esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 2 Related Work  
For cross-network data mining, network alignment is a crucial issue with numerous application scenarios, such as re-identification [20; 21; 22], bioinformatics [23; 24; 25; 26], translation [27; 28], and pattern recognition [29]. Recent embedding-based methods of network alignment can be broadly classified into structure-based methods and attributes-structure hybrid methods.  
The structure-based methods only use network structural information to learn node representation [11; 12; 16; 19; 30; 31]. For example, PALE [12] first learns the individual embeddings of two networks from existing single network embedding methods (e.g., LINE, node2 vec). Then, it uses an MLP (Multi-Layer Perceptron) and labeled anchor nodes to match the latent space of the two networks. CrossMNA [16] simultaneously performs node representation learning and network alignment by introducing two type specific vectors. Specifically, the intra-vector keeps the unique structural feature for an anchor node in its chosen network, on the contrary, the inter-vector represents the shared characteristics of the anchor nodes in different networks. MC [30] uses a matrix factorization-based network representation learning method to obtain node embedding vectors to capture the local and global structural features of nodes. IDRGM [32] estimate the distribution of perturbed graphs and maximize the distances among the perturbed nodes within the same graphs, for separating the nodes in a narrow space into a wide space. DHNA [31] designs a VGAE-based network alignment model, and two carefully thought-out restrictions are used to incorporate anchor nodes' degree differences across different networks into the encoder module.  
The attributes-structure hybrid methods use both network structural information and node attribute information to learn node representation [13; 15; 33; 34; 35]. Take social networks as an example, node-attribute information consists of the user profile, such as user name [36; 37], and other content generated through user activity on the social platform [4; 38]. For example, [PERSON] et al. [33] proposed a GNN-based network alignment model, in which a convolutional neural network incorporates attribute embedding and structural embedding. Then they trained a classifier to predict whether two nodes are in alignment. CAMU [15] introduces an attributed social network embedding (ASNE) method that incorporates both topological and node attribute information in each network. Subsequently, a mapping function is learned to project the representation from the source network onto the target network. CoLink [34] consists of two distinct models, namely the attribute-based model and the relationship-based model, along with a co-training algorithm that reinforces them iteratively.  
However, these methods are looking for the consistency of anchor nodes in different networks, but this situation is not always met in real networks. Therefore, this paper proposes a more general representation method to adapt to the different connection statuses of anchor nodes.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 5 Route Discovery in the Suggested Connectivity-Based Clustering Scheme  
The disclosed clustering scheme was based on V2I communications, where the RSU performs the process of grouping. The route discovery to a particular destination depends on the location of the target vehicle. It is possible that the destination node within-cluster or outside the cluster depends on the node's physical location. In light of the destination node location, the suggested scheme has two types of strategies in route discovery, i.e., intra-cluster discovery and inter-cluster discovery. The CEG-Dijkstra algorithm uses an intra-cluster discovery inside the cluster [3]. On the other hand, in frontier-cluster discovery, i.e., inter-cluster discovery, our scheme uses the Frontier Access Protocol (FAP). Detailed descriptions of each discovery strategy is given in their separate sections below.
## 2 Results and Discussion
### Characterizations of Odor-Active Compounds by GC-O Analysis  
The results of the olfactometry analysis are summarized in Table 2. Ethyl butyrate, myrcene, limonene, p-cymene, and linalool were the major odor-active compounds according to their high aroma intensity; these compounds were generally associated with pineapple, floral, woody, lemon, green and leaf aromas in the sensory descriptions of the panelists (Table 2). Among the hydrocarbon compounds, limonene (7.5-7.8) was present with the highest aroma intensity in all five samples, having a typical lemon aroma. Meanwhile, \(\alpha\)-pinene (1.6-2.8) and myrcene (1.3-6.3) were also present in all samples. 4-isopropyltoluene (1.6-7.5) had the highest aroma intensity in ZG, MS, and JY, and was an important volatile compound contributing to the green and herbal aroma in orange samples. In addition to hydrocarbons, esters were another important class of odor-active compounds, with apple, pineapple, pear, etc., fruity aromas. Ethyl butyrate (5.3-7.5) possessed the highest aroma intensity among these compounds. Ethyl isobutyrate (1.7), ethyl isovalerate (2.5) and propyl butyrate (1.9) were only detected in QY.
