esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Measurement Results
### Harmonic Mixing Performance  
For a harmonic mixer,  
\[f\mathrm{IF}=\left|f\mathrm{THz}\pm\eta\mathrm{fLO}\right|, \tag{10}\]  
where \(n\) is the harmonic number, especially  
\[f\mathrm{IF}=\left|f\mathrm{THz}-\eta\mathrm{fLO}\right| \tag{11}\]  
as in Ref. [17], for the down-conversion mixer in our experiment. In order to detect the maximum harmonic number, we kept LO power \(P_{\mathrm{LO}}\) at a higher level of 11 dBm, and adjusted the frequency \(f_{\mathrm{LO}}\) from 10 GHz with a decreasing step of 0.002 GHz. Finally, as recorded in Figure 6a, when the mixer was coupled with Antenna 1, the power of IF output \(P_{\mathrm{IF}}\) was \(-47\) dBm at 944 MHz, which was generated from the maximum harmonic number of 30 that we could detect in the mixing between \(f_{\mathrm{THz}}\) = 0.207 THz and \(f_{\mathrm{LO}}\) = 6.932 GHz. If we decreased the \(P_{\mathrm{LO}}\) to 5 dBm, the \(P_{\mathrm{IF}}\) of \(-44.6\) dBm at 1017 MHz was recorded in Figure 6b, with the maximum harmonic number decreasing to 22. On the other hand, when the mixer was coupled with Antenna 2, the maximum harmonic number attained in the mixing was 88, with the \(f_{\mathrm{LO}}\) of 2.36 GHz and the \(P_{\mathrm{LO}}\) as low as 4 dBm, shown in Figure 6c. Thus, under a similar level of LO pumping, the maximum harmonic number from Mixer 2 is much higher than that from Mixer 1.  
\[\eta=P_{\mathrm{IF}}/(P_{\mathrm{THz}}\cdot G_{\mathrm{IF}}) \tag{12}\]  
The conversion efficiency of the HTS JJ THz mixers can be calculated on Equation (12) [10], where \(G_{\mathrm{IF}}\) is IF amplifier gain, which is around 50 dB in our experiment. \(P_{\mathrm{IF}}\) can be read from the spectrum analyzer directly. Additionally, \(P_{\mathrm{THz}}\) has been calculated through numerical simulation. Thus, the conversion efficiency of the mixer coupled with Antenna 1 is \(-73.4\) dB in the 30 th harmonic mixing, while the other one, coupled with Antenna 2, is \(-69.6\) dB in the 88 th harmonic mixing.  
The mixing parameters of HTS JJ mixers coupled with different traditional antennas with the detected maximum harmonic numbers are listed in Table 1. Coupled with traditional antennas, the JJ with bowtie antenna achieves the highest harmonics. Generally, the conversion efficiency increases with the decrease in harmonic number. However, the JJ mixer coupled with the bowtie antenna (described herein) maintains the similar value of conversion efficiency while the harmonic number is nearly twice of that with a log-periodic antenna. The application of stubs on bowtie antenna has been proven to improve the performance of HTS JJ mixer.  
Figure 6: Measured frequency spectra of the IF output from the JJ (**a**) Mixer 1 coupled with Antenna 1: bowtie loaded by lumped-element stubs when \(f_{\mathrm{LO}}\) = 6.932 GHz, \(P_{\mathrm{LO}}\) = 11 dBm, \(n\) = 30, and (**b**) when \(f_{\mathrm{LO}}\) = 9.392 GHz, \(P_{\mathrm{LO}}\) = 5 dBm, \(n\) = 22; (**c**) Mixer 2 coupled with Antenna 2: bowtie loaded by open-end stubs when \(f_{\mathrm{LO}}\) = 2.36 GHz, \(P_{\mathrm{LO}}\) = 4 dBm, and \(n\) = 88 under a 0.207 THz radiation.
## 3 Optimization Models
### Nondominated Sorting Genetic Algorithm II (NSGA-II) Optimization  
In SCR systems, there is a trade-off between NOx emissions and NH\({}_{3}\) slip. Increasing the urea injection rate will reduce NOx emissions but increase NH\({}_{3}\) slip, which is not allowed by emission regulations. In this study, the objective of the optimization is to find the optimum urea injection rate to maximize the NOx conversion efficiency of the SCR while meeting the regulatory NH\({}_{3}\) slip limits.  
