esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spark High-Speed Imaging  
To accomplish the goal set out in this investigation, PIV and flame imaging was carried out simultaneously with high speed spark imaging. As previously described, the PIV and flame data were captured in one dataset using one camera, and hence, the spark can be imaged using another camera. As illustrated in Figure 2, the two cameras for these diagnostics were positioned perpendicular to each other with the PIV/flame camera directly in-line with the pent-roof window. The light emissions from  
Figure 2: Simultaneous setup for PIV/flame imaging and high-speed spark imaging, together with their sample raw images.  
the combustion chamber through the pent-roof side window was split by a 45\({}^{\circ}\) dichroic mirror with a cut-off at 490 nm and then captured by the cameras. The dichroic mirror was orientated such that light above the cut-off of 490 nm was captured by the PIV/flame camera, whereas the signal below 490 nm was used for imaging the spark. This was due to the fact that the signal for PIV/flame was mainly the scattering of the laser light at 527 nm, while the spark emission was broadband with strong UV emissions. Moreover, this split also separated the interference of the laser signal from the high-speed spark imaging data. To be able to focus on the spark tip location for the spark imaging, a 200 mm macro lens was used together with 35 mm extension tubes. For the spark imaging, the area of interest was 20 \(\times\) 10 mm around the spark tip. The camera was capturing at 50,000 frames per second to ensure all the spark movement was captured. It is also worth noting that the spark plug was installed in such a way that the J plug was behind the line of sight for the cameras, as this ensured the spark was not blocked. The spark plug used in this case was a small-tip iridium performance spark plug. The dwell time was around 3 ms, which translated to roughly 50 mJ ignition energy.
## 7 Conclusions  
Based on indoor hydraulic fracturing simulation experiments and three-dimensional optical scanning, this paper comparatively analyzes the characteristic differences in mineral composition, spatial morphology of hydraulic fractures, fracture area, and roughness parameters between marine and continental shales. The main conclusions are as follows:  
Marine shale of the Longmaxi Formation is predominantly composed of brittle minerals such as quartz, accounting for approximately 74.92%, while continental shale of the Dongyue Temple Formation has a higher clay mineral content, reaching 47.7%, with a relatively lower proportion of brittle minerals.  
During hydraulic fracturing, the average initiation pressure for marine shale is 19.45 MPa, whereas for continental shale it is 17.875 MPa, which is 8% lower than marine shale. Additionally, marine shale exhibits overall brittleness, leading to more complex hydraulic fractures, whereas continental shale, due to the development of bedding planes, tends to initiate fractures more easily along these planes, resulting in a more uniform fracture morphology.  
The average three-dimensional fractal dimension of hydraulic fracture surfaces in marine shale is 2.24397, while in continental shale it is 2.087065. The average complexity of hydraulic fractures in marine shale is 7.52% higher than that of continental shale, indicating greater roughness in marine shale fractures and a more complex structure of hydraulic fractures.  
X.W.: conceptualization, data curation, formal analysis, validation, writing--original draft, and funding acquisition. X.C.: methodology, data curation, formal analysis, validation, writing--original draft, and writing--review and editing. Y.J.: methodology, resources, supervision, and validation. Y.G.: resources, supervision, and validation. K.W.: data curation, funding acquisition, and investigation. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (No. 52104046, No. 52174017).  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author ([EMAIL_ADDRESS]). The data are not publicly available due to private concerns.  
Conflicts of InterestAuthor [PERSON] was employed by Research Institute of Petroleum Exploration & Development, PetroChina. The remaining authors declare that the research was conducted in the absence of any commercial or financial relation-ships that could be construed as a potential conflict of interest.
# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 4 Spatial Crime Analysis
### Clustering Using K-Means Clustering  
Clustering is the technique of data mining that groups the objects in sets of similar features or properties and each set differs from others in its behavior [4]. It can help in the prediction of crimes based on spatial distribution by analysis of the clusters [12]. In this study, we used the k-Means algorithm to perform clustering using a crime dataset because it is applicable over the large datasets and has less complexity as compared to other clustering algorithms [19]. The Weka tool is used in this study for performing k-Means clustering. In k-Means clustering, k clusters are formed from n observation based on the nearest mean. The process of k-Means clustering involves:  
1. Declaring the number of clusters as \(k\).
2. Choose the centers of each cluster.
3. Each instance is assigned to the cluster, which is the nearest.
4. The centroids of clusters are recalculated.
5. The process is iterated.  
Table 4 shows the centroids of each cluster formed through the k-Means algorithm. The total data is divided into eight clusters numbered as 0 to 7. Table 5 shows the distribution of clusters based on the crime type. Clusters' names are assigned based on the centroid. Figure 5 has been built using the Weka tool and illustrates the clusters of crime with respect to their latitude. We just included the centroid of the central cluster in Table 4 which is obtained as a result of K-means clustering in Weka and helps to identify the ratio of different crimes among cities of Pakistan.  
Figure 4: Visualization of crime cases on the basis of each crime type using ArcGIS.
## 5 Conclusions  
Based on electromagnetic theory, this study has developed a finite element model centered around the helical loop antenna to focus on the signal transmission mechanism within cased wells. Through in-depth research of electromagnetic wave wireless transmission theory in cased wells, we have successfully derived a comprehensive attenuation theory, which has been verified and applied in an underground wireless real-time control system. Using the finite element method, a theoretical model of helical loop signal transmission was constructed, and the calculated results were extensively compared and analyzed with actual field measurement data. The results show that the model is not only highly reliable but also demonstrates widespread applicability in practical applications. The frequency of the electromagnetic signal determines its propagation characteristics. Low-frequency signals, due to less attenuation, can cover a broader area, whereas high-frequency signals are limited in long-distance transmission but more efficient in short-distance transmission. Electromagnetic wave signal transmission is significantly affected by the resistivity of the drilling fluid. With low-resistivity drilling fluid, signal transmission over short distances is enhanced. The magnetic permeability of the drill string has a notable impact on electromagnetic wave transmission, particularly at high magnetic permeability, where it may limit the effective propagation of signals. The diameter of the drill string is crucial for signal transmission. A thicker drill string diameter facilitates rapid signal transmission over short distances, while a thinner drill string diameter is more suitable for long-distance communication. Similarly, casing size also affects signal propagation, with larger casing sizes aiding signal transmission over longer distances and smaller casing sizes exhibiting higher rates over short distances. The placement position of the helical loop antenna is crucial for signal transmission efficiency. If it is too close to the bottom of the drill string, it may hinder the effective propagation of electromagnetic waves, thus affecting communication quality.  
The research presented in this paper not only deepens our understanding of the rules of electromagnetic wave transmission in cased wells but also provides more efficient and intelligent guidance for future oilfield development, contributing to the ongoing development of the petroleum industry.  
Writing--original draft preparation, J.L.; writing--review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Henan Province Science and Technology Research and Development Plan Joint Fund Key Project (225200810046).  
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interests.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 6 Research Challenges and Future Directions
### Improving Modulation Recognition Accuracy in Small Sample Scenarios  
The recognition rate of modulation recognition algorithms based on deep learning is not high in the case of small samples, although some scholars have initially solved this problem through data enhancement and transfer learning [99]. However, the data quality cannot be guaranteed, the generalization is not strong, and it is difficult to solve the identification problem in the field of radar communication. Therefore, improving the accuracy of AMR models in small sample scenarios is still an important problem to be solved and has great research value.
