esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model
### Overall Structure  
The HCAE model takes as input all relation quadruples and attribute quadruples in the TKG. It consists of four parts: (1) Attribute embedding layer: using long short-term memory (LSTM), it maps semantically related attributes to nearby positions in the vector space to capture attribute correlations. (2) Embedding propagation layer: entity and attribute vectors are used as inputs, and graph attention embedding is employed to learn high-order connectivity between entities. (3) Temporal encoding layer: multiple layers of GRU are set up to learn historical information at different time granularities. \(k\) represents the time window size used to control the length of historical information. Finally, multi-granularity temporal features are fused. (4) Decoder: the entity vectors and relationship vectors generated by the structure mentioned above are used as inputs to compute entity-relation scores for all quadruples. The model's overall structure is shown in Figure 2, and the notation used in the paper is summarized in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(\mathcal{G}\) & The temporal knowledge graph \\ \(\mathbf{s}\in\mathbb{R}^{d},\mathbf{r}\in\mathbb{R}^{d},\mathbf{o}\in\mathbb{R}^{d},\mathbf{v} \in\mathbb{R}^{d}\) & The embedding vector of subject entity \(S\), relation \(r\), object entity \(O\), attribute \(v\) \\ \(T_{R},T_{A}\) & The dimension of the representation \\ \(\mathcal{E}=\{s,o|(s,r,o,t)\in T_{R}\cup(s,r,v,t)\in T_{A}\}\) & The set of entities \\ \(\mathcal{R}=\{r|(s,r,o,t)\in T_{R}\cup(s,r,v,t)\in T_{A}\}\) & The set of relations \\ \(\mathcal{A}=\{v|(s,r,v,t)\in T_{A}\}\) & The set of attribute values \\ \(\mathcal{T}=\{t|(s,r,o,t)\in T_{R}\cup(s,r,v,t)\in T_{A}\}\) & The set of timestamps \\ \(t\) & Timestamp scalar \\ \(k\) & The length of the time window \\ \(\mathbf{w}_{i}\in\mathbb{R}^{d}\) & The word embedding of the word \(w_{i}\) at position \(i\) \\ \(\mathbf{N}_{i,t}=\{o,v|(s_{i},r,o,t)\in T_{R}\cup(s_{i},r,v,t)\in T_{A}\}\) & The neighborhood of entity \(s_{i}\) at timestamp \(t\) \\ \(\mathbf{h}_{i,t}\in\mathbb{R}^{d}\) & The embedding of entity \(s_{i}\) and updated embedding \\ \(\widetilde{h}_{i,t}^{(l)}\) & The embedding of the entity \(s_{i}\) in \(l\)-th layers \\ \(\mathbf{W}\in\mathbb{R}^{d\times d}\) & The learn-able linear transformation \\ \(\mathbf{W}^{l}\) & The linear transformation of the \(j\)-th attention head \\ \(\mathbf{H}_{i,t}\in\mathbb{R}^{|\mathcal{E}|\times d}\) & The temporal representation of short-term, mid-term and long-term history \\ \(\mathbf{\hat{H}}_{i,t}\in\mathbb{R}^{|\mathcal{E}|\times d}\) & The output of the temporal encoder \\ \(\mathbf{\hat{\varepsilon}}\) & The number of entities \\ \(\mathbf{N}_{1},\mathbf{N}_{2},\mathbf{N}_{3}\) & The length of short-term, mid-term and long-term history \\ \(\lambda_{1},\lambda_{2},\lambda_{3}\) & The weight coefficients for three timescale histories \\ \(\mathbf{\omega}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used in the paper.  
Figure 2: Architecture of HCAE.
## 3 Results and Discussion
### Prediction of Fatigue Parameter (G*.sin\(\delta\)) Using Unaged Parameters  
A major objective of evaluating the methods for predicting fatigue resistance in asphalt binder is to eliminate the laborious laboratory works involved in measuring fatigue performance in the binder. The modelling of fatigue resistance of the asphalt binder is critical in prolonging the service life of the pavement. The evaluation of binder fatigue resistance requires short and long term aged conditioning resulting in physical and chemical changes in the binder.  
