esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
Based on the position information of the feature, each feature is processed through an SA mechanism, that is, features at different positions introduce different weights. This allows the model to distinguish the importance of features at different locations, thereby helping to better process the features. The new features obtained in the fusion stage of the attention mechanism are inputted to the fully connected layer (FC) for feature shrinkage operations so that the first dimension is equal to the output. Its function is to select information related to the output features from the fused features. Then, the feature shrinkage operation is performed through a feature extraction module composed of three sets of CNN, BN, and ReLU activation functions so that the second dimension is equal to the output feature. The function of the output feature module is to reduce the dimensionality of the data, that is, from 256 to 3. The CNN-GRU-SA derivation network proposed in this study expands the input features through CNN and GRU modules, decreasing the probability of feature loss during training. In addition, the attention weight of the fusion feature is extracted through the SA module to accelerate convergence during training. The final output result is obtained by shrinking and extracting the data through the FC and output feature modules. The main purpose of this feature processing method is to introduce more nonlinear transformations and increase the representation ability of the network while reducing the feature dimension to reduce the computational complexity and prevent overfitting. The combination of dimensionality expansion and compression enabled the neural networks to model complex data effectively within a relatively small hidden space. These operations produce a flexible and efficient network for learning feature representations, improving the generalization ability of the model and reducing the consumption of computing resources when processing large-scale data.
# An Overview of Terahertz Imaging with Resonant Tunneling Diodes
## 3 RTD THz Technology
### RTD Device
The RTD operating frequency is limited by both extrinsic RC (resistance-capacitance) time constant and the intrinsic quasibound-state lifetime. Efforts have been reported to minimize the device series resistance (R) and capacitance (C) as well as the dwell time (\(\tau_{\text{dwell}}\)) and transition time (\(\tau_{\text{dep}}\)) [33; 34; 35]. RTD has been demonstrated with very wide broadband intrinsic frequency response from 120 GHz to 3.9 THz [35] and the estimated intrinsic cut-off frequency is 4.6 THz with respect to \(\tau_{\text{dwell}}=33\) fs and \(\tau_{\text{dep}}=38\) fs for the specific epilayer design in [36]. Therefore, RTDs can be embedded in resonators to build up sub-millimeter-wave or terahertz continuous wave sources and highly sensitive detectors.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 3 Overview of Counterpart Schemes  
AEDG and LVRP are chosen for comparison because of their conceptual relevancy with RE-AEDG. In LVRP a model is designed which considers multiple mobile sinks following unconstrained sink trajectories. In the data transmission process control packets are generated in order to track the recent sojourn stop of sink. In LVRP, the major objective is to minimize the packet overhead, caused due to the flow of these control packets. Therefore, at LVRP the packet overload is reduced through proper linking of an anchor node to the sink. Anchor node gathers the data on the behalf of sink and the sink issues new routing updates only when it switches its sojourn stop and no more remains in its transmission range. It is selected through proper election from the nearest member nodes, moreover; it is a casual node and is responsible for making a connection between nodes and sink.  
Whereas in AEDG, heterogeneous nodes are deployed in vicinity of a predefined elliptical AUV trajectory, nearer nodes are GWs and the farther ones are MNs. GWs gather data from MNs and hold it until AUV approaches them. Deployment of GWs reduces the control packet overhead as lesser number of nodes track the recent AUV locality. MNs form a chain among themselves with a GW node as chain head. Moreover, number of nodes connected in a chain is also restricted to avoid longer intercommunication distances. Conservation of energy is the distinguishing factor of AEDG. Thus the design objective of our proposed technique is the effective utilization of that conserved energy to intensify network's operational efficiency in terms of network lifetime and throughput rate. Pictorial view of AEDG and LVRP with single AUV is shown in Figure 2.
## 1 Introduction  
Industry 4.0 refers to the next evolutionary step of industrial development that enables interconnection, automatization, and digital management of industrial ecosystems [1]. Industry 4.0 has become the focus of many researchers, manufacturers, and governments as a way to make factories _smart_ by optimizing energy consumption and resource use [1, 2].  
