esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This research provides valuable insights and practical solutions to address the challenges associated with resource limitations and network latency, contributing to the advancement of edge computing in the eHealth domain. The rest of the paper is organized as follows. The next section surveys existing work on resource-aware FL and FL-inspired resource profiling. Section 3 provides a classification of the edge resources while Section 4 introduces the proposed resource-aware profiling model with a detailed formulation of the edge workload profile and node selection. Section 5 discusses the challenges encountered in the implementation of the proposed model and presents feasible solutions. In Section 6, the model is evaluated using the fetal health monitoring dataset distributed over various edge nodes. Section 7, 'Future Directions', elaborates on ongoing efforts to enhance FL systems, focusing on various advancements. Finally, Section 8 concludes the paper by summarizing the key findings and contributions of the study.
## 4 Case Study
### Impact of Capacity Confidence Coefficients on System Cost  
Taking into account uncertainty when modelling reserve capacity gives more dexterity spare for power system scheduling.  
To capture different degrees of net power fluctuation, different confidence levels are set. Figure 7 shows the hourly deterministic reserve capacity in comparison with the probabilistic reserve constraint at various confidence levels. Besides, as shown in Figure 8, the overall cost rises with confidence level increasing.  
Figure 6: The hourly dispatch of the energy storage system (ESS) in a unit commitment (UC) problem.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ESS Parameters (Energy Storage Upper** **Limit/MWh, Power Upper Limit/MW)** & **Total Cost/5** \\ \hline
80, 16 & 1.3337 \(\times\) 10\({}^{6}\) \\
100, 20 & 1.3336 \(\times\) 10\({}^{6}\) \\
150, 30 & 1.3264 \(\times\) 10\({}^{6}\) \\
200, 40 & 1.3239 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total cost under different ESS parameters.
# Evaluation of Algal Control Measures in Eutrophic Reservoir Based on Aquatic Ecosystem Models
## 2 Materials and Methods
### Principles of Hydrodynamic Modeling  
The two-dimensional numerical model MIKE 21 FM, developed by the Danish Hydraulic Institute, was utilized to examine tidal flow in engineered sea areas [40]. The model employs an unstructured triangular grid for calculations, allowing for a better fit with the land boundaries and the software possesses several advantages: it has reliable algorithms, stable computations, a user-friendly interface, and powerful pre- and post-processing functions [41]. For horizontal spatial discretization, MIKE 21 FM employs the standard finite volume method, while a first-order explicit Euler difference format is used for the discretization of momentum and transport equations in time [42].  
\[\frac{\partial\xi}{\partial t}+\frac{\partial p}{\partial x}+\frac{\partial q}{ \partial y}=0 \tag{1}\]  
\[\begin{array}{c}\frac{\partial p}{\partial t}+\frac{\partial}{\partial x} \left(\frac{p^{2}}{R}\right)+\frac{\partial}{\partial y}\left(\frac{pq}{R} \right)+\beta\frac{\partial x}{\partial x}+\frac{pq\sqrt{p^{2}+q^{2}}}{C^{2}h^ {2}}-\frac{1}{\rho_{w}}\left[\frac{\partial}{\partial x}\left(h\tau_{xx}\right) +\frac{\partial}{\partial y}\left(h\tau_{xy}\right)\right]-\mathbf{\Omega q}\\ -f\mathbf{VV}_{x}+\frac{h}{\rho_{w}}\frac{\partial}{\partial x}(P_{\mathbf{\alpha}})= 0\\ \\ \frac{\partial q}{\partial t}+\frac{\partial}{\partial y}\left(\frac{q^{2}}{R} \right)+\frac{\partial}{\partial x}\left(\frac{pq}{R}\right)+g\mathbf{h}\frac{ \partial x}{\partial y}+\frac{pq\sqrt{p^{2}+q^{2}}}{C^{2}h^{2}}-\frac{1}{\rho_{ w}}\left[\frac{\partial}{\partial y}\left(h\tau_{yy}\right)+\frac{\partial}{ \partial x}\left(h\tau_{xy}\right)\right]\\ \\ +\mathbf{\Omega p}-f\mathbf{VV}_{y}+\frac{h}{\rho_{w}}\frac{\partial}{\partial y}(P_{\mathbf{ \alpha}})=0\end{array} \tag{3}\]  
where \(h(x,y,t)\) is the water depth (m); \(\zeta(x,y,z)\) is the tidal level (m); \(p,q(x,y,t)\) are the single-width flows in the x and y directions; \(C(x,y)\) is the chezy coefficient (m\({}^{*1/2}\)/s); \(g\) is the gravitational acceleration (m/s\({}^{2}\)); \(f(V)\) is the wind friction factor, \(f(V)=\gamma_{\alpha}^{2}\rho_{\alpha}\) where \(\gamma_{\alpha}^{2}\) is the wind stress coefficient and \(\rho_{\alpha}\) is the air density; \(V,V_{x},V_{y}(x,y,t)\) are the wind speed and its components in x, y directions; \(\Omega(x,y)\) is the Coriolis force coefficient (s\({}^{-1}\)); \(p_{\alpha}(x,y,t)\) is the atmospheric pressure (kPa); \(\rho_{w}\) is the density of water (kg/m\({}^{3}\)); \(\tau_{xx},\tau_{xy},\tau_{yy}\) are the effective shear force components.  
