esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Strain Distribution
#### 5.3.3 The Influence of Buried Depth  
Figure 20a shows a comparison of the strain-time (\(\varepsilon\)-\(t\)) curves at the mid-span position (B-B)2 between the predicted results in FEA and the measured results in the experiment (EXP) with various values of buried depth (\(d\)); additionally, a comparison of the peak strain (\(\varepsilon_{\text{max}}\)) at the position among the FEA, EXP, and theoretical (THE) results is also presented in Figure 20b. It can be found from Figure 20 that a generally good agreement was also achieved among the FEA, EXP, and THE results. Similar to the above experimental results, \(\varepsilon\) reduced with the increase of \(d\). It can also be shown that, when \(d\) = 0.25 m, the \(\varepsilon_{\text{max}}\) obtained from THE and FEA was more than 1410 \(\mu\varepsilon\), which indicates that the deformation of the pipeline reached the yield strain. It may be concluded from Figure 20b that a buried depth of the pipeline from 0.5 m to 0.6 m was more reasonable for the pipeline designed in this paper.  
The strain distribution of specimens under \(d\) in FEA is presented in Figure 21. It can be seen from Figure 21 that \(\varepsilon\) at the mid-span of the specimen with \(d\) = 0.9 m (D114 \(\times\) 2.5-d0.9-H1.0) was much smaller than that with \(d\) = 0.6 m (A114 \(\times\) 2.5-d0.6-H1.0) and \(d\) = 0.3 m (D114 \(\times\) 2.5-d0.3-H1.0), which reflects that the soil plays a buffer role in dispersing the impact load, and indicates that the pipelines cannot be buried shallowly.  
Figure 20: The influence of burial depth (\(d\)) on strain: (**a**) comparisons of strain–time (\(\varepsilon\)–\(t\)) curves between FEA and EXP results under different \(d\); (**b**) comparisons of the peak strain (\(\varepsilon_{\text{max}}\)) among FEA, EXP, and THE results under different \(d\).  
Figure 19: The influence of diameter (\(D\)) and wall thickness (\(T\)) on strain distribution: (**a**) \(D\) = 89 mm; (**b**) \(D\) =114 mm; (**c**) \(D\) = 139 mm; (**d**) \(T\) = 2.5 mm; (**e**) \(T\) = 3.0 mm; (**f**) \(T\) = 3.5 mm.
## 5 Energy-Harvesting Applications
### Cognitive Radio for Wireless Sensor Networks
#### 5.1.1 Overview of Cognitive Radio  
Cognitive radio is an innovative communication technology that enables radios to automatically and dynamically access and utilize available spectrum bands [91, 93]. The primary goals of CR are to increase spectrum utilization and reduce interference by allowing radios to adapt to their present spectrum environment. The primary characteristics of cognitive radio are the following:  
* Spectrum Sensing: Detects the presence of primary users (licensed users) and identifies unused spectrum bands.  
Dynamic Spectrum Access: Allows secondary users (unlicensed users) to access spectrum bands when primary users are not active.
* Adaptive Transmission: Adjusts transmission parameters based on the spectrum environment.
###### Abstract  
(1) Background: The success of prosthetic treatment with implant support depends on the combined effort of the team doctor-technician, each of them being responsible for the validation of execution stages. (2) Methods: we composed an online questionnaire with 18 multiple choice questions, using the Google Forms application. It was filled out by an equal number of prosthodontic specialists and dental technicians. Differences and associations were evaluated by Likelihood Ratio test, Linear by Linear association test, Kruskal-Wallis H test, Pearson Chi-Square test and the Fisher's Exact test. (3) Results: Differences and similarities were found between the statements of prosthodontic specialists and dental technicians. Years of experience are correlated with the number of restorations, impression techniques and types of restoration (\(p\leq 0.05\)). Similar answers for both groups were registered for preferred screw retained type of prosthetic abutment and most frequently reported complications. (4) Conclusions: The different perspectives of the two members of the prosthodonic team regarding the leading role in the treatment plan, type of abutment, impression technique and prosthetic design of implant fixed restorations has been revealed in our study. Similar education curricula and standards for continuing training courses after graduation are necessary for prosthodonic specialists and technicians in Romania.  
