esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Example
### Extracting Feature Data
#### 4.2.3 Data Classification  
For data classification, we extracted the time labels of various work states according to the task log and divided the dataset accordingly. Then, we performed clustering on the telemetry data under each work state using the DBSCAN algorithm to determine the abnormal boundaries. Figure 16 shows the visualization effect after data partitioning and clustering, with the seven-dimensional telemetry data under each state reduced.  
In order to perform anomaly detection for the CMG system, we selected clusters with more than 50 data points as a group of feature data and extracted a total of 329 groups of feature data. Each group of data are classified according to task status and time labels as a type of feature node and then fused with the spacecraft equipment knowledge graph. This improves the accuracy and reliability of anomaly detection.  
The extracted feature data are stored in the Neo4j database. Figure 17 provides a visualization of a portion of the spacecraft equipment knowledge graph.  
Figure 16: Visualization of feature data for each group.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Indicator-Based Approach
#### 4.1.4 DHS Infrastructure Survey Tool  
In 2009, the US Department of Homeland Security began using questionnaires to evaluate the resilience of critical infrastructure. The Infrastructure Survey Tool (IST) is one of the most widely employed questionnaire tools, functioning as an indicator-based method that assesses individual protective measures and vulnerability within the protective measure and vulnerability indices (PMI/VI). The IST was enhanced to serve as a resilience measure by developing a comprehensive methodology that applies uniform and consistent data to generate a resilience index (RI). The \(RI\) value is derived from three components: robustness, resourcefulness, and recovery. The \(RI\) has a value range between 0 and 100, where 0 indicates the lowest resilience [40]. The \(RI\) leverages the results of this tool to compare resilience levels across smart critical infrastructure sites.  
Upon calculating the \(RI\) for each component, an advanced resilience index is presented in the form of a dashboard. Figure 4 illustrates an example of the DHS dashboard, displaying the resilience index at the component level. The bar mark represents the observed infrastructure, while the low, average, and large dots indicate the comparative value in relation to other smart infrastructures. The implementation of this approach and its transparent representation enables facility administrators to make informed decisions more easily.
# Modeling the Impact of Atmospheric Warming on Staple Crop Growth in China in the 1960s and 2000s
## 2 Methods  
### Research Area  
China is a nation of multiple climate zones that support diverse cropping systems. Rice, wheat and maize are the three staple foods in China. Rice is the staple food for more than 65% of the Chinese population, accounting for 40% of caloric intake in China (State Environmental Protection Administration (SEPA), 2003) [17]. Wheat is also a staple food for half of the world's population, and the second-largest crop in China [32]. Maize is one of the staple crops, also the most vulnerable crop to climate change among the staple crops in China [33]. These three cereal crops accounted for approximately 54% of the total sowing area and 89% of the total grain yield in China until 2009 [8; 34]. These sites, Changshu (31.53\({}^{\circ}\) N, 120.68\({}^{\circ}\) E) with a winter wheat rice rotation, Taoyuan (28.92\({}^{\circ}\) N, 111.45\({}^{\circ}\) E) with a double rice rotation, Fengqiu (35\({}^{\circ}\) N, 114.4\({}^{\circ}\) E) with a winter wheat maize rotation, Sanjiang (47.58\({}^{\circ}\) N, 113.52\({}^{\circ}\) E) with single rice cultivation and Hailun (47.43\({}^{\circ}\) N, 126.63\({}^{\circ}\) E) with single maize cultivation, represent the typical cropping systems of the main cultivation regions of China. We conducted the analysis at the 5 sites in four typical crop rotation areas of China (Figure 1) to simulate crop productivity in different crop rotation systems. Detailed information on each site is shown in Table 1.
# Sporiasis Pathogenesis and Treatment
## 3 Therapy
### Biologics
#### 3.2.2 IL23/Th17 axis  
As previously mentioned, IL-23 drives the expansion of Th17 cells whose inflammatory effects are in turn mediated by IL-17A, IL-17F, and IL-22.  
