esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges to Developing a Remote Sensing Based EHA System  
Remotely sensed data can be used to retrieve a variety of ecosystem health indicators as surveyed above. However, different indicators may require different remote sensing data for higher accuracy of estimation. Hence, there are multiple challenges to combining indicators of vigor, organization, and resilience for establishing a comprehensive, temporally and spatially explicit EHA system. In addition to the lack of a good solution to estimate NPV and BSC cover using remote sensing data as reviewed in Subsection 3.1.2, there are other challenges such as: (1) scale issue; (2) transportable difficulty; (3) data availability; and (4) uncertainties in retrieved ecosystem health indicators from remote sensing data.
## 2 Data Sources and Study Methods  
### Study Area and Data Sources  
The Yangtze River Delta Urban Agglomeration (Figure 1) is one of the regions with the most rapid urbanization development [3; 36], as well as one of the most prominent tourist destinations in China [37]. As the strongest economic growth pole in China, its high-quality development is a crucial focal point to construct a new \"dual circulation\" development pattern. The tourism and urbanization systems are complex in structure and extensive in content, exhibiting a coupling and coordinated development relationship that is strongly interactive. The Yangtze River Delta Urban Agglomeration is a demonstrably representative region worth discussing in terms of the coupling coordination degree of tourism and urbanization. The premise of analyzing the coupling coordination between tourism development and urbanization development is to construct an index system that includes comprehensive content and reasonable levels. Approaching high-quality development with regard to performance improvement, the key factors to focus on are innovation driven by new requirements, data availability for indexing what is typical, scientific merit, measurability, and the principle of system relevance. Drawing lessons from the results of extant studies [38; 39], the project has identified 26 cities of the Yangtze River Delta in 2008-2018 as the study area, selecting 24 specific indicators (Table 1), respectively, from the tourism and the urbanization systems. Among them, economic benefit and development support in tourism are indicators that can reflect the quality and efficiency of tourism, while \"ecological\" indicators are added to the widely used \"population, economy, society, and land\" indicators of urbanization [36]; these factors also fulfill new requirements of structural optimization, performance improvement, and innovation-driven development for high-quality development. The data selected in this paper primarily originate from the statistical yearbook, the tourism statistical yearbook, and the statistical bulletin of each city. Because of disparate criteria for the selection of index data, there are differences in dimension and magnitude between sets of data. Range standardization has been used for data processing, and missing values have been supplemented by interpolation. Figure 1: Location of the Yangtze River Delta Urban Agglomeration. Note: The location of the Yangtze River Delta Urban Agglomeration comprises the province-level administrations of Jiangsu, Zhejiang, Anhui, and Shanghai (Jiangsu Province includes the 9 cities of Nanjing (NJ), Yancheng (YC), Yangzhou (YZ), Taizhou (TZh), Nantong (NT), Zhenjiang (ZJ), Changzhou (Czh), Wuxi (WX), and Suzhou (SZh). Anhui Province includes the 8 cities of Hefei (HF), Chuzhou (ChZ), Ma’anshan (MAS), Wuhu (WH), Anqing (AQ), Tongling (TL), Chizhou (CZ), and Xuancheng (XC). Zhejiang Province includes the 8 cities of Hangzhou (HZ), Huzhou (HuZ), Jiaxing (JX), Zhoushan (ZS), Shaoxing (SX), Ningbo (NB), Taizhou (TZ), and Jinhua (JH)).
## 2 Precision Agriculture  
Precision agriculture (PA) helps farmers make crucial decisions at the right time by analyzing a vast amount of data regarding the environment and crop details. Thus, PA helps the farmers marching towards more production with quality to meet the required demand. Remote Sensing (RS) plays a vital role in crop evaluation and soil health conditions. It indicates the problems at the right time and helps to resolve the problem wisely. Figure 1 describes various remote sensing platforms used for precision agriculture.  
UAV is flexible for most applications and addresses the solutions for the problems faced by other RS platforms [8].  