The performance of the models is presented in Table 4 and a graphical representation of the point by point comparison of the predicted and measured values for the simulation dataset is in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Training Results**} & \multicolumn{6}{c}{**Simulation Results**} \\ \hline  & & & & \multicolumn{3}{c}{**3\% Late Dataset**} & \multicolumn{3}{c}{**6\% Late Dataset**} \\ \hline  & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE \\ \hline ANN & 0.98 & 0.028 & 0.020 & 0.49 & 0.116 & 0.090 & 0.70 & 0.039 & 0.032 \\ \hline GPR & 0.96 & 0.036 & 0.024 & **0.60** & **0.120** & **0.019** & **0.76** & **0.38** & **0.032** \\ \hline RNN & 0.98 & 0.028 & 0.020 & 0.61 & 0.142 & 0.121 & 0.70 & 0.071 & 0.061 \\ \hline SVM & 0.96 & 0.037 & 0.025 & 0.49 & 0.116 & 0.090 & 0.70 & 0.039 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results for the prediction of fatigue parameter (G*.sin\(\delta\)) parameters using unaged dataset.  
Figure 3: The comparison of predicted vs. measured G*.sin\(\delta\) parameters using the unaged dataset.  
The results in Table 4 show that all model training performed substantially well. However, high model training accuracy does not translate to performance with a simulation of a new dataset that was not previously used in the model calibration. All the models did not perform well with M-L-3 mix simulation dataset. However, the proposed GPR within the two mixes (M-L-3 and M-L-6) used for simulation performed better than ANN, RNN and SVM models. The poor performance of the models at 3% latex modification could be attributed complex chemical and physical changes that takes place during short and long term binder ageing conditioning.  
With 76% correlation between the predicted and the measured value of G*.sin\(\delta\) parameter using the GPR model, it is possible to eliminate the ageing conditions (PAV and RTFOT) test required in the selection of suitable binder that meets specified PG temperature criteria for medium and low density traffic. This could be beneficial to the developing countries within the tropical region who need to use the manufacture specification data to design pavement structures.
## 2 Basic Models and Theory
### Baseline Control System Model
#### 2.3.2 Blade-Pitch Controller  
In the region of above-rated wind speed range, the method of proportional-integral-derivative (PID) is used to control on the speed error between the rated generator speed and the generator speed. The related equation about the small perturbation of the blade-pitch angles about their operating point \(\Delta\beta\) is shown as:  
\[\Delta\beta=K_{P}N_{g}\Delta\Omega+K_{I}\int_{0}^{t}N_{g}\Delta\Omega\text{d }t+K_{D}N_{g}\Delta\hat{\Omega} \tag{12}\]  
Figure 1: Blade element forces and velocities in the BEM model [28; 35].  
where \(\Delta\Omega\) is the small perturbation of angular velocity about the rated speed; \(K_{P}\), \(K_{I}\), and \(K_{D}\) are the proportional, integral, and derivative gains, respectively.  
During normal operation with the blade-pitch controller working, the blade-pitch angle can be adjusted by adding \(\Delta\beta\) to the current blade-pitch angle to regulate the power output.  
For the 5-MW NREL wind turbine used in this paper, we implemented the wind turbine's BCS as an external dynamic link library (DLL) [31]. The source code for DLL can be found in the OpenFAST web site of NREL [37]. Based on the flowchart of the overall integrated control system calculations and the properties of the BCS [29,31], the executable program file can be obtained by using Visual Fortran Compiler [27], and then combined with the input file, the dynamic analysis of wind turbine under external load excitation can be realized. It should be pointed out that the gains for the PID control are 0.01882681 s, 0.008068634 and 0, respectively. More details about compiling source codes [37] and obtaining the appropriate gains of PID can be seen in [27,29,31].
