esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The high-valence Ag(II)PRO nanoparticles and AgNPs were synthesized with similar size and characterized by spectroscopic methods. The antibacterial efficacies of both the nanoparticles were tested against three Gram-positive and Gram-negative strains responsible for DFU. The newly synthesized high-valence Ag(II)PRO nanoparticles showed higher antibacterial activity compared to silver-only nanoparticles (AgNPs). The MICs of synthesized Ag(II)PRO nanoparticles were much lower than the AgNPs. Due to their stability, high monodispersity, and higher antibacterial properties, Ag(II)PRO nanoparticles are expected to be the next generation topical agents for diabetic wounds.  
The following are available online at [[http://www.mdpi.com/2076-3417/7/7/736/s1](http://www.mdpi.com/2076-3417/7/7/736/s1)]([http://www.mdpi.com/2076-3417/7/7/736/s1](http://www.mdpi.com/2076-3417/7/7/736/s1)). Figure S1: 1H NMR and FT-IR spectra of the propamidine (a) and newly synthesized silver(II) propamidine complex (b); Figure S2: Visual observation of freshly synthesized Ag(II)PRO nanoparticles (a) and AgNPs (c) TEM image of Ag(II)PRO nanoparticles (b) and AgNPs (d); Figure S3: Petri dishes initially supplemented with 10\({}^{7}\) CFU/ mL of Gram-positive/negative strains (a, b, c, d, e, f) and incubated with different concentrations of silver(II) propamidine nanoparticles, from left to right 1.5, 3, 6.25, 12.5, and 25 uM; Figure S4: Petri dishes initially supplemented with 10\({}^{7}\) CFU/ mL of Gram-positive/negative strains (a, b, c, d, e, f) and incubated with different concentrations of silver nanoparticles, from left to right control, 20, 40, 60, and 80 uM.  
This work was supported by National Research Foundation of Korea (NRF) grant funded by the Ministry of Education, Science and Technology (MEST) (2015R1A2A1A05001842 and 2016R1A4A1010796). We are grateful to the Research Institute of Pharmaceutical Sciences at Seoul National University for providing experimental equipment and in vitro assay facility.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bacterial Strains**} & \multicolumn{3}{c}{**AgNPs**} & \multicolumn{3}{c}{**Ag(II)PRO NPs**} \\ \cline{2-5}  & **MIC (μM)** & **MBC (μM)** & **MIC (μM)** & **MBC (μM)** \\ \hline _Pseudomonas aeruginosa_ & 20 & 60 & 6.25 & 12.5 \\ _Staphylococcus aureus_ & 40 & 80 & 6.25 & 6.25 \\ _Acinetobacter caloaceticus_ & 40 & 80 & 6.25 & 6.25 \\ _Enterococcus faecalis_ & 20 & 60 & 6.25 & 6.25 \\ _Klebsiella pneumonia_ & 20 & 60 & 6.25 & 6.25 \\ _Streptococcus agalactiae_ & 40 & 60 & 6.25 & 6.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MICs (minimal inhibitory concentrations) and MBCs (minimum bacterial concentrations) of synthesized high-valence Ag(II)PRO nanoparticles and AgNPs in broth dilution method.  
**Author Contributions**: [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 2 Position-Based Repetitive Torque Observer and Compensation Strategy
### Principle of Position-Based Repetitive Controller  
The purpose of the repetitive controller (RC) is to track time-based periodic references or reject periodic disturbances. Proposed by [PERSON] [10], the RC is based on an internal model principle (IMP) [7, 8]. RC is often used in a control loop to generate an internal model for the controller to cancel out the periodic error caused by periodic references or periodic disturbances. For a periodic signal with period T (sec), the corresponding plug-in type [11] RC is shown in Figure 5a, which can be simplified as Figure 5b. The learning filter \(Q(s)\) is a low-pass filter applied to avoid the infinite poles located on imaginary axis, which might cause a stability problem.  
Since the cogging torque is a position-based periodic signal, as the rotor speed changes, the period changes correspondingly, as shown in Figure 6. An ordinary time-based repetitive controller will fail to track or reject the cogging torque disturbance. The delay index must be changed from time-based dependent to position-based dependent. An alternative structure for the cogging torque is proposed as shown in Figure 7. In other words, the position-based (PB) [12] delay will delay input data for the time needed of a rotor turn \((2\pi)\).  
The learning filter \(Q(s)\) plays a key role in the trade-off design between torque estimation accuracy and stability of the RC observer. A low-pass filter is given by \(Q(s)=\omega_{Q}/(s+\omega_{Q})\) in this paper.
