esa-sceva/satcom-chunk-collection huggingface dataset:
# Symbol Error-Rate Analytical Expressions for a Two-User PD-NOMA System with Square QAM
## 2 System Model  
Our work considers only the two-layer PD-NOMA multiplexing in the downlink. The PD-NOMA system model includes a one-base station (BS), two user's equipments (UE\({}_{1}\) and UE\({}_{2}\)), and AWGN channel propagation between them. The downlink group signal \(s\) at BS output is given by the superposition of two user signals according to  
\[s=\sqrt{p_{1}}x+\sqrt{p_{2}}y, \tag{1}\]  
where \(x\) and \(y\) are the modulation symbols, and \(p_{1},\ p_{2}\) are the power weights of UE\({}_{1}\) and UE\({}_{2}\). The total power of the output signal is \(E_{s}=p_{1}+p_{2}\). We use a typical scenario for the PD-NOMA that UE\({}_{1}\) and UE\({}_{2}\) are located far from the BS and near it. In this scenario, the channel state of UE\({}_{1}\) is worse compared to UE\({}_{2}\); therefore, \(p_{1}>p_{2}\). We assume that users utilize signal constructions based on Gray coding and square QAM with the modulation orders \(Q_{1}\ Q_{2}\in 2^{R}\) (\(R=2t\) (bits/symbol), \(t\in\mathbb{N}\)). The possible states of \(x\) and \(y\) are given by the alphabets \(\mathbf{A}_{1}\) (\(x\in\mathbf{A}_{1}\)) and \(\mathbf{A}_{2}\) (\(y\in\mathbf{A}_{2}\)). Figure 1 offers an example of two 16-QAM (\(Q_{1}\), \(Q_{2}=16\)) user constellations and the superposed PD-NOMA constellation. The minimum Euclidean distance \(D_{k}\) between the nearest points on the power-weighted constellation of UE\({}_{k}\) is given by  
\[D_{k}=2\sqrt{\frac{p_{k}}{M_{k}}}, \tag{2}\]  
where \(M_{k}{=2/3\times(Q_{k}-1)}\) is the normalization factor.  
The group signal is transmitted to users via the AWGN channel with a complex additive noise \(w\sim CN(0,\,N_{0})\) that follows the Gaussian probability distribution function with zero mean and noise power spectral density \(N_{0}\). The PD-NOMA symbol received by the one of the users is given by  
\[s=\sqrt{p_{1}}x+\sqrt{p_{2}}y+w. \tag{3}\]To decode a PD-NOMA symbol, the successive interference cancellation (SIC) method is employed. Thus, the symbol \(x\) is decoded from the first non-orthogonal layer by the minimum Euclidean distance criterion from \(s\) and by treating \(y\) as interference. It can be expressed as  
\[\hat{x}=arg\underset{x\in\mathbf{A}_{1}}{min}|s-\sqrt{p_{1}}x|^{2}. \tag{4}\]  
Then, the symbol \(y\) is decoded from the second non-orthogonal layer using SIC by following these three steps. Firstly, the symbol \(x\) is decoded by treating its own symbol \(y\) as the interference. Secondly, the decoded symbol \(\hat{x}\) is cancelled from \(s\) as follows: \(st=s-\sqrt{p_{1}}\hat{x}\). Finally, its own symbol \(y\) is decoded from \(s\prime\) by  
\[\hat{y}=arg\underset{y\in\mathbf{A}_{2}}{min}|st-\sqrt{p_{2}}y|^{2}. \tag{5}\]
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 2 System Model  
### NTN Architecture  
Figure 2 shows the architecture of the NB-IoT NTN with service and feeder links. In this study, we apply the LEO-based Earth fixed cell scenario, where NTN provides a fixed service cell for a specific location on Earth for a particular amount of time. In addition to the LEO-based Earth-fixed cell scenario, 3 GPP has presented an LEO-based Earth-mobile cell scenario, in which NTN provides a cell moving in satellite orbit [21]. The reasons for applying the LEO-based Earth fixed cell scenario in this study are as follows. (i) The Earth-fixed cell scenario is more straightforward than the Earth-moving cell scenario, which requires frequent handovers; (ii) NB-IoT communication would be required at a specific time, such as a notification message transfer. Therefore, rather than finding a new satellite that can communicate each time, we decided that an LEO-based Earth-fixed cell scenario that can communicate with the same satellite every time is better.  