Keywords: dental materials; dental education; prosthodontics +
Footnote †: journal: Medical Informatics and Biostatistics Department, Faculty of Medicine, \"Carol Davila\" University of Medicine and Pharmacy, 050474 Bucharest, Romania; [EMAIL_ADDRESS]  
Footnote †: journal: Medical Informatics and Biostatistics Department, Faculty of Medicine, \"Carol Davila\" University of Medicine and Pharmacy, 050474 Bucharest, Romania; eugenia.  
treatment with implant support depends on the combined effort of the team doctor- lab technician, each of them being responsible for the validation of execution stages in order to achieve a rehabilitation with a good prognosis. Communication deficiencies in this relationship are reported as a major problem that interferes with ensuring optimal treatment for the patient [7]. There are just a few studies that identify prosthodontics specialists' preferred methods of implant supported restoration in certain areas of the world [8; 9], some of them not providing information regarding professional experience [10; 11]; only a few polls have been made in the area of the dental technique laboratory [12; 13].  
The present study aims to compare the prosthodontic specialists' and the dental technicians' perspective concerning materials, clinical and technological methods used in the fabrication of fixed prosthesis with implant support and also to determine the most common problems caused by this treatment option in Romanian practice.
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 3 Simulation Method and Experimental Verification  
Using ABAQUS finite element pretreatment module, a three-dimensional finite element model was established. Drill bit, DS, borehole, and rock were modeled by rigid body shell element R3D4, spatial two nodes beam element BEAM31, rigid body shell element R3D4, and three-dimension solid element C3D8R, respectively, by the meshing techniques (Figure 4).  
Make the following hypothesis:  
(1) In the analysis process, the DS is in line elastic state without considering plastic deformation.  
(2) Ignore the impact of DS joints.  
(3) The axis of the DS coincides with the axis of the borehole when the DS is not under gravity.  
(4) The borehole wall is rigid and does not deform when contacting the DS.  
Figure 4: Meshing. (**a**) Rock. (**b**) Drill bit. (**c**) Borehole. (**d**) DS.  
Figure 3: Contact state.  
(5) The weight of the DS is constant.  
The boundary conditions in the model are shown in Figure 5, and are mainly composed of three parts:  
(1) Apply axial force and rotational speed on the DS nodes at the entrance;  
(2) A free border is used at the bottom. The bit can freely cut the bottom rock;  
(3) The range of motion of the DS is limited to the inside of the hole, and the wall of the hole is constrained by fixed constraints in global coordinates.  
The principle of HDD is consistent with that of directional drilling in drilling engineering. Therefore, the reliability of the model can be verified by comparing the measured data of drilling engineering with the simulation results. In order to evaluate and analyze the reliability of the model, the actual drilling data is obtained through field drilling measurements, and the obtained data is compared with the simulated calculation data in Figures 6 and 7. The boundary condition set in the simulation is ideal, which is different from the real drilling. Therefore, the small error is reasonable and acceptable.  
By comparing the measured torque and the simulated torque, as well as the measured WOB and the simulated WOB, the reliability of the calculation results of the DS dynamics model is verified.  
Figure 5: Boundary conditions.  
Figure 6: Compare measured torque and calculated torque. (**a**) The measured torque. (**b**) The calculated torque.
## 3 Results and Discussion
### Nucleotide Composition Analysis  
To explore why the coding approach proposed in this study worked effectively, we used the Two Sample Logos software [27] on all six species datasets to analyze the position-specific preferences of the nucleotide composition of these species as a way to explore statistically significant nucleotide differences between sequences containing non-4 mC sites and 4 mC sites. Figure 2 shows the nucleotide preferences of 41 base pairs of DNA sequences from three-scale datasets, where the C base pair is centrally positioned. Information on nucleotide preferences for the other three datasets is referenced in the provided Supplementary Figure S1. As shown in Figure 2, _E. coli_ and _G. subterruneus_ adenosine and thymine are significantly enriched on sequences containing 4 mC sites, while sequences at non-4 mC sites exhibit significant cytosine and guanine preferences. In contrast, cytosine and guanine are significantly enriched on sequences containing 4 mC sites and show a clear preference for adenosine and thymine on non-4 mC site sequences on _D. melanogaster_.  
