esa-sceva/satcom-chunk-collection huggingface dataset:
# Mueller Matrix-Based Approach for the Ex Vivo Detection of Riboflavin-Treated Transparent Biotissue
###### Abstract  
Corneal collagen cross-linking is an established procedure for the treatment of certain eye diseases which is applied to enhance the mechanical stability of such biotissue without deteriorating its functionality. However, being transparent, the optical analysis of the outcome of such treatments is cumbersome and relies on relatively expensive experimental equipment. We aim to apply the Mueller matrix polarimetry for the detection of photo-induced collagen cross-linking in transparent biotissue after treatment with riboflavin and UV irradiation. A simple Mueller matrix polarimetry setup could provide a fast and non-invasive analysis of transparent media to sensitively detect small photo-induced cross-linking effects in biotissue. We demonstrated the current capabilities of the approach on non-planar porcine cornea samples ex vivo. We reported the distinction between untreated and riboflavin-treated samples. The differences observed were correlated with the variation of certain Mueller matrix elements and parameters derived from the decomposition. The measurement data show variation in the cross-linked and non-cross-linked samples, although the effect of the UV treatment on the riboflavin-treated samples was not at the same level of significance yet and needs further investigation. The Mueller matrix measurement represents a promising approach for the detection of the effects of corneal collagen cross-linking. Further studies with a larger sample number are required to validate this approach. In the future, this could enable the reliable and non-invasive detection of photo-induced effects in biotissue and open the possibility for in vivo application, e.g., in eye disease treatment or the detection of scar collagen development.  
Mueller matrix; biotissue; ex vivo polarimetry; polarization sensitive detection
# Deep Learning in the Biomedical Applications: Recent and Future Status
## 4 Omics
### Around the Protein
#### 4.2.1 Protein Structure Prediction (PSP)  
Nowadays, one of the most challenging problems in Omics concerning the protein study is to obtain a better modeling and understanding of the protein folding process [58]. During the gene expression process, every protein folds into a unique three-dimensional structure that determines its specific biological function. The three-dimensional structure is also known as native conformation and it is a function of its secondary structures. Protein structure prediction is crucial to analyze protein function and applications. Several diseases are directly linked to the result of the aggregation of ill-formed proteins [134]. We highlight five main orientations in the field of the protein structure prediction. Most of these studies concern the protein secondary and tertiary structure prediction:  
* **Backbone angles prediction**: Prediction of the protein backbone torsion angles (Psi and Phi) can provide important information for protein structure prediction and sequence alignment [135; 136; 137; 133].
* **Protein secondary structure prediction**: Prediction of the secondary structure of the protein is an important step for the prediction of the three-dimensional structure and concentrates a large part of the scientific publications [134; 138; 139; 140; 141; 142; 143; 144; 145; 146].
* **Protein tertiary structure (3D) prediction**: Protein tertiary structure deals with the three-dimensional protein structure and gives how the regional structures are put together in space [147; 148; 149; 150; 151; 152].
* **Protein quality assessment (QA)**: In the process of protein three-dimensional structure prediction, assessing the quality of the generated models accurately is crucial. The protein structure quality assessment (QA) is an essential component in protein structure prediction and analysis [153; 154; 155].
* **Protein loop modeling and disorder prediction**: Biology and medicine have a long-standing interest in computational structure prediction and modeling of proteins. There are often missing regions or regions that need to be remodeled in protein structures. The process of predicting missing regions in a protein structure is called loop modeling [156; 157; 158]. Many proteins contain regions with unstable tertiary structure. These regions are called disorder regions [159].
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 2 Theoretical Background
### SIS Model  
The process of information transmission is analogous to that of infectious diseases, and the SIS model may be utilized to describe the propagation and evolution of information.  
Figure 1: Construction of the aggregation-phenomenon model. New nodes and hyperedges are represented by a red color. At time step \(t\), the selection preference attachment is performed as follows: (**a**) New node \(v_{10}\) (\(m_{1}=1\)) preferentially select the existing nodes \(v_{1}\) and \(v_{2}\) (\(m_{2}=2\)) to form a new hyperedge. (**b**) The new node randomly chooses neighbors \(v_{6}\) and \(v_{8}\) of \(v_{1}\) or \(v_{2}\) to form the second hyperedge with parameter (\(m=2\)). Blue nodes \(v_{4}\) and \(v_{5}\) in the figure indicate that they are not available for selection by the new nodes since they are not neighbors of \(v_{1}\) or \(v_{2}\).  
In this model, individuals are categorized as susceptible (S-state) nodes or informed (I-state) nodes. Whenever an S-state node contacts an I-state node, the information will be transmitted to the I-state node with a probability of \(\beta\), and subsequently, the I-state node may revert to the S-state node with a probability of \(\gamma\) due to factors such as forgetting. Additionally, a node restored to the S-state may still acquire information and become an I-state node once again.