Equations (1)-(3) constitute the basic governing equations for solving the tidal flow field. In order to solve this initial boundary value problem, appropriate initial and boundary conditions must be provided.
# RNA Modification Related Diseases and Sensing Methods
## 6 Software for Analyzing RNA Modification  
There are several software tools available for the detection of RNA modifications. For example, RiboMethSeq is a tool that can detect m6A, m5C, and pseudouridine (\(\mathbb{F}\)) modifications from sequencing data with high accuracy [126]. It uses a two-step approach that first identifies ribosome-protected regions and then analyzes read coverage to detect modifications. RiboMeth-seq can detect a variety of RNA modifications, including m6A, pseudouridine, and inosine. The software has been shown to have high accuracy in detecting RNA modifications, and it can be used to identify the locations and abundance of modifications in specific transcripts. Another tool, exomePeak, can detect m6A modifications with high sensitivity and specificity from m6A-CLIP-seq data [127]. It uses a statistical model to detect significant differences in read coverage between modified and unmodified regions of RNA. The software is optimized for detecting m6A modifications, which are the most common type of RNA modification. However, it can also be used to detect other types of modifications. The tool, exomePeak, has been shown to have high accuracy and sensitivity in detecting m6A modifications. However, these tools also have their limitations. For example, RiboMethSeq cannot distinguish between m5C and \(\mathbb{F}\) modifications due to their similar chemical properties [126]. Additionally, exomePeak requires high sequencing depth to achieve optimal results, which may not be feasible in certain experimental settings [127]. In the future, it is likely that new software tools will be developed that can overcome these limitations and improve the accuracy and sensitivity of RNA modification detection. For example, a recent study proposed a new method called MAZTER-seq that can detect m6A and m5C modifications simultaneously from RNA sequencing data with high accuracy [128]. This method uses a deep learning algorithm to distinguish between the two modifications based on their distinct sequence contexts and has the potential to overcome the limitations of current RNA modification detection tools. Overall, while there are currently limitations to the available software tools for RNA modification detection, new methods are constantly being developed, and it is likely that the field will continue to progress rapidly in the coming years.
## 4 Discussion
### Changing O\({}_{2}\) Levels Set the Scene  
The aerobic microbial count decreased by approximately one log unit after 14 days of storage without acetic acid addition. In the case of storage in closed tubes, a rapid decrease in headspace O\({}_{2}\) levels was observed with an almost complete O\({}_{2}\) depletion after two days of incubation at 20 \({}^{\circ}\)C. A fast consumption of O\({}_{2}\) dissolved in the algae suspension can hence be expected. Indeed, _Nannochloropsis_ concentrates were stored in the dark, preventing the algae from producing O\({}_{2}\) through photosynthesis. Aerobic respiration will decrease O\({}_{2}\) levels and eventually lead to hypoxic or even anoxic conditions. The creation of an anoxic environment by the dark incubation of _Nannochloropsis_ concentrates was already reported in a previous study [34], though under different circumstances (24 h incubation at 38 \({}^{\circ}\)C). Obviously, such drastic changes will eliminate obligate aerobes and affect the aerobic microbial count as seen in this study. In contrast to the aerobic microbial count, the number of yeasts and molds steadily increased during storage. This might be due to the fact that most yeasts are facultative aerobes [35] and adapt well to changing O\({}_{2}\) levels. Yeasts and molds were not separately quantified in an unequivocal way, but the appearance of the colonies suggested that yeasts dominated at the start of storage experiment and that their contribution to the fungal flora increased during storage (results not shown).  
