esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Consumption
### Average Energy Consumed by RNc in the Sync Period  
During the _sync_ period, SNs exchange _SYNC_ packets to synchronize their _sleep-wake up_ schedules. The duration of the _sync_ period for \(Cc\) SNs can be determined as, \(T_{sc,c}~{}=~{}(W_{c}-1)+t_{SYNC}+D_{p}\), where \(W_{c}\) is the length of the contention window for \(Cc\) SNs, \(t_{SYNC}\) is the transmission time of a _SYNC_ packet, and \(D_{p}\) is the one-way propagation delay.  
The average energy consumed by a \(Cc\) SN during a _sync_ period, \(E_{sc,c}\), can be determined as,  
\[E_{sc,c}=\frac{1}{N_{sc,c}}\cdot\left[\left(t_{SYNC}\cdot P_{tx,c}+\left(T_{ sync,c}-t_{SYNC}\right)\cdot P_{rx,c}\right)\right]+\frac{N_{sc}-1}{N_{sc}} \cdot\left(T_{sync,c}\cdot P_{rx,c}\right)\,, \tag{2}\]  
where \(P_{tx,c}\) and \(P_{rx,c}\) are the transmission and reception power levels for \(Cc\) SNs, respectively.
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
## 3 Results and Discussion
### Shading Characteristics of Shingled Strings
In addition, the results indicate that when developing a PV module in an installation area with a high frequency of vertical shading, the shading loss can be reduced by configuring strings of modules in parallel or by placing diodes.
## 3 Results
### Hand Motor Function Outcomes  
Like paretic hand motor function (Figure 3, Supplementary Table S2), the rmANOVA revealed no significant intervention (F\({}_{2,34}\) = 0.49, \(p\) = 0.619, \({\rm{\eta_{p}}^{2}}\) = 0.03) and no intervention \(\times\) TOA effect (F\({}_{3,49,59.28}\) = 1.26, \(p\) = 0.297, \({\rm{\eta_{p}}^{2}}\) = 0.07) but a TOA effect on the aROM measure changes (F\({}_{1.43,24.3}\) = 4.65, \(p\) = 0.029, \({\rm{\eta_{p}}^{2}}\) = 0.22). [PERSON]'s LSD revealed that aROM values measured at 15 min (\(p\) = 0.016) and 30 min (\(p\) = 0.03) postintervention were higher than the pre-intervention aROM values. Figure 3 shows different aROM change profiles across TOA and interventions. The values are indicated in Supplementary Table S2. All three interventions seem to have induced paretic wrist extension aROM increases. ShamPAS/MI induced aROM increases, significant at 15 min poststimulation (\(\Delta\)aROM\({}_{15}\): \(p\) = 0.04); PAS induced a continuous increase in aROM that became significant at 15 min (\(\Delta\)aROM\({}_{15}\): \(p\) = 0.005; \(\Delta\)aROM\({}_{30}\): \(p\) < 0.001); and PAS/MI induced a significant gradual increase in aROM from 0 to 30 min poststimulation (\(\Delta\)aROM\({}_{0}\): \(p\) = 0.008; \(\Delta\)aROM\({}_{15}\): \(p\) = 0.003; \(\Delta\)aROM\({}_{30}\): \(p\) = 0.04).
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 3 Methodology
### Multimodal Data Feature Extraction and Fusion
#### 3.3.1 Image Feature Extraction  
For the pre-trained model, the related parameters are initialized by the pre-trained VGG16. As the pre-trained VGG16 is trained by the images of size 224 \(\times\) 224 pixels with RGB bands, the images of size 227 \(\times\) 227 pixels with RGB bands are resized to 224 \(\times\) 224 pixels and input the transfer DCNN. In addition, in order to match the number of POI semantic features and reduce the parameter of the model, we delete the network structure beyond the first fully connected layer and add a fully connected layer that outputs 64 dimensions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Residence** & **Commercial** & **Shantytown** & \
\begin{tabular}{c} **Public** \\ **Service** \\ \end{tabular} & **Development** & **Education** &
\begin{tabular}{c} **Green** \\ **Land** \\ \end{tabular} & **Total** \\ \hline Training set & 360 & 230 & 90 & 60 & 80 & 100 & 45 & 965 \\ Training set \({}^{1}\) & 720 & 690 & 720 & 600 & 560 & 500 & 450 & 4240 \\ Test set & 417 & 239 & 102 & 56 & 74 & 101 & 17 & 1006 \\ \hline \end{tabular} \({}^{1}\) Augmented training set.  
