esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Imaging of Bragg-Dip Profiles and Evaluation of Detailed Inner-Grain Information
### Measured Neutron Transmission Spectra of Three Evaluated Grains
#### 4.3.2 Different Crystalline Grain Detection through the Observation of Bragg-Dip Split  
Next, the widths of Bragg-dip at entire Grain 3 and Mini Region B were compared. At entire Grain 3, (101) Bragg-dip appeared at the wavelength of 0.284 nm on the neutron transmission spectrum. However, at Mini Region B, two Bragg-dips appeared around the wavelength of 0.284 nm, as shown by Figure 7d. Thus, the Bragg-dip appearance pattern changed at Mini Region B. The wavelengths of these two Bragg-dips were 0.2828 and 0.2869 nm. The FWHM of each Bragg-dip were 0.0011 and 0.0013 nm. These values were smaller than that of entire Grain 3, 0.0021 nm. On the other hand, the FWHM of (110) Bragg-dip at Mini Region B was 0.0024 nm. This value was larger than that of entire Grain 3, 0.0017 nm (see Figure 7e). The separation of (101) Bragg-dip and broadening of (110) Bragg-dip are considered to be caused by another crystalline grain at Mini Region B. The reason is explained as follows. The separation of (101) Bragg-dip at Mini Region B is considered to represent the existence of two crystalline grains which have different crystal orientations. On the other hand, (110) Bragg-dip does not separate because the difference in (110) Bragg-dip wavelengths between two crystalline grains is not large. Hence, it is considered that two (110) Bragg-dips are overlapping, and they are observed as a broad (110) Bragg-dip. Here, we considered a possibility that the Bragg-dip pattern at Mini Region B, which had three Bragg-dips at the wavelengths of 0.2828, 0.2869 and 0.3931 nm, may be caused by one crystalline grain. However, such a crystal orientation does not exist according to the database presented in the previous study [19]. Therefore, it was a more reliable conclusion that these three Bragg-dips were caused by two crystalline grains.  
The crystal orientations along the neutron transmission path of these crystalline grains were evaluated as [100 74 26] and [100 75 28] by using the database [19]. The (101) Bragg-dip at shorter wavelength was caused by the crystalline grain of [100 74 26], and the (101) Bragg-dip at longer wavelength was caused by that of [100 75 28]. The angular difference between these crystal orientations was about 0.9\({}^{\circ}\). Based on the above considerations, two crystalline grains, which are overlapping along the neutron transmission path, could be observed at Mini Region B. In the region where the angular difference is smaller compared with Mini Region B (e.g., Mini Region A), the Bragg-dips were just broad but not split, and such a region existed near Mini Region B. Therefore, at these regions, the crystal turned out to be imperfect. Mosaic blocks existed at Mini Region A as discussed in the former paragraph, and different crystalline grain existed at Mini Region B.
# An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level
## 3 Methodology
### Word Embedding and Data Preprocessing  
Our goal is to classify incoming packets into benign or malicious classes without pre-processing the packets into a specific flow. To achieve this target, instead of considering the whole flow (e.g., as a document), we consider each packet (e.g., as a paragraph) and construct the key sentence from each packet, in which each word is a field in the packet header. After that, we apply word embedding [18] to extract semantics and syntax features from this sentence. We choose to consider meanings on the sentence rather than the whole paragraph since the meaning of a paragraph usually can be captured by the key sentence. Here, the order of the fields in each packet (fixed for each packet type) plays the role of resembling some grammar rules which are decisive in building sentence patterns for malicious traffic (signature-based detection) or benign traffic (anomaly detection). Notably, this packet-to-sentence-based model can significantly accelerate the traffic classification, since the behavior and characteristics of one or several first packets can entirely reveal whether their flow is a malicious one.  
In general, a field in each packet could be _a byte of the packet header_, _a field of the packet header_, or _a block of the packet payload_. As the initial trial, we consider a field in the packet header as a word and trim a packet to a fixed length of \(n=54\) bytes. Depending on the field length in the packet, the word size can vary. The strict order of the fields in the packet structure constructs a potential grammar rule for the built sentence. Note that the extracting field stage can be done along with the packet reading/decoding (i.e., data pre-processing), thus the resource consumption is quite efficient. In addition, if the length of the packet is less than \(n\) bytes, it will be padded with zeros (as shown in  
Figure 1: The testbed of our Mirai-based DDoS dataset in the campus of National Chung Cheng University (CCU).  
