esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Wind Turbine Deterministic Power Curve Models +
## 3 Polynomial Models
### Quadratic Models  
Quadratic models (QM) are polynomial ones with values of \(n_{j}\) equal to or lower than \(2\), for all intervals. The most used one is that which is based on three intervals according to Equation (5) [12].  
\[P=\left\{\begin{array}{cl}a_{01}&v_{1}\leq v<v_{2}\\ a_{02}+a_{22}\cdot v^{2}&v_{2}\leq v<v_{3}\\ a_{03}&v_{3}\leq v<v_{4}\end{array}\right. \tag{5}\]  
A typical assignation of parameters in this model is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(j\) & \(n_{j}\) & \(a_{0j}\) & \(a_{1j}\) & \(v_{j}\) \\ \hline \(1\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \(2\) & \(1\) & \(-v_{ci}\cdot P_{r}/(v_{r}-v_{ci})\) & \(P_{r}/(v_{r}-v_{ci})\) & \(v_{ci}\) \\ \(3\) & \(0\) & \(P_{r}\) & \(0\) & \(v_{r}\) \\ (4) & - & - & - & \(v_{co}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship among parameters of the linear models and the parameters of the wind turbine (WT).  
For \(j=4\), there is no interval, but \(v_{4}=v_{co}\). The main drawback of this model is its accuracy in the area of the rated wind speed; in other words, the power values corresponding to wind speeds around the rated one are far from the expected ones.  
A second option for the QM was proposed in [48], and it uses a more complex expression, as shown in Equation (6):  
\[P=\left\{\begin{array}{cc}a_{01}&v_{1}\leq v<v_{2}\\ a_{02}+a_{12}v+a_{22}v^{2}&v_{2}\leq v<v_{3}\\ a_{03}&v_{3}\leq v<v_{4}\end{array}\right. \tag{6}\]  
where \(a_{01}=0\); \(v_{1}=0\); \(v_{2}=v_{ci}\); \(v_{3}=v_{r}\); \(v_{4}=v_{co}\); \(a_{03}=P_{r}\); and the values of \(a_{02}\), \(a_{12}\), and \(a_{22}\) are given in Equations (7)-(9), respectively.  
\[a_{02}=\frac{1}{\left(v_{ci}-v_{r}\right)^{2}}\ \left[\ v_{ci}(v_{ci}+v_{r})-4v_{ ci}v_{r}\left(\frac{v_{ci}+v_{r}}{2v_{r}}\right)^{3}\right] \tag{7}\]  
\[a_{12}=\frac{1}{\left(v_{ci}-v_{r}\right)^{2}}\ \left[\ 4(v_{ci}+v_{r})\left( \frac{v_{ci}+v_{r}}{2v_{r}}\right)^{3}-3v_{ci}-v_{r}\right] \tag{8}\]  
\[a_{22}=\frac{1}{\left(v_{ci}-v_{r}\right)^{2}}\ \left[\ 2-4\left(\frac{v_{ci}+v_{r}}{2 v_{r}}\right)^{3}\right] \tag{9}\]  
A QM with five intervals was also proposed, with three of these intervals expressed through 2-degree polynomials, as shown in Equation (10) [14].  
\[P=\left\{\begin{array}{cc}a_{01}&v_{1}\leq v<v_{2}\\ a_{02}+a_{22}v^{2}&v_{2}\leq v<v_{3}\\ a_{03}+a_{23}v^{2}&v_{3}\leq v<v_{4}\\ a_{04}+a_{24}v^{2}&v_{4}\leq v<v_{5}\\ a_{05}&v_{5}\leq v<v_{6}\end{array}\right. \tag{10}\]  
where \(a_{01}\)=0, \(a_{05}\)=\(P_{r}\), \(v_{1}=0\), \(v_{5}=v_{r}\), and \(v_{6}=v_{co}\). The other parameters of the model are not related with the parameters of the WT.