\end{table}
Table 3: UFZs self-built image dataset.  
Figure 6: Image cropping using a central windowing strategy.  
For the self-built network, four 3 \(\times\) 3 convolutional layers with a stride of 2 are used, followed by two fully connected layers. The output dimensions of the first and second fully connected layers are set to 1024 and 64, respectively. The self-built network input RGB + NIR + nDSM images with 227 \(\times\) 227 pixel size, to avoid the limitations of the pre-trained VGG16.  
Finally, the outputs of the pre-trained model and the self-built CNN are fused into a single layer. To avoid the instability of the model caused by feature fusion, a Batch Normalization (BN) layer and a Rectified Linear Unit (Relu) layer are added after the fusion layer. A fully connected layer with an output dimension of 7 is designed to match the number of categories of UFZs. After the softmax function, the categories of UFZs are provided. UFZs samples are fed into the ST-CNN model to train it, resulting in model parameters specifically optimized for UFZs recognition being obtained. The feature fused by pre-trained VGG16 and the self-built CNN are regarded as image features, with an output dimension of 128.
## 3 Obstacle Avoidance Scenario
### Detection Sensors
The different types of real-time obstacle detection sensors, and their advantages and disadvantages, are outlined in Table 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor Types** & **Ultrasonic** & **Laser/Initiative** & **Structured Light** & **ToF** & **Millimeter-Wave** & **Monocular Vision** & **Binocular Vision** \\ \hline Range & \textless{}10 & \textless{}50 & \textless{}10 & \textless{}10 & \textless{}250 & \textless{}10 & \textless{}100 \\ \hline Cost & Low & Medium & Very High & Medium & High & Medium & High \\ \hline Precision & Short Range & Very High & Very High & Medium & Low & Low & Medium \\ \hline Resolution & Low & Very High & Very High & Low & Low & Medium & Medium \\ \hline Reliability & Low & High & High & High & High & Medium & Medium \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Obstacle detection sensors for UAVs.
## 3 Proposed Model
### ROI Extraction Module
#### 3.3.1 Pre-Processing  
As the network needs inputs of \(224\times 224\times 3\) pixel size, the pre-processing step maps gray-scale palmprint images to suitable ones to be fed into our CNN architecture. To this end, the inputs are rescaled to square images by removing the extra pixels from both left and right sides of the inputs. Then, these gray-scale images are converted to RGB color space by the weighted method and are resized to \(224\times 224\times 3\) using the nearest neighbour method (nearest-neighbor interpolation, [[http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation](http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)]([http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation](http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation))). Figure 3 illustrates an example of the final pre-processed image.  
Figure 3: ROI extraction module. B\({}_{x}\) and B\({}_{y}\): center point; B\({}_{y}\): width and B\({}_{it}\): high of the box containing the palmprint ROI.  
Figure 2: Our proposed model for palmprint verification. ROI: region of interest.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
###### Abstract  
Ultrasonic guided wave (UGW) testing of pipelines allows long range assessments of pipe integrity from a single point of inspection. This technology uses a number of arrays of transducers, linearly placed apart from each other to generate a single axisymmetric wave mode. The general propagation routine of the device results in a single time domain signal, which is then used by the inspectors to detect the axisymmetric wave for any defect location. Nonetheless, due to inherited characteristics of the UGW and non-ideal testing conditions, non-axisymmetric (flexural) waves will be transmitted and received in the tests. This adds to the complexity of results' interpretation. In this paper, we implement an adaptive leaky normalized least mean square (NLMS) filter for reducing the effect of non-axisymmetric waves and enhancement of axisymmetric waves. In this approach, no modification in the device hardware is required. This method is validated using the synthesized signal generated by a finite element model (FEM) and real test data gathered from laboratory trials. In laboratory trials, six different sizes of defects with cross-sectional area (CSA) material loss of 8% to 3% (steps of 1%) were tested. To find the optimum frequency, several excitation frequencies in the region of 30-50 kHz (steps of 2 kHz) were used. Furthermore, two sets of parameters were used for the adaptive filter wherein the first set of tests the optimum parameters were set to the FEM test case and, in the second set of tests, the data from the pipe with 4% CSA defect was used. The results demonstrated the capability of this algorithm for enhancing a defect's signal-to-noise ratio (SNR).  