Industry 4.0 may benefit from wireless networks, which have proliferated in many environments as a low-cost and high-performing \"workaround\" to physical wired networks in environments where such infrastructure is prohibitively expensive or challenging to install and maintain [1, 3]. Wireless networks offer numerous advantages over wired networks, such as mobility and scalability, low installation costs, and independence from physical damage or deterioration of cables [4]. A wireless alternative that is particularly promising for Industry 4.0 is IEEE 802.11-based wireless networks [3, 4], which are similar to the Open System Interconnection (OSI) model and whose protocol architecture resembles that of Ethernet-based wired networks [4]. This ensures extensive, smooth interoperability between Ethernet and IEEE 802.11-based networks [4].  
On the other hand, IEEE 802.11 networks are vulnerable to interference or even deliberate jamming [3, 4], which can lead to network failure [2]. This may prevent IEEE 802.11 networks from fulfilling the Industry 4.0 requirement for seamless connection between industrial components and devices [3, 5]. Since these networks operate in unlicensed wireless bands such as the \"Industrial, Scientific, and Medical\" (ISM) band, they may receive interference from other devices operating on those bands, such as unmanned aerial vehicles (drones) flying over smart warehouses, audio/video transmitters, cordless phones, or Bluetooth handsets operating near industrial devices. Such interference can occur at any time and degrade the quality of network service, such as by reducing throughput or even interrupting wireless communication entirely [2].  
Previous studies have focused on the signal coverage of IEEE 802.11 wireless networks and neglected potential interference from non-IEEE 802.11 devices and the impact on network performance [6; 7]. Therefore, the present study measured the effect of wireless audio transmission on an IEEE 802.11 network in a laboratory environment. Our results may help network and IT professionals as well as end-users manage and troubleshoot non-IEEE 802.11 interference in IEEE 802.11 networks.
## 4 Test Results and Analysis  
### Measurement Point Vibration Velocity Response  
In order to explore the characteristics and propagation law of environmental vibration caused by traffic load at the Chengshang Village ruins, the peak vibration responses of each measuring point under different test conditions are shown in Table 3. It can be seen from Table 3 that the peak vibration response is positively correlated with travel speed; when the travel speed increases from 10 km/h to 60 km/h, the peak vibration response increases by 283% at 10 m away from the vibration source. The peak vibration response is inversely correlated with the distance from the vibration source, taking the travel speed of 10 km/h as an example. The peak vibration response decreases by 89.9% when the vibration source distance increases from 2.5 m to 70 m.
## 4 Lagrangian Relaxation-Based Solution Procedures
### Procedures of Steps 2 and 3: Decomposition and Solving Sub-Problems
#### 4.2.3 Sub-Problem 3 (Related to \(y_{\text{\tiny{TS}}}\))  
By considering the variables related to \(y_{\text{\tiny{TS}}}\), the optimization problem is shown as  
\[\text{Objective function}:\] \[\text{min}\ \ \sum_{\tau\in T}\sum_{s\in S}(E_{s}-\mu^{4}_{ \text{\tiny{TS}}}+\mu^{9}_{s})y_{\text{\tiny{TS}}}\] (5) subject to : (C. 13) and \[y_{\text{\tiny{TS}}}=0\text{ or }1.\]  
Equation (5) can also be divided into \(|T||S|\) sub-problems. When the corresponding coefficient is less than zero, the decision variable \(y_{\text{\tiny{TS}}}\) is set to one. Note that any value for \(y_{\text{\tiny{TS}}}\) is all feasible for constraints (C. 13). The running time is \(O(|T||S|)\), and the pseudo-code is illustrated as shown in Algorithm 3.  
```
Input : Given parameters \(A\) and Lagrangian multipliers \(\mu^{1},\mu^{2},\mu^{3},\mu^{4}\). Output : Decision variable \(x\).