## 4 Results and Discussion
### Real-World Multiplex Networks
#### 4.2.4 Run Time Comparison  
The average run time of **Mx-CRTSA** was compared to the run times of the other methods, including **CentroidCoreg, PM, SCML, RMSC, CPNMF**, **CSNMF** and **CSNMTF**. The reported results refer to the average run times over 10 realizations and are displayed in the bar graph shown in Figure 8. As the bar graph shows, **Mx-CRTSA** is slower than all the other methods except **CSNMTF**, and has a comparable speed to **PM** and **CPNMF**. Nevertheless, the proposed algorithm surpasses the rest of the algorithms regarding detecting the community structure as demonstrated by the experiments.
## 2 Materials and Methods
### Sensitivity Analysis of Vegetation Indices  
S-1 radar data are susceptible to the terrain, and plain areas and mountainous areas show different surface characteristics and different reflection and absorption properties for electromagnetic waves. Therefore, when distinguishing other features without vegetation, different vegetation indices are selected for different terrains.  
The Normalized Difference Vegetation Index (NDVI) is a calculation of the reflectance in the red and near-infrared bands, and its main application is to detect the growth status of vegetation and the degree of vegetation cover. The Enhanced Vegetation Index (EVI) is a remotely sensed index for the assessment of surface vegetation cover and growth. It is an improved vegetation index that overcomes the limitations of traditional vegetation indices under high vegetation cover and atmospheric conditions [47]. Compared with the NDVI, the EVI has better sensitivity and dynamic range under high vegetation cover.  
The NDVI reduces the atmospheric impact at the expense of vegetation index saturation, while the EVI is more sensitive to high-vegetation areas. At the same time, under high aerosol content, the EVI is more stable than the NDVI, showing higher resistance to atmospheric interference and insensitivity to background noise [48]. In the dense vegetation area, the NDVI tends to be saturated, while the EVI does not show this phenomenon and can better reflect the spatial variability of vegetation in the region. Therefore, it is more suitable to use the EVI to extract vegetation in mountainous areas than in plains. In the plain area, the surface features are complex, and the forest land, cropland, and residential land are staggered. The growth period, maturity period, and decline period of different crops are different, and the NDVI is a more suitable index for the extraction of vegetation in the plain region [49; 50].  
Figure 1 highlights the challenge of differentiating between various orchard categories solely by utilizing spectral features, the NDVI, and the EVI. In light of this, the present study proposes the development of a recognition model for orchard extraction by the integration of S-1 data.
# Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions
###### Abstract  
In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector.  
residual useful life prediction; contribution plot; performance estimation; prognosis; diagnosis; fault detection Article
# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 3 Methods
### U-Net Variants  
Figure 1a shows the architecture of U-net variants (n = 24), where n is the number of channels. The downsampling path on the left (encoder) extracts features using convolutions from the input images. The upsampling path on the right (decoder) uses deconvolutions to reconstruct the details for the final segmentation results. The skip connections are used to fuse features of different layers obtained in the downsampling path with those in the upsampling path to improve segmentation accuracy.  
To maximise the performance of U-net architecture, we used SE blocks with residual and dense structures to construct four types of blocks, namely residual blocks, residual SE blocks, dense blocks, and dense SE blocks. Their structures are shown in Figure 2. SE blocks can be conveniently added in a residual structure and a non-residual structure. For residual structures with SE blocks, several convolution blocks are stacked first. Then, we use SE blocks to strengthen essential features. SE blocks are introduced by Squeeze-and-Excitation Networks (SENet) [11, 41]. It improves a prediction accuracy through modelling the correlations between channels and adaptively strengthening important features.  
Figure 1: Architectures of U-net variants and DeepLabv3 plus variants.  
A dense convolutional network (DenseNet) [42] considers that the residual connections combining the input with the output of stacked convolutions by summation impede the information flow in the network. Then, a different connectivity pattern was proposed: concatenation rather than summation. The structure is also shown in Figure 2, where four-layer dense blocks precede SE block.  
Each layer takes all preceding feature maps as the input to reuse these features to take advantage of features gained in different layers to exploit the network's potential. For dense structures, we added SE block in a non-residual block (i.e., dense block). Thus, four types of blocks are formed, including residual blocks, residual SE blocks, dense blocks, and dense SE blocks. For the blocks in the encoder path and the decoder path, we fill them using these four types of blocks, respectively, to form four U-net variants.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Simulation Results
#### 6.2.6 Node Energy Consumption  
Figure 10 illustrates the amount of node energy consumption in the network. Energy is a scarce resource in IoT, as it contains the resource-constrained devices. It is noted that the energy consumption of RPL, LA-RPL, and EDADA-RPL are 7 mW, 6 mW, and 5.5 mW, respectively, for a network size of 120. The proposed EDADA-RPL reduces the energy consumption by 1.5 mW. It is also found that the node energy consumption increases, as the number of nodes in the network increases. It is due to the number of nodes required to sense, listen, accumulate, and transmit the data from one node to  
Figure 8: Packet loss ratio vs. number of failed nodes.  
