esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pre-Intervention Tests
#### 3.1.3 Personal Behaviors  
Attention
Exploring the personal achievements in ATT revealed three behavior groups that are based on the highest score following different condions: 1. Following aerobic exercise (N = 11); 2. Following bal&coor exercise (N = 12); 3. Following reading a journal (control condition) (N = 7) (see Figure 3).  
For group 1, those who scored the highest scores after the aerobic exercise, mean scores after the aerobic exercise (109.90 \(\pm\) 5.69) were significantly higher than the scores following other conditions [105.57 \(\pm\) 8.44 follow the bal&coor exercise; 105.98 \(\pm\) 6.89 fol-low control condition; 105.98 \(\pm\) 6.89 in baseline, F(3.30) = 4.24, \(p\) < 0.05, \(\eta^{2}\)\({}_{\rm p}\) = 0.30)].  
For group 2, those who scored the highest following the bal&coor exercise, mean scores after the bal&coor exercise (108.00 \(\pm\) 8.33) were significantly better than the scores after the two other conditions (104.75 \(\pm\) 9.26 following the aerobic exercise and 100.91 \(\pm\) 14.38 following control condition, (F(3.33) = 4.16, \(p\) < 0.05, \(\eta^{2}\)\({}_{\rm p}\) = 0.28) but not than baseline (105.37 \(\pm\) 7.00).  
For group 3, those who scored the highest scores after the control condition, mean scores after the control condition (109.52 \(\pm\) 6.36) were significantly higher than the scores after the other conditions [104.59 \(\pm\) 6.46 follow the aerobic exercise; 104.70 \(\pm\) 4.89 after the bal&coor exercise; 102.39 \(\pm\) 12.03 in baseline, (F(3.18) = 3.76, \(p\) < 0.05, \(\eta^{2}\)\({}_{\rm p}\) = 0.39)].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Condition** & **Mean \(\pm\) SD** & **95\% CI** & \(F\) (**df**) & \(p\) & _\(\eta^{2}\)\({}_{\rm p}\)_ \\ \hline \multirow{4}{*}{**Attention**} & Baseline & 102.54 \(\pm\) 11.88 & 98.11 \(\pm\)106.98 & & & \\  & Aerobic & **106.60 \(\pm\) 7.67** & 103.74 \(\pm\)109.47 & & & \\  & Bal&coor & **106.34 \(\pm\) 7.61** & 103.50 \(\pm\)109.18 & & & \\  & Control & 104.78 \(\pm\) 10.74 & 100.77 \(\pm\)108.79 & & & \\ \hline \multirow{4}{*}{**Executive Function**} & Baseline & 104.50 \(\pm\) 13.46 & 99.48 \(\pm\)109.53 & & & \\  & Aerobic & **108.40 \(\pm\) 12.28** & **103.81 \(\pm\)112.98** & 3.86 \(\pm\) 0.05 & 0.18 \\ \cline{1-1}  & Bal&coor & **108.08 \(\pm\) 12.69** & **103.34 \(\pm\)112.82** & & & \\ \cline{1-1}  & Control & **108.33 \(\pm\) 12.42** & **103.70 \(\pm\)112.97** & & & \\ \hline \hline \end{tabular} Note: Scores in **bold** are statistically significant from baseline.  
\end{table}
Table 2: Score of EF and ATT before and after 3 intervention conditions (mean \(\pm\) SD) and 95% Confidence Interval (CI).  
Figure 2: Means and SEs for the executive function scores at the different sessions. * Significantly higher than baseline (_p_ < 0.05).
## 2 Related Work, Research Gap, and Problem Formulation
* Multi-layered architecture comprising key generation using neural key exchange protocol from the shared secret key. * Improving nonlinearity of subkey using the Khazad function. * Combining encryption and encoding in a single step to provide secure and reliable data transfer. * Detailed analysis comprising statistical, differential, and key strength analysis.
