esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sensor Packet Loss Compensation
### Packet Loss Compensation  
Assuming that the time history of the process input and output is known at sample instant \(k\), then as \(E\{\
u(k+j)\}=0\)\(\forall\,j>0\), _i.e._, the expected future values of the unknown disturbance sequence are zero, Equation (3) may be used to predict the expected change in the process output at sample \(k+1\). Recursion upon this equation can then be used to obtain the change in the output at steps \(k+2\), \(k+3\), \(\ldots\) assuming that \(\Delta u(k+1)\), \(\Delta u(k+2)\), \(\ldots\), and so on are known. Assuming that the last successful packet reception was at sample interval \(k\), and \(j\) consecutive samples have since been lost, Equation (4) below allows the prediction of the \(k+j^{th}\) process output:  
\[\hat{y}(k+j)=y(k)+\sum_{i=0}^{j}\Delta y(k+i) \tag{4}\]  
Thus upon omission of a packet, the value of the lost process information may be reconstructed using Equation (4)successfully received being taken into account. Assuming that the model is of reasonable quality and the burst duration \(\beta\) is not excessive, the compensator will achieve a reasonable quality of control. In most real situations the coefficients of the model represented by Equation (1) may not be accurately known, they may only be partially known, or they may in fact be time-varying due to inherent process non-linearity and wear and tear of components. This warrants the use of an on-line parameter identification technique for their identification, to be described below.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 2 Preliminaries and Related Works
### Aerial Access Network
Motivated by this, in this paper, we propose a FRL-based system which utilizes pseudo-satellites for supporting the satellite communication network and increasing the capacity of network service.
## 4 Experiment and Analysis
### Analysis of Results  
#### 4.4.1 Quantitative Results  
In this study, classical trajectory prediction models including SGC-LSTM [24], S-LSTM [3], Sophie [13], and S-GAN are selected and compared with the SISGAN model. Table 2 presents the comparative findings of the Average Displacement Error (ADE) and Final Displacement Error (FDE) for each method, with measurements expressed in meters. Each row details the prediction error of each model across various dataset scenarios. Notably, the accuracy of the model's predictions is inversely correlated with the magnitude of the prediction error reflected in the table.  
To provide a clearer representation of the variability among the different results, we have converted the prediction errors from Table 2 into a bar chart, as shown in Figure 8, and will analyze them in detail.  
As illustrated in Figure 8, pedestrian trajectory prediction models based on Generative Adversarial Networks (GANs) generally outperform those based on Long Short-term Memory (LSTM) networks. This superior performance is primarily due to the adversarial training between the discriminator and generator in GAN-based approaches, which continuously enhances prediction capabilities.  
A comparison of the prediction performance among the SGAN, Sophie, and SISGAN models reveals that those who effectively learn both pedestrian interactions and environmental data yield more accurate predictions. In the case of the hotel dataset, which predominantly features linear pedestrian trajectories, the SGC-LSTM model surpasses the SISGAN model proposed in this thesis. The SISGAN model, with its social attention and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**ADE/FDE (Meter)**} \\ \cline{2-6}  & **SGC-LSTM** & **SLSTM** & **Sophie** & **SGAN** & **SISGAN** \\ \hline ETH & 0.82/1.72 & 1.09/2.35 & 0.70/1.43 & 0.67/1.13 & **0.63**/**0.95** \\ \hline hotel & **0.45/0.65** & 0.79/1.76 & 0.76/1.67 & 0.72/1.61 & 0.58/1.62 \\ \hline Univ & 0.53/1.10 & 0.67/1.40 & 0.54/1.24 & 0.61/1.28 & **0.50**/**1.10** \\ \hline zara1 & 0.40/0.92 & 0.47/1.00 & **0.30**/**0.63** & 0.34/0.71 & 0.31/0.68 \\ \hline zara2 & 0.36/0.78 & 0.56/1.17 & 0.38/0.78 & 0.42/0.84 & **0.30**/**0.73** \\ \hline Average & 0.51/1.03 & 0.72/1.54 & 0.54/1.15 & 0.58/1.19 & **0.46**/**1.01** \\ \hline \hline \end{tabular} Note: the bold data in the table are the best results predicted by the model.  
