esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Classification Results Using CCA Fusion
#### 4.5.7 Statistical Analysis of LPBoost Model on CCA Fusion  
This section has discussed the reported statistics of the most famous traditional machine learning algorithm, LPBoost model classifier, to classify the lesion on the vector of the fused feature. The statistics show that the accuracy of AML class is 100 percent, ALL reached the accuracy of 93.182 percent, and accuracy of MM is around 95.556 percent. Sensitivity, precision, F1-score, etc., are cited below in Table 22. This classifier's overall accuracy was very efficient and reached 96.30 percent, which is also shown in Table 23 given below; also, the confusion matrix on CCA fusion is shown in Figure 13.  
Overall accuracy achieved by LPBoost on ALL, AML and MM was 96.30%. The overall statistics of LPBoost are given below in Table 23.  
\begin{table}
\begin{tabular}{c c} \hline
**Statistics** & **Results \%** \\ \hline Accuracy & 95.19 \\ \hline Error & 4.81 \\ \hline Sensitivity & 95.12 \\ \hline Specificity & 97.60 \\ \hline Precision & 95.17 \\ \hline False Positive Rate & 2.40 \\ \hline \end{tabular}
\end{table}
Table 21: Overall Statistics of SVM Model of All Classes.  
Figure 12: Confusion Matrix of SVM model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **Accuracy\%** & **Sensitivity\%** & **Specificity\%** & **Precision\%** & **False Positive\%** & **F1-Score\%** \\ \hline ALL & 93.182 & 93.182 & 97.802 & 95.349 & 2.1978 & 94.253 \\ \hline AML & 100 & 100 & 99.438 & 98.925 & 0.5618 & 99.459 \\ \hline MM & 95.556 & 95.556 & 97.222 & 94.505 & 2.7778 & 95.028 \\ \hline \end{tabular}
\end{table}
Table 22: Statistical Analysis of LPBoost model on CCA Fusion.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Simulation Environment  
To perform the evaluation, we implemented our algorithms within the Contiki OS [30], an open-source operating system for networked, resource-constrained systems, mainly focusing on low-power wireless Internet of Things devices. The evaluation has been performed in the COOJA simulator, a dedicated simulator for Contiki OS nodes. The simulator parameters are presented in Table 1.  
The network is set up and left to reach a steady-state for 2 min. All sensor nodes are equivalent to Sky Mote nodes and have a 10 m radio range. The sources start injecting data to the network with a source data rate of 25 pkts/s for one minute. The data rate is then increased to 50 pkts/s, and it is constantly increased with a data rate of 50 pkts/s, for each source every minute. After 13 min, each source injects to the network 600 pkts/s, with an effective rate of 230.4 Kbps.
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 3 State of the Art on Visualisation and Modelling of River Flooding
### Flooding Visualisation
AR also allows users to interact with real objects in addition to virtual contents in an augmented scene, like a mobile phone screen, thereby enhancing user experience. [PERSON] et al. [12] conducted a comprehensive overview of stakeholder analysis and categorised cutting-edge technologies into five groups. They noticed a limited exploration on review research about how stakeholders can actively contribute to the advancement and improvement of visualisation tools such as AR, VR, DT (digital twin) and MR (Mixed Reality). They emphasised the importance of efficient communication and collaboration among stakeholders, as these processes are crucial for addressing the complexities of urban flood risk management. They stated that stakeholders can play in the development and enhancement of these tools. [PERSON] et al. [13] used an AR visualisation method based on a 3D-printed terrain model and explored techniques such as adaptive processing of flood data and hybrid virtual flood and terrain models. Their statistical results indicated that the methodology is useful for understanding the flood hazard and in providing a more intuitive and realistic visual experience at 60 frames per second. They obtained a better result compared to the traditional AR flood visualisation method.
