esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Spatiotemporal Variation Characteristics of NPP in Yangtze River Delta  
#### 3.1.1 Spatial Distribution Pattern of NPP  
The average NPP was 550.17 g C m\({}^{-2}\) a\({}^{-1}\) in the Yangtze River Delta from 2000 to 2019, with NPP values ranging from 68.21 to 1433.10 g C m\({}^{-2}\) a\({}^{-1}\) (Figure 3). Among them, the percentage of regions with average NPP values greater than 400 g C m\({}^{-2}\) a\({}^{-1}\) was 93.04%, and the percentage of more than 600 g C m\({}^{-2}\) a\({}^{-1}\) was 30.93%. Generally, the NPP in the research region shows the spatial distribution characteristics as low in the northwest and high in the southeast, and the high value areas were mainly distributed in the mountainous and hilly areas with relatively high elevation (Figure 3), such as southern Anhui Province and south-central Zhejiang Province. In particular, the NPP of Zhejiang Province was significantly higher than that of other provinces and cities, which may be due to the high forest cover and abundant vegetation species resources in Zhejiang Province. In addition, there is less impact on vegetation from human activities in the region, and vegetation enables natural growth. Low value NPP areas were mainly located in north-central Anhui Province, southern and northern Jiangsu Province, and northern Zhejiang Province, including Xuzhou city, Huaibei city, Luan city, Hefei city, Suzhou city, and the border area between Hangzhou city, Jiaxing city, and Huzhou city. These areas are flat and low in elevation, and the main land types are cultivated and construction land (Figure 1), which explains the low NPP values in this area.
## 5 Finite Element Modeling
### Comparison of Local-Level Deformations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\begin{tabular}{c} **String Pot** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Initial Length of** \\ **String Pot (cm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Measurement** \\ **Model Prediction** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Measurement,** \\ _\%exp_ \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Model Prediction,** \\ _\(\gamma_{m}\)_ \\ \end{tabular} } \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Cage \#1** \\ \end{tabular} } & SG01 & 142 & 142.15 & 142.16 & \multirow{4}{*}{0.064} & \multirow{4}{*}{0.065} \\ \cline{2-3}  & SG02 & 142 & 142.5 & 142.48 & & \\ \cline{2-3}  & SG03 & 157.5 & 157.48 & 157.5 & & \\ \cline{2-3}  & SG04 & 157.5 & 157.54 & 157.5 & & \\ \cline{2-3}  & SG05 & 212.06 & 218.84 & 219.01 & & \\ \cline{2-3}  & SG06 & 212.06 & 205.41 & 205.35 & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Cage \#2** \\ \end{tabular} } & SG01 & 142 & 142.33 & 142.41 & \multirow{4}{*}{0.056} & \multirow{4}{*}{0.054} \\ \cline{2-3}  & SG02 & 142 & 142.18 & 141.96 & & \\ \cline{2-3}  & SG03 & 174 & 174 & 173.98 & & \\ \cline{2-3}  & SG04 & 174 & 173.89 & 174 & & \\ \cline{2-3}  & SG05 & 224.59 & 230.8 & 230.34 & & \\ \cline{2-3}  & SG06 & 224.59 & 218.45 & 218.36 & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Cage \#3** \\ \end{tabular} } & SG01 & 142 & 140.85 & 141.99 & \multirow{4}{*}{0.263} & \multirow{4}{*}{0.264} \\ \cline{2-3}  & SG02 & 142 & 143.03 & 142.13 & & \\ \cline{2-3}  & SG03 & 160 & 160.17 & 160.07 & & \\ \cline{2-3}  & SG04 & 160 & 159.66 & 159.98 & & \\ \cline{2-3}  & SG05 & 213.93 & 239.85 & 240.14 & & \\ \cline{2-3}  & SG06 & 213.93 & 184.09 & 184.27 & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **Cage \#4** \\ \end{tabular} } & SG01 & 142 & 143.58 & 141.28 & \multirow{4}{*}{0.310} & \multirow{4}{*}{0.293} \\ \cline{2-3}  & SG02 & 142 & 146.74 & 142.6 & & \\ \cline{2-3}  & SG03 & 157.5 & 157.98 & 157.47 & & \\ \cline{2-3}  & SG04 & 157.5 & 156.98 & 157.48 & & \\ \cline{2-3}  & SG05 & 212.06 & 244.24 & 240.26 & & \\ \cline{2-3}  & SG06 & 212.06 & 178.06 & 179.76 & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Cage \#5** \\ \end{tabular} } & SG01 & 142 & 144.51 & 141.25 & \multirow{4}{*}{0.064} & \multirow{4}{*}{0.065} \\ \cline{2-3}  & SG02 & 142 & 147.39 & 142.77 & & \\ \cline{2-3}  & SG03 & 157.5 & 158.04 & 157.46 & & \\ \cline{2-3}  & SG04 & 157.5 & 156.97 & 157.46 & & \\ \cline{2-3}  & SG05 & 212.06 & - & 245.26 & & \\ \cline{2-3}  & SG06 & 212.06 & - & 173.16 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: String pot measurements and the corresponding FE model predictions for test scenario 3.
