esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Compact Differential Evolution for Optimization Applied to Image Segmentation
## 2 Background
### DE Algorithm  
The original definition of DE can be found in [10; 27]. A Gaussian bare-bones differential evolution algorithm can be found in [28]. The steps of DE algorithm include:  
1. Initialization: \(\{X_{i}(0)|x_{i,j}(0)\in[x_{i,j}^{min},x_{i,j}^{max}],i\in[1,Np]\cap N^{*},j\in[1,D]\cap N^{*}\}\), where \(X_{i}(0)\) represents the i-th individual of the 0-th generation, and \(x_{i,j}\) represents the j-th dimension of the i-th individual. Initialize the population through an uniform distribution method: \[x_{i,j}(0)=x_{i,j}^{min}+rand(0,1)*(x_{i,j}^{max}-x_{i,j}^{min})\] (2) rand () represents a random number.
2. Variation: The DE algorithm implements the mutation of individuals through a difference mechanism. A frequently-used difference mechanism is to randomly select two individuals from the population and add their scaled vector difference to the individual vector to be mutated: \[X_{off}^{{}^{\prime}}(gth+1)=X_{t}(gth)+F*(X_{r}(gth)-X_{s}(gth))\] (3) where \(X_{t}(gth)\) is the individual to be mutated in the g-th generation, \(X_{r}(gth)\) and \(X_{s}(gth)\) are two individuals randomly selected from g-th generation. \(F\in\)[0, 2] as a constant, represents the scaling factor. \(r,s,t\in\)[1, Np] and \(r\
eq s\
eq t\). \(X_{off}^{{}^{\prime}}(gth+1)\) is a temporary offspring produced by mutation. This difference mechanism is called rand/1/bin. Other difference mechanisms are proposed in [27].
3. Crossover: The binomial crossover mechanism is implemented for each individual of the g-th generation population \(\{x_{i}(gth)\}\) and its temporary offspring \(\{xoff_{i}^{{}^{\prime}}(gth+1)\}\) to obtain the final offspring. The formula is as follows: \[xoff_{i,j}^{{}^{\prime}}(gth+1)=\left\{\begin{matrix}xoff_{i,j}^{{}^{\prime}}( gth+1),&if&rand(0,1)\leq Cr\\ x_{i,j}(gth),&otherwise\end{matrix}\right.\] (4) where \(Cr\in\)[0, 1] is a predefined constant, which is called the crossover rate.
4. Selection: The DE algorithm employs a greedy algorithm to choose individuals for the next generation: \[x_{i}(gth+1)=\left\{\begin{matrix}xoff_{i}(gth+1),&if&fit(xoff_{i}(gth+1))\geq fit (x_{i}(gth))\\ x_{i}(gth),&otherwise\end{matrix}\right.\] (5) \(fit()\) is a fitness function. In the process of evolution, the individual with higher fitness value will be preserved. The pseudocode of the DE algorithm is shown in Algorithm 1.
## 4 Therapeutic Effects of Honey
### Antioxidant Effects  
The positive therapeutic effects of honey are mainly associated with its antioxidant capacity [125]. This property is related to the inhibition of free radicals that are responsible for the oxidative reactions inside the human body [126] that can damage the cells and cause various disorders [127]. The chemical compounds found in honey, such as phenolic compounds, organic acids, amino acids, carotenoids, proteins, Maillard reaction products or enzymes (glucose oxidase, catalase) are considered crucial contributors to this property [53; 128]. Therefore, these antioxidant elements found in honey are thought to be nutritional supplements against oxidative stress [129], improving the formation of stable molecules and neutralising the negative mechanism of the reactive oxygen and nitrogen species [9]. In this regard, several honey varieties, including Buckwheat honey and Manuka honey, have demonstrated strong capacities to prevent oxidative damage [130; 131]. Nevertheless, the phenolic compounds are the most representative antioxidants [126], as several authors have proven a high correlation between the phenolic content and this biological property. For instance, [PERSON] et al. (2018) [103] observed some positive correlations between the antioxidant activities and the total polyphenol (\(r\) = 0.945, \(p\) < 0.01), total flavonoids (\(r\) = 0.866, \(p\) < 0.01), and between total flavonoids and total polyphenols (\(r\) = 0.957, \(p\) < 0.01). These results are in agreement with those reported for Brazilian honey samples [88] that reveal the connection found between antioxidant activity and the content of phenolic compounds is very strong. Since the phenolic content of honey derives mainly from the nectar of the plants that bees feed on [46], we can infer that the antioxidant capacity of honey depends strongly on the botanical origin of plants [35; 89; 132]. Further evidence shows that the antioxidant capacity is also correlated with the honey colour intensity stating those darker-coloured honey possesses higher antioxidant properties and therefore a higher phenolic content [29; 133]. However, other factors including environmental and seasonal conditions, processing methods, packaging and storage conditions, can influence the antioxidant ability of honey and contribute to its changes [53; 60]. Considerable studies regarding the antioxidant activity of various types of honey have been carried out worldwide, as illustrated in Table 1.  
