esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Synthetic Data in Point Set Form
#### 5.4.1 Synthetic Point Set NBR-Clust Results  
The results for clustering the synthetic point set datasets are shown in Table 7 as an average percent accuracy over the 10 instances per dataset. Toughness and scattering generally performed poorly, hence only the results for VAT, integrity, and tenacity are presented.  
VAT attained a high accuracy on all four types of synthetic point set graphs. VAT clustered almost all of the noiseless graphs perfectly, achieving a 99% or 100% average accuracy on each series of 10 graphs. VAT did only slightly worse on the noisy graphs, clustering both equal and unequal density graphs at 95% or above. In general, VAT did not cluster in one step. Clustering took between 1 and 7 steps, and the number of steps did not vary greatly between the four data set variations.  
Like VAT, integrity clustered the noiseless datasets almost perfectly, achieving an accuracy of 99% or higher. On the noisy unequal density datasets, integrity did relatively well, clustering between 69% and 95% accuracy. Integrity was less successful with the noisy datasets, although it exhibited a relatively consistent pattern of increasing accuracy as the number of clusters increased, independent of the number of dimensions. Integrity clustered every noisy dataset, and most of the noiseless datasets, in only one iteration.  
Tenacity also clustered the non-noisy datasets with high accuracy. Tenacity did not perform as well on the noisy datasets. In contrast to integrity, tenacity performed better on datasets with a smaller number of clusters. Tenacity clustered every dataset in only one step, without requiring further recursive iterations.
# A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises
## 4 Discussion
### RQ2: Digitisation and Digital Transformation Strategy
In the case where predictive accuracy is sought, parameter values can be adjusted to assess the outcomes of certain scenarios. For example, for productivity, it will be far more important to measure the throughput of a process rather than the vibration of a machine. The DT models should always leave room for adding further details such as new parameters and key performance indicators. This will allow system engineering to generate plans to install sensors strategically to capture new information. Most digitally enabled PLM solution suites that have various enabling DT technologies, such as 3 DExperience, can handle this with its behaviour modelling applications that run simulations and present quantifiable data at all measurable instances. The challenges outlined in the demonstration unit of this work are good representations of what most SMEs are facing in their DT development processes. The incorporation of temperature readings in the DT model allows it to interact with external circumstances, which can then be generalised and applied to any systems that react dynamically with extrinsic/intrinsic parameters in real time. SMEs at the modelling stage should investigate modelling tool options and find one that fits their purposes and budget as this phase will be critical to the cost and time control of the whole project. Regarding the level of detail of both the systems model and the 3D model, SMEs must decide what aspects of the DT represent the desired functionality of the physical twin. An important note is that although detailed systems models may require deeper understanding of an asset's functionality, it also sets up for greater modelling accuracy and predictive simulation capabilities through variable adjustments. The outputs of such predictive simulations are entirely dependent on the inputs; therefore, SMEs must ensure data integrity when extracting data from their existing system. For DT developers with limited knowledge on the underneath software, there is an expected learning curve which may vary depending on the complexity of the task. There is potential for value generation via adopting the proposed framework. As seen in companies such as Tesla and General Electric, the opportunities created by real-time monitoring provide the basis for informed decision-making, increased transparency, and improved responsiveness. The effect of real-time monitoring and informed decision-making grants insights into the fundamentals of a business in areas such as supply chain management, the customer relationships, marketing strategies and the company's bottom line. This may lead to a transformation of the business model such as that accomplished by Rolls-Royce, as mentioned in the introduction, or it may lead to a leaner and less wasteful manufacturing processes--both positive results. Ultimately, companies that choose to incorporate the digital twin concepts will be those that value information obtained in a timely manner and are able to generate new revenue from it.
