esa-sceva/satcom-chunk-collection huggingface dataset:
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Multi-Energy Microgrids
#### 2.1.7 Energy Storage System (ESS)  
The energy storage system (ESS) can be effectively used to balance the intermittent fluctuation of the output power of renewable power sources and ensure stable operation. The mathematical model of the ESS is modeled as follows [33]:  
\[0\leq u_{t}^{ch}+u_{t}^{dis}\leq 1,\forall t \tag{10}\]  
\[\begin{split} u_{t}^{ch}P_{ch,\text{min}}^{ESS}\leq P_{ch}(t)& \leq u_{t}^{ch}P_{ch,\text{max}}^{ESS}\\ u_{t}^{dis}P_{dis,\text{min}}^{ESS}\leq P_{dis}(t)& \leq u_{t}^{dis}P_{dis,\text{max}}^{ESS}\end{split} \tag{11}\]  
where \(u_{t}^{ch}\)\(u_{t}^{dis}\) are binary variables, which represent the working state of the ESS at time slot \(t\). The constrains in Equation (11) mean that the ESS can't charge and discharge at the same time. \(P_{ch,\text{min}}^{ESS}\),\(P_{ch,\text{max}}^{ESS}\) are minimal and maximal charging power of ESS, respectively. \(P_{dis,\text{min}}^{ESS}\),\(P_{dis,\text{max}}^{ESS}\) are minimal and maximal discharging power of ESS, respectively.  
\[\begin{split} S_{ESS}(t+1)=(1-\delta)S_{ESS}(t)+(\eta^{ch}P_{ch}( t)-\frac{1}{\eta^{dis}}P_{dis}(t))\Delta t\\ S_{ESS}^{\text{min}}\leq S_{ESS}(t)\leq S_{ESS}^{\text{max}}\end{split} \tag{12}\]  
where \(S_{ESS}(t)\) is the energy stored in the ESS at time slot \(t\). \(S_{ESS}^{\text{min}}\),\(S_{ESS}^{\text{max}}\) are minimal and maximal energy stored in the ESS, respectively. \(\Delta t\) is the time scale of the operation (one hour in this paper).
# Current Context and Research Trends in Linear DC-DC Converters
## 10 Conclusions  
Linear regulators are no longer obsolete; instead, they have a dominant market in portable devices with the introduction of LDOs. LDOs come in the form of ICs, typically outputting up to about 5 A load current, and they are low in cost compared to the switch-mode stages within a PMS. LDOs have excellent EMI characteristics as well as low noise output and high current slew rates. With these desirable features, there is a dominant position for LDOs in portable device power supplies to be supplemented at post regulator stage to overcome major issues in SMPS.  
Figure 12: (a) SCALDO 12 V–5 V discrete prototype [5]; (b) SCALDO IC; (c) comparison of end-to-end efficiency of the 12–5 V SCALDO [73].  
Many researchers are currently working to improve the performance of LDOs in terms of efficiency, noise levels, compactness, and transient characteristics. Currently, the efficiencies of LDOs in the market are in the range of 96.25 to 98.55%, which are comparable to SMPS efficiencies. In larger systems, where larger currents are required, a discrete transistor-based LDO implementation is possible, provided the designer keeps the dropout voltage at a very low value to achieve high efficiency.  
A newer extra-low-frequency SCALDO design approach summarised in this article demonstrates a unique way to achieve quasi-linear DC-DC converters where a high-efficiency LDO can be combined with a supercapacitor front end for loss recovery, creating a high-end-to-end-efficiency converter.  
Conceptualisation, K.G.; methodology, N.P.; formal analysis, N.P., K.G., N.K. and K.S.; investigation, N.P., K.G. and K.S.; writing--original draft preparation, N.P. and K.G.; writing--review and editing, N.P., K.G., K.S., N.K. and T.T.L., visualisation, N.P.; supervision, K.G. and N.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Advanced Energy Technology Project (AETP) grant from the Ministry of Business Innovation and Enterprise (MBIE), New Zealand, to develop the Future Architecture Network (FAN), grant number [UOCX2007] And The APC was funded by FAN project grant too.  
Not applicable.  
We would like to acknowledge the support for our work from the researchers of Future Architecture Network (FAN) project.
