esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 2 Push-Out Test for Shear Performance Analysis  
### Test Specimens  
Two groups of specimens are designed according to the current codes in China (GB50917-2013 [22]). One is VS style another is HS style; each style has 3 layout forms and 3 specimens of each form were repeated. The longitudinal length is 500 mm. The major difference between them is the layout of the shearing studs, as shown in Figure 2. There are 3 specimens in each group and 4 rows of shearing studs in each specimen, as shown in Table 1. In the notation of the specimens, VS indicates the conventional specimen with vertical studs, and HS14 indicates the prefabricated specimen with horizontal studs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\begin{tabular}{c} **Stud Diameter** \\ **(mm)** \\ \end{tabular} } & \multicolumn{2}{c}{**Compressive Strength of Concrete**} & \multirow{2}{*}{\
\begin{tabular}{c} **Tensile Strength of** \\ **Steel Plate** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Stud** \\ **Position** \\ \end{tabular} } \\ \hline VS4-1 & & & & vertical \\ VS4-2 & & & vertical \\ VS4-3 & & & vertical \\ HS14-1 & 10 & 62.1 & 428.8 & horizontal \\ HS14-2 & & & horizontal \\ HS14-3 & & & horizontal \\ \hline \hline \end{tabular}
\end{table}
Table 1: Push-out test specimens.  
Figure 2: Sectional dimensions of test specimens: (**a**) HS14; (**b**) VS4. Units: mm.
# Three-Dimensional Reconstruction-Based Vibration Measurement of Bridge Model Using UAVs
## 4 Results
### Comparison of Homography Transformation and 3D Reconstruction  
Figure 14 shows the displacement obtained by the two correction methods. The displacement curves are extremely similar to each other, and their maximum amplitudes are about 9 mm. Therefore, for the case that the reference points and the target point are coplanar (\(Z_{w}=0\) mm), the results of the homography transformation method and the 3D reconstruction method are consistent.  
The following context will discuss the case in which the reference points and the target point are in different planes.  
By moving the fixed objected 1 m towards the UAV camera, the reference points and the target point are non-coplanar (\(Z_{w}=1000\) mm). Figure 15 shows the 6 DOF UAV motion in this case. As demonstrated in Figure 16, the correction result of the homography transformation is entirely unacceptable, while the 3D reconstruction method is still effective. As the displacement PSD shows in Figure 17, the first natural frequency obtained by the proposed method is 3.927 Hz, which has a relative error of 0.33% compared to 3.940 Hz, while the one obtained by the homography-based method is 0.103 Hz.  
Figure 14: Comparison of displacement corrected by 3D reconstruction and homography methods (\(Z_{w}=0\)).  
Figure 13: Comparison of PSD from 3D reconstruction and homography methods (\(Z_{w}=0\)).  
Figure 15: Six degrees of freedom motion of the UAV (\(Z_{w}=1000\)).  
By moving the fixed object 1 m behind the bridge model, the distance between the plane of reference points and the plane of the target point is \(Z_{w}=-1000\) mm. Figure 18 shows the 6 DOF UAV motion in this case. The correction results of the two methods are also compared (Figure 19). In this case, the homography transformation method fails again, while the 3D reconstruction method is still effective. As shown in Figure 20, the obtained first natural frequency by the proposed method is 3.966 Hz, which has a relative error of 0.66% compared to 3.940 Hz, while the one obtained by the homography-based method is 0.044 Hz. Finally, Table 1 shows the frequencies obtained by two methods.  
Figure 16: Comparison of displacement corrected by 3D reconstruction and homography methods (\(Z_{w}=1000\)).  
Figure 17: Comparison of PSD from 3D reconstruction and homography methods (\(Z_{w}=1000\)).  
Figure 18: Six degrees of freedom motion of the UAV (\(Z_{w}=-1000\)).
