esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 2 Materials and Methods  
### Experimental Equipment and Test Blocks  
#### 2.1.1 Ultrasonic Phased Array Inspection System  
The ultrasonic phased array detection system included an HS PA30-E ultrasonic phased array detector, ultrasonic phased array transducer (probe), wedge, and scan encoder, as depicted in Figure 1. Data were collected with the HS PA30-E ultrasonic phased array detector from Wuhan Zhongke Innovation Technology Co., Ltd. (Wuhan, China). The device offered 64-element Total Focusing Method (TFM) full-focus real-time scanning imaging, dual phased array Time-of-Flight Diffraction (TOFD) integrated imaging, real-time data transmission, and online evaluation, among other features. The probe used was model 5L32-0.6 \(\times\) 10-A30-P-2.5-IPEX-Z, and the wedge used was model SA30-60S-C(37). The wedge primarily serves to protect the probe and facilitate angle deviation. Prior to use, the probe must undergo inspection [26] to ensure it meets the following criteria: correct assembly and labeling without damage, geometric dimensions of the casing and cables in accordance with the drawing requirements, clearance between probe contact surfaces not exceeding 0.05 mm, and satisfactory flatness of probe contact surfaces. If the inspection results indicate normal conditions, the probe can be safely used. Before conducting the detection experiment, it was essential to assemble the probe and wedge and, if necessary, to apply a coupling agent to ensure proper coupling. Ordinary machine oil was chosen as the coupling agent in this case. A line encoder was employed to record the position of the probe signal and obtain data images for subsequent signal analysis. In this experiment, a wire encoder was used, which allows the adjustment of the base magnetism to meet the requirements of different detection scenarios.  
The main parameters of the probe and wedge are shown in Table 1.  
Figure 1: Ultrasonic Phased Array Inspection System.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 9 Lessons Learned, Suggestions, and Recommendations
### Suggestions and Recommendations
#### 9.2.6 Regular Vulnerability Assessment and Risk Monitoring  
This method is crucial for ensuring smart grid security. This involves conducting penetration testing and simulating attack scenarios to evaluate security levels and response capabilities. It also requires implementing risk management measures to mitigate identified threats through various security countermeasures. Constant testing of smart grid systems through vulnerability assessments, penetration testing, log monitoring, and advanced threat modeling helps identify and address emerging security risks promptly and effectively. These practices are essential for maintaining the resilience and security of smart grid infrastructure against evolving cyber threats. This also requires enhancing control access and accountability in addition to regulatory compliance, which is described in the following:  
1. **Enhancing Control Access and Accountability.** This step is needed to enhance accountability methods through deterrence policies, and limiting access for various users (i.e., attribute control access schemes) is crucial to reducing internal threats and fostering a safe and trustworthy environment. It also requires motivating all entities, boosting confidence, and discouraging bad actors. It also promotes the accountability of users and the ability to disclose any suspicious behavior or illegal activity quickly.
2. **Regulatory Compliance.** Compliance is also needed to enhance the cyber-physical security and safety of the smart grid critical infrastructure, such as the Critical Infrastructure Protection (CIP) and North American Electric Reliability Corporation (NERC) standards, the IEC 62351 standards series [306; 307], and the NIST Framework [17]. In other terms, strict authentication procedures and granular access controls must be implemented to guarantee that only authorized individuals can interact with vital systems to improve control access and accountability in smart grids. This strategy guarantees traceability and accountability in conjunction with thorough logging and monitoring of user actions. It lowers the possibility of illegal access and facilitates quick incident response and forensic investigations.
