esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 6 Conclusions  
To solve the problem of the fault identification of the rolling bearing, this paper proposes a fault diagnosis method based on improved VMD and a parallel hybrid neural network. The improved VMD and parallel hybrid neural network are combined to significantly improve the accuracy and applicability of bearing fault diagnosis through optimizing signal decomposition and enhancing feature learning. In the aspect of signal processing, the bearing data set is composed of vibration signals, and there is some noise interference. This paper uses the improved VMD algorithm and introduces the weighted kurtosis as the evaluation index, which can more accurately decompose the non-stationary vibration signal, effectively remove the noise, and retain the impact characteristics of the fault signal. In the aspect of fault feature extraction, two parallel BiGRU-CNN networks and BiLSTM-CNN networks are used for feature extraction, and a two-way cyclic structure is introduced, so that the model can consider not only the previous historical information but also future information. The feature fusion is carried out by a splicing method. The gap layer is used to replace the traditional full connection layer for data dimensionality reduction, and the features after dimensionality reduction are input into the SoftMax classifier for classification. Experiments were carried out on the public data sets of XJTU and CWRU, and the accuracy of fault identification reached 99.72% and 99.73%. Compared with other latest models, the results show that the fault diagnosis method proposed in this paper has certain advantages.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Condition** & **P (\%)** & **\(\boldsymbol{F_{1}}\) (\%)** \\ \hline Load 1 & 99.71 & 99.70 \\ Load 2 & 100.00 & 100.00 \\ Load 3 & 99.40 & 99.40 \\ Load 4 & 99.80 & 99.80 \\ Average value & 99.73 & 99.73 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Diagnosis results under different load conditions.  
**Author Contributions:** Methodology, H.C.; Investigation, Q.S.; Writing--original draft, W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [China Higher Education Institution Industry-University-Research Innovation Fund] grant number [2024 HY031] and the APC was funded by [China Higher Education Institution Industry-University-Research Innovation Fund].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://biaowang.tech/xjtu-sy-bearing-datasets](https://biaowang.tech/xjtu-sy-bearing-datasets)]([https://biaowang.tech/xjtu-sy-bearing-datasets](https://biaowang.tech/xjtu-sy-bearing-datasets)).  
**Acknowledgments:** Sincere gratitude is extended to [PERSON] for his valuable suggestions and professional guidance during the revision of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 5 Potential Applications of Water 4.0
### Pressure Control and Monitoring  
In water distribution networks, pressure sensors are mounted at nodes of the network to measure water pressures along the pipes and at each node. Pressure and water demand share a good relationship. When the demand increases, more pressure must be applied to a node to meet the increasing demand. Thus, for the satisfactory supply of potable water to the end users, service pressure must be sufficient to adequately satisfy demand at the node. Unfortunately, when considering leaks, the reverse is the case to reduce the level of losses in the system. The issue is therefore more complicated than originally assumed. More so, consumer demand is not linear, which makes pressure control a difficult task when dealing with leak reduction. Therefore, demand uncertainty is another issue affecting the accomplishment of good pressure control in systems. With the advent of new technologies, if the pressure and demand at a given node can be monitored in real-time through the integration of pressure and flow sensors, an active control system for pressure control at nodes having the leaking pipes could be achieved. This is one of the features of Water 4.0.  
Figure 10: Water 4.0 significant water supply system application domain.  
Thus, with CPS and IoT as well as the advanced control and actuator mechanism offered by this technology, optimal pressure control in real-time may be accomplished.
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 5 Results and Discussion
The maximum power allocated to the \(k\)th user under fractional power allocation is plotted for different precoding schemes in Figure 6 using the proposed algorithms. It is observed that maximum power is allocated to the users with the MCS algorithm, compared to the MDS algorithm. Depending upon the user distances and channel gains, each AP with a maximum transmit power of 200 mW allocates different powers to different users. Using centralized PMMSE precoding, each AP assigns a maximum power of 141.7 mW to a user with strong channel conditions with the AP and a minimum power of 3.1882 mW to the user with the worst channel conditions. Figure 7 gives a comparison of the spectral  
Figure 4: Comparison of spectral efficiency for correlated and uncorrelated channels for different precoding schemes. efficiency achieved by the three power-allocation methods under the proposed algorithms. As compared to MDS algorithm, the MCS algorithm results in better spectral efficiency for all the users. This is because the latter considers the users' channel gains as the selection criteria. Equal power allocation results in improved SE in the upper region, while sum SE power allocation is recommended in the lower region. The optimum performance is achieved with the fractional power allocation with which most of the users attain higher SE. Figure 5: Spectral efficiency with PCSI and ICSI for various precoding schemes. Figure 6: Maximum power allocated to the \(k\)th user for different precoding schemes with the proposed algorithms. Table 2 gives the maximum and minimum power values allocated to the users under different precoding schemes for different values of FPA parameters. It has been observed that the power allocated to the users varies with the precoding schemes and the FPA parameters. With MMSE precoding, a user is assigned a maximum power of 154.42 mW and a minimum power of 1.5627 mW for \(\mu=-0.5\) and \(\vartheta=1\). With PRZF precoding, these values are 159.65 mW and 1.5590 mW, respectively. Moreover, PMMSE precoding with the same FPA parameters (\(\mu=-0.5\) and \(\vartheta=1\)) assigns a maximum power of 154.42 mW and a minimum power of 1.5590 mW.
