esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 User Interface and Set Up
### Test Case: Voting Process
#### 5.2.2. Managing Political Parties and Candidates (See Figures 15 and 16)  
* **Admin Login**: The administrator securely logs into the system, using bcrypt for password hashing to protect credentials;  
Figure 11. Main screen of the e-voting system.  
Figure 12. Add voter ID and Ethereum wallet address.  
Figure 13. Voter is registered.  
* **Creating Political Parties**: The administrator inputs party details and deploys a smart contract that records these details on the blockchain. The party creation process is securely signed using MetaMask, ensuring immutability and traceability.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 2 Materials and Methods
### Methodology and Implementation
#### 2.2.1 Satellite Data Access and Processing  
Satellite data have been gathered for the vegetation cover classification and the comparison of PPI and NDVI over the Valais territory.  
For the vegetation cover classification, we used the Forest Type ([[https://land.copernicus.eu/en/products/high-resolution-layer-forest-type/forest-type-2018](https://land.copernicus.eu/en/products/high-resolution-layer-forest-type/forest-type-2018)]([https://land.copernicus.eu/en/products/high-resolution-layer-forest-type/forest-type-2018](https://land.copernicus.eu/en/products/high-resolution-layer-forest-type/forest-type-2018)) accessed on 17 August 2023) and Grassland ([[https://land.copernicus.eu/en/products/high-resolution-layer-grassland/grassland-2018](https://land.copernicus.eu/en/products/high-resolution-layer-grassland/grassland-2018)]([https://land.copernicus.eu/en/products/high-resolution-layer-grassland/grassland-2018](https://land.copernicus.eu/en/products/high-resolution-layer-grassland/grassland-2018)) accessed on 17 August 2023) high-resolution layers of the Copernicus Land Monitoring Service (CLMS). These data have a resolution of 10 m, characterizing broadleaf forest, coniferous forest, and grasslands. For mapping shrulands, we followed the methodology proposed by [49], using Sentinel-2 data in the Google Earth Engine to compute first the median of the Normalized Anthocyanin Reflectance Index (NARI) and the Normalized Chlorophyll Reflectance Index (NCRI) for each year between 2017 and 2021 from 1 September to 1 November and then computed the median of the five years. The detailed methodology is explained in the next section.  
All PPI and NDVI Sentinel-2 scenes for the years 2018 to 2022 were downloaded from the WEkEO platform ([[https://www.wekeo.eu/](https://www.wekeo.eu/)]([https://www.wekeo.eu/](https://www.wekeo.eu/)) accessed on 10 August 2023), corresponding to 4 tiles for covering the Valais region: T32 TLS, T32 TMS, T32 TMR, T32 TLR. There are 73 observations per year at 5-day intervals, beginning on 05-01-2018, resulting in 1460 images per index, 2920 in total. In addition, for each observation date, a quality layer (i.e., QFLAG) was downloaded. It corresponds to a bitwise encoded status map, which indicates if the pixel is water, land, cloud, snow, or shadow [34].
## 5 Assessing Dataset Availability and Practicality
### Stitching Defect Detection  
Stitching defects are another area of focus within textile manufacturing, and similar to fabric or textile defects, the development of machine vision in this area is a response to the increasing necessity for automated quality assurance that has predominantly relied on manual visual inspection methodologies.  
Few datasets containing images of stitch defects have been acknowledged by [42]. A dataset of 900 images of stitching defects was created to be analyzed using DeepLabV3+. Half of the samples were created with white thread on white fabric, with the rest created using blue thread on white fabric. No other details, such as the fabric types or the ISO stitch types or the machines used to create the stitches were provided. Focusing on image segmentation techniques, the paper employs a deep learning algorithm for defective stitch inspection in garment manufacturing. Specifically, their proposed DeepLabV3+ architecture, combined with EfficientNet models, is utilized to precisely identify and classify defective stitches.
## 1 Introduction  
Noise is common in everyday life, affecting communication not only for individuals with hearing impairments but also for those with typical hearing (TH), i.e., bilateral pure-tone thresholds \(\leq\) 20 dB HL for 0.5-8 kHz in octave bands. Although pure-tone audiometry is widely recognized as the \"gold standard\" for assessing hearing function, a normal audiogram does not necessarily guarantee satisfactory communication experiences in all settings. Over 10% of adults may experience hearing difficulty in noisy environments despite having normal audiograms. [PERSON] et al. [1] reported that 12% of 682 individuals aged 21-67 years with TH reported hearing difficulties. Similarly, 15% of 2015 subjects aged 20-69 years with bilateral pure-tone averages of \(\leq\)25 dB HL for 0.5, 1, 2, and 4 kHz experienced similar issues [2]. However, standard clinical guidelines from the American Academy of Audiology [3] and the American Speech-Language-Hearing Association [4] provide limited guidance for addressing these communication challenges for listeners with TH, aside from recommendations to use hearing protectors to prevent occupational noise-induced hearing loss [5]. [PERSON] et al. [6] found that many clients express frustration when told they have \"normal hearing\" while still experiencing hearing difficulties. Additionally, clinicians expressed frustration with a lack of adequate training or scientific resources to assist these clients beyond suggesting the use of communication strategies [6].  
