esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach of Path Optimization Algorithm for 3D Concrete Printing Based on Graph Theory
## 1 Introduction
This will lead to: (1) The printing efficiency will decrease when the nozzle runs with a longer idle strokes. (2) The forming quality of concrete components will be worse when the nozzle head-up times increase. Therefore, a path planning method is proposed to shorten the idle strokes and the head-up times of the nozzle running path to improve the printing efficiency and the forming quality of the concrete components. First, model the concrete components, and then partition the model based on the principle of graph theory. The algorithm in this paper divides the running path of the nozzle into partitions, and each partition can be printed once without the need for the nozzle head-up. After the partition is completed, the ant colony algorithm is used to find the shortest path between each partition, minimizing the idle strokes of the nozzle running path. The format of this paper is as follows: We introduce the current problems of 3D concrete printing technology and give the solutions, then use simulation printing experiments and the actual printing experiments to verify the feasibility of the method in this paper. In the experiments, the advantages and feasibility of the algorithm in this paper are compared with three conventional 3D concrete printing path planning algorithms [21; 22].
###### Abstract  
This paper presents a symmetry-driven broadband circularly polarized magnetoelectric dipole antenna with bandpass filtering response, where the principle of symmetry is strategically employed to enhance both radiation and filtering performance. The antenna's circular polarization is achieved through a symmetrical arrangement of two orthogonally placed metallic ME dipoles combined with a phase delay line, creating balanced current distributions for optimal CP characteristics. The design further incorporates symmetrical parasitic elements--a pair of identical inverted L-shaped metallic structures placed perpendicular to the ground plane at \(-45^{\circ}\) relative to the ME dipoles--which introduce an additional CP resonance through their mirror-symmetric configuration, thereby significantly broadening the axial ratio bandwidth. The filtering functionality is realized through a combination of symmetrical modifications: grid slots etched in the metallic ground plane and an open-circuited stub loaded on the microstrip feed line work in tandem to create two radiation nulls in the upper stopband, while the inherent symmetrical properties of the ME dipoles naturally produce a radiation null in the lower stopband. This comprehensive symmetry-based approach results in a well-balanced bandpass filtering response across a wide operating bandwidth. Experimental validation through prototype measurement confirms the effectiveness of the symmetric design with compact dimensions of \(0.96\lambda_{0}\times 0.55\lambda_{0}\times 0.17\lambda_{0}\) (\(\lambda_{0}\) is the wavelength at the lowest operating frequency), demonstrating an impedance bandwidth of \(66.4\%\) (\(2.87\)-\(5.05\) GHz), an AR bandwidth of \(31.9\%\) (\(3.32\)-\(4.58\) GHz), an average passband gain of \(5.5\) dBi, and out-of-band suppression levels of \(11.5\) dB and \(26.8\) dB at the lower and upper stopbands, respectively, along with good filtering performance characterized by a gain-suppression index (GSI) of \(0.93\) and radiation skirt index (RSI) of \(0.58\). The proposed antenna is suitable for satellite communication terminals requiring wide AR bandwidth and strong interference rejection in L/S-bands.  
s _symmetry_; broadband; circular polarization; filtering; magnetoelectric dipole ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis
### Comparative Test  
#### 4.3.1 CNN Structure Experiment  
This section aims to evaluate the 2D-CNN models with various convolutional architectures to obtain the optimal model. Inspired by [PERSON]'s comparative experiment, this paper conducted corresponding convolution depth tests on the AVE dataset. This article initially focuses on the depth of the convolutional layer in the model and tests the effects of various indicators of convolutional layers with varying depths after fusion. We made four groups of comparison experiments on each object from 1-layer, 2-layer, 3-layer, and 4-layer convolution depth, respectively, so as to obtain the optimal solution, as shown in Table 2.  
As shown in Table 2, when the convolutional depth is three or four layers, the metrics of Accuracy, Recall, Precision, F1 score, and G-Mean show little difference. In contrast, when the convolutional depth is one or two layers, these metrics are significantly lower. Therefore, the model performs better with a convolutional depth of three or four layers. However, as the convolutional depth increases, the computational cost also rises correspondingly, and there is a risk of overfitting.  
Therefore, considering the indicators of Precision, Recall, Accuracy, F1 score and G-Mean, this paper selects a convolution depth of three layers.
