esa-sceva/satcom-chunk-collection huggingface dataset:
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 2 Materials and Methods
### Our Approach  
We propose two deep-learning models based on Recurrent Neural Networks (RNNs) [22]. RNNs find application in signal processing due to the ability to model sequential data and capture temporal dependencies. This kind of network can effectively denoise and filter signals, making them more appropriate for further processing (i.e., classification task), and is an active subject of research [23; 24; 25]. The problems related to this type of network are exploding and vanishing gradients [26; 27]. Thus, we used the Gated Recurrent Unit (GRU) mechanism [28] in our solutions to avoid such an issue.  
Figure 1(a) illustrates the described neural network. The input to the model are 453 values of the accelerometer signal (151 values for each axis: X, Y, and Z), sampled at  
50 Hz. The architecture consists of a GRU layer with an output of 512 and a hyperbolic tangent as an activation function. It is followed by a batch normalization layer and the transition to a dense layer with 128 neurons and activation function ReLU ([29]). In the next step, a dropout layer with a rejection rate of 30% and a dense layer with a single neuron with a sigmoidal activation function at the output.  
The second proposed model is presented in Figure 2b. This model's architecture and its input shape are similar to the previous with the modification of the GRU layer to the bidirectional. The other layers were constructed in the same way as in the first model.  
For both presented models, we used the same binary cross entropy loss function. We chose the Adam optimizer for training because of its adaptive learning rates, resulting in accelerated training.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Range/Values** & **Step/Sampling** & **Best Value** \\ \hline hidden\_dim & 64–1024 & 64 & 768 \\ dense\_units & 32–256 & 32 & 64 \\ learning\_rate & \(10^{-5}\)-\(10^{-3}\) & log & 0.000347012 \\ dropout\_rate & 0.1–0.5 & 0.1 & 0.3 \\ \(l_{1}\) & \(10^{-6}\)-\(10^{-4}\) & log & \(1.7089\times 10^{-6}\) \\ \(l_{2}\) & \(10^{-6}\)-\(10^{-3}\) & log & \(6.3877\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter search space and the best values found in the study.  
Figure 2: The architecture of proposed models (for optimized hyperparameters, see Table 1). (**a**) Recurrent Neural Network-based model (RNN). (**b**) Bidirectional Recurrent Neural Network-based model (BRNN).
## 1 Introduction
Drawing upon the classical theory of Thomson scattering and the single electron model, we have conducted a comprehensive numerical simulation using MATLAB to investigate the spatial, temporal, and frequency spectra of the nonlinear Thomson scattering process experienced by relativistic electrons. This process is specifically induced by the interaction of electrons with a tightly focused laser pulse. Our approach is designed to capture the nonlinear dynamics that arise from the interaction of the laser's intense electromagnetic fields with the relativistic electrons, which exhibit velocities close to the speed of light. This study primarily focuses on elucidating the influence of various initial phases on the radiation characteristics of electrons with different energy levels.
## 4 Apertureless-Type SNOM  
Imaging of electromagnetic near-fields has also been widely accomplished using non-aperture SNOM tips (Figure 1c) which can obtain higher resolution images compared to c-SNOM. The requirement for a precisely machined nano aperture is waived, but is replaced with other challenges in terms of signal detection due to a large far-field background signal from an external light source.  
Figure 2: Electromagnetic near-fields probed with c-SNOM. (**a**) An example of magnetic near-field maps (\(H_{y}\)) of single nanostructures taken with a pyramid-probe c-SNOM, scale bar is 300 nm [28]. (**b**) Measurements of the resonance shift of a photonic crystal nanocavity collected with a circular aperture c-SNOM probe (left). The distribution of the out-of-plane component Hz of the unperturbed cavity can be calculated from the resonance shift map (right). Reproduced with permission from [8], Copyright Nature Photonics, 2014.  
Photonic crystal nanocavities are a suitable test environment with well-defined nanostructures that are ideal to make use of the high resolution capabilities of s-SNOM probes [13; 35; 36; 37]. Experiments examining a mid-infrared inverse bowtie antenna using s-SNOM in a polarization-resolved interferometric configuration have been used to reconstruct the near-field vector from the amplitude and phase of the electric field components (\(E_{x,y,z}\)) [36]. Tuning of high quality factor photonic microcavities over a wide range of frequencies has been accomplished by introducing a subwavelength dielectric tip [36; 37]. These cavities enable the study of tip-sample interaction to advantageously control their properties. This is made possible by varying the lateral and vertical position of the tip near the cavity, which can be used to tune the resonator frequency while maintaining the cavity quality factor [36].  