When O\({}_{2}\) becomes deficient, anaerbes and facultative aerobes use either anaerobic respiration or fermentation to balance redox and energy balances [35]. The formation of small organic acids (Figure 4) reflects the occurrence of fermentation processes [36; 37], while H\({}_{2}\)S formation points towards bacterial sulfate reduction which is a typical example of anaerobic respiration [35]. _Nannochloropsis_ cells probably resist anoxic environments well as they may encounter anoxic challenges in their natural habitats [38]. Accordingly, FFA levels remained low and stable during 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C storage without acetic acid addition (Table 1). This suggests that cell integrity was not jeopardized, since cell disruption is believed to onset lipolysis in algae [17].
## 2 Definitions, Principles, and Outline of Roots-of-Trust and Trusted Environments
In the first case, only the secure software can access the hardware security-critical components, and the insecure software can exchange data with the secure software to request security services. The most significant example relies on the usage of two distinct processing units. One of the processing units (e.g., a MicroController Unit, MCU) is dedicated to all and only the security-related functions and processes, and is eventually assisted by additional hardware accelerators or specialized circuits; for this reason, such a microcontroller is called a Secure MCU, and it is provided with its own RAM that is called Secure RAM (SRAM). The other processing unit (e.g., the main processor) is in charge of executing the operating system and general-purpose applications. The main processor can make a request for security services to the Secure MCU through dedicated interconnections(e.g., a mailbox system). In the second case, both secure and non-secure software share the same hardware resources, but only the secure software has access to the resources dedicated to the security functions. An example can be a unique processor that executes both security-related software and non-security-related applications, hence exploiting the same RAM for both of them. Furthermore, some memory spaces are dedicated only to security-related data and instructions, and their access is forbidden to non-secure applications. Section 3 reports an overview of the state-of-the-art RoT-based Trusted Environments.
# Application of the _Aliium_ Test in Toxicity Studies of Lead and Copper: A Cytological Perspective
## 3 Results
### Comparison of the Impact of Pb\({}^{2+}\) and Cu\({}^{2+}\) on the Abnormalities Index  
The ANOVA test allowed us to compare the impact of different types of metals (\(Pb^{2+}\) vs. Cu\({}^{2+}\)) on the abnormalities index (AI). The results obtained and presented in Table 6 highlight that there is a significant impact on and a notable difference between the results for the abnormalities index (AI), depending on the type of metal used (\(p<0.05\)).  
According to the results obtained, the number of cell abnormalities was higher under treatments based on copper compared to those based on lead (\(p<0.05\)) (Figure 6).  
The results obtained confirm a strong relationship between the mitotic index (MI) and the abnormalities index (AI) (Figure 6). As the concentration of the metal treatment increases, significant changes are observed in both the AI and MI.  
Mitotic disruptions are closely linked to, and often a result of, the stress caused by these metals, which induce chromosomal aberrations. Consequently, this leads to the inhibition of the mitotic index and therefore, the reduction in growth capacity (Figure 6).  
Copper induces abnormalities, even at the initial concentrations of 0.25 mM, while maintaining mitotic activity relatively within normal limits. This highlights that the integrity of the cell division spindle is affected, but without completely halting the cell division capacity. Lead, on the other hand, shows a decrease in the MI at treatment concentrations, with more pronounced effects, along with an increase in the AI. Thus, both metals studied exhibit dose-dependent toxicity, with different toxicity thresholds, depending on the metal. The effects are visible for copper, even at concentrations as low as 0.25 mM, whereas for lead, they become more evident starting at 0.50 mM and above (Figure 6).