The methods used for the measurement of honey's antioxidant capacity, such as 1,1-diphenyl-2 picrylhydrazzil (DPPH), Oxygen Radical Absorbance Capacity (ORAC), Ferric Reducing Antioxidant Power (FRAP), Radical cation (2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothiazazoline-6-sulphonic acid)) (ABTS) or the croton or \(\beta\)-carotene bleaching [140] are also summarised in Table 1.
## 6 Conclusions
### Summary  
1. The results of the comparison indicate the data from intrinsic sensors have inherent disadvantages. Compared to the audio sensor, the sampling rate is too low, which means that this data contains less information. However, for flight safety, it is dangerous to add too much load to the computer. There is still an apparent limit to the use of intrinsic sensors.
2. The classification ability and recovery rate decrease with the increase in the number of types of faults. In addition, this study found that there is a trade-off between the accuracy and running time. Real-time classification requires additional sensors or light and powerful algorithms for effective classification.
3. Vibration analysis requires a significant amount of data, and the frequency-domain analysis is required for advanced classification. The above demands require a large amount of computational power; therefore, complex vibration analysis may not be suitable for real-time diagnosis. Nevertheless, a health monitoring system can still be used on the server side, and the health states can be tracked using its powerful classification ability.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Actual** & **Prediction** & **A** & **B** & **C** & **D** & **B + C** & **B + D** & **C + D** & **B + C + C** & **E** \\ \hline A & 181 & 2 & 0 & 26 & 0 & 0 & 1 & 0 & 0 \\ \hline B & 1 & 196 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline C & 13 & 1 & 185 & 14 & 2 & 0 & 16 & 4 & 0 \\ \hline D & 5 & 1 & 0 & 154 & 0 & 0 & 1 & 0 & 0 \\ \hline B + C & 0 & 0 & 1 & 1 & 195 & 1 & 1 & 2 & 0 \\ \hline B + D & 1 & 0 & 0 & 0 & 1 & 203 & 1 & 0 & 0 \\ \hline C + D & 0 & 2 & 16 & 3 & 1 & 0 & 177 & 0 & 0 \\ \hline B + C + D & 0 & 4 & 2 & 1 & 7 & 0 & 2 & 197 & 0 \\ \hline E & 2 & 0 & 3 & 6 & 2 & 0 & 4 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Confusion matrix of model based on time-frequency domain data.  
**Author Contributions:** Conceptualization, W.-H.L., S.-T.T. and D.-L.C.; methodology, S.-T.T. and D.-L.C.; software, S.-T.T.; validation, W.-H.L., S.-T.T., Y.-R.L. and D.-L.C.; formal analysis, D.-L.C. and Y.-R.L.; investigation, W.-H.L. and S.-T.T.; resources, W.-H.L. and S.-T.T.; data curation, D.-L.C.; writing--original draft preparation, S.-T.T.; writing--review and editing, W.-H.L. and D.-L.C.; visualization, D.-L.C. and Y.-R.L.; supervision, W.-H.L.; project administration, D.-L.C.; funding acquisition, W.-H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology under MOST 109-2321-B-067F-001.  
**Data Availability Statement:** The data that support the findings of this study are available from the author upon reasonable request.  