# The Life-Cycle Environmental Impact of Recycling of Restaurant Food Waste in Lanzhou, China
###### Abstract  
The recycling of restaurant food waste can bring environmental benefits and improve food safety for urban residents. We here assessed the entire life cycle of the anaerobic digestion-aerobic composing technique of restaurant food waste recycling using Lanzhou as a case study. We used the CML2001 method provided with the Gabi software and compared the results to those produced using the traditional treatment techniques (landfill and incineration). This work includes a sensitivity analysis of the results. It is here concluded that the anaerobic digestion-aerobic composing technique had the smallest environmental impact of the methods here examined. The life cycle of anaerobic digestion-aerobic composing primarily consumes water, clay, coal, crude oil, and natural gas. The pre-processing phase consumes the most resources, and anaerobic digestion showed the greatest environmental impact. Specific environmental impacts in order from the highest to lowest potential to exacerbate global warming were found to be photochemical ozone production, acidification, eutrophication, marine aquatic ecotoxicity, human toxicity, freshwater aquatic ecotoxicity, and terrestrial ecotoxicity. The main factors associated with different environmental impacts and the environmental impacts themselves were found to differ across different phases. Some environmental impacts were shown to be sensitive to electricity, and the eutrophication potential and photochemical ozone creation potential showed the least sensitivity to all variables. To reduce the environmental impact of the anaerobic digestion-aerobic composing treatment technique, the energy structure and consumption of electricity, water, and diesel need to be optimized.  
life-cycle assessment; restaurant food waste; anaerobic digestion-aerobic composing; incineration; landfill
## 3 Numerical Simulation Verification  
### Numerical Calculation Model  
In this study, a numerical calculation method in FLAC\({}^{\text{3D}}\) was adopted to verify the correctness of the model (Equation (24)). The numerical model could simulate the stress and deformation response of rock materials under external force, and the radial displacement of the bolted rock joint in the shear test could be directly observed to determine the influence range of the bolt on the rock joint. Considering that the preliminary modeling in FLAC\({}^{\text{3D}}\) was relatively complex, this study intended to combine the convenience of Rhino modeling to establish the meshed numerical model and then import it into FLAC\({}^{\text{3D}}\) for calculation [34], as shown in Figure 7. The size of the model is 150 mm \(\times\) 300 mm \(\times\) 0.5 mm, and a total of 3973 tetrahedral elements and 8052 nodes are included. The section area of the bolt only accounted for 0.08% of the entire area of the model, and normal velocity limitation was adopted as a boundary condition on both the bottom and right sides. The radius of the bolt was set to 5 mm, the point load P of 1.3 \(\times\) 10\({}^{5}\) N was applied to the bolt element, and the change of \(u\), was observed by using the self-programmed FISH program. As this study aimed to solve the stress-strain elastic solution of the bolted rock joint at the compression-shear side, both rock mass and bolt adopt elastic. An interface contact unit in FLAC\({}^{\text{3D}}\) was applied to the contact between the rock mass and bolt, and cohesion \(c\) and the angle of internal friction \(\psi\) are equal to 0.3 MPa and 41.5\({}^{\circ}\), respectively. The mechanical parameters of the rock block refer to the calculation examples in Section 2.3. That is, the elastic modulus \(Er\), Poisson's ratio \(u\), and compressive strength \(\sigma\) are equal to 2 \(\times\) 10\({}^{4}\) MPa, 0.2, and 250 MPa, respectively. The mechanical parameters of the bolt [33] are shown in Table 1. It is assumed that the rock mass is in the elastic stage without plastic failure during the whole process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elastic Modulus** & **Poisson’s Ratio \(\mu\)** & **Bolt Radius/mm** & **The Compressive Strength/Mpa** & **Tensile Strength/Mpa** \\ \hline \(5\times 10^{8}\) & 0.1 & 5 & 400 & 570 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of bolt.  
Figure 6: Action range diagram of bolt under theoretical deduction.