# Diabetic Retinopathy and Ocular Melanoma: How Far We Are?
## 5 Conclusions and Future Perspectives  
Diabetic retinopathy is a compilation of vascular lesions that can be seen through ophthalmoscopy. A non-proliferative stage (vascular tortuosity, retinal haemorrhages, microaneuryss as well as lipid exudates) and a proliferative stage (development of new aberrant vessels) are shown. Diabetic macular edema is another aspect of DR, characterized by a collection of fluid in the neural retina, which in turn increases its thickness and conduct to macula cystoid edema. In the various states of DR, the DME can be verified, being the main cause for the vision loss in these patients. During DR, the BRB breaks down, the RPE cells are subjugated to HOS inducing responses in the RPE cells. HOS elicits an osmolarity-dependent response, alters cell specificities, allows collagen and elastn to develop, reduces cell growth by arresting the cell cycle and allows the release of angiogenic factors as well as pro-inflammatory mediators. In RPE cells, the impacts caused by HOS give the transcription factor TonEBP/NFAST5 a crucial role as a host for these effects. Thus, HSO and inflammation cause the lesions that occur in DR, and in order to improve therapy for this disease in the future, knowledge of the implicit molecular mechanisms is therefore required. Currently the treatment for DR consists of corticosteroids and anti-VEGF agents, in order to prevent the progression of the disease. New medicines to reduce sugar from natural sources have recently been proposed. It is well known that long-acting drug delivery systems and proteins need to be considered for successful pharmacotherapy of DR. This review highlights the crucial function of inflammation in early DR. Several studies on diabetic animal models are available in the literature and also on diabetic patients who exhibit the influence of the diabetic environment on the increase of inflammatory molecule manifestation in the progression of DR. Glial cells are located between the vasculature and retinal neurons and thus have a crucial role in normalizing the retinal environment that causes neuroretina damage in DR. Moreover, these cells also have the ability to initiate inflammatory cascade. Scientists assume that variation in the metabolic behaviour of glial cells and its outcome in retinal neurons precedes microvascular involvement. Thus, tools have been developed to identify early changes to the neuroglial unit. In addition, there has been a development of anti-inflammatory drugs aiming to act on vascular changes and neurodegeneration. However, there is still a need to know more about the molecular mechanisms responsible for the
# Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation
## 4 Experiments  
In this section, we introduce our training method for a low-level quadrotor control policy. The RL controller receives the information on the quadrotor state (position, velocity, attitude, and angular velocity) from sensors and directly outputs the control commands of four rotors. The training was first performed and tested in a simulator. The quadrotor simulator was established using Python according to the dynamic model described in Section 2.1 for training and verifying the flight performance.  
After verifying that the RL control policy was trained successfully, we transported the controller into our DCRL structure and performed a real flight with the quadrotor. To implement the proposed DCRL control algorithm in this study, PixRacer flight controller hardware was developed and implemented using Simulink. The DCRL control strategy was examined in an indoor environment by performing fixed-point hovering under an external wind disturbance. Then, the quadrotor was set to track a square trajectory in an outdoor experiment. The position and velocity of the quadrotor were obtained using an OptiTrack motion capture system on the ground station computer. These data were transmitted through Wi-Fi to the onboard PixRacer flight controller within 10 m range in the indoor experiment. The physical parameters of the quadrotor platform are presentedin Table 1. For outdoor trajectory tracking, position information was only obtained from an onboard global positioning system (GPS) sensor.
## 3 Results
### Shear Bond Strength between Epoxy Resin and Core Resin  
The results of shear bond strength test showed that shear bond strength was significantly higher for NTAPP-treated epoxy resin than NTAPP untreated epoxy resin, when each was bonded with core resin. The values of shear bond strength with core resin for NTAPP-treated epoxy resin and NTAPP untreated epoxy resin were (\(1.28\pm 0.20\)) MPa and (\(0.60\pm 0.08\)) MPa, respectively (\(p<0.05\)).  
The result of shear bond strength with respect to time passed from NTAPP treatment was shown in Figure 7. The shear bond strength value of plasma treated epoxy resin decreased with increasing aging time, excluding the time periods from day 0 to day 1 and from day 5 to day 7 (\(p<0.05\)).  
Figure 6: The surface chemical composition of the epoxy resin analyzed by X-ray Photoelectron Spectroscopy before and after the non-thermal atmospheric pressure nitrogen plasma treatment. Spectra for; (**a**) C1s; (**b**) N1s; and (**c**) O1.  
Figure 7: Shear bond strength between non-thermal atmospheric pressure nitrogen plasma-treated epoxy resin and fiber post with respect to the time passed after the treatment (different lower case alphabet letter indicates statistical difference, \(p<0.05\)).  
