esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, two-dimensional time-resolved particle image velocimetry (2D-TR-PIV) was used to investigate the effect of the external domain on oscillating jets from double-feedback fluidic oscillators. Two different cases with different Re numbers (2680-10,730), as free external domain and fully confined were studied. Time-averaged results showed although a self-oscillating jet was attained for the free external domain, it could not be achieved for a fully confined geometry. For a fully confined geometry at \(\mathrm{Re}=2680\), two symmetric vortices did not allow the jet to oscillate and at \(\mathrm{Re}=6440\), the flow pattern in the external region became non-symmetric due to the Coanda vortex, subsequently, the self-oscillating jet was not observed. At \(\mathrm{Re}=10\),730, the strength of the jet was inclined to cope with such vortices and tended to oscillate. However, strong vortices were created near the exit region of the fluidic oscillator, which led to an almost non-symmetric pattern. In addition, the proper orthogonal decomposition (POD) method and phase-averaged analysis were applied to obtain the unsteady behavior of flow and the most energetic dynamic structure. Interestingly, at \(\mathrm{Re}=6440\), the third mode was still energetic for fully confined, but for other cases, the first two modes were the most energetic modes, which showed vigorous coherent structures.  
fluidic oscillator; experimental measurement (TR-PIV); proper orthogonal decomposition (POD); phase averaged; fully confined +
Footnote †: journal: Applied Sciences
# Some Plant Food Products Present on the Polish Market Are a Source of Vitamin B12
## 1 Introduction
Fermented cereal products are also worth mentioning. Among them, white borscht is a liquid substance that is produced by the fermentation of flour, water and spices. The product, available in stores, is used to prepare a traditional Polish soup of the same name. Sour rye soup has a very similar use and taste; the only difference is that wholomeal rye flour is used instead of wheat flour for its preparation. Ready sourdough is used to prepare traditional Polish soups such as zurek and white borscht. A combination of yeasts and LAB from rye bread or cracked rye, wheat, barley and millet, with the addition of flour, produces a refreshing, nonalcoholic beverage named kvass. This drink effectively quenches thirst, stimulating appetite and digestion [24]. The works by [PERSON] [21] and [PERSON] et al. [12] were the inspiration behind starting research in this area. Both studies were conducted using various plant products. The first study showed that vitamin B12 was present in plant products subjected to controlled fermentation. On the other hand, [PERSON] et al. [12] studied single species of mushrooms and plants such as garlic, couch grass, parsnip, corn poppy and sea buckthorn along with related products, in which significant contents of vitamin B12 were determined. These reports prompted us to conduct a similar study, analyzing sea-buckthorn jam, sauerkraut and other items. One novelty in this work is the extended range of fermented products, which, according to available knowledge, had not been analyzed before. Moreover, the studied products were commercial and not prepared in a controlled manner. The aim of this study was to estimate the content of vitamin B12 in selected popular fermented plant products and confirm the significant content of this vitamin in the sea-buckthorn products available on the Polish market, using high-performance liquid chromatography with ultraviolet detection (HPLC-UV).
## 5 Conclusions  
In the presence of multiple disturbing factors in underground coal mines and facing the problem of a complex environment, an improved algorithm is proposed to transform different components by combining a bilateral filtering algorithm based on HSV space with a multiscale Retinex algorithm with colour recovery factors after homomorphic filtering, and the conclusions are shown below.  
(1) The method uses homomorphic filtering to enhance each RGB component of the image, converts the image from RGB space to HSV space, keeps the hue H component unchanged, uses an algorithm of bilateral filtering and multi-scale Retinex fusion to enhance the luminance V component, gamma corrects its incident component, and performs histogram equalisation correction and Laplace operator enhancement for the reflection component. An adaptive nonlinear stretching process is performed for the saturation S.  
(2) As verified by MATLAB simulation, the improved algorithm (BF-MSRCR) can effectively remove various noises in the image, significantly improve the brightness and contrast of the image. In addition, it does not cause a lot of haloes and edge distortion in the image, and the colour retention of the image is also more complete and high-definition.  
