esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Derivatization Reagents for Catecholamines and Amino Acids
## 1 Introduction  
Derivatization methods are commonly used for chromatography analysis to change the physical characteristics of target molecules, namely altering volatility temperatures, thermal stability, and avoidance of column adsorption in gas chromatography (GC), and to lower the detection limits in liquid chromatography (LC). Recent advances in mass spectrometry (MS) have been realized by implementing derivatization methods for multiplex protein profiling [1] so that protein content quantitation can be comprehensively compared among multiple samples. Other common derivatization reagents include ICAT (isotope coded affinity tag) [2] and stable isotope labeling by/with amino acids in cell culture (SILAC) [3]. A third approach allows analysis of multiplex protein profiling using isobaric tags for relative and absolute quantitation (ITRAQ) [4, 5]. However, improving the ionization efficiency of small molecules by using derivatized reagents has not received much attention.  
For instance, the small molecules L-dihydroxyphenylalanine (L-DOPA) [6], dopamine (DA), glycine, and \(\gamma\)-aminobutyric acid (GABA) are important chemical messengers involved in intraneuronal signaling in the central nervous system. Unfortunately, the native structures of these target molecules are not efficiently ionized and detected.  
To efficiently ionize target molecules, several polyaromatic hydrocarbon (PAH)-based derivatization reagents were recently developed for use in MS. 2,4-diphenyl-pyranyliumtetrafluoroborate (DPP-TFB) [7] and 2,4,6-triphenylpyrylium tetrafluoroborate (TPP) [8] can selectively react with primary amine groups. 2-Fluoro-1-methyl pyridinium (FMP)-based derivatization reagents target phenolic and primary amine groups [9]. In this study, the utility of these common derivatization reagents, along with the newly developed reagent 2,4,6-trimethylpyrylium tetrafluoroborate (TMPy) as a monoaromatic hydrocarbon, are evaluated for their utility in detecting catecholamines and amino acids. TMPy can also selectively target primary amine groups and can yield ions that are detectable in positive mode due to the positive ions in its structure. The primary difference between TMPy and other reagents is the size of the molecule (Figure 1). We investigated whether four target samples are capable of being derivatized by several reagents, and subsequently compared ionization efficiency by evaluating signal-to-noise (S/N) ratios.
## 2 Materials and Methods
### Satellite SIF and SIF-Based Downscaled Products  
We use Version 27 (v27) Level 3 (gridded) SIF derived from the Global Ozone Monitoring Experiment 2 (GOME-2) onboard the MetOp-A (GOME-2A) from February 2007-December 2016 [43; 44]. MetOp-A is in a polar orbit with an Equator crossing time of 09:30. The native spatial resolution of the GOME-2A data from launch to the middle of July 2013 was 40 \(\times\) 80 km. From that time until the present, the resolution has been 40 \(\times\) 40 km. GOME-2A provides global coverage in approximately 1-3 days. Here, we use SIF derived from the far-red fitting window: 734-758 nm. SIF is a very small signal (typically \(<\) a few mW/m\({}^{2}\)/nm/sr). Spatial and temporal averaging is employed to increase SIF signal-to-noise ratios (SNRs).  
The quality control filters for GOME-2 Level 3 SIF are described in [PERSON] et al. [44]. GOME-2 SIF data with effective cloud fractions \(<\)30% are gridded monthly at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) resolution. This cloud screening is less rigorous as compared with that applied to MODIS data. GOME-2 retrievals of canopy-level SIF, which utilize the filling-in of solar Fraunhofer lines, are themselves not as greatly affected by clouds as are vegetation indices such as the NDVI [65]. However, top-of-atmosphere (TOA) SIF is affected by clouds in that it is sensitive to both the amount of solar radiation reaching the surface, as well as the amount of SIF that reaches the satellite. Therefore, TOA SIF should be reduced by approximately the square of the cloud transmittance as compared with the value in clear skies assuming that SIF yield remains constant with cloud conditions. [PERSON] et al. [66] showed that a substantial fraction of the canopy SIF can be detected by satellite for small to moderate cloud optical thicknesses.  
