esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 1 Introduction
A jerk model for tracking highly maneuvering targets was utilized in [PERSON]'s work [16]. In cooperative tracking of the moving target, [PERSON] et al. [17] applied squared-root cubatCarrying out this study, we hope to deal with the three subproblems better, which means guiding UAVs to tracking target in a faster converging time, getting a more accurate estimation of target state, and making intervehicle phase converge to the desired value in a shorter time. This paper's principal contributions can be summarized as follows: Taking the trajectory curvature into consideration, an optimization algorithm based on the gradient descent method for searching the optimal guidance parameter \(c\) is developed, which means faster convergence and stable tracking can be guaranteed. When UAVs are tracking a moving target, an interacting multiple model based on unscented Kalman filter (IMM-UKF) estimator is built for predicting the target state. In this way, the stability of the uncooperative target is improved. For cooperative tracking, a new speed control-based phase-keeping controller is designed to achieved faster convergence to the desired intervehicle phase, and the stability of controller is proved. The remainder of the paper is organized as follows: Section 2 mainly introduces the problems to be handled with. In Section 3, the overall framework of the LVFG algorithm is presented, and two components of guidance are analyzed. The proposed solution is introduced in Section 4, and fast but stable tracking, estimator for target states, and phase-keeping controller are presented. To verify the feasibility and benefits of our methods, numerical simulation and Hardware-In-the-Loop (HIL)
## 4 Model Selection and Development
### Model Development
\begin{array}{c}max=\sum\limits_{t=1}^{T}c_{t}^{T}x_{i}\\ A_{t}x_{t}+B_{t}x_{t-1}\leq b_{t},\;\;t=1,\ldots,Tx_{t}\geq 0,\;\;\;t=1,\ldots,T( \text{state transition constraints})\\ x_{t}\geq 0,\;\;\;t=1,\ldots,T\;(\text{non-negativity constraint})\end{array}  \tag{11}\]  
where  
\(T\): The number of time periods (stages). \(x_{t}\): The vector of decision variables at time \(t\). \(c_{t}\): The cost/reward vector at time \(t\). \(A_{t}\): The constraints matrix at time \(t\). \(B_{t}\): Captures the inter-period dependencies between decisions at different stages. \(b_{t}\): The resource availability vector at time \(t\). Since the models created in optimization problems contain a large number of variables and equations, their solution creates complexity. For this reason, many software programs have been developed to facilitate solution [68; 70]. In this study, an interface was developed by using Microsoft(r) VBA 16.0 and solver plug-ins together. In this way, the constraint values are obtained for the dynamic linear programming problem (DLPP) model by recording the number of employees who use the shuttle service provided by the company each day and taking into account the number of passengers over the last four weeks using the moving average method. The next step is to determine the number of vehicles required to minimize fuel costs according to the constraint values updated each week. For the problem, which resembles a dynamic structure due to the variable constraint values in the problem and which is transformed into an instantaneous static problem using estimation methods, a solution was obtained using the linear programming method. The objective function of the model was obtained as shown in Equation (12), and the decision variables were obtained as shown in Equation (13). The coefficients (\(Z_{ij}\), \(B_{ij}\), \(Y_{ij}\) and \(\;A_{ij}\)) were obtained by the model analyzing the number of employees who use the shuttle service provided by the company each day and taking into account the number of passengers. \[Min_{Z}=\sum_{j=1}^{2}\sum_{i=1}^{7}112.7Z_{ij}+64B_{ij}+99.3Y_{ij}+56A_{ij} \tag{12}\]  
where  
\(i\): Day (\(i=1,2,\ldots,7\)). \(j\): Time (\(j=1,2\ldots,n\)). \(V_{ij}\): \(i\). day \(j\). time. The number of employees going west. \(X_{ij}\): \(i\). day \(j\). time. The number of employees going east. \(Y_{ij}\): \(i\). day \(j\). time. The number of vehicles with 17 capacity required for east direction. \(Z_{ij}\): \(i\). day \(j\). time. The number of vehicles with 27 capacity required for east direction. \(A_{ij}\): \(i\). day \(j\). time. The number of vehicles with 17 capacity required for west direction. \(B_{ij}\): \(i\). day \(j\). time. The number of vehicles with 27 capacity required for west direction. Using the interface visualized in Figure 8, the number of vehicles required to travel in the east and north directions is calculated according to demand after the user has entered the specified parameters. Figure 7: Algorithms for data inputs and calculations: (**a**) Algorithm 1, (**b**) Algorithm 2 and (**c**) Algorithm 3.
