esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Fractional Brownian Motion: Review and Extension
## 3 Proofs and Discussion  
In the field of stochastic process modelling, the fractional Brownian motion (fBm) is often recommended as a replacement for Brownian motion as the driving process, particularly when studying phenomena characterised by long-range dependence. The fBm, with a Hurst exponent (H) ranging between 0 and 1, is a type of Gaussian process that possesses several advantageous properties, including long-range dependence, self-similarity, and stationarity of increments. Notably, when the Hurst exponent is different from one-half, the fBm ceases to be a semi-martingale. This paper is somewhat reliant on an earlier work of the Doob-Meyer decomposition theorem that provides a representation of a submartingale as the sum of a martingale and an increasing predictable process. This theorem lays the foundation for the decomposition of submartingales into their components. If we let X(t) be a submartingale with respect to a filtration (a sequence of sigma algebras), then there exists a unique, increasing, predictable process A(t) such that the martingale process M(t) can be defined as M(t) = X(t) - A(t). In other words, according to [PERSON], the submartingale can be decomposed into the sum of a martingale M(t) and an increasing predictable process A(t). The proof for a continuous case is very complicated and can be found here [17]. However, there is a contradiction here, as it was said that the fBM is not even a semi-martingale.  
In this paper, we propose to review and extend proofs in mixed fuzzy stochastic Brownian motion as novel equations suitable for modelling hybrid systems. These equations provide a flexible framework that can effectively capture the dynamics of complex systems characterised by both stochastic and fuzzy components. By combining the concepts of fuzzy logic and stochastic processes, we aim to address the modelling challenges associated with hybrid systems. Applications of these processes can be made in the telecommunication industry, where measurements of variables suffer from multiple disturbances, and the financial industry, where participants are operating under uncertainty.  
To illustrate the applicability of our approach, we utilise the Liouville form of the fractional Brownian motion. The Liouville form represents a particular representation of the fBm that allows for an enhanced analysis and interpretation of its properties. Byleveraging this form, we can gain further insights into the behaviour of the mixed fuzzy stochastic Brownian motion and intuitional mixed fuzzy stochastic Brownian motion, thereby facilitating their use in a wide range of modelling scenarios.
# Remote Sensing for Irrigation of Horticultural Crops
## 1 Introduction
The institutional improvements \"_rely on the user involvement in scheme operation and maintenance; introduction of water pricing and legal frameworks to provide incentives for efficient water use and disincentives for inefficient use; introduction of integrated catchment management; improved training and extension_\". In 2005, [PERSON] [6] emphasized the importance at the farm level of the extension services for improving information or of cheap credit for adjusting to the changing environments associated with water policy reforms (water-pricing reforms, particularly). Because more efficient irrigation practices can optimize WUE [18], and drip irrigation reduced crop evapotranspiration losses and runoff [19; 20; 21], the technology was appropriate for applying water to orchards and vegetables [22]. Proper irrigation (timing and amount) increased the crop WUE and crop yield [23]. In contrast, improper irrigation can lead to the onset of crop water deficits causing water and nutrient deficiencies that reduce yield [23; 24]. Nevertheless, regardless of the strategy engaged, the benefits of irrigation scheduling can be reached only by applying the exact amount of water required [25]. [PERSON] et al. [26] define precision irrigation as \"the accurate and precise application of water to meet the specific requirements of individual plants or management units and minimize adverse environmental impact\". Monitoring water use and crop water status in the field is important for developing effective precautions, and for this purpose, some indicators are required [27]. A very large body of research, spanning almost four decades, has demonstrated that much of the required agricultural information can be derived from remotely sensed data [28] starting from crop water status detection. IN order to develop effective irrigation strategies, it is necessary to identify the appropriate indicators for monitoring crop water status at the farm level [27]. For these purposes, the required agricultural information may be derived from remotely sensed data [28].
