esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of Hap and Hap-FCF  
All materials were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), specific surface area (BET method), zeta potential (ZP), scanning electron microscopy (SEM), and bright-field transmission electron microscopy (BFTEM). XRD patterns were acquired using a Bruker model D8 Advance diffractometer with a Cu K\(\alpha\) radiation source (\(\lambda\) = 1.5418 A, 40 kV, and 30 mA), operating over an angular range of 20 at a scanning speed of 2\({}^{\circ}\)-min\({}^{-1}\). Crystal size (X_s) was calculated using the Scherrer equation:  
\[X_{s}=\frac{0.9\lambda}{\beta\text{cos}\theta}, \tag{1}\]  
Infrared spectra were recorded with a PerkinElmer Frontier spectrometer, Macedonia Alcala, CDMX, Mexico in transmission mode, covering the range from 400 to 4000 cm\({}^{-1}\). The specific surface area (BET) was measured with a Micromeritics Gemini VII instrument, Norcross, GA, USA following a pretreatment of 2 h at 80 \({}^{\circ}\)C and 6 h at 180 \({}^{\circ}\)C. Zetapotential (ZP) was measured using a Zetasizer Nano ZSP instrument, Monterrey, Mexico, with samples diluted to a concentration of 0.1 g/mL in deionized water.  
SEM micrographs were obtained using a FEI Nova NanoSEM 200, Madrid, Spain scanning electron microscope operating at 18 kV. TEM micrographs were captured using a JEOL JEM-ARM200 CF, Napoles, CDMX, Mexico transmission electron microscope at an operating voltage of 200 kV.
## 3 System Architecture, Software Services and Deployment
### Cloud-Based Services
#### 3.4.4 Asynchronous Notifications  
The asynchronous notifications service pushes notifications associated with alerts generated by the interconnected smart metering devices to all authorized users. Authorized users may also receive a notification related to data analytics and other relevant services. It should be stated here that smart meter-related notifications are only forwarded to users in the case of an alert, indicating that a user-defined threshold for a specific metric is violated. This approach allows notification to be issued in the case of an emergency or detected anomaly, i.e., due to a node malfunction or water leakage. The asynchronous notification service renders the framework administrators capable of defining complex criteria that potentially introduce additional computational costs for efficiently handling the alerts generated by the device (e.g., leak detected). Remote users get notifications through a smartphone application that integrates the available push notification mechanism.
# A Review of Transformer-Based Approaches for Image Captioning
## 7 Challenges and Future Directions
### Cross-Fertilization across Domains  
There has been a marked interplay of computer vision and natural language processing (NLP). A number of the advancements that have arisen in computer vision models have been inspired by advancements in the natural language processing domain, and vice versa. Machine translation in particular has had a great impact on image captioning systems. Out of machine translation was born the encoder-decoder framework that has come to dominate  
Figure 12: Ambiguous and overlapping region features from images with _couches_ and _dogs_. In the semantic embedding space for images,there is a lot of overlap. The word embeddings on the other hand are more distinct. Multimodal alignment can help disambiguate the image semantic embeddings.  
the field of captioning. The attention mechanisms and transformer models that are used in the current state-of-the-art models also had their origins in the language domain.  
The language domain has also benefited from some of the approaches used in the vision domain. An example of this is the pre-training of models on large amounts of data to create better-performing natural language processing models. Through this approach, the NLP models end up benefiting from transfer learning and domain adaptation [141]. This kind of transfer learning is a concept that was previously used in computer vision with great success. Many vision models commonly use the large ImageNet dataset [49] pre-training and initialization of the parameters prior to subsequent fine-tuning on downstream tasks such as image classification, object detection and segmentation. ImageNet greatly facilitates the learning of general low-level visual features. ImageNet has had a tremendous impact in the field of computer vision. Together with ImageNet, the Visual Genome dataset [58] and OpenImages [51] have also proven useful for such pre-training. The large-scale use of pre-trianing in natural language tasks resulted in the _ImageNet moment_ for NLP [142].  
