esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Nickel Oxide Nanoparticles  
Nickel is a white, ferromagnetic metal that serves as a part of various alloys and for surface protection of other metals against corrosion. Due to its toxicity, the practical use of this metal is limited [145].  
Nickel oxide nanoparticles (NiO-NPs) have magnetic properties and possess excellent durability and electrochemical stability with potential use in applications such as drug delivery, cell tracking, imaging procedures, or biosensors [133]. However, it was also supposed that due to the toxic properties of nickel, NiO-NPs might cause potential adverse health effects, limiting their biomedical applications [146].  
[PERSON] et al. reported NiO-NPs as a multimodal cancer therapy agent. They designed a cumulative drug delivery system, which consisted of light-sensitive NiO-cores, bovine serum albumin, and folic acid and was loaded with doxorubicin (NOP-DOX@BSA-FA). Human cervical epithelial malignant carcinoma cell lines (HeLa) were treated with NOP-DOX@BSA-FA, showing outstanding anticancer activity [147].  
Antioxidant, antibacterial, and anticancer activities of photomolecules-coated, green synthesized NiO-NPs using _Albutilion indicium_ leaf were tested. The phytomolecules-coated NiO-NPs showed tumoricidal action against the HeLa cancer cell lines, inhibition of growth of _Escherichia coli_, Bordetella bronchiseptica, Bacillus subtilis, and _Staphylococcus aureus_, antioxidant activity, and biocompatibility with fibroblast cells [148].
## 3 Materials and Methods
### Overall IoMT-Based Framework  
Figure 1 shows the framework of our introduced IoMT-based breast cancer diagnosis approach. It is divided into three major stages, which are as follows: First, histopathological breast samples are collected from patients and tested. The cell images obtained from a microscopy imaging device are forwarded to a cloud data server to provide additional options. Histopathological image samples, for example, including annotations, are recorded in the patient's electronic health record (EHR) database. The acquired sample features are then analyzed using our custom-built and hyperparameter-optimized CNN classifier. Using cloud computing services for the classification of uploaded samples is preferred in our proposed framework to reduce the use of computational resources and file storage. Third, the results of the breast cancer detection are transferred to the therapist's computer display or smartphone, where they are verified and finalized with medical recommendations.  
The system framework, described in Figure 1, represents the classification for detection of breast cancer using IoT, using hyperparameter-optimized and trained models. Medical doctor can use computers, laptops, and mobile devices to access the platform. The MIoT  
\begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Methods (Models)** & **Dataset** & **Accuracy** \\ \hline [PERSON] [39] & AlexNet & BreakHis & 96.70\% \\ \hline \multirow{4}{*}{[PERSON] et al. [40]} & Ensemble of deep learning models & \multirow{4}{*}{Breast Ultrasound Dataset} & \multirow{4}{*}{97.09\%} \\  & (SqueezeNet, VGG-16, VGG-19) & & \\ \cline{1-1}  & with Cat Swarm Optimization and & & \\ \cline{1-1}  & Multilayer Perceptron & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Schematic architectural diagram of the proposed medical IoT-based breast cancer diagnosis framework for automatic identification of breast cancer using the proposed hyperparameter optimized CNN classifier.  
system has a web interface that allows you to choose between two options: training or prediction. The user must supply the sample exam result dataset during the training stage. After that, feature extraction is performed, resulting in a feature vector. Classifiers are trained by using the feature vectors and then optimization of hyperparameters is performed. In the previously separated test set, trained models complete a classification step. The user can choose the optimal combination from the results of the experiments. The model is accessible via a medical cloud service that may be accessed from any Internet-connected device. The cloud-based e-Health care application server can efficiently detect and classify the malignancy of breast cells as data enters, using the proposed approach, and relay the results back to the local health care institution for appropriate medical action. The user can utilize an application programming interface (API) to perform a suggested diagnosis at the prediction stage and communicate the results to the medical doctor for a final clinical decision.