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 3 Derivation of Fault Location Approach
### Fault Location Algorithm Based on \(Z_{Bus}\)
In real-time application, the voltage derivation (\(\Delta V^{abc}_{S(i)-f(j)}\)) at bus \(j\) is caused by the fault current contributed by source bus \(i\), which represents the upstream utility grid or DG. The value of the measured voltage (\(\Delta V^{abc}_{S(i)-meas}\)) can be obtained by the synchronized voltage and current measurement device, with millisecond level response time installed in each fault current contributed source, from the energy management system of the MG, and m in Equation (14) represents the number of fault current sources. For an \(n\)-bus MG, the **VEM** can be established, as shown in Equation (15). The smallest value of the element in the **VEM** denotes the bus with the short-circuit fault. It is noteworthy that the fault current is generally about ten times the normal load current; besides, the fault current contributed by DG is two times the rated output current limited by inverter of photovoltaic. Therefore, the measurement device is able to distinguish the fault current and normal current contributed by load and DG, and then it can be determined whether the bus voltage deviation is caused by fault current or normal current. Additionally, the computing time depends on the bus number of the system; fortunately, this is not a problem for MGs, which the system is relatively smaller than a distribution feeder, such as a partial feeder MG or a community MG.  
\[ve_{j}\ =\ \sum_{i\ =\ 1}^{m}\sqrt{\left(\Delta V^{a}_{S(i)-f(j)}-\Delta V^{a }_{S(i)-meas.}\right)^{2}+\left(\Delta V^{b}_{S(i)-f(j)}-\Delta V^{b}_{S(i)- meas.}\right)^{2}+\left(\Delta V^{c}_{S(i)-f(j)}-\Delta V^{c}_{S(i)-meas.} \right)^{2}} \tag{14}\]  
\[\textbf{VEM}\ =\ \left[\ \ v_{1}\ \ \cdots\ \ \ v_{j}\ \ \cdots\ v_{n}\ \ \right],\ Bus\ \text{number}\ =\ 1,\ldots,n \tag{15}\]
## 5 Topologies in NCSs
### Distributed Topology  
The pictorial representation of the distributed topology is shown in Figure 8. One example of distributed topology can be found in Reference [155], where the load frequency control of a networked multi-area power system was discussed. There are two important characteristics of this topology: the subsystem's information is exchanged with the help of shared communication network, and the plant consists of a huge number of simple interacting units that are interconnected to achieve a desired objective. In this topology, each controller is allowed to share its local information with the corresponding neighboring controllers. Thus, the distributed controllers are capable to coordinate their behavior by transmitting/receiving information to/from other controllers within their corresponding neighboring area. Here, the controllers are distributed controllers. Advantages of this type of topology are: modularity, scalability, and robustness.
# Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
## 1 Introduction
However, image stitching involves many research fields, and these stitching methods have many problems in dealing with special scenes such as conveyor belt surface fault detection. Due to the characteristics of surface material and image acquisition of the conveyor belt, it is not easy to extract and match the feature points of the conveyor belt image. Moreover, due to a large amount of data in a multi-view image, it is not easy to accurately and quickly stitch online, affecting fault detection and recognition. The current image mosaic method is not suitable for multi-view conveyor belt surface fault online detection. According to the particularity of image mosaic, the accurate and fast image mosaic method is studied. This paper presents an adaptive multi-view image mosaic method for conveyor belt surface fault online detection based on grayscale and feature combination. This method has a high registration rate and reduces the amount of stitching calculation and the time of the stitching process. It solves the problem of the difficulty of performing accurate and fast online stitching of the collected multi-view conveyor belt images, which affects fault detection and identification.
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 3 FIVADMI: A Framework for Improving In-Vehicle Isolation and Resilience
### FIVADMI Design Principles  
The major objective of FIVADMI is to improve _in-vehicle resilience_ by (1) identifying _anomalies_ in _real time_ in the IVN operation, and (2) taking mitigation actions for the identified anomalies. FIVADMI aims to deal with different issues that may arise due to the application of future generation ECUs (e.g., virtualized as lightweight execution environments such as VMs and Containers) within the vehicle. More specifically, FIVADMI aims to ensure _system-level isolation_ of ECUs and provide protection against _side channel attacks_ to secure communications. Moreover, FIVADMI is designed to be _scalable_, _interoperable_ and _reusable_.  
