esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Identification of Sensitive Features
#### 4.2.2 Building Areas  
During the rainy season of 2021, the above methods identified a total of 875 houses that showed highly sensitive surface deformation, with almost equal numbers of houses with anomaly settlement and anomaly uplift. These houses are mainly located in the old and densely populated urban areas of the Guancheng and Erqi districts, and most of them are abandoned and old houses (Figure 7).  
Figure 6: (**a**) Shows the apparent error anomaly detection image region, (**b**) shows the cumulative shape variable change for all PS points in the sampling box in (**a**), the gray background is the rainy season period of the year, and the black boxes in (**b**) mark the points with apparent anomalous shape variable values during the rainy season, different colors represent the cumulative surface deformation of all PS points at different positions within the sampling area, the red line indicates the occurrence of surface deformation anomalies at the corresponding time point.  
In order to observe the identification of highly sensitive buildings more intuitively, another four highly sensitive buildings are randomly selected to observe the cumulative surface deformation changes at their respective nearest PS points, and the results are shown in Figure 8. House a is located at the intersection of Longhai Expressway and Yingxiang Road (Figure 8a), the building is a colored steel tile house (Figure 8e), and it is nearby bare ground under construction. This kind of house has no foundation and is easily subject to the settlement phenomenon by rainwater in the rainy season, and three anomaly settlements occurred during the rainy season in 2021 (Figure 8i). House b is the Zhengzhou Book Purchase Center (Figure 8b), a commercial building built in 1999, located in the busy downtown area, with many urban underground space projects and building construction projects nearby, and its own underground structural condition is easily affected (Figure 8f); similarly, three anomaly settlements occurred during the rainy season in 2021 (Figure 8i), and three anomaly settlements occurred during the rainy season in 2021 (Figure 8i). House c is a dwarf layer building next to Building 1 of the Family House of the Chinese Medicine College (Figure 8c), which is surrounded by dense old buildings, and it can be seen from Figure 8g that the structure of this dilapidated brick house is not very stable anymore; with the poor infrastructure and drainage capacity of these old urban areas, the house experienced three anomaly uplift phenomena in the rainy season. House d is similar to house c (Figure 8d), which is also a dilapidated building in the old urban areas, and it already has obvious cracks on its walls (Figure 8h), indicating that its foundation structure has been unstable for a long time. According to the actual situation of these sampled buildings, we verified the identification ability of potential damaged buildings, and the scheme proposed in this paper works well in terms of identifying surface deformation-sensitive buildings.  
Figure 7: Distribution of highly sensitive housing.
# Novel Methodology for Condition Monitoring of
## 8 Conclusions  
This paper presents a novel methodology based on noninvasive monitoring for assessing different levels of uniform wear in a gearbox transmission system. There are four important point that must be highlighted in this new proposed methodology. The first is the reliability of using infrared images and the statistical features of hot spots, since they allow us to obtain a more suitable characterization of the operation of the gears in terms of thermal behavior, due to the fact that the temperature increase in the gears depends on the work performance of the uniform wear condition of the gears. The second is the implementation of dimensionality reduction strategies for the optimization of the set of statisticalfeatures estimated from the infrared imaging, since it allows the elimination of less discriminant features and the compression of more significant statistical features. The third is the classification based on an artificial neural network, which is capable of identifying and classifying the different case studies proposed in this paper for the study of uniform wear in the gears of the gearbox transmission system. The fourth is the comparison that is made between infrared imaging and vibration signals to determine the reliability in the results for the diagnosis of failures, obtaining 100% of the total classification index when applying the novel methodology using infrared imaging, due to the good performance that was obtained both in the training and validation of the classifier. Meanwhile, for the novel methodology with vibration signals, a classification index of 81.2% was obtained.  
The obtained results indicate that the proposed condition monitoring strategy is suitable to be applied in the assessment of gearbox transmission systems in industrial applications, where the use of invasive sensors represents a critical limitation. Further research will implement an online diagnosis methodology for assessing faults in gears and other elements of the gearbox transmission system.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [PRODEP] and CONACYT [434850].  
