esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 1 Introduction
It differs from previous work mainly in that it connects the operational and strategic aspects: the digital twin and the advanced algorithm do not only focus on \"production efficiency\" but also on the correct sequencing of orders. All identified gaps are targeted: the model removes arbitrariness in priorities by introducing scalable coefficients, overcomes slow responses by using DT in real time, and moves from a theoretical framework to a practical solution ready for deployment in the industry. The aim of this article is to present and verify an innovative approach to dynamic production planning using digital twins, which integrates real-time data from tracking systems (RTLS) and a complex order prioritization model with variable coefficients. The proposed system is primarily intended for custom production, where it allows the immediate adaptation of production plans according to the current situation, thereby significantly increasing the efficiency and flexibility of the production process. In the context of this goal, the following sections contain a description of the methodology, presenting the proposed modular architecture of the digital twin; the data collection method (RTLS); the order prioritization model; and the simulation methods used to verify the system. The experimental part presents a case study on the production of roof windows, where the system was practically implemented and tested, including a detailed description of the simulations performed. The experimental results demonstrate the effectiveness of the proposed system using clearly defined performance indicators, such as the total production time (makespan) and time and cost savings.
## 5 Security Proof and Analysis  
The communication security of the proposed scheme will be analyzed in this part. In Section 2, we presented some of our security goals and the threats that may be met. Here, firstly, we introduce the definition of the elliptic curve discrete logarithm problem (ECDLP) upon which the entire analysis is based.  
Definition 1 (Ecdlp): Let \(P\) be the generator of group \(G\), with \(n\in Z_{q}\) and \(N=nP\in G\). The ECDLP states that it is computationally infeasible to determine the value of \(n\) given \(N=nP\). To establish the security model for the proposed scheme, a game is defined involving adversary \(\mathcal{A}\) and challenger \(\mathcal{C}\), leveraging the aforementioned network.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 4 Results
### Reconstruction Performance Evaluation
In terms of per-class metrics, CV-CNN exhibited higher F1-scores in 12 out of 16 defect types, suggesting superior and more stable classification capability. Notably, for several critical defect categories such as Class 0, Class 3, Class 7, and Class 12, CV-CNN achieved significant improvements in both precision and recall, resulting in overall better F1 performance. For instance, the F1-score for Class 3 reached 0.8541 for CV-CNN, compared to 0.8142 for CV-FCNN. These improvements can be attributed to the model's ability to exploit both amplitude and phase information from complex-valued eddy current signals, particularly with the use of magnitude-based pooling. In summary, the comparison in Table 2 confirms the superior classification performance and robustness of the proposed CV-CNN model.
## 4 Multi-Hop Data Forwarding Strategy Based on Annulus-Sectors
### Energy Consumption of Nodes in an Annulus-Sector
#### 4.2.2 Energy Consumption of the Cluster Heads  
The energy consumption of the CH is related to amount of date been transmitted as well as the distance between this CH and its next-hop forwarder. In addition, it is not difficult to know that nodes in the RCCH are all located near the center of the annulus-sector. Therefore, for two communicable CHs in adjacent annuli, it is regarded that the hop distance between them is approximately equal to the width of the annulus. For each CH, its energy consumptions when sending and receiving one bit of data are defined as \(e_{t}\) and \(e_{r}\). Without loss of generality, their values are \(E_{elec}+\varepsilon_{fs}(R/k)^{2}\) and \(E_{elec}\), respectively.  
The CH in the \(k\)-th annulus, only needs to receive data uploaded from the non-CHs in the same annulus-sector and forward them to the CH in the \((k-1)\)-th annulus. Therefore, during a round of data gathering, the energy consumption of this CH is:  
\[E_{CH}^{k}=l((N_{k}-1)e_{r}+N_{k}e_{t}) \tag{7}\]  
In Equation (7), \(N_{k}\) is the average number of nodes of each annulus-sector in the \(k\)-th annulus, and the area of the \(k\)-th annulus is \((2k-1)\pi(R/k)^{2}\). Thus, it is easy to know that:  
\[N_{k}\approx(1/n)(2k-1)\pi(R/k)^{2}\rho \tag{8}\]  
Figure 4: Analysis on energy consumption of the non-cluster heads.  
