esa-sceva/satcom-chunk-collection huggingface dataset:
# Direct Temperature Measurements of Cardiac Stent during MRI Examinations
## 3 Results  
Figure 2 shows the average temperature trend during MRI examination and standard deviation (indicated as gradient color).  
Figure 3 shows the temperature trend for each probe during the entire MRI protocol described in Table 1. The colored box indicates the RF exposure length. Each temperature-versus-time curve was characterized by an initial base value (no RF exposure) of 22 \({}^{\circ}\)C \(\pm\) 0.2 \({}^{\circ}\)C for a period of 350 s, followed by 715 s of exposure (temperature rise) and a 210 s cooling phase (temperature fall). The linear regression used for SAR calculation is also shown for each channel. In Table 2, the resulting SAR values together with [PERSON]'s coefficient, dTmax, and CEM43\({}^{\circ}\)C are shown for each probe. CH2 and CH4 were located at the tips of the stent and, for this reason, have similar trends. CH3 was located at the  
Figure 2: Raw temperature data as function of time: average temperature and standard deviation, as gradient color, acquired during MRI examination. CH1 (black line), CH2 (red line), CH3 (blue line), and CH4 (green line).  
center of the stent. CH1 is relative to the probe located in the phantom, away from the stent location: this trend reflects the heating of the material surrounding the implant.  
Figure 4 shows the reconstruction of the stent temperature during the test along the stent length (0-2.4 cm). The horizontal lines show the start, the middle, and the end of the RF exposure. Below is also shown the 1D plot of the temperature along the stent length at different exposure times.  
As expected, the maximum temperature changes during the RF exposure occur at the tips of the stent, while in the center of the stent, the temperature reaches a lower value and has a smoother trend.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Probe** & **dTmax (\({}^{\circ}\)C)** & **SAR (W/kg)** & **R Pearson** & **CEM43\({}^{\circ}\)C** \\ \hline CH1 & 1.642 & 8.965 & 0.982 & 0.011 \\ CH2 & 4.120 & 116.870 & 0.960 & 0.574 \\ CH3 & 2.845 & 10.739 & 0.971 & 0.059 \\ CH4 & 4.004 & 81.297 & 0.961 & 0.360 \\ \hline \end{tabular}
\end{table}
Table 2: Maximum temperature change dTmax, calculated SAR (with [PERSON]’s coefficient) and CEM43\({}^{\circ}\)C, for each measurement location.  
Figure 3: The temperature trend during the MRI examination. For each probe, the linear regression (red line) used for SAR calculation is shown.  
Figure 4: Reconstructed temperature trend along the entire stent length. (Goodness of fit: SSE: 32.2, R-square: 0.9964).
# All-Digital Computing-in-Memory Macro Supporting FP64-Based Fused Multiply-Add Operation
## 2 Overview
### The Algorithm of FMA Operator  
The operation a \(\times\) b + c is often required in engineering applications and completed in two steps with two rounding operations. The FMA operator is introduced for the single instruction execution of this operation with single- or double-precision floating-point operands. This operator is designed to reduce the latency and provide greater floating-point arithmetic precision since only a single rounding operation is performed with an FMA operator on the combined full-precision product and sum.  
The algorithm process of the FMA operator is shown in Figure 3. There are six serial steps to complete the execution of a FMA operation on traditional architecture [27; 28; 29]:  
Figure 1: Illustration of the typical digital CIM design.  
Figure 2: The storage format of FP64 in computer.  
\(\dagger\) Obtain the significands of a, b, and c, that is, add the integer bit and mantissa to obtain the significand.  
\(\dagger\) Add the exponents and multiply the significands of input a and b to complete the operation a \(\times\) b.  
\(\dagger\) Align the exponents of a \(\times\) b and c, that is, shift the significands of the number with the smaller exponent to the right to increase the exponent until it equals the exponent of another number.  
\(\dagger\) Add the significands of a \(\times\) b and c.  
\(\dagger\) Normalize the result, that is, shift the significand of the result to the left to reduce the exponent until the most significand bit is 1.  