**Resilience**: FIVADMI adopts the _active defence_ approach to improve in-vehicle resilience, i.e., security properties related to the communication among ECUs are continuously monitored to detect security threats. Proactive defence is not suited to handle common adaptive security requirements in the cyber and automotive domains: it recommends to design control features to meet security objectives at system design time, which are then integrated into the system life-time. However, this approach cannot address new types of threats once the system has been developed. Passive defence is also not suitable for safety-critical systems, like autonomous vehicles, as that approach detects attacks once they have occurred. Therefore, FIVADMI takes actions to mitigate the impact of and gracefully recover from detected security threats. In our context, recovery consists of rolling back (or forward) to a stable state to overcome an intrusion [68]. A full description of the rolling back process is out of the scope of this paper. Also, FIVADMI is based on an external rule engine for real-time detection of anomalies. That approach makes FIVADMI non-invasive, and is most suitable for the automotive domain.  
**Isolation and side-channel protection**: FIVADMI ensures isolation of ECUs in the in-vehicle network by deploying each ECU within a _Trusted Execution Environment (TEE)_. FIVADMI also deploys a component to detect _side channel attacks_ in a vehicular context.  
**Scalability, interoperability, reusability**: FIVADMI is based on the _AUTOSAR_ open standard for automotive software architecture [25]. This choice gives an assurance of scalability, interoperability, and reusability.
## 3 Results and Analysis
### Characterization of Modified Nano-SiO\({}_{2}\)
#### 3.1.1 Dynamic Light Scattering  
Nano-SiO\({}_{2}\) has strong hydrophilic properties, and it easily agglomerates between particles, thereby enlarging the particle radius. Figure 3 displays the particle size distribution of modified nano-SiO\({}_{2}\). All of the particle sizes of the modified nano-SiO\({}_{2}\) were less than 60 nm, and the median particle size was 15 nm, in line with the nanoparticle size range. The modified nano-SiO\({}_{2}\) had less agglomeration, and the obtained particles had smaller and uniform particle size. When it was injected into low-permeability oil reservoirs, it was not easy to block and it more easily adsorbed onto the surface of rock pores evenly.
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 4 Results: User Tests
### Methodology
#### 4.1.2 Test Condition: Use of Guiding Methods  
The second part of the tests consisted of adding a visual guiding method (arrow) that dynamically points at the target speaker, regardless of the user's position and viewpoint. The arrows were added to the Modes B and C, in a counterbalanced manner (Table 2). This test condition was not considered for Mode A, because in this mode it may be possible that the subtitles and the target speakers are in different FoVs, and therefore the use of the indicator may cause confusion in such situations.
## 2 Materials and Methods
### Tribological Characteristics  
In the point tribological contact according to the \"ball-on-disk\" scheme, dry sliding friction tests were carried out at a load (\(P\)) of 5 N and a sliding speed (\(V\)) of 0.3 m/s. In addition, an increased load of 10 N was employed for comparison. A CH 2000 tribometer (CSEM, Neuchatel, Switzerland) was used in accordance with ASTM G99. The tribological tests were conducted using a metal counterpart (a ball made of the GCr15 bearing steel, 60 HRC) with a diameter of 6 mm and an \(R_{a}\) surface roughness of 0.02 \(\mu\)m, which was assessed with a New View 6200 optical interferential profilometer (Zygo Corporation, Middledfield, CT, USA). The testing distance was 1 km and the tribological track radius was 16 mm.  
In the linear tribological contact according to the \"block-on-ring\" scheme, the dry sliding friction tests were performed using a 2070 SMT-1 friction testing machine (Tochpribor Production Association, Ivanovo, Russia). A load (\(P\)) was 60 N, while a sliding speed (\(V\)) was 0.3 m/s. In addition, increased load of 180 N and sliding speed of 0.5 m/s were taken for comparison A metal counterpart was made of the outer ring of a commercial bear (the GCr15 steel, a diameter of 35 mm and a width of 11 mm). The \(R_{a}\) surface roughness was 0.20 \(\mu\)m.  
The counterpart temperatures were assessed with a CEM DT-820 non-contact infrared (IR) thermometer (Shenzhen Everbest Machinery Industry Co., Ltd., Shenzhen, China).  
