esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking Sensor Location by Video Analysis in Double-Shell Tank Inspections
## 4 Conclusions  
We have demonstrated consistent accuracy of offline video analysis for tracking sensor movement through straight sections of an air slot mockup in our laboratory. The 3-car RAVIS mockup constructed from the dimensions of the commercial RAVIS currently used for the NDE of double-shell tanks at Hanford did not have consistent forward motion as it navigated the turns in the bifurcated air slot channels. The inconsistent sensor motion as the crawlers change direction in response to force applied to the walls of the air slot increases the error of offline video analysis to predict the distance traveled between sites of data collection. Modifying the RAVIS mockup to enable smoother motion through turns in the air slots, without radical deviation from the commercial robots currently being tested for the NDE of DSTs at Hanford [6], is one of our goals for future research. Achieving this goal will allow us to more completely test real-time sensor tracking, which currently has been limited to targeted movement in the initial straight sections of the air slot mockup. The main focus of future research in real-time sensor tracking will be more effective motor control to avoid the accumulation of errors in repeated movement between sites of ultrasonic data collection.  
\begin{table}
\begin{tabular}{c c} \hline
**Site** & **Position Error (mm)** \\ \hline
1 & 8.01 \\
2 & 11.9 \\
3 & 20.0 \\
4 & 23.7 \\
5 & 41.9 \\
6 & 38.0 \\
7 & 48.5 \\
8 & 60.7 \\
9 & 71.9 \\
10 & 82.1 \\ \hline \end{tabular}
\end{table}
Table 3: Position error in simulated data collection using experimentally determined errors in real-time video tracking with motor control.  
**Author Contributions:** Conceptualization, C.M. and J.M.; methodology, J.P. and E.A.; software, J.P.; validation, J.P. and E.A.; formal analysis, J.P. and J.M.; investigation, J.P. and E.A.; resources and funding, C.M.; writing--original draft preparation, J.M. and J.P.; writing--review and editing, J.M. and J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Pacific Northwest National Laboratory, grant number 516084.  
The data presented in this study are available on request from the corresponding author.  
The authors gratefully acknowledge assistance from Washington River Protection Solutions staff members who provided data on the structure of Hanford DSTs that enabled us to map the air slot network in relation to tank-bottom welds and the Pacific Northwest National Laboratory for the realistic air slot mockup provided to WSU-TC. This mockup enabled a demonstration of the validity and effectiveness of our proposed position-tracking techniques.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 9 Practical Applications and Use Cases
### Comparative Analysis of NFT-Based Global Digital Registries
Implementing privacy-preserving technologies, such as zero-knowledge proofs or zkSNARKs, can mitigate these concerns by enabling the verification of token ownership without disclosing sensitive information. * Ownership Rights: NFT-based tokenization introduces new challenges in terms of ownership rights, particularly concerning intellectual property (IP) and copyright. The decentralized nature of blockchain technology may lead to potential conflicts between jurisdictions and legal frameworks. Therefore, it is essential to establish clear guidelines and frameworks for handling IP and copyright issues to ensure that creators and rightsholders are adequately protected and fairly compensated. * Regulatory Challenges: The widespread adoption of NFT-based tokenization for global digital registers may face regulatory challenges, as governments and authorities may seek to impose controls and restrictions on blockchain-based systems. Regulatory bodies need to strike a balance between ensuring the technology's responsible use and preserving its core benefits, such as decentralization, security, and transparency. Developing appropriate legal frameworks and engaging in international cooperation will be vital to effectively addressing these regulatory challenges.
## 5 Discussion on Occurrence of SG and HG
### Direct Reason Why Onset of Galloping in SG Can Occur  
The reduced velocity \(U_{r}\) and the amplitude ratio at the lower limit of self-excited region (or the onset of VIV-galloping transition branch) are listed in Table 6 according to Figures 11-13.  
