esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Developing SNEP Model in AVISPA
### Counter Exchange Protocol with Strong Freshness  
In case A and B do not have the updated counter values of each other, the counter exchange protocol is executed between A and B. This is a bootstrap protocol. While we model in AVISPA, we develop a counter exchange protocol followed by data transmission that uses the updated counter value. Figure 8 shows the messages that we have modeled in AVISPA. As we have mentioned before, instead of using keyed \(MAC\), we use the keyed hash function. Similarly, we use {D}_(Kab.Ca) to replace encryption with the initialization vector (_i.e._, {\(D\)}\({}_{\langle K_{AB},C_{A}\rangle}\)), where Ca is the initialization vector. Note that we use two different keys, Kab and Kba in two directions, _i.e._, from A to B and from B to A. Here, the counters Ca and Cb are being used as nonces, which could be considered as challenges for authentication.  
In the AVISPA model we develop, there are two agent roles: alice for node A and bob for the other node, B. Figure 9 shows the partial HLPSL specification that we develop to model these two roles. The following security properties can be validated using AVISPA.  
1. Upon receiving the second message, [PERSON] authenticates [PERSON] through the counter (nonce) Ca. We use the label alice_bob_ca to identify the goal. To validate this security goal, we add witness(B, A, alice_bob_ca, CA') and request(A, B, alice_bob_ca, CA) statements inside the roles of B and A, respectively.
2. Upon receiving the third message, [PERSON] authenticates [PERSON] through the counter (nonce) Cb. We use the label bob_alice_cb to identify the goal. To validate this security goal, we add witness(A, B, bob_alice_cb, CB') and request(B, A, bob_alice_cb, CB) statements inside the roles of A and B, respectively.
3. The secrecy of data, D sent to B from A. This goal is validated using the fact secret(D', d, {A, B})). Here, the label d identifies the goal, and {A, B} is the set of agents that are allowed to learn the value D.  
Figure 8: Messages for the counter exchange protocol with data transmission.
## 3 Results
### AIGC Input-Output Classification
#### 3.3.10 Text to Knowledge Graph and Knowledge Graph to Text  
Text-to-knowledge-graph and knowledge-graph-to-text are two tasks that bridge the gap between natural language understanding and knowledge representation. Text to knowledge graph involves transforming unstructured textual data into a structured knowledge graph, capturing the semantic relationships and entities within the text. Onetechnique used for this purpose is Grapher [117]. Grapher [117] consists of two stages. In the first stage, it leverages the power of pre-trained language models (PLMs), such as T5, to generate the nodes of the knowledge graph. PLMs have the ability to understand and extract information from text, allowing them to identify and represent the entities mentioned in the input text as nodes in the graph. In the second stage, Grapher [117] focuses on generating the edges of the knowledge graph. This is performed using recurrent neural network models like LSTM or GRU. These models take the generated nodes and the original text as input and generate the edges that capture the relationships between the nodes. The LSTM or GRU models are particularly effective in capturing long-range dependencies and contextual information, which are crucial for accurately modeling the relationships within the knowledge graph. On the other hand, in the knowledge-graph-to-text task, the aim is to generate coherent and meaningful textual representations from knowledge graphs. One approach for this task is the GTR-LSTM [129], which is a technique for generating sentences from RDF data. It utilizes a triple encoder architecture consisting of subject, predicate, and object encoders, which capture the semantic information of the RDF triples.
## 1 Introduction
### Additive Manufacturing Process Monitoring
The novelty of the current work is that it investigates the potential for laser generated broadband Rayleigh waves to provide useful in-situ information about material nonlinearity that can reasonably be expected to influence mechanical performance of the deposited material. Moreover, the laser ultrasound system will perform process monitoring in-situ on rough surfaces.
## 4 Results and Discussion
### Wheel Load Variation  
The driving safety can be evaluated by predicting the possibility of derailment when operating the train. The derailment ratio and the reduction rate of the wheel are calculated in the following formulas.  
\[K_{Q/P}=\frac{Q}{P}=\frac{8}{3}\frac{\alpha_{h}}{1-\alpha_{v}} \tag{13}\]  
\[\eta=\frac{\Delta P}{P_{0}}=\alpha_{v} \tag{14}\]  
where:  
\(K_{Q/P}\): Derailment ratio.  