\(\dagger\) Round the result; due to the length of the result exceeding that of the data stored by the physical device in the computer, the lower bits need to be rounded to ensure that the result can be stored in the computer with relative accuracy, and the common method is to round to the nearest even number.
## 6 Results and Discussion
The results of this analysis are presented in Tables 3 and 4, respectively. These tables provide a comprehensive summary of the proposed model's performance and its ability to accurately predict the target coordinates. Figure 7: (**a**) is the figure for training and validation loss, and (**b**) is the figure for training and validation accuracy of the ANN Model. The results obtained from the proposed model exhibit a remarkable level of accuracy in predictions, with the lowest location error being 0.18 m. The largest recorded location error was 1.06 m, which exceeded the performance of the sensor. These results demonstrate the capability of the proposed model to precisely forecast the target coordinates. The average error distance determined using the MLT approach was 1.603 m, according to Figure 8. In contrast, the proposed methodology has a much lower average error of 0.5625 m. These findings demonstrate that the proposed approach outperforms existing MLT methods.
## 3 Computed Amplification Factors: Main Statistical Characteristics
### General Background of Computed AFs
It would thus be meaningless to aim to obtain extremely precise models with residuals between observations and predictions much below these values. Note that a few additional parameters are introduced to measure the variability of the results, as summarized in Table 3.  
The average \(AF\) for all profiles, noted \(AF_{0}(\theta,T_{i})\), and defined as the geometrical average of the \(n_{p}\) average \(AF\left(AF(P_{k},\theta,T_{i})\right)\), is noted for simplicity as \(AF_{0}\):  
\[\text{Log}(AF_{0}(\theta,T_{i}))=\frac{1}{n_{p}}\sum\limits_{k=1}^{n_{p}}[ \text{Log}(AF_{m}(P_{k},\theta,T_{i}))] \tag{18}\]  
The initial variability, defined as the initial standard deviation of the site average amplification factor over all profiles, is  
\[\sigma_{0}(\theta,T_{i})=\sqrt{\frac{1}{11*n_{p}}\sum\limits_{k=1}^{n_{p}*11} \left[\log(AF_{m}(P_{k},\theta,T_{i},PGA(lo)))-\log(AF_{0}(\theta,T_{i})) \right]^{2}} \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Total Initial Variability** & **0.2631** & \begin{tabular}{c} **Total Initial Variability** \\ **(Soil Type: Sand)** \\ \end{tabular}  & **0.3241** \\ \hline Maximum initial variability \(\sigma_{\max}\) & 0.3905 & Maximum initial variability \(\sigma_{\max}\) & 0.4696 \\ \hline \(\sigma\) (\(\theta=0\), \(T=0.01\) s) & 0.3061 & \(\sigma\) (\(\theta=1\), \(T=0.01\) s) & 0.3794 \\ \hline  
The maximum initial variability, defined as the peak value of the initial variability, \(\sigma_{0}\), over the whole period range, is  
\[\sigma_{\text{Omax}}(\theta)=Max_{T_{i}}[\sigma_{0}(\theta,T_{i})] \tag{20}\]  
The overall initial variability, defined as the average of the initial variability over the whole period range, is  
\[\sigma_{0\text{m}}(\theta)=\frac{1}{n_{T}}\sum_{i=1}^{n_{T}}\sigma_{0}(\theta, T_{i}) \tag{21}\]  
where \(n_{T}\) is the number of structural periods used, i.e., 100.
