esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods  
### Data Collection  
Twenty-four subjects participated in 207 different experiments, which consisted of three different physical states (PS) (sedentary state (SS), and two different exercises: treadmill run (TR) and stationary bike (SB)) and three psychological states (non-stressful (NS) and two different APS inducements: exciting-anxiety stress (EAS) and mental stress (MS). Experiments were conducted at the Illinois Institute of Technology under an Institutional Review Board (IRB)-approved protocol. The types of APS inducements were determined based on a literature review of APS inducement research and consultations with a psychologist at the University of Illinois at Chicago. SS activities were separated into three categories based on different APS inducements: SS-non-stress (SS-NS) experiments involved subjects watching a video, resting at home, laying down without APS inducement, and reading a book; SS-mental stress (SS-MS) experiments consisted of multiplications of two-digit numbers under time constraints [11; 39; 40], solving puzzles, taking IQ or Stroop tests [11; 41; 42], and playing chess; SS-exciting-anxiety stress (SS-EAS) experiments included watching thriller/horror movies [11; 43; 44], taking a class exam that did not involve mathematical calculations (to reduce MS) [45], participating in research meetings with advisor, making a presentation to an audience, and playing a video game while sitting [44; 46]. TR experiments were conducted on a treadmill while running at a speed in the range of 2.5 to 7.0 mph. The speed was determined by the desired speed of the subject. The TR activities were separated into three categories based on different APS inducements: TR-NS experiments were conducted during TR exercise while listening to calming music or watching natural videos; TR-EAS experiments consisted of watching surgery or car-crash videos during TR exercise; TR-MS experiments were conducted by asking for the mental multiplication of two-digit numbers under time constraintswhile running on the treadmill [11, 39, 40]. APS inducements during stationary bike exercise were similar to implementations in the TR-APS protocol. Information on the experiments is presented in Table 1 and Figure 3.  
EE is the total amount of energy an individual uses to maintain essential body functions and the energy expended as a result of PA. The gold standard measurement of EE is indirect calorimeter. However, collecting these measurements is uncomfortable, inexpensive, and practical for daily life usage. Previous studies showed that EE can be estimated with wearable devices using biosignals such
## Passive Radars
### Illuminator of Opportunity Selection: DVB-T Signals  
Most of the PRS parameters depend on IoO characteristics: availability, radiated power, coverage area, transmitted waveform. In the passive radar literature, digital commercial waveforms have been extensively analyzed to determine their impact on system resolution, detection ambiguities and expected coverage: DAB [20], Global System for Mobile (GSM) [21], Universal Mobile Telecommunication System (UMTS) [22] and DVB-T, the European Union standard, [23; 24].  
The Ambiguity Function (AF) is a powerful tool to analyze the detection capabilities of radar signals [25]. The AF allows the estimation of the bistatic range and Doppler shift of the target and provides the necessary signal processing gain to allow the target echo detection, performing as a matched filter for the radar system.  
DVB-T is the digital terrestrial television broadcast transmission standard most used in the European Union [26] and has been used as IoO in this study. This system transmits compressed digital audio, digital video and other data in an MPEG transport stream, using Coded Orthogonal Frequency-Division Multiplexing (COFDM) modulation. COFDM works by splitting the digital data stream into a large number of digital streams, which digitally modulates a set of closely-spaced adjacent sub-carrier frequencies. In the case of DVB-T, there are two options for the number of carriers, known as 2 K-mode or 8 K-mode.  
DVB-T signals are composed of a random component, generated by the interleaving and the OFDM modulation of the MPEG-2 signal, and a deterministic component generated by the presence of signaling elements (Guard Interval (GI), Pilot Carriers (PC) and Transport Parameter Signaling (TPS)). Deterministic components of the DVB-T signal give rise to ambiguity peaks in the AF. The range of ambiguities is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **GI Peaks (km)** & **Scattered PC (km)** & **Continual PC (km)** & **TPS (km)** \\ \hline
2 k, \(T_{ll}=224\)\(\mu\)s & Every 33.6 & Every 2.8 \& 138.6 & Every 11.2 \& 34.65 & 34.65 \\
8 k, \(T_{ll}=896\)\(\mu\)s & Every 134.4 & Every 11.2 \& 554.4 & Every 44.8 \& 138.6 & 138.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ambiguity peaks location in the range domain for the 2 k and 8 k modes, due to Guard Interval (GI), Pilot Carriers (PC) and Transport Parameter Signaling (TPS).  
