esa-sceva/satcom-chunk-collection huggingface dataset:
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 3 Circuit Implementation and Calibration  
### DLL in Clock Generation Circuit  
The delay lock loop is composed of a variable delay line (VDL), phase frequency detector (PFD), a charge pump, and a mux module (CLK_CMP_MUX), shown in Figure 5a. The delay lock loop is mainly used to generate an accurate delay time, and this delay does not change with external conditions such as temperature and voltage changes.  
The basic unit of VDL is shown in Figure 5b. It is composed of a basic inverter and a control terminal Vct. Increasing the load can increase the delay time of timing, but it will also slow down the rising and falling edges [25]. Therefore, Vct needs to be properly controlled by a charge pump. Multiple (even 4/8/16) basic units can be used to increase the overall delay. In order to adapt to the clock frequency of 1.25 GHz-250 MHz, four same cell delay lines can be selected by control code P/N!0:2? Adjusting the voltage of Vct can make the rising edge of each delay line unit steeper or flatter, so as to achieve the purpose of adjusting the delay of the entire VDL. Considering the adjustment capability of each VDL, when the clock frequency is low, multiple groups of the same VDL can be used in series to increase the overall delay, and the number of VDLs in series is controlled by P/N!0:2?. For example, delay line1 is selected in Figure 5c. The timing of the delay line is introduced. A clock CLKC with a 25% duty cycle can be obtained through the simple logic operation of CK\({}_{\text{A}}\) and CK\({}_{\text{C}}\). CK\({}_{\text{A}}\) is delayed by 25% more of the clock cycle than CK. It is difficult to directly generate CK\({}_{\text{A}}\) through CK, but it is easy to delay CK by 50% of the clock cycle to obtain CK\({}_{\text{B}}\) due to CK and CK\({}_{\text{B}}\) being inverse. CK\({}_{\text{B}}\) and CK\({}_{\text{C}}\) are also inversely related. The phase detector detects the relationship between CK and CK\({}_{\text{C}}\). If the delay in CK\({}_{\text{C}}\) is too large, the charge pump makes the Vct smaller. Otherwise, it makes the Vct larger until the delay of VDL is exactly equal to one clock cycle. At this time, the final output of the DLL is CLKC.  
Figure 4: Clock schematic, including current mode logic, CML to the CMOS logic voltage, and non-overlapping with the outputs \(\phi_{1}\), \(\phi_{1e}\), \(\phi_{2}\), and \(\phi_{2e}\). DLL is added to generate \(\phi_{3}\), \(\phi_{3e}\), \(\phi_{4}\), and \(\phi_{4e}\).
## 6 Conclusions  
A new assessment methodology based on strength reduction method, named SRAM, is presented in this paper. It is well suited for reliability assessment of existing critical large reinforced concrete structures. After validation using literature-based results, the method is applied to two hydraulic structure examples. The key conclusions are as follows:  
* Compared to sophisticated non-linear finite element analysis, the method is simpler, more robust and allows to compute conservative value of structural resistance, excluding any contribution in tension from concrete;
* The methodology is well-suited for the assessment of existing large RC structures, particularly complex 3D hydraulic structures.
* The methodology can be used to assess disturbed 3D regions, with important advantages over conventional Strut & Tie model: possibility to include loads other than mechanical loads and to consider the structural effects of pre-existing cracks.
* While the focus of this paper is on large RC structures, SRAM's applicability extends to various types of RC structures and can be seamlessly integrated into future design and assessment codes.  
O.A.: Writing--original draft, Visualization, Software, Investigation; M.B.F.: Writing--review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Oumaima Abra was employed by the company IDAE s.e.n.c. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Constitutive Model
The choice of the Yld2000 model for this study is based on two key aspects: previous work has demonstrated that the Hill48 model does not adequately capture the complexity of non-stable austenitic steel 1.4301, underestimation of yield stresses, and r-values. Hill48 with conventional isotropic hardening drastically under-predicts yield stresses in certain directions once plastic strain increases [30; 31], leading to its dismissal for this application; moreover, the Barlat Yld2000 model is the most widely adopted in the industry and was the first approach applied to this industrial application [32; 33; 34]. Its continued relevance in the field is due to its proven accuracy and reliability in capturing the complex mechanical responses of materials under various loading conditions. The model's capability to describe orthotropic yield loci and incorporate isotropic-kinematic hardening effects makes it particularly suitable for the study of sheet metals, such as the austenitic steel 1.4301 used in this research. By employing the Barlat Yld2000 model, this study leverages a well-established and validated framework to ensure the reliability and comparability of the simulation results. This choice not only facilitates a deeper understanding of the springback phenomenon in industrial applications but also enables a meaningful comparison between experimental data and numerical predictions, reinforcing the study's findings with a robust theoretical foundation.