This algorithm dramatically improved the enhancement of underground coal mine surveillance video images, supporting subsequent mine facility detection, personnel detection, and safety production. However, the algorithm used in this study is still a universal image enhancement method. In subsequent research work, we can consider using deep learning algorithms to train the surveillance graphics of underground mines and build a more adaptable image enhancement model for underground mines.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm Categories** & **Standard Deviation** & **Average Gradient** & **Mean Value** \\ \hline homomorphic filtering & 49.76 & 6.98 & 136.43 \\ Laplace enhancement & 51.98 & 4.70 & 145.21 \\ MSR & 33.77 & 4.32 & 125.85 \\ MSRCR & 39.83 & 5.24 & 152.56 \\ BF-MSRCR & 74.16 & 11.7 & 178.77 \\ \hline \end{tabular}
\end{table}
Table 2: Downhole image enhancement quality evaluation.  
**Author Contributions:** Conceptualisation, F.T.; methodology, T.C.; software, T.C.; validation, F.T., T.C. and J.Z.; investigation, F.T.; data curation, T.C.; writing--original draft preparation, T.C.; writing--review and editing, F.T. and J.Z.; Supervision, F.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the Project of Science and Technology of Shaanxi (No. 2020 GY-029).  
**Data Availability Statement:** No new data is created in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background and Preliminaries
### Federated Learning Algorithm
#### 2.4.1 Federated Stochastic Gradient Descent (FedSGD)  
FedSGD is an optimization algorithm used in federated learning (FL) to train machine learning models on decentralized data. It is a variation of the traditional Stochastic Gradient Descent (SGD) algorithm, adapted to the federated setting. FedSGD is a distributed version of SGD and uses the computation power of several compute nodes instead of one [26]. In FedSGD [27], the central model is distributed to the clients, and each client computes the gradients using local data. These gradients are then passed to the central server, which aggregates the gradients in proportion to the number of samples present on each client to calculate the gradient descent step.  
The key difference between FedSGD, described in Algorithm 1, and traditional SGD lies in the aggregation step. In SGD, the local updates from all devices are typically averaged to update the global model. Moreover, a fraction of devices is randomly selected to participate in each round of model updates. This selective participation helps reduce the communication overhead and computational burden.  
Since there is a need to send parameters to the main server after, each gradient calculation has a bandwidth cost; this may be a problem if the clients have limited connectivity access. This issue is tackled by federated averaging (FedAVG).
## 3 DR-integrated Framework for Optimized Design
### Predicting Reliability of the Obtained Optimal Design  
Once the DR and the optimization are exploited, the reliability of the acquired antenna's optimum design will be checked. This might be a noncompulsory step based on the ascribed design problem. Generally, when a multi-dimensional system is evaluated, it is frequently computationally exorbitant to analyze further response statistics such as reliability of the system. Hence, although ANN is employed for the function approximation during the optimization process in Step 3, in Step 4, another neural network method, called the probabilistic neural network (PNN), is utilized as a classification process is needed for the reliability assessment. As a major advantage, PNN does not require expensive computational costs because of the fast and straightforward training procedure when compared with ANN. The performance of PNN is assured by the Parzen nonparametric estimator and the Bayes decision rule, which lessen the predicted risk of misclassification [51; 52].  
The following section presents the applicability of the proposed framework by a design example of the stretchable MSP antenna.
# Performance Studies of High-Power Optical Parametric Oscillators Pumped by a Pulsed Fiber Laser
## 1 Introduction
By employing semi-external-cavity-structured OPO with pump-double-pass, 9.23 W of output power was obtained at 3.82 \(\upmu\)m with a neodymium-doped yttrium orthovanadate (Nd:YVO\({}_{4}\)) laser power of 48.2 W [21]. It is noteworthy that the setups in Refs. [17; 18; 19; 20; 21] contain isolators for avoiding pump source damage resulting from unwanted backward pump beam. Isolators with high damage threshold enlarge OPO's module and increase costs. This paper reports that a linearly polarized pulsed YDFL is used to pump a MgO:PPLN-based OPO. Based on pump-single-pass, the OPO's configuration can be simplified as much as possible to only contain the necessary components. Without the assistance of other nonlinear technologies, the compact OPO is able to generate high power. To improve its output power at \(\sim\)3.8 \(\upmu\)m, the influences, including pump beam waist diameter and location, curvature radii of output coupler and length of crystal, are investigated, respectively. Optimizing the above influences, we obtained a maximum average output power of 8.2 W when the pump power was 78.6 W with a repetition rate of 200 kHz. Under the maximum output power, the wavelength is 3768.4 nm with a full-width at half-maximum (FWHM) of \(\sim\)18.6 nm. The M\({}^{2}\) factors of the output laser along the horizontal and vertical directions are \(\sim\)1.84 and \(\sim\)1.69, respectively. At the output power of \(\sim\)6.3 W, its stability over 7 h is 1.6% root mean square (RMS). Simple structure and systematic analysis of OPO's performance provide a reference for compact OPO prototypes pumped by high-power pulse fiber lasers.
