esa-sceva/satcom-chunk-collection huggingface dataset:
# Getting Sugar Coating Right! The Role of the Golgi Trafficking Machinery in Glycosylation
## 3 Membrane Trafficking Machinery
### Tethers (MTCs and Coiled-Coil Tethers)
#### 3.1.1 Coiled-Coil Tethers
GRASP65 and GRASP55: The mammalian Golgi reassembly stacking proteins of 65 kDa (GRASP65/GORASP1) and 55 kDa (GRASP55/GORASP2) are two homologous Golgi transport proteins localized to the _cis_- and _medial-trans_ cisternae, respectively [120; 121]. GRASPs are required for the in vitro stacking of Golgi cisternae as well as Golgi ribbon formation by linking individual stacks [122; 123; 124]. Interestingly, a recent study reported that auxin-mediated acute depletion of GRASP55 and/or GRASP65 does not affect Golgi stack formation but causes the loss of lateral connectivity [125]. GRASP65 interacts with GM130 and GRASP55 forms a complex with Golgi-45 to regulate Golgi morphology and tethering [122; 126]. Recently, a CRISPR/Cas9 mediated GRASP double knockout was reported to disperse the Golgi stack into single cisternae and tubulovesicular structures, increase protein trafficking and impair proteins and lipid glycosylation in HeLa and HEK293T cell lines [127; 121]. Both the single and double GRASP knockout cell lines had reduced the cell surface binding of wheat germ agglutinin (WGA) that binds sialic acid and N-acetylglucosamine, and that of Maackia amurensis lectin (MAA), which binds \(\alpha\)(2,3) sialic acid. In addition, LAMP1 and LAMP2 glycoproteins' increased mobility indicated that they were not fully glycosylated [121]. Glycan mass spectrometry analysis confirmed that GRASP55 and GRASP65 depletion affects N-linked glycans in HeLa cells [127]. Furthermore, the altered cell surface binding of Shiga and Cholera toxins suggests that a GRASP knockout impairs the production and/or trafficking of glycolipids as well [121]. Besides protein and lipid glycosylation defects, GRASP depletion also causes sorting defects resulting in the production of immature Cathepsin D [127].
## 3 Results
### Picking Accuracy and Picking Efficiency  
In this study, we conducted a detailed analysis of the 10 selected picking sites, with the data presented in Table 3. Across the 10 different picking experiments, our findings reveal that the total number of tea buds in the target field was approximately 258. Among these, 219 tea buds were successfully detected, yielding a detection success rate of 84.88%. However, depth information could not be accurately obtained for 17 tea buds, either due to exceeding the set threshold or being occluded. Of the detected tea buds, 186 were precisely located, achieving a location success rate of 84.93%. Ultimately, 160 shoots were successfully picked, resulting in a picking success rate of 86.02%. In summary, our premium tea-picking robot achieved an overall success rate of 62.02%. This rate slightly surpasses the recently reported index level of 61.30% in China. Additionally, the average picking time per tea bud was 1.86 s, which is marginally higher than the reference level of 1.51 s documented in recent Chinese literature [21]. To provide a clearer context for the performance of our proposed robot, we conducted a comparative analysis with existing systems. This analysis highlights the competitiveness of our robot in terms of both success rate and picking time. While our robot's performance is commendable, there are still opportunities for improvement, particularly in reducing the picking time per tea bud. Such advancements could further enhance the efficiency and effectiveness of our premium tea-picking robot, positioning it as a leading solution in the industry.
## 4 Fault Location Principle
### Hybrid Line Positioning Formula  
When a single-phase grounding fault occurs at any point on the MT line, the zero-mode time difference in the line-mode measured by the M section ranging device is \(\Delta t_{\text{R}}\). In this paper, the hybrid line is equivalent to a single cable line for fault location. The specific steps are as follows:  
Firstly, the overhead line is equivalent to a cable line, and the equivalent proportional coefficient r is given, as shown in Formula (23). According to the distance of each section in the hybrid line, the equivalent single line section distance \(l_{mi}\) is given, as shown in Formula (24).  
\[r=\frac{v_{1}v_{0}}{v_{1}-v_{0}}/\frac{v_{h0}v_{h1}}{v_{h1}-v_{h0}} \tag{23}\]  
\[l_{mi}=\left\{\begin{array}{ll}rl_{i},&\text{if $i$ is overhead line section}\\ l_{i},&\text{if $i$ is cable section}\end{array}\right. \tag{24}\]  
Secondly, according to Formula (22), the fault distance \(d_{\text{c}}\) of the equivalent single line is obtained, and the fault section is judged to be the overhead line section or the cable section according to the equivalent single line section \(l_{mi}\) distance.  
