esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning to Assess the Quality of Food Products--Case Study: Coffee Bean
## 2 Materials and Methods
### Building Neural Model by Python  
This step required the design of a CNN model. A diagram of the model development is shown in Figure 2. A CNN was prepared in the Python environment, consisting of input layers, such as convolution layers (Conv2D), max pooling layers (MaxPooling) and a global averaging layer (GlobalAveragePolling2D), and an output layer (Dense) with 3 neurons, representing the selected class of coffee beans. The Conv2D layers, as the base layers in the CNN, determined the patterns using filters to move the window (convolution window) by a defined step (stride) across the coffee bean image. The MaxPooling layers significantly extracted the most important representative features from the image, affecting the change in the dimension of the objects as well as the computational complexity. The GlobalAveragePolling2D layer was used to reduce the dimensionality of the data contained in the image while still maintaining relevant information. A digital image in the RGB color space model was reduced by calculating the average of all pixel values on a given channel. The reduction in the image information on each channel of the RGB color model, compared to fully connected, prevented, among other things, the overfitting of the model (overfitting) and so-called gradient fading, resulting in further deepening of the CNN for better performance. Dense layer (Dense) usually used for multiclassification, i.e., for a dataset such as coffee beans, where there are a minimum of 3 categorical classes, in this case, overdeveloped, underdeveloped and standard. The output neuron of the output layer is responsible for each class representing the mentioned type of coffee. In the classification issue, the Dense layer transforms the information compacted in the image into a set to predict their membership in certain classes.  
Figure 2: A procedure scheme for preparing convolutional neural networks.
## 4 Cooperative Resource Management Algorithm  
### Optimization Problem  
The aim of our algorithm is to minimize the total time consumption \(t\) gained by all MTs in \(\mathcal{M}\), while ensuring all constraints are not violated. Thus, the corresponding optimization problem can be formulated as  
\[\underset{\mathbf{\alpha},\beta\
eq\sigma,\pi}{\text{minimize}}\ t, \tag{16}\] \[\text{subject to}\ \alpha_{i,j}\in[0,1],\quad\forall i\in \mathcal{M},\forall j\in\mathcal{R},\] (17) \[\beta_{i,j,k}\in[0,1],\quad\forall i\in\mathcal{M},\forall j\in \mathcal{R},\forall k\in\mathcal{K},\] (18) \[\alpha_{i,j}+\sum_{k\in\mathcal{K}}\beta_{i,j,k}\in[0,1],\quad \forall i\in\mathcal{M},\forall j\in\mathcal{R},\] (19) \[\sigma_{j}\in[0,1],\quad\forall j\in\mathcal{R},\] (20) \[\pi_{j,k}\in[0,1],\quad\forall j\in\mathcal{R},\forall k\in \mathcal{K},\] (21) \[\eta-\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}\big{(}(1-\alpha _{i,j}-\sum_{k\in\mathcal{K}}\beta_{i,j,k})p_{i,j}\epsilon_{i}c_{j}\big{)}>0,\] (22) \[\mu-\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}(\alpha_{i,j}p_{i,j}\epsilon_{i}c_{j})>0,\] (23) \[\mu_{k}-\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}(\beta_{i,j,k }p_{i,j}\epsilon_{i}c_{j})>0,\quad\forall k\in\mathcal{K},\] (24) \[\sum_{j\in\mathcal{R}}(c_{j}\sigma_{j})\leqslant s,\] (25) \[\sum_{j\in\mathcal{R}}(c_{j}\pi_{j,k})\leqslant s_{k},\quad\forall k \in\mathcal{K}, \tag{26}\]  
where constraint (17) is the value range of each \(\alpha_{i,j}\), constraint (18) is the value range of each \(\beta_{i,j,k}\), constraint (20) is the value range of each \(\sigma_{j}\), and constraint (21) is the value range of each \(\pi_{j,k}\). As aforementioned, Constraint (19) is the value range of the total probability that \(r_{j}\) is offloaded from C. Constraint (22)-(24) is hard constraint of the offloading queuing systems of C, MEC-BS, and \(n_{k}\), respectively. Constraint (25) and (26) is hard constraint of the caching queuing systems of MEC-BS and \(n_{k}\), respectively.  
