esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rule-Based Language and Verification Framework of Dynamic Service Composition
## 4 Verification Framework
### Model Checking  
The processes generated in Promela are simulated and verified using SPIN. When a new service is added, it is transformed into Promela and verified. The client process sends a request and waits for a result. When the client process receives the result, then the termination property for the called process (service) is verified. When for a verified service, all private ports receive the data, then the service terminates. The property of termination is therefore verified. In LTL (Linear Temporal Logic) this means: \([\;]\) (\(p?x\)) meaning that when a private port \(p\) is created, it always receives data. \(p\) being a created private port when running a service \(s\) and \(x\) being the received data.  
The formal verification of the model contributes to the demonstration of the soundness property which is too difficult to proof theoretically [8].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Specification** & **Promela Code** & **Comment** \\ \hline variable, value and port & mtype, byte, chan & use _mttype_ or _byte_ to define variable and value And _chan_ to define port \\ \hline \(x\gets u\equiv x_{s}\) & x = u & assignment the value (u) to the variable (x) \\ \hline \(\overline{x}_{b}^{x}\) & \(==\), \(<\), \(>\), \(<=\), \(!=\), \(>=\) & promela conditional expressions on \(\overline{x}\) for e.g., \(x_{1}==x_{3}\), \(x1>u\), \(x_{1}=x_{3}\). We will note them by \(c\). \\ \hline \multirow{6}{*}{\(s\)(\(\overline{x}\),\(\overline{x}_{b}^{x}\)) & **begin**\(\;\  
Table 2 describes an example of services and their transformations into Promela distributed on three user spaces. The Promela transformation is then simulated. Three scenario are presented  
**scenario 1.** The services were correctly specified (Figure 3); The client process calls the service \(s\) from _space1_ on his public port. The _space1_ process receives and instantiates \(s\). \(s\) calls \(s_{1}\) in _space2_ by creating private port 4 on the \(figure\), \(s_{2}\) in _space3_ by creating private port 5 on the figure and define \(y_{3}\) locally. Finally, the client process receives the reponse awaited.  
**scenario 2.** The parameters of the called services do not match (Figure 4). From Table 2, before test, the input parameters for the service \(s_{1}\) does not match the space 1 and 2;  
**scenario 3.** The called service is not defined (Figure 5). From Table 2, before test, \(s_{2}\) service has been removed.  
Figure 4: Not matching service: service parameters do not match. This implies the call of the client process does not execute until the end.  
Figure 3: Correct execution: the client process obtains the requested response.
# A Self-Calibration Method for Robot End-Effector Using Spherical Constraints
## 5 Simulation Experiment
### Data Measurement  
Before the experiment commenced, the actual diameter of the metal sphere was measured to be 61.4 mm, and the end-effector load weight was 1.535 kg. In accordance with the calibration method described earlier, two sets of data were collected to ensure  
Figure 10: Schematic diagram of the experimental equipment.  
Figure 9: Schematic diagram of the calibration system.  
both accuracy and speed in parameter identification. The first set was utilized for robot parameter error identification and calibration, while the second set was employed to compare the robot's end-effector positioning accuracy before and after error compensation, thereby validating the model's accuracy and the effectiveness of the compensation. During data collection, it was essential to ensure that the data points were evenly distributed across the surface of the sphere. After the robot's end-effector contacted the sphere, the joint angle data for each group were recorded. A MATLAB program was then written, using the parameter identification method described earlier, to identify the error parameters. The results of the robot's geometric error parameter identification are presented in Table 3, with the end-effector deformation error \(c_{t}=0.00036\). Based on the material and dimensional parameters of the end-effector, the identified deformation error \(c_{t}\) of the end-effector aligns with its physical significance.  