## 3 Results and Discussion
### Evaluation Results of the Failure/Loss Ratios of Respective Performance Criteria of SPRG
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Degradation Factors**} & \multicolumn{5}{c}{**Ratio per Screw Mixing Time (\%)**} \\ \cline{2-6}  & **NSM** & **10 min** & **20 min** & **30 min** & **60 min** \\ \hline Viscosity loss ratio (\(V_{l}\)) & 2 & 8 & 36 & 39 & 49 \\ Mass loss ratio (\(W_{l}\)) & 33 & 27 & 47 & 93 & 87 \\ Adhesion failure ratio (\(T_{f}\)) & 0 & 1 & 1 & 0 & 72 \\ Impermeability failure ratio (\(T_{ijf}\)) & 0 & 1.5 & 32 & 67 & 82 \\ Impermeability failure ratio after SMRT 1 (\(T_{smf}\)) & 0 & 20 & 48 & 48 & 80 \\ Averaged deviancy from expected performance & 7 & 11.5 & 32.8 & 49.4 & 74 \\ \hline \hline \end{tabular} \({}^{1}\) Substrate movement resistance testing.  
\end{table} 
Table 9: Estimation of the deviancy from the expected performance of SPRG (2 days storage). towards the center of the radar chart. Once the screw-mixing time reaches 60 min, all the property and performance factors are at a significantly higher deviation state. At 60 min of screw mixing: (1) relatively significant deformation and performance degradation is imminent, and (2) repeated screw mixing at any point of the storage time for reuse can result in a risk of even further performance deterioration and waterproofing failure.
## 1 Introduction
Similarly, the HPC4 AI project posits that federated clouds can support regional and national academic and industrial research through on-demand provisioning of artificial intelligence (AI) and Big Data analytics (BDA) services through collaboration between institutions with complementary resources [16]. Consequently, the use of the cloud for HPC may enable an acceleration of innovation resulting in a positive impact for industry and society as a whole. Despite the potential benefits, compared to the wider information and communication technology (ICT) market, public cloud computing makes up a relatively small proportion of overall revenues for the HPC market but is beginning to show evidence of strong growth [12, 13]. It is thus important to understand the reasons behind organizations' decisions to adopt or not to adopt cloud computing for HPC workloads. Research on HPC in the information systems (IS) literature, and specifically HPC in the cloud, is limited [18, 19]. Indeed, the extant research is largely technical in nature and focused on evaluating the potential of different cloud platforms and determining which HPC applications the cloud represents a viable solution for [14, 20]. While these studies have enhanced our technical understanding of the potential of cloud computing in the HPC context, there is a dearth of research which explores the factors involved in influencing initial decisions to adopt cloud computing for HPC. This gap in our knowledge is important as developing an understanding of the factors influencing initial decisions to adopt a new technology is paramount to the success of new technologies [21]. With this in mind, the paper explores the following research questions:  
_RQ1. What are the determinants of cloud computing adoption for HPC?_  
**RQ2. **_What are the critical issues that are currently impacting and are likely to impact HPC in the cloud in the near future (1-5 years) and in the long term (5+ years)?_  
This study seeks to advance the literature on technology adoption and assimilation in the under-examined HPC context through a mixed methods approach. Firstly, the determinants of cloud computing adoption for HPC are examined with a survey of 121 HPC decision makers worldwide [22]. Secondly, a modified Delphi method was conducted with 13 experts to identify and prioritize critical issues in the adoption of cloud computing for HPC. The remainder of this article is organized as follows. In the next section, we discuss the related literature in the cloud computing context. Two pertinent theories are integrated to develop the research model in Section 3. The methodologies and findings for the quantitative and qualitative phases of the study are presented in Sections 4 and 5, respectively. These are discussed in Section 6. The paper concludes with summary of the study's contribution, limitations and opportunities for future research.
## 2 Data and Methods  
### Data Collection  
#### 2.1.1 SIF Data at DM Site  
The SIF data used in this study were collected through continuous half-hourly observations in an irrigated corn field at the Daman (DM) site (100.372\({}^{\circ}\) E, 38.856\({}^{\circ}\) N), which is located in south of Zhangye, Gansu Province, China [17], as displayed in Figure 1. Weobtained SIF data from two growing seasons: 2018 (1 June to 29 September) and 2019 (1 June to 30 September). The spectrometer was installed on a 25-m flux tower to detect SIF signals and far-red SIF signals in the 740 to 780 nm range by fitting windows using singular vector decomposition (SVD) method. We excluded negative SIF values and converted all half-hourly data into a daily scale. More details about the retrievals and instrument specifications can be found in [PERSON], [PERSON] et al. [17].