1 Initialize :\(x_{\mathrm{rs}}\gets 0\), \(\forall\tau\in T,s\in S\)
2 for\(\tau=0\) to \((|T|-1)\)do
3\(n\gets 0\)
4\(g\gets 0\)
5 for\(s=0\) to \((|S|-1)\)do
6\(c\gets A_{s}-\mu_{\tau,s}^{2}P_{s}C_{s}-\mu_{\tau,s}^{3}M_{s}+\mu_{\tau,s}^ {4}\)
7 if\(\tau<(|T|-1)\)then
8\(c\gets c-\mu_{\tau+1,s}^{4}\)
9 end if
10 for\(i=0\) to \((|I|-1)\)do
11\(c\gets c-\mu_{\tau,i,s}^{1}\)
12 end if
13 if\(c<0\)then
14\(x_{\tau,s}\gets 1\)
15\(g\gets g+1\)
16 else
17\(r_{n,0}\gets c\)
18\(r_{n,1}\gets s\)
19\(n\gets n+1\)
20 end if
21
22 Sort r via \(r_{;0}\)
23\(n\gets 0\)
24 while\(g<G\)do
25\(x_{\tau,r_{n,1}}\gets 1\)
26\(n\gets n+1\)
27\(g\gets g+1\)
28 end if
29
30 end while
```  
**Algorithm 2** Sub-problem 2  
```
Input : Given parameters \(E\) and Lagrangian multipliers \(\mu^{4}\), \(\mu^{9}\). Output : Decision variable \(y\).
1 Initialize :\(y_{\tau s}\gets 0\), \(\forall\tau\in T,s\in S\)
2 for\(\tau=0\) to \((|T|-1)\)do
3\(n\gets 0\)
4 for\(s=0\) to \((|S|-1)\)do
5\(c\gets E_{s}-\mu_{\tau,s}^{4}+\mu_{s}^{9}\)
6 if\(c<0\)then
7\(y_{\tau,s}\gets 1\)
8 end if
9
10 end for
11
12 end for
```  
**Algorithm 3** Sub-problem 3  
```
Input : Lagrangian multipliers \(\mu^{7},\mu^{8}\). Output : Decision variable \(\alpha\).
1 Initialize :\(\alpha_{\pi i\varsigma}\gets 0\), \(\forall\pi\in\Pi,i\in I,s\in S\)for\(i=0\)to \((|I|-1)\)do
2\(n\gets 0\)for\(\pi=0\)to \((|\Pi|-1)\)do
3\(c_{s}\gets 0\)for\(s=0\)to \((|S|-1)\)do
4 for\(\tau=0\)to \((|T|-1)\)do
5\(c_{s}\gets c_{s}-\mu^{7}_{\tau,\tau,i,s}\)
6 end for
7\(c_{s}\gets c_{s}+\mu^{8}_{\pi,i,s}\)if\(\pi<(|\Pi|-1)\)then
8\(c_{s}\gets c_{s}-\mu^{8}_{(\pi+1),i,s}\)
9 end if
10 end for
11 Find \(p\) that \(c_{p}\) has minimal value \(W_{n,0}\gets c_{p}\)\(W_{n,1}\leftarrow\pi\)\(W_{n,2}\gets p\)\(n\gets n+1\)
12 end for
13 Sort \(\mathbf{W}\) via \(W_{:0}\)for\(n=0\)to \((|\Pi|-1)\)do
14 if\(W_{n,0}<0\)then
15\(\alpha_{W_{n,1},i,W_{n,2}}\gets 1\)
16 end if
17
18 end for
```  
**Algorithm 5**Sub-problem 5
# Experimental and Numerical Analyses of Timber-Steel Footbridges
## 3 Experimental and Numerical Analysis of the Structural Detail of Interest in a Real Footbridge  
### Footbridge Superstructure Description  
First, we focused on the analysis of this structural detail in a real timber footbridge with possible vehicle access. This bridge is located on the Bata channel in Hustenovice, Czech Republic. It is a simply supported plate-girder bridge with a bottom member deck. The main girders are made of glued laminated timber and have a theoretical span of 11.94 m. They are fixed at an axial distance of 5.28 m at both ends and at intervals of one-sixth of the span (i.e., at distances of 1.99 m) by steel cross-girders made of steel profiles IPE 360. On the cross-girders, continuous gullam stringers with a cross-section of 280/330 mm are placed at axial distances of 0.88 m. The stringers carry a pavement made of transverse oak planks with a cross-section of 160/100 mm. The longitudinal and cross-sectional dimensions of the tested footbridge are shown in Figure 5.  