It can be easily accessible and provides accurate data. Further, it is cost-effective and easy to deploy anywhere and can operate real-time spatial images compared with other  
Figure 1: RS Platforms in precision agriculture.  
traditional RS Platforms. Table 1 presented a detailed comparison of the quality of services provided by the various types of RS Platforms in Precision Agriculture.
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Desmoplastic Small Round Cell Tumors (DSRCTs)  
DSRCTs are highly aggressive tumors, typically with intra-abdominal localization (pelvic peritoneum, mesentery, surface of the liver and omentum), but can arise in many other sites, including meninges, scalp, pleura, paranasal sinuses, parotid gland, pancreas, scrotum, ovary, kidney and bone [134]. These tumors are typical of pediatric and adolescent age, with a male predilection. Histologically, they are composed of round cells with scant cytoplasm, indistinct cell borders and hyperchromatic round to oval, or slightly angulated, nuclei that have finely granular chromatin and small nucleoli. The cells are arranged in variably-sized nests, trabeculae, or lobules, usually separated by a prominent fibro-sclerotic stroma. In some cases, necrosis in the central portion and calcification may be seen. Mitoses are usually observed. In rare cases neoplastic cells with glandular or pseudo-rosettes formation and rhabdoid or signet ring appearance have been reported [134]. Immunohistochemistryistochemically,  
Figure 7: Peripheral nerve sheath tumor consisting of spindle cells arranged in a fascicular pattern (**A**,**B**). Immunohistochemistryistochemical cytoplasmic expression of WT1 (_N-terminus_) in neoplastic cells at low (**C**) and high (**D**) magnification.  
the neoplastic cells show a polyphenotypic profile, including desmin, vimentin and epithelial markers, such as epithelial membrane antigen and cytokeratin and WT1 (Figure 8) [134].  
In most cases (>90%) of desmoplastic small round cell tumors, strong nuclear staining with antibodies directed against the _C-terminal_ portion of WT1 protein (clone WT1 C19), due to a recurrent chromosomal translocation (11;22)(p13;q12) resulting in the EWSR1-WT1 fusion transcript, is observed [134; 135; 136]. In rare cases, unusual nuclear WT1 staining with _N-terminus_ antibodies was described, and was due, probably, to novel fusion transcripts [35; 43; 137].
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method
### SDM Calculation
#### 3.2.1 Gradient Map Calculation  
This study utilizes the Sobel operator to compute the gradient map. The convolution kernels used in this operator along the x and y directions are constructed as follows:  
\[S_{x}=\begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix},\;S_{y}=\begin{bmatrix}1&2&1\\ 0&0&0\\ -1&-2&-1\end{bmatrix}, \tag{3}\]The gradient maps \(G_{x}\) and \(G_{y}\) are computed as follows:  
\[G_{x}=S_{x}*I_{0}, \tag{4}\]  
\[G_{y}=S_{y}*I_{0}, \tag{5}\]  
where \(*\) represents the convolution operation. The gradient magnitude \(G(i,j)\) is computed as follows:  
\[G(i,j)=\sqrt{G_{x}{(i,j)}^{2}+G_{y}{(i,j)}^{2}} \tag{6}\]  
The gradient direction \(\theta(i,j)\) is computed as:  
\[\theta(i,j)=\arctan\bigl{(}G_{y}{(i,j)}/G_{x}{(i,j)}\bigr{)} \tag{7}\]
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
###### Abstract  
The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek's Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier's nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear.  
