esa-sceva/satcom-chunk-collection huggingface dataset:
# A REM Update Methodology Based on Clustering and Random Forest
## 3 Proposed Approach for REM Updates
### Overview
Therefore, at the \(k\)-th cluster, to replace the \(k\)-th RF model with a new RF model trained with dataset \(D_{k_{\mathbf{t}1}}\), we establish a condition calling for a minimum number of samples needed in the dataset to train a new \(k\)-th RF model: \(\left|D_{k_{\mathbf{t}1}}\right|\geq N_{\text{min}}\), where \(N_{\text{min}}\) is the minimum number of samples. If \(\left|D_{k_{\mathbf{t}1}}\right|\geq N_{\text{min}}\) is satisfied, the \(k\)-th RF model is trained based on the corresponding new dataset, \(D_{k_{\mathbf{t}1}}\), replacing the old RF model in the \(k\)-th cluster. On the other hand, if dataset \(D_{k_{\mathbf{t}1}}\) does not contain the minimum number of samples, \(N_{\text{min}}\), the previous \(k\)-th RF model is used to predict the points in the \(k\)-th cluster. Finally, we update the REM by using the current \(K\) RF models to predict the RSSI values of grid \(G\).
## 2 Data and Methods
### Elman Neural Network
#### 2.2.2 Model Selection Method  
Firstly, five metrics, Nash efficiency coefficient (\(NSE\)), mean absolute percentage error (\(MAPE\)), root mean squared error (\(RMSE\)), mean absolute error (\(MAE\)), and qualification rate (\(QR\)), were chosen to compare the performance of the models. The focus of each  
Figure 4: Structure of the Elman neural network (ENN).  
indicator is different, so the first five indicators can be used for the initial screening model. The \(NSE\) is capable of evaluating the predictive power of a model concerning measuring the degree to which the simulated process value is close to the observed value. The closer the value is to 1, the better the predictive power of the model. \(MAPE\) can evaluate the analysis for the accuracy of predicting the smooth component in the runoff series, ideally the value of which is 1. \(RMSE\) is applied to measure the degree of variability of the data, and the smaller the value, the better the accuracy of the model [30]. The range of \(MAE\) is \([0,+\infty)\), and zero indicates that the predicted value exactly matches the true value. \(QR\) is the ratio of the number of qualified predicted flow values to the total number of flow values. The formulas for calculating the above-mentioned indexes are as follows.  
\[NSE=1-\frac{\sum\limits_{i=1}^{N}\left(y_{i}-y_{i}^{*}\right)^{2}}{\sum \limits_{i=1}^{N}\left(y_{i}-\overline{y}\right)^{2}} \tag{19}\]  
\[MAPE=\frac{1}{n}\underset{i=1}{\overset{N}{\sum}}\left|\frac{y_{i}-y_{i}^{*}} {y_{i}}\right|\times 100\% \tag{20}\]  
\[RMSE=\sqrt{\frac{1}{n}\underset{i=1}{\overset{N}{\sum}}\left(y_{i}-y_{i}^{*} \right)^{2}} \tag{21}\]  
\[MAE=\frac{1}{n}\underset{i=1}{\overset{N}{\sum}}\left|y_{i}-y^{*}\right| \tag{22}\]  
\[QR=\frac{k}{l}\times 100\% \tag{23}\]  
where \(y_{i}\) is the measured value, \(y_{i}\)\({}^{*}\) is the simulated value, \(\overline{y}\) is the average of the observed value, and \(n\) is the total number of time series.  
Considering that individual models may exhibit better performance in one aspect, but cannot win across the board, a more integrated approach is needed. The composite rating index method (\(M_{R}\)) is based on the ranking of each rating index, which is a method to determine their consistency and obtain a composite ranking [31].  
\[M_{R}=1-\frac{1}{nm}\underset{i=1}{\overset{n}{\sum}}r_{i} \tag{24}\]  
where \(m\) is the number of models, \(n\) is the number of indicators to be evaluated, and \(r_{i}\) is the ranking of the simulation capability of each model in each assessment metric (\(i=1,2,3\)). The value \(r_{i}=1\) indicates a model with the strongest simulation capability. It can be seen that \(0\leq M_{R}<1\). The value \(M_{R}\) is closer to 1, indicating the stronger comprehensive simulation capability of the model.
