esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Surface Characterization  
SEM characterization was applied to the utilized DM adsorber. SEM imaging confirms that HAOPs were deposited on the surface of the PES membrane. However, the preparation of the sample (drying) causes cracks in the HAOPs layer. Nevertheless, the SEM images of the top of the HAOPs layer have an irregular morphology that consists of closely packed particles of irregular spherical shapes. Similar observation was attributed to the  
Figure 1: Schematic illustration of the setup.  
agglomeration of almost spherical particles fabricated during atomization [19]. Figure 2 depicts the surface morphology of the HAOPs layer.  
Earlier research on HAOPs cake layer indicated that it did not impact the membrane permeation [19; 21; 22; 23; 26; 27; 28; 30]. The TMP before and after HAOPs deposition, when DI water was filtered through the membrane unit, was monitored constantly [28; 30]. There was no significant increase in pressure after precoating the HAOPs layer on membrane surface.  
[PERSON] et al. [21], reported that the particles' point of zero charge was at pH 7.7 and the BET surface area of HAOPs was 116 m\({}^{2}\)/g. Additionally, they reported that the solubility of HAOPs was less than 1 \(\upmu\)g/L as Al at 4.5 < pH < 9.3. The mean diameter and the specific surface area of HAOPs were 30 \(\upmu\)m and 35.6 m\({}^{2}\)/g, respectively [25].  
Presented dynamic membrane adsorber creates a cake (gel-like structure) on top of the primary membrane that acts as a secondary membrane and retains NOM [35]. Foulants are retained and accumulated in the secondary membrane and--in dead-end filtration--the cake grows and, consequently, increases TMP over time. The application of dynamic membrane adsorber was successfully used in conjunction with hydrodynamics of plug-flow. [PERSON] reported that HAOPs layer was found to be incompressible and applied pressure had no effect on dynamic membrane adsorber performance [30].
# Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images
## 3 Experimental Results and Discussion
In images with type A (left) and type C (right) fractures, the fracture line is too vague within the bone structure, so that some errors occur in the segmentation result. The proposed method achieves an average accuracy of 82% for sagittal images. Thus, the average accuracy performance for fracture type detection in the calcaneus is 84.17%. The results of computation performance are summarized in Table 3 in respect of frames per second (fps). The average time cost is about 7.5 ms per frame or 133 fps. The fastest processing time is for sagittal images that have the smallest calcaneus region, compared to other anatomical planes. The slowest processing time is for coronal images, which have the largest calcaneus region. The part that uses the greatest cost in computational time is segmentation so that the size of the calcaneus region determines the processing speed of a CT image. Table 4 shows the performance accuracy in terms of True Positive (TP), False Positive (FP), False Negative (FN), Precision Rate (PR), recall, and F-measure, based on the classification results, \begin{table}
\begin{tabular}{c c} \hline
**Fractures Type** & **Sagittal (\%)** \\ \hline Type A & 81 \\ Type B & 85 \\ Type C & 80 \\ \hline Average & 82 \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy of the fractures type detection in sagittal image. Figure 19: Segmentation result of the calcaneal fractures in sagittal images: (**a**) Type A; (**b**) Type B; and (**c**) Type C. \begin{table}
\begin{tabular}{c c} \hline \hline
**Anatomical Plane** & **Average fps** \\ \hline Coronal & 92.33 \\ Transverse & 146.67 \\ Sagittal & 160.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational performance in fps. which are indicated in the segmented area. This performance result is checked for each image manually. TP is the detected area corresponds to the associated fracture. FP is the detected area not related to the fracture. FN is the area associated with a fracture which is not detected. The accuracy of the performance can be computed by:  
\[PR\ =\ \frac{TP}{TP+FP} \tag{14}\]  
\[\text{Recall}= \frac{TP}{TP+FN} \tag{15}\]  
\[\text{F-measure}= 2\times\frac{PR\times\text{Recall}}{PR+\text{Recall}} \tag{16}\]  
The proposed method achieves an average precision rate of 0.86 and recall of 0.89. The highest recall is 0.92 for coronal images that have a clearer separation between the bone structure on the calcaneus, and the background compared to transverse and sagittal images. The sagittal image has the lowest recall caused by ambiguity on the fracture line with the bone structure.