# Tonic and Phase Amoremetric Monitoring of Dopamine Using Microelectrode Arrays in Rat Striatum
## 3 Results  
### In Vitro Characterization of MEAs for Measuring DA  
The recording properties of 4-channel S2 (Figure 1A) and 8-channel DSPR8 (Figure 1B) MEAs configured for the measurement of DA were evaluated through in vitro calibrations in PBS (Table 1). Constant potential ampeometry (E\({}_{\text{applied}}\) = +0.35 V) was used to measure DA at individual recording sites over a DA concentration range of 0-6 uM (2 uM increments). Oxidation of DA with the MEA recording sites occurred at any potential greater than +0.2 V vs. Ag/Ag+ on the MEAs [40]. The applied potential of +0.35 V was chosen because it achieved a robust DA response while limiting oxidation of species that could be present like H\({}_{2}\)O\({}_{2}\) or NO that oxidize at higher potentials. Nafion(r)-coated DA recording sites on both S2 and DSPR8 MEAs had excellent linearity for DA detection while effectively maintaining high selectivity ratios for DA over 250 uM challenges of AA (Table 1) or DOPAC (data not shown). As shown in Table 1, both MEA types demonstrated high-sensitivity measurements of DA (range, in pA/uM: S2, \(-\)5.9 to \(-\)121.3; DSPR8, \(-\)3.5 to \(-\)107.2), nanomolar limits of detection recordedwithout self-referencing subtraction (range, in nM: S2, 2.7 to 430; DSPR8, 1.9 to 100.0) and showed similar sensitivity per unit area measurements of DA (range, in pA/uM\({}^{-1}\)/mm\({}^{-2}\): S2, \(-\)1181 to \(-\)24,277; DSPR8, \(-\)696 to \(-\)21,437). Importantly, sentinel recording sites (i.e., Nafion(r)-coated + m-PD) did not respond to additions of DA or AA. Sensitivity/area is comparable to previous work [29].
## 4 Case Study
### Experiment Results  
In order to exemplify some of the many results offered by the proposed analysis, Figure 21 shows a comparison of the orientation deviation obtained for the last assembled part (part_D), calculated with two alternative experiments in which the position of the fixture locators has been modified. In Case 1, fixture locators are defined in the positions shown in Figure 15. In Case 2, two-way locators (p2 and p4) are positioned closer to the four-way locators (p1 and p3), reducing the distance between them. Data shown in Figure 21 correspond to the orientation deviation in the third assembly station; so, the deviation has no value until the first finished product is processed. After this point, each value change corresponds to a different processed product. As expected, the obtained results show that in Case 1, the deviations of the incoming product parts results in a greater orientation deviation of the resultant assembly (higher mean value) compared to Case 2. However, a closer locator position (Case 2) subtly increases the variability (standard deviation) of this orientation deviation in the simulated products.  
Figure 19: Instance specification for the Data_Tables block.  
Figure 20: Modelica executable model.  
The simulations also provide data related to the productivity of the analyzed system, such as the throughput, blocked-station times, simulating stops for maintenance or repair of breakdowns, etc. Furthermore, these results can be crossed with aspects related to quality, for example, establishing limits on deviations for product acceptance and calculating the productivity of parts that meet the specifications. Table 1 summarizes some results of the Case 1 experiment execution with a simulation time of 5000 s (14 h approximately). During this time, 203 products were finished, which represent a throughput of 14.6 products per hour. Considering the geometric specifications and the calculated deviations for each simulated product, only 164 units meet these specifications, obtaining a real throughput of 11.8 products per hour.
