esa-sceva/satcom-chunk-collection huggingface dataset:
# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
## 3 Longitudinal Speed Tracking Controller Design
### ADRC Controller Design
The state space equation of the ESO is:  
\[\left\{ \begin{array}{l}\dot{z}=Az+B\theta_{thr}+L(y-\hat{y})\\ \dot{y}=Cz\end{array} \right. \tag{22}\]  
where \(L=\left[\beta_{1}\beta_{2}\beta_{3}\right]^{T}\) is the observer gain vector. To simplify parameter tuning process, [PERSON] et al. [30] used the method of pole configuration that set \(L=\left[3\omega_{0}\ 3{\omega_{0}}^{2}\ \omega_{0}^{3}\right]^{T}\). \(\omega_{0}\) is called the observer bandwidth. The idea of ADRC is to have \(f\) estimated and canceled, thus leaving an integral plant that can be easily controlled. The vehicle longitudinal system is simplified to a simplest second order system, and an ESO will estimate the real value of total disturbance \(f\). Thus, the system is rewritten as:  
\[\ddot{v}=u_{0}\approx b_{0}\theta_{thr}+f \tag{23}\]  
We can obtain the estimated value of \(f\) by the ESO, and we can neglect the error between them:  
\[\ddot{v}=f+b_{0}\theta_{thr}=(f-\hat{f})+u_{0}=(f-z_{3})+u_{0}\approx u_{0} \tag{24}\]  
where \(\hat{f}\) is the estimated value of \(f\). The vehicle longitudinal system is simplified to a series integral system, and it can be controlled by a proportional-differential (PD) controller. \[u_{0}=k_{p}(v_{aim}-\hat{v})-k_{d}\hat{\hat{v}} \tag{25}\]  
where \(\hat{v}\) and \(\hat{\hat{v}}\) are the estimated value of vehicle speed and vehicle acceleration, respectively, and \(v_{aim}\) is the target vehicle speed. The control law of ADRC is:  
\[\theta_{fb}=\frac{k_{p}(v_{aim}-z_{1})-k_{d}z_{2}-\int{\omega_{o}}^{3}(v_{act }-z_{1})dt}{b_{0}} \tag{26}\]  
where \(\theta_{fb}\) is the throttle angle calculated by ADRC and \(k_{1}\) and \(k_{2}\) are parameters of ADRC. We set \(k_{p}=2\omega_{c}\), \(k_{d}={\omega_{c}}^{2}\), and \(\omega_{c}\) is called the controller bandwidth.
## 2 Basic Structure of the SA Sensor  
### Fabricaiton Process and Mechanism of Sensing  
Figures 1 and 2 show the basic structure of the SA sensor [14]. The SA sensor roughly comprises of four parts: a sensor chip, polydimethylsiloxane (PDMS), a printed circuit board (PCB) and an Au/Cr electrode. A very thin 300 nm beam is formed on the center of a Si sensor chip. The sensor chip was fabricated from Silicon on Insulator (SOI) wafer to facilitate forming thin beam. At the first step of the fabrication process, the n-type piezoresistive area was formed with either Ion implantation or thermal diffusion on the surface of the wafer. Then, the Au/Cr electrode was deposited and patterned with inductively coupled plasma reactive ion etching (ICP-RIE). The double-supported beam was also made during the ICP-RIE process. The Au layer was etched again to expose the piezoresistive area and form the electrode. The backside Si layer was etched as a cylindrical shape with ICP-RIE, and the SiO\({}_{2}\) layer was etched with HF vapor to release the beam. On the surface of the chip, hydrophobic CYTOP (Asahi Glass Co., Ltd., Chiyoda-ku, Tokyo, Japan) was shaped as a band ring pattern. The liquid (silicone oil) was dropped on the beam. Taking advantage of the surface tension of the silicone oil and properly limiting the size of the gap between the beam and surrounding chip below 1 \(\upmu\)m, leakage of the oil was completely prevented. Furthermore, due to the hydrophobicity of the CYTOP ring, an appropriate amount of liquid was automatically kept inside the ring. The parylene film was deposited on the chip surface to stabilize the liquid. The chip was bonded to the PCB with adhesive material, and both chip and PCB electrodes were wire-bonded. Finally, an appropriate amount of PDMS was mounted on the parylene film to form a fixed area to receive the lower frequency vibrations.  
