esa-sceva/satcom-chunk-collection huggingface dataset:
# ZnO/Ag Nanocomposites with Enhanced Antimicrobial Activity
## 3 Results and Discussion
### Antimicrobial Activity of ZnO/Ag Nanocomposites
coli_ in the absence of treatment (Ct), with ZnO (Zn1) and with the ZnO/Ag nanocomposites (Zn2, Zn3, Zn4, Zn5, and Zn6). The bars on the graphs represent the standard error; the asterisks indicate significant differences. The fact that increasing the silver concentration does not correlate with an increase in its antibacterial effect could be due to the arrangement of Ag in the NPs when added at higher concentrations. At higher concentrations, Ag tends to agglomerate, thereby decreasing the area of its surface in contact with the bacteria. Moreover, at higher concentrations of Ag, the size of the NPs is larger, as shown in Figure 2. Related to this, Ref. [44] reported that AgNPs of smaller size could cause more toxicity to the bacteria and show better bactericidal effects compared to the larger particles as they have larger surface area. In addition to being able to penetrate into bacterial cells, it is thought that another mechanism by which Ag NPs are killing bacteria is by releasing silver ions [45]. The efficiency of this process depends on different factors, such as intrinsic AgNPsr characteristics and surrounding media. For example, smaller silver nanoparticles are more prone to Ag release, due to their larger surface area. These findings could also support the fact that in our results, the antibacterial effect is not correlated with the concentration of Ag since Zn2 NPs containing 1% Ag are the smallest and are the most effective against the tested bacteria.
## 4 Results and Discussion
### Evaluation of Generated Data
#### 4.1.2 Similarity and Authenticity
The development of these two models aims to comprehensively assess the similarity and authenticity of the generated data, providing robust evidence for evaluating the performance of the TimeGAN model. Table 3 presents the results of our experiments. After multiple trials, it could be observed that the optimizer produced better quality data when GRU was chosen and 10,000 iterations were performed. In particular, the optimum achievable result was achieved when the batch size and hidden dim were chosen, and the optimal combination of parameters was 256,180. As indicated in the table, after this round of experiments, the module achieved the optimal discriminative score of 0.0696 and the best predictive score of 0.0488, both of which are very close to 0. These results demonstrate the exceptional performance of our proposed data generation model. In this section, the quality of the generated scenarios was assessed qualitatively and quantitatively based on aspects such as diversity, similarity, and authenticity. The conclusion drawn is that the scenarios generated by TimeGAN exhibited high quality. Furthermore, they could capture scenarios not encountered in real-world data collection and even depict situations with higher levels of risk than those observed in reality. This provides an effective approach for expanding the testing scenario repository. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parameter Type**} & \multicolumn{2}{c}{**Score**} \\ \cline{2-7}  & **Module** & **Hidden\_Dim** & **Num\_Layer** & **Batch\_Size** & **Discriminative** & **Predictive** \\ \hline \multirow{3}{*}{**Combination**} & Gru & 180 & 5 & 256 & **0.0696** & **0.0488** \\  & Gru & 200 & 5 & 256 & 0.1119 & 0.0495 \\ \cline{1-1}  & Gru & 180 & 5 & 128 & 0.1252 & 0.0609 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for different parameter combinations.
# Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum
## 4 Control Results and Discussion
### Real-Time Implementation  
In this study, the real-time implementation is conducted using a \"real-time simulator\", which includes two STM32F407 boards and a RS232-to-USB converter. The advantage of this simulator is that it can be programmed using Matlab/Simulink via its code generation function which allows for rapid prototyping and real-time evaluation of control algorithms. The ST microelectronic  
Figure 7: Block diagram of model predictive control with measured disturbance.  
STM42F407 VG microcontroller (a product of STMicroelectronics, Geneva, Switzerland) is an embedded board with a 32-bit ARM Cortex-M microprocessor. This microcontroller provides 1024 Kb of ROM (read-only memory), 192 Kb of RAM (random-access memory), and can run at a maximum clock speed of 168 MHz. The dynamic model and the control algorithm are embedded in the boards which were developed in Matlab/Simulink, as descireded above.  