The high resolution of s-SNOM that enables the study of microcavity resonators are also ideal to examine near-field effects such as surface plasmons [38; 39; 40; 41] in graphene and metal nanostructures. Making use of IR light as the illumination source allows for collection of IR-SNOM images [42; 43], and may also be coupled to FTIR spectrometer to collect high resolution spectroscopy measurements [38; 44]. Imaging of plasmon effects and IR-coupled systems are discussed in the next sections.
## 2 Mechanism Introduction and Modeling Analysis
### Structure and Operating Principle
#### 2.1.2 Automatic Seedling Feeding Device and Its Supporting Technology  
The fixed seedling cup intermittent automatic grouping feeding mechanism is mainly applicable to the automatic seedling picking system of full-row automatic seedling pulling, such as the automatic seedling picking system of the 2 ZY-4 pepper pot seedling translanter shown in Figure 1 (Figure 2). It is mainly composed of a tray feeding mechanism, a pot seedling whole-row extraction and conveying mechanism, a fixed seedling cup intermittent automatic grouping seedling mechanism, an electrical control system, and a rack. The tray feeding mechanism, which is responsible for the step-by-step supply of the tray, is mainly composed of a tray chute, pusher cylinder assembly, top seedling cylinder assembly, and so on. The seedling tray chute limits the movement of the seedling tray along the established trajectory, and the pushing plate cylinder assembly drives the seedling tray to move along the chute intermittently. The fixed seedling cup intermittent automatic grouping feeding mechanism is mainly applicable to the automatic seedling picking system of full-row automatic seedling pulling. The whole-row extraction and transportation of pot seedlings is composed of a transverse pneumatic slide rail, pneumatic seedling components, and other connecting parts. After the whole row of pot seedlings is extracted, they are transported to the fixed seedling cup intermittent automatic grouping seedling feeding mechanism.  
Taking the Amerco Rishen series D220P vegetable tray as an example, the tray size (length \(\times\) width \(\times\) height) is 632 mm \(\times\) 215 mm \(\times\) 35 mm, 10 plants per row, 22 rows in total, and 220 plants per tray. The tray can bend at a large angle, and the limit of the bending radius \(R_{\text{min}}\) is about 150 mm. As shown in Figure 3, the pneumatic seedling picking component is used in the whole row of the seedling picking and conveying mechanism. Before the bowl plate enters the bending point, the whole row of seedlings is grabbed, and the seedlings are pulled up. When the whole row of seedlings is pulled up, the interval length is \(W_{1}\), corresponding to the width of the seedling tray. After the seedlings are pulled out, they move to the seedling dropping mechanism and gradually extend the  
Figure 1: Traction-based automatic pot seedling transplanting machine: (1) Rotary tillage ridging device, (2) height-adjustable frame, (3) rotary ditching device, (4) earthing device, (5) seedling tray frame, (6) automatic seedling feeding device.  
interval length to \(W_{2}\), corresponding to the width of the seedling dropping mechanism, thus disrupting the interlacing of the seedling leaves and preventing the seedlings from hanging on the spacer between each seedling tube within the seedling tray when the whole row of seedlings is dropped.
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
###### Abstract  
In the era of Industry 4.0, there are many emerging industrial Internet of Things (IIoT) applications that require ultrareliable and low-latency communications (URLLCs), such as time-sensitive networking (TSN), status reporting, and remote control. With the deployment of massive intelligent devices in IIoT networks, providing security for device physical-layer access is a challenging issue, especially for URLLC applications with strict latency and reliability constraints. We thus developed an intelligent radio frequency identification (RFI) framework to provide a lightweight and energy-efficient physical-layer access scheme for URLLCs via leveraging unique hardware-level imperfections of transmitter. We propose a novel semisupervised-learning (SSL) algorithm to realize intelligent RFI in URLLCs scenarios. One-dimensional network construction is also exploited to improve the accuracy of the proposed SSL algorithm. On the basis of the proposed RFI framework, we analyze the overall uplink transmission error probability and network availability of URLLCs with massive MIMO, which can achieve comparable symmetry performance with that of downlink, and experimental evaluation is also provided to gain comprehensive insight on RFI. Numerical and experimental results demonstrate the effectiveness of our proposed RFI framework and the impact of channel correlation, and provide design guidelines for supporting the radio frequency identification of URLLC applications in IIoT systems.  
