esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mechanism Analysis for Active Control
### On the off-resonant frequency  
The control mechanism on the off-resonant frequency mainly involves the pattern similar to tha in Section (b), i.e., the cavity modal suppression and rearrangement suppressing the modes of the base plate B. This is different from that of the single ribbed case, where the rearrangement of the cavity modes rearranges the base plate modes to make it as a weak radiator [23]. The kinetic energy of the radiating ribbed plate is likely to be enhanced in the controlled condition. However, this rarely appears in the orthogonally two ribs stiffened case. The complicated coupling relations and numerous energy transmission paths between the cavity modes and base plate modes guarantee that the base plate modes can always be suppressed only by adjusting several most important cavity modes that have well coupling relations with them. There is no need to further rearrange the base plate modes to achieve sound radiation cancellation [24].  
For validating this conclusion, the averaged quadratic velocity of the base plate B \(\left\langle V^{2}\right\rangle\) which represents the kinetic energy of the plate is calculated before and after control, as shown in Figure 9. The averaged quadratic velocity is defined as [25]:  
\[\left\langle V^{2}\right\rangle=\frac{\omega^{2}}{2A_{b}}{\int_{A}w_{b}w_{b}^{ \mathrm{H}}\mathrm{ds}} \tag{3}\]  
where H denotes the complex conjugate, and \(A_{b}\) is the area of the base plate. \(\left\langle V^{2}\right\rangle\) is expressed in dB referenced to \(2.5\times 10^{-15}\mathrm{m}^{2}/\mathrm{s}^{2}\). The kinetic energy decreases remarkably after control in entire low frequency band, which validates the conclusion stated above.
## 2 System Model
### System Model without Hardware Impairments
Consequently, the received signal from the cascaded link by the \((m,n)\)-th element of the \(i\)-th RIS can be obtained as  
\[S_{i_{m},i_{n}}(t)=A_{i_{m},i_{n}}(t)\sqrt{P_{t}}x(t-\tau_{i_{m},i_{n}}(t)- \frac{\phi_{i_{m},i_{n}}(t)}{2\pi f_{c}}), \tag{10}\]  
where \(\phi_{i_{m},i_{n}}(t)\in[0,2\pi)\) is the controllable phase shift caused by the \((m,n)\)-th element of the \(i\)-th RIS at time \(t\) and \(f_{c}\) is carrier frequency. Note that we consider all the elements as losses diffuse reflectors; thus, the amplitude of \(\phi_{i_{m},i_{n}}(t)\) is always \(1\). Let \(\Theta_{0}(t)\triangleq e^{-j2\pi f_{c}\tau_{0}(t)}\) and \(\Theta_{i_{m},i_{n}}(t)\triangleq e^{-j2\pi f_{c}\tau_{0},i_{n}}(t)\cdot\phi _{i_{m},i_{n}}(t)\) be the phase parts of the direct and cascaded links, respectively; thus, the corresponding complex baseband received signal without HWI is  
\[y(t)=S_{0}(t)\Theta_{0}(t)+\sum_{i=1}^{K}\sum_{m=1,n=1}^{M,N}S_{i_{m},i_{n}}(t )\Theta_{i_{m},i_{n}}(t)+w,\]where \(w\) is a white Gaussian noise process with power spectral density \(N_{0}\).
###### Abstract  
Narrowband (NB) powerline communication (PLC) is extensively adopted by utilities for the communication in advanced metering infrastructure (AMI) systems. PLC technology needs to overcome channel disturbances present in certain grid segments. This study analyzes improvement proposals of the physical layer of the main narrowband PLC technologies approved by international communication organizations that are currently deployed in Europe: Powerline Intelligent Metering Evolution (PRIME) 1.3.6, PRIME 1.4, and G3-PLC, in order to improve PLC performance under channel disturbances. This thorough study is based on simulations carried out by an innovative ad hoc Virtual PLC Lab, developed by the authors, applied in replicable, fully-automated, and cost reduced test scenarios. The analysis is performed by applying standardized test methods and metrics, and by evaluating the influence of a set of representative channel disturbances defined by the European Telecommunications Standards Institute (ETSI) and selected noises generated by distributed energy resources (DER) in normal operation. PLC performance improvements in terms of equalizer curve fitting, error correction codes, and noisy subcarrier suppression mechanisms are presented. The performance gain due to each physical improvement proposal is accurately measured and compared under the same conditions in a replicable and automated test environment in order to evaluate the use of the proposals in the evolution of future PLC technologies.  
equalization; G3-PLC; modulation; OFDM physical layer; PLC for smart grids; powerline communication; PRIME 1.4; Reed-Solomon; spline; tone map 1
# Using Python for the Simulation of a Closed-Loop PI Controller for a Buck Converter
## 5 Comparison--Proposed Technique versus LTspice  
To show the applicability of the proposed simulation technique, a comparison of the proposed technique with LTspice will be presented in this section.  
