esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 5 Discussion
The calculations are carried out according to the aforementioned intended use and are presented in the following equations:  
\[\text{Data Transmission in 1 minute }(\text{mAs}):\ 215\ \text{mA}\times 13 \ \text{sec}\times 2=5590\ \text{mAs}, \tag{1}\] \[\text{Sensor reading in 1 minute }(\text{mAs}):\ 100\ \text{mA}\times 34 \ \text{sec}=3400\ \text{mAs},\] (2) \[\text{Charge consumed in 5 minutes }(\text{mAs}):(5590\ \text{mAs}+3400\ \text{mAs})\times 5=44950\ \text{mAs},\] (3) \[\text{Charge consumed in 1 day in active mode }(\text{mAs}):44950\ \text{mAs}\times 4=179800\ \text{mAs}. \tag{4}\]  
When the device is in sleep mode, all the radios are turned off, and only ESP32 is powered in deep-sleep mode, notably only the Real-Time Clock (RTC) memory and RTC peripherals are powered up, Bluetooth connection data are stored in the RTC memory, and the ultra-low power coprocessor is functional. From the ESP32 datasheet [32], we find that the current consumption in this case is 150 uA. Let us calculate the period for the CTD in deep-sleep mode in 1 day:  
\[\text{Deep sleep time in 1 day(s)}:24\ \text{h}\times 60\ \text{min}\times 60\ \text{s}-60\ \text{s}\times 5\ \text{min}\times 2=85800\ \text{s}, \tag{5}\]  
thus, the charge consumed in one day in deep-sleep mode is:  
\[\text{Charge consumed in 1 day in deep sleep mode(mAs)}:0.015\ \text{mA}\times 85800\ \text{s}=1287\ \text{mAs}. \tag{6}\]  
The total charge consumed in one day by the CTD is given by the sum of Equations (4) and (6). Finally, we express in Equations (7) and (8) the total charge required for one year (365 days) of operation in mAh (milliampere-hour) and the required battery capacity, including 20% leakage:  
\[\text{Charge consumed in 1 year(mAh)}:(179800+1287)\ \text{mAs}\times 365/3600\ \text{s}=18360\ \text{mAh}, \tag{7}\]Required battery capacity, including 20% leakage(mAh) : 18360 mAh/0.8 = 22950 mAh. Hence, proposing a battery pack of 27,000 mAh (consisting, for example, of two LSH20 SAFT batteries [38]) is viable even in the worst-case scenario for the intended use of the CTD.
## 3 Proposed Scheme
### Training Stage
#### 3.1.1 Training Sample Production Method  
As the quality of the training samples has a great influence on the network model, we have to build a high-precision training sample set, that is, the effective edges of the training samples need to be accurately described. However, the hand-drawn edge images are far from accurate: Objects of the HRRS images with high resolution tend to have relatively thin edges, and they are difficult to accurately depict by hand drawing. Edges are often defined to be semantically meaningful, however, different people often have different definitions of the edges of objects, which makes the stability of the model different for different edge painters. Even the same training sample producer, its perception of  
Figure 2: Framework of the proposed scheme.  
the edge of the object at different times may be biased, while the sample production takes a relatively long time.  
Aiming at the above problems, we have proposed a training sample generation method that can satisfy the precise robust edge, the core idea of which is to combine artificial processing and high-precision edge extraction algorithm such as Canny operator. The training sample generation procedure is illustrated as follows.  
First, edge information of the sample image is extracted by traditional and sophisticated edge extraction algorithm. At present, the gradient-based edge detection algorithms have been widely applied. Among them, the Canny operator is a widely accepted edge detection algorithm. Therefore, we used the Canny operator to extract the preliminary edge information of the samples.  
Second, as the edge features extracted by Canny operator are prone to have false edge features, such as \"noise characteristics\", which have a certain influence on the robustness of the algorithm. We use an artificial approach to distinguish between \"effective edge features\" and \"false features\" and remove false edges from the corresponding edge images. An example of removing the false edge features for a training sample is exhibited in Figure 3, in which we can clearly see that the false edge features extracted by the Canny operator in Figure 3b are removed in Figure 3c by an artificial process.  