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
## 4 The Role of NSPs in Bread Making
### The Influence of NSPs on the Water Absorption of Flour
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Non-Starch** \\ **Polsaccharides** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Type of** \\ **Bread** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Addition** \\ **to** **Doough** \\ **[\%]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Increase in** \\ **Water Addition** \\ **to** **Doough** \\ **[\%]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Increase in** \\ **Bread Volume** \\ **Bread Volume** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Decrease in** \\ **Crumb** \\ **Hardness** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Increase in** \\ **Crumb** \\ **Moisture** \\ \end{tabular} } & \multirow{2}{*}{**References**} \\ \hline \multirow{4}{*}{Arabinoxylans} & Rye & 0.5 & 1–5 & up to 16\% & up to 14\% & up to 1\% & [163] \\  & Rye & 1–2 & 2.5–19 & - & - & - & [71] \\  & Wheat & 0.5–1.3 & 11–13 & positive effect & - & up to 29\% & [134] \\  & Wheat & 0.25–0.5 & - & positive effect & positive & positive & [15] \\  & Gluten-free & 3–6 & up to 10 & up to 20.5\% & - & up to 9\% & [155] \\  & Rye & 1–12 & 3–95 & up to 18\% & up to 88\% & up to 62\% & [83] \\  & Hydrolyzed & & & & & & \\  & arabinxylans & Rye & 1–15 & 0.2–22 & up to 55\% & up to 92\% & up to 33\% & [83] \\  & Cross-linked, & & & & & & \\  & hydrolyzed, and & & & & & & \\  & unmodified & & & & & & \\  & arabinxylans & Rye & 1 & 5–18 & up to 34\% &
\begin{tabular}{c} depends on \\ modification \\ \end{tabular} & up to 4.5\% & [154] \\  & Wheat & 0.2–1.4 & 5–13 & up to 37\% & - & up to 6.5\% & [164] \\  & Wheat & 2.5–10 & 6–19 & - & - & - & [165] \\  & Wheat & 0.8–1.2 & 9–1 & negative effect & - & up to 8\% & [166] \\  & Gluten-free & 1–2 & - & up to 16.5\% & - & - & [170] \\ \hline Galactomannans & Wheat & 5–10 & 18.5–35.5 & negative effect & up to 21\% & - & [168] \\ \hline \hline \end{tabular}  
* lack of information.  
\end{table} 
Table 3: Effect of the addition of NSPs on the water added to the dough and on quality features of bread.
# Engineering of Ti:Sapphire Lasers for Dermatology and Aesthetic Medicine
## 2 Experimental Results and Discussion
### Investigation of Ti:Sapphire Laser Operation with VBG and STG
In the case of the STG laser, THG efficiency reached 36% at E\({}_{932}\sim 12\) mJ and then, at larger E\({}_{932}\), reduced to 33%, restricting the growth of E\({}_{311}\) at \(\sim\)5 mJ. We estimate that the main reason for THG worsening in the STG laser was the growth of the Ti:Sapphire laser spectral width when average pump power increased. Later, this problem was eliminated by optimization of the THG LBO crystal length. Nevertheless, here, the required E\({}_{311}\) at 100 Hz was realized using the Ti:Sapphire laser with both gratings. We can conclude that the application of both types of gratings, in general, allows the required level of Ti:Sapphire laser characteristics to be obtained. Laser energy efficiency was larger with VBG, but the required E\({}_{932}\) here was obtained at very close to LIDT conditions. The maximum available E\({}_{932}\) was larger with STG due to the larger LIDT. Other characteristics were similar for both grating types. The main disadvantage of VBG for our application is the relatively low and unreproducible LIDT. Four VBG samples with the same specifications were tested, and all of them were damaged at E\({}_{\text{OUT}}>12\dots\) 14.5 mJ. On the contrary, consequent tests of 25 STG samples confirmed their high LIDT and reliability, so we rejected VBG in our devices in favor of STG. Basing on these results, we developed the first solid-state dermatological UV laser PALLAS with a pulse energy of 5 mJ and repetition rate 100 Hz. The PALLAS laser head is shown in Figure 3. In 2017, LASEROPTEK started the serial production of these devices. In accordance with customer preferences, the laser wavelength can be set at 308 or 311 nm.