We define the total time consumption without computation offloading and data caching as \(\tilde{I}\), and we expand \(\tilde{I}\) as  
\[\tilde{I} =\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}t_{i,j}^{\mathrm{C}}| \alpha_{i,j}=0,\beta_{i,j,k}=0,\sigma_{j}=0,\pi_{j,k}=0\] \[=\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}\Big{(}\frac{p_{i,j} \varepsilon_{i}c_{i,j}}{\eta-\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}(p_{ i,j}\varepsilon_{i}c_{j})}\Big{)} \tag{27}\]  
We can come to this conclusion that the sufficient condition of \(\tilde{I}\) is that \(\eta-\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}\)\((p_{i,j}\varepsilon_{i}c_{j})>0\) must hold. By comparing the condition with constraint (22), we have  
\[\eta-\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}\big{(}(1-\alpha _{i,j}-\sum_{k\in\mathcal{K}}\beta_{i,j,k})p_{i,j}e_{i}c_{j}\big{)}\geqslant\] \[\eta-\sum_{i\in\mathcal{M}}\sum_{j\in\mathcal{R}}(p_{i,j}e_{i}c_{ j})>0. \tag{28}\]  
Thus, constraint (22) always holds.
# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
## 4 Conclusions  
Originating from the pine wood nematode, pine wilt disease poses a significant threat, not only causing the demise of pine trees but also having profound impacts on the overall forest ecosystem. The accurate identification of infected trees stands as a vital first step in formulating strategies to prevent and control this disease. Nevertheless, existing identification techniques face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient management measures.  
In this study, we harness the power of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to identify diseased pine trees. This cutting-edge method skillfully captures the spatial and characteristic complexities of infected trees, converting them into high-dimensional features through multiple convolutions of the VGG network. This approach notably decreases the parameter count while expanding the sensing range. Our validation set results are remarkably encouraging, achieving a Mean Intersection over Union (_MloU_) of 81.62%, a Mean Pixel Accuracy (_MPA_) of 85.13%, an overall _Accuracy_ of 99.13%, and an _F1 Score_ of 88.50%. This method surpasses the other two approaches, highlighting its efficacy. The research technique introduced here facilitates swift and accurate monitoring of nematode-infected pine trees, offering essential technical backing for the prevention and control of pine wilt disease.  
Conceptualization, M.G. ([PERSON]) and J.N.; methodology, [PERSON] and J.N.; software, J.S., J.N., X.J. and M.G. ([PERSON]); validation, J.S., Q.X. and M.G. ([PERSON]); formal analysis, X.J., J.S., J.N. and M.G. ([PERSON]); investigation, J.S., Q.X. and M.G. ([PERSON]); resources, X.J., M.G. ([PERSON]) and J.N.; data curation, X.J., M.G. ([PERSON]) and J.N.; writing--original draft preparation, J.S., Q.X. and M.G. ([PERSON]); writing--review and editing, [PERSON], Q.X. and M.G. ([PERSON]); visualization, J.S., Q.X. and M.G. ([PERSON]); supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Program of Shandong Natural Science Foundation, grant number ZR2020 KF031.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request. The data are not publicly available due to privacy.  
The authors are grateful to [PERSON] and [PERSON] in collecting the datasets, and [PERSON] in revising the manuscript.  
The authors declare no conflicts of interest.
# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 7 Finding 5: Principles of Responsible AI
### Human Agency and Oversight  
Human agency and oversight focus on the importance of maintaining human autonomy and control over AI systems throughout their lifecycle [83]. This principle ensures that AI systems augment human capabilities rather than replace them entirely [71; 75; 76], while protecting human decision-making power and agency in AI-driven environments. Different levels of human oversight can be implemented to maintain control over AI systems [70]. Human in the Loop (HITL) requires direct human intervention in every decision cycle of the AI system. Human on the Loop (HOTL) enables human intervention during AI system design and monitoring phases. Human in Command (HIC) provides oversight of the AI system's activity and decision making about when and how to use the AI system [70]. These mechanisms ensure that humans maintain meaningful control over their digital experiences and make informed choices [71]. Human oversight becomes particularly decisive in scenarios where AI decisions have significant societal impact, such as healthcare, criminal justice, and autonomous systems [80]. In these contexts, human judgment is essential for addressing complex ethical considerations and contextual understanding that AI systems may lack [80]. The integration of human oversight helps prevent unchecked automation and ensures alignment with ethical and societal norms. The implementation of this principle faces several challenges. AI technologies can potentially influence human autonomy through recommendation systems and personalized marketing [71]. Therefore, striking a balance between automation and human agency becomes significant in the design and deployment of AI systems [71]. Organizations must establish clear protocols for human intervention, especially when AI systems encounter ambiguous situations or scenarios outside their trained domains [80]. To protect human agency effectively, policymakers and technologists must consider the ethical implications of AI-driven persuasion and implement appropriate safeguards [71]. This includes developing skills in AI oversight, promoting accountability, and ensuring transparency in human AI interactions [77]. By prioritizing human agency and oversight, responsible AI frameworks ensure that AI technologies remain under meaningful human control while serving human values and societal goals [83].
