esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Statistical Analysis and Geographical Differentiation  
In order to keep synthetic and reasonable outputs, unaffected by mathematical artifacts, we have decided to present the unsupervised multivariate analysis (PCA) over just 23 parameters (6 and 17 combined from the PO and PWS variables, respectively). These components were set as \"loadings\" for the definite principal component analysis (PCA). We highlight that the limited number of independent samples prompts the use of selected parameters with major significance and discriminating power. On another hand, in the case of bigger data-sets (more samples), the whole or wider consideration out of the 47 variables could turn out more effective. The effective geographical chemical distinction is reported in Figure 3 by combining the score and the loading plots: Californian samples are clearly separated from Iranian samples which are far apart from the samples coming from the Mediterranean area (namely Sicilian, Greek and Turkish); these could be also distinguished to each other by supervised (linear discrimination analysis type) clustering. We again point out that, beyond the geographical discrimination, possibly performed by sophisticated and supervised statistical approaches, this double phase extraction, followed by NMR quantification (PO and PWS MARA-NMR), is a promising tool for the comprehensive chemical analysis of pistachio samples and other nuts. According to quantitative results, the American samples display a remarkably higher presence of free glucose, as well as di-acyl glycerol and linolenic fatty esters at the expense of monounsaturated fatty acid esters (oleic acid esters). On the other hand, Iranian samples are featured by the higher presence of trans-methyl-hydroxy-proline. The Sicilian and Greek pistachios contain somewhat less \(\gamma\)-amino-butyric acid, raffinose and arginine, while Turkish nuts contain the higher amount of proline, malate, shikimic acid, gallic acid and oleic acid esters (Figure 3). Such discussion encloses a precious chemical information, complementary to that highlighted by [PERSON] et al. [13], which majorly focused on lipid compounds. The general aim of such a spectroscopic approach is to understand the intrinsic chemical nature of compounds even though affected by dynamic behavior [38]. Once these phenomena are fully understood, the definite NMR quantification (even though entangled into complicated datasets) is enabled [34].
## 8 Application Example: 2023 Bangladesh Flooding Season  
### Background and Data  
Banglades is a land of many rivers and, as such, the most flood-susceptible country in the HKH. Sitting at the confluence of three major rivers--the Ganges, Brahmaputra, and Meghnar rivers and their tributaries [58]--Banglades faces a number of flood-related risks, such as fluvial flooding due to the accumulation of water from upstream catchments, pluvial flooding due to heavy monsoon rainfalls, and local heavy rainfall enhanced by drainage congestion (Figure 15).  
Figure 14: WD30 Performance Assessments. (**a**) An example of a SAR-derived water level map and the water level gauge stations in Bangladesh. (**b**) Comparison of WD30 to water level information from the BWDB for the confluence of the Padma and Meghna rivers in Bangladesh. Difference colors correspond to different observation dates.  
Banglades's meteorological year can be divided into three seasons, including the pre-monsoon hot season from March to May, the rainy monsoon season lasting from June to October, and a cool, dry winter season from November to February. The country has an average of 136 wet days per year, and approximately 80% of its yearly rainfall occurs from June to September [4]. Most flooding occurs during the monsoon season, covering up to 31,000 square kilometers or approximately 21% of the country in water. During the most severe flood seasons, up to two-thirds of the country can be covered in water, as was seen during the 1998 monsoon season [59].  
Here, we analyze the progression of the 2023 monsoon season for the country of Bangladesh (Figure 15) using operationally generated HydroSAR HYDRO30 data products. The 2023 season was chosen as it was the most recently monitored monsoon season at the time of writing of this article. Furthermore, the 2023 season was impacted by the cyclonic storm Mocha, a powerful and deadly tropical cyclone which made landfall in southern Bangladesh on 14 May 2023. We evaluated the flood extent and flood impacts across the country of Bangladesh as a function of time. All data used in this analysis were retrieved through the ICIMOD's RDS services introduced in Section 5.  
A total of 719 Sentinel-1 SAR-derived HYDRO30 products were collected to derive full-coverage surface water extent information in 2 week (14 day) intervals starting on 1 January 2023 and ending on 31 December 2023. A total of 25 14 day flood area maps were compiled from these data. The 14 day integration period was chosen to ensure full wall-to-wall coverage of Bangladesh by Sentinel-1 SAR data. Each 14 day map combined information from an average of 29 individual scenes.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 3 Simulation Scenarios  
In this section, a number of dynamic simulations carried out on IEEE 39-bus test system are used to create proper scenarios related to short-term voltage stability phenomenas. The obtained data are then used to compute the introduced SVS indices for each scenario. The 39-bus system is a well-known power system in dynamic studies which has 10 generators, 12 transformers, 19 loads, and 34 transmission lines. The load flow and dynamics data for the 39-bus system are available in [23]. The DIgSILENT power factory program is adopted for time-domain dynamic simulations and to generate the required data for testing the proposed indices.  