## 4 Simulation and Analysis of Magnetic Field Distribution
### Magnetic Field Distribution for Offset Coils
#### 4.2.2 Phase of the Primary Current  
Due to the characteristics of coupling coils, when the current on primary side changes, the secondary current also changes along with resonance phenomenon, and lags behind the primary current. The transmission from primary side to the secondary side is a cyclical process, and the magnetic field is a superposition of field generated by currents in the primary and secondary coils. Therefore, the maximum instantaneous magnetic field strength occurs in one cycle. Figure 14a shows the phase fluctuation of primary current corresponding to the risk point A3 when the coils are completely aligned. The maximum magnetic field at A3 is at a phase of 130 degrees of the primary current.  
Similarly, the five phases with various offsets can be obtained, and the primary current phase corresponding to the maximum magnetic field is shown in Figure 14b. The principle of coupling shows that as the offset increases, the primary current phase of the maximum magnetic field is different, which shows an increasing trend within the offset range. The simulation of magnetic field distribution is based on the current phase corresponding to the maximum magnetic field.
## 5 Simulation Results
### Simulation Environment
For each packet transmission, the channel is randomly generated, and the NBI is introduced as a single-frequency sinusoidal signal. To ensure the simulation reflects real-world conditions as closely as possible, the NBI frequency was randomly generated in each simulation iteration to represent varying interference conditions. Similarly, the ETU channel model parameters, including time delay and gain profiles, were randomly generated for each packet transmission. The BER results were then averaged over multiple iterations to provide a robust evaluation of system performance under diverse conditions. While these simulations may not perfectly replicate all aspects of real-world environments, they aim to provide meaningful insights into the practical applicability of the proposed method. The verification of the NBI cancellation performance is conducted by analyzing the BER. Two key performance measures are critical in frequency-nulling-based NBI cancellation: how effectively the NBI is canceled and how minimally the desired signal is affected. The BER serves as a comprehensive metric that reflects both of these performance aspects.
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.7 Channel Utilization vs. Slot Size
This decrease in empty spaces is essential to increasing the PDR even further. To put it simply, the combination of increasing the number of deployed nodes and the increasing communication range results in a methodical enhancement of PDR, which builds a more reliable packet transmission infrastructure. The average number of collisions per packet for a range of typical traffic loads is shown in Figure 18. Higher average traffic loads are correlated with a proportionate rise in collision incidence, according to the observed trend. Notably, every protocol that is examined responds to the increasing average traffic load in a comparable manner. This homogeneity results from the fact that CHs experience an increase in accidents as the average traffic load increases because of the increasing interference brought on by more simultaneous transmissions. The suggested technique stands out since it shows a significant improvement over the current methods. More specifically, compared to CAPC, CSMA, SFAMA, and T-LOHI, our proposed protocol records a 27%, 15%, 31%, and 37% decrease in the average number of collisions per packet when the average triple load is 0.015. This enhancement may be ascribed to the protocol's proficiency in identifying and executing the best energy-efficiency tactics, that can lessen the effects of many interferences. As such, there are essentially fewer collisions on average for each packet. In contrast, the results of T-LOHI, SFAMA, CAPC, and CSMA highlight the negative consequences of insufficiently allocating energy for data transmission. In these situations, there is a rise in packet losses, and therefore, in the average number of collisions per packet. Specifically, a protocol's capacity to properly handle numerous interferences might be compromised by inadequate energy allocation for data transfer. It must be noted that the risk of collision is doubled when the range of interference exceeds twice the extent of transmission. The increased number of sensors falling under the enlarged interference range causes this phenomenon and raises the possibility of accidents. Figure 17: PDR vs. number of nodes.
