esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Injectable Thermosensitive Chitosan-Based Hydrogels for Cell Encapsulation
## 5 Conclusions  
In the present study, three injectable, thermoresponsive, biocompatible CH-based hydrogels were developed and characterized. The hydrogel sol-gel transition occurred at 37 \({}^{\circ}\)C and, at the gel state, the system presented a physiological pH compatible with cell encapsulation. The addition of BGP to SHC solutions conferred the CH-based hydrogel system with higher (i) stiffness in compression without any changes in the swelling ratio, (ii) degradation properties and (iii) biocompatibility. The introduction of ARG in the hydrogel system did not alter the overall properties of CH hydrogels, besides conferring a slightly smaller weigh loss percentage due to an increased stability in cell culture conditions. Further biological experiments will elucidate the role of ARG as metabolic boost in this system, as it was observed previously [38]. Overall, this study suggests that by varying the GA solution composition it may be possible to modulate the physical properties of CH-based hydrogel systems according to the target cell requirements. To the best of our knowledge, no studies have been reported on the bioencapsulation of SH-SYSY using a thermosensitive CH+SHC+BGP+ARG injectable hydrogel as 3D cell culture system.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/18/6550/s1](http://www.mdpi.com/2076-3417/10/18/6550/s1)]([http://www.mdpi.com/2076-3417/10/18/6550/s1](http://www.mdpi.com/2076-3417/10/18/6550/s1)), Figure S1, Figure S2, Video S1: CH+SHC+BGP injection at RT, Video S2: CH+SHC+BGP injection in PBS at 37 \({}^{\circ}\)C, Video S3: CH+SHC+BGP injection in PBS at 4 \({}^{\circ}\)C.  
**Author Contributions:** Conceptualization, F.G., L.M. and A.P.; methodology, A.S.; A.S. hydrogel preparation, swelling and stability tests and cell encapsulation, A.S.; compression tests, result analysis supplementary videosand picture acquisition, F.G.; supervision of cell study, contribution to analyze the biological results, V.L.P., A.R. and A.Q; writing--original draft preparation, A.S. and F.G.; writing--review and editing, A.P. and L.M.; supervision, G.G., L.M. and A.Q.; funding acquisition, G.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Regione Puglia, Progetto FISR--C.N.R. \"Tecnopolo di nanotecnologia e fotonica per la medicina di precisione\"--CUP B83B17000010001, and \"Tecnopolo per la medicina di Precisione, Regione Puglia\"--CUP:B84I18000540002.  
**Conflicts of Interest**: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Fungi and Arsenic: Tolerance and Bioaccumulation by Soil Saprotrophic Species
###### Abstract  
Increasing arsenic environmental concentrations are raising worldwide concern for its impacts on human health and ecosystem functionality. In order to cope with arsenic contamination, bioremediation using fungi can represent an efficient, sustainable, and cost-effective technological solution. Fungi can mitigate arsenic contamination through different mechanisms including bioaccumulation. In this work, four soil saprotrophic fungi _Absidia spinoas_, _Purpurecillium linacinum_, _Metarhizium marquandii_, and _Cephalotrichum manum_, isolated from soils with naturally high arsenic concentrations, were tested for their ability to tolerate different sodium arsenite concentrations and accumulate As in different cultural conditions. pH medium after fungal growth was measured to study pH variation and metabolic responses. Arsenic bioaccumulation and its influence on the uptake of other elements were investigated through multi-elemental analysis using hydride generation atomic fluorescence spectrometry (HG-AFS), inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectroscopy (ICP-OES). Considering the increasing interest in siderophore application for metal bioremediation, the production of siderophores and their affinity for both Fe and As were also evaluated. All species were able to tolerate and accumulate As in their biomass in all of the tested conditions and produced siderophores with different affinities for Fe and As. The results suggest that the tested fungi are attractive potential candidates for the bioremediation of As contaminated soil and worthy of further investigation.  
