esa-sceva/satcom-chunk-collection huggingface dataset:
# Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models
## 3 Results
### Validation of Numerical Method
#### 3.3.2 Numerical Analysis of 3D Fractured Model  
As illustrated in Figure 8, the resulting effective stiffness coefficients \(M_{12}\) and \(M_{44}\) are compared to the theoretical solutions obtained using the Hudson and Eshelby-Cheng models. As expected, these predictions exhibit a similar trend as factor densities increase. As illustrated in Figure 8a,b, there is a substantially better agreement between the numerical \(M_{12}\) and \(M_{44}\) and their theoretical counterpart solutions, especially at low crack densities. We also see that the numerical results closely match the Eshelby-Cheng model, with some minor departures between them at higher crack densities due to limited accuracies in the Eshelby-Cheng model. The simulation method provides a physical prediction at high fracture densities that control the overall elastic properties of rocks. We can analyze the fracture-induced anisotropy using these equivalent stiffness tensors.  
Figure 6: Effective stiffness tensors \(M_{12}\) (**a**) and \(M_{44}\) (**b**) for 3D fracture model.  
Figure 7: Comparison of numerical simulations and theoretical predictions for effective stiffness constants \(M_{22}\) (**a**) and \(M_{12}\) (**b**) for an isotropic background medium containing fractures saturated with brine.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 4 Results
### Dual-Polarization Roughness Uncertainty Impact on Soil Moisture Retrievals  
Analogously to Section 4.3, the same analysis is conducted here for dual-polarization SM retrievals. Equation (30) either for \(Q_{HV}\) or \(Q_{LR}\) already gives the amount of dB between the modeled value from SMAP SM and the measured polarimetric ratio.  
The impact of a bad estimation of the \(Q\) parameter on soil moisture retrievals is presented in Figure 8.  
In this case, an uncertainty or error of \(\pm\)2 dB (e.g., two times the uncertainty of \(Q_{HV}\)), for an incidence angle of \(\sim\)30\({}^{\circ}\), will produce a soil moisture error of \(\sim\)0.3 cm\({}^{3}\)/cm\({}^{3}\), but for incidence angles larger than 30\({}^{\circ}\) (e.g., 40\({}^{\circ}\) or 50\({}^{\circ}\)), the 1-sigma error would be \(\sim\)0.05 cm\({}^{3}\)/cm\({}^{3}\), and the 2-sigma error \(\sim\)0.08 cm\({}^{3}\)/cm\({}^{3}\). For the circular polarization case, a 2-dB error represents, for any of the presented incidence angle combinations, a soil moisture 1-sigma error lower than 0.05 cm\({}^{3}\)/cm\({}^{3}\) and a 2-sigma error lower than 0.08 cm\({}^{3}\)/cm\({}^{3}\).  
Figure 8: Soil moisture and bare soil reflectivity differences between two orthogonal polarizations as a function of the incidence angle, simulated from a soil composition of Sand = 40%, Clay = 20%, and Slit = 60% [30].  
Figure 7: Scatter-density plots of the \(Q_{HV}^{inc}\) and \(Q_{RL}^{inc}\) fits in Equations (30) and (31).  
However, the RHCP component cannot be always received at any arbitrary incidence angle due to its very low reflectivity.
## 2 Related Work  
### Fractal Geometry  
Fractal geometry, also known as \"nature's geometry\", is a kind of geometry that studies irregular geometry. Irregular geometry refers to those seemingly chaotic rules with specific internal laws, such as meandering coastlines and rolling mountains. The research object of traditional geometry is the integer dimension, e.g., zero-dimensional points, one-dimensional lines, two-dimensional surfaces, and three-dimensional solids. The research object of fractal geometry is a non-negative real number dimension such as 0.63.  
On that basis, fractals have the following characteristics [6].  
1. Self-similarity, which may include precise self-similarity--the same at all scales; quasi-self-similarity--similar patterns on different scales, where small copies of the entire fractal may contain distorted and degenerate forms; statistical self-similarity--random repetition of a pattern so that a number or statistical measurement remains across scales [7]; qualitative self-similarity--such as time series [8]; or multifractal scaling [9; 10; 11; 12]--represented by multiple fractal dimensions or scaling rules.