# Emergence of White Organic Light-Emitting Diodes Based on Thermally Activated Delayed Fluorescence
## 3 Approaches for WOLEDs Based on TADF
### Hybrid WOLEDs Based on Blue TADF and Phosphorescence
#### 3.4.1 Hybrid WOLEDs including Blue TADF Materials and Complementary Phosphorescence Materials
The triplets on blue EML can (i) convert into singlets via the RISC procedure and then generate the blue emission; (ii) transfer to the low-energy of yellow phosphor Ir(dmpppy)\({}_{2}\)(dpp) via the Dexter process and then generate part of yellow emission (the other part of yellow emission is originated from excitons on the yellow EML). However, the main exciton generation zone of W11 is narrow, unfavorable to the performance. Similarly, the main exciton generation zone of W12 is located at the mCP interlayer/Bepp\({}_{2}\) interface, as shown in Figure 11b. As a result, excitons are more easily harvested by Ir(dmpppy)\({}_{2}\)(dpp) instead of DDCzTrz since Ir(dmppy)\({}_{2}\)(dpp) is close to the main exciton generation zone. However, a part of electrons can pass through the thin interlayer via the tunneling process and then meet holes, which can generate excitons to guarantee the blue emission. For W13, by way of the bipolar interlayer and the suitable energy levels of 26 DCzPPy, both holes and electrons can be easily passed through 26 DCzPPy, as shown in Figure 11c. As a result, excitons can be formed at both the mCP/26 DCzPPy and 26 DCzPPy/Bepp\({}_{2}\) interface, leading to a broad exciton generation zone, which ensure the high performance of W23. Besides, since the Dexter energy transfer from DDCzTrz to Ir(dmppy)\({}_{2}\)(dpp) is also prevented due to the 3 nm 26 DCzPPy, the yellow emission mainly results from excitons on the yellow EML. To simplify the device architecture, single-EML WOLEDs based on blue TADF and phosphorescence have been reported. For example, [PERSON] et al. codoped a blue TADF emitter and a yellow phosphorescent emitter into a fluorescent host, achieving the WOLED with a maximum EQE of 22.4% and PE of 60.3 Im W\({}^{-1}\)[156]. The device structure is ITO (120 nm)/PEDOT:PSS (60 nm)/TAPC (20 nm)/mCP (10 nm)/DPEPO: 50% DMAC-DPS: 0.3% PO-01 (25 nm)/TSPO1 (5 nm)/TPBI (30 nm)/LiF (1 nm)/Al (200 nm). The emission mechanism of this single-EML WOLED can be described as follows. (i) Due to the low concentration of PO-01 (0.3%), the direct charge trapping is not the main emission process of PO-01 to generate yellow emission; (ii) The energy can be directly transferred from DPEPO to PO-01 or from the singlet and triplet excitons of DMAC-DPS to PO-01, producing the yellow emission; (iii) The energy can be transferred from the singlet excitons of DMAC-DPS generated by the RISC of the triplet excitons of DMAC-DPS to PO-01. Therefore, both the original singlet excitons and up-converted singlet excitons of DMAC-DPS transfer emission energy to PO-01 for white emission, as shown in Figure 4.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation
### Experimental Results and Comments
#### 4.4.2 Comments  
From the experimental results given in Tables 3-5, we can see that the Naive Bayes algorithm produces the lowest overall classification accuracy (ACC) and the random forest algorithm produces the highest classification accuracy among 4 machine learning algorithms. However, the differences in the classification accuracy among algorithms are not so great. Two machine learning algorithms, including kNN with weighted neighbors and C4.5 decision trees have roughly the same overall classification accuracy. For the kNN algorithm, the choice of parameter \(k\) plays an important role in the classification performance and is usually determined empirically. The Naive Bayes algorithm provides the lowest overall classification accuracy, but it has the advantages of being simple and low time and computation requirements for training and testing.  