[PERSON] and [PERSON] [67] implemented a method to downscale SIF with higher resolution MODIS data. Their approach used NDVI, as well as land surface temperature and evapotranspiration derived from MODIS to downregulate how much SIF is related to NDVI (or FAPAR). This product,referred to here as SIF*, is provided from 2007-2013 at a monthly timescale on a 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid. Version 1 (v1) SIF* uses GOME-2 SIF Version 25 as an input. It should be noted that since v1 was produced, there have been two updates to the GOME-2 SIF data culminating in v27. These later versions used the same first step retrievals, but addressed various biases as identified by [PERSON] et al. [58] by applying posterior adjustments [44]. To obtain a monthly full-day estimate of SIF* from the original observations near 09:30 local time, we first divide each monthly grid box value of SIF* by the monthly-averaged top-of-atmosphere PAR (PARTOA) at 09:30, then multiply by monthly-averaged daily PARTOA [68].
## 5 Seismic Performance Assessment through Nonlinear Time History Analysis
### Seismic Performance Assessment through Modal Pushover Analysis  
#### Story Drifts and Peak Displacements  
Figure 21 shows the story drift response and peak story response of the RC building model and the GSCCW building model along the building height obtained using both Model Pushover Analysis (MPA) and the average NTHA results. The MPA procedure analyzes structures using pushover analysis at several selected modes, and combines the modal responses using a certain combination rule to obtain the final response. Since this procedure is able to include higher mode responses, it is becoming more popular than conventional pushover analysis for the seismic performance assessment of high-rise buildings. In this study, the first five modes of the case-study building were selected for MPA analysis. Details of the theory and procedure for MPA are not addressed in this paper for simplicity.  
From the comparison between the MPA and NTHA results, it can be confirmed that the structural response calculated on the basis of MPA with five modes was in very good agreements with the results obtained from NTHA, especially for stories below 60. Comparing the RC building model and the GSCCW building model, it can be found that the deformation response between the two models is almost identical, and the requirements in the specification are satisfied no matter which method is used.  
Figure 21: Comparison of story deformation between the RC and GSCCW models: (**a**) story drift ratios; (**b**) peak story displacements.  
Figure 20: Comparison of story moments of each ground motion between RC and GSCCW models: (**a**) RSN138; (**b**) RSN728; (**c**) RSN737; (**d**) RSN5774; (**e**) RSN5783; (**f**) RSN5823; (**g**) RSN5829; (**h**) RSN5977; (**i**) RSN5991; (**j**) RSN6965; (**k**) Relative error.
# Design of Quadruple Offset Butterfly Valve Used in Power Plants
## 3 Finite Element Analysis
### Design of Number of Laminated Seal Layers
#### 3.1.4 Comparison of the Contact Pressures between QOBV and TOBV  
To compare the contact pressures of the QOBV and the TOBV, a thermal-structural coupled analysis of TOBV with the same boundary and load conditions at 350 \({}^{\circ}\)C was performed. The contact pressures distribution of TOBV at each position are 18.96 MPa (A), 22.622 MPa(B), 46.122 MPa(C), 29.906 MPa(D), 28.429 MPa(E), 13.19 MPa(F), 148.9 MPa(G), and 13.548 MPa(H), respectively. The results of the QOBV contact pressures distribution at each position are 22.169 MPa(A), 23.241 MPa(B), 25.343 MPa(C), 16.278 MPa(D)  
Figure 17: The contact pressure distribution of the five-layers model.  
Figure 18: The contact pressure distribution of the seven-layer model.  
29.498 MPa(E), 11.627 MPa(F), 93.469 MPa(G), and 9.6218 MPa(H), respectively, as shown in Figure 19 and Table 4. The maximum contact pressure of the TOBV was 148.9 MPa, and that of the QOBV was 93.47 MPa, and they occurred at an angle of 86.1\({}^{\circ}\) (G position). The maximum contact pressure was reduced by 55.431 MPa (37.2%).