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 1 Introduction
Reference [19] proposed an approximate gradient field calculation method without building an ESDF map, significantly reducing the trajectory generation time. Reference [20] presents an optimization-based framework for multi-copter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The framework can complete various flight path optimization tasks and achieve good flight effects. This kind of method has two problems: First, its security threshold is too conservative, thus sacrificing the smoothness of the trajectory to some extent; second, the security threshold of such methods is usually set to a fixed value, resulting in poor adaptation to different scenarios. In this paper, we propose an improved trajectory generation method based on the B-spline curve, which represents the trajectory of the UAV based on the B-spline curve, then define the optimization function composed of control points, and finally obtain the optimal trajectory by solving the optimization problem. Compared with the trajectory generation method proposed in [16; 19], the method proposed in this paper makes three important improvements: Firstly, this paper proposes a trajectory generation method based on the upper-bound principle of B-spline error, which uses the upper-bound property of the error between B-spline curve and control polygon to propose a more accurate calculation method of security threshold. Compared with the existing safety threshold calculation methods based on the B-spline convex hull property, the safety threshold is reduced by 60%. Then, a trajectory optimization method with dynamic parameter adjustment is proposed, which is different from the current work in which the safety threshold is set to a fixed value. The trajectory generation method proposed in this paper dynamically adjusts the distance optimization parameters according to the smoothness of the trajectory so that the method has better adaptability in different environments. Finally, a dynamic time reallocate method is proposed to obtain a better trajectory. The effectiveness of the trajectory generation method proposed in this paper is verified through simulation and real-world tasks. The test results show that the proposed method can obtain a smoother and shorter time trajectory and achieve a good flight effect in real-world tasks. The rest of this paper is organized as follows: Section 2 introduces the background of the paper, including the differential flatness property of multi-rotor UAV and the formal definition of the trajectory generation problem. Section 3 introduces the motion-planning method proposed in this paper, including the security guarantee method, parameter adaptive adjustment method, and time reallocate method. Section 4 introduces the experiment and experiment results. Section 5 summarizes the thesis.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 5 Conclusions  
In this paper, we propose a two-stage siamese framework based on hierarchical Swin Transformer for building damage assessment tasks named SDAFormer. In Stage 1 of the framework, building segmentation is performed to locate the buildings. Based on the building localization in Stage 1, damage assessment is then performed in Stage 2. Compared with the CNN-based frameworks, SDAFormer can extract long-range semantic information for damage assessment. Moreover, the spatial fusion module is designed to be embedded in the Swin Transformer blocks to facilitate spatial information exchange. SDAFormer is the first to introduce a pure Transformer architecture for a multi-temporal remote sensing interpretation task. Compared with existing CNN-based methods, the proposed framework achieves significant improvements on the large-scale building damage assessment dataset, xBD. Furthermore, four independent disasters are processed for evaluation. The results verify that our framework is robust and has good potential for transferring to other tasks.  
Conceptualization, Y.D. and Z.J.; Data curation, Y.D.; Formal analysis, Y.D.; Funding acquisition, Y.Z.; Investigation, Y.D.; Methodology, Y.D.; Project administration, Y.Z.; Resources, Y.D.; Software, Y.D.; Supervision, Y.Z.; Validation, Y.D., Z.J. and Y.Z.; Visualization, Z.J.; Writing--original draft, Y.D.; Writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 12: The distribution of disaster categories in xBD dataset.  
**Funding:** This research was funded in part by the National Natural Science Foundation of China under Grant 62171016 and 61871413, and in part by the Fundamental Research Funds for the Central Universities under Grant buctrc202001.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** IPublicly available datasets were analyzed in this study. This data can be found here: [[https://www.digital](https://www.digital)]([https://www.digital](https://www.digital)) globe.com/ecosystem/open-data, accessed on 19 December 2021.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
## 6 Models for the Latency
### The Most Suitable Low Power Mode for Any Application  
It is a fact that there is not only one low power mode that provides the lowest latency. The choice depends on the amount of hops included in the communication. The value of the inactivity period that matches the latency when working in park and sniff power modes can be calculated from the models defined in Section 6. The value of the inactivity period is extracted from the equation that defines that match. Two cases are considered: with ping data-packets (i.e., the data traffic goes from master to slave), and with pingback data-packets (i.e., the data go from slave to master).  