It can also be seen that the random forest algorithm yields significantly better results than the C4.5 decision tree algorithm. However, the training time of the random forest is longer due to the number of trees requiring training is more. However, the training of the random forest classifier can be done offline, so it does not affect the classification speed in the testing period. Thus, the random forest machine learning algorithm has the highest overall classification accuracy and is selected for implementation in the proposed botnet detection model.  
In addition, the ratio between the number of benign domain names and the number of malicious domain names has a significant impact on classification measures. Accordingly, the T1 training set gives the highest ACC and F1 measures. However, its FPR is relatively high. The ACC and F1 measures produced by the T3 training set are not high, but the FPR is relatively low. At the same time, the T2 set gives the worst results of low ACC and F1 measures and very high FPR measure. As such, the ratio of the number of benign domain names and the number of malicious domain names to be chosen as 1:1 (the T1 dataset) will produce the best classification results.
## 2 Materials and Methods
### A-VTTCES-KF Algorithm
#### Carrier NCO Update  
In this part, the Doppler estimation based on the A-VTTCES-KF for the carrier NCO updates will be introduced. Ignoring the subscript of the epoch index, given the Doppler frequency estimation as  
\[\hat{f}_{d}^{j}=-\frac{f_{r}}{c}\left[\left(\mathbf{\hat{v}}_{u}-\mathbf{v}_{ s}^{j}\right)\cdot\mathbf{e}^{j}+\hat{t}_{f}-\hat{t}_{f}^{j}-\hat{\eta}\right] \tag{24}\]where \(\mathbf{v}_{s}^{j}\) denotes the \(j\)th-SV velocity vector in the ECEF frame; \(\hat{t}_{f}^{j}\) is the satellite clock drift of the \(j\) th SV; \(\hat{\mathbf{v}}_{u}\) is for the velocity vector of the GNSS receiver; \(\hat{t}_{f}\) stands for the estimation of the local clock drift; \(\hat{\eta}\) represent the time-correlated error which is assumed to be follow 1 st-order GM process; the speed of the light is denoted as \(c\); \(\mathbf{e}^{j}\) denotes the unit vector with respect to the LOS direction along the user's position and the satellite position of the \(j\)th SV; \(f_{r}\) represents the radio frequency. \(\hat{\mathbf{v}}_{u}\), \(\hat{t}_{f}\), \(\mathbf{e}^{j}\), and \(\hat{\eta}\) are all obtained through (7). Omitting the superscript of the channel number, the estimation updating the carrier NCO is attained as  
\[\hat{f}_{\theta,nco}=f_{i}+\hat{f}_{d}+\Delta\hat{f}_{e} \tag{25}\]  
where \(f_{i}\) represents the intermediate frequency and \(\Delta\hat{f}_{e}\) stands for the filtered carrier error estimation after passing the 3 rd-order PLL.  
On the other hand, the code NCO will be updated using the carrier aiding strategy [42; 76].
