esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Lens Array Full-Field X-ray Microscopy
## 3 Results  
### Measurement Results  
The multiple images of the grating are shown in Figure 4. The exposure time was 400 s. The images are dark-field and flat-field corrected with the use of a background image taken with no sample in the beam. The areas outside the illumination cone of the poly-capillary are black. Inside of the tilted 50 \(\upmu\)m \(\times\) 50 \(\upmu\)m aperture, gold lamellas are shown in gray (magnified section in Figure 4, middle). It can be seen by the different vertical position of horizontally oriented structures that the FoV is slightly different for each CRL. This deviation could be overcome by a more exact alignment of the distance between sample and multi-focus CRL, leading to a larger effective FoV. The number of images on the detector here is limited by the diameter of the illumination cone of the polycapillary. The detector size would have allowed all 9 \(\times\) 9 images of the multi-focus CRL to be taken. Each of the 9 \(\times\) 9 sub-images of the sample is generated by a different single sub-CRL of the multi-CRL array.  
Out of the 9 \(\times\) 9 CRL array, a set of 3 \(\times\) 4 images is used to create one image of the effective FoV with high contrast (Figure 4, right). The contrast of the 2.4 \(\upmu\)m period gold lines in the final image was 12%, so the gratings' period was clearly resolved.  
As expected, the total number of photons detected by the detector was increased by a factor of the number of single images produced by the CRL array. The SNR of the image summed up from twelve single images increased by a factor of 2.5 compared to a single image of the sample (Figure 5). As the stochastic noise decreases with the root of the increase in intensity, theoretically a reduction by a factor of nearly 3.5 would be possible. In the experiment, the illumination was not completely homogeneous, and part of the radiation was lost due to rays passing through the material blocks between the CRLs, resulting in a lower improvement of the SNR. Of course, instead of increasing the SNR by a factor of 2.5, one could instead decrease exposure time by a factor of 6.25.  
Figure 3: (**a**) Liquid MetalJet (LMJ) setup at the LRM in Würzburg. In the upper right corner, the position of the metal jet is indicated by a white arrow. The Be exit window in the LMJ copper housing is hidden by two capillaries mounted on a hexapod. The left capillary is a polycapillary, used for illuminating an absorption grating (sample). For imaging the grating, an aperture (50 \(\upmu\)m \(\times\) 50 \(\upmu\)m) is placed as near as possible to the grating. (**b**) Light microscopy image of the absorption grating (period 2.4 \(\upmu\)m, 10 \(\upmu\)m gold thick-ness, microwoks GmbH) used as sample. The small differences in contrast originate from the light microscope illumination, not from the absorption grating.
## 2 Preliminaries
### CA-SCL Decoder  
The CA-SCL decoder [5; 6] is an improved decoder based on the SC decoder and utilizes CRC as well as a list of multiple candidate paths to improve the FER performance of the SC decoder with a single candidate path. The list size of the CA-SCL decoder is \(L\). Meanwhile, a simplified path metric (PM) [7] is adopted by the CA-SCL decoder to distinguish the correct path ion the list. PM can be computed using the following equation:  
\[PM_{\rm c}^{(i)}=\begin{cases}PM_{\rm c}^{(i-1)},&\text{if}\quad\hat{u}_{i}[l ]=\delta(\lambda_{N}^{(i)}[l]);\\ PM_{\rm c}^{(i-1)}+\left|\lambda_{N}^{(i)}[l]\right|,&\text{otherwise}.\end{cases} \tag{2}\]  
where \(PM_{l}^{(i)}\) represents the \(PM\) value of the \(i\)th bit in the \(l\)th candidate path, \(0<l<=L\) and \(PM_{l}^{(0)}=0\). \(\lambda_{N}^{(i)}[l]\) is the log-likelihood ratio (LLR) value of the \(i\)th bit in the \(l\)th candidate path. \(\delta(x)=\frac{1}{2}(1-sign(x))\). In addition, we define \(\mathcal{A}^{\prime}\) as a set consisting of the first \(log_{2}L\) of non-frozen bits.  
