esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 3 Experiments
### Experiment Result
However, the introduction of consistency loss also makes the loss function more complicated, thus affecting the speed of model training, making it slightly slower than other methods. This is also the disadvantage of MCDANN. Figure 9 shows the accuracy comparison of several methods using multi-source domains under different working conditions. As can be seen from the figure, under the four unknown working conditions, the MCDANN method proposed in this paper has achieved the best accuracy. All these methods have lower accuracy on working conditions 0 and 2 and higher accuracy on 1 and 3. On working condition 3, since other methods have already achieved high diagnostic accuracy, the improvement of MCDANN is not large. On working condition 2, the MCDANN has the largest relative improvement. In addition to the CWRU dataset, we also tested our model on DIRG bearing dataset. DIRG dataset is an open source dataset acquired on the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG) from the Department of Mechanical and Aerospace Engineering at Politecnico di Torino [38]. This dataset includes a variety of speed and static load conditions, each with 6 failure modes, including two fault locations, inner ring or roller, each location includes 3 sizes of indentations, 450 um, 250 um, and 150 um. Due to the lack of some data, we decide to set the static load to 1000 N, and the rotational speed to 100 Hz, 200 Hz, 300 Hz, and 400 Hz as the four working conditions of No. 0-3 to carry out multi-source domain adaptive experiments. Except for the rotation speed as the working condition and these 6 fault modes as labels, other details are the same as the multi-source domain adaptation experiments on CWRU. The experimental results are shown as follows: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Domain**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-6}  & **0** & **1** & **2** & **3** & \\ \hline Single DAN & 86.15 & 90.34 & 87.27 & 96.36 & 90.03 \\ Single DANN & 90.32 & 95 & 89.87 & 97.15 & 93.09 \\ Single DANN+MK-MMD & 93.15 & 96.02 & 92.16 & 96.87 & 94.55 \\ Multi DAN & 92.14 & 93.43 & 90.67 & 97.05 & 93.32 \\ Multi DANN & 94.54 & 97.32 & 95.33 & 99.15 & 96.59 \\ MCDANN & **96.17** & **98.56** & **97.74** & **99.23** & **97.93** \\ \hline \hline \end{tabular} Note: The bold number mean it is the largest in this column.  
\end{table} 
Table 3: CWRU classification accuracy of different models on unknown working condition without labels. (SNR = \(-\)4)  
Figure 9: CWRU classification accuracy comparasion of three multi-source domain adaptation models on unknown working conditions without labels (SNR = \(-\)4). Table 4 and Figure 10 show that our model achieved the best accuracy on most cross-domain conditions except one when tested on the DIRG dataset without fine-tuning, indicating that it is not by chance that our model can achieve a good result, proving the effectiveness of our method.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 3 Evolving Computing Paradigms and Related Concepts
### Mobile Cloud Computing  
NIST states that the cloud computing alliance with IoT devices and mobile devices facilitates data and CPU-intensive applications suitable for the IoT environment [56]. The concept of Mobile Cloud Computing (MCC) is that it combines cloud computing and mobile applications with sophisticated computing modules processed on the cloud [57]. It enables data processing and storage away from mobile devices, benefitting not just smartphones and a wide range of mobile subscribers. Major computational tasks are moved to the cloud with MCC, improving mobile devices' battery life [2]. With MCC being grounded on the notion of mobile offloading, mobile devices entrust storage as well as processing to remote units to achieve workload mitigation and optimization in terms of energy, cost, and longevity [45].  
The mobile devices' proliferation entails the efficient management of constrained resources with MCC, as it operates on the synergy between cloud computing and mobile computing. It is capable of operating data-intensive mobile applications as it prevails over battery, memory, and computation power restrictions from the user viewpoint. However, with cellular communication, long-distance data transmission to/from a core network results in higher latency, jitter, and network overhead. This can be overcome if the computation, analysis, and filtering of data occurs at the proximity of the data source, facilitating Fog Computing (FC) and MEC [58]. The limited bandwidth, flexibility, and control, along with an unreliable latency, security, and privacy issues are some challenges faced by MCC [28]. The partitioning of mobile applications by adaptive offloading during runtime allows the management of computer-intensive units of the application [59].
