esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes
## 7 Conclusions  
In this paper, MSs are classified based on their operational modes, where their transmission mode for polarization conversion applications is discussed in detail. A brief history of MSs and advancements in their modeling approach is presented. Further, detailed theories for polarization conversion and design strategies are discussed.  
The authors have only focused on transmissive MSs for polarization conversion. To review all the functionalities of transmissive MSs, or to discuss both transmissive and reflective MSs is possible but hardly constructive. Our observation of the trends of transmissive MSs for LP-to-CP converters can be concluded as the first trend of miniaturization and easy-to-fabricate MSs. The second trend is a multiband operation to facilitate the merging of multiple systems together. Design ideas from microwave MSs have been transferred to optical MSs but less frequent, and vice versa. The third trend is the controllability of MSs using structural change, and the state change of controllable electric components. It is hoped that the present review will help to establish a link between the advancements in MSs and polarization conversion performance in microwave and optical regimes. The authors also suggest new possibilities to control/tailor polarization conversion performance  
Figure 10: (a) MS-based polarizer with microfluidic channels (Reprinted, with permission [112]); (b) fabricated prototype (Reprinted, with permission [112]).  
using gravity [102] and MEMS [114], as these fields have not been explored for transmission mode LP-to-CP converters.  
Conceptualization, A.K.F. and C.R.; formal analysis, A.K.F., C.R. and R.N.; resources, C.R.; writing--original draft preparation, A.K.F.; writing--review and editing, A.K.F., C.R., R.N. and B.H.; visualization, B.H.; supervision, C.R.; project administration, C.R.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61831001, the High-Level Talent Introduction Project of Beihang University, grant number ZG216S1878 and the Youth-Top-Talent Support Project of Beihang University, grant number ZG226S1821.  
The authors declare no conflict of interest.
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 4 Conclusions  
In this paper, a novel, non-invasive technique for characterising the water content (WC), and in turn the health of plant leaves was proposed using THz waves. The electromagnetic properties of eight types of leaves were determined for four consecutive days through the measured scattering parameters. The weight and thickness of the leaves were also recorded at the same time. We observed that the leaves became increasingly transparent to the terahertz (THz) waves through the course of four days experiment, as seen by the peaks in the real part of permittivity. Similar decaying trends were observed in the peak values of the real part of the extracted relative permittivity as the decreasing weight due to loss of WC. The significance of this paper lies in the simple, cost-effective technique and other advantages such as: (a) This paper proposes a unique technique to characterise and estimate WC  
Figure 7: Transmission response of leaves on first and fourth days.  
of eight various leaves in terms of electromagnetic parameters at THz frequency range from 0.75 to 1.1 THz. (b) The electromagnetic parameters are measured in simple, fast, and non-invasive manner using a THz material characterisation kit. Moreover, The structural integrity and configuration of leaves were also considered by employing two polytetrafluoroethylene (PTFE) caps which were fitted internally to the waveguide. (c) This paper establishes a notable correlation between electromagnetic parameters with WC in leaves i.e., change in the WC of leaves is reflected in the electromagnetic parameters at certain frequencies. In the age of a climate change driven water conservation, the proposed scheme can be used to design efficient irrigation systems on-site without any need to remove the leaves from plants.  
Conceptualization, A.Z., H.T.A. and Q.H.A.; software, A.Z. and H.T.A.; resources, D.R.S.C. and K.A.Q.; writing-original draft preparation, A.Z. and H.T.A.; writing-review and editing, Q.H.A., M.A.I. and A.A.; supervision, Q.H.A. and M.A.I.; project administration, Q.H.A. This research was funded under EPSRC DTA studentship which is awarded to [PERSON] for his PhD. The authors declare no conflict of interest.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Data-Assimilation Algorithms  
Assimilation systems typically include process models, observational data, assimilation algorithms (or schemes), and observation operators. Data-assimilation algorithms are central to these systems, serving as the crucial link between observational data and models. This process involves dynamically integrating new observational information--characterized by various types, spatiotemporal scales, and varying accuracy--into numerical models. It takes into account data distribution, background field errors, and observation errors to provide the most accurate estimation of model parameters and state variables [127].  
