esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 2 Materials and Methods
### Wind Power Curves Estimation  
Once the SCADA data anomalies have been filtered according to the rule described in the previous section, we estimate the wind power curve. Of all the existing non-parametric methods, we choose to use a classical neural network of relatively small dimensions, since we understand that the filtering quality used to eliminate anomalies and clean the data used for the training has more influence on the results than the chosen method or increase the dimensions of the network.  
Then, considering \(s\) the wind speed and \(p(s)\) the power for such \(s\), the ANN model \(N\) hidden neurons takes the form:  
\[p(s)=\mathbf{w}_{o}^{T}\sigma(\mathbf{w}_{i}s+\mathbf{b}_{i})+b_{o} \tag{3}\]  
where the column vectors \(\mathbf{w}_{i}\) and \(\mathbf{w}_{o}\) of size \(N\) contain the input and output weights, respectively. The same size vector \(\mathbf{b}_{i}\) contains the input biases while the scalar \(b_{o}\) contains the output bias. Function \(\sigma()\) stands for the activation function, which, in this case, applies the hyperbolic tangent sigmoid to all the elements of its input vector.  
We use the Levenberg-Marquardt algorithm for training with 2/3 of the total available data. We chose N = 10. The test will use the last third of the available data.  
Once the network weights are calculated, p(s) provides the curve directly.
## 3 System Design
### Traffic Control Based on Deep Graph Reinforcement Learning  
The traffic control model based on deep graph reinforcement learning includes two phases. One is the data collection stage when the simulation network is running, and the other is the training stage. When the network is running, each node following the policy forwarded by the controller determines the next hop according to the current state of the WSN. After the forwarding task is performed, the next hop node will integrate the real-time state and reward value into a four-tuple record (\(LastState\), \(Action\), \(Reward\), \(NewState\)), and store it in the experience pool for training. For a detailed process description, see Algorithms 1 and 2.  
```
1:batch size,\(\theta^{\mu^{*}}\)
2:load the weights:\(\theta^{\mu^{*}}\) for target Actor model
3:Generating data packet \(p\), using \(\theta^{\mu^{*}}\) and stochastic OU process to determine its next hop \(a\), and recording it together with the current network state. \(p:=\{destination,location,state,a,data\}\)
4:while\(Length(Experience\ Replay)<batch\ size\)do
5:if packet \(p\) received then
6:if\(p\) has arrived at its destination then
7: done=True
8:else
9: done=False
10:endif
11: observe the network to get the new state, calculate the reward \(r\) for \(p\to a\) and append experience replay list with \(\{p\to state,p\to a,r,newstate,done\}\)
12:\(p\to state:=newstate\); \(p\to location:=location\)
13:if not done then
14: use \(\theta^{\mu^{*}}\) to determine its next hop \(a\)
15:\(p\to a:=a\)
16: send data packet \(p\)
17:endif
18:endif
19:endwhile
```  
**Algorithm 1** Running phase  
It should be noted in the Running Phase that \(\theta^{\mu^{*}}\) is obtained by the shortest path method if the deep reinforcement learning neural network model has not yet started training. After collecting enough data, the Training Phase will start. The weights of all the deep graph neural networks in the model will be updated by experience playback and gradient descent. After the training, each node will use the updated weights to choose a better next hop for the packets in the Running Phase.  
The weights of Policy network \(\theta^{\mu}\) updated in the Training Phase will be stored and used for the packet forwarding in the Running Phase. At the same time, weights of Target Q model \(\theta^{Q^{*}}\) and weights of Target Policy model \(\theta^{\mu^{*}}\) will be stored and used as the initial value for the next training phase.  
After many iterations of data collection and training, the controller will become smart enough to make the best next hop policy for all packets passing through the node at the packet-level. The policy considers the transmission delay, buffer occupancy, and the distance from the destination node at the same time. The data forwarding and traffic control policy based on deep graph reinforcement learning mentioned in this paper can be trained online by combining two phases, and the _Online Policy_ deep neural network used for decision-making can be updated in real time.