Using the second set of 30 data points, the geometric and end-effector deformation errors of the robot were corrected based on the identified error parameters. The positioning accuracy was then evaluated using the same criterion as defined in Equation (39). Figure 11 illustrates the positioning accuracy of the robot's end-effector before and after error compensation. The KUKA robot examined in this paper exhibits an absolute positioning accuracy of approximately 0.5 mm. However, this accuracy significantly diminishes when the end-effector was attached. After implementing the proposed algorithm for error calibration and compensation, the maximum positional error
## 4 Results and Discussion
### The Effect of Bern Geometry on the Ground Surface Settlement  
Figure 11 presents the effect of different berm geometric parameters on the ground surface settlement. According to extensive studies on excavation-induced ground movements reported in CIRIA C760 [16], the settlements behind the wall could be related to the horizontal movements of the wall considering the ground loss. Therefore, the effect of the geometric parameters of the earth berm on the ground surface settlement presented in Figure 11 is similar to its effect on the lateral pile deflection presented in Figure 7. With increasing the height, top width, and bottom width of the earth berm, the ground surface settlements tend to decrease, and their contribution to the reduction in ground surface settlement diminishes as well. Once the top and bottom widths of the earth berm reach 0.30 \(h\) and 0.45 \(h\), respectively, their impact on reducing surface settlement becomes essentially negligible. Therefore, although a relatively large-sized earth embankment can be effective in reducing surface settlement, there exists a critical value beyond which it is ineffective to increase the size of the earth berm.  
Maximum ground surface settlement is commonly associated with the safety evaluation of the excavation. The relationships between different berm's geometric parameters and the maximum ground surface settlements and their locations are shown in Figure 12. The maximum ground surface settlement decreases non-linearly with increasing normal  
Figure 11: Effect of different geometric parameters of berm on ground surface settlement: (a) height; (b) top width; (c) bottom width.  
ized geometric parameters of the berm. A comparison of Figures 8 and 12 indicates that the influence of the berm's geometric parameters on the maximum ground surface settlement closely resembles their effect on maximum lateral pile deflection. However, the range of positional variations where the maximum ground surface settlement occurs is not as large as that for the maximum lateral pile deflection. For maximum ground surface settlement, the range is 0.25 \(h\) to 0.28 \(h\), whereas for maximum lateral pile deflection, the range is 0.50 \(h\) to 0.70 \(h\).
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 3 Proposed Algorithm
### Work Overflow  
An encrypted algorithm was developed for data transmission in the cloud server. If used efficiently, it can save data from man-in-the-middle attacks, and whatever information is transmitted over the entire network can be transmitted in a secure and reliable form. First, the data are converted to different decimal values by different mechanisms, and then, the Caesar cipher encryption algorithm is implemented in these decimal values. After that, a Cipher matrix algorithm is implemented on the Caesar cipher encryption algorithm's results to obtain the cipher matrix text. After obtaining the cipher matrix text, a user-defined key is being implemented on it, and then, a nonreadable text is generated called ciphertext.
## 4 Plant-Bacteria Interaction Regulates Plant Protein and Metabolites Production
### Bacterial Proteins and Metabolites in Plant Stress Mitigation (Direct Mechanism)
#### 4.1.3 Bacteria-Derived Phytohormones  
Plant hormones are classified into several types, including indole acetic acid (IAA), gibberellins (GA), cytokins (CK), abscisic acid (ABA), ethylene, and the cofactor pyrrolquoline quinone (PQQ) [42]. It is widely established that the use of PGPB promotes plant development and makes plants more resistant to salt stress by enhancing physiological response through the involvement of various growth promoting metabolites. Under salt stress, IAA generated by halotolerant and halophilic plant growth promoting rhizobacteria (PGPR) boosted root and branch length and total fresh weight of wheat plants [43]. Microorganism-produced phytohormones promote root biomass and root surface development, allowing plants to absorb more nutrients from saline soil (Figure 2C). The endophytic bacteria _Cutrobacterium_ sp. SAK1 increased soybean salinity stress resistance by producing IAA and ABA under 300 mM salt stress [44]. Recently, it was discovered that the IAA producing _Bacillus velezensis_ FMH2 increased plant root length and lateral root production that improved tomato salinity tolerance [45]. To alleviate salt stress (100 mM), a halotolerant IAA generating _Nocardioides_ sp. NIMMe6 was employed as a seed priming agent for the wheat crop. The researchers utilized _Nocardioides_ sp. NIMMe6 and a phytohormone-rich bacterial culture filtrate extract that was discovered to boost wheat plant salinity tolerance [46]. Phytohormones such as IAA and ABA produced by _Arthobacter voluwensis_ AK1 increased the expression of stress-resistance genes (_GmST1_ and _GmLAX3_) in soybean plants cultivated under 300 mM NaCl [47]. In addition to the direct methods, some microbial osmolytes are directly engaged in the reduction of salt stress. A study using _Pseudomonas_ sp. UV4 trehalose gene overexpressing mutant, improved salt stress tolerance in tomato plants cultivated in 800 mM NaCl [48].