From an algorithmic perspective, data-assimilation methods fall into two primary categories: sequential assimilation algorithms and variational assimilation algorithms [128]. Modern techniques are further classified into variational methods based on optimal control theory and ensemble Kalman filtering rooted in estimation theory. Most contemporary assimilation methods can be expressed by the following formula:  
\[x^{opt}=x_{b}+W(y^{obs}-y) \tag{12}\]  
where \(x^{opt}\) is the optimal value; \(x_{b}\) is the background value; \(y^{obs}\) is the observed value; \(y\) is the simulated value; and \(W\) is the difference between the different assimilation methods, which yield different a posteriori weights [129]. Due to the limitations of instruments and observation conditions, there are some differences between the observed data and the \"real data\", i.e., the observation error, which can be expressed as follows:  
\[\hat{x}=x_{b}+\varepsilon_{b} \tag{13}\]  
\[y^{obs}=H(x_{0})+\varepsilon^{obs} \tag{14}\]  
where \(\hat{x}\) is the truth value; \(x_{b}\) is the background field; \(\varepsilon_{b}\) is the error between the background field and the truth value, and the background error covariance matrix is denoted by B; \(y^{obs}\) is the observed non-conventional data matrix; \(x_{0}\) is the variable to be solved by the model; H is the observation model operator; the observation error \(\varepsilon^{obs}\), denoted as matrix \(R\), includes the discrepancy between the observed data and the true value, the error caused by the observation operator, and the representative error generated when mapping the state variables to the observation space. These two types of errors control the operation of the assimilation process and ultimately yield the analysis error. The purpose of data assimilation is to minimize the final analysis error.  
This section first reviews the data-assimilation methods used in the early stage, compares the improvements of these assimilation methods and explains modern assimilation algorithms such as three-dimensional and four-dimensional variational assimilation, Kalmanfiltering, ensemble Kalman filtering, and neural network algorithms. Finally, it analyzes and comments on these assimilation algorithms.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Scenario Description
#### 4.1.3 Iridium Data  
The Iridium-like signal simulator of Section 3.2 is fed with the trajectory defined in Section 4.1.1 and an Iridium Next TLE file. The simulator was configured with a sampling frequency of 2 MHz and a data resolution of 8 bits. We assumed that each Iridium satellite broadcast one burst per second with a transmission power of 30 dBw. We recall that the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accelerometer** & **Gyroscope** & **Magnetometer** \\ \hline Noise & 0.1 m/s/(h) & 3.6 deg/(h) & 0.2 mG/(hz) \\ \hline Bias & 50 mg (0.5 m/s\({}^{2}\)) & 1000 deg/h & 1 mG \\ \hline Sensitivity & 2000 ppm & 2000 ppm & 2000 ppm \\ \hline Misalignment & 0.1 deg & 0.1 deg & 0.1 deg \\ \hline \hline \end{tabular}
\end{table}
Table 1: A consumer-grade sensor error specification (\(1-\sigma\)).  
Figure 8: The user trajectory in the local frame.  
effects of the atmosphere and multipath on signal propagation are disabled. We then generated two hundred synthetic Iridium signal files by changing randomly the bursts transmission times and the signal noise  
We processed the synthetic Iridium signals by the signal processing tool. This latter was configured as in Section 3.1 (i.e., the PLL integration time is set to 10 \(\upmu\)s, the PLL bandwidth is set to 2 kHz, and the frequency estimated by the PLL is averaged over the last 0.5 ms of the burst tone). A sky view showing the satellites positions centered on the user trajectory is given in Figure 9.  
Table 2 displays the satellite availability and the received signal power. It shows that eleven satellites are captured during the scenario with an availability between 28% and 100%. In average, the number of satellites in view in any time interval of 1 s is approximately eight satellites. This satellite availability should be sufficient for the observation of the 3D position, the 3D velocity, and the receiver clock bias and drift.