soil fungi; bioaccumulation; arsenic; arsenite tolerance; _Absidia spinoas_; _Purpurecillium linacinum_; _Metarhizium marquandii_; _Cephalotrichum manum_; siderophores; multi-elemental analysis +
Footnote †: journal: Applied Sciences
## 2 The Theory of the Smooth Local Subspace Projection Noise Reduction and PE
### Local Projection Subspace  
In the local projection denoising algorithm [46; 47], as for each point \(y_{i}\) in reconstructed \(d\) dimensional phase space, temporarily uncorrelated \(r\) neighborhood points \(\left\{y_{i}^{j}\right\}_{j=1}^{\tau}\) are determined. Local subspace projection denoising method deals with short strands in the reconstructed phase space trajectories. These strands are composed of reconstructed phase space points of \((2l+1)\) continuous time, among which \(l\) is a small natural number. Namely that each reconstructed point \(y_{k}\) has an associated strand, as shown in Equation (2).  
\[\mathbf{S}_{k}=[y_{k-l},\ldots,y_{k+l}] \tag{2}\]  
Meanwhile \(r\) nearest neighbor strands including \(\mathbf{S}_{k}\) constitute into \(\left\{\mathbf{S}_{k}^{j}\right\}_{j=1}^{r}\). These strands are constructed by seeking the \((r-1)\) nearest neighbor points \(\left\{y_{k}^{j}\right\}_{j=1}^{r-1}\) of \(y_{k}\). Smooth local decomposition is conducted to every strand to obtain the corresponding \(k\) dimensional smooth approximation to \(\left\{\mathbf{S}_{k}^{-j}\right\}_{j=1}^{r}\), the filtered point \(\overline{y}_{k}\) is determined by the weighted average of points \(\left[\overline{y}_{k}^{j}\right]_{j=1}^{r}\) in the smooth strands. Then, the filtered time series is obtained in local projection denoising. The algorithm can be iterated many times to further smooth and filter the noise time series to obtain better denoising effect.
# Cascaded Dual-Inpainting Network for Scene Text
## 3 Methodology
### Structure Generation Module (SGM)  
When scene text is partially missing or corrupted, the structural coherence collapses. This leads to severe degradation in recognition accuracy caused by inconsistent patterns. It is is essential to preserve structural integrity for scene text recognition. The SGM reconstructs missing text regions and generates a complete pixel-level text structure, which serves as the structure-guided condition for the SRM.  
Given a corrupted scene text image \(I_{cor}\in\mathbb{R}^{h\times w\times c}\) as input, the SGM learns to predict the intact text structure, \(\hat{S}_{SGM}\in\mathbb{R}^{h\times w}\), through training. The generation process \(\mathcal{F}_{SGM}\) of the SGM is mathematically defined as follows:  
\[\hat{S}_{SGM}=\mathcal{F}_{SGM}(I_{cor}) \tag{1}\]The model follows an encoder-decoder architecture [29] with residual blocks as the backbone, designed for image-to-image translation (input: 3-channel RGB, output: 1-channel grayscale). The encoder reduces spatial resolution through three stages: a residual block maintains input size, followed by two down-sampling residual blocks with convolution and ELU activation function that halve the resolution sequentially. The decoder employs two up-sampling residual blocks with transposed convolution and ELU activation function to restore original resolution, ending with a \(1\times 1\) convolution and Tanh activation for output. Residual connections with learnable shortcuts and ELU activations are used throughout to stabilize training, ensuring efficient feature propagation while maintaining spatial integrity across the network.  
SGM loss consists of two components: \(L_{1}\) loss and binary cross-entropy (\(L_{bce}\)). These components are formally defined in Equations (2)-(4):  
\[L_{SGM}=\alpha_{1}\cdot L_{1}+\alpha_{2}\cdot L_{bce} \tag{2}\]  
\[L_{1}=\left\|S-\widehat{S_{SGM}}\right\| \tag{3}\]  
\[L_{bce}=-\theta_{1}\sum S\cdot log\widehat{S_{SGM}}-\theta_{2}\sum(1-S)\cdot log \left(1-\widehat{S_{SGM}}\right) \tag{4}\]  
where \(S\) denotes the ground truth text structure, and \(\widehat{S_{SGM}}\) represents the predicted text structure. \(\alpha\)-terms are loss-balancing coefficients.
# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 3 Notebook Federator
### Technology Considerations  
To achieve the automation described in the architecture, we consider the following technologies.  
* **Jupyter environment--**We build the FL pipeline with Jupyter Notebook. By default, we suppose that AI experts use a Jupyter environment such as Notebook to design the architecture of the model and aggregation for FL experiments.
* **Component containerizer--**We encapsulate the model and aggregator as reusable FL building blocks based on a Jupyter Notebook. The component containerizer module is one of the Jupyter extensions in NaaVRE.  
Figure 1: An overview of the architecture of Notebook Federator with NaaVRE [8].  
* **Docker Hub--**We share model and aggregator with distributed users around the world. Docker Hub, i.e., a central repository of containers, is the easiest way to deliver reusable container applications anywhere.
* **Docker Engine--**We enhance the automation of the FL deployment and execution. In this work, we consider the Docker Engine as the critical technology for FL pipeline automation because of its popularity in the community and flourishing software tools such as Docker container, docker-nvidia, docker-compose, or even Docker Swarm. In this paper, we mainly utilize docker-nvidia for local client training with CUDA GPU resources for automating FL pipelines, as we suppose that client users have their demands for local autonomy, e.g., controlling their own data and computation for AI model training, although using Docker Swarm can also achieve large-scale automated deployment for client users.  
All in all, the proposed work leverages the available technology assets presented above to bring them together for a specific application, the deployment of FL systems. With all that, the Notebook Federator positions itself as a novel module within the previously developed NaaVRE toolbox [8], providing a Jupyter notebook structured baseline for data scientists to easily reproduce the FL pipeline.
# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 2 Establishment of Mixed-Domain Feature Library
### Feature Extraction of Vibration Signal in the Frequency-Domain  
For a complex vibration signal, the information extracted in the time-domain is limited. Therefore, the time-domain signal is often transformed into a frequency-domain signal by a mathematical method to reveal the frequency component of the signal, so as to extract more information from the signal. The mathematical basis for the transition from time-domain to frequency-domain is Fourier Transform. The process of Fourier Transform is called spectrum analysis. The commonly used spectral analysis includes amplitude spectrum analysis, phase spectrum analysis, logarithmic spectrum analysis and power spectrum analysis. Power spectrum is a commonly used method for frequency-domain feature extraction. It can be used to describe the change of signal frequency-domain features by describing the change of power centroid and the dispersion degree of power distribution [19].  
For a discrete time series (\(x_{0}\), \(x_{1}\), \(x_{2}\),..., \(x_{N-1}\)) with finite length, assuming the sampling frequency is \(f_{s}\), three commonly used frequency-domain parameters are as follows [20]:  
Centroid frequency (FC):  
\[FC=\frac{1}{2\pi f_{s}}.\frac{\int_{0}^{\pi}\omega S(\omega)d\omega}{\int_{0}^{ \pi}S(\omega)d\omega}, \tag{11}\]  
Mean square Frequency (MSF):  
\[MSF=\frac{1}{4\pi^{2}f_{s}^{2}}.\frac{\int_{0}^{\pi}\omega^{2}S(\omega)d\omega }{\int_{0}^{\pi}S(\omega)d\omega}, \tag{12}\]  
Variance frequency (VF):  
\[VF=\frac{1}{4\pi^{2}f_{s}^{2}}\frac{\int_{0}^{\pi}\left(\omega-2\pi f_{s}F \right)S(\omega)d\omega}{\int_{0}^{\pi}S(\omega)d\omega}=MSF-FC^{2}, \tag{13}\]  
where \(S(\omega)\) is power spectrum of the discrete signal, \(S(\omega)=X(\omega)\cdot\overline{X(\omega)}\), \(X(\omega)=\sum\limits_{i=0}^{N-1}x(i)e^{-j\pi\omega}\); and \(\omega\) is the angular frequency.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 3 Results  
### Superpixel Segmentation of Forested Areas  
A superpixel map of the NHAR forest areas in 2020 was generated using the SNIC algorithm, which was reduced by 2589 hectares compared to the original forest map after the de-fragment operation. One location (Figure 8a) was selected as an example to demonstrate the segmentation results (white polygons). Because the input data for the  
Figure 6: Frequency histogram of the \(\chi^{2}\) statistics of the m-test for all forest objects. With a threshold \(\chi^{2*}=7.88\) at the significance level \(\alpha=0.005\), the histogram is divided into two parts: natural forest (dark green bars) and planted forest (light green bars).  
