esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
###### Abstract  
With the aim of addressing the low volumetric efficiency of CO2 swashplate axial piston pumps, the influence of four volumetric losses--loss of the CO2 compression retention volume, leakage volume loss of the port pair, leakage volume loss of the plunger pair, and leakage volume loss of the slipper pair--on volumetric efficiency was analyzed using a transient numerical simulation method. The numerical simulation results showed that the real physical property model can accurately describe the compression retention characteristics of CO2 under high-pressure conditions. CO2 compression retention volume loss accounted for 28.6% of the volumetric efficiency and was the main factor causing low volumetric efficiency of the piston pump. Leakage volume losses of the slipper pair, the flow distribution pair, and the plunger pair accounted for about 3.4%, 1.5%, and 0.5% of the volumetric efficiency, respectively. These research results provide a reference for volumetric efficiency loss analyses of piston pumps.  
Mechanical Engineering College, Xi'an Shiyou University, Xi'an 710065, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
CO2; swash plate axial piston pump; compression hold-up; leakage loss; numerical simulation
## 3 Results and Discussion
### Genomic Characteristics
In addition, the KEGG analysis of the reactive oxygen species pathway (mapped in chemical carcinogenesis) revealed the microorganism's ability to detoxify arsenic through the antioxidant  
Figure 9: Predicted structures of: (**A**) monasorubrin, (**B**) nidulanin A, (**C**) histidyltryptphenyldidketopiperazine/dehydrohistidyltryptophanyldiketopiperazine/roqueforine D/roqueforine C/glandicoline A/glandicoline B/meleagrine, (**D**) YWA1, (**E**) choline and (**F**) the penicillin homolog. defense system and the action of arsenite methyltransferase [EC:2.1.1.137], glutathione S-transferase [EC:2.5.1.18], arsenite methyltransferase [EC:2.1.1.137], NADH-ubiquinone oxidoreductase chain 1 [EC:7.1.1.2], succinate dehydrogenase (ubiquinone) flavoprotein subunit [EC:1.3.5.1], ubiquinol-cytochrome c reductase iron-sulfur subunit [EC:7.1.1.8], cytochrome c oxidase subunit 3, F-type H\({}^{+}\)-transporting ATPase subunit alpha, superoxide dismutase, Fe-Mn family [EC:1.15.1.1], voltage-dependent anion channel protein 1, superoxide dismutase, Cu-Zn family [EC:1.15.1.1] and catalase [EC:1.11.1.6]. The additional screening of the AsgeneDB database also revealed some peculiarities related to arsenic resistance, namely, the presence of specific genes, such as ACR3 (12 count), GET3 (8 count) and arsB (4 count) [all are As(III) efflux pump/permeases for detoxification]; ACR2 (1 count) and GstB (3 count) [both can conduct the reduction of As(V) to As(III) that can be excreted through the As(III) efflux pumps]; arsM (6 count) [As(III) sadenosylmethionine (SAM) methyltransferase (arsM)]; arsH (2 counts) [methylarsenite-specific oxidase (ArsH) that can oxidize methyltransferase to methyltransferase]; and pstB (6 count), glpF (1 count) and PiT (1 count) [all are glycerol phosphate transporters that can absorb As(III) and As(V)] [56,85]. These results reinforce the strong genomic and metabolic organization and investment towards _Penicillium mexicacum_ arsenic transport and As (III) oxidation. In parallel, multiple siderophores (iron transporters), siderochromes (iron transporters), iron-sulfur cluster transporters and various other iron-related proteins were also detected in the genome annotation (Supplementary Table S1), also suggesting that they are essential for _Penicillium mexicacum_ survival, the maintenance of normal metabolic function and resistance to metal toxicity in these contaminated environments. In fact, the production of siderophores, siderochromes and metal-chelating compounds allows for fungal survival in extreme environments where nutrients are scarce, while also ensuring the neutralization of toxic metals [86; 87; 88; 89]. Complementarily, iron-sulfur clusters also facilitate the survival and adaptation to metal-rich environments, partaking in critical roles for fungal homeostasis such as electron transport processes, nitrogen fixation, sulfur assimilation, amino acid biosynthesis, ROS detoxification and DNA repair [89; 90].