## 3 Results and Discussion
### Effect of Different Factors on the Stability of the Hybrid Nanofluids
#### 3.1.3 Influence of Ultrasonic Power  
The absorbance was measured at different ultrasonic power for the hybrid nanofluid sample and graphene oxide aqueous nanofluid, at pH = 11, 0.1% nanoparticle concentration, 1% SDS and ultrasonic dispersion of 120 min. The relation between ARR and ultrasonic power is shown in Figures 8 and 9.  
Figure 7: Relation between absorbance reduction ratio and time with different concentration of SDS.  
Figure 8a shows rapid decrease in hybrid nanofluid absorbance in the first four days, indicated by the rapid increase of ARR, and relatively stable absorbance afterwards. Moreover, the absorbance firstly increased, then decreased as ultrasonic power increased, as can be inferred from Figure 8b. The results indicate that the stability of the hybrid nanofluid reached optimum at 375 W of ultrasonic power, where the ARR was 17.16% lower than that at 450 W after 7 days. This is because the ultrasonic cavitation became stronger as ultrasonic power increased, which reduced the size of the nanoparticles and increased the spacing distance between the nanoparticles by the electrostatic and steric mechanism. The van der Waals attraction between nanoparticles is thus reduced, which hinders agglomeration and enhances the stability of the hybrid nanofluid. However, when the ultrasonic power is larger than the optimum value, it leads surfactant monomers to form micelles rather than attaching on the surface of nanoparticle. The intensity and frequency of nanoparticle collision increased greatly, and colliding nanoparticles generated larger agglomerates and precipitates.  
For the GO nanofluid shown in Figure 9, the influence of ultrasonic power on the stability was consistent with the trend of the GO-Al\({}_{2}\)O\({}_{3}\) hybrid nanofluids. Nevertheless, the ARR of the GO nanofluid was lower than that of the hybrid nanofluid, which means higher absorbance and better stability of GO nanofluid as compared to the stability of the hybrid nanofluid, which was slightly reduced with the addition of the alumina nanoparticles.  
Figure 8: Relation between absorbance reduction ratio of the hybrid nanofluid and ultrasonic power in 7 days.  
Figure 9: Relation between the absorbance reduction ratio of GO aqueous nanofluid and ultrasonic power in 7 days.
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution** & \multicolumn{2}{c}{**Advantages**} & **Disadvantages** \\ \hline \multirow{3}{*}{Walking stick} & \(\bullet\)  Easy to use & \multirow{3}{*}{\(\bullet\)} & Detects only obstacles connected to the floor \\  & \(\bullet\)  Easy to maintain & & \\  & \(\bullet\)  Easy to learn & & \\ \hline \multirow{3}{*}{Guide dog} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)}  Few dogs exist \\  & & & \(\bullet\) Hard to train the dogs \\ \cline{1-1}  & & & \(\bullet\) Can have problems with other persons \\ \hline \multirow{3}{*}{Mobile applications} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)}  Easy to use & \multirow{3}{*}{\(\bullet\)}  Few are focused on indoor navigation \\  & & & \(\bullet\) Many application types with different solutions for \\ \cline{1-1}  & & & \(\bullet\) Usually impaired persons \\ \hline \multirow{3}{*}{Special lines mounted on the floor} & \multirow{3}{*}{\(\bullet\)}  Easy to use & \multirow{3}{*}{\(\bullet\)}  Limited information \\ \cline{1-1}  & & \(\bullet\) Work perfectly with the \\ \cline{1-1}  & & \(\bullet\) Walking cane & \\ \hline \multirow{3}{*}{Orcam glasses [9]} & \multirow{3}{*}{\(\bullet\)}  Specially designed to help & \multirow{3}{*}{\(\bullet\)}  Expensive \\ \cline{1-1}  & & \(\bullet\) users with visual problems \\ \cline{1-1}  & & \(\bullet\) Lots of options like facial \\ \cline{1-1}  & & \(\bullet\) recognition, etc. & \\ \hline \multirow{3}{*}{eSight glasses [10]} & \multirow{3}{*}{\(\bullet\)}  Especially designed to help & \multirow{3}{*}{\(\bullet\)}  Expensive \\ \cline{1-1}  & & \(\bullet\) users with visual problems \\ \cline{1-1}  & & \(\bullet\) Adjust light intensity & \\ \cline{1-1}  & & \(\bullet\) Limited quantity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing solutions for visually impaired persons.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Enabling Technologies for Short-Reach System
#### 3.3.1 Potential Modulation Formats  
This subsection briefly presents some advanced high-order modulation formats that can be exploited to considerably scale the transmission speed of short-reach systems.
