esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Distribution and Temporal Variation of Carbon Source/Sink
#### 3.3.2 Temporal Variation in Vegetation Carbon Sources/Sinks
In contrast, the total NEP of forestland has decreased by 0.37 Tg C, which indicates that the vegetation has degraded in the unchanged forest areas. This may also account for the decline in the average carbon sink in forests throughout the region. In areas where land cover types have changed, the total NEP of cropland and forestland increased dramatically, reaching 3.45 Tg C and 0.93 Tg C, respectively, which is approximately 4.5 and 5.5 times more than in 2001, and the total NEP of urban areas also increased by approximately 1.07. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Cropland** & **Forestiand** & **Grassland** & **Urban Areas** & **Bare Areas** & **Total** \\ \hline \multicolumn{7}{c}{Total NEP (Tg C)} \\
2001 & 24.64 & 6.69 & 23.50 & 0.03 & 18.82 & 73.68 \\
2018 & 33.00 & 7.24 & 25.86 & 0.11 & 18.85 & 85.06 \\ Change & 8.36 & 0.55 & 2.36 & 0.08 & 0.03 & 11.38 \\ Percentage & 33.93\% & 8.22\% & 10.04\% & 266.67\% & 0.16\% & 15.45\% \\ \multicolumn{7}{c}{Average NEP (g C m\({}^{-2}\) a\({}^{-1}\))} \\
2001 & 178.87 & 389.86 & 52.03 & 38.94 & 12.99 & 35.84 \\
2018 & 215.85 & 374.25 & 56.75 & 43.15 & 13.23 & 41.37 \\ Change & 36.98 & \(-\)15.61 & 4.72 & 4.21 & 0.24 & 5.53 \\ Percentage & 20.67\% & \(-\)4.00\% & 9.07\% & 10.81\% & 1.85\% & 15.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Total and average NEP in 2001 and 2018 in the ARNWC. Figure 4: Average NEP of vegetation in the ARNWC from 2001–2018. Subfigures (**a–d,f**) are displayed by raster, with the color of the raster representing its value. subfigure (**e**) shows the urban areas, due to their relative small proportion compared to the study area, and therefore uses dots to represent the corresponding raster, with the color of the dots indicating the value of the corresponding raster. 0.08 Tg C due to urban expansion, whereas the total NEP of grassland and bare areas decreased by 0.2 Tg C and 3.17 Tg C, respectively. Figure 5: NEP slope of vegetation in the ARNWC from 2001–2018. Subfigures (**a–d,f**) are displayed by raster, with the color of the raster representing its value. subfigure (**e**) shows the urban areas, due to their relative small proportion compared to the study area, and therefore uses dots to represent the corresponding raster, with the color of the dots indicating the value of the corresponding raster. Figure 6: Total and average NEP in the ARNWC in 2001 and 2018. (**a**) Areas with unchanged land cover types; (**b**) areas of land cover type change.
## 7 Nanomedicine to Enhance the Therapeutic Effectiveness of BC
### Nanoparticle-Mediated Drug Delivery
#### 7.1.1. Lipsosomal Nanocarriers  
Recent studies by [PERSON] et al. delved into treatments for TNBC using the drug cabazitaxel [98]. Women who experience brain metastasis from TNBC face severe therapeutic obstacles, mainly due to the tumor's heterogeneity. To address this, the authors employed a platelet-membrane hybrid liposome delivery system, known as pVAP-PL, to target orthotopic breast cancer [99]. They found that the pVAP-PL/CNC drug delivery system was effective in preventing the premature release of cabazitaxel and in extending the in vivo circulation time of CNC [99]. Further, [PERSON] et al. also developed a liposome responsive to matrix metalloproteinase-2 (MMP-2), which dual-targets and co-delivers AUNP-12 and NLG919 to the intended targets, enhancing the synergy in remodeling the immunosuppressive microenvironment [98]. They encapsulated the indoleamine 2,3-dioxygenase 1 (IDO-1) inhibitor, NLG919, into the liposome, resulting in a tumor cascade-responsive liposome drug delivery system named NLG919@Lip-pep1 [98]. This system targets both the T cells and tumor cells associated with the PD-1 signaling pathway, particularly at the tumor's invasive margins. Furthermore, [PERSON] et al. successfully introduced a novel MMP-2-responsive cascade target liposome delivery system [98]. Their findings underscore the potential of an MMP-2-responsive cascade-targeted immunotherapy approach for metastatic BC [98]. This method synergistically modulates the tumor's immunosuppressive environment, prompting a robust immune response against metastatic breast tumors [98]. Moreover, tumor-homing peptide-capeitabine liposomes (THP-CAP-LPs) were notably absorbed by cells, and the destructive impact of capecitabine was enhanced with THP-CAP-LPs by decreasing antiapoptotic proteins and increasing proapoptotic ones, as confirmed by Western blot analysis [100]. Thus, THP-CAP-LPs for delivering CAP could be a viable strategy to boost antitumor effects while minimizing unintended consequences [100].
