esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Latent Heat Storage (LHS) vs. Sensible Heat Storage (SHS)  
Using the bibliometric analysis method, [PERSON] et al. [26] reported that up to 2021, more than 27,016 TES-themed articles have been published in the literature. Explicitly, nearly 2900 articles dealt with the SHS, while a record number of around 12,152 concerned the LHS subject matter.  
Table 6 sets out a non-exhaustive comparison between the SHS and LHS techniques in terms of operating principle, advantages, most used materials and application domains [5; 60; 114].  
[PERSON] et al. [27] drew up and analyzed the technical parameters characterizing SHS and LHS (see Table 7).  
[PERSON] et al. [85] published a critical paper on the use of PCMs for cooling applications in buildings. It was noted that LHSMs store between five and 14 times more energy than SHSMs [129] and that certain materials (e.g., _rocks_) offer much higher storage volumes than that of certain organic or inorganic PCMs.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Operating Principle** & **Benefits** & **Materials** & **Application Domains** \\ \hline \multirow{3}{*}{**SHS**} & Temperature change & Inexpensive; & & Concentrated solar power (CSP) \\  & (_Increase/Decrease_) & Simple operation & Water, rock, concrete, etc. & Plants or desalination \\  & & & & Building heating \\ \hline \multirow{3}{*}{**LHS**} & Phase change & Large storage density; & & Solar applications \\  & (_Solid–Liquid_) & Large latent heat; & Paraffins, salt hydrates, & Building heating/cooling \\ \cline{1-1}  & (_Solid–Liquid_) & Stable temperature & metallics, etc. & Heat pump \\ \cline{1-1}  & & & Thermal control \\ \cline{1-1}  & & & Industrial waste heat storage \\ \hline \end{tabular}
\end{table}
Table 6: Comparison between SHS and LHS techniques [5; 60; 114], reprinted/adapted with permission from Ref. [114], 2022, Elsevier.
# Power Control Techniques for Interference Management-
## 4 Power Control Technique Evaluation
### Power Control Techniques: Case Studies  
Several case studies on power control techniques highlight their practical applications and benefits in 5G networks. This study presents a collection of case studies with real power control applications in interference management. The case studies outline strategies to address challenges, practical suggestions, or novel solutions. The follow ing case studies illustrate the application of power control techniques in 5G networks to address interference.
## 2 Background and Related Work
### Background
#### 2.1.2 Website Page Camouflage  
Currently, numerous illegal websites employ various camouflage methods to evade detection by automated monitoring systems. These methods include, but are not limited to:  
* Variant text: These methods involve the use of Chinese and English replacements, numerical substitutions, special symbols, artistic fonts, and other techniques. Such strategies often render text classification models trained on standard text ineffective.
* Anti-image: Although relatively uncommon, this method involves the addition of disordered symbols of varying colors, blurring effects, and other visual distortions towebpage images. Such techniques can significantly hinder the performance of certain image classification models.
* Verification code: Certain web pages implement verification codes as a security measure, requiring users to enter the correct code to access the authentic content. This approach presents a challenge for web crawlers without the capability to recognize and process verification codes, thus impeding their ability to obtain the genuine web content.
* Fake 404 pages: This deceptive method is relatively straightforward to implement. The page's content remains that of the illegal website, while only the title section is modified to display '404 Not Found'. This technique can successfully deceive models that solely rely on title detection, as they may mistakenly classify the illegal website as a non-existent or inaccessible page.
* Building webpages with images: This approach exclusively relies on images to compose webpage content, with only the <img> tag in the HTML referencing these images. Consequently, models solely reliant on HTML detection will be ineffective in identifying the webpage's actual content.  
