esa-sceva/satcom-chunk-collection huggingface dataset:
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 4 Discussion  
This paper focus on the modeling and analysis of sub/supsynchronous oscillation in D-PMSG based wind farm integrated to a weak AC grid. The small-signal harmonic linearization method is utilized to develop the positive sequence of the GSC. In this main step, the dynamics of PLL under harmonic voltage perturbation is analyzed. Moreover, the mechanism of sub/supsynchronous currents frequency coupling is revealed quantitatively. Based on the impedance model, the influence of converter parameters is further discussed. Finally, the influence of the SCR of AC system on SSO is well explained by impedance-based stability analysis. Simulation experiments are carried out to verify these theoretical discussions. Following conclusions can be made:  
1. The D-PMSG's equivalent inductance \(X_{PMSG}\) is positive at subsynchronous frequency while negative (capacitive) at supsynchronous frequency, which means the resonance between D-PMSG and weak grid occurs at the supsynchronous frequency. The relationship of coupled sub/supsynchronous currents could be quantitatively calculated.
2. The D-PMSG's impedance is sensitive to the PCC voltage and output current, which are decided by power flow. Further proof that when the output current is relatively small as a result of low wind speed, it is very likely to observe oscillations in this system. The influence of control parameters is also discussed. No obvious impact of current controller is found.
3. A simulation experiment was conducted to study the mechanism of SSO when SCR decreases. Both simulation results and theoretical analysis proved that when the grid side impedance increases, the grid-connected D-PMSG system will be more unstable.  
Future work based on the work in this paper will discuss the following several issues: The first issue is about how to obtain the reduced-order equivalent model of wind farm which contains hundreds of wind turbine generators. The second issue focus on oscillation suppression, proposing advice related to parameters adjustment or additional damping control strategy. The last issue will discuss the interaction with synchronous generator, further analyzing the accident in Xinjiang, China.  
Methodology, S.Y.; software, S.Y. and X.Y.; validation, J.S., T.Z., and S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, X.Y. and T.Z.; funding acquisition, Z.H. and J.S.  
This research was supported by China Huaneng Group Co., Ltd. Headquarters Technology Project: Research on Subsynchronous Oscillation Mechanism and Suppression Measures of Grid-connected Wind Farm (HNKJ18-H34) and Shaanxi Provincial Key Research and Development Program (2019 ZDLGY18-04).  
The authors declare no conflict of interest.
## 4 Trust Evaluation in VANET
### Trust Level Computation
#### 4.1.4 Fuzzy Integral for Trust Level Computation  
Machine learning has been successfully applied to a wide range of applications in recent years, such as security problems [25; 26; 27; 28]. Trust evaluation for each node aims to create a secure VANET that can significantly improve road safety. RSUs make independent decisions for selecting nodes that can exchange data over VANET. Fuzzy integral logic is adopted in our work to extract the final trust level for each node. Assume the set \(\Lambda=\{t_{1},\ t_{2},\ t_{3},...,t_{n}\}\) indicates the possibilities or solutions from which the RSU must select (i.e., trust level for a node). Let \(\psi=\{a_{1},\ a_{2},\ a_{3},...,a_{n}\}\) represent the limited set of criteria that must be considered while calculating a node's trust value.  