## 3 Results
### Sensitivity Analysis
In this case the calculation of the electricity use of each energy activity is estimated in an independent manner too. Figure 5: Estimated energy use by using efficient devices upon the Mexican residential sector. From Figure 7 it is seen that that the combination of using efficient devices and decreasing their time of usage achieves an ensured saving of 1.93 TWh for air-conditioning and 1.12 TWh for refrigeration, this is in accordance with other studies [36; 43]. By decreasing the usage of illumination & appliances and applying efficient devices for the same activity the saving could be 17.87 TWh. By decreasing the time of usage and using low-energy illumination & appliances, the probability of having a higher energy saving than the combination of low-energy and decreasing the usage time of air-conditioning is 96%, while having a 98% chance of having a higher saving than the usage of low-energy and reduction of use of refrigerators. In all cases, the saving rate for refrigerators and air-conditioning systems is very poor, i.e., less than 7% of the total national electricity use. Figure 6: Estimated energy use by decreasing the time of use of devices upon the Mexican residential sector. Figure 7: Estimated energy use by using efficient devices and decreasing their time of use upon the Mexican residential sector. Finally, when an integrated approach is used that involves a saturation of efficient appliances and a reduction of their time of use for all the three electrical energy activities being applied at the same time in the residential sector, Figure 8 can be plotted. As can be seen from Figure 8, by applying an integrated approach, there is a very high probability of obtaining considerable energy savings. This in addition to existing programs such as the Mexican Daylight Saving Time program (Horario de Verano, title in Spanish), which, since 1996 has gotten a yearly average saving approximately of 1 TWh [44]. Furthermore, the maximum energy saving is estimated at 19.67 TWh for the Mexican residential sector in 2015. With this maximum energy saving, and with an emission factor of 0.454 tons of CO\({}_{2}\)eq/MWh for Mexico [45], it is calculated that a total of 8.93 million of tons of CO\({}_{2}\)eq could have been avoided in 2015.
## 4 Modeling Process and Results
### Validation of Models  
The performance of SI model, GD-SI model, and GD-GPR-SI model was compared and analyzed based on the ROC curves. The accuracy on the testing dataset reflects the predictive ability of the model, and the ROC curves of three models were plotted based on the testing dataset. Figure 11 shows the prediction rate curves of the SI (AUC = 0.931) model, GD-SI (AUC = 0.936) model, and GD-GPR-SI (AUC = 0.943) model. Results show that all three models have strong predictive capabilities (AUC > 0.93), which corroborates the reliability of the SI model. Moreover, the GD-GPR-SI model has the highest AUC value, followed by the GD-SI model, and finally the SI model. Results highlight the superiority of the hybrid model. Therefore, both the factor screening method and the GPR optimization method proposed in this study improved the performance of the SI model and proved effective.
## 5 Reversibility Evaluation of VRIS Image Steganography Model
### Reversibility Analysis  
The experimental results are shown in Table 8, where the VRIS model shows high accuracy in extracting secret images from encrypted images with different noise factors. Specifically, on the Tiny-ImageNet dataset, the average extraction accuracy of the model without noise is above 96% and reaches up to 98.45%, while in the case with noise, the accuracy still remains above 80% although it decreases. Similarly, the average extraction accuracy of VRIS in the presence of noise remains high on Mini-ImageNet, 256-ObjectCategories and LEW. This implies that the VRIS model can effectively maintain the information integrity and accuracy of secret images throughout the encryption and decryption process.  
Figure 8: Residual image comparison: the first row is generated by the ISGAN model, the second row is generated by the model proposed in the literature [38], and the third row is generated by the VRIS model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Output\_S\_DENOSE/Input\_S**} & \multicolumn{2}{c}{**Output\_S\_NOISE/Input\_S**} \\ \hline Dataset/Noise Factor & 0.03 & 0.05 & 0.07 & 0.03 & 0.05 & 0.07 \\ Tiny-ImageNet & 98.45\% & 97.44\% & 96.05\% & 91.31\% & 82.04\% & 81.99\% \\ Mini-ImageNet & 97.27\% & 96.87\% & 96.34\% & 92.63\% & 87.74\% & 83.39\% \\
256-ObjectCategories & 97.94\% & 97.37\% & 97.47\% & 94.05\% & 92.97\% & 93.57\% \\ LFW & 96.14\% & 97.10\% & 93.76\% & 92.81\% & 87.13\% & 87.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: VRIS model extraction accuracy.  