## 1 Introduction
The active disturbance rejection control (ADRC) is very suitable for this system because it demands a relatively low modeling accuracy, and the extended state observer can observe the coupling disturbance in different degrees of freedom. Thus, the compensation can be achieved to decrease the influence of the disturbance. The damping for the maglev system is close to 0, which requires the controller to realize smooth control for stability. The nonlinear function curve of the traditional ADRC inevitably causes the system jitter. The new nonlinear function can make the ADRC controller more suitable for the maglev system. First, this paper explains the working principle of the moving-coil maglev planar motor with the winding structure, and then solves the planar motor electromagnetic model decoupling. Second, an improved active disturbance rejection control (ADRC) is designed, considering the control characteristic of the maglev planar motor. Finally, the disturbance is imposed on the traditional and advanced controllers, respectively, to test their robustness to random disturbance.
## 2 Background
### Metrology Infrastructure as a Part of the Quality Infrastructure
The ongoing efforts include the definition and development of digital formats for presenting metrological information, e.g., data models for presenting metrologically relevant data as semantic metadata [19, 20, 21] or formats for digital calibration certificates [15, 22, 23, 24]. For this work, the exchange of DCCs was tested using the DCC format originally defined by PTB and further developed in EMPIR SmartCom and Gemimeg research projects [4, 5, 25, 26, 27, 28]. Digitization of the data formats sets new requirements for the development of the infrastructure as its operation has been based on mutual recognition and trust between organizations [16]. Transition to a digital environment means that also this trust needs to be established digitally to enable its representation in a machine-understandable format [29, 30, 31]. For this purpose, data security and cryptographical solutions, e.g., the use of digital signatures, are relevant [15, 17].
## 4 Results and Analysis
### Vortex Formation and Velocity Distribution
At a step's vertex, the power law provides an approximation:  
\[\frac{V_{x}}{V_{0}}=\left(\frac{y}{\delta}\right)^{1/N} \tag{12}\]  
where \(V_{x}\) = velocity component in the \(x\)-direction, \(V_{0}\) = free stream velocity, \(\delta\) = boundary layer thickness defined in terms of 0.99 \(V_{0}\), and \(N\) = exponent. For all the \(\theta\) and \(q\) values, the velocity profile of this study is best fitted with \(N\) = 3.9. In the developing flow region, the numerical results are also compared with those by [PERSON] and [PERSON] [23], in which experiments were conducted for a stepped spillway sloping at 45\({}^{\circ}\) and with horizontal steps (\(\theta\) = 0). Six flow discharges were examined. Figure 3 shows comparisons of the velocity distributions in a normalized form (\(V_{x}/V_{0}\)). The results in this study refer to \(d_{c}/h\) = 4.8 at the five \(\theta\) values; the data from [PERSON] and [PERSON] [23] correspond to \(d_{c}/h\) = 0.9-1.7 at the same \(\theta\) value (\(\theta\) = 0). The latter is best fitted with \(N\) = 4.5. A similar distribution pattern is observed in the present research. Furthermore, at larger downward inclination (e.g., \(\theta\) = \(-\)20\({}^{\circ}\)), the velocity gets larger for \(y/\delta\) < 0.5, which is associated with the reduced bottom roughness. At \(y/\delta\) > 0.5, the velocity difference is insignificant.
# Health Project Prioritization in Meta, Colombia, Using Anp-Dematel
## 2 Theoretical Framework
### ANP Combined with DEMATEL  
The Analytic Network Process (ANP) represents a significant evolution from the traditional Analytic Hierarchy Process (AHP) methodology, as it allows for the modeling of reciprocal relationships and feedback loops within dynamic decision networks--thus overcoming a fundamental limitation of strictly hierarchical models. However, the method's requirement for exhaustive pairwise comparisons--which scales geometrically as \((n^{2}-n)/2\) for \(n\) elements--poses substantial operational challenges in complex healthcare applications. Integration with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method helps to overcome these limitations through three synergistic mechanisms: (i) clear discrimination of cause-effect relationships (using influence thresholds of \(\gamma\geq 0.35\)), (ii) optimized weighting of network connections, and (iii) significant reduction in the comparative judgment burden (demonstrating an average 62% reduction in the number of required comparisons while maintaining 94-97% of discriminative power).  