In the scenario under analysis, located in Madrid (Spain), the 8 K mode was adopted. In Figure 3, the zero Doppler cut of the AF of a single channel DVB-T signal is presented. The expected ambiguity peaks are shown together with the main peak located at the range-Doppler plane origin.
## 5 Discussion  
DTs connect the physical world with the virtual space and create the real world in the virtual space by using historical data, real-time data, and algorithmic models. This technology will greatly promote the comprehensive situation analysis of factories, equip ment operation and maintenance, production line operation, production management, and other changes and will become the technical basis and development trend of intelligent manufacturing, \"Industry 4.0\". From the construction of the \"urban brain\" and SC to the promotion of the \"digital transformation\" of cities and the recently discussed DTs cities, digitalization has not only been a concern of science and technology circles and entrepreneurs but has also become a topic for urban administrators of all countries. DTs SC can integrate the fragmented information of the IoT, solve all the needs and pain points at one site, and build a new map of the IoT industry in the form of a metaverse.  
The digital wave represented by the Internet of Things, big data, artificial intelligence, and other new technologies have swept the world. The physical world and the information world are developing in parallel and interacting, and the construction of DTs cities has become unstoppable. The development of DTs SC needs good urban planning, but various urban problems caused by unreasonable urban planning still exist significantly. DTs visualization technology can make things that cost relatively high in reality and are difficult to achieve through short-term experiments quickly, low cost, and multiple implementations in virtual entities. The model construction of various schemes on the virtual platform can be used to guide the construction in reality, which can be used to deduce urban planning. By setting spatial and event thresholds, it can be judged whether the city is different from the planned goals in its development. Therefore, in the application of urban planning, it is necessary to record the construction phase of the full cycle and operate after the construction is completed. For the application of DTs technology, it is necessary to record all elements in the dimensions of space and time. Through the construction of the DTs city platform, the perception and decision-making ability of the city can be improved, and future planning and development can also bring a broader vision. Under the premise that all social subjects can have access to the DTs platform, the DTs technology creates a collaborative environment of multiple cooperation and co-governance for all subjects of urban risk management, which can improve the social identity and the sense of belonging of the public, and improve their enthusiasm to participate in urban risk management.
###### Abstract  
Numerical investigation on a nanosecond repetitively pulsed dielectric barrier discharge (NS-DBD) in air is a temporal and spatial multi-scale problem involving a large number of species and chemical reactions. To know the effects of the species and chemical reactions on the discharge characteristics and energy balance, a high voltage repetitive plane to plane NS-DBD is numerically studied. Four groups of species and the corresponding chemical reactions are adopted in the investigation. The most complex one has 31 species and 99 chemical reactions that contains all reaction types, in particular, the vibrational-translational relaxation reactions, whereas the simplest one has only 4 species and 4 reactions, which represents the main kinetic processes. The others are in between. The discharge energy reaches to a periodic phase equality state after the second pulse in the repetitive pulses, and the present analysis is focused on the 7 th pulse. All the \(N_{2}/O_{2}\) mixture reaction models predict almost the same discharge energies, which are qualitatively similar with that in the simplified 4-species model. The prediction of the discharge energy is determined by the electronic excitation and the energy gain by ions, but the vibrational excitation, negative ions, associative ionization, dissociation of nitrogen and oxygen molecules have very weak effects. The gas heating is determined by the exothermic reactions and the ions. The main processes in the fast and slow gas heating are the energy release of ions and the exothermic reactions, respectively. The negative ions, vibrational excitation, and associative ionization have very weak effects on the gas heating during the high voltage pulse, but they have considerable effects at a larger time scale. The magnitudes of the fast gas heating efficiency (\(\eta_{GH}\)) are in the range of 41%\(\sim\)47% in the \(N_{2}/O_{2}\) mixture reduced kinetic models, but \(\eta_{GH}\) is higher in the 4-reaction model.  
