esa-sceva/satcom-chunk-collection huggingface dataset:
# Correspondence between Multilevel Graph Partitions and Tree Decompositions
## 5 Example Applications: Tree Decomposition of Road Network  
In this section, we visualize the key aspects of a tree decomposition of small width in a road network. Popular preprocessing-based speedup techniques for shortest path computations in road networks employ tree decompositions [14; 16]. However, the sole objective of this section is to illustrate our theorem on real-world data. For the shortest path algorithms, we refer to the cited articles.  
We downloaded a Germany OpenStreetMap [17] dataset from GeoFabrik [18] on 11 May 2019. We then extracted a routing graph for cars using RoutingKit [19]. The extracted graph has 10.73 million nodes and 12.95 million edges. Afterwards, we compute a tree decomposition for this graph using the FlowCutter PACE 2017 code [20; 21]. It ran for about 7.3 h on an i7-3520M laptop. The tree decomposition consists of 166,133 bags. In Figure 5, we visualize some of the results. The routing graph and the tree decomposition are available in the Supplementary Materials.  
In Figure 5, we depict eight of the 166,133 bags. Each bag is depicted as map of Germany with the nodes in the bag highlighted. The center bottom bag is the largest bag in the tree decomposition with 438 nodes. The whole graph has therefore a tree width of at most 437. The bags depicted around it are its neighbors in the tree backbone. Solid lines represent an edge in the tree backbone. Dashed lines represent an edge or set of edges to bags not depicted in the figure.  
Figure 5: Visualization of a tree decomposition of Germany road graph around the largest bag.  
In the bags represented in the center and on the left, one can clearly see the corresponding cells and their boundaries and the separators. The bags on the right differ. Their sizes are significantly smaller with nine and two nodes. What happens is that the partitioning algorithm tries to compute bags such that removing the bag nodes decomposes the graph into three parts of significant size. These are the bags in the center and on the left. However, sometimes, this produces additional tiny parts, as, for example, a dead-end street branches off a main road at one of the nodes in a bag. These tiny parts are depicted on the right.  
The partitioning algorithm recursively splits cells in half and therefore the bags usually have three neighbors, if we ignore the tiny parts on the right. Two neighbors are the cell's children and one is the parent cell.  
Tree decompositions do not require splitting each cell into two parts. Indeed, edges in the tree backbone can be contracted, by computing the union of two bags. This corresponds to splitting a cell into more than two parts in the partitioning step. However, the union of two bags is never smaller than the two individual bags. It is therefore not useful to split a cell into more than two parts, if the optimization criterion is the width of the tree decomposition. However, for applications that optimize different criteria, such as the diameter of the backbone, splitting cells into more than two parts can be useful.
# Research Progress of Polysaccharide-Gold Nanocomplexes in Drug Delivery
## 2 Biological Activity of Polysaccharides
Elaeagnus umbellata polysaccharides have scavenging or inhibitory effects on hydroxyl radicals and superoxide anion radicals and can be used as natural antioxidants. It was also found that polysaccharides with different molecular weights have different degrees of antioxidant activity, and that low-molecular-weight polysaccharides may have more significant antioxidant activity due to the fact that they have more reducing hydroxyl terminals [61]. Finally, polysaccharides can improve antioxidant capacity by inhibiting inducible NOS (iNOS) and reducing NO concentration. Angelica sinensis polysaccharide (ASP) inhibits H2O2-mediated oxidative stress injury in human chondrocytes by downregulating iNOS, SOD, and CAT for osteoarthritis (OA) treatment [62]. Although polysaccharides have a variety of biological activities in experimental studies, they have not yet been directly and widely used in clinical studies due to the following points: firstly, the complex way in which polysaccharides interact with immune cells, and their effects can vary depending on their structure, concentration, and source; secondly, different sources of polysaccharides may have different compositions and biological activities, which makes it difficult to standardize them for use in clinical studies; and finally, although polysaccharides have potential immunomodulatory effects, there may be safety issues with the use of polysaccharides in clinical studies. The safety of polysaccharides, including potential adverse reactions and interactions with other drugs, must be thoroughly investigated before using them in clinical trials. Despite these challenges, there is ongoing research into the potential therapeutic applications of polysaccharides, and with continued advancements in scientific understanding and technology, it is possible that polysaccharides may eventually be used more directly in clinical research.
