esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The temperature (T) measured at baseline (t0) was compared with the temperature at t1 for each TrPs location in the same subject: if at least 2 out the 6 areas of the template showed a \(\Delta\)T(t1 \(-\) t0) \(\geq\) 0.1 \({}^{\circ}\)C it meant that the subject did not show basal conditions before the PPT; therefore, the measurement of this TrP location was not processed furtherly. For each TrP location that has been validated, two temperature (T) variations were calculated: \(\Delta\)T\({}_{1}\) = T(t2) \(-\) T(t1) and \(\Delta\)T\({}_{2}\) = T(t3) \(-\) T(t1). As stated by [PERSON] et al. [20], differences in \(\Delta\)T range \(\geq\) 0.2 \({}^{\circ}\)C have been considered significant. The same template (Figure 1) was used to report the self-perceived referred pain intensity (VAS) and localisation, and to extract the temperature variation after PPT. Mouth and eyes regions were excluded as they might be confounding in the morphed files. Using the same template, it was possible to obtain superimpositions of the VAS and the temperature variation. Thermal IR images were processed by means of an in-home made software (IRI ImagePro) [21], based on the Matlab platform. The facial thermograms of each subject were projected and registered in post-processing over the same six facial regions template (Figure 1) used by the subject to describe the area of referred pain. The registration algorithm was based on a warping technique presented in [PERSON] et al. [22]. It allowed performing a spatial normalisation between subjects. Spatial normalisation consisted of warping images from several individuals into a common anatomical space (template) so that alterations of the thermal signal could be compared on a pixel-by-pixel basis. To match images from different subjects, a combination of local transformations is required. The method was based on the Local Weighted Mean algorithm. It allowed modelling the local varying geometry of the body [23; 24], based on a set of 28 corresponding points, drawn on the thermogram and template (Figure 2). Figure 1: Template showing the six areas based on the trigeminus branches innervation. Figure 2: Example of morphing system.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 2 Materials and Methods
### Methods
#### 2.6.2 Analysis of Anomalies  
The standardized anomalies of all variables (including temperature, precipitation, VPD, SM, NDVI, SIF, GPP, and NEE) are computed to facilitate the study of the spatiotemporal variations of these variables during droughts and heatwaves. We calculate the standardized anomalies for each variable at the pixel level. These anomalies represent how each pixel deviates from the average conditions over multiple years, adjusted for the standard deviation between 2018 and 2021. The specific formula is as follows:  
\[\mathrm{Y(i,j,t)}^{\prime}=\frac{\left(\mathrm{Y(i,j,t)}-\overline{\mathrm{Y }}(\mathrm{i,j})\right)}{\mathrm{std}(\mathrm{Y(i,j,t)})} \tag{1}\]  
where \(\mathrm{Y(i,j,t)}^{\prime}\) represents the normalized index anomaly of pixel \(\mathrm{(i,j)}\) at time \(\mathrm{(t)}\); \(\mathrm{Y(i,j,t)}\) denotes the original values of the index for pixel \(\mathrm{(i,j)}\) at time \(\mathrm{(t)}\); \(\overline{\mathrm{Y(i,j)}}\) is the mean data for pixel \(\mathrm{(i,j)}\) during the period 2018-2021; and \(\mathrm{std}(\mathrm{Y(i,j,t)})\) is the standard deviation for pixel \(\mathrm{(i,j)}\) from the years 2018 to 2021.  
The anomaly calculation formula (Formula (1)) is based on the robust satellite techniques (RST) approach. The RST approach is a widely used approach for analyzing multi-temporal satellite data that is primarily aimed at detecting anomalies in the spatiotemporal domain to monitor environmental changes. The core idea of this method is to establish a baseline normal state based on years of satellite observations, typically represented by the temporal mean and standard deviation of signals over specific time periods. By comparing current observed signals with this baseline normal state, anomalies can be identified [35]. The RST method has been successfully applied across various fields, including monitoring changes in soil moisture and dynamic monitoring of landslides using NDVI [36; 37].
## 5 Analysis of Experimental Results
### Detectability of Delaminations Using SNR  
Figure 8a shows an example of the thermal image of the back face of the specimen with delamination depths ranging (1 to 4) cm. The image is taken at 1000 s after turning off the heat source under 30 min heating. The yellow (bright) and green (dark) colors represent high and low temperature, respectively. Delamination can be clearly recognized, because the surface temperature above a defect is much higher than its surroundings. Figure 8b shows the temperature profile of line 1-1 illustrated in Figure 8a. This line crosses from left to right of the thermal image, cutting the four upper delaminations. It can be stated that under the same testing conditions, the surface temperature above the deeper delamination is smaller than that above a shallower one.  