NSGA-II, which was proposed by [PERSON] et al. [PERSON] et al. (2015), can significantly improve the performance and efficiency of the algorithm by introducing an elite strategy and an improved non-dominated sorting method. It uses a multi-objective genetic algorithm to solve the multi-objective optimization problem of NOx conversion efficiency and NH\({}_{3}\) slip of SCR system, from which the Pareto chart is obtained, and then finds the optimal solution, providing a better approach.  
In NSGA-II, it is usual to maximize or minimize all the objective values. Since the objective NOx conversion efficiency to be optimized in this paper is to be maximized, all the NOx conversion efficiencies are treated by taking negative values.  
In this paper, the multi-objective optimization process is carried out in MATLAB R2023a using the optimized SVM as the objective function and the returned scalar vectors are NOx conversion efficiency and NH\({}_{3}\) slip. Urea injection is used as the only variable of the system. The constraints on the system variables are defined in the form of upper and lower bounds on the system variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1200 rpm-75 N\(\cdot\)m**} & \multicolumn{2}{c}{**1800 rpm-300 N\(\cdot\)m**} & \multicolumn{2}{c}{**2400 rpm-450 N\(\cdot\)m**} \\ \cline{2-7}  & **CPO** & **PSO** & **CPO** & **PSO** & **CPO** & **PSO** \\ \hline R\({}^{2}\) & 0.99 & 0.96 & 0.99 & 0.95 & 0.98 & 0.95 \\ RMSE & 0.52 & 1.65 & 0.6292 & 2.14 & 1.12 & 2.37 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the accuracy of NOx conversion efficiency prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1200 rpm-75 N\(\cdot\)m**} & \multicolumn{2}{c}{**1800 rpm-300 N\(\cdot\)m**} & \multicolumn{2}{c}{**2400 rpm-450 N\(\cdot\)m**} \\ \cline{2-7}  & **CPO** & **PSO** & **CPO** & **PSO** & **CPO** & **PSO** \\ \hline R\({}^{2}\) & 0.99 & 0.93 & 0.99 & 0.94 & 0.99 & 0.97 \\ RMSE & 1.60 & 6.34 & 4.17 & 43.20 & 1.76 & 5.92 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of the accuracy of NH\({}_{3}\) slip prediction.  
lower bounds. The parameters of the genetic optimization algorithm and their values are shown in Table 9.
## 7 Simulation Results and Discussion
### Experimental Scenario (2): The Impact of Mobility Speed  
The impact of mobility speed on network performance is investigated by testing different speed values, ranging from 0 to maximum speeds of 5, 10, 15, 20 and 25 mps, which are equal to 18, 36, 54, 72 and 90 kmph, respectively. In this scenario, Figure 13 shows that the PDR metric in AFB-GPSR clearly decreases as the maximum moving speed increases once there is a rapid change in network nodes. In contrast, it is found that this AFB-GPSR achieves a higher PDR compared to both the conventional GPSR and AODV routing protocols.  
In Figure 14, AFB-GPSR also outperforms in evaluating the E2 ED metric. It can maintain nearly constant E2 ED with nearly 0.3 s on average when the mobility speed increases to 25 mps, and this occurs because of the randomness of the mobility. However, AODV registered a higher E2 ED compared with AFB-GPSR and GPSR. That is because the AODV strategy is not easy to adapt with the dynamic nodes mobility, and it may take a longer time to find a route when the link breakage occurs.  
On the other hand, Figure 15 shows that AFB-GPSR has a higher throughput than the original GPSR-1s and GPSR-2s at a fixed beaconing time and the original AODV. Meanwhile, AODV topology-based routing has a lower throughput resulting due to the high latency and excessive route discovery in AODV.  
Figure 12: Throughput vs. Node Density.  
Figure 13: Packet-delivery ratio vs. node speed.