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 4 Co-Simulation Analysis and Result Verification
### J-Turn Test  
Steady-state test, i.e., J-turn test, by loading the car with continuous large radius turns, can more accurately evaluate the rollover stability of high-mass vehicles, such as SUVs and heavy trucks, etc. The forward trajectory of the car in the test is similar to the shape of the letter J.  
curves for the steady-state tests were applied to the system. The curve output of the joint simulation of the LTR is shown in Figure 16a, where the green curve represents the uncontrolled state of complete rollover at approximately 18 s. Once the control effect is applied, the auxiliary braking system starts operating at approximately 14 s, and after some time, the LTR value tends to stabilize. Meanwhile, during the braking process, compared with the uncontrolled process, the yaw rate (Figure 16b), lateral acceleration (Figure 16c), side slip angle (Figure 16d), and roll (Figure 16e) are reduced with time and tend to become zero-valued (stable state), indicating that the vehicle is out of the danger of the rollover. Thus, the two control effects of the auxiliary braking system are confirmed, and they effectively reduce the rollover tendency of the vehicle.  
The fuzzy-PID controller has smaller overshoot, faster feedback and regulation, better sensitivity, and better overall control effect compared to the PID controller. The errors of both control results are stable within 0.5, and the final results can meet the vehicle anti-rollover. However, combined with the working characteristics of forest fire trucks, fast and stable braking is indispensable, so the final choice of fuzzy PID control is in line with the actual situation.  
The braking torque curve is shown in Figure 16f. When the braking torque is positive or negative, the left or right front wheel achieves braking, respectively. The maximum value of the braking torque is 1170 N-m.  
Figure 16: _Cont._
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 3 Method
### Model Training for Noise-Assessment-Based Pulse Signal Screening
#### 3.4.2 Support Vector Regression  
Support vector regression (SVR) is a widely recognized method employed in diverse applications, wherein it seeks an optimal hyperplane by defining data points as support vectors to train a regression model. Similar to other methodologies, noise assessment metrics (\(l_{k}\), \(k\), \(M_{k}\), \(SNR\)) are utilized as inputs with the objective of training a model that performs regression on the absolute error value between the ground truth heart rate and the rPPG-based heart rate. The model training was conducted using the LIBSVM method, employing a radial basis function kernel-based regression model. Additionally, the hyperparameter for learning, gamma, was determined using Equation (7), and an optimal regularization parameter was obtained through grid search.  
\[gamma=1/n_{features} \tag{7}\]  
where \(n_{features}\) is the length of the input. The grid search for the regularization parameter was performed over a search range of 0.01 to 1000, with intervals defined as a geometric sequence of 10 [24].
###### Abstract  
Approximately 4-\(\upmu\)m-thick GaN epitaxial films were directly grown onto a GaN/sapphire template, sapphire, Si(111), and Si(100) substrates by high-temperature pulsed laser deposition (PLD). The influence of the substrate type on the crystalline quality, surface morphology, microstructure, and stress states was investigated by X-ray diffraction (XRD), photoluminescence (PL), atomic force microscopy (AFM), transmission electron microscopy (TEM), and Raman spectroscopy. Raman scattering spectral analysis showed a compressive film stress of \(-\)0.468 GPa for the GaN/sapphire template, whereas the GaN films on sapphire, Si(111), and Si(100) exhibited a tensile stress of 0.21, 0.177, and 0.081 GPa, respectively. Comparative analysis indicated the growth of very close to stress-free GaN on the Si(100) substrate due to the highly directional energetic precursor migration on the substrate's surface and the release of stress in the nucleation of GaN films during growth by the high-temperature (1000 \({}^{\circ}\)C) operation of PLD. Moreover, TEM images revealed that no significant GaN meltback (Ga-Si) etching process was found in the GaN/Si sample surface. These results indicate that PLD has great potential for developing stress-free GaN templates on different substrates and using them for further application in optoelectronic devices.  
