esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 2 Related Work
In our experiment, to reduce the size of DRL systems and make them feasible for applying on physical UAVs (to be under 1 GB, as specified in Section 3.2), we use depth images as the only input source for decision making because they not only give a better sense of the surrounding obstacles but also have a smaller size than RGB images. RGB images have three channels (Red, Green, Blue) while depth images (greyscale) have only one channel; thus, reducing the size of depth images to one-third of the RGB images size. The simulation library we use AirSim [24] (described in Section 4.1) provides several APIs for accessing the depth images generated by the simulation ([[https://github.com/microsoft/AirSim/blob/main/docs/image_apis.md](https://github.com/microsoft/AirSim/blob/main/docs/image_apis.md)]([https://github.com/microsoft/AirSim/blob/main/docs/image_apis.md](https://github.com/microsoft/AirSim/blob/main/docs/image_apis.md)), accessed on 15 November 2022). In addition, to give the UAV better predictions of the target's movements, we pile a number of consecutive depth frames to create a short video that captures the historical movements of the target. Initially, we intended to use Long Short-Term Memory (LSTM) [25] to process the sequential movements. However, we realize that adding a LSTM will generate an unnecessary overhead cost to the computational resources of the UAV. Therefore, we elegantly switch to use a short video of depth images to accomplish the same goal, but with much lower computations. In the simulation environment, we use a segmentation map to localize the target object in the current frame because it is extremely lightweight and convenient, and some UAVs nowadays already offer the integration of a segmentation camera such as the DJI Phantom 4 RTK, DJI Matrice 300 RTK, Intel Falcon 8+, Parrot Anafi USA, etc. For UAVs that do not have segmentation cameras, we adopt the current lightest object detection model (YOLOv4-tiny) to detect the objects. Furthermore, we design an effective non-sparse reward function to help the UAV to learn and track the target effectively. In this paper, we test our LDVRL system with both DQN and DDPG algorithms to see the adaptability of our framework and, finally, compare their performance under the same settings. Since a DDPG is an extension of a DQN, it is expected to give a better performance. However, a DQN shows better metrics for this task surprisingly. The details are discussed in Section 5.
# Applicability Study on Modified Argillaceous Slate as Subgrade Filling for High-Speed Railway
## 3 Experimental Evaluation of Cement/Lime-Modified Soil
### Results
#### 3.1.5 Unconfined Compression Strength Characteristics  
UCS is a vital mechanical parameter used in soil stabilization and construction of subgrade. UCS tests were conducted on the \(\Phi\)50 mm \(\times\) 50 mm quicklime/cement-modified cylinder samples with optimum moisture. The samples were sieved through a 10 mm sieve.  
Figure 4 shows the relation between UCS and compactness of specimens cured for 7 days. The results suggest that UCS increases with both compactness and quicklime/cement content. The compactness enhances the UCS sharply, while the addition of cement is 5% and the addition of quicklime is 6%. Similar comparative analysis between the effect of lime and cement suggests that the UCS of soil-cement mixture is higher than the soil-lime mixture [51; 52]. This has been attributed to the increase in the concentration of Si+4, Al+3 and K+ ions on the addition of cement, in contrast to the addition of lime.  
Figure 5 compared the UCS of air-dried WAS and immerged WAS. For the specimen modified with quicklime, it collapsed easily if the curing time was less than 28 days. Even if the curing time reaches 27 days, the UCS of the specimen immerged in water is almost one-third of that which was not immerged in the water, demonstrating a very weak water stability. For the specimen modified by cement, the value of the increase in UCS due to increased curing time is greater than the value of the decrease in UCS due to immerging in water, indicating that the cement-improved soil with enough maintenance time has good water stability. Additionally, whatever the mixture is, the UCS after 7 days' maintenance is greater than 0.5 MPa, showing that the quicklime/cement-modified WAS can meet the static strength requirement for high-speed subgrade filling.  
Figure 4: Relation between UCS and compactness of modified WAS after 7 days of curing. (**a**) Quicklime-modified soil; (**b**) Cement-improved soil.  
Figure 5: The relationship between UCS (saturated and unsaturated) and compactness. (**a**) +6%, quicklime-modified soil. (**b**) +5% cement-improved soil.
