esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Demonstration of Beam Scanning of Dual-Metasurface Antenna
## 2 Materials and Methods  
### Principle of Operation  
The beam scanning mechanism was conceived as a _dual_ metasurface structure (see Figure 1a). Radiation is effected by a (static) transparent metasurface with metal texture on a dielectric (inspired by [23]); in a fixed-beam configuration, the radiating metasurface would be backed by a metal plane providing wave guiding. Here, the ground plane was replaced by a _tunable-impedance metasurface_. The principle of operation was based on the radiation of a guided wave, whose phase velocity was controlled by the variable-impedance surface.  
The present study considered an antenna with scan in the vertical plane; the transverse size can range from small to multi-wavelength, depending on the type of feeding structure.  
Figure 1: Structure of the proposed antenna. (**a**) Schematic architecture. (**b**) Transverse equivalent network: \(Y_{\text{MITS}}\) is the admittance of the upper metasurface, \(Y_{\text{RITS}}\) is the admittance of the lower (reconfigurable) metasurface, \(d_{1}\) is the thickness of the dielectric layer, \(d_{0}\) indicates the air gap, \(Y_{\text{UP}}\) and \(Y_{\text{DOWN}}\) are the admittances looking up and down from the interface between the antenna and free space, \(Z_{\text{s}}\) is the equivalent impenetrable surface impedance that approximates the whole multilayer structure.  
For the sake of simplicity, here the intermediate size of one wavelength was considered, which would allow for star-type 2D scanning [24].  
Radiation of a guided mode is conveniently framed in the leaky-wave paradigm; hence, radiation happens via the spatially-modulated upper metasurface, exploiting both well-known results [5] and recent advances in metasurface antennas [23]. The operation principle, and thus the first-pass design, can be conveniently understood for the simplest modulation of the radiating metasurface, i.e., for the sinusoidal one [23]:  
\[Z_{\mathrm{s}}(x)=\mathrm{j}X_{\mathrm{s,ave}}\bigg{[}1+M\cos\left(\frac{2\pi} {p}x\right)\bigg{]}, \tag{1}\]  
where \(Z_{\mathrm{s}}\) is the equivalent impenetrable surface impedance that approximates the whole multilayer structure and takes into account the effects of both metasurfaces (Figure 1b), \(x\) is the coordinate along the direction of wave propagation, \(M\) is the modulation index, \(p\) is the modulation period, and \(X_{\mathrm{s,ave}}\) is the average surface reactance.  
In this case, leaky-wave radiation happens for the \(n=-1\) Floquet harmonic of the traveling wave [5; 23]; for TM mode propagation, the radiation angle \(\theta_{-1}\) is linked to the average surface reactance \(X_{\mathrm{s,ave}}\) by the approximate expression [18; 23]:  
\[\theta_{-1}=\arcsin\left(\sqrt{1+\left(\frac{X_{\mathrm{s,ave}}}{\eta_{0}} \right)^{2}}-\frac{2\pi}{k_{0}p}\right), \tag{2}\]  
where \(\eta_{0}\), \(k_{0}\) are the free-space impedance and wavenumber, respectively.
