esa-sceva/satcom-chunk-collection huggingface dataset:
# Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation
## 5 Conclusions  
In this paper, the design of a flat Luneburg lens was proposed by introducing the reflection cancellation method. A dual-polarized FLL was designed and prototyped at the _Ka_-band, and it was demonstrated that the proposed design method can effectively enhance gain and aperture efficiency and suppress backside radiation.  
The proposed design is promising for multibeam antenna systems in 5G and beyond applications. For instance, the designed FLLA can be adopted in customer premise equipment in small cells to support multiple beams simultaneously, resulting in multiplied system capacity. As stated in the Discussion section, since the operating frequency band was chosen at the _Ka_-band, the number of adopted layers for the FLL is limited to 10 due to the small operating wavelength and available PCB substrates. If a thinner PCB substrate can be used to design the target FLL with more layers, the bandwidth is expected to be much wider. This will be explored in future work.  
Conceptualization, Y.Z.; methodology, Y.Z. and Y.L.; experiments and data curation, Y.L. and R.J.; writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable. All the design parameters are provided within this article.  
The authors are grateful to [PERSON] of State Key Laboratory of Millimeter Waves for his help in the measurements.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & \begin{tabular}{c} **Spherical LL \#** \\ **(in This Work)** \\ \end{tabular} & **FLL in [13]** & **FLL in [20]** & **FLL in [21]** & **This Work** \\ \hline \begin{tabular}{c} Operating freq. \\ (GHz) \\ \end{tabular} & 29 & 26 & 10 & 10 & 29 \\ \begin{tabular}{c} Electrical size \\ (\(\lambda\) at central freq.) \\ \end{tabular} & \((4\pi/3)\times 29^{3}\) & \(\pi\times(3.82)^{2}\times 1.73\) & \(3.29\times 3.29\times 0.47\) & \(3.14\times 3.14\times 0.5\) & \(3.1\times 3.1\times 0.56\) \\ Number of layers & 12 & 6 & 17 & 18 & 10 \\ \begin{tabular}{c} Fract. bandwidth \\ Comparison ratio \\ (\(R/b\)) \\ \end{tabular} & \begin{tabular}{c} \(>\)33\% \\ None \\ \end{tabular} & \begin{tabular}{c} -17.3 \% \\ -17.3 \% \\ \end{tabular} & \begin{tabular}{c} -21\% \\ -21\% \\ -17.3 \% \\ \end{tabular} & \
\begin{tabular}{c} -20\% \\ -21\% \\ -21\% \\ \end{tabular} &
\begin{tabular}{c} -17.6\% \\ -20\% \\ -20\% \\ -28\% \\ -25\% \\ -21\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparisons between designed FLLA, conventional LL, and other related works.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Additional Performance Enhancements
#### 5.4.2 Additional Preprocessing: Data Smoothing
Overall, the joint adoption of smoothing and the combination of data from multiple operators leads to a marked reduction of the minimum average positioning error, with errors below 16 m in all cases, as shown in the rightmost column of Table 11.  
It is interesting to observe that both techniques proposed in this Section, that is combining data from multiple operators and smoothing data, improve positioning accuracy with respect to the results presented in Tables 3 and 8 for Oslo and Rome. Combining information from multiple operators provides, however, in most cases, a more noticeable performance improvement than does smoothing. The only exception is total Smoothing for the Oslo dataset, which leads to a positioning error lower than the one obtained by combining operators; in this case, introducing the combination of information does not lead to any additional performance improvement. Two observations emerge from this result: first, that while total smoothing as defined here is not feasible, some form of processing on data provided by devices during the online phase may improve positioning accuracy. Second, a lower floor for positioning accuracy may exist that cannot be overcome; however, further investigation is required to assess this hypothesis.