# A Lightweight Module for Feature Fusion Based on Their Dimensions
###### Abstract
This may cause the problem of feature information discontinuity in the fused feature information, which affects the inference quality of the final model. Figure 1a-c illustrates the common information fusion methods. Inspired by classical networks such as U-Net [41], Ghost [40] and MobilenV2 [17], an efficient network hierarchical connection structure, called \(\varphi\)unit (as shown in Figure 1d) was proposed. Based on the bottleneck module of MobilenV2 [17] and the connection module proposed in this paper, an efficient network \(\varphi\)Net was built. To explore the effectiveness of \(\varphi\)unit and the performance of \(\varphi\)Net, this paper conducts extensive ablation experiments, comparative experiments and validation evaluations, and tests with different datasets. The results of various experiments confirm the performance advantages of the novel structure proposed in this paper. Figure 1: Different feature fusion methods: (**a**) concatenation of feature information; (**b**) convolutional modification of feature information; (**c**) summation of feature information fusion; (**d**) sequential fusion of feature information. The key contributions of this work are summarized as follows:  
* The \(\varphi\)unit information fusion module is proposed, which considers the spatial feature relationship, a factor that is often neglected in the traditional model building strategy. * Based on the \(\varphi\)unit, combined with the relevant research results of lightweight models, this paper constructs a network model \(\varphi\)Net that can achieve a better balance among model parameter amount, computation and accuracy. * The deconvolution operation is introduced, which is used to reduce the difference between feature information, and it is applied to the image classification domain. This paper is organized as follows. Section 2 gives a brief overview of the related work in the field. In Section 3, we introduce \(\varphi\)unit and \(\varphi\)Net and explain the network structure and related functions. Section 4 conducts a series of experiments and analyzes the results. Section 5 concludes the whole research process.
## 2 System Model
### Transmission Channel  
The wireless channel introduces distortions due to multipath fading and additive noise, which affect the integrity of the transmitted signal. The received signal (\(\mathbf{Y}\)) can be modeled as follows:  
\[\mathbf{Y}=\mathbf{H}\mathbf{X}+\mathbf{N}, \tag{7}\]  
where \(\mathbf{X}\) represents the modulated transmitted signal, and \(\mathbf{H}\) is the channel matrix (Rayleigh, Rician). \(\mathbf{N}\sim\mathcal{CN}(\mu,\sigma^{2}\mathbf{I})\) represents AWGN with a mean of \(\mu\) and a variance of \(\sigma^{2}\). The channel, denoted by \(p_{h}(\mathbf{y}|\mathbf{x})\), characterizes the probabilistic transformation of input symbols \(x\) into received symbols \(y\) (Figure 1).
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 2 Related Work
### Analysis of S-MAC Protocol's Backoff Mechanism
#### 2.2.2 The Disadvantages of S-MAC Protocol's Backoff Algorithm  
The backoff algorithm of the S-MAC protocol is known for its high efficiency and simple implementation. The last node that successfully sends out data has the shortest backoff time, so it can keep its advantage during next competition. However, these kind of nodes always have the maximum competition priority, so the actual condition of the channel is not able to be reflected, which leads to a poor fairness [16]. The contention window drops to the minimum value immediately after the successful transmission and it increases until it reaches the maximum. It is obvious that the wide fluctuations of contention window have adverse effects on the network. The contention window for calculating random backoff time during carrier sense is fixed, which leads to unreasonable channel allocation [17]. In an actual network environment, the load of the network is constantly changing. On one hand, a small contention window is enough for a network to achieve backoff when the network load is not large. Although adopting a big contention window can primely avoid collision, this will lead to too much idle listening. On the other hand, when the network load is large, a relatively small contention window will lead to fierce competition and data retransmission caused by collision. Both of these conditions make throughput and energy efficiency decrease [15]. Apparently, the S-MAC protocol cannot adapt to the dynamic changes of network load due to the fixed contention window.
## 3 Management of Electrical Grids by Means of WSANs
### Synthesis  
To conclude, Table 1 gives an overview of potential WSAN applications suitable for SG and MG management, in terms of data rate, latency and reliability.  
