esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Preparation
### Loading and Boundary Conditions  
Test specimens were subjected to uniformly distributed construction loads calculated in consideration of self-weights of deck plate and non-hardened concrete and workload, as shown in Table 2. The self-weight of deck plate with a thickness of 0.5 mm was defined as 0.25 kN/m\({}^{2}\) for all specimens; those of non-hardened concrete are determined depending on their slab depths and were 3.1 kN/m\({}^{2}\), 3.45 kN/m\({}^{2}\), and 4.6 kN/m\({}^{2}\) for 135-ED1-3.2, 150-ED3-3.8, and 200-ED5-4.5, respectively. In addition to these dead loads, workloads involving construction machines, various materials, workers, and so on were assumed to be 1 kN/m\({}^{2}\) for all specimens. The total construction loads, therefore, became 4.35, 4.70, and 5.85 kN/m\({}^{2}\) for specimens, 135-ED1-3.2, 150-ED3-3.8, 200-ED5-4.5, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimen Type**} & \multirow{2}{*}{
\begin{tabular}{c} **Clear Span** \\ **(mm)** \\ \end{tabular} } & \multicolumn{4}{c}{**Construction Load (kN/m\({}^{2}\))**} \\ \cline{3-6}  & & **Self-Weight of** & **Self-Weight of** & **Workload** & **Total** \\ \hline
135-ED1-3.2 & 3100 & 0.25 & 3.10 & 1 & 4.35 \\
150-ED3-3.8 & 3700 & 0.25 & 3.45 & 1 & 4.70 \\
200-ED5-4.5 & 4400 & 0.25 & 4.60 & 1 & 5.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Construction load.  
Figure 4: Experimental view.  
All three specimens were simply supported at 50 mm from both ends, indicating that the clear spans became 3100, 3700, and 4400 mm, respectively. The uniform loads were assigned to the deck plates using two kinds of loading plates of rectangular support and weight plates, as shown in Figure 5. Because these loading plates could not be simultaneously placed on the deck plates to realize the uniformly distributed loading conditions, they were assigned conservatively one by one from the center of the deck plate to the end. The weights of single support and weight plates were 80 and 40 N, respectively, which were measured prior to conducting the tests. The loading method is shown in Figure 6, which shows that the support plates are inserted between the lower re-bars and the deck plate, and the weight plates are attached to both sides of the support plates. The loading conditions for 135-ED1-3.2A is shown in Figure 7.  
Figure 5: Loading equipment. (**a**) Support plate (80 N for each). (**b**) Loading plate (40 N for each).  
Figure 6: Loading method.  
The tests were performed in two steps. The specimens were loaded until the specified construction loads, provided in Table 2, were reached and were unloaded completely to evaluate the restoring capability, respectively. The loads were then applied to the maximum in order to observe the failure modes of the specimens. The overall test set-up is shown in Figure 8.
## 2 Method
### Prediction Models
#### 2.3.1 Logistic Regression  
Logistic regression model is a kind of generalized linear model, which is basically the same form as the multiple linear regression analysis model, and both of them have \(w^{\prime}x+b\). Logistic regression uses sigmoid function to convert the predicted value \(z=w^{\prime}x+b\) generated by linear regression model into [0,1] interval value [23]. Binary logistic regression model is a classification model, and its distribution is as follows:  
\[P(y=1|x)=\frac{exp(w^{\prime}x+b)}{1+exp(w^{\prime}x+b)} \tag{1}\]  
\[P(y=0|x)=\frac{exp(w^{\prime}x+b)}{1+exp(w^{\prime}x+b)} \tag{2}\]  
where \(x\in R^{n},y\in 0,1\), w and b are weights and offset values. Given an input vector x, you can find the value of \(P(y=1|x)\) and the value of \(P(y=0|x)\). By comparing the size of the two values, you can divide x into the category with larger value.  
The process of MOOC learners' dropout behavior prediction based on logistic regression model is as follows: the first step is to define the range of logistic regression superparameters, the second step is to create a new logistic regression object, the third step is to set AUC score as the model measurement standard, the fourth step is to cross verify the grid search to select the optimal superparameters, the fifth step is to use the optimal superparameters to fit the model on the training set, and the sixth step is to use the fitted model to predict on the test set. Figure 2 is the flow chart of MOOC learners' dropout behavior prediction based on logistic regression model.  
Figure 2: Logistic Regression model prediction flowchart.