By using the SNR shown in Equation (7), the image contrast (or the detectability) is measured with different heating regimes, as summarized in Table 4. The results show that as depth increases, the specimen needs to be heated for much longer. For example, delaminations at 1 and 2 cm depths were detected quickly after just 2 min of heating, while it required 5 and 10 min heating to detect delaminations at 3 and 4 cm depths, respectively. In the case of the deeper delaminations from 5 to 8 cm, the required heating time increased significantly. For example, to identify the delaminations with depths of 5 and 6 cm, FD1, WTDR = 2.0 and FD2, WTDR \(\approx\) 1.67, respectively, the specimen needed to be heated corresponding to 20 and 25 min, while the heat source had to be kept on for 40 min for a depth of 7 cm (FD3, WTDR \(\approx\) 1.43). The deepest delamination, i.e., FD4 (WTDR = 1.25) at 8 cm depth, was undetectable, even when heated up to 40 min. The results indicated that infrared thermography makes it possible to detect the delamination with WTDR \(\geq\)1.43. Based on Table 4, the detectability of delaminations can be divided into two regions of negative and positive SNR values as illustrated in Figure 9. The green (dark) and pale-yellow (bright) regions depict the identified and unidentified delaminations, respectively. The triangular symbols with black borders represent the undetectable delaminations, while the red squares stand for the detectable delaminations.  
Figure 8: The test results at 1000 s after turning off the heat source in the case of 30-min heating: (**a**) thermal image and (**b**) the surface temperature profile of the line 1–1.
# Energy Transition on Sifnos: An Approach to Economic and Social Transition and Development
## 3 The Hybrid Power Plant of Sifnos
### Computational Simulation Data  
For the computational simulation of the hybrid power plant's annual operation, a new power demand annual time series was developed, as the aggregate of the current power demand and the electrical vehicles (EVs) charging load, anticipated due to the full transition to e-mobility. For the estimation of the latter, according to the CETA [6], the annual existing final energy consumption for the transportation on the island was calculated at 17,281 MWh. Considering the lower calorific value of diesel oil and gasoline at 10.0 and 8.9 kWh/L, respectively [56], an average fuel-specific consumption in conventional vehicles of 7.5 L/100 km and an average electricity-specific consumption of a corresponding electrical vehicle of 20 kWh/100 km [57], the total electricity consumption for the full transition to e-mobility for the onshore transportations was calculated at 1500 MWh.  
The new power demand for charging EVs was developed by introducing a daily charging profile, shown in Figure 5, based on existing literature approaches [58]. Different charging load daily profiles were introduced for EVs charging at home, at work and at public charging stations. The annual EV charging load time series was developed by simply replicating the daily profiles for the whole year.  
Figure 5: Daily charging load profiles for the EVs.  
The newly developed annual power demand time series is presented in Figure 6. The annual peak demand and the annual electricity consumption are calculated as being equal to 6.71 and 20,357 MWh, respectively.  
The wind potential at the wind park's installation site was evaluated with certified wind potential measurements accomplished over a period of 14 months, from July 2016 to September 2017, covering the summer months twice, namely the crucial peak demand period. The average wind velocity during the measurement period was 9.89 m/s, classifying the available wind potential among the highest in Europe [59]. Most importantly, during July and August, namely during the peak demand period, the average monthly wind velocity was measured at 11.8 and 9.1 m/s, respectively, in 2016, and at 10.1 and 12.0 m/s, respectively, in 2017, revealing concurrency between the availability of high wind potential and the peak demand period. In Figure 7, the wind rose and the wind velocity Weibull distribution (C = 11.2 m/s, k = 1.91) are presented, based on the available measurements [60]. Another important feature revealed from the wind rose graph is the almost constant wind blowing direction from the north-northeast, perpendicular to the mountain's ridge orientation. This feature, apart from the minimization of the wake shading losses, is expected to provide an important contribution to the insular grid's stability, due to the stable, scarcely fluctuating northerly winds, enabling the more secure and constant production of the wind turbines.
# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 3 Results
### Quantitative Analysis  
In general, classification problems are evaluated using the performance metrics of accuracy, sensitivity, specificity, and F-measure. The proposed saliency-based, multiple-kernel learning classification is also quantified by the performance metrics of _Accuracy (A)_, _Sensitivity (S)_, _Specificity (SP)_, and _F-measure (Fm)_.  
\[Accuracy\ (A)=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{13}\]  
\[Sensitivity\ (S)=\frac{(TP)}{(TP+FN)} \tag{14}\]  
\[Specificity\ (SP)=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{15}\]  
\[F\ measure\ (Fm)=\frac{(2 TP)}{(2 TP+FN+FP)} \tag{16}\]  
where \(TP\) is true positive, \(TN\) is true negative, \(FP\) is false positive, and \(FN\) is false negative. Table 2 presents the individual stage performance metrics.  
