esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach for a Spatial Data Attribute Similarity Measure Based on Granular Computing Closeness
## 1 Introduction
Similar theory descriptions and applied calculations require a foundation of mathematical methods, and it is necessary to integrate new methods on the basis of considering the nature of geoscience space. Granular computing, which was proposed in 1998 [22], is a new way of thinking about problems that comprise a superset of various methods in the field of artificial intelligence, such as rough sets, fuzzy sets, quotient spaces and cloud models [23]. Two kinds of distance and similarity metrics to measure granules were proposed for the searching and matching of granules, then researchers designed a granule classifier by the similarity metric [24]. Through the transformation of the traditional fuzzy set approximation axiomatization definition, a method to measure lattice closeness similarity reflecting the proximity between two particle sets has been proposed [25]. In addition to the geometric form, spatial entities have some natural-society-economic characteristics, and generally few different spatial types have identical characteristics [26]. These features constitute the attributes of spatial entities. Scholars have proposed a fast and effective two-step technique to measure similarity for the retrieval of spatio-temporal patterns, and used a database formed of 88 Landsat images to retrieve similar patterns of land cover and land use [27]. Characterizing and defining the similarities and closeness between different levels of different attributes of these spatial entities can be accomplished by means of the relevant definitions and the methods of granular computing. This paper will use granular computing closeness to calculate the similarity between spatial entities, introduce the granulation concept to study the spatial problem, expand the application field of granular computing and enrich the spatial data mining method system.
## 3 Results and Discussion
### Performance Evaluation of Published Measurement Models  
To verify that the performance of the proposed method compares with the existing methods, experiments were set up to compare several published methods for estimating moisture and nitrogen content of _Pinus massoniana_ seedlings. In this set of experiments, each method uses the same data to train the model, and determines the model parameters, and then model performance was evaluated using the same test data. The experimental results are shown in Table 4, in which the PLSR, SVR, and ANN models for moisture content measurement are taken from the literature [21], and the PLSR, SVR, and ANN models for nitrogen content measurement are taken from the literature [5].  
Figure 5: Measured vs. predicated nitrogen content for _Pinus massoniana_ seedling leaves by the regression models of CNN (**a**), MS-CNN (**b**), MS-SC-CNN (**c**).  
As shown in Table 4, in the test of moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings, using the five-layer neural network leads to a certain degree of model over-fitting, so the correlation coefficient is reduced compared with the traditional PLSR. The SVR method is not ideal because the classification hyperplane does not fully describe the characteristics of non-linear data. Although the ANN method adopts a multi-level linear structure, which makes its result preferable to that of the SVR method, it is easy to for overfitting and gradient extinction to occur because of the multi-level structure and the high number of parameters in the model.  
Combined with the model estimation performance results in Table 3, the traditional CNN model's regression effect appears to be better when processing non-linear correlation data. Similarly, in the test of the leaf nitrogen content of _Pinus massoniana_ seedlings, PLSR, SVR, and ANN are also not ideal methods for prediction due to various limitations of the models, which are inferior to the CNN-based methods presented in Table 3. The deep model based on CNN had a better prediction accuracy, while MS-CNN and MS-SC-CNN performed better in terms of the \(R^{2}\) value. Combining the results in Tables 3 and 4 shows that the proposed MS-SC-CNN achieves the best calculation accuracy for both moisture and nitrogen content estimation.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 1 Introduction and Definitions