# Improved Honey Badger Algorithm and Its Application to K-Means Clustering
## 5 Simulation Experiment and Analysis
### Simulation Analysis of K-Means for Improved Honey Badger Algorithm
Its average \(F\)-score was 0.83, significantly higher than K-means' 0.73 and HBA-KM's 0.82. This enhancement reflects the effectiveness of IHBA-KM clustering. In terms of maximum \(F\)-score, IHBA-KM reached 0.91, surpassing the other two algorithms, indicating that it can achieve higher classification accuracy. In addition, its minimum \(F\)-score remained at 0.77, showing consistency and \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Iris**} & \multicolumn{3}{c}{**Wine**} & \multicolumn{3}{c}{**Glass**} \\ \cline{2-10}
**Algorithms** & **Mean** & **Best** & **Worst** & **Mean** & **Best** & **Worst** & **Mean** & **Best** & **Worst** \\ \hline K-means & 0.73 & 0.76 & 65.05 & 0.81 & 0.88 & 0.61 & 0.40 & 0.50 & 0.32 \\ HBA-KM & 0.82 & 0.86 & 77.37 & 0.95 & 0.95 & 0.94 & 0.42 & 0.51 & 0.33 \\ IHBA-KM & 0.83 & 0.91 & 0.77 & 0.95 & 0.97 & 0.94 & 0.45 & 0.54 & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: F-score comparison for 30 runs of each algorithm. stability across different data sets. This advantage means that IHBA-KM can provide a reliable performance in a diverse array of application scenarios. In high-dimensional data, feature similarity often becomes less clear, negatively impacting clustering quality. Therefore, according to the Glass data set, the average \(F\)-scores for K-means and HBA were 0.40 and 0.42, respectively, while the average \(F\)-score for IHBA was 0.45. Compared to the other two algorithms, the clustering performance of IHBA on this data set was marginally superior; however, the relatively low values indicate a potential limitation of IHBA when applied to high-dimensional data. In summary, the IHBA's advantage in accuracy and \(F\)-score is unquestionable. By incorporating the HBA, IHBA reduces the reliance of the traditional K-means on the initial cluster centers. To address the issues of the HBA, including its vulnerability to local optima and slow convergence, the IHBA introduces several improvements. These enhancements help the algorithm to avoid local optima, speed up convergence, and ultimately enhance the overall efficacy of the clustering process.
## 2 Materials and Methods
### Validation and Interpretation of SAR Shorelines and Change Rates  
SAR-SLs and rates of SL change were interpreted and validated, when possible, for the three study areas considered in this work. The validation process was designed to consider the different environmental characteristics of each study site and included comparing coastal changes estimated from SAR-SLs and those estimated from ground-truth data. In the following, we describe the validation and interpretation assessment undertaken at Bull Island, Salinas beach, and Start Bay.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 7 Experimental Results and Analysis
### Federated Learning Scenario 2: IID--Partitioned Dataset
#### 7.5.2 Robustness to Anomalous Training Data
The robustness observed in our experiments derives from this aggregation mechanism itself. By combining updates, FedAvg dilutes the influence of any single corrupted or anomalous client. Thus, when most devices provide clean data, the aggregated global model is primarily shaped by reliable updates, reducing bias introduced by corrupted local models. This process allows gradual correction of biases introduced by anomalous updates, further enhancing resilience. Over successive FL rounds, this iterative averaging helps the global model to maintain robustness without added complexity.