## 6 Performance Evaluation
### Evaluation for the Designed Protocol
#### 6.1.4 One Path Lifetime  
In Figure 5b, we compare the performance of the constructed path's lifetime. Recall that the path lifetime is determined by the link with the minimum lifetime. As can be seen from the figure, LSGR produces the longest path lifetime since it prefers the neighboring vehicle that can maintain high connectivity performance. Next in importance, our proposed LBRP shows the second best path lifetime as a result of the consideration of both link lifetime and one-hop transmission distance information on the protocol design. On the contrary, without the consideration of the link connectivity property, the path tends to be fragile, as illustrated in DBF and RTF. Especially for DBF, with the largest inter-relay distance, the constructed path is the most easily broken.
## 3 Performance Metrics  
The performance of the proposed modified gradient boosting classifier and random walker segmentation method is verified and validated using the disc coefficient, Jaccard Similar Coefficients, precision, recall, F1 score, validation accuracy, and Receiver Operating Characteristics (ROC) curve, based on the following assumptions:  
The accuracy of the proposed segmentation method is evaluated using the Disc Coefficient (DSC) and the Jaccard Similar Coefficient (JSC) using the following equation:  
\[DSC=\frac{2\times(M\cap R)}{|M|+|R|} \tag{15}\]  
\[JSC=\frac{(M\cap R)}{(M\cup R)} \tag{16}\]  
where \(M\) and \(R\) are ground truth standard and segmented result of the proposed method, respectively.  
The performance of the proposed modified gradient boosting classifier is evaluated using the following metrics:  
\[Precision=\frac{TP}{TP+FP} \tag{17}\]  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
\[F1\ Score=\frac{2\times Recall\times Precision}{Recall+Precision} \tag{19}\]  
where \(TP\) is True Positive--object is present at the goal location and detected as a correct class; \(TN\) is True Negative--object is not present at the goal location and not detected; \(FP\) is False Positive--object is not present at the goal location and detected as a wrong class; and \(FN\) is False Negative--object is present at the goal location and not detected.
## 3 Methodology  
We first examined the response of SIF to drought by analyzing the relationship between SIF and several drought indices, and comparing the results of VIs and drought indices. As mentioned above, meteorological drought indices fail to indicate real drought conditions for terrestrial plants when they are irrigated, and therefore we selected CDs that were mainly covered by grass to study the SIF response to drought. Relationships were obtained and discussed by month (from June to September) according to the adjacent CDs over a six-year period. Although previous studies have shown that the GOME-2 SIF correlates well with ground-based GPP, contrasts and analyses between humid and drought years have rarely been made. Here we examined the seasonal cycle and the consistency of GOME-2 SIF and flux-estimated GPP for both dry and humid years. Finally, we applied a simple model using SIF and NDVI to monitor and assess agricultural drought, and made comparisons with the spatiotemporal pattern of the meteorological drought monitored by drought indices. Figure 2 shows our technical methodology.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site Name** & **Longitude** & **Latitude** & **Vegetation Type** & **Time Range** & **References** \\ \hline
**KFS** & \(-95.19\) & 39.06 & grass & 08–13 (09 missing) & [33; 34] \\
**Kon** & \(-96.56\) & 39.08 & grass & 08–13 & [33; 34] \\
**Ne-1** & \(-96.29\) & 41.10 & irrigated maize & 10–12 (May to October) & [35; 36] \\
**Ne-3** & \(-96.44\) & 41.18 & rain-fed maize & 10–12 (May to October) & [35; 36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flux sites used in this study.  
Figure 2: A flow diagram showing the methodology and principles used in this study.
## 6 Conclusions  
The empirical analysis has shown that the framework and methodology adopted for this research can detect both older and newer versions of the Zeus banking malware, which demonstrates the potential of the framework to detect banking malware that evolve over time. The framework and methodology can also predict other banking malware variants, which demonstrates the potential to detect a wide range of banking malware variants without the need to analyse each banking malware variant to learn about their features.  
For future work, there is a potential to further this research by enhancing the methodology to incorporate additional banking malware variants. Moreover, further research can be conducted to detect other malware variants and improve the prediction accuracy when detecting them. Researchers can also further this research by designing and building an IDS solution that could detect a wide range of malware, and the findings from this research could be used for this and by anti-malware vendors when they design malware detection tools. Action on the malicious traffic could also be taken once the malware has been detected. The findings from this research can be used by other researchers to develop their own malware prediction tools to enhance their research.  