Figure 10 shows the implementation of the circuit under analysis in LTspice.  
To assess the feasibility of the proposed simulation technique, four different operating conditions are considered: steady state (Figure 11), start up (Figure 12), load variation (Figure 13) and input voltage variation (Figure 14).  
Figure 11: Waveforms of v\({}_{\text{O}}\), i\({}_{\text{L}}\), i\({}_{\text{C}}\) and v\({}_{\text{ctrl}}\) obtained in steady-state regime, with LTspice (**left plots**) and with the proposed simulation technique (**right plots**).  
Figure 10: Circuit implementation in LTspice.  
Figure 12: Waveforms of \(\rm v_{O}\), \(\rm i_{L}\), \(\rm i_{C}\) and \(\rm v_{ctrl}\) obtained during the converter start up, with LTspice (**left plots**) and with the proposed simulation technique (**right plots**).  
Figure 13: Waveforms of \(\rm v_{O}\), \(\rm i_{L}\), \(\rm i_{C}\) and \(\rm v_{ctrl}\) obtained during a load variation (from 5 A to 10 A) at 35 ms, with LTspice (**left plots**) and with the proposed simulation technique (**right plots**).  
Figures 11-14 show the time evolution of v\({}_{\rm O}\), i\({}_{\rm L}\), i\({}_{\rm C}\), and v\({}_{\rm ctrl}\) obtained in LTspice (left plots) and also with the proposed simulation technique (right plots).  
Figure 11 shows the waveforms related to the steady state regime.  
Next, the results related to converter start up (Figure 12) will be presented.  
Figure 13 shows the behavior of the converter when subjected to a load variation (from 5 A to 10 A at 35 ms).  
Figure 14 shows the behavior of the converter when subjected to an input voltage variation (from 19 V to 9 V at 35 ms).  
Figures 11-14 show the waveforms of the output voltage (v\({}_{\rm O}\)), inductor current (i\({}_{\rm L}\)), capacitor current (i\({}_{\rm C}\)) and control voltage (v\({}_{\rm ctrl}\)) obtained with the proposed technique and in LTspice. v\({}_{\rm ctrl}\) shows a slight difference between both simulations, which is essentially due to the time taken by the MOSFET to switch. This time period is not taken into account in Python simulation.  
In summary, it is possible to conclude that the results of both simulators are quite similar. Thus, it can be concluded that the proposed solution can be used profitably to predict the behavior of DC-DC converters.
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 5 Semantic-Based Trusted Data Access Control Process
### Semantic-Based Trusted Data Access Control Process  
The semantic-based trusted data access process is shown in Figure 8. As mentioned above, the access control agent is responsible for access control judgement and the management of user access control policies. The access control mechanism is initiated when an access request is found, and it returns a decision to allow or deny. Also initiated is the SPARQL endpoint, which allows the user to retrieve RDF data using the SPARQL query language, which allows the user to query and retrieve data from the database in a semantic manner without having to understand the underlying relational database structure. Ontology mapping solves the problem of data heterogeneity by using a unified standard ontology to establish a semantic association with the data source. The access control agent and ontology mapping obtain ontology information from the ontology cloud manager. The set of access policies about the data is written by the data owner according to the SWRL standard, and each policy defines two possible outcomes: permit or deny. To resolve policy conflicts, the system comes with a policy conflict detection that helps the user to check if the rule is correct. The encryption and decryption agents utilize blockchain public and private keys for their encryption and decryption operations. The process ensures secure and authorized data access through several key steps:  
_Step1_: Identity Registration and Access Request. The data requester registers its identity in the blockchain, selects the needed content according to the standard data dictionary, sends an access request, then generates a token. The token carries the access request, the waybill number, the data owner ID, the data requester's attribute information, and the public key. The subject instance is automatically created based on the data requester's attribute information.  
