The cause of such neurodegenerative diseases can be either genetic or due to some medical condition such as a tumor, injury, or stroke. Other causes may include exposure to viruses, toxins, chemicals, or high alcoholism. Some of the common symptoms of neurodegenerative diseases include memory loss, forgetfulness, anxiety, mood, and behavioral changes. AD is the most common type of neurodegenerative disorder, first reported by [PERSON] in 1906 while analyzing an autopsy report of a 55-year-old woman [17; 18]. Later, [PERSON] called this disorder Alzheimer's Disease in his book published in 1910 [19].  
Amyloid precursor protein (APP) is a type I membrane protein whose metabolism plays a crucial role in AD pathogenesis. After its translocation to the cell surface, it either follows a non-amyloidogenic pathway or an amyloidogenic pathway depending on its cleavage via several membrane-associated proteolytic enzymes (Figure 2). The non-amyloidogenic pathway is considered to be expected, where APP is cleaved by \(\alpha\)-secretase. This cleavage generates three non-harmful fragments, namely AICD, a p53 fragment, and a secreted soluble AAP alpha (APP\(\alpha\)). On the contrary, APP is cleaved via \(\beta\)-site APP cleaving enzyme 1 (BACE1) in the amyloidogenic pathway. This pathway results in the generation of a soluble \(\beta\)-cleaved ectodomain (sAPP\(\beta\)) and toxic proteins termed amyloid-beta peptides. These toxic proteins are reported to be deposited as amyloid and neuritic plaques, thus leading to the loss of FC, resulting in memory loss and cognitive impairments. Several studies claim that this is one of the primary causes of AD. However, aging and genetic factors also contribute to the decreased FC in AD brains. In the upcoming sections of this review, we have discussed previous research showing improvements in measuring FC using fMRI and studies exploring resting-state fMRI results in clinical AD patients relative to healthy older patients.  
Figure 2: Pathophysiology of Alzheimer’s disease (AD) in the brain. The metabolism of APP follows either a non-amyloidogenic pathway through \(\alpha\)-secretase cleavage or an amyloidogenic pathway through cleavage mediated by \(\beta\)-site APP cleaving enzyme 1 (BACE1).
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
###### Abstract  
Explainable machine learning methods with a specific mathematical model provide insights into how the model works. We propose a new mode that contains a two-layer architecture for hyperspectral image (HSI) classification. In the front-end learning layer, superpixel segmentation and mathematical models are combined to achieve the band selection, which obtains the data re-expression in a lower dimension. The mathematical model uses the \(l_{2,1}\) norm and graph regularized term, which helps induce sparsity, improve robustness to outliers and noise, and enhance the explainability of the data re-expression. We employ the support vector machine or the K-nearest neighbor algorithms in the back-end layer to classify low-dimensional data. Finally, the two-layer mode classification method is applied to the three real HSI dataset classifications. Numerical results show that the overall classification accuracy of our method is improved.  
land cover; land use; superpixel segmentation; sparse space clustering; \(l_{2,1}\) norm; band selection Article
# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 2 Methods
### Statistical Filtering  
Point cloud data acquired by LiDAR scanning typically exhibit non-uniform density, and measurement errors often introduce sparse outliers. These outliers can lead to inaccuracies in local feature descriptions (such as normal vectors or curvatures at sampling points), which subsequently affect downstream processes like point cloud registration. Exploiting the spatial sparsity of outliers, statistical filtering [17] is employed for their removal. For each point \(P_{i}(x_{i},y_{i},z_{i})\) in the point cloud data, the k nearest points are selected as its neighborhood set, and the average distance \(d_{i}\) between point \(P_{i}\) and its neighbors is then calculated using the following equation:  
\[d_{i}=\frac{1}{k}\sum_{i=1}^{k}S_{i} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Value** \\ \hline Weight & 265 g \\ Dimensions & 65 (L) \(\times\) 65 (W) \(\times\) 60 (H) mm \\ Communication Interface & 100 BASE-TX Ethernet \\ Field of View (FOV) & Horizontal 360\({}^{\circ}\), Vertical \(-7^{\circ}\)\(\sim\)52\({}^{\circ}\) \\ Point Cloud Output & 200,000 points/second \\ Range & 0.1–70 m \\ Power & 25 W \\ Resolution & \(<\)0.15\({}^{\circ}\) \\ Frame Rate & 10 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Livox-Mid360 LiDAR main specifications.  
