esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & _Angelica archangelica_ **(G2)** & \multicolumn{2}{c}{_Ginkgo biloba_ **(G3)**} & \multicolumn{2}{c}{**Combined Dose (G4)**} \\ \hline  & **g/dL** & **\%** & **g/dL** & **\%** & **g/dL** & **\%** \\ \hline S-Hb & 0.08 \(\pm\) 0.006 \({}^{\ast}\) & 0.5 & 0.09 \(\pm\)0.003 & 0.5 & 0.06 \(\pm\) 0.002 \({}^{\ast}\) & 0.3 \\ Met-Hb & 0.64 \(\pm\) 0.04 \({}^{\ast}\) & 3.9 & 0.59 \(\pm\) 0.04 \({}^{\ast}\) & 3.8 & 0.39 \(\pm\) 0.02 \({}^{\ast}\) & 2.5 \\ Hb-CO & 0.62 \(\pm\) 0.03 & 3.9 & 0.57 \(\pm\) 0.03 & 3.6 & 0.36 \(\pm\) 0.02 & 2.3 \\ Hb-O\({}_{2}\) & 14.63 \(\pm\) 1.14 \({}^{\ast}\) & 91.5 & 14.23 \(\pm\) 0.94 \({}^{\ast}\) & 91.9 & 15.35 \(\pm\) 1.24 \({}^{\ast}\) & 95 \\ Total Hb & 15.97 \(\pm\) 1.23 \({}^{\ast}\) & & 15.48 \(\pm\) 1.21 \({}^{\ast}\) & & 16.15 \(\pm\) 1.02 \({}^{\ast}\) & \\ \hline \hline \end{tabular} \({}^{\ast}\) Significant \(p\) < 0.05 and ** highly significant \(p\) < 0.01.  
\end{table} 
Table 2: Hemoglobin derivative concentrations in rabbits injected with 2 MBq/kg Gallium-68 after seven days of protective oral antioxidant treatment as indicated (G2–G4) (Mean \(\pm\) SE). Figure 2: **(A)** Sulfhemoglobin, methemoglobin, and carboxyhemoglobin derivative concentrations in rabbits injected with \({}^{68}\)Ga after antioxidant treatment for seven days. **(B)** Oxyhemoglobin derivative concentrations of rabbits injected with \({}^{68}\)Ga after antioxidant treatment for seven days. \({}^{\ast}\) Significant \(p\) < 0.05 and ** highly significant \(p\) < 0.01. The rate at which hemoglobin is converted to the oxidized form, i.e., the hemoglobin autoxidation rate, was measured, and the results are shown in Figure 3. A highly oxidized pattern was recorded in the hemoglobin of rabbits that received Gallium-68 with no prior proactive treatment with antioxidants. Groups 2 and 3 revealed lower rates of autoxidation than the groups that received no treatment before exposure to radiation, but a lowest rate was observed in rabbits that received a combined dose of antioxidants (Group 4 (G4)).
## 3 System Model
### Design Goals  
Based on the identified threat model and security assumptions, we establish the following specific design goals:  
* Security: Our primary security objective is to ensure system availability and output integrity with high probability even in the presence of adversarial attacks targeting individual executors. The architecture must guarantee that compromised executors cannot persistently produce erroneous outputs without detection, thereby preventing attacks from propagating to physical control processes or upstream analytical systems. Specifically, the system should maintain correct operation as long as a threshold number of executors remain uncompromised and automatically identify and isolate compromised executors through behavioral analysis and output comparison.
* Privacy: The architecture must protect sensitive operational data, proprietary algorithms, and system parameters from adversaries capable of monitoring the arbitration process or conducting side-channel attacks. Even if an attacker successfully infiltratesthe arbitration module, they should be unable to extract the exact values of executor outputs or internal system states, thus preserving the confidentiality of critical industrial information. Privacy protection should extend to both the content of individual executor outputs and the aggregate results of the arbitration process.
* Efficiency: While enhancing security and privacy, the architecture must maintain reasonable operational efficiency without introducing prohibitive computational or communication overhead. The additional resources required for maintaining multiple heterogeneous executors, performing privacy-preserving arbitration, and executing dynamic scheduling should be proportionate to the security gains achieved.