## 4 Experiments
### Running Efficiency  
In order to explore the performance advantages of the framework proposed in this article, we added relevant experiments mainly to explore the performance comparison of our model compared to the baseline model in terms of average detection accuracy (mAP),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **RFEM** & **mAP** & **AP50** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline \multirow{2}{*}{**VisDrone**} & **w/o** & 35.24 & 60.92 & 28.33 & 44.52 & 63.12 \\  & **w/** & **37.43** & **64.02** & **28.38** & **48.11** & **66.20** \\ \hline \multirow{2}{*}{**DOTA**} & **w/o** & 63.85 & 85.47 & 50.12 & 77.23 & 91.31 \\  & **w/** & **65.06** & **87.15** & **50.24** & **83.72** & **94.82** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation comparison with and without the RFEM on the VisDrone and DOTA datasets.  
Figure 13: Loss curves for different loss functions during training process.  
parameter quantity (Params), computational complexity (FLOPs), and inference speed (Speed). The experimental results are shown in Table 7.  
Through the analysis of the experimental results, it can be seen that the system in this article has significantly improved the average detection accuracy compared to the baseline system, with an increase of 4.51% compared to the baseline model. However, the parameter and computational complexity of the model have slightly increased, and the inference speed has decreased compared to the baseline system. However, it can still meet the requirements of real-time detection. In future research, we will improve the model toward a more lightweight direction, further reducing the number of parameters and computation while minimizing the detection accuracy of the model, in order to accelerate the inference speed of the model.
## 4 Problem Definition and Optimization Approaches
### Greedy Approach  
In the greedy approach, we try to minimize the cost of individual tasks with the aim to minimize the overall cost of Equation (22). Firstly, as shown in Algorithm 1, we define a function to approximate the workload of a task using the average observed workload of \(M\) previously completed tasks. As is depicted in Algorithm 2, this procedure repeats until all tasks have arrived. This algorithm is applied to both mobile and edge devices, regardless of the type of device. In the algorithm, local will refer to the current device at which task has arrived and remote will refer to an edge device to which a task may be offloaded. At each repetition, if a task is arrived from the network and from an edge device, meaning the current device is also an edge, the task is simply inserted into the local execution queue. If a task is not arrived from an edge device, state information is retrieved from the current device, including the size of the local execution queue (\(l_{s}\)), the total size of the tasks in the transmission queue(\(s_{s}\)), the size of the current task (\(l(t)\)), and a cached value representing the total number of tasks in the remote device. To acquire this cached value, every edge device periodically sends the total number of tasks in its execution queue to every other device, including both edge and mobile devices. This mechanism is elaborated in Algorithm 3, which is also used in the deep Q-learning approach and is explained in more details in the corresponding section. ```
1:\(W_{l}\leftarrow\) an empty list \(\triangleright\) For storing task workloads
2:repeat
3: wait until a task result arrives
4:\(r\leftarrow\) newly arrived task result
5:\(w\leftarrow\) the workload of task \(r\)
6: insert \(w\) into \(W_{l}\)
7:while the count of \(W_{l}\) is higher than \(M\)do
8: remove the oldest item in \(W_{l}\)
9:endwhile
10:\(w_{t}\gets average(W_{l})\)
11:until all task results have arrived
```  
**Algorithm 1** Greedy algorithm--workload approximation  
Next, the delay and power consumption of local execution and transmission of the task is approximated. These approximations are based on the number of tasks and their size. Arguably, the difficulty in finding an accurate approximation to these values is one of the drawbacks of the greedy approach. Having calculated the delay and power consumption, the value \(\alpha.d_{l}+\beta.p_{l}\) represents the cost of local execution and the value \(\alpha.d_{r}+\beta.p_{t}\) represents the cost of offloading, where \(d_{l}\), \(d_{r}\), \(p_{l}\), and \(p_{t}\) are the delay for local and remote execution and power consumption for local execution and transmission, respectively. The values \(\alpha\) and \(\beta\) are set using similar variables in Equation (22). Finally, if the cost of local execution is less than offloading the task, the task is inserted into the local execution queue. Otherwise, the task is inserted into the transmission queue.
# Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection
## 3 Robofish Design
### Electronic Design
#### 3.4.2 Hardware Choices  
To fulfil the requirements stated in the previous section, while also making the platform upgradable in the future, the Xilinx Zynq 7000 SoC platform was chosen for the main processor of the system. The Zynq 7000 SoC is built around a hybrid processor and FPGA architecture. It consists of two ARM Cortex-A9 processor cores and Artix-7 FPGA-programmable logic, with a high-bandwidth AMBA-AXI interface between them. This platform enables rapid development of software systems using a Linux operating system on the processor cores, with the ability to offload processor-intensive tasks to the FPGA fabric. Offloading demanding tasks to the FPGA speeds up execution time for tasks like vision processing with potential power-saving benefits too, which is important for a battery-powered autonomous vehicle such as this. The FPGA fabric can also be used to create an inter-segment communications controller for communicating between the head and other segments without sacrificing processor time, resulting in higher-reliability communication. For the other segments in the robot, the STM32 platform was chosen. Each segment is a modular element of the system, which accelerates development and upgradability.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
###### Abstract
The contributions of this paper are the following:  
* It compiles a state-of-the-art survey on how 5G-related technologies and the requirements set for 6G affect the Industry 4.0 movement;
* It provides a comprehensive overview on model-driven engineering (MDE) techniques and tools to be used for planning, modeling, and designing complex systems;
* It outlines interoperability and integrability frameworks for cyber-physical system of systems to be applied in multi-level, multi-stakeholder industrial production;
* It extends the 5G service slicing guarantees with the offerings of telco-clouds;
* It provides an overview of speech recognition applications in the production area, including security-related issues and handling noisy environments. The rest of the paper is organized as follows. Section 2 describes the ecosystem of industrial production in broad and narrow senses, including the stakeholders and their interoperability. Section 3 provides a comprehensive overview and comparison of modeling approaches and frameworks in the field of Industrial IoT. Regarding operational production systems, Section 4 surveys how information flow is made smoother when 5G and 6G technologies are on board - and it is extended by an analysis of telco cloud possibilities in Section 5. Human-machine communication can be made more natural (for humans) by the use of speech commands - their usage in the industrial sector is surveyed by Section 6. Section 7 concludes the paper. For better visibility, the structure of the paper is illustrated by Figure 1.
## 1 Introduction
These simulations compare the performance of the proposed algorithm with two conventional methods that focus on maximizing persistence or optimizing received signal strength, respectively. The results demonstrate the superiority of our algorithm, showcasing that the overload of the satellites is more balanced. The algorithm can obtain a trade-off between remaining service time and capacity by dynamically adjusting model parameters. The rest of this paper is structured as follows: First, the selection problem of the UAV-LEO backhaul links is introduced in Section 2. Then, the derivation, the definition, and the heuristic algorithm of the optimization model are proposed in Section 3. Furthermore covered in Section 4 are evaluations and discussions of the simulation results. Finally, Section 5 contains a summary of the paper.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 1 Introduction
In Section 4, the ASYM-DBT method is described in detail, including tracking channel architecture and its theoretical thermal noise performance. In Section 5, a software-defined receiver developed by Tsinghua University is used to process BII and BIC signals from a signal generator. A simulated experiment is designed to verify the theoretical thermal noise performance of the ASYM-DBT method. Finally, conclusions are drawn in Section 6.
# A New Generation of Fast and Low-Memory Smart Digital/Geometrical Beamforming MIMO Antenna
## 6 Conclusions  
For the next-generation wireless networks, massive MIMO is the essential enabling technology, combining the antenna arrays at the transmitter and the receiver to offer exceptional spectral and energy efficiency. This paper elaborates on the overall performance of the proposed smart MIMO antenna system, namely, the new digital/geometrical MIMO. According to the mathematical formulation of the signal processor, software code implementation, and calculations, the overall gain of the proposed system is 25.52 (14.07 dBi). The signal processing gain at the receiver is 64 (18.06 dBi). Therefore, the overall power gain for this proposed new digital/geometrical smart MIMO system is 1633.28 (32.13 dBi). When we calculate the overall directivity by forming the beams towards a selected direction at the transmitter and the receiver, the overall power gain value increased to 134.56 (21.28 dBi).  
The proposed system has several advantages that make it suitable for use in M2M and IoT applications. It can radiate very narrow beams with low energy consumption, and it can form and steer beams in any desired direction. The phase delay shifters can optimize the beamforming to reduce side lobes and improve the overall directivity.  
The paper suggests future developments of the work, such as adjusting the transmitter weights to further increase the power gain of the transmitter, improving the performance of the receiver, and eliminating spatially distributed noise by beamforming at the receiver. The implementation of the physical antenna structure is planned to evaluate and fine tune its hardware performance, making it readily available for use in 5G and 6G wireless systems and beyond.  