Figure 1: Flowchart of the dynamic neighborhood-based feature point registration method.  
Figure 2: Lidar equipment.  
where \(S_{i}\) is the distance between point \(P_{i}\) and other points \(P_{m}(x_{m},\;\;y_{m},\;z_{m})\) in its neighborhood. To calculate \(S_{i}\) the following equation is used:  
\[S_{i}=\sqrt{\left(x_{i}-x_{m}\right)^{2}+\left(y_{i}-y_{m}\right)^{2}+\left(z_{ i}-z_{m}\right)^{2}} \tag{2}\]  
The average value of \(d_{i}\) is \(\overline{d}\), which is calculated using the following equation:  
\[\overline{d}=\frac{1}{n}{\sum\limits_{i=1}^{n}d_{i}} \tag{3}\]  
The standard deviation is \(\sigma\), which is calculated using the following equation:  
\[\sigma=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}\left(d_{i}-\overline{d} \right)^{2}}} \tag{4}\]  
where \(n\) is the number of points in the point cloud data.  
If the average distance \(d_{i}\) of a point satisfies \(\overline{d}-\rho\cdot\sigma\leq d_{i}\leq\overline{d}+\rho\cdot\sigma\), the point is retained; otherwise, the point is considered an outlier and removed. Here, \(\rho\) is a multiplier of the standard deviation.
## 4 Application in Crankshaft Bearing Design
### Comparison between Analytical Method and FEM
#### 4.4.3 Computing Cost  
Both the FE and analytic solutions in this study are obtained from a micro computing server with Xeon processors of 8 cores, 32 threads and 3.1 GHz main frequency, 127 GB of RAM.  
It takes 1 h to complete the calculation by FE method, and the analytical method proposed in this study costs far less than 1 s. The computational resources cost by FEM is several orders of magnitude higher than that of analytical method. Thus, analytical method takes minimal computational cost to obtain an approximate result from time-consuming FEM.
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 1 Introduction
To the best of our knowledge, Ambisonics-based surround AEC methods have not been well studied yet. This paper proposes a gated convolutional recurrent network (GCRN) model to suppress the echo signal for the surround sound reproduction system. The input features of the GCRN model are the compressed complex spectra of the microphone signal and the B-format signals of the far-end instead of the D-format signals. This setting is mainly under the consideration that the actual loudspeakers layout in the near-end room may be different from the desired layout due to the obstacles or artificial errors, leading to the mismatch of the Ambisonic decoding matrix. The output of the GCRN model was the compressed complex spectrum of the near-end speech and the cost function was calculated between the estimated and real near-end speech signals with regard to their real and imaginary parts of the compressed complex spectra, respectively. The proposed algorithm was evaluated using the perceptual evaluation of speech quality (PESQ) [20] in double-talk scenarios and echo return loss enhancement (ERLE) in single-talk scenarios. The GCRN-based algorithm showed its effectiveness in both the simulated and real acoustic environments. In summary, this paper has two main contributions. On the one hand, the surround AEC is taken into consideration and the Ambisonics technique is adopted to record the surround sound for reproduction. On the other hand, the B-format signals instead of the D-format signals are used as the references to achieve better generalization against different non-standard loudspeaker layouts. The rest of this paper is organized as follows. A brief introduction to the fundamentals of Ambisonics is described in Section 2. The surround AEC problem and symbols definition are formulated in Section 3. Section 4 presents the used network architecture and the compressed complex spectrum. Experimental settings and results as well as analysis are given in Section 5. In Section 6, we give our conclusions.
# Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks
## 3 System Model and Assumptions  
In this section, we give the system model and some key network assumptions. As shown in Figure 1, we studied a D2D underlying cellular network, in which uplink spectrum resources were orthogonally assigned to different cellular users and reused by D2D links within a cell. The coverage region of a BS was assumed to be a disk \(A\) with radius \(R=\frac{1}{\sqrt{\lambda_{B}}}\)[36], in which \(\lambda_{B}\) is the BS density per unit area. BS is at the center of a cell. The distance from the CU to its serving base station is defined as \(r,r\in(0,R)\). The probability density function (PDF) of \(r\) is \(\frac{2r}{R^{2}}\).  
Cellular users formed a PPP \(\Phi_{C}\) with intensity \(\lambda_{C}\) and were uniformly distributed in one cell. The locations of D2D transmitters were assumed to follow a 2D homogeneous PPP distribution \(\Phi_{D}\) with density \(\lambda_{D}\). The distance from a D2D transmitter to its corresponding receiver was fixed [36]. Further, each D2D transmitter was assumed to be equipped with finite buffers for storing the sporadic arriving packets, in which only the non-empty queues caused interferences with other links. As for D2D users with data to be transmitted, they formed a thinned PPP \(\rho\Phi_{D}\) with intensity \(\rho\lambda_{D}\), in which \(\rho\) indicates the probability of non-empty queue status.  
Based on the above-mentioned assumptions, the SINR at D2D receiver \(m\) can be written as:\[SINR_{m}=\frac{P_{D\mathcal{S}m,m}d_{m,n}^{-\alpha}}{I_{D}+I_{C}+\sigma_{N}^{2}} \tag{1}\]  
where \(I_{C}\) represents the interference from cellular users and \(I_{D}\) is the interference from the D2D transmitters with non-empty data queues. The expressions of \(I_{C}\) and \(I_{D}\) are \(I_{C}=\sum\limits_{l\in\Phi_{C}}P_{CU}g_{m,l}d_{l,m}^{-\alpha}\), \(I_{D}=\sum\limits_{n\in\rho\Phi_{D}\setminus\{m\}}P_{D}g_{m,n}d_{m,n}^{-\alpha}\). \(P_{D}\) and \(P_{CU}\) represent the D2D user and CU transmission power. \(g_{m,n}\) and \(g_{m,l}\) represent small-scale fading from D2D transmitter \(m\) to D2D receiver \(n\) and from D2D transmitter \(m\) to cellular user \(l\), respectively. We assumed the distance-dependent path-loss model, i.e., \(d_{m,n}^{-\alpha}\) and \(d_{l,m,n}^{-\alpha}\) in which \(d_{m,n}\) and \(d_{l,m}\) are the distance between D2D transmitter \(m\) and D2D receiver \(n\) and cellular user \(l\), respectively. \(\alpha_{C}\) and \(\alpha_{D}\) are denoted as the path loss attenuation factor of the cellular link and the D2D link, respectively. \(\sigma_{N}^{2}\) denotes the power of the additive white Gaussian noise.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 3 The Scheme of Network Architecture and Purification
### Purification Circuit for Different States
#### 3.2.2 Purification of GHZ States  
A purification circuit for GHZ states is designed in Figure 7.  
For bit-flip noise, assume bit-flip noise occurs with probability \(p\), then \(q=1-(1-p)^{3}-p^{3}\). The states shared before purification and the matching probability are shown in Table 2. \(\ket{\Psi_{i}}_{c}\) is the control group in CNOT gate, the other is the target group.  
If the measurement results of the target group are equal, we save the control group and judge purification to be successful. Otherwise, we discard the control group and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Shared States** & **Probability** \\ \hline \(\ket{\Psi_{1}}\) & \((1-p)^{3}+p^{3}\) \\ \(\ket{\Psi_{2}}\) & \(p(1-p)^{2}+p^{2}(1-p)\) \\ \(\ket{\Psi_{3}}\) & \(p(1-p)^{2}+p^{2}(1-p)\) \\ \(\ket{\Psi_{4}}\) & \(p(1-p)^{2}+p^{2}(1-p)\) \\ \(\ket{\Psi_{1}}_{c}\bigotimes\ket{\Psi_{1}}\) & \((1-q)^{2}\) \\ \(\ket{\Psi_{1}}_{c}\bigotimes\ket{\Psi_{i}}\) & \((1-q)q/3,i=2,3,4\) \\ \(\ket{\Psi_{i}}_{c}\bigotimes\ket{\Psi_{1}}\) & \(q(1-q)/3,i=2,3,4\) \\ \(\ket{\Psi_{i}}_{c}\bigotimes\ket{\Psi_{j}}\) & \(q^{2}/9,i,j=2,3,4\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shared GHZ states and the matching probability (bit-flip noise).  