## 2 Ageing of Samples and Measurements of Electrical Properties  
### Ageing of Silicone Rubbers  
We used 10 kV power cables with stress-cone cable terminals for the ageing experiments. The power cables were placed under a 10 kV ac power supply with a 5 th harmonic voltage component, and the amplitude of the harmonic voltage was 20% of that of the ac voltage. At the same time, a power frequency current superimposed with harmonic current was applied to the conductors of power cables, causing a temperature rise in the conductors, which reflects the actual operating conditions of power cables. The operating times of four power cables under power frequency superimposed harmonic voltage were set as 1 month and 4, 6, and 8 months, respectively. During the aging experiments, the temperature outside the cable body was about 45 \({}^{\circ}\)C, and the temperature at the cable terminals was very high, which was around 70 \({}^{\circ}\)C on the surface of silicone rubber insulation. The temperature inside the silicone rubber insulation would be higher than that on the insulation surface. Silicone rubber samples were taken from the external insulating layer of cable terminals. The specimens were around 2 mm thick by stripping the sheath. As for the control group, pristine silicone rubber with the same thickness from the same type of non-operational power cable terminals was also prepared for the following tests. Prior to the characterization experiments, all samples were cleaned with an ultrasonic instrument (KQ-100 KDE, KUNSHAN Ultrasonic Instruments, Kunshan, China) for 30 min to remove impurities on the surface of the sample, and then the samples were placed in a vacuum drying box (DZF-6055, Yiheng, Shanghai, China) at 80 \({}^{\circ}\)C. The samples were dried for 12 h to remove the moisture inside, avoiding the influence of moisture on the dielectric characteristics.  
The silicon rubber insulation matrix of the cable terminals is polydimethylsiloxane, and the filler is mainly white carbon (namely, silica) with a mass fraction of 26%. Due to the highly active OH groups on the surface of silica, it can form hydrogen bonds with the main chains of polydimethylsiloxane, which can strengthen and improve the mechanical strength of silicone rubber insulation. Polydimethylsiloxane and cross-linking agent can form chemical cross-linking through a chemical reaction under heating with catalyst. The OH groups on the surface of silica can form physical cross-linking through hydrogen bonding with the main chain of the matrix. Chemical cross-linking and physical cross-linking constitute a complete cross-linking system for silicone rubber insulation, so that silicone rubber has sufficient electrical and mechanical strength.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 2 Related Works
### Contributions of This Work  
While reviewing the existing literature, we identified a lack of systems that implement a fully comprehensive response mechanism for disaster management and response. To address this gap, our primary objective is to develop a website that can automatically collect streaming social media posts, apply a pretrained model for accurate inference, and visualize users' locations on a dynamic map. This integrated system is designed to provide emergency response teams with critical, up-to-date information, allowing them to assess situations more effectively and respond in a timely manner. By automating data collection, analysis, and visualization, the platform enhances situational awareness and supports faster decision-making during disasters, ultimately improving resource allocation and response coordination.
# Situation-Aware Survivable Network Design for Tactical Environments
## 3 Proposed Algorithm  
In this section, we describe the proposed algorithm, which comprises two parts: (i) the situational backoff reset algorithm and (ii) the branch node-based routing algorithm. The main objective of the proposed algorithms is to increase the survival in tactical networks through improvement of military operations by supporting the guaranteed delivery of important or critical messages and maintaining end-to-end network connectivity. The first scheme, the situational backoff reset algorithm, adjusts the channel access of a tactical message depending on its importance and urgency, i.e., priority of traffic. The branch node-based routing algorithm helps provide end-to-end path connectivity even in the harsh environmental conditions of a battlefield. The proposed algorithms are described in detail in the following subsections.
## 4 Performance
### Variation of Number of Sending Nodes
#### 4.2.3 PSNR Comparison  
PSNR (Peak Signal-to-Noise Ratio) presents the ratio between the maximum power of a signal and the power of corrupting noise. The PSNR is most commonly used as a measure of quality of reconstruction of lossy codecs such as image compression. High PSNR means high image quality, and thus image transmission protocols should guarantee high PSNR. Figure 12 presents the PSNR comparison. The PSNR of SRI and MintRoute is decreased in an inverse proportion to the number of sending nodes, because the number of dropped packets increased in proportion to the number of sending nodes as shown in Figure 10 (A). MintRoute shows the lowest PSNR, because MintRoute does not care about the image quality. With MintRoute, any packets could be dropped in case of congestion, and thus, image quality decreased very much. Although SRI shows 100% of image reception in Figure 10 (B), PSNR is decreased drastically, but shows higher PSNR than MintRoute because SRI guaranteed the packets with lower frequency by assigning higher priority. SRI could reconstruct the image with lower resolution image even if some packets were lost. Therefore, with SRI, image reception ratio was high and PSNR is higher than MintRoute. On the other hand, RAIT showed uniform PSNR because with RAIT no packet was lost during transmission.  