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 4 Protocol Design  
The key idea of our proposed NCHARQ is to receive encoded packets from all nodes in the network and thus the system can obtain a space diversity gain. We choose to send packets block by block and hop by hop. There are two aspects for the reason that we choose to transmit data in such mode:  
1. The propagation delay is much longer than transmission delay in underwater channel. Thus, it will be very inefficient to inform the sender of the ACK or NACK information of every packet, and the channel utilization would be very low due to the long end-to-end delay. The packet error rate will be much lower and a higher throughput can be achieved with the help of relays.
2. In a packet by packet transmission mode, if a node sends a packet to a receiver successfully but the sender could not receive a valid ACK from the receiver due to the high packet error rate, the sender has to send the packet again and the duplicated information will be useless for the receiver. For example, if a packet is transmitted successfully from node \(i\) to node \(i+3\), it will be wasteful for node \(i+1\) and node \(i+2\) to send the packet again. However, due to the high packet error rate and half-duplex system in UASNs, it would be hard for all of the nodes between node \(i\) to node \(i+3\) to receive the valid ACK from node \(i+3\). Thus, useless retransmissions will cost high energy and bring a long end-to-end delay.  
In a block-by-block transmission mode, the receiver may only send a minimum of one ACK packet back for several packets in a block and thus reduce much propagation delay. In addition, although a single packet may be able to skip hops in its transmission process, the probability is very  
Figure 2: A typical encoding process of LT codes.  
low that all the packets in a block can skip hops and reach a node through fewer hops. Thus, any node in the network can simply send ACK packets to its adjacent upstream node and thus reduce the useless retransmissions caused by long-distance ACK packets transmission failure. To achieve the max throughput, we also have to control the sending window size precisely. In this section, we first present a detailed introduction to our proposed NCHARQ protocol and then design an adaptive sending window size control algorithm.
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 3 Van Atta Array Design
### The Design of Transmission Line Network  
As shown in Figure 3, four grounded coplanar waveguide (GCPW) is used as the Van Atta transmission line network to reduce radiation loss and crosstalk between adjacent transmission lines. The length of each transmission line differs by an integer number of wavelengths to weaken transmission line losses. The length of the four GCPW lines are \(2.5\,\lambda_{\mathrm{g}}\), \(3.5\,\lambda_{\mathrm{g}}\), \(4.5\,\lambda_{\mathrm{g}}\), and \(5.5\,\lambda_{\mathrm{g}}\), respectively. The phase delay of four transmission lines at 1-6 GHz is presented in Figure 5. The same phase delay is obtained for four GCPW lines at 3.5 GHz. The working bandwidth designed for the VanAtta array is 3.45-3.55 GHz. By calculating the phase delay difference between the transmission lines within the working bandwidth, which is within \(5.15^{\circ}\), it can meet the backtracking requirements of Van Atta.  
Figure 4: The series-fed array antenna performance. (**a**) Antenna S11 coefficient; (**b**) Antenna E-plane and H-plane directional patterns.  
Figure 3: The designed Van Atta array.  
The GCPW line consists of the ground on the bottom layer of the PCB board and the coplanar waveguide (CPW) on the top layer of the PCB board. The CPW consists of the middle signal line and the ground on both sides. The two semi-infinite ground planes of the CPW are connected to the ground plane of the bottom layer of the PCB through vias.  
By controlling the rotating table in the range of \(-60^{\circ}\) to \(60^{\circ}\), the monostatic RCS of the designed Van Atta array at different incidence angles are tested and compared with the flat metal plate of the same size, the RCS characteristics are obtained as shown in Figure 6.  
The Van Atta array and the flat metal plate of the same size have the same RCS at normal incidence. As the incidence angle increases, the first side lobe of the RCS of the  
Figure 5: The phase delay simulation results.  