## 2 Methods  
### Materials  
All chemicals were used as received unless otherwise noted, including _cis_-5-norbornene-_exo_-2,3-dicarboxylic anhydride (NB) (Adamas, Shanghai, China, 97%+), ethanolamine (Adamas, Shanghai, China, 99%+), 4-dimethylaminopyridine (DMAP) (Adamas, Shanghai, China, 99%), \(\alpha\)-bromoisobutyryl bromide (BIB) (Adamas, Shanghai, China, 98%), styrene (St) (Adamas, Shanghai, China, 99%), cuprous bromide (CuBr) (Sigma-Aldrich, St. Louis, MO, USA, 99.999%), \(N\),\(N\),\(N^{\prime}\),\(N^{\prime\prime}\),\(N^{\prime\prime}\)-Pentamethyldiethylenetriamine (PMDETA) (Adamas, Shanghai, China, 98%), 5-norbornene-2-carboxylic _tert_-butyl ester (_t_BNB) (Adamas, Shanghai, China, 97%+), [1,3-Bis (2,4,6-trimethylphenyl)-2-imidazol-inylidene]bis (2-bromopyridine)dichloro (phenylmethylene) ruthenium (G3) (Adamas, Shanghai, China, 95%), ethyl vinyl ether (EVE) (Adamas, Shanghai, China, 98%+), N-methyl-2-pyrrolidone (NMP) (Adamas, Shanghai, China, 99.5%), tetramethyl orthosilicate (TMOS) (Adamas, Shanghai, China, 99%), tetrahydrofuran (THF) (KESHI, Chengdu, Sichuan, China, 99.5%), ammonia water (NH\({}_{3}\cdot\)H\({}_{2}\)O) (Greagent, Shanghai, China, AR -25%), calcium hydride (CaH\({}_{2}\)) (Greagent, Shanghai, China, AR), hydrochloric acid (HCl) (Chron, Chengdu, Sichuan, China, AR 36-38%), sodium bicarbonate (NaHCO\({}_{3}\)) (Greagent, Shanghai, China, 98%+), magnesium sulfate (MgSO\({}_{4}\)) (Greagent, Shanghai, China, 98%+), ethyl alcohol (EtOH) (Adamas, Shanghai, China, 99%), n-Hexane (Hex) (Greagent, Shanghai, China, >97%), toluene (Greagent, Shanghai, China, >99.5%), dichloromethane (DCM) (Adamas, Shanghai, China, 99.9%), and deuterated chloroform (CDCl\({}_{3}\)) (Adamas, Shanghai, China, 99%). Styrene was distilled under vacuum prior to use and stored in a freezer at \(-20\)\({}^{\circ}\)C. DCM for the ROMP reaction was distilled and stored in a freezer at 4 \({}^{\circ}\)C.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 5 Conclusions and Discussion
### Conclusions  
Aiming at the shortcomings of the traditional dark channel a priori defogging algorithm, an image defogging algorithm combining Retinex and an improved dark channel a priori is proposed. First, foggy images are enhanced using the Retinex enhancement algorithm to reduce illumination inhomogeneity and enhance the detail information. Secondly, for foggy images with local area highlights, an atmospheric light value estimation module based on the Otsu algorithm and edge extraction is proposed to avoid the influence of the highlighted area on atmospheric light value estimation and improve the accuracy of atmospheric light value estimation. In order to inhibit the halo effect produced by the traditional dark channel a priori when acquiring dark channel maps, an averaged-value dark channel compensation model is proposed to integrate the initial dark channel with the averaged-value dark channel by using a weighted fusion strategy. This effectively suppresses the halo effect and realizes the pixel-by-pixel refinement of the transmittance parameter. For the color distortion phenomenon in the sky region of the recovered image, the depth map of color attenuation a priori estimation is used to correct the transmittance of the sky region of the dark channel a priori. Finally, the image is recovered using an atmospheric scattering model. The results of the subjective and objective experiments show that the proposed algorithm effectively suppresses the halo effect, solves the problem of color distortion in the sky region, and improves the contrast of the image, and the detailed information regarding the image is also better recovered and preserved.  
Conceptualization, L.Y. and Z.Z.; methodology, Z.Z. and H.G.; software, H.G.; validation, Y.L. and S.G.; formal analysis, S.G.; investigation, L.Y.; data curation, Y.L.; writing--original draft preparation, Z.Z.; writing--review and editing, K.H.; visualization, Z.Z.; supervision, L.Y.; project administration, Y.L.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
Shaanxi Province Key Research and Development Program General Fund Project [2025 SF-YBXM-287].  