## 2 Derivation of Functionality Loss Evaluation Model for Railway Network Components  
### Procedure for Deriving Functionality Loss Evaluation Models for Railway Network Components  
Railway networks consist of structures on which tracks are constructed, which can be broadly classified into tunnels, bridges, and embankments. Each component exhibits different functionality loss characteristics depending on the earthquake. The functionality loss model for each railway network component during earthquakes can be determined through the following process. First, the probability of various damage states during earthquakes of varying magnitudes must be calculated to evaluate the functionality loss caused by earthquakes. The damage to each structure caused by earthquakes is determined by the characteristics of the structure and can be defined using seismic fragility curves. Seismic fragility curves are integrated with the results of probabilistic seismic hazard analysis (PSHA) and are used for seismic probabilistic risk assessment (SPRA) of target structures, showing the exceedance probabilities of each damage state based on the magnitude of the input earthquake [15]. Next, restoration curves are derived following the damage. Restoration curves can be constructed using the mean and standard deviation of recovery times for each damage state of the structure. By combining the derived seismic fragility and restoration curves, functionality curves can be obtained for each earthquake scenario over time. Functionality curves are derived as expected values by combining the probability of damage occurrence for each damage state and the degree of functionality degradation over time when the damage occurs, representing the predicted functionality recovery of the structure over time. Finally, the evaluation time for functionality loss is determined, and the area under the functionality degradation curve over time is calculated to derive the final functionality loss curve. The procedure, organized in sequential order, is as follows and the flowchart is described in Figure 1.
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
## 3 Proposed Secure Communication, Multi-User Cooperation, and User Clustering Strategy in PD-NOMA  
### Problem Formulation  
This paper focused on three major points in the PD-NOMA systems: (1) Secure PD-NOMA communication; (2) Multi-user cooperation relaying scheme; and (3) User clustering strategy. Due to multiple access and its non-orthogonal nature, NOMA faces more security issues than the other OMA techniques. Due to multi-user detection (MUD) in NOMA, the strong users decode the information of weak users to perform SIC for signal detection. Thus, the security of the NOMA system for the weak or cell-edge users becomes a challenge since it is not designed to conceal the individual's information. The issue is more challenging in the case of densely populated cellular networks. The multi-user PD-NOMA system is shown in Figure 3, where the gNodeB is surrounded by the registered (\(R_{1},R_{2},R_{3},\) and \(R_{4}\)) and unregistered users (eavesdropper, (\(E_{1}\))). Here, the \"registered users\" are the users of interest to whom the gNodeB wants to communicate. An unregistered user as an eavesdropper is able to steal or misuse the information transmitted from the gNodeB towards the registered users. The eavesdropper is able to decode the information of all the users that are located far from the gNodeB than the eavesdropper. In Figure 3, the downlink scenario for ordinary PD-NOMA is shown where multiple users are served in proximity of the gNodeB. All the users' signals are combined into one composite signal through different power layers and then transmitted. During signal detection, the strong users decode the data of the weak users so that they can do SIC. In traditional PD-NOMA, registered users decode the data of weak users, which is only used for SIC operations.  
Figure 2: Illustration of multi-user Uplink PD-NOMA.  
