esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Additionally, cyclones making landfall in adjacent coastal areas of West Bengal, India, often extend their impact into the interior coastal region of Bangladesh, such as Jessore and Jhenaidah [39]. Importantly, the spatial extent and severity of disaster impacts are not static but vary over time, underlining the need for dynamic assessment approaches [40]. With increasing urban expansion, flood exposure is expected to increase, contributing to elevated flood risk across all sources [41]. The current flood monitoring system in Bangladesh, led by the Flood Forecasting and Warning Centre (FFWC) under the Bangladesh Water Development Board (BWDB), presents several limitations that hinder comprehensive flood assessment. Established in 1972, FFWC primarily relies on river water level data from 110 stations nationwide [42],with updates disseminated via its official website [43]. However, this river-centric approach overlooks rainfall-induced flooding, especially in flat, low-lying coastal areas where localized precipitation can cause significant inundation. Furthermore, the FFWC's model domain excludes coastal and South-Eastern regions, limiting the applicability of its outputs for flood analysis and verification in these vulnerable zones [44]. While it produces inundation maps by combining river water level forecasts and digital elevation models (DEMs) till 2023, these maps lack the ability to capture the dynamic nature of floods. Initially, FFWC used MODIS optical imagery for flood mapping [45], but due to cloud interference during monsoons, it adopted Sentinel-1 SAR data in 2021 for more reliable flood detection [44]. Despite this improvement, operational constraints still limit the production of timely, event-specific inundation maps, underscoring the need for more comprehensive and adaptive monitoring frameworks that integrate diverse flood drivers and regions. Against this backdrop, the present study aims to analyze recent extreme weather events, namely Cyclones Mora (2017), Tithi (2018), Fani (2019), Amphan (2020), Yaas (2021), Sitrang (2022), Midhili (2023), and Remal (2024), along with seasonal heavy monsoonal rainfall events from 2017 to 2024. By utilizing SAR imagery and ML techniques, this research seeks to produce high-resolution inundation maps and examine flood dynamics across diverse coastal regions. Specifically, the study focuses on optimizing change detection thresholds using machine learning methods, mapping the temporal and spatial patterns of inundation, and delineating zones with elevated flood risk based on recurrence analysis.
## 2 Literary Review and State of the Art
### Fog Computing (FC) System  
FC is a computing paradigm that extends cloud services to the edge of the network, closer to data sources such as sensors, IoT devices and control systems in industrial plants. Unlike traditional cloud computing, where data are sent to centralized data centers for processing, FC allows processing, analysis and decision-making to take place closer to where the data are generated. This reduces latency, improves operational efficiency and enables faster real-time response, which is essential in dynamic, mission-critical industrial environments. In an FC architecture, fog nodes act as intermediaries between field devices and central processing or storage systems in the cloud. These nodes can be devices with intermediate computational capabilities, such as routers, gateways or small local servers, that execute specific tasks, such as processing data, filtering information or running artificial intelligence algorithms. In doing so, they offload the workload from the cloud and allow applications to run continuously and efficiently, even in environments with limited or intermittent connectivity [49].  
FC is an extension of the cloud computing model, bringing computational resources closer to the edge of the network, where data are generated and used. In a manufacturing system, FC acts as an intermediary between plant floor devices (such as sensors and actuators) and cloud services, enabling local data processing, task orchestration and real-time decision-making. This approach is particularly valuable in environments where latency, connectivity and workload are critical factors. Implementing an FC system in the context of an MES allows for efficient distribution and management of computational tasks, ensuring that shop-floor processes run optimally, even in unstable network conditions or under high demand.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 4 Classification of Self-Healing Coatings Based on the Polymeric Matrix
### Thermoset Polymers
#### 4.1.2 Polyurethane-Based Self-Healing Coatings  
Polyurethane stands out as a highly sought-after polymer type in the contemporary era due to its versatile characteristics that are suitable for a wide range of applications, including anti-corrosion coatings. Traditionally, polyurethane has been utilized to enhance the aesthetics, scratch resistance, lifespan, and corrosion resistance of various metallic objects. In the realm of polymeric coating systems, polyurethane (PU) takes a prominent position thanks to its numerous properties, including being eco-friendly, non-volatile, cost-effective, and sustainable. The utilization of polyurethane (PU) as a polymer matrix for anti-corrosion coatings has advanced through the development of various self-healing systems incorporating PU. In a study by [PERSON] et al. [47], a smart-lignin-based green coating was formulated with notable anti-corrosion performance and the capability to swiftly release the corrosion inhibitor (nicotinic acid, a green inhibitor) upon mechanical damage. The assessment of anti-corrosion performance, conducted through electrochemical impedance spectroscopy (EIS), revealed an increase in the corrosion potential value from 481 (uncoated sample) to +187 mV. The released corrosion inhibitor facilitated the formation of a passive layer on the metal substrate's surface in response to mechanical damage, underscoring the self-healing ability of the lignin polyurethane (LPU) coatings.
