esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Overview of GNSSs
### Gps
#### 4.1.3 GPS Ground Segment  
The ground segment, referred to as the control segment (CS) within the GPS system, consists of four main subsystems: main control station (MCS), alternative main control station (AMCS), ground antenna network (GA), and a network of globally distributed monitoring stations (MSs). The MCS is located in Colorado and is the central control node for the GPS satellite constellation. Operations are maintained 24 h a day, seven days a week year-round by the highly trained staff of 2 SOPS. MCS is responsible for all aspects of constellation command and control, including the following [69]:  
* Routine monitoring of the status of the satellite bus and cargo;
* Satellite maintenance and subsystem anomaly resolution;
* Support for launching, decommissioning, and disposal of satellites;
* GPS performance management to ensure compliance with all performance standards;
* Message operations as required to maintain performance in accordance with standards of accuracy and integrity;
* GPS anomaly detection and response;
* Communication with military GPS users.  
The GPS uses a variant that simultaneously measures pseudo-distance relative to as many satellites as necessary to determine position coordinates and correct the time scale in the user's receiver. Knowing the distance to one satellite makes it possible to determine the position surface in the form of a sphere on which the object is located. Determining the distance to a second satellite determines a second position surface, which is also a sphere. The intersection of the two position surfaces forms a position line in the form of a circle. The intersection of this circle with the third position surface, determined by measuring the distance to the third satellite, determines two points, one of which is discarded as improbable. GPS is used to determine the position of objects in three-dimensional space, so the procedure must use distances from three reference points. The GPS uses satellites moving in circular orbits around Earth as reference points [79].
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 3 Results
### The Wheat Growth Responses to the Climate Change  
Considering the two focal SSPs, the responses of wheat PY to climate change manifest two distinct stages, demarcated by the year 2060 (Figure 5). Under SSP126, wheat cultivation in the primary production zones of central China is anticipated to experience favorable effects stemming from climate change, resulting in an upswing in PY. This trajectory underscores the dominance of CO\({}_{2}\) fertilization benefits, outweighing the adverse impacts arising from rising temperatures. If robust measures are implemented to control greenhouse gas emissions, the escalating trend in ambient CO\({}_{2}\) concentration (Ca) is projected to peak around 2060, subsequently dampening the fertilization effect. Consequently, the increase in wheat PY within substantial regions is projected to diminish. For the majority of production areas, the downward trend in PY remains statistically insignificant (\(p>0.05\)). Under SSP370, characterized by a 0.6 \({}^{\circ}\)C elevation in temperatures by 2060, the temperature sensitivity (Ta-induced effect) is more pronounced, exerting a formidable negative influence on wheat growth. This effect offsets the benefits of CO\({}_{2}\) fertilization. While a majority of the northern production regions exhibit negligible trends, the escalation in Ca persists beyond 2060, fostering a significant nationwide enhancement in wheat PY.  
Figure 4: PY frequency comparison of EARTHSTAT, this study, and ensembled results of other crop models. The shade of model ensemble represents the 95% confidence interval of different model results.  
Figure 5: Wheat PY prediction considering the response to the climate change (with static sowing date). (**a,b**) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 126, respectively; (**c,d**) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 370, respectively. The grid with significant variation trend is labeled with the black points.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.7 Consistency  
Consistency in the KDN environment can happen due to changes in the network environment, such as the addition of new devices to the system or changes to network policies. If the KDN system cannot adapt quickly to changes in the network environment, incorrect decisions may be taken by the system. If the knowledge plane is implemented as a physically distributed system and if such a system has replicas of data, data inconsistencies in replicas may lead to contradictory predictions from the machine learning models that make predictions based on such inconsistent data.  
Organizations can implement change management processes to ensure that KDN systems quickly adapt to network changes. In the case of a distributed knowledge plane having replicas of data, such data should be consistent, either using eventual consistency or a strong consistency approach, to make sure that the latest version of updated data is reflected in each replicate.