But the privacy of such systems is compromised if an unregistered user comes close to the gNodeB, which is capable of misusing or stealing the information of remote users. To solve this problem, we came up with a secret-spreading-based method that works with regular PD-NOMA to protect the privacy of these systems. This method is explained in the next Section 3.2. Due to the high frequency of 5G wireless communication, the cell-edge users around the gNodeB are facing severe signal degradation. This signal degradation is less in pico and femto cells but is more in micro and macro cells. To address this issue, it is necessary to use repeaters or relay stations that are capable of aiding these cell-edge users. In this work, we are utilizing the available resources, such as the cell-center users that are used as relay stations and are capable of decoding and forwarding the signals for cell-edge users. This technique is discussed in the Section 3.3. Furthermore, the power allocation in the densely populated 5G cells without user clustering is critical to handle. In the Section 3.4, we offer a three-step low-complexity user clustering technique.
## 3 Design and Implementation of the Proposed Scheme
### Decryption Step for Proposed Algorithm  
The decryption process is described in the pseudo-code at the end of this paper in Appendix A. The steps for decrypting an encrypted image are provided below.  
Step 1. Input an encrypted image (C).  
Step 2: Perform a bitwise exclusive OR operation between image (C) and secret key (\(K_{3}\)) from the sigmoid logistic map to obtain an intermediate image (\(K_{5}\)).  
Step 3: Compress the intermediate image (\(K_{5}\)) using the Kronecker xor operation to obtain a new image (\(K_{4}\)).  
Step 4: Perform a bitwise exclusive OR operation between the new image (\(K_{4}\)) and secret keys (\(K_{2}\)) from the sigmoid logistic map to obtain another new image (\(K_{1}\)).  
Step 5: Decrypt the new image (\(K_{1}\)) using the inverse of the hill cipher and a key (K inverse) to obtain an intermediate image (H).  
Step 6: For each element in an even column of the intermediate image (H), perform a bitwise exclusive OR operation with the top value of the corresponding odd column. For each element in an odd column, perform a bitwise exclusive OR operation with the top value of the corresponding even column, while preserving the top values of matrix (H).  
Step 7: Determine the number of rows and columns in the intermediate image (H). Iterate over each row in the image (H), starting from the second row (the first row is not shifted). For the current row, shift it to the right by its index within the matrix by concatenating the portion of the row that was shifted off the end with the portion that was not shifted until you obtain the plain image (P).
###### Abstract  
Detecting obstacles in the rail track area is crucial for ensuring the safe operation of trains. However, this task presents numerous challenges, including the diverse nature of intrusions, and the complexity of the driving environment. This paper presents a multimodal fusion rail-obstacle detection approach by key points processing and rail track topology reconstruction. The core idea is to leverage the rich semantic information provided by images to design algorithms for reconstructing the topological structure of railway tracks. Additionally, it combines the effective geometric information provided by LiDAR to accurately locate the railway tracks in space and to filter out intrusions within the track area. Experimental results demonstrate that our method outperforms other approaches with a longer effective working distance and superior accuracy. Furthermore, our post-processing method exhibits robustness even under extreme weather conditions.  
multimodal algorithm; computer vision; obstacle detection; topology; railway transportation
## 4 Case Study and Result
### Examples of Data Exploration with the Geovisual Analytical System
#### 4.2.3 Model Validation between Simulation and Observations  
An important task of climate modeling is to evaluate the accuracy of the model. The evaluation against observations can be achieved in many ways. Often simple map differences are informative, but a compact way of representing spatial correlations and RMS errors is via a Taylor diagram. The first method retrieves data from the database directly and visualizes data on GE as two layers. Users can generally observe the difference between two layers by toggling operations and transparent color settings. Taylor diagram is calculated at the server side. Figure 9 shows the interface for conducting quality evaluation and the resulting map and Taylor diagram. The evaluation is based on global 5-year mean values of surface air temperature from 300 model runs. In the returned Taylor diagram, all 300 values locate within the 0.5 RMS error contour and with greater than 0.95 spatial correlation [a perfect simulation would be represented by a point at coordinates (1,0)]. This result illustrates that the simulated climate condition is very similar to the observations.  
Figure 8: Interactive plot of mean values of a climate parameter _vs._ time using ensemble mean of surface air temperature as an example.