## 2 Materials and Methods
### FEM Modelling  
FEM modelling was performed in _COMSOL Multiphysics_. A somewhat rare approach was employed by constructing a 2D model using cross-sectional electron microscope images of the real substrates as a reference for the geometry. This allows for the modelling of a more accurate representation of the real substrates, instead of being limited to geometrically simple shapes and trivial configurations. The SEM images were imported and contours were drawn on them using an edge threshold. There is an element of choice when deciding the threshold value and the curve tolerance value as these cannot be inquired precisely due to the limited resolution of the SEM. These are therefore chosen so as to minimise artefacts created by the edge detection algorithm while at the same time retaining the smaller details distinguishable in the images.  
Using the _electromagnetic waves frequency domain_ physics package, the steady-state solution for an incoming electromagnetic wave of different frequencies interacting with the nanometre scale structure was calculated. The power of the incoming electric field wave was fixed at \(P_{in}=1\)\(W\). Scattering boundary conditions were defined on the edges of the model, alongside perfectly matched layers at the top and the bottom of the models. The full field was calculated. The materials used in the models were representative of the real substrates, with gold and silicon defined for the nanostructures and the substrate, respectively. The interface used for modelling can be found in Supplementary Materials (Figure S1).
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 2 Sources and Impacts of Training Data Error
### Sources of Training Data Error
#### 2.1.1 Design-Related Errors
When integrating HR/VHR imagery acquired at different azimuth and elevation angles, features such as building roofs show offsets similar to those caused by topography. These offsets are particularly problematic for (a) training repeated mappings of the same features, and/or (b) when using an existing vector dataset such as OpenStreetMap (OSM) as TD [133; 134; 135]. TD collected by interpreting HR/VHR imagery is often co-registered with the coarser resolution imagery used as ML model data. This creates a potential spatial resolution conflict because the minimum mapping unit (MMU), i.e., the relationship between image objects and pixel size, may be different in the two imagery data sets. This potentially leads to situations in which objects delineated as spectrally homogenous areas in HR/VHR imagery are part of mixed pixels in moderate- or coarse-resolution model imagery. This mismatch is similar to the concept of H-resolution versus L-resolution scene models proposed by [PERSON] et al. [136]; in H-resolution models, the objects of interest are substantially larger than the pixel size, and vice versa for L-resolution models. The incorporation of mixed pixels may degrade classification model performance, or at least introduce undesired spectral variability within classes [137; 138; 127]. This situation may be alleviated by displaying both HR/VHR imagery and/or other ancillary datasets as well as coarser model imagery during training data creation [139; 140]. However, such practices may not be possible when training data are taken from previous research projects, or when they are to be applied in the context of time series analysis, in which spatial features change over time, e.g., [141]. Similar spatial resolution and scaling issues must be dealt with when combining in situ measurements with satellite observations for continuous variables. Field-collected data often cannot practically cover the entire area of a pixel in the model data, especially for moderate or coarse-resolution imagery, and can thus induce scaling errors related to the modifiable areal unit problem [142; 143]. Spatial representativeness assessments and interpolation methods are used to limit this problem for operational EO science products [144; 145; 146; 147], but this issue is likely to be a source of error for most in situ TD samples. Another design-related problem arises from large-scale data collection initiatives that are becoming increasingly common due to the expanding extent of modern EO analyses, e.g., [148]. These efforts, often conducted via crowdsourcing campaigns, typically enlist citizens to collect data via a web-based platform, e.g., [149; 150; 151; 66]. Examples include OSM, Geo-Wiki [66], Collect Earth [152], DIYLandcover [150], and FotoQuest Go [153]. In cases where the resulting data might be purely voluntary [76], the resulting sample may lack spatial representativeness due to uneven geographic contributions [28; 154].