From Table 2, it is ca be seen that the performance of elderly subjects decreased with mild Alzheimer's disease. A comparative analysis was performed using state-of-the-art methods using the same OASIS dataset.  
A comparative analysis was performed using the methods of [PERSON] et al. [38], [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al. [16; 17]. The feature-based morphometry of [PERSON] et al. [38], independent component analysis (ICA) of [PERSON] et al. [40], and the support vector machine of [PERSON] et al. [39] were used in the comparative analysis. All of these methods use the OASIS dataset with four different groups. An equal error rate was used to classify diseases. ICA and SVM were used in [40]. In this method, the performance metrics are calculated using different formulae that are not in the standard definition formulae. The comparison method of [PERSON] et al. [39] used a saliency map and SVM for disease classification. The average error rate was 0.725, and the average accuracy was 74.54%. The proposed method is also compared with the recent literature involving with wavelet-transform-based feature detection. [PERSON] et al. [41] used an extreme learning machine and dual-tree for concepts for AD classification. [PERSON] et al. [42] and [PERSON] et al. [43] used wavelet entropy, particle swarm optimization, and neural network classifiers. The proposed method produced reliable results in the performance metrics of accuracy(A), sensitivity(S), specificity (SP), and F-measure (Fm).  
The importance of the visual saliency map in AD classification is discussed and evaluated in the present study. Eight state-of-the-art methods for AD classification were used for the comparison. Visual saliency analysis and MKL were the most critical techniques adopted in the present study. The robustness of the proposed method is shown with respect to the performance metric scores.
## 3 Results and Discussion
### Characterization of Flame-Retardant Coating Formulations Using XRD  
The XRD profiles of F0 and F3 formulations are shown in Figure 7. The sharp peaks of both formulations at 2\(\approx\) 14.4\({}^{\circ}\), 15.6\({}^{\circ}\) (corresponding to APP) [41; 42], 19.9\({}^{\circ}\) (showing chitosan) [43; 44; 45], 25.8\({}^{\circ}\) (melamine) [46], and 28.0\({}^{\circ}\) (boric acid) [47; 48] represent the crystalline patterns. Only the F3 formulation shows extra peaks of low intensity at 30.6\({}^{\circ}\), 40.4\({}^{\circ}\), 50.2\({}^{\circ}\), and 59.1\({}^{\circ}\), which are characteristics peaks corresponding to ZrO\({}_{2}\)[8]. No peak shifting was observed with the addition of ZrO\({}_{2}\), indicating that 3% (_w/w_) ZrO\({}_{2}\) additions do not affect the lattice parameters or the crystal structure of the host material. Furthermore, the almost similar intensity of the characteristic peaks in the XRD pattern of both the formulations points to the similar morphology of these materials.  
Figure 5: Transform infrared (FT-IR) spectra of ZrO\({}_{2}\) NPs.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 4 Experimental Evaluations
### Simulation Results and Analysis with NAIE Channel
#### 4.3.2 The BLER Performance of the NAIE Channel  
Compared with the w/o compression scheme which fully attains the precoding matrix, the CNN-based PCQNet can enhance the recovery quality of the precoding matrix with adaptive feedback overhead and obtain near optimal reconstruction performance. Similar performance can be seen over the NAIE channel in Figures 8 and 9. The CNN-based PCQNet scheme performs close to the ideal w/o compression scheme when \(\text{CR}=1/2\). Moreover, there is a slight performance penalty when \(\text{CR}=1/16\) which is still superior to the 3 GPP codebook. With the deployment of the pre-trained PCQNet, the feedback overhead is substantially reduced while the performance degradation is acceptable. The PCQNet scheme has better BLER performance than the LloydMax scheme under the same CRs.  
The PCQNet achieves the near-optimal BLER performance when the NMSE of the recovered precoding matrix is lower than \(-20\) dB. The transmission tends to stop if the NMSE of the recovered precoding matrices is worse than the threshold of \(-5\) dB. We also observe that, when the NMSE performance exceeds a certain threshold (e.g., \(-20\) dB), the overall BLER performance is quite close to the ideal w/o compression scheme. From the results, we can see that it is a reasonable compromise to set the CR to \(1/4\) for the compression of precoding matrices.  
