esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We conducted a numerical experiment to analyze the crosstalk artifacts of different-order multiples in marine seismic exploration, and examined the two-way illumination energy distribution of the full wavefield under the combination of primary waves, surface-related multiples, and internal multiples. From the perspective of full-wavefield illumination compensation, we propose a combination of multi-order wavefield global normalization conditions and full-wavefield least-squares migration. The algorithm introduces downward illumination wavefields of internal and surface-related multiples in the least-squares iteration, effectively suppressing multiple artifacts. Numerical experiments using a simple inter-layer model and the Pluto 1.5 model indicate that, when simulated marine seismic data simultaneously include both primary waves and multiples, the algorithm still yields satisfactory imaging results. Compared to conventional imaging conditions, this algorithm offers the following advantages: (1) it is equally stable but is not constrained by the choice of parameter \(\varepsilon\) (compared to smooth deconvolution conditions); (2) it provides broader illumination and has the potential for deep imaging; (3) it can suppress most multiple interference artifacts. In terms of its computational efficiency and working memory, this algorithm is essentially the same as the traditional cross-correlation imaging condition, with no additional cost.  
H.Z.: conceived and designed the algorithm; performed the experiments; analyzed the data; wrote the paper. D.W.: conceived and designed the algorithm; performed the experiments; analyzed the data; wrote the paper. L.L.: analyzed the data. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (No. 42074151, No. 41374108), and Major Projects of the National Science and Technology of China (Grant No. 2016 ZX05026-002-003).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Author [PERSON] was employed by the company Jilin High-Grade Highway Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 16: Least-squares migration local magnification: (**a**) global normalization primary migration; (**b**) global normalization full-wavefield migration; (**c**) cross-correlation full-wavefield migration; (**d**) deconvolution full-wavefield migration. The solid white arrow is surface-related multiple artifacts, the red arrow and the red box indicates the area of interest.
# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 5 Dataset  
Although aviation data are published as open source in some countries, such a secondary data source is not available in most countries. For this reason, secondary data were obtained from an airport while conducting this study. The data records of 40,077 flights (8625 late, 31,452 on time) in the dataset covered three years (2016, 2017, 2018). The dataset we used is unique, which allowed for the generalization and evaluation of results when working on different data.  
While creating the dataset, care was taken to ensure that the features recorded at the airport were similar to those used in the literature for estimating flight delays. Table 2 shows the features obtained from the airport, the data types of these features, and the studies in the literature that made flight delay estimations using relevant data.  
The study included meteorological data and data pertaining to the airport. The relevant weather data were obtained via the online website rp5.ru. The weather data from the time zone closest to the flight were extracted from the dataset, which contained weather information updated every 20 min. In addition to these variables, those presented in Table 2 were employed in the estimation of flight delays in conjunction with meteorological conditions. Table 3 presents the weather-related features utilized and the types of data associated with each feature. Furthermore, the flight type variable was included, which indicated whether the flight in question was scheduled or not. This encompassed special, ferry, and technical flights.  
Encoding of categorical data and unbalanced dataset management tools were required throughout the data preparation process. There were 7 unique observations for the day of the week feature. For the other categorical values, there were 31 for day of the month, 12 for month, 97 for origin of departure airport, 17 for mean wind direction, 419 for total cloud cover, and 2 for flight type. For a proper modelling, all categorical features were converted to numerical values. One-hot encoding, which is frequently used for categorical data conversion, was selected for this. Using this approach, new features were created form the unique observations provided for each categorical feature. After encoding was applied, the number of features used in the study was 595. For the imbalanced dataset, another data preparation step was required. If the classification categories are not relatively equally represented, the dataset is imbalanced. Due to the lower number of delayed flights, this issue was present in the dataset of this study. SMOTE [67], one of the recommended approaches, was preferred to address this problem. The minority class disadvantage is minimized with this technique, which generates synthetic data similar to the minority class. The data acquired without utilizing SMOTE are also presented to further analyze the outcomes obtained.
## 1 Introduction  
The use of unmanned aerial vehicles (UAVs) for remote sensing has become more common in the study of natural hazards and geomorphological processes, especially due to the development of technologies and mapping systems at larger scales, leading to a more comprehensive understanding of natural processes. UAVs are especially suitable for studying and monitoring rockfalls [1, 2, 3, 4], due to their smaller spatial extent and the fact that the potential rockfall areas are often located on steep slopes above infrastructure and settlements, where the terrain is hardly accessible and dangerous for classical field observations [5; 6; 7; 8]. Structure-from-motion (SfM) photogrammetry enables the production of various high-resolution photogrammetric-derived products, such as point clouds, 3D models of the surface, digital surface models (DSMs), digital terrain models (DTMs), and orthomosaics [9; 10]. The use of a UAV is suitable for the acquisition of remote sensing data at a local scale (a few square kilometers), to which rockfalls are mostly limited [11]. As UAVs are less demanding to operate, compared to other remote sensing technologies (e.g., aerial photogrammetry, satellites, etc.), both from the temporal and financial point of view, the area of interest can be surveyed several times over shorter time periods, thus achieving greater spatial (i.e., centimeter resolution) and temporal resolution of data [12; 13].  