2. Fine or detailed structure on any small scale. The result of this structure is that fractals may have emergent properties [13].
3. Local and global irregularities are not easily described in traditional Euclidean geometric languages. For images of fractal patterns, this has been expressed by phrases such as \"smoothly stacked surfaces\" and \"whirlpools on vortices\" [14].  
Fractals can be created by the following fractal generating technologies.  
1. IFS--uses fixed geometric replacement rules; may be stochastic or deterministic [15; 16].
2. Strange attractors--use iterations of a map or solutions of a system of initial-value differential or difference equations that exhibit chaos.
3. L-systems--use string rewriting; may resemble branching patterns or turtle graphics patterns [17; 18].
4. Escape-time fractals--use a formula or recurrence relation at each point in a space (such as a complex plane); usually quasi-self-similar; also known as \"orbit\" fractals.
5. Random fractals--use stochastic rules.
6. Finite subdivision rules--use a recursive topological algorithm for refining tiling [19] and are similar to the process of cell division [20].  
Hence, in this paper, fractal geometry can be used to generate flower patterns with self-similarity, self-affine, and scale-free characteristics.
# Towards Domain-Specific Knowledge Graph Construction for Flight Control Aided Maintenance
## 6 Application for Aided Maintenance of Flight Control System  
After entity-relation extraction, we construct a domain-specific knowledge graph by organizing a set of triples. The knowledge graph constructed is stored in Neo4j that is a high-performance NOSQL graphical database. Different from a relational database to represent data in tables, Neo4j adopts nodes and edges to represent data, where nodes are specific entities, and edges are relations between entities. Based on Neo4j, we implement a web-based human-computer interaction system prototype aimed at aided maintenance for grass-roots engineers. The main interface of the system is given in Figure 9, where the search box takes the input of a query.  
In a real maintenance scenario, grass-roots engineers normally start with alarm messages, e.g., \"F/CTL L + R ELEV FAULT\", \"F/CTL SEC 1 FAULT\", etc. According to current processing specification, engineers first consult the alarms from TSM and historical records (in relational database). If the problem is solved according to official recommended operations or accumulated fault diagnosis experiences, the maintenance is complete. When engineers need more information from unstructured data, our prototype system works. As for the above two examples, the alarm messages will be typed into the search box. Named entity recognition is then carried out to capture all entities (\"ELEV\" and \"SEC 1\") from the alarm messages. Next, a query of \"ELEV\" and \"SEC 1\" is performed on the knowledge graph. For \"ELEV\", the query first finds that it is an acronym for \"elevator\" such that a recursive query of \"elevator\" happens automatically to obtain more information.  
In the knowledge graph, the triples related to \"elevator\" are (\"actuator\", _drive_, \"elevator\"), (\"elac\", _control_, \"elevator\"), and the triples related to \"sec 1\" are (\"sfcc\", _sendMsgTo_, \"sec 1\"), (\"sec 1\", _sendMsgTo_, \"bscu\"), and (\"sec 1\", _sendMsgTo_, \"spoiler\"). Because \"actuator\" drives \"elevator\" and \"elac\" controls \"elevator\", it could be the case that a failure appears in \"actuator\" or \"elac\", and it also could be the case the connection between \"actuator\" and \"elevator\" or the connection between \"elac\" and \"elevator\" suffered a failure. As for \"sec 1\", it could be the case that it fails to receive or send key messages such that the system recommends to check \"sfcc\" and the related connection cables. Query results from our prototype system are shown, respectively, in Figures 10 and 11.  
Figure 9: Main interface of flight control aided maintenance system.