## 2 The Proposed Hybrid Time Series Forecasting Methodology
### Modeling the Decomposed Sub-Series
#### 2.2.3 Autoregressive Moving Average Model  
The autoregressive moving average (ARMA) model includes both error terms and lagged values of the time series. In this work, the sub-series are modeled with a linear  
Figure 1: Ozone concentration in the metropolitan area of Lima (\(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\  
combination of \(x\) lagged values and delayed error terms [31]. Mathematically, the model equation can be expressed as  
\[\mathfrak{C}_{\text{n}}=\mu+\xi_{1}\mathfrak{C}_{\text{n}-1}+\xi_{2}\mathfrak{C}_{ \text{n}-2}+...+\xi_{x}\mathfrak{C}_{\text{n}-1}+\epsilon_{n}+\psi_{1} \epsilon_{n-1}+\psi_{2}\epsilon_{n-2}+...+\varphi\epsilon_{n-s}, \tag{4}\]  
where \(\mu\) is the intercept, \(\xi_{i}\)\((i=1,2,\ldots,x)\) and \(\psi_{j}\)\((j=1,2,\ldots,s)\) are the parameters for the MA and AR models, respectively, and \(\epsilon_{n}\sim N(0,\,\sigma_{\text{c}}^{2})\). In this work, the descriptive and graphical analysis indicates that, in the MA part, the first two lags are significant, whereas in the AR part, only lags 1, 2, 3, 4, and 5 are significant.  
In this research study, each combined model is denoted with the STLD method by \(\mathfrak{t}_{\text{n}}\)STLD\(\mathfrak{t}_{\text{n}}^{\text{n}}\), where the \(\mathfrak{t}_{\text{n}}\) in the top left corner represent the long-run component/sub-series, the \(\mathfrak{t}_{\text{n}}\) in the top right indicates the seasonal component/sub-series, and the residual component/sub-series is represented in the bottom right by \(\mathfrak{r}_{\text{n}}\). In the forecasting models, we assign the codes \"a\", \"b\", and \"c\" to the autoregressive, the nonlinear autoregressive, and the autoregressive moving average models, respectively. For example, \"STLD\(\mathfrak{t}_{\text{c}}^{\text{b}}\) describes the estimate of the long-term trend (\(\mathfrak{t}_{\text{n}}\)) with AR model, the seasonal series (\(\mathfrak{t}_{\text{n}}\)) estimated with the NLAR model, and the residual series (\(\mathfrak{r}_{\text{n}}\)) estimated by using ARMA. The individual forecast models are combined to obtain the final one-hour-ahead forecasts of ozone concentration.  
\[\hat{\mathfrak{C}}_{n+1} = \big{(}\hat{\mathfrak{t}}_{n+1}+\hat{\mathfrak{t}}_{n+1}+\hat{ \mathfrak{t}}_{n+1}\big{)} \tag{5}\]
## 2 Materials and Methods
### Cropping Intensity Computation  
All the GIS-related operations were done in ArcGIS 10 software. For the Kharif season, a unique field was included in the attribute table with a value of 2 for all the cropped area (polygons) and 0 for the non-cropped area (polygons). In the similar way, for the Rabi season, a unique field was included in the attribute table, and all the cropped areas were assigned a value of 3 and the non-cropped areas as 0. The generated vector layers were in union with each other so that a single vector file could be generated. In the generated vector layer, another column was added, and all the unique values of the Kharif and Rabi seasons were summed together. In this column, all the polygons with value 0 signified the non-cropped area, 2 depicts the Kharif season crops, 3 shows the Rabi season crop, and 5 depicts double season crops. A separate double-cropped area vector layer was prepared for future use. A 10 km \(\times\) 10 km fishnet was generated over the final vector layer, and (considered as net sown area) tabulate intersection analysis was processed on the data set to compute the cropped area under each of the fishnet blocks. A similar process was applied to the double-cropped area vector layer. For the computation of the gross cropped area of each of the fishnet blocks, the net sown area of the respective block was added with the area under the double crop of the same fishnet block.  
Figure 6: Double crop time series NDVI signature.  
For the calculation of cropping intensity, the following formula was used  
\[\text{Cropping Intensity}\left(\text{CI}\right)=\frac{\text{Gross Cropped Area }\left(\text{GCA}\right)}{\text{Net Sown Area }\left(\text{NSA}\right)}\times 100 \tag{3}\]  
For the spatial representation of the cropping intensity, centroids and their corresponding x and y coordinates were generated in each of the fishnet blocks along with respective cropping intensity values. Using Inverse Distance Weighted (IDW) method, a cropping intensity map was generated using five equal class intervals. For the validation of the results and accuracy of the classification, the net sown area was collected from all of the tehsil land record branches of the study area for the year 2020-2021.