## 3 Proposed Testbed for Cybersecurity Analysis of Nuclear Power Plants  
In this section, we present the requirements considered in building up the proposed testbed, and we discuss the idea that guided the validation of our design. Then, the testbed components are detailed and discussed. The followed methodological process can be seen in the flowchart depicted in Figure 1.  
The requirements that guided the assembly of our testbed were as follows:  
* Choice of a NPP simulator faithful to the physical processes associated with its operation;
* Use of the Modbus/TCP protocol;
* Employment of a realistic network simulator;
* Selection of open-source software for the OT and IT elements to be incorporated;
* Have the ability to perform cyber-attacks against the testbed elements;
* Having the capability to monitor and log events to record historical data.  
We consider this testbed capable of emulating a variety of cyber-attacks against Modbus/TCP-based nuclear power plant simulated ICS. In order to validate this hypothesis, we planned to use it to perform an insider cyber-attack. This consisted in simultaneously: (a) replacing a local PLC by a Rogue PLC (Level 1 of the ANSI/ISA-95 model [16]), to modify the values of the registers used to control an actuator critical to the NPP operation (Level 0); and (b) interpose and modify the communication between Levels 1 and 2, so that the SCADA shows a normal situation in relation to the physical process, effectively blinding the system to the attack in progress (men-in-the-middle attack or MITM). The described levels and their respective roles can be seen in Figure 2.  
Figure 1: Methodological flowchart.  
This testbed also allows the application of the IAEA concepts of defensive computer security architecture (DCSA) [18; 19] (not part of this study), a practical technique to protect facility functions that support safety and security that make use of, depend on, or are supported by digital technologies. Therefore, a researcher can implement the concepts of computer security levels (implementing a graded approach) and computer security zones (delivering defense in depth) and develop cyber-attack scenarios to assess ways in which an adversary could exploit vulnerabilities in systems performing facility functions.  
The requirements and envisioned cyber-attack in turn guided the choice of the components shown in Table 1 below.  
In what follows, we briefly explain the capabilities and justify the selection of the above listed components. We also describe the adjustments made in order to integrate them into the testbed and enable the proposed cyber-attack.
## 2 Background
### Innovative Aspects of the Study  
To overcome the constraints of prior research and enhance the forecast precision, this study merges data decomposition with deep-learning architectures, specifically LSTM models. Leveraging LSTM's capability to preserve historical data for assessing uncertainties arising from model misclassifications and data noise, it surpasses the drawbacks of conventional gradient-based networks such as sluggish performance and numerous input parameters. Consequently, the forecast accuracy is enhanced. The key contributions of this study encompass the following:  
1. Most of the existing methods are based on the meteorological features of numerical weather prediction as input features, and the input prediction model based on multiple correlation and target variables proposed in this study takes into account the historical patterns of correlation features and target variables, which can improve the accuracy of the model prediction.
2. Aiming to address the shortcomings of the traditional DBO algorithm in the application of power load forecasting, such as its time-consuming nature, reduced search capability, ease of falling into local optimum and low convergence accuracy, the traditional DBO algorithm is improved by using the lens inverse-learning strategy, the spiral search strategy, the optimal value-guiding strategy, and the positional dynamic updating of weight coefficients strategy in order to form the MODBO-LSTM algorithm with multiple strategies, and the optimization outcomes of various optimization algorithms (FOA, SSA, GWO, etc.) are compared. The simulation findings indicate that the MODBO-LSTM algorithm outperforms the conventional DBO-LSTM approach, exhibiting superior prediction accuracy.  
3. A hybrid MODBO-VMD-MODBO-LSTM (MVMO-LSTM) model is proposed, whereas previous studies need to reset the number of decompositions when the center frequency domain is determined and seldom explore the penalty factor and decomposition layer values. This multi-strategy improved dung beetle algorithm not only optimizes the penalty factor and decomposition layer values of VMD but also optimizes the optimal number of hidden units, the optimal maximum training period and the optimal initial learning rate of LSTM. The combination of these two improvements can effectively help the LSTM learn and adapt to the complexity and seasonal changes of the power load data, thus improving the prediction accuracy and making the prediction results more reliable and practical.
