esa-sceva/satcom-chunk-collection huggingface dataset:
# DanceCaps: Pseudo-Captioning for Dance Videos Using Large Language Models
## 4 Experiments
### User Study
As shown in Figure 9, the participants' responses to the frame extraction location evaluation were analyzed by music BPM and dance genre. After watching the video, the participants evaluated their preferences for temporal motion tags extracted at beat, half-beat, and quarter-beat locations. First, the participants' preferences did not significantly differ depending on the music BPM, and they preferred the half-beat location the most and the quarter-beat location the least in all music BPMs. This shows that tag preferences were not affected by whether the music BPM was slow or fast, and that tags at the half-beat location most appropriately described the video. Second, the participants evaluated their preferences for temporal motion tags according to the dance genre. Half-beat was the most preferred in most genres, followed by beat and quarter-beat, but dance genres such as middle hip-hop showed exceptions where tags at the beat location were preferred over tags at the half-beat location. In conclusion, regardless of the differences in music BPM and dance genre, most evaluators preferred temporal motion tags at the half-beat location. This is likely because tags at beat positions can miss important information about the movement, and tags at quarter-beat positions tend to be overly long, including excessive details that deviate from the main point. Meanwhile, regarding caption preferences, 71.4% of respondents preferred 'time series motion captions', 25.7% preferred 'dance summary captions', and 2.9% chose'multimodal captions'. Most respondents preferred captions that provided detailed descriptions of dance movements, but captions with additional information, such as music, were generally less preferred. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Professional** & **Containing Info. ** & **Video-Caption Match** & **Motion Detail** \\ \hline AIST dance & \multirow{2}{*}{4.279 \(\pm\) 0.944} & \multirow{2}{*}{4.603 \(\pm\) 0.694} & \multirow{2}{*}{4.632 \(\pm\) 0.667} & \multirow{2}{*}{4.529 \(\pm\) 0.801} \\ \cline{1-1} \cline{5-5} \cline{
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 4 Results and Discussions  
This section describes the results of feature-based change detection experiments carried out on the three test datasets, compares their performances in comparison to the MAD and discusses the advantages and drawbacks of our proposed approaches.
# A Thermomechanical Coupling Constitutive Model of Concrete Including Elastoplastic Damage
###### Abstract  
The thermomechanical coupling constitutive model of concrete is a critical subject for the theoretical investigation and numerical simulation of the mechanical behaviors of concrete members and structures at high temperature. This paper presents a thermomechanical coupling constitutive model for the description of the mechanical behaviors of concrete at different temperatures. The expression of the elastic strain increment is derived with the free energy function including the temperature variable. The expression of the plastic strain increment is derived from the yield function based on the Drucker-Prager strength criterion. The elastoplastic damage effect is included in this constitutive model. The damage variable is included in the yield function to consider the effect of the damage on the elastoplastic mechanical behaviors of concrete. The proposed constitutive model is validated by the comparison of the simulation results of the uniaxial compression tests of concrete at different temperatures with the corresponding test results. The simulation results accord well with the test results at different temperatures. This indicates that the proposed constitutive model can characterize the mechanical behaviors of concrete at different temperatures with considerable accuracy. The proposed constitutive model was applied to simulate an axially compressive concrete column. The simulation results are consistent with the essential mechanical response behaviors of concrete members at different temperatures.  
concrete; thermomechanical coupling; constitutive model; elastoplastic damage; thermodynamics +
Footnote †: journal: Applied Sciences
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
###### Abstract  
Localizing underwater nodes when they cannot be tethered or float on the surface presents significant challenges, primarily due to node mobility and the absence of fixed anchors with known coordinates. This paper advocates a strategy for tackling such a challenge by using visible light communication (VLC) from an airborne unit. A novel localization method is proposed where VLC transmissions are made towards the water surface; each transmission is encoded with the Global Positioning System (GPS) coordinates with the incident point of the corresponding light beam. Existing techniques deal with the problem in 2D by assuming that the underwater node has a pressure sensor to measure its depth. The proposed method avoids this limitation and utilizes the intensity of VLC signals to estimate the 3D position of the underwater node. The idea is to map the light intensity at the underwater receiver for airborne light beams and devise an error optimization formulation to estimate the 3D coordinates of the underwater node. Extensive simulations validate the effectiveness of the proposed method and capture its performance across various parameters.  
Keywords:underwater localization; visible light communication; air-to-water communication _et al._ Article
## 2 Materials and Methods
### Calculations of Torque and Power  
According to Figure 1, the blade of the wind rotor is subjected to forces \(F_{x}\) and \(F_{y}\). Therefore, a general formula for the torque generated by one blade \(M_{1}(\alpha)\) as a function of the angle of the rotor rotation \(\alpha\) is given by Equation (9).  
\[M_{1}(\alpha)\ =\ \left[-F_{x}\cdot\cos\left(\frac{\alpha}{2}\right)+F_{y} \cdot sin\left(\frac{\alpha}{2}\right)\right]\cdot R \tag{9}\]  
where \(R\)--the radius of the rotor.  