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
## 4 The Verifiability Blockchain Service Interface (VBSI)
### Audit: Storing  
Figure 2 illustrates the process of receiving an event and data storing in the blockchain. Let us assume that the Event API-Gateway receives an event \(Ev(\mathsf{m})\) with message \(\mathsf{m}\) to be stored within VBSI. The message is composed of:  
\[\mathsf{m}=\{\|A,B,D,Hx\|\} \tag{2}\]  
\(A\) denotes who is sending the message; \(B\) who is the receiver; \(D\) a set of data required to be stored in the blockchain; and \(Hx\) is a hashed of the previous messages, as follows:  
\[Hx=\mathsf{Hash}(A,B,D) \tag{3}\]  
Token _To_ (Section 4.4 for more details) is obtained from \(D\):  
\[\textit{To}\in D \tag{4}\]  
Storing service involves the following off-chain operations:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Event API-Gateway**} & \multicolumn{4}{c}{**Audit**} \\ \hline \multicolumn{2}{c}{**Type of Transaction**} & \multicolumn{2}{c}{**HTTP(S) Methods**} & \multicolumn{2}{c}{**Consulting**} & \multicolumn{2}{c}{**Storing**} \\ \hline
**t** & **CRUD Operation** & **GET** & **POST** & **PUT** & **DELETE** & **Read** & **New** & **Update** \\ \hline
01 & Create & & X & & & X & \\ \hline
02 & Read & X & & & X & & \\ \hline
03 & Update & & X & & & X \\ \hline
04 & Delete & & & X & & X \\ \hline \hline \end{tabular}
\end{table}
Table 2: CRUD operations sent via HTTP methods and the interpretation in the blockchain by services consulting and storing.  
Figure 2: Storing operation, illustrating communication between Event API-Gateway, _Storing, Users-Authorization_ service and the blockchain.  
* Verify with the _Users-Authorization_ service if _To_ has enough permissions to execute event _Ev_, using Boolean function \(b=permit(To,Ev)\). Returning true if it has privileges to store the event in the blockchain; otherwise, it returns false.
* Send the event to the blockchain, using function \(R=deploy(\{Ev(m)\})\). Returning \(R\), which is a tuple (calculated in the blockchain) of three elements, \(\{\!\!\{T0,A_{tr},A_{sc}\}\!\}\): (a) the token; (b) a transaction address and (c) a smart contract address.  
Storing service can work under two scenarios: (a) Creating users; or (b) Executing operations over resources. Both will be explained in detail in Section 4.4.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 1 Introduction  
Electrical steels are widely used as core materials for a variety of electrical equipment, such as transformers, generators, rotating machines, etc. Due to rapid development in the manufacturing and automation sectors, applications for electrical steels are continuously expanding [1].  
Electrical steels are commonly classified as grain-oriented (GO) and non-oriented (NGO) electrical steels. The main applications of GO electrical steel include transformer cores, power reactors, hydro-generators, turbo-generators, etc. Conversely, the main applications of NGO electrical steel include generator cores, electric motors, electrical meters, etc.  
In the case of NGO electrical steels, the core loss and the magnetic induction are critical features that have been investigated in numerous studies [1]. In electric motor applications, efficiency and torque are the most focused properties [2]. Core loss is directly related to motor efficiency. The lower the core loss, the higher the motor efficiency [2]. Magnetic induction is directly proportional to motor torque. Figure 0(a) shows that, in practice, it is difficult to develop products with both high magnetic induction and low core loss.  