## 3 Results and Discussion  
### X-ray Diffraction  
Figure 1 shows the X-ray diffraction pattern collected for the ZnO nanoparticles prepared by the precipitation technique. The observed diffraction peaks located at 37, 40.05, 42, 51, 67.05, 74.97, and 79\({}^{\circ}\) were indexed to ZnO and correspond to the 100, 002, 101, 102 and 110 diffraction planes observed in the hexagonal wurtzite structure, respectively [42, 44]. The diffraction pattern confirmed the synthesized nanomaterial was ZnO. The diffraction pattern was fitted using the Le Bail fitting procedure in the Fullprof software, and the fitting results are shown in Table 2. The fitting showed the ZnO had a hexagonal space group (\(P6_{3}/mc\)) with refined lattice parameters of a = 3.245 A, b = 3.245 A, c= 5.199 A with cell angles of \(\alpha\) = \(\beta\) = 90\({}^{\circ}\), and \(\gamma\) = 120\({}^{\circ}\), which are in agreement with results reported on ZnO. In addition, the \(\chi^{2}\) value (indicator of goodness of fit) of the fitting was 3.08, which indicated a good agreement between the data and reported results on the ZnO crystal structure [42, 44]. Further analysis of the diffraction data showed that the average crystalline size of the ZnO nanosorbent was calculated using Scherrer's equation, as shown below:  
\[d=\frac{0.9\lambda}{B\overline{Cos}\frac{2\theta}{2}} \tag{1}\]  
where \(d\) is the diameter or crystallite size, 0.9 is a correction factor for the Gaussian fitting, \(\lambda\) is the Co K\({}_{\alpha}\) = 1.789 A, B is the Full-Width Half Maximum (FWHM) of the diffraction peak, \(cos2\theta/2\) is the position of the diffraction peak. The average crystallite size was determined based on the most prominent diffraction peaks: the 100 (27.21 nm), 002 (30.0 nm), and 100 (23.9 nm) diffraction planes, which averaged to 62.0 \(\pm\) 4.06 nm.  
The SEM of the synthesized ZnO nanoparticles is shown in Figure 2. The image showed that the nanoparticles were clustered and consisted of a mixture of triangular-shaped platelets and small spherical particles. The observed clustering of the particles, more than likely, was due to the lack of surfactants used in synthesis. The exclusion of surfactants aimed at generating the ZnO nanoparticles with a clean surface available for the binding of the As(III) and As(V) ions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **a** (\({}^{\circ}\)**A**) & **b** (\({}^{\circ}\)**A**) & **c** (\({}^{\circ}\)**A**) & \(\alpha^{\circ}\) & \(\beta^{\circ}\) & \(\gamma^{\circ}\) & \(\chi^{2}\) \\ \hline ZnO\({}_{\text{syn}}\) & 3.245(8) & 3.245(8) & 5.199(8) & 90.0 & 90.0 & 120.0 & 3.08 \\ ZnO\({}_{\text{lit}}\) & 3.2417 & 3.2417 & 5.1876 & 90.0 & 90.0 & 120.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Le Bail fitting results of ZnO nanoparticle sample.  
Figure 1: Powder XRD and Le Bail fitting of the synthesized ZnO nanomaterial.
# Flexural Buckling Failure of Rock Slopes: A Review of Influencing Factors and Analysis Methods
## 3 Deformation Process of Flexural Buckling Failure and Its Influencing Factors
### Factors Affecting the Deformation Process of Flexural Buckling
#### 3.2.2 Triggering Factors
(3) Human engineering activities  
Among human engineering activities, reservoir water level fluctuation is a special factor influencing the buckling stability of bank slopes of reservoirs, such as the Three Gorges, Lijiaxia on the Yellow River, Yalong River, and Chihaxia [31; 45; 55]. The direct effect of reservoir water level fluctuations on buckling stability is mainly manifested in the changes in static and dynamic water pressure, buoyancy, and seepage field within the bank slope, as well as the water-rock interaction in the drawdown zone. By analyzing the monitoring data of the Muyubao landslide, [PERSON] et al. [56] believed that its deformation is mainly affected by buoyancy and dynamic water pressure, and when the rate of reservoir water level fluctuation is small, buoyancy controls the deformation of the landslide; when the rate of reservoir water level fluctuation increases, dynamic water pressure becomes the main controlling factor of landslide deformation. Through field investigations, [PERSON] et al. [57] found that the deterioration and damage of rock mass near the front edge of dip slopes induced by long-term water-rock interaction is an important factor triggering buckling failure of reservoir bank, and they proposed an approach for evaluating the buckling stability of bank slopes considering the effect of rock mass deterioration. In addition, excavations of dip slopes in road and mining projects can cause changes in the original stress state and boundary conditions of dip slopes, triggering the buckling deformation and failure of rock layers [1; 22; 43].
