esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to Synthetic Data
### Model 1
#### 3.1.3 Inversion Results  
The first test runs the inversion without any absolute equality constraints. The process met the convergence criteria after 19 iterations for the data fit shown in Figure 5.  
Figure 5: Final data fit for the first model at the three frequencies, after 19 iterations.  
Figure 6 shows a vertical section of the resistivity _cube_ on the \(xz\) plane at \(y=0\), and Figures 7 and 8 show vertical slices in the \(yz\) plane on \(x=-5500\) m and \(x=-2500\) m, respectively, with the resistivity values recovered by the inversion process. The white lines show the true contours of the resistive targets. Both bodies appear in the final section positioned approximately 250 m above the true depths. The highest resistivity recovered is 15.5 ohm-m.  
Figure 9 shows a horizontal slice at a depth of 2200 m, which is the depth at which the greatest resistivities were found in this case, underestimating the depth of the resistive target. The inversion has success in the delimitation of the left resistive body, albeit with overly smooth contours and at a depth above its true position. The delineation of the deeper resistive body is observed in the horizontal slice at 3150 m, as shown in Figure 10. Again, a good match with the true model is achieved, with smooth borders. The highest resistivities are found at the center of the bodies, which is also typically found in 3D inversions. In this case, this result is reinforced by the fact that in the points furthest from the borders, the behavior of the data is closest to the 1D approximation.
## 4 Biological Applications of EUV/SXR Microscopes Based on a Double Stream Gas-Puff Target Source
### Imaging of Dried and Fixed Biological Samples Using Contact Microscopy
#### 4.3.4 Imaging of Various Cell Lines  
A further study was devoted to investigating the effect of the SXR radiation absorbed energy on the contact microscopy imprint preparation. The following human cell lines were imaged: prostate DU145 prostate carcinoma cells (AHTB-81, from ATCC(r), VA, USA), HCC38 breast cancer cells (CRL-2314 TM, from ATCC(r), VA, USA), and human bone marrow-derived mesenchymal stem cells hMSC (Poieties(tm), PT-2501, from Lonza, MD, USA). Details about the sample preparation are reported in [86]. The optical microscope images (Figure 27a-c), acquired using a Primovert microscope (from Zeiss, Oberkochen, Germany) show the different morphology of the cell lines. In particular, the DU145 cell line (Figure 27a) present different shapes and sizes compared to HCC38 and hMSC cells (Figure 27b,c, respectively). HCC38 and hMSC cell lines are, indeed, much bigger and elongated. hMSCs cells, extending up to 400 \(\upmu\)m in length, have a thickness of 2.5-3.5 \(\upmu\)m. DU145 prostate carcinoma cells, instead, having a rhombic shape, are smaller and thicker (\(\sim\)6 \(\upmu\)m).  
Figure 27: Images acquired using an inverse optical microscope with a 20x objective of (**a**) DU145 prostate cancer cells, (**b**) HCC38 breast cancer cells, (**c**) hMSC (human mesenchymal stem cells). On the bottom of each image are depicted the respective SXR contact microscopy images (**d–f**). The images were obtained with exposure of 800 SXR pulses. Results were collected using AFM (scan size: 80 \(\times\) 80 \(\upmu\)m\({}^{2}\)). The insets (80 \(\times\) 80 \(\upmu\)m\({}^{2}\)) shows fragments of detailed cellular structures. Figure modified, based on [86].  
The PMMA of the samples was exposed to 800 SXR and then developed with MIBK: IPA, in an optimal ratio of 1:2. AFM imaging, using a CP-II, Veeco, CA, USA, with MPP-11123-10 cantilevers, was performed in tapping mode. In the rhombical DU145 cells, smaller in size and agglomerate, was not possible to visualize internal structures (Figure 27d), while, instead, in the HCC38 cells, larger, it was possible to observe the cell nuclei, distinguishing also several nucleoli (Figure 27e). Finally, in the hMSCs cells, which are the largest imaged, even though the nucleus was barely visible, it was possible to clearly visualize the edges of the cell membrane and the internal cytoskeleton elements (Figure 27f).
## 5 Conclusions  
By analyzing high-sampling-rate observations from the Ebre and El Arenosillo ionosonde stations with colocated COSMIC-2 RO measurements, we demonstrated how to create the combined ionospheric EDPs based solely on real high-quality observations from both the bottomside and topside parts of the ionosphere. Such combined EDPs can serve as an analogy for incoherent scatter radar-derived \"full profiles\", providing a reference for the altitudinal distribution of ionospheric plasma density. These reference profiles offer a valuable data source for validating and further improving empirical ionospheric models.  