When decoding the \(i\)th bit and \(i\in\mathcal{A}^{\prime}\), the CA-SCL decoder utilizes a list \(\big{\rfloor}_{best}^{(i)}\) to reserve all candidate paths. When decoding the \(i\)th bit and \(i\in\mathcal{A}\setminus\mathcal{A}^{\prime}\), the \(L\) paths in \(\big{\rfloor}_{best}^{(i-1)}\) are expanded to \(2L\) sub-paths, which forms an expanded list \(\big{\rfloor}^{(i)}\). Then, \(\big{\rfloor}_{best}^{(i)}\) can be achieved by selecting \(L\) paths with a smaller PM from \(\big{\rfloor}^{(i)}\). When decoding the \(i\)th bit and \(i\in\mathcal{A}^{c}\), the \(L\) paths in \(\big{\rfloor}_{best}^{(i)}\) are not expanded, and the values of the \(i\)th bit in all candidate paths are 0 by default.  
After all bits are decoded, the CA-SCL decoding algorithm performs CRC on the paths in the \(\big{\rfloor}_{best}^{(N)}\). If CRC succeeds, the CA-SCL decoder will output the candidate path with the smallest \(PM\) from among the candidate paths that passed the CRC check. If not, the CA-SCL decoder will output the candidate path with the smallest \(PM\) in \(\rfloor_{best}^{(N)}\).
## 4 Results
### In Vivo Brain
The NAA/NAAG and Glx peaks are properly localized within the VOIs of the MaxEnt and GS reconstructions to within the same levels of spatial aliasing seen in the fully sample image. The line widths and amplitudes of the GS\({}_{1}\) and GS\({}_{2}\) reconstructed peaks are the most qualitatively similar to the fully sampled scan. Figure 4: Select 2D COSY _in vivo_ spectrum from a central 2 \(\times\) 2 \(\times\) 2 cm\({}^{3}\) voxel of the brain from the (**a**) fully sampled 4D EP-COSI scan of a healthy 23 year old male (**b**) 8\(\times\) NUS and the (**c**) 8\(\times\) TV (**d**) 8\(\times\) MaxEnt (**e**) 8\(\times\) CS (**f**) 8\(\times\) GS\({}_{1}\) (**g**) 8\(\times\) GS\({}_{2}\) reconstructions. Table 3 shows the mean metabolite peak RMSEs calculated over the 4 \(\times\) 4 voxel VOIs of the 4\(\times\), 6\(\times\), and 8\(\times\) NUS reconstructions of the _in vivo_ 4D EP-COSI brain scan shown in Figures 4 and 5. The RMSEs were calculated using the ppm locations shown in Table 2 and the lowest values calculated for each metabolite peak and NUS rate are highlighted. As can be seen, the majority of highlighted RMSEs are from the GS\({}_{2}\) reconstructions with a handful highlighted for the GS\({}_{1}\) and MaxEnt reconstructions. These results are similar to what was shown in the metabolite RMSE plots in Figure 2, which shows that GS\({}_{2}\) and GS\({}_{1}\) have most of the lowest values, followed by MaxEnt. Additionally, the values of the RMSEs for the large diagonal peaks of NAA and Cr are more tightly grouped than the smaller diagonal and cross peak RMSEs, and the high GS\({}_{1}\) RMSEs for Cho 3.2 ppm indicate it had trouble reconstructing the metabolite peak, as in Figure 2.  
Figure 5: Spatial distribution of a 1.5 \(\times\) 1.5 ppm spectral region centered at 2.5 ppm that contains the Glx and NAA/NAAG diagonal peaks from the (**a**) fully sampled _in vivo_ 4D EP-COSI brain scan of a healthy 23 year old male (**b**) 8\(\times\) NUS and the (**c**) 8\(\times\) TV (**d**) 8\(\times\) MaxEnt (**e**) 8\(\times\) CS (**f**) 8\(\times\) GS\({}_{1}\) (**g**) 8\(\times\) GS\({}_{2}\) reconstructions.