## 4 Recommendation on Improving the Efficiency of Intensity Measures
### Analysis of Functional Forms
We now address the role of structural period in the IM-EDP relationship by computing efficiency and sufficiency by adopting Equation (26) for short-period and long-period structures separately. We classify all structures with fundamental periods less than 0.5 s as short-period structures (based on Figure 6a, in which correlations with \(PGV\) and \(PGA\) at \(T\) = 0.5 s are similar) and remaining structures as long-period structures. We skip intermediate-period structures in this analysis because of their similar behavior with longer-period structures. We adopt a combination of IM approaches, similar to vector-valued implementation of IMs proposed by [PERSON] and [PERSON] [102], and evaluate the efficiency and \(p\) values for selected IMs and their combinations as listed in Tables 9 and 10. We find that the listed IMs are more  
For long-period structures, a combination of IMs is more efficient in the IM-EDP relationship. However, we do not observe sufficiency with respect to epicentral distance (\(p_{R}\)) due to the lack of \(M_{w}<5\) events in our dataset. A combination of a peak measure, \(PGV\), spectral measure, \(VSI\), and energy measure, \(A_{RMS}\), provides good efficiency and sufficiency (\(p_{M}\) and \(p_{Vs30}\)) for \(T>0.5\) s.  
In summary, these multi-IMs coupled with seismological and structural parameters in the regression model provide an efficient and sufficient prediction of EDPs by IMs. However, a few limitations of our review on ground motion intensity measures and selection techniques need to be mentioned. First, the adopted 2D RC frame models simplify the additional modes of deformations that may include torsional effects. Second, the ground motion duration is likely to impact building response, especially for sites located on top of deep sedimentary structures. However, the building models adopted here do not account for fatigue effects due to long-period cyclic loading. Last, the consideration of a single response measure (ISD, in this case) may not always be sufficient to characterize the complete structural response. Other measures of EDPs, such as hysteretic energy dissipation and co-seismic frequency of structure, should also be investigated in the future.
## 4 Simulation and Experiment  
The proposed MIMO-MFA active fault-tolerant control method is validated by means of joint simulation of MATLAB/Simulink and CarSim and an SUV vehicle model of CarSim is selected for the simulation verification. The main parameters are displayed in Table 1. Moreover, the real-time algorithm performance is verified by a driving simulator experiment in typical working conditions. The simulation and experimental verification prove the effectiveness and real-time performance of the algorithm.
## 2 Architecture of the Microgrid-Based Base Transceiver Station (MG-Based BTS)  
In this section, the proposed architecture for the MG-based BTS is described. To evaluate the degree of the transformation, the conventional BTS is first introduced.
## 3 Experimental Results
### Study Cases  
The first plant analyzed (Figure 8a) is a photovoltaic plant located in an area categorized as grassland, where the solar panels are grouped in two areas separated by a building occupying an area of 62,808 m\({}^{2}\). The adjacent plots are lands with the presence of trees.  
The second plant (Figure 8b) has an area of 13,254 m\({}^{2}\). It is a photovoltaic plant that is characterized by the proximity between their solar panels. Regarding the vegetation, the adjoining plots are characterized by being grasslands.  
Regarding the third photovoltaic plant (Figure 8c), it occupies an area of 29,271 m\({}^{2}\), in which there are areas with great spacing between groups of panels. From the vegetation  
Figure 7: Standard report generated by the SDI INSPECTORMAP when the alert system is activated.  
point of view, the area where the solar panels are installed is a grass area, while the plots adjacent to the installation are arable plots.  
The fourth photovoltaic plant (Figure 8d) is the one with the largest surface area (155,204 m\({}^{2}\)). Unlike the rest of the solar plants, this one is in an irrigated arable plot.  
Figure 8: Photovoltaic plants analyzed with INSPECTORMAP to validate the tool performance: (a) plant 1, (b) plant 2, (c) plant 3, (d) plant 4, (e) plant 5, and (f) plant 6.  
For its part, the fifth photovoltaic plant (Figure 8e) occupies an area of 11,753 m\({}^{2}\) and it is located on an arable plot. The vegetation surrounding the plant is characterized by wooded and arable areas.  
Finally, the sixth photovoltaic plant analyzed (Figure 8f) occupies an area of 5303 m\({}^{2}\) and it is characterized by being located on a plot of grass. The adjoining parcel on the left has a wooded area with tall trees, while the adjoining parcel on the right corresponds with an arable land.
## 1 Introduction  
In daily clinical practice, patients often experience temporomandibular or cervical complaints after different oral procedures [1]. Root canal therapy, one of the most common dental procedures, is a lengthy treatment, especially for multirooted teeth. The temporomandibular joint (TMJ) and its related structures may be impaired as a consequence of prolonged dental procedures through iatrogenic posture or trauma [2; 3; 4]. There have been reports of complications after third molar extraction ranging from 3.4% to 4.6% [2; 5]. and this type of extraction appears to increase the risk of developing posterior temporomandibular disorders (TMDs) [6]. However, there is no consensus on the prevalence of TMDs after endodontic procedures.  