## 2 Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors (Year)** & **Country** & **Dataset Size/Type** & **Model(s) Used * & **Method Type** & **Conceptual Framework** & **Framework Proposed** & **Key Findings** \\ \hline [PERSON] and [PERSON] (2023) [23] & China & 32,824 records; 4 h interval & CNN, BiLSTM, and attention & Hybrid DL & CNN-BiLSTM-Attention & Hybrid DL & DO RMSE = 0.1938; attention improved performance \\ \hline [PERSON] et al. (2023) [13] & India & 19,925 records; 5-fold cross-validation & XGBoost, SVM & Supervised ML & WQI + ML & XGBoost vs. SVM & XGBoost 94\%, SVM 67\%; automated classification \\ \hline [PERSON] et al. (2022) & France & 45 records; multi-dataset compute benchmarking & Tree models vs. DL & Theoretical analysis & Inductive bias theory & N/A & Tree models outperform DL in low-data regimes \\ \hline [PERSON] et al. (2022) & China & 5 records; regression regression datasets & NNBoost & Boosted shallow NN & Taylor expansion + NN & NNBoost & Improved performance in low-data \\ \hline [PERSON] et al. (2021) [22] & China, UK & 20,032,020 records; hourly measurements & LightGBM & Supervised ML & Tidal + discharge inputs & Tidal + flightGBM & RMSE = 0.14 m; R\({}^{2}\) = 0.98; better than physics-based \\ \hline [PERSON] et al. (2021) & Russia & Multiple records, benchmark datasets & FT-transformer DL & Transformer & Tokenized features & FT-transformer & Consistent results vs. GBDT; attention-based design \\ \hline [PERSON] (2021) & USA & Synthetic + real data & TabNet & Sparse attention DL & Sequential attention & TabNet & Instance-wise learning, improved interpretability \\ \hline [PERSON] et al. (2021) & USA (UMD) & 14 records; SSI + supervised datasets & SAINT & Self-supervised + attention & SSL + inter-sample attention & SAINT & Contrastive learning; superior to GBDT \\ \hline [PERSON] et al. (2021) [6] & USA (Ama-zon) & 15 records; categorical tabular datasets & TabTransformer DL & Transformer & Embedding + Transformer & TabTransformer & Tokenization-based attention; robustness to missing data \\ \hline [PERSON] and [PERSON] (2021) [9] & Iraq & Multiple records: UCI and public datasets & Decision Tree (CART, C4.5, etc.) & Comparative review & Tree algorithm taxonomy & Decision tree family & Up to 99.93\% accuracy on UCI; interpretable models \\ \hline \hline \end{tabular}  
* RF stands for random forest; DL refers to deep learning; GBDT denotes gradient-boosted decision trees; SVM is support vector machine; MLP stands for multilayer perceptron; CNN refers to convolutional neural network; AUC represents area under the curve; RMSE is root mean square error; ANN refers to artificial neural network; LSTM is long short-term memory; GRU denotes gated recurrent unit BiLSTM stands for bidirectional long short-term memory; WQI indicates water quality index; DT is decision tree; and KNN refers to k-nearest neighbors.  
\end{table} 
Table 1: _Cont._model, the transformer autoencoder potability estimator (TAPE), and proposes specially optimized solutions for drinking water classification problems.
## 2 Materials and Methods
### Agent-Based Simulation for the Design of a Spectrum Auction Policy
#### 2.3.4 Identification Model of the Minimum Bidding Price  
The calibrated model can be used to identify the best minimum bidding price (or initial bidding price) at the spectrum auction. Let \(p_{min,1}\) be the minimum total bidding price at the first round, and \(\hat{p}_{final}(\bullet)\) be the final bidding price estimated by the simulation model. Equation (11) represents the optimization model for the identification process.  
\[Max\ W=\hat{p}_{final}(p_{min,1}) \tag{11}\]  
Subject to the following:  
\[\tau_{min}\leq p_{min,1}\leq\tau_{max} \tag{12}\]  
\[\varphi_{min}\leq\hat{n}_{finalround}\leq\varphi_{max} \tag{13}\]  
Equation (12) is a constraint on the range of \(p_{min,1}\). \(\tau_{min}\) and \(\tau_{max}\) are parameters regulating the lower and upper bounds of \(p_{min,1}\). The simulated round with the final decision (\(\hat{n}_{finalround}\)) is regulated by Equation (13), where \(1\leq\varphi_{min}\leq\varphi_{max}\). Note that the lower bound of \(\hat{n}_{finalround}\) (i.e., \(\varphi_{min}\)) should be greater than or equal to 1, because the auction requires at least one bidding round. Since all other constraints are involved in ABS (see Sections 3.1 and 3.2), the final bidding price can be accurately estimated under various values of \(p_{min,1}\) with two constraints in Equations (12) and (13).
## 4 Parallel Performance and Application in Actual Scenarios
### Performance on Tianhe III  
We tested the fully optimized LICOM on the prototype system of the Tianhe III supercomputer. We reached up to 245,760 PEs, which was the summation of the CPU cores and the Matrix-2000 cores. Figure 10 shows the performance of LICOM on various numbers of PEs. The speedup in Figure 11 is the running time on each PE count divided by the running time on 1920 PEs. The reason for why 1920 PEs were chosen as the reference was that the elapsed time on 960 PEs was too long, which may lead to an abnormal speedup diagram. The speedup diagram in Figure 11 shows that there is still potential for optimization, since the speedup fell when more than 61,400 cores were used.  