# Potentiometric Biosensor Based on Artificial Antibodies for an Alzheimer Biomarker Detection
## 2 Materials and Methods
### Selectivity Study  
The potentiometric selectivity coefficients were determined by mixed solutions method (MSM). The selectivity of the biosensor was assessed by quantifying LOD of each calibration curve through the presence of an interfering compound [62]. The selectivity coefficients were calculated according to this equation:  
\[\mathrm{K}^{\mathrm{FOT}}_{\mathrm{A\beta-42J}}=\mathrm{a}_{\mathrm{A\beta 42}}/( \mathrm{a}_{\mathrm{J}})^{\mathrm{Z}_{\mathrm{A\beta 42}}/\mathrm{Z}_{\mathrm{J}}} \tag{1}\]  
In Equation (1), a\(\mathrm{j}\) is the concentration of interfering species, \(Z\) the ionic charges of the interfering ions and a\({}_{\mathrm{A\beta 42}}\) the intersection of the extrapolated linear section of the plot Emf vs. the logarithm of A\(\beta\)-42 concentration. Alb, Crea and urea were evaluated as interfering species. For this propose, a fixed concentration of interfering species within the physiological level in serum was added as background, while the concentration of primary ion (A\(\beta\)-42) was varied. (Crea, 1.2 \(\times\) 10\({}^{-5}\) g/mL), (Alb, 5.2 \(\times\) 10\({}^{-3}\) g/mL) and (urea, 4.0 \(\times\) 10\({}^{-4}\) g/mL) were evaluated as interfering species.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**pH**} & \multicolumn{3}{c}{**Membrane Composition**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**LOD**} & \multirow{2}{*}{**LLLR**} & \multirow{2}{*}{**Response**} \\  & & & & & & & & \\ \multirow{2}{*}{**Active Ingredient**} & \multirow{2}{*}{**Plasticizer**} & \multirow{2}{*}{**Additive**} & \multirow{2}{*}{**Weight**} & \multirow{2}{*}{**(mV/Decade)**} & \multirow{2}{*}{**(n = 3)**} & \multirow{2}{*}{**(ug/mL)**} & \multirow{2}{*}{**(ug/mL)**} & \multirow{2}{*}{**Time (s)**} \\ \hline \multirow{2}{*}{6} & MIP/AAM & oNFOE & — & 2:60 & 62.2 \(\pm\) 1.3 & 0.970 & 1.23 & 1.20 & \(>\)60 \\  & NIP/AAM & oNFOE & — & 2:60 & — & 0.920 & — & — & \(>\)60 \\ \multirow{2}{*}{8} & MIP/AAM & oNFOE & — & 2:60 & 75.1 \(\pm\) 1.0 & 0.992 & 0.97 & 1.20 & \(>\)60 \\  & NIP/AAM & oNFOE & — & 2:60 & 44.0 \(\pm\) 1.0 & 0.970 & 0.99 & 0.75 & \(>\)60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Membrane composition of A\(\beta\)-42 sensors casted in 50 mg of PVC and their potentiometric features in 1.0 \(\times\) 10\({}^{-2}\) M MOPS buffer, pH 6 and 8.
# Stimulation Montage Achieves Balanced Focality and Intensity
## 2 Materials and Methods
### Experiment Design
In the real world, uncertainty in tissue conductivity can result in different electric field distribution from our expected, idealized models; this greatly affects the robustness of the computational model. The conductivity variations result in a different coefficient matrix \(\mathbf{K}^{\prime}_{3n\times m}\); accordingly, with the same current pattern \(\mathbf{s}_{m\times 1}\), it produces an electric field vector \(\mathbf{e}^{\prime}_{3n\times 1}=\mathbf{K}^{\prime}_{3n\times m}\mathbf{s}_{m\times 1}\) that is different from the expected electric field distribution using standard conductivity values. The dissimilarity between \(\mathbf{e}^{\prime}_{3n\times 1}\) and the expected distribution \(\mathbf{e}_{3n\times 1}\) reflects the robustness of the computational model. In this study, we observe how small changes in tissue conductivity can create differences between \(\mathbf{e}^{\prime}_{3n\times 1}\) and \(\mathbf{e}_{3n\times 1}\), thereby analyzing the robustness of our model. Based on the literature [37; 38; 39], we assumed that the conductivities of the main tissues have uniform distributions with a unit of S/m, i.e., \(P_{scalp}(x|\sigma)\sim U(0.2,\,0.6)\), \(P_{skill}(x|\sigma)\sim U(0.001,\,0.04)\), \(P_{CSF}(x|\sigma)\sim U(1.20,\,2.01)\), and \(P_{cortex}(x|\sigma)\sim U(0.05,\,0.71)\). An overview of both single and multiple targets with different algorithms is given to demonstrate the general effects of conductivity changes. The detailed influences are revealed by an example of single target stimulation with SBFI in the main text. Figure 3: Results of different optimization algorithms for the real multiple targets protocol. The color scale represents intensity of the electric field, in V/m. (Note: Results are shown at the same scale [0, 0.1107] V/m for different algorithms except for CLS. The intensity above 0.1107 V/m is saturated for visualization.) Again, the intensity of the CLS method is too weak, while the MI method almost activates the whole brain. Only SBFI provides a good balance between stimulation intensity, precision, and accuracy. The LCMV method was unable to find a valid solution for the multi-target case.