\(Q\): Lateral force.  
\(P\): Dynamic wheel weight due to track distortion.  
\(\alpha_{h}\), \(\alpha_{v}\): Horizontal and vertical accelerations of the vehicle body, respectively.  
\(\eta\): Wheel reduction rate.  
\(P_{0}\), \(\Delta P\): Static wheel load and variation in the wheel weight.  
In this paper, the criteria for the wheel load variation ratio were used by 0.372 according to [PERSON] and [PERSON] (2018), and the rules on safety standard for railroad vehicles by the Korea Ministry of Land, Infrastructure, and Transport and the design standard for railway structures by Japanese Railway Technical Research Institute were used.  
In Figure 8, the wheel force variation results are described. The maximum wheel force variation rate in ballasted track was 0.06. Meanwhile, this value of precast slab track Type I and II decreased 33.33% and 50% compared to ballasted track. These results showed that the train runs more stable in the precast slab track sections. The limited rate change of wheel load is 0.372 so that both types of precast slab track acquired the requirement. However, as can be seen in Figure 8, the train operated slightly smoother in the Type II than Type I section. There is a small change in wheel force when the train runs from the precast floating track to the ballasted track at the connection zone. Therefore, it is necessary to increase the rigidity at the transition part when designing the floating slab track.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 3 Results
### Influence of Various Factors on Mechanical Properties
#### 3.2.2 Effect of Recycled Aggregate on Mechanical Properties  
As shown in Figure 6, under the same curing age and stabilizer content (20%), \(q_{\text{u}}\) decreased with increasing recycled aggregate content, peaking at 20%. For instance, at 40 days of curing, \(q_{\text{u}}\) values of R20C20-40d, R40C20-40d, and R60C20-40d were 4505 kPa, 3642.4 kPa, and 3589 kPa, respectively. The R20C20-40d specimen showed a 0.24- and 0.26-fold increase in peak strength compared to the latter two.  
The analysis reveals that increased stabilizer content enhanced hydration reactions, producing cementitious materials that synergistically interacted with red clay, forming a denser internal structure and filling voids. At 20% stabilizer content, hydration reactions were most complete, yielding the most stable skeletal structure and maximum \(q_{\text{u}}\).
# Recent Progress on Underwater Wireless Communication Methods and Applications
## Abbreviations  
\begin{tabular}{l l l} No. & Abbreviation & Full Name \\
1 & AUV & Autonomous Underwater Vehicle \\
2 & BECS & Bio-inspired Electrocommunication System \\
3 & BER & Bit Error Rate \\
4 & CNN & Convolutional Neural Networks \\
5 & DBPSK & Differential Binary Phase-Shift Keying \\
6 & DL & Deep Learning \\
7 & DPSK & Differential Phase Shift Keying \\
8 & DRL & Deep Reinforcement Learning \\
9 & EMD & Empirical Mode Decomposition \\
10 & FEC & Forward Error Correction \\
11 & GAN & General Adversarial Network \\
12 & IoUT & Internet of Underwater Things \\
13 & LD & Laser Diode \\
14 & LED & Light-Emitting Diodes \\
15 & LMS & Least-Mean-Square \\
16 & MIMO & Multi-Input Multi-Output \\
17 & ML & Machine Learning \\
18 & NRZ-OOK & Non-Return to Zero On-Off Keying \\
19 & OCDMA & Optical Code Division Multiple Access \\
20 & OFDM & Orthogonal Frequency Division Multiplexing \\
21 & OAM & Orbital Angular Momentum \\
22 & QKD & Quantum Key Distribution \\
23 & QPSK & Quadrature Phase-Shift Keying \\ \end{tabular}
###### Abstract  
The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models' performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models.  
