esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming
## 4 Conclusions  
The G3P-SR and NARX-NN models performed similarly in terms of accuracy, but they both outperformed the TSF-ARX model, which was unable to capture the nonlinearities present in the system which were not due to changes in the rope length and mass of the payload. The resulting gray-box models obtained by G3P-SR only had 23 and 24 model terms for the sway and velocity submodels, respectively, as opposed to the best NARX-NN model, which required 103 and 86 parameters, respectively. The G3P-SR model was slightly more sensitive to parameter variations with regard to the rope length than NARX-NN, but it was notably less sensitive to variations in payload mass. This is an advantage as, usually, in practice the payload mass is subject to a higher uncertainty than the rope length and, therefore, can be used in practical applications such as model-predictive control of the overhead crane.  
This study shows that the G3P-SR model was able to find data-driven parsimonious and more interpretable models that produced accurate predictions of the overhead crane dynamics and could be used as a substitute for other data-driven modeling approaches when parsimonious models offer an advantage over complex black-box models, such as easing the implementation of model-based controllers.  
Future work includes an extension of this work by considering other working conditions and model structures to validate the universality of the method. Different types of models can be obtained by creating a new grammar to allow a specific type of model  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(l=1.7\) [m], \(m=10\) [kg]} & \multicolumn{3}{c}{\(l=1.1\) [m], \(m=50\) [kg]} \\ \hline  & \(k\) & \(\delta m=1\) & \(\delta m=-1\) & \(\delta m=2\) & \(\delta m=-2\) & \(\delta m=5\) & \(\delta  
structure, such as, for example, polynomial models, rational models, or a combination of model types, and can be used in practical applications such as model-predictive control.  
Conceptualization, T.K., J.S. and B.K.; Methodology, T.K. and J.S.; Software, T.K.; Validation, T.K. and J.S.; Formal analysis, T.K., J.S. and B.K.; Investigation, T.K. and J.S.; Resources, T.K. and J.S.; Data curation, T.K. and J.S.; Writing--original draft, T.K. and J.S.; Writing--review & editing, T.K., J.S. and B.K.; Visualization, T.K. and J.S.; Supervision, J.S. and B.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 5 Conclusions  
The results of the study support our hypothesis that chronic nitrogen enrichment could stimulate microbial residue and amino sugar accumulation in alkaline soil. Microbial residue and amino sugar accumulation increased with an increasing N rate; however, the high-N and medium-N treatments had statistically similar accumulation. Total amino sugar was significantly higher under high-N and medium-N treatments, where N requirement for microbial growth was sufficient. The ratios of glucosamine to muramic acid and glucosamine to galactosamine decreased over time as a result of the more substantial increase in bacterial residues than that in fungal residues. Thus, bacteria contribute significantly to soil organic matter stability, microbial necromass, and AS accumulation in alkaline soil. Nitrogen enrichment improved the soil biochemical properties by increasing soil organic carbon, total nitrogen, available nitrogen, microbial biomass, and dissolved organic carbon concentrations, while lowering soil pH. Microbial residues and AS components showed a positive and significant correlation with microbial biomass, dissolved organic carbon, soil organic carbon, and aboveground plant biomass. However, soil pH showed a negative and significant correlation with glucosamine, muramic acid, total amino sugar, bacterial residues, and total microbial residues in soil organic carbon. Chronic nitrogen enrichment improved the soil biochemical properties and aboveground plant biomass, which stimulated microbial residue and amino sugar accumulation over time. Soil enzymes and microbial turnover have been reported to influence microbial residues and AS accumulation; however, these parameters were not measured in the present study. Therefore, we recommend future studies to focus on how chronic N enrichment affects soil enzyme activity and microbial turnover, and their contribution to microbial residues and AS accumulation in alkaline agricultural soils.  