Figure 8 shows the scheme for the real-time implementation. The nonlinear dynamic model of the gyroscopic inverted pendulum (17) is embedded in STM32F407 board 1. The nonlinear equations of motion are constructed in Matlab m-file and then is solved using Runge-Kutta fourth order method for a sampling time of 0.01 s. The inputs for this model are the control torque \(\tau_{a1}\), and \(\tau_{a2}\), and the outputs of the model are the state variables \(\left[\begin{array}{cccccc}\dot{\theta}&\theta&\dot{\kappa}_{1}&\alpha_{1}& \dot{\kappa}_{2}&\alpha_{2}\end{array}\right]^{T}\). The state variables are then sent to the controller on board 2 and a personal computer (PC) using two UART (universal asynchronous receiver-transmitter)channels of board 1. The STM32F407 board 2 is used to embed the proposed MPC. The feedback signals for the controller are the state variables from the dynamic model in board 1 which are received via UART protocol. The outputs of board 2 are the control torques. To guarantee the'real-time' conditions, the same sampling time of 0.01 s is used for the MPC. In the real-time simulator, both of the boards use the maximum clock speed of 168 MHz.  
Figure 9 shows the hardware connections of the real-time simulator. This simulator includes two STM43F407 boards and a UART-to-RS232 converter. All of these devices are connected to a 5v DC power supply from a personal computer. STM32F407 board 1 is connected to the other boards using three wires. The purple and blue wires are used to transmit and receive the data via the Tx and Rx pins, respectively, of UART channels. The black wire is connected to the ground on each board.  
Figure 8: Scheme of real-time implementation. PC: personal computer; UART: universal asynchronous receiver-transmitter.  
Figure 9: Experimental setup.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 1 Introduction
In addition, deep learning methods have visualized the neck muscle pattern landmark and muscle-tendon landmark [35; 36]. Besides, deep learning has assisted measurement and tracking in the urogenital hiatus and puborectalis muscle [37]. Moreover, deep learning methods have been evaluated regarding the ciliary muscle for glaucoma interpretation and measured bladder wall thickness [38; 39]. Deep learning will be especially beneficial when considering ultrasound muscle images with different types of muscles, namely skeletal muscles, smooth muscles, and cardiac muscles [40; 41]. As far as we know, there has been no discussion about cardiac muscle tissue in deep learning from 2018 to 2020. Cardiac muscles are one of three types of muscle, with the other two being skeletal and smooth muscles [42]. We found seven studies on skeletal muscles and three studies on smooth muscles from 2018 to 2020 which had case conditions and deep learning performance parameters. For skeletal muscle ultrasound imaging, image noise and muscle movement require special treatment for deep learning architecture and segmentation [30,41]. Smooth muscle research has focused on measuring muscle thickness [38]; however, the treatment of image problems in skeletal muscles and smooth muscles has not been compared in order to understand the current state of knowledge. Therefore, it is necessary to comprehend the comparison of deep learning conditions and strategies between ultrasound muscle images. This study aims to look at the trends and challenges of deep learning to overcome muscle ultrasound imaging problems from 2018 to 2020 by comparing the conditions and strategies in skeletal muscles and smooth muscles to assess the current state of knowledge. This study will enhance knowledge of the deep learning conditions and strategies for handling muscle ultrasound images.
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 4 Experimental Results and Analysis
### Dataset and Evaluation Indicators  
In the experimental process, we used the 2016 high-resolution ship dataset (HRSC2016) [51]. All images are three-channel color images from six famous ports. In addition to using the default satellite images from Google Earth, historical images of the same locations were also collected. The images in the dataset range in size from 300 \(\times\) 300 to 1500 \(\times\) 900, and most images are larger than 1000 \(\times\) 600. The sizes of vessels in the dataset range from 49 \(\times\) 23 to 714 \(\times\) 506. The image resolutions are between 0.4 m and 2 m. The training dataset includes 436 images with a total of 1207 samples, the validation dataset includes 181 images with a total of 541 samples, and the test dataset includes 444 images with a total of 1228 samples.  