_Step2_: Access control and SPARQL Query Processing. When the system receives an access request, it calls the Access Control Agent and SPARQL Endpoint, respectively. The SPARQL endpoint stores the query results in a virtual table, while the access control agent, based on the set of access control policies pre-defined by the data owner, uses the reasoning engine to determine whether the data visitor is allowed to access. Based on the judgement result, the system will return an access decision of allow or deny. If access is allowed, the system will retrieve the corresponding data from the virtual table.  
_Step3_: Data Encryption and Retrieval. The data taken out are encrypted through the encryption and decryption agent. Authorized data are encrypted using the requester's public key before transmission. The requester then uses its own private key to decrypt the data.  
Figure 8: Semantic-based trusted data access process.
###### Abstract  
We evaluated the mechanical properties and torque/force generated during canal shaping by four NiTi files with innovative designs. Each of the 52 TruNatomy Prime, XP-endo Shaper, Spring Endo files with unheated finish (Spring Endo), and Spring Endo files with heated finish (Spring H) were subjected to bending, buckling, cyclic fatigue, and torsional resistance tests (\(n=10\) per NiTi file type). Canal shaping was simulated with J-shaped resin blocks (\(n=10\)). Phase transformation behavior was investigated using differential scanning calorimetry (\(n=2\)). Statistical analysis was performed by one-way ANOVA and the Games-Howell test. Spring Endo and Spring H files showed higher bending and buckling resistances, ultimate torsional strength, and elastic modulus than TruNatomy and XP-endo Shaper (\(p<0.05\)). XP-endo Shaper demonstrated the highest cyclic fatigue resistance and angle of rotation to fracture (\(p<0.05\)). The elastic modulus increased in the order of XP-endo Shaper, TruNatomy Prime, Spring H, and Spring Endo. During simulated canal shaping, XP-endo Shaper generated greater clockwise torque and less screw-in force compared to Spring Endo files, with superior cutting ability. TruNatomy Prime generated the least clockwise torque and screw-in force. At room temperature, TruNatomy and XP-endo Shaper files consisted of mixed phases of austenite, martensite, and R-phase; Spring H files consisted of martensite; and Spring Endo files consisted of austenite.  
differential scanning calorimetry; mechanical test; NiTi file; stress; torque +
Footnote †: journal: Applied Sciences
### Importance of Underwater Simulation  
The importance of simulations based on aquatic environments is especially evident under the autonomous systems domain. For air and land vehicles, we can assume some kind of stable wideband communication, such as cellular or satellite communication. That kind of communication gives the remote operators much-needed information during experiments or for monitoring the behaviour of the vehicle in the form of telemetry data and video. Due to the radio frequency propagation characteristics of the air medium, communication and location are usually available. On the other hand, in underwater ocean environments, radio frequency is rapidly attenuated which make it impractical for both geolocating and wideband communication. The underwater environment may also not be accessible in case of a malfunction and subsequent reclaiming of a ROV, such that investigating a malfunction may not even be possible. For those reasons, underwater ROVs are usually connected (tethered) with a cable to a controlling platform above the water.  
With increasing applications in underwater exploration, ROVs attached with RGB cameras or even stereo cameras have become more prevalent. The design of ROVs with attached computer vision systems calls for the development of visually realistic simulation models where vision, dynamics and control aspects of the systems can be tested.  
A full-scale system simulation that can perform underwater experiments in a fully controlled environment with access to the sensory data and ground truth helps to bridge that gap. Realistic visual effects can bridge this gap even more by providing realistic video for the purpose of autonomous computer vision capabilities. Moreover, visually realistic simulation provides the effects important to accurately recreate effects important in computer vision such as lights shadows refractions visibility and more.