Figure 3: Traveling wave propagation diagram.  
Finally, through Formula (25), the equivalent single line fault distance \(d_{\rm c}\) is converted to the hybrid line fault distance \(d_{h}\).  
\[d_{h}=\left\{\begin{array}{l}\frac{d_{c}-\sum\limits_{i=1}^{i-1}l_{mi}}{r}+\sum \limits_{i=1}^{i-1}l_{i},\text{ the fault section is overhead line sec tion}\\ d_{c}-\sum\limits_{i=1}^{i-1}l_{mi}+\sum\limits_{i=1}^{i-1}l_{i}\text{, the fault section is cable sec tion}\end{array}\right. \tag{25}\]
## 1 Introduction
This paper introduces a novel optimization method that combines machine learning with the evolutionary generation algorithm for solving the multi-objective building retrofit optimization problem. The method's effectiveness is evaluated in terms of efficiency and reliability, thereby contributing to the refinement of tools for building retrofit projects. A multi-objective design scheme for residential building envelope retrofits is proposed, taking into account economic factors, life-cycle carbon emissions, and energy consumption under different energy efficiency design standards for residential buildings. The primary objectives of this study are as follows:  
(1) To propose a novel framework that integrates ML models and heuristic optimization algorithms to address decision-making challenges in the renovation of existing buildings. This framework aims to provide a robust and data-driven approach to optimizing renovation strategies. (2) To evaluate the effectiveness and efficiency of the proposed method through comprehensive testing and analysis, including assessing its performance in terms of accuracy, adaptability, and scalability for real-world applications. (3) To provide policymakers with scientific evidence and actionable insights from the study's results. The findings aim to support informed decision making by enhancing energy performance and reducing life-cycle carbon emissions and costs.
## 2 Experimental Protocols
### Airport Application Rate  
In collaboration with Montreal Airports, we obtained access to a designated runway and observation of the use of maintenance tools to directly observe operational procedures. This unique opportunity allowed us to assess the effectiveness of RDPs in winter conditions.  
According to the AS6170 test method standard [20], melting tests should be conducted using a 14 cm diameter Petri dish containing 5 g of product, which is equivalent to 280 g/m\({}^{2}\) of RDP. However, during the field test campaign, we noted that the actual application concentration (see Figure 2) was much lower than the specified standard. Note, however, that the spreading trucks lacked accurate monitors to confirm the precise amounts of applied RDP.  
Photo analysis determined the distribution of RDP grains within a given surface area to approximate the application per square meter. The amount spread followed the manufacturer's recommendations, which can also be adapted to the actual weather conditions. We  
Figure 2: Sodium formate (NaFo) over a concrete pavement runway surface, illustrating the application concentration used at Mirabel airport, Quebec.  
considered the size and weight of each individual grain to determine an actual application concentration of slightly more than 25 g/m2; this coverage is more than 11\(\times\) lower than that of the AS1670 standard test, leading to a possible overestimate of the RDP's ability to melt ice when relying solely on the AS1670 standard. To align with actual airport use and conditions, we therefore used a reduced amount of product (0.5 g), corresponding to the actual estimated application rate for the ice melting test. Nevertheless, to maintain consistency with the standard and overestimated amount of RDP, the same test was also run using 5 g of product.
# Mechanical Properties of DNA Hydrogels: Towards Highly Programmable Biomaterials
## 3 Responsive DNA linkers
### Thermal Responsive  
The use of hybridized DNA as a crosslinker inherently makes hydrogels thermally responsive and has been demonstrated in various studies to produce changes in the dynamic moduli [36; 53; 88; 91]. [PERSON] et al. fabricated peptide based DNA hydrogels with identical crosslinking point density but with three different DNA crosslinkers to study the effect of the crosslinker \(\mathrm{T_{m}}\) on the rheological properties of the hydrogel [92]. The three linkers have melting temperatures of 20.8, 39.6, and 53.5 \({}^{\circ}\)C, which corresponded to linker lengths of 8 (1 mismatched bp), 8, and 12 bp. The crosslinker with a mismatch did not form a gel above room temperature while the 8 and 12 bp crosslinkers yielded a storage modulus of 4500 and 5300 Pa and gelling temperatures of 42.5 and 53.5 \({}^{\circ}\)C, respectively. Furthermore, when cyclically tested for thermo-reversibility between 25 and 60 \({}^{\circ}\)C, the 12 bp crosslinker maintained its storage modulus when returning to the gel state [92]. [PERSON] et al. similarly varied the sticky ends but used a 3-arm DNAns, in contrast to the previously mentioned peptide-DNA hydrogel, and observed that the lower melting temperature of the sticky ends significantly increased the frequency dependence of the storage moduli though each variation shared a storage modulus of 1700 Pa at a frequency of 3 Hz [88]. The DNAns gel showed similar thermo-reversibility as demonstrated by [PERSON] et al. and showed gelling temperatures of 38, 48, and 58 \({}^{\circ}\)C for sticky ends of 8 (1 mismatched bp), 8, and 12 bp. The previously mentioned pH responsive hydrogel by [PERSON] et al. which utilized an i-motif to induce stiffening at low pH also demonstrated an increase in the gelling temperature from 39 to 45 \({}^{\circ}\)C [31]. These examples demonstrate the unique thermal responsiveness of DNA hydrogels and the ability to exploit these interactions for fine-tuning of the mechanical properties (Figure 2d).
