esa-sceva/satcom-chunk-collection huggingface dataset:
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Toxins in Cancer Therapy
#### 3.3.2 Botulinum Neurotoxin Type A  
Botulinum neurotoxin type A (BoNT-A) is produced by strains of _Clostridium botulinum_, which can improve some kinds of cancer such as prostatic hyperplasia (BPH) via apoptotic activity, reducing the cell growth and proliferation. The molecular target of this neurotoxin is synaptic vesicle glycoprotein 2 (SV2) [78; 79]. This protein has a major role in exocytosis and the secretory process in both synaptic and endocrine cells and tends to be overexpressed in cancer cells [80; 81]. Furthermore, SV2 has been increasingly used, in recent years, as a molecular marker for several types of cancer. BoNT-A penetrates into the cancer cells through binding to a high-affinity SV2 receptor, which is exposed on the cell membrane during the exocytosis [82; 83]. The BoNTA/SV2 complex is formed and influences the distribution of SV2 and the function of vesicles. This toxin could induce caspase-3 and -7 dependent apoptotic processes in breast cancer cell lines [80; 84]. The inhibitory effect of Botulinum neurotoxin type \"A\" on SV2 expression has been determined on breast cancer cell lines including T47D, MDA-MB-231, and MDA-MB-453 [69; 70].
## 2 Related Works  
### Location Based Dual Image Reversible Data Hiding Technique  
Location based dual image reversible data hiding technique was proposed by [PERSON] et al. [32]. In the method, the cover image pixel pairs are taken as a set of coordinates on the \(X\) and \(Y\) axes, respectively. This pixel pair accepts the center point of the cross pattern in Figure 1 as consecutive pixels (\(X_{i,j}\), \(X_{i,j+1}\)). The pixel pairs on the four sides (top, bottom, left, and right) of the center pixel pair represent different combinations of secret data of two bits each (\(S_{1}\) or \(S_{2}\)). The pattern in Figure 1 is expressed using Equation (1). Pixel value changes that will create the first stego image are realized by using Equation (1). \[\left(X_{i,j}^{\prime},X_{i,j+1}^{\prime}\right)= \begin{cases}\left(X_{i,j}+1,X_{ i,j+1}\right)\text{ \emph{if} }S_{1}=00\\ \left(X_{i,j},X_{i,j+1}-1\right)\text{ \emph{if} }S_{1}=01\\ \left(X_{i,j},X_{i,j+1}+1\right)\text{ \emph{if} }S_{1}=10\\ \left(X_{i,j}-1,X_{i,j+1}\right)\text{ \emph{if} }S_{1}=11\end{cases}  \tag{1}\]  
According to the \(S_{2}\) secret data, the expression in Equation (1) is applied as in Equation (2) to the second stego image. \[\left(X_{i,j^{\prime}}^{\prime\prime},X_{i,j+1}^{\prime\prime}\right)= \begin{cases} \left(X_{i,j}+1,X_{i,j+1}\right)\text{ \emph{if} }S_{2}=00\\ \left(X_{i,j},X_{i,j+1}-1\right)\text{ \emph{if} }S_{2}=01\\ \left(X_{i,j},X_{i,j+1}+1\right)\text{ \emph{if} }S_{2}=10\\ \left(X_{i,j}-1,X_{i,j+1}\right)\text{ \emph{if} }S_{2}=11\end{cases}  \tag{2}\]  
In the data hiding process, for example, \(X_{1,1}=45\), \(X_{1,2}=46\) and secret data = 0011. So, \(S_{1}\) = 00 and \(S_{2}=11\). Since \(S_{1}\) = 00 according to Equation (1), the formula \(\left(X_{i,j}^{\prime},X_{i,j+1}^{\prime}=X_{i,j}+1,X_{i,j+1}\right)\) is applied. Accordingly, the first two pixels values of the first stego image will be \(X_{1,1}^{\prime}=45+1=46\), and \(X_{1,2}^{\prime}=46\), respectively. For \(S_{2}\) = 11 according to Equation (2), the formula \(\left(X_{i,j}^{\prime\prime},X_{i,j+1}^{\prime\prime}=X_{i,j}-1,X_{i,j+1}\right)\) is applied. Accordingly, the first two pixels values of the second stego image will be \(X_{1,1}^{\prime\prime}=45-1=44\) and \(X_{1,2}^{\prime\prime}=46\), respectively. In the data extraction process, first, the equality of the pixel groups in two stego images is examined. If the two consecutive pixel pairs are equal for two stego images \(\left(X_{i,j}^{\prime}=X_{i,j}^{\prime\prime}\right.\)_and_\(X_{i,j+1}^{\prime}=X_{i,j+1}^{\prime\prime}\)), it means that the secret data is not embedded there. To recover secret data, the difference between the pixel pairs values of two stego images is examined. It is observed that these differences are between the values of \([-2,2]\). In this way, the data extraction process is performed by applying twelve different combinations. These combination states and the secret data that can be obtained are presented in Table 1.