Figure 2). The rationale for examining 54 bytes is that most TCP packets have a 14-byte MAC header, a 20-byte IP header, and a 20-byte TCP header. Table 3 lists the header fields and their length of TCP and UDP packets.  
After applying the word-embedding technique to fields in the packet header, each header field is embedded, (i.e., to integer number format based on their index in a dictionary of all words) and reshaped, (i.e., dimension) and put to the LSTM-based training model for performing the classification task. In order to understand each attack type and remain consistent with the order of the fields in the packet header, it is important to maintain the sequence order of fields and a consistent word dictionary. Finally, the word size (hyperparameter) selection and further the word-embedding strategy are adjustable and can be specified by the deployment environment and IoT applications under the system's protection umbrella.
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 5 Structural Analysis of the Connection  
In the overall analysis, the connection structure of the FFF mirror has been simplified. This structure has a connecting pipe threaded to rod 1 and rod 2, as shown in Figure 16. The tilt angle of the FFF mirror is controlled by adjusting the coordination between the connecting pipe and the two rods. A detailed analysis and calculation of the connection structure are required.  
Figure 15: Results after applying surface pressure calculation: (**a**) Deformation contour plot of the reflective mirror under back wind. (**b**) Stress contour plot of the reflective mirror under back wind.  
The nut acts to lock the thread, so it is simplified in the calculation of the connection structure. Instead, the threads on each rod were simplified by using contact types. The material chosen for the connection structure is 6061.  
Mesh the model primarily using hexahedral grid elements with a size of 3 mm. A total of 27,471 mesh elements and 112,932 nodes were yielded, and the mesh quality exceeded 0.85, as illustrated in Figure 17.  
Concerning boundary conditions, it was chosen to fix the inner wall of the hole on Rod 1, subjecting it to full constraint.  
To investigate the structural strength of the connecting structure under wind load, as previously studied, the maximum displacement of the support rod under wind load is 0.05 mm, set as displacement load in Y-direction on rod2.  
The analysis results are shown in Figure 18. The maximum stress within the connecting structure is 31.27 MPa, located in the relief notch of Rod 1 and presented as the concentration of stresses in the root corner. A yield strength of 6061 (280 MPa) is eight times the maximum stress, so it can be concluded that this connecting structure remains safe under actual working conditions according to the principles of the fourth strength theory.  
A failure analysis was conducted to investigate the ultimate strength of the connection structure by increased displacement load. When the displacement reached 0.45 mm, the  
Figure 16: Connecting structure.  
Figure 17: Connecting structure of the support rod.  
Figure 18: Stress contour plot in displacement load of 0.05 mm.  
stress exceeded the material's ultimate strength (281.5 MPa), which means the structure deformation cannot be restored. Figure 19 illustrates the stress distribution contour plot at a displacement of 0.44 mm. The maximum stress still appears at the relief notch, and the region of elevated stress in rod 1 extends from the relief notch to the threaded area. Therefore, the thickness of the pipe should be increased if it is to be used in these extreme conditions.
# Eight-Port Modified E-Slot MIMO Antenna Array with Enhanced Isolation for 5G Mobile Phone
## 3 Results and Discussions  
The proposed eight-element array antenna simulation results, such as parametric analysis, envelope correlation coefficient (ECC), S-parameters, overall efficiency, antenna gain, and radiation pattern, are presented in this section.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Pressure Signals  
Pressure traces provide the most reliable method for identifying if an engine is knocking. It is important to note that pressure measurements have historically been used in research settings to understand the fundamentals of knock and determine knock mitigation techniques. Figure 4 shows how pressure signals are currently collected and processed to determine knocking conditions.  
Figure 4 also displays the two general trends in the collection and processing of pressure signals to detect knock. The first is the development of new sensors that would allow for pressure signals to be integrated into production automobiles. The second trend is new processing algorithms to be able to detect knock, especially at borderline knock conditions. The ability to detect knock is especially difficult given new engine trends, including the use of turbocharged downsized engines [17]. Studies by [PERSON] et al. [18] and [PERSON] et al. [19] found  
that the pressure oscillations associated with knock in these engines are substantially different from those in conventional engines, with more cycle-to-cycle variation.