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 6 Discussion  
This section describes the pairwise comparison of various methods and test datasets. For comparison we have selected some state of the art models which are widely used for crowd monitoring. Table 4 contains the summary of pairwise comparison using MAE and MSE as benchmarking parameters. The CNN based crowd counting and localization algorithms presented in [8; 22; 34; 35; 76] are compared with Scale Driven Convolutional Neural Network (SD-CNN) using UCSD, UCF-CC-50, WorldExpo'10 and ShanghaiTech Part A, B datasets. The MAE and MSE of SD-CNN are lesser than those of other models on UCSD, UCF-CC-50 and WorldExpo'10 datasets, respectively. Comparing these models, SD-CNN has the ability to count and localize the human heads in both low and high level density crowd images. Similarly, Density Independent and Scale Aware Model (DISAM) has the lowest MAE as compared to other models tested on UCSD and WorldExpo'10 datasets. Unlike previous models which can only count the people in dense crowd, DISAM has the ability to handle both counting and localizing people in the dense crowd. Finally, we have compared SD-CNN with DISAM on different datasets and concluded that SD-CNN has lower rate of MSE on UCSD dataset and MAE on WorldExpo'10, respectively. In crowd monitoring and counting problems, researchers try to explore the domain and have applied different machine learning techniques and methodologies to count and localize the crowd as well as their anomalous behavior. In [4] the authors have presented the approximate size estimation and counting the accurate number of people in the crowd. The model has achieved 90% accuracy and it has been shown that the efficiency is not affected by increasing the number of people. The Kalman filtering approach and KL-divergence technique have been used [77] to monitor and count the crowd in smart city. Similarly to monitor and estimate crowd density, deep convolutional network has been used in [60]. There are three different classifiers namely multiple linear regressions, back propagation neural network and regression trees which have been applied and used in [78] to monitor and count the number of people in dense crowd. In [79] the proposed model has been divided into two folds;
# SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm
## 2 Satellite Communication Earth Station Array Model
Assuming the number of peak values in the Capon power spectrum is G, the complexity of the algorithm proposed in this article is O(GSM\({}^{2}\) + GM\({}^{3}\) + N-CM\({}^{2}\)).
###### Abstract  
Lithium-ion battery on-line monitoring is challenging due to the unmeasurable characteristic of its internal states. Up to now, the most effective approach for battery monitoring is to apply advanced estimation algorithms based on equivalent circuit models. Besides, a usual method for estimating slowly varying unmeasurable parameters is to include them in the state vector with the zero-time derivative condition, which constitutes the so-called extended equivalent circuit model and has been widely used for the battery state and parameter estimation. Although various advanced estimation algorithms are applied to the joint estimation and dual estimation frameworks, the essence of these estimation frameworks has not been changed. Thus, the improvement of the battery monitoring result is limited. Therefore, a new battery monitoring structure is proposed in this paper. Firstly, thanks to the superposition principle, two sub-models are extracted. For the nonlinear one, an observability analysis is conducted. It shows that the necessary conditions for local observability depend on the battery current, the initial value of the battery capacity, and the square of the derivative of the open circuit voltage with respect to the state of charge. Then, the obtained observability analysis result becomes an important theoretical support to propose a new monitoring structure. Commonly used estimation algorithms, namely the Kalman filter, extended Kalman filter, and unscented Kalman filter, are selected and employed for it. Apart from providing a simultaneous estimation of battery open circuit voltage, more rapid and less fluctuating battery capacity estimation are the main advantages of the new proposed monitoring structure. Numerical studies using synthetic data have proven the effectiveness of the proposed framework.  