Conceptualization, I.C. and D.A.; methodology, I.C. and D.A.; software, I.Z.; validation, I.C., D.A. and I.Z.; formal analysis, I.C., D.A. and I.Z.; investigation, I.C., D.A., I.Z., D.H., V.d.P., V.N.-P., A.S. and I.G.; resources, D.H., V.d.P., V.N.-P., A.S. and I.G.; data curation, D.H., V.d.P., V.N.-P., A.S. and I.G.; writing--original draft preparation, I.C.; writing--review and editing, I.C., D.A. and I.Z.; visualization, I.Z., I.C. and D.A.; supervision, I.C.; project administration, I.C. and D.A.; funding acquisition, I.C. and D.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the PITHIA-NRF project. The PITHIA-NRF project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101007599. This research was partially funded by the National Science Foundation (Grant 2054356) and the National Aeronautics and Space Administration (Grants C22K0658 and 80 NSSC20K1733).  
Not applicable.  
The authors declare no conflicts of interest.
However, this procedure still requires a long breath-hold time for patients (10-15 s per breath-hold) and relatively long post-processing times (\(\sim\)1 min per slice, which may vary according to image resolution). Recently, DL models have been developed to estimate the PDFF of liver by separating fat and water signals, as well as considering the confounding factors of \(R_{2}^{*}\) and field inhomogeneity, \(\phi\)[18]. Most of the recently proposed DL models are trained with a given dataset to separate the input MRI data into water-only and fat-only images to accurately estimate PDFF [18], along with \(\phi\) and \(R_{2}^{*}\) maps. The U-Net architecture [19] has shown a good agreement in terms of water-fat separation with non-DL methods. Improved U-Net-based versions have also been proposed for MRI water-fat separation, such as the Multi-Decoder Water-Fat separation Network (MDWF-Net) that uses multiple decoders to facilitate the output of different components that represent different quantities or whose amplitudes have different measurement units [20]. MDWF-Net has shown an improved estimation of \(R_{2}^{*}\), field inhomogeneity, and PDFF compared to previous DL approaches [20] in the liver. Recently, we have developed a new DL model, the Non-Linear Variables Neural Network (NLV-Net) [21], that only estimates the \(\phi\) and \(R_{2}^{*}\) maps, which have non-linear effects on PDFF estimation (see (1)). The water and fat maps have a linear effect on PDFF and are calculated using least squares once the non-linear component is available. DL models have been validated for estimating the PDFF of the liver and to obtain accurate PDFF estimations using three-echo MR images to reduce the scan acquisition time and, thus, the breath-hold time for patients [20]. However, none of these models has investigated the accuracy of PDFF in the pancreas, which is a much smaller organ than the liver. Therefore, the primary aim of this study was to evaluate the accuracy of different DL models, including NLV-Net, U-Net, and MDWF-Net for quantifying PDFF [13] from MR images in the pancreas. The secondary objective was to investigate the relationship between the fat amount on the liver and pancreas in healthy individuals and with suspected MASLD, using the best DL model and the gold standard technique.
# Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
## 2 Materials and Methods  
### Study Sites and Forest Data Collection  
#### 2.1.1 BTP--Betampona Strict Nature Reserve, Madagascar  
The model parameterization study site is Reserve Naturelle Integrale de (RNI) Betampona (translated: Betampona Strict Nature Reserve, henceforth referred to here as \"Betampona\" or \"BTP\"), a protected rainforest site in eastern Madagascar (Figure 1). One of very few remaining tracts of primary lowland rainforest in the country [23], Betampona is protected and only accessible through the acquisition of a research entry permit from the Malagasy government. Situated approximately 30 km to the northwest of the port town of Tamatave/Toamasina, the 2.23 km\({}^{2}\) reserve is located at 17\({}^{\circ}\)15\({}^{\prime}\)-17\({}^{\circ}\)55\({}^{\prime}\) S, 49\({}^{\circ}\)12\({}^{\prime}\)-49\({}^{\circ}\)15\({}^{\prime}\) E. Though given the protected status by the Malagasy government in 1927, enforcement was virtually absent until the 1990s. Based on conversations with the local organization managing Betampona's protection, it is estimated that ca. 50% of Betampona (i.e., 1114 ha) is undisturbed primary rainforest, with an additional ca. 35% characterized as recovering primary lowland rainforest, and the remainder as secondary lowland forest. The inventory plots measured in this study were established in undisturbed and recovering primary lowland rainforest; no plots were located in secondary forest.