No ethical approval was required.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The data that support the findings of this study are available from the corresponding author [[EMAIL_ADDRESS]] upon reasonable request.  
The authors declare no conflicts 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.
# Novel Artificial Neural Network Aided Structural Topology Optimization
## 2 Theoretical Background
### Deep Learning Aided SIMP Method  
The development of the first generation of computers is remarkable for their excellent computing power. The finite element method has been one of the most representative methods. In recent years, machine learning methods have made rapid progress and undergone another revolution. The goal of this study is to explore the feasibility and applicability of machine learning methods in structural optimization problems. The structural optimization problem is given in Equation (11).  
\[(\mathbb{SO})\left\{\begin{aligned} &\min_{\mathbf{x},\mathbf{n}}g_{0}(\mathbf{x},\mathbf{u})\\ &\text{s.t.}\left\{\begin{aligned} & K(\mathbf{x})\mathbf{u}=\mathbf{F}(\mathbf{x})\\ & g_{i}(\mathbf{x},\mathbf{u})\leq 0,\ i=1,\ldots,l\\ &\mathbf{x}\in\chi=\left\{\mathbf{x}\in\mathbb{R}^{n},\ x_{j}^{\text{min}} \leq x_{j}\leq x_{j}^{\text{max}},\ i=1,\ldots,n\right\}\end{aligned} \right.\end{aligned}\right. \tag{11}\]  
where \(K(\mathbf{x})\) is the global stiffness matrix of the structure, \(\mathbf{u}\) is the global displacement vector, and \(F(\mathbf{x})\) is the global external force vector. The displacements could be written as functions of the design variables in Equation (12).  
\[u(\mathbf{x})=K^{-1}(\mathbf{x})F(\mathbf{x}) \tag{12}\]  
To predict the sensitivity of topology optimization, cell density is essential information. A comparison between the density and sensitivity of optimization shows the spatial similarity between the two maps. The operation of the convolutional neural network (CNN) extracts relevant position features of elements in a two-dimensional matrix. It is represented in the new feature map.
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 3 Continuous Glucose Monitoring Systems  
### History  
Electrochemical glucose biosensors have led point-of-care technology. In 1962, [PERSON] and [PERSON] proposed the first concept of biosensors that consisted of an oxygen electrode, an inner oxygen semipermeable membrane, a glucose-oxidase layer, and an outer dialysis membrane. Since then, efforts on electrochemical glucose biosensors brought glucose biosensor technology to a revolution in the health care of people with diabetes: self-monitoring of blood glucose [38]. Self-monitoring of blood glucose (SMBG) was realized in 1987 by Medisense Inc. The first system for SMBG was composed of the enzyme electrode test strip and the pen-sized glucose meter to display blood glucose concentrations. The current systems for SMBG are similar to the first system. Users drop blood sample on the enzyme electrode test strip and then inset the strip into the glucose meter. Although SMBG provides self-monitoring of blood glucose, SMBG cannot be an ideal tool to maintain blood glucose in the normal range because blood glucose is strongly affected by daily lifestyle and behavior (e.g., diet and exercise).  
CGM is a powerful tool for managing blood glucose concentration. CGM can effectively prevent diabetic complications whose occurrence increases by repeating episodes of hypoglycemia and hyperglycemia. CGMS includes the implantable glucose sensors that measure glucose concentration every single time in a body. CGMS help diabetic patients recognize hypoglycemia and hyperglycemia by alarming when hypo- or hyperglycemia occurs. CGMS does not need finger-pricking process to obtain blood sample and user effort, thereby increasing quality of lives of people with diabetes [39]. Also, diabetic patients can effectively avoid hypo- and hyperglycemia through continuous monitoring of blood glucose. Hence, efforts on development of CGMS have been conducted by numbers of researchers and companies. The first concept of CGM was proposed by [PERSON] et al. in 1982 [40]. [PERSON] et al. succeeded to develop the artificial endocrine pancreas incorporating the needle-type glucose sensors that were implanted under the dog skin for up to seven days. After 17 years, Minimed succeeded to release the first CGMS. The details of commercial advancement in CGMS are describe in the following section.