Validation studies conducted across twelve healthcare systems in Latin America confirm the superiority of the hybrid approach in both analytical accuracy--with 0.92 concordance with expert panel assessments versus 0.85 for standalone ANP--and practical feasibility, with implementation times 2.3 times faster. Additionally, sensitivity analysis confirmed the stability of results under a 10% variation in weight assignments, reinforcing the methodological robustness of the approach for public health planning. From a policy standpoint, integrated resource allocation--prioritizing accessibility and equity--is essential to optimize decision-making in contexts characterized by multiple competing priorities.
## 2 Methodology
### Texture Detection in Spatial Domain
#### 2.1.2 GLCM Feature Extraction  
To further improve the ability of feature description, GLCM features are extracted. An HOG-like feature is the statistical result of pixels at different gray levels, while GLCM describes grayscale relationships between pixels and their adjacent pixels [15]. GLCM provides information about the directions and the extents of grayscale changes, but it cannot provide the information in the form of descriptors. Therefore, statistical attributes are used to quantitatively describe texture characteristics on the basis of the GLCM matrix.  
Based on the priori knowledge that textures of image blocks containing floating objects change dramatically, while textures of other background blocks change relatively gently, four statistical attributes, i.e., Energy \(E_{g}\), Entropy \(E_{g}\), Homogeneity \(H_{o}\), and Contrast \(C_{t}\) are calculated. The definition is Equation (1):  
Figure 1: Fused Histogram of Oriented Gradient (FHOG) for feature extraction.  
\[\left\{\begin{array}{l}E_{g}=\sum\limits_{i}\sum\limits_{j}P^{2}(i,j)\\ E_{p}=-\sum\limits_{i}\sum\limits_{j}P(i,j)\log P(i,j)\\ H_{o}=\sum\limits_{i}\sum\limits_{j}\frac{P(i,j)}{1+P(i,j)^{2}}\\ C_{t}=\sum\limits_{i}\sum\limits_{j}(i-j)^{2}P(i,j)\end{array}\right., \tag{1}\]  
where P is the probability matrix obtained by normalizing the GLCM matrix. Energy, Contrast, and Homogeneity reflect the thickness, depth, and variability of the texture, respectively, and Entropy measures the amount of information in an image. At the same time, textures of the foreground image blocks with floating objects are more delicate and more dramatic than the background blocks only with water surfaces. Therefore, the energy, uniformity, and contrast of the foregrounds are small, while the entropy values are large. Through these four attributes, foreground, and background areas can be well distinguished. Our calculation step is 2, and the directions are 0 degrees, 45 degrees, 90 degrees, and 135 degrees. Finally, a 16-dimensional descriptor can be extracted from each sliding window. Experimental results (in Section 3) show that the combination of GLCM features and FHOG features is more conducive to detecting floating objects than traditional texture features.
# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
## 4 Results
### Category System
#### 4.1.7 Category 7--Organisation and Framework Conditions  
The use of remote technologies is not only influenced by human or technical aspects. Organisational factors and circumstances can affect the (efficient) use of remote solutions as well.  
* Subcategory 7.1 _Customer Dependency_ is described by statements regarding decisions, chosen methods in maintenance, as well as wishes or requirements for maintenance and repair services from the customer side.  
The role of the customer and the demand for remote maintenance have an increased influence on whether machine manufacturers offer remote maintenance services. The value of on-site maintenance is rated higher than that of remote maintenance. Accordingly, customers are not willing to pay the same price for both services (Interview B). The need for remote maintenance is rated less in the areas of maintenance and inspection than in the area of repair. In terms of customer dependency on remote maintenance, the cost of remote solutions is an important factor. They depend on the customers' willingness to pay and their general service requirements (Interview G). In addition, the required technologies,software or licences must also be purchased by the customer, which is why barriers to the implementation of remote solutions quickly arise (Interview H). The aspect of the lack of standardisation of the software used, which has already been mentioned twice in previous categories, is also related to customer dependency. It is often not clear whether the required software can be installed on the customer side. Furthermore, many companies insist on using their own software (Interview H).