Previous photogrammetric rockfall hazard studies have focused on the potential of photogrammetry for different applications, regarding the vertical and sub-vertical rock slopes, as well as rockfall transit and deposit areas. The purpose of these studies was to (i) identify potentially unstable blocks, wedges, joints, and blocks of the rockfalls; (ii) extract rockfall discontinuities; (iii) identify potential rockfall release areas; and (iv) obtain spatial data for rockfall modeling and rockfall risk assessment [3; 5; 7; 8; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Photogrammetric studies of rock faces have also included comparing and combining UAV photogrammetric data with data obtained by aerial and terrestrial laser scanning.
## 4 Case Study of Engineering Application
### Deployment of the Monitoring System  
To ensure a safe operation of the LPG storage tanks, we have installed nine sensors (sensor numbers 1-9) on three spherical LPG storage tanks as shown in Figure 19 to monitoring the inner temperature, the surface temperature. Three additional sensors (sensor numbers 10-12) were installed to monitor the environment temperature, which can also be seen in Figure 19. The sensor numbered 2, 5, and 8 are fiber optic temperature sensors as described in Section 2.2. The rest of sensors are wireless sensors. The sensor deployment was started on 16 February 2015 and completed on 17 February 2015. The monitoring system was powered on 17 February 2015 and all the sensors survived the installation process. The temperature data was transmitted to the monitoring room via wireless signal. Figure 18 also shows that several routers were used allowing for the wireless signal transmitted from the storage tanks to the monitoring room. The monitoring room was located 1 km away from the storage tank in the office building as shown in Figure 18.
## 3 Accuracy and Feasibility for Hardware Implementation
### Methods Evaluation  
Backscattered signals from simulation and experimentation were the input for CNREM reconstruction accuracy, using the minimum number of samples and the higher \(R^{2}\) coefficient obtained. The execution time in a computer with an Intel Core i7 processor and 32 GB of RAM was calculated afterward for the 21 scenarios.  
Now, the comparison starts with the accuracy of each method, i.e., the fidelity of the reconstruction. The CNRs calculated from the input signal are the base for the linear regression that leads to the signal reconstruction, so the CNRs calculated are reliable only if this reconstructed signal has a \(R^{2}\) coefficient (from 0 to 1) close or equal to 1. The average for these measurements is presented in Figure 18, where MPM outperforms in both simulation and experimental scenarios.  
When the higher \(R^{2}\) for each signal was obtained, the number of samples for the linear prediction matrix in time or frequency domain methods were compared and are shown in Figure 19. Here, frequency domain methods (Cauchy's and VCM) use significantly fewer samples to obtain an acceptable reconstruction. The execution time was the final approach for understanding CNREM as a signal processing tool feasible for hardware implementation in GPR operations. Figure 20 shows how time domain methods use, on average, less processing time than frequency domain methods.  
Figure 17: Buried objects measurement in a sandbox. Time domain late-time response for Prony’s and MPM-based reconstructions, and frequency domain response for Cauchy’s and VCM reconstructions.  
Figure 19: Average number of samples per CNREM in different scenarios.  
Figure 18: \(R^{2}\) comparison for CNREM in different scenarios.  
Time domain methods have a better time performance than frequency domain methods, and MPM in particular has a good reconstruction, with the highest \(R^{2}\) coefficient for experimental scenarios. However, for real-time implementation, the number of samples and the late-time response identification could use additional hardware resources. This consideration is not present in frequency domain methods.
## 3 Results and Discussion
### Liming Damage at Different Peak Seismic Accelerations  
In order to study the damage response of the tunnel at different peak values of seismic acceleration, the initial fault displacement in Case 6 is set to 15 cm, with the peak acceleration of the artificial ground motion adjusted to 0.4 g for input in the numerical model.  
As shown in Figure 14, with a peak artificial ground motion acceleration of 0.4 g, the maximum compression damage factor of the lining increases from 0.78 in Case 2 to 0.92 in Case 6, while the damage range expands from the vault and the invert to both side walls, indicating a significant increase in compression damage under stronger seismic impacts. Additionally, both the extent and severity of tensile damage in the lining increase markedly. In Case 2, tensile damage primarily develops circumferentially around the lining, whereas at 0.4 g of peak acceleration, the damage area at the fault plane further expands, and axial tensile damage appears in the right wall within the fracture zone.  