## 3 Materials and Methods
### Methods
#### 3.2.2 Geochemical Analysis  
Geochemical analysis was performed on all 45 samples, which were initially air-dried, crushed, and homogenized before final pulverization. Samples for whole-rock analyses of major oxides were performed by Activation Laboratories Ltd. (Ancaster, ON, Canada) and carried out by ICP-OES. Major element fusion was performed by a flux of lithium metaborate and lithium tetraborate and fused in and induction furnace. The molten melt is immediately poured into a solution of 5% nitric acid containing an internal standard and mixed continuously until completely dissolved. The samples are run for major oxides and selected trace elements (4B) on an ICP. Calibration is performed using 14 prepared USGS- and CANMET-certified reference materials. Standards used are: DNC-1, NOD-A-1. NOD-P-1, SY-4, and BIR-1a (see details in Table S2). Total organic carbon (TOC) was determined using the infrared LECO method. Carbon is measured as carbon dioxide in the IR cell as gases flow through the IR cells. The analysis is performed using ELTRA Instruments. Standards used were GS311-4, GS316-3, and GS317-5 (see details in Table S2). The determination of paleoweathering is based on the triangular plots of [PERSON] and [PERSON] [65] expressing the molar volumes of [Al\({}_{2}\)O\({}_{3}\)/(Al\({}_{2}\)O\({}_{3}\) + Na\({}_{2}\)O + K\({}_{2}\)O + CaO*)]100 (A-CN-K diagram) and the chemical index of alteration (CIA), where CaO* represents the CaO content of the silicate minerals. The complete description of the calculation procedure can be found in detail in [PERSON] and [PERSON] [5]. Provenance is determined using discriminant function diagrams by [PERSON] and [PERSON] [66]. The diagrams divide the sedimentary rocks into four different provenances (quartzose sedimentary provenance, felsic, intermediate, and mafic igneous provenance) and are based on the major oxides and the ratio between the major oxides, based on calculations given in [PERSON] et al. [67]. Tectonic settings based on [PERSON] [68] tectonic discriminant functions are founded on the ratios of TiO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) + MgO, and of Al\({}_{2}\)O\({}_{3}\)/SiO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) + MgO and of SiO\({}_{2}\) versus log (K\({}_{2}\)O/Na\({}_{2}\)O) after [PERSON] and [PERSON] [66]. Tectonic settings are based on discrimination diagrams of Verma and Armstrong-Altrin [3] and differ according to low silica content (>35%-\(\leq\)63%) and high silica content (>63%-\(\leq\)95%). The discriminant function of Verma and Armstrong-Altrin [2] separates the active and passive margin settings. The entire procedure with the calculation formulas is described by [2] and the supplementary files therein.
# A Multi-Period Optimal Reactive Power Dispatch Approach Considering Multiple Operative Goals
## 2 Proposed Mathematical Modeling
### Inequality Constraints  
#### 2.3.1 Generators Constraints  
Equation (11) corresponds to the reactive power limits (\(\overline{Q_{g}}\) maximum and \(\underline{Q_{g}}\) minimum) on the set of online generators and FACTS (flexible AC transmission systems) devices. In this case, \(U_{gt}\) represents the state of generator \(g\), where 1 indicates being on service and 0 otherwise. Equation (12) corresponds to the active power limit (\(\overline{P_{g}}\) maximum and \(P_{g}\) minimum) on the set of online slack generators (which guarantees the active power balance). The rest of the generators are considered as fixed sources of active power.  
\[U_{gt}Q_{g}\leq\ q^{\mathcal{G}}_{gt}\ \leq\ U_{gt}\overline{Q_{g}},\forall g\in \mathcal{G};t\in\mathcal{T} \tag{11}\]  
\[U_{gt}P_{g}\leq\ p^{\mathcal{G}}_{gt}\ \leq\ U_{gt}\overline{P_{g}},\forall g\in \mathcal{G}_{slack};t\in\mathcal{T} \tag{12}\]
## 3 Characterisation
### Laboratory Protocols  
Much has been learned to date, but the variability of nanosafety related protocols (exposure, release, dosimetry, characterisation, hazard, _etc._) renders the available information difficult to compare and interpret [47].  
The authors have been party to an online-space for sharing research protocols with other members within the project. The aim was to enable laboratories throughout the world to easily compare their methods and subsequently develop common protocols and strategies for the safety testing of NMs. While those in the field were motivated to discuss the harmonisation of nanoscience protocols, considerable effort and time are required for such harmonisation. It was not always evident who is the right or available individual, organisation or unit to perform this task.  
It also became clear in the project that protocols often need to be adapted to deal specifically with nanoscale materials and also that protocols suitable for one type of NM may not be applicable to other types [7].  