The operating point described in [23] is taken into account as the basic condition of the 39-bus system. A complex load model including both dynamic and static models is used for loads modeling in the studied system. In this model, a dynamic part consists of induction motors while a constant impedance model is considered for a static part of the model.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 5 Limitations and Future Works  
This section discusses the limitations and future works of the proposed ST-GAT-based PID controller method:  
1. The established ST-GAT model considers only the spatial relationships between different monitoring points on a single inclinometer without accounting for the spatial relationships between adjacent inclinometers. Future research needs to consider utilizing the axial forces and deformations of all struts in the entire diaphragm wall as well as their spatial relationships.
2. The proposed control method requires approximately ten days to achieve the desired deformation adjustment, which may limit its practical application. In the future, a more efficient deformation control method should be developed.  
Figure 19: Comparison of axial forces before and after adjustment.  
Figure 18: Wall deflection control under different ki settings.
Endophytic fungi are considered novel sources of biofungicides [10]. In Chile, endophytic fungi that inhibit the growth of _B. cinerea_ have been isolated from native, endemic, and introduced plants [11; 12; 13; 14]. It has been suggested that plants inhabiting high-elevation areas provide unique biotic environments that allow the isolation of endophytic fungi capable of synthesizing bioactive compounds that help plants resist adverse environmental conditions [15; 16; 17]. Recent studies reported that endophytic fungi, isolated from _Echinopsis chiloensis_, inhibited the growth _of B. cinerea_ by antibiosis [12; 14]. This plant was located at 1200 m above sea level, in a region with a Mediterranean climate, characterized by rainy winters and long dry summers [18]. _E. chiloensis_ is an endemic cactus inhabiting semi-arid environments in central-northern Chile [19].  
Previously, our research group isolated an endophytic yeast from _E. chiloensis_. The isolate, named Ec2, emitted volatile organic compounds (VOCs) with antifungal activity against _B. cinerea_. Therefore, the objectives of this work were (i) to identify to the genera level the endophytic yeast Ec2, (ii) to identify the VOCs emitted by the yeast and (iii) to characterize the antifungal activity against _B. cinerea_ of VOCs produced by the endophytic yeast.
###### Abstract  
To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network.  
MANETs; MRLAM; Q-Learning algorithm; MP-OLSR; RWP
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Synthesis of Fiber Bragg Gratings from a Targeted Transmission Specifications
#### 2.4.1 Minimum-Phase Transmissive Transfer Function  
As previously mentioned, the transmission spectral response of an FBG is necessarily minimum-phase (MP). In addition, as per the principle of conservation of energy, the transmissivity and reflectivity must accomplish \(T(f)=1-R(f)\). In this scenario, the given specifications of \(|H_{T}(f)|\) directly determine \(\phi_{T}(f)\) and \(|H_{R}(f)|\). Hence, the design problem reduces to the synthesis of an FBG whose reflection amplitude spectral response is \(|H_{R}(f)|\), while the reflection spectral phase \(\phi_{R}(f)\) appears as a degree of freedom to achieve the simplest grating implementation [38; 41]. For instance, if a quadratic spectral phase is considered (i.e., a linear chirp), the FBG reflection spectral response to be synthesized can be mathematically expressed by [41]:  
\[H_{R}(f)=W(f)\sqrt{R_{\max}\Big{(}1-\left|H_{T}(f)\right|^{2}\Big{)}}\cdot \exp\biggl{\{}j\Big{(}\frac{1}{2}\beta_{d2}(2\pi f)^{2}+(2\pi f)\tau_{d}\Big{)} \biggr{\}} \tag{34}\]  
where \(R_{\max}\) is the maximum reflectivity; \(\beta_{d2}=\ddot{\Phi}/L\) (s\({}^{2}\)/m) is the slope of the group-delay as a function of the angular frequency per unit length; \(W(f)\) is a windowing function employed to attain a band-pass response in reflection; and \(\tau_{d}\) is a time delay introduced to produce a causal response. The parameter \(\beta_{d2}\) determines the minimum grating length \(L\) as  
\[\beta_{d2}=\frac{n_{eff}L}{\pi Bc} \tag{35}\]  
being \(B\) the full-width (e.g., at 99% of the maximum) reflection bandwidth of the device (Hz). Hence, \(\beta_{d2}\) is a fundamental design parameter, which can be suitably designatedto guarantee that the synthesized grating apodization has a resolution attainable by the available fabrication method.  