## 2 Results and Discussion
### Method Validation
#### 2.1.5 Robustness
Moreover, the method allows the simultaneous analysis of two antibiotics belonging to different classes with dissimilar chemical and physical properties: Dox (hyclate form) being highly soluble in water (50 mg/mL) [25], while Amox is less soluble in water (54.6 mg/mL) [2]. To our knowledge, no analytical methods were reported for the simultaneous determination of Amox and Dox in the investigated matrices (pharmaceutical formulations and wastewater samples). Fourier transform infrared (FT-IR) spectroscopy [57], UV-VIS spectrophotometry [58; 59; 60], and near-infrared (NIR) spectroscopy [61] have the advantages of a reduced volume of chemical waste generated and low initial investment for the equipment. However, their disadvantages are higher LOQ (-5 mg/L), low specificity, strictly controlled laboratory room temperature conditions and they require a chemometric method for quantification of more the one compound [57; 58; 59; 60; 61]. The capillary electrophoresis for the individually determination of Amox and Dox showed similar LOD (0.4 mg/mL), but higher relative standard deviation and a longer analysis time (30-40 min) [16; 62]. Voltametric methods are frequently used for the individual, quantitative analysis of Amox and Dox. However, their applications focus on solid samples containing a high concentration of antibiotics, such as in capsules and tablets. Consequently, the concentration range and LOD are much higher than for liquid chromatographic methods [10; 11; 63].
## 5 Simulation Results  
### Test System  
The applied technique was tested in a modified IEEE 39 bus system shown in Figure 3[22], which is the conventional IEEE 39 bus system, in addition to four wind farms at buses 2, 6, 23 and 28 with wind speeds of 7.54, 8.12, 9.08 and 8.62 m/s, respectively, and active power of 200, 250, 350 and 300 MW, respectively. The system was simulated in the DIgSILENT power factory. The optimization process for the optimal size and placement of energy storage systems was applied using DIgSILENT power factory 15.1[23].  
Figure 2: Flowchart of PM application for optimal sizing and allocation of energy storage in a power system.
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 3 Methodology
### Adversarial Techniques Used
* Contribution to CAPTCHA Security: JSMA's saliency maps reveal which CAPTCHA features (e.g., character spacing, noise patterns) are most vulnerable to adversarial manipulation. By quantifying how few pixel changes are needed to deceive models, our work underscores the need for non-differentiable CAPTCHA designs (e.g., randomized distortions) that resist gradient-based attacks. * Perturbation Budget: maximum \(L_{0}\)-norm of 15% of total pixels. - Iterations: 100 steps or until misclassification. - Target Class Selection: least-likely class (untargeted) or predefined labels (targeted). - Pixel Constraints: modifications limited to \(\pm\)10 intensity values per step to mimic subtle adversarial alterations. ```
1:Input:
2:\(x\): Original image
3:\(t\): Target class
4:\(e\): Epsilon budget
5:\(\phi\): Mask
6:while\(F(x^{\prime})\
eq t\)do
7:\(x^{\prime}f\leftarrow\mathcal{F}(x^{\prime})\) [Fourier transform]
8:\(g\leftarrow\
abla L(x^{\prime}f,t)\) [Compute gradient in Fourier domain]
9: Calculate Jacobian \(f\) of \(x^{\prime}_{f}\) w.r.t. model logits
10:\(S\leftarrow\text{SaliencyMap}(g)\) [Calculate saliency map]
11:\(S\gets S\circ\phi\) [Apply mask]
12:\(p\leftarrow\arg\max S\) [Greedily select pixels p in S with highest values]
13:\(x^{\prime}f\gets x^{\prime}f+\epsilon\cdot\text{Perturb}(p)\) [Perturb selected pixels and neighbors]
14:\(x^{\prime}\leftarrow\mathcal{F}^{-1}(x^{\prime}f)\) [Inverse Fourier transform]
15: Update \(x^{\prime}\)
16:endwhile
17:Return \(x^{\prime}\)
18:Output: Adversarial image \(x^{\prime}\)
```  
**Algorithm 3** JSMA Adversarial Generation.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 4 Configurations
### EF OPAs
Additional off-chip cylindrical lenses were needed to collimate the diverging light of EF OPAs [41; 98]. To achieve upward emission, a 45\({}^{\circ}\) mirror was set to direct the beam [98]. Furthermore, a kind of hybrid three-dimensional (3D) waveguide structures offered 2D patterns [111; 112]. Main evaluation parameters of some EF OPAs in recent years are given in Table 1.
## 2 Materials and Methods
### Colony Count  
The detection method of the total colony count followed the method of [PERSON] et al. [37], with appropriate modifications. The colonies were counted as colony-forming units (CFUs) and presented as CFU g\({}^{-1}\).  
Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry (HS-SPME/GC-MS) Detection  
Volatile compounds in sweetpotatoes were analysed by HS-SPME/GC-MS technology. The method was referred to [PERSON] et al. [38], with appropriate modifications. A DVB/CAR/PDMS (50/30 \(\upmu\)m, Supelco, St. Louis, MO, USA) fibre was used to extract and concentrate the volatile compounds. Then, the volatile compounds were separated and identified by QP-2010 GC-MS (Shimadzu, Kyoto, Japan). Accurately weighed, 3.0 g of chopped sweetpotato tissue was used as the sample, and 5 \(\upmu\)L of 2-octanol (0.1 \(\upmu\)L mL\({}^{-1}\), as an internal standard) was added to a 20 mL headspace vial, which was then sealed with a silicone rubber lid. After equilibration in a water bath at 70 \({}^{\circ}\)C for 15 min, the fibre was inserted into the headspace vial. The fibre tip was pushed out and exposed 1 cm above the samples at 70 \({}^{\circ}\)C for 45 min. Finally, the fibre was injected into the GC for desorption at 250 \({}^{\circ}\)C for 5 min and the data collection was started at the same time.  
An HP-5 capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m) was used in the GC. The injection port temperature was 250 \({}^{\circ}\)C. Helium was used as the carrier gas at a constant flow rate of 1.0 mL min\({}^{-1}\). The splitless injection was carried out with the following heating procedure of the column. The initial temperature was set at 45 \({}^{\circ}\)C and held for 3 min. Then, the temperature was increased to 125 \({}^{\circ}\)C at a constant ramp-up rate of 5 \({}^{\circ}\)C min\({}^{-1}\). Subsequently, the temperature was increased to 175 \({}^{\circ}\)C at a constant ramp-up rate of 3\({}^{\circ}\)C min\({}^{-1}\) and held for 3 min. Finally, the temperature was increased to 230 \({}^{\circ}\)C at a constant ramp-up rate of 10 \({}^{\circ}\)C min\({}^{-1}\) and held for 2 min. The MS connector temperature was 280 \({}^{\circ}\)C. The MS was operated in the electron impact ionization mode (70 eV), and the data were scanned from 50 to 550 \(m/z\). The temperature of the ion source was 230 \({}^{\circ}\)C, and the solvent was extended for 2 min.  
The volatile compounds were identified by comparison with National Institute of Standards and Technology (NIST) standards, and the substances with matching degrees greater than 80% were screened out [39]. The internal standard of 2-octanol was used to quantify the volatile compounds in sweetpotatoes. The data for each group were from three replicate experiments and the results were presented as the means \(\pm\) standard deviation (SD) ng g\({}^{-1}\) fresh weight (FW) equivalent of 2-octanol.
## 3 Biological Activities  
### Antibacterial Activity  
One of citral's most relevant biological activities is its antibacterial effect, evidenced against Gram-positive and Gram-negative bacteria (Table 2). In this way, citral obtained from the _Litesa cubeba_ (L.) plant was used in the formulation of food packaging films, showing that citral coatings achieved bactericidal effects against _Escherichia coli_ (2.1 log) and _Staphylococcus aureus_ (4.3 log) at concentrations of 20%. Headspace equilibrium concentrations of 1.8 \(\upmu\)g/mL air were found for 20% citral coatings, resulting in a 3.8 log reduction against _E. coli_[45]. Citral, also obtained from C. _flexulosus_, was used against four strains of _Acinetobacter baumannii_ with resistance to antimicrobials (MDR = multi-drug resistant), finding that all the tested strains were susceptible to citral with zones of inhibition that varied between 17 and 80 mm. Additionally, citral's minimum inhibitory concentration (MIC = 0.14% \(v/v\)) and the minimum bactericidal concentration (MBC = 0.3% \(v/v\)) were determined. The ability of citral to inhibit and kill MDR _A. baumannii_ highlights its potential for use in treating drug-resistant infections; however, in this study, it is suggested to perform in vitro cytotoxicity studies before human exposure in vivo or ex vivo [46]. Figure 2: Citral biosynthesis involves multiple transformations from the universal monoterpene precursor GPP. _Cronobacter sakazakii_ is a foodborne pathogen associated with severe disease and high mortality in newborns and infants. For this reason, [PERSON], [PERSON] [60] evaluated the antimicrobial effect of citral against strains of _C. sakazakii_. They also determined the growth kinetics, membrane potential, and membrane integrity to elucidate the possible antimicrobial mechanism. Citral MICs against _C. sakazakii_ strains ranged from 0.27 to 0.54 mg/mL, and citral resulted in a longer lag phase and lower growth rate of _C. sakazakii_ compared to the control. Citral affected the cell membrane of _C. sakazakii_, as demonstrated by the decrease in intracellular ATP concentration, the reduction in pH, and the hyperpolarization of the cell membrane. These findings suggest that citral exhibits an antimicrobial effect and
## 3 Real-Time Small Pit Defect Detection Network  
### System Overview  
The proposed real-time small pit defect detection (RT-SPeeDet) model involves a candidate region extraction stage that uses image-processing technology and a CNN-based classification stage, as shown in Figure 4. To separate the function of an end-to-end detection model into two stages, RT-SPeeDet conducts localization in the defect candidate region extraction stage and then applies CNN binary classification to the extracted candidate regions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Input Image** & **Speed [s]** & **Deep Learning** & **Small Defects** \\ \hline [PERSON] [16] & 1928 \(\times\) 1448 & 3.41 & \(\times\) & \(\bigcirc\) \\ [PERSON] [18] & 2800 \(\times\) 300 & 1.57 & \(\bigcirc\) & \(\bigcirc\) \\ [PERSON] [14] & 1408 \(\times\) 512 & 0.11 & \(\bigcirc\) & \(\times\) \\ [PERSON] [15] & 180 \(\times\) 180 & 0.13 & \(\bigcirc\) & \(\times\) \\ [PERSON] [19] & 512 \(\times\) 512 & 2.29 & \(\bigcirc\) & \(\bigcirc\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of existing defect detection models.  