## 4 Results
### Method Comparison
#### 4.4.2 Robustness of Proposed Model  
To highlight the robustness of their proposed system against model underfitting and overfitting, the experimental results are summarized as following.  
Imbalanced Data Handling  
In the experiment, the SMOTE associated with IR metric can detect data imbalance situation by applying Equations (1) and (2). In our case, underfitting occurs when a model does not fit the input data samples enough for six minority classes which tend to decrease classification accuracy. From Table 10, the SMOTE increased over 3.34% and 4.27% of the average classification accuracy of the models on training and testing data set that are consistent to reports in [19].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **76 Features** & **36 Features** \\  & **(Training/Testing)** & **(Training/Testing)** \\ \hline RF & 96.87\%/88.16\% & 96.53\%/89.38\% \\ SVC & 94.28\%/84.38\% & 92.46\%/81.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Multiclass classification accuracy (CSE-CIC-IDS 2018).  
Removing Extra Features to Reduce the Risk of Overfitting  
From Tables 8 and 9, we observed that users can select fewer features (\(N_{f}=23\)) and retain the close accuracy with \(N_{f}=42\) in our experiment that made the model more flexible and reduced the risk of overfitting. In this experiment, the model overfitting is prevented from inputting additional variables to avoid learning the noise in the training data, thus causing it to decrease the computational costs and negatively impacts on the performance of the model on new data inputs.
## 3 Results and Discussion
### Effect of Carbonization Pressure on CO\({}_{2}\) Sequestration and Rheological Properties of CGBS
#### 3.1.2 Effect of Carbonization Pressure on Rheological Parameters of CGBS
The hysteresis loop area is shown in Figure 9. When the carbonization pressure increases from 0.1 MPa to 1.1 MPa, the hysteresis loop area increases from 895 Pa/s to 1730 Pa/s. This is because the carbonates and hydration products produced during the carbon fixation process make the slurry system complex and the thixotropic characteristics of the slurry increase [32]. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Rheological** & \multirow{2}{*}{**Fitting Results**} & \multirow{2}{*}{**\(\tau_{0}\)/Pa**} & \multirow{2}{*}{\(\eta\)/Pa\(\cdot\)s} & \multirow{2}{*}{**n**} & \multirow{2}{*}{**R\({}^{2}\)**} \\  & **Model** & & & & & \\ \hline CGBS1 & & \(\tau=127.16+0.0399\gamma^{1.32}\) & 127.16 & 0.0399 & 1.32 & 0.9995 \\ CGBS2 & & \(\tau=138.95=0.0596\gamma^{1.26}\) & 138.95 & 0.0596 & 1.26 & 0.9990 \\ CGBS3 & & \(\tau=146.88=0.0685\gamma^{1.23}\) & 146.88 & 0.0685 & 1.23 & 0.9992 \\ CGBS4 & & \(\tau=171.66=0.0998\gamma^{1.54}\) & 171.66 & 0.0998 & 1.54 & 0.9994 \\ CGBS5 & & \(\tau=192.24=0.1028\gamma^{1.70}\) & 192.24 & 0.1028 & 1.70 & 0.9995 \\ CGBS6 & & \(\tau=212.49=0.1145\gamma^{1.54}\) & 212.49 & 0.1145 & 1.54 & 0.9996 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fitting rheological models of CGBS at different carbonization pressures. Figure 7: Yield stress and plastic viscosity of CGBS at different pressures.
## 4 Experimental Results  
### Datasets  
The results of the experiments in this section sought to assess the proposed OFU-Ontology algorithm's efficiency and effectiveness, compared with the baseline EG-FedUnlearn algorithm. The experiments were carried out on very diversified datasets representative of different data types, including image classification, tabular data, transactional data, health records, network intrusions, and social media comments. In this way, the datasets act as a fuller evaluation framework for the unlearning algorithms across several domains.  