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 5 Experiment and Analysis
### Feature Selection  
To fully exploit data features and enhance the prediction accuracy, this study employs the maximum mutual information coefficient for an adaptive input feature selection. Specifically, one-year data from a region in Tetuan City are used as a case study, with Figure 7 illustrating the mutual information coefficients between the electric load and candidate input features (e.g., temperature, humidity, and historical load).  
In the figure, the star symbol denotes the feature with the highest correlation coefficient. Given the relatively low correlation between the wind speed and load, the remaining four features are selected as meteorological inputs to the model, as visualized by the circular markers in Figure 7.  
Figure 7: Correlation coefficient plot.
## 4 Discussion  
Considering the results of the developed experiments, we can see that the separation of pre-designed microscopic behaviors together with a macroscopic behavior (which must be learned) is feasible. We also observe how the macroscopic system is capable of adapting to unseen changes in the environment, operating coherently with its initial goal.  
We believe that the use of fuzzy logic in the microscopic system not only greatly simplifies the learning of the global system (as it significantly reduces the dimensionality of the problem to be solved) but could also provide a way to easily adjust these microscopic behaviors without significant macroscopic alterations. Thus, it also allows engineers to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Num. Robots** & **Correct Executions** & **Fail Distance Mean (cm)** & **Fail Distance Std (cm)** & **CV** \\ \hline
2 & 65\% & 173.68 & 75.54 & 43.49 \\
3 & 80\% & 182.13 & 80.15 & 44.00 \\
4 & 83\% & 192.22 & 113.61 & 59.10 \\
5 & 67\% & 221.05 & 114.64 & 51.86 \\
6 & 64\% & 216.78 & 125.35 & 57.82 \\
7 & 59\% & 223.71 & 130.62 & 58.38 \\
8 & 40\% & 236.16 & 123.38 & 52.24 \\
9 & 30\% & 258.02 & 137.79 & 53.40 \\
10 & 37\% & 241.39 & 134.42 & 55.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scalability tests. For each swarm size, 100 executions of the behavior were carried out. If the box did not reach the goal in 700 iterations, then the test was marked as incorrect. For all incorrect tests of each size, the box–goal mean distance, the standard deviation and the coefficient of variation \((CV=(std/mean)\times 100)\) are shown.  
develop safety-critical systems (the reactive behavior of the robots; e.g., their speeds related to obstacle avoidance and local goals) in a simple way, with the required methodology. However, delegating these tasks to an end-to-end network could add an additional control phase to verify that the trained system is safe in its application domain.  
End-to-end strategies have clear advantages, including the designer's lack of concern for microscopic tasks when dealing with only a global behavior. However, the dimensionality of the problem is critical in determining whether deep reinforcement learning can be used. Moreover, when dealing with robotic swarms, we have the added problem that this dimensionality can be much higher. Hybrid strategies such as those presented in this article can be of great help to allow the use of RL strategies in problems with high dimensionality and also reduce the difficulties derived from the existence of local minima and the intrinsic randomness of the actuators and sensors of the robots.
# Quantitative Analysis of Drag Reduction Methods for Blunt Shaped Automobiles
## 4 Conclusions  
Quantitative analysis made for drag reduction methods, where relative, and absolute drag change was used for evaluations. Drag reduction methods were divided into three main categories: active, shape, and topology. For better comparison, these were divided into subcategories: steady flow jet, synthetic actuation flow, fairings, flaps, and winglets. An additional category was made for those that was difficult to categorise due to its uniqueness. Based on the mean value of subcategories, it was shown that steady jet flow and applications of fairings should be the most favourable drag reduction methods. Additional fairings are slightly favoured at aerodynamic vehicles over steady jet flow. Winglets and flaps are the least favoured categories. Combination of different methods should always increase the magnitude of reduction.  
Based on the examined papers, it showed that synthetic jet actuation requires fine flow control, due to the manipulation of the Kelvin-Helmholtz effect. Active pulsating flow control was only viable at low velocities, while vortex generators favoured at higher velocities.  
The review is limited to low-Re wind tunnel measurements and a validated version of CFD models of ground vehicles. Based on the literature review, the following research recommendations were made.  
Steady flow control systems show promising results. Application on the leading should be examined to reduce corner vortices on bluff bodies.  
Further vortex generator topology optimization should be made to achieve comparable reduction magnitudes to other active or shape modification methods. Low-Re PIV examination amounts should be increased for vortex generators.  
As for further conclusions, connection should be made between the relative size of wake region change and drag reduction.  
Finally, on-road measurements are not highly researched compared to drag measurements or drag reduction evaluations.  