Temporomandibular disorders are a subclass of craniofacial pain problems affecting the TMJ, masticatory muscles, and related musculoskeletal tissues in the head and neck [7]. TMDs are regarded as one of the major contributors to non-odorbones, ligaments, joints, and muscles of the orofacial region. Nevertheless, myofascali pain, characterized by muscle tenderness associated with pain, is the most common disorder within TMDs [8]. TMDs have a complex etiology that includes biomechanical, neuromuscular, bio-psychosocial, and biological aspects [9]. Iatrogenic injuries during any dental procedure involving prolonged mouth opening may act as a precipitating or predisposing factor for TMDs [10].  
Decreased mouth opening is a well-known side effect of prolonged dental procedures, including root canal therapy [1, 11]. Because of its importance, decreased mouth opening has negative effects on function and quality of life. Speaking, swallowing, and chewing are all controlled by the TMJ. Several therapies and health professionals can help patients with TMDs [12], and approximately 50% of TMDs could benefit from musculoskeletal treatment [13]. Considering the anatomical and neurophysiological relationship between the TMJ and the cervical spine, the cervical component should be treated in order to improve TMDs [7].  
Although dental procedures are common, and their potential relationship with TMDs is widely recognized in clinical practice, evidence of temporomandibular and cervical complications after various oral procedures is lacking. Therefore, the purpose of this study was to investigate the effects and risk factors of 90 min lengthy root canal therapy on the TMJ and its related structures.
## 3 Results
### XRF Analysis of Modified Mt  
Table 2 summarizes the XRF results of the modified samples. A significant reduction in the amount of Na\({}_{2}\)O was observed for the Mt after the acid treatment. The P\({}_{2}\)O\({}_{5}\) content of the H-P-Mt, and the SiO\({}_{2}\) content of the H-Si-Mt, significantly increased, respectively, indicating that the surfactant and silane molecules had been introduced into the H-Mt. Moreover, the increase in the P\({}_{2}\)O\({}_{5}\) percentage in the H-P-Mt suggested that the H-Mt had better interactions with the TTPC molecules, in contrast with the Mt. The percentage of SiO\({}_{2}\), from 75.3 wt.% for the Si-Mt, increased to 83.1 wt.% for the H-Si-Mt, due to the grafting of more \(\gamma\)-MPS molecules onto the H-Mt. Compared with H-Si-P-Mt, a higher content of P\({}_{2}\)O\({}_{5}\) and lower content of SiO\({}_{2}\) were observed in H-P-Si-Mt, indicating that the grafting of the \(\gamma\)-MPS was suppressed, to some extent, by the intercalation of the surfactant. That is to say, the intercalated TTPC molecules will reduce the grafting amount of the \(\gamma\)-MPS. Similarly, the grafted coupling agent will also prevent the further intercalation of the surfactant. This result was also in accordance with the result obtained from FTIR analysis.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **BET Surface Area (m\({}^{2}\)/g)** & **BJH Pore Volume (cm\({}^{3}\)/g)** \\ \hline Mt & 40.36 & 0.173 \\ H-Mt & 105.93 & 0.259 \\ P-Mt & 30.12 & 0.163 \\ Si-Mt & 68.87 & 0.191 \\ H-P-Mt & 80.09 & 0.244 \\ H-Si-Mt & 108.29 & 0.260 \\ P-Si-Mt & 33.90 & 0.168 \\ Si-P-Mt & 31.62 & 0.154 \\ H-Si-P-Mt & 56.58 & 0.212 \\ H-P-Si-Mt & 95.25 & 0.238 \\ \hline \end{tabular}
\end{table}
Table 1: Results of nitrogen adsorption–desorption analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**Composition (wt.\%)**} \\ \cline{2-11}  & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **MgO** & **Na\({}_{2}\)O** & **CaO** & **Fe\({}_{2}\)O\({}_{3}\)** & **K\({}_{2}\)O** & **P\({}_{2}\)O\({}_{5}\)** & **TiO\({}_{2}\)** & **Others a** ** \\ \hline Mt & 72.9 & 14.7 & 4.1 & 3.2 & 1.5 & 1.3 & 0.3 & 0 & 0.1 & 1.9 \\ H-Mt & 81.0 & 13.2 & 2.7 & 0.4 & 0.2 & 1.3 & 0.3 & 0 & 0.1 & 0.8 \\ P-Mt & 71.6 & 14.5 & 4.1 & 1.5 & 1.5 & 1.5 & 0.3 & 2.9 & 0.1 & 2.0 \\ Si-Mt & 75.3 & 13.6 & 3.8 & 2.5 & 1.3 & 1.0 & 0.3 & 0 & 0.1 & 2.1 \\ H-P-Mt & 77.7 & 13.2 & 2.8 & 0.1 & 0.2 & 1.2 & 0.3 & 3.7 & 0.1 & 0.7 \\ H-Si-Mt & 83.1 & 12.0 & 2.5 & 0.3 & 0.2 & 1.3 & 0.3 & 0 & 0.1 & 0.2 \\ P-Si-Mt & 72.8 & 14.3 & 4.0 & 0.9 & 1.6 & 1.6 & 0.3 & 2.7 & 0.1 & 1.7 \\ Si-P-Mt & 71.5 & 14.5 & 4.0 & 1.1 & 1.5 & 1.5 & 0.3 & 3.4 & 0.1 & 2.1 \\ H-Si-P-Mt & 81.4 & 12.3 & 2.4 & 0.1 & 0.1 & 1.1 & 0.3 & 1.8 & 0.1 & 0.4 \\ H-P-Si-Mt & 78.7 & 13.3 & 2.7 & 0.1 & 0.2 & 1.3 & 0.3 & 2.5 & 0.1 & 0.8 \\ \hline \end{tabular}