WR levels were determined by measuring width and depth of wear tracks according to stylus profilometry (KLA-Tencor, Milpitas, CA, USA), followed by multiplication by their length. They were calculated taking into account both load and distance values:  
\[\text{Wear rate }=\frac{\text{volume loss }\left(\text{mm}^{3}\right)}{\text{load }\left(\text{N}\right)\times\text{sliding distance }\left(\text{m}\right)} \tag{1}\]
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 3 Methodology
### Stacked Hourglass Module
#### 3.3.1 Shuffle-Gated Block  
The existing pose estimation models with encoder-decoder structures, such as the stacked hourglass network [2] and soft-gated skip connection [8], propose unique encoder structures, typically composed of various convolution layers. These conventional convolution-based encoder blocks can effectively extract feature maps but require substantial computational resources. Our study proposes the shuffle-gated block to reduce the computational resources required for feature map extraction in the encoder (Figure 3).  
Figure 2: Overall structure of one hourglass network comprising encoder and decoder blocks, leading to keypoint prediction.  
The shuffle-gated block divides the input feature map (144 channels) in half and processes each part differently. The first half learns the importance of each channel through soft gating comprising learnable parameters and converts it into a weight. This converted weight is multiplied by each channel to generate a feature map.  
In the second half, the three feature maps are generated interactively. The first feature map is created by applying the Ghost module from GhostNetV2 [21] (Figure 4) to the input feature map, followed by depthwise convolution. This process efficiently extracts key features through the Ghost module and depthwise convolution while minimizing the computational load. The first feature map generated in this manner serves as input to create the second feature map. In the second process, a \(1\times 1\) convolution is followed by a depthwise convolution to generate a feature map. This feature map is then used to create a third feature map through an additional depthwise convolution. Finally, spatial weighting is applied to the three feature maps generated in each process. Section 3.3.3 explains spatial weighting. These three feature maps are concatenated channel-wise with the feature map created in the first half, and their channels are swapped among the feature maps via channel shuffling to promote interaction and improve expressiveness.  
Consequently, the shuffle-gated block combines soft gating, various convolution techniques, channel shuffling, and spatial weighting for efficient and elaborate feature extraction. This allows for the creation of optimal feature maps that minimize the computational load while reflecting high expressiveness and spatial importance.  
Figure 4: Ghost module (DW Conv: Depthwise Convolution).  
Figure 3: Shuffle-gated block composing the encoder (C: Channel Dimension, S.W: Spatial Weighting). The channel value of each block corresponds to the input dimension.
# Approximation of the Riesz-Caputo Derivative by Cubic Splines
## Appendix C  
Here, we list the analytical expressions of the known terms \(f(x)\) and \(g(x)\) of Equation (1) for the numerical tests presented in Section 3.  
Example 1. \(f(x)=1\), \(g(x)=x+\frac{x^{1-\gamma}-(L-x)^{1-\gamma}}{2\Gamma(2-\gamma)}\).  