## 4 Results
### Consistency of SIF and GPP  
To directly use SIF to assess the impact of drought on GPP, we examined the consistency of flux-estimated GPP and GOME-2 SIF over long periods. Figure 8 shows the inter-annual and seasonal patterns of GOME-2 SIF and GPP. For the four sites, the two variables displayed similar seasonal patterns. For grassland, a significant increase in SIF and GPP occurred in May, but for cropland GPP tended to increase in June, when SIF was already high. The significant difference in the spatial scale, which is \(40\times 40\) km for GOME-2 SIF and dozens of meters for flux measurements, also introduced uncertainties into the comparison of seasonal patterns for GPP and SIF. Although a slight difference was apparent in the seasonal patterns, the GOME-2 SIF was consistent with GPP at seasonal and inter-annual scales.  
There was an extreme drought in the central Great Plains in 2012. The seasonal rainfall failed to occur during May to August [45]. We found some anomalies in the patterns of GOME-2 SIF and GPP in 2012. For sites without irrigation, _i.e._, KFS (Figure 8a), Kon (Figure 8b), and Ne-3 (Figure 8d), the peak values of SIF and GPP in 2012 were significantly lower than those of humid years, taking 2010 as an example. Table 2 shows the statistical results of the decline in the percentage of peak values for SIF and GPP in 2012 compared to 2010. For all three sites, SIF and GPP were reduced by at least 20% in July, and by 10 to over 40% in August. A reduction in NPP in 2012 was also found by [PERSON] _et al._[27]. when using GOME-2 SIF to estimate crop yield in the adjacent area, with the decline considered to be the result of drought. The 2012 drought reached its peak severity in August and then eased from September onward [45]. A recovery of SIF and GPP in September 2012 can be seen in Figure 8. However, for the irrigated site, Ne-1 (Figure 8c), we found no significant anomalies in the pattern of GPP, although there was an obvious reduction of SIF in July and August. The maize grown at Ne-1 can be regarded as continuous and homogenous [36], but for GOME-2 SIF, the area of a single pixel (approximately as big as \(40\times 40\) km) is likely to be contaminated with non-irrigated plants such as grass or shrubs. Thus, the influence of the 2012 drought was found in the GOME-2 SIF pattern, but not in the GPP pattern.  
Figure 7: Relationship between the Palmer drought severity index (PDSI) and Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF). The “r” in the figure represents the correlation coefficient, while “**” and “***” represent the significance levels \(p<0.05\) and \(p<0.01\), respectively.  
Figure 8 shows that although the pattern of SIF may differ slightly from that of GPP, GOME-2 SIF remained consistent with the GPP during the growing season for both drought and non-drought years. Thus, it is reasonable to use GOME-2 SIF to assess the impact of drought on vegetation production.
Currently, two predictive models are being used as reliable tools to select the most suitable solvents for extraction of target BACs, namely the Hansen solubility parameters (HSPs) that explain the dissolution behavior of solvents and solutes, and the conductor- like screening model for realistic solvents (COSMO-RS) that is a statistical thermodynamics approach based on the results of quantum chemical calculations that provide explanations of the mechanisms of dissolution [13; 14]. Within the green extraction principles, various innovative technologies, such as high voltage electrical discharge (HVED), were developed in order to improve the extraction efficiency while reducing solvent, energy, and hazards, and accordingly impact less to the environment [15]. Specifically, HVED is a nonthermal technology based on the phenomenon of electrical breakdown in liquids, which induces physical (e.g., shock waves) and chemical (e.g., formation of free radical species) processes. During the treatment, a gas is flown in the reactor resulting in gas ionization, and accordingly, cold plasma is being formed. These phenomena affect the plant cell, by inducing cell disintegration, a process where electric field is increasing the permeability of the cell membrane, resulting in membrane pores and enhancing the release of intracellular components [16]. Several extractions of bioactive compounds from plant materials were reported with HVED, such as peanut shells [17], pomegranate peel [18], sesame cake [19], grapefruit peels [20], and orange peel [21]. However, to date, no literature data was found on HVED-assisted extraction of valuable compounds from wild thyme. Obtained plant extracts could be used for functional food production, but also in other spheres of food industry, as colorants, preservatives and other different kind of additives [22; 23]. Furthermore, plant extracts could be further preserved by encapsulation methods for controlled release of BACs [24]in food industry [25], but in other industries as well, such as cosme [26] or agriculture [27], and in various fields of pharmaceutical industry and medicine due to plants' natural biological properties [28; 29]. The aim of this study was to evaluate the potential of green solvents for extraction of BACs and essential oils from wild thyme. Both theoretical and experimental analysis were performed. For theoretical analysis, HSPs and COSMO-RS software were used. Further experimental analysis was performed by means of HVED as a green nonthermal technology to extract BACs and volatile compounds from wild thyme in comparison with modified conventional extraction (CE).