The provided numerical results show that our proposed PCQNet achieves a better trade-off between the feedback overhead and the BLER performance over the i.i.d. Rayleigh channel and the NAIE channel. This CNN-based compression scheme significantly enhances the BLER performance compared with the 3 GPP codebook scheme and the LloydMax scheme under the same CRs.  
Figure 7: The NMSE performance of comparison between the Lloyd-Max scheme and the CNN-based PCQNet scheme over the NAIE channel. (The x-coordinates represent the SNRs for the generation of the testing dataset, which are not directly correlated with the NMSE performance).
## 3 Methods
### Visualization Method Based on Density Partitioning Clustering Algorithm
#### 3.2.1 Construction of Density Partitioning Clustering Algorithm Model
However, if a meeting exists, it becomes imperative to compute the visible points of the buildings to ascertain the minimum distance of line segment \(XY\). The method of calculating the visible points is as follows: The line segment \(XY\) intersects with buildings \(O_{1}\), \(O_{2}\)...,\(P_{i}\) represents any vertex from the group of buildings. If the vertices on both sides of \(P_{i}\) are not situated on the opposite side of the line containing \(XP_{i}\), then \(P_{i}\) becomes the edge visible point of \(X\). 5. By following step (4), the edge visible points for both \(X\) and \(Y\) can be determined, with \(XP_{i}\) denoting the edge visible point for the trajectory point \(X\) and \(YP_{i}\) representing the edge visible point for the trajectory point \(Y\). When there are coincident points \(Q_{1}\), \(Q_{2}\)... in \(XP_{i}\) and \(YP_{i}\), then the points \(X\) and \(Y\) can be connected with the help of this point. Consequently, the real distance bypassing the building is \(\mathrm{Ddist}=\min(\{|XQ_{1}|+|Q_{1}Y|\},\{|XQ_{2}|+|Q_{2}Y|\},\ldots)\). 6. If there are no overlapping points, then find the visible point \(XP_{ij}\) for each point \(XP_{i}\) in the set of visible points \(XP\) of point \(X\). If the point \(XP_{ij}\) overlaps with the point in \(YP\) is recorded as \(H\). Consequently, the actual distance bypassing the building is \(\mathrm{Ddist}=\min(\{|XXP_{i}|+|XP_{i}H|+|HY|\},\ldots)\). 7. The data results are clustered into clusters after implementing the abovementioned method to improve the DBSCAN distance calculation. Then, the K-means clustering algorithm is applied by setting the parameter \(\mathrm{k}=1\) to determine the clusters' centroid coordinates O and their corresponding attribute values. The flowchart of the density-based partitioning clustering algorithm is shown in Figure 3. In this figure, 1, 2, and 3 respectively signify three distinct routes that exist between two data points. Each of them navigates around the obstruction presented by the buildings. Figure 3: Flow chart of density partitioning clustering algorithm.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 4 Results and Discussion
### Heat Loss
When these outer surface temperatures were compared with the chamber inlet temperatures, they reached the range of 48-65% of temperatures in Kelvin at the chamber inlet of the microthruster. Unlike the tendency for higher propellant weight percentages to record higher temperatures, the outer surface temperature, when compared to the chamber inlet temperature, was lower with the higher-weight-percentage propellant, regardless of thermal diffusivity. The outside temperature to chamber inlet temperature ratio was the smallest for 95 wt% hydrogen peroxide, as the heat transfer coefficient increased with the weight percentage of the propellant, although the difference between the ratios was smaller as thermal diffusivity increased. The temperature contours in the solid domain, including the symmetry plane of the fluid domain, are illustrated in Figure 9. It was observed that the temperature gradient in the solid domain of glass and LTCC microthrusters was larger than that of the other microthrusters, due to low thermal diffusivity and conductivity. In the solid domain of the glass microthruster, the temperature varied from 800 K at the surface near the fluid domain to 450 K on the outside surface, where the solid domain was in contact with air. Figure 7: Thermal energy loss across the microthruster with materials of different levels of thermal diffusivity and different propellant enthalpies (85, 90, and 95 wt% hydrogen peroxide). In contrast to the glass, the temperature in the copper varied between 625 K and 700 K. The temperature gradient was inversely proportional to the thermal conductivity, and thus the temperature gradient of a microthruster consisting of a material with high thermal conductivity was inevitably small.