## 3 The Model Processing and Analysis Forecasting Result
### Analysis of the Performance of Each Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Low Income Group**} & \multicolumn{2}{c}{**Lower-Middle Income Group**} \\ \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**CS-Combined**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**CS-Combined**} \\ \cline{2-2} \cline{5-5}  & **Training** & & & **Testing** \\ \hline
**Ploy2** & 1.169509 * & 1.809601 * & **Poly2** & 5.267367 *** & 1.7801 * \\
**Sin2** & 1.601233 * & 1.695902 * & **Sin1** & 5.386427 *** & 1.8042 ** \\
**Reci-ploy2** & 3.67126 *** & 2.146856 ** & **Reci-ploy2** & 4.876673 *** & 2.0909 ** \\
**Reci-exp2** & 1.399597 * & 1.642031 * & **Reci-exp2** & 2.287399 ** & 0.70121 * \\
**Power2-ploy2** & 2.113385 ** & 1.487737 * & **Power2-ploy2** & 2.592205 *** & 1.8995 * \\
**Power2-exp2** & 2.78006 ** & 1.524198 * & **Power2-exp2** & 2.223426 ** & 1.9920 ** \\ \hline \multicolumn{2}{c}{**Upper-Middle Income Group**} & \multicolumn{2}{c}{**High Income Group**} \\ \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**CS-combined**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**CS-combined**} \\ \cline{2-2} \cline{5-5}  & **Training** & & & **Testing** \\ \hline
**Poly2** & 1.92596 * & 1.711787 * & **Poly2** & 1.476395 * & 0.52254 * \\
**Sin1** & 1.500561 * & 1.744547 * & **Sin2** & 0.124479 * & 0.62806 * \\
**Reci-ploy2** & 0.6028 * & 1.873882 * & **Reci-ploy2** & 1.494776 * & 0.4238 * \\
**Reci-exp2** & 0.148387 * & 5.378944 *** & **Reci-exp2** & 1.285115 * & 1.06562 * \\
**Power2-ploy2** & 0.693713 * & 1.74556 * & **Power2-ploy2** & 1.492353 * & 0.44561 * \\
**Power2-exp2** & 0.024528 * & 3.932425 *** & **Power2-exp2** & 1.302547 * & 0.94512 * \\ \hline \hline \end{tabular}  
* is the 10% significance level; ** is the 5% significance level. ** is the 1% significance level.  
\end{table} 
Table 6: Diebold–Mariano (DM) test of five different models for four different income groups. 2 Table 7 indicates that the first-order and second-order forecasting availabilities offered by the proposed combined model outperform six individual regression models for the four income groups in tuberculosis prevalence rate forecasting. For example, for the low income group, the first-order forecasting availabilities offered by each forecasting model are 0.998405, 0.998663, 0.99874, 0.998651, 0.998815, 0.9988572, and 0.999445, respectively, while their second-order values are 0.998403, 0.998662, 0.99874, 0.99865, 0.998814, 0.998571, and 0.999445, respectively. **Remark:** The results indicate that the proposed combined model is more valid and significantly superior to the other models. Accordingly, the proposed combined forecasting model can satisfactorily approximate the observed tuberculosis prevalence rate.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
###### Abstract  
Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color.  
hyperspectral; denoising; water color remote sensing; sparse representing; bio-optical model +
Footnote †: journal: Applied Sciences  
Article
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 4 Experimental Works and Results
### Experimental Setup
#### 4.1.5 Attack Detection Time  
The attack detection time is the average time taken to detect attackers in the network. It is calculated as the difference between the time when an attack was identified and the time when traffic began. We compared our proposed mechanism (MMTM-RPL) with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The results are shown in Figure 8 for the three types of attack. The numbers of nodes are shown on the \(x\)-axis and the attack detection time is shown in milliseconds on the \(y\)-axis. Figure 8 shows that for a rank attack, the attack detection times were 18.50, 24.50, and 20.25 ms for MMTM-RPL, DCTM-IoT, and RBAM-IoT, respectively. Note that for MMTM-RPL, the time is a minimum. Similarly, it is lowest for sinkhole and Sybil attacks. This is because in MMTM-RPL, the clustering algorithm responds swiftly to the arrival and departure of nodes in the mobile networks. Secondly, the use of MAs also makes the proposed MMTM-RPL efficient [52], since they use dynamic itineraries. Thirdly, as the control message overhead in MMTM-RPL is low compared to the state of the art, the network performance plays an important role in decreasing the detection time.