**Recommendations 19**: Funding agencies should request specific protocol proposals from those applying for funding and could also require the definitive description of protocols in the final reports of funded projects. Research proposals should explain why a particular protocol was chosen over another. The development of nanosafety science would be supported if peer-review journals require authors to be absolutely specific in their description of protocols within the materials and methods section, not accepting a simple reference to previously published protocols that were \"slightly adapted\".  
**Recommendation 20**: Careful characterization of all exposures, and understanding of biokinetics and mechanisms/mode of actions, should be encouraged for proposals for academic research into nanosafety. This is because without the exposure characterization, and without understanding biodistribution and persistence and mechanisms of action, hazard data cannot be interpreted for risk assessment purposes. Standard protocols for dispersion of nanoparticles should be agreed and differentiation between intrinsic NM responses and realistic exposure scenarios (in which NMs are more likely to be present as agglomerates) should be made.  
**Recommendation 21**: For many protocols, we recommend that tracking and visualisation techniques, _in vitro_ and _in vivo_, be invented, improved and fine-tuned to allow NM identification, quantification and characterisation _in situ_[7].  
**Recommendation 22**: Overall, existing protocols are prone to artefacts and these should be well documented and publicized, so that all research publications include the control tests used to verify the applicability of the chosen assay to the particular NM being studied. Much can potentially be learned from the pharmaceutical and cosmetics industries, as well as from the current understanding of the biological hazards of ultrafine particles (transport pollution, _etc._) [7].
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 2 Related Work  
In this section, the machine learning-based SS schemes are reviewed and discussed. From Table 1, it can be seen that much effort has been made to possibly improve the performance of SS. In [31; 34; 36], machine learning (ML)-based schemes are considered for SS, including multilayer perceptron (MLP), support vector machine, Naive Bayes, directed acyclic graph-support vector machine, K-nearest-neighbor and generalized likelihood ratio test. Meanwhile, deep learning (DL) is utilized for SS in [30; 32; 33; 35; 37], where CNN with STFT, CNN with LSTM, deep reinforcement learning and a generative adversarial network (GAN) are considered. Although the sensing performance is greatly improved for the above-mentioned schemes, the ML-based schemes are devoted to the extraction of the shallow features, while the DL-based schemes aim at feature extraction in the frequency domain and complex features, at the expense of computational complexity. In this paper, three basic CNN networks are considered to improve the performance of SS, based on a novel SS dataset. The LeNet network, AlexNet network and VGG-16 network are trained and adjusted on the proposed dataset. Superb sensing performance is obtained based on the simulation experiments. Note that the proposed schemes in this paper balance sensing performance and sensing complexity. Although the considered CNN networks are classical, they are easy to implement due to their high popularity. In addition, the dataset considered in this paper is based on the energy value of the local SS node. Both the data acquisition and the network structure have low complexity. However, the sensing performance is at a high level according to the results of the simulation experiments. In summary, the proposed schemes in this paper are necessary and useful for the possible performance improvement of SS.
## 2 Materials and Methods
### Data
#### 2.2.4 Space-Borne Radar Precipitation Product (IMERG)  
The space-borne radar precipitation data utilized in this study is derived from the Integrated Multi-satellite Retrievals for GPM (IMERG) product, which is produced globally by the NASA Goddard Space Flight Center. IMERG combines data from the core Global Precipitation Measurement (GPM) satellite and a constellation of around 10 supporting satellites equipped with Microwave Imagers [5]. This dataset provides gridded precipitation estimates with a resolution of approximately 10 km (a grid size of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)) within the latitude range of 60\({}^{\circ}\) N to 60\({}^{\circ}\) S in the WGS84 coordinate system. The temporal resolution ranges from as frequent as every 30 min to daily and monthly intervals, with options available for 3 h, 3 days, and 7 days aggregations to meet various research requirements.  
IMERG offers data in different stages, namely \"Early\", \"Late\", and \"Final\" runs, catering to various application needs. Each run has a distinct latency period, with durations ranging from a few hours for rapid response applications like flash flood observations to several months for research purposes; approximately 4-5 h (suitable for flash flood observations), 14-15 h (agricultural applications), and around 3 months (research data) [9]. These runs facilitate the timely availability of precipitation information for diverse user requirements. The data production system is depicted in Figure 4, providing insights into the data and runs involved.  