The joint communication and positioning domain or JCAP has been seldomly addressed more in the existing literature so far, especially compared to the joint communication-sensing (ISAC/JSAC/ICAS/JCAS) and joint sensing-positioning domains (SLAM/JPAS). An example of a study addressing the JCAP concept can be found in [36], but JCAP is an abbreviation very rarely used nowadays, as many researchers address the joint positioning-communications aspects under the BAC/JSAC/ICAS/JCAS umbrellas, based on the broader definition of sensing term, which can also include the positioning aspects. In order to illustrate the current interest in the separate and the joint research areas encompassing the positioning, sensing, and/or communication research, an illustration based on a few simple keywords is depicted in Table 1. Only the most prevalent research areas are shown in the table, for the purpose of clarity (three separate and two joint research areas). The number of papers returned via keyword search is based on four selected databases: CORE ([[https://core.ac.uk/](https://core.ac.uk/)]([https://core.ac.uk/](https://core.ac.uk/))), Scopus ([[https://www.scopus.com](https://www.scopus.com)]([https://www.scopus.com](https://www.scopus.com))), with institutional access, Google Scholar ([[https://scholar.google.com/](https://scholar.google.com/)]([https://scholar.google.com/](https://scholar.google.com/))), and a search of MDPI publishers ([[https://www.mdpi.com/search](https://www.mdpi.com/search)]([https://www.mdpi.com/search](https://www.mdpi.com/search))); the search was carried out in all fields (when available) or in the metadata (when a search involving all fields was not available). As seen in Table 1, even if the numbers across different databases vary significantly, they are all highly correlated and illustrate the breadth and width of various research domains (e.g., the radar and wireless communications domain are very broad research domains, while positioning and SLAM are much narrower research domains). Table 1 also illustrates that some keywords are more popular than others when referring to the same research areas (e.g., the term \"Wireless positioning\" is much more popular than the term \"wireless navigation\", ISAC is preferred to JSAC as the terminology in use, the concept of radioSLAM is still very infrequently used in the research papers, etc.) Figure 2 illustrates three potential application areas for JPSAC-related research and provides further details about how the SLAM, ISAC, or the more generic JPSAC algorithms can benefit the end users in various application areas. The next section delves deeper in the details of what SLAM, ISAC, and JPSAC entail, what kind of methods are typically adopted, and which surveys and tutorials provide good overviews of these joint or integrated areas.
###### Abstract  
This study develops a sustainable biopolymer film derived from potato peel waste (PW), enhanced with low- and high-viscosity sodium alginate (SA) through a solution casting method. The effect of calcium chloride crosslinking on the PW/SA composites was also evaluated. Scanning electron microscopy (SEM) analysis revealed that SA incorporation improved the film's cohesion and uniformity, with both low- and high-viscosity SA showing nearly similar effects. Both the addition of SA and crosslinking led to enhanced tensile strength, as well as improved moisture barrier properties, by lowering the water vapor permeability (WVP) factor. The inclusion of high-viscosity SA (hvSA) resulted in superior mechanical and moisture barrier properties compared to the low-viscosity SA (lvSA), achieving a tensile strength of 5.34 MPa, with a 68% improvement compared to the pure PW film. The WVP analysis showed that hvSA had a superior impact, leading to a 32% reduction in WVP compared to the pure film. Crosslinking further boosted the tensile strength and moisture barrier properties. The crosslinked hvSA/PW composite shows the highest tensile strength among all samples, measuring 6.47 MPa, which accounts for a 104% enhancement compared to the pure film. It also led to a 34% reduction in WVP, reaching a value of 1.58 \(\times\) 10\({}^{-12}\) g/(Pa-cm-s). The findings demonstrate that PW/SA composites, especially the crosslinked hvSA/PW, offer the highest mechanical and barrier properties, making them suitable for biodegradable packaging and biomedical applications.  
potato peel waste; sodium alginate; crosslinking; biopolymer films Article
## 3 Method Implementation
### Pointer Meter Reading
#### 3.3.1 Semantic Segmentation and Dial Expansion  
The traditional U2-Net segmentation model employs a baseline network to extract deep features and derive more representative semantic features. The U2-Net network employed in this paper is specifically designed for salient target detection, allowing for the extraction of the most prominent features in an image. Thus, it offers a significant advantage in recognizing pointer meters. The specific structure of U2-Net in this study is as follows:  
**Input layer:** The model accepts an RGB input image with a size of (B, 3, H, W), where B is the batch size, 3 refers to the number of input channels (RGB), and H and W are the height and width of the image, respectively.  
**Downsampling Path (Encoder):** The downsampling part consists of 4 convolutional modules (DownBlock). Each module includes a convolution layer, batch normalization, and ReLU activation, followed by a 2 \(\times\) 2 max pooling layer to reduce the spatial dimensions.  