Figure 5: _Cont._  
The connection of steel cross-girders to the main timber girders is achieved using 15 mm thick end plates and twelve bolts 20 mm in diameter. Together with the cross-girder, a vertical stiffener made of a \"T\" profile 165 mm in height (1/2 of IPE 330), welded to the overlapping end plate and the top flange of the cross-girder, forms a steel half-frame that protects the main timber girders against lateral-torsional buckling.
## 3 Errors Identification Principle
### Calibration of the Relative Angle  
In order to identify the angle errors of machine tools, a high-accuracy angular standard should be provided by calibration. The workpiece coordinate system was established as shown in Figure 5, and the relative angles \(\theta_{xi}\) and \(\theta_{yi}\) are the tilt angles of the plane mirror of the \(i\)th target around the \(X\)-axis and \(Y\)-axis, respectively. The first target was selected as the reference target, and the angle of the other targets were determined relative to it. The calibration relative angles could be obtained through measurement compensated by laser interferometer.  
Figure 4: **(a)** Horizontal measurement with respect to the \(X\)-axis; and **(b)** vertical measurement with respect to the \(X\)-axis.  
The spot displacement on the image sensor was proportional to the angle of the reflective surface, i.e., each pixel corresponds to 2.27 arc seconds. Subpixel spot extraction can improve the angle resolution by one order of magnitude. By using an angle sensor to measure every plane mirror, the displacement between the spot reflected by each plane mirror and that reflected by the reference plane mirror can be obtained. Due to the surface error of a plane mirror, it is rational to compute the average value of multiple samplings. As such, the calibration relative angles could be obtained through measurement compensated by a laser interferometer with an angular lens.
## 3 Dual-PRF Waveform Design and Signal Processing
### Theory Analysis of the Designed Waveform  
In this section, we assess the sensing theory performance of the designed waveform by the ambiguity function with its PSLR and integrated side-lobe ratio (ISLR). And the ambiguity function can be expressed as  
\[\begin{split}\chi_{u}(\tau,f_{d})&=\int_{-\infty}^{ \infty}s^{H}(t-\tau)s_{t}e^{2\pi f_{d}t}dt\\ &=\sum_{l=0}^{L-1}\sum_{n=0}^{N_{\text{c}}-1}\sum_{n^{\prime}=0}^{ N_{\text{c}}-1}(T_{s}-|\tau|)e^{j\pi[(n-n^{\prime})\Delta f+f_{d}](T_{s}+\tau)}\\ &\quad\cdot e^{j2\pi n^{\prime}\Delta f+f}\text{sinc}\big{\{}\pi \big{[}(n-n^{\prime})\Delta f+f_{d}\big{]}(T_{s}-|\tau|)\big{\}}e^{j2\pi f_{d} tT_{s}},\end{split} \tag{23}\]  
where \(L\) denotes the number of OFDM symbols.  
The numeral experiments are obtained under parameters involving 5G communication subcarrier interval \(\Delta f=120\) kHz, bandwidth \(B=400\) MHz, and carrier frequency \(f_{c}=26\) GHz. Figure 7 shows the ambiguity function of uniform sensing, dual-PRF sensing timing under uplink-flexible mode, and uplink-fixed mode, respectively. The range \(r\) of the reflected object can be estimated by the CP duration \(T_{cp}\) in the 5G NR communication with OFDM waveform. And its range can be detected at 87 m with the assumed parameters. The difference between the three waveforms is the Doppler dimension. So they have the same range ambiguity function, and Figure 7a shows that they can estimate \(r\) unambiguously within the detected range. The uniform sensing ambiguity function of \(v\) is shown in Figure 7b with unambiguity \(v=\pm 83.08\) km/h. Figure 7c,d shows the velocity ambiguity function of \(2 Tr\) and \(3 Tr\) under uplink-flexible mode. It can be seen that their unambiguity velocities are \(\pm 41.5\) km/h and \(\pm 27.7\) km/h. Similarly, Figure 7e,f are the velocity ambiguity functions of \(2 Tr\) and \(3 Tr\) under uplink-fixed mode. It can be also seen that, comparing Figure 7c,d with Figure 7e,f, the sidelobe of the velocity ambiguity function under the uplink-flexible mode is smoother than that of the uplink-fixed mode.  