Overall, the proposed digital/geometrical MIMO system shows promising results for achieving high spectral and energy efficiency in wireless networks, and its potential applications in M2M and IoT make it an interesting technology to watch in the future.  
**Author Contributions:** Conceptualization, K.P. and P.R.P.H.; methodology, K.P.; software, K.P., W.H.S.C.P. and S.P.P.M.; validation, K.P., W.H.S.C.P. and S.P.P.M.; formal analysis, K.P., P.R.P.H. and X.F.; investigation, K.P., W.H.S.C.P., S.P.P.M., P.R.P.H. and X.F.; resources, K.P.; data curation, K.P., W.H.S.C.P. and S.P.P.M.; writing--original draft preparation, K.P., W.H.S.C.P. and S.P.P.M.; writing--review and editing, K.P., P.R.P.H. and X.F.; visualization, K.P., W.H.S.C.P. and S.P.P.M.; supervision, K.P., P.R.P.H. and X.F.; project administration, K.P.; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Particle Size Distribution Analysis  
The dynamic light scattering (DLS) method was used to analyze the particle size distribution of KKD-ZnO-NPs. According to DLS analysis, the hydrodynamic Z-average of KKD-ZnO-NPs was 676.65 \(\pm\) 47.23 nm with a PDI of 0.41 \(\pm\) 0.0634 (Figure 3). In the following discussion, the obtained hydrodynamic size was in accordance with the particle size range observed by FE-TEM (500-1000 nm). A zeta analyzer was used to test the nanoparticle's stability; the KKD-ZnO-NPs zeta potential observed was around \(-\)23.75 \(\pm\) 0.2 mV, confirming the nanoparticle's stability. The low PDI value indicated that nano composition was polydisperse with multiple size populations, which was confirmed by FE-TEM and FE-SEM.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### ML Techniques
#### 4.5.2 Deterministic ML Technique  
There are no assumptions regarding signal waveforms for the deterministic ML (DML) model (i.e., average signal waveforms have this form: \(\mathbf{E}\{\widetilde{q}(t_{i})\}=\mathbf{Y}^{H}(\mathbf{\Omega})\widetilde{\mathbf{s}}( t_{i})\)). The covariance matrix of the array depends on the noise term  
\[\mathbf{R}=\mathbf{E}\Big{\{}(\widetilde{q}(t_{i})-\mathbf{E}\{\widetilde{q}(t_{i})\}) \big{(}\widetilde{q}(t_{j})-\mathbf{E}\{\widetilde{q}(t_{j})\}\big{)}^{H}\Big{\}}= \widehat{o}_{a}^{2}\mathbf{I} \tag{51}\]  
when the average signal waveform is derived from the signals. According to [31], the DML likelihood function for different snapshots is expressed as  
\[p\Bigg{(}\frac{\widetilde{\mathbf{A}}_{N}}{\Omega},\widetilde{S}_{N},\widehat{o} _{a}^{2}\Bigg{)}=\prod_{i=1}^{N}\frac{1}{\pi\widehat{o}_{a}^{2}\mathbf{I}}\times \exp\Bigg{(}-\frac{\widetilde{\mathbf{q}}(t_{i})-\mathbf{Y}^{H}(\Omega)\widetilde{ \mathbf{s}}(t_{i}))^{H}\Big{(}\widetilde{\mathbf{q}}(t_{i})-\mathbf{Y}^{H}(\Omega) \widetilde{\mathbf{s}}(t_{i})\Big{)}}{\widehat{o}_{a}^{2}}\Bigg{)} \tag{52}\]  
where \(\widetilde{\mathbf{s}}_{N}=[\widetilde{\mathbf{s}}(t_{i}),\dots,\widetilde{ \mathbf{s}}(t_{N})]\) are signals due to multipath. Setting \(\Omega\) and \(\widetilde{\mathbf{s}}_{N}\) constant from the negative log-likelihood, the variance can be computed as in [31]:  
\[\widehat{o}_{a}^{2}=\frac{1}{\left(N_{h}+1\right)^{2}}\mathit{Tr}\big{\{}\bm {P}\widetilde{\mathbf{Y}}(\mathbf{\Omega})\hat{R}_{aa}\big{\}}. \tag{53}\]  
The localization function and signal estimates can be deduced using the negative log-likelihood, which gives rise to a nonlinear least-square problem [31]:  
\[\mathbf{P}_{DML}(\mathbf{\Omega})=\mathit{Tr}\big{\{}\mathbf{P}\widetilde{\mathbf{Y}}(\mathbf{ \Omega})\hat{R}_{aa}\big{\}} \tag{54}\]  
and  
\[\hat{\mathbf{s}}_{N}=\mathbf{Y}^{\sharp}(\mathbf{\Omega})\widehat{A}_{N} \tag{55}\]  
respectively.  
