esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The present article provides an overview of photobiomodulation (PBM), also known as low-level laser light therapy (LLLT), which has garnered attention in ophthalmology for its potential therapeutic benefits in various ocular diseases. Photobiomodulation involves the use of low-intensity lasers or light-emitting diodes to stimulate biological processes in target tissues without causing thermal damage. This article discusses how PBM has been explored across various ocular conditions, including ocular surface diseases, age-related macular degeneration, diabetic retinopathy, myopia, amblyopia, and glaucoma. It summarizes findings from human studies and clinical trials demonstrating positive outcomes of PBM treatment in these areas. Moreover, the article emphasizes the importance of establishing standardized treatment protocols in terms of session duration and frequency, light type, and patients' inclusion criteria to further validate the role of PBM in managing ocular diseases.  
PBM; LLLT; eye; dry eye; AMD; diabetic retinopathy; myopia +
Footnote †: journal:  
by [PERSON] et al., cytochrome C oxidase (Cox) acts as a light acceptor following initial photon interaction, leading to an increase in mitochondrial membrane potential. However, it is believed that tissue effects may vary and follow different pathways.  
A comprehensive analysis of potential mechanisms of action at the molecular, cellular, and tissue levels is provided in the article by [PERSON] et al. At the molecular level, various potential mechanisms of action have been identified, including activation of chromophores such as Cox, retrograde mitochondrial signaling, light-sensitive ion channels, and direct effects mediated by free light on the cell. Additionally, various signal transduction pathways are involved, such as ATP, cAMP, ROS, Ca2+, and NO, along with the activation of various transcription factors and effector molecules such as transforming growth factor-beta (TGF-\(\beta\)), oxidative stress, pro- and anti-inflammatory cytokines, brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), basic fibroblast growth factor (bFGF), and keratinocyte growth factor (KGF), along with heat shock proteins (HSP).  
At the cellular level, LLLT appears to have an anti-inflammatory and cytoprotective effect, promoting proliferation, enhancing collagen fiber migration in wounds, inducing protein synthesis, and being involved in stem cell proliferation [8; 9; 10; 11; 12; 13].  
This review aims at providing a concise analysis of its applications in treating various ocular pathologies, including dry eye and ocular surface disease, age-related macular degeneration (AMD), diabetic retinopathy, myopia, amblyopia, retinopathy of prematurity, and glaucoma while considering also other potential future applications.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 9 Conclusions and Research Opportunities  
In a little over two decades, submerged HFs have gone from a curiosity to the mainstream of membrane technology. The major applications are for low-pressure membranes (MF and UF) in the water industry. The submerged HF concept is ideally suited for the dead-end filtration of dilute feeds (surface waters, pretreatment for RO) because effective cake removal can be achieved by backwash flow from the lumen side of the fiber. The submerged HF concept is also well-suited for more concentrated feeds, such as in aerobic MBR, where bubble-induced fiber movement helps to control fouling. The development of the submerged HF system has seen some advances in membranes per se (e.g., improved strength, flexibility, etc.) but the major efforts have been in module design and process optimization, such as fiber geometry, looseness, packing density, bubbling characteristics, backwash protocol, and modifying feed properties (e.g., by adjusting the bioprocess parameters in the MBR). The current generation of submerged HFs is clearly very effective, but there are opportunities for further development that could improve the concept and its applications. These are briefly discussed in the following section.
## 1 Introduction  
The very recent development of new technologies, particularly in the field of artificial intelligence, is leading to important innovations in the healthcare sector [1; 2]. They range from surgical outcomes prediction to anatomical studies, the correlation between clinical and radiological findings, and even to genetic stratification and oncology [3; 4; 5; 6; 7; 8]. Specifically, the so-called \"image-based analysis\" is currently highly investigated as the role of the human operator in the setting and reporting of radiological examinations could greatly benefit from technological support [9; 10; 11].  
The preservation of noble anatomical structures is critical in surgery, and, in particular, in the head and neck district. Given the important number of surgical interventions daily performed on the mandible, we decided to focus our work on the segmentation of the inferior alveolar canal (IAC). To date, only a small number of studies regarding the automatic segmentation of the inferior alveolar canal have been conducted. Limited results in terms of accuracy have been achieved, with the exception of the recent paper published by [PERSON] et al. [12]. Moreover, the need to obtain accurate data in a quick and automatic manner is a major concern in modern radiology.  