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 2 State of the Art
### IEEE 802.15.4
#### 2.2.2 IEEE 802.15.4 Slotted CSMA/CA  
The slotted CSMA/CA algorithm is used by every node to transmit a new packet (command or data packets) in the beacon enabled mode. It is executed only during the CAP (See Figure 3) period [14].  
Three variables are specified within the slotted CSMA/CA algorithm:  
* Backoff Exponent (BE): it computes the backoff delay observed by the nodes before performing the CCA test on the shared medium. The value of BE is randomly chosen between 0 and (\(2^{\text{BE}}-1\)).
* Contention Window (CW): it indicates the number of backoff periods during which the channel must be idle before a node can access the channel to send its data. It has a default value of 2.
* Number of Backoffs (NB): it corresponds to the number of backoff executed before channel access. It is initialized to 0 and compared to a maximum value, macMaxCSMABackoffs, which is by default equal to 5. If the value of _NB_ is greater than the maximum value, a failure occurs.  
First, NB is initialized to 0 and CW is initialized to the CW\({}_{0}\) value corresponding to 2 by default. After the initialization, the algorithm tests the Boolean value of the battery life extension (BLE) variable.  
Figure 3: Slotted CSMA/CA algorithm.  
If the latter is fixed to true, BE is set to the minimum between 2 and macMinBE (by default fixed to 3). Whereas, if BLE value is fixed to false, BE is initialized to 2. After fixing the values of the different variables, a node implementing the CSMA/CA algorithm waits for the backoff delay and performs the first CCA test. Two possible channel states exist:  
* The channel is busy and consequently CW is reinitialized to CW\({}_{0}\), if it has been changed. Furthermore, a busy channel results in incrementing NB and BE. BE must not exceed the value of aMaxBE fixed by default to 5. When a node reaches the macMaxCSMABackoffs value, the CSMA/CA algorithm reports a failure to the higher layer. Otherwise, if NB has not reached the macMaxCSMABackoffs value, the backoff operation restarts and the CCA is performed again.
* The channel is sensed idle and CW is greater than 0: the CCA is repeated and CW decremented. Otherwise, the node attempts to transmit if the remaining time in the current CAP is sufficient to transmit the frame and receive the acknowledgement. If not, the process is deferred to the next CAP of the next superframe.
## 2 The Phytochemistry of _Artemisia annua_ L. (Qinghao)
### Higher Terpenoids  
#### 2.7.1. Diterpenes  
The structure of the diterpene phytene-1-ol-2-hydroperoxide (**553**) [9,260] was wrongly assigned as phytene-1,2-diol (**552**), when it was first described as a natural product from _A. annua_[261]. Structural revision was made on the basis of studies of the photo-oxygenation of commercially-available phytol, which produced both phytene-1,2-diol (**552**) (in racemic form), as well as its 2-hydroxyethyl analogue (**553**) [260]. Both phytol (**550**) itself and an authentic sample of phytene-1,2-diol (**552**) were subsequently obtained as natural products from the seeds of _A. annua_[9]. Natural phytol is expected to have the \(7R,11R\) absolute configuration [16], and natural phytene-1,2-diol (**552**) was therefore assigned as being a mixture of epimers at the 2-position on the basis of its NMR spectra and on the assumption that the configuration at the 7- and 11- positions of (**552**) remained fixed, as in phytol. The observance of epimers is most easily explained if both the hydroperoxide (**553**) and alcohol (**552**) are products of the spontaneous autoxidation of phytol (**550**), occurring within the tissues of _A. annua_ plants as is shown in Scheme 8.  