## 1 Introduction
Based on the flow field results, radiated hydro-acoustic fields are predicted using the Ffowcs Williams and Hawkings (FW-H) equation. The effects of viscous flux vectors on the cavitation flows and the associated acoustic fields, are investigated in terms of cavity structures, volume fractions, velocity distribution, acoustic pressure spectrum, and directivities. In Section 2, the problem is described using computational grids and boundary conditions. In Section 3, the governing equations and the numerical methods are described, with detailed explanations concerning the treatment of viscous flux vectors in association with the preconditioned implicit pseudo-time marching scheme. In Section 4, the numerical methods used in this study are validated. The predicted results of the cavitation flow field and its radiated acoustic field are then presented and analyzed, with emphasis placed on the effects of viscous flux vectors according to the types of viscous flux models employed.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomechanical Stress Biomarkers
#### 3.1.3 Adrenomedullin  
Mid-regional pro-atrial natriuretic peptide (MR-proADM) is a C-terminal inactive fragment of adrenomedullin, which mediates vasodilatory and natriuretic properties [81]. Being elevated in acute HF and severe chronic HF, mostly in HFrEF, it has been found to have potential diagnostic and prognostic utility, although the findings obtained in clinical trials were controversial [82; 83]. The BACH (Biomarkers in Acute Heart Failure) trial has shown that MR-proADM is superior to both BNP and NT-proBNP in predicting short term mortality [83]. Serum levels of mid-regional pro-adrenoomedullin (MR-proADM) were associated with 1-year CV events and all-cause mortality in patients with overt HF [84]. However, [PERSON] et al. (2020) [85] reported that MR-proADM was not superior to NT-proBNP in the prediction of chronic HF. In a recently published meta-analysis comparing the diagnostic value of novel biomarkers for HF, [PERSON] et al. (2020) [86] revealed that MR-proADM had a poor discriminative value to confirm or exclude HF. Whether a serial measurement of MR-proADM is able to reclassify a risk of death due to HF individually depending on the presence of other CV risk factors including T2 DM is not yet fully understood, because no correlation between T2 DM and levels of MR-proADM has been found for mortality or HF hospitalization [87]. Perhaps the evaluation of MR-proADM levels may be important for T2 DM patients with abdominal obesity because these individuals exhibit an NP deficiency in early stages of HF, and other biomarkers can be used for the diagnosis and risk stratification of HF [88]. However, uncertainty in the total cost of biomarker implementation does not allow clearly proposing MR-proADM in the strategy of HF care.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 1 Introduction
[26] focused on detecting aggressive driving behavior using deep learning techniques, and [PERSON] et al. [27] proposed an LSTM model for predicting traffic conflicts using connected vehicle data. These computational approaches offer advantages in processing complex, heterogeneous data typical of mountain road environments. Despite these advances, significant research gaps exist regarding mountain road safety. First, most existing studies focus on urban or highway segments, with limited attention to mountain roads' unique characteristics. Second, relationships between driving behavior patterns and traffic conflicts in mountain environments remain sparsely studied. Finally, predictive models specifically calibrated for these challenging environments are largely absent. This study addresses these gaps by combining driving simulation experiments with field observations to capture driver behavior and conflicts at sharp turns in mountain regions, employing the FP-Growth method [28; 29; 30] to identify behavioral associations with conflicts, and developing an LSTM-based prediction model [31; 32; 33] for mountain road safety management.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 2 Mathematical Model of the Mobile Photovoltaic-Diesel-Storage Microgrid System
### Diesel Generator  
The addition of generators to the mobile photovoltaic-diesel-storage microgrid system improves the reliability of the entire system in terms of power generation. The output power of a diesel generator, which can be critical in scenarios where new energy generation schemes are constrained by unfavorable conditions such as weather and wind, can be represented by the following formula:  
\[P_{gen}=\eta\cdot P_{rated} \tag{4}\]  
where \(\eta\) is the generation efficiency, and \(P_{rated}\) is the rated generating power. When the new energy generation scheme cannot function normally, and the remaining battery pack cannot support the task load, the diesel generator can switch to a stable power supply mode. The operational status of the diesel generator can be defined as follows:  
\[\left\{\begin{array}{ll}\text{OS}=1&P_{load}>P_{PV}+P_{battery}\\ \text{OS}=0&otherwise\end{array}\right. \tag{5}\]  
where \(OS\) is the Operational Status. In this model, the diesel generator is activated (Operational Status = 1) when the combined power from the photovoltaic system and the battery pack is insufficient to meet the task demand.