## 2 Related Works
### Revenue Prediction and Economic Behavior Modeling  
Revenue prediction constitutes a core problem in economics, particularly in sales forecasting and market analysis, where accurate prediction supports the development of optimized pricing strategies [56]. In recent years, deep learning methods--especially those tailored to sequential data such as long short-term memory (LSTM) networks and transformers--have been widely adopted for sales forecasting [57]. LSTM networks, designed for sequence modeling, introduce gated mechanisms that effectively address the vanishing gradient problem observed in traditional recurrent neural networks (RNNs). The fundamental components of an LSTM unit include the input, forget, and output gates, with the update equations as follows:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{5}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{6}\]  
\[\mathcal{C}_{t}=\tanh(W_{\mathcal{C}}\cdot[h_{t-1},x_{t}]+b_{\mathcal{C}}) \tag{7}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t} \tag{8}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{9}\]  
\[h_{t}=o_{t}*\tanh(C_{t}), \tag{10}\]  
where \(f_{t}\) is the output of the forget gate, \(i_{t}\) is the input gate, \(\tilde{C}_{t}\) is the candidate cell state, \(C_{t}\) is the updated cell state, \(o_{t}\) is the output gate, and \(h_{t}\) is the hidden state. The LSTM structure enables the modeling of long-range dependencies, which play a vital role in revenue forecasting. Transformer models, built on self-attention mechanisms, offer advantages over LSTM in handling long sequences through parallel processing [58]. The core operation of the self-attention mechanism is formulated as  
\[\text{Attention}(Q,K,V)=\text{softmax}\Big{(}\frac{QK^{T}}{\sqrt{d_{k}}}\Big{)}V, \tag{11}\]  
where \(Q\), \(K\), and \(V\) represent the query, key, and value matrices, respectively, and \(d_{k}\) denotes the dimensionality of the keys. This mechanism enables the modeling of dependencies between all positions in a sequence, facilitating more expressive representations. Although LSTM and transformer models have demonstrated strong performance in revenue forecasting, several challenges persist. First, the complexity and variability of temporal data make models susceptible to noise interference [59]. Second, these models often assume a fixed sequential relationship in data; however, in practice, user behavior is influenced by seasonal effects, promotional campaigns, and other external factors. Addressing the non-stationarity of time-series data remains a key direction for future research [60].
# Effect of Ultrasound on Fermentation of Thick Molasses Worts by Distiller's Yeast
## 4 Summary and Conclusions  
This research study comprised an analysis of molasses worts inoculated with two yeast strains and exposed to ultrasound treatment of various amplitudes. The study examined whether ultrasound facilitates the fermentation process of molasses wort with a density greater than 18 \({}^{\circ}\)Blg. Another objective was to determine whether alterations in the amplitude of ultrasound would affect the fermentation process.  
Based on the examination made, the following conclusions were reached:  
1. In the case of thick wort (22 and 24 \({}^{\circ}\)Blg), a positive effect of sonification on fermentation was observed. Therefore, it seems advisable to conduct further research to determine the optimal combination of exposure time, frequency, and, above all, ultrasound amplitude for fermentation.
2. Ultrasonic wort treatment with a density greater than 18 \({}^{\circ}\)Blg improves the final ethanol yield by 5 to 20%, depending on the strain used.
3. The higher the density of the wort, the more pronounced the positive effect of ultrasound on the dynamics and yield of fermentation.
4. Ultrasound causes visible indentations in the cell walls of _Saccharomyces cerevisiae_ yeast and stimulates the cells to clump together.  
From a scientific and technological point of view, it seems advisable to expand further research on the use of ultrasound during molasses wort fermentation. It will be interesting to examine the impact of extended, ultrasonically assisted thick wort fermentation on ethanol yield, byproduct formation, cell survival, and metabolic and physiological changes caused by ultrasounds. Even a 1-2 percent improvement in ethanol production efficiency or fermentation acceleration, under industrial conditions, can be worth millions of dollars globally.  
Conceptualization, A.M.P. and A.K.; methodology, A.M.P. and U.D.-K.; software, J.D.; validation, U.D.-K. and J.D.; formal analysis, A.M.P.; investigation, A.M.P., B.J. and A.K.; resources, A.M.P.; data curation, U.D.-K.; writing--original draft preparation, A.K. and A.M.P.; writing--review and editing, A.M.P.; visualization, A.M.P.; supervision, J.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 5 Conclusions  
This study proposes an improved convolutional neural network, RS-UNet, based on UNet. By incorporating deep residual network modules to increase the depth of the UNet network while keeping fewer parameters and integrating the SE attention mechanism to enhance the disease area, UNet was used as the backend network for feature fusion. While deepening the network model to prevent degradation, it could fully utilize UNet's effective combination of low-resolution and high-resolution features, achieving good results in the segmentation and recognition of potato leaf disease spots. In addition, a combination optimization algorithm was used, and under the premise of ensuring segmentation accuracy, it effectively shortened the network training time, making the segmentation results closer to the actual disease spots. Compared with the original UNet, the improved network model demonstrated superior segmentation performance. Specifically, this method achieved 79.8% MIoU and 88.86% Dice scores. Finally, by using a combination optimization algorithm, the network's Dice coefficient was improved from 82.29% to 88.86%, resulting in a nearly 6.57% increase in recognition accuracy.  