In this study, the reference position is latitude 36.35080\({}^{\circ}\) N, longitude 127.30122\({}^{\circ}\) E. The satellite orbits are implemented through 3D satellite simulation. The orbit can be determined through the TLE file and in this study, the Starlink satellite TLE file is used [29]. In real Starlink satellites, the Ka- and Ku-bands are used [8]. We utilized only orbital information from the Starlink satellite's TLE file because 3 GPP NB-IoT NTN in the S-band is currently under research and has not yet been commercialized. Therefore, the study was conducted using the parameters presented in the 3 GPP document, not the Starlink parameters [21]. We also adopted Starlink satellite orbits because they are currently the most known satellites and have a relatively large number of satellites and orbits compared to other commercial satellites, allowing us to experiment with various orbits. Satellite and UE parameters are summarized in Tables 2 and 3.  
Figure 1: Problems and solutions for the NB-IoT uplink in NTN.  
We consider the following assumptions regarding the architecture of the NB-IoT NTN. (i) The target UE is located within the spot beam; (ii) the satellite can steer beams towards fixed points on earth using beamforming techniques; (iii) assuming that the feeder and the inter-satellite link are ideal, the service link performance is analyzed; (vi) a minimum elevation angle of 10 degrees is considered for the UE and the satellite.
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 3 Experiment Description
Then, the signal rays passing through from the side face are extracted and divided to two parts, one of which are signals penetrating from the side face at a height greater than 5500 m. Additionally, the intersections of this part of the signal rays with the top layer, namely the layer of 8 km, are counted. The boundaries of the outer region in the east-west and north-south directions are decided by the distribution of these intersections. 4. To check the boundaries of the outer region. If the horizontal range of the outer region is within twice the length of a voxel, the reasonable boundaries of the outer regions are achieved. Otherwise, go back to Step 2, increase the value of the altitudinal boundary, and re-proceed to the next steps. After these procedures, the boundaries of the outer regions are determined in the Hong Kong experiment, the boundary for altitude is 5.5 to 8 km, and the boundaries of the four directions (the north, east, south, and west) are 22.59\({}^{\circ}\), 114.43\({}^{\circ}\), 22.11\({}^{\circ}\), and 113.84\({}^{\circ}\), respectively. Therefore, a total of 8 \(\times\) 7 \(\times\) 10 voxels in the research area and four extra unknown parameters outside the research area are obtained. Figure 4: Scatter plot and polynomial fit of water vapor density with altitude (upper for 45,004, middle for 59,280, and lower for 59,316, respectively). To evaluate the performance of the proposed method, we made tomographic solutions using the traditional method with 8 \(\times\) 7 \(\times\) 10 voxels, identified as Scheme #1, and the new method above-mentioned, identified as Scheme #2. In addition, Scheme #3 is to extend the size of the entire tomographic area, that is, the scope is changed to (113.81\({}^{\circ}\), 114.41\({}^{\circ}\)) for longitude, (22.14\({}^{\circ}\), 22.59\({}^{\circ}\)) for latitude. The new scope, which results in 10 \(\times\) 9 \(\times\) 10 voxels in the tomographic modeling, is nearly to the outer boundaries of Scheme #2. For Scheme #3, the horizontal resolution in the east-west and north-south direction are 0.06 and 0.05, respectively, and the vertical resolution is 800 m, which is the same as that in Scheme #1. Since the outer regions in Scheme #2 are included, Scheme #3 can also utilize the increased signal rays of Scheme #2 in the tomographic solutions.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 3 Results  
### Station-to-Station and Year-to-Year Cross Validation Results  
In order to compare different machine learning models, three ML models (i.e., RF, ANN and CNN) were used to carry out the validation.  