# Investigating the Influence of Feature Sources for Malicious Website Detection
## 3 Preliminary Experiments
### Feature Inspection & Experimental Results  
Upon studying the feature contributions in the original and the expanded dataset, we uncovered an anomaly in the JavaScript-based features. It was found that the distribution of values of the data were clearly separated and we concluded that there appears to be bias emanating from these features. Figure 1 outlines this clear malicious-benign separation caused by JavaScript features. It would be quite unrealistic to accept this separation as ground truth, especially for a classification problem of this magnitude and nature. We will be investigating and addressing this issue at a later stage of the study. At this stage, we evaluate several classic ML models on both datasets, with the addition of two more variations which have JavaScript features removed from the initial data. This gives us four dataset variations for investigation purposes:  
1. Original data provided in [21];
2. Original data with JavaScript features removed;
3. Expanded data with features and malicious samples described in this section;
4. Expanded data with JavaScript features removed.  
The ML models used for this phase and their results are outlined in Table 2. At first glance, the accuracy scores may seem optimistic. However, after a thorough inspection of the feature space given by [PERSON]'s data, we identified an anomaly in the JavaScript-based features which suggested the existence of bias in the data. In general, data is considered biased if the sampling distributions in the dataset do not reflect population-level distributions, and in this case, it may be considered sample selection bias. This therefore opened up an investigation into JavaScript code in malicious websites, which might further validate these results. The JavaScript code present in the raw information as part of the original data comes in a heavily processed state, compared to what we extracted revisiting the original malicious websites as part of our feature extraction pipeline. This may have skewed the feature extraction phase and elevated the bias to a high degree to render any application of machine learning techniques meaningless.  
To further emphasize, there are studies which display more accurate and logical representations of their JavaScript feature sets. Studies [33, 34, 35, 36] are of varying times and datasets which plot distributions and t-SNE plots of their JavaScript features, which count deobfuscated code length, number of events and various vector embedding representations. These features are quite similar to the ones considered in this study, which allows us to benchmark the extent of the bias. When this comparison to previous studies is made, we concluded that we would not proceed with the current variations of [PERSON]'s dataset, but move forward with creating our own new dataset based on the same malicious-benign URLs given by [PERSON], but extract the features ourselves.  
Figure 1: JavaScript features distributed by class for [PERSON] data.
# Research on Waterless Cosmetics in the Form of Scrub Bars Based on Natural Exfoliants
## 3 Results and Discussion  
### Scanning Electron Microscopy of Exfoliating Agents  
To characterize the morphology of the exfoliant particles and determine their shape and angularity, an examination using a scanning electron microscope (SEM) was carried out. The results are shown in Figure 2.  
Figure 2: SEM images of the exfoliating agents, magnification 60\(\times\).  
Sucrose particles exhibited a highly regular shape, appearing as cuboids with precisely defined edges. Their size typically ranged from 600 to 1100 \(\upmu\)m. Similar to sucrose, the morphology of microcrystalline cellulose was characterized by a very high degree of regularity, but its particles were more spherical in shape, with rounded edges. Typically, their dimensions fell within the range of 700 to 1050 \(\upmu\)m. _Coffea arabica seed powder_ stood out due to its substantially smaller particle size, typically ranging from 30 to 100 \(\upmu\)m. The particles had an irregular shape, with no visible sharp edges. High amorphousness was also shown for _Zea mays (corn) cob granules_. The photograph depicts a mixture of spherical particles and oblong particles with distinctly sharper edges. These particles also exhibited the greatest size variations, between 600 and 1600 \(\upmu\)m. The greatest regularity of shape was observed in sodium chloride crystals, which were nearly cubic and had sharply defined edges. These particles showed only slight variations in size, typically falling within the range of 350 to 450 \(\upmu\)m. Based on the criteria for classifying particle shapes proposed by [PERSON] [PERSON] [29] and [PERSON] [30], sucrose and sodium chloride can be categorized as angular-shaped, microcrystalline cellulose as well-rounded, and _Zea mays (corn) cob granules_ and _Coffea arabica seed powder_ as subrounded particles. The authors of research papers [31; 32] state that abrasives should be evaluated based on three factors: hardness, dimensions, and particle shape. In addition, they found that ground fruit seeds provide more abrasive exfoliation due to their coarse particles, while more spherical and smooth particles provide smoother exfoliation. The authors also warn of the negative effects of using larger, harder, and irregularly shaped abrasives that cause a rough feeling on the skin, which can cause irritation and sometimes injury.