Figure 6: The RCS measurement results of the designed Van Atta array in a microwave anechoic chamber.  
flat metal plate rapidly decreases by 13 dB. The RCS of the Van Atta array is attenuated slowly due to the directional retrospective characteristics at oblique incidence, its main lobe is wider, and the side lobe attenuation is about 4 dB. This is due to the attenuation of the retrospective signal on the transmission line and the direction of the antenna at oblique incidence. Compared with the flat metal plate, the RCS of the Van Atta array is more than 15 dB larger than that of the flat metal plate in a wider range of lateral incidence angles (greater than 10\({}^{\circ}\)).
## 3 Interaction Models for Waves and Currents
### Interaction Model for Regular Waves and Currents  
In the small amplitude wave theory [41; 42], the water surface elevation \(\eta(x,t)\) is normally expressed as follows:  
\[\eta(x,t)=a\cos(\kappa x-\omega t) \tag{1}\]  
with \(a\) representing the surface wave amplitude while \(\kappa\) is the wave-number.  
The resulting velocity field of a small amplitude wave propagating on a current is typically written as follows [43; 44]:  
\[u_{T}(x,z,t)=u(z)\cos(\kappa x-\omega t)+U(z) \tag{2}\]  
\[w_{T}(x,z,t)=w(z)\sin(\kappa x-\omega t) \tag{3}\]  
where \(u_{T}(x,z,t)\) is the total horizontal and vertical velocity with the related first-order terms \(u(z)\); \(\rho\) is the water density; and \(\omega\) is the angular frequency of the wave when the current effect is taken into account [43]. \(w_{T}(x,z,t)\) is the total and vertical velocity with the related first-order terms \(w(z)\)[45].  
The first-order vertical term \(w(z)\) is determined by the following equation [43]:  
\[\frac{d^{2}w}{dz^{2}}-(\kappa^{2}-\frac{\kappa}{\omega-\kappa U}\frac{d^{2}U} {dz^{2}})w=0 \tag{4}\]  
within the region of flow \(-h_{w}<z<0\). The corresponding boundary conditions are as follows:  
\[w(z)=0\text{ at }z=-h_{w} \tag{5}\]  
\[\left\{\begin{array}{cc}(\omega-\kappa U)^{2}\frac{dw}{dz}+\kappa(\omega- \kappa U)w\frac{dU}{dz}-g\kappa^{2}w=0\\ w(z)=a(\omega-\kappa U)\end{array}\right.\text{ at }z=0 \tag{6}\]  
where \(g\) is the gravitational acceleration.  
After solving \(w(z)\), the first-order horizontal term \(u(z)\) can be calculated from  
\[u(z)=\frac{1}{\kappa}\frac{dw(z)}{dz} \tag{7}\]  
When the value of the second order derivative of the velocity distribution of the current is zero, the velocity solutions for Equations (5) and (6) can be provided as follows:  
\[u_{T}(x,z,t)=a(\omega-\kappa U_{0})\frac{\cosh[\kappa(z+h_{w})]}{\sinh(\kappa l _{w})}\cos(\kappa x-\omega t)+U(z) \tag{8}\]  
Figure 3: Definition of coordinate system of interaction models for wave and current.  
\[w_{T}(x,z,t)=a(\omega-\kappa U_{0})\frac{\sinh[\kappa(z+h_{w})]}{\sinh(\kappa h_{w })}\sin(\kappa x-\omega t) \tag{9}\]  
with \(U_{0}\) denoting the current velocity at z = 0.  
The uniform current is considered in this paper. As a result, the dispersion equation is applied to obtain the wave-number \(\kappa\) as follows:  
\[(\omega-\kappa U_{0})^{2}=g\kappa\text{tanh}(\kappa h_{w}) \tag{10}\]
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 3 Results
### qRT-PCR Analysis  
The mRNA expression levels of osteogenesis-related genes, including _Runx2_, _ALP_, _BMP-2_, and _Bglap_ in RBM grown on the specimens for 3, 7, 14, and 21 d, were evaluated by qRT-PCR. All genes in the cells grown on plasma-treated titanium disk surfaces were upregulated compared to those in the cells on untreated titanium (Figure 8).