**Scheme 8.** Proposed formation of phytene-1-ol-2-hydroperoxide (**553**) by spontaneous autoxidation of phytol (**550**) and subsequent hemolysis/reduction of **553** to phytene-1,2-diol (**552**).
###### Abstract
[2] took the PS-InSAR results to a second level by applying statistical analysis of fragility curve to map damage probability assessment for a part of Pistoia, Italy. Here, we took the analysis to a higher level by applying spatiotemporal analysis for time series measurements surrounding collapsed buildings before the incident. Spatiotemporal analysis discovers interesting and previously unknown patterns from large spatial and spatiotemporal databases, which are potentially useful to understand phenomena [13]. Spatiotemporal pattern analysis was successfully applied in different applications, including climate science, neuroscience, environmental science, precision agriculture, epidemiology and health care, social media, traffic dynamics, heliophysics, criminology, and location-based services [14; 15; 16]. To the best of our knowledge, however, it was not used in surface deformation analysis, especially, with PS-InSAR in any previous research. This might be due to the applicability of the PS-InSAR time series results in most applied research. In a PS-InSAR building collapse application, time series results are sufficient when vertical and horizontal LOS (line-of-sight) deformations are integrated into a building collapse causality model, similar to landslide areas where cumulative displacement is the only significant reason for building cracks and collapse incident [17]. However, when horizontal deformation does not exist, or is very small in an area of interest like Alexandria [18], vertical displacement in a large spatial distribution cannot be taken as a sole reason for the collapse, because the whole building is subjected to subsidence at the same rate. Therefore, the detection of specific spatiotemporal patterns surrounding collapsed buildings before the incident in the city might be helpful in future building collapse prediction. Alexandria, Egypt, was selected as a case study due to the recently increasing number of building collapse incidents in the city. Alexandria is constructed on a carbonate coastal ridge of Pleistocene age covered by a thin layer of Holocene sediments at the western margin of the Nile delta. Historically, it was susceptible to high rates of subsidence due to short-term catastrophic events of powerful earthquakes and tsunamis, the continuous subsidence of the Quaternary water-saturated substrate,destructive winter wave surges, and anthropogenic influences, such as the load of city constructions [19]. Official records attribute collapse incidents in the city to violations of building specifications, illegal extensions, lax oversight, aging, poor maintenance, and very few incidents to land subsidence [20]. However, this study does not focus on the reason behind the collapse incident, because whatever the reason for the collapse, the effect of all causes are mostly the same [12].
# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 4 Discussion  
Based on the data in Tables 2 and 3, the registration time of the proposed method is only slightly slower than that of K4 PCSs for the Bunny dataset; for all other datasets tested, the proposed method achieves the fastest registration time. Regarding registration accuracy, KD-ICP performs slightly better than the proposed method on the Bunny dataset; however, the registration time of KD-ICP is approximately twice that of the proposed method. For the Dragon and chair datasets, the proposed method demonstrates both optimal accuracy and the fastest registration speed. These results indicate that, compared to the improved KD-ICP method [4, 5], K4 PCSs method [9], and the 3D-NDT method [10], the proposed approach achieves faster and more accurate data registration, significantly enhancing the accuracy and efficiency of point cloud data registration. Noise typically occurs at the edges of the main data, making it easy to filter out the noise that is far from the main data using statistical filters. For unevenly distributed data, the dynamic neighborhood is adjusted appropriately based on data density to include more feature information. In the registration of diverse point cloud datasets the proposed method offers a favorable balance of accuracy and efficiency, enabling the completion of point cloud data registration with high precision and speed.
## 4 Discussion
### Comparison with Previous Studies
#### 4.1.2 Comparison with Previous Studies Based on CHB-MIT Database

Table 5: Comparison with previous studies on Bonn database (interictal versus ictal). Based on the above discussions, our RR-DTDWT based detector is more suitable for realistic application scenarios compared with those listed in Table 6. [PERSON] et al. [57] also proposed an automated epileptic seizure detection method based on DWT and complexity measure via sigmoid entropy, achieving a sensitivity of 94.21% and an accuracy of 94.38%, which is less effective than our method. Previous studies such as [35] evaluated the performance of their method using both Bonn and CHB-MIT databases. Although their model has also shown a perfect performance on both of the two classification tasks (\"normal versus ictal\" and \"interictal versus ictal\") on the Bonn dataset (see Tables 4 and 5), its accuracy (97.5%) on more challenging CHB-MIT database shown in Table 6 is lower than ours (98.89%). Promisingly, our RR-DTDWT algorithm can further promote the development of automated seizure detection technology.