Figure 5: The timing modification case. (**a**) The 6 th modification. (**b**) The 21 th modification.  
Figure 6: Dual-PRF sensing timing under uplink-fixed mode.
## 3 Results
### Selectivity of Hydrogen Plasma Etching  
Figure 7 shows the details on the tops of uncovered metal wires after 3-s etching in ambient air plasma (a), nitrogen plasma (b), H\({}_{2}\)/N\({}_{2}\) plasma (50%) (c), hydrogen plasma (d) and after 1-s etching in hydrogen plasma (e). As seen, ambient air plasma and nitrogen plasma led, besides the polymer etching, also to significant damages of the metal wires. Sample (c) treated in a mixture of hydrogen and nitrogen showed a degradation of polymer around uncovered metal tops. Sample (d) was multiple treated with short exposures of 0.25 s and total treatment time 3.0 s (with a dead time 2.7 s between each plasma exposure and 29.7 s in total). The reason for such non-continuous treatment was to decrease the thermal load on the thermally sensitive PMMA-based substrate and to prevent the unwanted effect of plasma over-exposure. Generally, it is evident that only the pure DCSBD hydrogen plasma has the capability of selective etching of thin surface polymer accompanied by just a negligible effect on the metal wires in the polymer mesh foil.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**Concentration (at.\%)**} \\ \cline{2-4}  & & **A** & **B** \\ \hline C & Carbon & 34.5 & 92 \\ O & Oxygen & 6.5 & 8 \\ Ag & Silver & 58.5 & - \\ Cu & Copper & 0.5 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concentration of Carbon, Oxygen, Silver and Copper on the sample after 1 s etching at distance 0.35 mm measured in the areas A and B as shown in Figure 6.  
Figure 6: Composite element map in the area of single uncovered wire. C—yellow, O—green, Ag—blue, Cu—red (distance 0.35 mm, treatment time 1 s).
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 5 Results
### Implemented Result  
On the MAX78000 microcontroller, the NN models have two parameter layers with a total of 1360 parameters, which corresponds to 1360 bytes. With respect to the total hardware resources on the board, 1360 bytes out of 442,368 bytes (0.3%) weight memory were used. Six bytes out of 2048 bytes of total bias memory were utilised (the bias was not used). The records were sent, one after another, repeatedly. Utilising the power monitor built into the MAX78000 EVKIT, the power drawn was between 17 mW (loading weights etc.) and 18 mW (when the NN model is inferring).  
As introduced in Section 4, our NN models on FPGA are implemented using two different approaches: namely, HLS and Verilog. These implementations on Vivado version 2018.2 were deployed on a PYNQ-Z2 board, which includes Dual ARM Cortex-A9 MPCore and a Xilinx Zynq xc7z020-1 clg400c SoC (containing a total of 53,200 LUTs, 106,400 FFs, 140 BRAMs, and 220 DSP slices).  
Figure 6: The _MULx11_ block calculation architecture.  
Table 6 shows the hardware resource usage, timing and power consumption estimation of the implemented NN for the IoT-23 dataset. In terms of FPGA resource usage, the HLS implementation approach consumed 24.60% LUTs, 15.25% FFs; while the Verilog implementation approach used nearly double these resources, which are 41.97% LUTs and 26.60% FFs on the PYNQ-Z2 board. Although the Verilog implementation approach used fewer BRAMs (14.29% compared to 30.36%), this approach required 220 DSP slices, which is 100% of the on-chip DSP slices on the PYNQ-Z2 board. The maximum frequency is approximately 101 MHz in both implemented strategies. The power synthesised in the Verilog implementation is 0.3W less than the HLS implementation (1.53 W compared to 1.83 W).  