## 2 Background
### Interoperability in IoUT  
The Institute of Electrical and Electronics Engineers (IEEE) defines interoperability as \"the ability of two or more systems or components to exchange information and use the information exchanged\" [29]. As depicted in Figure 2, interoperability expands in six layers, based on the capability of interoperation between systems [30; 31]. Although, in our review we will focus on the first three layers, namely technical, syntactic, and semantic, in which network/connectivity and simulation/implementation are achieved. In UWSNs, where interoperability is inextricably linked to communication and achieved by the transmission of acoustic and electromagnetic signals, water as a medium is an important deterrent. With the aim to overcome the principal issue of IoUT, i.e., interoperability, the authors in [32] propose the SUNRISE model, which implements an abstraction layer for supporting the interconnection of various control software of different underwater vehicles. Motivated by the first initiative to define a common language, which is JANUS from NATO Science and Technology Organization--Centre for Maritime Research and Experimentation (STO CME) [9; 10], and its being limited to initial contact and emergency message exchange, the authors created possibilities for a heterogeneous network of mobile assets. Encoding and decoding of messages is mandatory, even if a common physical coding scheme exists; any interaction between underwater assets using different control software isn't possible. Therefore, a protocol named SSC (Software-to-Software) is proposed, supporting the cooperation of heterogeneous platforms, e.g., MOOS ([[https://oceanai.mi.tedu/moos-ivp/pmwiki/pmwiki.php?n=Main.HomePage](https://oceanai.mi.tedu/moos-ivp/pmwiki/pmwiki.php?n=Main.HomePage)]([https://oceanai.mi.tedu/moos-ivp/pmwiki/pmwiki.php?n=Main.HomePage](https://oceanai.mi.tedu/moos-ivp/pmwiki/pmwiki.php?n=Main.HomePage)), accessed on 20 November 2022), ROS ([[https://www.ros.org/](https://www.ros.org/)]([https://www.ros.org/](https://www.ros.org/)), accessed on 20 November 2022), DUNE ([[https://nt.nuopen.ntnu.no/ntnu-xmui/handle/11250/2350792?locale-attribute=en](https://nt.nuopen.ntnu.no/ntnu-xmui/handle/11250/2350792?locale-attribute=en)]([https://nt.nuopen.ntnu.no/ntnu-xmui/handle/11250/2350792?locale-attribute=en](https://nt.nuopen.ntnu.no/ntnu-xmui/handle/11250/2350792?locale-attribute=en)), accessed on 20 November 2022), etc. SSC provides simplicity, ease of implementation, extendibility, and expressiveness. However, the automated integration of an autonomous vehicle in a network of a swarm of UUVs/AUVs remains a challenging goal.  
Figure 2: Levels of Interoperability.
# Prediction of Soil-Water Retention Curves of Road Base Aggregate with Various Clay Fine Contents
## 6 Conclusions  
The SWRCs of soils with different _F_Cs (0%, 2%, 4%, 6% and 8%) were measured using a large-scale SWRC testing apparatus. Based on the test results, the evolution of the PSD of the inter-aggregate pores and the intra-aggregate pores were deduced: with the addition of fine content, the PSD of the inter-aggregate pores was shifted and scaled, and the PSD of the intra-aggregate pores was only scaled. Then, an SWRC model was formulated considering the effect of fine content on the PSD of the inter-aggregate pores and intra-aggregate pores. The values of the fitting parameters were obtained from the SWRCs of _FC_ = 0%, 4% and 8%, and then verified with two other _F_Cs (2% and 6%). In the verification, the relatively large \(R^{2}\) indicates that the agreement between the proposed model and testing results is satisfactory. The proposed model can predict the SWRC at different fine contents of the soil characterized by the coarse-grain skeleton.  
Conceptualization, Q.Z.; methodology, Q.Z. and Z.C.; validation, Q.Z. and C.G.; resources, Y.C.; writing--original draft preparation, Q.Z., Z.C. and C.G.; funding acquisition, Z.C. and C.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation for outstanding scholar in Zhejiang Province (No.LR21E08004), National Natural Science Foundation of China (Nos. 51978611, 51878513) and Chinese Scholarship Council (No. 202006320250).  
Not applicable.  
**Data Availability Statement:** Some or all data, models, or codes generated or used during the study are available from the corresponding author by request.  
The authors declare no conflict of interest.  
Figure 7: The predicted SWRCs versus the measured data for _FC_ = 2% and 6%.
## 3 Results and Discussion
### Test Results
#### 3.1.2 Cracking Pattern  
Two measures of energy data generated by acoustic emission technology were analyzed to reveal the fatigue cracking process. \"Cumulative energy value\" refers to the cumulative value of energy data monitored since the fatigue test started. Since a substantial increase in the proportion of observed high energy values indicates that a macroscopic crack is about to appear in the steel plate [27], the percentage of an energy value higher than 500 PJ per day was calculated for each monitoring point. The daily high energy proportion and cumulative energy at each monitoring point during the fatigue test versus the number of loading cycles are shown in Figure 11.  
Figure 11: Variation of monitored energy data during fatigue test: (**a**) Percentage of energy greater than 500 PJ; (**b**) Cumulative energy value.  
Figure 10: Stress time history during fatigue verification test: (**a**) Girder inner web; (**b**) Girder outer web.  