2.3 IL-23 is a dimer composed of p40 and p19. The first biologic to be approved for psoriasis vulgaris after the TNF-\(\alpha\) inhibitors was unstekinumab, which is a monoclonal antibody directed against the p40 subunit. P40 is not exclusive to IL-23, but rather is shared with IL-12. IL-12 is a dimer consisting of p40 and p35, and is involved in the differentiation of naive T cells into Th1 cells. By targeting p40, unstekinumab blocks two different T-cell activating mechanisms, namely Th1 and Th17 selection. Ustekinumab is also effective for the treatment of PsA and Chron's disease. It is available in two dosages, 45 mg and 90 mg, depending on a threshold body weight of 100 kg. Ustekinumab has extensive safety data, few side effects, good clinical efficacy, and long treatment drug survival was reported. At 90 mg, unstekinumab showed a PASI 75 response in 72.4% and in 61.2% at 45 mg [197]. Studies using real-life data compared unstekinumab with the anti-TNF-\(\alpha\) drugs, and unstekinumab was found to have a significant longer drug survival [198; 199; 200]. Frequent adverse events include nasopharyngeal origins, upper respiratory tract infections, fatigue, and headache. Among the serious adverse events listed in the label of unstekinumab are infections. Tuberculosis (TB) has only been reported in two psoriasis patients receiving unstekinumab [201; 202]. The clinical efficacy of unstekinumab and the further clarification of its mechanism of action highlighted the crucial role of IL-23 in shaping the Th17 response. On the other hand, Th1 signaling is important for the response against bacterial and viral pathogens, and a study showed IL-12 signaling to have a protective effect in a model of imqiumiod psoriasis-like inflammation [203]. This rationale fueled the development of drugs targeting p19, which is the IL-23-exclusive subunit. This more specific molecular targeting approach has also achieved successful clinical outcomes. Three fully human monoclonal antibodies with p19 specificity are available: guselkumab, tildrakizumab, and risankizumab. Guselkumab is licensed for psoriasis, and showed clinical superiority when compared to adalimumab, with 85.1% of patients reaching a PASI 75, and 73.3% receiving a PASI 90 response at week 16 [204; 205]. Patients receiving tildrakizumab showed a 74% PASI 75, and 52% PASI 90 at week 16. Tildrakizumab was compared to etanercept, and was more likely to reach PASI 75 at weeks 16 and 28 [206; 207]. Risankizumab showed the following PASI responses at week 12: 88% PASI 75, 81% PASI 90, and 48% PASI 100. Patients were followed for 48 weeks after the last injection at week 16, and one-fourth of them showed a maintained PASI 100 [208]. Whether IL-23 inhibition has the potential to modify the course of the disease after subsequent drug retrieval is currently under study.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 4 Experiments and Results
### Experiment on Constructing a Sentence Vector  
This experiment aims to determine which configuration is the best for constructing a sentence vector. There are three configurations: (1) The summation of word-embedding vectors, (2) the average of word-embedding vectors, and (3) the average of the word-embedding vectors with inverse document frequency (idf). We needed to compute idf, so we used our own UMBC word-embedding vectors. This experiment used the best configurations (i.e., context size of ten, sense definition and sense relations, _n_best_ = 1) from the previous experiment without the probability of sense distribution feature to show the actual effect of each configuration.  