## 4 Experiments and Results
### Experimental Setting  
#### 4.1.1 Our Used Datasets  
Our dataset comprises real sensor monitoring data collected from a ship's energy efficiency monitoring system over a two-month period. We have quantized the dataset according to the precision requirements and numerical limits of actual ship monitoring system, using quantization techniques discussed in Section 3.3. Our ship status data include 41 floating-point fields (Figure 5 shows a part of the data fields), which mainly describe the real-time fuel consumption related data fields, the rotate speed, the torque and the power of the main engine, the ship speed data fields, ship-drought-related data fields, and the real-time weather-related data fields. With data logged approximately every 10 s, this compilation spans 479,742 rows.  
Figure 5: The screenshot of our quantized ship status dataset.  
In applying our compression algorithm to the ship monitoring system, the number of fields for transmission in the practical application may not precisely match those in the experimental dataset. However, we assume similarity in the nature of the data between the application scenario and our experimental datasets.  
To avoid overfitting, we chronologically divided the dataset into training, validation, and test sets, adhering to a 7:2:1 ratio. The training set serves for predictor training in our predictive coding step and for estimating data frequencies applied in arithmetic coding. The validation set aids in adjusting hyperparameters and method comparison. Lastly, the test set is reserved to validate the efficacy of our QAAPA approach, mitigating potential overfitting concerns.
## 5 Performance Evaluation
### Experimental Results
#### 5.4.2 Experiment 2  
In this experiment, we evaluate the robustness of the DAE+OCSVM anomaly detector against benchmark adversarial evasion attacks (FGSM and BIM) using different attacker's model architectures (CNN and FFNN). Table 3 presents the experimental results. The results, including \(ACC\), \(DR\), \(PR\), \(F1\), and \(HD\) values, demonstrate the vulnerability of the DAE+OCSVM anomaly detector to evasion attacks. Taking the CNN-based attacker model as an example, we observe that the \(ACC\), \(DR\), \(PR\), \(F1\), and \(HD\) values decrease from  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model Type** & **ACC** & **DR** & **FA** & **PR** & **F1** & **HD** \\ \hline DAE [15; 24] (With-malicious) & 91.63 & 92.92 & 9.67 & 90.58 & 91.74 & 83.25 \\ DAE [15; 24] (No-malicious) & 51.32 & 3.33 & 0.67 & 83.29 & 6.41 & 2.66 \\ DAE+OCSVM (No-malicious) & 93.61 & 96.74 & 9.51 & 91.05 & 93.81 & 87.23 \\ \hline \hline \end{tabular} Note: Malicious samples are used during the threshold selection process.  
\end{table}
Table 2: The performance comparison between the existing ET anomaly detectors and the proposed ET anomaly detector (DAE+OCSVM).  
93.22%, 97.48%, 89.82%, 93.49%, and 86.43%, respectively, under no evasion to 70.21%, 51.47%, 82.33%, 63.34%, and 40.38%, respectively, under the FGSM attack and to 67.80%, 46.65%, 80.85%, 59.16%, and 35.56%, respectively, under the BIM attack. Additionally, the _FA_ metric remain unchanged and align with the values in Table 2 because the attacker generates adversarial evasion samples only from malicious ones. These results demonstrate the transferability of adversarial evasion attacks across different ML models, even when the attacker's surrogate model has a different architecture from the defense model. _The performance reduction after launching the adversarial evasion attacks proves the severity of these attacks._ This emphasizes the need to secure the DAE+OCSVM ET anomaly detector against adversarial evasion attacks while maintaining high accuracy, as is discussed in Experiment 3.