Conceptualization, H.Q. and Q.S.; methodology, H.Q. and D.K.A.; software, D.K.A.; validation, H.Q., D.K.A. and Q.S.; formal analysis, D.K.A. and P.G.; investigation, D.K.A., Z.L. and D.D.; resources, H.Q.; data curation, D.K.A., D.D. and Z.L.; writing--original draft preparation, D.K.A.; writing--review and editing, H.Q. and D.K.A.; visualization, P.G. and C.Z.; supervision, H.Q. and C.Z.; project administration, Q.S. and H.Q.; funding acquisition, H.Q. and Q.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Basic Research Program of China, grant number No.2015 CB150501.  
Not applicable.  
We are grateful to [PERSON], [PERSON], and [PERSON] for their support during the manuscript preparation.  
The authors declare no conflicts of interest.
## 3 System Model
### Link Quality
#### 3.2.1 Path Loss  
In this paper, we assumed that all UAVs flew at the same altitude. Transmitting and receiving UAVs that could communicate directly without any obstacles affecting their communication quality used line-of-sight (LOS) propagation. In free spaces, the path loss of transmitting and receiving nodes was caused by the transmission power's radiation and dispersion and the channel's transmission characteristics. Furthermore, the distance between the receiver and transmitter was proportional to the path loss, according to the Friis transmission equation.The path loss of \(L_{i,j}\) was expressed by the following:  
\[\text{Los}_{i,j}\big{(}d_{i,j}\big{)}=10\lg\left(\frac{4\pi}{\text{c}}\right)^ {2}+\alpha 10\lg d_{i,j}+20\lg f \tag{1}\]  
where \(\alpha=2.05\) is the path loss exponent [44]; \(d_{i,j}=\sqrt{\left(x_{i}^{2}-x_{j}^{2}\right)+\left(y_{i}^{2}-y_{j}^{2}\right)}\) is the Euclidean distance between node \(i\) and its neighboring node \(j\); and \(\left(x_{i},y_{i}\right)\) and \(\left(x_{j},y_{j}\right)\) are the location coordinates of nodes \(i\) and \(j\), respectively. The variable \(f\) is the carrier frequency, and \(c\) is electromagnetic wave speed, which is approximately equal to the speed of light.  
The above equation calculated the dB value of the path loss, and we converted it to a formal absolute value, \(PL\big{(}d_{i,j}\big{)}=10^{\frac{\text{Los}_{i,j}\big{(}d_{i,j}\big{)}}{10}}\).
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 3 Reviewing Positioning Systems and Their Capabilities
### Main Positioning Systems  
As described in Section 2.4.1, there are seven main positioning systems that we are going to emphasize. These technologies have their advantages and disadvantages, neither of which should be ignored. Factors that should be considered are their cost to build and maintain. Cost is essential when designing such complex equipment and cannot be taken out of the equation. Maintenance is a key factor in underwater vehicles and their systems, especially if we take into account the corrosion of saltwater [97]. Underwater positioning systems play a crucial role in a wide array of applications, driving advancements in fields such as marine research [8], offshore energy, environmental monitoring, and SAR. These systems are vital for accurately navigating and mapping the ocean's vast and largely unexplored depths, where GPS signals cannot penetrate. In marine biology, precise positioning is essential for tracking the movements of species, mapping coral reefs, and studying underwater ecosystems. The offshore oil and gas industry relies heavily on these systems for the installation and maintenance of underwater pipelines, rigs, and other infrastructure. Similarly, underwater positioning is critical in search and rescue missions, enabling the efficient location of wreckage, sunken vessels, or lost equipment. SAR operations, such as mapping hazardous or hard-to-reach areas and diver navigation assistance, also depend on these systems for covert and accurate navigation. The importance of these applications lies in their ability to enable safe, efficient, and cost-effective underwater operations, contributing to advancements in scientific discovery, resource management, and national security. So, another key factor that we must take into account is the application of the positioning system [19,42,98,99].