Example 2. \(f(x)=1\), \(g(x)=\begin{cases}x^{4}+\frac{1}{2\Gamma(5-\gamma)}\big{(}g_{10}(x)+g_{20}(x) \big{)}\text{ for }0<\gamma<1,\\ x^{4}+\frac{1}{2\Gamma(5-\gamma)}\big{(}g_{11}(x)+g_{21}(x)\big{)}\text{ for }1<\gamma<2,\end{cases}\)  
where  
\[g_{10}(x)=24x^{4-\gamma},\] \[g_{20}(x)=4(L-x)^{1-\gamma}\bigg{(}3(\gamma-2)(\gamma-1)L^{2}x- \Big{(}(\gamma-3)(\gamma-2)(\gamma-1)L^{3}\Big{)}-6(\gamma-1)Lx^{2}+6x^{3} \bigg{)},\] \[g_{11}(x)=24x^{4-\gamma},\] \[g_{21}(x)=12(L-x)^{2-\gamma}\Big{(}(\gamma-3)(\gamma-2)L^{2}-2( \gamma-2)Lx+2x^{2}\Big{)}.\]  
Example 3. \(f(x)=1\), \(g(x)=\begin{cases}\sin(\omega x)+\frac{\omega}{2\Gamma(1-\gamma)}\big{(}g_{10} (x)+g_{20}(x)\big{)}\text{ for }0<\gamma<1,\\ \sin(\omega x)-\frac{\omega^{2}}{2\Gamma(2-\gamma)}\big{(}g_{11}(x)+g_{21}(x) \big{)}\text{ for }1<\gamma<2,\end{cases}\)  
where\[g_{10}(x) =\frac{x^{1-\gamma}\,{}_{1}F_{2}\Big{(}1;1-\frac{\gamma}{2},\frac{3} {2}-\frac{\gamma}{2};-\frac{1}{4}\omega^{2}x^{2}\Big{)}}{1-\gamma},\] \[g_{20}(x) =(L-x)^{1-\gamma}\frac{\omega(L-x)\sin(\omega x)\,{}_{1}F_{2}\Big{(} 1-\frac{\gamma}{2};\frac{3}{2},2-\frac{\gamma}{2};-\frac{1}{4}\omega^{2}(L-x) ^{2}\Big{)}}{\gamma-2}\] \[-(L-x)^{1-\gamma}\frac{\cos(\omega x)\,{}_{1}F_{2}\Big{(}\frac{1} {2}-\frac{\gamma}{2};\frac{1}{2},\frac{3}{2}-\frac{\gamma}{2};-\frac{1}{4} \omega^{2}(L-x)^{2}\Big{)}}{\gamma-1},\] \[g_{11}(x) =\frac{\omega x^{3-\gamma}\,{}_{1}F_{2}\Big{(}1;2-\frac{\gamma}{ 2},\frac{5}{2}-\frac{\gamma}{2};-\frac{1}{4}\omega^{2}x^{2}\Big{)}}{(\gamma- 3)(\gamma-2)},\] \[g_{21}(x) =(L-x)^{2-\gamma}\frac{\omega(x-L)\cos(\omega x)\,{}_{1}F_{2} \Big{(}\frac{3}{2}-\frac{\gamma}{2};\frac{3}{2},\frac{5}{2}-\frac{\gamma}{2};- \frac{1}{4}\omega^{2}(L-x)^{2}\Big{)}}{\gamma-3}\] \[-(L-x)^{2-\gamma}\frac{\sin(\omega x)\,{}_{1}F_{2}\Big{(}1-\frac{ \gamma}{2};\frac{1}{2},2-\frac{\gamma}{2};-\frac{1}{4}\omega^{2}(L-x)^{2} \Big{)}}{\gamma-2},\] \[\text{where }_{p}F_{q}(a;b;z)\text{ is the generalized hypergeometric function.}\] Example 4. \[f(x)=x^{\gamma},g(x)=x^{\gamma}e^{x}+\tfrac{e^{x}}{2\Gamma(m- \gamma)}g_{m}(x),\] where \[g_{m}(x)=(-1)^{m}(x-L)^{\gamma}(L-x)^{-\gamma}(\tilde{\Gamma}(m- \gamma,x-L)-\Gamma(m-\gamma))-\tilde{\Gamma}(m-\gamma,x)+\Gamma(m-\gamma),\] \[\text{with }0<\gamma<2,\,m=\lceil\gamma\rceil.\] \[\Gamma\text{ is the Euler Gamma function and }\tilde{\Gamma}\text{ is the incomplete Gamma function.}\]
## 3 Results
### Objective Knee Evaluation
Values are presented as median \(\pm\) standard deviation. The minimum and maximum values are given in brackets. \(p\) values are indicated. STRD_EO—deviations from the resultant of static Riva test (STR) mean axes, eyes open; STRD_EC—deviations from the resultant of STR mean axes, eyes closed; RF—risk fall; DTRD—deviations from the resultant of dynamic Riva test (DTR) mean axes; IS—instability of the human-platform system; and VPC—visual-proprioceptive control coefficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Limb** & **Group A** & **Group B** & \(p\)** \\ \hline \multirow{2}{*}{60-PKTrq Ext} & Operated & 171 \(\pm\) 52 (70–249) & 162 \(\pm\) 50 (86–256) & 0.512 \\  & Intact & 209 \(\pm\) 54 (113–300) & 188 \(\pm\) 43 (110–247) & 0.296 \\ \hline \multirow{2}{*}{60-PKTrq Flx} & Operated & 100 \(\pm\) 31 (47–153) & 99 \(\pm\) 31 (41–153) & 0.678 \\  & Intact & 114 \(\pm\) 25 (62 –153) & 104 \(\pm\) 28 (68–151) & 0.175 \\ \hline \multirow{2}{*}{PKTrq/weight Ext} & Operated & 230 \(\pm\) 