## 4 Performance Evaluations  
In this section, we will evaluate the performance of our LSRR-LA in a simulative and experimental way. First, in Section 4.1, we analyze the complexity of those algorithms that include LSRR-LA and other classical algorithms, such as DV-Hop, Amorphous, PDM, LSVR, and MSVR. Then, in Section 4.2, we use the performance metric ALE (Average Localization Error) to evaluate these algorithms in C-shaped, L-shaped, and D-shaped network topologies. Finally, in Section 4.3, the positioning accuracy of LSRR-LA is verified in an outdoor experiment.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 1 Introduction
### Novelty and Contributions  
Several low-resource embedded devices such as RFID [2; 3; 4], WSNs [5; 6; 7; 8; 9], smart cards [10], WBANs [11], and the IoTs [12; 13] require low-resource and power-optimized accelerators of cryptographic algorithms. Therefore, the novelty of this work is the latency-optimized hardware accelerator of SIMECK block cipher and its FPGA demonstrations. To our best knowledge, there is no prior latency-optimized implementation of SIMECK. Therefore, our contributions are as follows:  
* _Hardware design._ We have presented a novel architecture of the SIMECK lightweight block cipher with an emphasis on optimization of latency using a block size of 32 and supported a key length of 64 bits. To reduce the computational time (or latency), we have created digits of the key and data blocks into smaller segments. Then, each smaller segment of the key is operated in parallel to generate the round keys of SIMECK. Similarly, each smaller segment of the data block is operated in parallel to produce the required encryption/decryption operations. This helps us reduce the required clock cycles. Hence, our circuit takes 16 cycles to execute 32 SIMECK rounds.
* _Pipeline registers._ A 2-stage pipelining is used to shorten the critical path and to improve the circuit frequency.
* _Dedicated controller._ A finite-state-machine based controller is utilized for efficient control functionalities.
* _Implementation and performance evaluation._ Register Transfer Level (RTL) implementation of the proposed hardware architecture is implemented in Verilog (HDL) using a Vivado IDE tool. The performance of our RTL design is compared with state-of-the-art architectures by defining different figures of merit.
# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## 3 Methodology  
### Preliminaries  
Personalized federated learning is an important branch of federated learning that aims to address the issue of data heterogeneity among different clients. In a pFL system, assume there are \(N\) clients and one server. Each client, \(i\in\{1,2,\ldots,N\}\), holds a private dataset, \(D_{i}=\big{\{}(x_{j},y_{j})\big{\}}_{j=1}^{M_{i}}\), where \(M_{i}\) represents the data volume of client i. Additionally, define \(M=\sum\limits_{i=1}^{N}M_{i}\) as the total volume of all data. Each client's decoupled model parameters are \(w_{i}=(\theta_{i},\phi_{i})\), where \(\theta_{i}\) is the parameters of the feature extractor and \(\phi_{i}\) is the parameters of the classifier. On the server side, the aggregation of the shared feature extractor is completed, and the parameters of the aggregated feature extractor are denoted as \(\theta_{g}\).  