## 3 Results  
In this section, we present the experimental results obtained from an evaluation of our edge detection method. The evaluation was conducted on a variety of image patches that vary in complexity, thereby enabling a more comprehensive analysis of the method's robustness and adaptability.  
To facilitate a more intuitive understanding of the model's behavior, we generated error visualization images in which different types of classification outcomes are color-coded: false negatives are shown in purple (RGB: 156, 39, 176), tolerated true positives are in green (RGB: 76, 175, 80), and real false positives are in orange (RGB: 255, 152, 0). Tolerated true positives refer to the predicted edges that fall within a dilated region (15 \(\times\) 15 kernel)  
Figure 3: Post-processing stages applied to the edge detection output. (a) Raw edge map obtained using an algorithm before any refinement; (b) normalized edge image after applying min-max normalization to enhance contrast and prepare for binarization; (c) binarized image using the Gaussian blur and Otsu’s thresholding to isolate prominent edges; (d) skeletonized version of the binarized image, showing a one-pixel-wide representation of the edge structure for further analysis.  
around the ground truth edges, allowing for minor spatial deviations. False negatives correspond to ground truth edges that were not detected by the model, indicating missed detections. In contrast, false positives represent predicted edges that do not correspond to any ground truth edge, highlighting over-detection or noise sensitivity.  
In addition, a comparison was made between our results and those obtained using the DexiNed model [31], for which a series of experiments were carried out on both the original input images and those processed through various preprocessing techniques. To ensure a fair comparison, the same post-processing steps were applied to the outputs of both methods. These visualizations and comparative analyses provide insight into edge localization accuracy, noise resilience, and computational efficiency.  
For a quantitative evaluation, the F1-score was used as the reference metric because of its balance between precision and recall. Furthermore, we calculated several structural similarity metrics, including the SSIM (structural similarity index measure) and FOM (figure of merit), to evaluate the perceptual and spatial accuracy. Standard classification metrics were incorporated as well. The following parameters must be taken under consideration: precision, recall, specificity, false positive rate (FPR), false negative rate (FNR), true positive rate (TPR), and true negative rate (TNR). These metrics offer a comprehensive perspective on the model's performance across various evaluation dimensions.
## 3 Result and Discussion
### Fourier Transform Infrared Spectroscopy (FTIR)  
Figure 6 shows the IR spectra of the raw materials and geopolymer sample 16 before and after casting. According to [PERSON] [(55)], networks of silico-aluminate-based geopolymers consist of SiO\({}_{4}\) and AlO\({}_{4}\) tetrahedral linked alternately via oxygen sharing. The main band of geopolymer falls within the region of 900-1200 cm\({}^{-1}\), corresponding to the Si-O-T (where T is Al or Si) linkages. The absorptions at 996 and 797 cm\({}^{-1}\) are assigned to Al (IV)-OH (6 fold coordinated) and Al (IV)-O (6-fold coordinated), respectively. As seen in Figure 6, the IR peaks of geopolymer before casting appeared in 1052 cm\({}^{-1}\) instead of 996 cm\({}^{-1}\). This means that the asymmetric bending of the bonds O-Si-O and O-Al-O are within lower frequencies, which is in agreement with [PERSON] et al. [(56)]. The broad bands at 3000-3500 cm\({}^{-1}\) and 1650-1655 cm\({}^{-1}\), which can be clearly seen in the geopolymer sample after casting, are attributed to the stretching and deforming vibrations of OH and H-O-H groups of water molecules. Bands at \(\sim\)1400 cm\({}^{-1}\) are assigned to the Si-O-Si stretching, while the bands around 1000-1070 cm\({}^{-1}\) and 750-790 cm\({}^{-1}\) in the raw materials are attributed to O-Si-O stretching vibrations.  
Figure 5: The XRD patterns of the raw materials and sample 16 before (16-BC) and after casting (16-AC), Q: quartz and C: Cristobalite.
