dataesr/scientific-paragraphs-categorization huggingface dataset:
A Python computer program was created to control stage movements and acquire images. Images were stored as bitmap les (. bmp) in one folder per experiment. After the experiment, the image folder was treated using OxyO software to create a le containing position information as a function of time. Stability and drift experiments were conducted by taking images at constant time intervals without introducing any voluntary stage movement. The vibration measurements were performed using an image maximum acquisition rate that ranged from 200 to 300 fps, depending on the illumination conditions and image size. The smallest image size required to obtain the nanoGPS position determination was determined using OxyO software. The trajectory provided by the OxyO software is expressed in the (O, X, Y) coordinate system of the scale.
It is often more convenient to express it in the coordinate system of the camera (U,V axes), as shown in Fig. 2. The transformation of scale coordinates into camera coordinates is performed by applying the rotation by an angle j z. As this angle between the scale and camera is determined by nanoGPS software, this is very simple. We applied a rotation to (X, Y) coordinates by the constant average angle < j z > to obtain the (U,V) coordinates. It can also be very relevant to provide experimental results in the stage coordinate system when investigating stage properties. Even though the stage and camera often have nearly aligned axes, accurate measurements require the determination of the residual angle q between the stage and camera. The angle [j z + q] can be easily determined by instructing the stage to perform a large displacement along the x stage direction and determining DY/DX = tan(j z + q).
Results The experimental results are organized as follows. First, we present investigations on the stability performance. Then, we assess the performance of some microscopy stages. Bidirectional reproducibility related to a single position, accuracy, and reproducibility over more elaborate trajectories are reported. Next, we investigate vibration issues. We show that vibrations are not only related to the external environment but can also be intrinsic to the operation of the microscope. We present additional results in the Supplementary Data showing how to quantify the turret reproducibility, how to determine the scale bar in a very simple way, and how angle measurements can be useful.
In this paper, the design of a small wind turbine emulator based on dSPACE 1104 system is presented. This system is composed of a DC motor driven by a buck converter coupled to permanent magnet synchronous generator controlled by a hardware-in-the-loop con guration based on the dSPACE 1104 board. The DC motor emulates the rotary movement due to wind energy, where all the characteristics of a real wind turbine will be taken into account. By means of this control system the effect of different values of wind speed and torque is simulated in MATLAB® / Simulink® software, in order to constitute a tool that allows the analysis and development of power converters. The results obtained demonstrated the e ciency of the proposed emulator, given that the behavior of the experimental bench was the same as that of the theoretical calculation results.
Author contributions Tarek Boutabba: Writing -Original draft preparation, Conceptualization, Methodology, Software. Said Drid: conceptualization and validation. Farouk Mechnane: Visualization, Investigation. Larbi Chri -Alaoui: Supervision. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. Power coe cient for different values of λ Figure 2 Power Coe cient for different models of wind turbines as a function of λ. Figure 7 the hardware setup of the system. Figure 8 The complete emulation platform with the power electronics interface and the MPPT algorithm. Figure 13 Wind turbine power Figure 15 Response of the rotor speed under with variable wind speed.
Isotopic niches of diadromous fishes Isotopic niche widths of diadromous species varied between species and life stages. Mean SEA_B [CI 95 ] was maximum for  A. anguilla (12. 314 [9. 853-15. 039]) and then decreased for P. flesus juveniles  (7. 675 [6. 251-9. 225   S3 ). Correction for sample size applied to SEA_B did not significantly alter the niche widths of diadromous species, but marginally for P. marinus (SEA_B = 0. 334, SEAc = 0. 086) (Table  S3 ). The isotopic niche of A. anguilla, which is the widest niche observed in this dataset (Figure  4 , Table  S4 ), overlapped with several other diadromous species as juveniles, such as P. flesus, S. trutta, P. marinus, and Mugilidae (Figure  5 , Table  S5 ). Juveniles of P. flesus, the second widest isotopic niche observed in this dataset, overlapped with sub-adults of A.