## 3 The Proposed Routing Protocol--CRD  
The operation of the proposed CRD protocol is split into sessions. Each session is divided into two consecutive phases including clustering and routing. In the clustering phase, each node first uses a common channel to exchange necessary information with its neighbors by using HELLO packet. Second, the CDS selection algorithm, as shown in Algorithm 1, is proposed to establish a set of intermediate nodes (CDS). Then the size of CDS can be reduced by using the CDS size reduction algorithm, as shown in Algorithm 2. Finally, cluster heads are elected from the CDS based on the CDS based clustering algorithm, as shown in Algorithm 3. After these processes, the CDS includes  
Figure 1: A multi-channel cognitive mobile ad-hoc network (MANET): Basic concepts of the system model.  
intermediate (gateway) nodes and cluster heads, and this CDS is considered as a routing space in routing phase. In the routing phase, we denote \(v\) as a node, \(\text{CH}_{v}\) as the cluster head of \(v\) and \(C(v)\) as the receiving channel of \(v\). The proposed routing algorithm can be explained as follows. First, a source \(S\) generates a RREQ packet and sends it to \(\text{CH}_{5}\) on channel \(C(\text{CH}_{S})\). If a gateway/cluster head \(v\) receives a RREQ packet, it uses the CFS algorithm, as shown in Algorithm 4, to choose a focus region with a sending channel in order to forward this RREQ packet to another gateway/cluster head. If a destination \(D\) receives a RREQ packet, it generates a RREP packet and replies it to the source in order to establish a route which avoids the affected region of the primary user. In summary, the proposed routing algorithm outlined as Algorithm 5.
## 4 Discussions
### Limitations of the Current Work and Prospects for Future Studies  
The proposed downscaling method in this work fully utilized valuable topographic and vegetation information from ancillary data at a 500 m spatial resolution to reduce uncertainties in spatial heterogeneities over rugged terrain. However, due to practical constraints, there were also several limitations in this study. First, the proposed downscaling algorithm was based on the MODIS GPP, DEM and LAI data, and any errors in the source data would propagate into the final downscaled results [49; 50]. Second, the resampling method for 30 m GDEM data influenced the quality of DEM data at 500 m and 1 km resolutions, and hence affected the accuracy of the topographic information [51; 52]. In addition, the choice of algorithm for slope and aspect also resulted in uncertainty [53; 54; 55], besides slightly affecting the downscaling procedure. Third, although our algorithm considered the main factors (i.e., vegetation density, altitude, slope and aspect) for GPP downscaling, several other factors (e.g., land cover, soil texture, and soil depth) were not included in the algorithm, which might result in slight uncertainties. Finally, due to the lack of reference data for validation at smaller resolution, MODIS GPP at 1 km resolution were scaled only down to a 500 m resolution. This study tested the thesis that the 500 m GPP could be obtained from 1 km GPP and spatial heterogeneity information. Based on this thesis, the relevant work of obtaining the GPP at a finer resolution (e.g., 30 m) will be presented in an additional study.  
To improve the performance of the proposed downscaling method, future studies should consider more factors in the downscaling algorithm, and attention should be given to the effects of accurate ancillary data (i.e., DEM and LAI data) on the final GPP downscaled results. Moreover, the proposed downscaling method should be applied in more heterogeneous areas to test the feasibility and stability of the algorithm.
# Biomimicry and the Built Environment, Learning from Nature's Solutions
## 2 Biomimicry in Architecture
### Potentials and Benefits of Biomimicry in Architecture and Design
#### 2.2.1 Wise Selection of Construction Materials  
Function  
Wise material selection with a high level of functionality is one of the major benefits of applying biomimicry in architecture. The importance of understanding complex systems results in consideration of the individual aspects, which leads to an improved understanding of the overall function.  