## 4 Results
### Correlation Analysis between TANSO XCO\({}_{2}\) and MODIS-Derived Indices  
The R\({}^{2}\) and RMSE between TANSO XCO\({}_{2}\) and MODIS-derived indices were analyzed across months on each continent. The following nine indices were considered in the analysis: LST (of MOD11C3), EVI and NDVI (of MOD13C2), LAI and FPAR (of MOD15A2), GPP, NPP, GN (GPP minus NPP) and NG (NPP/GPP) (of MOD17A2).  
Different correlation coefficients between TANSO XCO\({}_{2}\) and MODIS-based indices were found on different continents (Table 1). In Africa, the highest correlation coefficients were between XCO\({}_{2}\) and NPP (R\({}^{2}=0.61\) and RMSE \(=1.21\)). Although the values between XCO\({}_{2}\) and NDVI and LST were not very high (R\({}^{2}=0.39\) and 0.24, RMSE \(=1.53\) and 1.70, respectively), but they meet the model indices select standard (see Section 4.3). In Australia, because the higher rate of bare areas can increase the reflectance of background, the highest correlation coefficient was between XCO\({}_{2}\) and LST (R\({}^{2}=0.41\), RMSE \(=0.85\)). Although the bare areas in Eurasia are also extensive, the forests in Siberia and southern China increase the correlation between XCO\({}_{2}\) and NDVI and EVI (R\({}^{2}=0.44\) and 0.42, RMSE \(=1.73\) and 1.76, respectively). In North America, the correlation coefficients between XCO\({}_{2}\) and NDVI reached R\({}^{2}=0.65\) (RMSE \(=1.15\)) and the lowest values that were selected for the TVP model is R\({}^{2}=0.49\) (between XCO\({}_{2}\) and LST and NDVI). In South America, the highest correlation coefficients were between XCO\({}_{2}\) and LST (R\({}^{2}=0.64\), RMSE \(=1.39\)). The index of NG (R\({}^{2}=0.19\), RMSE \(=2.09\)) were also selected for the model build.  
Figure 2: _Cont._  
The different values indicate that CO\({}_{2}\) concentration was influenced by different factors associated with different climate and land-cover types [2]. Table 1 also shows that LST was important in all five continents, which indicates that LST is an important factor in CO\({}_{2}\) concentration research. With regard to the selected indices in the five continents, only Australia had an average RMSE lower than 1; Eurasia had the highest averaged RMSE (1.87).
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
## 3 Proposed Fault Diagnosis Framework
### Proposed Method  
With the help of the advantages of HLZC and SVM, a novel intelligent fault diagnosis scheme called HLZC-SVM is proposed in this paper. There are two stages in the proposed intelligent fault diagnosis framework. In the first stage, HLZC is employed to extract the fault features from the vibration signals of rotating machinery. In the second stage, SVM is adopted to identify different fault types. Five steps are included in the FDRM method as follows:  
1. Measure the vibration data for various conditions of rotating machinery;
2. Partition the measured vibration data into training datasets and testing datasets;
3. Utilize HLZC to extract fault features from the vibration signals. Note that the hierarchical decomposition layers of HLZC is set as \(k=4\), and thus 31 features will be obtained;
4. Train SVM classifier using the training features;
5. Test the trained SVM classifier, wherein the output of SVM can be used to recognize the different fault types of rotating machinery.  
The flowchart of the proposed fault diagnosis method is shown in Figure 9.  
Figure 9: Flowchart of the proposed fault diagnosis method.
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 4 Proposed Methodology
### Coverage Analysis  
The first step aims to establish which visual sensor combinations are capable of monitoring the interest area \(\mathbb{A}\), in order to provide the minimum viewing area required by the application, \(\mathbb{A}_{min}\). This information will be used to compose the NFC: whenever none of these combinations can be formed by working nodes, the application will fail. This procedure is described in Algorithm 1, where the coverage area (\(ca\)) of each combination of sensors is computed and tested in Lines 2 and 3, respectively.  