# Assessment of AC Corrosion Probability in Buried Pipelines with a FEM-Assisted Stochastic Approach
## 3 Probabilistic Evaluation of the Leakage Current Density through a Holiday in the Coating
### Influence of the Main Parameters  
In this paragraph, some plots are presented that describe the probability of exceeding the value \(J_{lim}=30\) A/m\({}^{2}\) (suggested by [10,35]) versus the induced AC potential \(V\) by varying the main parameters involved in the model that is: the soil resistivity \(\rho\), the resistivity of the material inside the pore \(\rho_{\text{p}}\) and the insulating coating thickness \(d\). As far as the parameter \(\rho_{p}\) is concerned, two cases are considered:  
1. The resistivity of the material inside the pore is equal to the soil resistivity, i.e., \(\rho_{p}=\rho\);
2. The resistivity \(\rho_{p}\) is one order of magnitude smaller than \(\rho\) i.e., \(\rho_{p}=\rho/10\). This hypothesis is suggested by some results based on field measurements and described in [38].  
In Figure 9, the probability, of the induced AC current density exceeding the value of 30 A/m\({}^{2}\) versus the induced potential is shown; in particular, the curves have been plotted in correspondence with different values of soil resistivity by considering both cases (1) and (2).  
Figure 9 clearly confirms what is also known from experience, i.e., the probability of exceeding the limit value of 30 A/m\({}^{2}\) is much higher for low and medium-low soil resistivities than for high and medium-high resistivities. Moreover, as one could expect, the  
Figure 9: Per cent probability of exceeding 30 A/m\({}^{2}\) versus induced potential and for different values of soil resistivity; \(d=3\) mm.  
case with \(\rho_{p}=\rho/10\) is associated with higher probabilities because the spread resistance R\({}_{\rm s}\) is smaller than the cases with \(\rho_{p}=\rho\).  
Figure 10 has been plotted by considering different values for soil resistivity and very different values of the coating thickness i.e., \(d=1\) mm and \(d=10\) mm, representing the extreme values inside the typical range for d. Moreover, it has been assumed that \(\rho_{p}=\rho/10\).  
By looking at Figure 10, since \(\rho_{p}=\rho/10\), one has that the second addendum in (4) contributes only in a minor way to the spread resistance \(R_{s}\); for such a reason, the probability curves corresponding to the same value \(\rho\) differ between them only slightly. Therefore, one can conclude that if the resistivity inside the pore is much smaller than the soil resistivity, the coating thickness has a scarce influence on the probability of exceeding the 30 A/m\({}^{2}\) limits.
## 4 Influence of the Duration of the Laser Pulse  
Although a systematic study of all the parameters involved is far from the scope of the present work, we have evaluated the effect of the TL laser pulse duration by repeating some of our simulations using a shorter laser pulse durations (17 fs), and then comparing the results to the ones obtained with the TL 30 fs pulse. Figure 5 shows the output obtained for five specific different laser pulse excitation wavenumbers (18,800 cm\({}^{-1}\), 19,400 cm\({}^{-1}\), 19,700 cm\({}^{-1}\), 20,000 cm\({}^{-1}\) and 20,600 cm\({}^{-1}\)). As it is indicated, in the left-hand part of the figure (Figure 5a,c,e,g) the simulations have been performed with a laser pulse of 30 fs, as in Figure 2. The right hand panels (Figure 5b,d,f,h) show the results obtained by keeping all the same parameters but using the shorter laser pulse.  
The results in the left hand side of Figure 5 are cuts of the density maps shown in Figure 2 at specific excitation wavenumbers. The right hand side panels obtained for 17 fs show basically the same qualitative behavior as the ones obtained for 30 fs, except that we observe some undulations of the enhancement factors as the chirp is varied. An overall increase in the value of their maximum is also observed. The observed undulation may occur due to the larger effect of chirp on the shorter 17 fs pulse, since its duration for \(\phi^{\prime\prime}\) = \(\pm\)10\({}^{3}\) fs\({}^{2}\) results in \(\simeq\)159 fs, i.e., it is longer than the 94 fs that correspond to the 30 fs pulse at these chirp values. The longer time broadening of the 17 fs pulse hence allows the upper state vibration to make several oscillations (\(T_{\rm vib}\simeq\) 60 fs) during the intrapulse interaction, and can thus explain the observed enhancement undulations. The overall highest value of the enhancement factors obtained can be explained by the broader spectral wavelength of the 17 fs laser pulse (\(\simeq\) 865 cm\({}^{-1}\)), which in this case covers all the resonant energies of the upper states, and therefore more resonant transitions become available. The maximum enhancement obtained with positively chirped pulses for the population in the upper state is in this case \(\simeq\)1.4, at \(\widetilde{\
u}\) = 18,800 and with \(\phi^{\prime\prime}\) = 168.5 fs\({}^{2}\) (Figure 5f). For the coherence in the upper state the maximum enhancement is \(\simeq\)3.2 at \(\widetilde{\
u}\) = 18,800 and for \(\phi^{\prime\prime}\) = 168.5 fs\({}^{2}\) (Figure 5h).
