esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### Fault Analysis of the DFIG-Based Wind Farm with LVRT Capability  
For a DFIG-based wind farm with LVRT capability, the wind farm remains connected to the power grid when a fault occurs on transmission line. The capacity of a wind farm is generally much smaller than that of the power grid. The equivalent impedance of the wind farm converted from 690 V to a high voltage level is much larger than the equivalent impedance of the grid. The activation of the crowbar circuit further increases the equivalent impedance of the wind farm. Therefore, compared with the traditional power system, the wind farm side is a weak power supply system.  
Figure 4: The real fault case when a phase C ground fault occurs on the transmission line connected to DFIG-based wind farm without low voltage ride through (LVRT) capability.  
According to the above analysis in Section 2.1, the fault current consists of a decaying DC component, a decaying AC component at rotor speed frequency, and a forced AC component at synchronous frequency. Especially when the voltage sag is deep, the AC component at rotor speed frequency is the main AC component at the beginning of the fault. The DFIG rotor speed is generally 0.7 to 1.3 pu. Thus, the main frequency component of the wind farm side current is a 35-65 Hz AC component in the early stage of the fault, although the voltage is mainly 50 Hz due to the grid support. That is to say, the wind farm side shows current frequency deviation during disturbances. Two real fault cases are provided to illustrate the fault characteristics of the wind farm: a single-phase ground fault and a phase-to-phase short circuit fault on the transmission lines connected to DFIG-based wind farms with LVRT capability. As for the balanced fault, the detailed work has been done in papers [28; 29].
## 4 Algorithms  
For the purpose of describing our pruning and quantization algorithms, we first introduce our notation. We denote the input data to a neural network with \(L\) layers as \(\mathbf{x}\) and, for discriminative tasks, its associated output label as \(\mathbf{y}\). We denote the activations of the network as \(\{\mathbf{h}_{i}\}_{i=1}^{L}\), where \(\mathbf{h}_{i}\) denotes the activations of hidden layer \(i\) and \(\mathbf{h}_{0}=\mathbf{x}\) for convenience. The set of weights of each layer are denoted as \(\{\mathbf{w}_{i}\}_{i=1}^{L}\), where \(\mathbf{w}_{i}\) is the set of weights for layer \(i\) with \(C_{i}\) output channels, which we refer to as neurons. Finally, we denote the binary mask used to prune each layer as \(\{\mathbf{m}_{i}\}_{i=1}^{L}\) and the importance estimates of each neuron as \(\{\mathbf{m}_{i}\}_{i=1}^{L}\), where \(m_{i,c}\in\{0,1\}\) and \(\mu_{i,j}\in\mathcal{R}^{+}\) are, respectively, a scalar value for the binary mask and non-negative importance estimate for each neuron \(c\) in layer \(i\), where \(c\in\{1,\cdots,C_{i}\}\).  
Figure 4: As discussed in Section 3, we evaluate the Kendall coefficient of rank correlation (top row) and the Levenshtein distance (bottom row) over 30 independently trained discriminative models (left column) and generative models (right column). We plot the correlation and distance between \(\mathbf{r}_{\mu_{i}}\) and \(\mathbf{r}_{\mathbf{m}_{i}|s}\) in blue, and the correlation and distance between \(\mathbf{r}_{\mu|s}\) and \(\mathbf{r}_{\mathbf{v}_{i}}\) in green.
# A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
## 4 Case Study  
### Data Collection and Augmentation  
The vibration signals used in the case study were collected from 1.5 MW pitch-controlled wind turbines located on a wind farm in northeast China, as shown in Figure 3. The transmission chain mainly includes a bladder rotor, a planetary gearbox with a ratio of 100.48:1, and a doubly-fed induction generator. The planetary gearbox contains two planetary transmission mechanisms and one parallel transmission mechanism. The accelerometer was attached to the ring of the second-stage planetary mechanism by magnetic. Figure 4 illustrates the internal structure of the gearbox and the location of the sensors. Table 1 lists teeth number information of the second-stage planetary mechanism.  
Figure 3: Planetary gearbox in the case study.  
The case study involves four health conditions: normal (NS), ring gear tooth pitting (RP), planetary gear meshing misalignment (MA), and pitting-misalignment concurrence (CO). All the above failures occur in the second-stage planetary mechanism. Each health condition corresponds to four planetary gearboxes and, thus, there are 16 gearboxes used in this experiment. During the data collection, the speed of the gearboxes continuously varied. The characteristics of varying speed operation of wind turbines and changeable operating environment bring more challenges to fault analysis and diagnosis.  
