esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 2 Methodology
### Deep Learning-Based Surrogate Models
#### 2.2.2 Depthwise Separable Convolution Neural Network (DSCNN)  
DSCNN (depthwise separable convolutional neural network) comprises two primary components: depthwise convolution and pointwise convolution. Depthwise convolution applies a separate convolution kernel to each channel of the input feature map, with the outputs concatenated to produce the final output (Figure 7(a)). The number of output channels in depthwise convolution matches the number of convolution kernels, with one kernel per channel. For instance, if the input feature map has 4 channels (Figure 7), employing separate convolution kernels results in four single-channel feature maps, which are combined to obtain a four-channel output feature map.  
Pointwise convolution, essentially a 1 \(\times\) 1 convolution in DSCNN, serves two key functions: flexibly adjusting the number of output channels and performing channel fusion on the feature map from depthwise convolution. It does not involve inter-channel computation between different input and output channels. By connecting 1 \(\times\) 1 convolution after depthwise convolution, DSCNN effectively integrates the convolutional information from separated channels for further processing.  
Figure 6: Schematic diagram of the five-layer convolutional neural network structure.  
To evaluate the computational efficiency of both CNN and DSCNN, assuming the input feature map size is \(D_{k}\times D_{k}\times M\) and the convolution kernel size is \(D_{f}\times D_{f}\times M\), with N convolution kernels, each point in the feature map requires \(D_{k}\times D_{k}\times D_{f}\times D_{f}\times M\) calculations, resulting in a total of \(D_{k}\times D_{k}\times D_{f}\times D_{f}\times M\times N\) operations for CNN. For DSCNN, the total computation for depthwise convolution is \(D_{k}\times D_{k}\times D_{f}\times M\), while for pointwise convolution, it is \(D_{k}\times D_{k}\times M\times N\). Consequently, the total computation for DSCNN is \(D_{k}\times D_{k}\times D_{f}\times D_{f}\times M+D_{k}\times D_{k}\times M\times N\). The ratio of DSCNN to CNN computational cost is \(1/N+1/D_{f}\), significantly smaller than 1 since \(N\) and \(D_{f}\) are integers greater than 1, indicating DSCNN's superior computational efficiency compared to standard convolution. As a CNN variant, DSCNN offers substantial computational efficiency improvements without significant computational accuracy loss.
## 1 Introduction
[25] studied the free dynamic properties of GPLs-reinforced sandwich composite beams lying on a viscoelastic medium based on HSDTs; whereas [PERSON] et al. [26] analyzed the dispersion of waves in GPLs-reinforced composite porous shells based on a FSDT. Furthermore, [PERSON] et al. [27] probed thermal buckling of GPLs-reinforced composite annular plates based upon FSDT and GDQM. The same computational approach has been applied, recently, in [28] to analyze the axially FG-CNTRC beams lying on an elastic medium. A similar analysis can be also found in [29] to examine the thermal post-buckling response of FG-CNTRC curved panels lying on an elastic substrate with elastic restrained edges. [PERSON] et al. [30] applied a refined HSDT to study the wave dispersion characteristics of plates reinforced with graphene oxide powder immersed in a thermal environment. Among nonlocal formulations, a nonlocal strain gradient theory was implemented by [PERSON] et al. [31] to solve the bending problem of porous GPL-strengthened nanocomposite beams based on finite elements. A nonlinear frequency analysis of thin-walled CNTRC shells was also performed recently by [PERSON] et al. [32], accounting for the effect of an elastic medium; whereas [PERSON] and [PERSON] [33] studied the effect of agglomeration and waviness factors of nano-reinforcements on the wave propagation behavior of embedded nanocomposite beams strengthened with multi-walled carbon nanotubes, as provided by HSDTs. Despite the large literature on the topic, to date, there is a general lack of works analyzing the wave propagation of longitudinally CNTs-reinforced composite beams lying on an elastic medium. A complete investigation about the problem, indeed, should include different environmental, loading, and boundary conditions. In the last case, we have to account for the possible interaction between a structural component and the surrounding elastic medium or foundation, whose properties can vary linearly and/or nonlinearly. This aspect is analyzed herein from a theoretical perspective for CNTs-reinforced composite beams resting on elastic media. The CNTs distribution is modeled according to a power-law function that can embrace from linear to parabolic reinforcement patterns. A numerical investigation is repeated systematically to check for the influence of various input parameters (i.e., wave number, power index, foundation parameters) on the overall response that could serve as benchmarks for design purposes and further computational/experimental analyses on the same problem.