## 1 Introduction
[39] developed hybrid fibre-reinforced cement composites utilizing a combination of carbon fibres as well as CNTs with micro-silica and NS to improve the dispersion of CNTs. They showed that compared to micro-silica, NS exhibited less effectiveness in promoting the modification effects of CNTs in cement composites. [PERSON] et al. [40] incorporated NS, CNTs, and fly ash into self-compacting concrete. They showed that the presence of NS worsened the fresh properties of concrete and CNTs contributed to more ductile concrete. The progress of nanotechnology in the field of building materials also shows the potential to integrate nano additives into lightweight cement-based materials. In this regard, however, limited literature has been published. Only [PERSON] et al. [41] incorporated NS lightweight cement mortar (LWCM) containing FAC, and examined the compressive strength, tensile stress, strain capacity, as well as microstructure. They showed that the addition of NS effectively enhanced the physical characteristics of LWCM. NS showed the potential to restrain the porosity, refine the pore structure, and ameliorate the interfacial transition zone between FAC and cement. [PERSON] et al. [42] investigated the mechanical properties and microstructure of cement pastes with FAC and NS, pointing out that the presence of NS enhanced the compressive strength of cement pastes owing to the increased interface, the formation of better bonding between FAC particles in the cement matrix, as well as the improved pozzolanic activity of FAC. [PERSON] and [PERSON] [43] implemented tests on the mechanical performance, durability, and microstructure of LWCM with the inclusion of FAC and NS. The results demonstrated that due to the presence of NS, LWCM showed excellent resistance to high temperatures and sulfuric acid intrusion. The studies concerning the synergic influence of NS-CNTs additives on lightweight cement materials with FAC are not available in the existing research. In this study, therefore, following sustainable development in the building and construction industries, a green LWCM was fabricated via the utilization of industrial waste FAC as fine aggregates and artificial lightweight aggregates. The type of LWCM belongs to the traditional lightweight aggregate cement mortar. CNTs and NS were added in various contents as synergic modifiers. The objective of the investigation was to discover the binary effects of CNTs and NS on the mechanical performance and microstructure of LWCM. Experiments including a flexural strength test, compressive strength test, water absorption, scanning electron microscopy (SEM), thermogravimetric analysis (TG), and X-ray diffraction (XRD) were carried out to highlight the evolution of mechanical properties as well as the physical and chemical transformation by the changes in morphology and hydration process.
## 3 Materials and Methods
### Methods
#### 3.2.2 Synchronization of Spatial Resolution  
Next, the spatial resolution of all input data was synchronized. As shown in Table 1, the orthophotos have a spatial resolution of 0.2 m as opposed to all other raster data having a spatial resolution of 1 m. There are benefits and drawbacks to using either as the standard spatial resolution. Using 0.2 m as the standard results in more detail in the four orthophoto layers. Greater detail can be beneficial for the deep learning algorithm to recognize individual tree crowns and thus helps it determine the density of the forest. In contrast, the resolution of 1 m would provide a broader field of view when maintaining the same input image size as that in the DCNN. The resolution of 0.2 m was chosen as the standard for all input data as, otherwise, the anticipated information loss would be too high. This assumption is based on the fact that the average tree crown diameter for a first thinning is approximately 2.5 m. Consequently, a pixel size of 1 m would imply that one tree crown is represented by about 3 pixels in contrast to the 13 pixels with a pixel size of 0.2 m. The determination of an individual tree crown using a 3-pixel resolution is much harder compared with a tree crown represented by 13 pixels. Consequently, all data with a spatial resolution of 1 m were converted using the GDAL library [60].