The piezoelectric accelerometer samples at a frequency of 16,384 Hz and each signal lasts for 10 s. Ten segments are randomly intercepted from each signal to calculate the T-F spectrum. In this paper, generalized S-transform [41] (generalized factor = 2) is used to generate 512 \(\times\) 512 T-F images from signal segments. Figure 5 shows the vibration signal and T-F spectrums corresponding to the four health conditions.  
The shortfall in dataset scale, accompanied by the possibility of overfitting, is one of the common problems in model training. 10 \(\times\) 4 \(\times\) 4 = 160 samples are insufficient to train deep learning models in this case study. Therefore, the Augmentor toolkit [42] is utilized to mirror or distort the original image to obtain 800 additional training samples. After data augmentation, the total number of experimental samples reaches 960. Referring to similar studies [43], datasets of this scale can effectively inhibit over-fitting.
# Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors
## 4 Conclusions  
This study provides an in-depth analysis of ignition processes and flame propagation performance in cavity-based integrated combustors, focusing on the effects of various tail-groove strut configurations. The main results are summarized below:  
1. The ignition process was successfully validated through comparisons of numerical simulations with experimental observations. This study identified and categorized the ignition process into four distinct stages: kernel generation, kernel propagation, flame refilling, and flame growth.
2. The tail-groove strut configurations demonstrate superior flame propagation distances compared to the normal strut, which is attributed to reductions in the flame refilling duration.
3. The tail-groove strut features three recirculation zones--A, B, and C--compared to two in the normal strut. Recirculation B significantly enhances fuel droplet breakup and atomization, improving radial flame propagation and reducing the flame refilling duration.
4. Increasing the strut length (\(L\)) from \(L12D6\) to \(L16D6\) lowers the turbulent kinetic energy, reducing the flame core temperature. Conversely, increasing \(D\) from \(L12D6\) to \(L12D10\) boosts the turbulent kinetic energy in specific areas, reducing the flame area but raising the flame core temperature.  
Conceptualization, S.Z. and H.C.; Methodology, S.Z. and H.C.; Software, H.C.; Writing--original draft, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Aero Engine Corporation of China, grant number HFZL2023 CX003 and Shenzhen Science and Technology Program (Grant No. RCBS20221008093126068).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.
## 4 Methodology
### Development of the Transformer-Attention RUL Prediction Model  
The transformer-attention RUL prediction model was designed to directly utilize the segmented vibration signals \(y_{i}(t)\) as input, and the inherent intricate temporal dynamics can be captured in the bearing's operational conditions [32; 33]. Each segment \(y_{i}(t)\) represents the filtered vibration data over a fixed time window \(T_{w}\) with an overlap of \(\Delta T\); the capture of local temporal features is essential for accurate RUL estimation. Figure 3 shows the structure of transformer-attention algorithm.  
The segmented signals \(\{y_{1}(t),y_{2}(t),\ldots,y_{N}(t)\}\) are fed into the transformer encoder, which processes these raw time-series segments using multi-head self-attention mechanisms and positional encodings. This approach enables the model to learn the temporal dependencies and patterns directly from the vibration data, and the potential loss of information can be avoided. In the transformer encoder, each vibration segment \(y_{i}(t)\) is first embedded into a higher-dimensional space through a linear transformation, and the addition of positional encodings is further used to preserve the order of the segments. The embedded sequence is passed through multiple self-attention layers, the model learns to attend to different parts of the sequence, and the key segments reflect significant degradation patterns in the bearing.  
Figure 3: The structure of transformer-attention algorithm.  