The detailed descriptions above in Table 1 highlight how each dataset contributes to evaluating the efficiency and effectiveness of EG-FedUnlearn and OFU-Ontology. For instance, the MNIST dataset contains simple image data for the baseline evaluation of unlearning efficiency, whereas as shown in Figure 8, the CIFAR-10 dataset is a more complex image classification to assess how ontology-driven relevance impacts unlearning and the UCI Adult census dataset is tabular data with mixed feature types to evaluate  
Figure 7: Detailed workflow of the ontology-enhanced federated unlearning (OFU) process, showing relevance scoring and weighted gradient negation.  
the effectiveness of ontology in preserving key features during unlearning. In contrast, the Purchase dataset contains transactional data used to test how well less impactful transactions can be unlearned while retaining critical information. MIMIC-III is complex healthcare data designed to assess the prioritization of sensitive clinical information during unlearning. The KDD Cup 1999 dataset involves network security data and is used to evaluate unlearning efficiency in real-time intrusion detection. The Reddit Comment dataset consists of text data for evaluating unlearning in content moderation and privacy-driven removal requests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Dataset Size**} & \multirow{2}{*}{**Use Case**} & \multirow{2}{*}{**Relevance for Unlearning**} \\  & & & & & & Used to test & \\  & & & & & image & dataset makes it suitable \\  & & & & & classification & for testing the efficiency of \\  & & & & & & both EG-FedUnlearn and \\ MNIST [17] & Image & \multirow{2}{*}{70,000} & \(28\times 28\) grayscale & \multirow{2}{*}{\(-50\) MB} & \multirow{2}{*}{\(-50\) MB} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\ MNIST [17] & Image & \multirow{2}{*}{70,000} & \(28\times 28\) grayscale & \multirow{2}{*}{\(-50\) MB} & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ MNIST [17] & Classification & & & & & \\  & & & features) & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular}
\end{table}
Table 1: Overview of the datasets used for evaluating EG-FedUnlearn and OFU-Ontology.  
Figure 8: Samples of the CIFAR-10 dataset.
## 4 Results and Discussion  
Following the solution procedure shown in Figure 5, the present solution was implemented in the numerical software MATLAB 2018b. Firstly, the present solution was compared with the previous solutions both in reconstituted and structured soils [22,36]. Subsequently, parametric analyses were performed to investigate the cavity expansion responses for the soils with different structure degrees \(R_{s}\) and isotropic over-consolidation ratios \(R\). Finally, the influences of the structural parameter on the cavity expansion responses were analyzed.  
The present solution was performed in Boston Blue Clay because it has been widely used in the analyses of cavity expansion [22, 23, 24]. The basic Cam Clay parameters (\(M\), \(\lambda\), \(\kappa\), \(v\)) and initial state parameters (\(R\), \(\sigma_{h}\), \(\sigma_{v}\), \(\epsilon_{0}\), \(\epsilon_{0}\), and \(u_{0}\)) of Boston Blue Clay are listed in Tables 1 and 2, respectively. Because of the absence of experimental data in the literature, the values of the structural parameters (\(b\), \(c\), \(\omega\), \(\gamma\)) were estimated in a reasonable range. In addition, three different initial structural ratios, \(R_{s}\) = 1.0, 1.2, and 1.5, were adopted, where the special case of \(R_{s}\) = 1.0, was chosen to highlight the difference between structured and reconstituted soils.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{5}{c}{**Basic Cam Clay Parameters**} & \multicolumn{5}{c}{**Structural Parameters**} \\ \hline \(M\) & \(\lambda\) & \(\kappa\) & \(v\) & \(b\) & \(c\) & \(\omega\) & \(\gamma\) \\ \hline
1.2 & 0.15 & 0.03 & 0.278 & 1 & 0 & 1 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil property parameters used in calculation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(R\) & \(\sigma_{h}\) (kPa) & \(\sigma_{v}\) (kPa) & \(\epsilon_{0}\) & \(G_{0}\) (kPa) & \(u_{0}\) (kPa) \\ \hline
1 & 100 & 160 & 1.09 & 4348 & 100 \\ \hline
1.2 & 100 & 160 & 1.06 & 4302 & 100 \\ \hline
3 & 120 & 120 & 0.97 & 4113 & 100 \\ \hline
10 & 144 & 72 & 0.80 & 3756 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soil state parameters used in calculation.