The adoption of mechanisms and models from one domain to another is an approach that will continue to happen as each of the domains becomes enriched. With the most recent approach in image captioning relying on transformers, it will be important to keep looking out for new developments in the language domain that can further improve performance of the captioning systems and computer vision in general.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 2 SVD Transform and Its Characteristics
Based on the analysis, in the proposed watermarking scheme, the singular values of each image block are served as the authentication watermark, while the left and right singular vectors are adopted to classify the image blocks into smooth blocks and texture blocks. In [32], [PERSON] et al. further found that the probability distribution of the values in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) of all the blocks satisfies normal distribution approximately. Specifically, for smooth blocks, the values in sub-image \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) are approximately equal to the reciprocal of the block size, while for texture blocks, the values in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) are scattering distributed. For example, when the image block size is \(2\times 2\), the values in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) of smooth blocks are approximately equal to \(1/2\) (0.5). Based on the above analysis and reference [32], a new block classification method based on SVD transform is designed. The detailed steps of this process are described as follows:  
* Step 1: The host image is firstly divided into \(2\times 2\) image blocks, and then the SVD transform is conducted on each block. * Step 2: For each block after SVD, the product of the left and right singular vectors (the first sub-image \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\)) is calculated. For smooth blocks, the values in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) are approximately equal to \(1/2\) (0.5). Based on this point, two thresholds around 0.5, \(T_{1}\) and \(T_{2}\) are identified to label the pixels in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\). For a pixel in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\), if the pixel value falls in the range of \(T_{1}\) to \(T_{2}\), the pixel will be judged as a smooth pixel, else it is judged as a texture pixel. * Step 3: Calculating the number of the smooth pixels in 2 \(\times\) 2 block, if the number is larger than 3, the corresponding image block will be determined as a smooth block, else the block is determined as a texture block. In this process, the interval between \(T_{1}\) and \(T_{2}\) plays an important role for the block classification result. If the interval between these two thresholds is too large, more image blocks would be determined as smooth blocks. On the contrary, if the interval is too small, more texture blocks will be obtained. However, if a texture block is falsely judged as a smooth block, the recovery data of this texture block would be insufficient and cannot adequately reflect the texture feature of this block. Consequently, the restored image will become severely blurred. Conversely, if there are too many texture blocks in the image, it will lead to the data redundancy for original smooth blocks in image recovery process. In this paper, the thresholds, \(T_{1}=0.48\) and \(T_{2}=0.52\), are manually selected through a large number of testing experiments.
## 3 Results
### BFP-MSC Interaction with Ti Disks Do Not Alter Cell Secretion  
It was evaluated whether Ti disks could interfere with BFP-MSCs' secretory profile. To this aim, we analyzed the release of a panel of cytokines, chemokines and growth factors in the medium of BFP-MSCs plated without disks and of BFP- MSCs cultured on D1, D2, D3, and D4. Among all the factors analyzed, IL-1 ra, IL-2, IL-5, IL-6, IL-8, IL-9, IL-10, IL-12 (p70), IL-15, IL-17, basic FGF, G-CSF, IFN-\(\gamma\), IP-10, MCP-1, MIP-18, CCL5/RANTES, TNF-\(\alpha\), PDGF-BB and VEGF were the factors more abundant in BFP-MSC medium (Figure 5). Overall, Ti surfaces did not interfere with cell secretory functions. However, when the cells were plated on D2 (chemically etched surface), a significant reduction of IL-1 ra, IL-2, IL-10, IL-12, IL-17, and G-CSF, and a reduced trend of basic FGF (\(p\) = 0.054) was detected, compared to D3 (sandblasted surface). Interestingly, BFP-MSCs, in presence of D4 (blasted/etched surface), displayed a slight increase of IL-1 ra (\(p\) = 0.09)-compared to D2- and a significant increase of G-CSF, compared to cells plated without a disk and to cells plated on D1 and D2 (Figure 5).  
Taken together, all our data demonstrated that BFP-MSCs plated on different Ti disks are able to keep their own viability, adhesion and secretory capacity. Cells plated on D4 showed high adhesion on the disk and released a higher amount of G-CSF.  
Figure 4: Number of BFP-MSCs harvested from disks and re-plated in 12-well culture plates for 72 h. Bars represent the number of live cells relative to those plated upon detachment from disks.  
Figure 5: \(Cont.\)
## 3 New Pulse Duration Limits of Modelocked Thin-Disk Lasers  
Most scientific applications targeted by the development of novel high-power ultrafast sources require high peak powers in combination with short pulse durations (typically sub-100 fs), and benefit from repetition rates in the MHz range, resulting in increasing needs for high peak power sources with high average power. Current state-of-the-art ultrafast TDLs that combine high peak power and high average power are restricted in this aspect to pulse durations longer than 500 fs. Extending the high-power capabilities of TDLs to the sub-100 fs regime is, therefore, a major milestone, which is intimately linked to the development of novel broadband gain materials suitable for this geometry. In this paragraph, we will focus on the main challenges of combining high average power and short pulse durations, followed by a review of the latest achievements. We will mainly focus on recent experiments in which record-short pulse durations were achieved with TDLs based on the promising family of cubic sesquioxide gain materials. In particular, the recent demonstration of a modelocked TDL with sub-100-fs pulse duration based on the mixed sesquioxide material Yb:LuScO represents a promising first step [21]. These are the shortest pulses ever demonstrated from a TDL to date, reaching for the first time the sub-100 fs milestone. This proof-of-principle experiment shows that such oscillators are suitable to access this regime. Furthermore, preliminary power scaling experiments indicate that much higher powers are within reach. In addition, the progress in terms of pulse duration enabled the first measurement of the carrier envelope offset (CEO) frequency of a TDL, which is a first step towards full stabilization of such high-power unamplified oscillators for use in frequency comb applications.  