Keywords:nanosecond pulsed dielectric barrier discharge; chemical reactions; discharge characteristics; energy balance; gas heating +
Footnote †: journal: Applied Physics Letters
## 3 Multi-AGV Path Planning Based on Time Window
### Types and Handling Strategies of Multiple AGV Path Conflicts  
AGV path conflicts can be divided into three categories: directional conflicts, grid conflicts, and fixed-point conflicts [29], as shown in Figure 5. The shaded areas in the figure represent obstacle grid cells, different colored dots represent AGVs, and arrows represent the direction of AGV travel in that color:  
1. Grid conflict occurs when two or more AGVs meet at the same grid location at the same time. There are roughly four types of grid conflicts, as shown in Figure 6a-d. The first and second types of grid conflicts have overlapping AGV transportation paths, and it is necessary to redefine the AGV path to avoid the occurrence of conflicts. The third and fourth types of grid conflicts can be avoided by determining the priority order of traffic, as there are no overlapping transportation paths outside the conflicting grid cell;
2. Fixed-point conflict occurs when the AGV is loading and unloading goods or experiencing sudden malfunctions, and the position of the AGV does not change in a short period of time. When other AGVs pass through the grid cell, fixed point conflict occurs, as shown in Figure 6e. At this point, it is necessary to set the fixed-point grid cell as an obstacle cell and replan the path of the AGVs to avoid conflicts.  
Figure 6: Schematic diagram of AGV conflict.  
In response to the above characteristics of conflict, the method of setting priorities for the AGV can be used to avoid collision of AGV paths in a predetermined grid cell:  
1. Replan the path scheme: When there is a conflict between the first and second types of grids, the AGV with higher priority maintains its original path scheme through the grid. The AGV with low priority sets the conflict grid cell as an obstacle cell in the grid graph, starts from the current position, replans the route, and drives according to the new path scheme.  
When a fixed-point conflict occurs, the AGV that is malfunctioning or loading and unloading goods remains stationary. The AGV traveling in a normal manner designates the grid cell containing the stationary AGV as an obstacle cell and initiates path replanning from its current position to follow the newly planned path.
2. AGV waiting scheme: When conflicts occur between the third and fourth types of grids, the AGV with higher priority maintains the original path scheme through the grid. The low-priority AGV waits in place, and after the high-priority AGV passes, it passes through the conflict grid cell according to the original path plan.
# A Study on the Amplitude Comparison Monopulse Algorithm
## 2 Conventional Amplitude Comparison Monopulse Algorithm
Under the assumption that all patterns are in phase, the gain functions of the antenna patterns directed into the other quadrants are approximately computed as  
\[g_{1}(\theta_{az},\theta_{el})\approx g_{0}\left[1+\frac{k_{m}}{\theta_{3 dB} }\ (\theta_{az}+\theta_{el})\right] \tag{8}\]  
\[g_{2}(\theta_{az},\theta_{el})\approx g_{0}\left[1+\frac{k_{m}}{\theta_{3 dB}} \ (-\theta_{az}+\theta_{el})\right] \tag{9}\]  
\[g_{3}(\theta_{az},\theta_{el})\approx g_{0}\left[1-\frac{k_{m}}{\theta_{3 dB}} \ (\theta_{az}+\theta_{el})\right] \tag{10}\]  
\[g_{4}(\theta_{az},\theta_{el})\approx g_{0}\left[1+\frac{k_{m}}{\theta_{3 dB}} \ (\theta_{az}-\theta_{el})\right] \tag{11}\]  
Then, the sum (\(\Sigma\)), horizontal difference (\(\Delta_{az}\)), and vertical difference (\(\Delta_{el}\)) voltages are calculated by  
\[\Sigma=A[g_{1}+g_{2}+g_{3}+g_{4}]=4 Ag_{0} \tag{12}\]  
\[\Delta_{az}=A[g_{1}+g_{4}-(g_{2}+g_{3})]=4 Ag_{0}\frac{k_{m}}{\theta_{3 dB}} \theta_{az} \tag{13}\]  
\[g_{1}(\theta)=g_{1}(\theta_{az},\theta_{el})\ \approx\ g(\theta_{s})\ \exp\left[2\sqrt{2}\left(\frac{ \theta_{s}}{\theta_{3 dB}}\right)\left(\frac{\theta_{az}+\theta_{el}}{\theta_{3 dB }}\right)\log 2\right] \tag{5}\]0  
Therefore, the azimuth and elevation angle deviations can be estimated as  
\[g_{1}(\theta)=g_{1}(\theta_{az},\theta_{el})\ \approx\ g(\theta_{s})\ \exp\left[2\sqrt{2}\left(\frac{ \theta_{s}}{\theta_{3 dB}}\right)\left(\frac{\theta_{az}+\theta_{el}}{\theta_{3 dB }}\right)\log 2\right] \tag{5}\]1  
\[g_{1}(\theta)=g_{1}(\theta_{az},\theta_{el})\ \approx\ g(\theta_{s})\ \exp\left[2\sqrt{2}\left(\frac{ \theta_{s}}{\theta_{3 dB}}\right)\left(\frac{\theta_{az}+\theta_{el}}{\theta_{3 dB }}\right)\log 2\right] \tag{5}\]2  
where \(\hat{\theta}_{az}^{c}\) and \(\hat{\theta}_{el}^{c}\) are the estimates of azimuth and elevation angle deviations, respectively, which represent the unknown target location estimate.