## 4 Control of Metal Melting Process
### Support Vector Regression Control of the Metal Melting Process  
Support vector regression (SVR) control of the metal melting is based on the development of four SVR models, which simultaneously perform the prediction of the alloying additives amount (Figure 10). Each of the developed models performs the prediction of the amount of one alloying additive. As well as in the case of the neural network development, a set of 280 data is used for the development of SVR algorithms, and a set of 20 data, which does not participate in development of algorithms, is used for testing of the developed SVR models. The inputs into SVR algorithms are: chemical composition of 2.5 tons of molten metal in the furnace: 1. C (%), 2. Cr (%), 3. Mn (%), 4. Si (%), chemical composition of 5 tons of steel waste: 5. C(%), 6. Cr(%), 7. Mn(%), 8. Si(%) and final chemical composition of molten metal: 9. C (%), 10. Cr (%), 11. Mn (%), 12. Si (%). The output from each algorithm is different and represents one of the alloying additives: carburizing agent (kg), FeCr (kg), FeMn (kg) and FeSi (kg).  
The developed SVR models are based on the use of the RBF kernel function. RBF, as the kernel function for SVR, has become the choice of researchers because of its accuracy and reliable performance. The parameters C from Equation (8) and \(\sigma\) from Equation (10) significantly affect the prediction potential and need to be carefully determined. The optimum values of \(C\) and \(\sigma\) parameters were determined by using the grid-based search technique proposed by [PERSON] et al. (2003) [PERSON] et al. (2003). Table 3 provides the parameter values and the corresponding mean square error for each model in three cases.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Carburizing Agent (kg)** & **FeCr (kg)** & **FeMn (kg)** & **FeSi (kg)** \\ \hline Mean error (\%) & 0.47 & 0.51 & 3.31 & 1.88 \\ Max. error (\%) & 1.97 & 1.92 & 9.42 & 8.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum and mean error in the testing phase of the neural network.  
Figure 9: Neural network architecture for prediction of the amount of alloying additives.  
Based on the training phase, the greatest prediction potential was shown by case 2 when the parameters \(C\) and \(\sigma\) had values of 10 and 0.005, respectively. In this case, the mean squared errors in the training phase for four SVR models are in range 0.38-0.47. As a measure of validity of the SVR models, the maximum and mean error in the testing phase was used. The testing is performed with a set of 20 data, and the maximum and mean error for prediction four alloying additives are given in Table 4.
# A Microscopic Traffic Flow Data Generation Method Based on an Improved DCGAN
## 2 Materials and Methods
### Improved DCGAN Model  
The overall structure of DCGAN is shown in Figure 1. In order to enable the network to learn the distribution \(P_{x}\) of the real sample \(x\), we define the random input of the generator as \(n\), and then map it to the data space \(P_{G}\) according to \(G(n)\); we also define discriminator \(D\), whose corresponding output \(D(x)\) is a scalar representing the probability that \(x\) comes from the real dataset rather than from the data space \(P_{g}\). In the process of confrontation training, we classify the probability of correctly classifying real samples and generating samples by maximizing \(D\), and then we can complete the training of \(G\) by minimizing \(log(1-D(G(n)))\), where they play a minimum and maximum game.  
DCGAN reflects this theory in the actual training steps to carry out partial training for the two networks. The first step is to train the discriminator with the mixed samples from the generator and training set to complete the training of one step and improve its ability to distinguish between true and false data. In the next step, the generator and discriminator are combined into a network, which is fixed to the \(D\) that completed the training in the previous step, and then the generator completes the training of \(G\) under the current step according to minimum \(log(1-D(G(n)))\), and then it turns into the next cycle of training. In the end, the two will fight against each other in the cycle of circuit training and improve their abilities to reach Nash equilibrium [22].  
Figure 1: DCGAN network diagram.  
The core of DCGAN is the training of the generator and discriminator against each other to make the generated data conform to the distribution of real data. In order to make the basic DCGAN adapt to the scene data tensor, we modified the basic DCGAN in this paper. First, the input and output dimensions of the tensor were modified so that it could output traffic scene data that met our requirements. Second, because the training set data have a certain time dependence and the longitudinal position of the vehicle increases monotonically with time, this paper combines the Gate Recurrent Unit (GRU) with the DCGAN to change the discriminator model of DCGAN to a model composed of the GRU network.
# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 3 Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Type of Lesion** & **AUC\_ROC** & **mAP** & **F1** & **Accuracy** & **Recall** & **Specificity** & **Precision** \\ \hline \multirow{3}{*}{U-Net [7]} & EX & 98.80\% & 43.59\% & 45.99\% & 99.91\% & 41.71\% & 99.96\% & 51.26\% \\  & HE & 94.26\% & 49.99\% & 47.37\% & 99.60\% & 35.67\% & **99.92\%** & 70.48\% \\  & EX & 99.03\% & 55.90\% & 48.85\% & **99.93\%** & 38.18\% & **99.98\%** & 67.81\% \\  & HE & 93.70\% & 52.87\% & 49.81\% & **99.61\%** & 38.27\% & **99.92\%** & **71.30\%** \\ \hline \multirow{3}{*}{DRNet [32]} & EX & 99.15\% & 41.3\% & 44.58\% & 99.91\% & 40.18\% & 99.96\% & 50.06\% \\  & HE & 95.54\% & 50.55\% & 48.95\% & 99.59\% & 39.11\% & 99.89\% & 65.40\% \\ \cline{1-1}  & EX & **99.59\%** & 53.67\% & 49.59\% & 99.92\% & 41.07\% & 99.89\% & 62.59\% \\ \cline{1-1}  & HE & 95.25\% & 52.54\% & **53.13\%** & 99.57\% & **47.68\%** & 99.84\% & 59.99\% \\ \hline \multirow{3}{*}{CAR\_UNet [42]} & EX & 99.60\% & 47.19\% & 44.95\% & 99.92\% & 35.04\% & 99.97\% & 62.67\% \\ \cline{1-1}  & HE & 96.07\% & 48.78\% & 45.03\% & 99.58\% & 33.63\% & 99.92\% & 68.13\% \\ \hline \multirow{3}{*}{Ours} & EX & 99.51\% & **57.90\%** & **52.77\%** & **99.93\%** & **41.95\%** & **99.98\%** & **71.10\%** \\ \cline{1-1}  & HE & **97.34\%** & **53.63\%** & 50.86\% & **99.61\%** & 40.35\% & 99.91\% & 68.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative segmentation of EX and HE on ultra-wide-field fundus images. We employ the lightweight MobileNetV2 as the encoder of our network to reduce training time and memory usage. Table 4 shows a comparison of the different lightweight model parameters on IDRID. As illustrated in the comparative results, our model boasts only 0.95 M parameters, the smallest number among all the methods evaluated.
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 2 Materials and Methods
### Accuracy of the Results
#### 2.4.1 A Priori Accuracy of the Block  
The estimation of the a priori planimetric error of the blocks with four GPSs at the edges (Scenario A) uses the next equation:  
\[\sigma_{\mathrm{B,L}}=(0.47+0.25\mathrm{n_{s}})\sigma_{\mathrm{M,L}}, \tag{1}\]  
where:  
\(\sigma_{\mathrm{B,L}}=\) estimated planimetric accuracy of the block (L = XY);  
\(\sigma_{\mathrm{o}}=\) sigma naught of the bundle block adjustment;  
\(\mathrm{n_{s}}=\) number of strips;  
\(\sigma_{\mathrm{M,L}}=\) estimated planimetric accuracy of a single model.  
Equation (1) was conceived for aerial photographs measured with analytical stereovolotters [47]. For digital photogrammetry with digital sensors, we can rewrite Equation (1) as the following:  
\[\sigma_{\mathrm{B,XYZ}}=\mu_{\mathrm{xyz}}*\sigma_{\mathrm{o}}*\mathrm{GSD} \tag{2}\]  
Figure 7: Processing of each scenario: (**A**) Scenario A, processing of photographs with conventional flight planning; (**B**) Scenario B, Scenario A plus the block perimeter; (**C**) Scenario C, Scenario A plus one cross strip at each edge; (**D**) Scenario D, Scenario B plus three cross strips at each edge.  
where:  
\(\sigma_{\rm o}\) = sigma naught of the bundle block adjustment can be taken as the mean reprojection error of the adjustment.  