The curve where the latency in the sniff mode equals the one in the park mode when ping data-packets are used, i.e., for master-to-slave direction of communication is shown in Figure 5a. The vertical axis represents the inactivity interval of the low power mode (T) measured in s, and the horizontal axis the amount of hops involved in the end-to-end communication (N). Below the limit curve sniff gives smaller latency, but above the curve, it is the park mode the fastest one. In the same way, Figure 5b shows the same limit for the sniff mode when pingback data-packets are used. Furthermore, the horizontal dotted line represents the range of period of inactivity recommended by the chipset manufacturer, i.e., up to 2 s.  
In the range of period of inactivity recommended, i.e., up to 2 s, when the multi-hop communication takes place from master to slave (ping data-packets), the park mode is always the best option for the amounts of hops depicted in Figure 5a, as it provides lower values of latency than the sniff mode. On the contrary, if the multi-hop communication takes place from slave to master (pingback data-packets) and the inactivity interval is 2 s duration, the park mode is the best option in terms of latency, when three or more hops are included in the multi-hop communication. As a summary, for inactivity interval below 1.5 s duration, the sniff low power mode provides the best latency results in Bluetooth multi-hop networking.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 3 Overview of MPTCP Congestion Control Algorithms
### Cubic  
Cubic [30] has been the default congestion control algorithm in Linux since 2006. Recently, Microsoft also adopted Cubic as the default congestion control algorithm in Windows [31]. This algorithm uses the following cubic window growth function, independent of RTT, to achieve better performance in long fat networks.  
\[Wnd(T)=c(T-t)^{3}+Wnd_{max} \tag{1}\]  
where \(c\) is a Cubic parameter, \(T\) is the elapsed time from the last window reduction, and \(t\) is the time period the above function takes to increase to \(Wnd_{max}\), when there is no further loss event.  
Note that \(Wnd(T)\), the window growth function, is executed upon receiving an acknowledgment (ACK) during the congestion avoidance phase. The cubic parameter, \(c\), determines the aggressiveness of the Cubic algorithm in competing for bandwidth with other congestion control algorithms. According to RFC 8312 [30], the value of \(c\) should be set to 0.4 to achieve a good balance between TCP-friendliness and window increase aggressiveness.  
Cubic operates in three modes (TCP friendly, Concave, and Convex) depending on the current congestion window (cwnd). It operates in a TCP-friendly region when the cwnd is small to ensure that Cubic is not penalized relative to standard TCP. When the cwnd is less than \(Wnd_{max}\), the window grows more slowly (concave region), whereas the window increases quickly (convex region) when the cwnd is greater than \(Wnd_{max}\).
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 2 Preliminaries of Conventional Soft Decision Decoder
### Data Transmission over Channels  
It is assumed that the data is transmitted over all additive white Gaussian noise (AWGN) channels, and the binary phase-shift keying (BPSK) modulation is used for transmission.  
The codeword \(c\) is transmitted by the BPSK modulation as shown in Figure 1. For the bit \(c_{i}\) in the codeword \(c\), the received signal \(y_{i}\) by BPSK over the AWGN channel is  
\[y_{i}=(1-2c_{i})+e_{i},i=0,1,\cdots,n-1 \tag{9}\]  
where \(e_{i}\) represents the white Gaussian noise with variance \(N_{0}/2\).  
Figure 1: Signal Constellation of Binary Phase-shift Keying (BPSK).  
Let \(y=[y_{0},y_{1},\ldots,y_{n-1}]\) is the received signal sequence and \(r=[r_{0},r_{1},\ldots,r_{n-1}]\) be the received word. Because of BPSK signaling, the received word can be determined by  
\[r_{i}=\begin{cases}1,y_{i}\leq 0\\ 0,y_{i}>0\end{cases},i=0,1,\ldots,n-1. \tag{10}\]  
The absolute log-likelihood ratio for \(r_{i}\) is derived as  
\[|L(r_{i})|=\left|\log\frac{p(y_{i}|c_{i}=0)}{p(y_{i}|c_{i}=1)}\right|=\frac{4| y_{i}|}{N_{0}} \tag{11}\]  
The bit reliability is thus defined as \(|y_{i}|\) for \(i=0,1,\ldots,n-1\).  