Figure 2 shows the station-to-station cross-validation results of each model. Two interesting aspects could be observed according to the obtained results. First, there were significant differences among the performance of the models. CNN led to the best performance for all three carbon fluxes, while RF had the worst. Meanwhile, ANN was slightly worse than CNN. Second, according to the obtained R\({}^{2}\), all models generally achieved the best performance in predicting GPP, followed by RECO and NEE. Though NEE had slightly higher RMSE than RECO for ANN and CNN, GPP generally obtained the largest RMSE in both experiments, followed by RECO and NEE. This could be due to the wider range of GPP data compared to the RECO and NEE.  
Furthermore, Figure 3 shows the scatter plots of the station-to-station cross-validation for the best model, i.e., CNN. CNN led to R\({}^{2}\) values of 0.82, 0.72, and 0.62. However, GPP had a larger RMSE (1.54 g C m\({}^{-2}\) d\({}^{-1}\)) than RECO (1.27 g C m\({}^{-2}\) d\({}^{-1}\)) and NEE (1.31 g C m\({}^{-2}\) d\({}^{-1}\)). In the experiments, the scattered points were close to the 1:1 line and no significant underestimation or overestimation was observed.  
Figure 2: The coefficient of determination (**a**, _R\({}^{2}\)_) and root mean square error (**b**, RMSE) for station-to-station cross-validation of three models. RF: random forest; ANN: artificial neural network; CNN: convolutional neural network.  
As the time range of the used data for training was from 1999 to 2017, the data in 2018 were left out for the year-to-year validation purpose. The validation results are shown in Figure 4. The two interesting aspects could be highlighted in station-to-station validation, as also observed in the year-to-year validation. However, models generally had worse performance in the year-to-year validation than those in the station-to-station one, especially for the prediction of NEE by RF and ANN. This indicated that the predictability at the space scale was better than that at the time scale, especially for NEE. All models had similar RMSE around 1.9 and 2.3 g C m\({}^{-2}\) d\({}^{-1}\). Nevertheless, according to R\({}^{2}\), CNN could still be considered as the best model in general for all three fluxes. In summary, CNN outperformed the other two machine learning models according to the results of the two validation scenarios.  
Figure 4: The \(R^{2}\) (**a**) and RMSE (**b**) results of year-to-year validation by leaving out data in 2018.  
Figure 3: Scatter plots of predicted and observed values of GPP, RECO, and NEE of station-to-station cross-validation for CNN model; (**a–c**) are GPP, RECO, and NEE results, respectively.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.6 QoS  
As with security, QoS is a major issue in IoT environments. Most solutions available contemplate a set of classes for guarantees, e.g., guaranteed message delivery. CoAP provides support for confirmable/non-confirmable QoS. AMQP can support high availability based on queueing, and broker redundancy. MQTT integrates 3 levels of QoS: Best Effort, guaranteed message arrival (once), and guaranteed message arrival (more than once). DDS has a finer-granularity QoS, being able to provide parameters on data, even at the granularity of one topic. Examples of QoS parameters supported are: durability; presentation; lifespan, destination order, among others. OPC UA supports Best Effort only as it was designed, having in mind a specific use-case (in-plant communication). NDN has specific forwarding strategies, such as the _QoS-Forwarding Strategy (QoS-FS)_[26]. Furthermore, each NDN node can adjust data transmission based on required QoS and hence, rely on different forwarding strategies over a path between producer and consumer.
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 2 Methods
### Improvement on Mask R-CNN
#### 2.3.2 Edge-Prior Net  
EP-Net is an innovative backbone network designed to incorporate EPCBs, which explicitly enhance edge feature extraction and significantly improve the network's performance in detecting and recognizing target details.  