# A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics
## 4 Framework Development
### Sensing and Connectivity Layer  
Figure 2 depicts the implemented IoT monitoring system used in motor rehabilitation sessions. Two classes of wearable IoT devices are deployed to monitor patients: (_i_) smart rings, worn by each individual to track vital signs, such as heart rate and oxygen saturation; and (_ii_) IMU-based devices, used to surveil body movement, with each patient potentially wearing multiple devices at various locations.  
The complete list of features collected by IoT devices for patient monitoring is presented in Table 2.  
Figure 2: Implementation of the first two layers of I-TROPHYS.  
At regular intervals, raw data from both smart rings and IMU devices are transmitted to the ES via wireless links. Smart rings utilize Bluetooth Low Energy (BLE) [27] technology for communication, while we use WiFi for IMU-based devices due to the bandwidth required to transfer the complete IMU time-series. Additionally, the Message Queuing Telemetry Transport (MQTT) protocol is employed, which enables IMU-based devices to publish data to the broker and subscribe to configuration updates, such as changes in acquisition frequency.
# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 2 Materials and Methods
### Methodology
The third approach selected, Random Forest, belongs to the category of ensemble models based on bagging. The latter technique involves the separate training of \(n\) weaker models, in this case standard decision trees, where the strong model is represented by the whole group and the regression output is obtained by averaging the results of each individual estimator. Formally, given a training set \(S\), the bagging procedure generates \(n\) new sets \(S_{i}\) such that \(|S_{i}|<|S|\) by sampling the original data. Subsequently, \(n\) individual models \(M_{i}\) are trained on each \(S_{i}\) and their results are aggregated by averaging in regression tasks, or by majority voting in classification problems. Despite the high demand in terms of computational resources and training time, Random Forests (and bagging in general) typically provide robust solutions without suffering from overfitting like simpler regression trees, thanks to the sampling procedure and ensemble learning. Because of their versatility and resilience to outliers, RF models have already been successfully applied in many different regression tasks, from calibration of air pollutant sensors [21] to air quality estimation in urban environments [22; 23]. Lastly, we employed a Deep Recurrent Neural Network (RNN), specifically a LSTM model. In general, RNNs inherently handle input sequences with varying length; however, the latter is particularly suited for the task given the internal architecture. LSTM units are in fact able to capture both long-term patterns and short-term variations thanks to a combined mechanism of input gates, where the information content from new examples is merged with the internal state of the network, and forget gates, where the decision of whether to keep or discard information from previous states is taken. Given their high adaptability to sequential inputs, LSTM models have been successfully applied to time series forecasting and estimation of air pollutants in different domains [17; 24; 25]. For our work,we made use of a LSTM network structured with three hidden layers with dimension 100, followed by a simple linear layer with a single output, corresponding to a specific estimated pollutant.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 3 System Overview  
The proposed multi-submap fusion mapping framework based on semantic road surface fitting comprises two main components: the mapping module and the multi-submap management module. The system architecture is shown in Figure 1.  
During the mapping phase, we first obtain sparse map points from the ORB-SLAM3 system, which are generated by feature extraction and matching in the visual SLAM process and contain 3D spatial information of the scene. These map points contain both ground and non-ground points (buildings, obstacles, etc.), so they need to be filtered to extract accurate ground feature points. We utilize a pretrained semantic segmentation model to semantically segment the current frame image to obtain pixel-level semantic labels, which include semantic masks for ground categories. The map points generated by ORB-SLAM3 are then projected into the current frame image to obtain the pixel coordinates of each map point on the image plane. For each map point projected to the image plane, we determine whether it belongs to the ground category by querying the semantic labels of the corresponding pixel locations in the semantic segmentation result, and if so, these map points are retained; otherwise, they will be excluded. Subsequently, we model the ground in the scene to fit the plane to the point set composed of these ground points. After obtaining the ground plane model, we rasterize this plane into uniform raster cells, and  
Figure 1: Framework of the proposed method.  
considering the balance between map resolution and computational efficiency, we set the raster size to 0.1 m \(\times\) 0.1 m.  