Then, due to the parameter setting of the Canny operator, some important edge information may not be detected by Canny operator. Such edges are also very important in integrity authentication, so these edge information needs to be manually added. In Figure 4b, the edge of the riverbank is not completely detected by the Canny operator, so it needs to be added manually as shown in Figure 4c.  
Figure 4: An example of adding the missing edge features for training sample processing: (**a**) The original image, (**b**) preliminary edge features extracted by the Canny operator, (**c**) edge features after adding the missing edges by manual operation.  
Figure 3: An example of removing the false edge features for training sample: (**a**) The original image, (**b**) preliminary edge features extracted by the Canny operator, (**c**) edge features after removing false edges by artificial process.
# CFD Estimation of a Resistance Coefficient for an Egg-Shaped Geometric Dome
## 2 Materials and Methods
### CFD Model  
The CFD simulation is conducted in Ansys Fluent CFD package with the use of VOF (Volume of Fluid) technique. Despite the fact that the VOF method is generally dedicated to time-dependent analysis, the steady state condition simulation is implemented. The initial interface between both phases (air and water) is known, and both fluids do not interpenetrate each other in a substantial matter. The fluid properties are given in Table 2 and the following assumptions are taken during the simulations:  
* both fluids are incompressible and homogeneous;
* both fluids have constant properties;
* there is no mass transfer between both fluids;
* model is in thermal equilibrium;
* phase changes in both fluids are neglected;
* the pressure-based solver is used;
* for the pressure-velocity the coupled scheme linked to the volume fraction is used;
* the implicit scheme is used with a standard finite difference discretization scheme: QUICK and Modified HIRC for volume fraction;
* the \(k-\omega\) shear stress transport (SST) model is used;
* the roughness of model wall is neglected.  
The CFD model, shown in Figure 5, is prepared for steady-state conditions with a fixed position of the egg model and forced fluid flow. In contrast, experimental tests are conducted for the moving model. Therefore, the CFD model's boundary conditions are specified to reflect experimental test conditions. The tank side walls are considered frictionless, i.e., the shear stresses are set to 0. The size of the fluid domain is chosen so that the flow field is not affected by boundary conditions. The domain is equivalent to the tank cross section dimensions of the MarinLab and the domain length from the inlet to the dome wall is four times larger than the dome diameter, while to the outlet it is approximately ten times the dome diameter. Additionally, half of the model is used. The simulations aimed to calculate the total resistance force and total resistance coefficient for different velocities and draught.  
The computational model is prepared with mixed cell types: polyhedral and hexahedral. The predicted interface between water and fluid is refined, as well as a region near the egg walls. The grid is locally refined to capture flow induced on the egg wall during fluid flow. The total number of cells is approximately 1.5 \(\times\) 10\({}^{6}\). Figure 6 shows a grid sensitivity study.  
The refined grid on a dome achieving \(y^{+}\) values below 200 is shown in Figure 7.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Entity** & **Notation** & **Value (Water)** & **Value (Air)** & **Unit** \\ \hline Density & \(\rho\) & 998 & 1.204 & [kg/m\({}^{3}\)] \\ Dynamic viscosity & \(\mu\) & \(0.1003\times 10^{-2}\) & \(1.825\times 10^{-5}\) & [kg/m s] \\ \hline \end{tabular}
\end{table}
Table 2: Fluid properties.  
Figure 5: CFD model: 1—pressure inlet (open channel), 2—pressure outlet, 3—symmetry, 4—pressure outlet (open channel), 5—wall, \(T\)—draught.  
Figure 6: Grid sensitivity.  
Figure 7: The \(y^{+}\) value.
## 6 M2M Application Protocols  
It is impossible to discuss resource efficiency in devices using mobile M2M applications without covering application protocols. M2M application protocols take a fundamental role in communication efficiency: protocol overheads, necessary number of management/control and information messages, reliability, security, _etc._, all impact the number and size of transmissions and, consequently, the energy and bandwidth consumptions in a mobile device.  