## 2 Material and Methods
### Airborne Hyperspectral Measurements  
Six flight lines were acquired between 09:50 to 10:46 and 13:10 to 13:55 on 11 July 2015, with HyPlant flying at an altitude of 690 m, resulting in a spatial resolution of 1 \(\times\) 1 m per pixel. The measurements were acquired during cloud-free conditions. The HyPlant sensor, composed by the two push-broom sensors, was installed on a Cessna Grand Caravan C208B (a turboprop aircraft) owned and operated by CzechGlobe, Czech Republic. The first broadband dual-channel module (DUAL) was able to capture surface reflected radiance with a spectral resolution of 3 nm in the visible and near-infrared (VIS/NIR) regions, and about 10 nm in the short-wave infrared (SWIR) region, covering the total spectral domain between 370-2500 nm. The second narrow-band spectrometer (FLUO module) provided a high spectral resolution of 0.25 nm (Full Width at Half Maximum, FWHM) and quasi-continuously covered the red and far-red region of the electromagnetic spectrum ranges from 670 to 800 nm. The high-resolution FLUO module covered red and far-red regions of the electromagnetic spectrum, which allowed retrieving SIF close to the two atmospheric oxygen absorption bands O\({}_{2}\)A (760 nm, F\({}_{760}\)) and O\({}_{2}\)B (687 nm, F\({}_{687}\)).  
The radiance images from DUAL and FLUO module were generated through a dedicated pre- and post-processing chain described in [PERSON] et al. (2016) and [PERSON] et al. (2018). Atmospheric and topographic corrections were carried out with the ATCOR (Atmospheric & Topographic Correction model, ReSe Applications Schlapfer, Langeggweg, Switzerland) to obtain Top-of-Canopy (TOC) reflectance and radiance values. Afterward, all the images were geo-rectified using CaliGeo toolbox (SPECIM, Oulu, Finland). Technical details about HyPlant, sensor calibration, image pre-processing, and post-processing, as well as sensor validation, are described in [PERSON] et al. (2016).
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 2 Early Endosome-to-Golgi Transport of STx and STx1
### Cytoskeletal Factors  
The early endosome-to-Golgi transport of STxB/STx1B also requires the actin and microtubule cytoskeleton, the dynein motor, the Rho GTPase Cdc42, and the Cdc42 GTPase-activating protein (GAP) ARHGAP21 [38; 39]. Indeed, transport of STxB/STx1B from the endosomal compartment to the Golgi is blocked by acute nocodazole treatment that disrupts microtubules, cytochalasin D treatment that disrupts the actin cytoskeleton, expression of constitutively active or dominant-negative mutants of Cdc42, siRNA-mediated knockdown of Cdc42, or short hairpin RNA (shRNA)-mediated knockdown of ARHGAP21 [38; 39].
## 3 Joint Simulation Process and Modelling of Vibration and Circuit
### Design of Array Energy Acquisition Circuit  
In this energy acquisition system, the energy of the vibration generator came directly from the relative motion between the coil and the permanent magnet fixed on the bridge. The power output of a single generator is very small; however, it has good scalability and can be expanded from multiple generators into an array form, as shown in Figure 1. In this research, four groups of generators constituted the 2 \(\times\) 2 array. These generators were connected in a series to increase the output voltage.  
Tina-TI software was used for the circuit design and simulation. In the circuit design, the electromotive force generated by the coil was equivalent to the series connection of a voltage generator and a resistance, and the resistance in the series is used to represent the internal resistance of the coil. Since the electromotive force signal generated by the coil was an alternating current, a voltage source and battery pack were used as the signal source. The common full bridge rectifier was adopted, and the large capacitor was used as the energy storage element. A voltmeter was set at both ends of the capacitor, and the voltmeter can also be replaced by an oscilloscope.  