By combining Equations (7) and (8), we get:  
\[E_{CH}^{k}\approx l\Bigg{(}\Bigg{(}\left(\frac{2k-1}{n}\right)\left(\frac{R}{k} \right)^{2}\pi\rho-1\Bigg{)}e_{r}+\left(\frac{2k-1}{n}\right)\left(\frac{R}{k} \right)^{2}\pi\rho e_{t}\Bigg{)} \tag{9}\]  
As for the CH in the \(i\)-th annulus (\(i\in[1,k-1]\)), it not only needs to receive data uploaded by the non-CHs in this cluster, but also receives data forwarded by the corresponding CH in the \((i+1)\)-th annulus. Then, it sends all these data to one of the CHs in the \((i-1)\)-th annulus (when \(i\in[2,k-1]\)) or to BS (when \(i=1\)). The total number of nodes in the \(i\)-th annulus (\(i\in[i+1,k]\)) is \(\sum\limits_{m=i+1}^{k}N_{m}\). Therefore, during a round of data collection, the energy consumption of this CH is:  
\[E_{CH}^{i}=l\Bigg{(}\Bigg{(}N_{i}-1+\sum\limits_{m=i+1}^{k}N_{m}\Bigg{)}e_{r}+ \Bigg{(}N_{i}+\sum\limits_{m=i+1}^{k}N_{m}\Bigg{)}e_{t}\Bigg{)} \tag{10}\]  
Similarly, \(N_{i}\) is defined as the average number of nodes of each annulus-sector in the \(i\)-th annulus, so:  
\[N_{i}\approx(1/n)(2i-1)\pi(R/k)^{2}\rho \tag{11}\]  
Also, according to Equations (10) and (11), it is known that:  
\[E_{CH}^{i}\approx l\Bigg{(}\Bigg{(}\Bigg{(}\frac{k^{2}-(i-1)^{2}}{n}\Bigg{)} \Bigg{(}\frac{R}{k}\Bigg{)}^{2}\pi\rho-1\Bigg{)}e_{r}+\Bigg{(}\frac{k^{2}-(i-1 )^{2}}{n}\Bigg{)}\bigg{(}\frac{R}{k}\Bigg{)}^{2}\pi\rho e_{t}\Bigg{)} \tag{12}\]
## 2 Introduction to Interval Analysis  
In this article, uncertainties in design parameters are represented as interval numbers. A comprehensive discussion of interval arithmetic basics is available in [45] by [PERSON] and in [46] by [PERSON]. This section will focus on presenting the most important fundamental information.  
The design parameters of the considered system can form a set, which can be denoted as a column vector \(\mathbf{p}=\text{col}[p_{1},p_{2},\dots,p_{s}]\), where \(s\) is the number of parameters. The values of these parameters are not precisely determined, but the lower and upper bounds of their variability are known, and they can be written as interval numbers. An uncertain parameter \(p_{i}\) can be represented as follows:  
\[p_{i}^{I}=\left[\underline{p}_{i},\overline{p}_{i}\right] \tag{1}\]  
where \(\left(\bullet\right)^{I}\) denotes an interval number, \(\underline{p}_{i}\) represents the lower bound of the design parameter, and \(\overline{p}_{i}\) represents the upper bound of the design parameter. It can also be expressed in the following way:  
\[p_{i}^{I}=\left[p_{i}^{C}-\Delta p_{i},\ p_{i}^{C}+\Delta p_{i}\right] \tag{2}\]  
or  
\[p_{i}^{I}=p_{i}^{C}\left(1+\delta_{i}^{I}\right) \tag{3}\]  
where \(p^{C}=\left(\overline{p}+\underline{p}\right)/2\) denotes the central value of the design parameter, \(\Delta p=\left(\overline{p}-\underline{p}\right)/2\) represents half of the interval determining the parameter, and \(\delta_{i}^{I}\) stands for the uncertainty of the parameter \(p_{i}\). In this article, the notation specified by Equation (3) is employed.  