The IMERG product continually evolves, with version 06 being the most recent iteration. It incorporates advancements and updates to improve the accuracy and reliability of the precipitation estimates. Notably, the integration of the Tropical Rainfall Measuring Mission (TRMM) satellite-based TMPA with IMERG has further enhanced the dataset, offering a comprehensive satellite-based precipitation record from 2000 to present [8]. This integration enables the analysis of long-term precipitation patterns and trends for a wide range of research applications, including hydrological modeling. The specifics of the IMERG data can be found in Table 3. The data are primarily provided in \".HDF5\" or \".nc4\" file formats, and the grid images are recorded in the WGS84 geographic coordinate system, allowing for seamless integration with other geospatial data and enabling various geoprocessing tasks such as coordinate projection. These characteristics make the IMERG dataset suitable for hydrological modeling studies and watershed analyses.  
Figure 4: Schematic diagram of the IMERG inputs that go into the IMERG HDF5 and IMERG GIS data products, taken from [9].
## 3 Results and Discussion
### Basic Oenological Parameters
cerevisiae_, with an inverse correlation between glycerol and volatile acidity [3; 4; 13]. [PERSON] et al. [13], building on the findings of [PERSON] et al. [32], stated that in _S. paradoxus_, acetic acid may be integrated into various metabolic reactions to a greater extent than in _S. cerevisiae_. One such reaction is the production of acetyl-CoA catalysed by acetyl-CoA synthetases. Acetyl-CoA is a precursor for fatty acids involved in cell membrane restructuring, which enhances ethanol resistance in yeast strains that are less adapted to alcoholic fermentation. It is also possible that _S. paradoxus_ and _S. cerevisiae_ possess distinct metabolic pathways for acetic acid production, transport, and metabolism. These differences may involve variations in the expression of the corresponding genes and the activity of enzymes, such as pyruvate decarboxylases, aldehyde dehydrogenases, and others. Total acidity was not significantly affected by the yeast strains, though a tendency toward lower values in SPA-21 compared to SCE-21 was noted. This likely resulted from a reduction in malic acid content in SPA wines across both harvest years (Table 1), a trend often associated with _S. paradoxus_ fermentations [3; 5; 9; 25; 33]. The ability of yeast to degrade extracellular malic acid depends on the efficient transport of the acid into the cell and the activity of the intracellular malic enzyme, which is coded by specific genes [34]. [PERSON] et al. [33] reported that _S. paradoxus_ reduced malic acid more effectively than _S. cerevisiae_ and _S. bayanus_ in the alcoholic fermentation of synthetic must and wine. _Saccharomyces paradoxus_ continued to degrade malic acid even after glucose depletion, suggesting that malic acid may serve as a secondary carbon source for this yeast in glucose-depleted environments. This enhanced malic degradation was linked to an increased expression of malic enzymes, potentially indicating a more efficient transport system for this acid in _S. paradoxus_. Fermentation by _S. paradoxus_ led to slightly elevated tartaric acid and decreased citric acid levels compared to control SCE wines (Table 1). The concentrations of malic, tartaric, and lactic acids were significantly influenced by harvest year. Higher malic acid levels were observed in 2021, while tartaric and lactic acid were more abundant in 2022.
## 3 Experiments
### Performance Evaluation Methods  
In the field of ML, the confusion matrix, also called the error matrix, is generally used to represent the performance of models. A corresponding matrix comprises class labels and subsets as in Table 2. In this study, the labels were classified into two classes: positive for warped samples and negative for unwarped samples.  
Four subsets were considered based on the classified labels. True positive is the correct prediction of a warped sample, and true negative is the correct prediction of an unwarped sample. False positive is the incorrect prediction of an unwarped sample into a warped sample, and false negative is the incorrect prediction of a warped sample into an unwarped sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Output**} \\ \cline{3-4}  & & **Abnormal** & **Normal** \\ \hline \multirow{2}{*}{**Actual Output**} & Abnormal & True Positive (TP) & False Negative (FN) \\  & Normal & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix representation for binary classification. Note that we defined “warping deformation” as abnormal.  
Figure 4: (a) A sample of the thermocouple data from each corner of the not defected specimen; (b) A sample of the thermocouple data from each corner of the warping deformed specimen.  
In this experiment, the evaluation metrics defined in Equations (6)-(10) were used based on the above-classified subsets to determine the classification performances of predicting and categorizing the occurrence of warping deformation.  