## 4 Experimental Setup
### Models  
In the experiment, we compared the existing unimodal deep learning model that utilizes only text data with MulDIC which is our proposed multimodal deep learning model. We aimed to investigate the contributions of image and code data in issue report classification by conducting three experiments: Text-Image, Text-Code, and Text-Image-Code. In these experiments, we used the Text Only model, which solely relies on text data, as the baseline for comparison. The experiment involves four models: Text Only, MulDIC\({}_{TI}\), MulDIC\({}_{TC}\), and MulDIC\({}_{TIC}\). In the rest of this paper, MulDIC refers to MulDIC\({}_{TIC}\). We will provide detailed descriptions of each model in the following subsections.
## 3 Results and Discussion
### E-Nose Analysis  
The E-nose is sensitive to aromas in samples, and slight changes in volatile compounds may result in differences in sensor response [36]. As seen in Figure 2A, the W1S sensor exhibited the strongest response to volatile compounds in the spoiled ham. Furthermore, the W6S, W2S, and W3S sensors also showed strong responses to volatile compounds in spoiled hams, indicating a potentially higher concentration of methyl groups and alcohols in these samples. Conversely, the W1C, W3C, and W5C sensors demonstrated stronger responses to normal hams compared to spoiled hams, suggesting a higher presence of aromatic compounds in normal hams. The e\(-\)nose was employed to differentiate between normal and spoiled hams, as well as Xuanwei hams with different muscle types.  
The PCA results are presented in Figure 2B. The first principal component (PC1) accounted for 74.9% of the sample variance and displayed a positive correlation with W5S, W6S, W1S, W1W, W2S, and W3S. The second principal component (PC2) explained 20.1% of the variance and exhibited a positive correlation with W3C, W6S, W5C, W1S, W1W, W2S, and W2W. The sample distribution demonstrated that PCA effectively differentiated the samples. Normal hams mainly occupied the second and third quadrants, positively correlating with the W1C, W3C, W5C, and W2W sensors, which indicated an abundance of aromatic, short-chain alkanes and sulphur-chlor compounds. Conversely, spoiled hams were predominantly located in the first and fourth quadrants and positively correlated with the W1W, W1S, W2S, W3S, W5S, and W6S sensors, indicating prevalent compounds such as methyls, sulphides, alcohols, aldehydes, and ketones. PC1 effectively discriminated between normal hams and spoiled hams, while PC2 distinguished between SM and BF. Consequently, the e-nose is an effective tool for distinguishing different quality grades of Xuanwei ham. However, identifying the specific compounds in these samples remained challenging.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 2 Methodology
### Deployment Protection Methods  
The deployment protection methods proposed in this paper extend the aforementioned approach by conducting interference analysis for all base station positions within a designated zone surrounding the aircraft. They entail an integrated model that incorporates all base station parameters, as well as flight dynamic parameters. Through this analysis, the prohibited zone for base station deployment is determined. The prohibited zone is not only horizontal but also vertical. The size of the prohibited zone varies according to various factors, such as the type of path loss, antenna parameters, radio altimeter type, interference thresholds, flight altitude and attitude, and base station parameters. Additionally, the deployment protection methods include interference mitigation methods. With the use of beamforming technology, base stations can reduce interference with radio altimeters by implementing power control and angle shutoff techniques.
## 3 Resource Allocation Problem  
The resource allocation problem is a joint optimisation based on user pairing and the power allocation problem. The optimisation scheme can be decoupled to reduce system design complexity. User pairing and the power allocation problem are addressed separately to reduce system complexity. Consequently, we firstly formulate the user pairing scheme by using ILP. Then, we apply PSO for power allocation optimisation. The system design is developed in three scenarios for system performance. The first scenario is applied without a constraint on minimum data rate and fairness. The second scenario is applied with only a minimum data rate constraint. The third scenario is applied with minimum data rate and fairness constraints.