## 2 Analytical Approach for the Joint Rotational Behavior
### Materials
#### 2.2.4 Composite  
* Concrete-EG-concrete (CGC) model  
According to the material models of concrete and EG, the stress-strain relationship of the concrete-EG-concrete composite material can be obtained by using the following equations:  
\[\sigma(\varepsilon)=\sigma_{c}(\varepsilon_{c})=\sigma_{e}(\varepsilon_{e}) \tag{7}\]  
\[\varepsilon=\frac{2\varepsilon_{c}l_{c}+\varepsilon_{e}t}{2l_{c}+t} \tag{8}\]  
where \(\sigma(\varepsilon)\) is the composite stress; \(\varepsilon\) is the composite strain; as presented in Figure 3c, \(l_{c}\) is the depth of the composite compression zone and roughly equal to the height of the compression zone (\(l\)) [9; 13; 27]; and \(t\) is the EG thickness.  
The maximum compressive strain (\(\varepsilon_{u}\)) of the composite can be calculated as follows:  
\[\varepsilon_{u}=\frac{2\varepsilon_{cu}l_{c}+\varepsilon_{cc}t}{2l_{c}+t} \tag{9}\]
* Rigid-EG-rigid (RGR) model  
Some existing analytical approaches for the segmental joint with EG are mainly proposed based on the assumption that the concrete is much more rigid than the EG. For this assumption, only the compressive strains of the EG are taken into concentration, and the compressive strains of concrete attached to the EG are neglected. Therefore, the composite in Figure 3c can be considered as a rigid-EG-rigid (RGR) model, for which the concrete strain is approximately equal to zero. According to Equation (8), the strain of the composite can be presented as follows:  
\[\varepsilon=\frac{\varepsilon_{e}t}{2l_{c}+t} \tag{10}\]  
* Concrete-concrete (CC) model  
For the segmental joint with EG, if the influence of the EG is neglected, it is regarded as the same as the joint without EG. Therefore, the composite in Figure 3c can be considered as a concrete-concrete (CC) model. In fact, this model is just a special case of the CGC model, and the thickness of the EG (\(t\)) in Equation (8) is equal to zero.
# Ring-Overlap: A Storage Scaling Mechanism for Hyperledger Fabric
## 4 Scheme Design
### Intra-Cluster Data Storage  
In [27], the authors divide all nodes in the network into multiple clusters by the dynamic clustering algorithm. Each cluster needs to store all data, but nodes within the cluster are not required to maintain data integrity. The new proposed mechanism in the paper differs from [27] in that each cluster only stores \(1/m\) of the block data. If the nodes in a single cluster also follow the random storage method, the integrity of all the data in the network cannot be guaranteed if some nodes in the cluster are corrupted. Therefore, based on the division of clusters, we proposed overlapping storage in a single cluster, which can still ensure data integrity even if some nodes in a single cluster fail.  
Block data is divided and stored in the cluster based on parameters such as the number of nodes in the cluster and the total data volume of the block. The nodes in the cluster store the block data in an overlapping manner, and the storage capacity available in a single node must be greater than the size of a single block, and the total storage capacity of all the nodes in the cluster must be greater than the total data volume of the blockchain. Still, with \(m=4\) and \(n=3\) as an example, it is divided into 4 clusters by static clustering, with three nodes in each cluster, and each cluster stores \(1/4\) of the block data, naming the block data as A. Using overlapping shard storage, A is divided into three parts, and two parts  
Figure 2: System structure of ring-overlap.  
2. The next generated block IDs are 2, 3, 4, then according to the storage cluster location formula yields 2\(\otimes\)4= 2, 3\(\otimes\)4= 3, 4\(\otimes\)4= 4, it can be determined that the block should be stored in clusters 2, 3, 4, node storage process is the same as step (1).
3. When the generated block ID is 5, so we get 5\(\otimes\)4 = 1, and it is determined that the block should be stored in cluster 1, with the result that \(l=[(5-1)4]+1=2\), then \(l\odot\)3= 2, \((l+1)\odot\)3= 3, it is determined that the block should be stored in nodes 2 and 3.  