From Table 5, the result shows that using the summation of word-embedding vectors or the average of word-embedding vectors provides the same result. Because cosine similarity only measures the direction of the vector, dividing or multiplying a scalar to the resultant word vector affects only the magnitude of the vector and not its direction, except for zero scalars which cancel that word vector out of the sentence vector. Idf was not useful in WSD, for example, a word sense \"university\" and the context contained a term \"student,\" because of the term \"student\" appeared most frequently in the UMBC corpus; thus it got demoted and made it less significant to help in disambiguation for word sense \"university.\" Notice that results from Google word-embedding vectors (48.7%) were better than UMBC word-embedding vectors (41.7%) because Google word-embedding vectors were trained from a larger dataset. Hence, it will be possible to obtain more accuracy by training word vectors from a larger dataset than the Google News Dataset, but it will require more training time and resources.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Configurations** & **Precision** & **Recall** \\ \hline Context size 2 & 0.446 & 0.427 \\ Context size 6 & 0.455 & 0.436 \\ Context size 10 & 0.487 & 0.466 \\ Context size 14 & 0.471 & 0.451 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of different context sizes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configurations** & **Sense Distribution** & **Precision** & **Recall** \\ \hline Sense definition & ✗ & 0.421 & 0.403 \\ Sense definition + sense relations & ✗ & 0.487 & 0.466 \\ Sense definition & ✗ & 0.480 & 0.459 \\ Sense definition + sense relations & ✗ & 0.509 & 0.488 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results with and without sense relationships/sense distribution.
## 3 Adaptive Time-Frequency Analysis
### Variational Mode Decomposition and Optimization Models
#### 3.2.2 Successive Variational Mode Decomposition
Moreover, compared to VMD, SVMD reduces the computational burden, especially when dealing with complex signals. In specific applications within the field of structural health signal processing, where certain modes are considered as interference or noise and are not of interest, the concurrent implementation of VMD may lead to a wastage of computational resources. On the other hand, SVMD can extract modes one by one, skipping unnecessary modes, which significantly improves convergence speed and reduces overall computation time [90]. Furthermore, SVMD overcomes the issue of VMD's sensitivity to initialization parameters. As SVMD is less sensitive to the initial values of the modal center frequencies, it can reduce the likelihood of converging to incorrect or duplicate modes, a problem that has been noted by [PERSON] and [PERSON] in their work [65]. This robustness enhances the reliability of the decomposition results in structural health monitoring, where accurate mode identification is crucial. However, SVMD is not without its drawbacks. In some cases, it may get trapped in local optimal solutions, particularly when dealing with signals that have highly similar modal components or when the signal-to-noise ratio is low. This can affect the decomposition effect and lead to inaccurate mode separation. Additionally, the sequential nature of SVMD may limit its parallelization potential, making it less efficient than VMD in applications requiring real-time processing. Despite these limitations, SVMD remains a promising method for structural health signal processing, offering a balance between accuracy, efficiency, and robustness.
## 5 Security Analysis
### Confidentiality  
Confidentiality is a security property that protects sensitive data from unauthorized access. In the proposed key management system, the shared secret keys used for message encryption between parties are themselves encrypted. To communicate the key securely, initially, both parties share the secret information through the Diffie-Hellman key exchange combined with the double ratchet algorithm during the [PERSON] and [PERSON] session setup phase. In the Diffie-Hellman key exchange, Curve25519 is deployed, which uses a 255-bit prime and offers 128-bit security when implemented securely; this is considered secure in the absence of a quantum computer. Additionally, the double ratchet algorithm is deployed in the proposed key management system to prevent access to future messages if at some point the key is compromised.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 4 Performance Analysis
### Performance of FH Sequence Recovery
In order to achieve more flexible and efficient anti-jamming communication, the frequency-hopping system had to adjust the frequency-hopping sequence in time according to the jamming. Therefore, when the length of the frequency-hopping received signal changed, the network in this section could be used directly without retraining, which greatly increases the scope of application of the network.
# An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers
## 2 Related Works
### Blockchain and Its Application in CDM  
Blockchain is a technology in which a number of transaction details are bundled to form a block. Additionally, several blocks are connected like a chain using hashes, and then a number of people copy and store them in a distributed manner [24; 25]. It allows anyone to make reliable and secure transactions. Blockchain can not only be used for cryptocurrency but also for all data processing that has online transaction history and requires history management. Blockchain-based smart contracts, logistics management systems, document management systems, medical information management systems, copyright management systems, social media management systems, game item management systems, electronic voting systems, identity verification systems, etc., can be used in various ways [25].  