## 3 Data Acquisition and Processing
### Selecting Performance Degradation Index
#### 3.2.2 Features of Wavelet Packet Space  
Vibration signals are processed using a wavelet packet method to extract features of wavelet packet space. The signal frequency band is divided into multiple sub-bands by wavelet packet decomposition; thus, the feature information of the original signal is also divided into each sub-band. The total energy of a signal in the time domain is equal to its total energy in the frequency domain according to [PERSON]'s theorem in [37]. The total energy of original signal is also divided into each sub-band.  
When a signal \(x(t)\) is decomposed to the \(j\)-layer by wavelet packet, the energy of each sub-band can be calculated according to Equation (17).  
\[E_{ij}=\int\left|S_{i,j}(t)\right|^{2}dt=\sum_{k=1}^{n}\left|x_{i,j}(k)\right| ^{2} \tag{17}\]  
The total energy can be calculated as follows:  
\[E=\sum_{j=0}^{2^{i}-1}E_{ij} \tag{18}\]  
The percentage of all sub-bands' energy can be as the feature vectors of the vibration signal, then the expression of the feature vector as shown as follows in Equation (19):  
\[T=\left[t_{1},t_{2},\cdots,t_{2^{i}}\right]=\left[\frac{E_{i,0}}{E},\frac{E_{ i,1}}{E},\cdots,\frac{E_{i,(2^{i}-1)}}{E}\right] \tag{19}\]  
The first principal component is extracted by PCA from the energy feature of vibration signals in different working conditions, which are shown in Figure 6b.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 1 Introduction
Unlike most previous studies, we used accurate nonlinear models for all components and considered variable pump and turbine efficiencies (variable speed); variable head (attributed to changes in the water level in the upper and lower reservoirs); variable losses in penstocks, water reservoir evaporation, and seepage; and the variable efficiency of the PV inverter. We optimised not only the size of all components, but also four variables related to the load supply and energy management for arbitrage (which helps decide the priority to supply the load using the grid or storage, when to store energy, and when to inject energy into the grid). A genetic algorithm (GA)-based metaheuristic technique was used for efficient optimisation. GAs are among the most powerful optimization algorithms, and they have been widely used in the optimization of hybrid renewable energy systems [32]. As it will be shown in the results, in many cases, the required computation time for evaluating all combinations of components and control variables would be inadmissible. Using the GA, the optimisation time was reduced to admissible values. To the best of our knowledge, none of the previous studies included all these features simultaneously. The remainder of this manuscript is organised as follows: Section 2 presents the modelling approach for the simulation and optimisation of the system. Section 3 presents the computational results and discussion, including the validation of the simulation approach compared to the results of a previous publication and the optimisation of representative cases with sensitivity analysis. Finally, the conclusions are presented in Section 4.
# Sim-Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning
###### Abstract  
Models trained with Deep Reinforcement learning (DRL) have been deployed in various areas of robotics with varying degree of success. To overcome the limitations of data gathering in the real world, DRL training utilizes simulated environments and transfers the learned policy to real-world scenarios, i.e., sim-real transfer. Simulators fail to accurately capture the entire dynamics of the real world, so simulation-trained policies often fail when applied to reality, termed a reality gap (RG). In this paper, we propose a search (mapping) algorithm that takes in real-world observation (images) and maps them to the policy-equivalent images in the simulated environment using a convolution neural network (CNN) model. The two-step training process, DRL policy and a mapping model, overcomes the RG problem with simulated data only. We evaluated the proposed system with a gripping task of a custom-made robot arm in the real world and compared the performance against a conventional DRL sim-real transfer system. The conventional system achieved a 15-57% success rate in gripping operation depending on the position of the target object while the mapping-based sim-real system achieved 100%. The experimental results demonstrated that the proposed DRL with mapping method appropriately corresponded the real world to the simulated environment, confirming that the scheme can achieve high sim-real generalization at significantly low training costs.  