In nature, efficient materials are defined as those that have effective exchange with expensive materials (which are generated from metabolic processes). Nature has created sustainable light shell and fold structures and systems that can grow and be stable. Natural systems established the building processes in both animals and plants. This building process considers the availability of local materials and aims to create an optimized and multi-functional structure. Examples of such building processes can be seen in shell structures of mussels and sea and folded structures of leaves, hornbeam, and palm varieties.  
\begin{table}
\begin{tabular}{c c} \hline
**Organism Level** & **Behaviour Level** & **Ecosystem Level** \\ \hline Mimicry of a specific organism & Mimicry of the way that organs behave of a larger context & Mimicry of an ecosystem \\ \hline \end{tabular}
\end{table}
Table 1: Framework for different levels of biomimicry.  
Lifecycle  
The life cycle is another great lesson learned from nature and be is implemented in architecture, whether the matured structures are occupied by new life forms or decomposed into basic elements, from which new life forms can emerge. Biomimicry in architecture has resulted in building materials and elements that can integrate themselves with a life cycle in nature. However, a tangible gap has been observed in the literature on how the life cycle of built environments can learn lessons from the natural processes and ecosystems in nature [22; 23].  
Weight  
The concept of lightweight structures is another potential brought by biomimicry in architecture and building methods. Natural structures react to internal and external loads differently. Thus, their forms are affected by such factors, which is also the case for human-made technology-driven built environments. One of the benefits of using lightweight materials for building envelopes is the high level of insulation and light penetration, diffusion. An example of these features in nature can be seen in polar bear fur. It provides good insulation for the cold weather of Antarctica and allows the penetration of light into the darkly pigmented skin of the bear [24]. Another similarity can be seen between the hairs and parallel glass fibers, acting as the insulator and light distributor [25].
## 3 Mathematical Modeling and Design Methodology
### System Design Methodology
#### 3.4.5. Whale Optimization Algorithm (WOA)  
The WOA is one of the nature-inspired meta-heuristic optimization algorithms. It simulates the social behavior of humpback whales. It imitates the bubble-net hunting mechanism of humpback whales. It was proposed by [PERSON] and [PERSON] in 2016 [57]. WOA is designed based on the spiral bubble-net feeding strategy, which is mathematically modelled in order to perform optimization.  
Humpback whales recognize the prey's location and surround them. The WOA algorithm presume that the current supreme candidate solution is the targeted victim, or it is close to the optimal solution. After the supreme search is determined, the whales update their positions towards the supreme search. This behavior is given by Equation (23) and the position vector is computed as in Equation (24) [57]:  
\[\stackrel{{\rightarrow}}{{D}}=\left|\stackrel{{ \rightarrow}}{{C}}\cdot\stackrel{{\rightarrow}}{{X^{*}}}(n)- \stackrel{{\rightarrow}}{{X}}(n)\right  
It can be seen that there is a slight difference in the value of IR obtained by PSO and WOA in the ISD, SD, and TD models. However, the TD model has the highest value of IR. This is because the TD model takes into account the recombination losses, which implies the need for more PV to satisfy the load demand. So, neglecting the recombination losses in both ISD and SD models will actually increase the number of solar panels, i.e., the size of the PV array. The percentage decrease of the number of PV panels is 16.67% and 7.93% for the ISD and SD models for the PSO algorithm, respectively, as shown in Table 3. Moreover, the corresponding percentage decrease in the case of WOA is 17.74% and 11.76% for the ISD and SD models, respectively.  
Tables 4 and 5 show the reliability indicators and energy results at the optimal value of IR. Table 4 shows that the most reliable model is the TD PV model in terms of EENS, LOLP, and LPSP. Table 5 shows that the PV energy, obtained using PSO, of the ISD and SD models decreased by 9.3% and 3.23%, respectively, from the TD realistic model. This means that the SD model is more accurate than the ISD model and close to the realistic TD model. Furthermore, the PV energy, obtained using WOA, of the ISD and SD models decreased by 10.47% and 7.26%, respectively, from the TD model.