adaptive filtering; leaky normalized mean square; ultrasonic guided waves; pipeline inspection; SNR enhancement; signal processing Article
# Experimental Study on the Dynamic Response of an Immersed Tunnel Element Moored inside the Harbor
## 2 Experiments
### Experimental Equipment and Measuring Instruments  
The instruments used to measure cable force, motion, wave height, water level, wind speed, and other physical quantities in the test are shown in Table 3. The instrument for measuring the motion of the immersed element is the non-contact FASTRAK motion  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cable Type** & **Linear Density in Air (kg/m)** & **Wire Diameter (mm)** & **Unit Stiffness (kN)** & **Breaking Load (kN)** & **Ultimate Elongation** \\ \hline Steel Cable & 20.5 & 70 & 267,540 & 3090 & / \\ Nylon Cable & 8.87 & 120 & 29,178 & 2501 & 24\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of cables.  
Figure 4: Simulation of nylon cable. (**a**) Model of nylon cable. (**b**) Curve of relative deformation with relative tension.  
Figure 3: Cable layout of each immersed element when mooring at the berth inside the harbor.  
tracking system, which is based on the electromagnetic principle. The size of the receiving end is less than 1 cm, and the receiving end can be freely installed in any part of the floating or immersed structures. The maximum sampling frequency can reach 120 Hz. When the distance between the transmitting end and the receiving end is less than 60 cm, the instruments can obtain high measurement accuracy.  
The wave-making system adopted in the test is an irregular wavemaker with a movable and shakable plate (as shown in Figure 5a). The JONSWAP frequency spectrum is used in the experiments, and a value of 3.3 is adopted for the spectral peak factor. The given significant wave height and peak period are input into the computer for a spectrum simulation. After correction, the parameters of spectral density, peak frequency, spectral energy, and wave height near the peak frequency meet the test requirements.  
Simulation of the wind is realized by a fan array composed of 8 independent fans (as shown in Figure 5b). The fans can be arranged and combined arbitrarily to realize the targeted array type. The wind speed is controlled by adjusting the motor rotational frequency; the maximum wind speed at the fan outlet can reach 9 m/s, which meets the test requirements. In addition, the effective width of the wind field completely covers the length of the immersed element.
## 3 Results and Discussion
### Energy Dissipation  
Energy dissipation is an important index for evaluating the seismic performance of a structural member. A high energy dissipation capacity can reduce the seismic response and demands. The energy dissipation was estimated by calculating the enclosed area of the hysteretic loops. Figure 11 shows the single circle and cumulative energy dissipation versus lateral displacement. The capacity of energy dissipation for all test walls was basically same until the lateral displacement reached 3 mm (0.11% drift). The slope had an obvious increase when the first major diagonal crack occurred, which implied the horizontal reinforcement worked to dissipate energy. Regarding the construction method, the cast-in-place walls had higher energy dissipation capacity than did the prefabricated ones at the same lateral drift. This indicated that the energy dissipation was related to the location and direction of the diagonal cracks. A larger compression zone in the cast-in-place walls contributed to better energy dissipation than in the prefabricated ones. With respect to cross section, the T-shaped cross section improved the energy dissipation capacity compared to that of the rectangular section. This is because the boundary flange increased the compression area of the wall, which improved the lateral capacity. Therefore, walls with a T-shaped section had a higher lateral capacity at the same lateral displacement.  
Figure 10: Normalized stiffness degradation of specimens.
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 2 Material and Methods  
### In Vitro Model of the Cardiovascular System  
An in vitro model of the upper cardiovascular circulation was developed to recreate the physiological conditions necessary to simulate the pressure in an intracranial environment. The model was a closed-loop circulation circuit, and its main components were a pulsatile pump (heart), silicone vessels (arteries), intracranial chamber, brain phantom, and a reservoir.  
The pulsatile pump, used in this study to recreate the pumping action of the left ventricle, was a custom-made piston pump which outputs a pressure/volume profile corresponding to a square waveform. The pump was driven by an ad hoc driving unit and software application through which the user could customise the output profile by controlling the heart rate (HR), stroke volume (SV), and the in-stroke/out-stroke ratio.  
As shown in Figure 1, the output of the pulsatile pump was connected to the aortic branch of the system, comprising a rubber tube with a diameter of 20 mm and a wall thickness of 0.5 mm. The aortic branch split into the carotid and descending aorta. The carotid branch was connected to the cerebral circulation within the brain phantom via the intracranial chamber's inlet, whereas a 'Y' hose connector separated the incoming flow from the descending aorta into the right and left iliac arteries. The iliac arteries were then connected to the reservoir, where the fluid returned before being redirected towards the pulsatile pump. The carotid branch (excluding the brain phantom, see the following section) and the iliac arteries were composed of silicone tubing of different diameters corresponding to the arteries' dimensions. Two valves at the output of the carotid and aortic branch enabled the control and regulation of the output resistance of the system (i.e., the systemic pressure of the circuit).  