The technical planaries of the oneM2M Partnership Project [52, 53] came to an agreement to take into account CoAP, HTTP, and MQTT for communications, strengthening the idea that these protocols are the _de facto_ protocols for mobile M2M communications. In the following sub-sections, we describe CoAP [54] and MQTT [55]. We do not review HTTP due to the vast literature already available [56, 57, 58]. Although there are other application protocols that can be used for M2M, such as the Advanced Message Queuing Protocol (AMQP) [59], or the Extensible Messaging and Presence Protocol (XMPP) [60], CoAP and MQTT specifically target constrained networks and devices, relying in an effective reduction of protocol overheads.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
###### Abstract
We then estimate their auto- and cross-power spectra and compute the transmissibility coherence, which measures the similarity of signals in the frequency domain. Next, using a two-dimensional histogram, we estimate the marginal and joint entropies and calculate the mutual information between the transmissibility coherences of the baseline (undamaged) and potentially damaged states. Finally, we compare MI value from an unknown to the average baseline MI to flag any significant deviations as potential damage events. The method described in the flowchart is applied to a standard data set containing experimental data from a three-story aluminum frame structure. The results suggest that the proposed metric is a more sensitive damage indicator than two existing damage indices derived from transmissibility coherence (\(TC\)) for both linear and nonlinear damage states. The remainder of this paper is organized as follows. Section 2 provides an overview of transmissibility coherence and discusses existing damage indices derived from it. Section 3 introduces the concept of mutual information as a measure of similarity, which can be seen as an extension of the concept of correlation. Section 4 describes the proposed damage detection approach. Section 5 presents an application using a standard dataset containing experimental data from a three-story aluminum frame structure and compares the results from the proposed approach to two existing damage indices derived from the transmissibility coherence. Section 6 presents a discussion, and Section 7 concludes this paper.
## 3 Dynamic Anchor
### Anchor Generator
#### 3.1.2 Anchor Shape Prediction  
As shown in Figure 2, the anchor generator is composed of a full convolutional network. Given the feature map \(P_{i}\) from the FPN, the generator will predict the optimal shape \(\left(w,h\right)\) for each location, which is calculated by Equation (2). Similar to the pre-defined anchor strategy, the generator does not predict the center coordinates of the anchors, but takes the location coordinates on the feature maps as the center so as to keep the alignment between the anchor boxes and the anchor features.  
In training, the longest side of the input image is up to 1333; since the anchor box is smaller than the image size, the numerical range of the shape targets is approximately \(\left(0,\,1333\right)\), which will lead to unstable prediction results and loss explosion. Therefore, we adopt the following normalization transformation:  
\[\text{dw}=\frac{w}{range_{i}},\,\,\text{dh}=\frac{h}{range_{i}} \tag{11}\]  
where \(range_{i}\) is the Maximum regression distance in \(P_{i}\) and was set to \(\left\{64,128,256,512,1024\right\}\) in our experiments. With normalization, the output range becomes \(\left(0,\,1.3\right)\), which makes the predictions more stable. In training and testing, the generator will output a two-channel map that includes the value of \(w^{\prime}\) and \(h^{\prime}\), and then \(\left(w^{\prime},h^{\prime}\right)\) will be mapped to \(\left(w,h\right)\) by Equation (11).  
Significantly, the previous methods [22,30] always prepare anchors based on initial boxes. Our design does not depend on any pre-defined boxes and avoids hyper-parameters related to the anchors. In addition, the anchor generator predicts only one anchor for each location, but the pre-defined anchor strategy will place multiple anchors. For example, the RetinaNet tiles nine anchor boxes at each location. Our approach will reduce the number of anchors by 89%, which alleviates the classification imbalance.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Layer Contour Extraction
#### 3.4.1 2D Layer Division  
With the observation of indoor point clouds, there are two types of point distributions among wall elements: inner walls and outer walls [17]. Accordingly, we segmented the point clouds in these layers on the basis of inner wall point distribution. In practice, the first step is to determine the positions of inner walls, and then the segmentation can be completed by extending centre lines of inner wall structures. Actually, the localization of inner walls could be implemented by searching for parallel structures in a given distance. In our experiment, we set up the searching range \(s_{r}\) as 0.5-0.9 m along the perpendicular direction of each linear primitive. Thus, the division line could be extracted from the bisector of two parallel linear primitives. However, sometimes the inner wall was labeled with different classes as indicated by the blue circle in Figure 12a. A mergence of adjacent division lines was required. By introducing division lines, we partitioned point clouds into several parts which could effectively convert a complicated floor plan to several simple sub-floorplans. As shown in Figure 12b, the representative layer extracted previously was divided into six parts. Note that some parts only contain one individual room while others involve parts of rooms or more than one room. As our reconstruction method was not based on the room, it did not affect the result of reconstruction. Our goal was only to obtain simple sub-floorplans here, not the room segmentation.