# Enhancements and Challenges in CoAP--A Survey
## 1 Introduction
The applications of CoAP are also briefly summarized in Table 1. Section 4 highlights evolution and enhancements in CoAP focusing mainly on congestion control mechanisms of CoAP. Table 2 represents the comparison of various techniques of congestion control in default  
Figure 1: An overview of CoAP architecture. and advance CoAP versions. The qualitative and quantitative analysis of the enhanced congestion control schemes is presented in the Section 5. Table 3 summarizes the quantitative analysis of enhanced congestion control schemes. Section 6 illustrates some open challenges and research directions. Finally, the concluding remarks are presented in Section 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Role of CoAP** & **Application** \\ \hline [PERSON]. [PERSON] et al. [9] & Direct compatibility of medical sensors with internet + interaction of sensors with other nodes using RESTful communication & Interoperability \\ \hline [PERSON]. [PERSON] et al. [10] & Implementation of messaging system with MQTT model using CoAP & Interoperability \\ \hline [PERSON] at al. [11] & Design and implementation of healthcare platform with IEEE 11073 PHD & Interoperability \\ \hline [PERSON]. [PERSON] et al. [12] & Integration of two healthcare standards ISO/IEEE 11073 and IHE PCD-01 for communicating between medical IoT devices & Integration \\ \hline [PERSON] et al. [13] & Integration of CoAP with Open Smart Grid Protocol (OSGP) for information exchange between devices in smart grids (SG) & Integration \\ \hline [PERSON] et al. [14] & Integration between AAA infrastructures and EAP & Authentication and Authorization \\ \hline [PERSON] et al. [15] & To provide communication with packets having low overhead in CoAP-based authentication and access control framework for IoT & Authentication and Access Control \\ \hline [PERSON] et al. [16] & For delivering the media segments to consumers in implementing dynamic streaming over CoAP & Streaming Services \\ \hline [PERSON] et al. [17] & To perform adaptive streaming for constrained wireless environments & Video Streaming \\ \hline [PERSON] et al. [18] & Transfer of data from IoT nodes to cloud & Cloud Computing Services \\ \hline [PERSON] et al. [19] & Observing resources (temperature values) in IoT environment and WSNs & Resource Observation \\ \hline [PERSON] et al. [20] & For advertising and demanding of resource directories using CoAP REST methods & Resource Discovery \\ \hline [PERSON] et al. [21] & Implementation of a remote healthcare monitoring system using CoAP client/server model & Real-time Remote Monitoring \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applications of CoAP
## 1 Introduction
Although minimum current stress was achieved under light-load conditions, full ZVS was not ensured for all switches. To overcome the inherent limitations of conventional modulation strategies, this paper proposes an enhanced integrated optimization strategy based on triple phase shift (EIOS-TPS). This strategy employs time-domain analysis and formulates current peak minimization as the optimization objective, solved using the KKT conditions. The ZVS criterion is simplified by using current direction determination, and TPS, as the highest degree-of-freedom modulation strategy, is adopted without modifying the DAB converter topology. The proposed EIOS-TPS balances the trade-off between ZVS realization and current-stress optimization (CSO), enabling full-switch ZVS and minimal current stress across the entire load range under both buck and boost modes. Furthermore, EIOS-TPS requires no hardware modifications, ensuring high software portability and ease of implementation.
# Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
## 2 Methodology  
### Velocity Inversion and Network Architecture  
The velocity inversion can be expressed as the minimization of the following objective function:  
\[\mathbf{J}=\left\|\mathbf{d}^{syn}-\mathbf{d}^{obs}\right\|_{2} \tag{1}\]  
where \(\mathbf{J}\) is the objective function, \(\left\|\ \right\|_{2}\) denotes the Euclidean norm, \(\mathbf{d}^{syn}\) is the synthetic data vector, and \(\mathbf{d}^{obs}\) is the recorded data vector.  
Conventional methods for velocity inversion include seismic tomography and full-waveform inversion, which are based on travel time and waveform, respectively. As mentioned before, the two methods rely on the data quality and the setting of the initial velocity model, both of which cannot be well satisfied in microseismic monitoring. In this paper, we use neural networks to solve this nonlinear function. Neural networks can create strongly nonlinear mappings between microseismic gathers and velocities by building multiple hidden layers:  
\[\mathbf{v}=Net(\mathbf{d};\mathbf{\theta}) \tag{2}\]  
where \(\mathbf{v}\equiv[\mathbf{v}_{p},\mathbf{v}_{s}]\) denotes the predicted velocity value, and \(\mathbf{\theta}\) indicates the total weight in the network. The training process of the network is realized through forward propagation and back propagation in the network models to update the \(\mathbf{\theta}\). The testing process involves directly predicting the velocity model by inputting waveform data to the trained model.  