\end{table}
Table 2: The Average Displacement Error (ADE) and Final Displacement Error (FDE) of different methods on ETH and UCY.  
environmental information modules, possesses a more complex network structure, which can lead to decreased accuracy in simpler scenarios.  
Conversely, for datasets such as ETH, Univ, and Zara, which include intricate pedestrian interactions and numerous static obstacles, the SISGAN model demonstrates superior predictive performance. This highlights the effectiveness of the SISGAN model in emphasizing pedestrian interactions and assimilating scene information.
## 3 Beamformer Design
### IRS Phase Adjustment
As a result of Algorithm 3, each user allocates about \(N/K\) IRS antenna elements with the best channel gains into its phase control territory so that all the channels through these elements are coherently added to the direct channel from the AP, resulting in the maximized sum channel gains while the sum channel gains among the \(K\) users are almost equalized. The remaining IRS antenna elements of size about \(N(K-1)/K\) outside of a user's territory belong to other users' territories so that their phases can be taken as randomly aligned for the particular user's point. In this case, the methods of order statistics [31] can be applied to analyze the impact of the above IRS beamforming. However, this approach is very tedious and does not produce easy and intuitive results, and we thus instead take an approximation approach to the channel gains through the IRS. Suppose a selection scheme where each user picks up the best channel among \(K\) independent identically complex Gaussian-distributed channel realizations. The resulting channel gain of this scheme is \(7\sum_{k=1}^{K}1/k\)[32], where \(\hat{\gamma}\) is the gain of an individual channel. Combining this with the result of Proposition 2 of [16] and the fact that only \(N/K\) IRS elements are aligned, the approximate channel gain approaches \(N^{2}\frac{P\pi^{2}}{K^{2}16}\sum_{k=1}^{K}1/k\) as \(N\) goes to the infinity. Compared to the result of [16], the channel sharing loss is reflected as \(1/K^{2}\), while the loss is compensated by the selection as appears in the term \(\sum_{k=1}^{K}1/k\). ```
1. Set \(\text{Cnt}=N\) and \(\text{sum}_{k}=0\) for \(k=1,\ldots,K\). 2. Set \(u=0\) and \(\text{Sl}(i)=0\) for \(i=1,\ldots,N\). 3. **while**\((\text{Cnt}>0)\)do
4. Set \(u=(u+1)\bmod K+1\). 5. Set \(t_{max}=0\). 6. **for**\(k=1,\ldots,K\)do
7. **if**\(\text{sum}_{k}\geq t_{max}\)then
8. \(t_{max}=\text{sum}_{k}\)
9. **endif if**
10. **end for**
11. **while**\((\text{sum}_{u}<t_{max})\)do
12. Set \(s_{max}=0\). 13. **for**\(i=1,\ldots,N\), do
14. **if**\((\text{Sl}(i)=0)\bigcap(|\textbf{h}_{u}(i)\textbf{h}_{0}(i)|\geq s_{max})\)then
15. \(s_{max}=|\textbf{h}_{u}(i)\textbf{h}_{0}(i)|\) and \(t_{idx}=i\). 16. **endif if**
17. **end for**
18. \(\text{sum}_{u}=\text{sum}_{u}+s_{max}\) and \(\text{Sl}(t_{idx})=1\). 19. \(\angle(\phi_{idx})=(\angle(g_{u})-\angle(\textbf{h}_{u}(t_{idx})\textbf{h}_{0 }(t_{idx})))\). 20. \(\text{Cnt}=\text{Cnt}-1\). 21. **end while**
22. **end while**
```  
**Algorithm 3**\(\cdot\)\(\boldsymbol{\Phi}\) decision algorithms
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 3 Methodology