The self-attention mechanism calculates the attention scores \(\alpha_{i}\) for each segment, and the importance relative to the current condition of the bearing is calculated [34]. The attention score for each segment is calculated:  
\[T\alpha_{i}=\frac{\exp\left(Q_{i}\cdot K_{i}^{\top}/\sqrt{Q_{k}}\right)}{\sum_{j=1 }^{N}\exp\left(Q_{j}\cdot K_{j}^{\top}/\sqrt{Q_{k}}\right)} \tag{3}\]  
where \(Q_{i}\) and \(K_{i}\) are the query and key vectors derived from the segment \(y_{i}(t)\), and \(d_{k}\) is the dimensionality of these vectors. \(\alpha_{i}\) is the attention score. Then, the transformer function for each segment is represented as follows:  
\[Z=softmax\left(\frac{QK^{\top}}{\sqrt{d_{k}}}\right)V. \tag{4}\]  
where \(Q,K,\) and \(V\) are the query, key, and value matrices derived from the vibration segments \(y_{i}(t)\). The function outputs a context-aware representation \(Z\), which captures the temporal interactions among all segments. In addition, the weighted sum of the segments \(y_{weighted}(t)\) is computed as follows:  
\[y_{weighted}(t)=\sum_{i=1}^{N}\alpha_{i}\cdot y_{i}(t) \tag{5}\]  
This aggregated representation \(y_{weighted}(t)\) captures the most informative aspects of the bearing vibration data, and the temporal patterns are most indicative of its degradation state. The results are fed into a regression layer to predict the RUL of the bearing. The regression function is defined as follows:  
\[\text{RUL}_{pred}=f\left(y_{weighted}(t)\right) \tag{6}\]
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 4 Results and Discussion
### Performance of Color-Based Segmentation
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Image** & **GT** & **Count** & **TP** & **FP** & **FN (-b)** & **Precision** & **Recall (-b)** & \(F_{1}\) **Score (-b)** \\ \hline DJI\_0225 & 107 & 106 & 107 & 0 & 1 (0) & 100.00 & 99.07 (100.00) & 99.53 (100.00) \\ DJI\_0226 & 115 & 114 & 114 & 0 & 1 (0) & 100.00 & 99.13 (100.00) & 99.56 (100.00) \\ DJI\_0227 & 117 & 115 & 115 & 0 & 2 (0) & 100.00 & 98.29 (100.00) & 99.14 (100.00) \\ DJI\_0228 & 115 & 115 & 114 & 1 & 1 (0) & 99.13 & 99.13 (100.00) & 99.13 (99.56) \\ DJI\_0229 & 119 & 117 & 117 & 0 & 2 (1) & 100.00 & 98.32 (99.15) & 99.15 (99.57) \\ DJI\_0230 & 125 & 120 & 120 & 0 & 5 (1) & 100.00 & 96.00 (99.17) & 97.96 (99.59) \\ DJI\_0231 & 130 & 125 & 125 & 0 & 5 (2) & 100.00 & 96.15 (98.43) & 98.04 (99.21) \\ DJI\_0232 & 139 & 130 & 130 & 0 & 9 (3) & 100.00 & 93.53 (97.74) & 96.65 (98.86) \\ DJI\_0233 & 141 & 138 & 137 & 1 & 4 (2) & 99.28 & 97.16 (98.56) & 98.21 (98.92) \\ DJI\_0234 & 143 & 142 & 142 & 0 & 1 (1) & 100.00 & 99.30 (99.30) & 99.65 (99.65) \\ \hline
**Average** & **125** & **122** & **122** & **0.2** & **3.1 (1)** & **99.84** & **97.61 (99.24)** & **98.70 (99.54)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performance metrics for each image (GT: ground-truth, TP: true positive, FP: false positive, FN: false negative, (-b): excluding errors in the borderline). palm trees are separated, but not in dense areas. They found that using object-based analysis is useful to reduce one-to-many errors, but not many-to-one errors. Using template matching and object-based analysis in a subset of the mosaic image, a total of 582 oil palm trees was detected for a ground-truth of 509 trees (\(F_{1}\) score of 87%). The authors in [28] reported a \(F_{1}\) score of 91%, also counting oil palm trees in a single RGB image with a ground-truth of 184 trees. They used an extreme learning machine (ELM) classifier on a set of extracted SIFT keypoints and local binary patterns (LBPs) to analyze texture information. In [58], a local-maxima-based algorithm on UAV-derived canopy height models achieved a detection of 312 trees for a total of 367 reference trees (\(F_{1}\) score of 86%) in a forest, which is a far more complex scenario.
# Preventing Black Hole Attacks in AODV Using RREQ Packets
###### Abstract  
Ad hoc networks, formed by multiple wireless communication devices without any connection to wired or intermediary devices such as by access points, are widely used in various situations to construct flexible networks that are not restricted by communication facilities. Ad hoc networks can rarely use existing infrastructure, and no authentication infrastructure is included in these networks as a trusted third party. Hence, distinguishing between ordinary and malicious terminals can be challenging. As a result, black hole attacks are among the most serious security threats to Ad hoc On-demand Distance Vector (AODV) routing, which is one of the most popular routing protocols in mobile ad hoc networks. In this study, we propose a defense method against black hole attacks in which malicious nodes are actively detected to prevent attacks. We applied the proposed method to a network containing nodes engaging in black hole attacks, confirming that the network's performance is dramatically improved compared to a network without the proposed method.  