## 4 Experiments
### Highway Crack Detection Experiments  
#### 4.1.1 UAVRoadCrack Dataset  
The original image data used in this experiment were all captured by a DJI Meridian M300 RTK UAV equipped with a 45-megapixel Zenith P1 camera as shown in Figure 10a. The UAV flight height is 60 m, flight speed is 18 m/s, and the resolution of the UAV images is 8192 \(\times\) 5460. The horizontal and vertical resolutions of the image are both 72 dpi, the ground sampling distance (GSD) is 0.75 cm/pixel, the bit depth is 24, and the color representation is sRGB. The heading overlap of the flight line is 80%, and the sidelap is 70%. The left and right extension distance of the strip flight is 30 m. The camera's focal length for shooting is 35 mm, and the exposure time is 1/1000 s. The road remote sensing images used in the dataset include images from various sorties, various road sections, different road characteristics, different flight times, and different lighting conditions. Figure 6 shows some of the UAV aerial remote sensing images of roads.  
The dataset is labeled with LabelMe's line strip, and the labeling results are shown in Figure 10b, where the red curves represents cracks. Since the annotation generated with LabelMe is a Json file, it needs to be converted to a mask image. In addition, because its size is too large, the image needs to be cropped and split into images of 384 \(\times\) 384 size. Then, the dataset is divided into a training set, validation set, and test set according to the ratio 6:2:2. Figure 11 shows some of the original images in the dataset with their corresponding label maps. The structural composition of the final generated UAV remote sensing road crack dataset is shown in Table 1.  
Figure 11: A part of the UAVRoadCrack dataset.  
Figure 10: (**a**) The data acquisition equipment, a DJI M300 RTK drone. (**b**) The labeling results using LabelMe.
## 3 THz Multi-Heterodyne Detection
We can then use the IBN value and the order of the various modes (as seen in Figure 6) to simultaneously measure the frequencies of all the emitted modes, confirming that these modes correspond to the most intense shown in Figure 1b, acquired with the FTIR spectrometer. With respect to those measurements, the multi-heterodyne technique permits to increase the accuracy in the retrieval of modes' frequencies by more than 3 orders of magnitude and, more importantly, allows a simultaneous measurement for all the detected QCL comb modes. As a matter of fact, it can be noticed that by performing faster acquisitions on a smaller frequency window, the accuracy on the frequency of a single HBN can be further improved down to the kHz level, but this would compromise the simultaneous acquisition of all the emitted modes. Figure 6: Acquisition of the heterodyne beatnotes signal (HBN) on the spectrum analyzer, resulting from the mixing of the OR- and QCL- frequency combs, characterized by their intermodal frequencies f\({}_{\text{rep}}\) and f\({}_{\text{IBN}}\), on the Hot-Electron Bolometer. The HBNs are: (**a**) Collapsed when f\({}_{\text{rep}}\) = (f\({}_{\text{rep}}\))\({}_{0}\), submultiple of f\({}_{\text{IBN}}\); (**b**) corresponding to the optical modes when f\({}_{\text{rep}}\) is slightly detuned from (f\({}_{\text{rep}}\))\({}_{0}\), where modes are equally spaced by D. Resolution bandwidths = 10 Hz, blue: Averaged signal, red: Most intense mode M highlighted.
## 6 Discussion and Conclusions
### Ice Impact Load Prediction Model  
The ice impact loads for the barge in the present case study was previously studied with the probabilistic method [6], with the predicted design loads 130 kN or 550 kN for ice thickness of 0.32 m depending on different reference dataset. From Figure 20, it can be seen that ice critical force varies from 100-200 kN at same ice thickness. The model in this paper indicates that the predicted impact load of 130 kPa with a contact area of 0.1485 m\({}^{2}\) from empirical methods can be the more valid one. Nevertheless, the ice breaking force of stem collision is higher compared to the shoulder collision, while the contact area of stem collision is lower. This contradicts the conclusion that a smaller contact area results in a higher force according to the probabilistic method. It would be valuable to conduct further investigates on this. The ice breaking force predicted from the numerical model in this paper shows the same magnitude as in other studies, e.g., [12; 30].  
For the ice model test, ITTC guidelines are used to calculate the ice breaking force. Ice resistance is simplified as a composition of ship resistance in pre-saw ice and ice-breaking resistance. Pre-sawn ice is level ice in which the approximate breaking pattern is cut prior to the model test. Thus, ice force from model test does not contain the ice breaking component. The guidance has barrier during application.  