## 4 Results and Case Study
### Case Study  
We used publicly available Amazon data [47] of user reviews related to games reviews for this case study. The online reviews contain different words used by the different users to express their opinion; some words form \"the long tail\" as depicted in Figure 13. In total, 122,502 numbers of words were considered after applying the preprocess for UX dimensions extraction.  
Figure 14 shows the frequency of user satisfaction score in terms of rating in the used dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimensions** & **UXWE-LDA** & **Human Expert 1** & **Human Expert 2** & **Human Expert 3** \\ \hline Attractiveness & ✓ & X & ✓ & ✓ \\ Dependability & ✓ & ✓ & ✓ & ✓ \\ Efficiency & ✓ & X & ✓ & X \\ Perspicuity & ✓ & X & ✓ & ✓ \\ Novelty & ✓ & ✓ & ✓ & ✓ \\ Stimulation & ✓ & ✓ & X & X \\ Aesthetics & X & ✓ & X & ✓ \\ Complexity & X & ✓ & ✓ & ✓ \\ Affect and emotion & X & X & ✓ & X \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of UXDs between UXWE-LDA model and human experts.  
Figure 13: Word distribution for games reviews.  
First, we applied the UXMCQ model to filter out the unrelated reviews. Then, we applied the UXWE-LDA model for the dimension extraction. Figure 15 shows the extracted dimensions from the online reviews.  
The user sentiment orientations towards each UXD of online user reviews are shown in Figure 16. The results show that the users have positive opinions in the extracted UX dimensions as compared to negative.  
We used the structured data with \(W_{i}^{pos}\) and \(W_{i}^{neg}\) vectors generated by part-2 of the proposed methodology to train the ENNM Model as shown in Table 6.  
According to Table 6 generated by the ENNM model, the category of each UXD of game reviews can be identified and mapped in the Kano Model, as shown in Figure 8.  
Figure 17 shows the threshold for determining whether a UXD is an indifferent UXD. As can be seen from Figure 17, the UXDs identified as excitement UXDs include: hedonic and perspicuity; pragmatic identified as reversed UXD; must-be UXD includes involvement; and efficiency. Finally, performance UXD consists of three dimensions (stimulation, attractiveness, and dependability).
# Dynamic Dark Channel Prior Dehazing with Polarization
## 4 Experimental Results and Discussion  
### Polarization Axis Calibration  
The relationship between light intensities recorded by the CMOS camera and the orientation of the polarization axis is shown in Figure 6. Here, the orientation range of the polarization axis varies from the formal \(0^{\circ}\) to \(180^{\circ}\), with an interval of \(5^{\circ}\) related to the  
Figure 5: Polarization axis calibration system. **(a)** Schematic diagram. **(b)** System optical path. LS: light source; A: attenuator; PBS: polarization beam splitter; BS: beam splitter; PSA: polarization state analyzer.  
horizontal direction. In order to reduce the artificial error caused by rotating the polarizer, the light intensity corresponding to each polarization axis orientation is measured three times, and the average value of them is taken as the final intensity in the curve. The obtained data is fitted by the Fourier series process. It can be observed that the light intensity achieves the maximum value of 10.1 at the polarization axis of 35\({}^{\circ}\) and the minimum value of 2.8 at the polarization axis of 125\({}^{\circ}\), which correspond to the horizontal and vertical directions of the polarization axis, respectively.