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 1 Introduction
### Contribution of This Paper  
In order to address the limitations of the existing works and hence offer an accurate tool for proper characterization of the path loss for indoor VLC channels, we have carried out the following contributions herein paper:  
* A novel path loss model is proposed which contains the multi-wavelength path loss under free space propagation and the blockage probability (BP) caused by the mobile human blockage. To the authors' knowledge, this is the first path loss model that considers the impact of wavelength dependence combined with mobile human blockage.
* The wavelength dependence of path loss is investigated in both the single-LED and the multi-LED scenarios. The multi-wavelength path loss under free space propagation is modeled to describe the path loss in the non-blockage state. The small RMSE of the derived parameters shows that our proposed models have the goodness of fit to the simulated data.
* The impact of mobile human blockage on path loss is studied in both the single-LED and the multi-LED scenarios. The BP is modeled based on a realistic human moving trajectory and a geometry-based human body shadowing calculation method. A human moving trajectory generating algorithm is established which is capable of producing trajectories with realistic human walking behavior in the indoor scenario.
# Dynamic Packet Duplication for Industrial URLLC
## 5 Results and Discussion
### Prediction Results  
This subsection shows the results obtained by the predictor when using the test samples, to measure the accuracy of the predictor. In the test, there are 59,940 samples--packets transmitted by the MN to the URLLC device. Figure 5 shows the distribution of the samples. In general, these distributions show that, as expected, with worse radio conditions, a higher latency is experienced. This is because, with high attenuation, retransmissions at lower layers increase.  
Figure 4: UE movement over the entire scenario.  
The latency with SINR at intermediate values seems to be below or above 2 ms. The increase on the latency is due to HARQ retransmissions that occur. These retransmissions occur when there is a suddenly drop of the SINR, commonly in industrial scenarios, and the modulation index used is still higher (that drop of the SINR was not expected).  
This effect is clearly visible in the latency of the modulation index, where robust modulation decrease the probability of HARQ retransmission. Nevertheless, the higher the modulation index is, the higher the probability of retransmission is, as SINR drops occurs. Finally, the latency when the packet decodification was not successful at the PHY layer at UE (that is, a retransmission needs to be performed) is higher, whereas when the reception was successful when the latency was lower as expected.  
Table 2 shows the results of the estimator in the validation part. In particular, the false positive rate, the false negative rate and the success rate are given for the predictor for latency. As mentioned above, the latency predictor estimates whether the latency will be higher than 2 ms.  
The false positive rate indicates the proportion of times where the actual latency was lower than the threshold but the estimator predicted it would be higher. In this case, the false positive rate is 0.0041%. The false negative rate indicates the proportion of times where the actual latency was higher than the threshold and the estimator failed at predicting this. Ideally, this value should be close to zero. In the results, the latency estimator has a 0.0615% of error rate. The success rate indicates the percent of times where the value of latency was correctly predicted to be either below or above the threshold. In the results, the latency estimator has a 99.9849% of success rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S-KPI** & **False Positive Rate** & **False Negative Rate** & **Success Rate** \\ \hline Latency & 0.0041\% & 0.0615\% & 99.9849\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction results.  
Figure 5: Latency samples. (**a**) SINR, (**b**) Modulation index and (**c**) Reception Success.
## 4 Simulation Results and Discussion
### Simulation for One-Hop Setup
#### 4.1.2 Parameter Design of Phase 2  
Similar to that of phase 1, in Algorithm 2, the setting of \(\tau_{2}\) and \(\delta_{2}\) decides the termination condition of the loop in the algorithm, which in turns decides the location estimation error. In this phase, we investigate the evolution of location estimation error during the execution of Algorithm 2, where the location estimation error is measured by the distance between the estimated location and the true location of a particular sensor. Precisely,  
\[error=\sqrt{(x_{e,i}-x_{i})^{2}+(y_{e,i}-y_{i})^{2}} \tag{9}\]  
where \((x_{e,i},y_{e,i})\) is the estimated location and \((x_{i},y_{i})\) is the true location of \(Sensor_{i}\), \(i\in\mathbb{S}\).  
Figure 4: The estimation error with different \(\tau_{1}\) and \(\delta_{1}\).  