# Linked Data Triples Enhance Document Relevance Classification
## 6 Experimental Description
### Experiments on Reuters and 20 News Groups  
To test the generality of the results obtained from the Energy-Hub dataset, the same experiments were repeated on two standardized datasets, namely Reuters sourced from UCI Machine Learning Repository ([[https://archive.ics.uci.edu/ml/datasets/reuters-2157](https://archive.ics.uci.edu/ml/datasets/reuters-2157)]([https://archive.ics.uci.edu/ml/datasets/reuters-2157](https://archive.ics.uci.edu/ml/datasets/reuters-2157)) 8+text+ categorization+collection, accessed on 5 February 2021) and 20 News groups. The Reuters corpus is a multi-class, multi-labeled dataset. It has 21,578 instances categorized into 90 classes. ModApte version of split data with 9603 training documents and 3299 testing documents of 10 largest classes is used in the experiments. The 20 News groups  
Figure 6: Performance results of classifiers from different inputs on Energy-Hub dataset. The first block of results uses GloVe-based representations. The second is models using the InferSent sentence encoder. The third block of results uses BERT-based models. \(*\) indicate the best model from each class. is a multi-class news-topic categorization dataset with 20 different classes. It has 11,314 training samples and 7532 testing samples. To maintain consistency with Energy-Hub corpus, one of the two standard datasets, namely Reuters, is restricted to two class labels. All documents consisting of particular label (\"earn\"), a label class with the dataset that allows it to simply convert to a binary class dataset, were considered \"relevant\", and the remainder were categorized as \"irrelevant\". Two thousand two hundred and eighty-four documents were sampled where the relevant and the irrelevant set contained 1142 records. The training set with 1462 documents was used for learning. The validation and test sets contained 366 and 456 documents, respectively. The training samples from 20 News groups dataset were partitioned into two sets, forming 9051 training and 2263 validation instances to measure loss after each epoch. All models were trained with the same neural network architecture and parameters used in the Energy-Hub tests. Table 3 and Figure 7 shows the results on the Reuters dataset. Consider the first block of results in Table 3, which shows the performance of the additional features compared to a baseline model consisting of Text (with GloVe) word embeddings. \"Text (with GloVe)+Triples\" achieved the best results with 89.9% accuracy and 87.7% F1 score. The \"text+topics+triples\" and \"Text (with GloVe)+topics+NEs+triples\" models showed better performances than the baseline text model with respect to all of the evaluation metrics. These results are, therefore, consistent with those from Energy-Hub tests, namely reinforcing that adding triples improves text classification performances.
# Electrochemical Removal of Chromium (VI) from Wastewater
## 3 Results and Discussions
### Electro-Reduction of Chromium (VI)
#### 3.1.3 Effect of Reaction Temperature  
Reaction temperature is an important parameter affecting the reaction during the diffusion process [8; 10; 22; 29; 30]. The effect of reaction temperature on the reduction efficiency was investigated while other conditions--the initial concentration of chromium (VI) of 1.000 g/L, current density of 50 A/m\({}^{2}\), concentration of H\({}_{2}\)SO\({}_{4}\) of 100 g/L, and stirring rate of 500 rpm--were kept constant. The results shown in Figure 3 indicate that the reduction of chromium (VI) could be easily achieved at a higher reaction temperature (\(\geq\)50 \({}^{\circ}\)C), which was partially consistent with recent studies [8]. A higher temperature would decrease the diffusion resistance and favor the contact of the free electron and chromium (VI), resulting in a high reduction efficiency. Thus, a reaction temperature of 70 \({}^{\circ}\)C was chosen to be the optimum.  
Figure 2: Effect of concentration of H\({}_{2}\)SO\({}_{4}\) on the reduction efficiency of chromium (VI).
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 4 Interfaces and Protocols in QKD Networks Enabled by SDN
### Northbound Interface Protocol  
Northbound interface is open to the application layer, and its goal is to enable application layer to conveniently modify the underlying QKD resources in QKD layer. The basic control functions of it include topology acquisition, service request, QKD link building, and QKD path calculation, etc. Since application layer needs to provide useful information, the related information model needs to be dealt in controller with physical data uploaded by QKD layer. While adopting more information model will increase the complexity of northbound interface and reduce their interoperability and scalability. General information model can be defined by UML modeling language, and YANG model can be used to construct data model. Moreover, information model is independent of the specific types of interface protocol used in northbound interface. To meet the notification function, the protocol in northbound interface can adopt RESTconf protocol defined by IETF draft-ietf-netconf-restconf-07. As an example, RESTconf protocol will support notification events defined by YANG model, and users can receive the notification by subscribing to the corresponding URL.
# Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale X-ray Security Images
## 4 Image Augmentation
### Experiment Setup  
We adapted a Faster-RCNN [24] detection model for the task of detecting five types of threat objects in X-ray security images. We used ResNet50 [8], with pre-trained weights from ImageNet [12], as the backbone network. We trained the model for 30 epochs with a batch size of 8 using Stochastic Gradient Descent (SGD) with a learning rate of 0.000125 and a momentum of 0.9.  
We trained eight detection models, which varies only with the augmentation approach used during training. The baseline approach (Base) used only the positive samples from the training set without any additional augmentation. The image translation approach (Transl) used the positive samples from the training set and 10,000 synthesized images containing the translated X-ray images of threat objects. The image generation approach (Gen) used the positive samples from the training set and 10,000 synthesized images containing the generated X-ray images of threat objects. The image transformation approach (Transf) applies random flipping and resizing to all images in the training set. Different variations of the combinations of these approaches make up the rest of the trained detection models. When augmenting the training set using the combined image synthesis approaches (Gen + Transl), we sampled 5000 synthesized images containing the translatedX-ray images of threat objects and 5000 synthesized images containing the generated X-ray images of threat objects.
## 3 Service Environment Clustering Method
### Cluster Center Updating Algorithm  
If the distance between the sample \(S_{i}(i=1,2,\cdots,q)\) and the \(k\)th cluster center \(M_{k}\) is \(d_{ik}=\left\|S_{i}-M_{k}\right\|^{2}\), calculate the similarity \(r_{ik}\) and then obtain the similar matrix \(R=\{r_{ik}\}_{n\times l}\).  
Define \(\mu_{k}(S_{i})\) to be the slave degree of the \(i\)th sample \(S_{i}\) in the \  
Update the \(k\)th cluster center:  
\[M_{k}=\frac{\sum\limits_{k=1}^{l}{\left[{{{\mu_{k}}({S_{i}})}}\right]^{b}}{{S_{i}} }}{\sum\limits_{k=1}^{l}{\left[{{{\mu_{k}}({S_{i}})}}\right]^{b}}},k=1,2,\cdots,l, \tag{9}\]  
where \(b\) is a fuzzy constant with \(b>1\) for controllable clustering results, such as \(b=2\).  
Update the cluster centers via iteration until the objective function \(J\) is less than a specified minimum threshold \(\varepsilon\):  
\[J=\sum\limits_{i=1}^{q}{\sum\limits_{k=1}^{l}{{{\left[{{\mu_{k}}({S_{i}})} \right]}^{b}}{{\left\|{{S_{i}}-{M_{k}}}\right\|}^{2}}}}<\varepsilon, \tag{10}\]  
or until after a specified maximum number of iterations.  
If the FCM converges, we can obtain all of the cluster centers as well as the slave degrees of every sample to each cluster. If a sample has the largest slave degree to the \(k\)th cluster, it is considered that the sample belongs to cluster \(k\); thus, the FCM clustering is completed.
## 3 Results and Discussion
### Study on the Performance of Uranyl Sensor
#### 3.4.3 Calibration Curve and Detection Limit of Sensor  
Under the optimal experimental conditions, a series of standard solutions of UO\({}_{2}\)\({}^{2+}\) with different concentrations were manufactured. Differential pulse voltammetry (DPV) was employed to detect and calibrate the UO\({}_{2}\)\({}^{2+}\) on the MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE at different concentrations, as shown in Figure 6C. The MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE sensor has a good linear relationship in the range of 1.0 \(\times\) 10\({}^{-9}\) mol L\({}^{-1}\) to 2.0 \(\times\) 10\({}^{-7}\) mol L\({}^{-1}\) with the detection and quantification limits of 1.08 \(\times\) 10\({}^{-9}\) and 3.23 \(\times\) 10\({}^{-10}\) mol L\({}^{-1}\), respectively. The linear regression equation was expressed as I\({}_{\rm P}\)(\(\mu\)A) = 86.77(\(\mu\)mol L\({}^{-1}\)) + 7.218 with a correlation coefficient of r = 0.9993.  