# University Student Dropout Prediction Using Pretrained Language Models
## 3 Methodology
### Modeling
#### 3.2.1 Formulating Numerical and Categorical Variables into a Natural Language Format
To leverage the regional information of the pretrained model learned from the general corpus during pretraining, we included the high school location from Wikipedia if possible. We formulated the high school, such as \"Yeokgok High School\", with regional information as in \"Yeokgok High School, a public high school in Yeokgok-dong, Bucheon-si, Gyeonggi-do\". We bound features that were more naturally expressed when described together (e.g., university-admission-specific features and high-school-specific features). For instance, we formulated university-admission-specific features such as admission year \"2021\", admission type \"early admission\", and detailed admission type \"writing test admission\", as in the sentence \"Admitted in 2021 with early admission-writing test admission\". One study [36] showed the effectiveness of using numeric values and text together. Therefore, we statistically divided the numeric features into five categories using natural language and described them using categories. For instance, we formulated the major's GPA, such as \"4.17\", as the sentence \"4.17 GPA in major, excellent\", because a score above 3.75 and below 4.25 corresponds to \"excellent\". We identified parental financial income using any national scholarship the student received. Because tuition varies by major, students who received the same number of scholarships may have different income levels. For accuracy, we calculated the ratio by dividing the national scholarships received by each student's major. We then formulated this ratio in the same manner as the other numerical features. In the case of the course evaluation, we formulated the course evaluation score, such as \"3.93\", in the same way as the sentence \"Course Evaluation : 3.93, negative\" and since comments were originally in textual form, we concatenated them. As a result, we formulated the course evaluation as in the sentence \"Course Evaluation : 3.93, negative, Too many assignments.\". If there were no comments, we formulated it as \"No course evaluation comments\" and if there were no course evaluations at all, we formulated it as \"No course evaluations\". We then tokenized the paragraphs using the WordPiece tokenizer [37]. In that process, we did not remove stop words, in line with existing research [27]. Finally, we obtained the token sequences as follows:  
\[S=\{[CLS],\hat{d}_{1},\hat{d}_{2},\ldots,\hat{d}_{k},\hat{a}_{1},\hat{a}_{2}, \ldots,\hat{a}_{l}\}. \tag{1}\]  
Note that \([CLS]\) is a classification token, \(\hat{d}_{1},\hat{d}_{2},\ldots,\hat{d}_{k}\) denote the tokens of the demographic token sequence and \(\hat{a}_{1},\hat{a}_{2},\ldots,\hat{a}_{l}\) denote the tokens of the academic token sequence. \(k\) and \(l\) denote the lengths of the demographic and academic token sequences, respectively. The sentence formats corresponding to each variable are listed in Table 3.
## 3 Data Source and Research Method  
The wind regime data used in this paper are hourly wind regime data collected by Qiemo and Ruoqiang Meteorological Stations in 2017-2020. The number of hours with a sand-moving wind velocity of \(\geq\)4.5 m/s was counted. The sand-moving wind frequencies of 16 directions were calculated on three-time scales, i.e., annual, seasonal, and monthly. Drift potential (DP) was further calculated from wind velocity by the following equation [26]:  
\[\text{DP}=\text{V}^{2}(\text{V}-\text{V}_{\text{t}})\text{t} \tag{1}\]  
where DP (VU) denotes DP; V and V\({}_{\text{t}}\) denote sand-moving wind velocity and critical sand-moving wind velocity, in knots (1 knot \(\approx\) 0.514 m/s); t denotes sand-moving wind time, expressed as the percentage of the number of hours with the sand-moving wind in the total number of hours observed. The critical sand-moving wind velocity of the Taklimakan Desert is 4-5 m/s (4.5 m/s, on average, adopted in this paper) [27]. According to the definition of Equation (1), five sand-moving wind velocity scales were introduced, namely, 4.5 m/s \(\leq\) V \(<\) 7 m/s, 7 m/s \(\leq\) V\(<\) 9 m/s, 9 m/s \(\leq\) V\(<\) 11 m/s, 11 m/s \(\leq\) V\(<\) 13 m/s, and V \(\geq\) 13 m/s. First, the weight factors of these five wind velocity scales were solved,  
Figure 1: Overview of the study area.  
respectively. Then, the DPs of Qiemo and Ruoqiang areas were calculated. Finally, the DPs of 16 directions were used to obtain the resultant drift potential (RDP) and resultant drift direction (RDD). RDP/DP denotes the direction variability index, which is used to characterize the directional variability of sand-moving wind in a region. According to [PERSON]'s [26] division of wind energy environments, DP > 400 VU means a high wind energy environment, 200-400 VU--a medium wind energy environment, and <200 VU--a low wind energy environment. The direction variability index \(\leq\) 0.3 is a small ratio, 0.3-0.8 is a medium ratio, and \(\geq\)0.8--a large ratio. A larger direction variability index means a more stable wind direction in the region and vice versa.