Grain size, inclusions, precipitates, defects, and grain orientation of electrical steels have a distinct effect on magnetic properties. These parameters are influenced by multiple factors, such as steel composition, thermomechanical processing, and so on. Therefore, a review of each factor's influence on the texture evolution of NGO electrical steels would be a valuable contribution to the steelmaking community. Knowledge of these relationships will help engineers control the texture and magnetic properties in NGO electrical steel production processes.
# Evaluating Learner Engagement with Gamification in Online Courses
## 5 Materials and Methods
### Data Collection and Statistical Analysis  
To answer our proposed research question regarding user engagement with gamification, we collected the following data (see Figure 5):  
Figure 4: Test of the initial motivation of both experimental groups: DynamicAG and StaticAG. Measures: IM—Intrinsic Motivation, A—Amotivation, G—Gamer.  
* _DashboardClicks_: Number of interactions with the gamification dashboard by a student.
* _GEDisplays_: Number of times that a Game Element was shown to a student during the experience.
* _GEClicks_: Number of user interactions with Game Elements during the whole experiment.
* _InteractionTime_: The total time spent with game elements by a student.
* _Evaluations_: The mean score that a user gave to game elements during the course.  
Note that we assume that the more engaged the students are with the gamification, the more they will interact with these GEs. Thus, all these metrics are related to the interaction with gamification elements and not to the interaction with other user interface elements.  
Data gathered during the experiment were analysed using both descriptive statistics and non-parametric comparative tests to evaluate differences between the experimental and control groups. Differences in means were analysed in terms of normality using the Kolmogorov-Smirnov test and variance equality using the Levene test. The Brunner-Munzel test was used to compare means for non-normal distributions and unequal variances. In measurements with equal variances, mean comparisons were computed using U Mann-Whitney tests. Tests obtaining p-values lower than 0.05 were considered as statistically significant. The calculations were computed using the SciPy package [48].
# Bar-Wave Calibration of Acoustic Emission Sensors
## 4 Conclusions  
Receiving sensitivities of AE sensors to bar waves were determined using a refined set-up with reduced radial resonance contribution. A long aluminum bar was used as the wave propagation medium, to one end of which a pulser-driven transmitter was glued. The basis of the calibration was displacement measurements using a laser interferometer.  
Receiving bar-wave sensitivities of 16 types of AE sensors were measured and compared to their receiving sensitivities to normally incident waves. The two types of the receiving sensitivity always differed for a given AE sensor. For the selection of AE sensors, one needs to use the appropriate type considering waves to be detected.  
The bar-wave sensitivities of Roa sensors resembled their surface-wave sensitivities, provided by the manufacturer. The bar-wave sensitivity was 9.0 dB higher at the maximum than the peak sensitivity of the surface wave response. The peak frequencies were within 1.9 kHz on average for a given sensor. This indicates that the bar-wave sensitivities can represent the surface-wave sensitivities in typical AE applications.  
Using Choi-Williams transform, some bar-wave modes were identified by comparing peaks found on observed spectrograms with the positions on the dispersion curves for bar waves, calculated with the SAFE procedure [9]. However, numerous bar-wave modes prevented exact identification, especially above 500 kHz.  
Aperture or waveform cancellation effects contributed to the sensitivity reduction at higher frequencies in larger size AE sensors and to more fluctuating bar-wave receiving sensitivities even for sensors with smooth or flat receiving sensitivities to normally incident waves. Spectral dips observed in bar-wave results can be accounted for by aperture effect predictions reasonably well.  
The authors appreciate useful discussions with [PERSON] of Aoyama Gakuin University (Sagamihara, Japan) and [PERSON] of National Inst. of Materials Science (Tsukuba, Japan) regarding FET buffer circuits.  
[PERSON] conceived and performed the bar-wave experiments and data analysis; [PERSON] performed SAFE calculations and [PERSON] performed laser interferometry. All participated in interpretation of reported research results.  
No funding was received for this work.  
The authors declare no conflict of interest.