[PERSON] et al. [52] developed a 6-O-palmitoyl-l-ascorbic acid (PAA)-modified graphite (GRA) electrode based on uranium concentration by non-homogeneous complexation followed by differential pulse voltammetry reduction for determination. As well, [PERSON] et al. [53] performed the electrochemical determination of uranyl by using a Schiff base modified carbon paste electrode. Although the preparation process is simple, the detection range is limited and the detection limit is high. In addition, [PERSON] et al. [54] investigated the binding properties of uranyl ions to four different benzosubstituted macrocyclic diamides and prepared novel polymer film (PME) and coated graphite (CGE) UO\({}_{2}\)\({}^{2+}\)-selective electrodes. The CGE was used for flow injection potentiometry (FIP) for the determination of trace uranyl ions in samples. Although this potentiometric sensor exhibitsthe advantage of fast response and good selectivity, it is cumbersome to operate. Moreover, [PERSON] et al. [49] prepared a potentiometric ion-selective electrode (ISE) by dispersing uranyl ion-imprinted polymer particles in 2-nitrophenyl octyl ether (plasticizer) embedded in a polyvinyl chloride matrix. Its detection limit is low, but the electrode preparation method is complicated. Comparing the method of this experiment with other uranyl sensor electrochemical methods, which can be summarized from the comparison in Table 2 that the MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE sensor fabricated is more innovative and sensitive. This leads to the conclusion that the sensor designed with nanomaterials can greatly improve the limit of detection and linear range compared to other modified materials. In addition, the preparation procedure is simpler, cheaper, and more sensitive, thus the technique contributes significantly to the trace analysis of other substances.
# 5G-MEC Testbeds for V2X Applications
## 5 Summary of Available Tools
### Mep
AdvantEDGE is compliant with the ETSI architecture and runs on Docker and K8s. It allows the sandbox creation to deploy the 5G-MEC scenario. AdvantEDGE provides ETSI-defined MEC services such as location services, RNIS, WAIS, application enablement, application mobility, and V2X services. It also includes AdvantEDGE-specific edge services and network models to run the experiments. Furthermore, this platform allows the network traffic between elements to be controlled.
## 6 Non-Linear Methods
### Quantitative Impacts and Alignment with the 2030 Agenda  
The quantitative benefits of non-linear methods in the food industry are significant. Studies have shown that the application of non-linear optimization can result in cost savings of 10-20%, energy reductions of 10-15%, and waste reductions of 5-10% [36]. These outcomes align with the SDGs, particularly those related to sustainable consumption and production (SDG 12), climate action (SDG 13), and zero hunger (SDG 2).  
Moreover, non-linear methods enable a more nuanced understanding of trade-offs among competing objectives, allowing decision-makers to prioritize sustainability without compromising economic viability. For instance, a multi-objective non-linear optimization model for a dairy processing plant demonstrated that energy consumption could be reduced by 12% while maintaining profitability, illustrating the balance between environmental and economic goals [40].
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 4 Results and Discussion
### Spatiotemporal Variations of Snow Depth on Antarctic Sea Ice in 2019
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sea Ice State** & **Antarctic Sea Area** & **MD/cm** & **RMSD/cm** \\ \hline \multirow{6}{*}{Winter (June to August)} & Bellingshausen and Amundsen seas & 5.1 & 11.0 \\  & Weddell Sea & \(-\)1.1 & 7.0 \\  & Southern India Ocean & 0.1 & 7.0 \\  & Southern Pacific Ocean & 2.6 & 8.6 \\  & Ross Sea & 1.9 & 8.0 \\ \hline \multirow{6}{*}{Annual (January to November)} & Bellingshausen and Amundsen seas & 6.3 & 13.2 \\  & Weddell Sea & 1.4 & 9.5 \\ \cline{1-1}  & Southern India Ocean & 1.9 & 8.9 \\ \cline{1-1}  & Southern Pacific Ocean & 2.6 & 9.7 \\ \cline{1-1}  & Ross Sea & 2.3 & 9.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._in the Bellingshausen and Amundsen seas, initially declines then increases in the Weddell and Ross seas, and steadily rises in the Southern Indian and Pacific oceans. Regional and seasonal changes in snow depth are affected by multiple factors, such as snowfall, rainfall, and wind-derived blowing snow [42]. Based on the HY-2B derived snow depth on Antarctic sea ice, it is possible to conduct an in-depth analysis of the impact mechanism of changes in snow depth and the related meteorological and climatic indicators in future work. Figure 6: Spatial distribution of HY-2B SMR derived monthly snow depth in 2019.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 3 Hardware Implementation of the Proposed SCbSC and Evaluation of Its Performance
### SPCNG Resilience against Statistical Attacks
#### 3.2.1 Phase Space Test  
We draw in Figure 7a the phase space or mapping of a sequence X(n) generated by the proposed SPCNG formed by 3,125,000 samples out of the 3,125,100 samples generated to deviate from the transitional regime \(T_{r}=100\), and in Figure 7b, we show the mapping of 1000 samples taken randomly from \(X(n)\).  