MitoSOX Red Staining After treatments, cultured CGNs were incubated with 1 µM MitoSOX™ Red reagent in HBSS with Ca 2+ and Mg 2+ for 10 min at 37 • C. At the end of the incubation, the cells were rinsed in PBS and fixed in 4% PFA (in PBS) for 20 min at 23 • C. Nuclei counterstaining was performed by Hoechst dye (0. 0002% v/v in PBS, 5 min, 23 • C) and slides were mounted with DPX reagent. To assess the specific staining of mitochondrial O 2 •-, the morphology of stained intracellular structures was verified by analyzing CGNs with a NikonEclipse Ni upright microscope with a 100× objective (Supplementary Figure  S1 ). For the quantitative analyses, images were acquired using a 40× objective and the red signal was quantified with ImageJ software developed at the National Institutes of Health  [38].
WB Analysis CGNs incubated in the presence or not of MPP + and GM1-OS were washed twice with cold PBS containing Na 3 VO 4 (1 mM) and lysed with Na 3 VO 4 (1 mM) PMSF (1 mM), aprotinin (2%), and IP (1%) in cold PBS. Proteins in equal amounts were denatured, run on 4-20% precast polyacrylamide gels by SDS-PAGE, and transferred onto PVDF membranes using the Trans-Blot ® Turbo™ Transfer System (Bio-Rad, Hercules, CA, USA). The presence of mTOR, mTOR P-Ser2448 , Akt, Akt P-Ser473 , GSK-3β, and GSK-3β P-Ser9 was determined by specific primary antibodies (diluted 1/1000 in 5% BSA in TBS-0. 1% tween), followed by reaction with secondary HPR-conjugated antibodies (diluted 1/2000 in 5% BSA in TBS-0. 1% tween). The data acquisition and analysis were performed using Alliance Uvitec (Eppendorf, Hamburg, Germany).
, 2021; Knuth et al. , 2023). Zhang et al. (2021)  introduce a complete workflow for processing specific historical aerial image datasets including the novel use of the Artificial Intelligence (AI) based feature matching method SuperGlue  (Sarlin et al. , 2020). These works depict the complexity of processing historical aerial image data and while providing comprehensive toolsets such as using AI, DSM coregistration and multi-temporal tie points, a general processing strategy for diverse datasets is not yet available. This is mainly due to the following persistent issues. Digitized historical (aerial) images often suffer from poor image quality resulting in close-to-zero correct feature matches  (Maiwald, 2019; Zhang et al. , 2021; Morelli et al. , 2022). Further, even when applying learned feature matching methods, the texture quality and visual changes, particularly in forested areas, remain challenging.
Another problem is the need for definition of hypotheses for every specific dataset. This includes assumptions such as that the topography is variable enough for automatic registration  (Zhang et al. , 2021; Craciun and Bris, 2022) , GCPs in the study area  (Giordano et al. , 2018)  exist, enough image information for finding distinctive features  (Feurer and Vinatier, 2018) , and flight information is provided  (Knuth et al. , 2023). Novel learned feature matching methods in combination with SfM are often able to initially process data without any assumptions. However, they are still challenging to use on historical aerial images. That is because of their original training procedure on mainly terrestrial data and low image resolutions  (Dusmanu et al. , 2019; Sarlin et al. , 2020; Tyszkiewicz et al.
, 2020; Sun et al. , 2021) , which also results in a lack of rotational invariance as reported in  Tyszkiewicz et al. (2020). Retraining the networks would require labeled historical aerial image datasets, high computational resources and the openly available training protocol, which is sometimes not accessible  (Sarlin et al. , 2020). Our approach provides a general solution for processing historical aerial images unlocking the potential of existing aerial image archives even for difficult datasets. The study deals particularly with the automatic detection and matching of tie points in challenging historical aerial image pairs. Finding these points enables a subsequent Structure-from-Motion (SfM) pipeline to model 3D geometry, including the estimation of camera parameters, in one single workflow.
We are using learned feature matchers based on deep neural networks due to the limited efficacy of conventional feature matching techniques applied to historical datasets. Specifically, we focus on a combination of two AI-based image matching strategies, i. e. , Su-perGlue  (Sarlin et al. , 2020)  and DISK  (Tyszkiewicz et al. , 2020) , while presenting strategies for dealing with high-resolution aerial images and rotations between subsequent image pairs. As this workflow initially operates in image space only, it avoids the need to make further assumptions while leveraging the potential of learned feature matching methods. With the aim of providing a complete workflow, all feature matches and images are imported into a open-source SfM software where all camera parameters and 3D points are calculated simultaneously using bundle adjustment.