There are two processes in pFL, the local update process and the global aggregation process. Let \(E\) denote the total number of epochs for local training in a global round, \(t\), and \(T\) represent the total number of global training rounds, while \(e\) denotes an intermediate epoch during local training. For client i, it first updates the local feature extractor using the global aggregated feature extractor \(\theta_{g}^{(t-1)}\), and the updated local feature extractor is \(\theta_{i}^{t}\). Then, \(\theta_{i}^{t}\) is concatenated with \(\phi_{i}^{t}\) to form the initialized model \(w_{i}^{t}=(\theta_{i}^{t},\phi_{i}^{t})\). This model is trained locally for \(E\) epochs, resulting in \(w_{i}^{t+E}=(\theta_{i}^{t+E},\phi_{i}^{t+E})\). The updated local feature extractor \(\theta_{i}^{t+E}\) is then uploaded to the server. In an ideal scenario, assuming all clients participate in training and updating and the model parameters are aggregated in proportion to the data volume, the updated global feature extractor is \(\theta_{g}^{t}=\sum\limits_{i=1}^{N}\frac{M_{i}}{M}\theta_{i}^{t+E}\), and the aggregated feature extractor is distributed to each client. Under the pFL framework, the overall optimization objective is as follows:  
\[\min\limits_{\theta_{i}\phi_{i}}\Bigg{\{}F(\theta,\phi_{i}):=\sum\limits_{i=1 }^{N}\frac{M_{i}}{M}\mathcal{L}(\theta,\phi_{i};D_{i})\Bigg{\}} \tag{1}\]  
where \(\mathcal{L}\) is the loss of client i on its private dataset, \(D_{i}\).
## 3 Electothermal Modeling of the HEMT
### Influence of the Dissipated Power  
The dissipated power in the active area is one of the operating conditions of the HEMT. To observe its effect on the thermal behavior of the transistor, we vary the linear density of this power in the finite element model developed previously, and we keep the other parameters constant. The reference temperature was 25 \({}^{\circ}\)C. Figure 2 shows the temperature distribution in the whole structure of the HEMT. We can see that the temperature is high in the AlGaN layer at the exit of the gate and around the passivation layer and along the gate.  
To study the evolution of the operating temperature as a function of the dissipated power, we performed several numerical simulations. The obtained results are presented in Figure 3 with the experimental results that were obtained by the optical Raman-Micro-Spectrometry method. According to the figure, we notice that the HEMT temperature increases according to the dissipated power by a non-linear variation; it reaches rather important values at a very high dissipated power. The curve of the results of simulations and experiments are in good correlation. The small deviation between the curves, and especially in the case of high power, is due to the fact that we neglected the other modes of heat transfer, as we have previously mentioned. We will consider that this criterion is sufficient to validate the finite element model developed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & \(\rho\)\((\mathbf{Kgm^{-3}})\) & \(\mathbf{K}\)\(\left(\mathbf{Wm^{-1}K^{-1}}\right)\) & \(C_{p}\)\((\mathbf{JKg^{-1}K^{-1}})\) \\ \hline Au & 19,300 & 310 & 137 \\ SiN & 3300 & 10 & 713 \\ AlGaN & 5470 & \(25\left(\frac{293}{273+7}\right)^{1.35}\) & 548 \\ GaN & 6100 & \(161\left(\frac{293}{273+7}\right)^{1.45}\) & 490 \\ SiC & 3220 & \(416\left(\frac{293}{273+7}\right)^{1.45}\) & 690 \\ Nucleation layer & 6100 & \(6.7\left(\frac{293}{273+7}\right)^{2.7}\) & 490 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Materials’ thermal properties.  
Figure 2: Temperature distribution in the HEMT structure.
## 4 Conclusions  
Quantum-chemical calculation search of the most effective anode material on the base of silicon-filled pillared graphene was performed. The search was carried out of several positions: (I) the optimal mass fraction of silicon (Si) providing maximum anode capacity, (II) the optimal Li:C and Li:Si ratios, when a smaller number of C and Si atoms imbibed more amount of Li ions, and (III) the conditions of the most energetically favorable delithiation process. It was found that for 2D-pillared graphene with sheets distance of 2-3 nm and step between SWCNTs \(\sim\)5 nm the optimal concentration of silicon in the composite's pores was \(\sim\)13-18 wt.%. Under such conditions the achieved capacity was \(\sim\)1500 mAh-g\({}^{-1}\). For such a small mass fraction of silicon, the amount of lithium intercalated into silicon was \(\sim\)27% of the lithium captured by the carbon cage for model I and \(\sim\)45% for model II.  
In general, according to our calculations, an increase in the volume of pillared grapheme pores did not lead to an improvement in the quality of the anode material. For the first model with short SWCNT, the maximum capacity was 10% higher in comparison to the second model with long SWCNT.  
Figure 5: Binding energy if Li atom with components of the anode material.  