To keep the preferred sound impedance matching, the PCB side of the sensor was placed on a measuring object. When an acoustic emission signal is propagated into the sensor from the PCB side, the beam vibrates in accordance with the liquid surface. The advantage of this structure is that the vibration in accordance with the liquid surface is completely independent of the intrinsic eigenfrequency of the beam in the air. By measuring the shape of the beam with a laser scanning vibrometer, it was revealed that the vibration shape of the beam was dominated by the surface wave of the liquid, rather than its structural eigenfrequency. As a result, the sensor can receive quite a wide frequency range of vibrations, which cannot easily be achieved by resonant-type MEMS sensors.
## 4 Methods
### Relationship Between the Number of Vehicles and Complexity  
The number of vehicles serves as the foundation for determining the complexity of an urban intersection scenario. A higher number of vehicles typically indicates a potentially more complex interaction scenario. However, due to the sequential driving characteristics of intersections, not all vehicles contribute equally to the complexity. As illustrated in Figure 2, vehicles at the back of the queue must wait outside the intersection until the vehicles in front  
Figure 1: Illustrative Diagram of an Urban Unsignalized Intersection Scenario.  
depart and vacate space before they can enter. Consequently, the primary contributors to the complexity of the scene are the vehicles within the intersection. Additionally, to account for the dynamic nature of the scene, we calculate the average number of vehicles in the intersection per unit of time as the final count of vehicles present, which is as follows:  
\[N_{\text{inter}}=\frac{1}{T}\sum_{t=1}^{T}(N_{\text{total}}\left(t\right)), \tag{2}\]  
where \(N_{\text{total}}\left(t\right)\) represents the total number of vehicles in the intersection area at time \(t\)  
The relationship between the number of vehicles and the complexity of the intersection is nonlinear. When there are few vehicles, the impact on complexity is minimal. As the number of vehicles reaches a moderate level, complexity increases sharply. However, when the number of vehicles becomes too high, the impact on complexity gradually diminishes. As shown in Figure 3, this phenomenon is analogous to population growth in biology, which is characterized by an S-shaped curve. Therefore, the logistic function is used in this study to describe the relationship between the number of vehicles in the scenario and complexity, which is as follows:  
\[f_{1}(N_{inter})=\frac{1}{1+e^{-k_{1}*N_{inter}}}, \tag{3}\]  
where \(k_{1}\) is the growth rate parameter of the logistic function, which can be adjusted according to the road structure and geometric dimensions of different intersections. It is important to maintain a consistent \(k_{1}\) value for road structures with the same dimensions to ensure the model's evaluation results remain consistent across similar scenarios.  
Figure 2: Schematic diagram illustrating the division between the inner and outer areas of an urban intersection.  
In addition, since the value range of the variable \(N_{inter}\) is \([0,+\infty)\), we need to map it to the input range of the logistic function \([\mathrm{x}_{min},\mathrm{x}_{max}]\) using a linear transformation. The values of \(\mathrm{x}_{min}\) and \(\mathrm{x}_{max}\) are determined by solving the logistic function such that \(f_{1}(x)<0.01\) and \(1-f_{1}(x)<0.01\), which represent the limit values of the function approaching 0 and 1, respectively.
## 5 Challenges and Open Issues
### Challenges/Open issues for Routing Protocols  
QoS-based routing protocols show the following shortcomings/open issues:  
Scalability is the main issue for these protocols. Routing updates and maintenance mechanisms create overheads due to inappropriate dissemination and \"hello\" message sizes. The timing of route updates is ignored, which ultimately affects mobility support. Most QoS-based routing protocols ignore the congestion control and avoidance mechanismsin conjunction with prioritised QoS. Route failure cases are ignored, and packets are dropped in the case where a node does not provide the required QoS.  
Cross-layer routing protocols show the following shortcomings/open issues:  
The heterogeneous nature of body sensors demands prioritised QoS, which is not considered in these protocols. Scalability is a challenge for these protocols as their performance decreases in dense scenarios. Routing updates and maintenance mechanisms create overheads due to inappropriate dissemination. Mobility effects are ignored, whereas most of the WBASN applications demand mobility. Link reliability consideration is ignored.  
Cluster-based routing protocols show the following shortcomings/open issues:  
The heterogeneous nature of body sensors demands prioritised QoS, which is not considered in these protocols and, hence, not appropriate for most WBASN scenarios. In large clusters, the cluster-head selection algorithm causes energy overheads.  