Already, from Figure 7b, the region looks like a totally disordered region, indicating the lack of correlation between adjacent sample values.
## 3 Results  
### The Interannual Variation of Water Balance and Forest Growth from 2001 to 2018  
We extracted the monthly SPEI12, EVI and GPP values for each constant forest pixel and then looked at the spatial average to draw the time series. As Figure 3a shows, the water balance for forests in North China significantly increased from 2001 to 2018 (slope = 0.003, \(p\) = 0.0011), which suggested an increase in water availability for this region. However, despite this trend, forests in this area were generally under water stress, since SPEI12 was negative for most months. The mean SPEI12 for 2001-2018 was \(-\)0.7191, which suggested slight droughs. Furthermore, during these 18 years, there were four severe drought events (2003: SPEImin = \(-\)1.59; 2007: SPEImin = \(-\)1.81; 2008: SPEImin = \(-\)1.98; and 2017-2018: SPEImin = \(-\)1.57).  
The EVI (slope = 0.0001, \(p\) = 0, Figure 3b) and GPP (slope = 0.015 g C/m\({}^{2}\), \(p\) = 0, Figure 3c) increased significantly as well over this time period. These results suggest that increased water availability influenced forest growth, but this impact might not have been linear or immediate. Moreover, the fluctuations in the summer values of the EVI and GPP in the four severe drought years were different. Although both the deseasonalized EVI and GPP had much lower values in summer in 2003 compared with the general season cycle, the EVI was higher than normal in 2007, an extreme drought year. Meanwhile, the deseasonalized GPP did not increase in the summer of 2007. Similar patterns were observed in 2017 (Figure 3b,c). These discrepancies might be explained by different physical characteristics reflected by the two indices, which influenced their response times to droughs.  
Figure 2: Example of calculating the correlation between the growing season EVI and SPEI with a 1-month lag step.
# Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China
## 2 Materials and Methods
### Data
Actual evapotranspirationration data (unit: mm) and the 0-10 cm depth, monthly average soil water-content data (unit: kg\(\cdot\)m\({}^{-2}\)) in GLDAS_Noah025_M 2.0 and 2.1 datasets were used, with 0.25 \(\times\) 0.25\({}^{\circ}\) spatial resolution and a time range from January 1960 to December 2017. GLDAS data are global land-surface characteristics and flux data generated by advanced land-surface models and data assimilation technology [63]. The validation NPP data are the MOD17A3 surface vegetation NPP data provided by the EOS/MODIS (TERRA satellite) of NASA. MOD17A3 has been verified and widely applied in research regarding vegetation growth, biomass estimation, environmental monitoring, and global change in different regions of China and the world. In this study, we used NPP data with a 0.5 \(\times\) 0.5 km resolution from 2000 to 2017. Data corresponding to the 693 meteorological stations were extracted using the neighboring grid method.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Semi-Bridgeless Boost Converter with Isolation  
Figure 9 shows a modification of the SBBC conventional topology [95]. Such modification consists of adding a switch (\(Q_{3}\)) between the half bridge and the capacitor to isolate the AC power supply and DC link bus. This topology presents a reduction of leakage  
Figure 8: Semi-bridgeless boost converter with soft switching.  
Figure 7: Semi-bridgeless boost converter topology.  
current to the ground line from the grid. However, power losses can increase due to the addition of \(Q_{3}\) that operates when the converter is discharging; in addition, this converter has a disadvantage that requires an additional control action for \(Q_{3}\) and a greater filter in comparison with the SBBC conventional topology.