\(\mu_{\rm xyz}\) = weight coefficients depending on the layout of both the GCP and the image network.  
This paper seeks to identify the weight coefficients (\(\mu_{\rm xyz}\)) in Equation (2) for the four scenarios acquired by UAV, A, B, C, and D, in order to determine the best equation for the a priori accuracy estimation valid for the UAV-DAP with absolute GNSS on flat areas. To the authors' knowledge, no similar equation exists in the literature.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 4 Experiments and Evaluation
### Machine Learning Method  
#### 4.2.1 Training  
After data quality control and collocation, 2913 samples were obtained in total in the training process, 1942 of which were used while the remaining 971 were used for testing. Three GNSS-R observables and six kinds of meteorological factors were input to the classifier as features; the samples with bloom coverage (NDVI \(>\) 0.05) were labeled as 1 or 0 for bloom or no bloom, respectively. The RUSBoost algorithm was realized through the Classification Learner in the Machine Learning and Deep Learning Toolbox of MATLAB R2021b. The numbers of weak learners, learning rate and the cost of misclassification were set as 100, 0.1 and 1, respectively, as they performed the best during exhaustive tests.
# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 3 Methodology
### Contamination Segmentation
#### 3.4.2 Semantic Segmentation Model Architecture  
To accomplish the semantic segmentation task, we used DeepLabv3+, a state-of-the-art semantic segmentation algorithm developed by Google researchers [47]. The DeepLabv3+ architecture comprises an encoder and a decoder. Multi-scale contextual information from the image is encoded, and object boundaries are precisely and accurately recovered by the decoder module. The encoder comprises three essential components: ResNet, atrous convolution, and atrous spatial pyramid pooling. In this study, we employed ResNet50 as the network backbone to extract features. Atrous convolution is a helpful technique that enables the model to directly modify the resolution of features generated by deep convolutional neural networks and alter the filter's field of view to collect multi-scale information. The mathematical expression of atrous convolution is as follows:  
\[y[i]=\sum_{k}x[i+r.k]w[k] \tag{10}\]  
where \(r\) is the atrous rate (by changing it, the field of view of the filters can be modified adaptively), \(w\) depicts the convolution filter, and \(i\) and \(k\) show the pixel locations.  
Atrous spatial pyramid pooling (ASPP), the other main module of DeepLabv3+, is used to resample the features extracted from the model backbone at several rates before convolution. This is equivalent to scanning the original image with several filters, each with a complimentary effective field of view, in order to capture objects and valuable visual context at different scales. We chose the default DeepLabv3+ dilation rate of 6 since contamination might occur in a very small or very large region. The outputs of the ASPP are concatenated and pass through a \(1\times 1\) convolution layer with 256 filters that can generate rich semantic information.  
A bilinear upsampling factor of four is then applied to the encoder's features before passing them to the decoder section and concatenating them with low-level features from the backbone. The low-level features from the backbone network are subjected to a \(1\times 1\) convolution layer that limits the number of channels to prevent the encoder features' importance from being outweighed and complicating the training process. Once the low-level features and the encoder's rich features are combined, a few \(3\times 3\) convolution layers are used to improve the generated features, and finally, a bilinear upsampling by a factor of four is applied to generate the segmentation output.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 3 Triple Modular Redundancy in FSMs
### Model 4--Triplicated the Whole FSM  
Model 4 presents a situation in which the entire FSM is triplicated. Here, we are dealing with a situation in which we treat the FSM as a black box. A diagram of the architecture is shown in Figure 12.  
1. If the error occurs in the output block \(\lambda\), it will be mitigated due to the voter;  
Figure 11: Model 3—transition block \(\delta\) is triplicated and independently registered.  
Figure 12: Model 4—the whole FSM is triplicated.  
(2,3) If the errors cause FSM to go to the wrong state, and hence, have an incorrect cycle, the voter will correct the operation of the entire block and give the correct result.  
However, the faulty FSM will not be corrected and will remain in the wrong cycle. If an error is injected into any of the others FSMs, there will potentially be two FSMs operating in the wrong cycle and the results of the entire block will be incorrect.