The total torque for three rotor blades \(M_{III}(\alpha)\) relative to the \(x\), \(y\) coordinate system is the sum of the torques for the three rotor blades \(M_{1}(\alpha)\), \(M_{2}(\alpha)\), and \(M_{3}(\alpha)\) as a function of the rotor rotation angle \(\alpha\), according to Equation (10).  
\[M_{III}(\alpha)\ =\ M_{1}(\alpha)+M_{2}(\alpha)+M_{3}(\alpha) \tag{10}\]  
The turbine power is given by Equation (11).  
\[P(\alpha)\ =\ M_{III}(\alpha)V_{0}/R \tag{11}\]  
where \(V_{0}\)--the rotor peripheral speed [m/s]; \(R\)--the rotor radius [m].
## 2 Preliminary Information
### Each Robot Uses USBL and Depth Sensors to Enable Assumptions (A1) and (A2)  
Suppose that a robot \(n\) transmits a signal to another robot, say \(m\in\mathcal{N}(n)\). The bearing of the signal is measured by the USBL of \(m\). The signal bearing is measured by the USBL of \(m\), which contains an array of transducers. The transceiver head normally contains three or more transducers separated by a baseline of 10 cm or less. A method called \"phase-differencing\" within this transducer array is used to calculate the signal direction. The equation for bearing measurement is  
\[brg=atan2(\mathbf{nm}[2],\mathbf{nm}[1])+N_{b}. \tag{5}\]  
Here, \(\mathbf{nm}=\mathbf{n}-\mathbf{m}\). Furthermore, \(\mathbf{nm}[j]\) indicates the \(j\)-th element in \(\mathbf{nm}\). In (5), \(N_{b}\) indicates the bearing measurement noise. In (5), \(atan2(y,x)\) define the angle of a complex number, say \(x+iy\). Suppose that a robot \(n\) transmits a signal to another robot, say \(m\in\mathcal{N}(n)\). Then, \(m\) replies with its own acoustic pulse. This return pulse is detected by \(n\). The time from the transmission of the initial acoustic pulse until the reply is detected is measured by the USBL system of \(n\) and is converted into a range. This is feasible, since \(n\) has the reference system to measure the signal speed in underwater environments. This range estimation is then shared by both \(n\) and \(m\). The equation for range measurement between \(n\) and \(m\) is  
\[rng=\|\mathbf{nm}\|+N_{r}. \tag{6}\]Here, \(N_{r}\) indicates the range measurement noise.  
Note that \(m\) has depth sensors to measure its depth. The depth of \(m\) is measured using  
\[d(m)=\mathbf{m}[3]+N_{d}. \tag{7}\]  
Here, \(N_{d}\) indicates the depth measurement noise. Also, \(\mathbf{m}[3]\) indicates the third element in \(\mathbf{m}\). We define  
\[rng^{p}=\sqrt{\|rng^{2}-(d(m)-d(n))^{2}\|}. \tag{8}\]  
Then, the relative position of \(n\) with respect to \(m\) is  
\[relPos=[rng^{p}*\cos(brg),rng^{p}*\sin(brg),d(n)-d(m)]. \tag{9}\]  
USBL sensors can be used to transmit information from a transmitter to a receiver. Hence, the relative position information in (9) can be shared by both \(m\) and \(n\). Moreover, \(r^{dc}\) information of \(m\) and \(n\) can be shared by two robots. Thus, Assumptions (A1) and (A2) hold.  
We next calculate the number of communication pings required, so that two neighboring robots \(m\) and \(n\) can share the relative position information. The bearing measurement in (5) requires a single communication ping from \(n\) to \(m\). The range measurement in (6) further requires a return pulse from \(m\) to \(n\). Then, the calculated range measurement is sent from \(n\) to \(m\). Also, the bearing measurement in (5) is sent from \(m\) to \(n\). In total, four communication pings are required, so that two neighboring robots \(m\) and \(n\) can share the relative position information.