Each coil voltage signal in the array was rectified first and then connected in a series to both ends of the capacitor. The designed circuit is shown in Figure 9. The function of the switch was to ensure that the coil could be closed after the capacitor was charged (the voltage will not change). At this time, the external resistance is zero, the electromagnetic damping force is at a maximum and the system will work with the best damping effect. Through calculation, it can be concluded that the coil internal resistance in the system energy acquisition circuit simulation is 15.45 \(\Omega\). The model of the diode is 1N1183 and the energy storage capacitor is 1 uF.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 2 Related Works  
With the recent development of deep learning approaches, especially DCNN, several DCNN-based methods have been proposed to solve the motion correction problem in a data-driven manner. For instance, variation auto encoders (VAEs) and generative adversarial networks (GANs) have been employed for the retrospective correction of rigid and non-rigid motion artifacts in motion-affected MR scans [1]. GANs have also been used for motion correction in multi-shot MRI [9]. A conditional GAN improved the image quality of predicted motion-corrected images to a greater extent than motion-corrupted images [18]. In [19], a retrospective motion correction method that combined the advantages of classical model-driven and data-consistency-preserving methods for fast and robust motion correction was proposed and evaluated. In a recent study, a DCNN was also used for estimating the severity of motion artifacts in under-sampled MRI data, providing useful information for use in the reconstruction method [20]. Finally, an encoder-decoder network was able to suppress motion artifacts with motion simulation augmentation [2]. A comprehensive survey of deep learning for motion correction has been provided in [21]. Although great progress has been made by applying DCNNs to the motion correction problem, there is still much room for improvement. To the best of our knowledge, the source codes of most studies [1; 9; 18; 19; 20] are not publicly available, making it difficult for researchers to build and improve upon prior studies. Also, many previous approaches directly utilized existing DCNN models for motion correction. Hence, we aimed to develop a customized DCNN model for motion correction using publicly available source code.
## 3 Systematic Error Correction
### Active Correction by Compensating Optical Power  
Figure 5 shows the result of active correction of the systematic error. The optical power received by the interferometer photodetectors in the measurement and reference arms varies while the target is translated on the motorized stage. The optical power fluctuation is detected within the lock-in amplifier as illustrated Figure 5a, and stabilized by controlling the gain of the RF amplifier installed in the measurement arm circuit. As a consequence, the amplitude of the measurement signal into the lock-in amplifier is kept constant with a maximum deviation of 0.014% as depicted in Figure 5b. As discussed in the previous section, because the non-periodic error is dominantly influenced by the amplitude-to-phase conversion in RF components, the optical power stabilization is able to actively eliminate the non-periodic error. As a result, the systematic error is observed only in the form of cyclic error without the non-periodic error over a 2-m travel range, as shown in Figure 5c. The residual cyclic error can be corrected by post-processing so that the total measurement error remains within the level of \(\pm\)3.5 \(\upmu\)m (1 \(\sigma\)).  
Figure 4: Passive systematic error correction. (**a**) Mean systematic error (blue) and residual error after passive correction—1 \(\sigma\) line (green) and 2 \(\sigma\) line (orange). (**b**) Fourier transformed result of the mean error (blue) and corrected error (orange). FFT means a Fourier transformed result.
## 3 Methodology
### Data Preprocessing
#### 3.1.4 Gamma Transformation  
Gamma transformation, a nonlinear operation, is commonly used in image processing to modify an image's brightness levels [52]. It involves modifying the intensity distribution of the pixels by applying a power law function to the pixel value. Let us consider \(I_{\text{input}}\) to be the image before the gamma transformation and \(I_{\text{gamma}}\) to be the gamma-transformed image. Therefore, the process of gamma transformation can be explained through Equation (10).  
\[I_{\text{gamma}}=I_{\text{input}}^{\gamma} \tag{10}\]  
The exponent of the power law equation, the gamma value, controls the overall brightness and contrast of an image. The image becomes darker when the gamma value is raised as the transformation compresses the lower intensity values while maintaining the higher ones. In contrast, lowering the gamma value widens the range of lower-intensity values and compresses the higher ones, making the image appear brighter. Due to the integration of unsharp masking, some of the less important regions are highlighted, which can have a negative impact on the model. Therefore, the gamma transformation is applied to reduce the brightness. After a thorough analysis, the value of gamma, \(-5.047\), is carefully selected for this study. After the transformation, the intensity values are stretched to the original range of 0 to 255. Let the image after gamma transformation be denoted with \(I_{\text{gamma}}\), the maximum intensity value after gamma transformation with \(I_{\text{max}}\), and the minimum value with \(I_{\text{min}}\). Therefore, the image after contrast stretching, \(I_{\text{stretched}}\), can be produced using Equation (11).  