While this method was effective in segmenting potato leaf diseases, the proposed model was not able to achieve segmentation in complex backgrounds. Future research will focus on segmenting potato leaf diseases in complex backgrounds, simplifying the model's new strategies, and calculating the severity of the disease, integrating the solution into the latest disease detection platforms. This approach aims to achieve higher segmentation accuracy and efficiency, making it more suitable for practical production applications.  
Writing--original draft preparation, J.F.; resources, Y.Z.; supervision, G.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The experimental data used to support the findings of this study are available from the authors upon request. The authors would like to show sincere thanks to those technicians who have contributed to this research. The authors declare no conflict of interest.
## 4 Experimental Results and Simulation
### Error Evaluation of Experimental Results  
Figure 11 shows the tracking error for 24 h. Figure 12 shows the error probability density for wind power tracking. As can be seen, the error probability is mostly concentrated around 0, and the error probability distribution in [0, 0.0007] is up to 60%, the maximum error range within 3%. Therefore, the wind tracking control algorithm proposed in this paper has an ideal accuracy rate. In addition, compared with other tracking methods, the algorithm has a more convenient control process and the diversity of loads can be maintained. [PERSON] et al. [23] proposed controlling the loads by adjusting the temperature settings of the air conditioning loads, whose error probability is controlled within 5%. However, when the load similarity is high, fine-tuning the temperature settings will cause the loss of load diversity, and the cluster load will fluctuate greatly. In addition, this method is a process of closed-loop control. It is necessary to adjust the control signal in real-time and constantly correct it. The information feedback channel is indispensable and the control cost is too high. However, our phased plan control method does not require real-time regulation of the load. Compared with [23], the accuracy of our algorithm is higher with the error controlled within 3%. The diversity of load states are always abundant and it makes the control process more stable. At the same time, our method is more convenient to realize. It can be used to track the output of renewable energy, which is of great significance to load control decision-making.  
Figure 11: Distribution of the error value.  
Figure 10: Twenty-four hour wind power output tracking.
## 3 Methodology
It is vital to note here that the cycle shown in Figure 5 was created for each cell chemistry based on the respective cell specifications. Moreover, separate cycles were developed for each applied C-rate (1 C, 2 C and 3 C) according to the specific capacity of each cell. The reason for the chosen range of the C-rate is that C-rates below 1 C do not generate enough heat inside the cells to achieve meaningful results in terms of measurement accuracy. Parameters such as the voltage limits of the cells were considered as the safety limits in the cycle. The Neware BTS recorded the charge energy (\(E_{\mathit{dB}}\)) and the discharge energy (\(E_{\mathit{dB}}\)). This allowed the determination of loss of energy. The current profile resulted in the cell heat-up, and this was measured by temperature sensors. With the temperature difference and the heat capacity of the setup, again the energy loss can be calculated to determine the internal resistance for the calorimeter method. After the measurement cycle, there was a third resting phase of 5400 s (90 min) again for the determination of the temperature differences. The temperature was recorded with the CTP-5000 temperature sensor with a time resolution of 1 Hz. Figure 5: Flowchart of the measurement cycle.
## 3 Settlement Calculation with Error Corrections and Resampling
### Total Settlement Calculation Overtime  
By combining the absolute GNSS rover settlement and the MDD settlement, an accurate estimation of the total settlement in the concrete slab track layers was obtained.  
\[S_{Total}(t)=S_{GNSS}(t)+S_{MDD}(t), \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Coordinate** & **Value** \\ \hline Latitude & \(35^{\circ}47^{\prime}11.5474^{\prime\prime}\) \\ Longitude & \(127^{\circ}12^{\prime}00.3094^{\prime\prime}\) \\ Height (m) & 104.090 \\ \hline \end{tabular}
\end{table}
Table 1: Reference point coordinates (WGS84) of the site.  
where \(S_{Total}\) is the total deformation of each layer, \(S_{GNSS}\) is the GNSS settlement and \(S_{MDD}\) is the computed deformation for each MDD laser position.
## 4 Experimental Results and Analysis
### Spatial Location Representation Map Construction Experiment
In terms of the absolute trajectory error shown in Figure 13b, the absolute trajectory error of the method in this paper at the end of the run is about 0.031 m, which is about 47.2% lower when compared to the competitive Hebb learning network and about 56.9% lower when compared to the RatSLAM algorithm. Additionally, in terms of the whole exploring time, the method in this paper shows a good performance in terms of the location estimation. Figure 12: The process of constructing the spatial location representation maps by the three methods. (**a**) 5 min for the RatSLAM method to obtain results. (**b**) 5 min for the competitive Hebb learning network to obtain results. (**c**) 5 min for this paper’s method to obtain results. (**d**) 30 min for the RatSLAM method to obtain results. (**e**) 30 min for the competitive Hebb learning network to obtain results. (**f**) 30 min for this paper’s method to obtain results. (**g**) The RatSLAM spatial location representation map. (**h**) The competitive Hebb Learning network spatial location representation map. (**i**) The SLRB method spatial location representation map.