To further validate the reversibility of the VRIS image steganography model, we conducted a comparative analysis of the histogram of each channel between the original secret image and the secretly reconstructed image. This comparison provides a visual observation of the distribution of image brightness or colors. The results (Figure 9) indicate that there is minimal difference in the histogram of each channel before and after extraction, conclusively demonstrating the excellent reversibility of the VRIS steganography model proposed in this study. During the process of extracting the secret image, the secretly reconstructed image maintains a high degree of integrity and accuracy, exhibiting virtually no distortion.
# Effect of Unit-Cell Size on the Barely Visible Impact Damage in Woven Composites
## 4 Results and Discussion
### Impact Testing  
#### 4.2.1 Impact Force-Displacement Analysis  
The impact force-displacement curves of the four impacted architectures are depicted in Figure 4. The displacement was calculated by numerically integrating the acceleration (the impact force divided by the mass of the impactor) as a function of time. The reproducibility of the test results and the repeatability of the impact response are confirmed by the small experimental scatter in all cases. In an impact test, the force increases linearly as a function of the displacement/time up to a maximum value when the impactor velocity changes direction. This can be accompanied by damage occurrence in the specimen, in the form of a sudden drop in the force. Afterwards, the force gradually decreases to zero and the impactor returns back to zero displacement. The stiffness of the specimen can be calculated as the slope of the initial linear segment of the force-displacement curve. A clear difference is noticed among the architectures in the sense that the NCF specimens experience the least maximum force of approximately 2500 N while all the other remaining architectures (2D-PW, ORT-PW and ORT-TW) have almost the same range of maximum force \(\sim\)4500 N. However, the calculated stiffness of all the architectures is comparable (NCF: 954 \(\pm\) 50, 2D-PW: 966 \(\pm\) 29, ORT-PW: 963 \(\pm\) 14 and ORT-TW: 1075 \(\pm\) 70 N/mm). This suggests that the crimp and undulation in the 2D and 3D woven architectures have a negligible effect on the stiffness of the impacted specimens.  
Figure 3: Optical micrographs of composites cross-sections: (**a**) NCF, (**b**) 2D-PW, (**c**) ORT-PW and (**d**) ORT-TW. For ORT-PW and ORT-TW architectures, two micrographs were captured: along the warp (top image) and along the weft (bottom image).
## 4 Experiments and Results
### Ablation Experiment
#### 4.4.5 Ablation Experiment on Parameters  
To investigate the impact of weighting coefficients \(\lambda_{1}\) and \(\lambda_{2}\) on model performance, parameter analysis experiments were conducted on the CrowdHuman dataset. As shown in Table 6, the detection performance is optimal when the weights are set to \(\lambda_{1}\) = 0.2 and \(\lambda_{2}\) = 0.8, meaning the model achieves the best fitting performance with DFL weighted at 0.2 and SIOU at 0.8. The experiments indicate that high IoU values are critical for accurate target localization and detection, thus requiring greater weight. Conversely, DFL tends to cause overfitting during training, which degrades the model's generalization capability, necessitating a less weight.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Value of \(\lambda\) and \(\mu\)** & **AP** & **MR\({}^{-2}\)** \\ \hline \(\lambda\) = 1.0, \(\mu\) = 0.0 & 92.9 & 40.7 \\ \(\lambda\) = 0.8, \(\mu\) = 0.2 & 92.1 & 40.1 \\ \(\lambda\) = 0.6, \(\mu\) = 0.4 & 92.3 & 39.7 \\ \(\lambda\) = 0.5, \(\mu\) = 0.5 & 92.5 & 39.6 \\ \(\lambda\) = 0.4, \(\mu\) = 0.6 & 92.8 & 39.2 \\ \(\lambda\) = 0.2, \(\mu\) = 0.8 & 93.3 & 38.7 \\ \(\lambda\) = 0.0, \(\mu\) = 1.0 & 93.0 & 39.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of parameters \(\lambda\) and \(\mu\).  