```
Data: nfc = NFC(\(VA_{min}\), VisualSensors[]); Input: Minimum Viewing Area, List of Visual Sensors and its parameters. Output: Network Failure Condition.
1 foreachVS \(\subseteq\) VisualSensors[]do
2 ca = CA(VS); // Algorithm 2
3 ifca \(\geq\mathbb{A}_{min}\)then
4 nfc.addExpression(VS);
5
6 end if
7
8 end foreach
9
10 end foreach
11
```  
**Algorithm 1**NFC  
Figure 10: Overview of the methodology for dependability evaluation.  
The coverage area computing routine is described in Algorithm 2, based on Definition 1, and is used in Line 4 of this algorithm. In that same line, the routine to compute overlapped areas (\(\mathbf{oArea}\)) of covered regions is invoked. This routine is described in Algorithm 3 and it consists of the identification of the polygon formed by the vertices of overlapped regions. After that, the polygon area is computed according to Equation (7), based on the Shoelace equation [35; 66], where \(|V|\) is the number of vertices of the polygon and \(Vx_{i}\) and \(Vy_{i}\) are its \((x,y)\) coordinates. Those vertices must be in a clockwise or anti-clockwise order, since it is a requirement for the Shoelace algorithm.  
```
Data:\(ca\) = CA(VS, mArea); Input: List of sensor, monitoring area parameters. Output: Coverage area.
1\(ca\) = 0;
2 foreachsubset of sensors \(Q\) in sensors set \(VS\)do
3\(n\) = size(Q);
4\(ca\) = \(ca\) + \((-1)^{n-1}\). overlapArea(Q); // Algorithm 3
5 end foreach return ca;
```  
**Algorithm 2**Coverage area  
```
Data: oArea = overlapArea(Q); Input: List of parameters of a subset of sensors. Output: Overlap Area.
1 oArea = 0;
2 nQSensors = size(Q);
3 ifnQSensors==1 then
4 oArea = getArea(nQSensors);
5
6 else
7 polyEdges = nQSensors[1].edges;
8 for\(i\gets 1\) to nSSensors-1 do
9 polyEdges =...
10 getInterEdges(polyEdges, nQSensors[i+1].edges);
11
12 end if
13 oArea = getArea(polyEdges);
14
15 end for return\(\alpha\)Area;
```  
**Algorithm 3**Overlap area  
\[oArea=\frac{\left|Vx_{|V|}.Vy_{1}-Vx_{1}.Vy_{|V|}+\sum\limits_{i=1}^{|V|-1}(Vx_{ i+1}.Vy_{i}-Vx_{i}.Vy_{i+1})\right|}{2} \tag{7}\]
## 2 Materials and Methods
### Lactic Acid Adsorption Isotherms  
The batch equilibrium results of different initial lactic acid concentration experiments at 303.15 K were used for the isotherm study. In this study, we chose three commonly used isotherm models (i.e., Langmuir (Equation (I)), Freundlich (Equation (II)) and Temkin (Equation (III))) to describe the lactic acid adsorption process [25; 28]. The linear and non-linear forms of the isotherm model equations are presented in Table 1[28; 29].  
The Langmuir adsorption isotherm model assumes homogeneous and monolayer adsorption with no lateral interaction or steric hindrances between the adsorbate molecules [29]. The nature of adsorption in the Langmuir adsorption isotherm model is represented by a dimensionless constant known as the separation factor (\(R_{L}\)), \(R_{L}\) = 1/(1 + (\(K_{L}\)\(\times\)\(C_{0}\))), where \(K_{L}\) = Langmuir isotherm constant (L/mg), \(C_{0}\) = initial concentration of lactic acid (mg/L), \(R_{L}\) > 1 (unfavorable), \(R_{L}\) = 1 (linear), \(R_{L}\) = 0 (irreversible) and 0 < \(R_{L}\) < 1 (favorable) [30].  
On the other hand, the Freundlich isotherm model describes a non-ideal and reversible adsorption process, which is applied to explain the multilayer adsorption onto a heterogeneous adsorbent surface [31]. The Temkin isotherm model assumes that the heat of adsorption of all the molecules in the layer would decrease linearly rather than logarithmically [29].