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## 4 Dataset, AE Design and Implementation
### AE Design
#### 4.2.1 Separate Autoencoding  
We found that the configuration reported in Table 4 guarantees an effective design, i.e, low RE on the validation set, for the separate AEs setting. The selected AE is made up of **three hidden layers** and a different number of neurons depending on the configuration (_A_ Table 4a or \(B\) Table 4b). We ended up with two different configurations because, despite the numerous fine tuning operations, it was not possible to find a _single working_ configuration for all IoT devices. In particular, the three densely connected layers include _N_-48-6-48-_N_ neurons for _Configuration A_ and _N_-64-6-64-_N_ neurons for _Configuration B_. In the rest of the paper, \(N\) (i.e, the number of neurons of the input/output layer of the autoencoder) is equal to 115, which is the dimensionality of the traffic records of the N-BaIoT dataset. The classical rectified linear unit (ReLu) has been selected for the encode layer, the decode layer and the bottleneck layer, while for the output layer the hyperbolic tangent (Tanh) activation function has been used. We train an AE on NORMAL data points of each IoT device for 100 epochs with batch size 512 using the RMSProp optimizer with learning rate value \(lr=0.0001\).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**IoT Device** & _Training Set_ & _Validation Set_ & \multicolumn{2}{c}{_Test Set_} \\
**NORMAL** & & **NORMAL** & **NORMAL** & **ATTACK** \\ \hline Danmini & & & 7432 & 96,874 \\ Ecobee & & & 1966 & 822,753 \\ Ennio & & & 5865 & 316,395 \\ Philips B120N/10 & & & 26,286 & 924,327 \\ Provision PT-737E & 389,158 & 83,387 & 9323 & 766,096 \\ Provision PT-838 & & & 14,777 & 738,367 \\ Samsung SNH 1011 N & & & 7822 & 32,3067 \\ SimpleHome XCS7-1002-WHT & & & 6987 & 816,461 \\ SimpleHome XCS7-1003-WHT & & & 26,286 & 831,285 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training, validation and test set size (all-in-one autoencoding).
## 2 Engineering Background
### 3D Laser Scanning and Detection Results  
Traditionally, the assessment methods for abandoned mining areas include geological drilling, geophysical prospecting, 3D laser scanning detection, and similar techniques. Among these methods, 3D laser scanning technology is particularly valuable, as it accurately captures spatial information in the form of point clouds at the area's boundaries.  
Figure 1: Engineering geology overview.  
Furthermore, this technology enables the generation of precise 3D models through specialized point cloud processing programs.  
The project utilizes the HVM100 SLAM 3D laser scanning system, which is a continuous scanner with SLAM technology developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) of Australia. It utilizes laser ranging to acquire information about the surrounding environment. The data acquisition speed is 300,000 points per second, with a weight of only 1.8 kg. It has a distance accuracy of \(\pm\)4 cm, an angular resolution of 0.1\({}^{\circ}\), and a measurement range of 360\({}^{\circ}\)\(\times\) 360\({}^{\circ}\) with no blind spots.  
The highway construction company undertook 3D laser scanning detection for the abandoned mining area. The detection process is depicted in Figure 2: A laser scanner was inserted into the abandoned mining area through a drillhole; the scanner, capable of a 360-degree rotation, continuously collected data on the distances and angles of the measured points during the detection process; data accuracy was meticulously maintained by controlling three parameters, including the scanning center's coordinates, the scanning angle, and the measuring distance. In total, over 400 drillholes were strategically placed near the highway, and the data collection for all drillholes was successfully carried out using a 3D laser detection system.  
The 3D point cloud data collected from these drillholes were processed through a dedicated 3D point cloud data processing system, leading to the reconstruction of a 3D spatial model for the abandoned mining area, as illustrated in Figure 2b. This approach facilitates the automatic calculation of the roof area and the volume of the abandoned mining area.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 3 An SK-Type Feedback Scheme for the Gaussian Relay Channel with Noisy Feedback
### Our Achievable Scheme
#### 3.2.2 Dither Signal for the Feedback Channel Power Constraint  
In the proposed scheme, we introduce a dither signal \(\
u_{n}(1\leq n\leq N-1)\) to ensure the transmitted codewords satisfy the feedback channel's power constraint at time \(n\). Here \(\
u_{n}\) is perfectly known by Source, and it is i.i.d. generated and uniformly distributed over \([-d/2,d/2)\). In addition, \(\gamma_{n}(1\leq n\leq N-1)\) is chosen to avoid modulo-aliasing errors occurring in Source.