# Lightweight and Privacy-Preserving Multi-Keyword Search over Outsourced Data
## 2 System Model and Security Requirements
### Framework  
A PRMS scheme consists of four efficient procedures, namely, Setup, Index, Trapdoor, and Search.  
* **Setup (\(\mathbf{\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4}}\))**: With input security parameters \(\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4}\), data owner generate public parameter _para_ and secret key \(S\).
* **Index (\(\mathbf{F}\), \(\mathbf{para}\), \(\mathbf{D}\))**: For each document \(F_{i}\) (\(i=1,\cdots,m\)), data owner generates ciphertext document \(\tilde{F}_{i}\), constructs plaintext index vector \(\tilde{T}_{i}\), and produces ciphertext index vector \(\tilde{I}_{i}\). Data owner uploads the ciphertext document set \(\overline{F}=\{\tilde{F}_{i}:i=1,\cdots,m\}\) and ciphertext index vector set \(\tilde{I}=\{\tilde{I}_{i}:i=1,\cdots,m\}\) to the cloud server.
* **Trapdoor (\(\mathbf{W},\mathbf{para}\), \(\mathbf{D}\))**: From the given search keyword set \(W\), data user generates encrypted search trapdoor \(\tilde{Q}\), and sets a search threshold \(\tau\). The search trapdoor \(\tilde{Q}\) and search threshold \(\tau\) are sent to the cloud server.
* **Search (\(\tilde{I},\tilde{Q},\tau,\mathbf{para}\))**: With the received search trapdoor \(\tilde{Q}\), the cloud server compute similarity score with each ciphertext index vector in \(\tilde{I}\) and return the document if the similarity score is larger than \(\tau\).
## 4 Results  
### Ramp Compensation Design  
We first sought to choose a suitable value of the ramp compensation slope \(A\), as it is one of the most critical parameters in the control design. To do so, we calculated the stability of the Period 1 orbit via Floquet multipliers in the biparametric space \((A,\ V_{in})\) with \(R=100\ \Omega\). This situation is depicted in Figure 3 for different values of \(V_{ref}\). An interesting observation is that, for small values of \(V_{ref}\), the system tends to lose stability when two conditions are met: (i) \(V_{in}\approx V_{ref}\); and (ii) \(A\) is rather large, as seen in the dark red area in Figure 3A, which corresponds to \(|\mu_{max}|>1\). Interestingly, at increasing values of \(V_{ref}\), the controller does not lose stability anymore at large \(A\), and the conditions for stability loss are: (i) \(V_{in}<V_{ref}\) (equivalently when the sZETA converter works as a boost); and (ii) \(A\) is small, which produces the red triangular areas in Figure 3B-D. Since one of the main attributes of the sZETA converter is the ability to work both as a step-down and step-up converter, we chose a value of \(A\) which guarantees stability for most of the cases depicted in Figure 3. This value corresponds to \(A=10\), which is shown with the magenta line in all panels. In the next section, a deep stability analysis considering continuous variation of not only \(V_{in}\) and \(V_{ref}\) but also \(R\) is presented.
## 4 Conclusions  
Numerical simulations confirmed that the proposed subwavelength transmit-array lens elements exhibited both phase modulation and polarization conversion functions with one unit. The phase-shift change in the transmit-array unit was less than \(10^{\circ}\), with an incident angle varying \(30^{\circ}\), which is tolerable for beam scanning of the transmit-array antenna. The lens element covers the frequency range of 24.5-27 GHz, modulates the transmitted wave with a three-bit phase shift covering \(360^{\circ}\), and converts the linearly polarized incident wave into a circularly polarized wave. The reflection coefficients of the unit cells were lower than \(-10\) dB, the transmission loss was lower than 1 dB, and the axial ratio was below 2.5 dB. In order to further verify the functions of the transmit-array lens element, the unit cells were arranged in a grid pattern to form a fixed-beam, transmit-array antenna. The simulated results showed that the gain of the transmit-array increased by 12 dB, and the axial ratio was lower than 1 dB. The proposed transmit-array lens element can be implemented into flexible transmit-array antennas for wireless communication applications.  
