esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Analysis (FEA) of Palatal Coverage on Implant Retained Maximly Overdentures
###### Abstract
Thus, threaded implants are preferential to cylindrical implants as a result of their ability to minimize undesirable force components when prosthetically loaded. Prosthetic engineering of the final implant appliance also greatly influences the distribution of masticatory force during function. Particular attention should be given to evenly distributing the masticatory load of the prosthesis, and protecting implant occlusion through assuring that the proper number of implants are placed for the span of missing dentition. When selecting potential areas for implant rehabilitation, consideration should be given to maximize the amount of available bone to allow for the placement of 10 mm or longer implants with 4 mm or wider diameter fixtures [42; 43]. Regarding the ideal number of implants to support maxillary overdentures, [PERSON] et al. [44] found in his study that when comparing a prosthesis supported by three or four implants, the difference in axial force was not statistically significant. The bending moments, however, were significantly higher with three supporting implants in comparison with four supporting implants. [PERSON] et al. [45] in his research states that bone function typically ranges between approximately 50-1500 (1-2 MPa) micro-strain. If peak load of bone results in strains of 1500-3000, a mild overload occurs, resulting in mechanical fatigue damage, but remodeling normally repairs the damage and thus prevents the damage from accumulating. Moreover, repeated micro-strain greater than 3000 (60 MPa) increases micro-damage. Normal bone fractures suddenly occur at forces of 25,000 micro-strain (120 MPa). The view that non-axial load is more detrimental to oral implants than axial load is further supported by findings in studies using 3D FEM analysis by [PERSON] et al. [39] and [PERSON] et al. [46], where non-axial loads resulted in higher stress levels in the peri-implant bone area when compared with axial loads. Therefore, the purpose of this study was to determine the effect of palatal coverage and implant splinting superstructures on their supporting structures by measuring the stress and strain forcesin implant supported maxillary overdentures along the bone-implant interface, implant-prosthesis interface, and within the prosthesis by applying axial and non-axial loads using finite element modeling for stress/strain analysis.
## 3 Results
### Comparison of Different Path-Algorithms  
To compare the proposed path finding with alternative methods, the 2D map from [24] is redesigned and added with the path provided by the skeletonization method in combination with A*. Figure 25 shows the original map, the simplified environment, the clearance width of each path, and the comparison of the paths with alternative path finding methods.  
In contrast to the classical path finding methods, the found path is maximally far away from the obstacles. While A* calculates in the example of [24] the shortest path of the described methods, it touches the boundary next to the obstacles. Such behavior also occurs in APF, PRM, RRT, and RRT-C, which is due to the too free choice of the environment. In the following, the method is applied to maps from real environments.
## 3 Study Area and Data  
### Study Area  
Considering that the dense distribution of the stations affects the modeling effect in local areas [52]. Most GNSS stations are deployed in the western coastal region, and the distribution is denser. In this study, we select the western part of the continental United States (32\({}^{\circ}\) N-39\({}^{\circ}\) N, 114\({}^{\circ}\) W-123\({}^{\circ}\) W) as the study area. We obtained soil moisture values for a total of 60 days from 1 January 2014 to 1 March 2014 through The International Soil Moisture Network (ISMN) [53] ([[https://ismm.geo.tuwien.ac.at/en/](https://ismm.geo.tuwien.ac.at/en/)]([https://ismm.geo.tuwien.ac.at/en/](https://ismm.geo.tuwien.ac.at/en/)), accessed on 6 March 2021) for the experimental study. Here are 50 stations in the experimental area, considering the density of GNSS station distribution and land cover type. In this paper, 44 stations (brown circles) were selected to participate in the point-surface fusion modeling training; 6 stations (gray circles) were selected as test stations, which were not involved in the modeling and only used as the last results verification. Figure 5a,b show the GNSS stations and the distribution of land cover types in the selected experimental area, respectively.  
As seen in Figure 5b, the primary topography of the region is high mountains and plateaus. The central and northern parts of the test area are mostly evergreen coniferous forests as well as grasslands. The central part is the central valley, which is cultivated mainly by agricultural land. The left side of the central valley is the Coastal Range; the right side is the Sierra Nevada, which is dominated by evergreen coniferous forests and savannas. The southeastern part of the test area is barren, primarily desert and open sagebrush. It can be seen that each direction of the experimental area has unique geomorphological features and the geographical conditions are pretty different. In terms of climate, the western coastal region of the test area has a Mediterranean climate, the Sierra Nevada region has a highland mountain climate, and the southern and southeastern regions have a tropical desert climate. Although there are multiple climates in the test area, the overall Mediterranean climate is still present due to the overall proximity to the ocean. In terms of rainfall, the northwestern part is near the coast and receives more rainfall. The central valley is a mountainous area with a high elevation and less rainfall. The southeastern desert region has no rainfall. Thus, it can be seen that the soil moisture in the test area will have significant differences both in space and time, and the experiment has strong feasibility.  