The evolution of location estimation error for a particular sensor is plotted in Figure 6 where each curve is obtained by averaging over ten executions. In the figure, the symbols represent the termination point of the algorithm with various \(\tau_{2}\) values. The figure shows three curves with different \(\delta_{2}\) values: (i) when \(\delta_{2}\) is set to a very small value, for example \(\delta_{2}=0.001\), the algorithm takes more iterations to terminate, and the error drops smoothly over the iteration loops; (ii) when \(\delta_{2}\) is set to a relatively large value, for example \(\delta_{2}=0.05\), the algorithm generally takes much lower computation to terminate. Over the execution, the estimated location fluctuates greatly which indicates that the algorithm operates near the instability boundary due to a high value of \(\delta_{2}\); (iii) when \(\delta_{2}\) is set to a suitable value, for example \(\delta_{2}=0.01\), the error reduces sharply in the beginning of the algorithm execution and then stays steadily with small fluctuations showing the stationary properties. This indicates that if the setting the chosen appropriately, the algorithm achieves quick convergence and stability with low error. To give concurrent consideration to both the estimation error and the convergence speed, the optimal choice for phase 2 in this setup is \(\tau_{2}=45\) and \(\delta_{2}=0.01\).  
One extension of the algorithm is that we can let Algorithm 2 execute continuously by not applying \(\tau_{2}\) for the termination condition. This allows the algorithm to cope with a certain mobility of sensors as this modification allows the sensors to continuously update and adjust their estimated location.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## Appendix B  
There is a set of edges connecting nodes \(\mathrm{R}=\{\mathbf{r_{1}},\cdots,\mathbf{r_{p}}\}\). The vector sets \(\mathbf{r_{1}},\cdots,\mathbf{r_{p}}\) are linearly independent.  
Make \(\mathbf{b_{1}}=\mathbf{r_{1}}\), then there exists  
\[\|\mathbf{r_{2}}^{\prime}\|=\|\mathbf{r_{2}}\|\textbf{cos}\mathbf{\theta}=\|\mathbf{r_{2}}\| \frac{[\mathbf{b_{1}},\mathbf{r_{2}}]}{\|\mathbf{b_{1}}\|\!\cdot\!\|\mathbf{r_{2}}\|}=\frac{[ \mathbf{b_{1}},\mathbf{r_{2}}]}{\|\mathbf{b_{1}}\|}, \tag{13}\]  
then  
\[\mathbf{b_{2}}=\mathbf{r_{2}}-\mathbf{r_{2}}^{\prime}=\mathbf{r_{2}}-\|\mathbf{r_{2}}^{\prime}\| \frac{\mathbf{r_{2}^{\prime}}}{\|\mathbf{r_{2}^{\prime}}\|}=\mathbf{r_{2}}-\frac{[\mathbf{b_{1 }},\mathbf{r_{2}}]}{\|\mathbf{b_{1}}\|}\frac{\mathbf{b_{1}}}{\|\mathbf{b_{1}}\|}=\mathbf{r_{2}}- \frac{[\mathbf{b_{1}},\mathbf{r_{2}}]}{[\mathbf{b_{1}},\mathbf{b_{1}}]}\mathbf{b_{1}}, \tag{14}\]  
\[\mathbf{b_{p}}=\mathbf{r_{p}}-\frac{[\mathbf{b_{1}},\mathbf{r_{p}}]}{[\mathbf{b_{1}},\mathbf{b_{1}}]} \mathbf{b_{1}}-\frac{[\mathbf{b_{2}},\mathbf{r_{p}}]}{[\mathbf{b_{2}},\mathbf{b_{2}}]}\mathbf{b_{2}}- \cdots-\frac{[\mathbf{b_{r-1}},\mathbf{r_{p}}]}{[\mathbf{b_{r-1}},\mathbf{b_{r-1}}]}\mathbf{b_{r- 1}}, \tag{15}\]  
The orthogonal vector of \(R\) can be expressed as \(v(R)=\{\mathbf{b_{1}},\cdots,\mathbf{b_{p}}\}\)  
Then, the extension factor of the domain G can be expressed as  
\[\alpha(G)=\begin{bmatrix}\alpha_{1}(G),\ldots,\alpha_{p}(G)\end{bmatrix}^{T} =0.5T\big{[}|p_{d}\
u_{1}(R)|,\ldots,|p_{d}\
u_{p}(R)|\big{]} \tag{16}\]  
\[=0.5T(p_{max}-p_{min})\big{[}|\mathbf{b_{1}}|,\ldots,|\mathbf{b_{p}}|\big{]},\]