## 2 Materials and Methods
### Experimental Data  
The historical data of GHI were obtained from observations taken every \(15\) min at the Zacatecas Meteorological Observatory (OMZ) during the measurement period from 2015 to 2018. The meteorological station operates at 2650 m above the mean sea level. The selection of this period is justified by the limited availability of continuous and reliable data beyond this time frame. The interruption of long-term measurements and difficulties in accessing various local databases have been limiting factors. To ensure the quality of the irradiance data used in this study, several quality tests were applied to ensure the reliability and consistency of the measurements [28].  
To explore how cloud cover affects the accuracy of time series forecasts, we classified each day into three categories based on the sky's conditions, clear, partly cloudy, and cloudy, using the daily clear-sky index, \(K_{c}(t)\), which relates the measured solar radiation, \(H(t)\), to ideal clear-sky radiation. To simplify and keep \(H(t)\) as the only measured variable, we applied the most basic daily model of [29] to estimate solar radiation in clear skies,\(H_{cs}(t)\). This allowed us to calculate the average daily \(K_{c}\) index, as shown in Equation (1). This approach helps us understand how variations in the sky conditions affect forecast accuracy using validated methods to characterize solar radiation in different atmospheric environments [30; 31].  
\[K_{c}=\frac{H}{H_{cs}} \tag{1}\]  
Using the index \(K_{c}(t)\), it was normalized to obtain a uniform distribution for the impact of cloudiness. The maximum value of \(K_{c}(t)\) is 1, which correspond to perfectly clear skies. The minimum value of \(K_{c}(t)\) is 0, indicating completely obscured skies. Thus, the ranges of values are defined for the three conditions:  
Cloudy day: \(K_{c}(t)<0.33\)  
Partly cloudy day: \(0.33\leq K_{c}(t)<0.66\)  
Clear day: \(0.66\leq K_{c}(t)\)  
Table 1 shows the number of days that belong to each condition, using the previously defined ranges.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 5 Intrusion Detection in Fog, Edge, and Cloud Computing
### Applications of Fog Computing in IoT
#### 5.3.2 Healthcare Activity Tracking  
Fog computing has the ability to provide processing and event responses (in real-time), being imperative for technological use in the healthcare industry--the technology also focuses on issues around the essential network connectivity and traffic needed to use storage remotely for processing and medical record retrieval in the cloud. The following work [41] highlights where fog computing can be noticed in the healthcare domain, where the researchers propose fog computing as the potential gateway for augmenting health monitoring systems. They used services embedded in fog computing, which include interoperability, distributed databases, a real-time notification mechanism, location awareness, and GUI containing access management. In addition, they introduce a flexible, lightweight template for an ECG feature (examples include heart rate, P wave, and T wave) extraction. When researching, they performed demonstrations and obtained results highlighting the success of using the smart gateway.
## 1 Introduction
[12] investigated through FEA the influence of static and 90\({}^{\circ}\) out-of-phase cyclic mode II loading superimposed to the cyclic mode I on the plasticity- and asperity-induced closure for an aluminum alloy. The applied mode II could either increase or decrease \(\Delta K_{\text{Ieff}}\) depending on the stress ratio and loading path; in some cases, it had opposite effects on the two closure mechanisms. In the case of 90\({}^{\circ}\) out-of-phase mixed mode loading, mode II drastically reduced or even suppressed the plasticity-induced closure. To clarify the incipient direction of the crack path observed by [PERSON] et al. [8], [PERSON] and [PERSON] [13] investigated several criteria while using the FEA approach; they concluded that a maximum tangential stress (MTS) range criterion based on an elasto-plastic stress field can appropriately predict the incipient crack growth direction in ductile metals. [PERSON] et al. [14] studied the FCG behavior in a thin-walled tubular specimen of aluminum alloy under non-proportional mixed mode I and II loading, showing that the FCG significantly differed from that under mode I or proportional mixed mode loading. Although they obtained a