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 4 Experiments and Discussion
### Baseline Methods  
To verify the predicting performance of TEGCRN, we compare it with typical time-series models and some representative graph-based spatiotemporal networks. The selected baselines are introduced as follows:  
* HA : Historical Average, which views the traffic flow as seasonal signals and predicts future speed with the average of the values at the same time slots of previous weeks.
* ARIMA: Auto-Regressive Integrated Moving Average model with Kalman filter, a traditional time-series predicting method.
* FC-LSTM [37]: RNN sequence-to-sequence model with LSTM units, using a fully-connected layer when calculating the inner hidden vectors.
* DCRNN [12]: Diffusion Convolutional Recurrent Neural Network, which combines the bidirectional graph convolution with GRU encoder-decoder model. Only the distance-based graph is utilized and the adjacency in different time steps remains static.
* STGCN [13]: Spatiotemporal Graph Convolutional Network, which alternately stacks temporal 1-D CNN with spectral graph convolution ChebyNet to form a predicting framework in a complete convolutional manner. Likewise, it relies only on the distance-based static graph.
* Graph-WaveNet [14]: Based on the alternate convolutional architecture in [13], this model applies dilated convolution in the time dimension and incorporates an adaptive adjacency graph in the graph convolution module.
* MTGNN [21]: This model introduces a uni-directional adaptive graph and mix-hop propagation into graph convolutions, and introduces dilated inception layers on the time dimension to capture features in receptive fields of multiple sizes.
# Application of Infrared Thermography in E-Bike Battery Pack Detail Analysis--Case Study
## 3 Analysis and Diagnostics of the Battery Pack
### Thermography in the Battery State Assessment and Analysis
The reason for building up the heat in some cells is supported by the fact that ohmic resistance of all cells in the block is not approximately equal anymore--i.e., it is noticeably different. Instead of equally distributed charge current among all (four) cells within the block, the current is unevenly, reversely proportionally distributed through the cells according to their ohmic resistance. In our case, only two cells take all charge current, and other two in the cell block have significantly higher ohmic resistance. Contrary to intuition with infrared thermography, the healthy cells warm up while the defective cells look cooler, Figure 11.  
Figure 5.0 Principal structural diagram and electric schematic of 4p10s battery pack. Figure 5.1 Disassembled battery pack (**a**), cells that produce the hot spots (**b**), a BMS circuitry (**c**). Further, in order to determine the condition of other cells in cellblocks, a different indirect thermal and electrical parameter observation method had to be used. For this purpose, a balancing resistor thermal throttling observation combined with the thermal imaging analysis was applied. The thermogram shown in Figure 12a illustrates that there is no dissipation on the two resistors, which means that only two parallel connections are still in the charging process and that most of the parallel connections are in a similar state. Additionally, that points to the fact that the battery blocks are not homogeneous in their electrical (resistive/ohmic) properties. Figure 12 visually describes the dynamics of BMS operation as well as dissipation, which leads to an increase in the pack ambient temperature of 6 \({}^{\circ}\)C, while the maximum temperature of the active resistor at the time of recording increases by 19.6 \({}^{\circ}\)C.