Both the prediction error (ERR) and accuracy (ACC) provided general information on how many samples were misclassified, and they are expressed in Equations (6) and (7), respectively. Error is the sum of incorrect predictions divided by the number of predicted samples, and accuracy is the sum of correct predictions divided by the number of predicted samples. Accuracy has an inverse relationship with prediction error and can be expressed in terms of error as in Equation (7). Precision is the proportion of the warped samples among the ones predicted as warped, while recall is the proportion of the predicted samples as warped among the warped ones. F1-score is the harmonic mean of precision and recall. Higher values of the following three metrics indicate better predictive performances. These metrics are expressed in Equations (8)-(10).  
\[ERR=\frac{FP\ +\ FN}{FP+FN+TP+TN} \tag{6}\]  
\[ACC=\frac{TP+TN}{FP+FN+TP+TN}\ =\ 1-ERR \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F1\text{-}score=\frac{2(Precision+Recall)}{Precision\ +\ Recall} \tag{10}\]
## 4 Noise Performance of \(R_{\rm c}(I)\) and \(R_{\rm ch}(I)\)  
Next, we proceed with the noise measurements, the results of which are shown in Figure 6. Higher excess noise temperature \(\Delta T_{\rm n}\) is obtained for the shorter channel (Figure 6a, closed triangles).  
The excess noise temperature \(\Delta T_{\rm n}\) taken at constant currents for the different length channels is depicted in Figure 6b (symbols). The decrease in \(\Delta T_{\rm n}(L)\) can be explained by the presence of two distinct noise temperatures \(\Delta T_{\rm nc}\) and \(\Delta T_{\rm nch}\) in the sample where the former is that of the contact resistances \(2R_{\rm c}\) and the latter is that of the channel resistance \(R_{\rm ch}\). It is easy to show that \(\Delta T_{\rm n}\) of the serial connection of \(2R_{\rm c}\) and \(R_{\rm ch}\) is:  
\[\Delta T_{\rm n}=\frac{\Delta T_{\rm nc}\times 2R_{\rm c}+\Delta T_{\rm nch }\times R_{\rm ch}}{2R_{\rm c}+R_{\rm ch}} \tag{5}\]  
It follows that if \(\Delta T_{\rm nc}>\Delta T_{\rm nch}\), then \(\Delta T_{\rm n}\) decreases from \(\Delta T_{\rm nc}\) to \(\Delta T_{\rm nch}\), as the ratio \(R_{\rm c}/R_{\rm ch}\) decreases from infinity to zero, while both temperatures \(\Delta T_{\rm nc}\), \(\Delta T_{\rm nch}\) and \(R_{\rm c}\) do not depend on \(L\) at a given current and only \(R_{\rm ch}(L)=(R_{\rm 2D}/W)\times L\) depends on \(L\). Thus, the  
Figure 5: The dependence of \(L_{\rm x}\) on the current density for different samples; AlGaN/GaN (squares), AlGaN/AlN/GaN (triangles and circles).  
measured \(\Delta T_{\rm n}(L)\) dependence can be used to extract the temperatures \(\Delta T_{\rm nc}\) and \(\Delta T_{\rm nch}\) by fitting the experimental data with:  
\[\Delta T_{\rm n}(L)=\frac{\Delta T_{\rm nc}\times 2R_{\rm c}+\Delta T_{\rm nch} \times(R_{\rm 2D}/W)\times L}{2R_{\rm c}+(R_{\rm 2D}/W)\times L} \tag{6}\]  
where \(R_{\rm c}\) and \(R_{\rm 2D}\) are known (see Figure 3b). The solid curves in Figure 6b are the fit with the \(\Delta T_{\rm n}(T_{\rm nc},\,T_{\rm nch})\) model (Equation (6)). Formally small signal resistances at \(\sim\)10 GHz should be used in Equation (6); however, this simple model gives a good qualitative picture of two different noise temperatures: the noise temperature of \(R_{\rm c}\) is higher than that of the channel resistance. The extracted \(\Delta T_{\rm nc}\) is indicated by the squares in Figure 6a.  
Figure 6: The dependence of the microwave excess noise temperature \(\Delta T_{\rm n}\) on the current for the different channels length of the same TLM in the AlGaN/GaN sample (**a**). \(L\) = 6 μm (closed triangles), 12.5 μm (open diamonds), 25 μm (open triangles) and 45 μm (open circles). Lines guide the eyes. Estimated \(\Delta T_{\rm n}\) at the constant currents is plotted versus channel length (**b**). \(l\) = 100 mA (circles), 150 mA (rectangles), 220 mA (triangles) and 270 mA (pentagons). Curves are the fit by the model (Equation (6)). Excess noise temperature \(\Delta T_{\rm nc}\) of the contact resistance \(R_{\rm c}\) is estimated as a fitting parameter and its current dependence is depicted by the open rectangles.