###### Abstract  
Based on the two-fluid model, a three-zone drag model was developed, and the kinetic theory of granular flows and the Schneiderbauer solids wall boundary model were modified to establish a new three-dimensional (3D) unsteady mathematical model for high-pressure dense-phase pneumatic conveying in horizontal pipe. With this mathematical model, the influence of the three frictional stress models, namely Dartevelle frictional stress model, [PERSON] and Sundaresan frictional stress model, and the modified Berzi frictional stress model, on the simulation result was explored. The simulation results showed that the three frictional stress models accurately predicted the pressure drop and its variations with supplementary gas in the horizontal pipe, with relative errors ranging from \(-4.91\%\) to \(+7.60\%\). Moreover, the predicted solids volume fraction distribution in the cross-section of the horizontal pipe using these frictional stress models exhibited good agreement with the electrical capacitance tomography (ECT) images. Notably, the influence of the three frictional stress models on the simulation results was predominantly observed in the transition region and deposited region. In the deposited region, stronger frictional stress resulting in lower solids volume fraction and a higher pressure drop in the horizontal pipe were observed. Among the three frictional stress models, the simulation results with the modified Berzi frictional stress model aligned better with the experimental data. Therefore, the modified Berzi frictional stress model is deemed more suitable for simulating high-pressure dense-phase pneumatic conveying in horizontal pipe.  
high-pressure dense-phase pneumatic conveying; horizontal pipe; frictional stress model; numerical simulation +
Footnote †: journal: Applied Sciences
## Numerical Simulation and Parametric Analysis
### Discussions
#### 4.4.3 Effect of Thickness of the Concrete Core  
Figure 16 shows the influence of the thickness of the concrete core on the compressive behavior of SDC walls with different thicknesses of rubber layers (\(\mathrm{t_{r}}\) = 2 and 5 mm). The concrete core thicknesses of specimens are 90, 100, and 110 mm. It can be found that increasing the thickness of the concrete core increased the ultimate compressive resistance of SDC walls. Increasing the thickness of the concrete core from 90 mm to 100 mm and 110 mm, the ultimate compressive resistance of walls with 2 (5) mm rubber layers is risen by 7.7% (9.0%) and 15.3% (16.3%), respectively. The increment can be attributed to the increase of the cross-sectional area of the concrete. It can also be seen that the DI ratio increases negligibly as the concrete becomes thicker, which means the thickness of the concrete core has less influence on the ductility of SDC walls.  
Figure 16: Influence of thickness of concrete on compressive resistance of SDC wall.  
Figure 15: Influence of thickness of rubber layer on compressive resistance of SDC wall.
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 4 Implementation and Analysis
### Example 2, EPS-Bearer with QoS between UEs  
The second example represents a case when an ultra-low latency communication between UE1 and UE2 is required, creating a specific EPS-Bearer. As can be seen in Figure 8, initially, the traffic between UEs is routed using the UE 1 default OF-Bearer and the UE2 default OF-Bearer. Next, the traffic requirement is detected and sent to the Controller, which decides on the creation of a new specific OF-Bearer with ultra-low latency between OF-NB 1 and OF-NB 2. Then, the Controller inserts the specific service flow entry in OF-NB 1 and OF-NB 2 to link the OF-Bearer created with the Radio Bearer to UE 1 and UE 2, building the EPS-Bearer with ultra-low latency (see Figure 8).
## 2 Mathematical Method  
### The Transient Reynolds Equation  
The Reynolds equation considering variable viscosity and coexistence of laminar and turbulent flow is written below.  
\[\frac{1}{R^{2}}\frac{\partial}{\partial\theta}\left(\frac{h^{3}}{k_{x}\mu} \frac{\partial p}{\partial\theta}\right)+\frac{\partial}{\partial z}\left( \frac{h^{3}}{k_{z}\mu}\frac{\partial p}{\partial z}\right)=\frac{\omega}{2} \frac{\partial h}{\partial\theta}+\frac{\partial h}{\partial t} \tag{1}\]  
The \(k_{x}\) and \(k_{z}\) are circumferential and axial turbulence coefficients, respectively.  
\[k_{x}=12+a_{1}Re^{b_{1}};\;k_{z}=12+a_{2}Re^{b_{2}} \tag{2}\]  
where the local Reynolds number \(Re=\rho\omega Rh/\mu\). The values of constants \(a_{1}\), \(b_{1}\), \(a_{2}\) and \(b_{2}\) in the Equation (2) are determined by flow state in the lubricating film. For turbulence (\(Re>2000\)), the \(a_{1}=0.0136\), \(b_{1}=0.900\) and \(a_{2}=0.0043\), \(b_{2}=0.960\) are given by the Ng-Pan turbulence model [24]. Otherwise, all the constants above are equal to zero and \(k_{x}=k_{z}=12\) corresponding to laminar flow condition.  