* Subcategory 7.2 _Sustainability_ includes statements on the perceived awareness of sustainability in the company.  
Sustainability is an important issue for the companies surveyed. In particular, preventive maintenance is mentioned here to counteract unplanned machine down-times. However, the principle and the implementation of preventive maintenance is not yet practised in any of the companies.
## 3 Network Background
### Smart Cities and Ultra-Dense Networks  
UDNs are a critical enabler of smart cities, supporting the ever-growing demand for seamless connectivity across IoT devices, autonomous systems, and smart infrastructure. These networks are characterized by a high density of wireless APs, often exceeding the density of user terminals, with inter-AP distances reduced to just a few meters. In smart cities, UDNs facilitate high-speed data transmission and ultra-low latency, features which are essential for applications such as intelligent transportation, environmental monitoring, and public safety systems [47]. By minimizing the distance between APs and users, UDNs reduce path loss, enhance signal quality, and improve overall network performance [48]. This makes them a fundamental component of modern urban ecosystems, providing a robust, scalable, and energy-efficient communication infrastructure that enhances connectivity, efficiency, and sustainability in smart cities [47].
## 2 Materials and Methods
### Methods  
#### 2.2.1 Method of Ultrasonic Velocity Measurement  
A non-destructive evaluation method using ultrasonic velocity measurements was used as a technique for characterizing defects and damage to materials by observing the scattering of ultrasonic waves. Generally, when the speed of the ultrasonic wave is high, the quality and continuity of the material are good, and, if the speed is slow, it can be determined that the concrete has many cracks and voids. The Ultrasonic Pulse Velocity (UPV) method generates ultrasonic waves on the concrete surface through a converter and records the generation and arrival time of the sound waves that pass through the material and are sensed by the receiver at the opposite end. If the distance between the two points is known, the velocity of the wave can be determined. The pulse velocity is determined by a simple Equation (1).  
\[\text{Pules Velocity}=\frac{\text{Path Length}}{\text{Transit Time}} \tag{1}\]  
The method used to measure the ultrasonic velocity is divided into indirect and direct methods, but the direct method was used exclusively in this study because of the variability problem of the measurement results. An ultrasonic irradiation device uses ACSA 1410 Pulsar to input the thickness of the core specimens at each measurement and records the average ultrasonic velocity measured three times into contact with both ends of the specimens, as shown in Figure 2.  
The ultrasonic velocity range that corresponded to the general concrete quality \"good\" was reported between 3500 m/s and 4500 m/s in accordance with the relevant experimental results and national control standards (Table 3) [10; 11; 12; 13; 14; 15].
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 3 Methodology
### Data Processing  
The nuScenes data is labeled using lidar data. Radar detections are not time synchronized with the labels. Due to the time delay, bounding box labels do not always coincide with the radar detections. Further errors can be introduced through slight angular miscalibrations of the radar sensors, which has a particular impact on far-range detections. The transformation from the radar and lidar frame to the car coordinate system can be another source of spatial offset between ground-truth (GT) bounding boxes and radar data. To account for spatio-temporal calibration errors, nuScenes added the so-called _whl_-factor to increase the bounding box size. For small bounding boxes, e.g., pedestrians, increasing the bounding box size by 50% might be a reasonable factor, whereas this would include too much environment information in the case of vehicles or buses. Similarly, the _whl_-factor scales the tolerance for the length and width of vehicles differently, as usually the length of a vehicle is larger than its width. We propose an absolute _whl_-tolerance in meters that is equally suited for all object categories. Figure 6 shows the misalignment of bounding boxes and the effect of the _whl_-factor vs the _whl_-tolerance. Although choosing a higher _whl_-factor could also include respective points in the bounding box in Figure 6, this would; however, increase the tolerance in longitudinal direction too much.  
Figure 5: High-level structure of the KPLSTM model. The time dependency is already considered in the encoding stage as indicated by the blue color-coding.  
Due to the sparsity of radar data, we process three consecutive time steps as a single point cloud. As these past time steps are not available for the first sample of each scene of the public data set, we omit the first sample, both for training and evaluation.