The first model is also supported by the significantly lower mass fraction of silicon (13 wt.%) and the small fraction of the imbibed lithium ions. This is very important for the process of delithiation, since, as was shown Section 3.3, the most energy-consuming is the detachment of lithium from silicon, and the most favorable is the detachment from the SWCNT. Successful elimination of the largest number of lithium ions from the anode material will ensure the battery's durability and a large number of charging/discharging cycles. The advantages of the first model are explained by the fact that the main role in lithiation process is played by the surface rather than by the volume of pillared graphene pores, as for any porous material.  
The capacity of the considered in this paper anode material is higher by 400% in comparison to the widely used graphite anode. This allows to predict great prospects for the application of the anode material based on the composite of pillared graphene and silicon in modern and future batteries.  
Conceptualization, O.E.G.; methodology, O.E.G.; funding acquisition, D.A.K.; investigation, O.E.G., D.A.K.; writing--original draft preparation, D.A.K.; writing--review and editing, O.E.G.; supervision, O.E.G. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the RFBR grant (project No. 19-32-90160).  
The authors declare no conflict of interest.
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 3 Experimental Results and Discussion
### Comparison with Contemporary Techniques  
The comparative analysis presented in Table 4 assesses the performance of the proposed model against modern techniques, employing various deep learning models. The first model, EfficientNet [36], demonstrates an accuracy of 83%, a _precision_ of 83%, a _recall_ of 71%, and an _F1-score_ of 77%. Moving on to the second model, CNN [37] achieved an _accuracy_ of 89%, a _precision_ of 87%, a _recall_ of 84%, and an _F1-score_ of 85%. This model outperforms EfficientNet in terms of _accuracy_ and _F1-score_. The third model, ResNet34 [38], displays an _accuracy_ of 70%, a _precision_ of 70%, a _recall_ of 70%, and an _F1-score_ of 71%. While this model achieves a balanced performance, it falls behind in accuracy compared to the other models. However, the Inception-V3 model surpasses all others with an accuracy of 92%, a _precision_ of 93%, a _recall_ of 92%, and an _F1-score_ of 92%. This indicates superior performance compared to the aforementioned models. The DenseNet121 model in the proposed system achieves an _accuracy_ of 91%, a _precision_ of 91%, a _recall_ of 90%, and an _F1-score_ of 89%. Although slightly lower than Inception-V3, it outperforms EfficientNet, CNN, and ResNet34. Lastly, the ResNet-50 model in the proposed system yields an _accuracy_ of 91%, a _precision_ of 90% [31], a _recall_ of 90%, and an _F1-score_ of 88%. It maintains competitive performance, particularly in _accuracy_ and _F1-score_, when compared to other models. In short, the proposed Inception-V3 model stands out as an effective model in terms of accuracy and _F1-score_, showcasing the efficacy of the suggested system in surpassing the state-of-the-art techniques.
## 2 Thermal Evaluation of Overhead Electrical Lines
### Static Line Rating  
In SLR, electrical grids are operated under the assumption that their conductors have a constant ampacity, which remains the same for every hour of the day, any day of the year, or any season. Entire regions with many overhead lines are considered areas with consistent meteorological conditions. Static assessment is a conservative estimate based on assumptions of weak convective cooling due to wind, high air temperatures, and solar radiation, where the calculation concerns theoretical rather than actual ampacity [12].  
SLR is calculated using a thermal model based on the thermal balance of the bare conductor, low perpendicular wind speed (e.g., 0.5 m/s), seasonal air temperature close to the peak (e.g., 35 \({}^{\circ}\)C or higher in summer), and full solar heating (e.g., 1000 W/m\({}^{2}\)), as described in CIGRE Technical Brochure 299 [13].  
Meteorological conditions used for SLR evaluation vary depending on the region's environment and energy companies' risk tolerance. Each transmission line may have several types of SLR. These include normal rating (or continuous), long-term emergency rating, and short-term emergency rating [14].  
Figure 1: Factors affecting the ampacity of a conductor.
## 2 Theory
### Wavefront Propagation
The images are evaluated using the three sparsity criteria to determine the reconstruction distance that produces the best focused image, as is shown in Figure 4. Note how the three criteria lead to very similar results in terms of the best focused plane for the evaluated image.