Similarly, Table 7 represents the hardware resource usage, timing and power consumption estimation of the implemented NN models on the UNSW-NB15 dataset. The used LUTs and FFs are 22.15% and 15.26% in the HLS implementation approach; these numbers are 52.64% and 31.93% in the Verilog implementation approach, respectively. The BRAMs and DSP resources used are approximately equal to those of the IoT-23 dataset implementations. The maximum frequency is approximately 102.4 MHz in both approaches, while the power consumption in the Verilog implementation is 0.23 W less than in the HLS implementation approach.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 2 Principles
### Beam Steering Based on WGAs
#### 2.3.1 2D Steering Based on Phase Differences  
As demonstrated by the Fraunhofer diffraction model and some implementations [10, 58], 2D beam forming and steering generate arbitrary and dynamic far-field patterns by manipulating phase differences of 2D WGA-based OPAs. However, in general, an OPA with \(N\times M\) elements using phase control [10, 58] requires a circuit of \(N\times M\) phase shifters, and it becomes a complex system. A similar 2D dynamic forming was demonstrated by 1D long WGA arrays [12], which had much fewer elements.  
Figure 2: (**a**) Schematic of an OPA based on WGAs. Reprinted with permission from [13], © Gent University. (**b**) An OPA based on EF structures. Reproduced from [18] published by De Gruyter, 2016.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results  
Using the methodology described above, we present time-averaged glacier surface velocity maps at a horizontal resolution of 50 m and an analysis of associated error for selected regions obtained with Sentinel-2 and Landsat 7-8 data. For New Zealand and the European Alps, due to the large size of the study areas, we have chosen to display the results only in close-up on the Mont-Blanc and Tasman regions for more clarity and because results can be more easily compared with other sources (Venus,Planet Labs, Pleiades, GPS). Nevertheless, the complete maps of these two regions are provided in Figures S2 and S3.
## 2 Module Design  
### Design of a Tunable Mid-Infrared Solid-State Laser  
The tunable mid-infrared solid-state laser that was investigated in the current study was an all-fiber laser pumped OPO, made up of an all-fiber laser pump module, beam control module and OPO module. The schematic diagram of the laser source is shown in Figure 1. The all-fiber laser pump module is based on the structure of the master oscillator power amplifier (MOPA), and includesmaster-oscillation and power-amplifier two parts. The master-oscillation stage uses a linearly polarized DFB laser to produce seed light at 1064 nm.The power-amplifier is made up of three-stages: linear, polarized and Yb doped fiber amplifier (YDFA) and can produce a 1064 nm laser at maximum output power of 50 W. The facula and polarization-state of the fiber pump laser are adjusted and isolated through a beam control module and then poured into OPO module on the MgO:PPLN crystal. The optical parametric oscillation process happens through the feedback effect of resonant cavity and obtains a near-infrared signal light and a mid-infrared idler. We used a dichroicmirror to filter out the signal light and finally achieved a mid-infrared laser at maximum output power of 5 W.  
There is a certain relationship between the polarization period and temperature of MgO:PPLN crystal with the output wavelength of mid-infrared laser. The variation in the curve of the mid-infrared laser wavelength with the temperature of the crystal during polarization periods of 29 \(\upmu\)m, 29.5 \(\upmu\)m and 30 \(\upmu\)m is shown in Figure 2. In our design, the wavelength of the mid-infrared laser was tuned by changing the temperature of the crystal. The MgO:PPLN crystal was fixed in the temperature control furnace, a film heater was used to control the temperature of the crystal and a PT1000 temperature sensor was used to measure the temperature of the crystal. We were able to obtain a certain wavelength from the mid-infrared laser by controlling the temperature of MgO:PPLN crystal at a specific value.