The following results can be clearly summarized from Table 6. As \(c\) and \(m\) increase, or \(K\) decreases, \(U_{r}\) at the lower limit of self-excited region monotonically increases (from 6.625 to 9.125, from 7.25 to 8.867, from 7.85 to 8.5). On the contrary, regardless of the variations of \(c\), \(K\), and \(m\), \(A^{*}\) is always maintained at an approximate constant of 0.8. Actually, the two results reveal that the amplitude of VIV is a direct reason for the self-excited galloping phenomenon in SG response. When the amplitude reaches a sufficient high value of approximately 0.8, the prism starts to transition from VIV to galloping.  
Since the VIV amplitude is the direct reason behind the induction of galloping in the SG response, the evolution of SG and HG can be explained as follows. As seen in Figures 11-14, when \(c\) and \(m\) increase, or \(K\) and \(\alpha\) decrease, \(A^{*}\) at the same \(U_{r}\) in VIV branches reduces, resulting in the failure to induce galloping. As we know, VIV is a self-limited motion. When the response is in the initial branch and upper branch, the amplitude increases with the increase of velocity. When the response goes into the lower branch, the amplitude declines rapidly. If the amplitude can be increased high enough to induce the galloping before declining, the SG response can be observed. Otherwise, the galloping cannot be induced and the HG response is observed.  
Therefore, the phenomenon of SG and HG observed by [PERSON], and the phenomenon of HG1 and HG2 observed by [PERSON], can all be explained by the amplitude of VIV.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{c}\)/\(\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{-}\mathbf{1}\) & \(\mathbf{K}\)/\(\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{ \cdot}\mathbf{\cdot}\mathbf{\cdot  
It is noted that, for the prism at \(\alpha=1\) and at \(\alpha=1.5\), the onset amplitudes (\(A^{*}=0.730\) and \(A^{*}=0.323\)) are lower than that at \(\alpha=0.866\) (\(A^{*}\approx 0.8\)). This is mainly caused by the change of lift force owing to the change of wake mode. This issue will be discussed in detail in future studies.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 3 Proposed Approach
### Evaluation Metrics  
We focused on the performance aspect of the machine learning models to evaluate them and decide our final base model. Performance refers to how well the models can classify the input images of Arabian camels based on their breeds, which is the goal of this work. We used a set of evaluation criteria to measure and compare the performance of various selected models. The evaluation criteria are based on comparing the predictions made by the models on a given dataset of Arabian camel images with true class labels verified by domain experts. The evaluation metrics that we used are as follows:  
Accuracy: This metric measures how often a classifier predicts correctly. It can be calculated mathematically using Equation (1).  
\[\text{Accuracy}=\frac{\text{True\,Positives}+\text{True\,Negatives}}{\text{ Total\,Number\,of\,Predictions}} \tag{1}\]  
Precision: This metric measures how many of the positive predictions made by a classifier are actually positive. To compute precision, we use Equation (2).  
\[\text{Precision}=\frac{\text{True\,Positives}}{\text{True\,Positives}+\text{ False\,Positives}} \tag{2}\]  
Recall: This metric measures how many of the actual positive instances are predicted correctly by a classifier. To calculate the recall score, we use Equation (3).  
\[\text{Recall}=\frac{\text{True\,Positives}}{\text{True\,Positives}+\text{ False\,Negatives}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Year** & **Number of Parameters** \\ \hline InceptionV3 & 2015 & 23.8 million \\ NASNet-Large & 2018 & 88.9 million \\ PNASNet-5-Large & 2018 & 86.1 million \\ MobileNetV3-Large & 2019 & 5.4 million \\ EfficientNetV2-S & 2021 & 22 million \\ EfficientNetV2-M & 2021 & 54 million \\ EfficientNetV2-L & 2021 & 121 million \\ \hline \hline \end{tabular}
\end{table}
Table 2: CNN architectures’ details.  
F1-score: This metric combines precision and recall into a single score. The F1-score is computed using Equation (4).  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{4}\]  
AUC: AUC stands for area under the ROC curve. The ROC curve shows the performance of a binary classifier as a function of its cut-off threshold. The ROC curve plots the true positive rate (Recall) against the false positive rate (\(1-\text{Precision}\)) for different values of the threshold. In our multi-class classification problem, we applied the one-vs-all method to adapt AUC. In this approach, each class is treated as the positive class and all other classes are treated as negative. The AUC is then calculated for each class and averaged to obtain an overall value.
# Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization
## 3 Gait Phase Estimation Model  
### MLP for Gait Phase Estimation  
This section describes the gait estimation procedure using TD features. First, the TD features are calculated from the original time-series data in the current time window, as shown in Figure 3. Notably, the LTW is different for each TD feature set. Notably, the feature values were not normalized because a previous study [22] showed that an accurate estimation can be obtained without normalization.  
Next, the calculated TD features are fed into an MLP consisting of two hidden layers. Because the optimal selection of TD features is one of the main objectives of this study, the number of input nodes can vary depending on the number of selected TD features. The two hidden layers were constructed with 15 and 20 nodes, respectively. In this study, _tanh_ activation function was applied to every hidden layer. The details on the MLP architecture are provided in Tables S4 and S5 in Supplementary Materials. The two output nodes represent continuously varying variables \(x\) and \(y\), which can be converted to the gait phase \(p_{g}\) as:  
\[\begin{array}{l}x=\cos\phi,\\ y=\sin\phi,\\ \text{where }\phi=\frac{p_{g}}{100}\times 2\pi\end{array} \tag{1}\]  
If the error in the gait phase \(\phi\) is directly used in the regression cost function, the result would be unsatisfactory. Because the gait phase discontinuously changed from 100% to  
Figure 3: Gait phase estimation procedure. \(Z_{L,i}\) and \(Z_{R,i}\) are the \(i\)-th TD features of the left and right lower limb, respectively.  
Figure 2: **(a1)** Angle and (**a2**) angular velocity data of left thigh motion for the five subjects involved in this study.  
0% at the end of each gait cycle, the error is much larger near 0% (and 100%) than at other points. To avoid this unequally distributed error, a conversion technique (1) was adapted from a previous study [22]. The error is very large around the end of the gait cycle. For example, if the ground truth is 1%, the MLP prediction lags behind by 2%. Then, the MLP result would be 99%, and the absolute error is 98%. Thus, this unrealistic error must be addressed using (1).  
This issue can be addressed by converting the gait phase into continuously varying variables \(x\) and \(y\), as shown in (1). After the MLP calculates \(x\) and \(y\), the current gait phase is obtained using the _arctan2_ function. A loss function \(L\) is defined using the mean absolute error as follows:  
\[L=\frac{1}{2n}\sum_{i=1}^{n}(|x_{i}-\hat{x}_{i}|+|y_{i}-\hat{y}_{i}|), \tag{2}\]  
where \(n\) represents the number of training data points. The Adam optimizer was applied with a learning rate of 0.001 and 5000 iteration epochs, which were fixed throughout the study. The estimation algorithm was trained on a workstation with a single graphics processing unit (CPU: Intel Xeon CPU E5-2620 v4, 2.10 GHz, and 16 cores; GPU: NVIDIA GeForce RTX 2080 Ti). TensorFlow was used as the deep learning framework.