## 4 Non-Linear Numerical Modelling and Structural Analysis
### Execution of the NL Dynamic Numerical Simulations for Investigated Building  
The set of non-linear equations of motion under seismic input related to the analyzed building has been solved by means of the method of direct integration in line with [46; 47]. Both geometric and mechanical non-linearities have been included in the analyses. As the modeling approach based on fiber-plastic hinges, as already described, does not account for the possible shear failure before the development of cross-section plasticity resources, specific shear verification has been performed according to EN1992 [44] for each step of the dynamic non-linear simulations.  
With particular reference to the comparison between the fixed-base and base-isolated buildings, the presence of the FPS devices allows preventing the shear failure in columns where partial infills are located, resulting in a significant reduction in the short column effect.  
The outcome of the NL dynamic simulations has been quantified in terms of the peak value of the inter-story drift index (_IDI_) for each story of the building for what concerns the in-plane directions (i.e., X, Y). In Figure 15a, the response of the corner FP device (that turns out to be the most critical one) to the seismic excitation associated with one of the scaled seismic inputs (i.e., the earthquake of \"Emilia Piantura Padana 29 May 2012\") [27] (that corresponds to the EQ5 with reference to the database reported by [26]) is reported to demonstrate its agreement with the theoretical model of Figure 1b. In the force-displacement graph of Figure 15a, the irregularity of hysteresis cycles is due both to the variability of the dynamic coefficient of friction with the sliding velocity and to the effects induced by the vertical accelerometric component of the seismic record. Moreover, in Figure 15b,c, the IDIs, computed as average values between all the seismic records, are reported as a function of the building height from the foundation level for each numerical model herein considered. It can be recognized that the IDIs along the Y axis are smaller if compared to the ones along the X axis due to the different stiffness of the frames oriented in the Y and X directions, respectively. The results show a significant reduction in the IDIs in the presence of the isolation system, demonstrating its usefulness also in the case of irregular buildings. Comparing Figure 15b,c, the increase in stiffness can be appreciated due to the presence of masonry infills.  
Figure 15: Response of one of the FP devices under seismic excitation (**a**); parallelism related to mean value of the _IDIs_ achieved for different stories for numerical models of fixed-base and base-isolated building without (**b**) and with (**c**) masonry infills.
## 3 Obstacle Avoidance Scenario
### Obstacle Detection Technologies
#### 3.3.4 Computer Vision  
Computer vision is a trendy way to detect obstacles, and many studies have been done in different regions [250; 251; 252; 253]. Computer vision or image processing method uses a different type of camera for obstacle detection and mapping, such as monocular vision, stereo vision, binocular vision, infrared cameras, etc. [254; 255; 256]. Computer vision does obstacle detection, recognition, and can measure the distance using different methods. Using vision as the only exteroceptive sensors is called simultaneous localization and mapping (SLAM) [258]. The first use of computer vision was for negation based on a binocular stereo configuration [259; 260]. However, because of the expensive binocular or multi-camera systems, monocular vision became more popular [261]. Vision-based obstacle detection is often used for complex environments, where getting the surrounding data is complicated for active sensors. Using computer vision for obstacle detection needs higher computational facilities, but nowadays the rapid development of microcomputers is filling this gap. There are various methods used for computer vision obstacle detection, and some essential techniques are discussed in the subsequent sub sections.
## 4 Analysis
### Security Analysis
#### 4.1.3 Wormhole Attack  
A wormhole attack [18] is a notorious attack against WSN routing protocols that is difficult to withstand. In a wormhole attack, instead of compromising any node, collaborative adversaries first create a wormhole link, essentially an out-of-band and low-latency channel, between two distant network locations. They then tunnel routing messages recorded at one location via the wormhole link to the other, leading to chaos in the routing operations. A variety of attacks could be launched by attracting a large number of network flows via the channel.  
To identify a wormhole attack, a node must verify if the message originated from its direct neighbor. Since location-based authentication is built in our scheme, the message authentication code (MAC) is attached to the data from a legitimate node. The message includes the data from the node concatenated with its location-based public key. The MAC is generated by signature of the message digest with the location-based private key. When a node receives a message, it first examines whether the public location-based key is from an authenticated direct neighbor node and will discard those tunneled from distant locations. Next it verifies the MAC with the location-based public key included in the message and will discard those cannot pass the verification. Thus the message and its generated location are simultaneously verified.  