**Acknowledgments:** The authors would like to appreciate Ministry of Science and Technology to provide the funding of this project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The interconnection of power and natural gas systems can improve the flexibility of system operation and the capacity of renewable energy consumption. It is necessary to consider the interaction between both, and carry out collaborative optimization of energy flow. For space-time related line packs, this paper studies the optimal multi-energy flow (OMEF) model of an integrated electricity-gas system, taking into account the dynamic characteristics of a natural gas system. Besides, in order to avoid the problem of large data collection in centralized algorithms and consider the characteristics of decentralized autonomous decision-making for each subsystem, this paper proposes a decentralized algorithm for the OMEF problem. This algorithm transforms the original non-convex OMEF problem into an iterative convex programming problem through penalty convex-concave procedure (PCCP), and then, uses the alternating direction method of multipliers (ADMM) algorithm at each iteration of PCCP to develop a decentralized collaborative optimization of power flow and natural gas flow. Finally, numerical simulations verify the effectiveness and accuracy of the algorithm proposed in this paper, and analyze the effects of dynamic characteristics of networks on system operation.  
Keywords:optimal multi-energy flow; line pack; dynamic characteristics; decentralized optimization; PCCP; ADMM +
Footnote †: journal: Applied Sciences
# Multi-Point Displacement Synchronous Monitoring Method for Bridges Based on Computer Vision
## 1 Introduction
This study proposes a bridge multi-point displacement synchronous monitoring method, which can obtain multi-point displacement monitoring results of the structure by a single monitoring camera. The monitoring effect is similar to full-field displacement monitoring. This method can greatly expand the data on SHM and solve the problem of limited point monitoring leading to difficulty in evaluating the safety status of bridges.
## 3 Results and Discussion  
### Metabolites of Mertensia stylosa and M. serrulata: LC-MS Profile  
3.1.1. Pyrrolizidine Alkaloids  
Using high-performance liquid chromatography with photodiode array and electrospray ionization triple quadrupole mass spectrometric detection (HPLC-PDA-tQ-ESI-MS), six pyrrolizidine alkaloids were detected after a comparison of the retention times and mass spectra data with reference standards (Figure 2, Table 2). The derivatives of lycopsamine (**1**) were found in the studied species of the _Mertensia_ herb, namely lycopsamine \(N\)-oxide (**2**), lycopsamine 7-\(O\)-acetate (**3**), and its isomer (**3***) and lycopsamine \(N\)-oxide 7-\(O\)-acetate (**4**) and its isomer (**4***) (Figure 3). Lycopsamine \(N\)-oxide was the dominant compound in the herbs of _M. stylosa_ (5.27 mg/g) and _M. serrulata_ (2.14 mg/g). Lycopsamine was found in the herbs of _M. stylosa_ and _M. serrulata_ at significantly lower levels of 0.94 and 0.30 mg/g, respectively. Isomers **3** and **4** were found in both studied species in trace amounts. The total content of pyrrolizidine alkaloids in the herb of _M. stylosa_ exceeded the content of the same alkaloids in the herb of _M. serrulata_ by 2.7 times. Previously, lycopsamine and lycopsamine \(N\)-oxide have been identified in _M. bakery_, _M. ciliata_[23], and _M. maritima_[24], whereas only lycopsamine has been found in _M. sibrica_[25]. Acetate derivatives of lycopsamine and lycopsamine \(N\)-oxide were revealed in the _Mertensia_ genus for the first time.  
Figure 2: Fragments of HPLC-ESI-MS chromatograms of _Mertensia stylosa_ (**A,B**) and _M. serrulata_ (**C,D**) herb extracts (Total Ion Chromatogram or TIC mode, positive ionization; (**A,C**)) and (SIM mode, positive ionization, _m/z_ 300, 316, 342 and 358; (**B,D**)). Compounds are numbered as listed in Table 2. (**E**) MS/MS spectrum of lycopsamine (**1**, positive ionization).
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 2 Background and Threat Model
### Flow Table Overflow Attack  
The storage space of most modern hardware OpenFlow switches is limited. If the flow table space is full, new rules cannot be installed, which is named \"flow table saturation\" or \"flow table overflow\" [25]. Malicious attackers can exploit this capacity bottleneck to degrade network performance. The attackers send a large number of packets to trigger the installation of new rules and exhaust the storage space. Even worse, an attacker can leverage the timing-based reconnaissance to gather the configuration of flow rules. Previous studies, such as [PERSON] et al. [10], proposed inferring match fields and controller timeout settings to launch attacks at the minimal feasible attack rate. Additionally, [PERSON] et al. [26] demonstrated that attackers can accurately infer the flow table capacity and usage. Furthermore, [PERSON] et al. [11] examined the cache-based behaviors of the flow table and designed attack parameters considering eviction strategies and background traffic. It is evident that by leveraging these techniques, attackers can carry out saturation attacks with minimal cost.