As shown in Figure 5, the proposed EP-Net backbone is divided into five stages, similar in structure to ResNet50. The first stage performs downsampling using an EPCB block with a stride of 2, which simultaneously conducts initial feature extraction. Each of the following four stages also begins with a stride-2 EPCB block for spatial downsampling, followed by multiple EPCB blocks with stride 1 for deep feature extraction and fusion. Specifically, the number of EPCB blocks in stages 2 through 5 are 2, 3, 14, and 2, respectively. The number of output channels for each stage progressively increases as follows: 64 (Stage 1), 96 (Stage 2), 192 (Stage 3), 384 (Stage 4), and 1408 (Stage 5). This gradual increase in channel depth enhances the network's capacity to represent complex features.  
A key distinction between EP-Net and ResNet50 lies in the design of their core convolutional units. EP-Net employs the EPCB throughout all stages, which integrates edge-enhancing operators such as Sobel and Laplacian filters in a multi-branch structure. In contrast, ResNet50 utilizes the BottleNeck module, which includes ReLU activations and residual shortcuts. Each BottleNeck in ResNet50 contains three convolutional layers, and the total network depth, including the classifier, reaches 50 layers. EP-Net, however, replaces all BottleNeck modules with EPCBs, thereby focusing on explicit edge information and achieving better structural adaptability for defect detection in CT images.  
In summary, the EP-Net backbone network enhances the extraction of defective edge features through the multi-branch EPCB convolution module during training. It also employs skip connections to accelerate model convergence and ensure efficient gradient propagation. Once training is complete, each EPCB--originally composed of four parallel branches--can be merged into a single equivalent convolutional operator. This transformation is mathematically equivalent to the original structure, preserving the same feature extraction capability while significantly improving computational efficiency. Specifically, the merged EPCB achieves approximately a fourfold increase in inference speed compared to its multi-branch counterpart and also reduces memory consumption. This design boosts CT image defect recognition accuracy and balances detection speed, thereby better fulfilling the requirements of industrial CT image defect detection.  
Figure 5: EP-Net architecture.
# Low-Cost Digitalization Solution through Scalable IIoT Prototypes
## 3 Materials and Methods
### Software
The chosen solution is easy to install and maintain, and is low cost when comparing it to the industry standard. For prototyping purposes that are very dependent on time, it is preferable to use a more \"user-friendly\" [61; 62] language, such as Python. Actually, Python is a high-level interpretative programming language that is gaining popularity in embedded development [63]. It has extensive support libraries and clean object-oriented designs that can increase the productivity of the programmer up to 10-fold [64]. The drawback is the lower execution speed. The most optimal solution is to have a combination of both. It is preferable if the solution can support data storage and visualization natively, because the implementation can be lengthy and the goal is to provide quick results. Having in mind that this solution must provide the platform for both the prototyping and production phase, the safety and security concerns are very important. Having in mind all of the above, the solution chosen in this work is the Zerynth platform [65]. The hardware element is the 4 ZeroBox, while the Zerynth Cloud provides device management and data visualization, as well as storage. It enables wireless connectivity and support for the most common industrial communication protocols. Zerynth OS is the base for firmware development, and it can be programmed in Python and C. It is easily connected to the cloud and provides a remote firmware update feature. The security requirement is fulfilled by the secure crypto element [66], and on the software side, the \"hardened\" TLS v1.2 and v1.3 protocol [67]. The storage is large enough so that data can be buffered and uploaded to the cloud in an appropriate format. The data are collected and sent to the cloud where they are available for export or visualization on the dashboards. The cloud solution has an option to visualize the data through an integrated dashboarding system. This system is based on Grafana [68], an open-source analytics and monitoring solution. The visualization support is native, and the time for setting it up is almost zero. Another very important benefit of a prototyping solution is its ability to be updated remotely. This capability is present in a chosen solution through the Firmware over the Air (FOTA) feature. FOTA allows for a new version of firmware to be installed remotely through the wireless connection. The operators need to be notified and FOTA can be scheduled during the downtime of the broaching machine operation, thus diminishing the intrusiveness of the update. Of course, for changes and improvements concerning the hardware and the broaching machine itself, these disturbances are necessary. Remote firmware update capability is important, because going to the site means disturbing the factory operation, as well as another cycle of installation and testing. Finally, the solution can be upgraded from a prototyping one to a full production level solution with almost zero effort. Both vertical and horizontal solutions are greatly improved, compared to the Arduino-based solutions.