Link-quality-based routing protocols show the following shortcomings/open issues:  
Link quality is usually measured as a single value, such as a received signal strength indicator (RSSI) or link quality indicator (LQI). However, LQI/RSSI only represents a snapshot at a specific point in time for one link between two nodes and lacks any additional information about the remaining energy, hop count and end-to-end information. Thus, there is still an urgent need to find a reliable scheme to estimate the end-to-end link quality based on information from different layers.
## 4 Efficient Intersection Solver
### Origin-in-Triangle Check  
Once rotated, it is necessary to check if the origin is enclosed by the facet's 3 vertices in the \(y\)-\(z\) plane. For independent inputs, the ACS is a hyper-cuboid in \(\mathbb{R}^{m}\) control space, when mapped to \(\mathbb{R}^{3}\) moment space, some edges of the ACS mapped to the interior of the AMS through the null space of the \(\mathbf{B}\) matrix, and parallel edges remains parallel through linear mapping. Therefore, the facets of AMS in \(\mathbb{R}^{3}\) space are parallelograms [2]. By using the MATLAB command _convhull_, each parallelogram facet is parameterized as two triangles in \(\mathbb{R}^{3}\) space, which directly serves our purpose of the origin-in-triangle check.  
Keep in mind that only facets in the positive \(x\)-axis domain after rotation should be considered As denoted in Figure 5, the easiest way to find if the origin is enclosed by triangle \(P_{1}P_{2}P_{3}\) is to calculate the barycentric coordinates of origin expressed by vector \(P_{1}P_{2}\) and \(P_{1}P_{3}\), then check if both coordinates are positive and their sum is smaller than 1. Therefore, if coordinate of \(P_{i}\) in the \(y\)-\(z\) plane is \((y_{i},z_{i})\)\((i=1,2,3)\), then the barycentric coordinate of origin is calculated by:  
\[\begin{array}{l}u=\frac{y_{1}(z_{1}-z_{3})+z_{1}(y_{3}-y_{1})}{(y_{2}-y_{1}) _{2}(z_{2}-z_{2})-(y_{3}-y_{1})(z_{2}-z_{1})}\\ v=\frac{-z_{1}(y_{1}-y_{2})+z_{1}(z_{2}-z_{1})}{(y_{2}-y_{1})(z_{2}-z_{1})-(y_ {3}-y_{1})(z_{2}-z_{1})}\end{array}, \tag{7}\]  
Figure 4: A brief process of the intersection solver. The process is to determine whether a facet is a candidate to solve for, and immediately loop to the next if it is not. This considerably speeds up the optimization compared to a brutal search.  
If \(u\geq 0,\ v\geq 0\) and \(u+v\leq 1\), then the origin is enclosed by triangle \(P_{1}P_{2}P_{3}\) in the \(y\)-\(z\) plane.
## 3 Results
### Spatial Resolution with the FLUKA Simulation  
We performed three sets of simulations to compare the experimental values for both the ESF and LSF methods. The study includes the simulation of the 2.5 cm thick lead block described in Section 3.2.1, the 3 mm thick copper plate with 5 mm diameter holes (Section 3.2.2), and the 1 mm thick copper plate (Section 3.3.2). The latter had 23 holes of several sizes and spacing, simulated in such a way that the central hole was aligned to the center of the active area of the image sensor. The 3 mm thick plate was simulated at 7 mm from the Mylar window. The lead block and the 1 mm thick copper plate were simulated at 4 cm and 3.3 cm from the Mylar window, respectively. Figure 14 shows two of the simulated set-ups. In all cases, the source-detector distance was fixed at 230 cm with the minimum collimator aperture of 1 cm.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 6 Simulation Results  
In this section, we present simulation results to evaluate the performance of the proposed online resource allocation algorithm. We consider a BAN system composed of five medical sensors. We set the system time \(T=5000\), the processing gain \(K\) is uniformly distributed between (0.9, 1.1), the maximum collecting data rate \(c^{max}=50\), the maximum transmission power \(p^{max}=0.02\) W, the upper bound of energy supply \(e^{max}=2\); \(\alpha=5\times 10^{-4}\) and upper bound of energy consumption \(a^{max}=0.04\). Similar to [23], we define a concave utility function \(U(c_{n}(t))=log(1+c_{n}(t)),\forall n\in N\) and \(\
u=1\).  
Figure 3 presents the data queue dynamics with different \(V\). As we can see from the figure, the larger the \(V\), the longer the data queue length. This means that more data buffer is needed. After a period of time slot, the data queue is stable around a fixed value with small range of fluctuation, as shown in Equation (17). This means the BAN system is stable. In the initial stage, the data queue is very short, and the battery is fully charged. It can allow collecting a lot of data, so the data queues are growing very fast.  