The research was financed by the Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary (ED_18-1-2019-0028), within the framework of the (Automotive Industry) thematic programme of the University of Debrecen.  
The author declares no conflict of interest.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 2 On-Board Ship Detection Based on Deep Learning
### Feature Extraction  
Feature extraction is the basis of object detection and the quality of the features have great influence on follow-up tasks. With the higher performance requirements, the Zeiler and Fergus (ZF) [36] and the visual geometry group (VGG) [37] networks used in the original Faster R-CNN cannot meet the demand. Hence more CNN networks with better performance have been proposed in recent years, such as Residual Network (ResNet) [28], Dense Convolutional Network (DenseNet) [38], Squeeze-and-Excitation Network (SENet) [39], etc. Due to the limited on-board computing and storage resources, we adopt ResNet-50 as feature extraction network, which has a good performance and light weight.
## 1 Introduction
Robust and efficient solid-state detectors, like SPADs (single-photon avalanche diodes) or SiPMs (silicon photomultipliers) [43; 44; 45; 46] have replaced delicate and fragile photomultiplier-based detection systems. A large addition of various data analysis methods of broadband time domain diffuse optics has been introduced to make the analysis simpler, accurate, and repeatable, thus providing a reliable method to analyze experimental data [47; 48]. These new tools have opened up the scientific community to pursue standardization approaches that would validate and consolidate the data collection methodology and analysis methods, thus bringing in a harmonized approach across laboratories [49; 50; 51]. On the in vivo side, broadband TD-DOS is aptly placed to explore non-invasively new tissue locations and provide new insights on human physiological and pathological pathways. In this direction, various explorations have been conducted to characterize organs like breast, bone, thyroid, manubrium, forehead, brain, and others [13; 23; 26; 29; 52; 53]. This review will provide a broad overview of the history and recent advancements of broadband TD-DOS as shown in the timeline Figure 1, covering the following aspects:  
1. Physics of photon migration,
2. Broadband TD-DOS Instrumentation,
3. Analysis methods of TD-DOS,
4. Applications,
5. Future prospects. The first part, the physics of photon migration, provides the fundamentals of temporal propagation of photons in highly scattering media. The gamut of broadband TD-DOS instruments and advancements is discussed in the second section. The next section elaborates on various analysis methods and methodologies to extract optical properties and estimate tissue constituents. A wide range of broadband TD-DOS applications is covered across various domains (tissue constituent characterization, in vivo studies, food, wood, standardization techniques) are deeply elaborated in this section. A brief summary on the future prospects of the broadband TD-DOS is presented at the end.
## 3 Validation through Comparative Analysis
The switching losses of the high-frequency operating transistor depend on the value of voltage and current at which they change their switching states, meaning on to off or off to on. These losses are also governed by their rise (\(t_{r}\)) and fall times (\(t_{f}\)). In this calculation, the value of the voltage and current may consider constant, and their variation can be negligible in one high-frequency switching period. The maximum losses of one high-frequency (\(f_{s}\)) operating transistor are calculated here as  
\[P_{sw(transistor)}=\frac{1}{6}f_{s}V_{s(p)}I_{o(p)}\Big{(}t_{r}+t_{f}\Big{)} \tag{19}\]  
The switching losses of the transistors operating at low frequencies are always ignorable, and only high-frequency operation transistors can be considered for this calculation. There is a high switching frequency operation of two and four transistors in the proposed converter and converter described in [23]. The maximum switching losses of these converters are calculated in Equations (20) and (21) of the proposed converter and converter of [23] in the following forms as  
\[P_{sw}=\frac{1}{3}f_{s}V_{s(p)}I_{o(p)}\Big{(}t_{r}+t_{f}\Big{)} \tag{20}\]  
\[P_{sw}=\frac{2}{3}f_{s}V_{s(p)}I_{o(p)}\Big{(}t_{r}+t_{f}\Big{)} \tag{21}\]  
The summary of the comparing performance parameters is given in Table 1 for a quick review and evaluation. Here it can be observed that for the generation of similar characteristics of the output voltage, the proposed topology offers low conduction and switching losses by using a low number of the operating transistor. Therefore, the proposed converter may be one of the favorable choices for circuit developers or designers as it is attractive in terms of overall size, cost, circuit, and control simplicity. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparing Parameters** & **Proposed Converter** & **Converter in [23]** \\ \hline Count of diodes & 8 & 8 \\ Count of transistors & 4 & 6 \\ Count of gate drivers & 4 & 6 \\ Peak switching voltage & \(I_{o(p)}\) & \(I_{o(p)}\) \\ Peak switching voltage & \(V_{s(p)}\) & \(V_{s(p)}\) \\ Average conduction losses & \(\frac{3V_{D}I_{o(p)}}{\pi}+\frac{3I_{o(p)}^{2}}{\pi}\big{(}R_{Q}+R_{D}\big{)}\) & \(\frac{4V_{D(p)}}{\pi}+I_{o(p)}^{2}\big{(}R_{Q}+R_{D}\big{)}\) \\ Peak switching losses & \(\frac{1}{3}f_{s}V_{s(p)}I_{o(p)}\Big{(}t_{r}+t_{f}\Big{)}\) & \(\frac{2}{3}f_{s}V_{s(p)}I_{o(p)}\Big{(}t_{r}+t_{f}\Big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Validation of the performance characteristics.