## 4 Case Study
### HAN Description  
Figure 2 shows a general overview of the presented HAN system. Looking at it from the functional point of view, the HAN system manages 14 binary sensors (e.g., door, window, flood, and motion sensors), and controls various devices such as light switches, an alarm siren, and relay switches connected to appliances such as the fridge, air conditioner, and washing machine. The system may operate in four different modes: \"day\", \"night\", \"guard\", and \"alarm\". During the \"day\" and \"night\" modes, the system manages lights based on occupancy and outdoor lighting conditions. In the \"guard\" mode, the system activates security measures, such as alarms and device shut-off, in response to threats like flooding or fire. A key feature of the HAN system is its Demand Side Response (DSR) functionality, where certain appliances are automatically turned off when electricity prices are high, thereby helping to shift demand away from peak periods.  
Sensors represent the system's inputs, detecting changes in the environment and allowing them to enable corresponding transitions in the Petri net. The system manages 14 binary sensors, including door sensors (D1, D2) which detect if the door is opened or closed, window sensors (W1, W2, W3) which detect the state of the windows, flood sensors (F1, F2) which identify potential water leaks in the home, a smoke detector (S1) which detects the presence of smoke or fire, a dusk sensor (U1) which senses outdoor lighting conditions, and motion sensors (M1, M2, M3, M4, M5) located in different rooms to detect movement. Actuators/devices are the outputs of the Petri net model, controlled based on the sensors' input. The devices managed by the HAN system that are assigned to places include light switches (L1, L2, L3, L4, L5) that control lighting in various rooms. The alarm siren (A1) is activated in case of an emergency such as smoke detection or unauthorized entry. Relay switches (R1-R8) control large household appliances: R1: fridge (kitchen), R2:  
Figure 2: General overview of the HAN system.  
dishwasher (kitchen), R3: freezer (kitchen), R4: air conditioner (living room), R5: desk lamp (living room), R6, R7: lamps in the bedroom, R8: washing machine (bathroom).  
The HAN system operates in different modes. The behaviour of those modes can be described as follows:  
1. Day mode: lights are automatically switched on or off based on motion detection and outdoor light levels.
2. Night mode: like day mode, but with more lights activated.
3. Guard mode: used for home security; alarms are activated, and devices are turned off, if necessary, during emergencies like fire or flooding.
4. Alarm mode: triggered by critical events (e.g., fire, intrusions), turning on all lights and the alarm siren, and switching off all high energy-consuming devices to facilitate safe evacuation.
## 3 Results
### Simulation Setup  
MATLAB is used for Simulations of PECR algorithm utilizing a couple of parameters given in Table 1.  
In the proposed PECR algorithm, 500 low-power, stationary, and position-aware sensor nodes are arbitrarily installed in a 200 \(\times\) 200 m\({}^{2}\) area as shown in Figure 3. The BS is located at the focal point of the field. The goal is the effective and balanced utilization of energy assets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Values** \\ \hline Field’s dimensions & 200 \(\times\) 200 m\({}^{2}\) \\ E\({}_{\text{th}}\) (Threshold energy) & 0.000005 \(\upmu\)J/bit \\ I\({}_{\text{e}}\) (initial energy) & 0.5 J \\ \(\epsilon_{fs}\) & \(10\times 10^{-12}\) J/bit/m\({}^{2}\) \\ e\({}_{\text{r}}\) (receiving energy) & \(50\times 10^{-9}\) J/bit \\
1 (data length) & 4000 bits \\ BS’s locations & Inside (center) \\ e\({}_{\text{t}}\) (transmission energy) & \(50\times 10^{-9}\) J/bit \\ d\({}_{\text{th}}\) (threshold distance) & \(87\) m \\ \(\epsilon_{amp}\) & \(1.3\times 10^{-15}\) J/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for simulations.  