Figure 6: The loss curve during training on the CrowdHuman dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AP** & **Recall** & **MR\({}^{-2}\)** & **FPS** \\ \hline DIOU [37] & 92.1 & 96.3 & 39.8 & 51.1 \\ CIOU [37] & 92.5 & 96.6 & 39.5 & 41.7 \\ EIOU [45] & 93.0 & 96.8 & 39.2 & 49.5 \\ Wise-IOU [46] & 92.9 & 96.6 & 39.1 & 52.3 \\ DFL-SIOU (ours) & 93.3 & 97.1 & 38.7 & 57.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment results for loss function.
## 3 Problem Modeling
### Solution Evaluation and Acceptance Criterion  
NHALNS allows infeasible solutions during the search procedure. Solutions are evaluated by utilizing a generalized cost function, i.e., evaluation function in this paper. Denote the solution (route strategy) as \(R=\left\{r^{1},r^{2},\ldots,r^{m}\right\}\), where \(r^{k}\in R\) indicates the route travelled by EV \(k\in K\). The set of customers served along the route \(r^{k}\) is denoted as \(r^{k}_{c}\), and the set of RSs visited is denoted as \(r^{k}_{b}\).  
For route \(r^{k}\), the total time violation is denoted as \(T^{k}\).  
\[T^{k}=T^{k}_{l}+T^{k}_{e} \tag{35}\]  
where  
\[T^{k}_{l}=\sum_{i\in r^{k}_{c}}\left(\max(t^{k}_{i}-l_{i},0)+\max(t^{k}_{n+1}- T,0)\right) \tag{36}\]  
And  
\[T^{k}_{e}=\sum_{i\in r^{k}_{c}}\max(e_{i}-t^{k}_{i},0) \tag{37}\]The total energy consumption violation is denoted as \(E^{k}\)  
\[E^{k}=\sum_{i\in\mathcal{F}_{c}^{k}\cup\mathcal{F}_{b}^{k}}\max(-b_{i}^{k},0) \tag{38}\]  
The total capacity violation is denoted as \(Q^{k}\)  
\[Q^{k}=\max(\sum_{i\in\mathcal{F}_{c}^{k}}q_{i}-Q,0) \tag{39}\]  
For a given solution \(R\), the evaluation function value is defined as the sum of the objective value and the penalty for all the violations, as indicated below.  
\[f_{eva}(R)=f(R)+\delta_{t}^{1}\sum_{k=1}^{m}T_{e}^{k}+\delta_{t}^{2}\sum_{k=1} ^{m}T_{l}^{k}+\delta_{\epsilon}\sum_{k=1}^{m}E^{k}+\delta_{q}\sum_{k=1}^{m}Q^{k} \tag{40}\]  
where \(f(R)\) is the value of function (1), \(\delta_{t}^{1}\) and \(\delta_{t}^{2}\) are the penalty factors for the time window violation, \(\delta_{\epsilon}\) is the penalty factor for the energy consumption violation, and \(\delta_{q}\) is the penalty factor for the capacity violation.  
A simulated annealing mechanism is adopted to decide whether the solution generated after each iteration should be accepted or not. If \(f_{eva}(x_{new})<f_{eva}(x_{current})\), \(x_{new}\) is unconditionally accepted. If \(f_{eva}(x_{new})\geq f_{eva}(x_{current})\), the solution \(x_{new}\) will be accepted with a probability  
\[e^{\{(\frac{(-f_{eva}(x_{new})+f_{eva}(x_{current}))}{T_{0}})\}} \tag{41}\]  
where \(T_{0}\) is the current temperature. We initialize the temperature to a value of \(T_{0}\) and multiply it by a cooling rate \(r\in(0,1)\) in each iteration. To increase the diversification of the solutions, we also reset the temperature to the initial value \(T_{0}\) after \(\epsilon\) non-improving iterations.