Figure 1: Schematic diagram of the in vitro model. A pulsatile pump on the far left recreates the pumping action of the heart and directs the blood flow into the rest of the system. The cardiovascular system comprises an aortic branch and a carotid branch leading to the intracranial chamber. The latter is composed by a rigid body in which the vascularised brain phantom is enclosed. The intracranial chamber also includes an intracranial pressure (ICP) sensor and a pressuriser, which allows for the control of the intracranial pressure inside the chamber. Blood flow circulates through the simile-arteries in the brain phantom and reaches a reservoir, where the blood is collected and returns to the pulsatile pump. At the output of the carotid and aortic branch, resistance valves are used to regulate the cardiovascular system’s resistance.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 6 Simulation
### Simulation with Image in Real-World  
A simulation of the proposed method is conducted using images captured in real-world scenarios. For the shooting environment, a spherical mirror with a radius of 0.05 [m] is placed at a distance of 0.1[m] in the depth direction and 0.02 [m] in the vertical direction from the camera. The omnidirectional image captured is shown in Figure 18. The image's resolution is 1920 [pixels] \(\times\) 960 [pixels].  
First, the position of the spherical mirror is estimated. The cropped image of the mirror image region is shown in Figure 19a, the segmented image of the mirror image region is shown in Figure 19b, and Figure 19c shows the estimated elliptical shape based on Figure 19b. The result of the position estimation of the spherical mirror was 0.0938 [m] in the depth direction and 0.0252 [m] in the vertical direction.  
Next, the estimation result of the 3D information is shown in Figure 20. The estimation map is visualized by changing the hue linearly, where the smallest value is blue, and the largest value is red.  
From the estimation results, the 3D estimation of objects placed on the side of the system is achieved. However, it is difficult to estimate the 3D information for objects that are farther away due to the size limitations of the system. Additionally, in the real-world scenario, it is difficult to estimate the 3D image in the area where the camera is reflected in the image.  
Figure 17: Change in 3D Information of the poster against Change in 3D Position of the Spherical Mirror along the Z-axis: Changes in 3D information are indicated by the solid blue line. The green vertical dashed line represents the ground truth of the 3D information, and the red horizontal dashed line represents the ground truth of the spherical mirror position. Since the poster is located at the edge of the mirror surface, if the position of the sphere is estimated to be large in the depth direction, the distance error rapidly increases.  
Figure 18: Input Images in Real-world Simulation: The shooting omnidirectional image. The mirror image is on the center.
## 2 Models and Definitions  
### Network Model  
Assume that \(N\) sensor nodes are scattered on the water surface randomly and uniformly and are floating on the water surface with buoys. Each node has communication, perception and mobility capabilities (perpendicular to the horizontal direction), and the positions of the sensor nodes are adjusted underwater by moving them to form a 3D coverage network. A typical UWSN architecture is depicted in Figure 1. In this model, the sink node communicates with the ground-monitoring station by radio; nodes communicate with one another by acoustic channels and maintain connectivity with the sink node via one- or multi-hop paths. Furthermore, these nodes anchor to fix their position once they have adjusted their depth. This study denotes the \(i\)-th node by \(s_{i}\), and the corresponding node set \(S=\{s_{1},s_{2},\ldots\ s_{n}\}\). The following assumptions are considered:  
1. The Boolean perception model is adopted to describe the node sensing. If the sensing radius of the node \(s_{i}\) is \(R_{s}\), the space sensed by the node is a sphere whose center is the node location and \(R_{s}\) as the radius. An example is shown in Figure 2. The sphere with radius \(R_{s}\) is the sensed space of \(s_{i}\). \(P_{1}\) is within the sphere and can be covered by \(s_{i}\). On the contrary, \(P_{2}\) is beyond the sphere and cannot be covered by \(s_{i}\).
2. All nodes are isomorphic before deployment, but can adjust their transmission power by themselves, _i.e._, the communication radius \(R_{c}\) of the node can be adjusted with an adjusting precision of 1 m, but not more than the maximum communication radius \(R_{c}\_Max\) determined by the physical device. The communication radius of a node will be changed by the distance to its next-hop node, _i.e._, the communication radius of a node is the rounded-down value of the distance between them when its next-hop node is determined.