Data 2. 1. 1. Congo dataset (1961)  The first dataset contains images which could neither be processed in conventional SfM software nor with the adaptations shown in several other works  (Feurer and Vinatier, 2018; Zhang et al. , 2021; Knuth et al. , 2023). Only limited information about the images is available. Flight plan, flight height, scan size, and the focal length of the camera were unknown. The site is located in the tropical rainforest and the images depict a network of rivers in Congo in Central Africa. All 31 images are taken in 1961 and the only external information is the fact that they are consecutively numbered from 176 to 192 (17 images) and from 222 to 231 (10 images) according to their positions in successive flight strips (Fig. 1 ). An overview of the data is given in the upper part of Table  1.
The original images have a resolution of 11 400 × 11 408 pixels. Resampling them, using the available fiducial marks, resulted in a final resolution of 10 500 × 10 500 pixels. Resampling was done using the ReSampFid function of MicMac which detects the center of the fiducial marks and aligns the images respectively  (Rupnik et al. , 2017). 2. 1. 2. Occitanie dataset (1971 1. 2. Occitanie dataset ( -2001) )  The second test site extends over an area of a 170 km 2 in the Mediterranean landscape in Occitanie in southern France (43 • 5N, 3 • 19E) (Fig. 2). The area of interest is mainly covered by vineyards with forests in its uppermost regions. The area exhibited a strong change in land use because vineyards transformed during the 1980s, from goblet to trellised vineyards, accompanied by progressive land abandonment and urbanization during the past 50 years  (Vinatier and Arnaiz, 2018).
The fact that these images were scanned with photogrammetric scanners is important in regard of the production of geometrically correct outputs as noted by  Sevara (2016). We used a sample of these data from four different epochs that allowed for a stereo coverage of the entire test site. The characteristics of the images are reported in the bottom part of Table  1. In order to test the different SfM approaches as if camera data is unknown, all scanning information and interior camera orientation parameters were initially omitted. In the post-processing step, when estimating the accuracy of the multi-temporal DSMs, the camera parameters were used as described in Section 2. 3.
Methods Two different methods were considered to process historical aerial images and especially to robustly find tie points between sequential image pairs. Both approaches have already shown good results on historical terrestrial images  (Maiwald, 2022; Morelli et al. , 2022; Maiwald et al. , 2023). The first method combines the tie point extractor SuperPoint  (DeTone et al. , 2018)  with the feature matching method Su-perGlue  (Sarlin et al. , 2020)  and will in the following be referred to as SuperGlue. The second method is DISK  (Tyszkiewicz et al. , 2020). Both methods are experimentally used to find mono-temporal tie points for the first dataset and mono-temporal and multi-temporal tie points for the second dataset. A short summary and explanation on the parameters and limitations are given in the following sub-sections.
SuperPoint+SuperGlue SuperPoint serves as method for the feature detection stage  (DeTone et al. , 2018). SuperPoint generates reliable feature points using a synthetic pre-trained dataset to train the so-called MagicPoint convolutional neural network. Because the model does not perform sufficiently accurate on real images,  DeTone et al. (2018)  use Homographic Adaptation to transform the input image to multiple random homographic representations. Subsequently, the feature points are calculated with MagicPoint for all representations and are eventually aggregated to one final set of SuperPoint features. Afterwards the extracted feature points are matched with Super-Glue. SuperGlue is a graph neural network designed for matching tie points by solving a differentiable optimal transport problem.
However, the methods come with several limitations especially regarding historical aerial images. The training procedure of SuperGlue is at the moment not openly available, which means that the user can only use the pre-defined configurations indoor and outdoor for feature matching. Sarlin et al. (2020)  use scenes from the MegaDepth  (Li and Snavely, 2018)  dataset to train the outdoor configuration which however mainly consists of urban scenes. Thus, the parameter configuration might not be optimal for our historical aerial images. We propose a modified outdoor configuration using a maximum edge length of 1600 pixels in SuperGlue in contrast to the default value of the outdoor configuration which is set to 1024 pixels. We use this value because it is recommended by  Sarlin et al. (2020)  to use images with a maximum image edge length of 1600 pixels.