Figure 9: Sensor value vs. sensor ID number.  
another. Thus, it increases the energy consumption among the nodes in the networks. However, the EDADA-RPL consumes less energy when compared to RPL and LA-RPL. The reason is that it reduces the number of redundant data in the network from one node to another.
## Human-Performed Classification
### Acoustical Conditions  
A panel of 72 listeners undertook the tests in the typical computer laboratories (untreated acoustically), using closed-back Sennheiser HD 215 headphones and Lexicon Alpha audio interface. Background noise was not measured. The listeners were requested to adjust the playback volume according to their personal preferences. The remaining 136 listeners carried out their listening tests in unknown and uncontrolled environments. However, they were requested to report the manufacturer and the model of the headphones used. From the responses obtained, the listeners used a variety of headphones, ranging from consumer-level headsets to professional ones. The examples of 79 models of the headphones, as reported by the listeners, are listed in A. Some models of the headphones employed an active noise reduction system, potentially degrading the faithfulness of a spatial audio reproduction.  
Due to a large variety of the headphones used in the listening tests and likely large variations in acoustical environments, the experimental conditions could be considered as representative of real-life binaural listening scenarios. To further maintain the ecological validity of the test:  
* a mechanism supporting head movements was not incorporated in the playback system (no head-tracking devices used),
* no headphones frequency-response compensation was applied,
* no individualized HRTFs were incorporated.
# Improved Gradient Descent Iterations for Solving Systems of Nonlinear Equations
## 2 Multiple Step-Size Methods for Solving SNE
### IGDN Methods for Solving SNE
More precisely, \(t_{k}\) is defined by \(t_{k}=\max\{1,s^{k}\}\), where \(s\in(0,1)\), and the integer \(k\) is defined from the line search  
\[f(\mathbf{x}_{k}+t_{k}\mathbf{d}_{k})-f(\mathbf{x}_{k})\leq-\omega_{1}||t_{k} F(\mathbf{x}_{k})||^{2}-\omega_{2}||t_{k}\mathbf{d}_{k}||^{2}+\eta_{k}f(\mathbf{x}_{k}), \tag{31}\]  
wherein \(\omega_{1}>0\), \(\omega_{2}>0\), are constants, and \(\{\eta_{k}\}\) is a positive sequence such that  
\[\sum_{k=0}^{\infty}\eta_{k}<\infty. \tag{32}\]  
The equality (28) can be rewritten in the equivalent form  
\[\mathbf{y}_{k}=-t_{k}\gamma_{k+1}\gamma_{k}^{-1}F_{k}, \tag{33}\]  
which gives  
\[\gamma_{k+1}=-\frac{\gamma_{k}F_{k}^{\mathrm{T}}\mathbf{y}_{k}}{t_{k}F_{k}^{ \mathrm{T}}F_{k}}.\]  
Further, an application of Theorem 1 gives the following additional update for the acceleration parameter \(\gamma_{k}\):  
\[\gamma_{k+1}=\left\{ \begin{array}{ll}-\frac{\gamma_{k}F_{k}^{\mathrm{T}} \mathbf{y}_{k}}{t_{k}F_{k}^{\mathrm{T}}F_{k}},&\frac{F_{k}^{\mathrm{T}}\mathbf{ y}_{k}}{F_{k}^{\mathrm{T}}F_{k}}\
otin(-1,0),\\ \frac{\gamma_{k}}{t_{k}},&\frac{F_{k}^{\mathrm{T}}\mathbf{y}_{k}}{F_{k}^{ \mathrm{T}}F_{k}}\in(-1,0).\end{array} \right. \tag{34}\]  
Corollary 1: \(IGDN\) _iterations (26) determined by (34) satisfy \(F_{k+1}\leq F_{k}\)._  
Proof: Clearly, (34) initiates \(\gamma_{k+1}\leq\frac{\gamma_{k}}{t_{k}}\), and the proof follows from Theorem 1. Further on, the implementation framework of the \(IGDN\) method is presented in Algorithm 1. ```
0: Vector function \(F(\mathbf{x})\), \(\epsilon>0\) and initialization \(\mathbf{x}_{0}\in\mathbb{R}^{n}\). 1: For \(k=0\) chose \(\gamma_{0}=1\) and \(F(\mathbf{x}_{0})\). 2: Check the output criterion; if \(\left\|F(\mathbf{x}_{k})\right\|\leq\epsilon\) is fulfilled then stop the algorithm; else, continue performing the next step. 3: (Line search) Compute \(t_{k}\in(0,1]\) using (31). 4: Compute \(\mathbf{x}_{k+1}\) using (30). 5: Determine \(\gamma_{k+1}\) using (29) or (34). 6:\(k:=k+1\). 7: Return to Step 2. 8: Outputs: \(\mathbf{x}_{k+1}\), \(F(\mathbf{x}_{k+1})\). ```  