## 3 Results
### Third-Order Korteweg-de Vries (KdV) Equation  
In the literature, one can find various forms of the KdV equation. Recently, [PERSON] and [PERSON] [56] presented an analysis of the existence and uniqueness of a solution to the third-order non-linear KdV equation. Here, we consider it in a typical form, very similar to the one studied in [56], given as:  
\[u_{t}-3(u^{2})_{x}+u_{xxx}=0. \tag{17}\]  
For the initial condition \(u(x,0)=-2\,\mathrm{sech}^{2}(x)\), the exact solution of (17) is \(u(x,t)=-2\,\mathrm{sech}^{2}(x-4t)\). One should mention that this problem was investigated by [PERSON] et al. [34], who solved it using the 6 th-order CD scheme with and without the 10 th-order low-pass filter. Their results were comparable to [PERSON] and [PERSON] [35] solutions obtained with the help of the LGD method. Here, we solve Equation (17) using the previously discussed CD schemes on the meshes consisting of \(K=33,49,65,\,\ldots\,,257\) nodes over the time period \(t\in[0,0.5]\) in the periodic domain \(x\in[-10,10]\). An important remark that should be made at this point is that the solution of (17) is not periodic in physical space. Its period is imaginary and equals to \(i\tau\), where \(i=\sqrt{-1}\). The consequences of the assumed periodicity are discussed later. Figure 8a shows the numerical and analytical solution at \(t=0,0.25,0.5\) on the mesh consisting of \(K=257\) points. It shows how the initial function moves along the 'x'-coordinate. Ideally, as the KdV Equation (17) does not include a dissipative term, the initial shape of the function should be preserved over the entire simulation time and only shifted to the right side. Figure 8b,c show the \(L^{2}\) norm of the errors computed based on the results obtained by applying various combinations of \(M\) and \(N\). As in the case of the LdW equation, the agreement is very good. It can be seen that for \((M+N)\geq 10\), the errors are almost equal and this means that a further increase of the approximation order would not increase the solution accuracy. One should also note that the minimum error level reached \(\mathcal{O}(10^{-7})\) is significantly higher than during the solution of the LdW equation and higher than one could predict based on the accuracy of the 3 rd derivative approximation (see Section 2.3, Figure 4). The reason for such behavior is the lack of periodicity of the analytical solution in the real space, as mentioned above. Looking at the solution behavior in Figure 8a, one can readily notice that as time passes, the peak of the function \(u(x,t)\) approaches the boundary at \(x=10\) at which neither the function itself nor their derivative is periodic. In such a case, the errors of the derivative approximations are overwhelmed by the errors stemming from the violation of the periodicity conditions. The obtained results show that they are at the level \(\mathcal{O}(10^{-7})\). This prevents both from obtaining the solution
# Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study
## 4 Experiments and Discussion
### Experimental Results and Discussion about Observable Phenomena
However, its performance significantly degraded in the densest analyzed topologies (i.e., \(d=10\)), and this algorithm was only better than the other configurations of GMH in these graphs. As further seen from the figures, BC performed poorly in many scenarios where the low precision of the stopping criterion was applied. As further seen, its performance can be generally considered average. However, for \(d=2\) (when its matrix was identical to the weight matrix \(\mathbf{W}\) of OW) and \(d=10\) (i.e., in the densest examined topologies), its performance was relatively pretty good. In both these cases, its performance was the second best among the analyzed algorithms. Overall, OW was the best-performing approach according to the presented experimental results since it outperformed all the other algorithms for each precision of the used stopping criterion except for the graphs with \(d=2\) (the performance was lower if the low precision of the stopping criterion was applied). CW was one of the worst algorithms in terms of the number of iterations required for consensus achievement. In addition, it was necessary to know the value of \(d\) to identify the optimal configuration of this algorithm, which was its other significant drawback. However, it performed relatively well in sparsely connected graphs with the low precision of the stopping criterion. GMH outperformed all its competitors in the sparsest graphs (i.e., \(d=2\)), but its performance was significantly degraded with an increase in connectivity (for \(d=10\), this was the worst algorithm among those examined). Similar to CW, knowing \(d\) was also required for the optimal configuration of this algorithm. What is most important is that all the examined algorithms except for MD and LD can achieve the consensus in each \(d\)-regular bipartite graph regardless of the precision of the applied stopping criterion. Thus, except for MD and LD, no other  
Figure 13: Convergence rate expressed as number of iterations required for consensus achievement in 10-regular bipartite graphs and for three precisions of applied stopping criterion. algorithm required the implementation of the mechanism presented in [52] or required to be reconfigured.