# A Hybrid Routing Protocol Based on Naive Bayes and Improved Particle Swarm Optimization Algorithms
## 3 Network Model and Assumptions
### Energy Model  
The first-order radio energy consumption model is adopted [26; 27], and it is considered that the energy \(E_{tx}\) consumed by the node to send \(l\)-bit data is  
\[E_{tx}=\left\{\begin{array}{c}1E_{elec}+le_{f}d^{2},d<d_{0}\\ 1E_{elec}+le_{m}d^{4},d\geq d_{0}\end{array}\right. \tag{3}\]  
where \(E_{elec}\) refers to the 1-bit data transmission power of the transmit-receive circuits, \(d\) refers to the distance between the transmitter and the receiver, \(d_{0}=\sqrt{\frac{\varepsilon_{f}}{\varepsilon_{m}}}\) refers to the distance threshold of the model, \(\varepsilon_{f}\) and \(\varepsilon_{m}\) refer to the energy coefficients for the power amplification in the two conditions, and \(l\) refers to the length of the data packet to be sent.  
The energy \(E_{rx}\) that nodes require in order to receive L-bit data can be calculated as follows.  
\[E_{rx}=kE_{elec} \tag{4}\]
# Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization
## 3 Performance Analysis of the Offshore Wind Resources in the Luchao Port Area
### Wind Energy Parameter Analysis  
The wind speed data of six time periods, including 1 October, 2 October, 27 October in 2005 and 1 April, 8 April and 20 April in 2006, are separately selected in this study for research. The corresponding wind power density is calculated, according to Equation (1) and listed in Tables 1 and 2, respectively. It is not difficult to know that to obtain following information:  
(1) The monthly average wind speed in Luchao Port is generally about 6-7 m/s, but the maximum daily one can exceed 10 m/s;  
(2) The standard deviation of the monthly wind speed is maintained at about 3 m/s, and that of the short-term wind speed changes from 1 m/s to 3 m/s;  
(3) The monthly wind power density is about 300 W/m\({}^{2}\), but the instantaneous one is also less than 100 W/m\({}^{2}\);  
(4) The effective hours of wind energy in Luchao Port account for about 92% of the whole month.
## 4 Experimental Results and Discussion
### Robustness  
Robustness performance for the proposed watermarking scheme is evaluated under zero and various common attacks by using Normalized Correlation (NC) and Bit Error Rate (BER) as performance metrics. NC is a good measure for robustness that gives normalized correlation between original and extracted images in terms of direction and strength relationship. BER measures pixel level difference between original and extracted images. The relation for NC and BER is provided in Equations (12) and (13), respectively:  
\[NC=\frac{\sum_{r=1}^{P}\sum_{c=1}^{Q}\left[W(r,c)-W^{1}(r,c)\right]^{2}}{\sqrt {\left[\sum_{r=1}^{P}\sum_{c=1}^{Q}W(r,c)^{2}\right]}\times\sqrt{\left[\sum_{r =1}^{P}\sum_{c=1}^{Q}W^{1}(r,c)^{2}\right]}} \tag{12}\]  