From \(H_{R}(f)\) given in Equation (34), which is obtained from targeted \(|H_{T}(f)|\) specifications, the synthesis algorithms presented in Section 2.3 can be applied to obtain the desired \(n_{FBG}(z)\). Results will show that a higher dispersion value, \(\beta_{d2}\), leads to a more relaxed spatial resolution and a lower peak of the refractive index profile, at the cost of producing a longer device. In general, any optical processing functionality can be implemented using this configuration provided that its transfer function is minimum-phase [38]. The synthesized grating profile obtained from this design approach is much simpler than that obtained from previous approaches (based on space-to-time-to-frequency mapping [39]) and readily feasible, even for a strong coupling grating.
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 2 Related Work
### Distributed Frequent Subgraph Mining Algorithm  
Distributed frequent subgraph mining algorithms typically fall into two categories [23; 24]: those based on MapReduce [25] and those utilizing distributed memory frameworks like Spark [26; 27]. Each category presents its own set of advantages and disadvantages. In the context of MapReduce-based algorithms for frequent subgraph mining, one challenge lies in the inability to share data between different jobs due to the nature of Hadoop. Consequently, data sharing between jobs necessitates reading from the Hadoop Distributed File System (HDFS), leading to frequent I/O access during iterative calculations, which can adversely impact the algorithm performance. On the other hand, frequent subgraph mining algorithms based on Spark offer advantages in terms of iterative computation. However, these algorithms may face challenges related to scalability. Notably, FSM-H [28] and MRFSM [29] represent distributed mining algorithms that leverage the iterative MapReduce, effectively combining Hadoop and FSM algorithms. T-FSM [30], employing a unique task-based execution engine design, ensures high concurrency, bounded memory consumption, and effective load balancing.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 5 Experimental Setup  
The experimental setup used to evaluate our approach is presented in this section. First, the architecture of the nodes that compose the testbed is presented. Then, details on the ULP WuRx implementation are given. Finally, the designs of two state-of-the-art MAC protocols, to which SNW-MAC is compared, are presented.
# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 2 Related Work
### Attention Mechanism  
Seq2 Seq is a model used in natural language processing, such as machine translation, that uses an encoder-decoder architecture to transform one sequence into another [15]. However, during the process of compressing the encoder-decoder structure of the seq2 seq model into a fixed-size vector, loss of information can occur and the gradient vanishing problem can occur when the input is long. To address these issues, attention can be used effectively to train the model.  
Recently, attention has been applied as self-attention in CNN as well as seq2 seq. In the field of computer vision such as object detection and semantic segmentation, objects to be detected and segmented are distributed in a specific area, not in the entire image [16; 17; 18; 19]. Features are extracted by emphasizing a specific region and ignoring the remaining unnecessary regions. These features extract location and semantic information-oriented features and utilize the semantic and location features of objects to improve network performance and show good performance even in noisy inputs. Attention used in CNN includes convolutional block attention module (CBAM) [20]. Figure 3 is the architecture of CBAM.  
Figure 4 is the channel attention module of CBAM. Channel attention creates an attention map by utilizing the relationship between channels of features. Channel attention compresses the space through _MaxPool_ and _AvgPool_ to efficiently perform calculations. The channel feature association between each feature map is calculat  
Figure 3: CBAM Architecture.  
Figure 2: Network Model: (**a**) Existing Neural Networks; (**b**) ResNet’s Residual Block.  
perceptron (MLP) of the compressed features. Afterwards, an attention map is created with the sum of the two outputs and the sigmoid between 0 and 1.  
\[M_{\text{C}}=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))), \tag{3}\]  
Equation (3) is the channel attention module of CBAM. \(\sigma\) is the sigmoid activation function, _MLP_ is the multi-layer perceptron, _AvgPool_ is average pooling, _MaxPool_ is max pooling, and \(F\) is the input feature map.  
Figure 5 is the structure of spatial attention. Spatial attention creates an attention map by utilizing the relationship between spaces. Spatial attention creates a one-channel feature map through convolution. Through this, unlike channel attention, relation between pixels is calculated. Afterwards, an attention map is created with a sigmoid value between 0 and 1.  
\[M_{\text{S}}=\sigma\Big{(}f^{\gamma\times 7}([AvgPool(F);MaxPool(F)])\Big{)}, \tag{4}\]  
Equation (4) is the spatial attention module of CBAM. \(f^{7\times 7}\) is a \(7\times 7\) convolution.