Through observation of numerous illicit websites, we have found that constructing webpages consisting of only images to boost traffic is more common. By examining the HTML source code of the webpages, we have observed that they only contain <img> tags which reference images. Moreover, valuable key text is embedded within the images themselves. As shown in Figure 11, the four gambling websites are all composed of images. We can see that there is the text \"(\"(translated into English, meaning \"Real person chess and card\") in Figure 12(a). The text \"(\"(meaning 'Kaiyuan chess and card\") exists in Figure 12(b), and the text \"(\"(meaning 'Real money, real entertainment, board games and sports') exists in Figure 12(c). The text \"(\"(meaning 'Lottery betting\") exists in Figure 12(d), and the key text on these images can effectively improve the accuracy of recognition.  
The practice of constructing web pages solely with images undermines the effectiveness of methods solely focused on detecting web page HTML. Hence, in this paper, we opt to extract text from web screenshots as features instead of relying on HTML.
# Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong
## 5 Conclusions  
This study aims to optimize the water transportation planning of Mic modules under uncertain situations, with the objective of minimizing the total transportation costs. The main contributions of this study are twofold. First, propose a stochastic module water transport planning framework considering the uncertainty of weekly module demands. The results based on a real-world Mic project involving 418 modules show that the optimal transportation plan derived from the stochastic model, incurs a total cost of HKD 148,951. Notably, when compared with the proposed model, the costs associated with the three deterministic models [DM1], [DM2], and [DM3], are higher by 38%, 7%, and 30%, respectively. Second, analyzing the influence of barge rental on the total transportation costs through sensitivity analyses, the findings reveal that when \(p_{k}\) is less than 1.4 times \(c_{k}\), opting for all round trips to be finished using temporarily rented barges is the most effective strategy for addressing uncertain demand. Conversely, when \(p_{k}\) is higher than 4.0 times \(c_{k}\), employing booked barges for all round trips emerges as the optimal approach under uncertain demand conditions. Additionally, as \(c_{k}\) continues to increase, the total transportation cost obtained from [M1] remains stable. Overall, regardless of any fluctuations in \(p_{k}\), the proposed model consistently delivers the most cost-effective transportation plans. The findings from this study have significant implications for transportation planners and project managers in the water transportation and construction sectors. Although this method was validated using a Hong Kong case study, it can be applied by contractors in other countries or regions with the integration of road transportation. The comparisons with three deterministic models highlight the economic advantages of a more nuanced, uncertainty-aware approach. They can adjust parameter values based on local maritime conditions. As long as the input parameters are correctly specified, our model can output optimal transportation plans applicable to diverse transportation planning contexts. By the proposed stochastic planning framework, Mic contractors can achieve substantial cost savings and improve their capacity to manage demand variability. Additionally, the sensitivity analysis on barge rental strategies offers clear guidelines for optimizing rental decisions based on cost fluctuation. The originality of this study lies in its novel integration of stochastic programming into the optimization of water transportation planning for Mic projects, offering a unique solution to minimize costs while accounting for the inherent uncertainty in demand.
# Investigation of the Mechanical Behavior of Polypropylene Fiber-Reinforced Red Clay
## 3 Results and Discussion
### Constitutive Model for Fiber-Reinforced Red Clay
#### 3.2.3 Model Validation  
To prove the validity of the model, the test results under cell pressures of 100 and 300 kPa were calculated according to the above-mentioned method. The calculation results of the parameters are shown in Table 6. The estimated parametric values in Tables 5 and 6 were substituted into Equation (5) to obtain the deviatoric stress of the fiber-reinforced red clay using the known axial strain. The comparison of the measured and predicted results is shown in Figure 6. The model results agree well with the test results with different cell  
Figure 5: Fitting of the measured results (\(\sigma_{3}=200\) kPa).  