## 3 Results and Discussion
### Trace Element Contents in Brassicaceae Species: Conventional Versus Organic
[19] reported higher Pb contents in conventional carrots (0.064 \(\mathrm{\SIUnitSymbolMicro g}\)/g) versus organic ones (0.043 \(\mathrm{\SIUnitSymbolMicro g}\)/g). However, [PERSON] et al. [16] found higher contents of this heavy metal in organic wheat (94 \(\mathrm{\SIUnitSymbolMicro g}\)/g) versus that in conventionally grown wheat (82 \(\mathrm{\SIUnitSymbolMicro g}\)/g). Finally, [PERSON] et al. [25] have indicated that, although the majority of organic vegetables may have lower Pb content, organic agriculture as such does not necessarily reduce the content of this heavy metal in organically cultivated products. Another heavy metal whose consumption is aimed to reduce with the development of organic agriculture is Cd. Nevertheless, in the present study, we only found statistically significant differences for Cd levels (\(p<0.01\)) between organic (0.07 \(\mathrm{\SIUnitSymbolMicro g}\)/g d.w.) and conventional (0.23 \(\mathrm{\SIUnitSymbolMicro g}\)/g d.w.) (Table 4) _Sinapis alha_ grown on Farm II. For the rest of the _Brassicaceae_ cultivated on Farm II, Cd values were very similar both in organic and conventional plants. Comparing our results with those in a previous study, [PERSON] et al. (2018) reported lower Cd concentrations in organic tomato, onion, carrot and potato but not in lettuce. [PERSON] et al. [26] for infant foods, such as weaning jars, made with organic vegetable ingredients, it was also found that Cd concentrations were considerably lower than those reported in weaning formulas which were not categorized as organic. [PERSON] et al. [19] showed slightly higher mean Cd concentrations in conventional carrots (0.066 \(\mathrm{\SIUnitSymbolMicro g}\)/g) versus organic ones (0.060 \(\mathrm{\SIUnitSymbolMicro g}\)/g), and [PERSON] et al. [11] indicated significant higher or lower concentrations and even non-significant differences in Cd concentrations, depending on the food matrix. A factor that again influenced the Cd concentrations in _Brassicaceae_ was the soil in which they were grown. Thus, we found statistically significant differences in Cd levels (\(p<0.05\)) for conventional _Eruca vesiculara_ grown on Farm I (0.72 \(\mathrm{\SIUnitSymbolMicro g}\)/g d.w.) and those conventionally grown on Farm II (0.48 \(\mathrm{\SIUnitSymbolMicro g}\)/g d.w.)  
According to [PERSON] et al. [25], organic agriculture could eventually lead to the production of foodstuffs with a lower heavy metal content, although organic agriculture as such is not able to secure low metal contents in its products. Our results show that this final content is also influenced by other factors such as soil, vegetable variety or even the presence of these elements in the air and in irrigation waters.
## 2 RPL in the Literature  
Routing in AMI networks is a critical challenge due to the lossy nature, constrained resources, and the routing requirements. RPL is recommended widely as the best routing protocol for lossy networks like AMI.  
The evaluation and adaptations of RPL that we proposed for SG networks were based on the previous related issues introduced via evaluation and analysis of multiple RPL scenarios starting from theoretical view, simulation and experimental executions. In order to identify the problems and the existing solutions; we made an in-depth study of the state of the art to express the most recent related works found in the literature. We observed from the previous related work that RPL was always studied for WSN, no RPL studies are mentioned for the RPL behavior within PLC [5; 6], and many RPL studies are based on Contiki Operating System.