## 6 Conclusions  
This study proposes a framework for biomass and yield mapping based on the new PROBA-V S1 products at different spatial resolutions. First, the STARFM and ESTARFM algorithms were employed to fuse PROBA-V 100-m and 300-m data. The results indicated that this fusion had excellent abilities because the blended reflectance was closely correlated with the observed reflectance (\(R^{2}\) was higher than 0.71, 0.50 and 0.88 for the RED, NIR and SWIR bands, respectively). The NDVI and LSWI based on the predicted surface reflectance also had strong correlations with the referenced images (\(R^{2}\) between 0.69 and 0.95 for NDVI and \(R^{2}\) between 0.84 and 0.98 for LSWI). Then, winter wheat fields were identified based on time-series NDVI clustering, and favorable results were acquired. In comparison with the PROBA-V 300-m images, the overall accuracy of the classification improved by 10%-20% for the blended 100-m data. Finally, the biomass and yield were simulated using a simple light use efficiency model. The estimation results agreed well with the field measured values (0.864 \(\leq R^{2}\leq\) 0.871, 168 \(\leq\) RMSE \(\leq\) 191 g/m\({}^{2}\) for biomass; 0.631 \(\leq R^{2}\leq\) 0663, 41.8 \(\leq\) RMSE \(\leq\) 62.8 g/m\({}^{2}\) for yield). Considering the relatively high spatial and temporal resolutions of the PROBA-V images, our approach will be useful for the timely and accurate monitoring of crop planting areas and yields based on the new data.  
The research was supported by the China Grains Administration Special Fund for Public Interest (Nos. 201313009-2, and 201413003-7) and the National High Technology Research and Development Program of China (863 program) (No. 2012 AA12A307). The authors would like to thank the reviewers for their constructive comments for revising this paper.  
[PERSON] contributed to the research experiments, analyzed the data, and wrote the majority of the paper. [PERSON] conceived the experiments, and was responsible for the research analysis. [PERSON], [PERSON] and [PERSON] collected and pre-processed the original data. All co-authors helped to revise the manuscript.  
The authors declare no conflict of interest.
## 4 System Performance Analysis  
### Subsection  
Traditional amplitude modulation by adding a Boost circuit before the inverter circuit or to be transmitted baseband signal [20] to control the input voltage amplitude to achieve signal transmission. The voltage fluctuation exceeds 30% of the amplitude during signal transmission, which limits the application of this method. The MASK modulation simultaneous transmission method based on PSFB has the advantages of amplitude modulation: simple structure and no power interference to the signal. At the same time, the demodulation circuit uses a differential amplifier to amplify the signal characteristics in the integrated power signal flow, which can reconcile the contradiction between the output voltage fluctuation and the signal distortion rate and reduce the impact of voltage fluctuation on the system. According to the parameters of Table 2, 8 ASK modulation is carried out. For observation purposes, the system transmits the signal '01234567', when the voltage fluctuation at the receiver side is shown in Figure 7, with a voltage fluctuation of about 5%.
## 4 Experiments
### Pooling Techniques  
According to our analysis in the third section, using min-pooling in AP-MDSC can help extract significant features for sentiment classification, which is different from the more general use of max-pooling and mean-pooling in many other attention models. Table 4 shows the testing accuracy of the AP-MDSC model on the Amazon sentiment classification dataset with three different pooling techniques while keeping all other network structures and settings unchanged.  
As shown in Table 4, using the min-pooling layer results in a better performance on the dataset than using the mean-pooling and max-pooling layers. This validates our previous analysis result that when calculating the distribution of attention at each moment in AP-MDSC. If a word is less important than others, it should be given less attention; specifically, the corresponding attention weight should be smaller. Therefore, adopting the min-pooling layer is more reasonable than using the other two pooling layers. The choice of pooling technique is one of the keys to efficiently performing multi-domain sentiment analysis tasks for our model.
## 5 Conclusions  
This study attempted to produce the yearly soil erosion map for the Jhelum watershed using the RUSLE model in a GIS-embedded environment. Various data sources in ArcGIS were used to create RUSLE input parameters. By merging the model RUSLE with GIS techniques, the results of each factor contributing to the erosion process are displayed. Regarding the many elements contributing to land degradation, GIS allows the management of huge amounts of data relatively simply. It's obvious from this study that the GIS approach is straightforward, has a low cost for modeling and mapping soil erosion, and is very valuable for assessing soil erosion.  
The combination of heavy rainfall and steep slopes in the study area results in significant erosion runoff. The primary cause of the huge soil erosion in our study area is the outcome of high runoff and soil particle dislodging. This study shows that 55.18% of this watershed has a low erosion risk, 18.62% of the area has a moderate erosion risk, 13.66% has a high risk, and 16% has a very high erosion risk. Moreover, this study finds that the annual average erosion for the Mangla watershed is 23.47 t ha\({}^{-1}\) yr\({}^{-1}\).  