Similarly, when the generated block ID is 9, it is determined that the block should be stored in cluster 1. Then, according to the formula for the location of the storage nodes in a single cluster, \(l=[(9-1)4]+1=3\), then \(l\odot\)3 = 3 and \((l+1)\odot\)3 = 1, it is determined that the block should be stored in nodes 3 and 1.  
After the network generates nine blocks, the data storage structure of cluster 1 is shown in Figure 4.
# A Cooperative MAC Protocol for a M2M Heterogeneous Area Network
## 5 M2M Heterogeneous Area Network
### MFDRR Architecture  
The most important and novel component of the proposed M2M area network is the MFDRR, which aggregates data and extends the transmission range, as well as increases the throughput of the heterogeneous network. Figure 7 shows the MFDRR protocol stack and the queue structure. As can be seen from the figure, the MFDRR consists of two types of protocol stacks: the 6 LoWPAN stack on the left side and the 802.11 stack on the right side. The former is developed in accordance with IEEE 802.15.4 and RFC 4944 standards [25]. Two 6 LoWPAN MAC layers and two physical interfaces are used to support two frequencies/channels as mentioned earlier. The 802.11 PHY and MAC layers  
Figure 6: Heterogeneous M2M access network architecture.  
are developed using the IEEE 802.11g standard [26]. A buffer is used to connect the application layers of the two protocol stacks; the 6 LoWPAN packets are stored in the buffer for the payload aggregation.  
Data packet flow within the MFDRR is processed in the following manner: upon receiving an end device packet from a 6 LoWPAN router, the MFDRR 6 LoWPAN stack strips off its headers and hands over the payload to the application layer. Subsequently, the payload is stored in an aggregation buffer where payloads from multiple packets are aggregated to generate a WLAN payload for further transmission to the sink. Once the aggregation buffer length reaches a certain threshold value, the 802.11 application layer encapsulates multiple 6 LoWPAN payloads and forwards the aggregated payload to the 802.11 MAC layer. The MAC layer then immediately generates a Blank Burst (BB) control signal to initiate an 802.11 packet transmission. The following section describes the BB signaling algorithm and 802.11 packet transmission technique.
## 4 Fault Protection Strategy  
The protective circuits are designed to improve the reliability of the system. The protection measures are implemented by CPLD and switches \(T7\) and \(T8\). The protection signals, i.e., bus over-voltage (OV), bus under-voltage (/UV), bus over-current (BOC), and phase over-current (POC), are generated by hardware circuits and act as the inputs of CPLD. Filter operation and logic synthesis measures were taken in the CPLD program to export the fault protection operation signals of \(T7\) and \(T8\). The hardware description language VHDL was utilized to realize programming. Therefore, this hardware protection scheme can improve the reliability of the system compared with the software type. Meanwhile, unlike the cycle-by-cycle method, which takes protection measures at each cycle of the pulse width modulation (PWM) signal, the proposed scheme greatly reduces the cost of the chip.
# Dual-Energy CT Applications in Urological Diseases
## 2 DECT Principles
The raw data-based analysis allows for a greater variety of dual-energy CT applications compared to image-based analysis. VMI, electron density, and effective atomic-number analyses require raw data analysis. The choice between raw data- and image-based analyses depends on the dual-energy CT hardware, with raw data-based analysis commonly used with fast tube-voltage switching, sequential scanning, and dual-layer detector systems, while image-based analysis is used with dual-source CT scanners.