Figure 4: Diagram of IP-CNN-based RT-SPeeDet system.  
Figure 3: Pit defects according to the difficulty of observability; (**a**) strong level, (**b**) medium level, (**c**) weak level.
## 2 Related Work
### Other Techniques for Distributed Training  
For efficient distributed training, many techniques have been studied from various perspectives. In general, the PS-based distributed training algorithms often suffer from the problem of PS being a bottleneck because PS aggregates training results from all workers. To mitigate this problem, parameter sharding [10; 29; 34] is generally applied to PS-based distributed training algorithms. This technique divides global parameters and distributes them into multiple PSs to process them in parallel. On the other hand, To speed up the model convergence in synchronous distributed training, various methods that control the learning rate in distributed training have been widely studied [26; 27; 35; 36; 37; 38; 39]. Linear scaling with warm-up (LSW) [27] uses the linear learning rate scaling rule that linearly increases the learning rate as the batch size increases, together with the gradual warm-up that gradually increases the learning rate from a small value. AdaScale SGD [26] further improves large-batch training by applying more reliable learning rate considering the variance of gradients. [PERSON] [17] and [PERSON] et al. [16] consider distributed training over different types of networks. Distributed any-batch mirror descent (DABMD) [17] speeds up the training over a heterogeneous network, by varying the batch sizes across workers to minimize the waiting time by faster workers. Reliable parameter server (RPS) [16] assumes unreliable networks where the delivery of the messages between workers is not guaranteed. Through theoretical analysis, [PERSON] et al. [16] shows the convergence rate of the PS-based distributed training algorithm over unreliable networks is comparable to that of reliable networks.  
POSEIDON [10], iBatch [19], G-Pipe [25], and PipeDream [15] were proposed to maximize the training efficiency by overlapping the computation and communication.
## Comparison of Neural Activation Area in Primary Somatosensory Cortex and Brodmann Area 3
###### Abstract  
In this study, we measured neuronal activation in the primary somatosensory area (S1) and Brodmann area 3 (BA3) using 3T functional magnetic resonance imaging (fMRI) while presenting a 250-Hz high-frequency vibrational stimulus to each of three phalanges (distal, intermediate, and proximal) of four fingers of the right hand (index, middle, ring, and little). We compared the nerve activation area between each finger and each phalange. Ten healthy male college students (\(26.6\pm 2.5\) years old) participated in this study. One session consisted of three blocks: a rest (30 s), stimulation (30 s), and response phase (9 s). In the rest phase, the vibrational stimulus was not presented. In the stimulation phase, the vibrational stimulation was presented at any one of the three phalanges of the selected finger. In the response phase, subjects were instructed to press a button corresponding to the phalange that they thought had received the vibration. The subtraction method was used to extract the activation area. The activation area in the S1 was the largest when the little finger was stimulated (for the finger comparison), and largest when the second phalange was stimulated (for the phalange comparison). The BA3 showed similar trends, and there was no statistically significant difference.  
high-frequency vibrotactile stimulation; finger and phalange; neuronal activation +
Footnote †: journal: Applied Soft
## 2 Materials and Methods
### Pre-Processing of Change Detection Results  
The bi-temporal remote sensing images are processed by the deep Siamese CNN change detection model to generate pixel-level change probability distribution maps. Based on the change probability value of each pixel, the proposed method first performs pre-processing on the results generated by the CNN model, such as probability normalization and mean confidence score calculation, to enable subsequent parcel aggregation and confidence-based post-processing.  