The results of push-out shear bond strength between commercially available epoxy resin-based fiber post and core resin are shown in Table 2 and Figure 8. The push-out shear bond strength values of samples treated with conventional chemicals of hydrofluoric acid only (Group 3) and silane only (Group 4) were significantly higher than samples without any treatment (Group 1) (\(p<0.05\)) as expected. The push-out shear bond strength value of samples treated with NTAPP for 1 min (Group 2) was also significantly higher than samples without any treatment (Group 1) (\(p<0.05\)), which was even significantly higher than those treated only with hydrofluoric acid (Group 3) or silane (Group 4) (\(p<0.05\)). Additionally, there was no significant difference between samples treated with hydrofluoric acid followed by silane (Group 5) and samples treated with NTAPP for 1 min (Group 2) (\(p>0.05\)).
## 5 Interpolation Model with Scalar Subdivision Schemes
Let us consider for instance the cubic B-spline scheme, whose first and second limit stencil are \(\{\tfrac{1}{6},\tfrac{4}{6},\tfrac{1}{6}\}\)\(\{-\tfrac{1}{2},0,\tfrac{1}{2}\}\), respectively. Then the corresponding matrices are  
\[ \begin{bmatrix}\tfrac{4}{6}&\tfrac{1}{6}&0&\dots&0&\tfrac{1}{6}\\ \tfrac{1}{6}&\tfrac{4}{6}&\tfrac{1}{6}&\dots&0&0\\ \vdots&\ddots&\ddots&\ddots&\ddots&\vdots\\ \tfrac{1}{6}&0&0&\dots&\tfrac{4}{6}&\tfrac{1}{6}\end{bmatrix} ,\]  
which is not singular, and  
\[ \begin{bmatrix}\tfrac{4}{6}&\tfrac{1}{6}&0&0&\dots&0&0&\tfrac{1}{6}\\ 0&\tfrac{1}{2}&0&0&0&0&0&-\tfrac{1}{2}\\ \hline 0&\tfrac{1}{6}&\tfrac{4}{6}&\tfrac{1}{6}&\ddots&0&0&0&0\\ 0&-\tfrac{1}{2}&0&\tfrac{1}{2}&&0&0&0&0\\ \hline\vdots&\ddots&\ddots&\ddots&\ddots&\vdots\\ \hline 0&0&0&0&\dots&0&\tfrac{1}{6}&\tfrac{4}{6}&\tfrac{1}{6}\\ 0&0&0&0&\dots&0&-\tfrac{1}{2}&0&\tfrac{1}{2}\end{bmatrix} \]  
which is singular with kernel of dimension \(1\). As a matter of fact, in the specific case of point and tangent interpolation with odd-symmetric schemes, the following proposition holds. **Proposition 5**. : _Using an odd-symmetric subdivision scheme and setting \(d=2\), the attained matrix \(\mathbf{M}_{n}\) is singular._  
Proof. : From (3) and (15), for odd-symmetric schemes we have that the \((1,1)\)-block of \(\mathcal{L}_{n}\) is:  
\[(\mathcal{L}_{n})_{1,1} =\sum_{j=0}^{\lceil\frac{p}{2}\rceil} \begin{bmatrix}\beta_{-2j} &\beta_{-2j-1}^{0}\\ \beta_{-2j}^{1}&\beta_{-2j-1}^{1}\end{bmatrix} +\sum_{j=1}^{\lceil\frac{p}{ 2}\rceil} \begin{bmatrix}\beta_{2j}^{0}&\beta_{2j-1}^{0}\\ \beta_{2j}^{1}&\beta_{2j-1}^{1}\end{bmatrix} \] \[= \begin{bmatrix}\beta_{0}^{0}&\beta_{1}^{0}\\ \beta_{0}^{1}&-\beta_{1}^{1}\end{bmatrix} +\sum_{j=1}^{\lceil\frac{p}{2} \rceil} \begin{bmatrix}\beta_{2j}^{0}&\beta_{2j-1}^{0}\\ -\beta_{2j}^{1}&-\beta_{2j+1}^{1}\end{bmatrix} +\sum_{j=1}^{\lceil\frac{p}{2} \rceil} \begin{bmatrix}\beta_{2j}^{0}&\beta_{2j-1}^{0}\\ \beta_{2j}^{1}&\beta_{2j-1}^{1}\end{bmatrix} \] \[= \begin{bmatrix}\beta_{0}^{0}+2\sum_{j=1}^{\lceil\frac{p}{2} \rceil}\beta_{2j}^{0}&2\sum_{j=1}^{\lceil\frac{p}{2}\rceil}\beta_{2j-1}^{0}\\ 0&0\end{bmatrix} , \tag{20}\]  
where we used the fact that \(\beta_{0}^{1}=0\) from (12). The block \((\mathcal{L}_{n})_{1,1}\) is singular, since it has a null row, and the matrices \(\mathcal{L}_{n}\) and \(\mathbf{M}_{n}\) are in turn singular because of (3).