In the present study, several parameters are studied for ice bending failure. The ship speed and the ice thickness have a positive correlation. The ship speed is shown to have a great impact on the ice breaking in comparison to the other parameters considered. It is also worth mentioning that this parameter is sometimes ignored in empirical equations. The proposed model could assist in quantifying possible differences and improve those equations. For a certain stem angle, the ice loads increase with increasing ship speed. However, the influence from variations in stem angle is not perfectly clear.  
The numerical model is sensitive to parameters such as number of wedges, number of collision event. As speed affects ice breaking length, contact case may change. Moreover, when a ship runs slowly, other ice forces, such as sliding or splitting, may become significant. Those possibilities can violate the assumption that bending is dominant and cause discrepancy. Moreover, it is also possible that continuous brittle crushing is happening at such speed. In general, the method works well once suitable parameters are selected. Thus, it facilitates the computation of ice forces for ship design purpose.  
The current model indicates that the Lindqvist [1] method is limited regarding the prediction of the ice breaking length. Lindqvist does not work very well for ice thickness below 0.5 m. For IWW light ice conditions, it could be better to find another method.
## 5 Evaluation
### Security Evaluation
#### 5.3.1 Data Integrity  
The primary focus of AES and similar encryption algorithms is to ensure confidentiality. However, these systems do not ensure data integrity, which means that they do not detect data changes during transmission or storage. An attacker could modify encrypted data, which leads to incorrect but successful decryption. Therefore, the AES algorithm requires an additional mechanism for data integrity. The proposed solution addresses this data integrity challenge by integrating a hashing mechanism with encryption. The proposed system computes the original data hash, a unique digital fingerprint of data. After decryption, it computes the hash of decrypted data and compares it with the original hash. If both hashes match, no tampering occurred during the encryption and decryption. The proposed data integrity approach ensures robust security against unauthorized modifications, improving stored or transmitted information security.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 3 Architecture for a Cement Factory Based on Blockchain Technology
### Blockchain Service Layer
#### 3.2.1 Private Blockchain  
There are three types of blockchain: public, private, and consortium. For industrial applications, we found the most suitable choice is a private blockchain, as this can provide a high level of security [15]. The connections between blocks enable P2P communication, where data transmitted directly between peers. The block architecture of our proposed scheme is illustrated in Figure 2, only authorized nodes can access the private network to read or add new ledger entries through the use of smart contracts that are executed autonomously, without the need for a trusted third party that considered to be as disadvantage point of centralized architecture which adopted in other works. Due to the irreversible nature of smart contracts, it is almost impossible for an unauthorized node to make changes to the blockchain ledger [33]. Different from other works, in our architecture, the blockchain network receives the data from lightweight nodes as encrypted data; this efficiently helps increase the security of the valuable data with overall system security by adding another stage of encryption. This efficiently increases the difficulty of disclosing encrypted data, even if any unauthorized user can access the blockchain network. In addition, all blockchain ledgers used the hashing function mechanism, asymmetric encryption, and digital signature, already adopted inside the blockchain network to verify incoming new blocks.
# The Limited Impact of Running Performance on Football Success in the Turkish Super League
## 4 Discussion  
In this study of the 2021-2022 Turkish Super League, the findings partially reject the initial hypotheses. Contrary to the first hypothesis, running performance metrics, including distances covered at varying intensity levels, were not significant predictors of match outcomes, as revealed by both the binary logistic regression and two-way ANCOVA analyses. While the LOW variable exhibited a statistically significant difference, its practical significance was negligible, failing to support the notion that superior running performance across intensity zones differentiates winning from non-winning teams. Conversely, the second hypothesis is supported, as situational factors such as match location (home advantage) and team/opponent strength significantly influenced the probability of winning. Our main finding suggests that running performance does not significantly impact team success, which aligns with broader research indicating the limited direct influence of such metrics on match outcomes. For instance, [PERSON] et al. [24] demonstrated that running performance had little correlation with team achievement in the UEFA Champions League. Similarly, [PERSON] et al. [22] found no notable differences in the physical performance of teams across different success levels in the Spanish first division, underscoring that tactical and technical factors might play more significant roles. The study by [PERSON] et al. [23] further supports this, indicating the absence of a significant association between players' running performance and the achievement of their teams. Conversely, research indicating that running performance can decisively affect match outcomes often focuses on specific contexts or match scenarios where physical exertion aligns closely with tactical objectives. For example, [PERSON] et al. [18] linked high running performance with the likelihood of scoring the first goal in Bundesliga games, highlighting situations where physical intensity can directly influence game dynamics. Similarly, [PERSON] et al. [21] demonstrated that specific running metrics significantly impacted winning odds, especially when considering the match location and tactical setups. This perspective is enriched by studies like those of [PERSON] et al. [19], which tied higher physical activity levels to winning phases within matches, and [PERSON] et al. [17], who found a positive correlation between effective playing time and physical metrics like sprinting distances in winning teams. These contrasting findings illustrate the complex interplay between physical performance metrics and soccer success, influenced by league characteristics, team strategies, and game-specific situations, emphasizing that the impact of running metrics is not universally applicable across all contexts of professional soccer [1; 5; 32].