Conceptualization, M.A.K.; methodology, M.A.K.; software, M.A.K.; validation, M.A.K., S.W. and D.G.; formal analysis, M.A.K.; investigation, M.A.K.; resources, M.A.K.; data curation, M.A.K.; writing--original draft preparation, M.A.K.; writing--review and editing, M.A.K., S.W. and D.G; visualization, M.A.K.; supervision, S.W. and D.G.; project administration, M.A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 15: True negative rates compared for all three algorithms when using the minimum number of features.
## 2 Results and Discussion
### Diagnosis Prediction Based on Random Forest Model  
Random Forest was employed for the development of a classificatory model, able to distinguish controls, halitosis, and abscess cases, based on pattern recognition of the detected volatiles. Initially, the 39 discriminating features posteriorly mentioned were included in this method, and the default number of 500 trees was generated. Figure 5 presents graphs used to assess the importance of variables to the preliminary model. Figure 5A shows the plot of mean decrease accuracy pertinent to each variable, which represents the impact on the prediction ability of the model, in case of exclusion of a given variable. Figure 5B is a plot in terms of mean decrease Gini, related to the relevance of the features in the purity of classes, thus indicating the compounds that are determinant to divide the data into pure nodes, in which the elements belong to a single class. In accordance to these observations, eight compounds addressed as the most relevant for model performance (namely: 2-butanone, methyl thioacetate, 2-methylbutanoic acid, S-methyl pentanetthioate, dimethyl tetrasulfide, indolizine, pentadecane, and octadecanal) were selected, and a new Random Forest was performed. The data was split in half, 50% dedicated to training the approach, and 50% applied for model validation step. The obtained \"out of bag\" score was 6.67%. Figure 6A shows an example of a produced decision tree, in which classification accuracy was 100%. In Figure 6B, overlaid receiver operating characteristic (ROC) curves produced from calculated probabilities in Random Forest model are presented, and they are related to the provided discrimination of each particular condition against all other cases. The results of the performance of this procedure and obtained areas under the curves (AUCs) are presented in Table 2.  
Figure 5: Assessment of variable importance, in terms of (**A**) mean decrease in accuracy and (**B**) mean decrease Gini or node impurity.  
Figure 6: (**A**) Exemplary decision tree produced in Random Forest approach; (**B**) Overlaid ROC (receiver operating characteristic) curves concerning discrimination of specific condition (in green-control / “healthy”, in red- abscess, and in blue- halitosis) against all others.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Attribute Extraction and Analysis of Tourist Attractions
#### 4.2.1 Extraction of Textual Entity Information
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attraction Dataset** & **Number of Reviews** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline WS Hot Snow Miracle & 149 & 0.9116 & 0.9538 & 0.9322 & 227 \\ East Lake Eye & 85 & 0.9024 & 0.9652 & 0.9327 & 111 \\ Dulu Dulu Cute Pet Science Theme Park & 121 & 0.9119 & 0.9565 & 0.9337 & 176 \\ Guiyuan Temple & 116 & 0.8442 & 0.9489 & 0.8935 & 130 \\ Hubei Art Museum & 39 & 0.8698 & 0.7843 & 0.8248 & 40 \\ \hline micro avg & & 0.8941 & 0.9434 & 0.9181 & 684 \\ macro avg & & 0.8880 & 0.9217 & 0.9034 & 684 \\ weighted avg & & 0.8949 & 0.9455 & 0.9190 & 684 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy assessment of viewpoint phrase entity extraction. Based on the \"4E\" model of tourism experience [45], the thematic types of viewpoints are categorized into ten categories: \"Transportation and Travel\", \"Environment and Landscape\", \"Services and Facilities\", \"Food and Shopping\", \"Price and Consumption\", \"Crowd Flow and Density\", \"History and Culture\", \"Family and Education\", \"Animals and Plants\", and \"Leisure and Entertainment\". Given that the core terms within viewpoint phrases strongly indicate their thematic affiliations, we annotated topic labels for high-frequency core words (appearing over 10 times across all attractions), resulting in a labeled dataset of 88,145 viewpoint phrases. Based on the GSPM-TRTE methodology outlined in Section 3.1.2, a PyTorch-based text classification model was developed to enable automatic topic identification of viewpoint phrases extracted from tourism reviews. The model is evaluated on the test set, with the accuracy rates detailed in Table 3.  