We also propose a multi-submap management module to represent large scenes using multiple submaps. This module implements dynamic loading and storing of submaps as the vehicle position changes, as well as integration between multiple submaps. This approach is chosen because, when the automated guided vehicle (AGV) moves far from the origin of the world coordinate system, it is often impossible to obtain the occupancy status of every grid cell on the ground when attempting to map the entire scene at once. Furthermore, when the scene has a relatively large scale, loop closure detection and graph optimization [30] can cause significant changes in the poses of many keyframes, requiring large-scale adjustments to the grid map to reflect these changes. This process is highly time-consuming. In practical applications, operations such as reading and modifying the entire map of a scene may not meet the real-time requirements of system operation.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 2 Methodology  
### Hardware Consideration  
At first, a hardware survey was conducted within the available market. Table 1 presents five short-listed products that support an LTE-based control system for a custom drone. The products' main criteria are having real-time control while sending and receiving direct application programming interface (API) telemetry data and live-feed video footage using an LTE mobile network.  
Based on the market surveys, all products offer almost the same features. However, Raspberry Pi provides unique characteristics. Raspberry Pi is a compact 64-bit microprocessor that can run a wide range of programming languages such as Python, Java (Debian Soft Float), C, Node.Js, and PHP. In addition, Raspberry Pi can run any Acorn RISC Machine (ARM)-based operating system [30]. It might be useful for the future development of the Internet of Drones (IoD). On the other hand, Raspberry Pi is very affordable and cost-saving compared to the other products.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.2.2. Energy-Efficient Grid-Based Clustering Routing Protocol (E2 GRCP)  
An energy-efficient clustered routing algorithm based on grid was presented by the author in [133] in 2016, based on the three-dimensional cube. In this routing system, the entire monitoring area was considered to be a cube, which was then further subdivided into several grids. In the E2 GRCP approach, each grid made up a CH. A cube-length message sent by the base station informs the communicating nodes about the complete grid monitoring region. The communicating nodes set a timer and send a message to their close neighbors. They also give information about the grid area, the remaining energy, and the distance from the sink node. The sensor Rx node, which is located in the same grid region, assesses its remaining energy and the distance to the sink node while considering the message it has received. The time runs out if there is a significant amount of energy left in the message after it has been received from the Tx anchor node. However, if the message's remaining energy, which is received from the Tx anchor node, is low, it continues. The communicating node that has a high remaining energy and is near to the TX anchor node will thus be proposed as the CH. A multi-hop method is used to transmit data packets from the Tx anchor node to the CH. However, selecting the next Tx anchor node is a bit challenging. The distance to the sink node and the quantity of remaining energy are calculated by the Tx anchor node for the CH in which the cluster value based on the grid is less. The CH with the lowest weight value is selected as the subsequent forwarder node for data packet transmission. According to simulation results, the EECRC outperforms other clustered routing algorithms as VBF in [134], ERP2R in [135], EL-LEACH in [136], and LEACH in [137] in regards to energy efficiency.
## 5 Immersive Interface Testbed: An Evaluation Example
### Apparatus  
The study was conducted using two types of visualization interfaces to perceive the workspace: a _traditional interface_ and a _immersive interface_.  
The _traditional interface_ consisted of one 22\" LCD monitor, the Samsung SyncMaster 2233 RZ with a native resolution of 1680 \(\times\) 1050 and vertical refresh rate of 120 Hz. This monitor displays images acquired through a webcam with a wide field of view, in the remote workspace from a single fixed point of view. The webcam used is the HD Logitech C270 with an optical resolution of 1280 \(\times\) 960 pixels (1.2 MP), configured to capture video (4:3 SD) with 800 \(\times\) 600 pixels, a focal length of 4.0 mm, and a field of view (FOV) of 60\({}^{\circ}\).  
The _immersive interface_ consisted of one HMD, the Oculus Rift DK2, with a display resolution of 960 \(\times\) 1080 per eye, an OLED screen of 5.7\({}^{\prime\prime}\), a refresh rate of 75 Hz, a persistence of 2 ms, 3 ms, full and viewing optics with a field of view of 100\({}^{\circ}\). Internal tracking includes gyroscope, accelerometer,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Display** & **Camera** & **Stick/Arm** \\  & & **Orientation** & **Control** \\ \hline
1 & Traditional & Fixed & stick/gripper \\  & 1 Monitor & & haptic feedback \\ \hline
2 & Immersive & head orientation & \\  & via HMD & from HMD IMU & stick/gripper \\  & (FPV) & & haptic feedback \\ \hline \hline \end{tabular}
\end{table}
Table 2: The two different test setup combinations for semi-teleoperation tasks. Fixed view, first-person view (FPV).  