network security; routing protocol; ad hoc network Article
## 8 Future Directions
Future studies could investigate the feasibility of hybrid energy solutions, the technical challenges of integrating them with IoT devices, and the cost-benefit analysis for different applications. * Edge and fog computing play a significant role in reducing data transmission energy costs by bringing data processing closer to the data source. However, more research is needed to understand the optimal distribution of computational tasks between edge, fog, and cloud layers, especially in multi-layered IoT architectures. Future studies should explore how to balance workload distribution across these layers for maximal energy savings while maintaining Quality of Service (QoS). By investigating adaptive frameworks that leverage the strengths of each layer, researchers can develop more efficient task allocation strategies, minimising latency and energy consumption in high-density IoT deployments. * As IoT systems continue to expand, securing data becomes increasingly crucial; however, traditional security protocols often impose high energy costs. Future research should focus on designing lightweight, energy-efficient security protocols that protect data integrity and privacy without significantly impacting energy performance. For instance, researchers could investigate adaptive security mechanisms that adjust protection levels based on data sensitivity or network conditions. This would allow IoT networks to balance security and energy efficiency, supporting applications with high security demands, such as healthcare or finance, while minimising energy usage. * Establishing standardised testing frameworks and benchmarking tools for energy efficiency research in IoT would streamline comparisons across studies. Such frame works could include guidelines on experimental setup, data collection, and analysis, as well as standardised datasets for benchmarking purposes. Benchmarking tools could facilitate consistent testing of algorithms, protocols, and hardware components, offering researchers a baseline for evaluating energy performance. Standardised tools and frameworks would encourage reproducibility, foster collaboration, and accelerate progress in energy-efficient IoT research.
## 2 Basic Theory
### Acmd
Therefore, in practice, in order to reduce the influence of noise, a low-pass filter is used to process the signal growth. The IF may eventually be updated as follows:  
\[\mathbf{f}_{i}^{j+1}=\mathbf{f}_{i}^{j}+\left(\frac{1}{\beta}\mathbf{\Omega}^{ T}\mathbf{\Omega}+\mathbf{I}\right)^{-1}\Delta\mathbf{f}_{i}^{-j} \tag{9}\]  
where \(\mathbf{f}_{i}^{j}=\left[\overline{f}_{i}^{j}(\tau_{0}),\cdots,\overline{f}_ {i}^{j}(\tau_{N-1})\right]^{T},\Delta\mathbf{f}_{i}^{-j}=\left[\Delta\overline {f}_{i}^{j}(\tau_{0}),\cdots,\Delta\overline{f}_{i}^{j}(\tau_{N-1})\right]^{T}\), \(\mathbf{I}\) is the identity matrix, \(\mathbf{\Omega}\) is the second-order difference matrix, and \(\left(\frac{1}{\beta}\mathbf{\Omega}^{T}\mathbf{\Omega}+\mathbf{I}\right)^{-1}\) denotes a low-pass filter. If too small a \(\beta\) is used, the output IF will be very smooth. However, rapidly changing IFs require a relatively large \(\beta\). Vector \(\mathbf{G}_{i}^{j+1}\) can be updated by employing \(\mathbf{f}_{i}^{j+1}\) (Equations (3)-(5)), and Equations (6)-(9) can be repeated in the next iteration. As mentioned before, the parameters \(\alpha,\beta\) control the components of the output signal and IF, respectively. In ACMD, in order to process the vibration signal with fast fluctuation IFs, it is recommended to use a relatively large \(\alpha\) and \(\beta\) (\(\alpha=1\times 10^{-3}\), \(\beta=1\times 10^{-4}\)), and the termination threshold \(\
u\) is set to \(1\times 10^{-8}\) according to the required accuracy. However, the choice of \(\alpha,\beta\) is artificially set, and there are errors in practical applications. Therefore, this paper employs the state transition algorithm (STA) to adaptively optimize \(\alpha,\beta\) to improve the signal-to-noise ratio (SNR) and resolution of the signal.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 2 RF Energy Harvesting Principles