robot arm; reality gap; sim-real; simulated environment; deep reinforcement learning
###### Abstract  
This study aimed to develop and evaluate the reliability of a surface electromyography-based (sEMG) index to quantify knee muscle coactivation in healthy recreational athletes during the Lower Quarter Y-Balance Test (YBT-LQ). A prospective observational repeated measures design was used to assess both intra- and inter-rater reliability. Forty males completed three trials, with two raters independently conducting assessments. sEMG signals from the vastus medialis, vastus lateralis, biceps femoris, and semitenthous muscles were collected to calculate four coactivation indices (CoAls) for different muscle pairs. The reliability evaluation of these CoAls was based on intraclass correlation coefficients. The results revealed that the reliability of the CoAls varied depending on the muscle pair, direction, and limb examined. The highest intra- and inter-rater reliability was noted for the left lower limb in the posterolateral direction. The lowest reliability was found for the right lower limb in the anterior direction. Statistical analyses indicated significant differences in specific CoAls across different YBT-LQ directions. These findings highlight the potential of sEMG-based CoAls for assessing knee muscle activity during the YBT-LQ in healthy male recreational athletes. However, choosing CoAls is critical for reliable clinical and research assessments of knee stability and neuromuscular control.  
americ
# Distributed Authentication and Authorization Models in Cloud Computing Systems: A Literature Review
## 3 Results
### Cloud Access Control Mechanisms Included in the Review
#### 3.2.6 Attribute-Based Encryption Access Control (ABEAC)  
In cloud computing to preserve data privacy and make more protection against threats, either symmetric or asymmetric encryption technology can be used [7]. However, these authorization mechanisms are not scalable and flexible, because of using shared secret keys or public keys by the data owner to publish his data to the cloud. To make the access control policy more representative and adaptable for adding new users in the cloud, [24] proposed a flexible and scalable attribute-based encryption algorithm based on an identity-based encryption algorithm (IBE). The flexibility in this model comes from the public key encryption mechanism in ABE where user attributes are used instead of user identity in IBE. The encryption algorithm in ABE generates encryption keys and a ciphertext based on the user's attributes. Scalability in ABE is enabled because the ciphertext can be decrypted based on the receiver's attributes, which must satisfy the encryption policy. This leads to implementing \"one to many\" encryptions such that encrypted data can be decrypted by users who match specific requirements [24]. Therefore, data owners do not need to do anything when a new user joins the cloud system [7].  
ABE has two approaches. The ciphertext-policy ABE (CP-ABE) and the key-policy ABE (KP-ABE). In CP-ABE, a user's attribute is associated with the private key, and a ciphertext determines an access policy. The plaintext can only be recovered if the user's attribute matches the specified access policy of the ciphertext. In KP-ABE, an access policy is defined with a secret key, and ciphertext is generated based on an attribute list. If the user's attribute complies with the access policy tied with the secret key, he/she can decrypt the ciphertext [7]. Conversely, CP-ABE is better than KP-ABE because the data owner decides the access policy.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 7 Case Studies
### Comparative Analysis of Prediction Techniques  
When selecting a predictive technique for smart building management, it is essential to evaluate the specific objectives, data characteristics, and application limitations. Linear regression and time series analysis are well-suited for trend analysis and basic forecasting applications, while Support Vector Machines, decision trees, and random forests offer enhanced adaptability in modeling complex, non-linear interactions. As for neural networks and deep learning models, they are effective for high-dimensional data analysis; nevertheless, they require substantial data and computing power. Clustering enhances the precision of data categorization in predictive modeling. Table 7 gives a comparison of prediction techniques in smart building management systems.
# The Cerebral Arterial Wall in the Development and Growth of Intracranial Aneurysms
## 3 Cerebral Arterial Wall Anatomy
### Adventitia and Vasa Vasorum  
The tunica adventitia is the most external layer consisting of connective tissue, fibroblasts, and vasa vasorum, and is thinner than in extracranial arteries. Its main function is to provide structural support and protection to the artery. Being the outermost layer, it has properties of communication with the surrounding environment through the different signaling pathways along with the pericytes to carry out its self-regulating functions [25]. The vasa vasorum that cross the tunica adventitia are a network of microvasculature made up of arteries, capillaries, and veins with nutritive and drainage functions, transporting oxygen and removing metabolic waste from the arterial vessel walls. It is widely demonstrated that vasa vasorum can play a passive role in the pathogenesis of atherosclerosis, aneurysm, and vasculitis linked to the transport of several inflammatory mediators [26].  