## 4 Modeling Approaches to Assess the Costs for the Last Mile for Direct Deliveries
### Estimation of the kilometers Driven for Each Tour (Round Trip)  
The input variables for estimating the distance \(d\) driven for each tour are the size of the area \(A\) (Figure 2a) and the number of customers \(n\) to be visited (Equation (1)) [58; 59]:  
\[d=0.765\cdot\sqrt{(}A\cdot n) \tag{1}\]  
Given the need to plan multiple round trips, the total area needs to be divided according to the expected number of tours. Given the daily number of parcels and the number of parcels per tour (the capacity of the vehicle), the area served within each tour could be estimated by dividing the total area of the city by the total number of expected tours. However, the assumption that the transport orders are evenly distributed over the city area is not realistic. Typically, the number of orders correlates with the size of the population.  
In this context, all of the necessary data are given for city districts for the example of the city of Dortmund. From the associated data, the number of tours per district is calculated. A possible estimation of the total distance traveled could be to simply divide the area of the district by the number of necessary tours per district. Then, Equation (1) could be applied to compute the average tour length, adding the distance from the depot to the district and back for each tour. However, following this strategy leads to tours that are usually not fully utilized. An alternative is to assume fully utilized tours. For each district \(A\), the remaining parcels for a last, not fully utilized tour are transferred to the next (neighbor) district \(B\) to be planned, increasing the tour of the neighbor district \(B\) by the respective ratio of left orders in relation to the total number of orders of district \(A\).  
More formally, the approach can be formulated as follows: The maximum capacity of the vehicle is \(C\), and the number of full-truck loads is \(f_{j}\), with \(floor(n_{j}/C)\) for district \(j\). The area \(A_{i}^{y}\) to be visited by full truck tours is given by \(A_{j}\times(n_{j}/(f_{j}\times C)\). For each tour, an area of \(A_{j}^{y}/f_{j}\) for \(C\) parcels is assumed. If parcels remain, these are transferred to a (previously defined) neighbor district that has not been planned yet. Additionally, the remaining area \((A_{j}-A_{j}^{y})\) is added to the total area of the neighbor district. An example with the detailed areas and the neighborhood definition is given in Figure 2b. Here, the first districts to be planned are on the outer districts of the city. The respective neighbor is indicated by a blue line connecting the district centers. Here, a converging structure towards the city center is assumed.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 3 ML for Wind Energy Prediction
### Machine Learning and Wind Energy Forecast
#### 3.2.1 Linear Regression
* Adjusted R\({}^{2}\) (0.6194): The Adjusted R\({}^{2}\) is slightly lower than the R\({}^{2}\) (0.6194 vs. 0.6199), which accounts for the number of predictors. It is close to R\({}^{2}\), suggesting that the added features are useful, but not overfitting. * MSE (0.0312): The low value of 0.0312 of MSE indicates that the model's predictions are generally close to the actual values, though it is harder to interpret MSE without comparing it to the scale of the data. * RMSE (0.1767): An RMSE of 0.1767 means that, on average, the model's predictions are off by around 0.18 units from the actual values. * MAE (0.1389): An MAE of 0.1389 means that, on average, the model is off by 0.14 units, which is slightly lower than the RMSE. This suggests the model is performing well with relatively small errors. Cross-Validation Results (Mean \(\pm\) Std): These results give insight into how the model performs across multiple data splits during cross-validation. They help confirm the robustness of the model. * R\({}^{2}\) (0.6299 \(\pm\) 0.0082): The average R\({}^{2}\) across cross-validation is 62.99%, slightly higher than the original R\({}^{2}\). The standard deviation (\(\pm\)0.0082) indicates stable performance across different data splits. * Adjusted R\({}^{2}\) (0.6290 \(\pm\) 0.0082): The adjusted R\({}^{2}\) is 62.90% with minimal variability, confirming that the model generalizes well without overfitting. * MSE (0.0303 \(\pm\) 0.0007): The average error across cross-validation sets is 0.0303 with a small standard deviation (\(\pm\)0.0007), showing that the model is consistent. * RMSE (0.1741 \(\pm\) 0.0021): The average RMSE is 0.1741, meaning the average prediction error is about 0.174 units, with slight variability (\(\pm\)0.0021). * MAE (0.1376 \(\pm\) 0.0015): The average MAE is 0.1376, indicating that, on average, the model is 0.1376 units off. The small standard deviation (\(\pm\)0.0015) shows good consistency. The cross-validation results validate the model's stability, as the metrics consistently align across several data splits. Figure 17 shows that there is a clear downward trend, which means that the residuals change systematically as the predicted values increase. Specifically, the variance of the residuals decreases with the increase in the predicted values. The model struggles to predict values consistently across the range of predictions.