Keywords:photovoltaic power forecasting; data-driven; deep learning; variational autoencoders; RNN +
Footnote †: journal: Applied Sciences
## 2 Theoretical Approach
### Entropy Production and Fluxes  
We start with examining the entropy production rate in the solution, \(S\) (i.e., its change with time, \(t\)), which is given by [1; 13]  
\[\frac{dS}{dt}=\frac{d_{i}S}{dt}+\frac{d_{e}S}{dt}. \tag{1}\]  
The first term on the right-hand side is due to irreversible processes inside the thermodynamic system and according to the second law of thermodynamics is always positive, or \(d_{i}S/dt\geq 0\). The second term accounts for the entropy change that results from exchanges of energy and matter with the systems surroundings. It is the irreversible part of the total entropy charge that is relevant to the current discussion. In the case of a solution, it reads [1; 13]  
\[\frac{1}{k_{B}}\frac{d_{i}S}{dt}=\frac{\dot{S}}{k_{B}}=-\int_{\Delta V}dV \left(\mathbf{i}\cdot\frac{\
abla\mu}{k_{B}T}\right), \tag{2}\]  
where \(\mathbf{i}\) is the diffusion flux in \(\mathrm{kg\,m^{-2}s^{-1}}\), \(\mu\) is the chemical potential, and \(\Delta V\) is some volume within the solution. If the volume \(\Delta V\) is small, Equation (2) becomes  
\[\frac{\dot{S}}{k_{B}}\approx-\mathbf{i}\cdot\frac{\
abla\mu}{k_{B}T}\Delta V, \tag{3}\]  
Following [PERSON] and [PERSON] [1], we can write Equation (2) (or Equation (3)) in the form  
\[\dot{S}=\int_{\Delta V}dV\left(\sum_{\eta}X_{\eta}\hat{x}_{\eta}\right)\approx \sum_{\eta}X_{\eta}\hat{x}_{\eta}\delta V. \tag{4}\]  
The quantities \(X_{\eta}\) and \(\hat{x}_{\eta}\) are the generalized thermodynamic forces and fluxes, respectively [1; 13; 14]. Comparing Equation (4) to (2) leads to  
\[X_{\eta}=\frac{1}{k_{B}T}\frac{\partial\mu}{\partial r_{\eta}},\ \hat{x}_{\eta}=i_{\eta} \tag{5}\]  
where we have set \(\mu=\mu_{1}/m_{1}-\mu_{2}/m_{2}\). The index \(\eta=1,2,3\) accounts for the three spatial coordinates \(r_{1},r_{2},r_{3}\). The total flux can then be expressed as  
\[\mathbf{i}=-\alpha\
abla\mu+\delta\mathbf{i}, \tag{6}\]  
where \(\delta\mathbf{i}\) is the fluctuating part of the flux \(\mathbf{i}\).
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 3 Satellite Attitude Control System Evaluation Indicator Set Construction
### Standardization of Performance Indicators  
Satellite data often exhibit inconsistencies in units and types. In order to rationalize the assessment results, it is essential to standardize the metrics before conducting evaluations. Based on the trend of metric values, these metrics can be categorized into benefit-based indicators, cost-based indicators, and range-based indicators [17]. For the satellite attitude control system, attitude pointing accuracy, relative attitude pointing accuracy, pointing stability, attitude control energy consumption, attitude control response time are cost-based indicators, and maneuvering angular velocity is a benefit-based indicator. For benefit-based indicators, larger values are preferable, while for cost-based indicators, smaller  
In Equation (15), \(i=1,2,\cdots,m\), \(\mu\) and \(I\) are the statistical mean and standard deviation, respectively. The probability distribution of the unit grey coefficient is determined by the source and nature of the uncertainty of the indicator. In the absence of sufficient information, it is generally assumed \(\gamma_{i}\sim N\left(0.5,\frac{1}{36}\right)\).  
This section describes accurately and comprehensively the control performance of the satellite attitude control system in tracking mode by selecting the indicators in four aspects: fast control, high control accuracy, stable control and low energy consumption. It also solves the problem of discrepancies in the scale of satellite indicator data through the standardization of the indicators, and finally eliminates the effects of data uncertainty and error caused by the space-orbit environment and sensor measurements through the grey-numbering processing of the indicators.