## 1 Introduction
Based on SCADA capabilities and communication infrastructure, in [16,17], a real-time reactive power control has been implemented to optimally control the switched capacitors in distribution systems in order to minimize system losses and maintain admissible voltage profile. In [18], a new real-time voltage control method has been discussed, using load curtailment as a part of demand response programs to regulate voltage of the distribution feeders within their allowable ranges. However, few studies have been done about the daily VVC problem in distribution systems, in which the cost of reactive power support by various types of DERs has been considered. Hence, the establishment of a fair payment method for reactive power ancillary service of DER is necessary. Also, the reactive power capability (Q-capability) of DERs, especially the Q-capability of WTs considering wind speed fluctuations, has not been taken into account in previous studies of the VVC problem. These deficiencies motivated us to formulate a new pricing model for reactive power service from DERs, including synchronous machine-based DGs and WTs. This article also presents a new day-ahead active power market to minimize the electrical energy costs and the gas emissions of generation units. Along with this active power market, a novel reactive power dispatch framework is introduced to minimize the total cost of the following components: adjustment of the initially scheduled active powers, total active power losses, reactive power provided by DERs and Disco, and depreciation cost of the switchable facilities such as OLTC and Sh.Cs in order to achieve an economic plan for the daily VVC problem. Due to the presence of control devices such as DERs, OLTCs, Sh.Cs, _etc._, the daily VVC of a distribution system is a Mixed Integer Nonlinear Programming (MINLP) optimization problem. The complexity of solving this nonlinear optimization problem comprising integer variables and a great number of continuous variables forced us to employ decomposition techniques, such as the Benders decomposition algorithm. The innovative contributions of this paper are summarized as follows:  
* A new methodology is presented to determine the cost of reactive power support provided by DERs. * The Q-capabilities of DGs and renewable energy sources are involved in the daily VVC problem. * A novel reactive power dispatch framework is developed for daily VVC. The remainder of this paper is organized as follows: Section 2 describes the cost of reactive power production from DERs. In Section 3, the day-ahead active power market is introduced. The proposed daily VVC model is presented in Section 4. Section 5 presents the Benders decomposition algorithm, which is used as a solution methodology to solve the proposed reactive power dispatch model. The simulation results are discussed in Section 6, and the conclusions of this paper are reported in Section 7.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 4 Experimental Setup
### Quantifying Non-IIDness  
To situate our split against standard FL benchmarks, we report in Table 1 each client's label proportions and compute the average pairwise Jensen-Shannon (JS) divergence between clients' class distributions. With \(\alpha=0.5\), the mean JS divergence is 0.37, indicating a moderate degree of heterogeneity (cf. [2]).). We additionally report the macro-averaged area under the ROC curve (macro-AUC) for each method, which evaluates ranking quality across thresholds and is less sensitive to class-prior shifts than single-threshold F1.  
In contrast, the PneumoniaMNIST dataset (a MedMNIST collection of chest X-ray images for pneumonia vs. normal classification) is split in a roughly _i.i.d._ fashion: the training set is randomly divided into 5 class-balanced subsets so that each client receives an equal proportion of each class. We use appropriate models for each task: a simple multilayer perceptron (two 64-unit hidden layers with ReLU activations) for the tabular UNSW-NB15 features and a lightweight CNN for PneumoniaMNIST (two convolutional layers with 32 and 64 filters, respectively, each followed by ReLU and \(2\times 2\) max-pooling, then a 128-unit dense layer, and a final output layer).