If every image should be considered for finding tie points, the overlap has to be set to 100% respectively. SuperGlue is only rotational invariant up to approximately 45 •  (Tyszkiewicz et al. , 2020). We use this property to automatically detect the beginning of the following flight strip. If there are less than 50 geometrically verified matches between sequential images with a matching confidence higher than 0. 5, image 𝐼 𝑖+1 is rotated for 180 • and feature matching is repeated (Fig. 4 ). If the repeated image matching results in a significantly higher number of matches, a new flight strip has been identified. The found feature matches are stored in the Hierarchical Data Format (HDF) H5, which is readable and processable by the SfM software COLMAP.
DISK DISK uses reinforcement learning for extracting and matching a set of local features in an end-to-end trainable approach. Features are extracted using a U-Net architecture and their optimal matching is obtained by using the policy gradient method. Again, training is done on the MegaDepth dataset and not modified during the experiments. Usually, the approach finds less correct matched features than SuperGlue but provides a higher absolute number of detected feature points  (Maiwald et al. , 2021; Morelli et al. , 2022). DISK deploys a set of adjustable parameters. Images can be used at full size but also internally downsampled to a pre-defined width and height that has to be a multiple of 16. In our experiments we used resolutions of 1024 × 1024, 1600 × 1600, and 3200 × 3200 pixels. Larger image sizes are not possible on the NVidia A100 GPU, used in this study, due to memory limitations.
COLMAP COLMAP (https://github. com/colmap/colmap, accessed on 03 February  2023 ) is an open-source SfM software that can be modified enabling, e. g. , the import of calculated feature matches  (Schönberger and Frahm, 2016). In this study, COLMAP serves as a tool to simultaneously estimate interior and exterior camera orientation (if unknown) and to generate a sparse point cloud representing the 3D coordinates of the tie points. However, similar to the feature matching methods, COLMAP is mainly designed to reconstruct urban landscapes and less to process aerial images. Thus, some modifications need to be made. For instance, if the principal distance (focal length) 𝑓 is known, it should be set as prior focal length to achieve valid results and if it is unknown it should still be initialized with a reasonable estimate, e.
Proposed workflow using a combination of methods For maximum efficiency a synergetic workflow is proposed using the contrary properties of both AI-based image matching methods (Fig. 5 ). Step 1: Data preprocessing: For faster preprocessing the images are downsampled to a maximum edge length of 1600 pixels using bilinear interpolation while maintaining the aspect ratio. Afterwards, our workflow proposes three different strategies for reliably finding feature matches in historical aerial images. This includes the default approach using a combination of SuperGlue and DISK, the computationally expensive but feature-rich tile-based approach, and the approach for texture-less images using DISK exclusively. Step 2: Determination of image rotation: To determine the image rotation, all strategies start with the use of the default SuperGlue feature matching method on the downsampled images because SuperGlue usually outperforms other learned matchers even on difficult datasets  (Maiwald, 2022; Morelli et al.
, 2022). If more than 50 feature matches with a 𝑚𝑎𝑡𝑐ℎ_𝑐𝑜𝑛𝑓 𝑖𝑑𝑒𝑛𝑐𝑒 > 0. 5 are found between sequential images, SuperGlue can be used to identify consecutive flight strips as explained in Section 2. 2. 1, Fig. 4. If SuperGlue is not able to find a sufficient number of feature matches, especially in textureless scenes like for the Congo dataset, DISK is used instead to determine the image rotation in the downsampled images. Step 3: Feature matching: Using the correct rotation, feature matching can be applied on the fully sized historical aerial images. Therefore, we propose two different strategies. If DISK was used to find feature matches (because SuperGlue failed), it should also be applied on the images with full resolution (Fig. 5 , DISK approach). In our experiments a parameter setting with an image resolution of 3200 × 3200 pixels and a maximum allowed number of feature matches 𝑛 = 32768 provided the most reliable results.