## 6 Conclusions  
1. Compared with the LM model, the MAPE of the testing set for the PA-LM model decreased by 4.27%, and the percentage of the paired points in zone A increased by  
Figure 11: Prediction of the COP values of the two CSS horizontal wells by the optimized PA-LM model.  
Figure 10: Comparison of the actual and predicted COP of the first cycle of the two horizontal wells: (**a**) well B1H; (**b**) well B5H.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Well Name** & **MAPE (\%)** & **MSE** & **RMSE** \\ \hline B1H & 8.66 & 1363.81\({}^{2}\) & 1363.81 \\ B5H & 5.93 & 1058.66\({}^{2}\) & 1058.66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of the accuracy of the PA-LM model by the actual COP values of the two horizontal CSS wells.  
2.8% in the Clarke error grids. In addition, the MAPEs of the training sets for the PA-LM and LM models stabilized at 267 and 304 epochs, respectively. Therefore, the developed PA-LM model had a higher accuracy, a stronger generalization ability, and a faster convergence rate.
2. The PA can be used to determine the optimal hyperparameters of the LM model. Therefore, the PA-LM model outperform the LM model in predicting the COP of horizontal CSS wells in offshore heavy oil reservoirs.
3. The MAPEs of the predicted and actual COP values of the first cycle of the wells B1H and B5H (two horizontal CSS wells in Bohai Bay, China) by the optimized PA-LM model are 8.66% and 5.93%, respectively, satisfying the requirements in field applications. More COP data need to be collected from the following cycles or other CSS wells to further verify the PA-LM model's accuracy with the subsequent development of the reservoirs in Bohai Bay, China.
4. The developed PA-LM model provides an effective method for predicting the COP values of horizontal CSS wells in Bohai Bay, China, which was validated by the initial data set and actual production data. However, when the method is used in other offshore heavy oil reservoirs, the establishment process of the PA-LM model shown in this study should be re-conducted based on the practical reservoir conditions.
5. According to the calculated PCC results, the liquid production rate, oil saturation, and reservoir permeability are strongly related to the COP of the horizontal CSS wells. The soaking time, steam injection volume, steam injection rate, and steam quality have weak correlations with the COP of the horizontal CSS wells.  
Conceptualization, X.S. and L.Z.; methodology, Z.F. and H.X.; formal analysis, J.H. and C.W.; investigation, Y.B. and H.X.; writing--original draft preparation, X.S. and Z.F.; writing--review and editing, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a project from Beijing Research Center of CNOOC (China) Co., LTD. (CCL2021 RCPS02978 SN) and the Major Science and Technology of China National Offshore Oil Corporation (YXXY-ZX 06 2021).  
Not applicable.  
Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
High power levels can lead to significant heat generation, which, if not managed properly, can adversely affect amplifier performance and longevity. Chip thermal management typically involves both die-level and system-level approaches. At the die level, layout optimization is required to balance thermal performance and cost. At the system level, it is necessary to compare the differences in overall thermal dissipation between flip-chip and wire-bond packaging processes. Through optimized layout design and advanced chip integration techniques, the overall thermal management of the chip can be effectively enhanced, consequently improving its comprehensive performance and reliability [13; 14; 15; 16]. In conclusion, addressing these fundamental challenges--nonlinear amplitude/phase distortion, bandwidth restrictions, efficiency degradation, and thermal management--requires a multi-faceted approach involving innovative design strategies, advanced circuit techniques, and thorough performance optimization. By overcoming these obstacles, next-generation Doherty power amplifiers can meet the stringent requirements of 5G handsets, paving the way for more efficient and effective communication systems.
### Related Works
#### 1.2.4 Benchmark Datasets  
When it comes to effectively benchmarking any proposed 6 DoF pose detector, using annotated datasets as ground truth (GT) appears to be a common practice. Among the most renowned is RBOT [35], a semi-synthetic dataset for the evaluation of monocular object pose tracking algorithms. The YCB-Video [21] dataset provides sequences of videos informed by accurate 6 DoF pose notations. [PERSON] et al. [36] propose BCOT, a markerless dataset composed of physical objects noted with GT data owing to an external multicamera system. Datasets such as OPT [37]--although presenting a real-scene dataset--leverage fiducial markers in the background for GT pose estimation. Similar to the last approach, we also make use of fiducial markers to obtain GT notations for our evaluation. However, the scenes in the datasets mentioned earlier differ significantly from our current focus on digital construction. Specifically, we highlight the lack of metallic parts and the presence of unstructured construction visuals that deviate substantially from woodworking shops or building sites. DIMO [38] is the benchmark dataset closest to the experimental conditions that we can report. It presents a hybrid synthetic dataset of industrial metal objects containing RGB frames with 6 DoF pose labels. Notations are obtained via a robotically controlled recording session, and the targeted objects present shiny textures and symmetric features. Despite the presence of metallic elements, DIMO lacks challenging characteristics, such as skewed, occluded, and close-range views, which are prevalent in our application domain.