**Downsampling Path (Decoder):** The upsampling part consists of 3 upsampling modules (UpBlock). Each module performs bilinear interpolation on the low-resolution feature map to upsample it, concatenates it with the corresponding feature map from the downsampling path, and applies a convolution to process the concatenated features.  
**Output layer:**  
* Input: A 64-channel feature map.
* Convolution Layer: A 1 \(\times\) 1 convolution layer with 64 input channels and 1 output channel.  
Figure 12: Pointer meter reading process.  
* Output: A single-channel output image with the size of (B, 1, H, W) representing the segmentation result. **Activate Function:** The output passes through a sigmoid function to map the segmentation result between 0 and 1. In MAMRS, the prepared dataset is used to train the U2-Net model, allowing it to learn the semantic information of the image during the training process, including the target position and the shapes of the dial scale and pointers. Upon completion of training, the image to be recognized is inputted, and through image erosion, the dashboard's scale and pointer are expanded into a rectangular image by scanning with a specified width, using the center of the image as the origin, as shown in Figure 13.  
The two images are transformed from a two-dimensional array into a one-dimensional array by vertically overlaying them, plotted as a line graph, and then binarized for the scale using the mean value, in order to finally locate the pointer's positional information on the one-dimensional array. The result is shown in Figure 14.  
Figure 13: Rectangular diagram of scales.
# Optimizing Roller Design to Improve Web Strain Uniformity in Roll-to-Roll Process
## 3 Roller Design Optimization for Minimizing Strain Deviation
### Design Factors and Their Interaction  
The design of experiments (DOE) is a method of efficiently planning an experiment to obtain the maximum information while minimizing the number of experiments required [15]. DOE is used to analyze the factors that affect the output of the system. To minimize the MD strain deviation of the web under uniform tension conditions, design optimization was conducted based on DOE. Design factors were selected and the sensitivity analysis was performed according to the level change of design factors.  
We varied the thickness gradient of the skin (A), the diameter of the entire roller including the core and skin (B), and the elasticity modulus (Young's modulus, C) of the skin. Considering the process with actual rollers, the minimum thickness of the skin was set to 3 mm.  
Next, we investigated possible interactions between the selected design factors (i.e., whether one factor depends on another factor). Table 2 lists the initial values of the design factors used to study the interactions with a two-way factorial design. One of the three selected design factors was fixed, and the deviation of the strain in the MD of the web was analyzed as the other two factors were varied.  
The results are shown in Figure 8. The graph shows that the curves representing the relationship between the thickness gradient of the skin (A) and the diameter of the entire roller (B) and the relationship between the diameter of the roller (B) and Young's modulus (C) of the skin intersect. However, the curves representing the relationship between the thickness deviation (A) of the skin and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sort**} & \multirow{2}{*}{**Factor**} & \multicolumn{4}{c}{**Level**} \\ \cline{3-6}  & & **1** & **2** & **3** \\ \hline \multirow{3}{*}{Control factor} & A (mm) & Thickness gradient of skin & 3 & 6 & 9 \\  & B (mm) & Outer diameter of roller & 40 & 60 & 80 \\ \cline{1-1}  & C (MPa) & [PERSON]’s modulus of skin & 2.85 & 7.85 & 12.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design factors and initial levels of each factor.  
Figure 7: Compensation rollers for minimizing the non-uniform strain.  
[PERSON]'s modulus (C) of the skin do not intersect. Hence, factor B is affected by the levels of factors A and C; these factors interact.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 2 NDN Architecture and Vulnerability to Interest Flooding  
### NDN Architecture Fundamentals  
Named Data Networking (NDN) operates through a fundamentally different paradigm than traditional IP-based networks, using two core packet types to facilitate content-centric communication. As shown in Figure 1, interest packets serve as requests for named content, while data packets deliver the corresponding information back to requesters. The NDN architecture employs three critical data structures (see Figure 1) within each router to manage communication flows effectively. The Forwarding Information Base (FIB) maps content name prefixes to outbound interfaces, enabling routers to direct interest packets toward potential content sources. When content is available in a router's Content Store (CS), which functions as a cache of previously retrieved data, the router immediately returns the requested information without forwarding the interest further. The Pending Interest Table (PIT) maintains state information for all forwarded interest packets that have not yet received the corresponding data packets, recording content names along with incoming and outgoing interface details to ensure proper routing of the data packet back to the requesters [12; 13].  