###### Abstract  
Visible light positioning (VLP) is a cost-effective solution to the increasing demand for real-time indoor positioning. However, owing to high computational costs and complicated image processing procedures, most of the existing VLP systems fail to deliver real-time positioning ability and better accuracy for image sensor-based large-area indoor environments. In this study, an effective method is proposed to receive coordinate information from multiple light-emitting diode (LED) lights simultaneously. It provides better accuracy in large experimental areas with many LEDs by using a smartphone-embedded image sensor as a terminal device and the existing LED lighting infrastructure. A flicker-free frequency shift on-off keying line coding modulation scheme was designed for the positioning system to ensure a constant modulated frequency. We tested the performance of the decoding accuracy with respect to vertical and horizontal distance, which utilizes a rolling shutter mechanism of a complementary metal-oxide-semiconductor image sensor. The experimental results of the proposed positioning system can provide centimeter-level accuracy with low computational time, rendering it a promising solution for the future direction of large-area indoor positioning systems.  
visible light positioning; light emitting diode; wide-area positioning; CMOS sensor +
Footnote †: journal: Applied
## 3 Performance Evaluation
### Comparison with Previously Obtained Models  
In this section, the performance of the developed ANNs is compared to that of a previous artificial intelligence (AI) technique developed by other authors [4], i.e., the evolutionary polynomial regression (EPR), for the prediction of \(P_{i}\). In this regard, the testing data series used in each of the ANNs was fed into the predictive EPR models, and the \(P_{i}\) were approximated. The results of the comparison between the EPR and the ANN predictions are presented. On the basis of the results, all the proposed MLP and RBF ANNs predicted the pressures more accurately than the corresponding EPR models. As an example, Figure 8 compares the results obtained from an EPR model to those predicted by the 15-5-10-1 MLP-ANN for the case of Mohr-Coulomb criterion and by the 17-5-15-1 MLP-ANN for the case of Hoek-Brown strength criterion. As shown, all the performance's evaluation criteria presented higher accuracy in the ANN-based predictions than in the EPR model.  
Figure 7: (**a**) Neural predicted \(P_{i}\) versus calculated \(P_{i}\); (**b**) differences between neural predicted \(P_{i}\) and calculated \(P_{i}\) for the best RBF-ANN (17-3-12-1) applied to the testing data series for the Hoek–Brown criterion.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 5 Simulation Results
### Parameters of DE  
We first show the convergence performance of the proposed DE algorithm. Figure 7 shows the best fitness value of each generation. The curve in the figure gradually decreases as the number of generations increases. The result shows that the operations of our DE algorithm can yield better solutions. The proposed DE algorithm also has good convergence performance, in which the improvement of each generation is reduced after 150 generations and the best solution is yielded in about the 700 th generation. The proposed DE algorithm can thus generate feasible results with reasonable computation cost.  
We further show the performance of the DE algorithm with respect to different values of scaling factor. As shown in Figure 8, when the value of \(F\) is within the range from 0.1 to 0.3, we can observe that the fitness value decreases as the \(F\) value increases. The value of \(F\) is related to the step size of the next generated solution. If the step value is too small, the DE algorithm could be trapped in the locally optimal solution. When the value of \(F\) is within the range from 0.4 to 0.9, the fitness value is increased for a larger \(F\) value. However, a large step value could degrade the convergence performance. In the following experiments, we set the \(F\) value to 0.3.  
Next, we show the performance of the DE algorithm for different values of hybridization probability, which can balance the tendency between global and local searches for the DE algorithm. As shown in Figure 9, when the value of CR is within the range from 0.1 to 0.7, the value does not fluctuate severely because a small CR value may cause the DE algorithm to become trapped in locally optimal solutions. When the value of CR is increased to the range from 0.8 to 0.9, the better solution could be yielded. However, a higher CR value may also lead to slow convergence. In the following experiment, we set the CR value to 0.8.  
Figure 8: The scaling factor of our DE algorithm.  
Figure 7: The convergence of our DE algorithm.