## 1 Introduction
Although some alternative strategies have been demonstrated to achieve multiple-band absorption response, the absorption strength and sensing performance of the multiple-band absorbers are far from satisfying, see Table 1 for detailed performance comparisons. The novelty of this paper demonstrated here is mainly reflected in two aspects. Firstly, it simplifies the structure design of multiple-band metamaterial absorbers. Secondly, it improves the sensing performance of multiple-band metamaterial absorbers, that is, improves the application potential of the designed device. Herein, a triple-band near-perfect terahertz metamaterial absorber is demonstrated by using a pattern array of Au strip and an insulator dielectric spacer backed with a bulk continuous Au film. Three separated and narrow-band absorption peaks are realized, and the maximum absorbance of the device can reach 99.89%. The physical origin of the triple-band absorption originates from the combined effect of the fundamental mode resonance and two high-order (3-order) responses of the Au strip resonator, which is entirely different from previous multiple-band light absorbers. Sensing performance of triple-band absorber (in terms of the surrounding refractive index, thickness and refractive index changes of the overlayer) is also analyzed. The results demonstrate that the FOM (figure of merit) of the third resonance peak can be up to 40.67, which is much larger than previously reported works [43; 44; 45; 46; 47; 48; 49; 50], see Table 1. More importantly, we found that besides the frequency shifts with the change of the refractive indexes of the surrounding, the absorbance of the third resonance peak has an apparent change. The absorbance sensitivity of the third peak can reach 646.3% per refractive index. To the best of our knowledge, this is the highest degree of sensitivity in the absorbance change. These resonance characteristics prove that the designed triple-band absorber has promise in many areas, in particular of sensors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Working Bands** & **FOM** \\ \hline
[43] & terahertz & 2.3 \\
[44] & terahertz & 1.2 \\
[45] & terahertz & 3.0 \\
[46] & terahertz & 7.5 \\
[47] & terahertz & 3.0 \\
[48] & terahertz & 1.5 \\
[49] & terahertz & 2.3 \\
[50] & terahertz & 1.5 \\ This work & terahertz & 40.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of FOM (figure of merit) values among terahertz devices.
## 3 Results and Discussion  
### Surface Roughness Prediction Result  
As the correlation analysis is processed, the features (including time-domain and frequency-domain) that have correlation coefficient of values higher than 0.4 (as shown in Tables 3-5 and Figures 5 and 6) are selected as the input layer of the prediction model. In this research, the Elman neural network [15, 23] was employed to form the prediction model of surface roughness during the milling process. Among the total of 116 measured data sets, 100 data sets were randomly collected to train the prediction model, and the remaining 16 data sets were utilized for testing. The training and testing steps were repeated for five rounds to execute the cross-validation. The prediction results of Elman  
Figure 5: Correlation coefficients among Ra values and power spectrum densities of spindle vibration signals as well as their corresponding envelopes in three directions.  
Figure 6: Correlation coefficients of order spectra of spindle vibration signals and their corresponding envelopes with respect to Ra values.  
neural network are presented in Figure 7. With the selected features of measured signals, it can be observed that the Ra values of Inconel 718 work-piece during the milling process can be predicted accurately. The predictive error was assessed to be 18.85% in terms of the mean absolute percentage error (MAPE) value in this case. On the other hand, the coefficient of determination (R\({}^{2}\)) of the predictive model is 0.671. Furthermore, the prediction accuracy can be enhanced to the MAPE value of 13.48% by combining the signal features and the cutting parameters (as shown in Table 2) as the input layer of Elman neural network, and the prediction result is shown in Figure 8. The R\({}^{2}\) value is 0.862 in this case.  
Figure 8: Prediction results of Ra values through Elman neural network by using signal features and cutting parameters.  
Figure 7: Prediction results of Ra values through Elman neural network by using signal features.