## 2 Preliminaries
### Forward and Backward Privacy of DSSE  
#### 2.3.1 Forward Privacy  
Forward privacy [16] ensures newly updated data entries cannot be related to the previous search queries. The forward-private DSSE scheme hides the linkability between an update operation and the past search queries [2]. Thus, if a client performs any update operations (i.e., document/keyword add or delete) on a keyword \(w\), the adversary, including the data server, cannot learn any information whether searches on keyword \(w\) have been made beforehand, mitigating the file injection attack [17] in DSSE schemes.
## 2 Temperature Variation in Particle Flow Modeling Based on Energy Analyses
### Friction Effect  
When there is a relative displacement between two contacted particles, the friction will take effect, and the friction energy will contribute to the heat and hence lead to a change in temperature.  
In particle flow modeling, friction energy \(E_{\mu}\) is defined as a kind of cumulative energy, updated in the program as Equation (5) [51]:  
\[E_{\mu}:=E_{\mu}-\frac{1}{2}\Big{(}\Big{(}F_{s}^{l}\Big{)}_{0}+\Big{(}F_{s}^{ l}\Big{)}_{1}\Big{)}\cdot\Delta\phi_{s}^{\mu} \tag{5}\]  
where \((F_{s}^{l})_{0}\) is the linear shear force at the beginning of the time step, \((F_{s}^{l})_{1}\) is the linear shear force at the end of the time step, and \(\Delta\delta_{s}^{\mu}\) is the relative sliding shear displacement.  
The friction energy in the loading process is thought to be completely converted into heat \(Q_{\mathrm{f}}\):  
\[Q_{\mathrm{f}}=E_{\mu} \tag{6}\]  
Thereafter, the temperature change \(\Delta T_{\mathrm{f}}\) caused by the friction effect can be obtained according to the heat calculation formula:  
\[Q_{\mathrm{f}}=C_{\mathrm{p}}m\Delta T_{\mathrm{f}} \tag{7}\]  
where \(C_{\mathrm{p}}\) is the specific heat capacity at constant pressure and \(m\) is the mass of the particle. It should be noted that the heat \(Q_{\mathrm{f}}\) is assumed to be equally distributed between the two contacted particles.
## 6 Application of Cybersecurity Optimizer in Training Multi-Layer Perceptions for Attack Detection
### NSL-KDD Dataset  
The NSL-KDD dataset comprises 41 attributes, with 125,973 training samples and 22,544 test samples, distributed across 5 classes. The statistical results of various algorithms applied to this dataset are presented in Table 8.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classification** & **No. of Attributes** & **No. of Training** & **No. of Test** & **No. of Classes** \\
**Datasets** & **No. of Attributes** & **Samples** & **Samples** & **No. of Classes** \\ \hline NSL-KDD & 41 & 125,973 & 22,544 & 5 \\ CICIDS2017 & 78 & 1,048,575 & 262,144 & 15 \\ UNSW-NB15 & 49 & 175,341 & 82,332 & 10 \\ Bot-IoT & 35 & 3,000,000 & 1,000,000 & 5 \\ CSE-CIC-IDS2018 & 80 & 1,048,576 & 262,144 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Specifications of classification datasets.  
The results indicate that Cybersecurity-MLP and MFO-MLP achieve the lowest average MSE, suggesting that both algorithms exhibit strong capabilities in avoiding local optima. Furthermore, Cybersecurity-MLP attains the highest classification accuracy, with a rate of 98.5%. This highlights the effectiveness of the cybersecurity-based trainer in improving the training process for attack detection using this dataset.