Taking a detailed example, at position 16 in the _E. coli_ dataset, adenosine and thymine are particularly significant on sequences containing the 4 mC site, while at the same position, cytosine and guanine have a significant preference on sequences of the non-4 mC site. Similarly, at position 24, guanine and thymine are particularly significant on sequences containing the 4 mC site, and cytosine and adenosine are particularly significant on sequences  
Figure 2: Sequence logos between 4 mC sites and non-4 mC sites of _Escherichia coli_, _Geoalkalibacter subternaeus_, and _Drosophila melanogaster_.  
in the non-4 mC site. Additionally, at positions 19, 20, 22, and 27, there are also significant differences between the samples containing 4 mC sites and non-4 mC sites.  
However, at positions 9, 10, 11, 12, 35, 36, and 37, there are no significant differences between the 4 mC sites and non-4 mC sites. Therefore, we considered coding for consecutive positions or spaced positions for dinucleotides or trinucleotides as a way to highlight the differences between these positions. For example, assuming we encode dinucleotides spaced three nucleotide residues apart, then, when we encode position 12, we are encoding both positions 12 and 16, and at position 16 there is a clear nucleotide preference between the 4 mC site sequences and the non-4 mC site sequences at this time. Therefore, this information will assist in the differentiation of nucleotides preferences at position 12. One-Hot encoding is the direct coding of the original nucleotide residues and, therefore, contains the most original nucleotide sequence information. The distinction of the 4 mC sites is further enhanced by combining consecutive, spaced nucleotide residues to assist in boosting the nucleotide preference at a particular position, which would be very beneficial in improving the accuracy of 4 mC site recognition.
## 2 Relevant Approaches to Digital Twins for Sustainability in the Existing Literature
### Particularization of Digital Twin Sustainability in Various Areas of Manufacturing  
The focus on specific situations is a strong component of developing a suitable customized twin in high-tech industries such as aviation manufacturing [12], satellite assembly [13], missile production [14], electronics [15], the automotive industry, robotics and medical equipment [16], as well as intelligent home products [17; 18], or more common domains such as the clothing industry [19]. There are also proposals for the generalization of the model to support enhanced performance at the level of product design and development [20], the production logistics system [21], the manufacturing supply chain [22], or the entire value creation chain [23].  
Concerning certain domains to be addressed, the authors of [24] propose a smart manufacturing approach based on a digital twin concept relying on the quantitative green performance evaluation of the production systems and its outputs. The model introduces as priorities for the digital system important environmental, health, and social functions, which are then evaluated with the help of expert and stakeholder input in a comprehensive review. At the same time, the authors of [25], who analyse 52 scientific articles in the field, underline the need to track product and process data and to visualize the operations and interactions of the digital and real components of the twin. Additionally, the authors consider that energy consumption monitoring, which can signal operational efficiency and environmental sustainability, should be a main function of a twinning system. In the paper [26], the authors describe 12 potential benefits of data-driven maintenance processes as they can be implemented with the help of a digital twin, with a third of these being related to savings and safeguards in the area of environmental impact. The possibilities of supporting difficult decisions and projects of manufacturers working to protect nature are considerably enhanced in a digitalized approach based on twinning the real and the virtual environments.  
The private sector is concerned with creating the most adequate strategy to deploy digital twins with minimal disruption [27] to increase the efficiency of complex technical systems as can be found in modern production systems [28]. The focus of companies is both on the impact and results of employing twins, and the operational changes required such as plant design [29].
## 3 Results and Discussion
### Characterization  
The IR spectrum of chitosan, alginate, and the three TMC materials were compared. The expanded IR spectra can be found in Figures S1-S5 in the Supplementary Materials. The presence of various functional groups in biocomposite materials can be characterized by vibrational spectroscopy, as described in other studies [29; 30; 31]. Therefore, identifying the appearance or disappearance of IR spectral bands or changes in spectral intensity can be evaluated to ascertain the nature of the bonding within composite materials.  