GaN; pulsed laser deposition; transmission electron microscopy Article]Optoelectronic Properties and Structural Characterization of GaN Thick Films on Different Substrates through Pulsed Laser Deposition [PERSON] [1][PERSON] [1], [2][PERSON] [3], [4][PERSON] [5] and [PERSON] [5]
## 3 New Concept for Assessing the Technical Condition of HV Switching Bay Devices
### A New Concept for Calculating the Technical Condition of a Circuit Breaker
#### 3.2.1 The Multiplicative Criterion for Circuit Breaker CR\({}_{-1}\)  
The multiplicative criterion \(CR_{-1}\) is considered, which depends entirely on the detected current deficiencies and internal defects. The calculation of the technical condition of the circuit breaker is influenced by the \(GCR_{-1}\) rating. The number of detected deficiencies and internal defects (\(NDID\)) is defined to determine the partial multiplicative grade. The size of detected deficiencies and internal defects (\(SDID\)) is also specified. Let us assume that this variable has four values, \(SDID\) \(\in\) {1,2,3,4}. If multiple deficiencies or internal defects of the circuit breaker are detected simultaneously, a set of deficiencies and internal defects (\(SETDID\)) is defined, with a range of \(NDID\). The content of the set that includes any level of \(RDID\) from Table 1 is defined using \(SETDID=\left\{RDID_{i,j}^{k}\right\}\). If no deficiencies or internal defects are detected, \(SETDID\) is an empty set, \(SETDID=\left\{\right\}\), and the set range is \(NDID\) = 0.  
The \(SDID\) value is set to 4 (\(SDID\) = 4), and the final rating is determined uniformly as \(GCR_{-1}\) = 0.5. If one or more deficiencies or internal defects are detected, the \(SDID\) is defined as the maximum rate of \(RDID\) within the set \(SETDID\), which is expressed as \(SDID\) = max(\(SETDID\)). The rating is then determined using Equation (3).  
\[GCR_{-1}=2^{-(SDID+1)}. \tag{3}\]  
A circuit breaker replacement is required if the circuit breaker experiences failure related to a fault, if an unplanned event and urgent inspection are identified to be a fault of such severity that operation is no longer possible, or if the inspections determine that the level of internal defects is such that operation is no longer feasible or economically justified. In this case, the attribute \(RPL\) = 1 is set, \(SDID\) = 0 is set, and the final grade is determined uniformly as \(GCR_{-1}\) = 0. There is no direct requirement to replace circuit breakers to classify deficiencies and internal defects in the measures. Figure 3 shows a flow chart that outlines the general procedure for determining the grade of the multiplicative criterion \(GCR_{-1}\).
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 4 Enabling Technologies for Transaction-Based Energy
### Information Communication Technologies  
Communication is essential in MG to facilitate the information exchange needed for the MG coordination. The presence of a communication system also helps the system operator to pro-actively detect anomalies before they result in disruptions or outages by making system-level information accessible. The communication infrastructure embodies protocols, networks and technologies that enable the distribution of measurements and commands within the power system and subsystems [48] supporting TE. The MG ICT infrastructure needs to be reliable, scalable, secure, available and easy to manage. The authors in [48; 49] discussed the role of communication in the smart grid, while a survey of communication infrastructure in MG is presented in [23].
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.4 The Mekong Delta  
The Mekong Delta is vital to the economy of Vietnam. Extensive floods severely influenced the delta during the last century [436]. It now faces significant changes due to the development of new infrastructures and facilities for irrigation, flood prevention, and electricity generation that have led to substantial hydrological alterations [437; 438; 439; 440]. In particular, the over-exploitation of groundwater in the Mekong Delta has caused the widespread hydraulic head decline and ground subsidence over a region of \(\sim\)1000 km\({}^{2}\)[392]. In [321], the flood risk in the Mekong river delta was analyzed via ENVISAT SAR images acquired from 2007 to 2011, which applied incoherent change detection algorithms. Those results showed that flooded areas most often coincided with the two-season rice crop areas in the delta. Another critical risk condition in this ecosystem is salinity intrusion, which occurs during the dry season.