Although the demand for the use of medical data is increasing, medical information contains personal information, so there are restrictions on its use. In the domestic health and medical field, as the need to provide medical services tailored to patient characteristics by integrating genomic information, treatment, clinical information, and lifestyle information is increasing, it is essential to secure a security system for the safe use of medical information.  
Blockchain has a function that can be used for safe and clean data distribution of CDM data in a collaborative research environment in which multiple institutions participate. In a collaborative research environment, CDM providers and consumers operate blockchain nodes and manage the process of transactions related to access to CDM data by researchers.  
SimplyVital [26] uses a private blockchain network to share patients' personal medical data with multiple medical research institutions but maintains it on the distributed ledger of the patient's medical information provider. However, maintaining medical information on the blockchain is an illegal matter of domestic medical information, and there is a limitation to applying it to the joint use of medical information. OmniPHR [27] operates based on a blockchain to convert and maintain the dispersed personal medical information of patients into a standardized data format and manage the authority to access the data from any device. As access control is performed, there is a limitation that access control must be performed for each individual researcher.  
In this paper, we present a study on blockchain technology for user identity management of medical data and a model for cross-institutional CDM data access control. The proposed model is based on a decentralized identity to provide self-sovereignty, and through this, proves the qualifications of researchers in an environment in which multiple institutions participate. The DID model for credentialing is currently an effective approach for accessing CDM data, a unique use case in the medical field.
## 2 Related Works
### Fuzzy Systems  
Fuzzy theory uses mathematical methods to address issues related to ambiguous semantics. In everyday life, people's descriptions of things can vary. For instance, one person might consider the weather cold if it is below 18 \(\lx@math@degree\)C, while another might feel it is cold when it is only below 20 \(\lx@math@degree\)C. Similarly, in restaurant reviews, some might rate the food as excellent with a score of nine, while others might rate it as good with a score of seven. These differences in perception create ambiguity and fuzziness in descriptions, which fuzzy theory can handle effectively.  
Fuzzy control utilizes fuzzy sets, fuzzy logic, and fuzzy inference to deal with ambiguity and uncertainty. Unlike traditional control theory, which relies on precise mathematical models and inputs, fuzzy control can function without such constraints. It uses expert knowledge, rules, and experience for control settings. The input values are fuzzified, transforming them into fuzzy sets. Through the fuzzy inference engine, based on a knowledge base and fuzzy rules, the inputs are processed and, finally, the results are defuzzified to obtain concrete outcomes.  
Fuzzification maps input values to fuzzy sets using membership functions to express the degree to which each value belongs to a fuzzy set. This process converts precise input values into a form suitable for fuzzy logic processing. Fuzzy sets are mathematical functions representing ambiguity and uncertainty. Elements are associated with the functions, and their membership values are calculated. The membership value indicates the degree of association between an element and the set; the closer the element is to the set, the higher its membership value. Common membership functions [11] include Gaussian, triangular, trapezoidal, S-function, Z-function, and Pi-function. The choice of function depends on the specific needs.  
After fuzzification, the fuzzy inference engine performs reasoning based on rules from the rule base and knowledge base. The engine [12] uses the membership functions and conditions of the input values to compute their membership values. The rule base consists of a series of IF-THEN rules, where each rule has an \"IF\" condition and a corresponding \"THEN\" action. These rules are defined based on expert knowledge, experience, or extensive data collection.  
The fuzzy inference engine uses the knowledge base and rules to infer fuzzy results. Defuzzification then converts these fuzzy results into specific outcomes, yielding the most reasonable results and achieving the purpose of establishing a fuzzy system. Common defuzzification methods include the Center of Maximum (CoM), Center of Area (CoA), and Mean of Maximum (MoM). CoM uses the highest membership value as the central point for defuzzification. CoA treats the membership function of the fuzzy set as an area and calculates the center of this area for defuzzification. MoM averages the elements with the highest membership values for defuzzification. These methods ensure that the defuzzified results are consistent with the inferred fuzzy outcomes.