# A Survey on Satellite Communication System Security
## 2 Existing Surveys and Motivation
### Motivations  
Although some existing works have tried to address various issues of SCSs, more work is needed to provide a comprehensive understanding of the SCS landscape.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multicolumn{4}{c}{**Security Attack**} & \multicolumn{4}{c}{**Security Defense**} & \multicolumn{4}{c}{**Policy**} \\ \cline{2-11}  & \begin{tabular}{c} **Eaves-**Dropping** \\ \end{tabular} & \begin{tabular}{c} **Jamming and** Spoofing** \\ \end{tabular} & \begin{tabular}{c} **DoS and** \\ \end{tabular} & **Energy** & **PLS** & **Blockchain** & **SDN** & \begin{tabular}{c} **Crypto and** \\ \end{tabular} & \begin{tabular}{c} **Anti-** \\ **QKD** \\ \end{tabular} & \begin{tabular}{c} **Anti-** \\ **Satellite** \\ \end{tabular} & \begin{tabular}{c} **Anti-** \\ **Stategy** \\ \end{tabular} & \
\begin{tabular}{c} **Anti-** \\ **Stategy** \\ \end{tabular} &
\begin{tabular}{c} **Anti-** \\ **Directions** \\ \end{tabular} \\ \hline
[8] & ✓ & & & & ✓ & & & & & ✓ \\ \hline
[9] & ✓ & & & ✓ & & & & & ✓ \\ \hline
[10] & ✓ & ✓ & ✓ & & ✓ & & & & ✓ \\ \hline
[11] & & ✓ & ✓ & & & ✓ & & & ✓ \\ \hline
[12] & ✓ & & & & ✓ & & ✓ & & \\ \hline
[13] & & & & & & & ✓ & & ✓ \\ \hline
[14] & ✓ & ✓ & ✓ & ✓ & ✓Insufficient Review Papers on Attacks and Defences for general SCSs: Numerous studies have tackled the potential risks and remedies associated with special-purpose SCSs. However, these studies do not provide a comprehensive overview of the entire SCS landscape. Furthermore, the existing research that discusses specific attacks or defenses lacks clear classification standards and detailed descriptions of the threats and solutions in SCSs.  
Lack of Discussions on Various Aspects of SCSs: Previous studies on SCSs have mainly focused on their technological aspects. However, SCSs are also a national asset, and it is essential to discuss their non-technological aspects, including policy and physical aspects.  
In terms of policy, conducting policy analyses regarding cyber attacks in SCSs is crucial as they can significantly impact future research. Unfortunately, existing works have not addressed these issues, so more research is needed. On the other hand, when it comes to physical aspects, anti-satellite (ASAT) attacks are the most common attacks due to various countries' recent policy movements and geopolitical situations. However, no existing work has addressed the ASAT landscape, so we need to make an effort to understand it.
## 5 Design, Manufacturing and O&M Integration Key Technology of Complex Product Based on Digital Twin
### Data Integration Fusion and Analysis Technology  
With the extensive application of automation, information, intellectualization, etc., in the manufacturing industry, the complex product would generate massive multisource heterogeneous data in the design, manufacturing and O&M process. Since different data sources have different operating systems and management systems, the storage mode and logic structure of data are distinct, which not only makes the data integration difficult but also impedes the construction of digital twin body.  
To provide better services for the design, manufacturing and O&M integration of complex products, it is necessary to carry out multisource heterogeneous data fusion and analysis. The data fusion analysis framework diagram based on digital twin, shown in Figure 7, is divided into five layers: data collection, processing, fusion, analysis and application. The full-feature data of a complex product's construction design, manufacturing and O&M digital twin body is mainly collected, including the physical entity data, virtual model parameter data and their interactive data. In terms of data processing, the processing of missing values of these data, noise processing, etc., are involved. In data fusion, the neural network and body modeling technology are mainly utilized for uniform formalization indication of multisource data structure and semantic heterogeneity, and the fusion of multisource heterogeneous data is achieved per the data fusion rule. Regarding data analysis, various data mining algorithms are used for the prediction, correlation analysis, evaluation, etc., of data. The data fusion and analysis technology could provide the needed value information for the product design, manufacturing and O&M integration process.  
Digital twin technology is mainly driven by data and could simulate the real behavior of the physical world in the virtual world via data fusion analysis. The data obtained via data fusion and analysis technology could be applied at the design stage to enhance the accuracy of product design, applied at the manufacturing stage to provide optimized production decisions for a product, and applied at the maintenance stage to provide a  
Figure 7: Data fusion and analysis architecture.  
favorable decision basis for product repair and fault prediction to effectively promote the reliability and utilizability of product.