# Mathematical Apparatus of Optimal Decision-Making Based on Vector Optimization
## 1 Introduction  
The problem of making an optimal decision that meets the modern achievements of science and technology is connected, firstly, with the release of high-quality products, and, secondly, with the solution of problems of social and economic human development. The decision making can be undertaken under the conditions of certainty (when the functional dependence of the purpose of the parameters of the studied object and systems is known) [1; 2; 3; 4], and under the conditions of uncertainty (when there is not sufficient information on the functional dependence of the purpose of the parameters of the studied object and systems) [5; 6; 7; 8]. The conditions of uncertainty are characterized by the fact that input data for decision-making, can be presented as random, fuzzy or incomplete data, [1; 2; 9; 10]. Research on this problem of decision-making began with the work of [PERSON] and [PERSON] [11]. Analyses of modern decision-making approaches (i.e., \"simple\" methods) are submitted in [6; 12]. One of the areas of decision-making automation is associated with the creation of mathematical models and the adoption of an optimal solution based on them [12; 13; 14]. Currently, the most common mathematical apparatus for model-based decision making is vector optimization [6; 12; 14; 15; 16; 17; 18; 19]. The purpose of this work is to build a mathematical model for an object or system of decision making in the form of a vector problem of mathematical programming. Vector optimization is considered as a mathematical apparatus of a solution to the problem of acceptance of an optimal solution.  
For the realization of the goal of this work, the study considered and solved the following problems.  
The construction of a mathematical model of the problem of finding an optimal solution in the form of a vector problem of mathematical programming has been shown previously [4; 6; 15; 20]. In the current paper, the theory and a mathematical apparatus of problem solving using vector optimization are presented. The theory includes an axiomatic principle of optimality of the solution of vector problems. The mathematical apparatus of the solution of vector problems is intended for the solution of vector problems with equivalent criteria [6; 13; 21] and with the given priority of a criterion [15; 20]. The research, analysis and problem definition of decision making under the conditions of certainty and uncertainty are conducted. The realization of a mathematical apparatus of vector optimization is presented for numerical problems of decision making with one, two, three and four parameters. The solution of the problem of decision making includes creation of a numerical model of an object in the form of a vector problem, the solution of a problem of decision making with equivalent criteria, and, the solution of a vector problem of decision making with a criterion priority.
## 4 Signal Processing Method
### Multi-Point Parabola Approximation and Moving Average Algorithm  
To determine the precise time position of the reflection peak, the traditional method used the time-amplitude data of the peak point and its adjacent two points to obtain the fitting curve by parabola approximation method and then used the maximum point of the fitting curve to represent the peak point [30]. By extending this idea, a multi-point parabola approximation method was proposed to extract the peak position of single detection signal data by using the peak point and its adjacent four points. Distinctly, the result of the five-point parabola approximation is more accurate in the circumstances of high SNR. After that, to reduce the phase fluctuation of the peak points detected at a specific temperature, the 10-points moving average algorithm was used in signal processing. The moving average method is a simple smoothing algorithm. Based on time series data, the average value of a certain number of items can be calculated one by one. The method can effectively reduce the degree of phase fluctuation caused by random noise of the operating environment.  
For the peak phase data from 100 wireless measurements at 20 \({}^{\circ}\)C, the results of different algorithms are shown in Figure 5. For the combination method of multi-point parabola approximation and moving average algorithm, the phase fluctuation is about 3\({}^{\circ}\). Meanwhile, the three-point parabola approximation and the five-point parabola approximation are about 12\({}^{\circ}\) and 8\({}^{\circ}\), respectively. Therefore, the phase fluctuation of the proposed algorithm is only one-third of that of the previous algorithms.  