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 4 Discussion  
### Significance of the Findings and Cross-Validation of the Results  
The main motivation of this study is to explore the capability of advanced machine learning methods to generate a reliable model aimed at easily predicting \(C_{s}\). Needless to say, \(C_{s}\) is one of the most indispensable geotechnical parameters required to estimate the settlement and the swelling degree in the every site. Firstly, in order to identify the optimal input parameters, which have the ideal influence on \(C_{s}\) three advanced methods have been used. _PSO_ proposed three lower-dimensional parameters as an optimal input. _GT_ indicated that _WL_, _PI_, _FC_, and \(W\) could formalize the optimal input set. However, _FS_ showed that _WL_, _Yd_, \(W\), and _PI_ are the best ones. The reason behind the difference between the three approaches lies in the philosophy of each one in handling the data. Based on that, ten advanced machine learning methods (_ELM_, _DNN_, _SVR_, _RF_, _LASSO_, _PLS_, _Ridge_, _KRidge_, _Stepwise_, and _GP_) have applied for modeling the three selected optimal input set (_PSO_, _GT_, and _FS_). The findings clearly indicate that the optimal input is the one chosen by _FS_ and trained by the _RF_ method (_FS-RF_). The latter presents the most appropriate model, which gave the minimum values of error metrics (_MEA_, _RMSE_, and _IOS_) and higher values of _NSE_, \(R\), and _IOA_ compared to other models. Furthermore, the emerging model was evaluated by the K-fold cross validation approach and compared with other proposed formulae. The conclusion is that the _FS-RF_ model could generate new data without over-fitting or under-fitting, and being more effective than the empirical formulae. The other most interesting aspect is the optimal input set related to the best _FS-RF_ model (_WL_, _Yd_, \(W\), _PI_). Interestingly, this also accords with several studies, which have showed that physical parameters indirectly affected the swelling phenomena [59]. It is known that the micro-scale features of swelling soils comprise the mineral composition of clay particles, their reaction with the water chemistry, and the cations attracted to the clay particle by electrical forces. These micro-scale factors influence macro-scale physical factors, such as density, plasticity, and water content to control the engineering comportment of soil [59]. Additionally, the last part consists of the sensitivity analysis, which gives an overview about the more influenced parameters on \(C_{s}\) according to the proposed model. The findings indicate that _WL_ and _PI_ are respectively the most affected factors on \(C_{s}\), meaning that swelling phenomena are primarily influenced by plasticity parameters. In addition, water content and dry density have little effect on \(C_{s}\).
## 6 Adaptive Thresholds for the RFD  
In Section 4, a training method for the MLP on the basis of the MNQOA is proposed. During the application of such a method the parameters estimate \(\hat{p}\) and its uncertainty \(\boldsymbol{P}\) for the MLP, are obtained. Such knowledge is necessary to develop a RFD approach. Moreover, the method developed in Section 5 enables to improve the MLP quality. It follows from the appropriate selection of training data which allows to minimize the volume of the feasible parameter set and in the consequence reduce the MLP uncertainty. The obtained matrix \(\boldsymbol{P}\) describing MLP uncertainty allows to calculate so-called the MLP output uncertainty interval. Such interval includes the model output \(y_{m,k}\). It should be underlined that the range of such interval depends on the orientation and size of the ellipsoid which describe the feasible parameter set \(\boldsymbol{P}_{m,k}\).  
Figure 4: Outline of the proposed strategy for enhancing performance of MLP.  
Taking into account the facts that the \(\mathbf{P}_{k}\) is an ellipsoid and the output error is bounded by \(\gamma\), the adaptive threshold is given by:  
\[\hat{y}_{k}^{m}\leq y_{k}\leq\hat{y}_{k}^{M}, \tag{47}\]  
where \(y_{k}\) is the diagnosed system output, whereas:  
\[\hat{y}_{k}^{m}=y_{m,k}-(\mathbf{r}_{k}^{T}\mathbf{Pr}_{k})^{1/2}-\gamma, \tag{48}\]  
and  
\[\hat{y}_{k}^{M}=y_{m,k}+(\mathbf{r}_{k}^{T}\mathbf{Pr}_{k})^{1/2}+\gamma, \tag{49}\]  
are the minimum and maximum admissible output estimate of the diagnosed system extended by the values of \(\gamma\). The abnormal work of the diagnosed system is detected when the monitored system output \(y_{k}\) extends over the adaptive thresholds calculated by (47) (cf. Figure 5). In other words, the system is fault-free when (47) contains real system response.  
For the purpose of further analysis the following model quality index is introduced and called Mean Uncertainty Interval (MUI) is introduced:  
\[\text{MUI}=\frac{1}{N}\sum_{k=1}^{N}(\hat{y}_{k}^{M}-\hat{y}_{k}^{m}). \tag{50}\]  
It can be perceived as a mean value of model uncertainty and it will be used to evaluate its evolution.