Keywords:lithium-ion battery; battery monitoring; extended equivalent circuit model; observability analysis; Kalman filtering +
Footnote †: journal: Applied Physics Letters
## 3 Results
### ROS/RNS and Cell Death Markers in the Presence of Different Concentrations of Botanical Extracts  
Reactive oxygen species (ROS) and reactive nitrogen species (RNS) were analyzed in neural stem cells submitted to oxidative stress by H\({}_{2}\)O\({}_{2}\). The results can be observed in Figures 2 and 3, respectively.  
Figure 2: ROS analysis of botanical extracts in neural stem cells exposed to H\({}_{2}\)O\({}_{2}\) and in the presence of the botanical extracts (bilberry, blueberry, bacopa, sage, saffron, arctic root, ginkgo, ginseng, gotu kola). Dash line indicates control with H\({}_{2}\)O\({}_{2}\) levels. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.005\), *** \(p<0.001\).  
NSC exposure to oxidative stress increased ROS on average by 20.5%. The cultures exposed to oxidative stress and the botanical extracts revealed different levels of effectiveness in reducing ROS.  
Specifically, bilberry, bacopa, saffron and gotu kola did not have any effect on reducing oxidative stress. Surprisingly, a pro-oxidant effect was observed in the case of saffron in both of the concentrations used.  
Both ginkgo and ginseng had antioxidant and pro-oxidant effects, depending on the concentration used in the cell cultures. The extracts where only an antioxidant effect observed were blueberry (at 0.001%), sage (at 0.01%) and arctic root (at 0.01%).  
Regarding RNS, H\({}_{2}\)O\({}_{2}\) treatment increased this parameter by 7.9%. In this case, the results, as observed in Figure 3, indicated that the following extracts did not have an effect on RNS: bacopa, saffron, ginseng and gotu kola. In turn, the extracts that significantly reduced RNS were bilberry (at 0.0001%), blueberry (at both concentrations), sage (at 0.01% and 0.0001%), arctic root (at 0.01% and 0.001%) and ginkgo (at 0.00005%).  
Finally, several genes related to cell death were analyzed, specifically caspase-9 All extracts significantly reduced caspase-9 in NSCs exposed to oxidative stress in all of the  
Figure 3: RNS analysis in neural stem cells exposed to H\({}_{2}\)O\({}_{2}\) and in the presence of the botanical extracts (bilberry, blueberry, bacopa, sage, saffron, arctic root, ginkgo, ginseng, gotu kola). Dashed line indicates control with H\({}_{2}\)O\({}_{2}\) levels. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.005\), *** \(p<0.001\).  
concentrations used (Figure 4). As caspase-9 is implicated in the initiation process of the apoptotic pathway, this result suggests that all the extracts contain active compounds that contribute to inhibiting this gene.
## 4 Security Proofs  
### Security Requirements  
* Unlinkability: No one except the judge can trace a user when she/he is using her/his ticket for roaming the mobile networks.
* Ticket Unforgeability: None can forge a ticket without performing the requesting ticket protocol of Section 3.3 with the system.
* Tamper Resistance: The triple \((\delta,\sigma,\gamma)\) in a ticket cannot be modified.
* Ticket Swindling Resistance: Anyone else cannot consume an eavesdropped ticket for communication services where the ticket is owned by some user.
* Mutual Authentication: Neither a mobile user without a valid ticket nor an illegal system can pass the authentication.
* Secure Authenticated Key Exchange: After mutual authentication, a mobile user and \(V\) can share a common session key unknown to any eavesdropper.