###### Abstract  
In this study, a method for the active shape control for carbon fiber-reinforced polymer (CFRC) reflectors using piezoelectric lead zirconate titanate (PZT) actuators is proposed. According to this method, a finite element model considering higher transverse shear deformation with independent voltage degrees of freedom is formulated by the Hamilton principle. An optimal shape controller that minimizes the discrete root mean square (RMS) error of a reflecting surface is applied. Then, the optimal arrangements of the PZT actuators are determined by numerical optimization methods, which are developed by modifying the classical Genetic Algorithm, with both single and multi-objective optimizations being studied. In the single optimization formulation, the number of actuators is considered as a constraint, and the RMS error of the reflector is regarded as the optimizing target. A hybrid method that combines the gradient projection method with an adaptive Genetic Algorithm is proposed to solve this problem. In the multi-objective optimization, the residual RMS errors and power consumption of the actuators are considered as the optimization targets. Pareto optimal solutions are obtained by an improved multi-objective Genetic Algorithm. Numerical examples are provided to show the effectiveness of the proposed methods.  
shape control; CFRC; PZT actuators; multi-objective optimization; genetic algorithm Article Shape Control of a Carbon Fiber-Reinforced Polymer Reflector and Placement Optimization of the Actuators  
1 China Manned Space Agency, Beijing 100071, China; [EMAIL_ADDRESS]  
2 School of Astronautics, Beihang University, Beijing 100191, China  
3 China Academy of Space Technology (Oi'an), Xi'an 710000, China  
4 HYS Information Technology Co, Ltd., Shanghai Branch, Shanghai 201108, China; [EMAIL_ADDRESS]
## 2 Results and Discussion
### Characterization of Silica Particles
#### 2.1.2 Thermogravimetric (TGA) Analysis of Bare and mPEG-Grafted Silica Particles  
Figure 2 shows the percent mass loss of bare and mPEG-grafted silica particles. mPEG-2000 decomposition starts at \(\sim\)400 \({}^{\circ}\)C, whereas mPEG-5000 started to decompose at \(\sim\)300 \({}^{\circ}\)C, which compares to previous research showing a decomposition temperature of 392 \({}^{\circ}\)C for mPEG-2000-grafted silica [36]. In order to quantify the mass loss due to mPEG decomposition, the mass loss of bare silica was used as a baseline and any additional mass loss observed in mPEG-grafted silica particles was associated with the decomposition of mPEG. The percent weight loss difference between bare silica and mPEG-2000 was 2.1%, whereas mPEG-5000 had 5.0% additional mass loss compared to bare silica (Figure 2). The additional mass loss observed for mPEG-grafted silica particles suggests that both mPEG-2000 and mPEG-5000 were successfully grafted onto the silica surface. The additional mass loss of 5.0% and 2.1% in mPEG-5000 and mPEG-2000, respectively, compared to bare silica correspond to 0.01 mmol mPEG/g silica for both mPEG-5000 and mPEG-2000. This suggests that grafting densities of mPEG-5000 and -2000 are similar and any differences observed in polyphenol adsorption capacity is associated with the chain length of PEG.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 4 Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
### Fault Location Case
#### 4.4.2 Test Case 2: Multiple Faults  
In Figure 8, assume that both sections \(x_{9}\) and \(x_{18}\) have a fault simultaneously. The nodes that reported the fault information are marked with red boxes.  
Figure 8: Multiple faults in the multi-branch distribution network.  