Finally, the issue of free access to source codes and datasets in the deep learning field has received considerable critical attention. Reproducibility of the experiments performed cannot be assured if these data are kept private. Previous studies in the field of segmentation of the IAC have not dealt with the open-source sharing of the dataset and algorithm used.  
The specific objective of this study was to develop a CNN which is able to overcome the results achieved so far and described in the literature, in order to quickly output a reliable and three-dimensional segmentation of the IAC. This research also sought to optimize the results obtained and to release them publicly together with the dataset and source-codes used to achieve them.
## 2 Research Methodology  
### Theory and Data Collection  
There is a need to effectively control the seepage that may occur in earthen embankments in a way to ensure their safety and minimize water loss. With collection of water behind an earthen embankment, a seepage line or saturation line (also called \"Pheratic line\") may appear. This is the line below which positive hydrostatic pressures exist. Right upon the line, pressure is of the atmospheric type, which means that the hydrostatic pressure equals zero, whereas above the seepage line in the capillary fringe, the pressure is negative. Remember that some flow takes place in the capillary fringe, which is normally overlooked in embankment seepage analyses. The location of the seepage line of an embankment needs to be well predicted for three purposes: (1) ensuring that the seepage line is not going to cut the dam downstream face and bring about the toe softening or sloughing; (2) finding the line that separates the wet and dry soil, which is necessary when computing the stability level; and (3) obtaining the top boundary line to draw the flow net, which is needed for calculating the seepage.  
The literature consists of a number of studies that suggest determining the discharge and the free surface through homogenous embankment. Such classifications typically utilize the Dupuit's assumptions. A number of these processes are discussed in the present study.
## 4 Implementation and User Scenario
### Evaluation  
#### 4.4.1 Experimental Questions and Evaluation Metrics  
Our experiments aim to evaluate the efficiency and effectiveness of CORES as a VR-based decision-making tool, exploring utility in everyday life. Specifically, we focus on the following two experimental questions:  
**Q1:** how does the contextual information provided by VR affect the performance of CORES and the decision-making behavior of users?  
**Q2:** how do different users' emotions affect the quality of CORES' recommendations?  
To address these questions, we evaluate CORES' performance based on the following metrics:  
* **Accuracy:** the proportion of correct answers among all answers produced by CORES.
* **Precision (Pre)**: the proportion of relevant and correct answers among all the answers generated by CORES.
* **Recall (Rec)**: the ratio of correctly identified answers to the total number of expected correct answers.
* **F1-Score**: a weighted average of precision and recall, providing a balanced measure of CORES' performance.  
\[Accuracy=\frac{\#\text{correct answers}}{\#\text{all answers of CORES}} \tag{13}\]  
\[Pre=\frac{\#\text{relevant and correct answers}}{\#\text{all answers of CORES}} \tag{14}\]  
\[Rec=\frac{\#\text{correct answers}}{\#\text{expected answers of CORES}} \tag{15}\]  
\[F1-Score=2\times\frac{Pre\times Rec}{Pre+Rec} \tag{16}\]
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample ID** & **400 \({}^{\circ}\)C** & **STDEV** & **800 \({}^{\circ}\)C** & **STDEV** & **Thermal** & **Thermal** & **Cracking** & **Cladstone** \\  & & & & & **400 \({}^{\circ}\)C** & **800 \({}^{\circ}\)C** \\ \hline GP 04 & 76.43 & 4.13 & 25.91 & 1.00 & No & Yes \\ GP 05 & 90.02 & 1.04 & 30.43 & 2.31 & No & Yes \\ GP 06 & 56.51 & 2.52 & 18.51 & 3.68 & No & Yes \\ GP 07 & 56.62 & 3.34 & 19.84 & 3.71 & No & Yes \\ GP 08 & 47.73 & 0.37 & 14.43 & 3.73 & No & Yes \\ GP 09 & 59.10 & 3.00 & 13.61 & 1.37 & No & Yes \\ GP 10 & 41.03 & 3.32 & 15.22 & 0.90 & No & Yes \\ Minimum & 41.03 & 0.37 & 13.61 & 0.90 & N/A & N/A \\ Maximum & 90.02 & 4.13 & 30.43 & 3.73 & N/A & N/A \\ Average & 62.05 & & 20.22 & & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Values of average residual strengths: Gladstone/Callide FA-based GP cubes. It can be noted that the majority of the unsealed Gladstone FA-based GP specimens exhibited average mass losses (up to 10%) when subjected to elevated temperatures (400 \({}^{\circ}\)C and 800 \({}^{\circ}\)C). Sealed Gladstone FA-based GP specimens, on the other hand, recorded consistent losses, with an average mass loss of approximately 17% after being exposed to similar temperatures. The low mass loss percentage in the case of unsealed samples can be attributed to the presence of relatively lower proportions of water content in the unsealed samples. It can also be noted that large losses were recorded from the latter half of the samples (i.e., for GP 06-10). They had a higher alkaline solution to FA ratio (ca. 0.57) compared to the former half (i.e., for GP 01-05). As previously mentioned, above 1.75 (ratio of Na\({}_{2}\)SiO\({}_{3}\) to NaOH), the silicate in the mixture inhibited the geopolymerization reactions [75]. Therefore, the two pertinent ratios (i.e., alkaline solution to FA ratio and Na\({}_{2}\)SiO\({}_{3}\) to NaOH), in combination, can lead to higher mass losses. It was also reported [84] that average mass losses of FA-based GP pastes was about 19 and 20% at temperatures of 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, respectively, which are similar to the findings of the present study. The values of mass losses for the Gladstone/Callide FA-based GP specimens were approximately 17% and 19% after being exposed to elevated temperatures of 400 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, respectively. The corresponding losses recorded for the Gladstone FA were somewhat similar. It can be assumed here that changes in the chemical constitution is not a key governing factor responsible for the loss of mass after heat exposure. In addition, GPs have been reported to remain chemically stable up to temperatures of about 800 \({}^{\circ}\)C [32].
## 4 Results and Discussion
### Results of the Support Vector Machine  
The results of the fifth and last applied model, Support Vector Machine (SVM), using different values for the Kernel parameters, width, and degree, are presented in Table 9.  
The parameters \(\gamma\) (gamma), C (cost), and degree were changed during the analysis of this algorithm, aiming for the best results for the learning of the model. During this exercise, it was noticed that as the values increased, the performance and complexity of the classifier increased.  
An accuracy of 75.78% was obtained using Kernel RBF, with C equal to 3 and gamma equal to auto. This was the most performative result among the other configurations of this algorithm. A substantial compromise in the algorithm's performance is noticed when inserting new parameters in this configuration. Its precision was 76.61%, with a sensitivity of 0.91320 and 0.83318 as the harmonic mean (F1 score). The ROC curve analysis for the parameters Kernel linear, C equal to 1, and gamma equal to scale presented the best ROC score of 0.73772, as shown in Figure 8. Using these values, a patient has a 73% chance of being correctly classified using the Support Vector Machine.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Support Vector Machine -SVM**} \\ \hline
**Metrics/Kernel** & **Accuracy** & **Precision** & \begin{tabular}{c} _Recall_ \\ _That_ \\ \end{tabular} & \begin{tabular}{c} _F1_ \\ _Score_ \\ \end{tabular} & **Accuracy** & **Precision** & \begin{tabular}{c} _Recall_ \\ _That_ \\ \end{tabular} & \begin{tabular}{c} _F1_ \\ _Score_ \\ \end{tabular} & **Accuracy** & **Precision** & \
\begin{tabular}{c} _Recall_ \\ _That_ \\ \end{tabular} &
\begin{tabular}{c} _F1_ \\ _Score_ \\ \end{tabular} \\ \hline
**Cost** & & 1 & & & & & 2 & & & & 3 \\ \hline
**Kernel =** & & & & & & & & & & & & \\ \hline
**LinearGamma = auto** & & 66.25\% & 66.25\% & 1.0 & 0.79699 & 66.25\% & 66.25\% & 1.0 & 0.79699 & 66.25\% & 66.25\% & 1.0 & 0.79699 \\ \hline
**Kernel =** & & 72.08\% & 73.35\% & 0.9086 & 0.81173 & 72.80\% & 73.83\% & 0.91320 & 0.81646 & 73.28\% & 74.27\% & 0.91283 & 0.81902 \\
**Kernel =** & & 74.92\% & 75.53\% & 0.91924 & 0.82927 & 75.58\% & 76.43\% & 0.91283 & 0.83198 & **75.78\%** & **76.61\%** & **0.91320** & **0.83318** \\ \hline
**Kernel =** & & & & & & & & & & & & & \\ \hline
**POLYGamma = scale** & & 66.25\% & 66.46\% & 0.99018 & 0.79539 & 66.27\% & 66.48\% & 0.99018 & 0.79551 & 66.33\% & 66.51\% & 0.99056 & 0.79581 \\ \hline