pressures, FL and FC, with a maximum error of less than 10%, thus proving that the model can appropriately express the stress-strain relationship of fiber-reinforced red clay.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\boldsymbol{\sigma_{3}}\) & \(\boldsymbol{FL}\) & \(\boldsymbol{FC\) (\%)} & \(\boldsymbol{a}\) & \(\boldsymbol{b}\) & \(\boldsymbol{a_{1}}\) & \(\boldsymbol{a_{2}}\) & \(\boldsymbol{R^{2}}\) & \(\boldsymbol{a_{1}}^{*}\) & \(\boldsymbol{a_{2}}^{*}\) \\ \hline
100 & 7 & 0.15 & 0.00304 & 0.00218 & 0.00304 & 0.00229 & 0.99941 & 0.00250 & 0.00229 \\  & 7 & 0.25 & 0.00242 & 0.00194 & 0.00242 & 0.00340 & 0.99989 & 0.00344 \\  & 7 & 0.35 & 0.00152 & 0.00182 & 0.00152 & 0.00446 & 0.99982 & 0.00458 \\  & 7 & 0.50 & 0.00171 & 0.00160 & 0.00171 & 0.00560 & 0.99901 & 0.00573 \\  & 7 & 0.80 & 0.00184 & 0.00124 & 0.00184 & 0.00694 & 0.99696 & 0.00687 \\  & 13 & 0.15 & 0.00318 & 0.00171 & 0.00318 & 0.00334 & 0.99947 & 0.00344 \\  & 13 & 0.25 & 0.00328 & 0.00148 & 0.00328 & 0.00481 & 0.99847 & 0.00515 \\  & 13 & 0.35 & 0.00296 & 0.00134 & 0.00296 & 0.00610 & 0.99853 & 0.00687 \\  & 13 & 0.50 & 0.00212 & 0.00121 & 0.00212 & 0.00787 & 0.99795 & 0.00859 \\  & 13 & 0.80 & 0.00205 & 0.00094 & 0.00205 & 0.00978 & 0.999713 & 0.01031 \\  & 19 & 0.15 & 0.00303 & 0.00314 & 0.00303 & 0.0382 & 0.99979 & 0.00389 \\  & 19 & 0.25 & 0.00252 & 0.00120 & 0.00252 & 0.00570 & 0.99988 & 0.00584 \\  & 19 & 0.35 & 0.00294 & 0.00105 & 0.00294 & 0.00698 & 0.99787 & 0.00779 \\  & 19 & 0.50 & 0.00257 & 0.00090 & 0.00257 & 0.00855 & 0.99734 & 0.00973 \\  & 19 & 0.80 & 0.00234 & 0.00071 & 0.00234 & 0.01079 & 0.99629 & 0.01168 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model parameters under 100 and 300 kPa cell pressure.  
Figure 6: Comparison of measured and predicted results.
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator
### DC Link Circuit and Overvoltage Protection System  
Figure 12 shows the DC link circuit model of a back-to-back MMC converter. The circuit model consists of two DC link capacitors, a DC chopper, and an overvoltage protection controller. The DC link circuit is a connection circuit that connects the stator-side MMC and the grid-side MMC. The DC voltage on the DC link circuit should be kept constant at the rated operating DC voltage so that the power flow from the generator to the grid system can be achieved smoothly. When a disturbance such as a short circuit occurs in the  
Figure 10: MMC Inner Controller.  
grid system, the active power on the AC terminal of the grid-side MMC decreases, while the active power from the generator is still produced. This condition leads to a significant increase of the DC link circuit voltage due to a power imbalance between the stator-side MMC and the grid-side MMC. When the DC voltage is larger than 1.05 pu, the overvoltage protection controller is activated, and then the active power from the generator is absorbed by a chopper resistance (\(R_{ch}\)). The value of the resistance can be adjusted according to the amount of active power produced by the wind turbine generator, which is represented by the reference power (\(P_{n\!f}\)).