For the speedups on the three supercomputers, we can see that on Era and Tianhe II, there were still good speedups when 4800 and 19,200 PEs were used. The reason for why we did not test on more PEs was that we did not get access due to the policy for the supercomputer. However, on Tianhe III, the speedup was lower when more PEs were used. This might have been due to the communication overhead. Additionally, on a similar number of PEs, Era achieved the best speedup. For instance, on 4800 PEs (7680 on Tianhe III), the speedup for the fully optimized code on the three platforms was about 4.6, 3.9, and 3, respectively. A possible reason might be that Era had the CPU with the best performance.
## 3 NB-IoT Frame Structure
### NPSS Sequence  
The signals of NPSS occupy 11 symbols in subframe 5 of every radio frame. To achieve excellent correlation property, the Zadoff-Chu frequency domain is used to generate the NPSS sequence. The sequence in each NPSS symbol is identical and the only difference is the code cover which gives a steep rolloff timing metric to the NPSS detector. The complex exponential expression of the NPSS sequence is shown as follows  
\[a(k)=e^{-j\frac{\pi\Phi(k+1)}{11}},k=0,1,2\ldots 10. \tag{1}\]  
The code cover is a defined zero-one sequence, and Table 1 gives the exact values.  
The NPSS subframe structure is illustrated in Figure 2. Each time domain symbol is obtained by multiplexing the inverse fast Fourier transform (IFFT) of \(a(k)\) with code cover.  
Figure 1: Diagram of the distribution of physical channels and physical signals in one NB-IoT radio frame.
# Dual-Energy CT Applications in Urological Diseases
## 3. DECT Benefits
### Spectral HUI Curves  
VMIs can be utilized to create spectral HU curves: a region of interest (ROI) is drawn in tissue and the average CT number in the ROI at each monochromatic energy level of the VMI, typically ranging from 40 to 140 keV, is obtained automatically. Spectral HU curves provide information about the mean attenuation characteristics of the tissue within the ROI, allowing for the characterization of specific tissue types and aiding in the differential diagnosis. The attenuation of soft tissue and high atomic number materials such as iodine and bone tend to increase at lower energy levels. Water has zero attenuation at all energy levels, while fat shows decreased attenuation at lower energies (Figure 4). The presence of fat can be suggested by observing a curve pattern in the ROI of a specific tissue that indicates decreased attenuation at lower keV. This observation can help diagnose fat-containing diseases, such as lipid-rich plaques, adrenal adenomas, and angiomyolipomas (AML). Spectral HU curves can therefore provide valuable information in identifying tissue characteristics, aiding accurate diagnoses in clinical practice.  
Figure 2: (**a**) Corticomedullary phase acquired in dual energy and corresponding virtual unenhanced image; (**b**) synthetic subtraction of the iodine.  
Figure 4: (**a**) Color-coded overlay-iodine map showing two large left renal cysts, visible as devoid of iodine signal. In (**b**), a region of interest in drawn within the cyst to calculate the spectral attenuation curve, and automatically provide the area of the region of interest, the value of HU (Hounsfield Units). In (**c**), the attenuation curve of the cyst in green remains constant, in comparison with the blue one, representing the aortic attenuation curve. The green curve, representing the attenuation behavior of the renal cyst, remains flat, whereas the blue curve of the aorta markedly rises for lower keV to higher Hounsfield unit value.  
Figure 3: Color-coded overlay-iodine map. Hypervascular hepatic metastases with a peripheral contrast enhancement from a clear cell renal carcinoma. The iodine map highlights the iodine as bright yellow (white arrows). Yellow represents high density of iodine. HU = Hounsfield Units.
## 3 Research Framework  
The interruption cost was examined by the interruption modes and task types with the hypotheses discussed below. The cost could be evaluated with three approaches: physiological measures (pupil diameter changes), subjective measures (self-reported cognitive workload), and behavioral measures (task performance). Considering data sources and methodologies, cognitive workloads measured by physiological and subjective approaches are taken as user-oriented interruption costs, and task performance measures, including task completion time and task accuracy, are regarded as task-oriented costs. We assumed that the interruption cost varies by both the interruption coordination modes and the task types. Table 1 shows variables and measures of interruption cost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interruption Cost** & **Variable** & & **Measures** \\ \hline User-oriented & Cognitive workload & \(\bullet\) & Physiological measure (pupil diameter variance) \\  & & \(\bullet\) & elf-reported measure (NASA TLX) \\ \hline Task-oriented & Task performance & \(\bullet\) & Task completion time \\  & & \(\bullet\) & Task accuracy (the number of errors) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables and measures of interruption cost. NASA TLX—National Aeronautics and Space Administration (NASA) task load index.  