\end{table}
Table 2: Distribution of the composition of Mt and functionalized Mt samples found by XRF measurement.
## 4 Experimental Design and Setup
### Prediction Models  
The choice of the individual models was based on their popularity and performance. Based on the authors' systematic review [7], we selected the most frequently used individual and ensemble models that are suitable for the software quality classification problems. These include three individual models (i.e., NB, SVM, and KNN), and one ensemble model, RF. These models are considered among the best five models for classification problems [36], but also have their own strengths and weaknesses. The main advantage of NB is its ability to estimate the parameters and a model from a smaller proportion of the dataset, and then it produces means and variances of the variables for each class. Nevertheless, the disadvantage of NB is to assume all features independent from each other [37]. SVMs have efficient execution without dependence on the dimensionality of the input space and superior performance with excellent accuracy prediction [38; 39]. KNN is simple to perform and easy to understand, but it does not work well if the datasets have outliers, noisy, or missing values neighbors [40]. RF is less sensitive to the parameter used, so it creates easily. RF are less sensitive to the parameter used, so are easy to create. In addition, RF can resolve the overfitting problem more efficiently and can perform tree pruning faster than individual decision trees [26]. The open-source Waikato Environment for Knowledge Analysis (Weka) tool is used to construct prediction models, and the default values of the models' parameters were used [41]. More details of the models are outlined in the following sections
## 4 Matrixes for Enzyme Immobilization
### Hybrid and Composite Carriers  
Hybrid and composite materials mix different components to produce improved qualities not present in each component alone. These substances provide biomolecules with the prefect environment, enhancing the stability, reusability, and resistance to structural alterations of the enzymes [88]. Therefore, for enzyme immobilization on hybrid or composite supports made from organic-organic, inorganic-inorganic, and organic-inorganic, these precursors are quite beneficial.  
Synthetic polymers (like polyalanine and polyacrylonitrile) can be combined with biopolymers (like chitosan and cellulose) or two biopolymers (like alginate and lignin) to create organic-organic hybrids [89]. These produce long lasting, reusable biocatalytic devices with high enzyme retention by utilizing the mechanical stability of synthetic polymers and the biocompatibility of natural polymers [90].  
Organic-inorganic hybrids combine natural or synthetic organic polymers with inorganic elements such as metal oxides, silica, or carbon-based compounds [91]. They work well for absorbing, covalently binding, or encasing enzymes like hydrolases and oxidoreductases because of their exceptional stability, mechanical strength, and high affinity for biomolecules [92].  
Inorganic-inorganic hybrids, which are made entirely of inorganic elements, offer remarkable mechanical resilience, chemical inertness, and stability at high temperature and pH values [93]. They frequently have functional groups (such as carboxyl and hydroxyl) on their surfaces that improve biomolecule binding and hydrophilicity [94]. They are promising enzyme supports because of their low synthesis costs and good immobilization efficiency.