## 3 Methodology
### Channel Spatial Attention Module (CSAM)  
A diagram of the CSAM block is shown in Figure 4, which includes two parts: a channel attention and a spatial attention mechanism. The upper part is channel attention, and the lower part is the spatial attention mechanism. The channel attention involves global average pooling (GAP), convolution with kernel size 3 \(\times\) 3 \(\times\) 3, and the Sigmoid Activation Function, which enable the model to automatically learn the importance of each channel and adjust the representation of input data based on the contribution of each channel. The spatial attention incorporates average pooling (AP), max pooling (MP), convolution with kernel size 7 \(\times\) 7 \(\times\) 7 and Sigmoid Activation Function, which can help the model better focus on the features of different regions in the image to improve the representation ability and performance of the model. The CSAM is an attention module that combines channel attention and spatial attention mechanisms to improve the representation ability of image features, and it achieves global perception and the importance adjustment of image features by simultaneously focusing on feature information in both channel and spatial dimensions.  
Compared to the CBAM [45] attention mechanism, it has two inputs: one is the information transmitted by the encoder, and the other is the information transmitted by the previous layer in the decoder. The advantage of the CSAM module is that it can integrate shallow information transmitted by the encoder corresponding to the decoder with deep information transmitted from the previous layer, which can guide the selection of detailed features transmitted from the previous layer by spatial and channel attention mechanisms. The channel attention can be represented as follows:  
\[Z_{c}=Sigmoid(Conv_{3\times 3\times 3}(GAP(Z_{in}^{L}+Z_{in}^{L-1})))\times Z_{in} ^{L} \tag{2}\]  
where \(Z_{in}^{L}\) denotes the shallow information transmitted by the encoder corresponding to the decoder, \(Z_{in}^{L-1}\) represents detailed features transmitted from the previous layer, _GAP_ is defined as global average pooling, and \(Conv_{3\times 3\times 3}\) indicates convolution with kernel size 3 \(\times\) 3 \(\times\) 3. Sigmoid is Activation Function.  
Figure 4: An illustration of building blocks of CSAM block.  
The spatial attention can be described as follows:  
\[Z_{S}=Sigmoid(Conv_{\gamma\times\gamma\times\gamma}(concat(AP(Z_{in}^{L}+Z_{in}^{L-1 }),MP\Big{(}Z_{in}^{L}+Z_{in}^{L-1}\Big{)})))\times Z_{in}^{L-1} \tag{3}\]  
where _AP_ denotes average pooling and _MP_ represents max pooling; _concat_ is concatenate operation.  
Our proposed CSAM block can be depicted as follows:  
\[Z=concat\Big{(}(Z_{c}+Z_{s}),\;\;Z_{in}^{L-1}\Big{)} \tag{4}\]  
where _concat_ denotes concatenate operation.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 3 Experimental Section
### TRNG Implementations and Tests  
In this work, the selected commercial FPGA is an Igloo AGLN250, manufactured by Microsemi. This FPGA is based on a 130 nm flash technology. The clock reference is the on-board 20 MHz crystal oscillator. A high precision voltage source is used to set the voltage operation in order to avoid influences in the randomness due to the power supply.  
On the one hand, a RO-TRNG scheme composed of 512 identical laid-out ROs has been implemented as described in [12]. Each RO consists of a chain of 5 inverters. On the other hand, a STR-TRNG consisting of 511 stages has been implemented following the design introduced in [13]. The STR has been properly configured in order to guarantee the correct distribution of events. The TRNGs have been placed in different FPGA zones in order to avoid mutual influences.  