# Decoupling Control of Multi-DOF Supporting System of MLDSB
###### Abstract  
Magnetic liquid double suspension bearing (MLDSB) is composed of an electromagnetic system and hydrostatic system and its bearing capacity and stiffness can be greatly improved. It is very suitable for occasions of medium speed, heavy load, and starting frequently. Due to the gyroscopic effect and interference between the supporting system, the space state of the rotor can be affected and the operation stability of the MLDSB can be reduced greatly. Therefore, the coupling features of the multi-degree of freedom (multi-DOF) system are analyzed and a decoupling controller is designed and verified in the paper. Firstly, the paper introduces the structural characteristics, stress forms, and control regulation mechanism. Then, the mathematical transfer function of the multi-DOF supporting system is established and the coupling principle is revealed. The state feedback decoupling controller is designed by means of a state feedback decoupling, and its decoupling effect is analyzed by the Simulink module. Finally, the single-DOF decoupling system is independently controlled by the use of the root locus method. The coupling characteristics between channel \(x\) and channel \(y\) are tested experimentally, and the decoupling controller is added and its effect tested. The results show that the state feedback decoupling controller can effectively reduce the coupling degree in the multi-DOF system and convert the multi-DOF coupling problem into a single-DOF independent control problem. The coupling effect of channel \(x\) and channel \(y\) is reduced by using the decoupling controller, with the subsequent displacement characteristic of the rotor increased, and then the operation stability of the MLDSB is improved greatly. This paper can enrich the support system of the MLDSB and provide a theoretical reference for stability control.  
MLDSB; multi-DOF supporting system; decoupling control; state feedback decoupling controller; root locus method +
Footnote †: journal: Applied Sciences
## 2 Theory of LS Inductive Thermography
### LS-Based Inductive Thermography  
Figure 1 shows the principle of induction thermography based on line scanning (LS). There is relative movement between the IR camera and the inspection object. If there is no relative movement, the shape of coil is recorded together with the thermal image as in [18; 19]. The thermal image detects defects due to relative temperature scale, and if the temperature of the coil is measured, the detectability may be affected. The coil area can be removed by post-processing, but this takes a lot of time.  
When the inspection object passes through the coil, eddy currents are induced, generating heat. The defective area generates a higher temperature than other areas, which is measured by the IR camera. The measured data are transmitted to the PC. As shown in Figure 2, the thermal image is generated as a single image by accumulating the scanning line area for all frames. The reason for performing this process is to acquire the maximum thermal contrast data of the defective area and sound area after the inspection object passes through the coil. In addition, since the data for the fixed scanning line area is accumulated, a uniform temperature distribution image can be acquired. For example, the number of images acquired at a moving speed of 5 mm/s is 2934, and the image of the sequence is a combination of 2934 scanning lines based on the process shown in Figure 2.  
Figure 1: Principle of line-scanning-based inductive thermography.  
Figure 2: Process of sequence image acquisition of scanning lines accumulated for all frames.
# Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications
## 3 Kernel-FastICA-Based Nonlinear Blind Source Separation Algorithm
The convergence rate is dominated by the non-Gaussianity of the source signals--higher negentropy leads to faster separation. Error Bound Derivation: The upper bound on the separation error \(|\Delta s|_{2}=\left|\hat{s}-s\right|_{2}\) can be expressed as follows:  
\[\mathrm{K}(x,y)=\langle\phi(x),\phi(y)\rangle, \tag{5}\]6  
where \(\kappa(C)=\sigma_{max}(C)/\sigma_{min}(C)\) is the condition number of the regularized covariance matrix (Equation (7)), suppressed by \(\lambda=10^{-4}\); \(L_{\phi}=\sqrt{10}\) is the Lipschitz constant of the cubic polynomial kernel (derived from \(\left|\
abla\phi(\mathrm{x})\right|_{2}\leq\sqrt{10}\)); and \(\parallel\mathrm{x}\parallel_{2}\) is the norm of the input signal. This bound explicitly quantifies the impact of numerical stability (controlled by \(\lambda\)) and input signal magnitude on the separation accuracy. Theoretical Performance Limit: Under additive white Gaussian noise \(\mathrm{n}\sim\mathcal{N}(0,\sigma^{2}\mathrm{I})\), the Modified Cramer-Rao Bound (MCRB) for the PNL model gives the minimum achievable variance for source signal recovery:  
\[\mathrm{K}(x,y)=\langle\phi(x),\phi(y)\rangle, \tag{5}\]7  
where the Fisher information matrix \(\mathcal{J}_{F}\) is  
\[\mathrm{K}(x,y)=\langle\phi(x),\phi(y)\rangle, \tag{5}\]8  
Here, \(p(\mathrm{x}\mid\mathrm{s},\mathrm{f})\) is the likelihood function of the PNL model. The MCRB is inversely proportional to the negentropy \(J(\mathrm{s})\), proving that negentropy maximization is an asymptotically optimal strategy.
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of Upper Level Distributed Coordination Strategy
#### 5.3.3 The Situation of Network Topology Switching  
In order to verify the time characteristics of cooperative attack in the case of the switching network communication topology of a multiple UAV formation, it is assumed that there is a switching network topology among the above three structures with a switching period of 5 s and a switching sequence of (1) \(\rightarrow\) (2) \(\rightarrow\) (3) \(\rightarrow\) (1). The simulation curves of the flight trajectory, the leading angle, the time-to-go and the acceleration command over time of each member of the formation are shown in Figure 11.  