Basic operations on interval numbers are presented in Appendix A.  
In general, computations involving interval numbers are significantly more complex and time-consuming compared to real numbers. This complexity arises from the need to consider all possible combinations of lower and upper bounds of parameters, followed by selecting the lower and upper bounds of the resulting value. Consequently, this leads to increased computational overhead.  
An additional challenge in analyzing engineering problems using interval analysis is the potential for significant overestimation resulting from the direct application of interval arithmetic. This occurs because, in formulating equilibrium equations for complex systems, one physical quantity may sometimes be represented by more than one interval number. Additionally, uncertainties of interval numbers may overlap, resulting in a much broader interval in the computation outcome than the one containing all possible solutions. Therefore, reducing overestimation is a key issue to address when employing interval arithmetic. This problem is discussed, among others, by [PERSON] in [45] and by [PERSON] in [47]. In this article, overestimation reduction was achieved through the application of the element-by-element technique and by transforming the solution into an iterative form, which will be discussed in detail based on the damping parameters of damping elements in Section 5.
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 4 Experimental Results and Performance Analysis
### Performance Analysis
#### 4.3.1 Different Methods to Optimize the Effect  
Figure 7 shows the performance comparison of different optimization methods. All three optimization methods outperform the serial LLL_FP algorithm. Among them, the assembly optimization method achieves the best results, closely followed by SIMD vectorization. The combination of instruction prefetch and loop unrolling marginally outperforms the serial LLL_FP algorithm. The results indicate that SIMD vectorization and assembly optimization effectively improve parallelism, as discussed in the algorithm analysis section. Additionally, techniques such as data prefetch and loop unrolling enhance overall performance by optimizing memory access.
## 4 Impact of Data Augmentation on Robustness against Adversarial Examples
### Robustness of Adversarially Trained Network with Mismatching Data Augmentation Strategy
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ModelAttack**} & \multicolumn{2}{c}{**FGSM**} & \multicolumn{2}{c}{**JSMA**} & \multicolumn{2}{c}{**C\&W**} & \multirow{2}{*}{**Average**} \\ \cline{3-4} \cline{6-10}  & & \(A\) & \(A^{\prime}\) & \(A\) & \(A^{\prime}\) & \(A\) & \(A^{\prime}\) & \\ \hline \multirow{4}{*}{Clean} & \(M\) & 41.74 & 71.05 & 56.80 & 65.61 & 38.17 & 92.20 & 60.93 \\  & \(M^{\prime}\) & 70.28 & 62.67 & 72.29 & 65.12 & 81.23 & 83.27 & 72.48 \\ \cline{2-10}  & \(M\) & 99.25 & 81.65 & 57.71 & 66.03 & 59.80 & 92.41 & 76.14 \\  & \(M^{\prime}\) & 81.05 & 99.10 & 73.72 & 72.32 & 84.38 & 93.35 & 83.99 \\ \cline{2-10}  & \(M\) & 48.60 & 69.61 & 96.37 & 95.95 & 72.71 & 94.92 & 79.69 \\  & \(M^{\prime}\) & 70.88 & 72.25 & 95.87 & 98.20 & 92.06 & 95.27 & 87.42 \\ \cline{2-10}  & \multirow{2}{*}{C\&W} & \(M\) & 66.96 & 70.38 & 63.05 & 68.56 & 99.40 & 98.94 & 77.88 \\  & \(M^{\prime}\) & 55.09 & 71.68 & 71.74 & 72.00 & 93.09 & 99.31 & 77.15 \\ \cline{2-10}  & \multirow{2}{*}{FGSM + JSMA} & \(M\) & 99.27 & 82.25 & 95.24 & 94.09 & 81.75 & 91.67 & 90.71 \\  & \(M^{\prime}\) & 82.29 & 99.00 & 95.79 & 98.15 & 95.57 & 98.03 & 94.81 \\ \cline{2-10}  & \multirow{2}{*}{FGSM + C\&W} & \(M\) & 99.32 & 88.34 & 63.17 & 69.91 & 99.63 & 98.52 & 86.48 \\  & \(M^{\prime}\) & 85.78 & 99.12 & 76.29 & 77.69 & 94.86 & 99.32 & 88.84 \\ \cline{2-10}  & \multirow{2}{*}{JSMA + C\&W} & \(M\) & 69.31 & 76.85 & 96.25 & 95.52 & 98.71 & 98.78 & 89.24 \\  & \(M^{\prime}\) & 68.94 & 84.26 & 95.75 & 98.29 & 96.89 & 99.16 & 90.55 \\ \cline{2-10}  & \multirow{2}{*}{FGSM + JSMA + C\&W} & \(M\) & 99.21 & 86.66 & 95.33 & 94.86 & 98.59 & 97.25 & 95.32 \\  & \(M^{\prime}\) & 85.12 & 98.96 & 95.15 & 97.84 & 97.30 & 99.19 & 95.59 \\ \cline{2-10}  & \multicolumn{2}{c}{Average} & 76.41 & 82.11 & 81.28 & 83.13 & 86.51 & 95.72 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test accuracy of LeNet5 networks on adversarial examples. \(M\) denotes a network model trained using data augmentation, while \(M^{\prime}\) is a network trained without data augmentation. \(A\) is the set of adversarial examples generated from a target network trained with data augmentation, while \(A^{\prime}\) is the set created from a network trained without data augmentation.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 1 Introduction
It supports a wireless 3D hexagonal lattice of a substantial number of processors, which can operate as a massively parallel platform. In previous work [3], our evaluations suggested that for a set of tasks and network attributes the ABC's processor utilities are comparable to modern HPC platforms. Our new work incorporates additional critical issues, specifically link utilisations, and we employ a store-and-forward packet-switching algorithm. Performance comparisons are then made between the ABC and a 5D topology (which is used in supercomputers like IBM's Blue Gene series). We will demonstrate that, when these new factors are included, for a specific set of tasks the ABC's performance is comparable to 5D topology. However, we also observe that a store-and-forward switching method has a negative impact, particularly on the link utility. The rest of this section is dedicated to a brief review over current short-range high-speed wireless technologies and packet-switching methods. The paper continues with a brief introduction to the ABC architecture in Section 2. Results from simulation experiments are presented in Section 3. The ABC's 3D topology and a 5D topology are compared based on both link and processor performances. The paper finishes with conclusions and considers ideas for future work in Section 4.
###### Abstract  
Pancreatic cancer (PaCa) is among the most aggressive malignancies of the digestive system. Inflammation plays a critical role in tumor growth and dissemination, with soluble cytokines serving as messengers that facilitate interactions between immune and cancer cells. The release of cell-free mitochondrial DNA (cf-mtDNA) into the bloodstream has been identified as a potent proinflammatory trigger, acting as a mitochondrial-derived damage-associated molecular pattern (mtDAMP). Whether a relationship exists between circulating cf-mtDNA (ccf-mtDNA) unloading and inflammation in PaCa remains unclear. In this study, we quantified ccf-mtDNA levels in plasma/serum samples from PaCa patients and healthy controls and examined their association with inflammatory markers. Analyses were conducted on 14 participants: 3 controls (mean age: 52.0 \(\pm\) 16.0 years, 67% women) and 11 PaCa patients (mean age: 69.1 \(\pm\) 10.0 years, 27% women). Circulating levels of ccf-mtDNA in PaCa patients did not show differences compared to controls (\(p\) = 0.06). In contrast, concentrations of interleukin (IL-8, IL-17, and interferon-gamma were significantly higher in PaCa patients. Stratification of PaCa patients based on the median ccf-mtDNA concentration revealed significantly higher levels of IL-4, IL-9, monocyte chemoattractant protein-1 (MCP-1), and macrophage inflammatory protein 1-beta in those with ccf-mtDNA levels above the median (\(p\) < 0.05). Significant positive associations were also observed between levels of ccf-mtDNA and IL-8, fibroblast growth factor, and MCP-1. These results suggest a potential association between elevated ccf-mtDNA levels and increased concentrations of proinflammatory cytokines, especially in PaCa patients with an unfavorable prognosis. Further research with larger cohorts is required to validate these findings and assess the prognostic value of these biomarkers.  