big data; multi-class imbalance problem; sampling methods; hyper-spectral remote sensing images +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Results and Analysis  
As shown in Table 4, P, R, F1, and Acc were evaluated on the IMCS-21 dataset. The baseline model employs three sets of baseline models, which are traditional deep learning models, generic domain pre-trained models and biomedical pre-trained models, while the CWIIF proposed in this paper will be validated on the medical dataset. In view of this, the CWIIF in this paper uses the generic domain pre-training model RoBERTa for the pre-training model. As shown in Table 5, the performance of the proposed CWIIF is compared with previous baseline models evaluated on the CAIS dataset for F1 of slot filling, Acc of intent detection, and OAcc of the whole task. In addition, this paper selects two baseline methods with the highest OAcc, i.e., Stack-Propagation and MLWA, and their embedding methods are replaced with RoBERTa to form two baseline models (Stack-Propagation + RoBERTa, MLWA + RoBERTa).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **P** & **R** & **F1** & **Acc** \\ \hline TextCNN (2015) & 74.02 & 70.92 & 72.22 & 78.99 \\ TextRNN (2016) & 73.07 & 69.88 & 70.96 & 78.53 \\ TextRCNN (2015) & 73.82 & 72.53 & 72.89 & 79.40 \\ DPCNN (2017) & 74.30 & 69.45 & 71.28 & 78.75 \\ BERT (2019) & 75.35 & 77.16 & 76.14 & 81.62 \\ ERNIE (2019) & 76.18 & 77.33 & 76.67 & 82.19 \\ MC-BEAT (2022) & 75.03 & 77.09 & 75.94 & 81.54 \\ ERNIE-Health (2019) & 75.81 & 77.85 & 76.71 & 82.37 \\ Ours & **78.23** & **80.86** & **79.11** & **82.39** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of performance on IMCS-21 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **F1** & **Acc** & **OAcc** \\ \hline Slot-Gated (2018) & 82.21 & 93.87 & 80.43 \\ SF-ID Network (2019) & 86.34 & 94.66 & 84.09 \\ CN-Net (2019) & 86.16 & 94.56 & - \\ Stack-Propagation (2019) & 87.65 & 94.57 & 84.68 \\ MLWA (2021) & 88.57 & 94.66 & 85.47 \\ Stack-Propagation + RoBERTa & 89.33 & 95.26 & 86.85 \\ MLWA + RoBERTa & 91.10 & 95.16 & 88.34 \\ Ours & **91.93** & **95.33** & **89.56** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of performance on CAIS dataset.  
On the IMCS-21 dataset, the CWIIF in this paper consistently outperforms other baseline methods in four metrics: P, R, F1, and Acc. In particular, it presents significant advantages in P, R, and F1. Specifically, compared with the previous ERNIE-health baseline, this paper showed a 2.42% improvement in P, a 3.01% improvement in R, and a 2.4% improvement in F1. Also on the CAIS dataset, this paper's CWIIF compares favorably with other baseline methods in terms of Acc, F1, and OAcc. Compared with the previous state-of-the-art SOTA model MLWA + RoBERTa, the model in this paper improves the slot filling F1 by 0.83%, the Acc of intent detection by 0.37%, and the OAcc by 1.22% on the CAIS dataset. These results show that the CWIIF in this paper can effectively encode interactions between characters and words and confirm the advantages of CWIIF for intent detection and slot filling in Chinese medical dialogues.
## Methods
### Comprehensive Semantic Similarity Calculation Method
#### 3.4.2 Feature-Based Similarity  
In the ontology model, each node is associated with several properties. The higher the degree of property overlap between two concepts, the greater the similarity between them. Tversky algorithm [25] calculates Feature-based similarity by performing weighted operations on the property union and intersection between two nodes, which determines the proportion of shared attributes between them. However, this method considers all properties to have equal weights, while in reality, different properties may contribute differently to the similarity between concepts. [PERSON] et al. [16] address this issue by dividing the property information into three elements: property name, property data type, and property value. They calculate the similarity of these three concepts separately and then combine them with weighted formula as shown in Equation (14) for Feature-based similarity computation.  