###### Abstract  
Deep learning has achieved remarkable progress in medical image analysis, but its effectiveness heavily relies on large-scale and well-annotated datasets. However, assembling a large-scale dataset of annotated histopathological images is challenging due to their unique characteristics, including various image sizes, multiple cancer types, and staining variations. Moreover, strict data privacy in medicine severely restricts data sharing and poses significant challenges in acquiring large-scale and well-annotated histopathological images. To tackle these constraints, Transfer Learning (TL) provides a promising solution by exploiting knowledge from another domain. This study proposes the Uncertainty-guided asymmetric Consistency Domain Adaptation (UCDA), which does not require accessing the source data and is composed of two essential components, e.g., Uncertainty-guided Source-free Transfer Learning (USTL) and Asymmetric Consistency Learning (ACL). In detail, USTL facilitates a secure mapping of the source domain model's feature space onto the target domain, eliminating the dependency on source domain data to protect data privacy. At the same time, the ACL module measures the symmetry and asymmetry between the source and target domains, bridging the information gap and preserving inter-domain differences among medical images. We comprehensively evaluate the effectiveness of UCDA on three widely recognized and publicly available datasets, namely NCTCRC-HE-100K, PCam, and LC25000. Impressively, our proposed method achieves remarkable performance on accuracy and F1-scores. Additionally, feature visualizations effectively demonstrate the exceptional generalizability and discriminative power of the learned representations. These compelling results underscore the significant potential of UCDA in driving the advancement of deep learning techniques within the realm of histopathological image analysis.  
Keywords:histopathological image classification; uncertainty-guided; asymmetric consistency; domain adaptation; source data privacy +
Footnote †: journal: Medical Image Analysis
## 3 Experimental Results
### Over Performance Comparison  
We comprehensively compare the performance of our proposed scheme with seven latest state-of-the-art schemes in Table 5. It can be observed that most schemes other than [29] use bit-XOR encryption, and all schemes achieve model recovery without loss and data extraction. For schemes that use XOR encryption, the model is processed vertex by vertex, resulting in time complexities of O(n). Our proposed scheme has a significant improvement in embedding rate compared to others, indicating that our scheme has advantages to extend the privacy and copyright protection of 3D models to a wider range of applications.
## 3 Simulation Results  
In order to validate the proposed TV-GPEF algorithm, a series of numerical simulations are conducted for limited-view scanning. First, straight-line scanning with different sampling points are carried out for the Sheep-Logan phantom which is a widely used phantom in biomedical imaging. Then, limited-angle circular scanning with varying sampling views is also tested. The reconstruction results for the TV-GPEF algorithm are compared to those of the TV-GD and the TV-VB algorithm quantitatively and qualitatively. The PSNRs of these three algorithms are compared and the noise robustness, the convergence speed of the algorithms are also studied. The simulations are conducted using Matlab R2013a on a personal computer with a 2.4 GHz Intel(R) Xeon(r) CPU and 64 GB memory. The speed of sound is set to 1500 m/s. The simulations for photoacoustic signals and image reconstruction are all carried out in a two-dimensional plane.
# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
###### Abstract
Clustering technique can group users with similar preferences, habits, and behavioral patterns [9]. By leveraging the advantages of the clustering technique to discover user activity in the Web pages, the advertising effectiveness of the online roadshow can be achieved by investigating the three user experience constructs suggested by [PERSON] et al. [3]. Hence, this paper investigates the existing clustering techniques used for different functional purposes of web usage mining, aiming ultimately to enhance the user experience of the online roadshow website. To carry out the review of the state-of-the-art clustering techniques in web usage mining, the review works are collected from IEEE, Springer, ACM Digital Library, and Science Direct. Google Scholar was also used to include articles published in other educational journals. The keywords include the name of the clustering technique and the term of \"web usage mining\" is used to narrow down the scope of the studies to the clustering techniques that have been applied in web usage mining. The referenced articles on the  
Figure 1: Web usage mining process. clustering techniques are published from 2010 to 2021, with the exception of an article on fuzzy c-means that is published in 2003. To the best of our knowledge, there are a few comprehensive reviews on the utilization of different clustering techniques in web usage mining. The differences between our paper and the others are highlighted:  
* In [10; 11; 12; 13], the clustering techniques are generally discussed without focusing on any specific application, while our paper specifically discusses the clustering techniques used in web usage mining. In [10], greater focuses are on time series clustering, similarity measures, and evaluation metrics. In [11; 13], both papers exclude the review of fuzzy-based clustering techniques. * Refs. [14; 15] introduce and discuss the clustering techniques used in data mining for big data, but do not review their similarity measures and evaluation metrics. * Refs. [6; 16] are dedicated to the review of clustering techniques in web usage mining. However, these papers do not discuss the suitable clustering techniques for different purposes of web usage mining. To conclude, the clustering techniques are analyzed from three perspectives in this work. First, our paper reviews the similarity measures that are used in different clustering techniques. Second, we discuss the evaluation metrics of the clustering techniques that are used in determining if the clusters are optimal. Lastly, the purpose of applying the techniques to improve the user experience of the online roadshow website is proposed. The rest of the paper is organized as follows. Section 2 presents the clustering techniques that are used in web usage mining. Section 3 presents the discussion. Finally, the paper is concluded with a discussion on the contribution of this work and future work in Sections 4 and 5 respectively.