## 2 Study Area and Data
### In Situ Data  
In situ SWE data are sourced from the National Water and Climate Center (NWCC) in the United States. Partial data are gained by the center's SNOwpack TELmetry (SNOTEL) network where the data are freely available ([[https://www.nrcs.usda.gov/programs-initiatives/sswf-snow-survey-and-water-supply-forecasting-program/national-water-and](https://www.nrcs.usda.gov/programs-initiatives/sswf-snow-survey-and-water-supply-forecasting-program/national-water-and)]([https://www.nrcs.usda.gov/programs-initiatives/sswf-snow-survey-and-water-supply-forecasting-program/national-water-and](https://www.nrcs.usda.gov/programs-initiatives/sswf-snow-survey-and-water-supply-forecasting-program/national-water-and)), accessed on 18 May 2024). The SNOTEL network comprises 867 automated observation stations for monitoring snow conditions in the western mountainous regions of the country, with 35 located within the study area. The stations measure the SWE using snow pillow devices and pressure transducers, with an instrument measurement accuracy of up to 0.1 inches. The remaining SWE data are gained through the 90 cooperator snow sensors of the center. All these observation records are used to extract the ROD. However, prior to extracting the ROD, it is necessary to screen the stations. Firstly, some stations cannot extract the ROD for all years due to the lack of observation records. In this study, we only choose those that can extract the ROD for that year when selecting stations for a single year. Additionally, the number of Sentinel-1 images corresponding to each station also can affect the extraction accuracy. Therefore, those corresponding to fewer than 20 Sentinel-1 images are excluded based on the length of the snowmelt period in the study area. After the above screening steps, the number of ROD stations under different land cover types in various years, the annual number of Sentinel-1 images within the study area, and the minimum and maximum number of corresponding images among all stations each year, are listed in Table 1.  
Figure 3 illustrates the variation in the SWE recorded at the Gold Lake station from February to July 2021, alongside the corresponding changes in the Sentinel-1 VV polarization backscatter. In late March, the snow wetness gradually increases as the melting season approaches, resulting in a decrease in backscatter. In early April, the runoff begins when the snow reaches its maximum water-holding capacity, leading to a reduction in the SWE. Subsequently, the SWE decreases rapidly with the further acceleration in melting, while the increase in snow surface roughness causes an increase in backscatter.
## 4 Conclusions  
In summary, we have developed novel nanogel-embedded contact lenses for extended ophthalmic drug delivery. First, a series of PSBMA nanogels were synthesized by the reflux-precipitation polymerization method, and their uniform spherical morphologies and good colloidal stability were demonstrated by SEM and DLS. Ophthalmic LEV was then incorporated into the nanogels with high drug-loading capacity. Nanogel-embedded contact lenses were obtained by the cast molding method after mixing the drug-loaded nanogels into premonomer solution. The prepared hydrogels exhibit a closed-type porous structure, and spherical nanogels could be clearly seen dispersed in the hydrogel matrix. The evaluation of light transmittance, WCA, EWC, swelling behavior and mechanical properties of the series of nanogel-embedded contact lenses revealed that with an increase in embedded  
Figure 10: Cytocompatibility test for the series of contact lenses. (**a**) OD values of viable 3T3 cells by CCK-8 assays; error bars represent mean \(\pm\) SD (\(n\) = 5). (**b**) Confocal fluorescence images of Calcein AM (green)-stained 3T3 cells incubated on tissue culture plate (TCP) and on the series of contact lenses from day 1 to day 4. The scale bar is 200 \(\mu\)m.  
nanogels, the critical quality of the lens decreased slightly. However, the key properties of the optimized contact lens (pHEMA/LEV-2) with moderate incorporation of nanogels (8 wt%) remained within the range of values recommended for contact lenses, with light transmittance above 90% and EWC of 65.5%, etc. In vitro drug release revealed sustained LEV release in the therapeutic range for 10 days with no initial burst release. Furthermore, a cell viability assay and cell morphology observations demonstrated the cytocompatibility of the prepared contact lenses. In conclusion, our prepared nanogel-embedded contact lenses have great potential to serve as novel ophthalmic drug delivery systems.  
Conceptualization, Z.W. and L.H.; data curation, Z.W., X.L., Q.L. and W.S.; writing--original draft preparation, X.L. and X.Z.; writing--review and editing, Z.W. and R.S.; supervision, L.H.; project administration, Z.W. and X.Z.; funding acquisition, Z.W. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province (Project No: BK20190113), National Natural Science Foundation of China (Project No: 51902145) and Jinling Institute of Technology (Project No: jit-b-201828).  