The TRNG outputs have been collected and transferred to a host computer using an RS232 communication protocol. The integrity of the transmitted information has been ensured using a CRC mechanism. The achieved throughput for both TRNGs is 25 Mbps because all oscillators (ROs and STRs) are sampled using the reference clock. Regarding the used resources of the FPGA provided by the synthesis tool, a total of 2976 Core tiles (1997 Combinational tiles and 979 Sequential tiles), occupying a 16.67% of the available resources in the FPGA, are used.  
Once the data is on the host computer, two NIST test batteries have been used to evaluate the TRNG quality [5; 18]. On the one hand, we have checked the final TRNG output using the well-known NIST test suite that includes several statistical tests as the frequency test, runs, binary matrix rank, etc. On the other hand, we have estimated the min-entropy by using some tests recommend in [18] for testing non-IID generators. We have discarded the measurement of a physical parameter (in this case the jitter) because of the complexities of the radiation set-up.
## 5 Conclusions  
A method for estimating the turbulence profile over a horizontal path using a Hartmann Turbulence Sensor and two source beacons has been described. This method's measurement technique is similar to SLODAR, but an entirely different methodology is used for turbulence profile reconstruction. Analytical expressions for the path weighting functions of crossing and non-crossing geometries have been shown to depend on subaperture size and separations, the separation of the source beacons, and the length of the sensing path. Each weighting function is constructed using varying subaperture separations. This gives them their unique shapes, which help with the pseudo-inversion process. The influence functions produced by these weighting functions suggest that the technique's spatial resolution is highest near the HTS end of the path and steadily declines until the final weighting function notch location as shown in Figure 6a (roughly 350 m for this experimental setup). Past this point, the technique's spatial resolution is effectively zero due to the lack of weighting function diversity.  
This is a phase-based approach that is not prone to the saturation issues faced by irradiance-based techniques [5]. Therefore, this approach may be applied over greater sensing distances than others presuming similar aperture sizes. This methodology and analysis provide a deeper understanding of surface layer turbulence phenomena. This technique may prove to be an effective tool in long range imagery or directed energy through the atmosphere when used in conjunction with a multi-conjugate AO system.  
Conceptualization, J.M., S.B.-P. and S.F.; methodology, J.M. and S.B.-P.; software, A.B. and B.W.; validation, A.B., J.M. and S.B.-P.; formal analysis, A.B.; investigation, A.B. and B.W.; resources, J.M.; data curation, A.B.; writing--original draft preparation, A.B.; writing--review and editing, J.M. and S.B.-P.; visualization, A.B.; supervision, J.M., S.B.-P. and S.F.; project administration, J.M. and S.B.-P.; funding acquisition, J.M. and S.F. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by grants from the Directed Energy Joint Transition Office (DE-JTO), the Air Force Office of Scientific Research (AFOSR), and the Office of Naval Research (ONR).  
The authors declare no conflict of interest.
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
###### Abstract  
Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee arthroscopy. We focus on the problem of initializing registration in the case of partial views. The proposed method utilizes a pre-initialization step of using the automatically segmented structures from both modalities to achieve a global geometric initialization. This is followed by computing distance maps of the procured segmentations for registration in the distance space. Following that, the final local refinement between the MRI-US volumes is achieved using the LC\({}^{2}\) (Linear correlation of linear combination) metric. The method is evaluated on 11 cases spanning six subjects, with four levels of knee flexion. A best-case error of 1.41 mm and 2.34\({}^{\circ}\) and an average registration error of 3.45 mm and 7.76\({}^{\circ}\) is achieved in translation and rotation, respectively. An inter-observer variability study is performed, and a mean difference of 4.41 mm and 7.77\({}^{\circ}\) is reported. The errors obtained through the developed registration algorithm and inter-observer difference values are found to be comparable. We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US based image guidance in minimally invasive procedures.  
ao 2022 125488. [[https://doi.org/10.3390/app12115488](https://doi.org/10.3390/app12115488)]([https://doi.org/10.3390/app12115488](https://doi.org/10.3390/app12115488))
## 4 DRPS: Fault-Tolerant Approach for Dynamic Redundant Path Selection  
**Service Migration**  
Based on the system model proposed in Section 3, for the service migration problem in the VEC environment, this paper considers the performance fluctuation of ESs and proposes a fault-tolerant approach called DRPS, which consists of path selection algorithm and service migration algorithm. This section describes the method in detail.