# Bridging the Vocabulary Gap: Using Side Information for Deep Knowledge Tracing
## 6 Results
### Results on the New Dataset  
To demonstrate the vocabulary gap problem in DLKT models, we set the problem length as the sum of the problem lengths from all datasets and used the DKT and SAINT models to directly predict performance on the new combined dataset. As shown in Table 5, the DKT model performed well on the original training dataset, with DKT-A09 achieving an AUC of 79.87 on the A09 dataset. However, when these DKT models were applied to new datasets, a substantial drop in performance was observed. For instance, the DKT-A09 model exhibited significantly lower AUC values of 60.92 and 52.63 on the A12 and EN datasets, respectively. Similarly, the generalization capabilities of the DKT-A06 and DKT-EN models were limited when applied to other datasets, showing considerable performance degradation. A similar pattern was observed with the SAINT model. Although the SAINT-EN model performed best on the EN dataset (AUC: 77.14, ACC: 70.13), its performance dropped sharply on the A09 (AUC: 51.03, ACC: 51.42), A12 (AUC: 49.56, ACC: 50.54), Junyi (AUC: 50.33, ACC: 40.12) and Eedi (AUC: 49.13, ACC: 46.82) datasets. This phenomenon underscores the vocabulary gap issue in DLKT models. The vocabulary gap arises because the new datasets contain SKQ that the DLKT models have not encountered during training, resulting in poor model transfer. The models' struggle to maintain performance on datasets with unfamiliar elements highlights the need for enhancing the generalization capabilities of DLKT models across diverse datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**A09-Test**} & \multicolumn{2}{c}{**A12-Test**} & \multicolumn{2}{c}{**EN-Test**} & \multicolumn{2}{c}{**Junyi-Test**} & \multicolumn{2}{c}{**Eedi-Test**} & \multirow{2}{*}{\(p.um\)} \\  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** & \\ \hline DKVMN * & 70.39 & 65.31 & 67.81 & 70.88 & 65.85 & 60.94 & 69.22 & 88.77 & 71.87 & 68.52 & 291.70 k \\ DKT & 79.87 & 72.72 & 72.82 & 73.12 & 68.67 & 64.67 & 69.99 & 88.83 & 74.45 & 70.04 & 651.62 k \\ SAKT & 79.78 & 72.20 & 71.78 & 73.06 & 70.31 & 64.41 & 69.51 & 88.81 & 73.84 & 69.56 & 847.36 k \\ SAINT & 75.62 & 69.18 & 65.06 & 70.02 & 77.14 & 70.13 & 70.20 & 88.61 & 63.66 & 62.62 & 71,184.90 k \\ SIKT & 80.73 & 72.31 & 75.90 & 73.20 & 72.95 & 65.64 & 72.71 & 88.49 & 78.32 & 53.87 & 57.24 k \\ \hline \hline \end{tabular}
* The DKVMN model was trained on the A09-train dataset and evaluated on the A09-test dataset; the same strategy was applied to other models.  
\end{table} 
Table 4: Results of different models on five datasets.
## 3 Multi-Scale Numerical Modeling of Rock Failure Process under Compression
### Macroscale Simulation
#### 3.3.1. Macroscale Numerical Model of Columnar Jointed Rock Mass  
In this section, combined with geological features of the Baihetan hydropower station and based on the CJRM Window images obtained through the sampling window method, a CJRM reflecting the real structure is constructed. Figure 12a,b is the transverse section information image of the CJRM obtained from the selected window on the right slope of the steep-descent basin of the Baihetan hydropower station [35]. Different colors represent different shapes of column-shaped joints, which cut the basalt into columns. In the columnar block cross-sections, the triangular, quadrangular, pentagonal, and hexagonal polygon frequencies are 39.9%, 45.3%, 9.9%, and 4.9%, respectively. Based on the image in Figure 12b, the finite numerical model of CJRM is constructed according to the method and principle introduced in Section 4, as shown in Figure 12c, which is represented by the elastic modulus diagram. Figure 12d depicts the spatial distribution of joints in the model, and the critical information of the numerical model is consistent with that of the digital image. The size of the model is 2.75 m \(\times\) 2.75 m \(\times\) 2.75 m; the number of image pixels in each layer is 275 \(\times\) 275 (the number of single-layer finite element grids is 75,625). A total of 275 layers of images are copied, so the size of the unit is 1 cm \(\times\) 1 cm \(\times\) 1 cm, and the total number of model units is about 20 million. The mechanical parameters are derived from [58], as shown in Table 2. The loading method is displacement loading, and the loading rate is 0.1 mm/step.  