The authors declare no conflict of interest.
# Metal Artifact Reduction in X-ray CT via Ray Profile Correction
## 4 Conclusions  
We present a new method for metal artifact reduction (MAR). It assumes that a prior CT scan taken before implanting the metal objects into the patient is available. Using this prior scan and a segmentation or a model of the metal implant, we utilize a novel ray profile correction scheme that computes an accurate estimate of the rays traversing the projection regions affected by the metal artifacts. Our experiments with clinical CT data indicate that the proposed method can clearly reveal the placements of implanted pedicle screws that were ambiguous before due to the significant metal artifacts.  
Future research will extend this work along three directions. Firstly, we will further investigate the behavior of the phenomenon of beam hardening for better metal artifact region prediction. Secondly, using a CAD model available for commercial pedicle screws in Figure 9, we believe a better localization of the implanted metal can be accomplished, leading to a better ray profile correction. Lastly, we also believe that utilizing existing algorithms that can reveal features hidden by metal artifacts in conjunction with a MAR method will be able to help resolve the occasional blurring effects around metals. Finally, we also plan to conduct more clinical case studies which will contain a wide selection  
Figure 8: Case Study II. From top to bottom, transverse, coronal, and sagittal views. (**a**) uncorrected images, (**b**) corrected images, (**c**) uncorrected - corrected (**a,b**).  
of different implanted metals and different amounts of metal artifacts as well as through comparison studies with other existing methods in terms of restoration quality and time performance.  
**Author Contributions:** [PERSON] and [PERSON] share the overall idea and collaborated in writing this paper. The first author did the implementation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially supported by NSF grant IIS 1527200 and the Ministry of Science, ICT and Future Planning, Korea, under the \"IT Consilience Creative Program (ITCCP)\" supervised by NIPA. We also thank Medtronic, Inc. for the data and support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 2 Solar Radiation Mapping Using GISs
### Solar Radiation Estimation for Stations with No Solar Radiation Records  
Because some nations have limitations on the number of stations recording solar radiation, due to the expense of acquiring data using precise sensors at all sites, the extent to which the accuracy of solar radiation mapping can be improved is limited. Therefore, attempts have been made to predict solar radiation at stations without solar radiation data by using the solar radiation measurements of other stations. This approach has the advantage that the accuracy of a specific point can be improved over that achievable using a mapping method in which spatial prediction is only performed via interpolation.  
Figure 1: Locations of seven abandoned mine promotion districts in Korea (modified from [PERSON] et al. [8]).  
ANN is used to perform training in areas where data are present and to perform testing on areas without solar radiation records. It is important to know which input variables other than solar radiation are used for training. Variables such as average temperature, average relative humidity, average sunshine duration, average wind speed, average precipitation, longitude, latitude, and month of the year can be considered to estimate the monthly solar radiation [15]. To increase the accuracy of ANN, a multi-regression analysis and a genetic algorithm can be combined to develop a monthly average daily solar radiation (MADSR) estimation model for locations without measured data [16]. This method can also be combined with the Kriging interpolation to improve the prediction accuracy [17; 18].
## 2 Methodology
### Selection of the Study Area  
The research focuses on the southeastern region of the Brazilian state of Goias, characterized by climatic homogeneity in terms of precipitation. Within this region, five specific sites, each spanning 15 \(\times\) 15 km in size, were chosen for the sampling of pastures exhibiting varying degrees of degradation (Figure 2). As per the Atlas of Pastures [41], the state of Goias encompasses approximately 14 million hectares of pastures, with the selected study sites accounting for around 53,000 hectares.  