Considering \(\lambda=2z/L\), \(\overline{\mu}=\mu/\mu_{a}\), \(\overline{p}=\psi^{2}p/\mu_{a}\omega\), \(\psi=C_{p}/R\), \(d=2R\), \(\overline{h}=h/C_{p}\) and \(\overline{t}=\omega t\), the dimensionless equation was obtained.  
\[\frac{\partial}{\partial\theta}\left(\frac{\overline{h}^{3}}{k_{x}\overline{ \mu}}\frac{\partial\overline{p}}{\partial\theta}\right)+\left(\frac{d}{L} \right)^{2}\frac{\partial}{\partial\lambda}\left(\frac{\overline{h}^{3}}{k_{z} \overline{h}}\frac{\partial\overline{p}}{\partial\lambda}\right)=\frac{1}{2} \frac{\partial\overline{h}}{\partial\theta}+\frac{\partial\overline{h}}{ \partial\overline{t}} \tag{3}\]
# Characterisation of Holographic Recording in Environmentally Stable Photopolymerisable Glass
## 2 Materials and Methods
### Holographic Recording and Grating Thickness Estimation  
Holographic recording of volume phase transmission gratings was carried out by exposing the PHSG layer to two beams of 532 nm wavelength from a Nd:YVO\({}_{4}\) laser (Coherent, model Verdi) as shown in Figure 1. The recording beam had a diameter of 6 mm, allowing the development of a grating with the area of about 1.13 cm\({}^{2}\). The angle of incidence of the recording beams (\(\theta\)) was 12.3\({}^{\circ}\) with respect to the normal to the surface of the sample. This recording geometry provided an unslanted grating with spatial frequency of approximately 800 lines mm\({}^{-1}\). The optimum total recording intensity of 20 mW cm\({}^{-2}\) was used. The exposure time was varied depending on the aim of the experiment; more details on recording conditions are provided in Section 3. For the real-time diffraction efficiency growth curve recording and Bragg selectivity curve measurements, a low intensity (1.8 mW cm\({}^{-2}\)) 633 nm beam from a He-Ne laser was employed as a probe beam. The laser beam intensity was monitored using an optical power meter (Newport, model 843-R) and the acquired data were transferred to a computer. The position of the sample was computer controlled via a motion controller (Newport ESP300). The diffraction efficiency of the transmission gratings was calculated as the ratio of the first-order diffracted beam intensity and incident beam intensity. The effect of Fresnel reflection on the diffraction efficiency value was taken into account by measuring the intensity of the incident probe beam after the unexposed layer.  
Transmission gratings with the thickness of about 88 and 118 \(\upmu\)m can be considered as volume gratings as the Klein-Cook Q-parameter [17] is found to be 126 and 169, respectively. Quantitative analysis of recorded gratings was carried out using a coupled-wave theory [18] and the thickness (d) of the grating was estimated from the experimental Bragg angular selectivity curves utilising the following equation:  
\[\Delta\theta_{\mathrm{FWHM}}=\frac{\Delta}{\mathrm{d}} \tag{1}\]where \(\Delta\Theta_{\rm FWHM}\) is the full angular bandwidths at the half of the maximum of the diffraction efficiency and \(\Lambda\) is the spatial period.
## 3 Results and Discussion
### TCO Layers on Glass
Figure 3 shows the j\({}_{\rm loss}\) vs. sheet resistance due to the absorption of the \(\approx\) 100-nm-thick ZnO:Al and ITO layers deposited on glass under the different oxygen concentrations described previously. As expected, both materials present a decrease in the j\({}_{\rm loss}\) value when the sheet resistance is increased. It can be observed that the prepared ITO layers feature better optoelectronic properties than the ZnO:Al samples: for two layers with a similar R\({}_{\rm sh}\), the ZnO:Al layer absorbs a larger amount of light than the ITO layers. In particular, in the region near the desired optoelectronic working point of these materials (100-150 \(\Omega\)/sq), the ZnO:Al layer absorbs an amount of photons equivalent to a current density between 0.6 and 2 mA/cm\({}^{2}\) while the ITO absorption stays relatively stable at a value between 0.5 and 0.8 mA/cm\({}^{2}\). Figure 3: Sheet resistance vs. j\({}_{\rm loss}\) due to absorption of the ZnO:Al (open triangles) and ITO (filled squares) single layers on glass. The sheet resistance values of this figure were measured with a four-point probe. The optical absorption is similar for samples with a sheet resistance between 100 and 200 \(\Omega\)/sq. Note the axis break in the vertical axis.