# Experimental Study on the Permeability of Ecological Slopes under Rainfall Infiltration Conditions
## 1 Introduction
Although the above research results have brought many new insights into the permeability characteristics of slopes, most of the existing research focuses on the permeability performance of slope soil through flat overburden layers and studies the internal water migration law and permeability of slopes using small bare soil slopes filled indoors. There is still a lack of research on the internal water migration law and permeability of large ecological slopes in outdoor natural environments. In light of this, this paper aims to conduct a series of experiments to study internal water migration law and the permeability of large ecological slopes. At first, four kinds of large overburden ecological slopes are established outdoors, and artificial rainfall tests are conducted on each overburden slope simultaneously to monitor the water migration law inside the slope in real time after the vegetation matures in a year's dry-wet cycle. Then, the soil-water characteristic curve of the test soil is measured by using the pressure plate instrument, and the matrix suction inside the slope is calculated. Finally, the unsaturated permeability coefficients of the shallow soil of the slopes are calculated by using the transient profile method and the influences of different vegetation overburdens on the permeability performance of the shallow soil of the slopes under the effect of rainfall are investigated in detail.
# Research Trends in Collaborative Drones
## 7 Networked Drones Using Cellular Technologies  
The classical pilot-to-drone connection is assured using Visual Line of Sight (VLOS) communications. A direct link is used between a pilot and a drone. The pilot-drone distance is limited by the capability of maintaining eye contact with a drone, such as 500 m. To go far off VLOS, it has been proposed that cellular network infrastructures be used for pilot-to-drone command and control and more. This concept is called BVLOS communications. Pilots maintain communications with drones across arbitrarily long distances over cellular networks. BVLOS enables the piloting of drones outside the line of sight of pilots. Drone traveling distances become limited solely by cellular network coverage. Drones may be guided in complex structures involving buildings, electric poles, and wiring. Because communications are maintained, pilots can always intervene if something goes wrong. Applications of BVLOS include inspection of infrastructures, such as pipelines, and delivery of parcels, food, and medical supplies in urban or rural areas. BVLOS communications are expected to be one of the important applications of upcoming generations of cellular networks. This realization of this concept enables drone applications requiring wide geographical area coverage.
## 9 Optimization
### Real-Time Optimization  
[PERSON] et al. employed real-time data analytics and artificial intelligence to optimize shale oil wells in Vaca Muerta's Loma Campana field [77]. The study focused on 130 active wells with the objective of minimizing production losses by detecting issues such as choke erosion and paraffin blockages in a timely manner. The field, which has over 540 drilled wells and 15 production facilities, employs a range of extraction methods and is remotely supervised. The study underscored the challenges of monitoring wells that primarily rely on wellhead pressure data, and also critiqued the limitations of existing alarm systems based on pressure thresholds.  
Predictive modeling was conducted using the Prophet package, which utilized six days of historical data to forecast wellhead pressure trends. This enabled timely interventions based on pressure curve anomalies. The model was validated in the Loma Campana control room and provided hourly anomaly probabilities. Data cleaning was emphasized as crucial, and the LOESS algorithm and a Tukey test were used for time series decomposition and outlier detection, respectively.  
In a related study, [PERSON] focused on optimizing coiled tubing (CT) operations in Vaca Muerta, addressing challenges like casing collapse during hydraulic fracturing. The study spanned 45 wells across 12 pads and aimed to enhance operational efficiency without compromising safety or environmental standards. Key performance indicators (KPIs), such as CT speed and plug milling time, were systematically introduced in order to improve operations. The study also discussed the impact of various factors like CT pipe weight and fluid resistance on these KPIs. Overall, the optimization efforts led to an 11% reduction in total well time during frac plug mill-out operations.  