The accuracy measurement uses the Average Precision (AP) at an IoU threshold of 0.5, which represents the average detection accuracy at that threshold. To calculate this accuracy, one needs to first calculate the Intersection over Union (IoU) for each detection box using the model's predicted results and the ground truth labels. IoU is a metric used to measure the overlap between the predicted and the ground truth boxes, calculated as the ratio of the intersection area to the union area of the two boxes, as shown in Figure 4. Generally, predictions with an IoU greater than the threshold are considered true positives (TPs), while those below the threshold are false positives (FPs). Any ground truth box that is not matched to any predicted box is considered a false negative (FN). If the IoU threshold is denoted as [i], the formula to calculate the Average Precision (AP) at that threshold is as follows:  
\[\text{AP[i]}= \sum\
olimits_{\text{n}}(\text{R}_{\text{n}}-\text{R}_{\text{n}-1 })\text{P}_{\text{n}} \tag{16}\]  
where the formulas to calculate Recall (R) and Precision (P) are as follows:  
\[\text{R}= \frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{17}\] \[\text{P}= \frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{18}\]  
Figure 4: Illustration of IoU calculation.  
In addition to detection accuracy, this paper also introduces MACs (Multiply-Accumulate Operations) and parameter volume to comprehensively measure the model. MACs stand for the total number of multiplication and addition operations in the neural network, representing the computational workload required during inference. For the calculation, the input image size is set to 1 \(\times\) 3 \(\times\) 1000 \(\times\) 600.
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 2 Research Methodology and Related Work  
### Research Methodology  
This review adopts a systematic methodology to comprehensively analyze the existing literature in the field of blockchain storage while examining the application challenges and future innovation directions of blockchain technology in decentralized storage networks. Initially, relevant literature from the past four years was retrieved from databases such as Google Scholar, MDPI, IEEE Xplore, Springer, and ArXiv, using keywords such as \"blockchain storage optimization\", \"decentralized storage network\", \"IPFS and blockchain storage\", and \"Erasure coding and blockchain storage\", with Boolean logic applied to  
Figure 1: Full replication storage method.  
optimize the search process. A three-step screening procedure was then employed: redundancy filtering (removing duplicate articles), relevance screening (selecting studies based on title and abstract for thematic relevance), and mismatch exclusion (excluding papers unrelated to the review's objectives). As a result, 105 core papers were selected for further analysis. During the organization and synthesis of the literature, a multidimensional analytical framework was utilized to categorize and summarize the papers from both a technical perspective (e.g., storage efficiency) and an application context (e.g., Internet of Things environments). The review provides a focused analysis of the research background, primary challenges, and proposed solutions for each technology, highlighting the key contributions and limitations of existing studies. These findings are systematically presented through textual descriptions and tables, emphasizing the resolved issues and technological bottlenecks. The conclusion section further highlights the practical challenges encountered when applying existing technologies in large-scale distributed systems and, based on the literature analysis, proposes future research directions aimed at improving security, query efficiency, and other critical aspects. The relevant process is illustrated in the Figure 2 below.