Figure 7: NDVI time-series (**top**) and Landsat imagery cutouts (**bottom**) of a planted forest sample. The top part of the figure is the NDVI time series (1990–2020) of the sample; the bottom part consists of 8 Landsat composite images from different years to depict the variation in the spectral reflectance of the sample during the period of study, and the location of the sample is indicated by a yellow polygon in the center of each image. The planting year of the sample in 2003 can be inferred.  
SNIC algorithm were the NDVI time series over three decades, the Landsat composite images for four years (1991, 2000, 2010, and 2020) served as backgrounds to illustrate the segmentation performance. Spatial segmentation captures the boundaries of the objects well in each image, which coincides with the visual inspection of the high-resolution images (Figure 8b) on Google Earth. In terms of the evaluation indicator, the ALV under the optimal scale (Figure 4) is less than 0.15, indicating that the homogeneity of the objects is reliable. Although some of the larger homogenous patches were divided into smaller parts, in general, the superpixels clustered by the neighboring pixels with the same NDVI trajectory patterns were consistent with the ground entities.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 3 Cloud Storage System Model Based on Erasure Code
### Data Encoding Storage and Recovery
#### 3.2.2 The Construction of the Generative Matrix \(G\)  
The generative matrix \(G\) used for encoding and decoding is a matrix constructed based on the Cauchy matrix \(C\). The first \(k\) rows are extracted from the Cauchy matrix \(C\) to form a \(k\times k\) identity matrix \(I_{k}\), which is used to represent the original data block. Then, the last \(m\) rows of the Cauchy matrix \(C\) are used as the generation part for redundant data blocks. Finally, the last \(m\) rows of the identity matrix \(I_{k}\) and the Cauchy matrix are combined to form the generative matrix \(G\). The representation of the generative matrix \(G\) is as follows:  
\[G_{(k+m,k)}=\begin{bmatrix}I_{k}&&\\ C_{k+1,1}&C_{k+1,2}&\cdots&C_{k+1,k}\\ C_{k+2,1}&C_{k+2,2}&\cdots&C_{k+2,k}\\ \vdots&\vdots&\ddots&\vdots\\ C_{k+m,1}&C_{k+m,2}&\cdots&C_{k+m,k}\end{bmatrix} \tag{3}\]
# 3D Shape Generation via Variational Autoencoder with
## 6 Conclusions  
In this paper, a novel approach for generating 3D shapes using 2D images as input is proposed. The proposed method, 3D-VAE-SDFRaGAN, leverages the power of variational autoencoders (VAEs) to learn the latent space of the 2D images and maps it to the corresponding 3D shapes represented by signed distance functions (SDFs). The experimental results demonstrate that the latent space of 2D images has a significant impact on the performance of the model and determines the amount of information that can be transferred from the 2D image encoder network to the SDF-generator network. To further improve the performance of the model, a relativistic average GAN loss function is incorporated into the proposed 3D-VAE-SDFRaGAN framework. This improves the stability of the model during training and results in higher-quality shape generation. The proposed method successfully generates smooth surface attributes of the corresponding SDF using features extracted from the 2D image. The proposed 3D-VAE-SDFRaGAN model exhibits superior quantitative performance over state-of-the-art methods, achieving an average chamfer distance score of 0.578 across the selected categories of the ShapeNet dataset. Additionally, the proposed model exhibits superior performance compared to existing methods, particularly in terms of qualitative output, which is supported by a visual comparison of the generated samples.  