In this savanna environment, the dry season (May to September) is marked by very low precipitation values (Figure 2). In the rainy season (October to April), rainfall is mostly concentrated between December and March. In contrast to the 20-year long-term average (2001-2021), the year 2022, during which the SuperDove images were captured, experienced reduced monthly precipitation levels at the studied sites. This discrepancy was particularly pronounced during the rainy season, notably in March and April. Meteorological station measurements at the five designated sites or municipalities corroborated this observation.  
Figure 1: Summary of the methodology used in the current work to discriminate pasture degradation with SuperDove satellite constellation data.
## 3 Results
### Comparison with a Different Benchmark Dataset Used by DeepTP
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline
**Method** & **Year** & **Feature \({}^{\star}\)** & **Evaluation \({}^{\text{b}}\)** & **ML \({}^{\text{c}}\)** & **FS \({}^{\text{d}}\)** & **Dimension** & **ACC** & **Sn** & **Sp** & **MCC** & **AUC** \\ \hline [PERSON]’s method [4] & 2008 & AAC & SCV & NN & None & 20 & 0.8940 & 0.8240 & 0.9300 & — & — \\ [PERSON]’s method [5] & 2011 & AAC, DC & JCV & SVM & ANOVA & 30 & 0.9327 & 0.9377 & 0.9269 & — & — \\ [PERSON]’s method [7] & 2011 & TCP, AAC & JCV & SVM & Genetic & 30 & 0.9593 & 0.9617 & 0.9569 & 0.9187 & — \\ [PERSON]’s method [6] & 2011 & AAC, DC & JCV & SVM & IFFS & 28 & 0.9390 & 0.9380 & 0.9410 & — & — \\ [PERSON]’s method [8] & 2016 & AAC, DL, PA & JCV & SVM & None & 460 & 0.9353 & 0.9850 & 0.9564 & 0.8600 & — \\ [PERSON]'s method [5] & 2019 & ADC, CBKSAGAG & 10 CV & VA & MRMD & — & 0.9303 & — & — & — \\ [PERSON]’s method [2] & 2020 & PC, ACC, RD & 10 CV & SVM & MRMD & 119 & 0.9602 & 0.9585 & — & — & — \\ SAFFHIRE [11] & 2022 & CTD, PAAC, & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \hline \end{tabular} \({}^{\star}\)AC: amino acid composition: DC: Dipeptide composition: PC: Physics chemical features: CTD: composition transition and distribution: EC: evolutionary information; Pey’s acid association: CCSAGAG: composition of k-space and carbon group muscle: DDG: Dipeptide addition from expected mean: ACC: auto-cross covariance: RC: reduced dipole; APACA: amphiphilic pseudo amino acid composition: PASC, pseudo amino acid composition: PASC, position-specific scoring matrix based on the composition: PKL: PSST: position-specific scoring matrix based on the matrix transformation: (SOS: quasi-sequence order descriptor: \({}^{1}\)SCV: Solid-cross-validation: ICC: total cross-validation: CV); k coordinate cross-validation: ML: machine learning method: VA: voting algorithm of LNSGG: \(\epsilon=2.9\); random committee and PARI: AAC, LNSG (ARI) parameters and Logistic of DDG: Simple Logistic of TDC and Multi-class classifier of CSSAGAG: PL: ensemble learning, contains LR, PLS, RF, SA, SVML, SVMBR: \({}^{2}\)S: feature selection, ANOVA: analysis of variance: IPFS improved forward floating election; MRMD: Max Relevance Max Distance; GA-SAR genetic algorithm utilizing self-assessment-report.  
\end{table} 
Table 3: Comparison of our model and previous models on cross-validation.
# A Metal Detector for Clip Location Tracking of Stomach and Colon Cancer during Laparoscopic Surgery
###### Abstract
More specifically, it is most effective by detecting the clip within 5 s. The next important factor is the ability of the detector to determine the location of tumors in both the stomach and colon. Detection of tumor in the stomach is extremely difficult because the stomach is made of a thick mucous membrane that poses difficulty in the transmission of magnetic field energy for tumor detection. Lastly, the module that detects the clip is directly attached without any wire. In other words, the technology of integrating the sensing module into the clip is crucial. Considering these three requirements, further extensive research is required to improve this technology. In this study, a clip-detector for tumor location determination using a magnetic coupling technique is proposed. The proposed clip-detector with the magnetic coupling method is inexpensive and features less side effects than the existing methods, and it does not necessitate high proficiency for practical implementation.