As the received word \(r\) is given, its corresponding syndrome \(s\) is calculated as  
\[s=r\mathbf{H}^{T} \tag{12}\]  
If \(s=0\), it is a codeword by (6). Otherwise, it indicates that the errors happen.
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 2 Related Work
### Multi-Objective Tracking Methods  
Multi-objective tracking (MOT) can be classified into a detection-based tracking framework [26] and a joint detection-and-tracking-based framework, depending on the method. The detection-based tracking framework is a common approach to tracking multiple targets; it relies on target detection as the first step in locating and identifying targets in each frame and crops the objects according to the enclosing frame to obtain all of the targets in the image. Then, it is transformed into a target association problem between neighboring frames, and a similarity matrix is constructed based on IOU, appearance, etc., and solved by methods such as the Hungarian algorithm. As the performance of target detection has improved by leaps and bounds, the field of MOT has revolved around detection-based tracking frameworks for quite some time [27]. Representative methods are SORT and DeepSORT. SORT is an algorithm for tracking objects in a video sequence in real time, which is similar to many modern tracking methods [23]. Consider the case where two targets are occluded. The trajectories of the matched targets cannot be matched for detection, and the targets temporarily disappear. When a target that disappeared briefly reappears later, the target will regain its ID number to stop changing. To enhance the SORT algorithm, the researchers added cascade matching and state estimation to it. In recent years, several joint detection tracking approaches [11] have been introduced to jointly enhance detection and a few other components. The joint tracker provides equivalent performance with minimal computational cost. However, any inconsistencies or inaccuracies in any of the components can propagate errors, which can degrade the overall tracking performance, due to the fact that there are too many components, causing this type of method to not perform very well. Therefore, the detection-based tracking framework remains the most suitable multi-target tracking method in terms of tracking accuracy.
## 2 Materials and Methods
### Operation Tasks  
Two tasks were designed to represent common activities repeated by workers every day, which were pushing and pulling efforts, as shown in Figure 1. To avoid the effect of fatigue, each participant was required to perform each of the tasks five times. Since the slight deviation in DHM joint displacement could produce a significant difference in ergonomics outcomes, some basic positional constraints must be defined during the task execution. Following the safety requirements provided by [17], the restrictions on vertical lifting height should be less than 30 in, and the horizontal distance for placing a load should be in the range of 10 in and 25 in. Hence, in our experimental design, the table height is 75 cm (29.5 in), and the anterior-posterior distance is 50 cm (19.5 in). These positional constraints were confirmed by some participants (including females and males) during the design phase to ensure the set-up was designed appropriately for participants to complete the tasks. Additionally, the set-up was kept consistent for all participants to fully understand their performance when they are required to complete the same task in an industrial setting, where the aforementioned distances cannot be adapted to the size of each worker. The introduction of such constraints ensures that the deviation in results is mostly due to the limitations in the posture prediction algorithm of the software rather than the experimental design. The details are listed below.  
Task 1: Participants needed to push a 40 kg load forward for a distance of 20 cm on a table. The table height was 75 cm. The distance between the starting position (Figure 1a) and end position (Figure 1b) was marked using tape without medial-lateral offset. The feet were placed parallel to each other and in two fixed locations. The distance on the floor between each foot in the medio/lateral direction was 45 cm, which is comparable to the average shoulder width [18]. The horizontal distance from the standing position to the initial pushing position was 50 cm in the anterior/posterior direction. Two grabbing spots were marked on the top corners of the box. These positional constraints could strongly reduce the bias between the experimental measurements and the simulation due to the experimental design.  
Task 2: Participants needed to pull the aforementioned 40 kg load back from the end position to the original spot. All constraints were also applied in Task 2. Each participant was required to push and pull the load as one cycle and repeat the cycle five times.