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion
### Gas Sensor Measurements
#### 3.2.1 Sensor Response in Air Atmosphere  
SnO\({}_{2}\) response in air was widely studied and reported in literature [14]. Generally, in air the surface of SnO\({}_{2}\) is covered with negatively charged oxygen adsorbates (O\({}_{2}^{-}\), O\({}^{-}\), and O\({}^{2-}\) ). The formation of such oxygen adsorbates extracts electrons from the conduction band of SnO\({}_{2}\) bulk forming an electron depletion layer on the SnO\({}_{2}\) grains surface (space-charge region) and a potential barrier at the grain boundaries (Figure 3b) [41]; the sensor has a high resistance value (\(\sim\)30 M\(\Omega\)). As soon as the sensor interacts with the analyte vapor, the diacetyl molecules are oxidized by oxygen species on the surface and the depleted layer electrons are fed back into SnO\({}_{2}\) bulk, thus a narrowed depletion layer and a reduction of the space-charge region is detected. Therefore, the sensor resistance decrease. In this case, exposing the sensor to 0.4 mg/L diacetyl solution vapors, the sensor response is equal to 1.43 and it results faster in the first seconds after the exposure, with a total response time of 3.0 min. Despite a very fast response being first observed, a delay in the sensor signal rise is detected; it demands a longer time to reach the final value. Indeed, the recording of the dynamic behavior of a sensor can be confusing, because the measured change in signal depends both on the intrinsic reaction of the gas sensing mechanism and  
Figure 2: (**a**) XRD diffractograms versus temperatures and (**b**) SEM image of SnO\({}_{2}\) synthesized sample.  
on a delayed change in test gas concentration [42]. Moreover, it should be highlighted that response/recovery times may be dependent on the measurement procedure.  
On the contrary the recovery time is constant and slow (10.2 min).
###### Abstract  
In this study, a reliability evaluation of the phased array ultrasonic testing (PAUT) method was performed to examine the applicability of the method for tube weld specimens with flaws having the same specifications as the tubes in the boilers of thermal power plant facilities. To this end, test specimens were fabricated by inserting flaws into tube welds with identical materials and specifications to those used in the thermal power plant. PAUT data acquisition was obtained using a round robin test (RRT) on the fabricated specimen, and the data were compared with the results of radiographic testing (RT) for a comparative evaluation of the flaw detection performance. In addition, for quantitative reliability analysis, the flaw detection performance (probability of detection; POD) and the error in the sizing accuracy (root-mean-square error; RMSE) were calculated with different materials of the specimens (carbon steel, stainless steel, dissimilar metal) and flaw types (volumetric, planar). In the analysis results, for high-risk planar defects, the PAUT technique exhibited superior flaw detection performance to the RT technique. A POD analysis of the PAUT technique indicated that flaws of 6.9 mm length were detected at 80% probability for total tube specimens. Furthermore, a reliability analysis was performed for test specimens of different materials and flaw types, and the results were derived. Through the findings of this study, the applicable range of the PAUT technique was examined, and a technical basis for PAUT in lieu of RT was established.  
pnased array ultrasonic testing; round robin test; probability of detection; power generation facilities +
Footnote †: journal: Applied Sciences
# Phased System Reliability Modeling and Assessment for Construction of Lunar Scientific Base
## 5 Example  
### Comparative Examples with Traditional Methods  
This section compares the proposed method with the LM and MML methods through two large sample examples. The two traditional methods have been widely used in engineering because of their simple calculations and easy-to-understand formulas. However, they are unsuitable for cases where the series system has zero-failure subsystems. In this case, the BBA method in this paper can still work well.  
Consider a series system consisting of two independent subsystems. Among them, the test result of subsystem 1 is: \(n_{1}=100\), \(s_{1}=99\); the test result of subsystem 2 is: \(n_{2}=100\), \(s_{2}=100\). With the confidence degree \(\gamma=0.9\), the LCLs of system reliability are calculated using the LM, MML, and BBA methods. The results are listed in Table 1.  
It can be seen from Table 1 that the LCLs of system reliability given by the LM and MML methods are the same as those of subsystem 1. It means that the two methods do not consider the failure possibility of subsystem 2. However, the LCL of reliability for subsystem 2 is only 0.977, i.e., the failure possibility cannot be ignored. Therefore, the two methods actually lead to a dangerous system reliability assessment. In contrast, the BBA method in this paper can solve this problem by simultaneously considering each subsystem's possible failure.  