Step 4: Bundle adjustment: The potentially rotated full-resolution photographs are imported in COLMAP and the image geometry is calculated using the feature matches. If interior camera orientation parameters (IOPs) are known a-priori, they will be imported. The bundle adjustment in COLMAP is performed allowing two-view tracks eventually resulting in the estimated interior and exterior camera orientation parameters (EOPs) for the historical aerial images and in a sparse point cloud. The source-code to our introduced automatic workflow to match historical aerial images, including an experimental data sample is available at https://github. com/tudipffmgt/HAI-SFM. The final compilation of all tested and compared methods on the respective datasets can be seen in Table  2.
Accuracy assessment Giving reliable estimates about the accuracy of historical data often proves to be difficult due to missing reference data also being visible in images of the past. Only the Occitanie dataset was explored to assess the accuracy of scaled 3D models because it was not possible to identify stable reference points in the Congo images. The area with the largest image overlap was considered to ensure a comparison across all four epochs (Fig. 6 ). Multiple procedures of accuracy assessment were carried out. Reprojection error and tie point accuracy: The interior orientations, camera poses and sparse point clouds derived from our workflow were transferred from COLMAP to Metashape for further analysis. Afterwards, the quality of our introduced SfM process was estimated using stable objects that were visible in all epochs.
These points were measured manually in the images of all epochs and then compared to the corresponding projected image point. This allowed the visualization of the tie point accuracies and the calculation of the mean reprojection error across multiple epochs. Multi-temporal comparison of scaled models: The model of the four epochs from Occitanie was scaled by introducing one control scale (Scale 1) into the adjustment of the camera alignment. During the alignment, the given flight protocol was also considered. For the independent estimation of the accuracy of the refined, i. e. , scaled model, an additional check scale (Scale 2) was used. The 3D point coordinates to calculate the scales were obtained as UTM coordinates from the French geoportal (https://www. geoportail. gouv. fr/carte). The image measurement of the scales in the historical images were performed using the dataset from 2001 to ensure using points in the dataset with the smallest likelihood of change until recently.
Results and discussion We use the Congo dataset to introduce our developed approach as a proof-of-concept regarding the possibility of leveraging learned feature matching methods (trained on urban, terrestrial data) for historical aerial images. The Occitanie dataset is used to assess the capabilities of our workflow to match images taken at different points in time (inter-epoch) simultaneously with images taken during the same epoch (intra-epoch). This dataset is also used to provide insights about the accuracy of the aligned dense point cloud, which is relevant to assess the suitability of our workflow for multi-temporal change detection applications. The Occitanie dataset is the least challenging in our study. Hence, the standard configurations of the SuperGlue and DISK methods could be applied.
Results in COLMAP Using this optimal configuration for DISK, the Congo dataset is processed in COLMAP to obtain the camera parameters and a sparse point cloud. It is recommended to only estimate one single camera model during intra-epoch reconstruction as we assume sufficient stability of the aerial cameras. Using multiple different camera models leads to strong variations of the estimated focal lengths and consequently of the flight heights (camera height). Additionally, it causes strong dome effects of the models due to overparameterization, especially considering the limited image overlap of the historical data  (Eltner and Schneider, 2015). With our method all images could be used in a single SfM workflow, producing a DSM of this region. Furthermore, the flight route of two connected flight strips and one single flight strip not connected to the others was automatically reconstructed in two separate COLMAP models (Fig.
Occitanie dataset: Intra-epoch and inter-epoch feature matching Feurer and Vinatier (2018) and  Zhang et al. (2021)  already processed the Occitanie dataset and evaluated their results using the Time-SIFT method and the DSM-based approach respectively. However, it is of high interest if our adaptations and the use of learned feature matching method exclusively in image space are also capable of processing these mono-temporal and multi-temporal data in one single workflow. Thereby, we could show that the default method of SuperGlue is already capable of finding reliable inter-epoch and intra-epoch tie points (see comparison of all methods in Fig. 14 ). Without further modifications, COLMAP generates several reconstructions for a different number of images. However, a single reconstruction using all images could not be calculated.