We adopt the Unet (Figure 1), as it has shown great potential for many geophysical inversion tasks [34; 35]. We make microseismic data and the associated velocity model \(\{\mathbf{d},\mathbf{v}\}\) in pairs as the network input. We use the leaky rectified linear unit (LeakyReLU) activation function, which alleviates the problems of gradient vanishing and allows for a better fitting of the model [36].
tested various 3D models with and without a ferrule, focusing on post length, and concluded that post length does not affect crown and dentin stresses [23]. Polyetheretherketone (PEEK) is a synthetic polymer with strong mechanical properties,making it suitable for use in dentistry and industry [24]. PEEK is thermoplastic and has a low Young's elastic modulus, similar to bone [24]. However, only a few studies have focused on using digital dentistry and 3D models to analyze the effectiveness of post and core systems with finite element analysis. Some studies have compared PEEK to other post materials. A 2017 FEA study examined the polyetherketoneketone (PEKK) post-core system. It showed better fracture resistance than metal and fiberglass post-core systems, with a lower elastic modulus and better stress distribution at the intra-radicular surface [25]. Another FEA study in 2020 concluded that carbon-fiber-reinforced polyetheretherketone (CFR-PEEK) is superior for endodontic post fabrication compared to a fiber-reinforced composite (FRC) because it exerts lower stress in the residual dentin. Glass-fiber-reinforced polyetheretherketone (GFR-PEEK) and PEKK materials also provide stress distribution similar to FRC posts [26]. [PERSON]'s study found that PEEK posts produced less stress than glass fiber posts, particularly in the models with composite veneer crowns [24]. A 2020 study suggested that PEEK posts are preferred over titanium and glass fiber posts, as they provide better stress distribution and lower fracture risk [27]. Finally, [PERSON]'s 2021 study found that PEEK posts showed lower stress values than glass fiber posts in central incisor restorations [28]. This study uses finite element analysis (FEA) to analyze displacement, von Mises stresses, maximum principal stresses, the factor of safety (FOS), and stress distribution in 3D models of post and core restored teeth with a ferrule design. The FEA simulation is conducted in an ideal environment to better understand the relative performance of the materials. A post-core-restored tooth with a 2 mm ferrule height is tested under specific loading conditions in FEA simulation software. The null hypothesis is that the stress magnitudes and distribution in the 3D model's components are the same for the PEEK and high noble alloy posts.
# Water Demand (or Specific Surface) of Aggregate as a Dominating Factor for SCC Composition Design
###### Abstract  
In the modern era of superplasticizer-based concrete technology, water demand (or specific surface) of aggregate is a significantly underestimated factor influencing cement paste demand in the self-compacting concrete (SCC) design process. The presented data show that it is the key factor for optimization criterion of SCC cement paste demand. Four models were taken into consideration (Bolomery, Stern, modified Loudon, and Relative Specific Surface), and all of them fit linearly very well (R\({}^{2}\geq\) 0.95) to the relative thickness of coating aggregate with cement paste (t\({}_{\text{rel}}\)). This means that all of these models may be used interchangeably in the process of SCC design without any alteration (so there is no need to develop a new model). Including the water demand of aggregate in the design procedure in its proposed version sets the bottom limit of superplasticizer dose for laboratory trials, leaving only small gap for eventual minor adjustments.  
self-compacting concrete; SCC composition design; water demand; specific surface; specific surface of aggregate
## 2 Requirements Specifications and Measurements Gaps
### Dominant Wave Direction and Significant Wave Height  
These variables can be measured with radar altimeters and SAR. In the time frame 2020-2030, the instruments capable of measuring these variables are Poseidon-4 (Sentinel-6/JASON), SRAL (Sentinel-3) and SAR-C (Sentinel-1), but the 3-h revisit time is not satisfied. The current Copernicus infrastructure delivers about 24-48 h of latency for the corresponding measurements.