\[Sim^{ele}_{asb}(a,b)=\alpha sim(a_{n},b_{n})-\beta sim(a_{d},b_{d})-\gamma sim (a_{v},b_{v}) \tag{14}\]In the formula above, \(a\) and \(b\) represent a pair of properties compared between nodes \(c_{i}\) and \(c_{j}\). \(sim(a_{n},b_{n})\) denotes the similarity of property names, \(sim(a_{d},b_{d})\) denotes the similarity of data types, and \(sim(a_{v},b_{v})\) denotes the similarity of property values. \(\alpha\), \(\beta\), and \(\gamma\) are parameters satisfying \(\alpha+\beta+\gamma=1\). Considering that property names and property data types are string forms, the cosine similarity algorithm is used to calculate \(sim(a_{n},b_{n})\) and \(sim(a_{d},b_{d})\). If there are \(k\) pairs of properties between concepts \(c_{i}\) and \(c_{j}\), the Feature-based similarity of \(c_{i}\) and \(c_{j}\) is expressed as follows:  
\[Sim^{de}=\sum_{i=1}^{k}\omega_{i}Sim_{asb}^{de}(a_{i},b_{i}) \tag{15}\]  
In Equation (15), \(\omega_{i}\) represents the weights assigned to different property pairs, satisfying the condition \(\sum_{i=1}^{k}\omega_{i}=1\). When constructing the ontology model described in this paper, properties that are considered important for defining concepts are arranged towards the end of the property list. Therefore, the weight of the back property pair will be greater than the front property pair.
## 2 PV Models and Problem Formulation
### Single-Diode Model  
In the SDM, only one diode (\(D_{1}\)) is connected to the light-generated current source in parallel, as shown in Figure 1. The SDM takes into account the current generated by the diffusion and recombination of electrons. Furthermore, it also considers the leakage current of the PN junction, effects of silicon and electrode inter-surfaces, electrode resistance, and flowing current resistance [35].  
Under [PERSON]'s current law, the output current (\(I_{L}\)) can be calculated by Equation (1)  
\[I_{L}=I_{ph}-I_{D1}-I_{sh} \tag{1}\]  
where \(I_{ph}\) is photovoltaic-generated current, \(I_{D1}\) is the diode current obtained by Equation (2). \(I_{sh}\) provides the shunt resistor current, which can be calculated by Equation (3)  
\[I_{D1}=I_{d1}\cdot\left(\exp\left(\frac{V_{L}+I_{L}\cdot R_{s}}{n_{1}\cdot V_ {th}}\right)-1\right) \tag{2}\]  
\[I_{\text{sh}}=\frac{V_{L}+I_{L}\cdot R_{s}}{R_{sh}} \tag{3}\]  
where \(I_{d1}\), \(n_{1}\), \(R_{s}\), and \(R_{sh}\) are reverse saturation current of the diode, the ideal coefficient, the series resistance, and the shunt resistance, respectively. \(V_{L}\) is the cell output voltage. \(V_{th}\) is the junction thermal voltage which can be obtained by Equation (4). q is the electric charge (1.60217646 \(\times\) 10\({}^{-19}\) C), \(T(K)\) is the cell temperature (Kelvin), and \(K_{B}\) refers to the Boltzmann constant (1.381 \(\times\) 10\({}^{23}\) J/K).  
\[V_{\text{th}}=\frac{K_{B}\cdot T(K)}{\text{q}} \tag{4}\]  
Figure 1: Equivalent circuit diagrams of SDM.  
For reasons listed before, we rewrite Equation (1) as follow:  
\[I_{L}=I_{ph}-I_{d1}\cdot\left(\exp\left(\frac{V_{L}+I_{L}\cdot R_{s}}{n_{1}\cdot V _{th}}\right)-1\right)-\frac{V_{L}+I_{L}\cdot R_{s}}{R_{sh}} \tag{5}\]  
There are five unknown parameters in Equation (5) that need to be extracted in SDM, including \(I_{ph}\), \(I_{d1}\), \(n_{1}\), \(R_{s}\), and \(R_{sh}\).
# A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture
## 3 Atmospheric Influence on Grapevine Development  
### Grapevine Cycles Versus Weather and Climate  
Grapevine development is associated with several stages of its vegetative and reproductive cycles. Under the conditions of many traditional viticultural regions (i.e., extratropical viticulture), the grapevine vegetative cycle extends over one full year, whereas its reproductive cycle lasts for two years. The reproductive cycle governs several important qualitative and quantitative properties, such as the number of grape clusters in the following year. The vegetative cycle encompasses two main sequential periods: dormancy period and growing season. The grapevine phenological development comprises several stages or phenophases (Figure 2). These stages of grapevine vegetative and reproductive cycles are largely controlled by atmospheric conditions [10].  