66 (121–357) & 202 \(\pm\) 74 (69–330) & 0.277 \\  & Intact & 280 \(\pm\) 62 (199–411) & 228 \(\pm\) 57 (75–309) & 0.044 \\ \hline \multirow{2}{*}{PKTrq/weight Flx} & Operated & 134 \(\pm\) 38 (59–219) & 122 \(\pm\) 40 (39–192) & 0.445 \\  & Intact & 154 \(\pm\) 31 (101–218) & 126 \(\pm\) 37 (40–189) & 0.024 \\ \hline \multirow{2}{*}{Mean Pw Ext} & Operated & 117 \(\pm\) 34 (49–182) & 108 \(\pm\) 31 (52–162) & 0.414 \\  & Intact & 137 \(\pm\) 36 (74–197) & 127 \(\pm\) 30 (76–174) & 0.550 \\ \hline \multirow{2}{*}{Mean Pw Flx} & Operated & 68 \(\pm\) 22 (27–108) & 68 \(\pm\) 24 (28–112) & 0.904 \\  & Intact & 78 \(\pm\) 19 (45–114) & 78 \(\pm\) 19 (45–104) & 0.194 \\ \hline \multirow{2}{*}{Flx/Ext Ratio [\%]} & Operated & 59 \(\pm\) 11 (46–80) & 63 \(\pm\) 18 (40–121) & 0.602 \\  & Intact & 56 \(\pm\) 8 (44–76) & 55 \(\pm\) 10 (41–78) & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Absolute and relative isokinetic strength in ACL patients. Values are presented as median \(\pm\) standard deviation. The minimum and maximum values are given in brackets. \(p\) values are indicated. PKTrq—peak torque; Ext—extensors muscles; Flx—flexors muscles; Pw—peak torque/body weight ratio.
## 3 Results and Discussion
### DEM-Based Surface Water Mapping Results  
The surface water maps that are based on the FwDET-GEE algorithm from each DEM product (ALOS, GMTED 2010, HydroSHEDS, HydroSHEDS Hydrologically Conditioned, MERIT, and SRTM) and the reference map are presented in Figure 7. Overall, the DEM products show a general consistency with the reference map concerning the water surface areas, while some local regions in the north (marked by A), middle east (marked by B), and south (marked by C) indicate an overestimation of the water surface areas, yet middle west regions (marked by region D) in GMTED, MERIT, and SRTM flood products underestimate the flooded area. The spatial resolution acts as a key role in affecting the DEM-based products, as indicated by the similar results from identical spatial resolution (e.g., 30 m from ALOS and SRTM) products. Meanwhile, the products in the same spatial resolution can also produce different results, such as the differences found among 90 m HydroSHEDS, HydroSHEDS conditioned, and MERIT flood products.  
Figure 8 further illustrates the ALOS DEM and its elevation differences from other DEM datasets. The red regions show an elevation lower than ALOS, and the blue regions are higher. The extensive red areas in HydroSHEDS conditioned map indicate that many regions are lower than ALOS DEM and, therefore, explains more flooded area than those retrieved from ALOS data. The DEM data also show that high slope regions marked by A and C drain the water faster and will not accumulate the water inundation as much as the model predicted. However, the local elevation values may not fully explain the results without considering their relative differences from surrounding areas, such that the region marked by D in MERIT has a lower elevation than that in ALOS, yet it shows no inundation compared with results from ALOS and other datasets, which may be due to its relatively higher elevation than the western area near the flood boundary.  
Figure 7: Flood water surface maps based on the FwDET-GEE algorithm from multiple DEM sources (ALOS, GMTED, HydroSHEDS, HydroSHEDS conditioned, MERIT, and SRTM), with flood reference map included, with flooded areas indicated by A, B, C and D.