URLLCs; industrial IoT; symmetry; intelligent radio frequency identification; semisupervised learning; short-packet communication +
Footnote †: journal:  
Article
## 4 Validation Experiments and Results
### Validation Experiment of Active Rehabilitation Training Mode of the Controller  
For those patients who already have regained motor control ability of the upper limb, they could move their arm; however, stability training of their arm is needed. The robot could be in a completely backward-driven state by adjusting the parameters of the controller proposed in this paper, and the patient can move the robot on any trajectory without any assistance force from the robot. The subject was required to complete the predetermined circular trajectory during the test, moving counterclockwise for a total of three turns. Similarly, the pose of the robot remained fixed during the testing, and the parameters of the controller was set as \(R_{W}\) is infinite, \(K_{0}=[0,0,0]\) N/m, \(K_{1}=[0,0,0]\) N/m, \(K_{t}=[500,500,500]\) N/m, and \(K_{2}=[0,0,0]\) N/m. Figure 17 shows the actual and training trajectories of the subject's arm. Figure 18 presents the filtered interaction force and wrench between the subject's hand and the handle. Figure 19 shows the filtered total force between the subject's hand and the handle as well as the deviation between the actual trajectory and the predetermined trajectory.  
Figure 17: The trajectory of the subject’s hand during the active testing. (**a**) A 3D view of the trajectory; (**b–d**) XOY, XOZ, and YOZ view of the trajectory, respectively.  
Figure 18: The interaction force and moment between the handle and the subject’s hand. (**a**) is the interaction force between the handle and subject’s hand; (**b**) is the moment between the handle and subject’s hand.  
As can be seen from Figure 17, the deviation between the subject's trajectory and the predetermined trajectory is relatively large, with the maximum value being 40.43 mm. It can be seen from Figure 18a that the RMS of the total interaction force between the subject's hand and the handle is 1.66 N. As the robot's pose remained fixed during the testing, it can be seen from Figure 18b that the RMS of the interactive wrench is 2.08 Nm. One can conclude that the robot does not provide any assistance force in the active rehabilitation training mode, so the subject can move freely and be allowed to choose his own trajectory. Therefore, active rehabilitation training can be performed under the guidance of a Unity scene.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 2 Overview of Marine Internet
### Typical Applications
#### 2.2.1 Extension of Terrestrial Internet to Oceans  
The original intention of marine Internet [1] is to extend terrestrial Internet services to offshore areas seamlessly for users such as seamen, fishermen, offshore platform users, scientists of marine research and exploration as well as island residents. This is motivated by the fact that the current satellite Internet services are not affordable to the ordinary Internet users, while such situation may still last in the future, and more discussion can be found in Section 4.2.
## 5 Networking Parameter Adjustment Method for Optimized Systematic Performance
### Systematic Performance Calculation of UWSN's
Assuming \(\beta\) = 20, the contribution proportion of the UUV to the network is about 14%, so Equation (9) can be rewritten as Equation (18):  
\[\Psi=\frac{1}{1.14\times 10^{7}}\cdot\left(\frac{e_{b}}{p_{b}}+20\cdot\frac{e_{u }}{p_{u}}\right) \tag{18}\]  
Figure 7: The ideal underwater sensor network. For the rapid-reactivity, suppose the waiting time of overtime retransmission is twice the round time transmitted along the diameter, and the waiting times of retransmission is \(l=3\), then we will have \(0.9382\cdot\alpha=1\), that is, \(\alpha=1.0658\). Hence, Equation (12) can be rewritten as Equation (19):  
\[\Omega=\frac{1.0658\cdot k_{b}\cdot b_{b}}{\frac{L}{V}\cdot(1+ \frac{s\cdot l}{\varphi})} \tag{19}\]  
It should be pointed out that this case is not unique. It can be redefined according to practical monitoring mission demands and the environment, and the corresponding parameters can be recalculated. In this equation, \(\Theta_{0}\), \(\Phi_{0}\), \(\Psi_{0}\) and \(\Omega_{0}\) denote the initial expectation values of the coverage, connectivity, durability, and rapid-reactivity. \(k_{b}\), \(d_{b}\), \(n_{b}\), \(c_{b}\), \(r_{b}\), and \(p_{b}\) are the optimum parameters which can be obtained by solving the equation. If the equation has not been solved, the current network does not meet the mission demands and needs to be optimized and adjusted, or more sensor nodes are required to fulfill the mission. However, if this equation can be solved, the responding parameters are sent to each node and their node values can be adjusted so as to satisfy the mission needs. After the first round of optimization, the control node needs to evaluate the systematic performance of network again. After the third round of optimization, if the demands of the mission is still not satisfied, the state of current node does not fit the demands of the adjusted parameters. The reasons for not achieving theoretical optimum parameters include the following: firstly, in practical deployment, the sensor nodes of a network do not always have an accurate body-centered cubic structure and the calculation result may have deviations. Secondly, the results of the evaluation of connectivity, coverage and so on are not so accurate, and the parameter values need to be calculated according to the practical deployment circumstances. Lastly, there may be no deployment schedule which is in exact accordance with the optimal result. For instance, perhaps a node must be placed at a certain location in theory, but no node can reach that position, hence the optimization will fail, and a new round of optimization is required. In order to adjust the parameters for optimized systematic performance of UWSNs, this paper employs a genetic algorithm to solve the non-linear optimal problem with constraint conditions.