Figure 1: The overall workflow of the proposed methodology.  
Figure 2: Change detection framework based on deep Siamese CNN.
# Noiseless Attenuation for Continuous-Variable Quantum Key Distribution over Ground-Satellite Uplink
###### Abstract  
Satellite-based quantum key distribution (QKD) has lately received considerable attention due to its potential to establish a secure global network. Associated with its application is a turbulent atmosphere that sets a notable restriction to the transmission efficiency, which is especially challenging for ground-to-satellite uplink scenarios. Here, we propose a novel noiseless attenuation (NA) scheme involving a zero-photon catalysis operation for source preparation to improve the performance of continuous-variable (CV) QKD over uplink. Numerical analysis shows that the NA-based CV-QKD, under attenuation optimization, outperforms the traditional CV-QKD, which is embodied in extending the allowable zenith angle while improving the effective communication time. Attributing to characteristics of the attenuation optimization, we find that the NA-involved source preparation improves the security bound by relatively reducing the amount of information available to eavesdroppers. Taking the finite-size effect into account, we achieve a tighter bond of security, which is more practical compared with the asymptotic limit.  
quantum key distribution; continuous-variable; noiseless attenuation; uplink Vol. 2021, 11, 11289. [[https://doi.org/10.3390/app112311289](https://doi.org/10.3390/app112311289)]([https://doi.org/10.3390/app112311289](https://doi.org/10.3390/app112311289))
## 4 Reed-Solomon Codes Effectiveness against Single-Frequency EMD
### Peaks of the Undetected Corrupted Data
The angle that leads to this category is simply obtained by subtracting the areas responsible for generating bit-flips (i.e., \(\Theta_{E}\)) from the period of the single-frequency disturbance (i.e., \(2\pi\)). Subsequently, the DTP ratio can be calculated with Equation (4). Please note that this ratio only depends on the EMD voltage, and it is independent of the symbol size value (\(m\)) and the data word length (\(k\)). Additionally, the CTP ratio can be determined via Equation (5). There are only two possible scenarios that could lead to a CTP at harmonics of the disturbance frequency, which only happen within the range of \(\Theta_{E}\). Either when the channel enforces a logical '1' and the transmitted bit-string is all '1' (i.e., all bits of all symbols are '1'), or when the channel enforces a logical '0', and the transmitted bit-string is also all '0'. Figure 3: Reed–Solomon Codes Effectiveness under single-frequency electromagnetic disturbances for the symbol size of 4. Furthermore, Equation (6) can be used to calculate the ratio of CFN. The first part of Equation (6) calculates the CFN ratio for all code words except all 0's and all 1's code words, leaving \((2^{m^{k}}-2)/2^{m^{k}}\) code words. The second part, however, only calculates this ratio for all 0's and all 1's code words. Note that the second part of Equation (6) uses half of the calculated angle since only one side of the sine wave (i.e., single-frequency disturbance) results in a bit-flip for each code word. In other words, the only possible CFN event at harmonics of the bit-frequency for the all 0's code word is all 1's. Likewise, the only possible CFN event for the all 1's code word is all 0's. Nevertheless, other code words can turn into both all 0's and all 1's code words at the harmonics of the disturbance frequency, and the factor of \(2\cdot\Theta_{E}\) indicates this fact. Equation (7) provides an overview on the occurrence probability of the mentioned CFN areas at EMD harmonics. \[ \begin{cases}P_{turn\ to\ all\ 0's/1's}=\dfrac{2\cdot\Theta_{E}}{2\pi}&\text{all code words except all 1's and all 0's,}\\ P_{turn\ to\ all\ 0's}=\dfrac{\Theta_{E}}{2\pi}&\text{all 1's code word,}\\ P_{turn\ to\ all\ 1's}=\dfrac{\Theta_{E}}{2\pi}&\text{all 0's code word,}\end{cases}  \tag{7}\]  
Accordingly, the purple points in Figure 5a-c validates the obtained results at harmonics (i.e., DTP, CTP, and CFN ratios at 200 MHz and 400 MHz in Figures 2 and 3) by representing the aforementioned ratios for different amplitudes based on Equations (4)-(6). Figure 4: Effectiveness of each area in a single-frequency disturbance with regard to bit-flips. Figure 5: Theoretically calculated DTP, CTP, and CFN ratios at harmonics of the disturbance frequency when fbit is 200 Mbps.