Figure 14: Intracavity peak power of modelocked TDLs demonstrated to date, showing the crucial advantage of operating such oscillators in a vacuum environment. The different colors indicate different gain materials that have already been modelocked in the thin-disk geometry.
## 3 Numerical Model
### Model Validation  
The numerical model was validated using experimental data from the trials using 4, 8, and 12 finned tubes, a flow rate of 9 LPM, and discharging temperature ranges of 65 to 21 \({}^{\circ}\)C and 55 to 31 \({}^{\circ}\)C. The HTF temperature was also ramped down using trial-specific functions made from the experimental data in Excel to mimic each trial as closely as possible. The simulation time was set to 7200 s with a strict maximum time step of 30 s. Figure 8 shows how the numerical results from this simplified geometry and model compare to the experimental results from [PERSON] and [PERSON] (2018).  
Based on the experimental conditions of each trial, the HTF convection coefficients inside each finned tube were calculated following the approach presented in Section 3.4. These convection coefficient values are presented in Table 3.  
From the validation results, some cases showed great agreement; for example, the validation study 1 (see Table 3 and Figure 8a) for 12 fins was able to capture the maximum power and the representative power decline over time. Others captured general orders of magnitude for the system equivalent power without fully capturing the maximum power. It appears clear that this simplified 2D model is not as accurate or predictive for cases using only four finned tubes. However, for the purpose of this study, the validation results from Figure 8 prove that the simplified 2D models capture enough physics from the process, providing general trends. Therefore, the models were found to be valuable for parametric studies on the impact of the variation in the PCM thermal conductivity in this case.  
For reference, during this work, simulations were performed using an Intel Core i7-10700k CPU (3.8 GHz with 5.1 GHz Turbo Boost) and 32 GB of RAM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Validation** & **\# of Fins** & \(\dot{\textbf{V}}\) **(L/min)** & \(T_{range}\) (\({}^{\circ}\)C)** & \(\dot{\textbf{h}}\) **(W/m\({}^{2}\)K)** \\ \hline
1 & 12 & 9 & 65 to 21 & 1590.32 \\
2 & 12 & 9 & 55 to 31 & 1624.05 \\
3 & 8 & 9 & 65 to 21 & 1953.44 \\
4 & 8 & 9 & 55 to 31 & 2285.66 \\
5 & 4 & 9 & 65 to 21 & 3809.42 \\
6 & 4 & 9 & 55 to 31 & 4180.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Validation study and convection coefficients used.  
Figure 8: System equivalent power as a function of time for six numerical validation cases compared to experimental results from [18]: (**a**) 12 finned tubes, (**b**) 8 finned tubes, and (**c**) 4 finned tubes. The case numbers are indicated in Table 3 for reference.
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 1 Introduction
The Ocean Color--Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) approach utilizes scientific machine learning (SciML) in conjunction with radiative transfer computations of the coupled atmosphere-water system to perform the AC step and retrieve water products from the resulting \(R_{\text{rs}}\) estimates [14]. The OC-SMART data processing platform employs a radiative transfer model for the coupled atmosphere-water system, AccuRT [30], that accounts for a variety of atmospheric and oceanic conditions likely to be encountered in nature. The OC-SMART approach resolves the negative water-leaving radiance problem, and is used to retrieve aerosol optical depths and remote sensing reflectances (\(R_{\text{rs}}\)) as a function of wavelength on a pixel by pixel basis. These \(R_{\text{rs}}\) estimates are then used to infer inherent optical properties (IOPs), including absorption coefficients due to phytoplankton, detritus, and Gelbstoff, and backscattering coefficients due to particulates. However, these retrieved IOPs do not contain uncertainty estimates at present. To address this shortcoming, in this paper we focus on how to quantify uncertainties in retrieval parameters produced by OC-SMART. Since the accuracy of the remote sensing reflectance \(R_{\text{rs}}\) is critical to obtain high-quality water IOPs, our main target in this paper is to determine \(R_{\text{rs}}\) uncertainties associated with errors in measurements and _a priori_ information. We chose to focus on a Bayesian inversion approach because of its ability to estimate uncertainties on a pixel by pixel basis and its flexibility to being updated if and when new _a priori_ and/or measurement error information becomes available. Section 2 presents an overview of OC-SMART in regards to its methods, advantages compared to previous approaches, such as C2 RCC and SWARM, and limitations. Section 3 presents the methodology that allows us to quantify uncertainties, starting with an introduction to Bayesian inversion (Section 3.1) and continuing to discuss the key elements involved in the uncertainty estimation, including a convergence check and computation of the Jacobian (Section 3.1), measurement errors (Section 3.2), _a priori_ determination (Section 3.3), and experimental setup (Section 3.5). Application of our methodology to estimate uncertainties associated with \(R_{\text{rs}}\) is provided in Section 4 for various regions and several sensors. Section 5 contains discussion of results, conclusions, and future goals.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Simulations