3. The network has only one sink node. Its position is fixed at the center of the water, and the transmission power and energy can be infinite.
4. A node knows its own position and can determine the distance to the source according to the strength of the received signal.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 6 Correlation and Redundancy-Aware Feature Selection Using a Genetic Algorithm
\[\rho(f_{1},f_{2}) =\frac{\text{cov}(f_{1},f_{2})}{\sigma_{f_{1}}\sigma_{f_{2}}} \tag{4}\] \[=\frac{\sum_{i}^{n}(f_{1i}-\overline{f_{1}})(f_{2i}-\overline{f_{2 }})}{\sqrt{\sum_{i}^{n}(f_{1i}-\overline{f_{1}})^{2}(f_{2i}-\overline{f_{2}})^{ 2}}}\] \[\underset{x}{\text{minimize}} g_{cor_{o}}(x),g_{cor_{f}}(x),k\] (5) subject to \[g_{cor_{o}}(x)=n-\sum_{i}^{k}\abs{\rho(f_{i},o)}\] \[g_{cor_{f}}(x)=\sum_{i,j}^{\text{C}^{\text{k}}}\abs{\rho(f_{i},f_ {j})}\] \[g_{i}^{\prime}=\frac{g_{i}-min(g)}{max(g)-min(g)}\] (6) \[g^{w}(x)=w_{cor_{o}}g_{cor_{o}}^{\prime}(x)+w_{cor_{f}}g_{cor_{f}}^ {\prime}(x)+w_{nb_{f}}\frac{k}{n}\] (7) \[0\leq g^{w}(x),g_{cor_{o}}^{\prime}(x),g_{cor_{f}}^{\prime}(x), \frac{k}{n}\leq 1\] (8) \[0\leq w_{cor_{o}}+w_{cor_{f}}+w_{nb_{f}}\leq 1\]  
**Input** Set of all features \(f\), Number of features \(n\), Objective weights \(w_{cor_{o}},w_{cor_{f}},w_{nb_{f}}\)  
**Output** Set of selected features \(f_{s}\)  
```
1:for all feature \(f\)do
2: Calculate all feature-to-output correlation \(\rho(f_{i},o)\)
3: Calculate correlation between features \(\rho(f_{i},f_{j})\)
4: Initialize empty population \(P(i)\) and archive \(A(i)\) at generation \(i\), where initially \(i=1\)
5: Generate random individuals with length \(k\) to \(P(i)\)
6: Calculate objective values of each individual in \(P(i)\)
7:repeat
8: Assign Pareto rank to each individual in \(P(i)\)
9: Selecting the highest ranked individuals with ties broken by preferring large crowding distances from population \(P(i)\) into archive \(A(i)\)
10: Form a mating pool from \(A(i)\) using binary tournament selection
11: Generate \(P(i+1)\) using crossover and mutation operator to the mating pool
12:until Maximum generation \(t\) reached
13: Get the non-dominated individuals from \(A(t)\)
14: Calculate the weighted and scaled objective of each non-dominated individual, \(g^{w}(x)\)
15: Get the individual with lowest \(g^{w}(x)\)
16: Get the set of selected features \(f_{s}\)
```  
**Algorithm 2** Correlation-based Feature Selection using GA.
###### Abstract  
It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural network (CNN)- and support vector machine (SVM)-based machine learning models to classify the emotional states and the attention level of the participants to a video conversation. This application visualizes their attention and emotion analytics in a meaningful manner. This proposed system provides an artificial intelligence (AI)-powered analytics system with optimized machine learning models to monitor the audience and prepare insightful reports on the basis of participants' facial features throughout the video conversation. One of the main objectives of this research is to utilize the neural accelerator chip to enhance emotion and attention detection tasks. A custom CNN developed by Gyrfalcon Technology Inc (GTI) named GnetDet was used in this system to run the trained model on their GTI Lightspeeur 2803 neural accelerator chip.  
artificial intelligence (AI); convolutional neural network (CNN); facial emotion recognition (FER); support vector machine (SVM) +
Footnote †: journal: Accepted: 25 April 2022  
This paper describes the design of a participant engagement level detection system with a neural accelerator chip to speed up the overall process. This research also discusses emotion and attention recognition using a novel set of features to train comparatively simpler SVM models without sacrificing accuracy compared to previous similar studies.