Figure 15a shows the OLDC distribution along the tunnel axis under earthquakes with varying acceleration peaks. As shown, the OLDC distribution range remains largely consistent between Case 2 and Case 6, with both peaking at the fault dislocation plane. However, under stronger seismic activity, the maximum OLDC value increases from 0.70 to 0.81, reaching the threshold for complete damage (DS4).  
Figure 14: Lining damage at different peak seismic accelerations. (**a**) Compression damage. (**b**) Tensile damage.  
Figure 15: Overall lining damage at different peak seismic accelerations. (**a**) Compression damage. (**b**) Tensile damage.  
Figure 15b shows the OLDT distribution along the tunnel axis under earthquakes with varying acceleration peaks. As shown in the figure, the OLDT distribution patterns for Case 2 and Case 6 are similar; however, under an earthquake with a peak acceleration of 0.4 g, the OLDT values across all lining locations increase compared to Case 2.  
The analysis indicates that more intense seismic excitations significantly alter the damage characteristics of the tunnel. Regarding compressive damage, a 0.4 g seismic excitation causes the maximum OLDC value at the fault plane to reach 0.81, a damage level suggesting that tunnel collapse is highly likely at this location.  
Considering the damage state of the tunnel in Case 1, the OLDC maximum value reaches 0.54 after the tunnel experiences a 15 cm fault dislocation. At this stage, the tunnel is in the DS2 damage state, indicating the presence of cracks only on the surface of the secondary lining. However, when subjected to a 0.4 g seismic excitation, the damage state escalates to DS4, implying a high likelihood of tunnel collapse.  
This demonstrates that even minor initial damage can lead to severe tunnel failure when compounded by more intense seismic excitations. Therefore, during the operation of tunnels crossing active fault zones, it is crucial to promptly repair minor damage caused by fault dislocation to prevent catastrophic failures.
## 2 Literature Review
### Classification of WBAN Routing Protocol  
WBAN can be divided into six categories, i.e., cross layered routing protocol, quality of service aware routing protocol (QoS), cluster-based routing protocol, temperature-aware routing approaches, postural movement-based routing protocol and energy-aware routing routing protocol.
# Evaluation of the Strength Characteristics of Sility-Clayey Soils during Freezing-Thawing Cycles
## 6 Conclusion  
In this work, a three-parameter paired experiment was conducted in order to analyze the influence of void ratio, initial moisture content, and the number of freezing-thawing cycles on the engineering characteristics of soil, such as angle of internal friction and cohesion. According to the results of the study, the following outcomes were highlighted:  
1. The factors under study greatly affect the cohesion of the soil and, together, can decrease it by up to three times. The angle of internal friction has an indefinite behavior when the parameters under study change, which is confirmed by the literature review. Consequently, taking into account the reduction in cohesion is mandatory in engineering practice, whereas taking into account the change in the angle of internal friction is only recommended.
2. A program based on the least-squares method was used to calculate the approximation coefficients of the dependence describing the changes in strength characteristics from the abovementioned parameters under study based on experiment results. Thus, by setting the values of void ratio, initial soil moisture, and number of freeze-thaw cycles, it became possible to calculate the angle of internal friction and/or cohesion for each type of soil.
3. Freezing-thawing cycles significantly decreased the bearing capacity of the soils: by 44% after 10 freezing-thawing cycles for soil with e = 0.55 and w = 16.5%. However, in the case of e = 0.75 and w = 22.6%, the bearing capacity of the soils increased by 33%, exceeding all expectations. Such changes in strength characteristics must be taken into account when designing structures, as they can lead to additional settlement or even subsidence of the foundations during the freezing-thawing cycles while exploiting.  
Conceptualization, Y.P. and A.Z.; Data curation, A.Z. and U.Z.S.; Formal analysis, E.B. and A.G.; Funding acquisition, T.M. and U.Z.S.; Investigation, E.B.; Methodology, E.B., A.G. and A.S.; Project administration, T.M.; Resources, U.Z.S.; Software, A.G. and T.M.; Supervision, Y.P. and A.Z.; Validation, T.M.; Visualization, E.B. and A.S.; Writing--original draft, E.B. and Y.P.; Writing--review & editing, A.S. and U.Z.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request from the corresponding author.  
The authors declare no conflict of interest.
# Protection of Personal Data in the Context of E-Commerce
## 9 Conclusions  
This study thoroughly investigates the relationship between more stringent data protection legislation and its perceived effects on consumer trust and shopping behaviors in Croatia's e-commerce industry. The study employed an extensive survey to gather consumer opinions and actions, offering a thorough demographic overview and insights into understanding personal data security.  