### NFStream Preprocessing  
In the NFStream preprocessing step, the process begins by sending pre-named PCAP files to the NFStream tool for traffic flow metering and exporting to CSV files. Based on the NFStream configuration's accounting mode, statistics are provided across four layers, IP, transport, link, and payload, resulting in over 800,000 flow records. In the second step, the NFStream tool, integrated with the nDPI library, detects the application name and application category for each flow, subsequently splitting them into two separate datasets based on these properties. These datasets require additional noise reduction and cleaning. Figure 2 illustrates the flow record counts and distributions in the protocol and application category datasets across the four accounting modes. In the application category dataset, the Web category accounts for over 71% of the sample volume, followed by Net  
work, Download, and System. Similarly, in the protocol category dataset, the TLS category dominates with over 72% of the samples, while BitTorrent, DNS, and SSDP constitute the second-largest group. However, only 64% of the parsed flow records are categorized, leaving 36% of samples labeled as Unknown or Unspecified in the Protocol and Application categories, respectively. This occurs due to the 180-s active timeout setting, which truncates metering and may prevent some flows from accumulating enough metadata for accurate classification. Notably, the sample counts and distributions remain consistent across all accounting modes. The nDPI-parsed application name feature encompasses major and minor protocols, with minor protocols typically referring to specific software application names, such as Amazon, Google, Microsoft, etc. This study focuses on major protocols and excludes specific application software or serviced traffic flow samples. Algorithm 1 outlines the nDPI-Detected Application Category and Protocol Category (nDAPC) dataset generation algorithm. A key step in this algorithm involves removing all flow samples where the detected application category is unspecified, as these flows cannot be assigned to a class in the given scenario. The next step counts the flow samples for each class and eliminates any class with fewer than six samples, as such classes cannot undergo further resampling operations. Although both application category datasets and protocol category datasets detail the distribution of each class and the number of parsed flow samples, they are closely related. For instance, an application named TLS.Microsoft is generally classified under the Cloud category, while TLS.Microsoft365 is placed in the Collaborative category. Since Microsoft365 is a suite of Microsoft Office applications, it is understandable, upon human inspection,  
Figure 2: Protocol and application category datasets sample distribution. why it falls under the Collaborative class. However, when the major protocol is the same, the subtle differences between minor protocols can make class determination challenging.
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 3 Results and Discussion
### Quantitative Assessment
#### 3.1.3 Influence of Signer Representation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Frame-Wise**} & \multicolumn{2}{c}{**Unit-Wise**} \\ \cline{4-9}
**Body and Face** & \multicolumn{2}{c}{**Hands**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**F1** (\(\text{PIR}\))} & \multicolumn{1}{c}{**F1\({}_{\text{w}}^{\star}(\mathbf{t_{w}}=\mathbf{12})\)**} & \multicolumn{1}{c}{**F1\({}_{\text{prR}}^{\star}(\mathbf{0},\mathbf{0})\)**} & \multicolumn{1}{c}{**I\({}_{\text{prR}}\)**} \\ \cline{4-9}  & **OP** & **HS** & & & & & & \\ \hline \hline \multirow{9}{*}{2D} & \multirow{4}{*}{Raw} & & 