Figure 2: Vegetative cycle and main vine phenological stages.  
The impact of atmospheric forcing on grapevine can be divided into two different time scales. On the long-term, climate, which corresponds to the statistical distribution of the different atmospheric variables over long-term periods (decades) at a given location [11], determines the bioclimatic envelope of that location and its viticultural suitability [3; 12]. Macroclimates are determinant of the wine geography and the distribution of grapevine varieties, whereas mesoclimates and microclimates promote different terroric units, with diverse wine identity and diversity [13]. The wide range of climate-driven scales, as well as the spatial complexity and temporal dynamics in viticulture, have already been documented [14]. On the short-term, weather considerably governs the whole grapevine development process, as it requires suitable temperatures, radiation intensities, and duration, as well as specific levels of water availability throughout its growth cycle, ultimately influencing yield, biomass production, berry attributes, and wine structure and flavour [15; 16]. Effectively, the evolution of weather conditions in a given location can be used to predict local/regional grapevine parameters, such as yield [17] or phenology [18].
## 3 Materials and Methods
### Determination of Free Amino Acids Contents  
A Thermo Vanquish Flex UHPLC (Thermo Scientific, Sunnyvale, CA, USA) equipped with a Q-Exactive hybrid quadrupole-orbitrap mass spectrometer (Thermo Scientific, San Jose, CA, USA) was used to analyze the free amino acids in durian pulp. The experimental methods and instrument conditions were based on the literature and some optimizations were made ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). The pre-column AQC (6-aminoquinol-N-hydroxysuccinidyl carbonate) derivatization of amino acids was accomplished using the Waters AccQ-FluorTM(Waters Corporation, Milford, MA, USA) kit. The derivatization of AccQ-Fluor reagent, the derivatization of amino acid standard samples, the construction of the external standard calibration curve and the derivatization of the durian sample (50 mg) were strictly performed in accordance with instruction manual from Waters (Waters AccQ Tag Chemistry Package Instruction Manual, Revision 1). The processed sample solution was diluted nine times. Separation of amino acids was performed on a UHPLC system equipped with a Waters BEH C18 Column (2.1 mm \(\times\) 50 mm, 1.7 \(\upmu\)m particle size). The column oven temperature was set to 55 \({}^{\circ}\)C. The mobile phase A was water/0.1% formic acid, and the mobile phase B was ACN/0.1% formic acid. The flow rate was 0.35 mL/min, and the injection volume was 1 \(\upmu\)L. The elution gradient was as follows: 0.0 min A/B (95:5 \(v/v\)), 5.5 min A/B (90:10 \(v/v\)), 7.7 min A/B (75:25 \(v/v\)), 8.0 min A/B (40:60 \(v/v\)), 8.5 min A/B (95:5 \(v/v\)) and 13.0 min A/B (95:5 \(v/v\)). The UHPLC system was coupled to MS equipped with electrospray ionization (ESI) and the MS conditions were as follows: sheath gas flow rate 40 arb, auxiliary gas flow rate 10 arb, ion spray voltage + 3000 V at 350 \({}^{\circ}\)C, ion transfer tube temperature 320 \({}^{\circ}\)C. The scan mode was single ion detection (SIM). Different durian samples were studied using the same analysis methods and analysis conditions. The software TraceFinder (5.1, San Jose, CA, USA, 2019) was used for data processing. Three parallel determinations were performed under the same conditions.
## 3 Influence of PWM Pulse Frequency on Overvoltage Distribution  
When studying the influence of input pulse frequency on overvoltage distribution, the influence of other conditions is ignored. For the input PWM pulse of the permanent magnet wind turbine, the pulse rising edge is set to 2 \(\upmu\)s, the pulse amplitude is set to 2800 V, and the pulse width is set to 5 \(\upmu\)s. The pulse voltage signals with frequencies of 100 kHz, 50 kHz, and 10 kHz are used as voltage sources to study the distribution characteristics of ground and inter-turn voltages of motor end windings at different frequencies.