As can be seen from Figure 11, the initial time-to-go of the three UAVs is different at, respectively, 30.29 s, 37.97 s and 22.96 s. However, after mutual coordination, they gradually become consistent in about 11 s. In the later stage, the cooperative guidance law degenerates into the UAV's own control item, and finally the saturation attack on the target is carried out simultaneously in 34.21 s. The leading angle and acceleration command converge to 0 at the terminal moment. It is worth noting that from Figure 11d, it can be seen that the acceleration commands of M1 at 10 s, M2 at 10 s and 15 s have obvious jumps. This is mainly because the coordination information obtained by the UAV has obvious changes when the network topology is switched.  
Based on the simulation results of the above three different situations, it can be seen that the upper-layer distributed coordination strategy and the lower-layer non-singular sliding mode guidance law designed in this paper can realize the impact time cooperative guidance under the fixed or switching network topology of the UAV formation through the information exchange between them.  
Figure 10: Simulation results of cooperative attack under chain network topology.
## 1 Introduction
Conducted research and comparative analyses allowed to formulate the conclusion that among the four ways of coupling accelerometers to the ground, the most reliable are measurements of surface vibrations carried out with the use of measuring bases rigidly connected to the anchors (ring and cross-shaped measuring bases) and embedded in the ground by hammering. There are many other scientific publications and research reports in which accelerometers during measurements of vibration were simply set on the ground surface (for example [32]) or which do not provide a detailed information about method of coupling measuring transducers to the ground [1; 4; 33; 34; 35]. The abovementioned publications [28; 29; 31] indicate significant, even several dozen percent differences in the peak values of vibration accelerations recorded using different methods of coupling transducers to the ground. The spectra of recorded vibrations in the frequency domain also differ significantly [28; 29]. Such large discrepancies in the results of conducted research significantly impede not only the comparative analysis of the results presented by various research teams, but also may indicate low reliability or even unsuitability of certain solutions. Too low peak values of recorded vibrations may indicate an insufficient degree of coupling of transducers to the ground. Too high level of vibration may in turn indicate the occurrence of resonance vibrations of the measuring base (anchored in the ground or not) with transducers mounted to it. The purpose of this work is conducting numerical simulations in Ansys Engineering Simulation Software and identification of modal parameters (in particular natural frequencies) of the selected three types of measuring bases in the form of spikes with attached to them various types of accelerometers, as well as determining reliability of surface vibration accelerations recorded with the use each of them.
## 5 System Operational Simulation  
### Simulation Platform  
The operational simulation of the MoDisNet system aims to give an overall evaluation of the hardware and software design. The simulation will realize not only the wireless communication processing and upper layer algorithms, but also the sensor unit itself, including the reasonability of all the function modules of the sensor chip, the lower layer driving programs, and the cooperation of different types of sensors (MSN and SSN). For this purpose, we designed a simulation platform which has the capabilities to simulate all the functions of a single sensor, and the functions of information exchange between different sensors.  
To simulate the functions of a single sensor, we designed and coded MSN/SSN units based on the TinyOS system. TinyOS is an open-source operating system designed for wireless embedded sensor networks. It features a component-based architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks [31]. To realize all the functions that a MSN or SSN requires, we have to choose suitable system components that the TinyOS system provides, as well as design reasonable user/application components based on the functional descriptions listed in section 4.3. Besides, we need to use different interfaces to link all the components to make them work together.  
According to the functional description, we designed MSN and SSN in TinyOS system as shown in Figure 6 (a) and (b). Here, the grey square stands for a component; the shadow square is the interface that a component provides; the dotted square is the interface that a component uses; the line with a black arrow means a command sending from the tail of the arrow to the head of the arrow; and the line with a white arrow means an event informing from the tail of the arrow to the head of the arrow.  
The components that a MSN uses are Main component (which is the access point of all programs); the user/application component MSN (which executes all the functions of a MSN); the sampling timer component TimerC; the sampling and A/D conversion component DemoSensorC and the data transmission and multi-hop routing component AODV. Similarly, the components that a SSN uses are Main, SSN, TimerC (which is used for polling timing) and AODV. Here, the AODV is the acronym of Ad hoc On-Demand Distance Vector Routing [32]. This is an on-demand distance vector routing protocol which can support the multi-hop routing scheme and are widely used in the Ad hoc networks.  
Figure 6: The Structure Framework of MSN and SSN.  
The AODV component is provided by the TinyOS system which helps to realize the sending and receiving of the control information and data messages easily between different sensor units.