## 3 Results and Discussion
### Effect of Allicin on Actin- and Tubulin Cytoskeleton in Arabidopsis thaliana  
To understand in more detail how allicin affects root development and whether this is associated with the putative effect on the cytoskeleton that was observed in Tradescantia, _Arabidopsis thaliana_ lines were used whose actin or tubulin filaments were visualized with by beta-tubulin-GFP fusion [38] and GFP-actin-binding protein expression [37].  
Confocal microscopy of the actin cytoskeleton clearly showed typical filamentous structures in the root of the untreated control (Figure 4A). After exposure to 2 mM of allicin for 24 h, the filamentous structures were destroyed; instead, GFP aggregate structures were visible at the margins of the cells. Allicin thus destroyed the integrity of the actin cytoskeleton in the roots of _Arabidopsis thaliana_.  
Figure 3: Allicin inhibited primary root growth and root hair development. Seeds were germinated for two days on filter paper discs, which were then lifted and transferred to fresh medium containing allicin (**A**) or cumene hydroperoxide at the stated concentrations. (**B**) Wild type Col-0 was compared to the GSH-deficient _pad2_ mutant. The scale bars = 1 mm. N = 20, experiment was repeated three times with similar results. The letters A, B above the respective graph columns indicate statistical differences as determined by ANOVA analysis.  
In addition to the actin cytoskeleton, the tubulin cytoskeleton was also examined in an analogous manner. Here, too, the typical filament pattern was seen in the untreated control; treatment with allicin resulted in the dissolution of these filament structures and the diffuse fine-particulate distribution of GFP fluorescence, in contrast to the aggregate formation observed for the actin.
## 5 Experiment
### Mobile Fingerprint  
In this experiment, we compare all the benchmarks, that is, SSL, SSC, LapERLS, and the additional supervised \(k\)-NN algorithm. The parameters of the proposed algorithm are defined as \(\mu_{1}=5\), \(\mu_{2}=2\), \(c=1\) in Equation (5), and the other parameters of the compared methods are selected for by best performance. For the experimental study, we vary the number of the used labeled training data points out of the fixed total of the training data. When 283 number of additional test data points are used, and the results using 283 and 93 training data points are shown in Figure 4a,b, respectively. From the results shown in both parts of this figure, we observe that our algorithm outperforms the compared methods. In the cases of 100% and 75% labeled data in Figure 4b, SSC provides a slightly smaller error than the other methods. However, considering the advantage given to SSC, that is, as it knows the locations of the Wi-Fi APs, this result is not noteworthy. The major contribution of our algorithm can be seen in the case where a very small number of labeled data points are used. From Figure 4a,b, our algorithm shows a slightly increasing error as fewer labeled data points are used, whereas the others exhibit a substantially increasing error when comparing the case with 25% labeled data to that with 10% case.  
Finally, Figure 4c shows the CPU running time of the compared algorithms with respect to the percentage of labeled data. SSC requires more computational time than the other methods. The proposed algorithm needs slightly more time than LapERLS and SSL (an additional 0.2 s at most), due to the additional time-series term to be solved in the optimization process.