As described above, this study aims to identify the most effective approach to evaluate interruption cost using cognitive workload and task performance, and to apply the approach to three interruption coordination modes according to [PERSON]'s classification [32] with two types of tasks that require different levels of cognitive demands.
###### Abstract  
Lithium battery health state estimation can help optimize battery usage and management strategies. In response to the challenges faced by traditional battery management systems in accurately estimating the State of Health of lithium-ion batteries and addressing issues such as capacity recovery and noise interference, this paper proposes a method based on wavelet decomposition and an improved whale optimization algorithm optimized deep extreme learning machine for estimating the SOH of lithium-ion batteries. Firstly, the lithium-ion battery capacity degradation sequence is extracted, and the wavelet decomposition method is used to decompose the battery capacity into global and local degradation trends. Next, the non-linear convergence factor and the whale optimization algorithm with adaptive weights are employed to optimize the deep extreme learning machine for predicting each trend component. Finally, the prediction results are effectively integrated to obtain the lithium-ion battery SOH. This experimental method is validated using NASA and CALCE datasets, and the results indicate that the root mean square error and mean absolute percentage error are both below 0.95%, with relative accuracy and absolute correlation coefficients exceeding 98%. This demonstrates the method's excellent accuracy and robustness.  
wavelet decomposition; whale optimization algorithm; deep extreme learning machine; state of health; lithium battery
## 5 Case Study
### Results Analysis  
We can see from the above results, the convergence and computational accuracy of MAPSO are better than PSO. Thus, ES configuration obtained by MAPSO is discussed below, which is shown in Table 8.  
The following conclusions can be drawn from Table 8.  
1. The energy capacity with the objective of EE is 811.1 MW\(\cdot\)h less than that of EUE, while the cost difference is 3.2333 \(\times\) 10\({}^{9}\) $. When the capacity of total stored energy increases by \(\frac{4600-3788.9}{3788.9}=21.4073\%\), the cost will raise correspondingly by \(\frac{4.5605\times 10^{9}-1.3272\times 10^{9}}{1.3272\times 10^{9}}=243.6181\%\). The results indicate that under the current ES technology level, if the capacity of stored energy is still increasing, a large cost will need to be paid after it reaches a certain degree. Consequently, it is suggested that decision makers give priority to EE when coordinating various ESs.
2. Compared with the results of EE, both the cost and the capacity of stored energy will increase with the implementation of multi-objective optimization. However, the cost will rise by \(\frac{1.8013\times 10^{9}-1.3272\times 10^{9}}{1.3272\times 10^{9}}=35.7218\%\) while the capacity increases by only \(\frac{3802.9-3788.9}{3788.9}=0.3695\%\). Both of the two conclusions perform alike, which indicates that decision makers should attach great importance to EE. However, if the diversity of ES is a must, the multi-objective optimization of EE and EUE is the best choice and the capacity of PS, LTTES, LAB, SCES, HTTES, CAES, SMES and FES can be configured as 1000 MW\(\cdot\)h, 502 MW\(\cdot\)h, 600 MW\(\cdot\)h, 800 MW\(\cdot\)h, 200 MW\(\cdot\)h, 500 MW\(\cdot\)h, 69.8 MW\(\cdot\)h, and 131.1 MW\(\cdot\)h, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\gamma_{min}\) & \(\gamma_{mean}\) & \(\gamma_{max}\) \\ \hline PSO & 0.27917 & 0.32659 & 0.98439 \\ MAPSO & 0.13562 & 0.15468 & 0.67486 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of results with objective function \(\gamma\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & \multicolumn{8}{c}{**Stored Energy of Each ES/(MW\(\cdot\)h)**} & \multirow{2}{*}{**Total Cost/\$**} & \multirow{2}{*}{**Total Stored Energy/(MW\(\cdot\)h)**} \\ \cline{2-2} \cline{6-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline EE & 1000 & 600 & 600 & 800 & 200 & 500 & 0 & 88.9 & \(1.3272\times 10^{9}\) & 3788.9 \\ EUE & 1000 & 600 & 600 & 800 & 200 & 500 & 400 & 500 & \(4.5605\times 10^{9}\) & 4600 \\ EE and EUE & 1000 & 502 & 600 & 800 & 200 & 500 & 69.8 & 131.1 & \(1.8013\times 10^{9}\) & 3802.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal allocation scheme of ESs.