**Kernel =** & & & & & & & & & & & & \\ \hline
**POLYGamma = auto** & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Benchmark for Support Vector Machine (SVM).
## 3 Results and Discussion
### Cytocompatibility of the DNA Hydrogel  
The proliferation and viability of the RAW 264.7 cells cultured on the hydrogel were researched in vitro. With increasing culture time (Figure 8A), the RAW 264.7 cells gradually proliferated in the DNA hydrogels and the culture plates without hydrogels (control group). The proliferation rate of RAW264.7 cells on the surface of the DNA hydrogel was higher than that that of the control group. Cell viability was also examined using a Live/Dead Assay Kit. As shown in Figure 8B, high cell viability (green) was achieved on both the DNA hydrogel sample and the control group (without hydrogel), but more cells were visible on the DNA hydrogel, indicating increased cell proliferation, which was consistent with the cell proliferation results. Compared to the other synthetic polymer hydrogel [45; 46], the DNA hydrogel showed less cytotoxicity and a higher cell proliferation rate. Thus, the DNA hydrogel that was constructed via the self-assembly of X-DNA, L-DNA and S-DNA has excellent cytocompatibility and potential application in cell study.  
Figure 8: (**A**) Proliferation of the RAW 264.7 cells cultured on the surfaces of the DNA hydrogels and the culture plates (without hydrogel). *\(p<0.05\). (**B**) Viability and morphology of the RAW264.7 cells cultured on the surfaces of the culture plates (a) and DNA hydrogels (b) for 24 h (green cells are live, and red cells are dead; the scale bar is 200 \(\upmu\)m).  
Figure 7: Profiles of hourly wise-stage release of thrombin in human serum with or without CS15 at different triggering times (0 h, 48 h, 96 h, and 144 h).
# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 4 Result
### Impaction of Target Domain Images' Number  
Various numbers of target domain images may affect the effect of the multi-space domain adaptation of the model and affect the segmentation on the target domain. To explore the causality between these two, we train the framework with different quantities of target domain images. In this experiment, the CAMUS dataset is the source domain dataset and the self-acquired dataset is the target domain dataset. Considering that there are a total of 1600 source domain images taking part in the training, we randomly sample 200, 400 and 800 unlabeled images from the target domain for the rate of 1:8, 1:4 and 1:2 between the target and source domain. In addition, we use 100 unlabeled target domain images to represent the situation of a small amount of data for the adaptation in order to perfect the experiment.  
According to Table 2, with the increase in the number of target domain images that take part in the training process, the DI and the JI of both the LVendo and LVepi are improved. After the number of target domain images reaches more than 400, the accuracy of the segmentation tends to plateau.  
The trends of the segmentation DI are visualized in Figure 5. With the increase in the number of target domain images for training the model, the average of the mean DI and the upper limit of the mean DI grows, while the median of the mean DI does not change significantly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**HD (\(\downarrow\))**} & **DI(\(\uparrow\))** & **JI(\(\uparrow\))** \\ \cline{3-5}  & & **mm** & **val.** & **val.** \\ \hline \multirow{2}{*}{_w/o_ DA} & LVendo & 6.38 & 0.8649 & 0.7738 \\  & LVepi & 7.94 & 0.8777 & 0.7889 \\ \hline \multirow{2}{*}{_w/o_ EDA} & LVendo & 6.41 & 0.8630 & 0.7680 \\  & LVepi & 7.70 & 0.9038 & 0.8278 \\ \hline \multirow{2}{*}{_w/o_ FDA} & LVendo & 6.56 & 0.8637 & 0.7700 \\  & LVepi & 7.73 & 0.8949 & 0.8138 \\ \hline \multirow{2}{*}{MACS} & LVendo & 6.30 & 0.8764 & 0.7881 \\  & LVepi & 7.59 & 0.9037 & 0.8263 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation studies on the proposed MACS. The fully domain adaptation (DA), entropy domain adaptation (EDA) and feature domain adaptation (FDA) are ablated, respectively. The up arrow indicates expecting a higher value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **HD (\(\downarrow\))** & **DI(\(\uparrow\))** & **JI(\(\uparrow\))** & **Target Images Number** \\ \cline{2-5}  & **mm** & **val.** & **val.** & \\ \hline LVendo & 5.70 & 0.8983 & 0.8183 & & \\ LVepi & 7.33 & 0.8874 & 0.7996 & & 800 \\ \hline LVendo & 5.65 & 0.9033 & 0.8265 & & \\ LVepi & 7.64 & 0.8792 & 0.7910 & 400 \\ \hline LVendo & 5.72 & 0.8967 & 0.8160 & & \\ LVepi & 7.45 & 0.8832 & 0.7973 & 200 \\ \hline LVendo & 5.69 & 0.8926 & 0.8094 & & \\ LVepi & 7.41 & 0.8824 & 0.7950 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of our MACS model trained with different numbers of target domain images. The up arrow indicates expecting a higher value.