**Keywords:** cytokine; damage-associated molecular patterns; droplet digital PCR; extracellular mtDNA; macrophage; tumor microenvironment; sterile inflammation
## 5 Real Vehicle Experiments and Analysis of Results  
In order to verify the performance of the proposed system in real environment, a private dataset was self-made for real-vehicle experiments, and the public dataset M2 DGR is used to compare the proposed method with several mainstream LiDAR SLAM methods.
# Power Management Strategy for an Autonomous DC Microgrid
## 5 Economic Analysis
### SC and Battery Bank Aging Tariff  
The expected lifespan of a SC (held continuously at rated voltage and 25 \({}^{\circ}\)C) is fifteen years [39]. In real application conditions, recharge and discharge can accelerate the aging process so that its expected lifespan is estimated at ten years and the SC aging cost is calculated as in Equation (14).  
\[C_{SC_{-}Aging}=\frac{1}{3.6\times 10^{6}}\sum_{i=t_{0}}^{t_{F}}c_{SC_{-}Aging} \cdot|p_{SC}(i)|\cdot\Delta t \tag{14}\]  
where \(c_{SC_{-}Aging}\) is the SC energy tariff, which is about 0.3 \(\upxi\)/kWh.  
Similarly, the electrochemical battery bank aging is also considered, and its cost is calculated as given in Equation (15).  
\[C_{B_{-}Aging}=\frac{1}{3.6\times 10^{6}}\sum_{i=t_{0}}^{t_{F}}c_{B_{-}Aging} \cdot|p_{B}(i)|\cdot\Delta t \tag{15}\]  
where \(c_{B_{-}Aging}\) is the battery bank energy tariff, which is about 0.07 \(\upxi\)/kWh for the battery bank used in this study (Sonnenschein Solar Block of Exide Technologies indicates five years as the expected life [40]).
## 4 Results and Discussion
### Simulation Results
#### 4.1.1 Step Response  
The response curve under step signal input with an amplitude of 20 N is shown in Figure 12.  
All three controllers could make the output response meet the target values. However, there were differences at the beginning stage and the approaching stage. At the beginning stage, the BPNN-SP-PID controller acted first, and the reaction time was almost zero. The action time of BPNN-PID and PID was almost the same, taking ~80 ms to start the action. At the approaching stage, the PID method produced the largest overshoot of 6.1% among the three, and the overshoots of BPNN-PID and BPNN-SP-PID were 0.45% and 0.15%, respectively. At the stable stage, the residuals of the three were all close to zero due to the integral part of the PID controller.  
By comparison of the three response curves, two phenomena could be observed. First, the use of a BPNN greatly enhanced the robustness of the system. Due to the self-adjusting ability of neural networks, the approaching processes of BPNN-PID and BPNN-SP-PID were more stable than PID, and there was almost no overshoot or oscillation. Second, the  
Figure 11: MATLAB Simulink simulation block diagram.  
application of the Smith predictor could significantly improve the speed. Though BPNN-PID and BPNN-SP-PID showed good robustness, BPNN-SP-PID had a much faster reaction than BPNN-PID, especially at the beginning stage. This was mainly because the Smith predictor based on ANN2 could fit the system model precisely, resulting in a compensation for the inherent time delay of the system.