# Atlas of Micromorphological Degradation of Archaeological Birch Bark
### Morphology of Birch Bark  
The portion of the bark of the birch tree that is used to manufacture objects is the outer smooth white layer, called the phelem in botany. The function of the birch phelem is to prevent transpiration and to provide thermal [1] and parasite [2] protection. It consists of two cell types produced by the phellogen, arranged in alternating and compact radial layers, providing the characteristic laminated structure of the bark. One cell type has thin walls and a broad lumen filled with a white substance, betulin, while the second cell type has thick walls and a small lumen filled with a red-brown material composed of phenolic compounds [3; 4; 5] (Figure 1). The ratio of thin- to thick-walled cell layers depends on the _Betula_ species and the specific tree history [3].  
The cell walls are composed of a primary, secondary and tertiary layer, and the individual cells are joined together by the middle lamella, often called the compound middle lamella, as it can hardly be distinguished from the primary wall [6]. The phelem cells are arranged in such a way that the radial middle lamella creates a line. The thin-walled cells are heavily compressed in the outer portion of the bark but start off with a square shape. The compression and folding of the thin-walled cells can be explained by the formation of the phelem. To adjust to the growing size of the phelem, the bark cells are pushed outwards: the cells stretch in a tangential direction, while the thin-walled cells are compressed [7]. The folding of radial cell walls has also been observed in phenlems of other trees, e.g., _Querus super_[8], _Pseudotsuga menziesii_[9] and _Prunus serrula_[10].  
The secondary walls of both types of cells are composed of suberin, a biopolymer that makes the cork impermeable. Gas and water exchanges occur through characteristic openings called lenticels [11; 12]. These are formed by fanned-out bands of continuous, heavily suberized, thick-walled cell layers and interrupted by void spaces filled with irregular cells. The intercellular spaces between the thick-walled cells visible in the lenticels' radial sections are absent in the normal phelem [12; 13].  
The suberization of the cell walls takes place after their generation from the phellogen [14]. In this phase, the channels connecting the cells, so-called plasmodesmata, become closed and the phelem cells die. Plasmodesmata with a diameter of 50 nm have been found mainly in tangential suberized cell walls of _Betula_ sp. phelem [12]. Dead cells are less nutritious and have better resistance to microbial decay [15].