Figure 4: (**a**) Adaptive LMS algorithm flow; (**b**) original and denoised response in time domain of the SAW sensor.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 4 Solution of the MINLP Truss Synthesis Problem
### The Modified OA/ER Algorithm  
The OA/ER algorithm comprises the solving of an alternative sequence of Non-linear Programming (NLP) optimization sub-problems and Mixed-Integer Linear Programming (MILP) master problems; see Figure 2. The former corresponds to the continuous optimization of parameters for a superstructure with fixed discrete/binary variables and yields an upper bound to the objective to be minimized. The latter involves a global approximation to the superstructure of alternatives in which new values of discrete/binary variables (new topology and standard dimensions) are identified so that its lower bound does not exceed the current best upper bound. A global linear approximation includes the linear constraints from the original MINLP problem as well as the linearizations of the non-linear objective function and the non-linear (in)equality constraints, accumulated at each NLP sub-problem solution.  
Figure 2: Steps of the Outer-Approximation/Equality-Relaxation algorithm.  
The OA/ER algorithm, like all other MINLP methods, does not generally guarantee that the obtained solution is the global optimum. This is due to the presence of non-convex functions that may cut off the global optimum. In order to reduce the undesirable effects of non-convexities, a Modified OA/ER algorithm was developed by [PERSON] and [PERSON] [4]. The Modified OA/ER algorithm allows the following modifications to be applied to the master problem: deactivation of the linearizations, decomposition and deactivation of the objective function linearization, use of the penalty function, use of the upper bound on the objective function to be minimized as well as a global convexity test and the validation of the outer approximations; see also [59].
## Materials and Methods
### Prediction Model Based on LSTM
Then the cell state is put through tanh (to push the values to be between \(-1\) and \(1\)) and multiply it by the output of the sigmoid gate so that the decoded output is achieved. The operator \(\odot\) presented in the formula for C\({}_{t}\) and ht denotes element-wise multiplication (labeled as the scaling of information in the diagram with multiplication symbol as shown in Figure 6). Thus, the output at each time step depends on previous inputs and past computation. In the context of occupancy prediction from the acquired data as the research is proposing the use of multivariate features, the input of data features is denoted as \(Xi=(x1,\ x2,\ x3,\ x4,\ x5)\) where 1, 2, 3, 4, and 5 represent temperature, humidity, CO\({}_{2}\), occupancy, and date respectively. The hidden state of memory block \(h=(h1,\ h2,\ h3,\..)\), depends on the number of LSTM state network used with the real output sequence \(O=(o1,\ o2)\) (where 1 and 2 denote occupant and time respectively) are being iteratively calculated by the equations mentioned in Figure 6. In case the data needs iteration bias is added. Series Prediction with LSTM Neural Network  
In the context of environmental time-series data, the prediction process, the construction of an LSTM neural network shown in Figure 6 using the LSTM series of the cell is unfolded in Figure 7. At time \(t-1\), the input of the network is the observed historical data X\({}_{t-1}\) and the output is the predicted future data where X is the observed occupancy time sequence. Figure 7 describes the series of LSTM gates to construct the prediction architecture. Figure 7: The structure of series prediction LSTM neural network.
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 3 Methods  
The proposed O-Inception Element-Wise-Attention GRU network (OI-EleAttG-GRU) architecture is designed for human activity recognition in edge computing environments. It consists of two steps. Initially, a dynamic scheduling algorithm (DTS) is utilized to assign human activity recognition tasks to suitable servers to alleviate local computing pressure. Then, the OI-EleAttG-GRU deep learning architecture, set on the server, executes the human activity recognition tasks and identifies corresponding actions before returning the recognition results to the local device. This architecture combines a novel O-Inception convolutional structure with a GRU block using an element-wise attention gate. The new O-Inception convolutional structure achieves a balance between training speed and recognition accuracy, while the GRU block with EleAttG adopts an effective attention mechanism to improve prediction accuracy. OI-EleAttG-GRU leverages the benefits of both, enabling accurate and fast human activity recognition.