## 2 Materials and Methods
### Numerical Schemes and Boundary Conditions
#### 2.4.3 Numerical Implementation
\begin{split}\text{TAWSS}=\frac{1}{T}\int_{0}^{T}\boldsymbol{ \tau}_{w}\,dt,\\ \text{OSI}=0.5\left(1-\frac{\left|\int_{0}^{T}\boldsymbol{\tau}_ {w}\,dt\right|}{\int_{0}^{T}|\boldsymbol{\tau}_{w}|\,dt}\right),\\ \text{RRT}=\frac{1}{(1-2\times\text{OSI})\times\text{TAWSS}},\\ \text{transWSS}=\frac{1}{T}\int_{0}^{T}\left|\boldsymbol{\tau}_{ w}\cdot\left(\mathbf{n}\times\frac{\int_{0}^{T}\boldsymbol{\tau}_{w}\,dt}{ \left|\int_{0}^{T}\boldsymbol{\tau}_{w}\,dt\right|}\right)\right|dt.\end{split}  \tag{17}\]The helicity \(H(t)\) of a fluid flow within a three-dimensional domain \(S\) is defined as follows [47]:  
\[H(t)=\int_{S}\mathbf{v}(\mathbf{x},t)\cdot\mathbf{\omega}(\mathbf{x},t)\,dV=\int_{S}H _{k}(\mathbf{x},t)\,dV \tag{18}\]  
In this expression, \(\mathbf{v}(\mathbf{x},t)\) and \(\mathbf{\omega}(\mathbf{x},t)\) denote the velocity and vorticity vectors, respectively, while \(H_{k}\) represents the helicity density. The sign of \(H_{k}\) serves as an indicator of the rotational direction. Specifically, helical structures with a positive (negative) \(H_{k}\) value are classified as right (left)-handed, with a clockwise (counter-clockwise) rotation when the thumb is oriented in the direction of the flow. The LVOT peak pressure gradient is predominantly used as a hemodynamic descriptor in patients with hypertrophic cardiomyopathy (HCM). However, in these patients, secondary alterations in aortic blood flow have not been extensively described. The presence of helical flow in the LVOT and the ascending aorta (AAO), as an indicator of flow derangement, can serve as a valuable marker. To assess the aortic bulk flow with respect to its helical characteristics and flow topology, we define six helicity-based descriptors as follows: [47]:  
\[ \begin{array}{llll}h_{1}&=&\frac{1}{TV_{i}V_{i}}\int_{TV_{i}}\int_{V_{i}}H_ {k}\,dV\,dt,&h_{2}=&\frac{1}{TV_{i}V_{i}}\int_{TV_{i}}\int_{V_{i}}|H_{k}|\,dV \,dt,\\ h_{3}&=&\frac{h_{1}}{h_{2}},&h_{4}=&\frac{|h_{1}|}{h_{2}},\\ h_{5}&=&\frac{f_{TW_{i}}\int_{V_{id}}dV\,dt}{f_{TW_{i}}\int_{V_{im}}dV\,dt^{ \prime}\,dt},&h_{6}=&\frac{1/V_{id}\int_{TW_{i}}\int_{V_{id}}H_{k}\,dV\,dt}{1/ V_{im}\int_{TV_{i}}\int_{V_{im}}H_{k}\,dV\,dt}.\end{array}  \tag{19}\]  
where \(V_{i}\) denotes the volumetric domain under consideration, and \(TW_{i}\) signifies the temporal window for integration. Additionally, \(V_{id}\) is the volume corresponding to the predominant rotational direction, while \(V_{im}\) is the volume associated with the less dominant rotational direction.
It is used to examine sensor nodes that get data from the patient's body using intelligent portable gadgets to assess their physical properties [19]. IoMT enables wireless and remote devices to communicate securely over the Internet, while integration with AI methods allow for rapid and flexible medical data analysis and diagnostics. When delivering data through the cloud, IoT devices handle several unknown elements such as network structure, energy transfer, and computing capacity [20]. Both caregivers or health care providers and patients have successfully adapted remote patient monitoring, disease detection, and efficient treatment via telehealth services. All these devices and services lead to the transformation of healthcare to Healthcare Industry 4.0 [21]. Compared to existing methodologies, the methods and techniques used in this proposed study are more favorable, since they improve and optimize the selection of pertinent characteristics that detect the tumor at its early stages utilizing ML techniques. This work makes the following innovations and contributions:  
1. A novel framework for the optimization of hyperparameters of the convolutional neural network (CNN) and artificial neural network (ANN) models for achieving optimal classification results. 2. An experimental comparison of SVM and MLP classifiers that have been trained and evaluated utilizing particle swarm optimization (PSO) for feature selection. 3. The suggested approach can be easily integrated into the medical IoT-based healthcare system and used to effectively diagnose breast cancer. The remainder of this work is divided into the following sections: Section 2 presents the IoMT-based framework for breast cancer diagnostics, explains our methodology, including the neural network models and tuning of their hyperparameters, and discusses the performance evaluation measures. The results of breast cancer diagnostic implementation with comparative analysis with state-of-the-art are discussed in Section 3. Section 4 concludes and provides recommendations for future studies.