# Mechanical Properties of DNA Hydrogels: Towards Highly Programmable Biomaterials
## 4 DNA-Polymer Hybrids
### Peptide Based Hybrids  
In addition to synthetic polymers, polypeptide chains have also been functionalized with DNA linkers to create hybrid hydrogel systems. [PERSON] et al. used a human serum albumin (HSA) derived polypeptide copolymer (PcP) functionalized with ssDNA and a dimer of trivalent DNAs was used as the crosslinker to form a hydrogel with tunable mechanical properties [96]. Additionally, by varying solid content of the PcP from 1% to 5%, they were able to change the storage modulus from ~1 Pa to 4200 Pa [96]. [PERSON] et al. used a similar approach to crosslink poly(L-glutamic acid\({}_{240}\)-co-\(v\)-proppargyl-L-glutamate\({}_{20}\)) (p(LGA\({}_{240}\)-co-PLG\({}_{20}\)), M\({}_{\text{w}}\) 34,060, PDI 1.4) with dimeric trivalent DNAs which yielded a storage modulus of ~0.4 to 400 Pa at 1-5% \(w/v\)[16]. Moreover, the inclusion of DNA nanostructures as the crosslinker in this hydrogel added self-healing and shear-thinning properties, which are required for bioprinting. Interestingly, these hydrogels were crosslinked with similar DNA nanostructures though exhibiting an order of magnitude difference in their storage modulus at similar concentrations, which is due to the helical structure of the HSA-derived-PcP. This demonstrates the potential of biopolymers to provide further functionality for tuning the mechanical properties in conjunction with DNA-based crosslinkers. Additionally, the previously mentioned peptide, p(LGA\({}_{240}\)-co-PLG\({}_{20}\)), further demonstrated pH-induced \(\alpha\)-helix formation of the peptide backbone when crosslinked with linear dsDNA, which increased the storage modulus from 1750 to 2000 Pa when the pH was lowered from 8 to 5 [92]. [PERSON] et al. functionalized oligo(ethylene glycol)-functionalized polyisocyanopeptide (PIC) with ssDNA to create a hydrogel with stress-stiffening properties. By varying the ratio of crosslinking DNA to anchored DNA sites from 0.1 to 0.9 the storage modulus was tuned from ~1 Pa to 30 Pa [100]. However, increasing the ratio to 1.2 decreased the storage modulus to ~23 Pa due to saturation  
Figure 3: Hybrid DNA–polymer hydrogel formation. (i) Polymer chain conjugation with modified ssDNA linkers to form ssDNA conjugated polymer. (ii) ssDNA conjugated polymers crosslinked by complementary ssDNA linkers to form hybrid DNA–polymer hydrogel.  
of the anchored ssDNA by excessive crosslinker. The importance of effective crosslinks was further corroborated by an increase in the storage modulus from 50 to 130 Pa after heating and cooling with a ratio of 0.9, indicating network reorganization and concurrently enhanced crosslinking.
# Prototype Design and Performance Tests of Beijing Astronaut Robot
## 3 Testing Results
### Motion Performance Test of the Robot  
After the motion performance test of a single arm and the system simulation test, we test the motion performance of the robot with the prototype. The central controller receives the revolving  
Figure 11: The simulation of the robot’s motion. (**a**) The motion planning data; (**b**) Display interface of robot motion simulation system.  
Figure 10: The motion of single arm under 20 kg load. (**a**) Motion start; (**b**) In the process of motion; (**c**) Motion complete.  
command from the simulation and visual processing computer, and send motion data of every joint to the prototype, so the robot revolves according to the data.  
We tested three motion types in total: walking, rolling, and sliding, as shown in Figures 12-14.  
Figure 12: Walking mode. In this mode, arm 2 is initially located to the right of arm 1. During walking, arm 1 grasps the handrail, then arm 2 releases, the robot rotates 180\({}^{\circ}\) around arm 1. Then arm 1 reaches the left side of arm 2, and arm 2 grasps the armrest to complete one step of the robot. In the next step of walking, the robot rotates around arm 2 to complete the walking action. Arm 1 and arm 2 move alternately to achieve continuous walking of the robot.  
Figure 13: Rolling mode. In this mode, arm 1 grips the handrail, arm 2 rises, the center of gravity of the robot is adjusted, and then arm 3 drops and grasps the handrail. Finally, arm 1 rises to complete a tumbling motion. The three arms alternately run to achieve continuous rolling of the robot.  
In the above modes, the joint most likely to get a large motion error is the joint 3 of arm 1 with the largest load in the rolling mode. The planned motion curve and the actual value read by the encoder are shown in Figure 15. The results show that the robot joint motion is stable, and its maximum error is 0.04\({}^{\circ}\), which is consistent with the one-arm test results. The actual motion of the robot has indirectly demonstrated the accuracy of the joint motion, so it is unnecessary for the motion data for every joint to be listed.  
In order to allow the robot to reach a specific working position outside the space station with the aid of a large space robot arm, an electric slide rail is arranged on the robot arm. The robot grasps the handrail on the slider driven by the electric slide rail to realize the sliding motion. In the test, a 2-DOF (yaw and telescopic) rocker arm is used to simulate the large space robot arm. The motion of the sliding mode is shown in Figure 14.