## 5 Results and Analysis
### Evaluation of Landslide Susceptibility Maps  
We used the ROC curves for the training and validation datasets to evaluate the machine learning models. The probabilities of landslides calculated for the training and validation datasets provide measures of the performance, and prediction accuracy of the models, respectively [11]. The \(x\)-axis, and \(y\)-axis of the ROC curves are, respectively, the sensitivity and 100-specificity indices. The performance and prediction accuracy of the three models are shown in Figure 7a,b, respectively. The performance of the SVM model is slightly higher (AUC = 0.988) than that of the BLR (AUC = 0.985), and ADTree (AUC = 0.977) models. The prediction accuracy of SVM is also slightly higher (AUC = 0.984) than that of the BLR (AUC = 0.980) and ADTree (AUC = 0.977).  
Figure 6: Landslide susceptibility maps generated by (**a**) SVM with the quantile method, (**b**) SVM with the natural break method, (**c**) SVM classified with the geometrical interval method, (**d**) BLR with the quantile method, (**e**) BLR with the natural break method, (**f**) BLR with the geometrical interval method, (**g**) ADTree with the quantile method, (**h**) ADTree with the natural break method, and (**i**) ADTree with the geometrical interval method.
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 6 ODI vs. MRR Qualitative Comparison
The required matching can be accomplished by means of electrically-driven micro-electromechanical system (MEMS) microactuators, which control the gap by deforming the straight waveguide against the ring [82]. The ring, waveguide and MEMS actuators can be patterned with standard optical lithography, thus rendering the fabrication of the whole system technologically feasible. Temperature sensitivity:  
Although the strong thermo-optic effect of materials used as a platform for constructing the considered filters allows one to efficiently tune them, it is also responsible for making them prone to temperature fluctuations [83]. This susceptibility may cause the bias point of the ODI and the resonance wavelength of the MRR to drift, which in turn could affect the process of suppressing the red-shifted spectral components without significantly impairing the input data carrier and eventually leading to temperature-dependent performance degradation. Although it was numerically verified above that the MRR is, by its principle of operation, more tolerant to detuning offsets than the ODI, still, special care must additionally be taken in order to enhance the resilience of both schemes to possible temperature changes while at the same time achieving this goal in an energy-efficient manner. To this aim, it has been identified [83] that while athermal operation of the ODI is possible without any extra energy consumption, there is no efficient passive solution to the problem for the MRR, as well. Furthermore, the exploitation of negative thermo-optic materials that could resolve the thermo-optic issue in a straightforward manner faces challenges with regard to compatibility with standard CMOS technology and is the subject of intense research. Integration potential:  
Both schemes are amenable to integration, which is highly desirable for keeping size and IL small while enabling interconnection even at a small scale level. Thus, both are compatible with planar light wave technology and can be co-packaged with the SOA in the same module in a monolithic (ODI) [80] or hybrid (MRR) [67; 84] platform. Especially, the capability for co-packaging is rather crucial towards compensating for the reduction in the amplified signal level due to notch filtering and hence for extending transmission reach. Since the MRR can be made more compact than the ODI [83; 85], the use of the former could be favored for mitigating the SOA pattern effect in applications where space availability is critical, as in the transmitter side for power boosting, especially of multiple wavelength outputs, or the receiver end for power preamplification, or within intra-data center optical links [85]. On the other hand, it should be noted that both filters are compatible with standard CMOS (complementary metal-oxide-semiconductor) processing in silicon photonics platform [85; 86] and hence can leverage the benefits that accompany this mature fabrication technology.