Figure 2: Traditional teleoperation setup (remote visualization through a fixed monitor and camera) vs. immersive interface (point of view transfer; the head-mounted display (HMD) controls the remote camera).  
and magnetometer sensors with an update rate of 60 Hz. It refines HMD's pose with a positional tracking sensor based on a near-infrared CMOS sensor. The HMD controls the pan and tilt unit coupled with a webcam, the full HD Logitech C920, with an optical resolution of 1920 \(\times\) 1080 pixels, configured to capture video with 800 \(\times\) 600 pixels and an FOV of 78\({}^{\circ}\). The pan and tilt unit supports this camera and orients it according to the HMD's pose orientation, that is conforming to the user's head orientation. The Computer-Controlled Pan-Tilt Unit (PTU 46-17.5) from Directed Perception has two-step motors for the pan and tilt movements: load capacity over four lbs, speeds over 300\({}^{\circ}\)/s, resolution of 3.086 arc minutes (0.0514\({}^{\circ}\)).
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 6 Case Study: Analysis of a Repeater Chain
### Two-Hop Path with Multiple Memory Positions  
This configuration benefits from all the changes introduced in Section 6.1 but does contain an arbitrary number \(m\) of entangled memory positions between three nodes, Alice,a switch, and Bob. This configuration is a sub-case of the network presented in [31], with two nodes instead of a generalized number of leaf nodes. An illustrative example of the behavior of this network for \(m=18\) can be seen in Figure 5. As we see in the figure, the fidelity is increased the more pairs we introduce into the system, allowing us to use this overload of memories to drastically improve the communication capabilities of our system at the expense of limiting the multiplexing capability of the device.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 4 Technical Requirements: from Automotive to Network Perspective
### 5 GCAR Use Case Technical Requirements
#### 4.3.3 Network Assisted Vulnerable Road User Protection  
Figure 3 illustrates the scenario we considered for this use case: a pedestrian is walking close to the road or is crossing the street. A vehicle is driving towards the street with bad visibility conditions, for example, its view hidden behind parked cars. The network, with the help of accurate positioning technology, can predict the collision probability and deliver warning messages to the vehicle to avoid the potential collision in order to detect the presence of vulnerable pedestrian users and notify the driver or the AD vehicle.  
The most critical requirements for this use case include: (1) localization, the most significant requirement and the associated error shall be less than 50 cm or 3% of the true distance between the object and the vehicle itself, in case the latter value is greater. Larger values will increase the false alarm rate, as the pedestrian might be far away from the crossing area and a warning is still sent to vehicle although being irrelevant for the driving decisions. 25 cm accuracy would be the ideal case, since the place of the smartphone will affect the final accuracy; (2) intersection crossing time, which is around seven seconds when considering a two-lane road approximately 10 m width) and a pedestrian speed of 5 km/h; (3) relevance area, with the assumption of driving automation Level 3, about 70 m in urban area and at least 400 m in case of country road at night, which is the most challenging case. Regarding communication or network requirements, the availability of coverage should be 99.99% to ensure that this safety critical service is available and treated as top priority by the network. A reliability level of 99.9% is required in order to guarantee a similar reliability as the local on-board sensors (one error every 1000 operations). The data rate requirement is around 128 kbps with 10 Hz updated rate of pedestrian trajectory messages with a size of 1600 bytes: each message contains a five second trajectory with 100 ms periodicity (10 trajectory samples per second) and 32 bytes per trajectory sample. This gives 320 bytes per second and a total of 1600 bytes for five second trajectory.  
In addition, the power consumption and privacy of the VRU device are among other factors that should also be considered.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 3 Experimental Results  
### Experimental DDM Data Set  
The proposed method is evaluated using GNSS-R DDM data produced on TDS-1 when flying over the Arctic region. The Arctic consists of the Arctic Ocean, adjacent seas, and islands. Land within the Arctic region has seasonally varying snow and ice cover, with predominantly treeless permafrost-containing tundra. Arctic seas contain seasonal sea ice in many places. Sea ice of Arctic has a significant effect on global climate and local development, so it is very important to study the ice coverage and its changing trend. The experiments include two parts. Firstly, a case study is implemented through analyzing 131 DDMs from one GNSS-R SP ground track collected on 26 October 2015 in Section 3.2. In Section 3.3, 17346 DDMs from 130 GNSS-R SP ground tracks collected in six separate days from 24 March 2015 to 26 March 2016 have been employed to detect sea ice and water. Meanwhile, the sea ice changing trend over one year can be derived from experimental results.
###### Abstract  
Additive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts--however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects 'controlled' surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with \(R_{a}<0.1\) um)--the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)--texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of \(S_{a}\approx 6\) um, were obtained.  
laser ultrasonics; microstructure imaging; additive manufacturing; selective laser melting; rough surface imaging; surface integrity +
Footnote †: journal: Applied Physics