Figure 5: Overview of the study area for this study (32\({}^{\circ}\) N–39\({}^{\circ}\) N, 114\({}^{\circ}\) W–123\({}^{\circ}\) W), (**a**) experimental area in green, modeling stations in brown, and validation stations in gray; (**b**) land cover type of the experimental area.
# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 3 Proposed Electric Power Load Forecasting Methodology  
Section 3 describes the proposed medium- to long-term power prediction methodology. Figure 5 shows the simulation flow chart proposed in this study. First, for the data reading process, the original data from years 2017 to 2019 are read for medium- to long-term power prediction. Second, the original data are divided into data partitioning processes for training and testing. Finally, after the proposed DNN and LSTM function are performed, they are evaluated using the prediction error calculation formula. Section 3.1 describes the multivariate model collected over three years (2017-2019) for medium- and long-term electric load forecasting. Sections 3.2 and 3.3, respectively, describe the proposed DNN and LSTM, which are the most commonly used methods for time series prediction during deep learning.  
Figure 5: Proposed simulation flow chart.
## 2 Materials and Methods
### Subjects, Eligibility Criteria, and Cone-Beam Computed Tomography (CBCT)  
The study sample consisted of CBCT images at 5 stages (T0, pretreatment; T1, presurgery; T2, postsurgery; T3, posttreatment, and/or T4, retention; at least 1 year posttreatment) of 26 adult patients (11 males, 15 females; mean age, 19.6 \(\pm\) 2.8 years) who underwent orthognathic surgery at the Wonkwang University Daejeon Dental Hospital, in Daejeon, Korea, from July 2007 to December 2017.  
All of the subjects met the following inclusion criteria: (1) skeletal Class III relationship (ANB \(<\) 1\({}^{\circ}\)), (2) had undergone orthognathic surgery; BSSRO with or without Lefort I osteotomy, (3) CBCT images taken at T0, T1, T2, T3, and/or T4. The exclusion criteria included subjects with a severe TMD and asymmetry, and any craniofacial syndrome.  
The CBCT (PSR 9000N; Asahi Alphard Vega, Kyoto, Japan) images were taken in C-mode (scan size, 2003 179 mm; voxel size, 0.39 mm; field of view, 19.97 cm). The radiologic parameters were 80 kVp, 60 mAs, and 17 s scan time. The CBCT data were saved in digital imaging and communications in medicine (DICOM) files, and Invivo6 v6.0.3 software (Anatomage, San Jose, CA, USA) was used to analyze the DICOM data to generate quantitative measurements.  
Institutional review board approval to conduct the study was granted by Wonkwang University Dental Hospital (No. W2101/001-001) in Daejeon, Korea.
## 2 Methodology
### Evaluation Metrics  
#### 2.3.1 Metrics  
To evaluate the quality of the models, several metrics were used in this paper:  
* Precision: the proportion of true positives (TPs) among the predicted positives. It measures the model's ability to identify TPs without including false positives (FPs).
* Recall: the proportion of TPs among the actual positives. It measures the model's ability to identify all positives.
* F1 score: a commonly used comprehensive evaluation metric that combines precision and recall, measuring the performance of models comprehensively.
* Accuracy: the proportion of correctly predicted samples accounts for all samples. It is often used to evaluate the overall performance of models.
* Confusion matrix: an evaluation index is utilized to intuitively visualize the performance of models. Each column in the confusion matrix represents the predicted category, each row represents the true category.  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\[F1\ score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
The _precision_, _recall_, _F1 score_, and accuracy can be calculated by Formulas (1), (2), (3), and (4), respectively. _TP_ (True positive) indicates the number of positive samples correctly predicted; _TN_ (True negative) indicates the number of negative samples correctly predicted; _FP_ (False positive) indicates the number of negative samples incorrectly predicted as positive samples; _FN_ (False negative) refers to the number of samples that are actually positive but incorrectly predicted as negative.  