Possible solutions for these challenges are reviewed. The related work is divided into three sections. Section 1.1 presents one potential technological solution, which is the use of clinically available remote microphone (RM) systems, designed to enhance the signal-to-noise ratio (SNR). Section 1.2 addresses the use of smartphones as RM systems. Smartphones equipped with built-in microphones and Bluetooth capabilities (Bluetooth Special Interest Group, Kirkland, WA, USA) have been suggested as components of RM systems for use in small-group conversations for adults. Section 1.3 includes a review of the available research regarding the acoustic properties and potential benefits of using the AirPods Pro (AP) as a hearing device.
## 3 Results and Discussion
### TG Analysis  
To investigate the thermo stability of sample slices in different regions of IPN sample, Imayers of the 75 th, 50 th, 25 th, EP and PU were selected for TG analysis (Figure 6).  
The rigidity of polymer chains and the crosslinking density of cross-linked polymers determine the thermal decomposition temperature (\(T_{\mathrm{d}}\)). Due to EP (LY-1564) is mainly composed of rigid chains such as benzene ring and carbon-oxygen bond, it has the highest Td (about 330 \({}^{\circ}\)C) and the largest residual mass (about 17%) at the temperature of 470 \({}^{\circ}\)C.  
Figure 5: Differential scanning calorimetry (DSC) analysis of the slices selected from the gradient IPN. Temperature sweeps from \(-\)40 to 95 \({}^{\circ}\)C at a slope of 15 \({}^{\circ}\)C/min in nitrogen atmosphere (50 mL/min).  
Figure 6: Thermogravimetric (TG) curves of epoxy (EP), polyurethane (PU) and the chosen layers from the gradient IPN sample. Heating rate: 10.0 \({}^{\circ}\)C/min, air atmosphere.  
In contrast, PU is mainly composed of ether bonds. Because of these weaker bonds and the lower crosslinking density, the thermal decomposition temperature of PU is around 210 \({}^{\circ}\)C. The thermal decomposition process of PU can be separated into 3 stages: 210-280 \({}^{\circ}\)C, 300-380 \({}^{\circ}\)C and 400- 450 \({}^{\circ}\)C. The first stage represents the decomposition of the C-O bond as well as the generation of isocyanate and polyhydric alcohols. Parts of the generated isocyanate would volatilize which led to the weight loss in the first step. Meanwhile, the rest isocyanate would be self-aggregate into dimer or trimer. The second stage is that the dimers/trimers resolve into amines, alkenes and carbon dioxide. In addition, the volatilization of these products caused the second weight loss. The first two stages lose about 55% of PU weight, which exactly equal to the percentage of the isocyanate in PU. The final stage is the pyrolysis of the polyhydric alcohols. In addition, the pyrolysis products are water and carbon dioxide. The final residual mass is about 7% of the original PU.  
The thermal decomposition temperature (\(T_{\text{d}}\)) of the 25 th, 50 th and 75 th slices in the IPN are 220, 247 and 268 \({}^{\circ}\)C, respectively. The residual weight of the 75 th slice (11%) is heavier than that of the 25 th (8%), and the residual weight of the 50 th (10%) lies between them. This result can be explained by the decrease of PU content in the IPN along with the gradient direction from bottom to top. Meanwhile, shape of the TG curves of the gradient IPN is different from that of PU. This may be explained by the synergistic effect between PU and EP in this IPN structure.