## 2 Sample Expansion Methodology Based on CVAE-Skegan
### Assessment of Sample Generation Capability  
In this study, a comparative experiment is conducted using the driving end data of the CWRU dataset, which contains different types of bearing faults, such as inner ring faults, outer ring faults, and rolling element faults, as well as bearing data under fault free conditions. The vibration data are collected at a sampling frequency of 12 KHz, and the faulty bearings are fabricated by electrical discharge machining. Each type of fault is distinguished by three different damage levels, so there are a total of 10 types of data. The data are converted into time-frequency images by Continuous Wavelet Transform (CWT) without pre-processing. A total of 100 time-frequency image samples are taken from each status type. The time-frequency images are generated using the WGAN-GP, CVAE-GAN, and CVAE-SKEGAN models, respectively. Maximum Mean Discrepancy (MMD) and Kullback-Leibler (K-L) divergence are used to evaluate the sample-generation capability and quality of the above three models.
## 5 Proposed Solution  
This research exploits the capabilities of passive NFC devices with embedded HSMs that are commonly available in the market (e.g., smart cards). Further on, this class of devices will be identified as a Secure Element (SE). The SE is used to assign a robust digital identity to parts tracked in the SCM. The aim is to design the physical integration of an SE into the overall part, effectively transforming it into a \"smart-part\".  
The SEs are protected with physical security measures that offer tamper protection and detection. For example, the SEs can be protected with a protective mesh and covered with security tape [46]. Moreover, being a secure hardware element designed, produced, and tested by a reliable company following the standard Common Criteria Evaluation Assurance Levels 5+ [47; 48], the risk of software attacks or side-channel attacks is reduced significantly.  
The SE contains, in a secure partition, a private key that can be used to perform asymmetric crypto operations such as a digital signature. All the operations are performed through secure-by-design APIs, always keeping the crypto material in a secure state.  
Once a part is physically tagged with the hardware-based digital identity, the crypto functionalities of the SE can be used to interact with the blockchain-based SCM system. The smart-part can send valid transactions to the blockchain, signing them with its specific private key. The crypto information required for the signature cannot be extracted from the SE; this means that only someone with physical access to the part can trigger these blockchain functionalities, which proves package ownership.
## 3 Research Model and Hypotheses  
The research model used in this study was based on Path Analysis (PA), which is a method that allowed evaluating the adjustment between the set of dependency relationships (direct and indirect influence) proposed from the study variables [29]. It should be mentioned that this method does not test causality but helps to infer causal hypotheses [30].  
Before the study's analyses, an exploratory analysis of the data was performed to assess their quality: treatment of missing data, control of tabulation errors, presence of outliers, variability in distribution, and strange patterns in the data [31].  
The PA was developed in six steps: (1) specification: establishment of the study variables; (2) identification: determination of the information to compare the model; (3) parameter estimation: obtaining the values that best fit the observed matrix; (4) fit assessment: determining whether the relationships between the estimated model variables reflect the relationships observed in the data; (5) model re-specification: improving the model fits; and (6) interpretation of results [32; 33; 34]. For this purpose, the model presented in Figure 1 was proposed, which integrates the study variables: Mentoring (MN), Motivation (MT), Communication and Feedback (CF), Learning Design (LD), and Learning Performance (LP).  
Following the objective of this study, the following hypotheses are proposed and will be tested:  
* **H1.** Mentoring explains the levels of Learning Design.
* **H2:** Motivation explains the levels of the Learning Design.
* **H3:** Communication and Feedback explains the levels of the Learning Design.
* **H4:** Learning design explains Learning performance.
* **H5:** The relationship between mentoring, motivation, and communication with learning performance is mediated by the learning design (there is no direct relationship between mentoring, motivation, and communication and learning performance).
## 4 The Dataset Usage
### Data Extraction and Feature Selection
#### 4.1.3 PCA-Based Dimensionality Reduction  
Word2 Vec vectors tend to be highly dimensional, which may lead to overfitting and increased computational cost. To mitigate this, PCA was applied to reduce the number of features while preserving as much variance as possible. The resulting features, pca_0,  
Figure 3: Feature correlation matrix.  
pca_1,..., and pca_n, represent the most informative principal components. This approach improves model efficiency while retaining essential information.  
Combining Word2 Vec embeddings and PCA-transformed features benefits the model from semantic understanding and optimized feature representation, leading to improved generalization.