long and stable shear mode growth, they could not apply the commonly accepted MTS criterion in most of the non-proportional loading cases. As indicated above, previous studies on coplanar crack growth under non-proportional mixed mode I/II loading cycles apparently focused on rail steel. Only [PERSON] and [PERSON] [10] considered also the wheel steel case, but their elucidation of the phenomena associated with crack growth under such loading cycles was not sufficient. In particular, the reason why the growth direction of the crack changes depending on the loading conditions and the type of steel was not clarified. Besides, the crack growth models by [PERSON] et al. [9] have been pointed out as arbitrary. Therefore, in this study, crack  
Figure 1: Schematic representation of how a fluid in the crack generates mode I and II loadings. growth data from non-proportional mode I/II loading cycles were re-constructed by using a reliable equivalent SIF range and FEA was performed to elucidate the crack growth behavior under these loading cycles. The reliable criteria for predicting the crack path direction of non-proportional mixed mode loading were used, and the results were compared with corresponding experimental results. This paper, part 1 of two companion papers, presents FCG under non-proportional mixed mode I/II loading and is organized as follows. The introduction starts in this section with a brief overview of past papers for the crack growth under non-proportional mixed mode I/II loading cycles. Section 2 describes the detailed method of the experiments conducted and presents the experimental results. In Section 3, the FEA model for predicting the crack path direction is presented and the results are indicated. Section 4 gives detailed considerations and discussions by comparing the experimental and FEA results. Finally, the important results obtained in this study are summarized in Section 5.
## 3 Overview of UHS Globally and in the Baltics
### UHS Aspects in Lithuania
#### 3.1.1 Simulation Model Outcome
The findings from Figure 10 also suggest that while injecting 9 \(\times\) 10\({}^{8}\) volume of H\({}_{2}\) in standard cubic meters (Sm\({}^{3}\)) by the end of 10 years for the homogenous model, the maximum production achieved was 5.6 \(\times\) 10\({}^{8}\) Sm\({}^{3}\). For the heterogeneous model, injecting 8.1 \(\times\) 10\({}^{8}\) Sm\({}^{3}\) volume of H\({}_{2}\) by the end of 10 years, the maximum production achieved was 4.2 \(\times\) 10\({}^{8}\) Sm\({}^{3}\). The total volume of H\({}_{2}\) in place during the 10-year cycle is shown in Figure 9. The findings show a decrease in H\({}_{2}\) recovery efficiency over time. It has been observed that using a single well for both injection and withdrawal drastically reduces the efficiency of H\({}_{2}\) extraction by nearly 50%. As a result, it is crucial to use separate wells for injection and production procedures.
## 2 Climatic Actions on Structures  
### Definition of Climatic Loads  
In Europe, climatic loads for structural design are defined in the Eurocodes, for snow loads (EN1991-1-3 [9]), wind actions (EN1991-1-4 [10]), and thermal actions (EN1991-1-5 [11]). In the verification of the structural performance, characteristic values of the climatic actions are associated with a given probability of exceedance in a reference period.  
As stated in EN1990 [3], characteristic values of climatic actions given in the Eurocodes are associated to a probability of 2% of the time-varying part being exceeded in one year, leading approximately to a return period of 50 years.  
Characteristic values of climatic actions and their maps are typically considered country-specific data, to be included in National Annexes to the Eurocodes, as Nationally Determined Parameters (NDP), so that maps for characteristic ground snow loads \(s_{k}\), basic wind velocity \(v_{b,0}\), and maximum and minimum shade air temperature, \(T_{\max}\) and \(T_{\min}\), are given in relevant National Annexes to EN1993-1-3 [9], EN1993-1-4 [10], and EN1993-1-5 [11].  
Although different procedures for the analysis of extremes are adopted by different countries, as it will be discussed in the following paragraph, the current definition of characteristic values of climatic actions is usually based on the extreme value analysis of past observations of the natural phenomena, under the already mentioned assumption of stationary climate conditions, disregarding potential effects of climate change.