Figure 7: The execution process of the algorithm under a single fault. (**a**) Divide process; (**b**) combine process.  
The divide process of the algorithm is similar to that of Test Case 1. The divide process of the algorithm and the calculation results of each section approximation gain are shown in Figure 9a.  
The combine process of the algorithm is shown in Figure 9b, and the detailed running procedure is as follows. Combine the fault candidate sets \(\mathbf{G}_{7}\) and \(\mathbf{G}_{12}\) to create the fault location result \(\mathbf{G}_{3}\). Find the section \(x_{6}\) with the largest approximation gain from \(x_{3}\) to \(x_{6}\) and construct the set of sections \(\mathbf{X}_{3-6}=\{x_{6}\}\) with the approximation gain \(\Delta_{\mathbf{X}_{3-6}}=\Delta_{6}=6\). Construct the set \(\mathbf{X}_{7k12}=\mathbf{X}_{7}\cup\mathbf{X}_{12}=\{x_{9},x_{12}\}\), whose approximation gain is \(\Delta_{\mathbf{X}_{7k12}}=8\). The largest approximation gain of \(\Delta_{\mathbf{X}_{3-6}}\), \(\Delta_{\mathbf{X}_{7}}\), \(\Delta_{\mathbf{X}_{12}}\), and \(\Delta_{\mathbf{X}_{7k12}}\) is \(\Delta_{\mathbf{X}_{7}}\); thus, \(\mathbf{X}_{7}\) and \(\Delta_{\mathbf{X}_{7}}\) are taken as the fault location results of \(\mathbf{G}_{3}\), i.e., \(\mathbf{X}_{3}=\mathbf{X}_{7}=\{x_{9}\}\), \(\Delta_{\mathbf{X}_{3}}=\Delta_{\mathbf{X}_{7}}=\ 9\). Combine the fault candidate sets \(\mathbf{G}_{3}\) and \(\mathbf{G}_{15}\) to create the fault location result \(\mathbf{G}_{1}\). Find section \(x_{2}\) with the largest approximation gain from \(x_{1}\) to \(x_{2}\) and construct the set of sections \(\mathbf{X}_{1-2}=\{x_{2}\}\) with the approximation gain \(\Delta_{\mathbf{X}_{1-2}}=\Delta_{2}=\ 2\). Construct the set \(\mathbf{X}_{3k15}=\mathbf{X}_{3}\cup\mathbf{X}_{15}=\{x_{9},x_{15}\}\), whose approximation gain is \(\Delta_{\mathbf{X}_{3k15}}=13\). The largest approximation gain of \(\Delta_{\mathbf{X}_{1-2}}\), \(\Delta_{\mathbf{X}_{3}}\), \(\Delta_{\mathbf{X}_{15}}\), and \(\Delta_{\mathbf{X}_{3k15}}\) is \(\Delta_{\mathbf{X}_{3k15}}\); thus, \(\mathbf{X}_{3k15}\) and \(\Delta_{\mathbf{X}_{3k15}}\) are taken as the fault location results of \(\mathbf{G}_{1}\), i.e., \(\mathbf{X}_{1}=\mathbf{X}_{3k15}=\{x_{9},x_{18}\}\),\(\Delta_{\mathbf{X}_{1}}=\Delta_{\mathbf{X}_{3k15}}=13\). After the algorithm is executed, \(x_{9}\) and \(x_{18}\) are determined to be the fault sections.
## 4 Implementation and Optimization
### QAT Offloading Module  
#### 4.2.1 Handling Buffer Overflow  
When QZRAM is inserted into the kernel, the QAT offloading module initializes the compression buffer, compression instance, and compression session and binds them to the underlying QAT accelerator. The QAT offloading module offloads compression requests to the request ring through a compression instance and obtains results from the response ring after the data are compressed. However, in the process of compression, the data may swell because the effect of compression is not obvious, and the header and tail are added to the compressed data for the sake of compatibility with software and hardware memory algorithms. The data inflation generated in the process of data compression may still have a serious impact. The compression buffer is a continuous physical memory area, if overflow occurs in it, QZRAM may have serious errors, resulting in an entire system crash or even physical damage to the QAT hardware. Therefore, to prevent buffer overflow, the QAT offloading module sets the buffer size to twice the original data. Such a design may result in a certain amount of memory wastage, but it is worth it compared to the potentially serious consequences of a buffer overflow.