**Algorithm 1** The IGDN iterations based on (29), (30) or (34), (30). **Remark 1**. : _The IGDN algorithm defined by (29) (resp. by (34)) will be denoted by IGDN (29) (resp. by IGDN (34)). Mathematically, IGDN (29) and IGDN (34) are equivalent. The numerical comparison of these algorithms will be performed later._
# Finite-Key Analysis of 1-Decoy Method Quantum Key Distribution with Intensity Fluctuation
## 4 Conclusions  
The 1-decoy state protocol has been proven to have better performance in practical applications under normal attenuation. In this paper, we presented a finite-key analysis of the practical 1-decoy method with intensity fluctuations based on Azuma's inequality. We derived the formulas to bound the single-photon events and the phase error rate with both finite-key effects and intensity fluctuations. Our results show that when the data size is relatively small, the single decoy state protocol is greatly affected by the finite-length key and strength fluctuations. The results can also be reflected in the process of deriving the formula for the secure key rate. In the process of implementing practical 1-decoy state QKD, it is necessary to set an appropriate data size before the post-processing to alleviate the influence of statistical fluctuations on the secure key rate. The set data size does not need to be too large, as it takes a long time to collect the data and the advantage of one decoy state over two decoy states will disappear. According to our simulation, the accurate estimation of the intensity fluctuations plays a significant role in the secure analysis, especially for a small data size of the total transmitting signals. For intensity fluctuations, we can measure the range of intensity before the protocol. If the range of intensity fluctuations exceeds a certain threshold, we have to replace the laser. In this paper, we propose a method to  
Figure 3: (Color online) \(R_{g_{g}}/R_{ideal}\) vs. global attenuation for \(N_{Z}=10^{8}\) and \(N_{Z}=10^{9}\) with \(\delta_{\mu}=0\), 0.01, 0.05, 0.09.  
estimate the secure key rate for the 1-decoy state protocol when considering the different intensity fluctuations; otherwise, it will overestimate the secret information and bring the hidden danger of information disclosure. Moreover, when we consider both finite-length keys and intensity fluctuations, the protocol with data size \(N=10^{9}\) performs better than the protocol with data size \(N=10^{8}\), especially when the intensity fluctuation parameter \(\delta_{\mu}=0.09\).  
Conceptualization, C.Z. and Y.Z.; methodology, C.Z., Y.X. and Y.W.; software, Y.Z. and Y.W.; writing--original draft preparation, C.Z., Y.Z. and Y.L.; writing--review and editing, Y.X., M.J. and X.Z.; project administration, W.B.; funding acquisition, W.B. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2020 YFA0309702), the National Natural Science Foundation of China (Grant Nos. 61505261, 62101597, 61605248 and 61675235), the China Postdoctoral Science Foundation (Grant No. 2021M691536), the Natural Science Foundation of Henan (Grant Nos. 202300410534 and 202300410532) and the Anhui Initiative in Quantum Information Technologies.  
Not applicable.  
The authors declare no conflict of interest.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
###### Abstract
On the one hand, the storage system is easy to deploy and highly reliable, ensuring the stable acquisition and storage of data [18]. On the other hand, the flexibility of wireless communication technology effectively solves the wiring problem and improves the real-time performance of the monitoring link [2]. Complementing each other's advantages, these two aspects can not only meet the testing requirements for key parameters, such as pressure signals, but also significantly reduce the system construction cost and effectively enhance the adaptability and reliability of the system in harsh and complex working environments [19]. Verified by practice, this system is easy to operate, highly automated, and has excellent characteristics, such as high-precision testing and high reliability, which is of great scientific value and practical significance.
## 4 Simulation Analysis of Roughness on Shot Peening Residual Stress and Surface Deformation  
### Effect Analysis of Roughness on Residual Stress and Deformation of Shot Peening  
#### 4.1.1 Maximum Residual Stress of Different Surface Roughness  
The generated rough surface is random due to the target material model being established based on the W-M fractal dimension. Different impact positions are selected on each surface roughness to prevent the randomness from affecting the simulation results. The chosen principle is the different contact positions between pellet and surface topography during shot peening, such as pit positions on the target material surface.  
The pellet impacts on different rough target material surfaces with a speed of 50 m/s, the average maximum residual stress produced by impact at other positions under five roughness conditions is analyzed, and the pellet impacts on four typical surface topography positions. The maximum residual stress produced by each rough surface after impact is averaged. The surface roughness of the target material is 0, 10, 16, 24, and 29 \(\upmu\)m, respectively. Eventually, the moderate maximum residual stress under different surface roughness after impact shows