Figure 7: Purification circuit for GHZ states. Assume that two groups of 3-qubit GHZ states are distributed to the source and the destination node. No rotation operator exists here that can help to solve phase-flip noise, but the design is still useful for bit-flip noise. The circuit helps improve the fidelity of the control group by measuring the target group.  
prepare new groups until purification succeeds. After purification, we obtain \(\ket{\Psi_{1}}_{c}\) with probability \(\frac{(1-q)^{2}}{(1-q)^{2}+q^{2}/3}\).  
The fidelity after purification is \(F_{purify}\)  
\[F_{purify}^{2}= \frac{(1-q)^{2}\langle\Psi_{1}|\Psi_{1}\rangle\langle\Psi_{1}| \Psi_{1}\rangle}{(1-q)^{2}+q^{2}/3}+\frac{q^{2}\langle\Psi_{1}|\Psi_{2} \rangle\langle\Psi_{2}|\Psi_{1}\rangle}{3(1-q)^{2}+q^{2}}\] \[+\frac{q^{2}\langle\Psi_{1}|\Psi_{3}\rangle\langle\Psi_{3}|\Psi_{ 1}\rangle}{3(1-q)^{2}+q^{2}}+\frac{q^{2}\langle\Psi_{1}|\Psi_{4}\rangle\langle \Psi_{4}|\Psi_{1}\rangle}{3(1-q)^{2}+q^{2}} \tag{14}\] \[= \frac{(1-q)^{2}}{(1-q)^{2}+q^{2}/3}.\]  
For phase-flip noise, a similar rotation operator does not exist. However, we can take advantage of quantum channel recognition by a quantum neural network [31]. If the noise is phase-flip, we can use the H gate to transform the noise into bit-flip before the transmission.  
For n-particle GHZ states, if \(p\) is the bit-flip probability, then \(q=1-(1-p)^{n}-p^{n}\). The fidelity before purification is  
\[F_{initial}^{2}=1-q. \tag{15}\]  
The fidelity after purification is  
\[F_{purify}^{2}=\frac{(1-q)^{2}}{(1-q)^{2}+q^{2}/(2^{n-1}-1)}. \tag{16}\]
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
## 3 Proposed Energy Harvesting Topology
### RF Energy Harvesting  
The core of RF energy harvester is the rectifier which supplies DC power by rectifying the input RF signal from the antenna. The 3-stage differential cross-coupled rectifier is adopted, and the circuit is shown in Figure 4.  
Figure 3: Schematic of proposed photovoltaic and thermoelectric energy-harvesting circuit.  
The rectifier performance is evaluated by the term of sensitivity and power conversion efficiency (PCE). Sensitivity is defined as the minimum input RF power required to generate a specific DC output voltage, and PCE is the ratio of the DC output power to the RF input power [16]. Circuit topology, device parameters, carrier frequency, the amplitude of input RF signal, and the output loading conditions affect the PCE and sensitivity of the rectifier. The input impedance of receiver \(Z_{t}\) is mainly decided by the rectifier. \(Z_{t}\) is capacitive and should be conjugate matched with the inductive impedance of RFID tag's dipole antenna.  
When operating in LP mode, the receiver is powered by the rectifier with the on-chip MOSFET capacitor. Assuming the power consumption of the whole system is several microwatts, the simulation of output voltage and PCE versus input power for different stages and load resistance is adopted and the results are shown in Figure 5. When the output voltage is larger than 1 V, the 3-stage scheme achieves -18.5 dBm and -17 dBm sensitivity for 5-\(\upmu\)W and 10-\(\upmu\)W output power, respectively. The maximum PCE is more than 40% for LP mode according to simulation results.