where \(W(r,c)\) t and \(W^{1}(r,c)\) are original and extracted watermarks:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **MSE** & **PSNR (dB)** & **SSIM** & **NC** & **BER** & **Alpha** \\ \hline Airplane (5.1.11) & 0.2134 & 54.81 & 0.9485 & 1 & 0 & 0.5557 \\ Clock (5.1.12) & 0.2541 & 54.07 & 0.9451 & 1 & 0 & 0.4590 \\ Resolution chart (5.1.13) & 0.1065 & 57.85 & 0.6537 & 1 & 0 & 0.0010 \\ Chemical paint (5.1.14) & 0.4021 & 52.07 & 0.9940 & 1 & 0 & 2.4258 \\ Couple (5.2.08) & 0.4415 & 51.68 & 0.9917 & 1 & 0 & 1.3770 \\ Aerial (5.2.09) & 0.4521 & 51.57 & 0.9936 & 1 & 0 & 1.8740 \\ Stream and Bridge (5.2.10) & 0.5907 & 50.43 & 0.9973 & 1 & 0 & 3.5762 \\ Male (5.3.01) & 0.4408 & 51.70 & 0.9909 & 1 & 0 & 1.5879 \\ Airport (5.3.02) & 0.4611 & 51.47 & 0.9925 & 1 & 0 & 1.6172 \\ Truck (7.1.01) & 0.4501 & 51.60 & 0.9925 & 1 & 0 & 1.6709 \\ Airplane (7.1.02) & 0.3204 & 53.02 & 0.9725 & 1 & 0 & 0.4961 \\ Car (7.1.03) & 0.4444 & 51.63 & 0.9948 & 1 & 0 & 2.1592 \\ Car and APCs (7.1.04) & 0.4155 & 51.89 & 0.9943 & 1 & 0 & 2.3555 \\ Truck and APCs (7.1.06) & 0.4430 & 51.66 & 0.9969 & 1 & 0 & 3.6641 \\ Tank (7.1.07) & 0.4351 & 51.79 & 0.9976 & 1 & 0 & 3.7002 \\ APC (7.1.08) & 0.4641 & 51.50 & 0.9891 & 1 & 0 & 1.2744 \\ Tank (7.1.09) & 0.4369 & 51.75 & 0.9972 & 1 & 0 & 2.9941 \\ Tank and APCs (7.1.10) & 0.4183 & 51.89 & 0.9988 & 1 & 0 & 2.6553 \\ Airplane (7.2 01) & 0.4031 & 52.03 & 0.9767 & 1 & 0 & 0.8965 \\ Fishing boat & 0.4486 & 51.57 & 0.9930 & 1 & 0 & 3.8765 \\ Level step wedge & 0.3216 & 52.87 & 0.9973 & 1 & 0 & 2.3144 \\ House & 0.4143 & 51.94 & 0.9677 & 1 & 0 & 1.9874 \\ Pixel ruler & 0.0924 & 58.47 & 0.8453 & 1 & 0 & 1.7432 \\ \hline Average & 0.3813 & 51.55 & 0.9767 & 1 & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._\[BER=\frac{EB}{TB} \tag{13}\]  
\[EB =\begin{cases}counter+1&if\sum_{r=1}^{p}\sum_{c=1}^{Q}W(r,c)\
eq W^{1}(r,c)\\ 0&otherwise\end{cases}\]  
\[TB=P\times Q\]  
where \(EB\) represents the number of incorrectly decoded bits in extracted watermark, and \(TB\) represents total number of bits and initial value of counter = 0.
## 2 Materials and Methods
### Pit Detection Method  
#### 2.3.1 Open-Loop Crossover Algorithm (OLCA)  
The OLCA is illustrated in Figure 6. First, as shown in Figure 6a, an end point B of the edge is calculated. Then, starting from point B, the coordinate vector sequence of all non-zero pixels is tracked. By4\({}_{1}\), by4\({}_{2}\),..., by4\({}_{t}\),..., by4\({}_{p}\) are remembered, and the corresponding coordinate sequences are \(x\)\  
Through point B, establish the initial straight line equation conjugates with the straight line of Formula (5):  
\[a_{1}x+b_{1}y+C_{1}=0 \tag{6}\]  
Keep the slopes of the two vertical lines constant, traverse the \(xl_{r}\) sequence, and update the two linear equations at the index pixels:  
\[\left\{\begin{array}{c}(x_{2},y_{2})\to ax_{2}+by_{2}+c_{2}=0,a_{1}x_{2}+b_{1} y_{2}+C_{2}=0\\ (x_{3},y_{3})\to ax_{3}+by_{3}+c_{3}=0,a_{1}x_{3}+b_{1}y_{3}+C_{3}=0\\ \vdots\\ (x_{n-2},y_{n-2})\to ax_{n-2}+by_{n-2}+c_{n-2}=0,a_{1}x_{n-2}+b_{1}y_{n-2}+C_{n -2}=0\end{array}\right. \tag{7}\]  
As seen in Figure 6a, the pixel at any index position \(i\), \(l\) (\(2\leq i,l\leq n-2\)) is substituted into the linear equation \(ax_{i}+by_{i}+c_{i}=0\), \(a_{1}x_{i}+b_{1}y_{i}+C_{i}=0\), and the \(c_{i}\), \(C_{l}\) is solved. The position constraint conditions for judging whether the index position point is a pit can be described as  
\[d_{i-2}=ax_{i-2}+by_{i-2}+c_{i},d_{l-2}=a_{1}x_{l-2}+b_{1}y_{l-2}+C_{l} \tag{8}\]  
\[d_{i+2}=ax_{i+2}+by_{i+2}+c_{i},d_{l+2}=a_{1}x_{i+2}+b_{1}y_{l+2}+C_{l} \tag{9}\]  
\[d_{i-2}\cdot d_{i+2}{>}0,d_{l-2}\cdot d_{l+2}{>}0 \tag{10}\]  
This sufficient condition constrains the index position \(i+2\) (or \(l+2\)) of the front point and the index position \(i-2\) (or \(l-2\)) of the back point to the same side of the linear equation.  