## 3 Method
### Non-Linear Mapping  
In this section, we mainly implement non-linear mapping by stacking local residual blocks (LRB), and use global residual learning to send the output results of each local residual block to the subsequent expansion layer, which can more effectively learn image details. Additionally, the local residual block can better solve the problem of image degradation and reduce network redundancy. The structure of the local residual block (LRB) [14] is shown in Figure 3b. In this figure, (a) is a DRRN [11] network structure with two residual units. The local residual block structure used in this paper is improved on the basis of the structure of DRRN. It mainly uses local residual learning and is composed of adding shallow convolution branches (SCB) and deep convolution branches (DCB).  
Figure 3: (a) The partially simplified structure of DRRN. (b) The structure of LRB with a shallow convolutional branch (SCB) and a deep convolutional branch (DCB).  
LRB can be written as:  
\[x^{u}=U(x^{u-1})=G_{D}(\sigma(x^{u-1}),\theta_{D})+G_{S}(\sigma(x^{u-1}),\theta_{ S}), \tag{4}\]  
where \(U\) represents the function of the residual unit, and \(x^{u-1}\) and \(x^{u}\) represent the input and output, respectively. The weight sets of the deep convolution branches and the shallow convolution branches were flagged as \(\theta_{D}\) and \(\theta_{S}\). \(G_{D}\) and \(G_{S}\) are residual mappings that need to be learned for deep and shallow branches, respectively.  
In this part, we use a \(3\times 3\) convolution kernel for non-linear mapping. The number of mapping layers is \(m\), which determines both the accuracy of the mapping and the complexity of the mapping. In order to maintain consistency, the number of convolution kernels is still \(n_{2}\), so thispart can be expressed as \(m\times conv(3,n_{2},n_{2})\).
# Screening Mechanism and Properties of a Cantilevered Vibrating Sieve for Particles Processing
## 4 Establishment of Screening Simulation  
In order to investigate the influence of geometric parameters and vibration parameters on the screening efficiency of the CVS, this investigation performed a numerical simulation of the screening process under various conditions, such as different length of cantilevered sieve rod, sieve inclination angle, vibration direction angle, vibration amplitude, and vibration frequency. These influence factors are common in studying screening properties of other kinds of vibrating screen in [1; 2; 3; 5; 6; 13; 14; 15; 16; 17; 18; 19; 20].  
In this investigation, all the simulations were conducted by the commercial Discrete Element Method (DEM) software named EDEM (Version 2.7). Figure 5 shows the CVS model used in the simulation. The diameter of cantilevered sieve rods (d) is 6 mm, the spacing between two adjacent rods (s) is 16.5 mm, which means the screen hole size is s - d = 10.5 mm. Besides this, the density of coal and cantilevered sieve rod, the Poisons ratio, the Young's modulus of coal and cantilevered sieve rod, and the restitution coefficient, sliding friction coefficient, rolling friction coefficient, and the simulation time step are all the same as those in Reference [18].  
The screening material is coal particle. Based on the screen hole size, this investigation sets six different coal particle sizes, and their corresponding mass ratios are shown in Table 2. The mass of the coal particles whose sizes are smaller than the screen hole size accounts for 81.4% of the total mass.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Particle Sizes (mm) & 8 & 9 & 10 & 11 & 12 & 16 \\ \hline Number ratio (\%) & 37.7 & 37.7 & 18.9 & 1.9 & 1.9 & 1.9 \\ \hline Mass ratio (\%) & 27.2 & 38.5 & 15.7 & 3.4 & 4.5 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sizes and their mass ratios of coal particles.  
Figure 4: Anti-plugging mechanism of cantilevered vibrating sieve (CVS) induced by (**a**) transverse shift and (**b**) longitudinal shift of cantilevered sieve rod.  
Figure 5: Discrete element method (DEM) model of a cantilevered vibrating sieve (CVS) without particles (**a**) and with particles (**b**).