Additionally, the study explored the optimization of CT string designs and emphasized the critical role of fluid management. It also discussed how elevated temperatures in the fluid system can adversely affect the rheological properties of additives, leading to excessive CT pressure.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 2 Background  
### Adaptation Scenario: Suis  
The ship supplying information system (SUIS) [15] is a web service-based system, responsible for handling supplying requirements submitted by ship-users, and it can automatically calculate and dispatch food, water, oil, and other supplying materials to ships. However, this web service-based system suffers the problem of Slashdot effect [16]: during the sailing task-intensive period, the supplying requirements increase rapidly, and the servers respond slowly or even crash; while during the rest period, there are rarely supplying requirements and most servers are freed. In order to improve the quality of the services and to decrease operating costs, SUIS was reconstructed based on the cloud computing architecture. By being redeployed with the MAPE-K [6; 17] architecture, SUIS was composed of two parts, i.e., the self-adaptation logic and the application logic, as shown in Figure 1. The former is responsible for monitoring and optimizing service performance by dynamically adjusting the servers in operation, while the latter is used to provide continuous and reliable web services to ship-users.  
SUIS operates under dynamic user requirements, and it is difficult to describe and analyze the time behaviors for the self-adaptation logic. For example, the self-adaptation logic periodically monitors CPU utilization of servers (i.e., virtual machines, VMs for short), and dynamically increases or decreases server numbers in operation according to such a self-adaptation strategy as \"if _Utilization_ > _UpperLimit_ for \(N\) seconds, then add \(P\) more VMs\". From the above example, it can be seen that the time constraints, such as the monitoring period and the analyzing and judging time (i.e., for N seconds) need to be specified and verified elaborately. Uncertainty time behaviors, such as the message processing time, the network delay time, and the server dispatching time need to be depicted. In addition, additional quantitative analysis metrics are needed to evaluate the performance and the response-time of the above self-adaptation strategies.  
In addition to the SUIS adaptation scenario, other classical adaptation systems, such as the self-adaptive firefighting system [2; 18; 19] and the ZNN.com adaptation system [20; 21], demand high requirements for time behavior modeling and analysis.
# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
## 2 Proposed Approach
### Classification Using a Deep Neural Network Classifier  
The obtained feature vectors in Section 2.4 were provided as input to the proposed deep neural network classifier (DNN). The proposed DNN (referred as Model-2 in the later sections) consisted of two hidden layers with 32 neurons per layer, as illustrated in Figure 5. A dropout layer with a dropout probability of 0.8 was introduced between each layer to control overfitting. The hidden layers used the rectified linear unit (ReLU) activation function, and the final output layer used the sigmoid activation function.  
Let \(x_{i}\) and \(b_{i}\) be the input and bias value of hidden layer \(i\), respectively, \(W_{i}\) is the weight vector connecting the nodes in hidden layer \(i\) to the nodes in hidden layer \(i+1\), then, hidden layer \(i+1\) is activated using the following equation:  
\[Z_{i+1}=f(W_{i}x_{i}+b_{i}) \tag{2}\]  
where \(Z\) denotes activation of the subscripted hidden layer and \(f\) is the ReLU activation function defined as:  
\[f(x)=\max(0,x) \tag{3}\]  
Figure 4: Difference between connectomes of random participants belonging to the ASD and control group.  
Thus, ReLU gives an output within the range of [0, \(\infty\)]. In the case of sigmoid,  
\[f(x)=\frac{1}{1+e^{-x}} \tag{4}\]  
where \(e\) = Euler's number. It exists between 0 and 1 and predicts the probability value as an output in the case of binary classification. Xavier and He weight initializers were used with sigmoid and ReLU activations, respectively. Adam, with a relatively low learning rate of 0.0001 and default parameters, was applied as the optimizer [52]. The binary cross-entropy loss function was used in this binary classification problem (ASD vs. control). This loss function is defined as:  
\[J=-\frac{1}{m}\sum_{i=1}^{m}[y_{i}\cdot\log(p(y_{i}))+(1-y_{i}). \tag{5}\]  
where \(m\) is the total number of samples, \(y\) is the label, and \(p\) indicates the probability of \(y\) belonging to autism or control group. The objective of the network is to minimize the value of loss function, \(J\).  
Since our input vector was high dimensional, we employed L2 regularization techniques and introduced a very small batch size of 10 during training so that our model generalized well and performed better on unseen data.