#### 2.3.1 Parabolic Flow in Vessel Phantom  
Parabolic flow through a rigid, straight tube with a peak velocity (v\({}_{\mathrm{max}}\)) of 1.5 m/s was simulated at flow angles of 75\({}^{\circ}\) till 105\({}^{\circ}\), with steps of 3\({}^{\circ}\). The vessel had a lumen diameter of 6 mm and was centered at 20 mm depth. Scatterers, representing the blood, had Gaussian distributed amplitudes (mean = 5, SD = 1). Plane wave ultrasound data were simulated for pulse repetition frequencies (PRFs) of 2.5 till 15 kHz, with steps of 2.5 kHz. Table 2 provides an overview of the simulated PRFs and resulting inter-frame displacements. For each unique combination of flow angle and PRF, a pre and post frame were simulated, where in between the scatterers were propagated according to the parabolic velocity profile. This process was repeated ten times, with random initial scatterer positions, allowing 10 independent velocity estimates. No clutter filtering or time averaging were performed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties L12-5, 38 mm** & **Experiments** & **Simulations** \\ \hline Center frequency (f\({}_{\mathrm{c}}\)) & 8.9 MHz & 9 MHz \\ Sampling frequency & 36 MHz & 180 MHz \\ Transducer element pitch & 198 μm & \\ Transducer element height & 4 mm & \\ Transducer element width & 173 μm & \\ Nr. of transducers elements & 192 & \\ Nr. of active elements & 128 & \\ Excitation signal & 3-cycle pulse at f\({}_{\mathrm{c}}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transducer properties.  
Figure 1: Representation of the system’s point-spread-function (PSF) visualized using radio frequency (RF) (**a**) and envelope (**b**) data. The data sampling points required to capture a circular-shaped peak in the cross-correlation function are indicated in the figure. Please note that the envelope data sampling grid is a downsampled version of the RF data sampling grid.  
Band limited noise was added to the beamformed RF data resulting in a signal-to-noise ratio (SNR) of 12 dB.
## 3 Results and Discussion
#### 3.1.4 Integrated Healthcare Systems  
A multi-physiological-parameter medical monitoring system based on the ARM-embedded platform [111] has been developed; this system can detect out-of-standard data and issue alarms when necessary, such as when the blood oxygen saturation is critically low.  
The investigation outlined in [112] developed a reliable and smart e-healthcare system for the monitoring of the intravenous fluid level, pulse, and respiration rate, particularly focusing on elderly patients in healthcare settings like hospitals. The system enables constant monitoring and provides timely alerts to healthcare providers, enhancing patient care and safety.  
The MiVitals system [85] is a mixed-reality tool enabling healthcare professionals to monitor patients using holographic visualizations of real-time vital signs. It was found to be highly usable in a study by medical doctors and healthcare practitioners.  
The research team in [113] developed a novel method for gait assessment in patients with [PERSON]'s disease by employing inertial measurement units (IMUs) to quantitatively analyze gait patterns and incorporate biomechanical measurements. A fuzzy inference system was integrated into the model and validated through qualitative evaluations from multiple experts. It assists in the monitoring of disease progression and the personalization of treatment plans.
## 2 Cross-Hole Grounded-Wire-Source Transient Electromagnetic System  
This paper introduces a novel transient electromagnetic (TEM) method utilizing a cross-hole grounded-wire source, as depicted in Figure 1. In this method, a grounded current supply conductor is placed in a borehole ahead of the working face. A step current is supplied through a connected transmitter, while a receiving probe is positioned in a borehole on the opposite side to perform point-by-point measurements. This configuration is designed to capture the distribution of the secondary electromagnetic field generated by induced eddy currents, enabling both the time and spatial signal observation of the geological target surrounding the borehole. The secondary field, which propagates through the rock mass with lower attenuation compared to air, produces higher signal amplitudes, leading to improved signal-to-noise ratios and lateral resolution. This results in the more effective detection of geological features. Furthermore, the grounded-wire-source configuration facilitates long-range, high-precision exploration. Depending on the number of boreholes in front of the working face, multiple survey lines and sources can be deployed [40]. This setup can generate a 3D observation pattern, which provides a theoretical foundation for subsequent 3D inversion. In practical engineering applications, following the completion of preliminary geophysical exploration, drilling verification is necessary. Hence, utilizing existing boreholes for our cross-hole method requires minimal additional investment. Moreover, this approach mitigates the limitation of relying on a single borehole, thereby providing more comprehensive data for advanced exploration.