###### Abstract  
With the intensive development of data transmitting and processing devices in the terahertz (THz) frequency range, an important part of which are integrated plasmonic components and communication lines, it becomes necessary to measure correctly the optical constants of their conductive surfaces. In this paper, we describe a reliable method for determining the effective permittivity \(\epsilon_{\mathrm{m}}\) of a metal surface from the measured characteristics (refractive and absorption indices) of THz surface plasmon polaritons (SPPs). The novelty of the method is the conduction of measurements on a metal surface with a dielectric layer of subwavelength thickness, suppressing the radiative losses of SPPs, which are not taken into account by the SPP dispersion equation. The method is tested on a number of flat \"gold sputtering-zinc sulfide layer-air\" structures with the use of the THz radiation (\(\lambda_{0}=141\) um) from the Novosibirsk free electron laser (NovoFEL). The SPP characteristics are determined from interferograms measured with a plasmon Michelson interferometer. It is found that the method allows a significant increase in the accuracy of the \(\epsilon_{\mathrm{m}}\) in comparison with measurements on the same metal surface without a dielectric layer.  
Keywords:terahertz plasmonics; surface plasmon polaritons; plasmon Michelson interferometer; dielectric permittivity of metals; conducting surface; thin dielectric layers; free-electron lasers +
Footnote †: journal: Applied
## 2 Materials and Methods  
The overall methodology consists of four main stages: (1) dataset generation via OpenDSS simulations with randomized load conditions; (2) robust preprocessing and normalization of complex quantities; (3) training of the PINN model using a combined voltage- and physics-based loss; and (4) evaluation across multiple test feeders under varying conditions including noise and partial observability.
## 3 Proposed System
### Demand Prediction Agents
#### 3.5.1 Regression Models Employed  
Regression models such as Random Forest Regressor [46], Gradient Boosting Regressor [47] and Extra Tree Regressor have been employed as they have obtained the best results in previous studies [22; 48; 49; 50]. In the case study, the tested algorithms and their results are described in-depth. In order to compare the quality of the estimators a dummy regressor has been employed as a baseline regressor, which uses the mean as a strategy for performing predictions.
## 5 Experimental Results and Discussion
### Test 1: Analysis of PLR in the Presence of Collisions  
In the first set of experiments, as reported in Table 1, we deployed two EDs, namely ED1 and ED2 nearly close to each other at distance of approximately 20 m from the GW. Both nodes asynchronously transmit RPs every 70 s using Ch5. In addition, both nodes are triggered to transmit synchronously the UPs at random intervals using Ch0. The SFs adopted for transmitting the UPs are set according to the DCP commands. In this case, we force the users to transmit at the same time using the same channel to assess the possibility of isolating the two transmissions in the SF domain.  
We summarize the results in Tables 2-4 where we report the PLRs for different cases. We neglect the PLR due to DL transmissions discussed in Section 4.3 and which will be separately assessed in Test 2. As expected, the results reveal significant packet losses when transmitting with the same SF. On the other hand, in the case of different SFs, the node transmitting with higher SF has a much lower PLR. i.e., it is able to often capture the packet even in the presence of interference owing to quasi inter-SF orthogonality. Nevertheless, in the SF7-8 case there is a residual probability (slightly higher than the constraint of 0.1%) that the packet is lost by both EDs, while this probability goes to zero for the SF8-9 and SF9-10 cases. Since it is highly probable that concurrent UPs will report the same information to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & \(N_{ED}\) & \(N_{GW}\) & \(N_{UP}\) & \(\Delta t_{RP}\) & \(\Delta t_{UP}\) \\ \hline
2 & 8 & 1 & 20,000 & 70 s & Random (120–130 s) \\ \hline
3 & 8 & 2 & 20,000 & 70 s & Random (120–130 s) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings for experimental tests.  
the server, e.g., a gas concentration is higher than the threshold, in many cases it could be sufficient to receive only one UP to prevent the accident. Under this hypothesis, the 0.1% constraint can be satisfied when a maximum of 3 nodes in a cluster are assigned the same frequency and different SFs, namely, SFs 8-10. Considering the 5 channels in sub-band g, a cluster can be composed of 15 EDs.