## 3 Results  
### Organoid Formation from OPSCC Cells  
Our first step was determining the conditions that could be used to obtain a reliable number of homogeneous organoids within a time range of 24 h. To this end, we compared seeding densities of 100 and 1000 cells/\(\mu\)L hydrogel in a total volume of 50 \(\mu\)L, and assessed organoid formation with HoloMonitor M4. As depicted in Figure 1, all seeding densities tested allowed for the generation of organoids within 24 h after seeding. The obtained number and characteristics of the organoids varied depending on seeding density, where 100 cells/\(\mu\)L resulted in 102 \(\pm\) 36 organoids/mm\({}^{2}\), with median area 1234 \(\mu\)m\({}^{2}\), optical volume 14,377 \(\mu\)m\({}^{3}\), and optical thickness 13 \(\mu\)m; 1000 cells/\(\mu\)L resulted in 47 \(\pm\) 34 organoids/mm\({}^{2}\), with median area 2879 \(\mu\)m\({}^{2}\), optical volume 71,651 \(\mu\)m\({}^{3}\), and optical thickness 22 \(\mu\)m. Together, these results showed that seeding densities impacted the size and optical properties of organoids, and that close contact between cells, and therefore exposure to chemical and biological signals, favored organoid formation. HoloMonitor M4 monitored organoid formation up to a volume of approximately 10\({}^{5}\)\(\mu\)m\({}^{3}\). For larger organoids, insufficient light for analysis passed through the organoid structures.  
Figure 1: Seeding-density conditions and optical properties of organoids imaged with HoloMonitor M4. Oropharyngeal squamous-cell carcinoma (OPSCC) cells imaged 15 h after seeding at densities of (**a**) 100 and (**b**) 1000 cells/\(\mu\)L, both obtained by digital holographic microscopy (DHM). (**c**) Scatter plot displaying optical volume and thickness of organoids obtained using seeding densities of 100 and 1000 cells/\(\mu\)L (\(n=3\)).  
After selecting the appropriate seeding-density range to obtain homogeneous organoids, we assessed 3D-structure formation over time. Single cells were identified using a cutoff diameter of 20 \(\upmu\)m and tracked over 15 h to assess events influencing organoid formation and growth (Figure 2). Organoids started forming 2 h after seeding, and overall growth was monitored by the increase in area and optical thickness over time. During the first 8 h, cell-aggregation events dominated organoid formation; in the last 7 h, organoids were fully formed, and displayed stable diameter and optical-thickness measurements.
# Concurrent Validity of the Inertial Measurement Unit _Vmaxpro_ in Vertical Jump Estimation
## 2 Materials and Methods
### Instruments  
#### 2.3.1 Motion Capture System Optitrack  
The direct determination of the VJH was performed using a MoCAP system (Optitrack motive, Corvallis, OR, USA) which consists of 6 _Optitrack Flex 3_ cameras (Optitrack motive, OR, USA). This device allows the 3D tracking of a marker, consisting of a reflective sphere capable of reflecting infrared light in the same direction in which it is received. The marker is placed between the L4 and L5 vertebrae, in the same place as the IMU, near the center of mass [27; 28]. The infrared light is emitted by the 26 LEDs surrounding the camera lens (IR 850 nm) in a ring configuration synchronized with the capture shutter. Furthermore, the 6 cameras operate at 100 Hz with a shutter speed of 20 \(\upmu\)s, achieving an overall resolution of 0.001 m. The height of the VJH was determined from the data obtained, calculating the difference in the position on the vertical axis of the marker before the execution of the jump, during the rest phase, and at the highest point recorded in the flight phase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean** & **SD** \\ \hline Age (years) & 22.23 & 3.26 \\ Height (m) & 1.72 & 0.06 \\ Body mass (kg) & 64.12 & 7.33 \\ Fat mass percentage (\%) & 16.49 & 2.50 \\ BMI (kg/m\({}^{2}\)) & 21.64 & 2.14 \\ Experience (years) & 8.77 & 2.01 \\ \hline \hline \end{tabular} SD = standard deviation; BMI = body mass index; Experience = years practicing volleyball.  
\end{table}
Table 1: Descriptive statistics of study participants.  
The signal was transmitted via a USB cable connection to a laptop where it was stored and analyzed using the _Motive Tracker 2_ software (Optitrack motive, OR, USA). In addition, this software synchronizes and calibrates the image collection systems as well as exporting the data obtained in text format separated by commas (CSV) for subsequent analysis in a spreadsheet and thus determining the VJH through the difference in positions.
# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 4 Experiments
### Data Sets and Quantitative Metrics
A smaller RMSE value indicates better quality:  
\[RMSE=\sqrt{\frac{||\hat{S}-S||_{F}^{2}}{LN_{c}}}, \tag{21}\]  
where \(L\) is the number of bands and \(N_{c}\) is the total number of pixels. Peak signal-to-noise ratio (PSNR) [1] is used to measure the numerical similarity of spatial reconstruction between the result and the ground truth. It is defined as follows:  
\[PSNR=10\times log_{10}(\frac{max(S_{i})^{2}}{\left\|S_{i}-\hat{S}_{i}\right\|_{ 2}^{2}/L}), \tag{22}\]  
where \(S_{i}\) and \(\hat{S}_{i}\) denote the pixel value of the \(i\)th spectral band separately in the reconstructed image and ground truth, respectively, and \(L\) is the total number of bands. The larger the PSNR value, the closer the spatial quality of the reconstructed image is to that of the reference image. Spectral angle mapper (SAM) [1] is a metric which evaluates the spectral preservation quality, indicating the spectral similarity between the result and the ground truth. The SAM of the \(j\)th pixel is defined as the angle between the vectors of the estimated and the ground truth spectrum, as follows:  
\[SAM=arc\ cos(\frac{S_{j}^{T}\hat{S}_{j}}{||S_{j}||_{2}||\hat{S}_{j}||_{2}}). \tag{23}\]  
A smaller SAM value indicates higher spectral quality. The relative dimensionless global error in synthesis (ERGAS) [1] is a global statistical metric, which is defined as follows:  
\[ERGAS=\frac{100}{d}\sqrt{\frac{1}{L}\sum_{i=1}^{L}\frac{MSE(S_{i},\hat{S}_{i})} {mean(\hat{S}_{i})^{2}}}, \tag{24}\]  
where \(MSE(S_{i},\hat{S}_{i})\) represents the mean square error, \(d\) is the downsampling scale factor, and \(mean(\hat{S}_{i})\) is the mean pixel value of \(\hat{S}_{i}\). Hypercomplex quality assessment (\(Q2^{n}\)) [37] is another metric used to evaluate HSIs. \(Q2^{n}\) has been improved based on the universal image quality index (UIQI), which has been widely used in many other related works. The UIQI is defined as:  
\[Q(x,y)=\frac{\sigma_{xy}}{\sigma_{x}\sigma_{y}}\frac{2\bar{x}\bar{y}}{\bar{x}^ {2}\bar{y}^{2}}\frac{2\sigma_{x}\sigma_{y}}{\sigma_{x}^{2}+\sigma_{y}^{2}}\]  
\[\bar{x}=\frac{1}{L}\sum_{i=1}^{L}x_{i},\ \ \bar{y}=\frac{1}{L}\sum_{i=1}^{L}y_{i} \tag{25}\]  
where \(x\) and \(y\) are the band of ground truth and band of the reconstructed HSI. The three components of \(Q(x,y)\) respectively represent the correlation, luminance distortion, and contrast distortion of the image. However, the UIQI was specifically designed for monochromatic images. \(Q2^{n}\) overcomes this problem by modeling each pixel's spectrum vector \(x_{j}\) as a hypercomplex number, which is formulated as follows:  
\[x_{j}=x_{j,0}+x_{j,1}i_{1}+x_{j,2}i_{2}\cdots+x_{j,2^{n}}i_{2^{n}-1}. \tag{26}\]  
Thus, \(Q2^{n}\) is a universal image metric, a larger value of which indicates higher quality of the image.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 3 Proposed Method
Subsequently, the features are expanded through upsampling to achieve super-resolution. Upsampling increases the size of low-resolution images to restore high-resolution features. By employing subpixel convolution, the proposed method avoids checkerboard artifacts, ensuring smooth outputs and preserving high-frequency details. Figure 7: Architecture of the proposed super-resolution method. The proposed super-resolution method can generate detailed and sharp images with emphasized high-frequency features. This hierarchical feature extraction process ensures that both shallow and deep features contribute to the final output, enhancing the model's ability to preserve fine details and reconstruct high-quality images.
## 1 Introduction  
The therapeutic efficacy of mesenchymal stromal cells (MSCs) is well documented, and it has been closely associated with the release of bioactive molecules, and among these molecules, extracellular vesicles (EVs) are considered to be of great interest. They are reported to be as effective, or even more, as their parental cells [1, 2], showing adequate advantages. Due to their small dimensions, they can cross the epithelium and blood barrier, due to the absence of nuclei, and they show a major safety profile [3], and they are not influenced by the surrounding environment. These characteristics make EVs good candidates for clinical approaches, and several applications of EVs in different settings are reported [2; 3; 4]. EVs can be collected by in vitro cultured MSCs as a result of stress conditions, such as starvation [3]. Little data are available regarding the MSC functional activity after starvation and culture condition restoration. It has been demonstrated that the complete starvation of MSCs may act as preconditioning, influencing the cellular metabolic state, and may enhance the MSC survival in vivo, promoting quiescence or decreasing metabolic demands [5]. During the in vitro culture, MSC glucose and oxygen consumption rise considerably, and this result in them having a short lifespan after transplantation [6; 7; 8].  