# 3D U-Net for Skull Stripping in Brain MRI
## 3 Experimental Results and Discussion  
The implementation of some popular and prevalent skull-stripping methods is publicly available. We considered two classical non-deep learning methods, i.e., BSE [31] and ROBEX [9], and one deep learning based method, i.e., [PERSON]'s method [10], for comparison with the presented algorithm. BSE uses anisotropic diffusion filtering, edge detection, and a series of morphological operation to recognize the brain. BSE is extremely fast and generates highly explicit whole brain segmentation. BSE in the BrainSuite16a1 [52] package have been chosen with the default parameters (diffusion iteration = 3, diffusion constant = 25, edge constant = 0.64, erosion Size 1) for skull stripping. ROBEX is a learning-based brain extraction system in which a discriminative (random forest) and a generative model (point distribution) are combined to achieve skull stripping. The contour of the brain is further refined by graph cuts. It shows improved performance without any parameter tuning. ROBEX1.2 package available at [53] was used for this comparison. The [PERSON]'s method is to use a specifically designed deep learning network for skull stripping [10]. Note that we also considered BET [32] for comparison but the results were worst on this dataset. Thus, we have not included it in this comparison.
## 3 Results
### Analysis of Drought Characteristics of Stable Land Uses in the Shendong Mining Area
#### 3.3.2 Stable Region Ecological Drought Characteristics  
Similar to meteorological drought characteristics, ecological drought characteristics are mainly studied in three categories: drought frequency, duration, and intensity. From the drought frequency graph on the left in Figure 10, we can find that the ecological drought frequency is generally low, with the median of each type being between 0.3 and 0.4, with no significant differences. On the whole, most of them are concentrated in the range of 0-0.5, and only a few cases are higher than 0.5, which means that only a few regions have a high frequency of ecological drought. The ordinate of the drought duration chart in the middle ranges from 0 to 40, indicating that an average ecological drought can occur for up to 40 months. The drought duration of grassland with different coverages showed a certain regularity: that is, the higher the coverage, the longer the drought duration. The average median duration of drought in the medium-to-high- and high-coverage grasslands was 18 months, and the median duration of drought in cropland was about 17.5 months, which was similar to that of high-coverage grasslands. The relatively long duration of ecological drought is related to the cumulative effect of drought and the resilience of vegetation. Contrary to the duration of drought, the drought intensity of grasslands with different coverages tended to decrease with the increase in coverage. Therefore, ecological drought duration and intensity are negatively correlated. The median ecological drought intensity of low-coverage grasslands was relatively high, reaching approximately 0.12. The ecological drought intensity of croplands was about 0.06, which is consistent with that of grassland with higher vegetation coverage. Consequently, we can infer that the attributes of cropland are roughly analogous to those of grasslands with increased vegetation density during instances of ecological drought. Generally, with the exception of croplands, increased grassland coverage correlates with a reduced frequency of ecological drought, an extended duration of ecological drought, and diminished ecological drought intensity.  
Figure 9: Characteristics of meteorological drought in stable land use types of the Shendong mining area.