## 3 Results
### Integrated System Performance: Gesture Control and Obstacle Avoidance
#### 3.4.3 System Latency Analysis  
The latency in gesture-based control primarily stems from the looping sequence of detecting a hand gesture and executing the corresponding drone movement. In each loop cycle, the system first detects the user's hand gesture, then issues a movement command to the drone, and only after completing the movement does it begin detecting the next gesture. To obtain prompt responsiveness, the movement distance per gesture is set to 0.05 m. This execution interval allows the drone's overall gesture-to-command latency to be approximately 209 milliseconds (\(\pm\)53 ms). This delay includes 200 milliseconds (\(\pm\)50 ms) for gesture recognition, 4 milliseconds (\(\pm\)1 ms) for command transmission via Crazyradio, and 5 milliseconds (\(\pm\)2 ms) for drone command execution. These values were  
Figure 10: **(a)** Image of AI-Deck streamed footage in color mode. **(b)** Image of AI-Deck streamed footage in greyscale mode.  
obtained through repeated experimental trials using custom Python commands designed to benchmark each phase of the control loop. A complete summary of these measured latencies is presented in Table 4.  
The latency associated with the AI-Deck's live video stream was measured at approximately 100 milliseconds, with a standard deviation of \(\pm\)50 milliseconds. Although slightly noticeable, this delay did not significantly impair situational awareness or the user's ability to issue commands based on the visual feedback.  
For the obstacle avoidance system, latency was even lower. Since obstacle detection and response are handled entirely within the internal logic loop of the Python program, without requiring gesture recognition or external input, the response time was limited to the drone's command execution delay. This resulted in a rapid, near-instantaneous obstacle avoidance reaction time of approximately 5 milliseconds (\(\pm\)2 ms), ensuring timely evasion of objects detected within the safety threshold.
# Crystallization Kinetics of an Equimolar Liquid Crystalline Mixture and Its Components
## 3 Results and Discussion
### Isothermal Crystallization
For MHPOBC, the minimum of \(t_{01}\) is observed at 315 K, and of \(t_{02}\) at 312 K. For 3F2 HPhF6, the shortest \(t_{0}\) is obtained at 298 K. However, the  
Figure 14: DSC curves collected at selected crystallization temperatures \(T_{cr}\) (**a**–**c**), isothermal crystallization degree vs. time (**d**–**f**), and DSC thermograms collected during heating at 10 K/min after crystallization (**g**–**i**). The corresponding \(T_{cr}\) values are given in legends in K. The results for MHPOBC, 3F2 HPhF6, and MIX2 HF6 are shown in panels (**a**,**d**,**g**), (**b**,**e**,**h**), and (**c**,**f**,**i**), respectively. value at 295 K is likely overestimated. For MIX2 HF6, only the upper branch of the TTT diagram is observed in the investigated temperature range. The activation plot of the characteristic crystallization time (Figure 15b) does not show a linear dependence for MHPOBC and 3F2 HPhF6. The \(\tau_{1}\) and \(\tau_{2}\) of MHPOBC initially decrease between 323 K and 320 K (\(E_{eff}<0\)), then the activation plot is almost horizontal (\(E_{eff}\approx 0\)). For 3F2 HPhF6, \(\tau_{cr}\) shows an approximately decreasing trend with decreasing \(T_{cr}\) (\(E_{eff}<0\)). The \(\tau_{1}\) values for MIX2 HF6 show a linear dependence in the activation plot, with \(E_{eff}=-141(3)\) kJ/mol. Thus, the first stage of MIX2 HF6 crystallization is controlled by the nucleation rate to a larger extent than the crystallization of the pure compounds, and by the second stage of MIX2 HF6 crystallization, where \(\tau_{2}\) shows a roughly linear dependence at \(E_{eff}=-33(6)\) kJ/mol. Comparing the \(t_{0}\) and \(\tau_{cr}\) values for all the samples, one can say that 3F2 HPhF6 shows the fastest crystallization. The first stage of MIX2 HF6 crystallization occurs faster than that of MHPOBC in most cases, but the second stage of MIX2 HF6 crystallization is much slower than the overall crystallization of MHPOBC. Additionally, the crystallization range of MIX2 HF6 is shifted to lower temperatures compared to the pure compounds. The last parameter is the Avrami exponent (Figure 15c). The lowest \(n\) values of 1-1.5 are obtained for the first stage of MIX2 HF6 crystallization, and are interpreted as mainly 1D crystal growth. For the first stage of MHPOBC crystallization, \(n=1.5\)-2.5, which means a mix of 1D and 2D crystal growth. The second stages of MHPOBC and MIX2 HF6 crystallization, as well as 3F2 HPhF6 crystallization, are characterized by \(n=2.5\)-4, which indicates a mix of 2D and 3D growth (\(n=2.5\)-3) or mainly 3D growth (\(n=3\)-4) of crystallites. The maximal \(n\approx 5\) for the second stage of MIX2 HF6 crystallization at 280 K indicates the contribution of sheaf-like crystal growth.