# Compact Trap-Assisted-Tunneling Model for Line Tunneling Field-Effect-Transistor Devices
## 4 Conclusions  
The literature lacks a compact tunable trap-assisted-tunneling model. This work presented a compact TAT model that captured important physical parameters that influence the TAT process including phonon energy, the Huang-Rhys factor, and tunneling masses. The model is divided into three parts. The first part includes the potential model, the second part includes the standard Schenk model equations, and the third part includes the compact implementation of the Schenk model where the TAT tunneling rate is expressed as a Gaussian distribution. The approximated Gaussian distribution is then integrated along the \(x\)-axis to generate a TAT-generated drain current. The model was tested against the TCAD data and was found to be in reasonable agreement. The model was also tested against \(I_{\text{ds}}\) from an experimental device. The model was found to follow the same trend as the experimental device. The result, however, was not truly accurate. This could be due to neglecting the electrostatic degradation caused by the traps which could be important at high bias. Another reason for the inaccuracy could be neglecting effects such as contact resistance in the model. In summary, this work provides a useful contribution within the compact trap-assisted-tunneling modeling domain of TFET devices. The compact implementation part of the model can be generalized for use in any TFET device and can easily be incorporated in the simulation program with integrated circuit emphasis (SPICE) framework. For the completed compact model of the TET for SPICE implementation, series resistance [22], SRH [23], ambipolar, quantum confinement [24], and breakdown models [25] should be added to this proposed model.  
Conceptualization, Y.S.Y. and F.N.; Data curation, Y.S.Y. and F.N.; Formal analysis, Y.S.Y. and F.N.; Investigation, F.N.; Methodology, F.N.; Project administration, Y.S.Y.; Supervision, Y.S.Y.; Visualization, F.N.; Writing--original draft, F.N.; Writing--review and editing, Y.S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Trade, Industry, and Energy (MOTIE), project number 10054888 and Korea Semiconductor Research Consortium (KSRC) support program for the development of future semiconductor devices.  
This work was supported by IDEC (EDA tool).  
The authors declare no conflict of interest.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 3 The Scheme of Network Architecture and Purification  
### Network Architecture Based on Bipartite Communication  
For a network based on Bell states, a Bell pair is pre-shared between adjacent nodes. Assume that A is the source node while E is the destination node, and they are not directly connected. Entanglement swapping can establish entanglement between them. However, physical experiment has proved that each swapping will bring about \(12.77\pm 4.17\%\) loss of entanglement fidelity [17; 18]. The number of times a qubit taking part in entanglement swapping should be as small as possible. Node A can select the node between the source and destination for entanglement swapping through transferring commands in the classical channel.  
Compared with the Bell state, a multi-particle state such as the GHZ state has a better entanglement property [27; 28]. Therefore, the GHZ state is more widely used in teleportation. However, once we measure one particle of GHZ states, the whole entanglement will be destroyed. To solve this problem in bipartite communication, fusion is used to form GHZ states based on Bell pairs [25]. The principle and circuit of fusion are shown in Figure 4.  
The fusion scheme in the 5-node network is shown in Figure 5. The first round of entanglement swapping in the network occurs in nodes B and D, while the second round occurs in node C. Thus, entanglement is established between nodes A and E.  
Figure 4: **(a) Fusion principle and (b) circuit. Assume that nodes A and B share a pair of Bell states b1 and b2, and node A holds the other Bell pair a1 and a2. Circuit in (b) can be applied in two entangled particles a2 and b1. One only needs measure b1 in the Z basis and apply the Pauli gate correction on a2 based on the measurement result. Then, a1, a2 and b2 are changed into GHZ states.**  
Figure 5: Entanglement swapping and fusion in a quantum network. Entanglement swapping can help to establish entanglement between nodes A and E based on Bell states. Fusion in node A can help to form GHZ states.