It has been reported that, under long-term starvation, MSCs employ gluconeogenesis, \(\beta\)-oxidation, and autophagy in order to overcome the in vitro poor metabolic conditions [9; 10; 11; 12; 13]. In particular, it has been observed that high amounts of growth factors, such as IGFBP2, IGFBP3, IGFBP6, HGF, TGF62, PDGF\(\alpha\), VEGFR2, and IGF2, are secreted for adapting to the in vitro harsh conditions [9; 10]. Moreover, concomitant starvation and hypoxia have been described to induce MSC quiescence and to enhance the autophagy process that induces lipid \(\beta\)-oxidation as an alternative energy source [12].  
Considering the growing interest in the clinical application of MSC-EVs and taking into account the above reported observations, we would like to understand the fate of MSCs after starvation for EV collection. For this reason, we investigated the MSC functional properties after growth factor deprivation.  
The aim of the study was to investigate the serum starvation impact on features of MSCs, otherwise discarded after EV collection.  
To achieve our purpose, after starvation, we added complete medium, and at confluence, MSCs were detached and evaluated for morphology, phenotype, differentiation capacity, immunomodulatory ability, and metabolic state and compared to non-starved MSCs, while Transmission Electron Microscopy (TEM) analysis was performed on MSCs after starvation to reveal the basic ultrastructural characteristics of cells and EVs.
# Object Detection-Based Video Compression
## 3 Proposed Methods
### Experimental Results
\(\Delta\)Y-PSNR, \(\Delta\)Cb-PSNR, and \(\Delta\)Cr-PSNR are the PSNR increments of Y, Cb, and Cr of the proposed method compared with those of VTM-8.2, respectively, in each picture, where Y is a luma component and Cb and Cr are chroma components. The small PSNR gain in the detected object areas is meaningful in the object detection-based video coding using the VVC standard because any machine analysis techniques on the decoder side can be applied to the PSNR-improved detected object areas for further processing, such as face recognition, segmentation, and shape extraction. In Table 4, \(\Delta\)Y-PSNR that is higher than 0% indicates that the proposed method increases the \(\Delta\)Y-PSNR value compared with the VTM-8.2 anchor for the object-detected areas, providing better image quality of Y-PSNR in the proposed method than in the anchor. In the RA configuration, the average \(\Delta\)Y-PSNR was improved to 0.18%, but the averages of \(\Delta\)Cb-PSNR and \(\Delta\)Cr-PSNR were slightly decreased by 0.11% and 0.04%, respectively, for all the test sequences in Table 4. In the AI configuration, the averages of \(\Delta\)Y-PSNR, \(\Delta\)Cb-PSNR, and \(\Delta\)Cr-PSNR improved to 0.71%, 0.30%, and 0.30%, respectively, for all the test sequences. Therefore, the overall performances were improved in the object-detected Y, Cb, and Cr areas. The sequence in which the proposed method showed the high average \(\Delta\)Y-PSNR value of 2.25% was the BQTerrace sequence in class B, where the QP value was 22 in the AI configuration. The sequence in which the proposed method showed the lowest average \(\Delta\)Y-PSNR value (\(-0.53\%\)) was the MarketPlace sequence in class B, where the QP value was 37 in the RA configuration. Figure 6 shows the example of objects detected (226 th, 235 th, 240 th, and 246 th pictures) in the MarketPlace sequence in class B, which show low average \(\Delta\)Y-PSNR values. In the MarketPlace sequence, the object-detection algorithm does not work well, as there are fast-moving people, which require the camera to quickly zoom in and out. Therefore, object detections are difficult in the MarketPlace sequence. The person standing on the far left disappears after a few pictures in Figure 6a and the size of the square box of the object detected in the picture changes continuously in Figure 6a-d. Because of these reasons, the PSNR quality of the reconstructed video worsens, as confirmed by the RA data in Table 4, which led to a decrease in the average \(\Delta\)Y-PSNR value by \(-0.53\%\) at the QP value of 37. However, in the AI configuration, the average \(\Delta\)Y-PSNR value increased because the compression was I (Intra) picture coding, which does not use the P (Predictive) and B (Bi-directional predictive) reference pictures.
## 1 Introduction
_Notations_: Throughout this paper, scalers and vectors are denoted by lowercase letters and boldface lowercase letters, respectively. For any vector, the superscript \((\cdot)^{H}\) denotes the Hermitian transpose. The notations \(\left|\cdot\right|\) and \(\left\|\cdot\right\|_{2}\) denote the absolute value and 2-norm, respectively. We use \(\mathcal{CN}\left(m,\sigma^{2}\right)\) to denote the circular symmetric complex Gaussian distribution with mean \(m\) and covariance \(\sigma^{2}\).