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.8 Cross-Layer Interference Management  
Another power control scheme is the cross-layer interference management (CLIM) which defines the interference-limited communication range around the receiver [24]. The transmission and interference range are modeled as a disk graph. The interference-limited communication range is calculated in two cases of the path loss exponent while ensuring successful communication links. If a WBASN receives interference from others, it adapts its transmission rate and power to reduce interference. The network performance is maximized in both physical and medium-access layers because concurrent transmissions are allowed.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 1 Introduction
### Methodology of the Review  
To attain a comprehensive understanding of explainable AI techniques, our methodological framework adopts a synthesis approach. Leveraging Google Scholar, we systematically retrieved titles using carefully chosen keywords, specifically \"explainable AI model-based techniques\", \"explainable AI representation-based techniques\", \"post hoc explainable AI interpretation techniques\", and \"Hybrid explainable AI interpretation techniques.\" Following this, we meticulously shortlisted titles by incorporating category-specific keywords, resulting in the identification of 15 pertinent documents sourced from academic journals, conference proceedings, and various Web of Science indexed platforms. The exploration of each technique type involved an inclusive process, encompassing surveys and comparative studies within academic literature. To refine our understanding, we compiled an initial list of interpretation techniques, further enriched by references derived from the short-listed titles. This systematic and inclusive approach serves as the foundation of our methodology, ensuring both relevance and document quality, and facilitating a thorough exploration of the dynamic landscape of explainable AI techniques.  
We structured the review by methodically segmenting it into distinct components for comprehensive analysis. Emphasizing historical context, we meticulously traced the evolution of each method by examining references and identifying the pioneering researchers responsible for each approach's inception. Notably, our review only considered publications up to 20 March 2023.  
This review is organized as follows: Section 2 provides a foundation for understanding the historical progression of AI. In Section 3, we review the origins, basic principles, and suitability of existing Machine Learning (ML) interpretation techniques for different types of data. The following two sections respectively analyse trends in annual publications on different types of ML interpretation techniques; and cloud-based implementation strategies for XAI. Section 6 explores examples and use cases of XAI in various fields, includinghealthcare, finance, environment, criminal justice, and autonomous systems. Section 7 discusses the challenges and limitations of XAI. Finally, in Section 8, we conclude with a summary of the findings and a discussion of future research directions.
# Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
## 5 Conclusions  
We have proposed a novel ELM called HGCELM for semi-supervised classification. The idea behind HGCELM is to combine hypergraph convolution with ELM so that embedding the high-order relationship of data. The resulting model extends ELM into the non-Euclidian domain and endows ELM with the capability of modeling structure data. The proposed HGCELM is characterized by a very light computational burden and good generalization ability, making it easy to implement and apply in practice. Extensive experiments on 26 datasets demonstrate that HGCELM is superior to many existing methods. The successful attempt clues a promising avenue for designing randomized neural networks and graph neural networks.  
Conceptualization, Z.L. and Y.C.; Data curation, Z.L.; Formal analysis, Z.Z.; Investigation, Y.M.; Methodology, Z.L. and Y.C.; Project administration, Y.C. and Z.C.; Software, Z.Z.; Supervision, Z.C.; Validation, Y.M.; Writing--original draft, Z.L.; Writing--review & editing, Z.Z. and Y.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Fundamental Research Founds for National University, China University of Geosciences (Wuhan) under grant 1910491T06, in part by Guangxi Natural Science Fund General Project under grant 2021 GXNSFAA075029, and Guangxi Science and Technology Base and Talent Project under grant Guike AD20159036.  
Not applicable.  
The UCI datasets used in this work are available from [[https://archive.ics.uci.edu/ml/datasets.php](https://archive.ics.uci.edu/ml/datasets.php)]([https://archive.ics.uci.edu/ml/datasets.php](https://archive.ics.uci.edu/ml/datasets.php)) accessed on 14 March 2021.  
The authors declare no conflict of interest.  
Figure 6: Parameter sensitivity analysis for \(\lambda\) and \(k\) on (**a**) Iris, (**b**) Wine, (**c**) WDBC, and (**d**) Segmentation datasets. The x axis is denoted as \(log_{10}(\lambda)\).