## Materials and Methods
### Estimating NPP with the CASA Model  
We estimated the monthly vegetation NPP of the SNP based on the CASA model, which considers the physiological and ecological characteristics of vegetation and the environmental conditions related to growth [14]. Vegetation NPP estimation was derived by using vegetation cover type, NDVI, monthly average temperature, total precipitation and solar radiation [15]. The basic calculation formula of the CASA model is as follows [21]:  
\[\mathrm{NPP(x,t)}=\mathrm{SOL(x,t)}\times\mathrm{FPAR(x,t)}\times 0.5\times \mathrm{T}_{\varepsilon 1}(\mathrm{x,t})\times\mathrm{T}_{\varepsilon 2}(\mathrm{x,t}) \times W_{\varepsilon}(\mathrm{x,t})\times\varepsilon_{max} \tag{1}\]  
where SOL(x,t) is the total solar radiation at pixel x for month t. FPAR(x,t) is the fraction of photosynthetically active radiation absorbed by vegetation. 0.5 indicates the proportion of solar active radiation (0.4-0.7 \(\upmu\)m) that can be utilized by vegetation to the total solar radiation. \(\mathrm{T}\varepsilon_{1}\)(x,t) and \(\mathrm{T}\varepsilon_{2}\)(x,t) represent temperature stress coefficients, W\(\varepsilon\)(x,t) is the coefficient of water stress, and \(\varepsilon_{\mathrm{max}}\) is the maximum light use efficiency under ideal conditions [22]. Comparison between the calculated NPP and the reported study conducted in Northeast China and the western part of Jilin Province [23; 24] as the cross checking and validation of the analysis. [PERSON] et al. [23] verified the NPP by comparing the simulated value with the flux observation data. The simulated value was close to the measured value, and the error was within 25%.
# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
## 6 Results and Discussion for Fine-Tuned MTM (\(Mtm\)\(\rightarrow\)\(Stm\))
#### Statistical Analysis of Mtm\({\rightarrow}STM\)  
The results were evaluated using [PERSON]'s statistical test as presented in Figure 11. The figure shows that all the variants of \(MTM{\rightarrow}STM\) produced statistically significant results against their STM and MTM counterparts. The results were statistically significant with the previously mentioned approaches, the MTM-CNN and MTM-CW. Nevertheless, the \(MTM{\rightarrow}STM\) did not generate significant results with each other.  
The models are also shown according to their Friedman statistical ranks and are given in Figure 12. The figure indicates that all the fine-tuned models produced higher Friedman statistical scores. The \(MTM{\rightarrow}STM\) generated the highest statistical rank indicating that this model covered a wider range of features, benefiting datasets suitable for both the MTL and STL approaches. The \(MTM{\rightarrow}STM\) produced the lowest ranks among the fine-tuned  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Datasets** & **MTM-CNN** & **MTM-CW** & **MTM\({}^{\text{th}}\)**, STM** & **MTM\({}^{\text{th}}\)**, STM** & **MTM\({}^{\text{th}}\)**, STM** \\ \hline AnatEM & 86.9 & 87.5 & 87.9 & **88.0** & **88.0** \\ BC2 GM & 80.8 & 81.5 & 82.1 & **82.2** & 82.0 \\ BC4 CHEMD & 87.3 & 89.2 & 89.9 & **90.4** & 90.4 \\ BC5 CDR & 87.8 & 88.5 & 88.8 & 89.0 & **89.1** \\ BioNLP09 & **88.7** & 88.5 & 88.5 & 88.7 & 88.5 \\ BioNLP11 EP1 & 84.7 & 85.3 & 85.3 & **85.5** & 85.4 \\ BioNLP111D & 87.6 & 87.1 & 87.6 & 87.8 & **87.9** \\ BioNLP13 CG & 84.2 & 84.9 & 84.9 & 85.2 & 85.1 \\ BioNLP13 CE & 79.8 & **80.9** & 80.1 & 80.1 & 80.2 \\ BioNLP13 PC & 88.8 & **89.1** & 89.3 & 89.2 & **89.3** \\ CRAFT & 83.1 & 85.2 & 84.9 & **85.3** & 85.0 \\ EMPTM & 80.9 & 81.7 & 81.7 & **82.0** & 81.8 \\ JNLPBA & **74.0** & 72.1 & 73.0 & 72.1 & 71.9 \\ LINNAEUS & 87.7 & 88.1 & 88.8 & 88.2 & **88.8** \\ NCBI & 85.6 & 85.0 & 86.2 & 85.9 & **86.2** \\ Average & 84.5 & 85.0 & **85.3** & **85.3** & **85.3** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results comparison of \(MTM{\rightarrow}STM\) with state-of-the-art MTMs.  
models, which showed that the base model (MTM\({}^{10}\)) for that experiment did not learn distinct features and therefore, using that model as a starting point for the STM did not benefit the model.