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 6 Security Objectives of WSN-IoT
### Confidentiality and Privacy  
Confidentiality is otherwise known as privacy To fulfill the security requirements, it is implemented to prevent unauthorized clients from obtaining information. Confidentiality gives recognizable proof of verification and authorization for any sensitive item in the IoT network. Numerous security modules ensure the security of information. Maintaining data secrecy is a critical security requirement as it is vital to keep the framework intruder-proof. Privacy guarantees authorized users' private data and preempts intruders from accessing network services or stealing any data. Privacy has to be implemented at many levels. Privacy for devices is necessary to maintain physical and data confidentiality, as a network can be exposed to data intrusion. Privacy during data transmission within IoT devices preserves sensitive information. Privacy is crucial during the processing and storing of data, as it is most vulnerable at this point. Privacy of location is intended to prevent the disclosure of the geographical position of IoT devices from intruders.
## 2 Structure Design, Working Process, and Dynamic Model Analysis
The Karnopp friction model is often used to analyze the friction force in a 1-DOF PIDM, as shown in Figure 3. A zero velocity interval is defined from \(-a\) to \(a\); then, the Karnopp friction model [22] of a 1-DOF motion is described as follows:  
\[f=\left\{ \begin{array}{l}f_{c}\text{sgn}(v),\ |v|>a\\ \min(f_{e}(t),\ f_{s}),\ |v|\leq a,\ f_{e}(t)>0\\ \max(f_{e}(t),-f_{s}),\ |v|\leq a,\ f_{e}(t)\leq 0\end{array} \right., \tag{3}\]  
Figure 2: Working principle of 1-DOF PIDM. where \(f_{c}\) is the Coulomb friction, \(f_{s}\) is the maximum static friction, \(f_{e}(t)\) is the external force, \(v\) is the relative velocity of the friction interface, and \(a\) is a nonzero artificial parameter. By extending the Karnopp friction model in a 1-DOF PIDM and considering the friction distribution in the \(x\)-axis and \(y\)-axis of a 2-DOF PIDM, the value of the friction in the two directions satisfy the following formulations of a 2-DOF Karnopp friction model:  
\[ \begin{split}\text{while}\ \sqrt{\left(\dot{x}_{p}-\dot{x}_{s} \right)^{2}+\left(\dot{y}_{p}-\dot{y}_{s}\right)^{2}}\ >a\\ \left\{\begin{array}{l}f_{x}=f\frac{\dot{x}_{p}-\dot{x}_{s}}{ \sqrt{\left(\dot{x}_{p}-\dot{x}_{s}\right)^{2}+\left(\dot{y}_{p}-\dot{y}_{s} \right)^{2}}}\\ f_{y}=f\frac{\dot{y}_{p}-\dot{y}_{s}}{\sqrt{\left(\dot{x}_{p}-\dot{x}_{s} \right)^{2}+\left(\dot{y}_{p}-\dot{y}_{s}\right)^{2}}}\\ f=f_{c}\end{array}\right.,\end{split}  \tag{4}\]  
while \(\sqrt{\left(\dot{x}_{p}-\dot{x}_{s}\right)^{2}+\left(\dot{y}_{p}-\dot{y}_{s} \right)^{2}}\leq a\)  
\[\left\{ \begin{array}{l}f_{x}=f\frac{\ddot{x}_{p}}{\sqrt{\left(\ddot{x}_{p} \right)^{2}+\left(\dot{y}_{p}\right)^{2}}}\\ f_{y}=f\frac{\ddot{y}_{p}}{\sqrt{\left(\dot{x}_{p}\right)^{2}+\left(\dot{y}_{p} \right)^{2}}}\\ f=\left\{\begin{array}{l}m_{s}\sqrt{\left(\ddot{x}_{p}\right)^{2}+\left( \ddot{y}_{p}\right)^{2}},\ m_{s}\sqrt{\left(\ddot{x}_{p}\right)^{2}+\left( \ddot{y}_{p}\right)^{2}}\\ f_{s},\ m_{s}\sqrt{\left(\ddot{x}_{p}\right)^{2}+\left(\ddot{y}_{p}\right)^{2} }>f_{s}\end{array}\right.\end{array} \right., \tag{5}\]  
where \(f\) is the friction between the slider and stage.