Although soil loss in this area is comparatively low, effective management policies and strategies must be adopted to further reduce it, with a special focus on C and P. This is because the LS factor is influenced by topography, which cannot be easily changed on a larger scale, while the other two factors, R and K, cannot be changed [88]. This shall increase the life of the Mangla DAM to a greater extent, positively impacting Pakistan's agricultural and power sectors.  
* Proper supervision of terraces will condense the length of the slope and, subsequently, the soil loss [89].  
* By adopting innovative and sound cultivation methods, the C factor can be reduced, and erosion can be reduced.
* Conservation practices (P), such as curvature against the slope and trees on the field's boundary, will ensure that sediments do not leave their source [90].  
Conceptualization, F.I. and M.W.; methodology, F.I., M.H. and M.U.L.; software, F.I.; validation, F.I., M.H. and M.U.L.; formal analysis, T.J. and M.K.L.; investigation, M.W.; data curation, T.J.; writing--original draft preparation, F.I., M.H. and M.U.L.; writing--review and editing, M.W. and M.K.L.; visualization, T.J.; supervision, M.W. and M.K.L. All authors have read and agreed to the published version of the manuscript.  
This study is not supported by any external funding. We are thankful to the University of Rostock, Open access program for their support in the payment of the article processing charges.  
Not applicable.  
To respect the privacy of the datasets generated and/or analyzed here, they are not publicly available. The author can provide the datasets upon reasonable request.  
The authors of the study extend gratitude to ESRI and USGS for providing the land use, land cover data, and DEM for this research.  
The authors declare no competing interests.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 5 Open Issues and Challenges
### Performing IoT Connectivity  
The smart health-care network can consist of millions of devices in future. The concept of this network can only succeed if the connectivity is provided to every single device [27]. These devices have the ability to provide information after sensing. In this network, any existing communication technology can be used by IoT devices, such as a cellular network (i.e., 5G and LTE), Bluetooth and Wi-Fi. However, there are many challenges to guaranteeing connectivity to each device in the smart health-care network, such as:  
* Guaranteeing connectivity to the devices with high mobility (i.e., moving patient, high-speed ambulance) in the network.
* Providing connectivity to every device deployed in the network with both short and long-range.
## 1 Introduction
Their results presented significant thrust production, reduction of ship motion responses, and generation of anti-rolling moment for ship stabilization, over a range of motion parameters. Further, [PERSON] [30] numerically studied the possibility of extracting energy from gravity waves for marine propulsion by a two-dimensional oscillating hydrofoil. The investigated results of the simulation explained the increase of propeller efficiency in gravity waves. In addition, [PERSON] suggested that when some key parameters are correctly maintained, the oscillating hydrofoil could take advantage of sea waves to increase its performance. In [PERSON]'s study, the hydrofoil is defined as rigid. [PERSON] [31] simulated unsteady, viscous and turbulent fluid flow around a plunging hydrofoil near the water free surface for different submergence depths and oscillation frequencies. The drag of the hydrofoil shows sudden increment in some critical unsteady parameters, such as Strouhal number and frequency. The main reason for this phenomenon is that waves under the above parameters transfer more momentum. In addition, the free surface affects trailing edge vortices from the foil, and causes the drag increment at all frequencies with submergence depth of 0.5\(c\), and at critical frequency with other submergence depths, where \(c\) is the chord length of the foil. As seen from the above research, most of the authors consider a rigid foil, rather than a flexible foil, as the object of study, even though the caudal fin of fish is usually flexible. In this paper, the ability of a flexible bionic flapping foil to take advantage of waves is numerically studied for UUV propulsion. The commercially available computational fluid dynamics (CFD) software Fluent is used with an unstructured grid, based on Reynolds-averaged Navier-Stokes equations. The free surface waves, as well as motion and deformation of the flexible flapping foil were all implemented by customizing the Fluent solver using a user-defined function (UDF) technique. In addition, dynamic mesh technology and post-processing capabilities was fully used. The aim of this study is to investigate the hydrodynamic performance of the flexible foil when it operates near the wave surface, and then analyze ways to reduce the adverse influence of wave movements on the foil performance to utilize wave energy propulsion [27; 30; 32]. Moreover, vortex forming, fusing, shedding and dissipating processes caused by the foil are discussed. The objectives are to develop a numerical estimation method that can predict the performance of the flexible flapping foil in a wave field.