Applied Sciences  
Mdpj Sciences  
Soft Tissue Evaluation of an Immediate Esthetic Zone  
Single Implant with a Stereolithographic Guide  
Using 3D Reconstruction and a CAD/CAM  
Customized Titanium Anatomic Abument  
[PERSON] 1\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\), [PERSON] 3, [PERSON] 4, [PERSON] 5\({}^{\copyright}\) and [PERSON] 6,*  
1 Department of Oral and Maxillofacial Surgery, School of Dentistry, Pusan National University, Yangsan 50612, Korea; [EMAIL_ADDRESS]  
2 Department of Prostodontics, School of Dentistry, Pusan National University, Yangsan 50612, Korea; [EMAIL_ADDRESS]  
3 Department of Prostodontics, School of Dentistry, Pusan National University, Dental and Life Science Institute & Dental Research Institute, Yangsan 50612, Korea; [EMAIL_ADDRESS]  
4 Department of Oral and Maxillofacial Surgery, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; [PERSON] Department of Oral and Maxillofacial Surgery, Oulu University Hospital and Medical Research Center, University of Oulu, 90220 Oulu, Finland; [PERSON]S]  
6 Department of Oral and Maxillofacial Surgery, School of Dentistry, Pusan National University, Dental and Life Science Institute & Dental Research Institute, Yangsan 50612, Korea  
* Correspondence: [EMAIL_ADDRESS]  
Received: 3 January 2020; Accepted: 25 February 2020; Published: 2 March 2020  
**Abstract:** The replacement of an unrestorable tooth with a single implant-supported prosthesis is a treatment with predictable and successful outcomes. The anterior maxilla region is a complicated site for such treatment, due to its esthetic, functional, and physiological sensitivity. The purpose of this study was to evaluate the changes in the soft tissue following an immediate implant procedure using guided surgery in combination with a computer-aided design and computer-aided manufacturing (CAD/CAM) customized titanium anatomic abument in the esthetic zone. A total of 13 patients who had been treated with an immediate post-extraction implant placement and an immediate provisional restoration were included in the study. The changes in the soft tissue dimension, interdental papilla, and esthetic score were evaluated with a follow-up of at least one year. There was no significant change in the midfacial mucosal status when compared to the pre-treatment situation. The recession of the mesial papillary height was 0.32 mm and the distal papillary height was 0.10 mm. The mean horizontal change in the labial mucosa was 0.32 \(\pm\) 0.83 mm. There was no significant difference between the mesial papilla index and the distal papilla index. The pink esthetic score value prior to treatment was 11.16, and at one-year follow-up, 10.25. Within the limitations of this study, the immediate post-extraction implant placement procedure with a stereolithographic guide and a provisionalization with a CAD/CAM customized titanium anatomic abument may be a treatment option with predictable outcomes for the replacement of teeth in the esthetic zone.  
**Keywords:** immediate post-extraction implant placement; esthetic zone; pre-fabricated customized abument; immediate provisionalization; guided implant surgery; stereolithography
# Combinatorial Olfactory Signaling in Short-Distance Determines Host Plant Recognition in Locust
## 3 Results
### Odorant Receptors in Palps Involved in Detection of Odors from Host and Nonhost Plants  
We systematically profiled the OR genes expressed on palps in previous transcriptomic analyses [27] and identified six novel ORs (Figure 3A), among which LmigOR12 was specifically expressed in the palps. We coupled RNAi with the POR, which was previously used to deorphanize the locust ORs by comparing the olfactory opening frequency of the palps (Figure 3B) [29]. Knockdown of the palp-specific OR12 (dsOR12) led to broad attenuation of the olfactory response among the tested odors, indicating that OR12 might be generally required to tune volatiles on the palps (Figure 3C). Similarly, we knocked down the other five ORs and observed strongly differentiated tuning activities between each other or a combinatorial coding pattern (Figure 3D,E). The dsOR12/dsOR17 injections reduced the POR for most of the tested volatiles, suggesting that LmigOR12 and LmigOR17 were responsible for the detection of host and nonhost plant odors (Figure 3C,D). In addition, LmigOR15 was weakly involved in the detection of (_E_)-2-pentenal, 2-methyl-2-pentenal, and three common volatiles. In contrast, suppression of LmigOR19 led to narrowly reduced responses to (_E_)-2-pentenal and the common volatile pentanal. LmigOR21 was involved in the detection of common volatiles, the host volatile (_E_)-2-pentenal, and the nonhost volatile heptanol (Figure 3D). LmigOR22 was involved in the detection of (_E_)-2-pentenal and 2-methyl-2-pentenal, two common volatiles (_E_)-2-hexenal and pentanal, and the nonhost volatile heptanal (Figure 3E). Notably, OR12, OR21, and OR22 were all needed to modulate the nonhost heptanal. Together, these results supported the conclusion that locust palps employ a combinatorial coding strategy to detect plant volatiles; that is, one receptor can recognize multiple plant odors, and one plant odor can be detected by multiple ORs (Figure 3F).