To evaluate the overall performance of the network model, this paper also introduces a macro-average F1 score (macro-F1 score) which is the arithmetic average of all categories based on the F1 score of each category. Precision, recall, F1 score, and accuracy reflect the model's performance from different visual angles. F1 score combines precision and recall and provides a more comprehensive evaluation of the model, while precision and recall measure a certain aspect of the model's performance, respectively. The macro-F1 score and accuracy evaluate the overall performance of the model. Therefore, F1 score, macro-F1 score, and accuracy are primarily utilized for evaluating the models.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
###### Abstract  
The recent release of standards for vehicular communications will hasten the development of smart cities in the following years. Many applications for vehicular networks, such as blocked road warnings or advertising, will require multi-hop dissemination of information to all vehicles in a region of interest. However, these networks present special features and difficulties that may require special measures. The dissemination of information may cause broadcast storms. Urban scenarios are especially sensitive to broadcast storms because of the high density of vehicles in downtown areas. They also present numerous crossroads and signal blocking due to buildings, which make dissemination more difficult than in open, almost straight interurban roadways. In this article, we discuss several options to avoid the broadcast storm problem while trying to achieve the maximum coverage of the region of interest. Specifically, we evaluate through simulations different ways to detect and take advantage of intersections and a strategy based on store-carry-forward to overcome short disconnections between groups of vehicles. Our conclusions are varied, and we propose two different solutions, depending on the requirements of the application.  
VANETs; dissemination; urban scenarios; broadcast suppression scheme; store-carry-forward +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Adapting a bandwidth-efficient information dissemination scheme for urban VANETs. In _Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information_; [PERSON], [PERSON], [PERSON], Eds.; Springer International Publishing: Heidelberg, Germany, 2015; pp. 72-83.
# Assessment of Imaging Protocol and Patients Radiation Exposure in Computed Tomography Colonography
## 4 Conclusions  
Patient dose during CTC has been assessed using two helical scanners, various imaging protocols, and CT machine settings. The variation in CT protocol results in an up to three-fold variation in patient-effective dose. The variation in the effective dose during CTC protocols is attributed to the high tube currents used in this protocol, ranging from 100 to 450 mA per CTC procedure. ATCM shows a significant increase in CTDI\({}_{\text{vol}}\) (mGy) (69%) and effective dose (mSv) (95%) compared to the manual tube current (mA) selection. This suggests the technologist's role to be invaluable and crucial in selecting a noise reference based on the patient weight and adjusting the tube current per slice to avoid the overexposure as seen in the use of ATCM protocol. The current patient-effective dose during CTC is greater compared to previously published studies. Individuals undergoing screening CTC have a greater risk of radiogenic cancer probability, arising from long-term repetitive exposures. Patient dose optimization is seen to be necessary in assuring patients receive the greatest benefit of the CTC procedure.  
Conceptualization, M.A., A.S., B.A.; methodology, M.A., R.M., A.A. ([PERSON]), and A.S.; software, M.A., R.M., A.A. ([PERSON]), B.A., D.A.B.; validation, M.A., A.S., H.A., A.A. ([PERSON]), and D.A.B.; formal analysis, M.A., A.S., R.M., A.A. ([PERSON]), B.A.; investigation, M.A., A.A. ([PERSON]), R.M., A.A. ([PERSON]), and A.S.; resources, B.A., A.A. ([PERSON]), H.A.; data curation, A.A. ([PERSON]), H.A.; writing--original draft preparation, M.A., R.M., A.A. ([PERSON]), B.M.A., D.A.B.; writing--review and editing, B.M.A., H.A., A.S., D.A.B.; visualization, M.A., A.S., D.A.B.; supervision, A.S., B.A., D.A.B.; project administration, M.A., A.S., B.M.A., D.A.B.; funding acquisition, M.A., A.S., B.A. All authors have read and agreed to the published version of the manuscript.  
Please add: This research was funded by The Deanship of Scientific Research, Prince Sattam bin Abdulaziz University, Grant number 2018/01/9398.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of King Fahad Medical City (protocol code FWA00018774/2019.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data available on request.  
The authors appreciated the support provided by the radiology department personnel at King Fahad Medical City, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
References
## 3 Results and Discussion
### Growth of S. aureus in Sourdough  
The growth curve (Figure 2) showed that the number of _S. aureus_ cells was \(10^{3}\) cfu/g after about 3 h of culturing, which was the limit set by China's national standard (GB19295-2011, China), and was about \(10^{6}\) cfu/g after 8 h of culturing and was kept constant thereafter, indicating that the cell growth was inhibited in this period.  
Many reports on the growth characteristics of _S. aureus_ in different food matrixes have been published. The level of _S. aureus_ in no-knead bread dough fermented by yeast has been shown to increase by 0.4, 1.1, 1.7, and 2.2 log cfu/g after 18 h culturing at 21 \({}^{\circ}\)C, 27 \({}^{\circ}\)C, 32 \({}^{\circ}\)C, and 38 \({}^{\circ}\)C, respectively [24]. The growth of _S. aureus_ in rice cake stored at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C reached the stationary phase within 24 and 16 h, respectively [9], whereas that in pork stored at 37 \({}^{\circ}\)C reached the stationary phase in about 14 h [10]. Additionally, the growth of _S. aureus_ in milk stored at 25 \({}^{\circ}\)C reached the stationary phase in 10 h [11] In this study, the growth of _S. aureus_ in sourdough (25 \({}^{\circ}\)C) reached the stationary phase in 8 h (Figure 2), indicating that _S. aureus_ could grow rapidly at the initial stage in wholewheat sourdough compared to rice cake, pork, and milk. This might be because LAB, including _L. curvatus_ and _Ln. citreum_, which are the heterofermentative species, in sourdough could effectively convert fructose into mannitol [25], which is conducive to the growth and proliferation of _S. aureus_.  