Before the fatigue test was loaded to 1 million cycles, no cracks or abnormal phenomena were found by visual inspection or acoustic emission monitoring. When the number of cycles was 1.1 million, both the daily high energy proportion and accumulated energy value at measuring point 1 increased significantly, as shown in Figure 11, indicating possible fatigue crack initiation in the specimen. The fatigue test resumed because a crack could not be identified either at the weldment between the anchor box and box girder or in the neighboring plates. Energy monitoring results were stable during subsequent loadings. After 2.6 million loading cycles, the energy parameters at four monitoring points increased significantly again. A visual inspection revealed that, within the range of monitoring point 1, there was an approximately 6 cm long crack on the box girder inner web's outside surface at the end of the extra stiffener. After 3 million loading cycles, the two parameters at all monitoring points again showed an increasing trend, the growth of point 1 being the most obvious. Meanwhile, it was observed that the crack at the stiffener end had propagated another 1 cm upward during the past 40 \(\times\) 10\({}^{4}\) cycles. Because of the severe vibration of the specimen, the fatigue test was terminated at 3.2 million cycles. The observed macroscopic crack and its propagation process during the fatigue test are shown in Figure 12. Upon finishing the test, no other cracks were observed from a thorough examination of the test specimen.  
The finite element analysis results of the test specimen are shown in Figure 13. As shown, an apparent stress concentration exists at the end of the web stiffener where the crack occurred. A discontinuous stiffener weldment in the test specimen may have led to a deterioration in the stress state, which eventually caused a premature cracking in the vicinity of the extra stiffener. The position of the maximum tensile stress point from analysis coincides with the location of the crack observed, demonstrating the effectiveness of the finite element model adopted.  
Figure 12: Crack evolution of test specimen.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 4 Experiments  
To verify the effectiveness of this method, the aeromagnetic survey data of the Canadian Geological Survey (data sources: Kluane Lake West Aeromagnetic Survey, Residual Total magnetic Field, NTS 115G/12 and parts of NTS 115G/11, 13, 14 and NTS 115F/9 and 16, Yukon) are used for analysis and research. There are abundant deposits and significant magnetic anomalies in the Kluane area. The regional resolution of the magnetic map is 50 \(\times\) 50 m, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variogram Models** & **ME (nT)** & **MAE (nT)** & **RMSE (nT)** \\ \hline Gaussian Variogram & 111.1194 & 13.8591 & 29.9843 \\ Exponential & 137.775 & 6.7182 & 26.3359 \\ Variogram & 138.4306 & 6.8211 & 21.8168 \\ Improved Variogram & 68.9824 & 1.3386 & 9.6925 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of Kriging interpolation errors using different variogram models.  
Figure 7: Comparison of the interpolation results and Error distribution using different variograms: (**a**) Gaussian variogram; (**b**) exponential variogram; (**c**) spherical variogram; (**d**) improved variogram.  
The aeromagnetic sampling data were obtained along the Y direction with the distance between the measurement lines (500 m), and the noise of 0.1 nT was added. We fit these measurement data using the spherical model, exponential model, Gaussian model, and improved variogram model, respectively. The interpolation results and error distribution are shown in Figure 9. Meanwhile, the ME, MAE, and RMSE were used to evaluate the accuracy of the interpolation results (as shown in Table 7). It can be seen that both the spherical and the improved variogram models can reflect the approximate position of the magnetic anomaly. However, each evaluation of the improved variogram is lower than the other models, which further proves the feasibility of the algorithm.  
Figure 8: A magnetic map of the Kluane Region, Canada.  