Our research indicates that although most people have a substantial degree of Internet usage and knowledge of information technology, there are still notable deficiencies in their comprehension and implementation of measures to safeguard personal data. Significantly, many participants demonstrated misunderstandings regarding the definition of personal data, highlighting the necessity for improved educational initiatives to address these gaps.  
The implementation of the GDPR has significantly influenced how middle-aged Internet users, who are most knowledgeable about the rule, perceive and trust online platforms. However, the survey found that a substantial portion of the population does not consistently follow suggested Internet security practices, which might undermine the effectiveness of regulatory efforts.  
From an economic standpoint, the research emphasized a careful attitude towards online transactions, as indicated by a widespread inclination to choose cash on delivery. This preference arises from a persistent lack of trust in digital payment systems. This highlights the essential requirement for e-commerce platforms to strengthen their security measures to enhance consumer trust and promote more secure online purchasing participation.  
As we consider the future, regulatory agencies and e-commerce enterprises must maintain their collaboration to advance a more secure online buying environment. This entails complying with rigorous data privacy regulations and actively participating in consumer education to guarantee that individuals are adequately educated about their rights and the safeguards implemented to safeguard their personal information.  
In conclusion, although the more stringent legislation has established a structure for enhanced security and confidence, the main obstacle is guaranteeing their successful execution and continuously educating the consumer population to achieve a genuinely secure and reliable e-commerce environment in Croatia.  
Conceptualization, Z.M. and D.D.; methodology, Z.M. and D.D.; validation, V.D. and D.R.; formal analysis, D.D. and D.R.; investigation, Z.M. and V.D.; resources, Z.M.; writing--original draft, Z.M. and D.D.; writing--review and editing, V.D. and Z.M.; supervision,V.D.; project administration, Z.M. and V.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are contained within the article.
## 3 Clustering Rewards Based on Compound Correlation
### Adaptive Weight  
To optimize the clustering result, in this part, we utilize the information of the cluster members to design the adaptive weights of the group correlation and communication link correlation in the compound correlation.  
According to Goal 2 of the clustering problem, we hope that each cluster has more UAVs belonging to the same group. For group \(g_{i}\) to which UAV \(i\) belongs, we define the adaptive weight of the group correlation item as the following:  
\[\alpha_{g}=m_{i,k}/m_{i} \tag{6}\]\(m_{i}\) is the size of group \(g_{i}\) and \(m_{i,k}\) is the number of UAVs belonging to \(g_{i}\) in cluster \(C_{k}\). It indicates that the more UAVs belonging to the same group in the cluster, the larger the group weight \(\alpha_{g}\) is, which further verifies the rationality of the defined compound correlation.  
For Goal 3 of the clustering problem, aggregating UAVs with higher communication link correlations to one cluster will increase the total intra-cluster communication link correlation. We give the adaptive weight of the communication link correlation item by the maximum and the average communication link correlation in the cluster, that is:  
\[\alpha_{c}=\bar{f}_{k}^{c}\max_{(i,j)\in\mathcal{E}_{k}}\left(f_{ij}^{c}\right) \tag{7}\]  
\(\bar{f}_{k}^{c}\) is the average communication link correlation of cluster \(C_{k}\). When the cluster size keeps constant, the high average communication link correlation of the cluster indicates that the total intra-cluster link correlation is also large.
## 3 The Proposed Multimodal Model
### Overview  
We propose MulDIC, a multimodal model for classifying issue reports. The proposed model's architecture is illustrated in Figure 2. In the illustration, \"FC\" represents \"Fully-Connected\". As shown in Figure 2, the input data consist of three modalities: text, image, and code. The approach MulDIC preserves the distinctive features inherent to each modality by using CNN channels.  
The proposed model, MulDIC, consists of a total of four stages:  
1. To prepare the input data, MulDIC pre-processes text, image, and code data, respectively.
2. MulDIC processes the pre-processed text, image, and code data with respective CNN\(-\)based channels. The second stage generates three feature vectors corresponding to three modalities of text, image, and code data.
3. MulDIC uses an element-wise multiplication operation that combines the feature vectors generated from all modality data. Then, the proposed model has text, image, and code representations in a common representation space.
4. MulDIC performs a Softmax operation on the representations.  
As a result, an issue report will be classified into one of two categories: \"bug\" or \"feature\", as shown in Figure 2. Because we experiment with the feasibility of performance improvement by adopting a multimodal model in issue classification, we limit the classification of issue reports to only two \"bug\" and \"feature\" categories in this paper.