\[I_{\text{stretched}}(x,y)=\frac{255}{I_{\text{max}}-I_{\text{min}}}\cdot(I_{ \text{gamma}}(x,y)-I_{\text{min}}) \tag{11}\]  
Finally, the images are resized to 224 \(\times\) 224 to fit into the pre-trained classifier's input dimension. Figure 4 presents the changes in the image after each stage. Although some images are visually less appealing to the human eye, in the context of the full image, the blood vessels and exudates are more emphasized than less important regions. The system, however, does not integrate any data augmentation techniques.
# SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance
## 2 Method
### Soft-NMS Loss Function  
Soft-NMS is an improved loss function used to suppress redundant bounding boxes more smoothly during the NMS process [30]. Previously, the traditional NMS method used a fixed threshold to determine if two bounding boxes overlap and to suppress them [31]. However, due to the fixed threshold, it may adapt to situations between different objects, resulting in some candidate boxes with low confidence but which incorrectly exclude significantly overlapping true objects. In contrast, Soft-NMS employs a smoother strategy to handle overlapping bounding boxes. For adjacent bounding boxes with an Intersection over Union (IoU) greater than the NMS threshold, traditional NMS methods set their scores to 0, effectively discarding them, which may lead to missing boundary boxes, especially in occlusion scenarios. However, Soft-NMS retains confidence information by reducing the scores of overlapping bounding boxes rather than immediately discarding them (Figure 4).  
Specifically, in Soft-NMS, the scores of overlapping bounding boxes are adjusted based on their degree of overlap and confidence level. Bounding boxes with higher overlap and confidence levels will receive smaller score penalties, while those with lower overlap and confidence levels will receive larger score penalties.  
Soft-NMS performs this by introducing a decay function. Soft-NMS can be more flexible in adjusting the weights of candidate boxes. For the candidate box that is highly overlapping with the selected box but has low confidence, there is still a chance for them to be retained, thus improving the accuracy of target detection.
# Zirconium-Containing Metal-Organic Frameworks (MOFs) as Catalysts for Biomass Conversion
## 5 Conversion of Carbohydrates  
### Levalinic Acid into Chemicals  
5.1.1. Levulinic Acid Derivates as Value-Added Chemicals  
Levulinic acid (LA) can be obtained from the deconstruction of several kinds of raw materials, including monosaccharides, polysaccharides, and renewable resources, such as lignocellulosic biomass [2]. LA can be used as a starting material for the synthesis of many other important products in food and chemical industries (Scheme 4), including alkyl levulinate esters, \(\gamma\)-valerolactone, and levulinic ketals [24].  
Alkyl levulinate esters have low toxicity, high lubricity, and flashpoint stability (90 \({}^{\circ}\)C), as well as moderate flow properties at a low temperature, which make them suitable additives for gasoline and diesel fuels [25]. Ethyl levulinate (EL) can be used as an oxygenated additive in diesel formulations to reduce soot formation by up to 20% of the total content. EL is also suitable in gasolines due to the high octane rating (RON 110, MON 102) and energy content (31.2 MJ/L). The properties of levulinate esters are similar to biodiesel fatty acid methyl esters (FAMEs) but without their drawbacks of low cold flow properties and gum formation. Besides their use as a fuel additive, levulinate esters are also used in other areas, as flavoring compounds and fragrances, solvents, and plasticizers [2]. Levulinic ketals are prepared by reacting LA with polyalcohols in the presence of an acid catalyst. The resulting compounds are used as building blocks for the synthesis of pharmaceuticals, fragrances, plasticizers, cosmetics, or fuel additives [26; 27; 28; 29; 30]. Meanwhile, \(\gamma\)-Valerolactone (4,5-Dihydro-5-methyl-2(3\(H\))-furanone, GVL) is used as a fuel additive to produce cleaner-burning fuels [24; 31], as a biomass-derived solvent [32], as a component of biocide formulations, and as an intermediate for the synthesis of fine chemicals, such as adipic acid, valeric acid [33; 34], or 5-nonanone [35].