In our mechanism, because only messages from legitimate neighbors which locations are verified will be accepted by nodes, the wormhole attack is effectively resisted.
## 3 Results
### The Optimized Parameters of Typical Vegetation Types
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Parameter** & **GRA** & **AM** & **Shrub** & **ENF** & **DBF** & **Crop1** & **Crop2-W** & **Crop2-M** & **Units** \\ \hline C-N of leaves & 20.32 & 30.95 & 35.29 & 28.00 & 13.27 & 26.50 & 45.00 & 64.62 & Kg C kg\({}^{-1}\) N \\ \hline C-N of leaf litter, after retranslocation & 58.59 & 65.64 & 116.23 & 92.00 & 43.33 & 65.50 & 95.00 & 88.37 & Kg C kg\({}^{-1}\) N \\ \hline C-N of fine roots & 74.15 & 72.63 & 63.21 & 100.00 & 43.22 & 80.00 & 120.00 & 120.64 & Kg C kg\({}^{-1}\) N \\ \hline C-N of live wood & 0 & 0 & 89.37 & 80.00 & 50.00 & 0 & 0 & Kg C kg\({}^{-1}\) N \\ \hline C-N of dead wood & 0 & 0 & 1003.42 & 729.00 & 442.00 & 0 & 0 & 0 & Kg C kg\({}^{-1}\) N \\ \hline Leaf litter labile proportion & 0.39 & 0.39 & 0.38 & 0.32 & 0.39 & 0.37 & 0.68 & 0.68 & DIM \\ \hline Leaf litter cellulose proportion & 0.44 & 0.44 & 0.45 & 0.44 & 0.44 & 0.46 & 0.23 & 0.23 & DIM \\ \hline Leaf litter lignin proportion & 0.17 & 0.17 & 0.17 & 0.24 & 0.17 & 0.17 & 0.09 & 0.09 & DIM \\ \hline Fine root labile proportion & 0.30 & 0.30 & 0.30 & 0.30 & 0.3 & 0.30 & 0.34 & 0.34 & DIM \\ \hline Fine root cellulose proportion & 0.45 & 0.45 & 0.45 & 0.45 & 0.45 & 0.45 & 0.44 & 0.44 & DIM \\ \hline Fine root lignin proportion & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 & 0.22 & 0.22 & DIM \\ \hline Dead wood cellulose proportion & 0.75 & 0.75 & 0.76 & 0.76 & 0.76 & 0.75 & 0.76 & 0.76 & DIM \\ \hline Dead wood lignin proportion & 0.25 & 0.25 & 0.24 & 0.24 & 0.24 & 0.25 & 0.24 & 0.24 & DIM \\ \hline Canopy water & 0.0180 & 0.0227 & 0.0354 & 0.0107 & 0.0164 & 0.001 & 0.0124 & 0.0772 & 1 LAI\({}^{-1}\)d\({}^{-1}\) \\ \hline Canopy light extinction coefficient & 0.4533 & 0.7359 & 0.5720 & 0.4744 & 0.6868 & 0.32 & 0.5431 & 0.4468 & DIM \\ \hline All-sided to projected leaf area ratio & 2 & 2 & 2.3 & 3.4250 & 1.0815 & 0.325 & 2.0 & 2.0 & DIM \\ \hline Canopy average specific leaf area & 30.4374 & 12.9271 & 35.9618 & 20.9445 & 37.6114 & 26.9 & 32.1638 & 25.8928 & m\({}^{2}\) kg\({}^{-1}\) C \\ \hline Ratio of shaded SLA: sunlit SLA & 2 & 2 & 2 & 2.3988 & 3.5 & 2.0 & 2.0 & DIM \\ \hline Fraction of leaf N in Rubisco & 0.2637 & 0.2505 & 0.5210 & 0.3035 & 0.0845 & 0.34 & 0.2956 & 0.1937 & DIM \\ \hline Maximum stomatal conductance & 0.0026 & 0.0057 & 0.0018 & 0.0010 & 0.0024 & 0.0081 & 0.0032 & 0.0043 & m s\({}^{-1}\) \\ \hline Cuticular conductance & \( \begin{array}{c}3.6\times\\ 10^{-5}\end{array} \) & \( \begin{array}{c}7.5\times\\ 10^{-5}\end{array} \) & \( \begin{array}{c}7.2\times\\ 10^{-5}\end{array} \) & \( \begin{array}{c}6\times 10^{-7}\end{array} \) & \( \begin{array}{c}1.4\times\\ 10^{-5}\end{array} \) & \( \begin{array}{c}1.1\times\\ 10^{-4}\end{array} \) & \(\begin{array}[
# Modelling and Experimental Testing of Passive Magnetic Bearings for Power Loss Reduction
## 3 Experimental Setup  
The test rig consists of a fully passive vertical rotor, supported by two thrust-radial PMBs and a ball bearing to stabilise the system in the axial direction; it is designed to investigate the thrust-radial support configuration, allowing for the variation of the relative position of the rings of the magnetic bearings. Figure 4 shows the overview of the system and a sketch of its components.  