## 3 Results
### Physical and Chemical Parameters of Cultivation
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**N-NH3 (mg/ L)**} & \multicolumn{3}{c}{**Total P (mg/ L)**} \\
**Time (Days)** & **Control** & **50\% SE** & **100\% SE** & **Control** & **50\% SE** & **100\% SE** \\ \hline
0 & 0.12 \(\pm\) 0.09 & 0.05 \(\pm\) 0.00 & 0.04 a  & 52.23 \(\pm\) 1.57 & 32.62 \(\pm\) 4.99 *  & 18.68 \(\pm\) 8.57 *  \\
2 & 0.12 \(\pm\) 0.04 & 0.08 \(\pm\) 0.02 & 0.19 \(\pm\) 0.04 & 51.33 \(\pm\) 2.74 & 27.34 \(\pm\) 5.34 *  & 0.97 \(\pm\) 0.31 *  \\
4 & 0.15 \(\pm\) 0.02 & 0.11 \(\pm\) 0.02 & 0.2 \(\pm\) 0.07 & 46.5 \(\pm\) 1.76 & 20.09 \(\pm\) 1.75 *  & 1.16 \(\pm\) 0.11 *  \\
6 & 0.171 a  & 0.22 \(\pm\) 0.03 & 0.2 \(\pm\) 0.03 & 43.76 \(\pm\) 4.25 & 19.44 \(\pm\) 7.63 *  & 1.06 \(\pm\) 0.35 *  \\
8 & 0.28 \(\pm\) 0.01 & 0.27 \(\pm\) 0.02 & 0.15 \(\pm\) 0.02 & 49.7 \(\pm\) 4.59 & 15.03 \(\pm\) 0.92 *  & 1.82 \(\pm\) 0.28 *  \\
10 & 0.28 \(\pm\) 0.06 & 0.26 \(\pm\) 0.04 & 0.26 \(\pm\) 0.04 & 52.5 \(\pm\) 5.66 & 17.23 \(\pm\) 2.67 *  & 1.68 \(\pm\) 0.47 *  \\
12 & 0.48 \(\pm\) 0.18 & 0.26 \(\pm\) 0.02 & 0.66 \(\pm\) 0.41 & 52.64 \(\pm\) 5.18 & 17.81 \(\pm\) 0.34 *  & 0.97 \(\pm\) 0.17 *  \\ \hline \end{tabular}
\end{table} 
Table 3: Nutrient availability in each treatment with secondary effluent (SE) (mean \(\pm\) SD; n = 3). The symbol * indicates statistical differences (_p_-value < 0.05) among the different effluent concentrations, in each sampling day, in comparison with the control. Figure 6: pH value of the growth medium, in which _C. vulgaris_ was grown for 12 days. Different concentrations of (**a**) secondary effluent (SE) and (**b**) sludge run-off (SR) were tested (mean \(\pm\) standard deviation; n = 3).
## 1 Introduction
Although selecting a suitable task for inter-node migration can reduce remote memory access, the selection procedure must evaluate all tasks in the runqueue, which involves a processing overhead. Since the selection is in the critical path for the kernel scheduler, the cost of more operations outweighs any benefit. Therefore, we further improve the procedure for selecting tasks by using thread group information to eliminate superfluous evaluations. Only a subset of movable tasks in the runqueue is evaluated, so the selection procedure is more efficient. The contribution of this study is as follows. First, multithreaded applications and their memory access in Linux are analyzed. The analysis indicates that the existing memory-aware MB policy is still effective for multithreaded applications. However, it requires the kMLB mechanism to track per-task memory usage on per NUMA node. Thus, its implementation needs to modify many kernel operations and data structures that are affected. Second, the thread-aware Exclusivity policy is proposed, which is a relatively lightweight task selection policy since it does not need the kMLB mechanism. Instead, it uses the exclusivity of the thread distribution on nodes in the thread group to determine the target thread for inter-node migration. Third, several methods to enhance selecting tasks for inter-node migration for multithreaded applications are proposed. Finally, the proposed Exclusivity policy and enhancement methods are practically implemented in the Linux kernel. Extensive experiments using the PARSEC 3.0 [6] benchmark suite run on the modified Linux kernel with various task selection policies. Compared with the unmodified Linux kernel, the results show that when the task selection procedure is enhanced, the Most Benefit Plus (MB\({}^{+}\)) policy, which requires the kMLB mechanism, increases performance by 11.1%. The proposed Exclusivity policy increases performance by 10.7%. This policy is competitive and does not require the kMLB mechanism. Besides, it is more easily adapted to a newer Linux kernel. The remainder of this paper is organized as follows. Section 2 introduces the technological background and related work. Section 3 presents the improvements for inter-node task migration for multithreaded applications and the new task selection policy. Section 4 details the experimental results, and Section 5 concludes.