Figure 12: PNSR. RAIT presents uniform PSNR while PSNR of MintRoute and SRI was decreased very much.
## 3 System Function Realization  
The driving environment intelligent monitoring system has two core functions, and the architecture of function realization is shown in Figure 5.  
Figure 4: The hardware architecture of the system.  
Figure 5: Architecture of function realization.  
From and rear distance monitoring:  
The monocular ranging algorithm provides a faster ranging capability because it processes a single image stream, which reduces the computational complexity compared to the binocular ranging algorithm that requires the analysis of two image streams. Nevertheless, binocular ranging offers higher precision than monocular ranging. The system captures image data in real time through the IP camera and uses the RTSP to transmit the image to the edge computing device. With the help of the YOLOv8 target monitoring algorithm, the system can effectively identify the vehicles in the image. Once the vehicle is detected, the system will input the pixel coordinates of the target to the monocular ranging algorithm to initially determine the vehicle distance. The algorithm uses the monocular camera installed before and after the vehicle body for rough measurement. If the measurement shows that the vehicle distance is less than the preset safety threshold (50 m), the system will start the binocular ranging algorithm and carry out a more precise vehicle distance measurement through the binocular camera before and after the vehicle body and issue an early warning.  
Surrounding distance monitoring:  
This function focuses on monitoring vehicles on the side of the vehicle and uses binocular ranging technology for measurement. When the system recognizes the surrounding target, the pixel coordinates of the target are immediately input into the binocular ranging algorithm. Using binocular cameras on both sides of the car body, the system can accurately measure the surrounding distance. Once the measurement result is lower than the set safe distance (2 m), the system will issue an early warning.
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 1 Introduction
With our proposed multi-chip packages (Section 3.2) and blade server design (Section 3.3), up to 1536 accelerators can be installed in a standard 42U rack. To go beyond 100 Gbps, we also apply 400 Gbps optical interconnects to the baseline system and evaluate its performance. We made and used an arithmetic performance model reflecting the microarchitecture and operation of the DaDianNao accelerator and statistical latencies in the 2D-mesh network [15] to estimate computation time, memory access time, communication time, and energy-efficiency (Energy-Delay Product (EDP): an implementation neutral metric demonstrating the balance of performance and energy efficiency of a system design [16]) of neural networks. In our evaluation of nine different CNN models with various batch sizes, 100 Gbps and 400 Gbps optical interconnects improve the training time (and EDP) by 20.6% (22.8%) and 35.6% (44.3%) on average or up to 41.1% (47.4%) and 72.2% (85.6%), respectively, compared to the baseline system with 25.6 Gbps electrical interconnects. Consequently, we clearly show that machine-learning accelerator cluster systems can benefit from high-bandwidth optical interconnect.