As shown in Figure 6b, another sufficient condition for judging that an index point is concave is the distance constraint, which is described as follows:  
\[D_{i-2}=\frac{|ax_{i-2}+by_{i-2}+c_{i}|}{\sqrt{a^{2}+b^{2}}},D_{l-2}=\frac{| a_{1}x_{l-2}+b_{1}y_{l-2}+C_{l}|}{\sqrt{{a_{1}}^{2}+{b_{1}}^{2}}} \tag{11}\]  
\[D_{i+2}=\frac{|ax_{i+2}+by_{i+2}+c_{i}|}{\sqrt{a^{2}+b^{2}}},D_{l+2}=\frac{| a_{1}x_{l+2}+b_{1}y_{l+2}+C_{l}|}{\sqrt{{a_{1}}^{2}+{b_{1}}^{2}}} \tag{12}\]  
\[H_{i}=\frac{|c_{i}-c_{1}|}{\sqrt{a^{2}+b^{2}}},H_{l}=\frac{|C_{l}-C_{1}|}{ \sqrt{{a_{1}}^{2}+{b_{1}}^{2}}}\] (In summary, OLCA synchronously traverses the edge coordinates with conjugate lines. Under that judgment of two constraint conditions, the pit detection is complete.
## 3 Definition and Motivation
### Preliminaries  
In this paper, we study the physical heterogeneous cooperative MARL problems using the decentralized partially observable Markov decision process (Dec-POMDP) [57] scheme. The problem is described with a tuple \(G=\langle S,A,O;P,\Omega,R;\gamma,N,K,T\rangle\). \(N=\{1,\ldots,n_{i},\ldots,n\}\) denotes the finite set of \(n\) agents, \(K=\{1,\ldots,k_{i},\ldots,k\}\) denotesthe finite set of \(k\) agent groups, and \(\gamma\in[0,1)\) is the reward discounting factor. \(s\in S\) denotes the true state of the environment with complete information and is not accessible for decentralized components. At each time-step \(t\leq T\), agent \(i\in N\), from group \(k_{i}\in K\), receives an individual partial observation \(o_{i}^{t}\) from the environment and chooses an action \(a_{i}^{t}\in A_{i}\) from its local available action set \(A_{i}\). \(A_{i}\) is masked from the global action set \(A\) with agent's local available action mask \(AM_{i}\). Actions of all agents form a joint action \(\mathbf{a}^{t}=(a_{1}^{t},\ldots,a_{n}^{t})\in A=(A_{1},\ldots,A_{n})\) at every time-step. The environment receives the joint action \(\mathbf{a}^{t}\) and returns the next state \(s^{t+1}\) according to the transition function \(P(s^{t+1}|s^{t},\mathbf{a}^{t})\) and a reward \(r^{t}\) according to the global reward function \(R(s,\mathbf{a}^{t})\). All local observations \(o_{1}^{t},\ldots,o_{n}^{t}\) are generated from the state \(s^{t}\) according to the observation function \(\Omega(s^{t},i)\), and form the joint observation \(\mathbf{o}^{t}=(o_{1}^{t},\ldots,o_{n}^{t})\in O\). Observation action trajectory history \(\tau_{i}^{t}=\cup_{1}^{t}\{(o_{i}^{t-1},a_{i}^{t-1})\}\) (\(t\geq 1;\tau^{0}=o^{0}\)) is the summary of the partial transition tuples of agent \(i\) before time-step \(t\). Specifically, \(\tau_{i}\) indicates the overall trajectory of agent \(i\) across all time-steps \(t\leq T\). Replay buffer \(\mathcal{D}=\cup(\tau,s,r)\) stores all data for batch sampling. Network parameters are notated with \(\theta\), \(\phi\) and \(\psi\).
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 3 Single-Anchor Localization Method
### (Quasi-)simultaneous Response
* Within a certain distance difference, the anchor can complete the data association between the response and the agent. (Because the anchor assigns a different \(\delta_{i}\) to each agent in advance.) Note that high measurement efficiency means reducing the number of times that the anchor turns on the receiver, and it also means that the measurement takes less time. For scenarios where there are more than 2 agents, we only need to allocate different delays \(\delta\) to different agents according to their device ID, so that the agents' responses do not overlap, and the measuring process can be achieved.