## 3 Results and Discussion
### Adsorption Isotherms of Biochar Samples
In this sense, we could conduct a comparison with the removal capacity of the biochar from _Undaria primattida_ generated by pyrolysis at 500 \({}^{\circ}\)C, which registered an adsorption capacity of 125.9 mg g\({}^{-1}\) of Cu\({}^{2+}\)[49]; from _Asophyllum notosum_, with a capacity of 223 mg g\({}^{-1}\)[50]; and to the steam-activated biochar from _Porphyna tenera_, with an adsorption capacity of 75.1 mg g\({}^{-1}\) of Cu\({}^{2+}\)[9]. These results could also be compared with other removal results obtained [51], where biochar was generated from bovine bone, which presented a high ash content (83.8-87.8%) and was determined to have a Cu removal capacity of 72.53-83.71 mg g\({}^{-1}\). In these cases, the pH was maintained between 4.5 and 5.5, which is contrary to our study where the pH values were variable during the adsorption experiments (i.e., <6.5); thus, the formation of other copper species in solution (such as Cu(OH)\({}^{+}\) or Cu(OH)\({}_{2}\)) could co-precipitate on the surface of BCs [18]. Notably, when the pH of the BCs is lower or higher than the pH\({}_{\text{PZC}}\), the solid can have a surface with a positive or negative pressure. Figure 3: Linear regression of measured adsorption isotherms using (**A**) Langmuir, (**B**) Freundlich, (**C**) Dubinin–Radushkevich, and (**D**) Temkin linearized models for the studied biochar samples (BC300, BC450, and BC600). negative charge, respectively (electrostatic attraction effect) [22]. Since the pH and pH\({}_{\text{P}\text{ZC}}\) obtained for the BCs in this work are similar, but are different from the pH at which the Cu\({}^{2+}\) adsorption was measured, the cation exchange mechanism would be involved in copper removal [52].
# The Influence of Arabinoxylan on the Properties of Sourdough Wheat Bread
## 4 Conclusions  
The addition of AXs increased the water absorption of the wheat flour and dough yield proportionally to the amount and molar mass of the AXs. The addition of 2% of the AX_CR preparation, which contains AXs with the highest molar mass, caused the highest (by 9.8%) increase in the water absorption of the wheat flour dough. The addition of 2% of AX_NM increased the water absorption by 8.1%, while the addition of 2% of AX_HYD increased the water absorption of the wheat flour dough by 5%.  
The partially hydrolyzed AXs in the AX_HYD preparation, with the lowest molar mass of 190,440 g/mol, was the most effective in increasing (by about 20%) the volume of the bread, especially the addition of 2% compared to the bread without added AXs. The addition of all the AX preparations had the effect of increasing the bread volume per 100 g of flour compared to the bread without AXs.  
The addition of the partially hydrolyzed arabinoxylans (AX_HYD), especially at 2% most effectively reduced (by 41%) the hardness of the bread crumb on the day of baking compared to the bread without AXs. The reduction in the hardness of the bread crumb was observed as a consequence of the application of all the AX preparations, except for the addition of 1% of unmodified AXs.  
On the day of baking, the AX_CR preparation at the amount of 2% had the strongest effect on increasing the moisture content of the crumb in the central (by 2.6%) and peripheral (by 5.1%) parts of the bread compared to the bread without AXs. Also, the other AX_NM and AX_HYD preparations at the amounts of 1% and 2% had an effect on increasing the moisture content of the bread crumb on the day of baking compared to the bread without AX addition.  
On the first day of storage, the addition of the unmodified and cross-linked AX resulted in the inhibition of crumb moisture loss in the central part of the sourdough bread compared to the bread from the day of baking.  
In general, the addition of all the preparations resulted in a higher moisture content of the crumb in the central and peripheral parts of the bread on the first and third days of storage compared to the bread without AXs.  
Conceptualization, K.B. and A.B.; methodology, K.B. and A.B.; software, K.B.; validation, K.B.; formal analysis, A.B.; investigation, A.B.; resources, A.B.; data curation, A.B.; writing--original draft preparation, A.B.; writing--review and editing, A.B. and K.B.; visualization, A.B.; supervision, K.B.; project administration, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 5 Simulation Result and Discussion  
### Simulation Setup  
In this subsection, we will describe the simulation stages, namely: (1) dataset preparation, (2) training with validation stage 1, (3) fusion deployment and validation stage 2, and (4) testing, as shown in Figure 12.  
In the first stage, the dataset was prepared. Then, in the second stage, each CNN model was trained using the transfer learning concept and validated. In the third stage, decision fusion was performed, and then the model was validated. Validation stage 2 aims to obtain the best CNN model fusion compared with the single-model performance. The last stage is the testing stage to measure the model's performance. The model's performance is evaluated using the metric of accuracy, sensitivity, specificity, and F-measure. A detailed description is provided as follows.