In Figure 1 the IR spectra reveal a characteristic -OH signal at 3500 cm\({}^{-1}\) from chitosan and alginate, which is also noted for all biocomposite materials. Notably, the strong C=O stretching band in chitosan and alginate near 1660 cm\({}^{-1}\) was not found in the TMC materials, which indicates that the C=O groups of chitosan and alginate may serve as strong chelation sites for the metal centers [32]. A notable IR signature was attributed to the nitrate counter-ion near 1390 cm\({}^{-1}\). The expected signal was observed for Al-TMC and Fe-TMC systems, but was not apparent for Cu-TMC. Instead, the Cu-TMC IR spectrum (cf. Figure 1) exhibits a set of signatures at 3540 cm\({}^{-1}\), 2450 cm\({}^{-1}\), 2336 cm\({}^{-1}\), 1420 cm\({}^{-1}\), 1350 cm\({}^{-1}\), and 1050 cm\({}^{-1}\), which are characteristic spectral features for Cu\({}_{2}\)(OH)\({}_{3}\)NO\({}_{3}\)[33; 34], as noted for IR results reported by Steiger and Wilson for a related Cu-TMC biocomposite [32]. This possibly signifies a fundamental difference for copper-based materials, where Cu\({}_{2}\)(OH)\({}_{3}\)NO\({}_{3}\) are formed in the synthesis and may be incorporated within the biopolymer network. While in Al-TMC and Fe-TMC, the metal centers are chelated to the functional groups of the biopolymers in their oxo-coordinated form (Al-O and FeO(OH)) and nitrate simply acts as the counter ion for charge balance [15; 16].
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 1 Introduction  
Alzheimer's disease (AD) is one of the leading causes of dementia, which is characterized by a gradual decline in cognitive functions, including memory, reasoning, and communication. It is more prevalent among older people, and its impact is profound, not only on those affected but also on their families and caregivers [1, 2]. AD is a major public health challenge, and the number of cases is expected to rise significantly as the world's population ages [3]. One of the oldest and most recognizable signs of AD is hippocampal atrophy [4], Which involves shrinkage of the hippocampus, an area of the brain important for memory formation. As the disease progresses, AD is often accompanied by cortical thinning and white matter disturbances, indicating more severe AD stages. These structural brain changes highlight the importance of early detection in managing and mitigating the effects of the disease [5]. Early detection of AD is critical because it allows timely intervention, which can slow the progression of symptoms, improve quality of life, and provide patients and their families more time to plan for the future [6]. Furthermore, early diagnosis enables therapeutic strategies to be applied when most effective, which may delay the appearance of more disabling symptoms [7, 8]. Figure 1 shows MRI images of a brain brain, a brain with mild cognitive impairment (MCI), and a brain affected by AD [9]. The normal brain on the left appears structurally intact, with well-preserved gray matter (GM) volume. In contrast, the [PERSON]'s brain on the right demonstrates significantly reduced GM volume, particularly in regions critical for memory and cognition, with certain areas becoming enlarged due to atrophy and brain tissue loss. In the case of MCI, an intermediate decline in GM volume can be observed between the two.
## 5 Spatiotemporal Characteristics of Existing Pipeline's Deformation
### Spatiotemporal Characteristic Curves Combined with LDC, DDC, and GRC  
We can implement the backfill grouting and compensation grouting during the shield tunnel excavation to reinforce the surrounding soil and control the existing pipeline's deformation. Therefore, the study on the spatiotemporal characteristics of the existing pipeline's deformation necessarily takes into account the effect of grouting reinforcement. To simplify this issue, we assume that the effect of grouting reinforcement is reflected in enhancing the coefficient of subgrade reaction. The coefficient of subgrade reaction before the tunnel face is set to be \(k\). The coefficient of subgrade reaction behind the tunnel face increases to 1.5, 2, 2.5, and 3\(k\), according to different grouting degrees. Subsequently, we provide a straight line, called the grouting reinforcement curve (GRC), to represent the linear relationship between the subgrade reaction force and the deformation based on the Winkler foundation model. Figure 19 shows the spatiotemporal characteristic curves combined with LDC, DDC, and GRC, respectively.  