0.80 & 0.58 (0.51/0.68) & 0.57 (0.75/0.46) & 0.75 (0.75/0.73) & 0.42 \\  & & ✓ & 0.79 & 0.16 (0.44/0.10) & 0.45 (0.87/0.30) & 0.34 (0.68/0.23) & 0.19 \\  & & & ✓ & 0.82 & 0.55 (0.56/0.55) & 0.83 (0.85/0.80) & 0.75 (0.75/0.74) & 0.45 \\  & & ✓ & ✓ & 0.80 & 0.19 (0.52/0.12) & 0.60 (0.86/0.46) & 0.42 (0.61/0.32) & 0.26 \\ \cline{2-9}  & \multirow{4}{*}{Features} & & & 0.86 & 0.68 (0.65/0.71) & 0.88 (0.81/0.87) & 0.78 (0.66/0.94) & 0.56 \\  & & ✓ & & 0.85 & 0.66 (0.61/0.73) & 0.85 (0.75/0.59) & 0.79 (0.67/0.95) & 0.57 \\  & & ✓ & & 0.84 & 0.63 (0.60/0.66) & 0.87 (0.78/0.89) & 0.75 (0.61/0.95) & 0.49 \\  & & ✓ & ✓ & 0.85 & **0.69** (0.60/0.82) & 0.87 (0.78/0.880) & 0.81 (0.69/0.99) & 0.59 \\ \cline{2-9}  & \multirow{4}{*}{Raw} & & & 0.81 & 0.42 (0.56/0.34) & 0.68 (0.81/0.59) & 0.57 (0.64/0.52) & 0.32 \\  & & ✓ & & 0.82 & 0.47 (0.59/0.40) & 0.82 (0.82/0.82) & 0.64 (0.65/0.64) & 0.40 \\  & & ✓ & & 0.83 & 0.45 (0.64/0.34) & 0.73 (0.87/0.62) & 0.62 (0.75/0.52) & 0.38 \\  & & ✓ & ✓ & 0.80 & 0.37 (0.51/0.29) & 0.73 (0.83/0.66) & 0.58 (0.66/0.51) & 0.34 \\ \cline{2-9}  & \multirow{4}{*}{3D} & & & 0.83 & 0.65 (0.35/0.78) & 0.84 (0.73/0.89) & 0.73 (0.59/0.87) & 0.51 \\  & & ✓ & & 0.87 & **0.69** (0.66/0.73) & 0.89 (0.81/0.98) & 0.80 (0.69/0.95) & 0.57 \\  & & ✓ & & 0.86 & **0.69** (0.64/0.75) & **0.90** (0.82/0.99) & **0.83** (0.37/0.87) & **0.60** \\  & & ✓ & ✓ & 0.83 & 0.64 (0.56/0.74) & 0.86 (0.76/0.95) & 0.78 (0.65/0.95) & 0.52 \\ \hline \hline \multirow{9}{*}{2D} & \multirow{4}{*}{Raw} & & & 0.92 & 0.24 (0.18/0.36) & 0.20 (0.14/0.34) & 0.22 (0.16/0.37) & 0.15 \\  & & ✓ & & 0.94 & 0.30 (0.24/0.40) & 0.35 (0.24/0.62) & 0.34 (0.23/0.59) & 0.21 \\  & & ✓ & & 0.92 & 0.10 (0.08/0.13) & 0.28 (0.19/0.56) & 0.28 (0.18/0.35) & 0.16 \\  & & ✓ & ✓ & 0.93 &
## 5 Statistical Analysis of the Protocols Based on Subsets of Responses and ECC
### Model for Heavy Noise Injections  
The information presented in Figure 8 shows that responses in the range of 10 to 100 bits are too small to handle a noise injection of more than 30% erratic bits. A similar analysis is shown in Figure 9 with responses in the 128- to 512-bit range, and \(q=0.5\). An example of the predictive use yields the following results:  
\(P=128\) and \(T=30\%\rightarrow\) E(K) \(\approx 3.5\times 10^{-6}\);  
\(P=256\) and \(T=35\%\rightarrow\) E(K) \(\approx 9.1\times 10^{-7}\);  
\(P=512\) and \(T=40\%\rightarrow\) E(K) \(\approx 2.5\times 10^{-6}\).  
Such bit values of the error rates in K, which are in the part per million range (ppm), are widely acceptable for most applications. However, such an improvement introduces a negative impact on the latencies of the protocol, which are approximately proportional to the length of the responses. The bit error rates in the 35% range are large; thus, the penalty on latencies remains reasonable.
## 2 Vehicle Configuration  
The EV was fitted with a 15-kW radial-flux PMSM that drove the two front wheels indirectly through a gearbox reducer and two identical 7-kW axial-flux PMSMs to drive the left and right rear wheels directly through the hubs (Figure 1a). The control strategy was validated by CarSim simulation software with 15 mechanical degrees of freedom (DOF) for the four-wheeled vehicle. The steering system had one DOF, each wheel had one spin DOF, each suspension had two DOF, and the sprung mass was simplified as a rigid body with six DOF. The vehicle variables for the longitudinal and lateral dynamics models are defined in Figure 1b,c.  