The stateful FIB mechanism supports several advanced networking capabilities, including robust in-network caching, multicast data delivery, and loop prevention. However, the stateful nature of the PIT introduces specific vulnerabilities that distinguish NDN from stateless IP forwarding approaches. The architecture's reliance on maintaining per-packet state creates attack surfaces that malicious actors can exploit through coordinated flooding strategies. Recent research has focused on optimizing PIT efficiency through improved data structures. The S-PIT system, introduced in 2023, integrates Stable Bloom Filters into the PIT (S-PIT) to verify the existence of a content name without exhaustive searching [14]. Early detection strategies, including HopCount-Based Filtering and adaptive satisfaction rate monitoring, have demonstrated effectiveness in identifying anomalous interest behavior [15]. Simulation studies using NDNSim continue to provide information on how prefix granularity and request volume patterns influence the performance characteristics of PIT [13].
## 2 Schemes of SR-ODBR-DCSK  
### Signal Structure  
In the SR-ODBR-DCSK system, as in the DCSK system, each time frame of the SR-ODBR-DCSK system is also divided into two time slots: reference time slot and information time slot. Different from DCSK, these two time slots are not equal. When sending signals, the DCSK system sends a reference signal and an information signal at the same time. The reference signal, however, does not carry useful information, so the reference signal consumes half of the energy when sending signals, resulting in a low energy utilization rate. SR-ODBR-DCSK system reduces the energy consumption of sending reference signals by shortening the length of the reference time slot and effectively improves energy utilization.  
In this scheme, the length of the former time slot is \(R\) to transmit the reference signal, and the latter time slot transmits a 2-bit information sequence with \(\beta\) (\(\beta\) = \(PR\)) length after \(P\) replication by the reference signal with length R. The 2-bit information transmitted in the second time slot is modulated on two different reference sequences with suitable orthogonality through the Walsh function. Figure 1 shows the frame structure of the SR-ODBR-DCSK signal. Figure 2 shows the frame structure of the DCSK system signal. By comparing Figures 1 and 2, it is obvious that the SR-ODBR-DCSK system is more efficient than the DCSK system by saving time and energy.  
Figure 3 is the even number of information bits demodulation structures for SR-OBDR-DCSK. At the receiver, the information time slot is composed of PR time slots, and each time slot contains even information bits and reference signal compliant, odd information bits, and orthogonal reference information compliant, both of which are collected and then operated with reference information correlation to obtain even information bits.
# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 4 Cross-Border Risk Transmission Model in the Global Shipping Network  
### Construction of a Coupled Global Air and Sea Transport Network  
#### 4.1.1 Mechanism Analysis  
Against the backdrop of increasing global population mobility and the development of maritime trade, public health events can be transmitted through freight and passenger transport between cities of different countries. In this process, passenger and freight activities, as well as the importance of each node within the network, play significant roles in cross-border risk transmission. Risk transmission stems mainly from the node's own risk and the risk propagated through cross-border networks. Denote node importance by \(\alpha\), passenger transport by \(x\), freight by \(y\), and node importance probability by \(g(\alpha)\). The own risk of a passenger transport node can thus be expressed as \(g(\alpha)x\), and the own risk of a freight node as \(g(\alpha)y\). \(\omega\) represents edge weight. Assume the transmission path is as shown in Figure 10 below:  
Thus, the own risks of nodes 1-N are expressed as \(g(\alpha)x_{1},g(\alpha)x_{2},g(\alpha)x_{3},\ldots,g(\alpha)x_{N}\) and \(g(\alpha)y_{1},g(\alpha)y_{2},g(\alpha)y_{3},\ldots,g(\alpha)y_{N}\).  