Figure 9: Comparison of the interpolation results and Error distribution using different variograms for measured data: (**a**) Gaussian variogram; (**b**) exponential variogram; (**c**) spherical variogram; (**d**) improved variogram.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
### Related Works  
Several related works are listed in Table 1. The key points of concern are listed in the table. RFID technology has been used in the trace system for supply chains for many years. [PERSON] [9] evaluated the uses of RFID tags on cigarette packs. The research explains how the RFID works as well as how the RFID can solve the products' smuggling problem and inventory control as the technology can be scanned to verify whether the taxes have been paid or not. [PERSON] et al. [11] redesigned a passive ultra high frequency (UHF) RFID tag and integrated it into a cigarette pack, where the tag was only 0.5 mm thick. Neither of these two studies mentioned how to use RFID to implement a supply chain system. Some research has been implemented in the supply chain of non-tobacco products. In 2012, [PERSON] et al. [8] deployed RFID technology in the case of fashion supply chain management (FSCM). [PERSON] et al. [12] also proposed a RFID-enabled trace system implemented with the Electronic Product Code (EPC) global network, which is a global data exchange standard for supply chain networks, where the user can query product information via scanning the RFID with the EPC. These studies have proposed a good architecture for implementation in the supply chain, but the studies lacked integrity and traceability of data. Aside from implementing RFID technology, global positioning system (GPS) can also be integrated into logistics tracking systems. [PERSON] et al. [13] proposed a logistics vehicle tracking mechanism in 2012, where the authors implemented a GPS device in the vehicle to record and track the real-time location and used the global system for mobile communication (GSM) to upload those data to the server. The GPS helps to bind and record the delivery location of the products, and this location can improve the data completeness of the logistics system. Unfortunately, the authors did not analyze or prove the security of their system. Regarding tobacco product-related systems, [PERSON] et al. [14] proposed a retroactive system with a database. Their system could solve the problem of product tracking, but the security of traditional databases may be challenged. For example, if the administrator's account is maliciously logged in, the data in the database can be easily tampered with by the attacker and hard to trace. [PERSON] et al. [15] briefly introduced a financial service platform for the tobacco supply chain. These studies have provided a framework of the tobacco products' supply chain system, however, although the second research mentioned the use of blockchain technology, the description was not complete, and it did not mention how blockchain was applied. Recently, [PERSON] et al. [16] applied blockchain technology and IoT in the logistics system. The research analyzed the advantages of applying blockchain and IoT technology, but they did not analyze the security issues. The studies listed above have advantages and disadvantages for us to refer to. Consequently, we proposed a system with a comprehensive architecture with security. In
## 4 Experimental Results  
One synthetic and two real datasets that are commonly used in HU tasks were selected and the datasets were obtained from [54]. Through quantitative evaluation and testing, the unmixing performances of several advanced unmixing algorithms were compared with the proposed EACNN. The network used the above datasets as training and the ground truth for verification; the details were as follows:  
1. Synthetic dataset: the first dataset contained \(200\times 200\) pixels and \(224\) effective spectral bands, while the ground truth contained five randomly selected endmembers.
2. Jasper Ridge dataset: this contained \(100\times 100\) pixels and \(198\) effective spectral bands, while the ground truth contained four endmembers: \"Road\", \"Soil\", \"Water\" and \"Tree\".  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Endmember Network** & **Unmixing Network** \\ \hline \multirow{4}{*}{Convolution block 1} & \(1\times 1\) Conv & \(5\times 5\) Conv \\  & Batch normalization & Batch normalization \\  & Dropout & Dropout \\  & Tanh & Tanh \\ \hline \multirow{4}{*}{Convolution block 2} & \(1\times 1\) Conv & \(3\times 3\) Conv \\  & Batch normalization & Batch normalization \\  & Tanh & Tanh \\ \hline \multirow{2}{*}{Attention block 1} & Efficient convolutional block & \multirow{2}{*}{Efficient channel attention} \\  & attention module & \\ \hline \multirow{4}{*}{Convolution block 3} & \(1\times 1\) Conv & \(1\times 1\) Conv \\  & Batch normalization & Batch normalization \\  & ReLU & ReLU \\ \hline \multirow{4}{*}{Convolution block 4} & \(1\times 1\) Conv & \(1\times 1\) Conv \\  & Softmax & Softmax \\ \hline \multirow{4}{*}{Convolution block 5} & \(1\times 1\) Deconv \\  & & Batch normalization \\  & & Sigmoid \\ \hline \multirow{4}{*}{Convolution block 6} & \(1\times 1\) Deconv \\  & Batch normalization \\  & Sigmoid \\ \hline Attention block 2 & Efficient channel attention \\ \hline \multirow{4}{*}{Convolution block 7} & \(3\times 3\) Deconv \\  & Batch normalization \\  & Sigmoid \\ \hline \multirow{4}{*}{Convolution block 8} & \(5\times 5\) Deconv \\  & Batch normalization \\ \cline{1-1}  & Sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific network details of the EACNN.  
* [PERSON] dataset: the last dataset contained 95 \(\times\) 95 pixels and 156 effective spectral bands, while the ground truth contained three endmembers: \"Rock\", \"Tree\" and \"Water\".
## 2 Materials and Methods
### First Part: Automated System Overview
#### 2.2.1 Stage 1: FHC Detection  
The first phase of the proposed methodology focused on the detection of the FHC. For this purpose, two different datasets were used to train the regression model: one containing the spatial location of the hip joints and the other comprising specific landmark coordinates around the femoral head.  