Figure 11. Internal fracture evolution in a shale meso sample sliced along different Z planes. (**a–f**) The slices are at different loading stages, corresponding to points A–F in Figure 10g, respectively. (organic matter, clay minerals, carbonate minerals, damage elements, fractures).
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### DR and Load Management in Operation
#### 3.2.1 Demand Response  
DR strategies, enhanced by digital advancements, are increasingly recognized for their ability to improve grid efficiency, manage peak loads, and reduce carbon emissions. Smart meters and specialized software are crucial for addressing challenges such as reverse power flow from distributed generators, which was highlighted by a successful DR initiative by an Italian DSO [59; 60].  
The review in [59] evaluates residential DR as a cost-effective alternative to traditional network upgrades, discussing activation strategies, challenges, and the future potential of residential demand responsiveness. Another study [61], examines the impact of bidirectional digital communication on DR management (DRM) programs, aiming to reduce operational costs and emissions through optimized DR programs.  
Further research, reported in [62], introduces risk-based planning tools to address uncertainties brought by new technologies like DERs, using a robust linear programming model for a DR that showcases the potential economic benefits. Additionally, a game theoretic model [63] explores competition among demand response aggregators (DRAs) to stabilize the grid amidst renewable energy fluctuations.  
Despite the benefits of DRs, challenges such as consumer hesitancy and the need for enhanced stakeholder collaboration persist. Future research will focus on evaluating the sector-specific efficacy of DR and the impact of advanced communication on market efficiency, particularly in developing countries. This underscores the need for strategic engagement, technical advancements, and pricing strategies to make DR economically viable, highlighting the critical role of digital tools in enhancing grid performance and sustainability.
## 3 Results
### Forest Degradation Maps in the Three-North Forest Shelterbelt
#### 3.1.2 Forest Degradation Maps from MODIS and Landsat ETM+ Data  
A forest degradation map with a spatial resolution of 500 m from the trend of time series MODIS products was obtained (Figure 4a). By using the Landsat ETM+ data to analyze the situation in the degraded areas from MODIS datasets, the forest degradation map with a resolution of 30 m was acquired (Figure 4b). Compared to use single time series data from MODIS, the degradation map from MODIS-Landsat demonstrated finer scale features with a clear identification by excluding the impact of non-forest in the coarse MODIS pixels.  
In general, most forest in the Three-North Forest Shelterbelt were in good condition without degradation. The severe forest degradation was mainly located in the middle areas in Inner Mongolia, the northwestern areas in Xinjiang, the middle areas in Inner Mongolia, northern areas in Shannxi and Ningxia. In northeastern areas, such as Liaoning, Jilin and Heilongjiang, slight and moderate degradation also occurred in the forest. In general, the primary causes for forest degradation were poor quality of forest site, poor natural condition, over-mature forest, improper selection of the forest species, forest pest and disease, and the improper management measures.  
Forest degradation area from MODIS data, forest degradation area from integrated MODIS-Landsat data, and the forest degradation area from ground survey in each province were shown in Figure 5. By excluding the non-degradation areas in a MODIS pixel, forest degradation areas in each province from integrated MODIS-Landsat data were lower than these from MODIS data. Due to the poor quality of forest site, drought, sandstorm, biological hazard, physiological aging of shelterbelt and other natural factors, ecological situation was grim in Inner Mongolia and Xinjiang (Degradation forest area was more than 80 \(\times\) 10\({}^{4}\) hectares from MODIS data, and the severe degradation forest area was about 20 \(\times\) 10\({}^{4}\) hectares). In Shaanxi and Gansu, forest degradation areas were more than 40 \(\times\) 10\({}^{4}\) hectares. It was found that the forest degradation areas from satellite observations data were larger than those from ground survey data in most province (Especially in Xinjiang, where the degradation forest area was more than 80 \(\times\) 10\({}^{4}\) hectares from remote sensing data, and the degradation area was only 13,000 hectares from ground survey data). The reasons for that were mainly the difference between indictors and standards to demonstrate the degradation of forest from remote sensing data and ground survey data.  
Figure 4: Forest degradation from MODIS data and MODIS-Landsat data. (**a**) was obtained from MODIS products (500 m), then Landsat ETM+ data were used to analyze the details in the degraded MODIS pixels, shown in (**b**) (30 m).