As has been seen, WSANs are a key element in the evolution of the traditional electrical grid to a SG, from the energy generation units to the end customer, passing through the transport and distribution lines. Low-cost, easiness of deployment, fault-tolerance and high flexibility are the main characteristics that make WSANs solutions suitable for SG monitoring and control. Nevertheless, the efficiency and reliability of WSANs to be optimally applied to the SGs mainly depend on communication performances such as Bit Error Rate (BER), latency and security in harsh environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrical Grid** & **Application** & **Data Rate** & **Latency** & **Reliability** \\ \hline \multirow{2}{*}{Generation} & Traditional power plant monitoring & High & Low & High \\  & Wind or solar farm monitoring & Medium & Low & High \\ \hline \multirow{2}{*}{Transmission and distribution} & Line or cable monitoring and fault detection & Low & Low & Very High \\  & Distribution substation & Low & Low & Very High \\  & Outage detection & Low & Low & Very High \\ \hline \multirow{2}{*}{Customer-side} & WAMR & Low & High & Medium \\  & ifREM/OREM & Low & Medium & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 1: Potential WSAN applications for SG and MG management.  
The importance of communication protocols on the performance of WSANs in a SG scenario that includes MGs will be analyzed in the next sections.
###### Abstract  
Multi-Object Tracking (MOT) techniques have been under continuous research and increasingly applied in a diverse range of tasks. One area in particular concerns its application in navigation tasks of assistive mobile robots, with the aim to increase the mobility and autonomy of people suffering from mobility decay, or severe motor impairments, due to muscular, neurological, or osteauricular decay. Therefore, in this work, having in view navigation tasks for assistive mobile robots, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, is presented. To improve the data association of both methods, which are solved as a linear assignment problem with a generated cost matrix, a set of new object tracking data association cost matrices based on intersection over union, Euclidean distances, and bounding box metrics is proposed. For the evaluation of the MOT by detection in a real-time pipeline, the YOLOv3 is used to detect and classify the objects available on images. In addition, to perform the proposed evaluation aiming at assistive platforms, the ISR Tracking dataset, which represents the object conditions under which real robotic platforms may navigate, is presented. Experimental evaluations were also carried out on the MOT17 dataset. Promising results were achieved by the proposed object tracking data association cost matrices, showing an improvement in the majority of the MOT evaluation metrics compared to the default data association cost matrix. In addition, promising frame rate values were attained by the pipeline composed of the detector and the tracking module.  
multi-object tracking; data association; autonomous mobile robot platforms +
Footnote †: journal: Applied  
_Article_  
Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics
## 3 Results and Discussion
### DTF Experiment Results  
#### 3.2.1 NOx Emission Propensity Analyses in DTF Experiments  
The concentration of NOx emissions during sample combustion as determined using DTF is provided in Table 2. The concentration of NOx emissions together with the conversion of fuel-N to NOx (CR) are provided in Table 2. In Figure 3, according to the definition of the Pearson correlation coefficient, the factors that had strong correlations and R-squared are displayed. The factors that had weak correlations were excluded. Coal samples were sorted in ascending order based on the results of the DTF test on the concentration of NOx emissions, but, as shown in Figure 3a, there was no correlation with the nitrogen content found considering the ultimate analysis of coal. However, as observed in Figure 3b, in the industrial analysis based on a received standard, the moisture content showed a strong negative correlation (R\({}^{2}\) = 0.7659). In general, moisture hinders combustibility and causes a delay in coal ignition. According to [PERSON] et al., the moisture content is believed to be related to NOx emissions and this factor can be used to define minimum NOx emissions from the coal [18]. Figure 3c shows the correlation between NOx concentration and fixed carbon from the industrial analysis. R\({}^{2}\) = 0.7063 shows a strong positive correlation. To consider the amount of the nitrogen component in the coal and biomass needed for NOx emissions, we determined the relation using CR, the conversion factor of fuel-N to NOx proposed by [PERSON] et al. [19]. CR consists of three types of indices--FR (-), nitrogen amount in fuel-N (FN, kg), and FC (-), which is drawn from the industrial analyses. CR is expressed as follows. \[\text{CR}=\frac{\text{C}_{\text{NOx}}}{\left(\frac{2.24\times 10^{-2}\times \frac{\text{FN}}{\text{V}_{\text{dry}}}}{\text{V}_{\text{dry}}}\right)}\times 10^{2} \tag{1}\]  
where C\({}_{\text{NOx}}\) is the NOx concentration at the furnace exit, and V\({}_{\text{dry}}\) is the flow rate of dry air per feeding rate of coal (Nm\({}^{3}\)/kg). The denominator and NOx concentration, in this case, suggest that all fuel-N is converted to NOx. Coal and biomass samples are provided at 0.29-0.34 g/min for a constant heat supply so that V\({}_{\text{dry}}\) is calculated according for each sample. Figure 4 shows the relation between the CR and FR/FN, and the dashed line indicates the data from the referenced paper [20]. It is known that bituminous coal with FR < 2.5 exhibits a linear proportional correlation with FR/FN, and it is suggested that CR is proportional to \((1-\text{VM})/(\text{VM}\times\text{FN})\). VM, in this case, refers to volatile matter from the industrial analysis. CR in Figure 4 shows an exponential relation to FR/FN, and we can consider the relation with FR by excluding FN, according to the CR definition.