## 3 Results
### Test Results and Analyses of Large-Sized Granular Materials
#### 3.2.2 Effect of Particle Diameter on the Shear Strength of Large-Size Granular Materials  
Table 3 shows the shear strength values of large-sized granular material specimens obtained from the large-scale triaxial tests. Figure 10 illustrates the relationship between the internal friction angle (\(\varphi\)) and particle diameter (\(d\)).  
Figure 8: The stress-strain curves of large particle glass beads with different sizes in large-scale triaxial tests.  
Figure 9: Variation curve of shear strength parameters with particle diameter(\(d\)) of single-particle-size specimens of large-sized glass beads.  
In Figure 10, the internal friction angle (\(\varphi\)) of large-sized granular materials with particle diameters (\(d\)) ranging from 10 mm to 30 mm exhibits a generally monotonic increase with particle size, albeit with some variations. Specifically: when \(d\) increases from 10 mm to 15 mm, \(\varphi\) increases by 1.06\({}^{\circ}\) (about 3.6%); from 15 mm to 20 mm, \(\varphi\) increases by 1.18\({}^{\circ}\) (about 3.7%); from 20 mm to 25 mm, \(\varphi\) increases by 0.1\({}^{\circ}\) (about 0.3%), indicating almost no change; from 25 mm to 30 mm, \(\varphi\) increases by 0.75\({}^{\circ}\) (2.4%).  
These results indicate that, overall, the internal friction angle (\(\varphi\)) increases with particle diameter (\(d\)) for large-sized granular materials. However, within the range of 20 mm to 25 mm, \(\varphi\) remains nearly unchanged, suggesting a plateau in the relationship between particle size and internal friction angle. This observation highlights the non-linear nature of the relationship between particle diameter and shear strength parameters, even in larger-sized granular materials.
# Linked Data Triples Enhance Document Relevance Classification
## 6 Experimental Description  
### Extracting Natural Language Features  
Latent Dirichlet Allocation (LDA) [38] is adopted for mining topics as semantic features in the Energy-Hub. LDA determines clusters of documents over a given number of topics. Gensim ([[https://radimrehurek.com/gensim/](https://radimrehurek.com/gensim/)]([https://radimrehurek.com/gensim/](https://radimrehurek.com/gensim/)), accessed on 5 February 2021) is a popular software library in Python that implements LDA. It efficiently manages memory allocation when dealing with large corpora [39], hence being a natural choice for our purpose.  
After collecting articles through a crawler, these are pre-processed by converting the documents to sentences. Special characters are avoided as they are of little use in topic modeling. Stop words are removed, and bi-grams are generated from the textual data. Lemmatization is performed on the processed text to consider different words with similar meaning as single items. LDA is then applied on the processed corpus to generate topic models.  
Identifying the optimum number of topics required helps with better segmentation. A coherence score measures the performance of a topic model by computing the semantic similarity between dominant words in each topic [40]. Energy-Hub iterates over 10 to 100 topics, calculates the coherence score for each model, and selects the optimal model with the highest coherence score.  
For Named Entity Recognition (NER), we used the Python-based library spaCy [41] on the document text. The library builds on top of a Convolutional Neural Network (CNN) using annotated elements and raw text. The performance of NER systems is evaluated by comparing the extracted NEs with human annotations [32]. spaCy achieved 86% accuracy ([[https://spacy.io/usage/facts-figures](https://spacy.io/usage/facts-figures)]([https://spacy.io/usage/facts-figures](https://spacy.io/usage/facts-figures)), accessed on 5 February 2021) for NER on the OntoNotes 5 corpus ([[https://catalog.ldc.upenn.edu/LDC2013T19](https://catalog.ldc.upenn.edu/LDC2013T19)]([https://catalog.ldc.upenn.edu/LDC2013T19](https://catalog.ldc.upenn.edu/LDC2013T19)), accessed on 5 February 2021), hence being considered suitable for our experiments. Named entities extracted for this work include persons, organizations, nations, events, locations, dates, times, numeric quantities, ordinal, and cardinal numerics.  
Figure 5: Deep learning architecture for classification of news-feeds with BERT using text, topics, named entities, and triples.  
For the extraction of triples, document sentences are annotated with \"subject, predicate and object\" using Open Information Extraction (OpenIE) from Stanford CoreNLP ([[https://stanfordnlp.github.io/CoreNLP/openie.html](https://stanfordnlp.github.io/CoreNLP/openie.html)]([https://stanfordnlp.github.io/CoreNLP/openie.html](https://stanfordnlp.github.io/CoreNLP/openie.html)), accessed on 8 March 2021) library. A topic-entity filter is applied over each document to identify semantically enriched triples (as discussed in Section 4.4).