For the first dataset, 7967 annotated VDHE images from the University of Copenhagen and the Danish Kennel Club databases were used, with bounding boxes indicating the spatial locations of the hip joints (Figure 2).  
For the second dataset, 571 VDHE images were obtained from the Veterinary Hospital of the UTAD and the Danish Kennel Club databases. These images had previously been uploaded to the Dys4 Vet 25 software as part of an earlier project focused on NA calculation, in which [PERSON] performed manual annotations to train an automated NA model.  
Although NA is not the focus of the present study, the annotations generated during that earlier work proved valuable for the development of the current FHC/DAE system. The NA was calculated using a semi-automatic method in Dys4 Vet. For each hip joint, four anatomical landmarks were annotated: P1, P2, and P3, positioned around the femoral head to define a circle accurately representing its curvature, and P4, marking the effective CrAR. Using these annotations, the software automatically computed the angle between the line connecting the centers of the generated circles and the line extending from each center to the respective ipsilateral CrAR.  
Figure 1: Overview of the system pipeline for FHC/DAE measurement. The process initiates with the input of a ventrodorsal hip-extended (VDHE) radiographic image, followed by parallel computational models for detecting the femoral head center (FHC) and the dorsal acetabular edge (DAE) using segmentation and keypoint detection models. The extracted coordinates are integrated into mathematical operations to calculate the FHC/DAE distance.  
Importantly, the three femoral head points (P1, P2, P3) were repurposed in the current study as ground truth for training the regression network used to identify the FHC. The image crops from both sides were combined due to the symmetry introduced by applying a flip, resulting in identical outputs.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 5 Ongoing R&D
### The Massive Project
Figure 17 shows the throughput and packet loss ratio (PLR) vs. offered MAC load comparison between two E-SSA and ME-SSA waveforms, both with the same FEC coding rate (3 GPP Turbo code with rate 1/3), a payload length of 1200 bits, a spreading factor of 16 and a chip rate of \(R_{c}=1.92\) Mcps, and for two power randomization (PR) cases: a power randomization uniformly distributed between 0 and 5 dB; no power randomization. In practice, the ME-SSA waveform corresponds to the adaptation of the F-SIM one. The minimum received \(\mathrm{E_{s}/N_{0}}\) is 6 dB for both waveforms. The only difference, as explained above, is that the E-SSA waveform adopts BPSK modulation and the SUMF receiver, whereas the ME-SSA one adopts QPSK modulation and the MSD receiver. Simulation results were obtained assuming six successive interference cancellation (SIC) loops and an actual packet demodulator with frequency and phase estimators enabled, but with an ideal preamble searcher. This means that all the burst timestamps at the receiver are known. As expected, in such a low-SF regime, the ME-SSA waveform outperforms the E-SSA one, improving the overall throughput. Without PR, at \(\mathrm{PER}\simeq 10^{-3}\), the throughput improvement obtained with ME-SSA is about 50%, with 5 dB uniform in dB PR, as expected, and such improvement decreases to about 25%. Figure 18 compares the performance, in terms of throughput vs. SIC loops, obtained by the MSD-based receiver for the same ME-SSA waveform introduced above, for two offered load cases and for two preamble searcher (PS) cases: the ideal PS and the actual PS. From Figure 18, it can be seen that, to obtain the same performance of the ideal PS case for the two offered load cases taken into account, more SIC loops are required as not all the interfering bursts are immediately known. Additionally, the maximum throughput obtained with a real PS was about 1 bit/chip, corresponding to a performance loss of  
Figure 16: Generic \(i\)th stage of the multistage despreader. about 15% compared to the maximum throughput obtained with ideal PS case, depicted in Figure 18. In summary, the actual receiver parameters (PS threshold, buffer size and dispatch, etc.) shall be carefully tuned to gain a trade-off between performance and latency when the MSD is employed. Figure 17: Simulated E-SSA and ME-SSA throughput and PLR performance with and without power randomization (PR), 3 GPP FEC coding rate 1/3 with block size of 1200 bits, spreading factor of 16 and \(\rm{E_{s}/N_{0}}=6\) dB. Figure 18: Simulated ME-SSA throughput performance without PR, 3 GPP FEC coding rate 1/3 with block size of 1200 bits, spreading factor of 16 and \(\rm{E_{s}/N_{0}}=6\) dB, for both ideal and actual demodulator preamble searchers (PSs).