# Landsat Surface Product Validation Instrumentation:
## 4 Thermal Sensor Accuracy and Precision
### Ground Sensors  
Table 11 shows the key results that were generated from each flight. Recall that no flights occurred on the second afternoon due to high winds. Water targets are considered to be the most stable thermal target in the campaign and are highlighted in blue. These data suggest that the UAS FLIR is consistently within 1 \({}^{\circ}\)C of the TidbiT sensor in the pools. In comparison, the asphalt roadway temperature (highlighted in yellow) was measured by a contact thermocouple and the consistency with the UAS FLIR in this case was 2 \({}^{\circ}\)C. The thermocouples used in the experiment are base-metal Type K, with an accuracy of \(\pm\)2.2 \({}^{\circ}\)C [36]. Lastly, the large alfalfa target results are highlighted in green. In this case, the UAS FLIR is within 1 \({}^{\circ}\)C of the ground-based measurement made by the \(\mu\)FTIR. Overall, the UAS FLIR instrument consistently provided 1 \({}^{\circ}\)C accuracy.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{Object} & \multirow{2}{*}{Time (CDT)} & \multicolumn{2}{c}{Ground-reference temperature (C)} & \multirow{2}{*}{FLIR temperature (C)} \\  & & & \\ \hline Concrete (contact & & & \\ thermocouple) & 11:23 AM & 36.13 & 33.94 \\ \hline ColdPool (TidBit) & 11:23 AM & 13.83 & 14.12 \\ \hline AmbientPool (TidBit) & 11:23 AM & 25.76 & 24.92 \\ \hline
**Flight 2—Morming—Stare—30 August 2021 (Estimated Pass 11:52)** & & & \\ \hline \multirow{2}{*}{Object} & \multirow{2}{*}{Time (CDT)} & \multicolumn{2}{c}{Ground-reference temperature (C) [Std.} & \multirow{2}{*}{FLIR temperature (C)} \\  & & Dev] & \\ \hline Concrete (contact & & & \\ thermocouple) & 11:52 AM & 38.83 & 37.39 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Thermal sensor accuracy measurements.
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 3 Results
### OK Fitting and Optimal Interpolation  
Similar to the RF prediction model, GridSearchCV was applied to various options and parameter grids defined for the OK semivariogram. This procedure was repeated until the minimum RMSE was obtained. The grid in this study consisted of three variogram models: 20 levels of nlags, 11 weight steps, two types of coordinate types, and 20 levels of n_closest_points, resulting in 13,200 combination options. A five-fold cross-validation approach was used to perform 66,000 estimations. The iterations were based on the RMSE metric to identify the choice with the lowest prediction error and highest determination coefficient.  
The optimization results showed that the optimal options and parameters for the OK semivariogram (Figure 7a) were as follows: Euclidean coordinate type, number of closest points set to eight, nlags to 10, Gaussian variogram model, and weight set to 0.1. These settings were analyzed to obtain the highest prediction performance. The optimized semivariogram components included a partial mill of 42,589.76979, full fill of 43,906.26968, a range of 0.00114, and nugget of 1316.49988. The sampling points predicted using the fitted OK semivariogram had an RMSE of 65.101 mg/kg and an R\({}^{2}\) of 0.871. The red dots in Figure 7 show the correlation based on the lag distance of the actual binned data.  
Subsequently, based on the optimized semivariogram, a prediction map (Figure 7b) and prediction standard deviation map (Figure 7c) were generated. The OK prediction map shows that the values in the central-left part were predominantly high, and the highest predicted value in this region was 739.966 mg/kg. The standard deviation map displayed a range of 40-180 mg/kg in increments of 10 based on the minimum and maximum predicted standard deviations. Evidently, the deviation in the upper-left area, where the sampling data points are clustered, is greater than that at other locations. This could be due to the greater variability in values in that area. Furthermore, in the central-upper region, where no sampling data were obtained, the deviation was as high as 180 mg/kg. This implies that additional samples may be necessary to obtain reliable results in the area, because a high deviation indicates lower confidence in the predictions for this area.  
Figure 6: Comparison of the actual and predicted heavy metal concentrations by RF.  
As with the RF model, Figure 8 shows a comparison between the 40 sampled data values and the estimates derived from the OK interpolation. As expected, the OK method tended to overestimate lower actual values and underestimate higher values. This trend is due to the nature of the Kriging method, which operates under the BLUE constraint and is designed to minimize the variance in the prediction errors, resulting in a smoother transition in the predicted values.  
Figure 7: Results of the optimal OK technique-based interpolation. (**a**) Optimal semivariogram, (**b**) heavy metal concentration interpolated values, and (**c**) standard deviations.