The results show that the model performs excellently in most categories. Specifically, the categories \"Transportation and Travel\", \"Environment and Landscape\", and \"Price and Consumption\" exhibit high F1 scores of 0.9089, 0.9388, and 0.9651, respectively. These categories have lower lexical diversity and focus on core vocabulary, allowing the model to more accurately capture their characteristics. In contrast, the categories \"History and Culture\", \"Services and Facilities\", and \"Animals and Plants\" have slightly lower classification accuracy, with F1 scores of 0.8118, 0.8894, and 0.8908, respectively. These categories present greater challenges for the model due to higher lexical diversity and significant differences in characteristic words across attractions. Overall, the model achieves a weighted average F1 score of 0.9181, indicating its effectiveness in identifying thematic categories of viewpoints and providing a solid foundation for subsequent semantic association tasks.
## 3 Results
### The Impact of Drought on NPP
#### 3.3.2 NPP Variation during Drought Events  
We used growing season total net primary productivity (NPP\({}_{\text{gs}}\); Pg C m\({}^{-2}\)year\({}^{-1}\)) value to determine the NPP response to different drought conditions. On average, the growing season total NPP\({}_{\text{gs}}\) accounted for 93.14% of the annual total NPP\({}_{\text{a}}\) in Mongolian rangeland from 2003 to 2018 and exhibited a near-perfect positive correlation with the fluctuations of annual total NPP (\(r\) = 0.98, \(p\) < 0.001). Thus, small changes in NPP\({}_{\text{gs}}\) caused by droughts can significantly affect annual NPP dynamics. Figure 6 shows the spatial patterns of percent NPP anomalies across Mongolia for growing seasons in the studied period. Our result showed a good match between widespread drought-affected areas and NPP anomalies. A total of 62.33% of the national area showed a decreasing NPP trend during the drought of May to September 2004. A 30-50% reduction of NPP was detected in severe drought areas, mainly in the eastern regions, and the NPP in a large fraction of the region decreased by 50-75% in the same areas. A decrease of NPP was observed over 60.55% of the total vegetated area during the drought period of May to September 2005. The country-wide NPP reduced by 10-30%, while a 30-50% decrease in NPP areas was observed for moderate and severe drought areas in Mongolia (Figure 6b). From May to September 2006, a 10-30% decline in NPP was found in mild and moderate drought conditions, while the NPP of severe drought areas, which were scattered across the country, decreased by 30-40% (Figure 6c). About 84.85% of vegetated pixels showed a decline in NPP to a different extent in drought areas in Mongolia from May to September 2007, while vegetation not affected by drought indicated an NPP increment of 15.15% (Figure 6d). A considerable decline in NPP (by 10 to 50%) was observed in mild to severe drought areas from May to September 2009, which constitutes 76.43% of the vegetated lands. Approximately 87.75% of the entire vegetated lands showed a 10-50% decrease of NPP in mild to severe drought areas during May to September 2017, although the NPP reduced by more than 50% in some areas (Figure 6f).
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 4 Experimental Validation
### Structural Model and Damage Cases  
An experimental seven-storey shear-beam type building model was employed to validate the hybrid system proposed as shown in Figure 8. The stiffness of each storey unit is k1 =... K7 = 375 KN/m; meanwhile, the mass of each storey unit is m1 =... m6 = 3.78 kg, m7 = 3.31 kg, respectively.  
The stiffness reduction in each storey unit was used for simulating the structural damage. Here, two damage magnitudes and three damage locations were set up for examining the capability of proposed system. The damage magnitude contains two types, small damage and large damage, while the damage location was designated corresponding to storey number. The stiffness reduction of 4.1% was defined for small damage, and 16.7% for large damage (shown in Figure 9). Thus, four damage patterns are described in Table 5.  
Figure 8: Seven-storey steel frame model.  
Figure 9: Schematic diagram of damage magnitude.