VPM is a well-established GPP model by the conceptual partitioning of chlorophyll and non-photosynthetically active vegetation within the canopy to simulate GPP [36]. The model used additional spectral bands, such as blue and shortwave infrared bands to calculate the Enhanced Vegetation Index (EVI) [37] and Land Surface Water Index (LSWI) [33; 36; 38]. For example, [PERSON] et al., used the improved EVI and LSWI as input data for the VPM model to better characterize the dynamics of vegetation. They also simulated GPP more accurately than other PEM models, which only used NDVI in Qinghai-Tibetan Plateau, China [23]. Recently, this model has been further developed and applied to several ecosystems, including forest, grassland, agriculture, and alpine wetland ecosystems [10; 13; 15; 23; 36; 39; 40], which demonstrated the potential to scale up in situ observations of GPP from the CO\({}_{2}\) flux tower sites. As the largest wetland ecosystem in the warm temperate zone of China, the Yellow River Delta Wetland is the most active area of atmosphere-land-ocean interaction in the world [41; 42; 43; 44]. Therefore, the accurate estimation of the C source/sink function and its influence mechanism for the targeted coastal wetland will help to quantify the interactions and feedbacks between global climate change and coastal wetland ecosystems. In this study, we have combined the analysis of EC flux data with remote sensing images in the Yellow River Delta during 2009-2010. We aim to: (i) characterize seasonal dynamics of reed by analyzing the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) and CO\({}_{2}\) fluxes data; (ii) examine the biophysical expression of VIs related to seasonal dynamics of observed GPP data; and, (iii) evaluate the dependability and the applicability of the VPM model for simulating GPP of coastal wetland ecosystems. We also conducted a variety of uncertainty analysis for the model simulations. This study could help to deepen our understanding of C budgets of coastal wetland and further improve the potential of RS technology for monitoring and simulating the dynamics of regional vegetation and C fluxes.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 2 System Model  
The system model is a three-dimensional layout including depth due to its impact on important parameters, like energy. A propagation model is implemented considering the underwater conditions and several works proposed by researchers. Moreover, it is worth mentioning that energy consumption is an important parameter to consider in the design of the protocol for any sensor network. Therefore, both models of energy and propagation of acoustic waves have been implemented in Matlab for measuring, including the energy dissipation during the network operation.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 4 Numerical Simulation
### Performance Comparison under Rayleigh Channel  
A transmitted signal \(s\) passes through the Rayleigh flat-fading channel, and the received signal can be expressed as follows:  
\[g_{\texttt{ReLU}}(z)=\max\{0,z\}. \tag{11}\]  
where \(h(t)\) is the fading factor, which obeys the Rayleigh distribution; \(\theta(t)\) is the phase; and \(n(t)\) is the complex white Gaussian noise. The comparison between the two decoding schemes and SC algorithm is shown in Figure 17.  
As shown in Figure 17, the decoding performance from poor to excellent over Rayleigh channel is as follows: SCD, LSNNAD, and KSNNAD.
# Novel Processing Methods of Low-Clinker Multi-Component Cementitious Materials--A Review
## 2 Ultrasound Treatment (US-T) Methodology
#### US-T of Cementitious Materials/Concrete and SCMs
Nano-silica also enhanced the homogeneity of the cement paste microstructure during sonication and triggered a secondary reaction between SiO\({}_{2}\) and Ca(OH)\({}_{2}\) crystals, resulting in a denser C-S-H phase. [PERSON] et al. [71] used sonication to alter CSF densification, creating a finer specimen with better granulometric distribution. Commercial SF with an average diameter of 59.6 \(\upmu\)m was transformed into samples with an average diameter of about 5 \(\upmu\)m and a higher volume percentage of sub-micrometric particles with a diameter of less than 1 \(\upmu\)m (over 50%) with longer sonication time (up to 20 min) and higher sonication power level (168 W). It is important to note that US-T can change the particle size distribution in favor of smaller particle sizes, increase the specific surface area, improve pozzolanic reactivity due to increased consumption of portlandite during paste hardening, achieve a high dispersion (particles uniformly dispersed into cement paste) and degree of de-agglomeration (particles become more stable and have a lower propensity to agglomerate). Additionally, US-T can speed up the heat-release rate during the hydration-acceleration phase. It was also revealed that the rapid cement hydration was what presumably produced the increased strength growth of the sonicated cement suspension, which was only observed for the first 16 h. Especially for SSF treated for longer sonication intervals (20-25 min) and higher sonication power levels (141 W), the fc of SSF mortar increased up to 15 MPa after 28 days of curing compared to SF prepared without the impact of ultrasound.
# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 4 Results and Discussions
### Determination of the Strength of the Signal  
The model represents the collection of signal strength from the existing cell tower location. During transmission, the signal follows direct or indirect paths from the cell tower to the user's location. This model is going to determine a relationship for transmission of losses due to distance, obstruction, etc., between the cell tower and users' locations.