Figure 2a,b provide the TNE maps from the experiments for the driving modes of the three traction motors. The braking modes were estimated from the mirror image of 75% efficiency of the driving mode. The maximum torque was 150 Nm and the maximum speed was 2400 rpm for the 15-kW front motor, and they were 122 Nm and 1200 rpm for the 7-kW rear motors. Three motor control units were responsible for driving the three motors, and three lithium-ion batteries were deployed for providing the power: one pack of 144 V, 72 Ah, and 10.45 kWh for the front drive and two packs of 72 V, 72 Ah, and 5.2 kWh for the two rear drives.  
Figure 1: Electric vehicle configuration and variable definitions (**a**) propulsion system of multiple motors, and (**b**) the longitudinal and (**c**) lateral vehicle dynamics models.
However, traditional PID controllers have limited degrees of freedom and are prone to integral saturation. [PERSON] et al. [24] incorporated PID control into the design of a model reference adaptive impedance controller to improve impedance relationships. Nevertheless, significant overshoot still exists during the contact process, and a stability analysis of a system after introducing PID has not been provided. [PERSON] et al. [25] proposed an improved fractional-order PID impedance control method based on traditional impedance control, achieving good force tracking control results. However, their proposed method lacks theoretical derivation of its validity and stability. In order to address the issues and limitations observed in the aforementioned control algorithms, such as overshooting during robotic contact with the environment, offline algorithm optimization, and the lack of corresponding stability theory derivation, this paper proposes a fractional-order PID adaptive impedance control strategy and provides theoretical stability and validity derivations. This strategy allows for the real-time dynamic estimation of environmental information in uncertain and changing environments. It updates the reference trajectory in real-time based on estimated parameters to achieve adaptive control. Additionally, the flexibility of fractional-order PID further enhances force tracking control performance in complex environments, minimizing overshoot and maintaining stable force tracking. This ensures equipment safety and smooth task execution in applications with stringent machining precision requirements within complex environments. The main contributions of this paper are as follows: a fractional-order PID adaptive impedance control strategy was proposed to enhance the performance of robotic interactions with the environment and the subsequent force tracking; a mathematical derivation of the rationality and stability of the proposed strategy; and the validation of its superiority through comparative simulations. The remaining parts of the paper are organized as follows. Section 2 describes the impedance control system studied in this paper. Section 3 analyzes the stability of the impedance model and presents the design of the adaptive control with an online estimation of environmental parameters. Section 4 proposes the design of the fractional-order PID impedance controller and provides the theoretical proof of stability. Section 5 sets up simulations to validate the proposed strategy. Finally, Section 6 provides the conclusion of the paper.
# Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
## 3 System Model  
### Network Topology of UAV Swarm  
Consider a UAV swarm consisted of \(N\) nodes distributed in a 3D Euclidean space, as shown in Figure 1, in which the pink circles indicate the UAV nodes. Let \(\mathbf{u}_{i}(t)=[x_{i},y_{i},z_{i}],i=1,2,\ldots,N\) denotes the position of the \(i\)-th UAV in the 3D space. \(\mathbf{U}(t)=[\mathbf{u}_{1}(t),\mathbf{u}_{2}(t),\mathbf{u}_{3}(t),\ldots, \mathbf{u}_{N}(t)]^{T}\in\mathbb{R}^{N\times 3}\) indicates the UAV nodes position matrix. The Euclidean distance between the \(i\)-th node to the \(j\)-th node at moment \(t\) is denoted as \(d_{ij}(t)\), where  
\[d_{ij}(t) =\left\|\mathbf{u}_{i}(t)-\mathbf{u}_{j}(t)\right\|\] \[=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}+ \left(z_{i}-z_{j}\right)^{2}} \tag{1}\]  
Then the distance matrix between UAV nodes is presented as  