Figure 19a shows the different LDC obtained by different backfill grouting when shield tunnel excavates to 0 m. The overall pipeline's deformation gradually decreases with the increase of \(k\). After giving a certain settlement control value, we can provide a reasonable  
Figure 19: Spatiotemporal characteristic curves between LDC, DDC, and GRC: (**a**) spatiotemporal characteristic curve between LDC and GRC; (**b**) spatiotemporal characteristic curve between DDC and GRC.  
Figure 18: Existing pipeline’s deformation contours extracted from front view and side view: (**a**) Longitudinal Deformation Curve (LDC) extracted from front view; (**b**) deformation development curve (DDC) extracted from side view.  
growting measure according to the spatiotemporal characteristic curves combined with LDC and GRC. For instance, we choose the backfill grouting scheme in which the coefficient of subgrade reaction increases to \(2k\) (blue dotted line).  
Figure 19b shows the different DDC at the monitored point of \(x=0\) obtained by different backfill grouting. The control effectiveness of the grouting reinforcement is unobvious before the tunnel face passes the monitored point, whereas the effectiveness is remarkable after the tunnel face passes the monitored point. After giving a certain settlement control value, we can provide a reasonable grouting measure to control the deformation at a certain point according to the spatiotemporal characteristic curves combined with DDC and GRC. For instance, by connecting the control value to the origin, the minimum coefficient of subgrade reaction can be determined. Alternatively, by connecting the control value to the settlement at any moment (solid red line), the minimum coefficient of subgrade reaction, required after that moment, can be determined.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 3 Data Collection and Processing
### Data Observation  
#### 3.4.1 Time Domain Data  
The raw accelerometer data is plotted in the time domain to explore different characteristics between the 10 conditions.  
The tachometer magnitude data in Figure 4(a) are about the same for all 10 classes because their rotating frequencies are similar. These data might not be useful predictors; however, they are not eliminated during this study and are used to confirm whether the model can detect them as useful or useless predictors.  
The microphone data in Figure 4(b) shows very different behavior between the 10 modes due to sound differences caused by the machine's different conditions. This information can be extremely useful for machine learning in learning the differences between classes.  
Similar to microphone data, the acceleration data for both overhang and underhang sensors for all three axes have very different characteristics among all ten classes. In the first accelerometer at the underhang position, shown in Figure 4(c), the class ten or overhang Ball Fault generates the highest magnitude signal in all three axes. In the second accelerometer, shown in Figure 4(d), class 3 or Vertical Misalignment has the highest magnitude signal on the x-axis. This overhang sensor also generated a significant signal in the y-axis during the class-10 fault condition. Classes 3, 7, and 10 generated the most standout signals than other classes. This indicates that accelerometers work extremely well in picking different frequencies when there is a fault with a rotating machine.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Mode** & **No. of Sequence** & **Total Record** \\ \hline Normal & 1 & 250,000 \\ \hline Horizontal Misalignment—1 m & 1 & 250,000 \\ \hline Vertical Misalignment—0.51 mm & 1 & 250,000 \\ \hline Imbalance—6 g & 1 & 250,000 \\ \hline Underhang bearing—Inner fault-6 g & 1 & 250,000 \\ \hline Underhang bearing—Outer race-6 g & 1 & 250,000 \\ \hline Underhang bearing—Ball Fault-6 g & 1 & 250,000 \\ \hline Overhang bearing—Inner fault-6 g & 1 & 250,000 \\ \hline Overhang bearing—Outer race-6 g & 1 & 250,000 \\ \hline Overhang bearing—Ball Fault-6 g & 1 & 250,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sampled data for this study.  
Figure 5: _Cont._  
Figure 5: Sensor data in Time Domain: (**a**) Tachometer data (**b**) Microphone data (**c**) Underhang Accelerometer Data in 3 axes, and (**d**) Overhang Accelerometer Data in 3 axes.