The highest _S. aureus_ cell number in sourdough was only about \(10^{6}\) cfu/g, which is 10 to 100 times lower than the numbers of _S. aureus_ in pork and milk, respectively [10; 11] This indicates that the growth _S. aureus_ in sourdough was inhibited after 8 h [26]. One characteristic of LAB is the ability to produce acid. This is beneficial to decrease _S. aureus_ risk, as the growth of _S. aureus_ is inhibited by low pH values. Furthermore, bacterios secreted by LAB (such as _P. pentosaccus_) can also inhibit the growth of _S. aureus_[27]. However, because the number of _S. aureus_ cells exceeding \(10^{5}\) cfu/g can lead to production of enterotoxins that can cause human diseases [11], the contamination by _S. aureus_ (even at a number as low as \(10^{2}\) cfu/g) in sourdough could thus pose risks.
## 5 Conclusions  
Integrated UAV imagery and the ResU-Net model (U-Net model with ResNet101 backbone) were used to describe the distribution of high-value Japanese oak crowns in an uneven-aged mixed forest. The RGB information of two UAV datasets, taken before and after the leaf color change of Japanese oak, was extracted to identify the optimal timing of UAV image collection.  
Our results showed that the ResU-Net model could be used together with both UAV datasets to detect Japanese oak in an uneven-aged mixed forest with reliable accuracy. In particular, more robust results were obtained with the UAV dataset taken after the change in leaf color (October UAV dataset). Our case study demonstrated that the ResU-Net model can be applied even with a small training dataset to map the distribution of high-value deciduous broadleaf tree crowns in a complex uneven-aged mixed forest. Data augmentation techniques were, however, not applicable to improve the individual tree segmentation results in the present study. Moreover, we also found that the importance of preparation of the validation datasets representing the full diversity and complexity of the whole sample datasets. These findings can therefore be a valuable contribution to forest management practices.  
Nevertheless, to achieve the full capacity of the ResU-Net model in identifying high value Japanese oak and to improve the classification results, preparation of larger training datasets should be considered in the future study. Application of UAV orthophotos at their original resolution rather than after resampling to a lower coarse resolution can improve the performance of the model. Furthermore, we will investigate the robustness of the ResU-Net model by comparing it with other deep learning models commonly used for the mapping of individual tree species distribution, such as Mask R-CNN. In addition, merging UAV images from multiple seasons will be evaluated, as it is expected to improve the performance of the ResU-Net model in detecting high-value tree species in uneven-aged mixed forests.  
Conceptualization, methodology, formal analysis, and writing--original draft preparation, N.M.H.; resources, supervision, writing--review and editing, and project administration, T.O.; review and editing, S.T. and T.H. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the collaborative research project entitled \"Single-tree management of superior Japanese oak in natural forests and data collection for the development of a method to select Japanese oak trees suitable for barrel wood\" between Suntory Spirits Ltd., Kitanihon Lumber Co., Ltd., and the University of Tokyo.  
Dataset in the present study will be available on request to the first author's email with appropriate justification.  
The authors gratefully acknowledge the technical staff of the University of Tokyo Hokkaido Forest (UTHF); [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], for collecting the UAV imagery and kind support during our field survey in Sub-compartment 68E.  
The authors declare no conflicts of interest.
### System Overview  
The proposed two-way ranging system measures the time-of-flight (ToF) between multi-antenna radio platforms, as depicted in Figure 1. The radio platforms operate with independent clocks; so, the measured time-of-arrival (ToA) is affected by both the propagation time \(\tau\) and the clock offset \(T\) between the platforms. Using each antenna on the platforms, we measure the ToF between each transmit-receive antenna pair, creating a spatial diversity that can then be used to estimate the relative position and orientation of the platforms. We can construct and solve (in closed form) a system of equations using \(\tau\) and \(T\) and their first derivatives. This closed-form solution is attractive for its computational simplicity and tractability but is sensitive to environmental conditions such as shadowing and fading that might disrupt one or more individual measurements. To mitigate these issues, we propose a family of EKF solutions that achieve comparable or better performance without increasing the computation time; we even demonstrate that the proposed second-order EKF solution reduces the computation time by an order of magnitude compared to its one-shot equivalent.