The authors declare no conflict of interest.
## 2 Methodology
### Point Cloud Registration  
After locating and segmenting the target point cloud, the corresponding template point cloud is selected based on the pose classification results from YOLOv8-URE, and registration is then performed between the template and target point clouds.  
Point cloud registration can be divided into two stages: coarse registration and fine registration. Coarse registration is used when the relative positions of point clouds are completely unknown. Its goal is to estimate an approximate rotation and translation matrix to align the target point cloud into a common coordinate system, thereby providing a reliable initial estimate for fine registration. Fine registration then refines this alignment by minimizing the spatial discrepancy between point clouds to compute a more accurate transformation matrix for precise alignment.  
In this study, the sample consensus algorithm is selected for coarse registration. This algorithm handles outliers through random sampling, and its procedure includes the following steps:  
1. Random Sampling--Randomly select a subset of points from the source point cloud.
2. Transformation Estimation--Compute a rigid transformation matrix based on the sampled points.
3. Model Verification--Apply the transformation to the entire point cloud and evaluate its consistency.  
As can be seen in Figure 12, both the registration result of the sample and that of the actual workpiece demonstrate that accurate alignment can still be achieved despite the presence of surrounding outlier points, indicating the strong robustness of the algorithm, which makes it more suitable for registration in industrial environments.  
To further improve the point cloud alignment accuracy and ensure computational efficiency, this paper selects the NDT (normal distributions transform registration) algorithm as the fine alignment stage algorithm. As shown in Figure 13, taking the workpiece point cloud used in this paper as an example, a comparison is made between the proposed registration algorithm and commonly used methods such as ICP and GICP in a single-object scenario, which shows that the results of this paper's alignment algorithm are more accurate and have strong robustness to noise and outliers.  
Figure 11: Target point cloud segmentation: (**a**) RGB target detection result; (**b**) segmented target point cloud (the green part indicates the segmented target).
## 4 Experimental Results and Analysis
### Ablation Experiments and Visualization Results  
In this section, we analyze the experimental results for the multi-scale feature fusion part, as well as the RFAC part of the visualization, as shown in Figure 8. It can be found that the kidney segmentation visualization results in (III) are quite different from the gold standard. And (IV) optimizes the expression of kidney features by fusing kidney features of different scales, which solves the problem of ambiguous kidney boundaries to some extent. Due to the addition of the RFAC module, (V) can not only better segment the outer boundary of the kidney but can also well segment the inner hollow region of the kidney compared to (IV).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Types** & **Dice** \\ \hline None & - & 0.936 \\ GCNet [29] & C & 0.957 \\ AttNet [30] & S & 0.952 \\ DANet [31] & C + S & 0.963 \\ FLANet [32] & C + S & **0.970** \\ \hline \hline \end{tabular} Bolded text is the optimal value for each column.  
\end{table}
Table 2: Comparative test results for attentional decision making.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Base** & **+res** & **+FAin** & **+FAout** & **+FAout+res** & **Dice** \\ \hline \(\surd\) & & & & 0.946 \\ \(\surd\) & \(\surd\) & & & 0.948 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 0.963 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.966 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **0.969** \\ \hline \hline \end{tabular} Bolded text is the optimal value for each column.  
\end{table}
Table 3: Test results of RFAC module structure.  
Figure 8: Visual comparison of kidney segmentation results. (**I**,**II**) are CT slices and corresponding labels, respectively; (**III**) experimental results are from the RFAC model without multi-scale feature fusion and using the base version of the RFAC module; (**IV**) multi-scale feature fusion is added based on (**III**); and (**V**) an experimentally refined RFAC module is used based on (**IV**).  
Figure 7: (I) Base residual convolution block (Base); (**II**) Add residual path (+res); (**III**) add attention module internally (+FAin); (**IV**) externally add attention module (+FAout); (**V**) add attention and residual path (+FAout + res).