### Impedance Matching Network  
The impedance matching network is employed to prevent power leakage, and it guarantees maximum power transmission between the source of energy and the load. To achieve maximum power transfer, the impedance of the output antenna should be identical to the load impedance. The incident wave at the load will be reflected when an impedance mismatch leads to reduced efficiency. In the RFEH system, the receiver antenna acts as an RF source, while the rectifier represents the load. The impedance matching network concurrently functions as a low-pass filter in suppressing higher-order harmonics generated by the rectifying circuit, which can be re-radiated by the antenna, causing additional loss [103]. A perfect matching network for the RFEH should achieve impedance matching between the source and the load with minimum insertion loss and at any input power, frequency, and load resistance. Matching networks are classified into three configurations, which are L, Pi, and T configurations, as shown in Figure 4. The commonly used configuration is L, with two components that ease the control process and design. Moreover, the quality factor Q is sustained in the L configuration. In comparison to the L network, the T and Pi matching designs are more complex. Additionally, organizing the T and Pi configurations into numerous stages preserves the final matching outcomes but will alter the Q factor. This method is advantageous for improving passive voltage boosting.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Antenna Type** & **Size** & **Substrate** & **Gain (dBi)** & **Frequency** & **SAR (I g** & **Average)** & **PCE (\%)** \\ \hline
[97] & Broadband & & & & & & \\ Implantable & 91.44 mm\({}^{3}\) & Rogers 6010 & –32, –34 & 0.72–1.504 & 921 & — \\  & Antenna & & & & & & \\ \hline
[98] & Compared & & & & & & \\ Implantable & 48.98 mm\({}^{3}\) & Rogers & –30.8, –19.7, & 0.402, 0.915, 1.2 & 293.7 & — \\  & Antenna & & & & & & \\ \hline
[99] & Broadband & & & & & & \\ Substrate- & Independent & \(0.312\times 0.312\) & Felt and & & & & \\ Textile & \(\lambda^{2}\) & Polycotton & 2.2 & 0.9 & 1.52 & 40 \\ Wearable & Antenna & & & & & & \\ \hline
[100] & A circular & & & & & & \\ microstrip & 42.92 \(\times\) 42.92 & Duroid & & & & \\ patch wearable & mm & 6010 LM & — & & & & \\ antenna & & & & & & \\ \hline
[101] & Wearable & 35 mm (outer & & & & \\ Bandenna & & radius) & silicone & 5 & 2.45 & — & — \\ \hline
[102] & Folded Dipole & & & & & & \\ Waterline & & & & & & \\ Antenna & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 6 Conclusions  
In this paper, we presented an empirical study based on neural networks that estimates the BER in relation to obstacles (primarily buildings) encountered by a mobile vehicle in a VANET network. This study is driven by the need for Quality of Service (QoS) in emergency communications within VANETs. By estimating the BER based on the geometric structure of obstacles, vehicles can optimize their data routing decisions more effectively.  
For our future work, we plan to integrate the mechanism for BER estimation with the CL-ANTHOCNET routing protocol we previously developed [35]. Subsequently, we will evaluate its performance in scenarios involving the transmission needs of emergency vehicles.  
The structuring, methodology, writing, and implementation of the simulation using tools such as SUMO, NS-3, and Matlab were carried out by [PERSON] and [PERSON]. The improvement of the writing and the analysis of the results were performed by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data supporting the findings of this study can be obtained by contacting the corresponding author, [PERSON], at [EMAIL_ADDRESS] upon a reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANN & Artificial Neural Network \\ BER & Bit Error Rate \\ BFG & BFGS Quasi-Newton \\ BR & Bayesian Regularization \\ BSP & Binary Space Tree \\ CBR & Constant Bit Rate \\ CGB & Conjugate Gradient With Powel/Beale Restarts \\ CGF & Fletcher-Powell Conjugate \\ CGP & Polak-Ribiere Conjugate Gradient \\ CL-AntHocNet & Cross-Layer AntHocNet \\ DSRC & Dedicated Short Range Communications \\ GDX & Variable Learning Rate Backpropagation \\ LM & Levenberg-Marquardt \\ LOS & Line of Sight \\ MAC & Media Access Control \\ MAE & Mean Absolute Error \\ MSE & Mean Squared Error \\ NS-3 & Network Simulator 3 \\ OBU & On-Board Unit \\ OSM & OpenStreetMap \\ OSS & One-Step Secant \\ PCA & Principle Component Analysis \\ PHY & Physical Layer \\ QoS & Quality of Service \\ RMSE & Root Mean Squared Error \\ RP & Resilient Backpropagation \\ RSU & Road Side Unit \\ SCG & Scaled Conjugate Gradient \\ SNR & Signal-to-Noise Ratio \\ SUMO & Simulation of Urban Mobility \\ TraCI & Traffic Control Interface \\ UDP & User Datagram Protocol \\ \end{tabular}