## Evaluation and Results
### Grid Network Formation  
#### 5.4.1 Static Network  
As a reference point, tests were conducted with the same simulation time but with the sink node immobile and remaining at the start position of the MSN, next to node 15, as shown in Figure 1. What was found was unless there was a large interference distance which could encompass more than one node, this one node used up the most energy. However, when there was an overlap of interference distance, this affected the number of frames to reach the sink node due to channel access contention. This highlighted the hotspot issue, as one node would run out of energy far sooner than the others and at that point the network was in danger of becoming redundant. Even in the event that neighbouring nodes could then take on the role of hotspot when a large interference distance was in use, this may have increased network lifetime but would not avoid the ultimate conclusion, that network failure was the likely eventuality.
## 2 Materials and Methods
### Chemical Analysis of Samples
#### 2.8.3 Measurement of Some Antioxidant Enzyme Activity and GSH in Blood Serum  
The activity of superoxide dismutase (SOD) was determined following the method described by [25]. Blood serum was added to the assay mixture containing 66 mM phosphate buffer with a pH of 7.8, 0.1 mM EDTA, 5.7 M nitro blue tetrazolium (NBT), 9.9 mM L-methionine, and 2.5% (\(w/v\)) Triton X100 and riboflavin (0.01 mL of 4.4%, \(w/v\)) was finally added to initiate the reaction. NBT reduction was measured at 560 nm in a Jasco V-670. The activity of SOD was calculated in units of enzyme/mL.  
The activity of catalase (CAT) was determined by the classical method developed by [26]. CAT decomposes H\({}_{2}\)O\({}_{2}\) (the substrate) and can be directly measured by decreased absorbance at 240 nm. Freshly prepared reagents prior to assays were phosphate buffer (66 mM, pH 7.0) and 30 mM H\({}_{2}\)O\({}_{2}\) in a phosphate buffer. The final volume was 1 mL and the reaction was started by the addition of H\({}_{2}\)O\({}_{2}\). To correct for any non-enzymatic reaction, a blank assay containing buffer instead of substrate was used. CAT activity is defined in specific units/mL.  
The level of reduced glutathione (GSH) was measured according to the method described by [27] and was determined based on the reaction of GSH with 5,5\({}^{\prime}\)-dithiobis (2-nitrobenzoic acid). The resulting chromophore, TNB (5-thio-2-nitrobenzoic acid), has a maximum absorbance of 412 nm. The TNB formation rate is proportional to the sample GSH level. Blood serum was treated with 0.6% sulfosalicylic acid and centrifugated. The supernatant was added to the assay mixture containing 100 mM phosphate buffer with a pH of 7.5. A 3 mM stock solution of the DTNB reagent was prepared in phosphate buffer with a pH 7.5, and diluted to a final concentration of 10 uM. The reaction between GSH and DTNB was monitored at a wavelength of 412 nm using a Jasco UV/Vis V-670 spectrophotometer. The concentration of GSH in blood serum was calculated with the linear equation generated from a GSH standard curve.  
Total antioxidant capacity (TAC) was analyzed by scavenging of DPPH (2,2-diphenyl-1-picrylhydrazyl) radical activity [28]. Blood serum proteins were removed with one volume of acetonitrile, incubated for 5 min and centrifugated for 10 min at 9000\(\times\) g. Supernatant (25 uL) was added to the assay mixture containing 970 uL of methanol and 5 uL of 10 mM of DPPH radical methanolic solution. After 30 min, the absorbance was read at 517 nm by a Jasco UV/Vis V-670 spectrophotometer. In parallel, a negative control with 25 uL acetonitrile, instead of deproteinated blood serum was prepared. All determinations were performed in triplicate and the serum scavenging effect (Sc%) was calculated according to Equation (6).  
\[\text{Scavenging }\%=\frac{1-A_{517\ sample}}{A\ _{517\ negative\ control}}\times 100 \tag{6}\]