Figure 4 presents the energy queue dynamics with different \(V\). As we can see from the figure, the energy queue is in a steady fluctuation. This is because the sensor consumes energy when it collects data and transmits data, and the process of harvesting energy is time varying and random. In addition,  
we can know that length of the energy queue will not exceed the capacity of the battery. In time slot \(t=0\), the value of energy queue is equal to the battery capacity, that is, the battery is full of energy.  
Figures 5 and 6 present the time-average data queue length and energy queue length. From these figures, we can see that the time-average queue length increases linearly with the increase of weight \(V\), as shown in Equations (16) and (17). From Figures 3-6, we can get that the queue quickly stabilized around a time-average length. From Figures 5-7, we can get that, if we want to get more user utility, we will need a longer data buffer and a larger battery capacity.  
Figure 4: Energy queue dynamics.  
Figure 3: Data queue dynamics.  
Figure 7 shows the user utility. Under different values of \(V\) ranging from 100 to 500, the user utility increases from 1.8 to 3.1. We can see that the user utility grows rapidly with the increase of \(V\) in the initial stage, and then the growth of user utility slows down. This shows that user utility is a concave function of \(V\), as shown in Equation (18).  
Figure 5: Time-average data queue length.  
Figure 6: Time-average energy queue length.
## 3 DR and AGC Unit Capabilities
### Modeling of Residential Smart Appliances
#### 3.1.3 EV Model  
Here, an on-off strategy is used for EV response, which means that each EV is charged by a constant and maximum power. The benefits of charging with an on-off strategy instead of adjustable power are as follows: First of all, it was suggested that charging the EV with a constant power could prolong the battery's service time. Secondly, smaller communication overhead is required to contact a small subset of EVs, and hence it is more practical to turn charging on or off rather than adjusting the charging rate when a large amount of EV charging is scheduled. Finally, it is expected that using an on-off strategy can fully charge the EVs in a shorter timeframe [24].  
To model EV charging profiles, three parameters are essential: the rated charging power, the plug-in time, and the battery state-of-charge (SOC). The plug-in time is related to the time of vehicle arrival at home and arrival at work.  
The calculation of the EV charging profile is described in Equation (8):  
\[p_{\text{EV}}(t)=P_{\text{EV}}\cdot N_{\text{EV}}(t)\cdot w_{\text{EV}}(t) \cdot s_{\text{EV}}(t) \tag{8}\]  
where \(p_{\text{EV}}(t)\) is the EV charge power in time interval \(t\) (kW); \(P_{\text{EV}}\) is the EV rated power (kW); \(N_{\text{EV}}(t)\) is the EV connectivity status in time interval \(t\) (\"1\" if EV is connected to the plug and \"0\" if EV is not connected); \(w_{\text{EV}}(t)\) is the EV charging status without control in time interval \(t\), which depends on the battery SOC as shown in (9) (\"0\" if EV is not being charged and \"1\" if EV is being charged); and \(s_{\text{EV}}(t)\) is the DR control signal for EV in time interval \(t\), 0 = OFF, 1 = ON.  
\[w_{\text{EV}}(t)=\left\{\begin{array}{ccc}0,&SOC(t)\geq SOC_{\text{min}}\\ 1,&SOC(t)<SOC_{\text{min}}\end{array}\right. \tag{9}\]  
where \(SOC(t)\) is the state of charge in time interval \(t\), and \(SOC_{\text{min}}\) is the minimum SOC limit of EV at the desired finish time.  
The battery SOC after EV charging completes should fulfill customers' demand, which is determined by:  
\[SOC_{0}\geq 1-\frac{L}{E_{\rm EV}\cdot Q_{\rm EV}} \tag{10}\]  
where \(SOC_{0}\) is the initial SOC of EV; \(L\) is the travel distance of EV (mile); \(E_{\rm EV}\) is the efficiency of driving (mile/kWh); and \(Q_{\rm EV}\) is the full capacity of battery (kWh).  
The EV battery charging model is as follows:  
\[SOC(t+1)=SOC(t)+\frac{p_{\rm EV}(t)\cdot\Delta t}{Q_{\rm EV}}\cdot\eta \tag{11}\]  
where \(Q_{\rm EV}\) is the full battery capacity (kWh), and \(\eta\) is the coefficient of charging.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 4 Experiment
### Experimental Settings  
Our experiments are based on the Python language and the TensorFlow platform. We used LTP as the tool for word segmentation POS dependency syntax analysis. Experiments are implemented in a Python 3.6 version on CentOS 7.4 running on a server with a system configuration 16-core Intel Xeon E5 processor (2.10 GHz) with 16 GB RAM.  