# Medical Devices for Tremor Suppression: Current Status and Future Directions
## 2 Early Innovations  
Over the past several decades, a variety of different orthotic and stimulatory approaches has been proposed to target or reverse the abnormal rhythmic activities in the neural pathways of the cerebellum and the thalamus. Beginning 1987, [PERSON] and colleagues proposed several devices that employed energy dissipation to suppress tremors. The damped joystick is a hand control device designed to facilitate the control of wheelchairs and other applications [41; 42; 43; 44]. This device consists of a sealed chamber filled with viscous fluid and a spherical ball that acts as a damping element to suppress the involuntary movements of the position-sensing actuator. The controlled energy-dissipation orthosis (CEDO) is a wheelchair-mounted device that provides velocity-dependent loading with magnetic particle brakes to a limb coupling cuff [45; 46; 47]. Similarly, the modulated energy dissipation (MED) manipulator also provides damping via magnetic particle brakes with real-time digital control [48; 49].  
The success of these early works in showing that velocity-dependent loading can attenuate tremor and involuntary motions led to the development of wearable orthosis with mechanical loading. Other approaches, including electrical stimulation systems and assistive feeding devices, have also been proposed. Most of these are classified as Class I medical devices, meaning that they are registered with the FDA but not subjected to any premarketing review.
## 3 Experimental Setup and Results
This is the first hint that there are optimal FWHMs and center positions for the speckles' pixel intensity distribution to achieve the best CNR for a given speckle size and object in microscale ghost imaging. Hence, the speckles' pixel intensity distribution, which serves as the pseudothermal light field in the conventional ghost imaging system, gives a better contrast to the correlation imaging system. This scaling can also be easily verified when viewing the ghost images in false color representation in Figures 2b and 3b. In our experiment, the retrieved ghost images with the highest quality as judged with the naked eye were in good agreement with the images with the highest CNR. Figure 3: The evolution of the CNR and reconstruction of the object image. **(a)** The measured speckles’ pixel intensity distribution with different peak positions. Only one symbol is displayed for every 5 data. **(b)** The retrieved images of the letter “\(\blacku\)” in different FWHMs of the speckles’ pixel intensity distributions. **(c)** Solid blue lines are the horizontal line-outs of the normalized data of the retrieved ghost images and red dashed curves display the object. **(d)** The CNR as a function of different peak positions of the speckles’ pixel intensity distribution from the nonuniform pseudothermal light.
# Analysis of Load-Settlement Curve Based on Load Transfer at Pile-Soil Interface
## Fundamental Principles  
### Soil Compression Settlement  
The earth beneath the pile foundation compresses and deforms due to the external load of the pile foundation, creating settlement, and the ultimate settlement \(S\) may be described as follows [31]:  
\[S=m_{s}\sum_{i=1}^{n}\Delta S_{i}=m_{s}\sum_{i=1}^{n}\frac{\sigma_{z=0}}{E_{si }}(z_{i}C_{i}-z_{i-1}C_{i-1}) \tag{1}\]  
In which, \(S\) is the foundation's ultimate settlement (mm); \(n\) is the number of soil layers in the compression layer; \(\sigma_{z}(0)\) is the value of extra stress at the base ground (kPa); \(\sigma_{h}\) is the base compressive stress(kPa), when \(z/b>1\), \(\sigma_{h}\) adopts the base's average stress, and when \(z/b<1\), \(\sigma_{h}\) adopts the compressive force at the greatest compressive stress point b/3-b/4 in the graph of average stress at the base; \(b\) is the width of the foundation (m); \(\gamma\) is the capacitive weight of soil; \(h\) is depth of base embedment (m); and \(z_{i}\), \(z_{i-1}\) is the distance between the bottom of the \(i\)-th and \(i-1\)-th layers of the substrate (m).  
The depth control standard for settlement computation is expressed as follows:  
\[\Delta S_{n}\leq 0.025\sum_{i=1}^{n}\Delta S_{i} \tag{2}\]  
In which, \(\Delta S_{i}\) is the compression of the \(i\)-th layer of soil; \(\Delta S_{n}\) is the settlement value of the corresponding soil layer (mm); \(E_{si}\) is the compression modulus of the corresponding soil layer (kPa); \(C_{i}\), \(C_{i-1}\) is the average coefficient of extra stress in the relevant soil layer; and \(m_{s}\) is the empirical correction coefficient of settlement.