Therefore, the passenger transport risk for nodes can be expressed as follows:  
\[g(\alpha_{1})x_{1}\omega_{F1}+g(\alpha_{2})x_{2}\omega_{F2}+\ldots+g(\alpha_{ N})x_{N}\omega_{FN}=\sum_{i=1}^{N}g(\alpha_{i})x_{i}\omega_{Fi} \tag{10}\]  
The freight risk for nodes can be expressed as follows:  
\[g(\alpha_{1})y_{1}\omega_{S1}+g(\alpha_{2})y_{2}\omega_{S2}+\ldots+g(\alpha_{ N})y_{N}\omega_{SN}=\sum_{i=1}^{N}g(\alpha_{i})y_{i}\omega_{Si} \tag{11}\]  
Let \(f\) be the total risk, thus  
\[\begin{array}{c}f=f(\alpha,x,y)\omega=f(\alpha_{1},x_{1},y_{1})+f(\alpha_{2 },x_{2},y_{2})\omega_{1}+\ldots+f(\alpha_{N},x_{N},y_{N})\omega_{N}\\ \qquad\qquad=\sum\limits_{i=1}^{N}f(\alpha_{i},x_{i},y_{i})\omega_{i}=\sum \limits_{i=1}^{N}g(\alpha_{i})x_{i}\omega_{Fi}+\sum\limits_{i=1}^{N}g(\alpha_ {i})y_{i}\omega_{Si}\end{array} \tag{12}\]  
Figure 10: Risk transmission diagram.  
By coupling the air transport network layer and the maritime network layer, the above network node risk propagation model is established to explore transboundary risk propagation under the shipping network.
## 5 Obliqueness and Slenderness  
Let us define two properties of the orbit, i.e., obliqueness \(\alpha\) and slenderness \(\beta\), as shown in Figure 7a and apply the image processing to extract these properties from it. The flowchart of extracting these properties from an orbit, as shown in Figure 7b, includes computing the forward kinematics of the TJL using Equations (1)-(11); plotting an orbit; capturing the image of the orbit [20,21] from the screen; and, measuring the obliqueness \(\alpha\) of the orbit, the length of its major axis, the length of its minor axis from its image. Subsequently, the slenderness ratio, \(\beta\), of an orbit is calculated by taking the length of its minor axis divided by the length of its major axis.  
After computation, the properties of the 64 orbits presented in the previous section are obtained and listed in the Table 1. Regarding the extraction of properties, it is involved with several standard techniques of image processing [22,23], i.e., calculating the centroid of the image, obtaining the  
Figure 6: Scatter diagram in three-dimensions (3D).  
Figure 7: (**a**) Obliqueness \(\alpha\) and slenderness \(\beta\); and, (**b**) Flowchart of property extraction.  
centroid-shifted image, computing the eigenvectors and eigenvalues of the centroid-shifted image, retaining the eigenvectors with the maximum and minimum eigenvalues, calculating the Euclidean distances and orientations or simply applying the Matlab command, regionprops.  
The question remains as to whether we can deduce the criteria in terms of mathematical functions that a computer can reason about. The answer is positive, as the sequel will show. When we plot the scatter diagram according to the Table 1, the Figure 8a unveils that the data of the legitimate class cluster within a region that is tightly related to our preference in selecting the desired orbits. As observing the location of the clustering region, it tells that our preference is indeed consistent with the requirements of shallow obliqueness and moderated slenderness. It also confirms our skepticism of the training data contaminated with some amounts of bias because few dots of blue are aberrated from the clustering region.  
In Figure 8a, the contours are plotted according to the score function of the SVM. The contour at level 1.2 encircles a region containing a family of orbits in consistency with our preference. The mathematical criterion obtained by imposing an inequality on the score function of the SVM is called the model-based criterion. However, this set of training data are only sampled from two-dimensional parameters, i.e., a and b, is not sufficient to stand for the overall data and the orbits classified by our human vision is not objective. Hence, this model-based criterion will not be used. Instead, imposing the upper and lower bounds on \(\alpha\) and \(\beta\), i.e., \