###### Abstract  
In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. First, pipeline monitoring points are modeled as graph nodes to construct the pipeline corrosion spatio-temporal information graph, with corrosion rate and auxiliary features (selected through feature correlation analysis) forming node attributes. Then, a dynamic adjacency matrix is adaptively learned to capture hidden spatial dependencies, while temporal convolution modules extract multi-scale temporal patterns, and the node sequences with integrated corrosion features are input into the adaptive MTGNN for prediction. To reduce the accumulation of errors in multi-step prediction, a \"chunked progressive\" training strategy is adopted, incrementally expanding prediction horizons. Finally, experiments based on real urban drainage pipeline data show that in six-step predictions, the model reduces MAE, RMSE, and MAPE by 6.59-32.16%, 4.38-27.95%, and 5.01-22.22%, respectively, compared to traditional time series methods such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and non-adaptive MTGNN. The results indicate that the adaptive MTGNN, which integrates multi-source node features, has higher prediction accuracy across the three evaluation metrics, highlighting its capability to leverage spatio-temporal synergies for accurate short-term corrosion rate prediction.  
corrosion rate prediction; multi-feature; graph convolutional network; temporal convolution; multivariate time series Article  
1
## 4 Case Study and Results Discussion
### Analysis 2: Covariate Selection Sensitivity
#### 4.3.1 Decision Rule Performance Comparison  
The maintenance policies derived from the models outlined in Table 15 are illustrated in Figure 9. Model A, employing a single covariate, exhibited a notably aggressive maintenance policy, suggesting immediate intervention for all equipment surpassing 4500 h. Similarly, model C, incorporating two covariates, suggested intervention for all transformers. However, a notable enhancement was observed with the addition of more covariates, extending the intervention threshold to around 7000 h.  
Models D, E, and F presented nearly identical policies, marked by significant improvements over previous models, suggesting intervention after 15,000 h of operation. The primary difference among these models lay in the number of selected covariates, indicating initially that as the model incorporated more covariates, it became less sensitive to variations induced by these factors. Furthermore, these models proposed a more conservative approach compared to model B, the top performer in the previous analysis.  
Upon closer examination, the policies derived from models D, E, and F bore resemblance to the reference case depicted in Figure 5(a), with the primary difference being the dispersion of the obtained curves and a more cautious intervention policy. This shows the framework's capability to generate effective policies even without expert criteria, proving its utility when dealing with data lacking such recommendations  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Model** & **Beta** & **Eta** & **Tint** & **DiffT** & **C2H4** & **C2H6** & **TGC** & **R.Die** & **\begin{tabular}{c} **\%** \\ **Hum** \\ \end{tabular} & **LL** & **\# Var** & **Akaike** & **BIC** \\ \hline Model 0 & 1.60 & 209,019 & & & & & & & 763.9 & 0 & 1527.86 & 1527.86 & 1527.86 \\ Model A & 2.75 & 150,534 & & 0.79 & & & & 748.5 & 1 & **1498.90** & **\begin{tabular}{c} **\end{tabular} ** **1501.05** \\ Model B & 2.78 & 144,572 & 0.00 & & 0.07 & & 1.96 & & 748.4 & 4 & 1504.71 & 1513.28 \\ Model C & 2.76 & 145,335 & & 0.74 & & & 0.21 & 748.4 & 2 & **1500.74** & **\
\begin{tabular}{c} **\end{tabular}** ** **1505.03** \\ Model D & 2.79 & 132,488 & 0.16 & 0.97 & 0.79 & & 0.54 & 2.39 & 0.35 & 748.2 & 6 & 1508.42 & 1521.28 \\ Model E & 2.79 & 132,491 & & 0.10 & 0.08 & & & 2.39 & 0.34 & 748.2 & 4 & **1504.42** & **
\begin{tabular}{c} **\end{tabular}** ** **1512.99** \\ Model F & 2.79 & 132,353 & 0.19 & 0.88 & 0.79 & 0.17 & 0.18 & 2.30 & 0.35 & 748.2 & 15 & 1526.41 & 1558.56 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Final model parameters along with their respective LL, Akaike, and BIC scores.  
Figure 9: _Cont_.