\[\mathbf{D}(t)=\left[d_{ij}(t)\right]_{N\ast N}\in\mathbb{R}^{N\times N} \tag{2}\]  
The connect ability indicator matrix can be defined as \(\mathbf{E}(t)=\left[e_{ij}(t)\right]_{N\ast N}\in\mathbb{R}^{N\times N}\), where  
\[e_{ij}=\begin{cases}1&d_{ij}\leq R\\ 0&\text{otherwise}\end{cases} \tag{3}\]  
That is to say, the \(i\)-th and the \(j\)-th UAV nodes are capable of establishing communication link if their distance satisfies the communication distance requirement, i.e., \(d_{ij}\leq R\), in which \(R\) is the maximum communication range of the UAV. The universal set of network topology of the UAV swarm can be expressed as a time-dependent function of \(\mathbf{G}(t)\), where \(\mathbf{G}(t)=(\mathbf{U}(t),\mathbf{E}(t))\). The universal set is determined by node positions matrix \(\mathbf{U}(t)\) and connect-ability indicator matrix \(\mathbf{E}(t)\). The topology construction step is essentially to find a \(\mathbf{G}_{d}(t)\in\mathbf{G}(t)\) that guarantees any two UAV nodes in the system reachable through one-hop  
Figure 1: An example of UAV swarm deployment.  
or multi-hop link. For instant, based on the UAV swarm deployed in Figure 1, a schematic diagram of the universal set \(\mathbf{G}(t)\) and typical topology \(\mathbf{G}_{d}(t)\) are shown in Figure 2.
## 5 Conclusions  
For planning/designing a wind farm, the layout optimization is an important step to be implemented in order to increase the profit return of wind farm project by alleviating the wake effect. In this paper, an irregular wind farm layout optimization is carried out with different layout design methods, including the discrete grid method and the continuous coordinate method. For the grid method, two grid densities of 20 \(\times\) 30 and 40 \(\times\) 60 are investigated to verify the performance of the method with the cell refinement. Techniques of handling the irregular boundary with different wind farm design methods are proposed to guarantee the feasible results of wind turbine placement. The optimization results show that the performance of a coarse 20 \(\times\) 30 grid method is the worst, with a large cost of energy and low wind farm efficiency, but the computational cost is lower. By applying the densified grid cells, the grid method achieves a significant increase of total wind farm power output, varying from 1% to 1.5%, with 40 to 46 wind turbines being installed. However, the better optimization results can be achieved at the expense of significantly more computational time (roughly two- to three-fold more time than the coarse grids). In comparison, the coordinate method has the best performance in achieving the largest total wind farm power output. The relative increase of the total wind farm power output with the coordinate method is between 1.5% and 2% for 40 to 46 installed wind turbines with respect to the 20 \(\times\) 30 grid method. The computational time of the coordinate method and the 20 \(\times\) 30 grid method is the least and they are very close to each other. In conclusion, the coordinate method is the best option amongst the wind farm design methods, with a good balance of the wind farm optimization results and computational cost. By further evaluating the wind farm power output with the original 15\({}^{\circ}\) and finer 3\({}^{\circ}\) angular resolutions, it is found that the overall power output decrease is approximately 0.1%-0.3%, depending on the number of wind turbines optimized.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Design Methods** & **Turbine Number** & **40** & **41** & **42** & **43** & **44** & **45** & **46** \\ \hline Grid method (20 \(\times\) 30 grid) & 8303 & 8484.4 & 8708.2 & 8899.5 & 9089.5 & 9274.2 & 9482.8 \\ Grid method (40 \(\times\) 60 grid) & 8316.1 & 8504.9 & 8719.2 & 8924.1 & 9132.9 & 9302.3 & 9528.4 \\ Coordinate method & 8316.8 & 8534.5 & 8745.3 & 8924.6 & 9155.5 & 9345.6 & 9528.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the optimized total wind farm power output (in kW) with different wind farm design methods in a finer 3\({}^{\circ}\) angular resolution of wind climatology.  
**Acknowledgments:** The computational resources by Queensland University of Technology (QUT) are gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.