## 3 Methodology
### STS Angular Configuration Modified by Topography  
The radiance \(L_{TOA}\) for horizontal surface under Lambertian assumption can be expressed as:  
\[L_{TOA}=L_{0}+\frac{T_{v}}{\pi}E_{h}(\frac{\rho_{m}}{1-S\rho_{m}}) \tag{1}\]  
where \(L_{o}\) is path radiance, \(T_{v}\) is total transmittance in the viewing direction, \(E_{h}\) is total solar irradiance on horizontal surface, and \(S\) is atmospheric albedo. The calculated \(\rho_{m}\) is surface reflectance under Lambertian assumption, and can be treated as the first-order approximation of bi-directional reflectance for non-Lambertian surface (termed as \(\rho_{s}\) in the following). When considering the BRDF effect in mountainous area, the radiance \(L_{TOA}\) can be expressed as [20,38,39]:  
\[L_{TOA}=L_{0}+\frac{T_{v}}{\pi}\left(E^{dir}\left[f_{v}\rho_{s}(i_{t},e_{t}, \delta\varphi_{t})+(1-f_{v})\overline{\rho^{\prime}}(i_{t})\right]+E^{dir} \left[f_{v}\overline{\rho}(e_{t})+(1-f_{v})\overline{\overline{\rho}}\right] +E\frac{s\overline{\overline{\rho}}^{2}}{1-S\overline{\overline{\rho}}}\right) \tag{2}\]  
where \(i_{t}\), \(e_{t}\) are local incident and exiting angle, respectively, and \(\delta\varphi_{t}\) the relative azimuth angle between incident direction and exiting direction in slope geometry (difference between local incident azimuth \(\varphi_{i}\) and exiting azimuth \(\varphi_{e}\) ). These angles, related to solar zenith/azimuth angle ( \(\theta_{s}\), \(\varphi_{s}\) ), view zenith/azimuth angle (\(\theta_{v}\),\(\varphi_{v}\)), and slope (\(\theta_{t}\)) and aspect (\(\varphi_{t}\)), can be computed by referring to the formulas in [40].  
In Equation (2), \(E^{dir}\), \(E^{df}\), and \(E\) denote direct irradiance, diffuse irradiance, and total irradiance on tilted surface, respectively; \(f_{v}\) denotes the ratio of direct transmittance to total transmittance in the viewing direction; \(\rho_{s}\), \(\overline{\rho^{\prime}}\), \(\overline{\rho}\) and \(\overline{\overline{\rho}}\) represent bi-directional, directional-hemispherical, hemispherical-directional and bi-hemispherical reflectance factors, respectively. Based on theory of iterative method, we combine Equations (1) and (2), and depict the relationship between \(\rho_{m}\) and \(\rho_{s}\) as follows:  
\[\frac{\rho_{m}E_{h}}{1-S\rho_{m}}=E^{dir}\left[f_{v}\rho_{s}(i_{t},e_{t},\delta \varphi_{t})+(1-f_{v})\overline{\rho^{\prime}}(i_{t})\right]+E^{dif}\left[f_{v }\overline{\rho}(e_{t})+(1-f_{v})\overline{\overline{\rho}}\right]+E\frac{S \overline{\overline{\rho}}^{2}}{1-S\overline{\overline{\rho}}} \tag{3}\]  
where \(E^{dir}\) and \(E^{dif}\) can be derived from direct and diffuse irradiance received on horizontal surface under the same illumination condition.