###### Abstract  
Due to their flexible deployment and movement capability, unmanned aerial vehicles (UAVs) are being utilized as flying mobile edge computing (MEC) platforms, offering real-time computational resources and low-latency data processing for a wide range of applications. This article aims to explore a UAV-assisted MEC system where multiple UAVs provide MEC services to mobile devices (MDs) using an ellipsoidal trajectory. Depending on the position, size, and orientation of the ellipsoidal trajectories, the coverage area of the UAV, the energy consumption, and the task transmission latency of MDs change. This has rarely been investigated in the existing works. Furthermore, unlike other studies, we consider that each MD has varying task offloading rates, which, together with varying user densities, makes the problem more challenging. Therefore, we formulate an optimization problem that finds the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectory of UAV-assisted MEC servers, to minimize the total transmission latency and energy consumption of mobile devices while taking into account the required data transmission rate, task transmission time, and energy consumption constraints. Then, we transform this optimization problem into a Markov decision process and propose a deep Q-learning-based ellipsoidal trajectory optimization (DETO) algorithm, to resolve it. The results from our simulations demonstrate that DETO efficiently computes the optimal position and trajectory for each UAV, and can achieve better performance compared to other baselines, leading to the reduced data transmission latency and energy consumption of mobile devices across a range of simulation scenarios.  
unmanned aerial vehicle (UAV); mobile edge computing (MEC); task offloading; UAV ellipsoidal trajectory; deep reinforcement learning; user association +
Footnote †: journal: Applied Sciences
## 2 Results  
### Features of the Complete Plastid Genome, nrDNA, and SNPs Sequences  
The 28 newly assembled complete plastid genomes of _Clematis_ ranged from 159,601 bp (_C. tubulosa_) to 159,775 bp (_C. stans_ var. _austrojaponensis_) (Figure S1, Table S1). The GC content varied from 37.9% to 38%. The length of the single large copy (LSC) regions ranged from 79,377 bp (_C. tugetorum_) to 79,559 bp (_C. tubulosa_ var. _ichangensis_); the lengths of the inverted repeat (IR) regions were from 30,875 bp (_C. tubulosa_ var. _ichangensis_) to 31,086 bp (_C. tusugetorum_), and the lengths of the small single copy (SSC) regions were from 18,045 bp (_C. subumbellata_) to 18,300 bp (_C. tubulosa_ var. _ichangensis_). The complete plastid genome of _Clematis_ included 112 unique genes consisting of 79 protein-encoding genes, 29 transfer RNA genes, and four ribosomal RNA genes. Among all the unique genes, 18 genes had introns, and 25 genes were in the IR region (Table S1). Gene number and organization of the newly sequenced plastid genomes were identical across _Clematis_ species.  
In this study, nrDNA sequences of 38 _Clematis_ samples were newly assembled. The nrDNA regions were 6469-6497 bp in length, including the external transcribed spacer (ETS) at one end and the non-transcribed spacer (NTS) at the other. Within the nrDNA region, the 188 rDNA was 1810 bp without length variation; the length of ITS1 was 176-186 bp; that of the 5.88 rDNA regions was 161-162 bp; that of the ITS2 was 236-237 bp, and that of the 26S rDNA was 3356 bp (Table S2). GenBank accession numbers of all the newly generated plastome and nrDNA sequences are presented in Table S3.  
The draft genome and the reference genome of _Clematis_ were assembled by our previous study [60]. The size of the _C. brevicaudata_ draft genome was 7.81 Gbp, and after removing the duplicate and low-coverage regions, the size of the reference genome was 616 Mbp. Using this reference, we mapped the genome skimming data of all 71 samples to obtain the SNPs data. Finally, we obtained a 1,186,844 bp Geneious-0.05 MS dataset for phylogenetic analysis. The proportion of missing data in sect. _Naravelia_ was significantly higher than that in other species. A high missing data rate was also found in SNPs of _C. fusca_ due to its low-depth (less than 0.5\(\times\)) genome skimming data (Table S3).