Each node is associated with a vector \(\Lambda_{i}\in\Lambda\) whose components \(t_{i}\in\psi\), \(t_{i}\) reflect the value of the \(i^{th}\) characteristic that will be considered in calculating trustworthiness. There would be an objective function for each of the criteria. A utility function is used to simulate the selection process preferences \(\eta\) based on these criteria:  
\[\text{O}:\Lambda\rightarrow\text{ I.R} \tag{13}\]  
Such that:  
\[\forall\text{ a, b}\in\eta\text{, a }\eta\text{ b}\Leftarrow\Rightarrow\text{O}\left(\text{a} \right)\geq\text{O}\left(\text{b}\right) \tag{14}\]  
The utility functions that are labelled \(O_{1}(t_{1})\),..., \(O_{n}(t_{n})\) transfer each criterion of the \(j^{th}\) node to a single satisfaction scale \(\varepsilon\in\text{I.R.}\) Each node's criteria must be combined into a single score or ranking for the \(j^{th}\) node, as follows:  
\[\forall\text{ }t_{i}\text{,O}\left(t_{i}\right)=H(\left(O_{1}\text{ }\left(t_{1}\right),\ldots O_{n}\left(t_{n}\right)\right) \tag{15}\]  
\(\lambda\)-fuzzy measure [29] is used in our work to compute the overall trust evaluation for the \(j^{th}\) node.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 3 The Equivariant Vector Network with Self-Supervised Learning
### Implementation  
#### 3.5.1 Pre-Training Tasks  
We combine all the previous modules for the pre-training task. The point clouds after preprocessing \(P\in\mathbb{R}^{N\times k\times 3}\) as input will be sent to equivariant layers for equivariant feature learning through patching, vector feature embedding, and masking, and then the autoencoder will finally receive the tokens \(T_{d}\in\mathbb{R}^{m\times C\times 3}\). \(T_{d}\) will be fed as input to the final reconstruction head reshaped into the shape of point patches, and finally the reconstruction results \(P_{pre}\in\mathbb{R}^{mN\times k\times 3}\) are obtained. The original masked point patches, \(P_{mask}\in\mathbb{R}^{m\times k\times 3}\), are used as the ground truth, that is, \(P_{mask}\) and \(P_{pre}\) are used to calculate the loss, which is used to realize the self-supervised training process. This whole training process can guide the network to learn equivariant features in the point cloud so that the latent features \(z(\theta)\) of the model can learn the intrinsic representation of the original point cloud sample \(X\). For the calculation of the self-supervised loss, the regular point cloud employs the \(l_{2}\) Chamfer Distance, which is formulated as follows:  
\[\mathcal{L}_{1}=\frac{1}{P_{pre}}\sum_{a\in\mathbb{P}_{pre}}\min_{b\in P_{gt}}|| a-b||_{2}^{2}+\frac{1}{P_{gt}}\sum_{b\in P_{gt}}\min_{a\in P_{gt}}||a-b||_{2}^{2} \tag{20}\]
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## Appendix B Levenberg-Marquardt Algorithm
### Algorithm Setup  
The algorithm is set by calling the InitLM method; it is the main function where all parameters of the algorithms are set:  
\[\text{LM}.\text{InitLM}(\text{pimg0},\text{[options]})\]  
The necessary parameters are as follows:  
_ping0_--the initial parameters for the model function (_Nx \(\times\) Ny \(\times\) Npar_).  
Optional parameters are as follows:  
* _dp_--fractional increment of parameters \(p\) for numerical derivatives (_Npar \(\times\) 1_). Default: 0.005. Additional info: 1. \(\text{d}p\)_(j) > 0 central differences calculated;
2. \(\text{d}p\)_(j) < 0 one-sided 'backwards' differences calculated;
3. \(\text{d}p\)_(j) = 0 sets corresponding partials to zero, i.e., holds _p(j)_ fixed;
* _p_min_--lower bounds for parameter values (_Npar \(\times\) 1_). Default values depend on the tissue type;
* _p_max_--upper bounds for parameter values (_Npar \(\times\) 1_). Default values depend on the tissue type;
* _weight_--weight matrices (_NI \(\times\) NI_) or a scalar weight value; weight must be non-negative. For more information about weights, see [36]. Default value: 1;
* _eps1_--convergence in the gradient. Default value: 1 \(\times\) 10\({}^{-4}\);
* _eps2_--convergence in parameters. Default value: 1 \(\times\) 10\({}^{-4}\);* _eps3_--convergence in \(\chi^{2}\). Default value: \(1\times 10^{-4}\);
* _eps4_--\(\lambda_{Im}\) update criterion. Default value: \(1\times 10^{-3}\);
* _MaxIter_--maximum number of iterations. Default value: 200;
* _L0_--initial lambda value. Default value: \(1\times 10^{-3}\);
* _Lu_--lambda increase multiplicator. Default value: 1.5;
* _Ld_--lambda decrease divisor. Default value: 5;
* _Update_Type_ LM lambda update method. Parameter values: 1 = Levenberg-Marquardt lambda update (default), 2 = Quadratic update, 3 = [PERSON]'s lambda update equations, 4 = geodesic acceleration;
* _epsJtWJ_--the minimum allowed value of the Levenberg-Marquardt damping term (Update_Type = 1). Default value: \(1\times 10^{-3}\);
* _dg_--geodesic acceleration FD step. Default value: 0.1;
* _galpha_--geodesic acceleration acceptance criterion. Default value: 0.75;
* _Broyden_--[PERSON]'s rank 1 update for Jacobian, preventing the calculation of finite differences, 1 = use, 0 = do not use rank 1 approximation. Default value: 1;
* _lambdaMax_--maximum dumping parameter lambda. Default value: 10;
* _muPenTer_--penalty term for the nearest neighbor's initial coefficient. Set to 0 if no term penalty is required. Default value: 1;
* _Nsit_--number of spectra fitted simultaneously. Default value: 10;
* _Verbose_--Display messages during execution. Parameter values: 0 = no messages, 1 = messages. Default value: 1  
The constraint (i.e., muPenTer) takes care to penalize individual parameter values that are significantly different from the parameter values in the neighbor spatial points.