Supervision, G.H. and X.Y.; Validation, Y.L.; Writing--original draft, T.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under grant number 62001280, Shanghai Pujiang Program (20 PJ1404300), 111 Project (D20031), Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing (GXKL06200213).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Network Heads for Building Extraction and Change Detection  
**BE head**. The adapted all-MLP head is illustrated in Figure 2 and consists of three linear layers and one upsampling layer. This head takes the multi-scale representations learned by the backbone, that is, the Swin transformer, as inputs and outputs a segmentation mask that has the same size with the input patch. Specifically, the first of the MLP combines multi-level features from the backbone into features of the same size by resize and concatenation operations. These combined features are subsequently fused by a second layer before the third predicts the buildings and a final upsampling operation is performed to recover the resolution. The output of each branch is used for calculating the binary cross-entropy loss together with the input ground truth of building labels.  
**CD head**. The employed CD head consists of four combinations of Convolution-BatchNormNorm-ReLU, progressively decreasing the feature channels, a final convolution layer to predict the changing mask, and a final upsampling layer to recover the resolution. The input to the CD head is a concatenation of the two output features from the multi-temporal inputs respectively, and the output is a one-channel mask layer indicating the changed pixels. The prediction output is used for calculating the binary cross-entropy loss together with the input ground truth during training. For inference, a sigmoid activation operation is applied to the prediction output, and the final pixel-level change map is obtained given a threshold. In our study, the threshold is set to 0.5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{Layer Type} & \multicolumn{1}{c}{Input Size} & \multicolumn{1}{c}{Output Size (Equivalent Size)} \\ \hline \multicolumn{1}{c}{} & Patch partition & \(512\times 512\times 3\) & \(128\times 128\times 3\) \\ \cline{2-4} \multicolumn{1}{c}{} & Linear embedding & & \(16,384\times 192\)\((128\times 128\times 192)\) \\ \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Swin transformer block \(\times 2\) & \(16,384\times 192\)\((128\times 128\times 192)\) \\ \hline \multirow{3}{*}{stage 2} & Patch merging & & \(4096\times 384\) \\ \cline{2-4} \cline{2-4}  & Swin transformer block \(\times 2\) & \(4096\times 384\) & \(4096\times 384\)\((64\times 64\times 384)\) \\ \hline \multirow{3}{*}{stage 3} & Patch merging & \(4096\times 384\) & \(1024\times 768\) \\ \cline{2-4} \cline{2-4}  & Swin transformer block \(\times 18\) & \(1024\times 768\) & \(1024\times 768\)\((32\times 32\times 768)\) \\ \hline \multirow{3}{*}{stage 4} & Patch merging & \(1024\times 768\) & \(256\times 1536\) \\ \cline{2-4}  & Swin transformer block \(\times 2\) & \(256\times 1536\) & \(256\times 1536\)\((16\times 16\times 1536)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main operations of the utilized large version of Swin (Swin-L) structure as the backbone.  
Figure 2: Architecture of the lightweight change detectio (CD) head.
## 4 Discussion and Limitations
### Discussion of Specific Topics
Even though a wide range of statistical metrics are commonly employed, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R\({}^{2}\)), distinct attention should be given to the inconsistencies that may arise in model performance assessment due to the different sensitivities and mathematical properties of each metric. As [PERSON] et al. [99] pointed out, even though some of these metrics are well-known and widely used in evaluating the predictive accuracy of the models, attention should be paid to various issues such as non-convergent assessments that might derive from the fact that different methods favor or penalize models when outliers are encountered. Furthermore, as [PERSON] et al. [100] showed, RMSE is more suitable for scenarios in which large errors are of primary concern, as the metric squares them; MAE is more appropriate for cases in which large errors should not be unduly penalized; MAPE emphasizes the importance of relative errors, while R\({}^{2}\) evaluates the model's ability to explain the variation in the dependent variable. As a result, in the scientific literature, recently, heavy emphasis has been placed on using composite measures that combine RMSE, MAE, MAPE, and R\({}^{2}\) and offer a more balanced approach to model evaluations [100].