Figure 2: The flow of the PECR algorithm.  
After the arbitrary installation of nodes in the sensing field, the K-Means clustering algorithm (run by BS) frames the non-covering diverse clusters of the nodes as displayed in Figure 4.  
Each shape in one tone addresses a separate cluster. The star shape in the center addresses the BS. A three-sided shape addresses the CH in each cluster.  
The PECR algorithm uses a multi-hop transmission mode from node to BS utilizing low energy assessed by the proposed energy consumption model.  
Figure 5 depicts the competitive performance of the PECR algorithm in terms of energy usage against the number of rounds. Initially, after the establishment of the network at zero rounds, all nodes have maximum energy. The number of transmission rounds and the energy usage of the network have a direct correlation. Energy utilization increases with an increase in rounds. There is no sudden change in the curve. The energy utilization level progressively expands by up to 1800 rounds.  
Figure 4: K-Means clustering in the PECR algorithm.  
Figure 3: Nodes positioned in a 200 \(\times\) 200 m\({}^{2}\) field.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 2 Materials and Methods
### PRISMA Image  
PRISMA is a small sized satellite mission targeted at qualifying space-borne hyperspectral technology and delivering image spectroscopy data to foster novel processing methods and to employ in a variety of resource management and environmental monitoring applications [46]. It has been successfully applied to remotely estimate water parameters [47]. We collected the PRISMA L2D surface reflectance data on 14 December 2020 to test the performance of our proposed algorithm on the satellite remote-sensing hyperspectral data. The image was rescaled from the original range [0, 65535] to the range [0, 1] and then divided by \(\pi\) to obtain \(R_{\rm rs}\) data, according to Equation (6).
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 5 Simulation and Experiment Results
### Discussion  
In Section 5.1, comparative simulation experiments were carried out among the three methods. Compared with Method 1 which decouples task assignment and trajectory planning, the proposed Method 2* has advantages in terms of shorter flight distance, shorter arrival time and more arrived number of UAVs. The disadvantage compared with Method 1 is the increased computational time. Compared with the Method 3 which solves a global TAP once collision avoidance movement occurs, the proposed Method 2* has slight disadvantages in flight distance, arrival time, and number of arrived UAVs. Yet, the proposed Method 2* has obvious advantages over Method 3 in terms of computational time and the scalability, as shown in Figure 10. In Section 5.2, an experiment with UAV-LS successfully verified the effectiveness of the proposed method in practice.  
Through the above analysis, the proposed method achieves a good trade-off between performance and efficiency which is desired for most multi-UAV systems with a number of controlled UAVs and limited computing resources.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{Initial states **X** of the UAVs} \\ \hline
**Variable** & **UAV 1** & **UAV 2** & **UAV 3** & **UAV 4** & **UAV 5** & **UAV 6** \\ \hline x(m) & 1.2 & \(-1.2\) & \(-1\) & \(-1.2\) & \(1.2\) & \(1\) \\ y(m) & 5.4 & 4.4 & 7.5 & \(-4.9\) & \(-3.9\) & \(-7\) \\ z(m) & 6 & 6 & 6 & 6 & 6 & 6 \\ \hline \multicolumn{6}{c}{Target formation states **T**} \\ \hline x(m) & 0 & 1 & \(-1\) & 0 & \(-1\) & \(1\) \\ y(m) & 1.5 & 3.5 & 3.5 & \(-1\) & \(-3\) & \(-3\) \\ z(m) & 6 & 6 & 6 & 6 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Coordinates of the initial and target formations.  
Figure 15: Onsite photos of the UAVs-LS with six quadrotors at 0, 28, 52, and 75 s.
## 3 Results  
### RT and Accuracy Rate  
The mean and standard deviations of RT and accuracy rate in each condition are summarised in Table 1. ANOVA on RT revealed no significant effect either on the main effects or on the interaction, _Fs_ < 2.2, _ps_ > 0.13. There was no significant effect for accuracy rate either, _Fs_ < 1.45, _ps_ > 0.7.