## 5 Simulation Experiment of pH Control of Fertilizer Solution
The control algorithm used float accuracy with a sampling period of 2 s. Two groups of pH adjustment processes were set up; the initial pH values of the two water and fertilizer regulation tests were 7.1 and 6.9, respectively, the target pH values of the water and fertilizer  
Figure 16: Structure of the fertiliser pH adjustment test system. Figure 15: Fertilizer pH adjustment and detection experimental platform. regulations were adjusted to 6.3 and 5.3, respectively, and an electromagnetic flow meter was selected to determine the instantaneous flow rate. The variable control test was read by sensors and data acquisition cards to test and compare different control strategies, setting the system flow rate to 4 m\({}^{3}\)/h. The purpose of conducting this test was to regulate the accuracy of fertilizer pH control by the four controllers. The results of the test comparison are shown in Figures 17 and 18. The indicator performance of the four controllers is shown in Tables 3 and 4. As can be seen from Figures 17 and 18, as the fertilizer flow rate increased, the performance of all four controllers gradually improved. Although the PID controller rose faster, it had the highest overshoot and a longer oscillation time, with a larger difference to the set pH value in a short time. The fuzzy controller had a smaller overshoot, but the response time was slow and could not keep track of the set pH value in time. The fuzzy neural network controller was considerably higher in rise time, overshoot and adjustment time compared to the first two controllers, but it had a significantly higher overshoot than the controller proposed in this paper, with the performance parameters tabulated as shown in Table 3. When the pH value was reduced from 7.1 to 6.3, the control proposed in this paper was the smallest in both overshoot and steady-state time. In terms of overshoot, the reductions compared to the first three algorithms were 8.1, 4.6 and 1.5 percentage points. In terms of steady time, the reductions were 39 s, 120 s and 34 s.  
The pH value was reduced from 6.9 to 5.3, with the performance parameters shown in Table 4. Compared to the first three algorithms, the controller in this paper reduced the amount of overshoot by 13.8, 5.4 and 3.3 percentage points. The IPSO-FBPNN-PID controller can, therefore, respond to the set pH value in a short time with minimal overshoot and can meet the control requirements.
## 3 Materials and Methods
### RED/BLACK Concept of Communication in Aviation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Applications of** & & **Description** \\
**RED/BLACK Concept** & & \\ \hline \multirow{6}{*}{RED Communications} & - & These are the channels or systems that handle classified, sensitive, and secure communications. \\  & - & Information transmitted via RED radios is often encrypted and requires authorized access. \\  & - & The equipment and wiring used for RED communications are designed to prevent any form of \\  & - & electronic eavesdropping or interception. \\  & - & RED communications must be processed, stored, and transmitted using secure methods that \\  & - & meet stringent government and military standards. \\ \hline \multirow{6}{*}{BLACK Communications} & - & These involve non-classified, public, or less sensitive information that does not require the same \\  & - & level of security as RED communications. \\ \cline{1-1}  & - & BLACK radios can include routine air traffic control communications with aircraft, where the \\ \cline{1-1}  & - & Information does not have implications for national security. \\ \cline{1-1}  & - & The systems and networks for BLACK communications are typically not encrypted or are \\ \cline{1-1}  & - & The separation is both physical and electronic. Physically, the equipment might be color-coded, \\ \cline{1-1}  & & with RED cables and devices clearly marked to avoid accidental cross-connections with \\ \cline{1-1}  & - & BLACK systems. \\ \cline{1-1}  & - & Electronically, the systems are designed so that RED and BLACK signals are processed through \\ \cline{1-1}  & - & separate circuits and can never intersect. This prevents any potential leakage of classified \\ \hline \multirow{6}{*}{Implementation in Air Traffic Control} & - & In air traffic control, the concept would be applied by using separate radio systems for RED and \\ \cline{1-1}  & - & BLACK communications. The RED radios might be used for secure military communications or \\ \cline{1-1}  & - & other sensitive information exchanges, while the BLACK radios would handle the routine \\ \cline{1-1}  & - & management of civilian air traffic. \\ \cline{1-1}  & - & Air traffic control centers that manage both civilian and military aircraft, or those located near \\ \cline{1-1}  & - & sensitive installations or borders, might require such a system to ensure the security \\ \cline{1-1}  & - & of communications. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main functions and applications of the RED/BLACK concept.