The drawback of traditional methods based on maximum likelihood theory is more obvious when all subsystems are zero-failure. For example, consider a series system consisting of three independent zero-failure subsystems with sample numbers of \(n_{1}=100\), \(n_{2}=150\), and \(n_{3}=200\). Similarly, the LCLs of system reliability are calculated using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Date/Results**} & \multirow{2}{*}{**Subsystem 1**} & \multirow{2}{*}{**Subsystem 2**} & \multicolumn{3}{c}{**System**} \\ \cline{3-5}  & & & **LM Method** & **MML Method** & **BBA Method** \\ \hline Number of samples & 100 & 100 & 100 & 100 & — \\ \hline Number of successes & 99 & 100 & 99 & 99 & — \\ \hline LCL for reliability & 0.962 & 0.977 & 0.962 & 0.962 & 0.948 \\ \hline \hline \end{tabular}
\end{table}
Table 1: LCL calculations for system reliability using the LM, MML, and BBA methods \((\gamma=0.9)\).  
Figure 7: Flow chart of the proposed BBA method for the LSB construction.  
the LM and BBA methods (this scenario is beyond the application scope of the MML method [32]). The results are listed in Table 2.  
As shown in Table 2, when all subsystems are zero-failure, the LM method equates system test results to subsystem 1 with the fewest tests, assuming the reliability of the other subsystems as 1. In other words, the LM method only utilizes test information from subsystem 1, discarding valuable data from the other zero-failure subsystems, which is particularly wasteful in the case of small samples. In contrast, the BBA method utilizes test data from all subsystems, making the reliability assessment of the system more objective and reliable.
## 3 Results and Discussion  
### Raw Materials Investigation  
The results of the particle size analysis are presented in Table 7 and Figure 6.  
The results for sand show average particle sizes in the range of 420.626 \(\upmu\)m to 423.788 \(\upmu\)m. We can also see a slight bimodality based on the two peaks of the particle size distribution. Fly ash particles are significantly smaller, with an average mean size ranging from 25.186 \(\upmu\)m to 27.782 \(\upmu\)m. The size of the fly ash particles can be classified as average compared to other literature results. [PERSON] et al. [45] found that finer particles improve compressive strength, and a smaller particle size distribution improves the geopolymerization process and reduces pores and cracking [45].  
All three materials are analyzed at SEM with different magnifications. Images showing the shape of individual particles and surface properties are shown in Figure 7.  
The analyzed fly ash particles have a high degree of roundness, and satellite-like deposits of smaller particles on larger ones can often be observed. The particle surface is mostly smooth except for deposits or fusions of several individuals. The sand shows particles with high similarity and surfaces that can be described as rough under higher magnification. Xanthan gum is characterized by small particles, some with extremely low roundness. The surface is rough to flaky. Because there is no particle size analysis for xanthan gum, an optical measurement is carried out under the electron microscope to provide insight into the rough measurements and dimensions. The maximum Feret diameters range from 161.3 \(\upmu\)m to 238.8 \(\upmu\)m for a typical grain.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Raw Material 1** & **D10 [\(\upmu\)m]** & **D50 [\(\upmu\)m]** & **D90 [\(\upmu\)m]** & **Mean Size [\(\upmu\)m]** & **Span** \\ \hline Sand & 196.794 & 382.365 & 489.673 & 422.662 & 0.766 \\ Fly ash & 2.867 & 18.820 & 57.531 & 26.692 & 2.907 \\ \hline \end{tabular} \({}^{\dagger}\) Average for 3 measurements.  
\end{table}
Table 7: Particle size analysis.  
Figure 6: Size particle distribution: (a) fly ash; (b) sand.  
Figure 7: Size comparison of SEM images (magnification 200\(\times\)): (a) fly ash; (b) sand; (c) xanthan gum.  
The results of the elemental XRF analysis show the sand consists mainly of silicon (94.3%), as SiO\({}_{2}\). The results for fly ash are much more complex and are presented are presented in Figure 8. Fly ash can be classified as class F fly ash (SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) > 70% and CaO < 10%) [46].  
The evaluation of the analysis shows that the proportion of silicon and aluminum oxides in the fly ash is 80%. These oxides are the most important ones as they are needed for the formation of the geopolymer. The CaO content is also important, as too high a proportion prevents the formation of the three-dimensional geopolymer structure [46; 47].