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 3 Importance of Carbon Nanomaterials for the Corrosion Mitigation
### Graphene  
Since its discovery in 2004, space applications of graphene included multifunctional coating materials and as communication and thermal control systems [86]. [PERSON] et al. used ab-initio molecular dynamics (AIMD) simulations to investigate the impact interactions of graphene with \(AO\). The impact results showed a correlation between the extent of the damage with the impact sites and incident angle. Ring hollow site and some angles on the \(C\) atom site either rebound or scatter the impacting \(AO\). Hence, the authors proposed the vertically faced graphene for enhanced impact protection [56]. The data are listed in Table 2 and illustrated in Figure 11.  
The effects of \(AO\), electrons and protons exposure on graphene were studied by [PERSON] et al. Minimum damage was observed due to high energy electrons because of the electrical conductivity of graphene sheets. It was found that high energy protons can form polymorphic atomic defects via collision and excitation effects [54]. Another technique to mitigate \(AO\) effect on graphene sheet is changing the electronic density on the surface by doping heteroatoms. [PERSON] et al. doped graphene sheets with nitrogen atoms and demonstrated their barrier properties experimentally and via DFT calculations [55]. [PERSON] et al. showed that the multilayer graphene is more effective than single layer graphene in an \(AO\) environment [87]. A graphene polyimide composite was synthesized  
\begin{table}
\begin{tabular}{c c c c} \hline
**Impact Sites** & **Incident Angle \(\varphi\), \({}^{\circ}\)** & **Incident Angle \(\theta\), \({}^{\circ}\)** & **Impact Interaction Results** \\ \hline Ring hollow & 0\({}^{\circ}\), 30\({}^{\circ}\) & 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) & Rebounded \\ \hline C–C bond & 0\({}^{\circ}\), 30\({}^{\circ}\),90\({}^{\circ}\) & 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) & Adsorbed \\ \hline C atom & 0\({}^{\circ}\), 90\({}^{\circ}\) & 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) & Rebounded first and then adsorbed on C–C bond \\ \hline \end{tabular}
\end{table}
Table 2: Impact interaction results between the \(AO\) and graphene with various incident angles and impact sites [56].  
Figure 10: Influence of atomic oxygen exposure on carbon nanotube–polyhedral oligomeric silsesquioxane–polyimide film. (**a**) Erosion yields of the composite films and Kapton H as a function of atomic oxygen fluence. (**b**) The response of sheet resistivity of the composite films to changes in atomic oxygen fluence. Reprinted with permission from [PERSON] et al., 2021 [85]. © ACS.  
by [PERSON] et al. An amount of 1.5 wt% of graphene reduced the mass loss by 47%. Co-doping of the composite with 3 wt% of SiO\({}_{2}\) nanofiller further decreased the mass loss to 71% [88]. Figure 12 illustrates this promising graphene/SiO\({}_{2}\) nanoparticles composite which is also capable to enhance atomic oxygen corrosion resistance.