# Prediction of Transient Temperature Distributions for Laser Welding of Dissimilar Metals
## 4 Results
### Isothermal Contours  
Any time within the welding process, the welds develop at different temperatures at different positions. Isothermal contours, as shown in Figure 6, provide a view at a certain time instant in which regions or areas occupying the same temperature are within the range of 400-3000 K.  
Figure 6 basically shows one of the three-dimensional representations of the transient temperature field. The isothermal elongated ellipsoid shapes form in the direction of laser source movement. The laser source is moving and focused, which results in elongated shape of the isothermal representation nearer the source. To reiterate, the transient isothermal contours show the heating and cooling phenomena and regions with metallurgical similarity that be predicted from the analysis of the contours.
# Research on Influence of Switching Angle on the Vibration of Switched Reluctance Motor
## 4 Experimental Verification and Analysis  
This experiment is tested on a 1.5 KW, 12-8 switched reluctance motor test bench. The control system scheme is shown in Figure 8. The load is adjusted by adjusting the loading current of the eddy current brake.  
Figure 9 shows the variation curve of the 12 th order current harmonic component and the second-order vibration amplitude with the turn-on angle when the turn-off angle is constant, at 120\({}^{\circ}\). Figure 10 shows the variation curve of the 12 th order current harmonic component and the second order vibration amplitude with the turn-off angle when the turn-on angle is unchanged at 24\({}^{\circ}\). Figure 11 shows the variation curve of the second-order vibration amplitude under the condition that the opening angle is in the range of 15\({}^{\circ}\) to 45\({}^{\circ}\) and the closing angle is in the range of 120\({}^{\circ}\) to 150\({}^{\circ}\).  
It can be seen from Figure 9 that the current harmonic components and vibrations change periodically with the change of the turn-on angle. When the turn-on angle is 21\({}^{\circ}\) and 24\({}^{\circ}\), the 12 th-order current harmonic components and vibration amplitudes obtain the minimum value, respectively. As can be seen from Figure 10, with the change of the turn-off angle, the harmonic components and vibrations of the 12 th-order current show  
\begin{table}
\begin{tabular}{c c} \hline
**Harmonic Order, \(k=3\ m\)** & **Speed** \\ \hline \(m=1\), 3 rd & 2034 r/min \\ \(m=2\), 6 th & 1017 r/min \\ \(m=3\), 9 th & 915 r/min \\ \(m=4\), 12 th & 763 r/min \\ \(m=5\), 15 th & 610 r/min \\ \(m=6\), 18 th & 508 r/min \\ \hline \end{tabular}
\end{table}
Table 1: Speed of SRM when the frequency of current harmonics coincide with the 2 nd order natural.  
Through Figures 9-11, the conclusions of previous sections can be verified. For the switched reluctance motor regulated by the strategy of the switching angle control method, when the turn-off angle remains unchanged, and with the increase of the turn-on angle, the current harmonic components increase first and then decrease. When the turn-on angle remains unchanged, with the increase of the turn-off angle, the current harmonic components show a periodic oscillation trend, and the oscillation period is related to the current harmonic order. For a specific speed, the current harmonic order that plays a leading role in the second-order vibration can be deduced according to Formula (25). By analyzing this harmonic order, the variation law of vibration with the switching angle can be obtained.
## Maps for Upscaling Studies
### Further Studies  
Other regions where satellite data classifications have been used for upscaling purposes are Zackenberg on Greenland, Svalbard and Kytalyk in Russia. [PERSON] et al. [53] used the classes dry tundra, moist tundra, fen and wet grassland derived from Landsat TM normalized difference water index composites to estimate fluxes in the Zackenberg valley, Greenland, in 2015. A more detailed, earlier classification by [PERSON] et al. [54], which was made for analyses of soil properties from airborne data, was used as the training dataset. This map also served as input for an estimate of soil organic carbon [55]. A similar upscaling method, but using GeoEye satellite images, has been applied over Kytalyk using a maximum-likelihood approach for the retrieval of land cover classes [56]. A vegetation map from Landsat TM/ETM+ has also been developed for Svalbard [57]. It was used for the selection of regions suitable for phytomass retrieval from NDVI over the Nordenskjold peninsula [58]. A further subarctic study in northern Finland utilized high spatial resolution IKONOS data [59]. The classes mite, deciduous forest, evergreen forest, tundra and bare rock and lakes were considered to characterize an eddy-flux tower footprint. The up-scaling is limited to the extent of the satellite image in that case.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Parameters for Measuring the Performance and Quality of the Data Transmission Network
#### 2.2.4 Received Signal Strength Indicator (RSSI)  
The RSSI is a radio frequency power indicator that reaches the input of the receiving antenna through a channel. It is considered an important measurement for maintaining a good quality of the data sent from the UAVs to the ground station. In this situation, the aircraft is exposed to some physical effects, such as large-scale and small-scale fading that can decrease the signal quality when reaching the receivers on the ground. Therefore, if the power is less than the receiver threshold, the data received will be more difficult.  