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 2 Basic Theory  
Regarding the mathematical model of hypersonic vehicles, early research work includes a hypersonic vehicle model with a wing cone configuration given by the NASA Langley Research Center in 1990. This model has published most of the aerodynamic data of wind tunnel tests and has become the main model used by scholars around the world to design flight control systems [33]. It is shown as  
\[\dot{V}=\frac{-D+T\cos\alpha}{m}-\frac{\sin\gamma}{r^{2}}\mu \tag{1}\]  
\[\dot{\gamma}=\frac{T\sin\alpha+L}{mV}-\frac{\left(\mu-V^{2}r\right)\cos\gamma}{ Vr^{2}} \tag{2}\]  
\[\dot{n}=V\sin\gamma \tag{3}\]  
\[\dot{\alpha}=Q-\dot{\gamma} \tag{4}\]  
\[\dot{Q}=\frac{M_{yy}}{I_{yy}} \tag{5}\]  
where  
\[L=0.5\rho V^{2}SC_{L} \tag{6}\]  
\[D=0.5\rho V^{2}SC_{D} \tag{7}\]  
\[T=0.5\rho V^{2}SC_{T} \tag{8}\]  
\[M_{yy}=0.5\rho V^{2}SC[C_{M}(\alpha)+C_{M}(\delta_{c})+C_{M}(q)] \tag{9}\]  
\[r=h+R_{E} \tag{10}\]  
In Equations (1) through (10), \(V\), \(\gamma\), \(h\), \(\alpha\) and \(Q\) are the velocity, track angle, altitude, angle of attack and pitch rate, respectively. \(L\), \(D\), \(T\) and \(M_{yy}\) respectively represent lift, drag, thrust and pitching moment.\(m\), \(\mu\), \(I_{yy}\), \(r\), \(R_{E}\) and \(S\) represent the mass of the hypersonic vehicle, gravitational constant, the moment of inertia, the radial distance from the center of the earth, the earth radius and the wing reference area, respectively. \(\rho\) represents the air density. \(\overline{c}\) is the aerodynamic mean chord length.  
When cruising in the state, the expression of \(C_{M}\) is as follows:  
\[C_{L}=0.6203\alpha \tag{11}\]  
\[C_{D}=0.6450\alpha^{2}+0.0043378\alpha+0.003772 \tag{12}\]  
\[C_{T}=\left\{\begin{aligned} 0.02576\beta_{c},\beta_{c}<1\\ 0.0224+0.00336\beta_{c},\beta_{c}\geq 1\end{aligned}\right. \tag{13}\]  
\[C_{M}(\alpha)=-0.035\alpha^{2}+0.036617\alpha+5.3261\times 10^{-6} \tag{14}\]  
\[C_{M}(\delta_{c})=c_{e}(\delta_{e}-\alpha) \tag{15}\]  
\[C_{M}(Q)=0.5\frac{\overline{c}Q}{V}(-6.796\alpha^{2}+0.3015\alpha-0.2289) \tag{16}\]  
where \(c_{e}\) is the torque coefficient, and the control inputs are throttle opening command \(\beta_{c}\) and elevator deflection angle \(\delta_{e}\). For the multi-input and multi-output hypersonic vehicle system in which altitude and velocity are the most important state variables, if the control inputs and outputs are arranged as one-to-one correspondence, the ADRC has natural decoupling for the cross-coupling term between each channel. Based on the literature, the longitudinal dynamic model of the hypersonic vehicle is deformed and deformed into a form in which the velocity subsystem and the altitude subsystem are coupled with each other with a clear order. The parameters of the hypersonic vehicle used in this article are shown in Table 1.
## 3 Spectral Scattering Imaging
### Analysis of Scattering Images
#### Multivariate Calibration  
Multivariate calibration is to build a quantitative model relating extracted scattering and spectral features with quality attributes of target, which is the final step in processing scattering images. The multivariate calibration techniques basically fall into two groups, i.e., linear and nonlinear. Linear techniques, such as MLR, principal component regression (PCR) and PLS, are suited to model a linear relationship and have been widely used for prediction of quality attributes in apples; while non-linear techniques such as neural network (NN) and support vector machine (SVM) hold great potential of fulfilling calibration purposes since they can handle both linear and nonlinear problems. Detailed descriptions of these methods are beyond the scope of this paper, and readers are referred to relevant literature [67; 68; 69].