Asymptotically, MUSIC is equivalent to DML for large sizes of samples and uncorrelated signals [31] (i.e., in the incoherent case).
## 5 Discussion  
### Overview of Methodologies  
The results of this research imply that the addition of information from SVI can improve the accuracy in slum classification when compared to the exclusive use of RSI. However, the effectiveness of this improvement is highly dependent on the method of integration of SVI into the FCN-DK network as well as the quality and characteristics of the dataset used. A simple combination of RSI and SVI in FCN-DK showed significant improvement compared to using RSI as the only input.  
Conversely, the implementation of slum mapping using the field survey method has been demonstrated to be the most reliable and effective approach for classifying the slum level of an area, in comparison to the utilization of RSI and SVI with FCN DK or FCN modeling alone.  
The following conclusion is drawn from comparing the results obtained using FCN and FCN-DK, focusing on evaluating the combination of RSI and SVI compared to the use of RSI alone. The findings show that incorporating SVI increases the accuracy of slum mapping in the absence of direct field surveys. As illustrated in Figure 8, the comparison is presented between slum visualization outputs generated by FCN and FCN-DK models separately.
## 6 Conclusions  
This paper proposes a speed-coordinated control method for multi-PMSMs based on the fixed-time consensus protocol for MASs combined with CSMC. The speed cooperative system of multi-PMSMs is regarded as a MAS, and an undirected topology structure based on communication between each PMSM is established. Under the framework of vector control for PMSMs, a fixed-time consensus protocol is designed to replace the traditional speed loop controller. TISMO is incorporated into the protocol to estimate disturbances and introduce feedforward compensation, obtaining the desired q-axis current, thereby achieving speed consistency among multi-PMSMs within a fixed time. Simultaneously, CSMCs are designed in the current loop, effectively improving the system's performance. The experimental results compared with the traditional DCC method are shown in Table 3. The results indicate that the new control method exhibits good tracking and synchronization performance, along with faster convergence and enhanced robustness. The next step will be to study the coordinated speed control of multiple motors under network delays, data packet loss, and other issues that may arise in suboptimal network conditions.  
**Author Contributions:** Conceptualization, L.H. and X.L.; methodology, L.H.; writing--original draft preparation, X.L.; writing--review and editing, L.H. and X.L.; visualization, L.H. and X.L.; project administration, L.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under grant number 52177047.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 3 Wide-TSNet
### Data Processing Layer  
Data processing begins with the Bitcoin price data being loaded from a CSV file. The data are read into a Pandas DataFrame, in which the 'date' column is converted into the DateTime format and set as the index of the DataFrame. This step ensures that the data are chronologically ordered, which is essential for time-series analysis. The number of samples and timestamps for the training set is then defined. The number of samples refers to the total number of data points to be used for training, while the number of timestamps refers to the length of each time-series data point. In this case, we used 2554 samples, each containing 50 timestamps.  
We also define the number of valid days and samples for our validation set. The valid days represent the number of days to be used for validation, and the valid samples represent the number of data points to be used for validation. We calculate the number of valid samples as one-fifth of the total number of samples and generate the start indices for our training and validation sets. These indices represent the starting points of our time-series data points within the original data frame. For the training set, the start indices are randomly generated in the range between 0 and the length of the DataFrame minus the number of timestamps and valid days. For the validation set, the start indices are randomly generated in the range between the length of the DataFrame minus the number of timestamps and valid days and the length of the DataFrame minus the number of timestamps.  
We then create our training and validation sets with the generated start indices. For each start index, we extract a time-series data point from the 'close' column of the DataFrame, starting from the start index and ending at the start index plus the number of timestamps. These data points are then appended to the respective training or validation set.  
Finally, we convert our training and validation sets into NumPy arrays. This step is necessary as Wide-TSNet, like most machine learning models, requires the input data to be numerical.