To ensure that the power at the receiver is higher than the minimum allowable value and the throughput rate as defined by the device datasheet, it is essential to calculate the maximum distance that the UAVs must maintain from each other, also considering the received power of the radiofrequency signal. Based on Equation (4), it is possible to determine the signal strength as a function of the distance between the UAV and/or ground base station. This equation can be properly modified by adding a random factor \(X_{\sigma}\) associated with the environmental obstacles to model the normal log shading since such effect affects the average signal strength on a large scale [43].  
\[P_{Rx}=P_{Tx}+G_{Tx}+G_{Rx}+20 log_{10}\left(\frac{\lambda}{2\pi d}\right)+X_{ \sigma} \tag{4}\]  
where \(P_{Tx}\) is the device transmission power; \(G_{Tx}\) and \(G_{Rx}\) are the gains of the transmitter and receiver antennas, respectively; \(d\) is the distance between transmitter and receiver; and \(\lambda\) is the wavelength [26].
## 2 Theory of Underwater Electric Field Communication  
### Analysis of Electric Field Sensing Mechanism of Weak Electric Fish  
Different fish use various communication and perception methods, including vision, hearing, chemistry, and the lateral line sensory system. Some fish can generate weak electrical signals using their own organs and perceive these signals, allowing them to sense their environment and engage in information exchange. The distribution of these organs is indicated by the red area in Figure 0(a). These weakly electric fish create an electric field by repeatedly emitting electric organ discharges (EOD) through their electric organs (EO), which polarize surrounding objects. Polarized objects alter the distribution of the electric field, and weakly electric fish detect changes in the electric field to perceive their surroundings [17; 18].  
The EOD waveforms generated by weakly electric fish consist of pulse signals lasting from 100 \(\upmu\)s to 20 ms, as illustrated in Figure 0(b), and their propagation underwater is nearly instantaneous with minimal delay. The electric organ of weakly electric fish is typically located in the tail, while electric signal receptors are found in the head and trunk, as depicted in Figure 0(c). These electric receptors guide the current into sensory epithelial cells, and the electric signal subsequently stimulates the central nervous system to produce appropriate responses.
# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 4 Discussion  
### Cultivated Land Changes Greatly Affect Landscape Patterns  
Agricultural activities have greatly affected local land-use patterns. In the West Songnen Plain, the percentage of cultivated land contributed to about half (53.66% in 2015) of the whole area. Agricultural land expansion could be associated with depletion of other important natural resources such as wetland, grassland and forestland, leading to the change of land-use and landscape patterns. Previous studies also indicated that natural landscape such as grassland and marshland all indicated a downward trend in the study area [17; 18; 40; 41]. [PERSON] et al. found that approximately 64% of grassland in 1954 was removed by 2000 and pointed out that agricultural activity was one of the main drivers of grassland loss [17]. Our trajectory analysis also captured the conversion from grassland to dry farmland (e.g., \"Grassland-Dry farmland-Dry farmland-Dry farmland-Dry farmland\" and \"Grassland-Grassland-Dry farmland-Dry farmland\") and paddy field (e.g., \"Grassland-Paddy-Paddy-Paddy\" and \"Grassland-Grassland-Paddy-Paddy\"). Marshall in the West Songnen Plain showed shrinkage and fragmentation characteristic since the mid-1950s, which had a relationship with climate change, population growth, economic development and government policies [40]. This study captured the conversion from marshland to cultivated land such as \"Marsh-Paddy-Paddy-Paddy\", indicating that agricultural activity promoted marshland loss to some extent in the study area. Previous studies in other study area also clarified that agricultural cultivation was one of the dominant reasons for marshland loss [40; 41].  
It should be noticed that cultivated land abandonment also accounted for a certain proportion with the acceleration of urban expansion. Labor force attracted from agricultural activities to other economic activities is one reason for cultivated land abandonment. Therefore, historical land-use change trajectories could be used for future policy decision to promote the sustainability of land-use. Facing the intensification of land degradation globally, it is necessary to optimize future land-use structure to achieve green development.