## 4 Discussion  
For all the samples, diffraction peaks at 31.8\({}^{\circ}\), 34.5\({}^{\circ}\), 36.2\({}^{\circ}\), 47.6\({}^{\circ}\), 56.6\({}^{\circ}\), 62.9\({}^{\circ}\), 68.0\({}^{\circ}\), and 69.1\({}^{\circ}\) have been observed. The peak observed at 34.5\({}^{\circ}\) corresponds to the preferred orientation (002) plane of hexagonal structure of ZnO, indicating that all the deposited thin films have the same structure type [22,23]. Any secondary phase is observed, confirming that all  
Figure 4: Transmittance spectra of ZnO doped and undoped thin films.  
Figure 5: (\(\alpha\)hv)\({}^{2}\) front hv curves of i-ZnO and ZnO doped with Al, Ga, and In.  
dopants are in the structure. The particle size varies depending on the dopant agent used, for aluminium and indium doped films, more plane particles were observed, however for undoped and gallium doped films the morphology changes appearing spherical particles. In the visible region with wavelengths ranging from 350 nm to 800 nm, samples present an average transmittance in the visible (400 nm to 800 nm) of 91.6%, 86.6%, 70.3%, and 87.1% for Al, Ga, In, and i-ZnO, respectively. All films present a high optical transmittance which decreases with the heaviest doping elements.  
Band gap values are larger than the band gap of pure ZnO film (about 3.3 eV) which can be attributed to the Burstein-Moss effect [12], which is caused by the increase in free electron concentration after M\({}^{3+}\) doping. In addition, a sample of ZnO treated in the tubular furnace (ZnO\({}_{1-x}\)) was studied to see the effect caused by the oxygen vacancies independently; a bandgap decrease was observed due to the oxygen vacancies generated [9]. Furthermore, it can be observed that aluminium and indium doped films, which present a similar morphology, have a higher bandgap than gallium and intrinsic ZnO, which had spherical particles.  
As is shown, the sheet resistance decreases when a dopant agent is added. Although generated oxygen vacancies help in the decrease of the sheet resistance too, the presence of the dopants is necessary to have better results, confirming that the good electrical properties were obtained from a combination of the oxygen vacancies generated and the dopant added. The best electrical results come from In and Al doped films, which presented a similar particle morphology.
## 2 Materials and Methods
### Trend Analysis  
The Theil-Sen median trend analysis method was utilized to describe the spatiotemporal patterns of the TVDI from 2000 to 2022:  
\[\beta=mean(x_{j}-x_{i}/j-i),\forall j>i \tag{5}\]  
where \(x_{j}\) and \(x_{i}\) denote the data points in a time series. A \(\beta\) value greater than 0 indicates an upward trend in the time series, while a \(\beta\) value less than 0 indicates a downward trend.  
Subsequently, the Theil-Sen median trend analysis results were subjected to a Mann-Kendall significance test:  
\[Z=\left\{\begin{array}{cl}S/\sqrt{Var(s)}&(S>0)\\ 0&(S=0)\\ S+1/\sqrt{Var(S)}&(S<0)\end{array}\right. \tag{6}\]  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sign(x_{j}-x_{i}) \tag{7}\]  
\[sign(\theta)=\left\{\begin{array}{cl}1&(\theta>0)\\ 0&(\theta=0)\\ -1&(\theta<0)\end{array}\right. \tag{8}\]  
\[E(S)=0 \tag{9}\]  
\[Var(S)=n(n-1)(2n+5)/18 \tag{10}\]  
where \(n\) is the number of data points. Notably, for \(n\geq 8\), the test statistic \(S\) approximately follows a normal distribution. The mean and variance were analyzed as follows: at a given significance level \(\alpha\), if \(|Z|>Z_{1-\frac{\alpha}{2}}\), the hypothesis of the absence of a trend can be rejected, indicating a significant trend change in the time series data. Moreover, \(Z_{1-\frac{\alpha}{2}}\) is the value corresponding to confidence level \(\alpha\) in the standard normal distribution table. When the absolute value of \(\alpha\) is greater than 1.65, 1.96, and 2.58, the trend passes the significance test at the 90%, 95%, and 99% confidence levels, respectively.  
The coefficient of variation (\(CV\)) of the TVDI was applied to explore the drought variability in Guilin city from 2000 to 2022. A higher \(CV\) value indicates a higher degree of variability:  
\[CV=(\text{Standard deviation}/\text{Mean})\times 100\% \tag{11}\]