# Analysis of Subharmonic Oscillation and Slope Compensation for a Differential Boost Inverter
## 4 Continuous-Time Modeling of the Differential Boost Inverter
### The State-Space Switched Model  
Let \(\mathbf{x}=(i_{1},\,i_{2},\,v_{o1},\,v_{o2},)^{\intercal}\) be the vector of the state variables of the power stage of the inverter. The system can be described by a piecewise linear switched model as follows\[\dot{\mathbf{x}} = \mathbf{A}_{1}\mathbf{x}+\mathbf{B}_{1}V_{g},\quad\text{for}\quad u =1, \tag{10}\] \[\dot{\mathbf{x}} = \mathbf{A}_{0}\mathbf{x}+\mathbf{B}_{0}V_{g},\quad\text{for}\quad u =0,\] (11) \[\dot{v}_{i} = v_{\text{ref}}-(v_{o1}-v_{o2})=v_{\text{ref}}-\mathbf{C}^{\intercal }\mathbf{x} \tag{12}\]  
where \(\mathbf{C}^{\intercal}=(0\quad 0\quad 1\quad-1)\) and \(v_{i}:=\int(r_{\text{ref}}-v_{o})\text{d}t\) is the integral of the error signal \(v_{\text{ref}}-v_{o}\). \(\mathbf{A}_{0}\in\mathbb{R}^{4\times 4}\), \(\mathbf{A}_{1}\in\mathbb{R}^{4\times 4}\), \(\mathbf{B}_{0}\in\mathbb{R}^{4\times 1}\) and \(\mathbf{B}_{1}\in\mathbb{R}^{4\times 1}\) are the system state matrices presented below. The variable \(v_{i}\) was deliberately separated from the rest of state variables to avoid matrix singularities appearing in the expressions of the system trajectories and their steady-state values at the switching time instants [25; 26]. The matrices \(\mathbf{A}_{1}\), \(\mathbf{A}_{0}\), \(\mathbf{B}_{1}\) and \(\mathbf{B}_{0}\) are as follows:  
\[\mathbf{A}_{1}=\left(\begin{array}{cccc}-\dfrac{r_{1}}{L_{1}}&0&0&0\\ 0&-\dfrac{r_{2}}{L_{2}}&0&-\dfrac{1}{L_{2}}\\ 0&0&-\dfrac{1}{RC_{1}}&\dfrac{1}{RC_{1}}\\ 0&\dfrac{1}{C_{2}}&\dfrac{1}{RC_{2}}&-\dfrac{1}{RC_{2}}\end{array}\right), \qquad\mathbf{B}_{1}=\left(\begin{array}{c}\dfrac{1}{L_{1}}\\ \dfrac{1}{L_{2}}\\ 0\\ 0\end{array}\right) \tag{13}\]  
\[\mathbf{A}_{0}=\left(\begin{array}{cccc}-\dfrac{r_{1}}{L_{1}}&0&-\dfrac{1}{ L_{1}}&0\\ 0&-\dfrac{r_{2}}{L_{2}}&0&0\\ \dfrac{1}{C_{1}}&0&-\dfrac{1}{RC_{1}}&\dfrac{1}{RC_{1}}\\ 0&0&\dfrac{1}{RC_{2}}&-\dfrac{1}{RC_{2}}\end{array}\right),\qquad\mathbf{B}_ {0}=\left(\begin{array}{c}\dfrac{1}{L_{1}}\\ \dfrac{1}{L_{2}}\\ 0\\ 0\end{array}\right) \tag{14}\]  
The output of the voltage PI voltage controller providing the current reference for the control signal \(r_{s}(i_{1}-i_{2})\) can be expressed as follows  
\[r_{s}i_{\text{ref}} = k_{p}(v_{\text{ref}}-\mathbf{C}^{\intercal}\mathbf{x})+W_{i}v_ {i}, \tag{15}\]  
Therefore, the switching condition when the signal \(r_{s}(i_{1}-i_{2})\) reaches its peak value \(r_{s}i_{\text{ref}}-v_{\text{ramp}}\) within a switching cycle is given by  
\[k_{p}(v_{\text{ref}}-\mathbf{C}^{\intercal}\mathbf{x})+W_{i}v_{i}-(v_{\text{ ramp}}(t)+r_{s}(i_{1}-i_{2}))=0, \tag{16}\]  
which can be expressed in the following form  
\[k_{p}v_{\text{ref}}+\mathbf{K}\mathbf{x}(t)+W_{i}v_{i}(t)-v_{\text{ramp}}(t)=0, \tag{17}\]  
where \(\mathbf{K}=(-r_{s}\quad r_{s}\quad-k_{p}\quad k_{p})\) is the vector of feedback coefficients.