During the training of the network model, the pre-trained embedding with dimensions of 100 generated by the continuous bag-of-words (CBOW) model is directly used as the input of the LSTM-CRF model and used the Sogou news data ([[http://www.sogou.com/labs/resource/list_news.php](http://www.sogou.com/labs/resource/list_news.php)]([http://www.sogou.com/labs/resource/list_news.php](http://www.sogou.com/labs/resource/list_news.php))) as the corpus to train the word embedding. To further improve the performance of the model and prevent overfitting, the dropout rate was fixed at 0.5 for all dropout layers in all experiments and the LSTM-CRF model was trained using backpropagation algorithms to optimize the parameters. In addition, the epoch was 20, and the batch size was 50. Stochastic gradient decent (SGD) algorithm was adopted with a learning rate of 0.05 for every epoch based on the training set.
## 4 Distributions of Path Delay, Path Attenuation, Normalized Doppler Shift of the Scattering Path  
In this section, we investigate the time varying distributions of path delay, path attenuation, and normalized Doppler shift of the diffuse scattering path, with the assumption that the scatterers obey UD and the PDF is as Equation (9). The structure of the scattering area is illustrated in Figure 2, where the specular reflection point is the center of the ellipse, \(l_{1}\) is the semi-major axis, and \(l_{s}\) is the semi-minor axis.  
Figure 2: The structure of the scattering area(in XOY plane)
## 3 Compressive Sensing Based Preamble Channel Estimation
### Adaptive Regularized Compressive Sampling Matching Pursuit Algorithm  
#### 3.3.1 CS Algorithms Overview  
A number of CS recovery algorithms have been proposed. One of the popular kinds of recovery algorithms is based on the iterative greedy pursuit. OMP, CoSaMP and SAMP belong to this class. According to whether the sparse K is known prior or not, this class of algorithms also can be divided into two types.  
OMP and CoSaMP are the first type of algorithms with the sparsity \(K\) is known prior. For the OMP algorithm, in each iteration, the atom maximizes its inner product with the residual signal. However, the results of each iteration may be suboptimal. CoSaMP is proven to be the most advanced greedy algorithm. CoSaMP introduces the idea of backtracking that reduces the chance of error accumulation, selects \(2K\) coordinates and utilizes an iterative checking to refine them, and overcomes the defects of OMP, so that the atoms could not be changed once deposited in the candidate set.  
In practical applications, the second type of algorithms has better prospects than the first. SAMP is the second type of algorithm. The sparsity of signal is not required in SAMP as an a priori condition, and SAMP attempts to evaluate the sparsity of source signal by iteration.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 2 Background
### Bio-Inspired Controls  
Bio-inspired controls are controls that aim to imitate the form of human interaction. The impedance and admittance controls are opposed in concept. The former aims to prevent the environment from disturbing the trajectory, whereas the latter aims to allow interaction [22; 30].  
The impedance control establishes a dynamic relationship between the interaction force and the position error, correcting the robot's position errors by increasing the force exerted [22]. This control is defined by (1)  
\[m_{d}(\dot{x}-\dot{x_{r}})+b_{d}(\dot{x}-\dot{x_{r}})+k_{d}(x-x_{r})=f_{e^{ \prime}} \tag{1}\]  
where \(m_{d}\)\(b_{d}\), and \(k_{d}\) are the desired impedance coefficients that regulate the performance of the controller, \(m\) is the mass of the system, \(b\) is the damping coefficient, \(k\) is the rigidity of the system, \(x\) is the real position, \(x_{r}\) is the reference position, and \(f_{e^{\prime}}\) is the external force. In this type of control, the force exerted is considered a variable with a high degree of priority. A practical example is in an environment in which robots work together with humans [30].  
Admittance control relies on allowing movement, so the actuator changes its position according to the inputs [22]. The controller tries to smooth the source from the rigid position, responding to the forces of interaction and introducing a deviation from the desired movement [22]. In this way, the electromyographic signals are used at the inputs as a force applied to the exoskeleton position [31]. Admittance control is defined by (2)  
\[u=k_{p}(x_{r}-x)+k_{v}(\dot{x_{r}}-\dot{x}) \tag{2}\]  
where \(u\) is the input control, \(k_{p}\) is the position constant, and \(k_{v}\) is the speed constant.