# HyperIO: A Hypervisor-Based Framework for Secure IO
## 4 Related Work
### External Device  
Fidelius [23], and its successor ProtectIon [24], provide the secure input and output of sensitive information, such as credential fields in web forms. Fidelius uses enclaves, which implement the network (web) protocols, and a secure external device to accomplish its tasks. The device acts as a mediator between the computer and the peripheral devices. It captures keystrokes and modifies the monitor's image. When sensitive information is required, the external device switches to a secure mode and transmits the keystrokes in an encrypted form to the enclave. Moreover, the browser renders the web form on the image sent to the monitor. Fidelius is superior to HyperIO in terms of usability because the sensitive information is displayed graphically in its natural position on the screen. In addition, Fidelius has better performance because the external device performs the additional computations. However, because HyperIO does not require an external device, its deployment is easier and cheaper.  
Bumpy [25] and BitE [26] provide secure input and output by leveraging Flicker [27]. Bumpy makes two assumptions regarding the input and output devices. Firstly, Bumpy assumes that the input devices can encrypt the user's input for later decryption by Bumpy's PALs protected by Flicker. Secondly, Bumpy assumes an additional external monitor is connected to the computer. This monitor is used to display sensitive information by Bumpy's PALs. Unlike Bumpy, HyperIO does not require special devices for its secure operation.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
###### Abstract  
In this paper, we consider relay-based broadcasting in wireless ad hoc networks, which can enable various emerging services in the Internet of Things (IoT). In this kind of traffic dissemination scheme, also known as flooding, all the nodes not only receive frames but also rebroadcast them. However, without an appropriate relay suppression, a broadcast storm problem arises, i.e., the transmission may fail due to severe collisions and/or interference, many duplicate frames are unnecessarily transmitted, and the traffic dissemination time increases. To mitigate the broadcast storm problem, we propose a reasonable criterion to restrict the rebroadcasting named the duplication ratio. Based on this, we propose an efficient mechanism consisting of duplication suppression and re-queuing schemes. The former discards duplicate frames proactively in a probabilistic manner to decrease the redundancy whereas the latter provides a secondary transmission opportunity reactively to compensate for the delivery failure. Moreover, to apply the duplication ratio practically, we propose a simple method to approximate it based on the number of adjacent nodes. The simulation study confirms that the proposed mechanism tightly ensured the reliability and decreased the traffic dissemination time by up to 6-fold compared to conventional mechanisms.  
broadcast storm; flooding; relay; reliability; IoT applications 1
## 3 Results of Literature Review and Economic Assessment
### A Novel Long-Duration Storage, Hybrid of Hydro-Pneumatic UPHES with PTES  
#### 3.3.1 Overall Considerations for Long Duration Storage  
Long duration storage faces the tough challenge that it needs to require a very low Capex for energy capacity (i.e., below 50 USD/kWh) because in such storage, most of the available energy is left idle over long periods of time and delivers actual work only a couple of times per day, per week or even per month.  
Another challenge specific of long-duration storage is to ensure a very low discharge rate (such as allowing energy losses of only 0.10%-0.25% per day).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component of Energy Capacity** & **Volume in m\({}^{3}\)** & **Cost in USD/m\({}^{3}\)** & **Cost in USD** \\ \hline LRC cavern (400 MWh @ 1.750 kWh per m\({}^{3}\)) & 230,000 & 300 & 69,000,000 \\ Receiving pond for Hydro water & 230,000 & 30 & 6,900,000 \\ Total Capex & - & - & 75,900,000 \\ Total with 5\% provision for CO\({}_{2}\) cost, etc. & - & - & 80,000,000 \\ Total Capex per KWh of energy & - & - & 200 \\ \hline \end{tabular}
\end{table}
Table 1: Capex of Energy Capacity for the UPHES system.  
Long duration storage shall also show a very low embodied-carbon. In such storage most of the energy capacity will be put at work only a limited number of times in the shelf-life of the storage, consequently any long-duration storage with embodied-carbon above a low threshold (in grams of embodied-carbon per kWh of energy capacity) would never be able to repay for its manufacturing footprint. This single issue, as much as Capex costs, is the main reason why most analysts conclude that electrochemical storages will remain restricted to the applications of short-duration (less than 1 h) and medium duration (1-5 h) timeframes. According from a year 2013 report from Stanford University, the Energy Intensity of batteries over their lifetime is only 2 to 10 despite the assumption of fast-cycling, while the Energy Intensity of pumped-hydro is around 210 [22].  
In addition to the three constraints above, long-duration storages must show a reasonably high RTE like every other storage (i.e., higher than 60%), failing which the operational cost of stored energy would be heavily penalized by the cost of having consumed an amount of energy significantly higher than the amount of delivered energy. A high RTE is a major factor to charge a low LCOS (Levelized Cost Of Storage) to power users.  
To this date, only conventional PHES fulfils this set of constraints, albeit with strong geographical limitations for additional projects, regarding new suitable locations as well as reasonably fast approval process.