## 4 Control Algorithms  
As discussed in the introduction, we propose two different sliding mode controllers: (i) a first order SMC for spacecraft position tracking, guaranteeing tracking in terms of positions and speeds and (ii) a super-twisting SMC for the attitude control, including the quaternion dynamics.
## 3 Methods
### Model Compression  
Model compression employed a quantization technique aimed at enhancing detection speed and reducing the computational load of the model, given its deployment on smartphones, which typically have lower computational capabilities than computers. Quantization techniques were applied to convert model weights into 16-bit floating-point (FP16) and 8-bit-integer (INT8) precision. The FP16 quantization process was conducted using the export feature of the Ultralytics YOLOv8 library by specifying the format parameter to 'tflite' and enabling the 'optimize' and 'half' options. The 'optimize' parameter optimizes the model for mobile devices, while the 'half' parameter implements FP16 half-precision quantization. In addition to producing models in the FP16 format, the export results also yielded models in the 32-bit floating-point (FP32) TensorFlow format, which was used for quantization to INT8 precision using the TensorFlow library.  
During INT8 quantization, a calibration process is necessary to estimate the range of all floating-point values in the model. Constant values such as weights and biases are easily calibrated. However, variable values like inputs, activations, and outputs require cycles of the inference process to be calibrated. Consequently, a dataset of approximately 100 to 500 samples is needed for this calibration process. In this study, a validation dataset consisting of 430 images was used as the calibration dataset. These validation samples were carefully selected to ensure representativeness in terms of class diversity, image quality, and real-world variability. This subset covered a broad range of image conditions, including different lighting levels, degrees of focus, and background complexities, typically encountered in practical mHealth environments. The representativeness of these calibration samples contributes to maintaining the robustness and accuracy of the quantized YOLOv8-small INT8 model despite the precision reduction inherent to quantization.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 3 Materials and Methods  
### Mc-Net  
The proposed deep-learning-based method (Motion Correction Net, i.e., MC-Net) takes a motion-corrupted image as input and outputs a motion-corrected image. This method has a modified UNet [22] (Figure 1) as its neural network structure. The MC-Net was trained with a two-stage training strategy using a hybrid loss function, \(L\), that combines \(L1\)-loss and \(TV\)-loss [23]. By integrating \(TV\)-loss, the hybrid loss function encourages the model to produce output images with low total variation that can have sharp edges and reduced motion artifacts. The outer exponent is 1.25, as suggested in [22]:  
\[L=alpha*L1+beta*TV \tag{1}\]  
\[L1=\sum_{i,j}\lvert I(i,j)-I_{0}(i,j)\rvert \tag{2}\]  
\[TV=\sum_{i,j}\Bigl{(}\left(I(i+1,j)-I(i,j)\right)^{2}+\left(I(i,j+1)-I(i,j) \right)^{2}\Bigr{)}^{1.25} \tag{3}\]  
where \(I\) and \(I_{0}\) are a corrupted image and a motion-free image, and \(i\) and \(j\) are row/column indices. During the first training stage, we used \(L1\)-loss only [(_alpha_, _beta_) = (1, 0)] to suppress overall motion-induced artifacts. The pre-trained stage 1 model was then fine-tuned in stage 2 by turning on the \(TV\)-loss component [(_alpha_, _beta_) = (1, 1)]; this penalizes the total variation in relation to boundary artifacts in addition to the overall artifacts.