# Investigation of Impulse Aging of Energy-Absorption Elements for Hybrid DC Circuit Breakers
## 3 Research on Impulse Aging Characteristics of MOVs
### Result and Discussion
At the same time, when \(\alpha\) was less than 30, the change rate of \(U_{\rm 1 mA}\), the change rate of \(U_{\rm 0.01 mA}\), and \(I_{\rm L}\) of the three groups of samples were: 1.96%, \(-\)0.40%, \(-\)1.75%, 5.37 \(\mu\)A; 2.64%, 0.29%, \(-\)2.74%, 6.03 \(\mu\)A; and 3.59%, 0.92%, \(-\)3.36%, 5.73 \(\mu\)A, respectively. The parameters of each group of samples did not reach the commonly used aging judgment standard value, except for the 200 A-20 group sample, which did not experience damage until the leakage current \(I_{\rm L}\) exceeded 20 \(\mu\)A. The states of the samples after the impulse test are summarized in Table 5. The failure modes of MOV valve sheets under impulse aging can be classified into three kinds, namely, \"no damage\", \"insulation breakdown\", and \"body breakdown\". Among these, \"no damage\" indicates that there was no visible damage to the MOV when the test was completed; \"Insulation breakdown\" indicates that there were obvious burn-through channels or breakdown holes in the side insulation coating. \"Body breakdown\" indicates that the MOV body was broken down and cracked, and there were obvious traces of current flow and breakdown on the cracked surface. Under the same test conditions, the failure modes of the MOV valve sheet were different. This was due to the fact that the quality and structure of each MOV valve sheet could not be exactly the same, so each MOV valve plate had different structural defects and different load-bearing capacities. According to Table 5, it can be seen that body breakdown and insulation breakdown were the two most common fault modes in MOV impulse aging tests, accounting for 33.3% and 55.6% of the total experiment, respectively. Insulation breakdown usually occurs at the interface between the edge of the MOV sintered body and the insulation coating. Initially, it only formed non-penetrated impulse holes, and then gradually developed into penetrated holes, which formed erosion marks as the insulation layer was destroyed due to energy release. The body breakdown could be observed on the surface of the MOV sintered body, with some puncture holes due to thermal expansion caused by thermal stress. The pictures of failure modes of insulation breakdown and body breakdown are shown in Figure 11.
## 4 Conclusions
In addition, increased speeds, particularly for high-speed bridges, may result in shorter measurement durations and, therefore, a lower frequency resolution. Many of these factors provide additional challenges for applications requiring high-speed vehicles. Several promising avenues are suggested for further development in this area. First, the development of more robust and accurate feature extraction methods is essential. Current methods are sensitive to environmental factors such as weather conditions, track geometry, and train speed. Improving the robustness and accuracy of feature extraction methods will significantly improve the performance of drive-by monitoring systems. Second, the development of more efficient and effective machine learning algorithms is critical. The existing algorithms can be computationally expensive and struggle to handle the large amounts of data collected by drive-by monitoring systems. The development of more efficient and effective machine learning algorithms will make drive-by monitoring systems more practical for field use. Finally, there is a need to develop more comprehensive numerical models that accurately capture the dynamic vehicle-roadway-structure interaction. Current numerical models often oversimplify the complexity of the real-world scenario. By developing more comprehensive numerical models, the accuracy of underride monitoring systems can be improved. Conceptualization, E.F.S., C.B. and D.R. ; writing--original draft preparation, E.F.S., C.B. and A.M.; writing--review and editing, D.R., A.M. and H.C.; visualization, E.F.S. and C.B. ; supervision, T.N.B. and D.R. ; project administration, T.N.B. All authors have read and agreed to the published version of the manuscript. The authors would like to acknowledge CNPq (Brazilian Ministry of Science and Technology Agency), CAPES (Higher Education Improvement Agency), FAPESP (Sao Paulo Research Foundation) for financial support under grant #2022/13045-1, VALE Catedra Under Rail, project \"FERROVIA 4.0\", with reference POCI-01-0247-FEDER-046111, co-funded by the European Regional Development Fund (ERDF), through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020) and the Lisbon Regional Operational Programme (LISBOA 2020), under the PORTUGAL 2020 Partnership Agreement\", and project \"SMART WAGONS--DEVELOPMENT OF PRODUCTION CAPACITY IN PORTUGAL OF SMART WAGONS FOR FREIGHT\", with reference C644940527-00000048, from the Incentive System to Mobilising Agendas for Business Innovation, funded by the Recovery and Resilience Plan and by European Funds NextGeneration EU. Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflict of interest.