Adventitial vasa vasorum in coronary and carotid arteries may contribute to atherosclerotic plaque susceptibility [27]. On the other hand, the ability of cerebral arteries to obtain substances from the surrounding cerebrospinal fluid could explain why vasa vasorum in brain arteries is very rare [28]. [PERSON] et al., in a postmortem study, discovered a higher incidence of progressive atherosclerotic lesions in V4 segments, with a larger plaque burden and severe luminal stenosis in artery wall specimens rich in the vasa vasorum network [29]. They postulated that the adventitial neo-vascular network acts as a conduit for inflammatory cells and mediators into the plaque, aggravating the atherosclerosis pathology, and they also noted that cerebral arteries containing vasa vasorum had higher rates of atherosclerotic plaques and bleeding [29].  
The depth of the vasa vasorum penetration into the tunica media depends on the thickness and number of its muscular layers [30]. Repeated bleeding from the vasa vasorum is thought to build different layers of intramural hematoma, which are then gradually reabsorbed. This causes a wall inflammation and the release of growth factors, which leads to vessel wall proliferation and weakening, though allowing aneurysm growth [31].  
Vasa vasorum has been reported, especially in large saccular, giant, and fusiform aneurysms, and they have been observed with modern neuroimaging, leading to the identification and characterization of intracranial vasculopathies such as atherosclerosis, aneurysms, dissections, and vasculitis that would not be possible with standard angiography [30].
# Rate of Penetration Estimation with Parameter Correction
## 1 Introduction
### Motivation, Novelty, and Contribution  
The motivation for this study stems from the pressing need to improve the accuracy of well depth (measured depth) and ROP estimation, especially in the context of modern, deep, and complex wellbores. Traditional methods typically assume that the nominal drill string length corresponds directly to the true well depth and that ROP can be derived by simply differentiating depth over time. However, these approaches often neglect the effects of drill string deformation, sensor uncertainty, and dynamic downhole interactions--leading to significant errors, suboptimal parameter tuning, and increased operational risk. The novelty of this work lies in a comprehensive and physics-informed methodology that integrates advanced well-depth correction models with a Kalman filter-based real-time estimation framework. Specifically, our method introduces two key innovations over conventional approaches:  
1. Integration of static and dynamic elongation: The proposed model separates and accounts for both static elongation (due to tension, pressure ballooning, thermal expansion, and friction drag) and dynamic elongation (induced by vibration, bit-rock interaction, and transient axial loads). This dual-component correction enables a more accurate determination of the bit's true position in the formation--critical for high-resolution control and monitoring in deviated or deep wells.
2. Kalman filter-based ROP estimation: A state-space model incorporating ROP, bit penetration, and dynamic elongation is formulated, and a Kalman filter is employed to iteratively estimate these variables in real time. This method not only filters out sensor noise and measurement drift but also improves robustness against modeling uncertainties, providing smoother and more reliable ROP estimates than traditional time-differentiation methods.  
By combining dynamic modeling and real-time filtering, the proposed framework offers a significantly improved solution for accurate depth tracking and ROP calculation. These improvements have direct implications for enhanced drilling automation, real-time decision-making, and safe, cost-efficient operations.
###### Abstract  
In this paper, a semisupervised speech data extraction method is presented and applied to create a new dataset designed for the development of fully bilingual Automatic Speech Recognition (ASR) systems for Basque and Spanish. The dataset is drawn from an extensive collection of Basque Parliament plenary sessions containing frequent code switchings. Since session minutes are not exact, only the most reliable speech segments are kept for training. To that end, we use phonetic similarity scores between nominal and recognized phone sequences. The process starts with baseline acoustic models trained on generic out-of-domain data, then iteratively updates the models with the extracted data and applies the updated models to refine the training dataset until the observed improvement between two iterations becomes small enough. A development dataset, involving five plenary sessions not used for training, has been manually audited for tuning and evaluation purposes. Cross-validation experiments (with 20 random partitions) have been carried out on the development dataset, using the baseline and the iteratively updated models. On average, Word Error Rate (WER) reduces from 16.57% (baseline) to 4.41% (first iteration) and further to 4.02% (second iteration), which corresponds to relative WER reductions of 73.4% and 8.8%, respectively. When considering only Basque segments, WER reduces on average from 16.57% (baseline) to 5.51% (first iteration) and further to 5.13% (second iteration), which corresponds to relative WER reductions of 66.7% and 6.9%, respectively. As a result of this work, a new bilingual Basque-Spanish resource has been produced based on Basque Parliament sessions, including 998 h of training data (audio segments + transcriptions), a development set (17 h long) designed for tuning and evaluation under a cross-validation scheme and a fully bilingual trigram language model.  
automatic speech recognition; multilingual speech; low-resource languages; code switching; semisupervised learning; spoken language resources +
Footnote †: journal: _Applied Sciences_