## 6 Result and Discussion
### Plastic Strain Energy (PSE)  
Figure 18 presents the increase in estimated plastic strain energy (PSE) for the five selected BRBs under the successive analysis of multi-hazard scenarios. Since the PSE of BRB is associated with the \(R\) factor as \(PSE\) = 150 \(R\)[48], for a certain (\(R\) = 1, 2, 3, and 4), the PSE is marked to illustrate the BRB's progress damage. It is observed that the fatigue life of BRB#1, in terms of plastic strain energy, is about 25 years and 80 years for \(R\) = 1 and 4, respectively. Meanwhile, the estimated plastic strain for the BRB#5 (\(CDI\) = 0.005, \(R\) = 1) is below 150 after a service period of 100 years. In contrast to the CDI, the PSE reveals that even the BRBs with an average strain amplitude of less than \(>\)0.03% are prone to progressive damage under multi-hazard scenarios.
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 2 Reactive Power Optimization Based on Load-Curve Segmentation
### Optimization in an Online Mode  
At each time stamp, hourly forecasts are made for the next 24 h. Due to daily periodic characteristics of load curves, forecasts are made using a multiplicative seasonal auto-regressive integrated moving average model (ARIMA) [19; 20]. With consideration of daily periodicity as well as short-term disturbance, ARIMA (0,1,1) with seasonal moving average part MA (24) are applied, i.e., ARIMA (0,1,1) \(\times\) (0,1,1)\({}_{24}\).  
\[(1-B)(1-B^{24})L_{t}=(1-\theta_{1}B)(1-\Theta_{1}B^{24})\varepsilon_{t} \tag{10}\]  
where, \(L_{t}\) is time series of load, \(B\) is backward shift operator, \(\theta_{1}\) and \(\Theta_{1}\) are non-seasonal and seasonal moving average coefficients respectively, \(\varepsilon_{t}\) represents a random component at time \(t\). The value of \(\theta_{1}\) and \(\Theta_{1}\) can be estimated from history observations using the maximum likelihood method.  
The predicted loads are then combined with observations in the past 24 h, and be filtered and segmented using the method aforementioned. Afterward, optimization is conducted if the current time stamp corresponds to a separation point. The optimization is based on average load level for current time segment. Figure 1 presents the flow chart of the online optimization strategy.
## 3 Results
### Infrared to Visible Upconversion
The second ETU, denoted as II, can be described as Er\({}^{3+}\)(\({}^{4}\)I\({}_{11/2}\)) + Er\({}^{3+}\)(\({}^{4}\)I\({}_{13/2}\)) \(\rightarrow\) Er\({}^{3+}\)(\({}^{4}\)I\({}_{15/2}\)) + Er\({}^{3+}\)(\({}^{2}\)H\({}_{11/2}\), \({}^{4}\)S\({}_{3/2}\)). A method to distinguish between ESA and ETU mechanisms is provided by the excitation spectra of the upconverted luminescence [38]. In ESA processes upconversion excitation spectra are the result of excited state absorption and the one photon absorption. In ETU, excitation spectra are proportional to the square of the ground state absorption. Hence, excitation spectra of the upconversion green emission were performed in the \({}^{4}\)S\({}_{3/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) transition at 550 nm in both Er-doped aluminosilicate glasses. Figure 9 shows these spectra for the sample doped with a 4 wt%. It can be observed that both excitation and absorption spectra are similar. This behavior was also observed for the sample doped with a 1 wt%. Consequently, upconversion mechanisms were due to energy transfer upconversion processes. Figure 8: Energy level of Er\({}^{3+}\) ions in aluminosilicate glass and possible upconversion mechanisms under excitation at 800 nm.
# A Channel Allocation Mechanism for Cellular Networks
## 3 Channel Allocation Mechanism in Cellular Networks  
In this section, this study will analyze the relation of communication behavior of MSs and the status of traffic. Moreover, this study uses this traffic information to provide a method that can help the cellular telephone system to decide the number of channels that can be reserved. Figure 3 shows the architecture of our proposed mechanism. The goal in the proposed mechanism is to get the number of communicating calls (\(C_{i}\)) that is derived from four factors. The factors include the number of Call Arrival (\(A_{i}\)), the number of Handover In (\(I_{i}\)), the number of Handover Out (\(O_{i}\)), and the number of Call Departure (\(D_{i}\)). Moreover, this study uses Formula (2) to estimate the number of communicating call (\(C_{i}\)) for Cell\({}_{i}\). In the following, this study will discuss the four factors, respectively:  
\[C_{i}=A_{i}+I_{i}-O_{i}-D_{i}. \tag{2}\]  
Figure 3: The architecture of proposed mechanism.