## 3 Results  
Once PIPET was implemented, it was decided to simulate a total of 400 phantoms. These included 100 NEMA-like, 100 Jasczack-like, 100 Derenzo-like, and 100 Shepp-Logan-like phantoms, as these different phantoms can provide enough variability and a diverse dataset to be used in future works as the training dataset to implement a CNN reconstructor. Given the large number of scan acquisition processes to simulate, it was decided to run the GATE simulations on a CPU cluster. Since these processes are single-threaded, performance scales with the number of available CPU nodes and threads. The more computational resources can be allocated, the more efficient the simulations are, as each thread handles a separate task. This approach ensures that the workload is distributed across multiple CPUs, significantly reducing the overall simulation time. By leveraging the power of parallel processing, the CPU cluster maximizes efficiency, making it ideal for handling the computational demands of GATE simulations. The total computation time required to obtain the data was equivalent to approximately 9000 h, with the most significant computational expense of the simulation being PET acquisition with Monte Carlo methods [27; 28], which took nearly 8600 h; then, data processing lasted approximately 100 h, and the reconstructions took approximately 270 h. It is well known that Monte Carlo methods are slow and require a large quantity of resources for computation.  
In the Figure 4, we can see some of the results obtained in the first step of the pipeline, the generation of the voxelized 3D phantom, and last step, the reconstruction in CaSTOR.  
Additionally, line profiles were obtained for the samples shown in Figure 5. The line profiles were obtained for each phantom in the region of higher intensity.  
The SSIM was computed for all the phantoms; then, the mean for each type of phantom was calculated. The results of the SSIM metric are reported in Table 6.  
Figure 4: **Top row: Voxelized likewise phantoms. Bottom row: Reconstructed likewise phantoms.**  
Figure 5: Line profiles for the phantom samples in Figure 4.  
The FID was computed for each type of phantom; as this metric must be used with datasets, we used each type of phantom as a different dataset. The results obtained are reported in Table 7.
# Tritium Behavior from Vine to Wine
## 2 Materials and Methods
### Tritium Measurement Procedure and Correlation Coefficient  
All tritium measurements were performed by a low-background liquid scintillation spectrometer Quantums 1220 TM (PerkinElmer, Waltham, MA, USA), using a low-background liquid scintillation cocktail (Ultima Gold uLLT, PerkinElmer, Waltham, MA, USA). Thus, 8 g of water sample (distilled water from precipitation, extracted water from air, soil, and vegetable material, or purified combustion water) was mixed with 12 g of scintillation cocktail mentioned above in 20 mL polyethylene vials. Samples, background, and tritium standards (Tritiated water provided by PerkinElmer; reference standard NIST SRM 4927E [25] with a certified tritium activity of 2,632,100 dpm/g \(\pm\) 3.208%, reference date 12 June 2017) were counted for 500 min (50 min/cycle, 10 cycles/sample) with a counting efficiency between 26.6% and 28.5% at the best figure of merit. In the same experimental conditions, the background varied between 0.675 \(\pm\) 0.016 and 0.798 \(\pm\) 0.016 CPM (counts per minute), following a minimum detectable activity of about 0.5 Bq/L, evaluated according to ISO 9698:2019 (ISO, Switzerland) [22]. The uncertainty due to the statistical nature of radioactive decay and background radiation was reported at 1\(\sigma\). Tritium-free water used as background was a deuterium-depleted water produced in our institute, with a deuterium concentration below 15 ppm [26]. To better visualize tritium variation during the observation period, the results were reported in tritium units (1 TU is equivalent to one tritium atom to 10\({}^{18}\) hydrogen atoms). One TU corresponds to 0.119 Bq/kg of water using the following numerical constants: Avogadro constant, 6.0221 \(\times\) 10\({}^{23}\) mol\({}^{-1}\); molar mass of water, 0.018 kg mol\({}^{-1}\); tritium half-life 4500 days; tritium decay constant 1.54 \(\times\) 10\({}^{-4}\) days\({}^{-1}\).  
The Pearson's correlation coefficient between the tritium concentration values recorded for precipitation and atmospheric water vapor and monthly atmospheric parameters (amount of precipitation, temperature, and air humidity) was calculated using the formula:  
\[r=\frac{1}{n}\frac{\sum\limits_{i}{(x_{i}-\overline{x})(y_{i}-\overline{y})}} {\sqrt{\frac{1}{n}\sum(x_{i}-\overline{x})^{2}}\sqrt{\frac{1}{n}\sum(y_{i}- \overline{y})^{2}}}\]  
where \(x_{i}\) is the monthly tritium concentration, and \(y_{i}\) is the monthly atmospheric parameter. This coefficient ranges from +1 to -1, with +1 indicating a positive correlation, \(-1\) indicating a negative correlation, and 0 indicating no correlation.