## 3 Methodology
### Dialogue Context Encoding  
In order to encode the dialogue context, which holds utterances of the user and system (from its previous turn), it is fed to BERT. The candidate that contains the slot-value pair is fed to BERT. The two inputs pass through the tokenizer layer to split into a list of tokens. Next, the model produces an output fixed-length vector by concatenating the token, position, and segment embedding. Next, at the start of each utterance, a special classification [CLS] token is inserted and a special token [SEP] is utilized to separate two utterances and candidates. The output vector, i.e., the hidden states \(U_{h}^{m}\) of each token is then computed by BERT as shown in Equation (1).  
\[U_{h}^{m}=BERT(S_{[CLS]},S_{1},S_{2},\dots,S_{n},S_{[SEP]}) \tag{1}\]  
where \(U_{h}^{m}\) denotes \(h_{[CLS]},h_{1}^{m},h_{2}^{m},\dots,h_{[SEP]}^{[m]}\), \(m\) represents the number of BERT encoder layers, and \(h_{t}\) is the embedding of token \(t\). The BERT model predicts the probability score for candidate slot value pairs that are only equal to or greater than zero at each turn. To  
Figure 4: A schematic view of the proposed model.  
obtain the predicted slot value pairs at the current turn, we used the newly predicted slot-value pairs to update previously predicted values. For example, a user enquired about food = Italian at the current turn. However, if the cuisine does not exist then it will be added as newly corresponding values. Likewise, if the user modified cuisine Italian into food = Turkish then the corresponding values will be updated. Finally, we pass the last hidden state \(U_{h}=U_{h}^{L}(L=12)\) to SBL and attention to predict the slot value pairs.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 4 Problem Formulation and Problem Solution
### Overall Algorithm Design
15: Calculate \(d=w_{ij^{\prime}}-\max\{w_{ij^{\prime}},\forall j\in\mathcal{K}\}\). 16:if\(d>d^{*}\)then
17:\(d^{*}=d\). 18:\(x_{ij^{\prime}}=1\). 19:endif
20:endfor
21: Remove node i from \(\mathcal{L}\). 22:endwhile
23: Update \(r_{2}=r_{2}+1\). 24:until The increase in the objective value is below a threshold, i.e., the convergence of BCD algorithm. 25: Obtain the CHs through CHs selection algorithm. 26: Update \(r_{3}=r_{3}+1\). 27:until CHs and sensor association \(\mathbf{X}\) no longer change. ```  
**Algorithm 1** Acquisition Time and Sensor Association Optimization (ATSAO). Figure 2: The flowchart of the ATSAO algorithm. In addition, in order to obtain the lower bound of the problem **P2**, we use a GREEDY algorithm with relatively low computation complexity. The GREEDY algorithm makes the best choice based on the current situation at each step, regardless of the global situation, which dramatically reduces the computation resource required to obtain the optimal solution. However, the GREEDY algorithm is proven to have a particular gap between the solution obtained when solving the 0-1 knapsack problem and the optimal solution. In this paper, we will compare the performance gap with other algorithms. In order to obtain the upper bound of the problem **P2**, we first relax the problem **P2**. After the relaxation, the problem is as follows:  
\[ \begin{split}\textbf{P2.1}&\underset{\textbf{X}}{ min}&\sum_{i}E_{CM_{i}}+\sum_{j}E_{CH_{j}}\\ =&\sum_{i}\left[\frac{1}{\eta}(p_{t}+\alpha)*l*t_{i} \sum_{j}\frac{x_{ij}}{r_{ij}}\right]+\sum_{j}\left[e_{c}*\sum_{i}(l*t_{i}*x_{ij })+E_{DA}*(\sum_{i}(l*t_{i}*x_{ij}))\right]\\ s.t.&\sum_{j}x_{ij}=1,\forall i\in\mathcal{L},\\ &\sum_{i}x_{ij}\leq N_{j}^{max},\forall j\in\mathcal{K},\\ & 0\leq x_{ij}\leq 1,\forall i\in\mathcal{L},\forall j\in \mathcal{K}.\end{split}  \tag{21}\]  
The objective function and constraints of the problem **P2.1** are affine. Therefore, it is a linear programming problem and can be directly solved through the CVX toolbox. The sensor association **X** is obtained as a continuous variable between 0 and 1, which means that a single CM allows the association of multiple CHs, i.e., \"fraction sensor association (FSA)\". This relaxation reduces the complexity of the problem and makes it no longer a combinatorial problem. Therefore, the optimal sensor association **X** is successfully found and the upper bound of the problem **P2** has also been obtained through relaxation. A lower bound of the problem **P2** is obtained by the GREEDY algorithm, and its upper bound is obtained by the relaxation method. They can replace steps 10 to 22 in algorithm ATSAO, and the difference in performance is also shown in the simulation results section.