As the underlying task involves generating 3D shapes using generative modeling algorithms, the use of accuracy metrics such as F1, which are typically employed in classification tasks, may not be suitable for evaluation purposes. Instead, we utilized chamfer distance (CD), a standard evaluation metric for 3D deep learning, especially for mesh generation tasks [46; 49; 50]. In response to feedback, we have included this metric in the abstract to highlight the accuracy measurement used to evaluate our work, and have revised the abstract accordingly for the reader's consideration.  
Conceptualization, E.A.A. and K.M.L.; methodology, E.A.A. and K.M.L.; software, E.A.A. and K.M.L.; validation, E.A.A. and K.M.L.; formal analysis, E.A.A.; investigation, E.A.A.; resources, E.A.A.; data creation, E.A.A. and K.M.L.; writing--original draft preparation, E.A.A.; writing--review and editing, K.M.L., S.-C.C. and C.P.L.; visualization, E.A.A. and K.M.L.; supervision, K.M.L., S.-C.C. and C.P.L.; project administration, K.M.L.; funding acquisition, K.M.L. All authors have read and agreed to the published version of the manuscript. The research in this work was supported by Telekom Malaysia Research and Development under grant number RDTC/221045 and Multimedia University Graduate Research Assistant Scheme (MMUI/190044) Malaysia. Not applicable. Data Availability Statement Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 6 Revised PS-Request Protocol  
We proposed the PS-Request protocol, the so called original PS-Request protocol, to avoid the mutual interference between WiFi and WiMAX systems. However, we found that the original PS-Request protocol cannot completely solve the coexistence problem of WiFi and WiMAX systems. In the original PS-Request protocol, a WiFi AP only informs its WiFi STAs of the time to the start of the next WiFi zone, which is given in the Duration/ID field of the PS-Request frame, as shown in Figure 4. From this time, the WiFi AP and its WiFi STAs may compete to acquire the WiFi channel based on the CSMA/CA procedure of the WiFi system.  
As shown in Figure 5, the WiFi STAs receiving the original PS-Request frame from its WiFi AP cannot know the time remaining in the current WiFi zone. They only know the time to the start of the next WiFi zone. Thus, the remaining time of the current WiFi zone can be wasted. A WiFi STA that acquires a wireless medium after competition within the next WiFi zone, may send a frame beyond the next WiFi zone, as shown in Figure 8, because it does not know the time to the end of the next WiFi  
Figure 7: Proposed coexistence zone within WiMAX frame structure.  
zone. In that case, the mutual interference may be inevitable. This degrades the performance of both WiMAX and WiFi systems.  
If WiFi STAs can know the remaining time of the current WiFi zone after receiving a PS-Request frame, they can decide to send frames either at a time or at several times by segmentation. By doing so, the mutual interference, as shown in Figure 8, can be avoided. Thus, we propose a revised PS-Request protocol that delivers both the time to the end of the current WiFi zone and the PSM duration to the WiFi STAs, as shown in Figure 9. The Duration/ID field in the revised PS-Request frame represents two values of NAV. One is the time to the end of the current WiFi zone, and the other is the PSM duration. The sum is equal to the time to the start of the next WiFi zone of the original PS-Request frame. In this paper, we assume that seven bits of fifteen bits represent the time to the end of the current WiFi zone and the other eight bits represent the PSM duration. The number of bits for each part is determined according to the desired resolution and maximum value of the time.  
Figure 8: Drawback of the original PS-Request protocol.  
Figure 9: Format of revised PS-Request frame.  
Figure 10 shows an example of the revised PS-Request protocol. A WiFi AP broadcasts a revised PS-Request frame through the transmission procedure of the WiFi system. WiFi STAs receiving the frame can send their frames until the time to the end of the current WiFi zone after a competition. After that time, all WiFi STAs are in the PSM, until the start of the next WiFi zone.