## 2 GMRES and GMRES(m)
Notice that \(\mathbf{V}_{k}\mathbf{y}_{k}\in\mathcal{K}_{m}(\mathbf{A},\mathbf{r}_{k})\). In addition to Algorithm 3, there is a restarted version (GMRES(m)) [11; 31] described as follows. ```
1: Give \(m_{0}\), \(m_{1}\), \(\mathbf{x}_{0}\), and \(\varepsilon\)
2: Do \(j=1,\ldots\)
3: Do \(m=m_{0},\ldots,m_{1}\) (\(k=m-m_{0}\))
4: \(\mathbf{r}_{k}=\mathbf{b}-\mathbf{A}\mathbf{x}_{k}\)
5: \(\mathbf{v}_{1}^{k}=\frac{\mathbf{r}_{k}}{\|\mathbf{r}_{k}\|}\)
6: \(\mathbf{V}_{k}=\left[\mathbf{v}_{1}^{k},\ldots,\mathbf{v}_{m}^{k}\right]\) (by Arnoldi process)
7: Solve \((\bar{\mathbf{H}}_{k}^{T}\bar{\mathbf{H}}_{k})\mathbf{y}_{k}=\|\mathbf{r}_{k} \|\bar{\mathbf{H}}_{k}^{T}\mathbf{e}_{1}\)
8: \(\mathbf{x}_{k+1}=\mathbf{x}_{k}+\mathbf{V}_{k}\mathbf{y}_{k}\)
9: If \(\|\mathbf{r}_{k+1}\|<\varepsilon\), stop
10: Otherwise, \(\mathbf{x}_{0}=\mathbf{x}_{k+1}\); go to 2
```  
**Algorithm 3**GMRES(m)  
Instead of \(m_{0}=1\) used in the original GMRES and GMRES(m), a suitable value of \(m_{0}\
eq 1\) can speed up the convergence; hence, \(m=m_{1}-m_{0}+1\) is the frequency for restart. Rather than the original GMRES and GMRES(m) [2; 11; 31], we take \(\mathbf{x}_{k}\), not \(\mathbf{x}_{0}\), in (7) of Algorithm GMRES and in (8) of Algorithm GMRES(m). Because \(\mathbf{x}_{0}\) is not updated in the original GMRES and GMRES(m), they converge slowly. In Algorithm GMRES(m), if we take \(m_{1}=m_{0}\), then we return to the usual iterative algorithm for GMRES, with a fixed-dimension, \(m=m_{0}\), of the Krylov subspace. The restart remedies the long-term recurrence and accumulations of the round-off errors of GMRES. In order to improve the restart process, several improvement techniques have been proposed, as mentioned above. Some techniques based on adaptively determining the restart frequency can be found in [32; 33; 34; 35]. For a quicker convergence, we can update the current value of \(\mathbf{x}\) at each iterative step. Because \(\mathbf{x}_{k}\) is new information for determining the next value of \(\mathbf{x}_{k+1}\), the result of \(\mathbf{x}_{k}\) cannot be wasted for saving computational cost.
## 6 On Control Messages Protection  
So far, our focus has been on protecting the integrity and confidentiality of application messages, namely, messages whose payload contains data sent by the application layer of the network, such as sensor readings or commands to AUVs. However, unless the surveillance communication sub-system fits within a single acoustic broadcast domain, a multi-hop, ad-hoc acoustic network is necessary. In such a case, we have to consider integrity and confidentiality of _control messages_ too, namely messages  
Figure 4: Key tree.  
whose payload contains data used to maintain the network services. These include, for instance, routing discovery messages and routing maintenance messages. All of these control messages are of fundamental importance for availability. However, contrary to confidentiality and integrity, we can not assume end-to-end security for control messages because the network is by definition a distributed protocol.  
Disrupting the functionality of the routing protocols compromises the availability of the network and services running on the network. Attacks against routing protocols include attempts to create routing loops or black holes (when attacker claims to be a short distance to all destinations and then selectively forwards payload traffic). Therefore, the same principles of service integrity and service confidentiality apply to control messages. To provide a secure communication infrastructure, control messages need to have authentication and replay protection. Therefore, these messages need to use message integrity codes. Also, to prevent an outsider from identifying the type of control messages being transmitted, we should have these messages encrypted.  
In the UAN project we have used FLOOD, a routing protocol that has been designed for, and tested in, an underwater acoustic networks [31]. Unfortunately, FLOOD does not provide any mechanism to protect the confidentiality and integrity of control messages and this makes FLOOD prone to attacks. For this reason, we have designed and implemented _secure FLOOD_ (SeFLOOD), a secure version of FLOOD where every control message is protected by means of the cryptographic suite described in Section 5. In order to protect routing control messages we need link layer keys shared by pairs of neighbouring nodes. As these keys are meant to protect routing control messages, they have to be established before the routing protocol starts.  
In Section 6.1 we provide a brief description of FLOOD. By way of example, in Section 6.2 we highlight FLOOD vulnerabilities and describe a spoofing attack against control message integrity that translates into Denial of Service (DoS). Finally, in Section 6.3 we describe SeFLOOD which incorporates the establishment of link-layer shared keys.