The stator and the structure around the rotor are made of Teflon to avoid interactions with the PMBs owing to their diamagnetic behaviour. The rotor consists of four brass disks mounted on an aluminium shaft. The rotor is always kept in contact with the base constraint since its full vertical levitation would lead the system to instability. Therefore, a thrust ball bearing is mounted at the bottom of the shaft to reduce the contact friction losses. The thrust ball bearing is in contact with an aluminium base support placed on a scale (precision \(9.81\times 10^{-3}\) N) that measures the downward force, which is given by the sum of the rotor weight and the PMB axial forces. The lower and the upper PMBs consist of four and two stacked magnetic ring pairs made of an alloy of neodymium, iron, and boron (NdFeB). Every magnetic ring is mounted with the same upward magnetisation direction (the blue and red colours of the PMB rings in Figure 4 and Figure 5 identify the north and south magnetic poles, respectively). The PMB stator rings are housed inside two hollow cylindrical supports that are placed above and under the brass disks. These cylindrical supports are mounted on the stator structure via threaded connections; their rotation allows for the variation in the PMB stator ring axial position, as shown in Figure 5. The variation of the support axial position causes a variation of the magnetic axial force, and consequently the rotor weight can be partially compensated for. The rotor and the PMB ring properties are listed in Appendix A, in Table A1.  
Figure 6 shows the rotor test bench with the actuation and measuring systems.  
The actuation system consists of a DC brushless motor (Northrop Grumman BN34-25 EN-02 LH, Falls Church, VA, USA) controlled by an external driver (Litton SCA-B4-70-10, Beverly Hills, CA, USA) and an electromagnetic clutch (MWM EMS-60, Mannheim, Germany). The motor maximum rated torque and speed are 0.45 Nm and 7500 rpm, while the clutch maximum transmitted torque and speed are 6 Nm and 8000 rpm, respectively.  
Figure 4: (**a**) Test rig of the rotor with PMB supports; (**b**) rotor component sketch.  
The rotor angular speed data are detected by a laser tachometer (LaserTach LT2 ICP, Centennial, CO, USA) and acquired at 2048 Hz by means of a Siemens SCADAS Mobile acquisition system.
# GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification
## 3 Preliminary  
### Notations and Problem Definition  
Let \(G=(V,E)\) denote an undirected graph, in which \(V\) is the set of nodes, and \(E\) is the set of edges. There are \(n=|V|\) nodes with features on each node represented by a real matrix \(X\in R^{n\times p}\), where \(p\) is the dimension of features, and \(m=|E|\) edges with weights on each edge represented by the adjacency matrix \(A\in R^{n\times n}\) of the graph. Additionally, suppose \(D\in R^{n\times n}\) is the diagonal degree matrix (i.e., \(D_{ii}=\sum_{j=1}^{n}A_{ij},i\in\{1,\ldots,n\}\)) and \(Y\in R^{n\times q}\) is the one-hot-encoding matrix for labels in which \(q\) is the number of classes. In terms of dataset partition, the node-set \(V\) with an index of \(\{1,\ldots,n\}\) is divided into an unlabeled node set \(U\) and a labeled node set \(L\) composed of the training set \(L_{t}\) and the validation set \(L_{v}\). Finally, the problem is to classify all the nodes. In other words, given \(G\), \(X\) and \(Y_{L}\), find the appropriate graph neural network \(f\) to satisfy \(Y_{L_{t}}=f(X)_{L_{t}}\) so that the unlabeled nodes are classified by \(f(X)_{U}\).